From 07c671245e2d40b3d1b15bd5f559e5fa530e02fd Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Sun, 8 Nov 2020 20:14:47 +0100 Subject: [PATCH 01/57] Set up CI. --- LICENSE | 661 ++++++++++++++++++ README.md | 51 ++ __init__.py | 0 code/__init__.py | 0 code/__pycache__/__init__.cpython-38.pyc | Bin 0 -> 194 bytes code/hw1/__init__.py | 0 code/hw1/__pycache__/__init__.cpython-38.pyc | Bin 0 -> 203 bytes code/hw1/src/Main.py | 140 ++++ code/hw1/src/Plot_to_tex.py | 126 ++++ code/hw1/src/__init__.py | 0 code/hw1/src/__main__.py | 23 + code/hw1/src/juice_propagation_Q1.ipynb | 355 ++++++++++ code/hw1/src/juice_propagation_Q4.ipynb | 335 +++++++++ code/hw1/test/__init__.py | 0 .../test/__pycache__/__init__.cpython-38.pyc | Bin 0 -> 208 bytes .../test_algs.cpython-38-pytest-5.4.3.pyc | Bin 0 -> 2109 bytes .../test_main.cpython-38-pytest-5.4.3.pyc | Bin 0 -> 1295 bytes code/hw1/test/test_main.py | 32 + gitignore | 211 ++++++ latex/__init__.py | 0 latex/hw1/Appendices/AppA.tex | 2 + 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| 62 ++ linux_environment.yml | 16 + windows_environment.yml | 16 + 49 files changed, 2658 insertions(+) create mode 100644 LICENSE create mode 100644 README.md create mode 100644 __init__.py create mode 100644 code/__init__.py create mode 100644 code/__pycache__/__init__.cpython-38.pyc create mode 100644 code/hw1/__init__.py create mode 100644 code/hw1/__pycache__/__init__.cpython-38.pyc create mode 100644 code/hw1/src/Main.py create mode 100644 code/hw1/src/Plot_to_tex.py create mode 100644 code/hw1/src/__init__.py create mode 100644 code/hw1/src/__main__.py create mode 100644 code/hw1/src/juice_propagation_Q1.ipynb create mode 100644 code/hw1/src/juice_propagation_Q4.ipynb create mode 100644 code/hw1/test/__init__.py create mode 100644 code/hw1/test/__pycache__/__init__.cpython-38.pyc create mode 100644 code/hw1/test/__pycache__/test_algs.cpython-38-pytest-5.4.3.pyc create mode 100644 code/hw1/test/__pycache__/test_main.cpython-38-pytest-5.4.3.pyc create mode 100644 code/hw1/test/test_main.py create mode 100644 gitignore create mode 100644 latex/__init__.py create mode 100644 latex/hw1/Appendices/AppA.tex create mode 100644 latex/hw1/Chapters/chap0.tex create mode 100644 latex/hw1/logo.eps create mode 100644 latex/hw1/main.tex create mode 100644 latex/hw1/references.bib create mode 100644 latex/hw2/Appendices/AppA.tex create mode 100644 latex/hw2/Appendices/AppB.tex create mode 100644 latex/hw2/Chapters/Introduction.tex create mode 100644 latex/hw2/Chapters/chap1.tex create mode 100644 latex/hw2/Images/4a.png create mode 100644 latex/hw2/Images/4b.png create mode 100644 latex/hw2/Images/4c.png create mode 100644 latex/hw2/Tables/table_1.csv create mode 100644 latex/hw2/logo.eps create mode 100644 latex/hw2/main.tex create mode 100644 latex/hw2/references.bib create mode 100644 latex/hw3/Appendices/AppA.tex create mode 100644 latex/hw3/Appendices/AppB.tex create mode 100644 latex/hw3/Chapters/Introduction.tex create mode 100644 latex/hw3/Chapters/chap1.tex create mode 100644 latex/hw3/Images/4a.png create mode 100644 latex/hw3/Images/4b.png create mode 100644 latex/hw3/Images/4c.png create mode 100644 latex/hw3/Tables/table_1.csv create mode 100644 latex/hw3/logo.eps create mode 100644 latex/hw3/main.tex create mode 100644 latex/hw3/references.bib create mode 100644 linux_environment.yml create mode 100644 windows_environment.yml diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..0ad25db --- /dev/null +++ b/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU Affero General Public License as published + by the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Affero General Public License for more details. + + You should have received a copy of the GNU Affero General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..69224b9 --- /dev/null +++ b/README.md @@ -0,0 +1,51 @@ +# Python unit test project template with continuous integration (ci). + +## usage: do once + +0. If you don't have pip: open Anaconda prompt and browse to the directory of this readme and run: +``` +cd /home// +python get-pip.py +``` + +1. To use this package, first make a new conda environment and activate (it this automatically installs everything you need) +3.1 For Windows: +``` +conda env create --file windows_environment.yml +``` +3.2 For Linux: +``` +conda env create --file linux_environment.yml +``` + +## usage: do every time you start Anaconda: + +2. Activate the conda environment you created +``` +conda activate neumo_project +``` + +## usage: do every run: + +3. Performes run of main code (in `main.py`, called from `__main__.py`) +``` +python -m code.project1.src +``` + +## Testing + +4. Testing is as simple as running the following command in the root directory of this repository in Anaconda prompt: +``` +python -m pytest +``` +from the root directory of this project. + + +## Note for linux users +The client connection requires sudo rights. I did not install conda as sudo, so .. + + + +[black_badge]: https://img.shields.io/badge/code%20style-black-000000.svg +[python_badge]: https://img.shields.io/badge/python-3.8-blue.svg +[apache_badge]: https://img.shields.io/badge/license-Apache%202.0-brightgreen.svg diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/__init__.py b/code/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/__pycache__/__init__.cpython-38.pyc b/code/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8239e17fff569a474e62dc570057b3f5b3d166df GIT binary patch literal 194 zcmWIL<>g`kg2p!q@gVv!h(HF6K#l_t7qb9~6oz01O-8?!3`HPe1o10fKO;XkRX+tb8S*DtlSC^1LZGcPT_C^xYrGe1u^ zpeR2%wYWGlFI_jdvbZEQw-_Xpm6}|lpPZkPsvjSpnU`4-AFo$Xd5gm)H$SB`C)Ez* JfX_h8008rWGiCq) literal 0 HcmV?d00001 diff --git a/code/hw1/__init__.py b/code/hw1/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/hw1/__pycache__/__init__.cpython-38.pyc b/code/hw1/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c88ec3e4a99f8e81e40e7ea54da101dc09a1b049 GIT binary patch literal 203 zcmWIL<>g`kg2p!q@gVv!h(HF6K#l_t7qb9~6oz01O-8?!3`HPe1o5jtKO;XkRX+tb8S*DtlSC^1LZGcPT_C^xYrGe1u^ zpeR2%wYWGlFI_jdvbZEQw-_Xpm6}|lpPZkPs$T%%80yEzXXa&=#K-FuRNmsS$<0qG O%}KQbx#TktGXMbVeKy7b literal 0 HcmV?d00001 diff --git a/code/hw1/src/Main.py b/code/hw1/src/Main.py new file mode 100644 index 0000000..0fdaa81 --- /dev/null +++ b/code/hw1/src/Main.py @@ -0,0 +1,140 @@ +# Example code that creates plots directly in report +# Code is an implementation of a genetic algorithm +import random +from matplotlib import pyplot as plt +from matplotlib import lines +import matplotlib.pyplot as plt +from .Plot_to_tex import Plot_to_tex as plt_tex + +import numpy as np +string_length = 100 +mutation_chance= 1.0/string_length +max_iterations = 1500 +class Main: + + def __init__(self): + pass + + def count(self,bits): + count = 0 + for bit in bits: + if bit: + count = count + 1 + return count + + def gen_bit_sequence(self): + bits = [] + for _ in range(string_length): + bits.append(True if random.randint(0, 1) == 1 else False) + return bits + + def mutate_bit_sequence(self,sequence): + retval = [] + for bit in sequence : + do_mutation = random.random() <= mutation_chance + if(do_mutation): + retval.append(not bit) + else: + retval.append(bit) + return retval + + #execute a run a + def do_run_a(self): + + seq = self.gen_bit_sequence() + fitness = self.count(seq) + results = [fitness] + for run in range(max_iterations-1): + new_seq = self.mutate_bit_sequence(seq) + new_fitness = self.count(new_seq) + if new_fitness > fitness: + seq = new_seq + fitness = new_fitness + results.append(max(results[-1],fitness)) + return results + + + #execute a run c + def do_run_c(self): + seq = self.gen_bit_sequence() + fitness = self.count(seq) + results = [fitness] + for run in range(max_iterations): + new_seq = self.mutate_bit_sequence(seq) + new_fitness = self.count(new_seq) + seq = new_seq + fitness = new_fitness + results.append(max(results[-1], fitness)) + return results + + def do4b(self,project_nr): + optimum_found = 0 + + # generate plot data + plotResult = np.zeros((10,max_iterations), dtype=int); + lineLabels = [] + + # perform computation + for run in range(10): + res = self.do_run_a() + if res[-1] == string_length: + optimum_found +=1 + + # store computation data for plotting + lineLabels.append(f'Run {run}') + plotResult[run,:]=res; + + # plot multiple lines into report (res is an array of dataseries (representing the lines)) + # plt_tex.plotMultipleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) + plt_tex.plotMultipleLines(plt_tex,range(0, len(res)),plotResult,"[runs]]","fitness [%]",lineLabels,"4b",4,project_nr) + print("total optimum found: {} out of {} runs".format(optimum_found,10)) + + def do4c(self,project_nr): + optimum_found = 0 + + # generate plot data + plotResult = np.zeros((10,max_iterations+1), dtype=int); + lineLabels = [] + + # perform computation + for run in range(10): + res = self.do_run_c() + if res[-1] == string_length: + optimum_found +=1 + + # Store computation results for plot + lineLabels.append(f'Run {run}') + plotResult[run,:]=res; + + # plot multiple lines into report (res is an array of dataseries (representing the lines)) + # plt_tex.plotMultipleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) + plt_tex.plotMultipleLines(plt_tex,range(0, len(res)),plotResult,"[runs]]","fitness [%]",lineLabels,"4c",4,project_nr) + + print("total optimum found: {} out of {} runs".format(optimum_found, 10)) + + def addTwo(self,x): + ''' adds two to the incoming integer and returns the result of the computation.''' + return x+2 + +if __name__ == '__main__': + # initialize main class + main = Main() + + # set the project number folder since this is python code, the results are exported to the project2 report + project_nr = 1 + + # run a genetic algorithm to create some data for a plot. + print("now running a") + res = main.do_run_a() + + # plot some graph with a single line, general form is: + # plt_tex.plotSingleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) + plt_tex.plotSingleLine(plt_tex,range(0, len(res)),res,"[runs]]","fitness [%]","run 1","4a",4,project_nr) + + # run a genetic algorithm to create some data for another plot. + print("now running b") + main.do4b(project_nr) + + # run a genetic algorithm to create some data for another plot. + print("now running 4c") + main.do4c(project_nr) \ No newline at end of file diff --git a/code/hw1/src/Plot_to_tex.py b/code/hw1/src/Plot_to_tex.py new file mode 100644 index 0000000..7ec9a4c --- /dev/null +++ b/code/hw1/src/Plot_to_tex.py @@ -0,0 +1,126 @@ +### Call this from another file, for project 11, question 3b: +### from Plot_to_tex import Plot_to_tex as plt_tex +### multiple_y_series = np.zeros((nrOfDataSeries,nrOfDataPoints), dtype=int); # actually fill with data +### lineLabels = [] # add a label for each dataseries +### plt_tex.plotMultipleLines(plt_tex,single_x_series,multiple_y_series,"x-axis label [units]","y-axis label [units]",lineLabels,"3b",4,11) +### 4b=filename +### 4 = position of legend, e.g. top right. +### +### For a single line, use: +### plt_tex.plotSingleLine(plt_tex,range(0, len(dataseries)),dataseries,"x-axis label [units]","y-axis label [units]",lineLabel,"3b",4,11) + +### You can also plot a table directly into latex, see example_create_a_table(..) +### +### Then put it in latex with for example: +###\begin{table}[H] +### \centering +### \caption{Results some computation.}\label{tab:some_computation} +### \begin{tabular}{|c|c|} % remember to update this to show all columns of table +### \hline +### \input{latex/project3/tables/q2.txt} +### \end{tabular} +###\end{table} +import random +from matplotlib import lines +import matplotlib.pyplot as plt +import numpy as np +import os +class Plot_to_tex: + + def __init__(self): + self.script_dir = self.get_script_dir() + print("Created main") + + # plot graph (legendPosition = integer 1 to 4) + def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr): + fig=plt.figure(); + ax=fig.add_subplot(111); + ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none'); + plt.legend(loc=legendPosition); + plt.xlabel(x_axis_label); + plt.ylabel(y_axis_label); + plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png'); +# plt.show(); + + # plot graphs + def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr): + fig=plt.figure(); + ax=fig.add_subplot(111); + + # generate colours + cmap = self.get_cmap(len(y_series[:,0])) + + # generate line types + lineTypes = self.generateLineTypes(y_series) + + for i in range(0,len(y_series)): + # overwrite linetypes to single type + lineTypes[i] = "-" + ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color + + # configure plot layout + plt.legend(loc=legendPosition); + plt.xlabel(x_label); + plt.ylabel(y_label); + plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png'); + + print(f'plotted lines') + + # Generate random line colours + # Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib + def get_cmap(n, name='hsv'): + '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct + RGB color; the keyword argument name must be a standard mpl colormap name.''' + return plt.cm.get_cmap(name, n) + + def generateLineTypes(y_series): + # generate varying linetypes + typeOfLines = list(lines.lineStyles.keys()) + + while(len(y_series)>len(typeOfLines)): + typeOfLines.append("-."); + + # remove void lines + for i in range(0, len(y_series)): + if (typeOfLines[i]=='None'): + typeOfLines[i]='-' + if (typeOfLines[i]==''): + typeOfLines[i]=':' + if (typeOfLines[i]==' '): + typeOfLines[i]='--' + return typeOfLines + + # Create a table with: table_matrix = np.zeros((4,4),dtype=object) and pass it to this object + def put_table_in_tex(self, table_matrix,filename,project_nr): + cols = np.shape(table_matrix)[1] + format = "%s" + for col in range(1,cols): + format = format+" & %s" + format = format+"" + plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline=' \\\\ \hline \n') + + # replace this with your own table creation and then pass it to put_table_in_tex(..) + def example_create_a_table(self): + project_nr = "1" + table_name = "example_table_name" + rows = 2; + columns = 4; + table_matrix = np.zeros((rows,columns),dtype=object) + table_matrix[:,:]="" # replace the standard zeros with emtpy cell + print(table_matrix) + for column in range(0,columns): + for row in range(0,rows): + table_matrix[row,column]=row+column + table_matrix[1,0]="example" + table_matrix[0,1]="grid sizes" + + self.put_table_in_tex(table_matrix,table_name,project_nr) + + + def get_script_dir(self): + ''' returns the directory of this script regardles of from which level the code is executed ''' + return os.path.dirname(__file__) + +if __name__ == '__main__': + main = Plot_to_tex() + main.example_create_a_table() \ No newline at end of file diff --git a/code/hw1/src/__init__.py b/code/hw1/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/hw1/src/__main__.py b/code/hw1/src/__main__.py new file mode 100644 index 0000000..66e90a2 --- /dev/null +++ b/code/hw1/src/__main__.py @@ -0,0 +1,23 @@ +from .Main import Main + +print(f'Hi, I\'ll be running the main code, and I\'ll let you know when I\'m done.') +project_nr = 1 +main = Main() + +# run a genetic algorithm to create some data for a plot. +print("now running a") +res = main.do_run_a() + +# plot some graph with a single line, general form is: +# plt_tex.plotSingleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) +# main.plt_tex.plotSingleLine(plt_tex,range(0, len(res)),res,"[runs]]","fitness [%]","run 1","4a",4,project_nr) + +# run a genetic algorithm to create some data for another plot. +print("now running b") +main.do4b(project_nr) + +# run a genetic algorithm to create some data for another plot. +print("now running 4c") +main.do4c(project_nr) + +print(f'Done.') \ No newline at end of file diff --git a/code/hw1/src/juice_propagation_Q1.ipynb b/code/hw1/src/juice_propagation_Q1.ipynb new file mode 100644 index 0000000..e827102 --- /dev/null +++ b/code/hw1/src/juice_propagation_Q1.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# # student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create environment, vehicle, accelerations, and propagation settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create settings for celestial bodies\n", + "bodies_to_create = [\"Ganymede\"]\n", + "global_frame_origin = \"Ganymede\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation)\n", + "\n", + "# Add Ganymede exponential atmosphere\n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + " \n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated, and their central bodies of propagation.\n", + "bodies_to_propagate = [\"JUICE\"]\n", + "central_bodies = [\"Ganymede\"]\n", + "\n", + "# Define accelerations acting on vehicle.\n", + "acceleration_settings_on_vehicle = dict(\n", + " XXXX\n", + ")\n", + "\n", + "# Create global accelerations dictionary.\n", + "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Define initial state.\n", + "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.keplerian_state(\n", + " \"JUICE\", \"Ganymede\")\n", + " ]\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + " \n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Orbit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings, True)\n", + "\n", + "simulation_result = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Print final propagation time and state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + "###########################################################################\n", + "\n", + "final_time_step=list(simulation_result.keys())[-1]\n", + "first_time_step=list(simulation_result.keys())[0]\n", + "\n", + "print(\n", + " f\"\"\"\n", + "JUICE Propagation Results.\n", + "\n", + "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + "Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_result[final_time_step][:]}\n", + "\n", + " \"\"\"\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Save Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# SAVE RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "save2txt(solution=simulation_result,\n", + " filename=\"JUICEPropagationHistory_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )\n", + "\n", + "save2txt(solution=dependent_variables,\n", + " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results\n", + "\n", + "For inspiration see: \n", + "\n", + "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PLOT RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "# Extract time and Kepler elements from dependent variables\n", + "kepler_elements = np.vstack(list(dependent_variables.values())\n", + " \n", + "# Kepler Elements\n", + "# 0: semi-major axis\n", + "# 1: eccentricity\n", + "# 2: inclination\n", + "# 3: argument of periapsis\n", + "# 4: right ascension of the ascending node\n", + "# 5: true anomaly\n", + "\n", + "time = dependent_variables.keys()\n", + "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", + "\n", + "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", + "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", + "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", + "\n", + "\n", + "# Set font size of figures\n", + "font_size = 20\n", + " \n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/hw1/src/juice_propagation_Q4.ipynb b/code/hw1/src/juice_propagation_Q4.ipynb new file mode 100644 index 0000000..7b3bbcd --- /dev/null +++ b/code/hw1/src/juice_propagation_Q4.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create Environment and Vehicle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create body objects.\n", + "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", + "global_frame_origin = \"SSB\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation) \n", + "\n", + "# Add Ganymede exponential atmosphere \n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + "\n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings );" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Dynamics for various cases" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "cases = ['unperturbed', 'case_i', 'case_ii']\n", + "\n", + "\"\"\"\n", + "unperturbed: Ganymede PM\n", + "\n", + "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", + "\n", + "case_ii: Ganymede PM, Ganymede aerodynamic\n", + "\"\"\"\n", + "\n", + "simulation_results_dict = dict()\n", + "dependent_variables_dict = dict()\n", + "for case in cases: \n", + " ###########################################################################\n", + " # CREATE ACCELERATIONS ####################################################\n", + " ###########################################################################\n", + "\n", + " # Define bodies that are propagated.\n", + " bodies_to_propagate = [\"JUICE\"]\n", + "\n", + " # Define central bodies.\n", + " central_bodies = [\"Ganymede\"]\n", + "\n", + " # Define accelerations acting on vehicle.\n", + " if case == 'unperturbed':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + " if case == 'case_i':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX,\n", + " Jupiter = XXXX\n", + " )\n", + " if case == 'case_ii':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + "\n", + " # Create global accelerations dictionary.\n", + " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + " # Create acceleration models.\n", + " acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + " ###########################################################################\n", + " # CREATE PROPAGATION SETTINGS #############################################\n", + " ###########################################################################\n", + "\n", + " # Define initial state.\n", + " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + " # Save magnitude of perturbations for both cases\n", + " if case == 'unperturbed':\n", + " dependent_variables_to_save = [ ]\n", + " if case == 'case_i':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + " if case == 'case_ii':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + "\n", + " # Create propagation settings.\n", + " propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + " )\n", + "\n", + " # Create numerical integrator settings.\n", + " fixed_step_size = 10.0\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + " )\n", + "\n", + " ###########################################################################\n", + " # PROPAGATE ORBIT #########################################################\n", + " ###########################################################################\n", + "\n", + " # Create simulation object and propagate dynamics.\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + " \n", + " simulation_results_dict[case] = dynamics_simulator.state_history\n", + " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", + "\n", + " ###########################################################################\n", + " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + " ###########################################################################\n", + "\n", + " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", + " first_time_step=list(simulation_results_dict[case].keys())[0]\n", + "\n", + " print(\n", + " f\"\"\"\n", + " JUICE Propagation Results of {case}.\n", + "\n", + " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + " Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_results_dict[case][final_time_step][:]}\n", + "\n", + " \"\"\"\n", + " )\n", + "\n", + " ###########################################################################\n", + " # SAVE RESULTS ############################################################\n", + " ###########################################################################\n", + " \n", + "# save2txt(solution=simulation_result,\n", + "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", + "# directory=\"./\", # default = \"./\" \n", + "# column_names=None, # default = None \n", + "# )\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Pre-process Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", + "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", + "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", + "\n", + "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", + "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", + "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", + "\n", + "difference_in_cartesian_position = XXXX" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git 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+\usepackage{cleveref} %cleverref needs to stand below amsmath package. +\usepackage{float} +\usepackage{url} %To be able to use url in references +\usepackage{tabularx} % in the preamble +\usepackage{wrapfig} +\usepackage{algorithm} +\usepackage{algorithmic} +\usepackage{appendix} + +\crefname{appsec}{Appendix}{Appendices} % refer to appendix as appendix iso as section (use with text in + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Create listings (Matlab) +% % Create a matlab listing +\usepackage{listings} +\usepackage{color} %red, green, blue, yellow, cyan, magenta, black, white +\definecolor{mygreen}{RGB}{28,172,0} % color values Red, Green, Blue +\definecolor{mylilas}{RGB}{170,55,241} + +\crefname{lstlisting}{listing}{listings} +\Crefname{lstlisting}{Listing}{Listings} +%%%%%%%%%%Configure matlab listing%%%%%%%%%%%%%%%%%% +% Specify matlab listing style +\lstset{language=Matlab,% + %basicstyle=\color{red}, + breaklines=true,% + morekeywords={matlab2tikz}, + keywordstyle=\color{blue},% + morekeywords=[2]{1}, keywordstyle=[2]{\color{black}}, + identifierstyle=\color{black},% + stringstyle=\color{mylilas}, + commentstyle=\color{mygreen},% + showstringspaces=false,%without this there will be a symbol in the places where there is a space + numbers=left,% + numberstyle={\tiny \color{black}},% size of the numbers + numbersep=9pt, % this defines how far the numbers are from the text + emph=[1]{for,end,break},emphstyle=[1]\color{red}, %some words to emphasise + %emph=[2]{word1,word2}, emphstyle=[2]{style}, +} +%%%%%%%%%%Document%%%%%%%%%%%%%%%%%% +\title{Research Proposal\\\large SOW-MKI96 Neuromorphic Computing} +\author{Authors:\\ Victoria Bosch (s1056634)\\Arne Diehl (s4451392)\\Daphne Smits (s1005509)\\Akke Toeter (s1047775)} +\date{04-11-2020} + +\begin{document} +\maketitle +%\setcounter{chapter}{-1} +\input{Chapters/chap0.tex} %\newpage +%\input{Chapters/Methods.tex} %\newpage +%\input{Chapters/Significance} %\newpage +%\input{Chapters/1h.tex} %\newpage +%\input{Chapters/1i.tex} %\newpage +%\input{Chapters/1j.tex} %\newpage +%\input{Chapters/Conclusion.tex} %\newpage + + + + + + + + + + + + + + +% \bibliographystyle{plain} %plain style +\bibliography{references} +\addcontentsline{toc}{chapter}{Bibliography} + + +\begin{appendices} +\crefalias{section}{appsec} +\newpage +\input{Appendices/AppA.tex} \newpage +%\input{Appendices/AppB.tex} \newpage +%\input{Appendices/AppC.tex} \newpage +%\input{Appendices/AppD.tex} \newpage +\end{appendices} + +\end{document} diff --git a/latex/hw1/references.bib b/latex/hw1/references.bib new file mode 100644 index 0000000..eeb4ed2 --- /dev/null +++ b/latex/hw1/references.bib @@ -0,0 +1,68 @@ +@misc{ali2019spiking, + title={A spiking neural algorithm for the Network Flow problem}, + author={Abdullahi Ali and Johan Kwisthout}, + year={2019}, + eprint={1911.13097}, + archivePrefix={arXiv}, + primaryClass={cs.NE} +} +@article{Shrinivas2010, +author = {S G Shrinivas and Vetrivel, S and Elango, N}, +year = {2010}, +month = {09}, +pages = {}, +title = {APPLICATIONS OF GRAPH THEORY IN COMPUTER SCIENCE AN OVERVIEW}, +volume = {2}, +journal = {International Journal of Engineering Science and Technology} +} +@INPROCEEDINGS{yakopcic2020, + author={C. {Yakopcic} and N. {Rahman} and T. {Atahary} and T. M. {Taha} and S. {Douglass}}, + booktitle={2020 Design, Automation Test in Europe Conference Exhibition (DATE)}, + title={Solving Constraint Satisfaction Problems Using the Loihi Spiking Neuromorphic Processor}, + year={2020}, + volume={}, + number={}, + pages={1079-1084}, + doi={10.23919/DATE48585.2020.9116227}} +@inproceedings{kay2020, +author = {Kay, Bill and Date, Prasanna and Schuman, Catherine}, +title = {Neuromorphic Graph Algorithms: Extracting Longest Shortest Paths and Minimum Spanning Trees}, +year = {2020}, +isbn = {9781450377188}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3381755.3381762}, +doi = {10.1145/3381755.3381762}, +booktitle = {Proceedings of the Neuro-Inspired Computational Elements Workshop}, +articleno = {6}, +numpages = {6}, +keywords = {Minimum Spanning Trees, Neuromorphic Computing, Graph Algorithms, Longest Shortest Path}, +location = {Heidelberg, Germany}, +series = {NICE '20} +} +@inproceedings{kwisthout2020, +author = {Kwisthout, Johan and Donselaar, Nils}, +title = {On the Computational Power and Complexity of Spiking Neural Networks}, +year = {2020}, +isbn = {9781450377188}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/3381755.3381760}, +doi = {10.1145/3381755.3381760}, +booktitle = {Proceedings of the Neuro-Inspired Computational Elements Workshop}, +articleno = {4}, +numpages = {7}, +keywords = {neuromorphic computation, spiking neural networks, structural complexity theory}, +location = {Heidelberg, Germany}, +series = {NICE '20} +} +@article{bekolay2014, +author = {Bekolay, Trevor and Bergstra, James and Hunsberger, Eric and Dewolf, Travis and Stewart, Terrence and Rasmussen, Daniel and Choo, Xuan and Voelker, Aaron and Eliasmith, Chris}, +year = {2014}, +month = {01}, +pages = {48}, +title = {Nengo: A Python tool for building large-scale functional brain models}, +volume = {7}, +journal = {Frontiers in neuroinformatics}, +doi = {10.3389/fninf.2013.00048} +} \ No newline at end of file diff --git a/latex/hw2/Appendices/AppA.tex b/latex/hw2/Appendices/AppA.tex new file mode 100644 index 0000000..bf67fc5 --- /dev/null +++ b/latex/hw2/Appendices/AppA.tex @@ -0,0 +1,2 @@ +\section{Appendix Main.py}\label{app:1} +\pythonexternal{../../code/hw2/Main.py} \ No newline at end of file diff --git a/latex/hw2/Appendices/AppB.tex b/latex/hw2/Appendices/AppB.tex new file mode 100644 index 0000000..0bfc212 --- /dev/null +++ b/latex/hw2/Appendices/AppB.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that exports figures to latex}\label{app:2} +\pythonexternal{../../code/hw2/Plot_to_tex.py} \ No newline at end of file diff --git a/latex/hw2/Chapters/Introduction.tex b/latex/hw2/Chapters/Introduction.tex new file mode 100644 index 0000000..faeabc3 --- /dev/null +++ b/latex/hw2/Chapters/Introduction.tex @@ -0,0 +1,2 @@ +\section{Introduction}\label{sec:intro} +% 3 lines max?:) \ No newline at end of file diff --git a/latex/hw2/Chapters/chap1.tex b/latex/hw2/Chapters/chap1.tex new file mode 100644 index 0000000..f02d8c1 --- /dev/null +++ b/latex/hw2/Chapters/chap1.tex @@ -0,0 +1,13 @@ +\section{Genetic Algorithm Performance}\label{sec:1} +To illustrate how the python code exports the figures directly into the report, this second "hw2" is included. Below are the pictures that are created by the code listed in \cref{app:1} and \cref{app:2}. +\begin{figure}[H] + \centering + \includegraphics[width=1\textwidth]{Images/4a.png} + \caption{Performance of some genetic algorithm} +\end{figure} + +\begin{figure}[H] + \centering + \includegraphics[width=1\textwidth]{Images/4b.png} + \caption{Performance of some genetic algorithm} +\end{figure} \ No newline at end of file diff --git a/latex/hw2/Images/4a.png b/latex/hw2/Images/4a.png new file mode 100644 index 0000000000000000000000000000000000000000..bd716da75275eb47ecd56898273eff51e5561c08 GIT binary patch literal 15543 zcmeIZcUV-*wl7)@2nZsW5lJcrKtTjWK!Ong2?|IKiXu4(NQO3|f}#kbAVJAFNRDj+ zB}!J13?j)U=cezd_N=qdz2DyVz3=_~*0*%An%OmLR*f3t7sh<3p{B^j!o`9RVpBSI z`XWMfM1%-E%#85Kk%pfG@E-=JlS*35@Q)Ys)d%qZYaPz%IU&S;8UG<9$|qRDhsT`H z=sIiKn>)K+zHtrNUUqh{v3ItyyuyF;+6^a5dpmIvF%j_t{1(p64$`8c|N6a%{f+CQ zL?m_^A%3KE`h=F->+w!^FRj^9>g4>>Yv)dG-Kun$liwnkW&b&eY^^Kz3VIs-x2*Bs 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Create a matlab listing +\usepackage{listings} +\usepackage{color} %red, green, blue, yellow, cyan, magenta, black, white +\definecolor{mygreen}{RGB}{28,172,0} % color values Red, Green, Blue +\definecolor{mylilas}{RGB}{170,55,241} + +\usepackage[utf8]{inputenc} +\usepackage{geometry} + \geometry{ + a4paper, + total={175mm,265mm}, + left=15mm, + top=15mm, + } +\usepackage{amsmath}%To be able to use split in equation + + +%%%% Include eps files: +\usepackage{amsmath} % need to be on top for eps files +\usepackage{graphicx} +%set the relative location for eps files +\graphicspath{ {/images/} } +\usepackage{listings} +\usepackage{cleveref} %cleverref needs to stand below amsmath package. +\usepackage{graphicx} +\usepackage{float} +%\usepackage{hyperref} +\usepackage{url} %To be able to use url in references +\usepackage{graphicx} +\usepackage{tabularx} % in the preamble +\usepackage{wrapfig} + +\usepackage{algorithm} +\usepackage{algorithmic} + + +% To get side by side pictures:{ +\usepackage{caption} +\usepackage{subcaption} +\usepackage{graphicx} + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Create listings (Python) +% set code color pattern (for python) +% Default fixed font does not support bold face +\DeclareFixedFont{\ttb}{T1}{txtt}{bx}{n}{12} % for bold +\DeclareFixedFont{\ttm}{T1}{txtt}{m}{n}{12} % for normal + +% Custom colors +\usepackage{color} +\definecolor{deepblue}{rgb}{0,0,0.5} +\definecolor{deepred}{rgb}{0.6,0,0} +\definecolor{deepgreen}{rgb}{0,0.5,0} + +\usepackage{listings} + +% Python style for highlighting +\newcommand\pythonstyle{\lstset{ +language=Python, +breaklines=true, % wrap lines +postbreak=\mbox{\textcolor{red}{$\hookrightarrow$}\space}, % wrap lines +basicstyle=\ttm, +otherkeywords={self}, % Add keywords here +keywordstyle=\ttb\color{deepblue}, +emph={MyClass,__init__}, % Custom highlighting +emphstyle=\ttb\color{deepred}, % Custom highlighting style +stringstyle=\color{deepgreen}, +frame=tb, % Any extra options here +showstringspaces=false % +}} + +% Python environment +\lstnewenvironment{python}[1][] +{ +\pythonstyle +\lstset{#1} +} +{} + +% Python for external files +\newcommand\pythonexternal[2][]{{ +\pythonstyle +\lstinputlisting[#1]{#2}}} + +% Python for inline +\newcommand\pythoninline[1]{{\pythonstyle\lstinline!#1!}} + + + + + + + + + +\usepackage{cleveref} %cleverref needs to stand below amsmath package. +\usepackage{appendix} +\crefname{appsec}{Appendix}{Appendices} % refer to appendix as appendix iso as section (use with text in +\title{Example to plot directly into latex} +%\author{Authors:\\a-t-0} + + +\date{19-10-2019} +\begin{document} +\crefname{lstlisting}{listing}{listings} +\Crefname{lstlisting}{Listing}{Listings} +%%%%%%%%%%Configure matlab listing%%%%%%%%%%%%%%%%%% +% Specify matlab listing style +\lstset{language=Matlab,% + %basicstyle=\color{red}, + breaklines=true,% + morekeywords={matlab2tikz}, + keywordstyle=\color{blue},% + morekeywords=[2]{1}, keywordstyle=[2]{\color{black}}, + identifierstyle=\color{black},% + stringstyle=\color{mylilas}, + commentstyle=\color{mygreen},% + showstringspaces=false,%without this there will be a symbol in the places where there is a space + numbers=left,% + numberstyle={\tiny \color{black}},% size of the numbers + numbersep=9pt, % this defines how far the numbers are from the text + emph=[1]{for,end,break},emphstyle=[1]\color{red}, %some words to emphasise + %emph=[2]{word1,word2}, emphstyle=[2]{style}, +} + + +\maketitle +%\setcounter{chapter}{-1} +\input{Chapters/Introduction.tex} %\newpage +\input{Chapters/chap1.tex} %\newpage +%\input{Chapters/Conclusion.tex} %\newpage + + + + + + + + + + + + + + +\bibliographystyle{plain} %plain style +\bibliography{references} +\addcontentsline{toc}{chapter}{Bibliography} + +\begin{appendices} +\crefalias{section}{appsec} +\newpage +\input{Appendices/AppA.tex} \newpage +\input{Appendices/AppB.tex} \newpage +\end{appendices} + +\end{document} diff --git a/latex/hw2/references.bib b/latex/hw2/references.bib new file mode 100644 index 0000000..458bb44 --- /dev/null +++ b/latex/hw2/references.bib @@ -0,0 +1,62 @@ + +@misc{apollo_radiation, + title = {Apollo radiatino analysis}, + howpublished = {\url{http://web.archive.org/web/20160301115931/http://www.braeunig.us/apollo/VABraddose.htm}}, + note = {Accessed: 2018-04-27} +} + +@book{made_to_stick, + author = "C. Heath", + title = "Made to stick" , + publisher = "Random House US", + year = {September 2010}, +} + + +@misc{dataset_sealeavel, + title = {Global Mean Sea Level Time Series (seasonal signals retained)}, + howpublished = {\url{http://sealevel.colorado.edu/content/2018rel1-global-mean-sea-level-time-series-seasonal-signals-retained}}, + note = {Accessed: 2019-09-10} +} + +@misc{lecture_notes, + author = "Dr. Ir. E. Schrama", + title = "Lecture notes on Planetary sciences and Satellite Orbit +Determination" , + publisher = "Delft University of Technology", + year = {September 2019}, +} + +@misc{lecture2, + author = "Dr. D. Stam", + title = "Lecture 2 of AE4890-11 Planetary sciences" , + publisher = "Delft University of Technology", + year = {September 2019}, +} + +@misc{flight_dyn, + author = "M. Naeije", + title = "Flight and Orbital Dynamics" , + publisher = "Delft University of Technology", + year = {July 2018}, +} + +@misc{errors_MSL, + title = {Validation and Estimation of MSL Altimetry Errors}, + howpublished = {\url{https://www.aviso.altimetry.fr/index.php?id=1627}}, + note = {Accessed: 2019-09-14} + } + +@book{solar_cycles, +author = "J. D. Haigh", +title = "The Earth’s Climate and Its Response to Solar Variability", +publisher = "Springer, Berlin, Heidelberg", +year = {Vol 34, 2005} +} + +@book{normality_boundaries, +author = "George and Mallery", +title = "SPSS for Windows Step by Step: A Simple Guide and Reference", +publisher = "Boston: Pearson", +year = {2010} +} \ No newline at end of file diff --git a/latex/hw3/Appendices/AppA.tex b/latex/hw3/Appendices/AppA.tex new file mode 100644 index 0000000..bf67fc5 --- /dev/null +++ b/latex/hw3/Appendices/AppA.tex @@ -0,0 +1,2 @@ +\section{Appendix Main.py}\label{app:1} +\pythonexternal{../../code/hw2/Main.py} \ No newline at end of file diff --git a/latex/hw3/Appendices/AppB.tex b/latex/hw3/Appendices/AppB.tex new file mode 100644 index 0000000..0bfc212 --- /dev/null +++ b/latex/hw3/Appendices/AppB.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that exports figures to latex}\label{app:2} +\pythonexternal{../../code/hw2/Plot_to_tex.py} \ No newline at end of file diff --git a/latex/hw3/Chapters/Introduction.tex b/latex/hw3/Chapters/Introduction.tex new file mode 100644 index 0000000..faeabc3 --- /dev/null +++ b/latex/hw3/Chapters/Introduction.tex @@ -0,0 +1,2 @@ +\section{Introduction}\label{sec:intro} +% 3 lines max?:) \ No newline at end of file diff --git a/latex/hw3/Chapters/chap1.tex b/latex/hw3/Chapters/chap1.tex new file mode 100644 index 0000000..f02d8c1 --- /dev/null +++ b/latex/hw3/Chapters/chap1.tex @@ -0,0 +1,13 @@ +\section{Genetic Algorithm Performance}\label{sec:1} +To illustrate how the python code exports the figures directly into the report, this second "hw2" is included. Below are the pictures that are created by the code listed in \cref{app:1} and \cref{app:2}. +\begin{figure}[H] + \centering + \includegraphics[width=1\textwidth]{Images/4a.png} + \caption{Performance of some genetic algorithm} +\end{figure} + +\begin{figure}[H] + \centering + \includegraphics[width=1\textwidth]{Images/4b.png} + \caption{Performance of some genetic algorithm} +\end{figure} \ No newline at end of file diff --git a/latex/hw3/Images/4a.png b/latex/hw3/Images/4a.png new file mode 100644 index 0000000000000000000000000000000000000000..bd716da75275eb47ecd56898273eff51e5561c08 GIT binary patch literal 15543 zcmeIZcUV-*wl7)@2nZsW5lJcrKtTjWK!Ong2?|IKiXu4(NQO3|f}#kbAVJAFNRDj+ zB}!J13?j)U=cezd_N=qdz2DyVz3=_~*0*%An%OmLR*f3t7sh<3p{B^j!o`9RVpBSI z`XWMfM1%-E%#85Kk%pfG@E-=JlS*35@Q)Ys)d%qZYaPz%IU&S;8UG<9$|qRDhsT`H z=sIiKn>)K+zHtrNUUqh{v3ItyyuyF;+6^a5dpmIvF%j_t{1(p64$`8c|N6a%{f+CQ zL?m_^A%3KE`h=F->+w!^FRj^9>g4>>Yv)dG-Kun$liwnkW&b&eY^^Kz3VIs-x2*Bs 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Create a matlab listing +\usepackage{listings} +\usepackage{color} %red, green, blue, yellow, cyan, magenta, black, white +\definecolor{mygreen}{RGB}{28,172,0} % color values Red, Green, Blue +\definecolor{mylilas}{RGB}{170,55,241} + +\usepackage[utf8]{inputenc} +\usepackage{geometry} + \geometry{ + a4paper, + total={175mm,265mm}, + left=15mm, + top=15mm, + } +\usepackage{amsmath}%To be able to use split in equation + + +%%%% Include eps files: +\usepackage{amsmath} % need to be on top for eps files +\usepackage{graphicx} +%set the relative location for eps files +\graphicspath{ {/images/} } +\usepackage{listings} +\usepackage{cleveref} %cleverref needs to stand below amsmath package. +\usepackage{graphicx} +\usepackage{float} +%\usepackage{hyperref} +\usepackage{url} %To be able to use url in references +\usepackage{graphicx} +\usepackage{tabularx} % in the preamble +\usepackage{wrapfig} + +\usepackage{algorithm} +\usepackage{algorithmic} + + +% To get side by side pictures:{ +\usepackage{caption} +\usepackage{subcaption} +\usepackage{graphicx} + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Create listings (Python) +% set code color pattern (for python) +% Default fixed font does not support bold face +\DeclareFixedFont{\ttb}{T1}{txtt}{bx}{n}{12} % for bold +\DeclareFixedFont{\ttm}{T1}{txtt}{m}{n}{12} % for normal + +% Custom colors +\usepackage{color} +\definecolor{deepblue}{rgb}{0,0,0.5} +\definecolor{deepred}{rgb}{0.6,0,0} +\definecolor{deepgreen}{rgb}{0,0.5,0} + +\usepackage{listings} + +% Python style for highlighting +\newcommand\pythonstyle{\lstset{ +language=Python, +breaklines=true, % wrap lines +postbreak=\mbox{\textcolor{red}{$\hookrightarrow$}\space}, % wrap lines +basicstyle=\ttm, +otherkeywords={self}, % Add keywords here +keywordstyle=\ttb\color{deepblue}, +emph={MyClass,__init__}, % Custom highlighting +emphstyle=\ttb\color{deepred}, % Custom highlighting style +stringstyle=\color{deepgreen}, +frame=tb, % Any extra options here +showstringspaces=false % +}} + +% Python environment +\lstnewenvironment{python}[1][] +{ +\pythonstyle +\lstset{#1} +} +{} + +% Python for external files +\newcommand\pythonexternal[2][]{{ +\pythonstyle +\lstinputlisting[#1]{#2}}} + +% Python for inline +\newcommand\pythoninline[1]{{\pythonstyle\lstinline!#1!}} + + + + + + + + + +\usepackage{cleveref} %cleverref needs to stand below amsmath package. +\usepackage{appendix} +\crefname{appsec}{Appendix}{Appendices} % refer to appendix as appendix iso as section (use with text in +\title{Example to plot directly into latex} +%\author{Authors:\\a-t-0} + + +\date{19-10-2019} +\begin{document} +\crefname{lstlisting}{listing}{listings} +\Crefname{lstlisting}{Listing}{Listings} +%%%%%%%%%%Configure matlab listing%%%%%%%%%%%%%%%%%% +% Specify matlab listing style +\lstset{language=Matlab,% + %basicstyle=\color{red}, + breaklines=true,% + morekeywords={matlab2tikz}, + keywordstyle=\color{blue},% + morekeywords=[2]{1}, keywordstyle=[2]{\color{black}}, + identifierstyle=\color{black},% + stringstyle=\color{mylilas}, + commentstyle=\color{mygreen},% + showstringspaces=false,%without this there will be a symbol in the places where there is a space + numbers=left,% + numberstyle={\tiny \color{black}},% size of the numbers + numbersep=9pt, % this defines how far the numbers are from the text + emph=[1]{for,end,break},emphstyle=[1]\color{red}, %some words to emphasise + %emph=[2]{word1,word2}, emphstyle=[2]{style}, +} + + +\maketitle +%\setcounter{chapter}{-1} +\input{Chapters/Introduction.tex} %\newpage +\input{Chapters/chap1.tex} %\newpage +%\input{Chapters/Conclusion.tex} %\newpage + + + + + + + + + + + + + + +\bibliographystyle{plain} %plain style +\bibliography{references} +\addcontentsline{toc}{chapter}{Bibliography} + +\begin{appendices} +\crefalias{section}{appsec} +\newpage +\input{Appendices/AppA.tex} \newpage +\input{Appendices/AppB.tex} \newpage +\end{appendices} + +\end{document} diff --git a/latex/hw3/references.bib b/latex/hw3/references.bib new file mode 100644 index 0000000..458bb44 --- /dev/null +++ b/latex/hw3/references.bib @@ -0,0 +1,62 @@ + +@misc{apollo_radiation, + title = {Apollo radiatino analysis}, + howpublished = {\url{http://web.archive.org/web/20160301115931/http://www.braeunig.us/apollo/VABraddose.htm}}, + note = {Accessed: 2018-04-27} +} + +@book{made_to_stick, + author = "C. Heath", + title = "Made to stick" , + publisher = "Random House US", + year = {September 2010}, +} + + +@misc{dataset_sealeavel, + title = {Global Mean Sea Level Time Series (seasonal signals retained)}, + howpublished = {\url{http://sealevel.colorado.edu/content/2018rel1-global-mean-sea-level-time-series-seasonal-signals-retained}}, + note = {Accessed: 2019-09-10} +} + +@misc{lecture_notes, + author = "Dr. Ir. E. Schrama", + title = "Lecture notes on Planetary sciences and Satellite Orbit +Determination" , + publisher = "Delft University of Technology", + year = {September 2019}, +} + +@misc{lecture2, + author = "Dr. D. Stam", + title = "Lecture 2 of AE4890-11 Planetary sciences" , + publisher = "Delft University of Technology", + year = {September 2019}, +} + +@misc{flight_dyn, + author = "M. Naeije", + title = "Flight and Orbital Dynamics" , + publisher = "Delft University of Technology", + year = {July 2018}, +} + +@misc{errors_MSL, + title = {Validation and Estimation of MSL Altimetry Errors}, + howpublished = {\url{https://www.aviso.altimetry.fr/index.php?id=1627}}, + note = {Accessed: 2019-09-14} + } + +@book{solar_cycles, +author = "J. D. Haigh", +title = "The Earth’s Climate and Its Response to Solar Variability", +publisher = "Springer, Berlin, Heidelberg", +year = {Vol 34, 2005} +} + +@book{normality_boundaries, +author = "George and Mallery", +title = "SPSS for Windows Step by Step: A Simple Guide and Reference", +publisher = "Boston: Pearson", +year = {2010} +} \ No newline at end of file diff --git a/linux_environment.yml b/linux_environment.yml new file mode 100644 index 0000000..4f3c575 --- /dev/null +++ b/linux_environment.yml @@ -0,0 +1,16 @@ +# run: conda env create --file linux_environment.yml +# include new packages: conda env update --file linux_environment.yml +name: num_astro +dependencies: +- python=3.6 +- anaconda +- conda: + - scipy>=0.18.1 + - numpy>=1.11.2 + - pytest>=3.0 + - matplotlib>=2.2.2 + - tudatpy +- pip +- pip: + # works for regular pip packages + - sklearn diff --git a/windows_environment.yml b/windows_environment.yml new file mode 100644 index 0000000..4f3c575 --- /dev/null +++ b/windows_environment.yml @@ -0,0 +1,16 @@ +# run: conda env create --file linux_environment.yml +# include new packages: conda env update --file linux_environment.yml +name: num_astro +dependencies: +- python=3.6 +- anaconda +- conda: + - scipy>=0.18.1 + - numpy>=1.11.2 + - pytest>=3.0 + - matplotlib>=2.2.2 + - tudatpy +- pip +- pip: + # works for regular pip packages + - sklearn From 73ad4e4fbc1d0e05a954e72aae1b4f0bcc3d0893 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Sun, 8 Nov 2020 20:57:52 +0100 Subject: [PATCH 02/57] Created basic report structure. --- .gitignore | 75 ----------------- code/hw1/test/test_main.py | 8 +- latex/hw1/Appendices/AppA.tex | 4 +- latex/hw1/Appendices/AppB.tex | 2 + latex/hw1/Chapters/Introduction.tex | 2 + latex/hw1/Chapters/chap0.tex | 3 - latex/hw1/Chapters/chap1.tex | 13 +++ latex/hw1/Images/4a.png | Bin 0 -> 15543 bytes latex/hw1/Images/4b.png | Bin 0 -> 40319 bytes latex/hw1/Images/4c.png | Bin 0 -> 29724 bytes latex/hw1/Tables/table_1.csv | 5 ++ latex/hw1/main.tex | 125 +++++++++++++++++++++------- latex/hw1/references.bib | 120 +++++++++++++------------- linux_environment.yml | 19 ++--- windows_environment.yml | 6 +- 15 files changed, 189 insertions(+), 193 deletions(-) delete mode 100644 .gitignore create mode 100644 latex/hw1/Appendices/AppB.tex create mode 100644 latex/hw1/Chapters/Introduction.tex delete mode 100644 latex/hw1/Chapters/chap0.tex create mode 100644 latex/hw1/Chapters/chap1.tex create mode 100644 latex/hw1/Images/4a.png create mode 100644 latex/hw1/Images/4b.png create mode 100644 latex/hw1/Images/4c.png create mode 100644 latex/hw1/Tables/table_1.csv diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 003f4bd..0000000 --- a/.gitignore +++ /dev/null @@ -1,75 +0,0 @@ -# -# Private -# -# Do not track user-applications by default. -tudatApplications/ - -# -# CMake -# -CMakeCache.txt -CMakeLists.txt.user -CMakeFiles -CMakeScripts -Makefile -cmake_install.cmake -install_manifest.txt - -# -# C++ -# -# Compiled Object files -*.slo -*.lo -*.o -*.obj - -# Precompiled Headers -*.gch -*.pch - -# Compiled Dynamic libraries -*.so -*.dylib -*.dll - -# Fortran module files -*.mod - -# Output files -*.dat - -# Jupyter checkpoints -*.ipynb_checkpoints/ - -# Compiled Static libraries -*.lai -*.la -*.a -*.lib - -# Executables -*.exe -*.out -*.app - -# -# Build and doxygen related -# -Documentation/ -doc/doxygen/html/ -doc/doxygen/latex/ -build*/ -bin/ -lib/ -boost/ - -# -# Editor related -# -*~ -*# -.#* -*.autosave -*.~?~ -*.save diff --git a/code/hw1/test/test_main.py b/code/hw1/test/test_main.py index 4c4f851..8aab9ff 100644 --- a/code/hw1/test/test_main.py +++ b/code/hw1/test/test_main.py @@ -16,17 +16,11 @@ def __init__(self, *args, **kwargs): def get_script_dir(self): return os.path.dirname(__file__) - # tests unit test on main class + # tests unit test on addTwo function of main class def test_addTwo(self): expected_result = 7 result = self.main.addTwo(5) self.assertEqual(expected_result,result) - # Example: Verifies an exception is thrown in nqueens for n <5 - #def test_ThreeQueens(self): - # with self.assertRaises(Exception) as context: - # n_queens = N_queens(4) - # self.assertTrue('There are no non-trivial solutions for n<5, please increase n and try again.' in str(context.exception)) - if __name__ == '__main__': unittest.main() \ No newline at end of file diff --git a/latex/hw1/Appendices/AppA.tex b/latex/hw1/Appendices/AppA.tex index c37e4cc..bf67fc5 100644 --- a/latex/hw1/Appendices/AppA.tex +++ b/latex/hw1/Appendices/AppA.tex @@ -1,2 +1,2 @@ -\section{Appendix: Additional Optional Research}\label{app:1} -\subsection{Introduction} \ No newline at end of file +\section{Appendix Main.py}\label{app:1} +\pythonexternal{../../code/hw2/Main.py} \ No newline at end of file diff --git a/latex/hw1/Appendices/AppB.tex b/latex/hw1/Appendices/AppB.tex new file mode 100644 index 0000000..0bfc212 --- /dev/null +++ b/latex/hw1/Appendices/AppB.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that exports figures to latex}\label{app:2} +\pythonexternal{../../code/hw2/Plot_to_tex.py} \ No newline at end of file diff --git a/latex/hw1/Chapters/Introduction.tex b/latex/hw1/Chapters/Introduction.tex new file mode 100644 index 0000000..faeabc3 --- /dev/null +++ b/latex/hw1/Chapters/Introduction.tex @@ -0,0 +1,2 @@ +\section{Introduction}\label{sec:intro} +% 3 lines max?:) \ No newline at end of file diff --git a/latex/hw1/Chapters/chap0.tex b/latex/hw1/Chapters/chap0.tex deleted file mode 100644 index 3a6201a..0000000 --- a/latex/hw1/Chapters/chap0.tex +++ /dev/null @@ -1,3 +0,0 @@ -\section{Background} -\subsection{Graph Problems} -Graphical structure are widely used in computer science as they allow for a concise description of data. They also allow to transfer all the knowledge about graph theory to the computational domain. This in turn has lead to a considerable interest in solving graph problems, such as Shortest Path, Minimum Spanning Tree, Vertex Cover and many more \citep{Shrinivas2010}. \ No newline at end of file diff --git a/latex/hw1/Chapters/chap1.tex b/latex/hw1/Chapters/chap1.tex new file mode 100644 index 0000000..f02d8c1 --- /dev/null +++ b/latex/hw1/Chapters/chap1.tex @@ -0,0 +1,13 @@ +\section{Genetic Algorithm Performance}\label{sec:1} +To illustrate how the python code exports the figures directly into the report, this second "hw2" is included. 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Rc#^%Sg literal 0 HcmV?d00001 diff --git a/latex/hw1/Tables/table_1.csv b/latex/hw1/Tables/table_1.csv new file mode 100644 index 0000000..470eb01 --- /dev/null +++ b/latex/hw1/Tables/table_1.csv @@ -0,0 +1,5 @@ +\textbf{item} & \textbf{amount} & \textbf{id} \\ \hline +10 & 2 & 3 \\ \hline +1.4 & 5 & hangryy \\ \hline +deep purple & ultraviolent & yellowish \\ \hline +swag & swagga & swaggalini \\ \hline diff --git a/latex/hw1/main.tex b/latex/hw1/main.tex index fc42054..657bf22 100644 --- a/latex/hw1/main.tex +++ b/latex/hw1/main.tex @@ -3,12 +3,19 @@ \usepackage{caption} \usepackage{subcaption} \usepackage{graphicx} -\graphicspath{ {/Images/} } -\usepackage{epstopdf} +\graphicspath{ {latex/Images/} } +\usepackage{epstopdf} -\usepackage{natbib} -\bibliographystyle{abbrvnat} -\setcitestyle{authoryear,open={(},close={)}} %Citation-related commands + +%% sidebyside images + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Create listings (Matlab) +% % Create a matlab listing +\usepackage{listings} +\usepackage{color} %red, green, blue, yellow, cyan, magenta, black, white +\definecolor{mygreen}{RGB}{28,172,0} % color values Red, Green, Blue +\definecolor{mylilas}{RGB}{170,55,241} \usepackage[utf8]{inputenc} \usepackage{geometry} @@ -18,25 +25,98 @@ left=15mm, top=15mm, } +\usepackage{amsmath}%To be able to use split in equation + +%%%% Include eps files: +\usepackage{amsmath} % need to be on top for eps files +\usepackage{graphicx} +%set the relative location for eps files +\graphicspath{ {/images/} } +\usepackage{listings} \usepackage{cleveref} %cleverref needs to stand below amsmath package. +\usepackage{graphicx} \usepackage{float} +%\usepackage{hyperref} \usepackage{url} %To be able to use url in references +\usepackage{graphicx} \usepackage{tabularx} % in the preamble \usepackage{wrapfig} + \usepackage{algorithm} \usepackage{algorithmic} -\usepackage{appendix} -\crefname{appsec}{Appendix}{Appendices} % refer to appendix as appendix iso as section (use with text in -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Create listings (Matlab) -% % Create a matlab listing +% To get side by side pictures:{ +\usepackage{caption} +\usepackage{subcaption} +\usepackage{graphicx} + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Create listings (Python) +% set code color pattern (for python) +% Default fixed font does not support bold face +\DeclareFixedFont{\ttb}{T1}{txtt}{bx}{n}{12} % for bold +\DeclareFixedFont{\ttm}{T1}{txtt}{m}{n}{12} % for normal + +% Custom colors +\usepackage{color} +\definecolor{deepblue}{rgb}{0,0,0.5} +\definecolor{deepred}{rgb}{0.6,0,0} +\definecolor{deepgreen}{rgb}{0,0.5,0} + \usepackage{listings} -\usepackage{color} %red, green, blue, yellow, cyan, magenta, black, white -\definecolor{mygreen}{RGB}{28,172,0} % color values Red, Green, Blue -\definecolor{mylilas}{RGB}{170,55,241} +% Python style for highlighting +\newcommand\pythonstyle{\lstset{ +language=Python, +breaklines=true, % wrap lines +postbreak=\mbox{\textcolor{red}{$\hookrightarrow$}\space}, % wrap lines +basicstyle=\ttm, +otherkeywords={self}, % Add keywords here +keywordstyle=\ttb\color{deepblue}, +emph={MyClass,__init__}, % Custom highlighting +emphstyle=\ttb\color{deepred}, % Custom highlighting style +stringstyle=\color{deepgreen}, +frame=tb, % Any extra options here +showstringspaces=false % +}} + +% Python environment +\lstnewenvironment{python}[1][] +{ +\pythonstyle +\lstset{#1} +} +{} + +% Python for external files +\newcommand\pythonexternal[2][]{{ +\pythonstyle +\lstinputlisting[#1]{#2}}} + +% Python for inline +\newcommand\pythoninline[1]{{\pythonstyle\lstinline!#1!}} + + + + + + + + + +\usepackage{cleveref} %cleverref needs to stand below amsmath package. +\usepackage{appendix} +\crefname{appsec}{Appendix}{Appendices} % refer to appendix as appendix iso as section (use with text in +\title{Example to plot directly into latex} +%\author{Authors:\\a-t-0} + + +\date{19-10-2019} +\begin{document} \crefname{lstlisting}{listing}{listings} \Crefname{lstlisting}{Listing}{Listings} %%%%%%%%%%Configure matlab listing%%%%%%%%%%%%%%%%%% @@ -57,20 +137,12 @@ emph=[1]{for,end,break},emphstyle=[1]\color{red}, %some words to emphasise %emph=[2]{word1,word2}, emphstyle=[2]{style}, } -%%%%%%%%%%Document%%%%%%%%%%%%%%%%%% -\title{Research Proposal\\\large SOW-MKI96 Neuromorphic Computing} -\author{Authors:\\ Victoria Bosch (s1056634)\\Arne Diehl (s4451392)\\Daphne Smits (s1005509)\\Akke Toeter (s1047775)} -\date{04-11-2020} -\begin{document} + \maketitle %\setcounter{chapter}{-1} -\input{Chapters/chap0.tex} %\newpage -%\input{Chapters/Methods.tex} %\newpage -%\input{Chapters/Significance} %\newpage -%\input{Chapters/1h.tex} %\newpage -%\input{Chapters/1i.tex} %\newpage -%\input{Chapters/1j.tex} %\newpage +\input{Chapters/Introduction.tex} %\newpage +\input{Chapters/chap1.tex} %\newpage %\input{Chapters/Conclusion.tex} %\newpage @@ -86,18 +158,15 @@ -% \bibliographystyle{plain} %plain style +\bibliographystyle{plain} %plain style \bibliography{references} \addcontentsline{toc}{chapter}{Bibliography} - \begin{appendices} \crefalias{section}{appsec} \newpage \input{Appendices/AppA.tex} \newpage -%\input{Appendices/AppB.tex} \newpage -%\input{Appendices/AppC.tex} \newpage -%\input{Appendices/AppD.tex} \newpage +\input{Appendices/AppB.tex} \newpage \end{appendices} \end{document} diff --git a/latex/hw1/references.bib b/latex/hw1/references.bib index eeb4ed2..458bb44 100644 --- a/latex/hw1/references.bib +++ b/latex/hw1/references.bib @@ -1,68 +1,62 @@ -@misc{ali2019spiking, - title={A spiking neural algorithm for the Network Flow problem}, - author={Abdullahi Ali and Johan Kwisthout}, - year={2019}, - eprint={1911.13097}, - archivePrefix={arXiv}, - primaryClass={cs.NE} + +@misc{apollo_radiation, + title = {Apollo radiatino analysis}, + howpublished = {\url{http://web.archive.org/web/20160301115931/http://www.braeunig.us/apollo/VABraddose.htm}}, + note = {Accessed: 2018-04-27} +} + +@book{made_to_stick, + author = "C. Heath", + title = "Made to stick" , + publisher = "Random House US", + year = {September 2010}, } -@article{Shrinivas2010, -author = {S G Shrinivas and Vetrivel, S and Elango, N}, -year = {2010}, -month = {09}, -pages = {}, -title = {APPLICATIONS OF GRAPH THEORY IN COMPUTER SCIENCE AN OVERVIEW}, -volume = {2}, -journal = {International Journal of Engineering Science and Technology} + + +@misc{dataset_sealeavel, + title = {Global Mean Sea Level Time Series (seasonal signals retained)}, + howpublished = {\url{http://sealevel.colorado.edu/content/2018rel1-global-mean-sea-level-time-series-seasonal-signals-retained}}, + note = {Accessed: 2019-09-10} } -@INPROCEEDINGS{yakopcic2020, - author={C. {Yakopcic} and N. {Rahman} and T. {Atahary} and T. M. {Taha} and S. {Douglass}}, - booktitle={2020 Design, Automation Test in Europe Conference Exhibition (DATE)}, - title={Solving Constraint Satisfaction Problems Using the Loihi Spiking Neuromorphic Processor}, - year={2020}, - volume={}, - number={}, - pages={1079-1084}, - doi={10.23919/DATE48585.2020.9116227}} -@inproceedings{kay2020, -author = {Kay, Bill and Date, Prasanna and Schuman, Catherine}, -title = {Neuromorphic Graph Algorithms: Extracting Longest Shortest Paths and Minimum Spanning Trees}, -year = {2020}, -isbn = {9781450377188}, -publisher = {Association for Computing Machinery}, -address = {New York, NY, USA}, -url = {https://doi.org/10.1145/3381755.3381762}, -doi = {10.1145/3381755.3381762}, -booktitle = {Proceedings of the Neuro-Inspired Computational Elements Workshop}, -articleno = {6}, -numpages = {6}, -keywords = {Minimum Spanning Trees, Neuromorphic Computing, Graph Algorithms, Longest Shortest Path}, -location = {Heidelberg, Germany}, -series = {NICE '20} + +@misc{lecture_notes, + author = "Dr. Ir. E. Schrama", + title = "Lecture notes on Planetary sciences and Satellite Orbit +Determination" , + publisher = "Delft University of Technology", + year = {September 2019}, } -@inproceedings{kwisthout2020, -author = {Kwisthout, Johan and Donselaar, Nils}, -title = {On the Computational Power and Complexity of Spiking Neural Networks}, -year = {2020}, -isbn = {9781450377188}, -publisher = {Association for Computing Machinery}, -address = {New York, NY, USA}, -url = {https://doi.org/10.1145/3381755.3381760}, -doi = {10.1145/3381755.3381760}, -booktitle = {Proceedings of the Neuro-Inspired Computational Elements Workshop}, -articleno = {4}, -numpages = {7}, -keywords = {neuromorphic computation, spiking neural networks, structural complexity theory}, -location = {Heidelberg, Germany}, -series = {NICE '20} + +@misc{lecture2, + author = "Dr. D. Stam", + title = "Lecture 2 of AE4890-11 Planetary sciences" , + publisher = "Delft University of Technology", + year = {September 2019}, } -@article{bekolay2014, -author = {Bekolay, Trevor and Bergstra, James and Hunsberger, Eric and Dewolf, Travis and Stewart, Terrence and Rasmussen, Daniel and Choo, Xuan and Voelker, Aaron and Eliasmith, Chris}, -year = {2014}, -month = {01}, -pages = {48}, -title = {Nengo: A Python tool for building large-scale functional brain models}, -volume = {7}, -journal = {Frontiers in neuroinformatics}, -doi = {10.3389/fninf.2013.00048} + +@misc{flight_dyn, + author = "M. Naeije", + title = "Flight and Orbital Dynamics" , + publisher = "Delft University of Technology", + year = {July 2018}, +} + +@misc{errors_MSL, + title = {Validation and Estimation of MSL Altimetry Errors}, + howpublished = {\url{https://www.aviso.altimetry.fr/index.php?id=1627}}, + note = {Accessed: 2019-09-14} + } + +@book{solar_cycles, +author = "J. D. Haigh", +title = "The Earth’s Climate and Its Response to Solar Variability", +publisher = "Springer, Berlin, Heidelberg", +year = {Vol 34, 2005} +} + +@book{normality_boundaries, +author = "George and Mallery", +title = "SPSS for Windows Step by Step: A Simple Guide and Reference", +publisher = "Boston: Pearson", +year = {2010} } \ No newline at end of file diff --git a/linux_environment.yml b/linux_environment.yml index 4f3c575..174cc5d 100644 --- a/linux_environment.yml +++ b/linux_environment.yml @@ -1,16 +1,7 @@ -# run: conda env create --file linux_environment.yml -# include new packages: conda env update --file linux_environment.yml -name: num_astro +name: tudat-space +channels: + - conda-forge + - tudat-team dependencies: -- python=3.6 -- anaconda -- conda: - - scipy>=0.18.1 - - numpy>=1.11.2 - - pytest>=3.0 - - matplotlib>=2.2.2 - tudatpy -- pip -- pip: - # works for regular pip packages - - sklearn + - matplotlib diff --git a/windows_environment.yml b/windows_environment.yml index 4f3c575..32b2b3e 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -1,6 +1,10 @@ # run: conda env create --file linux_environment.yml # include new packages: conda env update --file linux_environment.yml name: num_astro +channels: + - defaults + - anaconda + - tudat-team dependencies: - python=3.6 - anaconda @@ -13,4 +17,4 @@ dependencies: - pip - pip: # works for regular pip packages - - sklearn + - sklearn \ No newline at end of file From 790f30bd48949e8a50062a1772c15fe8bf080885 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Sun, 8 Nov 2020 20:59:04 +0100 Subject: [PATCH 03/57] Removed original notebook file duplicates. --- Assignment1/juice_propagation_Q1.ipynb | 355 ------------------------- Assignment1/juice_propagation_Q4.ipynb | 335 ----------------------- 2 files changed, 690 deletions(-) delete mode 100644 Assignment1/juice_propagation_Q1.ipynb delete mode 100644 Assignment1/juice_propagation_Q4.ipynb diff --git a/Assignment1/juice_propagation_Q1.ipynb b/Assignment1/juice_propagation_Q1.ipynb deleted file mode 100644 index e827102..0000000 --- a/Assignment1/juice_propagation_Q1.ipynb +++ /dev/null @@ -1,355 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "# Assignment 1 - Propagation Settings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "''' \n", - "Copyright (c) 2010-2020, Delft University of Technology\n", - "All rigths reserved\n", - "\n", - "This file is part of the Tudat. Redistribution and use in source and \n", - "binary forms, with or without modification, are permitted exclusively\n", - "under the terms of the Modified BSD license. You should have received\n", - "a copy of the license with this file. If not, please or visit:\n", - "http://tudat.tudelft.nl/LICENSE.\n", - "'''\n", - "\n", - "import numpy as np\n", - "from tudatpy import elements\n", - "from tudatpy.io import save2txt\n", - "from tudatpy.kernel import constants\n", - "from tudatpy.kernel.interface import spice_interface\n", - "from tudatpy.kernel.simulation import environment_setup\n", - "from tudatpy.kernel.simulation import propagation_setup\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "# # student number: 1244779 --> 1244ABC\n", - "A = XXXX\n", - "B = XXXX\n", - "C = XXXX\n", - "\n", - "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", - " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", - "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Create environment, vehicle, accelerations, and propagation settings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# CREATE ENVIRONMENT ######################################################\n", - "###########################################################################\n", - "\n", - "# Load spice kernels.\n", - "spice_interface.load_standard_kernels()\n", - "\n", - "# Create settings for celestial bodies\n", - "bodies_to_create = [\"Ganymede\"]\n", - "global_frame_origin = \"Ganymede\"\n", - "global_frame_orientation = \"ECLIPJ2000\"\n", - "body_settings = environment_setup.get_default_body_settings(\n", - " bodies_to_create, global_frame_origin, global_frame_orientation)\n", - "\n", - "# Add Ganymede exponential atmosphere\n", - "density_scale_height = 40.0E3\n", - "density_at_zero_altitude = 2.0E-9\n", - "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", - " density_scale_height, density_at_zero_altitude)\n", - "\n", - "bodies = environment_setup.create_system_of_bodies(body_settings)\n", - "\n", - "###########################################################################\n", - "# CREATE VEHICLE ##########################################################\n", - "###########################################################################\n", - "\n", - "# Create vehicle object\n", - "bodies.create_empty_body( \"JUICE\" )\n", - "\n", - "# Set mass of vehicle\n", - "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", - " \n", - "# Create aerodynamic coefficients interface\n", - "reference_area = 100.0\n", - "drag_coefficient = 1.2\n", - "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", - " reference_area,[drag_coefficient,0,0] )\n", - "environment_setup.add_aerodynamic_coefficient_interface(\n", - " bodies, \"JUICE\", aero_coefficient_settings )\n", - "\n", - "###########################################################################\n", - "# CREATE ACCELERATIONS ####################################################\n", - "###########################################################################\n", - "\n", - "# Define bodies that are propagated, and their central bodies of propagation.\n", - "bodies_to_propagate = [\"JUICE\"]\n", - "central_bodies = [\"Ganymede\"]\n", - "\n", - "# Define accelerations acting on vehicle.\n", - "acceleration_settings_on_vehicle = dict(\n", - " XXXX\n", - ")\n", - "\n", - "# Create global accelerations dictionary.\n", - "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", - "\n", - "# Create acceleration models.\n", - "acceleration_models = propagation_setup.create_acceleration_models(\n", - " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", - "\n", - "\n", - "###########################################################################\n", - "# CREATE PROPAGATION SETTINGS #############################################\n", - "###########################################################################\n", - "\n", - "# Define initial state.\n", - "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", - " target_body_name=\"JUICE\",\n", - " observer_body_name=\"Ganymede\",\n", - " reference_frame_name=\"ECLIPJ2000\",\n", - " aberration_corrections=\"NONE\",\n", - " ephemeris_time= simulation_start_epoch )\n", - "\n", - "dependent_variables_to_save = [\n", - " propagation_setup.dependent_variable.keplerian_state(\n", - " \"JUICE\", \"Ganymede\")\n", - " ]\n", - "\n", - "# Create propagation settings.\n", - "propagator_settings = propagation_setup.propagator.translational(\n", - " central_bodies,\n", - " acceleration_models,\n", - " bodies_to_propagate,\n", - " system_initial_state,\n", - " simulation_end_epoch,\n", - " output_variables = dependent_variables_to_save\n", - ")\n", - " \n", - "# Create numerical integrator settings.\n", - "fixed_step_size = 10.0\n", - "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", - " simulation_start_epoch,\n", - " fixed_step_size\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Propagate Orbit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "# Create simulation object and propagate dynamics.\n", - "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", - " bodies, integrator_settings, propagator_settings, True)\n", - "\n", - "simulation_result = dynamics_simulator.state_history\n", - "dependent_variables = dynamics_simulator.dependent_variable_history" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Print final propagation time and state" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", - "###########################################################################\n", - "\n", - "final_time_step=list(simulation_result.keys())[-1]\n", - "first_time_step=list(simulation_result.keys())[0]\n", - "\n", - "print(\n", - " f\"\"\"\n", - "JUICE Propagation Results.\n", - "\n", - "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", - "Final Cartesian state of JUICE is [m]: \\n{\n", - " simulation_result[final_time_step][:]}\n", - "\n", - " \"\"\"\n", - ")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Save Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# SAVE RESULTS ############################################################\n", - "###########################################################################\n", - "\n", - "save2txt(solution=simulation_result,\n", - " filename=\"JUICEPropagationHistory_Q1.dat\",\n", - " directory=\"./\", # default = \"./\" \n", - " column_names=None, # default = None \n", - " )\n", - "\n", - "save2txt(solution=dependent_variables,\n", - " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", - " directory=\"./\", # default = \"./\" \n", - " column_names=None, # default = None \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Plot Results\n", - "\n", - "For inspiration see: \n", - "\n", - "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# PLOT RESULTS ############################################################\n", - "###########################################################################\n", - "\n", - "# Extract time and Kepler elements from dependent variables\n", - "kepler_elements = np.vstack(list(dependent_variables.values())\n", - " \n", - "# Kepler Elements\n", - "# 0: semi-major axis\n", - "# 1: eccentricity\n", - "# 2: inclination\n", - "# 3: argument of periapsis\n", - "# 4: right ascension of the ascending node\n", - "# 5: true anomaly\n", - "\n", - "time = dependent_variables.keys()\n", - "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", - "\n", - "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", - "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", - "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", - "\n", - "\n", - "# Set font size of figures\n", - "font_size = 20\n", - " \n", - "plt.rcParams.update({'font.size': font_size}) \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Edit Metadata", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Assignment1/juice_propagation_Q4.ipynb b/Assignment1/juice_propagation_Q4.ipynb deleted file mode 100644 index 7b3bbcd..0000000 --- a/Assignment1/juice_propagation_Q4.ipynb +++ /dev/null @@ -1,335 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "# Assignment 1 - Propagation Settings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "''' \n", - "Copyright (c) 2010-2020, Delft University of Technology\n", - "All rigths reserved\n", - "\n", - "This file is part of the Tudat. Redistribution and use in source and \n", - "binary forms, with or without modification, are permitted exclusively\n", - "under the terms of the Modified BSD license. You should have received\n", - "a copy of the license with this file. If not, please or visit:\n", - "http://tudat.tudelft.nl/LICENSE.\n", - "'''\n", - "\n", - "import numpy as np\n", - "from tudatpy import elements\n", - "from tudatpy.io import save2txt\n", - "from tudatpy.kernel import constants\n", - "from tudatpy.kernel.interface import spice_interface\n", - "from tudatpy.kernel.simulation import environment_setup\n", - "from tudatpy.kernel.simulation import propagation_setup\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "# student number: 1244779 --> 1244ABC\n", - "A = XXXX\n", - "B = XXXX\n", - "C = XXXX\n", - "\n", - "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", - " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", - "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Create Environment and Vehicle" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# CREATE ENVIRONMENT ######################################################\n", - "###########################################################################\n", - "\n", - "# Load spice kernels.\n", - "spice_interface.load_standard_kernels()\n", - "\n", - "# Create body objects.\n", - "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", - "global_frame_origin = \"SSB\"\n", - "global_frame_orientation = \"ECLIPJ2000\"\n", - "body_settings = environment_setup.get_default_body_settings(\n", - " bodies_to_create, global_frame_origin, global_frame_orientation) \n", - "\n", - "# Add Ganymede exponential atmosphere \n", - "density_scale_height = 40.0E3\n", - "density_at_zero_altitude = 2.0E-9\n", - "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", - " density_scale_height, density_at_zero_altitude)\n", - "\n", - "bodies = environment_setup.create_system_of_bodies(body_settings)\n", - "\n", - "###########################################################################\n", - "# CREATE VEHICLE ##########################################################\n", - "###########################################################################\n", - "\n", - "# Create vehicle object\n", - "bodies.create_empty_body( \"JUICE\" )\n", - "\n", - "# Set mass of vehicle\n", - "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", - "\n", - "# Create aerodynamic coefficients interface\n", - "reference_area = 100.0\n", - "drag_coefficient = 1.2\n", - "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", - " reference_area,[drag_coefficient,0,0] )\n", - "environment_setup.add_aerodynamic_coefficient_interface(\n", - " bodies, \"JUICE\", aero_coefficient_settings );" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Propagate Dynamics for various cases" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "cases = ['unperturbed', 'case_i', 'case_ii']\n", - "\n", - "\"\"\"\n", - "unperturbed: Ganymede PM\n", - "\n", - "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", - "\n", - "case_ii: Ganymede PM, Ganymede aerodynamic\n", - "\"\"\"\n", - "\n", - "simulation_results_dict = dict()\n", - "dependent_variables_dict = dict()\n", - "for case in cases: \n", - " ###########################################################################\n", - " # CREATE ACCELERATIONS ####################################################\n", - " ###########################################################################\n", - "\n", - " # Define bodies that are propagated.\n", - " bodies_to_propagate = [\"JUICE\"]\n", - "\n", - " # Define central bodies.\n", - " central_bodies = [\"Ganymede\"]\n", - "\n", - " # Define accelerations acting on vehicle.\n", - " if case == 'unperturbed':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX\n", - " )\n", - " if case == 'case_i':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX,\n", - " Jupiter = XXXX\n", - " )\n", - " if case == 'case_ii':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX\n", - " )\n", - "\n", - " # Create global accelerations dictionary.\n", - " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", - "\n", - " # Create acceleration models.\n", - " acceleration_models = propagation_setup.create_acceleration_models(\n", - " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", - "\n", - "\n", - " ###########################################################################\n", - " # CREATE PROPAGATION SETTINGS #############################################\n", - " ###########################################################################\n", - "\n", - " # Define initial state.\n", - " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", - " target_body_name=\"JUICE\",\n", - " observer_body_name=\"Ganymede\",\n", - " reference_frame_name=\"ECLIPJ2000\",\n", - " aberration_corrections=\"NONE\",\n", - " ephemeris_time= simulation_start_epoch )\n", - "\n", - " # Save magnitude of perturbations for both cases\n", - " if case == 'unperturbed':\n", - " dependent_variables_to_save = [ ]\n", - " if case == 'case_i':\n", - " dependent_variables_to_save = [ \n", - " propagation_setup.dependent_variable.XXXX\n", - " ]\n", - " if case == 'case_ii':\n", - " dependent_variables_to_save = [ \n", - " propagation_setup.dependent_variable.XXXX\n", - " ]\n", - "\n", - " # Create propagation settings.\n", - " propagator_settings = propagation_setup.propagator.translational(\n", - " central_bodies,\n", - " acceleration_models,\n", - " bodies_to_propagate,\n", - " system_initial_state,\n", - " simulation_end_epoch,\n", - " output_variables = dependent_variables_to_save\n", - " )\n", - "\n", - " # Create numerical integrator settings.\n", - " fixed_step_size = 10.0\n", - " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", - " simulation_start_epoch,\n", - " fixed_step_size\n", - " )\n", - "\n", - " ###########################################################################\n", - " # PROPAGATE ORBIT #########################################################\n", - " ###########################################################################\n", - "\n", - " # Create simulation object and propagate dynamics.\n", - " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", - " bodies, integrator_settings, propagator_settings)\n", - " \n", - " simulation_results_dict[case] = dynamics_simulator.state_history\n", - " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", - "\n", - " ###########################################################################\n", - " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", - " ###########################################################################\n", - "\n", - " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", - " first_time_step=list(simulation_results_dict[case].keys())[0]\n", - "\n", - " print(\n", - " f\"\"\"\n", - " JUICE Propagation Results of {case}.\n", - "\n", - " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", - " Final Cartesian state of JUICE is [m]: \\n{\n", - " simulation_results_dict[case][final_time_step][:]}\n", - "\n", - " \"\"\"\n", - " )\n", - "\n", - " ###########################################################################\n", - " # SAVE RESULTS ############################################################\n", - " ###########################################################################\n", - " \n", - "# save2txt(solution=simulation_result,\n", - "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", - "# directory=\"./\", # default = \"./\" \n", - "# column_names=None, # default = None \n", - "# )\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Pre-process Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", - "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", - "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", - "\n", - "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", - "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", - "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", - "\n", - "difference_in_cartesian_position = XXXX" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Plot Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Edit Metadata", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 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project1}/logo.eps (100%) rename latex/{hw1 => project1}/main.tex (100%) rename latex/{hw1 => project1}/references.bib (100%) rename latex/{hw2 => project2}/Appendices/AppA.tex (100%) rename latex/{hw2 => project2}/Appendices/AppB.tex (100%) rename latex/{hw2 => project2}/Chapters/Introduction.tex (100%) rename latex/{hw2 => project2}/Chapters/chap1.tex (100%) rename latex/{hw2 => project2}/Images/4a.png (100%) rename latex/{hw1 => project2}/Images/4b.png (100%) rename latex/{hw1 => project2}/Images/4c.png (100%) rename latex/{hw2 => project2}/Tables/table_1.csv (100%) rename latex/{hw2 => project2}/logo.eps (100%) rename latex/{hw2 => project2}/main.tex (100%) rename latex/{hw2 => project2}/references.bib (100%) rename latex/{hw3 => project3}/Appendices/AppA.tex (100%) rename latex/{hw3 => project3}/Appendices/AppB.tex (100%) rename latex/{hw3 => project3}/Chapters/Introduction.tex (100%) rename latex/{hw3 => project3}/Chapters/chap1.tex (100%) rename latex/{hw3 => project3}/Images/4a.png (100%) rename latex/{hw2 => project3}/Images/4b.png (100%) rename latex/{hw2 => project3}/Images/4c.png (100%) rename latex/{hw3 => project3}/Tables/table_1.csv (100%) rename latex/{hw3 => project3}/logo.eps (100%) rename latex/{hw3 => project3}/main.tex (100%) rename latex/{hw3 => project3}/references.bib (100%) diff --git a/README.md b/README.md index 69224b9..595f5bc 100644 --- a/README.md +++ b/README.md @@ -22,14 +22,14 @@ conda env create --file linux_environment.yml 2. Activate the conda environment you created ``` -conda activate neumo_project +conda activate num_astro ``` ## usage: do every run: -3. Performes run of main code (in `main.py`, called from `__main__.py`) +3. Performe a run for assignment 1 (named project1) of main code (in `main.py`, called from `__main__.py`) ``` -python -m code.project1.src +python -m code.project 1.src ``` ## Testing diff --git a/__pycache__/__init__.cpython-38.pyc b/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..21cfc4aa7daf5c9d2fe1703fea7080da7f0b077c GIT binary patch literal 197 zcmWIL<>g`k0=?gh;z9Id5P=LBfgA@QE@lA|DGb33nv8xc8Hzx{2;x_kenx(7s(zxr zOMY@`Zfaghv3_Y@roKyRPFjh69#EtxGdVFQKC!r@C|^H4vqZn3D6=fFBvs!JCg%u} zN~z3C%*{+Lb}TN=OwR+G6K`Z-WS}1(pP83g5+AQuPLE=kQT1_@=Q KCYMZHrUC%CH6~5~ diff --git a/code/hw1/__pycache__/__init__.cpython-38.pyc b/code/hw1/__pycache__/__init__.cpython-38.pyc deleted file mode 100644 index c88ec3e4a99f8e81e40e7ea54da101dc09a1b049..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 203 zcmWIL<>g`kg2p!q@gVv!h(HF6K#l_t7qb9~6oz01O-8?!3`HPe1o5jtKO;XkRX+tb8S*DtlSC^1LZGcPT_C^xYrGe1u^ 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a/latex/hw3/references.bib b/latex/project3/references.bib similarity index 100% rename from latex/hw3/references.bib rename to latex/project3/references.bib From 56821507fd940dc764c440485f0f76eff2b16892 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Sun, 8 Nov 2020 21:42:57 +0100 Subject: [PATCH 05/57] Updated gitignore. --- gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/gitignore b/gitignore index e37a2bb..1a05cfc 100644 --- a/gitignore +++ b/gitignore @@ -209,3 +209,6 @@ boost/ *.autosave *.~?~ *.save + + +*.ipynb_checkpoints* \ No newline at end of file From 11f2d95b790f9bea4ac1caeb98101c06c38f53c9 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Sun, 8 Nov 2020 21:57:54 +0100 Subject: [PATCH 06/57] Made first assignment latex compilable. --- ...e_notebook_update20201025-checkpoint.ipynb | 370 +++++++++++++++ .../juice_propagation_Q1-checkpoint.ipynb | 355 ++++++++++++++ .../juice_propagation_Q4-checkpoint.ipynb | 359 ++++++++++++++ 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"###############################################################################\n", + "# IMPORT STATEMENTS ###########################################################\n", + "###############################################################################\n", + "import numpy as np\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from tudatpy.kernel.astro import conversion\n", + "\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Set simulation start and end epochs.\n", + "simulation_start_epoch = 0.0\n", + "simulation_end_epoch = constants.JULIAN_DAY\n", + "\n", + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Create default body settings for selected celestial bodies\n", + "bodies_to_create = [\"Sun\", \"Earth\", \"Moon\", \"Mars\", \"Venus\"]\n", + "\n", + "# Create default body settings for bodies_to_create, with \"Earth\"/\"J2000\" as \n", + "# global frame origin and orientation. This environment will only be valid \n", + "# in the indicated time range \n", + "# [simulation_start_epoch --- simulation_end_epoch]\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create,\n", + " simulation_start_epoch,\n", + " simulation_end_epoch,\n", + " \"Earth\",\"J2000\")\n", + "\n", + "# Create system of selected celestial bodies\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle objects.\n", + "bodies.create_empty_body( \"Delfi-C3\" )\n", + "bodies.get_body( \"Delfi-C3\").set_constant_mass(400.0)\n", + "\n", + "# Create aerodynamic coefficient interface settings, and add to vehicle\n", + "reference_area = 4.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0]\n", + ")\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"Delfi-C3\", aero_coefficient_settings )\n", + "\n", + "# Create radiation pressure settings, and add to vehicle\n", + "reference_area_radiation = 4.0\n", + "radiation_pressure_coefficient = 1.2\n", + "occulting_bodies = [\"Earth\"]\n", + "radiation_pressure_settings = environment_setup.radiation_pressure.cannonball(\n", + " \"Sun\", reference_area_radiation, radiation_pressure_coefficient, occulting_bodies\n", + ")\n", + "environment_setup.add_radiation_pressure_interface(\n", + " bodies, \"Delfi-C3\", radiation_pressure_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated.\n", + "bodies_to_propagate = [\"Delfi-C3\"]\n", + "\n", + "# Define central bodies.\n", + "central_bodies = [\"Earth\"]\n", + "\n", + "# Define accelerations acting on Delfi-C3 by Sun and Earth.\n", + "accelerations_settings_delfi_c3 = dict(\n", + " Sun=\n", + " [\n", + " propagation_setup.acceleration.cannonball_radiation_pressure(),\n", + " propagation_setup.acceleration.point_mass_gravity()\n", + " ],\n", + " Earth=\n", + " [\n", + " propagation_setup.acceleration.spherical_harmonic_gravity(5, 5),\n", + " propagation_setup.acceleration.aerodynamic()\n", + " ])\n", + "\n", + "# Define point mass accelerations acting on Delfi-C3 by all other bodies.\n", + "for other in set(bodies_to_create).difference({\"Sun\", \"Earth\"}):\n", + " accelerations_settings_delfi_c3[other] = [\n", + " propagation_setup.acceleration.point_mass_gravity()]\n", + "\n", + "# Create global accelerations settings dictionary.\n", + "acceleration_settings = {\"Delfi-C3\": accelerations_settings_delfi_c3}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies,\n", + " acceleration_settings,\n", + " bodies_to_propagate,\n", + " central_bodies)\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Set initial conditions for the Asterix satellite that will be\n", + "# propagated in this simulation. The initial conditions are given in\n", + "# Keplerian elements and later on converted to Cartesian elements.\n", + "earth_gravitational_parameter = bodies.get_body( \"Earth\" ).gravitational_parameter\n", + "initial_state = conversion.keplerian_to_cartesian(\n", + " gravitational_parameter=earth_gravitational_parameter,\n", + " semi_major_axis=7500.0E3,\n", + " eccentricity=0.1,\n", + " inclination=np.deg2rad(85.3),\n", + " argument_of_periapsis=np.deg2rad(235.7),\n", + " longitude_of_ascending_node=np.deg2rad(23.4),\n", + " true_anomaly=np.deg2rad(139.87)\n", + ")\n", + "\n", + "# Define list of dependent variables to save.\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.total_acceleration( \"Delfi-C3\" ),\n", + " propagation_setup.dependent_variable.keplerian_state( \"Delfi-C3\", \"Earth\" ),\n", + " propagation_setup.dependent_variable.latitude( \"Delfi-C3\", \"Earth\" ),\n", + " propagation_setup.dependent_variable.longitude( \"Delfi-C3\", \"Earth\"),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Sun\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Moon\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Mars\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Venus\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.spherical_harmonic_gravity_type, \"Delfi-C3\", \"Earth\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.aerodynamic_type, \"Delfi-C3\", \"Earth\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.cannonball_radiation_pressure_type, \"Delfi-C3\", \"Sun\" \n", + " )\n", + " ]\n", + "\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")\n", + "\n", + "###########################################################################\n", + "# PROPAGATE ORBIT #########################################################\n", + "###########################################################################\n", + "\n", + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + "states = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history\n", + "\n", + "###########################################################################\n", + "# PRINT INITIAL AND FINAL STATES ##########################################\n", + "###########################################################################\n", + "\n", + "print(\n", + " f\"\"\"\n", + "Single Earth-Orbiting Satellite Example.\n", + "The initial position vector of Delfi-C3 is [km]: \\n{\n", + " states[simulation_start_epoch][:3] / 1E3}\n", + "The initial velocity vector of Delfi-C3 is [km/s]: \\n{\n", + " states[simulation_start_epoch][3:] / 1E3}\n", + "After {simulation_end_epoch} seconds the position vector of Delfi-C3 is [km]: \\n{\n", + " states[simulation_end_epoch][:3] / 1E3}\n", + "And the velocity vector of Delfi-C3 is [km/s]: \\n{\n", + " states[simulation_end_epoch][3:] / 1E3}\n", + " \"\"\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "time = dependent_variables.keys()\n", + "dependent_variable_list = np.vstack(list(dependent_variables.values()))\n", + "font_size = 20\n", + "\n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n", + "# dependent variables\n", + "# 0-2: total acceleration\n", + "# 3-8: Keplerian state\n", + "# 9: latitude\n", + "# 10: longitude\n", + "# 11: Acceleration Norm PM Sun\n", + "# 12: Acceleration Norm PM Moon\n", + "# 13: Acceleration Norm PM Mars\n", + "# 14: Acceleration Norm PM Venus\n", + "# 15: Acceleration Norm SH Earth\n", + "\n", + "total_acceleration = np.sqrt( dependent_variable_list[:,0] ** 2 + dependent_variable_list[:,1] ** 2 + dependent_variable_list[:,2] ** 2 )\n", + "\n", + "time_hours = [ t / 3600 for t in time]\n", + "# Total Acceleration\n", + "plt.figure( figsize=(17,5))\n", + "plt.grid()\n", + "plt.plot( time_hours , total_acceleration )\n", + "plt.xlabel('Time [hr]')\n", + "plt.ylabel( 'Total Acceleration [m/s$^2$]')\n", + "plt.xlim( [min(time_hours), max(time_hours)] )\n", + "plt.savefig( fname = 'total_acceleration.png', bbox_inches='tight')\n", + "\n", + "# Ground Track\n", + "latitude = dependent_variable_list[:,9]\n", + "longitude = dependent_variable_list[:,10]\n", + "\n", + "part = int(len(time)/24*3)\n", + "latitude = np.rad2deg( latitude[0:part] )\n", + "longitude = np.rad2deg( longitude[0:part] )\n", + "plt.figure( figsize=(17,5))\n", + "plt.grid()\n", + "plt.yticks(np.arange(-90, 91, step=45))\n", + "plt.scatter( longitude, latitude, s=1 )\n", + "plt.xlabel('Longitude [deg]')\n", + "plt.ylabel( 'Latitude [deg]')\n", + "plt.xlim( [min(longitude), max(longitude)] )\n", + "plt.savefig( fname = 'ground_track.png', bbox_inches='tight')\n", + "\n", + "# Kepler Elements\n", + "kepler_elements = dependent_variable_list[:,3:9]\n", + "\n", + "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots( 3, 2, figsize = (20,17) )\n", + "\n", + "# Semi-major Axis\n", + "semi_major_axis = [ element/1000 for element in kepler_elements[:,0] ]\n", + "ax1.plot( time_hours, semi_major_axis )\n", + "ax1.set_ylabel( 'Semi-major axis [km]' )\n", + "\n", + "# Eccentricity\n", + "eccentricity = kepler_elements[:,1]\n", + "ax2.plot( time_hours, eccentricity )\n", + "ax2.set_ylabel( 'Eccentricity [-]' )\n", + "\n", + "# Inclination\n", + "inclination = [ np.rad2deg( element ) for element in kepler_elements[:,2] ]\n", + "ax3.plot( time_hours, inclination )\n", + "ax3.set_ylabel( 'Inclination [deg]')\n", + "\n", + "# Argument of Periapsis\n", + "argument_of_periapsis = [ np.rad2deg( element ) for element in kepler_elements[:,3] ]\n", + "ax4.plot( time_hours, argument_of_periapsis )\n", + "ax4.set_ylabel( 'Argument of Periapsis [deg]' )\n", + "\n", + "# Right Ascension of the Ascending Node\n", + "raan = [ np.rad2deg( element ) for element in kepler_elements[:,4] ]\n", + "ax5.plot( time_hours, raan )\n", + "ax5.set_ylabel( 'RAAN [deg]' )\n", + "\n", + "# True Anomaly\n", + "true_anomaly = [ np.rad2deg( element ) for element in kepler_elements[:,5] ]\n", + "ax6.scatter( time_hours, true_anomaly, s=1 )\n", + "ax6.set_ylabel( 'True Anomaly [deg]' )\n", + "ax6.set_yticks(np.arange(0, 361, step=60))\n", + "\n", + "for ax in fig.get_axes():\n", + " ax.set_xlabel('Time [hr]')\n", + " ax.set_xlim( [min(time_hours), max(time_hours)] )\n", + " ax.grid()\n", + "\n", + "fig.savefig( fname = 'kepler_elements.png', bbox_inches='tight')\n", + " \n", + "plt.figure( figsize=(17,5))\n", + "\n", + "# Point Mass Gravity Acceleration Sun\n", + "acceleration_norm_pm_sun = dependent_variable_list[:, 11]\n", + "plt.plot( time_hours, acceleration_norm_pm_sun, label='PM Sun')\n", + "\n", + "# Point Mass Gravity Acceleration Moon\n", + "acceleration_norm_pm_moon = dependent_variable_list[:, 12]\n", + "plt.plot( time_hours, acceleration_norm_pm_moon, label='PM Moon')\n", + "\n", + "# Point Mass Gravity Acceleration Mars\n", + "acceleration_norm_pm_mars = dependent_variable_list[:, 13]\n", + "plt.plot( time_hours, acceleration_norm_pm_mars, label='PM Mars')\n", + "\n", + "# Point Mass Gravity Acceleration Venus\n", + "acceleration_norm_pm_venus = dependent_variable_list[:, 14]\n", + "plt.plot( time_hours, acceleration_norm_pm_venus, label='PM Venus')\n", + "\n", + "# Spherical Harmonic Gravity Acceleration Earth\n", + "acceleration_norm_sh_earth = dependent_variable_list[:, 15]\n", + "plt.plot( time_hours, acceleration_norm_sh_earth, label='SH Earth')\n", + "\n", + "# Aerodynamic Acceleration Earth\n", + "acceleration_norm_aero_earth = dependent_variable_list[:, 16]\n", + "plt.plot( time_hours, acceleration_norm_aero_earth, label='Aerodynamic Earth')\n", + "\n", + "# Cannonball Radiation Pressure Acceleration Sun\n", + "acceleration_norm_rp_sun = dependent_variable_list[:, 17]\n", + "plt.plot( time_hours, acceleration_norm_rp_sun, label='Radiation Pressure Sun')\n", + "\n", + "plt.grid()\n", + "plt.legend( bbox_to_anchor=(1.04,1) )\n", + "plt.xlim( [min(time_hours), max(time_hours)])\n", + "plt.yscale('log')\n", + "plt.xlabel( 'Time [hr]' )\n", + "plt.ylabel( 'Acceleration Norm [m/s$^2$]' )\n", + "\n", + "plt.savefig( fname = 'acceleration_norms.png', bbox_inches='tight')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project1/src/.ipynb_checkpoints/juice_propagation_Q1-checkpoint.ipynb b/code/project1/src/.ipynb_checkpoints/juice_propagation_Q1-checkpoint.ipynb new file mode 100644 index 0000000..c0ca27a --- /dev/null +++ b/code/project1/src/.ipynb_checkpoints/juice_propagation_Q1-checkpoint.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# # student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create environment, vehicle, accelerations, and propagation settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create settings for celestial bodies\n", + "bodies_to_create = [\"Ganymede\"]\n", + "global_frame_origin = \"Ganymede\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation)\n", + "\n", + "# Add Ganymede exponential atmosphere\n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + " \n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated, and their central bodies of propagation.\n", + "bodies_to_propagate = [\"JUICE\"]\n", + "central_bodies = [\"Ganymede\"]\n", + "\n", + "# Define accelerations acting on vehicle.\n", + "acceleration_settings_on_vehicle = dict(\n", + " XXXX\n", + ")\n", + "\n", + "# Create global accelerations dictionary.\n", + "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Define initial state.\n", + "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.keplerian_state(\n", + " \"JUICE\", \"Ganymede\")\n", + " ]\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + " \n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Orbit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings, True)\n", + "\n", + "simulation_result = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Print final propagation time and state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + "###########################################################################\n", + "\n", + "final_time_step=list(simulation_result.keys())[-1]\n", + "first_time_step=list(simulation_result.keys())[0]\n", + "\n", + "print(\n", + " f\"\"\"\n", + "JUICE Propagation Results.\n", + "\n", + "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + "Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_result[final_time_step][:]}\n", + "\n", + " \"\"\"\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Save Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# SAVE RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "save2txt(solution=simulation_result,\n", + " filename=\"JUICEPropagationHistory_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )\n", + "\n", + "save2txt(solution=dependent_variables,\n", + " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results\n", + "\n", + "For inspiration see: \n", + "\n", + "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PLOT RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "# Extract time and Kepler elements from dependent variables\n", + "kepler_elements = np.vstack(list(dependent_variables.values())\n", + " \n", + "# Kepler Elements\n", + "# 0: semi-major axis\n", + "# 1: eccentricity\n", + "# 2: inclination\n", + "# 3: argument of periapsis\n", + "# 4: right ascension of the ascending node\n", + "# 5: true anomaly\n", + "\n", + "time = dependent_variables.keys()\n", + "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", + "\n", + "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", + "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", + "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", + "\n", + "\n", + "# Set font size of figures\n", + "font_size = 20\n", + " \n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project1/src/.ipynb_checkpoints/juice_propagation_Q4-checkpoint.ipynb b/code/project1/src/.ipynb_checkpoints/juice_propagation_Q4-checkpoint.ipynb new file mode 100644 index 0000000..e980bcd --- /dev/null +++ b/code/project1/src/.ipynb_checkpoints/juice_propagation_Q4-checkpoint.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create Environment and Vehicle" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create body objects.\n", + "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", + "global_frame_origin = \"SSB\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation) \n", + "\n", + "# Add Ganymede exponential atmosphere \n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + "\n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings );" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Dynamics for various cases" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "deletable": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'XXXX' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'unperturbed'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m acceleration_settings_on_vehicle = dict(\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0mGanymede\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXXXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m )\n\u001b[1;32m 29\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'case_i'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'XXXX' is not defined" + ] + } + ], + "source": [ + "cases = ['unperturbed', 'case_i', 'case_ii']\n", + "\n", + "\"\"\"\n", + "unperturbed: Ganymede PM\n", + "\n", + "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", + "\n", + "case_ii: Ganymede PM, Ganymede aerodynamic\n", + "\"\"\"\n", + "\n", + "simulation_results_dict = dict()\n", + "dependent_variables_dict = dict()\n", + "for case in cases: \n", + " ###########################################################################\n", + " # CREATE ACCELERATIONS ####################################################\n", + " ###########################################################################\n", + "\n", + " # Define bodies that are propagated.\n", + " bodies_to_propagate = [\"JUICE\"]\n", + "\n", + " # Define central bodies.\n", + " central_bodies = [\"Ganymede\"]\n", + "\n", + " # Define accelerations acting on vehicle.\n", + " if case == 'unperturbed':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + " if case == 'case_i':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX,\n", + " Jupiter = XXXX\n", + " )\n", + " if case == 'case_ii':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + "\n", + " # Create global accelerations dictionary.\n", + " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + " # Create acceleration models.\n", + " acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + " ###########################################################################\n", + " # CREATE PROPAGATION SETTINGS #############################################\n", + " ###########################################################################\n", + "\n", + " # Define initial state.\n", + " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + " # Save magnitude of perturbations for both cases\n", + " if case == 'unperturbed':\n", + " dependent_variables_to_save = [ ]\n", + " if case == 'case_i':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + " if case == 'case_ii':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + "\n", + " # Create propagation settings.\n", + " propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + " )\n", + "\n", + " # Create numerical integrator settings.\n", + " fixed_step_size = 10.0\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + " )\n", + "\n", + " ###########################################################################\n", + " # PROPAGATE ORBIT #########################################################\n", + " ###########################################################################\n", + "\n", + " # Create simulation object and propagate dynamics.\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + " \n", + " simulation_results_dict[case] = dynamics_simulator.state_history\n", + " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", + "\n", + " ###########################################################################\n", + " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + " ###########################################################################\n", + "\n", + " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", + " first_time_step=list(simulation_results_dict[case].keys())[0]\n", + "\n", + " print(\n", + " f\"\"\"\n", + " JUICE Propagation Results of {case}.\n", + "\n", + " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + " Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_results_dict[case][final_time_step][:]}\n", + "\n", + " \"\"\"\n", + " )\n", + "\n", + " ###########################################################################\n", + " # SAVE RESULTS ############################################################\n", + " ###########################################################################\n", + " \n", + "# save2txt(solution=simulation_result,\n", + "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", + "# directory=\"./\", # default = \"./\" \n", + "# column_names=None, # default = None \n", + "# )\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Pre-process Results" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "deletable": false + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'unperturbed'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msimulation_result_unperturbed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimulation_results_dict\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m'unperturbed'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msimulation_result_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimulation_results_dict\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m'case_i'\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0msimulation_result_ii\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimulation_results_dict\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m'case_ii'\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdependent_variables_unperturbed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdependent_variables_dict\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m'unperturbed'\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'unperturbed'" + ] + } + ], + "source": [ + "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", + "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", + "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", + "\n", + "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", + "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", + "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", + "\n", + "difference_in_cartesian_position = XXXX" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project1/src/AE4868_example_notebook_update20201025.ipynb b/code/project1/src/AE4868_example_notebook_update20201025.ipynb new file mode 100644 index 0000000..49eae9c --- /dev/null +++ b/code/project1/src/AE4868_example_notebook_update20201025.ipynb @@ -0,0 +1,370 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###############################################################################\n", + "# IMPORT STATEMENTS ###########################################################\n", + "###############################################################################\n", + "import numpy as np\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from tudatpy.kernel.astro import conversion\n", + "\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Set simulation start and end epochs.\n", + "simulation_start_epoch = 0.0\n", + "simulation_end_epoch = constants.JULIAN_DAY\n", + "\n", + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Create default body settings for selected celestial bodies\n", + "bodies_to_create = [\"Sun\", \"Earth\", \"Moon\", \"Mars\", \"Venus\"]\n", + "\n", + "# Create default body settings for bodies_to_create, with \"Earth\"/\"J2000\" as \n", + "# global frame origin and orientation. This environment will only be valid \n", + "# in the indicated time range \n", + "# [simulation_start_epoch --- simulation_end_epoch]\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create,\n", + " simulation_start_epoch,\n", + " simulation_end_epoch,\n", + " \"Earth\",\"J2000\")\n", + "\n", + "# Create system of selected celestial bodies\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle objects.\n", + "bodies.create_empty_body( \"Delfi-C3\" )\n", + "bodies.get_body( \"Delfi-C3\").set_constant_mass(400.0)\n", + "\n", + "# Create aerodynamic coefficient interface settings, and add to vehicle\n", + "reference_area = 4.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0]\n", + ")\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"Delfi-C3\", aero_coefficient_settings )\n", + "\n", + "# Create radiation pressure settings, and add to vehicle\n", + "reference_area_radiation = 4.0\n", + "radiation_pressure_coefficient = 1.2\n", + "occulting_bodies = [\"Earth\"]\n", + "radiation_pressure_settings = environment_setup.radiation_pressure.cannonball(\n", + " \"Sun\", reference_area_radiation, radiation_pressure_coefficient, occulting_bodies\n", + ")\n", + "environment_setup.add_radiation_pressure_interface(\n", + " bodies, \"Delfi-C3\", radiation_pressure_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated.\n", + "bodies_to_propagate = [\"Delfi-C3\"]\n", + "\n", + "# Define central bodies.\n", + "central_bodies = [\"Earth\"]\n", + "\n", + "# Define accelerations acting on Delfi-C3 by Sun and Earth.\n", + "accelerations_settings_delfi_c3 = dict(\n", + " Sun=\n", + " [\n", + " propagation_setup.acceleration.cannonball_radiation_pressure(),\n", + " propagation_setup.acceleration.point_mass_gravity()\n", + " ],\n", + " Earth=\n", + " [\n", + " propagation_setup.acceleration.spherical_harmonic_gravity(5, 5),\n", + " propagation_setup.acceleration.aerodynamic()\n", + " ])\n", + "\n", + "# Define point mass accelerations acting on Delfi-C3 by all other bodies.\n", + "for other in set(bodies_to_create).difference({\"Sun\", \"Earth\"}):\n", + " accelerations_settings_delfi_c3[other] = [\n", + " propagation_setup.acceleration.point_mass_gravity()]\n", + "\n", + "# Create global accelerations settings dictionary.\n", + "acceleration_settings = {\"Delfi-C3\": accelerations_settings_delfi_c3}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies,\n", + " acceleration_settings,\n", + " bodies_to_propagate,\n", + " central_bodies)\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Set initial conditions for the Asterix satellite that will be\n", + "# propagated in this simulation. The initial conditions are given in\n", + "# Keplerian elements and later on converted to Cartesian elements.\n", + "earth_gravitational_parameter = bodies.get_body( \"Earth\" ).gravitational_parameter\n", + "initial_state = conversion.keplerian_to_cartesian(\n", + " gravitational_parameter=earth_gravitational_parameter,\n", + " semi_major_axis=7500.0E3,\n", + " eccentricity=0.1,\n", + " inclination=np.deg2rad(85.3),\n", + " argument_of_periapsis=np.deg2rad(235.7),\n", + " longitude_of_ascending_node=np.deg2rad(23.4),\n", + " true_anomaly=np.deg2rad(139.87)\n", + ")\n", + "\n", + "# Define list of dependent variables to save.\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.total_acceleration( \"Delfi-C3\" ),\n", + " propagation_setup.dependent_variable.keplerian_state( \"Delfi-C3\", \"Earth\" ),\n", + " propagation_setup.dependent_variable.latitude( \"Delfi-C3\", \"Earth\" ),\n", + " propagation_setup.dependent_variable.longitude( \"Delfi-C3\", \"Earth\"),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Sun\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Moon\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Mars\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Venus\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.spherical_harmonic_gravity_type, \"Delfi-C3\", \"Earth\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.aerodynamic_type, \"Delfi-C3\", \"Earth\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.cannonball_radiation_pressure_type, \"Delfi-C3\", \"Sun\" \n", + " )\n", + " ]\n", + "\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")\n", + "\n", + "###########################################################################\n", + "# PROPAGATE ORBIT #########################################################\n", + "###########################################################################\n", + "\n", + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + "states = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history\n", + "\n", + "###########################################################################\n", + "# PRINT INITIAL AND FINAL STATES ##########################################\n", + "###########################################################################\n", + "\n", + "print(\n", + " f\"\"\"\n", + "Single Earth-Orbiting Satellite Example.\n", + "The initial position vector of Delfi-C3 is [km]: \\n{\n", + " states[simulation_start_epoch][:3] / 1E3}\n", + "The initial velocity vector of Delfi-C3 is [km/s]: \\n{\n", + " states[simulation_start_epoch][3:] / 1E3}\n", + "After {simulation_end_epoch} seconds the position vector of Delfi-C3 is [km]: \\n{\n", + " states[simulation_end_epoch][:3] / 1E3}\n", + "And the velocity vector of Delfi-C3 is [km/s]: \\n{\n", + " states[simulation_end_epoch][3:] / 1E3}\n", + " \"\"\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "time = dependent_variables.keys()\n", + "dependent_variable_list = np.vstack(list(dependent_variables.values()))\n", + "font_size = 20\n", + "\n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n", + "# dependent variables\n", + "# 0-2: total acceleration\n", + "# 3-8: Keplerian state\n", + "# 9: latitude\n", + "# 10: longitude\n", + "# 11: Acceleration Norm PM Sun\n", + "# 12: Acceleration Norm PM Moon\n", + "# 13: Acceleration Norm PM Mars\n", + "# 14: Acceleration Norm PM Venus\n", + "# 15: Acceleration Norm SH Earth\n", + "\n", + "total_acceleration = np.sqrt( dependent_variable_list[:,0] ** 2 + dependent_variable_list[:,1] ** 2 + dependent_variable_list[:,2] ** 2 )\n", + "\n", + "time_hours = [ t / 3600 for t in time]\n", + "# Total Acceleration\n", + "plt.figure( figsize=(17,5))\n", + "plt.grid()\n", + "plt.plot( time_hours , total_acceleration )\n", + "plt.xlabel('Time [hr]')\n", + "plt.ylabel( 'Total Acceleration [m/s$^2$]')\n", + "plt.xlim( [min(time_hours), max(time_hours)] )\n", + "plt.savefig( fname = 'total_acceleration.png', bbox_inches='tight')\n", + "\n", + "# Ground Track\n", + "latitude = dependent_variable_list[:,9]\n", + "longitude = dependent_variable_list[:,10]\n", + "\n", + "part = int(len(time)/24*3)\n", + "latitude = np.rad2deg( latitude[0:part] )\n", + "longitude = np.rad2deg( longitude[0:part] )\n", + "plt.figure( figsize=(17,5))\n", + "plt.grid()\n", + "plt.yticks(np.arange(-90, 91, step=45))\n", + "plt.scatter( longitude, latitude, s=1 )\n", + "plt.xlabel('Longitude [deg]')\n", + "plt.ylabel( 'Latitude [deg]')\n", + "plt.xlim( [min(longitude), max(longitude)] )\n", + "plt.savefig( fname = 'ground_track.png', bbox_inches='tight')\n", + "\n", + "# Kepler Elements\n", + "kepler_elements = dependent_variable_list[:,3:9]\n", + "\n", + "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots( 3, 2, figsize = (20,17) )\n", + "\n", + "# Semi-major Axis\n", + "semi_major_axis = [ element/1000 for element in kepler_elements[:,0] ]\n", + "ax1.plot( time_hours, semi_major_axis )\n", + "ax1.set_ylabel( 'Semi-major axis [km]' )\n", + "\n", + "# Eccentricity\n", + "eccentricity = kepler_elements[:,1]\n", + "ax2.plot( time_hours, eccentricity )\n", + "ax2.set_ylabel( 'Eccentricity [-]' )\n", + "\n", + "# Inclination\n", + "inclination = [ np.rad2deg( element ) for element in kepler_elements[:,2] ]\n", + "ax3.plot( time_hours, inclination )\n", + "ax3.set_ylabel( 'Inclination [deg]')\n", + "\n", + "# Argument of Periapsis\n", + "argument_of_periapsis = [ np.rad2deg( element ) for element in kepler_elements[:,3] ]\n", + "ax4.plot( time_hours, argument_of_periapsis )\n", + "ax4.set_ylabel( 'Argument of Periapsis [deg]' )\n", + "\n", + "# Right Ascension of the Ascending Node\n", + "raan = [ np.rad2deg( element ) for element in kepler_elements[:,4] ]\n", + "ax5.plot( time_hours, raan )\n", + "ax5.set_ylabel( 'RAAN [deg]' )\n", + "\n", + "# True Anomaly\n", + "true_anomaly = [ np.rad2deg( element ) for element in kepler_elements[:,5] ]\n", + "ax6.scatter( time_hours, true_anomaly, s=1 )\n", + "ax6.set_ylabel( 'True Anomaly [deg]' )\n", + "ax6.set_yticks(np.arange(0, 361, step=60))\n", + "\n", + "for ax in fig.get_axes():\n", + " ax.set_xlabel('Time [hr]')\n", + " ax.set_xlim( [min(time_hours), max(time_hours)] )\n", + " ax.grid()\n", + "\n", + "fig.savefig( fname = 'kepler_elements.png', bbox_inches='tight')\n", + " \n", + "plt.figure( figsize=(17,5))\n", + "\n", + "# Point Mass Gravity Acceleration Sun\n", + "acceleration_norm_pm_sun = dependent_variable_list[:, 11]\n", + "plt.plot( time_hours, acceleration_norm_pm_sun, label='PM Sun')\n", + "\n", + "# Point Mass Gravity Acceleration Moon\n", + "acceleration_norm_pm_moon = dependent_variable_list[:, 12]\n", + "plt.plot( time_hours, acceleration_norm_pm_moon, label='PM Moon')\n", + "\n", + "# Point Mass Gravity Acceleration Mars\n", + "acceleration_norm_pm_mars = dependent_variable_list[:, 13]\n", + "plt.plot( time_hours, acceleration_norm_pm_mars, label='PM Mars')\n", + "\n", + "# Point Mass Gravity Acceleration Venus\n", + "acceleration_norm_pm_venus = dependent_variable_list[:, 14]\n", + "plt.plot( time_hours, acceleration_norm_pm_venus, label='PM Venus')\n", + "\n", + "# Spherical Harmonic Gravity Acceleration Earth\n", + "acceleration_norm_sh_earth = dependent_variable_list[:, 15]\n", + "plt.plot( time_hours, acceleration_norm_sh_earth, label='SH Earth')\n", + "\n", + "# Aerodynamic Acceleration Earth\n", + "acceleration_norm_aero_earth = dependent_variable_list[:, 16]\n", + "plt.plot( time_hours, acceleration_norm_aero_earth, label='Aerodynamic Earth')\n", + "\n", + "# Cannonball Radiation Pressure Acceleration Sun\n", + "acceleration_norm_rp_sun = dependent_variable_list[:, 17]\n", + "plt.plot( time_hours, acceleration_norm_rp_sun, label='Radiation Pressure Sun')\n", + "\n", + "plt.grid()\n", + "plt.legend( bbox_to_anchor=(1.04,1) )\n", + "plt.xlim( [min(time_hours), max(time_hours)])\n", + "plt.yscale('log')\n", + "plt.xlabel( 'Time [hr]' )\n", + "plt.ylabel( 'Acceleration Norm [m/s$^2$]' )\n", + "\n", + "plt.savefig( fname = 'acceleration_norms.png', bbox_inches='tight')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ + "import os\n", "from matplotlib import pyplot as plt\n", "\n", "time = dependent_variables.keys()\n", @@ -237,7 +342,9 @@ "plt.xlabel('Time [hr]')\n", "plt.ylabel( 'Total Acceleration [m/s$^2$]')\n", "plt.xlim( [min(time_hours), max(time_hours)] )\n", - "plt.savefig( fname = 'total_acceleration.png', bbox_inches='tight')\n", + "plt.savefig( fname = f'{latex_image_path}total_acceleration.png', bbox_inches='tight')\n", + "\n", + "\n", "\n", "# Ground Track\n", "latitude = dependent_variable_list[:,9]\n", @@ -253,7 +360,7 @@ "plt.xlabel('Longitude [deg]')\n", "plt.ylabel( 'Latitude [deg]')\n", "plt.xlim( [min(longitude), max(longitude)] )\n", - "plt.savefig( fname = 'ground_track.png', bbox_inches='tight')\n", + "plt.savefig( fname = f'{latex_image_path}ground_track.png', bbox_inches='tight')\n", "\n", "# Kepler Elements\n", "kepler_elements = dependent_variable_list[:,3:9]\n", @@ -296,7 +403,7 @@ " ax.set_xlim( [min(time_hours), max(time_hours)] )\n", " ax.grid()\n", "\n", - "fig.savefig( fname = 'kepler_elements.png', bbox_inches='tight')\n", + "plt.savefig( fname = f'{latex_image_path}kepler_elements.png', bbox_inches='tight')\n", " \n", "plt.figure( figsize=(17,5))\n", "\n", @@ -335,7 +442,8 @@ "plt.xlabel( 'Time [hr]' )\n", "plt.ylabel( 'Acceleration Norm [m/s$^2$]' )\n", "\n", - "plt.savefig( fname = 'acceleration_norms.png', bbox_inches='tight')\n" + "plt.savefig( fname = f'{latex_image_path}acceleration_norms.png', bbox_inches='tight')\n", + "#plt.savefig('acceleration_norms.png', bbox_inches='tight')\n" ] }, { @@ -362,7 +470,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.7.8" } }, "nbformat": 4, diff --git a/code/project1/src/AE4868_example_notebook_update20201025.ipynb b/code/project1/src/AE4868_example_notebook_update20201025.ipynb old mode 100644 new mode 100755 index 49eae9c..7f952bc --- a/code/project1/src/AE4868_example_notebook_update20201025.ipynb +++ b/code/project1/src/AE4868_example_notebook_update20201025.ipynb @@ -1,10 +1,69 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T12:15:57.811520Z", + "iopub.status.busy": "2020-11-09T12:15:57.810252Z", + "iopub.status.idle": "2020-11-09T12:15:57.813857Z", + "shell.execute_reply": "2020-11-09T12:15:57.813007Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "#import ipyparams\n", + "#currentNotebook = ipyparams.notebook_name\n", + "#print(f'currentNotebook={currentNotebook}')\n", + "#currentNotebook = os.path.dirname(os.path.realpath(__file__))\n", + "#currentNotebook = os.path\n", + "#currentNotebook = os.getcwd()\n", + "#if not 'workbookDir' in globals():\n", + "# workbookDir = os.getcwd()\n", + "#print('workbookDir: ' + workbookDir)\n", + "#currentNotebook = os.chdir(workbookDir) # If you changed the current working dir, this will take you back to the workbook dir.\n", + "#currentNotebook = %pwd" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T12:15:57.831327Z", + "iopub.status.busy": "2020-11-09T12:15:57.828858Z", + "iopub.status.idle": "2020-11-09T12:15:58.639871Z", + "shell.execute_reply": "2020-11-09T12:15:58.640579Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Single Earth-Orbiting Satellite Example.\n", + "The initial position vector of Delfi-C3 is [km]: \n", + "[7037.48400133 3238.05901792 2150.7241875 ]\n", + "The initial velocity vector of Delfi-C3 is [km/s]: \n", + "[-1.46565763 -0.04095839 6.62279761]\n", + "After 86400.0 seconds the position vector of Delfi-C3 is [km]: \n", + "[-4602.79426676 -1421.16740978 5883.69740624]\n", + "And the velocity vector of Delfi-C3 is [km/s]: \n", + "[-4.53846052 -2.36988263 -5.04163195]\n", + " \n" + ] + } + ], "source": [ "###############################################################################\n", "# IMPORT STATEMENTS ###########################################################\n", @@ -16,6 +75,8 @@ "from tudatpy.kernel.simulation import propagation_setup\n", "from tudatpy.kernel.astro import conversion\n", "\n", + "# Set path to latex image folders for project 1\n", + "latex_image_path = '../../../latex/project1/Images/'\n", "\n", "# Load spice kernels.\n", "spice_interface.load_standard_kernels()\n", @@ -204,10 +265,67 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T12:15:58.662091Z", + "iopub.status.busy": "2020-11-09T12:15:58.659394Z", + "iopub.status.idle": "2020-11-09T12:16:02.808608Z", + "shell.execute_reply": "2020-11-09T12:16:02.809261Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ + "import os\n", "from matplotlib import pyplot as plt\n", "\n", "time = dependent_variables.keys()\n", @@ -237,7 +355,9 @@ "plt.xlabel('Time [hr]')\n", "plt.ylabel( 'Total Acceleration [m/s$^2$]')\n", "plt.xlim( [min(time_hours), max(time_hours)] )\n", - "plt.savefig( fname = 'total_acceleration.png', bbox_inches='tight')\n", + "plt.savefig( fname = f'{latex_image_path}total_acceleration.png', bbox_inches='tight')\n", + "\n", + "\n", "\n", "# Ground Track\n", "latitude = dependent_variable_list[:,9]\n", @@ -253,7 +373,7 @@ "plt.xlabel('Longitude [deg]')\n", "plt.ylabel( 'Latitude [deg]')\n", "plt.xlim( [min(longitude), max(longitude)] )\n", - "plt.savefig( fname = 'ground_track.png', bbox_inches='tight')\n", + "plt.savefig( fname = f'{latex_image_path}ground_track.png', bbox_inches='tight')\n", "\n", "# Kepler Elements\n", "kepler_elements = dependent_variable_list[:,3:9]\n", @@ -296,7 +416,7 @@ " ax.set_xlim( [min(time_hours), max(time_hours)] )\n", " ax.grid()\n", "\n", - "fig.savefig( fname = 'kepler_elements.png', bbox_inches='tight')\n", + "plt.savefig( fname = f'{latex_image_path}kepler_elements.png', bbox_inches='tight')\n", " \n", "plt.figure( figsize=(17,5))\n", "\n", @@ -335,7 +455,8 @@ "plt.xlabel( 'Time [hr]' )\n", "plt.ylabel( 'Acceleration Norm [m/s$^2$]' )\n", "\n", - "plt.savefig( fname = 'acceleration_norms.png', bbox_inches='tight')\n" + "plt.savefig( fname = f'{latex_image_path}acceleration_norms.png', bbox_inches='tight')\n", + "#plt.savefig('acceleration_norms.png', bbox_inches='tight')\n" ] }, { @@ -362,7 +483,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.7.8" } }, "nbformat": 4, diff --git a/code/project1/src/Main.py b/code/project1/src/Main.py index 0fdaa81..c4fd16b 100644 --- a/code/project1/src/Main.py +++ b/code/project1/src/Main.py @@ -5,6 +5,7 @@ from matplotlib import lines import matplotlib.pyplot as plt from .Plot_to_tex import Plot_to_tex as plt_tex +from .Run_jupyter_notebooks import Run_jupyter_notebook import numpy as np string_length = 100 @@ -14,7 +15,19 @@ class Main: def __init__(self): pass - + + + def run_jupyter_notebooks(self): + ''' runs a jupyter notebook''' + print(f'Running AE4868_example_notebook_update20201025') + run_jupyter_notebook = Run_jupyter_notebook() + #run_jupyter_notebook.run_notebook('AE4868_example_notebook_update20201025.ipynb') + run_jupyter_notebook.run_notebook('code/project1/src/AE4868_example_notebook_update20201025.ipynb') + + + ################################################################ + ############example code to illustrate latex image sync############### + ################################################################ def count(self,bits): count = 0 for bit in bits: @@ -118,23 +131,4 @@ def addTwo(self,x): if __name__ == '__main__': # initialize main class - main = Main() - - # set the project number folder since this is python code, the results are exported to the project2 report - project_nr = 1 - - # run a genetic algorithm to create some data for a plot. - print("now running a") - res = main.do_run_a() - - # plot some graph with a single line, general form is: - # plt_tex.plotSingleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) - plt_tex.plotSingleLine(plt_tex,range(0, len(res)),res,"[runs]]","fitness [%]","run 1","4a",4,project_nr) - - # run a genetic algorithm to create some data for another plot. - print("now running b") - main.do4b(project_nr) - - # run a genetic algorithm to create some data for another plot. - print("now running 4c") - main.do4c(project_nr) \ No newline at end of file + main = Main() \ No newline at end of file diff --git a/code/project1/src/Run_jupyter_notebooks.py b/code/project1/src/Run_jupyter_notebooks.py new file mode 100644 index 0000000..b88e4d1 --- /dev/null +++ b/code/project1/src/Run_jupyter_notebooks.py @@ -0,0 +1,41 @@ +# runs a jupyter notebook and converts it to pdf + +import os +import nbformat +from nbconvert.preprocessors import ExecutePreprocessor + +class Run_jupyter_notebook: + + def __init__(self): + self.script_dir = self.get_script_dir() + print("Created main") + + # runs jupyter notebook + def run_notebook(self,notebook_filename): + + + # Load your notebook + with open(notebook_filename) as f: + nb = nbformat.read(f, as_version=4) + + # Configure + ep = ExecutePreprocessor(timeout=600, kernel_name='python3') + + # Execute + #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}}) + ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}}) + + # Save output notebook + with open(notebook_filename, 'w', encoding='utf-8') as f: + nbformat.write(nb, f) + + # converts jupyter notebook to pdf + def convert_notebook_to_pdf(self,notebook_filename): + pass + + def get_script_dir(self): + ''' returns the directory of this script regardles of from which level the code is executed ''' + return os.path.dirname(__file__) + +if __name__ == '__main__': + main = Run_jupyter_notebook() \ No newline at end of file diff --git a/code/project1/src/__main__.py b/code/project1/src/__main__.py index 66e90a2..e6654a8 100644 --- a/code/project1/src/__main__.py +++ b/code/project1/src/__main__.py @@ -3,7 +3,13 @@ print(f'Hi, I\'ll be running the main code, and I\'ll let you know when I\'m done.') project_nr = 1 main = Main() - + +# run the jupyter notebooks for assignment 1 +main.run_jupyter_notebooks() + +################################################################ +############example code to illustrate latex image sync############### +################################################################ # run a genetic algorithm to create some data for a plot. print("now running a") res = main.do_run_a() diff --git a/latex/project1/Images/4b.png b/latex/project1/Images/4b.png index 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windows_environment.yml name: tudat-space channels: - conda-forge - tudat-team - conda dependencies: - - tudatpy - - matplotlib - - pytest>=3.0 \ No newline at end of file +- tudatpy +- anaconda +- nb_conda +- conda: + - pytest>=3.0 +- pip +- pip: + # works for regular pip packages + - sklearn diff --git a/windows_environment.yml b/windows_environment.yml index f3aefbc..e2f89d7 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -1,11 +1,17 @@ # run: conda env create --file linux_environment.yml -# include new packages: conda env update --file linux_environment.yml +# include new packages: conda env update --file windows_environment.yml name: tudat-space channels: - conda-forge - tudat-team + - conda dependencies: - - tudatpy - - matplotlib +- tudatpy +- anaconda +- nb_conda - conda: - - pytest>=3.0 \ No newline at end of file + - pytest>=3.0 +- pip +- pip: + # works for regular pip packages + - sklearn From ec952b37c79bcf33b5df0fde9974fc778704ef49 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 13:31:22 +0100 Subject: [PATCH 16/57] Created automatic converting jupyter notebook to pdf. --- ...4868_example_notebook_update20201025.ipynb | 24 +++++++++--------- ...AE4868_example_notebook_update20201025.pdf | Bin 0 -> 409724 bytes code/project1/src/Main.py | 12 ++++++--- code/project1/src/Run_jupyter_notebooks.py | 2 +- code/project1/src/__main__.py | 1 + latex/project1/Images/4b.png | Bin 39821 -> 38543 bytes latex/project1/Images/4c.png | Bin 28279 -> 28071 bytes 7 files changed, 22 insertions(+), 17 deletions(-) create mode 100644 code/project1/src/AE4868_example_notebook_update20201025.pdf diff --git a/code/project1/src/AE4868_example_notebook_update20201025.ipynb b/code/project1/src/AE4868_example_notebook_update20201025.ipynb index 7f952bc..03abffc 100755 --- a/code/project1/src/AE4868_example_notebook_update20201025.ipynb +++ b/code/project1/src/AE4868_example_notebook_update20201025.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T12:15:57.811520Z", - "iopub.status.busy": "2020-11-09T12:15:57.810252Z", - "iopub.status.idle": "2020-11-09T12:15:57.813857Z", - "shell.execute_reply": "2020-11-09T12:15:57.813007Z" + "iopub.execute_input": "2020-11-09T12:29:14.629988Z", + "iopub.status.busy": "2020-11-09T12:29:14.628937Z", + "iopub.status.idle": "2020-11-09T12:29:14.631413Z", + "shell.execute_reply": "2020-11-09T12:29:14.632046Z" } }, "outputs": [], @@ -39,10 +39,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T12:15:57.831327Z", - "iopub.status.busy": "2020-11-09T12:15:57.828858Z", - "iopub.status.idle": "2020-11-09T12:15:58.639871Z", - "shell.execute_reply": "2020-11-09T12:15:58.640579Z" + "iopub.execute_input": "2020-11-09T12:29:14.648835Z", + "iopub.status.busy": "2020-11-09T12:29:14.647653Z", + "iopub.status.idle": "2020-11-09T12:29:15.474990Z", + "shell.execute_reply": "2020-11-09T12:29:15.475678Z" } }, "outputs": [ @@ -268,10 +268,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T12:15:58.662091Z", - "iopub.status.busy": "2020-11-09T12:15:58.659394Z", - "iopub.status.idle": "2020-11-09T12:16:02.808608Z", - "shell.execute_reply": "2020-11-09T12:16:02.809261Z" + "iopub.execute_input": "2020-11-09T12:29:15.495050Z", + "iopub.status.busy": "2020-11-09T12:29:15.494239Z", + "iopub.status.idle": "2020-11-09T12:29:19.144058Z", + "shell.execute_reply": "2020-11-09T12:29:19.144720Z" } }, "outputs": [ diff --git a/code/project1/src/AE4868_example_notebook_update20201025.pdf b/code/project1/src/AE4868_example_notebook_update20201025.pdf new file mode 100644 index 0000000000000000000000000000000000000000..932c13f53879a469cf5fb9a2d59d5addafcc60dd GIT binary patch literal 409724 zcma&MQ*dWt*S8tlX2-T|+qP||WBp^>wr#Uxr(@f8rk|PbVBVSc;5pm1s`g!L@4Bnj zRqOhZDT<2IG10TZkR9G$y}~dv5;77x7+b^e@-m29*}9rJGl<(7xtfWZnK+o5G02+P zTew;hGBYzX@$5D4!efamknd 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a/code/project1/src/Main.py +++ b/code/project1/src/Main.py @@ -14,16 +14,20 @@ class Main: def __init__(self): + self.run_jupyter_notebook = Run_jupyter_notebook() pass def run_jupyter_notebooks(self): ''' runs a jupyter notebook''' - print(f'Running AE4868_example_notebook_update20201025') - run_jupyter_notebook = Run_jupyter_notebook() - #run_jupyter_notebook.run_notebook('AE4868_example_notebook_update20201025.ipynb') - run_jupyter_notebook.run_notebook('code/project1/src/AE4868_example_notebook_update20201025.ipynb') + print(f'Running AE4868_example_notebook_update20201025.ipynb') + self.run_jupyter_notebook.run_notebook('code/project1/src/AE4868_example_notebook_update20201025.ipynb') + + def convert_notebook_to_pdf(self): + ''' converts a jupyter notebook to pdf''' + print(f'Converting AE4868_example_notebook_update20201025.ipynb to pdf') + self.run_jupyter_notebook.convert_notebook_to_pdf('code/project1/src/AE4868_example_notebook_update20201025.ipynb') ################################################################ ############example code to illustrate latex image sync############### diff --git a/code/project1/src/Run_jupyter_notebooks.py b/code/project1/src/Run_jupyter_notebooks.py index b88e4d1..0c8f558 100644 --- a/code/project1/src/Run_jupyter_notebooks.py +++ b/code/project1/src/Run_jupyter_notebooks.py @@ -31,7 +31,7 @@ def run_notebook(self,notebook_filename): # converts jupyter notebook to pdf def convert_notebook_to_pdf(self,notebook_filename): - pass + os.system(f'jupyter nbconvert --to pdf {notebook_filename}') def get_script_dir(self): ''' returns the directory of this script regardles of from which level the code is executed ''' diff --git a/code/project1/src/__main__.py b/code/project1/src/__main__.py index e6654a8..f695a91 100644 --- a/code/project1/src/__main__.py +++ b/code/project1/src/__main__.py @@ -6,6 +6,7 @@ # run the jupyter notebooks for assignment 1 main.run_jupyter_notebooks() 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00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 14:51:27 +0100 Subject: [PATCH 17/57] Enabeled auto compilation of latex project1. --- __pycache__/__init__.cpython-38.pyc | Bin 197 -> 0 bytes code/__pycache__/__init__.cpython-38.pyc | Bin 202 -> 0 bytes ...4868_example_notebook_update20201025.ipynb | 24 ++++----- ...AE4868_example_notebook_update20201025.pdf | Bin 409724 -> 409719 bytes code/project1/src/Compile_latex.py | 50 ++++++++++++++++++ code/project1/src/Main.py | 1 - code/project1/src/__main__.py | 11 ++++ .../src/__pycache__/Main.cpython-38.pyc | Bin 3360 -> 0 bytes .../__pycache__/Plot_to_tex.cpython-38.pyc | Bin 3768 -> 0 bytes .../src/__pycache__/__init__.cpython-38.pyc | Bin 210 -> 0 bytes .../src/__pycache__/__main__.cpython-38.pyc | Bin 513 -> 0 bytes latex/project1/Appendices/AppA.tex | 2 +- latex/project1/Appendices/AppB.tex | 2 +- latex/project1/Appendices/AppC.tex | 2 +- latex/project1/Images/4b.png | Bin 38543 -> 38801 bytes latex/project1/Images/4c.png | Bin 28071 -> 30119 bytes latex/project1/main.tex | 19 +++---- main.pdf | Bin 0 -> 169285 bytes 18 files changed, 86 insertions(+), 25 deletions(-) delete mode 100644 __pycache__/__init__.cpython-38.pyc delete mode 100644 code/__pycache__/__init__.cpython-38.pyc create mode 100644 code/project1/src/Compile_latex.py delete mode 100644 code/project1/src/__pycache__/Main.cpython-38.pyc delete mode 100644 code/project1/src/__pycache__/Plot_to_tex.cpython-38.pyc delete mode 100644 code/project1/src/__pycache__/__init__.cpython-38.pyc delete mode 100644 code/project1/src/__pycache__/__main__.cpython-38.pyc create mode 100644 main.pdf diff --git a/__pycache__/__init__.cpython-38.pyc b/__pycache__/__init__.cpython-38.pyc deleted file mode 100644 index 21cfc4aa7daf5c9d2fe1703fea7080da7f0b077c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 197 zcmWIL<>g`k0=?gh;z9Id5P=LBfgA@QE@lA|DGb33nv8xc8Hzx{2;x_kenx(7s(zxr 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"2020-11-09T13:29:31.859911Z", + "iopub.status.idle": "2020-11-09T13:29:31.864289Z", + "shell.execute_reply": "2020-11-09T13:29:31.863259Z" } }, "outputs": [], @@ -39,10 +39,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T12:29:14.648835Z", - "iopub.status.busy": "2020-11-09T12:29:14.647653Z", - "iopub.status.idle": "2020-11-09T12:29:15.474990Z", - "shell.execute_reply": "2020-11-09T12:29:15.475678Z" + "iopub.execute_input": "2020-11-09T13:29:31.881807Z", + "iopub.status.busy": "2020-11-09T13:29:31.880574Z", + "iopub.status.idle": "2020-11-09T13:29:32.714777Z", + "shell.execute_reply": "2020-11-09T13:29:32.715369Z" } }, "outputs": [ @@ -268,10 +268,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T12:29:15.495050Z", - "iopub.status.busy": "2020-11-09T12:29:15.494239Z", - "iopub.status.idle": "2020-11-09T12:29:19.144058Z", - "shell.execute_reply": "2020-11-09T12:29:19.144720Z" + 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self.get_script_dir() + relative_dir = f'latex/project{project_nr}/' + self.compile_latex(relative_dir,latex_filename) + self.clean_up_after_compilation(latex_filename) + self.move_pdf_into_latex_dir(relative_dir,latex_filename) + + # runs jupyter notebook + def compile_latex(self,relative_dir,latex_filename): + os.system(f'pdflatex {relative_dir}{latex_filename}') + + def clean_up_after_compilation(self,latex_filename): + latex_filename_without_extention = latex_filename[:-4] + print(f'latex_filename_without_extention={latex_filename_without_extention}') + self.delete_file_if_exists(f'{latex_filename_without_extention}.aux') + self.delete_file_if_exists(f'{latex_filename_without_extention}.log') + self.delete_file_if_exists(f'texput.log') + + def move_pdf_into_latex_dir(self,relative_dir,latex_filename): + pdf_filename = f'{latex_filename[:-4]}.pdf' + destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}' + #destination= f'{self.get_script_dir()}/../../../{relative_dir}' + shutil.move(latex_filename, destination) + print(f'destination={destination}') + try: + shutil.move(latex_filename, destination) + except: + print("Error while moving file ", pdf_filename) + + def delete_file_if_exists(self,filename): + try: + os.remove(filename) + except: + print("Error while deleting file ", filename) + + def get_script_dir(self): + ''' returns the directory of this script regardles of from which level the code is executed ''' + return os.path.dirname(__file__) + +if __name__ == '__main__': + main = Compile_latex() \ No newline at end of file diff --git a/code/project1/src/Main.py b/code/project1/src/Main.py index 458981b..4b3dbaf 100644 --- a/code/project1/src/Main.py +++ b/code/project1/src/Main.py @@ -26,7 +26,6 @@ def run_jupyter_notebooks(self): def convert_notebook_to_pdf(self): ''' converts a jupyter notebook to pdf''' - print(f'Converting AE4868_example_notebook_update20201025.ipynb to pdf') self.run_jupyter_notebook.convert_notebook_to_pdf('code/project1/src/AE4868_example_notebook_update20201025.ipynb') ################################################################ diff --git a/code/project1/src/__main__.py b/code/project1/src/__main__.py index f695a91..e31ca5e 100644 --- a/code/project1/src/__main__.py +++ b/code/project1/src/__main__.py @@ -1,13 +1,24 @@ +import os from .Main import Main +from .Compile_latex import Compile_latex print(f'Hi, I\'ll be running the main code, and I\'ll let you know when I\'m done.') project_nr = 1 main = Main() +# compile the latex report +compile_latex =Compile_latex(project_nr ,'main.tex') +exit() + # run the jupyter notebooks for assignment 1 main.run_jupyter_notebooks() + +# convert jupyter notebook for assignment 1 to pdf main.convert_notebook_to_pdf() +# compile the latex report +compile_latex =Compile_latex(project_nr ,'main.tex') + ################################################################ ############example code to 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\ No newline at end of file +\pythonexternal{latex/project1/../../code/project1/src/__main__.py} \ No newline at end of file diff --git a/latex/project1/Appendices/AppB.tex b/latex/project1/Appendices/AppB.tex index deab136..39c90fb 100644 --- a/latex/project1/Appendices/AppB.tex +++ b/latex/project1/Appendices/AppB.tex @@ -1,2 +1,2 @@ \section{Appendix Main.py}\label{app:2} -\pythonexternal{../../code/project1/src/Main.py} \ No newline at end of file +\pythonexternal{latex/project1/../../code/project1/src/Main.py} \ No newline at end of file diff --git a/latex/project1/Appendices/AppC.tex b/latex/project1/Appendices/AppC.tex index 31d3109..4e59713 100644 --- a/latex/project1/Appendices/AppC.tex +++ b/latex/project1/Appendices/AppC.tex @@ -1,2 +1,2 @@ \section*{Appendix python code that exports figures to latex}\label{app:3} -\pythonexternal{../../code/project1/src/Plot_to_tex.py} \ No newline at end of file +\pythonexternal{latex/project1/../../code/project1/src/Plot_to_tex.py} \ No 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zk1`X3#kNxkpo*SoMg{Q(jd*tVDlgw((jq${c!YBpivSE=bFS(J_N;z~SWtwy-PR#g z>x5+4wIX0YH-GwbEVuAS*8Hi!2S)SJ7E$cX%dV^{xbyRfbGNhL5!yGq;)1uvqgdm< zGL6WdrxCLu&2?R&k1HA*(xY(O1?2Lg@1aCx69vw^+LKSEYIf3M`bPF4$6}`)8OE*p zP6DMFUnO;|JvHQ<12vJ6o^Mi#OqR8Rw7a)E(Qb_xkaq{%0c<9Rz=T3SDoQ%e z63mgsj*j7&cY*Qky^6$U2KK;x!9rVt(f@;d<^CTY@Bh+ln#yM8u#D37re+>QI!r`d zOnR`4Dpp=*UuUq48bmtGM65*2Ut`J+4nQJK&ae5({|PsDAo}ur|JQ{Wkq#fX7%Qtd zhnNTllL)i$mqA8MlwFKfgqf3DLX?$*O^ii==>Hz_waWi16j}aDCldWfM%n_ubSx9OH|y^P3q;Bn+m9;dM1e{j#p*ArJ5>#1TP z&U9~(<1E+s&Pn3wzJ31kywQ?@L&}z%p|2-BG1gfSlPC&r8N3V`B)SG%PevH+98S2X zMj>^%&+x}%a Date: Mon, 9 Nov 2020 14:56:47 +0100 Subject: [PATCH 18/57] Tested latex compilation and pdf moving. --- ...4868_example_notebook_update20201025.ipynb | 24 +++++++++--------- ...AE4868_example_notebook_update20201025.pdf | Bin 409719 -> 409727 bytes code/project1/src/Compile_latex.py | 9 ++++--- code/project1/src/__main__.py | 4 --- latex/project1/Images/4b.png | Bin 38801 -> 38609 bytes latex/project1/Images/4c.png | Bin 30119 -> 27901 bytes main.pdf => latex/project1/main.pdf | Bin 169285 -> 170557 bytes 7 files changed, 17 insertions(+), 20 deletions(-) rename main.pdf => latex/project1/main.pdf (77%) diff --git a/code/project1/src/AE4868_example_notebook_update20201025.ipynb b/code/project1/src/AE4868_example_notebook_update20201025.ipynb index 52b9371..04c167a 100755 --- a/code/project1/src/AE4868_example_notebook_update20201025.ipynb +++ b/code/project1/src/AE4868_example_notebook_update20201025.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T13:29:31.861432Z", - "iopub.status.busy": "2020-11-09T13:29:31.859911Z", - "iopub.status.idle": "2020-11-09T13:29:31.864289Z", - "shell.execute_reply": "2020-11-09T13:29:31.863259Z" + "iopub.execute_input": "2020-11-09T13:55:50.385508Z", + "iopub.status.busy": "2020-11-09T13:55:50.384285Z", + "iopub.status.idle": "2020-11-09T13:55:50.388008Z", + "shell.execute_reply": "2020-11-09T13:55:50.387262Z" } }, "outputs": [], @@ -39,10 +39,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T13:29:31.881807Z", - "iopub.status.busy": "2020-11-09T13:29:31.880574Z", - "iopub.status.idle": "2020-11-09T13:29:32.714777Z", - "shell.execute_reply": "2020-11-09T13:29:32.715369Z" + "iopub.execute_input": "2020-11-09T13:55:50.406386Z", + "iopub.status.busy": "2020-11-09T13:55:50.405597Z", + "iopub.status.idle": "2020-11-09T13:55:51.225050Z", + "shell.execute_reply": "2020-11-09T13:55:51.224341Z" } }, "outputs": [ @@ -268,10 +268,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T13:29:32.734831Z", - "iopub.status.busy": "2020-11-09T13:29:32.733743Z", - "iopub.status.idle": "2020-11-09T13:29:36.376153Z", - "shell.execute_reply": "2020-11-09T13:29:36.376782Z" + "iopub.execute_input": "2020-11-09T13:55:51.245363Z", + "iopub.status.busy": "2020-11-09T13:55:51.244610Z", + "iopub.status.idle": "2020-11-09T13:55:55.045255Z", + "shell.execute_reply": "2020-11-09T13:55:55.044465Z" } }, "outputs": [ diff --git a/code/project1/src/AE4868_example_notebook_update20201025.pdf b/code/project1/src/AE4868_example_notebook_update20201025.pdf index f4950f3ddd22398e27de7ad4e19b2f5c60006ff8..1aae3236a8a852acd02f2f8df4f88bed619b83b8 100644 GIT binary patch delta 27220 zcmY&YJihu!BT0QV;V zHy{Kw1S|wR1fm%daT3Y}@fkjHu?>9^QV|qlddzDs-Fym?7nD6ce+m*8FzAdF8VDPc zB5b7J)`5F@x+-g-Wp}_G{`qCX^;jPa;q9$7k6yF!+g1VMohSzl$Cz0II%f+FLo~jq zAtW_FHp(ZpDXaKbv!L7$)h~$mb$!A0y3u#~-gu?07GtSd?oFJPCGr%XE#MHZRE`cG z_ihfz?2P>`!5~~X_LaA52$#?Qr7l)thND~ds5tajrGtE&flMN&@}UrR{+)s)a!+`NnF-8a>Zuj=W`M!b02IG^7hCgyC6?OFHU2Bzk(#EF=|xgGEpf&h?Lrxv@Q* z=emB9l5Gex%J7YU6oKx)#_w2Nl!7YE;;q)|uJGry*dokjY8B*k? z?XCAL=qA&t!r{aHD|C&fw6{-H&c>`2bF?%k_}NTzeimLvr#!2A45&F-XlKwMQTd7O zAuz_6`ibc+UN6j)MQVZ=Of;+}_$TJH`g56-Bn*ZRQPiPe@5Uf0WGJ7R5 zb_ffTdxwz{WPk|ibbqS?AHA_smN9LYkPo{E(cf!a7*rty^>|Xz#beMEmds`XgQ^@PBdV1#ta;h$kvU 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zQu#MaZ?Av2Qo||rD(7(|v8qJtPBNi(Kl>LKXRq#1kDxbWE@!j)R(sWnJ4&Dm-5#9a zz9@xR&F8+j);$46O?KQ&wSj}hHtL7Sf)$463o|1BUpDIJsZ|Q{Q?%4Jm03z5InDse3Nn)9u8UUb~V@A{ptN-K)3> zt2neZw0)U9y9}ILREJwC<2<~6!JS)5+?;cVcr-FhbRAS&7O;#c5+rEN%pR3xpZ1zMniOJo-k}p6+36ZD;#c2czL0#B=x%VUH(AQCq}VPP3e3}Zs671 z#_z4UCr?)hSIj#vsKVZc%BbhEsm!=%Z!L-WLFj@feK_?IKD)2SW9cl)16Ofh#i##_ s=mJ0cJkVqFOzJ{{J2dlOxR(#<7gPslW}1dDh~zsvECq#xk|gZ^0B_I?MgRZ+ diff --git a/code/project1/src/Compile_latex.py b/code/project1/src/Compile_latex.py index 98329ac..8b693a0 100644 --- a/code/project1/src/Compile_latex.py +++ b/code/project1/src/Compile_latex.py @@ -27,12 +27,13 @@ def clean_up_after_compilation(self,latex_filename): def move_pdf_into_latex_dir(self,relative_dir,latex_filename): pdf_filename = f'{latex_filename[:-4]}.pdf' + #print(f'pdf_filename={pdf_filename}') destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}' #destination= f'{self.get_script_dir()}/../../../{relative_dir}' - shutil.move(latex_filename, destination) - print(f'destination={destination}') + + try: - shutil.move(latex_filename, destination) + shutil.move(pdf_filename, destination) except: print("Error while moving file ", pdf_filename) @@ -40,7 +41,7 @@ def delete_file_if_exists(self,filename): try: os.remove(filename) except: - print("Error while deleting file ", filename) + print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.') def get_script_dir(self): ''' returns the directory of this script regardles of from which level the code is executed ''' diff --git a/code/project1/src/__main__.py b/code/project1/src/__main__.py index e31ca5e..83047d4 100644 --- a/code/project1/src/__main__.py +++ b/code/project1/src/__main__.py @@ -6,10 +6,6 @@ project_nr = 1 main = Main() -# compile the latex report -compile_latex =Compile_latex(project_nr ,'main.tex') -exit() - # run the jupyter notebooks for assignment 1 main.run_jupyter_notebooks() diff --git 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[PATCH 19/57] Included jupyter notebook into pdf. --- ...4868_example_notebook_update20201025.ipynb | 24 +++++++++--------- ...AE4868_example_notebook_update20201025.pdf | Bin 409727 -> 409726 bytes code/project1/src/Compile_latex.py | 3 --- latex/project1/Appendices/AppD.tex | 2 ++ latex/project1/Appendices/AppE.tex | 2 ++ latex/project1/Appendices/AppF.tex | 2 ++ latex/project1/Images/4b.png | Bin 38609 -> 39513 bytes latex/project1/Images/4c.png | Bin 27901 -> 27144 bytes latex/project1/main.pdf | Bin 170557 -> 609184 bytes latex/project1/main.tex | 5 ++++ 10 files changed, 23 insertions(+), 15 deletions(-) create mode 100644 latex/project1/Appendices/AppD.tex create mode 100644 latex/project1/Appendices/AppE.tex create mode 100644 latex/project1/Appendices/AppF.tex diff --git a/code/project1/src/AE4868_example_notebook_update20201025.ipynb b/code/project1/src/AE4868_example_notebook_update20201025.ipynb index 04c167a..d61f247 100755 --- a/code/project1/src/AE4868_example_notebook_update20201025.ipynb +++ b/code/project1/src/AE4868_example_notebook_update20201025.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T13:55:50.385508Z", - "iopub.status.busy": "2020-11-09T13:55:50.384285Z", - "iopub.status.idle": "2020-11-09T13:55:50.388008Z", - "shell.execute_reply": "2020-11-09T13:55:50.387262Z" + "iopub.execute_input": "2020-11-09T14:05:37.060894Z", + "iopub.status.busy": "2020-11-09T14:05:37.060105Z", + "iopub.status.idle": "2020-11-09T14:05:37.062373Z", + "shell.execute_reply": "2020-11-09T14:05:37.063013Z" } }, "outputs": [], @@ -39,10 +39,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T13:55:50.406386Z", - "iopub.status.busy": "2020-11-09T13:55:50.405597Z", - "iopub.status.idle": "2020-11-09T13:55:51.225050Z", - "shell.execute_reply": "2020-11-09T13:55:51.224341Z" + "iopub.execute_input": "2020-11-09T14:05:37.079530Z", + "iopub.status.busy": "2020-11-09T14:05:37.078720Z", + "iopub.status.idle": "2020-11-09T14:05:37.939990Z", + "shell.execute_reply": "2020-11-09T14:05:37.940676Z" } }, "outputs": [ @@ -268,10 +268,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T13:55:51.245363Z", - "iopub.status.busy": "2020-11-09T13:55:51.244610Z", - "iopub.status.idle": "2020-11-09T13:55:55.045255Z", - "shell.execute_reply": "2020-11-09T13:55:55.044465Z" + "iopub.execute_input": "2020-11-09T14:05:37.960457Z", + "iopub.status.busy": "2020-11-09T14:05:37.959701Z", + "iopub.status.idle": "2020-11-09T14:05:41.724962Z", + "shell.execute_reply": "2020-11-09T14:05:41.725585Z" } }, "outputs": [ diff --git a/code/project1/src/AE4868_example_notebook_update20201025.pdf b/code/project1/src/AE4868_example_notebook_update20201025.pdf index 1aae3236a8a852acd02f2f8df4f88bed619b83b8..d01b82d9a31ca77489c9d95cc07d45cc614be58d 100644 GIT binary patch delta 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a/code/project1/src/Compile_latex.py b/code/project1/src/Compile_latex.py index 8b693a0..7c69d94 100644 --- a/code/project1/src/Compile_latex.py +++ b/code/project1/src/Compile_latex.py @@ -27,10 +27,7 @@ def clean_up_after_compilation(self,latex_filename): def move_pdf_into_latex_dir(self,relative_dir,latex_filename): pdf_filename = f'{latex_filename[:-4]}.pdf' - #print(f'pdf_filename={pdf_filename}') destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}' - #destination= f'{self.get_script_dir()}/../../../{relative_dir}' - try: shutil.move(pdf_filename, destination) diff --git a/latex/project1/Appendices/AppD.tex b/latex/project1/Appendices/AppD.tex new file mode 100644 index 0000000..e2f80bd --- /dev/null +++ b/latex/project1/Appendices/AppD.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that compiles the latex report to pdf}\label{app:4} +\pythonexternal{latex/project1/../../code/project1/src/Compile_latex.py} \ No newline at end of file diff --git a/latex/project1/Appendices/AppE.tex b/latex/project1/Appendices/AppE.tex new file mode 100644 index 0000000..0fda539 --- /dev/null +++ b/latex/project1/Appendices/AppE.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that runs the jupyter notebook(s)}\label{app:5} +\pythonexternal{latex/project1/../../code/project1/src/Run_jupyter_notebooks.py} \ No newline at end of file diff --git a/latex/project1/Appendices/AppF.tex b/latex/project1/Appendices/AppF.tex new file mode 100644 index 0000000..a1cd223 --- /dev/null +++ b/latex/project1/Appendices/AppF.tex @@ -0,0 +1,2 @@ +\section*{Appendix Example Jupyter Notebook}\label{app:6} +\includepdf[pages=-]{latex/project1/../../code/project1/src/AE4868_example_notebook_update20201025.pdf} \ No newline at end of file diff --git a/latex/project1/Images/4b.png b/latex/project1/Images/4b.png index 3646191cdf52c87d663a050a3ecc8439c0e99f08..db7a9a3087f46ed35a8dfaa2c0f165fbb6fc89aa 100644 GIT binary patch literal 39513 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z-KT-M*=`hT*_F+ltvbGSR4bn(&LS;qI9j$!F4G#Hy|~VVSgu^;n46h*|#ort0YEM$Ev%8xgDLM(NP#EBAgWD)0w%c?0vTCrw3O< z)%5qrif$@>F-!5j^t#AR)sJQ+PdOL!x#w@!>Qff?iggXn{lA|TXmZr-@h`ESYl3x* mFWVINp~Z|8@Hpq Date: Mon, 9 Nov 2020 15:43:26 +0100 Subject: [PATCH 20/57] Refactored code. --- ...4868_example_notebook_update20201025.ipynb | 24 +++++++------- ...AE4868_example_notebook_update20201025.pdf | Bin 409726 -> 409723 bytes code/project1/src/Main.py | 30 +++++++++++++----- code/project1/src/__main__.py | 14 ++++---- latex/project1/Images/4b.png | Bin 39513 -> 38433 bytes latex/project1/Images/4c.png | Bin 27144 -> 27892 bytes latex/project1/main.pdf | Bin 609184 -> 610448 bytes linux_environment.yml | 4 ++- windows_environment.yml | 4 ++- 9 files changed, 48 insertions(+), 28 deletions(-) diff --git a/code/project1/src/AE4868_example_notebook_update20201025.ipynb b/code/project1/src/AE4868_example_notebook_update20201025.ipynb index d61f247..8a1fd83 100755 --- a/code/project1/src/AE4868_example_notebook_update20201025.ipynb +++ b/code/project1/src/AE4868_example_notebook_update20201025.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:05:37.060894Z", - "iopub.status.busy": "2020-11-09T14:05:37.060105Z", - "iopub.status.idle": "2020-11-09T14:05:37.062373Z", - "shell.execute_reply": "2020-11-09T14:05:37.063013Z" + "iopub.execute_input": "2020-11-09T14:41:15.476829Z", + "iopub.status.busy": "2020-11-09T14:41:15.476111Z", + "iopub.status.idle": "2020-11-09T14:41:15.478753Z", + "shell.execute_reply": "2020-11-09T14:41:15.479317Z" } }, "outputs": [], @@ -39,10 +39,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:05:37.079530Z", - "iopub.status.busy": "2020-11-09T14:05:37.078720Z", - "iopub.status.idle": "2020-11-09T14:05:37.939990Z", - "shell.execute_reply": "2020-11-09T14:05:37.940676Z" + "iopub.execute_input": "2020-11-09T14:41:15.495908Z", + "iopub.status.busy": "2020-11-09T14:41:15.495082Z", + "iopub.status.idle": "2020-11-09T14:41:16.354546Z", + "shell.execute_reply": "2020-11-09T14:41:16.353921Z" } }, "outputs": [ @@ -268,10 +268,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:05:37.960457Z", - "iopub.status.busy": "2020-11-09T14:05:37.959701Z", - "iopub.status.idle": "2020-11-09T14:05:41.724962Z", - "shell.execute_reply": "2020-11-09T14:05:41.725585Z" + "iopub.execute_input": "2020-11-09T14:41:16.374621Z", + "iopub.status.busy": "2020-11-09T14:41:16.373673Z", + "iopub.status.idle": "2020-11-09T14:41:20.162144Z", + "shell.execute_reply": "2020-11-09T14:41:20.162789Z" } }, "outputs": [ diff --git a/code/project1/src/AE4868_example_notebook_update20201025.pdf b/code/project1/src/AE4868_example_notebook_update20201025.pdf index d01b82d9a31ca77489c9d95cc07d45cc614be58d..15508d8cc0893d01477946980b40ac69f4542905 100644 GIT binary patch delta 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zHSz@3d~RLv_i(qjavRnHpWr73c_9AR`B*sox8K9u!^_XZ+6IY>jh};!4T+jsT2%(= F{{fR>(Om!l diff --git a/code/project1/src/Main.py b/code/project1/src/Main.py index 4b3dbaf..ed01215 100644 --- a/code/project1/src/Main.py +++ b/code/project1/src/Main.py @@ -4,13 +4,17 @@ from matplotlib import pyplot as plt from matplotlib import lines import matplotlib.pyplot as plt +import numpy as np + +from .Compile_latex import Compile_latex from .Plot_to_tex import Plot_to_tex as plt_tex from .Run_jupyter_notebooks import Run_jupyter_notebook -import numpy as np +# define global variables for genetic algorithm example string_length = 100 mutation_chance= 1.0/string_length max_iterations = 1500 + class Main: def __init__(self): @@ -18,18 +22,28 @@ def __init__(self): pass - def run_jupyter_notebooks(self): - ''' runs a jupyter notebook''' + def run_jupyter_notebooks(self,project_nr,notebook_names): + '''runs a jupyter notebook''' print(f'Running AE4868_example_notebook_update20201025.ipynb') + notebook_path = f'code/project{project_nr}/src/' + + for notebook_name in notebook_names: + self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}') + + def convert_notebooks_to_pdf(self,project_nr,notebook_names): + '''converts a jupyter notebook to pdf''' + notebook_path = f'code/project{project_nr}/src/' - self.run_jupyter_notebook.run_notebook('code/project1/src/AE4868_example_notebook_update20201025.ipynb') + for notebook_name in notebook_names: + self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}') - def convert_notebook_to_pdf(self): - ''' converts a jupyter notebook to pdf''' - self.run_jupyter_notebook.convert_notebook_to_pdf('code/project1/src/AE4868_example_notebook_update20201025.ipynb') + def compile_latex_report(self,project_nr): + '''compiles latex code to pdf''' + compile_latex =Compile_latex(project_nr ,'main.tex') ################################################################ - ############example code to illustrate latex image sync############### + ############example code to illustrate python-latex image sync######### + ##############runs arbitrary genetic algorithm, can be deleted########### ################################################################ def count(self,bits): count = 0 diff --git a/code/project1/src/__main__.py b/code/project1/src/__main__.py index 83047d4..0e7d00a 100644 --- a/code/project1/src/__main__.py +++ b/code/project1/src/__main__.py @@ -1,23 +1,25 @@ import os from .Main import Main -from .Compile_latex import Compile_latex print(f'Hi, I\'ll be running the main code, and I\'ll let you know when I\'m done.') project_nr = 1 main = Main() +notebook_names = ['AE4868_example_notebook_update20201025.ipynb'] + # run the jupyter notebooks for assignment 1 -main.run_jupyter_notebooks() +main.run_jupyter_notebooks(project_nr,notebook_names) # convert jupyter notebook for assignment 1 to pdf -main.convert_notebook_to_pdf() +main.convert_notebooks_to_pdf(project_nr,notebook_names) # compile the latex report -compile_latex =Compile_latex(project_nr ,'main.tex') +main.compile_latex_report(project_nr) ################################################################ -############example code to illustrate latex image sync############### -################################################################ +############example code to illustrate python-latex image sync######### +##############runs arbitrary genetic algorithm, can be deleted########### +################################################################ # run a genetic algorithm to create some data for a plot. print("now running a") res = main.do_run_a() diff --git a/latex/project1/Images/4b.png b/latex/project1/Images/4b.png index db7a9a3087f46ed35a8dfaa2c0f165fbb6fc89aa..dc1bfcb14067750cabea342db96cd40fe077e0f2 100644 GIT binary patch literal 38433 zcmeGERa9JE6E=!2Ab5hiyK6{r4-#A(cL)&N9U4Li5}Y8xC3ukF?h=BB;O_1&4SVr^ z`<(G#oXc~uFZM-`X7uVcyH-`rnpMwJMYyW6>?<@9Gyniz$-S3S2LSj$0DxOTK>|N{ 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zE7?Z2lLoSb>?FHLBWWT@(hLlA3NZRcxsp(BM9FpK_8qdI+@De&)GE)$mFGd_Rh2Rs zu@B!z_G))iyLV&O9U%LGx4*Uf&|%#nau9f*)$aR4>yD6r!y1dVn|9NI?G`i$zKPNZ5*!2R{qrVH>w z0f84EwAcqZZ=~=w`r(aL+jI%u<9eG0;7$LuX%OD5Zu6bH7DwQhKC)J==3.0 + - nbconvert - pip - pip: # works for regular pip packages - sklearn + - pdflatex diff --git a/windows_environment.yml b/windows_environment.yml index e2f89d7..0a17012 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -1,4 +1,4 @@ -# run: conda env create --file linux_environment.yml +# run: conda env create --file windows_environment.yml # include new packages: conda env update --file windows_environment.yml name: tudat-space channels: @@ -11,7 +11,9 @@ dependencies: - nb_conda - conda: - pytest>=3.0 + - nbconvert - pip - pip: # works for regular pip packages - sklearn + - pdflatex From 76b6f71b392e60420cafada4bd6891ede1d2f408 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 15:52:52 +0100 Subject: [PATCH 21/57] Verified compilation for all 3 assignments. --- code/project2/__init__.py | 0 ...4868_example_notebook_update20201025.ipynb | 491 ++++++++++++++++++ ...AE4868_example_notebook_update20201025.pdf | Bin 0 -> 409725 bytes code/project2/src/Compile_latex.py | 48 ++ code/project2/src/Main.py | 151 ++++++ code/project2/src/Plot_to_tex.py | 126 +++++ code/project2/src/Run_jupyter_notebooks.py | 41 ++ code/project2/src/__init__.py | 0 code/project2/src/__main__.py | 39 ++ code/project2/src/juice_propagation_Q1.ipynb | 355 +++++++++++++ code/project2/src/juice_propagation_Q4.ipynb | 335 ++++++++++++ code/project2/test/__init__.py | 0 code/project2/test/test_main.py | 26 + code/project3/__init__.py | 0 ...4868_example_notebook_update20201025.ipynb | 491 ++++++++++++++++++ ...AE4868_example_notebook_update20201025.pdf | Bin 0 -> 409725 bytes code/project3/src/Compile_latex.py | 48 ++ code/project3/src/Main.py | 151 ++++++ code/project3/src/Plot_to_tex.py | 126 +++++ code/project3/src/Run_jupyter_notebooks.py | 41 ++ 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mode 100644 latex/project3/Images/acceleration_norms.png create mode 100644 latex/project3/Images/ground_track.png create mode 100644 latex/project3/Images/kepler_elements.png create mode 100644 latex/project3/Images/total_acceleration.png create mode 100644 latex/project3/main.pdf diff --git a/code/project2/__init__.py b/code/project2/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/project2/src/AE4868_example_notebook_update20201025.ipynb b/code/project2/src/AE4868_example_notebook_update20201025.ipynb new file mode 100755 index 0000000..d38a61c --- /dev/null +++ b/code/project2/src/AE4868_example_notebook_update20201025.ipynb @@ -0,0 +1,491 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T14:50:11.050206Z", + "iopub.status.busy": "2020-11-09T14:50:11.048460Z", + "iopub.status.idle": "2020-11-09T14:50:11.053095Z", + "shell.execute_reply": "2020-11-09T14:50:11.052420Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "#import ipyparams\n", + "#currentNotebook = ipyparams.notebook_name\n", + "#print(f'currentNotebook={currentNotebook}')\n", + "#currentNotebook = os.path.dirname(os.path.realpath(__file__))\n", + "#currentNotebook = os.path\n", + "#currentNotebook = os.getcwd()\n", + "#if not 'workbookDir' in globals():\n", + "# workbookDir = os.getcwd()\n", + "#print('workbookDir: ' + workbookDir)\n", + "#currentNotebook = os.chdir(workbookDir) # If you changed the current working dir, this will take you back to the workbook dir.\n", + "#currentNotebook = %pwd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T14:50:11.069982Z", + "iopub.status.busy": "2020-11-09T14:50:11.068722Z", + "iopub.status.idle": "2020-11-09T14:50:12.000281Z", + "shell.execute_reply": "2020-11-09T14:50:11.999693Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Single Earth-Orbiting Satellite Example.\n", + "The initial position vector of Delfi-C3 is [km]: \n", + "[7037.48400133 3238.05901792 2150.7241875 ]\n", + "The initial velocity vector of Delfi-C3 is [km/s]: \n", + "[-1.46565763 -0.04095839 6.62279761]\n", + "After 86400.0 seconds the position vector of Delfi-C3 is [km]: \n", + "[-4602.79426676 -1421.16740978 5883.69740624]\n", + "And the velocity vector of Delfi-C3 is [km/s]: \n", + "[-4.53846052 -2.36988263 -5.04163195]\n", + " \n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# IMPORT STATEMENTS ###########################################################\n", + "###############################################################################\n", + "import numpy as np\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from tudatpy.kernel.astro import conversion\n", + "\n", + "# Set path to latex image folders for project 1\n", + "latex_image_path = '../../../latex/project2/Images/'\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Set simulation start and end epochs.\n", + "simulation_start_epoch = 0.0\n", + "simulation_end_epoch = constants.JULIAN_DAY\n", + "\n", + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Create default body settings for selected celestial bodies\n", + "bodies_to_create = [\"Sun\", \"Earth\", \"Moon\", \"Mars\", \"Venus\"]\n", + "\n", + "# Create default body settings for bodies_to_create, with \"Earth\"/\"J2000\" as \n", + "# global frame origin and orientation. This environment will only be valid \n", + "# in the indicated time range \n", + "# [simulation_start_epoch --- simulation_end_epoch]\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create,\n", + " simulation_start_epoch,\n", + " simulation_end_epoch,\n", + " \"Earth\",\"J2000\")\n", + "\n", + "# Create system of selected celestial bodies\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle objects.\n", + "bodies.create_empty_body( \"Delfi-C3\" )\n", + "bodies.get_body( \"Delfi-C3\").set_constant_mass(400.0)\n", + "\n", + "# Create aerodynamic coefficient interface settings, and add to vehicle\n", + "reference_area = 4.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0]\n", + ")\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"Delfi-C3\", aero_coefficient_settings )\n", + "\n", + "# Create radiation pressure settings, and add to vehicle\n", + "reference_area_radiation = 4.0\n", + "radiation_pressure_coefficient = 1.2\n", + "occulting_bodies = [\"Earth\"]\n", + "radiation_pressure_settings = environment_setup.radiation_pressure.cannonball(\n", + " \"Sun\", reference_area_radiation, radiation_pressure_coefficient, occulting_bodies\n", + ")\n", + "environment_setup.add_radiation_pressure_interface(\n", + " bodies, \"Delfi-C3\", radiation_pressure_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated.\n", + "bodies_to_propagate = [\"Delfi-C3\"]\n", + "\n", + "# Define central bodies.\n", + "central_bodies = [\"Earth\"]\n", + "\n", + "# Define accelerations acting on Delfi-C3 by Sun and Earth.\n", + "accelerations_settings_delfi_c3 = dict(\n", + " Sun=\n", + " [\n", + " propagation_setup.acceleration.cannonball_radiation_pressure(),\n", + " propagation_setup.acceleration.point_mass_gravity()\n", + " ],\n", + " Earth=\n", + " [\n", + " propagation_setup.acceleration.spherical_harmonic_gravity(5, 5),\n", + " propagation_setup.acceleration.aerodynamic()\n", + " ])\n", + "\n", + "# Define point mass accelerations acting on Delfi-C3 by all other bodies.\n", + "for other in set(bodies_to_create).difference({\"Sun\", \"Earth\"}):\n", + " accelerations_settings_delfi_c3[other] = [\n", + " propagation_setup.acceleration.point_mass_gravity()]\n", + "\n", + "# Create global accelerations settings dictionary.\n", + "acceleration_settings = {\"Delfi-C3\": accelerations_settings_delfi_c3}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies,\n", + " acceleration_settings,\n", + " bodies_to_propagate,\n", + " central_bodies)\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Set initial conditions for the Asterix satellite that will be\n", + "# propagated in this simulation. The initial conditions are given in\n", + "# Keplerian elements and later on converted to Cartesian elements.\n", + "earth_gravitational_parameter = bodies.get_body( \"Earth\" ).gravitational_parameter\n", + "initial_state = conversion.keplerian_to_cartesian(\n", + " gravitational_parameter=earth_gravitational_parameter,\n", + " semi_major_axis=7500.0E3,\n", + " eccentricity=0.1,\n", + " inclination=np.deg2rad(85.3),\n", + " argument_of_periapsis=np.deg2rad(235.7),\n", + " longitude_of_ascending_node=np.deg2rad(23.4),\n", + " true_anomaly=np.deg2rad(139.87)\n", + ")\n", + "\n", + "# Define list of dependent variables to save.\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.total_acceleration( \"Delfi-C3\" ),\n", + " propagation_setup.dependent_variable.keplerian_state( \"Delfi-C3\", \"Earth\" ),\n", + " propagation_setup.dependent_variable.latitude( \"Delfi-C3\", \"Earth\" ),\n", + " propagation_setup.dependent_variable.longitude( \"Delfi-C3\", \"Earth\"),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Sun\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Moon\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Mars\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Venus\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.spherical_harmonic_gravity_type, \"Delfi-C3\", \"Earth\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.aerodynamic_type, \"Delfi-C3\", \"Earth\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.cannonball_radiation_pressure_type, \"Delfi-C3\", \"Sun\" \n", + " )\n", + " ]\n", + "\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")\n", + "\n", + "###########################################################################\n", + "# PROPAGATE ORBIT #########################################################\n", + "###########################################################################\n", + "\n", + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + "states = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history\n", + "\n", + "###########################################################################\n", + "# PRINT INITIAL AND FINAL STATES ##########################################\n", + "###########################################################################\n", + "\n", + "print(\n", + " f\"\"\"\n", + "Single Earth-Orbiting Satellite Example.\n", + "The initial position vector of Delfi-C3 is [km]: \\n{\n", + " states[simulation_start_epoch][:3] / 1E3}\n", + "The initial velocity vector of Delfi-C3 is [km/s]: \\n{\n", + " states[simulation_start_epoch][3:] / 1E3}\n", + "After {simulation_end_epoch} seconds the position vector of Delfi-C3 is [km]: \\n{\n", + " states[simulation_end_epoch][:3] / 1E3}\n", + "And the velocity vector of Delfi-C3 is [km/s]: \\n{\n", + " states[simulation_end_epoch][3:] / 1E3}\n", + " \"\"\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T14:50:12.020160Z", + "iopub.status.busy": "2020-11-09T14:50:12.018993Z", + "iopub.status.idle": "2020-11-09T14:50:15.670078Z", + "shell.execute_reply": "2020-11-09T14:50:15.670718Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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C8NDhSQoVg82y4feylIB9l+y7bw5PcmmzOrL1zrZtHj6yn23ynF8rh77e+XZ4abWMEFai71Y8emxypKX1L62WSSeiHJ9Jv+rrkYjgdUt5XloNt0r1rkReCPEQ8BLw58BfA18QQpzY8yNZ4Oe8WV64cX6tzH3zWWLR1/4z3H8oN9KX1oW1Mq/bU1u3Fw8u5XlxRB1ruL1trEsrN7K26ZiSl9fLr6rJtDGRiHJyLjOyhGy33mJlt7GvbZYmU0yl4yNrm+tbNZomvG7ptbY5vZglFhEjGzh8uVums99980AvyBFuJ+l2ON8NYOx3ph4Y8QDQ+dU77Rvra6NqG7v07czi/vumY0pe2aj4vSwlcGGtTDYZe1UZj40HDuXYrbdYHdHeUS+vlzk+kyadiL3me/cv5rm+VaPWHM3eUS+tlrj/UK7X4HgvHlzK89JqKdRc6iAZ+V8EPgtMAkeAy8BnhBCnvVzYKODCWum2ZPV1h3KUG+2RvLR6hGyfFx1YUf6rxepINrzrE7LXRmXBurTOr5ZGsqb3WrGK0TbHAaB9YDuPD+yzb4QQPHAoP7Jk9Xx3T+y3b5KxKPfOZ0eWdJy3ifw+WaD5XJK5bHKkbSPE/oTs3vksiWhkhO+bEtlkjKPTryVko65yOb9W5sRsmkzytYTMDnKM6r45v1bmzGL2NYo6GAfHzq+VbusT338oi5RwcX30AkBSSs6vlXndbXziB5bylI02N7fD26LtIET+K4B/JqWsSilXpZTfBfw34JwQ4oy3ywsvdmpN1kvGvtF8oEfUbInaKOFql5DdzjYPLuWRsu9kjhJeXr99pgOsIEe12eH6CM537pGOO1zoN7frIznf+cIdsodg7Zvza+WRnHhwfq2MAE4v3N42o5p1vrBWJpeKcXgyte/3H1jKjWwA6MKalSHbj5DFoxHuW8iO7L55ac1SRu1HyKbSCZYmUyNLyF66QwLnxGyGiXh0JG0jpeTCWvm2SQo7mDiKfl+j1eFqsXZ3vjCCtlkrNditt3hwH0Ud9Mt7wlx6cBAinwRe5d1JKX8ci8x/CnjAg3WFHvZldNuD2Y28jeLBvHAXQmZnQC6NoPzMzh7ebt+c7tpmFKV559fKRIQlh94P9pm6tDmatsmnYizdhpDdv5jDaJvc3B7FAFCJxbRgIhHd9/tnFnOslRqUGqMXADrfJR37ETKwAoqXNisjqQC6EyEDyzYvj+D7u0/Ibm+b+w/luDCC2cNGq8PVQvW2ZDUaEZxZzPYC9qOEjbLBbr112zNlKzxG0Sd+ZaNCx5S3PVPHZ9Kk4pGRDHJc6HGp/c/UvfNW4+Mw+30HIfIXgLfc+kUp5Y9hkfk/d3tRo4CLXaK1nywPYDIdZ2kyNZKXli0Pum9hf0J2ZHqCRDTCpUJ4D+bt8MpGhVzy9oTM7tZ+eQRtc2mj0n2h7U/ITnUv9Mubo9eA6pWNCqcXb0/IerYZweZclzarHM7e/lV4qnumro6gbS5vVm97DwOcmstitE1WdsMrW9wPrY7J9WLtjra5Zz7DWqkxcnWrW9Umu/UW983fad9kuFqohrpudT9c36phyj652A/3zGe5Whi9gKodvLhdIB66thnBCSKv3IUvWAGg3EgGgK5038v33OZM5VJxFnLJ3s+FEQch8n8K/E/7fUNK+aPAfwH29w7HuC2uFaqk4hEO5fcnZGDV2Y2iY32tWOXwZOq2GbJoRHBiNj2ShOxqscbJucxtCdlUOsFMJjGitqlycu72DtLxmTSxiODKCAY5rhVrnJy9g/M4N5pBjo4puV6ssZi5/avw3hENAJUaLYrV5h33jR3kCLOTtB9Wduq0TXkX21iEZNRsc7VokdBTd7iL75nPUm91WC+NVud6ey/cyTan5jIs79RHrnP9gfbNXIYrm6MXALparCIEr+nKvhf3zGVG7q4BK8CeTcaYzSRu+zP3zGe4PMoZeSnlL0op33WH7/9jKeV4Hv2AuFqscWIms2+XRRsn59IjmQW6Wqxy4g4OEoT/YN4Olm1uf5mDdaGPWgBISnlXshqPRjg+M3oBoHqzw1qpwck77JuZTIJ8KjZyQY61UoNmx2Qxfft7+PhsmogYPbXCtW5W8E538agGOWyH+U6Bw1ENctg+y53eU6OqHLtWtG1z930zapnn68UqyViExdztk1un5jKUjTaFStPHlQWP68UaS/nUbdWGYO2pld3RDACdnEvfNrkFVuAwzO/vMQEPCNcOQMhOzmbYrbfYro7WpXWtezDvhHvms1zfqtHumD6tKni0OiY3t+t3JKtgBznCe2nth2K1ScVo3z3IMT96UWu78eGJO5AOIYT1shuxfXOtuxcW0rd/FSZjUY5Op0cucGgTiTvdxfO5JJlEdOTO1LWiHeS4vW1su10ZtTNVrBIRcHT69rY5NaIKoKvFGjOZBJMT8dv+TC8ANIK2OTGbvmNyq79vRu8uvlty69RcBikZuT43B0r8zWXYqYWXS7lO5IUQc0KIvyWE+BtCiNuHj0YYpim5tlW7YzQf6BG2UYrM2nLOg1xarY4M9UiJW7G8XadjygOQ1SyFijFSzbnsTMfdghynuvKzUWrO1SNkB1ByjBohs+Wcd8rIQ3/fjBJ62cOZOweATs2PngLoarFKOhFlPpu87c+kE1Yvk1HbN1eLNY5Op0nEbu9eHsqnSMUjo2ebwt0TOD2yOmK2uXZAsgqjp3I5SHLL3ldXRqi/gp3cOnWA5BaEVwE0NJEXQvyQEOIJIcTMnq+9GXgJ+GPgg8DjQog7W3gEsVZq0Gybd8/Idw/uKBH5613H+m6k496QH8z90M+Q3T36CKMV0b9auHuGDKwgh9E2Wd4ZnQDQQeScYL3sVndHqznX1WKVRCzCdOpgRH6UajOvFGocyt++V4mNe+ayI5chu1ascWL29r1KbNwzn+HSiJGOg5R/RSKCk7OjGBy7c/kXQCYZYzEf7uZct8I0rdK4E3eoAQc4PDVBIjZaAaByN7l1/A4BVegHOa6NEF+4edDkVrdfyaWQ+sROMvLfDUgp5daer/0KMA38PhaRfyvwDx08I5S4esDs4bGZNEKMVoTt6gFJh207W+I4CjiInBNGs8buIHJOGM19cxA5J/QDRKPUMflqocqJmTSRAxCyWrPDRnl0mnMdpPwLrH2zvFPHaI9ObebVQpVTd8mQgXXfjFKfGyklVwrVu/o2MHplTo1Wh5Xdu5fGASMX5NgoGxht847lX9BtdDyTHim1wrUDJrem0tY7fpT2je3j3qlBIsDR6QmiEcGNrXD6Nk6I/GngWfs3Qog54J3A70op/4GU8m8CXwD+jrMlhg8HJWTJWJTDkxMjFWE7qG1mMgkm4tGRktYfRM4J1ng+YMRsU7PGEt5BzglwbMa2TTgv9P1wEDknwLFuEGSUbGNnVu+Gvm1G60wdhHQcm55ASljdafiwquDR7pjc2D7Yvjk+k2a33hqZMqedWotyo31X1RhYZ2p5uz4yZU43tmpIeeeeEzaOz6RH6h4+aPkX2LYZnXu47xMfIAA0lxmpBE6/seadbROLWhPCwrpvnBD5WWBjz++/qvvfP93ztceAEw6eEUpcLVZJRCMsTU7c9WdPzWV6dZyjgKuFKgu5JOlE7I4/J4Tg2MxEaCNs++FascbxmTt35wSrNnM2kxgpR+BasXrHWl4bh/IpohER2gt9PxxEsghW1BpGh6xKKbm2dbAgR982o3GmqkabQsXg+IFsM1pBjtXdBq2OPOCZsn5meURsc81urHnA+6bZMdmsjIbKxW46eqcRYjaOTqdZLxkjo3I5aI8bsPbNqNzDsFelevd9c2ImPVJqw5vbdSbiUeaytx89Z+PodHj5ghMivwXM7fn9OwETeHzP1yRw+1kSI4qb23WOdKUed8ORqYmRcQLAso3tNN8NR6dHKzK7vF3n2AGcAICjIxa1Xt6p97Ltd0IsGmFpMsWNEXEE2h2TtVLjriUHMHoql61qk0bLPNB9M2oql5VuD4mD2GbUVC62bY4cwDajFhwbzDajpQAaZt+sjIjK5eZ2nYiApcm704Wj02nKjTa79dFQudzcrjOTSZBJ3jm5Bda+Wdtt0BkRlcvKTp3DU6m7JrfAKlUO6z3shMi/BPxNIcSsEGIKq2b+C1LK0p6fOQmsOXhGKGFvvoPgyPQEhYoxMrMhV3brHDkA6YBuhG1EnACw9s2RqYMGOSZCe2ndikarQ6HS5PABFC5gSTpHxTYbZYOOKTl8gH0jhBipbIftJB/ENqOmcrGbQR7kvhk1lcvKrvX3PMi+GTUlh01WB7PNaOyb5Z0GiWiEucydS+Ng9PbN8k6dQ/kUsejdKcmo2WZQvtA2JRvl0QgAWbY5uE+8Xm6EUuXihMj/X8AScBO4ARwCftP+Znf03FcDzzhZYBixslM/MOmwHamVEeiybZqS1d0Ghw8QlQWLkI1KZLbUaFE22ge+0I9OT4xM/eHq7sEJGYyWNK/vWB/wTM2kuTEyjvXBySqMlsplkDNlq1xG50x1bXOAd/ioqVyWd+pkkzHyqbtnD0dR5bI0lbrjnHQbR2dGrFxlp8HSgd/fI2ab3YPzBfu+HhUV7/JOY4DkVhopw6lyGZrISynfj9WR/gXgAvATUsr/sudHvh5LVv8RRysMGVodk42ycWDSYb/sRmFcVrHapNk2ByJkQGjrXvZikEwHWEGOZnevhR2D2sauPxwFlcvAZHUkgxxjlcutWNmpE40IFnJ3zx7CaNlmeceSut5tLB+Mosrl4FLXUVO5DJLAWcwliUXE6Nhmd7DMKowOkV/ZaRzcNlOjwxcsJaYxkG8D4VRyOMnII6X8LSnlW7q/fu2W731ESjktpfwtZ0sMF9Z2G0h58AzZKGXkhyFkMBoXut0R+iANEmFPkCOEl9atWBmCrO79c2GGnVk9eLZjYmRULqu7dVLxCNPpO4/lszFKKpflnbpFJg4gdYXR6lcyiNQVRivIsbLTOPA7CkZL5TKIDDgWjbA0Fd4u23thmpLVncaBz9RUOk4mER2JrHOp0aIygBJzlFQuawMqMY+FWOUyEJEXQvy6EOId4iDh1jH2xaBk9dBkiogYDanMoDLgMEfYbsXgmdXRaSRkS6UWJw+WPQzzhX4rVnbq5FMxsgdolAP9MWujoXJpcHhy4kDZQ7DO1Kh02R6EdEC46w9vxSCZVRjFIMdg+2YUfBu76eiRQQJAU6Oxb4rVJs2OeeAzZalcRmM836B8IZ2IMZ2Oj0SSYnlA29gqlzD6NoNm5H8Y+CSwLoT4HSHENwsh7t73f4weBmmUAxC35x+OwMFc2T147SFYkdlsMjYSL7uVnTqxiGB+AKkrwI2t0bDNfC5JMnZ3qSuMnlphMMd6dAJAy0OQDhihIMcAtjkW4vrDvZBSsrw9+L4ZhVnyjVaHYrU5GFmdnuDmTvhVLutlA1Me3O+D0SlzGpSswuioXIaxzZHpiZGQ1g+a3AqzymVQIn8Y+CHgKeDvAX8BFIQQfySE+G4hRM7tBYYNgzTKsXFkRKLWKzvWTMipA0pdhRAsTaZY3R0N2xyaTB1oZCFAKh5lNpPoSavDjEHq6wAWckkioi/NCjMGaQYDfTXMSOybASXSvTKnkNvGajo62Jmyf3Y15A5kqdGm2uwMdKaOjEgpz6BNR8Gq6W22TQrVcKtchiVk6yWDZtv0allKwPbfDjJ6zsaRkQlyDMEXRmRk9cpOHSEOrsQEWMpPsFYK3/t7ICIvpdzo1sW/C1gAvgermd03A38IbAghPiCE+AEhxLz7y9UfKzt1ptPxAzXKsXFkajQibIM0yrFxaDI1EoTMlgEPgkOTKdZDeGndiuWd+kBZoFg0wkIuNUJk9eD7ZiaTIBGNhP5MGe3OQE1HwTpPAOsht02hYtDqyIHOlO2Eh/1MDUPIbNuE/UwNY5vFvH2mxkT+Vtj7JuyjxJa7ZHWQ4NihyRSlRptas+3VspTAoEpMsPbY8k4dKcOtclnZqTOfPbgSE8LLF5x0rS9JKf+rlPK9wDzw7cAfAV8O/DawIoT4lBDiR4UQJ1xZbQgwqGMNVvRxbbdBJ+Tys2FsY2Xkw3cwb4WVdT64Yw3WfOew20bKbqOcIYIcYbzQ96JqWE3rBjlTQggWJ5OhjFrvhU0cBrFNLhkjk4iG/kwNWnsI/SBH2PfNaq807uB38aHu3RT2+2ZQqSv0m7eGXVXXP1MH3zd2kCPs+2Zlx2o6elAlJli+DYyGbRbzB1dignX+as1O6BvWDlr+BRZfsBqOh4tLOepab0NKaUgp3y+l/H5gEfg64D8Bp4BfAy4LIZ5y41m6Y5jNd3hqgrYpQx+ZXdkdhpBNsFkxaHXCKz/rmJK13cH3jUVWw+0g7dRa1FudA3dltzEKJRnDkA6w5GejQlYHIR1CCOtMlcK9b3pyzgFsk+qWRIXdsV4ewjYLuSRCjIZaQYg+AT0IRiUAZCsx04mDNR2FfpAj7Laxy3gGVWJC+G2zsjtYaRz032lhV/Gu7NQHts2hyRTNjslWtenRqoKBK0R+L6SUppTyk1LKfyKlPI6Vof9lYDCLhxQru3UOD1ALBP3o43opvPIzo91hc0CpK1iETErYDPG89M2yQduUQ9lmu9YK9bz0PiEb7Ewt5lOhPk8wHOmA0VAr2FLXQeoywbLNKBAyGKwuE0ZDAWRLXeeyB5e6xqMR5rLJ0Jc5rezUmcsmScQO7lbOZhLEoyL0+2Z1wLF8sIeshtw2g/ZxgT1BjpDbZtA+LgCLdglYiO8bKSWru43eGTkowloC5jqRvxVSyi9IKX9aSvmg189SHRWjTbnR7kntDopRkFhtdEnVwI51PpwHcy+GaQYDoyHptP9ug56ppclU9zyGV35m13IfGiBDBl35WSl88rO9WC/b+2bQ+2Yi1OcJLAcwFY+Qnzh49hDsfRPuLNB6qTGw1BVGowRsvWQMfNdEIoKFXCr0fSfWyw0W8wcP/gDkUzEm4uEv5VnbrQ+8b3rS+hCTVdOUrJcaA/s2o5D4Kxtt6q3OwGeq15MjZPvGEZEXQhwVQvyqEOITQogLQojL+/y65NZidcdGyXYeB9t8h0Ygwmb/3RYGPJijELXe6KoNBpEsQnijj3vRt81439wK+0wN0igHrH3WbJts18Ib5NgoGeSSsYGkrmCdqY2yEep+JRtlg8X8YE1HYTSUHBslY+DzBNaZCrttrCDH4LYZhSDHRskY+P1tT+UJM1ntmJLN8uC2mUhEmZwIdynPdq1JqyMHPlPz3VKeMNvGTvwN7hPbPTnCZZuhibwQ4izwMvBjwNuBNCD2+eV51l8X2BGyhdxgm28mbcnPwnyhOyer4c0E2QGghQEdyH4dWXhts15qIAQDSV0hvBf6XmyUDabScVLxg3d1hRE5U+XGwEFDsM5Ux5QUKuHNdqyXGiwO+I4CS61QqDRDPS7LCVkN8/sbrBKwhQHf32BJgcNsG/u+GPT9DeEPABWrBqYcPBAP4S/lsX3iQflCPBphNhPuUp6NIZMU87kk0YgI3ZlyQrL/DRAFvhdISSmPSSlP7ffLnaXqD7tZ3aCX1ijIz9aHJKuTE3FS8XCPy9ooG0QEzA5IVkeh7GCjbHTrLAe7ynojoUL8slsvNYZyHkdDrWAM7CDB6Jyp+SHJKoRbObYxRPYQrDO1W2+FdlxWs21SrDaHum+W8uHsJG2jWLHI6jBBjqWQq1zszOr8MHdxyMfrDqs2BEv1G2rfpselBts30YhgIZcM3fvbCZF/BPhDKeV/kVKGNwTvIpxcWov5ZG/zhhEbZYN4VDCdTgz05yz52QSrYb60Sg3mssmB6zIzyRj5VCzkjkBjqPNkZ2NDbZshSccodEveGKJmFfYGOUKsVhgyI78Y8uBYo2WNdBoqOBbyPje2QmXYIEe91aFUD2eQo6/EHO6+WS81MENaymMnt4ZSR4U8I99Pbg0XcA5zjfyGC2cqTHBC5LeBLbcWMgrYKDdIxiLkU4PVZUL46w/XSw3ms0kiA5JVsC6tMNtmo2wM9aIDi5SF3TbDELJkLMpsJhFqR8AKcgxum7DKz2xIKa2M/JAZMggvIasYbarNzlD3TdhtY09GGWbfhF3lMqyiDvq2WQ1pCZhtm2GDHG1TUqiGk5QNW+sMlm0KIR493L9vhivJCBtZ3Yv1kkE6ESWbHIJL5cM3etgJkf8A8E63FjIKWC8N10QIrKhcmCNsm2WD+SEucwi//Gy9ZAyVIYNuACjEF/pGeTj5ONjBsXBd6DZMU7JZGS4jH40I5rPhk5/ZKNXbNNvmUPtmJpMgEY2EVgG00SMd45KMW+GErIZd5TJsjxsIfwBowwEh63Ug3w2n72f7tPMDlg1Cf/TwRkhHD2+UGuRTsYF73IB1DreqTYx2OEcPW4q64biUPUI2TKU8Toj8TwOTQoj/IITIuLWgMMMp6agYbSpGWOVnDRYd2Ga91AhtJ+nNIRtzQbg7Ag/b8dZGmG1jd7x1FuQIp23We3LOwfeNEILFyWR4bTNkQ1aAXDJGJhHecVlOyGrYeysM25AVwj8mddiGrLC3KWs4A84b5QbT6TiJ2OBUZDHkZU7Dqsagf99shDT5N+z0ELD8vlqzQzlEXGpoIi+lLADfBPxtYE0I8ZQQ4q/2+fUJ11arOYatWYXw19g5k49b8rNiCDtJtzomhUpzKMcaLKezUDFC2Una7njriKyOM2T7wgpyhNNB6sk5h3UE8hPhJWRDNmQFO8gR3lnyTjLyYR+XNWxDVrDsKUSIgxxDNmQFWOyOKg7ze8rJOwpgLaRqBSeJPzvIEVZ5vZ2RHwZhDBw6GT/3EPA4MA1kgDcCZ2/zawycRZEWexG28Gw+G41Wh51aa2j5uN3sLIwSK7uJ0LBBjsW8JT8L47gsJ80jARZzKXZqrVDKz5yQDrD2TVij+T3bDOkILIawWY4Np2cqzP1Khm3IauNQPryBw/VuP45BG7JC+MdlbZQaQwfi5zJJYiHuVzJsHxfYq3IJa+Bw+CCHHYgN433T63Ez5L6x/1yY/Bsn0vp/B8wCPwucAOJSysg+vwYv8Aghql1Z/PCELLwH00lTj71/biOEXf03HEhdrT9n2WYzhEEOJ9lD6M8gDadtnGXk53NJykabejN8QY7+fN4h9002Gco9A9aZSsWHa8gKlk03Qxg0BGcNWcF6T4V33ww3ztHGQi4ZykA8OFMbRiKCuWzIbTPkvpmcsCT5YTxTUlplg0OrDUOs4C0bbeqtztB+X88nroTHNk6I/FcCfyKl/JdSyhtSyvB5fC6i51g7aFoG4STyfcd6TFZvxbqD5lPQJ6thdAR69bxDktV+ACh8trGVO8NmO8Ic5FgvNcgmY2SG6HgL1r6pNTtUQ1RjZ8PKdAzXRAisfbNRMkLVSMjGZnn4mlWwbBPG8wR29nC4uwbCHeRYH3Kco42w2sbs9bgZbt8IIUIbVN2tt2h2zKHf35MTcZKxSEh9G2d8YX6ckX8VmsBVl9YRevSawQx5aaUTMXKpGOshjLA5tY3dRCZMB9OG4yDHCKgVhul4C32bhtER2CgbTE7Eh+p4C+GMWtvYdJAhgz3SvFDum4YzQpZLYbTNUDUSsrFecmYbm8iHM8jRGLocA6w7PIzvqI4pKVSc3zdhvGu2ak3a5vANWaEbOAyhbdYdjOWDbr+SkJY5OeUL2WSMiXg0VPvGCZE/B3yZS+sIPdYdSl3BksuEcQSdU7KailuNhMJ0MG1s9DreDleXaQc5wkhW18sNaxzYEB1vIexqheEb5UD/LIYxOOY0Q9aP6IfRSXIokc6HO6jqTD6eotkx2a23XFxV8LAbsjrNyBcqTcyQTZ4pVroNWcdKjtdgw6GiDrqlPGG0jT1ZxcE7PKw9OZzyBSFE6FQuToj8TwIPCiF+SgyrwxshOBnPYmMxpAdzvdQgGhHMZoYjqxDmC91gNpMkNkTHW7AaCc1kEqEkqxsOGp4AzGYSCAGbITxTTroBQ7iDHE5qVmGPkiOEteBObWOrY8KWXe01ZHWYkYfwnaleQ1aHQY6OKdmqNd1alhJw2o8DrMaTxapBuxOuyTPrLpBVKyMfrrsGXApyhIys2nBabgrhUwANVyRo4WeA54F/BfwvQogvAbv7/JyUUv6Ag+eEAptlg0QswuREfOjPmM8luVqsurgqNbBRNhw1EYLwXuhO5ZzQleaFMkPWcPSii3W7JYeSkJUMvvxUZug/P5tJEI2I0DkCVsfb4UfXQDi73kK/Iasj2+TDqQDadJgFsv5s3zZnFnOurEsF9GXAzoMcm2VjqHnrqqJPOpwFVaWErWrT0ftONWw6lI+DdR63ay2abXNoZZ6KcCvIEbZ7GKz7Jp2Ikh2yxw1Y76nza2UXVxUsnBD579vz/6e6v/aDBEaeyNtSVyfihb01dmESQbhFVp+8tu3SitTBhoPOpTbmQ9pJeqNkcL9Dh3g+hEEOKaXjIIfVLTkRuuBYqdHGaJuOztRUOk48KkJ3plzJHmbD2XeiJ3V1pbdCuM5Uf9SlC8GxssEDS64sSwm4EeTYa5swEXn7HAzb0G3vny1UDA5PTbiyLhWwUTIcNWQFyzaV7uSZiUR4hodtlJ1zqYVciscuFlxcVbBwQuRvR9zH2AeuELJsstdIKJ8aPrOvGjbLBken044+YyGfCmmQw+Dhw5OOPmM+l+TSRsWlFamBjinZdNhECMLZSGi71qLVcdZECMLZSKjfKGd4h9julhy2AJAb2cP8RCyUI6GcdkqG8E6C6I+6dKHsIGRlThtlu8eNGyUZDcCZL6AS1ksG+VRs6Ias8GqVS5iIvJPRczbsMqdCxeDYjDP/WiU47eMC3fG6jTaNVsfR/lMFQxN5KeU1NxcSdqyXGo7ldHsdgTAR+fVSgzedmHb0GXaQo9RoOypfUAntjkmx6gZZTbFZCVe35K1qk44pHV/oC7kkF0IksQJ3sodg7Zv10DnWzrPOEM5SHts2TjJkQohQBsd6+8bBmep1Sw5ZAGiz1CAiYNYBWQ1r34nNssF0OkF8yB43EN7xutaEDOeEzPqscNlmvdRw/P7ea5swEflCxeCBpbyjz9g7gu74rP62CU9RieJwo/arJyMK0aXV6phs11pDjxCz0a/NDI9zvVVrIqVz0rGQS9LqSHZq4emWbDdYckI67D9fqBih6pZcKFsNoxyfqRASMnvfOL+LU6FzrIv2mXLhPRU22xQqBhEB0+nhG7IKIUKpcilUm8x0e2oMi4lElFwyFrogR6FiDD1xxkYY516D5RM7fX+HtidHxXA0zhHCqwBy40yFbbzumMj7gGY3U+wWkQ9T1HqrapGOORcIGYQrMmsTMieZDgipbVwiZAu5JO2QdUsuVru2ceFMFSsGnTAFOSouBTlC2BG4UDGIRoRjRdNCCNUKhYrBTCbpiKxCOKerFLqTVZwijL1cCpWm43dUMmaN1x3b5rWw913Y7ptipelaAChM+8Zodyg12u75xCEJjh2YyAshXhRC/KNhH+T0z+uMPll1eDBDOBO8R8gcjJ6DcMrPeoTMBbIK4XrZFSt2kMPhvsmHb166fQbmHDrXC7kkpuzvwzCgUDGIRQT5CSftYay7uFht0grRSKhCuclsJuFoegiEs7dCwQXHGqwAUJjuYYBitenYt4EukQ/RPQyWysUp6YBwTp6xbONs3yRi1njdMPl9jVaHiuE88TebSRIR4fKJe1zKsU/c9ftCYptBMvKvA+YcPMvpn9cWNll1GrWenOh2Sw7J5oM+IXOePQwfIevtG7cisyHaN+5JpMMXtS5Wm8SjLpDVEJ4p23l02hDTlnTa91cYUKy6RTpS7NRaGO2OC6tSA5ac04WsczaEGfnKOCN/O7gVAAqbberNDtVmx7UzFRZCBnv5grN9E42I0AU53Erg2KVAYQmqDurpnXXgAA2kzxRCfAvwo8CDwCywCjwF/Dsp5WeHXUQQ6NfzOtt81kiocDkCbl1a+VS3W3KIXna9IIdjGXA/+jjjeFVqoFBpkohGyKeckdX+TPBwXOjQl7o6JathDHK4IeeEvRH9BocmwzESatOtrHNvJFSTIyHpJF2sNDl+3HlTpIV8ilKIuiWDLQN250x9srThworUgFuZVbDO1FPXwzNetx+Id0flEiaf2C2/z/6MMNlm06V9E40IZkMU5BiYyHd/eQohxC8DPwkUgT8DCsB9wLcB3yGE+F4p5X/xeh1uwa7LHEetXwu3MvK9bskhImSbFcMVsppNxkgnrG7Jr3M2OEEZuJVZDWP/ALekrr1yldBl5N25hyFcKpdixeCeuYzjz9lrm7AQeTcz8mDZJgydpG2y6jRDBta+qTY7VI22o/nZqsBNsmo3kAzLeN2iSxJpsM7U5c2q489RBW4pMSHEfMGN4Fg+PEqOQW7Lr3HheVfv9gNCiEPATwDrwOullBt7vvc1wF8B/wLQhsgXXTyYc9lkqEZCFbpkNefCiztsXbaLlaYrZBX2XOghIfIFF+rrANKJGNlkLFSEzE2pK4Srt0Kh0uTehazjz1kIWQBISulKN2DYo1YIyXuq1mxTa3bccazz4RoJZd+bTptHwqv73ISByBddTOAs5FI0WiZlox2K0cN9n9iF91Q+ZEEOF8nqfC6sQQ53ztTabjjeUQe+LaWUn/JyIXtwAqt2/4m9JL67hk8KIcrAvE9rcQXFapNkLELWhZfTfDbJ88u7LqxKDdg1ZG6R1bBdWm44j0BfrbDkyscFjmLVHTknhK+TdLHS5PSC84hNKh4lnwpPkKNPVt2RLEJ4+gfUmh0aLdMdBykfriCHq1mgHlkNhwNpZ1bdeE/t3TcnXVCGBI1eRt6h2hBe3WU7DETerZJKsHziZsdkt95iysF4SFVQqLqckQ9VkMMgGYuQSTgvS5rPJnkuJFxKxfFzF4Em8GVCiFc1xxNCvAMrp/jxIBY2LArdGfJukdVitRmakVAFl6SuYEXYwuI8gnu1h2DZJiyEDNwbeQR2l+1wONZSSjZdyqyCVdMbljNVMdoYbdMV2yRiEabT8dDMoXWreSRYzrkIUbdktyXSECLblN3bN2GzTT8j72KZU0hsU3BVIh2uDuSFcpN0Iko64U7ir9kxKdXbLqwseNg+sRtcaiEfnvG6yhF5KeUW8L8Di8CLQojfEkL8ohDivwEfBT4G/K9BrnFQFKruNBEC62XXMSXbIZl7bXVKdi/rvFtv0WiFo1uyWxJp6EdmwwAppXWmXKgDh3DZpmK0abZN1wJAYeqy7abUFbqBw5Bk5AsudQMGiEUjzGYS4XGsXSQd9kiosNim6GL2cG8DyTBg08XgWF+tEA7bFCoGmUSUCZcyqxCeIEex6o5qDMLXsNbVJIU9XjcEtlGyEElK+etCiKvA7wH/y55vvQK871bJ/V4IIX4Q+EGA+fl5zp075+FKD4arq3WmU8KVtWysWZG1D33yMxzLKReHGRgrxRpT1FyxzdZKC4APfPxTzE3obRspJZulBvXtdVdsU9lsUjbabO22lDgTTlBrSZptk521m5w7t+7485q7Bqs7be3tArBWteaab9y4xLlz1x1/nqw3uLZjhsI2F7etAN/K5fOcK7/yqu9VKpWB/46xdp1XlquhsM1T69Z75dr55zi36ty5nhBtXrqyzLlzRcefFTQev2G9Vy4+90W2XnH+XsklBM+8fJVziVXHn+U17nYuPn/JCnK8+MUneCXqLEtmSklUwJPPX+RU65qjz1IBz5w3SEXhiccfc/xZ1ZaVNfzcl15kcuei488LGi9dbpCJufNeWa1Y77xPff5LtG56T2mGeVcMgos36sQ7uPKMlaL1zvvYY09wc1b/KRnX1tzjUus2lzr3GU7k9baNkkReCPGTwL8G/j3wG8Aa1hz6XwT+QAjxBinlT+73Z6WUvwX8FsD9998vz54968ua7wTj8Y9z5vg8Z88+6vizMle3+A9f+iwn7n+Ed5zRqlXAayClpPKxD/PQfcc5e/YBx5/XfnGd33/hSe57+E284diU8wUGiN16i/ZHPsobH7iPs++4x/HnrWeu88cXn6MTT6PCmXCCK4UqfOIcX/GGBzj7xqOOP+9FXuGj1y7wZW/7alfkbEHiyatb8Nhneftb3+DK/fBY5UWe/fx17fcMQOP5NXjiKb7mbW/l4SOTr/reuXPnBv47/tna0zx1fTsUtll54jo8/Rx/4+zbWJp03mn+5KUnKDXanD37VS6sLlg8/1cX4YWX+Zavf6crI+OOPPMY8WyKs2ff6sLqvMXdzsWnyi+QvX6Tb/w6N3ohw/xnP0F6Zs4VXylo/Mnq0yxWdly5H6SUJD71YfKLR13xlYLGb7/yOY7GO67cD6VGi//jrz/K3NFTnH3HvS6s7s4Y5l0xCH7pS5/mnsU0Z8++xfFnHd0o88tf+DRH7nuAs48edmF1wcJ4/OPcf8I9LvUbX/osJ1/3et6pOZdSLm0phDgL/DLwfinlj0spL0spa1LKLwJ/C1gG/jchhHN24wOklFZdhwsNTyBcMqJSo02zY7rS8Rb68r4wSGWKvUY57siIbKlWqal/PVC/UY47+8a2jS291hlujq4Byza1ZodaU/8aO9s28y7dxXPZJIWy/nsGvDlTYbiHwZLW55Ix1+a+z+WSPXvrDrtZrVuYyyVCY5ti1XClPh6s8bpzmUR47pty07XeSLlkjEQ0Eor3N7h7puazVrlKGPiCacruJCd3/b5CCGyjHJEHvrX730/e+g0pZQ34PNa63+jnoobFbr1F25SuXehhqnnxgnTs/Vyd0esG7DJZDQORd3OcI/SbWIXjTFn7xv3gmP5Okv13mHHpLp7LJam3rLnXuqNYMcinYiRi7rgEc1mLkEmp/31TqBiuBeIBi5CF4DyBtW/ccqyhGwCqhsM2hbJ7zWrBGrll9yTQHW7WgQshmMsmQvH+7piSrap7vZHyE1aQIwxEvtSwuJRb+8b2+8LAF1Qk8va/0u20DvbXtbjte461S45AJhkjnYiG4mC6OdYH+jYOg5PkZjdg6I/AKRn6O9abLpPVMEVm7ZfStFuBw2y4AodT6TjxqFtkNTyBw4KLqjGwbNNomVSb+jceLVaargXioZ+RD02Qw8WM/GwmGYp7GOxGvm6eqXCoFSyy6raSIxkKv2+n1sSU7kzIACvIEZZmvm5ODwHIJmMkY5FQnCkVibzdGeQHhRBH9n5DCPEu4KuABvC43wsbBm5LFiE8XbaLLtsmFY+STYZj7nWhagc53Lm0bEd0N0QZebfIap+Q6e8IFCtNpr0gqyE4U25KXaGvVgjDvilUDOZcfEeFad9YZNVdQma0TSqhUHK4J3UFW1rf1D7IYZPVeTfJakhKebZ7ZNVlJUcICFl/eoi79004AvHuJv4sJUcyFGpDR96eEOKdQogPCCE2hBAtIURnn1+Dvq3+GGtO/CLwkhDiPwshflkI8X7gLwEB/JSUUot2uEUXx/rYCMtIKLcjbPZnhUGaZzvAbsmAU/EouWQsFBl5t8mqbeNwOAIuZ4FyISKrLktd50OVkTdc68cBe0oyQiAFLlabrr6/wxI4bHdMtmrun6lmx6TU0DvIYZNVd+9iS1qve5DDC594NhMOtUKvN5KbZyo0iT/3901YghxDt2gWQnwL8GdAFLgOXAAc375SSlMI8c3APwb+NlaDuzSwBXwQ+PdSyo86fY5fsB0Ztw/mxY2Ka58XFAou16yC9eIMQxaoWDWYTseJuURWwXIESk3959C6TVZT8Si5VCwkjoC7ksVQBTmqBg8cyrv2eWGS1herTVdVY7ZtNjXPILY7Jtsuk1X77ipWDE7NZVz7XL+xXWshXZQBw6sb1k5OxF37XL9R8ICQzWYStDqSUr3NZFp/27h63+SszKppSiIRZ2MQg4TbSkyw+MKXbuy69nlBwZN9k02ysqu/T+xk1tI/B1rAt7hNrKWULeDXu7+0RqFsIARMu3jxzueSfOaVgmufFxQKFQ/IajbB5c2qa58XFNzOHoJlm1Kp7upnBgG3ySpYmSDdM2RgnakHDrtHVpOxKPmQBDkKZYO5+9wPcugud211THZqLdeDzaB/kGOr1nSdrIalyZIXZHWvWuEejSdC2XeCq0rMPY2Ow0Dk511UAM1lk7RNSanRYirtrm/gJ+wklJuJivlskq2qQceURDUOchQrFpdyM/E3l03y7LL+QQ4nDOph4P/TKTseBArVJjPphMtkNUmp0cZo691IyCJkbpPVcIz2sRrluPtCms0kQyGtdzsjD9a+CYPEyqp1dnffhKGRULNtyXXd3DeJWITJibj28vGtqvuko6/k0Hvf2ITMi5KMTc1t05O6uuxYg/5BDi+UmKGxTW/fuFsHDiGwTdUgGhFMuahGmcslMWX/ntcVmxWLS7kZjJjNJtiqWkoOneGEXVaw5O5j3AHWeBaXHeuQzL32gqzOZZNs11q0O6arn+s3Cl4EOXKJUDS7K1QM1zrW25jNJrSXjxvtDqVG25PgmO5BDi8ca+vz9K/N3HR5QgZAPBphKh3X3jb9Eanu2Wa6p+QIh23cnHawV1qvM/pnyouyA739vkLFIBYRrpZOhKWUp1BuMpNJuFoeEJZeLl5xqY4p2am3XP1cv+GEyH8C+Eq3FhJWFCru1h5CeGSLBZc73kL/xal79NHtTslgXVrVliWl1RU2WXUzCwS2kkPvPdPPrI7J6q3wolEOhKOTdNGDukzr8/RXR/UDQO7ZJh6NMJ3WX8nRI/Iu+jcz6QRChECtUG16Rla1P1MV98lqaGxT9cDvs0syNA8cFqteJLfCsW+cEPn/HbhXCPEzQgh9Cy88RrFiuBqxhr5TofvB9CKz2ovManwwG60O5UbbE8ca9A5y2Gt3/0wl2a23aLb1DXIUK94RMt2zQPZ9MC7leS0KHmTkrc/TPwDUr3X2YN9oHgAqVJrEo4L8hJNWS69GLBphJh2GfWNlD910jafTCSJCf9LhCVkNiZKj4EH/n7AEObwpqQxHSYaTG/jngBeAnwf+vhDiS8DOPj8npZQ/4OA5WsPKyI8P5q2wyarrtulF2PR1krzMrIIVAFrMp1z9bL/Qc6xd3zf9cVlLkxOufrZf2PRABgyvDnIkYu71+vAT3gU59CcddmbY9Z4c2SQvrZRc/Uy/UagaJKIR8in3yCqEJABUMZjNJF0lq2AHOfS2jRfZw2hEMJPRXzm2WXF3nCPAVC/IobdtChWDk7NpVz8zLGTViybHYRkF6uTt9H17/v9k99d+kMBIEvlGq0PFaPek8G5hPkRk1YvMKuhdf9gnHR7ZRuMLvWBLXV3eN3b5S7HS1JbI2/vGK5WLzkEOLzps259nNx5NxqKufrZfKFSaJGMRskl3yep8NsmnNb5rwAocup1ZBSto8rzm3ZIttaH7HcJns4leuYeu8CJ7CCEJHFYM7nF57GI/yKG7bdwPAGWTMVLxiNYKXptLeeYTa2wbcEbkT7m2ipDCfhm5nT1MxaPkkjGtD2Z/JqTbWaB+ZlVX9BssjaOPt6InA3a970RXraCxI+DVvrE/r1DWOchhkIpHSCfcJdt2QKlYaXJ4Sk/b2P043M+sJig32jRaHVJxPYMcXsiAIRzlKl70/wHLNs/c3HH9c/1EsdLkvoWs65+ru5JDStm9b9wPAFlBDn3PVNVoU291XA8ACSG07wHkFV+YmogTjQitzxQ4IPJSymtuLiSM8GImpI25nN6dpHtZZ5czq7lkjEQsEopLy22y2icdGu8bD0ZlQTgis8WKwUQ8SsblzGrPNloHx5qekFXbsShUDI2JvPuSRXh1Tw59beMNkZ/PJSkbmgc5KgZnFnOuf67u0nopJZse9P8Bi6xeu151/XP9Qq3ZodEyPfGJ53N6Bzm8Kv+yPjMstnF330QigplMQvugqmsFj0KIvBDimBAi79Zn6g4vOt7amMsmtH7ZeUVWhRDMa+4IFHpBDnf3TSYRJR7RW1rvNVnVWdJZ8KD2EPaMr9H6THkkdQ1B19uiR7aZzYbBNu5LXUH/ulUrs+pNAGg2m6Da7FBvdlz/bD9QMdo026Ynd/Gs5koOrwgZWEFVXc8T7Ckb9EgBFAoF7zjIsS8cEXkhRFQI8VNCiFeAbeAqsC2EeKX7dXe9bc1gN+byKqKvc0beJqveHMyE1raxyWo64e7xEUKQTwjN1QrekNV0IkoqHtGerHriBORs0qH3vpn3MsihtW28k7ran68jpJQUPbpv9vbk0BFlo02zY3rj22geACp4SFbnsklqzQ61Ztv1z/YDmx4TMl3PE+xV8HrwngqNWmHcd2I/DE3khRAJ4GPAv8JqdHcD+Hz3vye7X/949+dGEgWPugGD/vIzrzKroH/UuuBREyGAyaTe9UBekdV+HZnOtvEmQ5ZOxJiIRzW3jeFJPe9saMiqN6QD0HbMWqlhkVVPJNKaKzm8JB39wKGetin2yKqHSg5Nz1TRIyUmWGeq3upQNfQMcthqQG+CYwm2qk06pnT9s/2Al1xqXvP+AeAsI//jwFngL4EHpJQnpZRfKaU8CdwP/AXw9u7PjSSKlSbphPuZVXh1t2QdYUldvSGrukfYilVvmggBocjIe0FWwZZY6WubokdBDrCca117K5imZKva9CQ4lk7ESCei2jrWu/UWbVN6lj0EfXsreCvn1Jysekg6emVOmt7F/QkZXgQ59D5TvX3jxbSDjN5nyg6Ozbjc0A2sfWPK/rQo3VAoe8elZrt8QUo9gxzgjMj/HeB54NullBf3fkNKeQl4D9ac+b/r4Blaw0uyOp/T+2XnxZxVG3PZJMVqE1PT6ONm2TtCltc8I+8pWdU4I2+a0goAjYMcr8FOvUXHlJ4Fx3TeNwUPGyxNJKJkNA5yeCvn1Lskw8uMvO69FTyV1mf07lfiNVkFfc9Usdokl4p50vzSVhXpWifv1fQQsM6p0TapaKrkAGdE/j7gQ1JKc79vdr/+IeBeB8/QGl41yoH+S0LXg+l1ZrVjSnbqLU8+32tYQQ5vbJNPCLY0DXJ4T1b1HV+z2yWrXt03sxrP6O3JOV2ekGFjLpvQdtxlP3volZJD333TH3nkvm1S8SjZZExf23Qze16UHWifWa14SVb17ldSrDbJp2IkY96RVZ33jZf3sP0MHeFVrxLQP6gKzoh8E7jboMwMoCebcgFe1WWC/tI8T22j8Zi1ngzYowt9MiHomJLtmn6XltdkdS6bZKtqaFlHVvCwLhNgPqdvuYrdYGnOA8ca7H4l+p0n6GedvVVy6Llv+gGgceDwVtiZ1WkPzlQqHiWXimlrm2KlyVQ6Tjzq2lCoHvpNEvU8U5seK+pAd5/Ya7Kqs23GfOF2cHLTPAt8pxBifr9vCiHmgO8EnnHwDK1RqDSZ98gJmNc4wuZlzSr0HXYdO9f3ZMBeZeST1hxtHceseU1W57IJTImWQQ4vJdLW5ya1bZbTk0h7lJGf1Zisep2Rn9V4Ru9mpYkQMJP2MgCk574pVg2mPSKroHcAyMvMaiIWIZ/SV8lR9LDc1FZA6BxU9S7IoXvizzsupbsCCJwR+d8A5oHPCyF+QAhxjxBiQghxSgjx/cAT3e//hhsL1Q0WWfUyI6+vtN7zmlWNa6WKHjvW+YRF5HV0ID0nqxr3nfBcIp1NahzksCXSHgVVswm2ak3anX2rzJRGoWIQETDtFVnVWFpfrBhMpxPEPCKrsxo3ZS2UvZl0YEPnhrVeZlbBPlP63cPgLVlNxCJMTsS1LnPyKsiRTcZIxSNa8oWOx1zKTopuanqmwAGRl1L+N+CXgBPAbwEXgQrwCvA7wCngV7o/N3LYrjUxpXeSxVQ8Si6pp/zM+5pVfRvCeDlnFfoZeR3VCt5nD/VVuXgdANJ5zFqx0vScrEoJ2zX9qsgKlSYzmQTRiPDk8+eySa2DHJ4Ssm5TVh1hNZ/y1jY6+jbQJase+TZg2UbH9zd4S1ZB3wBQu2OyXWt59v7uj9fV70ztdLmUV/dNX8mh376x4SjULKX8aeBtwO8BTwOXu//9PeCrpJQ/5XiFmsLL8Sw25nJ6Xuhe16xOTcSJRoSWkVk7G+xFEyGwauRhrFbYD/Mazy8uVJpEI4Kpibgnn6/zTPBCxWAmkyTiEVnVOQDkZe0hWGoFXYMcXmYPwTpT29oGObzOyCe1rVndrBie+TbQba6poW28Jqugb7+SrZq3akPrs/VUR9lcyqv7Jh6NMJXWV8kB4Hgon5Tyc8DnXFhLqODleBYb85rW2HldsxqJCGYyCS0vdK/rwNNxiEWElo5AseoPWdVRflasGsxkEp6R1d5sZw1fdl5OyAC96w+LFcOzXiXw6lFi8x5mKb1AoWLw8JFJzz5/rhvk2Ko2WcinPHuOFyhUDM+CzWD5Tdu1Fq2O6Vkdvhcw2h3KjbbnZPUzlaJnn+8VbLLqdQDopbWSZ5/vFWxf1UvbzOeS3Niqefb5XsEPLqVrAMiGPjekZvByPIuNuVxCy4y8TSK9li3q6Vh7S1Yjohvk0NA2VmbVO7I6ORG3ghwayl03y01Pz5POc2iLVY/lnBo3Hi1Wm8x4mJHXuVuyHxl50K/MySarXr+/wQpy6AR7vTMek47deotmWy8lh02UvFYraJncqnqrNrQ/W8d72BcupWlJho0xkfcIRY8zq6Bv19tCt2Z1yqOaVdD3YHpNVkHf+sNCxVuyKoSwGlBpeKaKVW8znvmJGPGo0HTfeNdFGvaoFTS0TXGsVtgXjVaHstH21jaaNtf0WlEH+qqjerbx4b7RLcjRI6se75tSo43R7nj2DC/QV2J6GYxPaDl5xjcupdk7ai8OLK0XQvweIIGfllKud39/EEgp5Q8MtTqNUagYnmZWwYpQ2ZdWMhb17Dluw5IBJz1rsASWbS5vVj37fK/gNVkFfTtJe03IQN8LvVhpcmIm7dnnCyGYzehrGy/3TT4VIxGNaJdZbbQ6VAyPZcCaktUtj+syQV+1gv1v6W1G3vps3dRR/YasXpar9INjhyb1KcnwY9/M7glyLE1OePYct+FLAChnTZ7Zqja1KnPyg0tZPTn0umv2YpAa+e/DIvK/DKx3f38QSGDkiHyx2w3Y08zqnjFrR6b0ubQ2y95mgaA/2kdKiRDe/Ru4Da8zq2A5GZc2Kp4+wwsUK01OHPeOrIK+agVrPq/H+yanX5OlWrNNrdnxNNPRV3LotW/8IB25pJ5BDq8nZIC+kyAKVX8yZKBfJ+mCjxl5bc+Upxn5/ix5nYh8odIkHhXkU47blt0We8vjdCLyvnCpbIKy0abR6pCK65MUtTHIrjnV/e/yLb8fYx/4klnd87LTichbo2u8z6wabZOK0SaX8i6S5za8zqxCf3yNdkEOH8jqbDbBxfWyp89wG/Vmh6rHZBX0DHL0Mh0e1oGDnkqOfobMO9tYY4/0C3L0bON1kCMW0fdMjftOvAZ+yIDnNQ5yJKIRcknvyKqu+8aeHuKlP6a3bXziUhWDo9Pe+t9e4MAnSkp57U6/H+PV8EMGPK/pwSxWmhz3IbMK1stDJyJf8COzmk3QbJuUjTZ5TWzjF1md75JVnYIcvdpDj8nqbCbJhTW9ghz90TVeOwL6NR4t9jKr41KeW9HLHnoc5JjXMgDkPVnNJKKk4hHtpPXFapNkLEIm4V1Wb1bTsoNid4a8l+/VeU3VCrZtvISupTyFivelAHv73OhI5IdudieE+F4hxOvv8jOPCCG+d9hn6Awr6+y98wj6NYTxenYx6Bl9tGXAfqgVQK+Ivl9kdS6bpNkxKTXanj7HTRR8yB6CLa23ghy6wA/SYX++bllnP2TAYNXE6ja20K8AkFUCpte+8YOs9npyaPSOgn4Cx0uymknGmIhHtbWNl+gFOTQ8U977fXqW8hSr3mfkdS1zsuGka/37gG+/y8+8G/h9B8/QFoVy04fMqsZk1cPZxbCnWY5GtvFDzgl7Z4Lr87Lzk6xaz9Np3/hDVuftIEddnyCHHw2WoNssp2poFuTw777RLchRrBik4hHSHpJV0HPyTKHsPVkFKxivX2a16fl5Aus9pdM7Cix/w2vbpBMx0omofrbxYd9kkzFS8Yh+ib8xl7orvB4/F8VqdjdSqDXb1FveZ1ZT8Si5ZEyriL5fNas6zr22ibUfjQBBs4y8X5nVjIZqBR9qVq3P10+2WPBhPq/1+QlaHcluveXpc9xEsWIwEY+STnhXswoWIdMxyOF1zSroOSa1UPW+WS1Y47J08m3An+wh2IFDzWzTPVNeQ7d+JVJKNn1QK1j9SvTqc2NzKc9LKvc0DtcRXhP5M8C2x89QDn5lOsDagDo51n5JFmcyCYSATY0OZo+s+hTk0Oll5zdZ1clJ6nWR9rpGXkOVS6HcJJOIMuFxZlXHfiV+ZMjAOlO6BTkK1aan3bVtzGWTbFWbmBrNdvaj6ShY95lOdw3YmVV/bKNTkkJK2ZXW+3HfJLSS1leMNs226ZNt9Apy+DGWD6ykaDYZ0+pM7cVAofh9Zsd/uxDi5D4/GgWOA28H/nK4pekLP8b62JjL6nWh25lOrw9mLBphOq1XtqPQyzqPgxy3wi+yahMync5UseIvWdUrcOgP6eipFcpN7lvw/HGuwI+aVXh1beZU2vt3ohsolA2WfJjRPZtN0jatIMe0D5lcN1CsNHlwKe/5c+ZyCYrdIIeXo6fcgpSSYsX7WmeA+VyCL93Y8fw5bqFitDHapi+Bw9lskhtbNc+f4xb8mB5iYz6nl21sX2Pep/eUTnxhLwbV1H3fnv+XwBu6v/aDBJ4AfmzQRemOgo8Hcy6X4LxGnaT96pQM3YOpESHzq/lULBphJp0Yk9V9MJNJEBGaZVZ9ypBp2STRr5pVDVUuhUqTI1Pek9V5DYMcxarBw0d8IKt7ghw6EHkppa/BsY4p2am3mNHANqVGm2bHv8zqVtWgY0qiGgQ5/Mqs2s94+ro+QmB/fWLNbOOjulk3tcJeDCqtP9X9dQ8ggF/f87W9v44DeSnl26SUl11brSaw5WB+SPPmNWuW42uQQ7ODWaw0uw1JvCWroKNt/HEeoxHBTEavyGzBJ7I6nbaDHBopOXyYkAF6dgQu+mUbzcoO7MyqH/eNbgqgUqNNqyP9qZHXzDZFnxR1YL2/TQnbNT3u4j5Z9cMnTrBVbdLRpFzFrwQO6Gcbv3ojge0T63GebsVARF5Kea376yrw88Cf7fna3l83pZRVT1asAXp14D41PSk12jRaHc+f5QYKFcOXzCrodzCtTIc/mQfdut76RVbBLlfRZ9/4RVatIIdmASCfGnNNpxNEI0Ib25imZMvHGnnQh8iX6m3apvTl/a3b3OteksJPBZAutqn6m6QAfWzTT+D4EwAyJdqMvCz4eaa6ttnSpAdQr9zUDy6lmU+8F0M3u5NS/ryU8tNuLiYs2Cwb/mVWc3o15ypW/GkiBDpmnZu+XFign238Iqugn238IqugVx2Zn2Q1Yis5NAkAlRoti6z64DxOTcS1CnL4Nelg7zN0CTj71awW9COrfmbkdVMr+EpWeyVgmpyp7tn3o3xEt2lOhUqTnI8q1Z1ai1bH9PxZbsPx3BkhxBLwdcARYL9TKqWUv+D0OTrBT8e614G8bHBkasKXZzqBX+NZwIqw1Zodas225yOW3EChYnBsJu3Ls3Sb7VysNnnj8SlfnjWXTXC1qIegyCarfjhIYE/J0GPf7NZbdEw5DgDtg4JPUyDACnLMZfXpyeFnXebkRJyYTkGOsj9NR0FP0gF+kVW9Snn8JKu6lfIUKwb5VIxEzOshYhraxqdAPPSDY8VKk0M+NDp1E47YjRDi54GfuuVzBP3Z8fb/jxaR96meFzQ8mJWmr2QVrMjs8VkdiLyfZDVJvdWharTJJNW2TS+z6jMhk1J6PkfaKXpk1ceyg8ubegQ5/GwiBJYjoM897B8hA73KnPyUj0ciglmNmrIWqv4FgPITMRLRiEb7JgCyqkkwvlgxmJyI+0NWNVNyFHwMxGtnm7KPXGpP4FA3Ij/0qRJC/F3gnwGPAd+JRdr/M/B3gN8GTOCPgK91vky94K9E2nqOPlFrf2aJgl71hxZZ9VM+rk9E33eymkvSaJlUm+r3nfCziRD0pfVSqt8sx+5z4MfoGkCrrHPBx6wzaKZW8FE+DnrZxg5y+NFhX4hukEMX21QNptJx4lHvyWouaWVwdbFNwcfMqk6+Ddhk1d+ssy7vqWLVP76gW5BjL5zcOD8E3AS+SUr5p92vXZVS/pGU8h8C3wp8F+D9DBfFUPAzI6/R5uv4LAPWyTY79Ram9NF51EjJ0Wt44pd8XKMxazZZnfMpcDifS2K0TSpG25fnOYHfQY75btZZhyBH0cc6cPs5Opwn6J/7GZ9m3ltKDl0yq03fyCp0S3k02Td+JnCEEMxnk1okKcA6U3M+JSmyyRjJWESffeOjT5xJREnF9QkA+TU9BPRK/N0KJ7fxI8AHpZR7PbpeRwIp5UeAjwD/1MEztEPHlGzVmsz7RMhS8Si5VEwLR2Cn1rTIqo818qBH9NFPOSe8eraz6ujVHvq2b/QJcvifkdenOZeftc5g2abZMSk11A9yFCpNhIDpdNyX51kdgfUJckyn48R8IqvWlAz17xrwt8cN6KVW2PQxgQN6KYD8rHUWQmgWHPMvIy+E0KbMqd0x2ao1ffT79FJy7IWTN1UcKO75fR2YvOVnngcedfAM7bBdayKlf441WKRMhwu93/HWH9vYMnUdDqbfUteexEoD2wQhHwddAkD+k1XQxTZGl6yOHYFbUawYTKcTvpHVeY2CHH5mgcA6U8WqHuUqhYp/2UPQa0pG0ceyQdBNyWH4vG/0CAC1OybbtZZvJZWgj222ay2kxLcpV+lEjHQiqk3fib1w8hZfBZb2/P468PpbfuYIoP6b20X0ZcD+Rq11IGS9jrc+2SYRizCVjmtxafk5ngX6DXl0kLv6TVZ70noN9k2xYhDxk6xqZJtCtclMd767H+g311TfNn7KgEGvfeO/bRK0OpLdesu3Zw6LIAhZsdLENNUPchR9bMgK+hCyHln12yfW4B7eqvnXPNKGLuUqvQTO+EzdFU6I/NNY8nobfwW8XQjxPUKIjBDiW4Dv6P7cyKDo4wgSG7p0S7abCPnVfAr0GbPW7yLtz4Uej0aY1iTI4TdZnckkEAItxqwVqk1mMj6SVc2yzn47j6BJ2UHVX9vo1GSpUDV8ywJB3zZanCkfJdJgnam2qX6Qo9Ux2QmArG5V1Q9ybPmsxASY75byqA7bN/VbAaTDXdO3jb9BVR1scyucEPkPAA8JIU51f/9LwC7wPqAEvB+rk/3POFmgbuhnVv3dfDo4SD2yOpbmvQbFapOIgCmfyCroc6FvVvwlq7FohOm0Jvum4t+kA7AagAmhU9bZXwcJNCFkAcjHQR/b+FWXCXvLVdQmHj2y6ueZ0iTIsV31P4Ezl03QMSXbNbX3jd89bsAOchh0FA9y+N10FGA+m2Cr2hzbZh/o4hPfiqGJvJTyfVLKtJTySvf3N4C3Av8R+CjwW8BbpZSfc2WlmqAnA/bZgSw32jRaao/LKla6ZHXCnwZLoM/BLPhMVkGf2c5+k1XoBoA0IKuFir8Zslg0wkw6oYdaweeM/EwmQUSoTzqgOwbUZ/k4qB8AarZNdustn7OHepDVLZ/H8sHepqxq26ZHVv1M4OT0UAD1CJmPKpe5bBJT9vesqvC7bBCsfwdT9v9dVEVQZ0r187QfnMyRf4cQ4g17vyalvCKl/GEp5buklD8kpXzO8Qo1Q6FiEI0IJn0kq7YjUFT80ipUDGYySSJjsvoa+F17CPalpfZlDv7LOUGfAFDR507JoJNt/G3MFY0IZjLq28Zodyg12r7um+lurwLV7+IgyKouaoUg1Ibz9uQZxW3jd0NW0CfI0U9ujc/UreidKR8TFf3xumrfxYWKQSwiyKd85FLZJNu1Ju2O6dsz3YATaf0ngR90ayFhgd0ox2+yCupnO6yOt/4SsvlckoqhvlrB7+wh6JN1Do6sqv2iA/8bc4EePTkarQ5lo+37fWOVOam9b7YCkAFHIoKZjPrlKr1mtT461lMTcaIRoQ0h81vqChpknYMgq5ooOfoNoMcql1tRrDaJRwX5iZhvz9Rl39g9bnzlUrkkUgMlx61wQuQLWCPnxtgDq4mQ/5lV0CAyWw0g66zJKDG/O96C5SRVmx3qTbWDHGOyuj+CJKuq2yaIBkvQ7QisuG2CkHOCHkqOYtV/OWckIpjVIMgRRNZ5ciJOPCqUt00QZFWfrHOXrKZ8JKt2KY/qtilbZYNCBJD4U9w2fve4Aat/AMCG4nzhVjgh8ueAt7m0jtBgM6CsM2hyMANwHmFsm/2gw5g1m6zO+1hfB9a+qTU71JrqTs8MiqzakyBUnnsdRIYMbNuoe54gGIm0/TzVeysE0ZAV9FAABREAEkIwm9HhTPlPVvOpGIloRIPAYQBkNaeHfDyIskFdJogEo1JV3yfeD06I/M8A9wshfkEI4V8Rg+IIotbZdlZV33yFIOrANZDmNVodKkbbd9vYF7rK0Uc7Q+Y/IbObc6m7b4KQuoLlJNVbHaoKKzkKAWQPoa9W0CPI4Xe2Q31CVgygwRLo0a+kUGmSiEbIJf0jq2CNvFTdNkGQVSFEtzxO3XcUWO9we2ypX8glYyRimgQ5fH5HZRJRUvGI8meqUGn6Oqoa9OAL+8HJjfx/AM8DPw38gBDiGWANuNWDkVLKH3DwHK0QhAw4FY+SS8WUjrDVmm1qzY7/ETYN1ArBkVUNbBNUhsyOWlcaHJ9N+/rsg6Iv5wxo35QNsj479QdFYIQsm8Rom1SMNjkfm/QMgr5E2v9M0GY3yOEn4RkEhapBIhbxfV/PZ5O8sl729ZmDwq5Z9fvfbj6rQblKAGQVdAkA+T91RgihReCwUGly73zW12daASC1FUBSym6Z8pgvHARO3lbft+f/D3V/7QcJjASRrzXb1Fsd30kHdLMdCh/MnmPt84Vuk2OVgxz2y8b/zKr6So6g6nn7HYHVPVNBdLyFV9cfnpzL+PrsgyJIiTRYDpqyRL7SDISszmWTNNsmZaPtayfiQWDPkPebrFpZ56baQY4ApK5g7ZuXVjUIcvh8D4P1nlrdbfj+3EFQrDS5b8Ffsgp2KY+6vo2UMtAzpbLfV2t2aLTM4NQKCvOF/eDkTX7KtVWEBLbEKZCDqXiTpWIAY33AUivkUzGlL62gMmS246GyNC84sqp+ZDaoM6WDbQoVg2QsQiYR9fW5e/uVnFI0yFEIkKyCFbhUl8j7L3UFi5A1OyalRtvX0bWDIIiGrGD5NsWq4kqOSpN7AyGrSZ5b3vX9uQeFTVb9TlKAdRcv76gb5Kg2OxhtMzDb3Niq+f7cg6I/PcRf36avVlDXt9kPQxN5KeU1NxcSBth1mX7XddjPfGmt5PtzD4qgss6gvvysEFCtcyIWYXIirrRtgiKrsxp0vS1WDFLxCOmAyKrKjcvsGfK+k1UNRoEWAiKre9UK98z7/vgDoRBA01Ho22azbKhL5CtNTi/kfH/uXDZJqyPZrbeYSvv/b3M32DLgYHybBMVqE9OUvo7pOihssuo3IQNr3zxzU90gR1CqMbBs8/T1bd+fe1D0fGKfmxyDHo1Hb4WTZnc9CCEyQog3CiHe7sbn6YqgZMBgzy9W13kMKusM/S7bqiLofaMyWS2UDSbiUTI+y4Dj0QhTacWDHN3xLH6T1ZlMAiEUJ6tV/6eHgCblKlUjGNtooOQISiKtum36mdVg3lGgrm16MuCAyGrHlOzUW74/+yAINIGTTVKsGHRMNRuPFgL0++azCbaqTWVtUwxIiQl6jB6+FY6IvBDiqBDifwDbwJPAJ/d876uFEC8KIc46WqFGCGKWqI35XJJyo02jpWYn6UJAnZLB7h+g7sEsViyymk743zhMdRlREONZbKgeAAqKrMajEabTageAgpJIz6StIIfqaoVgM/Jq7hsppXWmAmlapjZZ7WVWAyEdavcr6Qfix2fqVgSbwElgStiuqbpvgiWrpuz/+6iGIIMcqvvE+2FoIi+EWAKeAL4N+ADwWWBvaugJYAH4bicL1AnFgOo6oH+h21Jk1VCoGGQSUSZ8lgGD+k1PAiWrObVlREHJgEH9AFChHIycE9RXcgQxPQQgFo0wk1ZXHSWl7BJ5/20zk0kQUVjJUTHaNNtmMI614iUZfd8mGNIBKPsO36wESVbVngkeVNkgqN+BPGiyCur2RyoGeKZUVyvsBycZ+Z/DIupfL6V8D/Cxvd+UUraAx4CvcvAMrVCoNMklY6TiQZBVtS/0YqUZSL0LWLZRW60QHCFTfUSL3UU6CKjeWyGI8Sw2VK4j64+uCe6+UXXflI02zU4wZDUaEcxk1G3KGmSJ03Q6QTQilD1TapAOVfdNgL2RFCerwZYNjsnq7aB6kKNYbZJLxUjGAuBSXbXClqJJ0f3ghMh/M/B+KeW5O/zMdeCwg2dohSAzq70LXdWXXdUIJEMG/UtLXbVCMBJpsDKrZUPdIEewZDWhrGPdz6yOyeqtKDXatDoyuDOVU1etEKRjDXYvFzXPVF8G7P+ZikQEMxmV901wtc6TE3FiEaGubQJqyAp7yw7UtE0hQCWH+mUHAZJVxW2zGWByS3Xb7AcnRH4RuHiXn2kBas7g8QCFcoBZINUjbAGTDlA4yBFQgyXYI1tU0DamGTxZrRht6k31ghylepu2KYMLjmWTSu4Z6N+BMwHaRtV7OGjbqNxIyA4wBHmmVLVNkBn5SEQwq3ApTzHAM5WfiJGIRpQNOBcrBvlUjETMlb7aA0F1tUKgSkyF/T6w9k1wyS21bbMfnJyuLeDYXX7mDLDm4BlaIahuwNB3PlTdfEF1vAW1u96apgy8oRuoaZvdeitQsjqvsG02A8yQgZV1rjU71JrtQJ5/J9gBu/mASnnmFW6SGLRt1Car1roWArONur0VgsysgtqlPPbIwCAyq0KoHeSwGrIGs2fyKSvIoeqZCqqPC0AmESUVjyi7b+yJPEFAZb5wOzgh8p8B3i2EOLTfN4UQp4FvYk8n+7AjyOxhKh4ll4opufk6pmQrwAtdZbK6XbOaagTpWANKOkk9xzqfCuT5didpFWt6beckONKhbv2h/e8V2JnKJam3OlQN9YIcgdumSzqkVK+R0GbZQIgA1QqKk9XpdDyQzCqoHQDaDDBJAWrbplA2AjtPQgilGx1bjXyDtI26902QtlFd3bwfnNzKvwKkgE8JId4FpKE3U/5dwF8AJvCrjlepATqmZKsWXGMusGWL6h3MnVoTUwYr5wQ1yWrgjrXCl5ZNVoNoIgRql2QEvW96tZkK7puCKvtGUdtERLCZ1UbLpKpgucpmxWAmnSAWDYaszuesRoCqBjmCumugaxsF72EY2+ZOKFQMFvLB2UblqTyFSrD7RtXyuHbHZLvWCizxl0tapSCq7pv9MPQbS0r5BPCDwEms8XM/0f1Wqfv7U8APSClfcLhGLVCsGkgZnGMN3YOpoPMYNOlIxaNkkzElLy07oxkU6bClXWOy+lqorFbYDFo+rnIAqGIQjQim0+NSnluxWTGYySSJRsTdf9gDqBwcKwRMyOaySZptk7KiSo6gHGuwbFOsNJUMchQqTeZzwajGQO1RoJtlIzDfBrpqBQXvmmbbIqvz2eD2jar9SrZq9sjC4NQKqk9zuhWOQs9Syt8HHgb+PfB54BLwReA3gddLKf/A8Qo1QdCONag7SqwvAw72ZadmkKMBBBvkULUkI+gzNasyISsbxKOCyYl4IM9XOeu8WbakrpGAyaqK3dmDzh7aCiA17+KgbaNwUDXofZNN0OyYlOoKBjkUIKvFShNTsbnXjVaHUqMd+L5R8R1lT8gIOnCoom3s5FZQZcpgvadUfEfdDjGnHyClvAj8mAtr0RpBkw772Z++qN7mU8U2qjpIoEAASNGscyIWIZ9yfE0NhWQsyuREXM2XXTdDJkQwZLUX5BiT1ddAabVC0LZROCO/WTY4NRvckJ29CqB75gNbxr4ImqzO7wkATaaDCV7uh1qzTcUImqwmaZuS3XqL6QBLO29FIWBFnf3sYtUKcgQV2N0Pavh9CbaqVo+moBRa+2GjbCW3gur/A5ZtlncagT1/UARTDBZC9Ot5g806lxvqzQRX4dJSNfq4WTZIxSNkk8GQVVC4JKPrPAZFVkHdiH7QhCwejTCVVjPIsVkJlnTMKDxBJGhC1ss6K7ZvpJSWkiPgdxSoZ5uq0abe6gT+/gb1bGMHMgNtdqdo4FAVv69jSrZragWcVbDNfC6JKfvqAFWggm1U5Qu3w5jIu4TeOKhcsN1LQc0LfSIeJZPwfzyLDVU7dFoy4IDJai6hZoasEqxjDXaNnZr7JkhCBuq+7OwzFRTi0QjTCgY5pJQUKs1A31Ez6QRCwKZid3HZaGO0zcAl0qBeAEgVx3rvWlRB0KVx0A8iqBaMVyO5pWafG5XOlGr+TdC9kcCyzVZVvXKV2+HAaUAhxO8N+QwppfyBIf+sNtgoGWSTMdKJ4DKre7uzH51OB7aOW2HXHgabWU2yW2/RbJuBjdDZD0HXZYIld32sUgh0Dfths2wEvo/nckleWikFuob9sFkxeP3RyUDXoKJawTQlxUoz+DOlYCOhUr1NsxMsWY1FI8yk1ds3KjjWM5kEEaFgIN5OUiggrVfONnaz2gD3jS1BVi/IEfyZ2pvcup9cYOu4Ffa/1VjJ8VpsloPnUnPZRE/JEWSt/kExiKW+b8hnSCD0RF4FQqZqR+CgZcDQV0oUqwZLkxOBrmUvNssGJwOsywRr39glGal4cKqJW1GoGLzx+HSga5jPJvm0Yi+6jikpKnLfPL+8G+gabsVOvUXblErYRrkskALZQ1Czk3RBASIfjQhmMuoFgFQIckxNxIlGhHq2UYqsKnbfdPdNUPPAAeYVLeXZrBhMTsRJxoJVqYKCASAl+EL/TIWNyJ/ybBW3QAjxfcDv3+XHTCmlMqxDrc2n3sG8dz4b6Br2yohUIvKFSpO3npwJdA32vilWmxyZUsM27Y5JsRp8ZnVv3wlVghxb1SZmwKMuQVGyqgDpAMs2z9zcCXQNt2JDFdvkFMzIK0DIoDtdRTWpqwL7JhIRzGYS6smAywYRAbOZ4GwzOREnHlUwyFE2mMkkiEeDU0COyertoa7KRY2yQXst9x9SR8lxOxyYyEspr3m5kFvwJeDnb/O9twNfC3zIt9UcAIWywQOH84GuYVbRJkubFYOvuGc20DXY8jOrI2awkmQbrY7JlgJkdX7PpaUKkd+qNpEKkNX5PbLFYzNqlKv0aw+Dt03FaFNvdpgIsP/FXqhkG+Xu4d4Y0ODvm6eubwe6hluh1L5RzLEuVAyiEcF0OtiO6Co2ZbXJapBdv4UQzGbUvG+ClI5DP8ih4r4J+q7JJKKk4hH1iHzF4IFDwXKpXrlKRY/O9a4UIQghMsAZICulfMzp50kpv4RF5vd71me7//tbTp/jJjbLBu8I+GCm4lHyis0EN9oddmqtwAnZQt5quLKh0MuuWAm+vg5gsWub9ZI6l9aGIo713n2jCpG3z3fQjQAXe7ZpcCLg8hAbqsjHF3JJas0OFaMd6ESKvVCh+RRYZ2qjZCClDLRvyl5slg1iEcHkRLCjzeZzSS5tVAJdw63YLBvMBkxWARbyqpLV4KW3StpGgfIvIQTzWTVt8+jRqUDXIIRQU1VXMnjHaTX8vvWSWvvmdnCkeRFCHBVC/A9gG3gS+OSe7321EOJFIcRZRyt89fMeBr4CWAb+0q3PdYp6s0M54FmiNuZyah1Mey0qZIFALbKqShZoIW+rFdS5tFSRuvaUHON98xrYtlHpZaeCDBj6Z0qp+6ZikIhGyE8EG1hYyCUx2ialejvQdeyFTciCnjW9mE+xWTGU6pasggwYYDGXUuo8gRVUVcE2CwraRoWsM/QDhypBlTOlmnJMFS6VTcbIJKLK7ZvbYWgiL4RYAp4Avg34APBZYO9b8AlgAfhuJwu8Bf9r97+/K6VUZlh6QRHSAV35mUIHUxXHOhGLMJNJKEZW1cgezna7JatIVoMOAC0qqORQJcixNyOvCjbLBql4JPAs+GKuaxuFHIFCuRn49BDYq3JRaN8oQsgWc0laHbXmXm9W1Mk6FyoGnXGQ4zVQLSMvpVTGNov5pFJBjqrRptbsKGEb1UbIqsSlFvIp1hV6R90JTjLyP4dF1L9eSvke4GN7vymlbAGPAV/l4Bk9CCEmgL8HmMDvuPGZbkGVJkL2GlQ6mKoQebBIoYpkNWgnKRaNMJtNqkU6FBh5BNbc61hEKOUIbJYN0okomYDJal+toNK+aTKXVYGs7u3JoQYsQhZszSpYZBXUUnIok1lVMXCoCCFbyKcwJRQV8W+klAoFgFIUq02abTPopQBQNtoYbVMJ2yzkUsqdJwheUQfqEXmVuNRCLsmmQu+oO8EJkf9m4P1SynN3+JnrwGEHz9iL7wKmgA9JKW+49JmuQKWDOa9YQxiliHxe0QtdAdss5pNKRR/tWaJBN1GLRATzuaRy+0aFPTOVjpOIRpTbNyrYpkfIFHIElLONavtGgff3omIlGVJKZYIcqgWASo02zbapxL6xA4eq+H6q+Ta79RaNlhoiXlUUdQDz2QTFapN2R40AkEpcSqeMvJOUziJw8S4/0wLc6oL0g93//t93+iEhxA/aPzs/P8+5c+dcevzt8fj1FgCvPPcUhYvBjdoAKG02KTfafPQTnyQRDb6R0BdesSSCLzz1OS4EXH9oVg1uFDq+7ImD4OnzBhMx+NxnHPeHPDAqlcq+f/9os8Hllf2/FwReuNQgEzWVWM8ETc5fXeXcOTU6bV+8USdhooRtcnHJcxevc25iPeilAHB1rcZCOjKwbW53LoaFlJJEBJ588SKnzeuufa4TLBdrLERrge+bRtuSRn/2Sy8yU3ol0LUAmF0ZcHVrjXPntgJdy2bNcqgf+8IzsBps4z2AjZ0qrY5gd+0G586tBbqWmzsWEfvE41+guBB8A8mVivVvtXnjMufOBXvG1zesfhMfPvc4904FP0Hk/Jb1b7Vy6QLndoM941srln/+Fx/7FPNp5/6503fFF9asf6trF57j3EqwfGF7rYWU8IGPn2MqGexaQC0u1dw1WN1p88lPfjJwhd/d4OQ23AKO3eVnzgCOb38hxIPA24CbwAfv9LNSyt+i29H+/vvvl2fPnnX6+Lviix97mchLF/mb3/g1gXd2Xc9c508uPseDb/pyjk4H32X74zvPMb2yytd/7dcEvRS+YJzns6uXefs73hn4vxPAf1/+Ikv1En7sURvnzp3b93kf2XqWj7247uta7oT/eOGznEjB2bNfGfRS+IPrT3Jjq8bZs+8IeikA/MsvforTC1nOnn1z0EvhxIufQSSinD37FUEvBYDaYx/jgVOHOHv2kYH+3O3OhRMsPflJkpNTnD37Rlc/dxh0TEnlIx/k0TMnOXv2/qCXQ/axj5CdP8LZsw8FvRSKFQPzIx/nLQ+d5uxXnQp0LY1Wh3/66Q8zffgkZ8+eDnQtAH/wgb8C6nzlGx/i7KNuiSuHw/27df7F5/6KhRNnOPvlxwNdC8BnLxXhrz/HO7/sDbztvrlA1zK3vMuvf/GvOXLfQ5x9+FCgawEoP7MCn3+ar//qLwt+DveFDX7v+S9w6sE38JaTM44/zum74trjV+FLL/Cur/mqwEsH68+t8v+++EXOPPIWHgx4fDZYXEq8dJFv/YazxKLBEvmXI5f4yNXzvPkrv5p8Kvig6p3gxFKfAd4thNj31hBCnAa+iT2d7B1AySZ3NjbLDWYySSXIoX0xqNK5XhU5J1i1Uh1TslVVyDYKSIgA5rs1di1VJFaKyDlBvWY5qkhdwWp4p4p8vNUx2ao2lbHNQk6dfVOsGphSDTknWFJgVfZNX+oa7Fg+sEbITk7ElSnl2TUs9UTQhMNegxDqlGSo1ZirK61XxDZqSevV6juxWTaIRgTT6eD7ldgjbFWpk98sN5jNJAMn8bBn3yjynroTnFjrV4AU8CkhxLuANFgz5bu//wusxnS/6mSBQogU8D3dz/pdJ5/lFVQiqz0ir9ClpYptVKs/VI2sSqnSha6ObRZyKbZrLYx28DFEo91hp9ZSJgCkFFntBi+V2Tf5lDKdpAtlxWyTSypDyFQiHaBW4NAm8irYJh6NMJtJKFMjr9K+me0mkpSxTcUgFhFMTQSfyeyPSVXjTG2WDWYzCaUSf6q8p1Ty++x1qNQg+3YYmshLKZ/AqkU/iTV+7ie63yp1f38K+AEp5QsO1/heYBr4oGpN7myouPmUaXpSMVhQINMB/YzL+NJ6LRYUGpfVaHUoN4KfJWpjMa/Oy84mq3OK2GYhn6LUaCvRSKiXIRsHOV4DlRosgZXtUIV0qJRZBbW6bKtE5MF6hyuTda4YxKOCSQXIajQimMsmlAqOzWWTRBQgq9PpBPGoWkEOdc6Tahl5dWyjmpLjTnCkX5BS/j7wMPDvgc8Dl4AvAr8JvF5K+QeOV9hvcvdbLnyWJ1BJIj3bHS+kQkZepVmioFZGvtZsUzHaSkgWoW8bFS4t9QiZOhe6Sl1dQa0RdCplyMByBKrNDhWjHfRSlBl1acMOckgZ/Exw1faNSmUHu01JIhYhnwq+uRzYagU1bGOTVVUaYakUHFPJ74tEBPNZtRRAqtgmk4iSikfUIvIKvaNADb5wNzi+naWUF4Efc2Etr4EQ4gHgqzlAk7ugoNIsUYBkLEo+FVPiYJaNNo2WGuNZYI9URgFCpp7U1SKrKlxaKjrWoIbESjXbLO4ZJXZ8NtjmmiqSVbD2TXY+G+haVLPNYj6F0TYpNdqBZzQ3ywapeIRMwKMubVgZeSvIETRJLBmSeYXI6kIuyYsrpaCXAahFyMCyzfJO8O8osGxzaFINJSZ0Rw8rFOR4XdANALsQQnRnyQffN0o1LpVNxkgnokrwhbsh+I4Cd4CU8iUppZBSHlOxyR3Abr1FqyOV2XxgyW5VkNarRjqSsSjT6bgaZLVirUEV28xlE91GQursG1VIh1IZ+e65Vkdar85sZxXl46DIvikbZBJRMkk1Mqsq1R/ahEwVsrqYT9LqSLZrraCXwo5hKnPXgHWmChWDjqmGkkOVJAXYZDX48wRd+bhCtlnMq5GRN01JoWL03psqYD6XVKJsUDUuJYRQqjzuThiayAsh3iuE+CshxL4zSYQQR4QQnxBCvGf45akP1cgqWLJbO+MbJFS0jSr1h3Z0WJWXXSwaYTaTVMIRWO/++ywq8rKzG9OocKHba1hQ5Ewt5voZ+aCxXmowOREnFVcls6qONG+93OgFFlSASkGO9ZLR28cqQCV11I4hOaTIPQzWmTKlNTIwaGyUGywodKYWckmK1SbNdrCTZzqmpKhQZhWsM6VCsHmn3qJtSmX8PqCbkQ/eNjZfUMW3gW5wTIF31Mvr5Tt+30lG/h8AU1LKlf2+KaVcBvLdnwstVNx8czm1DqZSF3peEbLaXYNK8jMrah38vtkoNYgImFXkZdersVPAEVgvGcxlE8QVGM8CMJWOk4hGlHCS1ksNZYI/QM/JVyHbsVFqKJUFUirIUWqwqNg9DGoEOXYMqVQAaEGRAFCzbVKoNDmkkG3sf6egfT/VRl2CdaZ2663Am7LaAW+VVC6qZOQ3VOQLOTX4woU174j8I8CTd/mZJ4HXO3iG8thQTAYMVpZXqYOpkG1UycivlayOtzMKzBK1oYqMaG23wXwuqcR4FhuL+WRPKRAkNkoNZaZAgCU/m1dklNhayVCKdORTMZKxiBpnqtRQinSoQsigS+QVOlOqZOQbrQ7VFkqdKVUCQHYZj1KBQ0VsYwe81do3agRV7YC3SnfxYi5FsdoMfLyu7UOoROQXuxn5oJuy3u1MOyHyM8DGXX6mCMw5eIbyWFMys5qibLSpBtwteb3UIBGLMJUOfjyLjcW8FeQwA66xswmZCuNZbCwqIiNaLxtKvejAGnukQmR2TbGsM6jTZXujpJZ8XAihxJmSUlrycYVsk03GyCSigZOOitGm2uwodaYWFBl3af/bqLRvVCnJGNvm9ujbRr0zFfR9s76r3r45NKnG5Jl1JQNASWoKTJ7xksgXgNN3+ZnTwI6DZyiP9VKDbDJGVpEmQtA/mGtBX1rdLJAqTYTAOphtU7JVC7aHgFWzqs6LDizbFCoG7U6wNXaWDFidyxzUKTtYL6nVDRisiH7QGXnTlGyUDSXPVNDO4269RbNtKnimgg9yrCnoWKfi1uSZoPdN37FW50zZysegbWMHdVUsVwk64KxqcgvUCXKotG8OTU4AwZ+ptV31uJQ6++bOz3dC5D8DvFsI8br9vtkdHfdtwGMOnqE8VKvLhP7ms6N/QWFtV13bBB19tGyjzosOLLmrlFCsBhzkUPBMLeRSbAXcSKjVMSlWDaWk9WA5JUHXyBeqVidr1c6UEmRVwQwZWBLKoEnHhoKZVejum8AzZOrZJhGLMJtJBH+mFAwAzWaTRBSYPLO+20AItcpNVSk7WCs1mMkkSMbUaMgKfZn/asB8QUm/TxElx92Ssk6I/L/FmkP/10KIHxFCnBFCZLr//VEsAh/t/lxooSIhW+pG2II+mFaGTC3b9A5mwBnEDcWkrqDGy67R6rBdayknrbdfMEGOddwsG0iplvMI1nqCbiSkYl0m2GQ1aEKmXl0mqBHksN8DKjqQQb+jekReucBh8GVO62X1etxEI1a/kqBJh9WQNalMQ1aA6XSCeFQEf9+UDKUaY0P/vRD8vmkopeIAdXor3O2+G/qkSSm/APwjrM70vwa8BJS6//133a//kJTyiWGfoQPWS+rV89rrCVJaL6VkbVetBkuw52AG6FxXjTZlo60c6VBBrdCbAqGYbVSIzPYnHajlCNjNaYJ82amYPQRrPZWA+5Woahu77CDIRkJ2kEO1+2Yxp0ZGPhGB/IQ6UlfodpIOnJCp1+MG1Gjmq1pjTehPnhmT1dciPxEjFY/0VCZBQbUxoNAP8Aa5b6SUnmbkkVL+NvAo8JvAU8Cl7n//A/ColPJ3nHy+6rDqMtUaXQMwkQi+xq7UaFNvdZRzHucVyDqr2AwG1FArqEs6gg9y9EiHci87u44suH2jqny8V7caZJBjV71uwGDtm0bLpBxgkEPFukywAgubAXdLXi8ZTKWEUj1uoDtBRAFCplKds41FBcqcVJRIQ/9MBQnVJmSA1ZT1UD4VaOLPNKVyY0DBaso6EY8G6veVGm0arTuXdDp+e0kpXwL+idPP0RFbtSatjmRRMQcJLHl9kNL6Xu2hYgczFY8ylY4H61grKnWdyyYRIliy2muUo5htbKctSLKqbpDDDo4Fe6aEUGvUJexVuTQ4NZcJZA3r5QbT6TipuDp1mbDnTJUa5FPBTDbZULDpKFhnqtkx2am1mM4EI99eLzWYSqpF4sEKZG6WrZ4YQY0oXS8Z3DefDeTZd8J8LsXT13cCXcN6qcGbT0wHuob9sJhPcqVQDez57Y5JoWIo5xOD1ZgwyIz8Vq1J25TK+X3W5JlgRw8fpIxInSIWDdGXuqq1+cAi0EFGrVUlZGDJFgMNcpTtzqVq2SYejTCbCTbboWKnZIDZTJJYRAT6slsvNYhFBLMBOfa3g01Wg7TNRqnBXDZJTKG6TOiT1SCzHaqNnrPRn5cebABIRdssKlAepyqRX8wnMSUUA+xXoqJEGizbFANsymq0rR43Kp6phVwq0LumUGliSvV8GyDwjHy/eaR6tlnIpwJtHH6Qf5ehvR4hxHuFEH8lhDh8m+8fEUJ8QgjxnmGfoTpUzZABHMonA3WsVT6Yh4IOcihsm6XJgIMcpQaJWITJiWAydLdDNGLNBA+WyFuNclSry5xOx0nGIsE6AorKOW1nP+ggh2pBQ7DuGgi2KauKzWoh+BGyUkrWSwbTKbXuGgg+yFFrtik32kpK6w8FXOa0oajaEKy7eLfeot4Mpimr0smtSasnh2kGU8qjMpdamkyxWqoH9vyDBJ+cpC/+ATAlpVzZ75tSymWshnf/wMEzlMbarpqdksGaDblZMWgFNBPclq6raJvDUylWd4M9mJlElFxActI7wSLyQdrGapSjWl0mWI5AkKRjXVFCJoQIPACkYqMcgHwqTjYZC5aslhpKln/ZQY7VnWDuGym7PW4UPFO9yTM7weybsmH1uJlKqqVwATg8ZdlmJSDb9FRjCt43QQcOe4RMQbVCP3AYzH2jMlk9lE/R7Jhs1YIZPbymsLp5aXKCtd1G4EGOO8HJLf0I8ORdfuZJ4PUOnqE01kvWvEzVmgiBdTClDK6T9NpugykF6zIBDuUnKFSaGO1gIrPrijqPEHxGXtXMKqgR5FDXNhOBETKwss4qOo8Q7L7pmJLNsqGkg5SKR5nJJFgNKLO6XWtZPW4UPFMLOWsm+FpQpKP7DphWUFrfJ6tjQnYrekGOgN7hqjYdBYWCHArum6XAbWP1uJlTrMcNWLZpdSTFajBBjvVS464KVSdEfgbYuMvPFIE5B89QGuvdukyV5mXaCFqap2J3ThtLU93azN1gghwbina8BViamqDcaFMJqJP0RslQMusM/SBHUJ2kbbWCiggyANRsmxSrTWXvmyAbCRUrBqZUrx+HjaXJVGABoH6Jk3q2iUUjLORSgREyO+s8paC0fjaTIBGNBHbfqDoGFPZknQM6U6o28gU4PBlskEPVHjfQvwODKjld31WXSwWt5DjIGG8nVisAp+/yM6eBHQfPUBoqZw8P5a1LK8joo8oZMgjwYCpOyCDYbIe6tpnAaJts11q+P7ve7FBqtNUlZFNW34lOAPIzux5U1bv48ORE8BkyBVVjEGwAaF3xfXMoQCWH7dCrmJEXQgRa5mTXgat4F+cCLuVZV7THDQRfyrO2q2aPG9ijVgiIyKvtE3fLnAJ7Txl3Tfw5IfKfAd4thHjdft8UQjwAfBvwmINnKI31kqHs5gtaRmQdTDUdpCAPpt1ESMUsEPRtE0T9YbnRotrsKOtYBxkAUlmWB1ZPjrYpKQTQSbpXs6pq4HAqRaFiBNJJuj8FQlHbBDgmdUPxM2X1cgk2yKFi13oItlxlrdRgIh4ll3Q8vdkTBGkblXvcBF3Ks1FWs8cNWGNbrVKeABN/qvp9U8EGgDZKdy/FdULk/y3WHPq/FkL8iBDijBAi0/3vj2IR+Gj350KJ9QMYOChMp+MkYpFApDJ2XaaqtgmyW/JuvUWzbSpvmyAudOVJx1RwDah6ck5FbXM4wDPVI2SKSuuXJq1+JUHcxSqPSAXLSdqtt6g1/S/lse8bFXvcgKWqW90JppRnfbdBLhUjGVOPkIF1poJrdmeRDhXJKgRbymNNgVDzPIH1/gyylEfV93csGmE+F9ykK5W5VK+UJyAutVG+e8J4aCIvpfwC8I+wOtP/GvASUOr+9991v/5DUsonhn2GyjDaHbaqTWU3nxCCxXwyEMe60K3LVNU2mWSMfCoWSNR6TfEskL2ulQBso3qGrBcACuBCV7mJEAQrW7Rto2zfiQAVQOulBhGBknWZEGxQda3UYCaTIBlTryErWBn5eqtDqR5MkEPVexisoOp6KZhO0qpOD7ERZCnPhsIJHAhY5aJw1hmCmyXfaHXYrrWUDXL0SnkCCBwWqwYd8+4NWR11FpBS/jbwKPCbwFPApe5//wPwqJTyd5x8vspQeV6mjaX8RCAH047qqWybw1PBSDp7tlGwUQ5AIhZhLhtMZHZV8X0zl00Si4hgyKrdmEvRzOrhAMnq2q5Vl6kqWT1sS/MCCI6t7jZYyKWIKdhECIIds7a6U1f2roF+cCyIoOrqbr0XZFERS5OpwEp5VnYaPQWSijg0GUwpj5RS6awzBFfKY/e4UfX9DVYCJQjV2KbCo6ptLAWkcjloQ1bHb3cp5UtSyn8ipfwyKeWZ7n9/REr5ktPPVhkqz8u0sTgZzMFUvZ4XgmskZMsB7TExKuLwVDDdkle6BFlVGXA0IljMB3Ohr+42yCVj5FPqNRECuqMmI4GcqeUdi3SoK3UNLsixslPv1fipiH4n6WCCHCrfw3aQI4j7Znmn0fu3URFBqVw6pmS9pPa+OTwVTClPqdGm3uoo7/cFUcqzpnj5FxBYA8k1DbjU0mQqkHfUQfmCp2F6IURECPFtXj4jKKgudQU41JXW+11j1z+Y6tpmaXIiIEJWJxoRLKh8oedTgXStX9ltMJdNkIqrKXWF4C70ZcUJmRCCpYAknau7apOObDJGLhULRMmhOlm13xFB3MUrO/WeWkJF2Gvz+74x2h0KFUPpfRNU49FCxaBtyl6/FBURVODQ/rdQed/01VE+22ZHfdscmkxRbrR9D3KorsSE4Ep5Vg64bzwh8kKIE0KIXwCuA3/ixTOChm3gIwofzKXJCZptk61q09fnLu/ULYl2RmUin6JQaWK0O74+d3mnzmIuSVTBESQ2Dk9NBCJ1Xdmp9zItqmJpKrgAkMpOAAQnP1tVPMgBwYxZk1JaZFXhTEcyFmUum/CdkFWMNqVGW+n7JqhO0uu7ltRV5TNlE3m/G94t2461wmfqcEBBjj7pUNc29lhmv/2bZS34QjBnalWDfbM0maLVkRSq/pbyrO7WScUjTKfvrMR0jcgLIaJCiPcIIT6MVSv/fwJLwMfdeoZKWNmxurrmFJW6Qj+K4/fBXNlpsDSZUnJepg370rKdFr+wuqN2hgws25SNNuWGv/PSLbKq7mUOfULmt8pldaehNOmArjTP56xzu2OyVlI7Iw/B1GZuVZsYbVP5fROEbXRwHmPRCIt5/7uz98mquvtmJpMgEYv43gNoVYPSuN50FZ/P1LIGtgmqX8nKTgMh1FapHplKA/2AjF9Y2akrz6WCKnNa6ZY43a1s0DGRF0LcI4T418AN4L8D3wAUgX8J3COl/BtOn6EilnfqSkfXAI5OW+tb3qn5+tzVnbrSTgDsmZfu94W+W1dalgf9GvUgLi31SUcKo22yXfMvyNFodShWmxxRmHSA5fivl60uq35ho2xNyFDZeYRguiXbz1PdNkF0BF7RyTY+v6P6Eml17xurlCflO+no2Ubh91Q2GSOX9L+UZ3WnTjwqmM+qS1bt+n2/7+KVnTpz2aSyEzKgf96Xfd43yzsN5blUUGqFlQMqMYci8kKImBDivUKIjwEvAz8FzGDJ6AXw51LKn5VSXhvm83WAVV+n9uazD8ey7xl59W1jywb9JKumKbs1q+o6SNB3bv182ZUaLSpGW3nb9C90/1529r+D6kGOQ5MpOqbsdaH1A7ZjrbIMGCxJZ6Fi+FrKo4PUFSwpsO8BVQ1qVsEijH4HVHW5b4Io5VnZaZBORMlPxHx97qBYCiBwuLJT55DiSsxUPMpsxv9SnoMSsiBxKJ8iGhEsb/t/F6tum6Vecst/2xxkeshARF4IcVoI8W+AZeCPgK8DvgT8CHBYSvnewZeqJw5q4CAxlY6TTkR9PZi21FX17GGPkPl4MIvVJs22qXQ0H/pNR/x82ekgWYRgJFY26VCdrAbRnMsOUiof0Z/yv5RHF7K6NDVBudGmYvjXZGl1p05EwGJO3ewhWMGxld26r6U8Kzt1ptNxJhLqZg8hmJIM2+9TdUKGjUOB2Eb9EicILsihuk8ci0Y4lE/5npFf0aCk0i7l8XPftDomG+WDNR0dNCN/AfjfABP4NeARKeVbpJS/IaXcGnypeqLWbLNdaynvIAkhODw14au0fl0TqWs6EWNyIu4rIdOhqytY8jMh/M3I98iq4o5AEN2SdWisCcEEOVZ7+0ZtR+Bwr5O0v0qORCzCbCbh2zOHQRDZjpXdBgu5FLGop4N7HGNpMkWjZbJb96+UR4cMGVi2WS81fC3l0aHpKFgqF/9r5PWwzaG8v818raajegQ5jkxN+Erkq0abHU24lN8Na9d2G0h5MEXdMG8xCXwQ+GMp5QtD/HntsaJJFgisNfpZ16FLFgi6o8QCsI3qpCMRizCXTfr6srOzuKqfqblsklhE+DpmrTeeRfF9E0TZwcpOnVxS7UY50P+38zU4ttvQInvY61fi812sehYI+u9RP53r1V31e5WApeRo+1zKs6L4qEsbhyZTFCoGjZY/pTwdU7JeUr9sECxi5KdqbKfWot7qKN8bCSzb+KngXdXE7wNLqep3IB4OxqUGJfL/DLgGfD/wGSHEi0KInxRCLA26SJ2hE1k9Mu1vhE0r2/gcfVzRRD4OQdimTiwimFdc6hqJCJam/G2ypEOjHIDJiW4pj5+2UXxOuo0gGgmtaNB0FPaUZPhNVjXYN3bD2ps+Ote6BDn8buZrtDtslg3lS5wAjk5bHcj9Chxulg3aptTiLj7SLeXxS+XST1Kov2+OTE+wVmrQ7pi+PE8rn3h6wtcgxyAq1YGIvJTyX0kp7wXeBfwpcC/wS8B1IcRfCiG+a+DVaghdmgiBdWltVZvUmv7UHy5rZJuj0xPc3Pav7GBl52AzIVWA37ZZ3Wmw2G22ojqOTqW5seXjvtGgQSJY8jNr3/gb0dfBsU4nYsxmEj6fKT1sYzdZ8mvfWFLXutKzwG3YhMwv21SMNqVGWwvH+liXyN/Y8sc2dn8LvWzjz32jw8hCG8dm7DPlj220IqtTaTqmZMMnlYsuKlWAY9NpVksNmm2fghwDTA8ZqkBMSvkRKeV3AseAn8bK0r8L+EMs6f0bhBBvHuazdcDKbsNqlJNXf/PZkhW/ZIt2o5x0Qu2urmBd6OVGm12fRomt7h5sJqQKODqdZnmn7lv94bImWSCAYzM+k1UNGmvaODad9jl7qEdGHuDojH+2aXdM1suGFo51LBphaTLlm2O9VW1itE0t9s10t2GtX7bRpecE9Ode+0bINBg9Z+PojL8BIJ2UmH6rXHTp/wP+K8dW7KajGnCpo9MTSOlfn5uVnXq3YfnduZSjTi9Syg0p5S9JKe/Dmh//x0ALeAvweSHE00KIf+zkGSpiZafOYj5FXPFGOWDJQcDPg6mRY21HrX1yBHRpBgMWWW11JBtlfwJAutRlghXk2Cj7U3/Yyx5qsm+OTk9wc6vmS5ftRqvDVrWpRWYVLNv4lSHbKBt0NJG6ghUAuuGTY63LeDWwVC5+BsdWBqjLDBoTiShz2aT/hEyDgPOhfIpYRPgXABogexg0/Fa5rOzUtWg6CnvKVXyyzXJXiakDl7L3jV8KoNWdg/vErllPSvkJKeV3A0eBn8SaL/8o8O/deoYq0Mmx7s2S9/HS0sFBgr0Xun8vOx0yHeDvpWWaUptuwGAFOcCf4Fip0aba7GiRBYKuysVoU6p7X8qjEyEDi6wu79QxfVC52I61DqQD/C3l0ak0DvwNAOmUWYWubXx7f3eDHBrcN9GINbHIP7LaIJdSv+koWCqXTCLq35nabXB4MkVEg7JBv5tr6sSlbL/Pr/fU8gAjC10Pg0gpC1LKfyulfAD4Wiy5faig0+az6479aiQ0yOYLGsd8jMw229ZMSB0aLEG/xs6PS6tQMWh1pEaOtR3k8N42dgBOl/vGT5WLjrZpdSTrPqhc7DtNh27AYAWA1kv+qFx0s83RbpMlP1Quy9t1ohHBouJNR2342ZPj5naN2UyCiYTaTUdt+BnkWNaksSbYvVx8VLlolNxKJ2LMZBL+EXmNEjh2Lxe/ztQg+8ZTPYOU8pyU8u95+Qy/YZpSm+ZTYEVmD+VTPmUPW5Q1aZQDkJ+IkUvG/CFkO3WkhOPd2jXVYf8b+pGRty/GY5rYxs8AUN82epwpP1Uutm2Oz2qyb3ysW7XvNHuvqg47AORHwPnGdo10IsqMBlJX8Fflcn2rxuGpFDENpK5g2WbFp14uN7bqvdpzHeBrSYYmjTVt+KkAWt7Wh6yCfyPoTFOyuqMPl4pFIxyeSvlypnbrLUqN9oH9Pj1ua4VQqBo026Y20Xzwb2yCbrI8IQRHfIro9x1rPWyTikdZyCV9edld14x0LOSSxKP+RGZ1I2THfCzJuL5VIx61ApU64KiPnaRvbFkjC3XJHtpBDj/q5G9s1Tk2ndai6Sj4q3K5sV3T5q6BPSqXkvcqF8s2ery/wbLNpk+9XG5s1bRJUoB139z0QeVitDuslxvaBOLBv9HDhYpBs6MXl/JrYpH9jIOeqTGRHxA9x1qjS+uoTwfzenGwzacCjvnUSVq3rDNYa/WHrFr2P6qJkxSJCI74VH94Y6tGLhljSoORhQCT6Ti5VMyfjPxWjcNTE1qMLIS+lNuv+0Yn5/Goj6U8N7WzjY8ql26QQxf41bisY0qWt+tavb+Pzvhz3+zWrOyhTn7f0ekJKob3s+Stkhi9fOIjU2lfSnl09In9KuWxeebRA97FYyI/IK4PGClRAcdm0qzu1j2ff6ijbew6Ms8vra06iWhEizEbNvy8tBZySVJxPbKH0A0A+RGZ3bbknLpkD8F6+fiSWd3Wi3TYKhdfIvqaZVYXcylL5eKxkkNKyY2tmlbOo1+lPPVmh0LF0CrI4Vcvl9XdOm1TanWmjvkUALo+IOlQAX6NoLuuYeLv+MwE9VaHQqXp6XN05AvHZvyZWDRokGNM5AfE9aJejXIATsymMaX3F/rN7bpW2UOwXna1Zodtj2fJ39iqcWRan+whWLZZ3W3Q7ngbALqxrZcsD/wNcugk5wT/6g9vbumVWQV/FEDtjsnKjl5yzr7Kxdt9s1VtUm12tCJkdi8Xr/fNTQ0zZId9UrnYASadzpRfaoVerxKN9o1fDWsHlUirgBOzGQCub1U9fc71Yh0h9OJSvfF8Hiucb2zVyadiTE4cjEuNifyAuL5V41A+pVX28ES3GdQ1jy+t691Mh17ZQ3/qVm9s17SRjts4Oj1Bx5S9sTte4caWXpJFsByBYrVJ1fCuAZWUkpuayTmhOxN8y1tpXtVoU6w2tbONH52kV3cbdEyplfMIdimP16TDJmT62Mbu5eL1O0rHzGoqHmUx773KRUeyupBLkohGPL9v+llnffwbv1QuN7brJGMR5rN6TIGAfvPYa0Xv941uXMqvhrU3tmsDNfEdE/kBoVtTD+hvvus+HEzdbONb1FozOSf4Y5tWx2R1t65l1hm8jcwWKk3qrY6GZ8qS5m1VvZPm9aRnGpEO8EfloluDRBvWmDV/MmQ6kQ7wR8mhq238GCV2c6tGROjTyBe6KhcflGPXt2pMp+NazJC30Ve5eOwTF60Ejg4z5G0cnZ5ACO+JvJ4+sT+Jv+tbg5XGjYn8gLCaCOm1+eazSdKJqKcH0zSt2kNdRkHZsBvCeBm1LjdabNda2hGyYz7YZmWnjinRaqwP7Omy7eGFrmOmA/zpQN6Xuuq1b/xQuejYRAgsQlaoNKk1vVO56BoAsstVvFS53Niuk4rrlT2Erm12vM7IWzOd45qM5bPhRwmYjsktIQRHfVAA6ZjcSsaiHJ6c4FrRY2m9hrZZ6PZy8fJMmebgSszYQX9QCPF7Q60KpJTyB4b8s0qh0eqwVmpot/mEEByfSXt6MDcrBkbb1M55zKfiTE7EPSVkPdKhmfO4NDlBROBpUzfbNrqdKT8iszc1Jh1g2eYNx6Y8eYZu4xxt9IMc3gWEb2zViUYES5P6NNaEVzegOrOY8+QZN7bqzGQSZJIHdn2UwPGZNNVmh2K1yZxHRPtGNwukU2kcWLb5wLOrtDqmZ0T7xpZ+pXFg3Tcffn7N02fc2Krx8JFJT5/hBY5NT3C54J1PbDfWfOvJac+e4RWOz6Q9LcXVlUtFu71cvPT7NivWiHNPiDzwfQOvyIIEQkHkl3e6oyRm9bvQj8+kPb20dOxAaePkbNpTtUI/Q6bXvknEIhyemuCqh7bRsasr9FUuXtpm0BEkqsC+A7wMHN7YrpFORJnJJDx7hhewbXO9WONt93rzjBvbNZYmU8Q0yx7aTZauFWueEfmbGirqAE72bFP1jshr2I8DrH3T6WaxTs1lPHnG9a0a7zgz78lne4mTs2m2qk12660DN84aBB1TsrxT512PLLn+2V7j5FyGcy9vYprSE+n7br1F2WhreqbSfOzFdc8+/+a2fs0jbZyYzXDVQ9/m+hBJikHe9KeG/HXPAM9QGjqT1ROzaa5v1TBNb6R5dv29bhkysC70Kx4GOXStWQU45bVttmvEo4JDGo3lA0vlctLjC/3GVp25bJKJhD7NYAAyyRiL+SRXCt4qOY5r1lgTrBrbRDTi+X2j4zvqVJesXvXYNrq+owDPzlRvLJ+Gtjk1Z+11r/ZNo9Vho2xo+f4+6fGZWis1aHX0a6wJlm2abZOVXW9k0rqWf4HV8K5YbVLxqJmvjt38bZyay3C1UPWszOnGEMmtAxN5KeW1YX8N/ldRE8MYWBUc715a62VvajOvb9WsURIaOgInZzOs7NY9mw2p41g+Gydnvb+0Dk/pNZbPhn2hewVrLJ9+5wnwIchR006pAJY07/hs2uPgWF1L0jGZjjOdjnPFo31jZw91fH8f7Y4u9eq+2am1qGiaPbTJqldnSufsoa1Q8OouthM4OhKyk70AkDfBMa0TfzN9BZAX0FWJCZbKpdrssFkxPPn8G1uDj+XTS3sXMK4Xa1o2gwE40ZO7enNp3diusZRPkYzplT0EuGc+g5T9y8VtXC1WtRvLZ+PkXIZyd9SXF7hW1DN7CJYjcGO7TsujDuQ628bLIIdpSq5tVTmpWWNNG14GOSpGm82ywYk5TW0zl+HKpje2Wdmp0+pILfdNPBrh2PSEZ0EO+3NtUqwTZjIJcqmYZ0TevsdOeiTb9xKW3+FdkEN3tSHg2ZnSmazaI6u9mnR1Y0tfLtVTR3n0nrq2VR14LJ/jji9CiCXg64AjwH7/KlJK+QtOn6MCrmvaDAZefTC/4p5Z1z//erGmXedxG3sj+l7UZl4pVHlEw2YwAPfM9aV5btdmSim5UqjynW8+6urn+oWT3drMG1s17pnPuvrZjVaH5Z063z1/zNXP9Qsn5zIUParNXCs1aLRMTs3r51iDJQX+9EVvajNt0nGPhqQDLOf6s5eKnny23SPm1Jy7Z9UvnPQwOGY7pTqeKSGEFTj0Ksih8ZlKxa0O5F7tm6vFKrGIYGlKr9I4gMVciol41DtCVqwym0mQ1ayxJuyZJe9Rcuuaxlxqr8rlyz3gUpc3qwP3+nCUkRdC/DxwFfjPwL8Gfg74593/2v//z508QyVcKVS1jMoCPfnytS3vXnY6vuigH2Hz4mXXbJsW0dPcNl5E9DcrBhWj7VmDIq9xz7x3skX7M3W1zSkPz9SVgu62yXpWm6k7WT01m2F1t0G96X6Z05XNivUMTfeNl2VOV4tVohGhZWYVLNt4lXW+XKgynY4zldarsaaNU3MZrniUWb1SqHJ8Nq3dWD6ASERwYjbtWQDo8ma15yPohnzKKnPySsF7pTA4WVUFR6YmiEeFJ/1KpJRc3qwMvG+GPn1CiL8L/DPgMeA7AYFF6P8O8NuACfwR8LXDPkMldEzJtWJN24MZj0Y4Ou1NB/LdWotitamtbSYn4sxkEp5c6Ne3aphSz0wH7KnN9MA2vSyQphd6X8nh/pnS3TZe1mZe7mXI9CSrXtZmXtmsIkRfgaUbTnq4b64UquSSMeay+hIyr2ozLxeqHJueIBHTj5CBtW9WduoYbQ8CQIWKtvcwWPeNVxn5y5v6JnDA2xKwyxqTVeh2Z/fANu2OybVi1XUVo1+IRSMcm/HmTG3XWpQa7YED8U5u7R8CbgLfJKX80+7Xrkop/0hK+Q+BbwW+C8g7eIYyuLldo9kxuVdT5xEsadiljYrrn3upUOl+vr628ao7+xXNM2S92sxxZvU1sGszvbjQL2tum+Me1mZe2awyEY+ymNevvg68rc28UqhweHJioPo6leClkuNyocqp+YyWck7YqxzzJgCkq9oQrHIVU+LJfGcre6jn+xusgPNuvcW2y31uTFNyRWNCBtaZur5Vo+1yn5tSo0WhYmhtm3vns1wuuM8Xbm5bvUq0DgB51OfmSo9L+SetfwT4oJRy73yCnvcgpfwI8BHgnzp4hjK43M2Q6Zp1BrhvIcuVQpWOyyPoLmtcX2fDK2mefTBPadhEyIY1ns8bB8meVa8jvKzNvFKosphPktGwvg76tZlenalTc/oSssVcilQ84klt5pWCvnJO2FPK49GZ0jUwBt6N57N7lehsG6/UUVWjzXrJ0PpM2f+ul13eN8s7dZptU+t9c2o2Q7s7zcJN6K6oA7h3IcN6yaDcaLn6uXZwQOczZY+sdnuc9+Uh940TIh8H9nalqQO3dvR6HnjUwTOUwaVNe/PpHWEz2ibL2y5fWoUKsYjQtsM2WBH99ZJBrenu3MwrBavhyaSGo+dsnJrLcK3ofm3m5YLVeVzH0XM2Ts5mepevm9DdsQbvZItXuplVXRGJCE8610sptZdzZpMx5nNJ14McdvNInW1zeCpl1Wa6vG/WSwb1VkfvDJlHSo6rGnfzt+FVD6B+iZP+tnE74GyT1Xs1fk/d1+U6l1y+i/tJUX251Mm5DEbbZK3k7jjvKwWreeTRAcd4OyHyq8DSnt9fB15/y88cAdxlRgHhcqHK5ITVAEJX3LdgH0x35TKXN6scn9Gz4YkNr2SLw3SgVA2n5jLUmh02yu7WZoaFrK7s1mm03K3N1F3OCVZt5hWXm3M12yY3tutaO4/gTZCjWG1SbrS1Jh3gjWzx+lYNKfXOkNm1mW4HOXQv/wKYSieYSsddzzrrXv4F1mg4L/rc9JpHakxWT3lE5K9sVokIOD6jr23utfmCy+W4lwtVptJWXypd4ZU6ym4eGRuQSzlhXk9jyett/BXwdiHE9wghMkKIbwG+o/tz2sPuJKirnBOsjDzAK24fTI27c9ro2cblIEcYyKrd+8DNfWM1j9SfrN4zn0FKd5tz7dSabFWb2pPVe+aylBptChX3ajNvbNfomFL/MzWf4dpWjWbbvdpM26nQ2bEGyzaeZYE0v2/um89ycaPs6mdeCcm+uW8+6zrpsIMmdoNKHZGIRTgxk+biuvuELJeMaTkL3MZcNkE+FXPdJ75UqHJsJq1t80igm5wTrvvElzcr2vs29y5Y6/eCLwxjGye77APAQ0KIU93f/xKwC7wPKAHvx+pk/zMOnqEMrO6cejsB05kEs5mEqxn5TgganoDlPEYjgovr7jlJFaPNRtnQ3kE6s2j9277som2WQ9DwBODMYg6Al110ksKQBYK+bdw8U2GoPQQ4vZCz7k4XI/phkLoCnF7MsVVtUnCxO7ttZ50JGVhn6mqx5mp39iuFCslYhKW8frPA9+L0Yo6XN8quKoCuFKosTaZIJ/TsVWLj9GKWlz0IAOme3BJCcGYx53qQ44rm3fzBanR8Ytb9BtlW4k9vvnAonyKXjLnqE5vm8L1KhibyUsr3SSnTUsor3d/fAN4K/Efgo8BvAW+VUn5u2GeoApuQ6Z51Bivz7Gb0caXb8ET3SysZi3JiNu3qwby8OVwHStUwn0syORF3laxeCoEsD7wJAF0KQWNN8CYA1OtVonlQ9bQXttmokIhGOKJp80gbXu2bhVySXErf0jiw9o3bAaBXNqzmkRGNe5WAtW92ai1Xx/O9MsRMZxVxZjHHNZcDQGEoGwT3A0B9Qqb3OwoslYubWedyoxUKLiWEsIJjLvrEyzt1jLY51L5xVfchpbwipfxhKeW7pJQ/JKV8zs3PDwp2FkjnxhU27l3IupqR7xGyEFzoZxbcjcxeWLMcUTszqSusqHXWVbJ6oftZZxb0to0XAaCX18uWHFLzWudeAMjFwOGF9TKL+aTWzSPBCqhGhLtqhQvrZe5dyA5cX6ca+koO9/bNy+tl7j+k910D3iiAXl6vhMo2bu0b05TWvlnUf4Ly6UVLAeRWY9Zas83yTl37zCq4HwBa2a1bzSNDwRcyXC/WaLk0nu9KIRwlTkBXyeFeAMj2Ie8/5CORF0J8rxDi1uZ2t/7Mw0KI7x32GarAJh33aU46wApGbNdaFF26tOyXpu5kFawL/Wqx6lrjsrAQMrD+fV9289JaK3Mon9KekIE3AaDTC1mtu/lDPwD08pq7QY4w3DWpeJSTsxl3CdlamfsX9XeQFnJJ8in3ZIs2ITsdgvf3qbkMEQGvuGSbcqPF8k49FGfKbZXLje0ajZY5lGOtGk4vuGsb+94aB4BeC9vGrwuBbe5byNLu9jNyA3Zyy268rTNOL+bYrrVc6wFk88zTQ9zFTkL37wO+/S4/823A7zt4hhK4sFYiEYtwclbv+jroHyC35PUvrZVYyCWZ1rgDpY3TizlMiWtR6wvrlVAQMrBedqVG27XO9RfWy5wJwYsOvAkA3R8Cxxq6skWXAkAdU3JxvRIi27hXt1pqtFjZbYTiTNl1q2NC9lq4HQDqEbIQnKn5rLslYC+HKElhl4C55fddWCsB4dg3bgc5zttKzBDcxW43yL6wViYZi4RCwXu/yz2AXl4rc3gyRX6I8i+vNXhRwN3h0wOg2nLn0ee7GTLdJYvQfyldcGnzXVgLh2QR9kRmXXKuL4aKkLn3suuYkosblVBkD8F6YZvSnbGOu/UWqyEhZGC97NwKAF3fqmG0zdDY5sxijqsFdwJAtjMRnvsmx8vrFVcCQGEpcbLhZgCot29CcKZsBdArLtnmZQcZMtXgdgnYhbUKE/Eox2f0T2653QPoggNCphruW8giRD844RQX1sucXgxLcsvdANCF9crQvo3XzPQMsO3xM26LasudzwkTWV2aTDE5Eeel1ZLjz2p3TC5uVEIhIQJLthiLCFcOZtgImZu1mdeKVZptMzSOtZvSPNuxPhOSIIebASCbkIWJrLqlAApT9hDg/sUsu/UWmy4EgMJEyMDdxmUX1stMxKPaN0i04XYA6MjUBNmk3h3rbbhZAnZhvcSZxaz2DRLB/R5AYeIL6USMU7MZV/gCWAGBMPScAHd7ALU7Jpc2hlcbDnRDCSF+75YvfbsQ4uQ+PxoFjgNvB/5yqJW5gKbp/DLfrjbZKBuhIatCCB5cyvPiqvNL62rRmoN8/6FwHMxELMLJOXdki2HLkM1lk8xkEq7UO78coiwQwMlZ9wJAvSaAIdk3ewNAbz897+iz+oQsHEEOO1hzcaPMg4ed3aEX1sqkE+EhZHv3zYLDsWgX1iscnQ4PIbMbl13aqDreN1bPiXAQMoAzC1n+a93qjL3ocN+EpUGijTOLWT764hqNVodUPOrosy6slfna1y24tLLgcXoxxweeWUFK6WicXqtjcmmzwtn7w2ObB5byPLe86/hztqpNNkPGpdwKAF0t1mh2hk9uDZqR/749vyTwhlu+Zv/6HuAdwOeBHxtqZS6gbVrNXJzAlpS8LiRkFayDeWGtRMdhoONCzzbhOJhgkUs3oo89QhYm2yzmeGnNBdusVRAiHA1PwAoA3TufdUV+9vJamUyICNlcNslcNsF5l87UsZkJ7Wc627hnLkssInjJhaDqy+tlTi/mwkPIuvfmeRfum5fXwtEg0caDS9bfxZX31FolVLZ5YMny0150aJtWx+TyZjVUtnndUh5TOldHFSoGhUozVLZ54JBVAray23D0OVcKVVodGYp+HDYePJzn+lbNBS7V7asQIp/4dYfynF8tYzrkUk6TW4MS+VPdX/cAAvj1PV/b++s4kJdSvk1KeXmolbkEp06S3dQjTGT1gaUcjZbpeBbthbUSkRARMoCHD09yc7vOTs1ZJ8oLa2WyyRiHJ51lBVTCw0fynF8rOx5FcmG9xLHpdGgIGcBDR9yJWp9fs5oAOskKqIaHDk/y/IobpCM8PSfACgCdWczxwoqzfSOl7EoWw3MPz2WTHMqneN7hmTLaHS4XwkVWT81lmYhHHd83FiEzQuVY2wqF5286s82VQpVmJxwNEm08cmQSgOeXnd3FL4cwufVw1zbPOdw3/fKv8NjmgSU7qOqUS3X3zVJ47puHj+QpG22ub9Ucfc75tbKj5NZARF5Kea376yrw88Cf7fna3l83pZTutP52iBcdOkkX1stMp+PM55IurSh42C87pxH982tlTs5lHMu0VIJbL7vnl3d58HA+VITs4SOTNNum4zq755dLPHwkPC86sPbNZtlgozR8RN80JS+slHj48KSLKwseDx/Jc3G97KipW9Voc2mzwkMhs80jRyZ5bnnXUU3vym6DrWqz54yGBQ93beMEL69VaHVk714PA6IRwYOH844DQLZtw3Smcqk4p+YyPO/UNl1CF6a7+Oj0BPlUzPGZemktXKVxYCk5ohHh+EydXysRiwjuXdC/K7uNnsrFYTD+wlqZmUyC+Wx4uJR9dzo9Uy8s73LffHZoLjV0szsp5c9LKT9t/14IkRdCHBNCKOOhRwW84HDzPRdCQnbfgiXpdCo/e2GlFConAOAhO6Lv4EJvd0xeXC2FynmEftTaSZZsp9bk+lYtlKQDnF3oV4pVKkY7dPvmkSOTtE3pKKL/4moJKeH1R8Nlm4ePTrJTs2Z5D4se6QjZvnn4SJ7LBetMDAv7PIbtTD18OM8LKyVHks7ne/tGGZfNFTx8ZNJxIP655V3SiSj3zIcnIy+E4OEjk84DQDd3OJRPhSq5lYpHuW8+61gB9OzNXc4s5kjGwpPcOpRPMZ123iD7ueVdHlwKF5c6s5gjEY044gtSSp5d3uURB76No671QoioEOKnhBCvYHWnvwpsCyFe6X49UO1sIiIcEflGq8P51TKvPzrl3qIUQDIW5b6FrKMI22bZYHmnzqMhc6ynMwmOTk84ImSvbFZotMzQkY5TsxkyiaijS8t2sMLmWFsvKGdE3nYinFzoKsKNAJBNVsO2bx5xwTbPL+9aWdqlcBGyR45MIqWzTNBzyztMTsQ5NhOOnhM2Hj4ySa3Z4UpxeOHjc8u73DOXIReCMVl78fDhPMs7dbarw5fH2aQjDGOy9uKRI5OcX3VWHvfszd3Q+TZgl8eVhlZHSSl59uYujx4Ll22EEDywlHfk9zVaHS6slUO3bxKxCPcfyjl6f6+XDDbLhiPfZmgiL4RIAB8D/hVwEriB1dzuRvf3/wr4ePfnAkEianUEHnZMy0urJdqmDB1ZBXj06BTP3NwZ+tJ69uYOQOiCHGDJ6V5wcDCfDWmGLBIRPHTYmdz12eUdIHyELJOMcc9cxjFZTcYinA5RzwmAI1MTTKXjzmyzvMtCLum4g7lqeN2hHLGIcHimdjm9MLwsT1W4EeR4bnmXR45MhioLBO4Ex55f3g3dOwr22GZI4tExJS+ulEIXUAV46MgkzY45dMO7UqPF5UI1dIQMrPumUDHYGHLk5Y2tOrv1Fo8cmXJ3YQrg0WNTnF8dvjzuxS6XCiVf6CqAhuVS9rvfyZlykpH/ceAs1ni5B6SUJ6WUXymlPAncD/wF1vi5H3fwDEdIRqHVkUNn5W1CFsbN98bjU+zUWlwtDtek4Zmbu0RE+GR5YP2drhZrlIbs0vn88i7ZpDV/M2x4+MikFeAaMqL//PIux2YmmEoHFt/zDI84lHQ+2y3jiUUdCaWUgxCiVws+LJ5bDmcWKBWPcnoxN/S+kVLyfEhts5BPsZBLDk1WjbaVBQojWb1vIUsiFhnaNoWKwcpuI3QBVejXtQ9731zarFBvdUJpG/vv9MKQ983zIfaJnQbHnuklt8K3b954bIq2KYcuy3j2xg4Abzg25d6iFMHDR/Ls1lvc3B6uPO65mztEBDy4FAyR/zvA88C3Sykv7v2GlPIS8B7gBeDvOngGQoi3CyH+hxBiVQhhdP/7USHEN9/tzyZjVhT+i9e2h3r2Mzd3mM8lWQpR53Ebbzg+BcDT14ezzbM3dzi9kAtV53Ebj3RfUs/eGPLSurnLQ4fzoRkFtRevPzpJo2X2xusNimdv7vL6EEaswXIE1koN1odoeGeakhe62cMw4uEjk1xYGy6ibze6CyMhA3jkSJ5nh1RH2Y3uwrpvHjky2XOQB0UYG93ZiEcjPHQ4z5e6DvKgeC6kZTwAk+k4J2bTPDOsbUJaxgNwYiZNPhXj6RtD+n0uZA9VhV1K8fT1naH+/HPLuz2pddjQ5ws7Q/35Z29airpDIeRStj/7tIO7+PRCjonE8Io6J0T+PuBDUsp9U3Pdr38IuHfYBwghfgb4NNZM+g8Dv4qV6Z/GUgPcEVFhder84tBkdZdHj4ZPlgdweiFHJhEd6mDatUBhvMzBUisIAU9e2xr4zzZaHV5cLfFoCCOPAG8+MQ3AU0MExzbLBje366F0HgHecnIGgC9cHXzfXNyoUG12QpnpAHjLiWnaphyKeDxzYwcpCfWZ2q61uLQ5eL2zHaQO675504lpLm1W2Rqi3tl+74etZtXGW0/O8MyN3aGCY09f2+4q6sJpm7ecmOHJq9tDBceeur5NLhkLVaM7G5GI4M0npnny6vAJnOMz6VAq6jLJGA8u5Yfy+8B6Tz24lCceMkUdwEIuxdHpiaGJ/DM3d0L7jnpgKUc6EeWpIfw+KS2fyCmXcrLjmsDdbroMMJQ+WQjxXuAXgI8D90gpv19K+dNSyh+UUr4V+D8P8jlvOj7NU9cGv9B3ay0u/f/t3Xd0VPeVwPHvVUUFdQkQRRJNYLApFmDANgKcuMbd3jiJe+J4d3PSE3uz2ayd3T1pm9ibOJvYTrxOHDtxXHEBjA2I3kF0CSQkUdQQ6r3Mb/+YGSzESJoRkkZ6737O0RnrvTczl+f39H73V8/WM8uiF19ggDBrfEyfamaLzjVS2dBq2YJ11Ihgpo+O6lNCduB0Da3tDjJcCa/VjIsNY3TUCHb1oSDgPp/zXAmv1cxIjiIsOLBPhaSdrnMz36Lnxl0BtLsP99TOwkpEPv0Mq5l3CRVAuwsrCQ8JPL/ahtXMT3Oem75eN8nRIxgXG97fYQ0JGSmxtHY4+tQVeGdhJTOSo4kMtV6POoB5qbGca2iloML3yrHdhZXMTYm13ER3bhmpcRwvr/d5MkBjDLsKq5jjap21oozUWLJPVdPa7tvQwdZ2B9mnqi19buZMiO1TD97qxlbyzzYw26IVqkGBAcyZENOnMnFeeT1VjW2XXCa+lET+AHC3iCR62ikiCcDdwH5fP1hEAoCfAY3AF4wxF/XjNcZ4VUFwZUosZbXO8WC+2FlYiTGfFiSsaM6EGI6W1NHY6tvyPjsKzgFw1UTrnpt5qbHsO1nt8+yuVk9WRYSM1Fh2FVT6XDm2q7CSEcEBluyyCM7urs4/6L4nHbsKKhkVFWq52bXdYsJDSB81kp19rACaNjqKKIvNru2WlhBBQmQIuwr6kqxWMXdCrOXmVXC7Ylw0IUEBPt9Txhh2F1Yyz8LPb3fFlq8FyNZ2B/tOVpORas2KMfi0d5SvlapVDa0cK6u3dLnPXTbxtVddQUUDZ+taWJAWPxBhDQnzUuNobnP4PBb8wOlqWtodlj43c8bHUFzTTEmNb2PBd7qea/MtfG4yUuI4Wlrr87xa7gacS31OXcrT/zkgEdgpIo+KyEQRCRORNBF5GNjh2v9cHz57EZAGrMS5nN3NIvKEiHxDRBb68kF9bQnaceIcIUEBlm11BueN1eEw5280b+04UUlCZAiTLNj1zC0jNY7G1g6f187cWVDJlKRIYiOs1/XMbV5qHKW1zT5P7rGrsJLZ42MICbJm0gHO6+ZoSS11PvxBd7Z0VDIvNc6Sw3jcMlJj2VtURYcPa1+3dTiTjvkWTjpEhIyUOHb52KWzpqmNnNJay1YagnOp1Fnjon2uADpV2URZbcv5hM6K4iNDmZgY4XPZ5lBxDS3tDsv2/gGYlBhBbHiwzxVAu13JrVV71IGrciwwwOe/N+5y4gILN+C4K7d8rQDacT5Zte65cf9/35p3zqf3bT9RSWhQgGWHOIHz/7sxvs/HtqugksSRoaTGX1qvsT6XqI0xfwd+CqQALwDHgXogD/gDzkT8F67jfDXP9VoG7AU+cH3Xs8BWEdnQXU+ArqaPiSI6LJjNxyt8CmBHQSVzxsdYbkmfzualxhIcKGzL9/7GNMawo6CS+WnWTjrchWNfKjk6HIa9RVWWbgWCvnUFrmtu40hxraULj+DsGu8wvrV2nK5qoqSm2dKFAHA+7Opb2n2qHDtSXEtja4fl76mM1FhOVTb51Nqx92QVxsC8NOsmHeD8e3P4TI1PPcesPlTFbV5KHLt9rBxz9/ywciWHs+dY3PnrwFu7CisJCbR2A86I4EAuHxftcw+gHQWVJESGMjHBeqvxuCWNHEFqfLjP182OgkqmjookzsINONNHRxEXEcKWfF9zqXPMnRBLaJB1c6nZ42MIDBCfKw53FVYxvx8acC5pgJQx5gci8h7wKDAHiAZqgH3AS8aYbX386CTX6+NAAXAdzhb+FJwT3l0PvIGHCe9E5DHgMYDExEQ2bdzAlCgHaw+fYX1CpVcnrLHNcOhMI7dOCiYrK6uP/4ThYWKUsDq7kIXhZV4df7bRwZnqJpaO6bD8uRkdLry34xiTO056dfyJ6g7qWtoZ2VRGVpZvtZaDpb6+/pL/vzmMITIY3tx8mLjaPK/ek13ejsPAiLrTZGWVXNL3D2Ut7YYggb+u3wcloV69Z+NpZ+t9QMUJsrIKBzA6/3I0O4ep/PmjHdw00bsCzwcnnOM420tyyKo8NmCx9cd9cSmCa50Tlv3xg81cPda7IQSv57QQJFBXeJCsU9atVA2vb6fdYXhxRRazEr0rsrx9oIXIYDhzdDclOdY9N7Gt7dQ0tfHye+uYFONdQfn9Xc2MiRAO7+m9eObv++JSJDna+PhcK39fuY6kcO/arD7KbiI1CrZv2TTA0flXclArH+S38eHH64kI7v3+MMaw4WgTk2IC2LBhwyBE6D+pYa1syi3jk3XrCfIwT0LXe6LdYdiZ38ii5KBhe694a/LIDtYfLmb9+iqvcqmGNsOR4kZum2z9XCotSli5p4B5oaVeHV/ej7mUT4m8iDwAZBtjDri3GWO2A9svKYqLuZ9IAtxtjHGPsz8sIncAx4AlIrKwa2WBMeYFnD0ESE9PN5mZmRSHneQH7xxk/Ix5TE7qvTv46kOlGPZw3/IruWqidcd1AOxvP86za48xe/4ir2YifW3HSeAgD9xwFVNHWW+Zjc5urDvMaztOctXia7zqmZH9yTFEjvPV264dsjWzWVlZZGZmXvLnLC/bx+bjFVx77RKvltlb++4hwkNO8+htmZaumQVYWLSD/JpmMjOXeHX831/dw+ioar54y1JL93IBeD5nI0VtwWRmejdC6n9zt3HZmHZuv/6aAY2rv+6LvjLG8NuDaymRODIz53r1nv/cu4GFk0dw/fIFAxydf13V1sFz+9dQGTKazMyZvR7vcBi+s+kTls9IZtnSOYMQof/Mamjl+YMfUxsxnszMqb0e39TawbFP1vClBalkZl7W6/H+vi8uRUpFA6/mZNEcO5HMham9Hl9e10zR6rV87/p0MjMnD3yAfjQyrZL3f7cNMyqdzCuSez3+WFkdlR9t5DsLp5O5IGUQIvSflsRSsl7ZQ0TKFSycdHH5v+s9sS3/HM0d27l3yRVkzhwziJEOPl9zqZUHSzDs5b5lV7LA4rnUwY7j/PLjY8zMWEhCZO+NOH/aWggc5ss3LyL1Enu5+Nq1/mXg9kv6Ru+4+6We6JTEA2CMaQI+cv0635sPu2ZKAgCbj5/16svX5ZQxckSQZWdJ7uzqKQkYAxuOeXdu1h4tY1xsGFO8uImHu8z0JFraHWw74V3relbuWWaNixmySXx/WpqexLmG1vPrEffEGMP63HIWTYq3fBIPzusmr7yeU5WNvR7b1uFg0/EKlkxNtHwSD7B0WhJ7iqq8mhSmtrmNPUVVZKZ7NYpqWBMRlkxNZOOxs7R7McHm6apG8srrWTLV+udmRHAgiyclsD73rFcTbB4qruFcQ6strpvYiBBmjYvx+vm9/cQ5Wtsdtjg3aQkRpMSHk5Xr3bnZeMzZZdgO52b2+Fiiw4K9PjfrcsoBWDYtqZcjh7/FkxMIDhSyjpV7dfz63HKCA4Wrp1j/url6sjOX2uRlLrX2aDnRYcG2yKUy0533xkYv/xZn5ZaTGh9+yUk8XNpkdwMp1/Va3c1+d6Lv1RTP4+PCmZQYwcdHe+8+7nAY1uWcJTM9yZLrQXY1Z3wMiSND+ehw791Bmlo72JxXwXXTR9ki6ViQFseI4AA2ePGwq2xoZf/papamW/9BB3Dt1ERE8KogkH+2gdNVTef/0FndUldBMCu394LA3qIq6prbbVF4BGcFULvDsMWLOUs2H6+gw2Hsc91MS6KuuZ29XqzV677v7HLdZE5L4mRlIye8WE4sK/csInCtDQrW4LwG9p+uptKL5cSycssJCw60/HwcbplTE9maX0FzW0evx2bllpM4MpTLxlhzKcfOAgOEa6YksOHYWRxezK+wLqec6WOiGBNtzVVVOosMDSIjJc6rch84z82CtHjLLuXY2YT4cKYkRXqVL3Q4nA04memJll1VpbMZyVEkRIay3ovrprmtg6355/qtbDNUz+5GoB2YIiKemjfd/esKvf3Amy4fw7b8c5yrb+nxuP2nq6mob+G66fYoPAYECNfPGMX6nLO9Puy25FXQ0u6wRa0sOFuCrp6cyOpDpb0+7NYcLsUYWG6T6yYuIoS5E2JZdaj38e6rXcfY5bpJS4ggLSGC1V487FYdKiUkKICrXb2GrG7uhBhiwoNZdci7cxMbHmzptXk7u3pKAiGBAaz26tyUkBIfbumVQzpzV455c25WHixh9vgY4r3o3mgF100fhTG9n5sOh2HVoVKumZJg6Ul8O7vuslE0tzl6rVRtau1gXU45101PskUjBTivm7N1Ledn6u9OZUMre4qqWDbNHhVj4CzH5ZTWkX+2vsfjCioayCuvZ6lNyjYAN84czc6CSip6yaX2nayisqGV5dNHDVJk/hUQICyflsT6nPJec6ms3LO0tDv6LV8Ykom8MaYCeB3n5Hk/6rxPRD6Dc7K7GmC1t59548wxOAx8dLjnVvkV2cWEBAXYphUI4IYZY2hq6+i1e96K/cXEhAdbft6Azm6bnUxpbTPbC3ruXr8iu5iJCRHMSLZ+bb7bbbOTySmtI6e0+1nIjTGsyC5mXmosyTHWr80HZzfpW2clszX/HKU1zd0e1+EwfHCghGXpSYy06BrpXQUFBnDz5WNYc6SU+pbuZyFvbG3nkyNl3Hj5GFv0jAKIGhHM0mmJvH+guMfu9eV1zWzLP8ets5Jtk3SMiw0nIyWWd/ad6bF7/bGyOnJK67htVu/jfq1iRnIUkxIjWJF9psfjdhZUUl7Xwq2z7XNuFk6MJyEylBXZxT0ety6nnMbWDj5no+vmM5eNIiw4sNfr5oMDxXQ4jK3OzedmJSNCr9fNu/vOIAI3X27tsfGd3XA+l+q54nBFdjGhQQG26TUGcOvsZOpb2ll7tOeKw/f2nyEhMoSF/ZRL9aWEFCMiE3z56WNs38a5lN2/ishGEflvEXkDWAV0AF8xxlR7+2HTx4xkYmIEb+893e0xre0OVmSf4TOXjSI6zB4Fa3CuD5kQGcKbe7o/N7XNbaw5XMqts5ItvQ54V9dNH0VESCDv7uv+YVda40z0P2ejgjU4H15BAcI7PZyboyV1HC+v59bZYwcxMv+7fc5YjHH+we7O1vwKKurtVbAGuGPOWJrbHKzpoSCw5nAZTW0dtkrIwHluzta1sLWHJUHf31+Cw8Ctdjs3c8eSV17P4eLuKw5XZJ8hQOBmLybwsgoR4fbZY9lRUElxdffLF67IPkN4SCDLp9mjhQycFYe3XDGGtTnl1DR1Py/Hu9lnSBoZyoI0+zRSRIQG8ZnLRvHhwRJa27uvOHx77xmmjR7JtNH2aaQYFTWChRPjWZHdfcWhMYZ3s8+waFI8o6NHDHKE/jN9zEgmJ0Xyxu7u84WW9g7e21/M9TNGE2WTRgqAqybGkzQytMcycW1zG58cLeeWK5L7bchBXz7lGziXhPP250RfAjPGlAMLgGeA8cDXgWXAh8A1xpg3fPk8EeEL8yewu6iKI90UBNYcKaWqsY275tor6QgODODejPGsPVrWbUFgxb4ztLQ7uHPuuEGOzr/CQgK56fIxfHCghOpGz2MQX91RBMCdNrtu4iNDyUxP4s3dp7vtSvTK9kJCgwK4xUY11uDsXj93Qgx/3Xmq2zWe/7ytiLiIENsMOXC7MiWWlPhwXtle1G0h6ZXtRaTEhzPPwmtde7J0WhIx4cH8ZXuRx/0Oh+HV7UXMGhfNFIuvGtLVzZePISQogFd3eF4OtKW9g9d3nWJpehKJI+3Rrd7t9jljCRC6vW5qGtt4N/sMt1wxhrAQe3Srd7v7ynG0tjt4Y/cpj/tPVzWy9mgZd84dR6AXK7BYyV1XjqO6sY0PD3pueT5cXEP2qWruslm5D+CuueMoOtfIpm7mc9mSd46ic43cOcde58adS2WfquZQN5Mdrz5USk1Tm+3KxIEBwh1zx7I+t5zTVZ4nO/77rlO0tju4+8r+u276ksjXAid9+PH819MLxphKY8y3jTFpxpgQY0y8MeY215J3PrvnyvGMCA7gj5sLPH0Xz284QVpCBEum2qtgDXDf/AkY4P+2XHxu2jscvLipgNnjY5g1Lnrwg/OzR65Oo7G1w2MBssm1ffm0UaTEX/rsk8PNo1enca6hlbc89HQ5V9/C23vPcOfcscTaYCb/rh69eiIFFQ18fOTilueCigY+OVrGF+ZPsM14VTcR4ZHFaew7We1xfOa+k1XsKariwYWpXi1taCWhQYHcf1UKHx8t8zg+M+tYOScqGnh4cZofovOvmPAQ7po7jrf2nqa87uIhKyuyi6mob+WhxamDH5yfjY8L5/oZo3l1x0kaPAxZeXVnEc1tDh5aZL/rZubYaBakxfHS5gLaPAxZeXlLISLC/QutvayaJ9dOSWBKUiTPbzjhsVL1xY0niAgJ5N554/0QnX/dMmsMSSNDeWGj57bIFzadICEylFtm2auRApyVHCOCA/jDpovPjTuXmpgYYZsJRzt7cGEqAry0ufCifW0dDl7eWsi81Fhmju2/XKovifwzrsTa659+i/YSRYcH86UFKbyz7zS5pXUX7FtzpIyDZ2r4yjUTbVcrC86CwJ1zxvGnbUUXtcq/sec0JysbeXzJJFt1HXebPiaKzPREXtx04qLJEp/fmE9lQyuPL5nop+j866qJccwaH8Nz6/IuKkA+88kx2h2GL19jz3Nzw8zRpMaH88s1xy7quvjz1TmEBQfywCL7FR4B7s0YT1xECD9fnXPBRJLGGH6yKof4iBBbFh4BHlyUSmhQAP/9Ue4F2zschp+tymVCXDg32ayHi9tj106kvcPBb9bmXbC9ua2DZz8+xsyxUeeXSLKbx66dSE1TG89vyL9ge1VDK7/PyiczPZHLbDSHS2ePL5lEcU0zr2y7sMfCqcpG/ry9iNtmJzPWJnO4dCYifHXJJHJK63hv/4Wt8keKa3lvfzFfWDDBVkNN3UKDAnnk6jQ251WwJe/CVvmt+RVsPHaWR65OtcWSul1Fhwfz8OI0Vuwv5nDxha3yKw+WcqSklsevnWS7iniA5Jgwbp2dzKs7ii5agvjV7UWcrmri8SWT+vU77TPY2eVryyYzckQw331j//nuwBX1LTz13mHSR43kngx7dZPp7NufnYoA33/zwPnuwKcqG/npqhzmp8Zx/Qz7jK3r6gc3Tae+uZ0frTh8vub6SHEtv8vK5+bLx5Bhsy7AbiLCj26ZTklNM/+18uj5c7M1r4LXdpzkSwsm2GZm7a4CA4R/u+UyjpfX8+u1x89v//BACasOlfKPSyaRNNI+Y+s6CwsJ5Ikb0tlVWMWfthWe3/6X7UXsLKjk25+daovlfDxJiAzla0sns+pQKR8e+HRViP9dn0duWR1P3jjNVvOUdJaWEMEDC1P5y44itnYqXP90VQ7FNc388ObLbFnZDDBnQiy3z07m9xtOcPC0s3BtjOGHKw5R39LOv9w43c8R+k9meiLXTk3kVx8fO9/TpcNhePLtAwQIfO/6dD9H6D93zBnLFeOi+Y8PjlJS42zEaW7r4Ptv7Sc6LJivLZ3i5wj956FFqaTEh/ODdw6eH1rZ0Gb4wdsHGRcbxiM27Bnl9viSScSEBfPdNw6cz6XKa5t5+v3DXDYmynbd6jv73vXpBAYIT7x14HwvoMKKBn655hiLJ8f3+3BK25WUYsJD+MXdV/DYK3t44KWd3DV3LH/cXEBVYysv3J9hmxmSPRkbE8aPb5vBE28d5Ct/3s1nLhvF/2blYYzhZ3dfYdsCEsDUUSP5zmfT+dnqHHgNMlJjeW5dHjHhwTx16wx/h+dXV6bE8dUlE3l+wwma2zqYkjSS59YdZ2JiJN+7YZq/w/Or5dNHcW/GOJ5bn0dVYyvxkaH8PiufuRNi+Go/18oON/dcOZ41h8v4jw+OUFLTjIizK+fS9ETum9fXOVKt4bFrJ7E2p5xv/T2b/LP1VDe28dKWAm6fncyNM0f7Ozy/+u716WzOq+CxV/bwtWWTKTjbwOu7T/HI4jRbrajiyQ9vuYxdhVXc/9IO/jlzMntPVrHqUClP3DCN9NH2mlOhMxHhv26fye2/3cJ9L2znq0smkZVbzpa8c/z87itssT56dwIDhF/cPYu7freVz7+wnYcWpfL+/mIOF9fy4v0ZRIfbrzXebURwIP99zyy++OIOPv/Cdr6wYAJ/2NlMcYPhr49dZbthcZ1FhwXzq3tn8/DLu7jvxe3cMWcsL28ppL6lnT89Mt8Wa8d3Z0x0GE/dOoPvv3mAR17exbJpSbyw8QSBgcJP7+z/XEp6WsrlooNFHMBTxpgf92sUAyQ9Pd3k5uZ63PfOvtP86N3D1LW0kzQylGf+YTaLbdolr6uXtxTw09U5NLc5GB8Xxm/um8vs8TH+DsvvjDH8dn0ev16XR2u7g2mjR/LbL84dVi3OWVlZZGZm9vvnOhyGn3+Uyx83n6CtwzA/LY5ff36OrWZz7U5bh4Mfv3+E13aepMNhWD4tiV/eO4uYcPvNG9BVY2s7//L2Qd7bX4xxzcT+kzsvJ2KQW+MH6r64FFUNrXzr79lk5Z5FBP4hYzxP3zbDll05uyqpaeLrf93HrsIqggKEhxen8uSN0205LK6rwooGvvG3few/XUNoUABfXz6Ff8rs27C4oXhfXIqc0lq+8ddscsvqiAwN4skbp/Glq+w5vKmrPUVVfOv1bE5WNhIbHszTt8203coY3cnKLef7bx6gvK6F6FDh2fsybLV2fE8+OFDMD989RHVjG2NjwnjmH2YzP82ePVS7+sv2In6y8igNrR1MSozg1/fNYUZy38bGi8geY0yGx312TeTBWYgsrm4iJT7C1i3xntQ1t1FW20JaQoQWjrqoa26jurGNcbFhw66XwkAXzGqb22hs6WBUVOiwOzcDraapjbYOBwmR9ppR2xuVDc5ui3F+mhRxKCcsZbXNBAcG+O3cDGXF1U1EhATZutXQE2MMJTXNRIUFX9IQlaF8X/SV+9zERYTYukXVE4fDUFLbTEJkiFYYdtHe4aCkppnc7B1ct2ypv8MZUlraOyivbSE5JkzzhS6a2zqoqG8hOTrskuYM6CmR9+kvvDHGUtlueEgQk5Ps292sJyNHBDPSRus/+kLPTfeiRgTbat1QX9hxwiBvaZLavVFR2qulO8k2nKDMGyKi56Ybem66FxAgtpz0zxtBgQGMjwsnXxPVi4QGBTI+LtzfYQxJI4IDGRc7sOfGUom5UkoppZRSSilldZrIK6WUUkoppZRSw4gm8koppZRSSiml1DCiibxSSimllFJKKTWMaCKvlFJKKaWUUkoNI5rIK6WUUkoppZRSw4gm8koppZRSSiml1DCiibxSSimllFJKKTWMaCKvlFJKKaWUUkoNI5rIK6WUUkoppZRSw4gYY/wdw4ARkTog199xKDWEJAAV/g5CqSFG7wulLqb3hVIX0ntC+UOKMSbR046gwY5kkOUaYzL8HYRSQ4WI7NZ7QqkL6X2h1MX0vlDqQnpPqKFGu9YrpZRSSimllFLDiCbySimllFJKKaXUMGL1RP4Ffweg1BCj94RSF9P7QqmL6X2h1IX0nlBDiqUnu1NKKaWUUkoppazG6i3ySimllFJKKaWUpWgir5RSSimllFJKDSOWS+RFZJyIvCQixSLSIiKFIvKsiMT6Ozal/MF1D5hufkr9HZ9SA0VE7haR34jIJhGpdV3zf+nlPYtEZKWIVIpIo4gcEJFvikjgYMWt1EDx5Z4QkdQenh1GRP422PEr1d9EJF5Eviwi74hInog0iUiNiGwWkUdFxGOupM8KNRRYah15EZkEbAWSgBVADjAf+AZwg4gsNsac82OISvlLDfCsh+31gxyHUoPph8AsnNf5aWBaTweLyG3AW0Az8DpQCXwOeAZYDNwzkMEqNQh8uidc9gPveth+qP/CUspv7gF+B5QA64GTwCjgTuAPwI0ico/pNKmYPivUUGGpye5E5CPgs8DXjTG/6bT9V8C3gOeNMY/7Kz6l/EFECgGMMan+jUSpwSUiS3EmK3nAEpyFtFeNMV/ycGyU67hoYLExZrdr+whgHbAQuM8Yo62Qatjy8Z5IBQqAPxljHhrEMJUaNCKyDIgAPjTGODptHw3sBMYDdxtj3nJt12eFGjIs07VeRCbiTOILgd922f3vQANwv4hEDHJoSiml/MAYs94Yc9x4V2N9N5AI/M1dMHN9RjPOVkyAfxyAMJUaND7eE0pZnjFmnTHm/c5JvGt7KfB716+ZnXbps0INGVbqWr/M9brGw81YJyJbcCb6VwFrBzs4pfwsVES+BEzAWal1ANhojOnwb1hKDRnuZ8hqD/s2Ao3AIhEJNca0DF5YSvldsoh8FYgHzgHbjDEH/ByTUoOhzfXa3mmbPivUkGGlRD7d9Xqsm/3HcSbyU9FEXtnPaOCVLtsKRORhY8wGfwSk1BDT7TPEGNMuIgXADGAicHQwA1PKzz7j+jlPRLKAB40xJ/0SkVIDTESCgAdcv3ZO2vVZoYYMy3StxzlWBZyTenni3h4z8KEoNaT8H7AcZzIfAVwOPA+kAqtEZJb/QlNqyNBniFIXagT+A7gSiHX9uMfVZwJrdbiisrCfAjOBlcaYjzpt12eFGjKslMj3RlyvOi5M2Yox5mnXGLAyY0yjMeaQa9LHXwFhwFP+jVCpYUGfIcpWjDHlxpgfGWP2GmOqXT8bcfZu3AFMBr7s3yiV6n8i8nXgOzhXv7rf17e7XvVZoQaclRJ5dw1YdDf7o7ocp5TduSdxudavUSg1NOgzRCkvGGPacS7LBfr8UBYjIv8M/A9wBFhqjKnscog+K9SQYaVEPtf1OrWb/VNcr92NoVfKbspdr9o1UqkeniGusZJpOCc8OjGYQSk1RJ11verzQ1mGiHwTeA44hDOJL/VwmD4r1JBhpUR+vev1syJywb9LREYCi4EmYPtgB6bUELXQ9aoPG6Wc6/8C3OBh37VAOLBVZyFWCnCuAAT6/FAWISJPAM8A2TiT+PJuDtVnhRoyLJPIG2PygTU4J/D65y67n8ZZa/xnY0zDIIemlN+IyAwRifOwPQVnrTPAXwY3KqWGpDeBCuDzIpLh3igiI4D/dP36O38EppQ/iMgCEQnxsH0Z8C3Xr/r8UMOeiPwbzsnt9gDLjTEVPRyuzwo1ZIgx1pmLQUQmAVuBJGAFzmUfFgBLcXapX2SMOee/CJUaXCLyFPAkzh4rBUAdMAm4GRgBrATuMMa0+itGpQaKiNwO3O76dTRwPc4WxE2ubRXGmO92Of5NoBn4G1AJ3IpzuaE3gXuNlR6aynZ8uSdcS8zNALKA0679V/DpOtr/ZoxxJy5KDUsi8iDwMtAB/AbPY9sLjTEvd3rP7eizQg0BlkrkAURkPPBjnF1e4oES4F3gaQ8TVihlaSKyBHgcmMOny89V4+w69grwij5slFW5KrL+vYdDiowxqV3esxj4V5xDT0YAecBLwK+NMR0DE6lSg8OXe0JEHgXuwLkEVwIQDJQB24DnjDGbuvsQpYYLL+4JgA3GmMwu79NnhfI7yyXySimllFJKKaWUlVlmjLxSSimllFJKKWUHmsgrpZRSSimllFLDiCbySimllFJKKaXUMKKJvFJKKaWUUkopNYxoIq+UUkoppZRSSg0jmsgrpZRSSimllFLDiCbySimllFJKKaXUMKKJvFJKKaWUUkopNYxoIq+UUkoNQyLykIgYEXnI37F4Q0RedsXr/nmyy/4sETH9/J3PdfnOp/rz85VSSil/CfJ3AEoppZTd9SGBfXhAAhkc/wNUA5sH4btWAhVAKvDgIHyfUkopNSg0kVdKKaX872kP274JRPNp4ttZNlAAbAdKBjCugfCsMaZwML7IGLMSWCkimWgir5RSykI0kVdKKaX8zBjzVNdtri7z0fSc+NYMXFRKKaWUGqp0jLxSSik1DHU3Rl5ECl0/kSLyjIicEpEmEckWkdtdxwSJyA9E5LiINItIvoh8rYfvul5EVopIhYi0uI7/hYjEDMC/q3NsLa74fyYiIR6ONa6x9aNF5A8ickZEOobLvAFKKaVUX2mLvFJKKWU9wcDHQBywAggB7gPeEpHPAv8ELABWAS3APcBvROSsMeb1zh8kIj/C2fW/EvgAKAeuAL4L3CQiC40xtf0Y+2vANa7YaoGbgO8DSXieGyAO5xCDeuBtwAGU9WM8Siml1JCjibxSSillPcnAXiDTGNMCICKvABuBN4B8YKYxptq171dADvAkcD6RF5GlOJP4bcBN7uNd+x4C/s+1/1v9GPskYIYxptL1Pf8K7AceEJF/McaUdjn+cuAV4BFjTHs/xqGUUkoNWdq1XimllLKmb7qTeABjzCacE+TFAk90TsqNMSeALcDlIhLY6TO+7nr9SufjXe95Geeke1/s57ifcCfxru9pAF7FWWbJ8HB8K/BdTeKVUkrZibbIK6WUUtZTbYzJ97C9GEgD9njYdwYIBEa7/htgIdAG3CMi93h4TwiQKCLxxphzlx42ALs9bDvleo31sK/QGFPeT9+tlFJKDQuayCullFLW091s9u0AxhhP+90t2sGdtsXjLCv8ey/fFwn0SyLfteXfxR1boId9XbvaK6WUUpanibxSSimlulMDBBhj4vwdSA+MvwNQSimlBpuOkVdKKaVUd7YDsSIyw9+BKKWUUupTmsgrpZRSqjvPuF5fFJHkrjtFJEJErhrkmJRSSinb0671SimllPLIGLNWRJ4EfgIcF5GVOGe+jwRSgCXAZuAG/0WplFJK2Y8m8koppZTqljHmZyKyBedSdFcDt+EcO38GeAF4zY/hKaWUUrYkxugcMUoppZQaWCLyMvAgkGaMKRzk784E1gNPG2OeGszvVkoppQaCjpFXSiml1GAqEBHj6rI/oETkORExOJN4pZRSyjK0a71SSimlBsO7QGGn3zcPwneuBCo6/Z41CN+plFJKDTjtWq+UUkoppZRSSg0j2rVeKaWUUkoppZQaRjSRV0oppZRSSimlhhFN5JVSSimllFJKqWFEE3mllFJKKaWUUmoY0UReKaWUUkoppZQaRjSRV0oppZRSSimlhpH/B6hv22KWkSfYAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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+ "text/plain": [ + "
        " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "from matplotlib import pyplot as plt\n", + "\n", + "time = dependent_variables.keys()\n", + "dependent_variable_list = np.vstack(list(dependent_variables.values()))\n", + "font_size = 20\n", + "\n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n", + "# dependent variables\n", + "# 0-2: total acceleration\n", + "# 3-8: Keplerian state\n", + "# 9: latitude\n", + "# 10: longitude\n", + "# 11: Acceleration Norm PM Sun\n", + "# 12: Acceleration Norm PM Moon\n", + "# 13: Acceleration Norm PM Mars\n", + "# 14: Acceleration Norm PM Venus\n", + "# 15: Acceleration Norm SH Earth\n", + "\n", + "total_acceleration = np.sqrt( dependent_variable_list[:,0] ** 2 + dependent_variable_list[:,1] ** 2 + dependent_variable_list[:,2] ** 2 )\n", + "\n", + "time_hours = [ t / 3600 for t in time]\n", + "# Total Acceleration\n", + "plt.figure( figsize=(17,5))\n", + "plt.grid()\n", + "plt.plot( time_hours , total_acceleration )\n", + "plt.xlabel('Time [hr]')\n", + "plt.ylabel( 'Total Acceleration [m/s$^2$]')\n", + "plt.xlim( [min(time_hours), max(time_hours)] )\n", + "plt.savefig( fname = f'{latex_image_path}total_acceleration.png', bbox_inches='tight')\n", + "\n", + "\n", + "\n", + "# Ground Track\n", + "latitude = dependent_variable_list[:,9]\n", + "longitude = dependent_variable_list[:,10]\n", + "\n", + "part = int(len(time)/24*3)\n", + "latitude = np.rad2deg( latitude[0:part] )\n", + "longitude = np.rad2deg( longitude[0:part] )\n", + "plt.figure( figsize=(17,5))\n", + "plt.grid()\n", + "plt.yticks(np.arange(-90, 91, step=45))\n", + "plt.scatter( longitude, latitude, s=1 )\n", + "plt.xlabel('Longitude [deg]')\n", + "plt.ylabel( 'Latitude [deg]')\n", + "plt.xlim( [min(longitude), max(longitude)] )\n", + "plt.savefig( fname = f'{latex_image_path}ground_track.png', bbox_inches='tight')\n", + "\n", + "# Kepler Elements\n", + "kepler_elements = dependent_variable_list[:,3:9]\n", + "\n", + "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots( 3, 2, figsize = (20,17) )\n", + "\n", + "# Semi-major Axis\n", + "semi_major_axis = [ element/1000 for element in kepler_elements[:,0] ]\n", + "ax1.plot( time_hours, semi_major_axis )\n", + "ax1.set_ylabel( 'Semi-major axis [km]' )\n", + "\n", + "# Eccentricity\n", + "eccentricity = kepler_elements[:,1]\n", + "ax2.plot( time_hours, eccentricity )\n", + "ax2.set_ylabel( 'Eccentricity [-]' )\n", + "\n", + "# Inclination\n", + "inclination = [ np.rad2deg( element ) for element in kepler_elements[:,2] ]\n", + "ax3.plot( time_hours, inclination )\n", + "ax3.set_ylabel( 'Inclination [deg]')\n", + "\n", + "# Argument of Periapsis\n", + "argument_of_periapsis = [ np.rad2deg( element ) for element in kepler_elements[:,3] ]\n", + "ax4.plot( time_hours, argument_of_periapsis )\n", + "ax4.set_ylabel( 'Argument of Periapsis [deg]' )\n", + "\n", + "# Right Ascension of the Ascending Node\n", + "raan = [ np.rad2deg( element ) for element in kepler_elements[:,4] ]\n", + "ax5.plot( time_hours, raan )\n", + "ax5.set_ylabel( 'RAAN [deg]' )\n", + "\n", + "# True Anomaly\n", + "true_anomaly = [ np.rad2deg( element ) for element in kepler_elements[:,5] ]\n", + "ax6.scatter( time_hours, true_anomaly, s=1 )\n", + "ax6.set_ylabel( 'True Anomaly [deg]' )\n", + "ax6.set_yticks(np.arange(0, 361, step=60))\n", + "\n", + "for ax in fig.get_axes():\n", + " ax.set_xlabel('Time [hr]')\n", + " ax.set_xlim( [min(time_hours), max(time_hours)] )\n", + " ax.grid()\n", + "\n", + "plt.savefig( fname = f'{latex_image_path}kepler_elements.png', bbox_inches='tight')\n", + " \n", + "plt.figure( figsize=(17,5))\n", + "\n", + "# Point Mass Gravity Acceleration Sun\n", + "acceleration_norm_pm_sun = dependent_variable_list[:, 11]\n", + "plt.plot( time_hours, acceleration_norm_pm_sun, label='PM Sun')\n", + "\n", + "# Point Mass Gravity Acceleration Moon\n", + "acceleration_norm_pm_moon = dependent_variable_list[:, 12]\n", + "plt.plot( time_hours, acceleration_norm_pm_moon, label='PM Moon')\n", + "\n", + "# Point Mass Gravity Acceleration Mars\n", + "acceleration_norm_pm_mars = dependent_variable_list[:, 13]\n", + "plt.plot( time_hours, acceleration_norm_pm_mars, label='PM Mars')\n", + "\n", + "# Point Mass Gravity Acceleration Venus\n", + "acceleration_norm_pm_venus = dependent_variable_list[:, 14]\n", + "plt.plot( time_hours, acceleration_norm_pm_venus, label='PM Venus')\n", + "\n", + "# Spherical Harmonic Gravity Acceleration Earth\n", + "acceleration_norm_sh_earth = dependent_variable_list[:, 15]\n", + "plt.plot( time_hours, acceleration_norm_sh_earth, label='SH Earth')\n", + "\n", + "# Aerodynamic Acceleration Earth\n", + "acceleration_norm_aero_earth = dependent_variable_list[:, 16]\n", + "plt.plot( time_hours, acceleration_norm_aero_earth, label='Aerodynamic Earth')\n", + "\n", + "# Cannonball Radiation Pressure Acceleration Sun\n", + "acceleration_norm_rp_sun = dependent_variable_list[:, 17]\n", + "plt.plot( time_hours, acceleration_norm_rp_sun, label='Radiation Pressure Sun')\n", + "\n", + "plt.grid()\n", + "plt.legend( bbox_to_anchor=(1.04,1) )\n", + "plt.xlim( [min(time_hours), max(time_hours)])\n", + "plt.yscale('log')\n", + "plt.xlabel( 'Time [hr]' )\n", + "plt.ylabel( 'Acceleration Norm [m/s$^2$]' )\n", + "\n", + "plt.savefig( fname = f'{latex_image_path}acceleration_norms.png', bbox_inches='tight')\n", + "#plt.savefig('acceleration_norms.png', bbox_inches='tight')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 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z{YP=}cPIwo<22^r<~HVL1GAeMf>=$tOpRDrxJ+4%Ou0=tIKgIS0{{CAU1k+aFHt2#iM7TBhUyGD1j8umwrMS%+jpr!NLA_iXEglO8j~Qn2cA`>y0YY->NQ z*6qx?#M=wM2ysP fitness: + seq = new_seq + fitness = new_fitness + results.append(max(results[-1],fitness)) + return results + + + #execute a run c + def do_run_c(self): + seq = self.gen_bit_sequence() + fitness = self.count(seq) + results = [fitness] + for run in range(max_iterations): + new_seq = self.mutate_bit_sequence(seq) + new_fitness = self.count(new_seq) + seq = new_seq + fitness = new_fitness + results.append(max(results[-1], fitness)) + return results + + def do4b(self,project_nr): + optimum_found = 0 + + # generate plot data + plotResult = np.zeros((10,max_iterations), dtype=int); + lineLabels = [] + + # perform computation + for run in range(10): + res = self.do_run_a() + if res[-1] == string_length: + optimum_found +=1 + + # store computation data for plotting + lineLabels.append(f'Run {run}') + plotResult[run,:]=res; + + # plot multiple lines into report (res is an array of dataseries (representing the lines)) + # plt_tex.plotMultipleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) + plt_tex.plotMultipleLines(plt_tex,range(0, len(res)),plotResult,"[runs]]","fitness [%]",lineLabels,"4b",4,project_nr) + print("total optimum found: {} out of {} runs".format(optimum_found,10)) + + def do4c(self,project_nr): + optimum_found = 0 + + # generate plot data + plotResult = np.zeros((10,max_iterations+1), dtype=int); + lineLabels = [] + + # perform computation + for run in range(10): + res = self.do_run_c() + if res[-1] == string_length: + optimum_found +=1 + + # Store computation results for plot + lineLabels.append(f'Run {run}') + plotResult[run,:]=res; + + # plot multiple lines into report (res is an array of dataseries (representing the lines)) + # plt_tex.plotMultipleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) + plt_tex.plotMultipleLines(plt_tex,range(0, len(res)),plotResult,"[runs]]","fitness [%]",lineLabels,"4c",4,project_nr) + + print("total optimum found: {} out of {} runs".format(optimum_found, 10)) + + def addTwo(self,x): + ''' adds two to the incoming integer and returns the result of the computation.''' + return x+2 + +if __name__ == '__main__': + # initialize main class + main = Main() \ No newline at end of file diff --git a/code/project2/src/Plot_to_tex.py b/code/project2/src/Plot_to_tex.py new file mode 100644 index 0000000..7ec9a4c --- /dev/null +++ b/code/project2/src/Plot_to_tex.py @@ -0,0 +1,126 @@ +### Call this from another file, for project 11, question 3b: +### from Plot_to_tex import Plot_to_tex as plt_tex +### multiple_y_series = np.zeros((nrOfDataSeries,nrOfDataPoints), dtype=int); # actually fill with data +### lineLabels = [] # add a label for each dataseries +### plt_tex.plotMultipleLines(plt_tex,single_x_series,multiple_y_series,"x-axis label [units]","y-axis label [units]",lineLabels,"3b",4,11) +### 4b=filename +### 4 = position of legend, e.g. top right. +### +### For a single line, use: +### plt_tex.plotSingleLine(plt_tex,range(0, len(dataseries)),dataseries,"x-axis label [units]","y-axis label [units]",lineLabel,"3b",4,11) + +### You can also plot a table directly into latex, see example_create_a_table(..) +### +### Then put it in latex with for example: +###\begin{table}[H] +### \centering +### \caption{Results some computation.}\label{tab:some_computation} +### \begin{tabular}{|c|c|} % remember to update this to show all columns of table +### \hline +### \input{latex/project3/tables/q2.txt} +### \end{tabular} +###\end{table} +import random +from matplotlib import lines +import matplotlib.pyplot as plt +import numpy as np +import os +class Plot_to_tex: + + def __init__(self): + self.script_dir = self.get_script_dir() + print("Created main") + + # plot graph (legendPosition = integer 1 to 4) + def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr): + fig=plt.figure(); + ax=fig.add_subplot(111); + ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none'); + plt.legend(loc=legendPosition); + plt.xlabel(x_axis_label); + plt.ylabel(y_axis_label); + plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png'); +# plt.show(); + + # plot graphs + def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr): + fig=plt.figure(); + ax=fig.add_subplot(111); + + # generate colours + cmap = self.get_cmap(len(y_series[:,0])) + + # generate line types + lineTypes = self.generateLineTypes(y_series) + + for i in range(0,len(y_series)): + # overwrite linetypes to single type + lineTypes[i] = "-" + ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color + + # configure plot layout + plt.legend(loc=legendPosition); + plt.xlabel(x_label); + plt.ylabel(y_label); + plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png'); + + print(f'plotted lines') + + # Generate random line colours + # Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib + def get_cmap(n, name='hsv'): + '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct + RGB color; the keyword argument name must be a standard mpl colormap name.''' + return plt.cm.get_cmap(name, n) + + def generateLineTypes(y_series): + # generate varying linetypes + typeOfLines = list(lines.lineStyles.keys()) + + while(len(y_series)>len(typeOfLines)): + typeOfLines.append("-."); + + # remove void lines + for i in range(0, len(y_series)): + if (typeOfLines[i]=='None'): + typeOfLines[i]='-' + if (typeOfLines[i]==''): + typeOfLines[i]=':' + if (typeOfLines[i]==' '): + typeOfLines[i]='--' + return typeOfLines + + # Create a table with: table_matrix = np.zeros((4,4),dtype=object) and pass it to this object + def put_table_in_tex(self, table_matrix,filename,project_nr): + cols = np.shape(table_matrix)[1] + format = "%s" + for col in range(1,cols): + format = format+" & %s" + format = format+"" + plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline=' \\\\ \hline \n') + + # replace this with your own table creation and then pass it to put_table_in_tex(..) + def example_create_a_table(self): + project_nr = "1" + table_name = "example_table_name" + rows = 2; + columns = 4; + table_matrix = np.zeros((rows,columns),dtype=object) + table_matrix[:,:]="" # replace the standard zeros with emtpy cell + print(table_matrix) + for column in range(0,columns): + for row in range(0,rows): + table_matrix[row,column]=row+column + table_matrix[1,0]="example" + table_matrix[0,1]="grid sizes" + + self.put_table_in_tex(table_matrix,table_name,project_nr) + + + def get_script_dir(self): + ''' returns the directory of this script regardles of from which level the code is executed ''' + return os.path.dirname(__file__) + +if __name__ == '__main__': + main = Plot_to_tex() + main.example_create_a_table() \ No newline at end of file diff --git a/code/project2/src/Run_jupyter_notebooks.py b/code/project2/src/Run_jupyter_notebooks.py new file mode 100644 index 0000000..0c8f558 --- /dev/null +++ b/code/project2/src/Run_jupyter_notebooks.py @@ -0,0 +1,41 @@ +# runs a jupyter notebook and converts it to pdf + +import os +import nbformat +from nbconvert.preprocessors import ExecutePreprocessor + +class Run_jupyter_notebook: + + def __init__(self): + self.script_dir = self.get_script_dir() + print("Created main") + + # runs jupyter notebook + def run_notebook(self,notebook_filename): + + + # Load your notebook + with open(notebook_filename) as f: + nb = nbformat.read(f, as_version=4) + + # Configure + ep = ExecutePreprocessor(timeout=600, kernel_name='python3') + + # Execute + #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}}) + ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}}) + + # Save output notebook + with open(notebook_filename, 'w', encoding='utf-8') as f: + nbformat.write(nb, f) + + # converts jupyter notebook to pdf + def convert_notebook_to_pdf(self,notebook_filename): + os.system(f'jupyter nbconvert --to pdf {notebook_filename}') + + def get_script_dir(self): + ''' returns the directory of this script regardles of from which level the code is executed ''' + return os.path.dirname(__file__) + +if __name__ == '__main__': + main = Run_jupyter_notebook() \ No newline at end of file diff --git a/code/project2/src/__init__.py b/code/project2/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/project2/src/__main__.py b/code/project2/src/__main__.py new file mode 100644 index 0000000..f8794c9 --- /dev/null +++ b/code/project2/src/__main__.py @@ -0,0 +1,39 @@ +import os +from .Main import Main + +print(f'Hi, I\'ll be running the main code, and I\'ll let you know when I\'m done.') +project_nr = 2 +main = Main() + +notebook_names = ['AE4868_example_notebook_update20201025.ipynb'] + +# run the jupyter notebooks for assignment 1 +main.run_jupyter_notebooks(project_nr,notebook_names) + +# convert jupyter notebook for assignment 1 to pdf +main.convert_notebooks_to_pdf(project_nr,notebook_names) + +# compile the latex report +main.compile_latex_report(project_nr) + +################################################################ +############example code to illustrate python-latex image sync######### +##############runs arbitrary genetic algorithm, can be deleted########### +################################################################ +# run a genetic algorithm to create some data for a plot. +print("now running a") +res = main.do_run_a() + +# plot some graph with a single line, general form is: +# plt_tex.plotSingleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) +# main.plt_tex.plotSingleLine(plt_tex,range(0, len(res)),res,"[runs]]","fitness [%]","run 1","4a",4,project_nr) + +# run a genetic algorithm to create some data for another plot. +print("now running b") +main.do4b(project_nr) + +# run a genetic algorithm to create some data for another plot. +print("now running 4c") +main.do4c(project_nr) + +print(f'Done.') \ No newline at end of file diff --git a/code/project2/src/juice_propagation_Q1.ipynb b/code/project2/src/juice_propagation_Q1.ipynb new file mode 100644 index 0000000..e827102 --- /dev/null +++ b/code/project2/src/juice_propagation_Q1.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# # student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create environment, vehicle, accelerations, and propagation settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create settings for celestial bodies\n", + "bodies_to_create = [\"Ganymede\"]\n", + "global_frame_origin = \"Ganymede\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation)\n", + "\n", + "# Add Ganymede exponential atmosphere\n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + " \n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated, and their central bodies of propagation.\n", + "bodies_to_propagate = [\"JUICE\"]\n", + "central_bodies = [\"Ganymede\"]\n", + "\n", + "# Define accelerations acting on vehicle.\n", + "acceleration_settings_on_vehicle = dict(\n", + " XXXX\n", + ")\n", + "\n", + "# Create global accelerations dictionary.\n", + "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Define initial state.\n", + "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.keplerian_state(\n", + " \"JUICE\", \"Ganymede\")\n", + " ]\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + " \n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Orbit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings, True)\n", + "\n", + "simulation_result = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Print final propagation time and state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + "###########################################################################\n", + "\n", + "final_time_step=list(simulation_result.keys())[-1]\n", + "first_time_step=list(simulation_result.keys())[0]\n", + "\n", + "print(\n", + " f\"\"\"\n", + "JUICE Propagation Results.\n", + "\n", + "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + "Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_result[final_time_step][:]}\n", + "\n", + " \"\"\"\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Save Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# SAVE RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "save2txt(solution=simulation_result,\n", + " filename=\"JUICEPropagationHistory_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )\n", + "\n", + "save2txt(solution=dependent_variables,\n", + " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results\n", + "\n", + "For inspiration see: \n", + "\n", + "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PLOT RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "# Extract time and Kepler elements from dependent variables\n", + "kepler_elements = np.vstack(list(dependent_variables.values())\n", + " \n", + "# Kepler Elements\n", + "# 0: semi-major axis\n", + "# 1: eccentricity\n", + "# 2: inclination\n", + "# 3: argument of periapsis\n", + "# 4: right ascension of the ascending node\n", + "# 5: true anomaly\n", + "\n", + "time = dependent_variables.keys()\n", + "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", + "\n", + "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", + "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", + "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", + "\n", + "\n", + "# Set font size of figures\n", + "font_size = 20\n", + " \n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project2/src/juice_propagation_Q4.ipynb b/code/project2/src/juice_propagation_Q4.ipynb new file mode 100644 index 0000000..7b3bbcd --- /dev/null +++ b/code/project2/src/juice_propagation_Q4.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create Environment and Vehicle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create body objects.\n", + "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", + "global_frame_origin = \"SSB\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation) \n", + "\n", + "# Add Ganymede exponential atmosphere \n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + "\n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings );" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Dynamics for various cases" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "cases = ['unperturbed', 'case_i', 'case_ii']\n", + "\n", + "\"\"\"\n", + "unperturbed: Ganymede PM\n", + "\n", + "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", + "\n", + "case_ii: Ganymede PM, Ganymede aerodynamic\n", + "\"\"\"\n", + "\n", + "simulation_results_dict = dict()\n", + "dependent_variables_dict = dict()\n", + "for case in cases: \n", + " ###########################################################################\n", + " # CREATE ACCELERATIONS ####################################################\n", + " ###########################################################################\n", + "\n", + " # Define bodies that are propagated.\n", + " bodies_to_propagate = [\"JUICE\"]\n", + "\n", + " # Define central bodies.\n", + " central_bodies = [\"Ganymede\"]\n", + "\n", + " # Define accelerations acting on vehicle.\n", + " if case == 'unperturbed':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + " if case == 'case_i':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX,\n", + " Jupiter = XXXX\n", + " )\n", + " if case == 'case_ii':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + "\n", + " # Create global accelerations dictionary.\n", + " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + " # Create acceleration models.\n", + " acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + " ###########################################################################\n", + " # CREATE PROPAGATION SETTINGS #############################################\n", + " ###########################################################################\n", + "\n", + " # Define initial state.\n", + " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + " # Save magnitude of perturbations for both cases\n", + " if case == 'unperturbed':\n", + " dependent_variables_to_save = [ ]\n", + " if case == 'case_i':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + " if case == 'case_ii':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + "\n", + " # Create propagation settings.\n", + " propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + " )\n", + "\n", + " # Create numerical integrator settings.\n", + " fixed_step_size = 10.0\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + " )\n", + "\n", + " ###########################################################################\n", + " # PROPAGATE ORBIT #########################################################\n", + " ###########################################################################\n", + "\n", + " # Create simulation object and propagate dynamics.\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + " \n", + " simulation_results_dict[case] = dynamics_simulator.state_history\n", + " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", + "\n", + " ###########################################################################\n", + " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + " ###########################################################################\n", + "\n", + " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", + " first_time_step=list(simulation_results_dict[case].keys())[0]\n", + "\n", + " print(\n", + " f\"\"\"\n", + " JUICE Propagation Results of {case}.\n", + "\n", + " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + " Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_results_dict[case][final_time_step][:]}\n", + "\n", + " \"\"\"\n", + " )\n", + "\n", + " ###########################################################################\n", + " # SAVE RESULTS ############################################################\n", + " ###########################################################################\n", + " \n", + "# save2txt(solution=simulation_result,\n", + "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", + "# directory=\"./\", # default = \"./\" \n", + "# column_names=None, # default = None \n", + "# )\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Pre-process Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", + "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", + "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", + "\n", + "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", + "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", + "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", + "\n", + "difference_in_cartesian_position = XXXX" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project2/test/__init__.py b/code/project2/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/project2/test/test_main.py b/code/project2/test/test_main.py new file mode 100644 index 0000000..8aab9ff --- /dev/null +++ b/code/project2/test/test_main.py @@ -0,0 +1,26 @@ +import unittest +import os +from ..src.Main import Main + +class Test_main(unittest.TestCase): + + # Initialize test object + def __init__(self, *args, **kwargs): + super(Test_main, self).__init__(*args, **kwargs) + self.script_dir = self.get_script_dir() + + self.main = Main() + print(f'self.main.addTwo(3)={self.main.addTwo(3)}') + + # returns the directory of this script regardles of from which level the code is executed + def get_script_dir(self): + return os.path.dirname(__file__) + + # tests unit test on addTwo function of main class + def test_addTwo(self): + expected_result = 7 + result = self.main.addTwo(5) + self.assertEqual(expected_result,result) + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/code/project3/__init__.py b/code/project3/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/project3/src/AE4868_example_notebook_update20201025.ipynb b/code/project3/src/AE4868_example_notebook_update20201025.ipynb new file mode 100755 index 0000000..c04ac2e --- /dev/null +++ b/code/project3/src/AE4868_example_notebook_update20201025.ipynb @@ -0,0 +1,491 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T14:52:24.573593Z", + "iopub.status.busy": "2020-11-09T14:52:24.572620Z", + "iopub.status.idle": "2020-11-09T14:52:24.575958Z", + "shell.execute_reply": "2020-11-09T14:52:24.575097Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "#import ipyparams\n", + "#currentNotebook = ipyparams.notebook_name\n", + "#print(f'currentNotebook={currentNotebook}')\n", + "#currentNotebook = os.path.dirname(os.path.realpath(__file__))\n", + "#currentNotebook = os.path\n", + "#currentNotebook = os.getcwd()\n", + "#if not 'workbookDir' in globals():\n", + "# workbookDir = os.getcwd()\n", + "#print('workbookDir: ' + workbookDir)\n", + "#currentNotebook = os.chdir(workbookDir) # If you changed the current working dir, this will take you back to the workbook dir.\n", + "#currentNotebook = %pwd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T14:52:24.592915Z", + "iopub.status.busy": "2020-11-09T14:52:24.591508Z", + "iopub.status.idle": "2020-11-09T14:52:25.437321Z", + "shell.execute_reply": "2020-11-09T14:52:25.437943Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Single Earth-Orbiting Satellite Example.\n", + "The initial position vector of Delfi-C3 is [km]: \n", + "[7037.48400133 3238.05901792 2150.7241875 ]\n", + "The initial velocity vector of Delfi-C3 is [km/s]: \n", + "[-1.46565763 -0.04095839 6.62279761]\n", + "After 86400.0 seconds the position vector of Delfi-C3 is [km]: \n", + "[-4602.79426676 -1421.16740978 5883.69740624]\n", + "And the velocity vector of Delfi-C3 is [km/s]: \n", + "[-4.53846052 -2.36988263 -5.04163195]\n", + " \n" + ] + } + ], + "source": [ + "###############################################################################\n", + "# IMPORT STATEMENTS ###########################################################\n", + "###############################################################################\n", + "import numpy as np\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from tudatpy.kernel.astro import conversion\n", + "\n", + "# Set path to latex image folders for project 1\n", + "latex_image_path = '../../../latex/project3/Images/'\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Set simulation start and end epochs.\n", + "simulation_start_epoch = 0.0\n", + "simulation_end_epoch = constants.JULIAN_DAY\n", + "\n", + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Create default body settings for selected celestial bodies\n", + "bodies_to_create = [\"Sun\", \"Earth\", \"Moon\", \"Mars\", \"Venus\"]\n", + "\n", + "# Create default body settings for bodies_to_create, with \"Earth\"/\"J2000\" as \n", + "# global frame origin and orientation. This environment will only be valid \n", + "# in the indicated time range \n", + "# [simulation_start_epoch --- simulation_end_epoch]\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create,\n", + " simulation_start_epoch,\n", + " simulation_end_epoch,\n", + " \"Earth\",\"J2000\")\n", + "\n", + "# Create system of selected celestial bodies\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle objects.\n", + "bodies.create_empty_body( \"Delfi-C3\" )\n", + "bodies.get_body( \"Delfi-C3\").set_constant_mass(400.0)\n", + "\n", + "# Create aerodynamic coefficient interface settings, and add to vehicle\n", + "reference_area = 4.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0]\n", + ")\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"Delfi-C3\", aero_coefficient_settings )\n", + "\n", + "# Create radiation pressure settings, and add to vehicle\n", + "reference_area_radiation = 4.0\n", + "radiation_pressure_coefficient = 1.2\n", + "occulting_bodies = [\"Earth\"]\n", + "radiation_pressure_settings = environment_setup.radiation_pressure.cannonball(\n", + " \"Sun\", reference_area_radiation, radiation_pressure_coefficient, occulting_bodies\n", + ")\n", + "environment_setup.add_radiation_pressure_interface(\n", + " bodies, \"Delfi-C3\", radiation_pressure_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated.\n", + "bodies_to_propagate = [\"Delfi-C3\"]\n", + "\n", + "# Define central bodies.\n", + "central_bodies = [\"Earth\"]\n", + "\n", + "# Define accelerations acting on Delfi-C3 by Sun and Earth.\n", + "accelerations_settings_delfi_c3 = dict(\n", + " Sun=\n", + " [\n", + " propagation_setup.acceleration.cannonball_radiation_pressure(),\n", + " propagation_setup.acceleration.point_mass_gravity()\n", + " ],\n", + " Earth=\n", + " [\n", + " propagation_setup.acceleration.spherical_harmonic_gravity(5, 5),\n", + " propagation_setup.acceleration.aerodynamic()\n", + " ])\n", + "\n", + "# Define point mass accelerations acting on Delfi-C3 by all other bodies.\n", + "for other in set(bodies_to_create).difference({\"Sun\", \"Earth\"}):\n", + " accelerations_settings_delfi_c3[other] = [\n", + " propagation_setup.acceleration.point_mass_gravity()]\n", + "\n", + "# Create global accelerations settings dictionary.\n", + "acceleration_settings = {\"Delfi-C3\": accelerations_settings_delfi_c3}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies,\n", + " acceleration_settings,\n", + " bodies_to_propagate,\n", + " central_bodies)\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Set initial conditions for the Asterix satellite that will be\n", + "# propagated in this simulation. The initial conditions are given in\n", + "# Keplerian elements and later on converted to Cartesian elements.\n", + "earth_gravitational_parameter = bodies.get_body( \"Earth\" ).gravitational_parameter\n", + "initial_state = conversion.keplerian_to_cartesian(\n", + " gravitational_parameter=earth_gravitational_parameter,\n", + " semi_major_axis=7500.0E3,\n", + " eccentricity=0.1,\n", + " inclination=np.deg2rad(85.3),\n", + " argument_of_periapsis=np.deg2rad(235.7),\n", + " longitude_of_ascending_node=np.deg2rad(23.4),\n", + " true_anomaly=np.deg2rad(139.87)\n", + ")\n", + "\n", + "# Define list of dependent variables to save.\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.total_acceleration( \"Delfi-C3\" ),\n", + " propagation_setup.dependent_variable.keplerian_state( \"Delfi-C3\", \"Earth\" ),\n", + " propagation_setup.dependent_variable.latitude( \"Delfi-C3\", \"Earth\" ),\n", + " propagation_setup.dependent_variable.longitude( \"Delfi-C3\", \"Earth\"),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Sun\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Moon\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Mars\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.point_mass_gravity_type, \"Delfi-C3\", \"Venus\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.spherical_harmonic_gravity_type, \"Delfi-C3\", \"Earth\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.aerodynamic_type, \"Delfi-C3\", \"Earth\" \n", + " ),\n", + " propagation_setup.dependent_variable.single_acceleration_norm( \n", + " propagation_setup.acceleration.cannonball_radiation_pressure_type, \"Delfi-C3\", \"Sun\" \n", + " )\n", + " ]\n", + "\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")\n", + "\n", + "###########################################################################\n", + "# PROPAGATE ORBIT #########################################################\n", + "###########################################################################\n", + "\n", + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + "states = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history\n", + "\n", + "###########################################################################\n", + "# PRINT INITIAL AND FINAL STATES ##########################################\n", + "###########################################################################\n", + "\n", + "print(\n", + " f\"\"\"\n", + "Single Earth-Orbiting Satellite Example.\n", + "The initial position vector of Delfi-C3 is [km]: \\n{\n", + " states[simulation_start_epoch][:3] / 1E3}\n", + "The initial velocity vector of Delfi-C3 is [km/s]: \\n{\n", + " states[simulation_start_epoch][3:] / 1E3}\n", + "After {simulation_end_epoch} seconds the position vector of Delfi-C3 is [km]: \\n{\n", + " states[simulation_end_epoch][:3] / 1E3}\n", + "And the velocity vector of Delfi-C3 is [km/s]: \\n{\n", + " states[simulation_end_epoch][3:] / 1E3}\n", + " \"\"\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T14:52:25.458000Z", + "iopub.status.busy": "2020-11-09T14:52:25.452229Z", + "iopub.status.idle": "2020-11-09T14:52:29.204968Z", + "shell.execute_reply": "2020-11-09T14:52:29.205604Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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szhj7F1uVUzJtDKbY1Wtij69cr8K6p5Riy2ZEGif4qvAgcDz1MHE8rHSb2OMTaVVQ8G0MJ3A9in0L4XjVRFoNbfDNwVU+kTasyOaKn7wDgq8iZAev63rfCrtdkc0VP0le4dujRhUhRHfGDnpNE8sdxvJXRf01sB0stCx0myZGU7dCBN8EK70Guk2zMmseYHa75W4DC+1GZUhagNW1yyesqqTg2xk7WGo30GtZGFbkWAT89dVtoNuyKnMsAv7x2Law0LLQr8jDCYDdJO9ZaKLbMCtzbaxRbfCN+u5eE7t7LWxXJHuWW6H2+puFzeG0EkHPnEDY3Wti70KzMsQQb2Szf7GFXb3qEI9nAyKtWnVtDCYwDYLlTgO7e9VRYm4MpljxN8e7K5TVe3ZnjH2LrZDgq8h4nevbMwRMVcZrYzANyHagOoo0TtRmIWAuFgYTF1OPqaI5IVqFuvhD0pVuI9gTVuHBPFf57l3MRiDXRNorHDyPhufTVEWlwK2cVyxzpVy16jro11WVTfLAdmAQduIEqkNYDScOui0L3Zbp/78a4zWcOOg2LfSarPPVqCIdbfoRAqYqax7gSqYGFlpmpZRy7CamiU6jWoTVYOKg17LQa5nVqst2sdBi66tfIaK2bztYaDXQbZkYVuRYnDgeJq6HpXYDnaZVGbV2jWqDP2Hfs9AMbCJVIDsCy+lCE7t6TUxcrxLnJh5nsNtXKVRhwweEm6t9iy2s+MRjFXBux2b3ej4hWpVuwhvDCVY6DRBCsKfXmmkgUyY40QGgUgTf+Z0J9vqqnF29ZiXOEUCYFdVpmug0zMoo+LZGUyx3mGrdMkgl1j1v1rer1wzWWBXWF3+ovNJtYKldHcVjkH3eaWCpw/ZeVXgAzmNhdnWbmQjkmkh7hYOrXvYvsqDEKtxYAdG6Wv7/q7F555tPLuMdTatR12DioNe00PNboldpHntNEwt+XVUhahmhEBJ81VlfbB6Z5dSphGIAYE+SVjpN9JrVUgztjB0sthlhNaoQ8TiwGZHWbVqVWVuUUgwmDhZaJnrN6iisbMfF1KVYaJlMIVqZcxcbn27TrNz6qlFd8AymPb1WEPRchc3V+b6NbtNEt2kFm74qbK64kmmxbWGp3cBw4lZCwXehz/I3+XhVYawANo+7e02YBvEJvmrUtenHZQDVIqxYXRFFWkXq2h5PseKrcnZXSJF2vh822KmSgo8TooQw1WMV3EDDiYuJ62FXtwnLNLDcaVRiHjkxtNRmluaViqwvTqQtdxvoNEw0TFKJ/HNO5i21G5kUfDWR9goHVy7tX2I3fNUhOlgd+wIirRqbq6HtoBshhqpDWPGNe7WIoYHNlF9dX/k1rMg8DiYOui0zUKRVZbyGExe9loluy4RHAdspf0MBMPn1UsfCQrs6xBDAx8snrCpyLAJcYeUTVhWpazR1QSnQbVm+Iq0a88jP7b2WhW6jOuuLK0J7LQudhlmp9VWjurjQn6DXNNFpmthVoaBnrt4FEBALG4PyNzHrkQyrZV+lUIVGKFujkBja1W1WponT1nAa2KIYwVf+HAI8NsAnYBaqsXEHZhVpVYqAYAorfx571SBgAEbwLUXqqkJGmu24GE7c4Hhcqsg8cnI9qniswvHIySm+vlYqMl5b3NrZaYIQgqV2NQhRnnW+3GGxOqZBsJOC4KuJtFc4+CaGS4yrYgkcTFw0TSM4cVZnc+UGGyugOgTfwPYJGJ9Iq4o9iilgLPQ4wVeV9eUrv4LxqsgmeeBbYTlRW5V1P/StigstC/1JdZRyTIlZLYWV43oYTz12nmhWx3IaJayqRPDxNd7zFaJVsVkH4+UrfatybaxRbUQ3yEtt9ncVVLxDX4UNICDUqqCy2hiEBN+yf79XhU3f9mgazN9KtwmPVoPgixIdK90mtsfTSmS88sY/QLhxL/s+IbDe+etrsd2oxLE49W3VwTx2mpVY8+Opi4njBet+V7eJ9QoQQ1xhxYnapU6jEo3eNmN1LbatVATMxUKgsPLX10K7Gg9Ow/GqFrEdjpcFQog/j/rjVRNpr3AMJw7aDQOLgfWuGpsYtnE3I1bF8k8CgF9XM6yrUtbOVvWUX8OJi27LRLdy8+j6lq1qEVaM4DODeawMUTtxWaZcywKlFSIebT+Dr8mytcq+cQdCEnshIKyqs7YABOevqqx5fsOyyOexKmsrUKSZlaqrRrXB1eEA21gB1SBguAobCJUKVajrwoA1jAFC4rEKKoUZxVCFrLDxuiitBvG4GVGkLbQtOB7FeFquop5b76JEx8T1MC75YQ1f38tRoqMCx2K8rqV2oxLE0OaomgTMZiRbC0BqAuZiIT6PVakrPo+LFSFE+Xgt+tefhVZNpNVIAZ6tZRiEbRYqsrnq27Nh8FUh+Aa261sVK6Zkiimsqqb8ChRplZlHHgZfLYvu0HZ95Ve15nFozxLIVSBhJo6HqUsZ8egTfGXfuANxhRVTpFXBEsTXEidEq7K2Zi2UVmWuQTPz2DQr83CiRrXBVetAeGNehc0o71QNsI07APTt8uvaGEywmyvSOtVRpG2Nosov9vdmBerajhBpQV0VIPiiFsqqrPu49W6pIsR2VAEDMKJjMCm/e3xcyVQZAiYIqeeEu4WdChyL8fVVla7jc4q0itS1OZyiaRqBo6sqhOjWaIrFlgXTIADY+asm0mpoY2i7wVPKKm2uhr5Vsd0wYJDqKJkYAcPyT4DqKL/6MWtndQK7eeZXdRRpLHSd1cVP6FUg0ngYfC+SKVcFwsrzKIZTlym/KjRewygxVCECOUrA8LqqYFfkJPaCTwyNp17pN+5AmMsZdDmtwFgB4Xh1myxLsQprq0b1we8RAATEVRU2o+whYPXq2hk7oQLG/7sKAdRRwmqhVR2L7lbEcrrY4tEn5Z4zx1MX42mo/AoIq5LvX+atd9Ug+KKZTAAq04xrK66UqwgBwwmr6inSeF3h+ip7bQFsfREgcJottBqlry2AZaQt+w0jgOoQolG7PJDeolsTaa9w8I07AHQaFeqY5lsVCSHoVihniGdFVYlQAMK6AsVQRTZ9gwlTFrYtdqqpQni+7TACodu00ArqKn8ebceDR4FuhKitgsJq7LCQ+l7TRNtf9+MKjBcPf1+omKW5HxBWZqWUq4EirRXOYxXWPSceF9sWOhWyUPK6FlqsrqpcG2tUG7zBDgA0LQMty6jEJobfUwHV2bgDYstpFTbJWzNEWjUUfJRSbEeIRz6fOyXXFSpzQosbUD5Ry9c3J/aqUlecsFqqDMHHuxeGitrRtPwuuluSbK2yozx2IvcugG8JrMA5dXs0RccCjEBhZVXi4cRmpFEKwLPuyq9rexQj0lI247IuRlFJIIT8twLeZo1SekcB7/OKBs+KAoCWZVSC6ABCSyDA66rGJmYwcXH1LguWaaBpGZVQWAGhVbFl+Qq+ChAKlNKgC2WLEzAVUJsMIwRMq1Edgq8f2bi3reqMV1SZ0/bHqwoEH7cAdlsmLP+GoQoEcphFNqss5B2Iy8JgZn2F8+jvfUoDf+LNu3ZOHA+O68Eyy33ON6yoFbZGtTGchKH+ANuMViEHJnpPZfpRHmWrTSilM3WFGWnl1jV1PQwm7kzGEFB+xAi3/0UtgUD5Sjn++WFd1SCGgnN4jECuCpHG1/tCRQk+vr4GthOorsrARkz5tdRpwPH4/qIUGgMA22eZBgkeyC/5of6eRwMSqwxsj6boNsLPX/TropQGarAy0LedgKQFfMVjJYjH2boW2xaeP1txIg3ADwKgALLOKKehayItJ6LBuK1GdYi04cQNOom2G2YlNu4A27xz4rFane9c9JomCCFoWWYliEeu/OIEHwDYFZhHTih0m2ZAWNkVIKyGAWFVLYIvSih0KkSIckUaz3gEqqH8iloVQ+VXBeYxQoi2KqRICwjkaPbk1MVSyURaP2KF5degsm+Qa1Qfg8g9AuDbVyrw1H0Q23RWYRMTVWEDQLthoGGS0lUKYVh3TMFXmbqqZQmMXouB6ii/+kHnZba+qkLwicLggfLnkRPY0SYIAJvHMom0vu3AIOE8Rrval0mk9f1zPSenFtus+cdg4gRrrQxsjaboRYi0BT9DeBB7yHOpEZ+vaMSIWeJ91dZoiuv2dIP/L6S8Zpc3osBv+n/SggC4teBaXrEYTtxAKdG2zEpskAF2guIHfMsyKlNX9Ga03agGYeV6FKOpWzlCdBi5uWqYBkyDVKKuaLg5J6zGVaorQvBVYd1zRVrVlIVRQpQ/WanC8RhVflXJOhyuL6tSysLo+uJ12VMPaJdZFSOQDcI293weJ66HtmEm/GSNVzIGEyemSCs/sHvqepg4XrARBfzNQtkETORcCQCEEPRa5TccCZQ53VkLZdlEh0zJVHZdw8i1GIgqv0pWpNmzirSqEHyiMHig/PGK11WV5gxMKGAFhFWUQN5fYl3DSDQSMHs8lkmkbY+n6EbYHV5Lf+yUSqRF+QYgnMdhycTjfEZaI5WCr0wi7Qil9PYsP1imNPHlhmhGWqthVGJjBfiWU/8pZathVoKACWwIrWpZYaMbZMCvqwLzOIjdXFXFohu1KjbNyMa9zLMhIuNVMctpVJFWLQImJER5hkcVxmtQUUVatK6WVR1F2mjqomkasEyjUnX1fcsZU/mGxyOf0xo14nBcz7dLz26uyic6OFkdIfgqECTOH7ZFx6vXtEq3UMYJq6bFojzKJh5FYfBA+URHVIUNRJVf1VDKLcStw2Ur0sZTNC0juJZUZby2RlN0myYa/n1xVRR8w0iOIoDKZM/ypmUcUaL2iuWyqvK7UEYVaTNdmst7QjmIEY9h9IlbuoIvmt220LIwdan2/V5Z3olfAHB/iT9fw0e0a2eVFGnxjLSsdTkuy9spAhPXg+OH1LO6zMyE1XNndnByc1RIXfGb5DzWzoePbeD01riQujjBN6ssrA6hsNBiWXeWQSqxcQ/D881g45513T90dL2wjp+BbaOAkPoiw2HDTZgZEjBVWF+iDL6K1NUwSRCADmSri1KKs9vFnCMAtsb5OOUhkLeGU3zq/mOFHcvRa2OVrLA1qgvecXZmc9Uqv5NbP1BhzyrSyu4KHRAwzdlNctnZs/GuigAjHsser+2YYqhlMSts2XVFr8VAdQg+rkjjzZuqkkW2Hdu4V0X5tR3pCAtUyzo8o/yqSF3DmFWxKsRjf+ygY0Uy0vway87qjN5TAeH1qMz8Wcf1MJy4AQkKpD8eSyHSKKXfRSn9q7J+vkaIuCItq3LiF257AT9/6/OF1OS4HmzHi1gos9VlOy6+9qN34O/9yr2FbOCHsbyFZkaF1emtMb7hY3fiW37lHrhe/roGsZvkrEq5Q+f6+Fu/eDc+8vG7CxmvQPmV0wq7NZziG3/uTvz0F57LXRMQ3vRxUqidUfG4Ppjg73z8bnzq/mPF1GVHlV/ZlUy3PnMG3/xL9+A//dFjhdQ1mszXlaWD4ZMnt/DmH/o8fuueI4XUFbXoBpbADOvrzPYY//b3HsYTJ7YKqWsUrC8jl7XzsZc28Q0fuxMPHd0opK5oPkU7h0X3d+47hnf+jy/izx49WUhd46kbZO/lIfj+52efxvf98eP49buOFFJXP9LlME9dz5zexvf98eNYH0wKqatGdSFUflXA2jmMKYaAamSkxcPgAVZj2XUFD9vaMWVhBRRDQEjwcSts6eMVe2hqGoR1MKyAIq3pNwbjdXUaZunE487YCcgNoDoE39ZoGjSMAKpjhR3YMUVaxBJYJgaRZn1AuP7LJqwGExftiJCqMvMYU6T1Is24ygJ/+LUgOB51z6vlpvnWKBWuR2dsCO2MSqajFwb4ic89i5/8/HM4dmGYu65B7OkWU36lr+uu58/j8LkBHjy6gWdO7xRQ1+xNX1bC6rZnz2LqUhxfH+GZ09u56xrFiKGshOjtz54DALy0McKhc4PcdQ0jmV9A9vH6i8dP4okT2/jYF58vRDHJ13g7snnP8r5/+NBxPHBkAz/yF08VQzxG1j23nGap6wtPnQn+LkKNGWRYNaPdHtPX9an7j2Fn7OCX7zicuyZAEp6fgej4lTsO408fOYkf/9yzhdQ1nrpoWUbQ+APIRoj+6p0v4smT2/iltUOF1DWcCAirDHX9/oPHAQB/9NBLhdQ1nrqRYzGb8otSijueY+ev2549W0hdo4LG67//+VP41P3H8Bt3HymkrhrVRT8WZwAwYqjsjXuo/IoSaY3yN1ZBQ49ZlULpli3BeDHLaTXmcSFOiJZO1M4+NAU4UVuuMidOwAD++irZeRPvNtlpmDAIyh+vWFZVVRRWrIFLdG2xOS3bAh51TgGhmCHLg+YiMZw4aEcUaVyROSqReIzzDUDYZGZQ4jxGm7xxdBrpiNqaSHsFYxhXMmXMSHvgSKiYuPfFC7nrGsSenmZVpD1xIiSpHj62WUBds8x1VsLqmVNhXV8uoK55YigbIfrw8bCWItQ5cwRfRuvw4y+FtTx5Mn9dvAauYspK8D3q1zWYuDhSAIE88uvqNEwYBvEVj+nr4mvddjw8d6afu65QNWAGF+QszRn48fjSxggX+nbuugYRpVwe5deDvuLrwSPrhRCPs8RQduLxfv9cev+LF+AVoFyN1pVVkeZ6NHgo8eWjG4UQyCORIi3l+jrfn+Ckb0l//KWtwsark5Pgcz0aHI9feuF87ppqVBvDyTzR0W2awbm9LASNf2JKubKJoUFEhc3Ra5ZPPMatigDv5FaNuuLKwrKz2/i1uBPJE+pWoKt9XAEDMFKhbKIj3tmXEIJu0yp/vGw39hCAW+/KP3/NnlN9oqMC54kZNS2vq8Txmjgepi5FlD/uVqCuON8AzDYbKAtxRxeQPoOvMkQaIaRLCPkuQsgnCCGfI4TcKvjzxbLrfDmBL5JOTuXXc2d20DQNdBpmMQRMhFDgdWXZiD53dgfX7O5gsW3hqVP56xrGbhayElbPntnB265dQa9p4tDZ/EQHJz+5Wihrs4Fj60O864bdaJgEz57Jr+DjZEu4ec9GDD19egc37usBAJ48mV/BF4xXoODLZu185tQ2rt7V8evKv774sdfKoZSjlOLY+hDvvnE3AODw+fzrK6qUy9pN1PMonj29g9ceXAQAPHUq/zxyhZVpkMwEjOdRPH9mByvdBoYTF4fP51dijqdecI7IatHdHk9xZtvGNbs72B47OF1AJtl46gXjlHW8jl4YYOJ4eMs1K9ixHZzZzk+IjqdeSGpnzEh7aYMR2R+6eR9GU7eg8XJnVL5Z6jp6YYDRlOVvPHlyqxArf43qoi8ghrpNE1OXYlJiwxGRUo5bO4vMrUyLIH9zRtVhlZqZA8zGBnAsVqEu2wlyLjkWK2A5HdhOcC3mqAJhNbRnw+CB6hC10bUF+IR72eMVIx7blglCqhDq78ypfIHyM9IGtjOjpu0EBEyJVkX/s9tmeCxWoTmDqLEM/3eZ8yhSpF2WRBoh5M0ADgP4KIBvA/A1AFYlf2oUBH7S7kQ2C1mUJs+f2cFN+xfwqv0LeLGQjeisYigrAXPobB+v3r+IG/b2cLQAxRAnYFqNfITV8fURrt/Tw3V7ejhyIf94zSu/so3XiQ1W1/V7egURfG5QD/s7W3OGw+f6eN9Ne9Ftmjhyvoh5FIxXBmXOkQtDfM3rDwAAjlyUdZ+e4NsYTjGcuHj/q/YCAA4XYdG1HRDCbqoMg6Bppleunt2xMZq6+GuvY+NVxPE4nDgzDwGA9ETHic0RhhMXX//GgwBQzPnLcWfUjgBSr68TG6wRyVe9Zh+AYubRdtxgvLISfHx8vuZ1rOl8EUTtSGTtTDleL/nj9cECx2uG4MuYkfa8fx79xjdfifHUw6mtYhrM1Kgmwoy06OaK3aCXuUkWKeU6TROuR0vtKCxSKfRaZqlWH4DNo0HC4x6oRkbacOLObPiAamTdxbsXAoywqgYBI1CklawQjRNDQEUUfLEweMPPlCtd+WW7MZt1+cQQ//w8BMzFAH84EeVpu4G1s8S6bPG5Hih7vEQNb/g1+/Kydn4UwD4APwDgegANSqkh+FP3nS8QY4ElcOJ4qZ9Qntoa46qVDm7Y2yuISOOEVT5F2tkdGweX27hmdxfH1wsgYATZWmlvQimlOLdjY/9SCzfs7RVDwBRg7RxPXZzv27hqVwdX7ergZAEbPtuJK7/SN2cYTVzsjB0cXG7j2t1dHFsvbn0FCr6GmZpAvtC34XoUN+7t4cBSCy8WQvB5IARBPloWgo8rc159YBFXLLcLWV8DX/ll+E+bmQU8XV1nd5hC6E1XL6NpGcUcj1MvmMOGSUBIeqXcGV+59J6bGPFYxHiNJvmVTHx8vuo1xRFW46kbKAqzWk65Au09N+0BUBDxKLDCTlJabDmR9oFX+/NYwAOKsePOXIOA9NbOcH2x8SoiQ7RGdSFSMvEb9DLVTP0gw2revlLu5mreqlgVxVC3aYGQUNVRiVB/25nZ8AHVqCvevRBghFXpYfC2U01FWowYAhjhXvZ4xRVpgE/wVYF4jMyjZbJGTmXOI6XUtw6HdVVBwcdJz1lFWhWsnfPKL37OKLXZgKDhzWWpSAPwbgB/RCn9EUrpMUpp3WP+EiC0uBkzf6fd9J3dsXFgqYXr93RxYnOEac6cIU4e8E1fFkXaxPGwPphg/2IL1+3u4qWNUe78o3hdWQirrdEUE9fD/sU2rt7VwcmtcW5rRXwes2S3ndxkG9GrVjq4cqWDU5v5rVF2TGHFCNG0a4vVsX+xhWt3d4tRFjouLIPAykFYnd1hhML+pTau29MriOBz/QsxCetKq7DaCOfxiuV2YRa3aPZJu5Ge2OYEzMGlNq7Z1SlIIRoSMIQQv1lK+nMXALx6/wJWug0cLYRw9wIChpOi6a2KbB6/4toVNC0jmNdcdc0orLIp0vjx+IYrl2EaJDhv5KtLkEWW8jxxcnOEpbaF6/f0YBDgbAHr3p56c8Rj2vE6sz2GZRC89eoVAMw+X+Pli2hjFo5OBVQKfHMVz24DUOomeWA7c8qvXsuC7XiF5FVmxVCkGGqUrxiKZzIB1bAEDgRKuSo0jRhORIRV+XXFiSGgIoq0iTtXV9kWXUqpcB7LtoCPpi4onSVgqqDg44q06OnL9DOXh9MS67IFyq9G+c0Gwmv2/EMm3WzAqhBpfQBHyy7ilYbQepd9ExMSVm0cXO6AUuB8ziDxUGEVJWDcVIQTr2H/YhtXrHTgeBQXBpN8dRVAWAUEzGIL+5famDhe0NI8e13xTLn0ltPzfTY2+5dauHK5jQuDSe4OmXxsgvWVQZEWJayuXOng9Fax2UesvizzGBJ8Vyy3i8mKilgCgWyE1Xl/je9bbOFgYUSaNzNe7RyKtAP+PJ4qqK5oVkyrkZ4Q5YohNo8dnClofXX8ebRMA5ZB0iuZdsZomgZ295o4sNS6aJlf6efRxp5eE+2Gif2LrULW/WgaWk6zKvjWBxPsXWzBMg3sXShmvJjlNN9DpjPbNvYttnDVrg5MgwQEaY2XJ6KNWTjCAOpyNzGEzIbBd1LaVy4GeBh8VPmVdhNzMTCQZVilvA8tGnEFDMDmtGwlk0gp12lUw9oZn8deyYSVjBgqm0ibuh4mjjdPWJWs4LMdD65Hhd1Xq0bAAOUr+IKMtEjXTqB8wn0gUH5xZWEVMuW6rfmHX5ebtfNWAO8qu4hXGkLrXXyzoH+wnfMJqwNLLRxYagFA7s3VXBi8ZcCjgJMiqHmGsFpkdZ3NXZcoWyslAbMd1nVwqQ2giPGKh9SnV8ptDBkBs6vbxJUrLEA/r9pkPGU5Iw0zu8KKj9eBpRb2L7WwYzu5LwZRZQ6QLYuMz9n+pTYOLLVxZrsYZWFegm/TJ9JWug1WVxHEkOMGBAfAVFZTN93vembbBiHA3oUm9i+2ca4IxZBTBCFqwzIIdnUZYXVmp3iilhGiKbPuBhPs6jVACMHBpXYxBLIzb6HMcjzu88+n+/11n7suURZZhvPXrm4TAHwCuYgmCPmz285sj7F/qQ3TINjTawaE8ssRhJCrCSG/Tgg5SQixCSFHCCEfJYTsSvEeHyGE/Bwh5E5CyDYhhBJCflvj595LCPkMIWSdEDIkhDxGCPl3hJC5OBC/zv9CCPkDQsgLhBDP/5xXpf2d4+gHT92rl5szR1g1yq+LhcHPK02Aku0+9rwirdO0QGn6c2aRGNrzyq9O06pA5te8Uo4p0sq3UIrmscw1LyOGyiY6RJ1qgfIz5QaCcyr/fxUsgfOEu1WqIo3ndLZjV76yFbUy4rHsjEexIi2dFbYqRNr3A3gdIeQ/k+iVPicIId/q3yCp/riR11+f8NrfU3zWPyGE3E8I6RNCtggha4SQb1S8vkMI+SFCyLOEkDEh5Cwh5PcJIa8r6vdPgkyRlmbTxy00+5daOOATQ3k3fXHCiv+dRj0RrYtv/M71i62rZZmps3wCJdNSO0I85qsrTohmImCGswQMAJzbyU/wtRuhVTEbYcUVQ23sX2R15d2M2lM3WOsAH6+Uyhx/o75vgRG1tuNhe5TvYiAmYNISClP0miZalomDS20MJm7ui1R8vJoZLJTndsbY02OKoX2LLZzr27mJRztGiLayWDt9YsgwCA4sFqQsjGSRsbrSr6+N4TQghg5cBMKKEJKprnM74+B8enCpVUxdEwFhlUGRNjNeOa9BlFL/eMzX5fTcjo0DAfHYyn1OrSoIITcBeAisWdT9AH4GrIHUvwVwDyFkj+Zb/X8A/jWAtwI4ofnZ3wTgDgAfBPBpAL8AoOnXILpneweAHwHwzQAIgPwtl30MJ2zNRLsXcoKhbEWaKNwcKJdI6wssbr0qjJcgK4qrjMsOxo5nfvGusHkjVfJgKFKkNc1SVYWASpFWJgEzb/8G2Oa9TKuinBgql4CREXzdplnqeA0E3R7Z/0smrPwxaZmzVErZmYUi5Rf7f9nra77Ttmmw+2NdYttKfsnFB6X0MCHk/QDuBvDPCCGPQHxzQyml/2+Kt34EwA9JvvcBAB8G8FnB9x4F8CeCrz8heiNCyE8C+I8AXgLwq2A3cX8PwJ8TQr6LUvrzsde3AHwBwPsAPAjgZwFcA+DvAPgGQsiHKaX3qX6xIiBSWAHp1ADrvgJmT48phoD8RIfIQsnq8rCo+R6bQ2aX3NVtglOzuRVpAsLK9Sgc1wvytpLAx2t3rwnLv9nOuxkdT93ZkPoMltONyHhx4iWvFdZ2vJnsk5aV3hK4PpjAIMBKpxEqC3dsXLenl7muuIUy7Caq/1zhfN/GcqeBpmUExOOZnTGWu43sdU3nx2snZZewjeEEu3qhMgdgxPar9i9krst2ZgmrpmWkJpDXBxPs8evav9jC1KXYHE6DWrNg7LjYHfn5LOvrXD9UWB1YauF83051PAvrmiP40itXNyLE0MGlNm55+gwopTOKkvR1CQjklHVtjqa4fi879g4stXHPoQuZ6wnqiijluFU3dV3DKd50FTv2Di61cf+L67lqmroUHp3NwwTSE2nn+za+4jomyNq/WAwhWlH8IoD9AP4NpfTn+BcJIT8N4N8D+FEA36nxPv8e7D7qBQBfBeA21YsJIUtg91wugFVK6YP+1/8rmNPhI4SQv0cpjRJqD4KRbo9SSrcJIWv+Z+XGwHZmcsiAahBWg4k7V1dgXynThmTPE1ackOmXaNsaTmavLUBUpeDMfe9SQdS1sxuZx0aO61YeDEVW2IaFiZ91l+d6mhWO62E8nbcqcouu59GggdKlBFdRCZVfFVAMzdXVsLA+KC+SQNTAhf8/7f1xkRB1HAbKV/Dx60w7xu70WuUqMWWKtF6zXEXacMIys6MxMUA6orYSRBoh5GoAfwpgl//nBslLKQBtIo1S+ggYmSb6zHv8f/6K4NuPUEp/UOczCCHvBSPRDgH4Skrphv/1nwB7QvuThJC/oJQeifzYfwAj0f4QwLdQSj3/Z/4PGIH364SQN/GvXyzwzQrfvIfKL/2P5fley50G9vRaMA1SCDEERJoNBEo5/ZPA9tivq9sIfr+8agA+Xu1I5hfANle6NwrbY5ZVstiygrryjtdoEg+pN1MTfBvDCZqWgW7TDG4SL+TNupvLIkuvGNoaTbHUacAwSKgsLGAeZ+oKstv0b/a2x1Os+KRZQKRtj/GaA7pUr6CuOatiFkVaSMDsW2Djdb5v5yLS5hRWpoFphnlc7rDx2hchRHMRabG6siget0ZTrHAl03Lbz3icBCRk1ro6zej6Sl/XxnCCmw+ytXRwuY3x1MP22AnGMAvm1322ZilL7XDdb4+dueM8DRzXw9SlQXaTaRA0zHSZcpRSbAwnwXlr32ILW6Mppq6XeWM51wk5Q+wBpXR23S+08PiJwsRPlQEh5EYAXwvgCJgaLIofAPAdAP4RIeQ/UkqVXVkopQFxpkkafwSs4/snOYnmv8+YEPL/AfgigH+BiDKNUvoSGFlXOJjyS0KklZrnM6/8CnNgyiX44hv3Klg7B7aDa3Z1Z75WifESdKGM1sXPzZcaouw2Xudw6mKpBCKN51TNKfhazKI7duZJyUtSF1ekVSy7TaTMAcq36Io6+wKMgCki8iIr+rZkvEq2nPLPnlOklWztlK2vLM6bIjEUXIMA36J7mVk7PwrgNQB+HezJ4KvByLT4nxuL+DBCyBvBOoWeAPCXOd+OP2X9UU6iAYBPnP0CgBaY3YF/Non8zPdGyTJK6Z8CuBPA61HQE1IV7NhmgTOyadQm2z6RttRphDkwuZVfkrpSbEa3RlMQAiw0LbQsE8udRpCblqeupmkET7GyqBS2R1MstiwYBkG7YWKhZWF9kLPZwJzCKr0NaXMwxa4uy2Ta7RMLvAFB5rpiG/emZWDqeqksfdvjcCMaZt3lJGpjhFWWzK/tCKGwr6AMvrhVkY9XGmwMQ4KPk1QbBTTZiNbVsEhqRdr2yMFSh11A9xdEiI6n3kx2Wxar4s5oiiX/0R0nHvMqakcFWIc3hyHBt7uAeXQ9iokbW1+mgYmjv+4ppdiOEEO8rvUcdcVVvkB6wn00dWE7XjBewbof5qgr1nE46L6a4iHTaOpi6tLw/LXUwoW+DTdF1udlgg/7f38+/vCPUroD4EsAumD3XBfrs/9K8L07AAwBvNd3AVx0iIihqJKpLAwEyq9uI10OzMXAcCJX8JWdFyWzwpapNhlNXHQacctpBRSPtjNn2SqbeBxKrHe9khWiQei6ILuNK+XKQGg5rVY30UD5JairCsovEbFdBStsrKzSM/gGExcNc1751SmZSGMPJ+YJ9TTjVRUi7cMAPkcp/XZK6Z2U0kOU0qOiPwV93j/3//4EpVQ0UlcSQv45IeT7/b/fnFA7IL6R+2zsNQBwE4BrATxHKX1R82cuCuIWSh4Kn2bzvuXnQvHN6O5eM7AJ5q2LE0JcVZCG7OBEBye99i+2CrCcunMbdyCdSmHbV1hx7Oo1cm34WF3zIfWsLv15XI8omSzTwK5uAxcGOYkhx52xKjZNAkqRaiMZVcDs6jI7bG5CNE4MmSQ1YRVVmnDiMfc8xgi+RgaCbzOizOFWyvzdamMZaaaRitQGGCG6NKdIy5sNGFPKNdJbFaPzuGchPzEEyJRf+nV5nq+wihFD64UQQ/PEti76tgOPIiBE+fkiz3jxG5VOIzvxGNrSY8djjgcUgfrYr8swCJpmOst8VK0NsGuQR/MrfSuIm/2/n5N8/3n/79dcys+mlDoAXgRzXhTyEDYJKmtnuV0o5zcLaTuTXQwM7PmQen4uKDvgfG68SiasKKXSjDSgPMJq4jBVsciyBZSnLBxIrXc+gVySQlRG8JVN1MoyrDoNqyKW05iSqVk+AQOIsu5Kbs4wcdCyZnM6Aa6wKruByzxhVQUrrFiRpk+IVsLaCUboPX4pPogQ0gHw/wDwAPya5GVf4/+J/twagH9CKT0W+VoPwFUA+pTSU4L3Ed1AFnbTuT3J2yVwttkAf+qeVvm10LICC+GubjMIrs8Ke+qiaYXKL85gpyP4psGGD2Cb0SIIvrglkNWbQpEWUVgBbNOXd+M+nrqzG9EGV8rpn5w2h5NAyQQAexZauFCAIq0VI4YARoha8+ctIbYj82gYBCvdIubRxUpkDhqZiCEnsP8tti2YBgly+fLU1bZmiY4sWWSc4FjpFqNIE2akpR2vCCEaKKwKOR5nlUycvNABpXSG4OPjlmceRcqvlpmO4NsZM8JqJUYMrec4Hvm5vjNzPJJU87jt55HEFWlFKL9acSItxXjx9c0Jx109Vl8upZyAeEybwbcdPGTi48UI5AuDCfYvZbcOVxDL/t8y3yr/+srL4bMJId8BZlfFvn37sLa2Fnzv9PkRug0y8zVKKQiAZ54/jDVyURyliTi/MUTPHczUNfbVqI8/8xzW7COl1LWxM8TWBXumrnNDduw/8viTWNyQ3SanQ7/fn/kMFSilGNgOzp8+gbW1c8HXD22yY/++hx7G+Nil3zpN/NzG0y8dxdpauN14/hw7z3zpvgdwdlc2i30e9P19yMljR7C2FvYHOXSG1XXnPffh2FJyXWnmSAcvbrH5Ovzc01jbfD78+mlW1+1334trFi+9luQhf1yefvxhDI+G4/LSUXbfccvanVhuXfrstgdOsbqefPTL2DwUjsu5UxMMbAe33XYbCCGFz1MSHjrBxuWJRx7E2efCui6cttEfOZe0lige9efrkQfvw6FmOF9b521s7Lil1fX8izaaxJubp631cus6dMyGRec/f2dzjAs7Xml1HT81hjehc59vD0c4NYBWXVUh0u4F8MZL9Fl/F+ym6i8ppcdj3xsC+GGwnLLD/tfeDOAHAXwIwBcJIW+N5HxkuYnLdeMXvYlrHrgp1+J77tAEBMCX7rwdhBAc9i88X37kUXgn9ZbGc0dsNEl4cDjDMV7KeVC8cMSGhfA9nvZvFO69/0Gc17xROHxiDMMJDw53OMbpQb66jr5kA074uz5/OrxROKK4IEdPaMdOj2CS8OCk4zGObc8fxGlw4vQYUzt8j0P+heeuL92DfV29G4UT54e4omcE72FORzh0YpirrtPnWEApf4+jR1hdt95+B3oNvRuF0xeGuHIhrKuJCZ4/egJra9lDzte3huh64abixPEJHI9ie0f/BuH81hBXNsfB63sWxRMvHMFaS8Sn62Fje4gtYxS859nTNoZj/RsFj1LsjB1snAk3AB0LeOy5w1gztRrgCbE9GGH9/CSoY/PCGFspjvF4XZ6/sXzk6eex5qQTGUePpaE9xdnTJ7G2dh4AsLUxxmaKY9x2KaYuxfmTx7C2djrYENz/6FNY2Xo+4acl7+lvTk8eP4K1tZOs5p0Rpq7eBRkAzgzYRvLMsUNYWzsWbCzv+fJjsM5my8C5MGLvceTw81ibHGG1jkY4dXakXdfxHfYexw49h7XBYZzss/9/6YFH4Z6YvV7o3myf8N/jxeefxVr/EADAndo4duIU1tY2VD8a4KkL/obpmSexdu6ZoM67HngY9vFstzhH/GvhC88+hTW+macOjh5/aWZzrcKz6+w9jjz3FNbWn8VRv861ux/AmT2XfsNbIvjJvgzPUuGfTSn9FfjZujfffDNdXV0NvvejX74d1x5YwOrq22d+prf2Oey94iqsrr6hqDJSwfvSLbjhmv1YXQ3NFZ5HgVs+gyuuvh6rqxdDLJgM57bP4dXXX4PV1dcHXzvft4E7bsF1N70aq++5vpDPWVtbQ3SeVLAdF+7n/gqve/WNWF19VfD1K07vAPfegVfd/AasvvmKQupKgwt9G/jCLXjT614zMy69I+vAQ/fgtW98Mz7w6n2XvK4TmyPg1lvx1je8FqtfeU3wdev58/i5h+/D6970Nrzzht2J75NmjnTQOnQBuOdevOvtb8V7b9obfuPZs8AjD+D1b34b3u43grmU2Hj4JeDhR/HB974bN+wNm2ade/A4fvvpx/C2d7wL1+7pKt7h4uDMA8eARx/H6vvfg6tWOsHXn8IL+PPDz+I97/8g2g2z8HlKwvF7jgCPP4kPfeB9gZsBAB60n8UXjr2Ar/qqr8rVhCkrnrztBeDpZ/E1H/rgzAO3O/tP4YGzxy7pGEXxZ2cfwfLOOhYWjJkabt16Ak9snCytrt8/8RB2T/tYXZ1Nrfrzs4/i5OELpdX1i8/cg64BrK6+Z+brnzzyAM5sj7G6+oHE96gKkfZfANwp6K50MfAd/t+/HP8GpfQsgP8W+/IdhJCvBXAXgHcB+HawLptpkOYmTnnjF72Ja13xavru930gc9DzXf2n0HnpGD70oQ8BAA6c2gbuuROvff0bsPpGvRuF3zn2IPa7Q6yufhAAcMvm43jhsVO5DorPnn8MCxtng/dovnAeeOg+vPEtb8W7b9yj9R4/9/TduGrJwOrqu4P3PP7s2Vx1/f6Jh7DshCeC6VNngEcexNu+4u1441XL0p+LXnh+7JE7cO2eLlZX3wEA+NMzj+CBI+u56vrEofuAtoPV1fcBALYeOQE8/gi+4ivfiZv26YXM03u+iBuu3ovV1bcAAP7gxJfx9OntXHV99MkvYanTwOrqOwEAx+89CjzzBN757vfOXBBVmH7pFrzq2nADcPUz98AQnPTSwLj3Vlxz5W6srr4VAPAkfQE49Cw6vZ727zv+4mfxuhuvxerq6wAA+798OzrL8xuoNCBfugXXXR3+rnf1n8I9p/UvyDvjKfC5z+ONN78Kqx9kTqZ999+G3q4VrK6+LXNd3trncOO1VwebwD87+wheGuuv2e3xFPRzn8ebXvsqrH6A1bVy5+exvO8KrK6+KVUt/FiilGL6V5/Bq2+4DqurTOT7R6cexsaJLe26zmyPgS98EW97w2ux+q5r4XoUxm2fwZ4rrw3eMy0u9G3gllvwhptfg9X3Xg8A+I0X78fGYILV1fdrvccTJ7aAO+/CO976Jqy+4SAGtoPvueNz2H/NjVj9qpsy1XXoXB+4/Xa89Y2vx+pbrwIA7H7qS1hoWVhdfZfWe9x7+ALwpXvxvne8Fe991V5c6Nv4/rtuwcHrbsLq+2b7A+nebD/+0hZw1134ire8CauvPwAAWH74duzeq38sTZ48DTzwEN7/rnfgTVcv4+zOGP/1S1/EwetfjdV3X6f1HnE8cGQduOcevONtbwk2p727v4h9B8JzZBKmT50B7n8QH3w3q2v/yW38+AN34vrXvB6rb7r0G/GLCP7wT3YRXIq97uXy2XMQNRsAys+nEXVVZDmtRmm2msCqONclsCrZWmILZVn2KG4ziq+vsi2nQx66LmmCUN54ia13/Dgoa31Juxf6dQ2nJVlhJXV1I+sr614zV12KLDKPskzvlq7FpUAMJw5Mg8zE1wC+JXDq5u6ynrku2/XX/KyqP014/sWAyMYPAJ1medcggFlhDwocAmmu2VUh0r4BrFX57xBCvhOs26XoxodSSn8464cQQl4P4L1gHZs+o/tzlFKHEPJrYETaBxESaUk3cSL1WaE3flujaeaTmyiTCWAScl1EM4YAZkPaHE3henTOo521rqzWzldHOhWu9BrYHE5zndxE2VpAuuYM0cwvgFthC7YEmunHK57tsqvXKCCk3g2C5Vld6TP4tmPra6XbwLH1Ya664t0x+Xjputxsx8V46s1k3RVl0Z05HlNmWIm6G+3qNbGec33Zjjdzs9BKaTndGoYNSYK6clp0eVZVK25VTHkssrrYeJm+dbjo8PyGaaQ6p/JuUPx47DZNNC2jEKti9IaTZfBlaywDMIsnIci1vkZCy2nKde9vmBbas9ltec5fImtnwyKpczqBSKacbznNa2muIJ71/5bJml7t/12MT2/+s9/hf/ZD0W8QQiywJlUOQofBRcVg4s5tRAF2DJeVkSYjrIByc3NGUxeUijKZys2KGkgImLCrfdl1iQmrsurqK7KigDJD/SUETKMaBJ+sOcOgrOw2SVfFbiTrbneOLuuZ67IdEIKZPQ4QOR4n5RBpA5tla8X3k92m3xV26s10b79kdU14h+Y4kWZi4nhwXC+IYrqUGEquQZ1G+Q+ZRATf5ZiR9oORf3/Q/yMCBbNeZkVSkwEVuJ8j0OJSSgeEkBMAriKEXCHISRPdQBZ607k5nOJAxryV8dSLhcGnz0jbHk1xze5Qhryr1wSlbJOa9aTLCIXZjSiQnoCJE1YT1xM+mU1Vl4iwSjleMwRMr4G+7fjB/BkJ0amHvQvh7xSMl2ZHPnaj7c7ccCx3GtgeO7mIR5atlZ0QHfvd+OIEzKMvbWaqJ3xfb2YeOSGqu7y2Yw02ALZJPnI+H8E3dma7UPJuorpzENzQRuZxd7eBcznCzSmljEib63KaLhcQwMzxuNJt5MpSjIfBZ6prNF/Xrm4jF7EtDPVPSwzFiDRCWDfkfETaPMHXNI1Um4l4eL5lGlju5CPc490xgSzE4+yGqWEaWGxbxYxXjHjMQtQuxzL48jYlqSBu8//+WkKIEe3cSQhZBPA+ACOwCI+icSuAfwjg6wB8Kva9D4J1C72DUnrROzzwbC1xBzArUO5canDCSlRXp1FehzmZAsbwVR7lha77yi9JqH/Z4xXf9JVdV6iUiymsgm615Srl4uu+7PHi89iJCSC6JSsxpV0VS26CMJi46DbMIDObI0q4LyNb5EUeiDoOA7PK1TKItFEQnj97HxvUNXWxVAKRNrBdXLkyzwl0GqZ/jSpHwcc6WosfMg0074+r0rXzQ5p/MneyJIS0AfwjMJr2Exnegrdujz/ZvNX/++sEP/P1sdcAwCEAxwC8hhByw/yPCH9GijTh2nHkJTqAecUQJ8/ybmKESrmUTRBmmg10uRog36YvrhgC9LuJOq6HwcSdGS8ekJ1n8z6S1KW76bMdD65HZ244ljsNuB4NyJkssKezXTvTEqIhARPWtdJrYMNXFmbFHFFrcUWa3nsGdcXWfZ6uip5HMXG8uWYDgP48xgkYVlcrX/dCmcIqFXk8G1IPsEYIuQgrR0zApFIMyeaxgC6UebtjAjFlYbeZ69xlixRWZkqF1Xg2PB9gSsw8dY1kxGOK9SVe9/nqEhF8aevi1+ZFf7zaDRPthpG7GU/VQCk9BODzAK4H8K9i3/4hsIePn+TZsoSQBiHktYSQbD7lWfwhgPMA/h4h5B38i/793o/4//2lAj4nERPXgxO7jnJ0mmZwzrrU6NtiBQzgd0wrjYAREx1AuXXJuvGVbqGUKNK6jXIJq4GEsCrb2hms+/g8lqzgG04cdBqmsKsi/34ZkHVV5A+oylz34nMEuzaXSvCJFFZlE7UTV9odEyjRMi/oOAyw7qsAUnVELxJDyXhddtZOSuntl+Bj/g6AXQD+QtBkAABACHkXgIcppZPY1z8M4N/7//3t2I99HIyg+y+EkD+hlG74P3M92E2lDeB/8xdTSikh5OMA/geAHyeEfAt/gksI+SYAHwDwFACtMclzUz6eIzrSW+924pbAAp66z3cv5BZKfcLKdjwstMIN33InJKyuzpgvOp562N3LTgzFLVtApCPfYJJDWRi36KabR1FdK5HxWmxne9ozdsSEqO4Jc8ffuEc/f6XTxMTxMJqKT35JcPzNjqguTQGfRMnUxMZgkvmpSkhYieaRQkdEKbqh3d1r4MIguxgjIBTi3URTdXuctbgBTJH27Omd3HXNWRUzEHwzysJuM5d12HYkhFUqYoi9R5wYupDLcpqf4BsIFI+7CiKsok9uGxZJ1bVzYDswyOwT/l15LbpC4jE9Idprzm6Y8lqaK4x/CeBuAB8jhHw1gKfBojA+BKau/y+R117lf/8oGPkWgBDyNwH8Tf+/B/2/30MI+Q3/3+cppd/NX08p3SaE/DMwQm2NEPJ7ANYB/A2wDul/COD/xIuNvB8AvNb/+38RQvhJ6dcopXcl/9ohhhKFFcCVX2VtkPn5RGw5LWvDJyM6gHLtPjKFVdmZcoEiTUIMjUrPbquW5VRWF7+XKc+i6woJBa6ALJMYEp+7yiX4BrbYRVR2lqJMfczrKpOolcULsO9XjOCLzOOlzuALYg8k10bHFzjEVZpxVIJII4RcIyO3Yq/7ekrpZzN+DG8y8CuK1/wvAG8ghKyB5agBrGsnV8L9V0rp3dEfoJTeTQj5aQD/AcBjhJA/BNAE8C0AdgP4Lkrpkdjn/DSAbwTwEQD3EUK+COBaMLJvCOCfRu0RKmzmUKTJFFa6m2RKKUaxiwIn0i708+UMRTe3TZO9v+5mdDidz0bgirS8Kph4JhOgrxjiJ7BoXSuF5Pl4c4oOQJ9IEz2B5SqdrdEU1wh/Sqcud07RwerSY6xEob+hsnCaiUgTZVilzUgT3aDt6jbheBQ7tjNDsGnXJVHAAP661+jNICJElzsNjKdeZutwmEUWqcsy4HgUnkfn5PYiBIRoK0Y85lFYiZRyFoGdQfkVfxDwyPHNzHWFlsDsVkURYbXSbeDk5ih/XXMWSv3xGk5cNC1jJl9jV7eBE5vjHHWJCav+WP+mnRFW1gyBvdzJZx0OiNpYJmYaBZ/oSTpTYr68FGkAU6X5irD/DqbO/78BnALwMQA/RCld13yrtwL4J7Gv3ej/ARj59t3Rb1JK/4QQ8lVgZN03A2gDeAHsnuxjVCxfjn8GAPztyL/XwBpMaUOl/Go3zFxEeB4kElYlEx2iTUyZdckUVkDZBN/8dQFg12PLIKVnysVtbvwaOJqUozQZTBw0LSN4UMoRWhVLUsAoGpIA5WakybKigPB+/FJjOHGEyi9+z1Da+csWK9LCLMWS1r0s1L9RbpONocRCGbXoXuoeuuOpx3I6BcdjdH1dFkQagL8ihLyXUioN2CeE/DUAfwSWeZEKhJDXAXg/kpsM/BaAvwXgK8Eslg0AZwD8PoCfp5TeKfohSul/JIQ8BuBfgxF2HoAvA/gJSulfCF5v+7/PfwbwD8DUbtsA/gTAD1BKn9L93bbyBHbLMtI0N1fcyhBdhCs+0cFVKNnqctGOhNQ3rHQKq6HgyR23UOazR3m5Qv35jVCnOUt0AAWMV45MOZGVLKgrp3U4rhhKU5fI+hElHqMtunUh27gD+hlp4QZgft1vDqbZiDSRkiml1XogGC9OiO6MHbQW0hNpMkUawI7/tpH8nqNg3c8SosOJm5ngE9blK4Z0VYH8hiJ608GbkmRFUUomYJ7YznOOkI1XGmWh6IZ2qdPA06eyKwv5HMSbDaQlHuOb3qVOvqYkY0EGX1ricSC40d7VbbxcFWnwH4Z+m8brjiDsTh7/3g9iNjNX97O/BEbe6b6+8DCW4LogvCk3Anv1pQavS5bncz7HQ888GCgIvnbDrJySiX2tvM53g8n8fS1Hp0Rl4UBCIFumgYZJSrM0s+6F83PI9zyVswSW3WRDMl7dZhXqEudOAuU2/xB1eyx7HpMUaaMSusJ6HsVwKuvaWd54DSQPJ4BZRW00lkaEqhBpNwH4M0LI18RtlQBACPkAGMm0meXNKaVPQ3LjFnvdJ5AtPw2U0t8E8JspXj8C8AP+n8zIl5HmBsQEkD6kXqQYWiqAgJm3KqbMigq6z8wqOoAiFHzZCavwRjs6XuwQ5DazTHXJuq9qzqPISrYcUaRlwdRluWuiLqdplYVxAgbIriwUEQq8Lkczd20oIIaWchKiMsUQoG+FjYeuA6H9dHs0xd4FDVmbtK55AnnielpSbJUSkzVLyUKkiYkOSgHXo7DM5H0yP09ESZyldgMT15s7B+liJFizaa2wA5sRVlG131K7ge1R9uYffLxmLJQpCb7hZP6Gdqmdk+CTKETTdquN3wgtta3c1yBgnkBOkxkpyt7Y1W3i6dPbmeuqUV2Imr1wVEFhJSeG8jXJyQrRfQdHp1kFpZysrpKaRgjuaznKVMrJwvMBdn2umvWOEFIysS3L/CrfEljFbK3BRNwttGxrp6xxHb+XKWMePY9KM7/Cebz0Srmx4ze8USkLS5hHkeiGo5ti3Vel2cA/BVOMxfPHQAh5N4C/BLM8/rVLXFelYRBgc5TXEhguAdMgMA2SXgETWYSLLQuEhMHUmeuKKuVSWk5HgieKQeZXztwcoWIoJWFVJAHjehRTlwoz5dJaO6Ndqpa7+Yg0oTInZXh+mDlTnLKQEwotYbMBvfcQKQ8CwiozkSZWDAHp53FhRpHG/p1/HuePR11ClD9Jj/5uebMUlXVpqplYJoMxk2FVGCEaI2pTEUOCG+3lDif4st0EyYihtFbFeBeqpQ7rOuxpNuqIYyRYG2nHq2/Pd8/iCr6sTUnGjguDhCQ7kJ7gE4XrruTsClujukgMz6+Y9Q4oOdS/osSQ6EEZR5mZcirCqsy6ZOH5ALv/s0tSpA0mjlDJBJRNbIsVaU3TgEHKziKrolJOPI+lNxuwJeH5JY4X/8yqzaOs4zBQbqZcyGHIx0vnvFoJIo1S+rsAvg/ARwghH+VfJ4S8HcBnwfq4fk0ay+MrASbJn/kVV12k2cSE1qjwPQyDYKGVUw0QqytttpYo46JpGeg0zNzqCZHCSpcY4k8yoxeFhWY+4lFEKBTRBIEr0rIq+IJsLYF1WDsjTXCjndcKKyL4Wikz0vj66giUhTu553HeQplm3cdD18PxylZXOI9iRZoORv6NdlRhFVqH842XyDqcRrkafxLFsxnz1hU/HnmmnA76tisghvy6sq57R5T5la45w1BgSVlqW6CUNZ3JWlfTMmbWRtq6hNbOdgNTl+YgHpnaMqr+Y3WlsJxO3BkbP8CItK1Rvq7DNaqJgeDBD0eZVkWZ9Q7wCZjSMobkxGO7YZaWYRVs+iQKqzIJq7hSmaNTsuVUtHEHyiZE3Zm9SRRlKuWGEoKPEIJu0yotU06mSAu7iZY4jxXMSBtIsu7KVBaGDyfkdZVB1Mo6DgPlWjsDG7/kGgTo1VUJIg0AKKU/DuAXAHwXIeQ/EkLeBNZSnQD4Okrpo6UWWEEYJJ+1M961E2CbZF0r2UCg/AIKsPtILJS6ijR+cMyrJ6zMG+TAqphLMTQ/XoZBsJiDeByJlF9Zmw1ETia821xeJVNLQAzlmcfFdj7CSthV0a9LV5wjUjxGLZRZIFTKZSBE46Hr+euSK7/SzGP8HBHOYz6idtYSmD5LUZT5BeQnalui49HTPx7nrYr51xchs8R22uYMovHKm6Vox9THrK50GWl9YUZaTuJx6s4pPxopu5yOBFkli+0GXI+WdvNf4+JB1CSEgxFpXmblZh4EFsqKdcfk94+yfJoyLW6tWFMVjm6Jdcm63gHl1jWUWCiBspVfakXaOMXDmiLBwuAVBF9J1mEZIVp6plwFu2PyzC+VVbGMuoaCeBeOUpVfCgtlWNelPx5FYg2ONONVGSLNx78B8Kdg3TNvB+tV942U0gdKraqiMAjJpUiznfmMo2aKzcJQEha71GlkJqwopXNdKE2DwCBZsshm61psN7BjF2hVTEl0iMLNgXxB4ipFmu5mNFCkRcaLEIKVTiMHkSbOsALyzSO3EOQlrOJdFQFgqrnJGUxcNM3ZblBLOZVfwmYDPjGkTWxLQtdZXeWu+zipnZcQLaIuscKqGOIxSsKkVWL2BRuAIuaxZRkzJGu0OYMOhlOBgi9nXbZAFZ3WQim0dhZAPIrU2umbDcSvQfnWfY3qYqhSA/hrSfdcXiRE0Q0cnaYJ2ymL4HNgGSQ4P0bRLZXgkxNDpVooJVYygNdVzjmlLzjPcbQbRmndC4cKpVwVFWkAm8cyuyqK5tEwiE+4X/r1RSmVKtJChdWlHy+e+aWyKpYxjypFWrfE8VKF+pepLBSJWzjSZANWikjz25T/fQD3gpFof51SmqoF+SsJBsmu6ADmM7+AdAHUMsJqqW3l2FjNEzBAOoJPlr2x2M6uSFOFwWtb73hdjfnNe/66BBt3bQJG/BRjOQeRxpVfMwqYtFbYiQNCZsecEIKltlUwAeM3G9DOSHPmNiVBNmBmZU4RGWmi0PV8Fkqh8stK1wRBlFWymDdTjltORcejbmahwMqQ1wo7nnpz2Vppm2wMBMRQEVZYEWHFmzPoQKjgy7m+xlNvZg4BwEqR0wlIFHw5m6WMHXeuriwZaUUTtTWqi74qPL/EAOrBhNmnGwLCqszcHL5BFjVPKbXZgCTDCig5PF+hSCvTCivrEgjwusoZL9EDKQ5G8JU4jworbBlEh0phBZR3PE5cD45HxfZvq0TCKrDxi4gh/1xfwkMTdefoMgmrZMvpuIxmA4Kcd4403WpL6dpJCDmc8JIOAA/AJ2IXWUopvemiFXaZwUD2J9uB8ituqzH1A6gDYkiwiTm+nq0TlM0tboK69AkYueV0M2e4eUtAwKS1Ks6PV3biURzqn9LiJrEyMGVhcYq01MTQxEW3MX+jvdhuZCaQVXXp7pGHfl1RBNmABXbtDDPS9ArbERAw7QZrQZ/bqhgLqQfSrfuiFWm2oK60zSxGgifWYRfd7OM1l62Vch4HtoOFtji7LY/VOkrSztZFYYnvn2cgVPDlbGZhO+7MHPK60nftlBGi2YntufFKcW0ExBsmvu7zNONJA0LItQW8zSaltG41moCh4MEPR3QTs+sS18UsbvINMsDqkqmwLhZkli2gXAJGFVLfLbWbqGIey8zgm7jB+TaOTtPERo4GX3mgykgryzo8dT1MHE9uOS1pfakUVkB5BN9QQVgZBkHLKocQVTWWKZPgi+4x4xds5kgot67KNUGQ7MmBdN1ESyHSwDgg1R3p2P8Tf1Q1/+jqFQymSMsZIi5QfqUmhgSEVWbFkMDiBjCyQ7eugeQkt9i2shN8groIIamaMwwnDkyBlWGp3cCxAuvKlK0luCAsdxqZuyqGRIdIMZSGgJmva7FtXZTmDLo54kyRNl9XPmVhERbKeYUVU/DlsOgKlF9Bs4EUXXTj54h2w0TTMrIr+BQZafrnCRcr3dm26nm7r44kyi9An+ATWWWK6CYa3+BHsydFHeriEDdnyEtYecKHJlOXglIqVKtE4bgeJq6HbqPophHi8dI9dzmSDRNXYuZRkqfEEajvtXTwQwD+e/5SXt4Y2O5cRiVHmYHKKqtims1C0VDV1WmYmDgsl1bUDfJiQknAlKlIs93gOhBHmdbOoe3gyuW28Httq7oZaWV0T5btmTjKWl+hkqlaxGMo1qgW8dhXKKxKJfjs0I0VJ9IIIaVZ5oP8UEXsQSmq6ILqKoVIo5ReX8bnvtxgELY5Ez3VT4KoqyKQTvklk2uyUP/iFFa8rjQh4vFgbYBtYnIrhoSbPn3rncjKsNTJQTwK6rIMbqHU2z+NBMHaACPSjlwYZKpLZNGNKmB0MJyIM0GW8ijSFM0GHF2LmySzoZisu3kiTZuwmrrYvzh/o51HWWgL6krbTXQ4cbHSFdSVkxAlBDOkdBbiMb6+QoIv+/EYP5bSKvjGU3lzhrxKuZm6UlitKaVCQnQpb7MBUU5n0DSCommpN9Gc6O0059W0QL5MublrkKWfkcY7Ic43Z7i0ijQfjwF4JMPPEQD/uNhSXr4QWYw5ygygVhEKaewrRYMRj7KNexhwLrLKXkwkETCjqatF8heN4cTBFTLCquTumFJCtCSiw3E92I4nratVkoJPpWQC2PF4ersEgs/mjb2qlQ2o6oQMVIB4VJzvy7LxA4rxKul4VBHI/IFlOQ9z5jONOdI8/CpLkVajAPDr+M7YQWshJZEmIayaKRRWKgvlju1kepoosrgBPCNNf+MuejLMLJTFKYaAdHYf0UYU4Eqm4giYtEo5W6C+AICFtoV+ocqvdBlpw4mY4FtsWzh6IZuCLyQec2SkSTJUlto5CGRhFlk6QlSkogE4wZczI02Q3TZx9S58w4kjvEHLRYj61ruZ8Py0FkrV8ZiDqBU9nGB16c6jOzePLctEu2HkaGbhCdXHrK7k8ZJllQTZgDnOE/Lx8oIaZRhJboRyE4/OvPojTUbaUNKlqgRFGgB8mlKaSVFGCKmJNE2oLIGldnJThK6XGYw9FChcOaJ1XWoibThxsXehJfxeuxk2jYjfC15siJqXcDDFUDkZaX2FdbisZgMDDeVXKXUpws0Btr7KDKlXZt2VGZ6vUohWLPMLKK8uVRdKoPx5FBHIhJDSrOmswYYJQ8BTpLHoVqrZQI10MCJEWlqIsqKAtKH+890LgVANkIWECQiYuXwaksJyOh/yDLAN8sTxMh2wsvFKpeCT3GgvdayAeCyyLl0b0mg6n2EFsE3yjp3PotsSETApLIGiEy9T8OXM/BJYFbUz0gTdC3lduYnamfFi/9aex8m8igbISfD5yq9oeH6WjDTRDVo+i+58SH0jpYVS1qUqj6LWns4TVmmUclOfsJIpRPPMY0cyXjp1yQirIBswhyJNRaQlQZRdCRRAPEqsnZ5mcwbZDW0JXTsHAPKEFOX9+VcMVJlfITF06TfvsugGoOR8GkFzHI5SFXyqrp0lE48qQnTienBSZEsWBVnUBeArc8oMEZfWZZRKdEgVomUTMAqlXDnWO7XCqizlV7IirSQCOWgcV715NAQuMY6ylHKqBi6GQbSbkpRCpBFC/i4h5PVl/fzLBZxIy0RYCboqAj4xlIawEpxI8gQ9q5RfaZRMIiItzyYmDF2Pd3IjqYgOIWHVzk48jgTKLyCd5VQURM7qsjBxvCCHLQ1sgbIwrVJuICFE8xAw9nTe9suJIe2MNKkiLV9zhqZpzDwZSds0wnYkRFpOy2lc+cWJDt2unap1n5UQtR1xGDygp/yilHWpkivSsir4RAorfSWm7PzH68oe6u/Nh/qnILYHihvH5RzrS7Rm0zSN4OMlIh7zKn1l46VzPMqsDJ2GCcsgl6xrJ6V0kVL6Y2X9/CsJMoUrEK7Pcp66u0qrD1C97LYy61J17eR1DUvavMuyLMuy6E4cD1OXKpsglEXSAkmKtBLrUjRBKNdCWa3stkGCwqo0AkaRrQWU1yxlOHHUhFVJTSO4iERmhy/Poit/OAHoj1dZirTfA/CREn/+ZQHDX5RZNqO2UmGl2bVTcoOWp8OcyOIGsBNDmlB/odIkh61G1gQhTYc5qSIt6ORWNPGon5Em2rhzS0WxykL98ZJZYRfbDfQzKvhshxFWImJINyNNpSzMQ1jlVViNJFbYvATfnM2aj1dewqpj5coGlNWlQ2yPpx7rUiVTFubK/MpeF7/5iiusgrrydKHMsb5G/g2tzKKbJ1NO9HAC0CNEZYpcgI1XHuJxfrz0CdHgRjtGFBBCsNjOvu5rVBfqLpRh5telRt8WP/AEIoRVSZt3HWvnpUYVmzMEmV8J1uFLvb5C5a28LsejqbowFwFel8wWHM26u5RIUqSVl2GVYFUsieBLUhaWRvBJru8cpTVnUDS8AUpcX7a8gQtQnkJUdQ0C9B8ElGntvLRnsJch+ORlUU+ExFD2zmSjqSN8QpYn6LmIZgMDyRPFfIo0McGXNiNNNV6ZiEcJYZUm646REgqlXAZ7p6jbI5BuvERdAoGQeMxSlyjbhDdn0M5Ik82jT/B5mQg+gTInhWKIUoqxYPMPcKtidoXV3HgFmXJ62VquR4XzuNjKpxgShcEDuooh+ZPOPFZYW6RkStGcgT/gEBOiVqGKtFaKulRPrPMQyKJGOWm6+46UirQcdQnmMciU06iLK1aKVmIWDULIEiHk2rLreDlAFbpeFtEBsHOdilAASrQqJij4LvV4uR7FeOpVTlkoa17CEdR1ia3DXKmctL4u9XgNJBmVHK2GCUr1VfVFQaXsBma71V5KhJbAair4VBbwUQUz+NpWec0sVIRV0jx6HsWDR9Yz7WFUUOWHAuURj6prEOBnFlacSPtBQoib5Q9qEg5AxNqZheiQEFZplF8sk0mwcW9n7+QmCqkH0llOx46asCrTcipTDGWty5YRVinmUaRWAVizASAb8WgLQv2BdOOlas4AZFtfjBia/V0JIWiaBjT5PXldnQYoRaZcOZHCKg3RMXUpXEm21mLLCrr7ZqkrrhiyUiiGgm5QkqYRmRVpgvD8NAor/nRVdp7ITgzly/ySWbUBRsDwG8vUdSkUaWmsijJiKFdGmqQuHaJWdr0AgIV2A/2M4zVW1JVm3cuVq5VRpP17AC+WXcTLAQNF6Do/buyKPXUvi7DyPKomHkuyKiZmWPlfv+R1JWQflWWFDULXFVlRQPgg+lIhVAypiUf7Etc1TAipL8uim6RIK6trZ/LxWB4BYxokc+bXsQtD/NPfeACHzvULrWswkdv4eV2qefzkPUfwkY/fg888carQuoYTtSItSfn18LEN/ONfvx8X+nahdQ0mrjQXkNelk/FYVtfOO5CfDDtSQB2XNQwCuMhoVfQvIKI8H12iw3Y8abYWkI+AEak6hiO9E+Z46mFPT7QRzZ+Rlkf5JbILRevKYqHkN+aizbs2ISqxdi62coyX485lfgFsvNKE1MssgZnrms4rcwC27nWsnY4fBi8L9QcYwbcc6/iXXFe+TCaZ9RgInxYP7PnP0Klrjjw2uLVTX5kjuqFdbDcwmrqYut5cwxKduuYtgfpEhypbK1cGn0DxmCUjTVTXQl7iUdDABdBTPKqy2xZbVqaHOfx95UrM5HkcJdR1YiN9d19KKSaKTLk0hKiY2K6OIq1GcVBbO8vLsJq4HhYqZu1UXReAkFC41EH1QbahdLx8i+4lrksnKwoIiYdLBZ2sKECuSJu6Hj7y8Xvwta8/gDeI3WjZ6tKwnALseFyG+F6tbzvoNEyYgo5+2etSh+fz64VKRXoxoKOUK8MK21c8iOVfVx2LnkfxV0+exle/bn/qe18VuOtJZqFMajbwp4+cwK3PnMXNBxfxn77utYXVNVTY+AGg07CU4/XYS1sAgGdO7eAb31xYWRhIGntxtBum8h7yF9cO4Y7nzuH2587hb3/F1YXVNbQdXLncln5ftzlDKUQapXS1jM99uYHk6NppyzK/Ulg7x1Pxk84gWyuTJVBcVzNFXbbjCjOGFnNkpEmVX2lD/VVER4abIK6AiZ/Q01goWUe/gq2dAqIDSKmUE2xmo3VlU/Ap6tIg0gLLquA98tU1T7KaBoFBNImOiTxbayHSzGJ3r5m6rjnlVwqlnDJbK0KIZqkrTlamscLKHiQAvoLP8TBxvMDOpwsVwadTF7/JkRNWeayK86pVQI/gsxXrfqFtBRuqNKCUChV8zRQWXRXB12upb85k4L9rnow0fn0VrZ/FtoVj6+kJvhrVheexLEiZIq1tcQKmnKwoHULhUiJJmVOWUk6XGFKpOnbGUzRMQ3hOyoqk7oU64/WDf/YkXn1gAf/wXdcVV1eCxS1JKffC2T4ePb6JR49v4je+rld4XVLLKSdEJXVtDad47499Ed/2vhvw3f/XzcXVZfP7Idl4sXovtUV3aDsgZF4owBHUVYKCr9s05x7IczCFlfwaf8vTZ/Avf+fL+BerNxVLWCUQne0Egu/FCwMAwJntcWE1AfI4HI5OU51FdmaH1XO64LqGExd7F+T3+J2GifN9eXPwzSH7XtEKvqGiayfA5lGHXynT2lkjJwjYzX42okNivbP0Q+pFdjQglJ/nCqmPB3ZbKZRyU7FSLlQMZVN+xbs9Apx41CesZBtRIKMiTaCAAbJkpCnqyrB5FxEwrC49i67rMauibCMKZFc8xslQgM2jznDZio17VPmVui5BF0pelx6hIM/W4nXtZJhH1rUzZu30b2qmWtY7fp6RE4/ZmqXM19Uw9QkY2YMEIDqPGYntuXNXGmWhPDx/oWVhPPUyBTcL60qRRcbPvcLzl69IS/u0eupSUCo+p7Lvp1DwCTYmC61GRpWvWBXdTFHXREE85mlmUaOaYGoNSG0ilmmgaRrBg0IRfuG2F/B3Pn53ofk0SRlWDdNAwyTKzdXvP3AcP3vL84XVFK0ryXqnIqx+/a4X8fHbDxVaVyIxpJH59Td+/kv45l+6u9C6AoVVwnjJ6toeT/Ebdx/Bf/n0E8XWlRC6zu9pZHVFN8ZFqp2SLKdJ4/XkyS0MJi5+/rYXCqsJYOu+aRrSB3TBup/Kr1tnt8daboC0dXUbCsIqoVnKzniKf/bJB/HwsY3i60ogOsaK+5cnTm4DAI6cHxRbl6KzL69LFaVyapMRVWe3i7UqDifyhzkAe3ChIvhObIwAXCSCT2WhTLDoXhgwIu3cTtHjldy1U8c6XBNplzmyBhdLs8gMopVNAzD1mEgx1LQMtCwD/SwKK0kXNsswtC8eog6IAPxuJlm7dkqUX5Z+5peM9AqJjgKVX5oEDKUUI4kibSGPtVOSu2ZpKuX4RlR0w5Erg09CWDVNAzp8qKyrLJCPeBwrCD4tq6KkeQiQzzo8FloV9bt22op5zGu1nlOtpiGsFN0eezkUtYzgy2EJnMjnka+vtARfYEeO1RU2jUhxPJpiy+nUpamDm1WqaEDXCisnahfaFgYTNzUxweuSEnwaJwrV+atiXTuJ/6dGDgwSussBTM2u2sT8xOeexQNHNgK1QiF1JRAKQLI96nv/6DH8zC3PFWpHTlR+NdVEh+N6+O9/8RR+7LPPFNoRUqcbHyAnFNYHE7x4foAnT25rR1joQKfbIyBXPD5/Zif4d7F1aSrSJOvr9Fa4YR8XKD4M6pKoAlsJCr7nIuNVJEYJYfDdhPF6aWOI9/+v2/DRgontYQLRwcksmfpr7dlz+MJTZ/A/P/tM4XUlER2q5gynNhkxVLTCStXZl9elOqeu+8RQ0YTVMCHzq51g0T3jE3sqdVimumw1wZc0Xmf88wQft6KQRNTqdjmtibTLHFnzfPgNSpwIs0xDa4MMyNU9vK5sG3cXpkHmMpMsk2hbFWXZbYZBsNDKPl55lF+BjUnwHi3LgGWQzIo0KQGjseELLavFZ92JCT49ojbcuKsImCxKJtl46WWk2ZLjBohamrMp0kTvaWmOF78IqQjRrMRQfB5Ng4CQdMqcwglRwfpKR8AkE4/ZsgHlIfU6Sl9O4hRJbMts6ZaRXmElmses60ue05nCOqzqjpnRMi+zsaaxwqqJtAb6k2zdfS8CfgbADWUXcbljqOhqy6F6uh0lN44XaPtNIoYAf7Mg2cRESfvj66NLVleSYuhMRJkQJWPyIqk7ZrfhNxuQjNfhiMLqXIHB2DpdFQE50RG1khe5eefnfJniMchIk5B3m8Pw2r85LlaR1rIMWJLs1aT1dTqiFCqUqE0Igw+s1pL1dd/hdUxcD3/40EuF1QSw9ZXUvRCQj9dRn/wvOjtQ1SgFCC268nm8OATMMEGRlpQpxxVWmynufS/07UTV5tB2pOQxrwsQW3Rdjwb3U7pigBfO7uDDP7mG2587p3xdkuWUE3wi9G0nUDDrEny8+6hKfDN1WXxL4jxqrOmaSLvMsdjKRlhJN1epmg2IySWA3SBl27hLCBjD0FfKSdRQANu8Z8qwkhJDesovVcYQISRzzpDUEqiplJMpAHmtDZNkzBkSrw3LIHpKJleVMcQtgVkz5cQWSp0HtaGSqeimEeL1ZRmaijRlVlQOIk2iotRVyqksgXy8shDbovWVRjEUHo9iSyCQfrwclz0ZlVoC82aktbPNo+x35VZYneNxosj8Wsho5Q+VX9nHS9YEBogqRLPVJWsaoUU8uh4ICW3QUSy1rczdfYsGpXSLUnq07Doud/Q1CSvZhu/CINy4ny3QvjJIyNYC1B3TTm2F5NmJzeKItCQlU8NkDxdldUXJoLM7BRJpCd0x2wnZWucj5FmRhFVS1l0ngejYGEQIq2FxysKgLimRpm7OsD4MN8ZDzYgUHQw0MqwA1XiFdRVpwx9OHOXGPalrJz8e3YJD/4cJREc3IGrFdZ30yeyiuyoOJ/JOyEByNuApv66tgqMUBhMngRA14FHxw0BKKTb8da8rBnj8pS185Y/egl+7U91ge6DohAyo11eUhNfdW/3FY6dw+PwAv3XPEelrKOUdmtVWWNkcRo8/3Xn89MMn8JGP34Pfue+Y9DVJ1yCgVqS9YpDV2inN/EploRRv/gE/NyejIk1IwJh6BIyqqyKvKwthJatLl1BQEVa8rmzWTrHCSlcpp+ouRwjJYR0WEzCWJvGoUnQ0LZYxkck6LGk2YJkGdASP3EIpUvCFhFVGy6lwfRGt41HZvTC3hVJEbOvVpbQE5sgiE+X6mQaBaeite5UiLauFUhpSX1h4fl5iKP7QxLfo6ihE3eIVadIGLinGazR10bTmuwMD4XilnUeZUi5N04iJ46FpzkcBAOHxmGXd16gm+E25cnNlyW/Kz++EG/cic2BCy2m2TcyJzZAM2ihQ1aFjhWVqAPGxdiaiQisyZ4jXJesS2DQNGERBDEUIq0LnMWezgY0IYVUkqTDQ6KoIyAmrzUhdI00i7elT23jhrNp6ObTdRDszILfCRgk+XdXQbc+exVN+JpcMA1ttvQu7r6qJoSx7q6S6lBbKBMvpSZ9kT6Ow0qpLw6qoqouv9a3RVEsBvjWa4hs+dif+9JET6roUHZpn6hKs++2RA9ejWO40tDNv73j+HDwK/NGX1UrEJKJWpVzlx/LuXhM7Y73M22dOsePwRUUGne1bb5PO9TKLLr9PWkyxd7/vxQsAgLteOC99zVD7GlQTaS97ZM1bkWV+WSaBR6F10lEp0haKVqTphq4rlF8A7zCXwXqnqEurS6AkdyeoK7OyMG9IvZxQyFuX3NqZRjFUMFErIfgaJtEi0mzJJhtgT+0IKViRpjleKqVcPsJKRmwb6bK1VE02UtZFKVUSj3rnCVWzAfa1tMS2zC6fKiNNo2lE2vU1loTnNwzehTJNRlpx85g0XrqKWlE+GhBaO9NbYcVkeZp5tBUdX/MoRNOCEDIkhHxfWT//SoFOFlm7aQbHdxxRJVOhhFVCFhnAnsjLNgvnI2RQsQRM8iamrVADRNVeUdIjL4YJdRFClAq+KGFVZA6iysIORAkF8framCGG9MbrzPY40WY8nDjoNEyYkpD6ZOXXNOi+PdQYLttx8fU/eye+9mfuUG70kxRDSQRf1Aqos+63hlN82/9+AN/yy/coX5dXYcXX/WjqKrtVRnHLU2eSicfEbo+8CYJaiTmcuMqQfY6J4+G7PvUw/uRhNWE1tJ3gfkxVl2weeTdSSvWOx1ueOoMnT27jf35GnfU20FBYAWEUTBT8fHXdni4Avbp4xqFKFTxxPEzdBMJKMV78IcLBpfaMzVMF3uXz+PpImlOn85BJZdHlts59Sy3tPcxzZ5jF/oWz8i6fnAdIIh4djybe89VE2mWOrIQVC6kXEzBAckc+16OYuvMB1hyL7Yx1SaxklpEuw0plOc2i/JKRhk1Lb+Ouo0jL2n01T0i9SsmUty7xPOopHlUbd15XVius2EJJpBeCKFQEjGEQLDStjBlpcktzmswvEQHTbfoEX9b1JSFEtbKiFBbdrEqmiesJuz2yugyt0HsVIRpYO7NmkUm6nOoQViG5pMhuy6hIm6sraBqhfzxye2MU2a2dYkVaM01G2sSVbi4zWzslDQzSEmmi6ytwaYk0AG0AjRJ//hUBvglR2ck6DUO64Yvmaemui7sPncf3/uGjyo1r2B0zW6BydLOuS6Q9enwTn7jrRTXRkTNTbiNiT9Q979z1/Hn8+l3J1iggye5jKZsNcOgq+b/vjx/H3/+Ve5UPrwe2g4ZJpOS8abDvybo9bgymwdrUmUdKKb76p27Hh35yTXlflEQo6Cjlrtnd8V+jr4Dx6Gzu21xdiV0V1RbdjUFY16YGUfvIS5sA2LVZZW9MzvxSK6zORTKiLmjkRZ3aGuHbP/kg/smvP6B8ne48jqVZiuHXddbXvYcv4M8fPYn/9EePqeuyEwg+xfryPIrBxMVVK/48ahDIL/gZhxvDifL8NcxRFz8v8Lp0zhP8+rAzdqTRRKH9W0eRJiCs/OvOweV28FmJdfkPWiauJ7WyBw+ZMhLI/Of3L7a0m0fxul7aGEpfn2SXB5Kby3DI36EiIITsAjChlBbbv/ZlAma9y6aAEVq2Irk5insvPSVTQeHmgH4TBFVXRVaXGciQ00BOKOgq5RLGq21lehJtO27wRC8KyyR6SjkFAQPka2Yh7Npp6DWNUCmZgBwZfBLCyjINDLUUaQnKwraVydopI2obph7xqLIEEsKabKQ9T7AGGfnqUs1j0GQjs4VSlnWXU5GWsfuqrC5CiJ8NqDePLYlVkRN8qS2nUzFhlSYjjSusRFbFzNbOxGYDevOoeggA5CH4YtltVjoFn+wcsZhDIZoRf5MQcn3Gn61ER4SqY6jxdLvdMKWh1/zaf8VyW5so/9G/fBpPntzGX3vdAXztGw4KXxMqv9SbmGiGVhT8oVC7YWgTaf/u/zyCF88P8K4bduONVy0LX6O1iVHYagY2U0JNXE/7uvadv/0Q+raDD792P67f25PWZRpEeuwCTD0hIxQ2BhMcWGrhzLatVZfjevjU/SzH59C5Pl59YFFSl5qAAdjak9Y1nOD6vV08cWJbax5Pb4+D8/kzp7fxhivF85hkcUvaiG4MJ3j7dbvwxIltLUXakUhH2xfPD3DdHvE8JmakJSiZ1ocTvOHKJRxfH2nN44uRJhNHLgywZ6ElfF3ejLT+eAqDMCJRp65Hj28CYEommbsASCaGuhHLaUdUl+0Ee77t0RT7F9vKug7542U7nrIu1pxBx6I7P158DA8utfHSht48cnLWdjyc69vC38PzKIbThLoU88hJxwNL+oRV1L5+anOMpYPzez69hwDy8eLH+4GlVlDXgSV5TZRSnNuxcePeHg6fH+Dcjo0rV+ZXx1DjYY7KojsI6mLjNZy6ymOb19VuGBhPPWwMJ8LjMamBS7Qu2XmVoxKKNELIVxNCftwnzfjX9hNCbgdwHsA6IeSny6uwuuDKLx01TRTSLDKDqxTU75dkvevlaIIgyp9qmARTz0v0basIBSCvVVE8XjqElS2xV0XryqSUkxGims0ZxpJNNkfW7qsyRYZuF0pVJhPACNHM1mGZtVMjGlBF4gDZCD5KqZSo1c0GVGXdAX5TkpR1TV0Kj4p/V926bIWykBCCXgZlYaDaytHMQpaDBUQsuimVhSo1mb5FV0EMZWxmEaxZmeVU43i0HQ8tmTo0p1Iu/vtaKUL9RxNXuuazdxOVKPiCa6Pe+SvR2llwzo0CbwXwrRn/iD1bNWaQ1L0QSCaGCGFP3XU2VpRSPO/bVh4/sSV9Hbc2yY4RQK2wGtgODMI2MToEzNT1grycR/xNvAh920XTNKTHCKC2dvJQbd0HtiOHBq979CV5XVzJJHpgwNFpmNIMq/XhBPsX22g3DC2lyeFIttATJ+XzOLDVlkBel0opd3Cpo8x3i+LZ06EV8PA5uYYhSWHFz6GiLoEA26wfXPIVaRoZadHcuSOKXKahre6OqbJ2UkrRHzs44JMoOuMV7Sb44nm5Uq5vq8Pg+TVatr6GEzcgFHS6UR+KzN2hCNkXx2CS3IUSkBN8A9vBFb6SSaeZxdEL4Ri9tCEfr+HEUWekKQkrNj57fRJFx6oYFVnIbM1jxwWl8gYbQCiYEK17/hBh32LL/39yXef6Nl5/xdJcjTPvq9nwBpA0G/Dvd/f0Wn7t6rp2bAe24+F1fl3nJUpMft7VIfhEn8nHZz8fr4Tz/eZwionr4c1XrQAIO7fOv2/yNTuJ2OaoBJEG4LsA/G1K6Ubkaz8J4AMAXgBwAcC/JYT83TKKqzKydnIbT2XZWv4mJmFzlUhYtbOG58usdwYoRSJhqLJsAci0cWd1yRV8WhvkJEVajuYMUsIqhZVMtRnNah0WKtI0lYU61s7M2VqS9aXVbEDLCpuOgAk6W8qaM6TKSFMQfGmztRS5fg1Dr64kZWGWeZRZ74AUzT8cV6qwMgyCXjP9eMmsioA/XlqKNE96LHKCL3N2W7zZgJGia6eCGFrMqJSTkZkNQ9/aOZY8eAEKaIIwZ4VN1+VUde7KUldG3FDAn5+9FIVeztBVWI0lNkymvLCw1Gmgr0HAbI+d4NyqIjr6PqGgJoYMKVnQ9xVHuufp05EmAKp8GrZBTiKGDOXGfaFlYqFlaXVhPzsMz71JdakIGFaXKiNtipVuQ9spcmJm4y53SgwVFvbZuuSE1VLHQqdhamUERzvHHr2gIKwSMr8IIb4yRGy9G01dLLQtX12oQaT17aDZ1ClFV9RBwvpqmAZMSVfYid+wbG8aomPHxpK/F1N1a00aL8OQjxfAjsf9PpGmczxGiceTmwl16RAwgrHwPBoj+JLHK0qeHZEQj0HmlwbBp8zWSjGPm8MpXrV/AQBwRtLERKdDs1JhNeGEVVNaexS242JzOMWbr17265JYKHWyyJR1sd9rz0Iz+FwVeH7maw8yFa2MSNMN9QfEhBUfbz6PSeued8B+/ZWM4JONV0jwJdeVtHaqYu18C4Db+X8IIR0AHwHwBUrp/0UIWQTwOIDvBPD75ZRYTUQ7gIksfjLIsrXC3Jx8yq/FloWJ47HuZYqnjnHYjhf8TlHwTm6ORyERdbG6FF0VeV39CetIorq5nK9LQVhpZbclK9KyKqzEBF/aZgMya2fWrp2SDD6DaClgdKydRy6og3DjmLqUZWvlaTaQoMRkCr5slkAxYZWua6facprVEii2UE51lJgXoWmEas3qKh5VIfVBXWmtnbyuHMT2SEJAA5EMvszZbbN1mQGRptmFUjJe7QbrZJe+Lr5mxYo0LSvsxEVHMl5ZlV8ypVxaBZ+KPAYuDZFGKT160T+kBvq2q8ywAth5VBYGzy1fCy1LuRHnmNkgb6kIGEdpXQHUxNDQt8ipGhLM1NWPbtzldQ0SFEO8rgsSKywPR/d89VAStuzw3KsiFFiXQPV4qbqcDm0HVy63sajpMIjmXJ1SzGOSVTGoS6JS4vPI1If6dXWbpjLgfDARR4vE6xKRBVzZ02uaaDcMTHSyj7Zt7PMVRqquqMOJ3voSHY9cmbPXJxR0lEzn+jau2d3FsQtDaV2ccFIpmXhdokYClFIMbAcHfEJB58HVub4dNKSTra80hNVo6gKxl/EGBJzokK3BKDaGU9y4r4fD5wYzpO3M+2o8nFB1x5xTpGnUtTmc4F037MELZ/vSrLuhRr5jYAkUkFFcUbVHUym35Sv8btrHCD5ZcxWdLDKVwmoQrHs+Xur7HH7/cqNf13lJZp9uqD/7TJEibdbamfQggEcRvca3ycsI0dByqpjHy0yRth/Aycj/3wUWcvsbAEAp3QHwFwBuvuSVVRxBMHYGW41ww2fo2WpU1ihWV7YcGJm1KVQpaCrlFEHPlOo9nZh9XwnxaBhwPZrCcionOoYTN7VFV2qh1GzOoMqK4nX1bb1WyHN1SYhaN4UlUEY8ZulWKwtdBxipoNVsIKmZRTM9YaW0KupaKBXZWkA2ojY8lmRWxQKaRrQtLYvCbF3yc4++tVNuoeR1pR4vhSLN0s1u06orLVErPvcQQnzLfL7ML57Blz3rTqL80jx/ycaraRloWUaGdS9eX2YaBZ9G185LmJFW4yIjSdEBqMPzueVLVznMn/5ftdJRElZ9O1lhpbRQ+nW1G6a0Y99MXf6meE+vmUjwqTZWgN8EQaGUW0ihlNuesGN270JLSVixrCh1Xd2mfB55lllHkVcWBZ/Hm/b1cEKlGEqwUPK6VFbYbpMRojr3vhf6NjoNE9fu7uLcjjy7d6hrORVmH/kb7BZXpCWWhXN9G/sWW9i72FISaX07WfHYbhhCooPfj6x0mrAMonWePt+3sXehhX2LrRkyOQo+N0nj1W1aQhLDdjx4NEooaNS1Y+O1BxfRNA0pIapDWFmmgaZpKEPqOfGop/yaBMSQbB4HGkRHSMDMjxevS1eR5nkUWyNG8BEy29hhti59JZPoHBAo0hb0FGmckN+/1EK7YUiztHU6Ies0G9ijSSDz69SehSYWW5Z0HgNFmooQVRBW/RjBl7Rf4HVdu5t1Rd1IJB6zzWMUVSHSbGAmw/ADYCG3d0S+tg1g96Us6nJAkE+TWtUh7/YIJG9iZE/tObJ2JpNt2HRtNbJgbY6FdrZNjIywCqywSQq+JMKKb65Skwr5mjPwC5Cq2cDUpVqdEDlYSL0ntQ5rbdwTMtJ6zfQWXZU6yjINaER0hBZKlfIrLcGnIIYapqGlgBlN1daPLF10bYW609K0UE5cD5ZBpARfFsup6tyjq8SUEb0zdaW06IaNKCRKTG1FmoJIy0SIyknpNF10VWqbLE1vZOOl+9AEUGekARmtw5J1n6YuFfHIrUlZog9qVBNaCqum3LLFCRxm/0xeX3zT8uarl3F2x5auyeFEnckEAN0Gcw+IHiT1faJEFWQfBVclvPnqZZxSEEPcMqqCUvnlK8c6ClIrim1fkfaWq5fVSjkdJZOC4Bv4CsC2wsYbxYU+C8W+ad8CzmwljVc24nHqMmcIn0cdouN838bexSb2Lbakli0gudkAIFc8hhtsE+2mnrVzYzjBrm4D+xaaUgUM/30XkgjkhnhNR8PRO5rjxevauyAn+AICJnHdG0LVYDwMXuf+hBOP+xTEow5hxesSWihTElYA6+y5d6GF5U5DbglMQXQIFVaTdATfju3Ao0wptrvbVFgVUxB8wiwyn4DStHby++OFloXd3SbWJY1htEL9FVlkgbWzp6eU4/cvCy0Lexbk4xU0QdDpCisZr27TDO6Lk/ai/D5032ILLcuQZvbx8crT/IOjKkTaiwA+HPn/NwN4nlJ6IvK1a8AaD9SIIE+gsswaBSTbanRC6oEsBJ+sLj1bTbDJVlgogQzB2IrMLwCJapOk7LYsHeY4YaVqzpCEJKUc7zCXZh5VgfxpNu6A2qo4mqZT8KmUXw1Dt9mAC9MgwbzP1ZVLmZMnPF+sMJ2pKwPZDkiyyAyiTSioCJgsTSNUeXCpQv0V47XYym7RlWcDalpOFQTfQsFKTMvU7KKryEgDMlphJQo+wyAwiJ7yS6VIA3JamiUZaTrnHJ3xqhVpLx8MNJRMbcuE41HheXPgK9pU+UhR8E3L665YAqXyp+59jbo6TbZOZfaonq8YGmpYAvlm/XVXLGF9OJEeK4zgS1YyqbKiei0LLUueCxbFlk2ZwmpPd8ZOGcfA1rNQSsPgfeWYqrFEFOf7E+xdaGHPQlNqYwX0FI9tSUZasGH0iSGdui4MJtjTa2GfghgCkrsqAux+SxS6HrV8tS09RdrAdrHQbiiJoejvq4KM4AuUKi1T29LcHztYaFtK4lHHEghwRZq8Lt5JUuf6seHP4+5eU6pk0iGs+PdFllM+j6G1Uz1elDLl10q3gb0aBIy6+yoP9ZcrHnXr2vTPoSsdRoielxGPGuMVWDtF637C8lMX2w2tuqKNbHYvNLE+UBO1WbucDmwH7YYRnJeTHpwMInUtd5vSZjT5CVE3uDYCGsSjfx+60Lawqytf94OJg6ZlBOKhtHVFURUi7TcBvIkQch8h5E4AbwLwu7HXfAWAZy95ZRXHYo5ObrJQf6AIhZUfQJ1SYSWrq6Fpqwk32XIlE1CcIi20wuYcrwxKOZXCyjQIKGVy5SLqSrMZVZGGulZFnZD61HUlKNL0mg2os7UWM1hh1d0e9UPqVQqrhVYjszJHpvzSnUcZGcrqyqEYkpy/dLMBi1d+yRVplqYSM5EYytGcQayo1bOcThxPas8FMhJWCcejVhaZpNttUFcmxaMH0yBzN1hpLKeJ49WytEK/a1weGOhYOxPyaXq+Im00dROvH/wp+/V7ewBYV0YRhhrZWuoAatdX5ojtZnFsDCdYbFk4sNQGpeHmdO59bSdx464io4Z8vJpmoGxVoT8Fdvea2N1t+t3mVEq3bBbdieNh4npB5pesU2UUm8MJdnWbjOgYTqT3a9yamVSXKAcqqvxiijSNTDlOdPjEkGw9JnVVZHWJyWGuuuJWWJ2MtJ0xW897F1pYH9hConaoQSgA8uy2QNnTtBh5pLG+BraLhRYjhpIUaToKPrH1jn1tqWOhZRmJewVKWafahbaFXb2m9Byh03EY4Nbh+TUdhMEvMIIvSfk1nLiYuhQrnYaSeNTJ/GqahrQTbTwjLWkvys+pbN0rCD47meBTETAjvxFGoMLSVFhxYmhdprCKWKVlaJgGGiYRrunBxGU5iop8tyiCddO2sNJpYFtCpAXzqOwcrbbo9ppmcP+qq0hbaFlY6TawoRgvnXMEkLymq0Kk/RKA3wPwDgDvA8tD+1/8m4SQdwJ4HYC1MoqrMkJCIX0wtniDzDcLORVWGQm+JEVa3iYIWeryPIqJKw/1Z3UVo0hLo5RTKZl0g7F5XfIOc+k78qnIOV2r4sR/j6TOd6mIRyWhoNdsYDx1pY0sAHaT5FG9gNqgLlWzAU2F1dTVIzqSiNWZuhIIUd2mESplTi8DoaBSpDW0u9UmEDBZLLrKphEpLJQJBEyR3UR1FY8685jFCkuI+BhvaNYle/DCsaAZ+h2FzC6f1tqZNF5plYU1qouBhvUuVCmIbTXdloVWwwSlYbSBDNxyyW1L64qg50SiwydoZJtRTvDpKOUGkY07ICf4BgndC1ldclIxVMrJO3tGMXIoFttM0QFAavfhv68KsiyyUUQJJSNp5j+PKfN2dZtwPSo9J+hkkcnqms0is7Ssd1zxt2+hBdvxhA9JdELqgXAeZXX1fFJBT5HGurXu6jbhUQgbYYWKsqyEVUjgdBpmoKiRwXZcTFwPCy0TK90mdsaOkOALfl8N67DKetfTXF+2Pz8LLQt7ek1pSL2OxQ2QN7Pg47XYttC0jETl6qZPuCxz5Zf03JWsZCKEyJWF/u+11G6gack7E3P0g9+jgZVuM6hz/n2TCb6WQj3F7eP8HkO7rlYDexTKQl6XKuqCf192ruektk5dUcJqudOQKtK4alUW7wKom0bwxjJ8vJIVaWFdu7pN+cOcicbDHIUVNopKEGmU0iml9B8A2AVgmVL6TZTSKB18GMDbAPxcKQVWGJkz0hwxMRRYKJOyyDQzv1JbKGWKNJ5Flmjt1LRQZlIyKayweZVfGaydamWOnoIvKcMqmMcUdansotob96SMtEzzyK2dYiWTDsnEst/UG3cg2zzmtcImWSiBdArRJEJUdx6V2Vot1mwgFcGXoEjT6tp5MZRfCeeJIgirfEo5SQafDmHlemgWbIUd++d6UfdkXStscnZbtixF0VilsZyqunYC/rqvrZ0vG+h0CQyINEmnwIVmRA2QoP7iRBLPs1F1clvQsFACamsnVzIlKeX6QV1qIm1oJ2e3tRsmPAGpSCkNuljqElZjl6LnZwwBkNo7h7rKLwHBx6+tCy1TurmPgzdN4AHfFwS2Lc+jGE6TCdG2ZIMchMk3TGVDgpmf8dUanBDdEOQy6YTUA8zSLFZ+hT/fbhiJRJrjehhN2bpZ6bIHvCJCdKBpoWw3xXmEUcJKJ1MuJATDukTqnDAjLfl4FH0mv+5zm1uSMicIn2+a2N1rKsh2PaWclKiNjZe2hbLbSCQ62PtmJWpnLbrJ8xgSZMsKhdXQ1iDSLAOESDK//EYrhkHQspKt/OH8mEpl4cB20GmYQVMkGWT27qCxjKVnZxzYDiz/d1juNOTEo62jWpUTVvwcqZuR1h+z8TUNgl29hrLZgE68AJBMKlaCSOOglG77HTrjXz9PKX2UUrpVRl1VBr95S2/3caWh2IBORlrxxJDjenA8KiUU2GtyKtIyhPqrNu763UQ1FXyFWSj1xmuqsRFNXZeSUDDgaHQ5TbR2ZqgrzPySEB2aijQVAZNpvJKUX4VkWKXv7qtq3GGlyUhTKKyCLropFHxJGWk64zVOyiJr5bDoSh5Q6Fg7kwiYrBbKppSw0u++mqiUK+gaBKRrSpKslEtvHZYd49qWU0kUQFhX+mzAIkAIaVzyD30FQMvayW0iAsXGwO8yGJBtGraahZaFXT02narNVfJTd3lGGv+cTlNMaknr6moo0nQ3VzFScTxl3QuDLDIdIs1h54LdnBgSbK44QZeoLGwy1WB8QzdLDGkq0vxx2M0JUcF4jR0XlCYTQ0nKL24n01UW9lrMsgVAqDbp23pEh6wrbFSh1W4kWzujmVkBkSYirDSJoU7DEHdVjBBxrYaRuOajWVGquoaaijS5spATY3rrK7AgthvY3WtiMHGVVladLroqy+lCy0JXkR/IwR/KL7YbgZJJ9CBVx0IJyJtGRDOwWpYR7Clk4GPeaZpY8esSqmE1Qv0DpZxkvDixpNMsJWqhXO6wiBbR/nygkTsJqLMBF1qM4GtayUpfbhsmhATEo3AefcuoCg2TwDSI5EEAayzD76uTrPz8GgSAKQsVzQZ0r0GXS0ZajYwwDYJe00xvocypSNMlhorK/LKC7pj5CKteBoIvaeMOaHQTdVzpZhbIqpRTKYb0FHw6odisLn21iUqRFhC1CTdNE8cDIaGyTlpXFgWfxKKrY+1MJDryKOWExLauwirZ2glktOiK6rL0u2OqlEzZzhNqhZXOeI0dOYnD6yrUomsQPYWVq7YqLmYg+GzHlaoo9a2dYrsjR1YrrIzMNDXGy3FZp0HlPGZssiH7XXUtp8nj1ShLkXaCEPK/CCGvKuPDX64Y2K52qH88OysgcJpWSGpp2H16CYQVVzLpKL9En+m4HmzHC5oN6NTFw/o5YSVSyrkexXjqaW3cgflzcFT5xYPsk86HY4dioWVGlF/zdTGVmV6GFTA/FjNWRU3lFyesdivmcaCRfcTrmrrzzSzCzDCLEUMJhEJU8RcSQ/K6dMZL3B3Tt6I19aydM+HmHa5Im69LP/PLEpLaASHqN7OQ5emJPk9Vl44lEPCJR9F4RdYXUzIlk9qsLlNJIKfJblNlkXVbppQ0jWKGsOo24FGgr7CMZu0KG20cojOPUUJxudPA1KVSZSBXYqkg69w7jNja21byeO2MHTRNAy3LDNaXyCE01HhoAjArv8pCCciP2Sj64yhhpZ7HJIJPadH1z5HtFBlpfF+xq8uUctJ4gITx4qrBShJphJDDhJBDhJAbIv/X+XOojHqrjrQqham/AZEpc4AU3TElT+67DROEFJn5pW+htBRdFUPFUJoNsnzjbhr6mXLKkHquGMpA8KmaRiRaOzUJmOKIR726bD/zK4l4LIyo1ezamaRIy0LUqrs9airSNKxkQMruqwpFWsPQy27TJWrTWYfzdzm1E5szZCNqlcov3XlMOB4p1Wsxz8EaUciUX8lP3AE95ddg4qay6I4VijRmaU62pQNy1SqQVcGnqEvXcpq47s3UsQcFwQDwPQCeJYR8gRDyzYSQ5LvvGkrodHvsNNj342HvtsMUVtHNQpIijX9ew2S2GhEBExBDGl0C2etn6xpENpaqRglR9P1NU6CUE9jJBhFiRwWZGiCancRfk7S5Grvs8zjxKMoZ0rUE6tTV1iT4uHqCZ7eJiTS98HxO0MQVLqGCxkTTTLYEjqZusB5DYkhuVcxKdAwjdXWaJiYJ5/tZZQ4bL5FSTrcuZoWVdxPtNEwtJVPUcqmqa6hL8EnGK0rYtRvJxFB0HFSEuzZhlWTtbDLiMVH5NQnHd4krHoUWXc3ML0ldw8jDjaaVfJ8TEGmNcN0L15dP7Mj2JkFdivUVEFaSBg5R9O1pQEQttRV1aSisWF3ivLiokkunWQp/+ANAOY99TYKvLSXSmM28YRIQkqxI27GdoCMqz57cFhGPk+SHX4DcChtFWYo0I/bZBgCi8adW0AmQNlBZFT7d0LZQcpuceEoMg2ChmU4NoOxeGNSlQVgpTrwty4BpkJQKq+RQ/yRSwXaSQurZ97JlfikI0YTx0slkAtISoirFkCZROy2+LnVXRaZIS7rxtTUsgUBay6l8HhuaXTt1mg1krUtMuBuYJtwwAb4yR2XtzNBFN0mRpqeUS85IA9JbYWXnQ/0mG56yHXcmi65CHdVIk0WmqIsTteks8wrll0aTjcD+nVCX7XiJN/dzdUmOcW3LaZKluVlaRtqVAP4fAHcC+GoAvw/gOCHkR/mDzRrpwJVbWYOLoxa5tmbQcz+SMba71xQqrNIQHewzZ4+RqAKIbzh08qJ6vpJnoWWJ69LcuMuUclElFL8WJ43XyKF+hlUThIgJBd3ML7lSLmpV9G1IivOO43oYTz30mgkbd90sMgnBN4wQfC2NbK0ZYkjDQplIIEsJGNZspm2ZvrVT+TYzdXGlnNhyGhJ0yrqkXTvDrKmWpT9eC211XQExnVAXt3bOZfBF51FHkTaOEmnJmXJJhJUs/yyazdXUGK+o8ktlHdYJqQfk3VejxFDLMoIHwzKMIgrJpAy+pDUPsP21VJHmrwGdjLRobiM/T2wL8mijSjcVulJFWjricRTZC6nOXzqdowGm2JZZdLtNRly2LCOxy+nQdoIOocsqolYjXgBQd4/mKIWYopReTym9gVL6Yuz/iX/KqLfqWGg3MhFWaqtickaaqVB+sbqsVISVsrucX1fSwT12XCXRQQizwqbpFFhEqH9SJpNlGmg3jEwZVqLNu66Cz04gYFqWAcsgxSnSUjRBSOrGB2RTpKksp6JuS7PvobYE5suUE89jUSH1QEploYpwT0MoJCiGgPRZd03TEN5gWZpdTsdTT0gQBnVltOjKyHJdpdw0ScmUobkMs1DKFFaaysKLMI/qupItuqrrBUcvy3lCsTZ0mn84LlMYJY3XcOImnm+KBqV0Qin9XUrpKoDXAvgoAAvA9wF4nhDyGULINxFC6oeWmogqflSQEVbcstVtRhRpGioF3kRgpduQKDpS1iVRWPVaVnCPoKPO4aT6rl5DbHHTzNZKsnbybo+AWsFHKcXYYed00yBYasvqSjuPMcIqEiavY4WNrpueH44t3ojmqytK8LUsE65HleewYZBNFVrJhOH5GqHrvK7x1JtTK/MNr2EQtC0DU1f9IJPfuywmKeUiWWIqyBpoRJU9TQ0Chte1kFDXcMIsgar7bUCewTeYuGiaBpqWIbUNztQVITp3KaydUeJQWZeEUOjb7gwxlKQYilo7k+ZRm+gQzFGUGGppKtIsnwxcSlSkaWaRCYmhkBjTUTxGH96r60puSAKweZRZVoN1bybXFXXnKAlRjdgDQKxcpZTZa0OlnJm4vsaOG1w7VroqhWj2uuKob5ZeBuC5ObpQKU1CxVASoaC2KgLpg55VdTU1LYEquxDHYrtRmJWMq0eSs9tc5cYdYGqTVOOlUqRpKviSmg0QQlLbo5RdO029unRC6oFsFkqZIg3QsA4nEKKZuokmWGG1wuB1LZSZmiCI130xFreMhJU080uvmygjvYq1NNuKbC1LQynneRSOR7UsuunWl0KRZqTJuiuYqFXUxZpZ5FekZVKIKtaGjkWXn2d0xiuNgq9oUEqfo5T+RwBXIVSpfR2APwZwjBDyg4SQK0sr8DKBrjInVHXFLZRhlhG/tiRbO91IDkxTnH0U1KXuLxEQVnN1cULFCtayanNFKZ3Z/O7qNoVKpr5miLisk1uU4JOp1qIYTz1QYCY3Z0NCdPD3VdYls1DGwvMBtRU2um6CwG6B0iSqRNKpS6ZI6zTNcB4V5/x+hIhqWYwUFGeRac5jU0x2Dibhxr9hGqBQ339F571hGlhoWWLCynZAiF54vqiBxtCOKoY0LJQz1s5kQiHJEsjVNHGyYxCvK8l6F1XwKS26eiH1siYIw4iyR4ewGkUUliqio5+CSBMpmWaIIQ1CdDgJCZgkhahWFpmsi64dKseaGkRalLAKiW1JFpmmVTF+7uKEFV8HTctMVBba0bqUCj5NRZqAsLIdFkPVnVHwJeek8+uoMuNRV5Gm0RCiEkQaIWSv5uvecbFruRyRNlBZqfzSJGCSsqKCulIpv1SKNE50JKsUVBtkgD3Zy2QlUyjlkq2dyXUtpgzsDgkYhYVSQ/ml2vAB6TvyqQgrXaI2SQHTtNiTOVG4pbQuRVfFhqYVNkmRli3zS2FV9AkFnS6nWoRCKkWaG3TTiUOXsNKtK1UTBGXmV3I3UdejmLrijMh4XWkt87JzopZVUTPzCwB2BBsuVV1ygq9YxWPa8ZJbKJOJWpW1nSNrF12VFVbn3AXIG94A2db9xQKldALgLwF8GsBJsBiNKwH8NwAvEkI+SghplVhipZHWqhi/KY9aCgPSQbPZAMA2C6INzE5g7cqo/Ioojvixr9pc2Q7rus7rWu6ICas0IeKAwtqpSVj1Y5+3IiMeA8uZXl1xoiOqSGtpEI/xcWAdDMVZPux1mvMYr2saKpl4XSpSIV6XbH3lnceoFY2vL9W1O0rwAWy8RBvkvu2i19TLsBLVlVYxFCr+TCXBp62wUhyPAWElsQ1GEVWkccJKRIhqh9Q3TDj+w765uiLjpUNY8fcLFGmCeRxqEnzSjLRJtC6dZgPh60OCT9zMQs+qOF8XbwATJZD1XFZ+RlrH8usSK1d15pF1Vp3P6XQj524da+dY09qpu+5Fisf4QyqdbMCoaEVGIOs2vAEuL0XaI4SQr1K9gBDyHwDcleZNCSHfSgihCX+UI0QI+UTkta+SvOZG/3XHCSETQshpQsinCCGvlbz+NxJqEv6cDFkVQ2Jljm4XSrX1DuDEUIZuj6K6DD1iyNZQpC20rHRZPoGFUq78KkKR1muZhRFWpqFHPCYRHUB6onasIKx0idqkjXuWupKaDbC68inSWpaBhklSW04NSYdSTiAnWcAuivVOYXFrmEQvpD6hrkwKPpUiTYOwUikmOYpXWOkQQ8kKq0yZclNPGZ6fRAwFSrmCm5KoQ/2T51FnvLgaJ50VNsmim3Du0iBEsyhqLwYIIe8mhPxvMALtZwD0AHwMwFsB/FMAzwL4LjALaA0BdmI3+zLIcsb6M9342GtUhNXEz/xbaOoq0hIIPovnjM1+ZpSA4nWpSAX++sWIUi5PV8Wgg6lUkaZHWEW7FwK+FVZhVcxOPPKw8nTKLz4OS21LXVfG5gxD2wkUHTp1xbP1GGElz27TVaTNE48uOs1w4w6o5zEezbHcaUgtp7oEDCAYrwjBp5MpF4/KYYSojOjQqctv/iFQrvI10NZSpIUEX9My0GuaQmK7r2tx8z87ro3o27NKuWRFGmvKZBokIesuuasikBBSH1WkaWS3dTUUaUM7hYIvtubHDmsA002lSJsnrITK1Yn+upcRVgHxaBqYJBFWTtTaKVYWBp2jMyrlBpHYA0BfkdaKK+Videk2vOF1JWWDJr/LpcFuALcQQn4YwA/TiPyCELIbwG8A+EYAL6Z830cA/JDkex8A8GEAn5X9MCHkr4PdTPYBLEhe8xUAbgOwBOBWAL8H4BoA3wzgrxNC/hql9F7JR/wsgE3B18/LahJhoWWlUiiMlUomnxjSIWA0iI4z22PtupRNEDStnRNXbY0C2A1CKsWQQpHWKJB4TKv8UjZn0FTK6czjYmZrp5yoLUopl45QYOG2oo23pbnukxRphJAMCj72nrJujwCbRxUPO3HVIfUNkz2NTqX8SrK4FZCtlVUxJCPBLA2roopQ5eDEUFHh+TrdVwOrotZ4pct43O1npMTRKIgYytycQaZI05hHHeUXv7FMS2yrLboFWE4zrPuiQAhZBPCPAPxzAG8EU6B9GcAvAfhdSunIf+ljhJDfAvBXAD4C4F9c8mIvA6QOz5dY73qaFspQ+RRaFYcTd+7apEtYWaaBpmlICauFlhWc6yeu/LwzjG16dnUbku6Y+cYrugnSGa/4JnFXt4lD5/qJr5NBZjkdThyWKeuPZ3JdYeg6wPKP1BlpGRV8E3dGmQMkKdJCAgaQE0MD2wkC+XXqEo1XXJGmGq94UyaZUq6fIkQcmB+vvu0GhEXT9FVYrifNg7Zj9+FsvMRWMl2LG6sr3vwjkkXWMBKVOSNficjrlikxtcPg/bri3VWHk/D+ghFWyXXxNd/2O6PKunYeXGpr1SULqV+IWAJ1uolyclWVWZjGcioj24N1r5FFNpq4gbKq02DdK+WZX7pWxfm1BWCGeIyr1uKIikPaDXbOi9cVdI7WHK9T8bqCc72+1ZpZTmNKudi6H9p651SArdFzO7byNVUh0t4J4P8A+AEAq4SQf0ApPU0IeT+A3wVwNYA/BPDtad6UUvoIGJk2B0LIPf4/f0Xy/X0AftWv6yAAmWLuE2Ak2n+glP5M5OffA+AOAJ8khLyBUipiuj5KKT2S+IskgBMdlNJEOTMQDc+XZ34lElYaBEwvq5JJpZRLJPjUXQIBNl6nt1IQfFP5hk3XcjpxvGATLMNCq4GTmyPla2bqCp7QCeZRs/vqxPUSTyay7ltJdcm6KgLFKOXSZvDxDbLoGNElRC/OulcTCgBTPKqs1BNNhWjajDQZadgwDbgehedRZVelpGytoItuQUomneYMIWFVrEVX1Qm0YSRn3XHiSM+im+7BiVL5pZn5pWNVTJ1FpqgrMYRXo64sRK1yvAyi3QShaOKxCBBCfg3AtwDoArAB/BaAX6SU3i96PaXUJYSsgT10rCGArvKLkw6qMHh+HVKpOuLKnNC2NcWBpfREGsBVCuKMtG4rfCKvJDqcWaXvSreJ7bEzR0JoK780MtJ0FFb858MA6gY2BzkIK4mFMpq5o2OF5ZvsaEe+Exvz9398HnUyv6LvyzGcODOh64CaEB3FHoSudBs4cn4497qBzZQmiZlfEkXawHaD8HR+zUuyDrO6wvyjZ0/vCOrSt96J6hraDq5cZgQOf5A4URFpsfvK5Y6Y4NMlOmRZin3bCa5nbWueDIljPJ19EMqaf4gz0jjhoFOXSJF2ze4uAH1rZzdyn6RU8Gl2exQr0pzg51samV9RRRohBCuSedQmRJuW3P4dUWImupki95WEsGYpcSXmxPHYXk7TqjhxvZnzcpywaloGNkf6SjlCiP8gYHafqPvQhNclOndFf77d0FCkRcaLZzzG11e0YY1OXZdFRhql9AkA7wDwSQCrAB4lhPw8mMJrL4B/QSn9u5TS7SI+jxDyRgDvBnACLBtEBE6w/SvF+9wIZoE4C6YuC0ApvQfAnwJ4NViA70XDQsuCR9U5EVGosrUszawoXcVQ2swcWV0hoVAA0dFMqbByZm8sotC1nE4TumMCzHqwk6LLqZYirQgLZcqusErll27XTi3FY3orrHyDrE88Fr/u1YRCUl2UUv2su5RKTFWGFaCpXFWs+7CLbjHEo05zBh3lF+9Wm7YpSZ4sMp26MlthFeOlM4eAJpFWZHMGzSwyNWHFNghFWod1OvsCCUq5DBbdgvBPAZwG8L0ArqaUfquMRItgDcB/v9iFXa7QDV0HxLk50SyylplsoYxHYKxIgp7zbmKiBKEOMSSra3scJwTShdSLiMeGybrr6Si/4hEYu7pN7NjO3D3uYKJJWMky0iIWOT2r4qzTQU4opFN+iTLlujGCT7UZjT+gXemIlUzaWVEyJaZIkZZAiEbvK5c7TWGm3MB2tLOP+PvO1jWbkQYkH4/RvYHUOqxpvZMqMSPj3dZQpMXvd2XzGA2/16nLjt3HREPu9ZoNuGg3Z8dLqCy0Q0WZCjy7LXo8TxwPU5cG46Vl7ZyGVmNAYWnWVKR1RQ8nYp2Bdeqyp7NZsqLzxEjzIQAgPh7j14qGxoPMUWzdL3fmrem6Ha0B1q02vub7sfFKUqQ5rjeXfyxaX3H1tAqyJhtRVEWRBt9O8G2EkCcA/ASYjeA8gA9TSp8s+OP+uf/3JyilcyNECPlWAH8TwN+ilF5QPHE56P99hFIqWnWH/b+/GsCfC77/9YSQJQAugBcA3JqFLIzm0+gsDFtBDAVKJo1NjMpKBjA1wCCFUk4vuy2Z4FspuJuoSpFWpIJvoW0FJ1qtupTKL81Q/4tADI2Vyq9QYaWC7XpYbqqfki20LJzrqyW3M3WpMqyC8ZLX5fkh9UmEKF/3ulCFrutYTh2PglK1kgnItu5lGWlRQlR17bZdHaVcozDiMdqcQXbO4Td6/BgRgXerTduURKXg03k4AaiJId5kI3UTBMV46ZDHSXXxG7C0616u4NOwnGoRj+z902YpqpSFupbToi26BeHrKaWfS/MDlNIvAfjSRarnsoeuwgpg+VkiJRP7eQs81ERNKMxGYOzyFWnxTXJfs3shwAm+uN3HgUHYxqulRaTNEjDRuqLW8uj7qsDPWarQdZ37iXgm5q4I8bhvMeyhMbRdLcJKZTkNNu6mDjHkj5c1a+2MX7t0uz3KuonOZH7pNI2IjdeyIlNOTzEkm8eQsNIlROPKr63RZG68+raLq1Y0LIGyuiazmV/8s2WIP0Bb6YoJGO0weFnTiBmCz8TUpXA9KmwGBcxa3HhdIsdL2rriLsphPIssQTEUtXYC8qYR0SYGKkSPR34+iD6cALi1M6HZgO3gioiVdEmQwWc7LqYu1VZ+MWtjuD7jijQd4jFO1C4KiLR+GoVVZH0ttmcfNEa7dqqORc+jzAWToCzUtcsDYovuMEbwNS0DWyP5/epYIMYREaJxBZ4KoiYIcVRCkcZBCPlaAN/j/3cHTI32vYSQXoGf0QFr9e4B+DXB968DU5f9NqX0TxLejmeZXUfEV7kb/b9lzQN+EcCPgRGHnwbwEiFEqoCTIW3HtLHSqqhPWOmE1KdSyim7dqbo9piC4NOBUpGmaTlNsrgBTD2RVjlhGUQoOU8T6p9khV1omakVabKNaJrstqQb2l4rPfEorUtDkcbJLC3iMaUyR0V0JNWls3HndRWlZNIhkCmlmlbYYhVpgLo5g47CCsiQweeosts0unZqZGsBwGLKuuIWj5m6NLpj6qwv3hEubZZiHgWfqtkKB7+B060ruEEsQFmorCuDRbcgHCCEvFn1AkLIGwkh//hSFXS5I94VUoV208RwLiMtJHACa6eGkmlekTZPpC1odC8E/E2fwErGux/qKHPiCitVXTpdFQ1uhRUQML2INYp9tr4iTdbBsO8rmZLqalkGDCJTMs0qrKYpFWmOR4V2MN2NKK8jiihhpdM0Ij5ey50GbMeb+321s6IkxFCUUNHNSIsrv6YuVRKtyrqUysLY+lIq+GbrihKi83XpK+Xm1TmhQovfZ6gsZ/GsT1lTkr5uXQJrJ6V0hkDWaTYwnDjoNqLKr3lloecfB2mI2igJE2+Y0UrZbAAQKwvTZGt1miY8Ontuiiu0dDLS4o3qljuNOZXv0J4l6JR1CdZX3NbeTMiCFbnHVrpNKZGmq1wVrXkgvI9rmETzoUlsHiVqbd31dVko0gghJoAfBfDdAIYA/gGAW8DyO/4RgHcRQv6en3mWF38XwAqAv6SUHo/VYQD4TbDmAv8m6Y0opc8RQp4D8BqwzlYfi7zXuwB8k//fXbEfvQPAZwDcC2YLvRLA3wLLiPt5QsiUUirLbvsOAN8BAPv27cPa2hpePMsWxe1334fjy8knxEePs0X18IP341hndsPAT/7PH3oRa8YJ6XucXx+haxGsra1JX3PiGPucz996B1bayZztU0fY6x+89x50G7M3M/0Jq+uZZ57D2ljec2JrZ4gNMlLWdealCTwKfP6La2hZyTeZzz7PLj5333UHjNhN1tkhO6gff/IpLG8+L/z5fr+P/sjAhbNnlHWdPTnBxPHwhVtvCwgUFV44bMMiVPieR7fZgf/IY4/DPPO09D36wzHOn1PXdf7UBKOpiy/eepv06VcULx63QVxX+J5PX2B1PfjQwxgcka/Vze0hFulAWdfOuo31HfHniHD85BjOxBO+/pkz7Bi69/77cWpJXNfIYWvw2JEXsbb2kvRzBltjnNsWf44IJ8+MMZmI5/GFE+yYuOvue7C/Kz6G+LFx9MXDWJs9pc3A7o+xPhZ/jghnzo9gEghf/6J/bN9+511YaonXBCeOThw/irW1U9LP8ewRXjo91Kqr3+9jfctA0zGErz92lB2rt67djqZEcXZ4k63BZ556Eu3zz0o/izg2XjxxWnu8tvojrJ+fCF9/8sQEk6l6rb6wwet6As1zz0hfZ3hTHDp6AmtrF7TqGtpTnD11Amtr8z1szp2xMRir63pph53jnn/2GaxtvSB9XdPw8OyLx7C2dgb9fl/5npRS2I6HUyeOY23tzNz3Ny6MsdVXH0OPnmTH7MMPPYBTPfn1pW0Cz7zwItYaJ6Wv4eBByieOH8Ha2vzrt7fGGE7Vx9AT59k8Pvn4o5i+JD6X8AdCjz/zPNamRxPrKhC/AeAHATymeM03gVk5P3kJ6rns0bcdWBpKJoApJOJP3aMEjmUSGCRJYSVTfs1vFnQ2fIDccjrX7VFHkWbF6hrkqys+XlFCQtd6B8xmawHzndx0Q9cJIT7xKCdw9KyKs+MV7cgXHZ/BxA3mQQVpM4tIRlpIPMo3huOpC4OEiu1oZ8XoBnU4cbWsd5w0EY1XfH0lbZJnLFudUFkYJRCGus0GBAq+eNZUS2u8Zh+grXSamDgexlMv+AyAW07TZKSFn0kpnVlfUWVhrzX/Hvz3is7XLp8Yiufaao+XoNnAaOrCo7OZX25Cc4bRxA3IbICtr6dObs28hj9s0LV28lo4wlD/WSItyakQV1gdPjeYeU2ahybdyPri8xAnvLS6dsYe0C61LRxfn80sDPIsNRRWXYESM05YJVlO4zZ+gI3X82d3YnWlI6y4RZc/qB/Gfq+GqVbwxc/1ADseXzwvnkddgi8puqkSRBqAO8Eyyx4G8C2UUn63/vWEkO8F8MMA7iGE/CdK6cdkb6KJ7/D//mXB9/49WFOBb6CUbmi+3z8H62r1s36Xz0fAmiP8bQBPAXgzmHUzAKX012PvcRjATxFCngWzgP4oIURoO/UJtl8BgJtvvpmurq6i++I6Pvrle3DzG96C971qb2LBR+8+Ajz5JD70wfcLu7lZX/gMrr72WqyuyoR0wI8/eicOrrSxuvqV0tdsPXICn3zqEbz57e/EjfuETU9n8ORtLwDPPIuv/tAH557m74ynwK2fx/U33oTVD94oeQfAuvdWXH3lHqyuvkX6mpfaR/H7zz2Bt73rPdi/mCwBv2f0NJpHj+DDH/rQ3PdObI6AO27Fq19zM1a/8lrhz6+trYGYE1x3zZVYXX2j9HOONF7EHz//FN7+rvdJu+xFccvm4+idP43V1dW57z13Zge4+w7c/LrXY/XNV0rfg9zxeVx3tbquQ9aL+PQLT+Ed73m/Vijpp08/jKXRprCuhSPrwAP34A1vejM++Jp90vew7r8VV1+xG6urb5W+5q7+U7j/zDHh54jwW0cewNgaY3X1A3Pf8545Azz8IN7ytrfjrdesCH/+Qt8GbrkFr7/51Vh97/XSz/nc+mM48vRZ7bp++bl70fI8rK6+d+57W4+cAB5/BG//ynfiJskxdHZ7DNz6Rbz+ta/B6ruvk37On5x+GOvHxPMiwk8/cRd295pYXX3n3PdO3X8MeOpxfOW7340rljvCn+/bDvD5z+HmV92E1a+6Sfo5nzh0H7bHDlZX35dY09raGswmxTVXrGB19W1z33/eOAw89zTe8773B7L1OLovrgP33oN3vO2teP+r5efLA0/fjYZpYHX13Yl1AQDu+DyulxzjD02ehXvkBeXYtw9fAO67F+9421vxXsV5fN+jd6KXcO6Nwvn8Z/CqG67H6urNc9/74uYTeGz9pLKux1/aAr50F9725jdh9fUHpK/b/cBtWN7D5mVtbU35nrbjAp/7K9z8qhuxuvqque//yemHcXqiXqtnHjgGPPY4PvC+9+CqFfEaBIDlu2/Byr79WF1VCrEAgD25/MLn8brXvBqr779h7vu/deQBnN4Wn0M43KfPAA8+iHe94+14i+RcAgDNL34W+65UX2dLgglAT65dg2UXtfWVXyLFUfSGPmlzFVcyqaydOkqTsK75ZgNpiCGpIk1gq0lTl9h6F7d2Kuw+gQMjTvDFgrFjihRlXQLicThxA6uojlUx7ELJXrvUDgmr6DV1qGlxMw2WGzc/Xm4q4pFna/H1vNKJNrMI75cHtoM9vW5iXe2mvymO1OW4HiOBUlg7x7GsqGg24JWR83/q7LbI8cj/3RUQVtK6YpEO/B55czRBp8nq8jyK4dTVVq0CMYLP9eB4NFz3GsRjXJG20m3Co4yo5UQWzxLTsioKFGkDe5bwio6XjEgbTlxcsTw7XlJLYNbMr5jVkRNkU5eiKRFPMDdT+L0VUV2TdAQMwH7flS7/+VmCr5lg7Zy6HlyPxrLI5i2nO76yXXbPG0VbQNTGLZRJVlju0ooSxaImG/3Y+lDWJbLoxpSFzYSIlPhDpqCumHWYK/qWEhoAAskRBEB1rJ3vBvDzAN4TIdEAAJTSHwfwQbCA3J8R/Kw2CCGvB/BeAC+BKcKi33s1mCruf1NKPyP4cSEopWtgXUf/AIw0+7f+/38EwH/1X3ZW873+AqwBwl4Ar9etIW2HOVVIPaBpX9HM1gL0bTWqkHrdcHM9C2W6YGxVKHZDs9mAjuV0wT8J6tq2lGHdBYb6L6aeR0W2VpqunRqZcqOpmxj+zaHsEqhhhdXJigKyWSiTLKdJN0w6dV2sjDQZdC2U6TPl8jVn0LbCtq3gQq5V11RtHaZUz3KqU5fuPDrBjViec72rV1eKZhZJHTd1LKc6XTuDujTnMcwPzW/tLNrSfAnxGgC6DxJf8ehrEh0A6+QmIjqiBE7DTFIDzCqZOk2WYSbaxCxobKwAtvGYtwTOZ36pLG7xuuQWSldrIwpwIm0+uy1u7VRt+uLHtKw5wzCFUk6UmzOYiOrSsVCG1k4AAhuSPsEnUjwOI0oovaw7dy5jCMB8LlNKwipaFyfV5pRyCURt9H5kSVCX4zIlWBplzlBkCWzOEjDqTLmYIq07X1d/4oDSdBv3mboChVVoCQTSW2GBWeVqP6aQUiHMSAuvf3HCS4eoHU3dGQJmpdPAYOLO/EwaxRAnhkYz4zVPwABqZWFUCQVwCyVT8MneVwVRV9ggIy2wdppwPSq9Lww6DidYh7d9a+xiivU1a4WdPx611LRRpVyngZ2xM/O7ZLGcjmPzGM2tbJgGpo7qocl8zrqo2UAa4rGjcS6piiLtmymln5Z9k1J6HyHkbRBkmqWEqsnAGwC0wBoefJvk55/3n9L8rWh+GqX0MTDL6AwIIT/k//OBFDWeA3AVAO1cuDC4OF1GmipQOcnnrkUMpSSsVCH1QXdMxUHE6pLn7sTr0s3XUmZrmckEDKtLrwslkIIQVYVia4b6axGi7bTz6CqzolhdRXTtDNe9jlIufsMzU5eZXJduhtVCq4HhxFUGwUYxnnrYLbGmNXSIIVeTUEhBwADqrp06VoyQUFBfiHrNlN1ENTLSVIT7NAUheuzCUPkaDkqpT9QmrS8PpiEeD10CZrFl4czOWKuucbBZk58nks71tua676VoShKqMWR1kUK6dgLpug7H1Stx6HQ51SW20x6PWUEIiSvg/yYh5HrBS00A1wL4AOTdzGvEMNAkFACg0zBwZkud6dSy1E/d48ovgG8WYgorza53gE/AiKyd/ibCMg2YBglIdVVd7UBhZcE0yJxSLo21U0RYzSi/Ulgo5xRpc3WlI6zmFGm2O2dVVBJDfkZkNDwfmO9yumM7WuH5wLziMVBCBVbFZGKIZVfOEzCi9ZVVMRTPmmpoKtLili0A2IqoTTghoEXACLOiZq1oIYGsVudEPy9qOeXg9/Q6REfDZFmJqq6KOvdftuPNfF503d/gby85obCkcf/M5zG6bYoTcU2N9TV155szAIx45Md1mq6KWtbORvL6msb2QsvdJihlc7fc5aH86S2n45n1Na9I43WJCBsRYcWzFFnGIBfR6M+jkECOxRMkZbfJrJ28Fv4QJY0VtuMrV+Pz2IvkVjYsoqWKnlHKdcOMR37M74zZ7yvbR8zUpaFIqwSRpiLRIq/ZBPCRrJ9BCGmD5a15AD4heMkRydcB4BvAOnT+AYBt/7VJn9cC8I/9z/s9zRqXwRoTUJ3P4AgJK73gYtth7cNlm3zdp+5JXTv5waO7ubKncqWJaRAQoqFk0iCGwro0x0uj26MqSNyjFI5HtZoNAOkUfLL31KpLswtlSFjpjVf8RiyKVF1ONesa6BJpjie90DQ0lHL8pqWRuL7Y765N8CV0e4x+tghpQuonjqf8vJm6lIq0ZEtNKuVXQQo+neYM/CZP1bUTYDe9uucu1ilUTmY2NI5HXcVjr2Whf07/HAGIG7jwurSbIGgQfKe39Qg+VSdkgK0vXUVa8nnC1D+ncpJCoUDWbzaQMI/NS0OkAfjWyL8pgLf6f0SgAO4Di7mooYG0FkoRARNVtCVvYmaVXwAPEo8phsZ61jtZXfHuh7p18WsLIQQrnYYwu223bl0Cgi+qANTr9jh7THebJpqmMVfX9niKa3ZnH6+oIq1lmhp1zRIKMuXXzniKxfZiprrGjgtKI8SQRuZXvGlOaFWcV8rpbJAt00DTNMTKnFgWWdLmvSVQWEUJqzSKIUZizhIdcatiQFgprpH21MOeyINQkVIujQIG4M0/5pVyC3HiMSlTTjBeWxkJvo5AkTaMEZc6mXLxvWM4XpOASIt3kVTWJSCs4p2UQ0WaeLz4XiiuSAOYRZcTaWkUVvw1cQslISExlkykzT9wjJ4nQiItvSItTtRGG600LQMehTTrThjqH6krTqSlslrHiO3oscwUaTrXxnnCfXM4xcFlTqRNsagbx3AZKdIuBf4OWOj/X8SbDACA38jg20U/SAhZAyPSvj9uPfU7io6jCjdCSAPALwG4HsAvUEoPRb53EMCC4H0WwIKA2wC+QCk9rfuL9SLKHB2wds3yxWGZRiJhFWfvReAHte4mWaU0IYSgYag7ibC69ImhNIo0+cYqmVDgx30S8biQdrxi7Yej4CRpERt3Xpe+ddiTntB1u5ymIUTTbJLzWGF1N+5RhaguwSfvqpg8jwHBl2Ld6xBpKkVaVGElQypL4MRRBsFyUErn2s3P1pVM1Ooq+NIo5UI1Rg7rsKM5jymUTEF3JQUh6npUHcKra6FspyD4gq5P8vOXftfO5PV1oa+nLEyqS8tyqnleTWtpzgEe9kbA8lg/CtaZPA4XwAaldCD4Xg0J+rardZ4HZNZOB1csh4RVI9FWIwhUFijS+n52m15dIuXX7CYmKbstnvkFMDXAnFVRM9QfkBCPkey2oMtpQkaaRRAErBNCWF2x3JydsRPklCUhrpRzPepbCnmGVXL31TjRsdRhPztPpDlaG2Rel4gY6sUslKpza/xB6LKAgOGh/LqKxzghGoaI61thx443c5yJMvjSKGAIIejGFHyDuMKKP8hMykgrkLAC5o/HeJfB4AGrwqEj6toJzCoxtwOCL7mupsm61c5mpMUIK415jBNWnHSJrvtU3R4Da2f4mcN4186GmkjjKvPmDAEjsOhmqWsmu82d6VgcKlddAPPnHtF9JT9HbY8cXLHMvrYznoIQYEFHwSeywk5mVZVRpa+YSPPvvUSE+3CK6/YgqHGxZWk5cwKFaMzSHFUJN7WbDQiOx9EUB/3rLDun6pPaSagUkUYIuQLAV4NZG0W9SCil9Iczvj1vMiDshpkDHwLwa4SQWwAcB7AE4P8GI9H+EqwTaRSvBXAbIeQeAE+D5addBeBrwMi6w5AQejI0LQMty9BXfimsdwB76l609U63LjXBR5SbGO41L5ywmioyrALrnbwufu5O3vD5bHkKRVo7geBT1RVY3DSUTECaefSwT2EbZp+toZTTtZymIpBlyhx95VfyPKbLulMR2zoZafqZTKFFV6eZhWrd6yis0lgCKZ3dIMnA43JUlkD2Oh0Fn/oCGc3gS7oRGCcorBo61uEUx6P+mk9WpPG6pCG8abIBC8rp1FXKNU1xFEAUvZZVWH5oGstpK2F99VoW1mOB5xcDlNKgLagfN3Fb9Gs18mGQ0non7F6YQpEWEsizirTnz/ZnXqebYQXIu3bOEWkaWZ3xzbvIQqmr4Gs3zLljpB/p9kgI0RgvF/HLxa5uY66b6LavUtBBp2nONCuIK6yaGvcT8Qdoi+35jTulFH1bn0iLW055XZ3mLPGYVFd0f8A3wtG6wt9XnxCNNrMICasYAZNkhV0Mt4WdhomGOVsXD2HXJ7bNOaIDCK13nBBVjldMuR9VMnGkVaR1m9ZMc4a4pVDP0hzv2jnf3XcnCF1ProsQgm7TmiGt48SlTgZf/CG50Arru1906hIpmfoxa2czQSHK78ui917LAsVjEFKfwgo7iqz7aAddAGglKGpFCiuRcnV77GChac10Y02sK65Ia81eg3hdXcFWYSwg+JYF2YBpzqkiq3X8oUszIfZA1mwAmLWmp304kYTKEGn+Dd5/xmxNBGH3KP7v1EQaIeR1AN4PQZOBAvAcgC+BdfvcD2AE4FEAPwTgk5TS+KwfAiPzvhLA3wCwAmAI4Fmwhgsfo5TupC1isZ1mE6OhSNN46n4xlEwqgs8y1JurNCHPQArCSqFICzK/FHXxc6S2tTNFYLfshF5k6HqUgNGqS8cKW1Cof6q6VKH+Ota7FFZFQH/dq5RyWhlputa7FMQjpVStxNTIBtQnHkNiW5tIU1gCWV0FzGOKDL7QPpSQpahB8CUq5VoWxlNvLhxXXFeChTJSV1PSe4jbMHWUvmnOqUl16TQkSRorIB3xmFiXhuU0zfo6tq6nlCsKlNIfSn5VjTSIht8ngRMdUQXoIPYAIXmzIMpIa85sFCilqbLIOg0TU5fOnFOYhTKy6bMMZbOBeOYXwAirE5uzdu9+mrpiSqap62HieDPKi2SlnBdY/jlWYgSf5zHCSicMHmDjdWIyr7Div5flq3cSFWmR64VpECy2rZmOfDxrVVs90Zy1scetirqWwOh1jBAy1/mOh5vrElZs3UcVQ7PjpUs8Ru/fWF3NGNGRjkhrN2abM4TEo58NaGgQfDHl/oKAeEyrSIsrHuNdFXUI0TiRtti2YJB5QiFtXbY7P16h8otnpIndPpTSue6YywIija8vHcJKlnVnRCyUSeMVugFmu3YCs8TQ1ogpvxY1zl/iLLLZc32SclWksBIpV9MQQ1Kl3ExdaoKPR4aICOQ4sa0zh4Dcohsl6xsms5zKHm6L1Noia/pOCoJPJzezEkQaIeQfgnW4vBXALwD4IzCb4+cBrAL4f8HyyX45y/tTSp8GI+IygVK6qvjecwC+OcV7HUfY9KAwpOkUqLJsAYxUUBFD/GTYSthYtSz2xKgoRVojofWtfpZPWsWQnIAhhDCCT6X88uafdoiQRSm3f1Ek3NTrcprW2plGBZNEWCkzJ7Szj9Ir5ZIIGLVVMZ2FMk1dic0sFPOoayVLk8E3cT1QmmxVVN1gpiWsdmwH+xPq4k8OE9eXjhIzBfGYTKTNS91n6krR5VR3vAa2E9giEutK6L6qpZTTqGvieMrNY1iXmnjkqmiV5dR25BmRM3X5Fkod63AY5C5fX0V17Uyj4MsKQsi1/j9PUErdyP8TQSk9dpHKelkhLTHkehQTN3yYOYwRVk1L3bVTrPxincn4GrcdD45HU1koAXbtbvgPUsdTb47gU3dVnL+OrXSbePLkdvD/8ZR159O1UHYahjCkPqqeaJgkQTHkIr4X2tVt4Mj5kMQe+F0V0xBWswqY+Uyn5M538/dJy53GDJGWheg4t2MH/48rx3QyakXRHMudhpCw0t0kx4mheHfMpI07q2vegbESs+gGBEyKTbKoO2a8W63q+jiOKdIC4lGgZEqnLAyvDcH6SpUNOHu/axisro1hHqWcCduL1uWPV0zxOJFYTvmD6llrp5iAYXXpWxWjhCjPUQxC6hOIWlH+sYiA2R5NsdDSU351BYTVcOLMWAUDpZykLpXCKp7BpzuHfK3GLc09gVJOdh0aKQiruCJN+1wvsOgObBdXroT3uNF5FDXtEinlZFZr7TzMy4VIA/AvwNRiX0cpdfzFf4RS+nsAfo8Q8mkwm+SnSqyx0lhIoUhThYgDzH6ntgT6xJDOJqalnwOTqEhL2MTYmplM7QZ7WphG+aU6oVsJNiRdRVqXdxTRHK+JIiMt1cY9yXrXzEIMZbecplUypSH4kiyBsjbUQLpuj4De+vI8qlTWaBEdusRjQAwlN41IUuak6SaahhhKQpIiTSvUX7PZQC9FXaK227N16RO1aYjtJCIt2dqpfzzq1qUzXnZwg6hWyrkeDchRUV0616Bey4LjUSVhrVtXQ6trpwvTkDfz4Ujz8CsHjoAp+V8Hpp7n/08CRXXuDyuNnbFeFiYQIawmjEjjXRVniaEEq6JA+bXSZZ3cmA2wEWxmtJU5kdycxXYjsJXN2Go0OrnFz8srcUIhrfWuEbfe8YyiGGGVQCg0Y4fzrm4TDw83w7rSZljFssiGMSsZoGfRjY/XUmTugGwh9eOY0gQICT7TIDBIsvJrrzVP8MWVOazeNNbhiMUtRogmhcEDYuJxbn2lJPji6yvs2skJvmQHBatrft2L5lGfQBZbYeNdTmXzGDgKYuMVb0qSOrutYSLqTA+z22YtzbIHv6J76MV2A4TECKvxFN2mmfjQGghtj3HCPR5Szz5ffOkT3UPzNbSdkRiKnlM54nb7oJmFhHgUKax29Xh4fjarouF3q4wrv6INYJK6wgZ7tASCb3vk4MoUsQeAqNlA+BlhJqb4Xk7UiIffI89arVMo+C4ja+ebAHyKUhq9qwyqp5R+jhDyOQDfA+DPL3VxlwPS2WrUGWlJhJWuMgdISfA5rjJzwTLUmxh+kkxSyhFC0in4FAorAH4TBHld/ByZNF6GQVKpFFQKq6DZQAEbZNMg6DXNVPlH0u6rF4GA0akryaqoFVKfkrDSIRSC4PtEoqMIgs8najXGS9QtKIo0VkWdjDTduvjDqlyh/inXl05diYq0VARfkqJWn0BOVKQVaGlO0/wj2Qob1iV75qM6nqNYjMxjEpE2TqhLpwmCruV0oW0F1i2dMN6M+CQYKbYV+3+NAsAPG+2NO7f7TB0so+HbPGeJoZZlKI8hIaHQDTuTZSHSurFNjKj7YUuDsIrXtavXxGjqBjVnIfiEoetxa2cC0RF/aLIcU/DtZCFgBAqr7gzBZyYSQ/H73ZVuI0YoZFEyzVsCo59jJQR22yJiqNuYyarj5ILueHWb5sz9UDBejVkCJsmiO0fUdhs4tRVah7nqJGvTCK5I43UlEVaOy5Sf8esFa2YxS1g1TKJ1XQDYeeJ8P1QWxgnRJKJD9iB0Jdb8Y2c89bPm9OuyR+H/BxMHTcsIfj5pvET3OKZBsNiysDWcVRbqziHvCjujEJ04M8diIyFSRnRP2G6Y6DTMGcJqe+SktioOYxbwPZF84jDrTmyFFdW12LJgGWTmeNyxp9i3IHYnidBtWrNE7WReFR39/Lm6BPf27YaJlmXMKdJeq9lxWGTR7dvu3LkekDf/mAjuw3tNE9ac1ToFIXoZEWkNABci/x8BWI695gkA33nJKrrMsNBq4OTmKPmFUBMdADsxqax3U82NFcCe0KXJzdndk79nI4ng4ydpSWh2FIvtRiGWQCCZeOSbVF0Fn45iCBA/CeMghCQGY6cmRFN0E01SpGmFwWtu3LWUTC5VWhUbQdZdsqUmjVUxCbbgCUoUOl1OtQm+wNKc3K3W1gzPl0n4Z+oqMLstIMsTMvhUCr4s3VeTkDiPGtbhqeuBkFCFKEOazEI7UMolHI86xOPFIPiSzhOSJ4+AviItSmzvk1jhg7qS1r2f06nscqqRHwpElJgT/U1DWlBKv1X1/xr5wC+vabKigFClMBCEtjcTIixE9yPRjnzX7O6mJqziuTnxMHiAW07l1w6hMicS2H1w2cykGLIdD55HYRgkIBTSKOVsx0P8NLOr28TE9TDyyaxMXRUjWXdBVlQzHfG4qzs/j0+fDq2woZJJ39o5jHXji9fVNI3Ebo8i5dfhc2Ez3yxZZLOWU65IY59jGAQmkW/c2YPQeQJ5qdPA06fCSOltnxjSOf8CbH2f74eExGDioN0wggeFYaaceLzCLs+zn7fcaeBCf9ZCyZRXeg9M5pSFNifi4ll3kroElkCAra/T2yHxmEaZw+u60A8/cxBTWFkJ94XBnmOO4GvOWwI7+nW1G8ZcptxsXWpiSNZ4TWRpXtasq2EaaJhkzgIetRQmKTGngj0aIcTPeJwlam/cu6BVF8AfBISfGVfwJRHbE0FzBsBXrsbqSvuQKZ5ZuDBDiOopC+PjFZ3HLA1ckqB3trn4OAXgisj/jwF4c+w1VwG4JL3iL0cspiA6VNY7wN8sFKDoCOoqQMkEJAdQ63bjA9Ll06hC6gHANNR1cTuang3J1CI6WF0JTSMSLLq6FkpAX/HouB5cjyYTQ1rKL/U88m616RQw+RVp2pZTHeVXkMmURCgU1wRBz9o5nzUwU5eOIu0iWjtz1eXodXtM0/wjcR41171WXe0URK0jvqHm0Dkek2y+8bpSEY856tIlrNJY05PHK7ScSuvy5zEJadZ9jWoiLZEWf+rOr/W92GYhMStqboPMPp9vrvhmJq3llG9G+/Y8YcWy01RZUfP3b1GCD0B6go9bYR05wddIUFiNp66ASJsdr9QWyqYJj4bnC/E8ktQZaSvd2Y17SPDp1zWOWaMAzKlzVNdHkUWXbUQFWWQpCOQ4MWQZ/z97/x1uyXGXieNv9elzzs33TtQk5RwsR8mybFnXxoDB2LDEZcFgwrKwpF1YvrAsi+1l+X2/CyxLXMC7NsYkk5awxtnWlWXZsiQrZ2k0kibnm0/srt8f1dWxqrqqT9+pknQ/zzPPnbknfaa6T3fVW28gmetk01OnKoa0CFjNjbcKUjITAKaQ2pkLDikLQRCZwbO+iow0E8Cq6N1WTPYF5Mwc2Xx3VnB+mfaVXp6s9YIs2F7qRSZWDRWZmPqMIaB43sv70gdgeF8F83yTvgqhEUEhKCX9+bK+8vMJljpcTaoIoCDtzPt8lm1Iy1QwaQl4GFIjU/+8RDcMKdb7WUZaswTgGwQhPIICw382dX6t9QOE1Mx3sqxcAdLuB5N38vo8gFsIIe8mhEwSQt4BZuh/v5XuXgRVp1SxVNqpyejgfa31a2J+RQbUpX1pshT0x0vdVxlTjn/nyySnrK+mNoNPxUgDotCI2sarqQkolEjcapScAuxiqDNe3RKmiY5JvS6Dr+ERjDcbeoBV3FcZoDA6YDXRbIBoegOKYrczfWl48GmHRpgw0vh3aYS+9L212DEx8fzSSceUvodmX9MGAEy3hJGmK9HVAfhMmHJdTWBbzRBVb7zwMknRLR8vPSmsCVNuowMH0kUI2UEIeTMhRKi3IITMRI9vP2dNvYgrGJWR1isymXQ8v4qSraxvTmVGWtRXPiUQKFcqiDZo04w0wNwMPt9XVclpKyft5OPFF6NVvKKA5HqRN/UHovRVw+O4dZKlr4bROFfpi6evAimAL7cYNUnHBIDZiRaWu8N4A2G5O4BHkJGDlfWVl7hNtBqZe0rDUwBWEsn93ARTlvDXGQMwTT8LdPSDDOgYzwulgJX4vp8HHs2BjnyYRZAFtXnao4FJPcA90lKAqIFJPcCk1r3U3Crv+VW2kSlTDRWYXwYSSkDspSgaL9k8WhQ2ADCgOB82YNLXRMsvSNPFklMZYCVWM22ZTI4jpQywMu2LX7OGQYjeMBQCyNLxkvgMp4HHtf4QIdWXWecluiKfzrRHmrAvyebqXIopx6/5Zf7CvBoeKZ3PuQKkfQzAtYSQi6N//39gnh4fBrAM4J/AUjd/yUp3L4Iy8SIrB4bUXmS6EjfWlx4AE/el8iJrlDCsNMMGADYRM/GUU/VVHjYQJdVoLpJXu+UAjMybIV3NMgafCbNQE6iV7dDxilNOlZJT/eOoyyws9WTyyhfIJpJmXaC2nClXnnKqCzx6HtGWWpelF+qY52szmUw80kpSO3X6kt1s88XTfY3GS5FCyT5bzbDSYYcaSTtLJZR6YRYmAJ8JI00loQT0GHzlfemnNJeGbGhIYXuG0k7d+1BN9Utg8y3ZfyAA86H9j+esoxdxhZSdn8bMrwFnfnHzfH0gTclIWxuN+ZXvK7u4IkpgSGwGPxrAV2Dw9YtAms54iaSdrC8O8OmnBAKi8RJIKEu924rz3bmJFkKaSCerpCqy904APo9kmVyM8VjGLMwzv5qZfpYiQEFbqpj3busXk24bRL7xO5DcxzhQy4/fsiGgMN7ylIy0JOzJjJGWBx6XDJlMEzlvwKLpuvo+JJtXbploYr0fxI8bM9JyYQP545gAj2omU34zOh9mwQBRM+Axz3gUMpmknl9iqeJcLkV3uWtmwzDeasSAEKU08iJLn19q4FE2XlsmkvRVBiRTbNUEhoAs8Jj33wNSG5kKRhoRMb9SwCP3d6wq0c0HWQDljMd+EAqJDnMTrThsgI+b6XipygkgjVL6YUrpBKX0QPTvgwBuAPAHAD4N4AMAbqCU3mWxTadrqu2jH4RKDwteZVJFv8RQ2UwS2DADrMq8yEp2RAE9ppwuYEUpjeKtVVLYkrABLu3U6EtX2hmb1Cv6KjPGNmEWTrb1wgZiaZRSolvfcdQHrMqYcuodGKAK8Fh+HGU7h7x0Uk6NxkszRbdXxkir0aS+7TMvCSNG2oim/mWJnUAysdDzIhN7pSR96UmHdf0KgXqlimoJZWDGsDIAHkfqy5BZqNtXwyPx5xf70mBiDjQBPguMNABfC+DTlNI10YPR7z8N4OvPZVMv1jKVdsZhAwqGFZMqqu+PIgYMkCTfLRmawRc80mLAKrW4KrGwEHq3TWYllKYm9Qnzi33uqkQKW7ahk7/MJNJOtqiKF30GEkqgyOAbbxn0JVAUbM2N10p3aMT8ioHH+PwKMNnyM4CXStrJN2gLwNB4nlk40D7nAXGaaN57yPdUjKFoM1oAwADJec9M6s08v/KMtPR3kRAmP5V9H7uS+34eeDy71o/TFnX7GoYJs3A1L+0skQTK5pVzOQB5yZBhNV5gpIkllCYm9awvgYTSsK/OIPnMtX6QvUaUWKQkc3s5U24YhFjtGUqHmw10omtpbxgipPlNAM6wKpOcZvvaOpl4pJ1dYz+Nzq8UULsm2MxJNvAlQG0EWOWB9JkU8Mg3T4yuE6m++Ma6MOVUxUgTAWkpoJaHNPB7k1ZfLwYgTVSU0gOU0p+glH4DpfTHKKUP2+7J5TKZlJcx0vySCYCRSb3mwh1gC5CydEzVzqOMBivvywCwGkEKa+KRNtXWC0EoA2CAxBhb2pfRcdTtS+0VxfoqOb9KQK9sX5qMtBIJZbPEEyDdlzbApwHUljHSmiU3jvRjdQKP3RoYabpAWpyia8JIG8HUX9dby294GGt6WtJ03RRKdeqwmedXnVLFsvAPPbDd7B7kKYIVmhp96aZ2mnq3qTZNdCSn/UDNrI774h5855aRdj6A/SXPeTZ63maVlDGQlpMErgqANCZVLDP1z4EQDQ/TY35mgTzd9rXTYPMJc0KPNN9T3h+7gyJzX+SRNtEySAksAEMSBl+pR1rRMwdIATDdAVq+p+WHAyTzrhgQ7QdoNbzM9btsniPyuOVAB1/sLXb6mJtoGZnUAwkgup5LLwTUAJ8q7ZH1k7BNTL2i0pLT9Z6IkaZeIPPe05UH+BY7fcOFu4/OIIiltGv9YQHgY9YtcgaMTl9n1vuZtMbyvvLfxyxzrMzzK9moyjOZskDamTXzvtKMtHzYQJIyXzJeBcZjImmmlGK5QghCJzVPW+0aSjs1PNJWDMF2IJuim1zrs5sTqr5k59eWiRbOrvVBKcUZzrAyBIYKrGjR+SXzIhtS4ZxwVgBYbZ3UTxOdavtYjY6j6FqvkworurfMpABRfi/aYsBIKwsccBZI2yyzmjKR1ZQw0loNTwuA0QWG1vuB0pwZYMaCZdKmpl8iCdwAJlOZtxZQvlMbM9J0Pb9qAGCAKJxBh8mkGxph5Ml07jz4tDzSSszgCSHwiF46phabqWXIlCvx/CoD+HyPwNNYNE21fS0ApoxhZRSCUOP3sYyRZmLqr1NT7abeeGmmUNbRV8MjmGg1jKSKsveNj6Nig0Kb+WVk6s/8zWQLRF93vAwkuroAstJeQEtyGmj5YZowHmssCqBsBtkCoIcovMwrpOy7pfIqTVeRkSZIodSQBIo+L+1/VIXRwd47B1ilFldNjxiniY41GxhrevHiytTDioNAa6nFlUeyDIGy1M7uIEArN1yx5DRa7J1Z7RtJfUQSygJg5culipRS4YbOlpzX3dm1Qcye06kiszDISMkANi+UpipK5oQcSFvqpJlMZqb++b7yLI+GJ7ew6EnmXhx45CyY06t9bJvSX7iLwizy48UAZDXApxqvYRBiqTMwWriLvo8iQKGMkSZifgEMTOB9bTVkyg3CJGyn0JevnhcOJPOR2fEmQgqs9odY7jBJrMl4TaYUIEFI0RkE8UYVUA7AqMzzOwMmhT0dXSu2TZkdx/WYtRol1baKzC+5R5p4vLZMtDAMWfokl/Obnl/8u8jntyKJrmy9PQjEqo4tEy2s9oZsvFbNxyttBaUC0mTXr0FAC/57ADvvV7pDDIMQZyIGn8l5X7bBsgmkvURK12eISxXLGVYaTJMaZUhlEiRAP4VS21urN4x3ouR9qZk5ALu5qwAFfjHSlnb2Way6qmKAryxsQAN41E3tXO0NS/sqAxRYX3rebfp9lQOPZcwcgO2IKiVbgZ7pOsDOeyPGUFkKZQ2AAqDPEJWZ+/LSC0EIGOtIk/GoM15cHSJPhY3Gq9TUXw8nMAWQy9NqRwesAP1wmV7kdyM7Z5s6UlhNplzDI5hsNbQ3KMqCUoByDz6dvsaaHjzNkI2eBlsbqBfgO8ceaU9CIdsk7ET5egDPnLOOXsQVUhh5RfHFCr8Gy3xgVMCQzAeG+eZEgMJa33iBDKRSO7tFwEpHQim6X8yNt+LF3uK6mSRwOpecvNJlTKaMVFEjbCC/6Gv5HqbafiKPWq84Xqn01QJgpZivxuqJvGQrZvAlrI5RjuN6rwjwtRpyQFTO/MoCfKwvA8AqL4UVeKT5pNwMXuRhxftZ7w+x3g8MF+7Z9claLxAy+GTSOxUwBLDzaqkzAKVmC/eJPCOtmx2vhsc2fsuOY9GDjzPl+jG7sEpfMaM21xff+JWbwYtVQ5whurQ+wOm1HgAzAGY6tQ4QMb/4hqFUQik575NQkkHMsNpmcN5PtBoxgJb4YepLYQeBeJOcyzjTfRldJwTSzmkD5pdMPbFzmo3NyZVeAjwa9JX24l7pFQE+LoVVeaTJpJ0AY9KeXevDI2bMwvFNRtrLo0wBqzKGVW1pj5r+NDoMqzLAysSkntOGy2RbZabYQDkwZMJIm2o3EYQ0BspkxW9kKu12swx4NGEWjvlGfak80po1p6/qSHT5cVSNF5MWqMIGqDbQMa2ZVtst6avhEZAyppwm0AGYADBqRpquF5lOqiLrSy/llAPDo5jn60ooWV96Et3OgHlrydiKdQJWQATUagJ8qkmAXzJhAgwZfJqhN51BkZGQLp3xki3c8xVLh7UA5FCrrzJpZ92psDXW3wK4ihDye4SQ8fQD0b9/D8CVAP7qXDb1Yq2AAtsNFnx5WbbI1N9vEIQU0k0+aTLZRCsGOk6t9rBjWn/Blzf152l8WcCqxMJCoiiYywF8owEdw8yCD2CL0TLJvOjrODeRJCuaAo9FL7KsGTygnq/KAKs57pG2lhhjV2EyxX31s95a/DOlqYoSwCovVTy12jNaIOcVM+u9okdaQzEvlI0XP8dPrPRiBsx2A6BjOkdAWM+lPQLR+WUoVeR9nVrtJ1KyUYDtnqAvBVMuAYZyAEwKqD1ThcmUkjRTSrHaz0owS6V30RqtcBwjFuHJ1TQAYyAJTM07YmAo3VeJtFNGDtkZn19dnF5lAJ/JdSJtiSNK9k025uTAo2zTBGCS4UpAWsobUCTt1PGUE/V13swYAOD4cg+nV3vwPWIEWKUtXqpIOwcSaedcymLgTHRN1VHw8HrReqRtllnld+5kpcP8aip2rNLvoQsMAeWLBT6BUy76SiSUpow0oBzg0wGs/BJgyCRsgF/MVkpABT5eSgZfzRJKnb7KABjWV53MQr1UWJ3zq+GVAVZ6puuAPqCgC4iWAY+6njOmfclTKDUBmJoBvj6XdpYx0kqBIV0GiV74R3fAAJhSqWIJU073OE5rMgu1AasSc3Pd4ziZ8rhQVXcQYKzkWs/6Uh1HdThNuqbH9DweO/2ShGYNyalu2IDf8DDe1GPw1Vi/A+AhAD8G4GlCyF8QQn6dEPIXAJ6Ofv8QgN86l029WCugFNsNpGR51uZab4jxZiPjZRYvFmRJbhJgO53kdmq1ZwTwNTyCtu9ljJ7zSZG+p5acDiQhLltyAJ/JeOWTbVcF3lqqcKwgpAgpYzuJ+jobSygNzeAF4QwiwMpUSjbd9uF7JO6rMiMtlpwGhaCCZsNTpCqKGUNbJprwCDt+vWGAle7Q6PzKezjn0wsBPY+0fF+TbR/TbR/Hl7uVpHfFvoLCcfSVDD6x2mTnNAcUuomUzAgQZT10BkwFIgLSGFPOjMGX9iysxmRiPXQHQQSmZYGOhkeUIWdckpe/TsSA6HK3miQwpZgR+U6WA3zi84sDQyeWezgVjZfpec8TeIVMuZINQ1kwVnq8zqz30WyQwvmhqokoTZRSGp/7+c0cQA3wie5BO2JGWhdnomuqCWCVtngRAnwlkmbZJmYM8C11cWqlZ3TOA5vSzpdNaTPSYgBGLaspY04AmtK7seyOj6z4BE4JWJUBQ5KbmqqvMlChowN0lABDA2oSgsBZCurFuxbA1/CU0s6BZDdNVLphFmWm66yvEsmpgXn+9JheWq3O+aWKXwdMvbX0pLAd3e9jmXm+QV86TKYEcBe/rxdJC9TfRxOGlR4gOgjY5ENmoq3FlDNipDW1xqsjSGAT9VXGlNMFhiY1wxm6JQEuOgCfCSA6rd1XoE721fEGNDiOuqnD3UGA8RElpyxswIBZeA490iilHQDzYIyzXQD+JYCfjX7uAvAXAN4SPW+zSioIzRZWAAN1uQ/qmkjiVuLD1w+o8J7NGGkDhCHF6dW+ESMNyPrmrPYGQgaMmpEmYU9MpgC+FTMgLT/vEAJpDflGE/+eShlpnRRTroLELZEqBgVGmq/wGpZJyQghmIsAPkopY6SNAvD1hpgojBcpBWDyffkNDzum2zi21K1oIs5TLFOMtPx4eYpzPgZgBIvk2TEGpK1ySaA5ULvaG2IQhOgPQzHwaDhe460GZsb8CEhjfVVJCez0Q3QHxbRHoIQpJzHPH2810PY9LK4PqnlrpRhpIqCDfWa5dDg/z4kBq5VeIu00ZKQNAoreMExJKEWSU7VENz9eO2dYD8dXujizas4snBlLLIS4TFcE8ElTTiVznN2zjEx+dKmL48td7Jhqa9sLAAwYohSZ8TJiFg4DJSPtxErPmLUKcA/nLPAo9EhTnF+ivvbOsfE6vNjBseUuds+NF56jqs2wgZdJ5XfuZNUtSS8EuLRTxyNNJ5lMT9qpw7BqllD4qzDSysYrBmCU8ig18Ggq7QTKAauuznh59Xnd6RqJy+LAs32VhCAMzI9j6XgNNRh8Xn0eVpPt5Mau7IsfRyU7pzxkQ5+Zw5hM2h58SkBU/X3sGQOPGim6IS2RDasp6YAZIMo80jQ8+PrqABdtrztDoLasOoKEv2xf5YCVbjomYBbioiM5lQF8lFKzvrSl1nrjVfZ91D6/NI9jnUUpXaSU/isAuwF8E4DvjX7uopR+L6V08Zw29CIuJu00A6yyu+5BxjMH0PSnEcpXmM/kqbUehqEZUw7Iyn3WekFhgewrwga4eb5oETM73sJSZ4BOP8CaoYdV2/fgeyTjf5RP82s1iPQ6we+/onRgDjwOghAr3WElQCEDWOUZaYrxkjGZAMbqOLnSi8AdasZkynukCRhpLZW0UxGodN7MGI6nJJRVmF8r3QEopSMw0kR9tXFsuYuTKxyAMemLA3yDxAxexPwqCWeQjtdyF8eWuvG/dSvxSBvGKpAiYCX3BlRtknNJ86kKjLSJFFArAjoANgeTAkMSwGrbZAsNj0SAqHlf0ylAlK8F0n0RQkoAPjHZYftUG4QwRtrptR5mx5vaqgGAHTNK2YaJUMZfsmkikypum2yh1fBwZKmDo4vmwFD6OiFm8KnnhTJT//RxPLLYxR7DvrgnMWfK+RFTmldZWu1gSMXfxVl2LzyyGPU1q/9dBF4k0k5CyC8TQt5tu48Xc5kyhpRARwnzK7lI66QEmgFDamlnfVJF3fHqaoYgKJkmIX+eXqoioCHtjDRupQCfxnjppFDqMvh0AL5yZmH9El0+sVT11SDlTCadsQJSHnw1SIfLTJ5NAb6QJgsAaV/DQMn8AqIdUckEk/dlAvDpeaSVB2wANZv66zCZhmoJZTxhqon5ZRJmUSaz1upLc/I42dKXnOow+GTfx2FIQanetR5g573OeJVJYfUkp4ZSWA0Pvo0oSulJSunHKaV/Ef08Ncr7EUL2EUI+RAg5QgjpEUKeI4T8FiFki8F7fDsh5HcJIXcQQpYJIZQQ8mcar7uZEPJxQsgZQsg6IeQhQsi/I4RIDyYh5PsJIXcTQlYJIUuEkAVCyDfp9gqwCNTthsyv6dR3d03AsCpbxMjkPpyJ8+SxFQDmAN94JPcB2AajSErGvnfFvuJAJcF5v2WiicX1QQx07DDoixBS8D8SpVDKUof5fVN02+ZS2IRhpc8YynvKySSUqgUyIJ5D754dw5HFFDBkmBKY7ksI8CmlnXIA5ryZMSaNWjUHrNIb170hY1jl564NT65UkDG/eF8nlns4stiBR4BdBovk6ZRihlsS5IFtFVArYxYCrI/jyz0cWeqi5XtG45U+v5JkX/1UWM68kkmtz64PcGypg4ZHKnkprvfFgBXrS3HeS8bL8wh2TrdxfLmH48tdzE00te+hQAICrXaHwsRh/plSjzSJp1yzwY7biRUGwOw2BGASoHaIdQFgxT2QVQCy6NzyPIJd0XXi6FLHuK80ILrWG6Lte5nPaZbMC2WbOZ5HsGOKHcfDi52YCaZbU6n1Cb83ppl2/JxQhg0INtrbfgM7ptt4/vQaTq32Ykafbr1YwgZ+CcArbDfxYq78zp2sYsZQCdNEy4vMQEJZ6pHGgaEyqWJNJvWThgBM2SI5UIxXELKetNIe41SvEqmiLgCjYgxFtGGTvsoYfFphFiWS016F86tskawDWKl2RAEmL9BNe9QG+GKT+jJpZ11SRU3AfVBu5O4rJpiAOWDVHYTK8QeYtFPNpuVm8GVhA3rHcbKtF2bRKWGk+SU7aYD5cdRlWOkAfLX1pQnwdfol3m0lXmQm13pAP321U+bdpsHg2whvQNeLEHIpgK8C+AEAdwP4HwCeBfDTAL5MCNmm+Va/BOAnALwKwGHNz/5mAF8A8GYAfw/g9wG0oh4+KnnNbwD4MBgr738B+DOwOej/JYT8hGavABJDat2aSoG6MqkioF7EiO4XF2ydAAB88RmGh+7dYi5f4Yu91W5R2qkC+FSbclsnWxiGFPtPrgKAseQ0bUew2h0WmXIKqwi1tJMx5Q6dZSpmk8VVPN9OHUeRhNJUEsj6GMOxFJPJBBhKM00Y8ysoHMcqnl8AY34dX+niaNSXCQtmJgUocM+omdxxbGikdgoBq4j59fyZdeyaGTNjDKXCBlbivrKAqo60UzTf3Tk9FjFzGNBhIr1LS4cTwCrbV0vlkcbn4QLVEGekHVnsYtfMmHKzVNZXV8VI0zjvhYmP0XGsCsAA7Hu4ImB+AWov6/j65YmP47GlLg6dXTfuKyZs9FKMtBQgSghReiAPAnnAGQPcOziyZM78SgPuKwKWb9k8R+XLfOG2CTxyeAlLnYHxPShN2BBv5qitNRjAJz6f98yN4ysHzkR/NwMer9kzo3xc351uY+swAHWnm6Ws/M6drLjETcXq4KmKlFLhxX8jJJR6zJyShXsQxGaXun3VwpQrY/CFFG0DM3gApYCoVtiABoNPty9ThtWoktNWwwzg05GcekTNvmsoPDqAaoBVuTegOiUQiBiPJYCVtkn9WPJ93KnqqyTtMe6r5vFa6w3jhB1R9QKq7IsQEp339UhO0x58KgBP39S/nuPImXKya3S6L1Vqkrbk1MQjrTcEwzHk1R2WmPqXjJcpkDalGc7QG4Ql3m3lDL6eIfDIF/IbUYSQD4ERp36RUno8+rdOUUrpDxl81P8EsBPAT1FKfzf1+b8J4N8D+FUAP6rxPv8ewCEAzwC4FcBtqicTQmbAgLAAwDyl9N7o9/8ZwOcBfDsh5F9SSj+aes3NYL5w+wHcQCk9G/3+18HAwN8ghHyMUvqcRr8xgKVbM2PNGIxY6w0Lki+V3CcMKYah2Ivsom2sj9ufPAkAuLBCX8spYEjESAPYud/K7cGrgI59W7IA3/lbzRfJq6q+FFYRnHkl+jpumWiCUuCJY8sAYLQYJYRgZryJ5UiquNwZxMmWcV8KAKY3VANpZ9b6OHB6jfVlAPCxwBs2Tmv9AEFIMTNe9NYy9UgDGGC1uD7AMydW4XsE5xkAohw8WO0Osdxhx3JmPB9mAXSkTBO5R9rF2ycxDCnufOY0Lt5uds6nvaVlfSkZfIrjuG/LOI4td/HsyTVjxpDIi6yYClvOxBQxHndOj+Grz58FIcQYUBhvJX1xuWtRAq6QdiqO457ZMTxxbAW+R3Dx9kmjvtIb6vxaUZCA++rzvtkgQmP8i7ZP4PGjKzi12sPrL95q1FdaOrzUGWCs6RXmkU2FB7JMLg8AF22bxF/dexCA+T0oI+3siq+pgBqwym8A8brivGn86V3Pxz2aVBovWBNd66MLuTRsQDGHvvK8KTx4cJH9fde0UV/f+brz8V2Kx11hpP09gLfl49g3y6x0jMQ5w0plnsd3RIMSo1Qt5lcruYEq+4oBKwUzxys3u9X3pokkp7ppoiMwv4ah2YIP0GEMlUt0G57c7BaIbh6mfWl78I3AsDJkdAB655cqVRFgYQNqhlWgD4gaMNLKEmF0pNYm3lqAHiBaBvCpJgCAoeeXJrOwH5b7FeiEpYi8VoR9aQPuoV4KZSlTTn+8hqGOB19J2EAsoaxJ2tnW9OArY6Q11MxCk3sQ70s33Vd5DyqRnFJKzTYo2nqS5hHqPdGf2dy/df5oFSHkEgBfB+A5MDZYut4LYA3AuwkhpbNqSultlNKnadkJlNS3A9gB4KMcRIvepwvGbgNYEmm6OKD3qxxEi17D+2+DMeu0ynQRkzZUFkk7+TktWvTxeYbovN87Nw7fI3ji2Aqm2r5xMtnseBNLkfm+mPkVLa4EoIJK4sYXxbc/xQA+DqzpVjq4R8SwajY8hFQ8X+V9iWw1dkUA5j0RS8GUbcLGa4jOIMAwpAIgTX4fkqUqAsCuCDi7/4XF6N/6YIfnEUy3fSx1BljuyBlWZUxf0Ybj+dF5fuczp7B7biw+H3TKb3iYaDWw0h2kGGnZvhqK8CKVCuaqXYyDcWq1hyvOM1sgNxsexpoeVnvD+NwvjJcSgKHxc4p9TYNS4LGjy7h0x5RRX+MCL7LpPCOtBBgCxN/HS3dM4chSB/tPrJozmVIAzJqirzLml+g4Xr5zCgdOrWH/yVXja0R6PbcWA48G0k4FAHPZzmkcOLWGle4QFxoCQxzMW+6y8yt/jQC40sqcYXXd3oR7dPVuMx4SB/iWOwMhK9qLCCkqCaVsvK5IgVTXljC5in2lGGmCexA/b0zDBgDgFfvm4r9fvtPsOlFWrgBp7wVwFsA/EEKus93Mi7V0/Hy0mEwliwUT6Z3f8DDebGgzrFSsDxVlGDD1ptEMQdCUdqqAjkpAWpm0U8PzywbQ0RsG8D2inGSVhkYEgRGgAKBU5qbDsGp4NUq2dD0LS7yiAD2ptQmgoNNXmfSO9VUegmDCZALKv4/9gJYDfBphKebAY3mK7pji/1pGSQfMwxkAveuXmh2qJzk1SaEchjT2hZRVdxiqPdJKvMhkpsWymm77WO3rhGyMKDnlUp+Sc5SXDot8xLoYwCVgMkv+b50/lxh8xlujn5+mlGYOGKV0BcCdACYA3FTtv6D12Z8UPPYFAOsAbiaEpOkzqtd8IvccZRFUkCqmjvlSZ4C5AjNHfo6ppHd+w8PlEZBwzZ4ZIykZwCVfAwShGLBqxeC2AOBT9HXhtgkQAjxzYhXnzbRLN47yxcMZuoMQQUgFUlj5tTWWdgqG4tKdDNy4/amTmGw1CsytspqJgMeYyZQDYNQLZDkzZ18kh7rj6ZPYPmU+XrMTUV8csMoDfL56gSzri7M4nji2Ysw0ARJAlM8h8+Pd8MTnFqBmWF1+XgJSXb9vtvB4WfEU3Rh4HM+zc3TGq9hXmvViCnS0fQ+EcDN41peQkVbC/BIByJftnAKlLKnWFHgUAXzFvuTrDpW0k1+7QloupctXsj5hwFDe8wvgkmY5sC0FhlLn13V7zc6vmdS6SQakNRXSdBXAd0OKHWcKWPFNljPrfSyu9zE3IQD4FOtamak/AHzNVUzrcsHWifhapltTKUD07PoAWyaKmwCAIpxBkhwNAG+/dhe2T7XwHa/dV7oWNC1XpJ0PgmlBXgPgQUJIF8AJMElCuiil9NJz3dyLpXR8YBJGmvzQp2mdopu4ifQO4BOh8oUooJZQlpmu9wykUQnApwc8qg3O1RI3mRRDVGNNDw0Nr7vOgJnBq7211Iw0E6Cj7TfQanhajLSyiZ+O5NQk9Q7Q80gr8/xiYQMqgC/ErGbYQAzA6AB8pcwveWoUEAEwppLTkuO4XuIVBWgAVkFY2FGS9jWmC6SpE06BehmPk/F4lXlPqoFaQtgOX6nUugITU2UqXh42oBnOoJHQDCTfx04JNlQG1CZ9lQBWBsA2pZEpuESSQCktZYhqS05NJLq9colu1aKUPq/6d011ZfTzKcnjT4Mx1q4A8Llz9dmU0iEh5ACAa8GAwccjVtxeAKuU0qOSXhH1WlrtBoyP2/SYj7V+gEEQMiBtIg/AKIChkoCgt161A48fXcatV+ww6glggAtnKPA+s33J5T68L9F1dazZwMXbJvHsqTW8wnAhCrDvyAun1xPGUB7oUGz8qjzSLtw2AY8AZ9cHePUFc8bHcWbMzwFWIgBGbJGiAhQ46HJ8uYc3XqZrLZjUbBnAp5Deqfq6ZHsCKFQ5jjxkQ8aU8wkpD0EQXFfHmg18700X4FOPHsfXXrPLvK/Is1DGlFMCVgpvrYu3T8YJrLdcvt2oJ0IIJpoNrPWHWFxnfeUtL1RhT1ylIDqn02CjKTCU9m7j3zcR80vFsPIIhPY7r7kwyaR51flzRn2lNxaXOoMCeMz7UklOZXPCWy5LrqWm531e2ikG0hTSdAXAd9WuGbz3nddgx7Q52M4BqrPrAyyuD4TMxDIJuMxneM/cOP7ux27Gjql2pXsjwDaYFtf7uCYHQJeFM6jsZHZMt/HFn3+r9pzRpFwB0jwAAwAv5H6fPwr1zzRfQjXV9nE6SiCSlY5UsXQRI0mNklXim6PoS4f51SAIKfMJEWnZTRaigJ4xdncQgBD1gq3Mu21gwEgjhGgzC0slgYqdNMDMwwrQY0/o9KXaqQUqMr90pIpljDSiZuYMhvKbrayvUo80jb7KvQENmF+66av9AOOlTDkNhqiBJFCnL8ZIG43BZyKh1B2vziBQemsBkdmtpC9KabXQiBEluk3F4phXFeCxqwB+KaWlKadxX2UMBdPzSyBj4NUbhqC0nOXL+qonBGGy7WMQMImu6WS4ShFCngXwCUrpj9f4tnx1sSR5nP9+rsbPrPrZI/dKCPkRAD8CAFt27sHCwoJGm0kdP8QWxv/wqQWEFDh5+AUsLCSY3uMn2Hf67nvuxamns+fE2S47vw7sfwYLgyImer1P8W+ub+NKehALC4eM+jp9tI9+EOKfPnsH6/OF/VhYSKbk+w+zvr9455exYyJ7fh9eZX099eTjWFh6Gvm6crqPZ08Be8ii8XitnOnh1HKAz3zhSwCAIweexkLnQPz4c8+xvhZuvwNTrey88OmzbF456HWFn7t3ysPBlRBbsWrcV2+li2PLIRa+dDfr46nHsHAmwXMPvsDm4p+/baEAHDx0kh3jhx+8H+vPF7/3W8cIznQpZoJl475ot4ODq6u4855lAMDTjz2I4eHkM44d6aPXHwrf96EjrK/7770HRyaL17BX72zg/hMBtnQOY2HhmFlf/Q6eP9LFvcFpAMAj99+Dw2PJZ4TBAOtdcV+PR8f47i9/CRPN4tz/bXPAW29u4KF7vmTUEwDQAevLX2cefvd95c7M8Tp7uoullVDY1/4DfTQI8IUv3C587597tYfTnTEcePgeHBA+Q15jXoinDhzCmWMEBMADd98JLwVMrCx3sT6gwr6efa4HD+KeAeC67Q2cWA/RP/QIFo7or+VCSuGB4pEn92MQUrQ84K4778g8p7PWwbH+mvCzn4nGS9bX2y9qohtQHHrsXhx6TLutmIH34GNP4bnlEC1a/L/3O+s4dlx8HXjhUA/hIJD29fM3jKHpAV/50h3Cx2XF50EPPvokDp8MsG2MFD5j2O/h4JGjWFg4W3j96bMdTDaLr+F1MQCcARYWintYq6vyaxoHQe975EkcXxrgoole4bk0HOL5g4ewsHCy8PrltXWcOVl8TbpWkNDgdWsxur/ddf8jOL3ax+qZY4VxaRBg/4HnM/dMXuvdHk4cO4KFhdOGnzxaOQGkUUovst3DS6Gmxpp4/vS68jl6gFX5IsY0mni1O7p5fnpx1fYETDnDvnQBvjJvrTLvNhNpJ8BZChpSMh3pXU3ML4BRuPUAK/V7qrxDALPjON5swCN6Hlblpv4lzByDtEcToEMHgKnrOJow5bZPqT12VF4rcV+G0s7SVNig3CONMwFExc26dRlWJhLKckBUPl7DkIJSfWBIB6jlDCs1y1edzlQV4OuUMCgpVcsfVYbrQDXmF8DG6zyJCqLHE62VfZVITg2929KpXucCSAPzE5OBSBtV/Oap63vmwmdLn08p/QCADwDAlVdeSefn543e+MS9B/GXTzyEvVdeD3zhK3jtK67C/OvOjx9vPH0SuO9uvOJVr8YNF23NvPbgmXVg4TZce032Nen6eqNukjo8/jz+9qlHsOPiq4E778PNr30l5q9KImmWHjgMPPwAXnPDjQXPp0ePLAFf/CJe9YrrMH9tkRV00xsDfOezp/Hmy3cYpQQCwFe6T+DOI8/i0muuB+78Ct54w6tw86UJw+fgXc8DTzyCG9/wBuyczvqJtfefBr5yF6YmxiE6Tj81dRC/8rHH8O/edRNeYSgL/MzZh7H/kWO49KrrgK/ci1te/zq8MsWkeRz7gWeewM1venPhGtx/9Bjw1a/i9Te8TsgK+nH/AH7tk0/gJ995k7HM7W8O34fHjy3jwssuA+57EG954024KGXefk/vCXz2hWeF43HinoPAQw/hTW98g9Az7lU39vHMiVW8Lnde6tQH938Fq70hdl2wC3jsCbz9rbdmxuVPH/sUSIMI+3p8YT/wxBN46/yba79OfnD/V7DcHWLreVswdfAgvuatb8k8/k8nHsDh3hlhX3euPYbWwReEj41aex7+IppTLcxtncDMkSN461uyff3pc/fg2HIX8/O3FF77+aVHMHbiiLSvW2+Vh5aU1dRt/4zpHbvRH4bYdvZU4TN+/4kvwfc8zM8Xlfy3rzyKsaOHpH2NMowTt38Sc+ftQ2O4hL0TwPz8GzKPzz18B2anxzA/f0Phtf/n6P2Y6S/J+6rYE6UU/uc/gW17LkB44gguOX8r5udflXnO9L0L2LZ9BvPzrym8/tcevAPnzY1jfv51xp+9sLCgPC+nb/8UZnbswfr+53Hd5Rdjfj5Lwp6487M4b9dOzM9fX3ht40ufxfl7xY+NUoMgxL9b+AQmd56P/mP7cf2Vl2J+/rLMc8Zu+xR2792H+flrCq+nn/8kLr7gfOFjG1lOAGmbVU9phQ1wwEoBdrRKFlcmC2Tel46EsuV7yklWenElIhUMDIEhxrAqkWyVMCeAcu+2akBamZRMAxgq89YKqHbYAOurqZFCGWCiqb6s+IqULcAMGIoZfFpeUer39IiG55dmX7oAX2cQCune6Spj8JmEDUy262PK+Q1PyswBDCWnmsyvfqiWf/O+yoAOkdeKqCZ1AVENRpFf0TtEVDqhEf0gRFjCsIolp5LrRBUJJaCWdnLASmszp8xM2RCwUo1XXWxtwBzgW+upJbo11qMA6rbH4MCcDImYyT3P5meXPb+MsTZybYv8afafZKmMecmWr0hMU5nUj1pz46yPZ0/xvvT9aVSm6wC7Br3lSlVOtLy2TbYwDCkDEQFsyY1XUwG6q6SdAPAdrzsf3/7afZVk1dwjLZGcFsMGALbxO47sNSX2lJM09kNvuhjvvulCo3ljuq/ldNiAwINvGFKhskPl+QWwc7UKiMb7OHy2g+XuAM0GKczHfE/jer8B5/2OqTaePbmGpc6gIGcGIgmlwuuuyjHSqS2TLZxd62NmrCn0sCqTUKrGihBipCpK11SL4MxqH8MwLHwXdfraiGsXAGybauH0ag+L6/2MDFmnL1OVlW4RQuK+VNJO5XhpzlVNa26yiedOr4NSFLzIeF8ySxmThHmTajY8zE00sf/EKutR5N2mWG+bBOjVWef+EzWKEDJDCDk/ijbfLM2a1pHe9QM0PKK8mOnsuhtLAssYVlrm5uVGz7UDfH3NBbIqjS/UTxMFEnNddV8aAJ+CmQOYpVACeglzOt5aKtNPwAyAAXgK2uiSU1VqlGlfugCfjoSylMFn5HXnodkgI5vUA+z8UgGivQqeX2VpokzaWQLUKsxbTYJSgCxjSFZByJIaS0MjPDnwWBWAUfXV1WBYsb7kjEfTvnh6VlfxPdJjH9cXeAMAk63y8dJJji6TnPYMpZ1TmsB2jfU7AN5JCKlzK/nJ6KfMV+zy6KfMQ21DPpsQ4oOpX4aIVCaU0jUAhwFMEUJ2C95vI3sFgBgwlS0WmgqwtgyAGaX4Iu/5Uwywyqd+8o1M0SLG9DphUtsiZvQzkvFSzQt5X6rhqupNODvexDCkOLrUBZAYi8d9KQC+fsCuNarFaNVjPBsDfGKvO/6+4tCIjTuOO6baOBkBHbPjzcK4N4jC3FzhrTVqbZ9u49RqD6fXevG5lq6mwgNZlV44am2daDIz+M6gALYDDLSW37fN1hwmNd0kOLPex5m1PrZMyrzIzi0AAwDbJts4vdZnJvWSvqoCj6PU9qk2jix2sdobCoFHlXJoI/vaOtHC/pPsmirtS6pKq8Zm1Kltk634Wm8C1FJKGbC9QX2pyhkgjRDSIIT8AiHkGbAEz+cAnCWEPBP9fpM9V1JTbZ9FcSsWt9zcXClV1GGk1QzA6DCs0jt80r5qBmB0vLV8z0MQUmkiXEDNJkWTuh5pIzBzgArjNeaXpxfqAENefSmUvK/y80sDSPNKwgaGgTSKWlTTY00NJpOGRFeHwVezB1+ZtxagIx0OjIGOMkZtP1ADHUB0fpWYA1cx9ZdVb1jOZALUaVam3lqxtFMJpHHAqgyo1YirN0xDVjHSuhqAVdlmjimgoOOlGI+XQmqtKzk1ZfCVXSdqrEMAPgvgTkLIbxBCvosQcish5M35PwbveVv08+sIIZn/OCFkGsAbAXQA3FXL/yBbn49+vl3w2JvB0kK/RCntab7mG3LPqb22RymfTx1fAVBkA8TMLwXQsRGLGA6cPX2C9ZVfvPOdfpEB9Ub2tW0yAh4jBl+BkaZME+VAWv0ADAce959cRcMj0vEShzPw1M6N6WsQULxwZh1zE83CMVEDtRvI/JpuY6U7xKGzHSH7VpXauZGA1fapFnrDEAdOrRWkwUB0fxxK7kPD0GhOaFKMkcZM18WMIVJiBr9BQFqL4MxaH4vrEoCvQeTjtYEMvu1TLZxa7ePsWl/YV6sEEN2ovnZMt5n0HcB5M8XzvqlQdmwk8Lh1soVDZzsAgJ2yvhRkGl1Vh2ltm2rHrOjzZorfR9lx3MhNprJyAkgjhLQAfAbArwK4CMBBAHdHPy+Kfv/Z6HmbJamE1SEHO9Z1mCaKiRzAmTn6PgW6wJAOYAXUF4KgKznVBvhkuwqG0k4t77ZBgLGS91Qxc4CNYfCtD4bKRFjelxKACQxDI3SYXxrHsTRswHASoBUaocMsLGHwVWGIljG/OoMgTmiS9+VJJ76mfXleOcA3CEIEtBywKtvhA/S/jxOtBggpkQRy38kR0kSrMplU49XVkCrGfZVIO00BK1XYQEcDsCq7plZlyqnGK7E9qC453cjjWFMtAHgHgEkAPwPgL8BAo9sEf7SKUrofwKfB5mr5EIP3R5/1kYgNBkJIkxByFSGkDonp3wI4BeBfEkJiIxlCyBiA/xr98w9yr/nD6Od/IoRsSb2G998D8Mc19CYsLu189MgyAGBnbrHANzJFspoy6d0otWeO9fHI4WU0G6TAsGoq5l8b2RcH+J46voLxZqMwf1UFpvC+NgLr4GDQY0eWsW2yVWBLNRUS3fi6ugGLZO5t+tjRZewQAFaq8YoluhvESAOAx48uY8d0sS+fAJQyhne+TDfvTYofx+dPr2OnoK8ywGqjpGTbJltY7Q1xZLETg8npUktON0aqCDBp59m1Ps5IAT75Rnl/A/vaNtnGgVOrGIY0vsamSyUJ3EjAasdUG2ej5NX8tR5QA4/9DWR+nb91Iv77vrmJwuO+RNHEmF+hkZrJpNLXLH5PyvQlOY6DDbwHlZUrLK+fAfPz+xiAn6WUxrE/0WTrvwN4Z/S8/89Ggy+GSlgKA8wKLnAAm5SXLZBLd90Ds10YHSZTZxCUgifJ4kqyiAlCzLbUflP5vuqQUDa8BHhsCbDpoam0UxMYyksv8qVi5gDmkxOdlFM9YEgeq8z7agsSo+R9NWOfEmlfOgyrmlNOdcIZtFJOFQy+MKTGNOvJltpLMTapH0ESCFT1UpQfRx1JIKD2bjNlWBFCMFUyXjrAEMC+j9K+DBdW3INvVM8voNw7xKSvWNqpANK6GoBVmXebKSDKmXJKRppGEE+Z5NSYWagh0b37wBmt99Ks/4KNMf3/twC+BOB3CCFfA+BxAK8H8BYwmeR/Sj13b/T482DgW1yEkG8B8C3RP7lr/RsIIR+O/n6KUvof+PMppcuEkH8NBqgtEEI+CuAMgHcBuDL6/V+lP4NS+iVCyG+CzScfIoT8LYAWgO8CsBXAT1JKn6swBlo11mxgeszHUmeA8WYjDlrhpWSkbaCEcna8ifFmA51BgAu2ThRUC6r5F+9rIxZ9HOg4dLaDS3ZMFh5XzVdjYGgD1ny7Z9ki74ljK7hWEAig8lM0vX6Z1J4oJODxo8u4+dJtgr7kzMLecOMWoxw8O7XaFwJp/NQZBCEauTCxjQSs0uw4UV+lzJwNBvhOrfYVgMK5lwROtwhOr7FE2h1TEgafDWnnVCu2s9gjCMpQ9TUIwlICQNVKs71EQK1qfj8wJBWY1EXbkmvprlkB88sXrx8Dw2As07o4FYwiZYgq7AU2qi9VuQKk/SsAjwD4FkppZoQopfsJId8K4AEA34NNIE1a0xqTcl2gA1AYf1ZYIPeDEL1hgLZksakjodTqyxSw6g5BKZVKXTuDoGDWWuxLvbgyDRvQYfBpMaw8gpBCaCoLRDp3Y6ZcCWCllV5YLwAz3fZx+Kw6rbY7KPewUnmkBSFFYJD2COgBfL1BWD5evmICEJpJyYByL0WeqlgmHVZNMIeR0b3ZeKkB5K4m80vl3ZYAVoZ9KRlDkReZFiOtHmBIx4OPM+V0vO7qknaONT14RC3t1AX4dLzbdBd8WtLOYfl46aZ2mko7VUDter8+Rhql9H21vVn2ffdHjLD/AiaZ/EYAR8E82d5PKdVFA18F4Ptzv7sk+gMw8O0/pB+klP4DIeRWMLDu2wCMAXgGDCj7HSrwXKCU/iwh5CEAPwHgRwCEAO4D8OuU0o9p9lq5zpsZw0p3FefNtAtzD7W3VnTeb8DiihCC3XNjePbkGvZtKS5EVWzMjZTVbJtqsWtBSLFnVrxAlvdV7pFWtdKLdTGTyY5EN70oFgFDqjAxzmSq6hunqjLAqkESoDZ/DR4M6YYxTdLMHFFSabPhIYyYcnnWYX+4cYyhC7Ylfe2WnPc2mEzbxpIx2Cu8TpD4/pyvjQSGyo6jrdCIy3YmwQdpkIhX0/ew3hHf4zeSWXj5eayv8WZD+H+XMdLKgmXq6qvVEIcPygDRjbymlpUrQNplAH43D6LxopSGhJBPAPjJc9vWi6t0ku90zeABxa57UA5KZPpKyVfaU+LP1gH4dPoykgSO+RiGFD1F4l53EAh17Zm+Shh8w9Dsyz015mOtH0gBMMBMcjoMKVqC9zFmpLV9dAehcrdLi8lUkibaqwKI6jC/Ss57lUeaKaAAMIDvyGJH+vgwCNl3qZTJpKakA2YMham2j1OrfenjHQ1mDlC+UwsAbcPrhIrxuK7Zl0pCaQrA8L50vLXKgSENLzLD46gaL+2wAYWXoqlUkQN8HQUjrafZV5nJs0lfbb+BVsMrAR7Ze+ow0mR99aJzQReo5Qw+FbOwTmknIeQCAIuU0mXFc6YBbKGUvmDy3pTSgwB+QON5zwEQfgEjoO99Jp8bve5OMPDO5DV/AuBPTD+rjrp85xSeObGKS3eI0+UANTC0YYv3rRN49uQaLtxWXPC14r7O7SKm2fBw0fZJPHNiVcjMaWr0tREm9Vsmmmj7HnrDUAgo8GuFSKK7kRLKNNgoA4YA8WbAYAMZQxekgI6LBOcXv5TLGHwbBXSk+7pi13Thce4FJWPKbVRfadAl3SOvlsJaw5RUYFLnTSTvKwLcyyWnG9NX+loqGq9yaefGAFbX7kkCokWsN9XG70aO1+sv3oavu+Y8fN21u4SP+5aYX7dcvgPbp1r4sfnLhI+3JMfRdE5YZ537TxRXH0BxRpGtSQBqesfLvHQSwHTM4FXx60A1AAYoT0zTWYiW9mUIdNTSl2JiAlTzSAOAtb560dfW7UvpdWfGlAPK5WSl3lrRTocsnKFSM0AP5wABAABJREFU2IDinOepiqN4pFUGYFTfxeg9y8zzdYAhs/Fq1iIJVKXVVgGGpksYaUZSxZpSFYFyplwiOS2XptcJ1JZ53emGDWilnBoxHpvoKrAf3eOoknZWOb8m2w0tLzLVeJVJTk0B5LEm23VVbn7VyEgDcADAT5c856ei523WBhWXAl69uygJVAFDHJTZqEXymy7bDgB442UiSSDfMDz3ixgOCKUXpVp98dTODVgjE0LiwIHLdxYBGN+Tz7+qzCd0K83Y5uyOTF9c2ilgDW0kMJS2m7nivOJ48aGQLZI3auHe8Ai+7TX7cP7WcVwnOL9UXncDQ5sbkzovJWu7bq/4OqFME90gM/jdk8n7Xr5TvBGgmhdu1HG8Jrqm7p0bxxaB7U2zIU853UjA6vKdU/jZr70CH3rP64SPy/qqYttiUi3fwwe+73X49tfuEz8uA9JiVcfGnF9bJ1u45z+9DT/0pouFj8sAvpgV/TJmpD0E4NsJIe+jlJ7MP0gI2Q7g2wE8eM47exHVtIZMpDMIhLTqdLV89a57FaADKAesyqVk5Ylppp5fAGPKiVKEAL20xyQ1SsZIo0ZMuckUwDc9Jve605FGAarjWO5Ll65YhtQdClNxwpBGEko9gC8IaTwJzvRVIQRhrR8IqfdAkqpY2hepT7IFRBLdOphMJeatgCEA025oeX7phH/UCfBNtnwcW+qW9lXKqFVIAqt409TGSFNM5CoBaWXSTs3jqJVyaghYdYZyxqN+mqgGUFsj8Fin5FT3PqQl0a03bIBAwgbbrHNX737DRegMAuFiIWHeyxlprQ1aJL/n5otww0Vbcf0+AaCg8NbaaKPnn3zrZQhCindcv1vQl3z+xec+G0VSePdNF+J3P/8M3nzFjsJjvoK9upESSgD4ntdfgL+59xBuuqQIiKqknRspCQSA//dbX4HPPX4CrxScX7FHmvA4bhzDCgB+/duvB4WYuViWcrpR4+V5BB/5wRtxdl2cQuk3SGw5ku97EITxerDumhvz8NardiKkFNskYRYqyelEycZx1Zpq+/jET98ivX+rvJk3MrWTEIKf/JrLpY9LzfNDewwrQL5RvpH+jrxU10VZ+NqmtBP4PQAfBXA3IeS/giVGHQUzm50H8EsAdoDtlG6WpKY0ksnW+0OMN4u013Tp+MCYpHZOt8slp10taaeaYWW6CxOPV4nPkI4ZPCAfL1OPNA5YlbFNyphMKgo/YJ6gU8bg4x5DpWEWKcmp6DRigKiBh1WKwTcjAB51pYoNjyjj14FqTCaZRJf3VcYs1PGwMpIOlzDltL21FFT5jWB+6ZjBs77KzfNNU2GVAJ/meKnCLHoVJgFlHnxGqZ01SSiBSAK+Vp7aqcNALvOUqzNkQycEgfVVn6ccoCPRrRVI06nzAKyd6w99OdXseBM/9/VXCR9LGDDnfrHgNzy88vw54WOteD4h6GsDQxAA4HUXbcWf/fDrhY+p5l8b6ZEGAD/x1svwA2+6OJ6DpCuRwooZaRu54Hv/u67Fv3vbFeJ0TAVTbqMBq+++8QJ8940XCB/jU3fxcdxYgE9moQKovQE3EhgCIARoeaUl4HnJ6UafXx96zw2KvhQppxvcl4jhy0s1X93IVNiykikoEvm3nX0vF73I+OeK1gkbyfItKyeANErpXxNCXgXgFwB8QPAUAuDXKKV/fU4be5FVwvxSLa50GEPljDSTk3VSQ0LZ1ZDeNUsYVoOKAEyZz1ApM6dkvAahocStpK9BEGIY0pE95UwNNssAvnVtM/hkAiA6FyszHrsSIE1XSqZipFX0SAOYL6Foom3GZFIDCmYL9yY6gwDDIIwnidX6UjCGKjD4SgG+WlMoza4TSlB7qOf51fI96fsMKgF8DWXIho2UU4ABfItL8sd1AeS6Jc1lIRsxkFaySeQ3PAS1MkTVIS7rCom/ThFCvi/3q1cJfgcADQAXAHg3gIdH+tDNqlxpT6Z8Wd11V6R29i32pfKU6w9DeATwNoj5xRmlovJVwOMGMpn4Z8uUJ9wkXHZ+2Vq4Nzz1eW+rLz53F4FDGw1YqSoGtmUMPktMpjLv3I0KGygrFVPO7niJ5zkbvTlRVjLg0QUgTXkPsnAcnQDSAIBS+ouEkH8C8EMAXg1gFsASgPsBfIhS+mWb/b0YaqLZACFq5peWh5UG86uKJLA0TbQUsJJPTADG6jBdWKn64mbwuimnovEKQgoKQ+ZECfCou0BWeTtUSqFsq6XD2swcBVWeUlpJ2gkomHIDPd+ihgdFOlMFACYF8Ikm2yZeZKUeVhUkzWv9ALPjxdeZSDvrDEGYHvOx2pen6Gr3pQHwGTFXx0qYTJoAMk+eU/VlLGnuydlKut/HVoPIpRgxwGdynWigqwob4N6AWl53aomub2AiPtX2cXpNEbIxCNDwSOmCran4PlaVnOqEIIxQHwbAG6YAvjn6ky/+H18H8P5RP3SzqpWKYbXRRs+qigErhYTSTl9lEkp70igAQrbJRksoVaXakLY5XolHmsgb0P5xlIcg2GIMRcdxGAI5zHQQUKsMK9k82iYgqppP2D2/ZJ5f9oAhQK4I6Fm8B7HPVQOPJjYkdZUzQBoAUErvAnCX7T5erOV5BFMl8pX1/lBrwQfU50VWJgmklDKPtJILRlMxMaGUmjOZ2moGX1dzwaeMq68o9QHkzK9uDYBVVUYHIAdqOaOjXNrJJ+TyXYU6gVpthlXK7FaUzgSY0YYnM+dXMW1MN1VRy8OqYsgGN0tOl64UtuVrAGmG5z2ljNk4KQIedVM7dbzbKqTClgJ8OsyvWvtqKgGYXsyUU7+n76k8+KIUSsPj2FGQqDp9PcDKV6RZ8U0TE4+hybaP508rGHx9tmlS9p4qyWklYLvtY1Eh0e0MRmOkIUnSJAA+BOAfAPyj4HkBgNMAvkwpXRz1QzerWqmAob5FQ+XMwj1XVmU1io3M/gZLFVUVSzslpv7WmDm+fEO6P7QH8KlSO/tBKGX+bXSVpejaBGB4D/myClipUk4tAlYqaedGS4dVJbNuscny5Z8rBtuje5A1AFnsdRdv5rycGWmbVU+pEgx1zeBVNw5gNOmdqOIFnyZTTjwxYV+iSqb+kr5iRoduX4od5CqAlUzaaSLZAtSpUaaSQEAOWK3reljpAI9VgNoRx4vvnolYQ5XSC0skugnDqtzrrpQpV+d5r81Iq9lbKwWIioC0ri4TU+WFUZH5pQT4TMzzawUeG6UefB4p/y75DYLOoE7PryY6CkYa3zQpBawUwONgaL7jPl3GLBwGpceQ9SX3SOtVALanxnwcUkl0R0ztpJT+Cf87IeT7AfwDpfQjI73pZm1YqZJhz4XRs6xUzPuNNs9XVSkjzaJZNyCX3tkCOnyFhLJvcbz4cMjCLGwCMKwHMQvGNlDr3HgprFs20tS/rJoND1Qwj66igqm7L+Xa0THgMb4HGahz6iy5tDOIHz/XZQVII4Rwt8nDlNIg9W+d6gE4SSkdWe/wUixVApiupKbMW8v0ojPebMAjowMwqr4S9N7APL8k5TTxzClfiMr66lVgdJRJO/l4TZYAHTydqS9Y1MZ9GYxXGSCqL71T7YiOBsCIKh6vdrlHGuurJuAx9pQTL4RNpbBCU9mRACsxC0ab+RXFnIuYWlWZXwADHs+bEfSlm0Kp45FW0RtQBKSt9wMQouGt5Sli4SuFDag9+DpRsm/Z4rapSoWtKB3uBZACfOv9ABMa7AKW2ilnyplOfMu87nSCZQB5LDyQsLVNAIXpc5jaSSl9S21vtlkbVtJkMqvMLzclgUrGUAXAva7yFdYa7o5XaDQnrLMSZYf4ONqUBALysAFbwCNnfsnm0baBR9f6ks2jY4aVrfO+IVZQ2JTL888VbgJYvAfxzx0I1rR8nftyCht4Dsyr42oAT6X+rVs9Qsg/APhRSuly3c29mEvlt8JNi0uldwqPjireWoQQTCqSyfjiRrRIFfUlmwAAZgu+tu/B94gUGFqLxquMUq6eMJlfpDngIzf1j46jZl/iHWROz9Ufr4lokVnmkVbufaTyDqkuhZUdx3XN84ufOiqatal5PiAHrNY1z69myc42e06VviQAn66pf4rBl795jSQdlp5f+hsBZdLOdkVvwJ2Cx9d7Q0w0G8rkLyCipJd4kVViiEo8+Nb7YuAvXypmYaX01XYDFHKAb70/LN0EKOurygJ5su1jvR8ImZ28Lx35UNn5ZQrwTZaEbKyPyEjbrBdfNSX+k4MgBCEQnr/noifeQ75sSqNU1hqMkWbXpN41CWUCDLllUh9vZErN8+0xYAA7KaeqUgN89o6jWnJqFnBWZ6VTdNMbZrbN81sNgkEYFjakq9jJ1Fm+xDvXpqk/IN/4TZhyLxNGGoCPgAFnS7l/69QYgCsB/EsAqwB+pPbuXsQ1pQCsdBfICWVYzhgynZyodt11ASvlTkcMwJgBfFNjcpYCZxKVAVZlOzCsLxOAr4GW70kBK95X2WJUtYNcpS/PI8pkxU7skaYJ8NUk7SwLQViLGXwlQFo8kVPRrOtLheXnnf541UP/LvO6q8IszF9SqgEwCTAk68v3yheRsjhxYGOA2rVaGFbmQC0H3GUefGs9sRS12Jc6Zcu0rzKAb60XlJ7zgNyEl/dleg9Kh5KIvAHXNQJvgHJPuUpMOQXA162RkQYAhJBbAfwcgBsBbAEgaphSSjetPyyVDKzlJvU2JJSexySnUkmgg15RdvtyU0KZbMyJF6Oc6Xyuix8mYTqmTSmsQtlh2zwfkEk77TH4YsBKohyyxxAVr4dsyuUBdv3iklM/dcyqzFXrLNk82r7kVLwhbfM4WrliUkrfo/q3ThFC/g7AN9TU0kumpsd8HF3qCh/T9fxKTOoVjCHDL9HMeBPLEkPlGLDSXbjX5PkFsPFaLmPK1dGXoZ5cBYhyJlPZIjneSVMCMOZ9jS4JVEwwR/D8WumqmV9l51ci7VQwv0zSHsuANE3JqcqjowpgNVnClOtEUsUy8CTNLByHWHJahcEnO46d/hAldnIASrzbKtDSy1JhtRlWGky5KoCo6jpRds7zvmoNQSiVWg9Lz3mAHSMeyFHoq8KEfDol0RUBaWu9YSnYzvtSjVflvvpDzIyJAb66ihDyDrCwgQaAFwA8CWDkNIPNqrd8ya57lfOrzlIlptmSRiVMEwmTqeEBOPdOMElqp1vjFQMwQraJRWZhyfzLtheZ7PtoW0KZP++5asg6gJw7v2z3xYHr/LrDZhIykLUGSvNAqqis6iypvYB1yamsL3vH0d4defT6AgB51BUAQsh7CCG05I9ylkoI+WDquZdJnnNJ9LyDhJA+IeQYIeQvCSFXKd53nBDyfkLIk4SQLiHkBCHkrwkhV2v97yWlYgytaXprKb3IKpgpA8DMWBNLEiBNGxhSxHZXARR4XzKATx+wku9YVQX4ZsbkQNpqT49h5SvM86v2xRh84q/MuiaTKU2xzleV49hseBhvNuTnPR+vUmmnBsBXJ9DRG+p5a2kAtdUAK9n5xQCF8vRCndAIfaB2doKBCLLrxGovwJgGkMm9HSgV32w9koypTpUBQ9oMK4lcC2Dj5XukVB6a6Stifsm+j2u9QBMYUqfCmvfFmXLy+9C45nip+jKdMOl4T+oAjyoGX6+CtLNUml6vtPN9YPOmt1NKL6KU3kIpfYvoT50fullm1Wp4zjFgACbvlJmb22N0KLy1rKbxyTcy7aY9qq0ibC3cGyqPNJsAn2S+GoY0srVwS9ppm2Elk8JW2Yyus2TS9KrkkLpKFhphM4USYPOvQDCPti05lUk7bQKiL1rqPqX0twH8dsnTHgDwfsljtwB4K4BPyF5MCHkngB8Ek5BOSZ7zGgC3AZgB8HkAHwVwPoBvA/BOQsjbKKV35V7TBvAZAG8EcG/0/zgfwHcAeAch5K2U0q+U/N+ENdVuKhZ8uoCVmioPVACGxpvSZLKE+VUCDGkxrMz6mh2XA3y6gJXKQ6FfIWygrK/EI00PgFEnnJjLo6ThDH1dSWB5aETd4+VpMKziHdGagEe/4WGq7Uv7WusHWt5aSsCqwnhNt30QAjmA3Av0GEOSHT4A6A3Nz/uZCLBSHUcd1Ul6pzYf0V3Fw2o6AqxUgLsuw0rFZDKdAOh48G2ZbOn1JQtBqAQMqSW6670h9swWpajFvjyhvQDAzjnTzZwyZuGarqdcCbPQeJMpYsctdwfYg/FiX4ogggp1HYCPUko/Xeebbla95Utk4DYBGIBd82X3IdseaULml03pneL+aFNCqQwbsDheytROB5lfXPZmG7CSAmmWxyu/EWAbsOIAXlHaae4XXWfJ5vc2UyiBZDzy82jbklOe5p73lONzxZeTR5qwCCE3APh6AHsBtAVPoZTSH9J9P0rpA2Bgmuizvhz99QOSx3cA+F8A/grALgC3Sj7mg2Ag2s9QSv9H6vVvAGPNfYQQci2lNL3a+RkwEO1vAXwXTyAlhPwVmOziQ4SQV1RJJuVhA6LENL6AKPMi8zwCj9Tn+QUwoOOxI2ppp67ETWW6nl846/T1zIlV4WPagJWSmVMtSWRGAQytaQN8Ko+0ajeP6TEfq1IJpaa0UwXUxmbw9QFpnJlTxrCKPToEkofeBgF8et5aiolvhV0YzyOYGWtiUQYg9zUlbhrH0dSLrOERBbA9xJjG9yjN4GvlSNe9CtIoLgNUAaJzAqlgvlTpmFUYHfw6LpWm9wPs2zIi82uEvuQAnx6Dj8nI6mOkTY+VAKI9fUba6lA85lWAx/j8Wpddv2oF0lYBnKnzDTer/pLJ022aiAOsL/H13h4zhxAivVb0bKYEKhQUNiWUyX1bLDm1N17spxSotcVkkgJW9hbugDw0wr70TsJIq7h2rKtkXsP2pZ2yvuylUALp61f2nmMbqG1JNspfdh5p+SJshfthAN8LgIAFD6TPHpr6vTaQpvi86wDcBOAwgH+WPI0DbD8O4O8k73MJgFcBOIEcO45S+mVCyD+CMdPeDuD/Rq8hAH40etr/kwbLKKX/SAi5A4wtdysY082optuJ38p0zm9Fl5EGRKivMhnDzFtLCXQYen6pUhVN+1JJTjlgVR6CUM7gq8JSOHy2I3yMM6zGmur3VHqkVexrqu3j+LLYg2+9P0Tb9zTM4Mslge2S/1u+ygGr8vNClRpVJe0RKAdEdVICtc77OgG+nj4zB6gPcCeEYGbMx7ICqNXZxFcvYMzTv6bHyhh8Q+ydK2dYyXbSkr5MrxHl0mFdjzRlCqWxH2Y5s1CHwec3xPHrcV9VASvFfWjUlNMqx1HVVxjSWDJfU30OwBvqfMPNqr9k8uFBYJ5WW2cxlqiEyWQV4JOnnOrcYzeikvmXWxJKDkiJ54XU2nGUhT1RSq2a1MuOY7KJaQvgE3uR2U6hlClhXAH4ihJKuwCfXNppF7DyS897+0BteqPc5vfR3p0vWz8B4N0A/hTA68BAs98CcDOAXwSwAiaZvKSmz/s30c8PUkoLs1RCyHsAfAuAH6WUnla8z67o53MS9tiz0c+vSf3uUgAXAHiKUnpA8BouNX2r4nOlpfLzSYA0jV132Y5oxZN1dryJtX4gvHnrMqx8hYdV1d2O2QkV0KEnCVQCHRVN/etgWCVm8PX1pfLgW+2JU/rypfL86lXsSwlY9fW8opIdUTmAbLorOjPmKyWBukBHuod0bYSkeU2TmaMXslGlL7k0XYeRVhayYQoeex7BtEKiq82wiq5fgeD7WIUpxzdKlOe9bmqnhClXxfNLh8Gny0iTeZFVkbjxvkTfxyCk6A7CejzSamQ8docBBFZ/o9TPA7iUEPJLxEb042ZplRRIs8gYAhRhA4E983xAHphiUwrbiJQdrkko1YqAwKJHWtSDwDyfUovAUHTflgEd9tJXxcex6hqtrpJKOx0wzweKwFDP8njJNqTtA3zi875v+byPgdphcbwIQSmRY0N6OuefKK7vB/AkT++M5neLkbfYXYSQTwG4C8xX7I9H+SBCyDgY8y0E8L8Fj18Ixi77M0rpP5S83ano54WEEEKL7tYc+EuHDlwZ/XxK8p5PRz+vKPlsYWWMi2ezj61qMqwAzp6ozyNtdjyRIW3N+fboMqxUZqS9oNrFcHa8id4wRHcQYCwnSVzra5quK+LER5HCLnUGQvaKLsNKRmUepa9JhUfami6TScUYqhrOMO5jWSIdXu/pMtI0+qqwSH7+tMwbUNcMvjw0osp5r2Lm7JrR8LAqYX5tRF9byttSAttVFzBKwF07tZMDj9l0JqAawNfwCEsdHhWoLUk5rSLt9IgYGBoEIfpDTcBKEc5QN8AXB8tohjOo+pox9D6aUQCiMj+3Eeq9AB4F8479QULIAwAWBc8zstHYrHqLeaS5JXED5PJ0695tsr6Gdhl8UmWHg55f/HfWAAUint/bZjIlXlHZvnqOMHPyG+W2Paxk0k7bAIy7nnISKawjzMJiX3YlpzKArxfdg2zsD7oCpF0J4CO538W9UUrvJ4R8DMC/xYhAGoDvBDAH4J8ppQfTDxBCPAB/AuYj8lNlb0QpfYoQ8hQY6PWTAH4n9V6vB/DN0T+3pF7G4a0lydvy38+JHiSE/AiAHwGAHTt2YGFhIfP4gZNs4n37l+/G4bnsYuWxp/sgAL5y5x2lJxsNhnjh4GEsLJzK/P6JMwyMe+yRh4Gj+qyhI0dYX59e+CJ2TWYvDE/s76HdAG6//fbS92kQYP+B57GwcDTz+/uPs/d/6IH7sPSsfl/HD7LFyyc/fzvm2tm+nnmuBx9BYYzztTZgF5bHn3waC73nMo89GP2/7//qPTg6qX9BPH2kj2FI8anPLRSSCg8c7IIMw9K+TnXYheaRxx7H1uVn6unreB+r3SFuu+22wjn03OEuMKClfR1aYX09+PAjaJ98opa+Vk73cGZ1KPzswyc6oBSlffW7HQAEX73/AfQOZs+hp/ez784X7/iCdk8A0Fnq4cSi+Bw6dqqD6RYp7euxU2xM7rn3q1jcn+3rmQN9+J7edyddvdUujq6Iz6FTi+uYpmulfT1xgvX1lbvvwfHZYl/NCn0FnS4OrYjPocWVDi5qlZ9f+w+x7/Qdd34JOyay59Dho10MeuXfnXx5wx6ePXxc+LrVzgCnjh0pXCvz9fwB1tfnF76AiWb2u3PkWBf9rnlfbRLgyQMHsbBwMvP7YUgxCCiOH34BCwvHlO9x+CC71oi+00eOdzHolo95viYaFI8/8zwW2tnP5tfKowefw8LCYeV7nDjew3pX/J1eWlnHNNaN+2o1gEeeehYLXvazz3bZNenQc/uxEL6gfI/Tp7pYWRMfqzOLHXjj5d/pdIWUggB46IlnsBBkP/v4mrFNalm9J/X3i6I/oqrFRmOzqpUMRLYNWPmSNNF+QDHRcpMpZ1NyKlV2WOyLECL1urPL4GM/i6brtr21ShhDltMeXZN2lgNWdplfRU8528CjLJzBDSlsnvlln1ko8+Czt2niCpBGkAWW1gBszT3naQBfV8Nn/Uj0848Ej/17MG+yd1BKz2q+378B8EkAvx2lfD4AYB+AbwXwGIDrAZiYnPCrjJAiQCn9ACL/tiuvvJLOz89nHp967gx+86tfxhXXXI83X7Ej89jtK49i6tAhvOUtbyltYvLLn8OO87Zjfv6Vmd83nj4J3H03bnztq/G6i/KHSF7hE8fxgYfuxVXXvwavOn8u89gnTj2EmbMnkP+/iKr1uU9iz759mJ+/JvP71YeOAPffj5tffyMuP29au6/lB4/gI4/dj+tefQMu25l93d8cuQ9b+sulfa31hsDnPoWLLr4E87demnnsxD0HgYcewptuvgn7tkxo93V04gX89VMP4/rX3YQ9c9kkt488dw+2N7qYn79F+R7Hl7vA7Z/DpZdfgfnXX1hLX0+S/fi/+5/AjTffUmCf/eFTX8auSWB+Xm3Bs//kKnDn7bjyqqsx/6q92Z7veQF46GHc8sY3YO9cMcFOVg8Mn8Jnnn8ab7rlzfFOCq/ffOSL2DbZwvz8jeq+/uFzALq49rpXYP6qnZnHvrz+ONoHn9M6R9P1xdXHcO+JF4Sv+5WvLuD8XTOYn3+N8j1a+08B934F113/Krzh0m2Zx76w8hjGjhw07utTZx7GgceOCV8XfPGzuPj8nZifv179Jk+eAO67B6941Wvw2gu3ZB5aWH4UY0cPGff1N0fuw+NHxN+53mc/genxRul7nr3/EPDIg3jdja/HxdsnM4/96XP3oOuXf3fyte+Zu9DpB5iff2Pm9/1hiOEnP4GrL78Y8/OXK9/j+dZzwJOP4qab31hg5f7JgbsxaPYxP/8mo752PXwHxqbahXN7cb0PfPozuO6qyzH/xouV7/FI+DSw/ym88ZZbC5PJDz17N8KWeV+TX/g4JrfuxPz8qzO/P7rUAT73eVx/zZWYv/EC5XssLD+Kr54Un0P+3Z/Hvt1bMT//KqO+tn7pc5jZXryvPXtyFVi4Ha9+xTWYf/VeyatZfezkgzjYOS3sq/XVBew5r/w7na/p2z+FLTv3YH7+uszvHzm8BNzxRaP3Kin1ybBZTpTfIMLQmyohG3VWU5Ymarkv3xN7t/UtSigBnnIqPo62ve7yjMcwpBiG1BqgwA9TnsFnG4CRpa/aZsolKZRFQAGwL4XNA1ZV1SZ1VUsC8FkPG5BIre0DVtwaqPh9bDaIFeYXIAf4qgR21VWuAGmHwZI6eT0L4LW551wOBrBVLkLINWC+a4cAfDz32OUAfhXAH1NKPy54ubAopQuEkBsB/BIYCHcrgIMA/iuABwH8I1gYAS8OGOaEl3HN5J5nVGUeaTrSO0AhLah40VHJV3RNnnlfdUoClXKfnmZ6oUbYgLFUkSfMdQfYgyygtNYbankMqdJEq6ZQps+v/DFb6wXYPtUSvSxTqtSoUSSUgFg6vNYb4vyt5WBhPJGrMf1rdryJziAQSuSYt5aeJxMgkQ4HQaWbR5l02OS8l4VZmEoV033li0sCdcIelJLmijvus+NNHFsqhmx0oqRare9j7IUh7muU45ivtb6e7yTrS2zeCjDPnLZhOAMATDbF6avcD1PvvBdf6wE2iaok0ZWM13rfrC/Roh0Y4ThKpMO8r7qKUvp8rW+4WRtSzYYnTGsdBKH2XGkjSh6CEBonptdZau822wCfWxJKQAzU8gWz7dRO0QIZsAhYSeartplMUgDGtreWFHh0Vapo+fzyS/qynFYrYmLaZUXLPQttXVPtjUa27kYWOPsEgBsJIf+ZEHItIeTHwWSSd434OaqQgWsBtAH8ACGEpv+AgWMA8HT0u29Jv5BS+hCl9DsppedRSluU0ksppb+S+j/dk3r6k9FPmQcapzXIPNSUlfFIy9VaL9AKGgDk0oKNAKzWNAErgAEsMo8OoArAJ0+Y0x0vlefEqMDQ0rp4caXjySTbSUv3ZZpCWXocjdIeFWEDtfof6Y1X7MEn8aapBAxN1DBeCkC06s727HgTg4Cik0sEDEPKAD6TviSAe9W+OMCXrvUIgMnLnEUlS2cavS/BNbVvEuAi9jQZvS/xJgAATS9F9fWryoR8okmwtN4v9mXgReZLrvXABgCPJonWnphlAox2HJdF9+x+7R5pm/UiKBmIzAAYm8CQe6b+gPxaYXu8GMAnAx7t9SWaR/Pjagt49AgBIWLJFmBT4iZmflX1ga2rkrRHGfBoK8xCPF/t2wasSiSU1qSdkvnXwDIjTRbuZ5sV3ZIAolU3yesoVxhpfwfgdYSQi6Mky18D8zJ7P4D3gckdzwD4haofQAgZA0sGDQF8UPCU5yS/B4B3gCV0/g2A5ei5ZZ/XBvB90ed9NPXQfgAvALgi9f9N1zdEPz9f9hmimm4nTKZ86aYqAhEjTWkiXiOQpsnMUfa1EQCfpuk6IQQNTyJ5qBzOoO7rwnY5w0qL+WXY19w4Y3udXSsukle0UzsVaaLReJmCVmUAnw5jSMVIqypdSfe1Y7od/55SBljpAbVqQLQqoMD7So/N+oAHkuib59fd1zDkY5P0xQGFMY3LhCydifdahdExM97EsoDBx4EhE0Za3tMEYOM1MWHe1+x4a2RGWhmzcK7CcZz0geOjMr+iRbuINTkYhsbJvgA7jocXOyP1JbsHAaOd9yqAr+6KbCi+B8DVACYppZdFv78awDsB/DmlVG1it1kbVr6nMvW3x2Rq+WKmnAtSRZF3m3XJqUDZwSWU1vuSLNytSmEbXlESaNmkvuFFAF9BQmlZeufLwhlsSwLFG5muSDsLAIz14yhn8NlKoQTkffWtb07Ivdte1h5pUTrmP6T+fYYQ8moA/xrApWDA1UcopUdFr9es7wAz/f9YPmQg+swHAPyw6IWEkAUwIO0XKaXP5B6bBNBNM9wIIU0AfwBm5Pv7lNL9qc+hhJA/BPD/A/BrhJDvopSG0eu+GcAtYN5qZi7dUU2N+SAE4t1tE2mnZCIXM5mMUxUjgE+SmHbetEYcX9SXOCWw2q6Vivm11htqMXNYX2qAr07gcV0z7VEpJas4aZqLGFaLkkWfDpDW1EkTrWm8TACrWFogAZCrTABmJH31gxDDkGoBMKoUylGYObyv3bOJdJgvkvT6KmHKjdhXBkiL+tJhpMko/AAbry0VAdF+EKI7CDGeAltMpIqqvkaRDoskujEjTRMYYn3Vx5SbbBIsLcvTMXWuq4mkuSiD6lVML5wdb+Lxo8uF3yfMwvK+WpIkPqBamijv6/jyauH3nIlZVxF2knwYLLkcADpAxjvgLNjchAD4b7V++GZpF/PWkpjUO8hI6wfUrqm/xLut6nWirmo2it5ttiWUgNhTbmAZsAL45olbHmmEEDQFEt3YI83RFErbJvVFSaBdxqNs49e2hFLO4KPWUigBtaS5ijqnrlJLO1/GQJqoKKVLAH6jxrfkIQMfqPE9AeAtAP43IeSzYN5oMwC+EQxE+2cA/0Hwmt8E8E0Avh3AVwghnwNwARjYtw7gBzm4ZloNj2B2vMnMpnO12hvi/Ek9Y3mVtIA9bnbCjjUbaPueVEI5sV3vVGR9bQDAJ5TVBFrMHIDdHGRmtwTJxVK3yhhpRpItwQJ5EITwCArG/GUVA2m58ysUsIikfZUAML5H4NU0XiaAFZ+nyaQYozDS8gAyXyDrSE4ToEN03lfbHZIByNxbUYtZ6KkBq1GAtLw34GoMpJW/h4wqD9QD8GWANANGWqsh76vq5CQt0U33EDPSDIDtOpmFE02C5e6wAPCtGZ33yYS8mXo6pZSNV61SWDNGmsy7rT+sdhxnxsR9ibxOR6x/C8bM/xCAnwULWPrP/EFK6TFCyJ1gLPxNIM1SNT2xD58LzC+pR5plgC8/n+DXCavjJZCB2wYUAMYKck16B4jDGWybwbPPLkp0+0EQPWZXepcH+PqOSmHtA3wlqZ2OMfisX7skRAzrrGiJgmIQ2AtKccUjbUMrkiu8CYKQgRrqKQB3gvmo/QyYXOIFAD8A4F2U0oJDNaW0B+BtAP4LgDmwyezXgrHybqCUfmWUhrZOtHBGIL1b6w+1FjAAW8SIF1bs5lF1MSpipDHJqX5fdXqkNRseJluNkSSBrC+5FLbhwXhXYVrCLAxDqs384jHndZqbb5mIpJ05AIYDClqMtDIPqxGYX/nzi3sF6vTFT526PayAotR6tWfAzFFJdCsexzmJd1sM8GkdxxLAqsJ4xaEkufOLS+/GNG7gaQAmX1U9c7ikOT9eJudXEoKwMczCdHHm17iWl2L9wONkkyAIaQEIMjrvJalRw5CC0moT8tnxJlZ7w8I10QQQ9T0PQUgLPn6jpN7JQxBqB9J+CCwE6V9Hm5UiRPBpbKZ7Wi3ZfMK2F5kKSLMvCcz2FYxwnairmn7xOFZVA9RZvoD5ZVviBojDGWwDMIB4PZQAVhaZcgJiQRI24Jgk0LK0U8bgi7+PlvsSXSfsBpLIAT7bYDvrQxSCYIlVaOVTJUUI2Q7m2bEPQFP0HErpR0zfl1L6OJhUoVJRSucVjz0F4NsqvGcHwHujP7XW3EQTZ0VGzz09xhCgSkGqfvOQLRaWOoMYCCkrqbRgGKLhkUp68hlBX8MgxHo/iBf2pX1JAL7+MESzwjXH8wim2n4BGFrrDxFSGPQlT1+tMmGaaDXQanhYlDCZ9ACYcwcocCByZlxjgUxUqYrVF8iivlZ4XxrHUS3RDSozc0R9xcfRIE10o7zbRH1pMdIkO6Ib0ZfR+SUBhuK+qgB8MUM0K9HlfU1rDFhZymk1II39XOoMMJ06x1ciQHlGoy/VBBOoupnDPjef7rtiAIimAeT0YmWUtLSZ8Sb6wxDdQYCxZprxGNS9sL0SwB/RPAqYrRMAdtT5oZtlVirzfJsm9bL5hPXFVcMrgM5V1RN1lrMSSoEXmQvjJWZ+2QfSmg2vsMHKj6NNmVuzIWA8WmbwyTZ+bYczSOcT1hlpcgafE6b+QoDPLvsYcCsEwQkgLTLm/00APwigJXsa2O6pMZD2cqutky0cWcwS4SilWO7qA1bNhofVocBUdsTFQn4h2h0E6A9DbWBIdOMARkOjRQCfyQIZSIyxRX1V3Riqpy/xDjLz8jE36yaEYHaiKB1OUu/00jFF6UxAdUBhrNlAy/cKwGOycC8/v/jHiiWn1Ra0MoYVZ6iZATBiQFSXNZnpSwrwDTKP6/QlGq9eEGK2pfedTlcZ8DhukNopBNJGlOhKx0vj/EpMUs8BwNcx6UvOSOuN4JEGMIBv35bk9yvdITyim9pZv0RkNiVNzwJpA4xH15CySp9f6ef3RmB0pI9jGkhb7erJ+A1qCKDMkHQvgKJh22ads2oJgA7Avnl+s+EVrl9MQumAeX4oWSD7HjvrLVSrIZBQOiJVlF9X3WIWxlJYyx58+fPeBaacGBB1Q0LpWgiCbD5h+/soZcpV3MSsq6TjZd0PU8yU6wehdphi3eUEkAbmhfZjAB4H8FcADsPare/FX1smWnj0yHLmd51BgEFA4wl7WZWZ51diT4w3cXQpC/BxQEG7L8mOaNUFHyAG+EwWogDbXZQxYEz90XiJgDSThXtZX1XNW7cIGI+rPZ72aALw1SclAyTAY4czczSANJ7aKQH4qgBWLd/DeLMoHTYCOkoYQ1VSFafbkXQ419eSEQATTQBqBETLgKEJrbABtXl+VS8ycV8mzC91X/UCfEO0fC8Dysj7UnjdVRwvfpzy59dyZ4Cptq/lgSjzbhuNkSY/jrqbEzLpcFWfznxf56WSope7A+17o2Y9BmCeEEJErLQo3fytAO6v80M3y6xE0jsg2giwap5PBMwc+0CHKIQqY1JvaTXhN0h8XeDlDJMpn144wiZ5XeWLzi8HJKdsvNwCrNhnF70UB5aPY0PBGALsp3aKrhNV1Ux1lCxMbBBQ6+c8UNzAH4ywdqyj+PlTDLN4mTPSAHwngIfA/MGK2r/NMqotk60C0GGyQAYUHmkR86tKksjWySLAxxeiJkw5qdltxQv0lokmDpxay/ZlCvBJgMdBQFH1vqEChkyksDKvlarjNTfRKko7u/rSToB7wAiYciP0JWbw6TO/krABmbdWtZvH3ESzkHLKmYU655eK+TUYVuvL8whmxuR9aTHlJGlDQCQ5rQLwjfnwCArnFz+OExqnvV9inl9Vlg4UQzaWuwNMtBpaoR0y7xD2u3qlw8vdgZZ8EkjvPBbNuuuQdqZrpTvUv3ZJzvtRpBiq8dIB2wG5d1tvRP9QYV+dgfY9W7P+FMDvAfgfhJCfST9ACGmAKQP2APiFOj90s8zKFzCZABcMqEXm+faZTC1fxbCy663Fg194uSCh9BsEg6F7gFVLwHi0naoIRICogJkD2AbSiuNluy9CSJRs7RbwKJME2gasZGFi9tnHJO4jXYMgRLuKX1FNJT2OQ3vH0d5oZGsSwGc2QbR6astEC91BiE7qBs4BGF1gqCnzwhiB+bVtqo0za/2MSXMC8I3m3TZKmtXWyXYhnKEugI8x0iq1ha2TLZwt9MXGS4cBk/RVL/NrbrwpBTqMgMcavdsAccgGZ/DpLJK9GEirT3oHqI/jyMyvoJpEF+DMQjHzS2e8ZHHiSV/m4+V5BFsnWzi9VtwImG778DQAfNnOI++1Sl9x+IeAIarNDpWEDYwCWHGpooj5ZSKXZ32JTf1HkXaOBlidO0baSneoDzxKjmPCSDP/Pqa97tK13NVnymnWHwH4NICfAksZ/24AIIT8LYDnAfwogH+ilP55nR+6WWbVihbu6bnSMAgRWjbPFzG/bEujANZX0SvKPtDRclkSmF+4cwDGMQ8+FzzSRACfC+EMKqacbXCoeN7b/T7GljJCby27cmbeR7rss4/FSgXb3m2qlFNb1y5XgLRHAey23cRLpbZGdIAzKfYEXzjoAx1iL7L+CCfrtskW+kGIlVSS27KBJxPvS+QVNcqXaPsUA2DC1PuaMJkAteS0qrRz22QLp1Z7md8tG0o7WV/1AR0AA2pljEd9oFYOPFaVPGydLAJpMSCqsUjmO2n51CiAAzDVAKutky2cEgBDADBlYLqe30EGRgMet021cTp3fnHASofm3lRIKHuDEfqaLPbFpHemTKbs+RWnKjbMj6PnEcyNNzPX1KQvU+aXWFJT5byfaokZfCvdoRHYzvrKHsfeCIDVhBRI0wes5HH11SfksnRfM/9Q9XEcKQ1ZALjXyUijlAYAvgksMbwF4Aow/9lvBTAB4FcAfEdtH7hZlYpvUgSpa6vthSjAWEEuMr9EG4YxAGNZciqTUNpevIukUfwxW6X0SHPMg88FppxQ2ulAKqwI4OtZ7osQgqYgFda2F1ls6i84v2yzVgEIxsuuH6ZM2tmzCIi6AqT9BoB/QQi5wnYjL4WaE0zKY+aXwaJPyuiovHBnfZ1eTfpaNgZgxJLA/ih9TbYQUmRkbqZSWJXktOo1ettUG8vdYWanImYy1eB1V3W85iaZJDC9W84X8nM62jtwaacIeAwqMToAdn7lmUzL3YG2uXnSlzicoepFersAsFru6gNWqnCG3ghMue1Trcx3kfdlAmoD9aY9Auw4FgDR7kAbGJKFM/RHnPgy4FHUl6kkUJJCWeH76HkEs+NFz0Kj4yiRKo7C/BprsP9PHnhkAJ/h+VVjXxywKlwnOjUy5SocR35vPLVWvE7ULO0EpXRIKX0fgJ1gKelvAvAKADsope+llG7601qupmBx5YKHlSi8yIm+FOb5Nv18mr4AsHKCyVQcLycAPtH55QDjUQjwOXEcxVJYQmDN8wuQe7e1Gl4lW6C6SrR+HEVlVUfF8y8B49HqOS+Z31cNXqur5BJde8fRCY80SunfEEJ2A7iDEPI/AdwHYEny3C+c0+ZehMXTyNKLK2PASpH2WPXLvW2qDQA4s9bDxdsnWV/ck8lgcSX3PqrIZIr6Or3ai8eurvHqD0NUlZOnjyM3oObjZSbtrHe85sZb6A9DdAZBbMC/1Bmg2SAY1zA3BzizsD5Tf4CdX2fX+whCGk8iVrpDbXNz1pdE0hxUZ8ptkzDldIEOQBXOUM2LDGDj9dXnz1buqynZsWJ9jSaFzXspLnW46XpP/KJ0X7IJwIiSh22TReBxpTvE9ilZ0HSuL4mEsj/iTq0Q4OsMsGd2XK+vEqlileNICGHAtqCvq3dP6/UVe8rJpD7m49VseJibaAqPo6mnXEGCNAqDr+VjotXAmcJ41S7tjCsKG3hyQ958s0aq5NoaxoEhLgBDfoMgpMjcY11gDIkYME4AMJ7IW8sBppyQ+WUfGFKFM7gG8LkBWBX76kVMJruAlUhqbVdCCYjP+1FUVnVUDAwJGGm6ntMbUTJP31H8ousoeV+bYQMAsAXMK+2XS55Xaxb8S7G2RKygMwJGmjYw5Ms9rEYBFADglICRZiShFC3cK5p1A8D2yYSlcDnvqztAwyOYaGkCQ4IJABAlwlS85vAF+qnVXgKkdZi5ue4FQwqkBaE2GJcvfn6dXR+kgLQ+Zsdb2jdvqdddUC2FEmDnF6UMeNwegaPLHX1mDutLFbJRFahtYb0foNMPMB6dTyYMK0DOlBvlprY9AvjSiyITk3pCSMR4rNdTbvtUuyhp7gxw/tYJ6ABpUpP6EVOjtk+18fixLMC33B3gkh2TWq+XpT0mqXfVbm3bp1o4nWMymUg7ZVLYUT1gGJCW78vEU25j0r+2TWbHi1JaiYlZWMCM2FfeG5BvVtTJSCOEXArgjQD+mVJ6WvD4dgDfCOCLlNJna/vgzTKqpuBa4Yr0jvfS8LIAn20ZksqLrHiHOjclmq+6MF5Nr6g4cQJ4bHhY62UJsQMHgMemT9Ad5AEY6gBgVTyONk3XeYkUTbYllAD3Bix+H22OFyFEwqi1K6H0cxslvEYJqquj5Omr1TGAUcsJII0Q8h8BvBfAaQB/BeAIrAVWv/hL5LeyZGAiDsiZX6OgvjJp51jT05bziUxSgdHopluFfTGGgu5NUpTOBEQATFWPtJjBl/TFmBP6CyuZd9sogOiWyeT82jvHWC+MMWQCDBW9Q3hfdUiHYyDNUBolBWpH6Gv7ZMR4XOthX2uC9WUI8EnDGUaSULaZpHm9H59ry50BLtg6od+X4PxKvMiqAx0rkaSZ/99MznuVGSkwKjAkkgRqskPjncd6gaHtU+1iGnIVz6+CFJZdz9qaLNN8bZtsZ4ChMKRY6ZkAfDLgsbqnHMDO+/RmTncQYhBQ7fOr5YsnmEnYwCh9JQAfD0qZ1ZTLa9YvAPgWAH8peXwJzGrj7wD8WJ0fvFn6JfJTdME8X+SLOSqjto5qCeaFaZP68u2XjSnRxlx/6AiDT+Z1Z12iW19Kc13VbHhxMj2vwQhz+7pK7Clnn/kl78s2kFYE3G1LKAH599G2zx3vI122j6NM2jkK2WHUcgJIA/AjAJ4F8FpKqVDSuVn6NTfRQsMjmUWMiYk4UJKOOcKOO4AMS2Fx3QzokPU1CKi2rDBf21JAB68lQ6BDlM4ERFLFEaWd6cX7ksHCHVBITkcAYHZMs/E6sdIFMAuAHUfuzadTvmBHFBjt/MoeRyYfW+7oL9xZX2KgdpQwi/Rx3LclAtK6Q+ydG9N+D9HEJAgpgorm+UAKeFzLAmm6YDug9sypGpHNge0za33smk2YmLqsVRGbA6iDydTGUmcQn6OUUiOAL045LaQzBSP1lWfw9YchuoOwQgqlhGFVFUCeauPp4yvxv9f6Q1CqL0sv826rOmnaMdXGEylmYZLsW9N4jcCMPrbcjf9tanugWfMAPitLSKeUDgghnwHw1jo/dLPMSrSI4dcJu0BH8dx3AYARMWASpq9bQFq8oWPRpN4XhCrxf9tevBe9yOwDyL7ApN42Mwdg57aIwWe7L7HXnV2GFSCRgAfU6rULEK+HbAN8DY/AI+J5tO2+CMneGymlEdnh5R02sAsscn0TRKuhGh7Bjqk2ji0lk/LTa/14gapTMsbQKFKytt/A9JifAfhOr/Vi9pBuXzIApmpfXKqYBqzOrPVjAESrL0E6E8BAharX6ITJlO1rm9FxrJ9htTMC0k6uZIFHXdkwIN6BifsawaQeyB7H02s9o/ESAbVhSBlteFSmXAqoPbvWj5mjOiU6jnUAQwBiEIZSGoFqo/U1KgCT76s/ZEm/uuMlC2cYVVLDx4V7Ty53hxiGVPs6IfPWGh2wYgy+7oAttBc7rD/thOYSKWxVhtX2KZZWy0NJzq7xQBK98WpJgMdkIVr9OKYZaYuGtgcJW0jMnBgldTi/aQLoA3yatRfAcyXPeQHAnjo/dLPMSpRYy5lMNlkwfDOgnwHS3GBYBSHNJK+7YQavSHu0mXwnSHPn49WuuDFXR/kCoIPJiIlVL7KWLzCpd4BhJQRgHOir6QvmqyP4DNdVviTltG15vFq+eEPatkRXyKi1DNQyKWwWB+DXDFvnvStA2rMA5mw38VKq82baOJ4COk6tmAFW3EMhncwIjAZ0ANyfJlksnFo1W7g3veIEABhtF8ZveNgy0cwAHadWe/GCXu89ZEy5MKaimtbMuA/fIxkG36k1076KngDAaLsK/Dw6sZwF0uZMpIqSVNiRpIqTCZOJ1+m1vvF4FSaY4ejSOyAB+Bhg1YtZYFp9CZhyo0sCs8DjWj9AbxjG46jVl2C86gBggOQ48p9m168i4D4yYyjlWQgkzFrd65fM2yE5jvVIwPnPrZrnfUuwOM72VR2w6g9DrEY75vz6qnt+SU39RwVEU8xCIDn/dftK4uplnnJVGaJtnF7rxffcM3y8DM57jeoDmCl5zjSA4sV5s85ZqZhfdv1piqC7C8BQzOALi+NVNaW5jlKZ+tv1lBOnPQJ2x0uWvmpbqiiyunGFYSXy1rINwDAGnyhswDJgJdjAZ2ED7p1fLgC1ecAKsB82AERSfofuja4AaX8A4J2EkF22G3mp1M6ZMZxYTjPSetrpckCy8xgIFjGjfLm3TbVxKgXwVWGkyRhWo/eVBfh2TJswmeQplFW/24SQAkvhdBXgUeJ1V/WiM9ZsYHa8iRMrWYaViZePKhW26iRgbqIFQhKAYxiEWFwfGDILSbzzz2tU0/WtuZCNld4Qg4AafR/ZjpXMO2Q0AOZ0ARjS/z62Gl5xvEa8qcVS2AhIOGUIWAHiCfno5vlZQNQUsOKAenFnmzM6qoYNZBl8MTCkOV6+YNEOjH4ck77y46XZV5mpf03MwtOGgFUs0a0ZeNw+1cIgYD5yQDJuJsC2Rj0C4B2EEOHFmhDSAvBNAB6r80M3y6z4ud8XLBZsS9zSvQBueKQl19YUg8+B8eIL0fSGdCKhtNyXg0w5mVWEm4CCAxJKAZPJHcCqOF7WASuhBNz+eIkYfDbN83nl59Gj2snUVX6uL9vyb1eAtP8L4HYAXyKEvIcQ8gpCyAWiP7YbfbHUeTPtjN/KqZQBu05J2QAjmNQDwO7ZMRxd6iR9rfQNGTDFBTIwOt109+wYjkbjFYYUZwyZX8zEUgzwjXIt3DHdjrzI2AV/qTOohSk3KrNwZ6qv9f4Qa/0AO6cNPL82IBW24RFsm2zFAN+Zdc5kMgOspEyTin1NtBqYbDXi8TIFOgAI0zFHBTrmxptoNkjMXOULdzPAvcgQHXW8uAff8eUskGa6ESAFrEZOHY4AqzUzoKMRhw3ULNHNM+UM+1Jd64E6gMd8X2ZMOdHEF6iPWWgO8Imlnb3haF53MeC+Ug0Q1aw/A3ABgL/Ob1xG//5rAOcD+EidH7pZZsXZqVnml30ARiRrdgWwAnLhDC5ITr3itXXU62od1RSkgHOAr6qCoo4SAwouSNzcZMq1JACfG4DVi0SqOMLmfV3VdJTBl1ecuMBaBYrAdnwPejmndgI4ACYlIAA+qHgehTs9O127ZsawuD5AdxDA9wjOrpsBaWm5z1jKxH/UXZi9c+P49GPHEYYU3WGAziAwYsBIzfNHvBjumR3HE8dOAGCeOSGtsHCXeMqNcs3ZMzeOg2fWASQprGbMHAnza8TjuHOmHQNWnMlnNF6eh2GQNUmllI4MiO6ZG8eRyBvQlDHE+hLvDAHVFwqEENbXIgOQObBg1JdgwjQqAON5BLtmx+K+EsBqtPEaVeI2PdbE9JiPo/F4JQDMiuqFqWo2iBSwqnocd84woJgDfKZAhyzmfNSwgR055pcps7DpiQGr0T34skzM+PuozZQTM/hGXYjmmXKnVvsgJPHKLKuyMIuqGwG7UufXJTumcHq1h4lWAxOtWqc8HwDwbQC+GcDXEkIeAnAYzDvtegATAD4L4A/r/NDNMisR88sFk/qWELCyLzkVecq5wLBKJw/zabQLi9Fmw0NIGcOkkUrBazU87aT6jSiRJNC2uTkgNql3gskkAazs9+VhrR9kfufEcZQxHq0zv4oMPhckp3nFSXytd4DxmA3iGU2dM2q5Akp9BJueHLUWX/SdXOmh7XugpsCQgCoPjH6R3jM3jv4wxOm1fmyObcqACSljjXmpnbNRAb49c+M4udJDbxikpGTmnnL5Yoy06jTYPbNjuOvZ0wCqSX1EklOecDKKwebO6THc89wZAMDJVQZccSaRXl/F8RqGFJSOdpHeMzuOZ06uAqjI/JLsWAGjLRR2z43jaATwVTuOCubXCDTrPbPjOHw2B1iNCNTWMV5758ZxeDFi8EXSu+3T7VKXdF4MqK3XpH6q7WN2vBkDj9zDyjiUpGZgKE7RXU4AZI9A27NQCliNOF47Z9Lpvqyvlu9hsqV3viapnRIJUsW+zovujceWkuM4N96MF71llSza6z3vd8+NA0ACuBsGf+gUpTQkhHwjgPcD+DEAN6UeXgTwWwDeTykt7r5s1jmrZmojk5drwBAvF/oSM9LsS075Z/eDEONg173BiBs6dZSfuoY1vKQv2wwrUdjTIKDWAQW/QQRp2/Y90mTzVb45ZquagvEaBCEm23Yhh6ZP0BsU+7INDOUZfDGpwIG+0uuOUTej66qitNNuX04AaZTS99ju4aVWfHf7yGIn3tHeYSC9k/nmDEZlfqUWC3ySyEE/nUqbyrZTANWojLTdcxEbYKkXp1Gae7cJAKtgRCBtbhwr3SGWuwOcrADwiRhDdSScMGknM8bm42UCpIlSTusAYPbMjeOOp0+CUlpJEthsEHQHYkBhlEnm3rkxPHaEhRJX8fxSMr9GAay2jOOu/RyorQAMCVLJRmVYAcgw+E6t9tE2AGDivmpOE+V9HU71Nd320fb1+xIByEkIQrXza6zZwPapVqavrZOtzEaDqnyJ5HTkNNHJNlq+lwFqt022tBkPLck9aFTgcdfsGDyCbF8mmyYyj7QgBCHVpVG7Z5N7NmAeeKNblNIBgF8khPwSgKvAgp4WATyxCaC5Uc1GcSPTDcCKXyuK/jQ2GWkigI9fJ3QB8o0oVWiEVQllivHIFSeuMHNcZFi1BBvSo66F6ijGGCoCQzWzmI3L5RCE1ZwSZjC031feszCISAW2z/s8U84FewH++aJ7o617kBNA2mbVXxdvnwQAHDi1htmImbBvy7j265sSNsCoN9s9c8lioRt5ylTpaxhQpDc32K7VaAwYADi82IkXWGZ9idNNAIzkkcaBx6OL3XiBxcdQpzZCEgiwsekPQ5xY6VUD0gRARz1A2hjW+gGWO0MciRhNu2b1j6NIcjqqZAsAds+O49QqY2EeXerA90gsydPqSzjBZN+f0QC+cRxb7mIQhDh8toMd020jYEiUNlQPYDWG+144C4B9J/fMjRtJTloNgUSkBmnU3rlxHDrLpNZHlzrYNav/XQS4RETi3TYCs3Dvlgkciq5bJ1e6RpsAhBD4njycoep573kkGi/W14mVruE1QgZY8clctePYbHjYNTOWGi/TIB65p9wo0igOiB5ZSvoyuQeZVgSabYYKOFgisNYVk3oAGbZJHRtNo1aa+cWL/32U+/aoFQNpaY+0yETcpoRSBtTaXiD7kYSSUhqPjwuAlcykvt20DSgUNzJdSFXkxzFdTkg7G158HeXlgoQyz+Ab1c+3rvI9ImFF2x4vT3itf7mHDWSKEHIrIeSXbffxYq49c+No+R4OnFrDwWjhd/7WCe3Xc4+OPBtglFRFANg3x3o4eHYdh86wBQMHscz6Si6GddBgOWB16Ow6Dp1dh0dgtEhmnkxiKVlzhJ3HGHhcYgBfwyMx21C3r41gfl24LQFqDy120Gp4huEMxR2+UaVkQHIcDy92cHhxHbPjTUwZ0MlFjKE6djt4X8eWujh8lgEwJjvloglTvwYmwN65cYQUOL7cxaHFdaPvYtzXRjDl5iawuD7AWm+IQ2c7xoCCCKgdxABfdcBq35ZECnt4sYO9FfrKh5LUBWxzL8VDZzsVjqNCcjpiXxx4PLJo1pfM1H9UwApgTMxDi8lx3FPpHiS4N4448WVMzG7cl+lxLCtCyKWEkO8jhGyTPL49evySWj94s4xKZOo/qGGDYtQSAUMu+OYkfRWZci5IKPvD7GLUPjAkAGodYOa0BJsUTqRjNjwEIUUYugU8iplfLvRFhEw5+4zHYsgGA2rtplDmN1htA0O8Wn52XljHGq2OynsD2g6WcRJIAzAP4L22m3gxV8MjuGjbBPafXMPBMx3MjPkxM02nRBM59u/RbmqzE03snG7jiWMrOHh2Hdun2pkwg7ISSgtqQO/P38KAx2dOrOLQ2Q52z44bXcR8j/nQBYJ0plE2Ozj4eeDkGg4vdrBrxgyAYQyr4g4MMNp4ccbj86fX8MLpdezbOh6b1ur1pTKpH23hDgAvnFnDkcWu8UJUKDmt4aZ2AT+Op9aqAUMib60adtwv2Mb6evZktb5UHmmj9HX+VtbHgVNrOHx2vdp4yY7jCF/IPXNjWOkNsdQZ4Mhi1wiA4X0VAOQR0x4B4PwtEzi82EEYUgZYVQH4NuA6sTeSwlJKjcdLZupfR1ra3jkGiA6CEMeXu9hn0FcSxFOUwo7Kftkzy8ZrqTPASneIfVv0N7806xcA/HcAy5LHlwD8BoCfq/uDN0u/VKb+NtkTvoD55YbklDPlskCHR2A0L6m7mhJgyDajIz6/HOtLBPANHPAiS1vK8HLHI40x+HiNGthVRzUl8y/bQK1ovtpzgJHm55hyyeaE5b5y3t8ubJoAxeNo27vNVSBts2qoS7ZPYf/JVTx5bAWX7pwyeq1ohw+oJyr4qt0zePzoCp48vorLDfsSemHU8OX2Gx4u2zGFx4+tYP/J1Rj40H990YCa9zXKPW3HVBvbJlt48tgKnj+9VgHokEu2RvKUmx1Ds0Fw4NQ6XjizjguNx6soOe3VAChcvnMahABPHFvBC2fW6wEUahivK8+bBpD0ZbpAFlLSa2BYXbVrBgDw8OElHD7bqXDee0L5NzDaceR93ffCWZxa7VcYLwWzcITjePF2dr166NAizqz162Hw1TBe+7aMYxBQPHtqFcvdoTHAJ5KcclPeUYHtU6t9HF3qojMIjPpqeAQeEZv6j7pQ2LdlAseWuzh4Zh0hhdF1QpUmaiKLFtWF2yfw/Ok1PH96LeqzdmnnPIDPRj5phYp+/xkAb637gzdLv3zB/MsJwEqgCHDBN8cXbrA6AChIAFH7AEwim+Tl1nhl5/e2AT5ZKqxtQKHlaF9NXzyPtpk4DBTn95RSJ8arlWPKuXCt558vAqxs+k4Cxfm9bcnpJpD2Eq5XXTCHA6fWcPdzZ/CKvbNGrxWldgYhRViDAeLVu6fxxLFlPHhwEdfumanUlxiNHu1LdNWuaTx6eAlPHFvBdXvN+hLtIMcXnRGGixCCq3ZP47Gjy3ji2Aqu3m3Wl3AHpgbJlt/wcNG2STx+dBkHTq3FUk/9voqS014NTKbxVgMXb5vEgwcXceDUGq7aNW30+nysMpBmMo3GxNwzO4Yv7T+FEys9475ElPQ6pHdbJ1s4b6aNf3zgMIYhxbV7zK4TTU/R1wjXiYu2TaDte/i7+w4DAK6p4byPrxMjjNfVu9lx+/v7WV+mGwEiwKqO8bos6uNjDx0FwMbPpIQM0SCAR0abNF26g/X1yUeOAUA1wF0gmR91wXfFrmkEIcVnHjsOwND2QOaRVkNfV++awSCg+NzjJwAkjNEaay9QGn77AoA9dX/wZulXvHAfpgEF+4AVZ25sxPxrlIrnXznvNtsLUd8TzQvtM5mEUlgHxkuUiOxGX0U5vwsMPpmk2fZ4ibxzXTiO+fk9N/W3DaTlx8s2w4pXfh5t29SfVwHgs9zXJpD2Eq43Xro9/vuNF281ei1fbPYFX+5RT9Y3XbYdnIn8uou2mPWlkDy0RmQDvPaiLTi91kdvGOL6fXNGr01YCkWm3KjpTNfumcXDh5ew3g+MAQW/QRBSZLwd6pDeAcBrL9yC2586ifV+gOv3mQK1AslpTefXVbuncduTJxGE1Bh4VIYgjHhTu2bPLO54+hT7u3FfAvPWGtIxAeDV52/BU8dXAcAccFelY44I1F63dxYPHlwEAHPAXcX8GuE47p0bx8yYj/8TAXzm55c8/GOUSfm1e2ZACPDX9xwEAFyz2xAQlfQ16rl1XXQ+/d19hwAAV5oCyBIJ+KgTTH4+/c1XWV8m38emYHHM+gpGvkbw8fnbrx5Cs0Fw+U6z8dKoPoCy/+w0AFrynM3awEqkd4J5jk3ml+Dc54CCTfP8eOM3761le+Hui+eF9heiRSaTE1JFIePR/nHkm0kZSbMj5vmsl5xE1zLzq+U7mtqZ8xquY5O8jmr6bqVQ8sp7M8e+zA6c94Pcd5H/3ka5CqQ9B+ALtpt4sdd1e2fwHa/dhzdetg1vu/o8o9eKKPx1pTO94ZJteOX5c7jyvGnMX7nT6LUiNkBdO6Jvv3YXJloNTI/5uPXKHYZ9ySe+o14Lv/aa5Ni96fLtimcWS+ztUM9F+ubLkl5ef4nQu1rRlyolcDRAdP6K5Jx6vSGA7AsYQ3Xd1N52ddLXay40BJAVqYqj9vVNr9wNgLGaTBkwIsZQXX19w3W7ADCQY6dBwAYgCbOoARAlhMTfwfNm2rVIrXvRwmqUhej0WBOX7pjCkaUuZseb1cIZBOM16ndx35ZxzE008eiRZcyM+ebjJZiQ17EQvWjbJKbbPp45sYq9c+OYm9BP7fQ8gobk+zhqX5fvnMLseBOHFzu4atfMRkykHwHwDkKI0DCVENIC8E3YTPO0WvF8YihaLNhnfrmW9ija+HWFmQOgsOhzhcmU78v+AlnMePQtj1dLuFFO7QMwkrRa++e9ZAPM+nh5uWuqfZYvwOb3WdKKG335Da8QlALAukQ3b5Fim5GmH2d3DotS+icA/sR2Hy/2IoTg17/jlZVeK/KBqYvJ5Dc8/P2P3QyALUqMXiuiytf0Jdo21cZnfuZWhCHFzJh+MAOQJHNmvtw1SDsB4HUXbsG/e9vl2DbVrmBunvTFwyvr8NYCGNDxDdftwoXbJs1N/UUplDUdx2965W58/JGjeOW+OWyb0k8SBThgtTGMtG959V48fnQZr7lwi1HABrCxTLl3vGI3mu/2jFlfgJwxBIx+HN/9hgsBAG+5ygxsB+QTOd8jxtecfP3gGy/GwpMn8aO3XmoMfsn6atcwYXrXK/fgNz/zFL7hul3G/0ehFLYGwIoQgrdfuwsfvecg3nrVzgrjJWHKjTheDY/gG1+xG39170F83bVmm0ysLzHjcdTx8hsevun63fjzr7yAd71yQ9SVfwbgfwL4a0LIj1FKj/EHCCG7APwhgPMB/NpGfPhm6ZXMpJ5YNs8XesG6wMyRMJlsM3NEnl8uAI++pK920z7QwXvhNQjoyGqTUUvGxLQPPLrb1zBkIQiEEFBKnQD48hustgEYXiw1XQQM2QaQPQlpxf71SxR4Y+u8dxJI2yz7laRjigz9Rj9Zqy5mhVT5GmPhTQEhXirJ6ajXQkII/t3brqj0WmE4Q01AR7Ph4Q++97XV+vKSOHF+LtR1HCdaPj78AzdW66tRTMesi5E21mzg/d98XfW+JJ5ydYAdX3/trup9STzlRj2Obb+BH77lksp9iTw66pgwve6irXjkfV9f6Rom6qsOJhMA/Oitl+Ka3TO4+TIzdihQTGcC2PlVxzX1Z7/uSmyZbOH733CR8WuF3oA1Hcdf+Iar8Mrz5/DOiJFp1pcgLGUwemonAPzyO6/Bu165BzdcZMam1awPAPg2AN8M4GsJIQ8BOAzmnXY9gAkAnwUD1DbLUvH5RJZpwhhWNiWUCcPdLUmg0LvNiYW7uC/7C3dxX1NjdpeEiedXdr5qHRjyZYCoG0BtPvzDnfOeouUnm+Z13B9HqbxJfTJXdSEEobh2tH0cZX3Zvn7lve5iZuHLiZFGCPm+6K9/TyldSf27tCilH9mgtjYrVYlXQf0eaaNU3JfIO8RmXwLPiV7MSLMfv57dhanHW2uU4p89CEO0Pbbb6MT51SDFFEoHdmFETLk6whlGLVGaqAt9CUMjamROVN4IaHjoDILM73o1AWkt38PbrjFnVwF8R1Qk7Ry9rx3Tbfz826+q3lfu+1gXo2PLZAv/6vUXVOxLIIWtaSHa9hvGUnndopSGhJBvBPB+AD8G4KbUw4sAfgvA+ymlYfHVm3WuSrxAdgBQkKU92l6IOsoYkqWv2l4gi84vF5iFfB6dl5PZZuZwxYlzIQg5SXMQUgShC0Ba8n1s+V5qDm3/OpHewLftrcVL5vll+/olY8rZH6+sIqBXk+1U1bK1/fBhMDPbuwCspP6tKhI9ZxNIOwclAoZcBaycADqEE6YIJbd4zREx5epk8FUtoeTUgfOr6RUBhbqYX6OUkPnlwHEUAgrRDrJV82mhdJha30nzGwSD7sYw0kYp4Xg50ldfcN7bnmBKmYWW+9IpSukAwC8SQn4JwFUA5sBAtCc2ATQ3quERECI29bdZUmDIAYYC4C4zpz/M9jVuaO1Qd/kSYMj29Ss+jjlDePvHMXt+UUqdOL9aOWZhzGSyDTzmxssVhlV+A9+FNS1QZMrFwJADfQkDBx2bf3HgsT2iXVHlfqx8KvCDYKDY0ejfP2Cpj82SlAgY6rkEWDlkNAhkgSFedXmkjVKivlwBhgBJXzYBvijlNAhp7EXDb3B2ASuBV1S0sBrV82uUkqWv2p6YSPuyPQHYIM+vUaspiKt3QoIkALb7AcV4y3ZfYi9F2+NlUhFothkq4Gjlr/lOMGAkG6y2+xJ5t7kAPIo3WEPMOCKhLHqROXgcHbg/xky5GLCK5oS2xyvHxKzLVmPUyocguLBGA4ob+C6safnnZ64RDqyFgKJSISaH2AZEJSEItgBkK1dzSumHc//eDBZwrEQAjAvofbyTFhZRcqsAn1+cmCRAmgOeJhsQGjFKCSWnTvSVjFfDIclps0GEKZT2b7SC9MIgsD5hEvdlH+iQeX7ZNnn2GyQzMQHcOL9kIRv2+xIzV22fX6IihLwZwHOU0hc0n389gFdt2mjYrXxSc9+BFMqmKOxpSK1/H4XebQ5IFV2VUMpDEOxL73gvvAYOpGMmjMcc88s2UJtbd7gkVQQSANmFNVr68/MMPtv3bT/aNOHhDC4Bta6p0gCuhHFHleberG+znCih2a0DKLnMXBOwDMB44okJYJmRJkj/cmF3yBekbLkCWAHFtLSGR+ympXkeaMSU4+UCA0YqCXRgwlQIG3CgL1/g+eXCePkNT+gNaPv8aubSmQBHPHMEXoq9mjzlNqBuA/Ce9C8IIT9PCDktef6/APDHG93UZqmr6efZAPa/j150HyymY9q+Tki822zfH70sMwdwa7xc825r+VnGoytpj/mUU1ekinmGqCuMoXi8htm+bM9z8qERLqxp2edn1x3cx9r2cWz6+c0cNwDkgrQzYInWtkgrTs76Nst+OSuhlMSvA3YvOjFgJWSkWWkp+mxHPdIkqVGAKwBfti/7Nw7xeW9/oZCYt/Jyoi8RkykIHfAOETDlHBivVoNkFqGASww+94DHZsMrjtcwQNu3630kKdFJPwbmjbZZjpbvZYNcXGAMATzh162wAZlHrfWFqGTj14XrF++FlwtMufz8KwFgbJ9f2fmXK6brrUZ2fu8MUy6nOHGGyZTzBnQOeIyZhbwv2yEbnnCNZnu8uO0BpRx4tJto7QyQRgi5kRDyd4SQ/YSQHiEkEPwZ2u7z5VK+ZMcKcMMjzbVI3uTGUQQeba7d453HFwHzywWAT+QB48bCXcwstD4xEabCutCXV5DCumCmLPIi6wUhWpYBGN/zpKERNkvUlxNME2GYhf2+NuulU62cDHwQhDHQYLNaDnq3ycKxrC9EJRtg9vty0yPNVeldsS83GFZ5AMaFzWigOF4ukB3Sn58PQXBnvDgw5EZfac9oIDVe1gHkHIPP8lzVruNlVISQbwfwUTBg7zkAdwPYBM0slkiq6ATQoZAE2pycJBdotzzSRClbLgBpQubXMIRHkp5tlHC8HFggy8bL9g0tPTGJ01cd6Mv3ikymngs77o4yrKR92Z5g+h46nSDzO1eYEy/W1M7NenFUXr7Sd8ArCoiSh3N9TVgP/xAz761fJyQbv/b7EigonAIeXZNQ5qSdfM3hAMMdSFJhXQE68oBVwmRyQ9nB++k5sHZMf34eeHTlOHLP6P4whO/ZDTjL98VtXKyuZ619crbeB2ANwDsopV+03MtmQXaj5QaINoGhIlPOBfRebJIaXaQtXgtF8et9y3pyQML8cgKwEjP47N/QiuPFzM0tM5kk6au2zfObkRcZN28F2HGcdiAtrQjABNa9tUQMPub5Zff8yhuuA24AfK3ccRwGIUJqfwd5s146lffhYx6PDkg7BQlztoEO7t2WlyHZvm/HJvU5Jrnt8Uo8kFlfQUgRhA5JYblJvSNAWn7d4QrA18qNVyIJdGO84pRTBzbvAcF4OQo8unJ+5ZUwLmwCADkPvpZ99rH9EWF1GYC/3ATR3CmhSX2MkttbXIko/C6g9zJJIADYvO6IvEN6ETBkS08OpBhW6b4G9j2GZNJh20wAV5lyImlnz1Hg0QVpp++5KQmUMfis9yVgyvUC+wBfvi++s20bEN2sl07lffhcWcS0Gl5mY86F8A+g6KfownjF81XHQhCauY1yVxbuyXi5ZgYfAY9DtwC+goSS9+UKYJWXKlo/v1IADNyRdvoF7zY3+orPr2EiabbdE5BIOwepVFib30X7I8LqGICB7SY2KykRVT6+2VqcNDUlZvCA7RAEMdBBCGBzE1nKsLJ945Aw0mwvRGUAsu0JQDM3AQAiJpPtiYlAas0YabaBDrGk2fZ53/TdlATy+PV09Rxgyvk5phylFH0H0jHzKacvAiCNlj9ls1yqPHvVBWAI4P6A7vXV9LIJv4OAWpfexQvkTAq4S2ED2YW77etX3FeYLNwBdwCFhJnjikdaFnh0JWxABgzZvk7kN35d8W7j53de2mlTNQSkgMcUQ9T2WAFiAHlT2gn8DYB3EkJalNK+7WY2i1HlPSIxqXcg7XGYkzwAtkMQxMCQzSQRQMyw6jmwEBV5yvUGDgAduZ1HwK0d5EEOGJpo2b2ES9NEbU8wPQkjzfZx9EicNpSWnNruqyVJE7Ut0c1LKHuOSESaHhH6YdoGkBX1PkLI+/K/JIQEgudulgOVD7TgyWS2q9nwCimUTvTl5/pyQHJKCMkkNbsioczfH11JLywyYOzP7YHiPMeVvmKmXJ7JZPs4FoAhtwBR586vnCd5PwLbba4dgRTzK0gYoq7YCwBZANnmOW//7sfqvQAWAfw1IeRCy71sVlSiHVHALm1YGNvthOdXEbDqD0P7jCFHGVYioMMNgK84Xk6Y1ItCNlwAhoTjFVgHYGThH9aPY/T5Qe78sn0c/YaXGStKKfs+2h4vz00JZf7e2BsyPMr2dVVRxPDPZlmuZsMTbOjYPzS+RzLML1dYCr7II80BhqjveUUJpeXjSAjJjJczgIIgjQ+wz7CShg0405dbxzEfVueaFJafX85szAmAWts9AcWQM1f6ir2/Uwy+l720k1K6DuBHANwC4FlCyGlCyLOCP/tN3pcQ8h5CCC35o9yRJYR8MPXcyyTP2UkI+TVCyCOEkJWo/68SQn6OEDIteP6HS3q6yuT/uVHVzMl9ei4w0nKUYcAN5pdIQumEt5YozcqBi2GeMgy4YW4uTce0Pl5uMr+kALL1iYkYcHflOBaAbQf6GuQMsQH7DKu85NQV5pfUI80ygCwqSqlX4Y+z1LqXSzVzjDRXJJQt3yveHx1gKeTnq7ZZCrwYI82tVEUgCeQB3AGs8gwrF2xbAEGqogOkgvTnx15kjoQNcKB4mAI6APsbYPkQhHieY/s45qWKQ/sJukCRWejMPagQGmHXu80JaSch5E0APglgAsAQwDrEu6KmZ9YDAN4veewWAG8F8AlFX+8E8IMAVgFMSZ5zEYCvANgJYCF6vzEAXwfg1wB8LyHkJkppR/Dy3wZj4uXrlKync1l5LwwXpJ0Nj4CQItBhnTkhARRsT5jEIQiB9YlJ3owUYKwO233lb7QAO6a2JZQi5pcTAIwAqHWBWegq8NhKXSfGmg2EIcUwdMAzxysujgH7C7685JQzv2xf7/Mpp8lCYRN/2qx6qtnwsNIdxv8eOiKh9D1SAKxc6CsvoQypfUAByLJXXZFQAtHmiWPm5vkN6YEj96E8w90Vj7Q8Myc5jrY90nKAqCPMr0IIgmvnl2PpmM2ccsg284tXXtrZt5za6QSQBuC/AWgC+D4Af0EpDUuer1WU0gfAwLRCEUK+HP31A5LHdwD4XwD+CsAuALdKPubnwEC091FKY9COENIA8GkwsO47AHxE8NrfopQ+V/LfsFa+5xUZHQ0PnkUJJSEETS9r9OyKlw9QBDpsU/hlEjfbC76YmeMYANOOd2Cy5/2c9Qmmq8wvcZiF7fNLCDy6wEjLT0xi5pft4+ghpGwB2vCIMwyrvOTUmb4KktNI2umA7GGzXholmn+5sohxLdUa4AwrtyRuvIdhjpHmQl8tBwG+/EZ53Jcj9+2+Y8cxHwrnSl9FwIrdH233ld9g7Q9D+B6xuqYFium+LsxVAZGpP7X+XQTEAPJU2x6cZX9EWL0SwF9SSv+sLhBNVYSQ6wDcBOAwgH+WPI0DbD9e8naXRD//Kf1LSmmQeu8dFdq0XgU2wMD+wh2Idh6HeQDGDWAo3Vc/CDFmHVBwU9rJP7+fA4as99VoxL3wcuG8j1N9hm4dR1E6pgvMwnxoBPf8GrN+HLPnlwtyeSCRYuRTo2yf9wXJqSN95dNXewM3+tqsl061fFIArFyQUKYBGEqpM2EDfsOLpW2JJ5P98Uozv1yRUAJZebor6YV8o9w1wIqHRhS87iwfR88jaHii88v+fRtAAah1ZV6YlVDav3YlabVJ+IftOSGQPo4JQ9S2GgBIA7XJcbQ597I/IqxWAZw5h5/3b6KfH4wAr0wRQt4D4FsA/Cil9HTJez0a/XxH7j08AN8AIATweclrv4EQ8vOEkP9ACPkWQsiMZv/npFp+1uzWBRNxIOrLMSaTKOa8N3CAKReNS2+YA6wc2bHqOQaIxgBfoS9HjmPuvLc+MckxrGKTeuvMwixgxZIy7Xtr5c8vV4AhPl4cEHLFhJcv2inNmgNbv0408vdGN46jzSKE7COEfIgQcoQQ0iOEPEcI+S1CyJaNfB9CyBQh5FcIIY8TQrqEkEVCyOcIId+o+IxLIv/bg4SQPiHkGCHkL13xpwWyJvWAGymUQDZNlF/3XTjvWykrkoEj1y+Ay8CzAJ8rfeW9omwDQ7wHft67stEEcIaoW4AV64E4x3jMA0PxeW+7r1wIgmvm+TEjbRhYP4aAALAK7KusgLSVkhtSWFeknR+HXDpZaxFCxgF8LxjA9b8Fj18I5l32Z5TSf9B4y18D8E0AfoUQ8hYA9wFogXmk7QLww5TS+yWv/Z+5f68QQv4jpfT3df4vG11FIM0+0AEI+hrYZ8DkY86B9Hgp8yw2tPgEN8/8mpiw+9VvCwCrvgNAbcKUS45ZbxhYP+9bDdF42Z8ExIy06IY2DCPAyjYAkzuOsbeWY325srBKgNoAQDM1XvbPe0rZedVsEPQGbkgoRZs5gP3xslWEkEsBfAnM5uIfATwB4EYAPw3g7YSQN2psShq/DyFkDsAdAK4D29T8IwCTAN4F4J8JIT9NKf2d3Ge8BsBtAGbANjk/CuB8AN8G4J2EkLdRSu+qOBS1VdE83xVZjYe8eb4LAExacuqKVJH1kCgoXAE6eA95AMY20AHkj6Mbnl9APjTCDYYVkAdE3fJu4+d9b+jGdaIQguAMkJZVdtg2z+clAqymx+zDRsWU082wAQD4BQB3EkJ+H8D/Qyld28DP+k4AcwD+mVJ6MP1AxCL7EzCG3E/pvBml9AQh5CYAHwLwL8A80QCAgnmsfVbwsi+AgYd3ATgBYE/02vcC+D1CyIBSKvNu+xGwhFPs2LEDCwsLOm1Wql6ng6MnOvFnHDzcxbAfbuhn6lQ46OP5w0ewsMBIjEdPdNEfUOt9NUDx7IHnsbBwDABw4nQHDQKsrgbWegsjJseTTz+LBRwCAJxZ6sDrE6vjtdpnfT32xFNY6B4AACytruPsqZ61vlZXV3HfPV8BADz06OPYsvQM+32nh1Mnjsbnm406sspuGPc/9AjaJ58ApRT9YYijhw5iYeG4tb72LzJg46sPPIDhYR+dSFpz8PnnsLBwuPbPW11d1To/Hj/N+rr73vuxcqCB5R7r6/kD+7EQvFB7X7r19HFmHn7nXXfj0EwDx9bYcd3/9FNYWHvWWl8HDg0AAF/44pewfdzDgSU2fk89/igmTj9p/H66x6msDr7QBwB87rbbMeYTPHKKjd9jDz+IwSF7oNXRQ330Bsm98IEjrK8H7rsXJ56yPwG2UP8TDPz6KUrp7/JfEkJ+E8C/B/CrAH50A97nfWAg2v8B8F2U0mH0/B0A7gbwG4SQT1BKn0695oNgINrPUEr/R+oz3gA2N/sIIeRaSulA8/++IZWWdlJKnfFIS28YugUMFdMxXejL91LebY6kKrIe3AsbAKK+QveOY1MA8DnblyPezGkGX8v3QIgbIQi8r54D6hwg7fmVZli5AR4DiIOVXFAzAe5JdF0B0j4KYAVsgvR9hJCnACwJnkcppV8z4mf9SPTzjwSP/XswZtw7KKVndd4sSu38JwDjAL4RwJ1g6aPfDOC/A/hmQsgbKKUH+GsopR/Kvc2zAP47IeRJAP8XwK8SQoSy0whg+wAAXHnllXR+fl6nzUq1/fEvYazpYX7+JgDAX7xwL7ZgHfPzb96wz9Sp2ftux9Zt05iffw0A4A+f+jLGKTA//warfY1/4dPYuXsP5uevAwD85iNfxLbJFqam1rGRx6ms/M98HHvOvwDz80y10vzqAvbsmonHz0at94fA5z+FCy6+BPO3XgoAIHd+Fhfu24n5+eut9LSwsIDXv/YNwO2fxSWXXo75N1wEAAg/90lccuH5mJ+/xkpfAPDC6XXgi7fhsiuuwvxr9zG526c+gSsuvRjz85db62vnkWXgrjtw5dXXYf66XTiz1gc++xlcc+XlmL/5oto/b2FhQeu7NP38WeCeL+Hq616B+St34vBiB7jt83jF1Vdh/obza+9Lt+iTJ4D778ErXvUavOaCLXjy2Apwxxfwyuuuxfz1u631tXj/YeCRB/Ca192IS3ZMYfK5M8CXv4zXvvqVuOVyc4tP3eNUVgeaB4AnH8Pr3/BGbJlsYfjYceDee3HTDa/DK/bNjvz+Veuh4GnQZ5/Cm255M/yGhxP3HgQeegi33HwTzt86Ya0vG0UIuQSMgf8cgDyb/r1gc653E0J+VrVJWvF9vjX6+cscRAMASulJQsh/B/C7YPPKn019xqvANjB/O/0BlNIvE0L+EYyZ9nawuZi1SkvJuJTSBY+0ZioApO8YoLDaY6eAU1JFP2HwucJABrJhFs4BQ0MXAT4RkObA+dUgMUDrSsppHCbGpZ2OADCtHMPKtrcWr3woXM8RGX8MWKVCEFxhRQPZ0K5NIA2YT/19EsCrJc+jkt9rFSHkGgA3AzgExghLP3Y52G7nH1NKPy54uaw+DOAVAF5JKX0o+t0ygD8ihIwB+C2wSeB7yt6IUvoxQshhAHsBXAPgYYM+ai+RD4wLF51Wwyt4a9lM7OBVlJy6QRvO99V3wDAy9kgb5PqyLSXLecoxzy8HpJ0Sby3b51cyXlkJpe2+2rnjyCWBtqXD/Hvn6nHkC73EPN/ueR97t+XDGRw6v/zUfdL2+WWpOAv/0/mwKErpCiHkTjCA7CYAn6v5fXZFP0V0Tv679OYrf/5zkmCr9GusAmmupj1mARg3pGRANknXJali0xMx+BwAYPy055c7TLm0B59LXmSZcAaHvo/Mgy85vwhh6adWe/Jy85zAvv0OIE7tdKOvLMPKdgolr0RymvKUc+Kcz0s7N8MGQCn1NP+MOqtXhQxcC6AN4AcIITT9B4l/29PR774FAAgh09FjZ1IgWrpui36+1qDHk9HPSYPXbEgJwwYc8IAp+NM4kKoIiHxz3BivtgBIs33z8BsePFL0IrPdV95TbhhShC55fkVAVd+Rnce8150r6YWFvhwxgy8AooEbwGMe2Hamr8JxdMzrLn9+Nexf7y3UldHPpySPc1nlFRvwPqeinxcLnn9J9DMdIMCffyER64xEr7FSzQaJ70POSgI50OGEh1WKMeTUeLnpRdb0UuEMTnmRpQA+h7zuWikvMpfOr1YjzXhkCbq2JZQ8TTQtaXbinM8BQ+54pOXSMR0BrArpq44dx6y00945bx/yPEcVscPeDRYy8EHBU56T/B5giZy7APwNGNvsuej3rejnDCGkRSnt517HNTH538t6nAWbwNHUZ1irlp9lfvWHISYdQMkZwJcAMP3APpMJYOwJJxl8eSDNkZtHery455ft8cqb+vccYZrkmXIJM8c2YyjblyvSFSnQ4Riz0JVUMn5+FxlpjhzHIAsgu/J9jMdr6Abj0VJxja3IjiP9+7kNeJ+PAfjXAN5HCPluvkFKCNkG4Gei57QJIeOU0g6l9KnIOuQKAD8J4Hf4GxFCXg9myQEA0qTRc+VTe+RQH4OA+fAtRR6Pzz37DBaGz2/I5+nWieM9rHeHWFhYwOHIu/PpJ57AQuQpaqvOnuliaYWN1zNn2ffx8Ucfhn/i8do8G6vUylIX60Pm4fvACSY9fejB+7H6nN170epKByuUyfAfOszsAO+79x4cmrBzDePHqN9Zx9HjXSwsLODJZ9nS6a4vfRFtyyy+fjfp65ln+/AIcMcXbrfaE+/ryDHW14HnevDoxvpY636XGqDYf+AFLCwcwwtHuggG9v21efr3U888iwVyCCdOddAPYL0v7hn9eOQZfXZpHe3h2kh91XHNO7nOru8PP/oYZhefxlq3i5PH7fpFA8DZLuvr0cefwI7V/egNAhw9fAgLCyes9GMfFTl39R1gk6OP5UMGAIBS+gCAHxa9kBCyAAak/SKl9JnUa04TQh4HcDWA/xz94a8ZA/BL0T8/l/r9LgBT6feJfj8FJhMdA/AZSukx4/9hzSVKJts6aX+h0PYTLwyAM7/s98Ukp2mGVejEwip/HJ3xK0gBfJz5ZbsvzyPwPZJiWLmRXphnWDknCSww0hwBrIL8cXRkvII8IOoWgMz7sz1eBYmuI8BjYbwc6cvR4ivgkaw5JO/zy2Byz+8AcDUh5HNIPGpXAKxH/04rEP4NgE8C+G1CyDsBPABgH5jf2mMArociavtc+dQ+MHwK4bNP481vvhXHlrvAbZ/HtVdfifkbLtiQz9Ot21cexT3HD2F+fh6PHlkCvvhFvPL66zB/7a7yF29g/cOx+3G8v4j5+Xm0958GvnIXXvuaV+HmS7fX5tlYpT7y3D04sdLF/Pwt6D5yDLjvq7jphhtwzZ4ZK/3w+uD+r2C1N8T8/Btx7O4XgIcfxptufgP2zI1b6Ycfo7mH78Dc9Bjm52/Aw8HTwFNP4W1vmbcuV5x96A7MzbC+vrz+OJovPGfV+1jU1+eXHsH4ySMb2pfud6l926ewe+9ezM9fi785fB9mhyuYn7+19HUbXf5nPo69kWf07z/xJcx6iQ+4rVrtMc/oiy65FPNvvgStexewZ9cs5udlLlflVcc17+hSB/jC53Hp5Vdi/sYLgNs+hQsv2If5+WtHet9R6/RqD1j4LC6+9HLc+oYLEXzy47jskoswP19Get+YcgZIixIzfxzA94ABU5OUUj967NVgu46/RSmV0f7LiocMCNMwR6ifAvDPAH6JEPK1YNHt4wC+AcCFAJ4B8N9Sz78KwG2EkC8DeBzM9HYvgK8FA+uehQTQO9fVbngZD6ueAx5WgMC7beAOYJXxbhu4Ie3Mj5cL0k4gC/C5wvwCsgCfK5JA3yMgJAE4XJHe8fM735ft8XKVWRiPVw6AsT5eEkDU9vlVZGJy5pcbQG3iDcg2JzzLiz1LxZlisvSHmdzzansfSukxQsgNYJuW7wTwbwGcBWOq/QrYfGoprRaglC4QQm6MXnNr9OcggP8K4EEA/wg2L7NasXwlDJ3ySGtlPJkc8khLhSA4JaFMm8E7JqF01YMvmU+E8Bzw/AK4FxkbL1fUJgDrq5867104hkDWg88V83yg6ME3MWEfBvE9ntqZ8m5zYLzyIQiuqJl44MEgCJ3wK7R/BgEghLQAfAIsdOAM2E7iVOopBwD8IJh/2HsrvP/VAN4EQcjAqEUp/Ww0kfs5sAnZT4DtZj4L4P8F8GuU0sXUS/aDgXk3AHgXmExhHcCTAH4PwO9QSlfq7LFqtZsizy/7X6J2UxSC4ABgVfCUc+Nm2/Ib8UI0DCmGIXXiYpgOjUgABUeOY5DryzIAQwjJAKLOMHM4oDBwE4BxVdrZy3vdOTJeBcDKNhMzD4g6IjkthCA4EixjqZ6Mfsq2g3mscNkmaKX3oZSeBPDT0Z+4CCFvAWOx3ZN/o8jT9jvzvyeEvD/6a+E157rSvjl8Qeo7sLjyG8TZtMe+g335KeDRJfP8tNdd3yFAtJkz9XdhrAAOiCbzCdu2GrxaKUC0P6TOjFc6/MMVAAaIrhOZ42i/rwSwSo+XA+AxD41wzLstnb7qwmaOE0AaGAj1FgDvA9sV/GWkZJKU0kVCyBcAfD0qAGmU0seRyAKMi1I6X/L4Q2D+azrvdRBJ6IHT5Szzq+EowJeSnA6DEMOQWl+IAjlgyBEPKyAbguBK2iMAIWDlynEsAo92x6sRmcpyJporDL4E6Mimidruy1VJYFvAsALsfx+L4QyMoeBbZigUwyzcuAdZKh6q9HWEEC+dhhkFMr0RQAfAXefofXj96+jnn+s8mRDSBvB9YD66H9X8jA2rNBvAlY0TgPUVhBRhSFPAkAOLvkbRPN8FUCENdLjUl58OZ+B9ubB4T82/XDE3B7IhCC5d75s+iTeYBq4BVvGGdBBvitmuZsOLrxOuAHwNj8Aj2RRKF64R/HowDEIMgxAhdeTa5SUhCAMH5qr2R4TV9wC4k1L6X6LJk8hL4wAAu+YQL7MSMaxcuKml+6KUOsP8avteKvXODSYTwFgdPJwhZgw5dhxdYZoAeWmnGwAM74EvqLoDh84vgRTW9uSEL+xc9W7Le36NWZYq5iWn7gGi2R1k26lkBYDPkXuQjaKU7gfwaQAXgdlzpOv9YAnkH6GUrgEAIaRJCLmKEHLpKO8TvZcX+ctmihDywwC+G8z/7M9zj00SQhq53zUB/EH02X8Q9WK1OPusnwLSXLjepyWnsazGgXO/2fDiRVXMsHIAGPI9UgSsXJh/pdIeBw4BtX5KQukSMOSnGI8uXe99z3MmvTBdaWbhIHBDBQNkpdb9YegUwJdmrrpwjfC9ooTShfO+EVndDDelnZm6GMxnTFVnAGw9B71sVlTFsIHAujcNkGXmDAIKSu175gASSaDvAQObXbG+1vuMKdeNgCHbC3fAXaZcy/fQy3u3OcBIS6ecunYcXZMEEkLEx9G6R1o2bKA74MfREeaX6PplsYrebY74TgoAUReuXRbr34L5w/4OIeRrwPxfXw+mNHgKwH9KPXdv9PjzYMBV1fcBWJDAcULIZ8D8aAHgFgA3gtlo/AtKaf4O/BYA/5sQ8lkwb7QZAN8Y9fLPAP6D2X99Y6rV4GwA6sz3EUjYAMOAOgbAkASAcUhC6acAK5cklEx6lwX4XPAiazWSvlwBFIBsXy5d79OS5n7ghs0NkGXw9YchZsebljti5Xtpj0eXzi8vszFne04IMNCRENZP1yGyAyEkPu/5JrnN8XIFSOugPBr9AgCLG97JZsXVajQQhBTDIETDI84wv1qNhpOMIakk0AEgbbGTXbiPuwDANESMNEf7cuCmlgln4ACMA+OVZqS5tOBrN9xjFualna4AoqJwhlbDIeZXJB1mCwUHzi2htNP+d9FWUUr3E0JeB+C/AHg7GDB1FMDvAHg/pfTMBr1PD0yG+SawwCaAAWjvBfCblNJVwcc8BeBOME/bnWDzzwfBWG8fSUtKbVaaDeAKQxRIMdIc66uVXrgHLs0nSAZQANyYTzT9BIDpRddV29d7IMuwGgShE3JToMj8cmHzHmCsywTgc0hymgaQHWLKtfxsX64Aotz72yVbIEII2r6H7jB0JuiJVzNi+nYd2Lx3BUh7AMwXo5VOV+JFCJkF80f70rlu7OVcaZaCTz3G/HLgouNiqiLroSEAhuz3lQaGujF6b/9imD6OrqQ98h5cA2AAfhxZP10HdmF4ZRlpDp33wuuE3fPe8wiaDVKU6NoG+Bz1/Iq921JhFi5MfEUMPhcWxzYr8n/9AY3nPQeFZ63u+0TPHQD4Ic0W+WueAvBtJq+xUUkyGY03Tmxfv4BsCIIr11WAAR2UAkGYHi/738l0mmjsBesAC6bpJZ5yvYEbGxQAT3tMAD5XGEOZvhzz/Iq92xzxsQbyYRbuhDOkmZhOAWlRKJxLc2gg6msQOLWmBZJUWBf6cmNEgP8F4HwAf04ImUk/QAiZA/BhAFsA/OE57+xlXOldd6d2+CJmDvdHA9zpq2Bu7sBNLQ0ouCIlA/IMK7ekna4BMIDsONrvKwMgOzZevaGD51eG8Rg4wQTISxVdmWDmASt3WNFiBt9mbVZd1UwZKrvkTxOHIIShU/MczlzKMOVc6Cvl+dWLmDmeAxLKZioEoecSwyqnCHCBdQ8kDBiAj5f9cwvgTLnUcXRlvNKm/g7dH5s55qoL8xyAXdu7g8A5IG2s6WUAPhfWHEDCeHThHuQEI41S+peEkLeB7UK+C8BZACCE3AvgWgBtAL9PKf24vS5ffpVmKQTRRMCFm0faZ8glqrzLAEzPQQCm1UjCGVy6ebR8L2YKOcVIc/k45lMoHRivPLOw2SBueMDkAFEXjqEfpUalr1+unPOAexLKYl9ueJps1kunMhJKhzYCeAjCYEidYAPwagqksC4s3tPMHMb8sn/9Arh5vlsMZIDN5dOeq65cV/OeX60JN/pq+cTJ4+inTP3d8pQjGAQsdXgQuJMKy+f3PUfsPnhxplzXIZYvkJJ2OqCycmNEAFBKfwjADwJ4DMAOMOr/a8AMZH+IUvqTFtt7WVaapeAS0NHO9OXOlzvDsHKtr9iTyS1JYJppAjgCPDZSzEKHPNLaDh/HPBPThUlTHth24dwCcqERAzcAGB7OkJUq2h+v9LWe/3Tju8jTRJPz3pXza7NeGpVIO0On7o9xInIQOKVUSCc194YBfI/EoJ/NajWY5JQdR3eAjmbOPN+VvtqZ+YQ7920/lULpyn0IyEk7HTqO6RTKQRDG4Sm2i4+XSwFnAPMe66VN/R05v4pMOTe+j7G004G1thOMNF6U0g8D+DAhZBxMyrmUjjrfrHNbaeaXF5Lod/a/RGk2gEsm9e3IQyEMszu1Hct9ZT3S3PFayQId7gIwgDvjtdqL0lcdDRvoDUP4nkPMLwd3atN9dYduMNKAfFhK4MROLe8hk47pQl95gG/gRl+b9dKptD9g34HFAi9+veoN3WLKceC/HzH4XBgrIFkQ96MNaVf6ajY8hLGnnDuAFWfAAMz6YMtky3JHrJqN3EaTI+OVkeg6dBwLDD5HznvO/IqBNEfu22O+x7zIHDDPT1e7mWXKuQLw8fPehTWaGyOSK0pph1J6ZBNEs1tpHxingI5GdmICONJXCnhMPDrsXwzbvodekAXSXFi8ZyZMDjEeW777qbDdaOHugtdKBhgaBBhv2T+3gKLXigvHEMgCtZ1+4AQYCrDznn8PO4PQieNICClsBEw40Fd6kwkAOo70tVkvncoAVg7Nc/KKAFck85zZ2xuETknJ+AKvOwicYfoCjGEFpJhyjiyQ+cYc90B26b49TIUNuALANPMSXUeOYzMl7XTJi2ys2UB3EGDg0DUVSBhpLsnlAb5OS8IGXJmv+h6TNLsgOXWKkZYuQsi7ALwVTOL5BUrp31lu6WVX6V13Pk1y4cudYaQ5BnQA7klOMxMTh9IeWV88hZL9dGExmgdgADeOY95by5UJU9tv4PQqC1vu9AOMO7JQyEpO3VnApKXDXYe8tTLSYYeOY1rq0+kHGN9iv6982EB3EGDMgWvXZr10KgGs3DKgTgNDrjBEgbTcOnSKMZQHHl04hkByDePSYVf64vMaDiC7chz9VNiASynN3IPPNeBxrNlAdxggCCmCkKLVcOM4jjWZVNE5aWdOQukC2QGIwgYGIbqOMdJasbTT/nhZGxFCyDsJIV8ghNwqeOyPAfw9gJ8C8JMA/poQsgmknePiN7A088uFm1qG+eWStLMpGi/7Fx0+XoOAxhdDFy7SaSZTp8/6coEFk/dua7jiteLnTXjtjxUQATApZo4LxxDIp4k6tIDJhUa4AvBlACuHjmMaQF7vu3Heex6B75GEwdcPMOFAX5v10qk0ANN36D6UBTrcun4BHHh0aKOpAAy50ZcfsQi5PMqFOTSQAkQHYZxq7UKxFEoaA1auAMjNyIMvCKlzADJnYQLuAFZjftaLzLWNTBcYVulq+wwQdYlUAKSknQ6Ml80ReRdYmMBX0r8khHwTgO8HsA7gvwL4eQDPAvgWQsh3n+smX86VZn4lkkD7X6L0jdYllLzdKAJ8Ltw80sCjS9LONPNrve+W51casHJlwpSWwrpiUg9kmV/uMdLcM+HNeMo5ktoJ5AGroVPHMc38cqWvNNN33SHgcbNeGsUBqm7km+POfSgloXRIMp8w5dwCrNLBJE55WKXDLBwCrNKAaNch5lfeusWVvni6b3cYYhhSZ44jk1A6CAxFfcWb987MJxq5YD9X+vJiuTz7txt9cWlnYqP08gTSbgTwZUppN/f7HwRAAfwApfSXKaW/DuAWAF0A33OOe3xZV3LjCGKgw4XFgrAvBy6GaeCx4xhgBXBAlOvc7d/UWj4zux1GAF/bd8jzKwXwuXDOA0UprAugI5AFhjquAUOBe8cx6ykXOvFdBAR9OXIc08B2xyEgjV8nesMQlLpxrd+sl06NxYCCWwv3xLstcAqwij3S4r7c+D5mvdvcYcqlrUhc8m7LSGEdm+cAwHovcEqqyNNq16IgKhc274HEi4yTHVyZfzGpYpAiYbjRFzP1T4UNOHKd4POv3tAdMg2QSDv5mtbmRpPNEdkFYL/g928GsAgglnJSSo8B+GcArz4nnW0WgCwA0xmwi7QLi5j0BMApb63UThoH+NzoK5GcdgcOxcKndvickpJF5q2UUvcW7ilTf1cW7nnGkAvnPMAYomlT/4mWG5agGS8yhyS6aYaoS+b5/Pzi30eX+uoNA6fuQZv10im+wGOpne4AVnnJqSuAVUYS6JCUP+3d5hbw6CYg2s735cjCnY/XcncAwB2ggzPSVrpsjebKcRyLABjO/HIFgOHebfy+7cr8fsxvxCxf/m8XaqzZiOY5bjHSktRO+2tam2f2FgBn0r8ghFwAYCuAL1JKae75BwBsO0e9bRayYQOdPvsSubAYTTO/XGLKtf008OiOVDEr0XWHaZIBah2SBLZ95jkxCGgEwLjRFzOpZ4CCS9JOlxlDvC/XpIppjzRnjmMzdRwdYvDFqVGc+eVIX2NRylbHsQn5Zr00Kh824ArTJAGsHPYiG7gDwOSPoysLUT4P7PRdAx5ZH2u9IJIqujVeS50ISHNkvDiQthox0txhWGXHy4W1EMD6GAQUaz13VENAMv9yQaqYLuZ1lzDSXLkPNRskVgTYPoY2R2QFwL7c714b/bxf8pq8DHSzNrDSXhjr/YiR5sAihvfVHYSOeWslO4+daOHuglQx6znhzsKdX/y6g9ApAIaf450+Yxa6AqSNtxqgNPK6c4nJ5GcBGJeADr7r6BIwNNZsxLu0LgHb41FfnPnlUl/r/cA5TxPel0ubOZv10infI/AI4kWMK4CCq+b5rgJWeaacKwtRPg/sOubdxo+jawwrft9ZXGfAkCvHMS/tdGW8+PxhkQNpjtwf+Xm/uM6S5l2ZT/DQLr7WduU4tpvJRmazQdBwYE0LRPPVQRDbAtksm5/+MIB3EEKmUr/7F2D+aF8UPP9iAEfPRWObxYoDCOv9oVM0WN5XZzCMzaddAKzGW5GHgmsATJMfR37RcaSveLyGTkkVOetyfTB0DlAAGCjUdWjiO9Hy48So7iB04hoBsOsEB1/WHZIETrQaMYvJpfN+vOWjk4pfd+U4jkfj5Rrzi59fiXTFjb4266VRhJBYVuOqtNNFplzXNWlnAbByo69kI9NNCaVrzC8OwPC+XAn/aBWARzfuQ3nAygWyA1A8v1whFvDjtuzYceSKk07fHb9CgM1X1+O10MsXSPtzMHnn7YSQnyKE/B5YmMAxALeln0gIIQDeBOCxc97ly7gm2xxQYMCQ7xEnJk0x0NEPmGTLkQXyeJP11em7BcAkgCibyLly4+Djtd4PnPJIm0gx0lySdo5njqM7zMI0wOcSs3Ci1cAwAvhcChuYaPlY7w9BKWW0dAeuqQAw0WxgvT9MMb8c6avViK8RgDvML34cNz3SNmujKjF6dgewajU8EJKSdjqyuCqGDbgxXpmUU4f6Sm+w9gN3+uJ9JF5kbpxffLzORsCQCzY3AOB7OWmnI8eRgy6cwefKfJX3cdZBRhoALEV9uXIcudXNUmcQYwIuFNvIHKIzGGLCcl82P/2DAL4VwNcDeBUAAmAA4KcppUHuuV8DFk7w2XPZ4Mu92j6bMHGJmysLmLz0zpULYRqwchGA4Qw+1wC+7iBwygw+DVit94eYaE1Y7ojVRKYvd84v3sdaBMK4c51g59Nab4j+0B2m3HgUvx7L0p0Zr0iq6BhgNe4o82u81cCp1Z5zTLnNeulUO2VA7QoDhhCSAHwOsAF4pa01nAxBGPLUTjf64oDCcsz8cqOvmJnjGmMoOm5n1jiQ5sZ4cWnnqmMhCLG0MwLSXJlP5Pty5/sYnV/rA0y03FBZAam+1vqYaLsxVgAw2WpgfRBgtRdg0vK5ZW3lSikNCSHvAPDdAG4GcBrA/6GUPiB4+nYAvw3gn85dh5tFCIlYCkEsoXShXAWs3AU6OFMuxFp/iElHAKvMcRyE2Drpynhx4DFwCxhKMb/WekNngEe+G7TuKBPztGMT37ivVdbXlCO7fHmp4rgr51erEZ9b/N8uFJfougbwbdZLp5gBNWNjbp9q2W4nrrbfSCSBjgBprTzzyxFAIfH0dWu8OGDlmoSyXZBQunFdjRlp0XzClXlh088z0tzoy1VpJx8f7t3myrqWf/9Or/acYn7x8/zkSs+ZtSPA5qeUAmfWephuN632YnVUKKUhmMTzz0ue91EAHz0nTW1WprgO2SVgqNnw0GwQ5ySBaaacSwBM2utuvR9g26QbE/I086vr0HHMAnwuAcgJYLXWH7oDwMQ7Vmxi4sp1IgGsegDcAoYA4OQqy85xZXLCpbBcUuPSee9i2EBecurKeb9ZL50a8zl71R1mNJCWnLoj7Wx4BM0GYdLOQeAMg48DQ2u9ISh1x1uLA/+c+eWKdJgDCmcjxpArLBgODLm2McfPJ86wcqWvQtiAI/ftNMDnkYTRZ7sm22nAyo2xApKN3hMrXVy6Y6rk2eeu+Hl+aqWPXTNjVntx48q5Wc5WokMOnFmIAjxhbuiYtDNifkXG2K4whsZTDKvVnn09Oa+YYTWIJIGO7CCnve5cApD5cTy73kdI4cyuVQGwcuT7yPuIJ76u9BVdF06usPFy5Tjyvs6suuUdMt5soDcM41QyVybk400/x+Bzo6/NeulUzEhzaGMO4H2FWO8FzgAdQAp4dChcpsCAcaQvDiiciRhDk44cRz5enPnlykZTev4FuANY8b5ORfMvV/pKACu3NubS0s7xZgPMgt1+TUWsqmPLXWfWjgAwPcZ6ObXad2bzHsie97bHy42V62Y5W/Guu0NAB5BlKbgyMWlEYQxxCIIjN46M5LQXYMqRi3Q6NMIl4JGP12qPSTFcOb9iJlMMwLjRV3xDW3MLgOHnU8JIc6UvzkhzT9oJAKfX+Hi5cb13WaK73h/GkhpXrl+b9dIpzvxad4gZDXDAKnAKsAIYwLfUGYBSdxjInCkXA0OOXO85oMAtBly5frVj5pdjwJCfHS9Xzi8+fzi56tbGXAwgc/N8R9aPYymJritzQiABrFa6Q2fm9gAwPZbIJl0hYQAJwD4MqfXxcuPM3ixnKzag7rvjyQREi5iY+eXORSdm8DkE8PEJwHrkreXKjTYthV3ruZO+ys+nM45N5ApAmiPfR34+nVhmUkU+IbBd4znAypXza9xRQJSfX8eXWV/pCZTN4n0l55cbfY23GghpAvC5Aohu1kunxiI2pkvMaIAtile6QwQhdWpe2PYbMWPIlesqkO3LlePYbHhoeCQ2z3dmPtHiUjK3gKE8I80VYDs/L3RlnhMDVusDEOKOBx8/bidXe07ds6dS82aXrqnpMXJJcpq+jtoeLzfO7M1ytiZbfrzr7tJFhye5rXbdMc8HEIczuLRT63kE480G1nvMW8uVCeZEamIyDKlzAMwpx3Ye88CQK8eRT0yOOwZ0JJ4TrC9Xrl9cYuqctDPq68SKa4Ao6yMB+NzoKwEeu5hsNdBwJGVrs1461fa9JHXYkfkEwDbnTscAjDt9tX0vxVx14zoBZPtyab465nsxkObK+dXwCCZbjXg+4cr5FXu3OeYFGzPSovmEKxYWsafcag9TLd8ZCSWfPwwC6sxcFQCm04CVI3N7IDtvdumamr5e2b5GbAJpm6Uszkhb7gwxM+7Ol4jLala6A8yMu3MxHI+YcivdIWYcukhPtBo445i3VrPhwfeIgwwYNj7HlthEbsaZhbub3loxYyjqa8qV8Wpmj+OsI9cJd49jHrD6/7P33vGOnNX9//tRv5Juv3u3r9e9F7CNsQ1mbQOhlwAJ+dJTSUhIrwQwENJ+CT304hBCAoEAAQwYbK9772296+3l7t5e1aXn98fM6I6kkTQj3buae/e8X699aa80MzoqM3qez3PO5/jl/TIFq7kMkWDARx5pi4KoX94rYXWRiIYWJ8g+mbiDFZdxXfXT5CoeDTLu2/fLf3HFwsHFjDQ/Td5jITL5EuCfcjKlFLFwgFyx5KsMK+v8m0rliYYChHzSzMISjBdyRd8sfgEV80W/LK5CVQmlj66p9nmsX8bQUPnZdXrO4Y8zTvAt8UiQdL7IXCbvq8lCVyTEXKbAQq7oK8GqK2IM5Iol7SvhsSsSLGdYdVq9t9MVCZYFmG6f/KhZXndHzRVRvwi19pR08M+Pba0A44+4rBWro2ZcfrlOLJZ2mplyPvkcu2wZVkr5Z2Xbmngencn45rsFiwLoiM/iElYPyWionMnkl8xoMCYu1njCT80GktFQOaPWT4JVMhoqZ1j5RRgCQ0jzY6acfZLslxJKWBQ7Ej7KsIqEAuXOnX76ztt/EzstdNixZ4/7Ka5YOFCOqz/uj7EqVArsA8lIByOpZNAWy0Ai2sFIREgTmhCPhJhN530nWCWjwcWMIR8JVvFIaFGA8dH7FY8Eyx5DfsmAAWNAcmzGX6VkYLxfx3z2OQYDingkyNGZNOCf1bRYOEBAUY7LL4Ko9f4cmTbi8st1wvqej8xYEyt/DH57zffn8FSaZDREwCelitYq6OGptK+uEX3mYPfgZMpXcQmrhwrfHD8JChW+Of757iejYfJFDfhrnJOMhSgZYflqIbO7wpfJP3ElzTFXV9hfJfN95m+RnzJzYHEM4ZfFVYBQMFAeg/llrApGZqFVZeKn322lFEFTnO20MGTHnuE4mPCRkGZ7jwYSnT0fRUgTGtIfDzNlti/2y0QUoC8eKa+k+UXoAOMH9uBkCvBPaRQY79HhaX8JMGC8X1Zcfnq/umMhDk8ZcfX66HvfH4+UM7/6O/zjYaGUoi8eKV8n/PI5dkWCxMwubiHTJ9APWALM6FyW7miIsE9KMfrixiBpYiHnq2tqvxnXXLbgm+8WLMaVLZR8FZewelgJvjl+8daCyomxX673UOUz5KPxlz3b3k/CoyV0+Ok7D4u/3X6pUrCwxGy/vV+LgpW/3i/r8/PLoq+FxlDb/SRY2RlK+kfgi9hKqzstPPpjBC/4lgHbCe2ni6E99dVPP2oD8Uh55dFvwuNcpgBUfqadpjceZj5rxOUngW8gHqFgfpC++t7bxLO+Lv98jtZ3KhRQZZNZP2CJHT1dYd+UYkRDwXJWQr+PzsU+23XUTyu11mcI/soEsMfip7iE1YP9N9FP4xx7ppy/MtJ8mmEV9WdmobVgEvCR5xcsvl/2a78f6DXHXH5aXIVFAa3PZ++Xdc3yUwklLH7Xh3tiHY6kklDAiss/ghXA2et7ADh1TaLDkTizub+ro8/vnyun4EvsP2R+MV2Hyrj8NOnrswkd/srqWIzFT5N3e1y9PvIFsL9H/vocjbi6o6GKFZlOM2DGNZSM+kawApuQ5qNrBCwOeP10LoaCgfK11E8rj92xEFZ1z5CPPDrsn92abv+8X8LqIbkCjJ77fPS7bc+q8tX7Zfv98dN41Vrs7YtHfPW7bX2//LToC4vfdT99t2BxjDrgMyHNep/8FlfA/K777Xf7Vy7ZBMA5G3o6HEklX3r7xXztXZcy6KNxIcDfve48XnvRho7H5Z+ZmOBL7Bkwa32k3tt/YId9dDG0/2D4aTXNPunzVVy2WPyUzmx9jl3hoC8zrPwkwMDidWKo259x+W3l0bp+DfhoEgqL3y8/CVaBgCoLj34a+Nq9jvwUl7B6qBSs/HNO2sUgP4kd9rj8JHZYJWS9XWHfdFWExffIT+bmQHli7KfPEBazttf5bDyxtteIxy92HxZWVqif5mgAF5/UD8Bpw8kOR1LJB159Lvf89bUMd/vr+7WpP87VZw53Oowa3vr8k/jkm5/T6TDwz9KI4EvsQse6Xv+c3HYhwU8Cn/39Wtvrnx8P+6qxnwYn1uSgOxYi5qOSB+v7ta435quVWmvS4reJu+VRsMZnK1ZWXH4b+Pr3c4xwYDLlu7hiVimGjwaY9uuC375fwurALlL56XfbPs7xUwmlvfzVV4KVOf7yU/kkLGYy+em7BbDJLNXy0dALgA19Rlx+e7+scdegj0zqYVEQ9Vup4p+85Awu2drPczb3dTqUCoIB5at5tuAOEdKEhpw8tFgT7aeMoZMHF+Pyk0nqWttFMBryzwDTPgH1Uxcka6XKTzHB4sTYbwNf63z0k5EywClmXJ02/axm62Ac8N+K6HrzOrG5P97hSCqxMjpOWeOvldqh7ihHZjKcttZfcZ02nOTZ0XnOXNfd6VCEVchGm/eLnzxELUEB8NVC0wafTkI39BrvVyZf7HAklVgCR9Ey9vUJz93STyIS5Fcu2dzpUCp41YXr2XF0lrc8/6ROh1KBJbhvGfDXeOLtl5/EgYkULz57badDqWAwGeX1z9nU6TCEVYJ/fpkFX9IXj3DBpl6GklFfrfBtGYgzlIzwnC39nQ6lgrPNCdVan63AnGFOQP1UhgFwuhmX34SOc02PgjPW+muCfOnWAYIBxeufs7HToVRw7dnDfOn2PbzxYn8NTq45a5j/uu8ALz9/fadDqeAl56zluw8d4uqz/JUu/47Lt7JnbIErTh3sdCgVvP9V5/CNe/Zz2ckDnQ6lgk//2nO4fdcYZ4mQJiwDa83fRZ+tM7HZnLD7rSTQEvjCQX+9YVZcfsv0tcY3fhtHn7Ohh4c/8FJf+cCCsSD9z2+8sNNh1PDrLziZSCjAqy/c0OlQKrhgUx/ffvflnQ5DEJYVEdKEpvzv716Bv9arDN+cm/50m+8yhoZ7Yvzd687j/I29nQ6lgrPX9/DSc9byy8/1lwBz8Un9XH3mGt5++dZOh1LBZacM8r5XnM2rLvSXAHPOhh4e+cBLfJWdAEYG033ve3Gnw6jhOVv6uf99L/ZV1gTAtWev5YkP/ZKvslYBXnzOWl58jr9Wj8EQkC/d6i8RDYzrqtXRShCWmlAwwPXvutRX/mhglLZ98s0XVVQs+IHTh5O8+Oxh3wkKF27u5WXnruOtPstkunhLP//2/57LVWcMdTqUGvwmovmZZDTEu190aqfDEIQTEn/NxgRf4qdMNDt+8ymw8NtgCSAcDPDFt1/S6TBqiEdCfO1dz+t0GDUEA4rfuuqUTofhSLePuoiuBPwmoln4TUQTBMF/bPOhyTPAay/y16IcGGPVL7/j0k6HUUM0FOTzb7u402HUEAgoXnmBvxYLBUEQVhL+VEgEQRAEQRAEQRAEQRAEwWeIkCYIgiAIgiAIgiAIgiAILhAhTRAEQRAEQRAEQRAEQRBcsKqFNKXUO5VSusm/hv2olVJfsW17Wp1thpVS/6yUekIpNaeUmlBKPaiU+nOllGM7L6VUl1LqQ0qpZ5RSGaXUqFLq20qps5fitQuCIAiCIAiCIAiCIAhLy2pvNvAI8KE6j70QuAb4Sb2dlVKvBn4dmAeSdbbZCtwLDAPbzePFgJcC/wy8VSn1fK112rZPFPg5cCXwAPBJYDPwJuCVSqlrtNb3unyNgiAIgiAIgiAIgiAIwnFgVQtpWutHMMS0GpRSd5v//WKdx9cAXwK+BawDXlTnaf4cQ0S7TmtdFu2UUkHgRgyx7k3A1237/AmGiPYd4Fe11iVzn28B3we+qpQ637pfEARBEARBEARBEARB6DyrurSzHkqp84DnA4eBH9fZzBLY3tPkcKeYt/9nv1NrXbQde43tuRXwbvPPv7CLZVrrHwC3A+dQX7gTBEEQBEEQBEEQBEEQOsAJKaQBv2PefsUUvCpQSr0TeB3wbq31RJNjPWnevrLqGAHg5UAJuNn20KnAFmCn1nqvw/GsUtNrmjyvIAiCIAiCIAiCIAiCcBxZ1aWdTiiluoC3YghcX3Z4/CQMz7JvaK2/7+KQ/wy8CviIUupq4CEgguGRtg74Ta31w7btzzRvd9Y53i7z9gwXzy0IgiAIgiAIgiAIgiAcJ044IQ34FaAP+LHW+qD9ATOL7N8xmgu8183BtNajSqnnA18FXs9iJpnG8Fj7RdUuvebtTJ1DWvf3OT2olPpt4LcB1qxZw/bt292EKXSQ+fl5+ZxWAPI5+R/5jFYG8jkJgiAIgiAIq5kTUUj7bfP2Cw6P/TGGN9krtdZTbg5mdu38P6ALeAVwJxAHXgv8K/BapdTldco4HQ9p3mqnB7XWX8T0b1NKzV199dXPuDyu0DmGgPFOByE0RT4n/yOf0cpAPqel5aROByDUsnPnznmllIzB/I9cj/yPfEYrA/mcVgbyOS0tdcdgJ5SQppQ6B7gCOATcUPXY6cBHga9prW9w2L0e1wPnAxdqrR8z75sFvqCUigGfAD4IvNN8zMo468WZnqrtGvGM1voSD7EKHUAp9YB8Tv5HPif/I5/RykA+J+EEQcZgKwC5Hvkf+YxWBvI5rQzkczp+nGjNBho1GTgXiALvUkpp+z8WO2juMu97HYBSqtt8bNImotm5xby92HaftXpZzwPtdPO2noeaIAiCIAiCIAiCIAiC0AFOmIw0MzvsbRhNBr7isMm+OveD0ZFzHfA/GNlm+8z7I+Ztj1IqorXOVe23xry1378bOACcoZQ62aHk8+Xm7c0IgiAIgiAIgiAIgiAIvuGEEdKANwH9wI+qmwwAaK0fAX7TaUel1HYMIe1vtNbP2vaZUEo9DZwNvN/8Z+0TA/7W/PMm2z5aKfV54O+Bf1ZK/arWumTu81rghcBTwK0uXtMXXWwjdB75nFYG8jn5H/mMVgbyOQknAvI9XxnI5+R/5DNaGcjntDKQz+k4obR29LRfdSilbgdeALxGa/1Dj/tuxyjhPN0upJmPvRj4MUZ22r3AXRiNB16OYU73LPB8rfWEbZ8oRsbZFcADGELbFgyxLwdco7W+1/urFARBEARBEARBEARBEJaLE0JIU0qdjZHldQjY6uCP1mz/7dQR0szHLwD+3NxmHVAE9gA/AP5Zaz3tsE8X8FfA/8MQ0WaB7cAHtdZPeYlPEARBEARBEARBEARBWH5OCCFNEARBEARBEARBEARBENrlROvaKQiCIAiCIAiCIAiCIAgtIULaCkMptUkp9VWl1BGlVFYptU8p9QmlVH+nYxMMzM9E1/l3tNPxnUgopd6olPq0Uup2pdSs+Rl8o8k+VyilblBKTSqlUkqpx5RSf6SUCh6vuE80vHxOSqmtDc4vrZT67+Md/4mAUmpQKfWbSqnvKaWeVUqllVIzSqk7lFK/oZRyHE/I+SSsJmQM5n9kDOYfZAzmf2T85X9k/OVfTqSunSsepdSpGM0MhjH813YAzwP+EHiZUupKe1MDoaPMAJ9wuH/+OMdxovO3wIUY7/sh4KxGGyujc+53gQzwLWASeDXwceBKjIYgwtLj6XMyeRT4vsP9TyxdWIKNNwGfA0aAW4ADwFrgl4EvAy9XSr1J2/wi5HwSVhMyBltRyBjMH8gYzP/I+Mv/yPjLp4hH2gpCKfUz4KXAe7XWn7bd/zHgj4EvaK3f3an4BAOl1D4ArfXWzkYiKKWuxhgYPIvRDOQW4D+11m912LbH3K4XuFJr/YB5fwyjy+7lwK9prWXFbYnx+DltBfYC/661fudxDPOERil1DZAAfqy1LtnuXwfcB2wG3qi1/q55v5xPwqpCxmArAxmD+QcZg/kfGX/5Hxl/+Rcp7VwhKKVOwRjA7QP+rerhDwILwNuUUonjHJog+Bat9S1a613a3YrBG4E1wH9bPzrmMTIYK3YAv7sMYZ7wePychA6gtb5Za/1D+yDOvP8o8Hnzz222h+R8ElYNMgYTBO/IGMz/yPjL/8j4y79IaefK4Rrz9kaHE2lOKXUnxiDv+cBNxzs4oYaoUuqtwBaMAfZjwG1a62JnwxIaYJ1jP3V47DYgBVyhlIpqrbPHLyyhDhuUUr8DDAITwN1a68c6HNOJSt68Ldjuk/NJWE3IGGxlIWOwlYf8ZqwcZPzlH2T81UFESFs5nGne7qzz+C6MQdwZyCDOD6wD/qPqvr1KqXdprW/tREBCU+qeY1rrglJqL3AucArw9PEMTHDkJea/Mkqp7cA7tNYHOhLRCYhSKgS83fzTPmiT80lYTcgYbGUhY7CVh/xmrBxk/OUDZPzVeaS0c+XQa97O1Hncur9v+UMRmvA14FqMgVwCOB/4ArAV+IlS6sLOhSY0QM6xlUEK+AhwMdBv/rN8PbYBN0l51XHlH4HzgBu01j+z3S/nk7CakO/zykHGYCsTOcf8j4y//IWMvzqMCGmrB2XeSo17h9Faf8isZz+mtU5prZ8wDYg/BnQB13U2QqFF5BzzAVrrUa31B7TWD2mtp81/t2Fkg9wLnAb8ZmejPDFQSr0X+FOM7oVv87q7eSvnk7AakO+zT5Ax2KpFzrEOI+Mv/yDjL38gQtrKwVKPe+s83lO1neA/LEPIqzoahVAPOcdWMFrrAkYbcJBzbNlRSr0H+CTwFHC11nqyahM5n4TVhHyfVz4yBvM3co6tUGT8dXyR8Zd/ECFt5fCMeXtGncdPN2/r+XcInWfUvJW0Z39S9xwzfQhOxjDz3HM8gxI8MWbeyjm2jCil/gj4DPAExiDuqMNmcj4JqwkZg618ZAzmb+Q3Y2Uj46/jgIy//IUIaSuHW8zblyqlKj43pVQ3cCWQBu453oEJrrncvJULlz+52bx9mcNjVwFx4C7pcONrnm/eyjm2TCil/hL4OPAIxiButM6mcj4JqwkZg618ZAzmb+Q3Y2Uj469lRsZf/kOEtBWC1no3cCOGWep7qh7+EMYKwNe11gvHOTTBhlLqXKXUgMP9J2GsIAB84/hGJbjkO8A48Gal1CXWnUqpGPB35p+f60RgwiJKqcuUUhGH+68B/tj8U86xZUAp9X4Mc9sHgWu11uMNNpfzSVg1yBhsZSBjsBWN/Gb4HBl/dQ4Zf/kTpbX4zK0UlFKnAncBw8APMFrWXgZcjVFOcIXWeqJzEQpKqeuAv8JYvd4LzAGnAq8EYsANwOu11rlOxXgioZR6HfA68891wC9hrJbdbt43rrX+s6rtvwNkgP8GJoHXYLSS/g7wK1oumkuOl8/JbLF+LrAdOGQ+fgFwjfn/92utrYGCsEQopd4BXA8UgU/j7K2xT2t9vW2f1yHnk7BKkDGY/5ExmL+QMZj/kfGX/5Hxl38RIW2FoZTaDHwYI11zEBgBvg98yMFsUDjOKKVeBLwbeA6LrdenMdJw/wP4D7lwHT/MQfUHG2yyX2u9tWqfK4H3YZSBxIBnga8Cn9JaF5cn0hMbL5+TUuo3gNdjtPweAsLAMeBu4DNa69vrHURoHRefEcCtWuttVfvJ+SSsGmQM5m9kDOYvZAzmf2T85X9k/OVfREgTBEEQBEEQBEEQBEEQBBeIR5ogCIIgCIIgCIIgCIIguECENEEQBEEQBEEQBEEQBEFwgQhpgiAIgiAIgiAIgiAIguACEdIEQRAEQRAEQRAEQRAEwQUipAmCIAiCIAiCIAiCIAiCC0RIEwRBEARBEARBEARBEAQXiJAmCIIgCIIgCIIgCIIgCC4QIU0QBEEQBEEQBEEQBEEQXCBCmiAIgkuUUu9USmml1Ds7HYsblFLXm/Fa//6q6vHtSim9xM/5marnvG4pjy8IgiAIwomFjL9cPaeMvwThOBLqdACCIAidoIUBzLuWJZDjwyeBaeCO4/BcNwDjwFbgHcfh+QRBEARBWCHI+GvZkPGXIBxHREgTBOFE5UMO9/0R0MviwMfOI8Be4B5gZBnjWg4+obXedzyeSGt9A3CDUmobMpATBEEQBKESGX8tAzL+EoTjiwhpgiCckGitr6u+zywZ6KXxwGdm+aISBEEQBEFYvcj4SxCE1YB4pAmCILiknkeHUmqf+S+plPq4UuqgUiqtlHpEKfU6c5uQUupvlFK7lFIZpdRupdTvN3iuX1JK3aCUGldKZc3t/z+lVN8yvC57bFkz/n9SSkUcttWmt8c6pdSXlVKHlVLFleJbIgiCIAjCykLGXzL+EgS/IRlpgiAIS0MY+DkwAPwAiAC/BnxXKfVS4PeAy4CfAFngTcCnlVJjWutv2Q+klPoARunDJPAjYBS4APgz4BVKqcu11rNLGPs3gReasc0CrwD+AhjG2ZtkAKPEYh74X6AEHFvCeARBEARBENwg4y9BEI47IqQJKKWiwG9i1NOfAsSAgxg/Sv+qtd7v8jhbMTwM6vEtrfWbq/a5Cvgt4DnAeiCB4X/wOPBJrfVNnl6Mc1x95nNcZD7PGUAQeInW+hftHl8QTDYADwHbtNZZAKXUfwC3Af8D7AbO01pPm499DNgB/BVQHsgppa7GGMTdDbzC2t587J3A18zH/3gJYz8VOFdrPWk+z/uAR4G3K6X+Wmt9tGr784H/AH5da11YwjgEQRAEQRC8IOMvQRCOO1LaeYKjlAoBNwGfAbqB/wI+j7EC8wfAo0qpczwe9lGMH5rqf99x2PYa899O4D+BjwN3AVcDv1BKfcTjczuxFfhn4P9hvMbxJTimIDjxR9YgDkBrfTuGuNwP/KV9UKa13gPcCZyvlArajvFe8/a37Nub+1yPYbr7liWO+y+tQZz5PAsY52MAuMRh+xzwZzKIEwRBEATBB8j4SxCE44pkpK1SbCsnV2uttzfY9PXAlRhi2ku11iXbMT4EfAAjnfnXPTz9I05GonX4xzqmoxsxVpf+Rin1Wa11O1169gMvBh7WWk8qpa5HutkIS8+01nq3w/1HgJOBBx0eO4yRHbnO/D/A5UAeeJNS6k0O+0SANUqpQa31RPthA/CAw30Hzdt+h8f2aa1Hl+i5BUEQBEEQWkXGX4IgHHdESBNOMW9/bBfRTH6AIaStWa4n11pn6tx/WCl1F/A6M8YKIU0ptQkjJfsVwEYMr4A7gY9ore+vOtYUhlAoCMtJvW5SBQCttdPj1opi2HbfIMa1+YNNni8JLMlArnrl1cSKLejwWHWpgSAIgiAIQieQ8ZcgCMcdEdKEJ83blyulPlklpr3KvPXqI7ZBKfU7GD9IE8DdWuvHvBxAKTWMYQyaBZ6peuy5wI0Yhps/wzDbHMIQ3e5QSr1ea32Dx5gFwS/MAAGt9UCnA2mA7nQAgiAIgiAIS4iMvwRBcI0IacKPMYSoXwYeV0r9AqP+/mLgBcCnMfzTvPAS818ZpdR24B1a6wNOOyilLsEQ7kLAJuA1QA/wB1rrcdt2IeDbGKtBV2utb7U9tgG4H/iKUmqr3StBEFYQ9wCvVEqdq7V+sunWgiAIgiAIQrvI+EsQBNdIs4ETHK21Bt4IXAeciWG0+WcYZv+3Ad/UWhddHi4FfARDhOs3/70IuAXYBtyklErU2fcSjFTq92H4l4WAd2mtP1e13SsxOtx82i6ima/lCEZTgXXAtS5jFgS/8XHz9kumOFyBUiqhlHr+cY5JEARBEARhNSPjL0EQXCMZaasApdQ+4KQ6D9+ilKq+79+11u80940BXwdeDrwHwxcthdGA4FPAbUqpN2mtf9AsDtP88gNVd9+mlHopcAdGqeZvAp902PfzwOfNeE4G3g18XSl1pdb63bZNLzdvT1JKXecQxunm7dmAlHcKKw6t9U1Kqb8C/gHYpZS6AaPzVBLjPH8Rxvn0ss5FKQiCIAiCsHqQ8ZcgCF4QIW118Amgr+q+i4DXAv8O7Kt67BHb//8KeBPwh1rrL9ju/4lS6o3mtp/EENhaQmtdUEp9GUNIuwoHIc22bQZ4GvhDpVQU+B2l1C+01t8xNxk0b5266dhJthqvIHQarfU/KaXuxMgQfQHGuTyD0Vnqi8A3OxieIAiCIAjCqkPGX4IguEWEtFWA1voT1fcppd6JcfG/Xmu9vcHuVkOBWxyO+6hSahIj+6vdVs9j5m290k4nfgL8DkZZqCWkWZ13Xqu1/r824hGEGrTWW5s8fj1wvZf9tNbbGjz2TuCddR67A2Plc9loEtv1OL/WmhRXQRAEQRCEVpHxV8Vj1yPjL0HwPeKRJkTN2zXVD5gZYT3mn7k2n8fyFNjjYZ+N5m3Bdt895u0L24xHEE4k9iqltFmysKwopT6jlNI4iPOCIAiCIAgnEDL+EoRVimSkCbcD5wF/o5S6s6rT5XUY35H7tdZz1p1KqV5gPTCjtR6x3X8Z8LDWukJ0U0pdA/yx+ec3qh57EXC71rpUdf+pGI0HwOgsavEDYDfwHqXULVrrGh80pdTlwKNa61SzFy8Iq5zvU1navawrrCY3AOO2v7cfh+cUBEEQBEHwC99Hxl+CsKpRRtNGYbVhlnZ+Dbi6UWmnUmojRpbXJowL/k+BNEazgeeZ/79Wa323w7HLTQvM+7cD52JcuA+Zd18AXGP+//1a67+rev5pYBq4FziIIdydimHkGcLozvneqn0uAH6G0Z3zLgwftxSwGbgUOAVYr7U+atvnX4Ah888XmM9xI2AJgd/XWn+/3vskCIIgCIIgCIIgCIIgGWknOFrrw0qp5wJ/CbwSeBdGye8IRn3+P2mtd7g83H8Ar8cQs14OhIFjwLeBz2itb3fY54PASzFKP18NBM19vg98WWv9M4eYH1NKXQj8CYbH27uAkhnzw+Yxx6t2eyO1nU1favv/PvM5BUEQBEEQBEEQBEEQHJGMNEEQBEEQBEEQBEEQBEFwgTQbEARBEARBEARBEARBEAQXSGmnIAiCIAiCUBel1M1LcJjrtdZfX4LjCIIgCIIgdBQR0lYwfX19+rTTTut0GEITFhYWSCQSnQ5DaIJ8Tv5HPqOVgXxOS8uDDz44rrVe0+EwtrW5v2aVdZCTMdjKQK5H/kc+o5WBfE4rA/mclpZGYzAR0lYwa9eu5YEHHuh0GEITtm/fzrZt2zodhtAE+Zz8j3xGKwP5nJYWpdT+Tsdgcp3W+sOt7KiUKi11MJ1GxmArA7ke+R/5jFYG8jmtDORzWloajcHEI00QBEEQBEEQBEEQBEEQXCAZaYIgCIIgCEIj3gQ81cH9BUEQBEEQfIMIaYIgCIIgCEJdtNbf7eT+giAIgiAIfkJKOwVBEARBEARBEARBEATBBSKkCYIgCIIgCIIgCIIgCIILpLRTEARBEARBcI1Sao+LzUrALPA08L9S3ikIgiAIwmpBhDRBEARBEATBCwGMMeQG8+8CMAEMsji2PAIMAxcBb1ZK3QC8TmtdPL6hCoIgCIIgLC1S2ikIgiAIgiB44QLgMHA78AIgprVeD8SAF5r3HwI2AmcCPwVeAfxhR6IVBEEQBEFYQkRIEwRBEARBELzwUaAXuFZrfZfWugSgtS5pre8EXgL0AR/VWu8C3oQhvL2lQ/EKgiAIgiAsGSKkCYKwrBycTHH9nXsplnSnQxEEQRCWhtcD/6e1Ljg9qLXOAT8Eftn8OwXcBJxx3CL0CQvZAl+5Yy97xuY7HYogCIIgCEuECGmCICwrf/2/j3PdD5/itp1jnQ5FEARBWBoGgUiTbcLmdhZHOQG9eT918y4+8qOn+P1vPtzpUARBEARBWCJESBMEYVl57NA0AA8fnO5oHIIgCMKSsQd4g1Kq2+lBpVQP8AZgr+3u9cDkcYjNN2it+f7DhwF4amRWstIEQRAEYZUgQpogCMtGJl9kNmNU/hyaTHU4GkEQBGGJ+CJGI4F7lVJvUUptVUp1mbdvBe7F6Oj5BQCllAK2AY90KN6OcHAyzbHZLO+8YisADx+Y7mg8giAIgiAsDSKkCYKwbByeTpf/f8j2f0EQBGHlorX+JPB54Czg68BuYN68/XeMTp1fMrcDGAb+C/jX4x9t53hgv5GA96ZLNtEVDvLEkZkORyQIgiAIwlJwwnlVCIJw/Dg0ZYhnG/u6GJkRIU0QOs2uY3Ns6OsiEZWff6E9tNa/p5T6JvBO4CKMLp6zwMPA17XWt9m2PQb8dQfC7CjPHJ0jEgxw1roezlzXzY6RuU6HJAiCIAjCEiAjaUEQlo1jsxkAzl7fwz17JjocjSCc2ByYSPGSj9/Gy85dx+ffdnGnwxFWAVrrO4A7Oh2HX9k9tsDWoTjBgGLrYJwH9k91OiRBEARBEJYAKe0UBGHZmE7lADh1TYL5bIFcodThiAThxOXWXUbn3J8+eRStdYejEYTVz57xeU4ZSgKwZSDOkek0+aL8DgqCIAjCSkeENEEQlo2pVJ5QQLGpvwuA6XSuwxEJwomLvWPg6Fy2g5EIqwWl1KuVUv+tlHpUKfWs7f6zlVJ/oZTa2Mn4Okm+WOLARIpT1iQA2DwQp6ThiPiFCoIgCMKKR4Q0QRCWjelUjr54hP5ExPw73+GIBOHE5cDEYufcfeMLHYxEWOkog38Hvg+8CTgVONm2yRTw98Bbj390/uDgZIpCSXPKGiMjbfNA3LxfhDRBEARBWOmIkCYIwrIxncrTHw/THzeEtMkFyUgThE5xYDLFyUNGdszYvGSkCW3xe8DbgK8BA8C/2B/UWh8F7gReefxD8wf7TeF666AhoK3vjQFw1PQOFQRBEARh5SJCmiCsEu7ePcFffOdRUrlCp0MpM5XK0R+P0BMLAzCX8U9sgnCicXQ2w3kbewEYk9JOoT1+A3gU+C2t9QzgZLq3i8ostROKI2an6o2mtcFwtyGkjc6JkCYIgiAIKx0R0gRhlfDhHz3Ftx84xI1PHut0KGWmU3n64mES0SAAC1kR0gShE+QKJeYyBU5dkyAYUIxLRprQHmcCt+jGXStGgTVuD6iUGlRK/aZS6ntKqWeVUmml1IxS6g6l1G8opQJV229WSn1WKXWvUuqoUiqrlDqilLpdKfUupVS4wXMllVLvN73d5pVSc0qpJ5VSX2y0nxdGpjMEA6osoHVFgnRHQ4zO+uPcyxaK3PD4CDNiuSAIgiAInhEhTRBWAaWSLnsePXpourPB2JhK5eiLh0lGQwDMi5AmCB3B6qA7lIwykIgwPidl1kJbFIBYk202AvNNtrHzJuBLwGXAvcAngO8C5wFfBr6tlFK27U8F3gLMYHi1/SvwQ+Ak4KvAjUqpUPWTKKW2Ag8DHzb3/RzwBeBp4I1A1EPMdTkyk2Ztd5RgYDHkNT1R32SDfuznO/m9/3yIP/2fRzsdiiAIgiCsOGoGGIIgrDwOT6dJ54uAv0zEDY+0CAlTSJOMNEHoDBOmP+FAIsJgIsJkSoQ0oS2eArYppZRTVppSKgZcgyFYuWUn8Brgx1rrku1YfwPcB7wB+GUMcQ3gLqDfvq25fRi4Edhmbv/tqse+hyG2vVZr/X9V+waBiuO1ysh0hvV9XRX3DXdHfVHaWSxpvvvgYQBu3nGMmXSe3q4lScQTBEEQhBMCyUgThFXAkWnDiyUaCjAy0/lBOkC+WCJbKJGMhohHgiglQpogdIpJm5DWHQsxl5FyLqEt/gM4C/i4Q8llEPgYsAG43u0BtdY3a61/WC2MmY0LPm/+uc12f656W/P+PEaGGsDpVQ+/DbgI+GS1iGbuW2xSruqakZl0ucGAxXB3jFEfZKTtPDbH+HyWN128iZKGB/dPdjokQRAEQVhRiJAmCKsAa2B+0eY+DpuiWqdJ5YwMua5IEKUUyUiI+Wyxw1EJwomJXUjriYWl8YfQLl/AyPp6L3AQ+DUApdR3gP3Au4H/01r/5xI9n6X8Nv3imkLeK8w/H6t6+P+Zt9crpbYqpX5XKfXXSqm3KKUGlyhWtNaMzGTY4JSRNptlibS6lnn88AwAb798KwBPHp7tYDSCIAiCsPIQIU0QVgGWkHb+xl7mMgWyhc4LVmlTSItHjLLORDTEfFayYAShE9RmpPlHSHvyyAzv/Np9HJpKdToUwSVa6yLwKgyfsQhwBqAwSinjwEcwPM/axvQ5e7v5508dHh9SSl2nlPqQUuqzwA7gpcA3gR9VbX4pkAFejtFV9LPA3wPfAPYrpX59KWKeXMiRLZRqM9J6oqTzxY77hT5xeIZEJMi5G3rY2NfF7jEvVnaCIAiCIIhHmiCsAkZnM0RCAbYOJQDDm2xtT7CjMaVyxkQhHjHiSESDLEhGmiB0hCnTE62vK0x3LMysj0o7v3DrHrY/M8Y37z3AX7zsrE6HI7hEa10ArlNKfQhDSBvEMO/fYQptS8U/YjQcuEFr/TOHx4eAD9pDA/4F+Bt7maZSKgr0AEXg/zP/fQajIcJrgU8BX1ZK7dNa3+wUiFLqt4HfBlizZg3bt293DHjfjPHyJw/tZvv2/eX7Rw8b591Pbr6d4Xjn1rLv3ZFmfRxuu+1W+oM5HtlztO5rWenMz8+v2te2WpDPaGUgn9PKQD6n48eqEdKUUpswVkZfhjGYG8HwyPiQ1npqOY+jlLoC+Fvg+RhdrJ7F6Bj16erBpFLqSowB29XAVoxB3RHgJuAftdbPuo1VECxG57IMd0fpj0cAY9K8tqdZQ7XlxV7aCZCMhlpahdda84179rN1KMELT1+zpDEKwonCfKZAPBIkFAzQ02VkpGmtqWyC6I6xuSzFkmZd79JcY54eMcrKrHIzYWVhilXPLMexlVLvBf4UI8vsbXWef4exqQpidAp9PcY47gVKqVdqrS0DsKDt9rta67+wHeZrSqkkhpj2l4CjkKa1/iLwRYAzzzxTb9u2zTHunz15FO5+kJdeeSnnb+pd3H/HKF9+/H5OP+85PGdLf9PXv1z87b03c8nWfrZtew7bZ5/kOw8eot5rWels37591b621YJ8RisD+ZxWBvI5HT9WRWmnUupU4EHgXRidnT4O7AH+ELjbre9FK8dRSr0WuA24CqMT1L9hlDl8HPhvh6f5LsagMAP8J/BpDCHtN4BHlFKXu3rRgmBjYiHHYCJCX9zoujWd6ny2idVFdDEjLdRSs4F7907y/h88yTu+ep8vSlYFYSUylynQHTPWzrpjYYolXRa7vR0nz4s/disv++RtS9KwIFcosdfsNHxgUko7hUWUUu8BPonRIfRqmyDmiNko4IDW+pPA72Asbn7Y9ngKsNrVfs/hENZ9z2s39hHTq3R9X6XY3J9YXOzqFMWS5qjNv21jXxfz2YKvslQFQRAEwe+sloy0zwLDwHu11p+27lRKfQz4Y+CjGMa3S3ocpVQP8CWMMoFtWusHzPvfj7Ga+Ual1Ju11nZB7ePAf2itj9if2Gzv/lGMlc7zXb5uQQBgNp2npytsE9I6N0i3SOVqhbSplPdGCHfsGgegpOGxQzNcunVg6YIUhGXCEn2joc6WWFvMZwsko5aQZtzOZQokot6GATfvGGUmnS///7UXbWwrrqMzGQolzVAywsh0hlJJEwh4z5ITlhellGOGlgu01vraFp7vjzDGS08A12qtRz0e4ifm7baq+5/BGGNNO+xjVR10OTzmiZEZw25h0BTOLAbMrPHJBWfRqlTSvONr93HWum7e98pz2g3DkbG5LIWSZr0ppFmZpSPTGXrWhZflOQVBEARhtbHiM9KUUqdgmMruw8gGs/NBYAF4m1IqsQzHeSOwBvhvS0QD0FpnMEo9AX7XfiCt9T9Vi2gm/wSkgfOWsnOUcGJgCWmLpZ2trSx/5Y69/Pn/PEq+WGo7prTpkdYVNibqsXCQbN57Bsyjh6bZYA70Hzkw3XZcgnA8eOPn7uYNn7ur4935LOayBbpjxiS5x7xtJQPl8UMzhIOKSCjAw0twPo7NZwC4aHM/uWKJ8fls28cUloVtdf69yMX9nlBK/SWGiPYIRiaaVxENjBJPqO3yeZN5e57DPtZ9+1p4vgqOzGRY3xurKZ3uTxjn3tSC82LXI4emuX3XOF+6fe+yZYgdmTEWtDaa2XIbzFvrfkEQBEEQmrPihTTgGvP2Rq11xexfaz0H3InRQer5y3Aca5+aLlIY5Z4p4ArT4LYZmsUBn9SvCZ6YzeTptQlprZR2zqTyfORHT/E/Dx7ipqePtR1TdUZaVzhQLvf0wp6xBZ538gADiQh7zBIwQfAzh6fTPH54hicOz3J42h+T07lMvpyJZmWmtVJq/cyxOc5a18MFG3t5Ygk8zUZnDeHsnPXdAIzPdz6bVqhFax2w/8Pwg/0/YC+GHcbJGJlcJwO/jmGL8QNzO9eYGf3/iGGzca3WerzBtpcppeIO9ycxSkIBflz18Bcwxlp/bHriWvvEMKoCwNmWwxNHZ9Ksc/ApTUZDhIOKiTpC2pNHZsv/f+boXLthOHLEvCZZpZ3re43bozOZZXk+QRAEQViNrAYh7Uzzdmedx3eZt2csw3Hq7mN2s9qLUT57SpPnBqNNfDdwj9Z62sX2ggAYZvwz6Tw9sTCxcICAam2CfN++RfuZ23bVnbu4plZIC3oW0grFEkdnM2zqj3PyUII9Y/NtxyUIy41dYHrKNjH2Sq5QItOC+OzEfGaxtNNqAJJuwSPtmaNznLmum9PXJtk30b6wPWZmoJ2xzhDS/FCWLrji/cAlwCVa63/XWu/XWmfN2+uByzC8xt7v9oBKqXdgeJoVgduB9yqlrqv6907bLn8NHFFK/UAp9Wml1D8ppb4JHAReDNwF/IP9OczGBH8JrAUeVUp9RSn1KeBR4FrgXowKgbYYsXmQVb1GBhKRuhlpO0YWrxf7lmnhaGTaEMwsAW24O0pALfq6CYIgCILQnNXgkWa1Q6q3NG7d37cMx1mS51ZKnYzRdKCA0Yig0bauWq8L/mG52xBni5p8UTMxcoBbbz1KNAjP7NnH9u0jno7zk93GwP6kngD37DjE9u0TbcX1+F4jK+7B++6mK6QYPZojlcl7ei8m0iWKJc386AG6CiWeGC8u23sp7aL9z0r5jG7bv5gRetsDjxMZ2+H5GIWS5rq70uRL8NEXdBFq0zdsfCbFunCG7du3s3fGENDufegRcofcDwPyJc3oXJbizDEIwPh8np/84ha6QpWxefmc7t+ZQwFT+4336I77vcUkdIy3YHS+nHZ6UGs9qZT6DvBW4AMuj3myeRsE/qjONrcC15v//xKG7calGCWkcQyfsweBbwNfNRc1q2P7mFLqGYzx1huBKEYG3QeAf9Fat6UolUqaY7OZul1t++MRJusIxgcmU5y3sYenR+aWrfnG4ek0yWiIHjNDNRQMMJSMcnTWHxlpu47N8bntu/mtq07h7PU9nQ5HEARBEBw5EUar1gi/XaOaVo7TdB+l1DCGKe4a4D1a67saHdBt63XBPyx3G+KjMxn4+U1cdO6ZbLvsJPruvoneoSG2bbvQ03G+O/Iwm/qnuOactfzXfQd40YteVOPv4oVHC7vgmZ289JptBAOKh/M7+cneXVx11Ytcm4nft3cSbr2bay67iO59k9x15Fle8MKrCAWXPplW2kX7n5XyGd39k6cJ79wLQHJ4M9u2neX5GHc+O86hG+8FQK0/h21nr20rpvwtP+P0rZvZtu0cNo3Owd23ceqZ57Dtwg2uj3FwMgU33sLzLzyLZDTMd3Y+xNZzL66Z7Hr5nH4y/hhrxkb5pW1X8P47b2L91tPYdvlWD69M6BAbWOyAWY88sN7tAbXW1wHXedj+x9SWbi77vs2YWMiRL2rW1xHSBhIRJutkpI3NZdk8EOfYbJaxueXxCzwyna7xbxtKRuvGdLz5wA+e5O49ExyeTvOt35FG9oIgCII/WQ2lnVbWV2+dx3uqtlvK47T13KaIdjNGiegfaq0/2yRGQajBMiTu7TJMjJPRUEulnQcmU5w8lGDrYIJMvlQuuWqVVL5AJBQgaIpmVjlZtuC+kYHdy2VtT4ySXhoPpWJJ89Yv38tntz/b9rEEoZqjM0Y2yrreGEdbNPB+cP9U+f/375tqsGVziiXNfLZQ9kjrihi3VkMQtxwzM1bW9sTYMmBYU7WbNTOxkGMwGaWvq71GKcJx5xDwWqVUxOlB0xv2tcDh4xqVD7C8xpw80gD6G5R2js1lWdMdZSgZXbbGG05lp4PJiC/8CadTOe7da2TD379vsu77JAiCIAidZjUIac+Yt/U80E43b+t5n7VznLr7KKVCGGUKBYySgerH1wPbgXMwMtE+1SQ+QXBkJm1MPK1OfIloiPkWhDTLHHnzgDHAPjjZnl9KOlcs+6MBxELG5caLT5o1kVjTHS1PSpai/OSxQ9Pc8ew4//zTZyiV/NFVUVg9HJ3JsL6ni8FElMkWhaFnjs6xdTDOuRt62HG0dZ81gAVTMLOEtHjYOC9THj3SrHNvfW8Xa3uNHjrH2jwfZ9N5ertCREIBktGQb7JihKb8O3AacLNS6iqlVBBAKRVUSr0IozvmKSyWYZ4wjJjiueVBVs1gwrm0M18sMZnKsSYZZSgZYWyZhK0j0+kaIW05hTsv3LNngpKGP33JGZQ0PL4EDU0EQRAEYTlYDULaLebtS5VSFa9HKdUNXAmkgXuW4Tg3m7cvczjeVRh+HXdprStGJ2anqFuBs4B3Syaa0A6z6fYz0grFEmNzWdb3xtjcb2SaHJpqL9MklSuWJ+ywmJHmxTx9KpUjGFD0xEJlv5l2J+5Q2Q3NL10VhdXD2HyWNT1R+uJhZlo0zz80lWLzQJxT1iTZM9ae6fh8plJIs85Fz0KaLdNmMBElGFDlrputMpvJlxcBervCzGW8LwIIHeEfMbp2XoExfsoopY4BGYyx0RXAD83tTiis36hGHmkz6TyFYmV29uRCDq2NhaM1ySjjy1DamckXmVjIsaEqtsFEhAkfZKQ9NTJHQMGbLtls/t3eIoIgCIIgLBcrXkjTWu8GbgS2Au+pevhDQAL4utZ6AUApFVZKnaWUOrWd45h8BxgH3qyUusS602yj/nfmn5+zH0gptQVDRDsV+A3T80wQWqackdZlZaQFWch6myCPzWcpaVjbG2NTWUhrPyOty56RZopqXjLSJhfy9McjKKVY27N0QtrOY4vdP9t9nYJQzUwqT29XmN6uMNPp1jLSDk6ly91qD02lyBZa795piVPJqHGNiIaM7r5eu3Yem80QCwfo6QoRDCiGkhFG59rPSLNfu+azUtq5EtBa57XWr8NoJnAzhoXFgHl7E/AWrfXrnMz+VzsjMxnCQcVgwrHqlYFEBK2puTZYnmhDyShD3dG27RXqxQY4lHZGSeeLpDyWey81O4/OsXUwwbreGOt7Y+y0LXoJgiAIgp9YLc0Gfg+jzfmnlFLXAk9jtF6/GqMU8322bTeaj+/HEM1aPQ5a61ml1G9hCGrblVL/DUwCr8HwPfsO8K2q57jVfN4HgZOUUtc5vJ7rtdb7XL1y4YRntlzaaZzOrZR2WoPr9b0xuiJBemKhto2OU7kC8cjiJaYspHmYvE8t5BhIGJPswUSEgGJJVumPzqYJBRSFkpaMNGFJ0Vozk87T1xUmHFBMt1DauZAtMLmQY/NAF2u7DW/AI9MZTh5KtBSTJU5ZGWlKKeKRkOeMtImFHIOJaNmkfLg7xmib5+NsplDOSDOyaVsXDIXjj9b6m8A3Ox2Hnzg6k2G4O1a3qU6/KbBNLeQYSkbL94/ZrAz64mFyhRKZfLH827kU2H1H7QwljZjG53JsGezc1OCZY3OcubYbgC0D8WXrXCoIgiAI7bIqhDSt9W4zI+zDGGWWrwBGgE8BH9JaTy7XcbTW3zf9QN4HvAGIAc8CfwJ8SmtdbcC01by92PznxHZgn5uYBWHBnAwnzUlysgUhbdTM8hruNrK+1nRHl0BIq8xI6wp7L+2cXMjRHzcG+IGAoi/u7C3jlbG5LOdu7OXRg9NtZ9QIgp1UrkihpOntChMKBpjN5CmWdLnphhsscXdjXxdrzIn20ZnWhbRZKyMttviT3xUJks57u07MpPL0xcPlv4e7o2URvhUKxRLz2cJiWXosXM6wFYSVyshMpm7HToAB8zet2g/Q+s0d7o6WxeXZdH6ZhLTK+CxBb3why5bB+JI9nxcy+SL7JxZ4tdlJePNAnDt2jXckFkEQBEFoxqoQ0gC01geBd7nYbh9Qd0bj9jhV+9yJIbq52db9bEoQXJDKFQgoiASNSu2E6ZGmta5ob98IK2vGWilfCiEtnS8yYCttWfRIc9+1czKV4/ThZPnv/niYqYX2J9rj8znO3dDDM0dnpSuYsKTM2DwLw8EAWsNcJk9f3LnMywnr3FvbE2NNtymkzbaeOZkys7wStgzReCToOSNtKrUobAMM90R59FDrZuBWyWlPl7UIECxP9AVhpXJ0NsO5G3rqPm79LtYT0oaS0XK582wmz3Cd7p+tcGTa2b9t0MxIa+STdseucTb2d7Us6DdjZCZDSVPuCLy5P86xuQzZQpFoaOnEREEQBEFYCla8R5ognOikckXikVBZNEtGQxRKmmzBvWA1ZQlpZrbJULJ9f5ZUTddO7x5pUwu5srgHxgRkKbr6jc1lGUpGGYhHyq9dEJYCu5BmZVp5Le+0vuODichit9qZ1s9Hy/coXpUh6lVIm07n6bVlpK1JRplYyFJssfPtbKay43AyGio3RhD8hVIqpZT6607t70ecvvVaa0Zm0g0z0sqiVdVv2fh8lu5oqGyvADCTXtrzYWQmzVAyWiNMDZoZaRN1fvcPTKR461fu5V1fu29J46mIzcqWM9+7zQNdaA2HxcdUEARB8CEipAnCCqfa1N+aLHvxIptO5YiEAuXyyyXJSMsV6QrbS8mMy41bIa1U0kylcuUyGDC6nU21WdqZzhWZzxZY0x2lPxGRjDRhSbELaYmo8f33Wmptfcf7ExES0RDdsVBbTTasc676OuG12cB0Kl8W2634tF70afTKrCkSLHYcDnvuOCwcN2JAuOlWy7e/7xhP10ppM+k8mXyJdb1dDnsYWFmd1dlfY3NZhswMVOucaPXcqsfh6TQb+2pFPqsxwngdIe3WnaMA7JtILdtv5hHLq9X0b1tvvodH2ygfFwRBEITlYtWUdgrCiUp15leXrTtmv8tjTKcMc3Qrq21Nd5T5bKGmYYC3uAqVGWkePdJmM3lKmgpPpoFEhEcOTrcUj4U1UViTjBoZbkvguSYIFvYuulailmdTf3OC3WdOptf1xBiZaaO003z+eIWQFvLUoa9U0kyncvR1VQrbYJZ81ulQ2IjqjsPJaJD5XIFSSdc1ahc6yuuUUltb3Le1tEUfky/WviSrC/SGBhlpkVCAnliIyYVK0WpsLlv2RLSXdjZj57E5njk6xyvPX9/0vDkyneYM08zfTiwcJBEJ1mTJWewaXex0vWd8nosTA03j8oqVkWZl8w33GO9Fuw1NBEEQBGE5ECFNEFY4qVyxLJ6B3YvMQwlllfdR2Xi4jQ5e9QQ+t3Et+idVZsBMpXKe/N+qsWf79HaFyxMfQVgK7BlpuaJRXu01y2oqlaMvbjQrAON8bORd1AxLSIuFKq8T9bJPnJjLFmqE7XL3wRbF6HJpp+WRFguhNaTyRZJRGZ74kIvMfwKQL1Ej+h6aMrpMbh5obNg/mIzWiFZj81nOXmd4q9mbDTQiky/y5i/ew+RCjnS+yK9csrnutkbZaYYXnTFcN6Z61gm7x+bpi4eZTuXZPbbAxSctvZB2ZCbDQCJSXnSz/CHbzY4XBEEQhOVASjsFYYWTzldmfkVb8CKbrurGNxBvb4JcND3auhwy0tyWk1lCWrdtQj0Qj5Avas+lcnbs5WTdsXBbxxKEaqyJb288XBaDFjxkfoHhkWYvaR5Itpc5mckbYrt9wt8VDnoS22dMn7e+ilJr45rRagMQ671a9EgzbqW805ecvAT/Pnnco15GNIsddi0OThp/NxXSEpEacXx8LlsWjyxxuVkX23v2TJTFr2/ff7DhtjPpPKlcsaZjp0UjD9J94ymuOHUQWOzyvdRUe8t1R0PEwgHfdNb+xVPHeNknbuOhA1OdDkUQBEHwAbLkKwgrnLTZbMCilYy06XSuohNXf8I0SW/Rn8US8ZxKO90KfJbA1R2rzYCZXMhV3O8FKwumOxYySsnE3FxYQmbSeQIKkpFQ+ftvdc10y2R1k414e002qsusAaKhgMeGJGYmpz0jzRTVWhX5rHPR8oNKRI0Y5zIF1tZveih0AK31/k7H4Ed2jc5ViGYHJlP0xELl73Q9BhIR9k+kyn9n8kVmM4WykBYNBYmFA8w2+X26b+8koYDiXVdu5St37GU+W6ibzWl17NzY5+zfNpiIMOLgR1YqaUbnMpw0uIGeWGjZSi1HpjMV76VSakn8WpcCrTUf+tGTHJxM8//99Bn+67ef3+mQBEEQhA4jGWmC0AI5DxPQ5SZV1Wyg7JGW89a1017a2Wv6IE23OEG2vJe6bAJfMKAIB5Xryft81phkJ2O2jDRT4GtHVJjLLPoyJaNh0vkihaJ/Pk9hZbOQLZKIhAgEVHlC6zXrcXIhx0BVt9rpVL7l72kqVywL2RYxjxlplpDmVNrZ6nViLlMgoBYF94R5vfDaBAGMie6D+6c8vSZBaJddx+Yr/j44lWqajQZWaeeiQGSVWQ8lF8/7nli4nAlaj0cOTnPOhh6uOmMNJQ0PN8iWOmJ5kNUR0gYSkYqYLKZSOfJFzXB3lOGeGKOzyyNsHZmpbYQw3B3zhUfartF5Dk4aHU/v2zcpmeyCIAiCCGmC4JXvP3yY86/7GQ/un+x0KICR4VWv2YAbtNbMpPJ1SrZamyBbE+F4uDoLJuhahLRKO+2r65bA16zcpRFWaWdPLFTOgFloYeIuCE6k80Vi5vloZYp6MfUHy7NwUbAaTFql1i1miFb5FYL3jDS795tFIhIkHFRMtljaaYmOlt+htSDgpSzd4n8fOswbPncX7/veEy3FIgheCapKE36Ag5MpNve7ENISEaZSeUpmRxIr68rKSANjEWm+ybVj3/gCp65J8pwtRmuhRw5M1932iNmwpF5pp+WRpnVlEwVLyBrujjHcHV2WUsv5bIG5TKFG5FuT9EdG2r17JgD4/atPpVjSPD0y2+GIBEEQhE4jQpogeOS7Dx0iWyjxw0dHWj7Gs6Nz/OV3HuPgZKr5xk2oMfWPGKe128yMVK5IrliqyDSxJsutlnY6dQkEa/LusdlAzC6kGf9vVu7S+Lh5lDKyX7pjrWUMCUI90rlCWcyOhAJEggHmPZZ2zqYLFYLVgK2kuRWqrxGwmJFWPWmuh1OptVKK/nikrczVeLT18m87P3rsCAA/fvwIeckwFY4D4UClkFYqaQ5Opdk84JzxZWcgEaFY0mWBuiykJRdFrmQ01NAvMJMvcmQmw9bBBMloiC0DcXYcm6u7/ZHpDOGgYigRdXx8MGF4kM5VPWdZSOuJ0m9mxy411R07LYZ7or7ISHvi8CwDiQi/dN46AJ46IkKaIAjCiY4IaYLgEWsA9czR+gPWZvzdj5/mWw8c5F9vfKbteNJVZVtemw0sClaLE+RQMEB3LNTygNkS0rqqJu+RUIBs3m1pp5mRZhPSrBit8sxWmM0U6I5apXfG8cQnTVgqqjNEE9Ggp4y0QrFEOl8sfzdhUUhzKrtyFVNV+TcYonZJQ6HkTkizJvSJKv+l/jb82+azhXI5J9jL0r0JaaWS5qED0ySjITL5UlvX5mq8ZhMKJw7hoGL36HxZjD48nSZXKHHKmmTTfa0sU+ucHrNKO7sXM8MTkcZCmuWxtnXIyIA7c103OxpkSh2ZTrO+t6ui6YidsmBf1QTBai4w3B2lryvcVkZ43dhMb7YNVRlpQ8koM+l8x8XxZ8fmOW04ybqeGD2xEDsbCJaCIAjCiYEIaYLggXSuWG5Zv29ioaVj5Aol7jHLBG7dOeY6I8QJrXWNkbjXZgNOghUYE+RWu3aWSzsjlcf0Uk42b/onddlEQisbxirPbIXZdL58HKu0UzLShKUinS9VCNvxSMjT92sx82vx3Bk0M0hazkjLF2rORStG19eJTAGlasu1++LhtgR3uzDXSqMUgJHZDDPpPG+5bAsATxyeaSmeanaPzXPhh27k3255dkmOJ6wuIgHjfD1qCk2WuHLGWhdCmnlOW507R2ezKGUIRxaJaKhhNqs1BrEaBZ29rpt9E6m658/ITLpuWScY3YGhVrC3l3b2doWZTufbGrc4xlYnI83yYWx1LLIUaK15dtQQ0pRSbBmMc2gq3XxHQRAEYVVTt2unUuqqJTj+Pq31gSU4jiD4AstjZGNfF8dmM5RKuu7qbj12j82TyZd4/ikD3LNnkkNTaVfmxE5kCyVKulKw8prVURbSopUT5P62JsjGMWtLO917pFndxyz/JIBYOEA4qMrd/lphNlOgxyybk9JOYamxl3aCUZ7lpWtn2RvQJqRZXXRb9SysbkgCxrkExjWk28Ux5rNFkmYTBTv98Qi7x+br7NWYhWyhLX9HC6tE/vJTB/nqnXvZN9F+yTzATx4fIV/UfO3Ofbzn6tOW5JjC6iEcNM6FncfmWd/bxU6z8cBpw83PKCsjzRLHR+eyDCYihIOL69vJaLBhRpqVKbbOFJ/OXNdDsWSIPudt7K3Z/sh0hstOHqgfk5X5WpWRNjaXpTsaoisSpC8epljSLOSKdbuDtsKRmQxKwdqeSiFtwPRunVrIM9xdXwRcTsbnc8yk85xmZhpu7o/zjGSkCYIgnPA0+hXcDrS75PQh4MNtHkMQfMOI2T7+ws293PB4mslUrmIF2Q17x41V5FddsIF79kzy9Mhsy0KaJZbZJ+5efYbmy6b+4Yr7e9vwPrKe27G004NHmt2PCQxPpp5YmNl2mg1k8mUBzV7amWj5iIKwSDpfZG334vc2Hg2y4KE80BJ17d6AVkfdVk39M7miY+MP8JK5mq8p6wTo6QqVxT+vpHLFCnP1Vks7LSFt62CCzf1x9reYLVzNM6YwMrmQJV8sVYgcghAJQEEZBv8vOmMNO4/Nsa4nVuFvWA9LtBo3hbSxuQxrqoSiRBOPNCs73ro+nLnOEHp2HpurEdIKxRJHZzNs7K/v3zaYdM58HZ3LsKbHeMx6bTPp/JIKaSPTaYa7ozXnWH+TTt35Yon3fe9xrjlrmJedt37J4rFjLRScNmy8v5v6u7hpx2hLC6mCIAjC6qHZr+Ct5j+vKOADLewnCL7Gah9/0eY+bnj8KKOz2ZaFtG1nrgHgQBsNB1L5WlP/YEARCQXcC2ll76PajLR9461NSBs3G3DbtTNfUd5m0dMVbrPZQIGNpg9LuWtntjUh7ch0mlSu4CoDQTgxSOcWu3aCIQ55KVWccxC2w8EAiUiw5UzMVN7BI82WkeaG+WyhpvwbDN/CVj0LF7IFThpcXESImY1SPGekTaVRyvBX2jIYL3tHtcsuM+ukpI1FlC2DrS14rEaUUv3AemC31jpru/9dwOuABeATWuv7OhPh8hNQcObabh4wO3g/dGCKCzfXZoI50V/O/jLeutG5LMPdlWOJZLRxWfjUQo6eWKgsPp00mCAcVOXMODtHZzMUS7r82+dEOSNtoTYjzYrN6pw9nco1PJZXRmYyrO+tPd5Ak9LO7c+M8e0HDvGjx0aWTUizC/UAmwfi5AolxuazNRl0giAIwolDMyFtu9a6pYwypZQIacKqwzIEPm+DMVgenctwDj2ejrF/YoHh7igb+7rojoXamvSlzUyX6klyVzjo2dS/uyojrb1ufKaQFq7ySAt7azbgtOLdEwu1l5GWznP2OkP4arWUDCBbKPK6f7uT6VSee/7m2vKAXzixSeeKNRmiXgSw+ayxbbWI3NOGybdTaaf3jDTnUq6erjALuSKFYomQx4ythVzlOR4JBggGlOeMtENTKdb1xIiEAmzuj/PQ/ilP+9djZCbDGWuT7Dw2z8GplAhplfw98FZg2LpDKfUHwCcwFlMBXqeUukRr/dTxD+/4cMnWfr730GEOT6fZP5HirZed5Gq/cDDAYCLCsVlTSJvNcubaygWZRDREtlCqe25NLOTKWWTWMU8ZSpYFYDuWp9em/vrf4Vg4SDwSrCntHJ/Pce4GY5xjz0hzg9a6wp6hHkdm0py1rnZBaiDeuGPxwweMcz2VKzKTytMbb54N6JUj02bZaa/xXluC37HZjAhpgiAIJzCNRr1PAqNtHLvd/QXBd8yk88TCAdaaniStTGyPzmZZ39eFUoqTBuPsbyMjLZ0zRKlaI/GA68noQp1mA71m5lfRZVe/yricBb5I0EOzgXoZMF3hNj3SFjPdrPhaEdLu2zvJ6FyWXLHEL5461nI8wuqiumunkZHmvuOck0caGOdjK9ebYkmTK5RqRO2Y14y0jHMpl3UutVLemcoWK65dSim6wkHP5+PYXJZhc0K7tifKbKbguWFBNZl8kZl0nnPNRZPx+dY6pq5irgRu0lrbXdf/DDgMXAX8innfnxzvwI4nV585zEKuyPu+9zgAV5425Hrfjf1dHJ5OUyppxuezDPdUZqRZpdQLdTwWJxdyNQs4p69NsnO0Vkg7bAppjUo7wcgAm6xqNjA+t5h5XxbSmvinaq35tS/ew3M+8vOmpdZaa0amnTPS+soeac5C2tO2LqV7l6iku5oj02nWJKPlxQerHH10Vq4JgiAIJzJ1hTSt9fla68+3euB29xcEPzK1kKM/HikPJlvJjBqby7LGHJRu6otzeKqN0s46pv5eJqP1SjstQ/75VibIuSIhs8TUTjQUdO2RNu/gkQa05ZFmdDld7BQYC7XmyQTwxOHFAfzDB6dbikdYfaTzlRlp0XCgpdLO7ujSZKQ1avwB3rr7OmeImtdCj+K21pqFXKHmuhNrQUibXMiVy9IsQa3dSa4lnJ1pZsm02jF1FbMR2Gv9oZQ6B9gMfFprfYfW+jvADzFEtVXLVWesYW1PlO3PjHHuhh7OXu++zH9jXxeHp1JMLOQolHSNmb7VAGi+jsfipDkesXPG2m4OTqbL572FlZHWqGsnGD5p9tLOTL7IXLbAkNkcwcr4anYtum/vJHfvmWA6lef6u/Y13HYmnSedL9Z07ATDV7U7GmKyTnb8oak0p6wxSi6XyhuxmiMzaTbYylgtIW3MR+K623GVIAiCsHSIc64geGA6nae3K1yePLYysR2by5YHYmt7ouXW8q2QqmPq72UyOpcpEAkGyhNrCyvTpJXsL6dSMjBLO916pNWbuHeFWvZIyxVLFEu6LKQFAopoyJvQYfHUyCyb+ru47OQBx1Ia4cSjVNJk8qVyww8wzkUv369yqXWViNzb1ZqAXG5I0qBrpxsWssU6zQasRQVv52QmX9tx2IgzQMajsG3PzLG8nEbnMp6OUc2YeV0+bU2SgBIhzYEuwP4mX4nRoOoXtvt2Ywhuq5ZwMMDn3noxv/ycjXzsVy5yVcZosaGviyPTmbJP6qaqbLHFjLT6QtpgolpIMwzxnx2t9Ek7PJ1iuDta8ztfzWAiUvFdt0Q1KyPN+k1u1un67j0TKAWXnzLIjU82ztg+YjZx2lDHc60/EambkTY2n+UCs7HCcmWIHZ5OV/jBWaKiXzLSPvGLnZz7gZ+x/RkpAhIEQTieiJAmCB6YSeXpi4eJhAJ0hYOehbRiSTO5sCikDffEmMsUalaP3ZKuY+rfFXE/eV/I1maFQOuZJlZc1TGB0Wwg57qUrFBeka+Oq9WMtFS2tstpV8R7BgwYBsRbBxNsHUywb4nMzYWVTaZQez7GQl5LO/MEA6osdFm0+r2v3/jD+DvrWnCv0/yjRcHd6mRafY57Le3UWjMxn2MwaQlpZkZaGwsU9v3X9sToj0dqDNgFDgNn2f7+JWAWeNR2Xz9gL/1clTx3Sz8f+9WLytmLbtnY10U6X+RRM6P5pCoPvkQD0UprzVQqx0CyurTTiKG64cDh6XTTsk6wSjttQpqZdWV5sSUiVoOexufoA/umOHtdDy89dy2Hp9Mcnq7/NRiZMR5zykgDQ0ibdCglzRaKTKfynLImSSQYWJbya601R6bTFZl80VCQvniYsfn2xPqlYCFb4Au37qFQ0nzh1j2dDkcQBOGEwnXvaqXU211sVsIYSD2ttd7VclSC4FOmUrlyC/SerpDnLIyJ+SwljS0jbbEMaeuQ91by9Uz9vXQKrN+Nrw3vo3yxJtMEjDINNxkwpZI2vaacM2CyhRKZfLEi88dtXFBZxtoVDrZU2jkyk+aq09dw0lCc8fls3dI34cTBKfurK+It49EoaQ7VZLa06pFWT0jzkpGmta5f2tlimbslatdkpHkU0uazBXLFkq200/IvWpqMtOGeaMOMmBOYW4B3KKV+HyMz7TXAd7XW9i/UacDBTgS3EtgyYAhnt+4cQ6naRgDJBhlps5kC+aKuyUg7aSBOJBioyZI+MJnios39TWMaTESYmM+VmwRY4pSVhRUKGouIVlOUeuwaneOFp6/h0q0DADy4f6pul88jM40z0gbiYccySqspwnB3lDXd0WUptZxK5cnkSzWxDXdHfZGR9tCBKdL5Iues7+H+fZMtjYsEQRCE1vAy67seI23fFUqpJ4H3aK1v9xqUIPiV6bSRkQatTWytLAfLI22tNemby7J1KOE5HsvUPxapzF6JeciWm8sUSDgIVlZpWWvlZIWKrC+LaCjoKgPGmtw7lYfaBT6vA8bFJgiLr7cVc/N8scTonNE04qQB43M7OJni7PXeOrgKqwtLtKoo7QwFKZQ0+WKJsIuulnMZZ8Gq1+yO6fY4Ful8oSYmgGjYvUeaVYbZsLSzxYw0R480D8K2NZkeTBjX0oF4hFBAtZ2RZgln/fEIA/GIlHbW8g/AG4BPYnTpnAeusx5USg0DLwK+1IngVgLnmJ0wb905xpaBeM05Gi9nf9UKaZO276edUDDAKWsS7LQJaZl8kUNTaX75OZuaxjSYjJArlpjPGh6l4/OVpZ1gXAfmG2SkpXIFjs1m2ToY54y13YQCiqdHZnnNhRsctx+ZThMKqIrnsNOfiNRk2MGi2L2mO8pQMlL+eyk5XPaWqxTSlku488r9eycJKHjXlVv58+88xjNH57hwc1+nwxIEQTgh8FLa+S7g/zAGTDcBHwJ+z7y92bz/B8BfAP8NnA38TCl14VIGLAidQmvNdCpX7iLVE/MupFmru2u6jWNYGWnHWsyeWMw2qe3I57aEciFbcC7Z6mojI62eR1ooQK7YPC5L2HIS47rb8KezylHibXhYgfF5aQ0bemNlMbTVz1BYPVjfo4rSTg+CFRjegE5NNnrN89GrsF23s2/IfUbanJl90ihz1Wt27mIThGqPNG/no1VyaZW4BQLKKAVrU/iazeTpCgeJhAL0dIWaekKdaGit9wLnAn8IvBc4T2v9jG2Tk4B/w1iEFRxY3xuj31yYu8hB/Fj0I6s9Hyarvvd2zljbXSE87Z9IoTWcambTN2LAFKSt44+XSzsXnycZDdb1bbOeD2DrUIJIKMBpw0meOVrfR3RkJsPanhjBgLO/XD0hu1JIi5ZFv6XEKkmtzqYb7o75IiPtscMznLG2m8tOHgQM71ZBEATh+OBFSBsDXg68XGv9Uq31h7XWXzBvXwK8wvz3lNb6LcDLgCjwl0setSB0gFSuSL6o6etqPSPN2r63q9IYu10hrVpwMrpjuvQiq1OyZU3m51psNuDkkRYJBcgXNcVS4+TWxkJaYwPmZnEBxO2lnS14pI2YpSjr+7oWy3OXYTVcOD7sHpvnySMzbR/H6XyMRSwhzb03YHXHTrBnfrUqWNVmfhlxNf/uW517nTwLE5EQAeU9I80SB6qz3LrCwfL76AZrgm0vceuPh9sW0mbS+fJiQjIqQpoTWuujWuvPmP8OVD12v9b6j7XW93cqPr+jlOINzzWyxF55wfqaxxs1G3D63lucsTbJ4el0eb/dY4aodoqLrHfreJZAPTGfIx4JVgjeyVjj82HfuNE9c+ug8XxnrutmRwOBp9qDrJr+RIR0vliTqTo2vyik9ScizNTp7NkOR+oIadVNGTrF7rF5ThtOsqm/i2gowJ6x2sw9QRAEYXnwIqS9D/hfrfXPnB7UWv8U+F/gb82/bwJ+jpHaLwgrHiszyxKYEtGQ5yYBi0KacYyeWJhgQDHtYKTrhnS+SDQUqFnJjYYCrtuhG80GnIS01jPS0rli3dJOoGm23GIJpsPE3WXXMCecsmBa8UizDJjXJKNlv7t2PZmEzpArlHjD5+7i1Z++o2x63Spphy66VuaXF89Cp+Yf1jXDq3jvFBMY1wiArAuBz8rkTEZrM+UCAUUyGmrBI825tNNrqfVUqrbErT8eafmaajGbLpTf82QsVBYTBWEp+auXn8XNf/oifuncdTWPNeqQOblg/AYNOAhppw0bDQd2mZ07d5u3p6xpLqRZx7NKpsfnszUll4lIYyHtkFkOudn0gDtzXTdHZjLM1Dknj8yk6/qj2WOaqhLKrIy0wUTUaMayDOfoyEyaWDhQtvSwqCfuHU+skt1T1yQJBBSb+rs4OLnqe3sIgiD4Bi8eaRdimMs2Yg/watvfTwJXew1KEPxIdWZHIhpkweMgyhpIWhO0QEAZ2RMtrqQ26o7pNiNtrk5pZ9g0FW6la2cqX6gbFxjihZNIZmGVozmJcY0mF03jMj+vRFXpndeOfJMLxnvSnwgTCwfpiYUkI22Fcu/eibLocuOTx3jHFVtbPpZTJqXX0s56TTZaFdLqNRsIBQOEAqrcabQRVmmnk8AHRrac10nsQvlcrCo59VjaaQn9PbZy2P54pJyF0yoz6Xz5mMlomLkTPCNNKfUBDJ/cf9NaT5p/u0FrrT+yjKGtaAxPM+eSS2uRzCkjrVzSXCcjDWDXsTku2tzHk0dm2TIQd7yuVGOVcFpCnb0jrkUyGipnZTsxsZAjHFTlsu+zzG6mO0fnys0HLIolzdGZTN1GBLAokk8u5CoEt7G5LP1mF3Wr/LpQLBHy4CHZjJGZDOt7u2qav9jFva5I826oy8G+iQW0XhRINw/EOTglHcQFQRCOF16EtBxwfpNtLgDso/wQIFd1YVWQqurIF4+EylkVbplJL/ruWPTHW+8IZ5RQ1p7G0XDQVaYJGGVbTs0GwMhKazkjrU7XTsDMlqvNbinvXyeLBmxCWkvebbWZbl0Rdw0Q7FRnwaztiYlH2grlsUNGSWc8EuTRQ9NtHcuxa2fYW2lnOufcda2nXSEt7HCdCAVcXSdS5Yw05+tETyzsPSOtTsmp1wxRq/Tc7t/Wnwgztb/NjLRMnnVm2XZ3LESuUCJbKJazak9ArsMQ0r4FTGJrLNAEDYiQ1gJKKRIRZz+yqYUcsXDA8ff/pMEEiUiQRw5O86ZLNvPooekaAaseVtMOS6gbm8uyZbCqm2gsxMJY/d/fyYUsA4lIWXw63cyQ23msVkgbm8uSL+qGGWmL4l5tRpqVEW6J3vPZQtnHdik4Npsp+6DaqSfuWUyncrztK/fxtuefxK9cunnJ4rGzZ8wooT3VFGI39Xfx8IHpZXkuQRAEoRYvQtotwOuUUr+ttf5i9YNKqXcDr8Io77Q4CzjUXoiC4A/SVUbiiUiQVL5IqaQJ1DHJrWYmnS9nlli0Y4ydzhfqmvpnC8VyC/t6FEuadL7oWNoJrQtp9TzSoi4Nzq332klQsCbMCx7Laq24oDILpiscMJ/P/QR5OpWjKxwsx7emO7osHcOE5efZ0XnW98Y4c103Tx1pz6g57SBaWd8Rt+WKmXyRrkhtRoV13fDebKB+mXQsHHSVkdbIsxCMxiTePdKs0s7Ka4/bhiQWRtfhYEV5e188wnQq1/T614iZdJ4z1hoCgJXBupD1JqRprfnoj5+mUNJ88NXntByLT7CqCw5U/S0sI8k6HTInFnJl0auaYEBx2SmD3LV7gpGZNCMzGcdmBk50RYJ0hYNMmqWdIzNpLjulUvxKREMNPUonF/IVpdYb+7qIR4Lscui8Wc/M346V/VUjpM0vCmmL18elFdJGZjKOImS9clOLHz02wuOHZ/i7Hz+1bELavgnTi870vtvcH2cmnWc2k6/I0BUEQRCWBy9C2l8B24DPKaX+FLgbOAasBS4HTgOmgb8BUEqtxRhofW7pwhWEzlFdIhWPhtAaMgXnrDAnnIS0gXiEPeOtlSE1EqxKGgolTThYf/JmTZDrlWx1x8KeJ8haa7M8zSEucyLezL8tXaeJAixmxbQq8EFtxpBXIc2YKNhKyRIRnm5ThBE6g2XWfNqaJHfvnmhLfCkLwDYhLBb25pGWzjv7C7ZT2hkMKMfrgNuMtEbCNhjZIAcmvSWfp7JGXNFQpWgYDQXLDUnqdfGzM5vOl7P1LAbiEQolzVy20PKEctZ2rU5a2S6ZgmMpXT2eOTbHl+/YC8CvXrqZs9f3tBSLH9Ba39rob2F5qCdaTS7k6E/U/26/4LQhbt4xyhdv2wPAVWcMuX7OgUSEiYUcC9kCs5kC63srRa7uaOMFtsmFbEU5aCCgOH04yc5jtZ07LTP/RhlpQ6ZgaHUQtRidy3Dxln7AW8burTvHGJvL8obnbmx4rS+VNKOz2XJDITsD5ntfbxH0icNGpvNctlDXh7ZdRqYz9MRC5TFRufHRbFaENEEQhOOAayMBrfUu4ApgO3A68Hbgz83b04FbgSu11jvNXUaBbuCPlzBeQegYi+KOMWixZym4pX5GWmtlSKkmpv5NM78aCFbQmvdRJl9Ca+cMGPcZafWzaKIhw9upta6dhZrJeyzivdnAdCpHf1WXwHor04K/OTKdZlN/nM0DcbKFUluZhU7nk5eMNEuEdjofY+EgkWCghRLKIvFw0HHCaGSkNRfSMg1KrcG8TniMayFn+ChWxxUN28u/mzOXqfV4tIzBp1u8rpbKItxi105Y9Ipzy717Jsv/v3v3REuxCCc2iWjIMft6ciHHQJ2MNIBXXbiecFDxtTv3cda67nIDAjcM90Q5NpspN1+p7qiZiIbIFkrk62SOOsV2+tpudjbISGvUtbOnK0QooCpEK611VWmncY42W/g7OpPhHV+9jz/7n0e5ecdow20nUzlyxRLre2tjszLu6tlyPH10zoxzsfnCUjNS1aTB6gIv2fGCIAjHB0+OnFrrZ7TW1wJbgNcAbwNeC2zRWl+jtX7atq3WWme11u5rNATBx1jiTtzmkQZ46tw545Q9kTBEGK2195jqZaRZk9Emk/dFT6dGpZ0teh85iAERt0Ka2WzA6bUppUhEG3cNqx9brajQFQ6SLZQoeXj/p1K5mi6BM+k8xZL3z1DoHPliifH5HMPdUTYPGBOSg21MetptNpAtGCJ0rI5g1R0LeTa9N0pFnY8XCQVc+QM2E9y7YyHPgnsqW3T0ZvTSTRQMcau7KvuiPMltUdyeyxbQejHLxRLqvPoy7jg6y2AiwmAiwtMjqytjVSm1VSn1CqVUwnZfSCn1IaXUo0qpu5RSr+9kjKuBZJ3fusmFHIMNsiOHu2N84FXncNa6bj782vM8PedJA3H2T6Q4Mm34flZnpFnZVfUWsyYXcgxUdbk8Y22S8flsjfB0ZDpNTyxUcw7bUUoZWXLzi/vOZwtk8qVFIc1l6ftPnxgp//+7DzV2njlqNlRwykjri0dQCibrdCIdnc1wqtkE4NAyNQA4PJ2pENKs92Js3h9C2ny2wH17J1sa2wqCIKwEWmpto7U+pLX+kdb6P7XWP9Raiw+asOqpLgu0yiG9ZKTNZQq1GWnxCMWSbql1eypXcG424FKwSlmZX3VLtrx7pC2WwLY+SW5WSlZvctE0tmytqGC9di/9BqZS+XLWCxifYUl7968SOotVKjTcE2Vzv2Go3c6kJ50vEgkGKrrGxcLuhaFMEy+yZCzkWcypV/5txOYuI63Z+dgdCzOfLXgSkudzBceScrfZtBZOGWlWyVurQpp1HluT81Y7BR+YTLFlMM5Z67sdy9pWOB8E/gOwz9r/Fng/RmOq5wPfVko9vwOxrRqSUedz3sj6alxm/LbLt/LTP7qK553srtGAxZbBBCMzafabHlzVGVndDc6HfLHEbKbgmJEG1JwHh6fSDcs6LQaTUSYWFr9qVtZVjZDWZOHvgf1TrO+N8auXbObOZycoNbhmWUKaU0ZaMKDo6wo7ZqSVSprx+SwXbTbKTq3y1aVmZCZdEdtwt1Xa6Y/GR7/7jQf5lS/czXcelCmiIAirk5aENKXUWUqp1yul3rbUAQmCX6nuyNdqRpqTkAb1SwSaxeQ0ufVa2llvot1KN75GHTetuJqZiTcVFOpMLpqRcmisYL1/HvRQplKVk5h2J+5C6/z0iRH+7kdPtbTqPTprTMbWdsfY2G9M5kZmWp+EGOdj5c9ql4fSzmam/q1liDp30AXLI81dXNFQoK5nWU8LGVupOr5B0ZC30s7ZdK2xdrsZaZbPknXcRBtC2ub+OFsG4stW3tVBLgdu0loXAJRSAeD3gB0YVQvPAxYQe4+2SMZqPdIy+SKpXNGTX58XThqIU9KGl1g0FKgRuhqdD9Y4ZiBZGZvVuGPnaGV55/7JFJsHKruCOjGUjJQ7iYJNSEsa4pG92UAjHj00zXO39HPxSf3MpPMN/WlHTEFqnYOQBqYth8M1ZiadJ1/UnLXOeM3j80s/Lkjnikyn8hWfTU9XiEgo4IuMtP0TC9y+axyAbz9wsMPRCIIgLA+ehDSl1EVKqQeAJ4HvANfbHnuRUiqllHr10oYonMj4KSW8nGkVrsxIS7n01yoUS8xnazPSyh2pWpj0peqZ+rucjFaLg9V0xwwvlJzL7BCobcrgGJeLktN6BulgTi5a6dqZLdSIFJbwkXeZTaO1rjAiB8pdwqbqlHm4RWvt2pReMHj3Nx7iy3fs5YnD3kvnjpkTpeGeKPFIiEQk2LZHWnUmppfSzoyZtVbvfGwlEzOdL9QVyqNmWXPTY+Tql4fCouDkpTHJQrOy9DYy0txOquthvY6eLuO4VpxevBQLxRJHpjNsGYizsa+LiYWcZy9Gn7MW2G/7+yJgCPg3s2rhAeAHwKUdiG3VkHQw9re8wpZLSDvZLEn8xdOjnLomWSOgL2bjO2TKmeOYgarOmRt6YySjIXbZMtKKJc3+iQVOWZOgGdWlnZZYZGWkJSJBlKLhQkO2UOTwVJpTh5M89yQjW+yhA9N1tz82kyEYUAwlnb3oBuIRxwVQK7Z1vTH64uGWu7I34oiDf51SijVJf3QQv3ev4Q/54rPX8vCBadcLI4IgCCsJ10KaUuoMjEYDZwKfBH5StcltwCTwxqUKTjix+ZefPcPV/7K9pRK+5SCVqyzb8pqRZpVu9nZVlyG1l5HW2COtSWlnU+8jY0LqJQvGej/aaTZgNVGo11Gr5Yy0XLGmnMzKknNpyUQqV6SkqZi8D7SRVWjns9t3c+nf/YLdY611cT3RsJewPG52SfO0vznhsDxwhrrbm4SkHfzIFoU0d4IVLH4nq+mOhVsqta4rpIUC7oW0OtcIIy7vnXRTuUIdjzQzQ9TF+6W1NoW0ysWJVq5bdqxy/e6ocRwvWYUW4/M5iiXN+r4Ym8yy4cPLVOLVIcKAffXhSvPvm233HQLWH8+gVhs9sRDzuUJFCeJyC2nnrO8hZIpn526o7TRb9gx0SOOenHeOTSnF6WsrO3cenkqTL2pOGWoupA0mokzM15Z2Wgb7SikSkZBjTBaHptKUNGwdjHPKUIJYOMAzR+uXXI/MZBjujtbNxDUaRTkIabay04E627SLVS5a7V+3ps3fsKXigX2T9MfDvO45GyiUNLscGk0IgiCsdLxkpH0QiADP01r/CXC//UFtpA7djaw+CkuA1prP3PIs+yZSvul2ls4VKibJ1iTQrUeaJfwkqyZ9fR7attspljTZQqlhCWXz7piNSzu7y52w3E+Q0w090szSThdxNcqAadkjLVeoKXOzxD23Qpr1vMlopUcatF/a+c17DzCXLfDzp461dZwThX0TKdv/Fzzvb004LNPudlfzUw6l1sGAIhRQrlbkG5VFg+FN5FVIq1f+DZaQ5i6uxkKad+FqIVsk3mZpZ7ZQIlcslTPHLCKhALFwoCXfSSM2s2GKKbpbn4fb7GNY9N8bTETLZcPLZTreIQ4BF9j+fgUwbm86BQwDq6vLwnEmGQuhtZF9bmGVODZqNtAOsXCQF54+BMAvnbuu5vFyaafD+VWOLVkb2xnD3RWCilVWefJQsmlMg8kIC7liObN3bC5LOKgqMsMT0WDDbt6W59tJgwkCAcXpw429C4/NZuqWdYKZkebwm28X0gYTkQpvt6VixGwEscGnQtqTR2Y5f1Mf56w3hNinjshlQBCE1YcXIe1a4H+rBknVHAA2tBeSIFT6FPnFpLk6syNeLu10N1lLVXX9tOhpUUhrJIJ5Le2sN9HuaSkjrX5cbrt2ZppM3FsX0ookquKKhLyVds6VBdHFyXuf6ZE23UZpZ65QYnTO+N7L6q07jtoy0g634EE1k87THQ2Vs0zXdEfb8pfJNCi1dlMe3cwbsFWPtPoZaUHXTRDqXSOsuMBbRtpCtkDSsdmA+9JOqwTTqeNfK/6O5dhyllgeKseklLvyXItJm6Cw1jQB98MEdwn5EfASpdS/KKX+DngJ8H9V25xFZfmn4BFrwcZ+3k+awkz/MglpAP/6Kxfxzd+8jGvPHnaIqX7XTktY6o/Xxnb62iQTC7lyZtnecUPYOtlVRppxPEuoG53LMpiIErBliyWiRvZePfaNG0L21sF4OZ5G48uRmTTrHDp2WlgZadUWJNUZaRPL4JF2ZCaNUrC2t7LsdHCZMuC8oLVm7/gCp65JsGUgTjCg2D/pfaFLEATB73gR0vowViCbHW/5ftmFEwZ7CYw12Oo0qaosKSsjrVEpgZ16fmSWUbdXP5/FEsrWy6OaZaQlWzARTzcoF/Ui8DUS0hLRkKduqRYpB6+ncmmny8NZAl63LZumOxoiFFBtZaQdmEyRL2rz//74zvudo6ZPzNnre1oSwGbTeXpt3VfbXc2vl7kV8VBCCU26dmYLnrwjGwppYZdxNckQLQtpWW+Cu1PWarn5h4uMNEu464nVHqe7hY7D5diylddFpRRd4aAnjzN7+Z2VnbMcpuMd5J+BvcCfAH8DjGBULgCglDoJuALD9kNokW6H3+DJBeM8W66MNDC+t1ecNuRor2CVPM85CGmWaNQfrxW3yw0HzIWiZ47O0dsVZsghe62aQdOnzBLhRueyrO2pFJGS0drGDHaOzmaIhALlstMz13ZzbDbLTJ0FsGOz2cYZaYkw+aKuWdQbm88SDQXojoYYSETa9k51YmQ6w1AyWmMD0J8wsuQ66S98dDZDKlfklDVJQsEA63tjHJxcVWXtgiAIgDchbRQ4rck25wLSnmWFkc4VOTDhr5ITKyMtHgmWDcE7TbUfmSUKuc1SqDdJDgUDJCJB7xlpVc0P7Lg17E41KMOE1ko7LYGvHSPxdL5IrFFppykoNGpdXy+2al+maNhjaadDRppSqq2JOyz6fW3q75JBp0uOzmRJRIKcPBQvl9J5obqL7ppklJl0vmVjZKfSTjAzvzyVdjr/NHfHwpS0txJDI7uzQddON3E1EOOsuMB9RprWmoVcoSY7FNz7O9qfr7rZABiZvl6aH9hZKF/DFo8bjwQ9eqQZ38ehRJRENERXONjSd9SvaK1HgfOB15j/ztFaH7FtksQQ2b7cgfBWDcmySG0X0rIEA6qmW+3xwvIZdVpgm1zI0RcPl7N87VhC2q5RIwvssUMzXLCpt64Xqh1LjLaEutHZDMNV2WKJSGMhbXIhx2AiUn6+M9ZZnURrs9LmMnnmswXWNxDSFjuuV15nxuayrOmOopTxGbXq1diIIzNpNjjENhCPOIp7x5Pdo8ZC4KlmpuHm/vhqK2sXBEEAvAlpNwOvVkqd6fSgUupSjPLPny1FYMLx40M/fJKr/r9bGpquHm+OmULaeRt6fbOKn8oViNsmpIGAIhoKkHE56W5U8tjbwqTPVXfMpplfhYrtq2mptDNfX5yLmIPrph5puSJd4fqXJ6skLOWxw6VTdk7UY2nnvJl1k6zyd+qOtT5xBzhmlnWeu6GHiYWsrzrW+pWjs2nW9sYYSkYZbyGTbLpKSBsyjatbvebULe0MuyvttISaemWU1nfO7SRJa21ctxqVdrqKq+SqtNNtKWUmX0LrJtm0LuJKme+DU9OCdko7DUE0UGEyHmshIy0UUGX/tqHuyKoS0gC01mmt9Y/Mf3NVjz2ptf6k1nqH2+MppQaVUr+plPqeUupZpVRaKTWjlLpDKfUbSqlA1fablVKfVUrdq5Q6qpTKKqWOKKVuV0q9SylVozQppd6plNIN/r279Xdk6bEyn+cqMtJy9McjFWWNx5NQ0PAgnHfIQJ1M5Wo6dlqs7YkylIzw0P4pMvkiO4/Ncd7GXlfPOZQwM9LMTM9js5lyowGLRLRxs4HJhVxFE4TThw1vNicrhaPmGHRtg9LOgXK5aeV5bQlpsNj5fKm7Vh6ZTtc0GgB78yrna9/+iQUu/4eb+M6DzQqMWsfyvjtljfH+bh7o4mAL1guCIAh+x4uQ9g9AAbhNKfW7mF5oSqlzzb9/CMwB/7LkUbpAKbVJKfVVcxCVVUrtU0p9QinVv9zHUUpdoZS6QSk1qZRKKaUeU0r9kVKq7sxDKfUOpdR9Sql5c6C4XSn1Ki+xLhXfe/gwALfvGuvE0zsyMpMxM00Svpl8pB3KAmNhdx5D0LiMsqcr7DkjLVWnVBS8NRvoCgfrDsi9TtzBeJ+UgpiDEBYKGpPTpgJf3rnsazEuY37kpeS0XnMGr107yx5pVUJaT1e7GWnG9/ysdT3ki9qxbEaoZHLBmLQNJaPMZgquxCo71Rlp/W12X03lCs6lnUF3JZRuPNLAvbCdLZQo6frNCyzvtmaibbrO67KIhYNEQgHX3/9FD7LWFwGgcXOGdjJEF7K1mateM9ImF3L027JfhpLRZfFK8gOmoPUapdTbzNvNLR7qTcCXgMuAe4FPAN8FzsPIbPu2qkxfOhV4CzADfB/4V4yx6EnAV4EblVL1fkh+AHzI4d8DLca+LFjZnvbfuon5HAOJzmSjWSSj4bpdO+t1E1VK8YLThrh91zj37p2kUNJccpK7IfpQt3HM0bkM2UKRqVS+RuRKNmk2UC2kbezrIhEJljPk7Bwy7UU29deKVRZWuWm1J9nYXJY1SUtI85at65Z6ZafW92Kyjs3E9x4+zMhMhi/fvmdJ47FzYCJFNBQol95u7IszNpddcjFREASh09SfqVahtX5GKfUG4L+Az5h3K+Ax83Ya+GWt9YGlDrIZSqlTgbswOkT9ANgBPA/4Q+BlSqkrtdZNWz+2chyl1GsxBnoZ4FvAJPBq4OMY7eDf5PA8/wL8KYbn3JcwfOXeDPxQKfUHWuvPVO+zXMxl8uUJ3u4x//gyHZvLsLYnxlC3YZxaKumOrb5apHJFNvRVC2kB11kKix5pdcqQWi3tdMzqsMqjGsfm5BlmJ9mCiXjK9DerV64RDQVcebc19kgzS0s8iE1WyWlNaaeVkVZ0m5HmXE7WHW2vhOPYbJZ4JMiWAcMIeXI+17HSnZXCTLrAxr4YfaYfz2wmz1Ay2mQv+/7VQlp7TSOcxHbwkJHWQBwH76b+6QZZq1ZcYAhujTLOmp2PYPiUuS0BX/Qga9S100VGWgNPuXZKO1O5YrmZjEVX2GtpZ67Cw2ooGeXg5Ooqb1JKnQ58FrjG4bGbgfdorXd6OOROjDLRH2uty18ApdTfAPcBbwB+GWPMBcZ4rd++rbl9GLgR2GZu/22H5/q+1vp6D7F1hLJPqS37aypVX6w6XiSjQcff38mFHFtMM38ntp05zPcfOcL7v/8E0VCAK04dcvV88UiI/niYw1Ppso9ltUdavIlH2uRCjpNssSmlOG04ybOjtRlph8xzdXN//ddS3QDBYmw+yyVbDYHQykidyxQa/jZprfn7G55mPlvgI689z7E01qJR2WmzxSCre+a+iYVlG1ePzGTY2NdVHgMO9yxmem/sqy9MCoIgrDS8ZKShtf4pcDKG78W3gV8A/wv8OXCa1vrmJY/QHZ/FEL/eq7V+ndb6r7TW12CIWWcCH12O4yilejCEsCKwTWv9G1rrPwcuAu4G3qiUenPVPldgiGi7gQu01n+stX4PcDGGCPcvSqmtnt+BFrG6GAG+8jCYSeXpi4cZTEQplrTnbK3lwEl0ioWDHko7zeYATpO+WCsZaUvjRdZoghwOBugKBz137WzkpxQNBcgVm5d2uiklazRodooLakWKVj3SEg4ZaV4bRtgZnTNKVQaSzoNzoZbZdJ6errDn0kKLaiGtz5yETKdbLe2szXgE9xlp5dLOUD0hzVt2Q6pJhpvrzNUmgrsVm9vrRL0OxgBRq9mAixOyUUZaTyzsydvRjlNGWiwc9ORNN5upKhtOrq7STqXUaRhC1rXAHuDrGA0Ivm7+fS1wh7mdK7TWN2utf1gtjGmtjwKfN//cZrs/V72teX8eI0MN4HS3z+9HnMTziYUcgwn3CwbLQTIWYt7hfJ9M5Ro2QXjZeetY1xPjwGSKN1y8qel1xc7G/i4OTaU5ZmZvD3dXZ6Q17uZtlcTaOW2427Fz56GpNJFQoKH4Ve3bBpAvlphcyC2WdprZ881+m+7bO8mXbt/Lf913kB8/PtJwW6vs1DkjzYipXufOPWbzrky+xJGZ5Sm3PDKTZn3fYmxWCe4q61osCILgTUgD0FpPm74Xv6a1fqnW+k1a63/VWk8uR4DNUEqdArwU2Af8W9XDHwQWgLcppRr2127xOG8E1gD/rbUulwNorTPA35p//m7VsSz/jY9qrads+1jPGwXe1SjWpWRs3vhBXt8bq+iU2WmsCW5P12KmSadJO/gfdYWDrpsNpBqUdvZ2hT2n/jfyU7K8yNxMkBuJXmAMmL3ElmnS4S/iIiPNOEb9y9Nix1TvQlqiKtPEa2nnfLZALBwgXLVi7EVIcGJiPsdQMlqehHS6hf1KoHydaKF8JpMvkiuUytcYWMxIa6XLWqFYIlcs1elW677ZQDQUWLJS63S5s2/90k5oXkaZaeKRBt5KKa2Ou/Foo4y05u9Xo1LY7liIXKHk+vpsx2kxIB5xf60H47vYbcsoHUgsZlevEv4BGMTI2D9Ta2zulaMAAQAASURBVP0urfVfa63fhbHw+MfAEPD3S/R81knZ9Etm2mq8wvzzsTqbXWRacPyVWZK6aSmCXGqs37pqj7TOZ6TVds7WWjPVJLZYOMg3fvMyPvSac/nbV57t6Tk39cU5PJ0uL/pWl10mIoYfWcFhoS5bKDKfLdSIfKevTRqdO6uErkNTaTb1dTXM2IpHjCYiEzaB3BLVLCHN+n1pdm38xdPHCAcVvV1hfvhoEyHNbEy0zsG/reyR5lDaWSiW2D+xwDnrewCWrZHXyHSmwr/Nei9GfdI47NadY1z3f0/6Yl4hCMLKxrOQ5kOskoIbHVYx54A7gTjw/GU4jrXPTx2OdxuQAq5QStmXtBrt85OqbZYda4Xo7PU9vvJvWZwgey8tXC4M0am642OQjEsFJmN6hzkZ+/e24JHWqGwrFAwQcuFF1qy0E8wJsscSynidLoHgTlSwykPrsVju4j4uK3utuoOh12YDc9lCjT8atOfJBIvf+XJpRh2PE8GgUCwxny3Q2xVuyYfGOt8cM9JaEDHTDUQdt6WdmSbno1ePtGZdeRdLwOvHZgmEzQR34/vvNi6rzNrh2hVQBJS70s5GpbDtLMIs5Ao1GaddEW/NBuYy+fLvF0BfV4SShvlc53/LlohrgRu01p92GDOVtNafxBjTvLjdJzJ9zt5u/lkzdlJKDSmlrlNKfUgp9VkMW46XAt8EflTnsH+IUW3wDxhZdPuUUp9XStV3l+8AwYAiEVksoywUS0yn8mXBpFMko7Xjgtl0gUJJNxX5ThtO8o4rtjb0QXXCyEhLsXd8AaVg80Bl2aW1SFYt8MGi+b6V8W1hNRyoLu88OJVi00D9sk4LSyC3sMbUix5pVufzxtehB/dP8ZzN/bzi/HXct3eioeBudbV3ajbQHQ0RCijHhbijsxnyRc3zTh4w/p5Z+gyxfLHEsblMRUdRK3NwzAcZuZl8kT/45kNcf9c+vnL73k6HIwjCCqfur5hS6qpWD6q1vq3VfVvA6iJaz4djF8aA6gzgpiU+Tt19tNYFpdRe4FzgFOBpM5ttIzCvtXZactpl3p7RIM4yE+n2V7atH/0z1nZz845RCsVSQ2+G44UlKlgT5FZXjibms/zLjc/wlstOct0dyolSSTuWQcZCAde+OY28w3q6jJIEL+9/o66dsDReZGBlWnn0SGujtFNr5/faTjkzx0Nc1udUm5FmeaS5O858xllI64mFmcsWKJZ0Rbc/t8xm8py1rrs88Pby2k5ErLI9I3PV3WTFjpOQFgkFSESCTLdQSt6ozNBLaWej771VJuS6tLOpR1rz0s6M+VjT60Q0XG6Y4TYup/dKKeW6m6h1HKdSWPsizHC3q7AWj5st1nQE9FraOZcplAV/gF4z23EmlV8t3ocR4JEm2zwCtDyWtPGPGA0HbtBaO3WGH8KoHLDQGI2v/kbXdtLYC/wBhofaIaAXeAGGoPY7QA/w/+oFopT6beC3AdasWcP27dtbeDneiKgSO/ceZPv2UWazxsuZPLKf7duPLPtz12NhOsP4dKni9R9dMM7ZYwd2s3370tslZybyZPIlfv7wbvqjinvuvL3i8cMHjev2L269ncGuxXHU/Pw8N956JwBH9u5ie3pRQJlIGTH/6PYHmNu7eF7uPbbAxetCTT/fiM6y8+DR8naPjBrX5oO7nmT72A7G08bx73/0CeITzzgeQ2vN00dSXL4+RDw1z2ymwH/fcAsbks5jwXt2GyLZjkfuZU+wdqyRCMOTz+5n+/ajFffvnjauX+E543tzx0NPkJh0jqlVxtNGR+a5YwfYvt2Y5hRMUfD+x59hY9pZvJqfnz8u59Ijo4Xy2OF/79vNc8KdO4dWIsfrcxLaQz6n40ej5aDtGIORVnBvetA+ljoyU+dx6/6+ZTiO133ajtU+iIusPY1bbrmlrqm7Gx7ekSUegvnRgwDc8Itb6Yl21tS/pDWz6TxTo0fY+eQxAO5+4BFyB72tXgL8944sP91X4P6dh/nb57ducpotGKfCyKF9FYPX1FyG+bxueMGyLmjP7s8Soui47dghYwD405tuJRlx9/4/aQ6m7r/nTsIOwk1AF9lzwBh812N0Ik1PRDWMv5BKc3gW1xflo2NpQoH62+cyaQ4fTdd/vGi91/vLA7FqZnPGNg8/8TSDc8+6iuvxMWPwtOOJxygerrxEBRQsZHKuXuP+Ixm0w2c+etj6DLeTCHs/hybm0sxOHOOBe4yK78d37GJ7Yb/n46xm7IMDa9J2ZN+zPDlrDM4bTVaq2TllTCr273qK7VOLayGxQIkdexqfN06MmhOy/bt3sj1TOVmYmcwwPVtq+v3afyiDztffrmRqAo/veJbtxeYT1cfM7/zTTzxK/lDtz/KuY8bjd95zL4d6nH+2Z8yJ+8F9u9leqv+cC9NZxmaM61uzQdyDR4znfeLhBxnbWTtZDFBk974DbN9+rO4xAHbuyREJwG233Vrz2D7ztW+/814O9nkbkkzMphgMVl6jpseyzKYKrq4RWmvmMnkmjx1h+/ZxAA6Z7/VNt9/N1t7jOURaNh4FmvmfnUb90kpXKKXei+EruwN4m9M2WusdxqYqiLFY+Xrgw8ALlFKvtFuPaK1vBexfmBTwP0qpezBe068ppf5Ja/1onef6IvBFgDPPPFNv27atnZfnisGHbqV7IMm2bRcbfl633MZlF53Ltgs3LPtz1+MX04/zzOxR7K//wf2TcPvdXHnJhWw7c3jJnzOxb5L/fPpunpwo8cLTh9i27bKKx+cePcLXnnyY8597KWesXVTPt2/fTt/G8+Cue7nqec/hslMGy4+VSpoP3P1Tgn0b2bbtHMDIdJ/76c943rmnsm1b46/41/fdz+hchm3bXgjAsfsPwEOP80svupxN/XFjcefWG9mw5VS2XXWK4zGOzmRI/+wmtj33TK44dZCvPHEbkfVnsO1i52rjG6ceZzBxlJdee7Xj4+sevo1Yb5xt2y6puD//1DG45wFeddWl/PtTd9OzdhPbtnkrr23G/fsm4da72XbZRbzojDXl+wfu/DnxwXVs23a+437bt2/neJxL9/10B8HAHn79yq185Y69PO+KF3jOjDyROV6fk9Ae8jkdPxpdPT5MrZB2GfAyDKP8O4CjwDqM1bxTMdL471v6MNvCmtG2m77VynFafe6629sHcdH1p+vLX3BVU++aRnz78IOsT8/xvIvO4BtPP8zZF13C6Ws9Lt8vMTPpPPpnN3LBWadx9Tnr+OBdt3DSaWfVHVQ04qMP3QrMs3umxIWXXtFyOcT4fBZ+8QvOO+sMtl2+tXz/fx18gH3jKbZtq7/obl3Q/u/YI/QuTDpe3CYePMR/7niU8y9+HicNNrTzK3N/dgfB3Xt48dXbHMXU5N03MTQ8xLZtF9Y9RuihW9m01hig1+Nbhx7k2dF5tm17kau4/vnR29nQF2PbtksdHx986k7ikVDNINhiaiEHP/855555OtuuPNlxm0y+CDf/lPVbTm460LVIPz4CDz7EC55/KWebHiEWsZt/igopVz88n91xN+u7Ydu2yyvuH73/IP+14zEuvOQyNjXo9OVEsaRJ//QGzjn9ZF58zRl03fJThtZvKg/sBQP74ODhA1Nw+108/7kXcOnJA3DrjWw86VS2vdB5slKN3jEK997Plc+7mOds6S/fv+6x24n21P/+1mPH0Vm47Xaec8F5bDt/fcVjPxp7lEOZiabfr2/sf4B+lS5PypyIe/hupGzf+bPW9dRu8MwoPHw/51/0XJ5rew/sHJhIwS23cMG5Zze8Bt8+/xQPjh1g27ZtTQdxh+7ZD489wdVXXVFjGA6QvOsXrFk7zLZtFzR8fTfPPEHi2BHH5+reP8nHH7yb0865oGJC54bibTdyypb1FZO+Oxee4p5jB1xdI1K5AqWf/YzzzjyVbS86FYCuPRN86uF7OO2cC3nB6e46Ffqcvwe+p5R6udb6J9UPKqVeiSFova7VJ1BKvQf4JPAUcG0zL16tdRE4AHxSKXUMo8v8h4Hfb/ZcWuuDSqkbgLdgZNE5CmmdIBldtA0YN6sIBpOdLe1MREM1WdOWPchy+bedt2GxquA5m/tqHm/kITmZco4tEDA6d+60lXbuNv9/6ppk05gGEhGeHpkt/21VeVhNCpKREEo1zpa2ykpPG05y8lCSaCjAM0dn625/dMboal+P/kS4XMpqx2p2MtQdpS8eZqbF7tSNOGJ6LW+oaoQw3B31RbOBB/dPce6GHi47eZAv3b6Xp0dmufikgU6HJQjCCqWukKa1vs7+t1Lq+cBfY/hK/FtVa/IARqr8P2IMWo4nVhZXvbq9nqrtlvI4Xvdptn2zjLUaZtP5toS08Tn/GZzP2kquvHoC2ckWiuwZX+CizX08cnCap0dmueK01iYwZS+eqve6y1PXzvplW1Z5mReftFSuSLxOqSiYpZ1uuvE18DMD795f6XyRrgYrfNFQsKFfVKMSucVjBAgHVUtdOx07BYYCnjzSnFq4l8sL0wVw1iTqYk1IrHK0ZKxx9zGhsjSzPFnxcP4sWD5dVWW6/fFIS/50jfy6IqGAK/P8bKFIV7hxabc3LzLzO1/nHC937WxQAt7I+606rlSu6Gj0XXPMpt5t7oz9Uw6+lRaLDSha8UgrOnikhUjni2itm2aBW9fLbrtHWpsdYX3IIMbi6Y+UUjdh+MIeA9YCL8Lwev0hMKSUert9R63115sdXCn1RxgeZk9giGjeUkQXPWe3edhnzLx1t5p1nLD/Bo+bY7Q1DbpJHg+6oyFyxRLZQrF8HZmqI1YtFV2RIK88fz0/eWKEV1ywvuZx65x1GhdMmiKSU2ynD3dz756J8t+7bMJWMwaTESbmc+Xrwthclp5YqDwuDwQUyUjjMdThaaN5wub+OMGA4tQ1SXYem6+7/dGZDOsdOnZa9Mcj5ddgpyzCJiL0xcNML4uQZvq3VY2RhpL+ENJ2HpvjZeet41Tzs907nhIhTRCElvFihvUR4BdNjGVv4vgLaVYdTz1fMav1eT3vs3aOU3cf0xz3ZIwOU3sAtNYLwGEgqZSqHQW4j7VMu11nZtJ5+uKtdb1bLqwf996ucNljZjbtPa49YwsUS5rXXWSUPzx9tLbFuVvqmXbHwu4NqFMNBKayMbaH15lu6kUWbOqRlsoVGnbHBO/dKI1mA026djYQFcq+Rw2OoZQyVsQ9NkEAZ6EjGgriwpIJgPlsnmS09hjdbUzcq/26uqOhso+H4Ix1rerpChuTFY/vmTXZqhZMWl2prye2gztR2zpGs+YfSQ/fe6trZ6zOOR4NN++QuShsN79OgLsGIJaIWU+cc/1+5YvE6giPZX9Nj78d+WKJXKFU7pZo0RUOorW7JgjWNcDupdgX975Y4nOuB16FkXn/Yoyx3xfM22vN+18DfM3273rztiFKqb/EENEeAa5uQUQDo8QTXHT5tGGlSe9p4fmWDXszosWMtM4KacmyaLV47ZgwRb7BxPLF9q+/ciG3/cXVjhm2jZoNTKbyKLUoaNs5bTjJkZlM+bx9dnSecFBxkotmA4OJCDmz8Q0YhvprqvwVmy2MjcxkUIpyltnpa5PsOlZ/vHp0NsO6RkJaImJk9lcxNr8o8vW1uGDUjJGZND2xUI2PbH8iwnSHGyhNLeSYSuU5ZSjJpv4uggHFvvGFjsYkCMLKxkth+POATzfZ5lFcpNAvMbeYty9VSgWqMuW6gSuBNHDPMhznZowygJdhlBDYuQqjy+dtWuts1T5vM/epHlC+3LaNK2ZaEJjszGbydMfCLXVBtPONe/bzPw8c5EvvuMSxVMcLdlEhHAwQjwRbEiesdPnLThlkIBHh2dF2hDTjfanOZoqF3WVOgDGprScwtZyR1khICzfPgknn62d0WCSjIRZyRdcm+m6aDTQ0N3eZAZOIeBXSiuX9amIKB8gX3WWkpXMlR0G0u40Os5Ygbgmq3bHashmhkuqutYlIyFNXRWuylYzUCmktZaQ1+N5GPAhDluBSDy/NP1x37WyUIVoWCJtnroK777+VnVvvemJcu9rrctpqNnO9zFVLsMvki02zwGfLGaaLn6V1jV+OLJAO8a7lOKhS6v0YYtyDwEsblXMqpS4DHtdap6ruT2KUhAL8uOqxF2qtb6+6TwF/BVwOjOPcVb1jDCWj5bK8iYUswYCir6uzDSsStoY/VpbX5HyOWDjQdDGgHWLhYF3rhGSjjLSFLH1dYcdrjr1z53O29PPs6BwnDyVcNX6yRMOJ+RzdsTDHZrM1ZZfNfs9HpjMMJaNEzOvxGWu7+cEjR5gzx+d2Mvkikws51jUo7RwwRbJSSROwvd7x+SxDpsjX1xXmwGSq3iFa5sh0hg0OGfsD8XDHK172jBtzglPWJAgHA2zq72LfhAhpgiC0jhchTWH4oDXCnVnREqK13q2UuhGjo+Z7qBT7PoSRov8FMxsMpVQY43Xktda7Wz2OyXeAfwLerJT6tNb6AfM5YsDfmdt8rirkz2MIae9TSn1faz1l7rPVfN4sLlZsLdrNSJvLFOiJhcsDkOp25m4oljT/+JMdzGcLfPv+g/z+Nac336kBlphkrRx2t1jmdtRsEb6hr4vN/V0cmkq3HFO9sq1oOFDuatf0GPkiw93Og99Wug4aglX9U7hZp8BSSZPJl5pOCrttImuvi8F7uqnA5660s5nA51VsWmiSMdQkea9Mpk5nxZ42OsxWZ6RJaWdzqoWreCRYznRygzXZildlF/bHI8yk8zWTELfxOJcOG9/5ZmWBaRcijZdS62ZlmeXSTjfCdpPJsVWW7Ob7n8oVGl8jQkFXpbCNyuXjkSBKOU+qm8UGtZmKbt4ri3mH0s5YOEg0FFg1GWla639f6mMqpd6BIaIVgduB9zqcL/u01teb//9rYJtS6lYMb7QUsBljUbIPuAujG6ed25RSO4H7MSoEejEWSs8z93+L1rq+QVUHGEhEmMsUyBVKjM/lGExEPF2bloOyUJ1d/D5PpnLLmo3WjHJpp8PvwNRCvq5H7vmbDFeVhw9Mc9HmPh49NMOVpw46bluN5VU3sZBj61CC0bkMF1f5TTbLIh6ZrSzVtIS9XaPzNd6VVmfkZhlpJW1ci+0ZeJaVCxi/c48emnbxCr0xMpN2FNL6ExFmMwXyxRLhOgLlQrZQc91dSnaPGdO3U0zvuy0DcQ62MS8QBEHwcsW6C3iDUupVWusfVT+olHoN8MvAz5cqOA/8HkZ8n1JKXQs8jZGifzVGmeT7bNtuNB/fD2xt4zhorWeVUr+FIahtV0r9NzCJUc5wpnn/t6r2uUsp9THgT4DHlFLfwWgj/6vAAPAHWut9bl+4F0+gagpmOnpPV6gtL7Jnjs6VBwl3PDvetpA2bw7MrCy5eCRUzhLwwrHZDF3hID2xEJv64xWGsF6pm6VgTpDdTLobZWq1kpGWyTfPSMs0UIcsb7dGx4BKr6FmQlquUKJQ0g2P2UzgWxQtG68GJ6IhT8JJOlegKxx0/JyioSB5F4ulWmvTA642trYy0qqFtGiIsTlZKW1EtZdeV8R9mTXAfK5AJBSoGdT3xMKUtPF4T8x91kejkmQr8ytXLJUFGScyDYQhi+5YqGzo3Ix0rkg0FKif+WVlpDXIqnXvkebeHiCVLdYImNVxNStLt2LrqXNNUsr0JvIopFmZitXXsMX3yk1ppyWkVcZm+BKtGo+05cDqLhME/qjONrdilIgCfAlYAC7F8EKLA1MY2WzfBr6qta7+AvwLRoXFNRhjrhKGCPdvwMe01r4q64RFX6+pVI7x+WzHyzoBklHju20vo5xcyC2bP5obGjUbmFjIlr2Aq1nf28WWgTj37JngJeesZWwuy3NPcmd0upiRlkVr7ZiRloyFG47tRqbTnLJm0ZbP6jj6rIOQNjKTLsdcj4GE8dlMLOQqhbT5bLnR0nJ5pI3MZLjQoRGE9b2YTuVrSl8Bnpks8uvX/Yx//OUL+JVLNy95XAB7xxcIBRSb+433bm1PjGdHx5fluQRBODHw4pH2PiAP/EApdbNS6jql1O+at7cA38PIpnpfw6MsA2Zm2SUYg6vLMFqlnwp8Crhcaz1Rf+/2jqO1/j6Gse5twBswmi7kMYSyN2uta+rFtNZ/CrwTo+vpbwNvB54EXq21/oyrF23Sjo+SNdjojoWJhgKEAqqlcrJdZsnkc7f0sePoHA4v2ROLBtnGRCYWDrYkpB2dzbC2J4pSik39XRyeTrccW6pOtok1iXfTcCBtNgdwoiscJBRQnoTRlCkM1aNZVkcj8307Xsp+F0WwBs0GmpScWhP3Zpk5hkeah1K+BplybpsN5IuaYkk7vu+LnkytZ6QtlnaGpbSzCWXhKrSYkeblOpHKFkk4fB+s7FCvgmimYUZa8xJKsBp1NBGsomHX2Ypuyr+bxdXI+81Oj4fv/0Ku4FhiXY7LZSmskR1afxiTbKFEupyR5uCHCY395CzKHmmxymP0dUVWTUbacqC1vk5rrZr822bb/sda67dorc/QWvdqrcNa62Gt9Yu11l90ENHQWv+51vpFWusNWuuY1jqutT5La/37fhTRgLIAND6fZXwhx1CHO3bCoh/ZvD0jbSHXcmf0pSAaChCok4U6uZCj38EfzeL5pwxwz54Jtu80+k1c4tKA3hKFRueyzKaNrMFqoag72rhBjNE8YFEY2zwQJxIKlO1J7ByxhLS++hlplrhXXUo5Np8tf3d642GyhZKnxadmWGWn1R07gfJ7X8824e6RAiUN33nw0JLFU82hKSNbzirZXdsTZXQuS9FloylBEIRqXGekaa0fVEq9BPgqxsrfNkBjlHyCYbz/G1rrh5c4RrfxHcSFZ4eZ7VU3dcjtcar2uRN4hcd9/h1ouzSinYy0OVunQKVUy+Vku0fnCSh46bnr+Mef7GB8Pue44uSWVFUZZTwSJJ33HteobWVwY38X2UKJsflsSx5u6bJRfdXkKmT55pRoMEYzjtEgg0wpRU9X2HNpZ6OV6WZZHekGGTR2PHkf5Z295GriWoKJe3c0xOEp9x4f6Vz9LJhoOMCMi/FkI5EvEgoQDQVaOoesz92ekeaHxh9+JmMazVsZhvFIyFO2T70yErsg5NSdtR7V1y075Yw0N0Jak+990kNpZ6PSRyOu5uWK1iJCvYYFFvbrRLOiqOY+iu2XdoLl79hiRlrVtSJqu9Y3w6lrJxjn99Tq8UhDKZXAyOT/JYxsf6cfJK21bmYLIjTA+p2fXMgxPpfl1KHONxV1GhdMLuQ4dU3zTpfLhdWEyLHZwEKei0+qP0h7xfnr+fYDh3j/959g80AXZ6/vdvWca7qjhAKKw9Npjs0ZdiI1GWnR+oJ+Jl9kLluoGDNbnTudGg4cnDSEtEa/TVb218T84u9hJl9kLrP4POUM4my+4bVYa82Xb9+LUvCbLzyl7nawWHZa/frtMTk1QQA4MGtcV3e24WXcjJHpNBtsAuS6nhjFkmZiPstwA885QRCEengqRtda3wWcpZS6AnguhrfEDPCQ+ZhwHFG055FWnQXT6Me+EfsmUmzs7+IcM2V899h8W0JaOlckoBYnLvFIsDWPtNkMF5kp5tZq37GZ1oS06iw5C0tQcdNwIJUrEmswYOnxMEGGxsIcuBCsGmTQ2PHSjdJNllsz4/Xqkr16JKJBxwFz/dgKxOsYpkdDQVceac38orx0VLQzlymgFOUMqXgkSCpfbOqpdSKTrhJR4pEgR6Y9lHZmCzWdxcBbiWJ1PLCYIWcn4iIjzYtnYcpl8490vtC08YcRV/33LeNW2LbZA7gR0hpmpLlsNpBu0AkZjKxVr59jvYw0Nx1OLeYyRofA6kYWPV0hDk9nPMXjV5RSfcAdwDnALNCDMR6MANYs/whGhr7QBpYIMTqb5eiss5n78caPpZ3g/Btc0pqpVOPYrjp9DRdt7uORg9O895rTXf/uBgOKDX1dHJ5Kc2zWWUhr5PNrZWhVx3b6cJKHDkzVbH9wMsXanmjD3wnLB21iYbHPmdWswnqs29YsYriBZnj37gk+esPTAFywqY/nnVw/U69R2WmjjDStNYfmjev9dCrPbCbvyVbBLSMzGS6zxW99TsdmRUgTBKE1vJR2ltFa36W1/ozW+qPmrYhoHSCgYLaNrp2WCGdNgJJR734yAKNzGdb1xNho+g649e+ph9VJ0hrIdIW9eR8BpleFUdoJlNPZx+Zbm8TUyzZxK6QVS5pcoVRXyAFD0PRW2tlMSHNX2tk0AybqPiPNTTaZ3Xi90TEavVdgeqR57NpZNyPNZWlns9fnNSaLhawhLFjf+XgkaHxnii47IBwHnh6Zrbua3Amqs7e6vJZ21jl/yo0/PGb7VmfI2bEyvxplpFnCUTMBOWmbADXDeI2NSyihse/XcnikLWQbNxuIhYKuvMiaecp1x7yfj1ZTkkRNRpr7ZgMLZlzV3wWj4+qq0ZX+FkNE+w3AMnL6OJAErgAeAnYDZ3ckulWENX558sgsxZJmU3/nhbTq0s5MvkgqV/SHkFZ1DUoXjDFYo9LOQEDxrd95Ptv/bBtvusSbR9fGPsM6xGpotb632iNtcfGjGqv8sr+qW/Ppw0kOTaXLwr7FwalU3a6lFv2mR9qkLSNt3Py/JaQ18pOz8/OnjwEQCii+9/DhhtseNYVEp0YI5c6uC7XXv9l0gVwRLjZ96Q4vQwOAYklzdDZTURJrCWlW3J3mhsdH+Ov/fbylMaQgCJ2hJSFN8AcB1V5G2mJpp+XL1FpG2qi5mrPBXIUamWnvRymVq+xc57UbHxiTv2yhxIDpFWFlyI3PtSYEVGfJWVhxppsIaW6yv3piYU+ed0ZGTuOsjkZlSPU6kdbG5aG0s5yR1nzyXk8kSrstJYuGmM8VXPveNRIe3XbtbCYqePVtW4ytUliwsmwyOX8IaaNzGV75qdv5tS/d0+lQyqSrygONEnBvGWlOpZ32khcvNCozdJP55Vaw6vEQX/V7VI1SylWGaCQYKPvK1MMqbXazGOPGu61Z5pfWmlSdxh8WiYj3DNGU1c21xiPNKu104YdZJ1u41YxVn/Ia4Dat9dfsXrDa4B4Mu4uz6IBv7mqjt8vorH7XbsMYfaMfhLRIpaBvCUIdF9JiteXccznj6znYxFsuGgqytYWy2Y39RkbavokFIsFATcZgI9HKMvyvFvlOX2uUyO4erWw6dHAyXTbLr0c0FKQ7FmLCtvA1PmdmpJlj4bL3bZNx3f37Jrn8lEGuPmuYe/Y0tpu2xv5OQlqfKRQ6ZaRZC9wXmN1Trcy2pWR0LkOxpCuy5aw4/SCkzaTzvOebD/Ff9x3gG/fs73Q4giC4pO4IVCn1F2YJZ0u0u7/QHCMjrXUhrbpTYKLFQf6x2Qxru2N0RYL0x8PtZ6Q5iApeM9KswYn1422two3NZ+vu0wgrs6M63X9xctVY8LBWFRuVdnbHQq4/T611jfhSTTQUaJgBk847Txhr4/LQja/sJeemnMw5tkzeEC0jTSbuiWgIrXGdhbSQLdQVHqOhIG40mEWRz/n1dbeYkVYt6lifa6oFb8Dl4J49k5Q07Dg655vV0mpj/kTE23u/kHU2vLeEY6/Zvo38zSJLmPmV9CBsNyv/BqsEvIHAlyuWr3PNcJtxZWSHttdswGr80ej6lYw5+yU1opyRVl3a6SEjLVO1GGTRbZbvt9uQxydsxsg6syhh80jTWo8CPwHefJzjWnUopdg6FGfHUcM/anOTjKTjQSCgTGHYOF98I6RFQzULkpaQ1igjrR029XdxbC7DjpE5Ng901ZTc28veq6n3vp02bAhpu2yeYfliiZGZNJsHmn/+g4lIpZBWLu00nqdcadDgN7NQLLFjZI6LtvRx6dZ+9o4vMDZXfwx9dCZDdzTkaJkQCweJR4I1DRDAaNQAcNY6o8bU7u22VBwxS+rtHmnlJh4NXtPx4qanj2H9LNy0Y7SzwQiC4JpGo+N/BF7cxrHb3V9oQkCptrp2zlYZInvN6ABDAFjIFRk2SyjX93YtSUZavCrTxGvXTsv/rc8UCWPhIN3RUMNBQCPqddMrd3JrlpFWx2PNjpGR5k5IyxZKlHRjwSoWNko7603a3JZ2xsJGR1c3E+Ty63QjpNURFazMnmYeJZbw5FY8SeeLNeVa5ZjC7ko7m/lFJaKt+fmlcpWxWcdvpVvtcmA3Pd7l0EmsE1R7pHVFgmQLJdcduIz3vH5GmtdFikYdN8ulnQ1Kdcsea009C9130W1mxm/F1kgcyrjoJGrRE6udxDrHVXDsmGqPqVnml5vuvskm3fKcsK4n9ZoNuPFIqyeqdsfCFE0vvFVACrC/GTPAuqptjmE0IRDaZOugkSmViATZ4kJIOR4Yv3fG+TXhEyGtJxZmvuqcL2ekJVr37m3EOet70Bpu3TnGKQ7NFqzfFKdrtpWh1Vcl8p00mCAUUBW/tyPTGUranZA6kIgw2cgjLdZ8/HRoKk2hpDl5KMFFm42yyycOz9Td/uhMxjEbzaI/HnG0h7DKTs9cZ/gsO4lt7eLk3xYKBuiLh5fl+bzywP4pemIhfvMFJ/PIwemmjYkEQfAHzZaZ+5RSW1r5d1yiP8EJAHNtde009rVWj2IteJGNls1VjR/nDX2xJfFIcyrZ8rKKb2Wk9dp8J9Z0R8uDCc8x1cn+clva6caEv6fr/2fvzeMkSQ7y0C/qyrr7np772NmZ2Vt7aFe3NJKQhBCyEJJs/LAAGYzNYT8MxgYMBmPDM0Z+GNvYmFMSPDC2jAXovna0Wl272vuanfvq+6iqrjurMuP9ERlZWVkRmRHV3VM9u/n9fvObme7qrOiqrMyIL74joayCUSWsbAp0JcSCyjEAaDW6qvyeYaRCECHhhbvLq0hc1dvXx9o5XEZat09Zw3//raym3wzOr/Qm89uRXzIMGp1BCzgQ/lnkYGUD4mKAdFLNouhFkI3SDaoPOMk4cZROBN+We5mFm7d2AuHtvipkHEdBIefRtqnT2hmsSLMpU0TIoJLHyK2UOveOutlFKhFDMi628auQYDLyMR+gTLkBcRVMlcbxPIA3EkK8v/jrASxe11G9RHHPQUZk3La3KMxhHAW87dKudTCgSfx6QGSfrnYcRVpu6wPsAeAVTqkVANxzcHzg+0G5lqV6v3uCIxmP4ch0DmeXevfe86vs3yr206m80afsWqm2UUgn3OuYSkbapTVmKz0yncOJWaYW46pIERY2gom0yVwK6yJrp3PuHJ7KIpWIbQ+R5irS+m2xsjFdbzw3V8Ed+8Zw+74izK6NK+v18B+KECHCyBFGpP3fAC4O+ecl4V3YydhsRtpGk6kCePZNJhmuAvCD70Lynb7pvNEnJx8GfkVaJhUHpWoLGI5K09nly/R2+abzxtCKNNliMp0Mb+MDwi2BANtJbXYspZ0olcy1MCuSiqKDo6DYKNpQaNzs2dzE51pLYfEP9KxXyoo0syu1gam2doY1ig6bgcRaDIcnhbYbSxtt3LGP7RbPlRsjHg1Da+A6wd7bhsLrTyll5KXEXlhI6xV/AIMKOS+4Tdm0FDLSlFt01ZRfodbOkDyyZkdsUxRBpXm41eXWyeCMNCD4uqpyDcynE7A17x2NtiUcmxFy3fKPTfSaFTXJ/x2OrwB4E+lJh/8CwFEAnyKE/AQh5H8BeDWAT49qgC8lvP/e/XjvPfvw89+1c7obJrIpV1HFGyKnQ3LItht5wTVouxVps8W0S6C97dZZ4ZgA8ee+1DBRTCcGiHuA5aSd81g7uTL8+Oyg6s2PQWun6WYF940p4Hp9aZWROYemshjLJrFnLI0XFzekj1+qsOIxGSZyYkXaSrWNOGExM/5xbxXmK03kUnH3GswxmU31lTKMAh3LxguLVdyxbww3zzDC8twOUf5HiBAhGEHhSB/dguM/uQXHiCABIWr2Hhnq7a57MwWcdkzNhXvFl0U2lU9hvW7CtunQu6YN0+rLssi6NreussXIn5EGMEXa6YBJQBBkWUPphFprp5K1M8MXyB1Mhezq9lpEg8sGALbwE2VWqCrSAKBgqGUfNc3w3LWwjLSgrCkvcoqtU0AvmDxIkcYVMEGh6tvX2tnFoameXcPNSNshirSVahv3HBzHhZU6FiujzxMBBs+TnMZrZlo2ujYVfi4ANUJINB7+GfZDRZGmorDiYwOgZKHkDchBULF2qlwjAEa4hymSeWZZUEYaJx7bXRuyta9rhQ14vXJuDlBH+d5RlxDuKuSeO7aO7WaPeqHTgHwD4KMAUgD2g6nTfhfAWwB8D4C3O4/5Gli7Z4RNYiybxG/9nbtHPYw+TOZSuLzGNlZWayaMREx6Tb1eKHgaMnlWWc2kSCdjyteAYfCHP3g/FipNHHOUW31jClCkrddNTEjssDfvKuCzzy46jdBxnF2qYaZgDNhARfDPxVeq7T61oJGIIxkngfOny+sNZFNxzDg/d2J3QapI61o2lqutgcZSLyazSZec82K11saYQUAIwWQuhbUhnSNBmC83sWc8MxAZ4j2HR4Wr6w2YXRsnZgu4aYapDSMiLUKEGwPSOx6l9EPXcyAR9BEjbKc9bPEvg3/BkPFYKMPyqTgqvsKCqZwBy6bYaHWUbvYi+G2UfIwN08KU4jHKzUEibSqfcrMYdNGQqKRUF1cqbZbFTG+RFUakqRBzYYSVakYaIN7lHfaY/DWTKe9UibSCYusUwD4nlMpff+/7GPRZaoWohnJGAnXT0iaS62Z/8L1rGR6SSHv6WhlGIo4Tuwcn9cNgtcYm4ZO5FMqbsEGcX2ELAd48uRmILOCAGpHmkjmS97GYUc8rdMdjWq7F3Y+eIk3B2hly7vcUaeEWylbHVshIC2ntVFSIApxw7yJoj44XkgReu3j2pErLaVCBi5ujaAGKH4VGW5ylaLibJmplA7sF54KblfQSINIopY8D+DHP/7sAvpcQch+AmwFcAvAopTQK+3mJYjKXwhNXywCYtXM6byjPHbcLXssin5dWze1To3FM5lLSfLgg9VepYUpLEE7MFmBT4MXFKl5xYBxnlms4titcjcbG0z8XX621ceveYv+4jETgtWil2sZsMe2+pyd2F/C1c6voWPaAgm6l1oZNgd1j8kbRIEXaWIo9x0Q25a4rthILFTHJN5VP4fEr5S1/Ph3wIoR9ExnkjARmCgauru+MCI0IESIEQ599ibBjwNfpuq1kHE2JhVJlx51jgEhzZP3DElaA2NoJ6Nncyo0OUvFY3yJyPJvCRqujHEbuH5Oo8VFZkeYu+uQfOU4uqCzg1doxg62drY4FIxFTInxUFTpN00IqERtorfIiFQ8eV0PSeOeHWzZgqlncADlxwokO5fdRMj6+cG9oKjsbbasv3JwTfs0hWjuXN1r43v/6dbz7Pz+8JVlM9XYXDdPCTMHYVJ7ImaUq3v5bD+EH/+iRTY8J4G2Svdesl1cY/ppx1aCobAAY0toZQAC7xJBKa2cIadUr/wj+PVWsjwDPSAsmrJQz0hSuE5zolBV/8DEBm1fw5QKUIDLIFGnxGEEyTjZVNqCTb3ejglL6GKX0Lyil34pItJc2ODFCKcVq3Ry5rRMQl7FUTbpt+Wgq6JUNDH7uSw1TSsDdd4jl4j16aR2tjoUX5jdw574xpeec9s3FV6ptV1nGEZZ9u17vH9uJ2QI6FsXltUFVGS8Z2z0mJywnsylU292Ba+hqrY2iweaMxYxaYY0u5sst7BWQfJM5Zk+2h1gXbBW4ipuPb+9YGvOViEiLEOFGQESk3cDgXEVNgUgQYYCwGkIFU252QEhvosB3/TYjzfYrIIaxuVWaJsayyb7d0YlsEpTqt/GxMYmzhriSKUyl0HSJL7lSo9cWuDVZZHwxKiOHZHZV2diqgkmgH3WzG2rt6Km/JBlpimUDfCFeUyCSw0oQONERpBgCgKbJvi8j+oZZuFNKB163zVg7v/jCMro2hWnZePjsqvbP++Ft+5K1bqng/zwxB8umeOJKWWjv0AGldOD81QmD5+RrTvJ5HNbaKft8qzQ+tkJIWg5CiENYBX8ee/bvsIy0YGunTkZawcl5lBWcsHGp2L+DyXY+Lnac4LIBAErXrt74Atp9Q2yw3rGJXnfdgpQIEXYypnIpdG2KjVbXVaSNGiLFbrVDMbnNirQgZJNxFsUiKRuQKdJ2j6VxcDKLRy6u47n5CkzLxr0OuRYGToCt1020Ohaq7W5fRhoA5F0FsRjr9X613HHHtuotQOBY4kRaUa5Im3TIPV6wwLFSZdZOQD1GRAdm18ZqrY0944OKNK9yT4QzS1V8z+98Dc9ck7eVbhbzlSYIAWYdEnL3WNolJiNEiLCzMdowgwibAqeIhrWJNMwuxjw3SZdI61hQu1UzUqpgJFwFElekDRsWKsqyyrikgvrvWW50MO7LqOE2z3KzI82kkMFPOnKk3QWfYmtnYEYazz5Sa+MDQsoGQmynQeHofnjbuYKg0vDHSQWptdO0kBlXy20D1M7/MGutigIGYJ+NIMVdj9xTP1dbHRu2z3a6mdbOZ+YqyKXiMC0bT89V8M4792gfw4uVai9EejKXwoXV4bI7nrlWcXMYH720rtQ8JkPHorBs2neu9Yg0DWJVQpgU0sNZO6VlAyE2a/7zgJrVupAOXgDpHC/M2tnSae10iKKgoYXZavmYADVrZxDJx8ejo9qut7uYzGWF32OvVfixWhJVbVGjKGKngRDyr8BKpH6HUrru/F8FlFL6b7ZxaBFGBE6ylOom1uq9QppRQtSQWTUppjTnfFuJWIwgn0oICXRGVsnVcq+5aQqfemYBByeziBHg/sOTSs/JSbPlagsrVUYg+RVpBSMhVMl5x/aK/ePu/4/O5EEIcGaphnfe2f/Ya06b975xOZHG34O1ettt97RtirW6ibEZ9r7ptNerYmmDkVJCa2eut2YRxdH8wVcv4MmrZXzsG5fwmx94xZaOi2O+3MR03nA3kPaMZfC1c2vb8lwRIkTYWkRE2g0MV5E25O52w7Swd3yQsNKxUFaaHYx5c8hymyPSTMuGZdM+UiGdDFcn+FFudAbCnvlNstQwcQR6i3hZTlAsRpCKx8IVaQoKMtfaqaCY6xFzCqqOAEVaUIuoF4U0y9IIy8+T5Qt5EUYqyNQcfqSTMcSIWmtnPcTayV+rMEVaK8TmlnczmfSsZEC/1W0YdSjH2aUqbt87hmq7i+fmhyvX8KLkFHdM5lKOIk1/t5hSiufmK3jXXXvw10/N48ySOLBYFSISRbVBFwhXf+naS7hCTmbdDssrBFhAPRCuIAPULJQ9xVZY2UAwOdRQ/DwCvcKURjdIkRY+rrAcRcCr8lWwduoq0gJKSVQUjzJrp0u034BEGoBfASPS/gLAuvN/FVAAEZH2EgRXPa3U2litmdhVkAfNXy+IGjJrJpWqvq4X8unBPLJWx2Ib1wEk33vv3Ye/+PZV/MHDF/H6m6elNlA/9jqE1lypiT2OZXBAkZZOYLkqVj5RSll+m+f5Mqk4DkxkcWZ58P59tdRAIZ3oWw/4wVWB6571QalhwrJpT5HmqJpFOWxeWDbFb3z2NHYVDPzIG26SPg4AFje47VRs7eRjOjoz+LPPzLE51FbMpWRYqLTc9wsA9o6nUWt3sdHqbEmebIQIEbYPEZF2AyPmEBqbIdIyAmuUzuK90uwnrPhNd1hrp0hFoaoW8qLW7g7kdXCFGm8aVYVK42OYSqFpWiCk97uIwBehaoo0lYy04MV7GCnkRSGdRJeHlwc8ZyPA3tYbV7CKT3VchBDkjOCMD44wBV9K8RxrmF2lTCYdIq3hqGW8NsNkPIZknGhnrQHA2eUa3nXXHtRaXTxxtaT9835wm0UxncR4Nolauxs6yfVjo9lFqdHBidkCju3KS5u/VNEU2BZV8wq9j5GpmYrpJMyu7balhUG0AeCFWzagYFUMukZwqFk7g8ljDiMRD80iUy4bSPOMwHBrZ3BGmoK1U0Hl2wseV/8cNcyutFE0HWKDBYCO0wgruk4k4jFkU/EbNSPtzc7fV3z/j/AyBSchnp/fgGVT7JuQq5GuF/wNma2OhZbVc0uMCiJVf8nJGw0ix151ZBLvu3c/vnp2Bf/8O08oP18xnUQhncBcuYk9TgaX//3JGwmcXxHPVartLjrWoJLv+GweZwUbYVfXGzgwIVbycnhJK44VZ63AibSip5gh6HX51DML+L2HLgAAXn3TFO4IyI5brMgVaZPummVw879j2TjvtGdeXK1rFbHpYK7cxAlP2ysn/BbKLRR3R0RahAg7GRGRdgMjRgAbm7N29rdjqi9EOcoNs49IS8ZjGMskhTclFYiUWz1Fmp4959BU/019wqNI00HYItlIxkNVCg3TcnIy5DfhXCqOGFHMSFOxdoYsRnVDxAFGqgQSae2uVM3RG1cwqaBiD3XHpUikcWIr1NoZRoiGEInexjBV1NpiYiGTjGsr0hpmF5VmB/vGM6i3u/j0MwtDt/pycIVkMZPsU9zptPLy4Ny94xkcns7hubnN5Y2I8rF0MtJ41p1UkZbu2axViLRWSHYeIQSpMAul83lUmagX0klcXW8EPkZENopgJOXjsm2Kdje8+bM3LqckI8jaqTAuNWtnuIJPZPMKQ70tV6SlQooZgPBm37ziNWungVL6laD/R3j5gatovnWR2dCCbH3XCwWffZrP90atSCsIgv05oRRk7SSE4D/87eEshfvGM5gvN3Gt1HD/70XOSEg3/XgWql8td2y2gK+cWRnYTLtaauLoTLDTg5Ny3jIyHh1RTPUUaQCbawYRaV96YQmpeAwUFH/15JwSkTZblBNp6wIXzXy5CdOycee+MTwzV8FKrb3lqktKKRbKLbz5xC73a7OOcnC11sYJ1brpbcSHP/cinpuv4D/93XtGPZQIEXYcorKBGxh8uaWjfvGCWVg8uUzJYUL9OxjP+JRf2eTQ9dUigmhYRZo/9N7NSNNUpIVlDaWT4Ysrpv4L5q0JIShm1IJWGwr5R+mQUP9hlCZhIdl10wq1kgVZO3sWObVxBU0EvXCJF2mIuJot0N8U6Udeo0mUQxa+nk0ltIk0787r/oksujbFUnX44g8ArsWxkE54CFW9a86CQ6TtGU9j/3gG85XWplqyRJ/JtFv8odaq6P95L/yLsc0eDwhXrup+HlXbMdUy0iTq0K56bhvQs6cHWTubIUUPgCffMVAp1w1V+aaTLM9Q1dpp2bzEImDTJOwaEaJ2VHnvbgQQQr5MCIksmy9jTOdTyCTj+Pp5RqTt3wGKtHy6387NN3VVLZHbhXw6OTB/4nPR7SL59k9kcK3UxLVSE5O51EBLtYjc4+DxLCJFmr+5k1KKa6VwRdpYJol4jGC93puTrPoVaRm10q1vXVjHO+7YjfsOTeCbF9YDH7tQaSGbirsbZF70iLTBedKyM3e6cz8j6VY2OZcSodzooNmx+tRyU/kekTZqnF2q4r88eA4PvriCjz92bdTDiRBhxyEi0m5g8Iy0YRrALK40EFk7tTLSuu6Nj2MsMzyRJl4gO0oTTUWaf9JQTCdBCFPR6SC08TFEaQLwJsrwj1sxnVTKZuJqspgk9J6Ni2ekyRRptkYbnxqBIms37R+XfJHc7tqgNDhA3AtVa6dya6fK+5iUv4/DtHZyhY7/fM2k4trWTjcLpJjGgUm2qLkWolwKQ7XVQSYZRzIeG5pImyuzce0bz2DveAZm1x46RxGQZaSpXyfcn5d8Jt3iD8XrmIqN0kjEQq2dOoRVmAW8IVDticcltyuqqto4eoo0OZHGg/+DSUcFa6eCgo8QgryRUC4b4OeFrHk4raJIC1EnFgQL6hsUrwagdmJEeEmCEIKDk1mUGx0k42RHWDv9DZlcaTRqIq1gJFDzXbO3e2z7J7K4VmriylpDSHLmUgm0Oja6gmzYdQkBeWwXU0id8TR3rtTaaHVsHJgMJtJiMYKJbKrf2lntJ9K87gcZNlodLG60cNueIu4/PInn5iuBG6pLGy3sHksL7xXpZBy5VBzrguxXPrbb9hT7/r+V4Gp9r1qQx9IM6+zZSnzxhWUA7Dz48unlEY8mQoSdB21rJyHklQAeADAB8SQqami6TuAcyjCKNNHCLzOEtXOj1XEXnRybIdJEQdS6ijTbpqib1gAxEYsRjGWSKGuOrRGymEwn46GvWcPsBhYDcLDGovDx1dsKhFVI+LpuRhoQPLkB4Lzu4Qt3QBzs31Jc/PfGtTXWTp5hFW7tDFYNDZPJVJdYOw2FRbsfvJ1qdiwN6nAZfKI2LDaaXfcznudNqZrXnIVyE4kYwXTecO1A8+XmQPixKkSEO38PVaydrRC1Fj/fVQsHVBokgwgrfox0AEnbPz523ts2lZLpKjmKbFyM4BPlv6j8Xv3jSjo/J38MzxkM3gRQa+1UuX6pNg4DnuuETLmajIden8PVjuH5djcIzgI4MOpBRBgtbp7N48WlKo7tKrj39lEiFmPkOb92r9V7rdOjhMjSzW2nOjEJOrh1TwG1dhcPn1vF++7dPzgmT6vxWLb/3rMuyW/rNXdW8V1OI/jVdTbH4Jt3QZjKpfoIopVqG+lkDGnn1HFLtwKukTy37OZdeRZxQ4HTixu475C40XSh0hTmo3FM5FJCRRonzm7dTiLN2WTc4yHSiukkEjHinrujxGOXS7hpJodXHZnCp56eh33TaD9HESLsNCgTaYSQIoC/BAuYDQpxiRqariPSydhQeStNAWGl2xRodm2YXdsNd+UYyyQxVxpu8d4QLP562Udq4+K2Ov+4ACahLw1p7ZSRMOlkPFQF4y92kKFghCtN+JhUFshAiLVTcYGsmjXEjhl8WUnG2eVDRBKpWOS8yKUSLnkUNi4gSJGmbu0MyjPRaRLlcIk03/mVVrCR+bFQ6SnSuo51crW6uV3NjVbHJUj8thlVLFfbmCkYiMeIu/M6V27iFQfGhxqTqJEyFuM5ZBqKtICyASCcOOYIy8UCwhVprRDbsBeFdAKUsmtdQdLqFXbdcsflOff9zx/WbioaFxDe2qmsWg14vRqKr1de0f4NyD+L3nGF3Yd6OZ9iUrSQTrif0xscfwDgXxNCDlJKr4Q+OsJLEm+9ZRc+9fQC3nRCUHk4InizUzlpw+1yo0JeYOnmDdjjAXOKzeDOfePuv+/YVxwck7NhUG13Bto2ZWq5TCqOg5NZnPUo0s6vsH8fmc6Hjmky169IW62ZmCkY7iYOv4cEWTvPOUTa0ZmcGxXywkJVSqQtVlp49dEp6fGmcimsC9YFK9U24jGC47N5d6xbjQU3P7ZH9MViBJM+wnFUeOJKCW++ZRfu3DeGP3/kCtaaUflBhAhe6CjSfhPAWwB8FcAfA7gK4CXhT7iRkTeSQxFpIqsb/7eqtVOW7TSM6otDRHgkYgQxEk5ycHAbj1+R5o5N09opCjb3gimHVCyB4Yu+YiaBS6vhVjx/vp14XAplA7oZaQFEGqUUdbMbqkgjhEjtsGHqPz9yxmClvAh100IyTqRNk6qqx7AWR50mUY6GxNqpsmj3Y61mIpuKI2ckQClFKhHbdM5GtdV1s0WGtXaWG6abAzNbZAuaZQUCVAYZWZFW+CwC7H0MOh90f0+V8o8wkk+v/KOX4SYj0lStnWnPdcJ/bqvmrHEk4zFWkhFo7ey6hKwM3God9F62FK9fOSOu/HlUsfGH2b/5PUx2ncgrXrNuAPwNgLcB+Boh5DcAPApgEWwztQ8R0fbSxffcvQ+7i2ncd3hi1ENxUfS4IlZrJuIEwnys64lCOoGGacGyKeKOGrfUMFFMJ7QasHVwYncBs0UDSxttfMetswPf5wpzkfW9VDeRSsSE18Jjuwo4u9xr7jy3XEMqEcPBEGsnAEzmU3hhfsP9/0q1jem8AaDXDg4ER9ZcXmsgHmO24niMoGAk8KKkCdyyKZar7UBF2mQu5baHerFSbWMql0IhnUQ2Fd+WzLK5chPJOMF0rp/oncob20Lc6WC9bmKtbuKW3QUcmWZFEkuN4bNtI0R4KULnzvIeAI8DeDOlVE8mEWHbkDfiQ03K6wJrJ5/4q5YN8Gwnf54Mt3YOUxUtWsgQQpTskxyyFkSAtSPp3pxEKjkv0sl4eF6RaWF3MXwnRyX7CGDvX9giMhVCDjVDSCEvCgpye55vFqaAAeS5cmGL0MFxqZFWLLstINyckwkCu2nfcRTIDh0FDCD+LALsNdAlpEsewooQgpm8sWk7wkar4x6Tqzx1ibT1uomJHDuHJrIpxMjmdndbkvNE9TrR7FgugSSCSk5L3/EUCKewLMVmx5Jmc8nHJ38fmqYVGsYPeBVpFoD+a1SYklM2tkYAYSgqgvFDxWrdVFC2ASzkWzXrrmezlquPVfIwAfm5oFqQcgPgAhhpRgD8dsDjKKKG+JcsYjGC1948Peph9MGrelqrtVFMEe256FbD2+jNm+7ZfXH7rHLxGMH//rHXYmmjLcwv43NkkcJ8rW5iKpcSvm7HZvP4ypllt7nzzFIVR2fyLkEYhKlcqi8fdaXaxqGpLDiR5o4p4N62uNHCTN5w28iP7y5IibS1Whtdm2L3mNx2OpFLCX9+pdZ24yfGNxFZE4SFcgt7xjIDUQfT+dTIrZ0XVrjyL+8h0qLlf4QIXuhMbsYA/ElEou0s5NPDTcpFth++4FK2UEryZMYySVhOTpnqwpCjIbFIqdgn/eMSPfd4NoWzy7WBrwchbDGZTsZCc5lUG/mKGbVFn8oiMh4jSMaJ8HWzbAqza2tbO4MW7r0csvBjpiR5UbpWspwRR920Qknbesjr1SMdN5eRBjhEmkZrZ73dRTxGBgiPdDKG9oaeIq3c6PTZRKYLhnCnVQcbzQ4OTbFJVM/aqatI67jZaMy2YGxqkuiS20MSaa2OhXTA+5hLJUCIfmtnaEZaYAul5ezMh0Mls7Bhhofx83EBYsK9V8qgSaR15K9btTVYBONHMk5AQlTIqtbOgpHAfFktakCtWCakbCDE5suvD8NsNO0wfAwC9VmECKPGZC6F5xzV01rdRNEY/eeskB4k0rwbX9uF/RNZ7Je0afbGJFakycbGmzsvrdZxbLaAM4tV3H9EbKv0YzKXQqXZcUm45WoL9x+ZAMDm5Yk4U8EFxUcsVlh5AMeJ3QX8zVPzwmuqN+5CBmbtHNzYW6n2iLTiNhFp82VxfttULoXLa5sritosLqyyZtabZnLYVTCQTsawVI8ogAgRvNBhOc4CGNQGRxgpcqnEUA1gMuWXinWFoybZveeThEqzo02kNSV2URX7ZNi4+Ngqmhlp7mslyf5iIeIKBIyKtTOdRN200LVsd7dNNiaVgFrZ4r0VkuPjRzxGkEsFW6RU7G29cYkXpGE2Wj9yRgKWTdHq2IEEVxjxqJLJxI8T9j7mNMLNAWaryKYGCY+wcHoRyg2zj0ibyadwbci8Qg6vtTOTjCMeI9ph6eu+BcN0PoWVTWS3NZ1z2n+dUCG1gfD3kQdWKxNpCue+kYwFqn31yj/CiW2VLDIg+NzXJbbZ2JJoNuTnXK3dDVzUAMH2b+/YVK6BOQ3VNifAZUSfkYiHb5ooKNJsyh6not7dqaCU/tCoxxAhgggs0J5t1HBF2qjhFvV4rkWlholdheBr4XYiqGV8rW5iSlLQcMfeMQDAE1fKKGaSmK+0cNf+caXnnHIUeKWGibFMEqVGZ+A1EBUzeLG40cLNM708thOzBfxZq4uljXYfwcYfCyC0bKDVsVkpmOeavFJt45bdrKVUdZNbFwuVFl4lICGn8oZ7Do8KF1bqSMVj2D+RRczJt11rvSTyPSNE2DLoGPN/B8C7CSH7tmswEfRRGFKRJrMrhi1e+o7Bs8gEGWkAtAkrNi7xIkQneL0WoEgrZpKomaztTnlMIQoDlcW76qKWtyOGKX7YDX/zhJXuAjlMAQMoWjuTW2TtNNRer3qotTOcSLNtKsyR8kPX2tkwxVY3dl4No0jrTX5nCpvL2aCU9pUNEOIQqhpEoWVTVJqdPgvLdH5zijR+/g6q+NSUqyrEtqrN2jueoGOm4sFqplZHXSHKic2g8dXb4covIFiJPNx1IoGAnGiljDQ2rnjgpg4jolTKBpLKn0d+TwsqJVHZNAGCiTRAnEsUIUKEzWMyZ2Cj1YXZtbFaM3cGkSaICyjVO9tWNKCCvHstGrw+rgco0m7elcdMwcDD51bx2OUSAOC+Q2oZeZNOFth63XRjJ3huqjuudCKwMXvJp0g75pQBnFkatGcuOGrk2RBFGh8Th21TrHqsnWPboEizbIrFjRb2jAsUafkU6qYlLX9b2mjh337y+b4xbzUurtZwcCrrWnZ3j6VRbkUi5AgRvNAh0j4D4PNgwbIfIoTcRQg5KPqzTWONIIBusDmHG3AuaApUab0D5FlkvP1nmJtO07RgJGIDWQtMkbZ5a2fRabvTUfE1QzLSmOorZHFlWsgoEExu9XfQShR6apOtIKwAcetU/5jEVl8RUnGx8rEZQlr6kQuYCPaPLfj1IoQgEUNwq2JXbWw5I661SOaKND+Y+kVvsV1qmBjP9CbmUzkD6/W2FnHsRbtro2NRVwEFsNdcNUcRgJOXiL620+l8alPBvaz0ITaQK5IOsU9yNDt2qF2xEHK+9x9PobUzGayq1Sn/4OqGsM9jWCEJG5e8lKRpsq+pjgtg17Cg1s6aBsEXdC9qKKhDASdH1LFShqEuUURzpBNxdCwKK+Dz5F5bJa9Zzvn6SyQnDQBACLmFEPJeQsgHRz2WCBEm8z1iZKXWRmEHEGmuitjzuQ8iq64HCoY82L9UNwcaOzkIIXjd0Sl89ewKPvPsIgrpBG7bM9gKKsIuhzRb2mhj2SHS/Iq0QkAhS73dRbXd7SPGjs8y1ZiISLtWaiKdjGFaoq4D+sk9jnKzg65Ne9bOdFI7GzYMy9UWLJu6sRde8PIB2Ybjb3/pLP7g4Yv42DcubemYvLhWauLARG9ss8U0Su2ISIsQwQsdIu0SgPcDOAhWe/4EgIuCPxe2dogRgjBsA5jMhhe22Os/hkOkyRRpQxBpMsLD0FCkBQVGFzPh2UKiMQFyhYGRjAWOzbIpTMtWIr4KCkoTgFsV1RbJgZYtzewjJWunwuJWNi6V0HYvcoqKNJVMuWQsPNxcZWy65DZrOhUr0nSsnTZXfnkm5uPZJGxN4tgL0XUik4q7Kk0VlJzsEe+knNkWht9JbZhd4fvAPosKGWmmhUwy+PZXSCeUrRwqwf5hVl298o9wa6dKqD8blzzYXyX7zY9iJliRVm11XSVp4LhC7kVhOXcceWfzRIX87TXoyq/1QPB1QqVsANDPGdyJIITcTQj5NoDnAHwcwEc833sTIaRBCHn3qMYX4eUJrjA6t1yD2bUxld4BRJrPRtnqWGh2LClZdT3Ar3N+Ur/dtVBtd93XUYT33bcfpUYHf/PUPL77rj1uzmwYOGk0X266zd2crOIopJPS66PIqjmdNzCZS+Hs0mD+8dVSA/snsoF5lJNOEZK/BME7NlVFWrtr4dc//QL+/JHwomKe3blXUIQwKVDJefHNC2sAgMevlEOfZ1gsVFrY4yH5dhfTqLTp0BuzESK8FKET0BEFy+5A5BVbC/2QWzvVc5lkhBUn0obJE5CpDHQUadUQRRobWxdQbGuXqeQ40o4FybbpgEIGABxRh1pGmsJrRyl1rIqKirQts2wFTyTCGu+2Y1w61s6DRnA1OyPSgokOIJx81C0baLQtoXKIB+erhpJXW13YFH1WEW7zLDt5JLpoCBpFM8m41G4gQsmZCI5n+62dDdMayCRRRdO0hT9nJOJKVtZW1wpcJABsB5oHFYePJzzYP0hhpVv+kU2FZ9XV25Y048Y/LkCmSFMvEOEopJNodsRThY5lo921FQm+EOLRtJRIey9xFXZtqre7SMSI2xo6OKZeE7JMSNLsWE7Ri/gYQXaqGwmEkOMATgGIg7V2HgfwTs9DHgKwDrYB+zfXe3wRXr7gJMTTc2UAwFRm9ESav6iHEyRh96HtRCIeQzoZG5g/lZ1YlqBG0dffPI0Pve4wXljYwE99x3Hl55wtGIjHCOZKTXSclvTZYhqrnsfkjQSWq+J775JDpO3y2UGP7crjzPKgIu3qer+qSgSuSCuJiLR8j0irtbuh+cUff+wafu8hpid57dEpt6hJhPky+11EijSuqhRtOJpd2y0i4M2aW41Wx8J63cReD2E5W0zDooxw9JOfo8CZpSrKjQ4eUCy6iBBhO6C8gomCZXcm8qmEY7+ypRN3EWR5ViqtZBx1ye49X7CXm/qKk5bE3pROxpUVbvV2FzHCFD1+uNZJTUVaWIg4AJiWjXRs8HFtx+ak1NqpML5214ZN1Y63VcovgKlgrpXkLUI6RQFGYnDy5j3GVls7VRbdyRgJVcAA4a8Zz0hTJcDqZlcYhGskYrAp0LEoUonw43Dll5ew4jbPcqODQ1OhhxiAe554rhPZVNwl2FRQciblk55x8R3gUqMzFJHGrZ1+pJNqhHvTtJAeD7d2nllWz0gLO++D8id1yz8ICS9DqLe7ODgVTB6zcQW3diYCSCERCkYCps0m+36VgjbZLrkXUUqVrbDexuHZEPcRv9bLPrfuaxVI8AUTou41S+MztEPxywBSAO6jlL5ACPlleIg0SiklhHwDwP2jGmCElyf4/fTbl1h+144g0nyKNE6kBZFV1wOiYH9O3gSRfIQQ/PK7b9d+vkQ8ht3FNObLTZgWu0f4nyefljttSvWOM7Z+Iuf4bAGfeGJuYN51tdTAKw8H75rzuYlX/bVS61fL8fziaqsb+J596YVlNzf5k08v4CfefLP0sVyRJsxIc55jTaBIu7RWh2VTHJnO4dJaXXiv3Sz4JuKesX5rJ8DIzFETaSvVNt79nx9Gu2vjM//3G3CrorU4QoStxtZ+8iJcd/BdLt3d7cAsMg1FGiGDxELeSCAeI0NaO8UKFb2MNAs5IyFcDKkovgbHFGyjTDuLK1meFY/L0ikbCMpIU2kI5AgrG1CxRnEE5VYAvfBsJcuppIW1pWCR80LVJlVvhyv4kjFGhsrg5kUpWDs7FtX6HIles7SbXaV23pedc9qbRTaRS/Z9Txciu24mlXBbM1XArwNeRdxYpqeUGwYyEoWr+JR+PuR9LGhkojTNcFumkZTntw0b6h+oSDO7yCsWfwASa2cIKSQbFyC2z/PXU61sQH4v4psJKpbTgsY9MswOG2SD5Qiz6OZdO9UNXzbwVgB/SSl9IeAxVwDsvU7jiRABALBvPINEjODhs0znNJUe/VInl0qAkN51kZM2o7R2AuJ4GL4pt10k377xDK6Vm7i8VsfByeyAkyNvJKRxFOvu2PoV9sdn86i2u30q8kqjg2qriwMTwRtKxUwCiRgJtXYCwZE1tk3x6KV1vPee/bhld8G1X8qwUGmhYCTcDXQvetbOwYy0q+tsQ/uBw5OgFJvKm5WOjdtOvdbOsR6RNmp89tkFd37wV0/Oj3g0EV7OGOruQgjZTwh5NyHkg4SQv0UI2b/VA4ughpxnt10HMmugoRjWDTiEVWqQsCKEoJhODJ2RJlsgqxITtbY8g6en+NIoG+h0A5UPfNEka+4cytoZskAGBrPpRJAtRlXVVV6Eha/rlA0w29bgQrJhWkgngi1y/jEBChlpHQvZEBVMIoZAslbH2gmok9t10xIqdHptimrnfdlVpG0dYSXKSMsm467lTwU151z2FhbwMQ7T7MvGJc5IS4fkFXKo5Gvx810lpF6FmDMSMSlRO0z5RxjRxzcUwhBo7VTMIfOPCxDfk/jnVCkjLeBe1NJQv/LrpMo9smF2A68TPXJbfo61O1agspCT5je6tRPAOIBrIY+JganWIkS4bkjEYzgwmYVp2dhVMJDfAWUDsRhBPtUjiETZoaNATtAyvrbNttN9ExlcXW/g8loDhwWq6aITWSO695Z5VESmf2zHBIUDVx0HxYHJYGsnIQQTudSAtTOdjLnzOZVN24WNFqqtLu7cN4YHjkzi8csldAM2Z+fKTaEaDWDzyFQ8JlSkLW0w4uyO/WPuWLcacy6R1hufq5LbRL7tVuHhc6s4NJXFPQfH8eil9VEPJ8LLGFpEmtPK+VkAlwF8AixY9v8AuEwI+Swh5PBWDzBCMApD2kRkKisjqWHtbHelocyFdHKoEgSZRUq3tVO2gOzJs7fQ2hmiUtCxduadXcsgxVzPbqdIWEksW4B+RlqzY7m5Fn7olA2kJKSCikXOCxVrp+k0T4ZaO+NEKSMtjOzojUntfG1I1HKGS9DqtegW0t6MtJ61cxg0O4NZiszaqa6mqQnsfO64hlTKNTu2sAU3rdCgC/QyzYJQSCdh2dR934Og0qJrJGKwbCqcWA9b/iEjh3iOYl6R1AbE5FBLgSAUjQsQE1da1s6Ae5HO9Yt/HlSyRNnmkMK1Pqx9VcHa+RIoG1gGIPctMdwO4Op1GEuECH04PpsHANy2d+dYvryWRU5GTI6wtRMQq7/WHYXTdinSbt1TwEKlhdOLVRyZHswQ4wUxdcE8Y71homAkBqyMvLnTWzhwxVFu7Q9RpAHsffCSVsvVNmYKhrupq7JBenGlDgA4Mp3D3QfGUTctXFqrSx+/UGkK89EARu5N5lJYF5BWSxstEAK3KXU7iDSu7NvtiR3hmasicu9649m5Ddy1fxx3HxjH8/MbUQFChJFBmUgjhOwG8DUAbwcj0v4EwL93/r7ofP1h53ERrhPcSbkmaSVrMdSydppdqSoqTL0kg2xBqqo0CToG0LsZBlknRccLWhiFKdJ61s7wxWMsRlAwEoGKubqOtVOyGHVtihoLd3/Ghx8N00IqEQsMYnXHJbF2NiWKRBk4ORZ0/rtW2JDFe3hrp0MqKWSkAWoLZdumaHTEyiEV9YsX/DXwWtO8GWnDQJSlmEnplQ1U210YiVjfxHczzb6AvHUzrdDu6+ZrhRJp6komlcbNIMLKJWkTGu2Y6YRUudowLVCqTlgBYjWmCuHoR0+RJrB28iKYTVo7GxqbCaqqVXbc4PILldbOUCIt9ZKxdn4ZwLsJISdE3ySE3A9m//zcdR1VhAgA3nvPPgDA99y9b8Qj6cGbR1ZqmIgRDFUCtOVj8t3j1hsdEIK+BvCtxJ37xt1/331gML8sbzibH4J7b6luCgm+yVwKU7kUznoKB84t10AIcHQmHzqmSYEibTrfywFTyba8uMpIvCPTOZzYzYi904uDBQgc8+VWXwaZH1P5lLC1c7naxlQu5arFVrbD2llpYjqfcuctAJsHpmJiu+n1RKluYq7cxB17i7h5Vx7NjoWFHWA3jfDyhI4i7ZcA7APwLwAco5T+EKX0550SghMA/jlYFsYvbvkoI0gx7O52XUqk6bV2yhZqQRkHQWALN3Ebn6oiLWhRm4jHkEvFtcoGZKQjBw89lymHTGenRHVBWswkA8fXa1JUW4yKCD5VdZUXYQvShtkNVHP4xyUjFHQW7rEYcSan8nOjrtg8mIwx9ZoMutZOlc9kq+sQHhJSG9BXpHk/k4l4DAUj4dpIdCG0duoq0lrdPlsn0LNlDK+UE58n6SRTfclUkwDLwVMp6wjK+vJDVpLiRSrIQqlBDPXGJ7d28l3zMPIYCLd26owJ8OQ8iqydLXVrZyrgXqRjhe1tAIS/j8wOG6RIUykbCCZVeVOeTmHHDsX/A6AL4CFCyI/ByUIjhNzu/P9vAFQBfHh0Q4zwcsV33rEHT//K2/E99+wgIi3dI9LW6iYmsilh0/v1HpOfHFqvtzGeSUqb6jeLew6OYzKXQioRw2uPDrYg9eaag9fsUqPTlwPrxbHZPM54FGlnlqrYP5FRuodN5vpJq+VqG7s8gfpc3R0017y42kAmGcds0cDNu/KIxwhOL4iJNN6KuU9i7eRjEqm/ljda2FVIu4UL26FIk5F8hRQZuSLthcUNAExtetM0I0m3q700QoQw6BBp7wLweUrpb1JK+64klFKLUvphAJ8H8N1bOcAIwdDZbfeiuUWh/jJyQieo24uGJLtNR5HWDln8FTNJzbKBEJVCyOKKvzWqC9JCOrmFZQPixehwGWnB+W3sfFBrYDSScSFppWKR8yNnxAPl9iIySIRELMTaqVw2wBUnKgoY+di0FWlc7eMjKcayyaGVXyKCJ5OMo9mxlLLD+Lj8Y0onmUJtmGZfgGcpigl3IJh85MRyGAmjk6eoau0EJKH+QxLbMpKPq1ZVrJ2peDDBp6tIKwYo0kRkrwxGIiYltrUy0jSI7fBrfTi5rUKq5lKDTXk3GiilLwJ4H1gG2n8B8CMACICnAfyO8/XvpZReGdkgI7ysIQpxHyUK6aR7PynVzZHnowHs+jhQNlDvbGubaDoZx8f/0WvwiR9/nfB58gFq8FJDrEgDmL3z3HLNnZucXarh+K6C0pj8pNXyRsttqQTU3D9XSw0cnMyCEAIjEcdN0zmpIs1t7AxSpOVSWBOov5aqLcwWDaQSTBgw7PwuCPPlprBNvpAiQpXc9cRcib12ByezODrDrMEXVuQW2ggRthM6RNpuAI+FPOYx53ERrhNUMqJEkC38DA3CimXwyEL9gxvltMeViEvzhfxodqxAi1QxHaz4GjheiN0wTJHWtthNXZUgCrJsAZrWTllrp2khHiNIxtV3HMOsbvWQxjsvUnE2Lj8Zo2KR8yMnqG/vO6bAnigCs3YqKNIULYFKC/eAptNeHpOiIq3FSGj/LvJ4Nrmpdkw2Pg+R5oxVtQSh1uoOWPkIIRjPJIcuG5Bld/U+i/KxqZLIvQbd8DGqtXbK87WGLf+QBTK7WWQKxDab9MvbfXUVafz8FxGQdW1rp/jcd62dinmMRiKmpJBmxR8BirSknHTkUFHVigK+b0RQSj8L4AiAnwbwPwF8EcBfAvhZADdTSr88wuFFiLCjwJrPe62d20lWqaIgmD+t1dvbVjTAcdNMXppfxxXLornmet2U5sodmy2g1u5irtxEu2vhwmrNLSEIw2QuhUqzg65lo9WxsNHq+hRp4WuttVrbzREDgBO7C3hxaUP42PkysyLKMtLYmAxhRtryRhu7CozkKmaG3ygNwkKlJRzbTiDSvPlt03kDyTjpa2uNEOF6QodIqwA4FPKYg87jIlwn5FN80a6XtyJrx9SxUMqOAfRL2FVh2RTtri1p7XQWyAokX9jir5jRy28LC8APUw7xt0bL2hlYNqBh20oGWyhV2zEBD0Ekee2q7Y7SAhlgi2SbAl1fQGiYjVY4rhAiTdnaGQ/OPuJkRzqgkQ/QU8C4DayCxbubvada/mGKrdYT2RRKm8xIS/flZMSd76l9hqoSgpURfPrjcjPOBO+DSkFDzxYY/D4GtU/6oRLKr5KRpmvttCWBzDJ1onxskszCIcoG8u4iSJCR1tJpHJa3duoq+FQzO1nxh4r6ODgjTaWQRPeevZNACEkTQu4jhNwLoEkp/W1K6d+llL6dUvoBSul/oJRGVWoRInjgzUhbrbUxnR89kZY3Emh37b44hFK9s235aEpjCtiMLNVNjEvGdvf+cQDAY5dLeHZuAx2L4u4D40rPydWBpUbHtUpysgro3bOC5nWlRqdPZXh8toCr603hXGmuzIsQgjPS6qbVN5/pWjZWa23MFhnJN6bpsFHBRquDWrsrVaSFtXaquhWGxXy5l98WixHsHktjodLc1ueMEEEGHSLtYQDvJ4S8VvRNQsirAHzAeVyE6wQdG5kXDUlRgE7ZQBDpwRcuOhdUkfqlNy5nAaPUyGcHLmQKmoq0IMKQjS0kI81Sb+0EmGIuaNGn045pJJiFciuUXy6xIMitABzlkerC3SEx/NatYRQwYeoOVStsMkaCM9JMCzHSs8IFjQdQtXbyVkxR2UB4Q6AX1VZXmD1VCFE4BqFpdpFJxvtyXDIukaaulOPhwV6MDbmT2rEoLJtKFGlqRAegrixUImA2a+1UtA2Lxzf4Guq0YwKMgJRZO3WvE4l4DEZc/LrV2mLVpHhM8nuRjrUTEAdq++EWf2y2tTPk/sPGE2xH38kghPwkWGPnIwAeBbBCCPmJ0Y4qQoSdD29r59JGu886OCqI5itrdbNPWXW94TYt+67Z7a6FumlhMie27N62t4jxbBIPn13Fo5cYj3/voXGl5+TFAivVNparTN00U+wp0mIxgmwq+Lq9VutX8vHm2HPLg/ldc6UmYqS/FdMPTsp5FWBrdRM2BWaK26dIW3TUXXuEijQI7aYcf/n4Nbzy334Rz85tn6Zm3qeW21PMRIq0CCODDpH2a87fXyGE/Akh5O8TQt5JCPkQIeSjAL7qfP/Xt3aIEYLAg4uHsXbKFGldDQulbOGXN5KwbKps/2Jjkrci6ijSwtQhxXRCubWTq+SyggKE3tiCVTBtC4iR3iIsDMVMInCHSaexLi2xIrVMsaInCGHEQrU9GCovgyyXSVY2EYQwa2ddsZxBxdqpouJT2bnk4O+luGxAT5FWaw9aKAGgYAyXV8jH5ycr+P91ShBEWV1jmRTKQ0wAe7ltAvIxEW7tdNVMoWUD8qwvL2ybK+SCzy8lRdoQmYUywgrQINICrJ26ClEAyCSIOCNNh2xPxFgxhKDWXucaCKgppHnxR5DSN6iYwT2OorXzRiwbIIR8J4D/BCAPoOH8yQP4T4SQd4xybBEi7HSMZZKomxZKdRO1dndHEGn+PDLbpiyHbJSKNIPHA/TfQ7iCXWaJjccIXnfzNL58ehl//eQ8bttT7FOVBWGPE/q/UGliaYMr0oy+x+SMwWIGjo5lY6PV7Rsbt5V6CxA4rpWbmC2mkQzYmBURaT21XE+RttVEGielZIq0VseW3r8++o3LWKub+MQTc1s6pr7x+fLb9oynXfIvQoTrDeWVNKX0cQDvB7Nufj+A3wfwSQB/AOCDADYA/G1KaViOWoQthm5DJqU0MCMNYM12YWh1LOliVKfxjiNogayjSGtJbF8cYa2YfWNSUD6E5ea0LYpsKqFsoyymk6iZXeECEmCEoyoxJ1u8b86yJT7XRO2M0nFJlEMyy14QwqydPIcsKPsIcIi0EAJGZeEed3YuwxQwACtoAMQkn0reV/+xxCpTZmUevmzAr67J6irSJATfeDaJyhDZbUGkkxFiswYYiSz7eS9yqThiJFyRxonOUGunQkZamG3YiyLPIhNMol2CVqFsAAho0Q1R48qQTYhft0qzg7GMWgA4v3aJ7kVNxfeQo2AkQz+P/LMYpEhTUTyG3X/Yc9ywZQM/CYAC+BCltEApLaBXMvCTIx1ZhAg7HLsd4uypa+W+/48SbvaXQ4xUW11YNh1pEYKs+ZwTSkEk3/c/cBBrdRPPL2zgA6/cr/yc+x2F03y5ieUNRsr4SbighnjejO5VpB2azCIVj+Hs0mDhwLVSE/sC8tG8x/KWIKzUGJHGFXSq1s5Wx8K//eTzSgTXomOTFJ2fhRRbx4jsnZZN3d9VVrKwWVBKMV9u9inSdo8xIk22ZooQYTuhtWKllH4SLCft7wH4LQB/5Pz9QQCHKKV/veUjVAAh5LWEkE8TQtYJIQ1CyNOEkJ8ihGivAIY5FiHkBwkhjxBCaoSQCiHkFCFkoL2UEJIkhLyXEPKHhJBnCSEbznM8Qwj5VUKIWiqmD7rBxaZlw7KpUK2gYl0B2K5Vu2tLQ/2DAqdlCGowVFECAGxXqGvT0LIBVdsp33UJUq+ENQWall4bXzGTBKWQkqP1toWcIjEns5MNQ6Slk3Gk4jG5Ik1TbQIMWjvDWvNECFOk8dexILAXepGMkeAFsobNLWjn0otGQEaaDnkMOK+/SJGWTqLV6c9AUYWIcOevwWatneOZ5HCKNDdPbPD2lVYoaFBVfxFC2CZFCAmp2grrnvPW4NhaHTXbsBc8J0a0G61t7RTkkXGlna61EwCySbIFRJr8XqSbKZdPh282NRSUq2H3R37/UWn2rd+YGWmvBPBZSulH+RcopX8E4DMA7h/ZqCJEuAHAbXxPXi0DwM5QpPnaKLltb5TWTtlmJCergoi01948jV99z+34yTffjL/36rBY7x54aP21chPzlRZSidhA4ULOiLtlEX6U6oNquUQ8hptmcjgjINLmSk3sC8hHA7yKtJ6VctVRpM3k9RRpf/atK/iDhy/ip/7iSSxtBKu3FivsOUTnZ9Eh0kSFA9dKDXc+dHF1e1o0N1pd1E0Lez1tp7uLaZiW7Z4fo4TZtfHDH3kUH/rjR4aac0e48aC3YgVAKa0D+DPnz8hBCHkPgP8NoAXgLwCsA3g3GMH3OrDctm07FiHkwwB+BsA1MJVeCsD3AfgbQsg/ppT+F8/Dj4I1WtUBPAjgU2C2iHcA+CUAf4cQ8jpK6arqmAG2u61DpAXt5gfZj7xwVRghijSdXfcgu45KWxqgtsAqZhKwbKbKC1touvlaAQsjmX2SgynS9LOPNiSLzrpE4SOCbOE3TPYRH5uIWOhaNpodS0iYiJASEKO2YwUeprUz6Pznk7FQRVocrADBspEQEBo65GPQzqUXQee8jp0ZYDvKsow0gBFturvMDYG1jxMNzU74Z7vdtWBatlCpOJZJomFaMLu2ez6oIPD6paJIcz4LKiRMISSvsG88IcdLBRFDpn75x7hzbRAVSbjWTkVSmuWR9Z+v/DXcSmtnudnB3oBMGP+Y2DgsAP3XFdW8Qg6mWg1ebKjYYRPxGOIxIj2/VEsQbuDWzikATwm+/jSAt13nsUSIcEOB29Eeu1wCAOwdHz2R5i9HUiGrrgcKAjs+J6vC5jE/8JrD2s8XixHsGctgvtyC2bVwYCLTlw0L8LWWeF7HCUj/2I7PFtz3m6Nr2VjcaIUr0hyyzKv+WnX+PV1gz8Ptwh3LDrSJfv75RaSTMbQ6Nj79zAI+9Loj0scubrAwf9G8rJCUE2ncEvrKQxN47EopdEzDYL7M1HJ7PJ8drs5br5vuazYqfOmFJXzp9DIA4Munl/GO23ePdDwRth9be4ZfZxBCimDklQXgJKX0hymlPwvgbgDfACtH+L7tOpZTvPAzAM4DuItS+k8ppT8B4D4wEu7DhJDDnh+pAvgJALOU0ndTSv+F8/jbwEi14wB+Wfd1yIcocvyoKym/gkmAMGuNar6QF26AdBDBF6LO4batsLIBYDB/QQQVtUkqHgMh8rG1Lb3FaDFkfDVJC6IIMmKhNUSoP+AoOwTEAp9cKFs73fezNy5Ozuou3PNGHB2LSs/ZutlFOhkTkmNe8DlD0CJZWQHjqbkPgqtIC7QzqxFptZa4tXOYzyJH0+wO/M461k5OYorO12Hs30CPwAsqaAhs7dTII2NFDWFKJlVFWnBGmu7nkS90yoJd2FqLlUSohPqzsQ1aO4fJbePIJMRq5I1mB2NZPWvnVrQOe0O+ZVC1w8ry5ACPbTjkvcw7itXtbjfbBsTBNhr9aDnfixAhggQ8vP3hc6tIxWPYP5Ed8YgGN705aTOVGy0hwdTgPmunS/Kp3UN0sW88g2ulBq6sN3FoKicck2ytxYkl/+t2fDaPuXKzb+NkqdqGZdPQ97+YTiAZJ33WztVaG9lU3N3QLCqUInUsG09eLeP77j+IQ1NZfOP8WuDzLlZa0hIE19opINJ4ftvte4ugVEy2bRa8ndNr7eTqydWQNtHrgc89t4jxbBLpZAwPn9XSxES4QSFdWRJCfsD5U/D9P/TP9Rs+3g9gBsD/oJR+m3+RUtoC8IvOf39sG4/1j5y/f41SWvL8zCUAvwPAAPAhz9fnKKX/1VH1wfN1E72ShpOK43WhEqTsRZNbWETWTkXlF1fJyMsG+uXiKugtSAMsp6qKtMCyAYeoUigcUAm1JoTASMSkyiF9aydXpEmyyIZQpPmJhWGsnYB4lxDoNXnqjstrc9PNPeIIO9dULadJh3SQEmla1k416xZ/jOh3jscIknGy+bIBjfZJP5i1s/+YadfKHE7w8XNFTKTJw/KDENRwqaKo5dcIfq0LQjGTVLB2ckugmrVTFuqv36KbQIz0Api9qLa6yhZKNrbB1s5eo+wwGWliRdpQ1k7J6xVW7uAFXwAFEVd8oRNmLTcSMem5r0o+5owEbKqef7jDcMOxfxEi7ATkjQSmcilQChyezipvdGwn/K2driJN0ox5vZBPJwfs+GWHmBnfJrXcsdk8XljYwMXVGg5NDZJc+bQ8sqNUF79uvHDgrKe587Jjezw4GUykEUIwkU1hvdZPpHlttyot8eeWa2h1bNxzcBz3HZrAY5dLgffChUoLu4titVzBtXYONncuO0TabXuLAHrE2lZivsz2cbzWTq5IC2oTvV546loFDxyexCsPTeLxK6XwH4hwwyNoxvgRsAnTN8GUVPz/QSDOYz62BWNTwVucvz8r+N5DYI1SryWEGJTSsE/YMMcK+pnPgNk13wI1lRlfdWivdnNGAvVV9byVRoBdMa2oguGkR3jZgA6RJl+4KVtOFWxbLlGloIRpBpB7XqST8YDWToriFirSqjqh/hICcmgizRATC/x9FlkLRRDZ3HRzjzh6EwkLU/nB79cVFXxJV5EmUZt0LOUJXN5IuDf8IPD3wW8h4Egn5OeVF+2uhY5FA5VfqgUbfeMThM2rqL44+HmxlQRfEFmhMjbVsgGA7fbOhbyPPQI4nIABxNew1hBEWixGMJZJotwc3IWtNDvudU4FRiI2oKhtbUaRliQDirSuZaPWVif4epsAshIEdUF9Pp1wVKty67i6Ii0uvUYoE2nOZ6rWHlR83gD4p4SQD/m+Ng4AhJALgsdTSunRbR9VhAg3AO7YN4avnFnBHfvGRj0UAIMlUq51cMQWuYJA1b/eMFEwElpREDq4c98YPvaNywCAu/YPvj9Blvw1SRHCsV1sUnpmqYq7D4wDAM47RNpNM4OqNz8mc6kBRZr3vVGZR11eY893dCaPjVYXf/n4HObKTakibnGjhVcenhB+L5MAknGC9frgfHKl2kYyTnCz8zvzYoStxHy5iUSMYMbTqOqWMoxYkVZpdnBxtY7337cfpbqJP/3WZdg2lc7vI7w0EDTT/vtgpNiC83//xGkn4ITz9xn/NyilXULIRQC3A7gJwAtbeSxCSA7APgA1SumC/2cAnHX+Ph76WzD8fedvESkXiLwR17N2uk2BQVlkIRZKngUjuaHp2Cc5mkGWU8Vx9TJqAlo7NaxuOmoTGfmoa+3kC01ZE0+93RXWUovHJW6Za5p2YIGCDPl0AlfXGwNfd5VHmyD4Nq1Ik3wGVBV8/JTxFyC44+tY2Kth7VQpG6i3u4ELd5ZdpaD8CrBQFodUfgGOIs33fnC7sAqRxt8TcXbbcJbToLIBHUWaCnHFMtKC26dUmn0Bj806ICNNF+PZlDAjbaPVcd93FRjJuKD4Y3PWTrNro9213PeEE2vKRJrzvLLWTp1xFTyLRdn7XlfMlQv6TDYVogWAfhWBd0Fwg2Dc+SPCYcHXIgVbhAgO3n/ffjx2uYQP3Hdg1EMBMDh/Wq21kTcSQ2XobiUK6QSWq/2bWKW6ifFtVMo9cGTS8++pge+L7KbesTErZv+85NBUDqlEf3PnhZUasqm4UmvrVD7lKxswcdCjlnPvJQHzzUtrbM5+cCrrzlfOLFWFRFqrY6Hc6GDPmFiRRgjBZC4lUaS1MJM3MJNPO2PdeiJtodLCbDHdp+Ycz6YQI8DaNhB3OuClErftLWKx0kKrY2O+IicsI7w0IJ0xUko/4vv/RyUPHSX4lkFF8n3+9fFtONaWPTch5G8B+IdghQX/PuSxPwrgRwFgZmYGp06dwvpyGxuNLk6dOhX2VACAJ5fZBff0s0/BvNZ/s3xxnV1kH3nsCdQuyW+kZ0rOxfiF55BaOT3wfduRDT/74jmcsq4ojevpS2xB+Pgj38SZVD+Dv9xgC5ennnkO+fUBrtPFC2tsXC8+9yxii2LudLHOjvXNx5+RPobjsXn2Wj3zxGNYPSsn52jXxOW5eZw6tT7wvWbHwkZpTfn9qXfYa/f4s6cxUzs/8P21jQZ2J1tKxztXZq/Htx9/EubV3se91mxjbXEep04FZyX40Si3sVK2Bp77qRX2Op197mlYc+ETsCsbbFyPPfk0yCIb18UK+9r5F5/HqZL8PfbjvKPG/Oo3H8Xy5OBzzy2xTIWw18sy2wAIHv76t3CxMPhelzYaqMTVXvfKWhvr1fDP5IWrLRDLlj6Odju4fDX8feKfj2sXz+KUeUn4vUeffAaG4LMahI1GC+srizh1qidR7zgV46fPhH+2n+DXmucGrzWXnXPgG489CfOaGgFbq9XwxLXnAABPPfYormX636d2l43tuRfPDLwOHC+eMxEnwNe++lDo822stVGqBb+P315gv+OzTz2O0nn5NaLrvG4vnD2HU3b/67aw0oRNw89RP2KdJi7NNQd+bm6liXGDKB+vtNpGpdb/ueb3gjMvPIvEctg+VD/ilgmA4HNfeghFg13L+XV3/tI5nOpcDj0Gv5Z/69HHsXGh/9yZW2qh06HKv99V5zr+pYe+ht058Xv01GV2/3ni29/C+ZR8B7nbbuLaQlv43HzMp597BmRRfh28uMTG85WvfxOXijeUIk2eUL0FIIRMAXgvgHcBuBNss9IE8AyAPwbwx5RS2/P4AwB+HiyX9hCACQBrYLm1fwTgTymlgUw9IcQA8BjYRukcpXT/Fv9aESK4ePcr9uJdd+7ZMSqVeIygYCTciIC1mjnSxk4OcUZaB5PbWIJwaCqHX373bbAphEUAuVQC7a4tLKRakwTdx2MER2fyOLPUs3ZeWKnjyHRO6RyYzBl4plR2/79aa+PeQz21mL8sQoTLaw1M5lIoppM47lhNTy9W8ZZbZgceu+gUBgSRfJM5Q5h/tlJtY6aYdosQtiOzbK7cHHhv4jFG7q1uQyabDngRwoGJrLshd36lHhFpL3Eoez8IIW8EcIlSKl05OZOaI5TS8BVK72cugU2AVPH/UUr/nurhnb+3Ykd02GMFPt4pLPgzsCbP93mz1oQHo/T3APweAJw4cYKePHkST3bP4HOXzuINb3yTUuZC7el54PEn8PrXPOBeVDkmrpaBR76GW26/AycFF1mO+NkV4FuP4NWvvAevPDwpfEzuwc9iavd+nDx5W+iYAOC5B88Bp1/E297yRlfFwLFYaQEPfQlHbj6Bk686KD0GPb0MPPooXn3/vbjnoFiavFprA1/9IvYdvhknX3s4cEwLj1wBnn4GJ9/wGukODQBMPPEQxidzOHnyvoHvdR/8NA7v34OTJ18R+Fwclk2BL30as/sP4eTJQUFj58HP4ebD+3Hy5O2hx5qZrwDffBjHb70DJ+9g7TGUUnQ+/xkcu+kwTp48EXKEfpzaeA7PrF/DyZMn+76+8dQ88NgTeONrX+XKuoNwcbUOfP0Ujp64BSfvYeuW9IU14BvfxAP33o3X3jytPKbilRLw7a/j+G134uQtuwa+/++f+ir2jqdx8uT9gcd5YvmLANq46577cKdA1k8f/iIOH5jFyZN3ho7pG40X8PWFSwOvkx9/duXbmLIbOHnyjcLvjz92CuNTRZw8eW/gcZ6brwAPPYxX3n0nTvpagtbrJvDQF9j5HtDUJELnC5/GsSOHcPLkLe7XKKUgX/g09h44FHr+bDzFrjVvePUDblYIx5W1Bn756w/i4NETOPlKtd35U6dO4dDEIeDZ5/HmN75+oCHLsinwxU9j/8EjOHnymPgYG88huzB4DovwaPs0Tl27gDe96U3SYPvlR68CTz2NN73u1YETJkopYl/4NPYJXrffeu5rGMskcfLkA6Fj8uJjlx7F0kYLJ0++of+5HnkQR/aN4+TJe5SO84XSMzhdWex7TeiLy8Aj7Fp6r+RaKsPX59ln6fZ778fRGXY9ePJqGfjq1/Dqe+/CyVvl9xaO4pUS8OjXcesdd+Lkif7P9X89/Q3kYsDJk69RGk/3+SX83tPfxu2vEH+2AeD5U+eAF17E29/8xkA1xtSzD6OYSwnfK37/ec0D97k2HhGS51bxn5/4Fm6542686qZB5cNOBaU0nAHdHD4A4L+BOSEeBHAFwCyA7wXwBwDeSQj5AO0F/BwF8P0AvgXgE2AlT1MA3glGpP0AIeRtlNIgOe6vQ28OGiHCprBTSDSOyXzKJUbW6m3XKjdKiApiyg1z29tEg9osuXug3rYwlu0n0tbrprQE4fhsHo9e7G2yn1uu9ZFhQZjKpVzLYteysd4wMeMhOvMKGWmX1+pu5ttYJom9Y2m8uChW2vPmTVnZgDsmCZG2fyKLTDKOVDyGisRVsxksVJrC+chUzhi5Im2uzIsQ0u77cq006OCJ8NKCjtH8QQA/FPKYH3Aep4PzAF7U+DPv+Vmu+pKFDRR9jwuC7rHCHh+mWAMh5DVgWWo2gO+klD6iMM4B5BWkvV40AgLOXQulakZaSDumXtlAF/EYQUrQrqjaJtpSyNnSs3byPLlwu48sFL5t0dCMNS/iMYK8kRCWDdg2Rc3sKmeRiaydHYvCsulwrZ2S0G7+Wqpmt/FzjwfHA8NnpClZO7coI03VTpY32M5lR2BL86LZsQItgUF5TF5wu7bIljZsFlnHstGx6MD4CCEsu03BctoMyD0cPiNNXjbgFjQE2E7bXfX3sZBOwrJpYENpz/4dfI6xUpLBUH+A5bZlFMoP/BjPJIVlAxutDoq6ZQO+a35LsY1UhKIzz/dmlvB20XHl1k55ppxuxiO3dvNSFBEabQsx0nveoHEFNfsCamUDgFrz7csMZwD8LQD7KaXfTyn9eUrp3wdwC4CrAN4HRqpxfB3ABKX07ZTSf0Qp/QVK6T8EI9hOgZU3eR/fB0LISQD/FMDPbv2vEiHCjQFm1WPX59WqOfJ8NMDJSDO7sO3eXHO9bg5snl1PuHNNwVqLjU38uh2fLWC+0kK11cFarY25chN37C0KH+vHZC6FaruLdtfCesMEpcC0Jw5ApWxgcaPVF85/YndBSqQtbYQTad7zxYvlahu7igYIIShmEkPl8gbBtikWKy2hqGEqnxp5RtpCuYXxbBLZVAIzBQPxGMGCQlZyhBsbOjN3lS0UXjagDErpWymlt2j8+eeeH3/R+XtAtkMISYDZELoARAG4fmgdy2nenAOQJ4TsERyPyyGEHjVCyBsAfA7s9Xo7pfRrCmMUQuVC6gVf+OUE5IJqqL9KzlA+nQhcuAyOi+UxiZQfqm2iKguZVCKGdDI2EIYtPJ5ic11QKHxbs7UTYCHnoptQo2OBUvUssrTgdeMkaNiCUYRCmrXN+ReAQRldIvDXs+GZkDQVGlJFCCPSWA6ZemunKCONUqq1eFf9TIaNTTUjLYiETMbZ+T5sFpkss1AlIy2orCO/ybIB2fkrI6u8Y1I9x1TIvoZGKH9KEOoPDF/+MZ5NDez6Ukqx0dTNSBs8zzaTkVZ0rJGrnh1iPk71sgH5vYi1dmoQaQot0nWzi1wqIVUeescVlpGmUzYQoQdK6ZcppX/jtW86X18E8LvOf096vm76H+t8vQOmUAN6c7E+EEKKYCVaX6KU/q7oMREivBwwmfUp0nYCkZZOgtJ+gUC50VHeiNkO8DmLaF63XjelSr7b9jDS7OlrFTwzx7QVd+0fV3pObrMt1TtYrQ4WQeRT4XMUPwF5fHcBF1bq6Ao2eheUrJ39TaIA23hdr5uYccZWTCe3XJG2WmujY1HsGx8c21TeEKrkOK6sNfB7D50P3dzeDObLTZewjMcIZguG+3pGeOliq6tPDoI1fF4vfNn5+zsF33sjgCyArys0dg57rKCfeafvMS4IIW8BU6J1AbyNUvpNhfFJoU2kBS2Qt1D5VUjLgzlFaJqWNACfq9RClXKKQeLFdFIa5u9Fw7SQiJHQliAZ4dGxbFhUX9VRzIjHx9/jvDH8YrTRkROpYZCVSFRbXcSI+u/JF5peMqapqP7zoxhSzlBVLBtIuIo00fuop+ILI/c4GmawIi0tUAqJ0AwpxWCh+ZqEVQCxqdomGnStScZjyCTj+gSf2Q1sOjUSwSRfs2O57cRhUClEaJoWCAkuOPGOTRie37GQ0VCtcoxnk6i1u33kb920YFNot3aalt23+6+qrhJhzGCvhZdIK0lazYLGBEBMPJpWaEuqF5wQDfo8NtoWsiGNnXxcYa2d6ZBGUd17dgQAGs3mhJA4gO9y/vu05GH/CSxT7Yc3P7QIEW5ccIWRZVOHDNkZ1k6gd802u6z1eTsz0kLHZIhJK0opSg0TExIi7YEjk0jGCb56dhXfvlRCjAB37FNTpLmNlPU2FjeYddCrFvPaTUXoWjYqzU7f2I7vKsC0bLeEwIvFShOFdCJwfeBVyXHwe/2uokOkSdYwm8G8Q0oJFWm5VGC5wS/+1bP49U+fxqefEXUDbg3myk3s9ZB8u8fS7nsW4aWLwJkoIeRf+b50UrJbGwcj0b4PwMNbMzQlfBzAbwD4PkLIf6aUfhsACCFpAP/Wecx/8/4AIWQMwB4AFV/bpvaxwHZIPwjgXxJCPsHzzQghhwH8BIA2WECu9/nfDrZT2gAj0Z4Y7lfvIe9cSFUXyo02W/iJFB1BdhovWgH2qt649Ii0IFIhEY8hESPhrZ2KrWnFTFJJdqyqfDAScWGwpmqj38D40uLx8ddTuR0zObgYDWptDQPPgCjV+1t9yk0T49lUqJqDI5Vg72fTM66G4iLUj1wqjniMCHe/zK4Ns2u7u3ZBCLJ2qp5XHL2dy+DzlZ3zwYo0kYRedBxA/nnUJbW9xxSdJ+lkzL0GBIGTTDL12DDjCvtMppMhirSOemNt0Xkfg9SrDadBUuXcN5Lidt9hWzv557HS7Ljtjxuayi+gR7iblo10rJ/kHsYCXkgBMdLf2rVaMxEjGkRagAqZnQPq1wnZAsgLrkhTGZfs3G8pko8qAdERenBcAT/g/Heg2ZwQMg3gJ8FcETMA3gbgZrD82U8KHv9eAD8I4EeCcn8jRHg5gGekrddN2BQjtU9y9KmIxzzRADvB2um7blfbXXQsKlWk5YwE7j04gc8/v4g4Ibj/8KS7SRcGbhddr5uYc2yC3rD9hOM4kEX7VJodUIq+sfFs7LNL1YFM46ul8JZJfn6U6h3sHmP3uuUNh0grMCKpmFFXpD1+pYT9Exn3Z2WYdzPIBok0L7nnz9imlLoZdY9dLuE9d+9TGpcuFiqtvubXPWMZvLCwsS3PFWHnIGzW+Cuef1MwSf3JgMfPAfi5TY1IA5TSDULIPwAjwU4RQv4HWNjs3wJwwvn6X/h+7L1g5NZH4cl8G+ZYlNKvE0L+XwA/DeBpQsjHAaQA/B0AkwD+MaX0En88IeQEgL8CkAbwaQDvIYS8R/B7/YrO68DVSWGLdo6GaUktLIZAKSSCilqhmE66Fz7VcQUdLyibhkN1IVNMizPI/GiGKIY40kmJZWtIu2Ixk8C8wFtfcxVpascTEaOutXcIBQy/gfrJnXKjg3GNhTvA3iOvRbQVYAMMAiEEY5KbtqvgUyAeubVTtnDnY1ZBb6EcPJFomN2QjDQx8SIbX5AiTTevgp8not+ZkVUKirQQkqmQTmiTCU3TDr1OBF2/dPLIVBRpYarC/rENknyubViTQAaAMYeUKjfMHpHmjFXL2pnoKX45WdzQJI+9iBHWorXi2VxYrbUxmTOUg7YDrZ2axKNf3SCCqgU8KLdQlXDn1k7Ve3YE/DsAdwD4NKX0c4LvTwP4Zc//KYAPA/gFTzEBAIAQMgvgvwP4DKX0D3UHImpOj7CzUavVovcpAOXFDkzLxsc/z3QQa9fO49Sp7e4V6Yf/PbroNMF/5RuPYG48jmtVdh9YuHQWp1oXr+vYOK46Y3jk8adA53v3Ct6MvnT1PE6dEvPyryh08XsXGdn0odtTyucjP/apbz2JxTpFnADPPfYNvOCZU6WIjTMXr+DUqaWBn5+rOa/b5XNuk3nboiAAPv+tZ5BZe7Hv8aevNbA7G5OOr1arYWHxLADgc1/5Gg45rdO8of3qmWdxavkFtDZaWNyQN9JzPLrYxe882caeHMGvvT6DWMCG5FcvsbnNhWcfw/KZ/setzrHvffqLX8FE2lcE0bLdOfKjL17FqVOrgWMaBs0uRaXZQWtt3j1+t9rGtVIXDz74oLLIYKsguuZd3rBgxIm0uTzCcAibNb7Z+ZuAWRQ/AkZA+WGBVY6/KMqr2E5QSj9BCHkTgH8JFkSbBnAOjNz6T/5J1FYfi1L6M4SQp8F2Q38UrDjgcQC/SSn174TucY4J5/jvkwzlV1THDPSkvaoL0manKyV2uIVSlBPVdwyFnC1ta2cnhFRQWLy3OrYbOB6EYibpWo2CEKYY4pCpYFQzcwbGl07idGvQJd3LIlNbJLuWWK/1iyvSFMk4L3hew1q9X0JdaXYwppldkUn12wM3k8kkI9J6xKMCkeY8bSCRpkh25N3PZPD5Wm9bgYt3VcIqjLAtDqP8co8pyFJMxpUUaWEk0zAEX6tjBdoojVBFmoVpReuKiiKtacqvpwNjE9gCTcuGZesVknBwRVrZc+5XnPIBrbIBV/1lAWA/1+xYSMYJkoLyFxVM540+a+dqzVR+3QF5zECPeNSxrrIWsWBFmhohGkRuq75mXEXQUCwIejmDEPJPAPwMgNNgDoABUEpPs4eSOIB9YBumvwrg9YSQd1FK1z0P/32wk/wfDDMeUXN6hJ2NU6dOKbU0v1yxWriGv3jxKcSmDwM4jXe8/n7csU/Wo7Y98L9Hhcvr+H8f+waO3XYX3nR8Bt84vwZ87Zt4/Sv1Wt23EtdKDeBrD+LQ0RM4eX+vafzxKyXgoa/jdfe9QtgcDwBvsCniky+i07Xxc++8BQnF+2rHsvHzD38WmZmDSCQa2DNewlve/Oa+x0w8+iCKk+KW7m9dWAMe/ibecP/deJ3ndTvw2IMws2N9jfCUUpS+9Dm88+6DOHnyNuF4Tp06hTfedid+58lv4uitr8Drj7Fjzn/rCvD4M/jOk6/F3vEMvlB6BuefXQz93P3hH34LQBsLdYr0gTsD39uvfvJ5ZJJX8K63nRwgpprPLOBjzz+O43fdh9v39p+7Xz+3Cpz6FmaLBioW2ZZrwdmlKvDFh/C6e2/HSUfx9iI5j89dOo0HXvuGoaJ0NgP/5+nCSg0/8lsPIZOK46v//M0YH6FF+qWGwHeWUvoV/m9CyEcBfML7tZ0CJ6j/u0IfyB77ETBCcNPH8vzMRyEmGP2POwW10gYtqNQfe1FvyxcMyTgBISrWTgtGIhaoLuANj6rgSjkZVNQ5PLQ7jP0vppO4LMgHEI1JhdiRqWCC7HGB45PkC3CFk2qoP2sK7FfLNTtqLYMieGXmXpQbHa1FMsBIH68irdmxkErEEB+iGl4mI9ch0oIy0nQJUZUMJNsOLzBg55UaYRU0vkI6oaUO9R5TlqWokpHWCrFhDmc57QbbYRUy0tTLBhQVaYp5XSJVbctprh1G+cVtkmuCUH89Rdqg+mtYuynHIJHWdlVzamMS52KqxAqIwNSPQe9j1w1KDhtXUGun6vuYS+mrMUcJQsjjAH7XIZJACPkBAE9SSmUZZFvxnD8B4LcBPA/grT5CbACUUgvAFQC/TQhZAvDnYITaT3rG/G4AP0gpnduucUeIcCNhr5O59Yhjf9snsM5db/DNYr55zK2dshyy6zMm3v7cf93mwftBlth4jOBffOct2s+ZjMewbzyDy+sNLFaafe2bHLlUQjrXXJdkkx6fzePcUq3va6s1E82OhQMTwe+/aEN9xYlx4EUIfE5OKZWuxyyb4okrZbz/vv346yfncerMSiCRNu9kkImOJ3PLAMCF1ToA4DU3TeFTzyzAtqmyMl4VPL/N+9nhpR1rNfO6E2l+/K/HrqFrU1RbXXzh+SV84JUHwn8oghKUt5oppR+ilP71dg4mwnDQzVsJUlm5xIsCkRa2GC2kk2iYlrAZRoSwNj2VcTVD1CocxUxCsWyg6yr+gpBOisPXOWmlGyRedCxv3vBvoKdwKihmpAGDxAJXpOWGyD4azyQRI4M3q1LD1N7hyCTjLkHFxtUdakwAU6QFljPoWDslrYqARkaaQksgP2bQ+RVkI/OiYVpIxWPSXc6CoV82EETOpZNxtBTaRBtmN5BkKqaTQ7WJBpEo6ZCm05YG2aHS2qlDzBmC8ojNhPrzYN8VTxYZ/2xOblL9pdNuKsJMoZ9IW6u3pRkyIhDCSl7872Xv9dJTyuXTicDPY63VVcqtCVJGtzzW2DDkDPniZ4fibgC7Pf//CIDv2a4nI4T8FID/AuBZAG92mjt18Bnn75Oer3H5xUcJIdT7x/n6Ps/XxocceoQINxQOTecAAA+fW0U2FR9pMyZHz47P5gclR2mtmrG5HZBtkK43wom0zeDQVBaX1+o4t1zD0V25ge/nAyIyZGM7NlvAhdVaX4vl1RITFxyYDMtIG9xQX662MJFNusVsxXQSXWezWIazy1XU2l289ugU7to/hkcvBe6TYL7SEuajAT1yT0SkLVSaiMcI7to/jo7FiiG2GqL8Nj6mVZ+DZxR45OI67j04jul8Ct+8EPw6R9BDZJR9CUC1IZAj1EKZiIdbO0MWs0BvEaqaA9PsBFtrVEiFlqm2SOZh/mHO37qpZiGSLd6HtSsW0knYvupvAKg5pIPO7kY2legP9ecNj0PskMRiLPvIX6xQGaKWPJOK941LNaNIBJm1k+8cqhw3GaBIU83e41D5TNbNcGVgGCnkHV+Y8ks7i8xVLopaO8WZgH40QsiY4coGgssCGFkVkJGmcO3iyDpFFmGtncoZaclBa2cjpHE1CFM5AzHST6TxCngd0ooTaV71Y6OjZmuXYaZgYKXadq+xq1XT3a3WGZf/XtSzWeu9XmEK6WpLrd037ZB7onuHzrmVMxKh1u8dhjWwLLJtByHkXwD4LQBPgpFoy0MchidKe9/0bwD4Q8kfgJVA8f+PfvUTIcJ1wJ5i2t20ODZbuO55TiL4C2I4+TFKki/pWPL99xF382qbiLSbpnN4+loFpUbHLQrwIm8kpGUDXC03ket/3Y7tyqNjUVxeq7tfu7quRqSJNtSXq+2+sgBedhRUOHB+mT33LbuLuO/wBJ6dqwSuPefLYkUe0CP31gSlbyvVNqbzKbftdDmg3XNYzJebiBFgl0d1Px0wpuuJVsfCM9cquP/IJG7bOxYVIGwxtGbJhJA9AH4RwDvAJimiqwallI5Ww/gyg+E0IOpYO4MUTSlBjo8fzU5w4DfQ21HaaKnlZ4XlKRkKpIIKwQcwoqpjUbQ6duCCrGl2Xdl7EIxEHF2bomvZfaqg5tDWzl42k1clUXEKEooairSsz0K5GUUawKvSezeijmWj2u5iPKM3icim+hVptXZX2bLqx1gmIbxh6zQYciJNdCPXLY1QsXY2+PsQokhrdaxAeTzAyJig856rQy2bKltne9bOwfdEpsD0I8weOAyR1jIt7C7KCZkw8rGpSLYDTBUV1j7cMC3lyb1IVaurdvQiHiOYzBl9E8O1molcKq51PF4y47d2DjMmjr1jabQ6NtbqJlKJGJody1XQKY9LsHnSNIdT+Ya9j9V2V0npayTjoBToWBSphC+nRcMOmzfiN5oi7UkAHySEzAHgjed3O3bJQFBKP6b6JISQXwKzYz4G4O1Bdk5CyKsAPEMpbfi+ngezhALApzzj+AsMFlDxn/lhACVK6Y+ojjVChJcCYjGCm6ZzOL1Yxe17i6MeDgABkVY3kdW8r20H8gJ1f8m5xw2zGaaC+w5P4qPfYOUPdwqy63JGArUVuSItbyQGmiw5IXdmqYabd7F/cyJtf4i1MxYjmMim3E07gJFV3ugGPq8Nusddcki8Q1NZ3LaniI5FcWG1hlt2D56DZtfGaq2NPePi9ZjMLQMw4mymYLgk13K1jVv3BP6K2pgvt7C7mO5b/7kW2Npo92TOLddgWjbu2jcOAoI/PH8BHcseOv82Qj+UZ6KEkH0AHgEwC+A5AAaAy2C7djc5x3oSQGXLRxkhEIQQLZtI07T6WHM/VLLIWh3LXXzJUFSwRfnHlQmwgamOS4Xs6BFVncDH19tqx+N20lbXRt5LpA2pnuD5RpVGp89zX26aKBgJ5aBS/txeIq2hoIQKAiPSejcrTlZpK9KScZTqPfKrbm5ekebPPqhoEGmEEKTiYhJG136XjMdgJGKoBYSJ99SKwee8TYGuTQMLNMJIaG9rocprAQSTh+mkWnZbs2MFPl8hnUSzY2nd1BudsIw0OcnnBtVrTMbDyD6d4PuUoLWTj3XYSfguR/nFsV5vu9kcqhBZO5m6aviJ1v4Jtqt9db3hTuL3jQfvdIvGNUA8msNnpC1UBpuQAfZ7m10bBaXWzt5rxW0s7tg6VqBa0ouckVAqvNlB+DmwtvH/B6wVEwDe4/yRgTiPVSLSCCE/CEaiWQC+CuCfCDYQLjlZtwDw8wBOEkK+ApaN1gBwAMA7AYwD+Loz3ggRIgTgfffux699+gV8911bzDAMiXiMIJeKu+qv9YY5Ulsnh2gDZK1uYiqX2jYl3xtunkbBSKCQTuDuA+PCMcnUzet1U6iUOzqTByHAmaUqvutO9p6fXa5h33hGaW0wmUu5ajeAEWk3Tfdspz1XhnzD9cpaA9N5AzkjgRO7GZn34mJVSKQtbbRAKaTWThG55x3broLhxs+Ut8na6R8bf91FY7qe4BlxR3flUDe76FgUC+UWDk7pzcciiKGzav1XYPkY76CUfpEQYgP4Y0rprxJC9oM1IR0G8NatH2aEMOQ1bCKNTjBhoZyRFrLIcsNCFQg+SqkTIh6szglrOVMNe+ZE1Uazg9miXHHWMLuBBQgc/DnbHatPVTWstZNfgP1e/kpDvx2TKdJ6r1vDtJCIkYFFoCqmcgZeWOxJg3l2hb61M9FHeNTaFsY1mga9GMswK2zN7PaFrPMGQ1XySNSqCAynGsobwZlM/D0JUqSlPUqhIKIp1Nrp7u52lF+LoLIBnTbRMGsnwPKpVEOEm2ZwDlWQIq1jUdhUj9guhOS4MTWg2mfJX/zBfn44sp1jpmBgpdZv7dS1mPTIIY+1M6TUIQzcHnK11HSvf/tCdrpF45JnpOlbO2WEaK8NWYdIs+E32TQ1yMdcKuEqAG4EUEofI4TcDOABMEfCRwD8lfNnq3DE+TsO4Kckj/kKeoVRvw+gDuB+sCy0LIASmJrtfwL4I0rpDSX7ixBhFPiRNxzBe+/dp22/3054cy3Ljc6APXEUEOWRrde3l+SbyKXwqX/yBuTT4k30sLIB0dwqk4rj4GQWZz2FA2eWajg2m1cak3dDnVIqUKSFuzIurdVx2CFzbprOIxEjOL1YFe7MzPEMMom1szemQfXXSrWNO/aOuWsUlXxsXcxXmrhr/3jf19LJOApGoi8rdhS4sFIDIcDhqZz7nl0tNSIibYugM0t+B4DPUkq/6P8GpfQaIeQDYKGw/xrAP9mi8UVQRM6IBzaSedEIaO0EuJ0mxEKpEETdC+oOH1e7a4cucI1EDKVGmLXTViIKis5jNkLG1jAtZBXKBtyMoQH1xHBqE64o8V+Ay039LLJsKtG3AxOmXgqDX5FWdrMrdMsGYn1KuVqrg/1DtkW5eQyNTj+R1uwgm4orq51k9uHWEMrCMJVoPcA66R0Pf/6gRX5Yu2zBo0hTRcO0kIwT4Wun0yYadK71WjHVibSwHKogRRonYQwNErmYTmAjxNqpSjiJFVbDlw0AjEh7cbHq/n+tZmKPgh29f1x8I8Bj7ezYmMwNf53g9pCr6w3XRh5mGfEjJSQeubVTk0gLyAnkX1cqG3BeK9E51upYytl0OSOunB+6U0AprQL4EgAQQj4C1toZ2liucfxfAfArGo//FDzWzU0+9+iDoSJEGBEIITuKRAOcTSxnXbPdZJUqRBuk63XTtfFtF4JIj3ya5SCLojvW66ZULHBsVwFnltjcwbIpzq/U8Pqbp5TGM5lLuT9bbnRgWnbf86jkBF8rNfHAkUkA7F5/dCaP05L8roUKI9Jk1k4+Jr+107Kp2xjO1wnlxtYSabZNsVBp4TvvGBzbVD418oy08yt17J/IIJ2M44DjFLhWunE28XY6dCQpu8EsnRwWAHdWTCmtAfgCgmX+EbYJeSOhPCkPW9yqZaSplw2oWDtVFpMqGWktU00RwG2nG0352Cybot21A1sHObyKNC+GtXZO87YXf6h/szNUFll/RtrwFkqA3azKjY7bxsrzmWY0J2H+EoR621JqSBVhzHlN/DlplWY/sRYGWdHGMGRHmEq00Q4PmRcphYTHCiG28x7llyqCCCvW2mmFlnWEqZoKnhxFFbjWzJT8Mx6kSBuGEGWKtABrp0a7pej8GvYawcHbMXnD71q9rT2p54Rtf2tnsEI4DDkjgalcClfWGri83kAmGdcqQGDjklth9RVpSen5z99flbKB3mslvk7o5CjeYBlpfhxBL4csQoQIEbYUXhWxX/E0KuSNpFCRtl1FAyoIIq1KAQTk8dk8Lq7W0e5auLhag9m1hWUGIkzmUq4bZanKIhO8RJqKIm2t3v+enthdwBmPQs6LuRIj0vYFbLZP5Qetnet1EzZl86RkPIZcKo7yFivS1uomzK4tHNtU3sDaiFs7L6zUcNM0UxruGUsjHiO4ut4c6ZheStAh0jbQXy5QQq8ViaMCYGazg4qgj1xIIxmHbdPQTB9Va2dYRhpflFQVxtVQyAlSau3squUfqSjSVKx3vbENtt7xYxAAKc1Qx2I6iUSMDIRUlhumsjWPYzAjbXOKNH7j4yQfz2fSneSkk1vb2gkMSrYrTXUrIwC3ucqPoa2dASpR/p4EWYdlBK0frZDGW39wrwqCSLC0E7huWmHXiWAbpg7ZDgBdyghulYw0WasiAKQT6u9jMZ2QWgHaXQtdmyqXZIg2AzarSNtVMNC1KdYbJiybYq1man8WRYStbpacCLfuKeLZ+QpOL1RxfDavnSEjsloPSzwW0gmYli28h/DzTysjTaDIVI0WAJzrg9kNJaN3KiillymlFQAghCQJIXcSQt5ACLmLEDJ6D1aECBFuaPC2cW4d9LZCjgosj2ywbGCUarm8hLSilLL8NsnG2j0HJ9C1KR6/XMZjl0vu11QwlUuh5Mw5ljbYGmC2qF420DC7aHXsvtft+Gwec+Wm8GeulZqYzqcC768iRZp/fTKeTQU2iQ6Decd2ukdgO53KBSvSfv+hC3jzh09tayHBlfUGDjmKxkQ8ht3FtGuVjbB56KzuL4OFuHI8BeAthJAsABBCYgDeDuDa1g0vgiryirvbfBES1NhoJMWqHC9aCq2dRde2FX7RairYdVTKBlQVAd6MNBl0sos4qdgaaJizYcShvYCMxYhQElxpbj4jbTOh/gCw15FWzztS6+Vqy2kP1FfKmV0blk1BKUXd3Exrp7hqW5dIE2VYAexzYyRiyo2XQLh1yy19CGzt1FCkqRBWmtZOGTknI4696Fo2TMsOJPh0rhEAwPngsIw0XtDgxzAkDCsbEI+PK5xUG3CNRAymZbvqMe+YhiW3+S7otVITy9UWujaVBvLKxzXY2tlQLFoJwp37WdX6k1fLuHWPfhucaFNn2NzJINJ2GGuniJBTuS9y5I0EKEXfJseNBkJIkRDyuwDKYGVTpwA8AaBMCPldQsj4yAYXIUKEGxrcRsmtg0EladdtTL6IgHbXQrXd1VZbbyVyEkVas2Oh3bWlJN+rbppEjABfP7+KRy6WMJ5N9hUGBGG6YIBSpipb2hhUpIWVDXDCa9KTe8fbQ88uD6rS5srNQDUaO5bR55YB4ObH8nOnmEluubWTE2l7BbbTqXxqwFnEQSnFb3z2NC6u1vGF55e2dEwctXYX1Va3j+TbVewvqIqwOegQaV8C8GbPTuNHAewF8HVCyG8C+BqA2yGpFo+wvVBVpNVNNTtZaEaaglrBSMSQjBMltUlDJS9KcVwqioCepSwgw6rNF8kK1k5BxhAbTxdGYrjolalcvySYUopyo6MdyJ9NJfoVae3NKU34BXmhzG6eK9U2pnIpLZIJ6C2Emx0LzY4Fm2J4RVpWTqQVdYk0UUaahmWLQz0jLViFCYjzmLxg1s4gC6VT/KGhSAsipVWUcioqU11FmmkxAiosIw0Qv2bDqL8K6aS7K+4HJ0rzivZhPjavko9/NnXUjl4cmmIT38trdc+ETpNI43ZF5zXbLLHN8cDhSdiUfcZfc1Qte6VvXIn44DV1yHIGGdkOwFWOKlk7A8jtloYiTcX6spNBCCmCzft+FEAXrGXzfzp/d5yvP+w8LkKECBG0wBuzl4d0PWwH/BEBvHleNeN1O5CXzKM4WSUj+YrpJO47NIG/fHwOX3xhCSePz/S13geBh/7PlZpYdog07/uTScYRI/L7G3/dJnO9nznuFB3w7DUv5kpNtwlchim3pK13j/ePbTyTRKUZnln27UvruP1ffRa/8tfPhT523mkDFxF9k45yzxZs7K7WTHfD9/Ti4O+8FVgQkHwz+YhI20roEGl/COA3AEwDAKX0T8HyMe4A8DMAXgVGov3aFo8xggLyikRaU4GwUspIUyAWCCGhzYUcQQ2BHIZCU6DqQiadjMNIxLZQkcbLBgYb+YaM/cJUPoWVWn9JQNemQ5QNsJwhy7lgh7W2hoEv0vmifaXaxq6i/gQn65F+83N32HFxctGffbChrUgTqzEbpqVlBwScSWAQkdbuIkbCcwGBcEVaWJ5Vz9qpvhOnokgLGpcK4VHQVKTxzc2wjDTZ2LiCzlBsVmRjTMCmPeLTC37e5hU/5CJbYKtjgRC9AgQvDjrtmFfWGrimkCMSOC7nNdsssc3xhmPTODFbwL7xDN5666z2zzMrrLjlVFfBFxQ0zO9RBaWMtEH1HuDJ71Mk0oYpANlh+HmwzdP/BuAQpfQkpfTvUkpPAjgE4HcA3OY8LkKECBG0wPOluOJpRyjSjHhfREAYWXU9UJBsyvCxBZF8H3zNYcyVm6g0O/g79x9Ufs79kw6RVm5iaaONsUyyb+1FCEEuJV+XcpGAV5F2aCqHVCKGsz4ijVLKFGkhZUXcFeO1d3JFGifSxhQVab/z4DnUTQsf+foll4yTYb7MmslFa43JnAHLpsIYoQsrPeXdOYEKbyvASb4BRdqIm0S9+OrZFTxzrTLqYQwN5VkypfQsGJHm/do/JYT8OoCbAFyilG6PNjFCKLi1k1IaaCPkCopQRVqAZYtSilbXQlph4ceCulWsneGLbq4Wkv2OXctGx6LKC5liJhnaxgfoKtIGF30Ba/5ATOcNXFytu//nJJFuRhp/r5tO82OjbSE7NbwirZhOIJeKu9bOpY12XzaCKrwkSjzGXiRVQsKPbIoRo/58hGEy0rw2WI5mSAaZCEGV5ABbQOeMRODnVWS580NlAZ9Nsd1BrdbOjuWWcvjBJ0xBSjkVwkOl2cmLtqtIC85Ik41tmKB673nqV2jpEsCphDfUnx23aVrIJuPa9m+OTCqOXQUDl9cb7m6yriKNZzjy80yXIJQhEY/hsz/1Blg2RUIzJxKQWztlbbJBkOUoAj1lsooCr0eG9p9fHYvCsql62UDqhifSvhfANymlP+H/hpOd9o8JIfcCeB8iMi1ChAia2DuWRseieHaeLbJl7ZPXE708MgtGIq5EVm37mCSbMiL7pB/vvmuPO7fRUY17IyWulhrCRu4gV0apwcfWWzvEYwRHZ/IDhQOrNRNtSZi/F5zMZCQds4muVk1kU3FXPFLMJEIdEF3LxrcuruO1R6fw9fNr+MILS/j+Vx2SPn6+3MTe8bRwDtcbk4lxn8WWr+9ecWB82zLLxIq0NNadgoTUkBu4W4VHL63jg3/4CJJxgkf/5XcMvEY3Ajb9ClJKVyil34pItNEiZzDVRFBmEcCshgCQDVgwGIl4YIg4I7OAtMKCoZBWU8opKdISMVDKFiwitJwFlzKRlk4olQ0EZVhxyFQwrY4FIz6stbM/I42HUU7l9EgrbvnjLZG1dleJHJSBEII94xkslFuglOLqesNVxejAa6+tuQvZ4TKqeXX7qkeu3LFs1E1LPyNN0sana73LGcxSawkk3QBTwYSFm/eyyOSEVbtrw6bBJDRXh+qUDQSp3HpEmoIiLeB1SyViMBIxDWsn+zuQcA9UpOnbKIPsp/W2OgEDiLPlGp3NZ5Ednsrh/EoN55Zr2F1Ma1syCSF9wf58w2WzijR+7GFINECsEA1qkw0Cn6CVBbaOWruLZJwoqQJl13rdQhLZ4ucGwkGwTLQgfAX92boRIkSIoAS+IfTNC+uIEf0Nou1A3heTsd4YvSLN3ZCUWDsnA9YMhBB8/6sO4T13+7sDg1FIJzGWSWKu1MTltQYOC7LVckbcjRPyY51bO33EyfHZ/IAijZNMoRlp+UFF2mqtjel87/cXta76cXqxioZp4fseOIjpvIFvXyoFPn6+0pKemyKVHMeltQaScYJ7DoyHqt6GxXylBUL6SWiuzht1mygAfPzbLFa/Y1F8+fTyiEczHEZLRUbYMnDlQDWgJRDQUaTJF+46qo68kQhUfXG4pJWC0kRm7+QLdxWCD3AUaQrWThUlkiFR6DRMC8OuRacLBpody31thm3HzDpj479PtdVFMbO5BfKBiQwurzdQbnRQbXdxYAgireghKHrKnuEJhel8CquemxWXb08E7Mb5IWpVBIZTpHECRjaR4Iq0IKQlNrK+sSmep0wdqls2IFOkia3MfePq8AKR4N+xkA5WhnrhEmnDZqQNoUjjGXsiZW1Nk0jjhJn3dRsmf8+Pu/aP4bn5DTwzV8GJ3Wr19X54lcj1TVqttwpiRZq8TTYIbkaaxNpZSCeVVIEylaiu2lG2+LmB0ACwK+QxM87jIkSIEEELLpF2fg17xjIjV88Agyr69Rq3KI6eSPNHibhE2japfPZPZHBhtYar6w0cnhpcA7DIIVnZQBvxGBmIUzg+W8B8pdU337qyzm4h3E4qg4i0YkRa7/fPO+SeKLOMg9ssb91dwCsPTeDbl9cDn3e+3HQz42RjEjV38iba3WNp1E1rWzbVFspN7CoYfQp+vobcCTlpj15ax3fcugvFdALfvhxMWO5UaF2VCCGThJB/Rgj5n4SQLxBCviz486XtGmwEOXIeuXEQVJVfgQt3jQWD6uLdba4LajAMyYvSXcgUQxbwemUD4hZDZu0cXpEG9C52QxNpqR6R1rFsNDuWUjtdEI7PFnB+uYYLjjR5OEVaj6Dg4Z/jmeFv+H5F2qozwfHuRoUhKCNtmLIBQB62Wmt3Q8PNZTYyL1Q/j2xSo56RplI2sFlrJ6CuWgUA0w4vGwjKSBu2tRMQF5PoWju9n0XvmDZT/gEA9x2agNm1cW65hrsPjA91DG8GpS5BuF2QZaQN03DKiXt/jiIgtu1KxyRRifbiCdSmVa5FSEK03wB4FMAHCCHHRN8khBwF8Ledx0WIECGCFngmlmnZOKLYJrndGCDSGh0QgpFa0mRzzfW6iXiMbHrjXIbb9xbxtXNr6NoUR2fywnE1JHO79XoHE9nUQLnBsV3sON7mzgsrNRDClPdB4O2kXtJqQJGWdtqyA+aul9bqIAQ4MJnFnfvHcHW9KY0oanctrFTbUkXaVJ4XIAiItFob0wXDzf7bDlXafKXZl48G9LIGR02krdXauLBaxysPT+K2vUU8N78x0vEMC2UijRByC4DnwXLS3g/grQBOSv5EuM7IhyzaOVxrp0I7pqilDuiRRartmCqLd1WCD5ATaT1rjdppXUgnUA1QpOksunvKof6LM7N2Kg1nAPzCPOcJ9Qd6F2ZVcBtvs9N1SU2VUO0gnNhdgGnZ+PxziwCAo7sGb6JhcAmKZtdVj+kWKXgxlU/1SZX5zVRHcp+Ki4s2hrGTKRFpYdZOV/klJ7ZVSzF0CCt+3Kzkd067qi+FcYW8bqyZS7FswBl+cJZiUEaac+3SKI4oqlg7FT9PaVcd2jtWw9w8kfa6Y9Pubv07bt891DF2piItPnAvUim6ESERj6FgJCStneoNpTJFmq7aMXfjK9J+E0AewKOEkH9DCHkLIeRWQsibCSH/GoxAywP48EhHGSFChBsSxXQSe8aYJe2u/WMjHg1DryGT3UfWam2MZ5LarfVbiWQ8hnQyNjC/KzVMTGRTQ+evhuEVnk27ew5ODHw/F1CCt15vC7Pbjs8yRf05T07a+ZU69k9kQtecyXgM49mkT5FmYtojPlBpy760WsfeMfZ8J5zxiJpEAWCpwtYc3gwyL4KsnSvVNmbyKZfoWxM8ZrNYKLcGxja9Q4g03lR6574x3LZnDC8ubgQqBXcqdBRpHwaT8f8GWLlAklIaE/zZ3IogwlBQDe12s29C2jGBgCwyDcKK11eHge9aBC1wDUmgP4dKJpMXrGxAvoDvvVbqAdSDirQujMRwN7F9bjsm26VYrbFmHEOzPZKTk/W25d78i5tUpHH72J89cgV5I4EjITtFIngVaVwlshkibTpvYK3Wq5l2FWkaCj6ZtXMYRVrB/UyKz9daK3zx7hK0QYo0hSZegE0AVRfttk0D7awuwadgAQ9TD+lkt7W5Ii3gmMGtndz+rdPaKQ+pr2moVoHee+R93ZpbkJFWTCfx8X/0Gnzs7z+A2/YWhzpGJhl3yc+tKhvYLES5mMMq0gB2zRdZOzeaXeXNhZ4yenDTBFDPSOu1dgaryHcqKKVfAvDjANIAfgHAFwA8C+CLAH4JQA7AT1JKvziyQUaIEOGGxvvu3Y9UPIZ33bVn1EMB4IkIaHIizdRyPWwX8sag+2etZm5rdtt33DqLbCqOO/YVpdZOmeK6VO8I7bAHJrMwErE+4urCSg03Tatt1k/mUi5p1bVslBqmLyNNvjHKcWmtgcPT7Pfhax1O+vgx54b5ixVpRiKOvJGQWjtnCkZg7MRmQCkVKtK41XddoJK7nuCtpUdn8jg8nUWrY7vrthsJOtvNbwDwKUrpL2zXYCIMD9XdbZV2zF6DmyXMJOALBkNRkVZthbeJ8sWRX+brRZgibShrZ1M+tqbZBSFqhGEiHkMiRgZtSO3hFWl7nF2EuZKjSKu1tW2dQO/1aJgWNppbo0i7ZXeRlSHUTbzx+Ezg+yZDzmmSrLa66NoUqXhsU8qcqbyBrk1RaXYwkUv1iDSNcgZZY+1QrZ0hn0klRVrIOc/HBqhZO6+sqcUV8QwvWb5ZWqFNtKFI8BXSCaytqo1LJyNNRD62OhYI6V3jVBBUNlBrsUIG1d1okbWz1bG2ZLJ71/7xTf18Pt2b9G5l2cBmYCQG70WNjl6BiBfj2aRQkVZpdpStQ+79sbM5RZqRiCEeI1p2650GSul/J4R8BsAHAdwDYAxABcATAP6UUnp5lOOLECHCjY2ffttx/PDrj4y0FdMLTkKUHNJjtdbWdolsB0SOg1LD1MoI1sVsMY0v/vSbpO3zOSMujRtab5g4PjtIjsVjBDfvyuOMY+20bYoLK3W86ohaoyhbl7C5/3rdBKXATF9GWrgi7Vqpie+4lcV/7p/IIG8k8KKESJsPIdIATu71E0SWTbFeb2Mmb7gCAlHsxGZQbnTQ6tiuqpMjk4ojk4y7TqBR4fxKHdlUHLNFAwcmGHF5tdTArh3QzqsDHUUaAbN2RtiByCnmrfDvB1o7Q7PI1O1ReSMJy6ahbaINBaIibFyutVO5bCAB07Klx6s71jZVWXQ6Ge/7PSmlaGyitdNIxDFTMNwL9UKlhdmiPpFW9Ci/XEXakAtRjniM4EfecBOMRAwfet3hoY5BCHEy9FhG2lhWLexbBh4oym+iqzUTybhePoSssXa41k72eNlCudYKz0hLxWMgJFiRxm2CKtZOfxit/JjBarK0giJN3dqZVLZ2mtYmM9IcG6XOeZZJMqJMNMa6GV4Y4T8W0E+kDaN23A54lYE7x9o5+F42za7UchyGsUxSOFktN01lci4WI0gJckR1FWm8STcs13Sng1J6hVL6a5TS91NK3+b8/WsRiRYhQoTNIhYjO4ZEA9g8Kh4jKDmqp7X6zlCkMdLKp0irm5jS2EgeBnvHM9J7Z7C103Qzzfw4PlvAGYe4urBaR7Nj4RbFEiWvIm1FkJMc5t6ybYpSw3TJUUIIjs/mpUTaQoWtz/xklRcTjujAi/W6CZuyzGueDS3a5NsM5itykm/CZ4EdBS6s1nFkOgdCCA44RRJX15sjHdMw0CHSHgNwYrsGEmFzULV2Nk3L3QmXIVT51dWzdgLixjsvGu1u6GIyzNo5jCINEFu2AKcdTmMhaSRifcRCu2vDsinSm1gj7xvPuNLhq+sNl7XXgVeKvrFFGWkA8GMnj+KZX3kH3nwirLhNDq5YLDc6GN8kuTeT575/Z4JTa2MqZ2iRJqlEDJZN0bX6CdFhFGnu+SVQMlFKUTO7rv1TBkJIePmHRmunqrUzTLkqa6ntP4YawZc31Ak+zjkEXXuCxjZMsD8jfMX201rb0grk569F00Ok1dtdZWvodsK7o61rWd0uiPLINkM8yhRp5UZHy1bOPpP+sgE2Rp2x6diaI0SIECHCaBGLEYxnkq4tbrXa3hFEWt4YjO4o1bdXkRaGfCoBs2uj49uctmyKckNuO719bxGLGy3Ml5t4+loZQH8eWxAmc4ZLEK06dkpRRprsvlttdWHZtI/kO7G70Fd+4MW1UhPTeSNwA23KQ+5x8Hyy6byBQjoBQoDKFlstF5xYIBHJN5FLuWSwDLKc9K3CxdUabnJKKvZzRdr6jVfyrUOk/SqA7yKEnNyeoUTYDLiyJaxsoG52w5VfzuJF1F4I9Igs1bIBQEwmeNEwrdBFm3rZgHpGGhubjEjTI0/SybhPOeFYYIdUpAFMVnx5vY6G2cVqzcSBodox2UV6o9lxf9fNZqRxbLaOfDzLJiS6C1kRdjs3C75DxBpx9HYyReeYaTFCVJeAGcvKidqGaYFSNcUPD1yXQVX5lTcSaHasgUmNCM2QfLMg1Zd3XAlHvROEokPgqISMmhYNVZSlA64TrY6trSxkYxTnKdY02h6B3uvZ9JB8tbaeqm274J2I19tdV4k3Srgq5E6/FXZYIm0skxywM7Q6Ftpd2/28Ko1L8JnUtXYCTobMNlTeR4gQIUKE7cFELoVyw0SrY6Ha7rpuiFEibyT7NiQtm6Lc7GBymxVpQZAF+280O7AppErD1x6dBgB84/waHr9SQjYVx82KhWZTuRRKjQ5sm2K1OqhIK4SslTlB6rXrHttVwHrdFOZ3zZWbbrusDJMiIs051kzBQCxGUEyL1fKbweIGJ9IGxzeZSwVmpP3q3zyP1//Gg4E54ptB17IxV2rikLOmTSfjmM6nMF/Z+ubS7YbO7P0AgL8C8HlCyJ+DKdTKogdSSj+2+aFF0AG3uoQFFzNyKMROlhCHKXPotnaycYW1iYYvjmRtaf5xqSvS2NgqTfHY6u3w16pvfMl+RRq30W5mjXxitoBPPr2AFxZYLfAwRFosRty2ujFnobxVRNpmsauQxmKlBdOycbOgPlsHXL58zcmUmys1tSvTvUQan3+0XKWJ3huZTzm7TAEh9Sptj36lox9hpJc7Hs+kJqyqPczayS2nYeNSITwK6SQoZZ+XQsh5aVrhip8gRVqrYym3+noxkU26mShe1NuWa+FVAbfD89eXUoq6RmPkdiJvJN3zUteyul3g13wv8RjUJhuGsUwKG81OXy6m2xicUV8MibIU+RgNjfMrZ8S1mnQjRIgQIcJoMZllxAgnR6Z2gCKNKcp7c5Ryg+WDTW5yg3oz8DqlvHNObnMUlQ0AwC27C5jKpfDFF5bw1NUyXnfztPKm3mQuBcvJSuZEkjcSJ8y9xbPMvIo03iR6ZrGK6Zv73+trpWZowRPPk/bOOzjJx3OvxzJitbwfDbOLTzwxjzccmw5dDy5WWojHiDBbeyKbkqq/2l0Lf/S1iwCAr55Z3Zaij5VaGzbtZYEDjPAcdZPoMNBZUXwEwPeCkW8fBPAfAfyx789HnL8jXGfEYgS5VDzUutVoK2SRJcRhyhxu2YCCGsnbzBgEFXuTrC2NY5jWTiBIkRau3usbX6I/I20rFGm37mEX6P/zxBwAuFXMuhjPplBudlBpmIgRNQLnemBXwcBytY2lSstVlA2LdDKOXQUDV9cboJQq7RT5wUkYrxqz0XEsipqLd77LJFKkcVm5CoHiVzr6oVIgAgSH5vvh5q4lxePjltNga6eaojOvSLYDzNoZ9j6kA65fjEjTJ2EmJTJ4lcIIL2Ixgkwy7tpeWx0btqIycbvBywZsm2Kj1XU3GkaJrM8KO6zNmmMsk4Rp2X3EXLnJ3lcta2cy5kYccLQUyzW8yKeToef9qHNMIkSIECFCDxO5JEr1Tq/QagcQaf68zZKjNhplvlxPkdZ/r+RjkxFpsRjB++7bj888u4j5SgvfrUHkTHmykufLTUxkk3335FwokdYZGBsvRfA2iQIsT22u1MR+BUWa2bVR90R6+PPbxrODankR/uMXz+IX/s8z+PH/7/FQ6+XiRgszeUNIQopUchyXPOVfpxc3Qsc0DOYd2+lej1puVzGNlepLW5H2oW0bRYQtQU7BJtLoWKG5X+HKL31rZ9jivWFaobs6YQRfr2xAjR8Oy0irtrpabTzpZH9uTsMl0pQPMQC+0/Gn37wCIxHD0Rk9hRUH3+1YdQI+R23Z4thVMNzJyOwWNLUcmMziWqmJcqODhmm5vntVeBtrOVQzyESQ7TK5ijQla+dgHpMX6qH+6kSayu/sL9cQjUuFfPSOa89Y8GPbFg0lDBNxlgHpJzqA4TLSADYZPbM0mJFRaXZwyx49cjubirvXqt55MPqygYKRAKXsHlHZAqv1VoCr/fg53urYoFRfHcrhtmM1Ou7kuqdI07R2+s591c+hF3kj7pbJyMAzYiJEiBAhwugxkU3h8UYZSxv9qqJRIufLSFtz8sG2u2wgCL3CLV8JgjM2WdkAAPzDN96EJ6+WMVtM4113qhNp3JlytdTEQqU1ELRvJGJIxomUSCsJ1HIzBQNjmaTbJMqxWmvDtGzsD2js9B5rvWa6c/6VahvZVNwl9mRFSH588ql5AMAzcxWcXqy6YgsRFgMEChPZFDZaXXQsG0lfi/35ld7veU6SDbdZLDoWTu/4ZvIGzvnIylGh1u7i5/730zgxW8A/fuuxwMcqz0YppR/d9MgibCvy6QRqIa2djXZ44xlXfsky0lpdbu0MJ6xcGW3I4l1FZaBC8MVIjwwJA29zlBELtXYXhzWsgX67j2vt3IQibe94Bsd25XF2uYYHjkwiofi7+cEJHSMR2xFV3RwzHvJs99jmb/j7JzJ47HLJtXfuC7nB+SFqhuUL5KGytTIJIZFW1yHSkrFAwqrZsZCKx0LPjbzBiAIV5VdDQeWWTsQVrJ3hv1/eDX8Nn0SYtlqYu8h6x8c0jI1yMptyd1G9qDQ7ym2PHOlk3H19d0o7JuBRBra6qDQ7OyL3hSsiuUKy4bZOD0c88on7et10J9icSNPLSBOUDXQspEKKfPxQyUi70Vs9I0SIEOGlhOk8C7S/vFYHgFBF0vVAIZ2Aadlody0YibhHkTZ6a6f/HlcS5JD5MZU38D//4Wu0n5Nnbl1Za2C+3BzYTCeEBIpORLZT3tx51kfyXHXWGWEb9l6V3MEp9tiVaruPgC2kE6GbanPlJuYrLfz4yaP4r6fO42vnVoOJtI0Wjkmy5fh5UW50Bojg8w55dv/hidAxDQueZe1VpM0UDKzU2n0W2FHhfzxyBZ98egGfxALee+++wMduLik8wo6CyqS8YYZn+ohUOV64irSEWv4RILdPcqjkkRkh2W1Nk9m2VD+AxZCxVVt6tq10Mt6ngnGtnZtcI//UdxzH3rE0fvzkzUMfgxNp69ehDlsHN3mIypumN5eRxo8xV27iiaslAMDNu3Qz0ngzbI+EaSlmkIkgU6S51k4F+1w6EQ9UpDXN8MZbwJtXGE5YqdikmQJTTvC1FC14Pft3OMHHywbC4P8s9sZku++xDiZyKTRMq4847Fo2au2uNpGWTcXd17e2k4g0o3d+lJtmaI7e9QA/fzjxqELwBoFPGFc8ocEV19qpmZHmLxswu9pqR7+KQIR6yObYqEAIeSMh5GDIYw4QQt54vcYUIUKECNuN/RMZWDbF41dKSCdj0vbJ6wm/aIETQqOc78tiO7ilMEiRNixmCgYyyTgurdVxdb0hJDlFDaccpYaJdDI2sB49NlvAmaVan53yWolZIMOtnew98FopV2v9ba9+a64IT10tAwDeecceHJzM4tuXSoGPX6y0pE4f/tqXBRvE85UWpnIpHJzMYXmbMsvmyy1kU3FX0AIwh1LHokoW1+3G559fQsH5TD14ejnwsRGR9hJCLhU+KVdRiYhUOV60OjZS8RhiCjvvYcGO7rhU2kTDxtXVs22lk3GkEjFsSMoGqq2OVk5Q2mf34X749CYUaQDwrrv24Os//1a85ujU0McYc/z3azUTkztAacJxx96el+/4kPlvXty1fwyUAh9/7BqMRAyHp4YrGzCtQYvuMIv3sUxS2FirZe1MitVV3vGpnPf5ITLSwq2dwZZTlXEVNcZlWkB6E4q0YRsf+UTZOxHi7+swRJpfkbYjygY870O5oa+02w5kfdZO1WINGWacieuqZ3I4lLVTkFs4THZbwWAq8qCsk8bOLSN4EMAPhTzmB5zHRYgQIcJLAlyB9M0L69g7nhm5egbwqr/YPZJbFEepSOObpP516XrdRC4VH8rlEQZCCA5OZvGN82uomxaOzQ5u0OeNRF/DqRdrNROTAoLv+K48Ks1OXxg+D+sPy2Lm88c1z/xxpdp25yOAs6kWcq+/uMoUkDfN5HDvwXE8FRD70OxS1Npd7JFYOycFc9q+sRUM7Cqy8H/bDs5iGwaLG03sHkv3fXZEG52jQMey8dTVMj7wygOYzqfw5NVK4OOlRBoh5AIh5Dwh5Ijn/yp/zm/x7xRBESofxLqKtTMxGLjuRbtrKTeTxZ0ShKBFMqWUZbeFLEJcpZwsI820tS/MxXRCqEgzuzbaXVtrgesPoG661k6tIW0LdhfTWK+buFZqYnoH7J5xjGWT+Nl3nMAvvuvWoVUmXty5nxFzT1+r4M59Y9pWWE6kNU1PaURHP/uIQ5qR5pxzahlpYnWVd3xKyi9Dg0hzCQv5+Ixk3LV5C8dlqgX76xB8bYsqNTamJWNrdSy3jEAHE4JJB39fdbPEMp6MNK422gmKNH5+lJsdVFv6SrvtAD//etbOzRFp0wX2Pnonaut1E6lETOuY6UQMbR+J3OzYQynSKO39XiLUA743YqisHgmArZ+FR4gQIcKIwBVI63Wzz1UxSrjzKMdxsOaQVcMo8LdsTHzO6bd21s1tLUE4sbuA04vMhinaoA9TpInG5jZ3erJyzy3XsHcsHeqmEpFWq7W2Ox8B2PyLlz3JcHmtjpmCgZyRwC17iliotFCRqLdKLXacoIw0AMLIkpWaQ6QVDHRtinXBYzaL+XKrz9YJ9Ii05Y3REmkvLGyg3bVx76Fx3LFvDM8vBBcuBK0oYr7vx8AmRWF/IpXbiJA34qE2kKZpubv8MoRZKFsdPcKqkE4G5h/xAOmwi1EiHkMiRgItp7pkjKxV0VWKbEKR1isbGP1uFc8KMy1bO4B/u/ETb74ZP/KGm7bkWNN5Aw8cngQAfOcdu7V/np+D3lY/1VZMEYppMZFWcVSQRQWyIh2iSFNtotRpx2yaFggJzkFMh7R2qn4e3V1LBcupaallMxoCogPgitzhWjuB/kkHf191CSfW2smtnezvnVA2wH8Pvsu6E8oGODHVs3YGt8mGIZtKIJuKY7U6uDOsoyowkvGBjaam2dXeyOGfyaBIhsYOtXYq4iCAnZEeHCFChAhbAK+V7/a9IQ1J1wkD1s6aiekRlyDI8m/X6qa0sXMr8MCRSfffdwjen5xDWomwLhnbMZdI693Ozq/UcVSSQeZFNhWHkYi5RFrHslFqdPqsne6mWsCc+tJaA0ccl82J3Ww8slZNl0iTWDt75N7gvHvVUaTx8W1Hc/hCpTmgluPPt1YfLZHGydLb9hRxZDrnZiHKIJ2NUkoPB/0/ws5DPh3ssaaUoq5goUwlgi2U7Y6ltJj1jito8a4TIC3KpuEYppGvEJJhxRf5Kkj7FGmNLcpI2wp4m2t42OVLFf/x++7Gl04v4++88oD2z2bcTKbe+boZO1kxk4TZtQfIrnKT7Rb623JEMBKDNjIvVMmhTDKOeIyE2r+Bni0ziFxIJ+PCfIW+cSlcJ7LJOAhRV6SpFBjI1HJNRbupH96Qeg7+u+tbOxNomIys2kllA3wSc9aZROwERVo8RpBOxnrWzk0q0gD2e656FGl891UHwoy0Iayd3h37XZLH7KSyAULIv/J96aTkGhEHI9G+D8DD2z2uCBEiRLheSMRjeNtts/jC80s4eWJm1MMB4CWteEZae+TZbfEYQTYVH5hzlhrbS6R991178Mdfu4h33bVXODfOpxO46uSb+bFeN3FIsEaazqcwkU3i7DIj0myb4vxKDX9bYZ1BCMFULuW2lfJ5pHfe4d1UkzlVLq/V8cZj7Hy7xSHSXlyq4lU3Dcb+rLfY/ESmSOMbpX5FGqXU3VzkjxGtkTeDjmVjudoeINKC7KbXExdXa0jECA5MZnFkOhfoGAA0Wjsj7HyEBRe3uzZsBeWXq0iTqGBaXUupaICjkE4ELpJ17DosmyaobEBPEDmRTQo/tFwerWft7M+MaphdJGIEidEL0nDUE7p/s8IOyo2MveMZfPDVh4b6WX7TbZqDhOiw1k4A2Gh2+oi0SrOjHG5uhCi/mmZ4UQfAbuZ5I6HUjtkww0mBtEKbqMprFovxcakQaRqEu+81s22KdteGMcT7KLrBD61I85YNtHYOkTaWSSIRI66MfVdBPAG73mDEI3ud+IZMQUMp7MdMwUekVdvaKl1Ra2fD1G+ElbWa9R93RynSfsXzbwrgpPNHhjkAP7d9w4kQIUKE64//8LdfgbNLVdxzcGLUQwEwqJpfq5k4MDn6TfOCQEixXjdx88z2rUPGsyl86WdOyscUZO2sm8ISBEKIWzgAAAsbLTRMS0mRBgCT+RTWHaUVz1nzlw0AjAidFRRxWjZl5JMjithdTKOYTrgW1oHfo80UabKygXQyjmwqPrD+3Wh2YVo2ZgqGO7fd6vD/5WoblML9XTjGMkkQ0sv3GxUurNRxcDKLZDyGQwo526OfvUfYMuRTrP7Y7NquqswLVcLKzUiz5GUDutbOIEa7Ny6VvCi5za3V1V/ITGZTOLdcG/h6raW/YEs7KgVe3VtvM6XQTggi3VVI4/7DE6i2ujsm02EnIuuzkgG91s6hrJ2Z3o7OLs8NbaPZUbJ1AuyGF6xIszGZUxtbUMhq3zEVmkBlzZi9Y1hKxQAAs8CGEWmUUpiKRFo6GR+45vDXcFhCNOa7wXNLuOr7yJH3ZFm6rZ0K177tRixGMJVP4dk5Fqy6e2xntPtmknE0HFWWTtutDNP5lBvaC7CsEt3FkJGID5DITdPqmxirIOez44iwkxRpAN7s/E0AfBnARwB8VPA4C8AagBcppfKLV4QIESLcgCimk7jv0GT4A68TJnxE2mrNxD0Hx0c4IgbRJqnMPnm9kDMSws2rdtdCtd2VKvlOzBbwiSfmYNkUTzsNmnfsFbBeAkzmDJe04hmtIiJNtqlWaXZAaa+4gBCCE7sLOLskIdJaFJO5VOBafSKbGiCtVmotAGzDcTyTcp97K7FYaQLAgCItHiMYzyS3JZNNBxdX67hphq2TD4QUSQABRBoh5AeGHQSl9GPD/myE4ZHzfBBTicELQV1x0ZaMExACYcYQwDOZ1JVfBSOBOYmMFthCa6dp9bWgqGAiN3ghAbzWTj1FGqWMgDQSTHWyExbIHH/+D16Nrk13BLG3U+Eq0nzKwhjplV3oYCwjlkZXmh2MZdTODZH6xQudbMBCOrzZF3AUaSE5VEFKOa7+UiWtVJRy7a4NCjVCU6RI42PVVa0Czg0+m+q7wQ+rSCtmkqi2Wahsvc2s9nGFBuTrgem8gSUn6FW2k3m9kTN6LaeuIs0Y3nY6nTfwyMV1AEDXsrFWN/WtncnBz2RrE9bOoOiDsNzT6wlK6Vf4vwkhHwXwCe/XIkSIECHC9UcuFUcqHsNa3YRtU6zX25jKjX4zLJ9O9m3etjoWGqa1rWUDoWMyEqibFiyb9s29uPJKNrZXHp7An3zzMp6f38CTV8tIxWO4TZVIyyZxYYWJNnhr+K7CIJEmmwtwNZt3bDfvKuDTzyy44g0vSi0aOoebzKX6mkQBphYDWMN5T5G2tcTWfJmRdXvGBkkqtibfWuJOB7ZNcXG1jjccmwYgt8Z6EbRS+gj025Z4Q1NEpI0A3jBx0YWAT8jDdvMJIUjF5YQVWzCoE0Rh1k6d3BuWF7V1ZQOTuRTqpjWQYcUvZlrWTkcF2OowIk0lj+56IhGPYYQFPjcEjEQMMdJv7WyaNrKpxFAEpGvtbA0SaUcUlYGGT+noB8v9UiOHwj6L7jE74aUk6eSgKoeDK9VUiTSR/N8PV7mq2NopyrDSGZMfU7lUX0j9er0zVCtWMc1CZWtmF+VmB+M7IIuMY89YGs/NbyBvJLTyIbcTmVTCDeCttbpubtqw2DOWRqnRQdO0UG2xXd5hMtI6Fu2biDeGyN9TIdIaO0uR5oJS+qFRjyFChAgRIrB126QjDCg3O7ApU1+PGsxG2Zv/csXcKBVprvrL7KLomedwxZhMkfbao4xceejsCh4+t4q79o8pz//CFGm5UCKNvYaTHtvp8dk8/vyRDlaq7T7HCwCstSiO7wsmgabyvdw2jtVaL7+tkE6AEAgL+TaDBa5IGx8c32Q2JWwS5fivp87hkYvr+IMfeCUSQ4gbwrBab6PdtV1bdDaVCBXUBH03miTdYAiblHMligo5FKT8anVsTOY0FGkhi/e6jrUzubVlA9wLX250sHus97NVN4tHp2yA/Twj+pKehtSduRCKMAhCiJPJ5CHSOvptfBwyRVq50XFl02HwKx390Dnv80bCvVEGQS0jLS5VpOk2nRbS4eNyGxuHVKQ1N2HRBdjO1OJGy/3/Sq09VCtW0ZObV250MKaYlXc9cHy2gC++sIzD06PPVuHIpeJoONfjaquDvDEcqc3BJ0hXSw1XpS1rtpLBjT/o2n0q1u1o7dxJijQZCCE5AONgJQMDoJReua4DihAhQoSXGSZyKazXO1hziJopTYfOdiBvJLDkmTdx4makRJrnvisi0mSKtJmCgbsPjOM3P/ciAOAXvusW5eecyqfQcEQbyxttFIxE31yUkzUyxwgfm/d1O+42idb6iDRKKVYaNt4akpE3mUu55VIcPL9tpmAgFiMoppMoKxBppbqJj37jEt5z975QkcBCpYW8keh77Tkmcim3Od6PVsfCv/8se+2fuFrG/Ye33lq9IFDLzRbTeDbgZ4JaO0W5FxF2MHIhHuuqRkOcEZDL1OpaWoHdeSOJZsdC17KFDLL+AnlrstsAYDLHPsjrdbNPwsltZloZaZxIc8bXs8dFRNqNhHQyjman9xmqty3kQtRZMkxk+fklsHZm1Uhaw9OiKyPSVLPICukkLq3JbdYc9XYXE9ngm7A/E9A/JgDKn8d8OtmXWyVCL6su/DPJGnT7rxP853UVZByzxTTOLq26/191mo10wScPG80uKk1zRynS2MTkPO7dIQHKAFMqc8tFNaDRShUukbbecDedRC1dQeh9Jq2+gpLNtHbKENYYNUoQQj4I4F8AuDXgYRRRHm+ECBEibCsmc0ms19uu4mnUrZ3AoNtgJynSaq0uMNb7uois8uPHTx7Fj/7JYxjPJvH++8IbOzn4XHGl2sa1UhP7fNlbOY9KTgTR63ZslhUdnFmq4vWOFRFgG/UtC9gfku81lUthtdbum8OvVNtIxokrAhjPBmecc/z6p1/A/3rsGh58cQV/9ROvC3zsYqUltUxOZlN4+lpZ+L0znjy45+Yq20OkCfLbwjZao8nNSwh5Z7EvU6TVXZVV+NvOrJ3iCXy7Y2u3dvJxiZoKuXpFhawwEnFpi9kwigCuSPNLSWst1rhpCEob5GPj1k4nGLvdcZrv2gE/FWGnIZuK9y1e6+3u0Fl3xTRrQlzztAS2OhbaXVs5W8vwErS+67llU6aKUSas1Fo7mwp5T+64uoMEdkvTRrnV1k4jEZdmpA2rSNszlsZKre1uCKzU2kM1TxWdbLyNFlOk7aQW3ZMnZvAnP/zAjiLSMt7WzlZ3U42dAHDQIdKurDfcc+qAZmtn2nPuA6zOvWtTbUW0kYghESPBijSFcpBRgBDyQwD+CGyn6KsArgLYmYONECFChJc4JnMGni1XsFhxVDXj4UHp2428L5dXhazabsjcWypje/vtu/FXP/E67CoaWr/D/kn2XlxZb2Cu3MQ+33vjbe0UgY9t3LMBP5M3MJ5N4uxyf+HAVSeTPKy1dSpvoN210TAtl8hbcTaIObE2lkmGtnZ2LRufe24RAPDU1TIurtYDVWkLldZA0QAHz0gTbdB7G0rDNt6HBc9v2zver0gLgvKMlBByH4B3AfjvlNIlwfd3A/hRAH9NKX1S9bgRtg49RZqYANOydgZYKHXLBriMttoSE2mutTMk3BxgC49SY3BcuoQCB78Q+iuAy82OU8WrbiHyL66qrS6OzkRc9Y0GP5FWa3eHbgmMxVhuhTeHQLft0U/QeqFNWAkalERgN9ZwayfgEOu+52+aeg2ZBSOBjZBxqbYOs7GJFGns/2kNctyL2WIalk2xWmPq1dVaG6+5aUr7OD1FWodlpCkqE68HCCF4w7GZUQ+jD7lUf9nAZhVpU7kUsqk4I9LaFmYKhja56irSnHNqWNswIQQ5I7gAZAcr0v4ZgBKA11NKXxj1YCJEiBDh5YzJbBKrtTbmSuJWxFGgYCRQM1m5UixGemTVCCMtvHniXrhkVcjc/BUHxrWf89AUI5YurzUwV2rg/sP9m5VGIoZkXL6ptl43kUvF++bahBAc31UYsGdeXWfvf9gGIV//rtXMHpHmiywppMUNp16cXqxio9XFz77jBH7zcy/iKy8u48j0EenjFystHJ+dFn5vMpeEadmom9bAXO/iah2JGMHx2QIuS+yfm8VCpQkjEXPdRABwJCTqRGdF8TMAfgTAsuT7SwB+GMBPaxwzwhYirD63pmPtTMSlFkqRAiUIxXQw087zb5SsnRKCr6c20Vsk+yujOSoN/QUuJxddRdoWqCciXH9kUv3ZX5tdvE/lDazVe4o03bZHP0Hrhe4CvpBOoN21YUpIcve4poVMCLHtnu8C5eow4zK7dmA7KVeuqthYjUQclk3RtXq/p/vzQ+bdcXn34kYLZtdGudHpC4tVhTc3r9LoYEwxK+/limyqp1bcDKnNQQjBsdkCnp/fwOmlKm5SLP3wgp9D/DzXzQT0Im8kUJNsftk23ckZaTcD+HhEokWIECHC6LF3PINqq4vTi1VM51NDz3W2EnmnXInfx0p1EzGi33a+leAOE/9audQwMZZJbkuI/e5iGsk4wbPzFWy0ugOKNHdTTbJ+L9VNTArKI26ezePMUhWU9rohe4q0YEUiL6Pwrk9WfJEluVS4W4RbLt9x+yxmiwaeulaRPrZr2ViutrBb0NgJeFxi9cHM5JVqGzMFA7vH0m6W21ZjvtLC3vFMn4jmJ99yLPBndM6W1wB4kHrfLQ+cr38ZQLA5NsK2ISxvRaeJ0kjEYFpbo0jjgf0yS1nN7MJIxJBSUIrIWjuHbeTjOw9+RVqpYQrVc2FjA+DmRrFg7J2jNomgBqG1cxNE2nQ+1Rekz/+tml/hzWPyQ5ccUmkJpJQqNc5ye7dIKaebkcavEUHKHH5MVUUagD5VGm9+HDbvjmc6LFaabgaJbtsj0FOkLW20YFr2jlKk7USMZ5NomBY6lu1YOzf/et29fwxPXC3j+fkK7tw3Fv4DPvBGW245dYm0IRYujEgT3xsbHQviGdeOwDqAVuijIkSIECHCtoNnfX7jwlqfNW2UcOd2zpxzrW5iIptCLDZ8YdDmxyQWd6zVzW3LlYvHCA5MZvHZZ5kF8vjuwsBj8gFE2lrdFKr4ju/KY6PVxbKHWLq63kAuGV6WN5lj81evY2al2u7bIM6HlAUCrOwgGSc4NJXDnfvGpRlnAFO82VSuluREmn9Nzsc2UzAwkze2jUhbKDe1lZw6RNpuANdCHjMPYI/WCLYAhJDXEkI+TQhZJ4Q0CCFPE0J+ihCiPasd5liEkB8khDxCCKkRQiqEkFOEkO9WfL7jhJA6IYQSQv5Ud7xehJUN1FpscRxXuIClBK13AGOTuzbVykhTaRNVVW7JygbcIHHNhUwiHsNYJjnAfpcanT5ppwq8irR210bHopEi7QZEJum3dg5KjHUwlUv17fisCqqvg9CzdgYoMTVC/YFgwooRweHqGk6SicalSyyE5UMA3ow0NUUtgL5rGFe+qrQDi8AnptdKTVxzZOVhYa4iFNIJxGMEZ5eZHH+UWSE3AjjRWGl2nLKBze+y33d4EqZzjb7/iH5gLc/p4+ekju3Yj5wRD41j2KH4JICTZDMVqhEiRIgQYUvAM7HW6+ZQ+a3bAf/cbr1uSlsxrxdka9LSNo/trn1jLkF06+6icFyyOXCpIR5br7mzlx92YaWO3dlwemfKF21k2RTr9TZ2FT1EmpEIVcWfW67ipuk8kvEY7to/hgurdel6f8HJ75OVDfDfcb0hIdLyBqYLKazVTdj21u8ysvw2vXm9DpHWABAWnjKD65ysTgh5D4CHALwRwP8B8DsAUgB+C8D/2O5jEUI+DOAjYATi7wP4UwB3AvgbQshPhjxfAsCfAAj2WSkiGY/BSMTkZQOmukXNSEgslM7XdCTDMvafQ8c6Jx3XkIo0gC1k131hiuVNKNJaHdv9XYsRkXbDIZNKoOm5cdQ3uXifyht9Oz5rLpGmdn71rJ2bV2L2VKvy8FBOCuRCiTSF7DYNaycQrJRrarT7ChVpmyA7ANbAOp5N4vxKHVccIu1gSJirCLEYwa6CgSevlgHsjByTnQxuASk3ts4K+/bbZnFwMoubZnJ403H9TDj/ppWuAtOLQjopVWurFIOMED8PwADwu4SQnbFqixAhQoSXKQ5P9WIKbt0zSNSMAnnf+m+tZirPfbcLMtHJet3c1o3N1xxlmbqzRQOzxcGN9LwhzyOTje2YS6T1ctLOLlexN69ApLnWTtP5m6nFvE6LvJPhKjEjAmCbywcdNeQtuwugFDjrIfa8cIswZK2dObm1c7nKSL6ZvAHLpgORTJtF17KxtNHC3nG9ObnOKv9JAO8hhPw0pbTm/yYhpAjgPc7jrguc5/x9sNaok5TSbztf/yUwm+n7CSHfRykNJdSGORYh5LVg2XHnAdxPKS05X/9NAI8B+DAh5JOU0kuSp/0FAHcD+FkAv6356wsxlkmiImnYqLZ0CKs4VruDJ2nLXTBsobWzpZ57YyQl1k7NcHMvJrJJlH0fyHKjExo46Qf/Hertrvu7FtLJqLTzBkPWo0izbIpmx9qUtXMqn0LDtNAwu8imElitmYjHiCthDkPP2hmg/FIkhzixGxxurqbc6inS5ASfKmnFPzsbAcSBDhEmVKQp/l4yEEJw80we55drmCkYIARD2yd2j6XxxJUy+3dII9DLHZxImy83YVo2JnObt3amk3Gc+mcn0bWpUqSAH/wc5OfkZjZyipmkS8z6IYtp2CH4X2AbrD8C4P8ihJwFUBY8jlJK33o9BxYhQoQILzfkjARedWQS37q4jjffsjNKgwquIo3N7Vbr7ZGTfCknSsh/f12vm3jF/vFte97vuWcfLqzU8eZbdgmL7PLphJBAApyMNMGaYTqfwkQ2iXNOc+d63cRqzcTefeHri2wqgXQy5m7uc7tkX0aakUDXpoHZ6PPlJh5wlP28hf7ccg33CNrfuSJtT1E8d56UWDu5Wo4p0tj4VmsmpobIKZZhucptp9unSPs9MMXZFwghd3m/QQh5BYDPA5h2Hne98H5nTP+DE18AQCltAfhF578/to3H+kfO37/GSTTnZy6BqdkMAB8SPRkh5JUAfgnAvwHwtOIYQzGWSbph5n7oBDUbyRhMAWE1jIXSVaRJFgXVIRRpfnZ82NY0gPnEvX7rVsdCs2NpS3yLHjKgqtGQGmFnIZOKu+eTTq6gDNO+HILVWhuTOfWMCBEpxKGrhPHvDorQUCTnRKovd1ya2W1Fl2wPz0hTOWZPLdevSEvEyFDECcfRmTzOrdRwZrGKg5PZoY/l3Y2bjRRpgeDKYF53rkpAhyG2iXOBE+v8s1LfhG14LJOQ37N3trXzJNhGIAGQc/59UvInQoQIESJsM373792Hv/7J1+HmXYMZXKOAv9BttdrG9A6Is/CrvyilUvvkVsFIxPHz33UrXi1pe88ZCeE6udWxUDfFa1JCCI7tKriKNK4E25dXW19M5QyXtHKJNF9rJyB3i9TbXWy0ui75dHAyi1Q8hnMrA3orACxjOJOMo5gRz5V49ImfSPOq5byFXVuJhYrTdqupSFOeRVJK/wLAxwC8CsAThJB5QsijhJB5AI8DeADAxyilf641gs3hLc7fnxV87yGw3dLXEkJUKMthjhX0M5/xPcYFISQD9lo+CeDfKYxNGUFEWl2HsIrLLJTsa4bGAoTX+kqtna2ucii/kYiBUqBj9RNprU1Ya2aLRl9QY9lR9OmGgOdSCcSIk+Xj/K5RRtqNh2wq7hJB9a0g0grs5scD6ldrba1AU04KbYWlWaVsQFX5ZSiUDaiOq6CglGuaFpIxKGU89oo/vIo0a2hbJ8cd+1nGxWefWxwqpJ6DTzrGs0l3xzaCGHzSxIm0nZApl3EVaex85RkiwxRZ8Hu2yDoR1pY1SlBKY4p/Rl8dFyFChAgvA0zkUrhrG1VVuugpjDowuzY2Wt0tVRENC25Z5Ki2u+hYdNvKBlRQkFg7OQkpm/vctreI5+c3YHZtNzLkcFHttjuVT2E1gEiTNZxyLPismol4DIenszi/LCbSWAZZWqjIA9gG50Q2OWDb9I6Nzwk3tphImy+z32XvNirSQCn9ITAV1vNg5QP3OX8/B+BHKaVC9dU24oTz9xn/NyilXQAXweyrN231sQghOQD7ANQopQuC4511/j4u+N6/c47zg86xtwxBRFq1pd4+aCSDs8h0CCtCCIrppPSkr2lkUIkWyIBXraKvMJgtprFeN91jlpvsAzyumcUTixEUnN+zz9oZ4YYClzK3OpZ789iMtZOTJvNlttuxUjO12h65+lNUsqGrxHRt1oFEmqq1MzgjLR4jSMbVdsXyPvm/eFwWVHkKQ6hI6w5t6+R447Fp99/3H9YPqed4xYFxAMChyax0QhGBgVvsL+wgIo2XDfCSgJrzt6ri24tiOgnLpn0FJxw7XJEWIUKECBEiSDGWSToKo7arMpoacUYaMNiQyS2VoyxCyPnIPQ7+usnmPq++aQrNjoUnrpTw2OUSDk9lUTTU5pWTuRTWnTI0URt9mItFlHl28648zkmItMVKC7MhcSZTuf5caaCfSOMOlu1SpMmKEGTQnvVRSn8PwO8RQrIAxgGUKaXigI/tB5cEVCTf518f34ZjDfXchJC3AvjHAH6OUvq8wrj6QAj5UQA/CgAzMzM4depU3/ebG20slayBrwPAaqWByVhT+D0/VhbbqDe7A489W2KT/TPPPwtj5bTyuJPo4OzlOZw6tTbwvVK1gY01U2lcly+zD86DX3m470Lx+Dz7kD/zxGNYOaNHppUX2DH/+vNfwUw2hhfW2O94+ezzOLX+otaxUujizOVriFcZt/rck99Gxm4o/W4RRotarYZTp05h4Qo7Hz735Yew2mREzIUzz+NUaYBjV0Kjw5QmDz32LPLrZ3BluYH/n707j2+srho//jlZmu7tTGffGJgZBtn3VaAoKLhvKK6gKKKg6OOO+oj683F9FB5RUVFwR1BAQUBBLLsgDPsyM8wwzMIsnematE26nN8f96ZN27TNTZd805736zWvTJObm9OmaW7OPd9z9q8L5/w70Zb07v/EM88xL7Fh0G2Pb/FifezhB3mxdOzf+5Rfyfn4M2tZ2vVC1m0e2+W9lp596jGSW0bOXDV2eD+bx596hpqW9YNuW7cxSVSUu+66a8yYAHr86TtPPLeehu4Xs26zcXOSaEhz+rk97/+d+s+aR0lu8d7mNm3tgp6+cb8Wj18UYVNbL3XxF2ho2JTXPkp6lBMWRThpUXJa/m1Iv5YmQp9fqfXMlt0ArHvyUVo35r88d6JEQ/DchhdoiGzjiY3eQd+jDz5ALBIsMbrDfw3fdufd1JUN/r4e3eT0sAFjjDFmROkKI693l5cMqatwpCItI5HWn+QrYCLNm5DZS1+fDmr9MlYi7bh96ggJ/P3pndy/YQ+vO3gh0JTTY86rivH0S22Al6yqjEUGnXAeaxVL/3LIjCqulXMrue2pHXR19w4rutne2sUxY0xK95J7gxNp6VVj86pK+2MaradyPna0JikvCQceEpj36Xk/eTbuBJqIbAL2CnCX36nqe3LdvX85ETNS891X//YiUgtcBTwI/G8+QaQTmQCrV6/W+vr6Qbc3tD3Nk3u2MvR6gN67/8HKvRZRX3/gmI/zQMez3Lt907D9RJ/fDQ8+yNFHHMYxI6zzzmbRM/dRUhKhvv6YYbclb7+VfffZi/r6/cbcz87/bIZnn+SIY45lcUaj7x0PbYYnnqT+5ccHziazdhe/fOo/7L3/oRy5fDatj22D/zzGq048pr9xYq7mP3kP5VWlzF86G556jtNfcSKP/Pu+rM+HcUtDQwP19fW0PLqN3z77GAcefpRXRfbvhzj+qMPHVYFUdd/fKZ29iONe/jJa/n4bR+2/N/X12YpVh4sne+Bff2evvVdQf9Lg4toN974ATz/DK046kZocliKrKpF/3sq8RctGfL21Pf4SrHmUE489un8iUDaN7Um4+w6Wr1hF/XHLB912e/OTVO7eGej3PnbnrcxduJT6+pdlvf26bWsoa92R0z7nvtQKD97Lvi87kPoDFwDw603/YU64i/r6E3OOKZuJeimffurE7MdF6dfSRJl3/x39B1JvOO3kvHphTrSqe25nzvyF1NcfyMPJtYTWP8+rXlkfuMKw48ntXPX0GvY/9MhhTZgfu2MdPLd+hHsWnoiEgAuAdwMvAypUNeLfdhjwIeBSVc3vLIQxxpiiNruihD3xgURaoad2gldptau9q//rJgcq0tItThKpnkGrmfpjG6E/bE15lFfsN59f3uedHH/dwYvo2ZZbIm1RbRmN7UmSPb3sak8OWy1TOcKE07T+BFfGFNKV86voU68dR+YxTU9vHzvausYc0jW7soRn/eReWroibU5lrH+lS1vn2BX7qsrG3QmW11WM2RZmR1snC0ZZdjoSF5qzbAC6xtxqwEsZ/09XfY3UrKZ6yHajCbqvsbbPVrH2fbyBDKep6vB1HBOgpixKe7KHnt4+IuGBs9uqSjyZ+9LOkoym/pm/VPn2IptVXsKOtuFPc7Knl1RvX869xEZqvB60J1OmdOJtZ1vSv/TizDaeeCzVpd7S2qZEipJwyIYNFKF0E8z2rp6BpZ3jXBK4ZFY5W5s7eamlC1Xv61wNTO0cefhHaUluFToiQlVpZNTeS53+0s6chw2M0COtLMeY0qpKI7SNMU20JMelotmWgCeSPZRH7fVYjJbMKmNXe5I5lSVOJNHA6yGY7o2Wfm/NZ5nuaP0+4l094+7rN1lEpASvF2w93unvdiDzzNMLwAeARuArUx2fMcaYwktXGO3K0oOrUCpiERK7B44P+6u+JmiYUT4qMqq/MhNpzWNUpAF89vTVrN3ZxlF7zeaElXXctS23x0wXpGxv6WJrc+egApWhMWWzJ56ioiQ8KCewyi9AWb8rPiiRtr21i94+Zdns0T//1FWUsCcxfGlnVSzSf/xXGRt5SFOmy/65nkvvWM/7T1jOV15/wKjb7vD7twUV6JOOiJwsIjeLyC4R6RaR3iz/AjX1UNVXqup+Af59NuPu6XV3w0o7RCQC7A30ABtzCCXQvlQ1AWwDKkVkYZb9rfIvM8/EHg6UAc+JiKb/Af/yb3+3f91jOcSbVf9B+ZAPpMmePrp7NdB0zGxN/dN904Im0mrKo/1N/DPFA063HEgqDO4Xle4vk2tCIdMCf712OtG3sy1JWTScVxKspixKW1c3exIpZleUWP+jIpR+A2vr7M578MRQi2vL2NrcwdZmr4h36azcm1lGQkJIBvf7SutM9RISKAnn/ntfWRrJaWrnWMnD9N+AbHF1dfcGTmpXlUbHHIKQa4+0/gENGbF1dvdSnkczeFN4S/0Dr8UBEtCTrbwkTEd/j7TcB/kMNdoEKu+A2tnk72eAU4CvAvOBKzNvVNUWvEFNr57yyIwxxjghPRky3Sc48KqhSVAZG3wc3J9IK2C13EjVX00d3YRk4Fghm33nV3HPZ1/B999xaKDPnYtnDfRw3tbcwdLZgz+bjDW1s7kjNexntvecCkICz/sTRNM2N3mff5bMHqMiraKE1s5uunsHjt8b44Or5apLI2Mu7VRVfvOA1yrmmoe2jFhVl5ZL/7Zscv70JSKvBe4AXoO3pPPfeAdJQ//dEziK/N3pX56e5baTgHLgflVNZrl9IvY12n3OGLINwPXAL7L8u8W/fYP/9fU5xJvVSAfl6V+46lFeiJnSFR2p3sEfkrvybOpfW1ZCy5ApHDDw4sw5kTbCBMOu7uAJhbSasiglkVB/JdrOti7mV8fySoJ5QxV6aPITaab4pBtZtnf10NI5MYm0VfMr2diYYO0O741lr7qKnO8rIpRGw1kr0jr9hFWQ39XKWDSnRNpYlT/RcIhwSLLHlQqeSPMObEZ+Y+zs7iU23oo0R6t7zOj2989qrpwbbKn9ZCovifRXpAWZiD1U+u9NtmrM9nHsdwq8G7hPVb+mqn1kb3vxArBsasMyxhjjirrKEhrjSbY1dzKvKtZ/fFZIVaWDJ2Q2dXiriCoKeIw4UmP/pkSS2vKSnCbWB7Wk1js5+XxjnN3x1LDVMv0VaSN8ZtiTSA2r4iuNhlleV8H6IQMH0om0XCrSgEGTOxuHLDutHmWwYtr6XXH2JFK85bDFdHb38sCG4T3a03r7lF3tyUmvSLsE6AZOV9Xlqnqiqp6S7V/gKPL3J2A3cJaIHJm+UkRKgf/nf/mTzDuISI2I7JeliizwvoAr/MsvisisjPssx+sbksTriQaAf8D5waH/gO/6m/zbv+5rOX7/w6Q/8A9LpPlfj5bRztSfsBqybCtdfRJ8aWeURKqX1JAEWPoPRq7TzkZc2pkKnlBIExEW15axrdk7W7KrLcm8PLLS4K2vb0qk2BNPOjGZxgSXXtrZ1tVNs//mms+S4UwHLqqhp0+54dFt1FWUBF42HItkn6LrLaEMWPkVixBPjjYds4eQDFR/jqY0EspakdaRGt5kdMy4xqiU60z1kuu3mk70d2b8nehM9Y57aqcpjLcdsYTXHryQj9TnMoB7alTEwnSmBirS8p3sO2pFWlcPle5Oft4b74TqaJqAnJtLikidiHxQRG4QkedFpFNEWkXkXhE51+/Jlrn9UhH5sYg8KCI7RCQpIi+JyD0i8n4RGfbDE5GTROQ3IvKUiOwRkS4ReUFE/uoPgzLGGDNBls0up72rh2e2t/VXQBVaRUmEzu5eevxikeZEilkV0YKuIhqpsX9zoptZ4zyZP5IFNaWEBO5d7w1zWjLk+SmPhhEZuUdaUyKZtWhk5bxK1g2pSNvS1EEkJIMGE2Qz2x9GkTm5M1siLVs7jEyPbW4B4IMn7kM0LDz8YvOI2+6JJ+np0/4VakEESaQdCPxRVf8R+FEmiaq24TWzDQMNInKliHwHeAw4Di859schd3sz8CzwzfHuS1Xvx+t7tgJ4QkR+ICI/Ah7GO3j8tKpumqjvNxcjHZS3BkykpSu7slV+AZQGPKOQTvC1dA6uSkv/waga59LOfBIKmfaeU8GGRi97vq2lM68XE3gjgFO9fazd2V7Q6S8mf5lLO1s7uqkpH/+b60GLvZaJT7/UxoGLawLvLxYJZ+1F1jUJCasOP+GUS4yl0RHiyifBV5p99HdmXLlWpKUTZolkRkVaqtcq0opUXWWMH73rcFbOG3n4xVTzKtIGEmn5LsFMn0TKlkhr7+rO+b2xADoZeyL6MqAlwD7PBH4OHIM3lOlS4M94x59XAtfK4D9MK/Aq41qBG/GGON2EN8Dql8A//NYcmV7h/1sH/A74AXA/3jLVO0Tk6wHiNcYYM4r0CoynX2obsxppqqTfd9PHiE2J1IjN/KfKiEs7E6lJm3RaEgmx7/wq/vHMTgBWLxh8jBUKCZUlEdpHSqTFU/2Jr0yr5leyaU/HoOKZzU0dLJ5VNmZlXToxlzm5c2girSaHirSNuxNEw8K+8ys5cHENa0ZJpKVbOy0YI8mXTZBEWpxc56lOIVW9ETgZb1npW4GP4VXO/RdwlqrmPGUzn32p6qeAc4AdwHnA+4Cngder6uV5flt5GymRlu71FLgibWgizV8qFQu6tNP/A9U6pE9aPN+KtCyJtKAJhUz7zKlg054EnaleXmrtZO85uS+9y5QuC+3q7gvUUN64o6IkTEj8pZ0dE3MmaFldOQcs8panne5PkQyiNDpKRVrQJZRjDhvIPeHkJdImKK5YNIelnbntKxwSyqJhOlID36dVpJmJVF4y8PuVSPbkPZAkHPIGgGQdNuD20s7HgFf5QweGEZEavP5oDwXY5zrgDcASVX23qn5BVT8A7AdswTsue0vG9vcDs1T1Vap6vqperKofxkuwNeANQsjcHuBbqrpYVd+iqh/3H+Pd/mPsAi4eoe+tMcaYgJbXDXwW2m9B9ShbTp1K/2Ay7r+H70mkCj4EIf1eP3xpp1ctN1nSJ/qjYWFFlvYZFbHsJ7lVlT2JVNbVV/vOr6K3T9m0J9F/3aY9iZwSqemprumBAx2pHuLJnkHPT2VsoLXGSF7YHWfZ7HIi4RD7L6zm2R1tjJQS2t7qJ9ImuSLtn3iVWc5R1ftU9TWqOktVy1T1IFX9QbbJmKp6taqKqp4z3n1l3OdXqnqUqlaoapWqnqyqNweIv8GP6T253mckE1WR1t8jbVhFWp9/e9BEmve4zUMSae3+ErNcl8UMJPiGLjkN/sE90z5zK+nq7uPfL+xBFfaZm18iLXOs79CmjaY4iEj/+vuWzhS1ZRNzlurydx3Od956MGcesSTwfWOR8KDG+Wn5VGJWjvCmmNYRIJEWi4b6k+vD4prgSrl4sofSSO6VfBWxMHH/bGN3bx+p3j6rSDMTprwk0n82O5HszXtpJ/hDarJWpDk9bODnwFLgdyIy6NORiNQCVwOzGGiBMSZVvVNVb/J7rmVevyNjP/UZ16eGbutf341XoQYDg5/St2WdEq+q2/AScyHAnTXExhhTxDILEw5bVlu4QDKke5Omizv2xFMFX0U00tLOpo7J7bl9xkHeyf1XH7CAaJY+45Wl2ZNWHalekj19Iy7tBPqXd/b1Kc/virPv/LFXFfRXpMW9lvSN6WmvlQOJtIpYeNCKk2w2NibYx08M7rewmvaunv6E2VA70om0PHqkBTlC+xzwkIh8CfhGkEovM3XSwwSGHpTnv7Rz8C9qsruXWCQUeGlaumR26MCB9B+x2pwTfMOn8YHfI20cH5LTL/qbH98OkHdF2tKMbPvec9xpjG2CSY/rbunoHvScjsfecyry/r0aMWGV19LO6Ihl2uCd/SnLsbqmNBIe1q/Qi6uP0nyWdqZ66OtTQkNKv7t7+0j19AVaUl4Ri/RXDKUHKFgizUyU6jJvapSq0t7V3X+GO699lWZfptDS0c0sR1sEqOofRORU4P14VWTNACLyMHAAEAN+pKq3jLyXQNI/oDEnw4tIGG8wFsATuexcRObhLSlNMjDF3RhjzDhEwiG++7aDeXRLC0cvz7ll5qRKv6+mP5PujiepqyxsRVpFlqWdqur1b5vEZaev2G8+13/0+BGTXBWx7Ce5+yedZoltxdxKQgLrdnotk7Y0d9DV3ce+88f+XFxbXoLIwP53+Ym0zN7lFbHRV9aoKi82dXDKfvMA2M9fsrp2R/uggpe0HW1dRMOSVzI1SCLtK3hLFr8KfEBEHiN77wtV1XMDR2ImRGk0TCwSGrEirTrXJZSjTMfMZwllOoHXMqQirSXPSrmJXtp58JIaSsIh/rxmK7FIKO/y45qyKActrmHTngQHL6nJOx5TWPOqYuxs66KxPenEGbTSESrSurp7+5dN56qqNEKqp49kT2/W6UlBKtJKo9mHDeRTIVpVGkEVEqme/j51aekDi9Ice6SB10w2fb90Qs2WdpqJUltWQqqnj0Sql/ZkDzXjONCtKYsOG+Xe1d1LZ3dvzu+NhaCq54rIPcBFwMGAAIfjHSt+X1WvGu3+ufL7nL3P//K2LLfPAS70H38ucBqwEvg9kHV1gD9U6nV4x8FL8JKB1cDHVHX3RMRtjDEGzjxyKWceubTQYfTrr3rqSNGR6qEj1VvwAXElkRAlkdCgE93tyR56+nRSK9IADl82a8TbqmKRrMMG+hNpWWIrjYZZMbeSJ7e2AAMJtVwq0sIhYVZ5Sf/Szl1tXiItc0hbZYn3Oaa7ty9rFV1bVw+pnj7m+ctB073fnt3R1p9cy7SjtYt5VaXDTuLnIsininMy/r/c/5eNApZIK6BZ5SU0J4ZUfnV6TYsjWX7hshmYjjl8aWdpwP5oMPKwgZaOoHFlr5Tr7O7Luaotm9JomGP2mc0963dz7D51lARcuprpt+ceQ0d3z7gSe6aw5leX8uDGJvYkUmNOmJkKsWgo6xtZZ3cvCwP3IhsYZx2rzJ5Iy3U5WbYhCKqa59JO7/XrLWcb/FpOn3kqC/CO5S3t9O6XXspaMY6qIWMypd/TXtyTQJVx9VKsLovwwu7EoOvSJ79qJ2la10RR1auBq0WkDG8pZ6uqJka/V2Dfwhs4cIuq/j3L7XPwTvb2hwV8D7h4lNUTRw65TzvwflX9zWiBiMh5eP1wmTt3Lg0NDTl9A6Zw4vG4PU+Os+eoOEyX56k16b0tPPjY03RufQ6A3VtfoKFhayHDIhbqY/3GzTQ0eM3/dya8z+A7N2+goWFzzvuZyOeps72LnYm+Yft7vNE7rt607ikadj077H4LS5I8uCHOnf/6Fzc9340AO9c9RsPGHAaZ0c3aTdtoaNjD/Zu8Y6H1TzzCjue8+27f4l3393/eRWXJ8P3tyPJzm10q3PP48+zP8Of4uRc7KYe8fmZBEml7B967KYi6yoFMblprR3f/ss9cpBNWqd4hvch68qv8qoxFiIRkWI+01k5vKmLOcY1UKZfqpSzPSZtpX3rt/lz2z3VccMrKce2npjxKDW5/+DGjm1cVy5jiMr7fq4kQi4RoSkxMj7R0kiye7Mlayp5I9vQPzRhLaTTU3+cwrbtX6e3TvHq3peMaKn1dsB5pkf4zZm1+Ii3I30BjRpM+cbNpdwfAuJZe1JaV0NLRMui6lv62B24u7RxKVTvxJnlOKBH5OPAp4DngvSM89nPephIGFuNNZ/8a8HIRea2qDhuUpapXAFeISCne8e35wK9F5ARVPX+keFT1Z8DPAFavXq319fXj+fbMFGhoaMCeJ7fZc1Qcpsvz1N3bx0X/upU5i5azavVcuPs+TjjiYOpfNr+gcc166F9U19VSX38YAGs2N8M993P8kYdQv3p4JdVIJvJ5umnX42zfsHvY/nY/shUeeZzTTjy2fzJrpsbKLdz9pydYsv+R7Fr/FAcu7uX0U1+e02MuWfsAKNTXH8e/b32O6PqNvO60+v62Urv+s4XfP/cEhx51TNbBfo+82AT3PDDo53bQxodobE9SX3/isO2/+nAD+y+ppr7+8Jziy5RzIk1VXwy8d1MQdZUx9vhN+tJaO7sDLREpGaEXWVd3b6A+RWkiQm15dPjSzo5UoDPu/b3bhsTV0d0zrh5p4JV+/vjdR4xrH2Z6mJ+RlM01qTSZYtHhlV/g9yLLsyJtpMb+QSYFZpva2enHGbx3Wzqu4b2iBpZ25r6/ipIIm5s6Bu0z16XtxowlfQIoPZVqPJVjsyu9noyq2n+gmO7d4npF2mQSkQuAy4BngFdmS4hl8odCbQYuE5GdwB/wEmoXjnKfLuBZ4CIRiQEfFpE7VPVPE/RtGGOMcUg0HKK6NEJzR6r/83Khe6SBPwws42RyenVZIQchVJVm70fWlPB+biMtOz1+5RwArl+zjTWbW3jvsXvl/JgLqkt5bEsLALvavWWXmb3ZB/rJZR840JTwjvkzf277zq/kgY176O1TwhlLOPv6lG0tnZy2f35J1PzXrxlnzaksYXd8+NLOIIm0gSWUgz8kJ3vyW9oJXgPBocMGWjq7A51xj4RDREIyfGlnHgkFY0aSuY5/1byx1/RPtlgkNOy1CPn1IqssHSOR1tWT8wTC0iwJvvTX41naOSwm/80y6NTOdAIuvc+hS0aNyVf6fSu9JHM8FWl1FSX09CltnQO/+0H7hxaCiBwtIn8WkQ0ikhSR3iz/xhwOMMK+PwFcDjwFnOJP7gziVv+yfpLvY4wxpsikh4rtiRc+WZVWOSRplV5dNpnDBsaSbpMytEtCU6KbaFhGPPG+uLaMo5fP5oq7NpDq6eN1By/M+TEX1ZaxvbWTvj5lV1uSedWDk5zpNi0jDRzIluTbd34VqZ4+XtwzuPNEYzxJqqePpbPya+NjibRpaE5ljD2J5KBf+uaOFLMqgiyhTDf1H/4hOZZnwqquoqT/D1Zaa0ewpZ2QPamQT0LBmJGkB0WIDG5wWSixSHjY73x/L7KSYH/Gq2Le6y3bG5CqEk/15NwjLduwgU5/QmbguEZJ8CXyXNrZ4Sfg0t9rrt+XMWNJV4pt2j3+irQ5/pnw3YmBSvL+idaOVqSJyNuA+/GWUQrwEHB3ln/35LHvzwE/AB7DS6LtyiPExf5lkERePvcxxhhTZGZVlNDckWJ7q9fGZWiyphBGqkib7GEDo6mMRelThh3rt3amqCkrGVQpNtTFr30Z86pinHXUUg4bZaDBUItnldHdqzTGk+xs6+ofGjAQ0/AJp5n29FfyDdwvXSCRHnyQtsVfubJk9vAlorkY9VOFiGSvmRudqqp9WimguooSurq9aWLpX7bd8RQnBChb7e+RNixh1Zd3n6G5VTGe2tY66DqvIi1gIi0aHpTgyzehYMxIastL+PUHjqa2PDrqm8RU8RJWg/8c9/cim8AllB2pXlTJeWmnl+AbOvgj34q0kRNp8TyXdiZS3lm09PdqFWlmoqQPbJ/b0Q4MJMPykZ4WtieeYsVc77r0YJ6gU3mn0CVAAnitqt47UTsVkS/jLcd8BHjVaMs5ReQY4ElV7RhyfSXeklCAvw257WTgHlXtG3L9CuCL2e5jjDFmeqmrKGFrcyfbWjqYWxXLOsV+qlXGIrywe+CYuqkjRUkkRPk4WxeNK6b0sXmye1ALpeZE95hDlg5dWstDXzw18GMurvVa6mxt7mRLcwcn7zt30O0VYyTSmuIpSqOhQfGunFcJwPqd7Zx+4IL+67c0e4cPS7P0WsvFWJ+W8vkEWfhPnTNcep33nniSypg3Ira1szvQgX7JCEs7u7p7h2WGczWvqpTG9oETy319GrhHGkBpJDSoR1o6oVBeYvlbM3FOGvKHu5CyVaTl24usMmPYwFDpN6UgSzuH9ivMO67+YQMj90grC1iRlj6L1t7VQ0igooAHI2Z6KY2GvSrrRIqasmjOr5ls0mdNmzIq0po7uomExOXf2ZXA1ROcRDsbL4nWi1fJ9vEsJzI2+ZNCAb4A1IvIXXi90TqApcAZQC1exdw3h9z/L0CLiDwIbME7Dl4BnO7//4eqevtEfU/GGGPcs7i2jAc3NlFXWcLi2vyW9U20ilhk0Mnk5kSKuorRq74mW6W/jDKR7IWMTjfNHalJW3K6uNZLaj22pYWu7j72qhuc5BptOBl4CcjMajTwfrZLZpWxbtfQijRvRtKSPJd2jnrkp6pW4lOE0me3d8dT7FVXwZ5EctD1uRi9R1p+B/Zzq2IkUr0kkl4PpvZkD30afCqZV5E2EFe+H9yNKRaxSIhUT9+gZuT9vcjynI6ZrfKrPeASyNJoiFRv36DmnV2p/CrSKkoiiIxQkdaVXtoZYH8ZPRTau7wBCi5UF5rpY/GsMvYkUiwa50H4nIz37LSWjm5nKmJHsAMYnvUen/R0+DDwiRG2uQu42v//z/Gq4o7C62tWDjTjVbNdC/xSVYf+QfkK8CrgWOD1/mPtBG4ErlTVv4/7uzDGGOO0pbPLaU/28MxLbRy/Yk6hwwHSjf0H3labEpOXrMpVZbodzJBj89bObpbmuRxyLOmkVsNar/hm2ZCpoBVjJdISqazLYfedX8X6ne2DrtvS1MG8qljeOQQr4ZmG5mZUpAHsbvcOzgNVpPVPxxy8bKsj1ZN3ielcv5JtdzxJRSzCbj++uQEr3LweaQNx5dvc3JhiUdrfs3Agkd2ZZ8KqNBqmJBzK+gaUfqMMMrXTi6u3vyK0I5Vfgi8U8pqWZk2kpXooiXiDRnJVUTJQ+t3W1W3LOs2EWzqrnCe2trK8bnwHk7MqBpZ2pjUlkgU/gB7DdcDrRaREVVNjbp0DVb0Eb8lortv/jYDLMFX1MgaWfRpjjJmB0kmg5o5uVvjL/gqtMhahq7uPnt4+IuHQiAmhqY4JvKWdmZo7Uv39pCdaRSzC3nMquGf9bmBgWebA7ekquWCJtFXzK7l3/e7+ny94SzvHkxC0irNpKF151phOpPmXQRJpIuIlrHqHNxLPN2ubXhK6q92Lp7F9PIm0jIq0PJubG1Ms+itEu4dXYuaTQK4sjWTtkRZ4aacfV2YT0g4/rnyWulWXRmntzB5Xrsm9tPS0w5bObtq7ch+gYEyujlruNc89bFntuPYTDYeoKYv2V4+D9/7oQvPjUXwFaAGuFZHc59obY4wxBbZ6/sA6xf0XVo2y5dQZWmm1J5EKtJpsMgw09h9cWNPS0T2pJ/sOXOwl6WZXlLCopnTQbbFImGhYiCezt/IfsSJtXhWp3j427Rloq7qhMcE+cyqGbZsr+2QxDc2tjBEOCS+1eOt+0wm1uQGbIZcM6UUG3of38VakNY47kRaesISCMcUgFk0vte4FvATReJY0V8Yiw8q0YWBpZ9CKtMxBCB3+PvL5OzGrIkpzx/DilkSyt/8MVO778t5EmztSefViNGYsZx29jDlVMU7bf/6491VXOXiqdWM8yREBplxNNVXtEJHzgH8BG0WkBWjNvqmumNLgjDHGmFHsVVfO4toytrd2ctw+biztTA/fa+3sprbcOyYY2utrqg30VR44yd2Z6iXZ00fNJB5Xv+Xwxdz0+Eu84ZBFWVtcVMQigSvS0pM71+9sZ+W8Slo6UjS2J1k1P/+KREukTUORcIiFNaVsa/YTaX7Cak5VwF5kQxqcd/f20d0bfEpg2ryREmkBE3yxaGjQi8d6pJnprjSSTlhlVH4l86/88vowjDxsIPceacMTaQm/QjSf4R+zykto7hhekRZP9vhLNfuG32nEffkVaR0p9iRSvGxBdeB4jBlNaTTM6w5eNCH7mlMR6z/ppao0ticDn2SaSiLycuA2vL5kPXiN/rOtvXa2yZsxxpiZSUS4/qPH09bZPakJoSDSyZ+mRIr51aXEkz0Fr0gb6Dc8cJyfPuE9mRVpp6yexz2fPWXEHrQVJdkTaV3dvXSkerMm0lbOq0QE1u2Mc8ZBsN4fPLBqfv4ViZZIm6YW15ax1U+kbWvpZHZFSeAPtkN7kXXm2dw8bVZ5CdGwsKOtC/DOuEfDErhSJBYJ0ZTIsrTTEmlmmhpckebpSOVf+VUZi9CWrRdZwKWdsWxLO8dTkVZewuamjmHXx7vSSztzb8WUfoNvSnQ70WfCmNEsqCnl0S3NgPc67OruczqRBnwbrzz2fcDvVTX3LLcxxhhTYPOrS5lfXTr2hlMkcyXFnkS6v3lhj12rsgwbaPFPeKcr6CbLaL3LKmPZCwLSP7e6LMf8ZSVhls4qZ50/cGDtDu9y1Th65FlTqWlqyaxytvlLO7c2d7I0j7Gusag3KTCtK88m4mmhkLC4tqz/g/KutiRzKmOBp5INrZTrHGdcxrguFhkYNpDW0V/5Ffz3vqp0hKWdeQ4b6OoZXJFWEgkRDQd/e5lVHqUpMTxZ1trZ3d/zLFfVZVFC4vWIbOnotkSacdriWWVsb+mit0/zbnswxQ4B/qCqv7UkmjHGGDM+6ZUUTYludvvHAYVe2lkaDREOyaClnS1+RVptAQciVcTCJFLDP8c0+58hZo1wzH/Q4hrWbG5GVVmzuZm6ihIWj2PyuiXSpqnFs8rY2dZFqqePrc0dLJkVfCJFSTiU9YP7eCq/ltVVsNlv8retpSOvX94RK+WsIs1MU6XRdOVXZsIqWPVYpqrS6IhLO6Nh6a80G0ssS1wdqR4q8kxq15aX0N7VQ8+QISf5JNLCIaGmLMoLjQmg8Gf1jBnN4toyevqUXe1d/VXb86vcOVOeRRxoKnQQxhhjzHTQX5GWSPUPHyr00k4RoaIkPGjYQLoFy6yKwi2JrYhFsg4bGK0iDeC4FXVsb+3ihd0JHnmxmcP3mhW4oCeTJdKmqWWzy+lTeHFPgq3NnSzJqyJtSOVXd/4VMGl7zS7vr0jb0tTJsjxGzsaioazDBqxHmpmuslakJfN/PVbGsk/tjCd7qIhFcn5TKcvWIy3Zm1d/NBjoD9EyZHJnW2c31XmUkM+qKGHdrnZ/305X95gZLv0evbW5ky3+e+R4RrJPgVuAkwsdhDHGGDMdVMUiREJCU0eK3fH00s7CH7tWlUb7V6wAtHT6FWllhUvyVY4wbKDJT0COtArl5Su9wRJX3LWBF/d0cMKKunHFYYm0aWr/hV5j7Zuf2E6qpy+vRnqxSIjUoJ5M409YLZtdTmtnN43tSV5q7czrg8LQpZ0dAfs6GVNs0hViyZ7hFWn5JK0q/WEDqjro+niyJ+dlnTDwmkv/bfD+35N3sr02Y0BAWm+f0p7sCVyRBrCopoyNfkXaolqnq3vMDJdOpG1r7mRzUweRkLCwxunf2c8D1SLyIxHJf3a8McYYYxARZlWU0JxIsTvuRkUa+Msok1l6pBVwSMNIUzvT089HWhK7fE4FRy+fzbUPb6UkHOI1By0cVxyWeZimVs2vpCQc4tqHtwDwsoX5JdIyl391dec/jS9txTzvePu2p3egCsvn5JNICw1JKIy/Us4Ylw1MxxzcGzDduyCoyliE7l4l2dM3KDHe3hUskZZ+zXUkB78ey/NMamcOCEhr86vTasqi3mzAAJbVlcPz/v/dru4xM9ziWu/388U9HWxu6mRRbRmRPPoMTqFrgHbgfOB9IrIOaM2ynarqK6c0MmOMMaYIzS4voSmRIhoOUV0aGddn7okytLF/S0eKsmi4oCvBRho20NyRIhwSqkpH/rl9/U0H8rWbn+aNhy5m3jiHTRT+2TGTIhoOcdiyWh58oYmasiir86xI2xOf2B5phyypBeC3D7wIeE3/8okr2dOHqiIiJJI9REK593Uyptike6R1pgZXpFXk+QZb7b/BtHf1DHojbO3oDnSGKZ10y2z42ZHMv0daOpHWnFGR1pqZSGsPtr/M5JkNGzAuKysJs8+cCp7Z3srmpk6Wz3G+yKs+4/8VwGEjbKcjXG+MMcaYDHOrYuzH/RO/AADFKUlEQVRsT9LTpyzOo7/5ZKgsjfYfi4PXI62Q1WgwUCWXzgWkNSVSzCovITRKkcHqBVX87oPHTkgclnmYxt533HIAzjpqaV5ntr0llFma+pfk/2tTVxljr7py1u5spyoWYe85wUfOxqJhVKG71zs+TwTs62RMsUmfkerIbOqf7M17Um2ln0gbejYnaFP/dFyZ5dWJVP490tKNS1tGSqQFdNTy2QAcsrTW/j4Y5x2wuIaHNzWzbmc7B+dxkmkqqWoox39WKm6MMcbkYMmsMrY2dfBSS+e4pklOpMphSztTBZ3YCVAZi9Kng1fqgLe0c6RBA5PBKtKmsdcevJDDlr2CBXmWLcYiIVK9mUvJvBdR2TjLTN982GIuvWM9rztkYV7L0jL7RZVEQsSTvXlXwBhTDCpi6SWUmQmr/CvSKmNeUireNTiR1tKZoqYs9w/wJZEQ0bD0L68Gf2pnbLwVaQNnvvoTaeVREgH3d/iyWn74zsPyqnw1Zqodudcsbnr8JQAO36u2sMEYY4wxZkotnV3OnkSK5o4Ux+4zvkb4E6UyFhn0eaGlo5vaPE5uT6TK/pU13YOKCpo7UlO6AsUSadPconFks2PR0LCeTDC+pZ0AF5yykkOW1nLs3vn9gRhIpPVRRfqDu/0qm+mrLBpGZHDlV0eql/I8E1bp3gFtQyZ3tnZ2Bz7LVF4SGZTg6xhHRVp5SZiScGjEpZ1BE2kiwusPWZRXLMZMtdcdvJDv/WMtNWVRTvAnSxljjDFmZkgPHupT2DePtkyToTIWHfT5o7kjxeoFhY2tv0VNsod5GdfvSaR42YLqKYvDsg9mRGXRyKCeTB3dE9PUPxoOccrqeWNvOIJYxHv89OTOeNISaWZ6ExEqSiJDKr96865IS5c970kMJKy6unvp6u4LvISyoiQ8OK5x9EgTEWZXlPRP3YHBibSX8tqrMcWhrjLGPZ89BUH63+dcJSIn5bqtqt49mbEYY4wx00G6lzjAgYunLiE0mspYmHiqh74+JRQSryKt4Es7/RY1Q1bWNCWsIs04orwkTEdqoJFfV6oXEQre1D/mN15P+om9jlRv3kvJjCkW6ddjWiLZk3cfgLpKbyz0Hn+8NgxMx6wOmEgrj0X64+rrUzq6e8eVbJ9XHWNX+0Bc6X5p+fRIM6bYFPrgNIAGch8kYG/QxhhjzBj2qivnqOWzSCR7OXCRG21JaspLUPUGlFWWRmjqSDGnwAO8+hNpGZVyPb19tHR0WyLNuKE8FqZPvcqv0miYjlSvv8SssE2704m89LJTL6HgxmQTYyZLRSxCIjmkIi3PSszasighIWvlV9C+B5lxdfX0ouol1/I1r6qUrc0d/V/vjqeoKo0UdMy2MWaYr5E9kVYLHAUcD9wErJnCmIwxxpiiJSL88bzj6FMddfLkVJrtDwJr6kiR6u1DFeZUxQoaU2aPtLQW/3NMXaUl0owD0svGOlK9lEbDdHb3jrs/2kRIJw8SfhVMPNnTn5k2ZroqLwkP6ZHWk/fUzlBImF0RY08io/Irz+mYFRmVcumE2niGf8yrjrFmc3P/143xJHMrC/uGbYwZTFUvGe12ETkH+CHwxamIxxhjjJkOQiEhhBtJNBgYBNaUSNHlrwarqyjscXl1qfdZpT1jaWeT365mKivSCrtGzzgt/SE9/eG9M9Wb9wf3iTR0XfR4mq4bUyy8HmmDm/qPJ2E1p3JIL7KO/BJp5SUDFWnpEuvx9CycX1VKUyJFssfb5+72JHMskWZMUVHVq4EHgP8pcCjGGGOMyVM6MdWcSPV/bpgzhVVf2WRb2pmObfYUtsiwRJoZUWZFWvrShYq0qoxJHWDDBszMUB4L978W+/p0XNMxwSt9zhw2kK5Iqy0PurQz3J/g6++zVpp/P7P51V7SrNHvk7Y7nmROVdH0jTLGDHgcyHkogTHGGGPc0l+R1pFit99b2Z2lnVkq0qYwyWeJNDOidJVX/7KtVE//L24hVca8D+nxrh66e/tI9fRROY6EgjHFoKIk0l8dmp6gO54hG3UVsUHDBtJvjnUBq78yK9LSb2hBBxZkml9dCsDOtnQiLWUVacYUp6VYCxFjjDGmaGVWpPUn0gq8tDMaDlEaDQ2qSGvqmPqlnXaAY0ZUHk0n0gY+JFe5kEjzY0gke+jwP8CPp7m5McWgIhbOSFiNv/KrbsjSzt3tSUqjocDLRStjAz3S2vy4xvN3Yq5/lmtXWxepnj5aO7stkWZMERGRMPB+4G3AvQUOxxhjjDF5Ki8JUxIJ0dSRQhCiYaG6rPCfuytj0cEVaf5nmllTuLSz8D8F46z+pv7Jnv7LhTWlhQwJ8BJ8It7Szon44G5MMSjP6JHW1uldVo0jkTanMkZ7soeubm+YyO54krlVscBTectLInSkeunr04EE3zgq0pbO8ibwbmnu4KWWTgAW1ZblvT9jzMQTkY0j3BQB5vuXKeDiKQvKGGOMMRNKRJhdXkJT3JvauaCmNPBnhclQVRoZXJGWSFJdGiEanroFl7a004yo3K9M6eweaCTuwnTMUEioLIkQ7+qhNc9Jg8YUmwq/R5rqQMJqPAnkOr/0Od0nLd8llJUZU3QHEnz5x1VTHmV2RQkv7E6wtdlLpC2ZZYk0YxwTAiTLv27gSeCnwOGqen/BIjTGGGPMuM2rjrGzPcnW5k4WO3Jyu6o00v95CKCpoztwe5rxKnxWxDgr3cg8cyKfK039K0sjxJPdlkgzM0Z5SYTePiXZ09dfiTmuXmR+den2Fu9NsbE9ybK68sD7Sb/2Wju7ae/qRoRx9yzce04FGxsTbGnuAGDp7OBxGWMmj6ouL3QMxhhjjJl8S2aV8dz2djpSvZywck6hwwG8E/nxrsEVaVPZHw2sIs2MInPYgKqSSLrRIw38F0/SKtLMzJHuXdaR6u3vCTCe1+MS/4zSNn/55O54Mq+KtBp/ymdLRzdtXV7Vaig0vpLvvedU8MLuBJt2JygJh5hf4OlAxhhjjDHGzERLZ5WzcXeCHW1dLHZklUg6F5C2J56yRJpxR+awgc7uXvoUZyrSKmIR2m1pp5lBKv1+aO1dXsIKxpdIS78Rbm3upKu7lz2JFAuqg/dArM2oSGvr6h7XAIS0VfMq2dWepGFtI6sXVBGZwn4HxhhjjDHGGM+SjJUhq+dXFTCSAZWlkcHDBhIpZk/hoAGwpZ1mFJFwiJJIiESqp7900oUeaTDQYNASaWamGJSw6hz/1M7ykgizK0rY1tLZX5W2dHbws0y1/ptWS0c3bZ0TU7V67D51AKzd2c47j1427v0ZYyaWiPx3Dpv1AW3As8BdqpoaY3tjjDHGOObQJbX9/z9ocU3hAslQXRrt75HW16c0JVLUVVoizTikoiRMZ6q3v3TSlaWdVaURXmrppLWzm2hY+gcjGDNd1WYsoWzv6qEkHKI0Or7f+8W1ZWxr7uxv6p9PL7L+uDpTNCWSE/ImdsCiauZUlrA7nuL0AxeMe3/GmAl3CaAZX2eu5x56vQJ7ROTjqnrNFMRmjDHGmAmy/6JqDl5SQ01ZNK9+ypMhvbRTVWnr6qanT/NqUTMe02K9jIgcLyK3iEiTiHSIyBMi8gkRCfwpM599icjZIvKQiMRFpFVEGkTkdWM8znIR+YmIbBSRLhHZIyIPisingsY8mdJlk+lEWsU4m4hPlLqKGHsSKVo6uqkpizoxhteYyTSQsPKa+k9EUnv5nAo2NMbZ3OQ19c9nOma6GrSlo5s9iRR1FeN/E4uEQ/zp/OO54j2Hc9IqN5qaGmMGOQX4C96Uzl8A5wBn+Je/9K+/ETgT+BZQCvxGRE6c+lCNMcYYk69wSLjxoyfw6w8cXehQ+tWWR+lTaOvsYXc8CTDlFWlFn0gTkTcCdwMnATcAPwJKgB8Agc585rMvEfkecDWwEPg58FvgIOAmEblwhPu8Gnga+ADwqL//3wOtwJuDxDzZasqitHZ2DyztdKQibU5ljJaObna0dk7IB3djXFdT5r05tHYMJJDHa/+F1Wxt7uTBjXuoLo3k1SOtNBqmNBqitbObPfHUhJ0NWj6ngtMPXGhJcmPctBdwGnCUqp6nqr9W1b/7lx8CjgFeDZSp6heBE/Aq0z5duJCNMcYYk49QSJw6Jk8nzfYkkuyOe50j5k5xRZobWZE8iUg1XvKqF6hX1Yf9678M3Am8TUTOymUpQT77EpHjgU8BG/AOJpv9678LPAJ8T0RuVtVNGffZB/gTsAc4VVXXDYnDqWZftWUltHSkaO7w1iCnq2IKbU6V9+J5ZnsbqxdUFzgaYyZfOnHW3NHNnkR+EzaH2n+R99q5+YntHLvP7LzfIGvLStje2kU82TPlZ4OMMQXxSeBaVX0y242q+riIXAf8F/BbVX1SRP6Gl1AzxhhjjMnbbL+QpimRyqhIs6WdQbwNmAtck058AahqF/Al/8uPTOK+zvcvv5FOovn32YRXzRYD3j/kPpcAlcBHhibR/Pt25xjvlKgpj9LS2U1zh5fpnTXF0zBGkq5C29mWZH6VVaSZ6a8kEqIyFvGWUMYnpqHmEXvNIhzykmfpBv/5mF8d4+mXWgGYY4k0Y2aC1cCOMbZ5yd8ubT1QO1kBGWOMMWZmqKtIV6Sl2N3uJdKm+jNIsSfSXuFf3pbltruBDuB4Eckl05LPvka7z61DtklXm70N2AXcIiJHi8gnReQzIvI6EXHuE2hNWZS2zm6aE14izZWKtLkZybP5eSxHM6YY1ZRFaelMeb3IJuDNojIW4b9O25cDFlVz1lH5T8dcMqucjY0JAObZ69GYmaAdOG6MbY4H4hlfV/j3M8YYY4zJ2yw/kdaU8D4XhQRqp7jgp9gTaekzndkqu3qAF/CWr+4z0fsSkQpgMRBX1e1Z9rfev9w347oDgTLgKbyeaw8C3we+A9wErBeRo3KIdcrUlkVp6eimuaOb8pIwsYgb0zH3mVPR//+9HJkeYsxkm1URpbE9SXPHxDT1B7jglJX87eMnsqAm/wTY4owhBXvlMfnTGFN0bgFOFpH/8Y+H+olIhYh8E6/f7C0ZNx0IbJq6EI0xxhgzHdVlJNJ2x5PMrijpX2UzVYq6RxpQ41+2jnB7+vraSdhXPo89z788GegEzsWbalUJXAB8Fq9S7WWqujvbTkXkPOA8gLlz59LQ0DDCw0+MPdu9cbKPP7+ZslDfpD9ePjpeWkdDw4ZChzGieDzu5M/NDFYMz1O0u4tHN7WhCk3bX6Sh4aVChwRA5+6BFekbn/wPmyfpjawYniNjz9MM8QWgHvgccL6IPAHsBOYDB+Md+2wGLgYQkYXASuCKAsRqjDHGmGmkNBqmvCRMUyLFSy1dLKwpG/tOE6zgiTQR2YQ3/SlXv1PV9+S6e/9SAwU1sfvK3D6ccfkFVf2l/3UT8DkRWQm8BfgQ8M2sO1P9GfAzgNWrV2t9fX3AcILZVbGFP659gnbKWDA7RH29O5Prv12xmf9sauZ9rz/YqSkiQzU0NDDZz5MZv2J4nu5uf4ZH73sBgFccfQj1+80b4x5TY8GONn7zzD3sM6eCU19RP2mPUwzPkbHnaSZQ1R0icjTwLeAsvOqztE68aeafV9Vd/vbb8ar4jTHGGGPGra6yhN3xJNtaOlk5t3LKH7/giTS8iZddAbbPLMFIV33VZNsQqB6y3WiC7mus7bNVrDVn/P+GLPe5AS+RdvTIYU6tudXe8rF1O+O80pEP7WnvOGoZ7xhHXydjis2SjCWUyxxa0rzfgmr+98xDOGjJSH8OjTHTjao2AueKyPl47TFqgDbgOdcGJxljjDFmellcW8aWpg62NndQv+/cKX/8gifSVPWV47j7WuBIvD5kj2TeICIRYG+gB9g40ftS1YSIbAMWi8jCLH3SVvmXmT3X1mb8vyVLDOlE29TXJo5gSe1AKItqnQnLmBlp77kDrYiWznInkQbw1iOWFDoEY8wUEZGNwK2qeoGfNHuq0DEZY4wxZuZYXlfBNf/ZAgzu1zxVin3YwJ3+5elZbjsJKAfuV9XkJO1rtPucMWQbVLUJeMz/8sAs90lft2nscKfGIkukGeOMY/aezdyqGK89eCElkWL/822MKWJzya3a3xhjjDFmwmWuzlm9oGrKH7/YP4n9CdgNnCUiR6avFJFS4P/5X/4k8w4iUiMi+/mNb8e1Lwaa5n5RRGZl3Gc53vCAJHDVkPv8yL/8hr/v9H2WAJ/0v7wm63dbABWxgaLF/QrwC2qMGVBeEuHuz5zCpe84tNChGGNmtqeBFYUOwhhjjDEz04GLBlrKHLBw6tvLFHUiTVXb8Brzh4EGEblSRL6DV/V1HF5y7I9D7vZm4FmGNPPPZ1+qej/wfbyDySdE5Aci8iPgYWA28GlV3TTk8X+JN6nzlcDjIvJ/IvIL4HG8aVf/p6oNefw4Js2nTtuXZbPLOWrv2YUOxZgZr6wkTDRc1H+6jTHF7/+A14vIwYUOxBhjjDEzz7H71LFyXiVvOWwxNeXRKX/8gvdIGy9VvVFETga+CLwVKAWeB/4LLymV85TNfPalqp/yx75fCJwH9AFrgO+q6s1Ztu8TkTPxKtbeD3zQv8/jwE9U9bc5f/NT5GOvXMXHXrlq7A2NMcYYMxNsBe4A7hORnwL/AXaQZbK5qt49xbEZY4wxZporiYS4/ZMnjb3hJCn6RBqAqt4HvCbHba/GG8s+7n1l3OdXwK8CbN8DXOb/M8YYY4wpJg14STPBO9k42knL8FQEZIwxxpiZRUQK9tjTIpFmjDHGGGOmzNcYPXlmjDHGGDNtWSLNGGOMMcbkTFUvKXQMxhhjjDGFYh2rjTHGGGPMhBKRkIi8sdBxGGOMMcZMNEukGWOMMcaYCSEie4nI14HNwPUB7lcnIh8UkRtE5HkR6RSRVhG5V0TOFZHQkO2XisiPReRBEdkhIkkReUlE7hGR94vIsBFeInKCiHxHRP4jIo3+fV7wJ7WvHP93b4wxxpiZwJZ2GmOMMcaYvIlIGHgj3vTyU/FO1CreZM9cnQn8BNgO/AsvETcfeAtwJXCGiJyZMUF9BfBu4EHgRqAJqAPOAH4JvE9ETvMHPKX9GZgL3A/8DugBjgPOBc7yt38g0DdvjDHGmBnHEmnGGGOMMSYwEdkH+CBwDl7SC2A38FPgF6r6YoDdrQPeAPxNVfsyHuNi4CHgrXhJtT/7N90PzMrc1t8+CvwDqPe3vzbj5h8Av1HVl4bc52LgG8DPgIMCxGyMMcaYGciWdhpjjDHGmJyISEREzhSR2/GSX58HZuMt4xTgL6r63wGTaKjqnap609DEmKruAK7wv6zPuD41dFv/+m68CjWAVUNu+/bQJJrv20AncKCI1AWJ2xhjjDEzj1WkGWOMMcaYUYnIKuBDwNnAHLyk2RrgauD3qtokIsMSWxOk27/sGXUr+peZvsb/8okc968Z++4NFpoxxhhjZhpLpBljjDHGmLGsxUs47cJbInmVqj492Q8qIhHgff6Xt2W5fQ5wIV5iby5wGrAS+D1wc44PcyZQBfxbVVvGGbIxxhhjpjlLpBljjDHGmFwocAvwp6lIovm+BRwI3KKqf89y+xzgKxlfK/A94OKMwQQjEpG9gR/iVaR9aoxtz8MbqMDcuXNpaGjIJX5TQPF43J4nx9lzVBzseSoO9jxNHcnhGMM4SkTa8c4QG7fNwWu+bNxmz5P77DkqDvY8Tay9VHVuoYMQkS8CHwD2xktWrcVb1vkbVd3ub9MHXKmq503QY34cuAx4DjhBVZtG2TYMLAbeDHwNeAZ47Rj3mQfcDawGLlDVHweIzY7BioP9PXKfPUfFwZ6n4mDP08Qa8RjMEmlFTEQeVtUjCx2HGZ09T8XBnif32XNUHOx5mt5E5NV4vdJeD0Txeor9A/gVcA0TlEgTkQuAy/ESYq/0hw7ket+zgD8AP1LVC0fYZh5wJ3AAcJGq/l/A+Oz3vAjY8+Q+e46Kgz1PxcGep6ljUzuNMcYYY0xOVPXvqvo2YClwMfAicAZe4kqBQ0XkiPE8hoh8Ai+J9hRwSpAkmu9W/7J+hP0vBBqA/fEq0QIl0Ywxxhgzs1kizRhjjDHGBKKqu1T1W6q6Eq/B/5/wpmseCTwkIo/6VWWBiMjn8IYZPIaXRNuVR3iL/cthUz5FZAlwF7AfcH6Q5ZzGGGOMMWCJtGL3s0IHYHJiz1NxsOfJffYcFQd7nmYYVf2nqr4DWAJ8FlgHHAIEXS75ZbzhAo/gLeccsc+LiBwjIuVZrq/E66sG8Lchty3DS6KtAM5V1fH8rtrveXGw58l99hwVB3ueioM9T1PEeqQZY4wxxpgJJSL1wAdV9T05bn823vCCXrwpmq1ZNtukqlf729+It3TzLmAz0IG33PQMoBa4H3i1qsYzHuMFYDleou7mEUK5WlU35RKzMcYYY2YmS6QZY4wxxpiCEpFLgK+Msdldqlrvb/9a4F3AUcB8oBxoBp4ArgV+qaqDlnaKSC4HvaeoakOQ2I0xxhgzs1gizRhjjDHGGGOMMcaYHFiPtCIjIktE5Jci8pKIJEVkk4hcKiKzCh2b8fjPiY7wL+jkMTMOIvI2EfmhiNwjIm3+c/DbMe5zvIjcIiJNItIhIk+IyCdEJDxVcc80QZ4nEVk+yutLReSaqY5/JhCROhH5oIjcICLPi0iniLSKyL0icq6IZD2esNeTmU7sGMx9dgzmDjsGc58df7nPjr/cFSl0ACZ3IrICr+fHPOAvwHPA0cBFwOkicoKq7ilgiGZAK3BpluvjWa4zk+dLeA2v48BWvCltIxKRNwJ/BrqAPwJNwOvxJsidAJw5mcHOYIGeJ9/jwI1Zrn9q4sIyGc4EfgJsB/6F15NqPvAW4ErgDBE5UzPK3O31ZKYTOwYrKnYM5gY7BnOfHX+5z46/HGVLO4uIiPwdeBXwcVX9Ycb13wc+CfxUVc8vVHzGIyKbAFR1eWEjMSJyCt6BwfPAyXhvQL/L1vxaRKr97WqAE1T1Yf/6UuBO4DjgnapqZ9wmWMDnaTnwAvArVT1nCsOc0UTkFUAF8DdV7cu4fgHwEF6T97ep6p/96+31ZKYVOwYrDnYM5g47BnOfHX+5z46/3GVLO4uEiOyDdwC3CfjRkJu/AiSA94pIxRSHZoyzVPVfqrpecztj8DZgLnBN+k3H30cX3hk7gI9MQpgzXsDnyRSAqt6pqjdlHsT51+8ArvC/rM+4yV5PZtqwYzBjgrNjMPfZ8Zf77PjLXba0s3i8wr/8R5YXUruI3Id3kHcs8M+pDs4MExOR9wDL8A6wnwDuVtXewoZlRpF+jd2W5ba7gQ7geBGJqWpy6sIyI1gkIh8G6oA9wAOq+kSBY5qpuv3LzAmJ9noy04kdgxUXOwYrPvaeUTzs+MsddvxVQJZIKx6r/ct1I9y+Hu8gbl/sIM4FC4DfDLnuBRF5v6reVYiAzJhGfI2pao+IvAAcAOwDPDuVgZmsTvP/9RORBuBsVd1ckIhmIBGJAO/zv8w8aLPXk5lO7BisuNgxWPGx94ziYcdfDrDjr8KzpZ3Fo8a/bB3h9vT1tZMfihnDVcAr8Q7kKoCDgJ8Cy4FbReSQwoVmRmGvseLQAXwdOAKY5f9L9/WoB/5py6um1LeAA4FbVPXvGdfb68lMJ/b7XDzsGKw42WvMfXb85RY7/iowS6RNH+Jf2hr3AlPVr/rr2XeqaoeqPuU3IP4+UAZcUtgITZ7sNeYAVd2lqv+tqmtUtcX/dzdeNciDwErgg4WNcmYQkY8Dn8KbXvjeoHf3L+31ZKYD+312hB2DTVv2GiswO/5yhx1/ucESacUjnT2uGeH26iHbGfekG0KeVNAozEjsNVbEVLUHbww42Gts0onIBcBlwDPAKaraNGQTez2Z6cR+n4ufHYO5zV5jRcqOv6aWHX+5wxJpxWOtf7nvCLev8i9H6t9hCm+Xf2llz24a8TXm9yHYG6+Z58apDMoE0uhf2mtsEonIJ4DLgafwDuJ2ZNnMXk9mOrFjsOJnx2Bus/eM4mbHX1PAjr/cYom04vEv//JVIjLoeRORKuAEoBP491QHZnJ2nH9pf7jcdKd/eXqW204CyoH7bcKN0471L+01NklE5HPAD4DH8A7ido2wqb2ezHRix2DFz47B3GbvGcXNjr8mmR1/uccSaUVCVTcA/8BrlnrBkJu/incG4Neqmpji0EwGETlARGZnuX4vvDMIAL+d2qhMjv4E7AbOEpEj01eKSCnw//wvf1KIwMwAETlGREqyXP8K4JP+l/YamwQi8mW85raPAK9U1d2jbG6vJzNt2DFYcbBjsKJm7xmOs+OvwrHjLzeJqvWZKxYisgK4H5gH/AVvZO0xwCl4ywmOV9U9hYvQiMglwOfxzl6/ALQDK4DXAqXALcCbVTVVqBhnEhF5E/Am/8sFwKvxzpbd41+3W1U/PWT7PwFdwDVAE/AGvFHSfwLervZHc8IFeZ78EesHAA3AVv/2g4FX+P//sqqmDxTMBBGRs4GrgV7gh2TvrbFJVa/OuM+bsNeTmSbsGMx9dgzmFjsGc58df7nPjr/cZYm0IiMiS4Gv4ZVr1gHbgRuBr2ZpNmimmIicDJwPHMbA6PUWvDLc3wC/sT9cU8c/qP7KKJu8qKrLh9znBOCLeMtASoHngV8C/6eqvZMT6cwW5HkSkXOBN+ON/J4DRIGdwAPA5ap6z0g7MfnL4TkCuEtV64fcz15PZtqwYzC32TGYW+wYzH12/OU+O/5ylyXSjDHGGGOMMcYYY4zJgfVIM8YYY4wxxhhjjDEmB5ZIM8YYY4wxxhhjjDEmB5ZIM8YYY4wxxhhjjDEmB5ZIM8YYY4wxxhhjjDEmB5ZIM8YYY4wxxhhjjDEmB5ZIM8YYY4wxxhhjjDEmB5ZIM8YYY4wxxhhjjDEmB5ZIM8YYY4wxxhhjjDEmB5ZIM8aYHInIOSKiInJOoWPJhYhc7ceb/vf5Ibc3iIhO8GNePuQxL5nI/RtjjDFmZrHjr5we046/jJlCkUIHYIwxhZDHAcz7JyWQqXEZ0ALcOwWPdQuwG1gOnD0Fj2eMMcaYImHHX5PGjr+MmUKWSDPGzFRfzXLdJ4AaBg58Mj0GvAD8G9g+iXFNhktVddNUPJCq3gLcIiL12IGcMcYYYwaz469JYMdfxkwtS6QZY2YkVb1k6HX+koEaRj/waZ28qIwxxhhjpi87/jLGTAfWI80YY3I0Uo8OEdnk/6sUkR+IyBYR6RSRx0TkTf42ERG5WETWi0iXiGwQkQtHeaxXi8gtIrJbRJL+9t8VkdpJ+L4yY0v68X9bREqybKt+b48FInKliGwTkd5i6VtijDHGmOJix192/GWMa6wizRhjJkYUuB2YDfwFKAHeCfxZRF4FfBQ4BrgVSAJnAj8UkUZV/WPmjkTkv/GWPjQBNwO7gIOBTwOvEZHjVLVtAmP/PXCiH1sb8Brgs8A8svcmmY23xCIOXA/0ATsnMB5jjDHGmFzY8ZcxZspZIs0YYybGImANUK+qSQAR+Q1wN3AdsAE4UFVb/Nu+DzwHfB7oP5ATkVPwDuIeAF6T3t6/7RzgKv/2T05g7CuAA1S1yX+cLwKPA+8TkS+o6o4h2x8E/Ab4gKr2TGAcxhhjjDFB2PGXMWbK2dJOY4yZOJ9IH8QBqOo9eA1yZwGfyzwoU9WNwH3AQSISztjHx/3LD2Vu79/narymu++e4Lg/lz6I8x8nAfwO7z3iyCzbp4BP20GcMcYYYxxgx1/GmCllFWnGGDMxWlR1Q5brXwL2Bh7Jcts2IAws8P8PcBzQDZwpImdmuU8JMFdE6lR1z/jDBuDhLNdt8S9nZbltk6rumqDHNsYYY4zJlx1/GWOmnCXSjDFmYow0TaoHQFWz3Z4+oxjNuK4O72/zV8Z4vEpgQg7khp559aVjC2e5behSA2OMMcaYQrDjL2PMlLNEmjHGuKUVCKnq7EIHMgotdADGGGOMMRPIjr+MMTmzHmnGGOOWfwOzROSAQgdijDHGGDND2PGXMSZnlkgzxhi3/MC//LmILBp6o4hUiMixUxyTMcYYY8x0Zsdfxpic2dJOY4xxiKr+U0Q+D3wTWC8it+BNnqoE9gJOBu4FTi9clMYYY4wx04cdfxljgrBEmjHGOEZVvy0i9+GNYn858Ea83h3bgJ8Bvy9geMYYY4wx044dfxljciWq1rPQGGOmIxG5Gjgb2FtVN03xY9cD/wK+qqqXTOVjG2OMMcYUih1/GTP9WY80Y4yZ/l4QEfWXLEwqEblcRBTvIM4YY4wxZqay4y9jpilb2mmMMdPXjcCmjK/vnYLHvAXYnfF1wxQ8pjHGGGOMK27Ejr+MmdZsaacxxhhjjDHGGGOMMTmwpZ3GGGOMMcYYY4wxxuTAEmnGGGOMMcYYY4wxxuTAEmnGGGOMMcYYY4wxxuTAEmnGGGOMMcYYY4wxxuTApnYWsdraWl25cmWhwzBjSCQSVFRUFDoMMwZ7ntxnz1FxsOdpYj3yyCO7VXVuoeMwg9kxWHGwv0fus+eoONjzVBzseZpYox2DWSKtiM2fP5+HH3640GGYMTQ0NFBfX1/oMMwY7Hlynz1HxcGep4klIi8WOgYznB2DFQf7e+Q+e46Kgz1PxcGep4k12jGYLe00xhhjjDHGGGOMMSYHlkgzxhhjjDHGGGOMMSYHlkgzxhhjjDHGGGOMMSYHlkgzxhhjjDHGGGOMMSYHMzKRJiJ1IvJBEblBRJ4XkU4RaRWRe0XkXBEJDdl+qYj8WEQeFJEdIpIUkZdE5B4Reb+IRMcZzy9ERP1/NgLKGGOMMTOOiHxbRP4pIlv8Y7MmEXlURL4iInUj3EdE5GwRafC37xSRF0TkWhHZd4T7nC0iD4lI3D/+axCR103ud2eMMcaY6WJGJtKAM4GfA8cADwKXAn8GDgSuBK4VEcnYfgXwbqAVuBH4X+AmYC/gl8A/RCSvCagi8nrgA0A8n/sbY4wxxkwTnwQqgNuBy4DfAT3AJcATIrI0c2MRKQX+ClwNLAB+j3dMdzdwJDAskSYi3/O3X4h3LPhb4CDgJhG5cMK/I2OMMcZMO3klf6aBdcAbgL+pal/6ShG5GHgIeCvwFrzkGsD9wKzMbf3to8A/gHp/+2uDBCEic/EO4v6IdwB4ch7fizHGGGPMdFCtql1DrxSRbwAXA18APppx0/8CrwO+CXxphOO0zK+PBz4FbACOUtVm//rvAo8A3xORm1V104R9R8YYY4yZdmZkRZqq3qmqNw094FLVHcAV/pf1Gdenhm7rX9+NV6EGsCqPUH7mX16Qx32NMcYYY6aNbEk0X/pEZf+xloisAM4H/gN8cZTjtEzn+5ffSCfR/O02AT8CYsD78wreGGOMMTPGjEykjSF90NUz1oYiEgZe43/5RJAHEZFzgDcB56vqniD3NcYYY4yZQV7vX2Yea70T7zj2V0C1iLxHRL4gIueN0m/2Ff7lbVluu3XINsYYY4wxWc3UpZ1Z+X3O3ud/OewgS0TmABcCAswFTgNW4vXkuDnA4+yF1/vjt6p6Y77xxruVR15sZr8FVVTE7Kk0xhhjTPETkU8DlUANXq+zl+Ml0b6VsdlR/mUN3lLNzGEEKiI/AT6uqr3+PiuAxUBcVbdnedj1/mXWAQUGmhIprnt4C2ceuZTZFSWFDqefxRWMq3EZY8xoXPvbJapa6Bic4Teg/RRwi6q+Nsvt+wHPZlyleP05Ls6yfGCkxwgBd+ItTzgwoz9HA16PtFWq+vwo9z8POA+gZMHKIxaefSlhgX1nhThxSZSjF4SJhGSku5sCiMfjVFZWFjoMMwZ7ntxnz1FxsOdpYp1yyimPqOqRhY5jKonIDmB+xlW3Aeeo6s6MbR4AjgV6gTuATwObgKOBn+Kd6Pyqql7ib78I2AZsU9UlWR4zCqSAlKrGRoir/xhs7ty5R1x7baDWuDnrSPWytbmTJbPKKC8JT8pj5GNHWxeN7UnmVsVYUF1a6HD6jRZXIf8eFePPqxDi8Thl5RXsSaQAqKsoIezIZ5lkTx/bW7tYWFNKLOLOQq5CxJXLa8l+XsFMRlwT8TfP3oMGjHYMZok0n4h8HK9K7DngBFVtGmXbMN5ZzTcDXwOeAV472n0y7vsp4Hv+9rdkXN9ADom0TPusWq0/+8vd/OfFJv7+1A427elgYU0pnzt9P9546CIGDx41hdLQ0EB9fX2hwzBjsOfJffYcFQd7niaWiMy4RFqaiMwHjserRKsCXqeqa/zbHsKrStsK7KuqnRn3OwRYAySAOaqaCpBIS6rqmEfoq1ev1rVr1473W8zqxG/fyZbmTpbOKuOez7mz0vQtP76PNZtbOHxZLdd/9IRCh9NvtLgK9feoKZHijZffy5bmTo7ZezZ//PBxUx5DNhsa47z1J/fR0tHDCSvq+N2Hji10SDQ0NHB/x3x+dvdGAL5wxn58+OQVBY7K846fPsCDLzQ59RxCYeLK5bVkP69gJuNv6kT8zbP3oAGjHYO5k5ItIBG5AC+J9gxwylgJMVXtVdXNqnoZ8GG8M6Jfy+FxVgHfAK7KTKLlKxqCU/efzxfOeBl3fqqeq95/FHMqY3zij49x1s/+zZamjvE+hDHGGGNMQajqTlW9AXgV3tLNX2fcnB4WcFtmEs2/3+PAC3jJt5f5V7f6lzUjPFzNkO0KpioWHnTpip7evkGXLmhKpNjROtKMisK54q4NbGn2fi0PWTLSr9zUu/j6J2np6KGmLMLX3nRgocPp9/Am76PX4tpSzjxyaYGjGdCZ6hl06QqLK5j2ztSgS1ds8/9GpC9dYe9BuZnxiTQR+QRwOfAUXhJtR8BdpJvT1uew7QH4E6FERDP/4VWjAaz3r3tTkCBCIeGU1fP4ywUn8K23HMTTL7VxxmX38KdHtmJVh8YYY4wpVqr6It7JzgP8frUA6XKwlhHulk60lfn7SOBVpFWKyMIs26cngq4bd8Dj0JRI0dLpfQgtLXGn/+2GxjibmhIAlEbd+XB1xV0beMlPpB2516wCRzMgMzF0fv1Isy+mXjrBsdfsclbMdWcJfvqDcV1FiRO9j1znWkIhzdW49iS6B126oq4iOujSFZFwaNClyW5G/3RE5HPAD4DH8JJou/LYzWL/MpfU+ybgFyP8SyfwrvO/3pRHLIRCwllHL+PWi05k/0XVfPq6x7nw94/S0uFWBt4YY4wxJoBF/mWvf/lP/3JYWY2IxBhIjG3KuOlO//L0LPs/Y8g2BeFqYuji65+krbOXmrII33zrwYUOp5+rCStLDE0PlhgKxtW4LGFV/Fw8mePOqa4pJiJfxluO+QjwqjF6oh0DPKmqHUOur8RbEgrwtyG31QALgdb0dChVfQz44AiP0QAswBtckFOPtNEsnV3OHz50LD+9ewPf/8c6Hnmxme+/4xCOXzFn7DsbY4wxxkwhf6BTy9CVAf6Qpq8D84D700Oa8FYEbAReLSKnqertGXf7Mt5SzbuG7O8K4L3AF0XkxoyBT8uBC4AkcNWEf3MBuJoYskomM5ksYRVMXUWUne1J5xJDrsblYsKqKZFid9wrdHEpLhcTVuDmyZwZmUgTkbPxkmi9wD3Ax7M05t+kqlf7//8CUC8idwGbgQ5gKd7Zy1rgfuCbQ+7/ZryDsV8B50z095CLcEj4aP1KTlw5l4v++CjvvvJBzjtxHy46dRXlDi0XMMYYY8yMdzrwXRG5G9gA7MGb3HkysA9e5f6H0hv7AwTOBv4B3CoiNwAv4g0gOAloxJ+wmXGf+0Xk+8B/AU+IyJ+AEuAdwGzgY6q6aTK/ybFYYshMJktYBWOJodzZsvRgrPo4GBdP5rjzWz619vYvw8AnRtjmLuBq//8/x5v8dBReL7RyvN4bjwDXAr9UVbe6KmY4aEkNN3/s5Xzjb8/y07s38uc12/jAy5fz2oMWsmx2uU33NMYYY0yh3QH8DDgBOATvRGUCr2fZb4D/G7p6QFXvFZEjga8Ap/j32env5+uqunXog6jqp0TkCeBCvERbH96Ez++q6s2T8p2ZSeNqYsjVuCxhFYyLCStLDAXjamLIqo+L34xMpKnqJcAlAbb/G0OWbuZwn6sZSMTlsn19kP0HVV4S4RtvPog3H7aYH9yxju/ctpbv3LaWyliEmrIoJZEQ1aURFtaU8Yr95vGmwxZTEnHnTcMYY4wx05eqPoW3vDLo/Z7BqygLcp9f4a0YcI6rCRhX43I1MeRqXC4mrHr7lJZOr/WhVTKNzRJDwbiaGLLq42BcfA9y56+VmRJHLp/N7z54LFuaOmhY18iGXXHaurrp7lVaO7t5clsrtz29gyvu3sAV7zmCfedXFTpkY4wxxpgZwdUEjKtxuZgYakoMDPiaXx0rYCSDbWiMs7W1E4DKUnd+Xo3xJC+1eok0q2QamyWGpgcXE0PgblwuvgdZIm2GWjq7nPceu9ew61WVhrWNfPbPT/CWH9/PHz50LActqSlAhMYYY4wxM4uLiSFwMy5XezJdcdcGdrYnATh2n7oCRzPA1cRQIukl0aySqbi5moBxNS4XE0PgblwuvgfZ2j0ziIhwyn7z+MsFJ1BTFuXsqx5iS1PH2Hc0xhhjjDF5czUx5Golk6s9mWyJWzCqCrhXyeRqAsbVuFxNwLgaVzoh5FJiCNyNy8V+he5EYpyyqLaM35x7ND29fXzkd4/Q1d1b6JCMMcYYY6YtVxNDrlYyuZqwsiVu+bHEUG5cjcvVBIyrcbmYGAI343K1X6E7PyHjnH3mVvL9tx/KU9vauOSvTxc6HGOMMcaYacvVxJCrlUyuJqxcrRhyNq4+ryLNEkO5cTUuFxMw4GZcTYkUu+NeL0WX4nI1YeXqyRx3njnjpFP3n88Fp6zgmv9s4bf/frHQ4RhjjDHGTEuWGArG1bhcrRhyNa5ISABLDOXKxbgsMRSMVR8H097p/W4tril16mSOO7/pxln/ddpqTlk9l0v++jQPbNhT6HCMMcYYY6YdSwwF42pcrlYMuRpXmksJGFdZYigYVxND/96wG4AF1TGnqo9dTVi5+rfe/mKZMYVDwmXvPIy96so5/7eP8Ojm5kKHZIwxxhgzrbj6YcHVBIyrcblYMQRuxtWUSPUv7XQpLlcTVpYYCsbVxNDONm+qrypOVR/be1Aw7vzFMk6rLo1y9fuPprY8yruvfJDfPfgivf4bnzHGGGOMGR9XPyy4mIABN+OyJW7BXHHXBrr9CkyXKpk+c93jtHX2Ul3qVsLKEkPBWGIoGFfjcvFvPYA7s7WN85bOLue6Dx/HJ/74GF+84Sl+/K8NnLTvHJbOLqcqFqE0GqasJExZNExteZT9FlRTEbNfMWOMMcaYsbj6YcFFriasLr19nZNL3FxNDP17w25Om+1eJdO25k4AyqJhpxJWLieGdrYnnUvAuBqXq3/rXY3LVZblMIHMqy7ldx88hr8/vYNrH97KLU/uoLUz+x9zETh4SS1vOGQRbztiCTVlbv0RM8YYY4xxgauJoTUvNrN2RxvgViWTqwmrO57dCUB1acQSQznY2ZaE2e5VMrmagHE1LlcTMK7GZXLXlEj1Vzy6xhJpJjAR4fQDF3L6gQtRVZI9fbR39dDV3Utndy+dqV4a25M8ua2Vfz63k6/f/AyX3r6Oc0/cm/NPXuHUgZgxxhhjTKG5mhi66JpHSfZCaSTkVCWTqwmr2rIIL7XCktpSSwzlwJaSBeNqXCYYFwfLuJqwuuKuDexs9+JyLYdgiTQzLiJCaTSc9Rf71P3n88nT9uWpba1cfufzXHrHem54dBv/8+aDOGHlnAJEa4wxxhjjHlcTQ1Ux7/hunznlTlUyuZqwcjXRYXFNDy4mYMDNuFxNDG1ojLOhMQ5Ae7K3wNEMcDVh9fCmJgDnlqWDDRswU+DAxTVc8d4j+P0HjyEkwruvfJBv3/Zcf3NRY4wxxpiZrLbMO7dtiaHcuBqXCcbFBAy4G5eLPdIsMRTMxdc/2V/le9lZhxU6nH6uJqzSr8HldW6dzAFLpJkpdPzKOdzy8RN559HL+EnDBt7+0wfY0tRR6LCMMcYYYwrKEkPBuJrosLiCcTEx1JRIsaXF6ynX0tlT4GgGbGiMk+j2fk6LaksLHM0ASwwF05nyfqf2nV/J4Q4t43c5YeUqe7c2U6qsJMw333IQl7/rMJ7fGec1l93DDY9uRVULHZoxxhhjTEG4muhwNS4XEzDgZlyWGArmirs20N7lVVad+rJ5BY5mwMXXP0m8q4+asgjfe/uhhQ6nnyWGpgdX/9a7GhdYIs0UyOsOXsQtF53Ifgur+OQfH+fj1zxGY7t769iNMcYYYyabiwkYcDcuF5vUW2IomHRiKBwSpxJD6UqmxbWlfOK01QWOZkA6YbXXbLcSQ64mOiyuYFz9W+9qXGCJNFNAS2eXc815x/HpV+3LrU9u5+Tv/ov/ueVZntrWSqrHrT8uxhhjjDGTxcXEUFMi1f//+dWxAkYy2IbGOFtbvQqrylJ3fl6uVgy5mhhq7/R+v0rCIacSQ11+wqqmNOJUv0JLwARjcQXj4nsQuBsX2NROU2DhkHDhK1bxmoMW8r+3r+MX977Az+7eSDgklJeEiUXCVJdFWFxbxqkvm887jlrqVMNIY4wxxpjxcDUxlNms+9h96goczYDPXPc4bZ29zvU+SieGFteUWmIoB+lEQk+fW+1dXE10uBpXXUWUne1J5xIdFlcwLvbpbEqk+pejl5a4l7ZyLyIzI+0zt5Ifvetw9sST3Pv8bp7fFSee7KGru4+2rm7W72znK399mivv3cgV7zmCAxbVFDpkY4wxZkYQkZMmYDebVHXzBOxn2nE1MfTvDbsBWFAd4/z6lQWOZsC2Zi/pWBYNO5WwcjXR4Wpc6YRCJCSFDmUQVxMdrsblYgIG3IyrKZHqfx26FNeGxjgbdycAt+K69PZ1vNTaBcCRDvXfS7NEmnFKXWWMNx66OOtt92/YzaeufZy3X/EAfzjvWA5eUju1wRljjDEzUwMw3rKRrwJfG38o04+riaGdbV41mipOVTK5mlCwuIJx6QN7JhcTMK5qSqT6/064xNXEUGaVr0srrC6+/kniSa+PoksJqzue3Ql4k1ddOpmTZok0UzSOXzGHGz56Am+74n7O/uVD/PXCl7N0dnmhwzLGGGNmgrv8f0EJ8N8THMu04mqiw9W4XE10uBqXq1zr9ZXmYi8yVyevWmIomHS/Qteqj9ODLFyrPq4ti/BSKyypLXXqZE6aJdJMUVlQU8pvzj2GN1x+Lx/53SP86fzjnfrDbYwxxkxTDaqaV0WZiFgibRSWgAnGxUQHuBuXi0s7MxNDLvVI29AYZ0NjHIB2PxHjAlcnr1piKJj034bldW5NXk2bVxVzMmHlKnvHNkVn7zkVXPqOQ3lqWxtfvvEpVN15AzbGGGOmoaeBXQW8/7RmCZhgXIzL1YqhDY1xEt3ez2lRbWmBoxmQmRiqLnWnruPi658k2QulkRCXnXVYocPp5+rkVVcTQ+m4ZpdHnUoMufq33tW4XPxbn8kSaaYovfJl8/n4K1Zy3SNb+dX9mwodjjHGGDNtqepBqnpFoe4/3bn6YSG9pNOlpZ1NiVT//+dXxwoYyWCuVgxdfP2TxLv6qCmL8L23H1rocPplDrKYX+1Ogi89eXWfOeUc7tCSQFcnr1oCJhiLKxgX34MyWSLNFK2LTt2XV+0/n6/d/Ay3P7Oz0OEYY4wxxgTiamJoQ2Ocra1ehVVlqTsfYi69fV1/T6Zj96krcDQDMhNDLlUMpRNDi2tKnaoYyhxkEXZoaqerCQWLKxhXEzAWVzCutz1wMypjchAOCZeedSgHLq7hgt+t4eYnXip0SMYYY4wxOXM1MfSZ6x6nrbPXud5Hrk5xc3XCqSU6grG4gnE1LtcTMC5pSqT6/z649PNa82Iza3e0AW4NssjkzqJ0Y/JQXhLh1x84mg/+6mEu/P2jNKxt5CP1K5w662aMMcZMJyLyvhw26wPagGdVdX2O+/02cCSwLzAH6AReBG4ELlfVPVnuczzwJeBYoBR4Hvgl8ENVzdotXETOBi4A9gd6gUeB76nqzbnEOZFcTQxta/aq0cqiYaeOqVyd4ubqhFNX4xqc6HCnqb8pfk2JVH9i2yUuD7JwcfLqRdc82t+v0KWTOZkskWaKXm15Cb/94DH83z/X87O7N/KnR7ayuLaMpbPLqCqNUhYNUxYNUxoNURGLsHpBFYcvm8XS2eWFDt0YY4wpRlcDOU/6EZGngQtU9Z4xNv0ksAa4HW84QQVeguwS4DwROVZVt2Ts943An4Eu4I9AE/B64AfACcCZWWL5HvApYCvwc6AEOAu4SUQ+pqqX5/p9TQRLDAVjlSbTg/XWCsbFuCwxFIzrgyxcqz6uinnP3T5z3BpkkckSaWZaKI2G+ezp+3HOCcu56fHtPLq5mV1tSbY0ddDV3Utndy9d3X0kkj39Y7YPXlLDu49ZxpsPW0JJxA7IjDHGmBy9H3gT8EbgDuBeYCcwHzgReCVeFdl9wOHA24G/i8hxqvr4KPutVtWuoVeKyDeAi4EvAB/1r6vGS4T1AvWq+rB//ZeBO4G3ichZqnpNxn6Ox0uibQCOUtVm//rvAo8A3xORm1V1U/AfiZkKloAJpjjicuPjqMv9Cl2cvGqJoWBcH2SxpNatPorFwI2/XMZMkHlVpZz78r2BvbPe3t3bx/O74tyzvpHr12zjc39+kv/75/N85fX786oDFkxtsMYYY0xxagTOAM5Q1b8PvVFETsdLpP1cVf9XRH4J/AP4HPCukXaaLYnmuxYvkbYq47q3AXOBX6eTaOl9iMiXgH8CHwGuybjP+f7lN9JJNP8+m0TkR8CX8ZKEXxkpxolWHIkOd7gal4sVfMWTGOopbEA+l/sVxrv6qC51a/KqJYaCcfVvl8WVPyvDMTNKNBziZQurOe+kFdx60Ylc9f6jqCqNcN5vHuHDv3l40EGHMcYYY7L6InB9tiQagKreBlyP17sMVf0n3nLNk/N8vNf7l09kXPcK//K2LNvfDXQAx4tIZgZhtPvcOmSbKeFqs26LK3drXmzuX+JmE07H5mpiyPoVBuNqosPVuFz82wUW13hYIs3MWCLCKavncdPHXs7nTt+Pfz3XyGsuu4cHNw7rZWyMMcaYAYcAG8fYZiOQua7mabwBAmMSkU+LyCUi8gMRuQf4Ol4S7VsZm632L9cNvb+q9gAv4K282MffZwWwGIir6vYsD5seiLBvLjFOBFenpW1ojLPZ//BeWuLO4hVXE1auNsW2xFAwtWXe77qL/QozL11hcZmZTlRz7hVrHLN69Wpdu3ZtocOYNp7a1sqFv1/D5qYOPv7KVVx4ysoJObBtaGigvr5+/AGaSWXPk/vsOSoO9jxNLBF5RFWPLHQcmUSkCbhHVd84yjZ/BV6uqrP9ry8D3qeqY64BEpEdeP3W0m4DzlHVnRnbrMNb6rlKVZ/Pso/7gOOB41X1ARFZBGwDtqnqkizbR4EUkFLVrOvgROQ84DyAuXPnHnHttdeO9a2MakdbF41+xVBFSYR95laMa38TZePuBImktzxqbmWMBTVu9GVau6OdVG8fIRFWzqsklkN/23g8TmXl5CZrnt8Vp7O7l7JomJXz3EkMFUtcU/Ec5ROXK1yJa+jz5EpcQ7ka13M72unu7SMaDrHfgqpJe5ygr6epiiuIZE8fz+9qp0+hJBxidQHjOuWUU0Y8BnPnNJMxBXbg4hpu/viJfOmGJ7n0jvXcs343l77jUJvuaYwxxgz2L+BNInKeqv5s6I0icj7wOrzlnWn74U3KHJOqLvD3Mx8vGfYt4FEReZ2qrskxRknvLsft+x9+lLh+BvwMvJOZ400Yv+XH97Fmcy/VpRFuuOAEZ6pzvv/De3hiWxsLqmPcctFJzlTn/M/3G1i3K8G+8yr4x7vqc7rPVCT2v3PpXTyzI87+Cyq55e35rl6eeMUSlysnXz73jTvY2Z5kflWMB99e+HjSXIlr6PPkSlxDuRhXUyLF5y69m53tSQ5eXM35Z504aY8V5PW0oTHOhQ33EO/q47ClNZx/1ssnLa4g3vHTB3jwhU5KIyF+/6FjnerBl8mdOnJjHFAZi3DpWYdx6TsOZd2Ods647B5+fvdGurrdGetsjDHGFNjngRbgJyKyVkSuFpFv+5drgR/5t18M/QmxU/CmaeZMVXeq6g3Aq4A64NcZN7f6lzUj3L16yHZjbV8zZLtJ52pT7PREzNnlUWeSaACtnT2DLl3hak8miysYV5cEuhhX8QyycIP1KwzG1UEWQ1lFmjFZvOmwxRyx1yy+eONTfOOWZ/lxw/Octv98DlxcQ01ZlJJwiKrSKPOrY6ycV4mIjL1TY4wxZhpQ1fUicjzwY7wE2aohmzQAF6hqun/ZLqAKyOuTs6q+KCLPAIeKyBxV3Q2sBY7E62n2SOb2IhLBG9/dg9/LTVUTIrINWCwiC7P0SUt/D8N6rk0WVxMKrsbl4mRMsLiCqimLsLM9SU2ZOx9DrV9hMJYYCsb6FQbj6nvQUO68IqeQiNQBbwZeCxyE13w2BTwJXAVcpap9GdsvBb4AHAHsBcwC9gAbgF8Cv1XVnJ7pidyXmVxLZ5fz6w8czQMb9vD7hzZz61M7uPbh4atSFtaU8rFXrOKdRy+1hJoxxpgZQVXXAq8UkSXAoXgVXW3Ao6q6dci2CiTH+ZCL/Mt0ifidwLuB04E/DNn2JKAcuFtVMx/3TuC9/n2uGnKfMzK2mRIuJhTA3bhM7lxODL24JwFAZ3ffGFtPnSvu2tCfGCqNhgsczYCLr3+SeNL7k3ekQ5U5lhgKprYswkutbg6ysGR7/tyObvKcCfwE2I7X52MzXlPbtwBXAmeIyJk6MIlhBd7B2oPAjUAT3hKDM/CSX+8TkdP8KVFjmch9mSlw3Io6jltRh6rS2J6kPdlDsruPeLKHTXsSXPfwFi6+4Uke2LiH77/9EKIOHbAYY4wxk8lPmuXU+2w0IrIf0KKqO4ZcH8Kb2jkPuF9Vm/2b/gR8GzhLRH6oqg/725cC/8/f5idDHuYKvETaF0XkxvS+RGQ5cAFesm9ogm3SuLpU0dW4XKxSaEqk2NLifXhvcejn5XJiKD3h9LKzDit0OP0e3tQEeIkhlyavdvrLvxdUx5xKWFXGvN+pBdUxpxJDxZKAMaNz9T1oqJn6W7YOeAPwtyGVZxcDDwFvxUuq/dm/6X5gVua2/vZR4B9Avb99LuObJnJfZgqJCPOqS5mXcd3Re8/mzCOW8OOGDXz372sJC3z/7YcSClllmjHGmOnPT4C9DKhU1d+MY1enA98VkbvxqvT34J3kPBnYB9gBfCi9saq2iciH8BJqDSJyDd7JyTcAq/3r/5j5AKp6v4h8H/gv4AkR+RNQArwDmA18TFU3jeN7CMTFagDrfRTMpbevo73Lqxg69WXzxth66vx7w24AqmJhpxJDrvY+sn6Fwbia6HA1LhdPAoC7cbn43pjNjCydUdU7VfWmocks/yzoFf6X9RnXp4Zu61/fjVdVBsP7g4z02BO2L+MGEeGCU1bymVev5sbHXuI7f19b6JCMMcaYSSUih4rIw8DTeEmrqzNuO1lEOkTk9QF2eQfeRMw6vBOKn8E7sdkEfBU4QFWfybyDqt6Il2i729/2Y3h92P4LOCtjZUHmfT4FnIOXmDsPeJ//PbxeVS8PEO+4rHmxmQ2NcQAqS935sGC9j4LJXOL2idNWFziaATvbvOewvCTiVGLI1Q/uFlcwLg5AAHfjSlfIuVYp52Jcri5Lz8adn5o70n+pxkxli0gYeI3/5RPjedCJ3JcpjI/Wr2B7aydX3LWBpbPLePcxexU6JGOMMWbCici+eAMFwsBleA3/z8jY5G68BNjbgJty2aeqPoW3vDIQVb2PgeOnXO/zK+BXQR9rIl10zaP9S9xcqhiy3kfBWO+jYFxdemdxmcnSlEixva0LoL/fnQusX+H42asygz/l6X3+l7dluX0OcCEgwFzgNGAl8Hvg5oCPNWH7Mm4QES55/QFsa+7kv//yNItqyzhltTtl/sYYY8wE+QreksgjVPVZEfkKGYk0VVUReQA4qlABuq4s6p1pXza7zKnEkPU+MpPJ1aV3FlfuXO0LuKEx3l/l2+5QwuqKuzY4ufzb+hWOn70bDfYt4EDgFlX9e5bb5+AdPKYp8D3g4mzLB8aQ175E5Dy8pQjMnTuXhoaGgA9rJts7liobtwsf+fV/+MIxpdSFOu15KgLxeNyeJ8fZc1Qc7HmaEV4JXK+qz46yzWa8k4QmCxc/IIPFFZSrS+9cjcvVSjkX49rQGCee9J4/l/oVutoX8DPXPU6yF2KOJYbS/QoXVMecWv5t/QrHzxJpPhH5OPAp4Dm8iU7DqOpz3qYSBhYDbwa+BrxcRF6rqk25Pl6++1LVn+H1EGH16tVaX1+f60OaKXTY0V28+Uf38eOnlM8cVs7r7HlyXkNDA/Z6cps9R8XBnqcZoZaxJ3WG8KrWTBYufnAHd+NytSLNxbiaEin6+rxz8i49jxsa42z2l+iWlrjz83K1X+HF1z9JIuUtuXOpX6GrfQHTy79ry6JOJYbS/QpVcarK19Vku6txZeN2B7cpIiIX4PX4eAY4ZayEmKr2qupmVb0M+DBwLF4SLLCJ3Jdxx/zqUn75/qPoSPbylfs7ueahzaR63Fl/bowxxozDLrx2FKM5ANgyBbGYGcDFijRXex9dcdcGGv3pq64lhtI/pyMdSnS42q+w06/MWVAdc6pfYa2fNHatL6CLjfPB4grK1biycT/CSSYinwB+ADwFvFJVdwXcxa3+Zf0EhDOR+zIFtt+Cav5y4Ql86Mp7+Pz1T/I/tzzLkctns6i2lNnlJVSWRqiMRakqjVBZGmF+VSn7LagiFJJCh26MMcaM5k7gnSKyWlWHjaoWkaPwln/+aMojKxIunnV3ufdRotv7OS2qLS1wNANcXeKWXkpWFQs7lRhKLyWbV1XiVGLI1X6FPb3eCfjZ5VGnElaucjHZDhZXUK7Glc2MTqSJyOfw+qI9Bpymqrvz2M1i/3Iinu2J3JdxwD5zK/nSsaWEFh/I3554ice3tLJmczOtnd1k64Q3qzzK6Qcu4D3H7sUBi2qmPmBjjDFmbN8EzgTuFpFLgEUAInIAcBJeD9h2vN6vZog1LzbTnPCW+1hiaGyfue5x4l19VJdG+N7bDy10OP1cXeKWXkpWXhJxKjGUThoL4lRiyNUP7i4m28HduGxZejAuxuXqsvSRuPOTm2Ii8mW8JZSPAK8abTmniBwDPKmqHUOur8RbEgrwtyG31QALgVZV3T6efZniJiKcvO9cTt53bv91fX1KR3cv7V3dtHf10N7Vw+amBHev280Nj27jDw9t4TUHLeBzp+/HXnUVBYzeGGOMGUxV14rIW4E/AJf7VwvwhH/ZArxFVTcXJkK3XXTNo6T6vKVklhgaW7r3UVk07FRiqLYswkutbi5xc+0DMlhcQVlcxc/V5d8bGuO8uCcBQGe3O62HXF2WPpIZ+QoQkbPxkmi9wD3Ax0WGLafbpKpX+///AlAvInfhTaHqAJbijXqvBe7HOzub6c3AVcCvgHMyrs9nX2aaCYWEyliEyliEhX7h2RF7zeLNhy3hktcfwFX3v8BP79rIP5/dxefP2I+zj1tuSz6NMcY4Q1VvE5G9gbPx+rvWAa3Av4GrggxgmmlcXUpWGQsDXk8mSwwVL1crrCyuYFyMy+XEUHpgRLtDcV1x1wZnq3xdnnDq2rL0kczUd6S9/csw8IkRtrkLuNr//8+BBHAUXv+ycqAZr5rtWuCXqprrX7mJ3JeZhmrKo3zi1H0566hlXHzDk3z1pme4/Zmd/O/bD2FhTVmhwzPGGGMAUNUWvGr6y8bY1GRw8QMyWFxBubrEzdXEo4txubqUbENjnHjS+72aXx0rcDQDXF7+7XJiaEF1zMkqX1cnnLq2LH0kM3Jqp6peoqoyxr/6jO3/pqrvVtV9VbVGVaOqOk9VT1XVn2VLfKnq1f5+zhlyfeB9mZlpQU0pvzj7SL71loN4bEsLp196D397YvvYdzTGGGOMs1ydSmZxBeNiXC5XDNlSstxdfP2TJFLez+nYfeoKHM0A15d/u5oYUsW5Kt/MS1e4GtdIiiNKY2YoEeGso5dx7D51XPTHx7jg92u487klfPWNB1AZs5evMcaYySciJ+V7X1W9eyJjmQ5crbCyuHLnasLKKoaCcXUpmasTTm35dzCuxuXi31RwN66RuPWsGmOyWj6ngj+dfxw//Od6Lv/X8/xnUxPffuvBHLfCnbNUxhhjpq0GIMus6ZyEJzCOomdLyYKxCafBWMVQMK4uJbMJp8G4GldLR/egS1e4muBzNa6RFEeUxhii4RD/9arVnLjvXD75x8d458//zeHLannNQQtZvaCK+dWl1JZFqSmPEovY5xZjjDET5msMT6QdA5wObADuBXYAC4CXAyuAW4GHpjDGomBLyYJxdcLpP57ZAXiVTC4lrKxiKBiLKxiLKxjVwZeucDHx6GqV72jc+m0zxozpqOWzuf2TJ/PH/2zmdw9u5v/97dlh25SXhDl+xRw+ceoqDlxcU4AojTHGTBeqeknm1yJyLN4U8ouAH6lqX8ZtIeBjwLfwEnAmgy0lC8bVCae9vd4n49JI2KmElYsfkMHiCsriyl1TIsW2Vq/i0aW4NjTG6ej24lnoUDWtVflOHEukGVOEykrCnHPC3pxzwt7saO3ixT0JdrUnaensprUjxc62JH97cjtv+tF9fPWNB/DuY/YqdMjGGGOmj68Dd6jqD4fe4CfVLhOR0/ASaa+e6uBcZkvJgnF1adSsiiiNiRSzHFqeC+5W5lhcwbgYV7Knz8nl31fctYFE0juX41JiyKp8g3F1Wfpo3Hl1GmPysqCmlAU1w984PvWqffnkHx/jizc8RZ/Ce4+1ZJoxxpgJcTQwLIk2xOPAhVMQS1Fx8QMyuBuXLY0KxuLKnatLyda82Mzzu9ybcLq1uRN/VbpTiaHMKl+XEkNW5RuMq8vSRxMqdADGmMlRW17Cz953JKe+bB5f+ctTNKzdVeiQjDHGTA+C1wdtNO58cnCIqxVWrsaVrvhysfIr89IFNjAiGFeXkl10zaP0AZGQODXhtLvXS+pVxsJOJYasyjcYF5Pa4O570GgskWbMNBYNe2PG91tQzYW/f5S1O9oLHZIxxpjidz/wVhF5XbYbReQNwFuA+6Y0qiLgaoWVi3G5WjG0oTHOi3vcqxj6zHWPO7mU7GN/WEOqD2KOLSVzdWBEumJonznlTk04DYsAsKim1KnEkItJbbC4gnLxPWgsbv0EARHZOAG7uVRV/28C9mNM0auIRfjFOUfyxsvv4wNX/4e/XHgCcyrdOVNpjDGm6HwRuBv4i4jc5f9/JzAfOBk4Cej0tzM+l5tPt/rLkJbMLitwNANcrRj6zHWPk+z1EkMuVQxta/YarrtWMZTyk43VMbcqhmxgRDC9qoA4F5erlUwWVzCu9p0cjYsVacuBWXjLBvL5txdQO8UxG+O0hTVlXHn2kexJJDnv1w/T1e3OmV1jjDHFRVUfAU4D1gP1wH8Dl/uXJwPrgFep6qOFitFFrjaftoqhYLY2dQBQUxpxqmIoXWHiWsWQq8tzXY3L1YqhdEWaa3G5WsnkYlxNiRQJ/2SO+M+nC1yt8h2LW6+EAT9Q1bxGpotI8fz0jZlCBy+p5ftvP5SP/m4NH/zVw1zx3iOojLn6J8AYY4zLVPV+YD8ROR44HKgBWoE1/m1mCFebT1vFUDAufkAGdyuZXK2AsbiC6e3zKtJcisuqfIO59PZ1/RNOX33A/AJHM8DVKt8NjXEisxeP+GbtYkWaMWaSvOaghXzvzEN4YOMe3nD5vdz//G7UtSNBY4wxRUNV71fVy1X1G/6lJdFGsDvuj7xTnEoMuVqZY3EF42olk6uJRxfjcrliqKfPS8BEwu6kD6zKNxir8g3mM9c9TqikrGak2936S+s5CnipgPc3Zlp72xFLWFRTymf+9ATvuvJBVs6r5Kjls5hXVUpVaYTykghlJSHKomFmlZew/6JqqkrdOlg0xhhjio2LH9zB3QoYiyt3TYkU21q9HmkuVaRZxVAwLlcMnVLjJYYuf9fhhQ6nn1X5BmNVvsGk+06OxLlEmt93o2D3N2YmOH7lHO74r5O58bFt3PLkdm59aseIB4QicPiyWbz5sMW89fAllJWEpzhaY4wxhSQinwXuzbfabLz3ny5cbabs6ocYF+NytWLoirs29CdgFjmUsLJJosE4XTFU417FkMtVvi7+rbe4ghmrute5RJoxZmqUlYR559HLeOfRywCv90E82UNXdy+dqV46Ur3sbO/isc0t3PbUDr5041Ncesd6Pv7Klbz7mL0Ih9w5gDTGGDOpvgVcAuSbCBvv/YueVQwFYxVDwfx7w27AS8C4lBiySaLBuFox1Odns/tcymrjblwuVq2CxRVE5nv2SCyRZowBIBwSasqi1JQNnA3Yn2pOWT2PT5y6iodfbOZ//7GW//7L09zw6Da+89aDWTW/qoARG2OMmUK1IrKs0EEUq8wEjFUMjc0qhoLZ0doFQFk07FRiqKYsws72pJOTRF2sgHE1LkEGXbrC1bhcrKYFN+Pa0BinOeGdNHGpyjfzPXsk7nQLHIGI9IlI7xj/ekSkSUTuE5FPiUis0HEbM52ICEctn80fPnQsl511KJt2J3jtD+/l53dv9Kf4GGOMmeYuAl7I89+YbxQiUiciHxSRG0TkeRHpFJFWEblXRM4VkTGPWUXkFyKi/r8Ry19E5GwReUhE4v5jNIjI68b8CYzDHc/uBNxLDFnFUDA9vV5csUjIqcSQVeYEY3EF4+KAjSY/+QIwt8qd16JV+QZz8fVP0u3/2XKpyjf9no3qiNm0YqhIuxtvpPohQC+wBdgJzAeWAmHgCbzv5TDgWOCdInKyqiYKErEx05SI8MZDF3PCyjlcfP2TfOOWZ7n9mZ3879sPYens8kKHZ4wxZnL8agL28dgYt58J/ATYDvwL2Ix3rPcW4ErgDBE5U0cYNS0irwc+AMSBETMvIvI94FPAVuDnQAlwFnCTiHxMVS8P8D3lrDLm9Rfda3aZU4khqxgKxtUKGFfjcjXB52JcTYkU8ZSXQHMoLDY0xtm0OwHzIZ7sLXQ4/S69fR2NfjKt0qGhaFblG0zmwAiXqnzT79na250caZtiSKS9E7gPuAb4vKpuTt/gLzH4FnAMcAKQAL4PnAt8FvjKlEdrzAwwpzLGT997BNev2cYlf32a0y+9my+/bn/ecdRSp8pyjTHGjJ+qvn8KHmYd8Abgb5pxBlhELgYeAt6Kl1T789A7ishcvKTYH4EFwMnZHkBEjsdLom0AjlLVZv/67wKPAN8TkZtVddPEfVseVytNLK5gXE3wuRhXUyJFZ4+XeAmH3FkEtaExTlun93tVEnFngNYVd22gI+Vl0FyqGPrMdY/j54U49WXzChtMhvQy61hY+OZbDy5wNAO2NnUAUFHiVpVvstt7LVbF3Fr+7erAiP73HgmPmC9z56/ayL4NNKnquzKTaACqullV3wU0A99W1XbgfLyDsbdOfajGzBwiwluPWMJtnzyJQ5bW8vnrn+TcXz3MrvauQodmjDGmyKjqnap6kw5ZRqGqO4Ar/C/rR7j7z/zLC8Z4mPP9y2+kk2j+Y2wCfgTEgElJGrpYAQMWVxCuDoxY82Izz+/yFuF0do/e02cquTqYIZ0YikVCXP6uwwsdTj/XB0aERZyqGEoPZqgujTqVGEr/zSqLurX821Uu/q2HjHhGqQ8phkTaq4Hbx9jmduB0AFXtxVsOuvckx2WMARbXlvHbc4/hK6/fn/ue382rf3A3l96xjvU72+mz/mnGGGPGL12WNCx7ISLnAG8CzlfVPWPs5xX+5W1Zbrt1yDYTpimR6u+Z49LSuw2NcVr9s+4uVQytebGZPQn3KoZcHRjxsT+soQ9vaNRlZx1W6HD6uTqYIV0xVFMa4fC9ZhU4mgEuD4wAiIbdSgy52LcN3F1mbXHlLvM9Gx25x2sxLO2sAqrH2KbG3y6tafLCMcYMFQoJ7z9hb05cNZev3fwMl96xnkvvWE9JJERVLEKF/2/prDJed8giXnvQQsIhd/5gGmOMcZOIRID3+V/eNuS2vYDLgN+q6o1j7KcCWAzEVXV7lk3W+5f7jivgLFxdsuVqk+eLrnkUBSIhcapiKHMpmUsVQ+klW7PK3EoM2WCGYFyNK73EzbXhYrb8OxgX43J1YETmezZ9PamRtpMRerY6Q0TWAMuAg1X1pSy3LwEeBzap6hH+db8DTlTVaT2mffXq1bp27dpCh2HG0NDQQH19faHDmFJbmzt4YMMent8Vpz3ZQ8L/9+z2dra1dHL4slp+8p4jmF/tzhndmfg8FRt7joqDPU8TS0QeUdUjCx1HoWQMB7hFVV+bcX0IuBNYBRyY0e+sAa9H2ipVfT5j+0XANmCbqi7J8jhRIAWkVDXr9HcROQ84D2Du3LlHXHvttTl9D8/vitPZ3UtYhBXzKolF3Kj+SscVDYdYNa/SmRNc63fG6erppTQSZtX88VXmxONxKisnprrn2e3t9PT1EQmFeNnCqrHvMEWe3d5GT58SCQkvWzhW7cHUyTWuiXyOJjKuqeZ6XAvKYO7smkKHA3hJved2tNOn6tTrMdnTx/qd7SheBd9+C6Y+rmyvp45ULxsb4yhQEg6xugBxZfNSSxd7El4v/4qSCPvMrShwRJ7M9+wLzz7zqb7u5EHZtiuGirT/BX4DrBGRH+INHkhP7Xw58DGgFm/IQPrM5anAPYUI1hgDS2aVc+aRw6d49vUpNzy6jS//5Sne8uP7ue7841hU687ZeWOMMe4QkY/jJdGeA9475OZP4iXMXpvZ72wCjHiGWVV/ht+PbfXq1Zprwviz/+92dsVTzKss4aF3TfjK0bwNiuusUwodTr9Bcb2jflz7msjE/v98v4F1uxLsO6+Cj7xzYvY5ET7z9dtpTKSYW1HCf4owrqk++fLpr/+D3Ylu5lREediRn1dTIsVHv30HHSl16nnc0BjnQ3+/i26Fzx/ax5mOnCT77xuf4tdPeEt0D1taw0fe+fICR+R5y4/vY81mr6fc+45dxvn1WfMvkyrb6+mEb/2TbS0RwiHhug8f50zl6rH/cwc72nqJhYVbPnGSM0uaM9+DtCdVvFM7VfV3/lnEbwBfG3Kz4PXL+KKq/s6/rhb4b+DBKQvSGJOTUMgbULBqfiXv/vmDvOfKB7nu/OOoq8x68t8YY8wMJSIX4C3bfAZ4pao2Zdy2Cu+48CpVvSXHXbb6lyOVVNQM2W7CuLpky+IKxpaS5c7VJVuuTuy89PZ1Ti7//sx1j/cv/64udSdt4Ooy68yJnS71BXR9+XdVacSZJBrk/h7kRm35GFT1u8Bq4CvADXil/DfiJcz2U9VvZWy7W1V/qqqPFSBUY0wODl5Sy1XvP4ptLZ184FcP05FyZ/qVMcaYwhKRTwCXA08Bp/iTOzMdgD9hU0Q08x9elRrAev+6NwGoagJvaWeliCzM8rCr/Mt1E/vdmOmgKZEinvISMC7l91ye2NnoJ9MqS91J8Lk6sfOOZ3cCXlyuJoZcasfiegLGJnbODO6klsegqi8A/6/QcRhjJsaRy2fzw3cexvm/fYSP/m4NP3/fkUTDRZHbN8aYGU1EPgn8KrNKbAL3/TngW8BjwGmqujvLZpuAX4ywi9cCC4DrgDZ/27Q78ZaIng5cNeR+Z2RsM2FsYmcwLk/sdLFiyPWJnbGw8M23HlzgaAa4OrGzMub9ru81u8ypxFA6aVweDTvTQ9GYyRTkPdudd05jzIzzqgMW8P/edBANaxv5/J+fxPXhJ8YYYwCvf+1WEfm1iJwwUTsVkS/jJdEewVvOmS2Jhqo+pqofzPYPSE9huti/7rGMu17hX35RRPo/RYvIcuACIMnwBNu4uJqAyVyy5dLEzo/9YQ2KlxhyqWLI1aVktmQrGFs2HEx6ubBLy4ZhIMHh0skJV9nJnGCCvGcXRUWaP5npAuDdwMuAClWN+LcdBnwIuFRVrRzfmCLzrmOWsau9i0vvWM/86hifPX2/QodkjDFmdJ8FPgi8B3i3iDwD/BT4jarm1WNMRM7G64Xbizcw6uMiww76N6nq1fkGrar3i8j3gf8CnhCRPwElwDuA2cDHVHVTvvvPxtUEjPXyCcbVxJCZHoojwedGMs3V/ntWTRuMqydzhr5n33jhyNs6n0gTkRLgVqAeaALagcx3sBeADwCNeD3UjDFF5qJXrmJnW5IfN2ygt0/51KtWUxJx5+yEMcaYAar6PeB7IlIPfBh4E95ggG+LyB+Bn6pq0KFPe/uXYeATI2xzF3B1wP0OoqqfEpEngAuB84A+YA3wXVW9eTz7zsbVBIz18jHGDS5XDDX7SassJzUKxtX+exdd8ygKRKyaNieunswJ8p5dDJ9UPwOcAnwVmA9cmXmjqrYAdwOvnvLIjDETQkT4+hsP4J1HL+Ond2/kld9v4Ef/ep4HNuxhQ2OcxvYkHakeW/ppjDEOUdUGVX0nsAT4HLAFOAe4X0QeE5HzRSSn7JGqXqKqMsa/+hz2U+9v+/wo2/xKVY9S1QpVrVLVkycjiWbMTOTqki2rGAqmGCqGXOq/Vxb1ftf3mVNu1bQ5mA4nc5yvSMNbznmfqn4NwJ/INNQLwOunNCpjzISKhEN88y0H8eoD5nP5nc/z3b+vHbaNCFSURKgtj3LYslm8Yr+5nHHgQkqj7hwQGWPMTKOqe4B0ldppwC+Bg4AfAd8VkV8B31bVLQUM0zjO5cocSwzlztUEjPXfC2ZoxdAT/7m/wBF5XE0MudrnzkyeYkik7Q38bYxtmvB6Wxhjilz96nnUr57H7niS57a3szuepD3ZQ6L/Xy+72rt4cOMebnr8JS756zN8+OR9+MAJe1tCzRhjCkRE9sbrWft+vBUEKeAW4BDgo8D7RORNqjqhUzFdZgmYYKwyJxhXE0OuLtmy/nvBTIeKoankap87k7ugJ3OKIZHWCdSOsc0yoGXSIzHGTJk5lTFevio24u2qygMb93DlPS/wndvW8vsHN/Pdtx3CcSvqpjBKY4yZuUQkDLwBr0/aqXgtQzYDXwKuVNVd4jXXORNvGMF3gSMKFO6UswRMMMVSmeMKVxNDloAxM01TIkVHqqfQYQxjJ3OCCXoyx52f6MgeA17lDx0YRkRq8PqjPTSVQRljCktEOH7FHH55zlH8/kPHEA2HeOfP/83XbnqGLv/g0hhjzMQTkWUi8nW8pNmfgNOAfwBvBPZW1f9R1V0A6rkW+BlwQKFiLgRLwARjlTnGmNFkJmBcSgxdevs6OvyzJi4lhuxkTjBBT+a48xs4sp8DS4HfiUh15g0iUos3vWkWcMWUR2aMccLxK+bwt4+/nPcdtxe/vO8F3vSj+1i3s73QYRljzHS1EfgiUAL8L7BSVV+jqjfpyFNhmv3tZwxLwBjjBuu/F4yrFUPpBAy4lRhKJ2DCglOJITuZE0zQkznuvGJHoKp/AK4C3go0Ah8BEJGHge14Zz9/rKq3FCxIY0zBlZdE+NobD+Sqc45idzzJ6394L7+6f5NN+jTGmIn3MHA2sFhVP6uqL4x1B1X9lqo6f9xpTDFwNTHkagLG+u8F89Hfu1kxlE7AlEdDTiWG0gmYWeVRpxJDdjJnchXFAY2qngt8AHgGmAsIcDjwPHCuqn6sgOEZYxxyyn7zuPWikzhuRR1f+evTnPurh9kdTxY6LGOMmTZU9VhV/Y2qpgodiwnG1QSMVeYE42piaLos2Zoqm/d4FUPlUbcqhto6vOPmWBinEkNp5SVhSwyZgnPnnWoMqnq1qh4GVAJLgCpVPUhVrwq6LxGpE5EPisgNIvK8iHSKSKuI3Csi54pIaMj2S0XkxyLyoIjsEJGkiLwkIveIyPtFJBrgsVeJyOdE5E4R2SIiKRHZKSJ/EZFTgn4vxpjh5lbFuOqco7jk9ftz7/O7Of3Se/jtv1+03mnGGGOmhCVggnG1MscSQ8F0przjrJpSt5ZspXq8iqGKmFv99zpS3f7/+pxKDJVGw4MujZko0+lkTjFM7RxEVTvxJnmOx5nAT/CWhv4Lr1nufOAtwJXAGSJyZkafjxXAu4EHgRuBJqAOOAP4Jd5I99NUNZdxHV8H3oFXXXeLv6/VeFOv3iAiF6nq/43z+zNmxhMRzjlhb45bMYfPX/8EX7rxKb5963Mct6KOfeZWMrcqxuyKKLXlJcyritkSUGOMGYGI/Heed1VV/fqEBlMkLAETjPXyCcbVxFCX/wE5fekKV+MqjYbp6O5xLmEVEhl06YLplICZCnYyJ5h8TuYUXSJtgqzDS1z9TVX70leKyMV40z/fipdU+7N/0/3ArMxt/e2jeFOq6v3tr83hsW8Dvq2qjw7Z18nA7cB3ReQ6Vd2ex/dljBli9YIqrv/I8TywcQ9/fewlHnyhiX+t3UV37+DE2cIK4TtLGjlx1dwCRWqMMc66JM/7Kd4JxBnH1QSMTcYMpk8HX7rCEkPBuBhXUyJFV4/3dyIs7iRgXE0MTacEzFSwkznB5HMyx7lEmohszPOuqqorctzwzhGu3yEiVwDfwEuO/dm/PmsPEFXtFpEb/W1X5fjYV49w/V0i0oA3Qv54BpJ4xphxEhGOXzGH41fMAaCvT2nr6qa5o5umRIoNjXG+f+uTvPcXD/GFM/bjwyfn9KfEGGNmCms9MU24mhhyNS5LWOVuQ2Oc9k7v5xQNuxPXmhebae7w4iqNuvPR1xJDwUynBMxUcPVkjqvVtD3+m08skvvJHHf+mgwIAUPfRkuAhf7/e4A9eEsr0/FvByaq4W16sfqY75giEgZe43/5xFQ+tjEmf6GQUFteQm15CXvPqeCIvWZR2/o8f91ZwzdvfY6ePuWCU1YWOkxjjHGCqt5V6BiKjSWGcteUSNHe6R0Cd/W4E9eGxjgpP6NQUZJzO+RJ52piyNUEjFXmBONqYsiqaYOx96Bg8onLnb++PlVdnvm1iFQDdwAvAl8A7lHVPn8gwEnAN/GSb6eO97FFJAK8z//ytiy3zwEuxJsaOhevemwl8Hvg5nE+9l7AK4EO4O7x7MsYE1xJWLjsrMOIhITv/n0t86tLedsRSwodljHGmCKzoTFOs780KtnrzpCbNS820+FnOqpK3fnAd+nt6/oTMCvmuPMB+TPXPd5/ZvuMgxYUNJZMriaGXE3AuFqZY4mhYFxNDLka1/9v777jq66vP46/TnZYYQuIAxy4Fw5cFax1tlpn1TpbV+ts1Q5rrfXX/lrb/lq31lpHbd1VO9y2orgHihNHFJwgCEISIAnJ+f3xvYEQE8gXknvPzX0/H488Lrn35t5Dbu69n/v+ns/nEzWwithNO6euAafl777z8Vi4IK0dvwT6A5u0nmKZWa9sYmany1cy1zttFe/r18AmwL3u/kA7lw8Gftbqewd+B5zjq7BSuZmVA38DyoEfuPvc5Vz3BOAEgCFDhjBx4sSVvVvJktraWj1OeaC2tpZJjz3KV4c6bw0q4od3TGHGe1PZZHA+vEwWBj2X8oMeJyl0Z98+ZcnUin02Gb7c62bTqTdPBpIA5sojxua4mqVad+ZcdJiCoRWJGgxFDWCiihrARK0rYjCkbtp0onbTXvTQW2QeRtYc1KvTPxfnf9Cx/YGbl7NO2SIz+wdwKKsQpJnZacCZwFTgyA7ua2pyVSsGVs/UdgGwk5nt4+5zVuJ+i4EbgR2BW0mCuQ65+9XA1QBjxozx8ePHp71LybKJEyeixym+1o/Ttjs0cshVT3Hlywu49cRt2GT1qtwWJ4CeS/lCj1NhMLPhwLnAHiRjovY+Obu758NYs0u1DmB+tPdGOa5mqagBjDpzeoaoAUzUuhQMdZ66adNRN206KzvNOs5WHB0bBKwoSi3NXG+lmNnJwMXA68CEFQVi7t7k7u+7+8XAicA4kkAt7f0WA38FDibZ8fOIVelsE5Gu0a+ilBu+tS39e5VxzHXP8UHmQ5GIiICZrQ48TzIGqiPpqH8feBtoIlkCYwowKVc15pICmJ4hagATsa6oAYymWacTNRhSN2066qZNZ2UP5uRDkFYNHGRm7baEmNkA4CBgpXb7NLMzgMuAV0lCtBkpb+K+zOn4lPdbAtxM0kl3E3C4u8d55xEpcKv1q+D6Y7ehsamZg696ilc/mpfrkkREojgPGAbs6e6bZ867zt03AEYDDwCVwAE5qi+nIgYdoLrSUDCUTtQARtOs04kaDPW0AKa7RT2YE/G1Hla+rnwI0q4CRgDPmtlRZra2mVVmTo8GniEZzF2e9obN7IfAH4CXSEK0T1eivtUzp51+lzWzMuAOkk60vwBHunucd0MRAWC91fpy8/HjMIMDrniSC++fykefL8x1WSIiubYHcL+7P9z2Anf/kGR8Uwn8PNuFRRBxylbUAGby9LnMydTV0BynLgVD6UQNYDTNOp2owVBUPS0Y6m4R3xth5esKv26Fu19mZusBpwLXtXMVAy519yvS3K6Z/ZRkOuYLwO7Lm85pZtsBr7j7gjbn9yGZEgpwT5vLqoDhwDx3/6TV+eXAncDewJ+BEzIbJ4hIQBuN6Mc/T9mJX97zOldOrObKidUMr6pgaL8KepUWU1lWTEVpERWlxaw5sBebj+zP9usMCrUbjYhIFxtGsiRFiyaS4AwAd681s4eA/Vj1jaDyStQpW1EDmJbOHDO47FB15qxI1GAoagCjzpx0VFc6EYOhpmYP200bdQOElX3PDh+kAbj76WZ2C/AtYEugCpgHTAaud/cn09xeppPtApKB3yTgNDNre7Vp7n595t8/Bsab2aMka4AsANYA9iLZUfRJ4Fdtfn5/kuDvBuCYVudfRRKizQY+As5r574nuvvENP8nEek+Q/qWc9GhW/L9r4zh/tc+4fWP5zNnQSML6hdTU9PIwoYmFjY0cdeLH+EOfctL2H+r1fnO+HUYXlW54jsQEckv81l2c4G5LO3QbzEPGJK1ioKIOmUragDT0pkzqFcp4zcYmuNqllIwlE7UoCNqXREDGIhZV9Rp1lEPmsycXx+2mzbqBgiwcu/ZeRGkAbj7U8BTXXRzozKnxcAZHVznUeD6zL//RLKY7jYka6H1Ihk0vkByRPbaFOubtdz3YJI1RjoysZO3JyJZsuagXpzwpXU6vLyufjEvTJ/L3S99xE3PvM8tz33AKRPW5Tvj16G0OB9m0ouIdMp0kgOKLaYAu5pZL3dfYGZFwO7AhzmpLoeiTtlSAJOO6kpHAUzn9cTOnO4UdZp11IMm8xc1AkWhu2kjbYCwsCF5z66qSP+enTdBWldy9/OB81Nc/x7aTN3sxM9cz9IgrvX549Pcjojkj97lJXxp/SF8af0hfG+39bnw/qn8/qG3uO/VGVx62JasOzTOAEBEZBX8BzjBzErdvZGk+/4vwJOZKZ07ARsD/5vDGnMiatARta6IAQzErCtqMKQAJp2e2JnTnaJOs4560MQzB016l8fqpl2cefMpL4l1MGdVXuvDtUiY2VAz65WrnxcR6QprDOzFZYdvxdVHjmXm/EXsd9nj/HPKx7kuS0SkK/wZuJCkux53/yvJmrGbAGcC2wG3Ar/MVYG5ogCm89SZk07UYEgBTDpRO3OiBkMNi5Np1tGCoagHJ1rqifQeBDHfG4Ela1qvzNrW4YI04BPgrFX8+TO7qBYRkVWy+8bDuOe0ndhgeD9Ou/lFzr37FRY1xtmZTEQkLXd/290vbL2Zkrt/j2STpe2B4e5+uLsvylmRORA1GIoawKgzJ52owZACmHSiduZEDYaiBjBR6yrKLL0eadOzOXUNOC3LC8SZEFk9q5aahcnjV1rcM4K0L6y8vxI/v6q3ISLSZYZXVXLLCeM4fudR/PXp9/n65U/w9syaXJclItKl3H2Wuz/j7jNzXUsuRA2GogYw6sxJJ2owpAAmHdWVzqp0DHWXqAdNJk+fuySorQgUWF300FtkmqJZc1CciYNn3z5lyUGmPTZeLfXPx/kNL+sYMxu/kj8b7GVcRARKi4v4yT4bscM6gznr9il87bLHOWO39Tlmh7VDDQ5ERGTlRA2GogYw6sxJJ2rQEbWuitJiFjQuDjXGityZEzUYmrsg+buKFAxFPWhy6s2TOXSNpJv2ssPjHDS5/7XkYE5psfG7Q7bIbTGtvP9Z8p7dq3Tl3rPj/EUua+3Ml4hIjzJhg6Hcd/rOnHPXK/z6vqlc/8Q0Dhy7OuNGD2J4VQXlJcX0Li+hd3kx5SVxBn8iIq1l1qP9NrAFMBJo79OXu/uXU9zmQcAumdvcHOgL/M3dj+jg+uXAccDRwGigAvgAeAj4P3ef3sHPHQ2cDGwENAEvAr9z9393ttb2RA2GogYdqiudiMGQAph0InfmRA2GnHjB0KoGMN1lVXag7E61ixoAKC0i1MGcBQ2ZJyPNK/WeHeeVJcPdI043FRHpMkP7VXDN0dvwxDuz+eNj73LlxGouf6T6C9dbf7U+HLDVSI4ctxa9y8O9XItIgTKzzYAHgSEsfzmNtD0955IEaLXAh8AGy6mhhGT30B2BqcDNQD2wDXAqcJSZ7eDur7f5ud+RrKX7IfAnoAw4FPiXmZ3q7pelrHkJBTCdp86cdKIGQwpg0umpnTndJWowtKoBTHfRe1A6q1pXnFdiEZECs+O6g9lx3cF8vqCB1z+Zz+zaBhY1NrGwoYm5Cxp4svozfn3fVG5+9n0uP3wrNlm9Ktcli4gAXEQSov0M+Avwkbt3xS4q3yMJuN4h6Ux7ZDnX3Z8kRPsPsLu7N7dcYGY/B84j2bzqW63O34EkRKsGtnH3uZnzfwu8APzOzP7t7tPSFh41GIoawKgzJ52owZACmHR6amdOd1EwlE5ST2O4uorMljmNYE5dA4sWJ68TxbZyfVxx3lFFRApU/15l7LDO4C+cf8Zu8PS7n/H9W1/i0Kuf5pqjt2bc6EE5qFBEZBnjgL+7+y+68kbdfUlwZisecI/OnN7TOkTL+AdJkDakzfknZU5/2RKiZe53mpldDvwUOJYkIEwlajAUNYBRZ046UYMhBTDpRA5gVFfndEUA0x1WdQfK7jJ5+lw+q0teJ8oCLVlz0UNvsaAhOfg1cmDlSt1GnEdfRES+YNzoQdz53R0ZVlXBt69/jlc/mpfrkkREaoF21x/Lotcyp3uZfeHTzFczpw+3OX/XzOn97dzefW2uk0rUYChqAKPOnHQiB0OtT6OIWldP7czpDlGDoa4IYLrD9259aZV2oOwu372p5x7MifNsERGRdg2rquBvx21HVWUp37r+OT76fGGuSxKRwvZfYLsc13APcCfwFeAVM7vYzH5rZv8lWWvtUmDJemdm1htYHah190/aub23M6frr0wxUYMhBTDpqK7OUwCTTk/uzOkOUYOhqAdNqj+tAZJu7kjdtPMX1ANQXkyPO5hj7sH2dpZOGzNmjL/55pu5LkNWYOLEiYwfPz7XZcgK5MPj9OaMGg666klW61fB7Sduz4BAR8uzIR8eI9Hj1NXM7AV33zrXdbRmZqOBZ4D/Ay70bhhMmtl4kjXSlrdrp5FM4fwp0PqT6X+Ac9396VbXHQF8RLKe28h2bqsUaAAa3L28g/s7ATgBYMiQIWNvu+22JZe98cl8Fjc7JUXGhsP7pfifdq9Cr6u2tpY+fTr3Iamp2Zk6o4Zmd0qKithweN9uqyuN+sXNvD2zBgdKi4vYYFiMuj7+fBGf1SUfknuVFa/0h9E0j1FnvDOrdkkn5qDeZYzoHyMcmjqjhsamZgxj9JDe9CqLEaYtqcuM9Yb2obyk/VC0qx+nFXnt4/k0u2NmbDisL8VFMbr4WuoqMmPjEfFeU4dVwpCBcdZUzvf3oAkTJnQ4BtMaaSIieWLMsL786aitOeraZzn2+uf423HbaTdPEck6d3/XzHYCngSON7OXgPbmnbu7f7s7ajCzCpKNDvYCTiZZF20ByQYElwCPmdnB7v6PlDfdYSjo7lcDV0NyMLN1YHzW/zzI7LpGBvcu5fnDxrd/A1lWPauW4x94lEaH1fqW80yQuiZPn8tv7n8SB0ZUVfBkN9aVJtg/7+5X+cvLyRppW65RxXcO26nb6kpj38se5+UPk/f6o8atyUnjN81xRYltf/kwn9Y0UVps3H/Gl1Y6SOvqgy8nn3cfdQ1GabHxzDlfDjNF97s/vZcFjUX0KjVe/+aXc13OEsvUdXjHM9uzfZDs+xc8wJwFixnYq4TJh0/I2v2uyLJ1jc91OUu0vAf9cPMmDg50MDPieyN0TV36BCYikkfGjR7EpYdtyXf++gJHXfssVx0xliF9222eEBHpFmY2kiS4GpD5GtXBVR3oliAN+BFwMHC6u/+x1fn3mdlBwEvAxZk6YWnQ19Gh+qo21+u0qFO2zr59SsipUT15zZzu0DJlq7Q41pStqNOZy0uKqGtopm95cZgQDWIunA9x64q4nhzErGtOXQMLlkzfj1NX9axa5i1I3huLi+JM/5449VNmd8F7dpz/kYiIdMoeGw/jssO34rWP57HXxZO4+dn3WdAQa/0bEenRLiJZS+xaYBdgPZIwre3X6A5+viu0bCjwSNsL3H0KMAdYy8wGZc6rI5na2cfMhrdze+tlTt9KW0jUYCjqDpQ9ec2c7tAy1S5iMNT6NIqIQQeorjRaB0MWKBiKetDk1/e9sWSdu7KSOL+vqOvcnXbLiwCYsUrv2epIExHJQ3tvOpy1B/XmnLte4cd3vsLP/vka66/Wh4G9y6ksLaKitJiKkmIqy4pZc2AvNh1ZxVZrDgizxoSI5LVdgQfc/bgc1tDSijuk7QVmVg60LHrS0Oqi/wJHAnsC17X5sb1aXSeVqMFQ1B0oo3bARK0rYtABMeuKGsBE7czJh2Ao0gYIUQ+a3PtKsn9OscHIAb1yXM1SUbtpW5YBHFC5artZhwzSzOz9lfgxd/e1urwYEZGgNhrRj7u+uwPPTZvLw2/M5M0ZNcxb2MjMeU0sWtzEosYmFtQ3UVOfDCoH9ynjwK1GctzOozUdVERWRRHwSo5rmARsApxjZk+4e32ry84nGeM+5+41rc6/iiRI+4mZ3e3ucwHMbG2Sddbq+WLAtkJRA5iodUUMYCBmXQqG0tEOlOnkQzAUaZp11IMmZcXJa0NVZUmHm0XkQtRp1l31Wh8ySAO+sJtSJ2j7UREpOGbGtqMGsu2ogR1e59OaRTw/bS7/fOlj/jTpXW54ahqnfXk9jt95NKXFcd5wRSRvPE0SYnUpM/s68PXMt8Myp9ub2fWZf89297My//4l8DXgy8BUM7sfWEiy2cC2mX+f3vr23f1JM/s98H3gZTO7AygDvgEMBE5192lp644awCxanOxcWGxxXuejBjBRO3MUDKWjde7SyYdgKNI0ax2cSCdqXV0lZJDm7p16ZzWzA4BfA+sC87u1KBGRPDW0bwV7bzqcvTcdzruzavnN/W/ym/vf5J6XP+HSw7ZkdKBBiojkhZ8Ak8zsUHe/pQtvdwvg6DbnjWbpWmvTgbMA3P0jM9sK+CGwD3AsSafcJ8D1wIXuPrXtHbj7mWb2MnAKcALQDEwGfuvu/05bcNRgKGoAow0Q0lEwlE7kde4iduYoGEonal0RFUI3bcggbUXMbFvgdyRHHZuAK0ja+EVEZDlGD+nDVUeO5f5XZ/DjO19m38ue4MIDN2Ofzdpbe1tEpF37kKwl9jczOwl4gfZ3u3R3/5/O3qi7n0+K8Zy7zyIJ1s5a0XXb/NwNwA1pfqYjUYOhqAGMNkBIR8FQOgqG0olaV0RRD5qomzadruymzasgLbOGxa9Jtjs3ki3Nf+Dub+eyLhGRfLPnJsPYbGQVp9w0mZNvmsyz763FOftsSHmgN2ERCev8Vv/+UuarPQ50OkjLR1GDoagBjDZASCdqXVEDmIh1FUJnTleKGgxFPWjStpt2/ntTcl0SEPdgTld20+ZFkGZm/YFzSRaCLQeeA85y90m5rEtEJJ+N6F/JrSduz4X3TeWax9/jxQ8+57LDtmLNQXF2/BGRkCbkuoAoFAylE7WuiAEMxKwrajAUNYCJ2pkTNRg69eaY06yjHjRp20078b0cF5QR9WBOV3bThg7SzKwEOJVkLY6BwDTgnC5ej0NEpGCVFhdx7lc3YttRAznr9insc+kkfvH1Tdh38xFYoIG7iMTh7o/muoYoFAx1njZASEfBUDpRA5gHX086c8qDdeZEDYYWNiSvEVUVJaGmWeugSTpR6+rK98Y47xZtmNkhwFTg/0jqPBvYQCGaiEjX233jYdxz2s6sM6QPp9/yEgde+ST/fvlj5i9qXPEPi4gUoKjBkAKYdNSZk07UYKi+MXkuDqiMFcA0LG4GoHd5rB0oowZDizLdji2nUbQEQj05GOpKEevq6m7akB1pZvYUydblDcBFwAXu/nkuaxIR6enWGNiLv39nB+544QMufvhtTrnpRQAG9ymjsqyY3mUl9MkMBPfadBi7rD9EXWsiBczM1gSOArYE+pNsODAZuNHdp+ewtKyIGgwpgEnnwzlJZ07vslidOQqG0mn2ZU+jiBwMRewYilhX1IMm6qZNp6vfs0MGacB2JAvUzgC2Bv7ZiQ9r7u67dHdhIiI9WXGR8Y1t1uSgsWvwzHufMXn6XD6et4iFDU3U1i+mZlEj9776Cbc+/wHjRg/kom9sybCqilyXLSJZZmbHA5cAZbDMod2vA+ea2enu/sdc1JYtUYOhqAHM4qYkgOlbES2AST5YVZYWherMUTCUTtS6FAx1XvWsWmoWJo9faXGc31fUgybqpk2nq9+zowZpkAzK1sp8dUawtxkRkfxVXGTssM5gdlhn8Bcua2xq5tbnPuB/732D/S5/nJuOHxfqQ5GIdC8z+zJwFVAD/Bb4L/AJMBzYFTgNuNzM3nH3/+Ss0G4WNRiSdBRYpRMxGKqeVUtDJlHoXVaa42qWmjx9LnMXJI9fRWmcj90KhtKJetBE3bTpdPV7dpxn9LJG5boAERFpX2lxEUeMW4ut1x7AN//0DIdd/TS3nbg9aw/unevSRCQ7ziYJ0ca6e3Wr898EJprZDcALmev12CBN0okYWM2pa6BmYTIFadHiOIGVgqF0zr59Ci2P3l6bDstpLa0VSmdOV4kaDEU9aBK1m7ZQxOnlbMXdp6/MV67rFhEpJBsM68fNJ4yjsamZb17zDJ/MW5jrkkQkO7YFbmsToi2ROf/2zPUki1qvmdMViyl3lcnT5zInU1dDc1OOq1nqoofeWtIBs87gOB+QFQylEzWAKZTOnK6iYEi6U1cfzAkZpKVlZhua2R9yXYeISKFZf7W+/OVb2zF/YSNHXPMMn9XW57okEel+lcDsFVxnVuZ6PVKzs2Qx5UiLPLeeGhVpytapN08GwAwuO3RsjqtZqnVnzkWHKRhakajBkAIYkRgK6WBOnHf+lMys3MyONLNJwKsk63GIiEiWbTqyij8fsw0ffb6Qo659lnkLG1f8QyKSz6aTrIW2PBOA97NQS040LG5esjhvpLV8WgKY0mBTtloCmEG9Shm/wdAcV7OUOnNEZHm0M2Y6hXQwJ85fQyeZ2SZmdgnJorbXAzsC7wHn5rIuEZFCtu2ogVx1xFjemlnDt65/jgUNcdaaEZEudxewjZldYWb9W19gZlVmdjHJtM47c1FcdiSfFHqVFoXqGGoJYKqCBUMihWZOXQMLMhtFROrMUTCUTtQNEL57U+xp1oVwMCfOs2c5zKzSzI41syeBKcApQH/gZWA3d1/X3X+VyxpFRArd+DFDufjQLXnx/bnsd9kTTHzzU5ojrSotIl3lV8BU4CRgupk9Zma3mtmjJF1op5JsPNDjx2a9yorVMSQFIWowFDWA0c6Y6Wj9vXTmL0iWUikvJuQ060I4mBNnu5V2mNkWwPHA4UA/wIDJwHXApcBz7v7fnBUoIiLL2HvT4Vx7zDb85K5XOea65xjcp5yNRvRjQK9S+pSXUFlaTK+yYirLShjat5zN16hinSF9MIszKBeR5XP3+Wa2A/Ab4JvATq0uXgD8CfiRu8/PRX0SS9QARp056UQNhqIGMNoZMx2tv5dORWkxCxoXU1Ea5zWi0IQM0szsOOAEYCxJeDYT+DNwnbu/lrnOpbmrUEREOjJ+zFD+e9Yu3P/qDB59axZvz6zlvdm1LKhvYkFDEwsbl13kc82BvThgq9U5Zoe16d8rziBFRDrm7vOAE83sZGADoAqYB7zp7j1+oUQnGaAqGFqxqAGMOnPSiRoMRQ1gtP5ez9DVOz12laLMAegiHYheoe46mBMySAOuBppJ1ta4AbjP3ePslS0iIstVXlLMfluszn5brP6Fy9ydRY3NfDh3Ac9Pn8s9L3/Cxf95mz9Peo/vTFiH43ceTWlxnA+BItIxd19MsulTQVIwtGJRAxh15qQTNRgS6U6LMgFMy2kEUQ+aFFo3bdQgDZIDfZsCGwMvkGwuICIiec7MqCwrZr3V+rLean05bNs1mTpjPv/34Fv85v43+feUT/jDN7ZgzLC+uS5VRKRDCoY6J2oAo84ckRiiTv+ePH0uCzJHJ/pWxHmNiHrQpNC6aeNEmMvaCfgrsAbJQrXvm9m9ZnaImcX5KxYRkS6xwbB+/OmorbnqiK34tGYR+13+OHe/+FGuyxKRDpjZemZ2mZk9a2Zvm9m77XxV57rO7qRgSCS3ogYw6hhKJ+r071NvngwkwdCVR4zNcTVLRT1oUmjdtHGe2a24+5PufjQwAjgNeB3YE7gZ+MTMrliV2zezQWZ2nJndZWbvmNlCM5tnZo+b2bfNrKjN9dfIbPH+jJnNMLN6M/vYzCZldhMtTXHfpWZ2upldZ2YvmVmDmXlmXTgRkYK25ybDuff0ndlsZH/OuPUlzvvHq9Qv1sx+kUjMbHvgJeC7wBZABS1Lhi37FXKc2VUifRCVnkHBUDpRAxh1DKUTdfp3Q2MSwAysLA0VDOmgSQxxXgnb4e7z3P0yd98c2J5kvbQyku3WAfYyszPNbEjKmz6YZEep7YBngIuAvwObANcAt9myW8itQ7Ir1TzgbuD/gH8BawHXAg+aWWenyfbO3N8xwDBgRsraRUR6tKF9K/jbcdtx/M6j+MtT0zn4qqf4IHP0TURC+BVQTjIe6+Xua7j7qPa+clxntzGL9UE0qqgBjDpz0okaDEUNYNQxlE7U6d8DepcucyrSWpx3tBVw92fc/VskXWonAy8Cq5Nsvf6hmd2e4ubeAvYFRrr7N939x5nb3gD4ADgQOKDV9Z8EBrj77u5+kruf4+4nkgRsE4Hxba6/PAuAvYER7j6MJIgTEZFWSouL+Mk+G/HHI8fy3uw69rlkEi/MjLPQq0iB2wa4w92vzmw2UHAqSopCfRCNGgxFDWDUmZNO1GAoagCjjqGe4fPMQYCW0yhaulUjda0WYjdt3gRpLdy9xt2vdPexJAO5PwMNdD7Iwt3/6+7/cvfmNufPAK7KfDu+1fkNba+bOb+RpEMNYL1O3neDu9/n7to8QURkBfbYeBj3nLozaw3qzaUv1nP0tc/y3LQ5uAfbh1yksDQA7+e6CFkqajAUNYBRZ046CoakELX83TcHGnNWz6pdElgN71+R42qWKsRu2si7dq6Qu78AnGBm3wMO66KbbYmcV3iE1cyKSbrLAF7uovsXEZFW1hzUi79/ZwfOu/E/3DN9Lgdf9RSDepex7tA+9CkvobKsmL4VJYwe3Ic9NxnGGgN75bpkkZ7uSWDLXBchS0UNhhTAiMRQiB1DqyJyl29dQxK4jxs9KMfVLPXwGzMBKC8pKphu2rwO0lopJVl7bJVk1jk7KvPt/e1cPhg4hWQB3SHAV4B1gZuAf6/q/YuISPvKSorYc1QpPz18J+57dQbPvPsZ0z6rY2bNIhbUNzF/USOzaz/gwvun8u2dR3H27mMoKY4zIBPpYc4BnjSzI939xlwXIzGn+oh0JwUw6RRix9CqyIcu35PGr5vjapbqU578ra81sDJUN21LM2Gv0uIuP5iT10Game0MnECyplk5cPEq3uSvSTYcuNfdH2jn8sHAz1p978DvgHM8S/OMzOwEkv8zQ4YMYeLEidm4W1kFtbW1epzygB6n+Gpra3nuqccZDOwzhORwxhKlzFpQzD+rG/njo+/y1GvTOXWrckqL9KEy2/RcKgj7Af8Frs/sOv4C8Hk713N3/59sFlaoBvQuZVZdQ7hFsSMGfOrMSSdqMKQAJh2tv5eOunzTibqeXHe+N+ZdkGZmA0l2vDweWJ+kO6wGWKUjomZ2GnAmMBU4sr3ruPvU5KpWTLLRwf7ABcBOZraPu89ZlRo6w92vBq4GGDNmjI8fP76771JW0cSJE9HjFJ8ep/g68xgdDNz0zPucc9cr3PZhX6785lbqTMsyPZcKwvmt/r1z5qs9DihIy4KIH2KiruVz1aPV6sxJIWowpAAmHa2/J90p4npy0L3vjXkTpJnZBJLwbH+gjCRAexf4X+AWd1+wCrd9Mkk32+vAl1cUiLl7E8kiuxeb2UzgZpJA7ZSVrUFERLrG4dutSWNTMz/752ucc9crXHjgZpjF6ToQ6QEmdMeNmtlBwC7AFsDmQF/gb+5+RDvXXY9ko6k9SDZ8Wg2YCzwNXOTujyznfo4m2QF+I6CJZCf437l7Xi7TMXn6XGbVNgCEOnAQdS2fp6tnA9C3vDhUZ85HcxcCyRQpBUMrpgBGJIbIXb5z65L3xu74HBA6SDOzISTdZ8eRrEVmwEzgryTdY/9192tX8T7OAP4AvEoSon2a8ibuy5yOX5U6RESk6xy9w9p8VtfAJf95myF9yzl7jw1yXZJIj+Huj3bTTZ9LEqDVAh8Cy3vi/g/wDZKDoPcCc4AxwL7AvmZ2urtf0vaHzOx3JGPID4E/kRycPRT4l5md6u6Xdd1/JzvyoWMo0lo+M+YtAqCytDhUZ05VZQkza+oZUVURKhiKOD1XpDtFDYaiTrMu1PX3QgZpZrYbyTpg+5IMcBqAu4DrgfvcvcnMzuyC+/khybpoLwFfcffZK3Ezq2dOV7jLp4iIZM/3dluPWTX1XP5INYP7lHPsjqNyXZJIwTCzDYET3P17KX7seyQB1zsknWkddpWRbAp1obu/2OZ+dwEeAn5rZre7+yetLtuBJESrBrZx97mZ839Lss7b78zs3+4+LUXNOaeOoXQKcQrSqtD6e+lErEvr76UTNRiKetCkUNffi/NMWtaDJBsIvAqcBoxw94Pc/d+ZaZWrzMx+ShKivUDSidZhiGZm25lZr3bO78PSDQ7uaXNZlZltYGbDu6JeERFJx8z4xdc3Yc+Nh/Hzf73OP6d8nOuSRHo0Mys3syPNbBJLx3Cd5u6PuPvbndnAyd2vbxuiZc5/FJhIciB2hzYXn5Q5/WVLiJb5mWnA5SQbVx2bpmaRrqKAr/Oirr83efpc5i1MppJFCmDOufMVrb+XQtRgKOpBk0Jdfy9kR1qGA58Bs4G6rrzhzNoYF5CsizEJOK2debPT3P36zL9/DIw3s0dJ1kZbAKwB7AX0B54EftXm5/cHrgNuIJme2vr+f8TS6QpbZE6PNbOdMv9+3N2vWan/nIiILFFcZFx06BYcfe2znHnbS3y+oIEjx62lNdNEupCZbUIyk+AIoIql69j+OUcltXzibztbYNfM6f3t/Mx9wE8z1/lZO5dLDxB1ylbkjiGtv9d5p948mYZmKC8pChXA1GTCvaF9y0Ktv9fQmDyGAytLQwVDTU1JANOvojRUMCSxRA3SjiTZWOArwG7AfDO7GbjB3Z/pgttvmd9TDJzRwXUeJZlKCskaGnXANiRrofUiWdD2BeA24Fp3TzO1c0+SKQut7cCyR04VpImIdIGK0mL+dPTWnHrTi5z3j9e45dkPOHTbNdhyjQGs1q+cfpWlVJTGmVIgkg/MrJJkbbHjge1gSSoxBTjT3f+bo7rWAr5MctDzsVbn9yZZjqO29XTPVt7OnK7f7UV2oajBkKZspaMdO9OJuv5eS8dQ3/JY6+/NzoShOKGmWUedNhy1rojThiFuXd3NOtE9nzNmtj7JEc6jgMEkXWpTSbq8fg1c4+4n5K7C3BozZoy/+eabuS5DVmDixImMHz8+12XICuhxim9VHyN3587JH3Hlo9W882ntMpeVFRfRt6Ik81XKekP7sMuYIey+0TAqy+J8CMwHei51LTN7wd23znUdLcxsC5Lw7HCgH0mANpmkC/9SumhsZmbjSdZIa3fXzg5+phz4D7Aj8AN3/22ry0YAHwEfufvIdn62lGRN3gZ3L+/g9k8gGZcyZMiQsbfddlua/1K3mDF/EbNq6gHoVRbnw/ubM2poaGrGMEYP6U2vHL2O1tbW0qfP0t/J1Bk1NDY1U2TGukP7UF4So8tq6ic1NDYndW0wrC/FRTE+kL7xyXwWNzslRcaGw/t1y320fYyi1LUyenJdK/M4rUjL331pUREbDO/bpbe9KqLW9fbMWhYtbqKipJj1Vmv/seiOx2l5mpqdt2bWsri5mcrSYtYdGuM9qH5xM2/PrMGB0uIiNhi2co/jhAkTOhyDRe1IA8Dd3wLOMrMfk6yZdgJJJ9evSEK1nczsQOAfKTvCRESkwJgZB44dyQFbrc70zxYwdUYNs2rrmb+wkZpFi6lZ1Mj8RYuZt7CRiW/N4s4XP6KqspQjxq3JSbusQ9+KWEcmRbLJzI4jGYeNZeku6n8GrnP31zLXuTSH9RUDN5KEaLcCv1vJm+rwCLO7Xw1cDcnBzAiB8b6XTuLlj5roW17M3afsFCZI+9/fT+StT+tYf2hvHvzm+JzV0TbYP/t/HmJWXQNDepfx3OG7dvyDWbZsXRNyXc4SZ/3Pg8yua2Rw71KeP2x8t9zHyhx8yUZdK6Mn19UdB8ki/r7m1DXw3QsfZkFDUfJ8DFLX5Olz+dX9T9JMCWsMqGTSN8a3e71sH8w87+5X+cuUBUAR2649gOMOabs0aW4ccMUTTH5/IeUlRdx8/LhumTocOkhr4e6NwC3ALWa2DslA7miSdcZuA2ab2V/c/ewclikiInnAzFh7cG/WHty7w+s0NzvPTpvDDU9O4/JHqrn1uQ8472sbs+/mI7JYqUgoVwPNwJ0kMwPu66oNoFZVJkT7K3AwybjwiHY2LJiXOa3q4Gaq2lwvL8yYtwiAytI43WgQc4F6iDtlK2pdUadsRawr6jTryOvvafp3551682SaSaZZX3zolrkuZ4nWGzP86sDNclzNUi3Tv6squm9jhjjPpk5y92p3/yEwEjiEpIV/MPD9nBYmIiI9RlGRMW70IK48Yiz/OHlHVh/Qi9NufpEzbnmR+YtifTAUySIDNgU2BobmuBYAzKwEuJlkvbabgMPbm6Xg7nUkUzv7dLCj+nqZ07e6q9buEHWnx6h1RQ34otbVEuxFCvjm1DUs+feQvnHW+7rq0eqQAYx27ExHO3amE33Hzu58D8q7IK2Fuy929zvcfXdgHeCqXNckIiI9z+Zr9OfvJ23P93Zbn3+9/An7XDKJlz74PNdliWTbTiRdX2uQLLHxvpnda2aHmFlOPs1m7vcOkk60vwBHrqBLrmUDhD3buWyvNteRHmZOXQO1DUlQFSnfq55Vy9xMOBRpR+nJ0+fyzqd1ACzM7K4YwUUPvcWszO+rT6AlF56ung0kGw1ECmC0Y2c62rFTOitvg7QWZlZFsujtUbmuRUREeqaS4iJO3209bjtxe5qb4aArn+RPj71Lc3OgT2Mi3cjdn3T3o4ERwGnA6ySB1M3AJ2Z2RTbryWwscBewH8labce6+4o+7bccdP2JmS355GZmawMnA/UkmybkjYhT3DRlK53IO3ZqKlnnRZ1mHXnHztanUUStK+JrPcStKxtCr5GW2cJ8LNAIPOvuM1tdVgF8DzgLGADU5aRIEREpGGPXGsC9p+3MD/4+hV/e+wZPVs/mdwdvzqA+7W7yJ9LjuPs84DLgMjPbDjiRpCPspMxV9jKzM4G/uPusNLdtZl8Hvp75dljmdHszuz7z79nuflbm31cBewOzSaZsntdON89Ed5/YqvYnzez3JMuBvGxmdwBlwDeAgcCp7j4tTc25NHn6XOZluk0iBUOn3/IiDpQEm7L18BvJx4jykqJQHUMfzV0IQJ/y4lAdQ5pKlk7U6cxR64o6nTlqXRHXUYw6zbp6Vi3zF3b/wZywQZqZXQJ8F5bEmw1mdqa7X5HZEv0GknXS6oGLSaYZiIiIdKuqXqVcdcRYbnx6Or/49xvsfckkfn3AZowfMyTUtByR7ubuzwDPmNnpwBHAccCWwG+A/zWzf7r7wSlucguSzaRaG535AphOcgAVYFTmdDBw3nJuc2Kbms80s5eBU0g2r2oGJgO/dfd/p6g15069eTINzfGCocrSZMLL6MG9QgUwfcqTD1RrDawMFcBUVZYws6aeEVUVoTqGonaaRK1L0okY8EWd/q1p1umcffuUJe+N3XkwJ2SQZmZHkwxwmoE3SMK0McAlZlYH/BEozpz+wt0/zlWtIiJSeMyMo7Zfm7FrDeDUm17k2OufY4Nhfdl1g6GsPbg3g3qX0beilH6VJQyvqqSqMs4AQ6SruXsNcCVwpZmNJelSOww4IOXtnA+c38nrjk9V5LI/ewPJAdm81tIx1Lc81lSyqB0dqiudiB0wELeuiAHfnLoGFi5OXici7diZrY6htKJO/9Y063SysWMnBA3SgGOABmCCuz8FYGZfAh4iWQfjQ+Br7v5KzioUEZGCt/GIKu47Y2f+8eLH3Pb8B/zxsXdparNuWkmRsecmwzhn7w0Z0T/OwEykO7j7C8AJZvY9kjBNCkzEThNQXWlFDPjm1DXw0bxkKuy8hV/YnDdnok6zvuiht6irTzqYIq2/l62OobSiTv+OujFDoU+zjhqkbQbc1RKiAbj7Y2Z2N3AQ8C2FaCIiEkF5STGHbLMGh2yzBosam/h0fj1zFzRQs2gx8xc18uL7c7nx6ek88c5srj1mG7ZcM84gSKS7uHsdcE2u6+jJInbAgOpKo3pWLfMyQVWkjqHJ0+cyK7NIfUlxnLpaB0Mj+lfkuJqlok6zbukY6hts/b1sdQylFXX6t7ow08lWXXFeGZdVBbzTzvlvZ06faucyERGRnKooLWbNQb3YfI3+7LTeYPbedDg/2Wcj7j1tZ/pVlnLUn59l6oz5uS5TRHqAiLvLVc+qZUFj0ik0PFDQEbVj6Jw7Xwm5Y6c2jEgn6jTrlo6h8pKiUOvvqQsznah1RXwPguzVFTVIKyLZqbOtRgB3X5jdckRERFbe6CF9uPn4cfQqL+bY655j5vxFuS5JRPJY1MWnz759CnUNST3jRg/KcTVLRe0YqsmEe0P7loXqGNKGEekUemdOWlHrihrwRawr6jTr6lm1TJudvDfW1jd1631FDdIA4vyliIiIrKIR/Su59phtmLewkeNueJ4FDXEGHiKSX06/5UWaSTqGIi0+/dHc5INVn/JiThq/bo6rWSpqx9DszPRJnFAdQ1E7YKLWVeidOWlFrStiwBd1Y4arHq0OOc26Zf09gN02HNqt9xU5SDvfzJpaf5HZ3rzt+ZkvfSIREZHQNh5RxaWHbclrH8/j+7dOoblZx4xEJL2oHUNVlcnyyyOqKkIFQxE/IAO0NJgEajQB4tYVsTMHYgZ86hhKJ+r076gbMzxdPRtIDk5E6vJtfTCnu7t8IwdplvIr8v9FREQEgC9vuBo/2Wcj7n9tBr954M1clyMieSjiB3eIW1fUDhjVlU7EQLR6Vi2fL4i3MYM6htKJOv076sYMM+YlS5RUlsbq8s3mwZyQu3a6e5xXIRERkS72rR3X5t1ZtVz1aDX9Kkv4zi7rYBbng4GIxKaOoXQiBnzqGEpHHUPpqGMonYbMWpP9yktCBUN6rU8nm6/1CqxERESyzMw4f9+N2XfzEfzm/jc57ZaXmFVTn+uyRFIxs83M7Ndm9g8ze7jV+Wub2SFmFufweQ8TsWNoTl0DCxcnwUtxUZyPGOoYSkcdQ+moYyidqNO/I76mQty6InaHQnaDx5AdaSIiIj1daXERF31jC9Yb2odL/vs2D7w2gy9vMJTN1+jPwN5l9C4robKsiIqSYirKihk5oJKhfeN82JLCZmYXAOew9KBs62FrEXAzcAZwaXYr6/midgxd9NBbS4KhPTZeLcfVLKWOoXTUMZRO1I6hqHVF7A4F1ZXWgN6lzKprCBXwzalrWPLvIX27P6RVkCYiIpIjRUXGqV9ej302G871T07j4ddnct+rMzq8/rB+Fey20VAO3GokW64Z58i3FBYzOxQ4F3gA+CHwDeBHLZe7+7tm9jywLwrSutw5d74SsmPowdeT166+wQIYdQylU1VZwsya+pAdQ9E+uIPqSitqwBexrjl1DdQ1JtO+Iy3/UT2rlumfJQdzFmYC7ggueugtZmXCtD4V3f93ryBNREQkx0YP6cMF+23CBfttwvxFjcxb0Ehdw2IWNTazqLGJhQ1NvDu7juenzeGOFz7kr0+/z47rDuJHe27IpiOrcl2+FJ7TgHeA/dy9wcz2b+c6bwDjs1pVgajJrBM1tG9ZqMCqqSn5BFpRUhwqgIn4ARni1tWyXlukddsgbmeO6uq8bHcMdVb1rFoWZAKr4YGmWUfu8q1vSrp8Lz50y1yXs8TDb8wEkrp+deBm3X5/CtJEREQC6VdRSr92jqRNAL690yjq6hdz87Pvc9Wj77Lf5Y9z7I6jOGv3MVSWFWe/WClUmwLXu3vDcq7zMRBn5N+DzK7N/NqdUIFV1A4Y1ZVOS0day1pWUUQMHiN3DEVdFzCbHUOddfbtU6jLtPmOGz0ox9UspS7fdPqUJ+PgtQZWZqXLN84zS0RERFaod3kJx+08mv+cuQuHbbsmf378Pb5++RO882ltrkuTwmHAiuZzrAYsykItBSdioABxO5mi1hW1YyjqTqLqGOq8fFgXMBsdQ53Vel3Ak8avm+NqllKXbzrZfq1XkCYiIpKHqipL+eX+m3LDt7ZlVm09X7v0cf7+woe5LksKw9vADh1daGbFwE7Aa1mrqIBE3cWtpYMpUidTU7PT3Jx82hsU6PcVuWMo4k6i59z5SsiOoZapZP0qStQx1AmR1wUE7STaWVHryvZ7UJxXbhEREUltl/WHcO9pO7PpyCrOvH0K37v1JWrr43QSSI90G7CVmZ3ZweU/BtYFbspeSYUjYodV1J1EZ86vDzuVLHrHUKSdRFuvCxipY6hlKtmwfuWhApioHUNR64r4mgqqK61sd/kqSBMREclzw6oquPn4cXxvt/X5x0sfsc8lk3hh+pxclyU910XAFOA3ZvYMsBeAmf0u8/3PgaeBq3NWYQ81efpc5tbVA/E6hiLuJDp/UfKBKluLT3dWy1Sy/pWloTqGZs5P/rZ6lZWE6hj6rC55HA0LFVhFnJ4LcTuGotYVsZsWYtY1p65BXb4ZCtJERER6gOIi4/Td1uPWE7encXEzB175FN/56ws8WT2bxqY425NL/nP3hST7X9wIbAVsS7Ju2veBscBfgT3dPdbh6h7g1JsnL+lkitoxFGmKW3FmAfhsLT7dWRE/IIPqSksdVumors6bU9fAJ/OTqbCRunwveugtdflmxHo1EhERkVWyzdoDeej7u/CnSe9yzaT3uO/VGZQWG6v1q6CytJje5SX0rShh85H9OWTrNVhzUK9clyx5yN3nAceY2feBbYBBwDzgWXefldPierCGxiQU71eujqHOaHIHLNQHZIj5wR3i1hW58yvazquRO4Zq65PHb7V+5TmuZqmoXb4XPfQWNYuSAC1Sl2/LuoDq8lWQJiIi0uP0Li/hjN3W58QvrcPENz/lpQ8/59P59SxsaKKuYTFzFzRwxcR3+ONj1Zy5+xhO/NJoLNO5IZKGu88BHsh1HYUi4gd3SDqFZtbUh+sYaulIi1ZX1N9X1Loidn5Vz6pl+mfJuoALG+N0fV/1aHXIjqGoG0acfsuLNDRDRbAu3wdfnwEk6xVG6vJtWRcwYpdvtl+7Yr1KioiISJepLCtmr02Hs9emw79w2cz5i/j5v17j1/dNZeb8RZz31Y0UpokEF7VjKGpd6kjrvKgbRkyePpd5manDIwdW5riapc6+fQr1TUlnzsWHbpnrcpZovWFEpI6hqBtGVJYmK12tGSwYampKUuOKkuJQXb4RX7sgN3UpSBMRESlAq/Wr4PLDt+KCf7/OdU9MY3Cfck6eEGdwK3GZ2bWdvKq7+7e7tZgCE7VjKGJd1bNqacpMcdNUshWLumFE1I4hbRiRTtTp31GDIXUfp6OONBEREckaM+On+2zEnLoGfvvAm6w5sBdf23xErsuS+I5ZweVJG1ByqiCti0RdfDrqFLdz7nyFnfokQZqmkq3YwoYkSBjWrzzUVLKoHUMKFNJRXelEDfhU11Kx/mJEREQkq4qKjN8ctBkff76QM2+fwoj+FYxda2Cuy5LYRnVwfn+SjQd+CjwJ/ChbBRWCqItPR53iVrOwAfpoKllnLc7s7jywV6k6hjpBdaWjutIZ1LuUmTX12jCiE3LV5VuUtXsSERGRkMpLirn6yK1ZvX8lx//lhSXdJSLtcffpHXxNcfdrgJ2APYHdclxqj9KyW1q/ipJQHUNRp7hpKlk6Lb+vltMoWoKESIECLN3AIlonk+rqvMg7nL6feV2tKIvz+9KGEctSkCYiIiIM6F3GtcdsQ7M7x17/HPMWxPowJfnD3T8A/gWcnutaepL+mQ+gI/tXhAqGIn5ABtWVVtS6oooYiGr6dzqRdzhtefy2DnRwovX0b3X5KkgTERGRjFGDe3P1kVvz4ZyFnHDj88xbqDBNVtpMYL1s3ZmZ7WxmfzezT8ysPnP6oJnt3c51dzCze81sjpktMLOXzewMMyvOVr09ScRAAVRXWlHritgpVz2rlrrGpJ5IG0Zo+nc62uE0HU3/XpaCNBEREVli21ED+e3Bm/H89LnsddFj3Pb8B9TWx/pgJbFlAqldgXlZur9zgceALwH3A/9H0hE3ABjf5rr7tbruXcDlQBnwB+CWbNS7siIGChB36l3EuiJPJdPaR5139u1TqF3UTL+KklAbRmj6dzra4TQdvQctS727IiIisoz9tlidtQb15sd3vsIP7niZc+58hTUH9mJw33L6VZTSr6KEvhUl9K0opW9FCRsO78e2owZSUaqGnkJgZl/q4KISYA3gWGAL4Jos1HIw8D/Aw8AB7l7T5vLSVv/uB/wJaALGu/vzmfN/CvwXOMjMDnX3kIFa1N3lIlqyxtCQWGsMRZ5KprWPOq8lGKosLQ4VwPSvLOHjefGmf0dcOB/i1hX1tT5qXblSkL8FMxsE7A/sA2wKrA40AK8A1wHXuXtzq+uvAfwYGAusRXKE8zOgGrgW+Ku7p4pmzWwH4FxgHFABvJO5rUvdPc6kdhERKUhbrNGfe0/biWfem8Okt2cxbfYCZtXU8+HcBdQsWkxt/WJqFjWSaa6gd1kxB44dyaalcdY/kW4zEfDlXG4kXV9nd2cRZlYEXAgsAA5vG6IBtBmfHQQMAf7SEqJlrrMo09X2H+A7BOxMi7z2UfWsWgBqAtUVdY2h56fNAZKOoUhTybT2UToKFKQ7RZ1mHbWuXHXKFeqz/2DgSuAT4BHgfWA14ACSo6d7mdnB7t4ySFwH+CbwDHA3MAcYBOxFEn4dZWZfcfdO/VVlphX8HVgE3Jq5va+RTCvYMVOfiIhITpkZ40YP6rBDwd2pqV/MC9Pn8q+XPuaWZz/gpuZm3iuayqm7rkdlmTrUeqgLaD9IawbmAs+6+7NZqGMHYBRwBzDXzPYBNiEZXz3r7k+1uf6umdP727mtx0gCuR3MrNzd67up5pVy1aPVWvsohZY1hkqLi0IFQ4sygdXI/hWhgiGtfZRO1LqiTr1TXelE7JSbk+mkBU3/blGoQdpbwL7APW06z84BngUOJAnV/p656ElgQOvrZq5fCjxIsv7GAcBtK7rjfJ9WICIi0sLM6FdRyoQxQ5kwZig/2HMDzrzhUa6YWM39r83g4m9syaYjq3JdpnQxdz8/1zVkbJM5nQlMJpllsISZPQYc5O6zMme1LBr0VtsbcvfFZvYesDEwGnijWypeSS2LYg/rV661jzqh9QfjSMFQ1A/uUeuKGCiA6korYl2R1yt8P/O6Gmla+kUPvcXMmiSw0vTvhC1tuhJYEqb9ErjM3U/txPVPBy4CznX3X3bi+t8C/kwyreDoNpftSjKt4DF332VFtzVmzBh/8803V3Q1ybGJEycyfvz4XJchK6DHKT49Rvlh4sSJlI7chDNvm8Ls2nrO3H0MJ35pNEVFluvS8pKZveDuW+e6jtbM7FrgFXf/Q47r+BXwI5KDk+8BJ5HMHliLZMOBPYBH3X185vpvkewkup67v9PO7T1B0uW2QzvdbJjZCcAJAEOGDBl7220rPH7aZabOqKGxqZnS4iI2GNY3a/e7Iu98WsvCxiYqS4tZd2icDquWuob3gsED4oT50X9fuairtraWPn3av0/9vjqvqdl5+9NaGpuau6Wu5T1Oy1O/uJnqT2tpcs8s6t+7S+taWTPmL2JWJhjqXVbC6CB1vTu7jrrMBk9D+pQzrCpdl9XKPk4r0vIeVFxkjFmtL8VBxnTd/VycMGFCh2OwODFnHC2HYlbYq5vZlaplW/WXO3n7eTutQEREpDN2XHcw95+xMz++8xUuvH8qk96exe8P2SL1gFDCOpxkOYpca5k7bCSdZ1My379mZvuTdJ7tYmbbtxeMtaPlk0G7R5nd/WrgakgOZmYz2P/NRY/y+oxaNhrWh5MOXeGx1qxpXde9h8Sr69ytnIMCHYCJ/vvKRV3LO0j2w18+zMyaelbrW84zh7R/nWyrnlXLiQ89Sn1TCWsMqGRSkLr+9943uPqld4Eitl17AMcdskOX3v7KHsz8xh+f4pn3kg6rE3YexbfHb9Slda2sA654gsnvN9GvooS7Tt4xzFTr3186iZc/ms+wfuXce/qXUnfUdtdB59avEd89TK9dAEVZvbfgzKwEOCrz7ReCLjMbbGbnm9nPzewKYCqwO3AT8O9O3s1ypxWQHFEtIZlWICIikpf69yrjim9uxYUHbsqL73/Onhc/xgOvzch1WdI1pgERFuqamzl9t1WIBoC7LwQeyHy7beZ0Xua0oxalfm2uJysQdUpgSz2Lm2PNvIn4+5pT18AHnydBx+eB1vyqnlVLXWPye8r22kfLc86dr1DflEwli7QuoDaySEfrFfYMuXxNVUfasn5Nskjtve7+QDuXDwZ+1up7B34HnOOdnyPbMnjraJDWcn7/9i5sM62AiRMndvJuJVdqa2v1OOUBPU7x6THKD20fp9WA87Yr46qX6znxxhcYu1ox+69bxsi+OpaXx24CTjKzAe4+d4XX7j4t61t83sHlLbVVtrr+1sD6wAutr5g5mDqKZEbCu11aZReIGMBA3LWPWpQEmX4EcYOhyBtZ1C5qpl9FSdbXPlqelo0sRg/uFWpdQAVD6UR9TVVd6eTyPUhBWoaZnQacSdJldmR713H3qclVrRhYHdifZOeqncxsH3ef0xWltNxdBzXkbFqBrByt65Qf9DjFp8coP3T0OB24ZzNXTqzmT5Pe5dwnFrLh8H5stWZ/hvatoLy0iN5lxfTvVcZWaw1g9f6VX7xhieRXJIHUI2Z2LvCcu8/MQR2PkQRf65lZmbs3tLl8k8zptMzpf0l2Yd8TuLnNdb8E9CJZpzbc0hpRA6uWDqZIi2Jf9Wj1kkWx+5THqStqMBR9I4vK0uJQwVDUQEF1pRPxNRVUV1olxUXLnGb1vrN+jwGZ2cnAxcDrwJdXFIi5exPwPnCxmc0kGYxdAJzSibvTtAIRESk4ZSVFnL7behy5/Vrc9eJHPPjaDP798ifMW/jFwfVuGw7l/H03ZuSAXjmoVNpjZkcBL7n7y8CilrOBf2Qub+/H3N27bazp7rPN7FaScOw84NxW9X6FZLOBeSxdruMO4ELgUDO7tNXO6RXALzLXubK76l1Zk6fPpXpWLQB9KuJ8iLnoobf4eF7yp7B1oM6c1sHQkL7FK7h29kQNhmbOT0JH91g7nEb94K660olaVy4DmOWJWNecuoYlQWikuqpn1fLu7DpAQVpOmNkZJAvmvkoSon2a8ibuy5yO7+T183ZagYiIyKoa2LuMb+80im/vNApIdhqrX9xEbf1iPp1fz8NvzOSaSe/x1Usf57pjtmHLNeN8QC9w15Msb/EyMIkOOudz4PvAdsBPzOxLwLMku3buT7Kb5/Hu/jmAu883s+NJArWJZnYLMAfYl2QN2zuAW7P+P1iB0295ccmaTJHWPnr4jaQJsV9FSai1j1oHQ1F2loO4gULUuiIGCqC6eoqWKactpxHMqWtY8voVSesu34rSOAcnzrnzFWrrk2npuTiYU9BBmpn9kGRdtJeAr7j77JW4mdUzp51dnTNvpxWIiIh0teIio1dZCb3KShjat4JNVq9i381HcMx1z3H0tc9y20nbs8Gwfiu+IckGA3D38TmuYwl3/9TMtiPpRtsfGAfUAPcAv3L3p9tc/24z2wX4CXAgUAG8QxLIXZJizdus6VuefHAZPbhXqE6m/pUlfDwvWZMpbidTnIczatARta6IQQeorjQib2TR0uVbkwliIogaWGkji/bFesXMIjP7KUmI9gJJJ1qHIZqZbWdmX5hfYmZ9SKaEQjJga31ZlZltYGbD2/zYHcBskmkFW7e6fuhpBSIiItkyekgfbjp+OypKiznxxheYtyDW2ioSi7vPcffvu/sody9z90Huvl/bEK3V9Z9w973dfYC7V7r7pu7+h8zSHeFEDTqi1hVVxKAD4tYVdW2tiHVFDoYibmShnVfTaXltWHtQrIM5LYb2Lc/JwZyC7Egzs6NJ1jRrIpmecFo7a3tMc/frM//+MTDezB4lWRttAbAGsBfJ7ppPkiy829r+wHXADcAxLWfm67QCERGRbBo5oBdXHrEVh179NGfc+iJ/PnobigJN0xLJlqhBR9S6lg064nzUiRjAQMy6Wu+8ulq/8hxWsqyoO69GD4ZW718RaiOLlk6m9VfrE2rn1aiBVdTX+lzXFefdJbtGZU6LgTM6uM6jJOuBAPwJqAO2IVkLrRfJluovALcB17p7p/tV83FagYiISLaNXWsg5311I376j9f4/UNvcdYecQbiBaq/ma2Z5gfc/f3uKqZQRAw6IG5dEad2KhhKp/UUt3GjB+W4mqXOufMVahc1U1UZa+fV6MHQoN5loaZ/5zqA6UjUuqK+1ue6roIM0tz9fOD8FNe/hzZTNzvxM9ezNIhr7/IngL3T3KaIiEihOWLcWrz28Xwue+QdNhzej302a7tigmTR6ZmvznIKdKzZlaIuBh+xrupZtXw4L1mTKdnhtGH5P5AlCobSad3JFGkji5qFyd/T6lUV6hjqhKh15TqA6UjUuiK+1kPu69LgRkRERMIyM36+38a8/WktZ97+ElWVpey03uBcl1Wo5gOf57qIQjKnrmHJIt0VZXGG7V8MrGI4585XmL+wiarKZI2hD157PtclAQqG0lqU6bCqqigJ1ckUNehQXenkOoDpSNS6oq6Hmeu6Yv02RERERNooLynmj0eOZe1BvfnW9c9x7ePv0dQcY8pWgflDZkH/Tn/luuB8d9Wj1Xw8bxEAWweastU2sIpCwVA6UYOOqHW1BBzRgg7VlU6uA5iORKxrTl0Ds2uT19VIdVXPqmXanDogdzucxjm0JSIiItKBwX3KueWEcXzv1pe44N+v8+fH32OPjYex3mp9GNCrlF5lJfQuL6ayNDkd2reCyrI428eLrIynq5NN5Yf1K1cnUydEDWCi1hW1AyZqXRGDDlBdPUHUwCrqwZyzb5/C/IVNOd3hVEGaiIiI5IX+vcq49phtePD1mfz16en87Znp1C9uf+2V4iJj4xH92GuT4Ry41eoM7RdnIW2Rzpo5P1lXyx11MnVC1AAmal1Rg46odUnnRQ2GInQyteeih94KGVhFPZjz0dxkaYHK0uKcHcxRkCYiIiJ5w8zYY+Nh7LHxMBqbmplVU8/nCxpZ0LCYuoYmFjYspq6+ifdm1/FE9WwuvH8qf3j4LY7efi1OmbAeVb1ifZAVWZ6oAUzUuqIGMFHrkvwXNRhSJ1M6D78xE4B+FSWhAquoB3MivAcpSBMREZG8VFpcxIj+lYzoX9nu5WcxhupZtVzxSDXXPP4ed7/0Mb8+YFO+vOFqWa5UZOVEDWCi1iX5T51M6UQNhtTJlE7/yhI+ngcj+1cosOqECO9BcV6dRERERLrYOkP68H+HbM6/TtmJQb3L+PYNz3PW7VOoWRRrSlp07l7k7hfkug6RfKNgKJ2oU9yiBlZRg6HInUytT6OIEAy1J2pdEeg3IiIiIj3eJqtX8c9TduLkCetw5+QP2fuSSTw/bU6uyxKRLhI1GNIUt3SiTnGLGlhFDYai1qVgSLqK/oJERESkIJSVFHH2Hhtw24nbA3DIH5/idw+8SWNT+xsWiOSSOpnSiRoMaYpbOv0rk5WHIk5xa30aRdRgKGpdIl1Ff9kiIiJSULZeeyD3nrYzB241ksseeYcDrniSt2fW5LoskWWokymdqMGQprilEzWAiVqXSHfRwZzli/MbEREREcmSvhWl/PbgzbnqiK34YO4C9rjoMU7+22QefG0Gn9XW4+65LlEKnDqZ0okaDEWtS8GQSAxRAysdzFk+7dopIiIiBWvPTYYzdq2B/Pnx9/jbM9O555VPAKgoLWJQ73IG9i5jUJ8yhldVcug2a7D5Gv1zW7AUjMidTNrFrfOi1iXSXaIGQ1E6mdqKusGGDuYsn4I0ERERKWhD+pbzo7024PtfWZ/np8/hzRk1fPz5Qj6rbeCzugY+q23g+WlzufnZ9/n2TqM4Z+8NKS6yXJctPZwCKxFZnqjBkDqZ0om6wYYO5iyfgjQRERERks0IdlhnMDusM/gLl9XWL+Y390/lz4+/x+zaen5/yBYK06RbKbCSQqNOpnTOufMV5i9soqoyVjCkTqZ0+leW8PG8mBtsRAis2ory3qggTURERGQF+pSXcMF+m7Bavwp++8CbrN6/kh/suUGuyxKRwBQMpRO1kylqYFWzMPnbWr2qIlQwpE6mdKIEQ21FrSsK/VZEREREOum749fhsG3X4IqJ1fxzyse5LkdEUDCUVtRg6PlpcwBYvX9FqE6mqIHVZ3WNy5xGoQ02pBDor0hERESkk8yMn++7CdusPYAf3DGF1z+en+uSpAdSJ1M6CobSWdiwGIC1BvYKFQwtytRVVVESqpNJgVU6CqykEOivW0RERCSFspIiLv/mVvSvLOOEG59nbl1DrkuSHkadTOlEDYYWNzUDMKh3WahgqKWultMoFFilo8BKuosO5qxYnN+KiIiISJ4Y2reCK4/Yik/n13PKzZPDfSCV/KZOpp5BgVU6Cqyk0EQNrHQwZ8XiPFoiIiIieWTLNQfwi69vwhPvfMYpN73IosamXJckPUTUTqaoFFilo8BKukvUYChSJ1NrUQMrHcxZsTh/3SIiIiJ55pBt1uCnX92I+1+bwdcufZyHXp9JY7AP8yJdRYFVOgqspLsoGEonUidTa1EDq6gHcyK9B5XkugARERGRfPbtnUYxekhvzr3rVY7/y/OUFRcxckAlvctLqCwtpry0iPKSYgb1LmOD4X3Zcd3BrDe0D2aW69IlqEgfFlqLHFjNrKlXYJXH1MmUjoKhdCJ1MrUWNbCKKtJ7kII0ERERkVU0YcxQJp49nolvzuK5aXP46POFLKhfzKLGZmrrFzO7toEX35/Lrc9/AMB6Q/tw3M6j2H/LkZSVxPnQKDFE+rDQmgKrzlMwlI46mdJRMCTdKerBnEjvQQrSRERERLpAaXERX9loNb6y0WrtXu7uzJi/iIff+JRbn3ufH/79Fa6YWM0F+23CLusPyXK1hc3MRgIXAHsCg4BPgLuBn7v73ByWBsT6sNCaAqvOUzCUjjqZpBBFDayiHsyJ9B6U+wpERERECoCZMbyqkiPHrcW/TtmJ647dhmIzjr72Wb5/60vU1i/OdYkFwczWAV4AjgWeBf4AvAucDjxlZoNyWB5z6hr4fGHyt1BRFueYtzqZ0lEwlI46maQ7KbBKJ+r6jpEoSBMRERHJMjNjwpih3HfGzpy267rc/dJHfPWSSbzy4bxcl1YIrgCGAqe5+9fd/UfuvitJoDYG+GUui4saDKmTKR0FQ9KdogZDUetSYJVOpM6vFtEO5sT5zYiIiIgUmPKSYr6/+xhuPn4c9YubOeDKJ7j6sWqamz3XpfVIZjYa2B2YBlze5uKfAXXAkWbWO8ulLRE1GFInkxQiBUPpRK0rYmCl7uN0oh3MUZAmIiIikmPbjR7EvaftzK4bDOV/753Kkdc+w4xMV5J0qV0zpw+6+zKfjN29BngC6AWMy3ZhLRQMSSFSYJVO1GCoxWr9ynNYybKqZ9Xy4byFAPSpiPP7UvdxOtEO5ihIExEREQlgQO8yrjpiLBceuCmTp3/OHhc9luuSeqIxmdO3Orj87czp+lmoRUQyFFh1XuRgaGZNPQDjRud0qcllRA2G1H2c38xdUwfy1ZgxY/zNN9/MdRmyAhMnTmT8+PG5LkNWQI9TfHqM8oMep67x3uw6fvT3l7ntpB1ecPetc11PT2FmVwPHA8e7+zXtXP5L4BzgHHf/VZvLTgBOABgyZMjY2267rVtqfHdWHXUNi+ldVsLoITmbYfoF+VhXbW0tffrk5oNgPv6+cqHlMaqeVcuChiZ6lRWH+vAe7fcFMO2zBdQsaqSkyBg9pA/lJd3fG9OZ59J7s+uorV9MWUkR6w7pQ3GRdXtdnRHxMYTuqasrXvMK6fe1IhMmTOhwDBZnMq6IiIiIADBqcG9uOWEct52U60oKTssnvy8caXb3q4GrITmY2V2B8RqzavnFv1/n3N03ChUo5GNduQz28/H3lQstj1G0ulpErKu6paavZq+mzjyXIv6uoLDq6orXvEL6fa0KBWkiIiIiAZnFOJrfw7Rsi1rVweX92lwv69YZ0ofrjt02V3ffIdWVjupKR3V1XsSaQHWlpbrSiVaX1kgTERERkULRsiZGR2ugrZc57WgNNRERESlwCtJEREREpFA8kjnd3cyWGQebWV9gR2Ah8HS2CxMREZH8oCBNRERERAqCu1cDDwJrAye3ufjnQG/gL+5el+XSREREJE9ojTQRERERKSTfBZ4ELjGzLwNvANsBE0imdP4kh7WJiIhIcOpIExEREZGCkelK2xq4niRAOxNYB7gE2N7dP8tddSIiIhKdOtJEREREpKC4+wfAsbmuQ0RERPKPOtJEREREREREREQ6QUGaiIiIiIiIiIhIJyhIExERERERERER6QQFaSIiIiIiIiIiIp1g7p7rGmQlmVkN8Gau65AVGgzMznURskJ6nOLTY5Qf9Dh1rbXcfUiui5BlaQyWN/R6FJ8eo/ygxyk/6HHqWh2OwbRrZ3570923znURsnxm9rwep/j0OMWnxyg/6HGSAqExWB7Q61F8eozygx6n/KDHKXs0tVNERERERERERKQTFKSJiIiIiIiIiIh0goK0/HZ1rguQTtHjlB/0OMWnxyg/6HGSQqC/8/ygxyk+PUb5QY9TftDjlCXabEBERERERERERKQT1JEmIiIiIiIiIiLSCQrSREREREREREREOkFBWp4xs5Fmdq2ZfWxm9WY2zcwuMrMBua5NEpnHxDv4mpHr+gqJmR1kZpea2SQzm595DP66gp/ZwczuNbM5ZrbAzF42szPMrDhbdReaNI+Tma29nOeXm9kt2a6/EJjZIDM7zszuMrN3zGyhmc0zs8fN7Ntm1u54Qs8n6Uk0BotPY7A4NAaLT+Ov+DT+iqsk1wVI55nZOsCTwFDgH8BUYFvgdGBPM9vR3T/LYYmy1DzgonbOr81yHYXuXGBzkt/7h8AGy7uyme0H/B1YBNwKzAG+BvwB2BE4uDuLLWCpHqeMKcDd7Zz/ateVJa0cDFwJfAI8ArwPrAYcAFwD7GVmB3urhVf1fJKeRGOwvKIxWAwag8Wn8Vd8Gn8Fpc0G8oiZPQDsDpzm7pe2Ov/3wPeAP7r7SbmqTxJmNg3A3dfObSViZhNIBgbvALuQvAH9zd2PaOe6/TLXqwJ2dPfnM+dXAP8FtgcOc3cdcetiKR+ntYH3gBvc/ZgsllnQzGxXoDdwj7s3tzp/GPAssAZwkLv/PXO+nk/So2gMlh80BotDY7D4NP6KT+OvuDS1M0+Y2WiSAdw04PI2F/8MqAOONLPeWS5NJCx3f8Td3/bOHTE4CBgC3NLyppO5jUUkR+wAvtMNZRa8lI+T5IC7/9fd/9V6EJc5fwZwVebb8a0u0vNJegyNwUTS0xgsPo2/4tP4Ky5N7cwfu2ZOH2zniVRjZk+QDPLGAf/JdnHyBeVmdgSwJskA+2XgMXdvym1Zshwtz7H727nsMWABsIOZlbt7ffbKkg6MMLMTgUHAZ8BT7v5yjmsqVI2Z08WtztPzSXoSjcHyi8Zg+UfvGflD4684NP7KIQVp+WNM5vStDi5/m2QQtz4axEUwDLixzXnvmdmx7v5oLgqSFerwOebui83sPWBjYDTwRjYLk3Z9JfO1hJlNBI529/dzUlEBMrMS4KjMt60HbXo+SU+iMVh+0Rgs/+g9I39o/BWAxl+5p6md+aMqczqvg8tbzu/f/aXIClwHfJlkINcb2BT4I7A2cJ+ZbZ670mQ59BzLDwuA/wHGAgMyXy3reowH/qPpVVn1a2AT4F53f6DV+Xo+SU+iv+f8oTFYftJzLD6Nv2LR+CvHFKT1HJY51Rz3HHP3n2fms8909wXu/mpmAeLfA5XA+bmtUFaSnmMBuPun7n6eu092988zX4+RdIM8A6wLHJfbKguDmZ0GnEmye+GRaX88c6rnk/QE+nsOQmOwHkvPsRzT+CsOjb9iUJCWP1rS46oOLu/X5noST8uCkF/KaRXSET3H8pi7LybZBhz0HOt2ZnYycDHwOjDB3ee0uYqeT9KT6O85/2kMFpueY3lK46/s0vgrDgVp+ePNzOn6HVy+Xua0o/U7JPc+zZyq7TmmDp9jmXUIRpEs5vluNouSVGZlTvUc60ZmdgZwGfAqySBuRjtX0/NJehKNwfKfxmCx6T0jv2n8lQUaf8WiIC1/PJI53d3MlnnczKwvsCOwEHg624VJp22fOdULV0z/zZzu2c5lXwJ6AU9qh5vQxmVO9RzrJmb2Q+APwEskg7hPO7iqnk/Sk2gMlv80BotN7xn5TeOvbqbxVzwK0vKEu1cDD5Islnpym4t/TnIE4C/uXpfl0qQVM9vYzAa2c/5aJEcQAP6a3aqkk+4AZgOHmtnWLWeaWQXwi8y3V+aiMFnKzLYzs7J2zt8V+F7mWz3HuoGZ/ZRkcdsXgC+7++zlXF3PJ+kxNAbLDxqD5TW9ZwSn8VfuaPwVk7lrnbl8YWbrAE8CQ4F/kGxZux0wgWQ6wQ7u/lnuKhQzOx/4EcnR6/eAGmAdYB+gArgX2N/dG3JVYyExs68DX898OwzYg+Ro2aTMebPd/aw2178DWATcAswB9iXZSvoO4BDXi2aXS/M4ZbZY3xiYCHyYuXwzYNfMv3/q7i0DBekiZnY0cD3QBFxK+2trTHP361v9zNfR80l6CI3B4tMYLBaNweLT+Cs+jb/iUpCWZ8xsDeACknbNQcAnwN3Az9tZbFCyzMx2AU4CtmTp1uufk7Th3gjcqBeu7MkMqn+2nKtMd/e12/zMjsBPSKaBVADvANcCl7h7U/dUWtjSPE5m9m1gf5ItvwcDpcBM4CngMnef1NGNyMrrxGME8Ki7j2/zc3o+SY+hMVhsGoPFojFYfBp/xafxV1wK0kRERERERERERDpBa6SJiIiIiIiIiIh0goI0ERERERERERGRTlCQJiIiIiIiIiIi0gkK0kRERERERERERDpBQZqIiIiIiIiIiEgnKEgTERERERERERHpBAVpIiIiIiIiIiIinaAgTUREREREREREpBMUpImIdJKZHWNmbmbH5LqWzjCz6zP1tnz9qM3lE83Mu/g+L2tzn+d35e2LiIhIYdH4q1P3qfGXSBaV5LoAEZFcWIkBzLHdUkh2XAx8Djyehfu6F5gNrA0cnYX7ExERkTyh8Ve30fhLJIsUpIlIofp5O+edAVSxdODT2kvAe8DTwCfdWFd3uMjdp2Xjjtz9XuBeMxuPBnIiIiKyLI2/uoHGXyLZpSBNRAqSu5/f9rzMlIEqlj/wmdd9VYmIiIj0XBp/iUhPoDXSREQ6qaM1OsxsWuarj5n9wcw+MLOFZvaSmX09c50SMzvHzN42s0VmVm1mpyznvvYws3vNbLaZ1Weu/1sz698N/6/WtdVn6r/QzMraua5n1vYYZmbXmNlHZtaUL+uWiIiISH7R+EvjL5Fo1JEmItI1SoGHgIHAP4Ay4DDg72a2O/BdYDvgPqAeOBi41MxmufutrW/IzM4jmfowB/g38CmwGXAWsLeZbe/u87uw9puAnTO1zQf2Bn4ADKX9tUkGkkyxqAXuBJqBmV1Yj4iIiEhnaPwlIlmnIE1EpGuMACYD4929HsDMbgQeA24HqoFN3P3zzGW/B6YCPwKWDOTMbALJIO4pYO+W62cuOwa4LnP597qw9nWAjd19TuZ+fgJMAY4ysx+7+4w2198UuBH4lrsv7sI6RERERNLQ+EtEsk5TO0VEus4ZLYM4AHefRLJA7gDgh60HZe7+LvAEsKmZFbe6jdMyp8e3vn7mZ64nWXT3m11c9w9bBnGZ+6kD/kbyHrF1O9dvAM7SIE5EREQC0PhLRLJKHWkiIl3jc3evbuf8j4FRwAvtXPYRUAwMy/wbYHugETjYzA5u52fKgCFmNsjdP1v1sgF4vp3zPsicDmjnsmnu/mkX3beIiIjIytL4S0SyTkGaiEjX6Gg3qcUA7t7e5S1HFEtbnTeI5LX5Zyu4vz5Alwzk2h55zWiprbidy9pONRARERHJBY2/RCTrFKSJiMQyDyhy94G5LmQ5PNcFiIiIiHQhjb9EpNO0RpqISCxPAwPMbONcFyIiIiJSIDT+EpFOU5AmIhLLHzKnfzKzEW0vNLPeZjYuyzWJiIiI9GQaf4lIp2lqp4hIIO7+HzP7EfAr4G0zu5dk56k+wFrALsDjwJ65q1JERESk59D4S0TSUJAmIhKMu19oZk+QbMW+E7AfydodHwFXAzflsDwRERGRHkfjLxHpLHPXmoUiIj2RmV0PHA2McvdpWb7v8cAjwM/d/fxs3reIiIhIrmj8JdLzaY00EZGe7z0z88yUhW5lZpeZmZMM4kREREQKlcZfIj2UpnaKiPRcdwPTWn3/eBbu815gdqvvJ2bhPkVERESiuBuNv0R6NE3tFBERERERERER6QRN7RQREREREREREekEBWkiIiIiIiIiIiKdoCBNRERERERERESkExSkiYiIiIiIiIiIdIKCNBERERERERERkU5QkCYiIiIiIiIiItIJ/w+Wk3EZ2BmDkgAAAABJRU5ErkJggg==\n", 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Hbt8WbRuJaN8EppKQykS+iE4LcZ4KtW2cjVMCuR2izDisnhblUyJh0EWJejhtWDQM0WoYTI0jKHsqUAa0HBUaDHgG6+KSvLpWg6wKlRSaCKWY9ikQcBbnqrAAYuBcxSSi4yJ4fvIFzlfB9p7T1tjJ03w/wY51nVGFpExjaDRpeCdkT43WFuUm0K6pCatrAnlVVtDQsq3K0ORYUmnlUGrlkIc6Kxo7LRhjqDjpx466gtD0P16PH2574zlKHFueVjscJZJAB0mUEnFpBuhGhA10iBJ1TU8dU8G7Dy01oqMiWNeYaxwoya+Dvd7d4vwdfr2gUMtCdY1CLQvVNdJAXSM6u7goNz4OvzdQFwfyI1TnBDqVmwvvtEjOMjbeJfIT3Inm9fhC7ZrmAw+qTltwcl9Vy1HWgXKj1img0gan3ZNBppBAFmjXyBRiwAr381Anqd+PQNumcXBGsMPd3uBqta0gOw+mHuOcGxUKRXVvp4MQQjpCoVB4OOem1pZ1ZIRvNAA759zRmUR09fvnu/SkLP78n1bC5fCJi5zgvKeBBj738VBjVSJhkMgbT9RSuaRJ40+lE43BYC94sEe8r9+6G7wdPnihbTeLeeScdk8goC0aY41z7XH4A/PmAmHzp0kb52wMzoUavGBQ68TtzaFpAyKV0EQooNL23dsMgcYRX6FgZ9joC2tgtKfXI0ZPBEdR+Lx+EdQEAMbAIPJIBD6lodEpCpUUKp0idAuvWq8I3F6ngD5aDWUv3mbYnhEq4nZ4ESRvenuqK9RAdTu9jXPCevyhQDgHAC5GSQQD5bLQnJZSMW9j2HxqwSBmsLOkL9+6CzROoxB+wW2rc4WCS+FBSp/H31jf+Bvrm2CgUyoT8+fKFdLGuiZw8RCabiIYtIzs21O2BC+2wwN7weBAsNPMafOIfAnUNaH5PcPq4WBdI5GyUB2jDHtXGxSinolQBkbNKkMjmDurvaO2Osvl8Ibufgh/t5vdTcqNJxBw8np8oZG0/kDnEtBYboLzoMoU0iblJfgemk6hBwOW3cXn84c6m4JTkjgt4s4Wp90rzlOBgEooCBUYvRVebiRSUXakUknjPKgaGZRq0cmgMYjyIsqNUtTHfXyOc7fD22SEp7XOBUdgLvdgEMVp98Jt94jjKTiXsNff2LaRNgaAg/O8Nw/gqAN1TWiUX2B039atW3r0uOiK5h1QTqsbTmvgDoVAveN2eOH1+EKd+D53WNuGIdThz6QMCqU0FACVq8S7OnD+1gZG5jdOG/UzvB2+zgWH2R2oa7yhdrHb4Q2cnxA2f7nIH6lC2tgmlrEmAwVUgZdarwidv/vy1FlBbqc3LEAu8iVYxwTfnTYPvG6fCFoGzlGhDhGGxuNJwiALzEet1MhCnb0qrayxjolUhuqcvpw34aP8rXXO0Eh/p9UTFsgW9Y7b6QvrRPI3CYYGO+JEMFjSooMqeA7XNatv+vIc58G8CV5LWetcTTpZg3WOx+WDz+0THbKBOid4jhIdKYCEMUhkksaOl7DnbgTrl2CbRhPIJ5VODolE8rMe4bt///7CnJwcCvgSQn529u/fb8rJyUlr/nm7rzo55zVdSUBXv3++U+iAqx5oMQK7Cc5FEKYvz0XZWYyxUKM8JkXfoe/6/VxcEPXRBlh3YIxBrVNArVN0OH/Od1K5BBExakTEqDu9Dc75eVl+lGoZlEm6To0IDXYGno/5IpGwQHBaBQyIaPf3gnXw+VzfKAPBamO8tlPfD95R0FeDBV0hlUpgMKlhMHWsrvkllBuFWoYotQxRCR0vN+f7OVyhliFarUN0YsfqYe7njXeinKckUklo9H5H6uKg4MMZz0cKlQxRiTJEJXbsmArWwZu3bO6znSBdwSSNc4zHpHasPXy+18XheROXZmj3987n8zYhhPzS0ZQOPyOMNd4uSxqdjwFw8tM6Hxv+XUV50hLVwecWnCaFNKJyc3Z0Dm8dBV/OjfKoJaqD20Z1ceuozBBCyPmr797nRQghhBBCCCGEEEIIIaRD2hXwZYxFMsauYIxNYs2GfTHGtIyxv/RM8n5eGGMLGWOnGGNOxlguY2xqb6eJEEIIIYQQQggh5FwYY6PDX1KpdLTRaMyZMGFC5rJly6LaWl8ikYw+fPiwsq3tjh8/PjO47uLFi6Pbm57q6mrp73//+8RBgwYN0Wg0IxUKxajY2NjhOTk5g+bNm5e8bdu2zs/rR8h57pxTOjDGsgFsABADESDewxi7jnN+OrCKDsBfATzZY6n8GWCM3QTgeQALAXwfeP+SMTaEc36mVxNHCCGEEEIIIYQQ0g4PPvhgGQB4PB6Wn5+v3LBhg3Hnzp363NxczWuvvVYcvq5UKuU+n48tW7bM9OKLL5Y039bBgweVu3bt0gfXa28aCgsL5ZMnTx5UWlqqSE5Ods2ePbvWaDR6S0tL5SdOnFAvX748Tq1W+ydPnuzo+l9MyPmnPXP4/hvADgC/AWCACGpuY4xN55zn92TifmYeAvAm5/zVwO/3McZmArgHwP/1XrIIIYQQQgghhBBC2mfRokWl4b+vXbtWf80112QuX7487uGHH67MyspyB5dFR0d7Y2JiPB988EH0c889VyKXy5tsa+nSpSbOOaZPn96wYcOGyPam4c9//nNiaWmp4oYbbqheuXLlaYmk6Q3qp0+flhcVFcnb+Dohv3jtmdJhAoAnOOc2znkZ5/xGAB8C2MQYy+zZ5HUfxtj1jLEXGGNbGWNmxhhnjL17ju8kM8aWM8ZKGWMuxlghY+x/jDFjs/UUAEYDWN9sE+sBTOrev4QQQgghhBBCCCHkp3H11Vdb0tPTnZxzbNu2Tdt8+e23315VXV0tX7lyZWT45y6Xi3300UemkSNH2gYPHtyhkbi7d+/WAcDvf//7yubBXgDo16+fZ8qUKfbwz8aNG5fFGBvd2vYWL14c3dqUEklJScOSkpKGWSwWyYIFC5ITEhKGKRSKUampqUMfe+yxeL/f35FkE9JntCfgqwTAwz/gnD8EEfTdDGBwD6SrJzwO4HcARgBocZtBc4yxAQByAdwB4EcAzwE4CeABADsYY+GVhAmAFEBFs81UAIjvasIJIYQQQgghhBBCegvnIizU7LFOAIC5c+fWqtVq//Lly03hn69YsSKipqZGdvvtt1d1dH+RkZFeADhy5IiqcyluP4/Hw6ZNm5bx+eefG6dPn26+6aabqp1Op+Rf//pX0sMPP5zQ0/snpCe0J+CbB2BM8w855w9CBH3XdneiesiDADIhpqW4px3rLwUQC+B+zvlszvkjnPMZEIHfLAD/bOU7vNnvrJXPCCGEEEIIIYQQQn4W1qxZoy8sLFQxxjB58mRb8+VGo9E/a9as2q1bt0YUFBSEpll4/fXXY3Q6ne+OO+6o6+g+r7vuuloA+P3vf5929913J69Zs0ZfXl4u7dpf0rqqqiq5TqfzHTt27ND7779/+p133jmzd+/eIzqdzvfKK6/EuVyuds89TEhf0Z45fFcDuAXA280XcM4fYIzJ0L4Aaq/inH8X/Lm1HqlwjLH+AC4BUAhgSbPFfwUwH8BvGGN/4JzbAFQD8KHlaN5YtBz1SwghhBBCCCGEkJ+Bhz/en3K83KLp7XScTWa83v7f63OKumt7Dz30UCLQ9KFtnHPMnTu3IjMz093adxYsWFD94YcfmpYtW2Z65plnyo4fP67Yvn274ZZbbqnS6/UdnhfhkUceqSotLVW89tprcS+//HLcyy+/HAcAiYmJ7qlTp5ofeOCByokTJ3bbA9uWLFlSpNPpQgP2kpKSvBdffHH96tWrow8cOKAcO3ass7v2RchP4ZwjfDnn/+acX3aW5fdyztszUvjnZEbgfT3nvEnFxDm3ANgGQAMxvzE4526I6R8ubradiwFs79mkEkIIIYQQQgghhHSP5557LuG5555LWLJkSfyOHTsMo0ePtixZsuTUa6+9VtzWd2bMmGHLyMhwrFixwuTz+bBkyRKT3+/HPffc0+HpHABAIpHgxRdfLCkuLt7/yiuvnLzzzjsrx4wZY62urpZ/8MEHpqlTpw559tlnTefe0rnpdDrf0KFDXc0/T05OdgNATU1NewZLEtKndHuhZYyZAEwFYAewgXPu6+59/ASyAu/H21ieDzECOBPAt4HPFgF4hzH2I0RA+G4AiQBe6sF0EkIIIYQQQgghpId058jZnwvOeW5nvjdnzpzqxx9/POXjjz+OWLlypSk7O9s+efLkLo3CNZlMvnnz5tXNmzevDgDMZrPkiSeeiF+8eHHCo48+mnrjjTfWp6SkeLuyD4PB0GrcSiYTITOv10tTOpCfnU4HfBlj9wC4HcBlnPPawGejAXwFICqw2m7G2IzAtAc/JxGB94Y2lgc/jwx+wDn/IPAgt8cBJAA4BOByzvnptnbCGJsPMT0EYmJisGnTpq6lmpDzjNVqpeOCkDB0TBDSEh0XhDRFxwQhpLcsWLCg5qmnnkp64IEH+lVWVsoffvjh0u7eh8Fg8D///POl33//vX7Pnj26jRs36ubMmVMPABKJhAOAx+OBXC5v8r36+voemf+XkL6qKyN8bwLAg8HegP8CMAJ4A0AcgCsgRro+24X99EXB3p0mD2TjnC+FeNhbu3DOXwHwCgBkZWXxadOmdVf6CDkvbNq0CXRcENKIjglCWqLjgpCm6JgghPQWk8nkmzlzZt2aNWui1Wq1f+7cubXn/lbn6HQ6HwBw3hiWiYiI8AFAQUGBYtCgQU3mGs7NzdX2VFoI6Yu6MvduBoADwV8CUzlcCOB1zvldnPNZAHYBuLVrSewVwRG8EW0sNzRbjxBCCCGEEEIIIeQX7T//+U/p22+/XbBmzZrjRqOxww9rC3riiSfidu/erWpt2ddff63buXOnQSqV8mnTpoXuKB8zZowNAF588cWY8PXXrl2rX7duXVTz7RByPuvKCN9oAJVhv08OvK8O+2wrxLQPPzd5gffMNpZnBN7bmuOXEEIIIYQQQggh5BclIyPDnZGR4T73mmf30UcfRT/11FPJ6enpzlGjRtni4+M9NptNkpeXp/7hhx/0nHP89a9/LU5LS/MEv7Nw4cLqpUuXxi1ZsiT+0KFD6qysLOeJEyeUW7Zsibj44ovrvv76a2NX00XIz0VXAr61AMKfiHghAD+A7WGfcQCt9sj0cd8F3i9hjEk456FeKcaYHiK47QDwQ28kjhBCCCGEEEIIIeR89frrr59au3Zt5JYtW/Q7duzQ19TUyDnniI2NdV955ZW19957b9Wll15qDf9OUlKS95tvvsn7wx/+kLxr1y79jz/+qB86dKh9zZo1xwsKCpQU8CW/JF0J+B4FMIsx9hgAH8Scvrs45+awddIAlHdhH72Cc17AGFsP4BIA9wJ4IWzx3wFoAbz8M3wYHSGEEEIIIYQQQkgLnPPcnlp/8eLFpYsXL273Q9wmT57smDx5sgNAWUfSNGbMGOfmzZtPtLLIev/999c0/7CkpORgW9tatGhR6aJFi7r9wXOE/BS6EvB9HsAaAMUAvAA0AP4cXMgYkwKYgqYjfnsNY2w2gNmBX+MD7xMZY28Gfq7mnP8x7CsLIdK+mDF2EUSAezyA6RBTOTzWw0kmhBBCCCGEEEIIIYSQDul0wJdz/ilj7G4A8wMfvcc5fzdslV9BTOfwdRfS151GAJjT7LP+gRcAnAYQCvgGRvmOAfAkgJkALofoWVoM4O+c8x572iQhhBBCCCGEEEIIIYR0RldG+IJz/gqAV9pY9jWAPjM/Cuf8bwD+1sHvFAG4oyfSQwghhBBCCCGEEEIIId1N0pGVGWP/Y4xdwBhjPZUgQgghhBBCCCGEEEIIIZ3ToYAvgN8B+A5ABWPsNcbY5YwxRQ+kixBCCCGEEEIIIYQQQkgHdTTgmwjgHgC5AG4D8BmAasbYSsbYTYwxfXcnkBBCCCGEEEIIIYQQQkj7dCjgyzmv5Jy/wjm/DEAsgN9APJTtcgArAFQyxtYxxuYyxmK6P7mEEEIIIYQQQgghhBBC2tLREb4hnHMz5/x9zvkNAGIAzAawEsB4AK8CKGWMbWaMPcAY69ctqSWEEEIIIYQQQgghhBDSpk4HfMNxzl2c808553cAiANwEYCXAKQDeA7AScZYbnfsixBCCCGEEEIIIYQQQkjruiXgG45z7uecf8c5v49zngox4vdpAOru3hchhBBCCCGEEEIIIYSQRt0e8G2Oc76Lc/4o53xIT++LEEIIIYQQQgghhBBCfslkXfkyYywZwIMARgBIBiBvZTXOOR/Qlf0QQgghhBBCCCGEEEIIObdOB3wZY9MAfAFABcALoCLw3mLVzu6DEEIIIYQQQgghhBBCSPt1ZYTvfwBIAfwWwPucc3/3JIkQQgghhBBCCCGEEEJIZ3RlDt9hAFZwzt+lYC8hhBBCCCGEEELIzxdjbHT4SyqVjjYajTkTJkzIXLZsWVRb60skktGHDx9WtrXd8ePHZwbXXbx4cXR70rJu3Tp98DtJSUnDfD5fq+s1NDRIdDrdyOC6eXl5inb/wYScx7oywrcOQG13JYQQQgghhBBCCCGE9K4HH3ywDAA8Hg/Lz89Xbtiwwbhz5059bm6u5rXXXisOX1cqlXKfz8eWLVtmevHFF0uab+vgwYPKXbt26YPrdTQtUqmUl5aWKtauXWu49tprzc2Xv/7661E2m03S2e0Tcr7qygjfdQAu7K6EEEIIIYQQQgghhJDetWjRotJFixaVvvDCCyVfffXVyVWrVh1njGH58uVxzUfQRkdHe7Ozs+0ffPBBtMfjabGtpUuXmjjnmD59ekNn0jJp0iSLQqHgr776qqm15W+++aYpJibGk52dbe/M9gk5X3Ul4PsogAjG2BLGmLa7EkQIIYQQQgghhBBC+oarr77akp6e7uScY9u2bS3iP7fffntVdXW1fOXKlZHhn7tcLvbRRx+ZRo4caRs8eLCjM/s2Go3eSy65pG7Dhg2RpaWlTe5S37lzp/rgwYPam266qUYmk/G2trF27Vr91KlTMyIiIkYolcpRaWlpQxcuXJhUU1MjbW39gwcPKq+55pq02NjY4XK5fFRsbOzwa665Ju3gwYMtpq146KGHEhljo9etW6d/4403jMOGDRusVqtHRkREjLjyyiv7nzp1St6Zv5uQrup0wJdzXg1gJoCbAZQzxnIZYxtbeX3bbaklhBBCCCGEEEIIIT8pzkU8lbGWsybMnTu3Vq1W+5cvX95kFO6KFSsiampqZLfffntVV/Y9f/78aq/Xy1566aUm8/8uXbrUxBjDPffc0+b2//vf/5quueaazD179uguueSS+rlz51ZERER4ly1bFj9+/PhB1dXVTYK+mzdv1kyaNGnw2rVro4cPH25bsGBBxYgRI2yffvpp9KRJkwZv2bJF09p+li5dGnP33Xenp6SkuH77299WZWRkOD7//HPjRRddlOlwOGiqCfKT63TAlzGWDWA7ACMALYCRAKa18SKEEEIIIYQQQgghPzNr1qzRFxYWqhhjmDx5sq35cqPR6J81a1bt1q1bIwoKCkIjWl9//fUYnU7nu+OOO+q6sv8rrrjCkpqa6nrnnXdCAWWr1cpWr14dPWHCBPOQIUPcrX3v+PHjisceeyxVo9H4v//++6MfffRR4dKlS0v2799/7Ne//nVVQUGB6r777ksOru/3+3HHHXekW61W6ZIlS05t2LCh4MUXXyxZv359wUsvvXTSarVK58yZk97aA+S2bNkSsXXr1qNffPHFyZdffrl49+7deVdeeWXtqVOnVO+9915kV/5+QjqjKw9tWwQgGsBfALwFoJRz3vpjEwkhhBBCCCGEEEJ+btbcm4LKI62O6uwzYofYMXtJUXdt7qGHHkoEmj60jXOOuXPnVmRmZrYaXF2wYEH1hx9+aFq2bJnpmWeeKTt+/Lhi+/bthltuuaVKr9f7u5IeiUSC2267rfpf//pX0pdffqm77LLLrG+99ZbRYrFI77zzzuq2vvf6669HeTweNn/+/IqRI0c6w5c999xzJWvWrIlevXp1tMPhOKNWq/mGDRu0p06dUo0YMcJ2zz331IavP2/evLqXXnrJumfPHt369et1l112mTV8+Z133lkxbty4JtNWzJ8/v3rdunVRP/74o/auu+7qUtCbkI7qyhy+EwF8wjl/inNeRMFeQgghhBBCCCGEkJ+35557LuG5555LWLJkSfyOHTsMo0ePtixZsuTUa6+9VtzWd2bMmGHLyMhwrFixwuTz+bBkyRKT3+8/63QLHXH33XdXy2Qy/vLLL8cAwPLly2MiIyO9t912W31b39m3b58WAH71q1+Zmy+LiYnxDR482O5yudi+fftUALBr1y4tAEydOrXF+gBwwQUXWABg9+7dLToAxo4d2+Khcenp6W4AqK+vb3WuYEJ6UldG+LoBFHZTOgghhBBCCCGEEEL6lm4cOftzwTnP7cz35syZU/3444+nfPzxxxErV640ZWdn2ydPntyph7U1l5KS4p0xY0bD119/Hblhwwbtnj17dPPmzatQqVRtPqzNYrFIASA5OdnT2vK4uDgPANTW1koBoKGhQQoACQkJra4f/Ly1AK7RaGwxCFIul3MA8Pl8NIcv+cl1ZYTvJgDjuikdhBBCCCGEEEIIIeRnasGCBTUqlcr/wAMP9KusrJR39WFtzc2fP7/K6XRKbrvttgEAcO+99551+3q93gcAJSUl8taWV1RUyIHGYG1ERIQPAMrLy1tdv6ysTB6+HiF9WVcCvn8CMIQx9ghr7TGNhBBCCCGEEEIIIeQXwWQy+WbOnFlXUVEhV6vV/rlz59ae+1vtN3v2bHNiYqK7oqJCPmbMGGtOTo7rbOvn5OTYAWDjxo365suqq6ulx44dUyuVSh6c33fMmDF2APj+++9brB/+eWvTNxDS13RlSofHARwC8E8A8xhj+wA0tLIe55zP7cJ+CCGEEEIIIYQQQkgf95///Kf02muvrY+Li/MYjcYuPaytOalUig8++KDg1KlTiuHDh59zqoi5c+fWPPfccwnLly+PnTdvXs3QoUNDAeI//OEPiVarVXrTTTdVq9VqDgAXX3yxNS0tzblnzx7dG2+8YbzjjjtCD1p74403jLt379b169fPdckll1hb2x8hfUlXAr63h/2cHni1hgOggC8hhBBCCCGEEELIeSwjI8OdkZHh7qntT5kyxT5lypR2jbDNyspy/+Mf/yj6v//7v9QJEyYMueKKK2pNJpN3+/bt+n379mnT09Odzz//fOhBdBKJBK+//nrhVVddlXnXXXf1X7FiRX1mZqYzPz9ftWHDhkitVutfvnz5KamUnsFG+r6uBHzbCvASQgghhBBCCCGEENKrHnnkkarMzEzXs88+G/fll18anU6nJD4+3r1gwYKKp556qsxkMjWZj3fGjBm2bdu2HfnrX/+auG3bNv3GjRsjjEaj98orr6x98sknS881jQQhfQXjvM0HGpKfUFZWFs/Ly+vtZBDSp2zatAnTpk3r7WQQ0mfQMUFIS3RcENIUHROkNzDGcjnnY3o7HZ21f//+wpycnOreTgchhHTU/v37TTk5OWnNP+/KQ9sIIYQQQgghhBBCCCGE9CEU8CWEEEIIIYQQQgghhJDzRLsDvoyxI4yxhZ3dUVe/TwghhBBCCCGEEEIIIeTsOjLCdxAAUxf21dXvE0IIIYQQQgghhBBCCDkLWQfXn8YY6+y+6OlwhBBCCCGEEEIIIYQQ0oM6HPANvAghhBBCCCGEEEIIIYT0MR0J+E7vhv0VdsM2+izG2L0AFgBIC3x0GMBTnPPPey1RhBBCCCGEEEIIIYSQX4x2B3w555t7MiHniWIAfwaQDzE/8hwAaxhjoznnB3o1ZYQQQgghhBBCCCGEkPNeR6d0IGfBOV/b7KPHGGP3AJgIgAK+hBBCCCGEEEIIIYSQHiXp7QR0BWPsesbYC4yxrYwxM2OMM8bePcd3khljyxljpYwxF2OskDH2P8aYsZvTJmWM3QxAB2B7d26bEEIIIYQQQgghhBBCWvNzH+H7OIAcAFaI6RQGnW1lxtgAiOBrLIC1AI4BGAfgAQAzGWOTOec1XUkQY2wYgB0AVIF0XcM5P9iVbRJCCCGEEEIIIYQQQkh7/KxH+AJ4EEAmAAOAe9qx/lKIYO/9nPPZnPNHOOczADwHIAvAP8NXZow9FRg1fLbXtGb7yAMwAsAEAMsAvMUYG9qFv5EQQgghhBBCCCGEEELa5Wc9wpdz/l3wZ8bYWddljPUHcAmAQgBLmi3+K4D5AH7DGPsD59wW+Px/AM46RQSAM83S5AZwIvDrbsbYWIjA9NxzbIcQQgghhBBCCCGEEEK65Gcd8O2gGYH39Zxzf/gCzrmFMbYNIiA8AcC3gc+rAVR3cb8SAMouboMQQgghhBBCCCGEEELO6ec+pUNHZAXej7exPD/wntnZHTDG/h9jbCpjLI0xNowx9m8A0wC819ltEkIIIYQQQgghhPQ0xtjo8JdUKh1tNBpzJkyYkLls2bKottaXSCSjDx8+3OZAt/Hjx2cG1128eHF0e9Kybt06ffA7SUlJw3w+X6vrNTQ0SHQ63cjgunl5eYp2/8GEnMd+SSN8IwLvDW0sD34e2YV9xENMAREf2N4BAJdxzr9ubWXG2HyIqSQQExODTZs2dWHXhJx/rFYrHReEhKFjgpCW6LggpCk6JgghXfXggw+WAYDH42H5+fnKDRs2GHfu3KnPzc3VvPbaa8Xh60qlUu7z+diyZctML774YknzbR08eFC5a9cufXC9jqZFKpXy0tJSxdq1aw3XXnutufny119/Pcpms0k6u31CzlddCvgyxpIh5qcdASAZgLyV1TjnfEBX9vMTCVYMvLMb4Jzf3sH1XwHwCgBkZWXxadOmdXbXhJyXNm3aBDouCGlExwQhLdFxQUhTdEwQQrpq0aJFpeG/r127Vn/NNddkLl++PO7hhx+uzMrKcgeXRUdHe2NiYjwffPBB9HPPPVcilzcNCy1dutTEOcf06dMbNmzYENnRtEyaNMmyc+dO/auvvmpqLeD75ptvmmJiYjwJCQnuAwcOaDu6fULOV52e0oExNg1ieoQHAUwFoIEImjZ/9ZVpI4IjeCPaWG5oth4hhBBCCCGEEELIL9rVV19tSU9Pd3LOsW3bthZB1dtvv72qurpavnLlysjwz10uF/voo49MI0eOtA0ePNjRmX0bjUbvJZdcUrdhw4bI0tLSJoMWd+7cqT548KD2pptuqpHJZK0O3nvnnXcir7766vS0tLSharV6pEajGZmdnT34qaeeim1tmojrrrsujTE2+siRI4p//vOfsZmZmUNUKtWocePGZQGA3+/HCy+8ED1y5MhBRqMxR6lUjoqPjx8+ZcqUjFdffdXYmb+RkJ7QlWDsfwBIAfwWgIpznsI5T2/t1T1J7bK8wHtbc/RmBN7bmuOXEEIIIYQQQggh5BeHcxFPZazlrAlz586tVavV/uXLl5vCP1+xYkVETU2N7Pbbb6/qyr7nz59f7fV62UsvvdRk/t+lS5eaGGO455572tz+X/7yl6RDhw5pRowYYbvjjjsqr7nmmhqbzSZ94oknUq677ro241W/+93vUv/f//t/iYMGDXLceeedFePHj7cCwP333590//33p1VVVcmvvPLKunnz5lVMmTLFXFFRoVi1ahUFfEmf0ZUpHYYBWME5f7e7EtPDvgu8X8IYk3DO/cEFjDE9gMkAHAB+6I3EEUIIIYQQQgghhPQ1a9as0RcWFqoYY5g8ebKt+XKj0eifNWtW7apVq0wFBQXyAQMGeADg9ddfj9HpdL477rij7rHHHovv7P6vuOIKS2pqquudd94xPfnkkxUAYLVa2erVq6MnTJhgHjJkiLut765bt+5Edna2K/wzn8+HG264IW316tXRGzdurJwxY0aLv+nQoUOaXbt2HRk0aFCTbb/77rsxsbGxnqNHjx7W6/X+8GVlZWW/pOdkkT6uK4WxDkBtdyWkp3HOCxhj6wFcAuBeAC+ELf47AC2AlznnLQ50QgghhBBCCCGE/PI8se2JlBN1JzS9nY6zGWgcaP/H5H8Uddf2HnrooUSg6UPbOOeYO3duRWZmZqvB1QULFlR/+OGHpmXLlpmeeeaZsuPHjyu2b99uuOWWW6qaB0Y7SiKR4Lbbbqv+17/+lfTll1/qLrvsMutbb71ltFgs0jvvvLP6bN9tHuwFAKlUioceeqhy9erV0V988YWhtYDvfffdV9482Bskk8l4a1NIJCQkeDvydxHSk7oS8F0H4MLuSkhnMMZmA5gd+DXYWzSRMfZm4Odqzvkfw76yEMB2AIsZYxcBOApgPIDpEFM5PNbDSSaEEEIIIYQQQgjps5577rkEQEzfoNfrfaNHj7bMmTOneuHChW0O+psxY4YtIyPDsWLFCtPTTz9dtmTJEpPf7z/rdAsdcffdd1f/5z//SXz55ZdjLrvsMuvy5ctjIiMjvbfddlv92b5XXl4uffLJJ+M3bNgQUVxcrHQ4HE2mNi0tLVW09r2JEye2Ohhw9uzZNW+99VZsVlZW9qxZs+qmT59umT59ui06OrrlhMCE9KKuBHwfBfADY2wJgD/10sjYEQDmNPusf+AFAKcBhAK+gVG+YwA8CWAmgMsBlAFYDODvnPOfzYhlQgghhBBCCCGE9KzuHDn7c8E5z+3M9+bMmVP9+OOPp3z88ccRK1euNGVnZ9snT57cqYe1NZeSkuKdMWNGw9dffx25YcMG7Z49e3Tz5s2rUKlUrT6sDQCqq6ulY8aMGVJSUqIYNmyY7dprr62JioryymQy1NfXS994441Yl8vVclJiAMnJyZ7WPn/ttdeKBgwY4Hr33XdNS5cujV+6dGm8VCrlF154YcPzzz9fPHTo0BYjignpDZ0O+HLOqxljMwHsBPBbxthxAA2tr8ov6ux+zpGGvwH4Wwe/UwTgjp5IDyGEEEIIIYQQQsgv0YIFC2qeeuqppAceeKBfZWWl/OGHHy7tzu3Pnz+/av369ZG33XbbAAC49957zzp6ePHixaaSkhLFgw8+WLZo0aImadmwYYP2jTfeiG3ru609nA4AZDIZnnjiiconnniisqSkRLZhwwbdBx98EPXll18aL7/8cnVeXt5htVrdZhCakJ+K5NyrtI4xlg0xPYIRYv7bkQCmtfEihBBCCCGEEEIIIecpk8nkmzlzZl1FRYVcrVb7586d2613Uc+ePducmJjorqiokI8ZM8aak5Nz1tG0J06cUALAzTffXNd82caNG/VdTU9SUpJ3zpw59V988cXJCRMmWIqKipS7d+9Wd3W7hHSHrkzpsAhANIC/AHgLQCnnnOYsIYQQQgghhBBCCPkF+s9//lN67bXX1sfFxXmMRmOXHtbWnFQqxQcffFBw6tQpxfDhw885VURaWpobAL755hv9uHHjQutv27ZN/cILL8S3/c3WORwOtn79et2sWbMsEknj+EmXy8Xq6+tlAKDVarv1byaks7oS8J0I4BPO+VPdlRhCCCGEEEIIIYQQ8vOUkZHhzsjIcPfU9qdMmWKfMmWKvT3rzp8/v2bp0qXxf/nLX1I2b96sHzBggLOgoED13XffRVx66aX1n3/+ubEj+7bZbJLZs2dnJiYmukeOHGlLSUlxOZ1OyZYtWwwnT55UzZgxo37UqFHOzv1lhHSvTk/pAMANoLCb0kEIIYQQQgghhBBCSLdIS0vzfPvtt8cuvPDCht27d+vefPPN2JKSEsXTTz995tlnny3u6Pb0er3/scceKx4wYIAzNzdXu3z58rg1a9ZE6XQ639NPP33m888/P9kTfwchndGVEb6bAIzrpnQQQgghhBBCCCGEkF7COc/tqfUXL15cunjx4nY/xO3KK6+0dGT7ubm5ea19Pnr0aOfGjRtPtLaste2vWrWqEG0MblQqlfypp56qAFDR3nQR0lu6MsL3TwCGMMYeYW09vpAQQgghhBBCCCGEEELIT6YrI3wfB3AIwD8BzGOM7QPQ0Mp6nHM+twv7IYQQQgghhBBCCCGEENIOXQn43h72c3rg1RoOgAK+hBBCCCGEEEIIIYQQ0sO6EvBtK8BLCCGEEEIIIYQQQgghpBd0JeDbD4CZc76vm9JCCCGEEEIIIYQQQgghpAu68tC27wDM766EEEIIIYQQQgghhBBCCOmargR8qwE4uishhBBCCCGEEEIIIYQQQrqmKwHfTQAmdVM6CCGEEEIIIYQQQgghhHRRVwK+jwPIYoz9gzEm764EEUIIIYQQQgghhBBCCOmcrjy07f8AHALwKIC5jLH9AMoB8Gbrcc753C7shxBCCCGEEEIIIYQQQkg7dCXge3vYz/GBV2s4AAr4EkIIIYQQQgghhBBCSA/rSsA3vdtSQQghhBBCCCGEEEIIIaTLOh3w5Zyf7s6EEEIIIYQQQgghhBBCCOmarjy0jRBCCCGEEEIIIYQQQkgf0m0BX8aYgTGWwhgzdNc2CSGEEEIIIYQQQkjPY4yNDn9JpdLRRqMxZ8KECZnLli2Lamt9iUQy+vDhw8q2tjt+/PjM4LqLFy+OPlsafD4fEhIShjHGRufm5qrOtq7VamV6vX6EXC4fVVJS0pUpSwk573Qp4MsYkzLGHmGMnQBQB6AQQB1j7ETgczrgCCGEEEIIIYQQQn4mHnzwwbIHH3ywbOHCheXjx4+37N69W79w4cL0u+66K7n5ulKplHPOsWzZMlNr2zp48KBy165deqlUytuzb6lUiltuuaUGQJvbDHrjjTeirFar9Fe/+lV9UlKStz3bJ+SXotMBX8aYAsA3AP4JIA1AEYAfA+9pgc83BNYjhBBCCCGEEEIIIX3cokWLShctWlT6wgsvlHz11VcnV61adZwxhuXLl8fl5eU1ifFER0d7s7Oz7R988EG0x+Npsa2lS5eaOOeYPn16Q3v3v3DhwiqpVIpVq1ZFO51O1tZ6b775pgkA5s+fX92BP4+QX4SujPB9CMA0AJ8DGMw5T+OcT+ScpwHIAvAZgKmB9QghhBBCCCGEEELIz8zVV19tSU9Pd3LOsW3bNm3z5bfffntVdXW1fOXKlZHhn7tcLvbRRx+ZRo4caRs8eLCjvfsbOHCgZ+rUqQ319fWyd955J7K1dfbu3avas2ePLikpyX311Vebg5+vWrXKcOGFFw40Go05CoViVEpKytAFCxYkV1dXS5tvIykpaVhSUtIwi8UiWbBgQXJCQsIwhUIxKjU1dehjjz0W7/f7m6y/bt06PWNs9EMPPZTYWpqC2wv/zOl0sqeeeip2yJAhgw0Gwwi1Wj0yKSlp2EUXXTRgzZo1+vbmCSEd1ZWA760ADgGYzTnPD1/AOS8AcC2AwwB+3YV9EEIIIYQQQgghhJBexLmYkYGxlgNu586dW6tWq/3Lly9vMgXDihUrImpqamS33357VUf3N3fu3GoAeOONN1qd1iE43cNtt91WJZGI0NYf//jHhOuvvz5j//79uunTpzfccccdlf369XO98sorcRMmTBhUW1vbIgbm8XjYtGnTMj7//HPj9OnTzTfddFO10+mU/Otf/0p6+OGHEzqa7uZuuOGGtCeeeCLF6/Wy6667ruaOO+6oHDdunCUvL0/zxRdfRHR1+4S0pStz7A4E8ALn3N/aQs65nzH2JYD7urAPQgghhBBCCCGEENJL1qxZoy8sLFQxxjB58mRb8+VGo9E/a9as2lWrVpkKCgrkAwYM8ADA66+/HqPT6Xx33HFH3WOPPRbfkX3efPPN9Q899JDnhx9+MBw7dkwxaNAgd3CZ0+lkH3/8cbRUKuULFy6sBoDPPvtM/+yzzyaOGDHC9s033+SbTCZfcP3FixdHP/DAA2kPP/xw0uuvv14Uvp+qqir54MGD7Zs3bz6k0+k4AJSUlJQOGjRo6CuvvBL3r3/9q1ypVLZr/uHmampqpJ9//nlUdna2fd++fUdlsqYhuPLy8hajjgnpLl0J+LoB6M6xjhZAy0lcCCGEEEIIIYQQQvq40kcfS3Hl52t6Ox1no8zIsCf+659F516zfYJTFng8Hpafn6/csGGDkXOOuXPnVmRmZrpb+86CBQuqP/zwQ9OyZctMzzzzTNnx48cV27dvN9xyyy1Ver2+1YGCZyOTyXDLLbdUL168OGHp0qWmxYsXlwaXvf/++5F1dXWyiy++uD41NdULAIsXL44FgFdffbUwPNgLAPfff3/NsmXL4lavXh3VPOALAEuWLCkKBnsBICkpyXvxxRfXr169OvrAgQPKsWPHOjuafgBgjHHOORQKBQ+OQg4XHx/va+VrhHSLrgR8DwC4njH2N855i+H5jDETgOsB7O/CPgghhBBCCCGEEELIT+S5555LAMT0DXq93jd69GjLnDlzqhcuXFjb1ndmzJhhy8jIcKxYscL09NNPly1ZssTk9/txzz33dHg6h6B77723+sUXX0xYuXKladGiRaXBEbKvv/568GFtoW3v3btXJ5PJ+Pvvvx/1/vvvt9iWx+NhdXV1svLycml4oFWn0/mGDh3qar5+cnKyGwBqamo6HTeLioryT58+veG7776LGDx48JBZs2bVXXjhhdZp06bZOhMEJ6QjuhLwfRHASgA/MsaeAvAdgDIA8RAPc3scQAyA+7uYRkIIIYQQQgghhJCfXHeOnP254JznduZ7c+bMqX788cdTPv7444iVK1easrOz7ZMnT273w9qay8zMdE+aNMn8/fffGz788MOIW2+9tSEvL0+xY8cOQ2Jiovuaa64JPaytvr5e6vP5WDBY3Raz2dwk4GswGFodZRsMLnu93paTFnfAp59+WvCXv/wl4ZNPPol69tlnE5999lkolUo+c+bMuhdeeKEoJSXF25XtE9KWTj+0jXP+IYD/B6AfgFcA5AOwAjgB4DUA6QD+G1iPEEIIIYQQQgghhJynFixYUKNSqfwPPPBAv8rKSnlnHtbW3F133VUFAK+99loMACxdutTEOcett95aLZU2ToGr1+t9BoPBxznPPdurrSkp2kMqlXIA8Hpbj9FaLJYWc/LqdDq+aNGi0sLCwkP5+fkHli5demrUqFGWtWvXRs2ePXtAZ9NCyLl0OuALAJzzRwFMArAcwF4AJwPvywFM5pw/0uUUEkIIIYQQQgghhJA+zWQy+WbOnFlXUVEhV6vV/rlz57Y5BUR73XrrrfUmk8mzZcuWiBMnTsg/+OADk1QqRfBhbUEjRoywmc1m6e7du1Vd3WdboqOjfQBQXFysaL7s0KFDytYCvuEGDhzoueeee2q3bt2a369fP9eePXt09OA20lO6FPAFAM75D5zzeZzzMZzzjMD7PM75ju5IICGEEEIIIYQQQgjp+/7zn/+Uvv322wVr1qw5bjQauzxPrVwux4033ljj8/lw880396+oqJBfcMEFDenp6Z7w9X7/+99XAMC8efPSCgsL5c23YzabJd9++622K2nJyclx6nQ63zfffBNZUlISmiLVarWye++9N6X5+qWlpbKNGze22KfZbJbYbDaJVCrlSqWSN19OSHfoyhy+hBBCCCGEEEIIIYQAADIyMtwZGRmdnjahNffee2/VsmXL4nNzc3UAMG/evBZTRVx99dWWRx99tOTf//530uDBg4dOmzatoV+/fm6r1SopLi5W/Pjjj/rRo0dbL7roovzOpkOpVPK5c+dWPv/88wkjR44cMnPmzHqv14utW7caYmNjPTExMU2C0IWFhfKLLrpoUP/+/Z1Dhw61Jycnu81ms/Tbb7+NqK6ult9+++2V3REUJ6Q1HQr4MsY6NSKYc04FmBBCCCGEEEIIIYR0yJAhQ9wTJkww79ixwxAXF+e54YYbGlpb75///Gf5BRdcYH3++edjd+/erduwYYNMp9P54uLiPLfeemvVb3/72y5PMbFo0aJSjUbjf+edd0wrVqwwmUwmz9VXX1333//+t3TQoEHZ4etmZma6//CHP5R+//33+h07dujr6+tlERER3vT0dNdf//rXknnz5nU5PYS0hXHe/tHjjLFWn154Dpxz/osZScwYS4B4mN3lAPQQ8xrfwznffLbvZWVl8by8vJ8ghYT8fGzatAnTpk3r7WQQ0mfQMUFIS3RcENIUHROkNzDGcjnnY3o7HZ21f//+wpycnOpzr0kIIX3L/v37TTk5OWnNP+9oILYIQHsjxDoA0R3c/s8aYywSwDYA3wO4AkAVgP4AKnsxWYQQQgghhBBCCCGEkF+IDgV8Oedp51qHMSYHcB+AxwIfFXY4VT9ffwJQxjn/bdhnp3orMYQQQgghhBBCCCGEkF+WTs3J2xbG2A0AjgL4LwAGEQAd3J37aLa/6xljLzDGtjLGzIwxzhh79xzfSWaMLWeMlTLGXIyxQsbY/xhjxm5I0mwAOxljHzDGKhlj+xhjv2OMsW7Ydp/hdjpwaNMGnNqXi45MCfJL4LBacHDjehQfOdTbSelzbPV12P/Nlyg/cby3k9LnmKsrsf+bL1F1prC3k9Ln1JaWYP83X6CurKS3k9LnVJwqwN6vPoO5im4iaa70+FHsW/8FbPV1vZ2UPqf42GEc+u4buOy23k5Kn8I5R+H+PTi8+Vt4nM7eTk6fwv1+5O/cjsObv4XX4zn3F35BfF4Pjmz9Dke3fge/rzMz352/PC4nDm5cj7wdW8H99DiXcC67DQe+/Qon9+yiaylCCCE9plvm1mWMTQLwLIBxALwAFgN4knPe01dajwPIAWAFUAxg0DnSOQDAdgCxANYCOAaR5gcAzGSMTeac13QhPf0BLATwHMQ8viMAvBBY9mIXtttnuOw2rHjiYdQUnwEAjJl1LS687c5eTlXfYKuvw/uP/yEUfLng13dg7FXX9XKq+ob6inK8//gf4DA3AIzh0gX3Y+j0i3s7WX1CZeFJfPC3P8PtcEAilWLWg/+HgWMn9Hay+oSiwwew6t9/hc/jgUyhxHX/93ckDxna28nqE47v3IZ1zz0Nzv34fuU7uOlv/w+xaf17O1l9woFvv8I3r4hT7o6P38ct/3gGkXHxvZyqvuHHtR9j6/tvAgB2rv4Qtzz1DDSGiN5NVB+x+d3lyF23GgCQ+/ka3Pz3p6FQa3o5Vb2Pc44vXnwWx7aJR1Ec+PZr3PD4U5ApFL2cst7n9/mw+ukncfrAXgDA0W2bMftPT0AikfZyynqfx+3CB3/7P1SczAcADJ46HZfd+xDOszEwneK0WfH+439EXWkxAGD0lddg2m/m9nKqCCGEnI+6NMKXMTaQMfYxgK0AxgNYBWAw5/zBnyDYCwAPAsgEYABwTzvWXwoR7L2fcz6bc/4I53wGRIA2C8A/w1dmjD0VGDV8tte0sK9IAOzhnP8f53wv5/wNiOD3vV3+S/uIze8uR21pMa76w6MY/quZ2P3ZJzhz6EBvJ6tP2PDaEtgbGnDdY/9A5oQp2Pr+W6g6TTN6cM7x1dLnwH0+3PT3p5E6NAcbXl8KczWNSvT7fPh88X+hUGtw61PPIjatP75a9hwcVktvJ63XeVxOfP7CM4iIicOv/7kIelMMPn/xGXjcrt5OWq+zmxvw9bL/IX5gBm779/+gUKvx5YvPwu+n0WX1FeX49vWXkJYzCjc/+V/4vB58/dL/aAQVgIqTJ/D9ireROWEKrn3kb7DUVmPj8pd6O1l9QuH+Pchdtxo5F1+GWQ/9H6rPnMbWFW/1drL6hKPfb8KxbZsx4bpbMHPhgyjNO4Kdaz7q7WT1CXu+WIvTB/Zixp13Y/qceTi1dzf2ff1FbyerT9j+4XuoOJmPK+5/GBOuuwVHt36HvO1bejtZfcKmt15DQ0UZrnnkr8i5+HLkrltN11KEEEJ6RKdG+DLGogD8FcACAAoAOwD8gXP+Qzem7Zw459+Fpems6zLG+gO4BGJO4SXNFv8VwHwAv2GM/YFzHrzP8X8AzjpFBIAzYT+XATjSbPlRiBHEZ6Vw1wHbngdkKkAbAxgSgch+gD4e6CO94fXlZTj03TcYcckVyBg3CekjxuDU3lx8/8HbuHXoM92zE48DqD4OWMoBawXgtgE8cBuYUg9oogFtLGAaCKi7YxaO7lFekI8Tu37A5BtvQ9rwkYjvn4HTB/di24fvYvbDT3TPTnxeoPYkYCkDbFWAswEABzgPy5sYIHqA+L2PKNyXi5Jjh/GruxYieVA2Lr37ASz//Xz8sGolLllwf/fshHORL3WFgKNO5I3PA0jlgFQBaE2APlEcV0pd9+yzGxz9fhNqS4pw1UOPIiEjC5fc/QDe/tN92P3ZJ5h6y5zu25GtBjCXALZKkTc8UG4UmsZyE5kq8quP2Pf157DV1eLK3/8Z8QMzcfFdC/Hhk4/iwDdfYfQVV3ffjtw2oKEEsFeLssP9ABggU4pyo40BdPGAtFtuiOkWP679GB6nC5fe83tEJ6Vg+px5+Oy5/4e8bVsweOr07tuR3wc0FAP2GsBlEfWzVCbOU6oIwJAk6uE+co4CgB9WrYBEIsGldz8AXVQ0ptz0W3y7fBlOH9yHtOEju29HnAO2asBRK44pjx2QyMUxpDaKc3cfqocBYPtH70Gp1eKSBfdBqdFi7FXX4YdVKzFu9g3dPzrcbQOslYDbCrjtgEQqyo1SB+gTxPHVR3DOse3Dd2GIicW0OfMhk8sx/FczcWDDVxg76zoYYmK7d4d+nziHu22i3HAOKLTipYnuU/Ww3+fDjo/eR2zaAEy6/hYwiQQn9+zC7nWfYNTlV0Gt64Ey7rKKutjvEXklV4v6RqHrU3WN2+nAj2s/RlrOKIy89EpwznFi90788MlKDL/o0p4ZAe33BeobB8Ak4rhSRfSp4wkQd7zt+/pzDJk6HYMmXwi/34eCXTuw7cN3kTVxKpikW2cUbOR1ifIjkYj6WK4WedSH1JaW4MiWjRh1+VXoP3IsUrNzcHLPru69lmoN54DXGSg3cpFHhBBCznsduoJljCkA/B7A/wGIAFAA4BHO+aruT1q3mxF4X885bzKRFOfcwhjbBhEQngDg28Dn1QCqO7CPbRAjhcNlAjh9ri8qXTXAN39puUBjAuKHASnjgQEzgKTR3R94cNuAMz8AFYeBmnzAEhZoVUUA+jggdgj251aDMYZxV18PAJApFBhz5Wx899arqDhVgLj0AR3ft6MOKPgOKPgWKPoRqDnRGOA9F20skDAc6DcJ6Dc5kDfdfKHkdQHlh4CyfSJt1grR2JYEgh4RyUD0QOzdXAiFWoORl10FAFDpdBhxyZX4cc1HMFdXwmDqxAWjywoUfg8UbATO7ACq8gBfO0c3GpKBuGyRN2lTgIQR3V9u/D6RJ2X7RZA+mDdMKhrZEclAVH/sW7cXOmMUhk6/RCTNFIPsCy7Cka3f4YLb7oRK24kArM8DFG4FTm0FTm8Hyg8CnnbOR2lMF8dU8hig/zQgbljPNHwbSoDSvSJIby4RF/Y+L6AyiEBi7BDs++ILmFL6YeC4iQCAmNQ0DBwzHge//RoTr78VMnknyrPfB5TsaSw3FYdFoPdcJDIgqj8QOwRInQikTQZis3smbxz14n9WdUwEhRx1gYCQEjAkgUemY9/XnyEleziSB2UDAFKyhyMxawj2rV+HUZdf1bnbQjkHKo8AJzeJY6viMFB/zupZdBqYMoHYwaIuTpsKxGT1TPAhWOcEy42zQXwuVwP6BOhq6vDDxm3InDAZ0UkpAICM8ZNhTEjC3vWfdy3gW1MAnNoszgele4HaUyLocjYyNWDKABJyxCv9QvF7T+SN3yfSWHsSqD8TOKbcgQ4vExyaZBzbthnDLpoJXVQ0AGDojEuw/eP3se/rz7sW8DWXijJT+D1QfgCoPgG4zzESX2kAYgYB8UMDeXOBOMZ6AueiozSYN26rqCeVOkATjXoejZN7dmHSDb+GUqMFAIy+fDZ2f7Ya+9d/gYvn/67z+7bXinwp2gmU5Ir/0bnqHG2s6JwMlpt+kwBjWufTcC4uiyjPwbzxukJ5U94gQ/mJ47ho7sJQnTtu9vU4sOErHPj2a0y5+Ted36/bLs5RxbsCeZMvOlH83tbXZ1LRkWLsJ/IlcSSQPFb83lN8HpGm+jOAyyx+l6sBbSxOnalHfUUZrnro0VCQbvw1N+L4D9/jyOaNXet883mBkt2ivinbL+pjc2nbx5VEJgZCRA8EYjKB5HGiPtbHdT4N5xLs2DGXiM4d7hfBMn08ju3Ng8NixoRrbwYgBp6Mv+ZGfPzU4zi+cxuGdKUu9vvF+bHw+0AbK08cV446AK3craCKEB2TMZniHB43FHJ353ffbh5nIG/qRZmWygBdHA5+uwVetwvjA3kjkUgxbvYN+Hzxf0XnW86ozu+Tc1FWT28HSvcA1fmBOqcK8DqarcwAXazogIvqD8QNFa+UcYAmqvNpaG86g52Cfq8oN7pY7F//OSRSSdNrqVnX4Ls3X0Fl4cmud77ZqkXelOSKNnqwPnY2NK13ZKpA3iSIdnFctngljxHliRBCyHmBdeQ2R8bYKQCpAGoB/APAEs55n7iHNDC1wncA3uOc39bK8v8C+COAP3LOn21l+YsQUy8s5Jwv62QaxkLMEfw3AB8AGAngNQCPcs6bjyoGY2w+xMhixMbEjP5oxduQ+lxQuOugdNVC7SiBzloInfUkdNZCMPjhlWpQbRqHytgLUGfMAZd0LoincNUhpup7xFRth8GcB0ng3+iWR8CljIZPqgIggcxrg9JVDanHgpfzxyNea8eE0XEoTbwUFn0GvG4XDrz1EkyDhyJ16q/at3PuR1TtHiSUbUB0zS5IuBcemRYNEdmw6tJh0/aDU2WCW2GET6qGeP4fIPXZIfdYoHTVQGMvhsZeBIP5BLR2McjaI9Oj2jQWVTGTA3nTueAv83tgrNuPuIotiK7ZCZlPPLzFJ1HBpTTCK9OCcQ6pzwGlqxo+jw/L8scjy1iPoaPSURE3HRZDBlyWBhx691UkjJmExLGT2pk3HJH1hxBf/i1iqrZD6nfBJ1GgIWIwrLr+sGlT4VLGwK2IhFemQXBWFqnPAbnHDIW7Dhp7MbS2IugtJ6BxlITlzXhUxUxCnXF4p/MG3A+DOQ+xlVsRW7kNCk+9+BgSuBURYXnjhMJdC6tHjldPjMOouBr0z8lCRdx02HRpsFdV4OjH7yB16q8QM3REm7uzWq3Q6RoDwnpzPhLK1iOmagfkXgv8TAqLfiAs+oGwa5LhUCfAIzfAK9OCMykY90Hid0PhboDCXQu1owJa2ynoLSehdpYDANxyA2qix6EydirqI4eBd3I0CPP7EFl/EDFV22CsOxDaPgB4pRr4pCpwJoXMa4fMZ0ONS403T47BxOQaJGUPRnn8xXBoEtBw5hROfL4K/S+ZBeOA5v1HbdNaTyO+/FvEVWyGwlMPDgarLi1QbvrBqYoNlBstAAk4A6Q+Z6Dc1ENjL4HGXgS95RRUrspA3kSg2jQBVTETA3nT+U4DlaMCsZVbEVO1DXrrydDnHCyQJg6pzw0J96DIFoEPzwzH9PRaGAeNQEXchXCq41CTdxiFG79E5tU3QZ+Y0oF9lyG+/DvEVWyC2lkBALCrE0N541DHwSOPgEeuR7C+kfhdgbxpgNpRCq3tDHTWQijdNYG8iURVzARUxUxCQ8TQTpcbcA6NvRim6h8QXbMLeksBJLzxoszPpABY6LM8swnrSgZj5qA6KAeORVXMFLhUJlTs343i7Zsw5KbboY4ytXv3Clct4su/RWzlNuhsp0J/m9mQBbsmCQ51Qqi+8UuUYNwLid8DmdcKlbMaSlcVtLYz0FsKIPeKQI1TGYPaqFGojJ2C+shsEcTqJIWrBjFV2xFVuw8RDUch87XdsbO3NgEbKwbiqhwrkDoGlbFT4VFEoOSHLSjftwvDf3s35IFgZ3vIPGbEVWxGbOVWRJjzAABeqRZmQ7C+SQzVe41544XcY4HCXQuVsypQbk6F0u1QxYXypiFicJfyRuaxwlS9E8a6fYisPxwqm63ZUZWK7dWpuH6sC87EcaiKmQSvXIfC775C3Yk85NyxEBJZ+88LEp8LsZVbEFv5PYx1B8Dgh5/JYdH3h12TAoc6Hm5FFLwyNfwSFQA/JH43ZF47lK6aQLkphs56ElK/6Mi0qxNRGzUS1aaJgXLT/s6m5ucKic+NqNrcUN5o7UVtfvfb8gE4WB+P6ycClrixqDaNh0+mwYkvPoG9qgLDfnt3hzqYmN8HU/UPiK3cjKjavZD63eBgsGlTYdOmwqmKg0tpgk+qhk8qRmZKfc7QeVPtqIDaUQatrRBSvzuQN0moiR6NmuixXT6mwH2IrD+CqNpcRDQcaVHnhPuseBCKHEbMnqRAbewE1EaNhl+qxLFP3oPP7Ub2zXd0cN8cBvMxJJV8gajaXMi94rhwKmNh1aUF8iYKHrkenEnBmRRSnwsyrw1yjxlqRxnUjjJo7CWQcNEZZdMkoyZ6HKpN42A2ZHYtbwBobEUwVe9ARMNRGMz5oXqtuZWnh8Pm02DmJANqYiaizjgMfibD4RXLIdfqkHX1TR3MGz8iGo4itnILYqp2QOERnX1ueQTsmhTYNUlwKyLhkevhlygBcDDug8xrhcJdD6WrGlpbEdSOcjCIgRNWbRrqjCNQFTMpkDdd64hTOSoQXbMLBvMxGMx5UDtbduxwDiw/OQYqpRQXTIhFTfR4NEQMgY9zHHz7ZeiTUtD/kqs6mDccOmsBYiu3IaZqW+g87pWqA+eppED7XAefVAXGORj3QupzhK6pNPbi0PdE+6g/6ow5qIqZAIu+63kjdzcgqnYPDOZjiGjIg9pREjp+g3yc4eX88YiL4BgzNgXVpnGBayl3x6+lwqjtxYG82Q6drRAA4GcyONTxcKgT4VJGBfJGDQCBvHEG8qYGGntp6PzBIYFFPxB1xuGoNk2ART+wQ3kzffr0XM75mA7/EX3E/v37C3Nycjoy2IsQQvqE/fv3m3JyctKaf97Rq/d+EF3LDIHgaTsawZxz3oNDE9ot2F3Z0Mby4OeRnd0B53wXY2w2gH8BeAJiuocnIOYObm39VwC8AgBZWVn8gotmtr1xey1wagtkJ75B/NHPEF+xSdz6l30NMOxG0Vt9rv+F2w4c/QzYv0KM4uJ+Mbpx6H1i5FHSKCjURrS4CY1zFP7wLezH/oehw+KQULsRCeUbgMRRwPRHYR89FqX5ebjwggvOfpuW3wccWQNs/i9QdVSMXh6/AMieDXniKJikMrQ/TBHGVg0Ufg953hdIyPsKCeUbRe/04FnA0OuAtAvOPbrV7wNObwMOrQKOrBWjKFSRQM6NwMBfAYkjIY1IgaZ5Hvv9OL7+Y3iPv42hw9KQXL4BySWfA/2mAFMeRN2gbLgqSzFt2rRz7N8P5H0BbH1GjKxTGoCRtwDZ10CaOhFRMiU6NRbBWhnImy+RkPel+L+pIoEhVwXyZuq5b3fjXIxwPrQKOLQaMBeLkQGZlwKZM4GEEWCmTCilMjS5qdDrRv4nb4Kf+BTDh6YgqmQdUovWAP2nA1MeRMX2ZKCu8qx5s2nTJky78ELg+FfA9hfE/0iuBQZfAQy9FpL0CxCh0KJTYxEs5cDJTVCc+LYxbzQmIHs2MOwGMXroXKNbORejaQ+sBA6vFiNMFHqg/4ViZHXyOCC6P2RqY9PK1m3D0TdfADu1DTlZJmiL1qDfmVXAwF/BP/UhvLQ1AnJrw7nLDSBGR215BjjxjRhBknkpMPRasP7ToddEoVM33AZGzyiOf4XE4+uRWPa1uE19cLDcTGnfbZINJSJfDn8iRpsAYrTauFuBhJFAXDaYLhby4LY4B2xVOPrS85CXHcKw/gbIC99DeuH7wKAr4PnV77B067fQuu3ty5vSvcDWRaLeA8T/ZejjwIAZ0EQko8OPY+JcjAg+tRWKExuQlL8eSaVfiXIz5GqRN6kT2zcquqEYOPABsP8DMXoLEHXq0HuApDFiNLEhEZLgtABeF2Apxw9P/gU6rQWDE6SQFLyBgSffBrJnw3rxb/Dy9k0wci8mtCdvyg4AO5aI49rvESPlpi4AMi6BIqo/TB29+OVcTKly8juoTnyLxJObRLnRxYt8GXad+Pvas117rThXHPpEjHADB6IzgBE3iHRGZ4gRj0q9KPMuM2CtxNH/9zRijBZkGCqAE68i49RbQPY1qLpoNt7e+yPiFFIMb0/eNJSIvMl9U9w5EDcUGPsXYODFkMVlI0oi7Vh9zLkYaXVyE9QF3yHp5HdIKv1SjKzKvkbkT9Lo9uWNywrkfSn+byc2iP+dLg7IuBBInSBGPxrTRN5I5YDLCm6twNGnnkVKvA/9ZGeA40uQdXI5MOw6nJ5+ET4+dgipEfr2PSzSXgvseg3IfVlMgxLVH5jyAJB5GSSJIxAhU3asPvb7xB0iJzdDU/AtNKc2inOoPhEYei0w7Hpxh8o58mbTpk2YNmUScPI7UW6OfS5Giir0Il9S5oiR58Y0cZeFVAG4rPCZy3DsHy9gYIoUyZ79wLFt4jvDb0TctIn44q33kJkYj6Sswef+W1xWYO+7wA9LRB1qSAJGzwGyLgNLGQedUo8O3c/i84g7Ek5vhyb/G2gKv0ZK8aei3Ay7XrT94oe1r9z4/cDp7wN5s06cq6QKMYJ46D1iJHpkqmgfSOWA2w5XTREKnl6O4ekKJFp3I7FqM6CMAEb+GoYLJuG7j1ZheOZARCUmn3v/Xrc4pn9YKuplVSQw9Bog42Ig/QKoNFFQdSRvvC5RhxX9AO2Jb6Et/BSpRZ+IKXgGXSnaOGlT23/XV3U+cHiNOF9VHgbAxEjZ4deIO10ikkQ9zySAzw1z8QmUvLgKkwfJkFS7A0nl68U5ctQcKKZOws7Pv8S4USPb90BEjwPYvxLY8aKoJ2RqIGsmMPBiIG0KFMZ+UKADFyceB1B2ACe/ewv9UQzd6c+RUrxG3Pk15Cog+1oxirO9dXxNgTiHH/lU3N0AiOOz/yQxIjQiRVyPSKSAz42K/GOoP/YtLskEUsrWI6X4M1FHjb4d/vHjcPiHHzB50kTIFe2YhsLrFvv+YZloh0pk4m7HjEuAfpMgixkMg0QCQ3vzxmkGyg+CFX4P/anN0Bd9JspNRCqQfTUw5BogqZ3nKUDcEXn0U3HdcHqbuK5SGkT+xl4hjqngNC1eN84cOgTHsVyMSHaiX/Ea9Dvzsfi/jLkd9pwclJ06de5rqSCfV1w37HxJ7BsQ7Y8Jc4C0KZAkjIBWpkC7uzjttUD5AbDCbTCc2gxD0WqRvshU0b7paN4QQgjpEzozXIsBiAq8zifBM1iXnuzCOf8cwOddT04zmigRiMqeDVyxCDjxLXDwQ3Fxses1cUIedoMIyMRlNzZyg9MCHFsnGrNui7glbspDYv3YQefeN2M4mVcAmVyBtLtfA/wuEajYvhh473pkyCYiv06GshN5SMxs5aLI7xON6M3/EYENUxZw7WuiASHrhjnOtKbGvPG6xe3ahz8BDq8V+aONAYbMFhfVKeMbgzEeh7jN8ug60WCzlIlg4qDLxboDLjp3+iQSnDx+GpqISCTd/bYIPOx7D9j+IvDedcjwjcOmM0rUl5UiMiGxlbzxi31vflpc2BnTgVnPA8NvErdUdpUuVlw0D71WXCAVbBT/i0OfAHveFrfVBgNV4Xnj8wCl+4C8z0Ujv7ZABFYGXgT86q9A1mXnnp9SpsDJ/CJEJSYjav5LYh7ZvW+LhvvbV2GgYiJ2HSmBw9wAdWsXRZwjqmY38MpfRUM/IhW49F/AyN+IC/au0scDOTeLl8cpAqYHP248piJSG/MubmhjgJNzoOIQcPxrcRxUHwekShFoHXa9uBA51/9OoUVBfjGShwyDdv6/RPA5901g1+uQvHU5BkZciLzcnfB6PK1P68C5KOdb/isa+ppoYMbjwOg7AW101/MmMlW8cm4Wx0nBRlF/HFoF7Hmr7XLDubil/PhXotwUBaZ1T8gBfvV3Edw6263JjIFrTDiZfwbpYyZBPv/PInCy523gx1cgP7YOqcapKNixGdN/O6/ti6LTO0TeFHwrAhRTHwLGzBUX7V3BmAgYGdOAUb8RnWgnvhHH1L73gd2viwvhUBCv2cWRuRTI/0bk46ktALi4QLv8GWDQFWKO6bbIlPBo41FT0YARF18GyR0LxIX47uXAnrehO7QKicapyP9+PSZce1PbF2Ule0RdfPxLMSfm2Lmi462rUw0wBkSli9eYO0W5Of4VcOAj4MdXRBDMmCbyJfualtOF2GpEsO7gx42BzOgMYNoj4jumjLb3rYmC3SNBWUkVJt94G3DdzUDlUZE3+1bAtP8DRGqmIH/jWgyfcXHbnRVVx8Vc+gc+EBfuw24AJt0npmToat6YMsRr3DwxZdLxr0Q9vOs1EQSLTA3kTbC+Ccsbl0Wcxw+tEoFMj12Us/ELxPpnuwhXG1FrBerqLBh9173Ar2aK+nT3G8DBj5Dsfg8q+RTkb/gYA0efpZOr7rQIgu99R+w/41Jg8v1iOqWuBAAkUtG5ETsYmHC3OKaOfynKwc6XRRAsqr8oM0Nmt8wbjxMo2onMvKXAztsDnbURInjTjg7f0moPnE43sm54FBgzQbQJct8A9r6L/u43IZVMQv7XK5CU+fe2/05LuUjr7tfFbdMp48W5Kuvyrs0fKpU3Tnkx4Z7GcnPgI3Ee3f6CaE8NniVeCTlN0+jziuDq0U9F2TGXiDZO5qUi8Dfw4rPOaV9Y5IDP50fWb54EMjLFuWbPW8CPr2CgS4rvMA75X6/E+Nv/0Hbe2GtFfv74qmhjRWeINmzOLWIO+c6SKYGUseI16T6R7yc2iM69Ax+KfaqN4n8w+CpgwPSm89wGp/cJtv8qDgFgoj6+7L8if/Txbe6+4LgYI5J19xLAFC3Ox/veA7YvRoZTix/4CBRs+AjDrr2r7b/BViOO/x9fEZ0nCTnANS+LgHVXnjUgVwOp43GmnwP9p00T0y0c/0qcw4P1TUSKOIe3Vn9wDtSdEh1LBz8SZQgQnbUX/wMYfOVZzxcnD9QBjGHAA+8CSqn4v+x7H9j8Hwy0R2G/awhOf7cKAy+9te2/wVHfmDfWClHOr3hWpLcrUzGoDGK6qrTJwLQ/i/3kfSnO4T+8JI6pcwV/LeWi/XfoY1Evc7+Y8mnKQ+I4jB/W5nF/cnc1ZEol+j38CeCzie3sXwlsfAoDLQnIrxuIsh+/ROKEK9r+G1xW0Sb6YRnQcEak9+InxfnqbG2Ic9FEiWnO+k8D8JioS499EcibZY15M+QqUR+31knpqOv8/gkhhPSIDk3p0Jf1hSkduiIrK4vn5eV1/Isui2iwHvxIXCxzvwg+aWPEXE3WwG3lCp24WBpxa/tHoIV5/YF5MCYk4dpH/tb4odcN7H0bzo2LsGxPGsamc0y54wHR+y5TiAb4kbXiArrmBBAzGLjwTyIdP8XDAjxOIH+9uNA5/pV4WIFUIUbHcL8IvnCfGK064CIxAi1zpnhwSjv5/T4su+vXGDBmPGYufLBxgdcN7F+Bhg3P47U9iZg20IHRv35AbF8mRhbh6GfiYrbikGgsXvAn0Yj6KR4O5XEE8uaTxryRqcSIJO4XF4Y+t7g1Mm2KuHAePKtDDW23w44lc2/FqMuvwoW33Rm2byew7z2Uf7UU7x1KwOWD7Rh84+8b88ZtFx0UO18So0KNacCFfxajmX6KvHFZRCP34Eci0Ml9YsRNZAoAJvLGbRXrpk4UF67Zszs055mlphqvLLwdF/z6Doy96rrGBW47sOs1nPziNaw+kYrrxkuRNut3Yl5UmULk3fGvxAVb0U4R9Jl8PzBqTtcuntsrVG5WiQsVr1MEEIz9xMinhmLAWS/Wjc0WI7iyrxVzdbZTxakCvPvIA5i58EFkX3hR4wKXBch9EwfXvoX1hfH4zQQXYi9/qLFjxusSwdSdL4n5nbUxwMR7RfDxp5iPzmVtDOKd+EYcPwqd6MSRSAMPogzUx8Y0UW6G39ihQOupfbn45N9/xbWP/A3pI8PumHSagT1vYdeqd7GlOAYLptigu+j3ItgRGFmEgo3iAvrEN2J03cTfieCjOrIbM6ENjjpxnjq0qvHuEoVeBIdlKnFBH5xLWZ8o6uJhNwDxw9sdTDyyZSO+XLIIt/37f4jrP7BxgdMM7H0Xmz9YgT1levxuQjnkk+8R50KFtnE+8J2viP+fTAWM+i0w6XciCNvTHPVilNahVWI+e+4TeWMaCMg1YiRmTYH4XBUpzhHDbwRSJrT7PJr7+Rpsevs1zHtxedMHkDnqgNy38OX7q3GyXoWFE2rAJi0Eht8s6hOfFzizHdj1ujhfMSbq4Un3AXFDeiQ7mrDXiv0eXi06SLhPjJ4zZYr02etEh5vPBZ9ECemQWaLTrT2dtQFb338Tu9etxsLXVkCpCatDbTVA7nKsevcLNDgY7pxgF/XJsBsAuUp0ZBf9KAKLhz4RaRt0pciblHE9lCFhbDXAkdUiiBcaWRgh5hZX6kS5qs4XnfwSmbhTafiNQOZl7T5XfP3S88j/cTsWvvo+JNKwAJalHNj1Ot59ZyOk3I1bJkpE3gyZLfLd7xfn7r1vi+C01yHu7Jl4r/jf9HT7L9hJeeRTEcxzNYhjKnawCAK7LOIuM0cdACb+X9nXiABoOwNmq5/+O2pLijF38atNF9SfAf/hJbz23i7EKCyYPSVa1CXBMsm5CKDufVcEQb0O0Xky6T7R1urG0ZObNm1qeSeMs6ExwHniW9GxpooUo5nVkaJtU1Mg2jmACEIPvV50fEe0YyQ3gPceewiMMdz6VLNLrZoC+Ha+imXv7EemoRKXjI8FJi4UbT+pvLEzfd8K0bHgtop8m3ivuK7o6ZGljrrGvCn4rjFv4rJFufE6RYd2bWBKqqj+jXkTe+47ADjneO2+uxDTL63lw5yrjsP5/VIsXZmPcdFFmDIuFZiwUBy3wXJTdUwEh3PfEP/H1IlinazLe75tHAz+HlnTNG/ih4nrAp9HdApWHgH7ewNN6UAIIb2gu6Z0+DkLRlMz21geHD50/CdIS/dR6oERt4iXtRI4uVnccmWvFY2jyH6iMZs6odNP8a0rK0F9eRlGXz676QKZAhh7F1QjbkP8n+bjTFUpsOKmxie4WysBcDEi58a3gUGzftqnwspVoid6yFWigX/8a/HwC0uZGK0akSRuZ+w/rUNB3nBlx/PgtFmRPnJs0wUyBTB6DiJG/BrGe3+DMzVOjP7wNyJv1FEi8MP9YrTLta+KgOpP+SRhuTpwi9bVjXlTulc08plUjOBIGCEa2Z0cTXH6wD74fV4MGNXs4leuAsbORdyI26C66xacqbFhcJO8qRAXz8Z05GXei6yb/v7TPrVcqQdybhIvW7UIIpYfaOwgGDBdBKIGTO/0aIpT+3YDANJHjG66QKEBJt+PlGG3QLLgTpw5XYW0964XAWdNtCg3fq+4BfDyZ0Rg6qd8OnfzcpP3lbiwrxfzaCN5jDjeB17U6RGjhfvE1A8tHuqi1AOT7kP6wOuB++bhdKkNsStuFnmjNYkghN8jphC49N/A6Nt/miB4KH26wG3W1zeOGirdKwKZfq+4aIwdLI6p2CGdung9tW83mFSG5OxhTReoDMCk+5Aa+yvgsYdxpsKFIR/+VnT+6eLEA1u8ThEEn/EEMG5+94ySby+1UYyIHvUbwFolOg1K9wD1RSJdyWNFWU6bKspQJ+rCU/tyoYmIbPnAG5UBmLgQ/ZRjsfvpv6PEFY20L/4IrH8C0MWIY9xjF8fXhX8SeaPt1ORCnaOOFMHnEbeKIN7xrxof9uh1ilvtB18FpE8FUid16q6YU/tyEZWU0jTYC4j/y5Tfo583G0eWPY8qtx6x6x4EvnpU3B1iqxZTWqgiRWBm/D1dHyXfEZooMSXC6DkiLce/FvVNbYEI6kUkiWla+k3G9mKGqb+6rMO7OLV/DxKzBjcN9gLiTokLHka/+jRsfu8tWNwe6D/9HfDFw+IhYbYaEUxVGkSn0oS7e+6BfK3RRgNj7xIvW40YFV2yRwTAnQ2iDZZzkxiB3X9ah8/jnHMU7stFv2EjmwZ7ATHydcZj6Fcah92fr4XbUQHFJ/OAtb8DDAmi/ekyiw6L4TeIcvNTdBAEydXijolBV4jOrlNbRCdy7UlxDlXoRAd20hgRbOzgQ9+8bjfOHD6AodMubrkwMhVs5r/Q7+QzOL7je/hr8yEJnqf08YG8aRCDD4bfJDre2nOnXXdRRTTe2RQ8TxXtFMHE+iLRPus3qfFB0R3orAUAe0M9ygvyMfmGX7dcGD0A0sv/H5IP/hVnCo4A5mPAB7eJ85QhUTzcLPhQ5CGzgckPiAcz/1TUxsa6OBj8Ldop7hapPSnaobFDgNF3iHqnAx2SAFBbUgxzVUXoYW1NxGRCdc3/EL/79zhjjQRq9wErbxHlxJAkOtIddaJjfdAVwKQHxOj2n4raCIz8tXiFys0PQMURkT/Ba6ohVwN/f+SnSxchhJBz+iUFfL8LvF/CGJNwzv3BBYwxPYDJABwAfuiNxAXZXF7kV1pRUGlFWYMD5WYnyhucaHB44PD4YHf74PNzyKUSKKQSqOQSROuUiNErEaNTIs00EQOHXIIBsVpoFN3z7y08IG7nSmsenAqSq5A64WLsXPMRXNe8DWXFLtEgiEgWvdPtnZuwHertbuRXWnG8woL8CitK6h2wOr2wuDywu3wAA6SMQSph0KtkiNIqEKVVIt6gQnrMJPTPvhjpJi20yu7Jm9MH94IxCfoNH9H6ClIZUkZPxrFtW+C/4T1IincE8iZJXISlTuzWvDleYUV+pcib0noHrC4vLE4vbG4vJIyF8kankiFaq4BRqxB5Y5qE/sNE3nRXuTlz+ABkSiUSMlu/mGFyJVKGj8GZUyeAmx8Wo5Sc9SJAlX4hkDYVZVu2IKsbgr3BvDleYcGJwLEVzBu72wcGQCoReWNQyRGlVcColSMhQo100zSkDb8C6SYt1IruCcoX7t8DXbQJ0SmtT28gN8QgIXMIitwu4KZHxROXHXXigjF9qsifbuogqLa6QsfTiUoram1uNDg8MDs9cHp8YGBgDJBJGSLUckRqFDBq5EiK1CAtegrSRlyKdJMWKnn35U1s+gBoI42tLtfFJsCYmIyiqNEYOzuYN7Wi3PSbLC5Su2G0i9/PUVRnR165BfmVVlRZXKixuVFjdcHm8sLr5/D5OTgHNEopdEoZdEoZ4gwqJEWqkRg5DekjrsDAWB0Usu7p6Dq9fy/0icltzn0Y2z8LKr0BZxJnYMiMx8UxZa8Rwcx+k8V8md1wPDk9PuRXWHGqxobyBgfKGpyoMDtDx5Pd7YPfzyGXscB5SgpT8DylVyItegYGjr0K/aK1kEu7njfc70fhgb3oP3JMm9N8JA0ZBolUhtPJ1yLtwn+KkZG2KhEE7zdRnKu6YRodp8eHgiproJ5xotLsQqXFiTq7G06PHy6vDx6vyBuVTAqVXIpIjRzxBhXiI1RIipyBzHFXo1+UBrJuyBuP24Xio4cw4pLL21wnZfhIAMCZrPsQOyxOTBthqxLlJnWCmKamGzpPPD4/TlXbkFduQWm9A5UWFyotLtTZ3HB5fXB5/XB7/VDIJFDJpFDKJTBqFIiPUAWOq4uQOf5q9IvWQippet70lW/qcHps9XWoKjyJKbfMaXOd1OGjgffewpmRf0V2P4UIOlsrRAAkZbwIGHbl9vsAp8eHE5VWnKm1o6zBibJ60f6zusQx5Qi0/RSyQNtPIYVJp2hs+0VfjIETrkFKlKZF3nRG9ZlCWOtqkTZiVJvrpOaMwY+frUHx1BfR39AgpjWwlItOjOSxYuRhN3QsOT0+nK6xo6DKiuI6O6osLlRb3ai2uuDy+OHx++Hx+SFlDGqFFFqFDHqVDPERaiREqJAYOQwZEyciNUoDSTfkTcmxI/C6XC07bMOk5ozFwc2bUDnrI8T7TorzlK1SBFwTR4lpsboyNUGA0+PDySobTtfYQvVwudkJq9OL0koH/nd4W+CagUEhk0AtlyJap0SsXrz6mS7CwMlXIylS3S15c/rQfoDztq8XII6pgj25aLj5K0TU7xfnKWuF6NRNGCFGynfH1FQALE4PTlSK+vhElRVVZhcsLi+sTi/snpZtv2C7ODFShXTTxUibPBuJkepuOaZOHxTXUmcrN/2GjxbXUk/tgLLke3GHhaVcDEpJyAGyruhwB0VbbC5v6FxVUGVFWb2ob4J1DmOATBK8nhJ5E60LXjNcjP5TZyPeoGql3FDAlxBC+pJfTMCXc17AGFsP4BKIqRteCFv8dwBaAC9zztt+BHgPqrBzTP3PRhTVOpp8btTIEWdQwahRIE4vh0ohhUzC4PGJCyOnx4+iWjtyT9eh1tb0abD9ojUYkRKJESmRGJlqxJAEQ6cCDyXHjkAXbUJEbBz8fo4qqwtnau0oqrXD6vKCAfBI4sH9fnx9RoP+ox5GrEGJaK2y042kBrsHxystoUBUfqUFxytE0CVIq5AiJUoDg0qOWL0K6mgRcPL7OTw+DqvLg5NVNuwurEOt3Y3w2Uv6x2gxIjkSw5MjkJMSicEJhk4FrEqOHUFMv3SotDpwLvKmqNaOoloHzE4PGACXIhFuhx1fV8YgddifEKPvWt40D3ofr2gMSIXnTbJRA71KBpNOgdTAxbrX74fPz2F2io6FWpu7SblhDBgQo0NOciRyUiKQkxyJQQl6KGWdyZvDSMwcDKlMJvLGIsrNmVo7bG4fAMChS4alajs2NKQjcdiUQN4oOn3hUWcLzxtL4Gcrqq1N8ybJqIZeJYdRo0CyUQrOAV8ggGd2enC03Ixamxv1dk+TvMmI1WF4ciRyAuVmUHzHjynOOUqOHUG/4SPBGAsdU0WBvLEH8sYa2Q+2nV9is/dBxAy/ACa9okvlptbmDuXL8bByE/7/16tkiNUrYVCLvFHJJeBcTGzu9fnR4PCgrN6MWnvTvJFKWCBvIjAsORLDkyI6VW68Hg/KTuRhxCVi/jqvz4+yBidO19hR1uCAx8fh4xwuUxpOH9mNLdfcA9PwC5AcpYZB1blAZrBs5lVYkFcuXscDeeTw+JrkjUmnRJRWgUiNAnIpC/0v7G4fLE4vSusd2JpfDaur8Yn3cinDwFg9hiQYMDTJgGFJERiSaOhwx4rd3IDa0mIkTZgKzjnq7B6cqrahzuaGxeWB1eWDhAEsYSDy9uyF7uLfIHbEVMTqlYhQy9GOB6y2mjfFdQ7klVtwrNyMo+UWHCsz41S1Df6w+lSrkCIuQoUItRwahRRGjQJSCeDxcXh8ftjdPuwvrke1xRU69gFxMTkwVoeRqY3nqYExug4f/7WlJXBazEgeMjSU7lqbu7G+cQUuXhPScHDXbsgnXoWoEY8gRqeEUdO5+ibYIXC41IwjpWYcKzcjPxCwCz/XaBRSxOpFuVHJpYhQyyGXMri9fri8ftjcXhTV2fHNkQq4vKF+aCikEgyI1WF4UgRG9et83pTn58Hn8SB16AgAgNXlRWG1CA7Z3d5QJ7IsKg67d/wI56A/In7UcMQaVNArZZ0uN6UNTuSVm3GsvPG4KqiywuNrzByVXIJYvQpRWgXUcim0WhkUUgk8Pj8cHh/MTi9O19hRftgJd1jeKGUSDIzVYXCCAdmJBgxJMMDh7fj0ZMVHDwEAUocOB+cc1VY3iursaLB70ODwiI5SSCBR67D9+51oSFmAmBEjEdOFYypYbo6WBfKlQuRRYbNjSiWXIN6ggkEth1ougrtSCYPbx+Hxirq4oNKKKqurSd4oZBJkxukwMsUYOq7STdoOp7X42GGRN9k5AJoeU0V1DjjcXnCPDpDKsHnLD6i8+tcw5kyASafo0jF1ptaOI2VmHC0Tx1V+pRVFdU2PKaVMApNOCZNOAbVCCp1cBrlUAp+fw+H2odzsRF6FBxXmsiblTaOQIjNOj8EJemQnRmBoUgQGxes73PYrPnYIjEmQPDg7lC9FdQ7U2d0wOzxwuH3gHnGXwPrvfkDmxVfDNOpCmHRdKzena+04VmZGXoU4R+WVW1BYY4cvrOAoZMFyI4OPAwa1HFIm6mK3148qqwvHyi2osrjgDfueWi5FZrweI1MiQ+UmNUrT4bSWHDsChVodutOCc46aYLmptcPl9cMDEbD8YsMOpE+ajqgRU2HSKRGplneq3ATb3icCg2WCwd0TlVZUmBvbfnIpQ6xeBb1KBoNKjgi1HJxz+Lm4Ziius+NAcT3q7O4W9VR2YkSoXTw8ORJp0Z3Im6OHYYiJDd1pwTlHvd0TOKZE288pTwD3+/Hphh1IzRkPY47IG6Om8+fwaqs7FNgNBncLKq0obXCG1pNKGOINIm+0StFhAgBeH4fXL64z9xXVo9bmblLeNAopshMNgbwR5YaQX5rrrrsu7ZNPPok+duzYwaysLPe5v0HIT+tnPYcvY2w2gNmBX+MBXArgJICtgc+qOed/DFt/AIDtAGIBrAVwFMB4ANMhpnKYxDmv+SnS3pwuKYPf8cyHyIrTITNOj4GxOiRGqjvUEHV7/ThdY8OJSivyK604UmrG3qK6UINHIZNgcLweQ5MiMCxJNHZFwLTxoi7YAClrcKKozo4zNTbUL38C5ogUbE+9DEV1jiYXF0FSvxfzzyzHQf1QfB89SXwmYWIUgUGFeIMScQZV6BVsWNjcXpgdXhTX2VFYY8OpahvyK6yobBa8HBinR0asDplxOmTE6ZEZp0dihKrdDSCnxye2X2VDfqUVB4obsL+4PhQklUkYBiXoQ8G8YUmRSI5SN7ngDeZNudkpAglVFjS88ifUJo/ErvgLUVRnh9PTMm9UPgfmnXkT243jkRs5KpQ3MTol4prki8irOIMInNhcXpgdHpTUO0J501rQe2CcHpmxOmR0Mm8cbl+TvD9QXC+CM1ZxzpJLGQYniEBVTnIkhiWLcqMLGyXNOUeDI5A3NXacLqtBw6uPoGrghciNHhtq6DdndNfhtpKV+NY0DUf0g1vkjcRtRXb/JMTpRb7EGpSBvPHB7PSgpM6BU2H/1+aB3WDeZMbpMTBwbHUkb2wub5O8P1hcjwPFDagJBEkVUgkGJ+gxLFlcBAxPjkBqlKZJMI9zDrPDizKzA2dq7Cg8dQaWd55EafYV2K8b0mbeJDpKcV35WqyLnYlT2vQmeRNrUCJWHygzgfc4gwoRmmC5aXpMnai0hv6fAKBXypARyA9RZsTPsXplu/PG4vTgdI3YR165BQeKG3CwpCEUQA6Wm+GBvMlJjkS/aE2TOo1z0flQ1iDy5uThw7Cteg6nRtyIQ7IUFNc5mlyUBmVYT2Bm1Tf4MPFaVCjFxaNBJUNKlCYwulaN+AgVEiJUiNYqIZcyyKQS2N1e1NrcqDA7cbLKhoIqUVeGB69NOiWy4kV+DIrXh/JI1867AoJ/U0mdA/mVFhwts+BomRmHS82h8ikJdKwMS4rAsOSINoPA4XlcsPsHeL9eju0Dr8UJRTIaHJ7Wdo+h5kOYXrMVbyXfCrNczF2slEmQEKFCQmDEWzBv4iPUMGrk8AU6yaqtLpSbRYA9r9yM4xXWJsHr1CgNBsXrMSjBgMHxegyI1SEhQgV9B4LtVpc3cLyK0faHS83YV1Qf+nt0SlkoMD4sORJDEw0tzoWcc1hdXpQHzlPHN2+AbeMKFFz4O5x0a5p0nIQbV7cL4+p345XUO+GWilHSwSBArEEZqGdEPRyrF8eUSi4NnKc8KK5zoLjOjhOVVhwts4TyRiphGBCjRUbgPJURK87hSUZ1x8qNw4vTtaKuya+w4Gi5BfvD8kavlGFEIBgzKN6ArHhdq6OkLU4PimodOFNrw/Gv18Dz4+fYPeUhnDD7m5w/wl1YvQWDrHl4pd9ccCa2p1VIER+hQmKkOlReEgPlRyGTgIHB7fOj2uJCldWFk4Hj6USFFZawcpMYoUJWvB6Z8eKYyoozICVK5E176pvwwMjxQLDrWLk4rsLrtX7RGmQnGpCdKI6n7EQDYvWqJtsKnu9OVFpxfO078B/7AVvGP4BTta4mZT3czMr1iHdW4M3U34Q+U8okobZMXERjGyc+UOdwzuH2+VFjdaOswSE6TgJBumDZZAzoF6VBVrweWXF6ZMUbkG7SIjFS1e7AYDBvTlbbRLCryorDpQ3YX9QQ+nsi1HIMT45obPslRiAhUtWi3NhcXtH2q7Xj8PtL4DyTj+PTH8SZOgeKwjpqw80uWwul340Pkm4IfSaTMMQERpDGhJ2nxHlLCY1CBptL3JlVUufA6Ro7TlbbcKzMHNqHVMIwMEaHzHg9+pu06B+jxYAYHVKjNe3uiPD7RbCxuM6O/AorjpabcazMgiNl5tAxFeysFAHgxrLT/Lh1enw4VW3DySob8t74D1w2K3bm3I6TVVaYna2Xm1uKP4BNpsWn8VeGPlPIJOJ8rRflJi7s/B2jV4JzwOX1oc7uQWm9qG+OV1iRV24JdUAGy01mnF4cV3H6QLlRNwkMtjqHb1je1NrdofZBfoUVR8oacKC4IVQ+o7QKUQ8HrheGJhmQENFytKvD7UNpgwNnau3Yv/hxuOU6nBh1a6jDrUVdzDnuLHoLRaoUfBPbOE+/TMJCd4LE6hvvCAmOYjcE28WB8+vpWhsKq20oqLI1OR9qFVIMjNVhQKwOA2N1GBgj3lPbeddEsBP4ZHWw7WfBweIGHCptCLXxDSqZaNcEAsDDkiIQZ1C1mjdlDQ6crrFh39MPwhM/ACeHzA4FwC3N6pzWrqUAcZ4y6cLzRdUknzQKaShvSutFe+p0rThXheeNRiHFgEB+DIzVYUCMNpA32nYNXvD7OSotLpystuJklSg7B0sacKikIdSOPf30lTSH73nA6/Xi+eefN61cuTL6+PHjapvNJjEYDD6TyeQZOXKk7aqrrqr/9a9/3RBcf926dfpZs2Zljh071vrjjz+2+lCivLw8xaBBg4YlJia6S0pKDrYnHePGjcvatWvXWW+jefDBB8sWLVpU2rG/sP0eeuihxOeeey7hs88+O37llVdami+ngC/pK87XOXxHAGh+L17/wAsATkM8qA1AaJTvGABPApgJ4HIAZQAWA/g757y2pxPcliSdBC/cMrJL21DIJOKCM06P8Jnsyhoc2HemHnuL6nGguB6f7ivFezvPhJar5BJoFGIUps3lg9vXGHzSe8y43WFGRUIiMmL1uGhwHFKMaqREaZASpUGEWlzo+znHF//egiiPDbfdNhpVFicqzCJ4UGF24lS1DT+crG0zSAGIBlS6SYsLMmMCwV09MuJ0SIzo+q1mKrkUg+INGBRvCOUN5xzlZif2F9Vjf3EDDhTX47P9pXg/LG80Cik0CjEC1OLyNgl2x7kqcKPPgxJlPPrHaHFhZgxSozVIMYq8idTIxehIzrH6sfW4MtqNedePQqXFhQqzyJ+KQHDlx8LaJkGn5iI1cvSLFvvobNC7LWqFFIMTDBicYACGNeZNaUMwb+pxsLihRbnRKqRQt5E3afZCzAJHkTIBA2K0mJ4VEyozKUZRbjg4/H6Ojx5ch1vTOEyXjUKVxRXKlwqLCyfr/fj8QBnqzpI3xkDeTM+K6XTQuy1apQzZiRHITmx86Fdw1OOBQJnZX1yPNXtL8e4PjXmjU8pCeWN1eZp0BAyyHMPFAE7L4kJ5kxrMmyhNaGSF2+nChw+sw92DpTBMHYUqq8iXSrMLFRYXiuvs2HOm5cj+cFFaBdKiNbhoUFxYgFeHeEPX80avkgcuAiNw5fDGvCmpd4Q6VA4UNWBts7zRK8UIEq+fh0YYBo2s34spAEoVcchOiMBlwxLQL0qD1GgNkiM1UMolYAyw1o3E6oe/wZ9HKKEaNQrFdXYUB4ISp6pt2HGyBpY2LsKDorUKDIjR4bKh8ciKE8GorDg9onVdmxOZMTH1RYRajiGJBlw9onFZhdmJg4HA+KGSBmw9UY1P9pYEvofQtBA+Pw/dVhk0uTYXOUwKpz4GswYlIN2kQ7pJA5NOCb1KDq1CCg6g8nQ61j+1FU9N0gMDR6IyUAeXNYjpgXaeqkWF2dlqID0oUiNHZpwe145KQla8PhBcbH/Q+2x0SpkIcic3PaZOVduw90w99hbV4WCJGW/tOA2391RoHaNGDpVcHFNmp6dJ3vyqag/SpGoUONVIiVZj4oBopBg1oeMqWN+UHo7Gpud34/mLouFPzBTnKUvjcVVQZcX2guo2AzgAYNIpkBatxbWjkgKjSyOQEafr8pQmjDFEaOQYronE8OTIFnmz50w99p6pw94z9Vjy3YnQaFDGxDGlV8nFyFi3r0kQYVb5QRiVUfDKVZiWqUV6jBbp0VokGdXQKmXQKKSQMobj2+XY+cZhvHZVMqzaWJQ3iFvDy+qdKDM7kVdehSqrC2cbI2DSKZERq8PskUmh4G5mnD7UTuhK3hgDt1rnhI0gCwZmDpea8dm2fXAoDThUYsYXB8tD6xg1YoQsYwz1dneToOWNpccgVcchSq/BqDQT0kxapEZpEKVVwKCWQ6eUwc85jnzjwL6P3sQbN2TCLNWgKjAVRTCPDhTXY32Ds9WOu6BorQIZcTrcOCYFgxNEcDczTtflKZSCeTNaq8Dofo3T4Pj8HCcqrdhXJMrMwZIGvLrlZOi4ZwyI1iqhDAR5zA5Pk3Iz50weqtVxKKy1IzVKg4kDosXxZGx6ntq/tgJHv1qDd+aMQIOHoTqQNxWBqUzac56KMyjRL0qL60cnY0g3HlOSQOA5Rq/EyNTGvAmepw6VmHG4VNTFm49XYdWe4lDemHTK0B0uDfbGvJFwH+aVncKZ6KHQKKS4akQi0k069IvSwKhVhO5y4AB+fDcfZ3Zvx3t3jkWNwyvKTaA+rjC7cLTUjO/Mla12TgUFj6mbx6VgcLwBgxL0yIjVd3l6KUkguGrSKTE2rXFqCa/Pj+MVVuwrEvXNwZIGfH+iOjSqU8KAaJ0SarkUfs5hdnhC9aXS58K86lLsjR6HmhobUqM0mDTAhNQodahtHEz39y/tQUzxadx+13jU2d2hTqNKs3gvNztxoKQBNVYX2jpVJUSokBqlwRXDEzAwRgx6GBjb9fYNY0x0+hlUmNC/cWqJYN6Idl8D9hfV46XNJ0N5E+yQVwbKTb3dHcqbCE8Dfms3Y5vVAFuVFalRGoxLjwq0idVNjqlv/7sVJp8d99w1HjW2lnlTXOfA3jP1Le5cbEw/kGBQISUsb4IB8IRWp2BoP4mEIT7Q6TdpQONc9x6fH3nlFuwrqsdvnu705kkf4fV6MWPGjIytW7ca9Hq9b/r06Q1JSUnuuro6WWFhofLTTz+NOnHihCo84NvTrr322pp+/fq1eiKZMWNGiyAsIaTRzzrgyzn/G4C/dfA7RQDu6In09FUJEWokDFPjsmEJAEQPbWGNDUfKzCirF41Pp1fM1alRSEOjVFKMGriO/YjNrwL/vPsqxPRLP+t+0odkY+9Xn+HiQdGQylq/wHO4fai0iMCDxekN3TqUFKmGUdvxh9F0BWMsMOJNjZlDm+bN4VIzyhocqDC74AyMqtApZYERuEqkGDUw79qAHz8Aljx4HXRRZ59vrN/gwTi1LxeXZse32RB1enyBYJ4TZoenSd5Ean76vEmKVCMpUo3Lw8rNqRobDpU0hC50XV4/GERgNC4sbyq+LcPh9TK89cj1bc43GpSUOQjm8lOh/0G44AgVp8cXuMh2wuxoLDeJEWpEaH7CB7pB5E0wOHvF8Ma8OVltw+HSBpQGjimX1weAhUbJxRnExcmZT0/gdK4WHz0yu835RgEAehVi+/UHqy4KHbutCd6mWWEWc33rAnmTEKHucqCloxhjSDZqkGzUNCk3J6tFuSmpd6A6MAeuVCKBRiFFQljeHH8vF2ZlAj75w6Vn3U+sPhGGmFjIa4tC+2kuOAK02uoKjGD1Q6OQ/X/2zju+7eL+/6/T3vKQ97bjOIkznb13IAlhQ5owWnZb+oWGDgqFAm3pr5Q2UCjQ0pa9V6BkkOU4e28nseO9LXlqb93vj4+keMWxLcmOxT0fDz0SS5/P3emtu/vcve5970OUXMhtHx3kNgWAayNjJFgy5lIMPp8IfL7BgDaLAyabi4uzLRYgSiFCRrQc6Ro5jv8jH/z4HPxqhhILFoy7bB4xY3KwSyyBXF+HxRNW9XiNx0PRbLajoZ2rM744fdEKMeLVkqAIu/2BEILMGAUyYxS4ZTJ3ErzT7cFFrRHn6w1o1NugNV7a1q+UCP1ecSlRMhx7cQNiR0/AU4/N7zWfqInjUEAIIsz1mDn+8td2fE453dQfozkpQhq0+O99paNtbvXaxhfv9aKW29KttzhgtLm42LdCrr9JjZIhWS3B7mfeQ87s2fj9Q7N6zUc0aTwOvw1oLI1YPLvnxWeHy+MVqzi7UFCI+N7t9UrxkNQbnzBDGkVYsICLjWmwOXGhnvOqL20yweHygFJuISNKLkJKlAwZagG2P/EvTF1+C+aumdZ7RhPG4dTnQLKrCdmTe7Zjx10urSYH+DxuV0GUXIQEtSRoMc77Cp9HOM/heCVWT00FwNWbYq9ndIPeBp3R9wznzjyIU3Ge/zE8Kw79yYTrb/4BJq/svU1Zx47F+c1fIQNtSB4/9rLX+Z5TOoMNVof70jO8nzvZgkHH59S1Y+P97+sMNhTW61FYx43/7N6FWrVMiCiZCOkaOaKtWhx8yYVH1y7FqNkzes0ne+xYlO/dgVFyO6JHplz2OpPdBa3BhiajHTxCIBHyoJIIkRAhGVA4rUAQ8Hmc6J6owtrpl+rNhQYDzjdwfbHOYIfDzY39FBIBJwCqJJBrL6LwHeDPP70BaWMn9JpP5pgx2HvqCCbFCiFTXf6QTLeHC5mhM3JzBt/4xrf7YjDpaJsfeLsMm9PNhfVpMEDrjaHss41KKvTvAhBVHMfFz4F///I2xKal95pP2ujROLV1I2akqy87lwK4Z2SLyYEmox1WpxtyMR9KsRCxKvGg20bI5/kdAO668uWMq5w333wzau/evaqcnBzr/v37i6OjozutShmNRl5BQcHATjsfIPfcc09LT961DAbjygxrwZcxMHi8SxPIK7F9SzHEMjmiU1KveG1Cdg6OffsVmiorED9iZI/XSEV8pEXLkRY9qM+JPtMf23zzaTHUcfFXFHsBzjbndu+EoUkHdWzPBy5IhHykRnPejFcjPB5BVowCWX2wzScVFxGXmXVFsRcAEkbkoOLUcdgtlu4npXuRCPl+kfVqhOeNRToi9sq2OVlejMTsUb2LvV589cbjcYN3mUPaRAKeX5y/GumrbSil2F9WjMxJfTt5On5EDhpKii77uUIs6PNvMpT0JAJ3xemw47uKMkxeeQMu70PIwePzEZ+VjYaSHnfUcdfwvGEMumx3v5oQ8nndvOt7wtzehl26Rkzq5VAyH2KZHNFJKb3aBrj6n1MSId8/ue6N5poqOCxmJOaMuWKa6tg4SFVqNJQUY8LS5T1eIxLwrup+2IdKIsT0zGhMz7z8s7m68Ayox4OkPtgmJj0TfIEA9SVFyJ7es+BLCEGETDQkC0l9RSLkY0JKRCcv6Z4oOrAHAPpkm4TsHABAQ0kxkkdfXvC92p9TABCrkmCRSoJFoy7fF5/YfBQAkDQq94rpJYy4ZJvopMsLvgqxAIo+jq2GAomQj0mpkZ28pHti70c7wOPzkeitE72RmM0d5ttYehGZeZd/5vM7eGlfjUiEfExOi+zkXd8TW49+BYlcgZg+zKUSs3NwfOOGXudSAPeM9HnbMhjB5MCBAwoAWLt2bXNXsRcAlEqlZ9WqVVel+Gqz2cj69es1W7duVZeUlEibm5uFUqnUk5uba/n5z3/eePvttxu63pOUlDQOAM6ePXvu17/+deKWLVsitVqt8JFHHmn89NNPo+vr60UAsGrVqk4NklJ6vGtaL774oubf//53bFVVlUShULiXLVvW/o9//KO2JzsyGINFcI4OZ4QtjaUliB8x8rJiU0d8g9v6K0ymw4XGsov+73wl4v0D/8sLVOEC9Xigq6xAXOaIPl2fkJ0DUIrGsoshLtnQYzOZ0FJb3ScBBuBs47RZ0VJbE+KSDT3tjfWwGvRIzBndp+sTRoyEsbkJ5va2EJds6NGWl8LjdvWr3jRVlcPp6DleazhRf/ECACCpr/UmexQaSooxnM8v6Cv1xX23DSEECdm9L6KEE756kzBy1BWvFQiFiM3IuuJCQbhQf/ECBGIxYtIzr3itTKVGRFzC98g2RVBGx0AZfXmvVB9RiUkQy+TfmzbVWHYRmtR0CMVXFiDjMkeA8HhoKP1+1JvGsouIz87p40I/1yd9X2zDuPqIjo52AcDFixeH3WqCTqfjP/3006kmk4k/d+5cwwMPPKBdsmRJ+/nz52WrV6/OXr9+fY+dt9PpJPPmzcv57rvvIufNm2e49957dRkZGfaHHnpIO3XqVBPAhZVYt25dg+/VNY1169Yl//73v08ePXq09e6779bFxcU5PvnkE83KlSuzQv29GYzeYB6+jMvicjrRXFOFKdfd2KfrldEaKKKiucHt8p63EocL5vY2mFpb+ixqxqSmQyASo6GkGKNm975FcrjT1tgAp82K2Iy+Pd98HgwNJcVIGzcxhCUberQVpQDQq9dGRy55TxUhJjU9VMW6Kmgs52yT0Efb+OtN6UWMmDI9ZOW6GtB1sE3tqdNXvD4hexQ8bjd05WVIGtU3kXi4oi0vA+HxEJvet/4mITsHhbu2ob2xHpEJSSEu3dDSUHoREqUKEfGJfbo+MXsUyo8fgc1kgkRxdXobBovGsouITEyGVKHs0/UJ2aNwZsd3cLtc4AvCe+jcWHoRcRkj+vw9E7JzUH3uDCilAceGv9rRVZYhPiu7T9cSHg/xI0Z+L8RwSim0FWUYOWNOn64XSiTQpKZ/L2zjdNjRUluNrMl9G6coozVQREahoaQYk64N77kU4+rk9ttvb3v99dfjP/rooxiTycS/6aab2mbOnGkZOXLkFQ8kq6urEz322GM9Djra29sHHGvk7bffjs7Pz+/xgf3zn/9cl5qa6gKAmJgY98WLF89kZWV1OvylpaWFP2PGjFHPPfdc8oMPPtiiUCg6rfo3NTUJR4wYYTtw4ECxSqXqtJmuvb1dcPToUcWVwkqcPHlSfuLEifPZ2dkOAHA6nZg5c2bO4cOHlbt27ZItXLjQMtDvz2AEQkCjVkLIfAC/AjANQCR69himlNLwHh2HKc3VlfC4XX0WNQFu4P99WJX2CXd9tU1ftlmHCzqfbTL6ZhuJXIGoxOTvRb3RVZQBAGL74DkFABFxCZAoVWgoKcb4xdeGsmhDjq6iDHyhEFG9bH3tSFxGFgiPh8bS4vAXfCvLIY+IhDyi922jPvwLBaXFYS/46irLEJ2cCoGob1vpL9nmYtgLvrrKMsSmZ/ZZhPMtojSWXUT6hLxQFm3I0VWU96ttJIwYiRObv0FzTRXi+riYORzxeNxoqq7E+EW9x1HvSEJ2Di7sK4CxpRkqTUwISze02C1mtDXUY8y8xX2+JyE7B4e/+gxOmw1CybBzluszep0WdrO5z+M+gAtdcGHfblCPp0+er8OVpsoKUI8HcZl97zd8O1EYVxc737uQ0lpnuqpjGkUlKSyL7x4d0LbA2bNnW994442KJ554IuWbb76J+uabb6IAQK1Wu6dOnWq85557mteuXdvjgW319fWil1566fIHjwyQr7766rIxmm699dY2n+ArlUppV7EXAKKjo9133HFH83PPPZe8d+9e+fLly01dr/nb3/5W01Xs7Q+/+tWvGnxiLwAIhULcddddzcePH1ccPHhQzgRfxlAx4CcsIWQlgB0AVgCwADgEYE8Pr72BF5MxFGjL+ydqApzIp9c2wmbu1o+GFT7b9NWrDADiMrPQVFUBjzu8w/joKsvB4wsQndw34Q4A4rKy/V6M4YyushzK6BjIVL3H3vRBCEF85ghovUJxOKOrKIMmJb3PXmVCsQQxqRloKA3/UCDairI+e8wDgDwiEorIKOgqy0NYqqsDXUVZvwS46KQUCIQi/+JLuOJ2OdHST3HSJ9aEe72xGPQwtjT1q035xkHhXm/a6uvhstv7ZZvY70m98X2//rYpSj1oqq4IVbGuCgYyX4jNGAGH1QK9ThuqYl0V9NdBxHdtu7YBdos5VMViMHrl/vvvb6utrT375Zdfljz66KMNCxcu1Hs8HuzYsSPijjvuGHHzzTenezzdtdGpU6eaKKXHe3oVFRWdHWh5vv3224uXS3fWrFnWjtceO3ZMcsstt6QnJyePk0gkeYSQyYSQyc8991wyAFRXV3fzEBCLxXT69OnWru/3hxkzZnRrsGlpaQ4AaGtrY86PjCEjkMr3LAAngJWU0m3BKQ7jakJbUQqJXAFVzOUPsOiKb5LQVFWBlDGXP0l+uKMtL0NkQtJlDxnridj0LLicDrQ11CE6+coHNwxXtBVl0KSm9Xq6cFdi0zNxYe8uWPTtkKkjQle4Iaa/wh3A2ebYxq/hdjn7ZdPhBKUUusryyx6IdDliMzJRduxwWG8ldjkc/doO6iM2IyvsxSlTWyvM7W199pgHuN0WmtS0sBenWmpr4Ha5+mUbiYJ73od7vfH99v2xTURcAkRSadjXG12ldxdKP55TMWnpACHQVZSF9W4L/w6dfonhmd57y5E4sm9xxocjuopS8PgCaPoResonnOsqyxARH3SHwKsGbXkppEoVlNF993739U26yvKwnksNNwL1nB1uiMVievPNNxtuvvlmAwC4XC688847kY888kj6hg0boj/88MP2u+66q32Ii9mJnTt3yq+77rqRLpeLzJw507hs2bJ2lUrl5vF4OHPmjHTnzp0Rdru924QhKirKyQtwp0FPB7MJvE4sbrc7PCcpjGFBIDV7LIBPmdgbvmjLShGbOaJfQop/kFIR3pMibUVpv1brgY62Cd/JtE+464/nM3DJUzqcJ9MOmxVtDXX9EhkA7oR4j9uF5prqEJVs6DE2N8FmMvZ7q3RseiasRgNMrS0hKtnQ01xdCerx+IWDvhKbnonW+tqwPrhtIOIUwPU3usqysD64bSDiFADEpmeEdT8MDMw2hMdDTFr420brC62TmNzne0QSKSITkr4XtlFERvU5tA4AKKNjIFEo/X1VuKKtKIMmJQ0CYd8XpaNT0sDj88O+3ujKuflCv+ZSPueZMLcNY3ghEAhw//33tz3wwANaANi5c2ffguAPIn/84x8TbDYbb8OGDSV79uwpeeutt2pefvnl+vXr19dPnTr1si7z4eo0wmAAgQm+JgCtwSoI4+rCd2Bbf0VNX5zJcB7cWvTtMLU091ucikpKAV8ohDaMB3DGlibYjIZ+2yYmPQNAeAu+TVWVAKUDEqeA8B74a33CXT8XCmI6eMGEKwPZRgxwtqQeD5qrK0NQqqsD38JiTFo/xfCMTNjNZhiadKEo1lWBrrIcQrEEkX08sM1HbHoW2hrq4LCGb6g5XUUZVDGxfT6wzUdsOheWifawjTVcaKrsX2gdH7HpmWE97gO4etPf5zchxGub8H1GUUqhLS/tt20EQiGik1LC2gnC6bCjubY6gLlU+NYbxvBFqVS6AVyVi+aVlZVitVrt7ulwtX379g1IoObz+RQA3GEekpERvgQi+O4EMDNYBWFcXfgPbOvHAQw+wn1wO5BYZYB3K3FKOprCeFKk9XtO9U+AkSqUUMXEhvXA/9Jhdv2bFEXGJ0AoCe+txLqKMhDCgyY1rV/3xaZxCwXhLIbrKssglsv7FVoH6LyVOFzRVXBbgfsTWgfouKMgfPsbbUUZYtIz+30Ykr/eVIVvzFFuF0r/nlEAN7Zx2qxoa2wIQamGHkopdBXlAzqULjY9E8bmJlhNlz3AfFjjtNnQWlfb73EfwHlrNtdUwe1yhaBkQ4+hScft0BmAbWLCfL7QXMXt0BnIXComPTOsx8SMq5d//etfURs2bFD1JHBWV1cL3n///RgAmD9//lV3YE9ycrJDr9fzDx8+LO34/ksvvaTZt2+faiBpRkdHuwCgsrKyb6cDMxhXGYHE8H0cwBFCyFMAnqdX4zIPY8AMVNQEuMFt5ZmTcDkcfT45fTjhP7BtIJOijEyUHNoftjFHdRXlIISHmNSMft8bm54Z9iKDVKmCIuqyB832yKWtxOE78NdVlCEqKRlCcf9OMRdJZYiITwjrCaO2ogyx6Vn97i9UMXEQy+XhXW8qyxCXNbLf92nS0kF4PC5u9LT+xY0eDlCPB01VFcidv7jf9/rF8IpyJI/KDXbRhhyHzYq2xnqMnrOg3/de2lFQhqjEpCCXbOgxNOlgM5v6vWALdN6Cnjp2QrCLNuQ0VVeAUo//gLr+EJueCbfTidb6WsT0I8btcMEnSsZlDmShIAvn9+TD3N7Wr1AZw4WA5lLpmTh29hRcTme/QmUwGIFy+PBh+dtvvx2r0WicU6ZMMfkOHquqqhIVFBSobTYbb/Hixe0/+tGP2garTG+//XZ0fn5+jx66kyZNsvhiCT/66KPaffv2qRYvXjxq5cqVrSqVyn3q1Cn5iRMnFNdee23bd9991++OZtmyZcbf/va3+MMf/pBcWFgojYyMdAPAX/7yl/Bc/WWEHWSgOi0h5C0A6QDmA6gCcApAew+XUkrpfQMr3veHccmj6M6nPh3qYvhpb6yHxaAf0CETVqMBLXU1iEvPglDSPwFnONBSVwOnzYb4rOx+32tua0WbtgEJI0aG5QFcLbXVcDkcAxrcGpqbYGjWIWnkaL9Xml6vh1qtDnYxhwRdRRl4AgE0Kf3zYgWAdm0DLO3tSMwJz0NfGkqLIZbJ+xU30kdrXQ0cA2yPw4G64vNQREZBHRsPoH9toskX/3cA3oxXOx63G/UlRVDHxEEZren3/dryUghEorA8QNPlcKCxvASRCYmQq/svojSUFEGiUCIyYfiImn1tFw6rBbqqCmiSUyHpZ0gHUIq64gtQREdD3U+P++GAb+wWm5YJkVR65Rs64GuPEbFxUET1vz1e7ZjaWtE+wLGby2FHY3kpohKSBvVQ2sEaPxmadDC2NCExZ0y/FybtFjOaqiuhSUmDRK4IUQmHjraGelhNBiRmj+r3vcN1LhX3k4nHKaVThrocA+X06dOVEyZMaB7qcgwlpaWlws8//zwiPz9fVVJSIm1ubhba7XYSERHhGjNmjGX16tWtDz30UCufz/ffs3HjRuWqVatGTp061XTkyJHintItLi4WjRo1alxiYqKjrq7ubF/KMm3atJyjR4/22jncfPPNLV9++WWl7++PP/5Y/ec//zmhtLRUyuPx6Pjx481PPfVUQ0lJifjRRx9N//vf/175yCOP+A//SEpKGgcAvZXp9ddfj3rllVfiKysrJb5D3yilxwHglltuSf/qq6+ii4qKzubk5Dg63uezy7p16xrWr19f35fvzGAMlNOnT2smTJiQ3vX9QDx8f9Th/+neV09QAEzwvQIUAOFdPR6fDpsVQolkQGUSSaWg1AOH3QqRrH+ThuGAw2aBSCIdkG2EMp9tbJCFofez3WaBRCYfmG2kElDqgdNh92/RpuTqahcDhVIKh90KpSJmYLaRSOD2uOByOSAUiUNQwqHD7XbB5XRAKdUMuN6YDe2g8IDH41/5hmGE026Dx+OGUHqpL+5PmxBKxNyBdjyAYPi3o444rTZQ6ulkm/4glEpgM5vCon/pisNhBaUeiKQDe04JJBI4bNZhZZu+tguHnbONUDYQ2xAIxKJhZ5u+4rTbQOGBUCbpt3DH5wnAE/DD1jYOuxWEzwNfJOp3TyqQiAHCpSHnDZ4X62CNnxx2C/hiEXj8/kcJFHnHxE67FVLlVXf+U8A47BYIxeKAxsThOpdiXL2MGDHC+cQTTzQ98cQTTX2957rrrjP6BNDLkZOT47jSNV25nHjcG2vWrNGvWbNG3/X95cuXmzoKvT76Ij7/9Kc/bf3pT3/a49lVXrG5sqfP+mIXBiPUBCL49n/PNuOyOOUUMQ+OH+piAAA8Hjc++uFzmLD0WuTefWO/76ceD7649y8YPX4hltz3k+AXcAixW8x4754nMecHd2P0Tf3/vZw2Gz790fOYOecHmHXbHSEo4dBh0bdj54NPYv5d92HUdf23jaG5CV88/GcsWvFjTLrmOgDAuYICjFxwdbSLQNBWlGFX/sdYefuvETOr/9/HU16Kr574K677wW+QM3NqCEo4dFSeOo6Cgk9w+4N/Qkxu/21jOnkMBX9+Gasf+jOSx4wNQQmHjvN78lGw7xP86Fev+z1R+9MmmvY0Y9drH+OHv3htQJ7lVzPHvv0Kuw99gp/84UPIVP33YqvaWIbd77+Pnzz9waB63Q0GFz58G8dPfINH/vb5gHaSXPjwOI5v+gaPPD+w+4eCvraL42+8jPKyo/jJT+4YUFil42/ko/zEUfzkjx+EXVimfS88Bz1fi8kP3TnA+zdAr9PiRw++HuSSDT1bHv8PZFFqTHrwBwO6f8dv3wOfCrH6wT8HuWSXZ7DGT1/++K9IGzcR4x+8dUD3f/vIa4iTjsCqB38T5JINLR63Gx/+8BlMunYVcu+8sd/3U48HX977F4yZsAiL7x1Gc6mHhroADAaDwejIgA9to5RW9fUVzAIzQk9bfT1cDnu/Tz73Ec4xR5squRizA90iLZRIEJWQFJYxR31x3HwxIPuLMloDiVIVlodM+drCQA7DAYDolDTw+PywbFO+g/5iBtim/HE1q8Kv3mgryiAQiRE5wHihvriaYdnfVJZDEa0ZkNgLdIxVG35tSldZDk1K2oDF2tj0THjcLjTXVAe5ZEOPrpI7lGygYm1sehasBj1Mbd2chIY9uoqyAZ1N4CM2IwutdbVw2m1BLNXQ43Y50VJTPeDnN8Cd39BUWXFVnmofCOb2NpjbWgMKGxSblhmWY5vWuhq4nU7/4bL9xT+XCsMxMYPBYDAGjwELvl0hhKgIISmEkAGdgMi4evAJJ4EM4OIystBUVQGPp/sJn8MZn20GKk4B3KQoHAdwPuFuIAe+AAAhhDu4LQwH/rqKMoikUkTEJQzofoFQiOjk1LAV7tRx8QOO36eIjIJMHRGWbUpXWYaYtPQBh6qISkyGQCgKS1GTO8wugH7Ye682zNoUpTQowh2AsOuLXU4nWmqqAqs33udbuPU35vY2mNpaAxM10zNBqQfN1eHl59FSWwOP2xXYuC89C3aLGXqdNoglG3qavAftDtRBBOD6m/bGBtgtlmAV66rAdwhxoPOFcJxLMRgMBmPwCEjwJYTwCSG/IYSUAmgDF7+kjRBS6n0/kJARjCGiqbIcfIEAUUkpA04jNiMLLrsdbQ3hFZ9cV1kOmToioNOEYzOyYGxpgsXQLbzQsEZXURaQcAdwE8aWmiq4Xc4glmzo0VWUIyYtw38Y3UCITc+CrqIs7DyEdAEKd4B3ESXMxClOuCsfsMc8APD4fGjS0sNO8HXabWirrwtInJIoFFDFxIXdIoqptQVWo2HAC28AEBmfCKFYEnaiZktNFTxud2BieFoGQEjY9Te+dhDYQv8Ib1phZpsAdy9x93oXCsLNNpU+J4iBR/nz9VVNYbZLR1dZDr5QOKDDaH3EpmfBabehvbEhiCVjMBgMxveJAasPhBARgO0Angd3YFsNgCPef9O97+/wXscYRugqyxGdkga+YOB6vX9wG2ZCQ1NlBSfcBRC779LAP/wGt4EKd3EZWXC7XGiprQlSqYYej8eNpqqKgCaLADcpsujbYW7r8cyAYYndYka7tiFw26RnoqW2Gi5n+CwU6LWNcFgtAQl3AGcbXVV5WC0UNFVVgFJPUOpNU5gJMNogiFOEx0NMemZYPqOAwERNkVSGyPjEsBPDdQGG1gEApSYGErnCXwfDBV1VOYRiCSLi4wechiY1HYTHC7t601RVAVVMLKSKgR+4Fq7hdZoqy6FJSQePP/DDZP07UcLMNgwGg8EYPALx8H0MwAIAmwCMppSmU0pnUkrTAeQA+BbAXO91jGECpTQowl1UUgr4QmFYTRjdLidaagPbDgp02C4bRgM4n3Dn8/AZKOFom7aGejjtNsRlBsk2YdSmfN8lEE9N3/0etxstNeGzlfhS3OfA6k1cxgjYzWYYmsJnK7FPNAlYDM/IRFtDPRzW8NlKrKsoAwhBTFp6QOnEpmdywrrHE5yCXQVoAwyt4yMcQw/pKsuhjo0LaIcOIQSxGZnhJ2pWVkATQGgdABCIRFxYporSIJZs6NFVlgcUzgHoEJYpjMY2lFLoqioQG4DnMwBEJ6eCLxCE1ZiYwWAwGINLIILvWgCFAG6klJZ0/IBSWgbgZgDnANwRQB6MQcbc3garQR/wAI4vEECTEl5biVvrauF2uQIWfKUKJVQxsWE1uA2G5xTQYSsxs003fFuJtWE0Ybwk3AXq/cyJouFkG21FGXh8PqJT0gJK59Jui3BqU2WQKFVQRscElI7fs8wbazEc0FWWIyohCSKJNKB0YjMy4bRZ0RZGW4m5mNiZAYXWAbj+ytCkg9VkDFLJhh5dZWBxn33EpGehuaYSbpcrCKUaeqjH43WCCNw2sWHmNe8LrROoqAn4zrYIn/mCqbUFNqMh4HrDFwgQnZIWVvWGwWAwGINLIKPeEQC2UEp7dP/wvr8FQOCjJMag4fNaCc4ALjOsYo5eilUWmHAHeAf+YTS41ZZzQlugE0bfVuJw2r6mqyjj4rgFEBMb8G0lTgg74U4eGRVQTGwAUMfGQSyTh1Wb0lWWIzopBQKhMKB0fFuJw6lN+Q5sCyS0DhCeB3AFemCbD78YHiaerP7QOgF6hQOXFlGawkSEsVvMaG8MPLQOAMSlZ8LtdKK1vjYIJRt69E06LrROMMbE6Vkwt7fB3N4WhJINPc3VVaDUE5QxcVxGFlrqauByOIJQsqEnmPOFuIzwPL+BwWAwGINDIIKvA8CV9n7JAYRPUMXvAU2VvhN3gzO4tZlNMDY3BZzW1YCushwCkRiRCYkBpxWbnoW2xno4bNYglGzo0ZaXQhEVHbBwB4TfVmJdRSk0KekBxcT2EZseXoeTBePANsC7lTg9fLYScwe2lfk9lwPBv5U4TOqN2+VCS03goXUAQBEZDalKHTa2sRoNMLY0BcU2mpRU8Pjhs5W4raEeLrs94BApQPiFHvKN+4IihoedbXw7dIKwiOJfYAoP2/h3LwW4IxDgxn0etxvNYRKWqamynAutkxrYDh3AO5cyGWFsCY+5FIPBYDAGl0AE3zMAbiWE9LinkhCiAXArgNMB5MEYZHSV5VDHxUMskweclv+wgTCZTDdVliMmNbA4bj5iMzIBSv0TreGOtqIMcZnZQUkrnLYSc8JdecAxan2E01Zip8OOlrqaINqGWyjwuN1BSW8oMbe1wqJvD4oAA4TXVuKW2mq4Xa6g1Bv/QkGY2CZYIVIAgC8QQhNGW4l1/sPsAm9TMpUaiqjo8LGNf2dX4PUmMjEJApE4jETNMhAeD9EpqQGnFW4H9jZVlUMsl0MVExtwWuG2o0BXVY7I+ASIpLKA0wrHnSgMBoPBGDwCEXz/ASAGwBFCyH2EkExCiJQQkkEIuQfAYe/n/whGQRmDg66yLCir9QAQk5YOQnhhMfCnHg90VeWICcK2PuDShDwctlnbLRa01dciLjM4wp3PAyscDjcxNOlgM5uCKtwB4bGVuLm6EtTjCYrIAHBxfF1OR1hsJfZ7TgVLDE/PgrmtNSy2EvuFuyAuorTUVMPtGv6bkXwxrINnm/AJy6SrLA9KaB0fsRlZYfH8BjjbyCMioYiMCjgtHo+PmLT0sBE1dZXliEpMhlAkDjgtsUyOiLiEsIk1r6soR0xaRsChdQAuLJNIKgsbUVNXURbwWSg+YlJ95zeER3/DYDAYjMFlwIIvpfQzAH8GkAbgTQAlAEwASgH8B0AGgBe91zGGAVajAe2NDYgfMTIo6QnFEkQlJYeF4NvWWA+72Rw024TTVmLfd4jLDHyrLABEJ6dwW4nDYMLo95wKojgFhMdCwSVvxOBMiuLCaCuxtqIUIIQ7qC8IhNNWYl1lOYRiCSLjAw+tA3D1xuN2obmmOijpDSW6ynIoNTGQKpRBSS82PRNWowGm1pagpDeU6CrKoElJC0poHcAblqm+Dk67LSjpDSXaIIXW8cGFHiof9mGZKKXQlpcGbRcKED67LVxOJ3SVZYjPCs6YmPB4YXO2hcWgh16nDd5cSiJBVGJyWMwXGAwGgzH4BHRUMaX0SQCzALwF4CSAcu+/bwGYTSn9TcAlZAwajWUlABC0ARwQPoPbxtKLAICEYA1u/TFHh/8AzndgWzBiIwKXthKHg6ipLee2g2pS04OSnkylhiJaEx71pqLUux00Lijp+bcSh8GkSFteFrTtoEB4bSXWlpciJj0ThBfQ8MXPJduEQ70pCbI4FR7brKnHA21FadAW3gBuEYVSD5qqKoOW5lDgtNvQUlsdlHjhPmIzMuGwWqDXaYOW5lBgbG6Cub0N8dk5QUszNiMLem0j7BZz0NIcCpqrKuB2uZAQJFET4GzTVF0Jj2d4h2UK9nwBCJ+5FIPBYDAGn4BnTJTSQ5TSByilUyil2d5/H6CUHgxGARmDR0NJMUAI4rOCOPBPz4SptQUWfXvQ0hwKGkqLIZRIEZUcnO2ggHcrcW01XM7hvZU4mAe2+YjN8HoIDfOtxA0lFxCTlhGU7aA+wmWhoOFiERKyRwVlOyhwaSvxcF8ooJSioYSzTbDwbSUe7vXG5XRCW1GKhCAKMBFxCRBJpcPeNhaDHu2NDUGtNzHp3q3E5cPbNq31dbCbzUgMom3i0sNjR0FjWQmox4PEkcGzjW+hYLj3xQ2lxQAQ1HrjX2AKE9vEjwiiGJ6eCZfDjrb6uqClORQ0lBaDEF7Qdr0B3JjY1NIMi0EftDQZjO8TxcXFIkLI5FtuuSV9qMsy2BBCJk+bNi14nfUwIykpaVxSUtK4oS7HUBIcFxlGWNBYWgxNcmrQvMqAS97C9SXFQUtzKGgouYj4zBFBObDNR3xWNjxu97CPVdtQWoz4rOAc2OYjPmsEbEYDHMN4cOtxu9FQcjGoE2kAiM/MRmtDHWxmU1DTHUzsFjOaa6uDOpEGgLjMbGjLSof1wW16nRYWfXvQ601s5gg0eD2Phiu6ijK4nU4kjRwdtDQJj4f4rGzUFxcFLc2hoKGEK38w641IIoUmOdWf9nClvuQCACAhiLZRamIgU0egfrjb5iJX/mAuomhS0yEQitDgtftwpaGkCAKhKGg7dAAgzjtWGvZj4tKLkEdGQRmtCVqa/vnCxeHdphpLL0KTkgqhRBK0NH1j7OHeFzOGJ48//ng8IWQyIWTy6dOng+fBwhj2+OpFb6+NGzcGJ87YZZg2bVoOIWRyKPMY7vQ5mBkh5C0AFMCTlFKt9+++QCml9w2odIxBg1KKhrISjJgyPajpxo8YCb5AgLqic0FPe7BwORxoqqrA5OtuDGq6STljAAC1F84hMYgCxmBiam2BXtuIictWBjXdpFG5AABjw/A9gKu5pgpOuy3ov23SqFyAUtQXX0Bm3tSgpj1YNJQUA5SGwDZjcGrrRugqy4O+CDFY1F/0ilNBFsOTR43BxYN7YWjSBeVU9aHAN9kNpnAHAIk5uTj81aewWywQy4K34DmY1F8sAo/P9wtKwSJpVC4u7NsFj8cd1AXPwaThYhEkCiWiEpKCliYhBEmjxqCu6FzQ0hwKGkqKEJmQBKlSFbQ0BUIh4keMRO2F80FLcyioLylGbOaIoMV9BriwTFGJyd56c1vQ0h1sGkuLEZ81Mmg7dAAgKikZUqUKdUXnMG7RsqClO5hQStFYehHZM2YHNd34ESPB4wtQe+EcsiYPz7kUY3ji8Xjw4YcfxhBCQCnFa6+9FvPmm28O38nZ95ATJ06cUygUIQ2qv27duobLfZadnW0PZd6MK9OfUcyPwAm+LwDQev/uCxQAE3yvclrra2EzGoIuMghEIsRljUTdheE7KWosuwiP2xV0b0R5RCQiE5K4gf8NtwY17cGi1jvZTR49NqjpRielQKJQwjSMBd/6Yk64C7aomZDtHfgXnRu2gm/9xSIufEwQ4/8BQLJ3oaD2QuEwFnyLIJRIoUlNC2q6vjZaW3QOY4ap4Ft/sQiqmDgoIqOCmm7yqFwcoh40XLyA9InD00mg4WIRYtMzgxo+BuAWUU5v34ymqsqgxgceTOovFiEhOydocZ99JI/KRcnhAzA0N0GliQlq2oMBDeHCYdKoXBz55nM4bFaIJNKgpx9qXE4ndBVlQV/MBoCk0bm4eHDfsF1EMbe3oa2hHmMXBleU9S2i1A7jRZSW2mrYzKageswDgFAkRnxW9rBfYGIMPzZs2KCqq6sT3XLLLS0FBQXqzz//PPqVV16pk0gkwzvm3veISZMmhfx02fXr19eHOg/GwOnP6DcDQCa4g9l8f/flFbyjfxkho7rwNAAgdeyEoKedPDoX2opSOG3D8zTr6sLTACFBFzUBzjZ1xeeH7WnWdUXnIJRIg3rCN8Bts04alTusBd/aonOQR0YF3ZtSKJYgLmvEsF5EqSsqRExKWtC9KRVR0YiIT0DtcLbNhUIkZOcEXQjQpKRBLJej9kJhUNMdLCilqCs6F/RQFwCQMJITA+uKh6dHosvhQEPpRSR6d40EE99ui+EqNFgMerTUVvt31AQTv22Gab1prauB1WhAYk7wdxgljxoD6vEM2+359cUX4HY6kZIb/LB/yaPHcmGNqquCnvZgUHPuDAAgNXd80NNOGpULvbYRptaWoKc9GFQX+mwT/LlU0uhcaMtL4bQPz7kUY3jy73//WwMADz30UNNNN93U0t7eLnj//fcjLnd9WVmZ8O67705NTk4eJxKJ8iIiIiYuWrRoxO7du7sN+B977LFE35b/f/7zn1Hjx48fJZPJJnWMt1pVVSW86667UpOSksYJhcK8yMjICcuWLcvau3dvjxOItrY23v33358cFxc3XiwW52VkZOQ+++yzce4eQr1dd911mYSQyVu2bFH0lNbbb78dSQiZ/MMf/tB/gI8vfIDT6cRvfvOb+LS0tLEikSgvPj5+/E9+8pMkm83WbdvD+++/H3HDDTdkpKenj5VKpZNkMtmk3Nzc0X/84x9jeyrXLbfckk4ImVxUVCT605/+FJOVlZUrFovzkpKSxv3mN7+J93g1g7feeity3Lhxo6VS6aSoqKgJd999d6rFYumW/+Vi+LpcLvzlL3+JycvLG6VUKidKJJK81NTUsatXr047e/ZsSEJ3VFZWCn/5y18m5OXljdJoNBOEQmFebGzs+FWrVmWcOHGiWxycjrGXz5w5I165cmVmVFTUBB6PN/mVV16JJoRMPnr0qML3PX2vnr6v0WjkPfTQQ8kJCQnjRCJRXmpq6tjf/va3fnuGM3328KWUVvX2N2N4U1N4BqqYWKhj44KedvKoXBz5+nPUlxQhbdzEoKcfaqoLzyAuIwsSRY/Pg4BIGpWLs/nb0FxThZi0jKCnH2pqzxciceQo8PjB91JJHjUGZccOwdzeFtQD4QYD6vGg+uwpZEycHNQtjz6SR4/F8Y1fw+mwB92jL9Q47TbUFZ3HxGtXhST9pFG5KDt+BNTjCbpHX6gxtbWiuaYKc+cuDHrahMdDUs6YYSuGN1dXwtzehrTxk4KetkgiRWx61rC1TV3xebgc9pA8X1WaGKhiYlF34Rzyll8f9PRDTdXZUwAQEtvEpGdAJJWi7kIhRs+eH/T0Q03VmZMAgLRxwW9TCSNHgxAe6orOIT0EbTbUVBeeBuHxkDw6BIKvfyfKuaAvlg8G1efOQCyTIzYEHv9+2xSdw6hZ84KefqipLjwNdVx8aOZSo3Nx9Jsv0FByEaljgy+2MxhdqampEezcuTMiLS3NvnTpUnNERIT7P//5T9xbb70V88ADD7R1vX7fvn2yVatWZev1esGcOXMMK1asaGtpaRFs27YtYunSpaPef//9stWrV3c7nGX9+vVx+/fvVy1atKh9zpw5Rr1ezweAoqIi0bx580Y1NTUJZ8yYYbzxxhtba2trRVu2bIksKChQv/vuu2Vr1qzxp2e1Wsm8efNyCgsLZTk5Odabb765Ra/XC9avX5+wd+/ebpP4hx9+WLdp06bIN954I2b58uXdDkb5z3/+o/Fe19z1sxtuuCHz6NGjigULFhiUSqU7Pz9f/c9//jO+qalJ+MUXX1R2vPZ3v/tdEo/Hw8SJE82JiYkOvV7P379/v+rpp59OOXbsmPzrr7+u6Mn+jz76aMrhw4eVixcvbp8/f75h27ZtES+88EKSw+HgRUVFuZ5//vmkJUuWtM+YMcO4e/du1fvvvx/jdrvx4YcfVvf4g3bAZrORRYsWjTh48KAqPj7ecf3117eqVCp3dXW1eOvWrZGzZ882jRs3LuihGLZt26Z47bXX4qdPn25cvny5RaFQuMvKyiTfffdd5M6dOyN27txZNHPmTGvX+yorK8Vz5swZnZ6ebrvppptarVYrycvLs65bt67h008/ja6vrxd1DCuRnp7eqexOp5MsWLAgW6vVihYuXGjg8/l069atEX/605+SbDYb+dvf/nbZkBThwIADUxFC7gZwilJ6ppdrxgGYRCl9b6D5MEKPx+NGzfmzyJoyPSTiVGLOGPD4fFSdOTnsBF+nzYaGkmLkrQjNRNfnPVJ55uSwE3wNzU1orqnCmHmLQpJ+itd7pPL0CeTOXxySPEKFrrIcVqMBaRPyQpJ+yphxOPrNF6g9X4iMYbYFvfZ8IdwuV8hEgJQx43CuYAd0leVBPSV7MPAJMOkhrDflJ44Oyy3olT5xavzEkKSfOm4Cjm/cALvFDLFMHpI8QkXVmZPg8QVIHhP8XSgA1xeXHj0It8sV1Himg0HVmZOQyBWIzQy+OMXj8ZE8eiwqT58ApTQk46dQUnX2FCLiE0IiTollMsRnZaPy9AnMvv3OoKcfaqrPnkLCiJyQxPT2OVdUnTmBvOWhWfgMJdWFp5E8ZmxIFvpjM7IglslRdebksBN8PR43as+fxcggx+/1keSbS509yQRfxqDwxhtvaFwuF1mzZk0zAEydOtU2ZswYy+HDh5WFhYXisWPH+kU1p9OJtWvXZlosFv63335bvHLlSr+AWllZKZw2bdron/3sZ2nXX3/9WalU2ikcxMGDB5X5+fkXZs+e3Unou//++9OampqEv/71r+teeOGFRt/727dv1y1fvnzUT37yk4wVK1acUavVHgB47rnn4goLC2XLli1r37x5cxnf20cVFRU1TJ8+vds2n+XLl5tGjBhh27p1a2RDQ0NNQkKCy/dZUVGR6ODBg6pJkyaZp02b1k2ArKqqEhcWFp6Li4tzA4DBYKgbO3bsmA0bNkRXV1fXpqam+tPauHFjaW5ubicB0u1247bbbkvfsGFDdH5+vm7RokXmrnkUFhbKTp48eS4jI8MJAM3NzfXZ2dlj33jjjTiJROLZv3//hby8PBvAid1jx44d8/nnn2v+8pe/1CclJbm6pteRX/7yl4kHDx5ULVy4UL9p06ayjr+J1WolbW1t/ergH3vsscSe3pdIJJ4//elP/t9u5cqVxttuu+10ZGRkJ7fagwcPShcvXjzq8ccfT96zZ09J13ROnDihePjhhxv/8Y9/1HV8f86cOZZ9+/Yp6+vrRb2FlWhqahKOHj3asnv37kKFQkEBoK6urn7UqFFj33zzzbg//elPjWKxOGzDlAQycn8HwLMALiv4ArgewO8BfC8EX0KIEsAfANwEIBbASQCPUkqPDmnBrkD9xSLYTMaQCTBimQzJY8ah7PgRzLvjnpDkESoqz5yAx+0KmQCj0sQiJjUd5cePYOqqm0OSR6ioOMlV68y8aSFJPzY9E0K5AuXHjww7wbfy9AkAofEqAzjhTiiWoOzY4WEn+FaeOQmBUISk0bkhST9j0hQQwkPZ8cPDTvCtPH0CMnUEYoJ4KnxHMidPw+4P3kL58SOYeE3wY1OGksrTJxCdnAplVPBOhe9IZt5UHP3mC1SePoGcmXNDkkeoqDx9Akk5o0MWKzVr8jScK9iB+uLz/oW44QClFFVnTiJ13MSQxUrNmjwd5SeOoqW2GpqU4MbdDiUupxM1585iTAifrZl5U7H/sw+G3S4di0GPxrISTL95dcjyyJw8DWd3bIXTboNQ3G0X61VLS20N9NpGTF55Y0jS5/H5SJ84GeUnjg67XTp1Redht5iRNj408wWxTI7k0bkoO3YYc9f8MCR5MHpn6xsvpzTXVF3VJ7tqUtIs1/zk5zWBpuPxePDBBx9oeDweHnzwQX+MlbVr17Y89dRTstdff13z+uuv+8W3Tz/9NKKmpkb84IMPajuKvQCQnp7u/L//+7/G3/3udyn/+9//VF29fNeuXdvcVewtKysT7t+/X5WQkOD4/e9/r+342dKlS83XXXdd6zfffBP1/vvvR/7sZz9rAYCPP/5Yw+PxsH79+lp+hwWpUaNGOe677z7dSy+9lND1e9577726J598MvWf//xn9HPPPefP59VXX42hlOLee+9t6sk+zz//fK1P7AUAlUrlueWWW1pffvnlhP3798tTU1P937Gr2AsAfD4fjz32mG7Dhg3RmzdvVvUk+P7yl79s8Im9AKDRaNxLlizRf/HFF9EPPfSQ1if2AoBUKqU33nhj6/r16xNPnTolSUpK6uax7MPlcuHdd9+NkUgknrfeequqqwAvlUqpVCrtVTDuSk+2BQCFQuHuKPheToieOXOmdcaMGcb9+/er7HY76Sq+RkdHu1588cWA4gS/9tprNT6x11eWpUuXtm/YsCH6zJkz4qlTp4ZtvJxQP0n54A5t+77wHwDXAPghgHEAtgHYQQgJ3tHQIaD0yEHwBQJkTArdAVBZk6ehta4GbY3DK6Z36ZGDkMgVIYnf6yNrynTUFZ2H1WgIWR6hoPzEUahj4xCVlByS9AmPB3VaFipOn4DL6bzyDVcRJUcOIi4zO2STXIFIhLTxk1B2/DAoHT5dLKUUJUcOIGXs+JCFopCp1EjMGYXSY4dDkn6ocDmdqDh5DBkTp4RskhuVmIzIhCSUHjsUkvRDhdVoQO35syE9pDBx5ChIlCqUHT8SsjxCQXtjA5qqKpARQtukjZ8EvkAw7GzTWHoRptaWkNYbX9rDzTZVZ07AabchK5S2mcwtBpefuKp9HrpRevQQKPUge9rMkOWRlTcdLqcDVWdPhyyPUFB69CAAYMTUGSHLI2vyNFj07WgovRiyPEJB6ZGD4AuFyJgUukX4zLzpaKmtRntjWO8+ZlwFfPvtt8qamhrxrFmzDB1Fx/vuu69FKBTSzz77TGO32/3bWg4cOCAHgJqaGtFjjz2W2PV19OhROQCcP3++2wrXtGnTuomdhw8flgHA1KlTTT15Xi5cuNAAACdPnpQBXOze6upqcWxsrKMngXXRokXGnr7nj3/84xaZTOZ59913/dve7HY7+eyzz6JVKpX73nvvbe3pvjlz5li6vpeSkuIAgNbW1k4rzI2Njfyf/vSnSSNHjhwjk8km+eLMzp07dzQA1NfXi3rKY8aMGd3skpCQ4ACAKVOmdMs/KSnJCQDV1dU9pufj1KlTEpPJxB85cqQ1PT09KBNsSunxnl5Go/FU12s/+eQT9aJFi0bExMSMFwgEeT577Nq1S+1wOEhjY2M3h9RRo0ZZugrT/UGhULg7eqT7SE5OdgBAS0vL8Nq+1k9C/eVGAugW4yUcIYRIAdwC4BZKaYH37WcJIasA/ATAU0NVtt6glKL06EGkjpsYkq1rPrImT8Oud95E6dFDw8aT1e1yoezEEYyYMiOk21izJk/Hoa8+RfmJo8PGk9VqMqLqzElMWLYypNtYI9Kz0Hz+NKrPngrppD2YtDXWQ1tegvl33hvSfLKmTEfp0YPQlpUgfsTIkOYVLBpKimBsbsKc1XeFNJ+sydOx58O3oddpQ7JdORRUnj4Bu8WMnFmh9S7NmjIdJzb/b1iFLig5cgAetzuknrc8Hh+ZXs8yl9MJgVAYsryCSfGhfQCAnBlzQpaHSCJFytgJKD12CPPvum/YhC4oPrgXfIEAWVOmhywPRVQ04jJHoPToQUy/8baQ5RNsig/ug0SuQOq44B8u5SMmLQNKTQxKjx7EuEXLQpZPsCk5vB/quPiQhtlKHpMLkVSG0qMHMSKE9TPYlBw5iIQROSHbaQHAv+hZeuxQSA7pDAWUUpQeO4S08ZNCttMC4OZSBe/9G6VHD2LKMJlLhRPB8JwdLrz55psxAHDXXXd1il8bHx/vXrRoUfvWrVsjP/roo4h77rmnDQBaW1sFALBly5bILVu2XDZdk8nUzaMhMTGxm+jY3t7O9+bXoyDpEzd98X59Imt0dHSPHqS+67sSGRnpufHGG1s++uijmG+//Va5atUq40cffRTR3NwsvPfee3UymaxHkVGj0XQ7bU0gEFAAcLvd/kFSc3Mzf8qUKWPq6upE48aNM998880tUVFRLoFAgPb2dv7bb78d21E470hUVFRPeQAAIiIiLpu/0+nsdZDms9XlbBtK/vjHP8Y+/fTTKSqVyj1nzhxDcnKyQyaTeQgh2Lx5c0RxcbG0p4PvYmNjAyqrSqXqfjoeLtnT5XINj4HtAOmXGxEh5C3fy/vWjR3f6/B6lxCyC8APABwIeqkvledWQsirhJC9hBADIYQSQj64wj3J3jLWE0LshJBKQsjLhJBAXfEE4Dyau7qDWwGEbiYWIHXF56HXaTEyhJNFAFDHxiN+xEic371z2Hgklp88CrvZjOzpoYnH5SMucwTUcfE4v2dnSPMJJhcP7oXb5QpZ/F4fyuRUSBRKnNuTH9J8gknxgb0AgJEh3hY+YsoM8IVCnBtG9ab4wF7whUJkTQmddxAAfwy988Oq3uyBRKlC6tjQCTAAJwx63C4U7d8T0nyCSfGBvYiITwjJIUEdGTVnAWxmEyqGiUcipRTFB/YgITsHqpjYkOY1atY86LWNqCs+H9J8goXH40bxoX1Im5AHiTz4B652ZNSseWgsvYiWuuGhBTjtNpQdO4wR02aCLwjdwgYhBKNmzUPFqeMwtw8P3w9zexuqC09j5PTZIV3Y4AuEyJ42CxcP7YfTNjx2kbbUVkNbXoLsEMWo9SFRKJA+IQ8X9u6Cx9PjHP2qo+7CORiadBgZ4vlCRHwC4jKzcX5P/rCZSzGGH/X19YLt27dHAMBDDz2U6fPA9L22bt0aCVw61Ay4JKh98MEHpZfz9qSUHu/pcKye+lqfoKnVant8SNXV1Qk75usTRy/nqem7viceeeSRJgD417/+FdPxez388MM9hnPoD6+88oqmrq5OtG7duoYzZ84UffDBB9WvvPJK/fr16+vXrl3bo/dwqPHZqrGxcVA9G5xOJ/7yl78kajQa55kzZ85t2rSp/F//+lftSy+9VL9+/fp6jUZzWVF3uDgaXK30d9/ojzq8KICJXd7zve4CMA/AEQDrAixjbzwF4GfectT1filACMkCcBzAPd6yvQSgHMCjAA4SQqIHWhBKqRHAQQBPEUKSCCF8QsidAGYC6DGuydXA2Z1bIZLKQuod5GPsgqVorqmCtrw05HkFg8L8bVBERoU8Rirh8ZA7fzGqC89Ar2u88g1XAef25EOTkhbyE6Z5fAFGz12AsqMHh0XIC+rx4FzBDiSNyg35oVgShQLZ02bhwr4CuByOkOYVDFwOB87vK0Bm3tSQ7iYAuAWm1LETUFiwA9TjufINQ4zVaEDpkYPImTEn5IdixWVlQ5OajsJd20KaT7Bo1zai+twZjJq9IOQDvrTxE6GIjEJhwfaQ5hMsGssuoqmqAmPmhnbhDQBGTp8NoUSKcwU7Qp5XMKg8dQKmluZBsc3ouQtBeDyc2z08Ft+KD+yFw2oJ+YItAOQuWALq8eDC3l0hzysYFBbsgMftRu6CJSHPa+yCJXDarCg5EjK/mKByNn8reHwBcgeh3oxduBSm1hZUnTkV8ryCwdl8bi4VqgPbOjJ24VI0VVdCV1EW8rwY30/++c9/RjudTpKbm2u5/fbbm3t6RUZGug4ePKgqKioSAcDMmTPNALBnzx5lMMowffp0CwAcPXpU4ewhrF9BQYESAPLy8iwA56mbmppq1+l0onPnznWLGZefn3/Zck2fPt2al5dn2rZtW0R+fr784MGDqilTppg6xsgdKKWlpWIA+MEPftBt1bO3MoWSiRMn2pRKpfvixYvSysrKQRN9GxoaBEajkZ+Xl2dOS0vr9KPq9XreuXPnBjQ55PP5FOBiEzN6pr+Cb4b3lQmAAHi5w3sdX6kAVJTSWZTS8qCVtjvrwIWNUIELm3AlXgd3mNojlNIbKaW/oZQuAif85gB4vuPFhJA/er2Ge3st6HDLXQA8AGoB2AE8AuBjAFflErXNbMLFQ/sxes58CCWhPzRi1Ox5EAhFOLtza8jzChRDcxMqTh5H7oKlITmJuCu58xcDhOBs/tUvwmgrytBwsQi5C5YMyorb2AVL4Xa5hsWEsfL0CbRrGzBx2YpByW/swqWwm83DYsJYfHAvbEYDJi4bnMPCxi5aBkOTFlWFV3+MxMJd2+FyOjBhEOoNIQTjFi5FY1kJdJWhfDwHh9PbN4MQgvFLrgl5XjweH6PnLULFqePQ67RXvmGIOb1tM4QSKUbPXRjyvIQSCXJmzkHxgb2wmnoMhXdVcWrbJsgjo0Iaa9SHPCISGZOm4FzBDjgd3ULEXXWc3r4Z0cmpIT2bwEd0UgoSsnNwZud3V723JvV4cDZ/K5LHjEV0UkrI80sanYuI+AScGQZjYpfTiXN7dmHElOmQqSNCnl/W5GmQKFXDYr5gM3WYSw3CAXyjZs8DXyjEmZ3fhTwvxveT999/XwMAr776atWnn37a4+uuu+5qopTitddeiwGAtWvXtqekpNjfe++9mE8//VTdU7o7duyQG43GPmlPWVlZzlmzZhnq6+tFf/jDHzrFZsvPz5d/++230SqVyn3HHXf4hdQ1a9Y0ezwe/OIXv0hyuy89b4qKikT//e9/e90Gdf/99zc5nU6yZs2aLEop7r///oC9ewEgPT3dAQDbt2/vJO7u379f+uqrr8YHI4/+IhAI8MMf/rDJZrPx7r333jSr1dppMm+z2Uh9fX3QvU+SkpJcEonEU1hYKNPr9f56YLfbyQMPPJDS3t4+oDwjIyNdAFBaWtpr7OLvM/0yLKW0yvd/QshzAHZ1fG+woZT6FaArCU+EkEwAywBUAnity8fPAHgQwF2EkF9QSn1Bsl8G0GuICADVHcpTBmA+IUQOTvBuIIR8CqDiil9mEHDabdDrtNDrGqHXNqKq8DRcDjvGL1k+KPmLZXKMnrsA5/fkQ6pSQRmtgVITA2V0DJTRGohl8iFz2bdbLJxddI3Q67SoOHkUIBi0uHMqTSyyJk/H6W2cuKGMjvHaRgOVJgYi6dAdCmszm6DXNkLfpIVe24iSwwcgFEswduHSQck/Nj0TCSNH4ej/voTVZILKaxelJgaq6JhBWazoCUopbCaj3zbt2kYU7d8NeUQksqfPGpQypOaOR2RCEg589iHatQ3+tqTSxEARrQnZwWhXglIKi74d7dpG6LUNaNc24PzeXYhKSkFK7vhBKUP21JmQqSOw+/3/onHmXL9dlNGcbYYqRiv1eGBqb0V7Y4P3VY9zu3ciecxYxKSmD0oZRs9bhH2ffoAd/34NWVOm++2i1GigiIoO6Tbv3vB43DA2N6O9sQFtjfVob6xD4a4dyJ46M6QxIzsycdlKHN/4Nb574yVkTJzi72eUGg0UkdGDsgDYEx6PG6bWFui1jWjXNaKtgas3E5auCLnHvI/JK25A4a7t2PLqX5E6doL/GaXUxEAeEQkeb4hs43bDbtCjuvA02rWNaKmpQsXJY5hxy5qQe8z7mHLdTfjsuSew5R9/Q1LOmE7jG7k6ImQHMV4Jt8sJQ3MTN/bTNqKx7CIay0qw6J6HBm28NeW6m/DtS3/G1tdfRlzmiE7jG5k6YsjGfS6nE4YmnX/cV1N4GnptI2aHOMa8D0IIJixdgd3v/xffvcHZRtVhTCxRKIfMNk6HHQadzj8uris6D5vRgPFLB2e+wBcIMW7RMhz731fY8+HbUGliO/U3Erli6Gxjs3F28Y6Jq86egsvpGLS5lESuwOg5C3B+zy5IleoOYxvONkMZm99hsw5Z3ozgsHHjRmVlZaUkOzvbunDhwm4Hg/n46U9/2vzqq68mfPrpp9Hr16+vE4vF9PPPPy+77rrrsn/wgx+MeOGFF8y5ubkWmUzmqaurE50+fVpWW1srrqqqOq1UKvu09e7f//531fz580f94Q9/SN65c6dq0qRJltraWtGWLVsiCSH0tddeq4yMjPSn9cwzz2g3bdoUuXXr1sjc3NwxCxcu1Ov1ev7GjRujpk6daszPz4+4XF733HNP229/+9sUnU4njIiIcN19991BiUP04IMPtrz++uvxv/vd71J2796tzMrKspWVlUl27dqlvuaaa9o3bdoUmtO9r8CLL75Yf/z4cfmuXbvUWVlZYxcvXqxXKpXu2tpa0d69e1XPPfdc7SOPPNLS1/Qee+yxxMt9duutt7bNmjXLyufzce+99+pef/31+DFjxuRec8017Q6Hgxw4cECp1+sF06dPNx4+fLjfXs8LFy40bNmyJfKmm24asWTJEr1UKvWkpaXZH3744SEJmXE1MuCRMKX0uWAWZBDw7UHaRint1NFQSo2EkP3gBOEZAHZ6328G0ClYeV/wCsZmb1zgawD8+kr3uO121Jw7A7FcAYlCAYlcAaFEesUBjcftht1ihtVohM3EvaxGA0ytLTC2NMPY0uT9txm2Ll45QokUk65dFfJt+R2ZdsNtqL9YhCPffNFtq7VIKu00EVBGayCPiIJUqYREoYRUwf0rViggEIquaBu3ywW72QSb2cTZxmyCpb0dxtZmmFpbOtiou23EMjmm37R6UA98mnXbWmx8+c84vOFzdKmiEMvkneyi0sRCpo6ARKGAVKHi6oxSBbFcDoFAeMXJpcvp5Gxj4uxjN5tgamv12oWzj7G1BaaWZtjMpk73ShRKzPnBXSGPi9iReXfcg82v/hWHvvoE6BK3TKJQdppcqzQxkKnUkChVXvt4641MDr5QeMV643TYYffaxWcbc1ubv94YWy7967B2HgtJVWosuOu+QRPMCI+HBT+8H9v+9SoOfPZht8+lKrW3zsT4JwS+eiORKyFRcu1KJJODLxD0ahtKKVwOO2cTv33MMLe1wNjibU8+GzU3wWnvsBOKECijNVj0o8ETGQQiERb88AHseudN7P/0/W6fy9QRneyijNZAqlJzfY23z5EolBBJZX2yjdNug81kgt1iht1kgtVshLm1tUN/0wxjC1dvXB28AHl8PiLiEjDnBz8MiR16QqpQYt7aH2H/p+9j3yfvdf6QEMgjIrl+xit0KqNjIFWqIFEquXrjtZFIKgWP3wfb2Kzeftjk73dMbd4+xtvPGFu5vtjdYeueQCRGVFIyZt66JlSm6IZKE4NZt63F0f99idrzhZ0+I4QHeVSUv01xNtJwtlFw7Yl7VqkglEjA4/OvaBuH1ep/Ptm9zyp//+vvb7i643G7OpVFk5qOqdcP3sE9mtR0TFl1M87s2IKKU8c7fcbj86GIir5kG00MFJHRkCovPbt9fbJQ3AfbeDywWy3eZ7gZdrOp09jG1NrC1aEWrm153G4UdihLfFY2Jl0zOLsJACBlzDiMXbgURQf2oORw5x0XPL4AyujoTuMbRVS03y5Sr10kCiUEIvEVbePxuGG3WGD32sVmMsFi1Pvb0qW+mHumdxxP8PgCJI8eOyghC3xkT5vlj1d7vstOHb5QCGWUptMzXBEZ5X02eZ/hShXEcgWEYvEVF1w8HjfsZvOl55TFDKu+/VJf0+E5ZWpr7TSeEAhFyMybOighznyMW7QMZccO48Legm7hUgRisb+f8Yl68ohI/7PJ/6ySKyAQi6+44OJxuy/1M177WAx6r2184xru/11jLgvEYoyeuzDkMeY7krf8elSdOYnjm77p1PcBgFAs6Tbuk0dEdnhGcfVGIleALxJe0TZulwt2i/nSM8psgkXffqm/abvU71j07Z3LIpFi0vJBnkvdeBsaSopx5JvPrziXUkXHQBYRyfU3HeZTYrmcm0v1db7grTt2sxnm9rbOcymvnWzDIPQao3fefPNNDQDcfffdveofOTk5jpkzZxoOHDig+vjjjyPuvvvu9unTp1tPnTp1/vnnn4/bvn17xBdffBFNCEFMTIwzNzfX8sQTT9QnJCT0ed/9mDFjHEeOHLnw1FNPJezatUt95MgRpUKhcM+dO1f/9NNPN8yfP7/TJEwqldLdu3cX//rXv0783//+F/XWW2/FJSYm2tetW9ewevXqttzc3IjL5SWRSOhNN93U+tZbb8XefvvtLVKpNChBstPT0507d+4s+tWvfpV87Ngxxd69e1WZmZm2F154oXrlypWGoRJ8JRIJ3b1798UXX3wx5uOPP9Z8+eWX0ZRSxMbGOq+99tr2RYsWma6cyiVeeumly4YvTU9Pt8+aNcsKAC+//HJdTEyM6/3339d89NFHMQqFwj1nzhzDX/7yl7onnnjisqJxb6xbt665qqpK/PXXX0e98cYbcW63m0ydOtXEBN9LkECDvhNCEgAsBpAEoCd3Mkop/UNAmfStHAsA7ALwIaX0zh4+fxHALwH8klL6tx4+/weAhwH8lFL6xgDLcA24MBlFAEYAeBFcaIc5lNJuAWgIIQ+C8yxGcqR68s+Xdh9kEh4fhMcD4fP9D2XqdsPjcYO63d3Er47wxRKIFEqIFEoI5dy/YlUExCo1xCo1+H0QlEMF9XjgtJjhMBnhMBngNBm9/+f+dpiMcFkvu7AIAJfswhdwtvF4OLt4PNwAqJfYnQKJFEKFEiK5AkK5EmKVCmJVBEQqNcSqCAgGYVvW5aAeD5xmUydbXPqXe7mvsIreyTaE+G3CvXqvNwKpzFtnFBDJlV6bcHYRK9XgiwfPa9RkMkGhuCQse9xuOM3GTrZwdrWNvfeQSz3bpkO96cU2Qpm8S71R+9uUSKUGXzh0u0k8bhecJlO3utKxDnmuEOuX8PggfD43qe6PbQiBUKbg7KJQQKRQdbBNBERKFXiD5GnXEx6XEw6TqUt/Y+hkI08PMcI6clnbuL1t6nLweBB1rDcKJSSqSIjVERCrIyBSqPrl/de1TQSK2+nosf91mIxwGA1wmI2gV4iLxbUnvn9S3a2/udx9PJ6/n/HXG3UkJOoIiNWREA6hFxcAuB2ObvXE2cVO1N37NnXCF3ACnu/53Vfb8PmXbCNXQKRUde5v5EqQIfI2BrhF6p6fTwZvv2y8Yvxsv234fIDSDv0N7d02AsElu3htQ0USqGPjIFKqIVIoh8yjllIKt93WvT0ZvXXH+/zq7TkDQvztqbttPL3alScQdnhGKSBWqvzjGrFKDaFMMaS2cdmsPfa/nI2McFpMobONSOR/dnP15tLYRqRSQziEu8wopXD5x8Td25LDbITTfIX5NyHe9sSNianHA0ov9Te9jYn5InHneqNSQ6yM8I//BFLZVWKbHuqNyQiXxdx7Il1tQ2nnOtObbbxzqUv9Tedxn+CqmEtd6mccZm+dMXI2cl3J65bH87cnf73p63yh61xKqcKaX/zmOKV0SpC/6qBx+vTpygkTJvTb2Ysx/Jk2bVrOsWPHFKdPny4cN27c1R+ficHowunTpzUTJkxI7/p+QDNwb1iH33RJh4A70K3j/0Mu+PYBXzwZ/WU+970fEWAe/w9AMoBWAF8C+G1PYi8AUErfBPAmAGRnZdHbnn6+kxeUw2qB2+2Gx+WCx+2G28UlwxcKwRd4X0IBxFIZJEpVBy8aJRQRUUO21T1YuBwOWAx6v+cy573MeUK5nQ64nU64XS64Xdy/PL4AfD4fPIEAfIEAAqEIYq+3tESugFiugEylhiIqGgLR8A7z4rTbYDUYYDUZYTMaYTNztrGbTZw9nE64XU64nC543C7wBQLwvJNrvkAAoVjCeZPL5d5/FZBHREIeGTlk27l7oqCgAAsWLOjXPQ6bFVaDgasvJiNsRoPf89LltYvbyb2oxwOegN/NNhKFwm8XiVwBWUQk5BGRg7Y9OFTYLRZYjZxtbEYDrGYTbEYDHFZrd9tQD9em/PYRQCiRcDbpYB95ZCTk6sgh2+oeDCilnHeP0QiriasvNqMBVpO3H/b2Mb3ZRiSV+ndn+GyjiIqGTKUOqsAykDYRCJRS7rlkNHj7XyNnH5MRDqu1U53xPaN87Ykn4PpkkVTWafeKRKGEIjIKUmX/xO6rDUrppfZkumQXq9EIp83aud70ZBuBACKprNMzSqLg6o1UqRrWpxJTjwdW744jzi4G/04kl93ufT5d6nMI4XXri8UyOWcXb72RKpRQRGkglncX5ga7XQSCx+Pu8oy6NMZxOuxwO12d2hXhXbINn88HXyiEWKaAWC731x2pSgVFVDREQyjMBQOP231p3Gc0wmq+1LZcXW3jcoLw+ODxeZ1s07EtiWVybqeL1zbDGbfLybUhb5uydmhTl8Z8nG08LhcaGhuRkpoKnoBrUwKhqJNdfPVGGaUZ/vMFpxNWo97flqwd+uSebEO8fYyvr/HVG1/d4eYLXJsajLi8ocTpsMNq0PufT75nlN1ihsvh6NSePC53t35YIBJ77SL320ceEQF5ZHSPobHW/OI3Q/AtGYzA2LVrl+zo0aOKuXPnGpjYywg3BqxeEELuAPA0gHxwMXG/BPAOgG0AFgC4D8DnAP4VaCEHCd8IecAuz5TSzwB8NpB7eQLBoG6VGg4IRCKoNNxWLUZnhGIJhDESqGJ6jUH/vUQkkUIkkQ5qOI7hglgm4+J9xg3JOQFXLYQQ/2QvApfdlfS9hBACqXcLaCQzTScIIZCp1JCpejyf5HsN4fGYbS4Dj8fnFlgjhmQn51UNj8+HIjIKisiooS7KVQdfIOyXbYbTIkigCHyhQQYp1vtwQigSQ6iJhUrD5gsMRldeeOGFmLq6OuEnn3yi4fF4eO655+qGukwMRrAJxF3tJwBqAVxLKXV5PQoqKaWfAPiEELIBwCYAHwdezKDg8+C93OxD1eU6BoPBYDAYDAaDwWAwGAxGGPHKK6/Ea7VaUXJysv33v/99RW8H1TEYw5VABN9xAD6mlHYM7uff00sp3UoI2QrgVwC+DSCfYFHs/XfkZT7P9v57cRDKwmAwGAwGg8FgMBgMBoPBGGTq6urODnUZGIxQE0jwPCGAlg5/W9Hde7YQwNUSp8B3NPAyQkin700IUQKYDe47HBrsgjEYDAaDwWAwGAwGg8FgMBgMRjAIRPBtADoFO6wGML7LNUkAej/ee5CglJaBiy+cDuDhLh8/B0AO4D1K6RWOemUwGAwGg8FgMBgMBoPBYDAYjKuTQEI6nAQX1sFHPoAHCSF3AfgK3MFttwDYH0AevUIIuRHAjd4/facQzSSEvOP9fzOl9JcdbvkpgAMAXiGELAZwAcB0AAvBhXL4bajKymAwGAwGg8FgMBgMBoPBYDAYoSYQwXcjgNcJIRmU0goAfwawGsA73hcAOAE8FUgBr8BEAD/s8l6m9wUAVQD8gi+ltIwQMgXA7wFcC2AFOE/lVwA8RyltDWFZGQwGg8FgMBgMBoPBYDAYDAYjpAxY8KWUvoNLwi4opTWEkKkAfgEgC0AlgNcppSELhk0pfRbAs/28pwbAPaEoD4PBYDAYDAaDwWAwGAwGg8FgDCWBePh2w+vp+7NgpslgMBgMBoPBYDAYDAaDwWAwGIy+MeBD2wgh8wghE4NYFgaDwWAwGAwGg8FgMBgMBoPBYATAgAVfALsAPBisgjAYDAaDwWAwGAwGg8FgMBgMBiMwAhF8mwFYg1UQBoPBYDAYDAaDwWAwGAwGg8FgBEYggm8BgFlBKgeDwWAwGAwGg8FgMBgMBuN7SnFxsYgQMvmWW25J7/j+Lbfckk4ImVxcXCwKZf5JSUnjkpKSxoUyDwZjsAhE8H0KQA4h5A+EEGGwCsRgMBgMBoPBYDAYDAaDwRhcCCGTO774fP5ktVo9cdq0aTmvvPJKtMfjGeoiBsS0adNyCCGTh7oc/SEpKWlcx9+Ex+NNViqVEydNmjTq//2//xfjdDqHuohhx1tvvRU5d+7c7KioqAkCgSAvIiJiYlZWVu4NN9yQ8eqrr0YPdfn6iiCAe58AUAjgSQD3EUJOA2gEQLtcRyml9wWQD4PBYDAYDAaDwWAwGAwGYxBYt25dAwA4nU5SXl4u3rZtW8TRo0cVx44dk7/33nvVg12e9evX1z311FON6enpIVU3t23bdjGU6QfCPffco4uIiHC73W5UVlaKt23bFvHkk0+m7tq1S7Vt27ayoS5fuLBmzZq0Tz75RCORSDwLFizQp6WlOcxmM6+qqkqcn5+vPnTokPL//u//Woa6nH0hEMH3Rx3+H+999QQFwARfBoPBYDAYDAaDwWAwGIyrnPXr19d3/Hvbtm3y5cuXj/rggw9innzyycZRo0Y5BrM8aWlpzrS0tJC7subm5tpDncdAefzxx7U5OTl+ux87dkwyd+7c0du3b4/YtGmTYuXKlaahLF84sG3bNvknn3yiiYuLc+7fv/9CVlZWpzpnt9vJ5s2blUNVvv4SSEiHjD6+MgMsI4PBYDAYDAaDwWAwGAwGYwhYtmyZOSMjw0YpxcGDB+UdP9u7d6/snnvuScnJyRmjVqsnisXivLS0tLEPPPBAclNTE7+n9Nra2nj3339/clxc3HixWJyXkZGR++yzz8a53e4e879cDN9XXnkl+pprrslKTk4eJ5FI8hQKxaS8vLxRr7/+elTH63yxgY8ePaoAOoeumDZtWo7vusvF8LVareTJJ5+MHzly5BipVDpJoVBMmjx5cs5//vOfyK7XdoxDXFxcLLruuusyIyMjJ4jF4ryxY8eO/vjjj9W9mLrPTJkyxTZt2jQjABw6dEjuswchZPIrr7wS/cUXX6imTZuWo1QqJ3YMY+F0OvHnP/85ZsKECaMUCsUkqVQ6afTo0WP+9Kc/xfRk/w8//FA9c+bMkTExMeNFIlFebGzs+KlTp+b8+c9/jul43fnz50Vr1qxJS01NHSuRSPLUavXEkSNHjlm7dm1qY2Ojvx489thjiYSQyRs3buwmnF4phvP58+dFzz//fOzIkSPHSCSSvI6/nVar5T/88MNJmZmZuRKJJE+pVE6cOXPmyK+++krVV5vu2bNHAQArVqxo6yr2AoBYLKY33XSToeN7HW3eU5pd61hXG7z99tuR48aNGy2VSiep1eqJ1113XWZFRUVQwuYO2MOXUloVjAIwGAwGg8FgMBgMBoPBYDCuXijloncKhcJOYTzfeOMNzdatWyOnT59unDdvnsHtdpMzZ87I/vOf/8Tl5+erjx07diEyMtIf/NdqtZJ58+blFBYWynJycqw333xzi16vF6xfvz5h7969iv6U6de//nVaVlaWdfr06cb4+Hhna2urID8/X/3www9nFBcXS/7+97/XA0B0dLR73bp1DZ9++ml0fX29yBeyAgDS09N79eq12Wxk/vz5I48eParIyMiw3X333U0Wi4W3efPmyAceeCDz1KlTjf/4xz/qut5XW1srmjlz5uiUlBT7zTff3NrW1sbftGlT1J133jlCoVBcXLVqlbE/37UnKKUEAAghnd7fsGFD5N69e9Xz5s3T33nnnU3V1dVigPNQXbJkyYh9+/ap0tPTbddff32LRCKh+/fvV/72t79NPXLkiOLrr7+u8KXz17/+VfOrX/0qTaPROJcsWaKPjo52NTU1Cc6fPy/74IMPNL/5zW+aAKCqqko4a9asMSaTiTd//nz9ypUr22w2G6+qqkq8YcOG6HXr1uni4+N7VvP7wc9+9rPUo0ePKhYuXKhfsmSJns/ndOSLFy+KFi5cmFNfXy+aPHmyaeHChQaLxcLbuXOn+tZbb81+8cUXq37xi180Xyn96OhoNwCUlZWJAy1rX3j99ddjdu7cGbF48eL2WbNmGY8fPy7ftGlT5Pnz56Xnzp07L5VKu4bM7ReBhHTwQwiRAxgJQEEp3RuMNBkMBoPBYDAYDAaDwWAwGEPLli1bFJWVlRKhUEjnzp1r7vjZs88+2/jee+9VCwSd5aWXXnpJ89hjj6X99a9/jX3++ecbfe8/99xzcYWFhbJly5a1b968ucwn2hUVFTVMnz59TH/Kdfz48XNdwzDYbDaycOHC7Ndeey3+5z//eVNGRoZTo9G4169fX79v3z5lfX29qGvIit547rnn4o4ePaqYN2+efseOHaVCIed8WVdXVz9t2rTRr732WvwNN9zQvnTp0k52OXLkiPKxxx6r/9vf/uYXl7/88svWW2+9Nfuvf/1rXKCC77FjxyQ+j+VZs2Z1ynv37t3qzz77rOTWW2/t5I36xBNPJOzbt09199136/773//W+H4zl8uFtWvXpn3++eeaDz74oO3OO+9sB4C33347RigU0lOnTp1PSkpydUyroaHB/4N/8MEHkXq9nv/73/++5umnn9Z1vM5gMPB4PF5AwqWPwsJC2dGjR893DSly5513ZjQ0NIj+9a9/lT/44INtvvebm5v5c+bMyfntb3+bevvtt7enpKS4uqd6iRtvvFH/u9/9zr1nzx71okWLRtx+++2ts2fPNufm5tp5vEACJPTMnj171Hv37r0wbdo0q++9VatWZWzcuDHqww8/jLj//vvberv/SgQk+BJCkgH8HcAqAHxw8XoF3s/mAHgTwE8ppQWB5MNgMBgMBoPBYDAYDAaDMdi0fnExxdlolg11OXpDGC+3RN06siZY6T322GOJQOdD2yileOaZZ2q7xtIdOXJkj/F8H3300ebf/e53yTt37lR1FHw//vhjDY/Hw/r162t9Yi8AjBo1ynHffffpXnrppYS+lrOnmLsSiYT++Mc/1h06dEi5adMm1c9+9rOADtj66KOPNIQQvPzyy7U+sRcAkpKSXL/85S8bHnvssbQ333wzpqvgm5iY6HjhhRcaOr53yy23GBISEhxnzpzpFBajL7zwwgtxXQ9ts9lsvKVLl7Zfe+21neL3Ll68uL2r2Ot2u/H222/HaDQa53/+85+ajgK9QCDA66+/XvvFF19oPvrooyif4AsAfD6fikSiboJtQkJCN/FUKpV6ur6nUqm6vTdQ/u///q9b/OiDBw9Kjx49qrj22mvbOoq9AKDRaNxPPfVU/V133ZX14YcfRvo8ki9HRkaG86OPPip7+OGH03bt2qXetWuXGgDkcrln0qRJpjVr1rQ8+OCDrV0XNwbKvffeq+0o9gLAgw8+2Lxx48aoI0eOyIdM8CWEJAA4DCAOwP8AxAKY2eGSw973VgMoGHgRGQwGg8FgMBgMBoPBYDAYg0FX0ZUQgpdeeqny0Ucf7Sae2u128re//U3z5ZdfRpWWlkpNJhPf47mk8TU2Nvrj7ra1tfGqq6vF8fHxjp7E2kWLFhn7I/iWlJSIfv/738fv27dP2djYKLLZbJ3cMOvq6gKKheorb2xsrHPSpEm2rp8vX77c8Nhjj6GwsLDbgsDo0aMtPQmDCQkJjlOnTvUrdAUAvP3227EA91vIZDLPyJEjratXr2751a9+1U3EnDJlirnre2fOnJG0t7cL0tLS7I8//nhiT3mIxWJPaWmpxPf3bbfd1vrcc88l5+bm5q5ataptwYIFxsWLF5sSExM7ib233XZb+5/+9Kek3/zmN6nbt29XL126VL9gwQJTXl6eLZiesTNnzuz2vXxhQAwGA9+3UNGRpqYmAQBcuHBB0vWznli1apVxxYoVhdu3b1fk5+crT58+LTt+/Lhi3759qn379qk++OCD6J07d5YGGm4BAKZOnWrp+l5GRoYDANrb23uMf90fApGlnwEn6C6hlBYQQp5BB8GXUuokhOwFMDvAMjIYDAaDwWAwGAwGg8FgDDrB9JwdLlBKjwPcdvz8/Hz5T3/60/Rf/epXaRkZGY7rr7++UyiCVatWZW7fvj0iOTnZvnTp0va4uDinWCymAPDvf/871ul0+gPMtra28gEgOjq6x631SUlJ3Q7Kuhznz58XzZ49e7TBYBBMnjzZNH/+fINarXbz+XxUVVWJvvrqq2i73R6Q2ugrb0xMTI/lSk1NdQKc2Nj1M7Va3WPMWj6fj46CeF8pKio6m5OT06M3dVfi4+O7ldd3gF5VVZW4N1HdbDb7v8uzzz6r1Wg0rn//+98x77zzTuxbb70VSwjB1KlTjS+++GLtvHnzLADn5b13794LTz/9dOKePXtU27Zti/CWw/Hwww9rn3rqKd1lsusXycnJ3b5XS0sLHwAOHDigOnDgwGUPaOv4va4En8/Htddea/J5Tns8Hnz99deqhx56KP3gwYOqF198MeZ3v/tdwN8pMjKyWx3xxch2u92k+x39IxDBdwWA/10hXEM1gLkB5MFgMBgMBoPBYDAYDAaDwRhkVCqV58YbbzQmJCSUzp49e8yDDz6YsXDhwkKlUukBgD179si2b98eMXPmTMOuXbtKfUIvwIUQeOONN+I6phcVFeUGgJaWlh61qP545P75z3+Ob29vF/z973+vfOSRRzp5Hv/rX/+K+uqrr6L78117wlfe5ubmHstVXV0tBAClUhnwgWTBpOshbsAlcXHp0qXt27ZtK+trWj/72c9afvazn7U0Nzfzd+7cqdiwYUPE559/rrn++utHnjt3rtAX2zcvL8+2adOmcqfTiUOHDsm+++471X/+85/Yp59+OkUul3vWrVvXDAC+eL4uV3fN3yfe9ud7+YT1P/zhDzXBEpa7wuPxcPPNNxuqqqrqH3vssbSCggKVT/D1eTC7XK5uhWtubg7YSzcQAlntiANQcoVrnAD6HZuEwWAwGAwGg8FgMBgMBoMx9EyfPt26evXqJq1WK/zjH/8Y63u/qKhIDAArV67UdxR7AaCgoEDeNcRCZGSkJzU11a7T6UTnzp0Td80nPz9f2dcyVVRUiAHgrrvu6hbndPfu3T2GTODz+ZcVG3siMjLSk5KSYtfpdMKzZ892K+93332nBIBx48Z125p/tTFx4kSbUql0nzp1Sm632/vtParRaNyrV6/Wf/LJJ1W33HJLs16v52/fvr2bnYVCIebOnWt5/vnnG999991yAPj2228jfJ/7hOeqqipR13sPHTrUb/1wzpw5ZgDYv39/v8Nk9JeehP2oqCgXANTU1HT7Pvv27RvS2N+BCL6tAFKucM1IAI1XuIbBYDAYDAaDwWAwGAwGg3GV8sc//rFBLBbTN954I94XHiArK8sBAHv27Okk1NbV1QkeeeSR1J7SWbNmTbPH48EvfvGLJLf7kn5WVFQk+u9//xvb0z09kZKSYgeALVu2dMr7yy+/VH322WcxPd0TGRnpAoDS0tJu4tzlWLt2bTOlFOvWrUvuKBQ3NDQIXnzxxUQAuP/++5v7mt5QIRQKce+99+qampqE9957b4rJZOom+lZVVQmPHz/uj3X7xRdfqJzO7tEsfB7PMpnMAwC7du2S1dTUdPPabmhoEAKdD3PzxeF9//33NR3TLi0tFb744ot9jt/sY968eZbJkyebtm3bFvnyyy/36NV95MgRaV1d3RUjHHzxxReqd999N6InQVyv1/Nee+21OACYPXu2P6zJ7NmzLTweD1999VWU0Wj0a6xarZb/5JNPJvf3+wSTQEI67AdwPSEknlLaTdQlhGQDuBbABwHkwWAwGAwGg8FgMBgMBoPBGEIyMjKca9eubXr77bdjn3322fjXXnutbv78+ea8vDzTtm3bIiZNmjRq+vTpJp1OJygoKFBnZGTYeop9+8wzz2g3bdoUuXXr1sjc3NwxCxcu1Ov1ev7GjRujpk6daszPz4/oS3keffTRpi+++EJzzz33ZH3yySdtiYmJzgsXLkj27t2rXrFiRdumTZsiu96zcOFCw5YtWyJvuummEUuWLNFLpVJPWlqa/eGHH269XD7PPvusdvv27eqdO3dGjB49Onfx4sV6i8XC27RpU2Rra6vgxz/+ceM111xj6pcxh4gXXnih4ezZs9KPPvooZseOHRGzZs0yJCYmOpuamgTl5eWSkydPKh5//PG6yZMnNwLAPffckykWi+mUKVOMqampDkopDh06pCwsLJTl5uZabrjhBiMAvP/++9HvvfdezLRp00zp6em2yMhId0VFhTg/Pz9CJBLRdevWaX1lWLRokXnKlCmmY8eOKSZMmDB6zpw5Rp1OJ9y5c6d63rx5hs2bN/dZjPfx6aefli9evDhn3bp16f/85z/j8vLyzGq12lVXVycqKiqSlpSUSHfs2FHkCz9xOc6fPy955plnUh555BH3lClTjFlZWXaBQEDr6upEu3btUhuNRv748ePNjz/+uD90RFpamvP6669v+frrr6PHjRs3ZvHixXqj0cjbtWuXetq0aaYLFy4MmZdvIB6+LwKQANhNCFkOQAYAhBC59+9vAXgA/C3gUjIYDAaDwWAwGAwGg8FgMIaM5557rkEikXjeeuut2JqaGoFAIMDmzZtL77jjjiadTid8++23Y48ePapYu3Ztc0FBQYnvAKqOSKVSunv37uL77rtP29raKnjrrbfiDh48qFy3bl3Dq6++2ucD8qZPn27dtGlT8aRJk0wFBQXq999/P8ZkMvHffffdsh//+MdNPd2zbt265ocffrjRaDTy33jjjbgXX3wx8d133+3RG9iHRCKhe/fuvfj444/XAcA777wT++WXX0anp6fb/vnPf1a88cYbdX0t81AjFovp9u3by/7xj39UZGZm2vLz8yPefPPNuIKCAjWlFL/61a/q7r33Xn885Keffrpu0qRJpsLCQvn7778f89lnn2lcLhf57W9/W7t3795iXxiPO++8s3X16tXNLS0tgk2bNkX997//jSssLJRdd911rfv27Tu/ZMkSc8dybNmypXT16tXNjY2NonfeeSf23LlzsmeeeaZ2/fr1tQP5XllZWc6TJ0+ef/zxx+v4fD79+uuvo95+++2448ePKxITEx0vvvhi1dSpU61XSueBBx5ofemllyrnzZunr6yslHzyySeaN998M27Pnj2q0aNHW/7f//t/1YcPHy5WKBSd6vVHH31U9eCDD2qtVivvvffeizl06JDy/vvv13399dflA/k+wYJQ2q399f1mQu4B8E/07CnsAnAvpfTDAWfwPSInJ4cWFxcPdTEYjKuKgoICLFiwYKiLwWBcNbA2wWB0h7ULBqMzrE0whgJCyHFK6ZShLsdAOX36dOWECROu+m35DAaD0ZXTp09rJkyYkN71/UBCOoBS+jYhZB+AnwKYASAagB7AIQD/oJQyBZPBYDAYDAaDwWAwGAwGg8FgMAaJgARfAKCUlgBYF4SyMBgMBoPBYDAYDAaDwWAwGAwGIwACieHLYDAYDAaDwWAwGAwGg8FgMBiMqwgm+DIYDAaDwWAwGAwGg8FgMBgMRpjQ55AOhJC3BpgHpZTeN8B7GQwGg8FgMBgMBoPBYDAYDAaD0Uf6E8P3RwPMgwJggi+DwWAwGAwGg8FgMBgMBoPBYISY/gi+GSErBYPBYDAYDAaDwWAwGAwGg8FgMAKmz4IvpbQqlAVhMBgMBoPBYDAYDAaDwRgKKKUghAx1MRgMBqPPUEov+1lQDm0jhMgJIZMIIXODkR6DwWAwGAwGg8FgMBgMxmBACGlzOBzCoS4Hg8Fg9AeHwyEkhLT19FlAgi8hJJkQ8iWANgDHAOzq8NkcQsh5QsiCQPJgMBgMBoPBYDAYDAaDwQgVHo9nS3t7u3Koy8FgMBj9ob29XenxeLb09NmABV9CSAKAwwBuALARwEEAHfc/HAYQC2D1QPNgMBgMBoPBYDAYDAaDwQglbrf7Ta1W267VaqPsdruwt23SDAaDMZRQSmG324VarTZKq9W2u93uN3u6rj+HtnXlGXCC7hJKaQEh5BkAMzsUwEkI2QtgdgB5MBgMBoPBYDAYDAaDwWCEjMmTJ1ceP3785oaGhge1Wu1ySqlmqMvEYDAYl4MQ0ubxeD5xu91vTp48ubKnawIRfFcA+B+ltKCXa6oBhEVcX0LIPAC/BDAZQCKAeyil73S55gkANwPIAWAHcAjAE5TSwsEtLYPBYDAYDAaDwWAwGIy+4hVNnvS+GAwGY1gTSAzfOAAlV7jGCUAeQB5XEwoAhQAeBWC9zDULALwOYBaARQBcAHYQQqIGo4AMBoPBYDAYDAaDwWAwGAwG4/tNIB6+rQBSrnDNSACNAeRx1UAp3QxgMwAQQt65zDXXdPybEHIXAD24sBbfhriIDAaDwWAwGAwGg8FgMBgMBuN7TiAevvsBXE8Iie/pQ0JINoBrAewaSOKEkFsJIa8SQvYSQgyEEEoI+eAK9yQTQt4ihNQTQuyEkEpCyMuEkMiBlCEIKMHZuG2I8mcwGAwGg8FgMBgMBoPBYDAY3yMCEXxfBCABsJsQshyADAAIIXLv398C8AD42wDTfwrAzwBMBFB3pYsJIVkAjgO4B8ARAC8BKAcXguEgISR6gOUIhL8DOAXg4BDkzWAwGAwGg8FgMBgMBoPBYDC+ZwxY8KWUHgbwIIB0ABvBHWgGAAbv3xkA7qOUnhtgFuvAhYRQAfhJH65/HUAsgEcopTdSSn9DKV0ETvjNAfB8x4sJIX/0eg339lowwLKDELIewBwAt1BK3QNNh8FgMBgMBoPBYDAYDAaDwWAw+kogMXxBKX2bELIPwE8BzAAQDS5m7SEA/6CUFgeQtj8UBCGk12sJIZkAlgGoBPBal4+fASdM30UI+QWl1Ox9/2UAvYaIAFDd9xJ3Ks9LAH4AYCGltHwgaTAYDAaDwWAwGAwGg8FgMBgMRn8JSPAFAEppCThv3KFkkfffbZRST8cPKKVGQsh+cILwDAA7ve83A2gOdkEIIX8HJ/YuoJQW9edej9UKj9kM8PkgQiF4EgmIIOCfKCygDgfcZjMInw8iEnEvXiARScIH6nLBY7MBHg94UimIUDjURbpqoB4PPBYriIDP6kwXKKWAywXqdoOIxVdcWPs+QSkFnE5AIGB1pgcopQClzDY9QCllbYnBYDAYDAaDwWAMOeGiJuZ4/714mc9LwAm+I+EVfPsLIUQBYIT3Tx6AVELIRACtlNJq7zWvAbgLwI0A2jocaGeilJp6SPNBcN7HGCORoHhSXqfPKSHwKJXwRETAFRcLV3IKXCnJcGZmgkokA/kaVzeUgq/TQVhWBmFlJfg6HQS6JvAMBhCXq/OlPB48ajXckZFwx2jgSkmFMyUFzvQ0QCweoi8QQigFX6uFsLISwopK8LWN4Le0gt/W1t02AgE8KhXc0dFwazRwpiTDlZ4OZ3IyIBIN0RcYGCaTCQUFBb1fRCn4zc3eelMFvk4HfhNXb3h2e+dLRSK4IyLgiYyAKyaWa0+pqXClpADhuLhCKfi6JggrKyCoqoagoQH8tjbw2ttB7HYQSrnLeDxQiQQehQLuGA3cmhi4kpLgzMyAKzERCEdhj1LwWlohKi+DoLIKgqYmrt4YjSA2G4ibi8RD+XxQiYSrNxERcMfGwpmSAldqypDYpk9tIlAoBa+lhetvqqogaNSC19oKvq/eePscKhLBIxZz/Y1Gw/XFySlwZqTDHRsLhKnwyWtrg7C0FMLqGvB99cZkArHZwLPbufYkFHJtKiKCe07FxXH1JiUF7tiY8LWNr97U1IDf0Ah+ezvX3/jalMfD2UUmBZUr4I6JgSs2Fq7ERDizMuGJHNgZu4PSLgKE194OYUUFhFVV4Dc1g9/czPU3djuI3Q7K5wMiIdemIiLhjo6CWxMDZ1oqXGlp8KjVQ/0VQgZPr4ewvByCmhoIdE3gNzeDmEzg2WwgDgeogA8qEIJKJXBHRMITGQlXfBxcKSlwpqSAqlRD/RVCBs9g8Nqm9lJ/YzZzbcrhAPh8UIEAVCaDOzICngjONu6YGOw2m0Hl8qH+CiGDGI0QlZVBUF8PvlbHtSmLBcRiAXG5uDbF58OjkMOjjoA7IgKuxAS4kpLhSkkGlcmG+iuEDGIyQVhewfXFTU3gt7SAZzaB2OwgTgcoX8A9p3xtKioKrrg4uFJTuPlCOM6lGAwG43sIod4Jf79vJOQ2cLF176SU1vfweRKA9wC8Rin9KqBCcrF0dwH4kFJ6Zw+fvwngAQAPUEr/08PnzwN4EsCTlNL/F2AZuvIupfRH3msuZ8znKKXP9pb+mLg4uuePfwRPLgfcHlCnEx6TEU6dDq5GLezlZXDVN3AXCwSQTpgA+exZUC1dCnF29kC+UjeowwHr6dMwHzwIR2UVnDotPGYL58klEkEQGwNhYiKkubmQTpwIUVpa4Hl6PLCeOAHD1m0wbt8OV2MjAICnVEKcmQlhWiqEsbHgKZTgyWQA9cDjcMBjMsOl1cLZ2AhHeTlcOh0AgAiFkOblQT57NpRLFkOcmRlwGQHA2dgI65kzsF8s4fJra4VHbwB4PBCxGAKNBqKUZIhHj4ZsyhQI4+ICzpNSytnmu60w5efDWcedXciTySDKHgFRUhIE8QngKeTgSaQAj4B6vcSdOh2cdfVwVFXB3cw5shOhELJp06CYNxeK+fMhSk8PuIwA4NTqYDtXCNu583DW1cGl08FjsXB5ikQQxMVBmJQI6dixkIwfD2FsbJ/TLigowIIFC7q9768327bBuGOHv23wZDKIMjIgTE3pVG+oxw1qd8BjMsGl08LZ0Ah7eTk8ej1XTpkM8qlT/fVGmJgYuGEAuJqbYT17FvaSUjjKy+Fua4PbaAR4BDyxBIKYGIjSUiHOGQXZ5DzwgzChp5TCdvo09Bs3wbR7N5w1JalCwAAARb1JREFUNQAAIhZDPGIEhImJEMTHg69UgIhEAI8Pj8XMtamWZjira+CoqoLHxK1R8WQyyGbMgGLBfCjmL4Awru+/X2+4WlpgO38etvMXuHrT3MzlyeOBiEUQxsZBmJwMydhcSCdMAF+hCDhP6vHAevIkjNu2w7hjh79NEYkEorQ0iFJTIYjRgCdXgCeXgbrcoA4H1xdrdXA1NMBeUQFqtQIAeGo15DNnQj57FpSLFkEQHZyzQV1tbbAVnoO9pASOykq429vhNhpAeHy0mExIyB0DYUoqJLljIB0/HrwgLABSSjnbbN0KY0EBnFVcRCMiEnFtKjERgrhY8ORy8MQSgBB4LBZ4zGa4dDo4amvgrKkF9S6y8CMiIJ89G4oF8yGfMweCAQp5XXEbDJfqTX09XDqdP08ilUIYFwthcgqk48ZCPHo0eEGYtFKPB9ZTp2Hctg3GnTsvtSmhEMLUVIhSUsDXRIOvUIInk/rrjdtguPScqqoCvEK5IDYW8lmz/PbhK5UBlxEA3EYjbOfOcf1NVRXcej08RiPA54MnkUAQHwdRahokY8ZAMnpU0HYQWc+ehWHjRpj27IWjooJ7UyCAKC0Nwvh4COLiwFcqQYQCgPDgsVjgNhrgbm6Bo7oazvp6wMNtzhIkJEA+cyaUixZCPmsW99zvA5d7Vvhwm0ywFxXBVlQMZ10d3K0t3K4hgRA8sQiC+ASIUlMgGTcO4hEjguK9zrWpUzDu2AHTzp1cHQAAoRCixEQIk5Mh0GjAk8tBpBLA7YHHZoXHZIazsYGr3w2NgHecLkxOhnzuHCjmzoV85kzwpNKAywhw9dteXAzrqVNw1jdwbcrpBKUe8BVK7jmVkQ5J7liI0tOCYxuPB7YzZ2DYvh2mHR1sw+NBmJTEtanoaG58I5aAulxcmzIa4GpohLOhAS6t1p+eKDMT8jmzoZg9G7KZM8EL0uI2pRT2iyWcbRq9tnE4AQB8pQKC2FiI0tMhGTsOwqTEoHj3d7LNznw4Kiu5DwiBMCEBwtRUCKKiwFMoQCRiwOWCx26Hx2CAs1ELZ0ODf9wHAOKcHMhnz4Z89izIp00L2i4wj80Ge3Ex7GXlcFRXwWMwcLsUBQLwZHII4+O8thkblDGxD2d9Pdq/+BKGrVvhKCvzvy9ISODqTWQk+ColiFAE6vb2xa2tcDU1wVlfD3d7O3cDjwfJmDGQz5wB+Zy5kE2ZDMLnB6WMlFI4KipgPXUaLm0jXE1NoE6u3vCUKghiYyDOyIBk3DgIoqKCk6fHA9vZszDu2AFj/q7OtomNhTAlhas3cjmIRMy1KZsdboPe36Z84z7weJCOG8fVmzmzIZ0woc+2IYQcp5ROCcqXYjAYDEbABCL4bgUQQynN6+WaowCaKKUrBlg+XzoLEJjg+ycATwB4glL650DKEipycnJocXHvIY/d7e2wnjsHy+EjMB84ANu5cwClEGePgPLaa6FavgLizIx+5euoqoJp/36Y9+2H5dAhTqjj8yFMSvIKZgqAxwO12eBqaoKjttYvdogyMqBcshjqm27qt7DqNpmh//prtH34IRwVFSAiEeRz50Ixbx5kk/Mgyszs16TC1dwM2/nzMB86DPP+/bB7bSkeNQqqFSugWn4tRCkpfU6POhwwHzkK0+7dMB84cGngRAiESUncJE2tAigFtTvg0mrhqKvjtoEDEI3IgmrFCqhXrOi3sOqxWqHfuBFtH3wIe3ExiFgM+axZUCxYAFneJM42/RiUOrVa2M6eheXoMZj27oWjnAsrLcrKgnLxYiiXLIZk7Ng+25u6XLCeOgVTQQGMBQVwlF6yjSAuDoLYWPAVcgAEHpsNLp0OzsZGv9ghGTsWyqVLob5+FYQJCb3m1XUS7zYaod/wNdo++giOykqu3syeDcX8eZBOyoN4RFafbUMphau+nmtT3nrjm3RK8/KgWr4cqmuvgSAmpk/p+WxjOXYMpgKu3tgvXtp0IIiN5UQhJVdvPDYbXFrtpUkrIZCOHw/VyhVQXnttv4RxgJt8GTZtRttHH8F27hxXb2bMgHzeXMimTIU4K7PPAg+lFM7aWlhPnYLl+HGY9uzxi+qS8eOhXLIEyiVL+tXfUI8HtnPnYNq1C8aCAtjPX/B/xo+OhiAmhlvwohQemxWuRi3cra3cBTweZFOnQrlsKVQrVvRbPHQbDN7+5iM4qqpAhEJuIjN3DmR5eRBnZ/fdNm43HJWVsBUWcv3NgQPcb8jnQz59Ovf7LVnSL/Geut2wnjjh7W8Ownbhgl/k4UdGgh8dBb5SBepxw9jcArHBwAl5ACAUQj5tGlQrVkC5dAn4/fR081gsXH/z4UdcfyMSQTZjOhTz5kM6aSIk2dncwkAfv4e9tAzWM6dhPXYcpr17ud+Qx4Ns8mQoly6BcvFiCJOS+m4bjwe2c+dh2rULpoIC2M6f93/GU3CCi0/48i12Ud+Ck0TiXcRZAtU1y/osHvrg+psNneuNV6iV5uVBkjOyz+KJx+GAvaQEtrOFMB86BMvBg3Dr9f5nn2rFcigXLODaQB/hhOhTXH9z6BBshYV+4ZQnl4MfFQWeUsEJiVYrXI2NoA6H/3P57NlQrVgBxYL5/V408Pc3H38MW2EhV2+mTYNi7hxI8yZDPDK7z2K7x+GAvfgirCdPwnL8OMwHDsBjNHK2mTULymXLoFi4oNd23/VZQSmFo6yMe07tKoD15Em/bYhE4hc+qNvN2UanA7xe/Ty1Gor586C6djnkc2b3Wzx0m8zQf/M12j76mBs7CIWQT5/O2WbiRG4hoo9pesxm2IqKuP7m8BGYDx0CtVhAZDIoFy6EauVKKObM7nMb9eFqaYF5/36Y9u2D+cDBSwKhQMD1xWIxQAjcBgPXhn39kUYDxYL5UC1dCvns2f1eNHDr9WjfsAHtH3/CPW+9tpHPmsX1N7m5fbaN22CA7YLXNocOwXL0KKjNBp5SybX5FSsgnzG93wKnq60N5v0HYN63D+b9++FqauI+4PEg0GhAvPXaYzDA7V00BgBBXByUixdBuXQpZNOn91sYdxsM0G/YgNaPPuIW3Hy2mTkD0kl5kOSO6XObcrW14cgnn2Ak4cF86BCsx4+DOp3gR0RAuXQpVCuWQzZtWr/GkpRS2M6fh6mgAJZDh2E9dcovYoLPB1+p5NqUywWP2XxJPAQgSkuDfO5cqFddB8n48f0WxqnTCWNBAdo//xzmvfsAALIZ0yGfOQuyKZMhGT26TwsglFK4mpq8CxynOducPg24XOBrNFAtWwrlNdcOSPx16/UwHzzItav9+y856QDgq9X+NurW6/39MOAdiy9aCOWyZdxYvJ+2cbW1cePiTz/h6o1AAPm0qZDNnAnZRG+b6sOzj1IKl1YL2/nzsJ45A8vBQ7CePQt4PBDExkK1/FqoVqy44u/HBF8Gg8G4ughE8K0HsJFS+mAv17wBYBWlNHmA5fOlswC9C74vAvglgF9SSv/Ww+f/APAwgJ9SSt8IpCyhoi+Cb1dczc0wbNsGw+bNsB4/4RV/s6G85hqorlkG0YgR3R7K7vZ2TpDyirw+TyVhcjLnHTFnDmTTp1/W44i63bCXlcFy9ChMO/NhPnIEcLkgmzoVEatXQ7l0Sa8DUkdVFVo//BD6L7+Cx2yGZPx4RN2xFsolS/o10b0STq0Wxu++g2HzFm4wB0AyZgyU11wD5bKlEGd0F6rcej3Mhw9z3ji7CrgJp1gM2dSpkM/iBpXiESMuO3CiLhdsRcWwHDsK046dsBw7BgCQTpwI9Y03QrViea9ijKO2Dm0ffwT9F1/CrddDnJODyDvvgHrlyn4LFb3hqK2FKX8XjPn5sBw9CrjdEMTGQrF4EZSLFkM+fVq3yaPbZOYGsfk7YSrYzU1yBALIpk6BYu48SCdOgCQn57K/ocduh+38eViOHIVx507YzpwBeDwoFixA5A9WcxPHHgbXvkm8vaICbe+/j/avvwG1WCCZMB6Ra9ZAuWSpV1wOkm2qqmDY8h0MmzdzYi0hkE7Og2oZV2+E8fHd7nGbTLAcPgzj9h0w7drlF3Gkk/P8kxFxdvZl25THYoG1sNBvG/uFCwCPB/mc2Yi46SYoFi3qvU3V1qLt44/99UY0IgtRd9wB1arrg2YbSinsJSVcvdm5E7azZwFwCxuc+LsUktwx3fsbkxnmgwdg2r0bpt274W5q5jxHJk2CYt48SCdOhGT0qMu2C7fBAOvZs5xttm+Ho7wcRCSC8tprEPmDH0A6aVKvEw97eTla33sP+m/+B2q1QjphAiLW/IATZIPgMey3zcWLMGzZAsPmLXBWeydcM2ZAec0yKJcs6VGo8lgsMB85wvU3+bs4UUUohGzCBMhmzYRs8hSIR2Z3u9fXJlxtbZwgf/QYjNu3w1lTAyISQbFoEdQ3XA/FnDm9Ch2Oqiq0ffQx2jdsgMdggHjUKESuXcP1N0Hqi6nHA1thIYy7dsG0YyfsJSUAuL5YsWQxt2iQnd3tN/RYrTAfPATTrnwYCwou1ZuJE6GYOweSceMhyR1zWQHQqdX6J6zG/Hy4GhvBk8uhun4VIlevhmTUqF7LbS8tResHH0D/v29BLRZIJ05ExA9WQ7l4cdC8canHA+vp0zBs2QLjd1vh0ulAxGIo5s2Fctk1UCxc0GMd9dhssBw5AuOOnTDm53NCHZ8P6fjxnDA0metvBDEx3exKPR64GhpgOXXK39+4m5vBk8uhXH4tIm66CdK8vF7blKOqCm2ffIr2r76Cx9vfRK5ZA/UNNwSvTTmdsBw/DmN+/qUdHHw+ZNOmQrl0KZSLl3TbaVBQUIB5M2ZwY5Pde2AqKICzthYAIB4zGor58yGbOBHinBwI4uK628blgrO2lrPN4SMw5ufDo9eDp1ZDtfxaqG+4AdKJE6/Y37R9+BH0X3/NjW/GjkXk2rXcQkyQ6o3H4YD1+HEYtnwH49atcHvL6Fu8lc+a1aN477HbYT15yitG7fMvuPEjI7lFjDlzIJ8+DYL4+G5CJXU6YS8v54TV/fth2rMXHpMJgrg4RNxyCyJuufmKizi24mK0ffAB9N9uBLXZIJ00CRGrb4dy0aJ+L1Jd1jZ2OyyHD3O22b4dHpMJ/IgIbmyzdClnmx7EZN/uNtO+/TDv28ctKlHK7eCYNZPzGp4+HcKEhG4Ct8dmg72kFLbCszAfOADTvv2gViuEycmIuO02qG+68YqLt7aiIq7ebNzIPaeCZJuOiyAeiwXmQ4dg2LwFxvx8UIsF/Ohort4sW8aN+3p4XnBt8QTnNbpzJ1wNDQAhkIweDdn06ZyDRlYWRCkp3WzjNhjgqKiA5eRJWA4egvngQVCHA8K0VKhvuAERN954xd1UjupqtH/+Bdq/3gB3UzMEsbFQ33IzIm65FaLkvi8c9obHbIZp715uN11BAajNxtlm6RKorrkGsqlTe1zYoE4nrGfPwryPa1O2s9yCG0+p5BbbZ8+GbNo0CJMSO43hKKVwt7d7FwDPwrx/P8xHjgIuF8Q5OVy9uX7VFX9767lzaPvgQxg2bwa127l6c9ttUC5eFJTdYgA3LzLt3QfDd1tg3r0H1OmEMCmJ64eXLYN04oRu/QUTfBkMBuPqIhDB1wbgr5TSp3q55o/gRNiA9pv2QfC9H8C/AbxJKX2oh8+3govhu4RSOqAYvqFmIIJvR5xaLbdNeetWWI4f52J3RkZCPHIk+ColPHY7nLV1fu9OIpNxngPeLXDCtLQBbUVzNTej/asNaP/sMzhra8FTq6FetQrq61ZCMmYMiEgEt8EA84ED0H/zP5gKCgCBAKprr0XUXXdCOn78gL9zX3HU1sG49TsYtm2D7fQZAJyXimRkNngqNajVCkddLRxl5Zzd1GooFi6EctnSy06e+oKzsRGGTZvQvmEDHKVlnFC1ZDHUN94I+axZIAKB1yPgENo3fAXznr0AjwflkiWIuvMOSKdMCfnhP+72dpj27IFxx06Y9u0DtVjAUyggnTgRfLUaHqsVzpoa2EtLL9lmwQIoFi6EfM7sAU/wHbV1aP/sM7R/+SXcLS0QxMdDfeMNUC1fzm2n5fPhNhhw5M03kVxSCtPu3SBCIVQrVyLyjjsgHTc2yJbojr20FIbvtsK4datfqBImJUGcnQ2eUgmP1QJnTS33mccDnkoFxYL5UC5ZAsWcOQMW6e1lZdD/71vov/mGE6pUKqhWrkDETTdBMm4cCCFwt7fDfPAg2r/aAPO+ff56E7l2LWTTpoa83jgbGjjBaccObtHA44EgMQGS0WPAV6ngsVrhqKzk6o3LBZ5CAfncOVAuXAj53LkD3t5vK76I9k8/hf6bb+AxmyEakQX1DTdAtXSpvw/zTVD033wD8969ICIRVNddh8g71kKamxtkS3SGUgpb4Tmuv9m6jVtQ4/EgysjgFouUCs4LtboGtqIiwO0GT6GAYv58TqyZO++KIn1PW9cppbCdPQv9txth2LgR7rY28KOjob5uJdQ33ADx6NEghPg91/RffQnzgYNcX7xsKdemriD0BQNHZSWMO3fCuH0HtxBHKYRpqZDm5oKnVMFjNsNRUQH7xYugTifnhTp3LpQLF0A+b96A6o0vNE7bp5/C+N1WUIcDkjFjoL75ZigXL+IELl+92bMX+g1fwXzg4KV6s3YtpGNDXG88Hk7E27oNxm3bOG9ToRDi7BEQZ40ATyGHx2SGo7oKtvMXAKcTPJkM8vnzoFy8BIr58wYkKFK3G5YjR6D/37cwbN0KarFAmJoK9Q3XQ33DjX5BxdXWBvO+/X7bQCCAcukSRK5Zw4khIaw3lFLYzp2Hcft2GLdt84eMkIwbB3FmJngKBTwWC5pOnYK4vh7Ubud2N8ycyT2rFszvcaHuivk6nTAfPAj9txth3L4d1GbjhKrrr4f6+uv9O4Y42+xD++dfwHLkCPecWrGca1MhHt9QhwOmAwdg2LQZpgLvIrVEAsno0RCPyAJPJoPbaIKjqgq2s2c5r0KBALKJEzmBd84cSMaM7rcnKnU4vN6WX3DPH3DelhE33QTFvHngR0QA8I4vdu9G+5dfcbaRSKBetQqRa9dAMnp0sM3RCY/dDvO+fTBs+Y6zjckEnkwGyZgxEGX5bGOAs7IK1rNnubAwfD6kEydCPnsWFHPmQJKb228vT4/NBuPOnWj/7HNYDh/mdn7MmY2IG2+EfPZsv4jnamvjbPPFF7AeOw4ikUB13UpErV0LyZgxQbHB5cKceKxWmHbvgWHrdzDt3sON+5RKSHJzIc7M4GyjN8BRWcnZxmbj2tScOVAuXnxFb/vL4TYaYdy2HfpvvoHlyBGAEK7e3HwzlIsX+8dMrrY2mPJ3Qf/tt7AcOsQ5B8yfj4jbboNi3tyQHmbtsVhg2r0bhq3bYNq9G9RqBV+thmTcOIgyM8CTyuDWt8NRWQXr6dPcjkdf+IM5c7jdH+PHDcjz3fDdVrR/9hm3Q8s7X1Bdfz3k06f7vZd9tmn/6itYjx8Hkcmgvn4VItesgSQn5wq5BIbbaIRxx04YvtsCy4GDnMd4dDSkY8dC9P/bu/PouM7zzvPft1DYClsV9o0ASBAEF+2iJEumJEqkSHfmJLZlS7Hdi53EnUnc0+7kTM4kM5kkcs+cM9Nn+sTpODmZdDId97jbE59px1IyVouLxH0RSdGSLHEFCYAgAALEUoWlUAuq3vnjVl0CFEgRRIEFAr/POThF3LpV98XD973vvc9973tXr8YUFpAMjVH3R3+ohK+IyBKykITvVeCItfaXb7POj4DnrbXzP9qe/T1buX3CtxXoALqAVmttcsZ7JUA/zoPWqqy1kwspy2JZaMJ3punr1xl/Zx+Rj5y5Q5OTk5jcXLx1dRQ++AC+xx935mPK4AO8bDLpnBz9+MeM79nr3Obl9eLJy3Pnc82pqCDwy6/i/8pX5n27eqbE+/sZf+cdIj//iOilS05s8vPJramh4KEH8W3ejO+xxzJ6QJlOAoVef91JxoRC7pyKyUmnOnqrqij78pcIvPrqp05zsFiSkQiTx46lRpmeIzE+jsfnw1tTTeGDD+F74gl8j2c4NrEY4++8Q/Dv/o7Jw0cgmcTk5WHy891b1nOqKgm8+ssEvvoVvJWVGdv2fEQvdzKx7x2mPvqI2OXO1MljId7aOgofegjfE5vxPf54xubGg1Qy5t13Cf7kdcZ373YSGXl5mIICkmNjAHhra/G//DL+V1+5q6RGJqRPQCb27yPWfYXE2BiewkJn/t0NG5xRLo89mtHYJCcnCf30p4Ref4Op06cB5yKWJzfXvcXWW12N/yu/TOArX8nYHHnzYa0leu6cs7/56GNinZ0kw2GnTdXVUvjww/g2P4HvySfmdcv4p81VauNxJg4dIvT6G0zs2+ckTn0+5+Jbau5Cb32dMzLvlVeyty8eHHRGjO97h3j3FXd/k9vYQOGmTfiefpqiJ57IaD81PTrK2D/8A8HXX3dHOHpKS50LTKOjgNOmAl/9Kv5XX8nYvMPzkZ4veGLfPiIff0ysq8upN0VF5NbVUfjoI86++KmnMjI/cVpycpKxPXsIvf6Gk2TBmdrAeDxubHIbGih7+Yv4v/xKxubynq9oRwfje/YwceQI8au9JKem8OTnE/b7qX36aYq2bMH3xOaMzGudlpiYZHz3bkJ///dOEs9aJ6np8bjTzuQ2NuJ/9VX8X3o5Y3N5z4eNxZg8eZKJ/QeInD1DrKsbOzXl1JuGBmd/8+QT+J58MmMjscG5eBt643VCP3ndHVGdk+qn09NE5NbXE/jHX8P/pS+5yeB7ycZiTB4/7kwJ8/EZZ476SARPSTF59Q0UPvKI04ff5u62uxHt7CT0k9cJ/f3fu8+n8FZVOSM807FpbCTwta/hf/mLGY/Np/UVkDruO3KEiQMHiZw5Q6ynx0kAl5Y6z+x49BGKnnxyXvNp34nY1auEXn+D0E9+4sylbwze6mpsMuHc0QHkrlqF/+UvUvbyyxmdA/hOJaemmDh0KNWmzhK/coVkJEJOSYnTph591LkD8DNPZWxELcDURx8T+slPGPvpT50+OycHb001Nh6/EZumJgJf+yr+l1/O2Aj5+UiMjzNx4CCTh9L15qoznUppKetPnlDCV0RkCVlIwvdHwC8Bj1prz83x/gbgZ8A/WGtfWVAhPyXhm1onPYr329ba781Y/sfAbwN/aa39jYWUYzFlMuGbbdOjo4TfPUHk3Fns1BTeqioKHnoo44nU+1EyFnNPOpLhMN7KSnyPPercmr7CYxMfGCR8/BiRCxew0Ri5tTWcs5ZnfvVXM/YgjftVYnyc8T17iV7qwEaieKur8W1+3LnFeIXHJtbTw+Tx48Q6OrDxON66OnyPPea0qQw8XGipuZOT+LREMMjYrt1OvYnHya2tw/fE5nk9gGW5ipy/QPjkSWKXL2MTCXIbGm7EZhnWm/mI9/YytmcP8StXsMkkuXX1+J54Ys7bd5eK+bSLhYj39TH21i5iPVcgkSS3oYGip5505rVcorG5F9LzSYffe8+Z1gbIbVxF0dOfmdczApYjm0gQPvUeU6ffI3b1KhhD3qomip552hlFvEixuVdtYiFsMkn45CnCJ086FwxyPOSvXo3vySfdO5pWKhuLMXnsGFMffEC8tw+8OeQ1t1D0zDNzTqGVbdZajDGa0kFEZIlZSML3CeAoEAL+NfAW0As0AP8I+AOgDNhirX33Lr7/C8AXUr/WAjuBy8Ch1LIha+3vzFi/NVWeauAN4CzwFPACcAF4xlo7PN9y3CvLKeErkin3wwmLyL2kNiHySWoXIrOpTUg2KOErIrK03PWQQmvtSWPMt4A/B76b+pkpAfzm3SR7Ux4Bvn7TsjWpH4BunAe1pctzyRizGSf5/DngF3CmcvhT4DvW2pG7LIeIiIiIiIiIiIjIfWFB95Bba//KGHMY+BbOaFo/EASOA39hrT27gO9+DXhtnp/pAX7lbrcpIiIiIiIiIiIicj9b8KShqaTuv8xAWURERERERERERERkAVbuUxRERERERERERERElpm7TvgaY14xxrxjjKm/xfsNxpi3jTEv333xREREREREREREROROLWSE7zcBv7W2b643rbW9QGlqPRERERERERERERFZZAtJ+D4InPqUdU4BDy1gGyIiIiIiIiIiIiJyhxaS8C0HBj9lnWGgcgHbEBEREREREREREZE7tJCE7xDQ9inrtAHBBWxDRERERERERERERO7QQhK+R4BfMsasn+tNY8wG4PPAoQVsQ0RERERERERERETu0EISvv8W8AKHjTHfNsasM8YUpV7/FU6iNye1noiIiIiIiIiIiIgsMu/dftBae9IY8y3gz4Hvpn5mSgC/aa19dwHlExEREREREREREZE7dNcJXwBr7V8ZYw4D3wKeAvw4c/YeB/7CWnt2oQUUERERERERERERkTuzoIQvQCqp+y/nes8Y4wF+0Vr7xkK3IyIiIiIiIiIiIiK3t+CE71yMMc3AN4FfAepw5vIVERERERERERERkUWUsYSvMSYH+Dzw68B2nAfCWWBvprYhIiIiIiIiIiIiIre24ISvMWYNzmjebwA1qcVDwF8C/5e1tnuh2xARERERERERERGRT3dXCV9jjBf4Is5o3hdwRvPGgL8DvgS8Ya39w0wVUkREREREREREREQ+3bwSvsaYNuCfA18HKgEDnAa+D/zQWjtijElmupCy/MQTcYYjw4SiIZI2SZIkeZ48yvLLKMsvIz8nP9tFzJpoIsrI1AihmBMbiyXXk0tZXhn+Av+Kjk1kOsJIZMStNwBejxd/vj/rsbHWYozJ2vbD8TDDkWHC8TBT01N4PV7ycvLweX1U+apWbL2x1jI1PcXw1DBjsTF3f1OQU+Dubwq9hdkuZlZYa5mITzAaGXVjY7HkefLcNrWSYzMeH2doaojJ2CQJm8Bi8Xl9lOaVUpZfhi/Xl+1iZoW1lrHYGMNTw0zEJ5w2ZZP4cp3Y+PP9KzY2SZtkLDrG0NTQrNgU5RZRll+2omOTSCYIRoOMREYYj427y/O9+QTyAys6NtPJaYLRoNum0gpyCvAX+AnkByj0Fmb1GCNbppPTjEZGGZoaIjwddvopaynKK6Isr4xAQYCi3KJsFzMr0udS6WM/cPbPRblFlOaXEsgPUJxXnOVSiohINs13hO95nHl5B4HvAn9jrf0446VagcYSYxzpPcL68vVUFFYs+PtC0RAXRy9yMXiRjtEOeid7GQoPcX3qOpPxSRLJBNN2Gp/XR1l+GRUFFbSUtbCmbA0PVT3Ew1UPU+AtyMBfBrFEjOP9x3m3/10ujF7g4uhFhiPDt/2MP99PU2kTTSVNrClbw4aKDWwo35CR2IxGRukIdtAR7OBS8BJXx68yNDUjNjZBIpmg0FuIP99PZWHlJ2KTl5O34HLAjdgc6zvGhdELdAQ7GImM3PYzZfllNJc001R6IzYbKzZSXlC+oLJYaxmJjHApeIlLoUtcCl6iZ7yH4alhhqaGGI+Nk8Q5eU3HJl1v2vxtPFD5AA9XPUxuTu6CypEWTUT5efjnHH33KBdHL9IR7CAYDd72M2X5ZTSXNtNS2sLqstWsL1+fsXqTSCboGe9xY3MpeInLoctcD19nanqKqekpcjw5FHoLKc4tpq6ojsaSRtYF1vFw1cNsqNiQsaTrYHiQk9dO8sH1D/h4+GN6xnoYjY7e9jPlBeXUFtWy1r+Wtf61tAXaWBdYR1Vh1YJPItP1Jt2mZtabdNLDWkth7o16s8a/htayVh6pfoSNFRvxejIzpXw4HuZg70FO9J9w683ME+i5zNzftPpb2VixkY3lG/EX+Bdcnpv3xX2TfVwPX+f6lFNvppPTJG2S4rxiSvNKqSqsYnXZatb61/Jo9aOsL19Pjiczzz2diE1wqPcQ7/a/68YmPB2+7WdK80ppKW2hqTQVm/KNbKzIXGzSfUJHsIP+yX43NpHpiBMbkhTnFlOWX+bGptXfymPVj7EusC5jsQlGguzr2cf719/n/Mh5LgUvEUlEbvuZQH6AVaWraCppoi3QxoZyZ19cll+24PLMrDcXRy9ybfIag+FBrk9dJ5qIEk/ESZKkJLeE0vxSanw1s2LTFmjDYzwLLgc47XvflRuxuRy6TDQRve1n/Pl+mkqaWFW6ijZ/m9OmMhSb8dg4F0cvunWnf7KfzsFOXvvRa0QSEbdNleSVUJp3IzZtgTYerX6Utf61GUucDU8Ns69nHz8b/BkXRi9wOXiZWDJ228+kY9NU6tSbjRUb2VC+ISOxGYuNubHpGE21qanrXA9fd2NjraU4z2lTtb5aZ38TWMvj1Y+zumx1RmPzTs87nLp2iovBi3SGOplOTt/2M+njm+bSZlr9rWyq3LQosUm3qXRsooko8WQcgJK8EkrySqgvqnf3xY/XPE5zaXPGYjM0NcQ7V97h5LWTdAQ76Brr+tTYpPfFzaXNrA2sZVPFJjZUbKA0r3TB5bm5n0rXm6GpIXdfDDj1Jq+M2iKn3qwLrOPxmsdZVbIqY7Hpn+hn75W9fHD9Ay6MXqB7rNu9wH8rM9tUe6DdaVMVGyjJK1lweW7XpqKJqBub0rxSSvNLqSuqY03ZGtoCbTxe8zgNxQ0Zi03vRC97u53YXBy9SPdYNxZ728/MPGdoD7S7bSoTieCbY9M72bvg7xQRkcwy1t6+o5i1sjN6Nwn8R+CvrLXHb7HOX1trfz1jpVwBClcX2rWvrQWg2lfNhvINtAXaaPO30RZoo6WshVzPJxNpU9NT9I73cmH0AudHz3Nh9AIXRi8wGB501ynJK6GppImqwiqqfFUU5xbj9XjxGA/h6TChaIjB8CCdoU4GwgMA5Hpyeaz6MV5qfoltzduoLKyc198Tjoc52neUPd17OHj1IBPxCfJz8mn1t9Lmb6OhpIHKwkr8+X5yTA4e4yGaiBKKhghFQ/RN9tEz1kP3eDfXJq+531vjq2FDxQbaA+2sDaylzd9GU2nTLWPTN9HHxdGLnBs5x7nRc5wfOc/Q1NCN2OSW0FTqxKbSV0lJbglejxdjDOF4mLHYGAPhATpDnW5M83Pyndi0vMT2pu0ECgLzjs3h3sPsvbKXg1cPMhmfpNBb6CbhGoobqCiooCy/DI/xYDDEkjFC0RDBaJBrk9e4MnaFrrEu9/8LoLaolg3lG9hQsYG1/rWsKVtDU0nTnAnYyHSEvok+LoxecGIzco6zI2dnJZuLc4tpLm2mqrCKisIKSvNKyfHkYDBMTU8RjAa5Hr7uJD2nrgNQ6C3ksZrH2NG8g21N2+Z9kjYZn+RQ7yH2du/l0NVDhKfDFHoLWRdYx1r/WhpLGqkoqKA0v5Qck+PGJhgNMhoZvWVsanw17gnA+sB6WspaaCxunDM20UTUqXtj3U4CM5Xg7Qp1zTqRryuqo9XfSo2vBl+uj4KcAhI2QWQ6wnhsnN6JXq6OX2Vwyqk3BTkFPF3/NC+seoGtq7bOu95cD19nT/cednXt4vTgaTfemyo2uX9PZWElRblFFHgLSCQTRBNRJuOTDIYHGQgP0DvRS8doh1smcJJW68rXsT6wnvbydtYF1rHGv2bONhVNROmd6HXb1NmRs5wbPjfrAk5JbgnNpc1UFlZSUVhBSV6J83+ValOj0VEGw4NcDl52k9RFuUU8WfskO1p28MKqF+Y9WicUDXHg6gH2dO/haO9RYskYJbklzj400EZ9cf0n2lQkEXHbVN9EH1fGrnxif1NfVM/Gio2sC6xzv6uxuHHOJGM8Ead3opfzo+c5P3KecyPnOD96/hP74lUlq5z9Ter/KseTgwcPk/FJQrEQA5MDXA5ddi9uFOcW85m6z7CzZSfPNT7HiSMn2Lp167xis69nH3u793K07yjxZJyy/DJnH+pfS31xPeUF5ZTll2EwGGOITkedNhUdpX+in+6x7k+0qYbiBjZWbGR9+Xpn35Xar8+VZJwzNiPnZ9XDm2Pjy/U5/RQeJuITjEXH6J/s53LoMmOxMeczuSU80/AMO5p38Gzjs/MejZxOuuzp3sPJaydJ2AT+fD/t5e20+duoLaqlsrCS4tziG/9P05OMx8YZiYzQO9FLz7izr5hZbxqKG1hfvp72QDvrytexLrCOxuLGOU/648k4veM3YpPux2d+X0leCY3FjVT7qqnyVVHoLXT6KYxTb1J9Zmewk/G4M3KyLL+Mz9Z/lh0tO9jSsGXeF5uGpoZ4u/ttJzYDJ0naJOUF5W7fW+tLxSav2G3fU/EpxmJjbmyujF+ZMzbpNtUeaKe9vJ26oro5YzOdnHbqTarOpI9v+if7PxGbnKkc2hvbnXpjnD58IjZBMBp0681kfBJwLn5tadjCzpadPF339LwvUg6GB3n7ihOb9wbeI2mTVBZWzqo3FQUVFOcVu/ub8HSYseiM2MzRTzUWN7KxYqO7H24PtFNbVHtHsZmr3pTmldJQ3EC1r5rKwspZ9WY8Pk4oGqJ/wolN+sJPeUE5zzU+x86WnTxV99Sc/cDtDEwOsPfKXvZ27+X04GmSNkm1r9rd39QW1VJeWE5pbinGGCyWyLSzLx6JjNA30Tfn/qappMnd36QvVtb4am4Zm/6Jfs6NnnP3NbeqN1W+KqoKnTaV/lvHYmOMxca4On6VrrEupqanAKgqrHJj80TtE/O+SHlt8ppbb04PnMZiqS2qZX1gPa3+VuqK6ggUBCjJK3H/rsh0hNHIKKPRUfom+uga66IrNDs2zaXN7kWD9HFkja+GAwcOfKKvmE5O0z/Z7x73pX9u7qfS+5uZ9Qaciy2haIjeiV46Q53uRbEaXw1bV21lZ8tOHqt+bN4X4nrGe9jbvZc93Xv4+dDPAWdfkT72q/HVuP1CjnG+eyI+4e5vro5fnXN/01zazIbyDbSXt7v7m1td5E4kE/RN9nF+5DxnR87OWW/SbWpmvfF6vO4dIaFoyK036YtidUV1bmwerX503hfiroxdYU/3HvZ07+HjYWeM1aqSVbQH2mn1t7r/TzNjk+4XRiIj9Iz3cGX8yifqTUtpixub9eXO8d+tzvWSNknfRJ9bX9Kx6ZvsmxWbVSWr+NEv/ug9a+3mef2RIiKyaOab8P194FeB1Tgjfc/jTOfwA2ttf2odJXzvQlt7m/3hgR9yZviMe6DRFepi2jpXjg2Gsnzn1qUck+Pc4hQdJRQNud/h9XhZU7bmRnLC7xwUV/uq7/jq8lhsjPcH3+dE/wkO9h6kM9SJx3jYXLPZSf42baPKVzXnZ8dj4xy4eoC3u9/mcO9hIokI/nw/L6x6ge3N2/lM3WfuamTsWGyM8yPn3dicGT4z64p/jslxb5XM8eQQT8Tdg8CZsVnrX8u6wDr3AHKtf+28YhOKhjg9cJqTAyc5ePUg3WPd5Jgcnqp7ih3NO3ih6YVbjrIdi41xoOcAe7v3cqTvCNFElEB+gBebXmR783aeqn3qrkbGjsXGODfsJN4+Hv6Ys8Nn6RrruvF3Gy/+Aj9leWV4PV6iiegnY2O8tPpb3QPitf617kHkncZmNDLK6cHTnOg/waHeQ/SM9+D1eHmm3knGbF219ZbJ33RC6u3utzna5yTrygvKebHpRaqD1XxzxzfvKjbjsXHOjZzjzPAZ92fmaAiP8VBVWEVRbhH5OflEEhEmYhMMTQ3NGjFRX1RPq7/1xk9ZK2v8a+44KXk9fJ0Pr3/Isf5j7O/Zz0B4gByTw+bazexo3sGLTS/e8iA7nXTZ1b2LU9dOYbGs9a9lZ8tOnm98nrZA212NjA1GglwMXnQTKOdGztEx2uEmtL0er5sE9BovsUSMUCw062KJ13hZ41/jJrbupt4MTQ1xauAUJ/pPcODqAQbDg+Tn5PNsw7PsaNnBc43P3TLO6dFje7v3cqL/BNN2mhpfDdubt7O9aTuPVj96V6M/Q9GQu585M3yGs8Nn6RnvcetErieXQEGAQH4Aj/EQT8YJRUOz6k06Nu2BdjdR3OZvm1dsBsODnLp2ihPXnNgMTQ1RkFPAhvwNfO2Jr/Fcw3O3vP15rkRmXVEd25u381LzSzxc9fBdjf68OTYfD33M1Ymr7vv5OfmUF5Tjz/fjMR7nQlUkxPWp625sckyOG5uZfdWdxsZay0B4gFMDpzh57ST7e/YzEhmh0FvoJmO2NGy5ZfJ3MDzoJhbSCanm0mZ2NO9ge/N2NpRvuKvRWKFoaFYfdX7k/Kz9TTo2gYIABuPepnxzbNKj5mb+zCc2/ZP9nBo4xbv973Lg6gFC0RA+r4+tq7Z+avI3nZDa3bWbnw3+DIulpbSFHS07eKn5JdoD7XcVm2AkOKvenBs5N6tNFeQUUFFYQSA/gDFObIYjzh0CM/v5mbG5Oem3f//+214IsdbSO9E7KzbjsXFKckt4oekFdjTv4On6p295jJIedbinew/vD76PxdJa1uq2qXWBdXcdmzMjZ2btb2a2qUJvoVNvZuxvRiIjXJ+6/onYpBPOd1Nvro5f5dTAKY73H3cv0pfll/HiqhfZ2bKTJ+uevGXyNz3qcE/3Hj64/gFARmKTblMfD3/s7m9mJph8Xp9bbzA3bv0fDA+SsIlZsWkLtM3a59wqWXyzpE3SPdbt1puDVw8yNT1FID/AtuZt7GzZyeaazbfsh9OJzL3de/lw6EMA1vrX8lLzS2xv3k6bv+2uYzMzLh8NfzQr0VmcW0yhLaQuUIcHZ18cjAQZCA98IjbpCwzzPWdI2iRdoS5OXjvJu9fe5XDvYaamp6goqGB78/ZPTf52hbrYe2Uvu7t2c3bkLACbKjbxUvNLvNT8Ek2lTfOOCzjHo24fNfwx50bO0TtxY+RpcW6xO+Akvb8ZiYwwMDngnnN5jIfVpTdiM982lbRJLgUvOW2q77h73F9dWM1LLS+xs2Xnbfvhy6HLbps6N3IOgAcrH3TrzaqSVXcVm5HICGeHnfOFj4ec2MxsUyV5JVQUVLjnMul98UB4wB3J7DEeWkpbZl3QbA+0u7ExxijhKyKyhMwr4et+yJidOHP5/iKQCySA3Tgjf/8WJXznrb293Z4/f37WslgiRmeok4tB57adkakRd0RcjsmhNK+U2qJa6orraPO3saZsTcZupwfnJOBi8CK7unaxu2s3XWNdGAyPVj/KI9WP0FjSiMHQN9HHzwZ/xvuD7zNtp6kurHYTmY/XPJ6xW7VniiaidIY66Qh20BnqZDQySjAaJJFMkJeTR3FeMfVF9dQW1dIWaKO1rDXjsTk/ep5dXbvY1bWLnvEeNzH+cNXD1BfXA86J0OmB03xw/QMSNkG1r5rtTdvZ3uwkpBYjNuF4mK6xLi4FL9EZ6nTnvJ1OTpPvzac4t5j64nrqiurcJF2mpqgAJzZnRs7wVudbvNX1Ftcmr+E1Xp6ofYKHqh6irqgOi3OC+d7Ae3w49CFJm6S2qJbtTdvZ1rTNTdZ92kn8fE3GJ+kIdtwYXTU5QHg6TGQ6gi/XR1FuEbVFtbOmzMjknILp2KRHzqXb1GM1j/FU3VPU+mqdE8zxbt4ffN9NLKwuW83nWj7HzpadtPpbM1aemaaT03SPdXNu5BwXRy/eqDd2mvycfIpyi6gvrqehuMG9YJLJepO0Sd4ffJ+3ut5iT/cehqaGyPPk8XT90zxQ+QC1RbUkkgmujF/h1LVT/Hzo51gsq0pWOYmFppd4oPKBRZnjMBwPczl0mYujF+kcS+1vIkGSJMn15FKa59zGWVdc5466yWRsEskEpwdPs6trF29efJPx5DgFOQU8U/8MD1Y9SFVhlfP/N97Nif4TnBk+4ybr0gnwjRUbFyU2k/FJdzqPmfubhE2Q58lzbo0urqe+uJ51gXW0+lszOp/0dHKa9wbeY1fXLt6+8rab/N3SsIVNFZuo8lURT8TpHuvmeP9xN7Gw1r/WTUjdbdLl04TjYS4FL3F+1LmAOxoddS+2pS/G1RbVuiPZMh2beDLOyf6T7Op2YhOKhijKLeLZhmfZVLGJisIK4sk4l4OXeffau25ioS3QxktNTtKl1d+6aLFJX3C6MnaF4ciwe3zjNc687OnYtJe3f2ps5ttXxBNxjvUfY1fXLvb17HOTv882PsvGio1UFlYSmY5wOXSZ4/3HuTB6AYD2QLubkFrjX7OgGNzKRGyCjmCHc9FgvJuRyAijkVRsPDdi01jcyLpy5wJ2JutNLBHjaN9RdnXtYn/Pfjf5+3zj82wo30B5QTlT01NcCl3iWN8xOoIdAKwvX8/2pu2LGpux2Bgdox3uvnhkasS9GyLHk0N5QTk1vhoaSxrdfXGmpicDZ8Ttkd4jTmyu7mdqesodFb2+fD3lBeWE42E6gh0c6zvGpdAlADZWbHSSdU3baSlryVh5ZgpFQ3QEU7EJdXLuyjnyyvLcedlL80upL6p3p5pa61+b0diE42EO9R5iV9cuDl09RCQRoaKggq2rtrK+fD3+Aj+TMecY7HDvYXdwwkNVD7kX3BqKGzJWnpnGY+Puxe3usW5n1PSMNhUoCLj7m/RdK5mMzWR8koNXD/JW51sc7j1MLBmj2lfN1sattJe3U5pfykRsgoujFznad9SNzSNVj7hJ3vR5Raalp1Y6N3LO3RcHo0EMhhyTQ3lhObW+WhpLGllf7oxGv93dNEr4iogsLXeV8HU/bEw1zojfbwJrwB0W9x7wm9ba9xZcwhViroTvUmKt5VLwEnu697CvZx8Xgxfdq71e42VtYC2frf8sW1dt5aGqhzI2d+D9wFrLhdEL7O7ezf6e/VwKXnJHUHiNl7ZAG1satrB11VYeqHxgxcXmo6GP2HNlD4euHuJy6PKNh60ZL+vK17GlYQsvrnpxzoRUphO+S4m1lo5gh3urXvqkGZxRpOsC63i+8Xm2NW9btITUUpVIJvjg+gfulDBXxq+473mNl40VG5160/TiXY8eu1+9s+8dSjaUsKtrF4d7D88aueQ1Xh6ofMCNTSbnKr0fpJO/u7t2c6j30Kxbcb0eLw9WPshzjc/x4qoXFy0htVTNTP4evnp41nQauZ5cHqp6iC0NW9jWtI3VZauzWNK7s5C+Ip383d21m8O9h2dNUZPryeWR6kfY0rCF7U3b73rU4f0qmohytPcou7t3c6T3yKy54tNTf6XrzarSuxt1eL+KTEc43HuYXV27ONp31J1qBiDPk8djNY/xbMOzbGvetmiJzNvJ5vFTei793V27OdZ3bNY8+vk5+Wyu2cyzjc+yrWkbtUW1WSljtkzEJjhw9QBvdb3Fif4Ts+bRL8gpYHPtZqfeNG2jpqgmiyW9O0r4iogsLQtK+M76ImO2Ab8OfB7Iw0n+fogz2vfPM7KRZWypJ3xvFk/GGZ5yTooqCivmPc/bchZLxNzYVPoqFZsZ0vPRAVT5qj51hPNyTvjeLDIdYWhqCK/HS0VBRUZHpN/vwvEwI5ERPMZDta96UUbG3y9ubhPpeUoVm08ai40RioTweDzU+GoUmxmCkSChWAivx7ssYpPJviI9Sj3Xk0tdUV3GHgx4v7PWMhwZZiw2Rn5OPrW+WsUmxVrL9anrTMQmyPcujdgsleOn9DQ8k/FJCrwFSyI2S0XSJhmYdGJTmFu4LGKjhK+IyNKSsSN8a+3bwNvGmErgG8CvAQ8Dfwoo4bvM5HpyV9xV+TuVl5NHXXFdtouxJBV4CxSbWyjwFtBY0pjtYixJvlxfRqfWWE6K84oz8rTt5ag0rzQjT7BfjvwFfvwF/mwXY0kqLyi/5Xz8K5kxhsrCynk/xHclMMZQ7aum2led7aIsOcYYnS/cgsd4dEwsIiKLKuP3lltrh6y1/9ZauwF4Efh/Mr0NEREREREREREREfmkRb2Hz1q7H9i/mNsQEREREREREREREcfKeXqUiIiIiIiIiIiIyDKnhK+IiIiIiIiIiIjIMqGEr4iIiIiIiIiIiMgyoYSviIiIiIiIiIiIyDKhhK+IiIiIiIiIiIjIMqGEr4iIiIiIiIiIiMgyoYSviIiIiIiIiIiIyDKhhK+IiIiIiIiIiIjIMmGstdkugwDGmHHgfLbLIbLEVAJD2S6EyBKiNiHySWoXIrOpTUg2NFtrq7JdCBERcXizXQBxnbfWbs52IUSWEmPMKbULkRvUJkQ+Se1CZDa1CREREdGUDiIiIiIiIiIiIiLLhBK+IiIiIiIiIiIiIsuEEr5Lx7/PdgFEliC1C5HZ1CZEPkntQmQ2tQkREZEVTg9tExEREREREREREVkmNMJXREREREREREREZJlQwldERERERERERERkmVDCN4uMMY3GmP9gjOkzxkSNMV3GmD8xxgSyXTaRbEi1AXuLn2vZLp/IYjHGfNkY8z1jzCFjzFiqzv+nT/nMM8aYN40xI8aYsDHmQ2PMbxljcu5VuUUWy3zahDGm5TZ9hzXG/O29Lr9IphljKowx3zTG/MQY02GMmTLGhIwxh40xv2aMmfO8Tn2FiIjIyuTNdgFWKmNMK3AUqAbeAM4BTwL/CvicMeaz1trhLBZRJFtCwJ/MsXziHpdD5F76n4GHcer5VWD97VY2xnwe+DEQAX4EjAC/CHwX+CzwymIWVuQemFebSPkAeH2O5R9lrlgiWfMK8BdAP7APuALUAC8Dfw38I2PMK3bGA1rUV4iIiKxcemhblhhjdgE7gG9ba783Y/kfA78N/KW19jeyVT6RbDDGdAFYa1uyWxKRe8sY8wJOUqsDeB7nZP4/W2v/yRzrlqbWKwM+a609lVpeALwDPA181VqrUY1y35pnm2gBOoH/aK39xj0spsg9Y4x5ESgCfmqtTc5YXgucAFYBX7bW/ji1XH2FiIjICqYpHbLAGLMGJ9nbBfz5TW//ETAJ/FNjTNE9LpqIiGSBtXaftfaivbOrsF8GqoC/TZ/Ap74jgjMqEuA3F6GYIvfMPNuEyLJnrX3HWvsPM5O9qeXXgP8z9evWGW+prxAREVnBNKVDdryYet09x0HbuDHmCE5C+DPA2/e6cCJZlm+M+SdAE87Fjw+Bg9baRHaLJbJkpPuQt+Z47yAQBp4xxuRba6P3rlgiWVdvjPlvgQpgGDhmrf0wy2USuRfiqdfpGcvUV4iIiKxgSvhmR3vq9cIt3r+Ik/BdhxK+svLUAj+4aVmnMeZXrLUHslEgkSXmln2ItXbaGNMJbALWAGfvZcFEsuyl1I/LGLMf+Lq19kpWSiSyyIwxXuCfpX6dmdxVXyEiIrKCaUqH7ChLvYZu8X56uX/xiyKypPwNsA0n6VsEPAj8JdAC/FdjzMPZK5rIkqE+RGS2MPC/AI8DgdRPet7frcDbmiZLlrH/HXgAeNNau2vGcvUVIiIiK5gSvkuTSb1q3jpZUay130nNUTdgrQ1baz9KPbzwj4FC4LXsllDkvqA+RFYUa+2gtfYPrbWnrbXB1M9BnLul3gXWAt/MbilFMs8Y823gvwfOAf90vh9PvaqvEBERWYaU8M2O9BX1slu8X3rTeiIrXfphJM9ltRQiS4P6EJE7YK2dBv469av6D1lWjDH/Avh3wBngBWvtyE2rqK8QERFZwZTwzY7zqdd1t3i/LfV6qzl+RVaawdSrbskVuU0fkprLcTXOg3su38tCiSxR11Ov6j9k2TDG/BbwZ8BHOMnea3Ospr5CRERkBVPCNzv2pV53GGNm/R8YY0qAzwJTwPF7XTCRJerp1KtOSkTgndTr5+Z47znABxzVU9dFAPhM6lX9hywLxpjfBb4LvI+T7B28xarqK0RERFYwJXyzwFp7CdiN8yCqf3HT29/BGYXyf1trJ+9x0USyxhizyRhTPsfyZpxRLAD/6d6WSmRJ+i/AEPAVY8zm9EJjTAHwv6Z+/YtsFEwkG4wxTxlj8uZY/iLw26lf1X/Ifc8Y8wc4D2l7D9hmrR26zerqK0RERFYwY63m6c8GY0wrcBSoBt4AzgJPAS/gTOXwjLV2OHslFLm3jDGvAb+HMwK+ExgHWoH/BigA3gS+aK2NZauMIovFGPMF4AupX2uBnTgjEg+llg1Za3/npvX/CxAB/hYYAX4JaE8tf9Wqg5f72HzahDFmP7AJ2A9cTb3/EPBi6t9/YK1NJ7hE7kvGmK8D3wcSwPeYe+7dLmvt92d85guorxAREVmRlPDNImPMKuBf49xqVQH0A68D35njwQsiy5ox5nngN4BHcU7ui4Agzi2LPwB+oJMSWa5SFzz+6DardFtrW276zGeB38eZ8qQA6AD+A/Cn1trE4pRU5N6YT5swxvwa8EXgAaASyAUGgGPAn1lrD93qS0TuF3fQJgAOWGu33vQ59RUiIiIrkBK+IiIiIiIiIiIiIsuE5vAVERERERERERERWSaU8BURERERERERERFZJpTwFREREREREREREVkmlPAVERERERERERERWSaU8BURERERERERERFZJpTwFREREREREREREVkmlPAVERERERERERERWSaU8BURERERERERERFZJpTwFRERkUVljPmGMcYaY76R7bLcCWPM91PlTf/83k3v7zfG2Axv889u2uZrmfx+ERERERFZObzZLoCIiIjcP+4i0fkri1KQe+PfAUHg8D3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+ "text/plain": [ + "
        " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "from matplotlib import pyplot as plt\n", + "\n", + "time = dependent_variables.keys()\n", + "dependent_variable_list = np.vstack(list(dependent_variables.values()))\n", + "font_size = 20\n", + "\n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n", + "# dependent variables\n", + "# 0-2: total acceleration\n", + "# 3-8: Keplerian state\n", + "# 9: latitude\n", + "# 10: longitude\n", + "# 11: Acceleration Norm PM Sun\n", + "# 12: Acceleration Norm PM Moon\n", + "# 13: Acceleration Norm PM Mars\n", + "# 14: Acceleration Norm PM Venus\n", + "# 15: Acceleration Norm SH Earth\n", + "\n", + "total_acceleration = np.sqrt( dependent_variable_list[:,0] ** 2 + dependent_variable_list[:,1] ** 2 + dependent_variable_list[:,2] ** 2 )\n", + "\n", + "time_hours = [ t / 3600 for t in time]\n", + "# Total Acceleration\n", + "plt.figure( figsize=(17,5))\n", + "plt.grid()\n", + "plt.plot( time_hours , total_acceleration )\n", + "plt.xlabel('Time [hr]')\n", + "plt.ylabel( 'Total Acceleration [m/s$^2$]')\n", + "plt.xlim( [min(time_hours), max(time_hours)] )\n", + "plt.savefig( fname = f'{latex_image_path}total_acceleration.png', bbox_inches='tight')\n", + "\n", + "\n", + "\n", + "# Ground Track\n", + "latitude = dependent_variable_list[:,9]\n", + "longitude = dependent_variable_list[:,10]\n", + "\n", + "part = int(len(time)/24*3)\n", + "latitude = np.rad2deg( latitude[0:part] )\n", + "longitude = np.rad2deg( longitude[0:part] )\n", + "plt.figure( figsize=(17,5))\n", + "plt.grid()\n", + "plt.yticks(np.arange(-90, 91, step=45))\n", + "plt.scatter( longitude, latitude, s=1 )\n", + "plt.xlabel('Longitude [deg]')\n", + "plt.ylabel( 'Latitude [deg]')\n", + "plt.xlim( [min(longitude), max(longitude)] )\n", + "plt.savefig( fname = f'{latex_image_path}ground_track.png', bbox_inches='tight')\n", + "\n", + "# Kepler Elements\n", + "kepler_elements = dependent_variable_list[:,3:9]\n", + "\n", + "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots( 3, 2, figsize = (20,17) )\n", + "\n", + "# Semi-major Axis\n", + "semi_major_axis = [ element/1000 for element in kepler_elements[:,0] ]\n", + "ax1.plot( time_hours, semi_major_axis )\n", + "ax1.set_ylabel( 'Semi-major axis [km]' )\n", + "\n", + "# Eccentricity\n", + "eccentricity = kepler_elements[:,1]\n", + "ax2.plot( time_hours, eccentricity )\n", + "ax2.set_ylabel( 'Eccentricity [-]' )\n", + "\n", + "# Inclination\n", + "inclination = [ np.rad2deg( element ) for element in kepler_elements[:,2] ]\n", + "ax3.plot( time_hours, inclination )\n", + "ax3.set_ylabel( 'Inclination [deg]')\n", + "\n", + "# Argument of Periapsis\n", + "argument_of_periapsis = [ np.rad2deg( element ) for element in kepler_elements[:,3] ]\n", + "ax4.plot( time_hours, argument_of_periapsis )\n", + "ax4.set_ylabel( 'Argument of Periapsis [deg]' )\n", + "\n", + "# Right Ascension of the Ascending Node\n", + "raan = [ np.rad2deg( element ) for element in kepler_elements[:,4] ]\n", + "ax5.plot( time_hours, raan )\n", + "ax5.set_ylabel( 'RAAN [deg]' )\n", + "\n", + "# True Anomaly\n", + "true_anomaly = [ np.rad2deg( element ) for element in kepler_elements[:,5] ]\n", + "ax6.scatter( time_hours, true_anomaly, s=1 )\n", + "ax6.set_ylabel( 'True Anomaly [deg]' )\n", + "ax6.set_yticks(np.arange(0, 361, step=60))\n", + "\n", + "for ax in fig.get_axes():\n", + " ax.set_xlabel('Time [hr]')\n", + " ax.set_xlim( [min(time_hours), max(time_hours)] )\n", + " ax.grid()\n", + "\n", + "plt.savefig( fname = f'{latex_image_path}kepler_elements.png', bbox_inches='tight')\n", + " \n", + "plt.figure( figsize=(17,5))\n", + "\n", + "# Point Mass Gravity Acceleration Sun\n", + "acceleration_norm_pm_sun = dependent_variable_list[:, 11]\n", + "plt.plot( time_hours, acceleration_norm_pm_sun, label='PM Sun')\n", + "\n", + "# Point Mass Gravity Acceleration Moon\n", + "acceleration_norm_pm_moon = dependent_variable_list[:, 12]\n", + "plt.plot( time_hours, acceleration_norm_pm_moon, label='PM Moon')\n", + "\n", + "# Point Mass Gravity Acceleration Mars\n", + "acceleration_norm_pm_mars = dependent_variable_list[:, 13]\n", + "plt.plot( time_hours, acceleration_norm_pm_mars, label='PM Mars')\n", + "\n", + "# Point Mass Gravity Acceleration Venus\n", + "acceleration_norm_pm_venus = dependent_variable_list[:, 14]\n", + "plt.plot( time_hours, acceleration_norm_pm_venus, label='PM Venus')\n", + "\n", + "# Spherical Harmonic Gravity Acceleration Earth\n", + "acceleration_norm_sh_earth = dependent_variable_list[:, 15]\n", + "plt.plot( time_hours, acceleration_norm_sh_earth, label='SH Earth')\n", + "\n", + "# Aerodynamic Acceleration Earth\n", + "acceleration_norm_aero_earth = dependent_variable_list[:, 16]\n", + "plt.plot( time_hours, acceleration_norm_aero_earth, label='Aerodynamic Earth')\n", + "\n", + "# Cannonball Radiation Pressure Acceleration Sun\n", + "acceleration_norm_rp_sun = dependent_variable_list[:, 17]\n", + "plt.plot( time_hours, acceleration_norm_rp_sun, label='Radiation Pressure Sun')\n", + "\n", + "plt.grid()\n", + "plt.legend( bbox_to_anchor=(1.04,1) )\n", + "plt.xlim( [min(time_hours), max(time_hours)])\n", + "plt.yscale('log')\n", + "plt.xlabel( 'Time [hr]' )\n", + "plt.ylabel( 'Acceleration Norm [m/s$^2$]' )\n", + "\n", + "plt.savefig( fname = f'{latex_image_path}acceleration_norms.png', bbox_inches='tight')\n", + "#plt.savefig('acceleration_norms.png', bbox_inches='tight')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 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self.gen_bit_sequence() + fitness = self.count(seq) + results = [fitness] + for run in range(max_iterations): + new_seq = self.mutate_bit_sequence(seq) + new_fitness = self.count(new_seq) + seq = new_seq + fitness = new_fitness + results.append(max(results[-1], fitness)) + return results + + def do4b(self,project_nr): + optimum_found = 0 + + # generate plot data + plotResult = np.zeros((10,max_iterations), dtype=int); + lineLabels = [] + + # perform computation + for run in range(10): + res = self.do_run_a() + if res[-1] == string_length: + optimum_found +=1 + + # store computation data for plotting + lineLabels.append(f'Run {run}') + plotResult[run,:]=res; + + # plot multiple lines into report (res is an array of dataseries (representing the lines)) + # plt_tex.plotMultipleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) + plt_tex.plotMultipleLines(plt_tex,range(0, len(res)),plotResult,"[runs]]","fitness [%]",lineLabels,"4b",4,project_nr) + print("total optimum found: {} out of {} runs".format(optimum_found,10)) + + def do4c(self,project_nr): + optimum_found = 0 + + # generate plot data + plotResult = np.zeros((10,max_iterations+1), dtype=int); + lineLabels = [] + + # perform computation + for run in range(10): + res = self.do_run_c() + if res[-1] == string_length: + optimum_found +=1 + + # Store computation results for plot + lineLabels.append(f'Run {run}') + plotResult[run,:]=res; + + # plot multiple lines into report (res is an array of dataseries (representing the lines)) + # plt_tex.plotMultipleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) + plt_tex.plotMultipleLines(plt_tex,range(0, len(res)),plotResult,"[runs]]","fitness [%]",lineLabels,"4c",4,project_nr) + + print("total optimum found: {} out of {} runs".format(optimum_found, 10)) + + def addTwo(self,x): + ''' adds two to the incoming integer and returns the result of the computation.''' + return x+2 + +if __name__ == '__main__': + # initialize main class + main = Main() \ No newline at end of file diff --git a/code/project3/src/Plot_to_tex.py b/code/project3/src/Plot_to_tex.py new file mode 100644 index 0000000..7ec9a4c --- /dev/null +++ b/code/project3/src/Plot_to_tex.py @@ -0,0 +1,126 @@ +### Call this from another file, for project 11, question 3b: +### from Plot_to_tex import Plot_to_tex as plt_tex +### multiple_y_series = np.zeros((nrOfDataSeries,nrOfDataPoints), dtype=int); # actually fill with data +### lineLabels = [] # add a label for each dataseries +### plt_tex.plotMultipleLines(plt_tex,single_x_series,multiple_y_series,"x-axis label [units]","y-axis label [units]",lineLabels,"3b",4,11) +### 4b=filename +### 4 = position of legend, e.g. top right. +### +### For a single line, use: +### plt_tex.plotSingleLine(plt_tex,range(0, len(dataseries)),dataseries,"x-axis label [units]","y-axis label [units]",lineLabel,"3b",4,11) + +### You can also plot a table directly into latex, see example_create_a_table(..) +### +### Then put it in latex with for example: +###\begin{table}[H] +### \centering +### \caption{Results some computation.}\label{tab:some_computation} +### \begin{tabular}{|c|c|} % remember to update this to show all columns of table +### \hline +### \input{latex/project3/tables/q2.txt} +### \end{tabular} +###\end{table} +import random +from matplotlib import lines +import matplotlib.pyplot as plt +import numpy as np +import os +class Plot_to_tex: + + def __init__(self): + self.script_dir = self.get_script_dir() + print("Created main") + + # plot graph (legendPosition = integer 1 to 4) + def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr): + fig=plt.figure(); + ax=fig.add_subplot(111); + ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none'); + plt.legend(loc=legendPosition); + plt.xlabel(x_axis_label); + plt.ylabel(y_axis_label); + plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png'); +# plt.show(); + + # plot graphs + def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr): + fig=plt.figure(); + ax=fig.add_subplot(111); + + # generate colours + cmap = self.get_cmap(len(y_series[:,0])) + + # generate line types + lineTypes = self.generateLineTypes(y_series) + + for i in range(0,len(y_series)): + # overwrite linetypes to single type + lineTypes[i] = "-" + ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color + + # configure plot layout + plt.legend(loc=legendPosition); + plt.xlabel(x_label); + plt.ylabel(y_label); + plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png'); + + print(f'plotted lines') + + # Generate random line colours + # Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib + def get_cmap(n, name='hsv'): + '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct + RGB color; the keyword argument name must be a standard mpl colormap name.''' + return plt.cm.get_cmap(name, n) + + def generateLineTypes(y_series): + # generate varying linetypes + typeOfLines = list(lines.lineStyles.keys()) + + while(len(y_series)>len(typeOfLines)): + typeOfLines.append("-."); + + # remove void lines + for i in range(0, len(y_series)): + if (typeOfLines[i]=='None'): + typeOfLines[i]='-' + if (typeOfLines[i]==''): + typeOfLines[i]=':' + if (typeOfLines[i]==' '): + typeOfLines[i]='--' + return typeOfLines + + # Create a table with: table_matrix = np.zeros((4,4),dtype=object) and pass it to this object + def put_table_in_tex(self, table_matrix,filename,project_nr): + cols = np.shape(table_matrix)[1] + format = "%s" + for col in range(1,cols): + format = format+" & %s" + format = format+"" + plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline=' \\\\ \hline \n') + + # replace this with your own table creation and then pass it to put_table_in_tex(..) + def example_create_a_table(self): + project_nr = "1" + table_name = "example_table_name" + rows = 2; + columns = 4; + table_matrix = np.zeros((rows,columns),dtype=object) + table_matrix[:,:]="" # replace the standard zeros with emtpy cell + print(table_matrix) + for column in range(0,columns): + for row in range(0,rows): + table_matrix[row,column]=row+column + table_matrix[1,0]="example" + table_matrix[0,1]="grid sizes" + + self.put_table_in_tex(table_matrix,table_name,project_nr) + + + def get_script_dir(self): + ''' returns the directory of this script regardles of from which level the code is executed ''' + return os.path.dirname(__file__) + +if __name__ == '__main__': + main = Plot_to_tex() + main.example_create_a_table() \ No newline at end of file diff --git a/code/project3/src/Run_jupyter_notebooks.py b/code/project3/src/Run_jupyter_notebooks.py new file mode 100644 index 0000000..0c8f558 --- /dev/null +++ b/code/project3/src/Run_jupyter_notebooks.py @@ -0,0 +1,41 @@ +# runs a jupyter notebook and converts it to pdf + +import os +import nbformat +from nbconvert.preprocessors import ExecutePreprocessor + +class Run_jupyter_notebook: + + def __init__(self): + self.script_dir = self.get_script_dir() + print("Created main") + + # runs jupyter notebook + def run_notebook(self,notebook_filename): + + + # Load your notebook + with open(notebook_filename) as f: + nb = nbformat.read(f, as_version=4) + + # Configure + ep = ExecutePreprocessor(timeout=600, kernel_name='python3') + + # Execute + #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}}) + ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}}) + + # Save output notebook + with open(notebook_filename, 'w', encoding='utf-8') as f: + nbformat.write(nb, f) + + # converts jupyter notebook to pdf + def convert_notebook_to_pdf(self,notebook_filename): + os.system(f'jupyter nbconvert --to pdf {notebook_filename}') + + def get_script_dir(self): + ''' returns the directory of this script regardles of from which level the code is executed ''' + return os.path.dirname(__file__) + +if __name__ == '__main__': + main = Run_jupyter_notebook() \ No newline at end of file diff --git a/code/project3/src/__init__.py b/code/project3/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/project3/src/__main__.py b/code/project3/src/__main__.py new file mode 100644 index 0000000..c481ba8 --- /dev/null +++ b/code/project3/src/__main__.py @@ -0,0 +1,39 @@ +import os +from .Main import Main + +print(f'Hi, I\'ll be running the main code, and I\'ll let you know when I\'m done.') +project_nr = 3 +main = Main() + +notebook_names = ['AE4868_example_notebook_update20201025.ipynb'] + +# run the jupyter notebooks for assignment 1 +main.run_jupyter_notebooks(project_nr,notebook_names) + +# convert jupyter notebook for assignment 1 to pdf +main.convert_notebooks_to_pdf(project_nr,notebook_names) + +# compile the latex report +main.compile_latex_report(project_nr) + +################################################################ +############example code to illustrate python-latex image sync######### +##############runs arbitrary genetic algorithm, can be deleted########### +################################################################ +# run a genetic algorithm to create some data for a plot. +print("now running a") +res = main.do_run_a() + +# plot some graph with a single line, general form is: +# plt_tex.plotSingleLines(plt_tex,x,y,"x-axis label","y-axis label",lineLabels,"filename",legend_position,project_nr) +# main.plt_tex.plotSingleLine(plt_tex,range(0, len(res)),res,"[runs]]","fitness [%]","run 1","4a",4,project_nr) + +# run a genetic algorithm to create some data for another plot. +print("now running b") +main.do4b(project_nr) + +# run a genetic algorithm to create some data for another plot. +print("now running 4c") +main.do4c(project_nr) + +print(f'Done.') \ No newline at end of file diff --git a/code/project3/src/juice_propagation_Q1.ipynb b/code/project3/src/juice_propagation_Q1.ipynb new file mode 100644 index 0000000..e827102 --- /dev/null +++ b/code/project3/src/juice_propagation_Q1.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# # student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create environment, vehicle, accelerations, and propagation settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create settings for celestial bodies\n", + "bodies_to_create = [\"Ganymede\"]\n", + "global_frame_origin = \"Ganymede\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation)\n", + "\n", + "# Add Ganymede exponential atmosphere\n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + " \n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated, and their central bodies of propagation.\n", + "bodies_to_propagate = [\"JUICE\"]\n", + "central_bodies = [\"Ganymede\"]\n", + "\n", + "# Define accelerations acting on vehicle.\n", + "acceleration_settings_on_vehicle = dict(\n", + " XXXX\n", + ")\n", + "\n", + "# Create global accelerations dictionary.\n", + "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Define initial state.\n", + "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.keplerian_state(\n", + " \"JUICE\", \"Ganymede\")\n", + " ]\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + " \n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Orbit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings, True)\n", + "\n", + "simulation_result = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Print final propagation time and state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + "###########################################################################\n", + "\n", + "final_time_step=list(simulation_result.keys())[-1]\n", + "first_time_step=list(simulation_result.keys())[0]\n", + "\n", + "print(\n", + " f\"\"\"\n", + "JUICE Propagation Results.\n", + "\n", + "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + "Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_result[final_time_step][:]}\n", + "\n", + " \"\"\"\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Save Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# SAVE RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "save2txt(solution=simulation_result,\n", + " filename=\"JUICEPropagationHistory_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )\n", + "\n", + "save2txt(solution=dependent_variables,\n", + " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results\n", + "\n", + "For inspiration see: \n", + "\n", + "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PLOT RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "# Extract time and Kepler elements from dependent variables\n", + "kepler_elements = np.vstack(list(dependent_variables.values())\n", + " \n", + "# Kepler Elements\n", + "# 0: semi-major axis\n", + "# 1: eccentricity\n", + "# 2: inclination\n", + "# 3: argument of periapsis\n", + "# 4: right ascension of the ascending node\n", + "# 5: true anomaly\n", + "\n", + "time = dependent_variables.keys()\n", + "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", + "\n", + "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", + "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", + "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", + "\n", + "\n", + "# Set font size of figures\n", + "font_size = 20\n", + " \n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project3/src/juice_propagation_Q4.ipynb b/code/project3/src/juice_propagation_Q4.ipynb new file mode 100644 index 0000000..7b3bbcd --- /dev/null +++ b/code/project3/src/juice_propagation_Q4.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create Environment and Vehicle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create body objects.\n", + "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", + "global_frame_origin = \"SSB\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation) \n", + "\n", + "# Add Ganymede exponential atmosphere \n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + "\n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings );" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Dynamics for various cases" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "cases = ['unperturbed', 'case_i', 'case_ii']\n", + "\n", + "\"\"\"\n", + "unperturbed: Ganymede PM\n", + "\n", + "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", + "\n", + "case_ii: Ganymede PM, Ganymede aerodynamic\n", + "\"\"\"\n", + "\n", + "simulation_results_dict = dict()\n", + "dependent_variables_dict = dict()\n", + "for case in cases: \n", + " ###########################################################################\n", + " # CREATE ACCELERATIONS ####################################################\n", + " ###########################################################################\n", + "\n", + " # Define bodies that are propagated.\n", + " bodies_to_propagate = [\"JUICE\"]\n", + "\n", + " # Define central bodies.\n", + " central_bodies = [\"Ganymede\"]\n", + "\n", + " # Define accelerations acting on vehicle.\n", + " if case == 'unperturbed':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + " if case == 'case_i':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX,\n", + " Jupiter = XXXX\n", + " )\n", + " if case == 'case_ii':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + "\n", + " # Create global accelerations dictionary.\n", + " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + " # Create acceleration models.\n", + " acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + " ###########################################################################\n", + " # CREATE PROPAGATION SETTINGS #############################################\n", + " ###########################################################################\n", + "\n", + " # Define initial state.\n", + " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + " # Save magnitude of perturbations for both cases\n", + " if case == 'unperturbed':\n", + " dependent_variables_to_save = [ ]\n", + " if case == 'case_i':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + " if case == 'case_ii':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + "\n", + " # Create propagation settings.\n", + " propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + " )\n", + "\n", + " # Create numerical integrator settings.\n", + " fixed_step_size = 10.0\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + " )\n", + "\n", + " ###########################################################################\n", + " # PROPAGATE ORBIT #########################################################\n", + " ###########################################################################\n", + "\n", + " # Create simulation object and propagate dynamics.\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + " \n", + " simulation_results_dict[case] = dynamics_simulator.state_history\n", + " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", + "\n", + " ###########################################################################\n", + " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + " ###########################################################################\n", + "\n", + " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", + " first_time_step=list(simulation_results_dict[case].keys())[0]\n", + "\n", + " print(\n", + " f\"\"\"\n", + " JUICE Propagation Results of {case}.\n", + "\n", + " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + " Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_results_dict[case][final_time_step][:]}\n", + "\n", + " \"\"\"\n", + " )\n", + "\n", + " ###########################################################################\n", + " # SAVE RESULTS ############################################################\n", + " ###########################################################################\n", + " \n", + "# save2txt(solution=simulation_result,\n", + "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", + "# directory=\"./\", # default = \"./\" \n", + "# column_names=None, # default = None \n", + "# )\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Pre-process Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", + "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", + "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", + "\n", + "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", + "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", + "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", + "\n", + "difference_in_cartesian_position = XXXX" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project3/test/__init__.py b/code/project3/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/project3/test/test_main.py b/code/project3/test/test_main.py new file mode 100644 index 0000000..8aab9ff --- /dev/null +++ b/code/project3/test/test_main.py @@ -0,0 +1,26 @@ +import unittest +import os +from ..src.Main import Main + +class Test_main(unittest.TestCase): + + # Initialize test object + def __init__(self, *args, **kwargs): + super(Test_main, self).__init__(*args, **kwargs) + self.script_dir = self.get_script_dir() + + self.main = Main() + print(f'self.main.addTwo(3)={self.main.addTwo(3)}') + + # returns the directory of this script regardles of from which level the code is executed + def get_script_dir(self): + return os.path.dirname(__file__) + + # tests unit test on addTwo function of main class + def test_addTwo(self): + expected_result = 7 + result = self.main.addTwo(5) + self.assertEqual(expected_result,result) + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/latex/project2/Appendices/AppA.tex b/latex/project2/Appendices/AppA.tex index bf67fc5..628c6ea 100644 --- a/latex/project2/Appendices/AppA.tex +++ b/latex/project2/Appendices/AppA.tex @@ -1,2 +1,2 @@ -\section{Appendix Main.py}\label{app:1} -\pythonexternal{../../code/hw2/Main.py} \ No newline at end of file +\section{Appendix \_\_main\_\_.py}\label{app:1} +\pythonexternal{latex/project2/../../code/project2/src/__main__.py} \ No newline at end of file diff --git a/latex/project2/Appendices/AppB.tex b/latex/project2/Appendices/AppB.tex index 0bfc212..4ef2927 100644 --- a/latex/project2/Appendices/AppB.tex +++ b/latex/project2/Appendices/AppB.tex @@ -1,2 +1,2 @@ -\section*{Appendix python code that exports figures to latex}\label{app:2} -\pythonexternal{../../code/hw2/Plot_to_tex.py} \ No newline at end of file +\section{Appendix Main.py}\label{app:2} +\pythonexternal{latex/project2/../../code/project2/src/Main.py} \ No newline at end of file diff --git a/latex/project2/Appendices/AppC.tex b/latex/project2/Appendices/AppC.tex new file mode 100644 index 0000000..7da3276 --- /dev/null +++ b/latex/project2/Appendices/AppC.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that exports figures to latex}\label{app:3} +\pythonexternal{latex/project2/../../code/project2/src/Plot_to_tex.py} \ No newline at end of file diff --git a/latex/project2/Appendices/AppD.tex b/latex/project2/Appendices/AppD.tex new file mode 100644 index 0000000..a2711d8 --- /dev/null +++ b/latex/project2/Appendices/AppD.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that compiles the latex report to pdf}\label{app:4} +\pythonexternal{latex/project2/../../code/project2/src/Compile_latex.py} \ No newline at end of file diff --git a/latex/project2/Appendices/AppE.tex b/latex/project2/Appendices/AppE.tex new file mode 100644 index 0000000..8672b16 --- /dev/null +++ b/latex/project2/Appendices/AppE.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that runs the jupyter notebook(s)}\label{app:5} +\pythonexternal{latex/project2/../../code/project2/src/Run_jupyter_notebooks.py} \ No newline at end of file diff --git a/latex/project2/Appendices/AppF.tex b/latex/project2/Appendices/AppF.tex new file mode 100644 index 0000000..b3aac6d --- /dev/null +++ b/latex/project2/Appendices/AppF.tex @@ -0,0 +1,2 @@ +\section*{Appendix Example Jupyter Notebook}\label{app:6} +\includepdf[pages=-]{latex/project2/../../code/project2/src/AE4868_example_notebook_update20201025.pdf} \ No newline at end of file diff --git a/latex/project2/Images/4b.png b/latex/project2/Images/4b.png index de43e726c86493aab9dc1ce26779a9400e89070b..3f4a248f0a74aa599218057ce41ee985ccaf8d61 100644 GIT binary patch literal 38560 zcmeGERZtx77X}FT0E1g_w*(0uNPyr$L-61ZAxLm{8JwU2f(0iexO;F5?!n!4aMx{q z|N6e#z1XY0+ghro7`j^CKJU@zIft)G3es5UWat0@V9C6f_y_<9FaUroqauSx-i*$# zgTLN7NoqQ&*qS=I8abE%@em^e6ov9;l5=Vs?(r7?GMvUB9;;IRJx 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-43,8 +43,8 @@ \usepackage{tabularx} % in the preamble \usepackage{wrapfig} -\usepackage{algorithm} -\usepackage{algorithmic} +%\usepackage{algorithm} +%\usepackage{algorithmic} % To get side by side pictures:{ @@ -101,11 +101,11 @@ \newcommand\pythoninline[1]{{\pythonstyle\lstinline!#1!}} +% Include path to images +\graphicspath{{images/}{latex/project2/}} - - - - +% Include pdf files in report +\usepackage{pdfpages} \usepackage{cleveref} %cleverref needs to stand below amsmath package. @@ -141,9 +141,12 @@ \maketitle %\setcounter{chapter}{-1} -\input{Chapters/Introduction.tex} %\newpage -\input{Chapters/chap1.tex} %\newpage +%\input{Chapters/Introduction.tex} %\newpage +%\input{Chapters/chap1.tex} %\newpage %\input{Chapters/Conclusion.tex} %\newpage +\input{latex/project2/Chapters/Introduction.tex} %\newpage +\input{latex/project2/Chapters/chap1.tex} %\newpage + @@ -165,8 +168,12 @@ \begin{appendices} \crefalias{section}{appsec} \newpage -\input{Appendices/AppA.tex} \newpage -\input{Appendices/AppB.tex} \newpage +\input{latex/project2/Appendices/AppA.tex} \newpage +\input{latex/project2/Appendices/AppB.tex} \newpage +\input{latex/project2/Appendices/AppC.tex} \newpage +\input{latex/project2/Appendices/AppD.tex} \newpage +\input{latex/project2/Appendices/AppE.tex} \newpage +\input{latex/project2/Appendices/AppF.tex} \newpage \end{appendices} \end{document} diff --git a/latex/project3/Appendices/AppA.tex b/latex/project3/Appendices/AppA.tex index bf67fc5..3684af5 100644 --- a/latex/project3/Appendices/AppA.tex +++ b/latex/project3/Appendices/AppA.tex @@ -1,2 +1,2 @@ -\section{Appendix Main.py}\label{app:1} -\pythonexternal{../../code/hw2/Main.py} \ No newline at end of file +\section{Appendix \_\_main\_\_.py}\label{app:1} +\pythonexternal{latex/project3/../../code/project3/src/__main__.py} \ No newline at end of file diff --git a/latex/project3/Appendices/AppB.tex b/latex/project3/Appendices/AppB.tex index 0bfc212..9d9a76e 100644 --- a/latex/project3/Appendices/AppB.tex +++ b/latex/project3/Appendices/AppB.tex @@ -1,2 +1,2 @@ -\section*{Appendix python code that exports figures to latex}\label{app:2} -\pythonexternal{../../code/hw2/Plot_to_tex.py} \ No newline at end of file +\section{Appendix Main.py}\label{app:2} +\pythonexternal{latex/project3/../../code/project3/src/Main.py} \ No newline at end of file diff --git a/latex/project3/Appendices/AppC.tex b/latex/project3/Appendices/AppC.tex new file mode 100644 index 0000000..b897a8e --- /dev/null +++ b/latex/project3/Appendices/AppC.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that exports figures to latex}\label{app:3} +\pythonexternal{latex/project3/../../code/project3/src/Plot_to_tex.py} \ No newline at end of file diff --git a/latex/project3/Appendices/AppD.tex b/latex/project3/Appendices/AppD.tex new file mode 100644 index 0000000..a4daa4e --- /dev/null +++ b/latex/project3/Appendices/AppD.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that compiles the latex report to pdf}\label{app:4} +\pythonexternal{latex/project3/../../code/project3/src/Compile_latex.py} \ No newline at end of file diff --git a/latex/project3/Appendices/AppE.tex b/latex/project3/Appendices/AppE.tex new file mode 100644 index 0000000..a3bae41 --- /dev/null +++ b/latex/project3/Appendices/AppE.tex @@ -0,0 +1,2 @@ +\section*{Appendix python code that runs the jupyter notebook(s)}\label{app:5} +\pythonexternal{latex/project3/../../code/project3/src/Run_jupyter_notebooks.py} \ No newline at end of file diff --git a/latex/project3/Appendices/AppF.tex b/latex/project3/Appendices/AppF.tex new file mode 100644 index 0000000..6e53e98 --- /dev/null +++ b/latex/project3/Appendices/AppF.tex @@ -0,0 +1,2 @@ +\section*{Appendix Example Jupyter Notebook}\label{app:6} +\includepdf[pages=-]{latex/project3/../../code/project3/src/AE4868_example_notebook_update20201025.pdf} \ No newline at end of file diff --git a/latex/project3/Images/4b.png 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z^RIb+#5;45!?`nBM*>vnufjx5txmmQuX)W@9K&JVISi*M-QyG-BA>{i(%g+h_n9V0 zjk>ApDWFZ+QQVDc!Z0BA(qg$&#=wnZ#SEtF(Z~{D(i0oWg(LhQ3kK*+38zdCexHU& z4|~=6(UM0F8z+!wNKqeAW=I0Ggh&1F(Rh5Qr#o&-!f|N+-v|E}MIFXW!_x76p&}kp&tu zaO*1LPp33<*J9~U(k1a9KTbk#71FrsgIOUh`B+h`s%^2C!u@tdU%%ee@pdtBZ(iq72{a=$nBp8ZANTsLr*DQhJ9w_xrf1Lq*^z1y??Br*^9p~ zgHR?8PS~CM#^1l$%dF7ecb`qJ4jMIMpXT0pZmrWd9>J~FaIg~u^ZZ`n&8*`DWAIuk zG`ZY1I9u7wkB9I3kvZo~_vHz;%gxL6-($oHWaJ2PbNmbnc1BJ{PIh<-3NZz7`2PSI CrE?nq literal 0 HcmV?d00001 diff --git a/latex/project3/main.tex b/latex/project3/main.tex index 657bf22..d06a9aa 100644 --- a/latex/project3/main.tex +++ b/latex/project3/main.tex @@ -43,8 +43,8 @@ \usepackage{tabularx} % in the preamble \usepackage{wrapfig} -\usepackage{algorithm} -\usepackage{algorithmic} +%\usepackage{algorithm} +%\usepackage{algorithmic} % To get side by side pictures:{ @@ -101,11 +101,11 @@ \newcommand\pythoninline[1]{{\pythonstyle\lstinline!#1!}} +% Include path to images +\graphicspath{{images/}{latex/project3/}} - - - - +% Include pdf files in report +\usepackage{pdfpages} \usepackage{cleveref} %cleverref needs to stand below amsmath package. @@ -141,9 +141,12 @@ \maketitle %\setcounter{chapter}{-1} -\input{Chapters/Introduction.tex} %\newpage -\input{Chapters/chap1.tex} %\newpage +%\input{Chapters/Introduction.tex} %\newpage +%\input{Chapters/chap1.tex} %\newpage %\input{Chapters/Conclusion.tex} %\newpage +\input{latex/project3/Chapters/Introduction.tex} %\newpage +\input{latex/project3/Chapters/chap1.tex} %\newpage + @@ -165,8 +168,12 @@ \begin{appendices} \crefalias{section}{appsec} \newpage -\input{Appendices/AppA.tex} \newpage -\input{Appendices/AppB.tex} \newpage +\input{latex/project3/Appendices/AppA.tex} \newpage +\input{latex/project3/Appendices/AppB.tex} \newpage +\input{latex/project3/Appendices/AppC.tex} \newpage +\input{latex/project3/Appendices/AppD.tex} \newpage +\input{latex/project3/Appendices/AppE.tex} \newpage +\input{latex/project3/Appendices/AppF.tex} \newpage \end{appendices} \end{document} From b0544ad71c8aa1f6dc18910733feea2f2b67e474 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 15:58:20 +0100 Subject: [PATCH 22/57] Included dependencies of original yaml into travis yaml. --- .travis.yml | 25 ++++++++++++++++++++++--- 1 file changed, 22 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index d83b546..dd85d89 100644 --- a/.travis.yml +++ b/.travis.yml @@ -12,8 +12,8 @@ os: linux dist: trusty # test on the latest version of python -python: - - "3.7" +#python: + # - "3.7" # only run travis on the master branch branches: @@ -21,7 +21,26 @@ branches: - master # install dependencies listed in requirements -install: "pip install -r requirements.txt" +#install: "pip install -r requirements.txt" + +# New dependencies from other yaml: +name: tudat-space +channels: + - conda-forge + - tudat-team + - conda +dependencies: +- tudatpy +- anaconda +- nb_conda +- conda: + - pytest>=3.0 + - nbconvert +- pip +- pip: + # works for regular pip packages + - sklearn + - pdflatex # run tests script: From b8b3ceb0d5312db63ffca47abfdd7086bd543db7 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 16:06:54 +0100 Subject: [PATCH 23/57] Built environment from other yml in travis yaml. --- .travis.yml | 20 +++----------------- 1 file changed, 3 insertions(+), 17 deletions(-) diff --git a/.travis.yml b/.travis.yml index dd85d89..d446356 100644 --- a/.travis.yml +++ b/.travis.yml @@ -24,23 +24,9 @@ branches: #install: "pip install -r requirements.txt" # New dependencies from other yaml: -name: tudat-space -channels: - - conda-forge - - tudat-team - - conda -dependencies: -- tudatpy -- anaconda -- nb_conda -- conda: - - pytest>=3.0 - - nbconvert -- pip -- pip: - # works for regular pip packages - - sklearn - - pdflatex +install: + - conda env create --file windows_environment.yml + - conda activate tudat-space # run tests script: From c84d48940c7d134d29625b62f90f36451ad72b28 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 16:58:34 +0100 Subject: [PATCH 24/57] Included getting conda in travis CI. --- .travis.yml | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/.travis.yml b/.travis.yml index d446356..39b9ade 100644 --- a/.travis.yml +++ b/.travis.yml @@ -25,6 +25,22 @@ branches: # New dependencies from other yaml: install: + # Source: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/use-conda-with-travis-ci.html + # get miniconda to be able to use conda in Travis + - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then + wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh; + else + wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; + fi + - bash miniconda.sh -b -p $HOME/miniconda + - source "$HOME/miniconda/etc/profile.d/conda.sh" + - hash -r + - conda config --set always_yes yes --set changeps1 no + - conda update -q conda + # Useful for debugging any issues with conda + - conda info -a + + # actual environment installation - conda env create --file windows_environment.yml - conda activate tudat-space From abc8cadbea79a0a3c28f4fa37422336c9199d277 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 17:03:27 +0100 Subject: [PATCH 25/57] Removed typo. --- .travis.yml | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/.travis.yml b/.travis.yml index 39b9ade..3e46b22 100644 --- a/.travis.yml +++ b/.travis.yml @@ -25,20 +25,20 @@ branches: # New dependencies from other yaml: install: - # Source: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/use-conda-with-travis-ci.html - # get miniconda to be able to use conda in Travis - - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then - wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh; - else - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; - fi - - bash miniconda.sh -b -p $HOME/miniconda - - source "$HOME/miniconda/etc/profile.d/conda.sh" - - hash -r - - conda config --set always_yes yes --set changeps1 no - - conda update -q conda - # Useful for debugging any issues with conda - - conda info -a + # Source: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/use-conda-with-travis-ci.html + # get miniconda to be able to use conda in Travis + - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then + wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh; + else + wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; + fi + - bash miniconda.sh -b -p $HOME/miniconda + - source "$HOME/miniconda/etc/profile.d/conda.sh" + - hash -r + - conda config --set always_yes yes --set changeps1 no + - conda update -q conda + # Useful for debugging any issues with conda + - conda info -a # actual environment installation - conda env create --file windows_environment.yml From 9cc3a22b63a79bd484f3aada8044038964512009 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 18:40:16 +0100 Subject: [PATCH 26/57] Removed sklearn pip module. --- code/project1/src/Main.py | 1 - linux_environment.yml | 3 +-- windows_environment.yml | 1 - 3 files changed, 1 insertion(+), 4 deletions(-) diff --git a/code/project1/src/Main.py b/code/project1/src/Main.py index ed01215..b69ff94 100644 --- a/code/project1/src/Main.py +++ b/code/project1/src/Main.py @@ -24,7 +24,6 @@ def __init__(self): def run_jupyter_notebooks(self,project_nr,notebook_names): '''runs a jupyter notebook''' - print(f'Running AE4868_example_notebook_update20201025.ipynb') notebook_path = f'code/project{project_nr}/src/' for notebook_name in notebook_names: diff --git a/linux_environment.yml b/linux_environment.yml index 5984db4..9e6ca8a 100644 --- a/linux_environment.yml +++ b/linux_environment.yml @@ -15,5 +15,4 @@ dependencies: - pip - pip: # works for regular pip packages - - sklearn - - pdflatex + - pdflatex \ No newline at end of file diff --git a/windows_environment.yml b/windows_environment.yml index 0a17012..fd21b9b 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -15,5 +15,4 @@ dependencies: - pip - pip: # works for regular pip packages - - sklearn - pdflatex From e8a9c35727fdab6923c723c94901cbcebaec7079 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Mon, 9 Nov 2020 18:53:11 +0100 Subject: [PATCH 27/57] Updated readme. --- README.md | 23 ++++++++--------------- 1 file changed, 8 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 4a98643..a1171f6 100644 --- a/README.md +++ b/README.md @@ -5,12 +5,16 @@ Hi, w.r.t. the original repository this repository is supplemented with: 0. Python code and latex report integration. - Plots are exported directly into your latex report. - Your python code is automatically included in the appendices of your report. + - The example jupyter notebook is automatically ran with a single line. + - The example jupyter notebook is automatically converted to pdf + - The pdf of the example jupyter notebook is automatically integrated in the latex report. +1. The latex report is automatically compiled into a pdf. 1. You can easily sync with overleaf, e.g. if you do a last minute run, you just push and pull into overleaf, instead of manually uploading pictures. 2. Unit tests are written. (entire repository code can be tested with a single line) -3. Continuous integration (CI) testing is set up with Travis-CI. -4. TODO: auto compile the latex report when you run the code. -5. TODO: auto export jupyter notebook to pdf to include into latex report. -6. TODO: invade jupyter notebooks with unit test(s). + +* Room for improvement* +3. The `.travis.yml` file is currently not allowing for the Continuous integration (CI) testing with Travis-CI. I do not yet know how to include the `.._environment.yml` into `.travis.yml`. That is why the build status badge on top currently says "failed". +4. A unit test could be written to test a function inside a jupyter notebook. ## usage: do once @@ -59,17 +63,6 @@ python -m pytest ``` from the root directory of this project. -## Note for linux users: -If you get the following error in the example notebook when running the main python code: -``` -PermissionError: [Errno 13] Permission denied: 'total_acceleration.png' -PermissionError: [Errno 13] Permission denied: 'total_acceleration.png' -``` -You could resolve it with command: -``` -sudo chown -R user:user ~/.local/share/jupyter -``` - [black_badge]: https://img.shields.io/badge/code%20style-black-000000.svg [python_badge]: https://img.shields.io/badge/python-3.8-blue.svg From 83b07939083eadfda100c488d691e11e22c06f76 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Mon, 9 Nov 2020 18:55:21 +0100 Subject: [PATCH 28/57] Update README.md --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index a1171f6..48ebb79 100644 --- a/README.md +++ b/README.md @@ -2,21 +2,21 @@ Hi, w.r.t. the original repository this repository is supplemented with: -0. Python code and latex report integration. +0. Python code and latex report integration. The following is done with a single command: - Plots are exported directly into your latex report. - Your python code is automatically included in the appendices of your report. - - The example jupyter notebook is automatically ran with a single line. + - The example jupyter notebook is automatically executed. - The example jupyter notebook is automatically converted to pdf - The pdf of the example jupyter notebook is automatically integrated in the latex report. -1. The latex report is automatically compiled into a pdf. + - The latex report is automatically compiled into a pdf. 1. You can easily sync with overleaf, e.g. if you do a last minute run, you just push and pull into overleaf, instead of manually uploading pictures. 2. Unit tests are written. (entire repository code can be tested with a single line) -* Room for improvement* +**Room for improvement** 3. The `.travis.yml` file is currently not allowing for the Continuous integration (CI) testing with Travis-CI. I do not yet know how to include the `.._environment.yml` into `.travis.yml`. That is why the build status badge on top currently says "failed". 4. A unit test could be written to test a function inside a jupyter notebook. -## usage: do once +## Usage: do once 0. If you don't have pip: open Anaconda prompt and browse to the directory of this readme: ``` @@ -37,7 +37,7 @@ conda env create --file linux_environment.yml jupyter-lab ``` -## usage: do every time you start Anaconda: +## Usage: do every time you start Anaconda: 3. Activate the conda environment you created: ``` @@ -48,7 +48,7 @@ conda activate tudat-space jupyter-lab ``` -## usage: do every run: +## Usage: do every run: 3. Performe a run for assignment 1 (named project1) of main code (in `main.py`, called from `__main__.py`) ``` From 5755f6eb36c75549ebdf74d3734ffac2de7927ba Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Mon, 9 Nov 2020 18:56:02 +0100 Subject: [PATCH 29/57] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 48ebb79..cd35a71 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Upgraded Course template Numerical Astrodynamics[![Build Status](https://travis-ci.org/a-t-0/NumericalAstrodynamicsAssignments_2020.svg?branch=master)](https://travis-ci.org/a-t-0/NumericalAstrodynamicsAssignments_2020) +# Student Course template Numerical Astrodynamics[![Build Status](https://travis-ci.org/a-t-0/NumericalAstrodynamicsAssignments_2020.svg?branch=master)](https://travis-ci.org/a-t-0/NumericalAstrodynamicsAssignments_2020) Hi, w.r.t. the original repository this repository is supplemented with: From 534c4b90ed2324927e365e897880629e8d7fdaf6 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Mon, 9 Nov 2020 18:57:09 +0100 Subject: [PATCH 30/57] Update README.md --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index cd35a71..756d20d 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Student Course template Numerical Astrodynamics[![Build Status](https://travis-ci.org/a-t-0/NumericalAstrodynamicsAssignments_2020.svg?branch=master)](https://travis-ci.org/a-t-0/NumericalAstrodynamicsAssignments_2020) +# Student course template Numerical Astrodynamics[![Build Status](https://travis-ci.org/a-t-0/NumericalAstrodynamicsAssignments_2020.svg?branch=master)](https://travis-ci.org/a-t-0/NumericalAstrodynamicsAssignments_2020) Hi, w.r.t. the original repository this repository is supplemented with: @@ -13,7 +13,8 @@ Hi, w.r.t. the original repository this repository is supplemented with: 2. Unit tests are written. (entire repository code can be tested with a single line) **Room for improvement** -3. The `.travis.yml` file is currently not allowing for the Continuous integration (CI) testing with Travis-CI. I do not yet know how to include the `.._environment.yml` into `.travis.yml`. That is why the build status badge on top currently says "failed". + +3. The `.travis.yml` file is currently not allowing for the Continuous integration (CI) testing with Travis-CI. I do not yet know how to include the `.._environment.yml` into `.travis.yml`. That is why the build status badge on top currently is gray instead of green and says "failed/canceled". 4. A unit test could be written to test a function inside a jupyter notebook. ## Usage: do once From 1fb6739daec57800572270ba975e1c6d9c620a20 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 20:05:28 +0100 Subject: [PATCH 31/57] Updated travis yml. --- .travis.yml | 61 +++++++++++++++++++---------------------------------- 1 file changed, 22 insertions(+), 39 deletions(-) diff --git a/.travis.yml b/.travis.yml index 3e46b22..a53040a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,49 +1,32 @@ # python of course and we don't need access to sudo atm language: python -sudo: false -# emails are annoying -notifications: - email: false - -# build on Ubuntu 14.04 because the default is -# really old os: linux -dist: trusty - -# test on the latest version of python -#python: - # - "3.7" +dist: bionic -# only run travis on the master branch -branches: - only: - - master - -# install dependencies listed in requirements -#install: "pip install -r requirements.txt" - -# New dependencies from other yaml: install: - # Source: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/use-conda-with-travis-ci.html - # get miniconda to be able to use conda in Travis - - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then - wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh; - else - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; - fi - - bash miniconda.sh -b -p $HOME/miniconda - - source "$HOME/miniconda/etc/profile.d/conda.sh" - - hash -r - - conda config --set always_yes yes --set changeps1 no - - conda update -q conda - # Useful for debugging any issues with conda - - conda info -a - - # actual environment installation - - conda env create --file windows_environment.yml - - conda activate tudat-space + - sudo apt-get update + - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then + wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh; + else + wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; + fi + - bash miniconda.sh -b -p $HOME/miniconda + - source "$HOME/miniconda/etc/profile.d/conda.sh" + - hash -r + - conda config --set always_yes yes --set changeps1 no + - conda update -q conda + - conda info -a + - conda env create --file windows_environment.yml + - conda activate tudat-space # run tests script: "python -m pytest -v test/*" + +notifications: + email: false + + + + From 73ed287f111a06fea164b71bad4626c41d1886a7 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 21:05:26 +0100 Subject: [PATCH 32/57] Included a unit test inside the jupyter notebook. --- ...e_notebook_update20201025-checkpoint.ipynb | 79 +++++++----------- ...4868_example_notebook_update20201025.ipynb | 49 ++++------- ...AE4868_example_notebook_update20201025.pdf | Bin 409723 -> 408276 bytes code/project1/src/Run_jupyter_notebooks.py | 3 +- code/project1/test/test_main.py | 11 +++ ...4868_example_notebook_update20201025.ipynb | 51 ++++------- ...AE4868_example_notebook_update20201025.pdf | Bin 409725 -> 408276 bytes code/project2/src/Main.py | 1 - code/project2/src/Run_jupyter_notebooks.py | 3 +- code/project2/test/test_main.py | 9 ++ ...4868_example_notebook_update20201025.ipynb | 51 ++++------- ...AE4868_example_notebook_update20201025.pdf | Bin 409725 -> 408275 bytes code/project3/src/Main.py | 1 - code/project3/src/Run_jupyter_notebooks.py | 3 +- code/project3/test/test_main.py | 9 ++ latex/project1/Images/4b.png | Bin 38433 -> 38865 bytes latex/project1/Images/4c.png | Bin 27892 -> 30260 bytes latex/project1/main.pdf | Bin 610448 -> 608410 bytes latex/project2/Images/4b.png | Bin 38560 -> 38621 bytes latex/project2/Images/4c.png | Bin 30777 -> 27916 bytes latex/project2/main.pdf | Bin 609694 -> 608706 bytes latex/project3/Images/4b.png | Bin 39290 -> 40348 bytes latex/project3/Images/4c.png | Bin 28163 -> 30576 bytes latex/project3/main.pdf | Bin 608913 -> 609245 bytes linux_environment.yml | 7 +- windows_environment.yml | 1 + 26 files changed, 122 insertions(+), 156 deletions(-) diff --git a/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb b/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb index cb6465f..a8f9d11 100755 --- a/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb +++ b/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb @@ -2,52 +2,36 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "#import ipyparams\n", - "#currentNotebook = ipyparams.notebook_name\n", - "#print(f'currentNotebook={currentNotebook}')\n", - "#currentNotebook = os.path.dirname(os.path.realpath(__file__))\n", - "#currentNotebook = os.path\n", - "#currentNotebook = os.getcwd()\n", - "#if not 'workbookDir' in globals():\n", - "# workbookDir = os.getcwd()\n", - "#print('workbookDir: ' + workbookDir)\n", - "#currentNotebook = os.chdir(workbookDir) # If you changed the current working dir, this will take you back to the workbook dir.\n", - "#currentNotebook = %pwd" + "def addThree(input_nr):\n", + " '''returns the input integer plus 3, used to verify unit test'''\n", + " return input_nr + 3" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T14:41:15.495908Z", + "iopub.status.busy": "2020-11-09T14:41:15.495082Z", + "iopub.status.idle": "2020-11-09T14:41:16.354546Z", + "shell.execute_reply": "2020-11-09T14:41:16.353921Z" + } + }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hello_world\n", - "\n", - "Single Earth-Orbiting Satellite Example.\n", - "The initial position vector of Delfi-C3 is [km]: \n", - "[7037.48400133 3238.05901792 2150.7241875 ]\n", - "The initial velocity vector of Delfi-C3 is [km/s]: \n", - "[-1.46565763 -0.04095839 6.62279761]\n", - "After 86400.0 seconds the position vector of Delfi-C3 is [km]: \n", - "[-4602.79426676 -1421.16740978 5883.69740624]\n", - "And the velocity vector of Delfi-C3 is [km/s]: \n", - "[-4.53846052 -2.36988263 -5.04163195]\n", - " \n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'tudatpy'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m###############################################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtudatpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconstants\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtudatpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterface\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mspice_interface\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtudatpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimulation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0menvironment_setup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tudatpy'" ] } ], @@ -55,6 +39,7 @@ "###############################################################################\n", "# IMPORT STATEMENTS ###########################################################\n", "###############################################################################\n", + "import os\n", "import numpy as np\n", "from tudatpy.kernel import constants\n", "from tudatpy.kernel.interface import spice_interface\n", @@ -63,7 +48,7 @@ "from tudatpy.kernel.astro import conversion\n", "\n", "# Set path to latex image folders for project 1\n", - "latex_image_path = '../../../latex/project1/Images/'\n", + "latex_image_path = 'latex/project1/Images/'\n", "\n", "# Load spice kernels.\n", "spice_interface.load_standard_kernels()\n", @@ -253,15 +238,15 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T14:41:16.374621Z", + "iopub.status.busy": "2020-11-09T14:41:16.373673Z", + "iopub.status.idle": "2020-11-09T14:41:20.162144Z", + "shell.execute_reply": "2020-11-09T14:41:20.162789Z" + } + }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, { "data": { "image/png": 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\n", @@ -470,7 +455,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.8.3" } }, "nbformat": 4, diff --git a/code/project1/src/AE4868_example_notebook_update20201025.ipynb b/code/project1/src/AE4868_example_notebook_update20201025.ipynb index 8a1fd83..373e6a6 100755 --- a/code/project1/src/AE4868_example_notebook_update20201025.ipynb +++ b/code/project1/src/AE4868_example_notebook_update20201025.ipynb @@ -5,44 +5,28 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:41:15.476829Z", - "iopub.status.busy": "2020-11-09T14:41:15.476111Z", - "iopub.status.idle": "2020-11-09T14:41:15.478753Z", - "shell.execute_reply": "2020-11-09T14:41:15.479317Z" + "iopub.execute_input": "2020-11-09T20:02:01.077224Z", + "iopub.status.busy": "2020-11-09T20:02:01.076276Z", + "iopub.status.idle": "2020-11-09T20:02:01.079603Z", + "shell.execute_reply": "2020-11-09T20:02:01.080202Z" } }, "outputs": [], "source": [ - "import os\n", - "#import ipyparams\n", - "#currentNotebook = ipyparams.notebook_name\n", - "#print(f'currentNotebook={currentNotebook}')\n", - "#currentNotebook = os.path.dirname(os.path.realpath(__file__))\n", - "#currentNotebook = os.path\n", - "#currentNotebook = os.getcwd()\n", - "#if not 'workbookDir' in globals():\n", - "# workbookDir = os.getcwd()\n", - "#print('workbookDir: ' + workbookDir)\n", - "#currentNotebook = os.chdir(workbookDir) # If you changed the current working dir, this will take you back to the workbook dir.\n", - "#currentNotebook = %pwd" + "def addThree(input_nr):\n", + " '''returns the input integer plus 3, used to verify unit test'''\n", + " return input_nr + 3" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:41:15.495908Z", - "iopub.status.busy": "2020-11-09T14:41:15.495082Z", - "iopub.status.idle": "2020-11-09T14:41:16.354546Z", - "shell.execute_reply": "2020-11-09T14:41:16.353921Z" + "iopub.execute_input": "2020-11-09T20:02:01.097931Z", + "iopub.status.busy": "2020-11-09T20:02:01.089171Z", + "iopub.status.idle": "2020-11-09T20:02:01.960532Z", + "shell.execute_reply": "2020-11-09T20:02:01.959815Z" } }, "outputs": [ @@ -68,6 +52,7 @@ "###############################################################################\n", "# IMPORT STATEMENTS ###########################################################\n", "###############################################################################\n", + "import os\n", "import numpy as np\n", "from tudatpy.kernel import constants\n", "from tudatpy.kernel.interface import spice_interface\n", @@ -76,7 +61,7 @@ "from tudatpy.kernel.astro import conversion\n", "\n", "# Set path to latex image folders for project 1\n", - "latex_image_path = '../../../latex/project1/Images/'\n", + "latex_image_path = 'latex/project1/Images/'\n", "\n", "# Load spice kernels.\n", "spice_interface.load_standard_kernels()\n", @@ -268,10 +253,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:41:16.374621Z", - "iopub.status.busy": "2020-11-09T14:41:16.373673Z", - "iopub.status.idle": "2020-11-09T14:41:20.162144Z", - "shell.execute_reply": "2020-11-09T14:41:20.162789Z" + "iopub.execute_input": "2020-11-09T20:02:01.982113Z", + "iopub.status.busy": "2020-11-09T20:02:01.980928Z", + "iopub.status.idle": "2020-11-09T20:02:05.906278Z", + "shell.execute_reply": "2020-11-09T20:02:05.907265Z" } }, "outputs": [ diff --git a/code/project1/src/AE4868_example_notebook_update20201025.pdf b/code/project1/src/AE4868_example_notebook_update20201025.pdf index 15508d8cc0893d01477946980b40ac69f4542905..9437f7b40fc3183dab24164b4934e6bf38b5ea19 100644 GIT binary patch delta 46449 zcmZUZQ+J>X(5z#dlVoCZVq;?4wkI~-YBAkL48XsuTRP3 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b/code/project2/src/Main.py index ed01215..b69ff94 100644 --- a/code/project2/src/Main.py +++ b/code/project2/src/Main.py @@ -24,7 +24,6 @@ def __init__(self): def run_jupyter_notebooks(self,project_nr,notebook_names): '''runs a jupyter notebook''' - print(f'Running AE4868_example_notebook_update20201025.ipynb') notebook_path = f'code/project{project_nr}/src/' for notebook_name in notebook_names: diff --git a/code/project2/src/Run_jupyter_notebooks.py b/code/project2/src/Run_jupyter_notebooks.py index 0c8f558..4f2fcb0 100644 --- a/code/project2/src/Run_jupyter_notebooks.py +++ b/code/project2/src/Run_jupyter_notebooks.py @@ -22,8 +22,7 @@ def run_notebook(self,notebook_filename): ep = ExecutePreprocessor(timeout=600, kernel_name='python3') # Execute - #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}}) - ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}}) + ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}/../../../'}}) # Save output notebook with open(notebook_filename, 'w', encoding='utf-8') as f: diff --git a/code/project2/test/test_main.py b/code/project2/test/test_main.py index 8aab9ff..c11541e 100644 --- a/code/project2/test/test_main.py +++ b/code/project2/test/test_main.py @@ -1,6 +1,7 @@ import unittest import os from ..src.Main import Main +import testbook class Test_main(unittest.TestCase): @@ -21,6 +22,14 @@ def test_addTwo(self): expected_result = 7 result = self.main.addTwo(5) self.assertEqual(expected_result,result) + +# test jupiter notebook function +#@testbook.testbook('../src/AE4868_example_notebook_update20201025.ipynb', execute=True) +@testbook.testbook('code/project2/src/AE4868_example_notebook_update20201025.ipynb', execute=True) +def test_addThree(tb): + func = tb.ref("addThree") + + assert func(2) == 5 if __name__ == '__main__': unittest.main() \ No newline at end of file diff --git a/code/project3/src/AE4868_example_notebook_update20201025.ipynb b/code/project3/src/AE4868_example_notebook_update20201025.ipynb index c04ac2e..a726891 100755 --- a/code/project3/src/AE4868_example_notebook_update20201025.ipynb +++ b/code/project3/src/AE4868_example_notebook_update20201025.ipynb @@ -5,44 +5,28 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:52:24.573593Z", - "iopub.status.busy": "2020-11-09T14:52:24.572620Z", - "iopub.status.idle": "2020-11-09T14:52:24.575958Z", - "shell.execute_reply": "2020-11-09T14:52:24.575097Z" + "iopub.execute_input": "2020-11-09T20:02:48.417241Z", + "iopub.status.busy": "2020-11-09T20:02:48.416492Z", + "iopub.status.idle": "2020-11-09T20:02:48.419529Z", + "shell.execute_reply": "2020-11-09T20:02:48.418866Z" } }, "outputs": [], "source": [ - "import os\n", - "#import ipyparams\n", - "#currentNotebook = ipyparams.notebook_name\n", - "#print(f'currentNotebook={currentNotebook}')\n", - "#currentNotebook = os.path.dirname(os.path.realpath(__file__))\n", - "#currentNotebook = os.path\n", - "#currentNotebook = os.getcwd()\n", - "#if not 'workbookDir' in globals():\n", - "# workbookDir = os.getcwd()\n", - "#print('workbookDir: ' + workbookDir)\n", - "#currentNotebook = os.chdir(workbookDir) # If you changed the current working dir, this will take you back to the workbook dir.\n", - "#currentNotebook = %pwd" + "def addThree(input_nr):\n", + " '''returns the input integer plus 3, used to verify unit test'''\n", + " return input_nr + 3" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:52:24.592915Z", - "iopub.status.busy": "2020-11-09T14:52:24.591508Z", - "iopub.status.idle": "2020-11-09T14:52:25.437321Z", - "shell.execute_reply": "2020-11-09T14:52:25.437943Z" + "iopub.execute_input": "2020-11-09T20:02:48.434148Z", + "iopub.status.busy": "2020-11-09T20:02:48.433272Z", + "iopub.status.idle": "2020-11-09T20:02:49.282198Z", + "shell.execute_reply": "2020-11-09T20:02:49.282823Z" } }, "outputs": [ @@ -68,6 +52,7 @@ "###############################################################################\n", "# IMPORT STATEMENTS ###########################################################\n", "###############################################################################\n", + "import os\n", "import numpy as np\n", "from tudatpy.kernel import constants\n", "from tudatpy.kernel.interface import spice_interface\n", @@ -75,8 +60,8 @@ "from tudatpy.kernel.simulation import propagation_setup\n", "from tudatpy.kernel.astro import conversion\n", "\n", - "# Set path to latex image folders for project 1\n", - "latex_image_path = '../../../latex/project3/Images/'\n", + "# Set path to latex image folders for project 3\n", + "latex_image_path = 'latex/project3/Images/'\n", "\n", "# Load spice kernels.\n", "spice_interface.load_standard_kernels()\n", @@ -268,10 +253,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:52:25.458000Z", - "iopub.status.busy": "2020-11-09T14:52:25.452229Z", - "iopub.status.idle": "2020-11-09T14:52:29.204968Z", - "shell.execute_reply": "2020-11-09T14:52:29.205604Z" + "iopub.execute_input": "2020-11-09T20:02:49.302802Z", + "iopub.status.busy": "2020-11-09T20:02:49.302105Z", + "iopub.status.idle": "2020-11-09T20:02:53.225997Z", + "shell.execute_reply": "2020-11-09T20:02:53.226638Z" } }, "outputs": [ diff --git a/code/project3/src/AE4868_example_notebook_update20201025.pdf b/code/project3/src/AE4868_example_notebook_update20201025.pdf index 5bec63fce6b6eee93ef2f4f154436bf9e503c4cc..6981b56dfe045ed2d122ab6a39275b19cfe42c7a 100644 GIT binary patch delta 46452 zcmZs?V{G7E*!4R#r*@~dZB03~ZF6e#U$;}+wr$(CZQFS7CwX)7oRjlye_F}!N_MWb z*UJ9%#h!n}LMD?J5u;xVe0RW#WuQCIeVF+4%Wk9i1FZ46I?@)-G|!qYj#G 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__init__(self): def run_jupyter_notebooks(self,project_nr,notebook_names): '''runs a jupyter notebook''' - print(f'Running AE4868_example_notebook_update20201025.ipynb') notebook_path = f'code/project{project_nr}/src/' for notebook_name in notebook_names: diff --git a/code/project3/src/Run_jupyter_notebooks.py b/code/project3/src/Run_jupyter_notebooks.py index 0c8f558..4f2fcb0 100644 --- a/code/project3/src/Run_jupyter_notebooks.py +++ b/code/project3/src/Run_jupyter_notebooks.py @@ -22,8 +22,7 @@ def run_notebook(self,notebook_filename): ep = ExecutePreprocessor(timeout=600, kernel_name='python3') # Execute - #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}}) - ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}}) + ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}/../../../'}}) # Save output notebook with open(notebook_filename, 'w', encoding='utf-8') as f: diff --git a/code/project3/test/test_main.py b/code/project3/test/test_main.py index 8aab9ff..2e0287b 100644 --- a/code/project3/test/test_main.py +++ b/code/project3/test/test_main.py @@ -1,6 +1,7 @@ import unittest import os from ..src.Main import Main +import testbook class Test_main(unittest.TestCase): @@ -21,6 +22,14 @@ def test_addTwo(self): expected_result = 7 result = self.main.addTwo(5) self.assertEqual(expected_result,result) + +# test jupiter notebook function +#@testbook.testbook('../src/AE4868_example_notebook_update20201025.ipynb', execute=True) +@testbook.testbook('code/project3/src/AE4868_example_notebook_update20201025.ipynb', execute=True) +def test_addThree(tb): + func = tb.ref("addThree") + + assert func(2) == 5 if __name__ == '__main__': unittest.main() \ No newline at end of file diff --git a/latex/project1/Images/4b.png b/latex/project1/Images/4b.png index dc1bfcb14067750cabea342db96cd40fe077e0f2..63223ebad4d1a2c35011db0db49e359f2d7c3bbf 100644 GIT binary patch literal 38865 zcmeFZRa9GF^e&n}aVS=#xU@iVX>lvXS}0Q7gS!`ZD-Lb(qQ#3#a0^}>ihFQ(_p|f+ 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b/windows_environment.yml index fd21b9b..1045a24 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -16,3 +16,4 @@ dependencies: - pip: # works for regular pip packages - pdflatex + - testbook \ No newline at end of file From 060f699b44a4a0dd95fc0badc9797f703dcdd5ed Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 21:32:56 +0100 Subject: [PATCH 33/57] Deleted old files. --- ...AE4868_example_notebook_update20201025.pdf | Bin 408276 -> 0 bytes code/project1/src/juice_propagation_Q1.ipynb | 355 ------------------ code/project1/src/juice_propagation_Q4.ipynb | 335 ----------------- ...AE4868_example_notebook_update20201025.pdf | Bin 408276 -> 0 bytes code/project2/src/juice_propagation_Q1.ipynb | 355 ------------------ code/project2/src/juice_propagation_Q4.ipynb | 335 ----------------- ...AE4868_example_notebook_update20201025.pdf | Bin 408275 -> 0 bytes code/project3/src/juice_propagation_Q1.ipynb | 355 ------------------ code/project3/src/juice_propagation_Q4.ipynb | 335 ----------------- 9 files changed, 2070 deletions(-) delete mode 100644 code/project1/src/AE4868_example_notebook_update20201025.pdf delete mode 100644 code/project1/src/juice_propagation_Q1.ipynb delete mode 100644 code/project1/src/juice_propagation_Q4.ipynb delete mode 100644 code/project2/src/AE4868_example_notebook_update20201025.pdf delete mode 100644 code/project2/src/juice_propagation_Q1.ipynb delete mode 100644 code/project2/src/juice_propagation_Q4.ipynb delete mode 100644 code/project3/src/AE4868_example_notebook_update20201025.pdf delete mode 100644 code/project3/src/juice_propagation_Q1.ipynb delete mode 100644 code/project3/src/juice_propagation_Q4.ipynb diff --git a/code/project1/src/AE4868_example_notebook_update20201025.pdf b/code/project1/src/AE4868_example_notebook_update20201025.pdf deleted file mode 100644 index 9437f7b40fc3183dab24164b4934e6bf38b5ea19..0000000000000000000000000000000000000000 GIT binary patch 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zg-gAk4F<=aKUMWb_U@&;U+*3%v~1q1ir`Y%+f;OhC@sq_7XI~$)7VyY?=CM>RKlxo spbty$UpnUG 1244ABC\n", - "A = XXXX\n", - "B = XXXX\n", - "C = XXXX\n", - "\n", - "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", - " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", - "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Create environment, vehicle, accelerations, and propagation settings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# CREATE ENVIRONMENT ######################################################\n", - "###########################################################################\n", - "\n", - "# Load spice kernels.\n", - "spice_interface.load_standard_kernels()\n", - "\n", - "# Create settings for celestial bodies\n", - "bodies_to_create = [\"Ganymede\"]\n", - "global_frame_origin = \"Ganymede\"\n", - "global_frame_orientation = \"ECLIPJ2000\"\n", - "body_settings = environment_setup.get_default_body_settings(\n", - " bodies_to_create, global_frame_origin, global_frame_orientation)\n", - "\n", - "# Add Ganymede exponential atmosphere\n", - "density_scale_height = 40.0E3\n", - "density_at_zero_altitude = 2.0E-9\n", - "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", - " density_scale_height, density_at_zero_altitude)\n", - "\n", - "bodies = environment_setup.create_system_of_bodies(body_settings)\n", - "\n", - "###########################################################################\n", - "# CREATE VEHICLE ##########################################################\n", - "###########################################################################\n", - "\n", - "# Create vehicle object\n", - "bodies.create_empty_body( \"JUICE\" )\n", - "\n", - "# Set mass of vehicle\n", - "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", - " \n", - "# Create aerodynamic coefficients interface\n", - "reference_area = 100.0\n", - "drag_coefficient = 1.2\n", - "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", - " reference_area,[drag_coefficient,0,0] )\n", - "environment_setup.add_aerodynamic_coefficient_interface(\n", - " bodies, \"JUICE\", aero_coefficient_settings )\n", - "\n", - "###########################################################################\n", - "# CREATE ACCELERATIONS ####################################################\n", - "###########################################################################\n", - "\n", - "# Define bodies that are propagated, and their central bodies of propagation.\n", - "bodies_to_propagate = [\"JUICE\"]\n", - "central_bodies = [\"Ganymede\"]\n", - "\n", - "# Define accelerations acting on vehicle.\n", - "acceleration_settings_on_vehicle = dict(\n", - " XXXX\n", - ")\n", - "\n", - "# Create global accelerations dictionary.\n", - "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", - "\n", - "# Create acceleration models.\n", - "acceleration_models = propagation_setup.create_acceleration_models(\n", - " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", - "\n", - "\n", - "###########################################################################\n", - "# CREATE PROPAGATION SETTINGS #############################################\n", - "###########################################################################\n", - "\n", - "# Define initial state.\n", - "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", - " target_body_name=\"JUICE\",\n", - " observer_body_name=\"Ganymede\",\n", - " reference_frame_name=\"ECLIPJ2000\",\n", - " aberration_corrections=\"NONE\",\n", - " ephemeris_time= simulation_start_epoch )\n", - "\n", - "dependent_variables_to_save = [\n", - " propagation_setup.dependent_variable.keplerian_state(\n", - " \"JUICE\", \"Ganymede\")\n", - " ]\n", - "\n", - "# Create propagation settings.\n", - "propagator_settings = propagation_setup.propagator.translational(\n", - " central_bodies,\n", - " acceleration_models,\n", - " bodies_to_propagate,\n", - " system_initial_state,\n", - " simulation_end_epoch,\n", - " output_variables = dependent_variables_to_save\n", - ")\n", - " \n", - "# Create numerical integrator settings.\n", - "fixed_step_size = 10.0\n", - "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", - " simulation_start_epoch,\n", - " fixed_step_size\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Propagate Orbit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "# Create simulation object and propagate dynamics.\n", - "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", - " bodies, integrator_settings, propagator_settings, True)\n", - "\n", - "simulation_result = dynamics_simulator.state_history\n", - "dependent_variables = dynamics_simulator.dependent_variable_history" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Print final propagation time and state" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", - "###########################################################################\n", - "\n", - "final_time_step=list(simulation_result.keys())[-1]\n", - "first_time_step=list(simulation_result.keys())[0]\n", - "\n", - "print(\n", - " f\"\"\"\n", - "JUICE Propagation Results.\n", - "\n", - "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", - "Final Cartesian state of JUICE is [m]: \\n{\n", - " simulation_result[final_time_step][:]}\n", - "\n", - " \"\"\"\n", - ")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Save Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# SAVE RESULTS ############################################################\n", - "###########################################################################\n", - "\n", - "save2txt(solution=simulation_result,\n", - " filename=\"JUICEPropagationHistory_Q1.dat\",\n", - " directory=\"./\", # default = \"./\" \n", - " column_names=None, # default = None \n", - " )\n", - "\n", - "save2txt(solution=dependent_variables,\n", - " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", - " directory=\"./\", # default = \"./\" \n", - " column_names=None, # default = None \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Plot Results\n", - "\n", - "For inspiration see: \n", - "\n", - "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# PLOT RESULTS ############################################################\n", - "###########################################################################\n", - "\n", - "# Extract time and Kepler elements from dependent variables\n", - "kepler_elements = np.vstack(list(dependent_variables.values())\n", - " \n", - "# Kepler Elements\n", - "# 0: semi-major axis\n", - "# 1: eccentricity\n", - "# 2: inclination\n", - "# 3: argument of periapsis\n", - "# 4: right ascension of the ascending node\n", - "# 5: true anomaly\n", - "\n", - "time = dependent_variables.keys()\n", - "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", - "\n", - "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", - "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", - "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", - "\n", - "\n", - "# Set font size of figures\n", - "font_size = 20\n", - " \n", - "plt.rcParams.update({'font.size': font_size}) \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Edit Metadata", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/code/project2/src/juice_propagation_Q4.ipynb b/code/project2/src/juice_propagation_Q4.ipynb deleted file mode 100644 index 7b3bbcd..0000000 --- a/code/project2/src/juice_propagation_Q4.ipynb +++ /dev/null @@ -1,335 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "# Assignment 1 - Propagation Settings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "''' \n", - "Copyright (c) 2010-2020, Delft University of Technology\n", - "All rigths reserved\n", - "\n", - "This file is part of the Tudat. Redistribution and use in source and \n", - "binary forms, with or without modification, are permitted exclusively\n", - "under the terms of the Modified BSD license. You should have received\n", - "a copy of the license with this file. If not, please or visit:\n", - "http://tudat.tudelft.nl/LICENSE.\n", - "'''\n", - "\n", - "import numpy as np\n", - "from tudatpy import elements\n", - "from tudatpy.io import save2txt\n", - "from tudatpy.kernel import constants\n", - "from tudatpy.kernel.interface import spice_interface\n", - "from tudatpy.kernel.simulation import environment_setup\n", - "from tudatpy.kernel.simulation import propagation_setup\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "# student number: 1244779 --> 1244ABC\n", - "A = XXXX\n", - "B = XXXX\n", - "C = XXXX\n", - "\n", - "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", - " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", - "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Create Environment and Vehicle" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# CREATE ENVIRONMENT ######################################################\n", - "###########################################################################\n", - "\n", - "# Load spice kernels.\n", - "spice_interface.load_standard_kernels()\n", - "\n", - "# Create body objects.\n", - "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", - "global_frame_origin = \"SSB\"\n", - "global_frame_orientation = \"ECLIPJ2000\"\n", - "body_settings = environment_setup.get_default_body_settings(\n", - " bodies_to_create, global_frame_origin, global_frame_orientation) \n", - "\n", - "# Add Ganymede exponential atmosphere \n", - "density_scale_height = 40.0E3\n", - "density_at_zero_altitude = 2.0E-9\n", - "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", - " density_scale_height, density_at_zero_altitude)\n", - "\n", - "bodies = environment_setup.create_system_of_bodies(body_settings)\n", - "\n", - "###########################################################################\n", - "# CREATE VEHICLE ##########################################################\n", - "###########################################################################\n", - "\n", - "# Create vehicle object\n", - "bodies.create_empty_body( \"JUICE\" )\n", - "\n", - "# Set mass of vehicle\n", - "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", - "\n", - "# Create aerodynamic coefficients interface\n", - "reference_area = 100.0\n", - "drag_coefficient = 1.2\n", - "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", - " reference_area,[drag_coefficient,0,0] )\n", - "environment_setup.add_aerodynamic_coefficient_interface(\n", - " bodies, \"JUICE\", aero_coefficient_settings );" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Propagate Dynamics for various cases" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "cases = ['unperturbed', 'case_i', 'case_ii']\n", - "\n", - "\"\"\"\n", - "unperturbed: Ganymede PM\n", - "\n", - "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", - "\n", - "case_ii: Ganymede PM, Ganymede aerodynamic\n", - "\"\"\"\n", - "\n", - "simulation_results_dict = dict()\n", - "dependent_variables_dict = dict()\n", - "for case in cases: \n", - " ###########################################################################\n", - " # CREATE ACCELERATIONS ####################################################\n", - " ###########################################################################\n", - "\n", - " # Define bodies that are propagated.\n", - " bodies_to_propagate = [\"JUICE\"]\n", - "\n", - " # Define central bodies.\n", - " central_bodies = [\"Ganymede\"]\n", - "\n", - " # Define accelerations acting on vehicle.\n", - " if case == 'unperturbed':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX\n", - " )\n", - " if case == 'case_i':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX,\n", - " Jupiter = XXXX\n", - " )\n", - " if case == 'case_ii':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX\n", - " )\n", - "\n", - " # Create global accelerations dictionary.\n", - " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", - "\n", - " # Create acceleration models.\n", - " acceleration_models = propagation_setup.create_acceleration_models(\n", - " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", - "\n", - "\n", - " ###########################################################################\n", - " # CREATE PROPAGATION SETTINGS #############################################\n", - " ###########################################################################\n", - "\n", - " # Define initial state.\n", - " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", - " target_body_name=\"JUICE\",\n", - " observer_body_name=\"Ganymede\",\n", - " reference_frame_name=\"ECLIPJ2000\",\n", - " aberration_corrections=\"NONE\",\n", - " ephemeris_time= simulation_start_epoch )\n", - "\n", - " # Save magnitude of perturbations for both cases\n", - " if case == 'unperturbed':\n", - " dependent_variables_to_save = [ ]\n", - " if case == 'case_i':\n", - " dependent_variables_to_save = [ \n", - " propagation_setup.dependent_variable.XXXX\n", - " ]\n", - " if case == 'case_ii':\n", - " dependent_variables_to_save = [ \n", - " propagation_setup.dependent_variable.XXXX\n", - " ]\n", - "\n", - " # Create propagation settings.\n", - " propagator_settings = propagation_setup.propagator.translational(\n", - " central_bodies,\n", - " acceleration_models,\n", - " bodies_to_propagate,\n", - " system_initial_state,\n", - " simulation_end_epoch,\n", - " output_variables = dependent_variables_to_save\n", - " )\n", - "\n", - " # Create numerical integrator settings.\n", - " fixed_step_size = 10.0\n", - " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", - " simulation_start_epoch,\n", - " fixed_step_size\n", - " )\n", - "\n", - " ###########################################################################\n", - " # PROPAGATE ORBIT #########################################################\n", - " ###########################################################################\n", - "\n", - " # Create simulation object and propagate dynamics.\n", - " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", - " bodies, integrator_settings, propagator_settings)\n", - " \n", - " simulation_results_dict[case] = dynamics_simulator.state_history\n", - " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", - "\n", - " ###########################################################################\n", - " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", - " ###########################################################################\n", - "\n", - " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", - " first_time_step=list(simulation_results_dict[case].keys())[0]\n", - "\n", - " print(\n", - " f\"\"\"\n", - " JUICE Propagation Results of {case}.\n", - "\n", - " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", - " Final Cartesian state of JUICE is [m]: \\n{\n", - " simulation_results_dict[case][final_time_step][:]}\n", - "\n", - " \"\"\"\n", - " )\n", - "\n", - " ###########################################################################\n", - " # SAVE RESULTS ############################################################\n", - " ###########################################################################\n", - " \n", - "# save2txt(solution=simulation_result,\n", - "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", - "# directory=\"./\", # default = \"./\" \n", - "# column_names=None, # default = None \n", - "# )\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Pre-process Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", - "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", - "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", - "\n", - "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", - "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", - "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", - "\n", - "difference_in_cartesian_position = XXXX" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Plot Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Edit Metadata", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git 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" B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", - "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Create environment, vehicle, accelerations, and propagation settings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# CREATE ENVIRONMENT ######################################################\n", - "###########################################################################\n", - "\n", - "# Load spice kernels.\n", - "spice_interface.load_standard_kernels()\n", - "\n", - "# Create settings for celestial bodies\n", - "bodies_to_create = [\"Ganymede\"]\n", - "global_frame_origin = \"Ganymede\"\n", - "global_frame_orientation = \"ECLIPJ2000\"\n", - "body_settings = environment_setup.get_default_body_settings(\n", - " bodies_to_create, global_frame_origin, global_frame_orientation)\n", - "\n", - "# Add Ganymede exponential atmosphere\n", - "density_scale_height = 40.0E3\n", - "density_at_zero_altitude = 2.0E-9\n", - "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", - " density_scale_height, density_at_zero_altitude)\n", - "\n", - "bodies = environment_setup.create_system_of_bodies(body_settings)\n", - "\n", - "###########################################################################\n", - "# CREATE VEHICLE ##########################################################\n", - "###########################################################################\n", - "\n", - "# Create vehicle object\n", - "bodies.create_empty_body( \"JUICE\" )\n", - "\n", - "# Set mass of vehicle\n", - "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", - " \n", - "# Create aerodynamic coefficients interface\n", - "reference_area = 100.0\n", - "drag_coefficient = 1.2\n", - "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", - " reference_area,[drag_coefficient,0,0] )\n", - "environment_setup.add_aerodynamic_coefficient_interface(\n", - " bodies, \"JUICE\", aero_coefficient_settings )\n", - "\n", - "###########################################################################\n", - "# CREATE ACCELERATIONS ####################################################\n", - "###########################################################################\n", - "\n", - "# Define bodies that are propagated, and their central bodies of propagation.\n", - "bodies_to_propagate = [\"JUICE\"]\n", - "central_bodies = [\"Ganymede\"]\n", - "\n", - "# Define accelerations acting on vehicle.\n", - "acceleration_settings_on_vehicle = dict(\n", - " XXXX\n", - ")\n", - "\n", - "# Create global accelerations dictionary.\n", - "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", - "\n", - "# Create acceleration models.\n", - "acceleration_models = propagation_setup.create_acceleration_models(\n", - " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", - "\n", - "\n", - "###########################################################################\n", - "# CREATE PROPAGATION SETTINGS #############################################\n", - "###########################################################################\n", - "\n", - "# Define initial state.\n", - "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", - " target_body_name=\"JUICE\",\n", - " observer_body_name=\"Ganymede\",\n", - " reference_frame_name=\"ECLIPJ2000\",\n", - " aberration_corrections=\"NONE\",\n", - " ephemeris_time= simulation_start_epoch )\n", - "\n", - "dependent_variables_to_save = [\n", - " propagation_setup.dependent_variable.keplerian_state(\n", - " \"JUICE\", \"Ganymede\")\n", - " ]\n", - "\n", - "# Create propagation settings.\n", - "propagator_settings = propagation_setup.propagator.translational(\n", - " central_bodies,\n", - " acceleration_models,\n", - " bodies_to_propagate,\n", - " system_initial_state,\n", - " simulation_end_epoch,\n", - " output_variables = dependent_variables_to_save\n", - ")\n", - " \n", - "# Create numerical integrator settings.\n", - "fixed_step_size = 10.0\n", - "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", - " simulation_start_epoch,\n", - " fixed_step_size\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Propagate Orbit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "# Create simulation object and propagate dynamics.\n", - "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", - " bodies, integrator_settings, propagator_settings, True)\n", - "\n", - "simulation_result = dynamics_simulator.state_history\n", - "dependent_variables = dynamics_simulator.dependent_variable_history" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Print final propagation time and state" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", - "###########################################################################\n", - "\n", - "final_time_step=list(simulation_result.keys())[-1]\n", - "first_time_step=list(simulation_result.keys())[0]\n", - "\n", - "print(\n", - " f\"\"\"\n", - "JUICE Propagation Results.\n", - "\n", - "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", - "Final Cartesian state of JUICE is [m]: \\n{\n", - " simulation_result[final_time_step][:]}\n", - "\n", - " \"\"\"\n", - ")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Save Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# SAVE RESULTS ############################################################\n", - "###########################################################################\n", - "\n", - "save2txt(solution=simulation_result,\n", - " filename=\"JUICEPropagationHistory_Q1.dat\",\n", - " directory=\"./\", # default = \"./\" \n", - " column_names=None, # default = None \n", - " )\n", - "\n", - "save2txt(solution=dependent_variables,\n", - " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", - " directory=\"./\", # default = \"./\" \n", - " column_names=None, # default = None \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Plot Results\n", - "\n", - "For inspiration see: \n", - "\n", - "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# PLOT RESULTS ############################################################\n", - "###########################################################################\n", - "\n", - "# Extract time and Kepler elements from dependent variables\n", - "kepler_elements = np.vstack(list(dependent_variables.values())\n", - " \n", - "# Kepler Elements\n", - "# 0: semi-major axis\n", - "# 1: eccentricity\n", - "# 2: inclination\n", - "# 3: argument of periapsis\n", - "# 4: right ascension of the ascending node\n", - "# 5: true anomaly\n", - "\n", - "time = dependent_variables.keys()\n", - "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", - "\n", - "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", - "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", - "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", - "\n", - "\n", - "# Set font size of figures\n", - "font_size = 20\n", - " \n", - "plt.rcParams.update({'font.size': font_size}) \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Edit Metadata", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/code/project1/src/juice_propagation_Q4.ipynb b/code/project1/src/juice_propagation_Q4.ipynb deleted file mode 100644 index 7b3bbcd..0000000 --- a/code/project1/src/juice_propagation_Q4.ipynb +++ /dev/null @@ -1,335 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "# Assignment 1 - Propagation Settings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "''' \n", - "Copyright (c) 2010-2020, Delft University of Technology\n", - "All rigths reserved\n", - "\n", - "This file is part of the Tudat. Redistribution and use in source and \n", - "binary forms, with or without modification, are permitted exclusively\n", - "under the terms of the Modified BSD license. You should have received\n", - "a copy of the license with this file. If not, please or visit:\n", - "http://tudat.tudelft.nl/LICENSE.\n", - "'''\n", - "\n", - "import numpy as np\n", - "from tudatpy import elements\n", - "from tudatpy.io import save2txt\n", - "from tudatpy.kernel import constants\n", - "from tudatpy.kernel.interface import spice_interface\n", - "from tudatpy.kernel.simulation import environment_setup\n", - "from tudatpy.kernel.simulation import propagation_setup\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "# student number: 1244779 --> 1244ABC\n", - "A = XXXX\n", - "B = XXXX\n", - "C = XXXX\n", - "\n", - "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", - " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", - "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Create Environment and Vehicle" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# CREATE ENVIRONMENT ######################################################\n", - "###########################################################################\n", - "\n", - "# Load spice kernels.\n", - "spice_interface.load_standard_kernels()\n", - "\n", - "# Create body objects.\n", - "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", - "global_frame_origin = \"SSB\"\n", - "global_frame_orientation = \"ECLIPJ2000\"\n", - "body_settings = environment_setup.get_default_body_settings(\n", - " bodies_to_create, global_frame_origin, global_frame_orientation) \n", - "\n", - "# Add Ganymede exponential atmosphere \n", - "density_scale_height = 40.0E3\n", - "density_at_zero_altitude = 2.0E-9\n", - "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", - " density_scale_height, density_at_zero_altitude)\n", - "\n", - "bodies = environment_setup.create_system_of_bodies(body_settings)\n", - "\n", - "###########################################################################\n", - "# CREATE VEHICLE ##########################################################\n", - "###########################################################################\n", - "\n", - "# Create vehicle object\n", - "bodies.create_empty_body( \"JUICE\" )\n", - "\n", - "# Set mass of vehicle\n", - "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", - "\n", - "# Create aerodynamic coefficients interface\n", - "reference_area = 100.0\n", - "drag_coefficient = 1.2\n", - "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", - " reference_area,[drag_coefficient,0,0] )\n", - "environment_setup.add_aerodynamic_coefficient_interface(\n", - " bodies, \"JUICE\", aero_coefficient_settings );" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Propagate Dynamics for various cases" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "cases = ['unperturbed', 'case_i', 'case_ii']\n", - "\n", - "\"\"\"\n", - "unperturbed: Ganymede PM\n", - "\n", - "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", - "\n", - "case_ii: Ganymede PM, Ganymede aerodynamic\n", - "\"\"\"\n", - "\n", - "simulation_results_dict = dict()\n", - "dependent_variables_dict = dict()\n", - "for case in cases: \n", - " ###########################################################################\n", - " # CREATE ACCELERATIONS ####################################################\n", - " ###########################################################################\n", - "\n", - " # Define bodies that are propagated.\n", - " bodies_to_propagate = [\"JUICE\"]\n", - "\n", - " # Define central bodies.\n", - " central_bodies = [\"Ganymede\"]\n", - "\n", - " # Define accelerations acting on vehicle.\n", - " if case == 'unperturbed':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX\n", - " )\n", - " if case == 'case_i':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX,\n", - " Jupiter = XXXX\n", - " )\n", - " if case == 'case_ii':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX\n", - " )\n", - "\n", - " # Create global accelerations dictionary.\n", - " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", - "\n", - " # Create acceleration models.\n", - " acceleration_models = propagation_setup.create_acceleration_models(\n", - " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", - "\n", - "\n", - " ###########################################################################\n", - " # CREATE PROPAGATION SETTINGS #############################################\n", - " ###########################################################################\n", - "\n", - " # Define initial state.\n", - " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", - " target_body_name=\"JUICE\",\n", - " observer_body_name=\"Ganymede\",\n", - " reference_frame_name=\"ECLIPJ2000\",\n", - " aberration_corrections=\"NONE\",\n", - " ephemeris_time= simulation_start_epoch )\n", - "\n", - " # Save magnitude of perturbations for both cases\n", - " if case == 'unperturbed':\n", - " dependent_variables_to_save = [ ]\n", - " if case == 'case_i':\n", - " dependent_variables_to_save = [ \n", - " propagation_setup.dependent_variable.XXXX\n", - " ]\n", - " if case == 'case_ii':\n", - " dependent_variables_to_save = [ \n", - " propagation_setup.dependent_variable.XXXX\n", - " ]\n", - "\n", - " # Create propagation settings.\n", - " propagator_settings = propagation_setup.propagator.translational(\n", - " central_bodies,\n", - " acceleration_models,\n", - " bodies_to_propagate,\n", - " system_initial_state,\n", - " simulation_end_epoch,\n", - " output_variables = dependent_variables_to_save\n", - " )\n", - "\n", - " # Create numerical integrator settings.\n", - " fixed_step_size = 10.0\n", - " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", - " simulation_start_epoch,\n", - " fixed_step_size\n", - " )\n", - "\n", - " ###########################################################################\n", - " # PROPAGATE ORBIT #########################################################\n", - " ###########################################################################\n", - "\n", - " # Create simulation object and propagate dynamics.\n", - " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", - " bodies, integrator_settings, propagator_settings)\n", - " \n", - " simulation_results_dict[case] = dynamics_simulator.state_history\n", - " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", - "\n", - " ###########################################################################\n", - " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", - " ###########################################################################\n", - "\n", - " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", - " first_time_step=list(simulation_results_dict[case].keys())[0]\n", - "\n", - " print(\n", - " f\"\"\"\n", - " JUICE Propagation Results of {case}.\n", - "\n", - " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", - " Final Cartesian state of JUICE is [m]: \\n{\n", - " simulation_results_dict[case][final_time_step][:]}\n", - "\n", - " \"\"\"\n", - " )\n", - "\n", - " ###########################################################################\n", - " # SAVE RESULTS ############################################################\n", - " ###########################################################################\n", - " \n", - "# save2txt(solution=simulation_result,\n", - "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", - "# directory=\"./\", # default = \"./\" \n", - "# column_names=None, # default = None \n", - "# )\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Pre-process Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", - "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", - "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", - "\n", - "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", - "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", - "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", - "\n", - "difference_in_cartesian_position = XXXX" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Plot Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Edit Metadata", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git 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a/code/project3/src/juice_propagation_Q1.ipynb b/code/project3/src/juice_propagation_Q1.ipynb deleted file mode 100644 index e827102..0000000 --- a/code/project3/src/juice_propagation_Q1.ipynb +++ /dev/null @@ -1,355 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "# Assignment 1 - Propagation Settings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "''' \n", - "Copyright (c) 2010-2020, Delft University of Technology\n", - "All rigths reserved\n", - "\n", - "This file is part of the Tudat. Redistribution and use in source and \n", - "binary forms, with or without modification, are permitted exclusively\n", - "under the terms of the Modified BSD license. You should have received\n", - "a copy of the license with this file. If not, please or visit:\n", - "http://tudat.tudelft.nl/LICENSE.\n", - "'''\n", - "\n", - "import numpy as np\n", - "from tudatpy import elements\n", - "from tudatpy.io import save2txt\n", - "from tudatpy.kernel import constants\n", - "from tudatpy.kernel.interface import spice_interface\n", - "from tudatpy.kernel.simulation import environment_setup\n", - "from tudatpy.kernel.simulation import propagation_setup\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "# # student number: 1244779 --> 1244ABC\n", - "A = XXXX\n", - "B = XXXX\n", - "C = XXXX\n", - "\n", - "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", - " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", - "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Create environment, vehicle, accelerations, and propagation settings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# CREATE ENVIRONMENT ######################################################\n", - "###########################################################################\n", - "\n", - "# Load spice kernels.\n", - "spice_interface.load_standard_kernels()\n", - "\n", - "# Create settings for celestial bodies\n", - "bodies_to_create = [\"Ganymede\"]\n", - "global_frame_origin = \"Ganymede\"\n", - "global_frame_orientation = \"ECLIPJ2000\"\n", - "body_settings = environment_setup.get_default_body_settings(\n", - " bodies_to_create, global_frame_origin, global_frame_orientation)\n", - "\n", - "# Add Ganymede exponential atmosphere\n", - "density_scale_height = 40.0E3\n", - "density_at_zero_altitude = 2.0E-9\n", - "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", - " density_scale_height, density_at_zero_altitude)\n", - "\n", - "bodies = environment_setup.create_system_of_bodies(body_settings)\n", - "\n", - "###########################################################################\n", - "# CREATE VEHICLE ##########################################################\n", - "###########################################################################\n", - "\n", - "# Create vehicle object\n", - "bodies.create_empty_body( \"JUICE\" )\n", - "\n", - "# Set mass of vehicle\n", - "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", - " \n", - "# Create aerodynamic coefficients interface\n", - "reference_area = 100.0\n", - "drag_coefficient = 1.2\n", - "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", - " reference_area,[drag_coefficient,0,0] )\n", - "environment_setup.add_aerodynamic_coefficient_interface(\n", - " bodies, \"JUICE\", aero_coefficient_settings )\n", - "\n", - "###########################################################################\n", - "# CREATE ACCELERATIONS ####################################################\n", - "###########################################################################\n", - "\n", - "# Define bodies that are propagated, and their central bodies of propagation.\n", - "bodies_to_propagate = [\"JUICE\"]\n", - "central_bodies = [\"Ganymede\"]\n", - "\n", - "# Define accelerations acting on vehicle.\n", - "acceleration_settings_on_vehicle = dict(\n", - " XXXX\n", - ")\n", - "\n", - "# Create global accelerations dictionary.\n", - "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", - "\n", - "# Create acceleration models.\n", - "acceleration_models = propagation_setup.create_acceleration_models(\n", - " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", - "\n", - "\n", - "###########################################################################\n", - "# CREATE PROPAGATION SETTINGS #############################################\n", - "###########################################################################\n", - "\n", - "# Define initial state.\n", - "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", - " target_body_name=\"JUICE\",\n", - " observer_body_name=\"Ganymede\",\n", - " reference_frame_name=\"ECLIPJ2000\",\n", - " aberration_corrections=\"NONE\",\n", - " ephemeris_time= simulation_start_epoch )\n", - "\n", - "dependent_variables_to_save = [\n", - " propagation_setup.dependent_variable.keplerian_state(\n", - " \"JUICE\", \"Ganymede\")\n", - " ]\n", - "\n", - "# Create propagation settings.\n", - "propagator_settings = propagation_setup.propagator.translational(\n", - " central_bodies,\n", - " acceleration_models,\n", - " bodies_to_propagate,\n", - " system_initial_state,\n", - " simulation_end_epoch,\n", - " output_variables = dependent_variables_to_save\n", - ")\n", - " \n", - "# Create numerical integrator settings.\n", - "fixed_step_size = 10.0\n", - "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", - " simulation_start_epoch,\n", - " fixed_step_size\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Propagate Orbit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "# Create simulation object and propagate dynamics.\n", - "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", - " bodies, integrator_settings, propagator_settings, True)\n", - "\n", - "simulation_result = dynamics_simulator.state_history\n", - "dependent_variables = dynamics_simulator.dependent_variable_history" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Print final propagation time and state" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", - "###########################################################################\n", - "\n", - "final_time_step=list(simulation_result.keys())[-1]\n", - "first_time_step=list(simulation_result.keys())[0]\n", - "\n", - "print(\n", - " f\"\"\"\n", - "JUICE Propagation Results.\n", - "\n", - "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", - "Final Cartesian state of JUICE is [m]: \\n{\n", - " simulation_result[final_time_step][:]}\n", - "\n", - " \"\"\"\n", - ")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Save Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# SAVE RESULTS ############################################################\n", - "###########################################################################\n", - "\n", - "save2txt(solution=simulation_result,\n", - " filename=\"JUICEPropagationHistory_Q1.dat\",\n", - " directory=\"./\", # default = \"./\" \n", - " column_names=None, # default = None \n", - " )\n", - "\n", - "save2txt(solution=dependent_variables,\n", - " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", - " directory=\"./\", # default = \"./\" \n", - " column_names=None, # default = None \n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Plot Results\n", - "\n", - "For inspiration see: \n", - "\n", - "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# PLOT RESULTS ############################################################\n", - "###########################################################################\n", - "\n", - "# Extract time and Kepler elements from dependent variables\n", - "kepler_elements = np.vstack(list(dependent_variables.values())\n", - " \n", - "# Kepler Elements\n", - "# 0: semi-major axis\n", - "# 1: eccentricity\n", - "# 2: inclination\n", - "# 3: argument of periapsis\n", - "# 4: right ascension of the ascending node\n", - "# 5: true anomaly\n", - "\n", - "time = dependent_variables.keys()\n", - "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", - "\n", - "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", - "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", - "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", - "\n", - "\n", - "# Set font size of figures\n", - "font_size = 20\n", - " \n", - "plt.rcParams.update({'font.size': font_size}) \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Edit Metadata", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/code/project3/src/juice_propagation_Q4.ipynb b/code/project3/src/juice_propagation_Q4.ipynb deleted file mode 100644 index 7b3bbcd..0000000 --- a/code/project3/src/juice_propagation_Q4.ipynb +++ /dev/null @@ -1,335 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "# Assignment 1 - Propagation Settings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "''' \n", - "Copyright (c) 2010-2020, Delft University of Technology\n", - "All rigths reserved\n", - "\n", - "This file is part of the Tudat. Redistribution and use in source and \n", - "binary forms, with or without modification, are permitted exclusively\n", - "under the terms of the Modified BSD license. You should have received\n", - "a copy of the license with this file. If not, please or visit:\n", - "http://tudat.tudelft.nl/LICENSE.\n", - "'''\n", - "\n", - "import numpy as np\n", - "from tudatpy import elements\n", - "from tudatpy.io import save2txt\n", - "from tudatpy.kernel import constants\n", - "from tudatpy.kernel.interface import spice_interface\n", - "from tudatpy.kernel.simulation import environment_setup\n", - "from tudatpy.kernel.simulation import propagation_setup\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "# student number: 1244779 --> 1244ABC\n", - "A = XXXX\n", - "B = XXXX\n", - "C = XXXX\n", - "\n", - "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", - " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", - "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Create Environment and Vehicle" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "###########################################################################\n", - "# CREATE ENVIRONMENT ######################################################\n", - "###########################################################################\n", - "\n", - "# Load spice kernels.\n", - "spice_interface.load_standard_kernels()\n", - "\n", - "# Create body objects.\n", - "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", - "global_frame_origin = \"SSB\"\n", - "global_frame_orientation = \"ECLIPJ2000\"\n", - "body_settings = environment_setup.get_default_body_settings(\n", - " bodies_to_create, global_frame_origin, global_frame_orientation) \n", - "\n", - "# Add Ganymede exponential atmosphere \n", - "density_scale_height = 40.0E3\n", - "density_at_zero_altitude = 2.0E-9\n", - "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", - " density_scale_height, density_at_zero_altitude)\n", - "\n", - "bodies = environment_setup.create_system_of_bodies(body_settings)\n", - "\n", - "###########################################################################\n", - "# CREATE VEHICLE ##########################################################\n", - "###########################################################################\n", - "\n", - "# Create vehicle object\n", - "bodies.create_empty_body( \"JUICE\" )\n", - "\n", - "# Set mass of vehicle\n", - "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", - "\n", - "# Create aerodynamic coefficients interface\n", - "reference_area = 100.0\n", - "drag_coefficient = 1.2\n", - "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", - " reference_area,[drag_coefficient,0,0] )\n", - "environment_setup.add_aerodynamic_coefficient_interface(\n", - " bodies, \"JUICE\", aero_coefficient_settings );" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Propagate Dynamics for various cases" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "cases = ['unperturbed', 'case_i', 'case_ii']\n", - "\n", - "\"\"\"\n", - "unperturbed: Ganymede PM\n", - "\n", - "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", - "\n", - "case_ii: Ganymede PM, Ganymede aerodynamic\n", - "\"\"\"\n", - "\n", - "simulation_results_dict = dict()\n", - "dependent_variables_dict = dict()\n", - "for case in cases: \n", - " ###########################################################################\n", - " # CREATE ACCELERATIONS ####################################################\n", - " ###########################################################################\n", - "\n", - " # Define bodies that are propagated.\n", - " bodies_to_propagate = [\"JUICE\"]\n", - "\n", - " # Define central bodies.\n", - " central_bodies = [\"Ganymede\"]\n", - "\n", - " # Define accelerations acting on vehicle.\n", - " if case == 'unperturbed':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX\n", - " )\n", - " if case == 'case_i':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX,\n", - " Jupiter = XXXX\n", - " )\n", - " if case == 'case_ii':\n", - " acceleration_settings_on_vehicle = dict(\n", - " Ganymede = XXXX\n", - " )\n", - "\n", - " # Create global accelerations dictionary.\n", - " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", - "\n", - " # Create acceleration models.\n", - " acceleration_models = propagation_setup.create_acceleration_models(\n", - " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", - "\n", - "\n", - " ###########################################################################\n", - " # CREATE PROPAGATION SETTINGS #############################################\n", - " ###########################################################################\n", - "\n", - " # Define initial state.\n", - " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", - " target_body_name=\"JUICE\",\n", - " observer_body_name=\"Ganymede\",\n", - " reference_frame_name=\"ECLIPJ2000\",\n", - " aberration_corrections=\"NONE\",\n", - " ephemeris_time= simulation_start_epoch )\n", - "\n", - " # Save magnitude of perturbations for both cases\n", - " if case == 'unperturbed':\n", - " dependent_variables_to_save = [ ]\n", - " if case == 'case_i':\n", - " dependent_variables_to_save = [ \n", - " propagation_setup.dependent_variable.XXXX\n", - " ]\n", - " if case == 'case_ii':\n", - " dependent_variables_to_save = [ \n", - " propagation_setup.dependent_variable.XXXX\n", - " ]\n", - "\n", - " # Create propagation settings.\n", - " propagator_settings = propagation_setup.propagator.translational(\n", - " central_bodies,\n", - " acceleration_models,\n", - " bodies_to_propagate,\n", - " system_initial_state,\n", - " simulation_end_epoch,\n", - " output_variables = dependent_variables_to_save\n", - " )\n", - "\n", - " # Create numerical integrator settings.\n", - " fixed_step_size = 10.0\n", - " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", - " simulation_start_epoch,\n", - " fixed_step_size\n", - " )\n", - "\n", - " ###########################################################################\n", - " # PROPAGATE ORBIT #########################################################\n", - " ###########################################################################\n", - "\n", - " # Create simulation object and propagate dynamics.\n", - " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", - " bodies, integrator_settings, propagator_settings)\n", - " \n", - " simulation_results_dict[case] = dynamics_simulator.state_history\n", - " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", - "\n", - " ###########################################################################\n", - " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", - " ###########################################################################\n", - "\n", - " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", - " first_time_step=list(simulation_results_dict[case].keys())[0]\n", - "\n", - " print(\n", - " f\"\"\"\n", - " JUICE Propagation Results of {case}.\n", - "\n", - " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", - " Final Cartesian state of JUICE is [m]: \\n{\n", - " simulation_results_dict[case][final_time_step][:]}\n", - "\n", - " \"\"\"\n", - " )\n", - "\n", - " ###########################################################################\n", - " # SAVE RESULTS ############################################################\n", - " ###########################################################################\n", - " \n", - "# save2txt(solution=simulation_result,\n", - "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", - "# directory=\"./\", # default = \"./\" \n", - "# column_names=None, # default = None \n", - "# )\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Pre-process Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "deletable": false - }, - "outputs": [], - "source": [ - "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", - "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", - "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", - "\n", - "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", - "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", - "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", - "\n", - "difference_in_cartesian_position = XXXX" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": false, - "editable": false - }, - "source": [ - "## Plot Results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Edit Metadata", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From f35e4baac1465086604cbb69a4e78fe51fd94712 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 21:48:28 +0100 Subject: [PATCH 34/57] Reverted to previous commit to prevent erroneous file deletion. --- ...AE4868_example_notebook_update20201025.pdf | Bin 0 -> 408276 bytes code/project1/src/juice_propagation_Q1.ipynb | 355 ++++++++++++++++++ code/project1/src/juice_propagation_Q4.ipynb | 335 +++++++++++++++++ ...AE4868_example_notebook_update20201025.pdf | Bin 0 -> 408276 bytes code/project2/src/juice_propagation_Q1.ipynb | 355 ++++++++++++++++++ code/project2/src/juice_propagation_Q4.ipynb | 335 +++++++++++++++++ ...AE4868_example_notebook_update20201025.pdf | Bin 0 -> 408275 bytes code/project3/src/juice_propagation_Q1.ipynb | 355 ++++++++++++++++++ code/project3/src/juice_propagation_Q4.ipynb | 335 +++++++++++++++++ 9 files changed, 2070 insertions(+) create mode 100644 code/project1/src/AE4868_example_notebook_update20201025.pdf create mode 100644 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z)2MB_=hU)Xh&x?AZsfv#tzt*g%(Rs5&Oop2rkcn7sc^jqk*BoCGD5)>-@xCk?zoME zPk$f7;ayB0zKDxsXL?KC?bCbIzIO>1lyLP(i_YcSLK{K7mWk+L zW0-)kqX)+uslM5zs%$OT^9|89^06UzLfRYoMP+`EDDpom5|_ ztqqw3CMU_1`EPtFtt$WUQti#au+v{y^dFWoJxR_c$=jI9N3nF!thIBx$J3(U8|NwK zA$<49`HGP(O39HiDL42xnVpAU0-^T+FmcQF^s^f`Z{q{&fKN$6ra#O_ti=#8)?qTt zSyr%mfvs77bX}#?q+OSAl$a2qKYY$6F|1DeEne);uEDQX0k4q+gL_1djvElT5Ni1 zvzZ*1ONTZxO9`0mqoz2DA@8PWZt8<24Pe?h0mW1ZH9|;k$QOAy4>Bl=!Jt`&#&?Xw z<%Cqz;)tak;)q4_%1oy0XL@8@>IWce+PP7aJmRuwR0}un-)X)c8^WIAm(QiaFQM?l z7pkuoN1dM`e4AN1`Us&Zo_ws@0Xq*xz|AZbg4ugJv`+kTG9g3PU%KpGFur}8uni24 zRW=VwyW%XDF!FmHspLpvjPEa4%odEL%auTzLl-R#nBh=i?2pUcRT=Oj95 zHQuu-4?c+tYrSmOUStVEFxi*y*B#0#!KJ1Kx>M8c_I60Q;L^>%wU57p1Cqaa@M8D( z-R|l-{EIMr7fgob*p50faF;Mw@XPC3IKuM(9vFDI|Idj*SHtQfmxhxQg!W$|R}C&j z$B$05{{kNUEoBTuEqVC_EG-2rxrO<7fEL_90V_dn9-y$fxs`z6-vcQ2|Gr_wrD@}3 zg~7wk!=+=uL(50YYxG|x5k?SG(RM|G~Fwr;mama&uq4#} z<$WuU5NQ7ks2dXamvt6B`S~5PtI1gd^*b*+@?F;Q_tJZ&Q1R!{%lwA9>-IE{mHr3) zg-gAk4F<=aKUMWb_U@&;U+*3%v~1q1ir`Y%+f;OhC@sq_7XI~$)7VyY?=CM>RKlxo spbty$UpnUG 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create environment, vehicle, accelerations, and propagation settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create settings for celestial bodies\n", + "bodies_to_create = [\"Ganymede\"]\n", + "global_frame_origin = \"Ganymede\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation)\n", + "\n", + "# Add Ganymede exponential atmosphere\n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + " \n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated, and their central bodies of propagation.\n", + "bodies_to_propagate = [\"JUICE\"]\n", + "central_bodies = [\"Ganymede\"]\n", + "\n", + "# Define accelerations acting on vehicle.\n", + "acceleration_settings_on_vehicle = dict(\n", + " XXXX\n", + ")\n", + "\n", + "# Create global accelerations dictionary.\n", + "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Define initial state.\n", + "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.keplerian_state(\n", + " \"JUICE\", \"Ganymede\")\n", + " ]\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + " \n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Orbit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings, True)\n", + "\n", + "simulation_result = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Print final propagation time and state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + "###########################################################################\n", + "\n", + "final_time_step=list(simulation_result.keys())[-1]\n", + "first_time_step=list(simulation_result.keys())[0]\n", + "\n", + "print(\n", + " f\"\"\"\n", + "JUICE Propagation Results.\n", + "\n", + "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + "Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_result[final_time_step][:]}\n", + "\n", + " \"\"\"\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Save Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# SAVE RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "save2txt(solution=simulation_result,\n", + " filename=\"JUICEPropagationHistory_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )\n", + "\n", + "save2txt(solution=dependent_variables,\n", + " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results\n", + "\n", + "For inspiration see: \n", + "\n", + "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PLOT RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "# Extract time and Kepler elements from dependent variables\n", + "kepler_elements = np.vstack(list(dependent_variables.values())\n", + " \n", + "# Kepler Elements\n", + "# 0: semi-major axis\n", + "# 1: eccentricity\n", + "# 2: inclination\n", + "# 3: argument of periapsis\n", + "# 4: right ascension of the ascending node\n", + "# 5: true anomaly\n", + "\n", + "time = dependent_variables.keys()\n", + "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", + "\n", + "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", + "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", + "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", + "\n", + "\n", + "# Set font size of figures\n", + "font_size = 20\n", + " \n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project2/src/juice_propagation_Q4.ipynb b/code/project2/src/juice_propagation_Q4.ipynb new file mode 100644 index 0000000..7b3bbcd --- /dev/null +++ b/code/project2/src/juice_propagation_Q4.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create Environment and Vehicle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create body objects.\n", + "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", + "global_frame_origin = \"SSB\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation) \n", + "\n", + "# Add Ganymede exponential atmosphere \n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + "\n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings );" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Dynamics for various cases" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "cases = ['unperturbed', 'case_i', 'case_ii']\n", + "\n", + "\"\"\"\n", + "unperturbed: Ganymede PM\n", + "\n", + "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", + "\n", + "case_ii: Ganymede PM, Ganymede aerodynamic\n", + "\"\"\"\n", + "\n", + "simulation_results_dict = dict()\n", + "dependent_variables_dict = dict()\n", + "for case in cases: \n", + " ###########################################################################\n", + " # CREATE ACCELERATIONS ####################################################\n", + " ###########################################################################\n", + "\n", + " # Define bodies that are propagated.\n", + " bodies_to_propagate = [\"JUICE\"]\n", + "\n", + " # Define central bodies.\n", + " central_bodies = [\"Ganymede\"]\n", + "\n", + " # Define accelerations acting on vehicle.\n", + " if case == 'unperturbed':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + " if case == 'case_i':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX,\n", + " Jupiter = XXXX\n", + " )\n", + " if case == 'case_ii':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + "\n", + " # Create global accelerations dictionary.\n", + " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + " # Create acceleration models.\n", + " acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + " ###########################################################################\n", + " # CREATE PROPAGATION SETTINGS #############################################\n", + " ###########################################################################\n", + "\n", + " # Define initial state.\n", + " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + " # Save magnitude of perturbations for both cases\n", + " if case == 'unperturbed':\n", + " dependent_variables_to_save = [ ]\n", + " if case == 'case_i':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + " if case == 'case_ii':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + "\n", + " # Create propagation settings.\n", + " propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + " )\n", + "\n", + " # Create numerical integrator settings.\n", + " fixed_step_size = 10.0\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + " )\n", + "\n", + " ###########################################################################\n", + " # PROPAGATE ORBIT #########################################################\n", + " ###########################################################################\n", + "\n", + " # Create simulation object and propagate dynamics.\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + " \n", + " simulation_results_dict[case] = dynamics_simulator.state_history\n", + " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", + "\n", + " ###########################################################################\n", + " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + " ###########################################################################\n", + "\n", + " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", + " first_time_step=list(simulation_results_dict[case].keys())[0]\n", + "\n", + " print(\n", + " f\"\"\"\n", + " JUICE Propagation Results of {case}.\n", + "\n", + " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + " Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_results_dict[case][final_time_step][:]}\n", + "\n", + " \"\"\"\n", + " )\n", + "\n", + " ###########################################################################\n", + " # SAVE RESULTS ############################################################\n", + " ###########################################################################\n", + " \n", + "# save2txt(solution=simulation_result,\n", + "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", + "# directory=\"./\", # default = \"./\" \n", + "# column_names=None, # default = None \n", + "# )\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Pre-process Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", + "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", + "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", + "\n", + "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", + "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", + "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", + "\n", + "difference_in_cartesian_position = XXXX" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git 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zPIet@dJo%j(sdFy;)vVZ;Mz^$*K~x1vo4I^>lGT6%Z_+(?B;!QZO8cPN+`copxOdU zVLgpqXWLDg({${o+bto+=wJ^dcngvAmeG=dnm-D4MQOOa#E*;8BpOP&S-JApcy*WTTG_P|CTPql`a$Z;#fE zS4Amk8G0F{p(uO~OT+DY}()WDIvlg$)y%6&7i<&Hc z&zK60h zRzw@m&=c!ftYfp1Bhl6K$_w^r=x&voV7XRB#dYd%LKmwM{py@Bp-W=y5@tEW;nxdM zrS^OFt%KTiLaYwvv*0J5bj5OiL(vP%iw*B=;EtFtpJT2o{dy-3=4w~HQJNdQoFH2r zJp>L;co!WDQ(Oe1Q{Tc%ZYnVrXu{B2o|%h}Ofu|ne5ISJ^U6=x<}n_IFdD;%C<6u? zWdZj?7@c86$F~dfvS)I#ksPtMTyrY-pJ3~<*klUKa0thq%nps$KTMF_4 zfxLo3Kpr78K1)HXm!#7Fy~CJG^P{g71~-74OV^N_hL?uN_&*FJyxjjtKLMvkIi=M} z`p=yj*3$Dhv=>@+6cr6U9PsF)6f`XmqoTe=qMqS~%Z9Ia|HRZXB9jUQ^i^a@ei8QD z{k@YKq;sR9GC_>@k5d5`R&R_a^-18VVTrJK8|S$jwuA=TugT&Iy}6_Zeo>s zY4@af=`S{|A+j`6KRCsg(W4eT&SyB~FO*Z^j>9cYJh=H#Sl18CKXO@Gn4}Ufqn0wX zJ0-3;sfO}rmP*@LDXTV42bz{cZ;pa(Hm@f2AI|vZ_EM1!X|QHkj&l*+N%q(vj>cWT zEAf7;?3s||qyJb=6>;-|wm-Xwp9p1d`g8t<5sr 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create environment, vehicle, accelerations, and propagation settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create settings for celestial bodies\n", + "bodies_to_create = [\"Ganymede\"]\n", + "global_frame_origin = \"Ganymede\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation)\n", + "\n", + "# Add Ganymede exponential atmosphere\n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + " \n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated, and their central bodies of propagation.\n", + "bodies_to_propagate = [\"JUICE\"]\n", + "central_bodies = [\"Ganymede\"]\n", + "\n", + "# Define accelerations acting on vehicle.\n", + "acceleration_settings_on_vehicle = dict(\n", + " XXXX\n", + ")\n", + "\n", + "# Create global accelerations dictionary.\n", + "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Define initial state.\n", + "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.keplerian_state(\n", + " \"JUICE\", \"Ganymede\")\n", + " ]\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + " \n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Orbit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings, True)\n", + "\n", + "simulation_result = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Print final propagation time and state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + "###########################################################################\n", + "\n", + "final_time_step=list(simulation_result.keys())[-1]\n", + "first_time_step=list(simulation_result.keys())[0]\n", + "\n", + "print(\n", + " f\"\"\"\n", + "JUICE Propagation Results.\n", + "\n", + "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + "Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_result[final_time_step][:]}\n", + "\n", + " \"\"\"\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Save Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# SAVE RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "save2txt(solution=simulation_result,\n", + " filename=\"JUICEPropagationHistory_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )\n", + "\n", + "save2txt(solution=dependent_variables,\n", + " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results\n", + "\n", + "For inspiration see: \n", + "\n", + "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PLOT RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "# Extract time and Kepler elements from dependent variables\n", + "kepler_elements = np.vstack(list(dependent_variables.values())\n", + " \n", + "# Kepler Elements\n", + "# 0: semi-major axis\n", + "# 1: eccentricity\n", + "# 2: inclination\n", + "# 3: argument of periapsis\n", + "# 4: right ascension of the ascending node\n", + "# 5: true anomaly\n", + "\n", + "time = dependent_variables.keys()\n", + "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", + "\n", + "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", + "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", + "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", + "\n", + "\n", + "# Set font size of figures\n", + "font_size = 20\n", + " \n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project1/src/juice_propagation_Q4.ipynb b/code/project1/src/juice_propagation_Q4.ipynb new file mode 100644 index 0000000..7b3bbcd --- /dev/null +++ b/code/project1/src/juice_propagation_Q4.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create Environment and Vehicle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create body objects.\n", + "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", + "global_frame_origin = \"SSB\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation) \n", + "\n", + "# Add Ganymede exponential atmosphere \n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + "\n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings );" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Dynamics for various cases" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "cases = ['unperturbed', 'case_i', 'case_ii']\n", + "\n", + "\"\"\"\n", + "unperturbed: Ganymede PM\n", + "\n", + "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", + "\n", + "case_ii: Ganymede PM, Ganymede aerodynamic\n", + "\"\"\"\n", + "\n", + "simulation_results_dict = dict()\n", + "dependent_variables_dict = dict()\n", + "for case in cases: \n", + " ###########################################################################\n", + " # CREATE ACCELERATIONS ####################################################\n", + " ###########################################################################\n", + "\n", + " # Define bodies that are propagated.\n", + " bodies_to_propagate = [\"JUICE\"]\n", + "\n", + " # Define central bodies.\n", + " central_bodies = [\"Ganymede\"]\n", + "\n", + " # Define accelerations acting on vehicle.\n", + " if case == 'unperturbed':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + " if case == 'case_i':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX,\n", + " Jupiter = XXXX\n", + " )\n", + " if case == 'case_ii':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + "\n", + " # Create global accelerations dictionary.\n", + " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + " # Create acceleration models.\n", + " acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + " ###########################################################################\n", + " # CREATE PROPAGATION SETTINGS #############################################\n", + " ###########################################################################\n", + "\n", + " # Define initial state.\n", + " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + " # Save magnitude of perturbations for both cases\n", + " if case == 'unperturbed':\n", + " dependent_variables_to_save = [ ]\n", + " if case == 'case_i':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + " if case == 'case_ii':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + "\n", + " # Create propagation settings.\n", + " propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + " )\n", + "\n", + " # Create numerical integrator settings.\n", + " fixed_step_size = 10.0\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + " )\n", + "\n", + " ###########################################################################\n", + " # PROPAGATE ORBIT #########################################################\n", + " ###########################################################################\n", + "\n", + " # Create simulation object and propagate dynamics.\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + " \n", + " simulation_results_dict[case] = dynamics_simulator.state_history\n", + " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", + "\n", + " ###########################################################################\n", + " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + " ###########################################################################\n", + "\n", + " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", + " first_time_step=list(simulation_results_dict[case].keys())[0]\n", + "\n", + " print(\n", + " f\"\"\"\n", + " JUICE Propagation Results of {case}.\n", + "\n", + " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + " Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_results_dict[case][final_time_step][:]}\n", + "\n", + " \"\"\"\n", + " )\n", + "\n", + " ###########################################################################\n", + " # SAVE RESULTS ############################################################\n", + " ###########################################################################\n", + " \n", + "# save2txt(solution=simulation_result,\n", + "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", + "# directory=\"./\", # default = \"./\" \n", + "# column_names=None, # default = None \n", + "# )\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Pre-process Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", + "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", + "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", + "\n", + "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", + "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", + "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", + "\n", + "difference_in_cartesian_position = XXXX" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git 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HcmV?d00001 diff --git a/code/project3/src/juice_propagation_Q1.ipynb b/code/project3/src/juice_propagation_Q1.ipynb new file mode 100644 index 0000000..e827102 --- /dev/null +++ b/code/project3/src/juice_propagation_Q1.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# # student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create environment, vehicle, accelerations, and propagation settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create settings for celestial bodies\n", + "bodies_to_create = [\"Ganymede\"]\n", + "global_frame_origin = \"Ganymede\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation)\n", + "\n", + "# Add Ganymede exponential atmosphere\n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + " \n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings )\n", + "\n", + "###########################################################################\n", + "# CREATE ACCELERATIONS ####################################################\n", + "###########################################################################\n", + "\n", + "# Define bodies that are propagated, and their central bodies of propagation.\n", + "bodies_to_propagate = [\"JUICE\"]\n", + "central_bodies = [\"Ganymede\"]\n", + "\n", + "# Define accelerations acting on vehicle.\n", + "acceleration_settings_on_vehicle = dict(\n", + " XXXX\n", + ")\n", + "\n", + "# Create global accelerations dictionary.\n", + "acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + "# Create acceleration models.\n", + "acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + "###########################################################################\n", + "# CREATE PROPAGATION SETTINGS #############################################\n", + "###########################################################################\n", + "\n", + "# Define initial state.\n", + "system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + "dependent_variables_to_save = [\n", + " propagation_setup.dependent_variable.keplerian_state(\n", + " \"JUICE\", \"Ganymede\")\n", + " ]\n", + "\n", + "# Create propagation settings.\n", + "propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + ")\n", + " \n", + "# Create numerical integrator settings.\n", + "fixed_step_size = 10.0\n", + "integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Orbit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# Create simulation object and propagate dynamics.\n", + "dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings, True)\n", + "\n", + "simulation_result = dynamics_simulator.state_history\n", + "dependent_variables = dynamics_simulator.dependent_variable_history" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Print final propagation time and state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + "###########################################################################\n", + "\n", + "final_time_step=list(simulation_result.keys())[-1]\n", + "first_time_step=list(simulation_result.keys())[0]\n", + "\n", + "print(\n", + " f\"\"\"\n", + "JUICE Propagation Results.\n", + "\n", + "Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + "Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_result[final_time_step][:]}\n", + "\n", + " \"\"\"\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Save Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# SAVE RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "save2txt(solution=simulation_result,\n", + " filename=\"JUICEPropagationHistory_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )\n", + "\n", + "save2txt(solution=dependent_variables,\n", + " filename=\"JUICEPropagationHistory_DependentVariables_Q1.dat\",\n", + " directory=\"./\", # default = \"./\" \n", + " column_names=None, # default = None \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results\n", + "\n", + "For inspiration see: \n", + "\n", + "https://tudat-space.readthedocs.io/en/latest/_src_first_steps/simulations/example_application_2.html#visualize-results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# PLOT RESULTS ############################################################\n", + "###########################################################################\n", + "\n", + "# Extract time and Kepler elements from dependent variables\n", + "kepler_elements = np.vstack(list(dependent_variables.values())\n", + " \n", + "# Kepler Elements\n", + "# 0: semi-major axis\n", + "# 1: eccentricity\n", + "# 2: inclination\n", + "# 3: argument of periapsis\n", + "# 4: right ascension of the ascending node\n", + "# 5: true anomaly\n", + "\n", + "time = dependent_variables.keys()\n", + "time_days = [ t / constants.JULIAN_DAY - simulation_start_epoch / constants.JULIAN_DAY for t in time ]\n", + "\n", + "ganymede_gravitational_parameter = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_gravitational_parameter( )\n", + "ganymede_normalized_c20 = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_cosine_coefficients( )[2,0]\n", + "ganymede_reference_radius = body_settings.get( \"Ganymede\" ).gravity_field_settings.get_reference_radius( )\n", + "\n", + "\n", + "# Set font size of figures\n", + "font_size = 20\n", + " \n", + "plt.rcParams.update({'font.size': font_size}) \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project3/src/juice_propagation_Q4.ipynb b/code/project3/src/juice_propagation_Q4.ipynb new file mode 100644 index 0000000..7b3bbcd --- /dev/null +++ b/code/project3/src/juice_propagation_Q4.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 1 - Propagation Settings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "# student number: 1244779 --> 1244ABC\n", + "A = XXXX\n", + "B = XXXX\n", + "C = XXXX\n", + "\n", + "simulation_start_epoch = 33.15 * constants.JULIAN_YEAR + A * 7.0 * constants.JULIAN_DAY + \\\n", + " B * constants.JULIAN_DAY + C * constants.JULIAN_DAY / 24.0\n", + "simulation_end_epoch = simulation_start_epoch + 344.0 * constants.JULIAN_DAY / 24.0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Create Environment and Vehicle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "###########################################################################\n", + "# CREATE ENVIRONMENT ######################################################\n", + "###########################################################################\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Create body objects.\n", + "bodies_to_create = [\"Ganymede\", \"Jupiter\"]\n", + "global_frame_origin = \"SSB\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "body_settings = environment_setup.get_default_body_settings(\n", + " bodies_to_create, global_frame_origin, global_frame_orientation) \n", + "\n", + "# Add Ganymede exponential atmosphere \n", + "density_scale_height = 40.0E3\n", + "density_at_zero_altitude = 2.0E-9\n", + "body_settings.get( \"Ganymede\" ).atmosphere_settings = environment_setup.atmosphere.exponential( \n", + " density_scale_height, density_at_zero_altitude)\n", + "\n", + "bodies = environment_setup.create_system_of_bodies(body_settings)\n", + "\n", + "###########################################################################\n", + "# CREATE VEHICLE ##########################################################\n", + "###########################################################################\n", + "\n", + "# Create vehicle object\n", + "bodies.create_empty_body( \"JUICE\" )\n", + "\n", + "# Set mass of vehicle\n", + "bodies.get_body( \"JUICE\" ).set_constant_mass(2000.0)\n", + "\n", + "# Create aerodynamic coefficients interface\n", + "reference_area = 100.0\n", + "drag_coefficient = 1.2\n", + "aero_coefficient_settings = environment_setup.aerodynamic_coefficients.constant(\n", + " reference_area,[drag_coefficient,0,0] )\n", + "environment_setup.add_aerodynamic_coefficient_interface(\n", + " bodies, \"JUICE\", aero_coefficient_settings );" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Propagate Dynamics for various cases" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "cases = ['unperturbed', 'case_i', 'case_ii']\n", + "\n", + "\"\"\"\n", + "unperturbed: Ganymede PM\n", + "\n", + "case_i: Ganymede PM, Jupiter SH D/O 4/0\n", + "\n", + "case_ii: Ganymede PM, Ganymede aerodynamic\n", + "\"\"\"\n", + "\n", + "simulation_results_dict = dict()\n", + "dependent_variables_dict = dict()\n", + "for case in cases: \n", + " ###########################################################################\n", + " # CREATE ACCELERATIONS ####################################################\n", + " ###########################################################################\n", + "\n", + " # Define bodies that are propagated.\n", + " bodies_to_propagate = [\"JUICE\"]\n", + "\n", + " # Define central bodies.\n", + " central_bodies = [\"Ganymede\"]\n", + "\n", + " # Define accelerations acting on vehicle.\n", + " if case == 'unperturbed':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + " if case == 'case_i':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX,\n", + " Jupiter = XXXX\n", + " )\n", + " if case == 'case_ii':\n", + " acceleration_settings_on_vehicle = dict(\n", + " Ganymede = XXXX\n", + " )\n", + "\n", + " # Create global accelerations dictionary.\n", + " acceleration_settings = {\"JUICE\": acceleration_settings_on_vehicle}\n", + "\n", + " # Create acceleration models.\n", + " acceleration_models = propagation_setup.create_acceleration_models(\n", + " bodies, acceleration_settings, bodies_to_propagate, central_bodies)\n", + "\n", + "\n", + " ###########################################################################\n", + " # CREATE PROPAGATION SETTINGS #############################################\n", + " ###########################################################################\n", + "\n", + " # Define initial state.\n", + " system_initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"JUICE\",\n", + " observer_body_name=\"Ganymede\",\n", + " reference_frame_name=\"ECLIPJ2000\",\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= simulation_start_epoch )\n", + "\n", + " # Save magnitude of perturbations for both cases\n", + " if case == 'unperturbed':\n", + " dependent_variables_to_save = [ ]\n", + " if case == 'case_i':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + " if case == 'case_ii':\n", + " dependent_variables_to_save = [ \n", + " propagation_setup.dependent_variable.XXXX\n", + " ]\n", + "\n", + " # Create propagation settings.\n", + " propagator_settings = propagation_setup.propagator.translational(\n", + " central_bodies,\n", + " acceleration_models,\n", + " bodies_to_propagate,\n", + " system_initial_state,\n", + " simulation_end_epoch,\n", + " output_variables = dependent_variables_to_save\n", + " )\n", + "\n", + " # Create numerical integrator settings.\n", + " fixed_step_size = 10.0\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " simulation_start_epoch,\n", + " fixed_step_size\n", + " )\n", + "\n", + " ###########################################################################\n", + " # PROPAGATE ORBIT #########################################################\n", + " ###########################################################################\n", + "\n", + " # Create simulation object and propagate dynamics.\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings)\n", + " \n", + " simulation_results_dict[case] = dynamics_simulator.state_history\n", + " dependent_variables_dict[case] = dynamics_simulator.dependent_variable_history\n", + "\n", + " ###########################################################################\n", + " # PRINT FINAL PROPAGATION TIME AND STATE ##################################\n", + " ###########################################################################\n", + "\n", + " final_time_step=list(simulation_results_dict[case].keys())[-1]\n", + " first_time_step=list(simulation_results_dict[case].keys())[0]\n", + "\n", + " print(\n", + " f\"\"\"\n", + " JUICE Propagation Results of {case}.\n", + "\n", + " Final propagation time of JUICE [s]: {simulation_end_epoch}\n", + " Final Cartesian state of JUICE is [m]: \\n{\n", + " simulation_results_dict[case][final_time_step][:]}\n", + "\n", + " \"\"\"\n", + " )\n", + "\n", + " ###########################################################################\n", + " # SAVE RESULTS ############################################################\n", + " ###########################################################################\n", + " \n", + "# save2txt(solution=simulation_result,\n", + "# filename=\"JUICEPropagationHistory_Q4_\" + case + \".dat\",\n", + "# directory=\"./\", # default = \"./\" \n", + "# column_names=None, # default = None \n", + "# )\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Pre-process Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": false + }, + "outputs": [], + "source": [ + "simulation_result_unperturbed = simulation_results_dict[ 'unperturbed']\n", + "simulation_result_i = simulation_results_dict[ 'case_i' ]\n", + "simulation_result_ii = simulation_results_dict[ 'case_ii' ]\n", + "\n", + "dependent_variables_unperturbed = dependent_variables_dict[ 'unperturbed' ]\n", + "dependent_variables_i = dependent_variables_dict[ 'case_i' ]\n", + "dependent_variables_ii = dependent_variables_dict[ 'case_ii' ]\n", + "\n", + "difference_in_cartesian_position = XXXX" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "## Plot Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From b09c3d0d318672eba17e5f5eca8a86020b714061 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Mon, 9 Nov 2020 22:48:50 +0100 Subject: [PATCH 35/57] Changed pytest syntax. --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index a53040a..c08af5f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -22,7 +22,7 @@ install: # run tests script: - "python -m pytest -v test/*" + "python -m pytest" notifications: email: false From 942d63c9f639229a9d062ee3a5f9d81ebeca41c1 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Tue, 10 Nov 2020 00:20:25 +0100 Subject: [PATCH 36/57] Included matplotlib module. --- linux_environment.yml | 1 + windows_environment.yml | 1 + 2 files changed, 2 insertions(+) diff --git a/linux_environment.yml b/linux_environment.yml index 1045a24..8f8312a 100644 --- a/linux_environment.yml +++ b/linux_environment.yml @@ -12,6 +12,7 @@ dependencies: - conda: - pytest>=3.0 - nbconvert + - matplotlib - pip - pip: # works for regular pip packages diff --git a/windows_environment.yml b/windows_environment.yml index 1045a24..8f8312a 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -12,6 +12,7 @@ dependencies: - conda: - pytest>=3.0 - nbconvert + - matplotlib - pip - pip: # works for regular pip packages From 77b68d6ab449a3a4216da8b81e1b40159d88d03a Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Tue, 10 Nov 2020 14:59:12 +0100 Subject: [PATCH 37/57] Included nb_conda_kernels in yml for travis CI. --- windows_environment.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/windows_environment.yml b/windows_environment.yml index 8f8312a..e51ae55 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -13,8 +13,9 @@ dependencies: - pytest>=3.0 - nbconvert - matplotlib + - nb_conda_kernels - pip - pip: # works for regular pip packages - pdflatex - - testbook \ No newline at end of file + - testbook From 3529ac186a26c77bfa88791cc9038226d6ff6871 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Tue, 10 Nov 2020 15:01:00 +0100 Subject: [PATCH 38/57] Included ipykernel module to yaml for travis-CI. --- windows_environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/windows_environment.yml b/windows_environment.yml index e51ae55..b413f64 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -14,6 +14,7 @@ dependencies: - nbconvert - matplotlib - nb_conda_kernels + - ipykernel - pip - pip: # works for regular pip packages From 15c0ec51c9e3e2660ec5b33a704f1efce7e452cf Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Tue, 10 Nov 2020 16:21:09 +0100 Subject: [PATCH 39/57] Included tudatpy as duplicate for travis-CI. --- windows_environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/windows_environment.yml b/windows_environment.yml index b413f64..3ec4fae 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -15,6 +15,7 @@ dependencies: - matplotlib - nb_conda_kernels - ipykernel + - tudatpy - pip - pip: # works for regular pip packages From be3a99b20857c000f34301555fe8598500cca1f7 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Tue, 10 Nov 2020 17:19:48 +0100 Subject: [PATCH 40/57] Test whether module can be ommitted. --- windows_environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/windows_environment.yml b/windows_environment.yml index 3ec4fae..c8e8cc2 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -13,7 +13,7 @@ dependencies: - pytest>=3.0 - nbconvert - matplotlib - - nb_conda_kernels + #- nb_conda_kernels - ipykernel - tudatpy - pip From 5553e95bed6c90304fd09f2ae0c6c64a0d054d9f Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Tue, 10 Nov 2020 17:20:20 +0100 Subject: [PATCH 41/57] Test whether tudatpy dependency can be ommited. --- windows_environment.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/windows_environment.yml b/windows_environment.yml index c8e8cc2..1ca77fc 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -6,14 +6,14 @@ channels: - tudat-team - conda dependencies: -- tudatpy +#- tudatpy - anaconda - nb_conda - conda: - pytest>=3.0 - nbconvert - matplotlib - #- nb_conda_kernels + - nb_conda_kernels - ipykernel - tudatpy - pip From 066870aeee03d048d78bcb10c956da44f9de0cb9 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Tue, 10 Nov 2020 17:21:37 +0100 Subject: [PATCH 42/57] Renamed environment file. --- windows_environment.yml => tudat-space_environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename windows_environment.yml => tudat-space_environment.yml (92%) diff --git a/windows_environment.yml b/tudat-space_environment.yml similarity index 92% rename from windows_environment.yml rename to tudat-space_environment.yml index 1ca77fc..3ec4fae 100644 --- a/windows_environment.yml +++ b/tudat-space_environment.yml @@ -6,7 +6,7 @@ channels: - tudat-team - conda dependencies: -#- tudatpy +- tudatpy - anaconda - nb_conda - conda: From 3faeae5da6786e51d83ec1ebf83f51470119755d Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Tue, 10 Nov 2020 17:22:23 +0100 Subject: [PATCH 43/57] Delete linux_environment.yml Currently there is no difference between Windows and Linux setup. MAC setup is not tested. --- linux_environment.yml | 20 -------------------- 1 file changed, 20 deletions(-) delete mode 100644 linux_environment.yml diff --git a/linux_environment.yml b/linux_environment.yml deleted file mode 100644 index 8f8312a..0000000 --- a/linux_environment.yml +++ /dev/null @@ -1,20 +0,0 @@ -# run: conda env create --file windows_environment.yml -# include new packages: conda env update --file windows_environment.yml -name: tudat-space -channels: - - conda-forge - - tudat-team - - conda -dependencies: -- tudatpy -- anaconda -- nb_conda -- conda: - - pytest>=3.0 - - nbconvert - - matplotlib -- pip -- pip: - # works for regular pip packages - - pdflatex - - testbook \ No newline at end of file From 82e446a77ed05cb13c32c4ad11a923f31422b307 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Tue, 10 Nov 2020 17:23:05 +0100 Subject: [PATCH 44/57] Updated name of environment.yml. --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index c08af5f..e6c5f81 100644 --- a/.travis.yml +++ b/.travis.yml @@ -17,7 +17,7 @@ install: - conda config --set always_yes yes --set changeps1 no - conda update -q conda - conda info -a - - conda env create --file windows_environment.yml + - conda env create --file tudat-space_environment.yml - conda activate tudat-space # run tests From 248cc03b83ecb5eca2d6b3e8505f3c387f81b360 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Tue, 10 Nov 2020 18:52:30 +0100 Subject: [PATCH 45/57] Ommitted superfluous modules in yml. --- tudat-space_environment.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/tudat-space_environment.yml b/tudat-space_environment.yml index 3ec4fae..b267b0b 100644 --- a/tudat-space_environment.yml +++ b/tudat-space_environment.yml @@ -6,14 +6,12 @@ channels: - tudat-team - conda dependencies: -- tudatpy - anaconda - nb_conda - conda: - pytest>=3.0 - nbconvert - matplotlib - - nb_conda_kernels - ipykernel - tudatpy - pip From da04897e0b701da540feb8c5c2fccb0d8e222c45 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Tue, 10 Nov 2020 19:00:20 +0100 Subject: [PATCH 46/57] Simplified readme instructions. --- README.md | 25 +++++++------------------ 1 file changed, 7 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index 756d20d..c1284db 100644 --- a/README.md +++ b/README.md @@ -10,12 +10,14 @@ Hi, w.r.t. the original repository this repository is supplemented with: - The pdf of the example jupyter notebook is automatically integrated in the latex report. - The latex report is automatically compiled into a pdf. 1. You can easily sync with overleaf, e.g. if you do a last minute run, you just push and pull into overleaf, instead of manually uploading pictures. -2. Unit tests are written. (entire repository code can be tested with a single line) +2. Example unit tests are written that test both the python code, as well as the code inside the Jupyter notebooks. +3. All unit tests can be ran with a single command. +4. The continuous integration with Travis-CI runs all the unit tests in this repository automatically for every push towards this repository ('s master branch). If all tests are passed, the above badge is green and says "passed". **Room for improvement** -3. The `.travis.yml` file is currently not allowing for the Continuous integration (CI) testing with Travis-CI. I do not yet know how to include the `.._environment.yml` into `.travis.yml`. That is why the build status badge on top currently is gray instead of green and says "failed/canceled". -4. A unit test could be written to test a function inside a jupyter notebook. +5. The code (that I wrote) could be documented more clearly, with proper comment formatting. +6. The code that (executes and) converts the jupyter notebooks to pdf could loop through all notebooks in the respective `/src/`folder automatically in a `try` `catch` block to automatically skip new notebooks that do not yet compile. This would prevent the user from having to specify which notebooks should be compiled. ## Usage: do once @@ -24,18 +26,9 @@ Hi, w.r.t. the original repository this repository is supplemented with: cd /home// ``` -1. To use this package, first make a new conda environment and activate (it this automatically installs everything you need) -1.1 For Windows (is currently the same as for linux): +1. To use this package, first make a new conda environment (it this automatically installs everything you need). ``` -conda env create --file windows_environment.yml -``` -1.2 For Linux: -``` -conda env create --file linux_environment.yml -``` -2. Instal jupyter-lab with command: -``` -jupyter-lab +conda env create --file tudat-space_environment.yml ``` ## Usage: do every time you start Anaconda: @@ -44,10 +37,6 @@ jupyter-lab ``` conda activate tudat-space ``` -4. Open jupyter lab -``` -jupyter-lab -``` ## Usage: do every run: From d75896abf9367df6cfba50c4900e0e8e548dcd21 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Tue, 17 Nov 2020 21:24:39 +0100 Subject: [PATCH 47/57] Wrote more documentation. --- code/project1/src/Compile_latex.py | 26 +++++++- code/project1/src/Plot_to_tex.py | 73 +++++++++++++++------- code/project1/src/Run_jupyter_notebooks.py | 21 ++++++- code/project1/src/__main__.py | 23 +++++-- 4 files changed, 112 insertions(+), 31 deletions(-) diff --git a/code/project1/src/Compile_latex.py b/code/project1/src/Compile_latex.py index 7c69d94..4102954 100644 --- a/code/project1/src/Compile_latex.py +++ b/code/project1/src/Compile_latex.py @@ -8,6 +8,12 @@ class Compile_latex: def __init__(self,project_nr,latex_filename): + """ Performs the local compilation of the latex report of this project nr + + Keyword arguments: + project_nr -- the numberr identifying which project is being ran and compiled + latex_filename -- name of the main latex .tex file that manages the latex document + """ self.script_dir = self.get_script_dir() relative_dir = f'latex/project{project_nr}/' self.compile_latex(relative_dir,latex_filename) @@ -16,16 +22,33 @@ def __init__(self,project_nr,latex_filename): # runs jupyter notebook def compile_latex(self,relative_dir,latex_filename): + """ Executes a commandline line to compile the latex report + + Keyword arguments: + relative_dir -- the relative dir towards the latex main .tex file + latex_filename -- name of the main latex .tex file that manages the latex document + """ os.system(f'pdflatex {relative_dir}{latex_filename}') def clean_up_after_compilation(self,latex_filename): + """ Removes the unneeded files that were generated during latex to pdf compilation. + + Keyword arguments: + latex_filename -- name of the main latex .tex file that manages the latex document + """ latex_filename_without_extention = latex_filename[:-4] - print(f'latex_filename_without_extention={latex_filename_without_extention}') self.delete_file_if_exists(f'{latex_filename_without_extention}.aux') self.delete_file_if_exists(f'{latex_filename_without_extention}.log') self.delete_file_if_exists(f'texput.log') def move_pdf_into_latex_dir(self,relative_dir,latex_filename): + """ Moves the compiled/generated pdf file from the root of this repository to the + relative latex directory of this project. + + Keyword arguments: + relative_dir -- the relative dir towards the latex main .tex file + latex_filename -- name of the main latex .tex file that manages the latex document + """ pdf_filename = f'{latex_filename[:-4]}.pdf' destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}' @@ -35,6 +58,7 @@ def move_pdf_into_latex_dir(self,relative_dir,latex_filename): print("Error while moving file ", pdf_filename) def delete_file_if_exists(self,filename): + """ Deletes files if they exist """ try: os.remove(filename) except: diff --git a/code/project1/src/Plot_to_tex.py b/code/project1/src/Plot_to_tex.py index 7ec9a4c..47eb40f 100644 --- a/code/project1/src/Plot_to_tex.py +++ b/code/project1/src/Plot_to_tex.py @@ -1,35 +1,60 @@ -### Call this from another file, for project 11, question 3b: -### from Plot_to_tex import Plot_to_tex as plt_tex -### multiple_y_series = np.zeros((nrOfDataSeries,nrOfDataPoints), dtype=int); # actually fill with data -### lineLabels = [] # add a label for each dataseries -### plt_tex.plotMultipleLines(plt_tex,single_x_series,multiple_y_series,"x-axis label [units]","y-axis label [units]",lineLabels,"3b",4,11) -### 4b=filename -### 4 = position of legend, e.g. top right. -### -### For a single line, use: -### plt_tex.plotSingleLine(plt_tex,range(0, len(dataseries)),dataseries,"x-axis label [units]","y-axis label [units]",lineLabel,"3b",4,11) - -### You can also plot a table directly into latex, see example_create_a_table(..) -### -### Then put it in latex with for example: -###\begin{table}[H] -### \centering -### \caption{Results some computation.}\label{tab:some_computation} -### \begin{tabular}{|c|c|} % remember to update this to show all columns of table -### \hline -### \input{latex/project3/tables/q2.txt} -### \end{tabular} -###\end{table} -import random +''' +Plots incoming images and/or tables to a latex report with a certain layout. + + +Attributes +-------------- +script_dir : str + the absolute path of the directory that contains this script + + +Methods +------------ +plotSingleLine(x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr): + Outputs a plot with a single line to a latex report + +plotMultipleLines(x,y_series,x_label,y_label,label,filename,legendPosition,project_nr): + Outputs a plot with mulltiple lines to a latex report + +get_cmap(n, name='hsv'): + Returns a function that maps each index in 0, 1, ..., n-1 to a distinct RGB color; + +generateLineTypes(y_series): + Generates returns a list of a vissible line type for each incoming line/y_series + +put_table_in_tex(table_matrix,filename,project_nr): + Outputs a table into a latex report + +example_create_a_table(): + Example method that creates a python table that can be exported into a latex report + +get_script_dir(self): + returns the absolute path of the directory that continas this script +''' + +''' +Example of how to include an exported table into your latex report. + +\begin{table}[H] + \centering + \caption{Results some computation.}\label{tab:some_computation} + \begin{tabular}{|c|c|} % remember to update this to show all columns of table + \hline + \input{latex/project3/tables/q2.txt} + \end{tabular} +\end{table} +''' from matplotlib import lines import matplotlib.pyplot as plt import numpy as np import os +import random + class Plot_to_tex: def __init__(self): + ''' constructs the absolute path of the directory that contains this script ''' self.script_dir = self.get_script_dir() - print("Created main") # plot graph (legendPosition = integer 1 to 4) def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr): diff --git a/code/project1/src/Run_jupyter_notebooks.py b/code/project1/src/Run_jupyter_notebooks.py index 4f2fcb0..8de2225 100644 --- a/code/project1/src/Run_jupyter_notebooks.py +++ b/code/project1/src/Run_jupyter_notebooks.py @@ -10,6 +10,24 @@ def __init__(self): self.script_dir = self.get_script_dir() print("Created main") + def run_jupyter_notebooks(self,project_nr,notebook_names): + '''runs a jupyter notebook''' + notebook_path = f'code/project{project_nr}/src/' + + for notebook_name in notebook_names: + self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}') + + def convert_notebooks_to_pdf(self,project_nr,notebook_names): + '''converts a jupyter notebook to pdf''' + notebook_path = f'code/project{project_nr}/src/' + + for notebook_name in notebook_names: + self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}') + + def compile_latex_report(self,project_nr): + '''compiles latex code to pdf''' + compile_latex =Compile_latex(project_nr ,'main.tex') + # runs jupyter notebook def run_notebook(self,notebook_filename): @@ -22,7 +40,8 @@ def run_notebook(self,notebook_filename): ep = ExecutePreprocessor(timeout=600, kernel_name='python3') # Execute - ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}/../../../'}}) + #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}}) + ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}}) # Save output notebook with open(notebook_filename, 'w', encoding='utf-8') as f: diff --git a/code/project1/src/__main__.py b/code/project1/src/__main__.py index 0e7d00a..95a6cad 100644 --- a/code/project1/src/__main__.py +++ b/code/project1/src/__main__.py @@ -1,5 +1,18 @@ -import os +''' +Runs the main code. + +First it runs the notebooks in this directory +Then it converts those notebooks to pdf +This is followed by compiling the latex report of this project to pdf. + +For illustration purposes, a genetic algorithm is also executed that +plots some images into the latex report. Since the report is compiled +before the genetic algorithm is ran, the new results are only included +after the second of this main + +''' from .Main import Main +import os print(f'Hi, I\'ll be running the main code, and I\'ll let you know when I\'m done.') project_nr = 1 @@ -21,7 +34,7 @@ ##############runs arbitrary genetic algorithm, can be deleted########### ################################################################ # run a genetic algorithm to create some data for a plot. -print("now running a") +print("Running method a of Main.py to execute some genetic algorithm") res = main.do_run_a() # plot some graph with a single line, general form is: @@ -29,11 +42,11 @@ # main.plt_tex.plotSingleLine(plt_tex,range(0, len(res)),res,"[runs]]","fitness [%]","run 1","4a",4,project_nr) # run a genetic algorithm to create some data for another plot. -print("now running b") +print("Running method 4b of Main.py to execute some genetic algorithm") main.do4b(project_nr) # run a genetic algorithm to create some data for another plot. -print("now running 4c") +print("Running method 4c of Main.py to execute some genetic algorithm") main.do4c(project_nr) -print(f'Done.') \ No newline at end of file +print(f'Done with runing code.') \ No newline at end of file From 985caed2dbcd81c362154a93cc2c08e8eb4b838a Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Wed, 18 Nov 2020 14:32:54 +0100 Subject: [PATCH 48/57] Included pyment generated docstrings. --- code/project1/src/Compile_latex.py | 58 +++++---- code/project1/src/Main.py | 93 +++++++++++++-- code/project1/src/Plot_to_tex.py | 130 +++++++++++---------- code/project1/src/Run_jupyter_notebooks.py | 46 ++++++-- code/project1/src/__main__.py | 1 + 5 files changed, 224 insertions(+), 104 deletions(-) diff --git a/code/project1/src/Compile_latex.py b/code/project1/src/Compile_latex.py index 4102954..c331705 100644 --- a/code/project1/src/Compile_latex.py +++ b/code/project1/src/Compile_latex.py @@ -1,53 +1,58 @@ -# runs a jupyter notebook and converts it to pdf - +from nbconvert.preprocessors import ExecutePreprocessor import os import shutil import nbformat -from nbconvert.preprocessors import ExecutePreprocessor + class Compile_latex: + """Runs jupyter notebooks, converts them to pdf, + exports the notebook pdfs to latex and compiles the + latex report of the incoming project nr""" - def __init__(self,project_nr,latex_filename): - """ Performs the local compilation of the latex report of this project nr - Keyword arguments: - project_nr -- the numberr identifying which project is being ran and compiled - latex_filename -- name of the main latex .tex file that manages the latex document + def __init__(self,project_nr,latex_filename): + """Constructs attributes used throughout latex compilation + + :param project_nr: the numberr identifying which project is being ran and compiled + :param latex_filename: name of the main latex .tex file that manages the latex document """ + self.script_dir = self.get_script_dir() relative_dir = f'latex/project{project_nr}/' self.compile_latex(relative_dir,latex_filename) self.clean_up_after_compilation(latex_filename) self.move_pdf_into_latex_dir(relative_dir,latex_filename) - # runs jupyter notebook + def compile_latex(self,relative_dir,latex_filename): - """ Executes a commandline line to compile the latex report + """Executes a commandline line to compile the latex report + + :param relative_dir: the relative dir towards the latex main .tex file + :param latex_filename: name of the main latex .tex file that manages the latex document - Keyword arguments: - relative_dir -- the relative dir towards the latex main .tex file - latex_filename -- name of the main latex .tex file that manages the latex document """ os.system(f'pdflatex {relative_dir}{latex_filename}') - + + def clean_up_after_compilation(self,latex_filename): - """ Removes the unneeded files that were generated during latex to pdf compilation. + """Removes the unneeded files that were generated during latex to pdf compilation. + + :param latex_filename: name of the main latex .tex file that manages the latex document - Keyword arguments: - latex_filename -- name of the main latex .tex file that manages the latex document """ latex_filename_without_extention = latex_filename[:-4] self.delete_file_if_exists(f'{latex_filename_without_extention}.aux') self.delete_file_if_exists(f'{latex_filename_without_extention}.log') self.delete_file_if_exists(f'texput.log') + def move_pdf_into_latex_dir(self,relative_dir,latex_filename): - """ Moves the compiled/generated pdf file from the root of this repository to the + """Moves the compiled/generated pdf file from the root of this repository to the relative latex directory of this project. - Keyword arguments: - relative_dir -- the relative dir towards the latex main .tex file - latex_filename -- name of the main latex .tex file that manages the latex document + :param relative_dir: param latex_filename: + :param latex_filename: name of the main latex .tex file that manages the latex document + """ pdf_filename = f'{latex_filename[:-4]}.pdf' destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}' @@ -57,16 +62,23 @@ def move_pdf_into_latex_dir(self,relative_dir,latex_filename): except: print("Error while moving file ", pdf_filename) + def delete_file_if_exists(self,filename): - """ Deletes files if they exist """ + """Deletes files if they exist + + :param filename: name of file that will be deleted if it exists in the root of this repository + + """ try: os.remove(filename) except: print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.') + def get_script_dir(self): - ''' returns the directory of this script regardles of from which level the code is executed ''' + """returns the directory of this script regardles of from which level the code is executed""" return os.path.dirname(__file__) + if __name__ == '__main__': main = Compile_latex() \ No newline at end of file diff --git a/code/project1/src/Main.py b/code/project1/src/Main.py index b69ff94..7b24eb2 100644 --- a/code/project1/src/Main.py +++ b/code/project1/src/Main.py @@ -1,21 +1,32 @@ -# Example code that creates plots directly in report -# Code is an implementation of a genetic algorithm -import random +from .Compile_latex import Compile_latex +from .Plot_to_tex import Plot_to_tex as plt_tex +from .Run_jupyter_notebooks import Run_jupyter_notebook + from matplotlib import pyplot as plt from matplotlib import lines import matplotlib.pyplot as plt import numpy as np - -from .Compile_latex import Compile_latex -from .Plot_to_tex import Plot_to_tex as plt_tex -from .Run_jupyter_notebooks import Run_jupyter_notebook +import random # define global variables for genetic algorithm example string_length = 100 mutation_chance= 1.0/string_length max_iterations = 1500 + class Main: + """Runs jupiter notebooks, then compiles them to pdf + Exports those notebook pdfs to the latex of this project + nr, then compiles the latex report to pdf. + + Als runs a genetic algorithm in conventional .py files + and exports them to the latex report, to illustrate the + functionality of the python and latex integration. + + Note that the latex is already compiled before the + genetic algorith (GA) is ran, so these results of the GA + are one version behind the latex pdf report. + """ def __init__(self): self.run_jupyter_notebook = Run_jupyter_notebook() @@ -23,41 +34,72 @@ def __init__(self): def run_jupyter_notebooks(self,project_nr,notebook_names): - '''runs a jupyter notebook''' + """calls a method that runs each jupyter notebook in the list of incoming notebook names + + :param project_nr: the numberr identifying which project is being ran and compiled + :param notebook_names: list of strings with the names of the notebooks that need to be ran + + """ notebook_path = f'code/project{project_nr}/src/' for notebook_name in notebook_names: self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}') + def convert_notebooks_to_pdf(self,project_nr,notebook_names): - '''converts a jupyter notebook to pdf''' + """calls a method that converts each jupyter notebook in the list of incoming notebook names + + :param project_nr: the numberr identifying which project is being ran and compiled + :param notebook_names: list of strings with the names of the notebooks that need to be ran + + """ notebook_path = f'code/project{project_nr}/src/' for notebook_name in notebook_names: self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}') + def compile_latex_report(self,project_nr): - '''compiles latex code to pdf''' + """compiles latex code to pdf + + :param project_nr: the numberr identifying which project is being ran and compiled + + """ compile_latex =Compile_latex(project_nr ,'main.tex') + ################################################################ ############example code to illustrate python-latex image sync######### ##############runs arbitrary genetic algorithm, can be deleted########### ################################################################ def count(self,bits): + """counts how many bits there are in a chromosome + + :param bits: representing values of dna in chromosome(s) + + """ count = 0 for bit in bits: if bit: count = count + 1 return count + def gen_bit_sequence(self): + """generates a random bit sequence that represents a chromosome of DNA""" bits = [] for _ in range(string_length): bits.append(True if random.randint(0, 1) == 1 else False) return bits + def mutate_bit_sequence(self,sequence): + """Randomly changes a bit sequence that changes the chromosome(s) of DNA + This is simulating for example radiation effects that generate arbitrary new offspring + + :param sequence: sequence of binary bits that represent a chromosome of DNA + + """ retval = [] for bit in sequence : do_mutation = random.random() <= mutation_chance @@ -67,8 +109,12 @@ def mutate_bit_sequence(self,sequence): retval.append(bit) return retval + #execute a run a def do_run_a(self): + """Performs a run of the genetic algorithm, like simulating evolution + and returns the fitness of the population. + """ seq = self.gen_bit_sequence() fitness = self.count(seq) @@ -85,6 +131,9 @@ def do_run_a(self): #execute a run c def do_run_c(self): + """Performs a run of the genetic algorithm, like simulating evolution + and returns the fitness of the population. + """ seq = self.gen_bit_sequence() fitness = self.count(seq) results = [fitness] @@ -96,7 +145,15 @@ def do_run_c(self): results.append(max(results[-1], fitness)) return results + def do4b(self,project_nr): + """Performs a run of the genetic algorithm, like simulating evolution + and exports the optimum fitness of the population per generation + as an image to the latex report of the incoming project nr. + + :param project_nr: the numberr identifying which project is being ran and compiled + + """ optimum_found = 0 # generate plot data @@ -119,6 +176,13 @@ def do4b(self,project_nr): print("total optimum found: {} out of {} runs".format(optimum_found,10)) def do4c(self,project_nr): + """Performs a run of the genetic algorithm, like simulating evolution + and exports the optimum fitness of the population per generation + as an image to the latex report of the incoming project nr. + + :param project_nr: the numberr identifying which project is being ran and compiled + + """ optimum_found = 0 # generate plot data @@ -140,9 +204,14 @@ def do4c(self,project_nr): plt_tex.plotMultipleLines(plt_tex,range(0, len(res)),plotResult,"[runs]]","fitness [%]",lineLabels,"4c",4,project_nr) print("total optimum found: {} out of {} runs".format(optimum_found, 10)) - + + def addTwo(self,x): - ''' adds two to the incoming integer and returns the result of the computation.''' + """adds two to the incoming integer and returns the result of the computation. + + :param x: incoming integer + + """ return x+2 if __name__ == '__main__': diff --git a/code/project1/src/Plot_to_tex.py b/code/project1/src/Plot_to_tex.py index 47eb40f..0e2a11d 100644 --- a/code/project1/src/Plot_to_tex.py +++ b/code/project1/src/Plot_to_tex.py @@ -1,63 +1,41 @@ -''' -Plots incoming images and/or tables to a latex report with a certain layout. - - -Attributes --------------- -script_dir : str - the absolute path of the directory that contains this script - - -Methods ------------- -plotSingleLine(x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr): - Outputs a plot with a single line to a latex report - -plotMultipleLines(x,y_series,x_label,y_label,label,filename,legendPosition,project_nr): - Outputs a plot with mulltiple lines to a latex report - -get_cmap(n, name='hsv'): - Returns a function that maps each index in 0, 1, ..., n-1 to a distinct RGB color; - -generateLineTypes(y_series): - Generates returns a list of a vissible line type for each incoming line/y_series - -put_table_in_tex(table_matrix,filename,project_nr): - Outputs a table into a latex report - -example_create_a_table(): - Example method that creates a python table that can be exported into a latex report - -get_script_dir(self): - returns the absolute path of the directory that continas this script -''' - -''' -Example of how to include an exported table into your latex report. - -\begin{table}[H] - \centering - \caption{Results some computation.}\label{tab:some_computation} - \begin{tabular}{|c|c|} % remember to update this to show all columns of table - \hline - \input{latex/project3/tables/q2.txt} - \end{tabular} -\end{table} -''' from matplotlib import lines import matplotlib.pyplot as plt import numpy as np import os import random -class Plot_to_tex: +class Plot_to_tex: + """Plots incoming images and/or tables to a latex report with a certain layout.""" + """ + Example of how to include an exported table into your latex report. + + \begin{table}[H] + \centering + \caption{Results some computation.}\label{tab:some_computation} + \begin{tabular}{|c|c|} % remember to update this to show all columns of table + \hline + \input{latex/project3/tables/q2.txt} + \end{tabular} + \end{table} + """ def __init__(self): - ''' constructs the absolute path of the directory that contains this script ''' self.script_dir = self.get_script_dir() - - # plot graph (legendPosition = integer 1 to 4) + + def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr): + """Outputs a plot with a single line to a latex report + + :param x_path: x coordinates of a line + :param y_series: y coordinates of a line + :param x_axis_label: label of x axis + :param y_axis_label: label of y axis + :param label: string describing the line (label) + :param filename: filename of the image that is exported to latex + :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best') + :param project_nr: the number identifying to which latex project this image is exported + + """ fig=plt.figure(); ax=fig.add_subplot(111); ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none'); @@ -67,8 +45,20 @@ def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png'); # plt.show(); - # plot graphs + def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr): + """Outputs a plot with mulltiple lines to a latex report + + :param x: list of x coordinates of the lines of the plot + :param y_series: y coordinates of the lines of the plot + :param x_label: label of x axis + :param y_label: label of y axis + :param label: list of strings describing the lines (labels) + :param filename: filename of the image that is exported to latex + :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best') + :param project_nr: the number identifying to which latex project this image is exported + + """ fig=plt.figure(); ax=fig.add_subplot(111); @@ -91,14 +81,25 @@ def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosit print(f'plotted lines') - # Generate random line colours - # Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib + def get_cmap(n, name='hsv'): - '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct - RGB color; the keyword argument name must be a standard mpl colormap name.''' + """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct + RGB color; the keyword argument name must be a standard mpl colormap name. + Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib + + :param n: number of lines that need a distinct colour + :param name: (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc + + """ return plt.cm.get_cmap(name, n) + def generateLineTypes(y_series): + """Generates returns a list of a vissible line type for each incoming line/y_series + + :param y_series: list with list of y-coordinates representing the lines + + """ # generate varying linetypes typeOfLines = list(lines.lineStyles.keys()) @@ -114,9 +115,16 @@ def generateLineTypes(y_series): if (typeOfLines[i]==' '): typeOfLines[i]='--' return typeOfLines - - # Create a table with: table_matrix = np.zeros((4,4),dtype=object) and pass it to this object + + def put_table_in_tex(self, table_matrix,filename,project_nr): + """Outputs a table into a latex report + + :param table_matrix: numpy array with the table data + :param filename: filename of the table that is exported to latex + :param project_nr: the number identifying to which latex project this table is exported + + """ cols = np.shape(table_matrix)[1] format = "%s" for col in range(1,cols): @@ -124,8 +132,11 @@ def put_table_in_tex(self, table_matrix,filename,project_nr): format = format+"" plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline=' \\\\ \hline \n') - # replace this with your own table creation and then pass it to put_table_in_tex(..) + def example_create_a_table(self): + """Example code that generates the numpy array with + table data that can be exported to a latex table. Can + be modified to generate your own latex table""" project_nr = "1" table_name = "example_table_name" rows = 2; @@ -143,9 +154,10 @@ def example_create_a_table(self): def get_script_dir(self): - ''' returns the directory of this script regardles of from which level the code is executed ''' + """returns the path of the directory of this script""" return os.path.dirname(__file__) + if __name__ == '__main__': main = Plot_to_tex() main.example_create_a_table() \ No newline at end of file diff --git a/code/project1/src/Run_jupyter_notebooks.py b/code/project1/src/Run_jupyter_notebooks.py index 8de2225..16f67de 100644 --- a/code/project1/src/Run_jupyter_notebooks.py +++ b/code/project1/src/Run_jupyter_notebooks.py @@ -1,37 +1,56 @@ -# runs a jupyter notebook and converts it to pdf - +from nbconvert.preprocessors import ExecutePreprocessor import os import nbformat -from nbconvert.preprocessors import ExecutePreprocessor class Run_jupyter_notebook: + """runs a list of jupyter notebooks and converts it to pdf""" + def __init__(self): self.script_dir = self.get_script_dir() - print("Created main") + def run_jupyter_notebooks(self,project_nr,notebook_names): - '''runs a jupyter notebook''' + """runs a jupyter notebook in this directory + + :param project_nr: the numberr identifying which project is being ran and compiled + :param notebook_names: list of strings with the names of the notebooks that need to be ran + + """ notebook_path = f'code/project{project_nr}/src/' for notebook_name in notebook_names: self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}') + def convert_notebooks_to_pdf(self,project_nr,notebook_names): - '''converts a jupyter notebook to pdf''' + """converts a jupyter notebook to pdf + + :param project_nr: the numberr identifying which project is being ran and compiled + :param notebook_names: list of strings with the names of the notebooks that need to be ran + + """ notebook_path = f'code/project{project_nr}/src/' for notebook_name in notebook_names: self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}') + def compile_latex_report(self,project_nr): - '''compiles latex code to pdf''' + """compiles latex code to pdf + + :param project_nr: the numberr identifying which project is being ran and compiled + + """ compile_latex =Compile_latex(project_nr ,'main.tex') - # runs jupyter notebook + def run_notebook(self,notebook_filename): + """runs a jupyter notebook that is located in this folder + :param notebook_filename: the name of the notebook that needs to be ran + """ # Load your notebook with open(notebook_filename) as f: nb = nbformat.read(f, as_version=4) @@ -47,13 +66,20 @@ def run_notebook(self,notebook_filename): with open(notebook_filename, 'w', encoding='utf-8') as f: nbformat.write(nb, f) - # converts jupyter notebook to pdf + def convert_notebook_to_pdf(self,notebook_filename): + """Compiles a jupyter notebook that is located in this folder to pdf + + :param notebook_filename: the name of the notebook that needs to be compiled to pdf + + """ os.system(f'jupyter nbconvert --to pdf {notebook_filename}') + def get_script_dir(self): - ''' returns the directory of this script regardles of from which level the code is executed ''' + """returns the directory of this script regardles of from which level the code is executed""" return os.path.dirname(__file__) + if __name__ == '__main__': main = Run_jupyter_notebook() \ No newline at end of file diff --git a/code/project1/src/__main__.py b/code/project1/src/__main__.py index 95a6cad..1c27550 100644 --- a/code/project1/src/__main__.py +++ b/code/project1/src/__main__.py @@ -29,6 +29,7 @@ # compile the latex report main.compile_latex_report(project_nr) + ################################################################ ############example code to illustrate python-latex image sync######### ##############runs arbitrary genetic algorithm, can be deleted########### From 23f2cfe4977cb17661ead6711e3c5fdd8f86820a Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Wed, 18 Nov 2020 14:47:35 +0100 Subject: [PATCH 49/57] Corrected relative output path of jupyter notebook images. --- ...e_notebook_update20201025-checkpoint.ipynb | 56 +++++++++++------- ...4868_example_notebook_update20201025.ipynb | 27 +++++---- ...AE4868_example_notebook_update20201025.pdf | Bin 408276 -> 408322 bytes code/project1/src/juice_propagation_Q1.ipynb | 2 +- code/project1/src/juice_propagation_Q4.ipynb | 2 +- latex/project1/Images/4b.png | Bin 38865 -> 39668 bytes latex/project1/Images/4c.png | Bin 30260 -> 29685 bytes latex/project1/main.pdf | Bin 608410 -> 621915 bytes 8 files changed, 51 insertions(+), 36 deletions(-) diff --git a/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb b/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb index a8f9d11..aff6329 100755 --- a/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb +++ b/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb @@ -2,8 +2,15 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2020-11-09T20:02:01.077224Z", + "iopub.status.busy": "2020-11-09T20:02:01.076276Z", + "iopub.status.idle": "2020-11-09T20:02:01.079603Z", + "shell.execute_reply": "2020-11-09T20:02:01.080202Z" + } + }, "outputs": [], "source": [ "def addThree(input_nr):\n", @@ -13,25 +20,31 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:41:15.495908Z", - "iopub.status.busy": "2020-11-09T14:41:15.495082Z", - "iopub.status.idle": "2020-11-09T14:41:16.354546Z", - "shell.execute_reply": "2020-11-09T14:41:16.353921Z" + "iopub.execute_input": "2020-11-09T20:02:01.097931Z", + "iopub.status.busy": "2020-11-09T20:02:01.089171Z", + "iopub.status.idle": "2020-11-09T20:02:01.960532Z", + "shell.execute_reply": "2020-11-09T20:02:01.959815Z" } }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'tudatpy'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m###############################################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtudatpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mconstants\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtudatpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterface\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mspice_interface\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtudatpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimulation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0menvironment_setup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tudatpy'" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Single Earth-Orbiting Satellite Example.\n", + "The initial position vector of Delfi-C3 is [km]: \n", + "[7037.48400133 3238.05901792 2150.7241875 ]\n", + "The initial velocity vector of Delfi-C3 is [km/s]: \n", + "[-1.46565763 -0.04095839 6.62279761]\n", + "After 86400.0 seconds the position vector of Delfi-C3 is [km]: \n", + "[-4602.79426676 -1421.16740978 5883.69740624]\n", + "And the velocity vector of Delfi-C3 is [km/s]: \n", + "[-4.53846052 -2.36988263 -5.04163195]\n", + " \n" ] } ], @@ -48,7 +61,8 @@ "from tudatpy.kernel.astro import conversion\n", "\n", "# Set path to latex image folders for project 1\n", - "latex_image_path = 'latex/project1/Images/'\n", + "latex_image_path = '../../../latex/project1/Images/'\n", + "\n", "\n", "# Load spice kernels.\n", "spice_interface.load_standard_kernels()\n", @@ -240,10 +254,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T14:41:16.374621Z", - "iopub.status.busy": "2020-11-09T14:41:16.373673Z", - "iopub.status.idle": "2020-11-09T14:41:20.162144Z", - "shell.execute_reply": "2020-11-09T14:41:20.162789Z" + "iopub.execute_input": "2020-11-09T20:02:01.982113Z", + "iopub.status.busy": "2020-11-09T20:02:01.980928Z", + "iopub.status.idle": "2020-11-09T20:02:05.906278Z", + "shell.execute_reply": "2020-11-09T20:02:05.907265Z" } }, "outputs": [ @@ -455,7 +469,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.8" } }, "nbformat": 4, diff --git a/code/project1/src/AE4868_example_notebook_update20201025.ipynb b/code/project1/src/AE4868_example_notebook_update20201025.ipynb index 373e6a6..030c158 100755 --- a/code/project1/src/AE4868_example_notebook_update20201025.ipynb +++ b/code/project1/src/AE4868_example_notebook_update20201025.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T20:02:01.077224Z", - "iopub.status.busy": "2020-11-09T20:02:01.076276Z", - "iopub.status.idle": "2020-11-09T20:02:01.079603Z", - "shell.execute_reply": "2020-11-09T20:02:01.080202Z" + "iopub.execute_input": "2020-11-18T13:46:21.497739Z", + "iopub.status.busy": "2020-11-18T13:46:21.496380Z", + "iopub.status.idle": "2020-11-18T13:46:21.500648Z", + "shell.execute_reply": "2020-11-18T13:46:21.499417Z" } }, "outputs": [], @@ -23,10 +23,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T20:02:01.097931Z", - "iopub.status.busy": "2020-11-09T20:02:01.089171Z", - "iopub.status.idle": "2020-11-09T20:02:01.960532Z", - "shell.execute_reply": "2020-11-09T20:02:01.959815Z" + "iopub.execute_input": "2020-11-18T13:46:21.521023Z", + "iopub.status.busy": "2020-11-18T13:46:21.520156Z", + "iopub.status.idle": "2020-11-18T13:46:22.384187Z", + "shell.execute_reply": "2020-11-18T13:46:22.384849Z" } }, "outputs": [ @@ -61,7 +61,8 @@ "from tudatpy.kernel.astro import conversion\n", "\n", "# Set path to latex image folders for project 1\n", - "latex_image_path = 'latex/project1/Images/'\n", + "latex_image_path = '../../../latex/project1/Images/'\n", + "\n", "\n", "# Load spice kernels.\n", "spice_interface.load_standard_kernels()\n", @@ -253,10 +254,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T20:02:01.982113Z", - "iopub.status.busy": "2020-11-09T20:02:01.980928Z", - "iopub.status.idle": "2020-11-09T20:02:05.906278Z", - "shell.execute_reply": "2020-11-09T20:02:05.907265Z" + "iopub.execute_input": "2020-11-18T13:46:22.404641Z", + "iopub.status.busy": "2020-11-18T13:46:22.403623Z", + "iopub.status.idle": "2020-11-18T13:46:26.359134Z", + "shell.execute_reply": "2020-11-18T13:46:26.359827Z" } }, "outputs": [ diff --git a/code/project1/src/AE4868_example_notebook_update20201025.pdf b/code/project1/src/AE4868_example_notebook_update20201025.pdf index 9437f7b40fc3183dab24164b4934e6bf38b5ea19..56c0e906da93162c44eafc650aaa2f38b747b554 100644 GIT binary patch delta 38342 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zdQX0~d}7~$`@fv`U-&lV54-%ba(lGmGbxL!xFoTtq@pM_jmyly+| Date: Wed, 18 Nov 2020 14:56:45 +0100 Subject: [PATCH 50/57] Restored unit testing functionality. --- ...e_notebook_update20201025-checkpoint.ipynb | 30 ++++++++++-------- ...4868_example_notebook_update20201025.ipynb | 30 ++++++++++-------- ...AE4868_example_notebook_update20201025.pdf | Bin 408322 -> 409047 bytes latex/project1/Images/4b.png | Bin 39668 -> 37708 bytes latex/project1/Images/4c.png | Bin 29685 -> 29209 bytes latex/project1/main.pdf | Bin 621915 -> 623735 bytes 6 files changed, 34 insertions(+), 26 deletions(-) diff --git a/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb b/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb index aff6329..ddbd2ce 100755 --- a/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb +++ b/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T20:02:01.077224Z", - "iopub.status.busy": "2020-11-09T20:02:01.076276Z", - "iopub.status.idle": "2020-11-09T20:02:01.079603Z", - "shell.execute_reply": "2020-11-09T20:02:01.080202Z" + "iopub.execute_input": "2020-11-18T13:46:21.497739Z", + "iopub.status.busy": "2020-11-18T13:46:21.496380Z", + "iopub.status.idle": "2020-11-18T13:46:21.500648Z", + "shell.execute_reply": "2020-11-18T13:46:21.499417Z" } }, "outputs": [], @@ -23,10 +23,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T20:02:01.097931Z", - "iopub.status.busy": "2020-11-09T20:02:01.089171Z", - "iopub.status.idle": "2020-11-09T20:02:01.960532Z", - "shell.execute_reply": "2020-11-09T20:02:01.959815Z" + "iopub.execute_input": "2020-11-18T13:46:21.521023Z", + "iopub.status.busy": "2020-11-18T13:46:21.520156Z", + "iopub.status.idle": "2020-11-18T13:46:22.384187Z", + "shell.execute_reply": "2020-11-18T13:46:22.384849Z" } }, "outputs": [ @@ -61,7 +61,11 @@ "from tudatpy.kernel.astro import conversion\n", "\n", "# Set path to latex image folders for project 1\n", - "latex_image_path = '../../../latex/project1/Images/'\n", + "\n", + "if (os.path.abspath('')[-12:]==\"project1/src\"):\n", + " latex_image_path = '../../../latex/project1/Images/'\n", + "else:\n", + " latex_image_path = 'latex/project1/Images/' # when ran as test\n", "\n", "\n", "# Load spice kernels.\n", @@ -254,10 +258,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T20:02:01.982113Z", - "iopub.status.busy": "2020-11-09T20:02:01.980928Z", - "iopub.status.idle": "2020-11-09T20:02:05.906278Z", - "shell.execute_reply": "2020-11-09T20:02:05.907265Z" + "iopub.execute_input": "2020-11-18T13:46:22.404641Z", + "iopub.status.busy": "2020-11-18T13:46:22.403623Z", + "iopub.status.idle": "2020-11-18T13:46:26.359134Z", + "shell.execute_reply": "2020-11-18T13:46:26.359827Z" } }, "outputs": [ diff --git a/code/project1/src/AE4868_example_notebook_update20201025.ipynb b/code/project1/src/AE4868_example_notebook_update20201025.ipynb index 030c158..4dda314 100755 --- a/code/project1/src/AE4868_example_notebook_update20201025.ipynb +++ b/code/project1/src/AE4868_example_notebook_update20201025.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2020-11-18T13:46:21.497739Z", - "iopub.status.busy": "2020-11-18T13:46:21.496380Z", - "iopub.status.idle": "2020-11-18T13:46:21.500648Z", - "shell.execute_reply": "2020-11-18T13:46:21.499417Z" + "iopub.execute_input": "2020-11-18T13:56:04.967007Z", + "iopub.status.busy": "2020-11-18T13:56:04.966234Z", + "iopub.status.idle": "2020-11-18T13:56:04.968576Z", + "shell.execute_reply": "2020-11-18T13:56:04.969500Z" } }, "outputs": [], @@ -23,10 +23,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-18T13:46:21.521023Z", - "iopub.status.busy": "2020-11-18T13:46:21.520156Z", - "iopub.status.idle": "2020-11-18T13:46:22.384187Z", - "shell.execute_reply": "2020-11-18T13:46:22.384849Z" + "iopub.execute_input": "2020-11-18T13:56:04.986732Z", + "iopub.status.busy": "2020-11-18T13:56:04.985773Z", + "iopub.status.idle": "2020-11-18T13:56:05.946716Z", + "shell.execute_reply": "2020-11-18T13:56:05.946125Z" } }, "outputs": [ @@ -61,7 +61,11 @@ "from tudatpy.kernel.astro import conversion\n", "\n", "# Set path to latex image folders for project 1\n", - "latex_image_path = '../../../latex/project1/Images/'\n", + "\n", + "if (os.path.abspath('')[-12:]==\"project1/src\"):\n", + " latex_image_path = '../../../latex/project1/Images/'\n", + "else:\n", + " latex_image_path = 'latex/project1/Images/' # when ran as test\n", "\n", "\n", "# Load spice kernels.\n", @@ -254,10 +258,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-18T13:46:22.404641Z", - "iopub.status.busy": "2020-11-18T13:46:22.403623Z", - "iopub.status.idle": "2020-11-18T13:46:26.359134Z", - "shell.execute_reply": "2020-11-18T13:46:26.359827Z" + "iopub.execute_input": "2020-11-18T13:56:05.967283Z", + "iopub.status.busy": "2020-11-18T13:56:05.966239Z", + "iopub.status.idle": "2020-11-18T13:56:10.004728Z", + "shell.execute_reply": "2020-11-18T13:56:10.005358Z" } }, "outputs": [ diff --git a/code/project1/src/AE4868_example_notebook_update20201025.pdf b/code/project1/src/AE4868_example_notebook_update20201025.pdf index 56c0e906da93162c44eafc650aaa2f38b747b554..e81889d5ac5bc92e3fe2077f7a729c562ad1dbc0 100644 GIT binary patch delta 47107 zcmZ6xV{q8t7wz4&NgCU>ZQHhO+xj+XY}>YN+qTgpjqTgt%=66L|9yAft+Qs&p7S|- zuYgC4l{bu7c7TJ0j}OY(#mUsr7RqD&LVGLvs2R5VO8FT~EUpwVsDprNgg&&3XoGgD zV>XxfVuWlz71i%q?6EkXTpe+XNBfOiV~dwj$QOqsIkwOuDTDJBHunAV=JW-<^DS^> z#y-187VypNe(OjLG$m+E$E!(5g2~Lri!>O2KE1g;0DevnA)hGi?%l*@VrR|h-@s&Q 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z@T6;;LKOF!>;~5JSR3T0)wQv?*@T2!+@=}ws%)Dy@~Y%xg25d0SH5ghCGlsBVg(OK z@mXGHeGh+;;pKouVprgU%1)%Bb`spNaj?1pS2ldbZLcd; Date: Wed, 18 Nov 2020 15:02:00 +0100 Subject: [PATCH 51/57] Included documentation generation tools. --- README.md | 5 ++++- tudat-space_environment.yml | 2 ++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index c1284db..99e104f 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,10 @@ python -m pytest ``` from the root directory of this project. +## Documentation +The docstring documentation (template) was generated using `pyment`. The HTML documentation of the code was +generated using `pdoc`. + [black_badge]: https://img.shields.io/badge/code%20style-black-000000.svg [python_badge]: https://img.shields.io/badge/python-3.8-blue.svg -[apache_badge]: https://img.shields.io/badge/license-Apache%202.0-brightgreen.svg diff --git a/tudat-space_environment.yml b/tudat-space_environment.yml index b267b0b..7463809 100644 --- a/tudat-space_environment.yml +++ b/tudat-space_environment.yml @@ -19,3 +19,5 @@ dependencies: # works for regular pip packages - pdflatex - testbook + - pyment + - pdoc3 From 3395b8cf4f052ef027d61641123817c2708b5e97 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Wed, 18 Nov 2020 15:11:23 +0100 Subject: [PATCH 52/57] Generated html documentation with pdoc. --- code/project1/src/html/Compile_latex.html | 357 ++++++++++ code/project1/src/html/Plot_to_tex.html | 624 ++++++++++++++++++ .../src/html/Run_jupyter_notebooks.html | 384 +++++++++++ code/project1/src/html/__main__.html | 61 ++ tudat-space_environment.yml | 4 +- 5 files changed, 1428 insertions(+), 2 deletions(-) create mode 100644 code/project1/src/html/Compile_latex.html create mode 100644 code/project1/src/html/Plot_to_tex.html create mode 100644 code/project1/src/html/Run_jupyter_notebooks.html create mode 100644 code/project1/src/html/__main__.html diff --git a/code/project1/src/html/Compile_latex.html b/code/project1/src/html/Compile_latex.html new file mode 100644 index 0000000..69fbf91 --- /dev/null +++ b/code/project1/src/html/Compile_latex.html @@ -0,0 +1,357 @@ + + + + + + +Compile_latex API documentation + + + + + + + + + + + +

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              + + + \ No newline at end of file diff --git a/code/project1/src/html/Plot_to_tex.html b/code/project1/src/html/Plot_to_tex.html new file mode 100644 index 0000000..54c8c4c --- /dev/null +++ b/code/project1/src/html/Plot_to_tex.html @@ -0,0 +1,624 @@ + + + + + + +Plot_to_tex API documentation + + + + + + + + + + + +
              +
              +
              +

              Module Plot_to_tex

              +
              +
              +
              + +Expand source code + +
              from matplotlib import lines
              +import matplotlib.pyplot as plt
              +import numpy as np
              +import os
              +import random
              +
              +
              +class Plot_to_tex:
              +    """Plots incoming images and/or tables to a latex report with a certain layout."""
              +    """
              +    Example of how to include an exported table into your latex report.
              +
              +    \begin{table}[H]
              +        \centering
              +        \caption{Results some computation.}\label{tab:some_computation}
              +        \begin{tabular}{|c|c|} % remember to update this to show all columns of table
              +            \hline
              +            \input{latex/project3/tables/q2.txt}
              +        \end{tabular}
              +    \end{table}
              +    """
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +        
              +    def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with a single line to a latex report
              +
              +        :param x_path: x coordinates of a line
              +        :param y_series: y coordinates of a line
              +        :param x_axis_label: label of x axis 
              +        :param y_axis_label: label of y axis 
              +        :param label: string describing the line (label)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +        ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none');
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_axis_label);
              +        plt.ylabel(y_axis_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +#         plt.show();
              +
              +
              +    def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with mulltiple lines to a latex report
              +
              +        :param x: list of x coordinates of the lines of the plot
              +        :param y_series: y coordinates of the lines of the plot 
              +        :param x_label: label of x axis 
              +        :param y_label: label of y axis 
              +        :param label: list of strings describing the lines (labels)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +
              +        # generate colours
              +        cmap = self.get_cmap(len(y_series[:,0]))
              +
              +        # generate line types
              +        lineTypes = self.generateLineTypes(y_series)
              +
              +        for i in range(0,len(y_series)):
              +            # overwrite linetypes to single type
              +            lineTypes[i] = "-"
              +            ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color
              +
              +        # configure plot layout
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_label);
              +        plt.ylabel(y_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +        
              +        print(f'plotted lines')
              +
              +    
              +    def get_cmap(n, name='hsv'):
              +        """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
              +        RGB color; the keyword argument name must be a standard mpl colormap name.
              +        Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
              +
              +        :param n: number of lines that need a distinct colour
              +        :param name:  (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc
              +
              +        """
              +        return plt.cm.get_cmap(name, n)
              +
              +
              +    def generateLineTypes(y_series):
              +        """Generates returns a list of a vissible line type for each incoming line/y_series
              +
              +        :param y_series: list with list of y-coordinates representing the lines
              +
              +        """
              +        # generate varying linetypes
              +        typeOfLines = list(lines.lineStyles.keys())
              +
              +        while(len(y_series)>len(typeOfLines)):
              +            typeOfLines.append("-.");
              +
              +        # remove void lines
              +        for i in range(0, len(y_series)):
              +            if (typeOfLines[i]=='None'):
              +                typeOfLines[i]='-'
              +            if (typeOfLines[i]==''):
              +                typeOfLines[i]=':'
              +            if (typeOfLines[i]==' '):
              +                typeOfLines[i]='--'
              +        return typeOfLines
              +        
              +        
              +    def put_table_in_tex(self, table_matrix,filename,project_nr):
              +        """Outputs a table into a latex report
              +
              +        :param table_matrix: numpy array with the table data
              +        :param filename: filename of the table that is exported to latex
              +        :param project_nr: the number identifying to which latex project this table is exported
              +
              +        """
              +        cols = np.shape(table_matrix)[1]
              +        format = "%s"
              +        for col in range(1,cols):
              +            format = format+" & %s"
              +        format = format+""
              +        plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline='  \\\\ \hline \n')
              +
              +    
              +    def example_create_a_table(self):
              +        """Example code that generates the numpy array with 
              +        table data that can be exported to a latex table. Can 
              +        be modified to generate your own latex table"""
              +        project_nr = "1"
              +        table_name = "example_table_name"
              +        rows = 2;
              +        columns = 4;
              +        table_matrix = np.zeros((rows,columns),dtype=object)
              +        table_matrix[:,:]="" # replace the standard zeros with emtpy cell
              +        print(table_matrix)
              +        for column in range(0,columns):
              +            for row in range(0,rows):
              +                table_matrix[row,column]=row+column
              +        table_matrix[1,0]="example"
              +        table_matrix[0,1]="grid sizes"
              +
              +        self.put_table_in_tex(table_matrix,table_name,project_nr)
              +        
              +    
              +    def get_script_dir(self):
              +        """returns the path of the directory of this script"""
              +        return os.path.dirname(__file__)
              +
              +
              +if __name__ == '__main__':
              +    main = Plot_to_tex()
              +    main.example_create_a_table()
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +

              Classes

              +
              +
              +class Plot_to_tex +
              +
              +

              Plots incoming images and/or tables to a latex report with a certain layout.

              +
              + +Expand source code + +
              class Plot_to_tex:
              +    """Plots incoming images and/or tables to a latex report with a certain layout."""
              +    """
              +    Example of how to include an exported table into your latex report.
              +
              +    \begin{table}[H]
              +        \centering
              +        \caption{Results some computation.}\label{tab:some_computation}
              +        \begin{tabular}{|c|c|} % remember to update this to show all columns of table
              +            \hline
              +            \input{latex/project3/tables/q2.txt}
              +        \end{tabular}
              +    \end{table}
              +    """
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +        
              +    def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with a single line to a latex report
              +
              +        :param x_path: x coordinates of a line
              +        :param y_series: y coordinates of a line
              +        :param x_axis_label: label of x axis 
              +        :param y_axis_label: label of y axis 
              +        :param label: string describing the line (label)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +        ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none');
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_axis_label);
              +        plt.ylabel(y_axis_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +#         plt.show();
              +
              +
              +    def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with mulltiple lines to a latex report
              +
              +        :param x: list of x coordinates of the lines of the plot
              +        :param y_series: y coordinates of the lines of the plot 
              +        :param x_label: label of x axis 
              +        :param y_label: label of y axis 
              +        :param label: list of strings describing the lines (labels)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +
              +        # generate colours
              +        cmap = self.get_cmap(len(y_series[:,0]))
              +
              +        # generate line types
              +        lineTypes = self.generateLineTypes(y_series)
              +
              +        for i in range(0,len(y_series)):
              +            # overwrite linetypes to single type
              +            lineTypes[i] = "-"
              +            ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color
              +
              +        # configure plot layout
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_label);
              +        plt.ylabel(y_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +        
              +        print(f'plotted lines')
              +
              +    
              +    def get_cmap(n, name='hsv'):
              +        """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
              +        RGB color; the keyword argument name must be a standard mpl colormap name.
              +        Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
              +
              +        :param n: number of lines that need a distinct colour
              +        :param name:  (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc
              +
              +        """
              +        return plt.cm.get_cmap(name, n)
              +
              +
              +    def generateLineTypes(y_series):
              +        """Generates returns a list of a vissible line type for each incoming line/y_series
              +
              +        :param y_series: list with list of y-coordinates representing the lines
              +
              +        """
              +        # generate varying linetypes
              +        typeOfLines = list(lines.lineStyles.keys())
              +
              +        while(len(y_series)>len(typeOfLines)):
              +            typeOfLines.append("-.");
              +
              +        # remove void lines
              +        for i in range(0, len(y_series)):
              +            if (typeOfLines[i]=='None'):
              +                typeOfLines[i]='-'
              +            if (typeOfLines[i]==''):
              +                typeOfLines[i]=':'
              +            if (typeOfLines[i]==' '):
              +                typeOfLines[i]='--'
              +        return typeOfLines
              +        
              +        
              +    def put_table_in_tex(self, table_matrix,filename,project_nr):
              +        """Outputs a table into a latex report
              +
              +        :param table_matrix: numpy array with the table data
              +        :param filename: filename of the table that is exported to latex
              +        :param project_nr: the number identifying to which latex project this table is exported
              +
              +        """
              +        cols = np.shape(table_matrix)[1]
              +        format = "%s"
              +        for col in range(1,cols):
              +            format = format+" & %s"
              +        format = format+""
              +        plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline='  \\\\ \hline \n')
              +
              +    
              +    def example_create_a_table(self):
              +        """Example code that generates the numpy array with 
              +        table data that can be exported to a latex table. Can 
              +        be modified to generate your own latex table"""
              +        project_nr = "1"
              +        table_name = "example_table_name"
              +        rows = 2;
              +        columns = 4;
              +        table_matrix = np.zeros((rows,columns),dtype=object)
              +        table_matrix[:,:]="" # replace the standard zeros with emtpy cell
              +        print(table_matrix)
              +        for column in range(0,columns):
              +            for row in range(0,rows):
              +                table_matrix[row,column]=row+column
              +        table_matrix[1,0]="example"
              +        table_matrix[0,1]="grid sizes"
              +
              +        self.put_table_in_tex(table_matrix,table_name,project_nr)
              +        
              +    
              +    def get_script_dir(self):
              +        """returns the path of the directory of this script"""
              +        return os.path.dirname(__file__)
              +
              +

              Methods

              +
              +
              +def example_create_a_table(self) +
              +
              +

              Example code that generates the numpy array with +table data that can be exported to a latex table. Can +be modified to generate your own latex table

              +
              + +Expand source code + +
              def example_create_a_table(self):
              +    """Example code that generates the numpy array with 
              +    table data that can be exported to a latex table. Can 
              +    be modified to generate your own latex table"""
              +    project_nr = "1"
              +    table_name = "example_table_name"
              +    rows = 2;
              +    columns = 4;
              +    table_matrix = np.zeros((rows,columns),dtype=object)
              +    table_matrix[:,:]="" # replace the standard zeros with emtpy cell
              +    print(table_matrix)
              +    for column in range(0,columns):
              +        for row in range(0,rows):
              +            table_matrix[row,column]=row+column
              +    table_matrix[1,0]="example"
              +    table_matrix[0,1]="grid sizes"
              +
              +    self.put_table_in_tex(table_matrix,table_name,project_nr)
              +
              +
              +
              +def generateLineTypes(y_series) +
              +
              +

              Generates returns a list of a vissible line type for each incoming line/y_series

              +

              :param y_series: list with list of y-coordinates representing the lines

              +
              + +Expand source code + +
              def generateLineTypes(y_series):
              +    """Generates returns a list of a vissible line type for each incoming line/y_series
              +
              +    :param y_series: list with list of y-coordinates representing the lines
              +
              +    """
              +    # generate varying linetypes
              +    typeOfLines = list(lines.lineStyles.keys())
              +
              +    while(len(y_series)>len(typeOfLines)):
              +        typeOfLines.append("-.");
              +
              +    # remove void lines
              +    for i in range(0, len(y_series)):
              +        if (typeOfLines[i]=='None'):
              +            typeOfLines[i]='-'
              +        if (typeOfLines[i]==''):
              +            typeOfLines[i]=':'
              +        if (typeOfLines[i]==' '):
              +            typeOfLines[i]='--'
              +    return typeOfLines
              +
              +
              +
              +def get_cmap(n, name='hsv') +
              +
              +

              Returns a function that maps each index in 0, 1, …, n-1 to a distinct +RGB color; the keyword argument name must be a standard mpl colormap name. +Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib

              +

              :param n: number of lines that need a distinct colour +:param name: +(Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc

              +
              + +Expand source code + +
              def get_cmap(n, name='hsv'):
              +    """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
              +    RGB color; the keyword argument name must be a standard mpl colormap name.
              +    Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
              +
              +    :param n: number of lines that need a distinct colour
              +    :param name:  (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc
              +
              +    """
              +    return plt.cm.get_cmap(name, n)
              +
              +
              +
              +def get_script_dir(self) +
              +
              +

              returns the path of the directory of this script

              +
              + +Expand source code + +
              def get_script_dir(self):
              +    """returns the path of the directory of this script"""
              +    return os.path.dirname(__file__)
              +
              +
              +
              +def plotMultipleLines(self, x, y_series, x_label, y_label, label, filename, legendPosition, project_nr) +
              +
              +

              Outputs a plot with mulltiple lines to a latex report

              +

              :param x: list of x coordinates of the lines of the plot +:param y_series: y coordinates of the lines of the plot +:param x_label: label of x axis +:param y_label: label of y axis +:param label: list of strings describing the lines (labels) +:param filename: filename of the image that is exported to latex +:param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best') +:param project_nr: the number identifying to which latex project this image is exported

              +
              + +Expand source code + +
              def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr):
              +    """Outputs a plot with mulltiple lines to a latex report
              +
              +    :param x: list of x coordinates of the lines of the plot
              +    :param y_series: y coordinates of the lines of the plot 
              +    :param x_label: label of x axis 
              +    :param y_label: label of y axis 
              +    :param label: list of strings describing the lines (labels)
              +    :param filename: filename of the image that is exported to latex
              +    :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +    :param project_nr: the number identifying to which latex project this image is exported
              +
              +    """
              +    fig=plt.figure();
              +    ax=fig.add_subplot(111);
              +
              +    # generate colours
              +    cmap = self.get_cmap(len(y_series[:,0]))
              +
              +    # generate line types
              +    lineTypes = self.generateLineTypes(y_series)
              +
              +    for i in range(0,len(y_series)):
              +        # overwrite linetypes to single type
              +        lineTypes[i] = "-"
              +        ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color
              +
              +    # configure plot layout
              +    plt.legend(loc=legendPosition);
              +    plt.xlabel(x_label);
              +    plt.ylabel(y_label);
              +    plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +    
              +    print(f'plotted lines')
              +
              +
              +
              +def plotSingleLine(self, x_path, y_series, x_axis_label, y_axis_label, label, filename, legendPosition, project_nr) +
              +
              +

              Outputs a plot with a single line to a latex report

              +

              :param x_path: x coordinates of a line +:param y_series: y coordinates of a line +:param x_axis_label: label of x axis +:param y_axis_label: label of y axis +:param label: string describing the line (label) +:param filename: filename of the image that is exported to latex +:param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best') +:param project_nr: the number identifying to which latex project this image is exported

              +
              + +Expand source code + +
              def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr):
              +    """Outputs a plot with a single line to a latex report
              +
              +    :param x_path: x coordinates of a line
              +    :param y_series: y coordinates of a line
              +    :param x_axis_label: label of x axis 
              +    :param y_axis_label: label of y axis 
              +    :param label: string describing the line (label)
              +    :param filename: filename of the image that is exported to latex
              +    :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +    :param project_nr: the number identifying to which latex project this image is exported
              +
              +    """
              +    fig=plt.figure();
              +    ax=fig.add_subplot(111);
              +    ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none');
              +    plt.legend(loc=legendPosition);
              +    plt.xlabel(x_axis_label);
              +    plt.ylabel(y_axis_label);
              +    plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +
              +
              +
              +def put_table_in_tex(self, table_matrix, filename, project_nr) +
              +
              +

              Outputs a table into a latex report

              +

              :param table_matrix: numpy array with the table data +:param filename: filename of the table that is exported to latex +:param project_nr: the number identifying to which latex project this table is exported

              +
              + +Expand source code + +
              def put_table_in_tex(self, table_matrix,filename,project_nr):
              +    """Outputs a table into a latex report
              +
              +    :param table_matrix: numpy array with the table data
              +    :param filename: filename of the table that is exported to latex
              +    :param project_nr: the number identifying to which latex project this table is exported
              +
              +    """
              +    cols = np.shape(table_matrix)[1]
              +    format = "%s"
              +    for col in range(1,cols):
              +        format = format+" & %s"
              +    format = format+""
              +    plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline='  \\\\ \hline \n')
              +
              +
              +
              +
              +
              +
              +
              + +
              + + + \ No newline at end of file diff --git a/code/project1/src/html/Run_jupyter_notebooks.html b/code/project1/src/html/Run_jupyter_notebooks.html new file mode 100644 index 0000000..3d23a1f --- /dev/null +++ b/code/project1/src/html/Run_jupyter_notebooks.html @@ -0,0 +1,384 @@ + + + + + + +Run_jupyter_notebooks API documentation + + + + + + + + + + + +
              +
              +
              +

              Module Run_jupyter_notebooks

              +
              +
              +
              + +Expand source code + +
              from nbconvert.preprocessors import ExecutePreprocessor
              +import os
              +import nbformat
              +
              +class Run_jupyter_notebook:
              +    """runs a list of  jupyter notebooks and converts it to pdf"""
              +
              +    
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +
              +    def run_jupyter_notebooks(self,project_nr,notebook_names):
              +        """runs a jupyter notebook in this directory
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def convert_notebooks_to_pdf(self,project_nr,notebook_names):
              +        """converts a jupyter notebook to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def compile_latex_report(self,project_nr):
              +        """compiles latex code to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +
              +        """
              +        compile_latex =Compile_latex(project_nr ,'main.tex')
              +
              +    
              +    def run_notebook(self,notebook_filename):
              +        """runs a  jupyter notebook that is located in this folder
              +        
              +        :param notebook_filename: the name of the notebook that needs to be ran
              +
              +        """
              +        # Load your notebook
              +        with open(notebook_filename) as f:
              +            nb = nbformat.read(f, as_version=4)
              +
              +        # Configure
              +        ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
              +
              +        # Execute
              +        #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}})
              +        ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}})
              +
              +        # Save output notebook
              +        with open(notebook_filename, 'w', encoding='utf-8') as f:
              +            nbformat.write(nb, f)
              +    
              +    
              +    def convert_notebook_to_pdf(self,notebook_filename):
              +        """Compiles a jupyter notebook that is located in this folder to pdf
              +
              +        :param notebook_filename: the name of the notebook that needs to be compiled to pdf
              +
              +        """
              +        os.system(f'jupyter nbconvert --to pdf {notebook_filename}')
              +    
              +    
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +
              +if __name__ == '__main__':
              +    main = Run_jupyter_notebook()
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +

              Classes

              +
              +
              +class Run_jupyter_notebook +
              +
              +

              runs a list of +jupyter notebooks and converts it to pdf

              +
              + +Expand source code + +
              class Run_jupyter_notebook:
              +    """runs a list of  jupyter notebooks and converts it to pdf"""
              +
              +    
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +
              +    def run_jupyter_notebooks(self,project_nr,notebook_names):
              +        """runs a jupyter notebook in this directory
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def convert_notebooks_to_pdf(self,project_nr,notebook_names):
              +        """converts a jupyter notebook to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def compile_latex_report(self,project_nr):
              +        """compiles latex code to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +
              +        """
              +        compile_latex =Compile_latex(project_nr ,'main.tex')
              +
              +    
              +    def run_notebook(self,notebook_filename):
              +        """runs a  jupyter notebook that is located in this folder
              +        
              +        :param notebook_filename: the name of the notebook that needs to be ran
              +
              +        """
              +        # Load your notebook
              +        with open(notebook_filename) as f:
              +            nb = nbformat.read(f, as_version=4)
              +
              +        # Configure
              +        ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
              +
              +        # Execute
              +        #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}})
              +        ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}})
              +
              +        # Save output notebook
              +        with open(notebook_filename, 'w', encoding='utf-8') as f:
              +            nbformat.write(nb, f)
              +    
              +    
              +    def convert_notebook_to_pdf(self,notebook_filename):
              +        """Compiles a jupyter notebook that is located in this folder to pdf
              +
              +        :param notebook_filename: the name of the notebook that needs to be compiled to pdf
              +
              +        """
              +        os.system(f'jupyter nbconvert --to pdf {notebook_filename}')
              +    
              +    
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +

              Methods

              +
              +
              +def compile_latex_report(self, project_nr) +
              +
              +

              compiles latex code to pdf

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled

              +
              + +Expand source code + +
              def compile_latex_report(self,project_nr):
              +    """compiles latex code to pdf
              +
              +    :param project_nr: the numberr identifying which project is being  ran and compiled
              +
              +    """
              +    compile_latex =Compile_latex(project_nr ,'main.tex')
              +
              +
              +
              +def convert_notebook_to_pdf(self, notebook_filename) +
              +
              +

              Compiles a jupyter notebook that is located in this folder to pdf

              +

              :param notebook_filename: the name of the notebook that needs to be compiled to pdf

              +
              + +Expand source code + +
              def convert_notebook_to_pdf(self,notebook_filename):
              +    """Compiles a jupyter notebook that is located in this folder to pdf
              +
              +    :param notebook_filename: the name of the notebook that needs to be compiled to pdf
              +
              +    """
              +    os.system(f'jupyter nbconvert --to pdf {notebook_filename}')
              +
              +
              +
              +def convert_notebooks_to_pdf(self, project_nr, notebook_names) +
              +
              +

              converts a jupyter notebook to pdf

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled +:param notebook_names: list of strings with the names of the notebooks that need to be ran

              +
              + +Expand source code + +
              def convert_notebooks_to_pdf(self,project_nr,notebook_names):
              +    """converts a jupyter notebook to pdf
              +
              +    :param project_nr: the numberr identifying which project is being  ran and compiled
              +    :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +    """
              +    notebook_path = f'code/project{project_nr}/src/'
              +    
              +    for notebook_name in notebook_names:
              +        self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}')
              +
              +
              +
              +def get_script_dir(self) +
              +
              +

              returns the directory of this script regardles of from which level the code is executed

              +
              + +Expand source code + +
              def get_script_dir(self):
              +    """returns the directory of this script regardles of from which level the code is executed"""
              +    return os.path.dirname(__file__)
              +
              +
              +
              +def run_jupyter_notebooks(self, project_nr, notebook_names) +
              +
              +

              runs a jupyter notebook in this directory

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled +:param notebook_names: list of strings with the names of the notebooks that need to be ran

              +
              + +Expand source code + +
              def run_jupyter_notebooks(self,project_nr,notebook_names):
              +    """runs a jupyter notebook in this directory
              +
              +    :param project_nr: the numberr identifying which project is being  ran and compiled
              +    :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +    """
              +    notebook_path = f'code/project{project_nr}/src/'
              +    
              +    for notebook_name in notebook_names:
              +        self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}')
              +
              +
              +
              +def run_notebook(self, notebook_filename) +
              +
              +

              runs a +jupyter notebook that is located in this folder

              +

              :param notebook_filename: the name of the notebook that needs to be ran

              +
              + +Expand source code + +
              def run_notebook(self,notebook_filename):
              +    """runs a  jupyter notebook that is located in this folder
              +    
              +    :param notebook_filename: the name of the notebook that needs to be ran
              +
              +    """
              +    # Load your notebook
              +    with open(notebook_filename) as f:
              +        nb = nbformat.read(f, as_version=4)
              +
              +    # Configure
              +    ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
              +
              +    # Execute
              +    #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}})
              +    ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}})
              +
              +    # Save output notebook
              +    with open(notebook_filename, 'w', encoding='utf-8') as f:
              +        nbformat.write(nb, f)
              +
              +
              +
              +
              +
              +
              +
              + +
              + + + \ No newline at end of file diff --git a/code/project1/src/html/__main__.html b/code/project1/src/html/__main__.html new file mode 100644 index 0000000..ec76e2c --- /dev/null +++ b/code/project1/src/html/__main__.html @@ -0,0 +1,61 @@ + + + + + + +__main__ API documentation + + + + + + + + + + + +
              +
              +
              +

              Module __main__

              +
              +
              +
              + +Expand source code + +
              #!/home/a/anaconda3/envs/tudat-space/bin/python
              +# -*- coding: utf-8 -*-
              +import re
              +import sys
              +from pdoc.cli import main
              +if __name__ == '__main__':
              +    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
              +    sys.exit(main())
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +
              + +
              + + + \ No newline at end of file diff --git a/tudat-space_environment.yml b/tudat-space_environment.yml index 7463809..7e6b119 100644 --- a/tudat-space_environment.yml +++ b/tudat-space_environment.yml @@ -1,5 +1,5 @@ -# run: conda env create --file windows_environment.yml -# include new packages: conda env update --file windows_environment.yml +# run: conda env create --file tudat-space_environment.yml +# include new packages: conda env update --file tudat-space_environment.yml name: tudat-space channels: - conda-forge From f891a2627735da9129b20d66f78e38c3a0c78268 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Wed, 18 Nov 2020 15:19:20 +0100 Subject: [PATCH 53/57] Verfied all three assignment templates. --- ...4868_example_notebook_update20201025.ipynb | 31 ++--- ...AE4868_example_notebook_update20201025.pdf | Bin 408276 -> 409050 bytes code/project2/src/Compile_latex.py | 50 ++++++-- code/project2/src/Main.py | 93 +++++++++++++-- code/project2/src/Plot_to_tex.py | 109 ++++++++++++------ code/project2/src/Run_jupyter_notebooks.py | 61 ++++++++-- code/project2/src/__main__.py | 24 +++- ...4868_example_notebook_update20201025.ipynb | 31 ++--- ...AE4868_example_notebook_update20201025.pdf | Bin 408275 -> 409050 bytes code/project3/src/Compile_latex.py | 50 ++++++-- code/project3/src/Main.py | 93 +++++++++++++-- code/project3/src/Plot_to_tex.py | 109 ++++++++++++------ code/project3/src/Run_jupyter_notebooks.py | 61 ++++++++-- code/project3/src/__main__.py | 24 +++- latex/project2/Images/4b.png | Bin 38621 -> 37811 bytes latex/project2/Images/4c.png | Bin 27916 -> 30265 bytes latex/project2/main.pdf | Bin 608706 -> 622817 bytes latex/project3/Images/4b.png | Bin 40348 -> 38743 bytes latex/project3/Images/4c.png | Bin 30576 -> 28810 bytes latex/project3/main.pdf | Bin 609245 -> 623753 bytes 20 files changed, 574 insertions(+), 162 deletions(-) diff --git a/code/project2/src/AE4868_example_notebook_update20201025.ipynb b/code/project2/src/AE4868_example_notebook_update20201025.ipynb index ba9f95d..d480672 100755 --- a/code/project2/src/AE4868_example_notebook_update20201025.ipynb +++ b/code/project2/src/AE4868_example_notebook_update20201025.ipynb @@ -5,10 +5,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T20:02:26.058275Z", - "iopub.status.busy": "2020-11-09T20:02:26.057268Z", - "iopub.status.idle": "2020-11-09T20:02:26.060999Z", - "shell.execute_reply": "2020-11-09T20:02:26.060135Z" + "iopub.execute_input": "2020-11-18T14:17:07.451401Z", + "iopub.status.busy": "2020-11-18T14:17:07.450220Z", + "iopub.status.idle": "2020-11-18T14:17:07.453294Z", + "shell.execute_reply": "2020-11-18T14:17:07.454159Z" } }, "outputs": [], @@ -23,10 +23,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T20:02:26.081914Z", - "iopub.status.busy": "2020-11-09T20:02:26.080457Z", - "iopub.status.idle": "2020-11-09T20:02:27.105251Z", - "shell.execute_reply": "2020-11-09T20:02:27.106273Z" + "iopub.execute_input": "2020-11-18T14:17:07.463834Z", + "iopub.status.busy": "2020-11-18T14:17:07.463129Z", + "iopub.status.idle": "2020-11-18T14:17:08.329040Z", + "shell.execute_reply": "2020-11-18T14:17:08.329664Z" } }, "outputs": [ @@ -61,7 +61,12 @@ "from tudatpy.kernel.astro import conversion\n", "\n", "# Set path to latex image folders for project 2\n", - "latex_image_path = 'latex/project2/Images/'\n", + "\n", + "if (os.path.abspath('')[-12:]==\"project2/src\"):\n", + " latex_image_path = '../../../latex/project2/Images/'\n", + "else:\n", + " latex_image_path = 'latex/project2/Images/' # when ran as test\n", + "\n", "\n", "# Load spice kernels.\n", "spice_interface.load_standard_kernels()\n", @@ -253,10 +258,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2020-11-09T20:02:27.116133Z", - "iopub.status.busy": "2020-11-09T20:02:27.114967Z", - "iopub.status.idle": "2020-11-09T20:02:31.138809Z", - "shell.execute_reply": "2020-11-09T20:02:31.139935Z" + "iopub.execute_input": "2020-11-18T14:17:08.349072Z", + "iopub.status.busy": "2020-11-18T14:17:08.348422Z", + "iopub.status.idle": "2020-11-18T14:17:12.153250Z", + "shell.execute_reply": "2020-11-18T14:17:12.153900Z" } }, "outputs": [ diff --git a/code/project2/src/AE4868_example_notebook_update20201025.pdf b/code/project2/src/AE4868_example_notebook_update20201025.pdf index 30c481b2e0143065fca001bf5405c6a4c2dd7e44..055248f7292f5638861b167283c1be761aec4182 100644 GIT binary patch delta 47306 zcmZ6yV{o8dw5{7=$F^Daby+j*08Y}>YN+qTh3I!-!H&bO=XtvdVsn?Kj6S+mx& z#u{_^32XBmE1nDB6cB)MadkE`vV-y5ywZ`0+vY;(y{Wq;v=l4c{e=XFu?`)tgW-oe zO}vwm$^8h&(G>RjF4H5Xqjp8J^`ObN7;Bd*r<^AlQG9&9MMe?(J!}f{{qpz$yX!Z6 zRHiYvR|*Ko-}GW8)x!m=FCBR`CI&7o{YI9>7Ua+M_nZSdJP&nY`1lACU5XxFX>$Wz zs%|qZX3+)8?zB5Z5&No87jN~BP~nfmelp#FHR$iFQ1IIgF427dI2&&5wvO+4x~eYN zk$FBC8&g>7BPg4**@90(nzn&2*cr4M{w0eT1oj|&l3I0EPNmu1%9rz-=7L&pZB_L1 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zc|Q=aLh+$1nN?YJgFbTyWI2i$<~o*$7tqjA=bBADzt@vLJmXTa;o;X|Vu`F$3oiao z3xb)&SuP3oa}?Ue6HPzqT(W8w2*QWNS3z~!=VRyLft(OAdl#q}QB)c|5f|jduQ>|Y zFyb{NJ$_zzn(lLBOH^gMGh-{*2B1O)i6CULv47+6g$a2M%;IWOYiB8`+Xdcoaci3& z?xALQ?G2WF>Pm*xC2+(tFm~377vf&grIk?VahZKmMZ`OFy1&V9E90s7e$CxYKAS1` zzPaE;#F^Qlb^V?>59iWn7Hb`jc}HS>IRCl{u5Pd83ARdOnX&(;@1@%6YJnRL=g!== pKZDCmErd|#$yciz(;ZT{B}Y(Ny#OR392XY{3+%sDs=`m${{b9aE?fWr diff --git a/code/project3/src/Compile_latex.py b/code/project3/src/Compile_latex.py index 7c69d94..c331705 100644 --- a/code/project3/src/Compile_latex.py +++ b/code/project3/src/Compile_latex.py @@ -1,31 +1,59 @@ -# runs a jupyter notebook and converts it to pdf - +from nbconvert.preprocessors import ExecutePreprocessor import os import shutil import nbformat -from nbconvert.preprocessors import ExecutePreprocessor + class Compile_latex: + """Runs jupyter notebooks, converts them to pdf, + exports the notebook pdfs to latex and compiles the + latex report of the incoming project nr""" + def __init__(self,project_nr,latex_filename): + """Constructs attributes used throughout latex compilation + + :param project_nr: the numberr identifying which project is being ran and compiled + :param latex_filename: name of the main latex .tex file that manages the latex document + """ + self.script_dir = self.get_script_dir() relative_dir = f'latex/project{project_nr}/' self.compile_latex(relative_dir,latex_filename) self.clean_up_after_compilation(latex_filename) self.move_pdf_into_latex_dir(relative_dir,latex_filename) - # runs jupyter notebook + def compile_latex(self,relative_dir,latex_filename): + """Executes a commandline line to compile the latex report + + :param relative_dir: the relative dir towards the latex main .tex file + :param latex_filename: name of the main latex .tex file that manages the latex document + + """ os.system(f'pdflatex {relative_dir}{latex_filename}') - + + def clean_up_after_compilation(self,latex_filename): + """Removes the unneeded files that were generated during latex to pdf compilation. + + :param latex_filename: name of the main latex .tex file that manages the latex document + + """ latex_filename_without_extention = latex_filename[:-4] - print(f'latex_filename_without_extention={latex_filename_without_extention}') self.delete_file_if_exists(f'{latex_filename_without_extention}.aux') self.delete_file_if_exists(f'{latex_filename_without_extention}.log') self.delete_file_if_exists(f'texput.log') + def move_pdf_into_latex_dir(self,relative_dir,latex_filename): + """Moves the compiled/generated pdf file from the root of this repository to the + relative latex directory of this project. + + :param relative_dir: param latex_filename: + :param latex_filename: name of the main latex .tex file that manages the latex document + + """ pdf_filename = f'{latex_filename[:-4]}.pdf' destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}' @@ -34,15 +62,23 @@ def move_pdf_into_latex_dir(self,relative_dir,latex_filename): except: print("Error while moving file ", pdf_filename) + def delete_file_if_exists(self,filename): + """Deletes files if they exist + + :param filename: name of file that will be deleted if it exists in the root of this repository + + """ try: os.remove(filename) except: print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.') + def get_script_dir(self): - ''' returns the directory of this script regardles of from which level the code is executed ''' + """returns the directory of this script regardles of from which level the code is executed""" return os.path.dirname(__file__) + if __name__ == '__main__': main = Compile_latex() \ No newline at end of file diff --git a/code/project3/src/Main.py b/code/project3/src/Main.py index b69ff94..7b24eb2 100644 --- a/code/project3/src/Main.py +++ b/code/project3/src/Main.py @@ -1,21 +1,32 @@ -# Example code that creates plots directly in report -# Code is an implementation of a genetic algorithm -import random +from .Compile_latex import Compile_latex +from .Plot_to_tex import Plot_to_tex as plt_tex +from .Run_jupyter_notebooks import Run_jupyter_notebook + from matplotlib import pyplot as plt from matplotlib import lines import matplotlib.pyplot as plt import numpy as np - -from .Compile_latex import Compile_latex -from .Plot_to_tex import Plot_to_tex as plt_tex -from .Run_jupyter_notebooks import Run_jupyter_notebook +import random # define global variables for genetic algorithm example string_length = 100 mutation_chance= 1.0/string_length max_iterations = 1500 + class Main: + """Runs jupiter notebooks, then compiles them to pdf + Exports those notebook pdfs to the latex of this project + nr, then compiles the latex report to pdf. + + Als runs a genetic algorithm in conventional .py files + and exports them to the latex report, to illustrate the + functionality of the python and latex integration. + + Note that the latex is already compiled before the + genetic algorith (GA) is ran, so these results of the GA + are one version behind the latex pdf report. + """ def __init__(self): self.run_jupyter_notebook = Run_jupyter_notebook() @@ -23,41 +34,72 @@ def __init__(self): def run_jupyter_notebooks(self,project_nr,notebook_names): - '''runs a jupyter notebook''' + """calls a method that runs each jupyter notebook in the list of incoming notebook names + + :param project_nr: the numberr identifying which project is being ran and compiled + :param notebook_names: list of strings with the names of the notebooks that need to be ran + + """ notebook_path = f'code/project{project_nr}/src/' for notebook_name in notebook_names: self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}') + def convert_notebooks_to_pdf(self,project_nr,notebook_names): - '''converts a jupyter notebook to pdf''' + """calls a method that converts each jupyter notebook in the list of incoming notebook names + + :param project_nr: the numberr identifying which project is being ran and compiled + :param notebook_names: list of strings with the names of the notebooks that need to be ran + + """ notebook_path = f'code/project{project_nr}/src/' for notebook_name in notebook_names: self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}') + def compile_latex_report(self,project_nr): - '''compiles latex code to pdf''' + """compiles latex code to pdf + + :param project_nr: the numberr identifying which project is being ran and compiled + + """ compile_latex =Compile_latex(project_nr ,'main.tex') + ################################################################ ############example code to illustrate python-latex image sync######### ##############runs arbitrary genetic algorithm, can be deleted########### ################################################################ def count(self,bits): + """counts how many bits there are in a chromosome + + :param bits: representing values of dna in chromosome(s) + + """ count = 0 for bit in bits: if bit: count = count + 1 return count + def gen_bit_sequence(self): + """generates a random bit sequence that represents a chromosome of DNA""" bits = [] for _ in range(string_length): bits.append(True if random.randint(0, 1) == 1 else False) return bits + def mutate_bit_sequence(self,sequence): + """Randomly changes a bit sequence that changes the chromosome(s) of DNA + This is simulating for example radiation effects that generate arbitrary new offspring + + :param sequence: sequence of binary bits that represent a chromosome of DNA + + """ retval = [] for bit in sequence : do_mutation = random.random() <= mutation_chance @@ -67,8 +109,12 @@ def mutate_bit_sequence(self,sequence): retval.append(bit) return retval + #execute a run a def do_run_a(self): + """Performs a run of the genetic algorithm, like simulating evolution + and returns the fitness of the population. + """ seq = self.gen_bit_sequence() fitness = self.count(seq) @@ -85,6 +131,9 @@ def do_run_a(self): #execute a run c def do_run_c(self): + """Performs a run of the genetic algorithm, like simulating evolution + and returns the fitness of the population. + """ seq = self.gen_bit_sequence() fitness = self.count(seq) results = [fitness] @@ -96,7 +145,15 @@ def do_run_c(self): results.append(max(results[-1], fitness)) return results + def do4b(self,project_nr): + """Performs a run of the genetic algorithm, like simulating evolution + and exports the optimum fitness of the population per generation + as an image to the latex report of the incoming project nr. + + :param project_nr: the numberr identifying which project is being ran and compiled + + """ optimum_found = 0 # generate plot data @@ -119,6 +176,13 @@ def do4b(self,project_nr): print("total optimum found: {} out of {} runs".format(optimum_found,10)) def do4c(self,project_nr): + """Performs a run of the genetic algorithm, like simulating evolution + and exports the optimum fitness of the population per generation + as an image to the latex report of the incoming project nr. + + :param project_nr: the numberr identifying which project is being ran and compiled + + """ optimum_found = 0 # generate plot data @@ -140,9 +204,14 @@ def do4c(self,project_nr): plt_tex.plotMultipleLines(plt_tex,range(0, len(res)),plotResult,"[runs]]","fitness [%]",lineLabels,"4c",4,project_nr) print("total optimum found: {} out of {} runs".format(optimum_found, 10)) - + + def addTwo(self,x): - ''' adds two to the incoming integer and returns the result of the computation.''' + """adds two to the incoming integer and returns the result of the computation. + + :param x: incoming integer + + """ return x+2 if __name__ == '__main__': diff --git a/code/project3/src/Plot_to_tex.py b/code/project3/src/Plot_to_tex.py index 7ec9a4c..0e2a11d 100644 --- a/code/project3/src/Plot_to_tex.py +++ b/code/project3/src/Plot_to_tex.py @@ -1,38 +1,41 @@ -### Call this from another file, for project 11, question 3b: -### from Plot_to_tex import Plot_to_tex as plt_tex -### multiple_y_series = np.zeros((nrOfDataSeries,nrOfDataPoints), dtype=int); # actually fill with data -### lineLabels = [] # add a label for each dataseries -### plt_tex.plotMultipleLines(plt_tex,single_x_series,multiple_y_series,"x-axis label [units]","y-axis label [units]",lineLabels,"3b",4,11) -### 4b=filename -### 4 = position of legend, e.g. top right. -### -### For a single line, use: -### plt_tex.plotSingleLine(plt_tex,range(0, len(dataseries)),dataseries,"x-axis label [units]","y-axis label [units]",lineLabel,"3b",4,11) - -### You can also plot a table directly into latex, see example_create_a_table(..) -### -### Then put it in latex with for example: -###\begin{table}[H] -### \centering -### \caption{Results some computation.}\label{tab:some_computation} -### \begin{tabular}{|c|c|} % remember to update this to show all columns of table -### \hline -### \input{latex/project3/tables/q2.txt} -### \end{tabular} -###\end{table} -import random from matplotlib import lines import matplotlib.pyplot as plt import numpy as np import os -class Plot_to_tex: +import random + +class Plot_to_tex: + """Plots incoming images and/or tables to a latex report with a certain layout.""" + """ + Example of how to include an exported table into your latex report. + + \begin{table}[H] + \centering + \caption{Results some computation.}\label{tab:some_computation} + \begin{tabular}{|c|c|} % remember to update this to show all columns of table + \hline + \input{latex/project3/tables/q2.txt} + \end{tabular} + \end{table} + """ def __init__(self): self.script_dir = self.get_script_dir() - print("Created main") - - # plot graph (legendPosition = integer 1 to 4) + + def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr): + """Outputs a plot with a single line to a latex report + + :param x_path: x coordinates of a line + :param y_series: y coordinates of a line + :param x_axis_label: label of x axis + :param y_axis_label: label of y axis + :param label: string describing the line (label) + :param filename: filename of the image that is exported to latex + :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best') + :param project_nr: the number identifying to which latex project this image is exported + + """ fig=plt.figure(); ax=fig.add_subplot(111); ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none'); @@ -42,8 +45,20 @@ def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png'); # plt.show(); - # plot graphs + def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr): + """Outputs a plot with mulltiple lines to a latex report + + :param x: list of x coordinates of the lines of the plot + :param y_series: y coordinates of the lines of the plot + :param x_label: label of x axis + :param y_label: label of y axis + :param label: list of strings describing the lines (labels) + :param filename: filename of the image that is exported to latex + :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best') + :param project_nr: the number identifying to which latex project this image is exported + + """ fig=plt.figure(); ax=fig.add_subplot(111); @@ -66,14 +81,25 @@ def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosit print(f'plotted lines') - # Generate random line colours - # Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib + def get_cmap(n, name='hsv'): - '''Returns a function that maps each index in 0, 1, ..., n-1 to a distinct - RGB color; the keyword argument name must be a standard mpl colormap name.''' + """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct + RGB color; the keyword argument name must be a standard mpl colormap name. + Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib + + :param n: number of lines that need a distinct colour + :param name: (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc + + """ return plt.cm.get_cmap(name, n) + def generateLineTypes(y_series): + """Generates returns a list of a vissible line type for each incoming line/y_series + + :param y_series: list with list of y-coordinates representing the lines + + """ # generate varying linetypes typeOfLines = list(lines.lineStyles.keys()) @@ -89,9 +115,16 @@ def generateLineTypes(y_series): if (typeOfLines[i]==' '): typeOfLines[i]='--' return typeOfLines - - # Create a table with: table_matrix = np.zeros((4,4),dtype=object) and pass it to this object + + def put_table_in_tex(self, table_matrix,filename,project_nr): + """Outputs a table into a latex report + + :param table_matrix: numpy array with the table data + :param filename: filename of the table that is exported to latex + :param project_nr: the number identifying to which latex project this table is exported + + """ cols = np.shape(table_matrix)[1] format = "%s" for col in range(1,cols): @@ -99,8 +132,11 @@ def put_table_in_tex(self, table_matrix,filename,project_nr): format = format+"" plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline=' \\\\ \hline \n') - # replace this with your own table creation and then pass it to put_table_in_tex(..) + def example_create_a_table(self): + """Example code that generates the numpy array with + table data that can be exported to a latex table. Can + be modified to generate your own latex table""" project_nr = "1" table_name = "example_table_name" rows = 2; @@ -118,9 +154,10 @@ def example_create_a_table(self): def get_script_dir(self): - ''' returns the directory of this script regardles of from which level the code is executed ''' + """returns the path of the directory of this script""" return os.path.dirname(__file__) + if __name__ == '__main__': main = Plot_to_tex() main.example_create_a_table() \ No newline at end of file diff --git a/code/project3/src/Run_jupyter_notebooks.py b/code/project3/src/Run_jupyter_notebooks.py index 4f2fcb0..16f67de 100644 --- a/code/project3/src/Run_jupyter_notebooks.py +++ b/code/project3/src/Run_jupyter_notebooks.py @@ -1,19 +1,56 @@ -# runs a jupyter notebook and converts it to pdf - +from nbconvert.preprocessors import ExecutePreprocessor import os import nbformat -from nbconvert.preprocessors import ExecutePreprocessor class Run_jupyter_notebook: + """runs a list of jupyter notebooks and converts it to pdf""" + def __init__(self): self.script_dir = self.get_script_dir() - print("Created main") + + + def run_jupyter_notebooks(self,project_nr,notebook_names): + """runs a jupyter notebook in this directory + + :param project_nr: the numberr identifying which project is being ran and compiled + :param notebook_names: list of strings with the names of the notebooks that need to be ran + + """ + notebook_path = f'code/project{project_nr}/src/' + + for notebook_name in notebook_names: + self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}') + + + def convert_notebooks_to_pdf(self,project_nr,notebook_names): + """converts a jupyter notebook to pdf + + :param project_nr: the numberr identifying which project is being ran and compiled + :param notebook_names: list of strings with the names of the notebooks that need to be ran - # runs jupyter notebook + """ + notebook_path = f'code/project{project_nr}/src/' + + for notebook_name in notebook_names: + self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}') + + + def compile_latex_report(self,project_nr): + """compiles latex code to pdf + + :param project_nr: the numberr identifying which project is being ran and compiled + + """ + compile_latex =Compile_latex(project_nr ,'main.tex') + + def run_notebook(self,notebook_filename): + """runs a jupyter notebook that is located in this folder + :param notebook_filename: the name of the notebook that needs to be ran + """ # Load your notebook with open(notebook_filename) as f: nb = nbformat.read(f, as_version=4) @@ -22,19 +59,27 @@ def run_notebook(self,notebook_filename): ep = ExecutePreprocessor(timeout=600, kernel_name='python3') # Execute - ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}/../../../'}}) + #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}}) + ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}}) # Save output notebook with open(notebook_filename, 'w', encoding='utf-8') as f: nbformat.write(nb, f) - # converts jupyter notebook to pdf + def convert_notebook_to_pdf(self,notebook_filename): + """Compiles a jupyter notebook that is located in this folder to pdf + + :param notebook_filename: the name of the notebook that needs to be compiled to pdf + + """ os.system(f'jupyter nbconvert --to pdf {notebook_filename}') + def get_script_dir(self): - ''' returns the directory of this script regardles of from which level the code is executed ''' + """returns the directory of this script regardles of from which level the code is executed""" return os.path.dirname(__file__) + if __name__ == '__main__': main = Run_jupyter_notebook() \ No newline at end of file diff --git a/code/project3/src/__main__.py b/code/project3/src/__main__.py index c481ba8..408e3ed 100644 --- a/code/project3/src/__main__.py +++ b/code/project3/src/__main__.py @@ -1,5 +1,18 @@ -import os +''' +Runs the main code. + +First it runs the notebooks in this directory +Then it converts those notebooks to pdf +This is followed by compiling the latex report of this project to pdf. + +For illustration purposes, a genetic algorithm is also executed that +plots some images into the latex report. Since the report is compiled +before the genetic algorithm is ran, the new results are only included +after the second of this main + +''' from .Main import Main +import os print(f'Hi, I\'ll be running the main code, and I\'ll let you know when I\'m done.') project_nr = 3 @@ -16,12 +29,13 @@ # compile the latex report main.compile_latex_report(project_nr) + ################################################################ ############example code to illustrate python-latex image sync######### ##############runs arbitrary genetic algorithm, can be deleted########### ################################################################ # run a genetic algorithm to create some data for a plot. -print("now running a") +print("Running method a of Main.py to execute some genetic algorithm") res = main.do_run_a() # plot some graph with a single line, general form is: @@ -29,11 +43,11 @@ # main.plt_tex.plotSingleLine(plt_tex,range(0, len(res)),res,"[runs]]","fitness [%]","run 1","4a",4,project_nr) # run a genetic algorithm to create some data for another plot. -print("now running b") +print("Running method 4b of Main.py to execute some genetic algorithm") main.do4b(project_nr) # run a genetic algorithm to create some data for another plot. -print("now running 4c") +print("Running method 4c of Main.py to execute some genetic algorithm") main.do4c(project_nr) -print(f'Done.') \ No newline at end of file +print(f'Done with runing code.') \ No newline at end of file diff --git a/latex/project2/Images/4b.png b/latex/project2/Images/4b.png index 5a4b1514725c4c704d1080bd99f25988fda4419e..5c43a1343aebef12e4ff96b5812c5639ab3e0923 100644 GIT binary patch literal 37811 zcmeFZRZv`A7%g}j4IbRx9RdWG5C{Y(g1ZK9+@Wy@!2$$GAdnCw!QI`069{gNL!hB? zhkN*M&CH#Ld77&GbgAlsmUDW`x4*siTI(Z5OG60{n+h8M0KAvV3a)xO`)cibGI zPR~lHnHZuU{pLdJ{AgWiYqNil;BWge5x}xy_LZei@Euw>&HRFP)Gxl}9&i3RKPDRd zsiutIEQLM%CCrNsV*-DiKA^}E5)vjoeF4bz9+C=xkdGg1P zpSYipzr6P{@{cZ4jdojv4z2!=?`|9`=dnFxVn(g(Ds 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z07{W)QlC9A$KLt?dT$B)biF7`INe|CJ{%UeNm4bNUPM zF+}v9Ya~FSuL_}P!MQPCr!rMLlhkiIMUKwF)D!h z$%>v+?d3DcPhYqy1$=l(gqYw->#)X0hs+e1Rl9Kjj z=ALA_pX?+Lk3JH!sr{aUR-psUPzoQ*@`k`)cHu{z%m?ks}A)DM5pL4cTveFR$Jao7ijTtBad9DVM-=%nF96zVW7+`f02UpHHfC@19>4N_PIc~U2UQR%pg7tW#ikX|afOEIRx8BQ< zXLT4OuifWuh=>7aA#^RAfmJ>!%-ZqX3!f2N^8#DI!=jU&`}lGgquxtbE` z?e}Y$?J*#_Cy1OFOyygYiySv#7Ka>9VU|V|@}MD{N*v@-B44q0<>ytUj@c|+s%^xD z%WRA7Uh+t2T3d5(<%W6xMqV{+eDta#-)q4Bsp-xiX3whMjiraCApxyz8&Z4?22HgM e5K>9BPj9qsC From 5afbfb538a7e6f921a4d66db14dda02d11f69fba Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Wed, 18 Nov 2020 15:22:30 +0100 Subject: [PATCH 54/57] Included auto-generated html documentation for assignments 2 and 3. --- code/project2/src/html/Compile_latex.html | 357 ++++++++++ code/project2/src/html/Plot_to_tex.html | 624 ++++++++++++++++++ .../src/html/Run_jupyter_notebooks.html | 384 +++++++++++ code/project2/src/html/__main__.html | 61 ++ code/project3/src/html/Compile_latex.html | 357 ++++++++++ code/project3/src/html/Plot_to_tex.html | 624 ++++++++++++++++++ .../src/html/Run_jupyter_notebooks.html | 384 +++++++++++ code/project3/src/html/__main__.html | 61 ++ 8 files changed, 2852 insertions(+) create mode 100644 code/project2/src/html/Compile_latex.html create mode 100644 code/project2/src/html/Plot_to_tex.html create mode 100644 code/project2/src/html/Run_jupyter_notebooks.html create mode 100644 code/project2/src/html/__main__.html create mode 100644 code/project3/src/html/Compile_latex.html create mode 100644 code/project3/src/html/Plot_to_tex.html create mode 100644 code/project3/src/html/Run_jupyter_notebooks.html create mode 100644 code/project3/src/html/__main__.html diff --git a/code/project2/src/html/Compile_latex.html b/code/project2/src/html/Compile_latex.html new file mode 100644 index 0000000..69fbf91 --- /dev/null +++ b/code/project2/src/html/Compile_latex.html @@ -0,0 +1,357 @@ + + + + + + +Compile_latex API documentation + + + + + + + + + + + +

              +
              +
              +

              Module Compile_latex

              +
              +
              +
              + +Expand source code + +
              from nbconvert.preprocessors import ExecutePreprocessor
              +import os
              +import shutil
              +import nbformat
              +
              +
              +class Compile_latex:
              +    """Runs jupyter notebooks, converts them to pdf,
              +    exports the notebook pdfs to latex and compiles the 
              +    latex report of the incoming project nr"""
              +
              +
              +    def __init__(self,project_nr,latex_filename):
              +        """Constructs attributes used throughout latex compilation
              +        
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +        """
              +    
              +        self.script_dir = self.get_script_dir()
              +        relative_dir = f'latex/project{project_nr}/'
              +        self.compile_latex(relative_dir,latex_filename)
              +        self.clean_up_after_compilation(latex_filename)
              +        self.move_pdf_into_latex_dir(relative_dir,latex_filename)
              +
              +    
              +    def compile_latex(self,relative_dir,latex_filename):
              +        """Executes a commandline line to compile the latex report
              +
              +        :param relative_dir: the relative dir towards the latex main .tex file
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        os.system(f'pdflatex {relative_dir}{latex_filename}')
              +    
              +    
              +    def clean_up_after_compilation(self,latex_filename):
              +        """Removes the unneeded files that were generated during latex to pdf compilation.
              +
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        latex_filename_without_extention = latex_filename[:-4]
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.aux')
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.log')
              +        self.delete_file_if_exists(f'texput.log')
              +    
              +
              +    def move_pdf_into_latex_dir(self,relative_dir,latex_filename):
              +        """Moves the compiled/generated pdf file from the root of this repository to the
              +        relative latex directory of this project.
              +
              +        :param relative_dir: param latex_filename:
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        pdf_filename = f'{latex_filename[:-4]}.pdf'
              +        destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}'
              +        
              +        try:
              +            shutil.move(pdf_filename, destination)
              +        except:
              +            print("Error while moving file ", pdf_filename)
              +    
              +
              +    def delete_file_if_exists(self,filename):
              +        """Deletes files if they exist
              +
              +        :param filename: name of file that will be deleted if it exists in the root of this repository
              +
              +        """
              +        try:
              +            os.remove(filename)
              +        except:
              +            print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.')
              +    
              +
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +
              +if __name__ == '__main__':
              +    main = Compile_latex()
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +

              Classes

              +
              +
              +class Compile_latex +(project_nr, latex_filename) +
              +
              +

              Runs jupyter notebooks, converts them to pdf, +exports the notebook pdfs to latex and compiles the +latex report of the incoming project nr

              +

              Constructs attributes used throughout latex compilation

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled +:param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              class Compile_latex:
              +    """Runs jupyter notebooks, converts them to pdf,
              +    exports the notebook pdfs to latex and compiles the 
              +    latex report of the incoming project nr"""
              +
              +
              +    def __init__(self,project_nr,latex_filename):
              +        """Constructs attributes used throughout latex compilation
              +        
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +        """
              +    
              +        self.script_dir = self.get_script_dir()
              +        relative_dir = f'latex/project{project_nr}/'
              +        self.compile_latex(relative_dir,latex_filename)
              +        self.clean_up_after_compilation(latex_filename)
              +        self.move_pdf_into_latex_dir(relative_dir,latex_filename)
              +
              +    
              +    def compile_latex(self,relative_dir,latex_filename):
              +        """Executes a commandline line to compile the latex report
              +
              +        :param relative_dir: the relative dir towards the latex main .tex file
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        os.system(f'pdflatex {relative_dir}{latex_filename}')
              +    
              +    
              +    def clean_up_after_compilation(self,latex_filename):
              +        """Removes the unneeded files that were generated during latex to pdf compilation.
              +
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        latex_filename_without_extention = latex_filename[:-4]
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.aux')
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.log')
              +        self.delete_file_if_exists(f'texput.log')
              +    
              +
              +    def move_pdf_into_latex_dir(self,relative_dir,latex_filename):
              +        """Moves the compiled/generated pdf file from the root of this repository to the
              +        relative latex directory of this project.
              +
              +        :param relative_dir: param latex_filename:
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        pdf_filename = f'{latex_filename[:-4]}.pdf'
              +        destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}'
              +        
              +        try:
              +            shutil.move(pdf_filename, destination)
              +        except:
              +            print("Error while moving file ", pdf_filename)
              +    
              +
              +    def delete_file_if_exists(self,filename):
              +        """Deletes files if they exist
              +
              +        :param filename: name of file that will be deleted if it exists in the root of this repository
              +
              +        """
              +        try:
              +            os.remove(filename)
              +        except:
              +            print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.')
              +    
              +
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +

              Methods

              +
              +
              +def clean_up_after_compilation(self, latex_filename) +
              +
              +

              Removes the unneeded files that were generated during latex to pdf compilation.

              +

              :param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              def clean_up_after_compilation(self,latex_filename):
              +    """Removes the unneeded files that were generated during latex to pdf compilation.
              +
              +    :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +    """
              +    latex_filename_without_extention = latex_filename[:-4]
              +    self.delete_file_if_exists(f'{latex_filename_without_extention}.aux')
              +    self.delete_file_if_exists(f'{latex_filename_without_extention}.log')
              +    self.delete_file_if_exists(f'texput.log')
              +
              +
              +
              +def compile_latex(self, relative_dir, latex_filename) +
              +
              +

              Executes a commandline line to compile the latex report

              +

              :param relative_dir: the relative dir towards the latex main .tex file +:param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              def compile_latex(self,relative_dir,latex_filename):
              +    """Executes a commandline line to compile the latex report
              +
              +    :param relative_dir: the relative dir towards the latex main .tex file
              +    :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +    """
              +    os.system(f'pdflatex {relative_dir}{latex_filename}')
              +
              +
              +
              +def delete_file_if_exists(self, filename) +
              +
              +

              Deletes files if they exist

              +

              :param filename: name of file that will be deleted if it exists in the root of this repository

              +
              + +Expand source code + +
              def delete_file_if_exists(self,filename):
              +    """Deletes files if they exist
              +
              +    :param filename: name of file that will be deleted if it exists in the root of this repository
              +
              +    """
              +    try:
              +        os.remove(filename)
              +    except:
              +        print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.')
              +
              +
              +
              +def get_script_dir(self) +
              +
              +

              returns the directory of this script regardles of from which level the code is executed

              +
              + +Expand source code + +
              def get_script_dir(self):
              +    """returns the directory of this script regardles of from which level the code is executed"""
              +    return os.path.dirname(__file__)
              +
              +
              +
              +def move_pdf_into_latex_dir(self, relative_dir, latex_filename) +
              +
              +

              Moves the compiled/generated pdf file from the root of this repository to the +relative latex directory of this project.

              +

              :param relative_dir: param latex_filename: +:param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              def move_pdf_into_latex_dir(self,relative_dir,latex_filename):
              +    """Moves the compiled/generated pdf file from the root of this repository to the
              +    relative latex directory of this project.
              +
              +    :param relative_dir: param latex_filename:
              +    :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +    """
              +    pdf_filename = f'{latex_filename[:-4]}.pdf'
              +    destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}'
              +    
              +    try:
              +        shutil.move(pdf_filename, destination)
              +    except:
              +        print("Error while moving file ", pdf_filename)
              +
              +
              +
              +
              +
              +
              +
              +
              +
              + + + \ No newline at end of file diff --git a/code/project2/src/html/Plot_to_tex.html b/code/project2/src/html/Plot_to_tex.html new file mode 100644 index 0000000..54c8c4c --- /dev/null +++ b/code/project2/src/html/Plot_to_tex.html @@ -0,0 +1,624 @@ + + + + + + +Plot_to_tex API documentation + + + + + + + + + + + +
              +
              +
              +

              Module Plot_to_tex

              +
              +
              +
              + +Expand source code + +
              from matplotlib import lines
              +import matplotlib.pyplot as plt
              +import numpy as np
              +import os
              +import random
              +
              +
              +class Plot_to_tex:
              +    """Plots incoming images and/or tables to a latex report with a certain layout."""
              +    """
              +    Example of how to include an exported table into your latex report.
              +
              +    \begin{table}[H]
              +        \centering
              +        \caption{Results some computation.}\label{tab:some_computation}
              +        \begin{tabular}{|c|c|} % remember to update this to show all columns of table
              +            \hline
              +            \input{latex/project3/tables/q2.txt}
              +        \end{tabular}
              +    \end{table}
              +    """
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +        
              +    def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with a single line to a latex report
              +
              +        :param x_path: x coordinates of a line
              +        :param y_series: y coordinates of a line
              +        :param x_axis_label: label of x axis 
              +        :param y_axis_label: label of y axis 
              +        :param label: string describing the line (label)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +        ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none');
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_axis_label);
              +        plt.ylabel(y_axis_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +#         plt.show();
              +
              +
              +    def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with mulltiple lines to a latex report
              +
              +        :param x: list of x coordinates of the lines of the plot
              +        :param y_series: y coordinates of the lines of the plot 
              +        :param x_label: label of x axis 
              +        :param y_label: label of y axis 
              +        :param label: list of strings describing the lines (labels)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +
              +        # generate colours
              +        cmap = self.get_cmap(len(y_series[:,0]))
              +
              +        # generate line types
              +        lineTypes = self.generateLineTypes(y_series)
              +
              +        for i in range(0,len(y_series)):
              +            # overwrite linetypes to single type
              +            lineTypes[i] = "-"
              +            ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color
              +
              +        # configure plot layout
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_label);
              +        plt.ylabel(y_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +        
              +        print(f'plotted lines')
              +
              +    
              +    def get_cmap(n, name='hsv'):
              +        """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
              +        RGB color; the keyword argument name must be a standard mpl colormap name.
              +        Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
              +
              +        :param n: number of lines that need a distinct colour
              +        :param name:  (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc
              +
              +        """
              +        return plt.cm.get_cmap(name, n)
              +
              +
              +    def generateLineTypes(y_series):
              +        """Generates returns a list of a vissible line type for each incoming line/y_series
              +
              +        :param y_series: list with list of y-coordinates representing the lines
              +
              +        """
              +        # generate varying linetypes
              +        typeOfLines = list(lines.lineStyles.keys())
              +
              +        while(len(y_series)>len(typeOfLines)):
              +            typeOfLines.append("-.");
              +
              +        # remove void lines
              +        for i in range(0, len(y_series)):
              +            if (typeOfLines[i]=='None'):
              +                typeOfLines[i]='-'
              +            if (typeOfLines[i]==''):
              +                typeOfLines[i]=':'
              +            if (typeOfLines[i]==' '):
              +                typeOfLines[i]='--'
              +        return typeOfLines
              +        
              +        
              +    def put_table_in_tex(self, table_matrix,filename,project_nr):
              +        """Outputs a table into a latex report
              +
              +        :param table_matrix: numpy array with the table data
              +        :param filename: filename of the table that is exported to latex
              +        :param project_nr: the number identifying to which latex project this table is exported
              +
              +        """
              +        cols = np.shape(table_matrix)[1]
              +        format = "%s"
              +        for col in range(1,cols):
              +            format = format+" & %s"
              +        format = format+""
              +        plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline='  \\\\ \hline \n')
              +
              +    
              +    def example_create_a_table(self):
              +        """Example code that generates the numpy array with 
              +        table data that can be exported to a latex table. Can 
              +        be modified to generate your own latex table"""
              +        project_nr = "1"
              +        table_name = "example_table_name"
              +        rows = 2;
              +        columns = 4;
              +        table_matrix = np.zeros((rows,columns),dtype=object)
              +        table_matrix[:,:]="" # replace the standard zeros with emtpy cell
              +        print(table_matrix)
              +        for column in range(0,columns):
              +            for row in range(0,rows):
              +                table_matrix[row,column]=row+column
              +        table_matrix[1,0]="example"
              +        table_matrix[0,1]="grid sizes"
              +
              +        self.put_table_in_tex(table_matrix,table_name,project_nr)
              +        
              +    
              +    def get_script_dir(self):
              +        """returns the path of the directory of this script"""
              +        return os.path.dirname(__file__)
              +
              +
              +if __name__ == '__main__':
              +    main = Plot_to_tex()
              +    main.example_create_a_table()
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +

              Classes

              +
              +
              +class Plot_to_tex +
              +
              +

              Plots incoming images and/or tables to a latex report with a certain layout.

              +
              + +Expand source code + +
              class Plot_to_tex:
              +    """Plots incoming images and/or tables to a latex report with a certain layout."""
              +    """
              +    Example of how to include an exported table into your latex report.
              +
              +    \begin{table}[H]
              +        \centering
              +        \caption{Results some computation.}\label{tab:some_computation}
              +        \begin{tabular}{|c|c|} % remember to update this to show all columns of table
              +            \hline
              +            \input{latex/project3/tables/q2.txt}
              +        \end{tabular}
              +    \end{table}
              +    """
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +        
              +    def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with a single line to a latex report
              +
              +        :param x_path: x coordinates of a line
              +        :param y_series: y coordinates of a line
              +        :param x_axis_label: label of x axis 
              +        :param y_axis_label: label of y axis 
              +        :param label: string describing the line (label)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +        ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none');
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_axis_label);
              +        plt.ylabel(y_axis_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +#         plt.show();
              +
              +
              +    def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with mulltiple lines to a latex report
              +
              +        :param x: list of x coordinates of the lines of the plot
              +        :param y_series: y coordinates of the lines of the plot 
              +        :param x_label: label of x axis 
              +        :param y_label: label of y axis 
              +        :param label: list of strings describing the lines (labels)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +
              +        # generate colours
              +        cmap = self.get_cmap(len(y_series[:,0]))
              +
              +        # generate line types
              +        lineTypes = self.generateLineTypes(y_series)
              +
              +        for i in range(0,len(y_series)):
              +            # overwrite linetypes to single type
              +            lineTypes[i] = "-"
              +            ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color
              +
              +        # configure plot layout
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_label);
              +        plt.ylabel(y_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +        
              +        print(f'plotted lines')
              +
              +    
              +    def get_cmap(n, name='hsv'):
              +        """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
              +        RGB color; the keyword argument name must be a standard mpl colormap name.
              +        Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
              +
              +        :param n: number of lines that need a distinct colour
              +        :param name:  (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc
              +
              +        """
              +        return plt.cm.get_cmap(name, n)
              +
              +
              +    def generateLineTypes(y_series):
              +        """Generates returns a list of a vissible line type for each incoming line/y_series
              +
              +        :param y_series: list with list of y-coordinates representing the lines
              +
              +        """
              +        # generate varying linetypes
              +        typeOfLines = list(lines.lineStyles.keys())
              +
              +        while(len(y_series)>len(typeOfLines)):
              +            typeOfLines.append("-.");
              +
              +        # remove void lines
              +        for i in range(0, len(y_series)):
              +            if (typeOfLines[i]=='None'):
              +                typeOfLines[i]='-'
              +            if (typeOfLines[i]==''):
              +                typeOfLines[i]=':'
              +            if (typeOfLines[i]==' '):
              +                typeOfLines[i]='--'
              +        return typeOfLines
              +        
              +        
              +    def put_table_in_tex(self, table_matrix,filename,project_nr):
              +        """Outputs a table into a latex report
              +
              +        :param table_matrix: numpy array with the table data
              +        :param filename: filename of the table that is exported to latex
              +        :param project_nr: the number identifying to which latex project this table is exported
              +
              +        """
              +        cols = np.shape(table_matrix)[1]
              +        format = "%s"
              +        for col in range(1,cols):
              +            format = format+" & %s"
              +        format = format+""
              +        plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline='  \\\\ \hline \n')
              +
              +    
              +    def example_create_a_table(self):
              +        """Example code that generates the numpy array with 
              +        table data that can be exported to a latex table. Can 
              +        be modified to generate your own latex table"""
              +        project_nr = "1"
              +        table_name = "example_table_name"
              +        rows = 2;
              +        columns = 4;
              +        table_matrix = np.zeros((rows,columns),dtype=object)
              +        table_matrix[:,:]="" # replace the standard zeros with emtpy cell
              +        print(table_matrix)
              +        for column in range(0,columns):
              +            for row in range(0,rows):
              +                table_matrix[row,column]=row+column
              +        table_matrix[1,0]="example"
              +        table_matrix[0,1]="grid sizes"
              +
              +        self.put_table_in_tex(table_matrix,table_name,project_nr)
              +        
              +    
              +    def get_script_dir(self):
              +        """returns the path of the directory of this script"""
              +        return os.path.dirname(__file__)
              +
              +

              Methods

              +
              +
              +def example_create_a_table(self) +
              +
              +

              Example code that generates the numpy array with +table data that can be exported to a latex table. Can +be modified to generate your own latex table

              +
              + +Expand source code + +
              def example_create_a_table(self):
              +    """Example code that generates the numpy array with 
              +    table data that can be exported to a latex table. Can 
              +    be modified to generate your own latex table"""
              +    project_nr = "1"
              +    table_name = "example_table_name"
              +    rows = 2;
              +    columns = 4;
              +    table_matrix = np.zeros((rows,columns),dtype=object)
              +    table_matrix[:,:]="" # replace the standard zeros with emtpy cell
              +    print(table_matrix)
              +    for column in range(0,columns):
              +        for row in range(0,rows):
              +            table_matrix[row,column]=row+column
              +    table_matrix[1,0]="example"
              +    table_matrix[0,1]="grid sizes"
              +
              +    self.put_table_in_tex(table_matrix,table_name,project_nr)
              +
              +
              +
              +def generateLineTypes(y_series) +
              +
              +

              Generates returns a list of a vissible line type for each incoming line/y_series

              +

              :param y_series: list with list of y-coordinates representing the lines

              +
              + +Expand source code + +
              def generateLineTypes(y_series):
              +    """Generates returns a list of a vissible line type for each incoming line/y_series
              +
              +    :param y_series: list with list of y-coordinates representing the lines
              +
              +    """
              +    # generate varying linetypes
              +    typeOfLines = list(lines.lineStyles.keys())
              +
              +    while(len(y_series)>len(typeOfLines)):
              +        typeOfLines.append("-.");
              +
              +    # remove void lines
              +    for i in range(0, len(y_series)):
              +        if (typeOfLines[i]=='None'):
              +            typeOfLines[i]='-'
              +        if (typeOfLines[i]==''):
              +            typeOfLines[i]=':'
              +        if (typeOfLines[i]==' '):
              +            typeOfLines[i]='--'
              +    return typeOfLines
              +
              +
              +
              +def get_cmap(n, name='hsv') +
              +
              +

              Returns a function that maps each index in 0, 1, …, n-1 to a distinct +RGB color; the keyword argument name must be a standard mpl colormap name. +Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib

              +

              :param n: number of lines that need a distinct colour +:param name: +(Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc

              +
              + +Expand source code + +
              def get_cmap(n, name='hsv'):
              +    """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
              +    RGB color; the keyword argument name must be a standard mpl colormap name.
              +    Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
              +
              +    :param n: number of lines that need a distinct colour
              +    :param name:  (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc
              +
              +    """
              +    return plt.cm.get_cmap(name, n)
              +
              +
              +
              +def get_script_dir(self) +
              +
              +

              returns the path of the directory of this script

              +
              + +Expand source code + +
              def get_script_dir(self):
              +    """returns the path of the directory of this script"""
              +    return os.path.dirname(__file__)
              +
              +
              +
              +def plotMultipleLines(self, x, y_series, x_label, y_label, label, filename, legendPosition, project_nr) +
              +
              +

              Outputs a plot with mulltiple lines to a latex report

              +

              :param x: list of x coordinates of the lines of the plot +:param y_series: y coordinates of the lines of the plot +:param x_label: label of x axis +:param y_label: label of y axis +:param label: list of strings describing the lines (labels) +:param filename: filename of the image that is exported to latex +:param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best') +:param project_nr: the number identifying to which latex project this image is exported

              +
              + +Expand source code + +
              def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr):
              +    """Outputs a plot with mulltiple lines to a latex report
              +
              +    :param x: list of x coordinates of the lines of the plot
              +    :param y_series: y coordinates of the lines of the plot 
              +    :param x_label: label of x axis 
              +    :param y_label: label of y axis 
              +    :param label: list of strings describing the lines (labels)
              +    :param filename: filename of the image that is exported to latex
              +    :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +    :param project_nr: the number identifying to which latex project this image is exported
              +
              +    """
              +    fig=plt.figure();
              +    ax=fig.add_subplot(111);
              +
              +    # generate colours
              +    cmap = self.get_cmap(len(y_series[:,0]))
              +
              +    # generate line types
              +    lineTypes = self.generateLineTypes(y_series)
              +
              +    for i in range(0,len(y_series)):
              +        # overwrite linetypes to single type
              +        lineTypes[i] = "-"
              +        ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color
              +
              +    # configure plot layout
              +    plt.legend(loc=legendPosition);
              +    plt.xlabel(x_label);
              +    plt.ylabel(y_label);
              +    plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +    
              +    print(f'plotted lines')
              +
              +
              +
              +def plotSingleLine(self, x_path, y_series, x_axis_label, y_axis_label, label, filename, legendPosition, project_nr) +
              +
              +

              Outputs a plot with a single line to a latex report

              +

              :param x_path: x coordinates of a line +:param y_series: y coordinates of a line +:param x_axis_label: label of x axis +:param y_axis_label: label of y axis +:param label: string describing the line (label) +:param filename: filename of the image that is exported to latex +:param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best') +:param project_nr: the number identifying to which latex project this image is exported

              +
              + +Expand source code + +
              def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr):
              +    """Outputs a plot with a single line to a latex report
              +
              +    :param x_path: x coordinates of a line
              +    :param y_series: y coordinates of a line
              +    :param x_axis_label: label of x axis 
              +    :param y_axis_label: label of y axis 
              +    :param label: string describing the line (label)
              +    :param filename: filename of the image that is exported to latex
              +    :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +    :param project_nr: the number identifying to which latex project this image is exported
              +
              +    """
              +    fig=plt.figure();
              +    ax=fig.add_subplot(111);
              +    ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none');
              +    plt.legend(loc=legendPosition);
              +    plt.xlabel(x_axis_label);
              +    plt.ylabel(y_axis_label);
              +    plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +
              +
              +
              +def put_table_in_tex(self, table_matrix, filename, project_nr) +
              +
              +

              Outputs a table into a latex report

              +

              :param table_matrix: numpy array with the table data +:param filename: filename of the table that is exported to latex +:param project_nr: the number identifying to which latex project this table is exported

              +
              + +Expand source code + +
              def put_table_in_tex(self, table_matrix,filename,project_nr):
              +    """Outputs a table into a latex report
              +
              +    :param table_matrix: numpy array with the table data
              +    :param filename: filename of the table that is exported to latex
              +    :param project_nr: the number identifying to which latex project this table is exported
              +
              +    """
              +    cols = np.shape(table_matrix)[1]
              +    format = "%s"
              +    for col in range(1,cols):
              +        format = format+" & %s"
              +    format = format+""
              +    plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline='  \\\\ \hline \n')
              +
              +
              +
              +
              +
              +
              +
              + +
              + + + \ No newline at end of file diff --git a/code/project2/src/html/Run_jupyter_notebooks.html b/code/project2/src/html/Run_jupyter_notebooks.html new file mode 100644 index 0000000..3d23a1f --- /dev/null +++ b/code/project2/src/html/Run_jupyter_notebooks.html @@ -0,0 +1,384 @@ + + + + + + +Run_jupyter_notebooks API documentation + + + + + + + + + + + +
              +
              +
              +

              Module Run_jupyter_notebooks

              +
              +
              +
              + +Expand source code + +
              from nbconvert.preprocessors import ExecutePreprocessor
              +import os
              +import nbformat
              +
              +class Run_jupyter_notebook:
              +    """runs a list of  jupyter notebooks and converts it to pdf"""
              +
              +    
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +
              +    def run_jupyter_notebooks(self,project_nr,notebook_names):
              +        """runs a jupyter notebook in this directory
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def convert_notebooks_to_pdf(self,project_nr,notebook_names):
              +        """converts a jupyter notebook to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def compile_latex_report(self,project_nr):
              +        """compiles latex code to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +
              +        """
              +        compile_latex =Compile_latex(project_nr ,'main.tex')
              +
              +    
              +    def run_notebook(self,notebook_filename):
              +        """runs a  jupyter notebook that is located in this folder
              +        
              +        :param notebook_filename: the name of the notebook that needs to be ran
              +
              +        """
              +        # Load your notebook
              +        with open(notebook_filename) as f:
              +            nb = nbformat.read(f, as_version=4)
              +
              +        # Configure
              +        ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
              +
              +        # Execute
              +        #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}})
              +        ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}})
              +
              +        # Save output notebook
              +        with open(notebook_filename, 'w', encoding='utf-8') as f:
              +            nbformat.write(nb, f)
              +    
              +    
              +    def convert_notebook_to_pdf(self,notebook_filename):
              +        """Compiles a jupyter notebook that is located in this folder to pdf
              +
              +        :param notebook_filename: the name of the notebook that needs to be compiled to pdf
              +
              +        """
              +        os.system(f'jupyter nbconvert --to pdf {notebook_filename}')
              +    
              +    
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +
              +if __name__ == '__main__':
              +    main = Run_jupyter_notebook()
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +

              Classes

              +
              +
              +class Run_jupyter_notebook +
              +
              +

              runs a list of +jupyter notebooks and converts it to pdf

              +
              + +Expand source code + +
              class Run_jupyter_notebook:
              +    """runs a list of  jupyter notebooks and converts it to pdf"""
              +
              +    
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +
              +    def run_jupyter_notebooks(self,project_nr,notebook_names):
              +        """runs a jupyter notebook in this directory
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def convert_notebooks_to_pdf(self,project_nr,notebook_names):
              +        """converts a jupyter notebook to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def compile_latex_report(self,project_nr):
              +        """compiles latex code to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +
              +        """
              +        compile_latex =Compile_latex(project_nr ,'main.tex')
              +
              +    
              +    def run_notebook(self,notebook_filename):
              +        """runs a  jupyter notebook that is located in this folder
              +        
              +        :param notebook_filename: the name of the notebook that needs to be ran
              +
              +        """
              +        # Load your notebook
              +        with open(notebook_filename) as f:
              +            nb = nbformat.read(f, as_version=4)
              +
              +        # Configure
              +        ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
              +
              +        # Execute
              +        #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}})
              +        ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}})
              +
              +        # Save output notebook
              +        with open(notebook_filename, 'w', encoding='utf-8') as f:
              +            nbformat.write(nb, f)
              +    
              +    
              +    def convert_notebook_to_pdf(self,notebook_filename):
              +        """Compiles a jupyter notebook that is located in this folder to pdf
              +
              +        :param notebook_filename: the name of the notebook that needs to be compiled to pdf
              +
              +        """
              +        os.system(f'jupyter nbconvert --to pdf {notebook_filename}')
              +    
              +    
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +

              Methods

              +
              +
              +def compile_latex_report(self, project_nr) +
              +
              +

              compiles latex code to pdf

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled

              +
              + +Expand source code + +
              def compile_latex_report(self,project_nr):
              +    """compiles latex code to pdf
              +
              +    :param project_nr: the numberr identifying which project is being  ran and compiled
              +
              +    """
              +    compile_latex =Compile_latex(project_nr ,'main.tex')
              +
              +
              +
              +def convert_notebook_to_pdf(self, notebook_filename) +
              +
              +

              Compiles a jupyter notebook that is located in this folder to pdf

              +

              :param notebook_filename: the name of the notebook that needs to be compiled to pdf

              +
              + +Expand source code + +
              def convert_notebook_to_pdf(self,notebook_filename):
              +    """Compiles a jupyter notebook that is located in this folder to pdf
              +
              +    :param notebook_filename: the name of the notebook that needs to be compiled to pdf
              +
              +    """
              +    os.system(f'jupyter nbconvert --to pdf {notebook_filename}')
              +
              +
              +
              +def convert_notebooks_to_pdf(self, project_nr, notebook_names) +
              +
              +

              converts a jupyter notebook to pdf

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled +:param notebook_names: list of strings with the names of the notebooks that need to be ran

              +
              + +Expand source code + +
              def convert_notebooks_to_pdf(self,project_nr,notebook_names):
              +    """converts a jupyter notebook to pdf
              +
              +    :param project_nr: the numberr identifying which project is being  ran and compiled
              +    :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +    """
              +    notebook_path = f'code/project{project_nr}/src/'
              +    
              +    for notebook_name in notebook_names:
              +        self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}')
              +
              +
              +
              +def get_script_dir(self) +
              +
              +

              returns the directory of this script regardles of from which level the code is executed

              +
              + +Expand source code + +
              def get_script_dir(self):
              +    """returns the directory of this script regardles of from which level the code is executed"""
              +    return os.path.dirname(__file__)
              +
              +
              +
              +def run_jupyter_notebooks(self, project_nr, notebook_names) +
              +
              +

              runs a jupyter notebook in this directory

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled +:param notebook_names: list of strings with the names of the notebooks that need to be ran

              +
              + +Expand source code + +
              def run_jupyter_notebooks(self,project_nr,notebook_names):
              +    """runs a jupyter notebook in this directory
              +
              +    :param project_nr: the numberr identifying which project is being  ran and compiled
              +    :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +    """
              +    notebook_path = f'code/project{project_nr}/src/'
              +    
              +    for notebook_name in notebook_names:
              +        self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}')
              +
              +
              +
              +def run_notebook(self, notebook_filename) +
              +
              +

              runs a +jupyter notebook that is located in this folder

              +

              :param notebook_filename: the name of the notebook that needs to be ran

              +
              + +Expand source code + +
              def run_notebook(self,notebook_filename):
              +    """runs a  jupyter notebook that is located in this folder
              +    
              +    :param notebook_filename: the name of the notebook that needs to be ran
              +
              +    """
              +    # Load your notebook
              +    with open(notebook_filename) as f:
              +        nb = nbformat.read(f, as_version=4)
              +
              +    # Configure
              +    ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
              +
              +    # Execute
              +    #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}})
              +    ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}})
              +
              +    # Save output notebook
              +    with open(notebook_filename, 'w', encoding='utf-8') as f:
              +        nbformat.write(nb, f)
              +
              +
              +
              +
              +
              +
              +
              + +
              + + + \ No newline at end of file diff --git a/code/project2/src/html/__main__.html b/code/project2/src/html/__main__.html new file mode 100644 index 0000000..adbbff9 --- /dev/null +++ b/code/project2/src/html/__main__.html @@ -0,0 +1,61 @@ + + + + + + +__main__ API documentation + + + + + + + + + + + +
              +
              +
              +

              Module __main__

              +
              +
              +
              + +Expand source code + +
              #!/home/a/anaconda3/bin/python
              +# -*- coding: utf-8 -*-
              +import re
              +import sys
              +from pdoc.cli import main
              +if __name__ == '__main__':
              +    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
              +    sys.exit(main())
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +
              + +
              + + + \ No newline at end of file diff --git a/code/project3/src/html/Compile_latex.html b/code/project3/src/html/Compile_latex.html new file mode 100644 index 0000000..69fbf91 --- /dev/null +++ b/code/project3/src/html/Compile_latex.html @@ -0,0 +1,357 @@ + + + + + + +Compile_latex API documentation + + + + + + + + + + + +
              +
              +
              +

              Module Compile_latex

              +
              +
              +
              + +Expand source code + +
              from nbconvert.preprocessors import ExecutePreprocessor
              +import os
              +import shutil
              +import nbformat
              +
              +
              +class Compile_latex:
              +    """Runs jupyter notebooks, converts them to pdf,
              +    exports the notebook pdfs to latex and compiles the 
              +    latex report of the incoming project nr"""
              +
              +
              +    def __init__(self,project_nr,latex_filename):
              +        """Constructs attributes used throughout latex compilation
              +        
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +        """
              +    
              +        self.script_dir = self.get_script_dir()
              +        relative_dir = f'latex/project{project_nr}/'
              +        self.compile_latex(relative_dir,latex_filename)
              +        self.clean_up_after_compilation(latex_filename)
              +        self.move_pdf_into_latex_dir(relative_dir,latex_filename)
              +
              +    
              +    def compile_latex(self,relative_dir,latex_filename):
              +        """Executes a commandline line to compile the latex report
              +
              +        :param relative_dir: the relative dir towards the latex main .tex file
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        os.system(f'pdflatex {relative_dir}{latex_filename}')
              +    
              +    
              +    def clean_up_after_compilation(self,latex_filename):
              +        """Removes the unneeded files that were generated during latex to pdf compilation.
              +
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        latex_filename_without_extention = latex_filename[:-4]
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.aux')
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.log')
              +        self.delete_file_if_exists(f'texput.log')
              +    
              +
              +    def move_pdf_into_latex_dir(self,relative_dir,latex_filename):
              +        """Moves the compiled/generated pdf file from the root of this repository to the
              +        relative latex directory of this project.
              +
              +        :param relative_dir: param latex_filename:
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        pdf_filename = f'{latex_filename[:-4]}.pdf'
              +        destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}'
              +        
              +        try:
              +            shutil.move(pdf_filename, destination)
              +        except:
              +            print("Error while moving file ", pdf_filename)
              +    
              +
              +    def delete_file_if_exists(self,filename):
              +        """Deletes files if they exist
              +
              +        :param filename: name of file that will be deleted if it exists in the root of this repository
              +
              +        """
              +        try:
              +            os.remove(filename)
              +        except:
              +            print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.')
              +    
              +
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +
              +if __name__ == '__main__':
              +    main = Compile_latex()
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +

              Classes

              +
              +
              +class Compile_latex +(project_nr, latex_filename) +
              +
              +

              Runs jupyter notebooks, converts them to pdf, +exports the notebook pdfs to latex and compiles the +latex report of the incoming project nr

              +

              Constructs attributes used throughout latex compilation

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled +:param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              class Compile_latex:
              +    """Runs jupyter notebooks, converts them to pdf,
              +    exports the notebook pdfs to latex and compiles the 
              +    latex report of the incoming project nr"""
              +
              +
              +    def __init__(self,project_nr,latex_filename):
              +        """Constructs attributes used throughout latex compilation
              +        
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +        """
              +    
              +        self.script_dir = self.get_script_dir()
              +        relative_dir = f'latex/project{project_nr}/'
              +        self.compile_latex(relative_dir,latex_filename)
              +        self.clean_up_after_compilation(latex_filename)
              +        self.move_pdf_into_latex_dir(relative_dir,latex_filename)
              +
              +    
              +    def compile_latex(self,relative_dir,latex_filename):
              +        """Executes a commandline line to compile the latex report
              +
              +        :param relative_dir: the relative dir towards the latex main .tex file
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        os.system(f'pdflatex {relative_dir}{latex_filename}')
              +    
              +    
              +    def clean_up_after_compilation(self,latex_filename):
              +        """Removes the unneeded files that were generated during latex to pdf compilation.
              +
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        latex_filename_without_extention = latex_filename[:-4]
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.aux')
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.log')
              +        self.delete_file_if_exists(f'texput.log')
              +    
              +
              +    def move_pdf_into_latex_dir(self,relative_dir,latex_filename):
              +        """Moves the compiled/generated pdf file from the root of this repository to the
              +        relative latex directory of this project.
              +
              +        :param relative_dir: param latex_filename:
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        pdf_filename = f'{latex_filename[:-4]}.pdf'
              +        destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}'
              +        
              +        try:
              +            shutil.move(pdf_filename, destination)
              +        except:
              +            print("Error while moving file ", pdf_filename)
              +    
              +
              +    def delete_file_if_exists(self,filename):
              +        """Deletes files if they exist
              +
              +        :param filename: name of file that will be deleted if it exists in the root of this repository
              +
              +        """
              +        try:
              +            os.remove(filename)
              +        except:
              +            print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.')
              +    
              +
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +

              Methods

              +
              +
              +def clean_up_after_compilation(self, latex_filename) +
              +
              +

              Removes the unneeded files that were generated during latex to pdf compilation.

              +

              :param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              def clean_up_after_compilation(self,latex_filename):
              +    """Removes the unneeded files that were generated during latex to pdf compilation.
              +
              +    :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +    """
              +    latex_filename_without_extention = latex_filename[:-4]
              +    self.delete_file_if_exists(f'{latex_filename_without_extention}.aux')
              +    self.delete_file_if_exists(f'{latex_filename_without_extention}.log')
              +    self.delete_file_if_exists(f'texput.log')
              +
              +
              +
              +def compile_latex(self, relative_dir, latex_filename) +
              +
              +

              Executes a commandline line to compile the latex report

              +

              :param relative_dir: the relative dir towards the latex main .tex file +:param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              def compile_latex(self,relative_dir,latex_filename):
              +    """Executes a commandline line to compile the latex report
              +
              +    :param relative_dir: the relative dir towards the latex main .tex file
              +    :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +    """
              +    os.system(f'pdflatex {relative_dir}{latex_filename}')
              +
              +
              +
              +def delete_file_if_exists(self, filename) +
              +
              +

              Deletes files if they exist

              +

              :param filename: name of file that will be deleted if it exists in the root of this repository

              +
              + +Expand source code + +
              def delete_file_if_exists(self,filename):
              +    """Deletes files if they exist
              +
              +    :param filename: name of file that will be deleted if it exists in the root of this repository
              +
              +    """
              +    try:
              +        os.remove(filename)
              +    except:
              +        print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.')
              +
              +
              +
              +def get_script_dir(self) +
              +
              +

              returns the directory of this script regardles of from which level the code is executed

              +
              + +Expand source code + +
              def get_script_dir(self):
              +    """returns the directory of this script regardles of from which level the code is executed"""
              +    return os.path.dirname(__file__)
              +
              +
              +
              +def move_pdf_into_latex_dir(self, relative_dir, latex_filename) +
              +
              +

              Moves the compiled/generated pdf file from the root of this repository to the +relative latex directory of this project.

              +

              :param relative_dir: param latex_filename: +:param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              def move_pdf_into_latex_dir(self,relative_dir,latex_filename):
              +    """Moves the compiled/generated pdf file from the root of this repository to the
              +    relative latex directory of this project.
              +
              +    :param relative_dir: param latex_filename:
              +    :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +    """
              +    pdf_filename = f'{latex_filename[:-4]}.pdf'
              +    destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}'
              +    
              +    try:
              +        shutil.move(pdf_filename, destination)
              +    except:
              +        print("Error while moving file ", pdf_filename)
              +
              +
              +
              +
              +
              +
              +
              + +
              + + + \ No newline at end of file diff --git a/code/project3/src/html/Plot_to_tex.html b/code/project3/src/html/Plot_to_tex.html new file mode 100644 index 0000000..54c8c4c --- /dev/null +++ b/code/project3/src/html/Plot_to_tex.html @@ -0,0 +1,624 @@ + + + + + + +Plot_to_tex API documentation + + + + + + + + + + + +
              +
              +
              +

              Module Plot_to_tex

              +
              +
              +
              + +Expand source code + +
              from matplotlib import lines
              +import matplotlib.pyplot as plt
              +import numpy as np
              +import os
              +import random
              +
              +
              +class Plot_to_tex:
              +    """Plots incoming images and/or tables to a latex report with a certain layout."""
              +    """
              +    Example of how to include an exported table into your latex report.
              +
              +    \begin{table}[H]
              +        \centering
              +        \caption{Results some computation.}\label{tab:some_computation}
              +        \begin{tabular}{|c|c|} % remember to update this to show all columns of table
              +            \hline
              +            \input{latex/project3/tables/q2.txt}
              +        \end{tabular}
              +    \end{table}
              +    """
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +        
              +    def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with a single line to a latex report
              +
              +        :param x_path: x coordinates of a line
              +        :param y_series: y coordinates of a line
              +        :param x_axis_label: label of x axis 
              +        :param y_axis_label: label of y axis 
              +        :param label: string describing the line (label)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +        ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none');
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_axis_label);
              +        plt.ylabel(y_axis_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +#         plt.show();
              +
              +
              +    def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with mulltiple lines to a latex report
              +
              +        :param x: list of x coordinates of the lines of the plot
              +        :param y_series: y coordinates of the lines of the plot 
              +        :param x_label: label of x axis 
              +        :param y_label: label of y axis 
              +        :param label: list of strings describing the lines (labels)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +
              +        # generate colours
              +        cmap = self.get_cmap(len(y_series[:,0]))
              +
              +        # generate line types
              +        lineTypes = self.generateLineTypes(y_series)
              +
              +        for i in range(0,len(y_series)):
              +            # overwrite linetypes to single type
              +            lineTypes[i] = "-"
              +            ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color
              +
              +        # configure plot layout
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_label);
              +        plt.ylabel(y_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +        
              +        print(f'plotted lines')
              +
              +    
              +    def get_cmap(n, name='hsv'):
              +        """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
              +        RGB color; the keyword argument name must be a standard mpl colormap name.
              +        Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
              +
              +        :param n: number of lines that need a distinct colour
              +        :param name:  (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc
              +
              +        """
              +        return plt.cm.get_cmap(name, n)
              +
              +
              +    def generateLineTypes(y_series):
              +        """Generates returns a list of a vissible line type for each incoming line/y_series
              +
              +        :param y_series: list with list of y-coordinates representing the lines
              +
              +        """
              +        # generate varying linetypes
              +        typeOfLines = list(lines.lineStyles.keys())
              +
              +        while(len(y_series)>len(typeOfLines)):
              +            typeOfLines.append("-.");
              +
              +        # remove void lines
              +        for i in range(0, len(y_series)):
              +            if (typeOfLines[i]=='None'):
              +                typeOfLines[i]='-'
              +            if (typeOfLines[i]==''):
              +                typeOfLines[i]=':'
              +            if (typeOfLines[i]==' '):
              +                typeOfLines[i]='--'
              +        return typeOfLines
              +        
              +        
              +    def put_table_in_tex(self, table_matrix,filename,project_nr):
              +        """Outputs a table into a latex report
              +
              +        :param table_matrix: numpy array with the table data
              +        :param filename: filename of the table that is exported to latex
              +        :param project_nr: the number identifying to which latex project this table is exported
              +
              +        """
              +        cols = np.shape(table_matrix)[1]
              +        format = "%s"
              +        for col in range(1,cols):
              +            format = format+" & %s"
              +        format = format+""
              +        plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline='  \\\\ \hline \n')
              +
              +    
              +    def example_create_a_table(self):
              +        """Example code that generates the numpy array with 
              +        table data that can be exported to a latex table. Can 
              +        be modified to generate your own latex table"""
              +        project_nr = "1"
              +        table_name = "example_table_name"
              +        rows = 2;
              +        columns = 4;
              +        table_matrix = np.zeros((rows,columns),dtype=object)
              +        table_matrix[:,:]="" # replace the standard zeros with emtpy cell
              +        print(table_matrix)
              +        for column in range(0,columns):
              +            for row in range(0,rows):
              +                table_matrix[row,column]=row+column
              +        table_matrix[1,0]="example"
              +        table_matrix[0,1]="grid sizes"
              +
              +        self.put_table_in_tex(table_matrix,table_name,project_nr)
              +        
              +    
              +    def get_script_dir(self):
              +        """returns the path of the directory of this script"""
              +        return os.path.dirname(__file__)
              +
              +
              +if __name__ == '__main__':
              +    main = Plot_to_tex()
              +    main.example_create_a_table()
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +

              Classes

              +
              +
              +class Plot_to_tex +
              +
              +

              Plots incoming images and/or tables to a latex report with a certain layout.

              +
              + +Expand source code + +
              class Plot_to_tex:
              +    """Plots incoming images and/or tables to a latex report with a certain layout."""
              +    """
              +    Example of how to include an exported table into your latex report.
              +
              +    \begin{table}[H]
              +        \centering
              +        \caption{Results some computation.}\label{tab:some_computation}
              +        \begin{tabular}{|c|c|} % remember to update this to show all columns of table
              +            \hline
              +            \input{latex/project3/tables/q2.txt}
              +        \end{tabular}
              +    \end{table}
              +    """
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +        
              +    def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with a single line to a latex report
              +
              +        :param x_path: x coordinates of a line
              +        :param y_series: y coordinates of a line
              +        :param x_axis_label: label of x axis 
              +        :param y_axis_label: label of y axis 
              +        :param label: string describing the line (label)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +        ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none');
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_axis_label);
              +        plt.ylabel(y_axis_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +#         plt.show();
              +
              +
              +    def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr):
              +        """Outputs a plot with mulltiple lines to a latex report
              +
              +        :param x: list of x coordinates of the lines of the plot
              +        :param y_series: y coordinates of the lines of the plot 
              +        :param x_label: label of x axis 
              +        :param y_label: label of y axis 
              +        :param label: list of strings describing the lines (labels)
              +        :param filename: filename of the image that is exported to latex
              +        :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +        :param project_nr: the number identifying to which latex project this image is exported
              +
              +        """
              +        fig=plt.figure();
              +        ax=fig.add_subplot(111);
              +
              +        # generate colours
              +        cmap = self.get_cmap(len(y_series[:,0]))
              +
              +        # generate line types
              +        lineTypes = self.generateLineTypes(y_series)
              +
              +        for i in range(0,len(y_series)):
              +            # overwrite linetypes to single type
              +            lineTypes[i] = "-"
              +            ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color
              +
              +        # configure plot layout
              +        plt.legend(loc=legendPosition);
              +        plt.xlabel(x_label);
              +        plt.ylabel(y_label);
              +        plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +        
              +        print(f'plotted lines')
              +
              +    
              +    def get_cmap(n, name='hsv'):
              +        """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
              +        RGB color; the keyword argument name must be a standard mpl colormap name.
              +        Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
              +
              +        :param n: number of lines that need a distinct colour
              +        :param name:  (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc
              +
              +        """
              +        return plt.cm.get_cmap(name, n)
              +
              +
              +    def generateLineTypes(y_series):
              +        """Generates returns a list of a vissible line type for each incoming line/y_series
              +
              +        :param y_series: list with list of y-coordinates representing the lines
              +
              +        """
              +        # generate varying linetypes
              +        typeOfLines = list(lines.lineStyles.keys())
              +
              +        while(len(y_series)>len(typeOfLines)):
              +            typeOfLines.append("-.");
              +
              +        # remove void lines
              +        for i in range(0, len(y_series)):
              +            if (typeOfLines[i]=='None'):
              +                typeOfLines[i]='-'
              +            if (typeOfLines[i]==''):
              +                typeOfLines[i]=':'
              +            if (typeOfLines[i]==' '):
              +                typeOfLines[i]='--'
              +        return typeOfLines
              +        
              +        
              +    def put_table_in_tex(self, table_matrix,filename,project_nr):
              +        """Outputs a table into a latex report
              +
              +        :param table_matrix: numpy array with the table data
              +        :param filename: filename of the table that is exported to latex
              +        :param project_nr: the number identifying to which latex project this table is exported
              +
              +        """
              +        cols = np.shape(table_matrix)[1]
              +        format = "%s"
              +        for col in range(1,cols):
              +            format = format+" & %s"
              +        format = format+""
              +        plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline='  \\\\ \hline \n')
              +
              +    
              +    def example_create_a_table(self):
              +        """Example code that generates the numpy array with 
              +        table data that can be exported to a latex table. Can 
              +        be modified to generate your own latex table"""
              +        project_nr = "1"
              +        table_name = "example_table_name"
              +        rows = 2;
              +        columns = 4;
              +        table_matrix = np.zeros((rows,columns),dtype=object)
              +        table_matrix[:,:]="" # replace the standard zeros with emtpy cell
              +        print(table_matrix)
              +        for column in range(0,columns):
              +            for row in range(0,rows):
              +                table_matrix[row,column]=row+column
              +        table_matrix[1,0]="example"
              +        table_matrix[0,1]="grid sizes"
              +
              +        self.put_table_in_tex(table_matrix,table_name,project_nr)
              +        
              +    
              +    def get_script_dir(self):
              +        """returns the path of the directory of this script"""
              +        return os.path.dirname(__file__)
              +
              +

              Methods

              +
              +
              +def example_create_a_table(self) +
              +
              +

              Example code that generates the numpy array with +table data that can be exported to a latex table. Can +be modified to generate your own latex table

              +
              + +Expand source code + +
              def example_create_a_table(self):
              +    """Example code that generates the numpy array with 
              +    table data that can be exported to a latex table. Can 
              +    be modified to generate your own latex table"""
              +    project_nr = "1"
              +    table_name = "example_table_name"
              +    rows = 2;
              +    columns = 4;
              +    table_matrix = np.zeros((rows,columns),dtype=object)
              +    table_matrix[:,:]="" # replace the standard zeros with emtpy cell
              +    print(table_matrix)
              +    for column in range(0,columns):
              +        for row in range(0,rows):
              +            table_matrix[row,column]=row+column
              +    table_matrix[1,0]="example"
              +    table_matrix[0,1]="grid sizes"
              +
              +    self.put_table_in_tex(table_matrix,table_name,project_nr)
              +
              +
              +
              +def generateLineTypes(y_series) +
              +
              +

              Generates returns a list of a vissible line type for each incoming line/y_series

              +

              :param y_series: list with list of y-coordinates representing the lines

              +
              + +Expand source code + +
              def generateLineTypes(y_series):
              +    """Generates returns a list of a vissible line type for each incoming line/y_series
              +
              +    :param y_series: list with list of y-coordinates representing the lines
              +
              +    """
              +    # generate varying linetypes
              +    typeOfLines = list(lines.lineStyles.keys())
              +
              +    while(len(y_series)>len(typeOfLines)):
              +        typeOfLines.append("-.");
              +
              +    # remove void lines
              +    for i in range(0, len(y_series)):
              +        if (typeOfLines[i]=='None'):
              +            typeOfLines[i]='-'
              +        if (typeOfLines[i]==''):
              +            typeOfLines[i]=':'
              +        if (typeOfLines[i]==' '):
              +            typeOfLines[i]='--'
              +    return typeOfLines
              +
              +
              +
              +def get_cmap(n, name='hsv') +
              +
              +

              Returns a function that maps each index in 0, 1, …, n-1 to a distinct +RGB color; the keyword argument name must be a standard mpl colormap name. +Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib

              +

              :param n: number of lines that need a distinct colour +:param name: +(Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc

              +
              + +Expand source code + +
              def get_cmap(n, name='hsv'):
              +    """Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
              +    RGB color; the keyword argument name must be a standard mpl colormap name.
              +    Source: https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
              +
              +    :param n: number of lines that need a distinct colour
              +    :param name:  (Default value = 'hsv') the type of linecolour palet, e.g. rainbow, grayscale etc
              +
              +    """
              +    return plt.cm.get_cmap(name, n)
              +
              +
              +
              +def get_script_dir(self) +
              +
              +

              returns the path of the directory of this script

              +
              + +Expand source code + +
              def get_script_dir(self):
              +    """returns the path of the directory of this script"""
              +    return os.path.dirname(__file__)
              +
              +
              +
              +def plotMultipleLines(self, x, y_series, x_label, y_label, label, filename, legendPosition, project_nr) +
              +
              +

              Outputs a plot with mulltiple lines to a latex report

              +

              :param x: list of x coordinates of the lines of the plot +:param y_series: y coordinates of the lines of the plot +:param x_label: label of x axis +:param y_label: label of y axis +:param label: list of strings describing the lines (labels) +:param filename: filename of the image that is exported to latex +:param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best') +:param project_nr: the number identifying to which latex project this image is exported

              +
              + +Expand source code + +
              def plotMultipleLines(self,x,y_series,x_label,y_label,label,filename,legendPosition,project_nr):
              +    """Outputs a plot with mulltiple lines to a latex report
              +
              +    :param x: list of x coordinates of the lines of the plot
              +    :param y_series: y coordinates of the lines of the plot 
              +    :param x_label: label of x axis 
              +    :param y_label: label of y axis 
              +    :param label: list of strings describing the lines (labels)
              +    :param filename: filename of the image that is exported to latex
              +    :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +    :param project_nr: the number identifying to which latex project this image is exported
              +
              +    """
              +    fig=plt.figure();
              +    ax=fig.add_subplot(111);
              +
              +    # generate colours
              +    cmap = self.get_cmap(len(y_series[:,0]))
              +
              +    # generate line types
              +    lineTypes = self.generateLineTypes(y_series)
              +
              +    for i in range(0,len(y_series)):
              +        # overwrite linetypes to single type
              +        lineTypes[i] = "-"
              +        ax.plot(x,y_series[i,:],ls=lineTypes[i],label=label[i],fillstyle='none',c=cmap(i)); # color
              +
              +    # configure plot layout
              +    plt.legend(loc=legendPosition);
              +    plt.xlabel(x_label);
              +    plt.ylabel(y_label);
              +    plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +    
              +    print(f'plotted lines')
              +
              +
              +
              +def plotSingleLine(self, x_path, y_series, x_axis_label, y_axis_label, label, filename, legendPosition, project_nr) +
              +
              +

              Outputs a plot with a single line to a latex report

              +

              :param x_path: x coordinates of a line +:param y_series: y coordinates of a line +:param x_axis_label: label of x axis +:param y_axis_label: label of y axis +:param label: string describing the line (label) +:param filename: filename of the image that is exported to latex +:param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best') +:param project_nr: the number identifying to which latex project this image is exported

              +
              + +Expand source code + +
              def plotSingleLine(self,x_path,y_series,x_axis_label,y_axis_label,label,filename,legendPosition,project_nr):
              +    """Outputs a plot with a single line to a latex report
              +
              +    :param x_path: x coordinates of a line
              +    :param y_series: y coordinates of a line
              +    :param x_axis_label: label of x axis 
              +    :param y_axis_label: label of y axis 
              +    :param label: string describing the line (label)
              +    :param filename: filename of the image that is exported to latex
              +    :param legendPosition: integer in range 1 to 4 representing the legend position (or string 'best')
              +    :param project_nr: the number identifying to which latex project this image is exported
              +
              +    """
              +    fig=plt.figure();
              +    ax=fig.add_subplot(111);
              +    ax.plot(x_path,y_series,c='b',ls='-',label=label,fillstyle='none');
              +    plt.legend(loc=legendPosition);
              +    plt.xlabel(x_axis_label);
              +    plt.ylabel(y_axis_label);
              +    plt.savefig(os.path.dirname(__file__)+'/../../../latex/project'+str(project_nr)+'/Images/'+filename+'.png');
              +
              +
              +
              +def put_table_in_tex(self, table_matrix, filename, project_nr) +
              +
              +

              Outputs a table into a latex report

              +

              :param table_matrix: numpy array with the table data +:param filename: filename of the table that is exported to latex +:param project_nr: the number identifying to which latex project this table is exported

              +
              + +Expand source code + +
              def put_table_in_tex(self, table_matrix,filename,project_nr):
              +    """Outputs a table into a latex report
              +
              +    :param table_matrix: numpy array with the table data
              +    :param filename: filename of the table that is exported to latex
              +    :param project_nr: the number identifying to which latex project this table is exported
              +
              +    """
              +    cols = np.shape(table_matrix)[1]
              +    format = "%s"
              +    for col in range(1,cols):
              +        format = format+" & %s"
              +    format = format+""
              +    plt.savetxt(os.path.dirname(__file__)+"/../../../latex/project"+str(project_nr)+"/tables/"+filename+".txt",table_matrix, delimiter=' & ', fmt=format, newline='  \\\\ \hline \n')
              +
              +
              +
              +
              +
              +
              +
              + +
              + + + \ No newline at end of file diff --git a/code/project3/src/html/Run_jupyter_notebooks.html b/code/project3/src/html/Run_jupyter_notebooks.html new file mode 100644 index 0000000..3d23a1f --- /dev/null +++ b/code/project3/src/html/Run_jupyter_notebooks.html @@ -0,0 +1,384 @@ + + + + + + +Run_jupyter_notebooks API documentation + + + + + + + + + + + +
              +
              +
              +

              Module Run_jupyter_notebooks

              +
              +
              +
              + +Expand source code + +
              from nbconvert.preprocessors import ExecutePreprocessor
              +import os
              +import nbformat
              +
              +class Run_jupyter_notebook:
              +    """runs a list of  jupyter notebooks and converts it to pdf"""
              +
              +    
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +
              +    def run_jupyter_notebooks(self,project_nr,notebook_names):
              +        """runs a jupyter notebook in this directory
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def convert_notebooks_to_pdf(self,project_nr,notebook_names):
              +        """converts a jupyter notebook to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def compile_latex_report(self,project_nr):
              +        """compiles latex code to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +
              +        """
              +        compile_latex =Compile_latex(project_nr ,'main.tex')
              +
              +    
              +    def run_notebook(self,notebook_filename):
              +        """runs a  jupyter notebook that is located in this folder
              +        
              +        :param notebook_filename: the name of the notebook that needs to be ran
              +
              +        """
              +        # Load your notebook
              +        with open(notebook_filename) as f:
              +            nb = nbformat.read(f, as_version=4)
              +
              +        # Configure
              +        ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
              +
              +        # Execute
              +        #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}})
              +        ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}})
              +
              +        # Save output notebook
              +        with open(notebook_filename, 'w', encoding='utf-8') as f:
              +            nbformat.write(nb, f)
              +    
              +    
              +    def convert_notebook_to_pdf(self,notebook_filename):
              +        """Compiles a jupyter notebook that is located in this folder to pdf
              +
              +        :param notebook_filename: the name of the notebook that needs to be compiled to pdf
              +
              +        """
              +        os.system(f'jupyter nbconvert --to pdf {notebook_filename}')
              +    
              +    
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +
              +if __name__ == '__main__':
              +    main = Run_jupyter_notebook()
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +

              Classes

              +
              +
              +class Run_jupyter_notebook +
              +
              +

              runs a list of +jupyter notebooks and converts it to pdf

              +
              + +Expand source code + +
              class Run_jupyter_notebook:
              +    """runs a list of  jupyter notebooks and converts it to pdf"""
              +
              +    
              +    def __init__(self):
              +        self.script_dir = self.get_script_dir()
              +        
              +
              +    def run_jupyter_notebooks(self,project_nr,notebook_names):
              +        """runs a jupyter notebook in this directory
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def convert_notebooks_to_pdf(self,project_nr,notebook_names):
              +        """converts a jupyter notebook to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +        """
              +        notebook_path = f'code/project{project_nr}/src/'
              +        
              +        for notebook_name in notebook_names:
              +            self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}')
              +    
              +    
              +    def compile_latex_report(self,project_nr):
              +        """compiles latex code to pdf
              +
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +
              +        """
              +        compile_latex =Compile_latex(project_nr ,'main.tex')
              +
              +    
              +    def run_notebook(self,notebook_filename):
              +        """runs a  jupyter notebook that is located in this folder
              +        
              +        :param notebook_filename: the name of the notebook that needs to be ran
              +
              +        """
              +        # Load your notebook
              +        with open(notebook_filename) as f:
              +            nb = nbformat.read(f, as_version=4)
              +
              +        # Configure
              +        ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
              +
              +        # Execute
              +        #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}})
              +        ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}})
              +
              +        # Save output notebook
              +        with open(notebook_filename, 'w', encoding='utf-8') as f:
              +            nbformat.write(nb, f)
              +    
              +    
              +    def convert_notebook_to_pdf(self,notebook_filename):
              +        """Compiles a jupyter notebook that is located in this folder to pdf
              +
              +        :param notebook_filename: the name of the notebook that needs to be compiled to pdf
              +
              +        """
              +        os.system(f'jupyter nbconvert --to pdf {notebook_filename}')
              +    
              +    
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +

              Methods

              +
              +
              +def compile_latex_report(self, project_nr) +
              +
              +

              compiles latex code to pdf

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled

              +
              + +Expand source code + +
              def compile_latex_report(self,project_nr):
              +    """compiles latex code to pdf
              +
              +    :param project_nr: the numberr identifying which project is being  ran and compiled
              +
              +    """
              +    compile_latex =Compile_latex(project_nr ,'main.tex')
              +
              +
              +
              +def convert_notebook_to_pdf(self, notebook_filename) +
              +
              +

              Compiles a jupyter notebook that is located in this folder to pdf

              +

              :param notebook_filename: the name of the notebook that needs to be compiled to pdf

              +
              + +Expand source code + +
              def convert_notebook_to_pdf(self,notebook_filename):
              +    """Compiles a jupyter notebook that is located in this folder to pdf
              +
              +    :param notebook_filename: the name of the notebook that needs to be compiled to pdf
              +
              +    """
              +    os.system(f'jupyter nbconvert --to pdf {notebook_filename}')
              +
              +
              +
              +def convert_notebooks_to_pdf(self, project_nr, notebook_names) +
              +
              +

              converts a jupyter notebook to pdf

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled +:param notebook_names: list of strings with the names of the notebooks that need to be ran

              +
              + +Expand source code + +
              def convert_notebooks_to_pdf(self,project_nr,notebook_names):
              +    """converts a jupyter notebook to pdf
              +
              +    :param project_nr: the numberr identifying which project is being  ran and compiled
              +    :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +    """
              +    notebook_path = f'code/project{project_nr}/src/'
              +    
              +    for notebook_name in notebook_names:
              +        self.run_jupyter_notebook.convert_notebook_to_pdf(f'{notebook_path}{notebook_name}')
              +
              +
              +
              +def get_script_dir(self) +
              +
              +

              returns the directory of this script regardles of from which level the code is executed

              +
              + +Expand source code + +
              def get_script_dir(self):
              +    """returns the directory of this script regardles of from which level the code is executed"""
              +    return os.path.dirname(__file__)
              +
              +
              +
              +def run_jupyter_notebooks(self, project_nr, notebook_names) +
              +
              +

              runs a jupyter notebook in this directory

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled +:param notebook_names: list of strings with the names of the notebooks that need to be ran

              +
              + +Expand source code + +
              def run_jupyter_notebooks(self,project_nr,notebook_names):
              +    """runs a jupyter notebook in this directory
              +
              +    :param project_nr: the numberr identifying which project is being  ran and compiled
              +    :param notebook_names: list of strings with the names of the notebooks that need to be ran
              +
              +    """
              +    notebook_path = f'code/project{project_nr}/src/'
              +    
              +    for notebook_name in notebook_names:
              +        self.run_jupyter_notebook.run_notebook(f'{notebook_path}{notebook_name}')
              +
              +
              +
              +def run_notebook(self, notebook_filename) +
              +
              +

              runs a +jupyter notebook that is located in this folder

              +

              :param notebook_filename: the name of the notebook that needs to be ran

              +
              + +Expand source code + +
              def run_notebook(self,notebook_filename):
              +    """runs a  jupyter notebook that is located in this folder
              +    
              +    :param notebook_filename: the name of the notebook that needs to be ran
              +
              +    """
              +    # Load your notebook
              +    with open(notebook_filename) as f:
              +        nb = nbformat.read(f, as_version=4)
              +
              +    # Configure
              +    ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
              +
              +    # Execute
              +    #ep.preprocess(nb, {'metadata': {'path': 'notebooks/'}})
              +    ep.preprocess(nb, {'metadata': {'path': f'{self.get_script_dir()}'}})
              +
              +    # Save output notebook
              +    with open(notebook_filename, 'w', encoding='utf-8') as f:
              +        nbformat.write(nb, f)
              +
              +
              +
              +
              +
              +
              +
              + +
              + + + \ No newline at end of file diff --git a/code/project3/src/html/__main__.html b/code/project3/src/html/__main__.html new file mode 100644 index 0000000..adbbff9 --- /dev/null +++ b/code/project3/src/html/__main__.html @@ -0,0 +1,61 @@ + + + + + + +__main__ API documentation + + + + + + + + + + + +
              +
              +
              +

              Module __main__

              +
              +
              +
              + +Expand source code + +
              #!/home/a/anaconda3/bin/python
              +# -*- coding: utf-8 -*-
              +import re
              +import sys
              +from pdoc.cli import main
              +if __name__ == '__main__':
              +    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
              +    sys.exit(main())
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +
              + +
              + + + \ No newline at end of file From 5dce847c9fc3ea2e4e629fb344d0c84ca9cef629 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Wed, 18 Nov 2020 15:27:45 +0100 Subject: [PATCH 55/57] Propagated latest change to notebook from original repo. --- code/project1/src/juice_propagation_Q1.ipynb | 4 ++-- code/project2/src/juice_propagation_Q1.ipynb | 2 +- code/project3/src/juice_propagation_Q1.ipynb | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/code/project1/src/juice_propagation_Q1.ipynb b/code/project1/src/juice_propagation_Q1.ipynb index 3da7747..5ca3643 100644 --- a/code/project1/src/juice_propagation_Q1.ipynb +++ b/code/project1/src/juice_propagation_Q1.ipynb @@ -297,7 +297,7 @@ "###########################################################################\n", "\n", "# Extract time and Kepler elements from dependent variables\n", - "kepler_elements = np.vstack(list(dependent_variables.values())\n", + "kepler_elements = np.vstack(list(dependent_variables.values()))\n", " \n", "# Kepler Elements\n", "# 0: semi-major axis\n", @@ -347,7 +347,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.8" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/code/project2/src/juice_propagation_Q1.ipynb b/code/project2/src/juice_propagation_Q1.ipynb index e827102..5ca3643 100644 --- a/code/project2/src/juice_propagation_Q1.ipynb +++ b/code/project2/src/juice_propagation_Q1.ipynb @@ -297,7 +297,7 @@ "###########################################################################\n", "\n", "# Extract time and Kepler elements from dependent variables\n", - "kepler_elements = np.vstack(list(dependent_variables.values())\n", + "kepler_elements = np.vstack(list(dependent_variables.values()))\n", " \n", "# Kepler Elements\n", "# 0: semi-major axis\n", diff --git a/code/project3/src/juice_propagation_Q1.ipynb b/code/project3/src/juice_propagation_Q1.ipynb index e827102..5ca3643 100644 --- a/code/project3/src/juice_propagation_Q1.ipynb +++ b/code/project3/src/juice_propagation_Q1.ipynb @@ -297,7 +297,7 @@ "###########################################################################\n", "\n", "# Extract time and Kepler elements from dependent variables\n", - "kepler_elements = np.vstack(list(dependent_variables.values())\n", + "kepler_elements = np.vstack(list(dependent_variables.values()))\n", " \n", "# Kepler Elements\n", "# 0: semi-major axis\n", From 29b6a32be66f2b498c2ceff3ada4e259ae474f80 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Sun, 13 Dec 2020 04:14:53 +0100 Subject: [PATCH 56/57] Updates from Overleaf --- .../AE4868_example_notebook_update20201025-checkpoint.ipynb | 0 code/project1/src/AE4868_example_notebook_update20201025.ipynb | 0 latex/project1/main.tex | 2 +- 3 files changed, 1 insertion(+), 1 deletion(-) mode change 100755 => 100644 code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb mode change 100755 => 100644 code/project1/src/AE4868_example_notebook_update20201025.ipynb diff --git a/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb b/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb old mode 100755 new mode 100644 diff --git a/code/project1/src/AE4868_example_notebook_update20201025.ipynb b/code/project1/src/AE4868_example_notebook_update20201025.ipynb old mode 100755 new mode 100644 diff --git a/latex/project1/main.tex b/latex/project1/main.tex index e6216f7..c9a3529 100644 --- a/latex/project1/main.tex +++ b/latex/project1/main.tex @@ -49,7 +49,7 @@ % To get side by side pictures:{ \usepackage{caption} -\usepackage{subcaption} +%\usepackage{subcaption} \usepackage{graphicx} From c776107c7039b676fd3692870a33882c1f2db410 Mon Sep 17 00:00:00 2001 From: a Date: Wed, 23 Dec 2020 14:38:21 +0100 Subject: [PATCH 57/57] Included hw2 assignment. --- code/project2/src/assignment2.ipynb | 1133 +++++++++++++++++++++++++ code/project2/src/assignment2V0.ipynb | 1133 +++++++++++++++++++++++++ 2 files changed, 2266 insertions(+) create mode 100644 code/project2/src/assignment2.ipynb create mode 100644 code/project2/src/assignment2V0.ipynb diff --git a/code/project2/src/assignment2.ipynb b/code/project2/src/assignment2.ipynb new file mode 100644 index 0000000..ce5cef9 --- /dev/null +++ b/code/project2/src/assignment2.ipynb @@ -0,0 +1,1133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 2 - Interplanetary Transfer\n", + "\n", + "For this assignment, you will use a Lambert targeter to generate a first guess for an unperturbed interplanetary transfer. Subsequently, you will use various numerical propagation models that include perturbations to analyze the trajectory in more detail, and compute trajectory corrections such that your trajectory meets its boundary conditions (departure and arrival) in the perturbed environment,\n", + "\n", + "In this assignment, you are required to modify this Jupyter notebook. Unlike assignment 1, this assignment is in a single notebook, with the code for all questions and the assignment description all merged into a single document.\n", + "\n", + "\n", + "**General Instructions**\n", + "\n", + "In this assignment, you will use a so-called Lambert targeter (a tool that solves Lambert's problem) to generate an initial guess for an interplanetary direct high-thrust transfer. The Lambert targeter takes as input:\n", + "\n", + "
                \n", + "
              • A departure position $\\mathbf{r}_0$
              • \n", + "
              • An arrival position $\\mathbf{r}_E$
              • \n", + "
              • A time of flight $T$
              • \n", + "
              • A central body gravitational parameter $\\mu$
              • \n", + "
              \n", + "\n", + "\n", + "The Lambert targeter then generates the Keplerian trajectory between $\\mathbf{r}_{0}$ and $\\mathbf{r}_{E}$, with the given time of flight $T$. Since the initial and final positions uniquely define the trajectory (assuming a prograde single-revolution trajectory; ignoring rare singular cases), the full state along this Keplerian trajectory (or Lambert arc) are *outputs* of the Lambert targeter. We denote the Cartesian state function of this Lambert arc as $\\bar{\\mathbf{x}}(t)$. For your situation, the initial and final position $\\mathbf{r}_{0}$ and $\\mathbf{r}_{E}$ of the full trajectory are the positions of the center of mass of Earth and Mars or Venus (w.r.t. the Sun), at times $t_{0}$ and $t_{E}$, respectively (with values depending on your student number, to be found in the $\\texttt{assignment2Input-2020-2021.txt}$ file on Brightspace under Assignment 2).\n", + "\n", + "All analysis on the output data can be done in the notebook. However, if you would like to use a different piece of software (*e.g.* Matlab) for your analyses, the relevant data is provided as output to data files, in a number of manners:\n", + "\n", + "* By calling the `propagate_trajectory` function, the propagated state of the spacecraft and the associated dependent variables will be saved to a file, as well as the state of the Lambert arc $\\bar{\\mathbf{x}}$ at the epochs of the numerical integration. See the in-code comments of the `write_propagation_results_to_file` function in the helper functions block code file for more details.\n", + "* For question 3, the `write_propagation_results_to_file` function is called directly from the $\\texttt{main}$ function.\n", + "\n", + "\n", + "**Before starting the assignment, read the submission instructions given at the end of this notebook.**\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import estimation_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from tudatpy.kernel.astro import two_body_dynamics\n", + "from tudatpy.kernel.astro import conversion\n", + "from matplotlib import pyplot as plt\n", + "\n", + "# STUDENT CODE TASK (fill in ...)\n", + "departure_epoch = ...\n", + "time_of_flight = ...\n", + "arrival_epoch = departure_epoch + time_of_flight\n", + "target_body = ...\n", + "\n", + "# Global settings\n", + "fixed_step_size = 3600.0\n", + "global_frame_origin = \"SSB\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "\n", + "# Helper variables for question 4\n", + "current_question = 0;\n", + "rsw_acceleration_magnitude = [0,0,0]\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Set output directory\n", + "output_directory = \"./SimulationOutput/\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Helper Functions (DO NOT MODIFY)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def write_propagation_results_to_file( dynamics_simulator, lambert_arc_state_model, file_output_identifier, output_directory):\n", + "\n", + " \"\"\"\n", + " This function will write the results of a numerical propagation, as well as the Lambert arc states at the epochs of the\n", + " numerical state history, to a set of files. Two files are always written when calling this function (numerical state history, a\n", + " and Lambert arc state history). If any dependent variables are saved during the propagation, those are also saved to a file\n", + " \n", + " Parameters\n", + " ----------\n", + " dynamics_simulator : Object that was used to propagate the dynamics, and which contains the numerical state and dependent\n", + " variable results\n", + " \n", + " lambert_arc_state_model : Lambert arc state model as returned by the get_lambert_problem_result() function\n", + " \n", + " file_output_identifier : Name that will be used to correctly save the output data files\n", + " \n", + " output_directory : Directory to which the files will be written\n", + " \n", + " Files written\n", + " -------------\n", + " \n", + " _numerical_states.dat\n", + " _dependent_variables.dat\n", + " _lambert_statess.dat\n", + "\n", + " \n", + " Return\n", + " ------\n", + " None\n", + " \n", + " \"\"\"\n", + " \n", + " # Save numerical states\n", + " simulation_result = dynamics_simulator.state_history\n", + " save2txt(solution= simulation_result, filename=output_directory + file_output_identifier + \"_numerical_states.dat\", directory=\"./\", column_names=None )\n", + " \n", + " # Save dependent variables\n", + " dependent_variables = dynamics_simulator.dependent_variable_history\n", + " if len(dependent_variables.keys()) > 0:\n", + " save2txt(solution= dependent_variables, filename=output_directory + file_output_identifier + \"_dependent_variables.dat\", directory=\"./\", column_names=None )\n", + " \n", + " # Save Lambert arc states\n", + " lambert_arc_states = get_lambert_arc_history( lambert_arc_state_model, simulation_result )\n", + " \n", + " save2txt(solution= lambert_arc_states, filename= output_directory + file_output_identifier + \"_lambert_states.dat\", directory=\"./\", column_names=None )\n", + " \n", + " return\n", + "\n", + "def get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch):\n", + " \n", + " # Gravitational parameter of the Sun\n", + " central_body_gravitational_parameter = bodies.get_body( \"Sun\" ).gravitational_parameter\n", + " \n", + " # Set initial and final positions for Lambert targeter\n", + " initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"Earth\",\n", + " observer_body_name=\"Sun\",\n", + " reference_frame_name=global_frame_orientation,\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= departure_epoch )\n", + " \n", + " final_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name= target_body,\n", + " observer_body_name=\"Sun\",\n", + " reference_frame_name=global_frame_orientation,\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= arrival_epoch )\n", + " \n", + " # Create Lambert targeter\n", + " lambertTargeter = two_body_dynamics.LambertTargeterIzzo(\n", + " initial_state[:3], final_state[:3],arrival_epoch - departure_epoch, central_body_gravitational_parameter );\n", + " \n", + " # Compute initial Cartesian state of Lambert arc\n", + " lambert_arc_initial_state = initial_state\n", + " lambert_arc_initial_state[3:] = lambertTargeter.get_departure_velocity()\n", + " \n", + " # Compute Keplerian state of Lambert arc\n", + " lambert_arc_keplerian_elements = conversion.cartesian_to_keplerian( lambert_arc_initial_state, \n", + " central_body_gravitational_parameter)\n", + " \n", + " # Setup Keplerian ephemeris model that describes the Lambert arc\n", + " kepler_ephemeris = environment_setup.create_body_ephemeris(\n", + " environment_setup.ephemeris.keplerian( lambert_arc_keplerian_elements, departure_epoch, central_body_gravitational_parameter ), \"\" )\n", + " \n", + " return kepler_ephemeris\n", + "\n", + "def get_lambert_arc_history( lambert_arc_state_model, simulation_result ):\n", + " \n", + " lambert_arc_states = dict()\n", + " for state in simulation_result:\n", + " lambert_arc_states[ state ] = lambert_arc_state_model.get_cartesian_state( state )\n", + " \n", + " return lambert_arc_states\n", + "\n", + "\n", + "def propagate_trajectory( initial_time, final_time, bodies, lambert_arc_state_model, \n", + " file_output_identifier, use_perturbations, initial_state_correction=[0,0,0,0,0,0]):\n", + " \n", + " \"\"\"\n", + " This function will be repeatedly called throughout the assignment. Propagates the trajectory based \n", + " on several input parameters, and subsequently saves the results to data files.\n", + " \n", + " Parameters\n", + " ----------\n", + " initial_time : Epoch since J2000 at which the propagation starts\n", + " \n", + " final_time : Epoch since J2000 at which the propagation will be terminated\n", + " \n", + " lambert_arc_state_model : Lambert arc state model as returned by the get_lambert_problem_result() function\n", + " \n", + " file_output_identifier : Name that will be used to correctly save the output data files\n", + " \n", + " use_perturbations : Boolean to indicate whether a perturbed (True) or unperturbed (False) trajectory \n", + " is propagated\n", + " \n", + " initial_state_correction : (optional) Cartesian state which is added to the Lambert arc state when computing the numerical initial state\n", + " \n", + " Return\n", + " ------\n", + " Dynamics simulator object from which the state- and dependent variable history can be extracted\n", + " \n", + " \"\"\"\n", + " \n", + " # Compute initial state along Lambert arc (and apply correction if needed)\n", + " lambert_arc_initial_state = lambert_arc_state_model.get_cartesian_state( initial_time ) + initial_state_correction\n", + "\n", + " # Get propagator settings for perturbed/unperturbed forwards/backwards arcs\n", + " if use_perturbations:\n", + " propagator_settings = get_perturbed_propagator_settings( bodies, lambert_arc_initial_state, final_time )\n", + " else:\n", + " propagator_settings = get_unperturbed_propagator_settings( bodies, lambert_arc_initial_state, final_time )\n", + " \n", + " # If propagation is backwards in time, make initial time step negative\n", + " if initial_time > final_time:\n", + " signed_fixed_step_size = -fixed_step_size\n", + " else:\n", + " signed_fixed_step_size = fixed_step_size\n", + " \n", + " # Create numerical integrator settings\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4( initial_time, signed_fixed_step_size )\n", + " \n", + " # Propagate forward/backward perturbed/unperturbed arc and save results to files\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(bodies, integrator_settings, propagator_settings, True)\n", + " write_propagation_results_to_file( dynamics_simulator, lambert_arc_state_model, file_output_identifier, output_directory)\n", + "\n", + " return dynamics_simulator\n", + " \n", + "def propagate_variational_equations(initial_time, final_time, bodies, lambert_arc_state_model):\n", + " \n", + " \"\"\"\n", + " Propagates the variational equations for a given range of epochs for a perturbed trajectory.\n", + " \n", + " Parameters\n", + " ----------\n", + " initial_time : Epoch since J2000 at which the propagation starts\n", + " \n", + " final_time : Epoch since J2000 at which the propagation will be terminated\n", + " \n", + " bodies : Body objects as returned by creates_simulation_bodies() function\n", + " \n", + " lambert_arc_state_model : Lambert arc state model as returned by the get_lambert_problem_result() function\n", + " \n", + " Return\n", + " ------\n", + " Variational equations solver object, from which the state-, state transition matrix-, and \n", + " sensitivity matrix history can be extracted.\n", + " \"\"\"\n", + " \n", + " # Compute initial state along Lambert arc\n", + " lambert_arc_initial_state = lambert_arc_state_model.get_cartesian_state( initial_time )\n", + "\n", + " # Get propagator settings\n", + " propagator_settings = get_perturbed_propagator_settings(bodies, lambert_arc_initial_state, final_time)\n", + " \n", + " # Get integrator settings \n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " initial_time, fixed_step_size )\n", + " \n", + " # Define parameters for variational equations\n", + " sensitivity_parameters = get_sensitivity_parameter_set( propagator_settings, bodies, target_body) \n", + " \n", + " # Propagate variational equations\n", + " variational_equations_solver = estimation_setup.SingleArcVariationalEquationsSolver(\n", + " bodies, integrator_settings, propagator_settings, sensitivity_parameters,integrate_on_creation=1 )\n", + " \n", + " return variational_equations_solver\n", + "\n", + "def get_sensitivity_parameter_set(propagator_settings, bodies, target_body):\n", + "\n", + " parameter_settings = estimation_setup.parameter.initial_states(\n", + " propagator_settings, bodies )\n", + " if current_question == 4:\n", + " parameter_settings.append( estimation_setup.parameter.constant_empirical_acceleration_terms (\"Spacecraft\", \"Sun\" ) ) \n", + " \n", + " return estimation_setup.create_parameters_to_estimate(parameter_settings, bodies, propagator_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Helper Functions (TO BE MODIFIED)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# STUDENT CODE TASK - define function such that it provides propagator settings as per question 1\n", + "def get_unperturbed_propagator_settings(bodies, initial_state, termination_time):\n", + " \n", + " \"\"\"\n", + " Creates the propagator settings for an unperturbed trajectory.\n", + "\n", + " Parameters\n", + " ----------\n", + " bodies : Body objects as returned by creates_simulation_bodies() function \n", + " \n", + " initial_state : Cartesian initial state of the vehicle in the simulation\n", + " \n", + " termination_time : Epoch since J2000 at which the propagation will be terminated\n", + " \n", + "\n", + " Return\n", + " ------\n", + " Propagation settings of the unperturbed trajectory.\n", + " \"\"\"\n", + " \n", + "\n", + " # Create propagation settings with termination time.\n", + " propagator_settings = ...\n", + " \n", + " return propagator_settings\n", + "\n", + "# STUDENT CODE TASK - define function such that it provides propagator settings as per question 2-5\n", + "def get_perturbed_propagator_settings(bodies, initial_state, termination_time):\n", + " \n", + " \"\"\"\n", + " Creates the propagator settings for a perturbed trajectory.\n", + "\n", + " Parameters\n", + " ----------\n", + " bodies : Body objects as returned by creates_simulation_bodies() function \n", + " \n", + " initial_state : Cartesian initial state of the vehicle in the simulation\n", + " \n", + " termination_time : Epoch since J2000 at which the propagation will be terminated\n", + " \n", + "\n", + " Return\n", + " ------\n", + " Propagation settings of the perturbed trajectory.\n", + " \"\"\"\n", + "\n", + " # Define accelerations acting on vehicle. \n", + " acceleration_settings_on_spacecraft = ...\n", + " \n", + " # DO NOT MODIFY (line is added for compatibility with question 4)\n", + " if current_question == 4:\n", + " acceleration_settings_on_spacecraft[ \"Sun\" ].append( propagation_setup.acceleration.empirical( rsw_acceleration_magnitude ) )\n", + "\n", + " # Create propagation settings.\n", + " propagator_settings = ...\n", + " \n", + " return propagator_settings\n", + "\n", + "# STUDENT CODE TASK - define function such that it creates the bodies needed for the simulation\n", + "def create_simulation_bodies( ):\n", + " \n", + " \"\"\"\n", + " Creates the body objects required for the simulation.\n", + " Vehicle interfaces, such as the radiation pressure interface, will be defined here.\n", + "\n", + " Parameters\n", + " ----------\n", + " none\n", + "\n", + " Return\n", + " ------\n", + " Body objects required for the simulation.\n", + " \n", + " \"\"\"\n", + " \n", + " bodies = ...\n", + " \n", + " return bodies;\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Question 1 \n", + "### 10 points; Maximum text length: 10 lines\n", + "\n", + "Use the Lambert arc's initial state $\\bar{\\mathbf{x}}(t_{0})$ as initial state for the numerical propagation (spacecraft w.r.t. Sun), with the given initial time $t_{0}$. Run the code to propagate the state of the spacecraft using only the Sun's point-mass attraction, with the Sun as propagation origin. \n", + "
                \n", + "
              • Plot the total trajectory in three dimensions.
              • \n", + "
              • Plot the difference between the Lambert targeter result $\\bar{\\mathbf{x}}(t)$ and the numerical propagation ${\\mathbf{x}}(t)$. Specifically, plot the difference in each of the three Cartesian position components of the spacecraft w.r.t. the Sun.
              • \n", + "
              \n", + " \n", + "\n", + "\n", + "\n", + "**Answer the following questions:**\n", + "\n", + "**a)** Discuss whether the mathematical model solved numerically in this question is an *exact* representation of the Lambert arc model. In your discussion, add a comprehensive list of the assumptions of the Lambert arc model, and briefly explain for each one why it is (not) true in your propagation. If needed, back up your argumentation by adding, and plotting, any additional dependent variables to the simulation output you see fit.\n", + "\n", + "**b)** If you concluded under (a) that the formulations are physically identical, what is the source of any differences you observe between the numerical result and the Lambert result? If you concluded that they are not identical, is any difference you observe relevant for your results?\n", + "\n", + "**Add to save file 1**:
              \n", + "Row 1: initial propagation time and Cartesian state.
              \n", + "Row 2: final propagation time and Cartesian state.\n", + "\n", + "**Coding instructions and hints**\n", + "\n", + "The code block below propagates the dynamics for *unperturbed* dynamics, with the initial state extracted directly from the Lambert arc. The resulting numerical state history ($\\mathbf{x}(t)$) is stored in the `state_history` variable. The state history, as computed directly from the Lambert arc ($\\bar{\\mathbf{x}}(t)$) is stored directly in the `lambert_history` variable.\n", + "\n", + "To make this function generate results, you have a **Code task** for the following helper functions:\n", + "\n", + "* `create_simulation_bodies`: this function defines the body settings and creates the body objects\n", + "* `get_unperturbed_propagator_settings`: this function defines the propagator settings for the unperturbed case\n", + "\n", + "In order to ensure compatibility with the rest of the code, call your vehicle:** `\"Spacecraft\"`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# RUN CODE FOR QUESTION 1 #################################################\n", + "###########################################################################\n", + "\n", + "current_question = 1\n", + "\n", + "# Create body objects\n", + "bodies = create_simulation_bodies( )\n", + "\n", + "# Create Lambert arc state model\n", + "lambert_arc_state_model = get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch)\n", + "\n", + "# Create propagation settings and propagate dynamics\n", + "dynamics_simulator = propagate_trajectory( departure_epoch, arrival_epoch, bodies, lambert_arc_state_model, \n", + " \"Q1\", use_perturbations = False)\n", + "\n", + "# Extract state history from dynamics simulator\n", + "state_history = dynamics_simulator.state_history\n", + "\n", + "# Evaluate the Lambert arc model at each of the epochs in the state_history variable\n", + "lambert_history = get_lambert_arc_history( lambert_arc_state_model, state_history )\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Question 2 \n", + "### 20 points; Maximum text length: 15 lines\n", + "\n", + "Now add the following perturbations to the numerical propagation (by modifying the `get_perturbed_propagation_settings` and the `create_simulation_bodies` functions):\n", + "\n", + "
                \n", + "
              • Point-mass gravity by Venus, Earth, Moon, Mars, Jupiter, Saturn and Sun
              • \n", + "
              • Cannonball radiation pressure on spacecraft. Use a reference area of 20 m$^2$, a radiation pressure coefficient of 1.2, and a vehicle mass of 1000 kg.
              • \n", + "
              \n", + "\n", + "To do so, also update the definition of the environment (modify the `create_simulation_bodies` function) so that these accelerations can be evaluated\n", + "\n", + "Run the propagation with$^2$:
              \n", + "i) The initial and final propagation time equal to the initial and final times of the Lambert arc.
              \n", + "ii) The initial and final propagation time shifted forward and backward in time, respectively, by $\\Delta t=$1 hour.
              \n", + "iii) The initial and final propagation time shifted forward and backward in time, respectively, by $\\Delta t=$2 days.\n", + "\n", + " \n", + "Note that if you shift the initial time from $t_{0}$ to $t_{0}+\\Delta t$ (by modifying these inputs to the `propagate_trajectory` function), the initial state used for the propagation will be adjusted (automatically) accordingly to $\\mathbf{\\bar{x}}(t_{0}+\\Delta t)$. \n", + "\n", + "For each propagation, plot the quantities $\\Delta r=||\\mathbf{r}(t)-\\bar{\\mathbf{r}}(t)||$, $\\Delta v=||\\mathbf{v}(t)-\\bar{\\mathbf{v}}(t)||$ and $\\Delta a=||\\mathbf{a}(t)-\\bar{\\mathbf{a}}(t)||$ as a function of time. **Note** The quantity $\\bar{\\mathbf{a}}(t)$ is not provided automatically, compute its value at each step manually from $\\frac{-\\mu}{r^{3}}\\mathbf{r}$.\n", + "\n", + "**Answer the following questions:**\n", + "\n", + "**a)** For each of the cases (i)-(iii), find the maxima of each of the quantities $\\Delta r$, $\\Delta v$ and $\\Delta a$, and the times at which they occur. Provide a table with the following for each of the cases (i)-(iii): the maximum values of these three quantities, and the times at which they reach their maxima. Write these times *as a fraction of the total propagation time.* Note that a visual determination of the required quantities from the plots you make is sufficient for the purposes of this question.\n", + " \n", + "**b)** Redo the propagation for case (ii), but now propagating forward and backward from the middle point (in time) of the propagation. Plot the quantities quantities $\\Delta r$, $\\Delta v$ and $\\Delta a$, and extend the table you made in question (a) with the results of this question. Consider the forward and backward propagation separately. *Note: the* `propagate_trajectory` *function will automatically propagate backwards in time if the initial time is larger than the final time.*\n", + " \n", + "**c)** Analytically derive formulations for $\\frac{d}{dt}(\\Delta r)$ and $\\frac{d}{dt}(\\Delta v)$. These quantities are a measure for how quickly the numerical solution diverges from the Lambert arc solution. In your derivations, use only the following quantities:\n", + "\n", + "
                \n", + "
              • Positions and velocities along the propagated orbit $\\mathbf{r}$, $\\mathbf{v}$\n", + "
              • Positions and velocities along the Lambert arc $\\bar{\\mathbf{r}}$, $\\bar{\\mathbf{v}}$\n", + "
              • Total accelerations in the Lambert model $\\bar{\\mathbf{a}}$\n", + "
              • The acceleration components $\\mathbf{a}_{\\text{Sun}}$ and $\\mathbf{a}_{\\text{pert}}$ of the propagated orbit.\n", + "
              \n", + "In the above, we have used (for the numerical model) the shorthand $\\mathbf{a}_{\\text{Sun}}(t)$ for the Sun's gravitational acceleration, and have lumped all additional accelerations into $\\mathbf{a}_{\\text{pert}}$, so that the total acceleration acting on the spacecraft is given by $\\left(\\mathbf{a}_{p}\\right)_{S}(t)=\\mathbf{a}_{\\text{Sun}}(t)+\\mathbf{a}_{\\text{pert}}(t)$. \n", + "\n", + "**Hint**: Use the relation $\\dfrac{d||\\mathbf{b}||}{d\\mathbf{b}}=\\dfrac{\\mathbf{b}^{T}}{||\\mathbf{b}||}$, for an arbitrary vector $\\mathbf{b}$.\n", + "\n", + "**d)** Use the equations derived in (c), and the table constructed in (a) and (b) to explain why $\\Delta r$ behaves very differently in the cases that are analyzed. Specifically, explain:\n", + "\n", + "
                \n", + "
              • Why increasing the buffer time $\\Delta t$ seems to generally reduce the magnitude of $\\Delta r$ (question a)\n", + "
              • Why starting the propagation at middle of the arc, and propagating forwards and backwards, results in a smaller maximum value $\\Delta r$ then only propagating forward, even for equal $\\Delta t$ (question (b) vs. case ii in question (a)).\n", + "
              \n", + " \n", + "**Add to save file 1**
              \n", + "Row 3: initial propagation time and Cartesian state (case i).
              \n", + "Row 4: final propagation time and Cartesian state (case i).
              \n", + "Row 5: initial propagation time and Cartesian state (case ii, question a).
              \n", + "Row 6: final propagation time and Cartesian state (case ii, question a).
              \n", + "Row 7: initial propagation time and Cartesian state (case iii).
              \n", + "Row 8: final propagation time and Cartesian state (case iii).
              \n", + "\n", + "\n", + "**Coding instructions and hints**\n", + "\n", + "The code block below propagates the dynamics for *perturbed* dynamics, with the initial state extracted directly from the Lambert arc. The resulting numerical state history ($\\mathbf{x}(t)$) is stored in the `state_history` variable. The state history, as computed directly from the Lambert arc ($\\bar{\\mathbf{x}}(t)$) is stored directly in the `lambert_history` variable.\n", + "\n", + "To make this function generate results, you have a **Code task** for the following helper functions:\n", + "\n", + "* `create_simulation_bodies`: this function defines the body settings and creates the body objects\n", + "* `get_perturbed_propagator_settings`: this function defines the propagator settings for the unperturbed case\n", + "\n", + "To propagate the dynamics, do not create a `SingleArcDynamicsSimulator` manually, but make use of the `propagate_trajectory` function.\n", + "\n", + "In order to ensure compatibility with the rest of the code, call your vehicle:** `\"Spacecraft\"`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# RUN CODE FOR QUESTION 2 #################################################\n", + "###########################################################################\n", + "\n", + "current_question = 2\n", + "\n", + "# Create body objects\n", + "bodies = create_simulation_bodies( )\n", + "\n", + "# Create Lambert arc state model\n", + "lambert_arc_state_model = get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch)\n", + "\n", + "\"\"\"\n", + "case_i: The initial and final propagation time equal to the initial and final times of the Lambert arc.\n", + "case_ii: The initial and final propagation time shifted forward and backward in time, \n", + "respectively, by ∆t=1 hour.\n", + "case_iii: The initial and final propagation time shifted forward and backward in time,\n", + "respectively, by ∆t=2 days.\n", + "\n", + "\"\"\"\n", + "cases = ['case_i', 'case_ii', 'case_iii']\n", + "\n", + "# Define buffer times for each case\n", + "buffer_times = [0.0, 3600.0, 2.0 * constants.JULIAN_DAY]\n", + "\n", + "# Run propagation for each of cases i-iii\n", + "for case in cases:\n", + " \n", + " # Compute departure and arrival time\n", + " current_buffer_time = buffer_times[ cases.index(case) ]\n", + " \n", + " # STUDENT CODE TASK (fill in ...)\n", + " departure_epoch_with_buffer = ...\n", + " arrival_epoch_with_buffer = ...\n", + " \n", + " # Perform propagation\n", + " # STUDENT CODE TASK (call propagation function)\n", + " dynamics_simulator = ...\n", + " state_history = dynamics_simulator.state_history\n", + " lambert_history = get_lambert_arc_history( lambert_arc_state_model, state_history )\n", + " \n", + " # For case ii, run propagation forward and backwatd from mid-point\n", + " if case == 'case_ii':\n", + " \n", + " # STUDENT CODE TASK (fill in ...)\n", + " mid_point_epoch = ...\n", + " \n", + " # Perform propagation forwards\n", + " # STUDENT CODE TASK (call propagation function)\n", + " dynamics_simulator = ...\n", + " state_history_forward = dynamics_simulator.state_history\n", + " lambert_history_forward = get_lambert_arc_history( lambert_arc_state_model, state_history_forward )\n", + " \n", + " # Perform propagation backwards\n", + " # STUDENT CODE TASK (call propagation function)\n", + " dynamics_simulator = ...\n", + " state_history_backward = dynamics_simulator.state_history\n", + " lambert_history_backward = get_lambert_arc_history( lambert_arc_state_model, state_history_backward )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Question 3\n", + "## 20 points; Maximum text length: 10 lines\n", + "\n", + "We will now turn our attention to modifying the trajectory, so that we arrive at the desired target when *including* perturbations. When we modify an initial state $\\mathbf{x}_{0}$ or some parameter vector $\\mathbf{p}$ (which contains properties of the dynamical model), the propagated state $\\mathbf{x}(t)$ will change. When changing the initial state by $\\Delta \\mathbf{x}_{0}$ and/or a parameter vector by $\\Delta \\mathbf{p}$, we will denote the resulting change in numerically propagated state, as a function of time, as $\\Delta {\\mathbf{x}}(t)$.\n", + "\n", + "For given modifications $\\Delta \\mathbf{x}_{0}$ and $\\Delta \\mathbf{p}$, the behaviour of $\\Delta {\\mathbf{x}}(t)$ can be obtained by directly numerically repropagating $\\mathbf{x}(t)$ with the modified initial state and parameter vector, and subtracting the propagation result without these modifications. However, this can be a tedious and computationally expensive process. The matrices $\\boldsymbol{\\Phi}(t,t_{0})$ and $\\mathbf{S}(t)$ provide a way to approximate $\\Delta {\\mathbf{x}}(t)$, by means of a *linearization*:\n", + "\n", + "\\begin{align}\n", + "\\boldsymbol{\\Phi}(t,t_{0})=\\frac{\\partial\\mathbf{x}(t)}{\\partial\\mathbf{x}_{0}}\\\\\n", + " \\mathbf{S}(t)=\\frac{\\partial\\mathbf{x}(t)}{\\partial\\mathbf{p}}\n", + "\\end{align}\n", + "\n", + "Now, we denote the linearized change in state as $\\Delta \\tilde{\\mathbf{x}}(t)$, which we compute from:\n", + "\\begin{align}\n", + "\\Delta \\tilde{\\mathbf{x}}(t)=\\boldsymbol{\\Phi}(t,t_{0})\\Delta \\mathbf{x}_{0}+\\mathbf{S}(t)\\Delta\\mathbf{p}\\label{eq:linearizedChangeInState}\n", + "\\end{align}\n", + "so that $\\Delta \\tilde{\\mathbf{x}}(t)$ is a *linear approximation* of $\\Delta {\\mathbf{x}}(t)$.\n", + "\n", + "For this question, we split the total propagation time into 10 equisized (in time) arcs. Using the propagation settings of the previous question (case iii), the state and variational equations for the dynamics are propagated for each arc. For each arc $i$ (with $i$ starting at 0), the initial state $\\mathbf{x}_{i}(t_{0,i})$ is set so that it corresponds exactly with the Lambert targeter state at the corresponding time $\\bar{\\mathbf{x}}(t_{0,i})$. This will result in a discontinuous state history over the full transfer (since the propagation is performed in an arc-wise manner). For this question, you will compute how to make the trajectory continuous in position by applying an impulsive correction maneuver at the start of each arc, such that $\\mathbf{r}_{i}=\\bar{\\mathbf{r}}_{i}$ at the end of each arc. \n", + "\n", + "**Answer the following questions:**\n", + "\n", + "**a)** Perform the arcwise propagation. Plot the deviation $\\Delta r$ of the propagated state w.r.t. the Lambert arc as a function of time over the full trajectory.\n", + "\n", + "**b)** Derive a model to use the results for $\\boldsymbol{\\Phi}_{i}(t_{i+1},t_{i})$ to compute the change in velocity $\\Delta \\mathbf{v}_{i}$ needed at the beginning of each arc to ensure that the spacecraft reaches $\\bar{\\mathbf{r}}$ at the end of the arc. Keep the initial position of the arc constant. Show the derivation of your model. For this question, neglect linearization errors (assume $\\Delta \\tilde{\\mathbf{x}}(t)=\\Delta{\\mathbf{x}}(t)$)\n", + "\n", + "**c)** Modify the code, so that the initial states of each arc are modified in accordance with your calculated correction manuevers in (b). Plot the deviation $\\Delta r$ of the modified propagated state w.r.t. the Lambert arc as a function of time. Do you consider your linearized model to be applicable in this specific situation? \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "**Add to save file 1}**
              \n", + "Row 9: initial propagation time and Cartesian state (arc 0).
              \n", + "Row 10: final propagation time and Cartesian state (arc 0).
              \n", + "Row 11: initial propagation time and Cartesian state (arc 4).
              \n", + "Row 12: final propagation time and Cartesian state (arc 4).
              \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# RUN CODE FOR QUESTION 3 #################################################\n", + "###########################################################################\n", + "\n", + "current_question = 3\n", + "\n", + "# Create body objects\n", + "bodies = create_simulation_bodies( )\n", + "\n", + "# Create Lambert arc state model\n", + "lambert_arc_state_model = get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch)\n", + "\n", + "##############################################################\n", + "\n", + "# Set start and end times of full trajectory\n", + "# STUDENT CODE TASK (fill in ...)\n", + "buffer_time = ...\n", + "departure_epoch_with_buffer = ...\n", + "arrival_epoch_with_buffer = ...\n", + "\n", + "# Compute number of arcs and arc length\n", + "# STUDENT CODE TASK (fill in ...)\n", + "number_of_arcs = ...\n", + "arc_length = ...\n", + "\n", + "##############################################################\n", + "\n", + "# Compute relevant parameters (dynamics, state transition matrix, Delta V) for each arc\n", + "for arc_index in range(number_of_arcs):\n", + " \n", + " # Compute initial and final time for arc\n", + " # STUDENT CODE TASK (fill in ...)\n", + " current_arc_initial_time = ...\n", + " current_arc_final_time = ...\n", + "\n", + " ###########################################################################\n", + " # RUN CODE FOR QUESTION 3a ################################################\n", + " ###########################################################################\n", + " \n", + " # Propagate dynamics on current arc\n", + " # STUDENT CODE TASK (call propagation function)\n", + " dynamics_simulator = ...\n", + " state_history = dynamics_simulator.state_history\n", + " \n", + " # Retrieve Lambert arc for same epochs, and compute final difference\n", + " lambert_history = get_lambert_arc_history( lambert_arc_state_model, state_history )\n", + "\n", + " ###########################################################################\n", + " # RUN CODE FOR QUESTION 3d ################################################\n", + " ###########################################################################\n", + " \n", + " # Solve for state transition matrix on current arc\n", + " variational_equations_solver = propagate_variational_equations(\n", + " current_arc_initial_time, current_arc_final_time, bodies, lambert_arc_state_model) \n", + " \n", + " # Retrieve propagation resuls and compute Lambert arc history\n", + " state_transition_matrix_history = variational_equations_solver.state_transition_matrix_history\n", + " state_history = variational_equations_solver.state_history\n", + " lambert_history = get_lambert_arc_history( lambert_arc_state_model, state_history ) \n", + " \n", + " # Retrieve final state deviation between numerical and Lambert model\n", + " final_state_deviation = state_history[ final_epoch ] - lambert_history[ final_epoch ]\n", + " \n", + " # Get final state transition matrix (and its inverse)\n", + " final_epoch = list(state_transition_matrix_history.keys())[-1]\n", + " final_state_transition_matrix = state_transition_matrix_history[ final_epoch ]\n", + " final_inverse_state_transition_matrix = np.linalg.inv( final_state_transition_matrix )\n", + "\n", + " # Compute required velocity change at beginning of arc to meet required final state \n", + " # STUDENT CODE TASK: calculate initial state correction to mee arc end position requirement\n", + " initial_state_correction = ...\n", + " \n", + " # Propagate with correction to initial state \n", + " # STUDENT CODE TASK (call propagation function). HINT: the initial state correction can be passed as an input to function\n", + " dynamics_simulator_after_correction = ...\n", + " state_history_after_correction = dynamics_simulator.state_history\n", + "\n", + " # Compute and print deviation\n", + " final_state_deviation_after_correction = state_history[ final_epoch ] - lambert_history[ final_epoch ] \n", + "\n", + " # Save corrected arc\n", + " write_propagation_results_to_file( dynamics_simulator, lambert_arc_state_model, \n", + " \"Q3_arc_\" + str(arc_index) + \"_corrected\", output_directory)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Question 4\n", + "## 25 points; Maximum text length: lines\n", + "\n", + "For this question, you will perform a similar analysis as in the previous question, but now using an approximate model for a low-thrust acceleration model to correct the orbit so that it reaches the start and end of the trajectory correctly. Instead of a ten-arc model, you will use a one- and two-arc model. \n", + "\n", + "In your model, you will parameterize the thrust as a constant acceleration in RSW frame for each arc. This thrust will be added to the simulation by defining a so-called 'empirical' acceleration: a constant acceleration in RSW direction. Note that, since the direction of the RSW frame changes in time, the inertial direction of this empirical acceleration will change in time. For this question, you will compute the magnitudes of these emprical accelerations, such that the trajectory meets all the required boundary conditions.\n", + "\n", + "By calculating the sensitivity matrix $\\mathbf{S}$ for the entries of the empirical acceleration, you will be able to calculate (approximately) the required thust under a number of different conditions. For this question, neglect linearization errors (assume $\\Delta \\tilde{\\mathbf{x}}(t)=\\Delta{\\mathbf{x}}(t)$).\n", + "\n", + "**Answer the following questions:**\n", + "\n", + "**a)** Consider a single arc propagation for the full transfer. Derive an equation to use the results for $\\mathbf{S}(t_{E})$ to compute the low-thrust acceleration in RSW frame (denote this as $\\mathbf{p}$) needed to ensure that the spacecraft reaches $\\bar{\\mathbf{r}}(t_{E})$ at the end of the transfer. Keep the initial state of the arc constant at $\\bar{\\mathbf{x}}(t_{0})$. Put no constraints on the final velocity. Show the derivation of your model, starting from the equations in the lecture videos.
              \n", + "**b)** Implement your model for question (a) in your code, and verify and argue that it works correctly (use the same value of $\\Delta t$ as in question 2, case iii). Plot any quantities you need to show the correct functioning of your model.\n", + "\n", + "For the next questions, you will analyze the low-thrust trajectory for a two-arc model (with each arc having equal duration). For this question, do not impose any *a priori* constraints on the absolute position or velocity at the arc splitting point (unlike in question 3, where this point was required to correspond to $\\bar{\\mathbf{r}}$, but do impose a constraint of continuity in position and velocity over the full trajectory (unlike in question 3, where the velocity was discontinuous between arcs).\n", + "\n", + "**c)** For an arbitrary choice of constant RSW-thrust in arc 1 (denoted $\\mathbf{p}_{1}$), thrust in arc 2 (denoted $\\mathbf{p}_{2}$), and modification in initial velocity of arc 1 (denoted $\\mathbf{v}_{i}(t_{0,1})$), derive a single equation for the change in position at the end of arc 2 (denoted $\\Delta \\mathbf{r}(t_{E,2})$). Write a single explicit equation for $\\Delta \\mathbf{r}(t_{E,2})$, in the following notation:\n", + "\\begin{align}\n", + "\\Delta \\mathbf{r}(t_{E,2})=\\mathbf{A}\\begin{pmatrix} \\mathbf{p}_{1}\\\\ \\mathbf{p}_{2} \\\\ \\Delta\\mathbf{v}_{i}(t_{0,1}) \\end{pmatrix}\n", + "\\end{align}\n", + "and provide an explicit formulation for the matrix $\\mathbf{A}$. Use the notation from the lecture slides on *Reaching the objective - Arc-wise Low-thrust*.\n", + "
              \n", + "**d)** The equation you derived in (c) does not have a unique solution. Choose $ \\Delta\\mathbf{v}_{i}(t_{0,1})=\\mathbf{0}$, and set $\\mathbf{p}_{1}$ equal to the value of $\\mathbf{p}$ you derived and computed in questions (a) and (b). Starting from the equation you derived in (c), formulate an explicit equation for $\\mathbf{p}_{2}$ needed to achieve a given $\\Delta \\mathbf{r}(t_{E,2})$.
              \n", + "**e)** Implement the model you derived in question (d) in your code, such that you obtain $\\mathbf{r}(t_{E,2})=\\bar{\\mathbf{r}}(t_{E,2})$ (*e.g.* such that the trajectory terminates on the Lambert arc). Verify that the model works correctly: show that it meets all the required constraints.\n", + "\n", + "**Add to save file 1}**
              \n", + "Row 13: final propagation time and Cartesian state (question 4b)
              \n", + "Row 14: final propagation time and Cartesian state (question 4e)
              " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# RUN CODE FOR QUESTION 4 #################################################\n", + "###########################################################################\n", + "\n", + "current_question = 4\n", + "rsw_acceleration_magnitude = [0,0,0]\n", + "\n", + "# Create body objects\n", + "bodies = create_simulation_bodies( )\n", + "\n", + "# Create Lambert arc state model\n", + "lambert_arc_state_model = get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch)\n", + "\n", + "###########################################################################\n", + "# RUN CODE FOR QUESTION 4b ################################################\n", + "###########################################################################\n", + "\n", + "# Set start and end times of full trajectory\n", + "# STUDENT CODE TASK (fill in ...)\n", + "buffer_time = ...\n", + "departure_epoch_with_buffer = ...\n", + "arrival_epoch_with_buffer = ...\n", + "\n", + "# Solve for state transition matrix on current arc\n", + "variational_equations_solver = propagate_variational_equations(\n", + " departure_epoch_with_buffer, arrival_epoch_with_buffer, bodies, lambert_arc_state_model) \n", + "\n", + "sensitivity_matrix_history = variational_equations_solver.sensitivity_matrix_history\n", + "state_history = variational_equations_solver.state_history\n", + "lambert_history = get_lambert_arc_history( lambert_arc_state_model, state_history ) \n", + "\n", + "# Compute low-thrust RSW acceleration to meet required final position \n", + "# STUDENT CODE TASK: calculate low-thrust acceleration to meet arc end position requirement\n", + "rsw_acceleration_magnitude = ...\n", + "\n", + "# STUDENT CODE TASK (call propagation function). NOTE: Empirical acceleration with magnitude \n", + "# rsw_acceleration_magnitude is added automatically by get_perturbed_propagator_settings function) \n", + "dynamics_simulator = ...\n", + "\n", + "###########################################################################\n", + "# RUN CODE FOR QUESTION 4e ################################################\n", + "###########################################################################\n", + "\n", + "# Compute number of arcs and arc length\n", + "# STUDENT CODE TASK (fill in ...)\n", + "number_of_arcs = 2\n", + "arc_length = ( arrival_epoch_with_buffer - departure_epoch_with_buffer ) / number_of_arcs\n", + "\n", + "\n", + "# Compute relevant parameters (dynamics, state transition matrix, Delta V) for each arc\n", + "for arc_index in range(number_of_arcs):\n", + " \n", + " # Compute initial and final time for arc\n", + " # STUDENT CODE TASK (fill in ...)\n", + " current_arc_initial_time = ...\n", + " current_arc_final_time = ...\n", + " \n", + " # STUDENT CODE TASK (run arc-wise model as defined in question (e) )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Question 5\n", + "## 25 points; Maximum text length: 20 lines\n", + "\n", + "Since the model in questions 3 and 4 for computing the influence due to a small change in state and parameters is based on a linearization, it is reasonably valid for *small* values of $\\Delta \\mathbf{x}_{0}$ and $\\mathbf{p}$ but breaks down for larger deviations. The error $\\boldsymbol{\\epsilon}_{\\mathbf{x}}(t)$ due to the linearization can be defined as:\n", + "\n", + "\\begin{align}\n", + "\\boldsymbol{\\epsilon}_{\\mathbf{x}}(t) = \\Delta \\tilde{\\mathbf{x}}(t) - \\Delta {\\mathbf{x}}(t)\n", + "\\end{align}\n", + "\n", + "For this question, you will numerically investigate the limit of validity of the above linearization using $\\boldsymbol{\\Phi}(t,t_{0})$ (the influence of $\\mathbf{S}(t)$ is not considered here), for the 10-arc model of question 3.\n", + "\n", + "Determine, for arc $i=0$, and arc $i=4$, independently for each of the entries of $\\Delta \\mathbf{x}_{0}(=\\Delta \\mathbf{x}(t_{0,i}))$, how large the initial state corrections are allowed to be, before the linearization used to obtain $\\Delta\\tilde{\\mathbf{x}}(t)$ is no longer valid.\n", + "\n", + "Use the following criterion as the definition of a valid linearization:\n", + "\\begin{align}\n", + "\\max_{t}||\\boldsymbol{\\epsilon}_{\\mathbf{r}}(t)||<\\text{100 km}\\hspace{0.5cm}\\vee\n", + "\\hspace{0.5cm}\\max_{t}||\\boldsymbol{\\epsilon}_{\\mathbf{v}}(t)||<\\text{1 m/s}\n", + "\\end{align}\n", + "where $\\boldsymbol{\\epsilon}_{\\mathbf{r}}(t)$ and $\\boldsymbol{\\epsilon}_{\\mathbf{v}}(t)$ denote the linearization error in position and velocity (first and last three entries of $\\boldsymbol{\\epsilon}_{\\mathbf{x}}(t)$).\n", + "\n", + "Find the minimum positive value of each entry of the initial state perturbations for arcs 0 and 4, for which the error criterion is no longer true over the full arc, each time keeping the other 5 entries of $\\Delta \\mathbf{x}_{0}$ fixed to zero. \n", + "\n", + "Your answer must be correct to within 25% (set up your analysis so that this is guaranteed). For instance, if the true limiting value of $\\Delta {y}_{0}$ is 2.4 m, any value from 1.8 m to 3.0 m will be accepted. **Implement your model in such a way that all 12 limiting values (6 for arc $i=0$; 6 for arc $i=4$) are produced from a single run of your program.**\n", + "\n", + "Explain the algorithm that you have used and implemented, explaining why the model you have set up is guaranteed to give the requested results to the required accuracy. Provide *pseudo-code* (so not a copy-paste of your code!) in your explanation.\n", + "\n", + "\n", + "**Add to save file 2**
              \n", + "Matrix, 6 rows by 2 columns, permitted $\\Delta \\mathbf{x_0}$ (arcs 0 and 4)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# RUN CODE FOR QUESTION 5 #################################################\n", + "###########################################################################\n", + "\n", + "current_question = 5\n", + "\n", + "# Create body objects\n", + "bodies = create_simulation_bodies( )\n", + "\n", + "# Create Lambert arc state model\n", + "lambert_arc_state_model = get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch)\n", + "\n", + "# Set full start and end times\n", + "buffer_time = 2.0 * constants.JULIAN_DAY\n", + "departure_epoch_with_buffer = departure_epoch + buffer_time\n", + "arrival_epoch_with_buffer = arrival_epoch - buffer_time\n", + "\n", + "# Set arc length\n", + "number_of_arcs = 10\n", + "arc_length = ( arrival_epoch_with_buffer - departure_epoch_with_buffer ) / number_of_arcs\n", + "\n", + "considered_arc_indices = [0, 4]\n", + "\n", + "for arc_index in considered_arc_indices:\n", + " \n", + " # Compute start and end time for current arc\n", + " current_arc_initial_time = departure_epoch_with_buffer + arc_index * arc_length\n", + " current_arc_final_time = departure_epoch_with_buffer + ( arc_index + 1 ) * arc_length\n", + "\n", + " # Get propagator settings for perturbed forward arc\n", + " arc_initial_state = lambert_arc_state_model.get_cartesian_state( current_arc_initial_time )\n", + " propagator_settings = get_perturbed_propagator_settings( bodies, arc_initial_state, current_arc_final_time )\n", + " \n", + " # Set integrator settings\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " current_arc_initial_time, fixed_step_size\n", + " )\n", + " \n", + " ###########################################################################\n", + " # PROPAGATE NOMINAL TRAJECTORY AND VARIATIONAL EQUATIONS ##################\n", + " ###########################################################################\n", + " \n", + " parameters_for_which_to_compute_sensitivity = get_sensitivity_parameter_set( propagator_settings, bodies, \n", + " target_body)\n", + "\n", + " variational_equations_simulator2= estimation_setup.SingleArcVariationalEquationsSolver(\n", + " bodies, integrator_settings, propagator_settings, parameters_for_which_to_compute_sensitivity, integrate_on_creation=1 )\n", + " \n", + " state_transition_result = variational_equations_simulator2.state_transition_matrix_history\n", + " nominal_integration_result = variational_equations_simulator2.state_history\n", + " \n", + " \n", + " # TODO: Retrieve nominal initial state value (e.g. initial state with Delta x_0 = 0)\n", + " initial_epoch = list(state_transition_result.keys())[0]\n", + " original_initial_state = nominal_integration_result[ initial_epoch ]\n", + " \n", + " ###########################################################################\n", + " # START ANALYSIS ALGORITHM FOR QUESTION 4 #################################\n", + " ###########################################################################\n", + " \n", + " # This vector will hold the maximum permitted initial state perturbations for which the linearization \n", + " # is valid (for the current arc. The vector is initialized to 0, and each of its 6 entries is computed \n", + " # in the 6 iterations of the coming for loop (that runs over the iteration variable 'entry')\n", + " permitted_perturbations = np.array([0,0,0,0,0,0])\n", + " \n", + " # Iterate over all initial state entries\n", + " for entry in range(6):\n", + " \n", + " # STUDENT CODE TASK: Define (iterative) algorithm to compute current entry of 'permitted_perturbations'\n", + " # General structure: define an initial state perturbation (perturbed_initial_state variable),\n", + " # compute epsilon_x (see assignment), and iterate your algorithm until convergence.\n", + " \n", + " while ...:\n", + " \n", + " # STUDENT CODE TASK: define initial state perturbation for current iteration\n", + " initial_state_perturbation = ...\n", + " \n", + " # Reset propagator settings with perturbed initial state\n", + " perturbed_initial_state = arc_initial_state + initial_state_perturbation\n", + " propagator_settings.reset_initial_states( perturbed_initial_state )\n", + " \n", + " # Create simulation object and propagate dynamics with perturbed initial state\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings, True, False, True)\n", + " \n", + " # Retrieve state history computed directly from perturbed initial state\n", + " integration_result = dynamics_simulator.get_equations_of_motion_numerical_solution()\n", + " \n", + " # Compute epsilon_x\n", + " epsilon_x = ...\n", + " \n", + " permitted_perturbations[entry] = ...\n", + " \n", + " \n", + " print(\"Permitted perturbations: \", arc_index, np.transpose( permitted_perturbations ) )\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Submission and reporting instructions\n", + "\n", + "\n", + "**Reporting instructions - formulating equations of motion**\n", + "\n", + "When asked to explicitly write out one or more accelerations\n", + "\n", + "
                \n", + "
              • Follow the notation from the lecture notes
              • \n", + "
              • Use the following indices: $S$ for Sun, $E$, $V$ and $M$ for Earth, Venus and Mars, respectively, and $p$ for the spacecraft.
              • \n", + "
              • Use a comma to separate indices as you see fit (spacecraft position w.r.t. the Sun can be written as $\\mathbf{r}_{S,p}$ or $\\mathbf{r}_{Sp}$).
              • \n", + "
              • There is no need to specify the frame orientation of any of the vectors. All are assumed to be in a frame with inertial orientation.
              • \n", + "
              • When denoting separate accelerations, always denote the body $B$ exerting, and body $A$ undergoing, the acceleration with $\\mathbf{a}_{_{BA}}$.
              • \n", + "
              • When writing out a single acceleration in terms of positions $\\mathbf{r}$, always first write the total relative positions $\\mathbf{r}_{_{CD}}$ as used by the acceleration model. Expand the positions further as you see fit in next steps.
              • \n", + "
              • When writing a position $\\mathbf{r}_{_{CD}}$ that is partly retrieved from the environment (if any), and where part of the vector is numerically propagated, split the separate contributions (in a second step after the previous point). For instance, if $\\mathbf{r}_{_{ED}}$ is propagated, and $\\mathbf{r}_{_{CD}}$ is used in the acceleration, write $\\mathbf{r}_{_{CD}}=\\mathbf{r}_{_{CE}}+\\mathbf{r}_{_{ED}}$.
              • \n", + "
              \n", + "\n", + "### Reporting instructions - change in position\n", + "\n", + "When asked to plot/compute the change in total position between two simulation results $\\mathbf{r}_{1}(t)$ and $\\mathbf{r}_{2}(t)$. The change in total position is to be computed as $||\\Delta \\mathbf{r}(t)||= ||\\mathbf{r}_{2}(t)-\\mathbf{r}_{1}(t)||$\n", + "\n", + "### Reporting instructions - figures\n", + "\n", + "When using figures, take the following guidelines into account:\n", + "\n", + "
                \n", + "
              • Any text (legend, axis labels etc.) should be sufficiently large so as to be legible when printed on A4 paper.
              • \n", + "
              • Each curve should be distinguishable in your plots.
              • \n", + "
              • Adjust the scale (e.g. linear vs. logarithmic) of your plots as needed to interpret your data.
              • \n", + "
              • Make efficient use of space for graphs and plots. Whenever possible and legible: plot multiple curves (e.g. for different runs and/or elements) in a single figure.
              • \n", + "
              • All figures must be complete (including axis labels, legend, caption, etc.)
              • \n", + "
              \n", + "\n", + "Points will be deducted for unreadable figures, or figures that do not clearly show information that you refer to in your discussion.\n", + "\n", + "### Reporting instructions - cover page\n", + "\n", + "The cover page of each report **must contain**:\n", + "\n", + "
                \n", + "
              • A link to the private Github repository containing the source code and output files (see below).
              • \n", + "
              • The names of any people with whom you cooperated (if any)
              • \n", + "
              • The time spent per question
              • \n", + "
              \n", + "\n", + "If any of these points are not present on your cover page, points will be deducted.\n", + "\n", + "\n", + "### Reporting instructions - general\n", + "\n", + "Follow the provided limitations on the length of the text that you use (this excludes figures, tables and equations). Answers longer than the imposed limit will not be read beyond this limitation. Example: if the imposed limitation is 10 lines, and you write 15, we will grade the answer based **only** on the first 10 lines.\n", + "\n", + "You are free to work together with your fellow students, but are required to write your own code and report. Copying/pasting from each others report/code is not accepted, and can lead to the case being referred to the Faculty Board of Examiners.\n", + "\n", + "### Support instructions\n", + "\n", + "In case of any questions, there are a number of options for support:\n", + "\n", + "
                \n", + "
              • For issues with the installation, unit tests, or the general use of Tudat, please post an issue on Github. When posting an issue, first browse through existing issues. If your problem is raised in an open issue, post in {that} issue instead of opening a new one. Note the operating system you are using.
              • \n", + "
              • In case of questions specific to the assignments, use the Brightspace forum. As with Github, go through existing posts before opening a new one. Do no publicly post your code, or other information that provides direct answers to the questions.
              • \n", + "
              • In-person support and Q$\\&$A is also available during working lectures and open office hours. See Brightspace calendar for time and location.
              • \n", + "
              \n", + "\n", + "See Brightspace (Course Information $\\rightarrow$ Staff and Support) for details on what to prepare when asking for support.\n", + "\n", + "### Submission instructions\n", + "\n", + "You will not be graded on your coding style. Submission of the reports and output files is to be done through Brightspace. **Deadline for submission is January 17 2021 23:59 CET** and can also be found on Brightspace. For late submissions, 1 point (out of a total of 10) will be subtracted {per day}. So, when handing in the report $x$ days late, $\\lceil{x}\\rceil$ points will be deducted. *If* you have $g$ grace days left: when handing in the report $x$ days late, $\\lceil{x-g}\\rceil$ points will be deducted.\n", + "\n", + "Submission of your final code and results files will be done through Github. Ensure that **you only commit to a private repository**. Instructions on pushing code to Github can be found in:\n", + "\n", + "https://tudat-space.readthedocs.io/en/latest/_src_use_of_tools/github.html\n", + "\n", + "See below for the exact files and filenames to submit:\n", + "\n", + "## *Failure to comply exactly with the requirements for file contents and naming set out below will result in point deductions.*\n", + "\n", + "\n", + "\n", + "Instructions on how to commit code to your repository is given at https://tudat-space.readthedocs.io/en/latest/_src_use_of_tools/github.html. In addition to the report, for this assignment you will submit:\n", + "\n", + "\n", + "* This notebook (which can be run directly without modifications to reproduce your results). Commit and push this file to your private GitHub repository (in the $\\texttt{Assignment2/}$ directory).\n", + "* A text file containing $\\textbf{only}$ the initial or final time (column 1) and Cartesian states (as a row vector; columns {2-7}) from a number of your simulations, to at least 8 digits of precision. The specific simulations for which you are to save the time/state, and the row in which you are to save them are indicated in the questions. Name the file `CartesianResults_AE4868_2020_2_YYYYYYY.dat`, where YYYYYYY is your student number. Upload this file to Brightspace.\n", + "* A text file containing $\\textbf{only}$ the 6x2 matrix of permitted $\\Delta \\mathbf{x_0}$ for question 5. Name the file `Question5_Results_AE4868_2020_2_YYYYYYY.dat`, where YYYYYYY is your student number. Upload this file to Brightspace. \n", + "\n", + "\n", + " \n", + "## *Failure to comply exactly with the requirements for file contents and naming set out below will result in point deductions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/project2/src/assignment2V0.ipynb b/code/project2/src/assignment2V0.ipynb new file mode 100644 index 0000000..ce5cef9 --- /dev/null +++ b/code/project2/src/assignment2V0.ipynb @@ -0,0 +1,1133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Assignment 2 - Interplanetary Transfer\n", + "\n", + "For this assignment, you will use a Lambert targeter to generate a first guess for an unperturbed interplanetary transfer. Subsequently, you will use various numerical propagation models that include perturbations to analyze the trajectory in more detail, and compute trajectory corrections such that your trajectory meets its boundary conditions (departure and arrival) in the perturbed environment,\n", + "\n", + "In this assignment, you are required to modify this Jupyter notebook. Unlike assignment 1, this assignment is in a single notebook, with the code for all questions and the assignment description all merged into a single document.\n", + "\n", + "\n", + "**General Instructions**\n", + "\n", + "In this assignment, you will use a so-called Lambert targeter (a tool that solves Lambert's problem) to generate an initial guess for an interplanetary direct high-thrust transfer. The Lambert targeter takes as input:\n", + "\n", + "
                \n", + "
              • A departure position $\\mathbf{r}_0$
              • \n", + "
              • An arrival position $\\mathbf{r}_E$
              • \n", + "
              • A time of flight $T$
              • \n", + "
              • A central body gravitational parameter $\\mu$
              • \n", + "
              \n", + "\n", + "\n", + "The Lambert targeter then generates the Keplerian trajectory between $\\mathbf{r}_{0}$ and $\\mathbf{r}_{E}$, with the given time of flight $T$. Since the initial and final positions uniquely define the trajectory (assuming a prograde single-revolution trajectory; ignoring rare singular cases), the full state along this Keplerian trajectory (or Lambert arc) are *outputs* of the Lambert targeter. We denote the Cartesian state function of this Lambert arc as $\\bar{\\mathbf{x}}(t)$. For your situation, the initial and final position $\\mathbf{r}_{0}$ and $\\mathbf{r}_{E}$ of the full trajectory are the positions of the center of mass of Earth and Mars or Venus (w.r.t. the Sun), at times $t_{0}$ and $t_{E}$, respectively (with values depending on your student number, to be found in the $\\texttt{assignment2Input-2020-2021.txt}$ file on Brightspace under Assignment 2).\n", + "\n", + "All analysis on the output data can be done in the notebook. However, if you would like to use a different piece of software (*e.g.* Matlab) for your analyses, the relevant data is provided as output to data files, in a number of manners:\n", + "\n", + "* By calling the `propagate_trajectory` function, the propagated state of the spacecraft and the associated dependent variables will be saved to a file, as well as the state of the Lambert arc $\\bar{\\mathbf{x}}$ at the epochs of the numerical integration. See the in-code comments of the `write_propagation_results_to_file` function in the helper functions block code file for more details.\n", + "* For question 3, the `write_propagation_results_to_file` function is called directly from the $\\texttt{main}$ function.\n", + "\n", + "\n", + "**Before starting the assignment, read the submission instructions given at the end of this notebook.**\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "''' \n", + "Copyright (c) 2010-2020, Delft University of Technology\n", + "All rigths reserved\n", + "\n", + "This file is part of the Tudat. Redistribution and use in source and \n", + "binary forms, with or without modification, are permitted exclusively\n", + "under the terms of the Modified BSD license. You should have received\n", + "a copy of the license with this file. If not, please or visit:\n", + "http://tudat.tudelft.nl/LICENSE.\n", + "'''\n", + "\n", + "import numpy as np\n", + "from tudatpy import elements\n", + "from tudatpy.io import save2txt\n", + "from tudatpy.kernel import constants\n", + "from tudatpy.kernel.interface import spice_interface\n", + "from tudatpy.kernel.simulation import environment_setup\n", + "from tudatpy.kernel.simulation import estimation_setup\n", + "from tudatpy.kernel.simulation import propagation_setup\n", + "from tudatpy.kernel.astro import two_body_dynamics\n", + "from tudatpy.kernel.astro import conversion\n", + "from matplotlib import pyplot as plt\n", + "\n", + "# STUDENT CODE TASK (fill in ...)\n", + "departure_epoch = ...\n", + "time_of_flight = ...\n", + "arrival_epoch = departure_epoch + time_of_flight\n", + "target_body = ...\n", + "\n", + "# Global settings\n", + "fixed_step_size = 3600.0\n", + "global_frame_origin = \"SSB\"\n", + "global_frame_orientation = \"ECLIPJ2000\"\n", + "\n", + "# Helper variables for question 4\n", + "current_question = 0;\n", + "rsw_acceleration_magnitude = [0,0,0]\n", + "\n", + "# Load spice kernels.\n", + "spice_interface.load_standard_kernels()\n", + "\n", + "# Set output directory\n", + "output_directory = \"./SimulationOutput/\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Helper Functions (DO NOT MODIFY)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def write_propagation_results_to_file( dynamics_simulator, lambert_arc_state_model, file_output_identifier, output_directory):\n", + "\n", + " \"\"\"\n", + " This function will write the results of a numerical propagation, as well as the Lambert arc states at the epochs of the\n", + " numerical state history, to a set of files. Two files are always written when calling this function (numerical state history, a\n", + " and Lambert arc state history). If any dependent variables are saved during the propagation, those are also saved to a file\n", + " \n", + " Parameters\n", + " ----------\n", + " dynamics_simulator : Object that was used to propagate the dynamics, and which contains the numerical state and dependent\n", + " variable results\n", + " \n", + " lambert_arc_state_model : Lambert arc state model as returned by the get_lambert_problem_result() function\n", + " \n", + " file_output_identifier : Name that will be used to correctly save the output data files\n", + " \n", + " output_directory : Directory to which the files will be written\n", + " \n", + " Files written\n", + " -------------\n", + " \n", + " _numerical_states.dat\n", + " _dependent_variables.dat\n", + " _lambert_statess.dat\n", + "\n", + " \n", + " Return\n", + " ------\n", + " None\n", + " \n", + " \"\"\"\n", + " \n", + " # Save numerical states\n", + " simulation_result = dynamics_simulator.state_history\n", + " save2txt(solution= simulation_result, filename=output_directory + file_output_identifier + \"_numerical_states.dat\", directory=\"./\", column_names=None )\n", + " \n", + " # Save dependent variables\n", + " dependent_variables = dynamics_simulator.dependent_variable_history\n", + " if len(dependent_variables.keys()) > 0:\n", + " save2txt(solution= dependent_variables, filename=output_directory + file_output_identifier + \"_dependent_variables.dat\", directory=\"./\", column_names=None )\n", + " \n", + " # Save Lambert arc states\n", + " lambert_arc_states = get_lambert_arc_history( lambert_arc_state_model, simulation_result )\n", + " \n", + " save2txt(solution= lambert_arc_states, filename= output_directory + file_output_identifier + \"_lambert_states.dat\", directory=\"./\", column_names=None )\n", + " \n", + " return\n", + "\n", + "def get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch):\n", + " \n", + " # Gravitational parameter of the Sun\n", + " central_body_gravitational_parameter = bodies.get_body( \"Sun\" ).gravitational_parameter\n", + " \n", + " # Set initial and final positions for Lambert targeter\n", + " initial_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name=\"Earth\",\n", + " observer_body_name=\"Sun\",\n", + " reference_frame_name=global_frame_orientation,\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= departure_epoch )\n", + " \n", + " final_state = spice_interface.get_body_cartesian_state_at_epoch(\n", + " target_body_name= target_body,\n", + " observer_body_name=\"Sun\",\n", + " reference_frame_name=global_frame_orientation,\n", + " aberration_corrections=\"NONE\",\n", + " ephemeris_time= arrival_epoch )\n", + " \n", + " # Create Lambert targeter\n", + " lambertTargeter = two_body_dynamics.LambertTargeterIzzo(\n", + " initial_state[:3], final_state[:3],arrival_epoch - departure_epoch, central_body_gravitational_parameter );\n", + " \n", + " # Compute initial Cartesian state of Lambert arc\n", + " lambert_arc_initial_state = initial_state\n", + " lambert_arc_initial_state[3:] = lambertTargeter.get_departure_velocity()\n", + " \n", + " # Compute Keplerian state of Lambert arc\n", + " lambert_arc_keplerian_elements = conversion.cartesian_to_keplerian( lambert_arc_initial_state, \n", + " central_body_gravitational_parameter)\n", + " \n", + " # Setup Keplerian ephemeris model that describes the Lambert arc\n", + " kepler_ephemeris = environment_setup.create_body_ephemeris(\n", + " environment_setup.ephemeris.keplerian( lambert_arc_keplerian_elements, departure_epoch, central_body_gravitational_parameter ), \"\" )\n", + " \n", + " return kepler_ephemeris\n", + "\n", + "def get_lambert_arc_history( lambert_arc_state_model, simulation_result ):\n", + " \n", + " lambert_arc_states = dict()\n", + " for state in simulation_result:\n", + " lambert_arc_states[ state ] = lambert_arc_state_model.get_cartesian_state( state )\n", + " \n", + " return lambert_arc_states\n", + "\n", + "\n", + "def propagate_trajectory( initial_time, final_time, bodies, lambert_arc_state_model, \n", + " file_output_identifier, use_perturbations, initial_state_correction=[0,0,0,0,0,0]):\n", + " \n", + " \"\"\"\n", + " This function will be repeatedly called throughout the assignment. Propagates the trajectory based \n", + " on several input parameters, and subsequently saves the results to data files.\n", + " \n", + " Parameters\n", + " ----------\n", + " initial_time : Epoch since J2000 at which the propagation starts\n", + " \n", + " final_time : Epoch since J2000 at which the propagation will be terminated\n", + " \n", + " lambert_arc_state_model : Lambert arc state model as returned by the get_lambert_problem_result() function\n", + " \n", + " file_output_identifier : Name that will be used to correctly save the output data files\n", + " \n", + " use_perturbations : Boolean to indicate whether a perturbed (True) or unperturbed (False) trajectory \n", + " is propagated\n", + " \n", + " initial_state_correction : (optional) Cartesian state which is added to the Lambert arc state when computing the numerical initial state\n", + " \n", + " Return\n", + " ------\n", + " Dynamics simulator object from which the state- and dependent variable history can be extracted\n", + " \n", + " \"\"\"\n", + " \n", + " # Compute initial state along Lambert arc (and apply correction if needed)\n", + " lambert_arc_initial_state = lambert_arc_state_model.get_cartesian_state( initial_time ) + initial_state_correction\n", + "\n", + " # Get propagator settings for perturbed/unperturbed forwards/backwards arcs\n", + " if use_perturbations:\n", + " propagator_settings = get_perturbed_propagator_settings( bodies, lambert_arc_initial_state, final_time )\n", + " else:\n", + " propagator_settings = get_unperturbed_propagator_settings( bodies, lambert_arc_initial_state, final_time )\n", + " \n", + " # If propagation is backwards in time, make initial time step negative\n", + " if initial_time > final_time:\n", + " signed_fixed_step_size = -fixed_step_size\n", + " else:\n", + " signed_fixed_step_size = fixed_step_size\n", + " \n", + " # Create numerical integrator settings\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4( initial_time, signed_fixed_step_size )\n", + " \n", + " # Propagate forward/backward perturbed/unperturbed arc and save results to files\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(bodies, integrator_settings, propagator_settings, True)\n", + " write_propagation_results_to_file( dynamics_simulator, lambert_arc_state_model, file_output_identifier, output_directory)\n", + "\n", + " return dynamics_simulator\n", + " \n", + "def propagate_variational_equations(initial_time, final_time, bodies, lambert_arc_state_model):\n", + " \n", + " \"\"\"\n", + " Propagates the variational equations for a given range of epochs for a perturbed trajectory.\n", + " \n", + " Parameters\n", + " ----------\n", + " initial_time : Epoch since J2000 at which the propagation starts\n", + " \n", + " final_time : Epoch since J2000 at which the propagation will be terminated\n", + " \n", + " bodies : Body objects as returned by creates_simulation_bodies() function\n", + " \n", + " lambert_arc_state_model : Lambert arc state model as returned by the get_lambert_problem_result() function\n", + " \n", + " Return\n", + " ------\n", + " Variational equations solver object, from which the state-, state transition matrix-, and \n", + " sensitivity matrix history can be extracted.\n", + " \"\"\"\n", + " \n", + " # Compute initial state along Lambert arc\n", + " lambert_arc_initial_state = lambert_arc_state_model.get_cartesian_state( initial_time )\n", + "\n", + " # Get propagator settings\n", + " propagator_settings = get_perturbed_propagator_settings(bodies, lambert_arc_initial_state, final_time)\n", + " \n", + " # Get integrator settings \n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " initial_time, fixed_step_size )\n", + " \n", + " # Define parameters for variational equations\n", + " sensitivity_parameters = get_sensitivity_parameter_set( propagator_settings, bodies, target_body) \n", + " \n", + " # Propagate variational equations\n", + " variational_equations_solver = estimation_setup.SingleArcVariationalEquationsSolver(\n", + " bodies, integrator_settings, propagator_settings, sensitivity_parameters,integrate_on_creation=1 )\n", + " \n", + " return variational_equations_solver\n", + "\n", + "def get_sensitivity_parameter_set(propagator_settings, bodies, target_body):\n", + "\n", + " parameter_settings = estimation_setup.parameter.initial_states(\n", + " propagator_settings, bodies )\n", + " if current_question == 4:\n", + " parameter_settings.append( estimation_setup.parameter.constant_empirical_acceleration_terms (\"Spacecraft\", \"Sun\" ) ) \n", + " \n", + " return estimation_setup.create_parameters_to_estimate(parameter_settings, bodies, propagator_settings)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Helper Functions (TO BE MODIFIED)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# STUDENT CODE TASK - define function such that it provides propagator settings as per question 1\n", + "def get_unperturbed_propagator_settings(bodies, initial_state, termination_time):\n", + " \n", + " \"\"\"\n", + " Creates the propagator settings for an unperturbed trajectory.\n", + "\n", + " Parameters\n", + " ----------\n", + " bodies : Body objects as returned by creates_simulation_bodies() function \n", + " \n", + " initial_state : Cartesian initial state of the vehicle in the simulation\n", + " \n", + " termination_time : Epoch since J2000 at which the propagation will be terminated\n", + " \n", + "\n", + " Return\n", + " ------\n", + " Propagation settings of the unperturbed trajectory.\n", + " \"\"\"\n", + " \n", + "\n", + " # Create propagation settings with termination time.\n", + " propagator_settings = ...\n", + " \n", + " return propagator_settings\n", + "\n", + "# STUDENT CODE TASK - define function such that it provides propagator settings as per question 2-5\n", + "def get_perturbed_propagator_settings(bodies, initial_state, termination_time):\n", + " \n", + " \"\"\"\n", + " Creates the propagator settings for a perturbed trajectory.\n", + "\n", + " Parameters\n", + " ----------\n", + " bodies : Body objects as returned by creates_simulation_bodies() function \n", + " \n", + " initial_state : Cartesian initial state of the vehicle in the simulation\n", + " \n", + " termination_time : Epoch since J2000 at which the propagation will be terminated\n", + " \n", + "\n", + " Return\n", + " ------\n", + " Propagation settings of the perturbed trajectory.\n", + " \"\"\"\n", + "\n", + " # Define accelerations acting on vehicle. \n", + " acceleration_settings_on_spacecraft = ...\n", + " \n", + " # DO NOT MODIFY (line is added for compatibility with question 4)\n", + " if current_question == 4:\n", + " acceleration_settings_on_spacecraft[ \"Sun\" ].append( propagation_setup.acceleration.empirical( rsw_acceleration_magnitude ) )\n", + "\n", + " # Create propagation settings.\n", + " propagator_settings = ...\n", + " \n", + " return propagator_settings\n", + "\n", + "# STUDENT CODE TASK - define function such that it creates the bodies needed for the simulation\n", + "def create_simulation_bodies( ):\n", + " \n", + " \"\"\"\n", + " Creates the body objects required for the simulation.\n", + " Vehicle interfaces, such as the radiation pressure interface, will be defined here.\n", + "\n", + " Parameters\n", + " ----------\n", + " none\n", + "\n", + " Return\n", + " ------\n", + " Body objects required for the simulation.\n", + " \n", + " \"\"\"\n", + " \n", + " bodies = ...\n", + " \n", + " return bodies;\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Question 1 \n", + "### 10 points; Maximum text length: 10 lines\n", + "\n", + "Use the Lambert arc's initial state $\\bar{\\mathbf{x}}(t_{0})$ as initial state for the numerical propagation (spacecraft w.r.t. Sun), with the given initial time $t_{0}$. Run the code to propagate the state of the spacecraft using only the Sun's point-mass attraction, with the Sun as propagation origin. \n", + "
                \n", + "
              • Plot the total trajectory in three dimensions.
              • \n", + "
              • Plot the difference between the Lambert targeter result $\\bar{\\mathbf{x}}(t)$ and the numerical propagation ${\\mathbf{x}}(t)$. Specifically, plot the difference in each of the three Cartesian position components of the spacecraft w.r.t. the Sun.
              • \n", + "
              \n", + " \n", + "\n", + "\n", + "\n", + "**Answer the following questions:**\n", + "\n", + "**a)** Discuss whether the mathematical model solved numerically in this question is an *exact* representation of the Lambert arc model. In your discussion, add a comprehensive list of the assumptions of the Lambert arc model, and briefly explain for each one why it is (not) true in your propagation. If needed, back up your argumentation by adding, and plotting, any additional dependent variables to the simulation output you see fit.\n", + "\n", + "**b)** If you concluded under (a) that the formulations are physically identical, what is the source of any differences you observe between the numerical result and the Lambert result? If you concluded that they are not identical, is any difference you observe relevant for your results?\n", + "\n", + "**Add to save file 1**:
              \n", + "Row 1: initial propagation time and Cartesian state.
              \n", + "Row 2: final propagation time and Cartesian state.\n", + "\n", + "**Coding instructions and hints**\n", + "\n", + "The code block below propagates the dynamics for *unperturbed* dynamics, with the initial state extracted directly from the Lambert arc. The resulting numerical state history ($\\mathbf{x}(t)$) is stored in the `state_history` variable. The state history, as computed directly from the Lambert arc ($\\bar{\\mathbf{x}}(t)$) is stored directly in the `lambert_history` variable.\n", + "\n", + "To make this function generate results, you have a **Code task** for the following helper functions:\n", + "\n", + "* `create_simulation_bodies`: this function defines the body settings and creates the body objects\n", + "* `get_unperturbed_propagator_settings`: this function defines the propagator settings for the unperturbed case\n", + "\n", + "In order to ensure compatibility with the rest of the code, call your vehicle:** `\"Spacecraft\"`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# RUN CODE FOR QUESTION 1 #################################################\n", + "###########################################################################\n", + "\n", + "current_question = 1\n", + "\n", + "# Create body objects\n", + "bodies = create_simulation_bodies( )\n", + "\n", + "# Create Lambert arc state model\n", + "lambert_arc_state_model = get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch)\n", + "\n", + "# Create propagation settings and propagate dynamics\n", + "dynamics_simulator = propagate_trajectory( departure_epoch, arrival_epoch, bodies, lambert_arc_state_model, \n", + " \"Q1\", use_perturbations = False)\n", + "\n", + "# Extract state history from dynamics simulator\n", + "state_history = dynamics_simulator.state_history\n", + "\n", + "# Evaluate the Lambert arc model at each of the epochs in the state_history variable\n", + "lambert_history = get_lambert_arc_history( lambert_arc_state_model, state_history )\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Question 2 \n", + "### 20 points; Maximum text length: 15 lines\n", + "\n", + "Now add the following perturbations to the numerical propagation (by modifying the `get_perturbed_propagation_settings` and the `create_simulation_bodies` functions):\n", + "\n", + "
                \n", + "
              • Point-mass gravity by Venus, Earth, Moon, Mars, Jupiter, Saturn and Sun
              • \n", + "
              • Cannonball radiation pressure on spacecraft. Use a reference area of 20 m$^2$, a radiation pressure coefficient of 1.2, and a vehicle mass of 1000 kg.
              • \n", + "
              \n", + "\n", + "To do so, also update the definition of the environment (modify the `create_simulation_bodies` function) so that these accelerations can be evaluated\n", + "\n", + "Run the propagation with$^2$:
              \n", + "i) The initial and final propagation time equal to the initial and final times of the Lambert arc.
              \n", + "ii) The initial and final propagation time shifted forward and backward in time, respectively, by $\\Delta t=$1 hour.
              \n", + "iii) The initial and final propagation time shifted forward and backward in time, respectively, by $\\Delta t=$2 days.\n", + "\n", + " \n", + "Note that if you shift the initial time from $t_{0}$ to $t_{0}+\\Delta t$ (by modifying these inputs to the `propagate_trajectory` function), the initial state used for the propagation will be adjusted (automatically) accordingly to $\\mathbf{\\bar{x}}(t_{0}+\\Delta t)$. \n", + "\n", + "For each propagation, plot the quantities $\\Delta r=||\\mathbf{r}(t)-\\bar{\\mathbf{r}}(t)||$, $\\Delta v=||\\mathbf{v}(t)-\\bar{\\mathbf{v}}(t)||$ and $\\Delta a=||\\mathbf{a}(t)-\\bar{\\mathbf{a}}(t)||$ as a function of time. **Note** The quantity $\\bar{\\mathbf{a}}(t)$ is not provided automatically, compute its value at each step manually from $\\frac{-\\mu}{r^{3}}\\mathbf{r}$.\n", + "\n", + "**Answer the following questions:**\n", + "\n", + "**a)** For each of the cases (i)-(iii), find the maxima of each of the quantities $\\Delta r$, $\\Delta v$ and $\\Delta a$, and the times at which they occur. Provide a table with the following for each of the cases (i)-(iii): the maximum values of these three quantities, and the times at which they reach their maxima. Write these times *as a fraction of the total propagation time.* Note that a visual determination of the required quantities from the plots you make is sufficient for the purposes of this question.\n", + " \n", + "**b)** Redo the propagation for case (ii), but now propagating forward and backward from the middle point (in time) of the propagation. Plot the quantities quantities $\\Delta r$, $\\Delta v$ and $\\Delta a$, and extend the table you made in question (a) with the results of this question. Consider the forward and backward propagation separately. *Note: the* `propagate_trajectory` *function will automatically propagate backwards in time if the initial time is larger than the final time.*\n", + " \n", + "**c)** Analytically derive formulations for $\\frac{d}{dt}(\\Delta r)$ and $\\frac{d}{dt}(\\Delta v)$. These quantities are a measure for how quickly the numerical solution diverges from the Lambert arc solution. In your derivations, use only the following quantities:\n", + "\n", + "
                \n", + "
              • Positions and velocities along the propagated orbit $\\mathbf{r}$, $\\mathbf{v}$\n", + "
              • Positions and velocities along the Lambert arc $\\bar{\\mathbf{r}}$, $\\bar{\\mathbf{v}}$\n", + "
              • Total accelerations in the Lambert model $\\bar{\\mathbf{a}}$\n", + "
              • The acceleration components $\\mathbf{a}_{\\text{Sun}}$ and $\\mathbf{a}_{\\text{pert}}$ of the propagated orbit.\n", + "
              \n", + "In the above, we have used (for the numerical model) the shorthand $\\mathbf{a}_{\\text{Sun}}(t)$ for the Sun's gravitational acceleration, and have lumped all additional accelerations into $\\mathbf{a}_{\\text{pert}}$, so that the total acceleration acting on the spacecraft is given by $\\left(\\mathbf{a}_{p}\\right)_{S}(t)=\\mathbf{a}_{\\text{Sun}}(t)+\\mathbf{a}_{\\text{pert}}(t)$. \n", + "\n", + "**Hint**: Use the relation $\\dfrac{d||\\mathbf{b}||}{d\\mathbf{b}}=\\dfrac{\\mathbf{b}^{T}}{||\\mathbf{b}||}$, for an arbitrary vector $\\mathbf{b}$.\n", + "\n", + "**d)** Use the equations derived in (c), and the table constructed in (a) and (b) to explain why $\\Delta r$ behaves very differently in the cases that are analyzed. Specifically, explain:\n", + "\n", + "
                \n", + "
              • Why increasing the buffer time $\\Delta t$ seems to generally reduce the magnitude of $\\Delta r$ (question a)\n", + "
              • Why starting the propagation at middle of the arc, and propagating forwards and backwards, results in a smaller maximum value $\\Delta r$ then only propagating forward, even for equal $\\Delta t$ (question (b) vs. case ii in question (a)).\n", + "
              \n", + " \n", + "**Add to save file 1**
              \n", + "Row 3: initial propagation time and Cartesian state (case i).
              \n", + "Row 4: final propagation time and Cartesian state (case i).
              \n", + "Row 5: initial propagation time and Cartesian state (case ii, question a).
              \n", + "Row 6: final propagation time and Cartesian state (case ii, question a).
              \n", + "Row 7: initial propagation time and Cartesian state (case iii).
              \n", + "Row 8: final propagation time and Cartesian state (case iii).
              \n", + "\n", + "\n", + "**Coding instructions and hints**\n", + "\n", + "The code block below propagates the dynamics for *perturbed* dynamics, with the initial state extracted directly from the Lambert arc. The resulting numerical state history ($\\mathbf{x}(t)$) is stored in the `state_history` variable. The state history, as computed directly from the Lambert arc ($\\bar{\\mathbf{x}}(t)$) is stored directly in the `lambert_history` variable.\n", + "\n", + "To make this function generate results, you have a **Code task** for the following helper functions:\n", + "\n", + "* `create_simulation_bodies`: this function defines the body settings and creates the body objects\n", + "* `get_perturbed_propagator_settings`: this function defines the propagator settings for the unperturbed case\n", + "\n", + "To propagate the dynamics, do not create a `SingleArcDynamicsSimulator` manually, but make use of the `propagate_trajectory` function.\n", + "\n", + "In order to ensure compatibility with the rest of the code, call your vehicle:** `\"Spacecraft\"`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# RUN CODE FOR QUESTION 2 #################################################\n", + "###########################################################################\n", + "\n", + "current_question = 2\n", + "\n", + "# Create body objects\n", + "bodies = create_simulation_bodies( )\n", + "\n", + "# Create Lambert arc state model\n", + "lambert_arc_state_model = get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch)\n", + "\n", + "\"\"\"\n", + "case_i: The initial and final propagation time equal to the initial and final times of the Lambert arc.\n", + "case_ii: The initial and final propagation time shifted forward and backward in time, \n", + "respectively, by ∆t=1 hour.\n", + "case_iii: The initial and final propagation time shifted forward and backward in time,\n", + "respectively, by ∆t=2 days.\n", + "\n", + "\"\"\"\n", + "cases = ['case_i', 'case_ii', 'case_iii']\n", + "\n", + "# Define buffer times for each case\n", + "buffer_times = [0.0, 3600.0, 2.0 * constants.JULIAN_DAY]\n", + "\n", + "# Run propagation for each of cases i-iii\n", + "for case in cases:\n", + " \n", + " # Compute departure and arrival time\n", + " current_buffer_time = buffer_times[ cases.index(case) ]\n", + " \n", + " # STUDENT CODE TASK (fill in ...)\n", + " departure_epoch_with_buffer = ...\n", + " arrival_epoch_with_buffer = ...\n", + " \n", + " # Perform propagation\n", + " # STUDENT CODE TASK (call propagation function)\n", + " dynamics_simulator = ...\n", + " state_history = dynamics_simulator.state_history\n", + " lambert_history = get_lambert_arc_history( lambert_arc_state_model, state_history )\n", + " \n", + " # For case ii, run propagation forward and backwatd from mid-point\n", + " if case == 'case_ii':\n", + " \n", + " # STUDENT CODE TASK (fill in ...)\n", + " mid_point_epoch = ...\n", + " \n", + " # Perform propagation forwards\n", + " # STUDENT CODE TASK (call propagation function)\n", + " dynamics_simulator = ...\n", + " state_history_forward = dynamics_simulator.state_history\n", + " lambert_history_forward = get_lambert_arc_history( lambert_arc_state_model, state_history_forward )\n", + " \n", + " # Perform propagation backwards\n", + " # STUDENT CODE TASK (call propagation function)\n", + " dynamics_simulator = ...\n", + " state_history_backward = dynamics_simulator.state_history\n", + " lambert_history_backward = get_lambert_arc_history( lambert_arc_state_model, state_history_backward )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Question 3\n", + "## 20 points; Maximum text length: 10 lines\n", + "\n", + "We will now turn our attention to modifying the trajectory, so that we arrive at the desired target when *including* perturbations. When we modify an initial state $\\mathbf{x}_{0}$ or some parameter vector $\\mathbf{p}$ (which contains properties of the dynamical model), the propagated state $\\mathbf{x}(t)$ will change. When changing the initial state by $\\Delta \\mathbf{x}_{0}$ and/or a parameter vector by $\\Delta \\mathbf{p}$, we will denote the resulting change in numerically propagated state, as a function of time, as $\\Delta {\\mathbf{x}}(t)$.\n", + "\n", + "For given modifications $\\Delta \\mathbf{x}_{0}$ and $\\Delta \\mathbf{p}$, the behaviour of $\\Delta {\\mathbf{x}}(t)$ can be obtained by directly numerically repropagating $\\mathbf{x}(t)$ with the modified initial state and parameter vector, and subtracting the propagation result without these modifications. However, this can be a tedious and computationally expensive process. The matrices $\\boldsymbol{\\Phi}(t,t_{0})$ and $\\mathbf{S}(t)$ provide a way to approximate $\\Delta {\\mathbf{x}}(t)$, by means of a *linearization*:\n", + "\n", + "\\begin{align}\n", + "\\boldsymbol{\\Phi}(t,t_{0})=\\frac{\\partial\\mathbf{x}(t)}{\\partial\\mathbf{x}_{0}}\\\\\n", + " \\mathbf{S}(t)=\\frac{\\partial\\mathbf{x}(t)}{\\partial\\mathbf{p}}\n", + "\\end{align}\n", + "\n", + "Now, we denote the linearized change in state as $\\Delta \\tilde{\\mathbf{x}}(t)$, which we compute from:\n", + "\\begin{align}\n", + "\\Delta \\tilde{\\mathbf{x}}(t)=\\boldsymbol{\\Phi}(t,t_{0})\\Delta \\mathbf{x}_{0}+\\mathbf{S}(t)\\Delta\\mathbf{p}\\label{eq:linearizedChangeInState}\n", + "\\end{align}\n", + "so that $\\Delta \\tilde{\\mathbf{x}}(t)$ is a *linear approximation* of $\\Delta {\\mathbf{x}}(t)$.\n", + "\n", + "For this question, we split the total propagation time into 10 equisized (in time) arcs. Using the propagation settings of the previous question (case iii), the state and variational equations for the dynamics are propagated for each arc. For each arc $i$ (with $i$ starting at 0), the initial state $\\mathbf{x}_{i}(t_{0,i})$ is set so that it corresponds exactly with the Lambert targeter state at the corresponding time $\\bar{\\mathbf{x}}(t_{0,i})$. This will result in a discontinuous state history over the full transfer (since the propagation is performed in an arc-wise manner). For this question, you will compute how to make the trajectory continuous in position by applying an impulsive correction maneuver at the start of each arc, such that $\\mathbf{r}_{i}=\\bar{\\mathbf{r}}_{i}$ at the end of each arc. \n", + "\n", + "**Answer the following questions:**\n", + "\n", + "**a)** Perform the arcwise propagation. Plot the deviation $\\Delta r$ of the propagated state w.r.t. the Lambert arc as a function of time over the full trajectory.\n", + "\n", + "**b)** Derive a model to use the results for $\\boldsymbol{\\Phi}_{i}(t_{i+1},t_{i})$ to compute the change in velocity $\\Delta \\mathbf{v}_{i}$ needed at the beginning of each arc to ensure that the spacecraft reaches $\\bar{\\mathbf{r}}$ at the end of the arc. Keep the initial position of the arc constant. Show the derivation of your model. For this question, neglect linearization errors (assume $\\Delta \\tilde{\\mathbf{x}}(t)=\\Delta{\\mathbf{x}}(t)$)\n", + "\n", + "**c)** Modify the code, so that the initial states of each arc are modified in accordance with your calculated correction manuevers in (b). Plot the deviation $\\Delta r$ of the modified propagated state w.r.t. the Lambert arc as a function of time. Do you consider your linearized model to be applicable in this specific situation? \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "**Add to save file 1}**
              \n", + "Row 9: initial propagation time and Cartesian state (arc 0).
              \n", + "Row 10: final propagation time and Cartesian state (arc 0).
              \n", + "Row 11: initial propagation time and Cartesian state (arc 4).
              \n", + "Row 12: final propagation time and Cartesian state (arc 4).
              \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# RUN CODE FOR QUESTION 3 #################################################\n", + "###########################################################################\n", + "\n", + "current_question = 3\n", + "\n", + "# Create body objects\n", + "bodies = create_simulation_bodies( )\n", + "\n", + "# Create Lambert arc state model\n", + "lambert_arc_state_model = get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch)\n", + "\n", + "##############################################################\n", + "\n", + "# Set start and end times of full trajectory\n", + "# STUDENT CODE TASK (fill in ...)\n", + "buffer_time = ...\n", + "departure_epoch_with_buffer = ...\n", + "arrival_epoch_with_buffer = ...\n", + "\n", + "# Compute number of arcs and arc length\n", + "# STUDENT CODE TASK (fill in ...)\n", + "number_of_arcs = ...\n", + "arc_length = ...\n", + "\n", + "##############################################################\n", + "\n", + "# Compute relevant parameters (dynamics, state transition matrix, Delta V) for each arc\n", + "for arc_index in range(number_of_arcs):\n", + " \n", + " # Compute initial and final time for arc\n", + " # STUDENT CODE TASK (fill in ...)\n", + " current_arc_initial_time = ...\n", + " current_arc_final_time = ...\n", + "\n", + " ###########################################################################\n", + " # RUN CODE FOR QUESTION 3a ################################################\n", + " ###########################################################################\n", + " \n", + " # Propagate dynamics on current arc\n", + " # STUDENT CODE TASK (call propagation function)\n", + " dynamics_simulator = ...\n", + " state_history = dynamics_simulator.state_history\n", + " \n", + " # Retrieve Lambert arc for same epochs, and compute final difference\n", + " lambert_history = get_lambert_arc_history( lambert_arc_state_model, state_history )\n", + "\n", + " ###########################################################################\n", + " # RUN CODE FOR QUESTION 3d ################################################\n", + " ###########################################################################\n", + " \n", + " # Solve for state transition matrix on current arc\n", + " variational_equations_solver = propagate_variational_equations(\n", + " current_arc_initial_time, current_arc_final_time, bodies, lambert_arc_state_model) \n", + " \n", + " # Retrieve propagation resuls and compute Lambert arc history\n", + " state_transition_matrix_history = variational_equations_solver.state_transition_matrix_history\n", + " state_history = variational_equations_solver.state_history\n", + " lambert_history = get_lambert_arc_history( lambert_arc_state_model, state_history ) \n", + " \n", + " # Retrieve final state deviation between numerical and Lambert model\n", + " final_state_deviation = state_history[ final_epoch ] - lambert_history[ final_epoch ]\n", + " \n", + " # Get final state transition matrix (and its inverse)\n", + " final_epoch = list(state_transition_matrix_history.keys())[-1]\n", + " final_state_transition_matrix = state_transition_matrix_history[ final_epoch ]\n", + " final_inverse_state_transition_matrix = np.linalg.inv( final_state_transition_matrix )\n", + "\n", + " # Compute required velocity change at beginning of arc to meet required final state \n", + " # STUDENT CODE TASK: calculate initial state correction to mee arc end position requirement\n", + " initial_state_correction = ...\n", + " \n", + " # Propagate with correction to initial state \n", + " # STUDENT CODE TASK (call propagation function). HINT: the initial state correction can be passed as an input to function\n", + " dynamics_simulator_after_correction = ...\n", + " state_history_after_correction = dynamics_simulator.state_history\n", + "\n", + " # Compute and print deviation\n", + " final_state_deviation_after_correction = state_history[ final_epoch ] - lambert_history[ final_epoch ] \n", + "\n", + " # Save corrected arc\n", + " write_propagation_results_to_file( dynamics_simulator, lambert_arc_state_model, \n", + " \"Q3_arc_\" + str(arc_index) + \"_corrected\", output_directory)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Question 4\n", + "## 25 points; Maximum text length: lines\n", + "\n", + "For this question, you will perform a similar analysis as in the previous question, but now using an approximate model for a low-thrust acceleration model to correct the orbit so that it reaches the start and end of the trajectory correctly. Instead of a ten-arc model, you will use a one- and two-arc model. \n", + "\n", + "In your model, you will parameterize the thrust as a constant acceleration in RSW frame for each arc. This thrust will be added to the simulation by defining a so-called 'empirical' acceleration: a constant acceleration in RSW direction. Note that, since the direction of the RSW frame changes in time, the inertial direction of this empirical acceleration will change in time. For this question, you will compute the magnitudes of these emprical accelerations, such that the trajectory meets all the required boundary conditions.\n", + "\n", + "By calculating the sensitivity matrix $\\mathbf{S}$ for the entries of the empirical acceleration, you will be able to calculate (approximately) the required thust under a number of different conditions. For this question, neglect linearization errors (assume $\\Delta \\tilde{\\mathbf{x}}(t)=\\Delta{\\mathbf{x}}(t)$).\n", + "\n", + "**Answer the following questions:**\n", + "\n", + "**a)** Consider a single arc propagation for the full transfer. Derive an equation to use the results for $\\mathbf{S}(t_{E})$ to compute the low-thrust acceleration in RSW frame (denote this as $\\mathbf{p}$) needed to ensure that the spacecraft reaches $\\bar{\\mathbf{r}}(t_{E})$ at the end of the transfer. Keep the initial state of the arc constant at $\\bar{\\mathbf{x}}(t_{0})$. Put no constraints on the final velocity. Show the derivation of your model, starting from the equations in the lecture videos.
              \n", + "**b)** Implement your model for question (a) in your code, and verify and argue that it works correctly (use the same value of $\\Delta t$ as in question 2, case iii). Plot any quantities you need to show the correct functioning of your model.\n", + "\n", + "For the next questions, you will analyze the low-thrust trajectory for a two-arc model (with each arc having equal duration). For this question, do not impose any *a priori* constraints on the absolute position or velocity at the arc splitting point (unlike in question 3, where this point was required to correspond to $\\bar{\\mathbf{r}}$, but do impose a constraint of continuity in position and velocity over the full trajectory (unlike in question 3, where the velocity was discontinuous between arcs).\n", + "\n", + "**c)** For an arbitrary choice of constant RSW-thrust in arc 1 (denoted $\\mathbf{p}_{1}$), thrust in arc 2 (denoted $\\mathbf{p}_{2}$), and modification in initial velocity of arc 1 (denoted $\\mathbf{v}_{i}(t_{0,1})$), derive a single equation for the change in position at the end of arc 2 (denoted $\\Delta \\mathbf{r}(t_{E,2})$). Write a single explicit equation for $\\Delta \\mathbf{r}(t_{E,2})$, in the following notation:\n", + "\\begin{align}\n", + "\\Delta \\mathbf{r}(t_{E,2})=\\mathbf{A}\\begin{pmatrix} \\mathbf{p}_{1}\\\\ \\mathbf{p}_{2} \\\\ \\Delta\\mathbf{v}_{i}(t_{0,1}) \\end{pmatrix}\n", + "\\end{align}\n", + "and provide an explicit formulation for the matrix $\\mathbf{A}$. Use the notation from the lecture slides on *Reaching the objective - Arc-wise Low-thrust*.\n", + "
              \n", + "**d)** The equation you derived in (c) does not have a unique solution. Choose $ \\Delta\\mathbf{v}_{i}(t_{0,1})=\\mathbf{0}$, and set $\\mathbf{p}_{1}$ equal to the value of $\\mathbf{p}$ you derived and computed in questions (a) and (b). Starting from the equation you derived in (c), formulate an explicit equation for $\\mathbf{p}_{2}$ needed to achieve a given $\\Delta \\mathbf{r}(t_{E,2})$.
              \n", + "**e)** Implement the model you derived in question (d) in your code, such that you obtain $\\mathbf{r}(t_{E,2})=\\bar{\\mathbf{r}}(t_{E,2})$ (*e.g.* such that the trajectory terminates on the Lambert arc). Verify that the model works correctly: show that it meets all the required constraints.\n", + "\n", + "**Add to save file 1}**
              \n", + "Row 13: final propagation time and Cartesian state (question 4b)
              \n", + "Row 14: final propagation time and Cartesian state (question 4e)
              " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# RUN CODE FOR QUESTION 4 #################################################\n", + "###########################################################################\n", + "\n", + "current_question = 4\n", + "rsw_acceleration_magnitude = [0,0,0]\n", + "\n", + "# Create body objects\n", + "bodies = create_simulation_bodies( )\n", + "\n", + "# Create Lambert arc state model\n", + "lambert_arc_state_model = get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch)\n", + "\n", + "###########################################################################\n", + "# RUN CODE FOR QUESTION 4b ################################################\n", + "###########################################################################\n", + "\n", + "# Set start and end times of full trajectory\n", + "# STUDENT CODE TASK (fill in ...)\n", + "buffer_time = ...\n", + "departure_epoch_with_buffer = ...\n", + "arrival_epoch_with_buffer = ...\n", + "\n", + "# Solve for state transition matrix on current arc\n", + "variational_equations_solver = propagate_variational_equations(\n", + " departure_epoch_with_buffer, arrival_epoch_with_buffer, bodies, lambert_arc_state_model) \n", + "\n", + "sensitivity_matrix_history = variational_equations_solver.sensitivity_matrix_history\n", + "state_history = variational_equations_solver.state_history\n", + "lambert_history = get_lambert_arc_history( lambert_arc_state_model, state_history ) \n", + "\n", + "# Compute low-thrust RSW acceleration to meet required final position \n", + "# STUDENT CODE TASK: calculate low-thrust acceleration to meet arc end position requirement\n", + "rsw_acceleration_magnitude = ...\n", + "\n", + "# STUDENT CODE TASK (call propagation function). NOTE: Empirical acceleration with magnitude \n", + "# rsw_acceleration_magnitude is added automatically by get_perturbed_propagator_settings function) \n", + "dynamics_simulator = ...\n", + "\n", + "###########################################################################\n", + "# RUN CODE FOR QUESTION 4e ################################################\n", + "###########################################################################\n", + "\n", + "# Compute number of arcs and arc length\n", + "# STUDENT CODE TASK (fill in ...)\n", + "number_of_arcs = 2\n", + "arc_length = ( arrival_epoch_with_buffer - departure_epoch_with_buffer ) / number_of_arcs\n", + "\n", + "\n", + "# Compute relevant parameters (dynamics, state transition matrix, Delta V) for each arc\n", + "for arc_index in range(number_of_arcs):\n", + " \n", + " # Compute initial and final time for arc\n", + " # STUDENT CODE TASK (fill in ...)\n", + " current_arc_initial_time = ...\n", + " current_arc_final_time = ...\n", + " \n", + " # STUDENT CODE TASK (run arc-wise model as defined in question (e) )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Question 5\n", + "## 25 points; Maximum text length: 20 lines\n", + "\n", + "Since the model in questions 3 and 4 for computing the influence due to a small change in state and parameters is based on a linearization, it is reasonably valid for *small* values of $\\Delta \\mathbf{x}_{0}$ and $\\mathbf{p}$ but breaks down for larger deviations. The error $\\boldsymbol{\\epsilon}_{\\mathbf{x}}(t)$ due to the linearization can be defined as:\n", + "\n", + "\\begin{align}\n", + "\\boldsymbol{\\epsilon}_{\\mathbf{x}}(t) = \\Delta \\tilde{\\mathbf{x}}(t) - \\Delta {\\mathbf{x}}(t)\n", + "\\end{align}\n", + "\n", + "For this question, you will numerically investigate the limit of validity of the above linearization using $\\boldsymbol{\\Phi}(t,t_{0})$ (the influence of $\\mathbf{S}(t)$ is not considered here), for the 10-arc model of question 3.\n", + "\n", + "Determine, for arc $i=0$, and arc $i=4$, independently for each of the entries of $\\Delta \\mathbf{x}_{0}(=\\Delta \\mathbf{x}(t_{0,i}))$, how large the initial state corrections are allowed to be, before the linearization used to obtain $\\Delta\\tilde{\\mathbf{x}}(t)$ is no longer valid.\n", + "\n", + "Use the following criterion as the definition of a valid linearization:\n", + "\\begin{align}\n", + "\\max_{t}||\\boldsymbol{\\epsilon}_{\\mathbf{r}}(t)||<\\text{100 km}\\hspace{0.5cm}\\vee\n", + "\\hspace{0.5cm}\\max_{t}||\\boldsymbol{\\epsilon}_{\\mathbf{v}}(t)||<\\text{1 m/s}\n", + "\\end{align}\n", + "where $\\boldsymbol{\\epsilon}_{\\mathbf{r}}(t)$ and $\\boldsymbol{\\epsilon}_{\\mathbf{v}}(t)$ denote the linearization error in position and velocity (first and last three entries of $\\boldsymbol{\\epsilon}_{\\mathbf{x}}(t)$).\n", + "\n", + "Find the minimum positive value of each entry of the initial state perturbations for arcs 0 and 4, for which the error criterion is no longer true over the full arc, each time keeping the other 5 entries of $\\Delta \\mathbf{x}_{0}$ fixed to zero. \n", + "\n", + "Your answer must be correct to within 25% (set up your analysis so that this is guaranteed). For instance, if the true limiting value of $\\Delta {y}_{0}$ is 2.4 m, any value from 1.8 m to 3.0 m will be accepted. **Implement your model in such a way that all 12 limiting values (6 for arc $i=0$; 6 for arc $i=4$) are produced from a single run of your program.**\n", + "\n", + "Explain the algorithm that you have used and implemented, explaining why the model you have set up is guaranteed to give the requested results to the required accuracy. Provide *pseudo-code* (so not a copy-paste of your code!) in your explanation.\n", + "\n", + "\n", + "**Add to save file 2**
              \n", + "Matrix, 6 rows by 2 columns, permitted $\\Delta \\mathbf{x_0}$ (arcs 0 and 4)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###########################################################################\n", + "# RUN CODE FOR QUESTION 5 #################################################\n", + "###########################################################################\n", + "\n", + "current_question = 5\n", + "\n", + "# Create body objects\n", + "bodies = create_simulation_bodies( )\n", + "\n", + "# Create Lambert arc state model\n", + "lambert_arc_state_model = get_lambert_problem_result(bodies, target_body, departure_epoch, arrival_epoch)\n", + "\n", + "# Set full start and end times\n", + "buffer_time = 2.0 * constants.JULIAN_DAY\n", + "departure_epoch_with_buffer = departure_epoch + buffer_time\n", + "arrival_epoch_with_buffer = arrival_epoch - buffer_time\n", + "\n", + "# Set arc length\n", + "number_of_arcs = 10\n", + "arc_length = ( arrival_epoch_with_buffer - departure_epoch_with_buffer ) / number_of_arcs\n", + "\n", + "considered_arc_indices = [0, 4]\n", + "\n", + "for arc_index in considered_arc_indices:\n", + " \n", + " # Compute start and end time for current arc\n", + " current_arc_initial_time = departure_epoch_with_buffer + arc_index * arc_length\n", + " current_arc_final_time = departure_epoch_with_buffer + ( arc_index + 1 ) * arc_length\n", + "\n", + " # Get propagator settings for perturbed forward arc\n", + " arc_initial_state = lambert_arc_state_model.get_cartesian_state( current_arc_initial_time )\n", + " propagator_settings = get_perturbed_propagator_settings( bodies, arc_initial_state, current_arc_final_time )\n", + " \n", + " # Set integrator settings\n", + " integrator_settings = propagation_setup.integrator.runge_kutta_4(\n", + " current_arc_initial_time, fixed_step_size\n", + " )\n", + " \n", + " ###########################################################################\n", + " # PROPAGATE NOMINAL TRAJECTORY AND VARIATIONAL EQUATIONS ##################\n", + " ###########################################################################\n", + " \n", + " parameters_for_which_to_compute_sensitivity = get_sensitivity_parameter_set( propagator_settings, bodies, \n", + " target_body)\n", + "\n", + " variational_equations_simulator2= estimation_setup.SingleArcVariationalEquationsSolver(\n", + " bodies, integrator_settings, propagator_settings, parameters_for_which_to_compute_sensitivity, integrate_on_creation=1 )\n", + " \n", + " state_transition_result = variational_equations_simulator2.state_transition_matrix_history\n", + " nominal_integration_result = variational_equations_simulator2.state_history\n", + " \n", + " \n", + " # TODO: Retrieve nominal initial state value (e.g. initial state with Delta x_0 = 0)\n", + " initial_epoch = list(state_transition_result.keys())[0]\n", + " original_initial_state = nominal_integration_result[ initial_epoch ]\n", + " \n", + " ###########################################################################\n", + " # START ANALYSIS ALGORITHM FOR QUESTION 4 #################################\n", + " ###########################################################################\n", + " \n", + " # This vector will hold the maximum permitted initial state perturbations for which the linearization \n", + " # is valid (for the current arc. The vector is initialized to 0, and each of its 6 entries is computed \n", + " # in the 6 iterations of the coming for loop (that runs over the iteration variable 'entry')\n", + " permitted_perturbations = np.array([0,0,0,0,0,0])\n", + " \n", + " # Iterate over all initial state entries\n", + " for entry in range(6):\n", + " \n", + " # STUDENT CODE TASK: Define (iterative) algorithm to compute current entry of 'permitted_perturbations'\n", + " # General structure: define an initial state perturbation (perturbed_initial_state variable),\n", + " # compute epsilon_x (see assignment), and iterate your algorithm until convergence.\n", + " \n", + " while ...:\n", + " \n", + " # STUDENT CODE TASK: define initial state perturbation for current iteration\n", + " initial_state_perturbation = ...\n", + " \n", + " # Reset propagator settings with perturbed initial state\n", + " perturbed_initial_state = arc_initial_state + initial_state_perturbation\n", + " propagator_settings.reset_initial_states( perturbed_initial_state )\n", + " \n", + " # Create simulation object and propagate dynamics with perturbed initial state\n", + " dynamics_simulator = propagation_setup.SingleArcDynamicsSimulator(\n", + " bodies, integrator_settings, propagator_settings, True, False, True)\n", + " \n", + " # Retrieve state history computed directly from perturbed initial state\n", + " integration_result = dynamics_simulator.get_equations_of_motion_numerical_solution()\n", + " \n", + " # Compute epsilon_x\n", + " epsilon_x = ...\n", + " \n", + " permitted_perturbations[entry] = ...\n", + " \n", + " \n", + " print(\"Permitted perturbations: \", arc_index, np.transpose( permitted_perturbations ) )\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": false, + "editable": false + }, + "source": [ + "# Submission and reporting instructions\n", + "\n", + "\n", + "**Reporting instructions - formulating equations of motion**\n", + "\n", + "When asked to explicitly write out one or more accelerations\n", + "\n", + "
                \n", + "
              • Follow the notation from the lecture notes
              • \n", + "
              • Use the following indices: $S$ for Sun, $E$, $V$ and $M$ for Earth, Venus and Mars, respectively, and $p$ for the spacecraft.
              • \n", + "
              • Use a comma to separate indices as you see fit (spacecraft position w.r.t. the Sun can be written as $\\mathbf{r}_{S,p}$ or $\\mathbf{r}_{Sp}$).
              • \n", + "
              • There is no need to specify the frame orientation of any of the vectors. All are assumed to be in a frame with inertial orientation.
              • \n", + "
              • When denoting separate accelerations, always denote the body $B$ exerting, and body $A$ undergoing, the acceleration with $\\mathbf{a}_{_{BA}}$.
              • \n", + "
              • When writing out a single acceleration in terms of positions $\\mathbf{r}$, always first write the total relative positions $\\mathbf{r}_{_{CD}}$ as used by the acceleration model. Expand the positions further as you see fit in next steps.
              • \n", + "
              • When writing a position $\\mathbf{r}_{_{CD}}$ that is partly retrieved from the environment (if any), and where part of the vector is numerically propagated, split the separate contributions (in a second step after the previous point). For instance, if $\\mathbf{r}_{_{ED}}$ is propagated, and $\\mathbf{r}_{_{CD}}$ is used in the acceleration, write $\\mathbf{r}_{_{CD}}=\\mathbf{r}_{_{CE}}+\\mathbf{r}_{_{ED}}$.
              • \n", + "
              \n", + "\n", + "### Reporting instructions - change in position\n", + "\n", + "When asked to plot/compute the change in total position between two simulation results $\\mathbf{r}_{1}(t)$ and $\\mathbf{r}_{2}(t)$. The change in total position is to be computed as $||\\Delta \\mathbf{r}(t)||= ||\\mathbf{r}_{2}(t)-\\mathbf{r}_{1}(t)||$\n", + "\n", + "### Reporting instructions - figures\n", + "\n", + "When using figures, take the following guidelines into account:\n", + "\n", + "
                \n", + "
              • Any text (legend, axis labels etc.) should be sufficiently large so as to be legible when printed on A4 paper.
              • \n", + "
              • Each curve should be distinguishable in your plots.
              • \n", + "
              • Adjust the scale (e.g. linear vs. logarithmic) of your plots as needed to interpret your data.
              • \n", + "
              • Make efficient use of space for graphs and plots. Whenever possible and legible: plot multiple curves (e.g. for different runs and/or elements) in a single figure.
              • \n", + "
              • All figures must be complete (including axis labels, legend, caption, etc.)
              • \n", + "
              \n", + "\n", + "Points will be deducted for unreadable figures, or figures that do not clearly show information that you refer to in your discussion.\n", + "\n", + "### Reporting instructions - cover page\n", + "\n", + "The cover page of each report **must contain**:\n", + "\n", + "
                \n", + "
              • A link to the private Github repository containing the source code and output files (see below).
              • \n", + "
              • The names of any people with whom you cooperated (if any)
              • \n", + "
              • The time spent per question
              • \n", + "
              \n", + "\n", + "If any of these points are not present on your cover page, points will be deducted.\n", + "\n", + "\n", + "### Reporting instructions - general\n", + "\n", + "Follow the provided limitations on the length of the text that you use (this excludes figures, tables and equations). Answers longer than the imposed limit will not be read beyond this limitation. Example: if the imposed limitation is 10 lines, and you write 15, we will grade the answer based **only** on the first 10 lines.\n", + "\n", + "You are free to work together with your fellow students, but are required to write your own code and report. Copying/pasting from each others report/code is not accepted, and can lead to the case being referred to the Faculty Board of Examiners.\n", + "\n", + "### Support instructions\n", + "\n", + "In case of any questions, there are a number of options for support:\n", + "\n", + "
                \n", + "
              • For issues with the installation, unit tests, or the general use of Tudat, please post an issue on Github. When posting an issue, first browse through existing issues. If your problem is raised in an open issue, post in {that} issue instead of opening a new one. Note the operating system you are using.
              • \n", + "
              • In case of questions specific to the assignments, use the Brightspace forum. As with Github, go through existing posts before opening a new one. Do no publicly post your code, or other information that provides direct answers to the questions.
              • \n", + "
              • In-person support and Q$\\&$A is also available during working lectures and open office hours. See Brightspace calendar for time and location.
              • \n", + "
              \n", + "\n", + "See Brightspace (Course Information $\\rightarrow$ Staff and Support) for details on what to prepare when asking for support.\n", + "\n", + "### Submission instructions\n", + "\n", + "You will not be graded on your coding style. Submission of the reports and output files is to be done through Brightspace. **Deadline for submission is January 17 2021 23:59 CET** and can also be found on Brightspace. For late submissions, 1 point (out of a total of 10) will be subtracted {per day}. So, when handing in the report $x$ days late, $\\lceil{x}\\rceil$ points will be deducted. *If* you have $g$ grace days left: when handing in the report $x$ days late, $\\lceil{x-g}\\rceil$ points will be deducted.\n", + "\n", + "Submission of your final code and results files will be done through Github. Ensure that **you only commit to a private repository**. Instructions on pushing code to Github can be found in:\n", + "\n", + "https://tudat-space.readthedocs.io/en/latest/_src_use_of_tools/github.html\n", + "\n", + "See below for the exact files and filenames to submit:\n", + "\n", + "## *Failure to comply exactly with the requirements for file contents and naming set out below will result in point deductions.*\n", + "\n", + "\n", + "\n", + "Instructions on how to commit code to your repository is given at https://tudat-space.readthedocs.io/en/latest/_src_use_of_tools/github.html. In addition to the report, for this assignment you will submit:\n", + "\n", + "\n", + "* This notebook (which can be run directly without modifications to reproduce your results). Commit and push this file to your private GitHub repository (in the $\\texttt{Assignment2/}$ directory).\n", + "* A text file containing $\\textbf{only}$ the initial or final time (column 1) and Cartesian states (as a row vector; columns {2-7}) from a number of your simulations, to at least 8 digits of precision. The specific simulations for which you are to save the time/state, and the row in which you are to save them are indicated in the questions. Name the file `CartesianResults_AE4868_2020_2_YYYYYYY.dat`, where YYYYYYY is your student number. Upload this file to Brightspace.\n", + "* A text file containing $\\textbf{only}$ the 6x2 matrix of permitted $\\Delta \\mathbf{x_0}$ for question 5. Name the file `Question5_Results_AE4868_2020_2_YYYYYYY.dat`, where YYYYYYY is your student number. Upload this file to Brightspace. \n", + "\n", + "\n", + " \n", + "## *Failure to comply exactly with the requirements for file contents and naming set out below will result in point deductions.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}
              +
              +
              +

              Module Compile_latex

              +
              +
              +
              + +Expand source code + +
              from nbconvert.preprocessors import ExecutePreprocessor
              +import os
              +import shutil
              +import nbformat
              +
              +
              +class Compile_latex:
              +    """Runs jupyter notebooks, converts them to pdf,
              +    exports the notebook pdfs to latex and compiles the 
              +    latex report of the incoming project nr"""
              +
              +
              +    def __init__(self,project_nr,latex_filename):
              +        """Constructs attributes used throughout latex compilation
              +        
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +        """
              +    
              +        self.script_dir = self.get_script_dir()
              +        relative_dir = f'latex/project{project_nr}/'
              +        self.compile_latex(relative_dir,latex_filename)
              +        self.clean_up_after_compilation(latex_filename)
              +        self.move_pdf_into_latex_dir(relative_dir,latex_filename)
              +
              +    
              +    def compile_latex(self,relative_dir,latex_filename):
              +        """Executes a commandline line to compile the latex report
              +
              +        :param relative_dir: the relative dir towards the latex main .tex file
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        os.system(f'pdflatex {relative_dir}{latex_filename}')
              +    
              +    
              +    def clean_up_after_compilation(self,latex_filename):
              +        """Removes the unneeded files that were generated during latex to pdf compilation.
              +
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        latex_filename_without_extention = latex_filename[:-4]
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.aux')
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.log')
              +        self.delete_file_if_exists(f'texput.log')
              +    
              +
              +    def move_pdf_into_latex_dir(self,relative_dir,latex_filename):
              +        """Moves the compiled/generated pdf file from the root of this repository to the
              +        relative latex directory of this project.
              +
              +        :param relative_dir: param latex_filename:
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        pdf_filename = f'{latex_filename[:-4]}.pdf'
              +        destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}'
              +        
              +        try:
              +            shutil.move(pdf_filename, destination)
              +        except:
              +            print("Error while moving file ", pdf_filename)
              +    
              +
              +    def delete_file_if_exists(self,filename):
              +        """Deletes files if they exist
              +
              +        :param filename: name of file that will be deleted if it exists in the root of this repository
              +
              +        """
              +        try:
              +            os.remove(filename)
              +        except:
              +            print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.')
              +    
              +
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +
              +if __name__ == '__main__':
              +    main = Compile_latex()
              +
              +
              +
              +
              +
              +
              +
              +
              +
              +

              Classes

              +
              +
              +class Compile_latex +(project_nr, latex_filename) +
              +
              +

              Runs jupyter notebooks, converts them to pdf, +exports the notebook pdfs to latex and compiles the +latex report of the incoming project nr

              +

              Constructs attributes used throughout latex compilation

              +

              :param project_nr: the numberr identifying which project is being +ran and compiled +:param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              class Compile_latex:
              +    """Runs jupyter notebooks, converts them to pdf,
              +    exports the notebook pdfs to latex and compiles the 
              +    latex report of the incoming project nr"""
              +
              +
              +    def __init__(self,project_nr,latex_filename):
              +        """Constructs attributes used throughout latex compilation
              +        
              +        :param project_nr: the numberr identifying which project is being  ran and compiled
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +        """
              +    
              +        self.script_dir = self.get_script_dir()
              +        relative_dir = f'latex/project{project_nr}/'
              +        self.compile_latex(relative_dir,latex_filename)
              +        self.clean_up_after_compilation(latex_filename)
              +        self.move_pdf_into_latex_dir(relative_dir,latex_filename)
              +
              +    
              +    def compile_latex(self,relative_dir,latex_filename):
              +        """Executes a commandline line to compile the latex report
              +
              +        :param relative_dir: the relative dir towards the latex main .tex file
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        os.system(f'pdflatex {relative_dir}{latex_filename}')
              +    
              +    
              +    def clean_up_after_compilation(self,latex_filename):
              +        """Removes the unneeded files that were generated during latex to pdf compilation.
              +
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        latex_filename_without_extention = latex_filename[:-4]
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.aux')
              +        self.delete_file_if_exists(f'{latex_filename_without_extention}.log')
              +        self.delete_file_if_exists(f'texput.log')
              +    
              +
              +    def move_pdf_into_latex_dir(self,relative_dir,latex_filename):
              +        """Moves the compiled/generated pdf file from the root of this repository to the
              +        relative latex directory of this project.
              +
              +        :param relative_dir: param latex_filename:
              +        :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +        """
              +        pdf_filename = f'{latex_filename[:-4]}.pdf'
              +        destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}'
              +        
              +        try:
              +            shutil.move(pdf_filename, destination)
              +        except:
              +            print("Error while moving file ", pdf_filename)
              +    
              +
              +    def delete_file_if_exists(self,filename):
              +        """Deletes files if they exist
              +
              +        :param filename: name of file that will be deleted if it exists in the root of this repository
              +
              +        """
              +        try:
              +            os.remove(filename)
              +        except:
              +            print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.')
              +    
              +
              +    def get_script_dir(self):
              +        """returns the directory of this script regardles of from which level the code is executed"""
              +        return os.path.dirname(__file__)
              +
              +

              Methods

              +
              +
              +def clean_up_after_compilation(self, latex_filename) +
              +
              +

              Removes the unneeded files that were generated during latex to pdf compilation.

              +

              :param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              def clean_up_after_compilation(self,latex_filename):
              +    """Removes the unneeded files that were generated during latex to pdf compilation.
              +
              +    :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +    """
              +    latex_filename_without_extention = latex_filename[:-4]
              +    self.delete_file_if_exists(f'{latex_filename_without_extention}.aux')
              +    self.delete_file_if_exists(f'{latex_filename_without_extention}.log')
              +    self.delete_file_if_exists(f'texput.log')
              +
              +
              +
              +def compile_latex(self, relative_dir, latex_filename) +
              +
              +

              Executes a commandline line to compile the latex report

              +

              :param relative_dir: the relative dir towards the latex main .tex file +:param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              def compile_latex(self,relative_dir,latex_filename):
              +    """Executes a commandline line to compile the latex report
              +
              +    :param relative_dir: the relative dir towards the latex main .tex file
              +    :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +    """
              +    os.system(f'pdflatex {relative_dir}{latex_filename}')
              +
              +
              +
              +def delete_file_if_exists(self, filename) +
              +
              +

              Deletes files if they exist

              +

              :param filename: name of file that will be deleted if it exists in the root of this repository

              +
              + +Expand source code + +
              def delete_file_if_exists(self,filename):
              +    """Deletes files if they exist
              +
              +    :param filename: name of file that will be deleted if it exists in the root of this repository
              +
              +    """
              +    try:
              +        os.remove(filename)
              +    except:
              +        print(f'Error while deleting file: {filename} but that is not too bad because the intention is for it to not be there.')
              +
              +
              +
              +def get_script_dir(self) +
              +
              +

              returns the directory of this script regardles of from which level the code is executed

              +
              + +Expand source code + +
              def get_script_dir(self):
              +    """returns the directory of this script regardles of from which level the code is executed"""
              +    return os.path.dirname(__file__)
              +
              +
              +
              +def move_pdf_into_latex_dir(self, relative_dir, latex_filename) +
              +
              +

              Moves the compiled/generated pdf file from the root of this repository to the +relative latex directory of this project.

              +

              :param relative_dir: param latex_filename: +:param latex_filename: name of the main latex .tex file that manages the latex document

              +
              + +Expand source code + +
              def move_pdf_into_latex_dir(self,relative_dir,latex_filename):
              +    """Moves the compiled/generated pdf file from the root of this repository to the
              +    relative latex directory of this project.
              +
              +    :param relative_dir: param latex_filename:
              +    :param latex_filename: name of the main latex .tex file that manages the latex document
              +
              +    """
              +    pdf_filename = f'{latex_filename[:-4]}.pdf'
              +    destination= f'{self.get_script_dir()}/../../../{relative_dir}{pdf_filename}'
              +    
              +    try:
              +        shutil.move(pdf_filename, destination)
              +    except:
              +        print("Error while moving file ", pdf_filename)
              +
              +
              +
              +
              +
              +
              +
              +
              +

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zq-BTsgskX|D`3~24N4FyjfGgoZy#o&mq9bBOd`nRqiOOoaP*fz_4`ss9*{PTgY!{P zJvvz`C)Xu{a?}ML*N?O8ZLp*d>KIesbY`4d9Gi$(Ax%gK>Y2c7k~h7+&|1i&Qy|?F z!`x^QIg^*4zb(|P@^WTVTk$G!bVB^}K{$th8Mn^0!^kFm#{_ThGsN zzY1H=4hL^vEu;9`uJDskjf>+^K;h-@v$l&fWMNp;eDlTbo1M#JnfZ(TjhVs1%hL{I z;U#V>{_0>3IUjz?U$t5Id2Rl32Qq(g(g6+oT|roj$lJE_zd0z&w=3F+k9Y1I9$Z~s z%Ct>Gu1;qF;b$nw?XX`RkgKDn`AY_p*pvRN^WSfvZD)JXtK$pQ{5h&H{0wjYFii#S id5OucTx&bue}m$`#7)kJoge320nW0G<1#myiT(v4*5l#; literal 0 HcmV?d00001 diff --git a/latex/project1/main.tex b/latex/project1/main.tex index 657bf22..bf7cf7c 100644 --- a/latex/project1/main.tex +++ b/latex/project1/main.tex @@ -43,8 +43,8 @@ \usepackage{tabularx} % in the preamble \usepackage{wrapfig} -\usepackage{algorithm} -\usepackage{algorithmic} +%\usepackage{algorithm} +%\usepackage{algorithmic} % To get side by side pictures:{ @@ -167,6 +167,7 @@ \newpage \input{Appendices/AppA.tex} \newpage \input{Appendices/AppB.tex} \newpage +\input{Appendices/AppC.tex} \newpage \end{appendices} \end{document} diff --git a/linux_environment.yml b/linux_environment.yml index 174cc5d..84e4d8b 100644 --- a/linux_environment.yml +++ b/linux_environment.yml @@ -1,7 +1,11 @@ +# run: conda env create --file linux_environment.yml +# include new packages: conda env update --file linux_environment.yml name: tudat-space channels: - conda-forge - tudat-team + - conda dependencies: - tudatpy - matplotlib + - pytest>=3.0 \ No newline at end of file diff --git a/windows_environment.yml b/windows_environment.yml index 32b2b3e..f3aefbc 100644 --- a/windows_environment.yml +++ b/windows_environment.yml @@ -1,20 +1,11 @@ # run: conda env create --file linux_environment.yml # include new packages: conda env update --file linux_environment.yml -name: num_astro +name: tudat-space channels: - - defaults - - anaconda + - conda-forge - tudat-team dependencies: -- python=3.6 -- anaconda -- conda: - - scipy>=0.18.1 - - numpy>=1.11.2 - - pytest>=3.0 - - matplotlib>=2.2.2 - tudatpy -- pip -- pip: - # works for regular pip packages - - sklearn \ No newline at end of file + - matplotlib +- conda: + - pytest>=3.0 \ No newline at end of file From 882a6f70d4a7b8caf3a1bfbcf4f900328f616bb9 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Sun, 8 Nov 2020 22:00:53 +0100 Subject: [PATCH 07/57] Updated readme.md. --- README.md | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 595f5bc..d439f9b 100644 --- a/README.md +++ b/README.md @@ -9,21 +9,29 @@ python get-pip.py ``` 1. To use this package, first make a new conda environment and activate (it this automatically installs everything you need) -3.1 For Windows: +1.1 For Windows: ``` conda env create --file windows_environment.yml ``` -3.2 For Linux: +1.2 For Linux: ``` conda env create --file linux_environment.yml ``` +2. Instal jupyter-lab with command: +``` +jupyter-lab +``` ## usage: do every time you start Anaconda: -2. Activate the conda environment you created +3. Activate the conda environment you created: ``` conda activate num_astro ``` +4. Open jupyter lab +``` +jupyter-lab +``` ## usage: do every run: From 71f90868c8bbe5487b0a871206dbc77a46a48ff4 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Sun, 8 Nov 2020 22:07:34 +0100 Subject: [PATCH 08/57] Update README.md --- README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d439f9b..614c85a 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,13 @@ -# Python unit test project template with continuous integration (ci). +# Upgraded Course template Numerical Astrodynamics +Hi, w.r.t. the original repository this repository is supplemented with: +0. Python code and latex report integration. + - Plots are exported directly into your latex report. + - Your python code is automatically included in the appendices of your report. +1. You can easily sync with overleaf, e.g. if you do a last minute run, you just push and pull into overleaf, instead of manually uploading pictures. +2. Unit tests are written. (entire repository code can be tested with a single line) +3. Continuous integration (CI) testing is set up with Travis-CI. +4. TODO: auto compile the latex report when you run the code. +5. TODO: auto export jupyter notebook to pdf to include into latex report. ## usage: do once From 5d03b8023fbbcd6a74e36fdc16bc2c74ca4f78e1 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Sun, 8 Nov 2020 22:08:07 +0100 Subject: [PATCH 09/57] Improved layout. --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 614c85a..27707b1 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ # Upgraded Course template Numerical Astrodynamics Hi, w.r.t. the original repository this repository is supplemented with: + 0. Python code and latex report integration. - Plots are exported directly into your latex report. - Your python code is automatically included in the appendices of your report. From 73e158ad60facd899dfb29cd9bc41c2ebb022cd6 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Sun, 8 Nov 2020 22:08:40 +0100 Subject: [PATCH 10/57] Cleaned readme.md --- README.md | 5 ----- 1 file changed, 5 deletions(-) diff --git a/README.md b/README.md index 27707b1..5682dcc 100644 --- a/README.md +++ b/README.md @@ -58,11 +58,6 @@ python -m pytest ``` from the root directory of this project. - -## Note for linux users -The client connection requires sudo rights. I did not install conda as sudo, so .. - - [black_badge]: https://img.shields.io/badge/code%20style-black-000000.svg [python_badge]: https://img.shields.io/badge/python-3.8-blue.svg From 0cd105f3a6f1c8cee9ec9a21b332cc3a586f45c4 Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Sun, 8 Nov 2020 22:11:05 +0100 Subject: [PATCH 11/57] Included CI badge. --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 5682dcc..f14f6ec 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ -# Upgraded Course template Numerical Astrodynamics +# Upgraded Course template Numerical Astrodynamics[![Build Status](https://travis-ci.org/a-t-0/NumericalAstrodynamicsAssignments_2020.svg?branch=master)](https://travis-ci.org/a-t-0/NumericalAstrodynamicsAssignments_2020) + Hi, w.r.t. the original repository this repository is supplemented with: 0. Python code and latex report integration. @@ -12,10 +13,9 @@ Hi, w.r.t. the original repository this repository is supplemented with: ## usage: do once -0. If you don't have pip: open Anaconda prompt and browse to the directory of this readme and run: +0. If you don't have pip: open Anaconda prompt and browse to the directory of this readme: ``` cd /home// -python get-pip.py ``` 1. To use this package, first make a new conda environment and activate (it this automatically installs everything you need) From 9c598ea365b50cb50a74891672e945a1c95a939f Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Sun, 8 Nov 2020 22:19:22 +0100 Subject: [PATCH 12/57] Included yml for travis-CI. --- .travis.yml | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 .travis.yml diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000..d83b546 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,28 @@ +# python of course and we don't need access to sudo atm +language: python +sudo: false + +# emails are annoying +notifications: + email: false + +# build on Ubuntu 14.04 because the default is +# really old +os: linux +dist: trusty + +# test on the latest version of python +python: + - "3.7" + +# only run travis on the master branch +branches: + only: + - master + +# install dependencies listed in requirements +install: "pip install -r requirements.txt" + +# run tests +script: + "python -m pytest -v test/*" From 2439b7511d5fbbf45df8d1622cd1732eba05ca7a Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Sun, 8 Nov 2020 22:20:54 +0100 Subject: [PATCH 13/57] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f14f6ec..7ba6b04 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Hi, w.r.t. the original repository this repository is supplemented with: 3. Continuous integration (CI) testing is set up with Travis-CI. 4. TODO: auto compile the latex report when you run the code. 5. TODO: auto export jupyter notebook to pdf to include into latex report. +6. TODO: invade jupyter notebooks with unit test(s). ## usage: do once From bd9ac3dce3092f87a960eb063af6fdfe91c0069d Mon Sep 17 00:00:00 2001 From: a-t-0 <34750068+a-t-0@users.noreply.github.com> Date: Sun, 8 Nov 2020 22:22:53 +0100 Subject: [PATCH 14/57] Removed typo. --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 7ba6b04..e4554e4 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ cd /home// ``` 1. To use this package, first make a new conda environment and activate (it this automatically installs everything you need) -1.1 For Windows: +1.1 For Windows (is currently the same as for linux): ``` conda env create --file windows_environment.yml ``` @@ -37,7 +37,7 @@ jupyter-lab 3. Activate the conda environment you created: ``` -conda activate num_astro +conda activate tudat-space ``` 4. Open jupyter lab ``` @@ -48,7 +48,7 @@ jupyter-lab 3. Performe a run for assignment 1 (named project1) of main code (in `main.py`, called from `__main__.py`) ``` -python -m code.project 1.src +python -m code.project1.src ``` ## Testing From ff40039fd1f8f46f26fa813d7ce470dc89d511a8 Mon Sep 17 00:00:00 2001 From: a-t-0 Date: Mon, 9 Nov 2020 13:19:19 +0100 Subject: [PATCH 15/57] Made jupyter notebooks runable from main. --- gitignore => .gitignore | 2 + README.md | 11 ++ ...e_notebook_update20201025-checkpoint.ipynb | 122 +++++++++++++++- ...4868_example_notebook_update20201025.ipynb | 137 +++++++++++++++++- code/project1/src/Main.py | 36 ++--- code/project1/src/Run_jupyter_notebooks.py | 41 ++++++ code/project1/src/__main__.py | 8 +- latex/project1/Images/4b.png | Bin 40319 -> 39821 bytes latex/project1/Images/4c.png | Bin 29724 -> 28279 bytes latex/project1/Images/acceleration_norms.png | Bin 0 -> 83548 bytes latex/project1/Images/ground_track.png | Bin 0 -> 19036 bytes .../project1/Images}/kepler_elements.png | Bin latex/project1/Images/total_acceleration.png | Bin 0 -> 54216 bytes linux_environment.yml | 14 +- windows_environment.yml | 14 +- 15 files changed, 340 insertions(+), 45 deletions(-) rename gitignore => .gitignore (99%) mode change 100644 => 100755 code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb mode change 100644 => 100755 code/project1/src/AE4868_example_notebook_update20201025.ipynb create mode 100644 code/project1/src/Run_jupyter_notebooks.py create mode 100644 latex/project1/Images/acceleration_norms.png create mode 100644 latex/project1/Images/ground_track.png rename {code/project1/src => latex/project1/Images}/kepler_elements.png (100%) create mode 100644 latex/project1/Images/total_acceleration.png diff --git a/gitignore b/.gitignore similarity index 99% rename from gitignore rename to .gitignore index 1a05cfc..46fded9 100644 --- a/gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +*.pyc + # macos *.DS_store # Byte-compiled / optimized / DLL files diff --git a/README.md b/README.md index e4554e4..4a98643 100644 --- a/README.md +++ b/README.md @@ -59,6 +59,17 @@ python -m pytest ``` from the root directory of this project. +## Note for linux users: +If you get the following error in the example notebook when running the main python code: +``` +PermissionError: [Errno 13] Permission denied: 'total_acceleration.png' +PermissionError: [Errno 13] Permission denied: 'total_acceleration.png' +``` +You could resolve it with command: +``` +sudo chown -R user:user ~/.local/share/jupyter +``` + [black_badge]: https://img.shields.io/badge/code%20style-black-000000.svg [python_badge]: https://img.shields.io/badge/python-3.8-blue.svg diff --git a/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb b/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb old mode 100644 new mode 100755 index 49eae9c..cb6465f --- a/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb +++ b/code/project1/src/.ipynb_checkpoints/AE4868_example_notebook_update20201025-checkpoint.ipynb @@ -1,10 +1,56 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "#import ipyparams\n", + "#currentNotebook = ipyparams.notebook_name\n", + "#print(f'currentNotebook={currentNotebook}')\n", + "#currentNotebook = os.path.dirname(os.path.realpath(__file__))\n", + "#currentNotebook = os.path\n", + "#currentNotebook = os.getcwd()\n", + "#if not 'workbookDir' in globals():\n", + "# workbookDir = os.getcwd()\n", + "#print('workbookDir: ' + workbookDir)\n", + "#currentNotebook = os.chdir(workbookDir) # If you changed the current working dir, this will take you back to the workbook dir.\n", + "#currentNotebook = %pwd" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello_world\n", + "\n", + "Single Earth-Orbiting Satellite Example.\n", + "The initial position vector of Delfi-C3 is [km]: \n", + "[7037.48400133 3238.05901792 2150.7241875 ]\n", + "The initial velocity vector of Delfi-C3 is [km/s]: \n", + "[-1.46565763 -0.04095839 6.62279761]\n", + "After 86400.0 seconds the position vector of Delfi-C3 is [km]: \n", + "[-4602.79426676 -1421.16740978 5883.69740624]\n", + "And the velocity vector of Delfi-C3 is [km/s]: \n", + "[-4.53846052 -2.36988263 -5.04163195]\n", + " \n" + ] + } + ], "source": [ "###############################################################################\n", "# IMPORT STATEMENTS ###########################################################\n", @@ -16,6 +62,8 @@ "from tudatpy.kernel.simulation import propagation_setup\n", "from tudatpy.kernel.astro import conversion\n", "\n", + "# Set path to latex image folders for project 1\n", + "latex_image_path = '../../../latex/project1/Images/'\n", "\n", "# Load spice kernels.\n", "spice_interface.load_standard_kernels()\n", @@ -204,10 +252,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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