diff --git a/.gitignore b/.gitignore index 35b972c2..355c8095 100644 --- a/.gitignore +++ b/.gitignore @@ -72,6 +72,7 @@ instance/ # Sphinx documentation docs/_build/ +docs/source/tutorials/images/ # PyBuilder .pybuilder/ @@ -164,5 +165,6 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ -# Calculation dumps +# cached data *.data +examples/tutorials/data diff --git a/.travis.yml b/.travis.yml index cf8b77cc..f04e622d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2024 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) @@ -47,27 +47,29 @@ env: before_install: export ARCH=Linux-aarch64 SED=sed install: - - | # install Python via Miniconda + - | # install Miniconda travis_retry wget https://repo.anaconda.com/miniconda/Miniconda3-latest-${ARCH}.sh -O miniconda.sh - bash miniconda.sh -b -p $HOME/miniconda - export PATH="$HOME/miniconda/bin:$PATH"; hash -r + bash miniconda.sh -b -p ${HOME}/miniconda + export PATH="${HOME}/miniconda/bin:${PATH}"; hash -r conda config --set quiet yes --set always_yes yes --set changeps1 no - - | travis_retry conda update -n base -c defaults conda travis_retry conda update --all conda config --set solver libmamba conda info -a conda list + - | # install executables via Miniconda: Python, Pandoc, Codecov travis_retry conda create -n test-env eval "$(conda shell.bash hook)" conda activate test-env - travis_retry conda install -c conda-forge python=${PYTHON} - - - | # install dependencies - travis_retry conda install -c conda-forge numpy scipy python-igraph h5netcdf tqdm + travis_retry conda install -c conda-forge python=${PYTHON} pandoc codecov travis_retry conda update -c conda-forge --all - travis_retry conda install -c conda-forge tox flake8 pylint pytest-xdist pytest-cov codecov - travis_retry conda install -c conda-forge networkx matplotlib cartopy sphinx + conda info -a + conda list + - | # install Python libs via Miniconda + travis_retry conda install -c conda-forge \ + numpy scipy python-igraph h5netcdf tqdm \ + ipython networkx matplotlib cartopy sphinx nbsphinx nbsphinx-link \ + tox flake8 pylint pytest-xdist pytest-cov conda info -a conda list @@ -77,12 +79,10 @@ script: ${SED} -i '/-j ./ {s//-j 2/; h}; ${x;/./{x;q0};x;q1}' setup.cfg ${SED} -i '/-n auto/ {s//-n 2/; h}; ${x;/./{x;q0};x;q1}' pyproject.toml ${SED} -i '/jobs = ./ {s//jobs = 2/; h}; ${x;/./{x;q0};x;q1}' pyproject.toml - - # install self (and dependencies, if on Windows) - - travis_retry pip install -v -e ".[tests,docs]" - - # run test suite - - tox -v + - | # install Python libs via Pip: self (+ dependencies, if on Windows) + travis_retry pip install -v -e .[tests,docs] + - | # run test suite + tox -v # report statistics after_success: codecov @@ -99,8 +99,9 @@ jobs: language: shell before_install: - export ARCH=MacOSX-x86_64 SED=gsed - - export HOMEBREW_NO_AUTO_UPDATE=1 HOMEBREW_NO_INSTALL_CLEANUP=1 - - travis_retry brew install gnu-sed + - | # install executables via Homebrew: GNU `sed` + export HOMEBREW_NO_AUTO_UPDATE=1 HOMEBREW_NO_INSTALL_CLEANUP=1 + travis_retry brew install gnu-sed - os: windows language: shell @@ -108,6 +109,7 @@ jobs: - export ARCH=Windows-x86_64 SED=sed - export PATH=/c/Python${PYTHON/.}:/c/Python${PYTHON/.}/Scripts:${PATH} install: - - | # install Python via Chocolatey + - | # install executables via Chocolatey: Python, Pandoc, Codecov travis_retry choco install python --version ${PYTHON} travis_retry python -m pip install --upgrade pip + travis_retry choco install pandoc codecov diff --git a/CONTRIBUTIONS.rst b/CONTRIBUTIONS.rst index 21b5042d..6e99bb66 100644 --- a/CONTRIBUTIONS.rst +++ b/CONTRIBUTIONS.rst @@ -12,13 +12,17 @@ BSD (3-clause) URL --- -http://www.pik-potsdam.de/members/donges/software +https://www.pik-potsdam.de/members/donges/software-2/software Mail ---- Jonathan Donges, Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, D-14412 Potsdam, Germany +Related publications +-------------------- +See `Publications `_. + Authors ------- Written as part of a diploma/PhD thesis in physics by `Jonathan F. Donges @@ -27,10 +31,6 @@ Institute for Climate Impact Research (PIK) and completed at the University of Potsdam, Germany. Substantially extended by `Jobst Heitzig `_. -Related publications --------------------- -See `Publications `_. - Contributors ------------ - Jakob Runge (extended ``core`` and ``climate``) diff --git a/MANIFEST.in b/MANIFEST.in index 27c7da18..a8fb7461 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -4,6 +4,8 @@ recursive-include src/pyunicorn/*/_ext *.pxd *.pyx src_*.c include *.rst *.txt docs/Makefile graft docs/source -recursive-include examples *.py +recursive-include examples *.py *.ipynb *.png +prune docs/source/tutorials/images +prune **/.ipynb_checkpoints recursive-include tests *.py diff --git a/README.rst b/README.rst index 3eed7617..fedda118 100644 --- a/README.rst +++ b/README.rst @@ -12,22 +12,27 @@ About ===== ``pyunicorn`` (**Uni**\ fied **Co**\ mplex Network and **R**\ ecurre\ **N**\ ce analysis toolbox) is an object-oriented Python package for the advanced analysis -and modeling of complex networks. Beyond the standard measures of complex -network theory, such as degree, betweenness and clustering coefficients, it -provides some uncommon but interesting statistics like Newman's random walk -betweenness. ``pyunicorn`` also features novel *node-weighted (node splitting -invariant)* network statistics as well as measures designed for analyzing -*networks of interacting/interdependent networks*. - -Moreover, ``pyunicorn`` allows one to easily *construct networks from uni- and -multivariate time series and event data* (functional (climate) networks and -recurrence networks). This involves linear and nonlinear measures of time series -analysis for constructing functional networks from multivariate data (e.g., -Pearson correlation, mutual information, event synchronization and event -coincidence analysis). ``pyunicorn`` also features modern techniques of -nonlinear analysis of single and pairs of time series, such as recurrence -quantification analysis (RQA), recurrence network analysis and visibility -graphs. +and modeling of complex networks. Beyond the standard **measures of complex +network theory** (such as *degree*, *betweenness* and *clustering coefficients*), it +provides some uncommon but interesting statistics like *Newman's random walk +betweenness*. ``pyunicorn`` also provides novel **node-weighted** *(node splitting invariant)* +network statistics, measures for analyzing networks of **interacting/interdependent +networks**, and special tools to model **spatially embedded** complex networks. + +Moreover, ``pyunicorn`` allows one to easily *construct networks* from uni- and +multivariate time series and event data (**functional/climate networks** and +**recurrence networks**). This involves linear and nonlinear measures of +**time series analysis** for constructing functional networks from multivariate data +(e.g., *Pearson correlation*, *mutual information*, *event synchronization* and *event +coincidence analysis*). ``pyunicorn`` also features modern techniques of +nonlinear analysis of time series (or pairs thereof), such as *recurrence +quantification analysis* (RQA), *recurrence network analysis* and *visibility +graphs*. + +``pyunicorn`` is **fast**, because all costly computations are performed in +compiled C/C++ and Fortran code. It can handle **large networks** through the +use of sparse data structures. The package can be used interactively, from any +Python script, and even for parallel computations on large cluster architectures. License ------- @@ -69,8 +74,8 @@ Mailing list Not implemented yet. -Usage -===== +Getting Started +=============== Installation ------------ @@ -103,8 +108,7 @@ Optional *(used only in certain classes and methods)*: To install these dependencies, please follow the instructions for your system's package manager or consult the libraries' homepages. An easy way to go may be a -Python distribution like `Anaconda `_ -that already includes many libraries. +Python distribution like `Anaconda `_. Official releases ................. @@ -148,7 +152,7 @@ Test suite ---------- Before committing changes or opening a pull request (PR) to the code base, please make sure that all tests pass. The test suite is managed by `tox -`_ and is configured to use system-wide packages +`_ and is configured to use system-wide packages when available. Install the test dependencies as follows:: $> pip install .[tests] diff --git a/docs/source/conf.py b/docs/source/conf.py index 5c6b8f9d..168872df 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -38,7 +38,9 @@ 'sphinx.ext.mathjax', 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', - 'sphinx.ext.githubpages' + 'sphinx.ext.githubpages', + 'nbsphinx', + 'nbsphinx_link', ] # Add any paths that contain templates here, relative to this directory. diff --git a/docs/source/contact.rst b/docs/source/contact.rst index 7af6250c..02470a95 100644 --- a/docs/source/contact.rst +++ b/docs/source/contact.rst @@ -4,8 +4,7 @@ Contact .. include:: ../../README.rst :start-after: (3 clause). - :end-before: Usage + :end-before: Getting Started .. include:: ../../CONTRIBUTIONS.rst :start-after: BSD (3-clause) - :end-before: Acknowledgements diff --git a/docs/source/development.rst b/docs/source/development.rst index c77e0b6f..37db9c53 100644 --- a/docs/source/development.rst +++ b/docs/source/development.rst @@ -1,3 +1,3 @@ -Development -=========== +.. include:: ../../README.rst + :start-after: $> cd docs; make clean html latexpdf \ No newline at end of file diff --git a/docs/source/download.rst b/docs/source/download.rst index e1a71c90..f5a72cd3 100644 --- a/docs/source/download.rst +++ b/docs/source/download.rst @@ -1,11 +1,4 @@ -Download -======== - -.. include:: ../../README.rst - :start-after: ``pyunicorn`` is `BSD-licensed `_ (3 clause). - :end-before: Test suite - .. include:: ../../README.rst - :start-after: graphs. - :end-before: License + :start-after: Not implemented yet. + :end-before: Development diff --git a/docs/source/index.rst b/docs/source/index.rst index 6c2d0371..5a5cfcb3 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,21 +1,16 @@ +============ Introduction ============ .. include:: ../../README.rst - :start-after: ========= - :end-before: Reference - -For example, to generate a recurrence network with 1000 nodes from a sinusoidal -signal and compute its network transitivity you simply need to type + :start-after: :target: https://codecov.io/gh/pik-copan/pyunicorn + :end-before: License -.. literalinclude:: ../../examples/modules/timeseries/recurrence_network.py +Example +======= -The package provides special tools to analyze and model **spatially embedded** -complex networks. +To generate a recurrence network with 1000 nodes from a sinusoidal +signal and to compute its network transitivity, you can simply run: -``pyunicorn`` is **fast** because all costly computations are performed in -compiled C, C++ and Fortran code. It can handle **large networks** through the -use of sparse data structures. The package can be used interactively, from any -Python script and even for parallel computations on large cluster -architectures. +.. literalinclude:: ../../examples/modules/timeseries/recurrence_network.py \ No newline at end of file diff --git a/docs/source/license.rst b/docs/source/license.rst index 3283412c..407b9068 100644 --- a/docs/source/license.rst +++ b/docs/source/license.rst @@ -2,9 +2,4 @@ License ======= -.. include:: -.. include:: ../../CONTRIBUTIONS.rst - :start-after: ============= - :end-before: Mail - -.. literalinclude:: ../../LICENSE.txt +.. literalinclude:: ../../LICENSE.txt \ No newline at end of file diff --git a/docs/source/methods.rst b/docs/source/methods.rst index 17587530..3118e837 100644 --- a/docs/source/methods.rst +++ b/docs/source/methods.rst @@ -1,6 +1,6 @@ -Methods -======= +Package Overview +================ A brief introduction to the methods, measures and algorithms provided by ``pyunicorn``. @@ -21,15 +21,15 @@ possible. Spatially embedded networks --------------------------- ``pyunicorn`` includes measures and models specifically designed for spatially -embedded networks (or simply spatial networks) via the GeoNetwork and Grid +embedded networks (or simply spatial networks) via the ``GeoNetwork`` and ``Grid`` classes. * :doc:`api/core/geo_network` * :doc:`api/core/grid` -Interacting/interdependent/multiplex networks / networks of networks --------------------------------------------------------------------- -The InteractingNetworks class provides a rich collection of network measures and models specifically designed for investigating the structure of networks of +Interacting/multiplex networks (networks of networks) +----------------------------------------------------- +The ``InteractingNetworks`` class provides a rich collection of network measures and models specifically designed for investigating the structure of networks of networks (also called interacting networks, interdependent networks or multiplex networks in different contexts). Examples include the cross-link density of connections between different subnetworks or the cross-shortest @@ -39,8 +39,8 @@ the degree of organization of the cross-connectivity between subnetworks. * :doc:`api/core/interacting_networks` -Node-weighted network measures / node-splitting invariance ----------------------------------------------------------- +Node-weighted (node-splitting invariant) network measures +--------------------------------------------------------- Node-weighted networks measures derived following the node-splitting invariance approach are useful for studying systems with nodes representing subsystems of heterogeneous size, weight, area, volume or importance, e.g., nodes @@ -52,8 +52,8 @@ as well as interacting networks. * :doc:`api/core/network` * :doc:`api/core/interacting_networks` -Climate networks / Coupled climate networks -------------------------------------------- +(Coupled) Climate networks +-------------------------- ``pyunicorn`` provides classes for the easy construction and analysis of the statistical interdependency structure within and between fields of time series (functional networks) using various similarity measures such as Pearson and Spearman correlation, lagged linear correlation, mutual information and event synchronization. Climate networks allow the analysis of single fields of time series, whereas coupled climate networks focus on studying the @@ -66,8 +66,8 @@ FMRI and EEG data) or financial data (e.g., stock market indices). * :doc:`api/climate/coupled_climate_network` * :doc:`api/climate/climate_data` -Recurrence networks / recurrence quantification analysis / recurrence plots ---------------------------------------------------------------------------- +Recurrence quantification/network analysis +------------------------------------------ Recurrence analysis is a powerful method for studying nonlinear systems, particularly based on univariate and multivariate time series data. Recurrence quantification analysis (RQA) and recurrence network analysis (RNA) allow to diff --git a/docs/source/publications.rst b/docs/source/publications.rst index 941f02a2..9a057636 100644 --- a/docs/source/publications.rst +++ b/docs/source/publications.rst @@ -7,9 +7,12 @@ detail and applying the methods implemented in the ``pyunicorn`` package. General complex networks ======================== -*Review papers* -~~~~~~~~~~~~~~~ -[Newman2003]_, [Boccaletti2006]_, [Costa2007]_. +- Review papers: [Newman2003]_, [Boccaletti2006]_, [Costa2007]_. +- Further network papers: [Watts1998]_, [Newman2001]_, [Newman2002]_, + [Arenas2003]_, [Newman2005]_, [Soffer2005]_, [Holme2007]_, [Tsonis2008a]_, + [Ueoka2008]_. + +.... .. [Newman2003] M.E.J. Newman. "The structure and function of complex networks". @@ -29,11 +32,6 @@ General complex networks `doi:10.1080/00018730601170527 `__ -*Further network papers* -~~~~~~~~~~~~~~~~~~~~~~~~ -[Watts1998]_, [Newman2001]_, [Newman2002]_, [Arenas2003]_, [Newman2005]_, -[Soffer2005]_, [Holme2007]_, [Tsonis2008a]_, [Ueoka2008]_. - .. [Watts1998] D.J. Watts and S.H. Strogatz. "Collective dynamics of small-world networks". In *Nature* vol. 393, 440–442 (1998) @@ -93,9 +91,12 @@ General complex networks Proceedings of NOLTA (2008) http://tsuzuki.ise.ibaraki.ac.jp/MyPaper/Meeting/08NOLTA.pdf + Spatially embedded networks =========================== -[Bartelemy2011]_. +- [Bartelemy2011]_. + +.... .. [Bartelemy2011] M. Barthelemy. "Spatial networks". @@ -103,11 +104,13 @@ Spatially embedded networks `doi:10.1016/j.physrep.2010.11.002 `__ -Interacting/interdependent networks / networks of networks + +Interacting/interdependent networks (networks of networks) ========================================================== -*Introduction to structural analysis of interacting networks* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donges2011a]_. +- Introduction to structural analysis of interacting networks: [Donges2011a]_. +- Random graph models & network surrogates for interacting networks: [Schultz2010]_. + +.... .. [Donges2011a] J.F. Donges, H.C.H. Schultz, N. Marwan, Y. Zou, J. Kurths. "Investigating the topology of interacting networks - Theory and @@ -117,11 +120,18 @@ Interacting/interdependent networks / networks of networks `doi:10.1140/epjb/e2011-10795-8 `__ -Node-weighted network measures / node-splitting invariance -========================================================== -*Introduction* -~~~~~~~~~~~~~~ -[Heitzig2012]_. +.. [Schultz2010] H.C.H. Schultz. + "Coupled climate networks: Investigating the terrestrial atmosphere's + dynamical structure". + Diploma thesis, Free University, Berlin (2010) + + +Node-weighted (node-splitting invariant) network measures +========================================================= +- Introduction: [Heitzig2012]_. +- Analysis of node-weighted interacting networks: [Wiedermann2011]_, [Wiedermann2013]_. + +.... .. [Heitzig2012] J. Heitzig, J. F. Donges, Y. Zou, N. Marwan, J. Kurths. "Node-weighted measures for complex networks with spatially embedded, @@ -131,19 +141,6 @@ Node-weighted network measures / node-splitting invariance `doi:10.1140/epjb/e2011-20678-7 `__ -*Random graph models and network surrogates for interacting networks* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Schultz2010]_. - -.. [Schultz2010] H.C.H. Schultz. - "Coupled climate networks: Investigating the terrestrial atmosphere's - dynamical structure". - Diploma thesis, Free University, Berlin (2010) - -*Analysis of node-weighted interacting networks* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Wiedermann2011]_, [Wiedermann2013]_. - .. [Wiedermann2011] M. Wiedermann. "Coupled climate network analysis of multidecadal dynamics in the Arctic". Bachelor's thesis, Humboldt University, Berlin (2011) @@ -162,9 +159,12 @@ Node-weighted network measures / node-splitting invariance `doi:10.1209/0295-5075/107/58005 `__ + Climate data analysis (general) =============================== -[Bretherton1992]_. +- [Bretherton1992]_. + +.... .. [Bretherton1992] C.S. Bretherton, C. Smith, J.M. Wallace. "An intercomparison of methods for finding coupled patterns in climate @@ -173,11 +173,21 @@ Climate data analysis (general) `doi:10.1175/1520-0442(1992)005<0541:AIOMFF>2.0.CO;2 `__ -Climate networks / Coupled climate networks -=========================================== -*Comparing linear and nonlinear construction of climate networks* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donges2009a]_. + +(Coupled) Climate networks +========================== +- Comparing linear & nonlinear construction of climate networks: [Donges2009a]_. +- Studying the dynamical structure of the surface air temperature field: + [Donges2009b]_, [Radebach2010]_. +- Introduction to coupled climate networks & applications: + [Schultz2010]_, [Donges2011a]_, [Wiedermann2011]_. +- Review of climate network analysis (in Chinese!): [Zou2011]_. +- Visualization of climate networks: [Tominski2011]_. +- Evolving climate networks: [Radebach2013]_. +- General: [Tsonis2004]_, [Tsonis2006]_, [Gozolchiani2008]_, [Tsonis2008b]_, + [Tsonis2008c]_, [Yamasaki2008]_, [Donges2009c]_, [Yamasaki2009]_. + +.... .. [Donges2009a] J.F. Donges, Y. Zou, N. Marwan, J. Kurths. "Complex networks in climate dynamics". @@ -186,10 +196,6 @@ Climate networks / Coupled climate networks `doi:10.1140/epjst/e2009-01098-2 `__ -*Studying the dynamical structure of the surface air temperature field* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donges2009b]_, [Radebach2010]_. - .. [Donges2009b] J.F. Donges, Y. Zou, N. Marwan, J. Kurths. "The backbone of the climate network". In *Europhysics Letters*, vol. 87 (no. 4), 48007 (2009) @@ -201,32 +207,16 @@ Climate networks / Coupled climate networks structure of the Earth's climate system". Diploma thesis, Humboldt University, Berlin (2010) -*Introduction to coupled climate networks and applications* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Schultz2010]_, [Donges2011a]_, [Wiedermann2011]_. - -*Review of climate network analysis (in Chinese!)* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Zou2011]_. - .. [Zou2011] Y. Zou, J.F. Donges, J. Kurths. "Recent advances in complex climate network analysis". In *Complex Systems and Complexity Science*, vol. 8 (no. 1), p27-38 (2011) -*Visualization of climate networks* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Tominski2011]_. - .. [Tominski2011] C. Tominski, J.F. Donges, T. Nocke. "Information Visualization in Climate Research". In *Proceedings of the International Conference on Information Visualisation (IV), London*, p298-305 (2011) `doi:10.1109/IV.2011.12 `__ -*Evolving climate networks* -~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Radebach2013]_. - .. [Radebach2013] A. Radebach, R.V. Donner, J. Runge, J.F. Donges, J. Kurths. "Disentangling different types of El Nino episodes by evolving climate network analysis". @@ -234,11 +224,6 @@ Climate networks / Coupled climate networks `doi:10.1103/PhysRevE.88.052807 `__ -*General* -~~~~~~~~~ -[Tsonis2004]_, [Tsonis2006]_, [Gozolchiani2008]_, [Tsonis2008b]_, -[Tsonis2008c]_, [Yamasaki2008]_, [Donges2009c]_, [Yamasaki2009]_. - .. [Tsonis2004] A.A. Tsonis and P.J. Roebber. "The architecture of the climate network". In *Physica A: Statistical Mechanics and its Applications*, @@ -285,11 +270,12 @@ Climate networks / Coupled climate networks In *Progress Of Theoretical Physics Supplement*, vol. 179, p178-188 (2009) `doi:10.1143/PTPS.179.178 `__ -Power Grids/Power Networks -=========================================== -*Resistance based networks* -~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Schultz2014]_, [Schultz2014a]_. + +Power grids & Power networks +============================ +- Resistance based networks: [Schultz2014]_, [Schultz2014a]_. + +.... .. [Schultz2014] P. Schultz "Stability Analysis of Power Grid Networks". *M.Sc. Thesis*, Humboldt-Universität zu Berlin (2014) @@ -299,12 +285,14 @@ Power Grids/Power Networks Spatially Embedded Infrastructure Networks". In *Eur. Phys. J. Special Topics: Resilient Power Grids and Extreme Events* (2014) -Time series analysis and synchronization -======================================== -*General* -~~~~~~~~~ -[Pecora1998]_, [Schreiber2000]_, [Bandt2002]_, [Kraskov2004]_, [Kantz2006]_, [Thiel2006]_, -[Bergner2008]_, [Pompe2011]_, [Ribeiro2011]_, [Runge2012b]_. + +Time series analysis & Synchronization +====================================== +- General: [Pecora1998]_, [Schreiber2000]_, [Bandt2002]_, [Kraskov2004]_, + [Kantz2006]_, [Thiel2006]_, [Bergner2008]_, [Pompe2011]_, [Ribeiro2011]_, [Runge2012b]_. +- Event synchronization: [Quiroga2002]_, [Boers2014]_. + +.... .. [Pecora1998] L.M. Pecora and T.L. Carroll. "Master Stability Functions for Synchronized Coupled Systems". @@ -369,10 +357,6 @@ Time series analysis and synchronization `doi:10.1103/PhysRevE.86.061121 `__ -*Event synchronization* -~~~~~~~~~~~~~~~~~~~~~~~ -[Quiroga2002]_, [Boers2014]_. - .. [Quiroga2002] R.Q. Quiroga, T. Kreuz, P. Grassberger. "Event synchronization: a simple and fast method to measure synchronicity and time delay patterns." @@ -388,11 +372,22 @@ Time series analysis and synchronization `doi:10.1038/ncomms6199 `__ -Recurrence networks / quantification analysis / plots -===================================================== -*Review of recurrence plots and RQA* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Marwan2007]_. + +Recurrence quantification/network analysis +========================================== +- Review of recurrence plots & RQA: [Marwan2007]_. +- Introduction & application of recurrence networks in the context of RQA: [Marwan2009]_. +- Thorough introduction to recurrence network analysis: [Donner2010b]_. +- Discussion of choosing an appropriate recurrence threshold: [Donner2010a]_, [Zou2010]_. +- Review of various methods for network-based time series analysis: [Donner2011a]_. +- Introduction to measures of (fractal) transitivity dimensions: [Donner2011b]_. +- Applications of recurrence network analysis to paleoclimate data: [Donges2011b]_, + [Donges2011c]_, [Feldhoff2012]_. +- Theory of recurrence networks: [Donges2012]_, [Zou2012]_. +- Multivariate extensions of recurrence network analysis: [Feldhoff2012]_, [Feldhoff2013]_. +- General: [Ngamga2007]_, [Xu2008]_, [Schinkel2009]_. + +.... .. [Marwan2007] N. Marwan, M.C. Romano, M. Thiel, J. Kurths. "Recurrence plots for the analysis of complex systems". @@ -400,20 +395,12 @@ Recurrence networks / quantification analysis / plots `doi:10.1016/j.physrep.2006.11.001 `__ -*Introduction and application of recurrence networks in the context of RQA* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Marwan2009]_. - .. [Marwan2009] N. Marwan, J.F. Donges, Y. Zou, R.V. Donner, J. Kurths. "Complex network approach for recurrence analysis of time series". In *Physics Letters A*, vol. 373 (no. 46), p4246-4254 (2009) `doi:10.1016/j.physleta.2009.09.042 `__ -*A thorough introduction to recurrence network analysis* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donner2010b]_. - .. [Donner2010b] R.V. Donner, Y. Zou, J.F. Donges, N. Marwan, J. Kurths. "Recurrence networks -- A novel paradigm for nonlinear time series analysis". @@ -421,10 +408,6 @@ Recurrence networks / quantification analysis / plots `doi:10.1088/1367-2630/12/3/033025 `__ -*Discussion of choosing an appropriate recurrence threshold* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donner2010a]_, [Zou2010]_. - .. [Donner2010a] R.V. Donner, Y. Zou, J.F. Donges, N. Marwan, J. Kurths. "Ambiguities in recurrence-based complex network representations of time series". @@ -439,10 +422,6 @@ Recurrence networks / quantification analysis / plots In *Chaos*, vol. 20 (no. 4), 043130 (2010) `doi:10.1063/1.3523304 `__ -*Review of various methods for network-based time series analysis* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donner2011a]_. - .. [Donner2011a] R.V. Donner, M. Small, J.F. Donges, N. Marwan, Y. Zou, R. Xiang, J. Kurths. "Recurrence-based time series analysis by means of complex network @@ -452,10 +431,6 @@ Recurrence networks / quantification analysis / plots `doi:10.1142/S0218127411029021 `__ -*Introduction to measures of (fractal) transitivity dimensions* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donner2011b]_. - .. [Donner2011b] R.V. Donner, J. Heitzig, J.F. Donges, Y. Zou, J. Kurths. "The geometry of chaotic dynamics -- A complex network perspective". In *European Physical Journal B: Condensed Matter and Complex Systems*, @@ -463,10 +438,6 @@ Recurrence networks / quantification analysis / plots `doi:10.1140/epjb/e2011-10899-1 `__ -*Applications of recurrence network analysis to paleoclimate data* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donges2011b]_, [Donges2011c]_, [Feldhoff2012]_. - .. [Donges2011b] J.F. Donges, R.V. Donner, K. Rehfeld, N. Marwan, M.H. Trauth, J. Kurths. "Identification of dynamical transitions in marine palaeoclimate records @@ -484,10 +455,6 @@ Recurrence networks / quantification analysis / plots `doi:10.1073/pnas.1117052108 `__ -*Theory of recurrence networks* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donges2012]_, [Zou2012]_. - .. [Donges2012] J.F. Donges, J. Heitzig, R.V. Donner, J. Kurths. "Analytical framework for recurrence network analysis of time series". In *Physical Review E: Statistical, Nonlinear, and Soft Matter Physics*, @@ -502,10 +469,6 @@ Recurrence networks / quantification analysis / plots `doi:10.1209/0295-5075/98/48001 `__ -*Multivariate extensions of recurrence network analysis* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Feldhoff2012]_, [Feldhoff2013]_. - .. [Feldhoff2012] J.H. Feldhoff, R.V. Donner, J.F. Donges, N. Marwan, J. Kurths. "Geometric detection of coupling directions by means of inter-system @@ -521,10 +484,6 @@ Recurrence networks / quantification analysis / plots `doi:10.1209/0295-5075/102/30007 `__ -*General* -~~~~~~~~~ -[Ngamga2007]_, [Xu2008]_, [Schinkel2009]_. - .. [Ngamga2007] E.J. Ngamga, A. Nandi, R. Ramaswamy, M.C. Romano, M. Thiel, J. Kurths. "Recurrence analysis of strange nonchaotic dynamics". In *Physical Review E*, vol. 75, 036222 (2007) @@ -544,11 +503,14 @@ Recurrence networks / quantification analysis / plots `doi:10.1016/j.physleta.2009.04.045 `__ + Visibility graph analysis ========================= -*Introduction* -~~~~~~~~~~~~~~ -[Lacasa2008]_. +- Introduction: [Lacasa2008]_. +- Application to geophysical time series: [Donner2012]_. +- Tests for time series irreversibility: [Donges2013]_. + +.... .. [Lacasa2008] L. Lacasa, B. Luque, F. Ballesteros, J. Luque, J.C. Nuno. "From time series to complex networks: The visibility graph". @@ -556,10 +518,6 @@ Visibility graph analysis America*, vol. 105 (no. 13), p4972-4975 (2008) `doi:10.1073/pnas.0709247105 `__ -*Application to geophysical time series* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donner2012]_. - .. [Donner2012] R.V. Donner and J.F. Donges. "Visibility graph analysis of geophysical time series: Potentials and possible pitfalls". @@ -567,10 +525,6 @@ Visibility graph analysis `doi:10.2478/s11600-012-0032-x `__ -*Tests for time series irreversibility* -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -[Donges2013]_. - .. [Donges2013] J.F. Donges, R.V. Donner, J. Kurths. "Testing time series irreversibility using complex network methods". In *Europhysics Letters*, vol. 102.1, 10004 (2013) diff --git a/docs/source/sitemap.rst b/docs/source/sitemap.rst index 5919dace..c828217f 100644 --- a/docs/source/sitemap.rst +++ b/docs/source/sitemap.rst @@ -3,16 +3,15 @@ Sitemap ======= .. toctree:: - :maxdepth: 2 - :includehidden: + :maxdepth: 3 index download - tutorials methods + tutorials api_doc - development - changelog publications + changelog + development license contact diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst index 1b5badc9..643d5707 100644 --- a/docs/source/tutorials.rst +++ b/docs/source/tutorials.rst @@ -2,13 +2,14 @@ Tutorials ========= -The tutorials are designed to be self-explanatory. For further details on the -used classes and methods please refer to the :doc:`api_doc`. +The tutorials are designed to be self-explanatory, and are set up as Jupyter +notebooks that can be accessed read-only through the links below. The original, +executable notebook files can be found in the folder ``pyunicorn/examples``. +For further details on the used classes and methods, please refer to the +:doc:`api_doc` documentation. .. toctree:: - :maxdepth: 1 - - tutorials/climate_network_1 - tutorials/recurrence_network_1 - + :maxdepth: 2 + :glob: + tutorials/* \ No newline at end of file diff --git a/docs/source/tutorials/ClimateNetworks.nblink b/docs/source/tutorials/ClimateNetworks.nblink new file mode 100644 index 00000000..b72da4b8 --- /dev/null +++ b/docs/source/tutorials/ClimateNetworks.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/ClimateNetworks.ipynb", + "extra-media": [ + "../../../examples/tutorials/images" + ] +} \ No newline at end of file diff --git a/docs/source/tutorials/CoupledClimateNetworks.nblink b/docs/source/tutorials/CoupledClimateNetworks.nblink new file mode 100644 index 00000000..7910a142 --- /dev/null +++ b/docs/source/tutorials/CoupledClimateNetworks.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/CoupledClimateNetworks.ipynb", + "extra-media": [ + "../../../examples/tutorials/images" + ] +} \ No newline at end of file diff --git a/docs/source/tutorials/EventSeriesAnalysis.nblink b/docs/source/tutorials/EventSeriesAnalysis.nblink new file mode 100644 index 00000000..87275733 --- /dev/null +++ b/docs/source/tutorials/EventSeriesAnalysis.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/EventSeriesAnalysis.ipynb", + "extra-media": [ + "../../../examples/tutorials/images" + ] +} \ No newline at end of file diff --git a/docs/source/tutorials/RecurrenceNetworks.nblink b/docs/source/tutorials/RecurrenceNetworks.nblink new file mode 100644 index 00000000..84941f59 --- /dev/null +++ b/docs/source/tutorials/RecurrenceNetworks.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/RecurrenceNetworks.ipynb", + "extra-media": [ + "../../../examples/tutorials/images" + ] +} \ No newline at end of file diff --git a/docs/source/tutorials/VisibilityGraphs.nblink b/docs/source/tutorials/VisibilityGraphs.nblink new file mode 100644 index 00000000..86687d51 --- /dev/null +++ b/docs/source/tutorials/VisibilityGraphs.nblink @@ -0,0 +1,6 @@ +{ + "path": "../../../examples/tutorials/VisibilityGraphs.ipynb", + "extra-media": [ + "../../../examples/tutorials/images" + ] +} \ No newline at end of file diff --git a/docs/source/tutorials/climate_network_1.rst b/docs/source/tutorials/climate_network_1.rst deleted file mode 100644 index 66d3a9d3..00000000 --- a/docs/source/tutorials/climate_network_1.rst +++ /dev/null @@ -1,20 +0,0 @@ - -Constructing and analyzing a climate network -============================================ - -This tutorials illustrates the use of ``climate`` for constructing a -climate network from given data in a commonly used format, performing a -statistical analysis of the network and finally plotting the results on a map. - -For example, our software can handle data from the NCEP/NCAR reanalysis 1 -project like this monthly surface air temperature data set (a NetCDF file): -ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.derived/surface/air.mon.mean.nc - -You can use ``PyNgl`` for plotting the results on maps -(http://www.pyngl.ucar.edu/Download/). Alternatively, the tutorial saves the -results as well as the grid information in text files which can be used for -plotting in your favorite software. - -This tutorial is also available as an ipython notebook. - -.. literalinclude:: ../../../examples/tutorials/climate_network.py diff --git a/docs/source/tutorials/recurrence_network_1.rst b/docs/source/tutorials/recurrence_network_1.rst deleted file mode 100644 index b9caaf5e..00000000 --- a/docs/source/tutorials/recurrence_network_1.rst +++ /dev/null @@ -1,10 +0,0 @@ - -Recurrence network analysis of the logistic map -=============================================== - -This tutorial demonstrates how to use ``timeseries`` for a nonlinear time -series analysis of a realization of the chaotic logistic map. - -This tutorial is also available as an ipython notebook. - -.. literalinclude:: ../../../examples/tutorials/recurrence_network.py diff --git a/examples/tutorials/ClimateNetworks.ipynb b/examples/tutorials/ClimateNetworks.ipynb new file mode 100644 index 00000000..0b43b821 --- /dev/null +++ b/examples/tutorials/ClimateNetworks.ipynb @@ -0,0 +1,467 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c79b8e2b", + "metadata": {}, + "source": [ + "# Tutorial: Climate Networks" + ] + }, + { + "cell_type": "markdown", + "id": "677ae7d7", + "metadata": {}, + "source": [ + "The objective of this tutorial is to introduce climate networks, and to explain and illustrate their application with the `pyunicorn` package. First some theoretical background for understanding general climate networks will be given, and then some methods provided by `pyunicorn.climate.ClimateNetwork` will be illustrated. An introduction and application of coupled climate networks will follow. For a detailed discussion and further references, please consult __[Donges et al. (2015)](https://aip.scitation.org/doi/10.1063/1.4934554)__, on which this tutorial is based. " + ] + }, + { + "cell_type": "markdown", + "id": "76c98668", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "a56c11e0", + "metadata": {}, + "source": [ + "_Climate networks (CN)_ are a way to apply complex network theory to the climate system, by assuming that each node represents a varying dynamical system. Of interest is then the collective behaviour of these interacting dynamical systems and the structure of the resulting network. This approach was first introduced by __[Tsonis and Roebber (2004)](https://www.sciencedirect.com/science/article/abs/pii/S0378437103009646)__.\n", + "\n", + "CN analysis is a versatile approach for investigating climatological data, and it can be used as a complementary method to classical techniques from multivariate statistics. The approach allows for the analysis of single fields of climatological time series, e.g., surface air temperature observed on a grid, or even two or more fields. It has been successfully applied in many cases, for example to dynamics and predictability of the El Niño Phenomenon (__[Radebach et al., 2013](https://arxiv.org/abs/1310.5494)__)." + ] + }, + { + "cell_type": "markdown", + "id": "05e76cc7", + "metadata": {}, + "source": [ + "## Theory of Climate Networks (CNs)" + ] + }, + { + "cell_type": "markdown", + "id": "fcc79d2d", + "metadata": {}, + "source": [ + "CNs are a typical application of _functional networks_, which allow to study the dynamical relationships between subsystems of a high-dimensional complex system by constructing networks from it. `pyunicorn` provides classes for the construction and analysis of such networks, representing the statistical interdependency structure within and between fields of time series using various similarity measures." + ] + }, + { + "cell_type": "markdown", + "id": "1860c9d0", + "metadata": {}, + "source": [ + "### Coupling Analysis" + ] + }, + { + "cell_type": "markdown", + "id": "30cd9555", + "metadata": {}, + "source": [ + "CNs represent strong statistical interrelationships between time series of climatological fields. These statistical interrelationships can be estimated with methods from the `funcnet.CouplingAnalysis` class in terms of matrices of _statistical similarities_ $\\textbf{S}$, such as the _(lagged) classical linear Pearson product-moment correlation coefficient_ (CC). The CC of two zero-mean time series variables $X,Y$, as implemented in `funcnet.CouplingAnalysis.cross_correlation()`, is given by \n", + "$$\\rho_{XY}(\\tau)=\\frac{\\langle X_{t-\\tau}, Y_t \\rangle}{\\sigma_X \\sigma_Y}\\,,$$\n", + "which depends on the covariance $\\langle X_{t-\\tau}, Y_t \\rangle$ and the standard deviations $\\sigma_X, \\sigma_Y$. Lags $\\tau > 0$ correspond to the linear association of past values of $X$ with $Y$, and vice versa for $\\tau < 0$. " + ] + }, + { + "cell_type": "markdown", + "id": "70377c40", + "metadata": {}, + "source": [ + "### Similarity Measures for CNs" + ] + }, + { + "cell_type": "markdown", + "id": "fadb2909", + "metadata": {}, + "source": [ + "By thresholding the matrix of a statistical similarity measure $\\textbf{S}$, the interrelationships between time series of climate networks can be reconstructed:\n", + "$$A_{pq} = \\Theta(S_{pq}-\\beta)\\quad \\text{ if } p \\neq q; \\qquad 0\\quad\\text{otherwise}\\,,$$\n", + "where $\\Theta$ is the Heaviside function, $\\beta$ denotes a threshold parameter, and $A_{pp} = 0$ for all nodes $p$ to exclude self-loops. A CN that is reconstructed using the Pearson CC from above is called a _Pearson correlation CN_." + ] + }, + { + "cell_type": "markdown", + "id": "9c64c013", + "metadata": {}, + "source": [ + "## Constructing CNs" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ff7f5d81-129e-4966-a7fc-a0d25aea87f3", + "metadata": {}, + "source": [ + "Having established some basic theoretic background, we will now use `pyunicorn` to construct a CN. We start by importing the required packages, by downloading an example __[NOAA dataset](https://psl.noaa.gov/repository/entry/show?entryid=synth%3Ae570c8f9-ec09-4e89-93b4-babd5651e7a9%3AL25jZXAucmVhbmFseXNpcy5kZXJpdmVkL3N1cmZhY2UvYWlyLm1vbi5tZWFuLm5j)__, and by specifying some metadata for it." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e793f1a2", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from pyunicorn import climate" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2a8e9a1d-982d-4216-8483-4f372f40d918", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./data/air.mon.mean 100%[===================>] 27.46M 10.1MB/s in 2.7s \n", + "2024-02-05 03:01:16 URL:https://psl.noaa.gov/repository/entry/get/air.mon.mean.nc?entryid=synth%3Ae570c8f9-ec09-4e89-93b4-babd5651e7a9%3AL25jZXAucmVhbmFseXNpcy5kZXJpdmVkL3N1cmZhY2UvYWlyLm1vbi5tZWFuLm5j [28791289/28791289] -> \"./data/air.mon.mean.nc\" [1]\n" + ] + } + ], + "source": [ + "DATA_NAME = \"air.mon.mean.nc\"\n", + "DATA_URL = f\"https://psl.noaa.gov/repository/entry/get/{DATA_NAME}?entryid=synth%3Ae570c8f9-ec09-4e89-93b4-babd5651e7a9%3AL25jZXAucmVhbmFseXNpcy5kZXJpdmVkL3N1cmZhY2UvYWlyLm1vbi5tZWFuLm5j\"\n", + "DATA_FILE = f\"./data/{DATA_NAME}\"\n", + "![ -f {DATA_FILE} ] || wget -O {DATA_FILE} -nv --show-progress \"{DATA_URL}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6f79efc6-63ca-4726-bb8f-003744170bcc", + "metadata": {}, + "outputs": [], + "source": [ + "DATA_FILENAME = \"./data/air.mon.mean.nc\"\n", + "# Indicate data source (optional)\n", + "DATA_SOURCE = \"ncep_ncar_reanalysis\"\n", + "# Type of data file (\"NetCDF\" indicates a NetCDF file with data on a regular\n", + "# lat-lon grid, \"iNetCDF\" allows for arbitrary grids - > see documentation).\n", + "FILE_TYPE = \"NetCDF\"\n", + "# Name of observable in NetCDF file (\"air\" indicates surface air temperature\n", + "# in NCEP/NCAR reanalysis data)\n", + "OBSERVABLE_NAME = \"air\"\n", + "# Select a region in time and space from the data (here the whole dataset)\n", + "WINDOW = {\"time_min\": 0., \"time_max\": 0., \"lat_min\": 0, \"lon_min\": 0,\n", + " \"lat_max\": 30, \"lon_max\": 0}\n", + "# Indicate the length of the annual cycle in the data (e.g., 12 for monthly\n", + "# data). This is used for calculating climatological anomaly values.\n", + "TIME_CYCLE = 12" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9f54ffe5-02a5-47e4-a459-870e1e8afef6", + "metadata": {}, + "source": [ + "Now we set some parameters for the CN construction, the first being the threshold $\\beta$ from above, and create a `ClimateData` object containing our data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "baf245bd-2f3e-401d-bc1d-9943fee4bbf2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading NetCDF File and converting data to NumPy array...\n", + "Global attributes:\n", + "description: Data from NCEP initialized reanalysis (4x/day). These are the 0.9950 sigma level values\n", + "platform: Model\n", + "Conventions: COARDS\n", + "NCO: 20121012\n", + "history: Thu May 4 20:11:16 2000: ncrcat -d time,0,623 /Datasets/ncep.reanalysis.derived/surface/air.mon.mean.nc air.mon.mean.nc\n", + "Thu May 4 18:11:50 2000: ncrcat -d time,0,622 /Datasets/ncep.reanalysis.derived/surface/air.mon.mean.nc ./surface/air.mon.mean.nc\n", + "Mon Jul 5 23:47:18 1999: ncrcat ./air.mon.mean.nc /Datasets/ncep.reanalysis.derived/surface/air.mon.mean.nc /dm/dmwork/nmc.rean.ingest/combinedMMs/surface/air.mon.mean.nc\n", + "/home/hoop/crdc/cpreanjuke2farm/cpreanjuke2farm Mon Oct 23 21:04:20 1995 from air.sfc.gauss.85.nc\n", + "created 95/03/13 by Hoop (netCDF2.3)\n", + "Converted to chunked, deflated non-packed NetCDF4 2014/09\n", + "title: monthly mean air.sig995 from the NCEP Reanalysis\n", + "dataset_title: NCEP-NCAR Reanalysis 1\n", + "References: http://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis.derived.html\n", + "Variables (size):\n", + "lat (73)\n", + "lon (144)\n", + "time (913)\n", + "air (913)\n", + "ClimateData:\n", + "Data: 10512 grid points, 9597456 measurements.\n", + "Geographical boundaries:\n", + " time lat lon\n", + " min 1297320.0 -90.00 0.00\n", + " max 1963536.0 90.00 357.50\n" + ] + } + ], + "source": [ + "# For setting fixed threshold\n", + "THRESHOLD = 0.5\n", + "# For setting fixed link density\n", + "LINK_DENSITY = 0.005\n", + "# Indicates whether to use only data from winter months (DJF) for calculating\n", + "# correlations\n", + "WINTER_ONLY = False\n", + "\n", + "data = climate.ClimateData.Load(\n", + " file_name=DATA_FILENAME, observable_name=OBSERVABLE_NAME,\n", + " data_source=DATA_SOURCE, file_type=FILE_TYPE,\n", + " window=WINDOW, time_cycle=TIME_CYCLE)\n", + "print(data)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2fade6f6-8457-436f-a52a-77464e92fd54", + "metadata": {}, + "source": [ + "Next, we construct a CN based on the Pearson CC, without lag and with fixed threshold. Alternatively, several other similarity measures and construction mechanisms may be used as well." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c5326b90", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating a Tsonis climate network...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n" + ] + } + ], + "source": [ + "net = climate.TsonisClimateNetwork(\n", + " data, threshold=THRESHOLD, winter_only=WINTER_ONLY)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2cdee7ef-7d2f-46d9-83ed-c9253e0100c0", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a climate network based on Pearson correlation without lag and with\n", + "# fixed link density\n", + "# net = climate.TsonisClimateNetwork(\n", + "# data, link_density=LINK_DENSITY, winter_only=WINTER_ONLY)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e6bf3b44-193b-48c0-b7be-f056bd35d72c", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a climate network based on Spearman's rank order correlation without\n", + "# lag and with fixed threshold\n", + "# net = climate.SpearmanClimateNetwork(\n", + "# data, threshold=THRESHOLD, winter_only=WINTER_ONLY)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5cdcfc8a-6448-4df3-b49f-f3e5d220f0f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a climate network based on mutual information without lag and with\n", + "# fixed threshold\n", + "# net = climate.MutualInfoClimateNetwork(\n", + "# data, threshold=THRESHOLD, winter_only=WINTER_ONLY)" + ] + }, + { + "cell_type": "markdown", + "id": "b443476e", + "metadata": {}, + "source": [ + "We finish by calculating some basic network measures for the resulting CN, optionally saving them to text files." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4f568b0f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Link density: 0.026892009342390742\n", + "Calculating closeness...\n", + "Calculating node betweenness...\n", + "Calculating local clustering coefficients...\n", + "Calculating average link distance...\n", + "Calculating angular great circle distance using Cython...\n", + "Calculating maximum link distance...\n" + ] + } + ], + "source": [ + "print(\"Link density:\", net.link_density)\n", + "\n", + "degree = net.degree()\n", + "closeness = net.closeness()\n", + "betweenness = net.betweenness()\n", + "clustering = net.local_clustering()\n", + "ald = net.average_link_distance()\n", + "mld = net.max_link_distance()\n", + "\n", + "# Save the grid (mainly vertex coordinates) to text files\n", + "#data.grid.save_txt(filename=\"grid.txt\")\n", + "# Save the degree sequence. Other measures may be saved similarly.\n", + "#np.savetxt(\"degree.txt\", degree)" + ] + }, + { + "cell_type": "markdown", + "id": "15af9941", + "metadata": {}, + "source": [ + "## Plotting CNs" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b26e5953-53c6-418a-b08b-509ad415081f", + "metadata": {}, + "source": [ + "`pyunicorn` provides a basic plotting feature based on the __[cartopy](https://scitools.org.uk/cartopy/docs/latest/)__ and `matplotlib` packages, which can be used to have a first look at the generated data. We start by initializing a `MapPlot` object:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b823297c", + "metadata": {}, + "outputs": [], + "source": [ + "# create a Cartopy plot instance called map_plot\n", + "# from the data with title DATA_SOURCE\n", + "map_plot = climate.MapPlot(data.grid, DATA_SOURCE)" + ] + }, + { + "cell_type": "markdown", + "id": "422af668", + "metadata": {}, + "source": [ + "With `MapPlot.plot()`, we can now plot some of our previously calculated measures on the given grid." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "056f3a92", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot degree\n", + "map_plot.plot(degree, \"Degree\")\n", + "\n", + "# add matplotlib.pyplot or cartopy commands to customize figure\n", + "plt.set_cmap('plasma')\n", + "# optionally save figure\n", + "#plt.savefig('degree.png')" + ] + }, + { + "cell_type": "markdown", + "id": "b8feb1e0", + "metadata": {}, + "source": [ + "Try plotting more measures if you like." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8b67424d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot betwenness\n", + "map_plot.plot(np.log10(betweenness + 1), \"Betweenness (log10)\")\n", + "\n", + "# add matplotlib.pyplot or cartopy commands to customize figure\n", + "plt.set_cmap('plasma')\n", + "# optionally save figure\n", + "#plt.savefig('degree.png')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyunicorn", + "language": "python", + "name": "pyunicorn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/tutorials/CoupledClimateNetworks.ipynb b/examples/tutorials/CoupledClimateNetworks.ipynb new file mode 100644 index 00000000..e69aa680 --- /dev/null +++ b/examples/tutorials/CoupledClimateNetworks.ipynb @@ -0,0 +1,920 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9b3f935f", + "metadata": {}, + "source": [ + "# Tutorial: Coupled Climate Networks" + ] + }, + { + "cell_type": "markdown", + "id": "41e36ae2", + "metadata": {}, + "source": [ + "The objective of this tutorial is to introduce *coupled climate subnetwork analysis*, which uses *interacting networks* in order to study the statistical relationships between several fields of climatological observables, or between a climatolical obervable at different vertical levels.\n", + "\n", + "First, some theoretical background on *interacting networks* is given and the method of *coupled climate network analysis* is explained. Then, some methods provided by ``pyunicorn`` are illustrated by the example of the Earth's atmosphere’s vertical dynamical structure. An introduction to (single layer) *climate networks* and their application with the ``pyunicorn`` package can be found in the tutorial [Climate Networks](https://github.com/pik-copan/pyunicorn/blob/master/notebooks/tutorial_ClimateNetworks.ipynb). For a detailed discussion and further references, please consult [Donges et al. (2015)](https://aip.scitation.org/doi/10.1063/1.4934554) and [Donges et al. (2011)](https://link.springer.com/article/10.1140/epjb/e2011-10795-8), on which this tutorial is based." + ] + }, + { + "cell_type": "markdown", + "id": "122ca935", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "64969606", + "metadata": {}, + "source": [ + "*Coupled climate networks* are a very useful tool for representing and studying the statistical relationship between different climatological variables, or between a single variable at different physically separable levels. This can be useful for finding spatial as well as temporal patterns that account for a large fraction of the fields' variance. The method can also be applied to study the complex interactions between different domains of the Earth system, e.g., the atmosphere, hydrosphere, cryosphere and biosphere, which still remains a great challenge for modern science. The urge to make progress in this field is particularly pressing, as substantial and mutually interacting components of the Earth system (tipping elements) may soon pass a bifurcation point (tipping point) due to global climate change. Mapping the complex interdependency structure of subsystems, components or processes of the Earth system to a network of interacting networks provides a natural, simplified and condensed mathematical representation." + ] + }, + { + "cell_type": "markdown", + "id": "5d0b07ef", + "metadata": {}, + "source": [ + "## Theory of Interacting Networks" + ] + }, + { + "cell_type": "markdown", + "id": "6fd973d9", + "metadata": {}, + "source": [ + "The structure of many complex systems can be described as a *network of interacting, or interdependent, networks*. Notable examples are representations of the mammalian cortex, systems of interacting populations of heterogeneous oscillators, or mutually interdependent infrastructure networks. \n", + "\n", + "``pyunicorn`` provides the class `core.InteractingNetworks`for constructing and analysing all kinds of interacting networks. *Coupled climate networks* are the application of *interacting networks* to *climate networks*, and hence, the class `climate.CoupledClimateNetworks` inherits from `core.InteractingNetworks` and `climate.ClimateNetworks`." + ] + }, + { + "cell_type": "markdown", + "id": "0eab5909", + "metadata": {}, + "source": [ + "*Interacting networks* can be represented by decomposing a network $G=(V,E)$ into a collection of $M$ *subnetworks* \n", + "$G_i=(V_i,E_{ii})$. Here, $(V_i)_i$ denote the disjoint sets of nodes corresponding to each subnetwork, such that $\\bigcup^M_{i=1}V_i=V$. The internal link sets $(E_{ii})_i$ contain information on the connections within a subnetwork,\n", + "and disjoint sets of cross-links $E_{ij}\\;(i\\neq j)$ connect nodes in different subnetworks, such that $\\bigcup^M_{i,j=1}E_{ij}=E$. \n", + "Alternatively, a network of networks of this type can be represented by a standard adjacency matrix $A$ with with block structure." + ] + }, + { + "attachments": { + "Systems-of-interacting-networks-or-networks-of-networks-are-a-natural-representation-of_W640.jpg": { + "image/jpeg": 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tqOUH6JA3o54B6c7idN9tOPcBBN11vtktL2Mut4t9A94BY2pqWRF2zoa5iN9eirvuVvZa33V1dTC3siMzqoyt8oRgbLy7euUAb2vIe5V1bcq+avuNXPWVc7ueWeeQvkkd7ucepK7bGcouNpjFsmq6+px6eoiluNojrXww1rGPDix3Kem9a5tbCDeeuuF78Q92mtNiqK2zcK6V/JX3FrDHPfHg9Yot9WxDsT6+u+wnew2i2WGzUlms1FDQ2+kjEUEELdNY0eg/r33J6lQhimc8YMlxu3ScOeFeO49j0lMz6uqbtdWvi8rXwkQwac1oGun/APi7McP+N9/BOT8YoLNEe9Lj1qa3v7TP08a+4oLbjBDa6/xN8L6a7R0L6OhoLtV1IqYmuYWuiDG85d8Og4bG+x+9ZdkvGzhLi7TDcM3szXRN15FHJ9Jc3X5vLEHa+7ooUdwWxep8UFvxLI6zIcwpWYq+71Mt6uT5ZHSGpMTfibynlH6Pz676rYnF+HuDYx5Zx/EbJbpIztssFEwS79y/XMT8yUGsNw4iHHOJlZxG4QYZmFfj9bE6XKKSS0vgt8/KNiojfo8knUkkt9z6uUlYzmfHriNYaW94rYsHxqz18fm09VW3B9dLyH2EQ5eYeocBogggHopqul6sdC4wXO7W6lLhosqKhjN9O2nH2P8ASoEnlHA3JX5XjczbnwlvtUTcqejPnNstSXFpni5NjyS4crmjsRruGghkX9iTiTfhzZnxvyDkf/CU2P0sdva0foiQbLh8yFfWnw48KaWq+mXGyVWQVvQGpu9fLUuI+YLg0/5KyS5cX+F1umdDWZ9jkcjWB5aK+Nx0RsdAT3BB/FZZZLpQXq0Ut2tVVHV0NXE2annj+zIxw2HD5FBbY/jeO49EYrBYbXaWEcpbRUkcII9jygLtVEd58SPB+13SptU2Tzy19NUOppaeG2VTnNka4tcN+Xo6II6E/JSrb6uGvoKeupnF0FRE2WMkEEtcNjoe3QoK6KG/3QNuknENHww4qVr9bcIMbJLRvXUF4Kl24TSU1BUVENO+pliic9kLDp0hAJDR8zrX4oK6KIbRxR4jXG509OOB1+pKWSojikqaq5Qx+U0kBzy3WyGgk9O+lJ2SVF1pbBXVFjoIrhc44HOpaWSYRNmkA+Fpefsgn1QdgiinFL3x3q8ht0WQ4VitstD5B9NmiuTpZY2a68rQdF2+nqFnedR5TJitYzC57XBfiY/oslzDzTt/KN5+cMBd9jn1r116IO7RRpg9t42RZVT1OZZJiNRZGtcJqS20UrZHnldykOf207lPzG1lXEC05LebB9DxTLDi1x85r/p31fHWfAN7Z5cnTrsddgjSDIUUecOsLz6w319xyrirWZXTvie36G60Q0kYcSCHjkJI1ojXbqu54k4lV5faIKCjy2/4y6KbzHVFonEUsg5SOQkg9Ou+nqAgytpLXAj0V60ggEeqjfhrw4GF19TWuzjNslkqIREWX66/So4tHfMwco5SfvPRXvEjhljXEhttbkkl08u2ukMUdHWvp2v8zl3z8v2vsDXts+6DO1190vdltcrIrnd7fQyPbzNbUVLIy4e4DiNhY7ww4ZYlw5ZcW4tSVUBuT431Tp6uSdzywEN6vJ1oOPZcs44YYFm90gueWYzR3arp4fIiknLvhj5i7WgQO5J/FBkVlvNnvVO+os11oblDG/y3yUlQyVrXaB5SWkgHRHT5roMw4mYDiFebfk2W2m1VgiE30eonDZCwkgODe5B0f1LsMKw7F8LtsluxWyUdopJZPNkjpmcoe/QHMfUnQA2fZVbvimL3it+m3bG7NcKrlDfOqqGOV+h2HM5pOggYdlOP5fZW3nGbrT3S3ukdGJ4SeUub3HX2WEZzx74Y4bldTit7vdSL3Slnn0kFuqJXM5oxI3q1nKdtcD0J1vr6qR7dQUNuphTW+jpqOAEkRQRNjbs9zoDSuEHUYbkVtyzGqPIbOZ3UFY1zoTNC6J5AcW7LXdR1B7qPcl4401nyK42Sm4ZcTb1LQSuifUWywedTvcCRtr+cbaddDpSyiDr8aubrzj1vuz7fW219ZTRzuo6yIxz05c0ExyNPZzd6I9wopvXE7im2tqaW2cB7tWsiqnwwzy3yngZM0OLQ/wCJu2ggbGx6qQc/z3D8DtzK/LsgorTFJzeU2Z+5JuXW+Rg25+tjfKDrY911vDLiHbeI1rrrlZ7PfaKgp5WsgqbjRGBlY0jfPDs/E0dt9EGRUElRNQ081VTfRah8TXSwc4f5TyAS3mHQ6PTY7ry2495LkmU8Vr1c8rpoqS5slFO+lhk54oGxjlDWO2QW9ObYOjzE+q9BOIvCU55kr6m/5rkbcdMLWfUFDOKeB7hsOMjm/E8Hfbpr39FFfig8OVlreHVLcOHVmjorjj8Dg2jhLnGrptue5uyS50jSS4Ekk7cOpI0GiqIry1Wu5XWpbTWygqq2Zzg0Mgic92z2GgPkg23/AGOLH7s2TKModVTxWh7Y6FkA/g55gecvPzY0gD/1pW4618wvw8XnEsVtzMQ4n5NjN4FMx1dE1zKqgfU8oL3CBwA1zbHc9APnvt/rTxF4kP8AhGwYxxBomdTLbqg0FYW+pc145Cfk0FBNiKGaPxF4jRVUdDnVlyXBq1zuXlvFueIXH+LIzYI+ZA9fvUnYvlWNZRSmpxy/2y7xAbc6jqmS8v8AKDSSD8ig7hWl5t1Jd7PW2mviE1HW076eeM/nxvaWuH4glXahrxF8fbVwgrLZbn2SW93GvhfP5DKoQCKMHla5zi132jzAaH5p+WweFC4VdJiN44dXeXzLphVzktrnHvJTFxdTyfcW8wHyaFMq88qnxN3um4uXLiHjmL263T3O2soayjqJ31EczmO/JzOLQw87WhrfbQPv0nLwu+Jar4gZVU4tm8NsoLlUnzLVJSMdHFLofFCQ5zjzdNt69eo762GzaIiAr5vRoCsmDbgPcq9QEREBERAREQW9UfiDfYKiucx3K771wQFoj+yL0FZFxYsNzka76HUWRsELyOnPHPKXtH3CRh/xlvcsB458LrJxVwt9hur3U1TC4zUFaxu300uiN6/Oaezm+o9iAQHlmilPNOAfEPG+IFLhbKCnutxroZKih+h1DdVETCeZw5y0tIA3o/htS/wO8It9kvlNd+Jv0ait9O8SfVUMzZpagg7DZHN2xrD66JJ7dO6CVeEvDO/3zghgZOfZVizoLTt1Pa5xGJBJI6Rjn8wPUMc0aUs8NMNfhdnnoJcrybJXzzea6ovld9JkYeUDlYdDlZ03rr1J6rKI2MjjbHG1rGNADWtGgAOwAXJBrVknD+w8SPFlk9FfZa9tPbseo+lHVuhc5zn704t9Nfm/cVOmAYdZMHsH1HYGVLKLznTBtRUvmcHO1v4nknXTso64Tc1Z4l+MVwJf5cAtFHFsADpTOLx7/aG9/P8AVMyDA8q4O8NMpyOfIcgxKhuF0qA1ss8rn7eGtDW7Adro0AdvRZDacSxq1YiMSoLNRw2Hy5IvoHJzRFkjnOe0g72CXOJ+9d2iDV614rjfBHiH9R5VjlmueBZFUgWu9VlvilltlQegp6iVzd+WfzXOPQD25tbOUkFPS0sVPSQxQU8bQ2OOJoaxrR2AA6ALrcxxuz5bjVbj1/o46y31sZjljcO3s5p9HA6IPcEAqIuFuRXnhnmNPwhz6tkqqGfYxK+zfZqoh2pZXdhK0aA99gerdhOUcbI28sbGsbsnTRobJ2f6VyREBERAREQEREBERAREQFVpjqTXuFSX2M6eD80F6iIgIii3OePfDzGLm+yQV9Vkd+DzGLTYqc1lQXju08vwtI9QXA9+nQoJSVOoqKen8vz54ovNeI4+d4bzvPZo33J0eitJ/PumPSfQ5qm11FZSHyZXwjzaZz2fC4sd05mkg8p9RoqMsQ4C49b8io8qyy/X/Ncko5Wz09ddqxxZTyAggxRNIa0AjYB5gP1IM84hXq92DFai549jM+TXJjmMht0NQ2F0hc4N2Xu2ABvZOug2Vi3DFvGetyB944gy4zabS+ncyKyW5jppWPLmlr5JydcwAcNNJaQewPUSSiDrbvYLHd6yirLrZ6CvqKFznUklTTtkdA52uYsLgeUnQ6j2XZEbaR7oiCxI0SD6LqM0vQxzEbtfjTTVRt9HLUNgiYXvlc1pIYAOuyQB+K72pZp3MOxWPwZTYJ8yqcQhuUUl8paRlZPSNBLo4XO5Q4nWh1103vRadaI2EZ+GfhnZbTwisdZkGNWma/3CN1fWVE9DE6bmmcXtaSRsaY5o16aKmOGKKGJsUMbI42/ZaxoAH4Bc0QEREFGspqaspn01XTxVEEg0+KVgc1w+YPQqMsm8PvCy9VP02nx76huAO2VllmdRyMPuAz4N/e0qU0QQjLw94vYhC+fDeL/1pQwtLvoWWUwnaGt6/FUt+MDQ66A9T92iPF7ObxxDzyuye+Gl+kyhkTWUocIWMY3lAZzEnlOi7r124r1MyW001+x25WOsfNHTXClkpZXQvLHtY9paS0jsdFeW3GLh9eeGudVuMXhpd5Z8ykqQ3TamAk8kjfv1oj0II9EGHKtRVVTQ1sFbRzyU9TTyNlhljcWuje07a4EdiCAdqiudPDLUTxwQRullkcGMY0bLnE6AA9SSg9MvC7xOn4o8MIbvcKd0V1oZTRV7gzUc0rWtPmMPb4muaSPQkjtrcqrCOBOERcPuFVjxcNaKmCnEla5vXnqH/FId+oDiQPkAs3QVaZu5N+yuVTp2crOvcqogIiICIiAiIgsSdklERAREQQxxdP0DxI8HroDpk7rrQzbI681O3kHX+MT/AEBTOoX8Trvq678L8kedQ0GY0sM7t/Zjma5rnHp2HL/T+qaEBERBC3h01U8Q+MFzaWESZSaXYfzH8izl0f1/h1HoppUMeEs/SMcze7bLvrPNrnVB3LrYLmDp8vh/rUzoCIiAsV4p4HY+IeI1GO3yNwa8iSmqYwPOpZh9mWM+jh/SNj1WVIgh3grnd9pMgn4U8SpGty63R89DX6IjvVKO0zCe7wB8Q7nRPcO1MSwDjXw3pOIFghMFUbVkVrk+lWW7R9JKSdvUbOt8hIHMPkD3AWt198Y2Q2qyQ2eHGbXVZLRl9PcK59QZKKSRji3nibGQXNdrm3zADfTY6oN0EWjWJ+NHMae4MGUYzZbhQl3x/QRJTzNB9i572nXtodu/qtxOHeZWDPcUpclxus+k0NQCPiHK+J4+1G9v5rh6j7iNggkMhREQEREBEUEcbfE7hvDu7zWGho58kvVOeWohp5mxQwOGwWPlIdp49QGnXY6PRBO6LUjFfGxa6i4thybCKmgpHv19Ioq0TuYPnG5rd69SHfcFsnSZ5h1VhMWaR5HbmY9LGHtr5ZhHGOuuU82iHb6cp676a2gyRFHWAcYsVzvK5rJilNeblSwQvfJd20D2UIe0geX5jtHmOyR00eU6JXY8UsKuma01DQ0ebXvGaGNzzWstRayWra7Wm+aerNad22DzdR0CDPYKiCZ8kcU0b5IiBI1rgSwkbAI9OnVRvxJyPi0zJDjvDzB7dPGYWyOv13ruSkiLtjl8pn5RzgQd63rp06rvOFnDfE+HVsqaXGKKeF1a5stZPPUvmlqHjenPLievU9gAsxQdHgVJk9FitJT5jdaO63tvOamppKfyYnbeS0Nb7NaQ3fTet91dWjH7FZ6qsqrVZ7fQ1FdM6ermp6djH1EjiXOe9wG3Ekk7O12SICIiAiIgIis73dLdZbRVXe7VkNFQUkTpaieV3KyNgGySUFerEppZRTtjdNyHyxI4taXa6bIBIG/XRUW8CeH1zxChu97yyogrsyyGsdV3aqiO2NAJEcMZIH5Nrew0O+uwGrvgXmuU8QW3jKq2209txGonDMcjfG4Vc8Tdh08hJ0GuPVo1vv3GiZJljD2/P0KC0RfXAtOj3XxAREQEREBRT4meEtv4p4I+nD6ekvluDprbWynlaw6+KN59I3ADZ9CAeutGVlHHiNkyuThjU2TDLdU1d1vs8dqEsTSRRxTEtkneR9lobsc35pcD6INV8e8GmaXCliqqrLsYiglAcx9I+WpBadevI0H17Ejp3Uo8JvCLbcQzS05PeMuden22cVLKNtuEMZkaNsJcZHE8rtO7DelsLg+O0OJYfacZto/ettpI6aN2tF/KNFx+bjsn5kruUBVaePmdzHsFxijLz8vUq6aA0aHZB9REQEREBERAREQWKIRokIgIiIIh8YdDJVcAL7V07uWqtklNXwO39l0c7CT/AJJcpTstfFdLPRXOD+Cq6eOdnXfwvaHD+gqxzmyR5JhV7x6UAsuVvnpDv08xjmg/eNgrB/Cle33vgHjD5ifpVBTut1Qx32o3U7zEAfnytafxCCUV8cQ1pc4gADZJ9F9XT5xVfQcKvtbsD6PbqiXZGwOWNx7fggjHwXNMnAO23Mt065V1dVu28uJJqZG9d+vwKZlF3hPpfofh2w6Ll5eaidL9nl+3K9//AM3f17qUUBEXwkAbJ0Ag+ooxzTjtw8xys+q4LpLkV6cS2O12OI1k73fo/B8LT26FwPXsse+t+PuejVlsls4aWh/aruhFZcHN/SbCBysP8V4B6d0Hf+KvIp8Z4A5VcaSoENXJTNpISHaduaRsTuX5hr3Hp21v0XmKvRW5eGnHLzZbi/K8hv2V5HU0skVPdLpVvLaSRzSA6KJp5WgHR5TzdloNnOJ37C8mqsdyOgloq+mdpzXD4Xt2dPYfzmHXQjug6NbD+CzPs0xm7X2yYvhtTl8FXFHVTUkVc2nNNyO5TKC8EEnnAI6E6b16LXhb6eBHhZcMRxWtzK/U76a4X2ONtJA/7UdKPiDiPQvJB0ewa33KDL3cXOI8E3l1XATJmgt2DBcIZvX5DX9KM42ZPGyT6ZwN4hsewnpBSslBA9iCNn5DamdEELnxARwwskruEHFulBID3Pxz4Gk/MyDf6lyf4jcShkDKzFc9o9jbTPYJBv7tElTMiDXniJ4nMQgwO+mxMvtLfm0j2ULau1SxBsrhpry4jQAJDupG9Lz+mkkmlfNNI6SR7i573HZcT1JJ9SvWHipizM14c37FXyNjdcqJ8MUjuzJNbjcfkHhp/BeVWRWa549fa2yXmjlorhRTOhqIJB1Y4f1j1BHQggjoUFgtpPAHcoL1eMk4b3+jprrYaujbdG0dZG2WETRSxt3yO2CTzsJ6f3sLVtbofsenDy5W6K7cRLnTSU0NfTChtvOCDNFzh8smv0eZjAD66d+Iba0tPBS07KalgiggjHKyONga1o9gB0AVVEQXkf8ABt+4LkvjRpoHyX1AREQEREBEVs64UDbmy1urqZtfJEZ2Uplb5rowQC8M3stBIG9a2QgoZFerTjtkqr1fLhT2+3UjPMnqJ38rGD/tJPQAdSSAOpXV2+oxbiXw+gqjSx3bHr3TB4hq6cgSsJ2OZjhsEEbB9wCD2K6DPuE9pzrN7Ze8puVdcbNbItwY+8gUTqjZ/LvA6vOjrldsfgSDIMUbIomxRMayNjQ1rWjQaB2AHoEHGmghpqeOmpoY4YImBkccbQ1rGgaAAHQAD0VREQcZGNeOvf3VtJG5ncbHurtEFiiunQsd6aPyVN1O70IKCii5mGT9FPKk/RKDgsbwHNbLm1JcquxOqZKe33Ga3ySyRcrJJIiA50Z7PZ16OCvM9tV/uuFXe245XQW671VI+Gkqpt8sL3DXP02djZIPXrpW/CjBqHAuH1nxSiLSygpw2WRo15sp6ySH+U4k/jpBkCrRQE9X9B7KsxjWfZH4rkgAADQGgiIgIiICIiAiIgIiILOUakcPmuKq1I0/fuFSQEREBQn4fj+1rifxO4dSczI4LsL5b2nsYKtoc4N+THBo+8nv1U2KEuKjxh3iMwDONeXQX2KXGblIOgDnnzKbf3yb6+gagkjiZmtj4f4ZW5TkEzmUdKAAxg3JNIejY2D1cT+A6k6AJWiHE7xTcScvbX2+3zUmP2WsifA6kp4WSyOieC0tfK8E7IPdgYpV/ZJbjWsosKtLS5tDNJWVDxvo+Rgia3p7tEjv8taaINjuAPikv+FxWvGcppYLnjNKxlMySKIMqaWIdARrQkAHoRs6+177l5FxR4fY/j9LfbxltrpaGsp21NKTNuSeNw21zIxt7gQR2C8plvN4EcExCv4YMy24YzQVV8bcJoWVtSzznhjA3lLA7YYRsjbQD0QZqOMGcZp+S4T8N62opX9GXzISaOiH8ZrPtyt7fZIPyX1vBfKcw1Lxe4i3K8wO6ustn/eVAP4ruXT5R1PU8p+fvNyIMewrCMRwui+iYrj1vtMZGnup4QJJP5bz8T/8YlZCiIC6LMcOxbMKJlHlFgt13hYdxiqgDzGfdru7T9xC71EEa0XAbhFRUlXT0uCWlgqoXwvkewyyMa5paSxzy4sOj0LdELHfD1ebji99unBPKal0twsDBNY6uU9a+2OOoyPd0f2SB27D7JKmxRP4i8NutytdvzvDmcuZYnIaug5W9aqH+/UztdXBzd6HvsDXMUEsIsb4ZZlas9we2ZXZ3/vauhDnRl23QSDo+N38Zrtj59x0KyRAREQFgvE/hHgPEdrH5VYoqmriZyRVkT3RTsHtztI5h1PR2x17LOkQQli/hZ4QWO5Mr3WWruz43czI7jVGWIH5sADXD5OBCmqCKKCFkEEbIoo2hrGMaA1rQNAADsAuaIC+sG3Ae5XxVKYbl+7qgukREBEVjfrzabDbJbne7nR22hiG5KiqmbFG373OICC+VtdLhQWqglr7pXU1DRwjcs9TK2ONg9y5xAH4rqcDzLHM5sbr3i1ybcbe2okp/PbG5oL2HTtBwBI9j2IPRYBduCMGW5tVX/iRk9yyi3R1TpLVYnfkKGlj/ND2N/hXj9I6367QSTlkN7rMXuEOMXClobvLTuFDVVEXmxRyEfC4t9R+v30exwnhRwes2GXOXJrrcKvJ8yq2FtXfLg4ukIPdsTdkRM100Ouum9dFJFPDDT08dPTxMhhiaGRxsaGtY0DQAA6AAei5oCIiAiIgIiICIiAiL49vMxzdkbGtg6IQR1w14g3HNOI+aW2ioqT9q+PVEdvgr2ud5tRWAbnb3LS1nQeh7HrvpIyxzhvhViwHFYsbx6KZlFHLJMXTSeZJI+R5c5z3d3HrrZ9AB6LI0BERAREQEREBERAREQEREFKpbuPfsrZXxGwQfVWTgWuIPog+IiICj7xEYhLmvCC+WejD/rKKEVlvczfOKmE87A35u5S3/GUgog104hYz+6P8NVgvtpliGQwwiqgBcGtdUtBjngJ7NDnA6PTqGnoNrQ/ILLdsfu09pvduqbdX07i2WCojLHtP3H0+fYr0I4Yk8OePuScOJB5Vkybmv9g+HTWSnpUwt9tcvMB00G/NSPfbhg91zaDB73TW+4Xt9vNwhpKqh84fR+csLw5zS0fE3sSCfTaDzG4c4Lk2f5HFYsYtk1ZUPI8x4GooG/pyP7Nb9/fsNnQXp3wkwqi4e8O7RiNDJ5zKCEiSYt0ZpXOLpH69NucdD0Gh6KOeLOB1+GXiDirwqtkNPc7bF5d3slLGIobtRjqWhjRoStHUEDZ0O5ABlHh7l9jzrEqLJseqhPQ1bNgHXPE786N4BPK5p6Ef1jRQd+iIgIiICIiAiIg1/dPBwM41TfSpY6Ph5m87pRI9wZBa7nrbt+jWSAb9APkGFZTJ4juCrLj9AOd0hm3y8zaWoMX/APII+TXz2tSPGnxQueX8Tq/FIKmSOwY/Uupo6drtNlqWbbJK8erg7maPYA61zFQGg9fLHd7VfLZFc7LcqS40Uw3HUUszZI3fc5pIV8vNbwn8ULnw+4nW6jdVSOsF4qWUtwpiSWDnIa2YD0c0kHfct2PUL0pQEXGR7I43SSOaxjQS5zjoAD1KPkYyIyve1sbRzFxOgB779kHJFHtfxu4TUdXFSvz6xzzSvDGNpKj6TzOOtDcXMPVZrfq99rsdfc46GruD6SmknbS0rOeacsaXckbfVztaA9SQgvVcUrdNLvdQ/i/EPibkeSUVPBwduFnsjp2tq6+63GKKWOMnqWwfacQOvQn2UlZtZbjfcUq7RaMgq8erJgwR3GlY18kOngu0HdOoBb8t77hB3ixLijntvwCy09yrrRfLs6qqBTU1LaqI1E0kpaXBugQBsNPc+ix7h3wXsuKZPHlddkeUZPkLI3sbW3e5PlDA8acGsGmga6aO9enopOQYFwpyvOcqmuFZk+AS4ja+Vhtoqq1slVN1PMZIwPyfTl0D1HXv0VbN+FOD5rlVBkWV2j63qKCDyaenqJXGmA5i7mMW+Vx6n7QI1rp0WbogpUdLTUVLHS0dPDTU8TeWOKJgYxg9gB0AVVEQEREBERAREQEREBERAUTcTshv0/HTh5glguU1BFK6ou94fGR+VpoW6ZEQe7Xu2D+BHUdJZVg6y2d2QMyB1rojeGUxpG13kN88QF3N5fPrm5Obry71sn3KC/REQEREBERAREQEREBERAREQFQqWfnj7iq6EAgg9igsUX17SxxaV8QEREEWeJLDrnf8RpckxfmZluKVH1naHtBJkLeskGt9Q9rR09S0DsSuk4TTVef8XaPjLQxRMx6swxlrjAlaZI6r6V5ksTmg7BaQ4b1o9Pxm5axWPMrNwl8TGQYbTTTMxK91VPJVNc3UNqudSwvaGnehHK0de2unYN2Q2dUA5tb6zgdnNTxGx2klnwW8zD9tFrgbzfQpSdCtib6Df2gPf5jkn5Uqunp6ukmpKuGOennjdHLFI0Oa9jhotIPQggkaQUrTcKK62ymudtqYqqiqomzQTRnbZGOGw4H2IV0tfbDUVXADOYsZuU0knDG/1R+qK2VxIstU8kmne70icdkE6A2SezytgQQRsHYKD6iIgIiICIo340cTmYXHSWGw0JvuaXg+XaLTEdlxOx5sv6MTdEknW9HqAHOaHn/4kMerMb45ZdQVkbm+dc5qyAkk80MzzKw79fhcAfmCo9W/td4XbdlmKVNZneQ1tXntxlFTVXmJxcyF+tCFkR00xNHTXQ9OhaNARPL4KczFeWRZfj7qPfSV0czZNfyOUj3/ADkGv3C7GKzMeIdixmhbKZa+tjjc6M6Mce9ySb9OVgc7fyXq1YrdHaLJQ2qKepqI6Kmjp2TVMnmSyBjQ0Oe785x1sn1O1r5jmAY54ZcXZlrLLcstrJZmQXi6xMa11BSnfNJHF1IYCG7AJJ9XAALYGw3e2X6zUl4s1bDXW+rjEsFRC7bXtPqP9ncHoUEWVPhq4TVtbLV3az3G6SSyPlIqbrUcoc47Ogx7dKXIaeGKkZSsYPJZGIwx3xfCBrR336e6qogx6zYNhVmcHWfEMftzgdh1LbYYjvtvbWhZCi+taXOAHqgq0rNuLj2HZXC+MaGtDR6L6gIiICIiAiIgIiICIiAiIgIiICIiAoa8NlXUXbKOK94kqZZoP241FBAHP5mtbTtaPh6nQPPvp06qZVQoRRmEyUIgMUji8uh1yvd2J2O56IK6IiAiIgIiICIiAiIgIiICIiAiIg4TR87encdlaK+VGeLfxN7+qC3REQFED+Djb5lPFKoy99FV2jM/q9tLHTud51OKWFzA8ktAa8OII0XDp176UvrEeHedUeZV+UUtHRy0/wC168y2mV73hwmkj1zObrsNnWigwXgpmN6x/JJeDvEWp57/AEEfNZbnIeVt5oxsNcCe8rQNOGyeh7lpJmhYTxf4bWbiNYI6OukmobnRP8+1XSmJbPQzjRD2kEbGwNj116EAjEuFHE26Ul//ALGfFXybdmVPptHWfZpr1F2bLE7oOc66t6dewB21oSXmWNWbLsZrsdv9Gyst1bGY5Y3dx7OafRwOiCOoICibhBkt4wHL28Gs/rn1Lw0vxa9THQuNMP7w4n+/MGhrrsdP0S6cVhvGDh9auIuISWave+lrIXiottfF0lo6hv2JGnv36Eeo9jogMyRRTwL4g3S7VFdw/wA7jbR53YGhtW3WmV8PQMqovcOGidAaJ7DehKyAiKN+NHE5mFx0lhsNCb7ml4Pl2i0xHZcTsebL+jE3RJJ1vR6gBzmg40cTmYXHSWGw0JvuaXg+XaLTEdlxOx5sv6MTdEknW9HqAHObT4LcMX4pJWZVlVcL7nV4HNc7m8bEY6aghH5sbdAdNb0OgAADgtwxfiklZlWVVwvudXgc1zubxsRjpqCEfmxt0B01vQ6AAASYgIiIOE0cc0T4Zo2yRvaWvY4bDgehBHqFj2BYXjeBWertuN0rqGgnq5Kx8TpnOjjc/XMGBx0xo0NAaA0skXx7WvYWPaHNcNEEbBCCzsd2tl8tUF1s1fTXChnBMVRTyB7HgHR0R7EEfeFeqKMM4Z3fAuJ81ZhdwpqfBrsJJrnZJ+bVLU6+GSl10aHHW2nQAB7/AAhsroCuaePlbs9yuNPF+e78Aq6AiIgIiICIiAiIgIiICIiAiIgIiICIiC1uz5YrTWSQEiVkD3MIGzzBp1/Sov8AB5bqi1+G/EqKrilinbHUveyWMsc3nqpX6IPUfaUr1E0NPTyVFRKyGGJpfJI9wa1jQNkknoAB6qhabjb7tboLlaq6lr6Kobzw1NNK2WKRvu1zSQR9xQXSIiAiIgIiICIiAiIgIiICIiAiIgIiIKM0W9ub39QrdXypyxB/UdCgtVhHCLB6nCIMobVXOO4SX3I6u9FzITGI/P5PyetneuTv81nDmlp04aWD8EM6fxG4e02VvoI6EVFRPE2NkpkaWxyuYHAkDvy77IM3WLcTMAxniHjzrLktCJ42nnp6hh5Z6aT0fG/u09vkfUELKUQQFBlPEzgyz6HntHU5vhkOxFkdCwuraSMHQ+lRn7QA7vB+Zc49BL+F5ji2ZWxtyxa+0N2piASYJQXM36PYfiYfk4ArvSARojYKizMeA2DXq5OvdkjrMPyAdWXOwTGlfvv8TG/A4Egb6An3QWnicxenfip4kW25sseUYhE+tobjrpIxoJdTSa6uY/ZaB7u9iQdJOLPHbiHxDuEjq68z2y2EAR2y3yuigaP42juQ+u3E9+mh0WwviYxTjRYuC97huXEChyzGWGA1Qntwpq5jBMzl0WbDwHcuy49tn0WlSDKMP4hZtiVzZcceye6UMzSC4NqHOjk16PYSWvHycCt0/BHV2PLbRfc7r6ie555U1ZivFXVaL4oz1iZCB0ZEWjsPVhHZrQNBFtp+xuRVhybMJ2Nk+hCjp2SHfw+YXuLN/PQf/T7oN1kREBERARF9a0uOgNlB8UUcYbjxBwPJKfiJaXT5Bh8MAhvlgbG3zaaMEk1cBABcRv4gT2Ht1ZMEUIb1d1KqkAggjYKCzsdypbxZqK7UJkNLWwMqITJG6NxY9oc3bXAEHR7EK8WM8UMpq8NwqtySkx2uyA0XK+Wko3NEgi5vjkG+/K3btAEnXoNkXeDZVYs0xejyTG6+Ott1WzmZI3u0+rHDu1wPQg9kHdoiICIiAiIgIiICIiAiIgIiICIiAiIg6DiRFVT8OslgoYppquS01TII4mlz3vMLw0NA6kk60Auh8OtorLFwOxC1XGlqKSsgtkfnwVEZZJE8/EWuaeoIJ1orOKyogpKSarqZGxQQRukle7s1rRsk/cAVZ41fLTkljpr3Yq6Kvt1U0ugqIt8sgBLSRv5ghB2KIiAiIgIiICIiAiIgIiICIiAiIgIiICIiDjMwvicwHlcQQHa3o+6wzhFgMPDnh1bMOo7hJcYbeZuSeWMMc8STPl6gEjpz6+etru88vEmPYNf7/CITLbLZU1jPNBLNxxOeOYAg6+HrohWXCfIqzLeGmO5PcYYIKu6W+KqljhBDGl7QdN2SddfUoO5IIOiNIr1zQ4aIBVJ0DT9kkILdFUdC8em/uXAtcO4IQWF/tNBfbHXWW6U7aihroH09RE7s9jgQR+orz74x+GDP8QvFTNjVsqsnsTnl1PNRs8yoY30bJEPiLh220EHW+m9D0SUb8T8yvFn4l8PMRsckAlv9fO6tD4+c/RII+eTXX4Sd9D8ig0Kw7gJxaya5toqfCrtbWkgPqLrTvo4ox7kyAEj5NBPyW/XAHhbbOFGCMsNJMKyunk8+4VnJymeUgDoPRrQAAPvPclZ9VwmopJqcTSwmWNzPMiOns2NczT6Edwoh4GX7JrHk914SZvLW3G42qM1dpvMrXO+saBztNL3dfyjSeU79tfm7ITGi+gE9gSubYZD6a+9BTQAk6HVXDacfnHf3Kq1rW/ZACChHAT1d0Crsa1o00aX1EBERAWKYPw9xjC7xfrljlJLQm+VDKiqpmSn6OyRoILo4/ssJ2Sdd/uAAytEFnabrbLtDNNa7hS10cE76eV1PK2QRysOnsdo9HA9weoV4og/sX3nEOLVNl3DOopKGz3ioDcnskxLaZ7ev75hA+zKPYdCT7EqX0BERAREQEREBERAREQEREBERAREQWt4hfUWetp42875ad7Gt2OpLSAOqwHww2K9Y1wJxmxZFbZLbdKOKaOeme9rnM/LyFp20kdWlru/qpIXQ4Jl9izWxvvGPVMk9IyplpnmSF0bmyRu5XNLXAHoUHfIhIHcgLiZGfpBByRcPNj/SC+h7D2cP1oOSIiAiIgIiICIiAiIgIiICIiAiIg6vLrHS5Nil2xyulnhpLrRTUU74C0SNjlYWOLS4EA6cdbBX3ErJS41itpxyhkmkpLVQw0UD5iDI5kTAxpcQAC7TRvQA36BYl4istueDcF8iyqzTQw3CgiiNO+WMPaHPmYzqD33za/FZrZZJ5rNRTVLw+d9PG6VwbyguLRs69Ovogu0REBERB8LWnu0fqXQV2G4/W5vbsyqKNz7zbqWSlppvNcGsjk+0OTfLs++trIFE3hvyS85fHm+RXC4zVNskyeqprPC87ENLFytaW+o5iSdehCCV/LZ+iP1L6GMB2GtB+5fUQYzgWdY7mouzbFUyvltFdJQVsE8RilhlYddWO6gHR0SOuj6grJlHdVwz+j8baXiTj11+qn1NK+lv9GIudlyaABE7uOR7SB8XXoANd9yIgIiICIiAiIgIiICjHjfVcTLBJb8wwUw3e3Wtj/rfHXQjzK2IkEvik7iRoB037+jvsmTkQdJgWUWvNMPtuUWbz/oNwh82ITxGN7epBaQfUEEdNg66Ejqu7XUZjd5cdxK5XmktFXdpKCmdNHQ0YHmzco+w0e/6/kCeisOGed43xDxaHIsYrhU0r/hljcOWWnk0CY5G/muG/uPcEggkMmREQEREBERAREQEREBERAXwkAbJ0FTlmDejepVu5xcdk7QV3zj80b+ZUZcAcSveFWLJLVdmwsinyWurLf5UvOHUshaWE/onfN8Pp+KkVYvjWb2q/ZvlGIUtPWw3HGnUwrDOxojkE8Zex0ZDiSNDrsDuO6DKEREBFgPF/i5h/C36r/bXLWx/WfnfRvo9OZd+Vyc2+o1/CNWAfuueEH/Or1/o8/7UE/AkdiQubZnjv1+9a/fuueEH/Or1/o8/7VJXCXiXjHE6zVd2xaSrfTUlR9HlNRB5Z5+UO6DZ2NOCDPWTMd3+E/NVFYrnHI5nzHsgu0XGN7XjY/UuSAiIgIiICIiAiIgIiIOiz7ErJnGJVuLZHTvqbXW+X58bJXRl3JI2RvxNII+JjSu9UV+JbJr3jWN4x9QXOS3Vd0yqgtrpI42vc+OQv52AOBHUN/oUqICIiAiL4SB3ICD6uvx6x2fHrY22WK2UlsoWPe9tPSxCONrnuLnENHQbJJUU+L671cPCiDH7TUy09yyS9UNopZYXFsjXPlDyWkdd6jI/FS3TvZT08cEYcWxsDGlztnQGup9Sgu0Vv9Id+iE+kO9gguFDd1zbJcB46stOZVwq8KyxzI7FXGJrBbaxo0aWQgDYf3DnbO9eziJcFQfVoVKshoa4RCuooKgQytmiEsbX8kjfsvbvs4b6EdQgvUXRw5djcuYyYey705v0VIKx9FsiQQl3Lz+3fXTe+oXeICIiAiIgIiICIiAsRx/h1jFg4gXfNbPTz0VxvEDYq6GGYtppXNdvzTEOnmHsXff6ucTlyILelrqKrnqYKWsp55aSQRVDI5Q50Ly0O5XgH4TyuB0fQg+quFDmdcMsjtHEEcSOE1bS0N4rJY2X20VTi2iusfMNyOIB8uRoJPMBs9fUkPmNAREQEREBERAREQFbzTb21p6epX2ok7safvVBAREQFgNhwe4WzjlkmdCspjbr1bKamdTjm80TQ9A49OXXL077WfLDOI+dswy84lRVFsdU02Q3dlqNV5/IKaR4JjJHKebmII1sdkGZoiINOv2Sv/zA/wDeX/0q06W4v7JX/wCYH/vL/wClWnSAt6f2OT+1hkf89f8AcxrRZb0/scn9rDI/56/7mNBtCiIg+tJadg6KuYZA8aPRytUaSDsHqgvkXCJ4e3fqO65oCIiAiIgIiICIiDrMgx+zX/6v+ubfDW/VtdFcKPzN/kaiPfJINeo2f1rs1EXGi8XWl4zcIrJb7tXUUFyuVa+rgp5XMbUxwwNdyyAHTmgnsd99+ilxzg0bJ0EH1cHytb67PsFRkmc7o3oFSQVHzPPboFTJJ7lEQW0s1BLWNoJZaZ9S1onEDnNLwAej+XvrY6H3CuVDGFxTV/izza73CKSm+g2SlttqbUMLHVMPM2SeSIEfExsjmtLhsbeOvVTOgIiICIiCM+NvDu45RLZ8pxCsgteaWCoElvq5diOaInUkEpAJMbgT7+o/OKkqmfO2Fnmlol5RzhhPLzeut+i5KMeOGbZJw9qLHlEdHFW4bFOYMhZHCXVNOx+hHO071yNPca31A9egSqyf0ePxCrNc1w207XV0FXS19DBXUU8dRS1EbZYZY3czXscNhwPqCCrhpLTsHRQXqKjFMD0f0PuqyAiIgIiICIiAsB42O4i0dho73w5kpqmrtdR9IrbRNECbpBykOia/uxw7jXUnXto58iDFOFGe2biLiEWRWZlTC3zHU9VTVEZZLSzs1zxPB9RsdR06rK1bVT2UFvqaiGkkl8pj5jDTsBfK7q4ho6Aucfc9SVjHCXiLYOJGNG72V0sE8Ehgr7fUt5KiimHeORvoeh0ex/AgBmCIiAiIgLhM/kZ07nsuatJnc7yfQdAg4IiICLBeI/F7h5w8ulNbMwyD6sq6mHz4Y/oU83MzmLd7jY4DqD0J2sW/dP8AA7/Df/qqt/3SCY1hvF/A4uIOL09nfcXW2aluFPX01U2ESGKSJ2xppI7guG99N+qw790/wO/w3/6qrf8AdKR8Gy3H82xyHIsYuH0+2TueyOfyZIuYtcWu+F7WuGiD6IO8RRzwWze8ZPX5lY8lipIbzjl7ko3Npmlsb6Zw3BIAST8QDu/t+AkZBBXix4K33i9+1r6kuttt/wBU/SvN+mc/x+b5OuXlae3lne/cLWGDwxZZNxXqOHjL9ZjW09nbdZKr8r5IY6XyxH9nfN69taHdeiahfCA2r8XfEKpBaTb7HbqQ/FsgyDzNfLt2Qa/fuK87/wAK8b//ALv/AALYrwscKLxwmxC62a83Ggr5qyv+lMfSc/K1vltbo8wB3tpUvogIiICIiDlE8sdv9avAQRsdlYq4pXbBYfTsgrIiICIiAiIgIi4yPDG7Pf0CCzr7da6i4UVyq7dSVFdQF5oqiSFrpacvbyvMbiNs5m9DrWx0K+vcXnZKh2+1dTUeMSwW9tZMIafEZ6uSBpPI4uqDGC4dj669tfNTCgIiIMF4jcWsF4eXWjt2Y3Se1vrYnS00rqKaWKQNOnAOja4bGxsHsCD6hY/fPEZwkoMarrxSZbRXKSli546KAuE87uzWNa4A7J9ew7nor7xJcM4OJ/DKss8bGC70oNVapXdOWdoPwE+jXj4T7bB9AvMORjo5HMeNOaSCPmg2O4EcXL7lni6tWS5FUaF2EtuZAxx8umiew+VEwe3OGb9yST1K36Xj/aLhW2m60t0ttTJS1tJM2enmjOnRyNO2uHzBAW1nArxU8Qbhd6XHL7i0uZTS9Gy2yER1mvVxYB5bgPuZruSg3URU6aR0tPHK+GSB72BzopC0uYSPsnlJGx26Ej2JVRAREQFb3OhpLnbam3XCnjqaOqidDPDINtkY4EOaR6ggkK4RB09L+13FLZa7LDJQWij2yit1MZGxhztfDGwE/E7QPQbJ6ruFhvGPALdxGwiosNZI6lq2uFRbq5n8JR1Ld8krSOvQnR1rYJ7HqLjhPLmMmB29mfUcFNkMLXQ1RhmbI2bkJaJdt6AvADtDtv07AMqVWGUt6O6j+pUkQXwII2OoRWsEnIdH7JV0OvZAREQEREBERAWGQcNccpeKjuI1vFXQXeeldTVsVNNyU9bvXK+ZgHxvbrofuJ2QNZmiCm2op3VT6Vs8TqiNjXviDxzta4kNcR3AJa7R9dH2VRRDxi4cX5+TQcTuGNTHRZtRRiKoppX8tNeKcf3ibZADtAcrunYbI01zZYoH1MlDBJWwxwVTommaKOTnax5HxNDtDmAOxvQ37BBWREQcJncsZP4BWir1R7N/FUEBERBot+yNf2z8d/mX/v5Fq8tof2Rr+2fjv8y/9/ItXkBejXga/ucrL/7TV/69y85V6NeBr+5ysv8A7TV/69yCULZh9kt2b3fMaSKVl1vEEMFY7zTySNiGmHl7cwHTffSyBYlxitN/vXDK+0GLXSrtd8NMZKGopZCyTzWHmDAR2D+XkPycqPBTNY8/4Z2fJgAyqmh8uui1ryqlh5ZW69PiBI+RCDM1DHA0mr45cZ7o7mJkudBSbcR2gp3NHT8f6lM6hfwugVVdxRvQDdVec18UbhGW80cYYGnZ6n7R+47QTQiIgIi6/JboyyY5c71LE6aO30ktU6Np0XiNhcQD7nSDsEWqP7tnGv8AAi7/AOdx/wCxP3bONf4EXf8AzuP/AGINrlyidyvBWt/DjxY2DNM5tGK02JXOkmudQIGTSVLHNYSO5A6nstjkF8i4xHmjB+SIOSIiAiIg+OIa0k9grR7i9xJXOofzO5R2CpIKDfojq1xHkGqbGA4jXmBm96PrrarqHcFpqiXxWcRa+SKdsMNpttNE90Rax+2c7gHEacR07b7/ACUxICIiCIfFvxAGA8G7jLSz+XdrsDb6DlcA5rng88g/ks5jv3LfdeclJYb1VWauvNNaa6W2W8tbWVjIHGGnLnBrQ9+tNJJAAJ2dr0X8SGSSwiz4Vi1BSV2eX5zobXJJC17rdAf4aq2QSwAN6H3bvryaWI8esKtGB+F2iwO0tLqequtDTVEr38rqmV87XvkeR1JcWdvQAa+yEGjGJ21t5ym02hxcG11bDTEtOiA94b0J7d1vtj/h+yHh46ebhJxHqLUJ3B01JdrbBVMn16Oka1rgPuCxPxW8CqagNRxdwNkdBc7U9twuFC1uopfLPOZ2AfZeNczh2IBPQ75tnMYu9LkGN2y+0R3TXGkiqouu/hkaHD+goIk/bhx8xZusl4bWbLaZp0avHLgYpNe/kygucfkNK6tniN4ffSWUOUx37DK93T6NfbZJAdjv8TQ5uvmSFMStrnb6C50b6O5UVNW0z/tw1ETZGO+9rgQUFnjmR4/kdJ9Kx++W27Qa2ZKOqZMB9/KTr8V2qijIvDzwqutT9NpMfdj9eCSyrslQ+jezf6LWHkH+SurHDPi3jLd4Rxiq7hA37NDlFI2rBHpudvxj8AgmtFCn9kDjZjLNZfwkhvsDftVuL1/mb+6nfuQ/rCv7H4i+GNbVfQbvca/Frh60l+oX0r2/e7qwdfdyCXFjHFO85Fj+BXS94rZobzdKKLzmUcj3N8xjTt/LyjbncuyGjuf1HubLeLTeqMVlmulDcqY9pqSobKw/4zSQqzK2jfWPo2VcDqmP7cIkBe3oD1b3HQg/ig6fh3l9mzrDrflFhn82irY+YNP24njo6N49HNOwf6OmlkCwb6BgnCHHskyZrW2e21NQ641/5RxYZSANRx70C46Aa0DZIHtqDvDTxyvHErxD36K4udS2mqtTvqqg5ttgEUjSN+73Nc9zj8tdgNBtUq1NJr4D29FRRBfIuED+dnXuO65oCIiAiIgIiICxLi1DnUmGTS8Oq2hp7/Tysnjiq4Q+OrY07dBsnTC8dOb07bbvmGWogwbgzxIt3EbHZqqOkntd5t0v0W8WqpaRNRVA7tO9badHR9dHsQQM5VOGnp4ZJpIYIo3zv55XMYAZHaDeZxHc6AGz6Aeyw/hlxLx7PJrtRW5tXQXWz1T6autlfH5VTDpxDXlmz8LgNgj7u6DKqg7lPyCprnN/CuXBAREQay+LrgZmvFLNLRd8ZktTaakt30aT6XUOjdz+Y93QBp6acFC37jziz/6fG/8APn/7tegaIPPz9x5xZ/8AT43/AJ8//dqX+GPCbjJwhxCmueL3eju1Y18j7tjE05dS1TeY8rqeQgeXLyAA7ABIGyQOU7SIgwLhZxWxrPfOoKf6Racgo9trrJcGeVV07h3+E/ab1+0Pcb0eiyqWssNhfR2+Sot1sdXTujpIC5kRnlO3EMb05nHqTrqsR4rcJcaz50NxlNRZsjpOtDfLc7yquBw7bcPtt/in3OiN7WrPixv/ABax/HsexrNo6Sokt9x+l23JqFjR9MMbfhLgRuKZuwTy6Dt+utkN51C/g3/LcJKu6FwcblfrhVlweXk7mLdk+p+H/tWoH7p/jj/hv/1VRf7pdNhPHbiphmM0uN41lP0C1UnP5EH1fTScvO9z3fE+MuO3OJ6k9/ZB6gItfPBNxGzLiJjORVmZXj6zno6yKKB30aGHkaWEkaja0Hr7rYJxDWlziAANkn0QfVjfFT+1hlX8y1n+pesazHjnwxxmc0dRk0FyuJdyMoLU01k7n/o6j2Af5RCwDO+IPFnMMGv37WeGDsesLrZUGouOSz+VMYfLdzclO34g7l3okkbI/EPP1ERBJHhf/ugcM/nNn9RXqEvL3wv/AN0Dhn85s/qK9QkFzTH8no+6KlEdNPX1RBdIiIC4yu5GE+vouStql238vsgpIiILK33a1XGqrKW33Oiq6ihk8qrigna99O/W+WQAksdr0Oir1RRwHsl2t2YcT7ldaCspBcsmkkpHTwljZoGtAa9mwOZp6jfrpSugLoOIeW2jB8NuOUXyYR0dDEXluwHSu7Njbvu5x0B9679a/wBFvjrxd+nuLpOHOF1eqdhH5O7XNu9yezo499PfY7h50Hf+HvELtLU3DivnEHLluTNDo6dwP/BlF0MVO0HqDoNLvXsD1Dt0vFPuqj4bWdvN+/s6tok0B/Bt8wu7+o6H8CpmUL8etVXGLg3a9gmS91VWGnZ2IIQ7evlzd/RBL13oKa6WmstdazzKWsgfBM39Jj2lrh+olRP4RK+oHC2fEri/muOJXWqs1RvoSI5C5jte3K4NH8lTEoUxLeJ+LLK7F1ZQ5faILzTju0VEJ8qRo+ZG3n8PkEE1oiICIiArC+WWz3yjNFe7VQXOlPeGsp2TMP8AiuBCv0QRDefDnwzqap1dY6O54ncT2q7DcJKZ49Rpuywfg1QH4o/D5ldusFRnjM0rcsFsha2obXU4FXHTtJ+MytP5Xl3s7AIGzvotuM1znEMMo/pWU5FbrUzW2tnmAkf/ACWD4ndj2BWvHHXxMxP4e3CPDMXvMlDc430MF9r4Po9MXPYdmFrhuUhvX01sE+xDSypvF3qaH6DU3SumpOYP8iSoe6PmG9HlJ1vqevzK7zhLm9fw64gWzL7bTxVM9CX7glcWslY9jmOaSPk469iAfRYqu8wHHKnL81tGMUk8dPPc6tlMyWQEtYXHXMQOuh3Qb68LvFRw3y8w0d3mlxa5yENENceaBzv4swHLr5vDFPAII2DsFRBwV8POCcNmQ1zaUXu/NAJuNbGCWO94mdRH9/V3zUwIOcL+R+/Q9CrtWKu4Hc0Y9x0Qc0REBERAREQEREBYRkXDKw3biTZeIEMtXa77bCWTT0Twz6dAWkeTONfG0HR99DXtrN0QWFRNC2t+jmWMTPaXtjLhzOaNAkDvoFzdn5j3X1Rtx04b3DJK2hzHDbkbPnFkYfq6qLvyVRHsl1NMOxY7r92/YlZlhdZe6/FLbW5Jao7TeJqdrqyjjmErYZPUBw6H37nW9bOtkO3REQEREBERAWqP7JB/yMxP+cJv9WFtHeLnbrPbJ7ndq6moaKnYXzVFRIGRxt9y49AtHPGhxPo+Itms37XLPcDjlDXTMjvVRGY4q2bl0WwtPUtAGy4+4Gh6hrGiIg2f8F0/FV+N5BbeHNDj0UE1bE6sul2kcRAeQ6ayJnVztddkEdgp+PAi4ZOWzcVeJGRZUD1fbqV4oKA/IxR9TrtvYKj79je/5GZb/OEP+rK2uQY3hWB4bhlP5GLY3bbUNcrpIIB5rx/GkPxO/Eldnk9rbe8aullfMYG3CjmpTIG8xYJGFvNr11vel2KINR/3Edq/6Qa3/Rjf94n7iO1f9INb/oxv+8W3CINaeGnhLt2F55Z8qizWrrX2ypE7YHW9rBJr0Luc6/UtlkRBVhaS0/eiqUw/JfeUQVUREAnQ2rInZJPqrqc6id+pWiAiIg6jFMlseU2+W4WCvbXUsNRJTPkaxzQJWHTm/EBvR9ey7dRh4ZcXvmJ8NJLdkdG+juc11rKmaJz2O1zzHlILCRotAPf1WQ8Xc7t3DzB6vIq5hnmBENDRtPx1dS7YjiaB1JJHXQOgCfRBg/H/ACa7Xm7UPBzCKry8hv8AGX3OrYf/AMLt3aSUkdnOHwtHfr6FzSpNwjGbTh2J27GbHT+Rb7fCIom+rvUucfVziS4n1JKwfw+4HcsdtVdlmYPbVZtksgq7rMW9adpHwUzfZrB00Om+nUNClFAUMcQSazxY8MKY82rfbLrVjqANyRCL8e39XzUzqGJ/3941KZgALLZgrpC7yydSSVnLy77D4Tsa+Y+4JnUJ+JgjG8l4dcTGkRssd9FFXyb1y0dW3y5HH3A0P8r07rBvE1xh4tcIc9jFFHZrljdzj823urKFx8twGpIXOY9pJaSHA+zm99FQTxk8SuYcSsJOKV1stdso5pWyVZpA8mcMIc1vxk8oDgHdOpIHpvYbp8HOLtk4nX3K6OwMJorFUQwxVJPWqa8P/KAejSWO16kaJ1vSkheefgcz+yYRxMuMOSXWC2Wy62/yvPncWxidkjSzmPZo5TJ1PTr3W2mUeIHh3a636rslbVZheXj8lb8egNY957D42/B30PtE/JBLCsb5eLTY7e+4Xq50dto4xt89VO2Jg/FxA9FDxr/EFnfS3W2zcM7TIek9cRXXEtPqIx8DT36O0e3VX1j8PeIG4MvGcXC757dwebz73UmSFp9mQj4Q3QHwu5ggo3HxB2S41kls4a43fc/uLDyuNup3R0kZ/jzvGmjqOuiOvdW/7W+PecHmyTLbbw+tbx1oLCz6RWkezp3HTD82E/cpnttDRW2ijobdR09HSxDUcFPEI42D2DWgAK4QRlhXAnhtjNX9Y/Upvl2J2+43qQ1k7nfpfH8LT82tBXYcUeFmE59V0NxzWCpraS0xSeTTGrfDTs3ove4MIJOmjudab277z1Rn4n8ilxvgfkU9JzGvroRbaNjPtOlqHeV8PzAc534II58LPDbC8j4Y5Bfbnidufb8outU+jp5acbgoWP5Io2OPxN0WuPMDvej36rB8c8PF44deKDEa23Ce54pNWyywVhHx0zmQyPEc2ux+Ho7oHfI9FtVw3x2LEsAsWMxAattBDTuIO+Z7WgPd+Ltn8VkCAiIgKtSu04t91RXKI6kafmgvEREBERAREQEREBERBQqh0DvwWPZxDkdRiNzhxGso6K/OgP0Gari8yJsnpzD59RvrokHR1o5NI3mjIVmgjngrxLGa09bZb7bnWPMrKRFeLVJ05HeksZ/Oid3B2dbHUghzpGVGOkpY6yWsZTQtqZmNZLM1gD3tbvlBd3IHM7QPbZ91pHxn8TPE/GuK2S4/Zqq3xW+3V8lPAyegaXhrTrqT1O9bB9RpBvEi87/3W3GL/n9o/wBHM/2p+624xf8AP7R/o5n+1B6IKM+KHGOxYhcmY3aaOqynMJx+97HbQXy/fK4AiJuup31111rqsBx7KeLfGixW+nxyGXCcakpo23DIp49Vdc/lHmfRI/zGl3Np/wCIIILVLPC/hpiXDu2vpset/wC+putXcKg+ZV1bidl0khGzs9dDQ32CCP7RwmyXPbnBkfHC5RVzIn+bRYtQvLbfSH080jrM8fMkdxtwOlK15xDFLzb6W3XjGLJcaKj/AOLU9XQRSxwdNfA1zSG9OnRd2iDDv7FPC/8A6N8O/wBB03/gXVZfwR4ZX/GLjZo8Kxy1S1cDo462itUEU9O8/ZexzWg7B0db69j0JUjIghLwo1tFbrNd+H9wslqsuX43O2nuraOljg+nx6/I1Xwgc/MzW3HrvROuYKbVCniGstyxm9WzjXilM6W5WBhhvlLENGvthO5Afd0euYH07/mgKWsavVtyLH6C+2epbU2+vgbPTyt/Oa4bG/YjsR3B2Cg7FERAREQERc4W80gHp3KC5jHKwD5IuSICIiCjVH4APmrdV6v838VQQFb3GrgoLfU19U4tgponTSuA3prQSTod+gKwDO+N3DvBsqdjeXXWptNb5TJo3SUM0kcrHdnNdG13TYc3rrRafxwrjB4iuGcHCy+Px/Jaa8XKrpJKSkpacOD/ADJWOaHuDgNNb9ok+2u5GwljDs4xnKrDFerTc4jTSUja0if8k+OBzntEj2u6taTG/RPT4Som4ewzcaeKR4l3OF4wvHJn0+K00g0KuoB1JWuB7gEab7EDsWnepXhxp8kzDNpOHlFepKK35HTsprrNzbkFHB+ULI9noS1pZrRGnHY1tektitVvsdmo7PaqWOkoKKFsFPCwdGMaNAIL1Fa3S4UFron1tzrqahpY/tzVErY2N+9ziAFFd88RHD+Cuda8YN1zW7A6FHYKJ9Ts+nx9GEfMEoJeUKYlLFJ4t+INdNMxkVtx+gpnvfLoRh/5U/IDpv8Ap9V8+uvEJmOxZ8Zx/h9b3npU3ao+mVnL7tjYOVp/ivH4+8ccKOD9szjipxHPEe93TKprZcKSmml8w0cNbI2LqZI4z+boNaAdAfeg7fxkcReEt64W3TGJL9RXe/xvZLb4qB3nOhnDtcxkaCxoDS4OBOyCQOvbRFb0cdfDpeMyvdrsmDW3GMTxW2Q875A3kkqp3/acWxsJfytDQC8g9Xe6jbi/4Vabh/wgu2XftuqLpcrd5UhhbRthhcx0jWOHV5OwHc299eXWuvQNabVb6663Gnttso6itral4jgp4Iy+SRx7BrR1JW6/hS4A8QMNrosjyHKq3H45C18lkoZWyeeB2E5O2D16NBOj9ppUA+GPhzS5XxtGI5bQVkUMNHPLPG1zopYnNaOR7XDqNOc1wPUHp3BW2Hl8Y+EQHkmfihh0Q+w74bxRsA9D2nA/Fx/ihBPCLDuGnEzDuIVG+XG7qySph/4zQTjyqqmIOiJIj1HXpvqPmsxQEREBQnxs/wDKjjhwzwEAPpqaqkyO4sPbkpwRBseoL+Zv+1TYoT4P6yrxBcSs5dp9Na3w4zb3d9CIB9QN/wDrOU9Pf8SE2IiICIiAiIgvh1G0XyP+Db9wX1AREQEREBERAREQFazs5XkjsVdLjIznYR6+iCzXl74oP7oHNP5zf/UF6hEEHR7heXvig/ugc0/nN/8AUEEboiIPWbhV04X4qB/+S0f+pYskWN8K/wC1hiv8y0f+pYskQEREBERBwmjjmifDNG2SN7S17HDYcD0II9QoI4XyycIuLVVwor5HNxa/vkuGJTPPwwvJLpqPZ9idtH3er1Zfsgf9oaL+eqf/AOCVefiD2KReOq2r/Y3/APl1lX82Rf61Bu+iIgK5pmcrOY9yqMLOd3yHdXaAiIgIiIKFV3aqCuKodGlW6CDPGbwwizvhhPeaKONt8x+N9XTvJDfMgA3LESfTlHMPm0D1K86F6AePDPnY5wxhxC3yH6zyWQwuDPtNpWEGT8XEsZr1Dney0oj4c56+wVN/GHX5tqpY/Nmq30MjY2s9XbI6gepG9DqUHU4tf7xi9/pb9j9wmt9zpHF0FRFrmYS0tPfoQQSCD0IJWx/D7jzx34lXKhxO0PMbttbWXO0WaOapawnXO/zXthZ69fg+XXS174eY7cMrzW149a6WCrrKybljgmkdGyTQLi1zm9WggHqO3yW9fD7ijivDWhpsRzLh5WcL+VwYyUQme31D9a5hUsB5nHXUu3rptx7oO+tnh3w6atZcs3ud/wA6uTSXCW9V73xNPs2JpDQ35HY/oUqWKy2ew0DaCx2qhtdI3tBR07YWD/FaAFzs11tl5t8dxs9xpLjRyjcdRSzNljcPk5pIKvEBQv4YeWru/FW8Asd9Izmup2uB3zMiDA079tO/rU0KGfB4Hy8Lrlcn8xNxyK41W3a2dy8u9Dt9lBMywXxB283TgbmtI1pc76lqZWgb2XRxl4HT5tCzpdfklALrjtytZAIrKSWn0f47C31+9BhPByy4/d8UwviB9WwC/PxmmpnVjW8r3MdFGXMd25tOZ0J7ddd1Iyibwf130/w54o9x/KQQzUz2nu0xzyMAP4AH8VLKCN+JnBrEs1rmXtgqsfyaA81Pe7TJ5FSxw3rmI6PHX1666AhYh+3vibwp1BxQtLsrxqM8rcos0GpYWdAHVNOO3zc3p/KKndfCARojYKDp8OyrHcvssd4xm8Ul1oZO0sD98p/Rc3u138VwBHsu5UQZnwOt7rxLlXDa7z4JlDvidNQN/elUep1NB9lwJ7kD5kOXXW3jJf8ACq+Kxcb8d+pHPd5dPkdva6a2VR9ObW3ROOux+8hoQStnl/hxbCb1klRymO2UM1UWn84sYSG/iQB+KwfwqWGax8DbC+t5jcLsx91rHuADnyVDvMBPz5Cwfgui8Ul2gyLh7juH2Kviqhm96paCOemlD2mmDw+SRrhsFo5W7I30JU1UlPDS0sVLTRtighYI42NGg1oGgB8gAgqoiICIiAiIgvI/4Nv3Bcl8YNNA+S+oCIiAiIgIiICIiAiIgpTx8w5m9/61GGRcDeFWQ3yrvd5w+krLhWSGWondPMC9x9SA8D9SlRUZoubbm9/b3QRF+504L/4CUX+cT/8AjT9zpwX/AMBKL/OJ/wDxqVeyILe20VLbrdTW6iiENLSwshhjBJDGNAa0devQAK4REBERAREQa8fsgf8AaGi/nqn/APglXn4vRTxx2K95DwWit9gs9xu9YLvBIaehpnzycoZLt3KwE6Gx1+YWjn9inih/0b5j/oOp/wDAgw5bV/sb/wDy6yr+bIv9aoF/sU8UP+jfMf8AQdT/AOBbLeAPDstxrMslnyPFr5ZoZrfGyKSvt8tO17hJshpe0AnXoEG4a+saXu0F9YxzzoBXUbAxuh+JQI2hjeULkiICIiAiIgp1I3Fv2KtVevHMwj3CskFq+30D7iy4voaZ1axnltqDE0ytbsnlDtbA2T0+ZVy9rXsLHtDmuGiCNghfUQQLSeHa0Y94g7DxGxPyKO1RSTvr7Xy6ZE91PI1r4fZvO5u2enp06CcrjQ0VxopaK4UlPWUso5ZIZ4xIx49i07BCuEQQ1eOAFmobjJeuGWQXXh/d3nbvq5/mUUp//Up3nlI7dAQPkrMZ5xgwDbOIeENyu0R973jA5pWt39qSmOj26kt5WjXqpxRBgOLcXcDy2wVtxxnI6KqnpaaSaSkl3HUR8jS480LtP0NdwNdDorofBxRCi8N2Jx8unSR1EzjycpPPUSuG/foQN+wC4eI/htgV44fZLlV0x+lZeLba6msguFMfo9R5kcT3N29uufZGtO2OqwPg/Z+OGF8LcYumKV9nzKxVdrp6wWKuApammbJGJCyGcdHfa/P7b0B6oNm0UOWbxDYkJpLXmNtu+F36KNz/AKuu8HlCYj0imOmO300XFu9/irG2eKzg1Vu5aq93C2u7EVVtmJB9j5Yd9yCv4SZI7fgmV2eeZsbbFllzo3l7tCNrXh5PU6A+In27qX7PcqC8Wqlulrqoquhq4mywTxO2yRjhsOB9ivOzi5xaqIbln+MYTdGSWDJr464T1sPM100b2N3G3YBDS7fN76A7E72y8EV9F68PNmhLuaW1zz0Mh3+i8vaP8iRiCbEREBW9yoaK5UE1BcaSCspJ28ksE8YfHI32c09CFcL49zWML3uDWtGySdABBqDceGtdb/E3VUvBg0dufidtbc3UNylfNRCrqNtdExvUxeZEQd77tGtDREwYdxxt/wBcxYrxLtE+CZO7oyOvP7zq9dOaGf7JBPoT7AFxVp4UmPvduzDiTOHF+W36eamc4Hf0OAmKEde+tPClHMcVx3L7LJZ8ms9JdaGTvFOzfKf0mu7td/GaQR7oOyrKykoqb6TWVUFNBsDzJZAxuydDqenUqpDLFNE2WGRkkbvsuY4EH8QtdMy4Q5ziuK3KxYTXuzLCq2F0VRit4qNTQMOz+9Kg/ZLTotDugIB+IrRmtZfMXvdVQP8ArSz1sEnJLFIH08zO+g5vQg6P9KDcTxm8fpbIZuHmE1/JczoXauhd1p29/IYf0z05iPsj4e5PLs3h14ZkOI2e/wAQaGXKhgq2hp6ASMD9D9a8jJZHyyOlle573kuc5x2XE9yT6lbVeHvxWUWJYrasOzCxVElDb4hTw3GifzvDATrnjcRsAEDbXdh2QbvL6wbcB7lY5gGcYrndmN2xO9U10pWkNkMew+JxG+V7Hac069CAsmphuT7uqC6REQEREBERAREQEREBERAREQcJI2v+R91bPjczuOnurxEFiiunwsd26H5Kk6B47aKCki+ljh3af1L4gIiAE9ggIuTY3ns0/iqjac/nHX3IKKqxwud1d0CrsjazsPxXJB8a0NGmjQX1EQEREBERAREQFaTN5ZCPxCu1SqW7bzD0QWyIiAiIgIiII18UlUaPw+ZnMDrmtzou2/tuDP8A5ll3D2lNDgOPUTmlpp7XTREHuOWJo/7FG3jQkcfD3eaCNzWyXCqoqVhJ11NTG78ejT09tqYoY2QxMiiaGsY0Na0egHYIOuyfHbDk1sfbMhtFDdaN/eGqhbI0H3G+x+Y6rQXxlcHbTwzye23LGW+RY7wx4jpHzF7qeaPXOG8xLiwhzSCSdHYJ7L0OXnF4vMyrOIXGGsdbIqips1okFpoHRxuLJJQTzka6Fzn8wGu7WtQQks24X8Vs64cTudil9mpaaSTzJqOQCSnldoDZY7oCQAOYaOgOvQKwznh9mmEMo35XjldaWVreandOwcr/AHGwSA7+KdH5LY/9j/4e4vkFLfMrv1lguVZb6yKChdUt544Ty8znBp+Eu6t6kEjprW0E/eHTiNmnEPHTccqwSWwR8gMFc2Tlhq/myJ/5Ro+fxA+hUrIiAo38TORvxjgdk1dTucKyopDQ0oaCXulnPlDlA9Rzl34KSFCfHf8A8qOL3DLh4346f6wff7izfTyqVp8sO9w55c3Xvr7wEj8LMbbiHDjH8ZaAHW63xQykfnSBo8x3T3cXH8VkqIgLzG8UueM4g8ZrtdqV7H26j1b6BzR9uGIu+PfrzOL3D5OA9F6Bcbswx3D+H9wqciqqljK2J9HT01I7VVVSPaWiOEfpHff07qH/AA4eHKy2tz8yzjG6NtfV/FQWKcGoit0R7eZ5m/Mm11O+jT2APRoaGLarwneHDHs3xWkzzL62eqoJppG09rh3G1/lvLCZJB8RBIPRuu32vRSl4nfDbbc2o5MjwmlprbksMbQ6mYGxQVrGjQaQOjJANAO7HQB9CJA8K9lrMf4BYvaLhSTUdZBFOZ4Jmlr43uqJHkEHqDt3ZBINjtFrsdqgtVmt9Lb6GnbyxU9NEI42D5AdF2tK3TC73VBoLiAPVXjQGtAHog+oiICIiAiIgIiICIiAiIgIiICIiAiIgIiIPmh7BfURAREQEREBERAREQEREBERAQjY0URBZyNLHkfqXFXU0fO3p3HZWqAiIgIiIIZ8WoNRi2G2oB7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+ } + }, + "cell_type": "markdown", + "id": "43bc2905", + "metadata": {}, + "source": [ + "![Systems-of-interacting-networks-or-networks-of-networks-are-a-natural-representation-of_W640.jpg](attachment:Systems-of-interacting-networks-or-networks-of-networks-are-a-natural-representation-of_W640.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "9a3e271c", + "metadata": {}, + "source": [ + "In the following, we introduce some local and global measures for interacting networks. \n", + "The indices $i,j,k,l$ always denote subnetworks, while $v,w,p,q$ designate single vertices, and we always assume $v\\in V_i$. The formulae explicitly account\n", + "for the general case $i \\ne j$, but can be nevertheless easily modified to suit the special case $i = j$. Furthermore, the term *cross* refers to the interaction between subnetworks $G_i,G_j$, whereas *internal* refers to the structure within a single subnetwork." + ] + }, + { + "cell_type": "markdown", + "id": "cc54fec3", + "metadata": {}, + "source": [ + "### Local Measures\n", + "\n", + "The **cross-degree centrality** $k_v^{ij}$ gives the number of neighbours of the vertex $v$ within subnetwork $G_j$,\n", + "$$k_v^{ij}=\\sum_{q\\in V_j}A_{vq}\\,,\\quad v \\in V_i.$$\n", + "\n", + "The **cross-closeness centrality** $c^{ij}_v$ measures the topological closeness of $v$ to subnetwork $G_j$ along shortest paths,\n", + "$$ c^{ij}_v = \\frac{N_j}{\\sum_{q\\in V_j}d_{vq}}\\,,$$\n", + "where $d_{vq}$ is the shortest path length between vertices $v$ and $q$,\n", + "and $N_j$ denotes the number of vertices in the subnetwork $G_j$.\n", + "\n", + "For any vertex $w \\in V$, the **cross-betweenness centrality** $b^{ij}_w$ indicates its role for mediating interactions between two subnetworks $G_i$ and $G_j$,\n", + "$$ b^{ij}_w= \\sum_{p\\in V_i,\\,q \\in V_j,\\,p,q\\ne w}\\frac{\\sigma_{pq}(w)}{\\sigma_{pq}}= b^{ji}_w\\,,$$\n", + "where $\\sigma_{pq}(w)$ denotes the total number of shortest paths from $p \\in V_i$ to $q \\in V_j$ that include $w$.\n", + "\n", + "### Global Measures\n", + "\n", + "The **cross-edge density** $\\rho_{ij}$ measures the density of connections between distinct subnetworks $G_i$ and $G_j$,\n", + "$$ \\rho_{ij}=\\frac{|E_{ij}|}{N_i N_j}= \\rho_{ji}\\,.$$\n", + "\n", + "The **cross-average path length** $\\mathcal{L}_{ij}$ measures the average length of existing shortest paths between two subnetworks $G_i$ and $G_j$,\n", + "$$ \\mathcal{L}_{ij}= \\frac{1}{N_i N_j - M_{ij}}\\sum_{v\\in V_i,\\,q\\in V_j,\\,d_{vq}<\\infty}d_{vq}= \\mathcal{L}_{ji}\\,, $$\n", + "where $M_{ij}$ is the number of pairs $(v,q)\\in V_i\\times V_j$ which are not mutually reachable.\n", + "\n", + "The **global cross-clustering coefficient** $\\mathcal{C}_{ij}$ is an estimate of the probability of vertices from subnetwork $G_i$ to have mutually connected neighbours within subnetwork $G_j$,\n", + "$$ \\mathcal{C}_{ij} = \\langle \\mathcal{C}^{ij}_v \\rangle_{v\\in V_i} = \\frac{1}{N_i}\\sum_{v\\in V_i,\\, k_v^{ij}>1}\\frac{\\sum_{p\\ne q \\in V_j}A_{vp}A_{pq}A_{qv}}{\\sum_{p \\ne q \\in V_j}A_{vp}A_{vq}}\\,.$$\n", + "\n", + "The **cross-transitivity** $\\mathcal{T}_{ij}$ is the conditional probability that two vertices in subnetwork $G_j$ are connected if they have a common neighbour in subnetwork $G_i$,\n", + "$$ \\mathcal{T}_{ij}= \\frac{\\sum_{v\\in V_i,\\,p\\ne q \\in V_j}A_{vp}A_{pq}A_{qv}}{\\sum_{v\\in V_i,\\,p\\ne q \\in V_j}A_{vp}A_{vq}}\\,.$$" + ] + }, + { + "cell_type": "markdown", + "id": "75c19d62", + "metadata": {}, + "source": [ + "## Application: Vertical Dynamical Structure of the Earth’s Atmosphere" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9a58a8a0-4c84-4377-bb78-2a54eeb5e47a", + "metadata": {}, + "source": [ + "In the following, coupled climate network analysis is illustrated by the example of the dynamical structure of the Earth's atmosphere. \n", + "In order to treat a climate network as a network of networks, an *ab initio* physical separation of the climatological fields is necessary, i.e., a separation of processes into those responsible for internal coupling within a single field and those mediating interactions between fields.\n", + "\n", + "For the Earth system, there are distinct physical processes behind *quasi-horizontal* and *vertical* atmospheric dynamics: We have a stable isobaric quasi-horizontal stratification, while local heating of the Earth’s surface and atmosphere induces minor disturbances of the system. Therefore, we can treat the considered climatological field variables at different isobaric quasi-horizontal surfaces as separated subnetworks of an interconnected network.\n", + "The small vertical disturbances of the system due to convection processes lead to vertical movement, resulting in pressure gradients that are balanced by quasi-horizontal geostrophic winds along isobares." + ] + }, + { + "cell_type": "markdown", + "id": "61de5550", + "metadata": {}, + "source": [ + "We consider the discretised and vertically resolved geopotential height field $Z^i_v(t)$, sampled at predefined points $v$ on isobaric surfaces $i$, as the climatological field variable to construct coupled climate networks. It reflects global weather and climate dynamics to a good approximation, as it captures the dynamics both of the geostrophic wind field and of convection processes.\n", + "We specifically focus on the interaction structure between near ground and upper level atmospheric dynamics, which is particularly interesting because a large portion of the solar forcing that drives atmospheric dynamics takes place on the Earth’s surface." + ] + }, + { + "attachments": { + "Illustration-of-a-coupled-climate-subnetwork-as-it-is-constructed-in-this-work-where-V1_W640.jpg": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "id": "58970dbf", + "metadata": {}, + "source": [ + "![Illustration-of-a-coupled-climate-subnetwork-as-it-is-constructed-in-this-work-where-V1_W640.jpg](attachment:Illustration-of-a-coupled-climate-subnetwork-as-it-is-constructed-in-this-work-where-V1_W640.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "668d5926", + "metadata": {}, + "source": [ + "## Computing a Coupled Climate Network\n", + "### Loading the Climate Data" + ] + }, + { + "cell_type": "markdown", + "id": "0fe0dac8", + "metadata": {}, + "source": [ + "For this tutorial, we download [Reanalysis 1 data](https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html) provided by the *National Center for Environmental Prediction / National Center for Atmospheric Research* (NCEP-NCAR). This data set contains the monthly averaged geographical height potential for 17 isobaric surfaces $P_i$ of the atmosphere, on an equally spaced spherical grid with a latitude and longitude resolution of $2.5° \\!\\times\\! 2.5°$." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d5602280-eeae-4de7-9d9f-20f08471b3e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./data/hgt.mon.mean 100%[===================>] 296.75M 5.21MB/s in 28s \n", + "2024-02-05 03:21:41 URL:https://downloads.psl.noaa.gov/Datasets/ncep.reanalysis/Monthlies/pressure/hgt.mon.mean.nc [311163603/311163603] -> \"./data/hgt.mon.mean.nc\" [1]\n" + ] + } + ], + "source": [ + "DATA_NAME = \"hgt.mon.mean.nc\"\n", + "DATA_URL = f\"https://downloads.psl.noaa.gov/Datasets/ncep.reanalysis/Monthlies/pressure/{DATA_NAME}\"\n", + "DATA_FILE = f\"./data/{DATA_NAME}\"\n", + "![ -f {DATA_FILE} ] || wget -O {DATA_FILE} -nv --show-progress \"{DATA_URL}\"" + ] + }, + { + "cell_type": "markdown", + "id": "0ddc28ac-3014-4295-afd8-f451ff6cd641", + "metadata": {}, + "source": [ + "Now we will start with some imports and some specifications regarding the data set." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "695c5769", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from pyunicorn import climate\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2bd80f13-10bd-4865-8f65-55ec8c2b4f3f", + "metadata": {}, + "outputs": [], + "source": [ + "# Indicate data source (optional)\n", + "DATA_SOURCE = \"NCEP-NCAR Reanalysis 1\"\n", + "# Type of data file (\"NetCDF\" indicates a NetCDF file with data on a regular\n", + "# lat-lon grid, \"iNetCDF\" allows for arbitrary grids - > see documentation).\n", + "FILE_TYPE = \"NetCDF\"\n", + "# Name of observable in NetCDF file (\"hgt\" indicates the monthly mean of the geopotential height\n", + "# in the NCEP/NCAR reanalysis data)\n", + "OBSERVABLE_NAME = \"hgt\"\n", + "# Select a region in time and space from the data.\n", + "# If the boundaries are equal, the data's full range is selected.\n", + "# For this tutorial, we choose a window for latitude and longitude corresponding to North America.\n", + "WINDOW = {\"time_min\": 0., \"time_max\": 0., \"lat_min\": 45, \"lon_min\": 80,\n", + " \"lat_max\": 60, \"lon_max\": 120} \n", + "# Indicate the length of the annual cycle in the data (e.g., 12 for monthly\n", + "# data). This is used for calculating climatological anomaly values.\n", + "TIME_CYCLE = 12" + ] + }, + { + "cell_type": "markdown", + "id": "67822736", + "metadata": {}, + "source": [ + "We will first generate a separate `pyunicorn.climate.ClimateData` instance from our data file for each of the 17 vertical levels, which will then be coupled to the near ground level in the following steps. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "33aead33-118a-4e55-a96a-e69c5f3036ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n", + "Reading NetCDF File and converting data to NumPy array...\n" + ] + } + ], + "source": [ + "# number of total vertical levels in the data file\n", + "VERTICAL_LEVELS = 17\n", + "# loop over all levels to create a `ClimateData` object for each level\n", + "data = np.array([climate.ClimateData.Load(\n", + " file_name=DATA_FILE, observable_name=OBSERVABLE_NAME,\n", + " data_source=DATA_SOURCE, file_type=FILE_TYPE,\n", + " window=WINDOW, time_cycle=TIME_CYCLE,\n", + " # The `vertical_level` argument indicates the vertical level to be extracted from the data file,\n", + " # and is ignored for horizontal data sets. If `None`, the first level in the data file is chosen.\n", + " vertical_level=l) for l in range(VERTICAL_LEVELS)])" + ] + }, + { + "cell_type": "markdown", + "id": "a7b51a1a", + "metadata": {}, + "source": [ + "One can use the `print()` function on a `ClimateData` object in order to show some information about the data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0afb1744", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Global attributes:\n", + "description: Data from NCEP initialized reanalysis (4x/day). These are interpolated to pressure surfaces from model (sigma) surfaces.\n", + "platform: Model\n", + "Conventions: COARDS\n", + "NCO: 20121012\n", + "history: Created by NOAA-CIRES Climate Diagnostics Center (SAC) from the NCEP\n", + "reanalysis data set on 07/07/97 by calc.mon.mean.year.f using\n", + "/Datasets/nmc.reanalysis.derived/pressure/hgt.mon.mean.nc\n", + "from /Datasets/nmc.reanalysis/pressure/hgt.79.nc to hgt.95.nc\n", + "Converted to chunked, deflated non-packed NetCDF4 2014/09\n", + "title: monthly mean hgt from the NCEP Reanalysis\n", + "dataset_title: NCEP-NCAR Reanalysis 1\n", + "References: http://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis.derived.html\n", + "Variables (size):\n", + "level (17)\n", + "lat (73)\n", + "lon (144)\n", + "time (913)\n", + "hgt (913)\n", + "ClimateData:\n", + "Data: 119 grid points, 108647 measurements.\n", + "Geographical boundaries:\n", + " time lat lon\n", + " min 1297320.0 45.00 80.00\n", + " max 1963536.0 60.00 120.00\n" + ] + } + ], + "source": [ + "print(data[0])" + ] + }, + { + "cell_type": "markdown", + "id": "9eea2cec", + "metadata": {}, + "source": [ + "### Constructing the Network" + ] + }, + { + "cell_type": "markdown", + "id": "fb13be4b", + "metadata": {}, + "source": [ + "The `pyunicorn.climate.CoupledClimateNetwork` class provides the functionality to generate a complex network from a similarity measure matrix of two time series, e.g., arising from two different observables or from one observable at two vertical levels. The idea of coupled climate networks is based on the concept of coupled patterns, for a review refer to [Bretherton et al. (1992)](https://journals.ametsoc.org/view/journals/clim/5/6/1520-0442_1992_005_0541_aiomff_2_0_co_2.xml). The two observables (layers) need to have the same time grid (temporal sampling points). More information on the construction of a `ClimateNetwork` based on various similarity measures is provided in the tutorial on [Climate Networks](https://github.com/pik-copan/pyunicorn/blob/master/examples/tutorials/ClimateNetworks.ipynb)." + ] + }, + { + "cell_type": "markdown", + "id": "e5e230be", + "metadata": {}, + "source": [ + "For our example, we construct 17 coupled climate networks from the data by coupling the lowest level with each other level, based on Pearson correlation without lag and with a fixed threshold. For the construction of a coupled climate network, one needs to set either the threshold $\\beta$ or the link denisty. In this example, we set the threshold $\\beta = 0.5$." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8872d035", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/stw/code/pik/pyunicorn/src/pyunicorn/core/interacting_networks.py:962: RuntimeWarning: invalid value encountered in scalar divide\n", + " average_path_length = path_lengths.sum() / norm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n", + "Calculating daily (monthly) anomaly values...\n", + "Calculating correlation matrix at zero lag from anomaly values...\n", + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "Calculating all shortest path lengths...\n" + ] + } + ], + "source": [ + "# for setting a fixed threshold\n", + "THRESHOLD = 0.5\n", + "\n", + "cross_link_density = []\n", + "cross_average_path_length = []\n", + "cross_global_clustering = []\n", + "cross_transitivity = []\n", + "cross_degree = []\n", + "cross_closeness = []\n", + "cross_betweenness = []\n", + "\n", + "for l in range(VERTICAL_LEVELS):\n", + " # generate a coupled climate network between the ground level and the level l\n", + " coupled_network = climate.CoupledTsonisClimateNetwork(data[0], data[l], threshold=THRESHOLD)\n", + "\n", + " # calculate global measures\n", + " cross_link_density.append(coupled_network.cross_link_density())\n", + " cross_average_path_length.append(coupled_network.cross_average_path_length())\n", + " cross_global_clustering.append(coupled_network.cross_global_clustering())\n", + " cross_transitivity.append(coupled_network.cross_transitivity())\n", + "\n", + " # calculate local measures\n", + " cross_degree.append(coupled_network.cross_degree())\n", + " cross_closeness.append(coupled_network.cross_closeness())\n", + " cross_betweenness.append(coupled_network.cross_betweenness())" + ] + }, + { + "cell_type": "markdown", + "id": "583b18dd", + "metadata": {}, + "source": [ + "## Plotting some Coupled Network Measures\n", + "### Global Measures" + ] + }, + { + "cell_type": "markdown", + "id": "3b27db00", + "metadata": {}, + "source": [ + "We first determine the average geopotential height for each vertical level (in *km*),\n", + "which we will use as an independent variable to plot the global measures that we calculated for the 17 coupled climate networks." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c1ead3e6", + "metadata": {}, + "outputs": [], + "source": [ + "# read out the observable hgt (geopotentential height) and average it for each level\n", + "hgt_averaged = np.array([\n", + " np.round(data[l].observable().flatten().mean() / 1000, 1)\n", + " for l in range(VERTICAL_LEVELS)])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6cd75bf5-8c14-4705-920e-8adcbfaeee15", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_global_coupling(\n", + " measure: np.ndarray, title: str, xlabel: str, xindex: str,\n", + " set_ylabel=True, ax=None):\n", + " \"\"\"\n", + " Plot the geopotential height over a coupling network measure.\n", + " \"\"\"\n", + " ax_ = plt if ax is None else ax\n", + " ax_p = lambda p: getattr(*((plt, p) if ax is None else (ax, f\"set_{p}\")))\n", + " ax_.plot(measure, hgt_averaged, 'o', color='blue')\n", + " ax_p(\"xlabel\")(r\"${}\".format(xlabel) + xindex + r\"$\")\n", + " if set_ylabel:\n", + " ax_p(\"ylabel\")(r\"$Z_{\\,l}$ (km)\")\n", + " ax_p(\"title\")(title)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c8006820-d57b-41a2-ace7-f5355836ef5f", + "metadata": {}, + "source": [ + "Note that the cross-link density and cross-average path length are symmetrical ($\\rho_{1l}=\\rho_{l1}$, $\\mathcal{L}_{1l} = \\mathcal{L}_{l1}$), but the global cross-clustering coefficient and cross-transitivity are not ($\\mathcal{C}_{1l} \\neq \\mathcal{C}_{l1}$, $\\mathcal{T}_{1l} \\neq \\mathcal{T}_{l1}$). When computing asymmetric measures for two coupled climate subnetworks $G_i$ and $G_j$, the methods in `climate.CoupledClimateNetwork` return tuples such as $(\\mathcal{C}_{ij},\\mathcal{C}_{ji})$ or $(\\mathcal{T}_{ij},\\mathcal{T}_{ji})$." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "67126dbc-54e4-4b20-a227-9569b1c2d609", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_global_symmetric_coupling(measure, title, xlabel):\n", + " plot_global_coupling(measure, title, xlabel, r\"_{\\,1\\,l}\")\n", + "\n", + "def plot_global_asymmetric_coupling(measure, title, xlabel):\n", + " fig, axes = plt.subplots(1, 2, layout=\"constrained\")\n", + " fig.suptitle(title)\n", + " # plot both vertical directions of coupling\n", + " for vert in range(2):\n", + " plot_global_coupling(\n", + " measure[:, vert],\n", + " f\"pointing {['up', 'down'][vert]}wards\", xlabel,\n", + " [r\"_{\\,1\\,l}\", r\"_{\\,l\\,1}\"][vert],\n", + " set_ylabel=(vert == 0), ax=axes[vert])" + ] + }, + { + "cell_type": "markdown", + "id": "05c2ddbb", + "metadata": {}, + "source": [ + "#### Cross-Link Density" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2a017cc2-9fee-4447-8cd6-2cd055f9bad2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_global_symmetric_coupling(cross_link_density, \"cross-link density\", r\"\\rho\")" + ] + }, + { + "cell_type": "markdown", + "id": "a20a0dc7", + "metadata": {}, + "source": [ + "#### Cross-Average Path Length" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4ea4c9b0-f483-4646-b61a-52ce1ddd8ba4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_global_symmetric_coupling(cross_average_path_length, \"cross-average path length\", r\"\\mathcal{L}\")" + ] + }, + { + "cell_type": "markdown", + "id": "f87b8bdf-6188-4896-b258-d08409b209c8", + "metadata": {}, + "source": [ + "#### Global Cross-Clustering Coefficient" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "24355550-7635-4f2f-a301-804369555685", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_global_asymmetric_coupling(np.array(cross_global_clustering), \"global cross-clustering\", r\"\\mathcal{C}\")" + ] + }, + { + "cell_type": "markdown", + "id": "1e334508-8705-489b-bdfa-a7f190850486", + "metadata": {}, + "source": [ + "#### Cross-Transitivity" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a660fabf-8672-48e4-8488-6a6b3d6a071b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_global_asymmetric_coupling(np.array(cross_transitivity), \"cross-transitivity\", r\"\\mathcal{T}\")" + ] + }, + { + "cell_type": "markdown", + "id": "711d6aef", + "metadata": {}, + "source": [ + "### Local Measures" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "401c42a2-7077-4ce4-8654-4aa22a10132b", + "metadata": {}, + "source": [ + "For visualising the three-dimensional fields of local cross-network measures $m^{ij}_{v(\\vartheta,\\phi)}$, where $\\vartheta$ and $\\phi$ denote latitude and longitude, we choose to focus on their variation with height and latitude, and therefore consider zonal averages along circles of constant latitude,\n", + "$$ m^{ij}(\\vartheta)=\\langle m^{ij}_{v(\\vartheta,\\phi)} \\rangle _\\phi \\,. $$\n", + "\n", + "When computing local asymmetric cross-network measures for two subnetworks $G_i$ and $G_j$, `climate.CoupledClimateNetwork` returns values for all vertices $v$ of the coupled network in form of a pair of Numpy arrays, i.e., tuples $(m_{\\,\\cdot}^{ij},m_{\\,\\cdot}^{ji})$ where $m_{\\,\\cdot}^{ij}$ is a 1-D Numpy array ordered by latitude *and* longitude. In order to select the measures of the nodes by latitude and longitude, we need to reshape the output array into a $N_{\\vartheta} \\times N_{\\phi}$ matrix, with $N_{\\vartheta}$/$N_{\\phi}$ being the number of latitude/longitude values. In our example, these numbers are the same for all 17 generated coupled climate networks." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "aac4fd1e-1fae-48c7-9d26-692bf5a7a05c", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import List, Tuple, Callable, Optional\n", + "\n", + "lat, lon = [\n", + " np.unique(coupled_network.grid_1.__getattribute__(f\"{coo}_sequence\")())\n", + " for coo in [\"lat\", \"lon\"]]\n", + "X, Y = np.meshgrid(lat, hgt_averaged)\n", + "\n", + "def plot_zonal_coupling(\n", + " measure: List[Tuple[np.ndarray,...]], title: str, clabel: str,\n", + " vert_labels: Tuple[str, str] = tuple(\n", + " [f\"pointing {v}wards\" for v in ['up', 'down']]),\n", + " vert_indices: Tuple[str, str] = (r\"^{\\,1l}\", r\"^{\\,l1}\"),\n", + " transform: Optional[Callable[[np.ndarray], np.ndarray]] = None):\n", + " \"\"\"\n", + " Zonal heat plot of a coupling network measure.\n", + " \"\"\"\n", + " fig, axes = plt.subplots(1, 2, layout=\"constrained\")\n", + " fig.suptitle(title)\n", + " Z = np.zeros((VERTICAL_LEVELS, len(lat)))\n", + " \n", + " # plot both vertical directions of coupling\n", + " for vert in range(2):\n", + " # average over lon with same lat\n", + " for l in range(VERTICAL_LEVELS):\n", + " Z[l] = measure[l][vert].reshape(len(lat), len(lon)).mean(axis=1)\n", + " Z = Z if transform is None else transform(Z)\n", + " ax = axes[vert]\n", + " pcm = ax.pcolormesh(X, Y, Z)\n", + " ax.set_title(vert_labels[vert])\n", + " ax.set_xlabel(r\"Latitude $\\vartheta$ (°N)\")\n", + " if vert == 0:\n", + " ax.set_ylabel(r\"$Z_{\\,l}$ (km)\")\n", + " fig.colorbar(\n", + " pcm, ax=ax,\n", + " label=r\"${}\".format(clabel) + vert_indices[vert] + r\"(\\vartheta)$\")" + ] + }, + { + "cell_type": "markdown", + "id": "6bc0c10e", + "metadata": {}, + "source": [ + "#### Cross-Degree" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4d00102f-8f4b-4788-8cd6-99ec9f87ba6f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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GDcP//d//ITU1FUlJSfj111/x5Zdf3ncbr7/+OtRqNZ599lnk5+djwYIFGD16NFJTUwEA//73v5Gfn48///wT77zzDgDAzc3tnussLy9HbGwsIiMj8dZbb2Hnzp14++23ERoaismTJwMAdDodBg4ciEOHDmHy5MkICwvD5s2bMXbs2Np+XfeUnp6OESNG4KmnnsLYsWORnJyM4cOHY9u2bXjooYfg6emJ8PBw7Nu3D9OmTQMAHDhwACqVClevXsXp06fRsmVLALeLw+7du0vr3rBhA27cuIHJkyfD29sbhw4dwpIlS/Dnn39iw4YNeu24desWYmNj0a1bN7z11luoW7cugNu/48cff4zHH38cXbp0we7duzFgwIBK+/HUU0/h888/x5QpU9CiRQtcuXIFBw4cwK+//op27drJ8t0RGcKMMiwnJwe9e/fGrVu38OKLL8LV1RUrV66Ei4tLpWXnzJmDV155BTExMZg8eTLS0tKwfPlyHD58GD/88AMcHR3RvXt36HQ6HDhwAI888giA21mkVquxf/9+aV0///wzCgsL0aNHD71trF+/HtevX8eTTz4JlUqFBQsWYOjQofjjjz/g6OiIbt26QaVSYd++fWjVqpXe+g8cOCCt5/Lly/jtt98wZcoUad7y5cvRsmVLDBo0CHXq1MGWLVvw9NNPQ6fTIT4+Xq8daWlpGDVqFJ588klMnDgRDz74IG7evIk+ffogKysL06ZNQ2BgID766CPs3r1b77OlpaWIjY1FSUkJpk6dCn9/f5w/fx5ff/018vLyoNVqq/XbkBUQZLTZs2cLAGLQoEF6859++mkBQPzyyy9CCCGOHTsmAIh//etfess9++yzAoDYvXu3NK9nz56iZ8+e0uvvv/9eABDNmzcXJSUl0vx3331XABAnTpyQ5g0YMEA0atSoUjszMzMFAJGcnCzNGzt2rAAg5s6dq7ds27ZtRfv27aXXX3zxhQAgFi1aJM0rLy8X0dHRldZZlYrv6G7JyckCgMjMzJTmNWrUSAAQX3zxhTQvPz9fBAQEiLZt20rz4uPjhZ+fn/Q6MTFR9OjRQ/j6+orly5cLIYS4cuWKUKlU4t1335WWu3HjRqV2JCUlCZVKJc6dOyfNq/huXnzxRb1lK37Hp59+Wm/+448/LgCI2bNnS/O0Wq2Ij4839LUQmQUz6v4ZlZCQIACI1NRUad6lS5eEVqvVy6hLly4JJycn0bdvX1FeXi4t+9577wkA4sMPP5S27eHhIZ5//nkhhBA6nU54e3uL4cOHCwcHB3H9+nUhhBALFy4UarVaXLt2Te878Pb2FlevXpXWv3nzZgFAbNmyRZrXsmVL8dhjj0mv27VrJ4YPHy4AiF9//VUIIcTGjRv1fmMhqs7A2NhY0aRJE715FVm8bds2vfmLFi0SAMRnn30mzSsqKhJNmzYVAMT3338vhBDi559/FgDEhg0bqvrKyYZwGNqE7j4imzp1KgDgm2++0fvfO4cgAOD//u//AABbt2697zbGjx+vd55QRY/ZH3/8UctW3/bUU0/pve7evbveOrdt2wZHR0dMnDhRmqdWqyvts6kEBgbi0UcflV57eHhgzJgx+Pnnn5GTkyO1MTc3F2lpaQBuH1X36NED3bt3l47cDxw4ACGEXs/inT0FRUVF+Ouvv9ClSxcIIfDzzz9XaktFz0WFit+xokezwt0n6wOAp6cnUlNTceHChZrsPpEsmFGGffPNN+jcuTM6deokzfPx8ZFOPamwc+dOlJaWIiEhAWr13/8JnThxIjw8PKTvSK1Wo0uXLti3bx8A4Ndff8WVK1fw4osvQgiBlJQUALdzKzw8HJ6ennrbGTFiBOrVq6e3v4D+93hn1l2/fh2//PILJk2ahPr160vz9+/fL43EVLgzA/Pz8/HXX3+hZ8+e+OOPPyoND4eEhCA2NrbSdxUQEIBhw4ZJ8+rWrYtJkybpLVfRc7h9+3bcuHEDZLtYLJrQAw88oPc6NDQUarVaOh/v3LlzUKvVaNq0qd5y/v7+8PT0xLlz5+67jYYNG+q9rgiTa9eu1brdzs7O8PHxqbTeO9d57tw5BAQESMOwFe7eF1Np2rRppXMcmzVrBgDS91kRnvv370dRURF+/vlndO/eHT169NALSg8PD7Ru3VpaT1ZWFsaNGwcvLy/p/KeePXsCQKWgrFOnDoKCgvTmVfyOoaGhevMffPDBSvuxYMECnDx5EsHBwejUqRPmzJlj9H80iWqLGWXYuXPnKn0/QOW/64rv4O75Tk5OaNKkid531L17dxw9ehQ3b97E/v37ERAQgHbt2qF169Z6B7R3HsxWqM732L17d1y8eBEZGRn48ccfoVKpEBUVpVdE7t+/H127dtUrbH/44QfExMTA1dUVnp6e8PHxwUsvvQSgcgaGhIRU+V1VldF3fychISFITEzEf/7zH9SvXx+xsbFYunQpz1e0QSwWZVTVBR33ml8dhq4SFHdc0GGqdZqSoX2++wT1mggMDERISAj27duHlJQUCCGkoMzOzsa5c+ewf/9+dOnSRQrK8vJyPPTQQ9i6dSteeOEFbNq0CTt27JBOfr/7ZHaNRqMXsjX12GOP4Y8//sCSJUsQGBiIN998Ey1btsS3335b63USmQozSl7dunVDWVkZUlJS9M6drijmfvvtN1y+fLnKYrE632O3bt0AAPv27cP+/fvRrl07uLq6SusvLCyUDqIrnDlzBn369MFff/2FhQsXYuvWrdixYwemT58OoHIGVnXOZk28/fbbOH78OF566SXcvHkT06ZNQ8uWLau8IJCsF4tFE0pPT9d7nZGRAZ1OJ13p16hRI+h0ukrL5ebmIi8vD40aNTJJO4wJekMaNWqEixcvVhpKuPMqx3upOCq+88pAAAZ7KjIyMir9x+X3338HAOn7BP4O3f3796NNmzZwd3dH69atodVqsW3bNvz00096J46fOHECv//+O95++2288MILGDx4MGJiYhAYGFit/QD+/h3PnDmjN79iOPxuAQEBePrpp7Fp0yZkZmbC29sb8+fPr/b2iEyFGXXvz9+930Dlv+uK7+Du+aWlpcjMzNT7jjp16gQnJycpoyqKth49eiA1NRW7du2SXtdGw4YN0bBhwyrXf/bsWWzYsAHl5eV669+yZQtKSkrw1Vdf4cknn0T//v0RExNTo6KwUaNGOHPmTKWMNpSBERERePnll6Wi9vz581ixYkUt9pgshcWiCS1dulTv9ZIlSwAA/fr1AwD0798fACo9uWDhwoUAUOXVtLXh6upq8m7+2NhYlJWV4YMPPpDm6XS6SvtsSMWQbcX5O8Dt8wXXrFlT5fIXLlzQu/KyoKAAa9euRZs2bfRug9O9e3ecPXsWn376qRSUFecKLVy4EGVlZXpH1RVH63eGnBAC7777brX2A/j797zztj1A5d+1vLy80u/g6+uLwMDAKm8xQSQ3ZpRh/fv3x8GDB3Ho0CFp3uXLl7Fu3Tq95WJiYuDk5ITFixfr5ciqVauQn5+v9x05OzujY8eO+O9//4usrCy9nsWbN29i8eLFCA0NRUBAQK32uWJdu3fvxqFDh6T1Vxw4v/7663BxcUH79u2l5avKwPz8fCQnJ1d7m/3798eFCxfw+eefS/Nu3LiBlStX6i1XUFCAW7du6c2LiIiAWq1mBtoY3jrHhDIzMzFo0CA8/PDDSElJkW6tUnG+XOvWrTF27FisXLkSeXl56NmzJw4dOoQ1a9ZgyJAh6N27t0na0b59e3z66adITExEx44d4ebmhoEDBxq1ziFDhqBTp074v//7P2RkZCAsLAxfffUVrl69CuD+PQV9+/ZFw4YNMWHCBDz33HNwcHDAhx9+CB8fH2RlZVVavlmzZpgwYQIOHz4MPz8/fPjhh8jNza0UaBXhmJaWhtdee02a36NHD3z77bfS/ckqhIWFITQ0FM8++yzOnz8PDw8PfPHFFzU6n6pNmzYYNWoUli1bhvz8fHTp0gW7du2q1INx/fp1BAUFYdiwYWjdujXc3Nywc+dOHD58GG+//Xa1t0dkKswow55//nl89NFHePjhh/HMM89It85p1KiR3r0MfXx8MGPGDLzyyit4+OGHMWjQIKSlpWHZsmXo2LEjnnjiCb31du/eHa+//jq0Wi0iIiIA3D5ofPDBB5GWloZx48YZtd/du3fHunXroFKppGFpBwcHdOnSBdu3b0evXr30Ljjq27cvnJycMHDgQDz55JMoLCzEBx98AF9fX1y8eLFa25w4cSLee+89jBkzBkePHkVAQAA++uijSueL7t69G1OmTMHw4cPRrFkz3Lp1Cx999BEcHBwQFxdn1H6TmVnkGmw7U3FbitOnT4thw4YJd3d3Ua9ePTFlyhRx8+ZNvWXLysrEK6+8IkJCQoSjo6MIDg4WM2bMEMXFxXrLGbotxd23IKjqVhOFhYXi8ccfF56engKAdIsKQ7elcHV1NbhPd7p8+bJ4/PHHhbu7u9BqtWLcuHHihx9+EADEJ598ct/v6ejRoyIyMlI4OTmJhg0bioULFxq8dc6AAQPE9u3bRatWrYRGoxFhYWEGb7/g6+srAIjc3Fxp3oEDBwQA0b1790rLnz59WsTExAg3NzdRv359MXHiRPHLL79U+7sRQoibN2+KadOmCW9vb+Hq6ioGDhwosrOz9W6dU1JSIp577jnRunVr4e7uLlxdXUXr1q3FsmXL7vtdEZkSM6p6GXX8+HHRs2dP4ezsLBo0aCBeffVVsWrVqkoZJcTtW+WEhYUJR0dH4efnJyZPnizd/uZOW7duFQBEv3799Ob/61//EgDEqlWrqvy+3nzzzUrrujNfKpw6dUq6ZdGd5s2bJwCImTNnVlrPV199JVq1aiWcnZ1F48aNxRtvvCE+/PBDg1lclXPnzolBgwaJunXrivr164tnnnlGbNu2Te/WOX/88Yf45z//KUJDQ4Wzs7Pw8vISvXv3Fjt37qxynWS9VEIYcdYxAfj7Bq2XL19G/fr1Ld0cs9q0aRMeffRRHDhwAF27djXJOhs3bozw8HB8/fXXJlkfkdIxo0ybUURKw3MWqdrufhxeeXk5lixZAg8PDz6NhIgsjhlFJA+es0jVNnXqVNy8eRNRUVEoKSnBxo0b8eOPP+K1114z+vYKRETGYkYRyYPFIlVbdHQ03n77bXz99dcoLi5G06ZNsWTJEr1njhIRWQozikgePGeRiIiIiAziOYtEREREZBCLRSIiIiIySFHnLOp0Oly4cAHu7u6yPG6KyJKEELh+/ToCAwPv+Tzr4uJilJaWmmSbTk5OcHZ2Nsm6qOaYaWTPmGnWQ1HF4oULFxAcHGzpZhDJKjs7G0FBQVW+V1xcjJBGbsi5VG6Sbfn7+yMzM5PhaiHMNFICZprlKapYdHd3BwB0Q3/UgaOFW0NkWrdQhgP4Rvp3XpXS0lLkXCpH5tFG8HA37iyUgus6hLQ/h9LSUgarhTDTyJ4x06yHoorFimGaOnBEHRWDlezM/+5rUJ3hSA93tdHBSpbHTCO7xkyzGooqFonotnKhQ7mRN80qFzrTNIaIyEjMNHmxWCRSIB0EdDAuWY39PBGRqTDT5MU+WyIiIiIyiD2LRAqkgw7GDrgYvwYiItNgpsmLxSKRApULgXIjn/Rp7OeJiEyFmSYvDkMTERERkUHsWSRSIJ4MTkT2hJkmLxaLRAqkg0A5g5WI7AQzTV4chiYiIiIig9izSKRAHLIhInvCTJMXi0UiBeKVg0RkT5hp8uIwNBEREREZxGKRSIF0JpqIiKyBJTJt3759GDhwIAIDA6FSqbBp0ya994UQmDVrFgICAuDi4oKYmBikp6frLXP16lWMHj0aHh4e8PT0xIQJE1BYWFjDlsiPxSKRApX/78pBYyciImtgiUwrKipC69atsXTp0irfX7BgARYvXowVK1YgNTUVrq6uiI2NRXFxsbTM6NGjcerUKezYsQNff/019u3bh0mTJhn1XciBxSIRmYWSjsKJyP7169cP8+bNw6OPPlrpPSEEFi1ahJdffhmDBw9Gq1atsHbtWly4cEHKvl9//RXbtm3Df/7zH0RGRqJbt25YsmQJPvnkE1y4cMHMe3NvLBaJFKhcmGaqCSUdhROReZky0woKCvSmkpKSGrcnMzMTOTk5iImJkeZptVpERkYiJSUFAJCSkgJPT0906NBBWiYmJgZqtRqpqanGfSEmxquhiRTIFOcc1vTz/fr1Q79+/ap87+6jcABYu3Yt/Pz8sGnTJowcOVI6Cj98+LAUrkuWLEH//v3x1ltvITAw0JjdISIbZspMCw4O1ps/e/ZszJkzp0brysnJAQD4+fnpzffz85Pey8nJga+vr977derUgZeXl7SMtWCxSERGKSgo0Hut0Wig0WhqtI77HYWPHDnyvkfhVQ0FERHVVHZ2Njw8PKTXNc0ze8RhaCIF0kGFciMnHVQAbh+Fa7VaaUpKSqpxe+ztKJyIzMuUmebh4aE31aZY9Pf3BwDk5ubqzc/NzZXe8/f3x6VLl/Tev3XrFq5evSotYy3Ys0ikQDpxezJ2HQCPwonI8kyZaaYQEhICf39/7Nq1C23atAFwexQmNTUVkydPBgBERUUhLy8PR48eRfv27QEAu3fvhk6nQ2RkpOkaYwIsFonIKBVH38a48yg8ICBAmp+bmysFrS0dhROR/SssLERGRob0OjMzE8eOHYOXlxcaNmyIhIQEzJs3Dw888ABCQkIwc+ZMBAYGYsiQIQCA5s2b4+GHH8bEiROxYsUKlJWVYcqUKRg5cqTVnYPNYWgiBTJ2uKZiMpU7j8IrVByFR0VFAdA/Cq9grUfhRGRelsi0I0eOoG3btmjbti0AIDExEW3btsWsWbMAAM8//zymTp2KSZMmoWPHjigsLMS2bdvg7OwsrWPdunUICwtDnz590L9/f3Tr1g0rV6403RdjIuxZJFIgUxR7Nf28ko7Cici8LJFpvXr1grjH86RVKhXmzp2LuXPnGlzGy8sL69evr9F2LYHFIhGZxZEjR9C7d2/pdWJiIgBg7NixWL16NZ5//nkUFRVh0qRJyMvLQ7du3ao8Cp8yZQr69OkDtVqNuLg4LF682Oz7QkSkJCwWiRRIJ1TQCeOOwmv6eSUdhROReVki05SExSKRAlliyIaISC7MNHnxAhciIiIiMog9i0QKVA41yo08Viw3UVuIiIzFTJMXi0UiBRImOL9H8PweIrISzDR5sVgkUiCe30NE9oSZJi+rOWdx+fLlaNWqlfQ0iKioKHz77bfS+8XFxYiPj4e3tzfc3NwQFxdX6ZmLRETWgplGRPbCaorFoKAgvP766zh69CiOHDmC6OhoDB48GKdOnQIATJ8+HVu2bMGGDRuwd+9eXLhwAUOHDrVwq4lsU7lQm2Qiw5hpRObDTJOX1QxDDxw4UO/1/PnzsXz5chw8eBBBQUFYtWoV1q9fj+joaABAcnIymjdvjoMHD6Jz586WaDKRzdJBBZ2Rx4o6GL5nIjHTiMyJmSYvqyyjy8vL8cknn6CoqAhRUVE4evQoysrKEBMTIy0TFhaGhg0bIiUlxeB6SkpKUFBQoDcREZkbM42IbJlVFYsnTpyAm5sbNBoNnnrqKXz55Zdo0aIFcnJy4OTkBE9PT73l/fz8kJOTY3B9SUlJ0Gq10hQcHCzzHhDZhoqTwY2d6N6YaUTmwUyTl1UViw8++CCOHTuG1NRUTJ48GWPHjsXp06drvb4ZM2YgPz9fmrKzs03YWiLbxfN7zIOZRmQezDR5Wc05iwDg5OSEpk2bAgDat2+Pw4cP491338WIESNQWlqKvLw8vSPx3Nxc+Pv7G1yfRqOBRqORu9lERFViphGRPbDqMlqn06GkpATt27eHo6Mjdu3aJb2XlpaGrKwsREVFWbCFRLbp9sngxk9UM8w0Inkw0+RlNT2LM2bMQL9+/dCwYUNcv34d69evx549e7B9+3ZotVpMmDABiYmJ8PLygoeHB6ZOnYqoqCheNUhUCzoTPBqLVw7eGzONyHyYafKymmLx0qVLGDNmDC5evAitVotWrVph+/bteOihhwAA77zzDtRqNeLi4lBSUoLY2FgsW7bMwq0mIqoaM42I7IXVFIurVq265/vOzs5YunQpli5daqYWEdkvU5zMXS54FH4vzDQi82GmyctqikUiMh8d1LyBLRHZDWaavKz6AhciIiIisiz2LBIpULlQoVwYd+WfsZ8nIjIVZpq8WCwSKVC5Ca4cLOeQDRFZCWaavDgMTUREREQGsWeRSIF0Qg2dkVcO6njlIBFZCWaavFgsEikQh2yIyJ4w0+TFYWgiIiIiMog9i0QKpIPxV/7pTNMUIiKjMdPkxWKRSIFMcwNbDkwQkXVgpsmL3wwRERERGcSeRSIFMs1zVHmsSUTWgZkmLxaLRAqkgwo6GHt+D592QETWgZkmL5bRRERERGQQexaJFIhDNkRkT5hp8mKxSKRAprmBLYOViKwDM01eLBaJFEgnVNAZe08yIz9PRGQqzDR5sYwmIiIiIoPYs0ikQDoTDNnwBrZEZC2YafJisUikQDqhhs7Ik7mN/TwRkakw0+TFb4aIiIiIDGLPIpEClUOFciNvQGvs54mITIWZJi8Wi0QKxCEbIrInzDR58ZshIiIiIoPYs0ikQOUwfsil3DRNISIyGjNNXiwWiRSIQzZEZE+YafLiN0NEREREBrFnkUiByoUa5UYeRRv7eSIiU2GmyYvFIpECCaigM/L8HsHbTBCRlWCmyYtlNBEREREZxJ5FIgXikA0R2RNmmrxYLBIpkE6ooBPGDbkY+3kiIlNhpsmLZTQRERERGcSeRSIFKoca5UYeKxr7eSIiU2GmyYvFIpECcciGiOwJM01eLKOJiIiIyCD2LBIpkA5q6Iw8VjT280REpsJMkxeLRSIFKhcqlBs55GLs54mITIWZJi+W0URERERkEHsWiRSIJ4MTkT1hpsmLxSKRAgmhhs7IpxUIPu2AiKwEM01e/GaIiIiIyCD2LBIpUDlUKIeRJ4Mb+XkiIlNhpsmLPYtECqQTf5/jU/vJ0ntBRHSbuTOtvLwcM2fOREhICFxcXBAaGopXX30VQvy9EiEEZs2ahYCAALi4uCAmJgbp6eky7L38WCwSkVkoLVyJyH698cYbWL58Od577z38+uuveOONN7BgwQIsWbJEWmbBggVYvHgxVqxYgdTUVLi6uiI2NhbFxcUWbHntcBiaSIF0JjgZvKafrwjXNWvWoGXLljhy5AjGjx8PrVaLadOmAfg7XNesWYOQkBDMnDkTsbGxOH36NJydnY1qLxHZL3Nn2o8//ojBgwdjwIABAIDGjRvjv//9Lw4dOgTg9oHvokWL8PLLL2Pw4MEAgLVr18LPzw+bNm3CyJEjjWqruVlNz2JSUhI6duwId3d3+Pr6YsiQIUhLS9NbplevXlCpVHrTU089ZaEWE9kuHVQmmWriznBt3Lgxhg0bhr59+xoM11atWmHt2rW4cOECNm3aJMO3IC9mGpH5mDLTCgoK9KaSkpJK2+vSpQt27dqF33//HQDwyy+/4MCBA+jXrx8AIDMzEzk5OYiJiZE+o9VqERkZiZSUFDN8I6ZlNcXi3r17ER8fj4MHD2LHjh0oKytD3759UVRUpLfcxIkTcfHiRWlasGCBhVpMZLsqnnZg7ARUL1gB5YUrM43IfEyZacHBwdBqtdKUlJRUaXsvvvgiRo4cibCwMDg6OqJt27ZISEjA6NGjAQA5OTkAAD8/P73P+fn5Se/ZEqsZht62bZve69WrV8PX1xdHjx5Fjx49pPl169aFv79/tdZZUlKi9x+ugoIC0zSWiCTBwcF6r2fPno05c+ZUWu7FF19EQUEBwsLC4ODggPLycsyfP99uw5WZRmSbsrOz4eHhIb3WaDSVlvnss8+wbt06rF+/Hi1btsSxY8eQkJCAwMBAjB071pzNNQur6Vm8W35+PgDAy8tLb/66detQv359hIeHY8aMGbhx44bBdSQlJekdHdz9HzUipao4v8fYCbgdrPn5+dI0Y8aMKrd5Z7j+9NNPWLNmDd566y2sWbPGnLtuMcw0IvmYMtM8PDz0pqqKxeeee07qXYyIiMA//vEPTJ8+XeqFrDgAzM3N1ftcbm5utQ8OrYnV9CzeSafTISEhAV27dkV4eLg0//HHH0ejRo0QGBiI48eP44UXXkBaWho2btxY5XpmzJiBxMRE6XVBQQHDlQj/O7/H2Edj/e/8nopAvZ87wxUAIiIicO7cOSQlJWHs2LF64RoQECB9Ljc3F23atDGqrZbGTCOSlykzrTpu3LgBtVq/v83BwQE6nQ4AEBISAn9/f+zatUvKr4KCAqSmpmLy5MlGtdMSrLJYjI+Px8mTJ3HgwAG9+ZMmTZL+f0REBAICAtCnTx+cOXMGoaGhldaj0WiqPCIgIvNTWrjeiZlGZF8GDhyI+fPno2HDhmjZsiV+/vlnLFy4EP/85z8BACqVCgkJCZg3bx4eeOAB6e4OgYGBGDJkiGUbXwtWVyxOmTIFX3/9Nfbt24egoKB7LhsZGQkAyMjIqDJYiahqohZXM1e1jppQWrhWYKYRyc/cmbZkyRLMnDkTTz/9NC5duoTAwEA8+eSTmDVrlrTM888/j6KiIkyaNAl5eXno1q0btm3bZpO3AbOaYlEIgalTp+LLL7/Enj17EBISct/PHDt2DAD0hqyI6P4qnlhg7DpqQmnhykwjMh9zZ5q7uzsWLVqERYsWGVxGpVJh7ty5mDt3rlHtsgZWUyzGx8dj/fr12Lx5M9zd3aWrH7VaLVxcXHDmzBmsX78e/fv3h7e3N44fP47p06ejR48eaNWqlYVbT0T3o7RwZaYRkb2wmmJx+fLlAG7fpPZOycnJGDduHJycnLBz504sWrQIRUVFCA4ORlxcHF5++WULtJbItlniCS5Kw0wjMh9mmryspli88/mwVQkODsbevXvN1Boi+2aJYWilYaYRmQ8zTV4so4mIiIjIIKvpWSQi86nNs52rWgcRkTVgpsmLxSKRAnHIhojsCTNNXhyGJiIiIiKD2LNIpEA8Cicie8JMkxeLRSIFYrASkT1hpsmLw9BEREREZBB7FokUiEfhRGRPmGnyYrFIpEACxt8m4t63nCYiMh9mmrw4DE1EREREBrFnkUiBOGRDRPaEmSYvFotECsRgJSJ7wkyTF4ehiYiIiMgg9iwSKRCPwonInjDT5MVikUiBGKxEZE+YafLiMDQRERERGcSeRSIFEkIFYeRRtLGfJyIyFWaavFgsEimQDiqjb2Br7OeJiEyFmSYvDkMTERERkUHsWSRSIJ4MTkT2hJkmLxaLRArE83uIyJ4w0+TFYpFIgXgUTkT2hJkmL56zSEREREQGsWeRSIE4ZENE9oSZJi8Wi0QKJEwwZMNgJSJrwUyTF4ehiYiIiMgg9iwSKZAAIITx6yAisgbMNHmxWCRSIB1UUPFpB0RkJ5hp8uIwNBEREREZxJ5FIgXilYNEZE+YafJisUikQDqhgoo3sCUiO8FMkxeHoYmIiIjIIPYsEimQECa4cpCXDhKRlWCmyYvFIpEC8fweIrInzDR5cRiaiIiIiAxizyKRAvEonIjsCTNNXiwWiRSIVw4SkT1hpsmLw9BEREREZBB7FokUiFcOEpE9YabJi8UikQLdDlZjz+8xUWOIiIzETJMXh6GJiIiIyCD2LBIpEK8cJCJ7wkyTF4tFIgUS/5uMXQcRkTVgpsmLw9BEREREZBB7FokUiEM2RGRPmGnyspqexaSkJHTs2BHu7u7w9fXFkCFDkJaWprdMcXEx4uPj4e3tDTc3N8TFxSE3N9dCLSayYcJEExnETCMyI2aarKymWNy7dy/i4+Nx8OBB7NixA2VlZejbty+KioqkZaZPn44tW7Zgw4YN2Lt3Ly5cuIChQ4dasNVERFVjphGRvbCaYeht27bpvV69ejV8fX1x9OhR9OjRA/n5+Vi1ahXWr1+P6OhoAEBycjKaN2+OgwcPonPnzpZoNpFtMsGQDThkc0/MNCIzYqbJymp6Fu+Wn58PAPDy8gIAHD16FGVlZYiJiZGWCQsLQ8OGDZGSklLlOkpKSlBQUKA3EdHfTzswdqLqY6YRyYeZJi+rLBZ1Oh0SEhLQtWtXhIeHAwBycnLg5OQET09PvWX9/PyQk5NT5XqSkpKg1WqlKTg4WO6mE9mEipPBjZ2oephpRPJipsnLKovF+Ph4nDx5Ep988olR65kxYwby8/OlKTs720QtJCKqPmYaEdkyqzlnscKUKVPw9ddfY9++fQgKCpLm+/v7o7S0FHl5eXpH4rm5ufD3969yXRqNBhqNRu4mE9keoTL+/BwehVcLM43IDJhpsrKankUhBKZMmYIvv/wSu3fvRkhIiN777du3h6OjI3bt2iXNS0tLQ1ZWFqKioszdXCKbxvN75MdMIzIfZpq8rKZnMT4+HuvXr8fmzZvh7u4unbOj1Wrh4uICrVaLCRMmIDExEV5eXvDw8MDUqVMRFRXFqwaJyOow04jIXlhNsbh8+XIAQK9evfTmJycnY9y4cQCAd955B2q1GnFxcSgpKUFsbCyWLVtm5pYS2QE+SFV2zDQiM2KmycpqikVRjf5fZ2dnLF26FEuXLjVDi4jsFx+NJT9mGpH5MNPkZTXnLBIRERGR9bGankUiMjMOuRCRPWGmyYY9i0QKxBvYEpE9sUSmnT9/Hk888QS8vb3h4uKCiIgIHDly5I42CcyaNQsBAQFwcXFBTEwM0tPTTb3rZsFikYjMRknhSkT269q1a+jatSscHR3x7bff4vTp03j77bdRr149aZkFCxZg8eLFWLFiBVJTU+Hq6orY2FgUFxdbsOW1w2FoIiWywJWDFeHau3dvfPvtt/Dx8UF6enqV4bpmzRqEhIRg5syZiI2NxenTp+Hs7Gxkg4nIbpkw0+5+5npVN8N/4403EBwcjOTkZGnenfdSFUJg0aJFePnllzF48GAAwNq1a+Hn54dNmzZh5MiRRjbWvNizSKRIKhNN1XdnuHbq1AkhISHo27cvQkNDAVQO11atWmHt2rW4cOECNm3aZPwuE5EdM12mBQcH6z2DPSkpqdLWvvrqK3To0AHDhw+Hr68v2rZtiw8++EB6PzMzEzk5OYiJiZHmabVaREZGIiUlxeR7LzcWi0RklIKCAr2ppKSkyuWUFq5EZJuys7P1nsE+Y8aMSsv88ccfWL58OR544AFs374dkydPxrRp07BmzRoAkG7C7+fnp/c5Pz8/6T1bwmKRSImEiSZU7ygcUF64EpEZmTDTPDw89Kaqnseu0+nQrl07vPbaa2jbti0mTZqEiRMnYsWKFfLup4XwnEUiJTLh+T3Z2dnw8PCQZlcVrMDtcO3QoQNee+01AEDbtm1x8uRJrFixAmPHjjWyMUSkaGY+DzsgIAAtWrTQm9e8eXN88cUXAAB/f38AQG5uLgICAqRlcnNz0aZNGyMban617lksKytDdnY20tLScPXqVVO2iYhsSHWOwgHD4ZqVlQVAP1zvlJubK71HRGQNunbtirS0NL15v//+Oxo1agTg9sUu/v7+2LVrl/R+QUEBUlNTERUVZda2mkKNisXr169j+fLl6NmzJzw8PNC4cWM0b94cPj4+aNSoESZOnIjDhw/L1VYiMhWhMs1UA0oLVyIyIzNn2vTp03Hw4EG89tpryMjIwPr167Fy5UrEx8cDAFQqFRISEjBv3jx89dVXOHHiBMaMGYPAwEAMGTJEpi9BPtUehl64cCHmz5+P0NBQDBw4EC+99BICAwPh4uKCq1ev4uTJk9i/fz/69u2LyMhILFmyBA888ICcbSeiWhLi9mTsOmpi+vTp6NKlC1577TU89thjOHToEFauXImVK1cC0A/XBx54QLp1jq2GKxGZj7kzrWPHjvjyyy8xY8YMzJ07FyEhIVi0aBFGjx4tLfP888+jqKgIkyZNQl5eHrp164Zt27bZ5G3Aql0sHj58GPv27UPLli2rfL9Tp0745z//ieXLl2P16tXYv38/i0UikigtXInIvj3yyCN45JFHDL6vUqkwd+5czJ0714ytkke1i8X//ve/1VrO2dkZTz31VK0bRERmYIGbcgPKClciMiMLZZpS1Ppq6OLiYhw/fhyXLl2CTqfTe2/QoEFGN4yIZFSLcw6rXAcRkTVgpsmqVsXitm3bMGbMGPz111+V3lOpVCgvLze6YURE5lBWVoacnBzcuHEDPj4+8PLysnSTiIhqRa48q9Wtc6ZOnYrhw4fj4sWL0Ol0ehMLRSLrpxKmmWwV7+xAZF+UnGnmyLNaFYu5ublITEys9KQFIrIRJnzaga1ZuHAhGjdujOTkZMTExGDTpk04duwYfv/9d6SkpGD27Nm4desW+vbti4cffhjp6emWbjIR3Y9CM81ceVarYehhw4Zhz549CA0NrdVGiYgspbp3dlixYgWSk5N5ZwcislrmyrNaFYvvvfcehg8fjv379yMiIgKOjo5670+bNq02qyUic1HwyeDVvbODRqPhnR2IbIVCM81ceVarYvG///0vvvvuOzg7O2PPnj1Qqf7+glUqFYtFImun4NtMZGdnIyEhAbt378aNGzfg6+uLiIgI9OrVC8OGDUOTJk0s3UQiqimFZpq58qxWxeK///1vvPLKK3jxxRehVtf68dJERGY3fPhwaLVaJCcnw9XVFWfPnsWUKVOQnZ2Nl19+GQMHDsTixYvRoEEDSzeViOiezJVntSoWS0tLMWLECBaKRLZKoUfhAHD69Gn89NNPaNq0KQDg1q1bmDx5MrZv3w4nJyfMnTsXkZGR2L9/P0JCQizcWiKqFoVmmrnyrFbV3tixY/Hpp5/WeqNEZGEKvXIQALp06YIPPvhAer1lyxY4OTnB398f9evXx+LFi5GQkIBnnnnGgq0kohpRaKaZK89q1bNYXl6OBQsWYPv27WjVqlWlC1wWLlxoVKOISGYKPRkcuH2B3kMPPYQvvvgCWq0Wx44dw/jx4/VGSv7xj39gzpw5lmskEdWMQjPNXHlWq2LxxIkTaNu2LQDg5MmTRjWAiMicmjZtihMnTmDjxo1IT0/HtGnT0LNnT71lkpOT4e3tbaEWEhFVj7nyrFbF4vfff2/URonIskzxtAJbfdoBALi5uWHMmDHSa3d3d7i5ucHPzw+5ubnIy8tDcnKyBVtIRDWh5EwzR57V+tY5o0aNqvK95557Dm+++aZRjSIimSn0ZHBDUlNTcerUKVy5cgXu7u7o3bs3AgMDLd0sIqouZppEjjyrVbE4efJkeHp6ol+/fnrzp0+fjk8++YTFIhHZlBYtWqBFixaWbgYRkdHkyLNaXQ29bt06jBo1CgcOHJDmTZ06FZ999hmHqInIqmVlZdVo+fPnz8vUEiIi45grz2pVLA4YMADLli3DoEGDcPToUTz99NPYuHEjvv/+e4SFhdWqIURkPir8fY5PrSdL70QtdezYEU8++SQOHz5scJn8/Hx88MEHCA8PxxdffGHG1hFRbSg108yVZ7UahgaAxx9/HHl5eejatSt8fHywd+9e6aaQRETW6vTp05g/fz4eeughODs7o3379ggMDISzszOuXbuG06dP49SpU2jXrh0WLFiA/v37W7rJRERVMleeVbtYTExMrHK+j48P2rVrh2XLlknzeJ9FIiun0HuSAYC3tzcWLlyI+fPnY+vWrThw4ADOnTuHmzdvon79+hg9ejRiY2MRHh5u6aYSUXUpNNPMlWfVLhZ//vnnKuc3bdoUBQUF0vsqle192USKwysH4eLigmHDhmHYsGGWbgoRGUvhmSZ3nlW7WOSFK0RkyxISEtC6dWu0atUK4eHh0Gg0lm4SEZHJpaamIjIy0qTrrPU5i0RkwxR4FB4dHY3jx4/j22+/xalTp6BSqdCyZUu0atUKrVq1wsCBAy3dRCKqLQVmmiHDhw+v8VXS91PtYjErKwsNGzas9orPnz+PBg0a1KpRRCQvJT7tYNCgQRg0aJD0uri4GCdPnsTx48exe/duFotENkxpmfbYY49VOV8IgatXr5p8e9UuFjt27IghQ4bgX//6Fzp27FjlMvn5+fjss8/w7rvvYtKkSZg2bZrJGkpEZErOzs7o0KEDOnTogNTUVEs3h4io2nbu3ImPPvoIbm5uevOFENi3b5/Jt1ftYpG3myCyIxyy0SPHsA0RmZHCMq1Xr15wd3dHjx49Kr3XqlUrk2+v2sUibzdBZEcUFqyA+YdtiMiMFJZpGzduNPjejh07TL69Gl/gwttNEJEtMvewDRGRXKZPny5dnGeOuzvwamgiBVLayeCA+YdtiMh8lJZpvXv3NuvdHVgsEimRAp92YO5hGyIyI4Vlmrnv7sBikYiIiMiG3Xl3BzmoZVkrEVk3YaLJxl26dMnSTSAiU2CmyZpnRhWLCQkJSE5OxtGjR1FSUmJ0Y/bt24eBAwciMDAQKpUKmzZt0nt/3LhxUKlUetPDDz9s9HaJlKbi/B5jJ1s3bNgwlJeXV/nerVu3jFo384zIfJhp8uaZUcVidHQ0zp8/jzfeeAPt2rVDeHg4RowYgfnz52PLli01Xl9RURFat26NpUuXGlzm4YcfxsWLF6Xpv//9rzG7QEQK5unpWeXDA65cuYKYmBij1s08IyJzkjPPjDpn0dQnWPbr1w/9+vW75zIajQb+/v61ai8R/Y/C7klmyNq1a9GpUyd8+OGH+Oc//wkA+PXXX/HII48gLCzMqHUzz4jMiJkma56Z9AIXuU+wBIA9e/bA19cX9erVQ3R0NObNmwdvb+8qly0pKdEbHi8oKJCtXUQ2xRRDLjYerMDtI/EvvvgCvXr1Qnh4OK5du4YRI0ZgwoQJePPNN2Xffk3yDGCmERnETJM1z2pULI4aNQr//ve/LfaUlocffhhDhw5FSEgIzpw5g5deegn9+vVDSkoKHBwcKi2flJSEV155xQItJSJrNXToULRp00aaIiIi8N5776F///4oLi7GkiVLMH78eNnbUdM8A5hpRKTPXHlWo2Lx008/xe7du7Fr164qC0YhBIqKiio9IcFURo4cKf3/iIgItGrVCqGhodizZw/69OlTafkZM2YgMTFRel1QUIDg4GBZ2kZkUxQ8ZBMaGor9+/fjvffew19//YV69eqhdevWEELg8ccfR7t27VBWVgZHR0dZ21HTPAOYaUQGKTTTzJVnNR6GbtOmDaKjo7F79+5KBeOlS5fQoEEDo6+6qa4mTZqgfv36yMjIqDJcNRqN7I/AIbJJCg1WAHrDMefPn8exY8dw7NgxeHt74/vvv8eqVatQp04dhIWF4ZdffjFbu+6XZwAzjcgghWaaufKsRsWiSqXC6tWrMX36dERHR2PXrl2IiIjQW0an09W6MTX1559/4sqVKwgICDDbNonIfjRo0AANGjTAgAEDpHmFhYU4duyYWQtFgHlGRMaRM89qVCwKIeDg4ID169dj9OjRUg/jnQWjSlX7x+UUFhYiIyNDep2ZmYljx47By8sLXl5eeOWVVxAXFwd/f3+cOXMGzz//PJo2bYrY2Nhab5NIiZT2HNWacHNzQ7du3dCtWzej1sM8IzIfZlrVTJVntbrPolqtxrp16xATE4Po6GgcP37cqEZUOHLkCNq2bYu2bdsCABITE9G2bVvMmjULDg4OOH78OAYNGoRmzZphwoQJaN++Pfbv389hGSKyOswzIrIXNR6GrlBRMD7xxBPo06cPdu3aBT8/P6Ma06tXLwhhuLTfvn27Uesnov9R6Pk95sQ8IzIjZpqsatSzeHfwqdVqfPzxx3jooYfQp08fHDt2zJRtIyIiIiILq1GxuHXrVmi1Wv0V/K9g7Nu3L+Li4kzaOCKSB5+jSkT2hJkmrxoVi/369avyfBq1Wo2PPvoIgwcPNlnDiEhmwsiJiMiaMNNkU6sLXKpc0f96GFNSUky1SiIiIiKyMJM+G1qlUqFTp06mXCURyYEngxORPWGmycqkxSIR2Qbek4yI7AkzTV4mG4YmIiIiIvvDnkUiJeKQDRHZE2aarFgsEikQh2yIyJ4w0+TFYWgiIiIiMog9i0RKxCEbIrInzDRZsVgkUiIGKxHZE2aarDgMTUREREQGsWeRSIF4MjgR2RNmmrxYLBIpEYdsiMieMNNkxWFoIiIiIjKIPYtESsSjcCKyJ8w0WbFnkUiBKs7vMXYiIrIGls60119/HSqVCgkJCdK84uJixMfHw9vbG25uboiLi0Nubq7xO2sBLBaJyCLsPVyJSBkOHz6M999/H61atdKbP336dGzZsgUbNmzA3r17ceHCBQwdOtRCrTQOi0UiJRImmmpJCeFKRGZkoUwrLCzE6NGj8cEHH6BevXrS/Pz8fKxatQoLFy5EdHQ02rdvj+TkZPz44484ePBg7ffTQlgsEimQJYdslBKuRGQ+psy0goICvamkpMTgduPj4zFgwADExMTozT969CjKysr05oeFhaFhw4ZISUmR5TuQE4tFIjJKTYIVUE64mpVKLf9EpBDBwcHQarXSlJSUVOVyn3zyCX766acq38/JyYGTkxM8PT315vv5+SEnJ0eOZsuKV0MTKZEJrxwMDg7Wmz179mzMmTOnyo9UhOvhw4crvWdv4UpEZmTCTMvOzoaHh4c0W6PRVFo0OzsbzzzzDHbs2AFnZ2cjN2z9WCwSKZGZg7ViOSWFKxGZkQkzzcPDQy/TqnL06FFcunQJ7dq1k+aVl5dj3759eO+997B9+3aUlpYiLy9P7wA4NzcX/v7+RjbU/FgsEpFRqhOsgPLClYjsV58+fXDixAm9eePHj0dYWBheeOEFBAcHw9HREbt27UJcXBwAIC0tDVlZWYiKirJEk43CYpFIgVT/m4xdR00oLVyJyHzMnWnu7u4IDw/Xm+fq6gpvb29p/oQJE5CYmAgvLy94eHhg6tSpiIqKQufOnY1sqfmxWCRSIgs87UBp4UpEZmSFT3B55513oFarERcXh5KSEsTGxmLZsmWm3YiZsFgkIqthT+FKRMqyZ88evdfOzs5YunQpli5dapkGmRCLRSIFMsXj+kzxuD97DlciMh9ryTR7xWKRSImscMiGiKjWmGmyYrFIpFQMRiKyJ8w02fC2/ERERERkEHsWiRSI5/cQkT1hpsmLxSKREvH8HiKyJ8w0WXEYmoiIiIgMYs8ikQJxyIaI7AkzTV4sFomUiEM2RGRPmGmy4jA0ERERERnEnkUiBeKQDRHZE2aavBRZLKrqOEKlcpR9O6K8XPZt3N6QzjzbIfvBIRu7olKroFKpZN2GMFOc2SWVeQbxVGp5/w1UMNt/22qCmSYrDkMTERERkUGK7FkkUjwehRORPWGmyYrFIpEC8fweIrInzDR5cRiaiIiIiAxizyKREnHIhojsCTNNVlbVs7hv3z4MHDgQgYGBUKlU2LRpk977QgjMmjULAQEBcHFxQUxMDNLT0y3TWCIbphLCJBMZxjwjMh9mmrysqlgsKipC69atsXTp0irfX7BgARYvXowVK1YgNTUVrq6uiI2NRXFxsZlbSkR0b8wzIrIXVjUM3a9fP/Tr16/K94QQWLRoEV5++WUMHjwYALB27Vr4+flh06ZNGDlypDmbSmTbOGQjO+YZkRkx02RlVT2L95KZmYmcnBzExMRI87RaLSIjI5GSklLlZ0pKSlBQUKA3EdHfVw4aO1Ht1CbPAGYakSHMNHnZTLGYk5MDAPDz89Ob7+fnJ713t6SkJGi1WmkKDg6WvZ1ERPdTmzwDmGlEZBk2UyzWxowZM5Cfny9N2dnZlm4SkXUQJprIrJhpRAYw02RlVecs3ou/vz8AIDc3FwEBAdL83NxctGnTpsrPaDQaaDQaczSPyKbwBraWVZs8A5hpRIYw0+RlMz2LISEh8Pf3x65du6R5BQUFSE1NRVRUlAVbRkRUM8wzIrIlVtWzWFhYiIyMDOl1ZmYmjh07Bi8vLzRs2BAJCQmYN28eHnjgAYSEhGDmzJkIDAzEkCFDLNdoIlvEKwdlxzwjMiNmmqysqlg8cuQIevfuLb1OTEwEAIwdOxarV6/G888/j6KiIkyaNAl5eXno1q0btm3bBmdn5xptR+VUByqVo0nbXqVS+TcBAKLcPNuB0JlpQyQ3DtnIz1x5BgAqBweoVA4ma3tVRLm5goZqS+Ug77+BCkJnrj9+dbULOGaavKyqWOzVqxfEPe6grlKpMHfuXMydO9eMrSIiqjnmGRHZC6sqFonITDhkQ0T2hJkmKxaLRArFIRcisifMNPmwWCRSIiFuT8aug4jIGjDTZGUzt84hIiIiIvNjzyKRAvHKQSKyJ8w0ebFYJFIingxORPaEmSYrDkMTERERkUHsWSRSIJXu9mTsOoiIrAEzTV4sFomUiEM2RGRPmGmy4jA0ERERERnEnkUiBeKVg0RkT5hp8mKxSKREvIEtEdkTZpqsOAxNRERERAaxZ5FIgThkQ0T2hJkmLxaLRErEKweJyJ4w02SlzGJRmOJf1f2pHBxk34Y5qdTm+ecidOb7ixW3ysy2LSK5qLUeUKudZN2GuJon6/ql7djj36Qwzw38RHm5WbZDyqPMYpFI4ThkQ0T2hJkmLxaLRErEKweJyJ4w02TFq6GJiIiIyCD2LBIpEIdsiMieMNPkxWKRSIl45SAR2RNmmqw4DE1EREREBrFnkUiBOGRDRPaEmSYvFotESqQTtydj10FEZA2YabLiMDQRERERGcSeRSIl4sngRGRPmGmyYrFIpEAqmOD8HpO0hIjIeMw0eXEYmoiIiIgMYs8ikRLx0VhEZE+YabJisUikQLzNBBHZE2aavDgMTUREREQGsWeRSIl45SAR2RNmmqxYLBIpkEoIqIw8P8fYzxMRmQozTV4chiZSIp2JJiIia2DmTEtKSkLHjh3h7u4OX19fDBkyBGlpaXrLFBcXIz4+Ht7e3nBzc0NcXBxyc3ON208LYbFIRGahtHAlIvu1d+9exMfH4+DBg9ixYwfKysrQt29fFBUVSctMnz4dW7ZswYYNG7B3715cuHABQ4cOtWCra0+Rw9AqBweoVA7yb8jJDNuA+W4kqtI4mWdDZhwKKL+Wb5btiFtlZtlOdVliyKYiXDt27Ihbt27hpZdeQt++fXH69Gm4uroCuB2uW7duxYYNG6DVajFlyhQMHToUP/zwg1FttXe6Bj7QOWhk3YaDk3n+/nWXr5hlOwCgKyk227bMQZSXW7oJpiWq39Vn7kzbtm2b3uvVq1fD19cXR48eRY8ePZCfn49Vq1Zh/fr1iI6OBgAkJyejefPmOHjwIDp37mxUW81NkcUikeKZ8GTwgoICvdkajQYaTeXCRWnhSkRmZIFMu1N+/u2OBy8vLwDA0aNHUVZWhpiYGGmZsLAwNGzYECkpKTaXZxyGJiKjBAcHQ6vVSlNSUlK1PlfTcCUiMoeaZppOp0NCQgK6du2K8PBwAEBOTg6cnJzg6empt6yfnx9ycnLkarps2LNIpEQmfNpBdnY2PDw8pNn3OwIHlBGuRGRGFsy0+Ph4nDx5EgcOHDBu+1aMxSKRApnyaQceHh56wVodSghXIjIfS2XalClT8PXXX2Pfvn0ICgqS5vv7+6O0tBR5eXl6B8C5ubnw9/c3rqEWwGFoIjKrinD9/vvvDYbrnWw1XInIfgkhMGXKFHz55ZfYvXs3QkJC9N5v3749HB0dsWvXLmleWloasrKyEBUVZe7mGo09i0RKZMIhm+ovLjB16lR8+eWX2LNnzz3DNS4uDoBthysRmZGZMy0+Ph7r16/H5s2b4e7uLp0qo9Vq4eLiAq1WiwkTJiAxMRFeXl7w8PDA1KlTERUVZXMXtwAsFokUSaW7PRm7jppQWrgSkfmYO9OWL18OAOjVq5fe/OTkZIwbNw4A8M4770CtViMuLg4lJSWIjY3FsmXLjGukhbBYJCKzUFq4EpH9EtXohXR2dsbSpUuxdOlSM7RIXiwWiZTIQsPQ92NP4UpEZmSBTFMSFotESmTCG9gSEVkcM01WNnU19Jw5c6BSqfSmsLAwSzeLiKjGmGdEZCtsrmexZcuW2Llzp/S6Th2b2wUii7PEs6GpMuYZkWkw0+Rlc8lUp04d3nONyFg8v8cqMM+ITISZJiubGoYGgPT0dAQGBqJJkyYYPXo0srKyDC5bUlKCgoICvYmIyFrUJM8AZhoRWYZNFYuRkZFYvXo1tm3bhuXLlyMzMxPdu3fH9evXq1w+KSlJ72HgwcHBZm4xkZUSAHRGTjwIN0pN8wxgphEZxEyTlU0Vi/369cPw4cPRqlUrxMbG4ptvvkFeXh4+++yzKpefMWMG8vPzpSk7O9vMLSayThXn9xg7Ue3VNM8AZhqRIcw0edncOYt38vT0RLNmzZCRkVHl+xqNBhqNptJ8lYc7VOrK803O3VX+bQDQuZphXwCUeppnOzf8nMyyHQAo9lSZZTvqW/Jvo7y0GEjeLP+GSBb3yzPAcKZd7OoBB42znM2DU4FW1vVXqPtX0P0XMhHHAjP8YQJwuGmm7Vy/aZbtqG6WmmU70JUAmebZFN2bTfUs3q2wsBBnzpxBQECApZtCZFsE/j4hvNaTpXfCvjDPiIzATJOVTRWLzz77LPbu3YuzZ8/ixx9/xKOPPgoHBweMGjXK0k0jsi1Gh6oJrjxUOOYZkQkx02RlU8PQf/75J0aNGoUrV67Ax8cH3bp1w8GDB+Hj42PpphER1QjzjIhshU0Vi5988omlm0BkH3QAjD1dU2eKhigX84zIhJhpsrKpYpGITINPOyAie8JMk5dNnbNIRERERObFnkUiJeKjsYjInjDTZMVikUiJGKxEZE+YabLiMDQRERERGcSeRSIl4lE4EdkTZpqsWCwSKRFvM0FE9oSZJisWi0QKxNtMEJE9YabJi+csEhEREZFBiuxZvBXsA9Rxln07NwLk3wYAFPmbp+a/HmKeoy6v5n+ZZTsAoBbm+e5KyuT/Uyu/UQIkV3Nhnt9jV0o6FkFdt1zWbRSWOMi6/gpXChzNsh0AcLqmMct2HIrNsx1NnqtZtlPnpnn+9stLi4HMai7MTJOVIotFIsXTCUBlZDDqGKxEZCWYabLiMDQRERERGcSeRSIl4pANEdkTZpqsWCwSKZIJghUMViKyFsw0OXEYmoiIiIgMYs8ikRJxyIaI7AkzTVYsFomUSCdg9JALrxwkImvBTJMVh6GJiIiIyCD2LBIpkdDdnoxdBxGRNWCmyYrFIpES8fweIrInzDRZcRiaiIiIiAxizyKREvFkcCKyJ8w0WbFYJFIiDtkQkT1hpsmKw9BEREREZJAiexazo+vCwdlZ9u3omhfJvg0AaNPgvFm2MyVwl1m284BjoVm2AwDZtzRm2c7vpX6yb+Nm4S08Wd2FBUxwFG7cx8l0YkJ+h5Obo6zbaOt2Ttb1V2ipMU+eAcCZMl+zbGfrlVZm2U76NR+zbCf/hvz//QSA8hvFwEfVXJiZJitFFotEischGyKyJ8w0WXEYmoiIiIgMYs8ikRLpdACMvAGtjjewJSIrwUyTFYtFIiXikA0R2RNmmqw4DE1EREREBrFnkUiJeBRORPaEmSYrFotESsSnHRCRPWGmyYrD0ERERERkEHsWiRRICB2EMO7KP2M/T0RkKsw0ebFYJFIiIYwfcuH5PURkLZhpsmKxSKREwgTn9zBYichaMNNkpchisfyBGxB15e9u7h2SIfs2AKB/vV/Msp22TsVm2Y6b2s0s2wGAy+U3zbKdMiH/n1oZc06xnB1KoXGQ9x9AsOMVWddfobljuVm2AwDhTjlm2Y63w3WzbOcbp9Zm2c5vBfI/6x4AbjmX4IxZtkT3o8hikUjxdDpAZeQBE8/vISJrwUyTFYtFIiXikA0R2RNmmqx46xwiIiIiMog9i0QKJHQ6CCOHbHibCSKyFsw0ebFYJFIiDtkQkT1hpsmKw9BEREREZBB7FomUSCcAFY/CichOMNNkpchi8cGAS3B0dZJ9O+3cz8m+DQDo6Gyee4XVVbuaZTuXygvNsh0AOFwcapbtnCgKkn0bJUVlAFKrt7AQAIy9zQSD1VqkF/rCUcibaaHOl2Rdf4UIp3SzbAcAfM10T9d2mnyzbOe8a7ZZtqM2tiirphKHMuyu7sLMNFnZ5DD00qVL0bhxYzg7OyMyMhKHDh2ydJOIiGqFeUZE1s7misVPP/0UiYmJmD17Nn766Se0bt0asbGxuHTJPEe9RPZA6IRJJjIO84zINJhp8rK5YnHhwoWYOHEixo8fjxYtWmDFihWoW7cuPvzwQ0s3jch2CJ1pJjIK84zIRJhpsrKpYrG0tBRHjx5FTEyMNE+tViMmJgYpKSmVli8pKUFBQYHeRESWxWHX22qaZwAzjcjaKCXPbKpY/Ouvv1BeXg4/P/2HmPv5+SEnp/JFHklJSdBqtdIUHBxsrqYSWTVLDdlw2PVvNc0zgJlGZIglMk1JeWZTxWJNzZgxA/n5+dKUnW2eK8WIrJ6Fhmw47GocZhqRARbINCXlmU3dOqd+/fpwcHBAbm6u3vzc3Fz4+/tXWl6j0UCj0Uivxf8ui79VVCpvQ//nZuEts2znupnOs3B1MM92rpeb77yRmzfM8xvdvq2NvEr/tw1Rjds/3EKZ0Q87uIXb27t7KPTuvzupff8bdp0xY4Y0737DrvaspnkGGM60MjNk2k0X+8ozAHB2KDfLdq7rzLNPN4vMlGc35M8zwLozTXF5JmxMp06dxJQpU6TX5eXlokGDBiIpKem+n83Ozq54HhAnTnY7ZWdnG/wbuHnzpvD39zfZttzc3CrNmz17dpXbPn/+vAAgfvzxR735zz33nOjUqVP1AsDOGJNnQjDTOCljssZMU1qe2VTPIgAkJiZi7Nix6NChAzp16oRFixahqKgI48ePv+9nAwMDkZ2dDXd3d6hUqmptr6CgAMHBwcjOzoaHh4exzbcK9rZP9rY/QO32SQiB69evIzAw0OAyzs7OyMzMRGmpaXqihBCV/paq6lWkqhmTZwAzDbC//QHsb59quz/MNOthc8XiiBEjcPnyZcyaNQs5OTlo06YNtm3bVukk8aqo1WoEBdXuSRoeHh528Ud7J3vbJ3vbH6Dm+6TVau+7jLOzM5ydnY1pVq3UZtjV3hmTZwAz7U72tj+A/e1TbfbHWjNNaXlmkxe4TJkyBefOnUNJSQlSU1MRGRlp6SYR0X04OTmhffv22LVrlzRPp9Nh165diIqKsmDLLIt5RmR7lJZnNtezSES2y9hhVyIia6GkPGOxeB8ajQazZ8+2q3MW7G2f7G1/APvcJ8D4YVcynr3927K3/QHsb5/sbX8qKCnPVEJU45p0IiIiIlIkmzxnkYiIiIjMg8UiERERERnEYpGIiIiIDGKxSEREREQGsViswuuvvw6VSoWEhARpXq9evaBSqfSmp556ynKNvI85c+ZUam9YWJj0fnFxMeLj4+Ht7Q03NzfExcVVurmotbnfPtnabwQA58+fxxNPPAFvb2+4uLggIiICR44ckd4XQmDWrFkICAiAi4sLYmJikJ6ebsEWky1iplkfe8wzgJlmr3jrnLscPnwY77//Plq1alXpvYkTJ2Lu3LnS67p165qzaTXWsmVL7Ny5U3pdp87fP/f06dOxdetWbNiwAVqtFlOmTMHQoUPxww8/WKKp1XavfQJs6ze6du0aunbtit69e+Pbb7+Fj48P0tPTUa9ePWmZBQsWYPHixVizZg1CQkIwc+ZMxMbG4vTp0xZ5CgvZHmaa9WaaPeUZwEyzZywW71BYWIjRo0fjgw8+wLx58yq9X7duXZt6jE+dOnWqbG9+fj5WrVqF9evXIzo6GgCQnJyM5s2b4+DBg+jcubO5m1pthvapgi39Rm+88QaCg4ORnJwszQsJCZH+vxACixYtwssvv4zBgwcDANauXQs/Pz9s2rQJI0eONHubybYw06w70+wpzwBmmj3jMPQd4uPjMWDAAMTExFT5/rp161C/fn2Eh4djxowZuHHjhplbWDPp6ekIDAxEkyZNMHr0aGRlZQEAjh49irKyMr39DAsLQ8OGDZGSkmKp5laLoX2qYEu/0VdffYUOHTpg+PDh8PX1Rdu2bfHBBx9I72dmZiInJ0fvd9JqtYiMjLT634msAzPNujPNnvIMYKbZM/Ys/s8nn3yCn376CYcPH67y/ccffxyNGjVCYGAgjh8/jhdeeAFpaWnYuHGjmVtaPZGRkVi9ejUefPBBXLx4Ea+88gq6d++OkydPIicnB05OTvD09NT7jJ+fH3JycizT4Gq41z65u7vb3G/0xx9/YPny5UhMTMRLL72Ew4cPY9q0aXBycsLYsWOl3+LupwFY++9E1oGZZt1/K/aWZwAzza4JEllZWcLX11f88ssv0ryePXuKZ555xuBndu3aJQCIjIwMM7TQeNeuXRMeHh7iP//5j1i3bp1wcnKqtEzHjh3F888/b4HW1c6d+1QVa/+NHB0dRVRUlN68qVOnis6dOwshhPjhhx8EAHHhwgW9ZYYPHy4ee+wxs7WTbA8z7TZbyjRbzzMhmGn2jMPQuD2EcenSJbRr1w516tRBnTp1sHfvXixevBh16tRBeXl5pc9ERkYCADIyMszd3Frx9PREs2bNkJGRAX9/f5SWliIvL09vmdzcXJs6P+bOfaqKtf9GAQEBaNGihd685s2bS0NRFb/F3Vd02trvRObHTLvNlv5WbD3PAGaaPWOxCKBPnz44ceIEjh07Jk0dOnTA6NGjcezYMTg4OFT6zLFjxwDc/uOwBYWFhThz5gwCAgLQvn17ODo6YteuXdL7aWlpyMrKQlRUlAVbWTN37lNVrP036tq1K9LS0vTm/f7772jUqBGA2yeG+/v76/1OBQUFSE1NtanficyPmWZ7mWbreQYw0+yapbs2rdWdQzYZGRli7ty54siRIyIzM1Ns3rxZNGnSRPTo0cOyjbyH//u//xN79uwRmZmZ4ocffhAxMTGifv364tKlS0IIIZ566inRsGFDsXv3bnHkyBERFRVVafjA2txrn2zxNzp06JCoU6eOmD9/vkhPTxfr1q0TdevWFR9//LG0zOuvvy48PT3F5s2bxfHjx8XgwYNFSEiIuHnzpgVbTraImWZd7C3PhGCm2TMWiwbcGaxZWVmiR48ewsvLS2g0GtG0aVPx3HPPifz8fMs28h5GjBghAgIChJOTk2jQoIEYMWKE3rkuN2/eFE8//bSoV6+eqFu3rnj00UfFxYsXLdji+7vXPtnibySEEFu2bBHh4eFCo9GIsLAwsXLlSr33dTqdmDlzpvDz8xMajUb06dNHpKWlWai1ZMuYadbFHvNMCGaavVIJIYSlezeJiIiIyDrxnEUiIiIiMojFIhEREREZxGKRiIiIiAxisUhEREREBrFYJCIiIiKDWCwSERERkUEsFomIiIjIIBaLRERERGQQi0UiIiIiMojFIhEREREZxGJRQXr16oWEhASrX3d6ejpiY2Ph7u6OTp064dy5c/dc/sqVK/D19cXZs2dNsv0KI0eOxNtvv23SdRKR6TDTaoaZRrXFYtFKjRs3DkOGDKn156sKuo0bN+LVV1+95zKWdv36dTz00EOIiIjA0aNH4enped82zp8/H4MHD0bjxo0BAEVFRRg5ciQCAgIwatQo3LhxQ1p23LhxUKlUeP311/XWsWnTJqhUKr15L7/8MubPn4/8/HyT7BuRkjHTmGlku1gsKoiXlxfc3d0t3Yx72rhxI9RqNd588000a9YMkydPxu7duw0uf+PGDaxatQoTJkyQ5i1atAhubm747rvv4OLigkWLFul9xtnZGW+88QauXbt2z7aEh4cjNDQUH3/8sVH7RETyYKbdxkwjubFYtEHbtm1Dt27d4OnpCW9vbzzyyCM4c+aM9P64ceOwd+9evPvuu1CpVFCpVDh79qzeUbehZRo3blwpiNq0aYM5c+ZIr4uKijBmzBi4ubkhICCgymENnU6HpKQkhISEwMXFBa1bt8bnn39+331LT0+HVquVjohdXV31jqLv9s0330Cj0aBz587SvGvXrqFZs2aIiIhAWFgY8vLy9D4TExMDf39/JCUl3bc9AwcOxCeffHLf5Yio9phpf2OmkTVisWiDioqKkJiYiCNHjmDXrl1Qq9V49NFHodPpAADvvvsuoqKiMHHiRFy8eBEXL15EcHCw3jqqs4whzz33HPbu3YvNmzfju+++w549e/DTTz/pLZOUlIS1a9dixYoVOHXqFKZPn44nnngCe/fuvee6mzZtimPHjmHjxo0oKyvDuXPn4Ovra3D5/fv3o3379nrzpkyZgvfffx+Ojo5ITk7GM888o/e+g4MDXnvtNSxZsgR//vnnPdvTqVMnHDp0CCUlJfdcjohqj5n2N2YaWaM6lm4A1VxcXJze6w8//BA+Pj44ffo0wsPDodVq4eTkhLp168Lf37/KdVRnmaoUFhZi1apV+Pjjj9GnTx8AwJo1axAUFCQtU1JSgtdeew07d+5EVFQUAKBJkyY4cOAA3n//ffTs2bPSek+dOoXIyEgcP34c48ePx+OPP46ysjI4ODggOjraYHvOnTuHwMBAvXmNGzdGeno6Ll26BD8/v0rn7QDAo48+ijZt2mD27NlYtWqVwfUHBgaitLQUOTk5aNSo0b2/HCKqFWba35hpZI3Ys2iD0tPTMWrUKDRp0gQeHh7SSdBZWVmyb/vMmTMoLS1FZGSkNM/LywsPPvig9DojIwM3btzAQw89BDc3N2lau3at3tDSnU6dOgVXV1c0adIEH374IQoLC3Hu3Dk0a9YMvXr1MtiemzdvwtnZudJ8tVoNf3//KkO1whtvvIE1a9bg119/NbiMi4sLANxz2IiIjMNM+xszjawRexZt0MCBA9GoUSN88MEHCAwMhE6nQ3h4OEpLS41et1qthhBCb15ZWVmN1lFYWAgA2Lp1Kxo0aKD3nkajqfIzV65cgZ+fn/S6Tp06cHZ2RlpaGoYNG2ZwW/Xr17/vSd2G9OjRA7GxsZgxYwbGjRtX5TJXr14FAPj4+NRqG0R0f8y0vzHTyBqxWLQxV65cQVpaGj744AN0794dAHDgwIFKyzk5OaG8vPye66pqGR8fH1y8eFF6XVBQgMzMTOl1aGgoHB0dkZqaioYNGwK4ffL177//Lg3FtGjRAhqNBllZWVUOz1TF3d0dubm5evPmzp2LRx55BE2bNjX4ubZt2xp1Zd/rr7+ONm3a6PUi3OnkyZMICgpC/fr1a70NIjKMmaaPmUbWiMWiFcvPz8exY8f05tWrVw/e3t5YuXIlAgICkJWVhRdffLHSZxs3bozU1FScPXsWbm5u8PLyqtYy0dHRWL16NQYOHAhPT0/MmjULDg4O0mfc3NwwYcIEPPfcc/D29oavry/+/e9/Q63++4wGd3d3PPvss5g+fTp0Oh26deuG/Px8/PDDD/Dw8MDYsWMrtaV3797Iy8tDUlISxo8fj48//hiff/45UlNT7/kdVRxFX7t2DfXq1bvfV1pJREQERo8ejcWLF1f5/v79+9G3b98ar5eIKmOmMdPIRgmySmPHjhUAKk0TJkwQO3bsEM2bNxcajUa0atVK7NmzRwAQX375pfT5tLQ00blzZ+Hi4iIAiMzMTNGzZ0/xzDPP3HOZ/Px8MWLECOHh4SGCg4PF6tWrRevWrcXs2bOlz12/fl088cQTom7dusLPz08sWLCg0rp1Op1YtGiRePDBB4Wjo6Pw8fERsbGxYu/evQb3ed26dSIoKEi4uLiIAQMGiIyMjGp9V506dRIrVqyo9vc6ePBgvXmZmZnCyclJ3P3ncPPmTaHVakVKSkq11k1EhjHTmGlku1RC3HUyB5GN2bp1K5577jmcPHlSrzfAWMuXL8eXX36J7777zmTrJCK6H2YaWRsOQ5PNGzBgANLT03H+/Plq31etOhwdHbFkyRKTrY+IqDqYaWRt2LNIRERERAbxPotEREREZBCLRSIiIiIyiMUiERERERnEYpGIiIiIDGKxSEREREQGsVgkIiIiIoNYLBIRERGRQSwWiYiIiMggFotEREREZND/Awcas9Mcd0epAAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_zonal_coupling(cross_degree, \"cross-degree\", \"k\")" + ] + }, + { + "cell_type": "markdown", + "id": "a9035fe5", + "metadata": {}, + "source": [ + "#### Cross-Closeness" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "31c0412b-736e-472f-b77a-f71e228728f3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_zonal_coupling(cross_closeness, \"cross-closeness\", \"c\")" + ] + }, + { + "cell_type": "markdown", + "id": "c72290fc", + "metadata": {}, + "source": [ + "#### Cross-Betweenness\n", + "\n", + "In contrast to the previous two local measures, cross-betweenness $b^{ij}_w$ is defined for $w\\in V_i \\cup V_j$ and is *symmetric* w.r.t. exchanging the involved subnetworks. Therefore, in the following we analyse the zonally averaged fields of cross-betweenness\n", + "for vertices taken from a specific isobaric subnetwork $i$,\n", + "$$ b^{ij}_i(\\vartheta)=\\langle b^{ij}_{w(\\vartheta,\\phi)}\\rangle_{\\phi,w\\in V_i}\\,.$$" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "80f0b4a6-4505-4e15-b3c3-63208e3f93e6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def clip_log(Z):\n", + " neg = Z <= 1\n", + " return np.where(neg, 0, np.log10(Z, where=np.logical_not(neg)))\n", + "\n", + "plot_zonal_coupling(\n", + " cross_betweenness, \"cross-betweenness\", \"log_{10}\\,b\",\n", + " vert_labels=(\"near ground\", \"upper level\"),\n", + " vert_indices=(r\"_{\\,1}^{\\,1l}\", r\"_{\\,l}^{\\,1l}\"),\n", + " transform=clip_log)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyunicorn", + "language": "python", + "name": "pyunicorn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/tutorials/EventSeriesAnalysis.ipynb b/examples/tutorials/EventSeriesAnalysis.ipynb new file mode 100644 index 00000000..1149e36d --- /dev/null +++ b/examples/tutorials/EventSeriesAnalysis.ipynb @@ -0,0 +1,573 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "12877375", + "metadata": {}, + "source": [ + "# Tutorial: Event Series Analysis " + ] + }, + { + "cell_type": "markdown", + "id": "efc240da", + "metadata": {}, + "source": [ + "*Originally written as part of a PhD thesis in Physics by Jonathan F. Donges (donges@pik-potsdam.de) at the Potsdam Institute of Climate Impact Research (PIK) and Humboldt University Berlin.*\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "f2f2eb59", + "metadata": {}, + "source": [ + "synchronization measures of time series have been attracting attention in several research areas, including climatology and neuroscience.\n", + "synchronization can be understood as a measure of interdependence or strong correlation between time series. \n", + "The main use cases of synchronization are:\n", + "- Quantification of similarities in phase space between two time series\n", + "- Quantification of differences in phase space between two time series.\n", + "\n", + "A research example of synchronization phenomena is the analysis of electroencephalographic (EEG) signals as a major influencing factor to understand the communication within the brain, see [Quiroga et al. (2001)](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.65.041903).\n", + "\n", + "Two widely accepted measurement methods of synchronization are **Event Synchronization (ES)** and **Event Coincidence Analysis (ECA)**. The non-linear nature of these two methods makes them widely applicable for a wide range of utilizations. \n", + "While ES does not include the difference in timescales when measuring synchrony, when using ECA a certain timescale has to be selected for analysis purposes.\n", + "For more background information consult [Odenweller et al. (2020)](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.052213) and [Quiroga et al. (2001)](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.66.041904)." + ] + }, + { + "cell_type": "markdown", + "id": "5424b801", + "metadata": {}, + "source": [ + "## Event Synchronization (ES)" + ] + }, + { + "cell_type": "markdown", + "id": "a0721685", + "metadata": {}, + "source": [ + "As mentioned before, the parameter-free method ES offers a fast and reliable way to measure synchronization between time series.\n", + "The fundamental idea is illustrated by the picture below ([Odenweller et al., 2020](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.052213)):\n", + "\n", + "![Event synchronization](./images/EventSynchronisation.png)\n", + "\n", + "Two events $l$ and $m$ from timeseries $i$ and $j$, respectively, are considered synchronous if they occur within a certain time interval $\\tau$ which is determined from the data properties. The time interval $\\tau$ is defined as:\n", + "\n", + "$$\\tau_{lm}^{ij}=\\frac{1}{2}\\min\\left\\{t_{l+1}^{i}-t_{l}^{i}, \\; t_{l}^{i}-t_{l-1}^{i}, \\; t_{m+1}^{j}-t_{m}^{j}, \\; t_{m}^{j}-t_{m-1}^{j}\\right\\}$$\n", + "\n", + "Thus, given an event in timeseries $j$, the occurrences of synchronized events in $i$ can be counted as\n", + "\n", + "$$c(i|j)=\\sum_{l=2}^{s_i-1}\\sum_{m=2}^{s_j-1}J_{lm}^{ij} \\,,$$\n", + "\n", + "where $J_{lm}^{ij}$ counts the events that match the synchronization condition.\n", + "Finally, we can define the strength of event synchronization between the timeseries $i$ and $j$ by\n", + "\n", + "$$Q_{ij}^{ES}=\\frac{c(i|j)+c(j|i)}{\\sqrt{(s_i-2)(s_j-2)}}\\,.$$\n", + "\n", + "In the usual case, when the timeseries are not fully synchronized, $0 \\le Q_{ij}^{ES} \\le 1$, and total or absent synchronization correspond to $Q_{ij}^{ES} = 1$ or $Q_{ij}^{ES} = 0$, respectively.\n", + "To generate an undirected network from a set of timeseries, we can consider the values $Q_{ij}^{ES}$ as the coefficients of a square symmetric matrix $Q^{ES}$. It should be noted that fully synchronized time series will adapt a value of $Q_{ii}^{ES} = Q_{jj}^{ES} = 1$.\n", + "\n", + "The advantage of ES is that no parameters, such as a delay specification between the two timeseries, has to selected *a priori*." + ] + }, + { + "cell_type": "markdown", + "id": "1efab43e", + "metadata": {}, + "source": [ + "## Event Coincidence Analysis (ECA)" + ] + }, + { + "cell_type": "markdown", + "id": "fae1001e", + "metadata": {}, + "source": [ + "In contrast, ECA incorporates *static (global) coincidence intervals*. For a chosen tolerance interval $\\Delta T$, an *instantaneous event coincidence* between two events $t_{m}^{j} < t_{l}^{i}$ is defined by the condition\n", + "$0 \\leq t_{l}^{i} - t_{m}^{j} \\leq \\Delta T$, and is generalized to a *lagged event coincidence* via a *time lag* $\\tau \\ge 0$ on timeseries $i$. The fundamental idea is illustrated by the picture below ([Odenweller et al., 2020](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.052213)):\n", + "\n", + "![ECA](images/ECA.png)\n", + "\n", + "When computing the coincidence rate with ECA, the *precursor* and *trigger* event coincidence rates should be distinguished. The former refers to events in $i$ that precede all events in $j$, and the latter referes to events in $j$ that precede at least one event in $i$. More precisely, the precursor event coincidence rate is defined as\n", + "\n", + "$$r_p(i|j;\\Delta T,\\tau) = \\frac{1}{s_i-s_{i}'}\\sum_{l=1+s_i'}^{s_i} \\Theta \\left[\\sum_{m=1}^{s_j} 1_{[0,\\Delta T]}\\left[(t_l^i-\\tau)-t_m^j\\right]\\right] \\,,$$\n", + "\n", + "and the trigger event coincidence rate as\n", + "\n", + "$$r_p(i|j;\\Delta T,\\tau)=\\frac{1}{s_j-s_j''}\\sum_{m=1}^{s_j-s_j''}\\Theta\\left[\\sum_{l=1}^{s_i} 1_{[0,\\Delta T]} \\left[(t_l^i-\\tau)-t_m^j\\right]\\right] \\,.$$\n", + "\n", + "For details on the calculation of $s_i', s_j''$, consult [Odenweller et al. (2020)](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.052213).\n", + "By changing the indices in the precursor or trigger rate, one obtains the opposite rates, e.g., $r_t(j|i; \\Delta T, \\tau)$. Therefore, the ECA yields a total of four coincidence rates.\n", + "\n", + "By computing the mean or the maximum of the two directed trigger coincidence rates $r_t(i|j; \\Delta T,\\tau)$ and $r_t(j|i;\\Delta;T,\\tau)$,\n", + "one arrives at the *degree of event synchrony* $Q_{ij}^{ECA, mean}$ or $Q_{ij}^{ECA, max}$,\n", + "which can be used as a statistical measure of uni- or bidirectional dependency." + ] + }, + { + "cell_type": "markdown", + "id": "8a82f258", + "metadata": {}, + "source": [ + "## ES/ECA for Simple Random Event Series" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "6e06f934-ec77-4034-a57b-8da719b5bb1a", + "metadata": {}, + "source": [ + "`pyunicorn` provides a class for ES and ECA. In addition, a method is included for the generation of binary event series from continuous timeseries data. First, we import the necessary packages." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "82ab6709", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from pyunicorn.eventseries import EventSeries" + ] + }, + { + "cell_type": "markdown", + "id": "1274c566-566b-41a0-a7b5-5a925ce824ea", + "metadata": {}, + "source": [ + "### Input: Event Matrix" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1c7c6d9c-4348-4588-af02-55b630044382", + "metadata": {}, + "source": [ + "Next, we initialize the `EventSeries` class with a toy event matrix, in which the first axis represents the timesteps and the second axis covers the variables. Each variable at a specific timestep is either `1` if an event occurred or `0` if it did not." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1be1ea26-cdb7-44c5-9a7f-00d1adf7dcff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EventSeries: 2 variables, 10 timesteps, taumax: 1.0, lag: 0.0\n" + ] + } + ], + "source": [ + "series = np.array([[0, 0],\n", + " [1, 1],\n", + " [0, 0],\n", + " [1, 1],\n", + " [0, 0],\n", + " [0, 0],\n", + " [1, 0],\n", + " [0, 1],\n", + " [0, 0],\n", + " [0, 0]])\n", + "\n", + "ev = EventSeries(series, taumax=1)\n", + "print(ev)" + ] + }, + { + "cell_type": "markdown", + "id": "c350db9f", + "metadata": {}, + "source": [ + "**Caution:** The argument `taumax` represents the maximum time difference $\\Delta T$ between two events that are to be considered synchronous. For ES, using the default `taumax=np.inf` is sensible because of its dynamic coincidence interval, whereas for ECA, a finite `taumax` needs to be specified." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "59b6d71d-4852-4441-8174-f57e8c500cb8", + "metadata": {}, + "source": [ + "For variables $X,Y$, the return values of the synchronization analysis methods are:\n", + "\n", + "- ES: $\\left( Q^{ES}_{XY},\\, Q^{ES}_{YX} \\right)$\n", + "- ECA: $\\left( r_p(Y|X),\\, r_t(Y|X),\\, r_p(X|Y),\\, r_t(X|Y) \\right)$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9295b844", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.5, 0.5)\n", + "(0.5, 1.0, 1.0, 1.0)\n" + ] + } + ], + "source": [ + "print(ev.event_synchronization(*series.T))\n", + "print(ev.event_coincidence_analysis(*series.T, taumax=1))" + ] + }, + { + "cell_type": "markdown", + "id": "43a272de-8ee9-4734-925a-a45965c70c18", + "metadata": {}, + "source": [ + "### Input: Timeseries" + ] + }, + { + "cell_type": "markdown", + "id": "80260d7e", + "metadata": {}, + "source": [ + "If the input data is not provided as an event matrix, the constructor tries to generate one from continuous time series data using the `make_event_matrix()` method. Therefore, the argument `threshold_method` needs to be specified along with the argument `threshold_values`. `threshold_method` can be `'quantile'`, `'value'`, or a 1D Numpy array with entries `'quantile'` or `'value'` for each variable. If `'value'` is selected, one has to specify a number lying in the range of the array; for `'quantile'`, a number between 0 and 1 has to be selected since it specifies the fraction of the array's values which should be included in the event matrix. Additionally, one can specify the argument `threshold_type`, if the threshold should be applied `'above'` or `'below'` the specified `threshold_method`.\n", + "\n", + "Here is a simple example for finding the synchrony between two continuous time series variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "aca1e9f0-1892-42e0-ae31-13321c30589b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[4 8]\n", + " [3 5]\n", + " [2 2]\n", + " [3 6]\n", + " [2 0]\n", + " [3 3]\n", + " [6 7]\n", + " [5 3]\n", + " [6 1]\n", + " [4 0]]\n", + "EventSeries: 2 variables, 10 timesteps, taumax: 1.0, lag: 0.0\n" + ] + } + ], + "source": [ + "series = (10 * np.random.rand(10, 2)).astype(int)\n", + "ev = EventSeries(series, threshold_method='quantile',\n", + " threshold_values=0.5, threshold_types='below', taumax=1)\n", + "print(series); print(ev)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c92101e6-85e5-4dad-84c1-55a3d9331a62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ES: (0.43301270189221935, 0.43301270189221935)\n", + "ECA: (1.0, 1.0, 1.0, 1.0)\n", + "ECA[p]: (1.0, 1.0)\n" + ] + } + ], + "source": [ + "print(\"ES:\", ev.event_synchronization(*series.T))\n", + "print(\"ECA:\", ev.event_coincidence_analysis(*series.T, taumax=1))\n", + "# only compute the precursor event coincidence rates\n", + "print(\"ECA[p]:\", ev._eca_coincidence_rate(*series.T, window_type='advanced'))" + ] + }, + { + "cell_type": "markdown", + "id": "3ad4d332-4909-4fa9-9ed7-8539decd7ff9", + "metadata": {}, + "source": [ + "### Output: Event Series Measure / Functional Network" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8b13dcec-2393-439e-94c9-c261d489760a", + "metadata": {}, + "source": [ + "The `event_series_analysis()` method, with its argument `method` set to `ES` or `ECA`, can be used to construct a measure of dependence (*functional network*) as described in the introduction. Such matrices can then be subjected to complex network theory methods as a part of non-linear timeseries analysis, see **Tutorial: Recurrence Networks**.\n", + "\n", + "The return value is a $N\\!\\times\\! N$ matrix, where $N$ is the number of variables. For detailed information on the calculation of the matrix and the required arguments, please consult the API documentation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0f5cf4b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ES:\n", + "[[0. 0.20412415]\n", + " [0.20412415 0. ]]\n", + "ECA:\n", + "[[0. 0.66666667]\n", + " [0.66666667 0. ]]\n" + ] + } + ], + "source": [ + "matrix_ES = ev.event_series_analysis(method='ES')\n", + "print(\"ES:\"); print(matrix_ES)\n", + "matrix_ECA = ev.event_series_analysis(method='ECA', symmetrization='mean', window_type='advanced')\n", + "print(\"ECA:\"); print(matrix_ECA)" + ] + }, + { + "cell_type": "markdown", + "id": "6376d465", + "metadata": {}, + "source": [ + "### Significance Level Calculations" + ] + }, + { + "cell_type": "markdown", + "id": "d28b190a", + "metadata": {}, + "source": [ + "The signifcance levels of event synchronization can also be calculated using `pyunicorn`. The methods `_empirical_percentiles()` and `event_analysis_significance()` respectively estimate the $p$-values via a Monte Carlo approach and the signifcance levels ($1 - p$) via a Poisson process. For detailed information please consult the API documentation." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4e4dc503-53c9-4aad-9f32-c5b05569642c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MC[ES]:\n", + "[[0. 0.2711]\n", + " [0.2691 0. ]]\n", + "MC[ECA]:\n", + "[[0. 0.2847]\n", + " [0.2847 0. ]]\n", + "Poisson[ES]:\n", + "[[0. 0.2683]\n", + " [0.2684 0. ]]\n", + "Poisson[ECA]:\n", + "[[0. 0.2941]\n", + " [0.2941 0. ]]\n" + ] + } + ], + "source": [ + "print(\"MC[ES]:\")\n", + "print(ev._empirical_percentiles(method='ES', n_surr=int(1e4)))\n", + "print(\"MC[ECA]:\")\n", + "print(ev._empirical_percentiles(method='ECA', n_surr=int(1e4),\n", + " symmetrization='mean', window_type='advanced'))\n", + "print(\"Poisson[ES]:\")\n", + "print(ev.event_analysis_significance(method='ES', n_surr=int(1e4)))\n", + "print(\"Poisson[ECA]:\")\n", + "print(ev.event_analysis_significance(method='ECA', n_surr=int(1e4),\n", + " symmetrization='mean', window_type='advanced'))" + ] + }, + { + "cell_type": "markdown", + "id": "cb3c47de", + "metadata": {}, + "source": [ + "## ES/ECA for Generating a Climate Network" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7211ee7d-e2b3-4e6d-bf72-3b8c2dbe50fa", + "metadata": {}, + "source": [ + "A possible further application of ES and ECA is the generation of climate networks from the event series measures above, and is implemented in the `EventSeriesClimateNetwork` class.\n", + "\n", + "**Note:** If more general applications of event series networks are desired, use the `EventSeries` class together with the `Network` class." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ea03f3ea", + "metadata": {}, + "outputs": [], + "source": [ + "from pyunicorn.core import Data\n", + "from pyunicorn.climate.eventseries_climatenetwork import EventSeriesClimateNetwork" + ] + }, + { + "cell_type": "markdown", + "id": "c33df5f1", + "metadata": {}, + "source": [ + "We shall use the small test climate dataset provided by `Data`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9b8c2e94", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data: 6 grid points, 60 measurements.\n", + "Geographical boundaries:\n", + " time lat lon\n", + " min 0.0 0.00 2.50\n", + " max 9.0 25.00 15.00\n", + "\n", + "[[ 0.00000000e+00 1.00000000e+00 1.22464680e-16 -1.00000000e+00\n", + " -2.44929360e-16 1.00000000e+00]\n", + " [ 3.09016994e-01 9.51056516e-01 -3.09016994e-01 -9.51056516e-01\n", + " 3.09016994e-01 9.51056516e-01]\n", + " [ 5.87785252e-01 8.09016994e-01 -5.87785252e-01 -8.09016994e-01\n", + " 5.87785252e-01 8.09016994e-01]\n", + " [ 8.09016994e-01 5.87785252e-01 -8.09016994e-01 -5.87785252e-01\n", + " 8.09016994e-01 5.87785252e-01]\n", + " [ 9.51056516e-01 3.09016994e-01 -9.51056516e-01 -3.09016994e-01\n", + " 9.51056516e-01 3.09016994e-01]\n", + " [ 1.00000000e+00 1.22464680e-16 -1.00000000e+00 -2.44929360e-16\n", + " 1.00000000e+00 3.67394040e-16]\n", + " [ 9.51056516e-01 -3.09016994e-01 -9.51056516e-01 3.09016994e-01\n", + " 9.51056516e-01 -3.09016994e-01]\n", + " [ 8.09016994e-01 -5.87785252e-01 -8.09016994e-01 5.87785252e-01\n", + " 8.09016994e-01 -5.87785252e-01]\n", + " [ 5.87785252e-01 -8.09016994e-01 -5.87785252e-01 8.09016994e-01\n", + " 5.87785252e-01 -8.09016994e-01]\n", + " [ 3.09016994e-01 -9.51056516e-01 -3.09016994e-01 9.51056516e-01\n", + " 3.09016994e-01 -9.51056516e-01]]\n" + ] + } + ], + "source": [ + "data = Data.SmallTestData()\n", + "print(data); print()\n", + "print(data.observable())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e591bcf2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "EventSeriesClimateNetwork:\n", + "EventSeries: 6 variables, 10 timesteps, taumax: 16.0, lag: 0.0\n", + "ClimateNetwork:\n", + "GeoNetwork:\n", + "SpatialNetwork:\n", + "Network: directed, 6 nodes, 0 links, link density 0.000.\n", + "Geographical boundaries:\n", + " time lat lon\n", + " min 0.0 0.00 2.50\n", + " max 9.0 25.00 15.00\n", + "Threshold: 0\n", + "Local connections filtered out: False\n", + "Type of event series measure to construct the network: directedES\n" + ] + } + ], + "source": [ + "climate_ES = EventSeriesClimateNetwork(\n", + " data, method='ES', taumax=16.0,\n", + " threshold_method='quantile', threshold_values=0.8, threshold_types='above')\n", + "print(climate_ES)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bbb03017", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting network adjacency matrix by thresholding...\n", + "Setting area weights according to type surface ...\n", + "Setting area weights according to type surface ...\n", + "EventSeriesClimateNetwork:\n", + "EventSeries: 6 variables, 10 timesteps, taumax: 16.0, lag: 0.0\n", + "ClimateNetwork:\n", + "GeoNetwork:\n", + "SpatialNetwork:\n", + "Network: directed, 6 nodes, 0 links, link density 0.000.\n", + "Geographical boundaries:\n", + " time lat lon\n", + " min 0.0 0.00 2.50\n", + " max 9.0 25.00 15.00\n", + "Threshold: 0\n", + "Local connections filtered out: False\n", + "Type of event series measure to construct the network: directedECA\n" + ] + } + ], + "source": [ + "climate_ECA = EventSeriesClimateNetwork(\n", + " data, method='ECA', taumax=16.0,\n", + " threshold_method='quantile', threshold_values=0.8, threshold_types='above')\n", + "print(climate_ECA)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyunicorn", + "language": "python", + "name": "pyunicorn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/tutorials/RecurrenceNetworks.ipynb b/examples/tutorials/RecurrenceNetworks.ipynb new file mode 100644 index 00000000..a73e2b1f --- /dev/null +++ b/examples/tutorials/RecurrenceNetworks.ipynb @@ -0,0 +1,882 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "d35172b4", + "metadata": {}, + "source": [ + "# Tutorial: Recurrence Networks\n", + "\n", + "This tutorial introduces the analysis of non-linear timeseries via a complex network approach. The implementation of this methodology in the `pyunicorn` package is illustrated with two examples, showcasing the basic use of the classes `timeseries.Recurrence{Plot,Network}`." + ] + }, + { + "cell_type": "markdown", + "id": "5fb6ee17-1bb9-401a-a574-6e05565640ef", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "### Recurrence Plots for the Analysis of Complex Systems\n", + "\n", + "So far, the analysis of complex networks in different scientific fields has been performed by studying the adjacency matrix $A_{i,j}$. Recent work has been focussing on studying time series using a similar approach, by transforming the time series into a complex network and then analyzing the phase space and its properties using the **recurrence plot (RP)**.\n", + "\n", + "The **recurrence matrix** can be considered as the adjacency matrix of an undirected, unweighted network. With this approach, it is possible to characterise the local and global properties of a network. \n", + "In particular, it can be applied to *(i)* both univariate and multivariate time series (phase space trajectories), *(ii)* with and without pronounced oscillatory components, and *(iii)* with or without embedding. Moreover, similar to traditional **recurrence quantification analysis (RQA)**, studying network properties for sliding windows in time also allows for coping with non-stationary time series. Consequently, unlike for most of the existing techniques, there are no fundamental restrictions w.r.t. its practical applicability to arbitrary time series.\n", + "\n", + "For more detailed background information, please consult [Marwan et al. (2009)](https://www.sciencedirect.com/science/article/abs/pii/S0375960109011852)." + ] + }, + { + "cell_type": "markdown", + "id": "ba9af2d3-8273-485e-9fa3-a0848b74ce9b", + "metadata": {}, + "source": [ + "### Visualization of a Timeseries and its Recurrence Network\n", + "\n", + "Before we get started, we shall visualize a timeseries based on a three dimensional chaotic oscillator example described by the Lorenz system:\n", + "\n", + "$$\\frac{d}{dt}(x,y,z)=\\left( 10(y-x),x(28-z)-y,xy-\\frac{8}{3}z \\right) \\,.$$\n", + "\n", + "The following figures represent the timeseries in phase space and its RP.\n", + "\n", + "![Recurrence Plot](images/REcurrencePlot_v2.png)\n", + "\n", + "__(A)__ A state at time *i* (red dot) is recurrent at another time *j* (black dot) when the phase space trajectory visits its close neighbourhood (gray circle). This is marked by value 1 in the recurrence matrix at *(i, j)*. States outside of this neighbourhood (small red circle) are marked with 0 in the recurrence matrix.\n", + "\n", + "__(B)__ Graphical representation of the corresponding recurrence matrix (recurrence plot).\n", + "\n", + "For literature review and background information, see [Donner et al. (2010b)](https://www.worldscientific.com/doi/abs/10.1142/S0218127411029021)." + ] + }, + { + "cell_type": "markdown", + "id": "17927a78-ff98-42c6-8d8f-ee7c81554df2", + "metadata": {}, + "source": [ + "### Correspondence Between Recurrence Networks and Phase Space Measurements\n", + "\n", + "The following table shows how some of the characteristics measured from a recurrence network translate into corresponding phase space features of the time series. For more background information, consult [Donges et al. (2012)](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.85.046105).\n", + "\n", + "\n", + "| Scale | Recurrence Network | Phase space interpretation |\n", + "| :--- | :--- | :--- |\n", + "| Local | Continuous local $\\epsilon$-clustering coefficient | Local dimension | \n", + "| Local | Continuous $\\epsilon$-matching index | Local density gradient | \n", + "| Global | Continuous $\\epsilon$-transitivity | Global dimension | \n", + "| Global | Continuous global $\\epsilon$-clustering | Average local dimension | " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ec852748", + "metadata": {}, + "source": [ + "## Example: Logistic Map\n", + "\n", + "First, we import the necessary packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "111c44d0-ab2b-4aca-8c96-44eb7be9fa10", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from tqdm.auto import tqdm\n", + "\n", + "from pyunicorn.timeseries import RecurrenceNetwork, RecurrencePlot" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "e4994e9a", + "metadata": {}, + "source": [ + "We create a function to calculate a logistic map timeseries:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "39494417", + "metadata": {}, + "outputs": [], + "source": [ + "def logistic_map(x0: float, r: float, T: int, spinup: int = 100):\n", + " \"\"\"\n", + " Returns a time series of length T using the logistic map\n", + " x_(n+1) = r*x_n(1-x_n)\n", + " at parameter r and using the initial condition x0.\n", + " \n", + " INPUT: x0 - Initial condition, 0 <= x0 <= 1\n", + " r - Bifurcation parameter, 0 <= r <= 4\n", + " T - length of the desired time series\n", + " spinup - number of spinup-timesteps before storing results to output\n", + " OUTPUT: numpy array of timeseries under given parameters\n", + " \"\"\"\n", + " # spinup\n", + " for n in range(spinup):\n", + " x0 = r * x0 * (1 - x0)\n", + " \n", + " timeseries = np.zeros(T + 1)\n", + " timeseries[0] = x0\n", + " for n in range(T):\n", + " # get current timestep value\n", + " xn = timeseries[n]\n", + " # calculate next timestep value\n", + " xstep = r * xn * (1 - xn)\n", + " timeseries[n + 1] = xstep\n", + " return timeseries" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9c841c22", + "metadata": {}, + "source": [ + "After choosing the bifurcation parameter $r$, the initial value $x_0$ and the length of the timeseries $T$, we can now generate a timeseries. Play around with the parameters if you like! For an interesting regime, we should stick with $r >3.6$.\n", + "**Note:** Our `logistic_map()` function includes a spinup of 100 timesteps by default, so the resulting timeseries will not start at $x_0$." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4e4391fc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zidDnAxNCDgT5QHQ6S0TVnU4TGxiyHM4E1/JandNZxIKxcyf7rszQCH3LlB072Kvp3riW12Y5/jPPANdcA/z0p/L3I9OphssgVhHzycqVwPLl8jHlm+n0WZxiaCgJNLmW14bYb++9l81ZTz3V/F4ZTOfGjcmc6eP8WeF6jbetru/i/HlzMmNOpxq+59q8iE5nI6LTWSJaRV7bijmdrgsJ6b5fhKEQAluYt+0yY6Esljbr8cn4Mxk6kelshkuns4iqxPRsTRLSkSajpAAdYGY6W9GA1o35MhzmhQuBZcvk74V871zfG90aCLhdS/I61EXKa6vo3Dz7LAveyhByn7aBb1VKaIhOZ4moOtPZytVr8zKd1P68TmeVJapVbnvW45uczsh0qhF6jrju2fqOvvs0ZHin0zRmW9GA1ikzXPeL/fuBJ54A1q9Xv+/y/HngOuhuYjpdOi95g1xFymur6Nx8/essTeX++5vfC7lPmzA87LeYYIiITmeJqLrTaVv4o4ry2qJyOn3Ia/v7k2P4kKi6zOksqwhS1uObouu8c1B0HpEJValea7p3vuYTnRHr0xAaHEz29PMxV9JzAyLTKcJ1222VFa7OnwdlyWt9MJ0hyWur6Nxs3sxet29vfq/K1xbyePSF6HSWiKqX8bdlJqoor/VZvTbvYkiLLeCHlSmK6TS1PcQtU9IYgeQoFInh4cbnzyP0xTr0nE6fucY6hDJXim0h2Mw3Tz0FbN0qf893ANIEn+kAZJTbsHmh3buyHHJTEAsIT17LM50qG+KKK4D3vlf+nu85IS+oX/PON6HK1xbyePSF6HSWCNfMQ5WZTt+Ds1WczrLZwiJYl1CYzixj0lZeC7hp/9VXs0qasv1fQ1+sXQax8j7Xej1cea3vuTIN06l6dmedBbzlLfL3fF+fCT6DEXRumXFexvnzIKRCQqHJa22+//3vMxmqDKGPGRNCnWttcdVV1VyDfSA6nSWiTHltiPt0/va3wDnnyL/vWwpoktfq5Kv897I4nXkXDJPTWVYBhXo9G5vnM0eqKKbTRtLlol/fdx87d19f83uhGyIhy/X37Uuelw8Z5e7dwFFHMWNG1jaX5zbBxHTa9LuNG4ENG+TvhW6o+TSQW4Xp9CGvdXn+MpxOSqPxUaHeNXT9OvRre+QR4LzzgP/5n+b3Qldt+EB0OktEmfJal4WEZJM6v2G26tw33ABcfz3w6KPN7/leLMmQGhqSO04uczrzOiZp5LUuI7xZj2+bO+cyHxVwW0go6/FNoL0CqyhLcllIqKjnCvhxLh5/HHjgAeDWW5vfE59r2bnCReR09vfbsXUhGmo+q9eGntO5fTvwu9/J3yurkFAVq9fayGvp/6HlmBcBW6czxGujfFTZNkahr8E+EJ3OElGE0/m61wEXXeTu+CoMDOgdL/58pklTtnmz78Fpqh5XlLzWhQHrU14rXk/RxXiqwnT6MgKffZa9ygz40KOsIed08gaED9m37rmK7XGRK6xDEdVrbZ3O0Ay1wcFkrXC1/ZUOoTOdX/wi8PKXy5UXrucjGrN8ygePqstrqf1VDDCaUOWcTgr82ti1ZQcIQ0R0OktEEZG+m24C/vIXd8dXoYjiEbpJ07eBbLv3XIg5nSYDuawIb5bj1+ut63Ty7H/W45tgy3RW0en0Wb02DdPpM5gAlP9sRzLTyV97iEynbwP9iSfY69NPN79XlrxWPFcZ5/ftdPq2n/JCF0wJPaczzVxddoAwRESns0TknfQGB9nglEVU6nW3k2pRhgYQJtNp63RmZWXKyul0JVHdt08+YeY1gPnjujLi1q8H7rlHff48x9c5zOLxfDJiIUaI88prh4aAG2+UR4+LKhAlHotQFtNpmitdnV8Hfi3IEiiiYIzK6fSds6pDmn7hg+n0baA/8wx7lfXbsuS14rkIIW+ZkkZeW8W53oQq53SmUaWE6DSXjeh0loi8Bq6Oxh8YaDS+iu7crcx0Dg83tqlqTKfrQkL79gGHHgpceqn8PR5pj5+m7VkXnPe8B3j1q+XvuaxeK16PiwWzlZlOevZDQ/LiGT/7GXDmmcCvftX83khiOss2xGwDdKr3qb179zYHDFwHT/PCp6qEP//AQHMQcHCwcZz4MNB1TmdITGfR9ybvfFOkvDa0MWMCL1mvYk6nrbwWCNNpLhvR6SwR+/YBPT3s9yyDxyaKSMcvunMXUSbfxHR2drLfy55YxEnc5HTKmJVWdjo3bmTJ8g880Pwe3buurmzHL0MS1dfH2i97bnv3At3d2Y9PRmB/f7NjVEaU02bBa2sLb7EbHrYv3gXI7x3Nh1TIgQf1y+7u6uVJA3ZMp6/5Mq/qhX9fNUbanrNMQuu3vhlwYoTEc8n+9mGg6/rtvn1ARwfQ3u6e6XQVEHjsMfU6AgC1Wn6mM6vTWaux30MbMybwY6qKLK4N09nRwV5DdJrLRnQ6S8S+fcDYsez3LIOHjCw+0kygzk3HrxrTuX8/c5hdLUg6iAukzlBSbQtSlLzWRfXavMenTdx5Y1M83/jx2Y5fRpXQrVvZc1OxRuPGZT8+337RABf/Lrpf1+t2C97YseEtdiLLJd4rm3xYel82H1JfGjfODdPp2hCyfa6uzq9D3rWAf1+ce12vY3nhs1I4oO+X4hgKUV7b08MMcJcsMJ1LRN61ZPVq4LDDmMJCBI3T8eOzM53kNGaV144Zw34PbcyYwAdSqsh02qhSfM3VIaI0p3Pt2rVYtGgR5s2bh4ULF2LNmjVNnxkeHsayZctw1FFH4bjjjsPpp5+OdevWldVE59i3j00MWaNhNKH39zd3Xt7IAvwynVlyOvftY6xEZ2f5E4totGZhcluZ6SSn02TcZzl+GfJaKmqhan9RTqe4YLpmHnbvTgIgqgWvrQ0YNSq8xU58FrKKrKZ0AZ9Op2sZpYkxAvw5ZnlVL/z/xH7r26E2ISSmUzXfkOokRHmtyzXetTqBlD6yLd/yzjd797J5WndvTFumkNMZ2pgxweR00nWPGRPmtdmkuIQaRPOB0pzOiy++GBdddBEeeughXHLJJVi6dGnTZ66//nrccsstuPvuu3HPPffgFa94BT784Q+X1UTnoEhf1kmXJnSguYO7NkTSFI/IGqmje+Mrck9yNZOhpLt+k/zWh9OZd7G1cTqz9jvXbd+zJzGAxPbX643qg6KdE3quWaXHJtBiB6jHVHe3O2YhD0TWPItU0MbpHDvWXSEhV88VqDbTaRqzaZzO0PqtbU5nR0f5TCe1xxfjNTCQtM/EdJYtrxXzwrOcf8sW9ipT/NgwnffdB7zrXfJz79kDjB5t53Sq5oRQx4wJvNOpm+/GjAnz2mxSIar6bFygFKdz8+bNWL16NZYsWQIAWLx4MdavX9/EYtZqNezfvx/79u1DvV7Hjh070NvbW0YTS0HeSZd3OsuWJZmKR9g4B6acTl8GMrVn0iT2mjZ6L0oBdfJbF46VDStD0p2inU5aJIpgOnVRzqzqAL50v9h+Kr6VRx2Qxggs2tCixQ7QO50+AjkmiIabSZqsc150su8imE6Vc+HS6QvZkCkyp1MVPA2VtbFlOl0ZyDZMp69gBB8E8810qu5Nezt7zXL+vE7nNdcAX/kKcO+98u8T05lXXhvamDHBVl47enSY15aG6Qyx/WWjFKdz/fr1mDFjBjqey6at1WqYPXs2+oQdhM8++2ycdtppmD59OmbMmIHf/OY3+M///E/pMZcvX47e3t4DP7tkM0FgKJLpFCf1suS148a5q17ry0AmZ2TiRPaa1ukUpYBpt1wpQ16bZ0HyKa/Na8RR24FmYyFv24eH9QoAXhaU5fgmtALTSUWcVPeOkIXp7OzMXkjIxrlwySiFnCe0axcwYQL7PYvqpVWYTlWQrL09e79Lc35VATxfTKfOPgEa7R/XLLCLuZgKlmUNclEb+DWPQExnV5f8+zxT22pMp81c6yv1ygZp5uoQ2182gioktHr1atx333144okn8OSTT+IVr3gF3vGOd0g/u2zZMmzYsOHAz1h6qgGjDKfTtbx28mS5hDRvTifJa10ayLfeKs/HyMt0mv7m/6cz0np68hcSUjlueRZbl4WEbHM6s+Zz6JhOPjoNpL83u3c3joOymQcbp9OXZN0Efj4B8jGdKgY+z1xr41y4ZBZCZjp371Y/N/F/WeW1obI2NnMtBXpc7tMJlK+sMIFXXpRdSIivhk3n4iE6na7ktePGsbbItnjSOZ0mplM3ZmirnKqyaTY5nS7HVF7YzNWhzmc+UIrTOWvWLGzcuBGDz93xer2Ovr4+zJ49u+Fz3/rWt/Dyl78cEydORFtbG9761rfid7/7XRlNLAUunc6y5LWTJzNDWxw8ReR0umQ6h4eBV74S+MAHmt9Ly3TmYWV0jlXWKqM7dyZsnclAziNRdcF0knE/bpy5iEBeplNsf1FtF49HcM08mOS1pB4Imekk58U0pnRjUtUvTYU5dAiF6Rwaaj6+b8kWMZ21Wv7qta3IdLpiZer1sHM6TUynS8aK5pPRo5NziecG8t0bk9PZ0cHmHNXxqd+L6wZ9XzdfVXnMmGCT09nVFSbTOTyctH9goDm1qurPxgVKcTqnTZuGE088EVdddRUA4Nprr0Vvby/mzp3b8LnDDz8cv/3tb9H/3Aj72c9+hmOOOaaMJpYClzmdRUmunnpKHqVLw0wMDuqL6eiq17oykDdvZsbpU081v0ftySqvNRnM/P9cSEh37gSmTNEfP0+/4OW14nMtKqdzyhT38lqV05l1QSDjgaSGZTMPaXKoQouw0nxy0EHsVTWGaL/GLPLaPHPtzp1mCakr437//kbjq2xVC8D69urV8vd272bn7upyV702VGaAxvykSfoCUy5Ymd272dpMY0IV5PIVjNAVOgTcymtpHZk6lb36kNeS06g6fh55bZXzoE2wyekMlencsaPRJjIV+Ayt/T5Qmrx2xYoVWLFiBebNm4dLL70UK1euBABceOGFuP766wEA73rXu3DYYYfh+OOPx3HHHYff/OY3+OpXv1pWE51icJD9hCyvXbcO6O0Frr66+T2e6QTUkzol6ssGl231WhdG1IYN7JWf4Ah0ba7ktXyhIZOE1IXTmddAJseNqr3yKKp6rcrpdCmvzZsHTQbotGnsNeZ0NqJeB/7t34Abb2x+T3Q6VYEcXb+ycTo7O5tzrm2wY0fyXHWGkIt9hXkGGzA7Zi6e7aWXAiefDGza1Pzerl3suXR3j0ymc9Qo1j7Vtbti82i+obm+qkyni/mI7g05nS4CgCamk+YbIL3TmUdeG/qYMYFssvHjq5fTKc7VVdt32Ac6yjrR/PnzsWrVqqb/X3HFFQd+7+7uxuWXX15Wk0oFdT7X8to8hYTuvZcZaA8+2PyeLdM5ZgxbAAYGkglY/IyJ6VTJc/PgiSfYK2+oE/IWEjLJa3mjt2h57fAwW8RUiy0dn2RHRbCFJCHiz5c1L5J3Ol1UgyxDXjttGrB2bflGIC147e16pxPwE2HdsgX47GeBhx8Gzjij8T3bIBY/n4iwldcC7Pu0xYkNduxgG8HLniu115UhJM5RJkPGxbN98EE2bz31FHDwwcn/h4dZe8aM0TOdNsY3UD1mYOdONtd1d+uZzs5O+VyfB2ScT5vGWLfQcjrTFBKSvZ8H/DoCqOeTrMb/vn3JOVROp4nppDaJ8tqhoWSN7uyUz2dp1AFVc2z4fl01plOcq6um3PCBoAoJtTJoMBXldBYtFQSAxx9nr2L0BmATbVdXMnhMk7rOcRMH5tAQ+3HJdJLTKWM6XRcSEiP/onzZxrFatYqxDyKoH6hkivS/rJNevZ7fcdu9m+1PJmNNdu5kCwrtpyjeG75fqQo06GDT9jFjsm3JYhtdd11I6OCDw2Q6H3uMvarmE8Asr9UZUrZMp+r7OuzYweS1XV3Nz5WUC65klHS/6NrL3pMZAKiwPL/mAMlcSUynbK7kHS/TXFo1ZmDHDuZ09vSUX/SE5hsKApjmm5CYzsHBZI3PMx/deCPwq181/9/kdOZ1zIjlBIqX19L4HjUqm7yWrs2V8sI1duxg/WL8ePk6RnNtyEwnBU9bjYV2geh0lgTe6cyyIA0Psw5OjJILea3K0ACSPB5iTlRyOBsjUWVouMw/I6dz587mZO+iCgkRm5jVKR09Wu1YffWrwIc+1PxsaAGbOFEeXadS66NGsVygtP1i+/bG+yUuuGJOp+zZ3XQT25/sBz9ofo+Yg54e9rfp3qbtGzqnk1/sdQva5z8PXHutvO2AX3lteztzekOsXktBLNl8kkY5AWSrXmsyAm+/Hfj3f2+W3g4OsjmK+qX4XOlYrphOMmQOOYS9mgwZF89WFYCk50Y5nSrlhs5IDD2n8667gJe/XN5veadTFeBzZSDzjBDgpkJrHvAGuGmNz3pvLroIeN/7mv8v5nSq7k3WdcTkdOaR14rrUFZ5bagSVBO2b2cBvlGjqsd0Up+fOZO9uqq10kqITmdJyMt07tjBnIfeXva3C3ktOZ0qZoIkVfz5CGmMRFXk3iUrQzmdQLO8xcR08jmZsvdN125beEhnLNB3eCcKSBawcePkki/6HrEyae8tne+5LXYzMZ3UBj6/krBjR9J2/nj8dzs7szNWTz+d9FlV201j8uMfB5Yvl7cdcJtHpMOzz7Jgw6hRzWOK8m99Mp0m5QSQj+mk79gwnbJ7v3Il8KlPJYwsgcaUitGitrlmOlWGjOvo+Z49ybgXnx3d6zFj1EwnOZ15WBufzMAPfwj87ndsiy0R/Hzliul829uAf/5n+bkBM9PpM6dz1Ci50ynOtVnuzd69wPr1SUEfHuJcXLS81sbptGU6VfYHyWvzMJ0hOmYAcyw//nH5mCGnU6UeCDmnk9RGNFeHOJ+Fhuh0loS8TidFXVVOZxnyWh3TKRqJOsdNZUS5ZGWI6QSaJbamQkLUHiqSpLp2lbRY/FtmLLS1JWyfbtExOZ2qxTbrpE3no34nc9w6O5N+kabt1H7e6ZS1n5gD1fFN7Z81S912wCz56u9vNDoIYiEhV1umqIrgbNvG+qzM6aQK0j6r19ownapCQmnk+qbqtYDeaRWfLT1XVe6ea2bBxHRSe7KyNiasX9/cFkJaprOK1WvXrWOvYr+gLUt08tq8TOfwMPDd7wI/+1nzeyam0/e9e+YZ5nCOHq1nOrMGwWiP7WeeaVYrmZjOvA45ObodHWwMyKq4hyCvDdExA4Bvfxv42MeAn/+8+T1bpzNEh1oMEMqeDW/bhdZ+H4hOZ0nIK68tw+m0kdeamE7V+fnrNUXqXOZ0As3J32S00hYJKgOY2Ly8Tqfs3tGCAWR3OmWSL56VyeJ80PkOPZS9yhw3k3Fv43Sq5LV8lFN1fFP7dW0H9MGOep21Qed0ujJ0AOAPf2DP7t57m9/btk3NdJYxpkwgp3PXruYxsGsXaxeNGVUOlk312n37Go1QsVK46fsic8I7nTqmM++9/fa3gTvvbP6/DdOZJ1/VBFoH+LYQ0jCdVd1zcO1a9iqO+f37Wb/SyWvzVq9dv571N1n+u4npdC3nN+GZZ1gQTOZ0FsF0Pvwwe63Xm/ulbSGhrA459YU5c9j5ZYGgvPJaYjrzyGtDdMyApDilbMs60ekUHXqXFaFtUK8D73gH8KMfNb9nO1fr7KORhuh0lgRbpvOaa4Avf7n5/yanM6+8du/exPjSyWtNTKfKWNAVj+DltT6Yzj172HOhqqxZnUqbaweysXm2TKdOXpuH6VQ5bmmq9tkwnTJDir83afrGnj2sfTNnMpZalEXZjEk637Ztzc8x7ZYpWfr1r37Fvnf77c3v8fLaffsac4HF6He9nr4IU16Q0wnIGTNeOZFHrg809ktROWH6vo7plDkX4pjKuufyW97CJGcibHI6+UBM0fOlzum0ZTq7u/PtOegrB6peVzudsmCEaCAT05nV+P/rX9nrrl3N62RaptOFgXvXXcBLXiJ3inVMp62qRAdyOgH1OqiS1xZVSOiww9grv5aQE5pXXqurLWDjdLosxJgX5HSKAT6qvj9+fGJ/8c+Ogr40pnytYytWAP/7v83vmeS1lOLiKkBYRUSnsyTYOp3LlwMf/nDz/10znbykatu25oFty3Ta5DXqJk1Xe3jt3JkMfBnTSRszi23l/1axMrZOKW3qncXpVDlutIDZyGt19/a3vwUeeKD5/5SHSU6nzHGzNe7Ftg8Osr5gktdS21XHX7MG4HZeamr7lCns2agKCenGJP8sVffeJdN5333s9cknG/8/NMTOT/Ja8fyi05n1/HmQxunMU0gIaHy24lyr+r5KXmub05kn+k7MNR8MI9gUpzCNiTzgnU5R9dLqTOeWLcnzVzmd/HzFXwsZyHn6BTmduvMT0+mjwNT3vw/86U/ALbc0/p/YRxt5bdZ7o3M6TXNx3pxOcpZkTiddW1HyWkqNkH2X/7x4/iownWKwgqTKxHQCjc9OTBMByp8TaK4W12DATl7Lz9UhPpuyEZ3OkmDrdPb3s0lJjIa5djrJ0GhrYw4nf/6BATZ48uR08n/v398oh3Odk0CG3fz57FXGdPJFkvIynSZ5rkpCqpuYTExnXlZm8WJ58Qpbea2OdVG1nRZuk7zW5JBfdhnw9rc3S3fofFOmsOerktfaRphlRmB3dyLLdlFNkpzOjRsb/099eOLEJLePX/BEZiHr+bNi+/bGcSZzXvIEsfjP0PEIYjVI0/ez5nTmYbRMhkx7e8JoyQwZ05jLA1oLOjpGXk4nsZyAmekEGq+f2loE0wk0G+g0nnzKa++6i73yhfmApCo8OZ3iGi/aP1kYK97pVAWKbOW1snszNAQsWybfp3zLFtZuUh/wTqe4jqiOr3I6xUJCsu9XOadz587EBlP1ad7p5K9PdKiB8ucE01w9alRiA5SdClFFRKezJIgVWlUDhwaZOKmT0TZ9OnMMi94wnAwNcsx4Y4OPbmfN6dQV0xEN5KIlFDThHX00ezUxnaq2m3I6TSyvyul0Ka+1ZWV27mxc1AlpnU5T2/kIrmjc0/HE75rktTQWxCqk1PaDDtI7nTqGXed08hvFy9qel+ncvRt45BH2u+h0Uh8meS2gXqx9LHjEcs6ezV5Fp9OlvNbW4bbN6VQFcvIYeffcw16feqq5bc8+m+TGAeUznY8/zoyoGTPUOZ3kdMqcSgoU5SmK4isvkYoIAWYGHNCrC7LsK/zQQ8nvqn7pq1o2oHY6aXyT0wmYnYe0z9Ykr63Vkm3PVPdGV3xrzRrgC18Avv715ve2bGH3ndZw3ukUFTNAvi1TZN/nx4yusFmITCffp22cztAUO+R0PvVUcwErKuYn6/NAs7w2tGfjA9HpLAlipE+1INHkoprUyYBWMZ1Z92MkI3HBAvYqczrz7NMpfp5vf1ETy9q1zY4H0Ox0hsZ0Ful06goJqRyroSH209fXPKlu3cpYF4rwyhw32wjvvn2Nz11sO2BmgXXHVzmdJqZTV9zCxHTq9hjNKxV84IHESRejrOR08vLakCLENJ+ccAJ7VTmdpjHnUl5rm9PpYssUcjqHh5udC3I6Zc8VyF9cy4S+PhYsmDRJzXSSvHZgoHkdK0JeS+uYL6azp8fMgAONfUMs2gakfzY80yn2i+3bG1UhZed0PvVUoiZR2SeqYIntmFRhaIhVr50+nf0tWwfHjWN9RrcO6uZi+g5VyeWxeTNzOun7MqfTdh3cv18+BkheK/u+TR50qEwnzxzrnE5Zmog41wL+mM48c3UsJJQgOp0lIa0hxOdYAs2RRJkB3dbGOrdORvmjH8llAsR0Hn984/mARkOjiJxOQC4FzBMRGh5mG3r//d83v2fDdKZxOrPmdJrktS6q19oUEqL/DQw0M2pbt7JAB12bLC/Stnqt2H6x7Xx7+e/aOuR8DiHQmNNp43RmkdeqWA/+u1mZB75irfhcyBnwzXQ+8oi8qIjodKYtJCQ67CaZpgunkx9TPEOfl+kcHm58tmJeJxWIUjGdYkXELEbY1q1Mli4GmYaH2dpjcjr5gIFsrreV1+q2z/JRdZmczpNOMvcLoLHf5g307NnD1uHnPY/9LWM6J0xgjJ4sGOI6p5NYTsCO6TQ5nWnat3496wsnn8z+luV00vqqWwdt9v0ldQmPLVuY3F3mdKaV1wKNbKdMXqtLT6paTicFUg4+WB5IAezVA0C5c0J/f2MgSLSd+WJ+gHw+i0xnI6LTWRLSGkKySb2zk02aqkT9nh62IKkMoS1bgHPPBT760eb3SFJFMkre2OANDdvqtar3azX2KmM685SWvuMOds/uvrv5u3QvjzqKvcr26UxTSKjK8lrZpMe3R3Tctm5NCvEA2eS1/PHJEZS1nY4ntt/WIZe1HcjndPJtlxmB48Yl/aZo5oHyOZ//fLbY8Y5PGnmtqyhrfz8zApcsaX5PVE7wQSxZjngWZqIMplPWvryFhB55hM1/lH8mM2R00fMiKiJ+6lMsh/vXv278/+bN7Pp4p5NnMsVCQoB6PjPJa2Vb/ZRRyRxoXl8J69axugmzZ7P5ib82WU6njJXJ+mzI4X3pS9mrjBUaP579rmPgXTGd5HR2dKR3Om3no/vvB266qfn/JK190YvYq4rppHNkCQDSZx55pHGu3bePHd/EdNrON9Re8fs28tqxY6uX0/ngg8zuO+UUdt18+0PP6XzwQXY+Unrxc3W9bpbXigHC0J6ND0SnsySkzTOSTeqTJ7PBqypJTkaAKkJME+Xq1c3v9fUxh3PyZPa3Sl6bl+mkRdPEyqSdWG64ITkvXwwCYExCRwcwdy77m3c66/VEXmuKMvqU15qq15IBPzgo3zpDJ6/l2yuTqJLTBuSrXiu231Zea8rpNDmdfE4nb0yIi31WprOtTV5UxZbpFHN8CPfdx+7tKaewY/Fj0lZe67LgzK9+xe7JqlXNEkt6FjLlhCxHvEh5ra3cTVW91lQwJm8hIWI5/+Zv2CtvyAwMsGvR5XTmrYhYrwPXXst+v+OOxvdI8UJOJ21pQJAxnfxzqNfZNdjIaydMUBvQuvkqL66/Hpg1K1kz+LavXcuCPJQ3yfcNk7xWbDsgb/8Xvwi85z3N/6fct1NOYa8qphPQM52ucjrvvDNxHp54onHMF8V0vv/9wJlnNs8H5HQedxw7hsnpzJMjvmtX4/GpDxQlrwUaCzXy+3Sa5LUTJiT7EBNE+8kHm/b008D558tZ4gcfZPubEqHBB1PoPvjO6RSrBRN0c/Xevey5pAkQZn02MsVIVRGdzpIgMwJ1hpDK6QTUTicNWlN+2po1jZ1YlFTR+QhiHg9/LP7YnZ12kyYgX5DysDK8AUEMEeGJJ1hRjI4OZjDw8lraa230aJa72N5udhqLdjrJsbIJRjz7bOP7O3ey59LeLjeQbVgZvr284zY0xPrBlClJ+3jjnhY/2+q1QHZ5ra5f0HdU8lpyOut1eQEr3XYyKqeT8nJ0zMP+/ey4qjEBsAV5woTECeBx332MnaeK1bzE1iSvLWJMmXDNNex19+7GYhEAexYzZiQ5WCblRFYjkbYhylNIaM+exu/LZJQ6Rkt1X887D/jXf23+P+VznnEGe+XltXSfTHlCeSoi3nZbkr7BSyaBZqeTbxMgZzr5MUJtoXujk9eqnM7OTvZcXbE2X/oSe73xxsb/03Ypc+fKnU5ZISHdXAvI+x3txS06TiTjO+44dg6ZFNE03wCJ05fl3m3aBFx4oZwJvusu5pAfcQS7Vr791EfyMp07drDvif2SnM7nPY+tRzqn01T8q73dPNfzzhP1gbzyWr5NMnkt/32VDUH2k44N9MGmXXYZ28dSLMI0NMTWhiOOSKpx806nbU6nS6ZzcJDJ6d/2tub3dE6nSW0EFJPTuWIFuz98kbMqIzqdJaGInE6d00mGCKCeeGhyGhhgjidh06ZGSRWQjenkGSnVpEnV5VTyWlNESBaR2rSJGVJHHMH+5q8NYEYdGe4TJjQyndQOMm5lFRnTMp1Z5LW2ElKgMSAgRngBfXELk1PIM520XyvJAEWJapZcFpnTqWIOxH3vTMd//PHG/rF1Kzt2V1fyfMX2d3XpDVyV08mzHoBa7mZaLB99lLX5e99r/P8zz7AF7phjkj3ATAueqThXkYv1/v3Aj3+cOH0iY/b44yyy3dPD2icLYhVRSIiMsDzyWqD52YqBHB2jpbqvN94IfO1rzf3innvYfXvlK9nfsuc6aRJre3t7oyFDgZM8z5UCHD09jL3iwTudMtXLrl2M7eKLnsgcL5LXDg2p9xxUOZ28YqdoA/ORRxJJ8R//2PgeKWRsmE5Tv7Dpdzff3Ph/cjrnz2cGOu90Dg2xPq5jOmkNbm9nzyjLvfv3fweuvBL4ylca/79rF7s/CxYkaynvmBZVSIjuzapVjf9/+GF2TXPmNDud9XqiOgHMaSaqfmVyOvPIa6lYHyGrvNbkdPpgOvfvZ/McAPzud43v9fWx9484ItnqR+V05hlTefCd77B58Hvfa+43997Lzn3qqexvfq7mA4TUdr7Pk/2SR220bx/wsY+x1x/9KN13Q0V0OkuCTfSdpElA44Rer9sxnbRY2xjQvLFBhoZKXmub00mGBqCeNMnpzJJ/9r73AYsWNRe/+L//Y6/LljFjjmc6BwbYJEea/IkTG5lOPnIPyKPzolNpKlLgQl6rc9xEp1MlBbTJW+TZQj4nElA7nVnltbLN1vm2kMFqK6/dtavRuSFpMLUdaG6/rToAULMegHo/R9vn+qtfNZ6fAifHHMMYQ6CR6eSdE1dbFADAVVcB3/xm8/9/+Uv2/Cg6zDude/eyMTdnDvt78mS100nqgrQ5nfU6+wzNV0U6nbQVDqAP5JiYzv5+Nk///veN/7/nHmDePGaEjR6tZjrJuePnen6zdN1z3bQJeOtbk7mdUK8DP/whmw/PPps5ErwBbMN0jhnD2iZbC8Qgl6x9/FqgczrzMJ0PPyyvDn/FFez1kEPYc+BljrZOpyoH3ZaVob4u9ou//pUpA8aPZ31DJkM0MZ157t1DDwErV7Lff/KTxvfuuYf1HZPTmVdeS/dG5nTOmsWuT3Q69+5lz1onr027DvJOJzn/aeS1og1A5yP7iO93fCEhW6WYD6ZT5TB9//tsnHR3A7ff3nhtVLl2/vzE6eSDKbJCQmXmdA4Nsfx2Ou8f/tD4/r33AkceyYJA3d1qp7OtjbWfbzuv+si6Bn/jG0nFaLJzq47odJYEG0OI/3v79sQY2L2bvSc6nWKyexoDWuZ0zp6dTGoyp9NUvdbG0DDJa3VG4urVwK23JrI+wg03MCPo9a9nBgPvdG7cyO4TOZ0i00nGKi2URTCdaZzO4eHGHChAbWATVE6nSfKVNqeTr/4K6J1OF9VrZUZcGnkwVd6ltgPNsih+zORlOlUMto00ePt21rcJ1Id5p1O24PGypKLzpL/0JSYRfdvbmiXr3/8+e/3Qh1i/v/325D0+iAU0O528coLamLbaJAUkyDGSyd3yMJ38c+Xbw/9OTKdp+yte+r97NzOgjzuOzVmHHKI2ZAA2L2XJ1f3d74BvfYvlyPG4+25mUC9eDJx4YvI/wuOPsyDAzJlyp5OqDtP1823ir9mkemlrS4qiiJWBTeuYDvU665Nz57IgJI+BAWbEHXYYe294uHHMkXxNJa/dsYM9szFjzP3Cpt/xBm69zpy+efPY39OmsXNTv+Jz3wC100nPRGcDvOMdwF/+0vzef/wHG1cnncQK+vC1EUjuesIJaqezvZ2NmzzyWhnTWa+zMUNVfadOZfeDPsuvI4B5myObuZ7fNqUIeS0dm9YjE9OZRV6r2/orL66+ms0HH/944//rdSatHT0a+PCHWf/hFQTkdOZlOvPkdG7fDlx8MfCudzXP09dcw8bd3/4t+/vnP0/e27aNKQ6PPZaN+5kz1WojoLkwmu2uDGvXAqef3qw66e8H/uu/WH8/7TR2X1X1H6qE6HSWBBtDSDS8aFLno4hAMqmLg9Mkr1U5nfxG7u3tbAJQGYm6fTpto9uAetK0iRB/6lPJ5DEwwFiXF72IDc5jjmHGAx2fWATe6eSZzjTy2tGj2eRTpLxWzIGSfZ/uBUkZxQqwOnmtaAiZghF9fYkRKDKdY8cWy3SaqtfaGnEqpvbppxvbDuiZzoGBZikgf+xnnknOb8s82LK0QGOOGTl5xx6byGtFprOnJ5GvAtmYzv/8T3YOsYrpN78JvPe9zHEcHgY+8IHkvb17GRPywhcChx/ODNE77kjGJD0DcjrFrTd4ppPaaJLXqt4nxyhvv+Sj7zKn02QIyVQr9Lkbbkj61Zo17Pdjj2V/i4aM6HSKTKdtri6d+wc/aHQwfvhD9rp4cbKdDZ8/19fH5sqODrPTKVsL+DFr2lJl1KjmgJpNQbyhIRYM+dCHGts2MAAsXQpcein77mWXNUpYf/rTJGeRKsTyBjI5Wc97ntrpHD8+2bKE2stfF90XG8fqnnuS9m/ezIzj+fPZ39OmsfFE6zDPCAFmplN17+69l+WInXlm41YQd97JDPDXvCZxLHi2k/qIjukkdj4P00n3pq8vGRdbt7K1gpxOmtNpLRGdTl0hobzyWlnw0kZeS8emtsucTpvv52E6H3uMpSGJVdwfeYQFGM89F/h//6/RvhgeZuNsyRJ2zR//eKOE9k9/YnP/W9+aOG78+9THdE5nRwebC2xzOmXX95vfsDVKTK3685/ZPPf1rzPJOL9zw/AwsyXHjgUuv5wx6bzTya/BQPq52nYNXrWKqR7OOadxHbrqKjYOli1jz2ZggF1n1RGdzpJgYwjRAGtvZ68mp1Oc1HlpjWlrjLvuSmSqMmYiL9NpitSZ8s90i/WaNSyfDGBGw44dbLEEmNM5PJxE2MjppIVy4kR2HHoeaZjO7m79+yYDmQwGEzMgPjv6DE3aouMmSgHzFBLaty9ZFPjqr3R9KlmRKVjQ3c0m5yxMZxrpMTk8e/aw9unktXv3JgsdtV+UbosRalqQ08jd2trYj6ntotM5YQJzAA4+mBlzotPJL3Z0PQRbI2/VKnauV72KGexPP81y/t72NjYf/PGPzOC48UYW3AGAX/yC9YM3vIH9fdJJ7H6QoSY6ncR0klMqOp02lX9Vc6XM6UzTL+m5mphOXZ60rH18LuOjjybzERUROu449jpzJrs31GaZIWNiOk396pJLkrZcey1zaF7yksTpFFUvs2c3tkEmr6Xr59vEn9cUgCSnE2i+PtM69uSTzCC79FLmiPz3f7Pnd/bZjN197WuZKqanh/VjWmsuv5ytreefz5yn0aOBW25Jjrt2LTM8R41SFxLSya5lQTLdOlyvJ04xn88JNBddkTGdojSZlBWA2QZ45hnmeNLxP/IR9vqpTwGveAV7xrzTeeedbB6aPj0J4IpOp84+Sct0AgnbyRcRAtROp051YiOvVTmdvLyWgilpq9eK64gor+3pSaqg675vk9Mpy6MGGFv2whey9XbuXObkHHkku6/vfS/LGfzEJ1haxIc+xO7B61/PxtmLXsSY+dGjWcCHbNLLLmOv73kP2wf9oIManU4qlDdtWjKmRNk4v/cskD7ABwBf+ALwuc8x++9VrwJ+9jPW7lNOYfPFF78IvOAFwCc/yQJxALveNWsYA3rQQcBZZzHCgoJPVESIdzo3b06eTRZViqmOy9/9Hft7cBD49KfZsd/5TjZWgdaQ2EansyTIItQq54IWfZXTaZOfpuvcU6aw75KcqK+PtYkqTeqYCRc5nWmioDNnssn9k59kEytJ13inE0iiVHQPeaYTSNjONEwnXV+R8lr+2KrFmD5PjBcttlRB1UZeS8ev19WO1axZ7JWchjQ5nbWanmHv6mrOxdm5ky20o0fr81FN+Rz9/eq2i/Ja3ZgB1P2WghZkhPL5XdRGVWEPQB1dp2ucNo1JVDdvZs/ovvtYX6Z9d6dObY6y8rIeIBvT2d/P7s0ZZzB284gjgDe/mbXn179m1/3pT7P79P73s75D8va/+zv2SjJNyuuUOZ381hsyplPmdNpUw87KdBLDRvMCPVdZURL+mIBdvxQDRT/7GXsVnU46PwUUZIaMyXjXsTZHHMGi6DfeyCSTDz7Ioubt7ewZz5yZOJ179rBxIzqdYj6ujum0ldfaOJ0mxc5LXsLm80suYRL0X/wCuOACZkwefzxzoNatY7LRxx5j7599NvtsZyfbY/bWWxOFw7p1ybZakyaxeyQGI2xTGUxGJhngJLEVnU4x/01kOkeNSiqv8/eOn29M9+7RR5mDfuONzJh985vZfevpYdU6b7mFnX9wkBngFKQYO5bNPbzT+eyz5qA4YFe9n+6NrdOpmov5e9Pfz+6JrmgcPctDDmEOAN2rLVvYd8h2GDu2eHktjYWs8tq2NnZ9ujlh82YmLf/7v2f95IYb2L278ELguuvYM/zOd9haSgGdn/6UOZm//z3wspcxRvSJJ4C3v53do+uuA179aua8trUxx/bOOxP76sEH2RxUq7FzTprUzHTygRTZtQF2FexHjQLe9Cbm9J59NnOc581j7O573sPmhYMPZsHVO+9kDvaoUYkM/6yz2CuxnTKnE0hyLNPIa20UfC96EQtC/fM/s/SVhx9mdUzGj2fz0vOfz8apanuXqiA6nSVBrJQJqCcmmlypgq0N05lGXksbLJOx8fjjzLgkhnXSJLW81kVOp62BvH8/Mxguuoi1/ec/ZxPnzJnJJvTkdJLMQpTX0gRBi3gappOczrRFT0SnM23OAn1fdDplsiLV8XXPhj5D+USU1ylzOvlcYt6QAPTBDpXTOXZsY1GStIwSfe7ww1n/1TnMgNnpVDkPKqfTFF23Le519tns9Re/YIvaM88kfRlg/V5kOm2dThPrMm4cG0vf+Q57FuPGMVaTDPBZs9jCfO+9TKJ0/fXMYCen0uR0is6LjOlUOSamMTFqFLvGtE4nsQHic92zp7EoSVbngj7/qlexNlJw7J572LHp3tCYpnnKxHTa5r/T+T/xCXYNl1ySBAsWL04+t2BBsoUWrTfkdKr2bLZlOm3ktUDzWmDL1r385cyo/f/+P9bmj32MsZm0hrzvfcCLX8xYkHe9iz3viy5KjnPKKcn2HJs3s/no+c9n77W1MQchDQMuM5BVAYHjj2dzExUToi2H+JxOIHE6ZUzn8HDzfo22881b38qklKtXM8ezo4NJ7Qmvfz27Xz/7GXOI9+9P1liAjRu+ABbJawG902maj/r7mZE/cWJ6ppNfB+v15ntj65AfcQS7t6QA27KFOcK1GvtbdDrzymv37EnuWRZ5rahyA9T3dsEC5tDcfz87xoYNbMz87d+ye/7mN7OA5zXXMAn65z7HgpHU35cuZU7rddexNWtoiI0zwumns3t3001sjdq0KdlVAGBOn1hIKG8qA31u3Djgu99l9stHPsLmvNtuS5zG3l7meA4OMuf47ruBf/qnZKy9/OWsj/BO58SJie0oVpFPI6+1YTr/+79ZG776VTZfjRvXuJ/vmWeyOfr++5uPUSVEp7MkpGFVDj+cvdrKa2m/RNvF+uST2Ss5nX19iRFE59mxI2HEbPfptHGcdEynzYLU3c20+11dLCL04IMsQkULwty57D1iOmlhpAlDZDrF6rV5mM4s1Wtt5Wj8NZicTpOBrHKsyODSOW71evLssjqd5LSaCrbYMgf797MxccghicMsK4IEZGc6RUYszZYpgNnQed3r2OuNNzYWESKQ00n3ziSvTcN0dnWx8fPmNzNJ1dq1yUJNuOQStjj/y7+we/jGNybvHXEEawMVE3r8cTaHkFMpOi/8fEJtVAWxVG3nx02Wqso889DdrX+u/DEBO+eCNxJf8QomU962jRkyVJgCSPqVaMjQPKliOm33ID38cGYU3nsvK0oxeXJS/h9g7FV/P/DAA40F5fg2UJvqdXafxUJCJqYzq7zWNGa6u9nP+97H+u1HP5rcV4AFob7xjcSQnD2bsTKEl7yEvf7xj41FhAhTp6qdTpuqxrJr56/x1FOZw7t9O3PsOjoYEwU0O52ynE7Z+U3bzfB992MfY87n0BBjiPlrf81r2P37yU8SO0F0OjdsSNaDvXvt5LU2Red6elhg/Pbb2d9pnU7VWmJTZAlInCSS2G7enLCvgNrpzCqv5ZnOrPJa/rnLvk9bttDxAdZWfrwQ2tqYY3nTTWyfYf4ztRrLCZ41izltz39+st8wwJxOgLGNInsPNFdl5plOWU5nmnWM7kFvL1PCXXpp0hcJL34x295lxw72eb7Q2tixbEz+4Q9snhPnapPTqZLX2jKdY8awgMCcOey+vOtdyZgCWkdiG53OkmBj4FLnmz6dfdbW6eQndMC8WB93HBvgd96ZbDNBhgaQDCLeMSMjgvYAUxmJpn33dEynzYLU1cUMtbe9LTEUSFpL137EEY1O5+TJyYQmMp1p5bWqoif8nn6unM6JE9k50iy2NvIUOr6M6ezsTI4vFuPhF1vATl47MJC0O+12L4DaWOjqYoGTvEynrdMp2zJl/3613M0UCJo9mzkAv/hFUk2UdzpnzmT3e/t29p29e5uZziz7dPLGGMCeMb/QEcaPZ0UkKBBF0lqA9f0FCxjTWa8ne3QS6HgqplM2psiQUsm2eQM/j9PZ3c0MSpFRssnptAmydXWx+WloiG1H8cwzibQWkBsytVqjjFJlyNBcrGM6u7pYwGDSJHYNr3990magMa9TdDo7Otj4JOOKKs3ywQL+XKp7k1Zea1uFne+3KhxxRMLgXXhhouYBWPC1rY3JSPntUgi80zk8zPptmqrGgH4+PPVUdtw//pEZ6M97XnLPbHI6gWYD3ZbppLF1+eWsuNTnP9/4ucmTGdP1y18mea/UVwA2H+7Zw2wE6h828lobxqqrizkH+/ezfvnww8xZo2s35XSqFD+290Z0OrdsSZ4HkM/pJLWYKK8VmU6VDUHXqMuDBtRBMJsxY8KkSayabU8P8MEPJgUOASazPfhgxuDzlWsJBx+cFOSr183yWpugOdC41ppw/vms33/zm0lleMKZZ7JjfetbrG188FWcq7dtY/MJzYd58+/JRvr5zxmh8m//1vi5U09lx4pOZ4QVeAPXFB3v7k4iiUB6p9M0qY4axQwf3tDgjUSZHI4MRJJCZpXD6arXdnbaTyyXXMLuY1dXssk64ZhjmOG7Ywe7hyShA5IJLo+8VnXttOdgGqfTRv7K9wteomojr01jIM+axdrBO25TpiSRPrFyXxamk45L7ae203NVyWtVRhxJzMjpfOYZ1j6bnE5ZISGT02lyTkTWx5a16epiEeOnn2YLHsAKMxD4vTrFXBLdPp2mQA5vsJhw4YWsGMNZZzWOKYAVE3rmGWaoPfFEOqdTJVnnHfYsTKeukBD/fd65EJ9rXsk6OZ1AYtjLnE5eXjtxYmLIjR7N2k7H5413wFyJk/KoqGrjP/xD4+d0TifQmN8ve278ucTz2sprVVJB1ZjhHW8bvP/9TN4sGnHjxzOZ6x//qHY6n32WtYGuXQzwmeZa8dkMDbE5q6uLSfwAljv9yCPNjBCQjukUJaQ2DntnJ5Nb0/zI45xz2PG/+U32PjGNQGMFW9v0H1JU6Ni44WF2/178Yva/Vasac20Bu5xOOid/3SZ5LX3+yCPZ6yOPsOvfuVPPdKYJcnV1sWcoymvFnE7Z9zs71XO9ielMO2ZMeOlLWZ+kfZoJtVoiXSV5tOh0Aqxf79nDnrkpkALYB3JsceGFjWodAuV1fu5z7FXndD77bFKxGUjktWL6ERUTBOzG5JFHspQAssMJo0YxJvnmm6u9dUp0OktCGlalu5s5ALY5naIhYhMhPuEENmlTFJM3NGRyOH5R0hmJWXI6ydDQLUh0DLrGOXOA5ctZkRMyggh8XucTTyQOA5AY6nkKCWU1kKmdWeWvYl6kKsJrYlLF4/MOyqGHNjKdtMADzY6bzOnUBQt0TidV7lMVElL1K/q7u5v1CYA5zSamc3CQLXi26gObnE5AbQSaxmR3dyJTuvdetjjzhg6/4ImyHjLmskrWbRfrjg5WeIWK4vCgvM6f/pTdV1kQi9pts0+nOKZ0Y1J0OtPkWHV1JXsiAvJKmIB6zNoEEGfNYo4mOZYmppM3NkTHLG2AkeaE976XGe+veEXj5+bMYdd6113NubhAo9Mpe258m/jzZpHX0p7FadYxG7S1MWNSZnCfcgpjE6lyNKW2AMn4e/rp4oMR3d0sqDR5MmONBgcTpQnA1qmODn1Op3h+fn00FcuxcT7OOYe97t3L+izPEuucTmqbuMbb2j9dXUxeW6sxh3zTpkaHlwKJJsWP7t7o+tXcuazPPPJI4x6dhDxMZ1cXa6eqkFCWQE3ZTCdBdazTT2eO19VXsz7DPzuewRf7tElea5N6lRfz5rE5gFhuG6eTQHY5tZlvOxUEzDsmzzyTHeO3v7W7nhBRmtO5du1aLFq0CPPmzcPChQuxRtxQB8DKlSuxYMGCAz9TpkzBueeeW1YTnSLtpNvbyxwjkr+2tSULnsrpNMlr+UWRItxUFl0mr+WNDd6xU8nh+MVWx/aJBrIoqQLU1Sb5ie4972E5ByLI6bz5ZnZs3ul0yXSavk9GsMpIM7FtItMpRnizbljOH58kqvV6NqdTFyzgnc56vdHppPOndZj5z5Cx/Pjj5pxOWdt1x58xg/Vb3umkjeL549Bxqb/aSrpIUkb3g5fW0vkBOdNZq+llmEUxnUAi6RRBTud117FXG6aTxpxOOQGw17RMZ3s7u24bJnLqVPb9vXvT53TazOVAYwoA/2x7etj94ZlOmSEjqlps8/fp/LVao/FHaGtj0ui77mLBpgkTkmsHGovKibm4MqZTdm9s5bWio2yzjuUF5XXecUeyXQqB3zYlrezahmFva2MVQWlO4ZnOtrZG2bct05mGATfhsMOSAAmfzwk0Op1iEIz26tRt6Qaog59dXawfHnUUSzcAGvtudzebJ9PUNhADgLq5eOxYdn280ykynQMDyeep/9rK7ceNa94yxUZey48Z3XM3nd81iMHftYs5cPw5eaaT+jTZZJRjmrbQIn2uiGur1RK2E2icq8eNY3MfL6+lNRiwCxDmHZOU18nvJ1o1lOZ0Xnzxxbjooovw0EMP4ZJLLsHSpUubPnP++efjrrvuOvAzffp0/OM//mNZTXQKmbzW5HQCzBihynC85ArQy2tNrAo5nbQpvEleq2M6eYmjKRG+u1te5UucNMX2Dw4yQ95mYNJEQQtWWqZT91zyOJ2UE5rWKeQ/M2UKW7D6+9PLa2363Zw5zADfvJkZE0U5nTzT+fTTbGLmq4RSG9O2nb831IcfeywxSFTbDKmcTl2V1MmTG51O2iie2sgfl45jK+mi509S8TROJ7UvS/XatLIkFY46ip3rj39kf5ucztGjE+bEhbw2TYAvrXNhU0hIdIzI6Tz00MTIIhxyiD3TKZPXmoIZJpxwArvuv/ylMfgIsGe3bVuS0wjYb5liYm1EqaCtQy06p3lATifQKOEE7PpFWmWG+FzIQAcanU6AsUJ8TmdHR9IfxH4pFooxsSq2Y57YTj6fE9AznYC8AFYa+wdgQTh6/mLARJdmolInpBkzhx/Ockn5PToJ1P/5NBPaNswmsMsznfU6+76NvLarS72tiCl4WrS8Vod585L1ipfWAonTuWlTcyCF9upU5XSaCqcVdW3kdM6e3ThX12qM7TTN1aoAYRFz9dy57KfKW6eU4nRu3rwZq1evxpIlSwAAixcvxvr167GOKsFI8Oc//xmbN2/G66isY8Vhsz0DvyDQpL5+PZvUSVIC2Mlrh4eTzdgJfOc+9lhm9NE5aZ9DQC6v1TGdvIFts9iKVb5kUVDdpG3CoYcyQ5Q23rZhOnmnc3Cw8d6JTqPO6dQZyB0dajbP1lDhHTfbCC99Nw1bSBULeafTVEgoTU6nyNICzduOpGWUeKZz61a2mNFzsclH1R2fCs7wTqfYdr7NYn+1NaApkslLMAG9vBaQO520L12RuTAqdHayNtNCaHI6xflELMKUxukcO5ZdOxU5SpvKQAbl5s12Msr+/iR/23a+OvlkxhxRdUceM2ey4OLgIBvTNkxnmgCjCcRi7d3b7HROmpTsXSqbK/k28efNIq+VGWkUbJRdWxH9trc3keXz+ZxAo9OZdnsqG9UK0FhJWHQ6+e0laGsJCnKJTqfq3olI67C/611sc3q+cBiQ3ulMsz0VtY3yOgG506nbM5nOSUib6nD44ey4VIFVlNcCyVoi22dTFxzlczqpjbzSSvZ9W3ltCExnrZbMc7I+DTQ6nbxjJ1aBL5vpBFggaMKEZGtBHuR0Up63jum0TXtLGxCo+tYppTid69evx4wZM9Dx3Iio1WqYPXs2+qhygQRXXnklzjvvPHTypfYqjLTRd3ICaVIXJ3RAL681Hb+nh7ETAHNoeSbTJK8VmQlbQ4M+I2M6bYss2UwsbW0sX4bawBc9EZlOmbxWbH8RTCfl3dlISG2czq1b7SO8/KbYpuOTAbZ6NXtNw3TaVK8V285L+fLeGzKYSV7Lt52qfWZlOokRE5lOgsoINDGdYr9euhS44gpAFHhMn85e0zCdJjmb+Lm8IIkt0Oh0jhvH+p8qiEXXnmbPQZHpBBrnQ1sD15bpFIMhaY289nZWUfvrX0cTqDIx5VIXwXSmYbR4FkvmdALs2aVlOtPKa2VMJ5AEEwhFszannMJedU6n2C86OtgzTVtISHwutCflhAmNbBrAHJ1du1i/3rGj2TgHkvPLglx5WRWAOQn/8z/NRU0mTGDjLg3TaRtYprYtWpS8JzqdU6c2pmnwRcNMhYRMLHBnZ5Lb++c/J+cjZHE6RXntnj2sb9A9Er9vO2ao3bbS5TKYToDtNwkkNibB5HSOGpV979uinM5Ro5jc/mtfa35vxgzW30mBIAsQ6oJoRYzJ889nldB5MqVKCLKQ0O7du/G9730PF1xwgfIzy5cvR29v74GfXXxmd4BIG30XI4kyp5MMaFn0GzBPPGRs8AYi0CivHRxk3+OdUpHtky22OompbD8j04KUVhbEyxP5wUl5lfyWKfyEJstTEp1OnRTQ5JS6cDpNrAzv+MiOz/cL6gu056LM6cxavZYvACE6zHT+tEUE+L7X08MWNWI6+bZT/qVuuxfAXHDm6aeZEbxzZ7PDzN+TrLl3nZ1szzzROKD79+STdk6njZyfrq+oxfqkk9jrmDGNyoy2tubcQJHpBNIZiTKnk3+2aZ8rkC53L01+GmH06OTzPGh+osh1GkPG5FzYxGyPOippp87pTMt0pmVtsgRPiwCxMqKkXed0UjvzKDMA5rh+6lPNe4wCjf2SmE7+3IA+yGUz32RFrZZU2E/LdNqkSgBMpjlpEjsWBd0IU6awY+7Z01wbwCSv1Y0ZyitM43Ty10YpAyank74vUwupvl8VphMAzjsP+MpX2N7PPPhCQkUynUXKawH2/GVbh5HiSDZXq+S1NoUW6X0bnHACC07z63+VUIrTOWvWLGzcuBGDz93xer2Ovr4+zBZXuOfwgx/8AEcffTSOEsMkHJYtW4YNGzYc+BkrljANCENDbKCkYfPI6Vy7tnHjZUAe2QfSTzzkdMryeIBGQ8OW6WxvZ0ZmGqYzi/TGBH67Cd7prNXYJMcznfwGwjZOZxZ5rcrpTCt/1TGdNgUUAHdMp0621NWVbAWhcjpFI45vl62hMmdO4nTyjg+1Py3TKRacqdeTapY6IzAN88BLYHWYOZMxnSp5rYlZkOVJ0/YNRYCYzkMPbTagJ09W54gX7XQWkdOZRqqnO77NfEWGDNXWS2PI6GSUpK4wobMzcbhcMJ38veHz/005UGkc+jx461tZ/v/f/E3j/01Op00Ous069s53Av/yL83tEit9iowQkD0YUYSBLjqdvBGcpZCQ2La2NnZf3vGO5n4sroNi8JLOSeDHrGnMAInTSVVMbeW1qiql/LigPrRzZ/L8ipLXllm9VoeuLuCf/imZpwijR7P7x6cyiE6nqSCeeG38nOIa4lydRl6rYzo7Oxv3O21llHKZ06ZNw4knnoirrroKAHDttdeit7cXc8XM/edw5ZVXalnOqkHFRJoYre5u4J572P/SyGtto6wqp3PcOOY8ygwNQM102hqJOqbT1rkwgYyo7u7miNXEiY1Mp1gkiW8v/V6rsXvS1cUmOV7yVQTTaVvoJ4u8Ns2CNHMmawftEcs7bjJGCdDndFKBC5K3Tp6sZzrzFuY49FDmmO3b1+gwU/uzVq/lnZNNm5qZTvHe2zIPaZjGGTManU7RyMsa/S4qQnzMMawd/NYPBB3TqRpz/HyiU05kcTrFYAIgz+k0MZ1FpANQUOy++9ir+FwBfZ5QETlOlNepU72IAUgd06lSvfD5/zY5nfx3ZOcoAu3twKtf3ezY0Nwny+kE1KyMjulMs46JlT7TBrmK2G5Gh95eNl4ef5zdF56pKaKQEAD8x38k+9vyEOsDyFQnunWwXm+WbfOf4bfO6exsPD71f+oTvNNJn7dhOnfsSC+vpUASjRna+svWfipLXqvDwQfLCwkBzWOKD8ra2gAuoWM68xRGKysYEAJK861XrFiBFStWYN68ebj00kuxcuVKAMCFF16I66+//sDn/vrXv+Kuu+7CG2U7t1YUMkMB0C9IJF+hzq1zOtPKa/mcife8R77B78SJzNAQy+TT91VsHZ1fNjGQ4yZjOouO1JHTecghzcZEFqaTnokqul+GvLa7u3mx7exM2mQT4dUdn5hqvqiUrpCQjbxWfG5UddBGXiu7N6Z+wRvNWZxOVfs7OxPn5NFH2WsRTGca52DmTNZ+2r9XZD58R7+7u4GbbgK++MXm9yZPZs5yvS7fgonaDDRHrru69HNlkUznzp3yMZVWPZDGucjCdNoUjEnzXC+4gFUqFauU8qoXmy1TTPOZqHgBzDmdvlibjo6kYrWNvDZtANEEYtfWr2ff0+V0qvLHxCJMRTofpMa6777mwC45nXT+tIEgE3RMp3hvREWH7vz8OkVz1NSpjTYEL48Vr42Ob+N08kynrbxW3B4rtDFjA9HpNOV0mgJ8ZTrUNnO1assUXf59CMGAsmAh6ioG8+fPx6pVq5r+f8UVVzR9bie/c24LIEulTIBN6g8/zH7nJ3Vx8+W08lp+gpIZiECyKbhMXmtiOnWOF+3hpSr5XVR0e8YMNkGI1dMA5lBT4eTduxsNCVmkUXQqqc00yYhGaF55rW4xtpEVmZhOG4kqOVY28lrbgi90vIcesqteayOvFfsF73TK5LVUACDNmKQ8H3JOaEza5HTyDn/eXBQqQ3///ezc/GbttFjX66yt+/cnY9b03Itc8CivU8TkyWy+ou03ZE4nPUueDQPcymu7u9m97OxkzsWuXY1VQtva2DmyMp1p5LUPPMBebaLn/PXxe/7J2miDRYuAH/+4+f828to0OZ1pnM6iVC95QMXDbOS1+/ez/sJXNc7TL8jpXLu2+dymnE46//Bw4zxRNNMJsHEnqhtGj07Sijo70wXBbO5NmnVQvGbdmKXPUF7nPfc0SmsBvbyWrs9WXkvPxlZeC+idzrLUAXlw8MHArbcmih2TvDZNepBriEynTF6rSxfgr40Qmc6IwqEqkGCKRvFVV3mns62tkS3MwtqYQMyEDdNpK6/lJ83+frYo1etuCgnVaox1+cY3mt+bMIEZEcPD9vJa0enMynTqouM2k6pYjMemgII4aZsWexVbKDPuOzqS/ixzrGROJ+VEAm7ktbK2U/tFh9mm4AzdF53TmWfLlDTyWoAxH2IRAXHTcFkgx2f0m+YvYmll8lpRmmwbzJD1S7ofbW1sLtBde63GDEtyLvjnSu3ImtNpK6Nsa0u+o2M605ThL+K52hQS4uc7WRBNFaDMy3SWYUCLTqdOXpslwKdDFqezzIIyfK0EGdMJsH5LjGuRBaJobn/ySXbtabevUp2fP/dhh7FXsapwXqeTl9eqmE6dDZGF6SwzUGPCwQcz2+uRR9jcy68FMsl6kQG+vKA1mFhaXYBQRgZFeW10OktB1pxLldMJNOZM2MpredbGBGI6VTmdlKsna7fJ6eQHJ0lfisyRIjzvec1V7wBmsFOp9TTyWtv3Xclru7rYMx47NnE6eUOEzpHXQKZiQj09jfdGrF67d286WRHAjIWhocT5EJkDylERv5uGpSWonM56PVkYbMYkHZuMQHI6dZIuG8k5fS6NvJYgbmEgK2JgY2TxbXQJaq/M6TQxE2mczuHhRofb5vtAo3MhOp06Q6iIe9ve3jhPFVWGvwgjTMd0isECOi+9Z5LXihvd+6peq8PUqSxItm0bu5/8PZVVr7Vh04BsTmfaLVMAt+oGk30CMBtFlV6UxzGiuV2W6mBSnejGLH9fKK9T53QODrKfLE6nrJCQzTqUh+kMwbmhfv3QQ+y58QV0aEyRLLvoQE5ejB3b2Nds5LU2KTYhMNBlITqdJSBLpUygMbdO53TaymvTdO5Jk5gRRxIIXeEPm/w1cdIEWPvTFlkqYnDS4r1tW36mc2ioOV/ERl5Lkyr/zG0NFT4vknd8aC9OlbzWtCDRvSe2cMqUxgBFZyf7kTFK9H69zu6Hru1AYizoItQ2levSMp3klNgWEuINSRumU2UEdnQkzL7Y/rRMJ6BmOvkFL6RcGJq/aGtmm/kki9Mpzic23wfYs6VCQjKn0zSm8jILPGtUVBn+Ioww6mc800ltam9nPyom0ySvbWtj9zEra1MW0zk8nBTL4SHLQS8yGNHTw/oiVVBNu2UKoJ4vbaplm8A7nWIQjHc6ZbltQDFMp2wdUQWxbOwj/tzkdOrkteI6AqST14qFhGRjpl43O51pt8vzCb5AljjX8ilL9OqCkMgDPvhrI6+1rTQ+UhCdzhKQNYJry3SmqcRp27npfFTFVLbFgcpINLF9fPReFalzKQ+hiWLLFuYIpGE6ba+ddy5svt/dzYww03YzgNrpBPTRd12/6OhIIo7EFopOG9AsURUXW6Cx/eJzy2IskASyvd0cjBg3LjGAZDmdAGt/GvUBtZ2O99hj7DVLdF12720NAdViB+idzhCYTp3TWSTTmcUIBJhzsWsXY7VsmE5b9UCWZ8szWjaSLRXTWcRzbW9n94OKyo0Z08hM6PL7TfJaoDG/P0TWhgJNjzySrl8UVUV02rTkGGkKCZmK5dionUyYMiW5Xhums8jA8qRJ7Bp0wcssc7HM6dQxnaI8lo5fpLyW2tkqTCc5nUBjnwaa1Q8u5tq8UK3DefZUDuG5lIXodJaAtNsz8IWECFnktaacBR10cjhRVpVWXstHhFwsSCbQRPfEE+w1C9OpiqJ2dTWXY5c5nVkNbIAt9ps3s0WviOi7yIDzTKeIsWPTOZ3i/bFxOkW2MM290bWflwdncTo7O9m4oM/YbJmSNrqug0qCCTSOKYqMh1SQhdqb1emUbVNEn8nidIrXTobl0JA5p9OG6Uxr5JEhM2FCc4EooHGu5/d11eUKF2WEUX7/7t2NcyXAri+rvBYIPz+N+sW+fekY8KKqiPIsW1plher8RfULqrAP6J3OtLm6Nvemo4PNKVmYTlv76LTTgAsvBMSNFGRMJ+90dnXZy2tpXNP9ks3VRYyZqjmdfMCgTELCBjRXy4r5AY0BwlqtseaFSpUSAgNdFqLTWQLSSkhlTKfIbIwera4iWoSBa2MkppXD2TCdZUyadC+ffJK95pHXyq4daLz+gYF8Tqd4j6ZMSZ65zOlUFRLSRUH5+9rby1g92X6LPNMpy+kUj6+S1+7Zw77Ly7xMjpvMwJYtOPPmse+IhpDOObG9N3zUO+2WKUA+9UFPTzIuZfMBwJ5JmnPzn3MJsZCQbszJ1AOAul+Z5G6yxV48h8q4p2NRP6PCZ6boe1Z5rSlXlwwUYqp0ZfiLMsL4/H5+HQDUTGd3t/m5AeFX4lSNdzr/0FDSt4ouMAU09ssimM6iWRUbp9N2rk07ZqZOTe6nzulUSY9NOZ2jRwOXX85qQ/Do7mbOBs90FrVlSq3W/P0ixkyI8logndMZGtOpUhvxAcKenmSujkwnQ3Q6S0DWQkJTp7LfJ05sjKgAzGjLIq+1HZg6ea3I9snkKzp5rSyns0xWhiY6cjrzFBJSGcgmea7u3tkwnYQs8lqTIdLZCdx7L/Df/40mjBnTuD8ZH+G1idLq2m5yyG2q4wKs3b/8ZXNf4fcZTbM/Gj9mVM6JSV5ra+iYYFrw9u4NsyCLrnqtakzojA0T02krd6Nz6JwLXkZJc2rRjhE9V11uHNBYIApIri1PrrAJOqdTxXTym7nr5LV8Ffa0AUibKux5wfcL2VwLNAaaiq60yRvoReR0Fs2quGA6bdvHryU2AcC08loVqOJqFnltd3djTqdYSIi+bxoze/cmATA6runa+GP4hM7pFKuw24wpX0ynaq6WpbgA+vz7EIIBZSE6nSXAdssUcfC0tbFJXZzQgcbNl9Mk6hchr7XJa7RlOrNKj/PAJdMpvi8WAbCR/tgUEiLopIBU5CgL2zZjRrOUDkif05m27XRcILlHdNw08tqXvUzediB7TidgZjpNkq48TCeQFBPSMWIhFmQRg1hplROAndOZhXkAzM5Flvww/nMmqJjOzk62DvCGjMjiAo3Fu+j8RT3XSZPYFgE7dzbPCTKms6srYWwAe9YmDSNGRYxcwxSMoPbQq00qA5Cf6aQc/LTVa10xnbpgSdaguwmqAGZWeW0adUBap5N/7j09rO/u2NFcSIg+oxszfL+rItM5blzSDtWYCrU2AaB2OnV1FQD3+fdVQQE1zCJMyDPpfv7zzQYF0Lj5sqokuez4YmRJBRpQW7eyVx3TmVZem4XpLHJiycN0pjWQxSIARbB5JrZw27bGc9hIAW3vq43TyRvgYtv54j4q5oC/N2R00/HzMEo2TqfJKbSV17q494Ad0xlicQmaT+j+ps0RB9SGGH0ubyEhgi6Qk8aA5Y9vguq51mrN+fti9JzOT06YKAHOi0mT2DE3bwaOO67xPZHp5M+bV15bhGInL0z9AmjsGzbKBiCb08mfv1azk1nmDXKZcOaZTFUi9osy5LWqdVCVpqF7NmIVehPI6Uw733R0sGc3bpxcXkvfNzGdADt3Glk1fwyfqNUY29nXZyevNbH3ochrOzvZHKxTpVB1f74YW3Q6IwpHGqeTLxIBAK9/vfyYWffBsu3cqnw4/jw6xytvTqdLeW0RTKfKQDYxoSopYRo2z1ZeK07GRUg8x45lfW54OFv1WiqUMjRkJ68VZb95GKUsTKc4ZlSMWJbCHvU6O1+axZKYzjROJ1VF9ilLIkaSAhZpCwkBauelo4O96pxOvhCQ7Bw2OZ28qqRoQ2j2bPYM+X1mCSIbKEbPxfPT1jxFOp0AO6aM6dyxI/mbH7O28tosOZ1lGWk6ZYZN/lnefmHql1mCXEUa56edBtx5Z/P/XRcSAtTroC0LrAuOmjB2LKt0nUZeSwoAgD1LnbzWFKgBGpViRRc2cw0bp1MsiKeq7h+KvBZons9kqhR+zRf3qR8JiE5nCcgq5dNBnNTF4hKq49t2bn5A8WwC/Q2onZuurkZDgz4jYzptpcdFRrNootu4kb3KmE5x0s8qrzU5nWnYPOo3JqYzSy6LeBwV+AqwAwPp5bW1Gmv/pk1qp1NmxAFyFjjNglNkIaGensacMlNOp6rAlG3bCfPns1d+P1JA73QCega9rAVv8mS905lWXkvb6ACJQ2tbSEh8PjYyyv5+d8zC+PHMeOf3YiWI24qIBq54/qKfKx+AtMnptA0WAHb5aa4dJx0oB2/HDjtlho3TB9j3C8p/6+lp/o7M6bRxPmzn+jzQMZ201uWdj0ypGrq6CeL505577Fi2d2tap5Mwblx2ea1NKkXI8log6de6nE66BnEd8+1QH3oo8N73Auee2/yeOFfLVCmDg81rXijBgDIQczpLgGrSzSN9ESd1VefOenze6ZRFt+l4/GsRTGcZUkBaoLZsYa86pnN4mN1HW6dSnPRtnE6RzZNde2dnElQwOZ1pFltZG3Sge/X00+w1iwFM7U9jxNHx8/QL0ens6GjcekJsu6wNxDyIRk5bGztGGpY5S4T2vPOA228Hjj++8f8yQ0QMCPherFXOi628VpU7CDQ7nWn2zQOY8UPn0TFaNgw2XUtbW7q8w/nzm89N12KS1/LnLzryr1sLxACjLEBnMqCHh9ln0jBiZRppFJCwyUHnWRmguC1TZGkxvNOZRvZdxr3TpdBQvq8rphPQ35si5uIs8lrR6SSms6urcZ6wldem2ec8NOdG5XTyOZ0yR9mmur9rtLUBl10GnHpq83v8XK1Spejmw5GA6HSWAHFikpXFBtItCKLTaWKcgPROLX1WFt2m9tJx+f93djJDgnJRRcdNJw0uQwrY0dF4TTqnU2SjbIoo6d6XyWtNjpVoaJokX2nltWlzOoEk11clHyHIJlWV02lzb/Iw4GLBGdOYEfstoDZAgfQFZ7IwUh0dwIknNv9fN6boe74Xa955SVO91qQ+APRMp00uMDHwgN65SBPIKeq+8tFzH4aM6rkBjUEuOrcoPc7K2hQxXxUB1ZjngxFiTqBqjc8qr00735SxT6cOOqYTMBfbsYFt8NVG8ZNFXrtvX1LJXQxy8fYPHZ8/Nslr9+xp/C59v9WZTlUwhR9Tsv4QAtOpgyivVeXfE0J7LmUgOp0lII0hZNv5RHmtyfgH0smSarXE2JAZGtRe/lVliImOmywKmtZIzAt+stMVEhInPpPDnUVeK05Mpsg+zxjJFtvBQWYE2Tg+sjboIDKdaQsJAWanU1YuHbDfMsXUdnJOTG2XyXtsnU4b5qHIxdIkrw2J6Wxvl8v108prZU5n2uq1vETa5Fzs32/PKBXJxolMp+2YK2qu1DmdOqaTNka3ZW1cbP1VBGz6ha2kPa1jNWkSGy8mpjON7LuM8W5yOouQ+9M60tHR/J2s8to0TieQqKVsFD8yplPmdGaR19qmJ/HznU/Mncte+b3oAX2RJKAc2zAPRHmtzC7PE+xoBUSnswSkmXRdyWvTVmcDEmNDJqkC1JO6yYjUyWup/a4NZL4QS5aN6l3Ja236RWdnYoTo2EIXBjIttjqn05bpNG1BYCOvzZPTmaXtOqeTz6e1YR6KZO/z5HSW7XSOHZvIYoHshYTyMp1UcISgYpV00XedvLao+5qW6SxTXitjOvn2mQxo2VpAzzuEQkJAMuZ1QTLZPS9iHWtrA045BVi4sPk9myCX6306VdAVEqL2FSWvHTeucT4BsgcA0+R0AnLFj63TCTCnlQ960/ezymt1a3xZ2wzZ4C1vAf78Z+CEExr/b5LX2uTn+4RNKsRIl9fGQkIlQGUIFZHTKTOgi5Jc8UYij7RMp2rSTBMFLXpisWU60zqdWZhOk2Ml6xdTprC983R5keK5izDiRHmtbU5nVnktb/AWldNJuThpK+9S22s1NfOgkoi6LviShen0Ja9VzSeunE6VJEvsM9Ony5+tLqezDHktGTJUbKdsQ8bEdA4Ps6Bme7t5PjMZ0HxBPJ3jxG+95Bo28lrZWC4q0PP738v/b1NIiD+/uGe0S2RhOrPKa1UBQN74549bRAAwL9NJbd60iVWu5pFHXqsLgoXABBI6OoAXvrD5/6YxldcGcA2+MJpNpfGRKK+NTmcJcCGvJQOaIok8c2drQJtgy3SmdTrzMJ1FDU5XTKfpfdkeYlmdzocfThd9t81b1MFGXmvjuAHpt0zJm88hMp28oWAbqOnqAq64AjjqqObjpy1eUaa8NjSmk0dR8to9exJD0yZ/TLzuj3wEOOsstVOchuks0rgfPZodX3ZtOsmWi+q1uqJyo0bJ57M0rI2JxaVjlGmknX02cNttwDHHNP5fJq81MZ1UYKqjAMsr7ZYp4p7RLtHZmexZmGaNJ0m2DSZMYJ+VVePt6QGefTY5Ln9+X/Javp30+86dzUxnFnltKLLqvJCpSmyZzhAcN1KlDA4yxzOLmqrVEZ3OEiAr7KEyhGzLmaeV12YxRPLmdKre11W2A8pnOm2cTmqfeO2mxd6G6eTbYut0Hnwwu08qI1Anr82TU6ArJGTL5tkUEaBrsHHIRZmkCj097HPkdPIR8jQLwtveJj9+dzczIqjt/HddVxnV5cbR+X3LkkzKiTxMJx3zmWfYq2gEkhFALJpsTB1xBPsRkSWns0h5LV3Ltm2N51ad38VcWaux+6crKjdqVPN9tTWg+a2/CDqms0wj7SUvAX7zm+b/88GINKxMUW0np9N2/9gyjfNajdkoaQoJ0b0RpbK6c8yZk+ybyINPdbCdT/jPmFCUvBaQFxLKKq8tQ+7vErKcThsbQPycL4waxdpHAcLIdDYjOp0lYN++JPJHULE2WQoJuZLXqpxOFdOpqjYpYwPb2tIZyHSMohLheaZT3JiZP5+KySyykJDNpCoGIz72MWDJkmZnSxd9L0LiqWM6bfvd618PfOITjEHgYVNkKY8RV6s1FpzJUgRJB7FyMO8Mu2Y6SZYY+j6dgDmIpYrei4aYTK1gKnDFH8v2um1yOmX3VgwIZQXNT8TcmAyZop9rWxtzPLdts9s+K6+8lhAK06mCTaVN104nHbO/n91rfr9nwC+ronM6VYHltG37v/9rPC5BV0hINxenzenMK68Vv0vfd8F0hjBmTMiS01n2OqYDPRvZXB3CmAwB0eksAaJTCOQ3oHXVa4vq3GQkZtmnkz+/+H6tluQppTGQ+UU1L4hl6+hojszzbS5aXivLW7SRZPHl4QGWgC8m4fPH1+0pWGSEV7bYmhy30aOBf/93ddt1DnneCC6f+8ePGQoI5TFERLmb2HbAnXNQq7Hz6yTru3c3fqfsBU8VxKJ7YwrkmOS1gJl54J1O2+uWyWtNzEJ/f2MuZB6IhkwWSXteTJrEnE6bAGRWea1NRWnZOXxBx4AD6uBpkbm+QNIvRYcXcLt/qwnkdKaR16ZtG1VBFZG2yFIZ8loV01mkvLYMub9L2OR0iutYSIWE6FnKVCmxei1DrF5bAlw6nbt328mSstD4aZlOGlQmp5Pan3YD4CIHJrEksgkfyO5U2sprdduCDA0xyRQhi4EsKyRUpLw2T/VaFfgFR1Y0haKcWe8NtV/mdMr21cvCdKqeaxnOgc7IC2GfThXTWauxe5C3kBCQbiufNM8VkBtClIPmcr7KynQW7XQCepZalh+e1oA2MZ1DQ+wnBCPNVL3Wdb8QWSFbVqWs8Z6W6SzSIe/uZseTSY+LmItpHKiUWuLx08pr+XXOFKgB7OT+ITCBJtjkdIYurwXSBwir8GyKQnQ6S4BLp1PWuV3La1WSKrHqoMoxAxKmM0+1yTwgplNVJCmt0ylKi/PIawH9gqWDTl4rY/PSLrYunU7+3pDjbcMWppmwx4xheZf9/c1jUlzQ0o4ZyiMiQ0fGPLiU1lDlvLTFucparFVOJ7XBNpBDn7F1OlX3vogxRcd3OV+JOZ1Z8qjzwqaoHLWhKHmta3VAXtiwMi77hXj+0FgVE9PpUnrM2yguFT90PL74kcq54K/f5HTy3zeNGb74UtWZTpt9OkMuJGQjr41MZ4RziIV+gGZDRWao6qBzOovq3Cp5rch0iu0W5XBFMJ1F5ySomM72dibhTeM08u8XIa8FshvIOnmtjJVJa8SlLSSUxqnl742qiACQnbECWPtlxWbo+HkccroXAwNqptOlAS06nWVvQ2TCIYcAixcDr3td83t8DlZep5OYU0JRgZy0BWOKeq46ptN1gSqCTeVh1b2xldfaMJ0hGWkmVsZ1vxDPX7aywgSe6SQlCd8+l8VuZGPWRvGTNqcTMDuNMgUAn9OpUlvZOp2Uzw/IA8t0jBDGjAmdnc21CUzqgZDmBF3Rt9Dns7IQczpLgA3TSRNg2kJCZEC76NwveQkr9nLGGY3/tykewZ9fxXRu26au7Ou6+pqK6aR2upbXEps3PGwni8oir7Up459VXptGVmR7fJt97+j4NLlnyemUMUZAMfJaQJ9j5dIIFNUDNqyLbeXfItDRAfzwh/L3inI6t21j94GvgKkywLMEcnwynTp5rWtDZsEC4Oabm/NUdXJ++r1IpjMkVsN07apAj+ikZIXNfOOTJeadzp6exjFZhrwWaFwHdRLUPEynbB3hjy9TANgwnf39bF4T2ybKqsU0EdW2IiGMGROoNkEaprO/n61hfKFOX0hLBoU0n5WFyHSWABunM+2kRxMVOZ0utOOTJgHXXw/Mm9f4f5HpzOJ08kynuG+Z66p/QHan07a6rQ3TKbsvNlFSHWyKW+Tpd+L9yrLXpQp8v7K5N3T8tE4nQTYm8zjkonNStrRGJ68tY0zlAT/mTEaibEyYniv/fSCdgZuW6RweZn+XKa917Vx88IPA+vXNx5QxnTbyWmp3qzOdvuS1IVTKpDV+797mflPWveHXEronRQQA0zCdsvuuKySUVl4ru7chz/UmkNOpCjyHnK8amU4zotNZAmROZ978sc5O9qNzOl0ZuCam0+R4AY3Va3l5CLXfZdU/QC2vpXaq2k4yIddOJ12/LEqqg0xeq4u+p+0XHR2Nz8Gm37W320Uh+YIyKiMOcOt0FsV0pjECXclrbdQDoSzWNkwn/V82JrI4nVkCOSr1AH9s+r0MeW0ZDDrQHBgk2CgrRHktz67TtdlWrw2pUqVOwgmUt2WKLMgVwp6AfCVPm5oWRaqZxAAg3+eKrF4LZHM6dVumZJHX8qgy0wmw6/MVyMmLtDmdkel0iLVr12LRokWYN28eFi5ciDVr1kg/d++99+K0007DkUceiSOPPBLXXXddWU10BhXTmXcx5fPTyoyoyJhOnYGtYzptHHI6VwhMp+n9NPJalVSP/37aScnGCMxroKoM/CKcQjHKqXPI6fhpCwnx5+KRN99VdE5s5W5lMJ2hR7+p2iTQLJcyGWGA+bkCbqrX0vFdP1cgzL3fTEynTF4rBh8BYNculm5gu46FYKTpJJyA+0CPTl7ru18AjcESmWNURiEhCu4WHQDkg9VZnM4xY5JAu05eK/s+FS6SBRvo+yHP9SaIW3/ZBHJCmA+A9AHCyHQa8NBDD+Hqq6/GLbfcgmfprlri4osvxkUXXYSHHnoIl1xyCZYuXdr0mT179uCcc87BJz/5STzwwAO477778NKXvjTVeUJEGnltmsEzerSfvdtkbF5aee2oUez93bvLl94ATDpcqzVGHAkmp1PHypiYTmL9dPJXQH/vdEi7d1yWfufS6aR7a+OQZzk+3/a0m3KbkJbpdJHTKSujDxSz3YxLEMMNZJtP0jCd9Xo6+assp1Pn0Bf9XG0qlfvK3bNRbtg4nTI5WujVHqkNuiBZSEynL6fzmWfMQXegWOchbX592nvT1pbMOab5RnbsWi1hS9PKa4HGAKOJ6QxpmyEb2OR08utY0YREHmStNB5K+8tAKqfznHPOQb1ex+rVq/GBD3wA55xzjtX3Nm/ejNWrV2PJkiUAgMWLF2P9+vVYt25dw+e+853v4OSTT8Ypp5wCAGhvb8fUqVPTNDE4DA+zjlW0lA9gkxVNLmXm+ZDE1GQk6hw3XnpjGwUt0ogaPRr4zneAD32o+T0+Op+W6bSRFpNjpWPzsjqdJnltEf1O5bgV6XTaOORA+gXHVAAij/MgOie2hk7RMszt29mrrF8NDSX/C0lyJQZy8jidJuYhrWRdlrsnPlvXVYkBe6azTAmqiekUjcQ0TmdZ0uGsMBU9KVNemybIVba8dscOufrApfNgw3TmVZrRWpKF6QSSgHdaeS19xzanMyR1gA1MOZ1A4zoWUvDUZj4b6fLaVNVrTzrpJPzDP/wD2lKWO1y/fj1mzJiBjufueq1Ww+zZs9HX14e5c+ce+Nz999+P7u5uvPa1r8WGDRtw3HHH4fOf/7zU8Vy+fDmWL19+4O9du3alalNZkG1fALAOODzMfvgtOtI6nYSyaXxRDic6Zfz5VZMmwAwpsSKi63wPwpveJP+/S3ktoGfzinI6VZKvvDmdQDqmM+1zyyKvLaLtdHxiCenYYht0EFlmH4WEgCSQI1aLpPPT7yEt1rr5ROxXsnGTpppkWgOTf67Dw0l7+ePTMan9aY5vgo1kyxejxef3q+ZKIGGWi2I6QzHSTAFEcqxqNfbqks0LjSVW2SdAYxCsoyO5N66cThfS47FjgU2bsjudVExIxXTqbJA0TGfV2DRTmgjQuI6FFDy12d4qMp0p8Oijj+L000/HihUrcOuttxbu6A0ODuLXv/41VqxYgTvvvBOHHHII/umf/kn62WXLlmHDhg0HfsbKdhwPALKtJYDmRSGv08kfv72dLXIuJx5eDmfKJ9Exnap8D3LI+faXNTDTOp21WnP+mfh9uidAOjavCHmtzBAi5Inw8ucD1MGONAuC6JDrgikkkywqp7MoeS0tmCZDx4W8FpCrB4oICLiEOJ+kHRNp5LV5ZNM2eUYunyvfHqCcAlU60DWmUW7w77e1sb+rmgNlYjqBhJVJy7CbQP1iz57mas4hsMQq+wRo7hdpt4wzwbW8FkjWwazzDTmdWZzWNExn1dg0mzGVR03lEjbbW1U5IFAErJzOW265BQBw88034/LLL8f06dPxu9/9Du9+97utTjJr1ixs3LgRg8/d7Xq9jr6+PsyePbvhc7Nnz8bpp5+OQw45BLVaDUuWLMGtt96a5nqCg4rpzBt9B/SRRJUhVNTEk4aZ0DGdMqehiIIxeZDW6ezqSlilouS1WYMRsvwzG3ltFsetvb2xomURTGQa6XEWIy5LIaG0zgnF4mwXnKJlmCrJOpAvIOASRcprTYWE0t53G8m6y+dK8/yePc3H9V3hVcZ0ytqnWiuAZM9m8btVkKOlUWYUbWBSP9+xg70WXSwnL0z2CZA9EGSC60JCQD6nEUgnr+UD2/SdVmU6RafTRu0UynyQNkAY2nxWBqyczre//e34zW9+g7a2NsybNw/nnHMOjj76aPzv//6v1UmmTZuGE088EVdddRUA4Nprr0Vvb2+DtBYA3vCGN+C2227Djudm0Z///Oc4/vjjU1xOeDAxneLElLaQEMFFzqgOPDOhMhJ1jpdNFJQmlrIT4U1Op42BrPt+T0/66rVZ5LWqnIii5LVFb01BxzQtOHkCNVmYzrTOCRmBLiRdOtgwnXkCAi7R1cXaRjnwacdUZ2fyuaKZzra2RHq9f3/zFkCu51rRKA2JDTQxnTIDWud0ihvdt7eHzXTqlBlFrPE60L2iHO7QWJU0a3zRgRJdJfGipMd5czrTyGv5wDadk4rG2eZ0hjJmTBg1ij0zWUG80BU79CxVxfyAagcEioCV03njjTfiX/7lX/CTn/wEd9xxB0477TR8/OMfT3WiFStWYMWKFZg3bx4uvfRSrFy5EgBw4YUX4vrrrwfAmM4Pf/jDWLRoEY477jj89re/xde+9rWUlxQWVE6nKvpehLwWUEffi4wk8pMiP7hkhoZ4bn6ito2Chsx08u8BdjmdtnI0+o4N0spr8zidpsWWjp+H6dSxOnnaDrirXqszAsuQYe7Zo55vxPOHEmEVGbO0YwrIV03SBAoUyaLqIrPgqnot3xaCjuks49nyz802R13mdO7c2Xg8/vuh7tMJ6JnOvAFEm3MDicNuK+f3wXSa5qOi13hRncAflwJGeefiMuW1sjFD2wypijQRqsam6Rj8vNtfuYYuQBhzOhmsCgnNnj0b3/rWt7Bo0SJMmDABn/3sZ/GP//iPqU40f/58rFq1qun/V1xxRcPf5513Hs4777xUxw4ZaZlO1/LaoplOGQuZJqdT1nbXsiQT8jid7e2NhaFUTue2bW5yOkV5Lb8pNh0/ZKZT55AXIclKw3RmLTijk7uVIa+VHTN0plNk6MkgA+zHxJgxbFxlZR50IOeio6P5e66rRXZ2sjEsK2IUOtNpK68lmNaxsgOQJvT0sC1BTEVPAHfy2iownWWv8bpCQrVaMYqfophOG3mtyukEWo/p1PVrVTAllPlAfJam7a2qFhAoAlZM54c+9CGcccYZ+MAHPoDp06eju7sbNZ7rj1CiLKfTxT6gOhDTKcurS5PTCZijoGVHt9M4nTJph60813X1WtliVUQwwjbCS8fPI68tWq5mcjrzGGnUDtmWJWU4B2nUA0BYsiRqh4zpTON0Am7m2u5u9WbsrnOoajW1Ae+7Sqn43MTz2rI2BBPTGZoBrZvLXbN5WXM6Q5LXumaBVWO2iH5l63SqHIs3vxm44ALg0EPl388TqAlZHWAC73SKQfPQmU6xn4c2JkOAFdO5detW3HXXXZg+fTre85734DWveQ127dqF888/33X7Kg+TvNY0MengU15LTKcuj0fneGWJgpYpr+Xzy+h//PuqnE6g0eHv728uAlBGISFZLovYNv74WRw3k2Scjp/WuB8aYvkqQLH3Biineq0t00l9iK9snAdpxtTQEOvfoURYxX6bhf237ZdZjDCS19br9tUiizQkeGbDVDFapnBwBVkOuUkaLd4Xn4qdvNDtKVhW3qIsyEXrjU9WJaRCQjbrYGdnY96kCXnltaecwn5EFBGoCVkdYAJd2/bt8ucGJNc3PNxcudkn2tqSOQGwswHKmqtDgdWlUsVaAJgyZQp++ctfHsjJjNDD5ZYpvAFtK0sqclJPE90WPxMy08m3X8VU6qKQMvktv5gRa+JCXlursc/Kclno+K5yOovaMgWQF+Mpol+YqpzW68kWB3mdTlOOVX9/sQtOljzpUBZrXU6nmINVFNOZpl/yDHzZ8lrAzHT62kLAlunMytq4rgycF1mUGWXIa4uSkOZBmkJCLnM6ZWNWFsxIe1/yymtVsJXXElqxei3A+rVqHfNFSNhA9WxC3+6lLGQydyZMmIBf/OIXRbelJeFTXusyyklsX1aJqA0r48tANhlSXV1JLqvMqZI5nTyySLLSsoVpIrxpj68y7kVDJ8uG32kkY3kDNUUbC1kKIBS5WKbJ6QyNMaJ2qHKwRPUA/x2C60JCqkAOBSso59LFfGUyZHxtIWBiOtOyNkVvY+Qa3d2sffv2NStaymLzZPMNnd+nzNIn00n9aPdutTohr0TTldNpE6jhx4mt8iIkx0wHndPp2za0gWo+C72uQlnIHGMfJY60CCl8OZ0hMJ06ea0NK+NTXkvnpXPzEkg+Emkjr5U5nfV6su+ezkjLsmDwW7LI7u3QEDs/f/winE6g8drp+aV1mIGkmqVNMCPNvSFDASieEUvD0gLF51SmUQ+EFiG2kcPp5hPAvqpy3pxOk+TLhXFPc71qb1xfubqyuZLvU/xcSZLuPExnaAY0H2jq7m5UtKgCPUW1vb2dnUPGdNL5fcosfW6ZoltH6Px5FDlAfnmtCnnlta3CdO7da7YNQ5sPgMZ+z9uNoVeQLwsjSEnsB7Y5nXmdzrIrmBGbJ1ssZPIQahPBZ5EBE0RDSpRAiu/byGt56CLURRrIKuMdyOfQ2zqdWdsO2OVFZmWsyDC0kXy1tTWyF6Zjq9re1sZ+XEpr0jCdoRki1I49e1hARDamfDOdui1TALf3lp6t6dx0/rKeqy3TqQpQAtny00Lpt/yYl0k4AbesTE+PPMhF5w+leq0LybsOurkYkAfl096XqVPZ60EHNf7ft7y2jBxzl+CvzRTgC20+ABrnalkQKsprI5zCNqezrEJCfJQ8D6idu3c3/k3nBhonBnFDdRvpjc8tU+i8OqdRxcrYOp1p2Lws8lobVibL8VWyIjp+XuMecJfTWavZOydZKu8C8sIedHxf8toiCpe5RBpmQtWvVMxDEfOJKacTcMsi03xpu01RWc+VdypNqRZZnM7QCwnxQTIfgZ6eHvlWOnR+nzJLnznmOtUJIGfQ0577Na8BrrsOOPPM5mMDbuW1JqdzeLhZ7h/KXG+CTjrs2za0AT0bF4UWWwHR6XSMkPbpFCMveUDttHWc0k6a4vdln3MFGyZT976NvBbQ37u8BrKJlSkiL1LGdPLR9SwGomuHHGDtpzxBHrJ7k8dhtjECI9PJID533b3Ly3Rm7Ze6LVPyHt8EFdOpqohY1nOlwmUqJlNMRRDfB6pfSAiQM51FpEqYkEVmWVS1bBPa25M2+ZLX6pzOvIxTezvwt3/brIQJQV4LhDvXm2DKVwXCDZ4CiV1uajsQ5bURDlCG0ynm+dDxXdL4NFCojL9KUqU6t898DxPyOp1p5bU6Nq8seW0aQyStvDbNpOpaXguw9vMyW/H4WZ3OtIU9ypTX+q4IbYIuiAW4KSSUNk8aYPOd7Zgq8t6qDJkQKiJ2dbmT14ZeSEjM6eRRlryWYONYdXYWF3i2ga0B7prptOlXRRn/NjUtdChCXguEnfeoQ6swnaGlQoSC6HQ6himnM8/gUUmugGKqs+ngkun0vb2DrdO5b598j6i08loXOZ2qQkKySKEofTbBZU5nGnltVtZjzBi1NFg8fpq2U/CHtlyxMQJdSDCB8rcoyIsi5LWuczoBdSVM2fFdMFritcn2YyxTXgvYFZWzZTptq7CHYqjRfd6zx08wwsQKifeu7PGuslFcFzajuVg3n7iyj8qW17qoF+ITaVRwITrUJlWKr1SIUBCdTseQbRILFCP50jmdKnltUdAxneLWGSrGjQym0AxkW6eT8lldyGuLKnoifk+2IKW9r3PmAEuWAOee2/xeGYWE8i6m06cDU6Y0/1/W79LeG1OU1lchodANEd18Atj1q4MPZq/isy3S6eTbSvBZSEi2H2OZ8lpAz3TmldeqmM5QDDWbfuHSQPY539jAlIvsUs3U3W2f6hCi0zkwwPIyZYHtKqdSmKBj76vAdJoCLb5SIUJBQWVlIlRIK6/NUkhI9h3X8tq0cjjx/VqNTZy7doUnBRQNpTROI/2dVV5bRCSPzz8z5Rll6RcdHcC3vy1/ryin08Yhz9ovLr88GZc8irg33d16x8nlgkM52zo2LlTJlc18QlsMqfrVm97EAgovfGHzd4F8hgp/n0xjyuWWKT7mehNovnMhrw2d6TTJWwG/TCfdLzq/L6fTl/RYV9nXVaCGgulFVK+lY+RRioU215uQJqczRKdTFyBsb/cfCPKNyHQ6hsstU0wyR5fy2ryOF+Av38MEW6ZT5VyEIK/du5dFSW2ioEXe16LltXyuaVFRzkMPBebPb/6/bLFOe+w0m3YXLa2p1ZLzV5XpzCOv7eoCXv3q5py1Iq49C9NZhryWzu9TRkly/qzy2jQF8fr706cDuATfXh/rmIkVClVeW4aaiYKvsuO6nIupSF0R8tqsgRogbDZQhzRzbYgOtcrpBMIYk74RnU7HmDSJyRFtJ90i5bVDQ4z1oOOHxHQC9qWlQ5XX2koBxffpel1Wr6XnbsPKFNkviqpeW6+ztvL7o7rOnSuC6UxjBLqIctpWOQ2NMRLnE7H9XV3Zn3sRTKQNo+XSuagK00lyX/49oNjqtaH0WcBOCliW01mmhNQWPgPLpjHrsl8V4XTmkaTT94Hw5noTsuxsENK1hTxXh4DodDrGZz4DPPposoccocjqtarODTQamS5zOnULXl6mM9TqtTqmc3iYOf1p5blFVa+V/a46fpH9oih5rex7rhecop1OkxHoIp/DVDnPZbGbPCiC6VShyOq1su+VMV/ZRs+HhthPmYYMz3R2dck3RM/K2vjOVzXBpl+4ZGVMrJBvVsW2kJCrnE6CTl7rYszw81Xa515EoAYIt2icCVXP6bRVpfiYq0NAdDo9QWaoiFFiE7q6GBOk6tz88X0wnSZ5rW1p6VDltTppMcCeqawIAG9gi8+8KHmt2Bbx+GXKa4s27l2xdUXldBJMho5M/pwXqgUvdMmVD6ezqDFVhjLDVKncZzCBmE5TDrnJgG5rk2/9JW50H5LxbNMvQtoyxRfT6UteK/udzu/yuciOz6eKmL5L3yuC6Qxtrjeh6vt06gKEvufqEBCdTk9QOYVp9tCq1RiD6mL7BxNM1SZFOZyO6XRRZCkPTE6nTaVNQF3dlpeQqpiBovLPbNjCUGRFgF8jrgiH3HYLA1eGgC3TGZrkKq1kXfYZFVxXr1Ud39bItIHJkPEZTBCZTh5pWBsqhMVD5tCH0meBdEGysuW1Zcj5TfBdSIggm08o/agMp7Oz0962yyuvDb1onAm+K4XnRchzdQiI1Ws9oSjG6UtfAnp73R1fBRum0zanM7T8syLktbr300pIqeqZLdLKaydNsj+2CT7ktUUtpkVIj03SINdOn0myHqrkKgvTaevUFWGopC1ukcbItIEulULcngoo15AhplM2V6ZhbUxpIl1d4RXe8M3KpJXzl33vJk5kr6r0Itdbpsh+F8/vYi60sX9UoPlkpDKdVd+n03Yrw6o9l6IQnU5PKIpxestb5P+XMRsuczplxsbevez3tDmdvidNXh6bR15bhNNJzy2NAZv2+CExnT6Zg6LltSExnaEXYLBxOvv7E2YijVOXdwsDIL16oCzZNJ3fJ6vR3W12Om2ZThGu00Tywre8ljfQZef3zXT+678CL3sZMG1a4//LmI9sn00ZTGeaY/PVb/MwnaGqWkyguV229VcrMJ0hO8xlIDqdnlCGzJGOT4Za2Uzn9u3sdxPT6SPfQwdq6969iQRW9r6uiBJgltfqvutS4ikev8j7WlT1Wtn3ypLXDgywaxgeLv7eu5aM2+ZJh7bgifOJqt+qinPpkHcLA7E9NkGyshhsOj89T19M5/798gBWWnmtCBkjJrJmPhGyvDaEnM5DDmE/IsoobGb7bFw4ZXmcTv77qj7DX49N9f+Qthkygbb+2rvXXu1UFaeTt49CbHsZiDmdniAzAouWd9DxKXfBRRRRx/YNDCQFU/Iwnb7ktSb5rAumswgDNq1EtejFlgp/FG3cu47g2rAyJthumeKqT5sCOaEu1jbzCZA8m6xGHJCtaJsN08k/27IYbDq/T9k0nWvPnnKYzlACJUC6LVN8VK+lubheD0uaXLa8Nku/zIO8TicvWZe1ra0tuT4bVUso87wtqho8Bey3TAmx7WUgOp2e4Hpi4Bc8F53bhuk0TegvexnwohcBU6Y0fxfwZyDT+U1Oo4uczrY29uOygipQDsNehowx7fF1kOX5FOl0uq6YCFS3kBC1l4wJk5GYx+nMUrQtbSGhog2JadNYe0WZIlBOrrAO/Fqgujc2+Wkq6TAQbiGhtKoV/v9FgO5ZR0fjnsbi+V0EnvOgjDXeNiAQWk4n/33dfUnjmFXNsaFnV7U0EcB+y5QQ214GorzWE8qS1+YxoHUQHS+VtEd37re+lf2I8B3NsmU681avFX/nv+9yr0ggkY8ODLh3OtM8NxuH3NWkXUSeD7VfJmeSFREoS17rW7JugmqMEfI6neJiXzSDDbh1jObMAf78Z+Doo5vfKyOYoQO/FhQtr5UFS0Iy0tIGyWTOYRHnl90Tvl/W6+rP+UAZEtC0hYRCYjptnc7t2+XbDAHVZjpVTqdv29AGJP8nxpOH77k6BESn0xNkjNOYMW6O76Jz07Fo/zSZsaGLbuvgO5qV1+nMw3QC+RcsW1kRncOFrDtrvzM55KLjVqShktdhBtSLJR2f5G6u5bWhFecygYxx1XzigulMg7Sybxf3deFC+f9l0fMyjTC61qzy2o4O9jmbnM6Q5bU26oKi226ab+j8NK5CuXdlOEYhyWvT2nY29tOoUfIigzLHLJR53hZVZjoXLAA+9zngzW9ufk+WYhPKmCwL0en0BNeyIdeTqjhQsshrVfCdCG/rVLrI6QSaq1G6kte6WmzzHJ//rMkhd1F5Fygmp1PVdsBddB0AjjwSGD8emD5dfW4gzAWvqwvYty/5nQf/bLI89yLVAzZMpyzK7QqyPCEf8lrZeW3ktQAweXKyvQaP0KvXpsn1ddF2ndPJ90uVbN0XymCwbQMCZRQSSrstmS3TaQo20PlDmudtQMFTm/Qg2ed8olZjVZtliExniTmda9euxaJFizBv3jwsXLgQa9asafrM73//e4waNQoLFiw48LOX9t1oMai2xij6+Hx1Nhc5naq/aXBlmdDLYg5UCE1em5Vtkx3ftePDGzpZjl+rJW02OeQuAzVZDRFbI9AVI3XxxcCmTc0GfBUWaxs2kZwXn06nj5xOHXznCRXBKP34x8Dy5c3/F9MBBgfDMqB9VzW2CXK5quuQB2U45Gn7ZUg5nV1d2Z3OMuT+rmFiOkMOnurge64OAaUxnRdffDEuuugiLF26FD/84Q+xdOlS3HbbbU2fmz9/Pu66666ymuUNZRYS8sV0AkxylfbcvotH2DKVruS1ooTUlbzWVYQ37/F7etRBGPHeuArU5M3pNBmBrhactja7giyhFRIC0hmJWZkDIFsQK43TWXaQzHdFRB3TSVWCTX3+5JPlx3a9juVFW1ty/0NjOsuYb7KijECNzXziKvhKTiOQPUi2c6e+bUuWAE89Jf8u0DgnhLTNkA1MOZ0hB0918D1Xh4BSmM7Nmzdj9erVWLJkCQBg8eLFWL9+PdatW1fG6YNE6FVETeCPVas1S1/pfRXbp4PvMvm2TKfqvhYhr3WVfxa6vBYwO24hjxkbptOVw69DFRZrnfNCfxdVSCjtfGIjWfcVJJNtmRIK0wmw9uXN73e1n2IRsM0/KzunM6/qxCXKyDu0lddWtZDQ+94HfOYzzf+XzUdVc2zSMp2h9GsTItNZktO5fv16zJgxAx3PjYZarYbZs2ejr6+v6bMPP/wwTjzxRCxcuBBf+cpXymieF5SV0+lKXisaGmIyu0liqoNsYgmJ6bSRFgPqa+/oSO6XC8fKtqKiK1lR3uOr9h+j44esDrBhHlzKa1WQzTehbRiehumM8toE/H6MPgwZXbCA/pc3v9/VfFUEVEGyMuW1JqYzNFYlpEJCrhQ/fNG4tPc9z5jxrbwoAmlyOtva0u257BOynM5QxmRZCOpRnXjiidiwYQMmTJiADRs24KyzzsKUKVPwhje8oemzy5cvx3IuCWQXeQgVAT8xUL5KkZ2vrOq1quPmcTp9F48wsbS2f6uc1lqNGQt797pxrHzKisooxuMqGFGkw+xLXqtC6FtPAGE7nbZMZ73uR15L5/fhXNgqN/IynaEyA7asTH8/MG5csedWGedANZhOV9X7AfsiTy6Dr/39cum1zfezqgNGEtNZtXzVjo5kz9yqsbRFoRSmc9asWdi4cSMGn+sp9XodfX19mD17dsPnxo8fjwkTJgAAent78eY3vxk333yz9JjLli3Dhg0bDvyMrZhonRgG1zJH1/t0ir+L51c5Xjr4Zg7a21n0LCvTaXI6AbfFcmy3dwi13+nuTd58Vx3Kktf6kApWwRDRBbJ8O53t7UkbdGOKDIoyDQnfeY/8uVRrQV4D2ock3Ra2+Wc+t0wJ7d6VWUios1O9rYjrdTBLTQv6flFMZ9UcM8DvmHKJKgTRXKMUp3PatGk48cQTcdVVVwEArr32WvT29mLu3LkNn9u4cSOGn9tQaufOnfjZz36GE044oYwmlg7bAgtZ4ZvpNLGFOoSwz1RXl9ppFOUcaeW1gF3eIjHgRcprXRtxsn5H98MWaeS1rqXBRbO04vHLWjAp7zpkQyTk6rV8m3RjyodkyrchY2I6i5AK+pCk20LVL3wXEiqjWnZWiPORizXetI4A7p3OLPYPfb6oLedCVLWYoOrX1G+qynT6VqWEgNK2TFmxYgVWrFiBefPm4dJLL8XKlSsBABdeeCGuv/56AMwZPfbYY3H88cfj5JNPxqte9Sqcf/75ZTWxdBCj5Vrm6KJz8/lgRctrQ4jU2TrVsvfTMJ26Cq15JZ6y75YlK6J+J4sym2Arry0jD9oF0+mLkRKlyaEtdvy9EAMVRTAHefuNjeTLB6MkCySFUr0WaGZt0uRfVZnpbGtjPyFsmRIiq+KyEjlgv2eyyyruWZ3OIopvkfJiaCi8ud6EqVPZ2FHt3RtymogOvm2AEFBaTuf8+fOxatWqpv9fccUVB35/97vfjXe/+91lNck78kgobI4NuI1+d3WxvMSinc62Nuao+JQC2jidKgO4CHltEdVfZd8tK8KbVQYJ2DnkQPELThELgk1Op0/npApMpyxQkdeIE/tNlvlEZcT6Nu59nz9N9dqurnRBqCownTrnpoh+Z3NuWzl/SPfOZQARMKdpAO4L6uVxOoeGsq0T/HOnvhfaXG/C+94HvPKVwIwZze+J/SakPm2C77k6BJTGdEY0gxYkFwtCGREVGwM7S04nfd9nNCsP01mUvDavxFP23bK3TClSxkjHr3pOZyhMZ2iLne6555HrA0m/GRrKJlnn26fKM/LlGPkuGGMzV+bJxQXCNtJMMk6XjtXo0awAz9Sp8nMD4d4714xV1eW1Wb8fctViW0yYACxaJH8v9OCpDqEHgspAUNVrRxrKktcSXDCdquMWYST61O2bDClddD8kprNsKaC4mBfJKNHxq57T6dM54e9d0ZU080I3nxTBHOSVaFaB6QxdXpuFoQaqsU+njukkmWPRbe/sBFatkjNCvoMRJtAaPzzMXn1Ij13bX3nmKyBb0D70554XYvC06KrHLuF7rQgB0en0iLLktc/VZircELFhOouQw4UqryWIbbNxuG2i41n7hQ0LW5asu2h5Ld2ber0cpjNtvwu5mqTvQI4JLucT+v7evdm+D7BnK9vb1LdjFHohIVFemwY+i2/ZwjTmXT+XY4+V/993MMIEsn9onXdln1SR6SyqJkaogZo8EG3DSZP8ticNWj0gYIMor/UIl05nGVI+Ol7RW6bQ98lhzrLPVV6kcTrFwhh07fW6+vsumc5azSwFdGXEiccvWl5LCw45Ty7GTBHbvdhWkyyzX4degMHGSMxrxGUtRAQw58LWgPUhrw1hy5Si5bW+r80GaebykSS7NkFMLyqT6XRdoMqnvNa33N81Qg+e6hB6IKgMRKfTI1xumVIGje8yB4vuja9EeB2Tyb/f1dVcGEMlt+VhcqyGhoB9+9SfMYEvysKjCoWETMyBK0OBL+Of9fgkWZVJV307J6EXYHApryVDLOv3AWDmTHVhC8C/vNZXnpAN05k3pzPkHChTHrfvfhGqw+6aBbYt6hZqISEgCdqL67jNd0N97nkh1vsIbT7QIfRAUBmI8lqPoEnXZSGhwUHmwBR9fKCcHCxfkTp+ktc51SYWVPWZNKxOlmvv7pZLAcvKZSHHbfz49McwGQsuJ+y8LPO0acD11wMnndT8nu/tH6rCdOrmkzzKCSDfmPrqVxN5Lg/f8lrfbKApQJdHXuv72mxwxBHAlCny7R3KkNeqEHrREtdrfKvIa2XruA6+5yPX6OhoDaazFVloG0Sm0yPKyOn0Xb02DzPhc9I0ScZsrj3v9/OwMiopYFmyIpfy2no9Mf6LnrCLGJNnn81YMdmxAX9G6EhmOvM6rQBw0EFAb2/z/8kg9MlgAyOH6QzNyHz/+4ENG4BRo5rfc1mh3oTQGS9a4121zVZeG6LTySvFWjEPOg9872yQB60eELBBdDo9oqxCQq5zOl0xEz4XS9uczrxMpyuns7tb/j1+w3KXsqIiqte6yO0zoexAkI/qta4qaeaF6y1T8nxfh1rNvQGtg2hAt7WlY0bywmauJCOrCKYzNAO6rU3dJp9MZ+j5Yy5TJQB7ppPOn0bCakKRQbKsY6ZVHRuaa6nqcUh92gTRBih7rg4B0en0CJf5Hr736Swip9OnLKgsp1O3IOYpetLd7ccQKrJ6rSuHXAeXki/RGKAc0rLg0wC2QRnVa133G5/Va/OoC/LAhukE8rE2IfdbHXwGT0PPH3Od75omp7OzkzkARSGvnL+IMRNyoCYPxKrHIfVpE0QboJWeiy2i0+kRohHoinFy5biVmdNZJaZTVbyHh2vHasIE9T6MZVZNznLssWPZ6+jRze+5dh5cMlYy50AsQuUSopEX2oJH99pFnjP1yzyBHNPxQ3AufBgyprkyjwHtOw86L6K8Vg2X9g8d76yzgFe8ovm9ItYpHfIqvfLIa0MPNuSFb0IiD3wHCENALCTkES6j42VEiMvI6ayi09nezpyJel1dBMCUtwjkc6y+/GV50RM6fsg5nf/4j+x14cLm98R74yqn08W9EQ0dH8WxQjZEqs50+tqiwLchY2I66X9Z5LWuq4y6RiwkpIZrdUCtBtxwg/rcgHunM+98NTyc/rtkf1Q1UGOCb0IiD8SAQJXaXhSi0+kRVZfX2jATWTdjFxek0KL3OgO5VmP/1xlZuiIHRRjIJ5ygfq8Mhn3/fnaOLG2fMgV43/v0x69yTqevBccn62KDMp3OKgUrTPBtyNgynar3deDXMQreVclQi1umqOEzsCwWqArV6czyXaD53oY21+eB7yKTeSAqEFvpudgiyms9oozcOpdGpk1Op+pvE3yzMnmYTiC5/6r3bXI6SZoTYoVW3bEBt04h4E4mKd4bF8UlaEyW3ad9jykTbCTrebdMyVO9VocQDGhfhkxHR5IPV7TT6bsyb17QmPPR9tBllq63TNHBteqkqOq1Wb5L5x8pTGfV5gNgZMtro9PpEWWyKi6qZKXJa8wS4Q4hp1NV7MXkdJrenzaNvR50UPN7rh0rl/e2jJxLl8fnxyRvTBcB0Qj0Ia8N2RCpurw2hC1TfBkyOtVLHgO6Cvt06uBTCigLPId071ymeZhQFXltlu8CI4PprOJ84NsGCAHR6fSIzk62dcG+fexvF5UyXUZU0uxVmZYxCkVe29UlL/aS1+lcuhRYtYptLC6iFSSk5DC7iiC7Yqx4NtCVIeIryhl6ISHX1bABt4EcX5KvMuZ6E2wDkEUwnVUzMkMoMOUiiJYXIWwn43qu9+V0Vjnv0YTQg6c6+FY7hYCApqCRB5esTRnRb52hwf+vszN9lU7f0ay8TqVJXtvTA5x8svy9Mtg8V0ZcGW0H3Dm1vJHo4tiAuzwiEzo7WWEKCnKFtuDp2DJ67vV642dtUeVAjs25Ab9l+G0DkEUwnaEFS3RwOdfanBsIN3/M53xUVvVaX/LaKuc9mtDRwciaKsrtfeffh4DodHqESxklSUJdLjiuDA36fgjyWlNOZlanVIcyDWRVdd08xwZaQ17bikwn4I7ty4s0yonQnU6f+WmhMZ15DOgyqrC7hE8DOXQDV5yPfFV8dmEfhcZ0VskxM8F1+pFLhB4IKgPR6fQIl4ZQrdbI2pTNdBaRk+AzmpWX6QzZ6XTZL8oqJOTaeXDBRIawZQrgroJrXtjOJ6rP6FDGmPLNaPl0LspgOqvI2vg0kH0HuUxwPSZtzt2q8tpWZzqBcNcxHUIPBJWB6HR6RBkGtMvO7ZrpBPwsSPz5TE6lasIzyWt1qHK/KMvpdJnT6doh9yWvdc0S54VuPqG95wh5nc4qVYQ2QXTMfBhhZeR0UkE8ut4qwOc6Jsr5QzPOXc/lOpQlrx0YYK9ly2t9K8VcwrdtmAehB4LKQHQ6PcJ1xKYsprPoioWAX+kNEBbT6XJPwao6nS4LFbVqIaHQmU6TZL0Ixsw10+mzeq1PQ8Z2+6y0baPCN1U10nyyMiH0Cx18rvEUxHLtdBJ8MZ2tKK8NPXiqQ+iBoDIQnU6PKFMqWMWcTiB8prPq8lrXuSyuivFUUXrsu1x66Iv17NmswNb8+fL3i2DMyphrXRxfB561GRgIL6czz3Or1RqL8VTNSAuF6QzR6fS9xrvsV76dzipXeDUh9OCpDtT2/n7W91rpudiiQkKV1kOZMsoJE4o9NuA2p9P3gmRyGl0WEiqreq0LQ4SKElU5p5MqKk6eXPyxAXbfQ2A6Q1vwpk8Hnn1WbUR0dYW7T6fPatuu2X8bdHer9zQugrUJla0zwWegRyxa4sIGyAO6Nz7ktQC7P/v3s9oRoTGdRSjFItMZHkIv5lcGotPpEeLgESeqIo7vcrGeMoW9yozzIgwNwF80Ky+TGXpOp6t+QQWsqux00vFdMZ2+FhzfY8oGPT3q96rAdPqsXuvTCOvqShxP2Xuy323BM1JVM9J8sjKhy2t9B0vKWKcIaa+t6tX/XaIVmM4qtr0oRKfTI/hJt6sr/V6WNsd3Ka895xzgl78ETj21+b0iDA0gXKazDHmty43saUEaNarYYwOs/S7bDlTT6fRtZFW51DxQHaezra3YbYhszg34dTqPPhrYtk3+XpFMZ9WMNJ8BgSivNZ+/LKczLaEQq9eq4Tt4mwchBAh9IzqdHuHSwAUaJRaujv+qV8nfo0T9ej1MiakJJqayDKdTPFZR4IMRLiRXnZ3A3r3sd9dMp6tcnCpW9jWhCkynDkU4na6YSN7I8y2b9vFcv/hF9XtFsDahOk4m+BzzbW3sJ9Q9AUNY48twOjs6koJYtigiaN/q+3RW0XELYa72jVhIyCP4kuEuBo5LGaXt+YFimM6RKK8Fsi1Ytsffu9fNfc1rZNoc22VuHqFqLK0JVV6sgWKq12b9vgmdnSzAtnev/2BCaM+1iPw0V9W2XcP3mHMdeM6DEO6N6+AlkN8GyMt0lq28cI0qB09Dn6vLQGlO59q1a7Fo0SLMmzcPCxcuxJo1a5SfrdfrePnLX46JEyeW1Twv4GUCreh0hlxMxwRT210WEnLptPHHd8WwV9npdNl234ul7zGVF/RsshhRrtUD/FzuSzYdqhFWJNMZ2rWZ4HvMu06xyYMQ7k2o60iROZ2hPfe88B2syIMqt70olOZ0Xnzxxbjooovw0EMP4ZJLLsHSpUuVn/3CF76A5z3veWU1zRuoAw4Pu5O/kszRx8QTcjEdE3w6nS7ZNv74roIdfPtdbZniWr7r4thU3TMUprNqxkhR6gHZ33nhOpCjQ+jBhKJyOqvMdPosHrZ/vzsbIw98r/Euaw/k7fNFqQNCZLjzIvT5Toeqr8FFoBSnc/PmzVi9ejWWLFkCAFi8eDHWr1+PdevWNX12zZo1+PGPf4wPfvCDZTTNK/iJyZXM0Vc5cjp/1nP7joKanMaTTwbe+U7g3HPl7xdlIFeNiXR9/LxVAdMc39WYDCWns0qLNVCceqC9vXi5metUCZtzh/pci8hPaxWmU5R5u4bP+cYEsd8WHQgygRwzoPh7w88vvpjOqlZ8NqHKjlvV1+AiUIrTuX79esyYMQMdz93xWq2G2bNno6+vr+FzAwMDePvb344VK1ag3WAVLF++HL29vQd+dpF3VSGUYfz7rPDVykxnTw/wP/8D9PZm+74OZTqFVc3pLOP4rlhgn3IyoLoLXsiBHJ/yWt8BOhOKrF5b1T67a5d6SxmX8DnfmMCPmc7O4msXmOByHazV/M5XHR1Jjnlozz0vquy4VX0NLgJBFRL6+Mc/jnPPPRdHHnmk8bPLli3Dhg0bDvyMHTu2hBYWizJklBTJ8zHxFOF4kdNcdoQ4T9vzfr8sA9nV8cvIiwTcFEgo497EfTqzoQjlBOA20OJDXhu6ITOSWRt+HfMV+A29X/gYM/z5AbfroA95re976xKtxHRWqe1FoRSnc9asWdi4cSMGBwcBsEJBfX19mD17dsPn/vCHP+BLX/oS5syZg1NOOQU7duzAnDlzsGXLljKaWTqqLHNMc/688lofEeK8TmfIrEyVj1/lttPxfTmdoTsnJoQ8pvj5ylcwIdR964owoPv7meNZNSONns3AgJ/n0tERbr+ge5N1W7Wizg+E53Tmlef6nI9cIzKd1UYpTue0adNw4okn4qqrrgIAXHvttejt7cXcuXMbPnfzzTfj8ccfx2OPPYY//vGPGD9+PB577DFMnTq1jGaWjqozWiYUJa/10fZQmM6qyV9dH7/q98Y142Zz7qpGWUN2Ol33Sx3EAlWhPdcipIKhOk4m+OwXdP5W7Rehnz/PfFWUPNeH3N81quy4iQHCVns2NihNXrtixQqsWLEC8+bNw6WXXoqVK1cCAC688EJcf/31ZTUjKJQl+QKq53TSvfFVdW/mTGD2bGDBgmzfz+N0Vln+Crjt11V2mMs4vs25q27Ah+h0hhDge05IFNxzLUJeG6rjZILvfsHndIbcL3xW13d1/jzzFf+9KgbtXaLKwdP2dhZQqOoaXARKy5SbP38+Vq1a1fT/K664Qvr5OXPmYNu2bY5b5RetHH0Hwmb7TBg3Dnj88ezfr4qBXLVgR5kOuYt749MI5aV+VdwwPOQx5TvA59u50SGvvLajg8lrs37fJ3z3C9/rqA6++2zI8tq83+e3FgvtuecF3Zf+/kTlUSW4rJpcBQRVSGikYSRE37Oe23fb8yLkQkJVPn7Vx0wITCdQTUOkKuqBVmRt8qAIpjPP933C93MJeR31/VxDltfm/b7ve+sSYtC87HofeeF7TvCN6HR6RJWN/zTnH4mTZnQ63Ry/ym0Xj+8rpxOo5pgqaj5pNQY7hPPrUEROJ6FqRprv5xLyOurb+C5rrs96bSEH2Xwi5D5tg1Z+NjaITqdHVFnmaIOiJs2qGRpA2A53ldm8qo8ZnwuO7/kgL0JWTvi+t77Pr0NeeW3I12aC77aHvI76Nr7LWkt8M52hPfe88N1v8sL3nOAb0en0iKrn7pkQsuPlGlVhOqvG+rQS0+krpxOopiGStxokXX8rzrW+z69DkUxn1dYC320PeR313bZWltf67ncuUXWHOuS5ugxEp9MjWtnA5c8fouPlGkcfzSaUI45I/92qs3l0fBdJ/lUfMz4XnKqPqbxytbxGoM2xXR0/9PPrUGROZ9WMNN9tD7lf+Da+Q3c6q1yI0SWq7lCHPCbLQGnVayOa0cpSPqC4SF0VJ80XvADYty/bd9va2I+r7WLKkhq6SPKvutMZCwllR14jziXT6duQ8D3X60As8+DgyGNtQuoXoY35kO5NiE5nZDrl8N1v8qKVn40NItPpEa1eUTFkiWnoqDIr47Ltro2oqrPMOlR9sfNpxJng27j3Pdeb0NmZfZue0K9NB99jLuR11HfbXPerUHI6Q3vueeF7rs2LKs9nRSA6nR5R1qQHVJvpbLVJ0wbR6ZSDN1qreG+ivDY78gSxgGqPKRNCny87O/Mz1ECY16aDbwMzZAPd93MNfR0cyYUYdfA91+ZF1dufF1Fe6xFRXmv+LtB6k6YNyjKQQ4zw6lCrseMPDFTTeQiF6azimCqKOaha8SwbhD5f5rknVTbSQuoXod073302dKczMp1yVH0dq3r78yI6nR4xUsr4R6YzPcqSAro8vqvn1tFRXaczbpmSHSHLa33fW9/nN4FvX1pU2Ujz/VxCXkd9ty30nM7IdMrhe0zlRdXbnxdRXusRVd8aw4RYfS07qlz0xKVx7/r4rTwmq26IhFxIyPe99W3Am5BHXltlI833Oub7/Dr4fq6hr4OR6ZSj6tcW+lztGpHp9IhWZlWAyHTmgUspYOiLre3xqyiTjExndkSm0+78oTkXQFJIKAuqvBb4brvv8+vgu8+GnmYS7Sc5fAf48sJ3v/eN6HR6RCwkZP5u1u9XHVWutOnSKQTKY4FbbUxW3RCJTqca/LPtCHBV7+piW6ZkQZWNNN/9wvf5dfA9H4Uur501Czj44GzS9CqPGRNC7tM2oH7nYh/zKiDA5WnkoExWpWryWt9t940qG8hRXuvv+LbnruKYCtnp9D1f8ddW9N64RWDZMrbvcBb4dk7ywHe/8H1+HXw7D/z58+Qcm46f9do+8xngIx/JNp6rPGZMCLlP2yD0udo1otPpEa1s4ALAi14EPP/5wPOel/67vtvuG63gWEWnsxm0YHZ0ZJcb5j03UM0xFXL1Wt/zFT3bUI2wCy/M/t0qB0t89wvf59fBd9vo/K7m4jxBdwAYM4b9ZIHve+sSVb+20Odq14hOp0e0evXaM84AHnoo23d9t903YvVaNarsPLh2yG3O7ev8eZHXiKvymDLBZ79yDd/3Ng98j7mQWSHfbXNt/LtOM9HB9711iSrPB0Brz9U2iNVrPaJWczvxVXlwtvKkaYNYvVaNsqqQVtEhtzk3UM0xFXL1Wt9snM9+5Rq+Hbc88L0Gh3zvfLetyooc23P7Or9L+J5r82KkM53R6fSMVjaE8qCVJ00blCUhDbFqn8/jl+WQ+67WWMUxFTLT6fve+uxXrlHlYInvNdi306sDL2mNTmexqPKYMSHkPm2DyHRGeEVZhpCLRHmXqPrEkhetICF1LVuqovPgc8HhjbwqGiKnnQZccAFw1lnZvh/ltdWEb4c+D0LpF77Or0OtFoYEtRWdzpCfe17wFV+reG2trEqxQczp9IwyDKH29uqVZvYdIfaNKhvIVZbXUtt56XuR8LngkJE3MFDNBW/8eOCKK7J/v8rqARNa2ZDx7bjlAW2LMDTk1/kI1QbwOR9VOThqQpXHjAm0Ng8OVtM2bGVVig0i0+kZZRjQVZx0fEtvfCOyeebju5YGuyhn7nvBaWXnxASX9963kee7X7mEb4c+L1qZzcsLn+1rZXlt1ceMCVW2bUMfk64RnU7PKMOAruKkw0tvRuLgrHKub5VlS67b7nvBqfKckBdVLs5lgu9+5RK+Hfq8CMGxCnW8t7JDPnly42uZqPqYMaHKxXhGsl0LRHmtd1TZgHaNjg4mvanixJIXZfSLtjY3kqsqR5Db2thPFdtug6rPCXlQlmTdZ5GoVnyuVWdtQpBZhtovQnDIXZ37vPOAuXOBY491c3wdqj5mTKjyfBd6IMg1ItPpGWXIKKs4MIHqtz8PqtwvqpzTCbD2V1EaXIXz+0SVJesmVDnybwJdG+VHVg0+n03oa2gIElRXz6WnBzj9dDfHNmGkMJ1VvLYqt70IRKfTM8rYc7CqhkjoC6ZLRKfTfHyXBSCq6jCHfn6faGWns5XnStd51q4RAtMZqg0Qwr1p5TEDtPb1hdqvdWjludoG0en0jCo7F64R+oLpEmUUPamyUwi4bX9V2x76+X2ileW1I8GAruo6EAKbF2q/iCywG/iej1yjyvPdSLZrgRKdzrVr12LRokWYN28eFi5ciDVr1jR9ZtWqVViwYAEWLFiAo48+GhdffDH2799fVhO9oKxKnFVE1dufBy6vnSRqVXWsqiyv9b3g+D6/T7i8dt/MQtUdMx2qbGACftsf+niPDrkb+J6PXKPK810r9zsblOZ0Xnzxxbjooovw0EMP4ZJLLsHSpUubPnP88cfjtttuw1133YV7770Xmzdvxle+8pWymugFsZCQGlVvfx7MmgWMHQtMnFj8sakysKv7OnMmK8Yze7ab45fh1FZVehz6+X3CdQEqQpQKFosqG5iA3/aHPt5DKCRU1X6lQ8zpDBehB4JcoxSnc/PmzVi9ejWWLFkCAFi8eDHWr1+PdevWNXxu9OjR6HxuJujv78fevXtRq2ISRwpQB+QjU0Wh6pNq1dufB//6r8DatcD48W6O79LpnDsX6OsD3vAGN8d3bUj9wz8Af/d3bo7te7H0fX6fePnLgZe9DDjssOKPzW/x5GIuN6GV58qq99kQmM5Q710rb5niE7F6bbioctuLQClO5/r16zFjxgx0PDfKa7UaZs+ejb6+vqbPPvbYYzj++OMxZcoUTJgwAe985zulx1y+fDl6e3sP/OzatcvpNbhCZyeb/NocPImqd+5WXhRM6O4Gpk93d3yXbB4AHHKImz4NuDdUPvtZ4EMfcnNs386B7/P7xKtfDfzhD8CoUW6O39nJflz1e9O5gdacK6t+bSFISEMd7yHcm6r2Kx1GCtMZar/WYSTbtUCAhYTmzJmDu+++G0899RT279+P6667Tvq5ZcuWYcOGDQd+xo4dW3JLi0F3Nyut7QJV79ytvCj4hstiOa5R5X7tu09X+d6FDteBHNO5gdZ8rlU2MIHI5ukQgrw21HuTB3RtbW2NDmiroMrPLvRAkGuU4nTOmjULGzduxODgIACgXq+jr68PszVJX2PHjsWb3vQmXH311WU00Rs++EHgssvcHLvqnbvqxkbIGDeO/VQREycyOaMr6bFL+DYCqz4nhAyXknWbcwOt+VyrbGAC0bHSIW6Z4gatfG1Ata+vym0vAqU4ndOmTcOJJ56Iq666CgBw7bXXore3F3Pnzm343Lp16zAwMACA5XT+6Ec/wnHHHVdGE73h1FOBt73NzbGr3rlDXzCrjO9/H6hqja73vx+45RZg6lTfLUkP333a9/lbGR0d/qsSt+Jzrfq1heBYhRqM8BksmTkTOPFE4JRTyj+3a7S1sZ+qjhkTqhxkG+lrcGnE+4oVK7B06VJ8+tOfxvjx47Fy5UoAwIUXXojXve51eN3rXoff/va3+OIXv4j29nYMDg7iFa94Bf7jP/6jrCa2HKreuave/pDxwhf6bkF2TJgAvPjFvluRDb6NwKob8CHDVz4nnRtozedaZQMTiEynDj7vTU8PcPvt5Z+3LPgMgrlGldcx3zaAb5TmdM6fPx+rVq1q+v8VV1xx4PeLLroIF110UVlNanmMHQscd1x1HYyRPjgjWg++jcCqG/Aho6PDX/5UK8+VVTYwATbm2tuT/ZHLPjcQbr/o7m7dvEPf8Cn3d43Q+7UOvm0A34hDvYXR3g7cfbfvVmSHzy0IIiJcgOqd+ap7VnUDPmT09Pibq1rZkKmygQkAJ50EPPusn3OHPt7/+Z+B009nOfoRxSIynWGilQOENohOZ0SwIObAl2QtIqJoHHcc8J3vAGec4ef8reyc+MZnP+uHzQKqbYSZUPVr++QngU98ws+5Qx/vL3sZ+4koHiOB6azi9VW57UUgOp0RwWLpUuDYY323IiKiONRqwJvf7O/8Iz3K6hKvfa2/cx99NHDEEawwSquhFYw0X0wejXNXe9NGhAufWzi5RpXXsTFjGl9HGqLTGREs3vAG9hMREVEMXvAC4IEH3O0NHOEHc+aw59qKGDMGmDEDmD/fd0uqhzlzgC98AXjd63y3JKJsjB/Piu61Ig45BJg8uZrr2GmnAd/4xsgdk7V6vV733Ygi0Nvbiw0bNvhuRkRERETQqNdjDlVEtdDfzxjP2G8jIuxw++3A6NHAkUf6bknxGBwEduxgjmdEeND5Y5HpjIiIiBhBiIZ7RNXQqjLBiAhXOOkk3y1wh46O6HBWFbFES0RERERERERERERERIQzRKczIiIiIiIiIiIiIiIiwhmi0xkRERERERERERERERHhDNHpjIiIiIiIiIiIiIiIiHCG6HRGREREREREREREREREOEN0OiMiIiIiIiIiIiIiIiKcITqdEREREREREREREREREc4Qnc6IiIiIiIiIiIiIiIgIZ4hOZ0RERERERERERERERIQz1Or1et13I4pAd3c3pk6d6rsZUuzatQtjx4713YwIxGcRCuJzCAfxWYSD+CzCQHwO4SA+i3AQn0UYCP05bNmyBfv375e+1zJOZ8jo7e3Fhg0bfDcjAvFZhIL4HMJBfBbhID6LMBCfQziIzyIcxGcRBqr8HKK8NiIiIiIiIiIiIiIiIsIZotMZERERERERERERERER4QzR6SwBy5Yt892EiOcQn0UYiM8hHMRnEQ7iswgD8TmEg/gswkF8FmGgys8h5nRGREREREREREREREREOENkOiMiIiIiIiIiIiIiIiKcITqdEREREREREREREREREc4QnU6HWLt2LRYtWoR58+Zh4cKFWLNmje8mjRjs27cPr3/96zFv3jwcf/zxeNWrXoV169YBAE477TQcdthhWLBgARYsWIAvfOELnlvb2pgzZw7mz59/4H5///vfBxDHR9l4+umnDzyDBQsWYN68eejo6MAzzzwTx4RjvPe978WcOXNQq9Vw1113Hfi/bgzE8eEGsmehWy+AuGa4gmpcqNYMII4LF5A9B916AcQx4Qq6uWjz5s0444wz8PznPx/HHHMMbrrppgPf070XFOoRznD66afXV65cWa/X6/Uf/OAH9Re84AV+GzSCsHfv3voNN9xQHx4ertfr9fqXvvSl+qmnnlqv1+v1U089tf6jH/3IX+NGGA499ND6nXfe2fT/OD784rOf/Wz9ta99bb1ej2PCNf7whz/U169f3zQWdGMgjg83kD0L3XpRr8fx4QqqcaFaM+r1OC5cQPUcePDrRb0ex4Qr6Oai888/v/7Rj360Xq/X63/5y1/qhxxySL2/v9/4XkiITKcjbN68GatXr8aSJUsAAIsXL8b69esboqcR7tDT04OzzjoLtVoNAHDyySfjscce89uoiAOI48M/rrzySlxwwQW+mzEi8LKXvQy9vb0N/9ONgTg+3EH2LOJ64QeyZ6FDHBduYPMc4npRDnRz0TXXXIN3vOMdAICFCxdi5syZ+MMf/mB8LyREp9MR1q9fjxkzZqCjowMAUKvVMHv2bPT19Xlu2cjEZZddhnPOOefA3x/84Adx7LHH4o1vfCMeeeQRjy0bGXjLW96CY489FhdccAG2bNkSx4dn/OlPf8Kzzz6L1772tQf+F8dEudCNgTg+/EJcL4A4PsqGuGYA0a7yBdl6AcQxUQZoLnr66acxMDCA6dOnH3hvzpw56Ovr074XGqLTGdHy+PSnP41169bhM5/5DADg29/+Nh588EHcc889eOlLX9o0kUYUi5tuugn33HMP7rjjDkyZMgVvfetbfTdpxOPKK6/EW97ylgPGWxwTEREM4noBxPFRNuKaERbE9QKIY6IMyOaiysO3vrdVsWnTpvq4cePqAwMD9Xq9Xh8eHq4ffPDB9bVr13pu2cjCZz/72fpJJ51Uf/bZZ5Wf6e7urm/durW8Ro1gPPnkk/WxY8fG8eERO3furI8dO7b+wAMPKD8Tx4Qb8DlTujEQx4d7yPLXbNaLej2Oj6KhyyWkNaNej3aVa8ieg816Ua/HMVE0ZHPR6NGj6xs3bjzw98KFC+u/+tWvjO+FhMh0OsK0adNw4okn4qqrrgIAXHvttejt7cXcuXM9t2zkYPny5fjud7+LX/3qV5g4cSIAYHBwEJs2bTrwmWuvvRYHH3wwDjroIE+tbG3s3r0b27ZtO/D3d7/7XZxwwglxfHjE97//fRx//PE44ogjAMQx4Qu6MRDHR/mQrRdAHB9lQ7VmANGu8gFxvQDimHAN1Vz093//9/ja174GALjtttvwxBNP4NRTTzW+FxJq9Xq97rsRrYq//vWvWLp0KZ5++mmMHz8eK1euxLHHHuu7WSMCGzZswKxZs3D44Ydj3LhxAIDu7m789re/xamnnor9+/ejra0NU6ZMwfLly3H88cd7bnFr4pFHHsHixYsxNDSEer2Oww8/HJdddhnmzJkTx4cnLFq0CG9/+9tx/vnnA2BGXhwTbnHxxRfjhhtuwFNPPYWDDjoI48aNw7p167RjII4PN5A9i9///vfS9eLPf/5zHB8OIXsWv/zlL5VrBhDHhQuo5iegeb0A4prhEirb9c9//jM2bdqE8847D48++ii6urrw5S9/GaeffjoAaN8LCdHpjIiIiIiIiIiIiIiIiHCGKK+NiIiIiIiIiIiIiIiIcIbodEZEREREREREREREREQ4Q3Q6IyIiIiIiIiIiIiIiIpwhOp0RERERERERERERERERzhCdzoiIiIiIiIiIiIiIiAhniE5nREREREREREREREREhDNEpzMiIiIiIiIiIiIiIiLCGaLTGREREREREREREREREeEMHb4bEBERERERMZLxkY98BJs3b8bWrVtx//33Y8aMGfj5z3+O0aNH+25aREREREREIYhMZ0REREREhEfcfvvt2LJlC66++mr89a9/xe7du3Hrrbf6blZERERERERhiExnRERERESER9xxxx245ZZbDjCb/f39mDx5sudWRUREREREFIfIdEZERERERHjC+vXr0d7ejuc///kAgH379uHhhx/G0Ucf7bllERERERERxSE6nREREREREZ5w++23Y+HChQf+vvvuu3HUUUehs7PTY6siIiIiIiKKRXQ6IyIiIiIiPOH222/HC17wAuXfERERERERrYBavV6v+25ERERERERERERERERERGsiMp0RERERERERERERERERzhCdzoiIiIiIiIiIiIiIiAhniE5nREREREREREREREREhDNEpzMiIiIiIiIiIiIiIiLCGaLTGREREREREREREREREeEM0emMiIiIiIiIiIiIiIiIcIbodEZEREREREREREREREQ4Q3Q6IyIiIiIiIiIiIiIiIpzh/wfp20YwGOnifwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Parameters of logistic map\n", + "r = 3.679 # Bifurcation parameter\n", + "x0 = 0.7 # Initial value\n", + "# Length of the time series\n", + "T = 200\n", + "# Generate the timeseries\n", + "time_series = logistic_map(x0, r, T)\n", + "# Plot the time series\n", + "plt.figure(figsize=(14, 4), dpi=80)\n", + "plt.plot(time_series, \"b\")\n", + "plt.xlabel(\"$n$\")\n", + "plt.ylabel(\"$x_n$\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ca7ab08c", + "metadata": {}, + "source": [ + "### Recurrence Properties Under Fixed Recurrence Threshold $\\epsilon$\n", + "\n", + "Now we can create a RP from our time series using the `timeseries.RecurrenePlot` class. Following [Marwan et al. (2009)](https://www.sciencedirect.com/science/article/abs/pii/S0375960109011852), we choose a fixed recurrence threshold $\\epsilon$ that is 5% of the time series' standard deviation. For that purpose, we set the parameter `threshold_std`. **Note:** For a one-dimensional timeseries we do not use embedding." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ad52fdac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating recurrence plot at fixed threshold in units of time series STD...\n", + "Calculating recurrence plot at fixed threshold...\n", + "Calculating the supremum distance matrix...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Fixed recurrence threshold in units of the time series' standard deviation\n", + "EPS_std = 0.05\n", + "# Default distance metric in phase space: \"supremum\"\n", + "# Can also be set to \"euclidean\" or \"manhattan\".\n", + "METRIC = \"supremum\"\n", + "rp = RecurrencePlot(time_series, metric=METRIC,\n", + " normalize=False, threshold_std=EPS_std)\n", + "plt.matshow(rp.recurrence_matrix())\n", + "plt.xlabel(\"$n$\"); plt.ylabel(\"$n$\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "61d57bf6", + "metadata": {}, + "source": [ + "Some of the main properties of a RP can be easily extracted with the `rqa_summary()` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4d22a95e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'RR': 0.05061755897131259,\n", + " 'DET': 0.7494577006466949,\n", + " 'L': 3.971264367701975,\n", + " 'LAM': 0.10953545232220277}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rp.rqa_summary()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8ef17b28", + "metadata": {}, + "source": [ + "#### Network Calculation\n", + "\n", + "Now we can use the recurrence properties, which mimic the phase space properties of the original time series, to calculate quantitative characteristics of the time series implicitly. We shall distinghuish here between *local*, *intermediate* and *global* properties. In order to construct the complex network we use the `timeseries.RecurrenceNetwork` class, which combines the `timeseries.RecurrencePlot` and `core.Network` charactistics. For more information on the individual properties, see [Donner et al. (2010a)](https://iopscience.iop.org/article/10.1088/1367-2630/12/3/033025/meta). \n", + "\n", + "Our main focus will lie on determining the local and global clustering coefficient as well as the network transitivity. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6b5a9d37", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating recurrence plot at fixed threshold in units of time series STD...\n", + "Calculating recurrence plot at fixed threshold...\n", + "Calculating the supremum distance matrix...\n" + ] + } + ], + "source": [ + "rn = RecurrenceNetwork(time_series, metric=METRIC,\n", + " normalize=False, threshold_std=EPS_std)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8c74cd38", + "metadata": {}, + "source": [ + "#### Local Clustering Coefficient\n", + "\n", + "The local scale network properties consider only the direct neighbourhood within a defined $\\epsilon$-ball of a vertex $v$.\n", + "A __local clustering coefficient__ $C_v$ characterizes the density of connections in the direct neighbourhood of $v$ in terms of the density of connections between all vertices that are incident with $v$. In particular, we consider the clustering coefficient by Watts and Strogatz,\n", + "\n", + "$$C_v=\\frac{2}{k_v(k_v-1)}N^\\Delta_v \\,,$$\n", + "\n", + "where $N^\\Delta_v$ represents the number of closed triangles including vertex $v$, and $k_v$ stands for the local recurrence rate around $v$." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "72890ded", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating local clustering coefficients...\n" + ] + }, + { + "data": { + "image/png": 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/bbIiPuM6n3kQn+V0Pq1zb5Cx+sUXvd9P47X774+/V3v/fplrKT5JIKqx4m3cY1asLF4sg4ROLCpE1YU54QQ5lsJtg3cUrCtW1SY+VcDV1XmLzxNPlEergFOHfdYsue/6+pJNuxkaEpfpmGOK2+rUdmtbleFhaY8GBE7ic+JEccyB5PuM3tfvepc8piE+W1vl/DUgukOWVMCwa5ecu6j7tidPNlOO42AYwG9/K/8fHAzm1KUpPvX3n3yyrJCnJT47OqRIWhzns9rEZ5LO586d/r9v0yagvb34KIE0g8046DzT1ibjcdLzmJWXXpLF3O5ud2fQ+rz2z2oiKfGp84qm3QLh+2wlj1oJ6ny2t7u/R57Ep935nDRJ+kLazmeQsdov9VZjoF274qfeVvkZnwDFZ3nRsz6rSXzGPWbFyqJFEqzqyq3V+QRMEZXkXqdqFp9bt4p4mzEjmPi0Op/WSTeN+27nThGQeu3szqd+PWtWcVuVvXvlXpgyBejsLBWfVtcWSMf5HDPGbH/SRYd275Z0p3nzRORFdcj6+pKpaLlrV/H+kEJBgte44uy55+Rat7bK10FcljQLDul4NXWquB1p7fkcO1b63ZtvOhfbciNoKtfpp8tjlsRnuQsObdpUnHILZFd8qlhqazNFTlp7dl96SRb9RoxwHxus4lMzE6oJ/TyDLFJ4oXPirFlSrGrGjOp1Pnt6SrOXdB5rbnZ/Dx3387jnM6l5zImkx2prDBS3T1J8klCkFUinSZLOpwb62klfeEFSqsaPl6+jpsV4Ue1pt5MmiQPoNHF4OZ9W8akBXJL3nd4XXV0yqbuJT3VG7e3Xr8eNE5FgbfuBA/Lzs2aZxUbScD47OiTde8qU5J1PLQZQXy/O/nPPBU97Pny4uHR8Eg6KOp9WurvlnoizF1onUa1WHaT6q/49hw87p+lqP+3vD1+oyerOzZmT/HErVvEZZbwKspre2Cj35syZyQuYdeukkIvujQ2DfrYTJ7p/T1Lic3BQtqdUi/jUQHTUKHM8TqPoUG+v/DvuOLlP3JxPqyh96KHsno3qhGGYn2eSzicgfXbVKlNQBiEL4nPCBBkv7WNGkHoceXY+gWTmMSeCjNWtrTLXTJ3qP1br3NHSAtx7b7y26XtRfJJAVGPabZLOp7Xi7eHDUnlTnwOip8V4Ue3O55QpIhq8nM9jjpFAxE18puF8Wu+LKVP8xae9/fr1uHHSxu3bzQBJC9fMmmUeUZSG86n39IIF4nwmuSdWD4AG5L7etSt4QR57kJDEivXu3aXic84cEWZB2+XEb34j7/vxj8vXQcSntU86nc9m7adhgkSgeF9id7cEJEnuebOuOEcZr6xBjNtqelubrOgvWiT3ZZJB1X33SeBz333hf7anR4JgrzO3Gxulz3qJz7o6f1GxbZvcm7r4pIwfL8I/q+Kzrc0snpfGvk/NGjruOAnC/ZzPyZNlrn3wweTbkhaHDpmLUnGdz/XrRSCoQDvxRLmv/PbnWcmC+NTCOvbU2yAnEeRJfNqdT0DG+bjzmBNBnM+2Nvn/okWyqOFVIEtjng9/WJza1avjt43ikwRi/HjJT69V53PKFAleVqyQoGpgwHRDAdMFo/MpQmjbNlN89vWVDmw6mE2YIC6JVXxaCy2k6Xx2dJji0yregorPsWPNVWlts/4dRx0lj0lX6x0akkqKVvG5f3+yZ0Jq2i1gOiFB72trkG79Oipa8MnJ+QSiB/JvvCF/0wc+IPcfEM75BJz7pPW5sKm31vsyjUqIVudz4UJxtpN2PkePlv8vWiTB1qpVUVtbigbzYYJvJWjV8zFjvMXn9On+4tOp0i2QbppdHKxpt2nMY4pVfHo5n/r8hz4kj3GdlnJinbOTEJ9HHWXudY+SrZAF8Tl3rjxa99QPDso8FtT5zEParZvzCSS/IBV2rD582Hv7zq5dMqdrllCcPknxSUJRKKR37EVaJOl86mr+Sy+Zg79VfAIyObz6avQD5u1Uq/O5c6cMtJ2dpmhwS10dM0YE3IYNpgBcv16KEIwaJcFeoZCe89nZKRO09bPetk0CMa1U6ed8aputj/r8jBkiZpM6UH3HDvmcdNKeP18ek9r3efiw3GsqPsM6ZDqx6FmScYMG62dtJe6kre7Z+edL2hEQ3vlMWnz29Mg939pqis8khYq1zzU3y+JKGOczzGq6ZoUkmXobR3wGPe/ZS3w2Ncl4EVV8AnLfbt2a3ByRBNa0W0AWnJKcx5Sgzqc+390tiyT33ResEnUWsIrPOGm3Q0My5+k8AkTLVsiC+HRyPnfulHnMr0+OGiWiJ8/OJ5CO+NQF4CDOJ+A9VmvdhbPPFhMqzr5PvZY855MEZsYMmViTPvYiLXp6RLh47fMJw+LFMiH/6lfm11Y0LSapQhvV6nyqc6jOJ+As4MaMEfdl1iz5+3Ry0oO1ASlM0dmZrvNpbbP+36/tgLP4tBaJAMwjipJyJu0LKnr8SFL7Pu0Tw9FHi1AJutquAkfTpeMGDWmJz9/8RgLg971PUi0nTEje+Qy7V80qkPTvS1p8jhplrr6feKL8zUHP49yzx2xfkNV0INmiQyo+X3gh3Bw0MCBCIK7zOWaMZAD5iQpNlban3QJmIG4vUlZJrGm3gIicJOcx5aWX5P5pbw/mfDY2yuLQ9u3A008n25a0SMr5fOMN+Rys4lP354VxPnUB4dChZPePB8HL+Qxy9BEgAmr06HyIz4GB8orPzk75v99Ybd1S5obWXWhuBt77Xql4G/X+pvNJQtPVJfucohR8qAS9vRJU2lMdoqIB1e9+J6s/s2cXv5500aFqdT6Dik99zSrg+vulCqd10k3acbfv+bS2Wf8/ZYopwLzEp6bXWp3PhgbTNU06bdieSq7OZ1Li05qaCcjfsnBh8KJDOrGo+EzL+ZwyRSbCKJN2fz/whz9ImXn9O6dP9xef1mIiQDppt3pdZ8xI/rgVazo1YI5Xzz8f7Of37DHva7/V9Jkz5f9JChgVfTt2OB/d4IbOV3GdTxWfBw96j8d+zieQrX2fKpiszieQbP2CoSHg5ZfF9QSC7flsaBDxCVRP1Vtrv4jjfNozaJQTTpDP0Wm/uRPWfedBfyYpvJzPIEcfKW59stoYHCyNRePMY24Yhoz1uvhlH6t1HtOxurtbFmCDiE9A+uTwsJz5GQWKTxKaaqt4GzTVKigqPgcHZRKts92CSRcdqkXxaXcOAbnv3nwzucmzt1cc1TFjSsXngQMycAd1PqdMkfeyik8VDtp2IDnxbHc+OztlBTOptFunSnQnnigBfBBnUCcWDbyTcj7tKTp1dbL4E0WcPfSQ3Esa2AIiqrZs8XYHDh0qdmuSFJ/Dw/IZ63VtaJA+kGRQogJKCSMyhodltVxTlO2r6YODEujqanpdnSxarFiRXKaMdaU9TOptUJcFkM9n377SNE+r+AS8hcWmTeJwTJ5c+loWxaeT8wkku+9z/XrpGyo+GxvlvnBKp1VR2tgoZ95OmlQ94jMp59NNfJ54onw+L78c7H2s4rPcMcSePbKFQMcMJ+czSHw2dmx+9nzanc+6uuT3gR88KL9LFwrtY7XOYzpW19dLv/Qaq63x2gc/KBmFUfskxScJTbkq3u7alYzQCFpkIigLFpiDhz3lFpB0ounTk5u0qzXtVs8O7Oz0dg91MLO6h/aCPYB53yWZutrRIQOoik9tsz5OmSLudkODt/isqzOPWzEMs0iEve1pOZ+FgrifaTmfQLhgtFzOJyCT9saN4ffT6qRpF58DA95ZHRqkawGQJNNu33pLAnFrMKbHrSS1383ufC5aJPdvkOu6f7/c3+PHS0BpX03Xv1UDGkDGyN27vSv2btkSXJzu3Gku+On+wSCEdVmA0r8vjPjcvFkCbvviJJCs+HRyn6NgLTgEyDaVrq5kxader4UL5VEX55zcT2vabV0dcN55Ul0+K6nKhw65X/+0xWfYhYFKi88xYyT7rK7O2fmMkwpfbTg5n0D0ecwN/awmTZI+ZB8n9Gv7WL1jh/O5z4ODcl/rHNzRAZx2GvD730uNiKjto/gkgSmX87lwIfD//D/x3uPQIQl8knQ+R4yQPXBAabEh5cQTZaIMe8yCE/v2yaoUUF3i08/5HB6WASiM8wkkt+hhXZTQfRHaZn3s7BSR4XRUjF0QzZolk0dvrwTpabbdacV4wQJ53ulIm7A4iU91yJYv9/95u/OZtvjUwhxh+OMf5WdVCADmKrHXAoeuIOse8iSdTyd3rrtbJvekjluxi8/WVrl3wlzXMWMkaLGvpuvXKmAA/32fL74o/eP//t8grZdgXo8CSdP5BIqD3eFhGYvDOJ9OKbeALFC2tcUXn488Iu3585/jvQ9QWnAIkM955cpowaUT1mJDgLmI6xRwW9NuAeCCC+QxK+7nN78JzJvn/NkkVXBIxadW4lZUfAZJZx8Y8M/USBMVn/X1MmZGdT5VfFZLrRE3nJxPIPo85kbSY7X1XHPlggtk3Igy/uzeLZ9Dc3P4n80IFJ/lphzO5/CwBIBxzhECTAcjSecTMB1PJ+cTkBWhoaFgAZ0ffX3egW5W2bpVJpz2dmfxqROJvqZB3YYNziu+SbqHhlGcjq2pcXbxqY6om/gcOdKcSI46Spz6J54obbs6RUkKZ6B40k6y4q1TJbpjj5W/4ckng/98UgWHnCY+JepxJNu2lToKQcSnBuk6piQpPp2ua5LHrRw6JPeoPX35tNPk3rSfdWvHGtC0tQVbTdeAxm1P6YMPynj/3//t3/7Dh+V3dHfLtQojPqM4n/bKvoYRTHzu3y+vuYlPPW4l7jW96y5pU9D9ul7Y024BGdOGhpydkCi89JK4X7p46+V8WtNuAWDpUvM9ssDGjeISOS32qfhsbo7nfG7YIHNTa2vx85Mnyz0UpEiYfXyqlPgEZMyM43wODFRXDOSEl/MJJJeKH2esdqp467T15dRT5TFKzKH3hWYQVSEUn+VGU4nSdD51pS5u0JrkMStW/vqvgc99znSD7CxZIo+PPx7v9xiGTGRegW5W2bpVJsn6euejVpzcrFmzzLRba8EeIFn3sK9PgnD9XEeMEJHsJT6djomxtx2QIjbWrwEZYLVKdBL09MiRD9ZAUSveJiE+nZzPxkbglFOAp55yLxKiaL/VsSJt5xMIN2nv3y//7EFPGOdTxxQ38altjet8Jnncilt5ex2vdOHE7+fDrKYvXCj3qtt76/OPP+7vaqjYmzBB3tfvUHQrcZ1P69/uJz69Kt0q3d3yfVG3lhiGFL0DkhGHfX0yTlmFju7Te+ON+O8PiHDUwiZAOOdz/HhxZbNyzJtXjKKBfVeXjAVR521rxXcr9fXSh4MIW3v2VSXF56RJpc5nU1OxAHJDx6xqT731cj6BdMRn0LFa2+CUZeM0B+scGKbwm7V9VZxyC1B8lp+GBpmU0pwEkhKfYVbWwnDKKcAPf+g8iADASSfJa37BnB/9/RJktLfL19UkPrdtKxZvQPEqsZv4fOMNCSpnzjTTjYFknU+nRYkpU8I7n0HFJyDtT+qIot5eabt11TDJirdOBYcAESn79/u7D3v2SNtGj06mRH7S4tMtI0LFQhDn0018GoYICs1WCLPn08mdS/K4Fa/rCgQXn3pdg6ymNzfLCvnjj5eKDMMwF+i2bvXv23bxefgwsGaN988ocZ1PazA3YUJxe+x4VbpVurvl79ctBmFZvVrcN0AKscVl3z7J5LDuUU1SfBqGfC7WcVHnT789n4C5gKd/c6XR9jkdYaF9Xq9/lNTbvj65Z53EJxDsuB/AXPwaOVIeyxlDDA7K71fh2NEh/UgXXKx1F/xw6pPVhmFUxvkMOlZPmODuqDvNwTp/Bj2my96+Kj7jE6D4rAwzZlSH+EzL+fSjpUX2ZTzxRLxztYKk+GWR4WERn7qXcvRoGdSCiE/DkAIr9kl37Fh5nyTuO6dAtLNT2mwYxcWStI39/cX7e9zEpwbD9vbPmCHXb8eO+O3XSdvK7NkSqCUpPu2Tg6a++YmU3bvlWtXVJVOlcNcuWYiw7kdTpk0T5zrMpO22KKXBttf+Sg023fqkBlYqPuM6n11dcl2TFJ/26zpvngSzfpka9lSuvXuLF1OcVtMB4MwzJZi2F0jZsKH4Xva7r9TpUfEJBE+97ekRV0+DcC/iOp9BxScQPdhU1xNIxvm0HrugaH/wS8cOwv790jd0IRUwg3An59OedgvIguSmTeU/q9IJrxhFxadm60RJvXWqe2BlwoRwzqcumJQzhtDxwOp8Aubiny6iBkHfo5or3mrROCfTIso85oXTWG3FaaxuaJD7JKj4HD9e5mU6n6RszJghA1/YM+yCkrT4TNr5DMKSJRKcBF2Zd0LFZ7U5nzt2SPCgzqGKECfxaQ2ErRVirf9X1D2Mi9N9MWWKfL579kiwNWaMmYJmd24NQ/7v1vYxY0pduqT2Stv3qyoNDdGPHbHjlp552mnyGESk6MSSRJVC/aydVsjr68MfR+ImPkeOlOsWx/nUr7XPRhGf1mub5HErbuKzUJDx6rnnvMcYazA5erT08UOHzNedVtMB4Iwz5PGRR4qf1/vof/yP4q/diCs+wwa6aabdxk2n/t3vJGVxypTk0m7tiztJOp+66GYVn0GcT6tLNGOGLAAm4fTGJW3n06niu5Wgzmclxae9oqmOtz095jwWNDbLQ9qt3udOzmeUecwLu/N54EBxP3Mbqzs6govPujr37/fCMKTfUHyS0CR9dIQdHdgPHoxXaS9MqlXSqEsUZ9+nTmLVJj7taatAaeqqm/Pp9H9F903GXfl2S7sFpO1bt5a23drmvj5ZxbS2ffRoc4J3azsQv8/s3St9wmnSnjTJ+5iQoOzeLYGtvRLd+PGytzRIeqZOLEk5n04pt0p3twRrfntRFa90fD3r0w0/51O/juJ89vZKQG4Xh/r3xT1uxU18AiI+BweBZ55x/3n7ajpQHHy7OZ+nnSZ/l1186n108cVy74ZxPufOFacgqPgMc+SWn/jUQhmVcj7375fP8owzRJwklXZrv246BiYhPnVcCup82tNuAbPqaxb2fWbB+ezv998znCXxqfNtb685j8VZEKo2nO5pK2HnMS+sWyy0X1tTb93Gajcx6Vb0z15EKgh9fRLDUXyS0KR93Ip1Mooz2FTS+XzHO+Qxzr5PHSzGjJFAi+JTJqwoaR5WnMSHl/i0n1PqtgdR2+wlPuMGTl6p5O3t0ra4k9fu3e4Tw9Kl8jd4BaROzmecva5BxOfAQPDjSIKIT7f2BnU+NdgLs+fTbQ/UnDly38cdb73OVguSUm1fTQeKAxq31fTWVtkn/9hjxQL6iSekn82YIeL3hRe8Py8N4sePlwDu6KODic/h4Wgpfm7iUwu+eInP0aO9gyutYhpFfP7xj3I/vP/9sjVg+/b45wM6ic+WFul3SYpPXZQBvJ1Pp7RbHUOzsO/Ty/nct0/ma922Ecf59NrzGeS9VXxWomK+m/js6Qm/JSoP4tPL+QTCz2NeRB2rOzpkvrWbPm4xz6RJ4eOxHJzxCVB8Voa0j1tJSnz29koqkb1UeTmYMkVWauOITw3ERo2SQKBaxKd9zyQQTHxOn24WGXKadJNy3L2cz7VrJaDwcj6jiM+k2u4lnCZONFOC42A/C9JKkOI0dudzcDDembf2ysJ2wh5H4nUE07Rpkkrq5lYEdT5HjpQ+G9b5dArG1PGJG5R4OZ8nnSRBUVDxGcb5BMSl27vXLOO/Z48Urlq6VMT20qUiEp9+2v33W51PQM6L3LLF/37fvVvuwSScT/3svNIeN2/2TrkF4h23ovs9VXwC8RfknNJuAUm9TSvtNojzaQ3Uq8n5HDXKFIhRnM/160XAWuchK9oH/N47S86nNe02bDFI7XfVvOfTz/lM8lgt63gVZqzW62GvTeEW83R0mNXjg+JW+K7KoPisBNXkfFYi5VZZulSOvoh61pf17LVqEp9uzufevabz4ZTG0dhoirQ03cPeXgn+rKvwGsQ9+2zx19Y2xhGfeuxIms6n/j1xU2+9KtH5OWSHDsk/q/Op7xmFgQEJ5vycTyD4pK2BjzUQVvwq3vodtaJft7SIAA06KXvtgdLn4l5XL/HZ2moWSXNzfe3VbgHngMbp6AT7vs+nn5bfo4sZQRY17OJT9336VV9OwmWxB9Ju4lOrunql3Crd3TIehNlaokesHHWUpB7rGcVxUm8HBqTPOi0aqPiMW6XbKe02TLVboHqcTxWfQQUiIPutb7nF/PfCC3KN61xC3KDOp44/WRCf1rRbOp+lJFnxds8eWcgfOTLcWO1WwVZjHvv3W69pmLYBFJ8kAuV0PuOsdIXZ55MGGlA9+WS0n1fnMy/iEzCvp1PBIQBYvFiCDKcAOUnnc8KE4klA27p8efHXQHDxuXixPOphzVb0iKKknE+3tFsgfkVdL+dzzhz57Nz2Mtsnlrgr1m57TaxEEZ9uGRF+Z336FQGzi8+gabf798vPel3XKCXtrXiJT0DGq5073YukaUDT2uq8j8i6WOb03vX1wJ/+JF/r/aOLGSecIPuMvfbIv/WWBON6bwUtOhTWZRkxQvY7RxGf27eLkAsqPoeHw4mpNWtkP+D73y8LaLpIFqfokHWesTN1qlmILQ5Oabdhq912dMh1qTbnM0ja7fnnA1ddZf7bsgU45hj376815zMP4jPInk8gOfGppwx4jdX2bAcv8albDqxQfJKyogNrlp3P4WGZ8CrtfALRiw5ZB4hqE58NDcWBhpOAa2srXQW89VZ39yNJ59M+6em5mS+/LF9HEZ8f+xiwcqW539dOEkcUee1j1s87jvgcHJT7zk2gaGXU5593TqW1pyfGDRq8zvhUurrkPgojPp1cT8AUn24prnv3mim1hUJyzqfXddW2JuFoA+6Tvi6WuY1XWqFQz3DV56yvjxghItLOqFHAyScDjz4q2Q9PPCGfkS7YNDVJ6u+TT7oXFNu5U+YddYOCis8oR27ZqzQ7ic++vlLXMkilWyVKsGlNuQVM8RnH+XQLRIHkKt4mUe02S2d9BhGfo0dL+/0E4r59Mme+5z3A739v/rvlFvefCbvnMwvis7lZPpM4zmc1p936OZ9h5zEvrFtf3MbqkSNLxaSX+HSag60LCmHaBvCcTxKRrq5s7/l86y0JcirpfB57rExCUfd9VmvarZ7xaU0Zcira4zSYjRvnvs9l8mQZnJNwPu2TXmOj3Cs6QUQRn4WCFEFxo6tLgrA4+x+9nM8kxKf9bDYnli51r4xq388R1/l0c8itNDRIiloY8ek2LgRxPnVFubnZW3yOGhVcfHpd1yTTbpubncUh4J9SbQ1o3FbTndwz5YwzpA0rVgBPPSVi1OoCLFki99+qVc4/v3OnGUQD8lm1twd3PuOKz6Ym87PT4N++3zRIpVslqvgcMQI46yz5WtNu4zifXo51UhVvt283CzUpYavdArLv8/XX46cBx8Uv7batTcaIIEeiaGXbd7wDeN/7zH9eC27V6HwCZnXUsMUgW1rkXggSD65Z43xdKo2f8xl2HvMi6lgdVnzS+SRlp6tLArQ0DnxOQnxGWe1Omvp6OWbgL3+JdmRMtRYc2rq1eM8k4Jx26zW5OlFfL/fd6tXR23b4sLTBadKzttlpz6e2PUgqqBNaMCPO2a89PSLqrUG4ksSeT7/UTMB7f559YimH8wnIGafr1vkHpYYRT3zu3WtO2k59Mg3nU69rEmm3XtdVK896pVT7raY77fdUdN/nv/6rjG0qdhW/TBF1PpVCQdzPl17ynoeiVD0fM6Z4wcReAdrNeQojPmfPlsd164K1qb/fPGJl5Eh5Lsm02zSdz+3b5T62VnIOW+0WkPvzwIFkjpSKg5vzaRgS2OtnOX68v0D0q2zrRDU6n4DEY729znUXvCgUgp0Z3d8vKfxf+1rwdj7zTPxq0UHwcz6B4POYH1HH6nI6nxSfJBJjxoiz6HfOVBT8xOfQEHDJJZKa4kYlj1mxsnSpfEZa5TEM1eh8Dg1JCpjdvXRyD8OKNwA4+2wRn6+8Eq19Xi6Itc1W8dnWJsLXz/n0493vlsdf/zrcz1np6ZEJ254uAySz5zNISsxJJ0lQGER8JuV8+n3WWqU2buXTtjaZlP2cTyCY+Ay659PrvmxslL8/CefTL9XJq0haXOdz6VJZOPnZz+RrXcRQvI6nMoxS5xMQ8dnf7+0WJJV2G0R8hkm7Vdcy6KLC44/LPX7uueZzOhYkkXbrtucTSCbt1p7qHrbaLZCdirduzuehQzIHqvicMMFffKrzedRRwX9/WOezkketWEVOR4eMY9u2uc9jbtgXhJzYtk0W/IIu8L74ohwD9YUvBG9HVPycT0DGDa9q60EwjOLFsrjO5/Cw3OdezifFJykbWqwjTgqhG37ic+NG4I47gL/+a/czDaOkWqWB3z4qL6rR+dy+XSZfL/E5POx/fIYbH/+4PP7yl9Ha51XoQNs8bpx83kqhIEG7XXyG3bPwrnfJ7/3FL6KvbHq5dhqQxBGfQZzPlhazMqrdcaqU86mLBVrsyo0ghS6mT0/G+Rw1Spz2IOeu+i2Wtben73wC7kXSDMMsYgFEcz5Hj5b7Rj8P+97ojg4paOU0Vvb1ybxgF5+afnrrre6/t7dXgtww482YMRKgaXXuoOJz0yYZL1S0eTFihFyPoNf1+efl8ZRTzOfq6+VzSyvtNknn0y4+w1a7BbJT8dbN+bS7yJp26zXeR3E+R4+WhZygzqfer+WMIXbvljjReg0nTZI545VXwsdmY8f6zyM6jgZdjHnhBXm8+Waz0n1aBHE+reeNR+XAAfldUZzP0aNla4F1TNJzup3GzyjF8Cg+SSwqKT51VWjDBuDOO53fIyvO52mnSTASZd/nvn0ywbS0yOc9OBgskK0kGgR5ic99+2QCirLh/KyzJAD9xS+itc/LBdE2O+05tZ5TumuX7J1rbg73uxsagIsukol35cpwP6t4HR/U2ir/kki79ZsYliyRwMe+wlyJareAec38gvAg4nPaNBGf9oBRU+rCOJ9AsNRbP3dOHYM42AWUE24p1faAxm013Ut8Ambq7fz5zqnjS5ZI2pl9JV2DbPvPfPCDUhX0hz90D8T1CBu3Yyuc0L9T/74w4nPyZPd9tXZ0D1wQNHtGCy0pnZ3ppd22t4t4iBMMDwxIP7anWIatdgtk3/m0f5YTJpjHRbnhd6anE3V1wVJ6dexpbXXeo54mTuONjrtuW1+8CJJ2q30paH/QLTyGAVx7bTrbyJQgzmcSiz327CX7WG2fx6wUCnJdrGLSa7F9xAiZm8M4nzznk8QiC+ITAL79becBIyvO5+jRchh6FPGpVfMKBdOJy7r7qUGK257PXbuip60CpoBbuTKagAuSdhtEfEZpO2A6t1HE88GDEux4Tdrt7ek7n4B7cZpKOZ9BV4xVwPmJz/7+0hTegwclKA6z5xMIJj69zh7V53fsiB4cDQ7KeOJ3XY87zrlImv26anCtwffgoPztXmm3AHDmmfJoT7lV3JxX+xmfSl0d8OUvy9/2gx84v2eUI7es9+3wsARrQcVnkJRbxR7oebFihewJsweNnZ3i9ETNpvByPuvq5P3jBMN67aI4n3aXKGvOpy6kKvZja/R+9XIo16/3PtPTjSDFjPr75XNubCx/9pST+LTOu2FjszFjZLwJsr97+/ZgC/WrV0t89dnPytnD//Zv4doUhiDOZ5Li075QqGO1fR6z4yY+3eZg3ccbpn16nFUVQ/FZKSopPjW4PuYYcZH+8z9LvycLBYeUhQslKA66/0ux5uVXm/j0cj7jiE8A+MQn5DFK6q2XI66C2S6cgdK026htf+c7xRmJknobZEFl4sT093wC5lmm9sqkbuIz7T2fSabduhUd0iA9rPMZpN87nT1rpb1dgq4gZwY6EfS6NjRIle5XXy1+3l4FWQ8w18/E/tm4cc45wBVXAJ//vPPrxx8vj/b7yk18AjIezJoFfP/7xU6s4pUt4IZVfO7bJ33VT3z29orjMn9+8N8zaZL0V03vdaO/XwJlPZrGyuTJkt7tt9/ZDS/xCchYHicY1gWfKHs+7S5RZ6c8lxXn0zCK+7f92Bq9T9wcyuFhyeAKk3KrBNlP2t9vxmrVLj7HjpXPy2s81fndMIJliqxZI276jTdKe66/Pno/8qPczqd+9lqlO+hYreJT4xO/OThM9oa2r8pdT4Dis3KkKT6tK1Zezuc3viED6re+VRrI9/ZKJ8/CWUJa+TDsESFash2oHvGp6S52AaeDTRLiM87eSS8BpwO/036tcePMfWdxxGd9PfDRj0og+dJL4X62HOIzqPM5Y4as1Ot+JcUucpqaZIUzjvNpPVfSjaTTboFS8akCLMyeTyC48+l1XeMetxL0ugISjPX0FI/tTvt0Ro82PxP7Z+NGc7OcX6gi045bBVgd863VbpWGBnE/d+0CfvSj4tf275d/cZxPp7/dSVTo0UOnnhr893R0mMWUvHj5ZQm8ncRn3Iq3Xmm3gIyHPT3RK4LqPWtPu41S7bauTubTSjufbjGKU9ot4H59t22TAjNRxGeQ/aRZE5/WfhinT7phFUF++z6Hh4HXXgPmzpX3/j//R+bOMJVyw1CuPZ9O41VbW/CxuqND7hOdt4I4nzt3Bt8StmdPNuLymFB8VoospN0uWABcdZUUYrBXvtV9PtbS7pUiqvi0lmyvFvGpA5Z9YKuvl8EwCfEZZ++kl/N5/PHAP/2TpODYsR63Ekd8AtFTb4PsY25vl2sQ9T4JKlIaG+W+tovP3bslQLQGsmPHxnM+x4zxT0mbNEn6eiWcT2vwF2fPp991tbY/LGHEp1bdtLpLTpUrR48O73z6MX68tNFNfDo5nwBw6aVy3W66qfjej7r9wk98av+3Op9/+Ys8WgsC+eF2tIEd3e/p5nwC0Sve+jmfU6fKPW5//7vuks/bD10Mi+J8OvX7Sp/1OTRU/Lut+z6dCg4B7hkLUSrdKhMmiHD1isEqJT4HB2XcSzrtFvAWn9Z71K8/bN4sn8e8efL1JZfIdpKbbzYLESVJEOdz4kRJSU3S+QTCjdX2MSmI8wkEXxil80lioQNaGoNZUPE5YQLwD/8gnfl//+/iCSHKPp+00H0qUcRntTmfXgOs7puMKz6B6AKut1cCA71/rdTVAV/8ovMZfdrWN96QvzFO25cuFbcirHMb1PkEorufYYoBzJol4tP6N2hFVOuiT5BCEW4EFfoNDcGqfupn6HW+nO7ZC+J8GkbxGb5R0m41ZTJN5zNMhUENhDUwdvv5MKvpYZg9u/ToFD/xOWKEzAU9PcWVb4MsNjjhJz4bG+VvtYvPESNKCwJ5kYT4LIfzCZQGxF/+sqQpHjrk/f5uabc6R7gVHGpsdF48njlT5sa00iP9sLc3jvMZpdKtEuSsz0qJT3uavpKE8+m1kGl1Pv36gxbLmztXHgsF4IYbxBGNcxyaG0Gcz0Ihfpp7Es4nYI5JfkX/wh63QvEZjddeew1LlizB3LlzcfLJJ2Olg/MyPDyM6667DkcffTQWLlyIs846C2u9ziGrRsrhfLa2eovP8eNltfszn5ECGSpEDCPaPp+0iJN2W23OpwbiI0aUvqbiM2gFUy/e+U65vmEFnJ/D5Ia2VYOFOG2vqwM+9jFJ+QmzwhrE+YwrPvfsMffz+TF7tgSB1t/lNLHEdT6DftadncGcT6+9lUA45xMo7pP6/+bm4M5nkCJIlXA+/cRnGs4nIPfVG28Uf65u1W6tXHmlfE7/5/+Y1y7q3n8/8QkUF3wxDBGfxx/vPPa5EUZ8TpjgvCUgrvi071O0o7/T2re2bZP7Y3DQP/vELe1W+6BbwSE3h0gXc+37PnfsKI8bqvGJCmMn5zNowaE44jPIWZ/9/eY4VE7x6dZnxowx+0eUPZ/W93aip8d0y/2cT610q84nIFkL9fXmsUZJEsT5BER8Bk27dcpyKrfzqdfRPoY5bSfQM0MpPsNzzTXX4Oqrr8aaNWtw/fXX47LLLiv5nnvvvRePP/44XnjhBbz44ot497vfja985Svlbmq6+InP//xP4Cc/ifbe2kknTpTKXFZnAZAbeswYsxN/6UsywH7yk3IA9+OPS4fMivOpTkoY8XnokHwOeXM+NW1Vv46Kde/kiy8G/7moixLaVg3I47QdMJ3bu+4K/jNBAmkN8OLsDRw7Nli6ugZM1tRbJ/FZDucTkEl72zbvADRIRsTo0RKI2/urk/MJlIrPpqbi1GM/8RnE0S7nns+wzqdhJOt8dneX/n4/5xOQOelrX5N0ujlzxJXTADNp5xMoFp/r18v/w+z3tLbLyzUYGpIxbtEi536ZRNptS4v7gozuQ7O6MdZqyH6BulvarZfzOTDg3h49bsW67/Pxx83FyCC89hpwzTXhiwBq2wBzXLKObWELDunYGSXtNojzuX9/ZZxPtz6jR3kA6e351IJffv3B7nwCsmh49NHpiM8gzicgiz29vaVxrxPnnAN85CPFzzllL8VxPoOm3drHsF/9Sl6zLrD39ZUWb6tSyio+e3t7sXz5clx88cUAgIsuugibN28ucTULhQIOHTqEgwcPwjAM7N27F9N0NT0v+InPb39bUhijYBWfQOlgs2NHcRAye7YUZLjkEuCBB8QVA7LjfLa1SccNU6HPnr5TLeLTz/ncvducLJMScD//ebDvHx6W4D2O85mU+HzHO2SS+cUvgh+fESSFUAO8OGm3QYsBBBWfY8fK/Rz2jNqhIZkowzifhw55p+P19rofZ6IUCua+MitBnU99PqjzGXQvL1Ae53P6dBHPVvHnlEY3erRco4MHk3c+geJ9nzt3yueqn60b/+N/yP7/efPEAdU5KG3nM8p+TyCY87lundxDTim3QDJpt26uJ+Ccdvv44+b//QL1KM6npt064eR83nSTjKNBjjQbGgI+/Wngxz8G/vQn/++3o/GJxiBeez6DpN1OnBit3wR1Pu3isxzusFeav/bFpMVnf798/scdJ2N4EOezpcXMdFGOP14WHv2KgIUlqPOp/c2vP/f3y/Ewjz5aHEO4OZ8HD0obojqfboLRzfn8059K+2ROzvgEyiw+N2/ejM7OTjS8PWgWCgV0dXVhk22F/Pzzz8eZZ56JyZMno7OzEw899BC++c1vOr7nTTfdhGnTph351xdlJa4S+InPvj650YKs3tixD+72lL2dO0tXwGfOBG6/XSbCc8+V5449NvzvTosZM8I5n/YiENUiPr0G2LFjZeLToCGugFu6VD7X73xHzvjzm1S3b5fAQ52CMCSZdgtIcP/pT8v7XXhhMGdw61YZtL3Ox0piz2fS4lO/th/I7od+JmGcT8B90h4clLEjSNAzY4bcp9ZJPajzaReffmO6Bjle+1B1vIu75zPItR0xQgIgq7Pk5nwC8rkkvecTKBWfXq6nUigA73sf8NxzwM9+Jtdx5EjTLQuKNcXPS3zu2SP3VVTx6Ra4WVHnwE18NjdLe+Ok3XpdNzfxOWGCfAZBxKc1U0nxcz7dgnS787lpk7lHz35EkBP/+q9mZeIo/clrcdxp0bipyTvtNkrKLeDvfA4NyWKcVXwOD0evWhwGL/F57LHSx/0Wkuxon3TbwqGLeFOnyrUJIj7nzi0taqWVuHWfdVKEcT4B/9TblSsl5tm/X7I9FKexXvv3vn3RnM9Ro9z7o5vzqRlp1rT8MLUHMk4mCw4tX74cL7/8Mt544w1s3boV7373u/FZpwqaAK677jps2bLlyL9RXiuQWcJPfOpqf5Qg2M/59ApEFi0Cfvc76bhvO9SZoKtL9iH5neemVLvz6ZZ2C8iEO3Kk/wqgH/X14nLMng38zd/I+YFexS/iVBZMWnwCclTQZz4D/Pa3wGmnmWlAbmzY4N/2JNJug04MdpGgKfJOzqe+dxh0xTWoGPYrU68iL4j4nDlTxiFrQK+Ttp/zqWNj0LRb7TNNTe7f09gowWZc5zPotT3qKOe0W3u1W6BYfCbpfFozinbudD5mxY26Ohn/V6+WvyPMzwLBnU9APtu//EXuU00ZDsrYsRKMel1Xr2JDyuTJ8dJuvcTnyJHyt6v47O8Xcb9kibRpxQrveW3HDudsA789n15pwPX15iLmzTeLqGpqkgroXmzaBHzlK2bfjdKfwjifhYL7eZwHDsj4EmU+sv5+N4dOxyWr+LQ+nyZeIuOHPwSWLw//nn7Op3VbyuTJ3osxBw7IvWBNuVVUfCadehtmzyfgX3TIut3ILvDq64uLKoYZq+1ZNn5bX5wKDhkGxWeSTJ8+Hdu2bcPg24OlYRjYtGkTumzVMW+//XacffbZGDt2LOrq6vCZz3wGf/zjH8vZ1PQJKj7jDO5O4vPAAfnntwre2el/PEM56eqSSTZogFDNzmd9vfNnb01dTeqcp/nzJfXk/e8HbrsNOOMM90lHg+koK832tNsk2t/cLG3+7ndFeJ5yiiycODEwIKubfm2Pk3arxQCC/m3jxsn3qiB3S+0MUijCibB7g/3SD8NUPlV3xZra55Z2ax0D+/vDp916papbaW8vz55PQALiXbvMa7ZnjwTSVpFiXU33O64jDFOmiJCI4nzaaWryT7N2YsQI83xaP/HZ0yNi7JRTwh/tpXvg/MTniBHmXjYnOjvTS7sFxI3RYPiZZ2QuW7IEOOEEuedfe839Z7dvd74GQardOtHQIKnhGzfKgtdPfiLj4oc+JGOkW6aBYQDXXiv98bvfleeSEJ9ezqd+n5M7qc5tWs6njkuVFJ9O401ra7T5M6z49Iq11q6V+8FabEjRRZ6kxWdY5zOM+Fy1yvy/Zh9Zx6IwY3VTk/x8UPGppwdY+9KWLeacY28bwHM+w9LR0YETTjgBd9xxBwDgnnvuwbRp09BtW+2cNWsWHn74YRx+O6j47W9/i2OzlAKaBJUSn0FS1LKILlAE3fdZreLz8GH3IFoHsMOHk3EOlbFjgd/8RgpPPf207PtyIk5lQR0sVSgk1f5CAfjbvwX++79FsH/kI87pqZs2iTj0a/u4cfKeUcRnX5/8jjATgx63AnhXOASiO59h027dnM8w4lP3lVlTT+3pSk7HTUXZ8xlUfHZ0xBOfjY3BU93sRYf27JG/27qolJbzWVcn7qeKz6EhaX8U8RkHLZTlJz4feUREUNiUWyWI+Dz2WG/HpLNT2hllfvBzPgEJiLVf6X7PpUv9XSLDkLHIab6OWu0WMNPi77pL3v/aa6VQDOCeQXL33ZJl8qlPSX0IIB3xOWJEcV8eP97ZnYwzH1l/v5vzmQXxmaTD5TeP2MVnX5/7QoRTsSHr75k1SxaUkiTsns8g4lOzZezuov1zDztWW8ek3bv95+COjmLnU4Vxc7O8j8YjdD6js2zZMixbtgxz587FjTfeiNtuuw0AcOWVV+Lee+8FAFx77bU46qijsGjRIixcuBAPPfQQfvSjH5W7qeniJT61CAWQvPjUm7jcgUhcwp71Wa1pt15Bg3UAS1J8AuK2fvvbEqS57dWIM9nbVxKTbv855wB///fSb5yCp6Btb2iQtkURn2HdMUBEwpYtku7sNrHkzfn06pNR9nwGDUra2+W6Bk3dtxKmijFgik8V304BTVrOJyD31caNIkz0Pqik+FQn1IqKz9//Xh7TEJ+9vSL6vFJugegVb4eGZA73cz6nTJH7eO9eKSDS2AicdJK/+NQ9sWGdT6+0W0D6565dwD/9k8Qil18OLFggrzml3u7eLYuS48eL69naKn9znPhk7FhZKLEuFu7bV/pZTpggbbUXlosrPkeNks/IzfnURS/rUStA9YrPESPkbwjifOpc4FZF2umYFSvHHy/f47dwGIagzqffIipgprWeeKL0fT/x6TRWe/V5HZMMI5j4nDSpuC+p+PzgB+VR3c8ciU+fq5g88+bNw5NPPlny/C233HLk/01NTfhJ1GNGqgWnlDPF+lxazme1ic+wZ326OZ9pnKuaJEGcT/v/k2TuXCk+4dSO9eslYIjyu+vqZMBM4oxSN3QVds0aCeyshCnJHzU9M0olulmzZILauLHyzuekSSKu0nQ+R40y3T8/8dnSIu1J0vk0DAk2w6aShj3YW8W3Op9OZ7PZV9ObmsKdcenF7Nlmqrl+PpUQn2++6f7Zqfh8+GF5PPnkaL+no0OEnbUyqaLFhhYt8n4P68JLmD2Eem8GcT4BWWh64gkJeltaZMxqbXUXnzoOhd3zOTjofdaw9s9XXgGuvlrGCE1Ldio6dNddIkSWLSs+6iNOfDJihPQBu/NpD+rHjxfhuWdP8VgWV3x67ScF8ud86vsFTbsFpP/qHnIrKj6dnE9A0snvuUdE1DveEa/NStBFRk1L9nI+t26VeWDhQlkUe/ppmRsKBfl89Hg/xT5WW+cxJzo6gCeflPcaGgrmfD73nNkGFZ8f/7h8jitXAu96V67EZ4Y29dUY9fUSbDiJIWuwFWdw1wmrFsUnnc9ozJkjE721Aquyfn304g6A2WZdgU2aOXPkMY7zCciiTRTnM8p+DGvF20o7n42NMmYkIT7b2+Ua251Pa6qSvU8aRrH4LBQkgPYTn9Zg1q9N1r8jDGGqGAPOabd+q+lJuZ5AcTGrSo35VufTS3zu3y/je5Qq2oD3cStBig0BpvgM63wGdaxVfD70kPTLpUvl6/p6CYCff9652rjbMStA9Gq3QHH14s9/Xh7nzJGA2sn5fOopebzwQvO59vZ48Uljo4wH9oJDTs4nUCoSN2wQAR7nGD7rcT92siA+k0jDt2JdALaj4rO93eyLblkwa9ZIv3MbE9MoOhTU+QSK91g7oeJu4ULgmGNkDNq0yXQq447VHR0SR+nWhyDO58CAeW1efFFig9NOk69z6HxSfFaS1tZ0xWeenM/Jk2Wyiut8Zl18Hj5cWfFpdQ/t7QpSsMcLbbPuq0wa3TvuJj4LBXPF3wsVn2HPc4uSdhtEfEZ1PqO4zFOmJJN2q5+13fm0Ttr2PjkwIBO2dWFi5Ej/tFuvCtFWVHxGdbXDXNcpU6Q9VvFpDyTtq+lJBpraF6ziM2zF2riMGSPj8K5d3uITAE49NfrvCSI+/ZxPv2DbDacCOU6o+PzFL+RxyRLzteOPl2tkPe5B0UWwJKvdAuY4eMYZcq4jIA7QUUc5O59PPy0/Yz3v1ZpaGAar+LQ7cV7i0y4S16+XRYsgYsSNLDufra3xK9rbGTvW2/mcMEF+p1caumGI8+mWcgukIz6DOp+Aucfa7d60ik/d67xqlVzzoSH/LBW/sVrHJHWI/eYOa8Xbgwfl5xYuFAd21CgzLZjnfJJEqKT4rLaCQ3V10hGDFhyqZuez0mm3QKmA01XBpMRnGowaJZOOk/jcsEFWyL2O41Da280iLW688YbsM/3e98znou75BEQkpOV8hmlPZ6f7pN3bK4Fe0PebOVP6q76Xn/Opj3bxmWTaLeA9pg4PA1/+spx1qe8btooxII7WjBly3x0+LAFFLTqfhiGCzumzs4rPqPs9Ae+zPlesEEHlF6z57XcG5CzkRYuKRVBY5/Oxx+RRnU/AO1D3SruNWu0WELF/4YWyz9/K/PlSedcqaPfsEUGqLozS0SHfF3ZRzM/5tH+Wep9YRaJhxDvj0/reb73lPN5VWnymITD80m61L3mJz507ZW5xS7nVn588ubLO5/797mdjq/g89lhxPgEReG5zcBTnEzDFZ5C0W0CuwSuvSPyxcKEs4h59NJ1PkjBu4tO60h/nEGd1mPJQcAiQVc68O59e6VLWAC5t8Wkv/R93fw2QvvgEpP2vvVYaTIQJVHRhxi31du1a4PTTJYXuf/0v82zUKOJz+nQRKmk4n1HE55Qp8vc4/a7eXpkkg7rWM2aI6FJR4Od8OonPUaOCp93GdT4HB4HLLgNuvBF44AHg/vvNdhtG+PL2Rx3lvZc3TedzxgxZsKu0+AScnQSgeByIIz7dFhUOHBDR5JdyC3in3RoG8I//KGchv/iinEmqBBWfWgQFkIUBq4MYRHwmXe125EjZ22/fjzd/viyWWM+ofeYZ+Qzs7rR+7mFjFDfn0zDcCw4BxeJz+3YZF+KKzwkT5PPT62glr+Kzr8/5nrGKT6/+4FdsSDn+eOCll5wXR6IQxvn0O+vzxRdlcXTMGNP5tIpP+1gf1/kMknYLyBj20kvy/4UL5fGYY+Ta7Nwp7WtqCraInnEoPitJ2s5nU5NMinlIuwVEfO7eXbqa9dZbpROgPShobJQgP+vi06vgUEOD+fekJeDa22VAtruH1SQ+9+4t7je7dsm/oPtVvcTniy+K8Ny0CXj3u+Xeu+8+eS3KqmRDgwiF9evdxasWN/ByPgcGSvfp7tol90uYtDSvSoHbtwdLuVV0X9nGjWZgmQXn0ylYPngQ+OhHgZ/9TBztujrgpz+V16KuNh91lLRd9/34raYnKT5HjJDxcu1a06mrlPi0/19paZF/dXVSoCQqbuJz5Upxrf1SbgHpc01Npc7n8DDwhS8A3/ymGSBax8agabeTJsn8AxS7noC4L/X1zuIzSNptlGq3bjgVHXr6aXl0E59hYxS783nwoPThQ4dkocKp4BBQ7DgnMR+5vbfiJj7LUbQwLfGpc4s9hjp4UH6n3uNjxkh/iCs+Dx923kMchbDOJ+A8jx0+LPe3irsJE+TvXrUq2EJhkLFa+4aOFWGcT2tKMFCcFrxnTy7O+AQoPitLmuKzvl4cCnuaxc6dsrfDXhWwGtCiQ/a9MR/8oKTJWdGgwFrxr6Ul++LTb8U6bQFXKIiAq2bxCRS3X1fx4zqfTz0le6R27QJ++Uvg9tuLRUoU51Pb5eV81tXJZOflfP74x1IwxHq2mt/h1k54pR+q8xkU63ErBw5IYBnW+Qyz5zNqwaF9+2QM+a//Ai6+WBzPc86RRYUdO6JfV/37dd+h/bpqkL1zpwR/SabdAuZZn5V2Pu3/tzJ1qohDP/HmhZsIevlledQ9jV4UCpImaL3vBwflCJIf/ED6vVbltY4tQZ3P+nozldEuPpubxd0Im3ZbVyf/3KrdRtkvqMet2MVnQ4Pp0CpJiE+9L/budRfyTs5nUuLT66zPSh+1kpbzqe9vRa+hik+n/qB4nfFpJel9n2H3fALOzuerr0r/UHEHSP9btcp9T6X2797e0nnMiTjO54svSnyu97Y9LTgHKbcAxWdl8ROfbW3yetizkqwCxkl8VqPrCZhFEqz7Pnt6RBS88IJ5NiogQUFrq7naDFSH+PRyPoHyCbitW4uDfi3YowsAUaiU+AwbqDilZw4PAx/+sPSt++4DPvIRcQnPOUfEyvbt0UXK7NnSx197Te5XpyMSvApFAJKqMzwsgliJIj7dnM8DB6RPhTmixHrcigbpYZ1PTbv1KmoSNCiZMEHuYbvz+c1virC49lpZSGhsBC69VN735z+Pfl3VaVfxaV8tr6uTMV4/66QrW3Z3y2enzkOa/c6JIOLzV7+SYzzi4LaooEU61DnwY/Jk0+kZGgIuuUT603nnAb/7nbiCra3O4jOIeNaA2C4+AQnUN28uXfDavl0cKLdjUxobo1W7dUOdT71nDEPE56JFpRXKk3I+gWDi0+pOhl1QdCOM86mPaccQQ0PyWZRTfFqPWVGs/cHK6tUyT/l99prNYF0QjUMY59Mr7dbuLAIyRvT1mQtW9s++sVEWifT9gjqfeg8FFZ/qfGo2hLYNMJ1Pik8SGz/xqcFLlMHdS3xWW7Ehxem4lYceksfh4dJ0KPskVg3i0y9o0AA4zdQLp32f69fL/sQ45xCWs+1xxKeT87l6tUzE11wjglP5zGdkUrzzThEphUJ4B0vbtWKFTGpOeyq9SuQDZp/4+c8leAGSdT5VsEV1PjXNK4rzOTRkuptOBHU+Gxok2LSPp3/8owiYH/zAPLvtwx+W8eOnP40vPvWsSaegoa3NDGjScD4BYPlyabt1Ia4cBBGfxx1nHpEUlZYW+ew0gFZWrpR7Qiv/+tHZKe8xMABccYX0pQ9/WASypgfPmeOcdhvk2p16qoxP6i5acXOJduyQe9Ntn3VDQ6nzaRjSZ6Kk3U6YIOOfOp+vvy79xakacZLO55497p+lU8GhMOc2e+HlfFZqz6eOlWmKT/tc4iY+e3oktrKyZo187n7jrRb6yprz6SQ+1V188kl5jDtWjx9ffA6o3zw8bpyMzy+9JJ+5tW1dXWbFW4pPkgitrRI42SePuOLTmnJjF587dlSv8+kkPh980Py/dW+BU0WyahCfWXA+nc7L3LAh/ipzOdp+1FEyiCeddquTkr1Ax4c+JPfZT39qHqfhdfi0E9qu/n73icXP+dQ+0dMjYmp4WAKMpJzPMMesKJMmyb0cx/lUx8cr+yPoUSuABPJW57O/X8ThO95RHOC3tgIf+5gIN6+AxAsdvzXYcfr50aNNdyFp51PFZ39/Zcb8IOIzKfTYDyurVsm+tKAirLNT+s2nPiX9+YMfFAFqva/mzpW+pvdq0LRbAPj+96VNTuODm/jcvt0728DJ+dR4IuoxHQsWiPhU1xNIVnxa22ffSweULhqPGCHPqUDcsUMW6saMiT+XRNnzmXYMkWZFU7fK6So+rWftdnbKIoZVmA8NyT5yv/2egIynixfLtbIL2CiEcT47OiQOcNrz+eKL4mJaF6XUXXziCXmMO1bX1RX3W7+Fy7o6abMWM7NuFdCKty+9JH2E4pPExi2NI2nn8+BB2cw/OCiDTrWKz+nT5VEDbcMQ8amTgpajBvLrfC5aJJNCmtfQ7h7u2iVCJq74PPZYGWStq3pJ09go/cbufLa2BhdOTmm3KkCs5/MB8r4f/7ikFj37bDRXV0UC4D6xqPPplH5qGOJQqNP4H/8hk1SUCq2TJslkZ3c+o4jPujpJvXVzPuvqJLAMIj699n1qcZUgVXjtImX5chkX7dcVEFcbAH7yE3kM+1m2txdnt7itpus1Tcv5BPIvPidNKr6ufX1y36mjEQQNvO++G3jPe+TRvhA4d65cLy0iFSbttlBwd5+1Iq+T+PTKVHJyPsM4RE7Mny9jfm+vt/jUdiXtfDp9luPHi/Pz/vfLdXruORHscc+LzqLzmab4DJt2CxSn3q5bJ4t9QcQnINdo377SYnhRCHNf19dLnOTmfB5zTHFf1HFC/9Ykxmr9LFtaglWn7egws5bsMdLRR8tYYBgUnyQBdFCzp94mLT4BGWwqVfUwKUaOlAlPxefq1TK4fPrTMgnVgvP59a+Lkxcn/dUPu/OZVHGHY46R62JNW02DuXNldVYH8vXrpS8FDVRGjZLP1+p8PvGEOO/WIxOUSy+Vx127oolP6+fq5XwODjrfv7t3S+B29tmyz+aee0zxGNYZaGwU0ZSE8wmI+Ny40RSf9hVja5902/MJ+DufQftDe7uMgxqw60q33dEGgHe+UwS9jpthr22hYC4IAO6r6U7/T4JaEp8dHRKcqcOiC5FhxKcubp5xhhxD0txc+j32LQlh0m69GD1anJinnjID3AMH5L4P63zq11HSboHiirdPPy33vVNqdEOD3Fdp7/kERAi98Qbwhz8A554rVal/85twv9eJWnM+w6bdAsXi849/lEen8dIJ3fepi7dxCHtfT5lSKj63b5e50S7uJkwontuSGKv1/YLOwdbP3l4kzTqOUXyS2JRbfFbzMStKV5dZcEhTbi+4QD4ru/isNudzeFgEk9fKXn19+mc8jR4tE48GWEmJT6A8VZbnzhVBsnmzfJ4bN4Zre6EgixwqPnfvlmDWbcI9/XSzr0YRn2PGmEGQl/OpbbGjizFdXbIQs3cv8O//Ls9FSUubMiUZ5xMQ8bV/v1wDwHtBKGra7cBAcPHZ0SHBvY6FTz4pwcxJJ5V+b12dFJ1Rolxb6540t9V0p/8nwahRZkBTC+JzaMg821aLDYURnx/9KHDzzcBvf+s+TtmzQsI4n35ceKH0k0cfla+9jllRnJzPJNJuAUnze+45OYPVbSuBU7qzH1Gcz3/9V+Df/k1E0m9/K1Wpk/jM/ZxPzc4A8iE+/dJureO7ik/rXPDgg/KZnH12sN/3/vfLdbYWwovK4KBZ4TkIU6eKcNZFaKD0DE0rfgIv7FgdVnzq90+dWjpeW4umUXyS2LidG6WDsAbMSYvPai04BEiA/cYbMhD94Q8ixs44QybM1avl+aEhmSCqzfnUSTlNVzMoc+fK52kYyRV3KBfWAHHLFrknwgpn695ATT1zE591dab7GbWYkrbP7efdggagWHx+4hMinjVVNIr47OwU59Oa4hvH+QTMST+s8xl0z2fQQNuaUm0YIj4XLXIXG3pdgfji02m1PE3nEzDdz1oQn4B5n0YRn21tUlDMS9TYxWdfn9x7SSwIXnGFPN56qzzq+OM1X3s5n3HSbgGpQnzwoHPKrRJXfDo5n06B/UknSRq8LtIlhaZEOjmf+/fL+KMZM3kQn15pt+PGFccedudzaEgKPJ50UvDrMHGiLKr84Q9m7YWohK3gPHWqLOhbC5E5FRtSVOA1NDjPB+VyPv2EMc/5JLHxcz41ZSsp8akrqdXufA4NScD9xz8Cp50mA8GCBfJ3r1/vPom1tMje1yQ2v6dB3KAhSebOFZdt587kytqXC2uAGLXtVufTbb+nlUsuMc9Gi4KKhLjO59SpwJlnmhNuVOfz4MHi3+V13qAXOobppB/V+fTa8xkm7VYDgu3bZazYvt07hay7W5ztkSOjOS1+4jNN5xMw76ukg/YgNDWZoqzc4nPVKrknkh6zxo+X+dPqfCZ13RYskHvxl78snq/DOp9x0267uiTl+LHH5Gs/8blzp/NZo264OZ9JushBKRTkero5n1YRUl8vbc6r+LSmfQJm5XMVn8uXy5zw3veG+526qHLbbeF+zs7gYLh72qnirc5DTmf/qsAbM8Z5i07azqeX+NSKt9q+HEDxWUm8xKee7dXWxrRbK1rx9u67i/cP6qrVK6+4p+9oUGs9DzRLhKnamTZWAbd+vdyLYYVHpbC3HQjv2k6cKH1mYED2BTY3i0PmxuzZki73j/8Yrc0aJHvt+QTMtEIrVvEJSLVOJarzCRSnW/X2yj3gdt6gGyo+1YlKY89nmBVx65mQbhWM7dx5p6z4Ryluovdda6tzG/PsfALm/Vwu8amLLitXioMXVYB5YT1uxWl7RxyuuEL6wZ13BlvwSaPabX29OYYCknbrhn7u9vNJvXByPv3SbtNk/Hj3PZ92B6wc2VNpik/9vJ32fNrFp36t4vOBB+TxPe8J9zvf8x7ZT33bbcUpsGEJ63zaK7fv3y/j+JQpzn1KY0i3zz1t53PaNHnUytdWCgUzHZ7ik8TGS3xqkBc1rUUn3byJT03j+7//Vx51INSOuWqVe/n7cqXNRCVrabeAKeBmzYpfWbBcTJ0q19oqPqM4n4AEgE8/LalGftdl6VJTuIXFT3yqiFu7tvQ1FZ9aMOWii8xJOqrzCZiT9oEDkjYbxdXV/qriMY09n2ELDgFyXYM42oAEBV7ujxcqPt2ua9rOpwZU6gKUmzFj5No4Fe9JEqvzuW+f9IkwKbdhmDtXfo8KpiSv28c/Lvf8rbcGS7tNo9otYM6ns2Z5i1/rYk5QnJxPv4JDaRLU+QSqX3zW18tC5pYt5nOHD4v4tovPpiaZP1R8PvigXJvTTgv/Oy+/XH6n9Wi8sMR1Pv/hH2Rc+Pu/d/5+q/PpRNrO50UXyd7Yiy6K1r4qg+KzkqQpPvPufK5eLQOArsrqZBnE+cy6+MyC86nVDVetkgJP1ZJyC8gezO7ueM6nBlV//rMERkGr+0XlxBNF3DtVlQSK7287mzZJ4KAB/rhxwAc+IP+Pkm5pdz5vvFEm8M9+Nvx7TZlSHDBE3fOZdNptb6842pMnmwI5DXTRIKnV9LB85CPAr34FnH9+8u8dhHHj0j3XV7Fe1yiVbsNgrXibZNotIO/1yU9KiuPDD8tz5a52C5j7Pv2ERpSzPq3zXFOT9N1KO5+7dpVux8mj+AQkbfapp8zCjXrt7OITkPFx2za5z598UrZ0RFkcv/xymd90P3MUouz5BGTu+t3vgB/9CDjrLOBv/9b5+ydOlCNk3ObgsGO1fp5B5+ARI2T7jlu/Pf10EfLVUnvDB4rPShJUfFpLyAch7wWHlDPPLP47p0ypbudT026z4HzOni2TxcMPRyvYU2nmzpXKka+8IhNo2Cq72kfuvVce/dyxuBx/vKwMX3ih8+udnXKPW8+yVTZtKu4XAPCd7wD/9E/uE6kXVufztddEfB57LPCFL4R/r/p605EFStN2k6p2GzbtdsMG2f/zjnek6+iPHSuLfW4Lfmk7n/X1wIc/7H6+ZNp8+9vAD3+Y/u/RQK+3N1qxoTBYs0KSTrsFzD1yepRIuavdAqZjnqb4tGZnVdr5HB4u3QdZKfGp759WZXgtonbHHfLodMyKMnmyOJ9/+pPcV2H3eyozZwLvfjfwX/9VfH52GKI6ny++CPzVX8l99m//5l0t98knzaw6O9bxOcj2k5NOkrlT+3NcLr9c5nqKTxKboOJzaMi50Igb1mDMWiVzxw4JQqrZtu/oMMWZ/bzIBQvkbDI9U5DOZ3SammTCePZZ+braBjw9DH7FimjCWcXn734nj2k7n4CIPjchpHs+7M7nwICIRLv4nDMH+OIXowkrdT63bgWuvVYWRX70o+j3pbp/bW2lE39LiwQV1jNM0zznc8IE+Ux+/3sJOMtxXe+6C/je95xf0xX05uZs9PukOftscV/TZvx4ubfK6XyuXp182i0ggk8zHQoFb+c4jWq3gCxY/Mu/+AfO1gJeQbG3b/Ro0/kcMaL8i69uZ33u318Z8Zn2IvR73ysLGrffLnOkn/jcvdtchA2739PKFVfItf/Zz6L9fFjns61N5o/f/lYE9L/+a+k8aWfcOHdhqWO10zzmRF0dcP31yW15qKtzPme8SqH4rCRBxScQfmXRzfkcP7569u45UVdnDiD2gfDoo+Wz0wCdzmc8rEUnqtH5VKIIZ3Ubdu+Wn3eamMvNggUS5FmLe2zdKiLKb1INg+7tvPNO2aNz+eWS8hMVTWt1SlWy9slynPNZXy8CVD/DcojPd7/b+RxRwByj0nA9a4m6Oumz6nw2NaU3ZnV3y+OKFRK8J+3UFQqm6Jswwdu1TqPaLSDxw7XX+rtvcdNuAdP5TMNFDoLTWZ/DwzIeeWVqpMXhw3I/p1EsC5DP/VOfEuf+mWe8xacuRP7yl5LBMm9e9N/7oQ+JuLv11uJjvIIS1vkETOH38Y8XF+KLAsfqRKH4rCRu4rOvzxyE44rPtjaZzFR8VvN+T+WUU2RVW/elKLpa/Je/yGO1ic8sOZ9AfsRnHOcTKI9ACYK1orNir3SbBI2NZrr/uHGSvhsHq/Npxy4+GxqKA4ygez7D9BkdUxsbZa9tJVFBnsZ+z1pD6yNopdu0Uo1HjpQiVJoVkkYwqnu//CqMp1HtNgxJiE+r81kJ8enkfGpF/Eo4n4cOpb8Aram3t9/u73wCcn3e8554xkVzs9zXq1bJnuawhHU+AWDxYln8/NGP4psuHKsTheKzkjiJz6EhGfiiOp+GUdxJ6+pkcsyT+PzpT2Xwsg8mdvFZbWm3WXY+VUBUC0mKz7T3ewbFqehQGuITMFe8/+mf4h+xE8b5tLqeQPJpt4D59xx/fOnvKzcMaJKjo0P6w+bN6aXcKnPnmlU00xCfHR0SMPsd3ZRWtdugjB0rbcib86kxWaXSbtOOAY4/XhYz77xT+gvgLT6B6Ps9rXz4w/L4yCPhfzaK83nHHSJ2kzjnmGN1olB8VhIn8an/jyo+9Rwl68QzZoykD771VnUXG1IaGpxL96sz5BYUZF18ZtX5nDKl8kF6WCZMMPdK5cX5LKf4/NSngE9/OpliCWGcT/t9NmKE9Pek0m4BU3xm4boylSs5OjrENQLKIz6VtATTlVcCn/iE9/ekVe02KIVC+Ir8Ts7n0JC8RyX6gZPz6SU+BwbinVfpx6FDkjaeJoWCuJ9vvSV70gFv8VkoyPaBuJx0khgiTz8d/mejOJ8NDckVbuJYnSgUn5VEO4VVDGmQFVV8OgmYMWNkdWtwMB/Opxvt7cUrXHQ+46EBVrUVGwJksozT/hEjJChqbQUWLky2bVGZMUPuYWvFWy2Xn7T4/OIXZdU4SGEFP1R8RnE+ARkL3cSnYURPu82C+ORqenLodQXKKz4rGYyq82ndQ1fOtFsguvi0n0W+a1d1OJ9AujFEOZxPQBYXCwX5u0ePdl7QV/F5/PHJGBejRknffOqp8D8bxflMEo7ViULxWUmcnM+0xKfm9edZfBYKpvsJ0PmMy/TpEmSddValWxKNc86R9ketEHfaaXL0SSUnPCv19VLwwe58NjXFT41Nk2nTpEjLCSeUvhZUfLrt+dRAO0ywduqpMg6eeWbwn0mLkSNlzKr03tM8YHVu0haf1iOMKik+da6wOnHlnkeiiM+GBnPbjDWYr4T41PtGM6aAyorPcjifgIzL6ma6FdSbNUteu/ji5H7vqafKsWJbt4b7uSjOZ5KMHi3zL8fqRMhIVFWj6EqTl/jUowHiik8lz+ITkNTExx6T/1er85kV8VlfL8cJVCv/+3/Lv6j8939Hq8qXJgsWyD4dLc6hZ3xmuYJ1Q4OcF+qEXXzq0VBWRo1ydz51vAsjPi+9VApfZOEzKxTMcylJPHShtrk5/WyNcqTdBkEXxqyuUDnTbgH53Pv6nM/FdMIuIqzxSSU+y4kTZYvGq6+az+l4k2fnE5Bx8A9/cBefI0cC27Yl+ztPPRW45RZJvdU9oEGotPNZX198j5BY0PmsJIWCDG5e4rO+XgZHis9g6L64xsbS1cOsi88ogTRJlywIFCvq7L/6qgjj119PPuW2nMRNu426YJO160rio+IzzUq3ylFHmb8jC86ndd9nJdJugeBnfWZNfOoZylZhYa+9oeTJ+QTkDN6JE4szxuwUCsmOl6eeKo9h931W2vkkiULxWWn8xCcQLq3FT3zmoeCQFzqIOk1iWRefWXM+SfbQxZVVq8zjCWpBfLql3WZtnzSpHCqC0k65BWSM1kJmlXQ+ncRnJdJugXAxirVt1rTbSgn5+fPl7F89/7cW9nwCcu+++irw/e+X5/cBEqONGhVefFba+SSJQvFZadzEp3VCCyM+nVY9a9H59KusmUXofBI/rBVv06p0W07iOp9Z2ydNKsf8+bIX88ILy/P7NPW20gWHgOLjViqRdguEi1Gy5HwC5riq7melxWe5nE9AYkKnYkNpUV8vVW+XLw9XNZjOZ66g+Kw0dvGpK/x253PXLnOV34taT7udPl0+u2oWnxxgiRvd3RJUWsWnnqNZjWif3L9fxjcn8TlqlIyRTvtv6XwSZcwYYM0a4GMfK8/vO/ZYSUesZLGvLKXdJuF8Vlp8ajG3Shccyvt4dtppEutaK7d7YRgiVOl85gaKz0oTJO1WJzdNCfGi1sVnoQBcfz1w1VWlr2VdfDKQJn6MGCECdNWqfDmfu3YVf21l5EgJPpz6LbMFSKX40peAhx4Cpk6tXBu8nM9qEZ9ZcD7nz5fHrDifeR/Pwu77LPeCCkkdis9KE3TPJxBscPcTn9ZzMPPK178OfOELpc9nXXzS+SRBWLAAWLfOrCCbB/GpB7y7iU/Aed8n90mTSjF2bOWPofLa81kul0gXx6vZ+Zw5U1Jd1fmsZLXbchYcqhRRxSedz9xA8Vlpyik+x4yp7SBN9zVkVXzS+SRBOPpoYHhYXBdAzmurVoKITw1InfZ9ss+QWsbJ+Sy3SxRXfGbB+ayvlz28lU67HR6W65f38ayzU7ZIPfVUsO/nwnzuoPisNCo+dT9TmuIz7ym3fhQKIkCzKj45wJIg6P6kl16SscFJsFULYZxPJ/HJtFtSy2Sh2u3IkfIvqvi01meoZPGmBQvk6Kr+/sodtaKLaXl3PgFxP1euBPbt8/9eOp+5g+Kz0rS2ykZqnTC8xGeQc7QoPr1pacmu+KSLQ4Kg4hOo7pRbIL74ZNotqWWyUO0WkBgl6jmf9fWm41nJY2vmzxcTYM2ayjmftRQDnHqqfN7Ll/t/LxfmcwfFZ6XRwU0Hu6ScT+vEQ/FpkmXxyQGWBGH+fPPQ71oSn157PmshWCPEThaq3QLhzyK3t033fVZSfFqPW6mU+Dx0SB5rxfkEgu37pPOZO8ouPl977TUsWbIEc+fOxcknn4yVK1c6ft9LL72EM888EwsWLMCCBQvwq1/9qswtLRNpiU/r4D5+vASrkyfHa2seyLL4ZCBNgtDaah6vUgvi02vPJ9NuSS2ThWq3gCk+nY5DsjMwUCoidIG80s4nIPs+NR6zn39J5zM5TjxRXO8g4pML87mj7MsI11xzDa6++mpcdtlluPvuu3HZZZfhmWeeKfqe/v5+XHjhhbj99ttx+umnY2hoCG9pcJI3nMRnU1Px4Dx6tAxGUcVnWxtwzz3AwoXJtLmaybL45ABLgrJgAbBxY/WLz8ZGoK4O2LlTvmbaLSHByUK1W0DE58AAsGePVAH2IqvO57x5skivzmdrq5lhonDPZ3K0tgLHHSfi0zBKP2srdD5zR1mdz97eXixfvhwXX3wxAOCiiy7C5s2bsXbt2qLv+4//+A+cdtppOP300wEA9fX1aK/kQc5pYheffX2lm9wLBRnct2zxX1l0EzAf/jAwe3b89lY7ra2VFZ+9vcCXv2xOMlYYSJOgHH20PFa7+CwUpE/qvW9PcwOYdkuIG1modguY2Vlbtvh/r5P4zILz2dIiR6688oosdDmNReVKu62V8ezUU4Ft2/zvGy7M546yis/Nmzejs7MTDW8PmIVCAV1dXdikh6W/zapVq9DU1ITzzjsPixcvxqWXXortLpvZb7rpJkybNu3Ivz6nACXLODmfdvEJAKecAqxYAfzd33kLUHZSbyrtfN59N3DjjcCjj5a+xhRCEpQPflAOt9d9M9WM1e10cj7tY6QVjneklslCtVtA4hMA+MQnREx44SQ+zzwTeNe7Kj/3zZ8vBYf27auM+Ky1xbTjj5dHl+13R6DzmTsyWXBocHAQf/jDH7Bs2TI8//zzmDp1Kj73uc85fu91112HLVu2HPk3qpIrZ1EIKj5vu00G5+9/H7jqKqmQ6wSDMW8qLT71dzu1gc4nCcpZZ8lqcTWf8an4iU/dd6WugJVaC9YIsZKVarcf+hDwz/8MrFoFvPOdsiXACcOQttrnuC9/GXjkkbRb6c+CBTLOrF7tHIex4FCy6Py1dav39zGuzR1lFZ/Tp0/Htm3bMPj2QGkYBjZt2oQuW+pYV1cXzjrrLEydOhWFQgEXX3wxngp6GG21EVR8jh4N/O53wLnnArfeCnzqU86pm+yk3qj4DFIYIQ10cnEKpOl8klrET3xqIMY+Q0gxWal2CwDXXQcsWwasXw+cfrrsnbRTqbYFRYsO6Z5POyNGyFYBOp/JMGWKPPqJTzqfuaOs4rOjowMnnHAC7rjjDgDAPffcg2nTpqG7u7vo+z7+8Y/jmWeewd69ewEA999/PxYtWlTOppaPoOJTv/fXvwYuugj4xS+Av/3b0u+h+PSmpQUYHi6erMvJwYPFj1Z47UgtEtT5dOozzBYgtUxWqt0qV18N3HEH8Oabkkpr3waV9TnOeoayk/gsFNLNnqo15zOo+Mz6fUNCU/a022XLlmHZsmWYO3cubrzxRtx2220AgCuvvBL33nsvAHE+v/KVr2DJkiVYuHAhHn74Ydx8883lbmp5CCM+ARmUfv5z2e/lVKKandSbtNNm/PByPg8flmDCq+obIXmDabeERMPL+ayUS/SpT0ltip4e4PXXi1/LenyizifgLD6BdMVnrY1n7e1yn77xhvf3VfqeJolT9is5b948PPnkkyXP33LLLUVfX3LJJbjkkkvK1azKYRWfw8MyqPntW21okOpwdM/CYxWfWmGvnPg5n7xupNYImnbrNd7VSrBGiBU357NQkDMUK8WECfJo77NZj08mTpR/O3ZURnzWmvNZVwd0dtL5rEEyWXCoprCKT3U/vZxPpbmZ4jMKWXc+GUSTWoNpt4REw63abaX7g1ufrYb4RN1POp/lYcoU7vmsQSg+K40GW/395iHqQcRnUxPFZxQqLT7pfBJSTJyCQ7UYrBGiuJ3zWekg3S1boRriE933WUnxWSvOJyDi8803i+9hO9Vw35BQUHxWGqvzqZvzgzqfXtUf2UmdqbT41Gvm5uIwiCa1BtNuCYlG1p1Pe4xSDfGJik+3OKwcabe1NJ5NnSpbznp73b+HzmfuoPisNFbxGcb5dEu7zXop80pTafGp18xt4YDXjdQa2icLBeegS5/3cj7Zb0gt4rbns9L9wS3tthriE6bdlpcgFW+rYdGChILis9JEFZ9Mu41GVsQnnU9CBO2TLS3ulZ799riz35BaxK3abaUdompOuz3tNGDRIjmr1AkWHEqWIOKTzmfu4JWsNHGcz6Gh0ommGgb3SlJp8elVcCgLK9aElBur+HSjqYnOJyF2su58VmPa7bhxwIoV7q+r+DSM5I9Fq0Xnc+pUefQ6bqUa7hsSCjqflaaxUUqiRxGfQHUO7pWk0uLTz/nkdSO1RhDx6eZ81mKwRoiS9T2f1eh8+qHjlNNiWFzofDpD5zN3UHxWmkJB3E+r+PQ75xPI9+CeJtbqwpXAz/lkEE1qjTjOJ9NuSS2T1Wq3eY5P0lzArsXFNO75rEkoPrOAXXwG3fMJ5HNwTxM6n4RkiyScT/YbUotk1fnMc3yiW6XSiCFq0fkcM0Y+U6+0WzqfuYPiMwtEEZ95XllMk0qLTzqfhBQTd89nXZ1sXSCk1uCez/LjF0MMDgJ//rPsCQ1LLTqfhYK4n3Q+awqKzyyg4jPsOZ+A++DOFSJnKi0+vZzPLAQNhJSbOM4nF2xILZPVard5Xhz3iyF+9SvgjDOAp54K/94qPmvJ+QT8xWc1HNFDQkHxmQVaW2UgS9L5rPTkk1UqLT69nE8etUJqkbjOJwMSUqtk3fnMY3ziF0P09BQ/hkHHuFqLA6ZMAXbudF5gBPJx35AiIonP733vezjodpOQ8CS957OhIfkS4Hmh0uKTzichxcTd81lrgRohCvd8lh+/GMJrjvejFtNuAfO4lW3bnF+n85k7IonP7u5ufPKTn8SNN96IPk0VJdGJs+fTKe2WHdSdSotPN+dzeFjOba21SYeQMM6nfR8V025JLZP1are1uOdTn48SY9Sy8wm4p97S+cwdkcTneeedh1//+td4xzvegauuugrf/OY3k25XbRFnz6fTymI1D+xpU0nxOTxsDqJ5XBEmJApBnc/h4eIgG2DaLaltsup81vKeT30+qvNZX197BdRUfLpVvKXzmTsiLSPccccdWLNmDdasWYN9+/bh8ccfT7pdtYWW7t65U1a8gqzueKW1sIO6k4T4fP11SRMJuwpnXQW2rwjXaroNIUGdT0D6jXV8o/NJapms7vlsaJAq1LUoPvVvjup81lqxIcBMu6XzWTMEupL/9V//hT179uDSSy8FABQKBVxwwQWYN28e2traUm1gTaDic/t2YNSoYD9D5zMa9fXy+UQVn6+/DsyeDdx8M3DlleF+1nqt8jgpExKF8eNlj/rEie7fYx3vrGMknU9Sy2gwnrVqt4DzPu08zHNppt3W6h52v7RbOp+5I9AIdeONN+Lf//3fj3z96U9/GoCI0okTJ2Lp0qXptK5WsIrPICm3APd8xqGlJbr4fOMN2Zv5+uvhf5bOJyGlTJ0K/PGPwKJF7t9jdT6tHD4cfMwkJG8UCrKgqsG5Ycj8lIUYoLm5Nvd8xi04VIvOJ/d81hyB9nweOnQIs2bNKnl+/vz5+J//838m3qiaI474pPMZnjjiM86qJp1PQpw54wxg7Fj3173GOy7YkFqmsdGcP7I0j+Td+ezvd349bsGhWhzPWlqAceO457OGCCQ+B+1FHt5m3rx52Op1MCwJhorPwcHg4pN7PqNTKfFpXQU+fLi4cqc6n7x2hJTi5Xyyz5BapqHBDM71MQsOUVNTvsVnWs5nLYpPQNxPOp81QyDx2d3djd/97neOrzXVYopA0qj4BOh8loMsOJ9AcSCtg2utTjyEeOE23tVysEYIkG3nsxbTbuMuUNdqTB1EfFbzfUOKCCQ+v/GNb+Dqq6/GnXfeCcPi1jzzzDMYFbRADnEnjvjkns/wZMH5tH9N55MQd9ycT6bdklqnoSG74pPOZzhqeTFtyhQ5bnDv3tLXsuTok0QIdCWPO+443H333bj44otx/fXX48QTT8Tw8DAee+wx3HPPPWm3Mf/Q+SwvWXE+Dx4ExoyR/9P5JMQdL+eT4x2pZRobs5l229wM9PQUP1cL4jPuArVX1e88Yz1uZfTo4teYdps7Al/JU089Fa+++iruv/9+vPDCC2hra8M///M/o7u7O8321QZRxKfbns/Bweoe2MtBpZ3PtjZg377ia5eHSZmQtHAb72rZKSAEyK7z6bTnMw+FY4KKTzqf4bBWvJ0/v/i1wUGp6lwolL9dJBVCLSPU19fj/PPPx/nnn59We2oTq/jkOZ/pU2nnc8wYEZ9Oabe1OvEQ4oXTNoOhIWB4mH2G1DZW5zNL4jOvez51LPJLu+Wez3Co+HSqeMu4NncE2vNJUoZ7PstLS4t8TkND4X82CedTU23pfBISDCfnk32GkGLnM2tpt3nc81lXJ+NRGmm3tex8WtNu7QwOZuOeJolB8ZkFkky7pfj0xy9txouknE+AzichQXFabOM+aUKy63w2NckCr/Woviy1Lw5e2VNxCw7VuvPpJD4Z1+YOis8sEEV8NjTIChzFZ3gqJT7pfBISDafFNlaIJiS7ez6dtgZlqX1x8BKfUWOEoSH5V6uLaZMmyZ5Op7RbOp+5g+IzC0QRn4VCaVqLYbDgUBDofBJSXTg5n+wzhGS72i3gnK1Q7TGKm/g0jOjOZ62PZ42NIkDpfNYEFJ9ZIIr4BEo39Oehklw5oPNJSHXhteezVoM1QgA6n5XATXwePiwCFAgfI2h8UKtpt4Ck3nLPZ01A8ZkFoopPeylznoUUDDqfhFQXXs5ntQeyhMQhy3s+gdIYpVCQYzOqGTfxaf1b6XyGR8Xn8HDx83Q+cwfFZxZQMQSEdz7zuKqYNkmIz0OHSgdIP+zik9eOkGA4uSgM1gjJdrVboDTtNgtti4ub+LQ+R+czPFOmyD2yc2fx83Q+cwfFZxaorzcHnKDnfAIUn1FJQnwC4Vc27Wm3dHEICYaOj6x2S0gxWXU+3dJus9C2uAQRn4ODxZV+/eBimlnxdtu24ufzct+QI1B8ZgVNvY2z5zNLE0+WSUp8hv35IM5nLU88hLjh5XxyvCO1TLXt+cxC2+ISJO3W6Wsv6HwCbW3y2NdX/Dydz9xB8ZkVoohPtz2feRjc06RS4pPOJyHRcHI+6RQQkt1qt257PvMwx7W0yLEoGnMp9pggjPjkeGbGv/v3Fz+fl/uGHIHiMyuoIOKez/Sh80lIdeFVOZN9htQyDQ0ihAwjWzGA257PLLQtLm4xhI5PhYLz616o+Kxl51NNmP7+4ufpfOaOsovP1157DUuWLMHcuXNx8sknY+XKla7faxgGzj77bIwdO7Z8DawUTLstH1l0PnntCHFHBSazBQgpRu//wcFszSN5T7sFSmMA/Vpj1jAxgo5ttbyYRuezZii7+Lzmmmtw9dVXY82aNbj++utx2WWXuX7vd7/7XcyePbt8jaskTLstH5V0PuvqzGvMyp2EBKOuTvoGnU9CitH5fmCAabflws/5HDeu+OsgMAag81lDlFV89vb2Yvny5bj44osBABdddBE2b96MtWvXlnzvypUr8etf/xpf+tKXytnEytHaKoNOmA7W3FxcUY3iMxiVdD6bm93TkQBeO0LcaGqi80mIHY0Z6HyWDz/nU8VnFOezltNu6XzWDGUVn5s3b0ZnZyca3h4sC4UCurq6sGnTpqLvGxgYwFVXXYVly5ah3ucw4ptuugnTpk078q/PXiWrWvirvwL+/u/D/YxdxGRp4skyUcWnYRRPpFGcz6Ym5xVhrnoS4o19jzv7DCHFzmeWYoBa3vM5fnzx10HgeEbns4bIZMGhb3zjG/jIRz6CBQsW+H7vddddhy1bthz5NyrMOZlZ4tOfBm64IdzPUHxGI6r41IlEF0TofBJSPuzOJ9NuCSl2PrOUdkvnk85nWOh81gxlFZ/Tp0/Htm3bMPj2AGkYBjZt2oSurq6i73vkkUfwgx/8ADNnzsTpp5+OvXv3YubMmdi+fXs5m5t97A4aBUwwoopP/X5d1aTzSUj5cHM+Od6RWiarzmct7vm0i086n+FQ8Wl1PoeH5V8WFlRIYpRVfHZ0dOCEE07AHXfcAQC45557MG3aNHR3dxd936OPPorXX38dGzduxGOPPYbRo0dj48aNaG9vL2dzs499ZTFLE0+WqZT4VOezsVFKsdP5JCQ4bns+azlYIyTrez5rMe2Wzmc0NO3W6nyqm5+H+4Ycoexpt8uWLcOyZcswd+5c3HjjjbjtttsAAFdeeSXuvffecjenurGLT3bSYFTS+WxuFuHJ/WuEhMPtXGP2GVLLZLXarVPa7eBgPuITOp/p4OR8ZumeJolR9qs5b948PPnkkyXP33LLLY7fP3PmTOzevTvlVlUp3PMZjREjRABWwvmcOFH+73ZMDgdYQpxhtVtCSsm681lLabdxnE8dz2rZ+WxqktjM6nxm6Z4miZHJgkMkINzzGY1CQSaPSjmfgDzaA+mGBmkbIaQUOp+ElMI9n+UnzYJDtTyeFQriftL5zD0Un9UM93xGp7W1Ms6nTshOzmctTzqE+ME9n4SUkvVqt7W855Npt+FpbaXzWQNQfFYzFJ/RyaLzyetGiDvqfBqGfM20W0Ky63za4xPDAIaGstG2uPColfSg81kTUHxWMzpIcc9neMotPoeH5frQ+SQkGk1N0o80GGHaLSHZ3fOp20jyuDhO5zM96HzWBBSf1Qydz+iUW3zqAoGb85mXdCRC0sKexkfnk5DsVru1V3XPU3zi5XyOGGFWbaXzGR46nzUBxWc14yY+2Un9iSM+x44t/joI9onF7nwePswVT0K8sBcwoVNASHadT6B4kTVrbYuDl/hsbnau9OsHxzOBzmdNQPFZzdD5jE4c8dnaKp99mJ/Xa0Tnk5BouB0tVevBGqltnJzPrMwlTs5nHhbHvdJuW1qinSVO51Og81kTUHxWM9zzGZ244jNstVw6n4TEw8355HhHahkn5zMrgbp1nstTfOLnfDY0AHV1dD6j0Noqn9vQkHydp/uGHIHis5qh8xkdFZ9aOTMIOtHoyqaT+Ny8GTjxROCVV4qfd3I+Dx82fz+dT0K8cdvzWevBGqltslrtFshv2m1jI1Bf7+58RjlLnOOZYN8vS+czl1B8VjMUn9HRlUtr6qsfQcTnU08Bzz0HPPpo8fNOzqf1eTqfhHhjdz6z5vIQUgns53zW1cm/LJDXgkOAcwygzidQ/LcH4dAh+WwKheTaWI20tsqj7vvM231DAFB8VjduwRg7qT9R9mQEEZ979xY/Kk7OJ1C8KszrRog7Ts4ngzVS69idzywtxuQ17RZw3nqjzicQzfnkArTpfOq+z6ztYyaJQPFZzbgV4GAn9ScJ8WndFK/s21f8qLg5n9b9a5x4CHHHabGNfYbUOvY9n1ma/2vd+QxbF6LWiw0B7s5nlhZVSGwoPqsZpt1Gh84nIdWFk/NJ8UlqHXu12yzNI3nd8wk4xwB25zNswSGOZ3Q+awSKz2qG4jM6ccRnc7O7+IzjfPK6EeIO+wwhpagjlMW021pyPg1Dvo6adkvnU6DzWRNQfFYzPGolOlHFZ3NzcSU7e7XcOM4nVz0JcYd9hpBSdL7PYtptU5NZCClv8YldXOq4FLXgEJ1Pgc5nTUDxWc1osY28riymiYpPp32bbthXNQ3DLI+uuDmfeo2cnM+hIWB4mNeNEC/ofBJSitX5zGLaLSDCLG/xiV186rhE5zMedD5rAorPaqZQyHdaS5pEdT6tE4vTz7s5n06rovq8XjeuehLiDvd8ElKK3fnMUpBeS+LTui1HH+l8hofOZ01A8VntWAc4dtLgpCU+1fF0S7t1cj7zNikTkgasdktIKfY9n1maR6x1KfIWn7S0iKgeHpavnZxPTTkOAsWnoOKTzmeuofisdpqaivdAFQpAfX1l21QNRBGf/f3BnU+3gkNOzqem7nLiIcQdt3M+CallrM5n1tJu87zIqjGAik4n59P6uh9MuxU07ZbOZ66h+Kx27Gm37KDBKHfaLZ1PQuJhD+boFBCS/Wq3QH7TbgEzBnByPq2v+8HxTKDzWRNQfFY7FJ/RSDvtdt++4kq4bs7nwYN0PgkJglN1b/YZUutkudqtdZ7Lu/h0cz6Dxhh0PgU6nzUBxWe109RE8RmFtJ3PwcHidBs35zOPK8KEpIGT88k+Q2qdLFe7zXOGT1DnM0jarVbO52Ianc8ageKz2mluLnYC8jKwp01Y8el0gLT95w8fNq8FULzvk84nIfGg80lIKdVQ7bYWxKc+Rkm7HRyUGIPOJ53PGoHis9ph2m00worPgQGpauc1sdiLDFn3fdL5JCQe3PNJSCnVUO02j/Nc0LTbIM4nF6BNeM5nTUDxWe1QfEYjrPgMsqppF590PglJDuuCjWFwvCMEyHa121pyPuMUHNIYgM4nUFcn9w2dz1xD8Vnt2Pd8cnUoGGmIT3U629uLvwbofBISF+v+MQ1IuGBDap0sV7t12vOZpfbFIUnnUxenOZ4JI0fS+cw5FJ/VTnOzBGJDQ3QCwpCm8zl1avHXgEwudXXmAOrkfPLaEeJOXZ30EfYZQkxY7bYypOF8UnwKra10PnMOxWe1Y99TwQ4ajLBl0MM4nyo+7c5nczNQKBT/fqvzyYmHEG+0wBqDNUIEXdA8fFjqEmQpBuCez3DOJ9NuBTqfuYfis9qxryzmZWBPm7o6GejL6XxaJxZrOhJdHEKCodsMuGBDiFBfL486F2UpSKfzSeczCnQ+cw/FZ7Vj31PBDhqclpbyO58KnU9CwmN3PjnekVqnUBDBqXNRlvpELZ3z6eZ8Bokx6HwWQ+cz91B8VjtMu41O0uIzqvOZt0mZkLSg80lIKY2N2RSfdD551EoU6HzmHorPaodpt9GppPPZ2Cgr1ty/RkhwuOeTkFKszmeWHKJa3PMZJe2Wzmcx6nzqkVpAtu5rEhuKz2qHabfRqaTzWSiUuji8doR4o32GabeEmDQ2mk5RlvpEnjN8NAbQzz1OwSEuphXT2irCM4+LFgQAxWf1Q+czOmk5n52dxV8Dpc4nQBeHkLBon2HaLSEmWd3zWYtpt/o3Ryk4ROdTGDlSHvfvN9Nu6XzmCl7Naod7PqMTR3zW1xfvswFMp3PMGGDUKG/nE6DzSUhY7M4nxSchxXNRloJ0a3wyNCT/z8s855R2O2KEVNIHoh21wvFMaG2Vx/5+Mz7Sqs4kF5Td+XzttdewZMkSzJ07FyeffDJWrlxZ8j0PP/wwTjnlFBx99NE45phj8MUvfhHDw8Plbmp1oANcf3/2zvjKOnHEp9PP790r16OxEWhro/NJSNKw2i0hpdD5LD9Ozqc9PrC+7gVjgGLszmdDg3lGOskFZRef11xzDa6++mqsWbMG119/PS677LKS7xk3bhx+/vOfY9WqVXj22WfxxBNP4Pbbby93U6sDddP6+uQxLwN7OUhafO7bB4weLf8fPZrOJyFJw2q3hJSSVedT57Q8znNNTSKIrM6n03FqYZxPpt0KduczL/cMOUJZxWdvby+WL1+Oiy++GABw0UUXYfPmzVi7dm3R9x1//PGYNWsWAKC5uRmLFy/Gxo0by9nU6kEHOIrP8LS0SCqQTopeBHU+29rk/1bnc3hYVjbpfBISj+Zm6U9ZLK5CSKVoaMjmkRSFgvTZgwelfXV1ZlpqtaN/m5vz2dgoqaJ0PsPj5HySXFHWUWDz5s3o7OxEw9s3UqFQQFdXFzZt2uT6M2+++SbuvvtunHfeeeVqZnWhgkZdtixNPFknTFpMFOdTxadbMQE6n4SEQ/uQjncM1ggpnjuyNo9Yi4RlrW1xscYAducTMIW3H3Q+i1Hnc//+fN43JNvVbvfu3Yvzzz8fX/ziF3HSSSc5fs9NN92EadOmHfnXpw5grUDxGZ2kxafd+dy3T8qF26vgKdy/Rkg47OMdxSchxc5Q1lwiFWB5FBF28WmND+yve0Hnsxh1Pvv76XzmlLKKz+nTp2Pbtm0YfDs9xDAMbNq0CV1dXSXfu2/fPpx77rm48MILcd1117m+53XXXYctW7Yc+Tdq1KjU2p9JuOczOmk7n0ND8rrbqib3rxESDrvzyfGOkGw7n9Z5Lmtti4s1BrCn3dpf94LOZzHWtNs83jekvOKzo6MDJ5xwAu644w4AwD333INp06ahu7u76Pv6+vpw7rnn4txzz8XXvva1cjax+qDzGZ0kxefhwzKBWJ1PQK6Ll/NpPTaC144Qb+h8ElKK1RnK2jxSS85n1LRbOp/FWAsO0fnMJWVPu122bBmWLVuGuXPn4sYbb8Rtt90GALjyyitx7733AgC+//3v4y9/+Qt+9atfYfHixVi8eDG+9a1vlbup1QHFZ3TCis+GhuJB0Drx6OdvdT4BScXVycfJ+Tx8mBMPIUHRPqT7qdlnCCme97MWqNfKns84zqdbXYhahc5n7in7KDVv3jw8+eSTJc/fcsstR/7/1a9+FV/96lfL2azqhWlo0QkrPp0mloEBSa/VYNguPvftM4MBp1VRgCnThASFi22ElJJl57OpCdi5M58iQsWl1nZwmuN37fJ/H0275WKaQOcz92S64BAJAAVMdJIQn/qaik972q2f8wkwhZCQoLDPEFJKlvd81kLarYpHFhxKBjqfuYfLCdWO3QngClFwkhSfbmm3+/bJWV+Au/NJF4eQYHDPJyGlZL3arabdZq1tcVFxqTEEj1pJBjqfuYfOZ7VDAROdrDmfHGAJ8YbbDAgphc5nZWhpkZTbPXvMr+2v0/kMD53P3EPxWe0wGItOuZxPXdX0cj4bG4FCIVz7Cak16HwSUkrW93wODMg8mLW2xUVjAN3X6TTHDw2Je+cFnc9i6HzmHorPakeDL+75DE+WnE8G0YT4Y692y/GOkOxXuwUkRslbf9UY4K23ir+2v+4XY6jzmbVrVykaG+Ufnc/cQvFZ7RQKMrjT+QxP1pxPQog3dD4JKSXrabdAPuc5u/h0m+P9YoxDh2RhjdlPJq2tdD5zDMVnHmhuNtM68ja4p0lWnM++PgbRhASB1W4JKSXLabcqwAYHs9e2uNjTbt1iBL+iQ4cPcyyzM3Iknc8cQ/GZB6yihp00OEHF59CQTA5pOZ/Dw7xuhARB+4z2KfYbQrKddpvn+CTJtFvu9yyGzmeuofjMA1ZRk7fBPU2CTgy6auk2sfT3lzqfWq0tiPMJ8LoREgT7Ag7dAkKqw/kEste2uAQpOAT4O5+HDnEss0PnM9dQfOaBPA/uaRJUfOrrQZxPFZ91dfL/IM4nwImHkCDYF3DYbwipjj2fQPbaFpcknU+OZcW0tsqWJMOg85lDKD7zAB20aGg57yTE59698rV1kGxro/NJSJLYF3DYbwgpnneyFqjneZ5L0vlk2m0xI0ea56fm7b4hFJ+5IM8ri2mStPOp+zyV0aPpfBKSJNYArb5eMgwIqXXofFYGOp/p0dpqfm55u28IxWcuyPPgniYNDfIvqPhUp1SxOqd795opt4rd+fQSn7xuhPjDBRtCSuGez8pA5zM9tG4GkD03n8SG4jMP5HlwT5uWlnSdz717TefTK+2WgTQh/uQ5hY+QqGS52m2e4xNdgKbzmTzWxf683TeE4jMXMCCLTlLi08353LfP/Hk6n4TEg84nIaVk2fnMc3xiT7ul85kcdD5zDcVnHqCIiU7azufwsJmSQ+eTkHjkOZAlJCrc81kZNAbo6yv+2v46nc/w0PnMNRSfeSDPg3vaJCE+9+yRycPJ+QSA7dulMIp99Y7XjZBw1NWZfYXBGiFClqvd5nmec4sJ7F9TfIaHzmeuofjMA3ke3NMmCfHZ0yOPTs4nIOKzqQkoFIpfp/NJSHi037DPECLQ+awM9pggStqtYYj4ZNptMVbxmbf7hlB85gKmokUnjvhsbBQnprdXvvYSn/ZJCcj3pExIWmi/YZ8hROCez8qQhPM5MCCPXEwrxpp2S+czd1B85gGKmOjEEZ+Fgjynzqdb2u2ePc6rmnmelAlJCzqfhBTDareVwR4T2Of5IM6nWzX8WofOZ66h+MwDeR7c0yaO+NTntm+X/7s5n4C/88lAmpBgaL9hnyFEUMFZVyf/skSe4xNrTNDUVPrZB3E+Dx+WR45nxdD5zDUZG6VIJKwrZuyk4WhpkZXH4WH37/ETn0ND8n835xNwXtXM8j4dQrKK9iX2GUIE7QtZ7BN5jk/q6kzR6LXATOczPHQ+cw3FZx7I88pi2qig9Joc/MSnEtb5LBTo4hASFvYZQopRUZdFcZf3+ERjAK/4gM5neOh85hqKzzyQ98E9TYJMDkHFZ1jn0/o8rxshweCeT0KKybLzmff4RGMApwXmhgagvp7OZxTofOYais88kPfBPU104ujvd/+etJxP6/MMpAkJBqvdElKMOkNZ7BN5j0+8nE99ns5neOh85hqKzzzAqqnRCeJ8qjCl80lI5aHzSUgxOn9kMUi39tM8znMUn+lA5zPXUHzmAV1ZzGKlu6wTNO22UHCeHOh8ElJe6HwSUkyWnc9CId+LrF5pt/o8027DQ+cz11Cp5AEGY9EJKj5bWmQSdft5oNT5HDnS/Bk6n4QkA51PQorJ8p5PIN8xCp3PdKDzmWsoPvNAngf2tAkjPr1+HigVn4WC+RydT0KSgX2GkGKyXO0WyHeMQuczHZqazMX7rN7XJDIUn3mA7ll0khKfra1S1c6Oik86n4QkA/sMIcVk3fnMc5+l85kOhYLpfubxvqlxKD7zQJ5XFdMmKfFp3++p6PN0PglJBvYZQorJ8p5PIN8xCp3P9NB9n3Q+cwfFZx7I88CeNkmJT3vKrULnk5Bk4Z5PQorJcrVbIN8xCp3P9KDzmVsoPvMABUx0suJ88toREgz2GUKKybrzmecYJa7zqeKTzmcpdD5zC8VnHmAwFp2sOJ9c9SQkGOwzhBST9T2feY5RgjifQ0PAwIDz65p2y/GsFDqfuYXiMw/keWBPGzqfhFQX3PNJSDGsdls5gohPwN39ZNqtO3Q+cwvFZx7I88CeNn7i0zCSEZ90PglJhjyn8BESBTqflSNI2i3gHmOw4JA7dD5zS9nF52uvvYYlS5Zg7ty5OPnkk7Fy5UrH77v11lsxZ84czJ49G1dddRUG3FIWCIOxOPiJT50Y4qbd0vkkJBnofBJSDPd8Vo6gzqdbjEHn0x06n7ml7OLzmmuuwdVXX401a9bg+uuvx2WXXVbyPRs2bMDXv/51PProo1i7di16enrw4x//uNxNrR7yPLCnjd/EoM/T+SQkG3C8I6QYVrutHEGdT7e0Wzqf7tD5zC1lHal6e3uxfPlyPPDAAwCAiy66CJ///Oexdu1adHd3H/m+u+++GxdccAEmT54MAPjsZz+LG264Addee205m1s9FAoycLGDhkcnjvXrgT/+sfT1t94q/j63n6fzSUh5oPNJSDFZdz7zPM8FdT4ffRTYurX09bVr5ZHjWSkqPrO6qEIiU9YrunnzZnR2dqLh7RupUCigq6sLmzZtKhKfmzZtwowZM458PXPmTGzatKmcTa0+JkwAxo2rdCuqj5EjZUK87z7554bbZ9veLo9vL5SUoM+PH+/8+oQJ3u9PCCmGfYaQYnTx2W0RtNJMmADU1wOjRlW6JcmjMYCOS3bGjpXHq67yfh+37Klapr1dzBV+Nrmj6pcTbrrpJtx0001Hvu7r66tgayrI/febgxwJzogRwO9/D6xe7f49DQ3ARz7i/NqJJ8rPn3mm8+sXXgj89rfAOec4v3755cD8+cDRR4dqNiE1y1lnyULRe99b6ZYQkg0aG4EHHgDmzKl0S5z5h38AzjvPfRG2mnn/+2WOf//7nV+/9FLZu+h11mdXF9DRkU77qpkvfEHG+87OSreEJEzBMAyjXL+st7cX3d3deOutt9DQ0ADDMNDZ2YnHHnusyPn8zne+g3Xr1uHmm28GANx///244YYb8Nhjj/n+jmnTpmHLli2p/Q2EEEIIIYQQQpzx0mNlLTjU0dGBE044AXfccQcA4J577sG0adOKhCcge0HvvfdevPnmmzAMAzfffDM++clPlrOphBBCCCGEEEISpOzVbpctW4Zly5Zh7ty5uPHGG3HbbbcBAK688krce++9AIBZs2bhG9/4BpYuXYru7m60t7fjmmuuKXdTCSGEEEIIIYQkRFnTbssB024JIYQQQgghpDJkJu2WEEIIIYQQQkhtQvFJCCGEEEIIISR1KD4JIYQQQgghhKQOxSchhBBCCCGEkNSh+CSEEEIIIYQQkjoUn4QQQgghhBBCUid3R600NTWhvb290s1wpK+vD6NGjap0Mwh4LbICr0N24LXIDrwW2YDXITvwWmQHXotskPXrsH37dhw6dMjxtdyJzyzDM0izA69FNuB1yA68FtmB1yIb8DpkB16L7MBrkQ2q+Tow7ZYQQgghhBBCSOpQfBJCCCGEEEIISR2KzzJy3XXXVboJ5G14LbIBr0N24LXIDrwW2YDXITvwWmQHXotsUM3XgXs+CSGEEEIIIYSkDp1PQgghhBBCCCGpQ/FJCCGEEEIIISR1KD7LwGuvvYYlS5Zg7ty5OPnkk7Fy5cpKN6lmOHjwID70oQ9h7ty5WLRoEd7znvdg7dq1AIAzzzwTRx11FBYvXozFixfju9/9boVbm29mzpyJefPmHfm877rrLgDsH+Vm586dR67B4sWLMXfuXDQ0NOCtt95in0iZv/mbv8HMmTNRKBSwYsWKI8979QH2j3RwuhZe8wXAOSMt3PqF25wBsF+kgdN18JovAPaJtPAai3p7e3Huuedizpw5OPbYY/HnP//5yM95vZYpDJI6Z511lnHbbbcZhmEYv/zlL42TTjqpsg2qIQ4cOGDcd999xvDwsGEYhvGDH/zAOOOMMwzDMIwzzjjD+M///M/KNa7GmDFjhvH888+XPM/+UVm+853vGOedd55hGOwTafPII48YmzdvLukLXn2A/SMdnK6F13xhGOwfaeHWL9zmDMNgv0gDt+tgxTpfGAb7RFp4jUWXX3658Y//+I+GYRjGX/7yF2Pq1KnG4cOHfV/LEnQ+U6a3txfLly/HxRdfDAC46KKLsHnz5qLVVJIezc3N+MAHPoBCoQAAOO2007Bx48bKNoocgf2j8tx666244oorKt2MmuBd73oXpk2bVvScVx9g/0gPp2vB+aIyOF0LL9gv0iHIdeB8UR68xqJf/OIX+OxnPwsAOPnkkzFlyhQ88sgjvq9lCYrPlNm8eTM6OzvR0NAAACgUCujq6sKmTZsq3LLa5Pvf/z4uvPDCI19/6UtfwnHHHYdPfOITWL9+fQVbVhtceumlOO6443DFFVdg+/bt7B8V5oknnsCuXbtw3nnnHXmOfaK8ePUB9o/KYp8vAPaPcmOfMwDGVZXCab4A2CfKgY5FO3fuxMDAACZPnnzktZkzZ2LTpk2er2UNik9SM9xwww1Yu3Ytvv3tbwMAfvazn+HVV1/Fiy++iHe+850lAypJlj//+c948cUX8dxzz2HixIn4zGc+U+km1Ty33norLr300iNBHPsEIYJ9vgDYP8oN54xsYZ8vAPaJcuA0FlU9lc77zTs9PT1GW1ubMTAwYBiGYQwPDxuTJk0yXnvttQq3rLb4zne+Y5x44onGrl27XL+nqanJ2LFjR/kaVcNs3brVGDVqFPtHBdm3b58xatQo45VXXnH9HvaJdLDuqfLqA+wf6eO0vy3IfGEY7B9J47XXUOcMw2BclTZO1yHIfGEY7BNJ4zQWtba2Gtu2bTvy9cknn2w8+OCDvq9lCTqfKdPR0YETTjgBd9xxBwDgnnvuwbRp09Dd3V3hltUON910E+688048+OCDGDt2LABgcHAQPT09R77nnnvuwaRJkzBhwoQKtTLf7N+/H7t37z7y9Z133onjjz+e/aOC3HXXXVi0aBHmz58PgH2iUnj1AfaP8uM0XwDsH+XGbc4AGFdVAvt8AbBPpI3bWPSxj30MN998MwDgmWeewRtvvIEzzjjD97UsUTAMw6h0I/LO6tWrcdlll2Hnzp0YPXo0brvtNhx33HGVblZNsGXLFkyfPh2zZs1CW1sbAKCpqQkPP/wwzjjjDBw6dAh1dXWYOHEibrrpJixatKjCLc4n69evx0UXXYShoSEYhoFZs2bh+9//PmbOnMn+USGWLFmCq666CpdffjkACfbYJ9LlmmuuwX333Yc333wTEyZMQFtbG9auXevZB9g/0sHpWvzpT39ynC+efvpp9o8UcboWDzzwgOucAbBfpIHb+ASUzhcA54w0cYtdn376afT09OCSSy7Bhg0bMGLECPzLv/wLzjrrLADwfC1LUHwSQgghhBBCCEkdpt0SQgghhBBCCEkdik9CCCGEEEIIIalD8UkIIYQQQgghJHUoPgkhhBBCCCGEpA7FJyGEEEIIIYSQ1KH4JIQQQgghhBCSOhSfhBBCCCGEEEJSh+KTEEIIIYQQQkjqNFS6AYQQQggBvvrVr6K3txc7duzAqlWr0NnZifvvvx+tra2VbhohhBCSCHQ+CSGEkAzw7LPPYvv27fj3f/93rF69Gvv378dTTz1V6WYRQgghiUHnkxBCCMkAzz33HB5//PEjTufhw4cxfvz4CreKEEIISQ46n4QQQkiF2bx5M+rr6zFnzhwAwMGDB7Fu3Tocc8wxFW4ZIYQQkhwUn4QQQkiFefbZZ3HyyScf+fqFF17A0UcfjcbGxgq2ihBCCEkWik9CCCGkwjz77LM46aSTXL8mhBBC8kDBMAyj0o0ghBBCCCGEEJJv6HwSQgghhBBCCEkdik9CCCGEEEIIIalD8UkIIYQQQgghJHUoPgkhhBBCCCGEpA7FJyGEEEIIIYSQ1KH4JIQQQgghhBCSOhSfhBBCCCGEEEJSh+KTEEIIIYQQQkjq/P9iFISW72r4mAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "C_v = rn.local_clustering()\n", + "\n", + "plt.figure(figsize=(14, 4), dpi=80)\n", + "plt.plot(C_v, \"r\")\n", + "plt.xlabel(\"$n$\"); plt.ylabel(\"$C_v$\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2e0821fe", + "metadata": {}, + "source": [ + "#### Global Clustering Coefficient and Network Transitivity\n", + "\n", + "In contrast, global scale network properties take all vertices into account.\n", + "The __global clustering coefficient__ is defined as the average value of the clustering coefficient taken over all vertices of a network,\n", + "\n", + "$$C=\\frac{1}{N}\\sum_{v=1}^{N}C_v \\,.$$" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "85ed81cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating global clustering coefficient (C_2)...\n", + "C = 0.7532331807249787\n" + ] + } + ], + "source": [ + "print(f\"C = {rn.global_clustering()}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "6c9458f8", + "metadata": {}, + "source": [ + "The __transitivity__ of a network measures the probability that two neighbours (i.e., recurrences) of any state are also neighbours. It can be calculated from the link matrix $A_{i,j}$ of the network as\n", + "\n", + "$$T = \\frac{\\sum_{i,j,k=1}^N A_{i,j}A_{j,k}A_{k,i}}{\\sum_{i,j,k=1}^N A_{i,j}A_{k,i}}\\,,$$\n", + "\n", + "where $A_{i,j} = R_{i,j} - \\delta_{i,j}$ with the recurrence matrix $R_{i,j}$ and the Kronecker-delta $\\delta_{i,j}$." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "02a5687e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating transitivity coefficient (C_1)...\n", + "T = 0.8021460350693536\n" + ] + } + ], + "source": [ + "print(f\"T = {rn.transitivity()}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d88f8295", + "metadata": {}, + "source": [ + "### Recurrence Properties Under Fixed Recurrence Rate $RR$\n", + "\n", + "For comparison, we now calculate the same recurrence properties using a fixed recurrence rate." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e5b29970", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating recurrence plot at fixed recurrence rate...\n", + "Calculating the supremum distance matrix...\n", + "Calculating recurrence plot at fixed recurrence rate...\n", + "Calculating the supremum distance matrix...\n", + "Calculating local clustering coefficients...\n", + "Calculating global clustering coefficient (C_2)...\n", + "C = 0.7544336150477271\n", + "Calculating transitivity coefficient (C_1)...\n", + "T = 0.8005944339367739\n" + ] + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# choose fixed recurrence rate\n", + "RR = 0.05\n", + "METRIC = \"supremum\"\n", + "rp = RecurrencePlot(time_series, metric=METRIC,\n", + " normalize=False, recurrence_rate=RR)\n", + "plt.matshow(rp.recurrence_matrix())\n", + "plt.xlabel(\"$n$\"); plt.ylabel(\"$n$\");\n", + "\n", + "rn = RecurrenceNetwork(time_series, metric=METRIC,\n", + " normalize=False, recurrence_rate=RR)\n", + "# Local measures: \n", + "# - Local Clustering Coefficients\n", + "C_v = rn.local_clustering()\n", + "plt.figure(figsize=(14, 4), dpi=80)\n", + "plt.plot(C_v, \"r\")\n", + "plt.xlabel(\"$n$\"); plt.ylabel(\"$C_v$\");\n", + "\n", + "# Global measures:\n", + "# - Global Clustering Coefficient\n", + "print(f\"C = {rn.global_clustering()}\")\n", + "# - Transitivity\n", + "print(f\"T = {rn.transitivity()}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "479d003e", + "metadata": {}, + "source": [ + "## Example: Lorenz Attractor\n", + "\n", + "In this example, we will be using the chaotic Lorenz system as defined in the introduction." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6178bd02", + "metadata": {}, + "outputs": [], + "source": [ + "def Lorenz_timeseries(dt=0.01, num_steps=10000, x0=0., y0=1., z0=1.05,\n", + " s=10., r=28., b=2.667, spinup=1000):\n", + " \"\"\"\n", + " Given:\n", + " dt: length of timestep\n", + " num_steps: number of timesteps\n", + " x0, y0, z0: initial values for timeseries\n", + " s, r, b: parameters defining the lorenz attractor\n", + " spinup: number of spinup-timesteps before storing results to output\n", + " Returns:\n", + " timeseries: numpy array of three dimensional timeseries on Lorenz attractor\n", + " with length num_steps\n", + " \"\"\"\n", + " # define integrator\n", + " def step(x):\n", + " v = np.array([s*(x[1] - x[0]),\n", + " r*x[0] - x[1] - x[0]*x[2],\n", + " x[0]*x[1] - b*x[2]])\n", + " return x + v * dt\n", + " # initialize\n", + " X = np.array([x0, y0, z0], dtype=np.float64)\n", + " # spinup\n", + " for n in range(spinup):\n", + " X = step(X)\n", + " # observe timeseries\n", + " timeseries = np.zeros((num_steps, len(X)))\n", + " timeseries[0] = X\n", + " for n in range(num_steps-1):\n", + " timeseries[n + 1] = step(timeseries[n])\n", + " return timeseries" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cdb6dfe6", + "metadata": {}, + "source": [ + "Now we calculate a timeseries on the Lorenz attractor with the defined function. Try different timesteps, initial values and Lorenz parameters, or just use the default ones defined above." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "05eb6b57", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lorenz = Lorenz_timeseries()\n", + "\n", + "ax = plt.figure().add_subplot(projection='3d')\n", + "ax.plot(*lorenz.T, lw=0.5)\n", + "ax.set_xlabel(\"X Axis\"); ax.set_ylabel(\"Y Axis\"); ax.set_zlabel(\"Z Axis\")\n", + "ax.set_title(\"Lorenz Attractor\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "4bea44c2", + "metadata": {}, + "source": [ + "### Recurrence Properties\n", + "\n", + "We first calculate the RP of this three-dimensional timeseries for a fixed recurrence rate." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "07feab8f-6bdf-4c24-98a2-65f4dec2764a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating recurrence plot at fixed recurrence rate...\n", + "Calculating the supremum distance matrix...\n" + ] + } + ], + "source": [ + "RR = 0.05\n", + "lorenz_rp = RecurrencePlot(lorenz, recurrence_rate=RR, metric=METRIC)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "db9d16c4-5154-4371-ae3c-54e88f79e092", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yM2LZ1nCoLjfIb1FyBClAagQZ5PfIVM+EKOCaPkEJoz9lm6mQjbK+ujBlAxnlUpQ9ysKdIBEkO0gBwjrIbLg2YLimT1Bcc5IqiJu+frAN5BLWnpxiJY6q9FyYZ8ogbGpKtmw26S8jnafS5rJTxSL6Uk5vypDdRedoqs8BelLiHEEKIDuCTODajNI1fXTANrMHritxKNuKI0hLUDUDMjUTp7LIgYIMopiymawyKWQOdGF7FKwTKmNBVNhBGkZFpzPZOCnMGkVS2JQ6r8z6CrIaVwam0stRiNIO8tlNd/un1I4z0V3PKsriFKsAqlKsqahY6EE5zcG4hW0Llbhv6IOarTnFagG59vkEuSfqM0WxNbWkexFBkGfLWjAj654g96tov6Jly8D21LaMslTJonIcyleWbDiCFEBHBJnA5D40ajM93cjSn8pewqhyxLU9xFVvl8hVh7wPUjI6HSTAHZSRBztJhkmHU6yWo2q1nKyjmsIQpUxZaVKVKU2Vz8n3zFzlhN1zm+/+MM+Kek8Y/Nq8yr2gOtL3VF9n+GFa1qjlcwQpgO4IMgGVVB2FsimnP+OAzXazWfZMXNJFBSL24QjSEcLO3sNEFPmQMROM8rI+yL1R9ZcdQaqeReuoT9NbM6IQNYoOgmobpQ7+lOqDiizZFi6GlY8dJAGipMjynXyf7ZlBkblvTsa9UW0mWk4UMn+lIBcq9+bJvEdFulvXqtXM8lREYjqju8z6MOmkdE5CghCkD/rBKVYBTKVYUwmz5zHu6Rjb9LdFXlvkVAklG7AsweAUqwXITo2GiSRlQmGhhkn9wyC6L1BlmWGuU3XYPmVk1ZUKG1CTRWckyfsgCUAhgkxgwwyNGi7YzIQOLtiNCqZsGYc6DKojR5AOQz0qoogLNjMxyKkq0/a6CIMpJ+VC28+HSh3ZQRIl18t3FatbVaM6nZrve+oDhc2y2bYiNmrZpusq6MIckxGkLlup0pFTrALYeli5rHtkIbts2/S3mTCLxKI+UyXcDvRBzdacYrWQMAc3q/qpIVXRp+zIIcyCABP7IGXeI2txjOx2o6otqkJGO6CUpTG9SE5225BF1HI5ghTA9CIdFbP3qGVSeja1GWoYUnWgrI9tbU0FlORlWYLDEaRjqJi9Ry2T0rNNv9OSQerAQnl7jm1tTQVBD35QSbaTY0xgWz2KwA7SEDr2QeZ7Zj5UHksne6YpQ/8otpJtJ1WDsOh+PhUTMqppOBHCLAozgYmtQJnl66xH3gdJANMp1iDEfeEDBRkYN+G25QacYrUIl5bQU0g9UYxEVC8K0rVAQ9eiFGr1l0DnAi/boLRgSSbsIA2jInVK0UkkkDUD151ujoLI4ehRU+Eq70nIJvM4w6jp5SiotLXpFKcpTB91qQpOsQpAYR+kbfvMKGCb/rbIK0tOKvpSkSMslOSnJEs2OMVqAUFnW2HSO9RncKbSrVRsImPBjCpUROhUFrIkSI2MRa+VVaZMKB0ez4eVxxCdi3Tits+MkmyuRUa2Y5MdbZI1DuSqjyARJDtIAWxaxZoN7sAMwzCcYnUCV1a3JsqMspDD7xkqdaGcTjNRDvWVqrlSelHanyxZwtwvohOTn6i24ghSAFMRpGsLd6LIyxGwWWy0v61tJ5e8tupECY4gHSC1I2QjDgdEJwYBmXLrWrxAOWqxqd2IoqPtBJUhDKmOL9vCO2r1QUmWVDiC1IAL7yAz0TUD9SsnStmynxekHMrYlk0wgSwbqcjsMPrgCJLJi64ZqN9AEaXsbBuSZUNthp4P27IJJpBlIxM/HsBEI2x9sIOMMSb3CUZxarlSUDKR6XhF5YyS/rXx1CXdZcs6dYiakzSx55EaKuqDU6wCuJhiTSVISojTR8Hg9K/bcLqVPpl1wClWJhBBZlf5OrutM1VVC3P83l/JeGaYiMGWKMOmiJZaJJmKqeyQafL1lyU7Ngo/iyNIAWw/SSdK2XGaActaPEQlaox6j+66p9LWqNSfKijJoopcOvJJOpJxPcWaShw6T5zg1a3hcN0GrusHZNeRU6wxQtbJHQmoH/CdiYo9gCb3QUaVTUY6Nuyh6bL0pbBnMWwaO9/3qnRT0S5koqMsFTpyBCkA9QjStpSQ7GfHYTasAirRJdcfoxOOIGOGitmgyhmm7GerkpVq1CyLMAuI4rjnlLJsmdgkqw2wg3QEGYNMlBRFmNV/Mgmrf+o9UQ4goOigbdoTadpJymoHppGRGg4CpRW6KuAUqwDUU6yq4RSYGJx+jI7N+lKSXacslPQWgVOslkJl75rpF/pBkGUzGfrpTHUHKUfX4hldezxlkU9H01mRKIjIIsvOuqPWfMgsiyNIAeIeQSawbaYoA1k6x9F2DH3iFGkmyud9kJJx3UEGabimGznDMEwUOMVqgERYn/lv5n9TJIjDS02nUNdLNrnqOOgzXCcueopgoy1yjWMq9dFRLh81J5lEBHlgx8ko7VXg+6OlmZ+l/gu4F3m5po8ILtcnwwDIOZ5lXhel/WeOnwnyjZ8y+h2nWCWTmWIVqUCXnaTMxmobogMIw9iGyHiW7fqo5WV+ptJJWpNinT17Ns455xz06tUL/fv3x5VXXont27enXfPll1+ivr4effv2Rc+ePTFhwgTs3bs37Zrdu3dj/Pjx6NGjB/r374/bb78dR44cSbtm9erVOPvss1FcXIxTTz0VixYtCi13onJSKynzM7+KpLwaNAiZOsaFRH361THFfZAmy7TpFYMObFjFKRLRZX4XBb/7c/UtE+OO0QjykksuwcSJE3HOOefgyJEj+OlPf4pNmzZhy5YtOO644wAAkydPxiuvvIJFixahrKwMDQ0NKCgowJtvvgkAOHr0KEaOHInKykrcd999+Oijj3Ddddfhpptuwr333gsA2LVrF4YPH45bbrkFP/rRj9DY2IgpU6bglVdeQW1tbV45oyzSUTHzooZr+uSC65NxgaB1bKpN5Co3rEzWplg//vhj9O/fH6+99houuugitLe34/jjj8czzzyD7373uwCAbdu24YwzzkBTUxPOPfdcvPrqq/j2t7+NDz/8EBUVFQCAhQsX4s4778THH3+MoqIi3HnnnXjllVewadOmZFkTJ05EW1sbli1blleuqKtYRQfVfBVuopGKlkl1UNVlUxV2ipJSMl0ftsls2l4qkK2TaHukvnXEmhRrJu3t7QCAPn36AACam5tx+PBh1NTUJK8ZOnQoBg4ciKamJgBAU1MTRowYkXSOAFBbW4uOjg5s3rw5eU3qMxLXJJ6RycGDB9HR0ZH2F4VsqYSgKQudm3+DlJm4jkIqLYxNZcgdxE5BnxlmsDE92NsmM5X2KxPZ9hRNcaqsR9lp3nyQcZCdnZ2YMmUKzj//fAwfPhwA0NraiqKiIpSXl6ddW1FRgdbW1uQ1qc4x8X3iu1zXdHR04IsvvjhGltmzZ6OsrCz5N2DAACk6ZqKiU4ZtMLKchOlBJowMMuXWpX++ckzXQxRMyR62Hdhs67CY6uu6gwQyDrK+vh6bNm3C4sWLTYuCGTNmoL29Pfm3Z88eZWXJToGYliNIx5HdwTJf5gdBpv6iRK2vMLN4GwZzkxOtMHajMDE0AVW9ZY6pJBxkQ0MDli5dilWrVuHEE09Mfl5ZWYlDhw6hra0t7fq9e/eisrIyeU3mqtbE/+e7prS0FCUlJcfIU1xcjNLS0rQ/GzCdVksg2nFUpYBsQcUqQNVlpqIy2qJWlzJef7iIzRMxEYw6SM/z0NDQgCVLlmDlypUYPHhw2vejR49Gt27d0NjYmPxs+/bt2L17N6qrqwEA1dXV2LhxI/bt25e8ZsWKFSgtLcWwYcOS16Q+I3FN4hmMfKjOLiliq51Mp7NNYLPsOrG9nhMYdZD19fV46qmn8Mwzz6BXr15obW1Fa2tr8r1gWVkZJk2ahGnTpmHVqlVobm7GjTfeiOrqapx77rkAgIsvvhjDhg3Dtddei5aWFixfvhw/+9nPUF9fj+LiYgDALbfcgp07d+KOO+7Atm3bMH/+fDz//POYOnWqMd1NoHtvWtB0a+LabP9m/rcryFpFq+OeTHREsYm2ke8vca3fv5n/HRaRNh2knGzyp/6333c24EJUbXSbR5cuXXw/f+KJJ3DDDTcA+OqggNtuuw3PPvssDh48iNraWsyfPz+ZPgWA999/H5MnT8bq1atx3HHH4frrr8ecOXNQWFiYvGb16tWYOnUqtmzZghNPPBEzZ85MlpEP1w8rV02UrQ1x2HfI5Cdbu0htCzrbjsw2KCJvlC0/TDrW7oOkigkHqWKvka37zGQNdLr3dIaZGIS5TnW92twWczlSWc/O95ksbHSSpmX1K58dpGRMRZA2D0yyy6Y4848TNtvNpSyErXJTwtqDAph0RJby+31PdUl6lLKD6BpV/zjsiVSxuIbqHsJsh3LYqA/VxS+6+lRQopbLEaQA1N9BqogkdaSKqD7PBCZ0sC0DYYIw0adOG8WtPmTAEWTMULHcXuVMVfazVcmqc9ZrYiUrpW0aFKMiIHv0GfQeVVCNKF2BHaQhZKdvwp4Akoso7wvzocJJhpUl1zNVDj4yUp+pyNxyoWLyFKUtmiTsRMIPFe1JRX1EwSWHzSlWAainWPNBOQ1DWTaGUQG3ebNwipVJg3Jn5BQREzco90cmncL8lzBMeERmyyJO0rb9X4w8ok6gqLadMHpR08F1OIJkyJPqQDnaZIJA1TkCwZ0dZ1v0ww4yhuhenRm1vNQBjuJAx9Al9fQZagSViaqjdxl2kDFEZyeTfQIOxYGOoQ/FthOlX1DTxVXYQTJKkTEw+R1GzTAiJNoLxegrytYRarq4CjvIGGHCsUQdmFLfIVF+n8TQw6/tyMD0sWk8QdQHO8gYYSL6knH+auoz2DkyIqhsOzqe4/cdTxD1ww6SKKqO8zJ13qfo6Tqp/516r42pVdvkpYqII3Gh7aTqktlXbdPFFdhBEkVFRzAVQYqUnTko+A1wts2cbZOXKtnaTbb2YWvbySa3rQ7fBdhBEkZ2RzDduXKVnfp+Jd/MWbX8tg0+uuSlZpfM9kAh6pK1pSlbepidpF74LFYBbD+L1Q9ds+og5YjM/CnKreM5KsqxJbLKJFe7sK3tiMibK0pm8pNpqyBnsbKDFMBFBwnQHDBy3WNbyiyBbXa2HducpMhzTOriGnxYuQOkpk/8Uin50isi9+jqXGHSQdlW8Yk+L4zNVCFiZxmyhalPGbKFkV1lXfgt3En9TkYaVFSGqPjpkpp2pZRmpdTnZMERpABUI8i4zyBdi5goy5YP2+oiatk215VuqNmKI8iYoGo2rHImKHNGGTYyVRERyXiOKtl06KOqLlQR1dZhZVelr+mMigpbyZAharkcQQpANYJMoGL2rnLWJ/vZ1GaothBm0YcKW3P9uYEt9cgRZMyQ9Y4v6jODlG06khTB9vcn+fDbEiFyj2y7UHuXlgll2TIxKasNzjEo7CANITs9Q2ng0rmIIfV5UVMsuhcx6VgsEuQeUX1VtAvKTlJnWj4qup2U6YU52cqSJQOnWAWgnmJNxeaFD67IQB22ERNnOMVqEVFnpn5Rj6l0I4WZtgwZZC9qUV2ersUxuiIDKtGYH6bbN1Wo6x1WPo4gBVAdQYZZMGNykQ0FVNjMJJRlS0WWXeOe6ZAFJV1s6XN8ko5kKKRYbRuEKGCb/rbIa4ucotiuDyX5KcmSDU6xWkDQl9thUoc6Fz5QSNmZ1D8MoouCTOhg0x7AqOgc0FXYIGy7USWLzoU7qtsUR5ACUIggE9gwQ6OGCzYzoQPvebSfONg7qI4cQToM9aiIIi7YzMQgp6JMF+rCJuJgb5U6soMkQL6URJjUIbVOoTqdmu97EwNFlD2YYa8zsYJZ1ypaWZjYf6oKE3uOg6DLVqpSu5xiFcBUitW1laqy5eWFS/pwzW6u6UMZarbmFKulBI16VP3sk6qX7LqOmFNhM12Y2sOa7x6dB6nrsD/1dhAU04umVCzqogBHkAKY3gfJyCeIzbl+xLHNVrbJy0SHI0jL4A6qnyCzWq4fcWyLFmyTl9ELO0gN2NwBZS0eoYjMwVGFnWy0KWDfPknXnKTqPYcu2SofnGIVQEaKNQ6pHFt1lLXYx1b9VWGbPWyTlwkHp1gJQn2WKmPBAnUdsyHr9zR1HhRvwwKTuESSVOvC1AIo08jUkR2kRkR/sT0s2e6N4twSnwX5rUBTRLGdLOem6nc5g5aj+8ivXHLouEcWYSdL+erCBNnajUwoOlyZfZBTrAJQOmqOUYusNBun6xiGJpxidQDRF+JhFn2YPI1FdoQsO/1IOZLMhup9rSoXZ5i0ka59nbKhGLVRlAngk3S04GIEaWuEo0tu2+wT5tQl23SMiq6Tl6jalapcuuEIksmL6ZNEwj5b109CmdgCEoUw7yRtXVQVlrDvQ4P2E6p2jbNzDFsf7CBjTL4O4xdxyCxb9vNkIzPdqoOg9Zn4jOJgTkkmmXZVvUcx7PeuE7adc4pVABdTrHGHj5pTg222sk1eFcTNBkFSrOwgBWAHycRtEGEYVwniIAs1yeQES3ZsRGmvYFnpRGjv0uCaujfSNd2ykaprqs5x0T8obBf7yOzXqcju66Lpzsx+l/hMF/wOMiKJysr8N/HfmZXrArVVI53VLRemO6tNxKlduISfI9TR10XGz8S4oxN2kBHJrEC/6Mo1R+I3u4wDrtanCthG9pEtS+L3ncpyE1Dob/wOUoDEO8gDO07Om2LNlopwLeXkkj5hFuy4pL8qbLORbfLKJtUBqR7H8jm7bOXKKJ/3QSokTErVtdm0a/oE6XBRZ7Su2CwfNraRODvHBPlen8iyUbbxM/Fv6ulNJiNJjiAFSF3F2vjR5pypCCD3bMe1Wapr+uRCVn3GyWZA/PS1GdHxLGqdmhw/eZuHZMJs88hVka4MGJxu/Ap2kvmJm75+2GwD3ZN9lU6SU6wEyFWBqtIEUZ8Z9OQP0y/QZSBD7jD6i3RwEzaVVWaY1JzJNqSjbF3OMZ8uYXTNTHsmPlOFXwrWTxbVcAQpQNwPCrB55iuCzIUHsu3kuu0zMamvS7ZWpYsLr404giSKipmdLEQixajPoYqsGanKJfCZUGxLYeTMxGQ2QtXCKxP6qLJjtoguH6psIKPN5YIjSAF0RpAmoxDbZoKyoRxJ2iyHbfA7ZXGo6p1LLl6kI5m4p1jjhGtOkgkH15/9ZKtDTrE6QOYL8XzXmXqm7LJz3Sv6XbbrRcqmnG4FwqWUoqahotSd6LNV4Se7rHagOr0XpFzTUJRJRh/kCFIA1yNIm2bLNskK0Fm4Y5vdbMGUXbk+xcm0FUeQDqFi0UAmNnW0sDP7MFCNJMMs3LF5K45MZPensIuookKtPinLEsVWZBzknDlz0KVLF0yZMiX52Zdffon6+nr07dsXPXv2xIQJE7B37960+3bv3o3x48ejR48e6N+/P26//XYcOXIk7ZrVq1fj7LPPRnFxMU499VQsWrRIg0ZyCOO8gtwTJtVmmnz6yT4OS8ZzVDjJoOXkOrhCFlTaSDZU9CdTe/VU9fMwUJhk59qfGbY+SDjIdevW4Xe/+x3OOuustM+nTp2Kl19+GS+88AJee+01fPjhh7j66quT3x89ehTjx4/HoUOH8Oc//xlPPvkkFi1ahFmzZiWv2bVrF8aPH49/+Zd/wfr16zFlyhT86Ec/wvLly7XpR5kgDZtCJ7AVXTN+VYcWmHiW7VCyBSVZVBF2gpgL4w7ys88+Q11dHR577DH07t07+Xl7ezt+//vf44EHHsC//uu/YvTo0XjiiSfw5z//GW+99RYA4I9//CO2bNmCp556CiNHjsSll16Ku+++G4888ggOHToEAFi4cCEGDx6M+++/H2eccQYaGhrw3e9+F7/97W+16ahykYMsokaSlHXLRRS5Ve/xy9VuqKdTbWjzqahsB6ZsoKqfUtInE9lt37iDrK+vx/jx41FTU5P2eXNzMw4fPpz2+dChQzFw4EA0NTUBAJqamjBixAhUVFQkr6mtrUVHRwc2b96cvCbz2bW1tcln+HHw4EF0dHSk/UUh9dgkqgNG1EjS1hlqFLnDRmui9+Q7bkvkXlPY0OZTidoOVD07Cqr6qak6FS1Xpr2NOsjFixfjnXfewezZs4/5rrW1FUVFRSgvL0/7vKKiAq2trclrUp1j4vvEd7mu6ejowBdffOEr1+zZs1FWVpb8GzBgQCj9/LBlwGDEUO0kXSBu+lJD1Yk6lJ2kLIw5yD179uDWW2/F008/je7du5sSw5cZM2agvb09+bdnzx6pz1d1fBiV1KdombJlM6V/2IUfUWVTucJZdtrQdicpyx6mnIqq55pawasLYw6yubkZ+/btw9lnn43CwkIUFhbitddew7x581BYWIiKigocOnQIbW1tafft3bsXlZWVAIDKyspjVrUm/j/fNaWlpSgpKfGVrbi4GKWlpWl/svFrXFFTNVRSn6JlypaNiv6iRHUaKlc4i7Q1l7cTZSJrAYjNNvDD1ApeXRhzkOPGjcPGjRuxfv365N+YMWNQV1eX/O9u3bqhsbExec/27duxe/duVFdXAwCqq6uxceNG7Nu3L3nNihUrUFpaimHDhiWvSX1G4prEM0yi87QVE1B+mU8F6pFkLlQd7k0VlwZ+1bhiK2MOslevXhg+fHja33HHHYe+ffti+PDhKCsrw6RJkzBt2jSsWrUKzc3NuPHGG1FdXY1zzz0XAHDxxRdj2LBhuPbaa9HS0oLly5fjZz/7Gerr61FcXAwAuOWWW7Bz507ccccd2LZtG+bPn4/nn38eU6dONaV6GjKOtJK5MVYmqbPLoEd95YPi5u2wmIokw6QFZbQ10+0zqq0ptSPKslCzVRhIHTX3rW99CyNHjsSDDz4I4KuDAm677TY8++yzOHjwIGprazF//vxk+hQA3n//fUyePBmrV6/Gcccdh+uvvx5z5sxBYWFh8prVq1dj6tSp2LJlC0488UTMnDkTN9xwg7BcFI6a4+PFjiWffrbpb0JeWWXGrX1Skp2SLH5Qk49/zUMyFBxkNqI2Pr/7dTVoGeW4NjCrcPq6JhK21UVUu4T5XpW+lGQRLd9UuewgJWPaQboWKTG5oVyfKmSjrK9NmLaj6fJF4cPKHUPFikKGLpTrU4VslPW1CdN2tME5BoUdpAao7sUTQXQBka0DnCy5Ze+DMzXYyTqpJE4LdygRdtGfCJT6ui4ZOMUqgOkUq2lsSZ2YhtOP0Ymbvqow9a7TBjjFaikm90SG2TYRphzd6DzhRIWdVJ26lA/dkXUCjiTloKrPUtv7qno7F0eQAuiMIE0uyHFhduiHC3rpWiGaeo8LdqMCpVWcrtVrUH14Fatk4p5iZWhgYhuFa4OpSSg5yTjDKVYH0JUa1HXijM4Ui24ZZJYnO9UdNdWmOuVJOaUZNUXpVxeqMFmHVFAxlnEEKYDrESTPMNXBC3fchvco0yezDjiCZAIh84W+rTNVVYtSMm0rI6qUcX6v6D0msGmRjsivfJjSR1d2iBph+ks2OIIUQEUE6XJkYeu7Ft1ym5BXlo4myzYBR4r+UNU7l1y8SEcyrqdYGXuJm6OiTFztapvenGJ1ANFTK4KkUUyehCGjbJMLl1Q+J0p5On5uymS7kYWOOtWVTqVWH5QXAfEiHQ24GEHqmvXJLsdWuVUTJgVom45RkRVtc7rVbjiCZPKia9aXbaAIW7ZOuXVHklEQWSwS9B7XkBVtU1mYE6e6i0pYW7GDJICphh72GDNZZeu+N8zgKANdzjZMfbKTFLsn6HN02DVI+6S0ktZEWWHrg1OsAriYYmX0w+lhhlGLSNvnFKul2LT/K9v9tkYjlBdxxC0qtK0fUN1fSqkNqJJF5p5HPziCFIDSYeW6y45TNGJyj6CKZ9q2L5QKruvtun6AvH2QHEESw+TsP9uChLgg852jbHRs50iQuCdOdZ+K7RF4PlzXD5CnIztIggSpXNl7/lQv3JHdMSmnxGTLpuuw8rArj1XUhUpk2zro9zKRVcc2oyLdyilWAXiRTvygnG4NUw7v3QuHidS2KqjKpZpMvTnFygTG9ZRLUGSlaFSls4JGBBS2JdiIjKidil3jWsdR9GYHaQhqqUGZG/ptXbWnKg0l+7CExDNlv5NUva/O1sE5zOQi1ZaUojYVi8dEPjNNWL05xSoAp1jtIW5pJJdSgIyd2NaeOMVqEdlmW6L7CsPM4KgujJCBjDSS7kU61CLJTGzf4wroq1MdNqJWH5RTt7xIRwMuRpC6Fm3I3lupa6+m7lmxiT2Lts38oyLLRpQWPMWtDmXAEWQMMfk+Kl85QcvO9zwdS9Z1z4qjlmdin6RtuHQOq4my4khgB7lu3TqMGzcOZ511Fq6++mr86le/wksvvYTdu3erkI8RJIyTiNth5WHK0Zlujbr3Ttdh5TYv6NJ1AD01J2l6MY3O1LNMGQKnWIcOHYqBAwfi8ssvx65du7B+/XqsX78eBw4cQO/evfHJJ58EFoI6LqZYXYLTTPmhlBa0CbaLWyz/cD3GnXCmuhTrnj17sGDBAjQ0NOD+++9HY2MjPvnkE+zatQuPP/54aMEZs5ieYUYhjmkmFSn1uNlQBNtO1MkHJVlMUFs1Ekt2bBS+PrCDrK6uxgcffHDM54MGDcIVV1wR9HGxg2oDzfZuz5bOb3qWr/owABnvXaMeJhAEihvlwyJit3zfU0lnRpGFUj1GkeWq00cIXxs4xfrKK6/gt7/9LZ5//nn06dMnsHA2EqcUK6+GdAsq9clthJYNKMkSlrA6KF3F+p3vfAerVq3C6aefjkmTJuG///u/0dzcjEOHDgUWNM6o2L8oI00aZgFGlC0bqqGcOta1TzLMPbLbGsUUrokFQi5EkmHKVVGWDnsGjiB37tyJlpaWtL/33nsP3bp1w5AhQ7BhwwZVshqD0u9Bqpz5uTCr9MMFvUxEgi7YjWEyCRJBSjkooKOjAy0tLdiwYQPq6+ujPo4culOsPDAxfphoF9wWGdfQflBAaWkpLrzwQiedowlUrZwzmeKSXbbKPXc69z4GuVbmvlXRe1Qt1NLVFlX2A919LPFsaqnqXJiWNWr5fNScAKYW6ahIt5qMCChEIxRksBHX7GarPrbKTQk+as4Rwu5di/pyXtXMOOpLdVG5VOgfBpNRUr7vZe+jDPPMsPeEQdcWJtX6UHWOVLeDcQSpAdPbPMJEkpQXaMh+tgr9dZMqL2XZZclmi77UoWRH0+WLon2RjuuYdpCAGSepEnaSudElO5U0vc11xXyNDfXIKVYLkJ0aDHsSjilky2ab/kD0vaUyUoBh973KTj9SH1RlpRApt8cwiPY5nXsuZcIRpAAUIkjbsGEmSQGb7UQl+mToQbmeOYK0CBUzIAqzVOrRGxVstpPO36Ckgs2y64TSqUFRYAdpGJGUhIqVmjpInUHq3gdJbR9oLqjUVzZktzXT+kYp27TsmVCWRfdvYqqAU6wC6EixcroqOK7pb7M+trVfm22dCXVdqMnHKVYLCbMvS+deLop7A1UsFokKxehEVrStK5LUEXWY+ik3Fc80ubdZBGpRdxA4ghSAF+m4R5BZrYwZMLVZtCps09M2eZnocATJMHkIMquVMYDGZRC2LVqIS70w4WAHKYlcKQybBgwR/BYQ2ahj0ME8U9d8C6lyPUP2tbKQUaZtThKws/1mI4ouudq27r4uo79FhVOsAuRLsSbSNLnSNZzKoUuYdCvXZ35ss5Ft8uYiyqIpiuOYzHI5xaqZRMXlWnRg46w6LgRNt+aqT12LpmzApjavso+asEHYRVOp/wK5Fwbq1MtUW+IIUgCZi3RcmqUC7ukTFNu2N+RDhWyU9dUFpcjLhfpI1SGoPnxYuWRkr2J1oYEyX8NO0swzbYOSk4wznGIljqr0nMm9TmGQLZvJPXVh7okqm6p0rYo9gjalW/2QYQ9TNghzIL0oMvbQyi5fJhxBCsD7IINDadZKSRaXcC1yZuShs55Vplg5gjSMq4s2KEUMlGTxg7JsubDxHNao2Cy7TnSfw6qqLHaQhglzWDn1o6USUDqsnPLATFk2IHpbzMS0vlHKNi17JpRl4cPKY4Kth5W7ns5yTT+b9bEt3Rq1bEp1RUkWP6jJxylWC1Gx0EPkmbpfssucUao6rNyUrjoPnw/zzHxtTZa+uhZWRbELpSwOJVn8MBl1Ry2XI0gBTC/SMREpUpv15cImWUWgrE/c2xo1ouwHVCkLZTiCdIx8jU7FDE31rI9CJEkVyvrY2NZcJnVsMG1H0+WrgB2kISgOMlFesodJr8lEVbpV93MSUB5sVEQJpvXNtRDJJnTb0W/MMFGuKow7yA8++AA//OEP0bdvX5SUlGDEiBF4++23k997nodZs2bhhBNOQElJCWpqavDuu++mPWP//v2oq6tDaWkpysvLMWnSJHz22Wdp12zYsAEXXnghunfvjgEDBmDu3Lla9MuGqkFG9v2iHY5CaiXse1xZZcfJSarApL6ZkZjNuLLFIl+5OjDqIA8cOIDzzz8f3bp1w6uvvootW7bg/vvvR+/evZPXzJ07F/PmzcPChQuxZs0aHHfccaitrcWXX36ZvKaurg6bN2/GihUrsHTpUrz++uu4+eabk993dHTg4osvxqBBg9Dc3Iz77rsPv/jFL/Doo49q1TcfJqMZWUv5KQzophYs6HSSuhYaBUFGBK9zwJXVDii0+UyoOUlVsqhefGR0kc706dPx5ptv4k9/+pPv957noaqqCrfddht+8pOfAADa29tRUVGBRYsWYeLEidi6dSuGDRuGdevWYcyYMQCAZcuW4bLLLsPf/vY3VFVVYcGCBbjrrrvQ2tqKoqKiZNkvvvgitm3blldO04t0bMOWl/UiuKSLTVCzu0x5KOmmUxYqeluzSOell17CmDFj8L3vfQ/9+/fHqFGj8NhjjyW/37VrF1pbW1FTU5P8rKysDGPHjkVTUxMAoKmpCeXl5UnnCAA1NTUoKCjAmjVrktdcdNFFSecIALW1tdi+fTsOHDigWk3jmDwgwHZc0sUmqKWXZbYDSm1KpyyU9BbFqIPcuXMnFixYgNNOOw3Lly/H5MmT8eMf/xhPPvkkAKC1tRUAUFFRkXZfRUVF8rvW1lb0798/7fvCwkL06dMn7Rq/Z6SWkcrBgwfR0dGR9mczNjZMhqHmJJn4UWiy8M7OTowZMwb33nsvAGDUqFHYtGkTFi5ciOuvv96YXLNnz8Yvf/lLY+W7hGhaRXRTNjv7eBHVSVJuO6ZXfjP5MRpBnnDCCRg2bFjaZ2eccQZ2794NAKisrAQA7N27N+2avXv3Jr+rrKzEvn370r4/cuQI9u/fn3aN3zNSy0hlxowZaG9vT/7t2bMnrIoMoqd4OYpgwkLlvZcMuB/ox6iDPP/887F9+/a0z3bs2IFBgwYBAAYPHozKyko0NjYmv+/o6MCaNWtQXV0NAKiurkZbWxuam5uT16xcuRKdnZ0YO3Zs8prXX38dhw8fTl6zYsUKDBkyJG3FbILi4mKUlpam/THhcWWAYuzDpTQt9yP9GHWQU6dOxVtvvYV7770Xf/3rX/HMM8/g0UcfRX19PQCgS5cumDJlCn7961/jpZdewsaNG3HdddehqqoKV155JYCvIs5LLrkEN910E9auXYs333wTDQ0NmDhxIqqqqgAAP/jBD1BUVIRJkyZh8+bNeO655/DQQw9h2rRpplSPFa4MUIx9cATJRMHoO8hzzjkHS5YswYwZM/CrX/0KgwcPxoMPPoi6urrkNXfccQf+8Y9/4Oabb0ZbWxsuuOACLFu2DN27d09e8/TTT6OhoQHjxo1DQUEBJkyYgHnz5iW/Lysrwx//+EfU19dj9OjR6NevH2bNmpW2V5JRh8wByqUBj2EY2vBh5QLwPsjwiC6QEJ0ds3OMH7xI5yuoyW8r1uyDZOKBjI7NgwMTBpcyDpxi1Q87SEY5UTu2SwstGL0k2o4LTpL7gX7YQcYICgdBh8GVAY7RS6pzdMGxuKSLLbCDjBG2OpnUQYEHB0aUVIdCse0HlYmyLq7CDjKG6HQysn6nMjEo8ODABIFy1BVUJnaO+mEHGUN0djJZnZryQMfQh2LbCdIvMq+lpoursINklCJjYEoMDuwkmaAk2gvF6CvI1qbMtk9NF1dhBxkjTP2IbpTOnDo48DsYJgh+bUcGuvtR6vt3niDqhR1kjDDRuaKUmW3mzAMEk4rfhEll1KXyObnetfMEUT/sIImiwgmY6FxBZr2psmUOcDaml1xx5Kb18HMaqZ9ltutcbYca+eTO9h0jRlRb8VFzApg6ak6FQzM5A81Xdur3fgOcLY6RkYtfG8jVPmxqO0HltkEn6vBRcw6gYrZocgaar+xs35uYOdv2ripMObrukUXqezi/d3I2t53UeyjqFGc4ghTAxcPKdc1Aw5QjMoNWjW0z9Ch2dgmX2o5NkbBNcATJ5EXXDDRMOSILFVQjcyCibGfXohBdbSfXc2SVk+s9az4ZmHTC2oodJAH8Ki8z7SJyT5DvAX2LXWQPxCLPC2Mz2QRZVCRrQA07GcmFjLYm4x4ZyGiLIpGc7L5lcrIYBZ3y5erzYeuDU6wC6EixxiFFJlveONiMCkEWWMl6pkq4HeiDmq05xWoh2WaDUVM5smb3piIc2c9TZTNZqChbRhvIZzeZdUElJU2lLlQ9V/YCwGzYEOlmgyNIAVRHkNRmWHEgiM25fsSxzVa2yctEhyNIy1DVQW2dtclC1qyWB1BxbLOVzdENox52kBowuSAhKqKyUxxkRBZS6N7zGKQ8ijYVJXWvYtB7dOOak1SdxqVgK10ycIpVABkp1jikcmzVUdYCE1v1V4Vt9rBNXiYcnGIlCPVZqqxtI5R1zIas7RGq9Fe1zUc1Ye3hWiRpSh8KW51MIFNHjiAF0HmSDrWl73GaVcs+AcU0VOQQhYq8tm1ZCYpMWanqnUuuIBEkO0gBXDxqLhtUG7xtULEjFTlsg+1mP9nqkFOsDmBqcYzqvWmy5VWRwjO551O2bDoXItmUTs1XNuX9shRT7hRSt0H37orAEaQALkaQ1FK51LBBxlTCnHST+plt+urCpXQrVbl0wxEkkxeTM2QViyF0RdJhoHAyjN/3qYOlrQusVBOmn1C1JVW5dBBWb3aQhtCVfss3aOYi7GzTxIpXFZ1f1mw723OiypvL4WWTI0p7yFW27HsoDeRRJx+pmNZLdgRpy0pZPqxcIapSrCpSHpxGsQuuL3vgunIDTrFagqqoh8oMjtIiBqrI+vklHfdQK1s3lPqWDbhgK3aQhpGRxgyaavO7RwVRV8Tmui5MqpBqh4068Kr6VRPqZecibNvJh8l397akMxO4EG1zilUACqtYbU7HqirHtZRXVH1sW3Fpomxdh0HI1i3X81zrB6rhFKsD6Hjpr+v3+FRFDNRTXioiqzB796ju9zNRf7IWIoVZuBOFXHJT3rOpExVjGUeQAlCIIFXCM1B12Bz5M/nRHUlGgZIsOsnUmyNIJhC27flTgazZZ749cbre+4XRh0rdUZEjAaVIMgqUZFGJzH2p7CANoaKhZnum6L5EGZjshLLTzWFsIrIhP4HKxSJhfm2Eyj5I01FO0AFWpl1VE4d9kLL6MsApViFcT7HmI66pGRvguqFDXOvCNr05xWoR+aI+mak00WdmImvVX5iy/Z4h6zpV98t+Ti5UHYrud63pyCAKlOsiKNTqg3LqlhfpaMDFCNLkFo8oZdu+NUUVYRaL2KZjVGRtg7FpYQ5zLBxBxhCKm7UT5cgsW6fcLkWS2d7LUJ35q0DWoQZUFubEqe6iwot0Yo6sBSWAvgHdxL1By9G16jSByn2QsicrMqC8DzLXPRScZJAFX3Fwpirqg1OsAriYYk2FU0J2wfVFB0630of3QTKRCDq7cnE2qiudGrd9kLa1FdmvKkxH6JnIlIWSXqnk2+KxZMdG4WdxBCmAzAgy6oxS5YxU1iIGRj42L07iNmLOBq4uzkrVIag+QSJIdpACyE6xutBAma+x2XnpKofbPDtJKnCKlThhDx5Xvd8tyj1hkFGO6rShyP1RtqwEKY/S4qQwslM7dUX3awWV6dag7Ua3LCb0lgFHkAK4vkjHJHGeyTLZcblduKybDXAEyVgDDxSMHy63C2oLd5jsFJoWgGGAr2fVqf8Cbg+UjBiiaW6b2k4uJ5mpS+q/jF44xSoAp1jDI9qxg8yoeaCIF1GiLcoOM8yWEiY6nGJlSCEzncSDBBMElyIvTsvqhx0koxyZAxQPEkwYXHGSCbgf6IEdJKMUGQsSsv3gMMPkI9H+XHAolNPFrsIOMkaYGCRkpLhcSpMx+nBtcUvmIiRGPewgY4SJziVjcHJtoGP0QH0FaFCZKOviKuwgY4SNnctv+T7DiEC97QSVibIursIOMkaYOgcy6v3UBzqGJtTbTpQIkpoursIOMobo7FwyOjP1gY6hC+U0q2g79vvVCmq6uAo7yBiis3PJHJioDnQMLXQfzh0WkXacrc1T08VV2EEySpG1zSN1eTsPDkw2MttH5hYhWW1Hxy/RJK7Jdowe9wP1sIMkiorGb3KbR9CfjUpNJUX5cVQmOqYHYr+0YuZn2dKQqbLLajuynlNbNTKQTjadNesKfBarAKbOYnXlh2tF3wPl+mFXdoxmoWp/v/Zh60HfueS2TRfK8FmsDiDiTMI+08ReSJGy8znHzGhAFZTScKrKCXIP5UHZz2lk+zWMoOi6J0Eup+6ni+nI3gai2ogjSAFc+jUPyoNdApciSdvktZ1cbYcyQdu8DTpRIdNW1kSQR48excyZMzF48GCUlJTglFNOwd13341Un+15HmbNmoUTTjgBJSUlqKmpwbvvvpv2nP3796Ourg6lpaUoLy/HpEmT8Nlnn6Vds2HDBlx44YXo3r07BgwYgLlz52rRMSqyZ4k2vNzPtQrRBvlT0SFvmIjCJhsGId8KVop1AWSXO/Gv37tUV+tQNlFsZdRB/uY3v8GCBQvwX//1X9i6dSt+85vfYO7cuXj44YeT18ydOxfz5s3DwoULsWbNGhx33HGora3Fl19+mbymrq4OmzdvxooVK7B06VK8/vrruPnmm5Pfd3R04OKLL8agQYPQ3NyM++67D7/4xS/w6KOPatU3G/nSjn7f50s3Bkll6iZsY802YIiWYWpAEbF31NScaDmp98hKIUZ9jo56CdJ28iHSt2T3sVwL2KhAqc9lErY+jKZYv/3tb6OiogK///3vk59NmDABJSUleOqpp+B5HqqqqnDbbbfhJz/5CQCgvb0dFRUVWLRoESZOnIitW7di2LBhWLduHcaMGQMAWLZsGS677DL87W9/Q1VVFRYsWIC77roLra2tKCoqAgBMnz4dL774IrZt25ZXTgop1jApFcppGB2yUdbfD5MLqEw8x7b6SYWS7JRksQFrUqznnXceGhsbsWPHDgBAS0sL3njjDVx66aUAgF27dqG1tRU1NTXJe8rKyjB27Fg0NTUBAJqamlBeXp50jgBQU1ODgoICrFmzJnnNRRddlHSOAFBbW4vt27fjwIEDx8h18OBBdHR0pP2ZJlsHCBN95rtf14xe9V6yMDaLcm1UwtZXPqK2ERHCPMd0BBTF1pT6FiVZgpRvQ7lGHeT06dMxceJEDB06FN26dcOoUaMwZcoU1NXVAQBaW1sBABUVFWn3VVRUJL9rbW1F//79074vLCxEnz590q7xe0ZqGanMnj0bZWVlyb8BAwZI0DY8UdKlYb7XNRuVtZdM5T26Z+ZBV/qKPjPK96mokI1q6tumvhWm3ajYQhakfB1ELdeog3z++efx9NNP45lnnsE777yDJ598Ev/5n/+JJ5980qRYmDFjBtrb25N/e/bsMSqP6ndYOp+pCptkzYfpyCoXKgY6yvrahGk7upjmNeogb7/99mQUOWLECFx77bWYOnUqZs+eDQCorKwEAOzduzftvr179ya/q6ysxL59+9K+P3LkCPbv3592jd8zUstIpbi4GKWlpWl/UdC5CEH2M1Wk/FQQVn+qex5NDXam7GF6cKfSjqOiss9SspEuWYw6yM8//xwFBekidO3aFZ2dnQCAwYMHo7KyEo2NjcnvOzo6sGbNGlRXVwMAqqur0dbWhubm5uQ1K1euRGdnJ8aOHZu85vXXX8fhw4eT16xYsQJDhgxB7969lemXwOaZlYqUHyVkya9igDd1qIOs59jkJG1vx6mo6rOmJzGp6JLFqIP8zne+g3vuuQevvPIK3nvvPSxZsgQPPPAArrrqKgBAly5dMGXKFPz617/GSy+9hI0bN+K6665DVVUVrrzySgDAGWecgUsuuQQ33XQT1q5dizfffBMNDQ2YOHEiqqqqAAA/+MEPUFRUhEmTJmHz5s147rnn8NBDD2HatGmmVPfFZJo0aoei0nEykaG/KCpm7yLbfGSUI/t+wD4nCdBtx0FRFUnqPJUo3zPC9o0gGN3m8emnn2LmzJlYsmQJ9u3bh6qqKlxzzTWYNWtWcsWp53n4+c9/jkcffRRtbW244IILMH/+fJx++unJ5+zfvx8NDQ14+eWXUVBQgAkTJmDevHno2bNn8poNGzagvr4e69atQ79+/fAf//EfuPPOO4XklLnNI+qSbF7SHRwXbGazDjbLLgtTNoiD7YPqGGSbBx81JwCFfZCpxKHRy4aazXjfYPxgJ6mOIDpasw+SCYfMxm56f1RURGWltoiH0r5WHQtzZOxJ1U1Uu4nWuQx07HWljKqUMkeQAlCLIBn1uDbrdk0fXeSzm012tUlWVSz/cD3GnXAmR5BMMFyfYeZD12yf8jaKuLcBQHwhiOj3fs80RRwiyXzUVo3Ekh0bha9nB8kA0LMijDJRHaKKVG/qM8M48KD3qBpAbWpDfjaQcaIOlWPdXHGSUXS46vQRwtdyilWAOKVY47Z4xHW4PulAya6UZAlLWB14kY6lUFiAESaSNLkARoXNTCJb3rCRpoq9btTqwsQhDKrKDNNnqUS1YdGhA0eQAsQpgsyFC7NOxg64relBp52p1ClHkIwSKDRuE1COOF0lrm1NNzojSRvrlB0kw+TBxo7NMKK4snBHBYWmBbCJJTs2orSX+Jwi0fBcHWBd1s2PxCCSOqDESf+gxK192IyIk1R9QEmuck21JY4gFZKoVBdnZy7rlotM5xg3/UVh5+gWptp6qnM0UT47SIWYrlwdxGkQ9JvJxkn/ILjc5uOGyQgudfzkCNIxTFeuKlL1itMgmFmfcdI9KK61+TiT2d51tnvT/Y0dpCJqq0Yar1xVpOoVt0HQ1UmPbNg2buCXLdFZt6YnpewgFZAaXaU2KlecpItOPwhx1z8IbCN7MT3BT4ydJp0kO0jJ5IouXBlUTXcck6RGzXHUXxRe5WsvFBxTtvf9ujM37CAl4ZdSDXPwsS3E0Ulmq8+46B8EV9p53Mh8fWLCSfqVm/mdLvioOQFEjprzm9m4+r4qVR/bdQwit5+uKvU3YVNZZdrWHmyTNxdRdNHdxoPI4idTGIIcNccOUoAoZ7Ha7kCYdHLVpytOUhY2y247sm1PxUn6fRcUPouVEC6mIv30sFW3oHJH7axByrO53dgmuy1yihLV9tleDZl8F2ni9QZHkALwr3kci64IijphdI6jnRi3MH1wQBQ4giSKS7NUXrDyFWF0jpOd4qJnLly0gcmtH7zNw1FSZz4qKln3M6ltZYlabtj7/SJpkXtMyCurbkSfE6TNU3EkYV4hBO0nLpCtDauuR5E2JUsGTrEKEPcUK6cExbBl1Snl+qQsWz5slj0KtunNKVaHCDNzlT1TlxkVyp5dqtBfdiSpckatM8Wrcw+cDij3E5sQ0dvWrBJHkAKYjiDzzdDiuGAm354o2/SXtcdLNbJsbYu+1KFkR9Pli8L7ICVj2kEC7CTDEHf9VcH7PZls2FCPnGK1gKCpwXxpDNOrSimk7GxbVWtCNtEyo7TFsM+kjKwUIuX2GAbRPqdKb94HSQBVEaQNs62wuKybTGy2k2sLhBh5UK5njiAtQVWEQ2GWSj16o4LNdorjHlCbZdeJ7npWVRY7SMOIpCRkp2N1oXLfp4rVvaagvEo43zNtdJJRyqYWFVFt04DeelZVFqdYBdCxSIfTVcFxzWaUZcuHbXVhs61tg5qtOcVqIWH2Eqnaf6Qy+pI5y1NlsyhEjU5k2T7IPaJl5spqyKwL1/ZbpqJzsYrOvYlR24aMcnN9FhaOIAWgsM2DAtRmgoy7cFvTg047U6lTjiAZJVBo3Ew84LamB512Nv3uOQzsIBmGYRgtUHCSS3ZsFL6WU6wCJFKsB3acjNJeweYUrs6EqaRLdJLt3Urc7MC4ST7HJbOdh3WSMmTgFCshEg3B9KxJNhRmgqZh58gwbsMOUjGJAdS1gZSdA08SGEYXpsYadpAKSR1AeSB1Byq/nsAwccDkqwx2kApJVCoPpO7AUSPD6CV1HNUNO0iFpDpHHlTdIHWyw5MehlEPR5CO4upgGmenz2lzhtELR5AxwKXBNM5p49R6jOskgYkfpvu6KSfJDlIxiQp1aTCNcwSZgNPnTFyg0sZN9Dd2kIpJrVAqDS0qcXYOqTPpOEfS+Yhbu3AV03099RWVif7GDlISfpXmV6GuDKamO44JMuszbvoHwZV2Hjcy10xkrsTX2db9ytXdrthBRiC1Mfk5QlcH0MyIOC6DYbb6jIv+QXCtzceBzDFLl5PMtpDRpHNOwA4yAFedPiL5336NKfFvtu9cweSMziTZBhDmWFxr83Eg13gGqJkQ5+pTFMZRdpABSD0FPnWpf65UgItO0jV9RJHRWeNis7i2EUoEtX0+h6SiTrM5ZVGZVMO/5iGA6A8m56tsl/DTzWV9gXT9otSx63Zi3CBXG1fZhkXSvFHgX/MwRD7n6NJsOo7v4mS9Y+bIirGBXG1cZRumtKaDHaRk/F4427zNI5e8Nuojk8w0eph7c2HCtrLKzEzN6Sw7DKrL1qlbvrKCyJKrjaucEGfbFZBNFlVwilUA0RSrKK6l2FzTJ+5wfdpPvjoMW8eU2kZYWTjFSpxsM6CoM78wz5QBlUjSlP6qy9O9GEikPlXKRKEtqUKXbvnqMGyf9Vu4k4mufhh2HA0CR5ACyIwgo87AKM3gbMEFm9msg99ii7hhSu842DuojhxBEiZqY6USrdmECzajOsiJ2NVvsUXcMFV/Ju1NJVqOAjtIAqhY4BGlbJsGMFFZTQ/MKha/iH6vIl2aIExbNO3sZaV6VdpVJqbavs5yVb0W4BSrALIX6YgSh/RInNBVn9xumLgi0vY5xeoIOgc5WbMvKrPmoKiM8KJu8wkaFZqOlqNiW6RPNZKk1AZUyaJ6fyZHkALojCBVzP5Fn2l75EFlARSVSNH2+jRFGLvF1dZU9c4lV5AIkh2kAKZSrCag2uBtg4odqchhG2w3+8lWh5xidYBEmkBm+kbkmarSc6L6hH2uzGtlyKjqMHNZ6VbZ7SYsqtOAYXUPu+hDR1qT4sI60+X7IWMs4whSANcjSJtmyzbJCphNmauWgzGX5ub6FCfTVhxBOoTKmVnUxSMm0HmGqclIMswzXV64IwvZUX2+ulDlxLg+xYliK44gBXA5ggwyE43jrNW1hTuy7mHyQ8mulGRRhWg0z4t0JOOyg2QYIB4DaNzhOv7KBuNOOJMdpEza29tRXl6OC3AZChHeQS7ZsRFXnT5ComTyCCIbZT2Y8HC9qoGSXSnIYlqGJ99pwaCz30NbWxvKyspyXssOUoCdO3filFNOMS0GwzAMI4k9e/bgxBNPzHlNoSZZrKZPnz4AgN27d+edccSdjo4ODBgwAHv27MmbvogzbCdx2FZisJ3E8DwPn376KaqqqvJeyw5SgIKCrxb7lpWVccMTpLS0lG0lANtJHLaVGGyn/IgGOrzNg2EYhmF8YAfJMAzDMD6wgxSguLgYP//5z1FcXGxaFPKwrcRgO4nDthKD7SQfXsXKMAzDMD5wBMkwDMMwPrCDZBiGYRgf2EEyDMMwjA/sIBmGYRjGB3aQDMMwDOMDO0iGYRiG8YEdJMPEnP/7v/9Dly5dsHTpUowbNw49evTAkCFDsGbNGtOiMYxR2EEyTMxpaWlBly5d8MADD2DmzJloaWnBwIEDMX36dNOiMYxR2EEyTMxpaWlBeXk5nnvuOXzrW9/Caaedhssvvxwff/yxadEYxijsIBkm5rS0tOCKK67A8ccfn/xs165dOPXUUw1KxTDmYQfJMDGnpaUF1dXVaZ+tX78eI0eONCMQwxCBHSTDxJj29na89957GDVqVNrn7CAZhh0kw8SaDRs2oLCwECNGjEh+9v777+PAgQPsIJnYww6SYWJMS0sLhgwZgu7duyc/+8tf/oLy8nKcdNJJ5gRjGALwz10xDMMwjA8cQTIMwzCMD+wgGYZhGMYHdpAMwzAM4wM7SIZhGIbxgR0kwzAMw/jADpJhGIZhfGAHyTAMwzA+sINkGIZhGB/YQTIMwzCMD+wgGYZhGMYHdpAMwzAM4wM7SIZhGIbx4f8B+TWSPCGZOjQAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.matshow(lorenz_rp.recurrence_matrix())\n", + "plt.xlabel(\"$n$\"); plt.ylabel(\"$n$\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "68cbfc9c-f3f7-4d28-b69f-e9e77969ebe4", + "metadata": {}, + "source": [ + "#### Local Clustering Coefficient\n", + "\n", + "From the local clustering coefficient we can calculate the local clustering dimension:\n", + "$$CD_v=\\frac{log(C_v)}{log(\\frac{3}{4})}\\,.$$" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f24dfd26-1259-4c5b-bd92-88ff5b35df94", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating recurrence plot at fixed recurrence rate...\n", + "Calculating the supremum distance matrix...\n", + "Calculating local clustering coefficients...\n", + "C_v = [0.81391066 0.81240007 0.81127381 ... 0.53332127 0.53707695 0.53951554]\n", + "CD_v = [0.7157369 0.72219436 0.72701667 ... 2.1851597 2.16076691 2.14501963]\n" + ] + } + ], + "source": [ + "lorenz_rn = RecurrenceNetwork(lorenz, recurrence_rate=RR, metric=METRIC)\n", + "lorenz_C_v = lorenz_rn.local_clustering()\n", + "print(f\"C_v = {lorenz_C_v}\")\n", + "CD_v = np.log(lorenz_C_v) / np.log(3/4)\n", + "print(f\"CD_v = {CD_v}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "12015724", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot trajectory on Lorenz attractor\n", + "ax = plt.figure().add_subplot(projection='3d')\n", + "ax.plot(*lorenz.T, lw=0.5, color=\"grey\")\n", + "# plot local clustering dimension\n", + "scat_plot = ax.scatter(*lorenz.T, lw=0.5, c=CD_v, s=1, cmap=plt.colormaps[\"jet\"])\n", + "cb = plt.colorbar(scat_plot, pad=0.2)\n", + "ax.set_xlabel(\"X\"); ax.set_ylabel(\"Y\"); ax.set_zlabel(\"Z\")\n", + "ax.set_title(f\"$CD_v$ for fixed $RR = {RR}$\");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "e3d9bb11", + "metadata": {}, + "source": [ + "#### Global clustering coefficient" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "74a04d72-2177-4734-a38d-f4276dd8f9d4", + "metadata": {}, + "source": [ + "Similarly to the local case, we can calculate the global clustering coefficient of the Lorenz timeseries and obtain its global clustering dimension as:\n", + "$$CD=\\frac{log(C)}{log(\\frac{3}{4})}\\,.$$" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "24aee056-ec76-4f2c-a21b-2459812a5ce8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating global clustering coefficient (C_2)...\n", + "C = 0.6420799584495237\n", + "CD = 1.5400418712226018\n" + ] + } + ], + "source": [ + "C = lorenz_rn.global_clustering()\n", + "print(f\"C = {C}\")\n", + "CD = np.log(C) / np.log(3/4)\n", + "print(f\"CD = {CD}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c192e4cc", + "metadata": {}, + "source": [ + "#### Transitivity" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "0a7c3def", + "metadata": {}, + "source": [ + "Lastly, we can calculate the transitivity $T$ and its corresponding dimension $TD$ analogously." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "807bc13e-1c35-485e-84ea-11d820bc9dd9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating transitivity coefficient (C_1)...\n", + "T = 0.7217193610758386\n", + "TD = 1.1336087419104863\n" + ] + } + ], + "source": [ + "lorenz_T = lorenz_rn.transitivity() \n", + "print(f\"T = {lorenz_T}\")\n", + "TD = np.log(lorenz_T) / np.log(3/4)\n", + "print(f\"TD = {TD}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2fea4b6b", + "metadata": {}, + "source": [ + "### Bonus Exercise: Transitivity Dimension as a Function of Recurrence Rate\n", + "\n", + "We can also calculate the transitivity dimension for an array of different recurrence rates. **Note:** This might take a while to finish." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "11ee1348-8720-4632-a64f-3552b017e998", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3a0ffc57aa0c46bda8f6e4ac8ec9ec07", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14, 4), dpi=80)\n", + "plt.plot(rr, TD, label= \"Lorenz attractor (10000 timesteps)\") \n", + "plt.xlabel('recurrence rate $RR$')\n", + "plt.ylabel('transitivity dimension $TD$')\n", + "plt.legend(loc = \"lower right\");" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyunicorn", + "language": "python", + "name": "pyunicorn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "vscode": { + "interpreter": { + "hash": "45ca354fb3f6330470f28203103c66b355f6bc9fd4170de504cc6983f9e7e887" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/tutorials/VisibilityGraphs.ipynb b/examples/tutorials/VisibilityGraphs.ipynb new file mode 100644 index 00000000..52a1c51c --- /dev/null +++ b/examples/tutorials/VisibilityGraphs.ipynb @@ -0,0 +1,761 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6154b988", + "metadata": {}, + "source": [ + "# Tutorial: Visibility Graphs" + ] + }, + { + "cell_type": "markdown", + "id": "86362e1e", + "metadata": {}, + "source": [ + "The objective of this tutorial is to introduce the network-based method of visibility graphs for nonlinear time series analysis, and to illustrate its implementation in the `pyunicorn` package. First some theoretical background for understanding visibility graphs will be given, and then some of the methods in `timeseries.VisibilityGraph` will be highlighted. For a detailed discussion and further references, please consult __[Donges et al. (2015)](https://aip.scitation.org/doi/10.1063/1.4934554)__, on which this tutorial is based." + ] + }, + { + "cell_type": "markdown", + "id": "f5ecd3c6", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "82630840", + "metadata": {}, + "source": [ + "_Visibility Graphs (VG)_ encode visibility relations between data points in the one-dimensional time domain, by drawing upon analogies between height profiles in physical space and the profile of a time series graph. More precisely, VGs are based on the existence or non-existence of lines of sight between well-defined objects (__[Donges et al., 2015](https://aip.scitation.org/doi/10.1063/1.4934554)__).\n", + "They can be applied to investigate purely temporal features such as long-range correlations (__[Lacasa et al., 2009](https://iopscience.iop.org/article/10.1209/0295-5075/86/30001)__) or time reversal asymmetry (__[Donges et al., 2013](https://iopscience.iop.org/article/10.1209/0295-5075/102/10004)__)." + ] + }, + { + "cell_type": "markdown", + "id": "e02236af", + "metadata": {}, + "source": [ + "## Theory of Time Series Visibility Graphs (VGs)" + ] + }, + { + "cell_type": "markdown", + "id": "7083e755", + "metadata": {}, + "source": [ + "### Standard VG" + ] + }, + { + "cell_type": "markdown", + "id": "4d45f2a5", + "metadata": {}, + "source": [ + "In a time series context, well-defined objects between which lines of sight can be established are the sampling points of a (univariate) time series graph. These sampling points are uniquely characterised by pairs $(t_v, x_v)$ with $x_v = x(t_v)$. From a practical perspective, we can identify each node $v$ of a standard visibility graph with a given time point $t_v$. For $t_v < t_p$ (and, hence, $v < p$) a link between the nodes $v$ and $p$ exists iff\n", + "\n", + "$$\\forall\\; q \\in (v,p):\\quad x_q < x_v + \\frac{x_p-x_v}{t_p-t_v}(t_q - t_v)\\,.$$\n", + "\n", + "Put differently, the topological properties of VGs take into account the time-ordering of observations explicitly and are thus closely related to the roughness of the underlying time series profile. An illustration of a timeseries with respective visibility relations (grey lines) can be seen in the figure below, taken from __[Donner and Donges (2012)](https://link.springer.com/article/10.2478/s11600-012-0032-x)__. The VG of a time series stays invariant under arbitrary affine transformations." + ] + }, + { + "cell_type": "markdown", + "id": "dbec9eef", + "metadata": {}, + "source": [ + "![visibilitygraph](images/SimpleVG.png)" + ] + }, + { + "cell_type": "markdown", + "id": "a0c4fc91", + "metadata": {}, + "source": [ + "### Horizontal VG" + ] + }, + { + "cell_type": "markdown", + "id": "1b60e2a4", + "metadata": {}, + "source": [ + "A notable algorithmic variant are _horizontal visibility graphs (HVGs)_ that facilitate analytical investigations of graph profiles (__[Luque et al., 2009](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.80.046103)__). For HVGs, the more restrictive condition\n", + "\n", + "$$\\forall\\;q\\in(v,p):\\quad x_q<\\text{min}\\{x_v,x_p\\}$$\n", + "\n", + "is used and the linkset of a HVG is a subset of that of a standard VG. A HVG remains invariant only for uniform translations and rescaling of the original data.\n", + "An illustrative example of a HVG can be seen in the figure below, taken from __[Luque et al. (2009)](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.80.046103)__. In the upper part a time series is plotted with the respective horizontal visibility relations (arrowed lines), and in the lower part the resulting mapping to a HVG can be seen." + ] + }, + { + "cell_type": "markdown", + "id": "c5ba3df8", + "metadata": {}, + "source": [ + "![horizontalvisibilitygraph](images/HVG.png)" + ] + }, + { + "cell_type": "markdown", + "id": "ddda7dcd", + "metadata": {}, + "source": [ + "### Testing Time Series Irreversibility" + ] + }, + { + "cell_type": "markdown", + "id": "40135bf0", + "metadata": {}, + "source": [ + "By decomposing degrees and local clustering coefficients for VGs and HVGs into contributions from past and future observations, statistical properties under time-reversal of time series can be analysed. Statistically significant deviations are often found between the distributions of time-directed vertex properties for non-linear systems that are known to be time-irreversible, but not for linear systems (__[Donges et al., 2013](https://iopscience.iop.org/article/10.1209/0295-5075/102/10004)__)." + ] + }, + { + "cell_type": "markdown", + "id": "de11a1f8", + "metadata": {}, + "source": [ + "#### Time-Directed Vertex Properties" + ] + }, + { + "cell_type": "markdown", + "id": "fe181077", + "metadata": {}, + "source": [ + "To account for the fact that the time-ordering of data is intrinsically interwoven with the resulting network structure of VGs and HVGs, a set of statistical network quantifiers can be defined, based on two simple vertex (node) characteristics:\n", + "\n", + "- Decomposing the degree $k_v$ of a vertex $v$, i.e., the number of edges incident to it, into contributions due to other vertices in the past and future of $t_v$, one obtains the time-retarded ($k_v^r$) and the time-advanced ($k_v^a$) degree:\n", + "$$k_v^r = \\sum_{p\\in V:\\,pv}A_{vp}\\,.$$\n", + "\n", + "- In a similar manner, the local clustering coefficient $C_v$, which characterises the likelihood that the neighbours of $v$ are also connected, can be expressed in terms of past and future contributions:\n", + "$$C_v^r = \\left( _{2}^{k_v^r} \\right)^{-1}\\!\\! \\sum_{p,q\\in V:p,qv}A_{vp}A_{pq}A_{qv} \\,.$$" + ] + }, + { + "cell_type": "markdown", + "id": "71ebb8e0", + "metadata": {}, + "source": [ + "#### Irreversibility Test via Kolmogorov-Smirnov (KS)" + ] + }, + { + "cell_type": "markdown", + "id": "d82fb93b", + "metadata": {}, + "source": [ + "Looking at the frequency distributions of the retarded and advanced sequences for one vertex characteristic,\n", + "in case of time reversibility it is expected that they should be drawn from the same probability distributions, e.g., $p(k_v^r) \\approx p(k_v^a)$. Thus, rejecting the null hypothesis that $\\{k_v^r\\}_v$ and $\\{k_v^a\\}_v$ (or $\\{C_v^r\\}_v$ and $\\{C_v^a\\}_v$) are drawn from the same probability distribution, respectively, is equivalent to rejecting the null hypothesis that the time series is reversible.\n", + "\n", + "For sufficiently long time series, the samples of individual vertex properties approximate the underlying distributions sufficiently well, and the Kolmogorov-Smirnov (KS) test can be used to check this hypothesis. Specifically, a small $p$-value of the KS test statistic (e.g., $p<0.05$) implies that the time series has likely been generated by an irreversible process.\n", + "For illustration purposes, the distributions of time-directed vertex properties of a linear and a non-linear timeseries will be analysed in the following." + ] + }, + { + "cell_type": "markdown", + "id": "41f104c7", + "metadata": {}, + "source": [ + "## Application of VGs" + ] + }, + { + "cell_type": "markdown", + "id": "0ddeee77", + "metadata": {}, + "source": [ + "As an example for the application of VGs and HVGs and of the class `VisibilityGraph`, a linear and a non-linear time series will be tested first visually, and then with the KS test for irreversibility." + ] + }, + { + "cell_type": "markdown", + "id": "90a819f1", + "metadata": {}, + "source": [ + "### First Steps" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "745b1e50", + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from pyunicorn.timeseries import VisibilityGraph" + ] + }, + { + "cell_type": "markdown", + "id": "b9b4e18e-2d3d-4405-9119-91637e15702d", + "metadata": {}, + "source": [ + "#### Synthetic Data" + ] + }, + { + "cell_type": "markdown", + "id": "757844b5", + "metadata": {}, + "source": [ + "First, we create and plot a linear time series, based on a linear-stochastic first-order autoregressive model (*AR(1)*) with an additive Gaussian noise term:\n", + "$$x_t = \\alpha x_{t-1} + \\xi_t \\,,$$ \n", + "where $\\alpha = 0.5$.\n", + "For more background on the construction of time series, consult __[Shumway and Stoffer (2017)](https://link.springer.com/book/10.1007/978-3-319-52452-8)__. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5a01c592-ba8c-4f87-addd-7e2513b0ac4f", + "metadata": {}, + "outputs": [], + "source": [ + "# we set a seed to reproduce results later\n", + "np.random.seed(42)\n", + "# process parameters\n", + "a = 0.5\n", + "mu, sigma = 0, 1\n", + "# sample size\n", + "N = 5000\n", + "# Gaussian noise\n", + "x_t = np.random.normal(mu, sigma, N)\n", + "# AR(1)\n", + "w_t = np.zeros(N)\n", + "t = np.arange(0, N, 1)\n", + "for i in t[1:]:\n", + " w_t[i] = a*w_t[i-1] + x_t[i]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3eb4edb9-9707-4787-b94e-a334d4f2f995", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OF154IQ4//HAsWLAAF1xwgbH30s1327Ztcd111+G6665DbW0trrrqKjz77LN4+OGHXZ3eEQRBEARBpBMylUAQBEEQBBEBXn31VUydOhXvv/8+nn76aZxxxhn4y1/+YrOBmtCYZDVjZ8yYgWnTpgWSpy1btuCbb75J/l1WVoZPP/0UvXr1wn777cd9hpdHy7LwxhtvaKd/ySWXoEuXLnjhhRcwYcIEJW3bjRs3YuLEibj22mtx9dVXp/x3++23Y/Xq1ZgxY4Y0nk6dOuHPf/4zRo4cifnz5yevn3766bAsC3PmzNF+H5bS0lKsWbMGZ5xxhmvYtm3boqSkxFd6KuTm5uJ3v/sdvv76ayxevDjl/o4dO6TPFxcXp1zr1asXAKCmpgYAcO2112Lz5s3o379/Stiqqirs3r070Hzv3LnTdq9ly5Y47rjjYFkW6urqtNMmCIIgCIIIEtK4JQiCIAiCCJjhw4dj+fLlKdfPOOMMdO/eHcuWLcOjjz6K2267DZdffjkA4OOPP0avXr3w17/+FYMHDwbQeIR/6NCh+O1vf4tLL70Ua9euRb9+/XDcccehoqLCeL6POuoo3HHHHZg1axa6du2KDz/8EIWFhfjoo4+EzxxzzDE4/PDD8c9//hObN29Ghw4d8PXXX3syL9CiRQtcf/31ePvtt5Gbm4sbbrjB9ZmBAwfCsixcccUV3Pu//vWvkZeXhwEDBtgcdfG477778Prrr+P555/Hl19+CQA466yz0LlzZ4wZMybpUCvBsGHDsGDBAgBAXV0dFi5ciGeeeQYAcMUVV9jMDIwZMwaWZeHKK690faef/exnePfdd/HMM8/giCOOwL777puStimef/55jBs3Dr1798Yf//hHHHfccSguLsbcuXMxZswYrnA2wVNPPYWJEyfi0ksvxSGHHILt27ejb9++6NatG8466ywAwO9//3sMHjwYd911F8aNG4czzzwTDQ0NWL58OQYPHoyRI0faTFOYzvdFF12E/fbbD2eeeSa6du2KZcuW4e2338all16K9u3beys0giAIgiCIgCDBLUEQBEEQRMA89thj3OsfffQRDjnkENx6663o0qULXn/99eS9I488En369MF9992HwYMH49prr8Vtt92Gbdu24b333sPIkSNx3HHH4fPPP8eQIUMwfvx44/k+8sgj8dZbb+HBBx/EihUrcNhhh2HQoEG4+OKLhc+0aNECw4YNw9/+9jf06dMHrVq1wm9/+1vcc889SmYBnNxyyy14++23ccEFF2D//fd3DT9gwAAcfPDBwrQ6deqEs846C4MGDRLaWU1wwAEH4MYbb8Rnn32GNWvW4PDDD0fLli1x0003YciQIXjuueds4b/++mt88sknyb/nzZuHefPmAWh0GMYKbocMGYKzzjoLhx9+uOs7PfbYY1i/fj1efPFFlJeX49xzzw1McNu1a1fMnDkTTz31FIYOHYq+ffuic+fO6NGjB1544QXps1dccQXWrVuHDz/8EEVFRejSpQvOPfdcPPnkk0mzEjk5Ofj222/x2muv4dNPP8U333yDNm3aoHv37rjvvvuSTsqCyvef//xnDBgwAK+++ioqKirQrVs3/O1vf8MjjzziKV2CIAiCIIggiVmm3OISBEEQBEEQzYZDDz0UPXv2xPfffx9qPhYsWIBevXrh008/xe9///tQ85KgoKAAxxxzDIYPH44LLrhA+/lt27bhsMMOw5dffqmkcUsQBEEQBEFkJ2TjliAIgiAIgogs/fv3R7t27WxO2sKme/fuuOOOO/D88897ev7111/H8ccfT0JbgiAIgiAIQgqZSiAIgiAIgiAix7Bhw7B06VK8//77uOeee9C2bduws2Tj3Xff9fysV4EvQRAEQRAEkV2Q4JYgCIIgCIKIHPfeey8KCwvx61//Gk8++WTY2SEIgiAIgiCItEM2bgmCIAiCIAiCIAiCIAiCICIG2bglCIIgCIIgCIIgCIIgCIKIGCS4JQiCIAiCIAiCIAiCIAiCiBhZZeM2Ho9jy5YtaN++PWKxWNjZIQiCIAiCIAiCIAiCIAgii7AsC+Xl5TjggAOQkyPXqc0qwe2WLVtw0EEHhZ0NgiAIgiAIgiAIgiAIgiCymI0bN6Jbt27SMFkluG3fvj2AxoLp0KFDyLkhCIIgCIIgCIIgCIIgCCKbKCsrw0EHHZSUU8rIKsFtwjxChw4dSHBLEARBEARBEARBEARBEEQoqJhxJedkBEEQBEEQBEEQBEEQBEEQEYMEtwRBEARBEARBEARBEARBEBGDBLcEQRAEQRAEQRAEQRAEQRARgwS3BEEQBEEQBEEQBEEQBEEQEYMEtwRBEARBEARBEARBEARBEBGDBLcEQRAEQRAEQRAEQRAEQRARgwS3BEEQBEEQBEEQBEEQBEEQEYMEtwRBEARBEARBEARBEARBEBGDBLcEQRAEQRAEQRAEQRAEQRARgwS3BEEQBEEQBEEQBEEQBEEQEYMEtwRBEARBEARBEARBZAwNcQt//HQ2Xhu9MuysEARBBAoJbonIs7W0CpNXFYWdDYIgCIIgCIIgCCICTFi5HaOXFuKNsavCzgpBEESgkOCWiDyn9/kJN/93Biau3BF2VgiCIAiCIIiQePunVfh4ytqws0EQRASorouHnQWCIIi0QIJbImOYXrAz7CwQBEEQBEEQIbBpVyVeHrUSTwxbinjcCjs7BEGEjEXdAEEQWQIJbgmCIAiCIAiCiDSVtQ3J37FYiBkhCIIgCIJIIyS4JTIG2lQlCIIgCIIgSNOOIAiLVocEQWQJGSu4ff755xGLxXD//feHnRUiTdAknSAIgiCaB0u2lOKlkctRUVMfdlYIgiCIDITWhgRBZAt5YWfAC7NmzcJ7772HE044IeysEGmEdlUJgiAIonlw6ZuTAQC7axrwxBU9Qs4NkWnQjJAgCIIgiGwh4zRuKyoqcNNNN6F///7Ya6+9ws4OQRAEQRAE4ZGlW8rCzgJBEASRgdAGDkEQ2ULGCW7vvvtuXHrppbjwwgtdw9bU1KCsrMz2H0EQBEEQBEEQmYtFZ6QJgiAIgsgSMspUwpdffom5c+di1qxZSuH79OmDJ598MuBcEWmD5ugEQRAEQRAEQRBZD23gEASRLWSMxu3GjRtx3333YcCAAWjVqpXSMw8//DBKS0uT/23cuDHgXBJBQkMzQRAEQRBEdkIyGoIgCIIgspGM0bidM2cOtm/fjpNPPjl5raGhARMnTsTbb7+Nmpoa5Obm2p7Jz89Hfn5+urNKBATtqhIEQRBE84IcjxJeoFpDEAQtDQmCyBYyRnB7wQUXYNGiRbZrt99+O4455hg89NBDKUJbgiAIgiAIItrQwpsgCMI7y7eVYcrqnbj19EOQl5sxh2kJgiAIDTJGcNu+fXv07NnTdq1t27bo3LlzynWieUKLO4IgCIIgCILmhATRyK9enwQAyIkBt595WMi5SS90YoMgiGyBtuWIjIGGZoIgCIJoXtDYThAE4Z/Fm8vCzkLaoQ0cgiCyhYzRuOUxfvz4sLNApBEanAmCIAiCIAjStCMIOzmxsHNAEARBBAVp3BIEQRAEQRAEQRBEhhLLQsEtKfUQBJEtkOCWIAiCIAiCIIiMgQQ2BGEnJwslt9QNEASRLZDglsgY6FgcQRAEQTQvLJLAEQRB+CaWhYJbgiCIbIEEt0TGQGs7giAIgiAIgiAIO9lo45Y2/giCyBZIcEsQBEEQBEEQBEEQGUo2mkogCILIFkhwSxAEQRAEQYQC6UsRqpDJLIIQk41yW+oRCILIFkhwS2QMdByGIAiCIJoXNLQTXqB6QxB2slLjlvoBgiCyBBLcEhkDjc0EQTQHLMtCdV1D2NkgCIIgCILIWEgLnyCIbIEEt0TGQNoVBEE0B27+7wwc/8RIlFTWhp0VgggdGtoJL5DAhiDsZKXGLUEQRJZAgluCyABKKmvREKdFCkE0B6as3om6Bgtjlm0POysEQRChsGlXJYYt2II4zW0IwggktyUIgmi+kOCWyBiyVbuiYEcFej01Gr97d2rYWSEIwiBktzt7mbdhFy55YxKmrikKOysEEQpnvTAO934xD1/N2eTpeeo+CcJOThYKbqkfIAgiWyDBLZExZOvg/O28zQCA+RtLws0IQRDNkpr6Blzx9mQ89t3isLOSNdzYfwaWbS3Djf1nhJ0VggiV6QU7w84CQTQLstFUQpYuDQmiWTJnfTEuem0Cpq4mpQYeJLgliIiTk41b6ASRBURlwTF22XYs3FSKT6etDzsrWUMVOadrIlt3ZYlGPE5xqNYQhJ1YNgpuqSMgiGbD9e9Px8rCCtz4ASk18CDBLZExZOvYnI076ARBpA+yn00QRFjEvEpuCYKwQcsFgiAymboGWo/IIMEtQUScXNK4JQiCIAiiGULCJoIwQzYuF7LV/wlBENkHCW6JjCFbj8OQxi1BNFMi0qdRF0OESUSaARESXrsfcu5IEHaycb1A3QBBENkCCW6JDCI7R+ds3EEniEwkTiYHCEIbWnhnN1koayKIQKCmRBAE0XwhwS2RMWTr4i4bd9AJItMoq67DmS/8hIeHLpKGY7XEonLEj2xMEgQRFl7nONHoPQkiOmSlc7KwM0AQBJEmSHBLEBEnh1RuCSLyfDV7E7aWVuOLmRuk4UgplyAIookslDURRCBkZVtiN8OzVcOHIIisgAS3RMaQreMxyW3DZ+rqIjzy7SJU1taHnRUioqgumBri7CIjoMwQRAYRFc1zIiw8atxStSEIG3RCT581Oyrwr68WYP3O3WFnhSAIQkpe2BkgCFWydXFHE7HwufGDGQCATq1b4p8XHx1ybogootpK4xGUNlAXQxBEWFD/QxBmyEZFD3ZGZVn6/cl1701HUUUNZqwtxoQHzzOaN4LIFOoa4li2tQw9D+hIJ30jDGncEkTEoQ40OmzcVRl2FoiIompbrj4EWwm19XH0m7AGy7aWpT1tgiAIGZ5nONHbA8s4dpTXYHtZddjZSKG8uo6OvSvCOkXNRhu3fimqqAEArN9J83sie/nH4AW44u0peHvc6lDzQSIPOSS4JTKGbJ3D5dJEjCAijxdTCeniwylr8fzw5bjkjUnc+9TDEGGSrWM70QhNccKhviGOU58dg9OeG4vquoaws5Nk/sYSHP/EKPxjyIKws5IRZHv3yY4f2V4WBOGV/y3YAgB4d/yaUPNBm09ySHBLRALLsjJyd72mvgG3fjgT/ScWBJYG7T6ZY8Tibbiq7xRsLKaddSIc4iEIbhdvLlUOm4n9MEEQmQuZgwqHSkZYW1JZF2JO7PTdo/E1dO7mkHOSGWT7mG2RczKCMEbY5txI5iGHBLdE6FiWhWvfm4Yr35kiFWpEcTj+es5mTFi5A8/+uEwp/ORVRRg8a6NWGuyihiYl/rjr8zmYu6EE/x66MOysEM0M1blGA9OG0yXD1dnBpi6GIIh04nWdlq1+DwiCaIJ6AYIwR9hrgBidAZRCzsmI0KmsbcCsdbsAAFvLqnFgp9bccGF3Jjwqa+u1wt/830YnVz0P7IjjDuig9Axr4zZuAbnUp/mmtCo62iVEM0FROMpuTjWkqVNzyxmb9bhlIYcmTkQaieLYTqQPOhoZDlFtdxHNVmSh8mqCyoIg/BH6hihNB6SQxi0ROjahgVTjNnpDsteJb2G5ujMI9thAGPYxCYJwx5PGbZras45chLoYIt1EtcptL6vGvA27ws4GwWCzZxnVipOBREl2Tt/VO9l4Ki8LX5kgAiPs9kSmEuSQ4JYInWxUi9dxOJZr07ilGQpBRBEvzsnq0yW41QhBfQxBNHLac2Px275TsWBjSdhZadZESWiYrVC3749Fm0qxpaQqlLSz/duxr5/tZUEQfgl7DZCNMiEdSHBLRAppfxHBAdmrFnCuxpYSa+OWNG7DhYYTwi/xOPs7XRq3ZONWREPcwgeTCrBwU0nYWSEiysy1xWFnoVnjdaGWZV1VFpFZX7ZgRwUuf3syznj+p1DSZ9ch2TZ+EwRhlrDFDKRxK4cEt0SkkAlCozgf8TpJ0vGibBPc0qyMICKJqvCB3c2OjsZtE0Hvtm8vr8brY1Zia2k42klOvp67Cc/8sAxXvD0l7KxkLdl4vDdsdu2uxaPfLo6ENjEt1MxRUVOPZVvLtJ8jrWfvLNxUmvz97bzNuOXDmeRHIY2w40cUTeoRBKGOjnwkGyHBLRE67EArk2NEcXGnkyNWu05noZKjaAOYIIjwUJ1rsC04XUeSdDRug87Tnz+bg9fHrMLtH80KNB1VVm4rDzsLBJF2nhy2BJ9NX48r3wl/w8LrOi2Kc8KwuejVCbjkjUmYuroo7KxkDeyYef+g+Zi4cgfe/mlV2tKnZtAElQVBZDgkt5VCglsidNiBNmzbKkHCatfpmEpgiULx7K6pJ5MNBOFAtUWzfVy62lGUnJPN21ACAFgeEYFpDqn7EVnIqu0VYWchic7GEiFnS2mj49vhi7eFnBPvRGGeqwMvv6RxSxBEJvPYd4vx90Hz064wRrMBOSS4JUInkw3L6+SXFdLoLFTYoGEXz/ayavR4fCSue29ayDkxS2VtPeasL468RnND3MIDg+bjs+nrjcf9zrjVGDxro/F4swVljdsQTCW4Yct7NLKUNkhmRLhhwcLAGRswd8OusLNiDK+bx0EQnZwQhD5ZNmRGjkxbNxJE1InHLXw6bT2+mbcZc9I87yFlCjkkuCVCx37cLbNs3OpQz3gl8rpoClsj+cdFWwEAs9c3nwUsANz0wQz87t1pgQhETTJyyTYMnbcZj3672Gi8q7eX46WRK/CvrxcajZdIxXbCIE2CW53uJuw+Jt2QB1vCjUmrivCfbxbhqr5Tw86KMdIhuFU2ZeDVVIK3x7IOy7Lw3fzNWLMjOlrWzQnemBnWuEJtgiAIv7D9SEGaxw2akcshwS0RKeQ2btOXD1V0DOGz3uS9rpnCLoOoaAiaJnF8e1DENU7LAjp+V1pVH0i8zYWNxZUYs7RQKohQd07W9Dt9zsmiY+M2aoS1uU+avpnDGkWzAm+NXYWr+k5BVW1DwDnyT27AFXDw7I04+enRmK/g/Eynf8qy7skIIxZvw31fzscFr0wIOytKZNon5s0L0tm/Z3ubYNdh2V4WRHhU1TZgyuoi1DXE3QNnEOle9pPpJDkkuCVCR9VUQhQFCjpZYjVudbwmsmmE7TGVbNuGS1Cln+5xcsTirfhy5ob0JuqDs18chzs/nY1xK7aLAyk7J2OdMaZ+0WVby3DxaxMxaok5G4Vu39duZ9xYshlBWB5saWra/Hhl9ErM3VCCL2dFv28Lut7/66uF2FVZh7sHzFXIi7c0IjgljCRzRCekDJdfVW1DVjqMC/uVs11wmY3vTESPuz6fg5s+mIFXR68MOyu+8dKPfztvMwbM8H9qlSwlyCHBLRE6qoLJ+obMHp29Cj0t4R/pp7lq3CaI+ts1lwnqXZ/Pxb+HLsKmXZWuYTfsrMRn09ahpj58LbZZ68QmQpSdkzGb8bw+7a7P52BFYTn+9NkczdyJcRfcWtzf2QBNEsOnuVW56rroa9yky8atyoa7jgw57M3rTEQ0bWPL0m9tWL29HMc+NgL/GLzAZ0yZB698SWksHKh/IMJiwsodAIDPp0Xb5F4QxOMW7h80H//3zWJs2+Mg0yukcSuHBLdE+NhsPoqD1Ub8+IGb/TBW6Ol1oRq23JQ0bsMlKpNSy7Jw/5fz0Gf4Ml/xbCutdhUUnvfKeDz63RK8/dNqX2kFDTvZkL2Tm8ZtSWUQ5jDkEyE2F9nWxGmSGD5R6ddE6ObulVEr8P3CLYHkxRTpEtyqzHW82gONer2JCirl5Lck359YAAAYOm+zz5jUNg9HLtmG69+fhq2lVb7T80vYpwGjdCovDDLZwTVBRBHdZtTANLyKGm+m90qr6vDm2FXYUV7j6flsgQS3ROjYjvlIuosoaNw5YSeYbvbDWKGnzuQqXZOyjcWV2Fgs14Bs7hq32Qq7bFZZNC3aXIpv52/BexMKfKV7db9p+McQuYZOot1MXbPTV1rpRNVWN28jJAiHZW6ySXbhGfYiNN2EZiqBBMbNlvq4hXsGzgs7G1LSVf1EcxZ2nNHSuM2u7skIojLL5LL882dzML2gGI9+uyTsrGShqJQgCBHNrT9QGSfYtYzXucVj3y1uFmYmgoYEt0Ro1DXEUVZt1y6TdRC19dHWuHWjwaPGbTrsV1XXNeDsF8fh7BfHScu5QaYSnUae+X4p3hy7yni8buMNT9iysbgSIxZvC/SI+XM/LsPrY9IzoKm8RplBZ2ZD56pp6ET9CD9bM2TCT/YWbyMkiM0Rt3rNJpltglvRJHN3TT2WbCmNfL1rDlARp5+8tJlK4F9nv7nnnFC9UULUh7FX/dYGr1rTfimqCF87i1++6SuPbG8GduUWgiD8ojsnY9cNXnu+GQXFHp/MLkhwS4TGL1+dgBOeGGWbeGWa4FbPORmrcesxPY/PucEez66srbf9Xr6tLPl3FKxVbNhZiQ8mr8Wro1dGQqhy9ovjcNfnczBisTlnUiybdlXi/YkFeH3MqrTYeVZJobquSftd9xt4/Wbhf2n5hIQVAMqEnzbtVo5UoyGAOq1n49Z48pFGJL+67K3JuPTNyRizTOKQjsgKmmObEJlKWLKl1PXkjQ6isrP1kaR9HijpOChl8hPqZDcKG41BnJIh1MlG8xBEdGkOo5lum7Jr3DaHEoguJLglQmPdzsbFwcSVRclrclMJEZAaOtDp2uwat95MJQQ1QRT1s5e9ORm/en0Sxq9oFF5EQeOWNZkRgTl7khlrg9ktZB3dBCWoZgdalYVQtY9vkKn2nd1g25DsHdny5WnXBmFH2k0bytbHRKlRpQHRJHNt0W4AwP8WBGOrlKa2RJjwTIQUllXj0jcn4+wXxxlMid+fsBtUXpV/s6un8o6KuQqW2vo4HvtuMcYsLQwyW0bgjZc/LtqKFdvKfcetqiwSdj2MggJDmNg0brO8LLKZXbtrcfW7U/HZ9MxzDvbT8kIMnbsp7Gx4hhUNeJ3b0gaMGiS4JSKFTGYRdedkbrACEZ3uKd1dGTvvKXAIL6Jm4zbI3GwpqcJNH0zH2GVNi5dv5m3G7HXhHecI6n3tNm7dw7PCZF0NUc/vEPEJOSsclWWVvcUTkgYiuHXTuLU5TDOefKQJy8Yt0USWVblIwNO4dXOw6gU1UwnqbTDiw0AkEX4Dwe+BM9bj02nrceens5XTCKsbdY6XU9cU4a8D5uLi1yf6infiyh046pHh+HDyWtewvDoZVnlQ+yCylQEz1mP2+l149NvFRuJ79oel6Ds+PU6R//DxbDwweAFWFvrfcDKBbj/CrgNpSh0sJLglQsdNkJFAZfc73butOsnZBLc6Nm7TcIzZJriThAtCqKSLXbMxuPz855tFmLJ6J+74xL54ufa9aaGZ7UhH9VbZ9WS1nnU1NL1qdKaj5n02fT2+nLnBdzxyG7dyjdsgcLVxy1TnbNO4TZOpTyKDaY4LkZyQK348ogu9+gxXEOAhtHEr6Oq3llZ7SMXcR/Q6rwaApVvKBCH1uH/QfADAU98v1c5DusmuEVsOlUX20jKvSaTld224srAc/SetxYsjVmg/6yflBRtLfDwdHjZTCR7HgrDspGcaJLglIoUfG7fT1uzEqc+ODczWqF/s7+ZVeBW85JY34CU61Gho3LLH+s3GzEYncnoRt4CjHhmOt38y7xzNjaAWCKrH/JPh2W+guc71bioh2Lq3s6IGj367GP8eusgmmFZF1cZtOkyfOHGzOWXfVIpCG08fYWncRklYRQTPzLXFWBURbRoRQSycRP0J2/XppBqks9YtJVU4/olReOTbRWYjThMfT+Frh6qM0X7LMioat6bsK2rVyZCHTDb9N8auQvHu2vAyEwLZbKOfaKJrh1bJ32XV/hwo7wqpDe3M0LZrP1EcMflGM4MEt0ToWIpCAzcbt3d+MgtFFTW46/M5xvLmhteOxqu8JjiNW7VpakManGPp4LX81+/073zl5VErfcehS1o0blUEt4pCSm78Xgf1gN+9spbRIvapdCVr3+y9KGiwA85TD6FlIxRIgBo+Ud8sEGXPsiz0m7AGU1YX8QPsYWNxJa59bxp++Vrq8e0Ri7di1rritJ/i4FV7WVv4Zt4mnN5nLBZvLtVKR/Rlo6hx239SAarqGvD5dP+nLsLgiWF87VChjduILpR1cuUcr0xVJZ06yStH01Xasix8MnUdZhTsdA3bf1KB4dQJIvrk5+Umf28pqfIVV1WdvvKGCaKyJmBRGSfYfEfwFZoVJLglIoXUxm0EnZPpYDeg7+25tGhcSu5HQeNWVzuUR3l1PUYtkWtmR/HYRjqO5KkM0uwpW20bt541br09ZxLZYo7V9JEJokIxleBm49ZiJ13qeaqqbcDv/ztDqOmVCZAHXH8MnLEBt380E1W17gud2vo43hq7CvMdxwEj0LSliPrEkUsK8fzw5bjpgxnS59ft3M29XrCjAnd9PhfX9JuGXk+NQkWNPy2hIPn7oAXYWlqNv30xT+s50akCi3VmotEGbfMo5ruUVtal7QRDpiHs0gVl6aUUw+pFUzVuTcWsHlE6qt2ElTvw+P+W4Lr3p6fedKSfbe3AVr+z69WbJTPXFmODJ+Wapo+/s8Kf5mq1D8Gtny4oKoJbbRu3cW9rCD9pZiskuCWM8/7ENXh++HLl8GxjlXVabs7JWrfMU07TFFoCWNsRv2jZ+VR1TpVuW14zCnai34Q1tvLSdaQlwrTn0eC0xvgLKpPpqTrWSoZnVkfpWiQEqZVXVduAOz9xd8QiE+ar1ssGj0JSP7iZA7Cbb1CPd8CM9Zi0qkio6ZUJ5Ng2gty/x8biSlTW+hewRXFjyAv/+WYRxq3YgU+nrXMN+8XMDXhl9Er85p0pwWcsDfj1XL+Z0QqqrG3AtDXu2nRhU6d5HEHUokw6M1lVWI4TnxqFWz+a6S+iZorKEN1cTCWYQud9eOO46fLQOiXWPIYWZZqLvGdnRY0vgWFzYGVhOa59bxrOeWmc9rOiTT0vVCpsRAdBZAS3muWXzebW0g0JbgkjvD5mJa7qOwXVdQ147sfl6DdhjScPxbIG79ahtW6Z/uqs0z3ZBxWdNIK332TfsBYf+0q3xu1170/H88OX438LtnDv+xmc3bR8oniMkB0cgxrgVYSJrCBQNxte63CQc4HPpq/DCsb+pF+Bquj5hriFG/s3aedFxjmZR63+KGsIqsLWZbc2VbCjAme/OA7nvzzBf8IhLq6nF+zERa9NwMy1xcbiVKkLBR7mBFFmt6IAX9SkwhTee9U017YJLXh3E04qE1EMmNFo2mDSKrnJimzl67mbuNeFirga3yYetzzZhE+wfufulNN0Wuk7whozlaARlpdd021b5kvQOVdtLpuCXojivF2FraVV+NkzY3CuB4Flc2LZVu/OBXljg1dYUwm6gkg/SYfp6LBlrncZihdTCVW1DSivrkv+nZktN/2Q4JYwwutjVmHuhhLbBFHl6CRgH2j9yDFat8h1D2QajU7W66BiwqmZVhq8SeieeWCDi7ZNeXUdSivrpGG8sGxrk1At5kNoqMoT/1uCxZvNeCc2id1sRkBpuNyfUbATO8qbHLfpCpC924UOblgvrbLXWb8piYpk+TZ7nYrKkUb7brmB+OJWxnhn1zH7MXmPLdNtZV68rkeH69+fjpWFFbj2vWnG4lQRFbQSjdHRaAbaqG5ciE+q2++EranCfsMRi7dyx3JdkZCo3zbd5xD6iJQJdL7Hle9MwYlPjkJVrX5/P27Fdpz70njc2J9z/F+RIJyTWZaF7eWpzmmnF+z0rWXvGY33yjbrP17N0EWJiSt3AAAKy/hOkbOFFj6Ehya/fX2D+vi0fFsZFm3Ss/0uIkzBbYtcvZOXLGy+Vd7Bsiz0fGIkjn9iVNZrmeuS/rPlRLPGrx1atwY/f2MJ9mmfjwM7tU65xxomjyImvC4GJ6xTE57XS5yTWZaF458YBQBY9tSv0Lqlue+xW7BA9rPQlc1tP566znO8QeLVFqkbqnaDp63ZiRsciyzt3WivGrfeHvOEl7L1Ysai3q8XNEVyZOo6cDon81/S1743DRuKKzHpofMi3y/bzX7Iw3Zpl5/8Xd8QR56PRUazW1srSAvyw9hcNYCoSVQa1jhPZx/n9rXu+nwujj+wI4bde5b9OU2pkFirUysa5jl2HkX4wYR24qI9zurmrNfX3h+4R1N69vpdntMPwsbt4NkbU65t2FmJ6/fYl133/KW2e+nYgJW9lrMtNbuxJQvIZi1pllyXuaqMeEBjgyyuhriFX70+yVhaYeo7tMjLATyaiGDzrbKssaymvnvTrkocsW97agGKkMYtYRQvk3Ed51u/eWcKznz+J+69MHaZvZpK0HkwLbvJit9Apl3J3tvs06Onk8SR1MraeqxgtBb9zJczUSshHXVBJnScsTbVBqO2czLtHO15Lo070X6TUi2TNMltXSdEsg2BxZtLcebzP+G7+ZuV05u9fhe2l9cY00Lwg2VZ0g1Fm6kEl+/WqU2L5G+nljbhTqsWzWvKWedTYONcrIetLeYUyiaEcvYwZtKyaehE5ORB2OUfJn7H1y0l4ZxC0J1/qPDBpLUp19YKHAwC/Hmo6fmlzESJM/lMnNv6wa9jPSI6sFqfupqYXpQnhHEp2myt40ha/TknC09yy2o765Yeu/5X2RDkhaC2q0bzmkUToeNfU8172tGfdHubXNjlvWZesnh3rW2xpKpxp2qT07SQrbKmcQC//K3JuOvzuUxCRpOJPF5tkeogi7ZdfuohDX0bt97yHWT7ThWgeOjHPDyTLo1bt5mkzATHv4cuxOaSKtz35XztZKPQPG/sPwMnPz1aeKydXeQm2pTIzI+OkJdIpRWjfW1qjLAsKzIOPfwT/ffQXZSKPjP7ybzOhwh/iDaCvZSxm/PgoIhC20+HXVUdYWy2aW/SUNx8yM1pEku9M2611rMmT2N41LPylpbNd0nAiUloIdB2VmlfuqaP7OvX7Oqv/JJRgts+ffrg1FNPRfv27bHvvvviN7/5DVasWBF2tggGvxq3UZiE6eDVVi1rkNv9Ob0O0Y15G3bh5KdH40+fzebGK3O0IPs+xbtrk79Nf8bExHjNjt3c68bSieAMUKSRHpTg9m9fzhN+5w6tWqRc09WW4oVW2VlP55fxW7SqzogklkeM4raQk3mE1XZGxBCF5jStYCcqauoxedUO7n12rpqoyyc9Pco13hAVIzKWfEbjtl6wcajLPV/Mw+l9xjYLR3npxGuz1jWVILRxG/c/r4nieJ1JhF16puyps4QhAkjHskVuKsFRBhkqB1m9vRyrt/uzIUx9glm2llbh+4Vb7Key4hb6/LgMo5ZsM54eKzyctmYn1u/cHco3VW3TvPFNN7c6p46DhB3bdcucXTM+P3y5a3huuVHTVSKjBLcTJkzA3XffjenTp2P06NGoq6vDRRddhN27xUdYiPTit9Px83QY3kR10mRD3vX5XKHdVhkmOvVP9thvHbNse/Ka7aiRJAmZ4PZXbzTZ+TH9LUTCI1+mErw/Ghr2I+3m4mWLd9KqIoxfsZ0bri1X41ZTcMsJ/uSwpQrPpa99i95JtiDyorGkIvTu8+MynP/yeK3NHiduCzmZ9lunNi09pxst+IXACrUT/Vt1nbtU1m9fnKmLaxEqr8OWNa/86hriGLZgC7ZrOH/7YeFWbC+vCWQRmUD0pVU/oajvctYB3SrFO6apin2zVj1hbY1bwXWvNv9Vs1pbH8f4FdtRWRuuQL9gR4XQVMu749fgh4Vb05yjaDJtzc7knFinHaScfFDoWGetK8bcDbuE93X7Zl77Md298/IkMteTiUNLdV0DLnx1Ii58daKvI/JRo8/wZXhwyIKMEijPWleMpVsaTdL95fO5uGfgPPSbUJC8//2irXhvYgH+9Nkc42mzfgNmr9+Fc18aj9dGr1R61qs5Qn5camtiE5+VjSJM5TWbrxPNZ9l+OOHEV0YGNYfIkVGC2xEjRuC2225Djx49cOKJJ+Ljjz/Ghg0bMGeO+c6D8IZN+V1x9mAXGobTmhdvLsUDg+dji6ZtVq8atwCwanuF0vvaxiIDxcNzFuS245f4lsoat4a10UR1yZdzMk6kJjwSB4m9LljYWVGD2z+aiRGL/QktnMVYJZg4s/anEmhPNDjBv5i5wfWxdM5nvCTlZbPCaXrkw8lrU8K8N7EABUW78eXMVIcpqmjZuHXkqbUPu6R+2ufQuZvw4ojlxsYEFZ8XbuYP0q0ZsaqwHCMDFEiaRF/Ywf5u/KP/pALc+8U8XPKGvrMPUfom7Kf61sBXjFcnmTFLC3HcYyMwhONISZdEPlS+ofYQKXgp22aRVvmqLahfHLEct300y25aKc2MXlqI81+ZgJs+mJ5yb3rBTrwwYjnuHhhe/kR9axjT8Bv6T8ftH83Sfs4513SrnhU19bim3zRc1XcqaurVBIQrC8vx4JAFwvvpKC/n3PS10Stx4pOj8N38zalNLOLzWB6sIov26QnFI/K7a+rTfjLjvQkFGDJnU8ppwRQC/GQTVu7Ab/tOwapCd23m7WXVuKbfNPz6zcYxeP7GEgDAh1Oa5qaFpcHZs+b5e33zJzWTCXZbx+YapSwuE6nYTCWEqnHr/Vntk5fc4CTNVSGjBLdOSksbHSfsvffeIeeESOB25N7tGT9rLD/93WVvTcbQuZtx35fzvEfignOhv2lXJU57biz6jncZlAz3Za1bcgS3CsntKK9RtslpWqghEqhmmGUN33zPaOfELaDP8OUYt2IH7vrc7OaVzvF4bY1bjxU6nRr1Xurvsz8sS/5W1bBjJzvLtpbhqe/Fmsd+JnRun1PWB8uSDdKW3gODF6Dv+DWYsVbfWzkPUZ1m65XOhlM6TCX88rWJ+PNnczB7nZkyiBJsG6vbYzNk7J5TIDuZTUBVeHXxg0kF6PXUKCxnHFqmm7qGOD6ftt54vHd+Oht1DRYe/Gqhp+e9atfomk6JWxbKq+vw1ZxNNg3BoDyAJ/hsemOZT1zJN5GSDgbOaMzDrHWp2p0JjbYw8TJfN5wD218zFfs5VsinOy6yGti7a9QEt1f1nYrt5TXC++nYxGPbXVVtA94YuwoA8Mg3i1PCZp7Y1nlMW+9ZFeWW+oY4ejw+Ej0fH+nrpIJWvpjMqG4SBMGtH87EvA0l+MsA902iTQLlpXQpVflJxmQWVftGE22fjSGKjjotuH9/XQUettwycJ8pVDJWcBuPx3H//ffjzDPPRM+ePblhampqUFZWZvuP0GdraZVyp63aiYnjS72eq6IqpcDQuZtw98C50mM4q7dXaMWp01U5X/nFESuwo7wGL45Qt9NsROOWo0En8yoPAF/O2ohTnx2DuRtK/GfAA6Ia4EeYZ3qsEOWkriGO4Yu2oqhCPPFXZW1R0659fI/GrQmcn1y0OOcJ0PWdk+mFdz7XELeCn0R6iL6oQl/YxG6E7JAsDP0iErCOWLwND3210LaocJatblGY/ja7PAjxeOQIZjtsdl01biHvJ3XQEXov2+bP7l86UHkfm7CQKb7NJVX4x2CxRptS+pzkn/lhGcqq6/Hot6nCjXTx6bT1GLucb3rGr6kEUyTqchDrJwvAv79ehH8OWYC/DmjaYLRUV8bO+DhBed/e06kJwx9AFtsOzti9tbQqbUIlJ0Fpq0nTFCTjln6f4U2bpE6BgZsQoCWj0qcqTHPT0uTNgUyf3mJjO/axEbYbHqxFRA42y7r1T2UsLq9u+oa7Ks3MKdxQ7eK2lVajtNK7KSxVSgy9d5D1y0/P4/0UBy8firIMw921qgPwIMgR7Oau2V6Bk58ejfcnrhE+q7uBJlBt0YojW8lYwe3dd9+NxYsX48svvxSG6dOnDzp27Jj876CDDkpjDpsHA2dswOl9fpJqgnlB1DdxJ+WG0nxg8AL8sHArPp8u1n7x44jHDedAoDwwGBQWAHbP3sk0bAOe/zSc+bQsy9dOomgS/P6EAqwr8mbjOl2T2/cnFuAvA+biyrenGI3X5M6ssy7qbJb42WlNoPItLKvRbuE5L47D796dqpWmLn6LVtSEUjRuNdLxU11F5XvX53MwaPZGfDy1qU80+e4maqipWi7qQ7xqO6TzSJuhvUtfjFyyDb2fG4OZhjSgnf3A13M3+azj4qeDGtdVhDMyW5pOvArMNu2q1H6GzXniW7B2DBPMWW/Pv25ZWpaFHxY1nhSZsnonkyYTRitG9XSdLNhYgo3F+mXlPQ9Nv53fyDluzlpXjNP7/ITr3081q5BuTHRtk1cV4Z6Bcz1tLrulP23NTuE9tw0kNu4agS1z3ZMk6RB0i5pdjJN+kCdhgiJml9xqYZ9zRENjEVBzJlxaWYef9xmLZ39cxr0fFdI13fGzrjHpyFtVCGyivtlOvIVp45b5zdbXj6euw67KOjz3o9jpmO4JNH57iE7bjTIZKbi955578P3332PcuHHo1q2bMNzDDz+M0tLS5H8bN/q3BZZtPLdnMPloyjql8KqdjtC2luI1fpxq4WS7rbq75FqDg6JWoywNE91aqxapglsWE+OGfdCzcEP/6bjsrcmeDa+LBBcfTF6L818Z7ynOdJGwUblZ035yAlGJxS1zw5yzHsctC32GL8OkVfZjprzPoG8qwRuWZWHZ1jJsLqlK0fyeuqYIs5hjliWVtfhgUoGWkyN7HoOZQDgXVKqmR3yn69LXsNrgqZsuemmxwd2eLa+ucxWk+Ds61/SwUGufnTS7JWZ4g0uVIDcU6xviuPOT2Xh3vFibAgD+/NkcFJbV4Nr3puFfXy3wdILAvjDQftwzfsvPz+O5adghPOuFcb6etyxgY3ElxiwrTLn39k+rbH9r2zKGfSNwXdFuvDl2FUoYDTOdpqTTv7Cs37kbV74zBWe/6K+svDJyib1snf1Hws67U1AeJH5NJcj6wJv/OwPfL9yKZ34QC6R4TzvtcBbvrsXYZYW2uaNsPHM1C8T8rjWl3ZyGvkznFFRmatwyphI0n42quCdu8X+zrN6hdsrz2R+W4oFB81FbH8ec9cWoZ+ruim3luHvAXCUbtlHHz7e0BL+DxMQ8hl1v8BQCCsuq02Jqg+1LdNd12hq3nK63rDpcR6KZQqqL8AhjWRbuvfdefPPNNxg/fjwOO+wwafj8/Hzk5+enKXfNE7fF6cbiSpvRctVOzCnYa/qtlT0bskfrFSdo+osSe6rjV2zHwk2luPf8I1ImVM78eZlbsWU1YeUOfDN3E568oic6tmmhHAfPwZRpW2dsp98QtzC9oFGoVrCjAkd2ba8dn2zh7X3gNDu7VdXkKa+uw4NDFiJuWdinfT6eurKnVMNV9D1M2lVzxjRk9kaMWbYd700owLrnL5U+qyt79GwqAfz2WVZdhxv7zwAArHzmErTMy8HfB83HuBU78NWcTRhx/znaaaVLqMSWXZBJssXWf2IBlm8rx0tXn8DPU8oH0pyQ2WxXyp895ZkxqKmPY/w/f4FDu7Tlx+ejZNjvqGLjVmdjyW8d0RlrgtS4HbFkG8YsK8SYZYX4yy8OR3l1HbaVVuOHRVsxf2MJPrjlFJunZwAYPLvRXul7vz8leU3XsRXXE7uP95Q9KjKToYqfrlbWtzvvhGkqYXctf9GUMm/x4ISuVYsc7K5tXHhe8sYkVNU14Kiu7Zg0zL+4syyXbXU3lWY6F2x8Ydq4FOG33FXqq67D38f/t8QW7+VvTcbmkio8cumxuPPs7gD89Yc2u6MCjVtd0mHjVqhx26hya78WeG7MY3dS7R6+viGOd8evwRlHdLaHV3g2HRrJlbX12MmYz/JbR/pPalxnL9tWjmVby3DXuYfj35ccA6DRsV/x7lrMWFuM2Y9cKIlF/7REulEpp+q6BsQtC21aOkRYBrOrqsVtxMatRMC/srAcF702EYfv0xZj//EL32nJYGuHriBW2zmZo0wnrtyB2vpwzARlGhkluL377rsxcOBAfPfdd2jfvj22bWvUZOvYsSNat24dcu6aJ25N8ff/nYF1O5sEVso2bgWDNO95E4PHgBlNXutlg3Z9QxwjFm/F6Yd3QcfWCsJQR9Zu2+MV97yj98Xx3Trag3rVuBX8vvXDmQCAdq3y8MxvjleKKyVuy0IsFvNkjiHxLP9e0292APBqvyddE1E/dW3cih1YuqUMxx3QQRru7XGrMYLxFN+7e2dcceIB4jwJWqHzO93w/nScdtje+Psvj+KGf+irhaiPW3jl2hNT03DExbZpe17c8+EG731Uvm/csrhtpoxxeFNT34CWeTkYt6JRU3i5R9ugQudinGvbSqvRMs8uGVItElZQ6Fb3/PSCbLEljuT97mcH8tPx2d3qPF6zZ6I2rWCnUHDrB7Z8hYJbJsM19XFc/tZkYXzsu3k9PZBAp08LUuO22iG8OPvFcTZtyDHLCvGrnvunPFfg5iXbBdObI7Iykt2rrmtATiyW0oZNIft0ziJQ0cAvr65Du3x/U/e6hjh2VbKOwtSFGV6EHq1a5CYFt1V7fAysLGzSNNPSuFVcUKdsP4WslucmJAz7eLuX4lEZ+/32XYmTSs/8sCwpuJWVlVtqdo1bvjBdN8t8G7d6cXiFV76ZqHGra+5g0OyNeGX0SrwyGvjzOd0DzJk3Tnt2rM02sqrDWjcSG1D9JqxJCm6L9/gBMOFHw7QSj34G5LfjcQsnPDkKtfXxpKJG06Pq82n3bKhtJJjetHHmO+GQeo3P+ZYSTF3UVcjRN5lnT/bFkWIzDISdjDKV8O6776K0tBS/+MUvsP/++yf/GzRoUNhZiywllbW+FphufZJTwKPaWYqO/+vktLY+jm2lakehWQ++sknBrso63PX5XNyyRyjqlWqOdkVKuooDtttAurXE23FwNj6t48KSvDTdYwTBzABQ3+BRcBvATDSIye3opalHTZ04nVAVu0y2ROXsvD6tYGfS07CT8uo6DJq9EV/P3YRCjvkAZxKiOsBr39rH/b1q3FrOvxsvsAsXXlfHe1/dtBK8+dNqLNxUkvy7rLrRPtnJT4+2Py8yve+oc4my8ysEdDJ2WSF+23cKCvYcw+MtdOsEbTFu+XP+ptp/pGNxyeZFpKXF5van5duxaHOpdtwsFTX1yXI3RZCCW2fMJQ4nKTUCLQhnlnRzaHrBIysi0fhR1xDHSU+NxqnPjknRGLG3AfWNHCdOUwmytvX3QQukx11nrSvG8U+MwiM+na1d/tZkTFjZZAZH9i2ct7xUxXwXobhp+cDbP60y3qd6we5VPu64F2zaqnbjm37rZ0jlibkbdgm1jXlpyjTU1yTGM2lbl8fPXnJuWnmFV3SmBfFCUwngacVnnuTW1tsqVKxV7MaPIJ4wcTq089MdhaH5CoRTlm5pVtc3JDUznXN7k2YFVeMy8WlCF5bvwY/GrbaGbtg7qRlMRglurT0LSud/t912W9hZiySrCsvR66nRuLG/d2cH+t49m35LNU2EwijOREsQxxVvT8bP+4xNCmVNDm4LNpYohROluKO8JiU/zoHby2LcsizMWlfsy5YRO6mzHP82pqGYF8k99l3tGrfeJsoZOA9N4luDUfC814GP57XaGRUrxEjs5gP8by4SAKamYWH+xhJU1no7Mhq37PUgkUV2ocdbrHs5fiMr2js/mZ387dUxXoKq2gY8P3w5TnxyFDbu8mYDmccdn8zGvA0luH/QfAD89sMzmQIA41Zsx0lPj05uQuhWM9Vji6pN2k/7UbKLyCTgVldsG1yCoBe+MgHnvzIBi10EwDqLa79H/eX5CC5uJ24LlKC0Dieu3MG14buttBpVdQ0orapLaoLK8ucFpyCKjZf3ti+MWCGM69VRKwHYTxB5wXkSQcczthehkJtdfT2NW/fAL+8pJ9tzzG+RPWe/gkwZ0TSVkEptfRyrtqvNL1XmIDX1cdwzcB5GLdmG6jr3MsjLiQnXHZU17s/bbKVyovFqGkeGSSHEuqLd+GBSAaoc8ySxqYQMnhwzsO1t/c5KXPjqBAyereaX5v2JTU4VVT5FGA7M/DndMpgRrXRFCgjB1Tk/bcnLOlbEUsa0jmwsMK5x67FuFu+uxQ8Lt/oaZ2w2bnU1aHVNJdiUxbQezXoySnBL6DFkziYAwAwfXqB1G5Rqp8N2dl77vcTi47v5m7XylY4jaX8dMBcPD11kz4PHF2XzvmlXFa7pNw2/fG2ir/w58+TFzrDsfdh7DQ3+J8pB2HgMshYs3lyKa/tNU3I0IiqRz6avxzkvjsO6nXzhoE5RspsE/M8m3mQ47+Xx0mdVtagHz96I37wzhes1W20iaDm0axvTZZ80tTFw3fvThItMVvCsqg0tYndtA/pNWIOKmnq8JdCW9kNC6M4r3Ra5/OH/8+kbUFJZhz9+2iig5r3Kd/M3Y+rqIl95U538+5nT6Wrc6kzCRWG37dECGcmYQ2HZXlaNfwxekKKR40TFzINfYjH3uEVF4hxHVbIosm1vArfkXxiRehTPJkR1RGDPnffyd7N17yRPZhM3oEFr7PJC5bi9ZEHU1yTwuljlCuaEp0WafvPqghPTi8kUjVuz0aegveG2599bPpyBKat3Gk1j9NJC/OmzOXhy2BJumiy5CrtU0rHDtrnLldzK73uAV+dE/b8bv3h5PJ75YRleG2PffBCtW2IxM1rxfli9vQJfz9nk86RO0+8hczZi9fYK/OurhZLwemmFLd/2pXFrLhtahCFQc/us6TBbUFlbjx/2mChww7RzMq+vcN1703D3wLl4dXTqpqUqbBMJ3DmZqoYHkQIJbgk5frStFKN167Tc4lTxDBvUjqVMaPXlLPtusTOkF5MEBRwNP90Jib1zTr2vbqdYjEjjVlU700kQwnZeuS3fVo6/fD7Hd9w3fTADM9cV43fvTnUNKyruR79djA3FlcKB2OskhSc8T9G4ZS6UVtVBBk+Dl8fg2Y0bSds8mC4AGvMY4yzK2NfhtUeVYnJWha2l1Rg0i6/twZaf0KSEYjosQRwdSkTJ17j1NvwX7KjAfV/Ox40fzODeVzaVoJier8UgUzVV7HG75V3HFrhoXPrX1wvx9dxN0mcB+yZEYIJbqHhhN1cv7eUnyJBOfMw38KIFJNvQNbWwcMph3epzrkATvjGuYOrB62PUN428ZEF2/B2AVvGyQbeXp5oZEp3o0D895r/es1E4bdyGLWxrzEPqOyYcySaYubYYf/h4FjZw7N7rFtEXMx1zYoHg3ZmH5L0931BVu4sXyutGnW46m0uqlDSMRTgVbkRNqNFUgnObKb2V6cJXJ+AfQxZgmKKwiwf7Drx1QmFZNa7tNw3/W7ClMbxo0zyiQiA/85j0mkpg1uMhlKWfFO3FpB/TU8OW4sb+021O5dxi8qNJnYzfwGbSqu2NpkO+X+C9DbLzC10lK1n4V0etwOPf2c07xS3777BtvGcSJLglpPgxlSAPxxcMsddr6hvwwJ6jvjISgiNZfxfU8FMp8MSslAkPQm7TEujE9/UymZWWN6txy1QKVSGfM44gjgqLBorhi71pSgBN5ekm7NShpUDA1mgqRj8+le8rDpJ6Q2XjBPCv4Ry3LO4xSJspDo8bA6M4tomdR6d51022Rq+O+1TgCbVkmn0szkWDm+CdDb58Wznu/WJe0uarn0mul2fZulG8u1Zg85DtqzTy49KORHVR1alXejRuY9hk0ESHGyYWKCyqpplU8pNyT+F5lTRTTCXYIkgNL2uXOUEcPUHje4hinrByBzYWNwntvNTFPIkwGvDej977xVwA9rHcTZNdBlu8pm3kej0NEiS2rQnB61773jT8tHx7sqztz5sfs8Yu3+4aZoXEVBhb02atSxUAq5i70UXUl/ka0x1xijameJfD0i5dxNj/14Z53TaMaZWE8LvPj8swc10x/vbFvMa5b0QFtCJEVUHtrFk4hGGiISxTCZZl4cMpazF1zU7MXl/suCd7Ti8dbhyG4/MKTzFGFVH4eNzCmz+txifT1qPPj8swdI/Sggkt42yFBLeEFF0NSdXBlG2oH01Zx73+2bT1GDrP3QxCXb1eHk1OanZr2Ot0lo2XAcpE/2Z33mD/VycNWf7tu2msMF5HcNv0u7nY8eLhVt6srSUWnTWBm2DeeUW0aPVjKkEWSnXymsOZWLACvTrOSsyChU27KqULcafNR0A+mUjYwBabSrAwZPbGlOOSsmrc4FHo7BXVJiX9bpw42PBPfb8UwxZsSTp7ZAWpyulbwHM/LsNpz43RdjTHfvO7Pp+DF0em2g+1T5rl38AueJSnLdqkUpW9sWOvR+VoV2IAXuKUCYvqMKXSR9ucVhoR3DL1ycPztm/vqOmmFhNOQadbvCLt1Nr6OCYyDsVM4iaMZW1IeilnN41bHY0yNmhhWaPGLfvtRIJbURKbdlWi/8QClFfX2e3/G+6OneNkugRPMq1PnXfcXJK6wRN124TvTShIuaZzakIVUTROx4RacTr+FkWVE4ulam97TtUfKmYuRLCvkN+iKZ6Es1C2XZ/1wjh8Pp1v51s8J/OcNSP4EkiGlHdRukHVr5r6hhTnzU6kRcFkWLfI2LV86xZ5yomaOZkhzrduWfvT7G76raPEIAo/ePZGnP782OTf700swAODFyAet+xmszJsEyZsSHCbJaTLw65NACjdpRIIhpjfqot0FS3OoAa+Sg3tDmceVLO024cGiRtNeWJ3vxp/+znmxXbErNZBg4aKg99FuRtByIJ59cz1KLLHyqkzYXDTKlQ9tslLUUeLWoSSfcy4xXVOZp9spOZw5JJtOOuFcbh7QKrGkAwLFsYsLcRXc1KPtSe8W4ta8ZaSajz41UL8+bMmsxuN5SR+0SA0bhPfnStgDahP5NXnhFankrMwZ3yw8P7EAhRV1OKzaeuF4UYvLcS1703Dpl1N2oHONsJzSuRWf0S4aQB/Ksir6nt7KStddD3P6z6bEhfz28ScxG8ZyWy7m1pM6GqothAIPz6bLq77fnHL4ZhlTScSvHx3N+1+k33R1hL+vFGUxBVvT8GzPy7Dk8OW2o+KmliQ245/h6Nx+/xwd3u+jahvWjVd81dGuk+rJMf2A7xq56b13xC3uBu5MkRdmZ8+RFUYy7selp6D6ikeHqK14zX9pu2Ju6lf5G0iJJ/VvJ4uRPN1pQ1Pl9wr2yfXHO/TLVC75PVJUrvGgGNdKLVLb6euIY5Ppq7D6u0V3Pu7GCfMrVrYx2BZOZgoIR3lAZ249J/1vqnFmw//66uFyc1VlgbLsoWPx8O3QZ1JkOC2GcO2AxVnDCZQtj0l0VRLoCrISBzVTucgk/DcKLKnxsP5Oiod47bSao2Jtxo8A+Q8TbKBLp6rZdm3adwyf+iMBX6PwTZ3dAQfbEiuxq3jmkjYwPt+3yo4BzSBZTm8niY0bplM8RbHCWHdCE1nIZYF3PnpbPxzyIKUewkNMlF9tpl/iVsoLKtGj8dHcuNKEMTmWiJGnlkQ1bYoC/fhlLUp12Sv4WUzhk2/Tb7YO/0fP52NmWuLbU4hVcqUDeE25tgEjx4n2Kp9WX0aBD3pPsnAFtm5L41Pua+vXeL9WcCpcSuO2yuWZaVoS7vNU0Q2bpcLTl7owp2jxVxOA7ACcg8l7eoATyMuXvmxebr5v3zb26IFccKB49TVRYGaSnDGly4tySGz+bba9+Si6ZeH1426xi2v3rFjEC//A2Z42SARKKL4KB9nPRe1oVgsxtHSC2fC7MeUi0jZI0GLPIPiihDqbZAat340u/2kaxqeD5cESiYRBcJ/APhg0lo8/r8luPDVCdxny6ubFKRSNnGZvzfsrMTIJduS44lp3xR+o/PV53hUYgD05sMpY2Ho2yqZBQlumzFsU2CPugWJW1tftrUMf/x0NpZt4y9CvHQcKh26SZZtLcPRj4zA498tVrbvCaQuHFTy++Miu6Fx0+9oOf4FmhZ2brbiZJ0tO5ix31En+3aP8O4Tk9r6ON4cuwoL/djZ8omXz+P1m+o8x4Zdz3MyohoPJ+T4FanHdxviFh4cssColpgF++LW2tP02HrCM9tQXedNACbbhEoIvERBnvlhafJ3g2Xh8+nrUVsfx/w9Jha88NbYVRi+SM/xQKJo+CYNFDfZJPdKKjm2nCUP2DUklZK31d0OrVq4ht9V2aQ5oSS49Xh832u7VdXArLdteAUzuKXbvp7pRY5M+0YFr4vAZJoK8TuFGW5FINJaa5ufx73O8vGUtRi/Qm4jlGdOxu09bN/Ni8atm43bCKzbYrGY7VuZaHNsFHUhSTllqfp+RcHzQTpTcjtKrdImE/D6Iy/OfUQHyfyUguopKB6ZoHG7vbwav//vDIxY3FjeNofGnAJt4dKHJPh23mbuBn46HXzN3bAr5VqQ+7BB2T73M14v3VKGv30xD+t3qtn0dyNhZk/mkNRucqDx99qi3Vi8uRSTV8vNDLE2yGXzxnNeGoc/fzYHY5ZtT0nTK259kg6mbAT7dU4mK5eGuGXLZxTG/0yCBLeEUdyEAde/Px2jlxbixv4CrQiXXVceKnZ4Te3olFTW4o09Hpg/kRzb5fGO45iu2rEv+9/899AbtHlai/bjMY2426WT3Wz66fUIuO5asf+kArw6eiWueHuKUvxR0eL1Wje1Bmgm6F2fz0m97WORwGPsskIMmbMJj37b5ElUFqWKhohzItCkcdt0jecAJqEdr4tsUyah4cAKCVm2ljYd1/XqMM3J7toG/MWDuQeAf2TUqxzB7VtJN3SYIlUVqLPO4NopCK9s6eke93IJL3K6qINq02LjV01q6uoi9J9YoLyYUDs6qRbXuOXbcU2/qVzv801x+c8Pi1/BrV3jsFEzPrHwtznP8BL1nudkgnpeWxKNu251f876YjwxbClu+2iWNBxvvuSmeW3XuNXHzfal1hgY4CKPfTfzGrfhmEpQdjZrMG710xz6qbKbojzYquzWJyS+cU19gy+TZOJy8CFEUZyTxWLpFUrKYPuuUUu24Z6Bc1Fenbq5u620Ghe9NhGTVhXhrs/nYvHmUldzXiITMk5eGrkCH3FOArEl9N38LTjz+Z+waFOpUpy6XNV3aso1P/1JaBq3Pp694u3J+N+CLfjjp7Pd07Es/Pkzebi6encFLZvd1D2/z3t5PC57azJXYcUWPzMuOtszL8mEAzM/w0Q8bmF6wU6UMSf0xnEUYXTwt1nkTYmBF15W3xssi/utCDVIcNuMCUMuZRO2cTLAHiF2e17VWY+u7TCv5TJo1gb0emq07ci1zoRpgUPbTuVZE9+wqrYBs9YVczvShLdonm0bP8cb44IBQM9UgnhnlcfMtakehGWk6zhZUIOSyXWlc+EstHGrmCZ77MgUccteJ3mmEnhCUq/lVCtxpJcTa1zssTZsRdTH44qajcHNXnh1faCnI6HuqJpKYJGVjxctXd6zIuynPNTj9qrZ4EXjViWtuoY4bvxgBp79cRkWKC5IUx1nqb+Ts0+evX4XZq3bhX9+JTYHYrqGs7IwL/253UEG8KvXJ+L+QfPx3oQ1xvpt56Laq8Zth9ZywW3CjrQbPBMcq7dXYLtEk9FPGwTcNfFUTWz5wa3cN5dUYda6pjmEERu3TBSsYKAhbtkW65ZlBbKRXF3XkNIH/rS8MLmZ6fcNRc8H+TXdfF/Yy1FuIijx+/Q+P6HH4yNRWVuvNOm2CTji4pF7+bZyz74iLDTOQRLvK+rfePVGNF+Oxy38Y/AC/HdyqmDTBGw7/9Nnc/D9wq1466fVKeF+9cZE22mdy96a7BiHORq3eeoNZNKqopRrbPzP/rgMm0uq8MDg+SnhHv12MS5/a7LWhv/U1UX4zsVkmC/BrUuLUtV0rqiux7Q1O32bNFTpqxLzlzU73DVut5ZWY+SSQmmY5MlaSRjZPbfxkR0XnfHI5kUqpueclFTW4pOp69Bv4hpc//50/NYh6Hc7VSDDlKkE3fmts37Lxs+GBourHU2oQYJbwsau3bWYs77Y8w6uXzV/9mlVTc1EktLQBvqFh75elHLN1+6WQpjUoyD66dz20Uxc028a+k9KNZeRcNjEm8y6eTKXD2ZNv+2mEtRfQNcDfRlnZ19KVDRuPVaiuKVemq4hHbc3FvMnOarpBeL4zZF6olrZBLceJseieizTuM3JifHNBHBIl2NIHrK6NXh2qtM1fhx6+ZcekRJpJkni8zOmqDxrr1ep4WOCuyrZ4pWFFxu3KlWoqKJpsp/YdFhZWI7lArNEQGoXaKKulgi00AHzmmF+j/A7x71de9r0uBU7bN+6mHFcovMOlmWlmkpwW4QLBt4u7fKlz6mbluKHE52CAvxvErqd3tERkgbZm87dUNKUjoGEbCfImPZ864czMXRek6AniM3d3TX16PH4yJTv/YePZ+OpYUtT0vWSB1FbMG0ShYVXly59c1Jy454VcHKdkzHfJNFmEu17VSHfcZGIZ39YitOeGyMUJl/Tbxpu+kDcrmRYloXfvDMFvZ8bi6VbZH14TNle8sRVO/D13E14+nu51rJXeN9mW2lq2bjNnXhdVAu3BYkLvH6XV0s/m74eizaXYtxydc3HGz+Ygfu+nM84rU1F1MfxvtW1/abZxnO35qRqKqGqrgE39J+OL2aJ/ZfY5zgW97oOKllT6S8S83FZWD/5tW2Up9hgFaNykGLM0kJMXdO0mXDvF/Pw+P+W4MURKwDY524AUFolnkO5Y2a+rGvawzn/kJVLfdyucRu3IrMczwhIcEvYOOelcfjdu9MwYaU3dX2/8zW3XVf5s5IO3WuGXNMM9lkTArAZeya0CWdjbJzr9hwf4U3g3TTDZIs50ZFirdP9zOdX0VKr0nAU15hA4075zgrvu5ufTlvniNPCCk2PxLwiUREO6AkQ9PPA8r8FW4RptvQ5oVbFmXTSOQBTT7wsGEWPyDRuc2Mx5YVEXYMVml2OMETG8kmu6mYcM4n28RIqz3ptGzxBmfP9eGGCsnHrdOZYWx/HRa9NxK9en4RtpdXc/tG5McgrL1HKbm9RXl3nya67DrrO7sYsLcTzw5cnv5NtMS/oX5xojWFIXbh6PfbqVpdVBbdehOdex/AEbhph6djccgpu3DQh/eZpyuoiTC9o0uBl2/Pk1XaNwCDefsZa/ikrABiwZy5oNweinwtREambStBOksuSLWW49r1pKdcTTcmyrOR4LtMqi8Wg9DESj/WftBZFFbVcDc8Ec9bvco9QwNI9DgmHLdwi/D68piUaYnScKXuBJ7hVtevvqnHrd57JKb5WLczOXWXa4Drr2ZnrivHKqJXJv92qpNvGmBP1TXutaLmYcoDKa7/SNb9m5tn+2akAwosqsUHEW3Ow71xYVo07P51t2xiV9ReAP3vIfr6ZSOFK7VlL+rfznk3ITrYStCDBLWEjccSZ53BIBb8Gp207PgbbsqhjmLO+GJe/NdlcQhpscznyBZjdhUqMJbw47baFG0cN18mA5PuINW7V0V2UywQi8biFdQ6PpT8s2oobP5iB81+ZoJErO499t8T2twVgzDL5kR8nvKqp0nYKy2owUXGDxS06t/T+9sU84b36eBz1DXG8NHI5pu5ZkPKP70kSUDqeaH8Pnsatl8W26Amp4DYnpqRJoJOnIOYuTacR0jcxkr2HqtaJ6uTcDTWNW366boHjnImnc8LP+/SKpvps9caLo4hq5qjnz/uMxYlPjkoJ5yx3U5pyS7eU4fgnRuHugXabzG71UNfcgdPUgRt3fjob/Saswfd7nPzZ6pkjBlF8ieulVXUY79L/Wpa+/UGxmRr5GwZpy9Sv2QC3uYSOHXCvWXE+5yZQ8iO4ra5rSNG0lPlisCzLuOkmL/1mwnQWNz7uNcHmRohHX9m0E2V6+8ez0OupUSitqtM+NcFPwztvjFmFT6auc0+DSaRlbo7k2HpqvRHVJNVNQx3YzUreBs0GSZ2yxeMyj9NxfKb6XVvl5Qrv8fosy7KwsbgS5740Dq+NXsl5SoyojxN9EtY2sFtb1v2u1YoCfK+btiym/KYl+k/bZlPKZqv3+O0nnJxzAZ5wVi1NVpivqrjgz6yGd3hzWlWcwmaenekE9XG3c26EDBLcZinxuIXl28qEHYnuDl4Cvw2QfT4dzhx+9+40LNrs3UB94B2O01SCexBxVJJ7PC0EV+dkkre3adx6HAzYQUTlKJBMIPLY/xbjFy+P595zs7usi+7cmFeOKovwv2o4qpKV++rt5Rg8e6NiPKnX4hbw5ayNeGfcGty4Z7EahP1gC/ZdWp5jPW8at/xn3EwlqCalauM2GPz3ULpF6uZNVikOH+kv3lyW9Oqs8qzX48KJurZoUyl6PzcWX8/ZlHpcjKeNoVgbWCHwA4MXYCRjW52H0+6iE259dmSFl99hC7ZwzR/I+rlfvzkJAPDjInueTStWel1obCut2vNM0zXVRWAinZs+mK5kLiXVwahLeMF1t9dTLVsvfaRfG7SuphJCsHEbg9y8kh8hAE+bVzafDeLtVeJk33F7WQ3OfnGcXhrCNqIVjVHYtBNtb/yKHaisbcDopYXCuWnTQ8HlbV3Rbrw2ZiUe/98S17ArCptObrXME09uY+DUbUHn7HVdJ6OOqdeqR/Z52Nd+qd/Fb955VbJVC5ngNvXaI98uxtkvjsP6nZV4Y+yqlPuysV3XkR/7DUXN6bPp63Hm8z/Zjtqv3+luU7ZKctrA73zaianNgqSNW0mW/GwYsRtrOhuJbjZubRvwiuXpx6a8KYeIupu1znJ4eZR4YyMet3wJibMdEtxmKc/+uAy/en0SXhi5nHs/NyeW0pg27aqUaqEB9gbIa4uux6otb50nIJ+oZmq3YFbjtjE2t7JIlLurczLpANqE10WfzmPby6ulmk2fTxfbdDKJZZmZqJhex8qiu/DViUlTCF5ZvV3PNpwq3y9sypezTKzkdVZgpZ+GqGzcnJOpfqIwbdwm8DMv0p0Mi0JX1NS7jh/JODgC+sbranlIeHWWeftOCCPdjgsv38q3L5j4rPd+MRfby2vwjyELkqcVZPlV19S2x6XiCM+ZNzdSNW5Tw0xaVYRbPpypnPbuGrVFIY/VEhuBPPye8JHWbZFQas+/izeL7U7K4mfrNneoEIwfbu0wSM3+wDVuNfpIr4tz51M5OTEsk9gO1X3nt8auwqBZCXNUqe8r+z5BrFuVThswQZZIykLleZXrKeEMjS2itPdpn++4Z0/RWUalVXVKiXgRNJRX1+F37051D8ghP0+scctbJIimoEFYtWLXaTpasU7YMuX1B35n1bzyS5hK4JubsF9cuqUsaWKEZczSphN2sqm/rv8Fm219wbTp0W8XY3OJ3R/F13PczSDITGa4reEBPfMHKidOVOKr5QhunfmLS+65wc7dUvpqyf6OKJma+gas2VHh6eRUnQ+lNZUUVm+v4J6u8HPKS2edXx+3bGs1ktvqQYLbLCXhVfS9CakOq4DGibZzoDnrhXG4hmNHiuXbeU2CFt6kLC/XTYuzCW1HQ6rBTR4XculxWIPkXkjR1jHQwfHi8KZxK0Z07Ekn/zo2cK5+d5qv3X6T6ObCtssZt1J2I6NC46KHny+nNp9uE2ODJ+rLuqLduGfgPEf6TVTVNpp1YRfYXgQMokfkgtvUjS0RdQ3mPYarpl1TH8c/Bi/AsIX+BPMqJPIkylrPx0fi4tcnqsXF/LYfh1f/vjX1DUKbc6+NXoleT43GDwu3umrcrtpegWlrdqakn2ijNfXiCb+zHa/fuRsLNqmd8NDeuGT7EcX64ewzRf3OQk6evdRpt++n603Z60ItEdb27Z1hRMfANdNJcTCq/rgNt+lQkKYS/A5HQ+cG53FdFWefGYO8THXytLKwHK+MXpl0YMubikh9AgSgWqD7zby05+8F40pQphIsy3J9L/b2fh1bpdyzz3Xt9eL3/52JmeuK4YYFuT1THu+MW4OdHp0ctszLkR5bd5a3SBAWhKmEOmbul6tqB4gD+wbctucz77w6mb9H4zaPk29W4LixuDJ5isQWp2Xhzk9nK6UvEmyJXovtQ3Tak0q3lZg7uz1voh2bqnJJG7eSjXbLkXedNsbWuRQbt3v+3bU79eSRaNy9sf8MXPDKBIxY3HTqSFWmoargwMPtlUur6nDhqxO4pytsSjDaGrfqYRviccdcGqH5AclESHBLcFm6pQxH/t/wlOsLXOyCyY5gAO47snFJ5+kHWx9kUCjmFpPMU7MKzqM3fgbS5A4h72gHUsvd1cStpBwTn66sug7VzCCkk38dIdyG4kptW4JBIctGSWVt0vuxk/qGOM57eTwue2uy8R1IUwJ/UTw1dWbMmkxYuQMnPDES383fnHKMNW4BH09Zl/z7+eHL9+RLXcDvZEtJFRZsKuHek5lKSORHhYa4d/uFvPf5w8ezcN1705V2uMur6/H13E1K2oHiPOiFk034ZHYeRWl63cS45I1J+MPH/EXVmz+tBgD837eL7OkK4uKZKUhM9GUTfmcfdtmb6vbU/Yx/qmWWonEbsADNdL/G5tevQDJoR2p+43V7TvXbBf2NveDUVJfhufwcf8di8vmIzliiYjJjbdFuLBaY5fJb1yzLSvFMrvQc8/5e9r2f+WGZID/6cZlCajLGSv4ved9rXns/N1YrvFPoo5Nuo41bkeAv9cOJviWrjGGqH2DHdD8avU4FBic61ZN/0iE1XOuE4JajUMSW1ZIt/HY7QzCX5yEaz1W0WrU2CxVGwkqpqQR2Pq2ergh2c9iyLIxeWogtDi1hFRIbBLLNWpn9W/f4mx5wztsSf57/yviU58T+c3YBaDRnkYxXcf5b40twa0/jnXGrceuHM5PC4M27xGVv902jma7O2j4un3sRckhwS3CZoOj0SCak4rVFN8+g7CO6Ewte6Iqa+kC1OVT7m0WKWlZOnOXra7KVMKbOucWbNLmaSpDdsyxMXlWEE54YhVuZY7Y6/bOuNlVUNG5l/PqNSdgkGDjX7azEhuJKLN1ahp279RdhMu76nH/M+iuFY1UJGiTaLqkat96+xa0fzsTu2gbc9+V8tGmZan+MnQSNWbYdgL/JxhnP/4Rr+vFPEcgmTzX1DXh46EKlND6Ztk4vUwy88v5p+XbMXFes7PTDL6oTLMvxr680bRPw1Ml4fUMcTw1bitFLxY4AC3a423orr65XmjTytImbBNVN15xjjfOIY3mNWNPFiS/BreKzzj5eJ0kvmxGmJ+he7aQlQtqeF4RJfVYjHe5gK39GVKpuwvgIymOV8eNFG1BU1nGUTywmt3fj1zwEj38OWSC8x3uH7eXVStpXT32/FKc8M8Z2TSX37Cua1MZU1v4OoM6yUTrrVaMWXtPfccubKoSXfIu0+FSQ2bjNiaXmZ/HmMlRytCpZ5QZT9ZvddPETpaupBI3qycsHL2tJwS1n7aBiUzfhzFsF0TpUVGQxhTA8VMYB2XdibxkR3DIfbtjCrfjjp7NxxvM/acfTdEpGMmY7BbmC/I9csi1lE411Ttbg6DgSvcQuZoMu8Vp85fCmd663beyrDXS+NG4df780cgUmrNyB4YsbHbLK5kzp0ritj8elcy9CDgluCV/IJnu8xuhqKoFdGCs251gMqKytx9oi+0J9Z0UNej4+Epe+OckeUwiamV4dYDlzOmzBVtcwbnE5++Nnvl9qK5+VheX41esTMXyx3BmOrGOPWxZu/m+qtrGW4FZzJRoFjVsLlrRNbClNPV7XJBRqet+zXhhnLk+Wldz9dSJbSDqJW5bwyH1tvX0HX/dLJIqMtYGtuphn64lJExNVEjtgA6ZvSAqO3Rg4Y4PnLkf2PmFMdmTFG+fUYxPp8DZwvpm3GR9OWYs/Kh5TFNGgqHHFC5JYiLHfaNQSpzMu72Wh65yTTerDKWvx5P+Wuj7jrJdrNG3M8vMhqbOGK21cUE9UkS1SRe+h8w6bdlVinWNe4javEfUVbu1Kta757SOD6Hd06rqp9E2aSkjR1haE03FQVLCjAqc9Oxa/UjAv8xFzEsUrXjdbeaiWnOn+YPHmUvzrq6YNVZ7mnF2wq3ecOhmPh1qY4qleI90WuTJTCbGUe1/M3IBrOWbtWOUGPwotA2asT26asiZ9Pp22PqW/U4XNTbpOBSRs3PIUirw4QxNp1AMSwa1Q5VYhDId3x6/ByyNXKId3YheoaeaZA1uMk1fZFcKqahu0HdYqKxJY4n7oz5/NwWVv2U8/1clMJcjmvZz8i5yT/eebRSlhefhyzC7Ia2I9w1M+SGD3F6L+jaesLsKXM9X9yMQdGrdRNA0YZUhwS/hCNrTxOlU3LR374K2WB8sCnvsx9chWQmt4+bZybl6WujhjGLfCXTCjOoEzpQy6TdOuFovIOdkHk9diesHO5N/vjFuD5dvK8dNyl/eXDmbaj6SGtQnx3WHNVEXxOKiIxHsGoeEDyI5o6aVnWY3emXk4d4hjHied+Yxmiboggv1trgwrJNqRWzkCeL/wvpPsbdJ1vMhp11UsWLL/a4oGjmB+u6YtVBmD52xM/hbl3bIaNTJ4k002f49+Z/cW7qc+6tq4ZZmyeie+nquiTW//mCLtc+6THsY0P91ywY4KXPH2ZJvNOHv5qkf+/PDl+GbeJuki1UQ1/uVrEzFE41SDDLf8hKnl6BeTZrFE8OyAyuZv8XjjMdMfF23FyCXbUCDZ1HDGIipj0Wvy8jFizyZQgVdBmGaR+jGN5TdtHfbv2Fp47++D5tv+ds4DLaQKLtLVHPxo3DY6i+bfE/XDPPNIrDDSa5sr2FGB//tmcXLTlLVxO39jCX7x8nhP8bLvF0R/wKu/rfZo3HoR0gKp61+R6RBAMg8Xxh1zDSPi7XGrNZ9g8qOwGfrEMPdN4QQikw+7dtfi2MdG4Kq+U5TiaVIMYPLqCOPUxtXps9i+QmdTQ6eqjlwiPiHG4ucEiig7ietOTdeKmnr85p0peH74cpvJH9F69Jp+U21tHgBu+mCG1pzcqXFLKrd6kOC2OZMGBUSpqQQ0doC3fjgTfTiCVe4zCrt9PEZxOkRZv7BoUynX2DzL7R/NQnm1N01ZJ16P8QfhTIA3mO30ZB9NjMybuyp+PIYHJQR1w7L0NVeSA6oZM7EpiCYhOzS/uUwwILMHm0hfxfFQSw+CW5tNO4PfXXYMTldbxWsrjpzGrfRe6sRal1WF5Xhg8Hzb6Qne0TiT3eLG4ibTJaIxZ8GmEhzz6Ai8O2ENk6/Gf2WbRH7Wnyp1bGNxJQbO2ICa+gZPWmBBHFJQqSMqON//vi/nY+GmUtz1+RyUV9fhT5/OxnfzmpxeWRZQXdeAcSu2o9rF1j4A/H3QAk/awX67GPZ5XvE7N7erahswbMEWlLrYUa1QPLYbRe0WUV1fvb085Vt63bByPpYTk7fPpVtL8dLIFfjrgLn482dzcP4rE5TjFlVzmcZtEI5o3WDTMDrXVMy7lz7ryK7thPec8z5eeTs3Ir2Us5dnUoTI2nHI+iq1yNhvLGpzy7aWSU8IsvOiuoa4MSErz/Eni1c/Acn4OdlMnPDiadyqKH+MWaYmiJPFJ/p0NudkaeyyvZofEsGehGSLIKEYtWBTqZ65KokdW6c2rk7ubVqyCicoEvWxpt59rqGLn/WrqCwLy6qxtmh3yvf9Zt5mzN9Ygn7M3BYQ9w+z1u1yV+pyoSFupZyWCv+8bOaQF3YGiMwmtsenKQ/LAqauKcKElTswYeUOPPzrY13jk6nxC/MQA/JbqO9BxABlL+tuznRU8+h5UmywN5NFpeo0iEV6fES0QEGjttOBe7XG739+iO1edV1DcgdcFocKYS1OxywrRM8DOmo9kxQABZRn5+5ogtOe1XOuIZvHypyTxS1g6uoirNsptsmamATZBLeKgmx2kuPXViJLRY148ZKujQFZMumq4qp9clLj1odI+XfvTkVZdT0mrSpKXrObSmj8IyizKKL3W7jHRjlrciRRB9xMxnilTmHheO5L4xC3gKKKGlzZ6wDtNPyUouhZU3V24qodOO/ofZN/s1ru745fg1FLCzGKsXFsAfj31wvx7fwt+E2vA/D69Se5pmErYkfeRF7j/XradnvaWbUf+26xktZuwuGe3/TDgCf8Gb9iO277aBaO2a89Rtx/ju80nCnEEJMKDHTGErZOzN2wC927tOWH0xDoBn1qqL4h7hCCmEvPhDd6YdwaUXNNJVj2+0HmNUFJZS1+WGQ3c6aT7l8HzBXeU1UUqK2P205h8AQzQ2ZvxINfLcSZR3TGgDt/zo2nbX7THH13Tb1wfqmLTePWsR555vul6NC6hZF0bGnu+Zdnwk+l+X05a2PKtWKHE7oEuiffbKfW0tRrL9hYgts+mtWUrkKylmVJ66BdAN0UYUxTMN20Tmq6VlplL+sUX4QaxWbzVdDg7DfEEYnmCX7w0/eLHn19zCq8PmYV+t9ySvKaBfspR3s84jz4scELNPY9eYwLk3TV7+YCadwSSVQnbp9NX5/cLZPPGSztBu7VK2R+XqojI3Ea6ked3bSeVPPo1duqSfFE4lvx8izTmBQhPWYouLV4cyn6TViDR79dbLu+cI9G2zPfNx3BUbG1JEyfeR1TE0sVVhZWYCijBaaCCU1FW3yOiEw559PRuGW1I+KWhQ8VbfB50bi17dwaXOxWy4TRuhq3HhuyvAjSP9mRe2G3/+uFsj3aPKx2tt1hQuO/QZxE0CVuNY5vsk2voG3cJspjesFO5UUWi99i5M0ZpFriGuXhXLxXMc52dlWmLo7jloVv5zduyCb+dYNtx2xqCzaWpNjAS4bz2ezcysD5SUyZWmhK32h0RuCNUd/sGUeXbyu3XTeVfRffZHrmrZiIruo7FWsEjhHXFu3GzR9wbP9zwvofyuQRXOywneumOamTnSBlzjp9qnOcfnX0CpsNXKcgNyj8+nuQUVPfoFQm/ScVYOCMJjuU9fE41hbttvVH385vbHNTVu9MeT4BO/aWV9crKX1sVxBu2QTqju/2weS12KjhjJVXHLJrPOdkXjcyXhyxnHtd1zmZfiD/XPXuVHuyGgLVBP+dvNZWBrGYfS2QIEdgQkFEovjY7+Lc0LDJD9yjdMTf9ISOjdvtZaknCf3aC1e2+2s12uhesLFEOW7Wj4FlAV3ateSGk00//a4pLfB9VxBqkOCWSKJ65OXRbxfj9j27clLnZJzo3Poz9hmdCVorjsatJfwDqFE4SqmSB9UcehU0qAwAqlEnhGk84Uudhx00Lxq3oiNYL+0xpv/B5LVMHGpp8WA1LdZLND2jgGmNW2c0XrSpufFKqohT45atkzrvxTon8+JsJ12a1mnTuFUQlAafB7U0E3n18g1kfRivH/BqesYkb45dhaMeGS7d9PIzv9WxcWtZamORs/8N4vipLB86xdG6hX0zttJlzPbSHkRjjGwu5LfZhb1GSZdtbB0S5T1u+XZ8PGWtPLDH7DtfOxaTb8Dp9PHOkEu3iv0nTF5dlHKNl5RvJ3Iuj6/ZsVtrvm1ZjWY77vxkNgZzNA1ZgrK3rFsizq65qKIWixgHUl4FELr53lnB18I0QcGO3fht36mu4aY46t2ro1bivJfHJ+fegNp4wBZZRU096hUUI057biy+2OO8aCqn/gNACaM9yWt7fhUweHOpxDWejVuvY7fI1qeoPxHVJZtg01tWtHG2BxWFma1l1bj0zUlJ51RPf78Ufcc3HbvfXFKFuz6bg22l1RL7q+7pJG3cMtcKHUJTW1la3jU5VfqvxOfhzRVk/jFUUBl7Siprcc5L43DBKxNw5TtNdoLd3tm5XsrL4YsBZXlg39mTc0cLgTmVzgZIcEsk8TKJ4R0xSWDB38RM51EdjdtGb8JqsbuVyTLJBJ1FR3C7aFMp7vxkFlZvL3cPrEFVXQOGzt1kM0CewJvGrRh9e068iZP3zjxuWZizvhijlxZyd9OjROItjQluHX+b0riVDeSpGrdNqCSf+Py6GreJHWeVPJpE28atx40bWTJpm+oobqAkNSK8JCF7T45gPqgmrTMJdS4aeHjVAC+qqDHWbll6PTUaYxm7fH7LkZdD+bdUj7ul4wif/VmOhpR61EyczAIkTS3KrQyCViaP4hIpoV1++8ez8MSwpZi3YZfxNFKckyEmFdDrtD/nN9VezPI2QHyOZSqbW2wKblPAuGXho6lrMWZZIf719UJp2M27qqT3/aDTp7rNIV4dvRJlhvxYyOB9iXTZ4xeROObPCtlU+h62XtY1yE+csDz+vyWob4jjRo7GOQA8MGhB8jev7enMoUz05X58bPDYWlKF/05em7J5qmQqIaROWyXdl0Ysx5ItZfj30EXCtjliyTY89PVC23xW5LTMLS9Su/S232r2q7eUVOH8l8fjI+ZU4Mgl28QPSNI0RTxuobCsGn8dMAczCoq5YT6bth4bi6tSnFe6vbPzvqgfkvWzK7Y1yT28zFOdBmqiOCeJMiS4JZJ4GRx4Rt0TbNpViVdHr/ScB9WJa2lVnc32IDdeR9eg2teYWjzrCG6vfGcyxizbjlv+O9OoqYQNxZV4YPAC20QtgRebNf/bcxx1e1l1yuRX7D2ZD2+Noeusy6Y9Erfwu3en4Y+fzsaW0uAWESYxJaf5aMpa9J9YkPybp6ngZVHo1eaRThvKsR2rcg8/aVWRbcGZBufkjemkKaEoaMfNXNc0cZRrAO/RiDCcZdsEz0rVkIlCGYnwkrVPp63DKc+MwetjVqmnA0u5HJ4fzj/GqE0sJugTxPnQEVZc+940VNXytWx52fbWp7HPqz0TdH3z+k16H7a3UjjfAiPO4wU7KtBvwhpU1nrTNHJmSWY30KtQhqdxKxubtAS3zvml5vjQ+HzMcc0fuhvWKhq3MqdVLDt36zu7VcGy9MZ4lbp+lYK2ako+DIga0j1smdoQstkCjVuoV5yoN8QtFHNM3CTYxrR51Th14J4OkXwDFQeXPETF/O38LXj6+6X455AFtuviLLAatx6EYwYqmIqjMtakmExxYnNJlS0+u3KHmpJG47/SQK7xsFz+1mT85p0pKCjajc0lTevElYUVtnAm01ShwbLwn6GL8OOibZhWwDdbItp0rHFZy9s2qy1x/ZN9y/6T1rrmQ56J1LoVAetnGQM5JyOSeJnQt5Bo3P590ALhPZU8qPYHCccxqsRi3o5h+0GnU0q895bSajzF2HwNEi+C2xdGLMeFx+6LX742MeWe0J4Tpzwty3LVuNWdhLDpvzVWzWlL2JjaJHjmh2UAgBt6H4x2+XncgdVLWrK24Jws2LUF1NNasqWpLassfosqamztP10C1XRp9ko1bi3n33InEV5wniiQ5SduNZb/XMPacjw7pOx7Njo6MPPepr9qg2Xhg0kF+HjqOgz+8+lKzzz23RIA9oVsgvLqOoxZVogzDu+Crh1aJa/rVMdV25sWJf5t3KZeM6klPnNdMc49ap+U67xsezOVkFq33PBbR9zs8JlYcMv6giC6rvNfmQCg0d7fY5cfp/38ki1l9naehu7VsuTHr31p3HrIixO/c0+Vps3WtTXbKyQh9+RHMUt3fSZ2pmVLXy06xzPqHYxKGW7yoB1son5Gd7tRjvOotargpiFuoahczWSE0zkU4N/fh2yLkWci4q7P56Cgz6Xa6biNqWOYEy+AuC751biNW4DfadEGBbvCLZiTMbI+MwZ7f2N3vuaOyoku+ya/e7mxZlNk8PqcRPaDWHL83zeLuddNzPHZIc+CuE9XfS8vglsr+b89f2dqZxgSJLglkniZKIrso5jAZFt2vprqpDwMjVsWVQ0Hv6gOYE5YbTwWUV1yXt5eVo3L357MPXasfdqQ1QRg/hDtWKYL193yPXk1rWWQ+AY8J0eeBlvJI6l5twvW3OBNglSEow1xCx9PXZf8O202btPk705+LKzp3tTVRfjLgLl45jc9cfmJBxhLn3Vk4JYfWMDnM9YnBY86qNq4TQh32OD1Dg+1fjBdfeKWldxIeZmxJeiVfw5ZgJFLGhd+k/51nu2el6x7EVokiIHft8mdk3lOzhUvWkle+gvf7+DyvNcph92uuHjRHmQfOWc9fz6gwv8WNDmUky7OOTdVxhhnCAuWtCxEcRZV1GDvNi1tpghS4tadu3Cu+Z16qjzOhnFzqhq31FuYsuktjwIpVaIiEOAJW6J0UqS0qg4dW7dQEgo5lWt03kN1PcOd+wXgSDNRQXivHZQOAG/DnQebJS9ZiVsWcn0W2v2D5qdcc+aX9RcjW1fkxGK2U5S6zslEPhQ+mrIW8zeW4NVre9nieWX0Ss9a0ylpW8D7E1NPqrL5UqXch2kWdlz3Wj2dJkC8mEpIsLW0Cp9NW6+dh9Q2oB1FVkOmEogkXgYqmcatF2yDgo/WfNdnc1BT39RpO6NKp8ZtaVUdvpsvnxT7wa+DGUBsUN+NeRtKuNdFxeYsz77j1whtRepqQzU4BqRMIZFV0zYtE87EeDbIdI5hJ5AuclPSsE/uvaDyDZ1lFoRdUB66/YLXRZpUTsrc+/2HM1FaVYd7v5jnKR0Rzr5Fru1g4VMPkzg3nAvFOet34ZFvmzQSvNjnFmG69rDf3dORMgcJoS0ADJ0b3Jiiiu7xU912wAvfMi9HYCpBK+qUZ5TzZlCgJiq/mvoGfDZ9Pdbv3J0aQCWNkITnflS4dU9OsbTLV9BBcby4m0YWr732Hb8apzwzBvd84fBm7ojIy/jgLDq/c0+d48dq8aVec9qhDh6Xj+YgMOFbROLQQbZO0KkHNgWJuJod0QQ6ZhWc6KxzuP0qL5xqfMopA7oSZlHcNrNhHiqyibkwzx9KYiM6wailTXMSnqZ0gljMLuS0CaYVKlFS49YR9MlhS/Hd/C0Yu6zQ1uftKK/BE8PMnFi1ADz343L+Pc1ifsjFPrg0H4ZO4yR/Q9xHqsxXr39/Otf0omseHJum5JxMD9K4bcZoC/Q8CW7NTtzY9is/JizP7Igl22zHPFJtkKnlx4RM4C+fz8HUNeFqfQbFV3M2ca+relCVfUddQSw7uUmXAE+X/Tq0SjkCnXg3E4IdliaN29R4+03QH2xl2XPm3d6O3d/Lq3M6Z57SNQFQ8aTM4jVbqtqLQWnvOM0kWnHxsiRueff8rCrsswDcO9AuNKlXdI6ilg/TbbDpt2kbXi3yHEL1NHd5ovdRdR7ilTYtc7lzG2/HSfU3+1YUluPLWRs46atFYGu3nBJ5bcxKDJq1AVtKxXZe3RgyZxNuOO1gz897xVQV31Ii1gTnC2UUhJSaeeAJS14c0ag1/+Miu/Mav/WaK1TyGanKdGL9Tvej0AmcjkD7TyzAUV3bYfFmNQe9JmjUENN6IpB8mJhnpLO/rmuIY/LqIiNxOQUuWoJbxbGa75xMPR1VoiAvEuXB7/sGNRf+7+S1wnsywXwsFhPOh1TatJuN28raBmOt/cBOrW02b7lj+54X0E3TOXboYGKJaLfrLz51ojKf0Rk/7HF7M1NFNEIat1kK1xmUh44+z7Dg1nYs1ufAs0tiCF/VRqWJ4+vNVWgrQ3j8QmtnX+/7s9/UpDDHJDy7lYlFp+k8J8rDqzDNiWzX39medNtxfovclO+tdvxVnG6Q6G4MeM2W7DmRrUyTwkfn0UmZgGRGwU5PtrLdcE40nXmK8m49W09MrDvzGQ23lszYazH/Tyeqmk1N4fXy+NDXC/HDwq22a60EdjG8vL2XqnPte9O42s7qCrvuAf0IbQFIjy+aaC/1DXG8/dOqFKewpoQrz/ywDMW71exhAt422L1o3IojS41bNy9O/NprV2lrDwxW90PhzM6zPy7zLLStrmvAPQPnCk1tibCgO4fUzJgi72pqmXHbRRq7608Yc1IyVJovO8YXVai3UUB9Lsprezpdi+pGSKIvNukbQDcq0Xhg10jVz0cYuivuNm6dVxp5aSRfm5WlvLrR8aWo/cdi5tq7jgXIdE4/TThFdApMRfkPUvnJQurJI/JNpg4JbrMUrxpuTnRMJazfudu1cdqEEVItLPf0ZAOf6qQ4AOemWYHo++jUMJvARuFJNnwQXmmDIqE9ZnqgXLGt3Gi8sv7B2Z7YsCrp5+fleLJ75Iw6XZrWus7Jgta4ZXl5lH9bqgmcw4TsPf4yYC62ehQ4yRY7bFnHLStlUm1ScGu69ti8KLus6EYu2YZ3xskdKbJHwtN/TNlODIKylxSibvMsLKvB3Q4N621l1QKbhPbIVQRXzsVDOtjJCDtMpsnGJatqftO0YOHLWRvx8qiV+N27U233/Cy+nGP88j3jV2q4VHh23FOec44vLrMKP/2KtqkEWCll59vGreH63BBXd0TlxufT1+N7x4aMCpal10dHRXDLI50bjvM2lkjv62Tl6n7Tkr//9sU8re+hWn946zNdgWjqXDA1zsR7mxQa6cYl1rhlbWjr1xWduXB9Qxz/9nGEPxmPTHAbs4/J7Pccs2y7a9yseSweObGYEcEmkLrul20kmEpThTnrdrkHcsGuCCGeJwXpgNl5eiNK9r4zARLcZil8wa1+PDqmEs59abxrGPsxQkk4hfREC+UYYsqTpnR5j29uiCYNzuPddZJKp6txwoY3bXbAD+omS8zm+aYPZgAwt/OuZSqB+T119c4UT7pOWublpLRJpTbqQViTwI+QV3dfwPPkzsNj74zzv6hM4Ky5YbQqp7f5XKcWsMlMGX5BmzBNGs7Cnz+bg5dcHJi1yW/SNnU6gQtjqOIlKXN2ITtmKePhofZFZcEOju1XDxs/djMc/gpQ9ekr35kCAPhx0dZkH20C1fRNCIxWb6/gXjepuaajva8y3ju1/dyKQWd8cNYd7RJOqbtuYmV3ghAMjlvuLmBRQUebmkV2tJcbPiKHcFX2t7aUVOG3faekJT+ivHhpvjpzLn8at+qZ27CzEr2eGoU+PzbZY5VlU/TeDwyej/u/nKc1tqqWYXl1HQbN2iA9GZrAk8atRv/1zbzN+HLWRv1EHMj6zJyYfd3tdaQQlUVOoxFdIzj7mItem5gS5p1xqxHXtPHslxs/mIEpCZMnHhN2CkxFn8zviQ9pHmBfQ0Wjl84cSHCbpfCde+g3n1yezQUf9J9UgJWF5Z7zw8K+ozMm1T4pqrZSWYKw/eQXFbs59wych4EzUm0FNsXBPueeJjvZC+LIdtAENQEwtZvp1czFvxR28vPzcjy10UcdwivVI7Pfzd+MHo+PUAvMIV0bOkFphKni3OCLWxY27RLbnwwCtg+OW/w86docTheq44fqp2zbUuCWIIRhKhaLcfsW0St/4+K1XsYXM+2LylXbU7UxvQjPvNi4FaHqSKwhbuGRbxfhrwPmugfWgcn/ki1l2OFwONoQt9Bn+DKMW7HDd1KiOYfJqYhIyMOrc3KBQeO/TgGJJYhLJc7UPMn/diPVVrs9jiGz9YUqQUxdeY5OvcBTHFGh8ZtphI/I9J1Xl5x17+Ghi4TOfoMm0Q96+So6Razapvyuu7aVVaO8uh7vTSxIXuPFmLi2sZhvq3Po3M34dv4WFCsIV3V5cMhCPPT1Itz35Xzufdn6VYVL3pikLCjf6XEjxYmbxq3dxq23PkA0782JmeufVOpffdzC5NVFadcWHb+icfOssrbBJSQftvzKquoxaRV/PiArgqO7tveUdoLa+ritTkfZ3FkUIcFtlsI/aqgfj1P7yS+VtQ3J3S25qQT3zLJC5cWb7R6LVTsK6lC8IapL7OUfFsmPyjmdErnBTlKipHGrSlA5NhWvngdqvVR5GrdeJu+qAtX7vpyP6jrvwj5tG7ceP4LsuXRU8aIKu/DHsoB/DFG3i2gCu+A21fu6ZQEjl8g1ulUxraFli012fF0xvlYt+PZddeIwCX8xzM/J6h18LU0vVHEWLanCM/cSMdmGzn9lgnLYz6eLNyy94iz3F0fY7QYOnr0R700owJtjV/lOSyR4Mzkd1LHNLluwJ/JavNvuIb1Rq1WMH8GtJ1MJrKDGoVn64FdNm5//W7AF7yk4Fw1CoKBjGk2G13oiO9oLpPZHUZkF8uaj7JXqugZMWOl/Q0WIS0H4WefoPKrqx8Hk/F3m1MqygPkbS1C2x36qCB39JBXN4HkbdmHEErmTKjYeL215W1k1ZhSIbUh/OdN9DNI3TyFxTgZ7NfSq8yUqiVgshqo6+XdURbU93PLhTGPCYlXiVuMJpA88nl5im9bV/aYKNa1l45/TMa4uf/5sjtFN82yDBLfNGFmny5t46y5aLcsyrnHLIrXvqPA8mzOn7UVlUwkZKACMAqJjFjoTRF2tRlbLNrErGQWWbpU78Eg0xagPXlq+WjTfhWfjttarx5kIEkSu0rGp9O+hi2x/h2E72tkPOMecuGWlOElyQzQ2mi5Sm003yYJO9VuKBCdhHQnmZVu0IWJypsBLw9k/qZQIO75Hs+dQx/n+pVV2QeUagXkDL4i+pc5xZjdEC2LeVZmQN2dPf7G7xr6od/veqsKjqtoGPPqd3f6irqAlVfAr7hP+9sU89Bm+3LXPC6I+m3JG7PmYNMRHe4HUco/KdIA3brJ5e5/RDA2DRF68aEH2HS+3y85S52P+MMiD1jngogAEC/+bv8U1Dh0NcZWgC1xsDjem2fTbazWWzQucczseuopZ0j7TcULH++aNWOOWt6HrBZ21jpsCkmksK/UEkg7suLLdcSpHFC4InIpZor6H7N+mQoLbZoyzvrNapyZs3MatpkmxKjoTA7/NVZaWqjdc03Zeuu3V2mh8bgRpp0aarqCzXVWovnjUNZXALt5eH+Nfq8gUyselAxIdmBr3dAZy3WqXz/EU/7cv5ulFgqY+o3uXttrPBopXe1SSOhHGhGZAAJqCbtg0buNWythlWcCHU/S0D0Qjg+kitR8NFIdTTXfvti19x2GKRi/R6oma1MbkbeqkmEpQyNoXjNZRpi8QnPnPcwj5K+vMLGot2bwvBLNN745fg6/mbBLeT2SJ67xO8slVx7t+E9akKAboVqVUTVG5gBIAlrlsCAdRnXV8WsjwbAvZ0jSVEJHtmAbOJgSbt+Xb1NYjQZEYY718lYWbSt0D7UFV49YkiXbMrQuKcwfjG7oKYewa+B7TUXxOFE5X81n2fXNiDmetHgcLUZZisZhn8wFOdBS2qg2Nq6r4FaiqPi4rAxPtgY3DsizhJkGGT8sCgQS3WQTrIIhrKkH7+K8FQyenuHjxqM5iYqFo2pZluu3RmjA47wVRVdpcom4f0/793b+DztHKKBKYjduAPK1Kw2q+TNzSczoiIhFF1MZ6zxoTUsGCWjiTTC/YmZ6EGNh6EbdSF/1e6o1JJ0pymvIm2+NUbaOd2+Xzn7fCEVJE6UBKiqkEhfKYuqapPkfoVTzhzH9ejn16b0obqcGybEtu9qRLOlqV8zu/4DAJ4SSx0aNrh1ZVaLGBYx9Tt11YluU4Gu2eP6cZm9Q8mK/RbsJiVbx2v1tKq/XeywpmQ0b3oGEVR7izZvtuvDNutbF26Qe3Mq2sNXMEPYw5euLNuKYSFOPQqXMqdVulf7DPUTJjdJKdkmvc6DWRCj8Sy7K47cwLWrKQNH8av/2Z6qm5oP14jGUcXVpW6kZz8l6guchMSHDbjHFOLtgdDV4T0W2ncbnCgn8kkassykQ7RjqTRtOmElSO3OhOChNRrt5ejgteGY/v5jc5gWF/pxN/NrMs27+q1GSgQ7IElmXxPaUbidxMNFqTbu2+xIx31kS/EDXtuSCyE4Y2fdt8gXMsn0gF1DbvsxacSl9eylZUP0wLP20atxKx1s4KNecgUarWsVg4pjNEpGgtagvPjGUlFJzdQV5ODDMKdqJkj2MdU8KXuAWb05+BM9Ynf3t1OgWolf/q7eVYukVdww9oMq3Cc14na++q/StvrqNvdsz+98AZG1zHMLfyirKZL6/15J9DFmjVYwvBtGvd/PM2AW7oPx0vjVzhuvGQDppMJaTee+7HZTjusZGYuqbIdzph+J5IatxyklY/DWcWlbm0TWwb3aZso06yBsuJxWz1y+saUfTJ4pZldHNSlXR/Gr/pqS7jpCZGDLw0e0ombllCMzxRW8tFARLcNmOckws3swZeHCoE2ab8Rr1+J99TqE6eVxu0CweoaaQ4NWVUaIhbuPDViVizY7fQS2k68SNUSghgbQIbhegyWeP2rZ9W47UxKwOJW+dLdGzdQnjvynemKMejrXEbN6MhFF2NW285kj0Vxro8KAcqsndhJ9FxK3Vc81JvhM4TDZcpG59srX/G8z+pxqiUVrrQEQ6ZtH/KxXcBRK3X0MTx/qOWFuK696fj4tcbnb1uKFY/7SKj3jHOFjJ28vwosru1Y8tqnOO8+ZO6TU2gac7lrKtuWq2qdZsXzG9VfOr7pa6npdySiPJ6109PkNBwvvaUbq5h3RzQeUXXRJyMUS5OqtJBU9tLfa+E/d3nh/sXMIexmdA0J0xNW1lwa7gxKZWlARu3qphqJTLBvHNs8FoVZGYd6gzVL505ZboFi37XSapr83S2VcsCWgj61AgPY6FBgttmjLOjZBe8zqOia4t245YPZ2rF72IizDd+TSWY4JkflmHehl3G4nM7olvXEIcHuS1mrOUfYQ6r0/Ozs57YNdX1Oplu754meXV0MEJbQK+tmDpBrvv5GywLM9eKPeCqYqGxDQVxTPT8Y/b1/Kx3G2WyPjBz67sTVc03y7I4phICy5ZvTNdD3rFsAJi9fpftyHo6iCEWis1CEVGuB+nA+foVe5xxFZY1ClZNHXPf6KiDogWXLm5NxXNT2pO9FOd1LhGqC245Gre6ShCWh7HXTdAd0SVvPG75mmeUVTXWaxXHyBaCMRlh0idzebUZTXg/qFR1E8UYquCWk7Rq3dBRRDFV3ZymU4JEJX6VLMhNJcQc76T/UuXVdcLnXhu90lhB6RwkSrvGrVJbtWz/sqhqE0tt3CrFoI4FiamEaA5joUKC22aMs+HJ/Arc8cks4aJQRKPGQnCtKqiof9T0Ajlisbkdcdl87/nhy3HUI8OFXrll1IieCanT83OENmGnSLfco3w0MEx0FnCm1iO6i8b6uIU7PpntO93Z64pxzKMjsNGQdhnLWUd0waXH7+/p2SBq5rM/LsP5L49HeXWde+CII+vr622CW6TYVTc5BhmfkCpq3KoyZbXYxvD7k9LvmVxL4zZwhdtUjUq95w1mJgTSlf8yh6Apl9lp9mMqwVXj1mO8SedkTo1blzhVN595/Y9uXr0IWd2eiOp0KM7ZfNOhtKpxvFOpayq2gr3gp547aZEX/jI8aA/y6U6HRda2gjCVMNzQejHHpnHrrdz+9dXCtG7wyzZyTWjcDp69CfM2lnDvrdmx29j8TctUQpqrtMpmeaJsuZsVPjYkE5iuU1JTCRHdgAyT8EcMIjCc7dOucWu/58W+ppdB2LtzKjtOL746rCgs1wpvcrJRUCQu534T1ngeBEQTkJnr/GsxeqGu3ofG7R7BLXssO0qd9w2nHeQ7jtvOONR/RhSoqKnH1hL1tmLKaZPupMyUvdZZ63YFJsCPxbwLn7xr3IrvLdlShoKi3RgUkgNCk8iKx+6czOKYSggoUwawT3CDlVya0qhUZea64qRWpxvVdQ1C00WmcFYD3TFjZaFZs0jpJqyNyxZ54nmlDkHlvqy6Hnd8PAvljrrqaipBWRsv9Zpuf/8T46hFFbc0whCSqRD3ol3MkNioVBLcIpi5o0nBbTpMfLmVQWKckr2WiVcOo4uSpamscRtCvm32YD1WkW1l1b7WyrrI6nLq/Fm/UL+cuUFqZsJUl6dlKiHNa9NBs93n+0mNW849Exq3xrHsPphst6I5jIVKMJ5GiEjg7HxyYrHkUVO74XNvLcNCsI1KFvd5L48PLmEHUTedGkMscA+QuviZjCZMJVzUoyu+mNk4SEXp9fpcdQIO36cdnvlhmec4jtu/Q/J3kOKc0/uM1TqKZ0zjVvODZYK2dE4s5nnBFqWNh0giqS9s3eDZuDVZtsZt3DK/g9Y4DYO7Pp+jFO6YR0cEnJPUb6f7Le8eONdcZkIgLEFdCy+2nTi4m0rw/n5jOYJRt37Dj2aS7rd4cthS3Nj7YK1n3IVxWtGlDb/1NKHxzTOVkLJ5YwXjh6OF4FivF8qr6/HjonDt3Cam60EPUWGYd2o6Np56T9XUTxj5tpkV8DHHUTEpYgqp4Bb+N9xXuficMTUG6kQTxX62SeM2NXPfzd+iFQcP0++8o6IGuyrVHPQSJLht1jiFIdX1DTjnpXE4vXtn2/Uxy/R3+4E9nuADFEhExYZjQ4Q8Z4sIw8O8DJmtIzcSRxSjLMw7fN92vp5P1yJb136aKeGSrnfXqGoHsfjRuA3S/qhJ7Z+wkDonY00lwEqxAa7bPa+VnHowrffH1uvM/0qpbNpl3iSJV5x9SPR7FDt7t22J4t3eFy+yPvS7+Zs9x+sGKxjwc2LDzZ636e9ZWFYtnb+qmkrgOifzkJ+563d5eCo682RVLM7mmxdUogiqZKJgl9YkIjvNpk5gydIJmqQQi+ecTFnjNgTBLWsqwUfy6cy6zM+IsyoFka8M6woDI1Ff/Syh0yn3eGnkirSl1RwgUwnNGOdgM3JJITYWV2Hw7E2264s2l3qKPyj7Ucn4g4taiwjLD5M0J43bhKCG3Q2P1tv5F8Kw76PrJTsoPrr9VGNx6R47Tq8HU29pxeB9wfnRlHUp1y5QcHamktV0alQEheybOJ0UpppK0PuestMaxjVumfiagXw9o8g0gZbfZizrc014hBfB1ms/ryD7XJNWFRlvm38ftACvjV4lvO/H43wQgp5/DlngSLfxXz+OYMMgbllGNrFER2ttBLRGybQydyNRX23H8wN4xTCK7clhSwD4tfdpMkdqpNPfhOpY6eaou16y7vM7b1MhjFYZ5Z7Az3anbAlvwcJPyws9x61Dhk3j0gIJbpsxzkGphSHNiARBHUNqij+4uHVo4ExqokbUtFP9aBgmBnSnU6Io4VdjJGrvAwD7tMuHqeliRY2ew6x01d8JK3ckzW/oEovFjPUBR+7bDi0VnJKoTLxymoPgVnLPZiohnmrj1mRbMl0L06mpk/m1wB8pphLCyYZnTGu3sezTPj+wuPPzcpO//byCrK0s2lwayOmuoooa4T0/jou8NHu3OcVXczbZNsQTSage944KPDvlXlDZsLQQ7KnA5kKivrLH2YMYu8LQXB06t/G0AS9l1aYTxny9VcumfjUq64U/fio3jVQn6TMbT6wFW79C2ayNyLdhSZStn+KQfZ/dNQ34w8f+nUmrQP13KiS4bcY4+9A8xi6TiSWCZQXbqKJyfDohADdly80028qqIye4Xb5NzwEcS1LjNsImKvyuO6JSt52Ykh3oVsd0aYzf+uFM/OebRZ6ejcVS7XR5RdXsgkqxNAO5LV4dtVJ4j11cWUhdsJscg0y3SzY2U3Un01DRarrjrMN8p+OsBxHtYoUEWTvatgzOKlobRsAQA1BSWevJdJPskbycWNq/p7rjIv4xc11UppfO0wdAtOdJPOJxM/OMDq1buIYJ+lRgc4FXh2sCMO8UtW8xbIGqvc/0Z5zts/3McUxlvb7Bws7d4o0uAKiT1JkgNyYTRK1+hYUJcYBMppBwIB401/ysG1rkRlPuEiZUIs0YpzCEbQAmjrfFA9S43V1Tb+t88kKUTiQ6sKgeSZ6zflfkBLd+PJnurKjFm2NX2byQR23Xza8Q5uC92xjKSTTRXbhHrf7yiCFmTEgaQ8zYRLasKvPt7Tk9vrOwtrbiloVZ64pt901WHfOmEpoiDHr4iGoLqlMQLD162XE4sFNrX+mk1IOoFoiAIG1VTyvYGVjcbN+9srACvZ4ajVs+nKkdj2yMD2PuparJyhu7vPRJKt+fbUr9JqwBkJkatyboqCq4NZIan5bNRKiQqK9sFfxk6jrz6YQ4z/OjjRmKczJTNm79ZwVAo7DOrY+SbSLFYJc1BGMqIf3fKWprU0DukE+VKJhfvOpkEtzyIOdkzRjnICmaZLwxVmznS4aF4CZFPR4faRPW5uXGQrMrlejA8nJjgN4J8LQRVQ1OL9w/aJ7UyH0U8LPG7t6lrU1LKSo0apSaQXfQj5pzPR45MbPCFZWYVErlhRHB2a9MF933aSu8x9rasiyg0uH4btnWMmP5MF0L7TZuo7nxFzSqmzJ+iyfVVEL0+xSWiO4Lu8LOPTaXNDqrm7y6SDse2ZCRE0u/xq1bva2srUervFxMWpX6rl7yqtI/8AQjmWZv1ZSphL3atHQNY8EKVOh22mF7e6rr6cbN6SHPxu0KH6fmRIQpDPKTchhNjC0qP8mr1H+Vz9IQt9AiNwaZrqVs3ZYTs5djc3FOFsXuN5EnP3KBhgiswaOqLBc2JMpuxjgbrWlbiPGAt7PZCWmYZgoSQqUwtX7dyLTJuwzu4B+x1/Oz7ujYpoX26xx/YEfvCSrSqAVqJi5dDdoo7O66oWreQDUulcVrpjlY8sphncWCW1aozyuPh4d6M33Bw3R5N6Nu2TOqY5Nvu+GZbiohQwX7psp5zFKxs5PcnFjaBfFuY9LNH8zA7PW7uPe8LJhVppejOWWUcaYSLDPjKG/zm2f/PMhakwn25T+eshbTC4qlYXjjXhDrnTD75M+nr/f8bCimUy35vEc9HhO5acRtjHJzSs32i0HMjVZt13OKbIJIztEt2z+ekI1/6XrnDOheQ4E0bpsxzj7UeBuw0qfVwtrnTTcJOWKQC6u/D5rv6/lM0Fj0Q+TezkeGvNSil685ERe/PtF7ogqYsuF65TtT0LqF3kZLJphKsCyzfQBNSpqQCToaAp7sq+bDC+wEN2i5XMGO3cEm4BHVo9wmNW5jsQiOGS5kqNzW2KabzFxKGAIytzFp7oYSlFbxj2B5snGrUAEeGLwg5VqmmUqoqm0wshbh1QnnJQvBCt1CXJYoceGrE7BaQZiVWCuy878g1lxhngxMOCnzQtgnGqPSwt36RJngdtyKHba/Iynw9EAU36LJOZn33MlkCularmXqZnbQeFZjrKqqMpkPIgCki2EDLS9upW8nMjdMjds0vOQ387xPKoDU79kcjk9HGT+L1ZJKfXsb6Rq/TKSzYGOJq4aHk4wQ3MKssFVlUhL1UjFVL2XvadfSCLZETCuvsdU6W52TqWoEmjZDkmkLwyBt3AZJOuZHObH0a72p1B/RF/OSVa/1NdNOW/3hk1lGBPG8KNq3stu9tSwEOohG/SivitAWYNow8zpBrLnCFoB6JYyxhE1x065g5S2qb+cuuFUvpwytChlBUnDrIw7ZGjddimJR71/DwnPPfNZZZ6VcW76chEVRIsVUArMwMCEosdJ4eK1FiFvbmaDN6sziu+PXhJORgPhm3mb8uGhr2NlI4qf9FBTtRodW7o41WNIxfpm0cauLbnm2b5X+wyKWoSOeQKPQViWuqE9uVRzEqCD7/DZTCUZSE2N+RIv4B0wD09aoOcby27TYehKLxTKu5DN1jZKO6VFeTvq/p8p73fnpbO51Z7/9p3O6G0mPRyZserKs3l5hZJ6RyxlAO7VxjkdWoMLC5iJYSJYRU1RBmErIsKqaJOx8/+2LeZ6fVan+qmO0G8MWbFEOq9oubz39EK/ZSQtRnKMnsmT5UESQjSvpMm3XTLpX42gLbocNG4YXXngBFRUV2Lhxo+3eddddZyxjhH+cDY/dQDUxEDVq3DZ/UwkJjYZM0+Bpbvx1wNyws5DE72Lg6P3aaz4RfP1vtHEbTjvT7Y/CWDCZcqoCJDzsZv6sRHcDQoSsb2UniUH3waYXaDaN28z/3J6478v5SuF8m0pw/p1hw3WmatymY17U6JwsvR/Uj0DUuQHUPt99o9Hr+1XXydwFRRMT8wxeHK3y7HZvg7Zx21wEt0mFW+Z1Du0itjvvlRGLtxmPUwW/fUcYji5NdXcqeZ9WYEZwu7W0Wjms6uuddeQ+3jKTxTRp3HqvRFLBbZp2MjJ1ThQ02oLbnj17on379igqKsKtt96Kww8/HOeccw6uu+46tGhhZhEn45133sGhhx6KVq1aoXfv3pg5c2bgaWYqso7fzYi4Wvxp1LgN0VTCjIKd+GLmhozT4Jm6JvrebjMVA80Hf1bQwkmQLo3bsNCdCIQxoFuG01X7ptHudfLzzPTLso2Qmrqmxha4Hx7DxW2zu2o26maHX2GOs6zDWGz7IkMrSDoWcbkhaNwu3Vrm+Vlnd6ZiGsCLFtPizaW47K3J2s+FjYlhVGX8tBDsxkJzESzwjlY7y82ELeXi3bW+4/CC3yqwZIv3vsArpmptVDcwVZVfmsneiBJ9bzrZSDyWBbw4Yjl6PzdW+9nEZpRsXE+XyZPm0r+aRvm86erVq3HEEUfgsMMOw1//+lf07NkT55xzDgBg8+bNWL9+PXr27BlYRgFg0KBBeOCBB9CvXz/07t0br7/+Oi6++GKsWLEC++67b6BpZyLOhsdqeNXUmxDcpm9QCFPjtqy63tVzeW5OLHJH1m7sPyPsLESS//v1sXj2x2W+4mgwIUHSqNLp0ISNQX9BlRMzo6WYEYJbg51dLKb2DlGddCcw9RlkzWl7eU3yd13AklvTwj52gktzUDl+i4f9drFGyW1GkamLlLQJbjPoe3opEi9d22ujV+o/FAFMnDZRshEfsHJJprZZJ4n6ys5xnLaTl24twzYNjcoo4VfQNN7hWCuTiGq3qWqCMAzHlGFhqj+xLKCvR3OJubEYGlxMzKRN4zY8fb1Io1wsPXr0wOWXX46xYxsl+AmhLQAceOCBOOOMM9ChQwfzOWR49dVX8cc//hG33347jjvuOPTr1w9t2rTBhx9+GGi6mYpzB9/0Lkk8jRq3eWlowd19HA3SsQd1x1mHeU6H8M/h+/o/AmbCIYjO4iVdcxfdeYOpdqmrbZQbwoDeEDdnKgFQK+uoTroTmDL3oDo2BT1hNB09Gx15yA0WpyO4qLcdJwfv3SbsLHhCxyGNV3Jiseh3hgzOTT6VccOPhm+mYWI+w4vDuSJp1Lj1n5aI5mIqockLfdO154cvx8y1diezL47MTD82GdR1JHn6+6UordJ3ZOwkqib+VOdyzWVzRAVT/YkfWU8iD7I1brp01LLp2+ugvPxdvXo1TjzxRNx0003o2bMn+vfvj+rq9O2+1dbWYs6cObjwwguT13JycnDhhRdi2rRpactHplDfEMfopYW2a6YXveXV9WlTCUuHc7JaH+ffdQS3t51xqOd0CDunHLKX9jMmBCgm2pJONtJhD9VLsZiaaGSCxm19g2VMgB6LqWoMmUkvKEx9BtX3NHFck6Vz25a2v01vbrKLpvcnFhiNu7nhuy45bCVEve04OePwzmFnwRNGTp+40OiczP2D3tj74MDzooKz7gU1XGXqutZEvlXHzw8mBdfvNhfBrcUR3ALAjf2n2/6urMk8e8oAsLKwPOwseOLd8WsiK3j1i+pUjueEsLli6mTxlNXezSQm+rQoOGVvJt2rcZQFtwcddBCeeeYZbNy4Ef/5z3/wySefoFu3bnj44YdTnJQFQVFRERoaGtC1a1fb9a5du2LbNr7B85qaGpSVldn+yxZ4go1RDkGuXy57azIWbCo1GqeIvDSo2PkRCuhM4LJoHAqc/956qvYzXicCb1zfCz/941wAZgQ8OrlIT52JaQuIwzJhEobgti4eN/YdYogpTUrSZUsqbFTf08/mGg/n4t+4xm12fD4j+N2cSnFOlkF6Vl42IKNCWjRuFU0l/O7kA9H3ppNx2qF7B54nGc66F9QCdMyy7cFEnAFwNW4ddcQC8F6AG2bNRaiUGFad47BT687ESbMwuPTNzLMDDQDzNuzCqc/q2yllieoXUzaV0DyamBIdW5vxE/XgVws9P5soby82101DGrd8lKVhtbW12L59OwoKCtC9e3f85z//we233463334bRxxxRJB59EyfPn3QsWPH5H8HHXRQ2FlKG83NLkw6drbrfWiOtNAQLNORWYOkUUv0yl4Hovs+7QCYcU6mpXGbJudk+qYS5A90ahOMw8owbB81atya+RCLNpcqlXXUBbemyiNuWeg7frVrONOnRlKqbwDmhAg1/FalFOdkGVb0mTovMOHo1g3VfiY3Jwe/Pn5/XNHrAGGYdJzecnZTtAC1Y6JtqtmID7YTaC7rLJ5zMh7p0K4nmpixthhFFTXuASVs2FlpKDdmURUMNpc2psJebVq6BwqYJudkIWcENG6KUHZO1qpVK7Rr1w5dunRBhw4d0KFDB3Ts2BFXXHEFOnbsGGQeAQBdunRBbm4uCgvtWqOFhYXYb7/9uM88/PDDeOCBB5J/l5WVZZXwtjmhY4rAK7U+HLbpDC5BvEm29m9e3ttEWZmYwOpomEX1SF6uiwQ1KNvUYWi61DfEjQpXyDlZE3ELeHHECtdwpoVEzm9gWqEoQxWUMpI1OyqSvzPMJCosZK5mUTocleQofk+VMoxxPNf9ptcBWLS5FGt27PaUPye0YSPHROnwxk9nvJNWeT8yrEIYtvaDwLIs1NQ3pJjXc5IG5fpmxdFd22Pdzt1GnIF75faPZ4WWtgyycZvKXgEpuuiQWNPVR0Bym03fXgflYefaa69FixYtcMUVV2Do0KGYOHEihg0bhgEDBqBv375B5hEA0LJlS/zsZz9LOkcDgHg8jrFjx+L000/nPpOfn58UMif+IzKT9Gjc+jCVQB1MKHgpdRPf6ozDu/iOQycb6aj/XlJw014yueHCHicOYxe+Lm4Z3SBRiSrq639zNm7VXtS0jVtnNTJ1vL7HAY1zDdJQSh/DFzeZzIohllG2AS3LSoMVcz5+u9J0mEoA1PoIlYUeb/yPxWJmNZ5J41aKCaFAFIq0ucz74xbw3bwtruGiIMyJGjKnkiP/fg6eurJHGnOTOaiaSmgumyMqpMMkpBuJLKzaXiEPmAbCOFmZCSgXy5dffokFCxagVatW6N27N37zm99g/PjxAWYtlQceeAD9+/fHJ598gmXLluEvf/kLdu/ejdtvvz2t+SDSTzomvn60uXQWP0G8SgatUY3iZbHVMs//aHAQZ7L2G8nxTB46OW9hcATr0o5/HMdLWboJlFvkBaOhGoqN2/q4Ua04lfJOt+bWhcd2dQ8UAKp7Zn7M2fBIsXFrIPqp/z4fXTu0AhCN42aZgknBWSwDnZOFYSohJwYsfepXvuJIh6kEyzKnQc3rw2MwexJq6LzNrmlmM49+t8R3HFyN2zS3+Uw1b+IkblmoqnN3PGZ647Q5cPyB8hPH6XAs7Ae/phi8omoqobm0MUIf2vDkoyUN6NatG55//nmsX78eF198Me666y706tULH3/8cUDZs3Pdddfh5ZdfxmOPPYZevXph/vz5GDFiRIrDMqKRe88/Au1b5UX2mLUO6XgFHTu1TnQGlyAG8mydTnkpyfy8XOP5AICXrzlR7wGNOpNr0C7f7Ed+yb3uJQU3jVqTAmc2g0GZTrn7vMOF9+rj5mzcAmqTknQLbv/v0mPTml4C1fc0rd3nrJ6mjvAmvixp3BKqhDFNi8ViaNVCPB72u/lkPPDLo6RxmN5MEaHSRaj0qaK5WpBrxGyy05guoqCN1RzWVkB4G6dZQYSryK7dtTjlmTGhpK3unCzCBWiYKLxpZY37Bk66yKZvr4Py0Pf222+jT58++M9//oOHHnoIM2bMwDHHHIOCggLccccdQebRxj333IP169ejpqYGM2bMQO/evdOWdqbxj4uOxvzHLsJhXdp6jqO7j2dNEmQD7r5PWxzYqTU++cNpnuMI29HU098vNR9pMyW/RTAzft1jLqrV4Oiu7dNi49kLbguXPIHA2Uu/wiYV1C78WUfsI7xX1xBP+WYv/u4Ez2mpvEJJZZ3n+L2wf8dWWuFNyZXDWjg6xxUTx+sbnfwlHDxk65aaPiZbdKY5J7OAUM5+u6WYn5frGqa2PviCtqBmxkRFmMct5liwmnGkNWYe/vdKb6MXzX+uPzWzfKlYlqU09qmak9tSUuU3S82GKLf8ZVvLQktbWePWYJqHdBabtYgCURgmTCoJ+SUKm3NRRNk52YABA9CpU6fkf/vvvz+OPfZYXHLJJejUqVOAWST8kJsT86XJ0bplMNqJugSpsXDBMfvi/y49zlccOoLl6HSLmY+XgS7fgKkEE6jkfcZ/LsDebVumRfPSy/FiNy11oXMy5t1zYmqCO3ahFlQbkn2Ts47oguXbym3XDvYxEVTp0l4Ysdxz/F7QbU+m5JLh2bh1OiczJbht/E0nS9Uxaj86FjNmrzgdWJZ6n3bVSQemHMX3iluZW3C3671zd/BHbS1FWwkq8zBemBhigS6aac5nnijsZYvqzKUn7I8vZ21Mb2Z8oLrBWFOntnF6xvM/KYUbcGdv3PTBDKWwQXPiQZ2wYGOJ9nOiceaMwzsDiPamTftWwTrDkq0pVC3smFTa6hDw+zYHoqQkRBq3fJQFt9OmTQsyH0SA+Kn8UWk3QToBMCETi1Bfl1V40ZIxZSrhkUuPxTM/LPP8vEreO7dtibzcnLRo7nkpS3cbt+5C8tycGOIKEi5WBhxUdyCL9lc998OKQrvg1s9RSZUJfWFZtef4vRCWPTZ1Uwmmbdza/+Zlo/s+bXHs/h3ww8Kttus/7743phcUp8aJps1S1eOAhGHBLYCv52wyF2HAWFB//4d/faw5wa1Le1dploVlaRDcQk2XUqU7DmOuRgtQ8/DGzyD2t9vl56Gipp6fB0H7ibpdUyeqw5SoHLwSpVMRQQmsolwTXhwZrGLAGYd3xvKt5di5uzblXkWN2mkyk13n4i2l5iILgCj0G1Ey/+LHfGVzhkolC4jyjp8qbfOV9xi0MbG21irjzP8ckcGTxq0hUwl3nt3d1/M6eU/HWOqlLN3MQ7RV0NhXXdQGPanJzYlJNftjsVhKXpvDphiLbp5MmBYA1I9gqoZTJcVUguP+8Qd2xPD7zsZzvz0+5dl+N/9MEGdTXTWd3+aM0fYdA978abW5+HST9/Aqqn1JG6ZPPfvILvoJsbhp3Fruc5vtadhcUu1mVOZhvDBB98VR7OszHd5QHURv+/I1J+KN63sp5yFoft59b9x2xqFG44xbaucTSqvMmm7KpFMRukRJKC1i0qqiQOPPy8nBkLtO5977fPqGQNMmvCE8JRkC7QKU+2Qy0flCRGBEaAPFMx1bB3fEwdTxWOWwJLkNlciYSlAJs6dipWvz5eFLjtEK72YOqU1L/sDLPqa6w8sWQRDFcfaRXVy/iTOrfrQ0/DjSCYqweiZVzVTTphKcJznYsaBlXg6G3XuWUENfWLdjsaR2eLqdy2UyJqt6ebVZzTBdtDd0LEup7V16/P62Tezf//wQdN/Hux8CVxu3CpucPG2qIFBpSiplKOqyg+xroz4H733Y3mFnQRve99pWan4ToV1+Hq7sdSD3nqidBzls//fWU/HEFT3w2R3efXI4Ud2ANa1xG6V9Ta+fTFR0CaF0Nm/axGL+TxuYLL82EkecUSAKdSVKGrdRykuUiIYEgwgUP51BVISMrQJyKAWYWVxr2biNRpFmPP1vOcVTWbbMoOMXvNe74Jh9A0vvkuP31wrvNrC24WjcHry33S6suuCWtXFrvhGpaJc57/uZWKwqrHANk+6uIi83B6ceupdyeFnXeWWvA5K/b/75wdJ4VB1VmHZOpmIqgRdOdA1waNySkdusRLdbUDWVcOfZh9n+5p0C0EH27OUnHoAzD/ep0WsMy5h2HlfjFsH2taJx5eiu7QNMVZ2oO+3hwSvRpQE4W5LVO+EmgPFcNJHYuDGpGReWANXUiR0TBGZ+K4vXezEYENwabE2tBZvtRBN+haWmTwMQqWSOBIPwTHM4zhtkPkzYD6WNofTTu/vengb1qJgOmb52p2uYdGaVl9awe87CV4KjToB739I2P1Vw63xEdaIQdBtTEZ447/vpW8sUjh2GUVefurKncljZopbN+QXHdJXGoyqPVbGn2amN+umMFFMJgoWkzleIockxVnEaHDc1F6LRK5vBS7tVGcuc2t/H7NfeV7mJstm+VR7euuEk5OQE67RLlcKyGqxU2OhSgTeOBG4qIdjofZOJmk1RqJfCdp6GvJn8ZHHLCuVov6kkrzr5QN/Hqr3OtYQat3uuR0X5KQxisWiMHwl4iiRRoHPblnj2t+rz7iDx+70u6bmfmYwQQmj7IQvws/iPSp8b5OBnxMatRv6iUqaZzJ/P7Y4OrVp4dlaUEwv/mFZlbYNrmHQK7nhpHd+tI3ZLjsfludhK4B0zd2q2qjoeZEMFUSyWwnFlp6DPz4JXRXs0SpNeHrLFnk1D2uU9TDrfG3bPWTj7xXFKYZ1KS2w27PWNr6XHI5YD/LhoGwDgk2nrlfJBIPqVXQMv3YLK6ydMF0z99/nYVVmLg/Zu42vz6KFf6ZnGCYvH/7dEKZxKLyLSVAzSrInoG0XFxmdGCm4jMJMWmkrQyJvXuajMHr8uzvRPOrgT5m0oMRa/CFMatzHEXOei7nF4w60NN6NhTZsY/NdTk31kC591JAgO6dwG4//5C8RiMdTUu68Jg8avI3hTa9bRfz/HSDzNEdK4zQJ89ZsRGXWCzIaJyYOWjduIlGkm8/ufH+Lr+Sh4efaaBz9ZP7BTa3G8guuyfLq9A29B6JyIqU7s2LRiMD8JUzKV4Pjbj9UNFWHljvL0a2zq1C/ZG7DRuNUTVVMJbnRolYeD9lY/9uvM14biyuRvm01ljTyE37MQQXHMfmpH23WFSip9D9Bkn/2ATq3R44COjWl5rHD9bzkFtyoca4yCgMwkPLltDLFgBbeCcSIqJ8X9LtbDIF1Zln0jsb1k9fjdxsaJD57HtaVvcg5rOZyTtUiTgyJT9T8W8+dvIBGHSSLStEOl0catenhTG0iXn3gAWrfIxUkHd7JdbxFBM3mqY3+68NuvmNpPOjIiZoSiSPRqMWGcKAip/BLkG5gxlWBe49aP05Gg+N3J3QKL+/HLj1MOm3Ta5TGtKDSJMJRcRirsYh67fwfb37KycptocT0/O5qbssatI9i/LjarLRa3LNdv4mznfvrW+rBVvgXoCGp4m14nHtSp0c4VK/h0iVLVOZkbuhNg1fB8G7f8Z02Ptz/vvjf2aZ9vNM4oEoEu2RXV76DtmwxqzslacZyreF30Ha44v4jCWKmKiiCIp3EbC/gETtTn4Ca1N5sbsmohKjed0mTrBs9/wcGd23A3FUx+soa4hekFTaa70qWBbUxwC/95Nr5BlTCVEPG274WfHbKXYkg9G+ym6t2/Lj4ay57+FS7uYT+2r6uVPfDO3kbyI4PdMonCJqnf6toMq3vkIMFtFuBL4dZYLnwSYG/Aansdf2BHT3EEsUEt2/VW1fwxTc8DO7gH8shJB6tOBprwOimKwmTKbZAWZ9Fb3g/au7XUDlgivQbHEX7ZxMtN6Mpb2FiW/Q28OCcLoj+wLODIfeXtyqt9Xh4mzQOYRFfjtt/NJ9uufXf3mXjiih62+p0ujdvAHIxw2pwoqVgM6NLOnKD1ulMPwpNX9DAWX1SJQJfsirKg31Pc7mF4/bdXJaJ8mYftaHZNRhD12UFq3IrqTVSK2W0cP6BjqzTlRB2//YXqxoUXdOaX7DS/jWB+xqsnJjcD4hYwemlh8m+/ZgdU8Vr/27eyl1Ojxq2/RVhwc4cmwt6A3ddQ+vecd4RSuFhMbywMWvNfN/5TDt0bfa46PqDcNMIOO1GYA/kXnkfgJZo5JLjNAs45ap+wsxAJnIN9ArbjfP+Wn3mKW3Wn7J0bT1bunGVaEFHX4PCCzngRc/yrS1Cl16qFepfq6ghLcL2ixt2pFQ+3dWmiDjs1QWX5dNPU4dVTp5amsuBWKZR3LFho3TIXCx6/SJyHLNC41cJqPLbNQ0fObkqI/c+LjlYK9/xVx+Om3gfjxG5qG3W6R18fvexY9Qfc0vaohxEVj/WqZMKIpppH3X7BstSe4WncehVYJMwuuJEJ30UH3pHqWMyc1j+PqCu0pkvD8mANMzZu+N1873/LKb7zILRxy7m8v0D4zcbRQvAdeHM306YSWPyaHfCaripO3wkxxEKz0yx0TrZHLM1+prBNknx11xlmIooBe7dtqRJMq53y6p2XKpJI0m9pB1Wl2LoaFXM5Cfy2o6iPdc0BEtxmASp2zERERj4YYO/Galp4XQSpdlYndOuovAyvrBU7hfK6uex3MW9Z6gs+EXecdRj3uhfxhNf6GZTgW2di5iq4FQTYHpDd00Ry9Q0OG7R+NG45ty3Y312l7Tx22XEpNm5Nk1i3t5doJTvzmqkatzL7wDpvZEHcbmO23/JYTQlNVE25XH/awXj2t8f76gdkj/rV/nGm4yWbsZi8LkeNKJyCcEM1i16/lxe8CllaqgpuBdH/86KjPKUbNvw2HwvUVIKo/zPlnMkvbhuwptrmveeraeoBwK+Pbzrm3KlNC1zzM3vf7jdHXRS1D2XfSKTtzstbt734G5xsfdTRdDXZXTq1zdNmKsHjcy9dc4Lt71jMv5awcRu3CVMJTG0I2wlgh9Zm5gM5sRjeuL6XYlj1eHMNaXoL+yvNj5zudWIUZkB++/pMmMdlOiS4zQKi6ElRFxPTW9H8ixWgeO1ztDorxaDrd1YK73kdUFq1lByPVMACMOaBc33FIUKrCBM7qh7LIaj5k46tON1veNe5hwMA/u/X3rT53DVuG3EKFGWv5DYR5U1QnPlwK7Pf//wQ/OGsw7Q0OD2RtEkmDuL8ZipaByLqHSYp0oms7mmZSrAsYXgd4XzkTSVoatyqLNCO27+Dhskbby9WF2IdM0WUjmmrfgXdcUmlvnx026nc614FFq0lphJUWmPPAztGZ2NfA1F5BbmRJiqnaIht06MJeFPvg7XaxQndOiV/N2ru2e/7zbLqO0tt3Gpo3IqE9xU1TQoabD/QoVUeHrlUPNczbSohqLhleB32zzvabgu40VSCT4GTYbFZ4tVs86CQJS6mhGoxAHu1UZv7atm4NZQ/U+u7oGx/h10PZPh9ZdK4DZ4IVx/CFH4GJNNt8IzDO3t6zsS6XrRzzu42e31f1UmDzrh07Sli7TGvA3Arn9qylmVpeW3XQc/BWzR3BbXewU3j1vH3Q786GvMf+yUuOLZroEfZnAJFWVlV1zVI48rlzFAsWDYzAW5llvAEG/RiItEPyN7XWey8o8sqPPSrY9DQEN6yXS4oUi9n5xuwtl3ZNurW3sKSLyprUPJs3EoW7SqCuPsuPFKpL43FYp6EFJYVXTvKPESvGBUNjqeu7OHLmZ2M3JyYa9yHdeHb5PSq3a3qYVs01ubEYpE65rl325ZJu6WyouS1zVgsWO3XqC9m0+GcTLd0nXMc5/jvdw4YrPYjV3LrCvuOQ+46A3ee3V0c1uDqPSyNW3NbFzHuXFMrBo+v7PYGbLRhm0ow9V1zNOYkYTgnS/QNJoo7iC/G1gN23InCXMdvHXX2y2FrmTdHSHCbBfhph1HoSAC750WviGxKskIDr++reuywMQ21cHu3FR/l8qpE3dqnxm2QBLELuV8HvrZWUNVaZ5Bym9CkapjE0Elxl9sTe9JzmkqQcbKLd1medlPcsh+Nd5soJG+zGrcAjjLsoO/gzmpCNB1EZkVyc4C6EIVqpoTgTlnHq9eemPydCRq3qot/XY1blY2VGNQWBarheGSSHWVDpxsDy8ctpx+q/B3O1fQrkBuLubYRUb6CdiQUle/ixmd3nKbUP3Nt3CJV69AkUZlHi/C7WL/+1IOUwumk4pxTm9a4DXIjmBe1ytSQnT+ms8ronoIyxS8cmrNeicWivzkCBNvHsFx10oHc66bKqLG8FedOGus6Y4LbPdE453dRqSJs+2KrRBTy57ftO6tFJrTLTIMEt4SUdLU5tzHAsoAbex/sKw3RIvbso7o05cNj3MraKzF1PQFZh+d10rmPQW/nptHRoFAV5A/96xmY8OAvUhz0BTVpNxmv6aNbqunpCHvycmLoIHD6B/DNtFiWPQ1VzWObjdtYDOcc2YX/AEPPAzu4hknw6KXHuYbRnVjWNvBVSWOI4YFfNtqI/Hn3vbXiNIGszLVMJcBuKqFD6xbceNziNKUZqttmpOXgsf3FoDb5lWnSOsvOi+CnUYtQ+zEpEx78BS7u0dVspBz+9asmJ3NpF9zK7inm5enf9NRKMyfH3QWd6L4XjdvjD1Rzyicjag5S2fL5eXfxyS5RH+7UOjSJsKwisq/iNn11+9TP/+4EeQDFeFi7tuycurH/MyuEUR7LJd9IVGV4MfPWCM4s6LQpnQ12N14aucJzPvzQqkWuETvsOQbGOtObK1byBFfTtQ3FYvN3Jnn5mhO5101918Y5iVrYcDRu9/wbrSEqSZSdk/n9BDyFI8IsJLjNAjKh3bjt+FsAntVcDDlxCgf63nQy3r3pZFx/apNAWGeQ+d3J3XDZCftj8J9PV3f0AfWOTBbM6wB8z/lH4LTD9salJ+zv6fkgBxmdAUNVznNAp9Y4pHPblLiDahNGdxclcQXxGRJlktggOVtBMNroaEycUZ5QoUu7lra26DZZa9o9d153L+yv7joD3/xVzZPuXgr2at2+70e3nYrLmLYlXNjFgBtOOxgTHzwPL/6OP8kOEmeZ/+38I/DD385qzJtmXDaTCKIwaRqE9DVuzYZL5EFd45Yfzu6Iz5sI2W9f/eYNJ6VcO6RzW1xwbDCCW/YtD+zUmnvdC4kNEicf3c63GSuvq2p56dCqhXsghrwc96OnQo1bR137mcspCEC8sNchatNKtnwO36ed0Ba/yFRCkIJbUVlFZc3u95i5KioKGgmcgs6UZ31WQHW5rf5X4vUhXTmnv9658WRHntzH0gRBnqZIp1sUE9q9JpQcvMYg6jb8G+DzjqhMddaNj18uVmSIacxKtJyTGdO49RbP+ceY0QB3w2YqAepKLOnAr3DfWS/+dfHRgpCEV0hwmwWkW3PPC26dRdyylDvjfwgWak66dmiFS47f3z5YaBTVvh3y8faNJ+O0w/ZGS2WNW/X4TTkQYunYugUG//l03ORRe9mEyQoROoOtrhf6lKNgAY2Qhygct0+g6iws3Tzwy6Pw6R9OQ7+bf6YUXjbXcmrcHtq5Dd656WSb4Jb3Lf50TpNtt0S9iHlopnk5MZvdVb+41ZucnBiO3V9dy/fgzm1sR517H7a3srdePzg3yh646Gj0OKBRC0+nHV583H4SzVr1RagptIXOig/olEksFlPqX2RaKymaWCHM1LoLbKqaZuAfezf+sNWjpj/8ruUO2pvvzf2ornxTK7L0gjSx4zVqtv84Yt92Qu/1LDrvIfbQrR5HEDzj2Mh3ZvOIfdtxnxMJKdNtVWTgnb0DtaurA2/6yvpYMJHNg/Zq47oWsQtu7WFTNt/T5OfgsC78egSI58O8mHnKKU7hNPunW/bqBKd5VOi+j7xvj5o2vRuxWJibIPKUo1SUbmPpbWccyoQVB86RzF1Sw6oXgCkbwIloOrZuwb0uYj+OI1SvWRLZpAccphKY6hMF7VS/wnPn8HrHWYehVQsSNZqESjML8NMXpKsfcU1HY1Q+WtH2ZT1n4uP1fXlHwnnoaE/JOnGvnWtSCOZx0us2gb/w2H1xSc/95IEEBGkLxym8Cyqp167rpRw2SA0fHm6LxESZtMjNwTlH7YO2ikfYZPXUuTAZ/OfTcfg+7eyCW86H79SGOXK/59/Csurktep6uVM0lbx5gW13fW9q1JYZft/ZyWu19XHtTQU2zrzcGK7sdaCR44MyTNmw++fFRzsE6oywlrmeroWg7vdWPv2gmQ8Vu6MywS2br1gsnM3XdDmVOOPwRs1+NjXbb591R9TtiV4vhhie+U1P9DqoE048qJPjXjCoOCdT0bhlNZX9oCJQDFu4c/g+doGaahvh27iNBerIj9eWzjjC/URLuuB9yxevNncS5MJj98UfzjrUXeOWmeizyhAxpH7fdFS/O886TCqAkZ2ocZLLGROc4ezfwWWTOMACcJsfqGwOqWLiNWLw71zQdHEmsuOM9qd/8E8CmOJQifKI25jO2maXmpGK6axjFQPCvKmE3whs/YowuSST1cewndTJ8DvfSu2nYzi0c3qUALIFEtxmAX6aoZcF498vFGu8Whbw9o0noUOrPNxz3hHJ66Y67L/84nBux8PTvqjj2IjSycWFzJFRZVMJGjuVsmBeJ21BjxexWMyzUEjnnXSFng//+hicf8y+eP/3jVqkbB257YxDtW0Siui2l7rGrds7GJ9Iutz3MmBblrye5jlVefYErrdp3HLyYlO/a0qLTRdoMudw1cn8CVoMZsuRraMJwTarYVtbH7e9W3vG/u+ph+7VlC+bRiH/d5Dc8vNDhPdUc5ATa7RRZ3s29bPtCZsmwa1CmE/+cJpWeEC/DqmMZ42LH364lEMghovPmT2eVno6BLcfM+YKRJrbbC5UN0hVENXJWAy4+eeH4Nu7z0QnTY0dr+TGYqiPyzXoRP0z28fmxNSqijOMzCyOKL6wl57O+qn6bdJtKuHVa0+MvGdtZz/u5HCB9rIqj1/eA/l57o5xWdm5c+7g/L7pKNEzFcxF8eD16zxhjbMP0pmDnXRQJ5tZJpO4CZY+ZcbQKGBig56N4TiNU1OWhaSZKW68jrx138dfW5Ix8I+9hSZieHlxwq7dZGED07jl9JNeuuVE3lPMrbg+6X8MuPSE/dGhVR5ev/4kYRibjVvfKZpFNlS5aeqLno/IwZJmAwlus4B0q98f5tK4LzvhACx4/CLbYkHFxi3gbnezuq6B2zmP/vs5KdfqOAsldpD56q7TcbTgOOWJB3Wy2ZJTdk4GdWF4EKYSEo95fd6t/82JARcd580Gos4ArzsQdGmXjw9vOxUX9dhvT1pN937efW/8XiLICgo3BR8vmyZHdW3n+V289hJyjVvHwmRPKqxWKq/ts4voxDM8g/4f3XYqpvz7fJxzJN+Le+NGiUFhDzux5dyvj8dtRxjPOqILbjvjULx49Qn4O2PChX2W1QBLtIGg5zld2ufjihMP4N7TNR8gqqdiEwrB4ZbOwD/2tmmVqFZ63TqkolEhE8jaHfGZF1K0bdm0oXDtKd0w4z8XSPMQFKxXcbYeOd8/geo4q4Lo9Wrqm9qvsx2yeXTrZ/949mHKecnJcdf4FH2NPMdi28t3O+ngvWx//+2CI5k4BfnRTOfqn3VzD6RB6tF5NURtM6jF5VUndxOWYVTWs6JTat/89Qw8cumxvgWEifd3qzMH79208e3UjHY+mY51jVsKou/H1bjl71A7wsjjYMnJieHtG0/GfUxbNYWbaZ50jA0ndlN3oGjCwWssFsOs/7sQ3997lvKpzQQ9DuiIfdrbNz+Tzsl850yd1i1yU5Ul9qCyd8T2jbLgMcXdZAvhOidLuS7JS15OTHkMkB39v+2MQzH/sYvQy3Fah4VtX1ETasrmroP+dLrr87zH2ZMUV/birzsA4OSDO+G1607E/+450zWdbIYEt1mAr64woFHHqZnp1rerHj+urY+nxHX1z7pxO+y6ermphPy8XGEHfTtjCwjQW1Cq71TK7nn7MInnRE8/99vj8RbHKU0Ct0EmJxYTCoTc0Hklv9oxYRzjduJWp2XZ8nIsLKgJgiyfTudkiTpd72LjdunWspT4ebvUebk5OLBTa2PCBTdEbfLaU7rh4L3b4JfHdbUJbi0LeOKKHrj2lIPEZgRC0MaKQXycX9fphFhTUr1/N4Xb93bWx6BMEKhr3AqedxyZNV2PWTMo+Xm53PyqOFjzwjlH8TdZWGxvz7y7lz5MbCpBYeHpeJh95BRGg57Hf359rLK96tyYguBWkF22HecoblTJgoy8/xybjXERutXjGE1hiBvO91TWuOUdWUeq81qTiFp6EGNym5bumq0sb1zfS9gPnXTwXrjz7O6+50hu884EpzDKEM5xJeV7u8R1zH7t8eQVPTRymYpbW9L5frw+1lmubL/PK3NeG/JShfJyYjiqq1jz09WWv8HxSBTTM785Xun5jq1b4OIe3syzOfOxT/t89Dywo6f5tfM9EnPcdC4vZN9FZU6iYuIJ2DN3UXwvndfnmrHxUH4NnO/Hi+f4A5s2B3JzYkrrypyYvN23zM1xndPb53fRkty6aVorxCC9e83PDpLe/+1J3XBCt04qCWUtJLjNAkwOHF3a5eOfF8mdf6kOeuwg4zaoqHZttRxhrChqvqkEpxpH6sP9bj45ZddI1VSCzigm+27ebdwm/uU/f2Pvg3G5RPAqs/cFNH5Tr0IGHQGW36EujOPpTlxNJaQpH8n0PCYoe8w5EUzUDbuN29TneN+EvaYzsTZZjjaNBCbiF68+ERMe/AXatMyz9Ss2zWFBRthqL2oCXdrl4+kr3Rehpyh4lE/mR0FTVvo8ZzEuEtZGxUFmipaeJFt+ugUlwS3E/XCK0MJDHm6UOKBsk+8u3AnieHcsBqEjT9EGAJsNL84xfyWwua4muLX/zT7iVj6xWEzZaWlurorGLT89djMiFotxHU25wrzokfu2s9VL0Vvqtg/TQsrU8lfLEF84oLZoDxpVx7oydN/jihMPCFy4lBTcStLp0i5f0r5SBUVuee7QqoVvpzhuxaKzucU10eH4W3SiZ+CdjSdF+t9yinJ6MiwLGHHfOfjb+Udw77v3bUayIUU0Rt151mG2vy88titiMXVtSRHsO+ns4SSCOsukgSO4lZkRNIH8lKb7R2O/u3RuBPU5iU5dMaHEcMbhnbmmn3I4G+Wf3dFk8iM/L0eorcySmxOTzkJUlLhEzsmigCz7bv3C739+iKupBJ5QndCDBLdZgB9tHeeTj152LPbtkOp50Qt2gYWZne3ahlSNW1HcPJtyKZNDx/32rfLwq577e9b20BFg+N/5EqfvpUrc2PtgXNxDbgbBz4RO51G/fb/OpkFQpNuLtZvQw4twzYIlbbupphIaYQdvvpC26XdiIcozlZCMVzphFd7SRp5O4z3WDlRc8J62JanSJoJaZTlob3Uby37LxV3jlgkbkZmGW/9uCpUFvUwg6xSc6XyrWKzxiLPsKD9rKkGEcBGVFs199v29L3Iu7tFV6GRRpdt3CsHYvKiYw1BdiPrRuHX2K343InU3boKAtQ0uwln+yhq3gm8S5HpSNW91HIe5uujOKxq/Y7BzoKTCgCSdoooaWz5Svi+c39tlzQDLdx11e/wogRY57zkVjVuRQsEZR3TBJ384jT++ezl9hca+KV9g27iryxovHXNmkeb43efxhc3+YcYZT0/byyQxdrDX925rt5luGqmyj6bgVjaO6Cjo6LRB57zpH788Srt6P/dbvqa2s8q+ecNJ6NSmZfLv/Ba5yFdQwMqJxaQVpGWewtyP+R01Mab0u7u0+6eu7MH93uw76jpvJlKJyHKKiCpcIaihdscOEh1bywc0VS0CnsZtoiPZv2PTZKT7Pm1x0XHux2tUF/qyCSnrvCgWU5/E+9095T+3518Pz/7hzMNc01U6pim4rrPg7Ny2pXsgRUJSuHXVGpWVpdC2mp8FmNfNAMlzKUfT94RlhRS8RQA7yUwM9DxhLi98Sv4MLkptGgmCeG84rUnTkZ2j2AWc7AS56XpiYuSsG3FLrdvVEvCpB+VyLMeBh+gdTWvcnt69M37l6XikN2GPLiqaGzGOFlmCHEc5OsuvvUAYCTRqtJ508F7SSbbKcWpVbbLzj9nXPRDD7WceCkBuMsH+/lrR23D2PywqY5Wziy7eXZv8rSKUVR3TcnNiNvMxPEQxsVVNfUFtD8OmnLopzY8vBruDVjd0tKVnP3KhaxivmzCieh2kxq14O86e5obiSt9peTrmbaAfPEFik1S1HcQ4Y2Hiuq7GrU66wvy41KqcWFN/5vYcbzPCmT1VjUe/JOoIry38+vj9lE7WBU0bweaiqN/1chqDhX0ltzb0hzMPkz4PMHNc5npuwDvYcmGr+/M2e+mScLGYWny6XRGb/+evOh73XnAk8jW15oVORx1v5DTpZ1lQcqCYlxOT1rWWuXqmaqKGbO7q1u4bbeynXmfbk2yDOt3+mDIVEtxmCTrtQWbsXmXAlglh2Q6Pjeu+C4/EaYf6NzBfWx9P6aATHcmXf/o5/nj2YZjxnwsw9oFz0ZqzeLUdx4al7BBBVizVdQ1N4aAuxJDF6VnjNmb/18uzMvxsxKs+2yI3hr18Cm7Z+VNYGrd/cBz5cuI1V14nsF7qhGXJ85nq+bvx79+d3Oio5swjOnPbFHutSQmJEeY6BbeSkczkXEDlGDJ7VMruZI2P3SkTP4yqUEFnQSUK6hbFid064tpTuuGdG09OPNH0rOAtTTaxVi1y8MWffo6D9m6t/WyqsCeYtq9yRFemcZvrWEA58y3r/1SqSjuJ4DeBal3SPY585hFdMP3hC/DRbafartvNbPB/6/ZszldgHcgoLTwdKU4r2Pn/7Z13mBzFmf/fnt2d2ZyTNmm1K2mVd6WVtMoZBYQsEGAhBEgiCkQWYAE2wcYWBs4BjAmHz7L9Oxub8zlxNiATDSYZI8BgZIPxwRFNlBCgtP37Y3dmqnuqqqvTdM/M9/M8ejQ7U131dnfFt95638RnNQsm6zIGZLG2uBVVFrOCy+vhTKTE0DSNOhWiTNtlbFO50gLaXD9VF33cI+ua8yOcV6wYY51IIJq5SJUNHyucGDR5UWV+fvoM4W/xZ271itifU+YOkrRVxalrDl133xas5dWoiGO1yruusii1z+YaxiiWHceNupJX31b2NFs+Ny/7GFG7LY7mcZ9BOqbrsk3L69dMpMs4bd58Ba8/98tvfBzZWsbLjcYIZzOZh93NMKO/9oHPI+pLac1Uo1/Ub63uEcsm6EKtjKZ0XVezuLUIYlagYnHLzmnS6Drg6MEAobJgk7JnoFJ9reoFXCW4B4rbHMHOcLGW8Y3HVYJKMls/o90YtVsCO0jUlMToZxutIxZase9gf4p8cf+zQ2tK6NLlY6ihvFBZAavqEkH2fA2KWzuKFclvTne8/fY16Sp3xYunddS4KWWwqGRhKgtwP1jZ00zf3zBFnMCGWPWDCom108R+La3Gy4Mcn88q2HFTEP/7K4ePpRvWTKSbjuslXjwEnnWt0SLCnF4ig/AX+2g2F1bs5N1ojcr/HJ94m9+E6lzHzr2KfdzKcxnZUEbXHNVNTZVFg+nZa60/uyXeXk+b22n72pTFv09Nv1BB8TSgkFV7B+ZUsgWgymLJGOiPn97PDa3GikLOpg7zmfifeaJOaqsUlmN+js2VSWW/yhgqU4KpPB/VcTo/4tzHrfl4tVpgPCWxiEj8DKzmgqr53HPeHMPfX1bw5U2U+vxVq6vI77DT9WRUqa2r1wO3OLEcZtvJLcf30h+3LLCdh8y3Y7zPtro7mdsg2dy8dyjf6EO1nteWxrj+4VUu551e4JVbzdlsExmZDHz2r/+N1xCzK6sBmdQs6/ymIC/ClcNcdnz8cu3jlvnMM+rhpRsoNz4/Nf4SV1Cx3/odiNZtQOv6cmZj0wM/x1anSMwYLJK1eDkabV01wRCYjy3b3GeK7tPq/vt1nWvda26jVj5uVf3ax0mnGvMrh4+j72+YQtcd3S1MI9qIbyiPSZ9h3Pc0LwnbNquKvTstm6tAcZsj8Aba+rJUB97mtFaKTDNXfC7Vx8n6Ge3J65lOkPUFY7XYUJ2M8ia+ZYXqfoVSFvYWf8eRdWjs4DUwKVKTxcrHkBOSZdu/XuUKN0eB0hnAyHwUOSiG1YgtlmRSmZvDD0+aSr/cNJOO6xP7tbRqQQWqAfZMyC3D+Qqo4mg+rehuovLCAstAZDyFhnnXNl1W03aV/EYxNc4ns4KTn78fFrdendoTKdlEFpRuiS8oaktj9N9niK28eNiNTO4U2cKPLZwt/3Am4KXZVYBZbtmiSqWusGOSKHlQG1pEYqW/3dME8cd0zVETaEZnDZ3J+EdUaSufnzxg6dPNOQbuqQWTQkRrUVaG96Q5mxvIipa55bFTlkgxbT5Cru47MVUeFSo5C0c3cw83zeSrJn+MXoxjThRYbKkTWysTm3K8352gOtaw5ZhPHXTWlQrTcpUFpN4WygrzaSJvA8jick0jKuRZ3Jou7BtWTQtHp7qUMb9uJxa3TojXEZ4rmYimpQSVTU3jh1Sp8MYgv1yvs0Xx3qkIUbnxECpsf6ayMePGDZzbuCjlhQV017mz6ffnz5WO/6rt6oBNn91stVOtYjI/0SyfMgZUPHQiGm7qY3jkaZrUStbuOiqdBqiFBXk0v6teWr9Fp12s5Lz40NFExO+39jPxhKa0V9FGB0YXIAkUtzkCrysTLgRkxy0cDNiXDDZoM6wvGKsJq1Wnce1RE2hkQ+mA4tj0m8wfoBnzwMfuQBJJ/OdIxL9gsTGSqBN/X2ac7twmIsErXs4uqtSikqrlGz8qb5RN7VovlEC84GRe+s1VRXYrdu6zqCCPelorHdeL76+fonR8msf/ffCp8DeV46xWMvMsF1IsbmV5CH5yUo1EAcZEGFwlCFaaRlcJ/FxVJ3d2lLEy35UyzKKINvpEFpRE5MotjqofNh4p6X1aHSsHuWCKnzWizvjbIAOKW+O15kXVzcdNSnxWMXJhgyAJ5wEWi3cVeFZsIkTWdm5eUfzSz09upR+fMs00nllff+SkZvr1mTPp9lNTTwPZDfYiT2dtnSTKie37BnzcWpdnTiJTiItdJdibD4oUt6LNPSvM6VTrCW+c83uPQpT/3JF1VFuanHd4YWW5iKMgJCJaN128qWscm1yLkILoJIlMDnPdGmra5Faq5zaeJ6+aWyn0NeJv0rHFTmqrpJ+eNp1rkSzbkFOtC24U9TwFraaw+aMq23LJkWyzLDxOn5eq4PHLEpl91zz3F4l0ivPJeH8n2owwc8rsYTS1vZqWjnPiu18um1XZLKMay2l4fanF2sQvi1t23FddEBr/jErmXrL2rOtEh4yx9tnu1OJ2DCcmxEC54XIdILK4tdoIS7rDSU3Dxh7SNC1FJ5L4zYacuQwUtzkCr72p+CNM9SWp2d7aFHWkBQaLW3keVpY2R09upXvOm5syuSMiKlWIUBzH/ESuWDGWZo+oTf4uGrQF+W3bMIWG1SZ38TRNXenqdveUm6fpfxH/tXE6HdXbQlcdPi7lWhnxzlsU2TPOdUdPoGuPmqCQYyqedO6cBcK2DVNpZEOpZaA8L5EHoPMmnzii+UHfsGqabzPAkCrmds2rt1xn9kx750UhNfcHMksGlSPGqhgUowqXHzRZ2/NgxRDpynRdV1qgjWrkTw55ZarIw5dFmjPnU2pfprKIaK7k+7B1NLkfxI6SyE0/oxqkka2brLLXuEGgmZ5l6jtaOs56gcxSW5rckBS1D7cWt18/cjxd8TnjsXflHA39c/KP46aJlU88zO9bl/zGFUPTaEJLJVc5ozJOKAcn0xRcJQjyYt9TREHpYheRJfBAH6Jeluj+zOJ6sbEto7zI2QalCJV+WSYqa/XoJsBSSTSPbj2+l9YxJ9xYvnTYGPrNmbPo/53Ux5FP435W4ebjei3TqCqNNEG7JyKqKjG2Nys5z5jXSQUuTUMt65jGd4ujEdFvzpxFK3ua6Po1E4WXm8UzWBymQYvBc5Wg4m5FdQ1T69IYQhZvJYFHei/2eTeUF4oTmouPxyAzK245Lr5k89RLl4+hn22cruTfW4R0PqNQoQwxXiTPVTUIpm2LW+XgaMZ0rCGQWfHY1TDgYoENTs6jX9dJ0zQa2SC3us2z8HErajs/PiXZ77IpwqW2FZ+a1XVdqU/irdf2H+TokYBjoLjNEexMxthOsd/U77pdFLATU3YH2tr/jHoZ5gHl0PHqi1r2Ul0nqi8vpB+eOJVNYX2hLH8b78GtvyJu+Vr8f/n1k9ur6bqju6mmhO9OQ0S8Qz62T+xrdWhtCWmaZggUQ2TD0saDPt/gF3BQ5vEtFXTPeXNtRUk/adCvT9zpOxHRt4/pkUZYVsWpv7swbeCmHk1PvSfeIM6294TlAruhZOqXZEom0U9OHq/ddse+C5G1Lq8umt+hSv83Zkg5N3I1j7FNFZJNKHv3aFYqcj+brrE6iklE9KOTpnK/Fz3HhnLrvsp8z+lYHIvQNGN04uaqIuY3NqGxHblVqNaVxejiZaMSf4vagdB6W7F4jdSsP7nZMvWdnSecvSB1IS9rGin+EA3tUV02HmZFEg91i9uIgo9b0bXJz0+/+qFSmWZFtNRVguC3AVcJlkUlECqATXemXmeMCWWWVixzODEY/O4GZPOtA8xg5mbsLi8qoMVjG7nH34kGAlGNb6mgWSNqUywhZZauVqhYCKr2WYbYA6age8Nq1S1ur1gxhhaOblBzWUMD79/po+cdjda0gfnkt4+ZSC1VxeJyzYpbg+JK7Zk5UfYnfdxy6opm3W+p1hGlDUzZOieipQQhFZ9KcjfxZbM9xhQMS+l60/tKWtzy67SKHLZlkFlEunwX5nQqSS0Dbpowt3lh+SZZWOMqs+L7e+sn0zFTWk3r+FTi1SdFZE4bndEpjrMies7pNAjyg4Fgj9ZvnTfO7z9gXLAF6Z4wG4DiFqQgC1yiaeTZLJedNHipa2LFG9dcbqvDtIpwb9fiNuW+nC5kU2RK/U62s5+8TrMlhkgRI0I2OVg2rpHOXjCc1kwZmBTJjtHLjth6Uf28CgJx8bJR9PPTZ9DXViUtjFf2NNPWVWKL41uOT1qoyIp+f88+ZTn8DnogwmqybBVgZOA7nuI2mS8vCqm5XNn9i35x8t7tLqzYYAcqSk3hooR0y2ddGsu3XBg8eOE8+vEpfTSuuYKOnTpgvThzuL1gf9wxIf7Z8L2431TZlBBN7t7ZvZeb7+bFXYnPixWOvBHZV1LbgV1McMvWiHZ9eiDxd1MFP3CWRibFisu2/qOTplI9Y1Ekes4iBZAqmmavjbFJ2QUfW6dVFP6iPImMdVckGxsERQbPV6oZ1VeVFyFLSx/hBhRTyKvvfyKt0ZcdNoY2HzKShlTwrdl5iNbemrliWiA6NltVXEAljIKtXDEmgbloFfckRN77blbJTpaGtUZyMw+Ouz/hlbXWtJEun1t63yeKxqUmsxUck8x8TSw/z2C1JiPev5Uoun/Sia80t/Q7TRpFuX2SoqJa02jT/E7D33HSMaXjBv8i6zbiZ+A0K3zzcWs4/ZJH//jaofauN8nFMzjw29JQlrtK0arSRTS1fs9saWkFa+0pVdwa5pyawfWT+Rm3VBXT1UdOoBENZdIbjM+vrdp8XkSTBvfizc+aK4vE/WqIDG1k6CR+fOymKW+Y32vT8hrIgeI2R+DtyqpYotmJ3m4X1hcMz7z+i8uTvnGndaQqF3jHfFLTeFvFRSUqDzI2Hp9sgc6bWNl5M3Z2VpP5J//4xuf5A5dM5r5h1XT+4i7KH3wnTt0EmAfASw8dTbWlUbp381zxReY82F1wF3U6Py9CvUOrUuqZSCF0dG8LLRmbtFCRLZJsWZmzn4VZpn+GoDJZ5D5/RtR+juWC+U5kE2LRM3bkKkHxmjPnD6ehNcX0lZWsqxG+0tcwsR/8w9xfq9YFq0X30JoSmtE5oFAc01ROf/7SIfTDE42LYTaLZYPWVEvGJhWhk00RvEVByDRBGiK1IIZqCw1+O+ZF+h6QT/63l3yZefdcWchobceOZ8Yxw/j08jS1Y4oilANmmZItHcu3rFsxoYn7vabZC17Fwir5jM/FXn5S19eC38Y2iU9LsP2Mik9wVSV7JKIZxgUeqi4tZM/oxFnD6CyV48cMooWsrtubc4isr/LzItTAKPCqS9WOV5vLVg4oxBH6udc/UrvWIbLnxB4ndmM0GPcjaC7riUsWGlxeWcGT1X3gp8FxzZTRJNMGPVs2r+9g25xKVyDzVWqGt0ayeh+axt/gUp5fE9H0Dr4rNuUgfS42H/muq6xdJZhFK+ec9HnggnmKz8Ge/Omy1hP13ebnrSe+N8LzcavknkdRPu61NtZNPNTHa7MDJz4qFreLRifnluyGfoqbI8kGoqov3Q2DbmR4py70lA98IppGTZVFqZtOEvqGmebMrI5FOZdg6Ze4SnjmssWGdGb2Q3HrKVDc5jCibpftMM2N0EvFLbsg4/W7J8/uoEe2LKCbj+ulFYNHu9gOYF4X/0i7n+O62OJWdZKlzr4D4s4uXTveovtaxQkuRmTha1TB+lKUVsYpczroyUsXpUQdluFUme4kf9n3Xm3Aq/qmSjcqFre8Z2C0uJX/zitHBSfvPaJYby5Y0kUPXjif2pnjneI6wVfYsei6ThNaK23Jp0J1STRlkcb+tWpSC91/wTy68dhJ9MAF8+i6o7tp9RTjMUKRxS1JntXGuR1EJFYGEqn6IE1+VjqGaD6WbXmFc6zy1jSiA0zlFrmA0Mhbqx3z5Sr1ctP8Tvrm6h5uOtFR6YimHqySyHjPbPtmFSN27zxl/GK6DdEYc8Dsh4W93GbnqropGFfGv/iVpfQf6yfzEwmyMtcHJ3vVToaMft36FACLbBG/Z2/S8lw1mKz5/ala3PLmNI+/8r7h78MUgirFYTeJ/rhlgfJ1cfYbnov4GVlZgh9IWPgZ76++vNByPqUb2oU0qaekzgmZjTiL0y2yOXf8F9EGHi89ryqruC/hukpQKnXgfkX3qe6OQLEwQfm8/KzGGLZP7qgtobvOnZOSpr22xJMTLeb3IszRvcmtEi1V/NMK5meZqDvmwdxKDDfvU1KAl24aVC1uZWNpnMri5AkLZf/mBiMIdcXgsvFD6IEL5tF/rEsdY+P1zKoaxUu2tYEgSerUxce5i0bYCv4qgn3+MnRdfM+sSxpuTBLOLZpdJQJ1oLjNEbgRUxUs0cxtMKKRZ1tE7EAimiA1VxbR0nGNCVlZZeb6wd0z2ZFUJ2Ng/PY761MVgaKBUaqEdCADkYVzeE7LFSVv5Dja590Hz6WEqqIqKZdMcWvxt+BzSj7cvO09ZdECwStUFfxeHdVWuf0gFLoqcuVzNA1sd6AyEZBa3Aq+d2Jp7VVdsVLs83zcTmqrom0bpgiPaemke1OfTIrYYbUllJ8XofbaEjqqt8XCupm9Vpyuu7WSnrl8Md24dpJSXiqwfY+oqqdsnPh4dNFafs2wsBEtWga+Fi/of3yy2vFhUTkqdWbm8Fqhv0hR36tp9vplNu1BocWtcnZElDpOqnSBByTHO+12oaoL0fiGZ2FBniGYKYsoK3MZXm/qyhaWPDc2wrQSJdjHnyUVt+qWhqa/Va9TmcfYeIbjm5MW2qJAuLLs2Ocie5xWC2xRkCS+PLI+3L8+0cofK1uy1fisImZxVN1VAg+rY9NE/JN/qs8wYlKSOrO4dQ53Lk3WFrf5JplVDRWcwL6Cq1eN923MbhEEQ2W58nNjqVuwgW6Wiufj1u89EdnzVjrBpNyHqs00VSxhK5l1p7FeGUtg+w5zO7FjWd8+OJ81E2/r5jafcp+DX9jxJCVb9zldlp27aCRd4zDAt1EWE8ITNmqS8gLExwPE1TDBCh/5wgK6aGlXSlpgDRS3IAWDqwTzbx4OmoYgaIqdAuszZ+bwWnrs4oW0bYPR6bjo6K4qz1+5hJ65bDH3KKR48ZT8/PUjjf5NnUzGiOTPxE4+vFfGu/zbx/S4KofInjLMuasEOxKJyk5+9kVxK5jWmAd7873coOCnmFuepM0m0/g9bUyFfceLxzRwI+byfDsaLG55wcnMilsHdclJXyayilRBaJlqTMX9Nv485nXVpwRpieOHYt5ulRFZDPE2aSqKCoSLiflddbYtbtm8+obx/faas5S523HbXqzkN1vcapLZmMzidsZwuS9dK7n82vSJaPZcJbApjUo+ts2l5jdgCSLI07z4U7gPmfWO3eegurhj+yLRExN9b64PKsGi7MC+CrMbLTsnIGXzmT37DtqWy8/hzGlcBI3I4LOUDL/wYZ+Lk2jliWsp9Wi2E7x6rirWrqnKd3U5NMFn9lq3cztLH7eaZnD5JpJHeL1ESar8Hly8MF7/HNGsxy62v8qLiPt5r5voMVNTgx7rpv/t0tVQRkf3tii5kOEFLUsotEw3u6J7wIWQ3fWfm3mHVHHr4TpnwOLWOj/RJiiroGX7WpVx0IxGA3FFFo9poCs/N1bxqlSSwcnkNSle173yle5m3u7Fmk41D1Uxo/kRmmpyDXHbusm0Zmor/WzjdEM6FXdTIBUobnMEXqNTseBICQKkaa5GY1En1S5QRpgxL6oaKwpTj/q67MuKo/lUIbBuEC6qmEIN/k5Vj/lwkHWUvEFDtCNmZxfVjF2LW6mrhJS8ZWllhbkfrNiyzSIfM3gUvNfFMRRVRYI5WWu1OAKxtDyFZ+Kxu2cisp50sM9Z5POzsijVpyGbL0+RkhqcTCyD6NlMbKsUXyTAoLi1a+Vt2FASpBF8r3qU1Yu5pIqcojLZz6xVg+hIrOgZfm/dFEV/cMwCkkm/ekorHTctdZFnzlPF165faGS0SBHVLY2MC2P3rhLkfRCPGkW/o2ZUgtAl5GCSiix1RFVCtIgyf6sSgV3VX54Kqu8qT1PpG/g/mBfkY5sqLAPjmZH14+xC1hy41s4xT7vPtaNOPid0almsctV5h4x0lrem0YVLRnG+F1/DPkJZ/bS6XzsWtzK8Uu9cfOjolO+sqktehN/Pxe/JOA6qzHkU577Enz9bnfTWNH4cDdV3oGlGGZ0EzXXyvuL3yi1CsxcEUubtNJ22Ak6PnM/tqqNrj+5WUiJJDQSYzxcu6aKvHD425Xu/H4esTXh5EkMjzZWrhMntybUVu9Y2W3KLyzf+Ec2P0K0nTKZ1g6dwnRDvey39Wg/+r/I8Owb1Git7jHEA7L6K7pYKuvHYSSl+db14o8p52GheZrdFrdXFtHXVhBR3hmnsHrIKKG5zGBVLNPPxNq+NE+/bPJf++4wZ1KxwTIVI7veVh9cdg9vJop0OW7bzxxZRXRKljXM7afSQcuUyud8pXitDuqtrysxJ4BgnMlnlYR6A+zpq6I9bFtDtp05znr/g+1T/kurPRIZBwS5I4zZKPA+rsVzteBYv32TO736817JcqeJa8NMFi+0f03HzCL16/P5uaZgUsQo5ivrEfIn1hFWukYimVHfYZ2q2AlrZ05yS3pwla3G7Zqp65HUVrPqpiKYZxlfRKQDzXmlEc+cQwyyXrM++7YTJ9JXDx9GoRv7YIiOiabYUAE3MHOAgs+Czeo46ie9BNcAJi5eBNFQXy3msOwjB2xXlxFMmtHOOKzqFfWb1ZYWG71WCz8Q5aDPC+G/Pnq2cVtUvLpG4v2K/ri5R36hgFUZCBZZyXuLfLP3U2ihNtqHh2ckchcamaUQTWhhXE8x7tAyQxRYl+FXVKk7n5qHmCoSruFV847WlMdM4yVfiynDzunj9k0aaLWtCTTP7aWZ/U5k/KBflC3aKl58STP52VG8L102H0ka4ghzflbiYEmGnr7ZC1X5LVCarz2UtbmV+rUXGC14ppOOiqgQkNMsg4s6zZ9Hd587hBkOLI9qsYwMCk6bR8glDqK3GmWGPjFSXFHzs1B7lPZQUgw4bheQwUNzmCLzdSJXJRYpjeI9bVkddKU1qq7JOOMg+hUWVYYczTR2B/DiM9aKMh+ok/k+XLqIty0bRyIYyrrsD0eQsNU95OSrv3p6FlTMFt6yEmcP5R6RlZfOeT1NlEXdC7harY8pOVTIGK3lRmgB6e5XAW7x6wE7seAs486aGzFJJVG5xNE8YaMJMfGPJrjWqQQ6FfiD+rWzeI2o3ugOZZDI4SW+0uBU/KzVLXhUFBKOsNaXn+UY2Z8luZngdLEHFVQKrJGTTm5XebFZuLW5lwejMLBrTQMdPG+qoHLM1mRWbF3fRyp4m+v6GKYYj+CrW5qJinLQHmY9bu6guKntaKi3TCK2NmX49Xm+O6h0IHqoa+VrWf7Jzx+8xgdP6dV3Zx21bdbEtf7hEA/5+ecfQ47DPw46vP7fKVWneLjOR6VYs90A8srj1yjhDRU800LclCyxhFLdWbomUxhAbcx5e9bSy4tRI5ONWrcy6spixjzB8VMvEzTYeT86IZu9YfUTTaO9+vquTdCy/nFraJrAhJNfIgOMpQXZ6xgt5Gsr5cxXZpa+8u8c6Y0U0TW1+tl8wlu76bH/ic3khPzhZOjYuWOL1yLrND24KKQhYHM2nLougkqLiohy3cimyeHDvqs9Z1Z2lHaCndQYUtznMWQuGW6ZJjd7ulzRq2LWG8Sr4UxyR8kt1p9xORysbQAwDHFPgjM7UI5LqFrc8BS//s1AuG64S5MpZZ0rdW44XROQ24buPW6GCQa40cToQq1znlU8mO6hYAvO+Z/udrasGFuWs+GalnFMfnFaPpKO2hM5eMJx+etq0wWvslxNHs9uYnJVi+OtrR4wXpPOwRPYdM38UMJ1lSnAGhbpo11rb3Dc3cpVWxkxZBamdTScVrG5RI6PFrUjpTZp5fHEnZ8r1PtXFiKalWPnLZK8oKqBvHzOR5nfVGyxuWUTjgqhvM5dXqBDERCUStioqY8s3V3cb/BQLN7gUXlS83nS3VtJjFy+k/z5jppqgUlcJyc/xICPxS1T7XU3jb6TEaR+0JJrXZbRM8srPvwpeWG6Jg9eq5S1ToKfNx62NHC45NNUtREIexcrBBicqibKKW0YmnvGBRLEb/0l1zqMR/9lbLTc0jW9xq8LGuZ2UFzGeSjBMERRfg7s5Cf+52nVxM7SmhMbwTvylccrpVK1kp77L2vHf3/k48VkccM6ebHZR8Sv9xeWpLkzsonrqR2Rx+wnj05x9VrLNfmNO8k0dJ8RFNYuc6uZr4H+vLH2FhjYaJdrUkZNST48ReaPfSHnOAoHi35/v0I2QStlADShucwRzW9x51VLHxx+DRGXh5edkQTwxT342Wj/qjjsnqfWFSIHMKYtrcauozDXuHIvlSchlw4rWcXAyyQsujeUrud1wMkG2h+KCwaOjImw+ooHXr2i8LGaLZ7sBpuKwt8DbsTbfotQ6XfC9imwVxQV0/uIuaqkaUCwIjGOUUEmfyN/B/RiuH2TVpGaur1e5DM5vkk0uWryooubjVpx+aE0JfefYibSZmWimWNwyCwWvm4fVpFrTxD5u80xKb7PFrZs+K8Vdi08DpkapvhLrFa2a7fq4FQbHMX09pb2KVk1spguXiN2kiKyEZIjGHJU6tWSsWjAx0b2zj4odfxsrCqXB91RhlafmcUb1+K1VW/7RSX109sIR9I3P9xi+l548UCqZc53NOmQmHnTITt7KsrpxlaCrpRtI4+w3MydMbxfLo3C9pml01eHjaEJLBX37mB4qYhRPXviatrMpz5tDWFuZaVSQnzoZV9FZb1k2oPQWWmcqiu7lWBDPz846L6INBFi786xZNMo0V7M7hrvBqUGgH/N/g89ytiyFa9XG49Q01x41gSqLrV28nDy7QyF/i9I1tecm2gQ1umZJfm9sr2rrQ6838FQtS93oQgwyC4rTiOhnG6fTT0+dRsf1DR38zpv1orEctUzi7+ywCUOs81Ttu2Bz6wgobnOUWH6eUuMy92F+WCfa4TtrJtGw2hK6+bheYRpDZ+D1YlxhYu4k+AsPuY9bwUKVm1atPF4yuwODnfohSyp3E+FeBi+D/fAQyWheSJuTOQ644lKp7hWrJrYY/hYdAxSmGWRt34CycWp7Mjop267N9UPuKkFNqcPDrJjwKuKvmzfhtwWHm4UGK0+BwJLIip7WSiKyr/TntePDJjTRDGYzwZxCFkDNLSrdCruwYe+3QBCwhsiD4GQeB/MUwbPcWjC6XulakUJQJOqoIfzjiDxL72+s7qFN88Wnjez4Apw0GOBQZIUiq8PnLBxBFyweyfWFaAe2L0wN1Kq6KHPym7qrBI2Izl88khrKY1yleWt1MZ1/yMgU37Jyi1ulojmyuNOuOilWVVbZ07Rq9vFn5UQ+P5RevGaU6pt+4N3/+sxZtLKnmcoL82np2EaaM7JOeZOHK9fg/24NTVSUOLzNkf02rPZF8wLl4GRu5iSc2hLR1MaYC5d0UV5EoytXDgTh4hkGOJEsmh+hk2cNc3BlMPDmnuxBE8PrETyQM+Z12iqT98qPntxqKw8r5H2RLCRdEhW3QyJ3V+rKP2+x3KoZFMwr13OytUtpLJ/6Omp8NbpRHpsGxewwBRiTpbVbNhS5aribMYKMQbUh3XnWLMPf5omL3TlCjY0gDyqMb6mg+y+Yp5w+Xd2A0eJ2YDK3/6BOE1oq6bX3P+Gms0K2fhQro1K/L+Icn+F2kJyvjH7GrIWXBmYzXe/U4tYKtaBG/MmCmdrSKL378T5l37lxRDmad8TNz8DpQkPlunRsupgVKIYJrIK1epx5XfX00IXzaUiloo9GpxaqFnXabH1nmIPbfpzspFS08aKgrGTSXL1qPH3h589x5Rsox56E5muiHGsiWXrDJNz48pX53roBdydq/glV2rH4uatGMSYa8Bf6xkefqQhlLpb/s2Zc2IiDk2kp44u7/lG+eeQVmkkB0NNaSZdwIs3zOHJSC11/799p/qh62rM3eaxS1G6uXzOR/u2ev9Gh4xvpxG1/SnzvpMuz447pP0+eRi+98zGNay6nzXc8k/K7rM89T6DsFW4OC75n52jmY9tedPmivrWiqEDq/sCARtRSVUyPXbyQNE2ja+/e6arsgSwda265OH1WSpalqlZNkhu2dpUwWJZCUbIkduYgsvty4ntU0zS6+XixUYaZaH6E9h3op9kjaumeF95O+d2WxS3nOyvFraYR1w+znT5E9Lz9PN0Yvyv+41FzlbBp/nA6ZXaHYY7g5ASZOc3zVy5x5H5CpgCTlu/oqiQ85aRx3mS9hmIDo7ptv+lAUxRCeHKGubixIrlB8xnjK1k+b2c+u3wYoxrL6MW3difc9FgGJxv8329DGK8NCe7bPJdfjuL13nu4Db4eZyqwuM1heP3CuOYKw99miwq7k4lDxjRYJ/KYNBgWpmB0qq7RjssW05OXLqLa0pjJF5dcuIuXJX2GyX3cqn9fXKAW3VTlaK8VUsWteXfN6brL4jqV3ckixuWGbIC8bd0UWjO1jb57rPpiwpznNUcmA6ekRKv26pkopPFjIWCuogV5EZo4aIXmtsy2mmLjBJ7Jytw2Rg0po9rSGNfPmptjoWZfm+6ORzGfHedizKd3aFXis67rqQsnByWx18RUFLfMZ7bpGX33qctRUxobzEtBic0+UwXLanPXwC5Qrcp74ML5lvIYyrXqT0mjPXsPJP9myje7cDAqxN214xRXCZzsRJN8e+VoBj/H5y4aoWxdWlUSpT9/6RD69jETDd+L7nxIRRFdd3Q3TWytMqZ38KjsHNEuiubR+JYKw7s7vCd5lN7Zxom9TR02qJ4X7d+Mua/91uoeuuTQUTS8vkzZOjkuhZeLUU0jKi8cqE+TmZMZKtfxuOm4XtK0VL/gMzrVN23d3p7UNYRF5vHX5Hact2dcIJa4tlTBYtamqA3lxo3cBy+cR985diIdO3iUOJFt3CrORv4TTGsfImOQVB4a8X3c7j+g3ocYffka81bBzevm+7hVd6tltbHrpP8xP0/XwccscNtmuwdPCLFEDSd57JWlIo6maYZ6M3dknTixQ6SKU9NmshuG1yeNPXZ/xp8PERnrgfE3d4L88MSpdOmho+mbCTc9YmONgbLNH+zDXmmnevcwayu7IogsZZXHZB+aYRC6mmwAFrc5jNKgmuKo2/j3Hy6aT7OvuV+5TH+H4ODLIxpYWJfE8g0RcuNYdVSnze2krb97kYjkHbrYVULq97GC1MkV72pelnYXAV5Za7i5TmUnlHXiL1OE9LRWJo5u24HNcUJrckEwvcO4CPRq4ArK4tZs5WAuQsVHsheL+Vh+Hj168QLK0zTquOS3xvxdTOyaTL4rjaL61zbUrUfMihrTr5q7+4/m2fQpznwukASaUEFpAcN8FtVvo3jGNOwiMeVy09/R/AgVFkTos/1q1lRWzU3TiD5mFLfGa43Pzqh8VjumKJTLJJi5Xi4b16h0HI6lqCCPPt1/kCa2VdLTr35IRAOPjy3LblTiuBsLy4Uvk29envzeZKya1Ez//efXlYK2ynDbn4muFmW7aDS7OW5adCqaZsiUI+b3dvjEZKAUZVcJPqzQNCJ64tJFtGfvgcRmjxvmd9XT365alqI4sho3Zf1L4nvVtbGDOV/yd7UyrODODTnfjW0ql8acWNHdRH9+9QOaOkxdqW5FY0Uh/eDEqVQ2qLAfUlFEh00QxzSwU++OntxKW/47eXqloTxGS8bJ/U9rmpbix5uIqLXaOs5CHNEcSVV0N2OBqAyvrAnVFJUWGxIW17td4zl9fv/vpD7a9sdX6JgpqS4KRIHzRCU5OQqfF9Gof9Dad9uGKbavt0K+ieSd8QHLnn2M4lZ2vUJeqtSXF9Ipc5J+f632IoOM83P2ghFUGstPjPluxtWLlnbRNXftpKuPHE/Hf+8Jy/R25m/p6LtyGShucxiVxpXiKsHU0Fqri12XwcNVpFTm8xOvvO88Iw6ivks1qICd25L7uFUvgGc1x7e45Xxn0wJA6v7AnNYkVmmhYndkIYjKwMouOPzwIGB8bho9celC+mDPfmqvLTGmM11nV7nBK0805UqHqwRN04STKuGkVXmQl+M0ujOPn502nbb98RW67LCxhu/tButjUZls2j1aaGXF7uiNMxepuEowXpq8OCJYvKgiasfGY4XJNLwFtDmNOcs8icWt29ZiNanWNPEiwejCITU4mRtE9xk/MrhqUkvqRRbcefYsuuNP/0dH9TbTom88NFCOqeqo+LuzwuqZpigcbDyq647qpvMPGZkIROgUVgQvjcVEtyJ3T+Qe2T2oukrwZeTRBsZxpYC1Jlkqiwvow0/2p/zGG0PsHP8W/abaj+pENHloFf3pfz9I+c0qzlxcTrUxxJiIfYuqiolbLFwa5EU0+vLKccZyLNYTIliRVKwL7da3oTXFhnY0e0QtbdswVamvZU8VXHPUBJo2rEYpSBQPo794+8/GLrx3ret6ygaYKuar0qmWSXdwslkjamnWiFqF/K0LsKuI1Cj+7nTlMrwkomm+lPnJvqSrBPP84bqju2ntbY/TRUu6PDu9xkPFPYpbDHMEURrOd0XRPIN/fjeinDFvOJ00axjF8o3jp8hoRCTntUdNSPlOuS0G2WFkMHCVkMOYOyD2mH4cs+VNumKTeRaxMU2wJcoUAHZkk62JRDu0vOzNHXOqVPFrU79TCS6lijl7Nu+fnjrNsGiSBiWxKKe91nrhXchYIfvh+N0csKm+rJC6GlMD6Jjrio2YFsbyFG4hLYpbMg7wRiWjoM46qFh25uhOuoOpw6rpu2t7qbFCzceuXTmEitvB/536dkxV5Gr2la9MHipR6UXBycx+WkXcc94cSzlY2KPLbBKRXz7Wb5pZMWNw55CioHHXXqyujmhawvLf7Ccx32AJrHkaTNF8+YSWSiIi+uWmmXT3uXMcuTfqrCulLctGURWjsDDXU6ebUlawuZqfja3o6BHNtdKWyL0ViZcnE7yYC8nmIcoubk1i8PyCpgtN0+je89VdgVg1N8N4J0ij4jOUaEBp9v0NU2jJ2NQ2aFWXtcT//tQ/Fa5eNeBmwkqp60VZXvDz02fQ4T1N9PUjU5UPKv2sRsb5Y3VxlNpq7PUhbk4lxGVwCu/59+tyi9vjprUJf7tgyYDf7mMHg8t68YKtHklNSUwpnQi/q6DK+s+2EYDmbh6g0v/Kch9QHDsuXsjYpqSrM3M/NrGtip67YgmtnznMFFTWWxmU65FH8xk3rkDc3ntcN/DTU6dZpuXJ2VJV5CooHvS0zoDiNodhO8ZvfL6bTpubGtly/qh6Q8Rk+wsBZ00zyOMIMkS7UaoWUXbuSnZcWjWgwZKxDVzlC+9ynthmy1Er5NbG5kV18nOVOZq0RIFpVQevOnw8fa67iW6XDEasMtuPuqaq75YVfcfG6erlKbwbv53pE/E2LdzVGWM6PxTsNtO72McwKDg9Ot7I5sLrLTQaiFg8on5AsWYX+8HJkhj9x4qvH9mQuqExcE3yohH1yQ1Eo0KYuN+zsKdCGsqNR6rzGbMO89X8Uwk2FIFMBrwgnRoR/efJfTR1WDX9/PQZht8M0ZVNskVMFrh2ict17+a5dMOaibRwdD0RDVgv8jaX7CBrozbi9diCXU+kbg7Krz1v0cDc5pyFIzyThy3TydgiPG6vcK15baW6uJZa1Up+VHaVYJKeFzDVD756xDga11xObUwfoBHZcq1gZ9wU1X/V0yBFBXlUVlhA87rqU36z2mCO1zW3wyTvHlRVC8dMbaO/XbWMloyVuxjwG1V5e4dW0beOmUj15c42aM2P6oCDnffiAndzUatLvrm6W/gbr0r19+vSNczZkr5ywagGevpLh9BXDx+wtPZixiZaB920dhItHtNA5yzyru/2A7UNe3tzQ400V+uWh7fMt0wja0NW7poWjBrov6783Fju7+Yrf3/+HNq6ajwdyZ724WQfr5cFEXvPyw5WAdm9WIeoWNymU6vZ12Htx92X4GQh1fOEHShucxijZZWgKug6nTA96fg/Xe3MfEzCDkH0BWyRxaZFiWN5pP7OrOVYM7WNbjl+snIkdFVlrlN2fWY8mijyA0VkXBD+5JRpCUsOXlozdWUxun7NRJomGYxYZbaqNYxT7Ciz2UnqeE6wDCfEA7HNH5W6GPSaFMUtO0ERLPKdPH0713jZH3gVnMwNIivm1qrilPqkaQNKiu3nz6WNnI05HgeZI+12rXVZ8gxKUfs3zzbLPIESmM03XzBoNFcW0Z1nzaLHL1mYMlE0BwFjcf26mAyuXzMx9WdtwIrkZ6dNp/EtxrbOKpjNilqvXCV01pXSiu4m25Nn2VFlTfiHupLPLuxCy6xks6p3Zy8cTn+4aD6d6+Hin+0jGspjtHRsI63obpJcYURlc1h8rekaK38LqAAAR+1JREFUn1d/rKuExRJLbbPscXcnbHBFP1jbN5TuPGu2IYCbXVIDvhkxBIcT5KHaj164tEuYj2WzH/xd9ai1X9gZM1TlCOv63ty+9jlwB1NVEqWLl42iLy4fbTgJ5lQGMwtHp7bLeJfJ6/sP6nLFrdUcqKokastthxUiq/5l44fQrSdMpoqiAiLiBP5VRSKkk83ulOwNSlk+xTE2WLJavnbnATcwc5D6skKl01RCNLmcG2a2047LDqF1M9qVshteX0ZrpraZAtqKEW3ee4JFEw5TV5ROH7G86ZvbKV2YnmUmAcVtDmO24uGhk9rAQySwUDJ9N1rRoseVgsTxldao+LgtNQUlEx0ltkLqLsDGkZsz5g0obI7qTe5m8q4uLyzg5SjNW5w6lZf/tcfwt8xfKKvkG9VYZrSY8eAFs4+W5wPYW8QCp9538rOdiZlM+XzPeXPo28f00Mmzhynnp4rVwM2+Y6EvJ9NDEFmIsKlsuUqQPn97lUmkNLWLm+PQ5v749lOn0RETm+nyFWM86fwKmPZQErX2Oy2qA4YFvAO5VE4VqCo0xzVXpEQkJzL6JzRfz3sXcWujIxX8wFq/ytQEHYM+sFmLtYF8kmntWABeuGRAETRzeHITy+0+VUN5Id183CTubzKL9DoPAkiZyyAi2s+Y8prrjHWAOI1aq4s9tfww9xE3H99rWDRbIfIFrCJjig9RTxQnEotbRquyldlcNWOW/fxDRtK/nzCZ/mO990F1rLD7TKyMZcsLC+ie8+bQ/RfME1rFqm4O15eJLT+tFKJa4n/7L93NcV175Rj/9lLpIuqT0skBh8cKTpvbSSfP7nCkCLF6htI1G+c73cJVgi0LdIW6eMXnxkh/V62b3z6mR7oRpDKvNLNxbmfCetQLeI/uxmMnGdZeKk/XiasEs9svVwFOFS6V+XkWjWXGuZ24EIOrBGtRbKHq49ac6rAJQ+jGY9X6IPbZu+l6/djQStNQQETh3ZALOwhOlsOoHOHQBzS3TDqNhpoCksWjMccVhDzuPGsW3fnsm3SmYsTmsLpKEMGKWxITNyunPm6j+RHadyA5KRQN2jylxryuenrikoVUyyyczXJcsHgkjeAcWXZzNNxM1LTDK/vLvMseMRyNcY/RD6v3dU2lbfF+k1mPifjDRfMNz8c88A6pKKSVPc3kB+Yx/qCuGzYu2DsQTYjY21zR3URHTLQfHEmG6DE6CWaWLotbq4i+7OdpHTUJ63JzEU7qdmksn248dhJFNHlflpQ1KS1bHrsh4uSxiZTkIsWtE8t5mR9eXm4b53bQwtH11Gny/c7D0icl5+dfnTmT9h7op/f37DOkY9PaOY2yaf5wOrq3hf761m565KX3lORSYem4IXTr8b3UWS9+DvHnedsJk+lv7+ymaR3eRJf/j3VTaMO2JxN/s+NiiuIsgHmE2yIPqDqO5ZBicasoi6xEuRuF5Gc7LqKi+RFHvpSNeao/6A8+YdqTzRlEXkSjKz83li7/9fPCNCJ3L3EKXGwOnz6vkw7265ZB2OLvOsOmzp4hO2HlF/FnPay2hF55dw/NGm4drMp7GazGmdTfk3Ul9bd+XZe65bAzfqgkXTpuCG0+ZCT92/a/0YaZ7Rx51MrqqCuln58+g9q3/A/390Ucy2MVGSuLeQYt6hjzNxY2sa2Slk8YYk+gQeyO426Gxs66EoPhjTlgqlfkKa7zCiSxCdxiVd94pUU0ou8oKm1VUXOZkdmYX12m30+6gOI2p2EXw/wUw2pLUpR3fR01tHXV+IR10NePnEDrprfTOMnR7nHNFdLfUyRz0YL9nLiKFjGslYyKlZrdso6d2kbb/vjPxN92dRRmH17s5YeMaaAzF/CPirLplKwBpUpK448yi9vywuQzLCvM91yR7/euokjRlJJOorBWveXWankwjHT6ETrY309fO2I8nf6fT9EpszuM9yCyVmc+yyzT7N7GaXM6UvJnaawotD1R0ASfVVC1JrAnj0nZ6FG+KYsJBxgUtw6uF92LSNnqxIVAQZ64nfIXvBpXSXPo+EZ65rWP6IvLRyfTstdxyuZ9V1ZYQGVEBsXtgD+5JHbvs768kF54c1cyP49cwyzm+LHkLTYWjWmgRS6VdCxm+fdLjienK5iqEXeF7vfQGbCqktKpj1v2N1m98mMIspPnPwxKB7vlaLS2r02quLXCbjA2to1/YenAce1vbv+b9JqEj1uVAjx4H574elQURNXvZxwrK02ZkYkT7jlvDn2y9yBVuFTyOZmWWj0ZXrNMuErgpD9oobmys3GoOs8+a+EIOmJSMzVXFnHy8GayLho3LeuWzeJHSU6VmpsMzzhDZcwasLi1J1eKqYyN5ss7ycIPeu1EEr5MsnWTyC2WF8hiyxCldz1lSYhEcUI6XT1kE3CVkMPIdgL/+4wZ9LUjxtPsEbVclwprprYlHFoX5EWou7XS06j1YbW4FYm1l7H6KYwam5XdWxk9ZCCy5sqepF88szWZMICJYlnGAVKWzp7wso54n2lBKrPmjWga/eXKJfSXK5dQfl7E6NfSg7phNTi7RVXBJ7O49U65Z/G7h03tYP+AIvnOs2bTyp5mwz2oukpQQWUu/87uvcLfhg1uOtnFD/cIPGSLFWl9Ui/Cd6IGi1vvJBP1G/kOfLbJohPbEXlSWxU9smUBLRufVHiz13MDx0k3dIxymDeC7N5p2k6/Sfp0rzAvds3jCksQ8wi30yDzkVY7OA5OJqkhMj0Oq+Rhn/W/nzDZoBwL6XROiTxNM7Q/J32Z3dMAK7qbaGxTOZ00axhTrvya+M9OnrXT8TBUKN7345csTLiQcV3kYJkFeRHXSlsiZwYF1vXCXoWw4/7KCjZY21ePGCdN21LFd1nj1dglktvqdqyOzptpMimfpescTr8gel8zOmsMaewGG061bpRfP6mt0vLaW47v5V7rVNeuek8GX7gejy1WsvOKs3u7KjJn8pipahxgvsc0emnIaKC4zWGMClnjb5PaqujYvrbBIxH2O8lVEweOZrMTTzu0VqfuvKrjX48nUk5LA6TYFOfXZ86kxy9ZmFDgEqkfgVT2R2ywtlZUIEjyU4E90kpkcn9gkqF/8Mh9/Ni9LJCZE46ZMhAchZ0MeYlhsiZTjJv+TpevOWOZ3uVlttZg7100AfbaKm72iIHjiocP9kFcy0mHeau+V+61im3TTxnSCWuR4VZM9npRYAonG4esQsXr4GRWCyPVDR23FrdElLYZcTrqptnYRmahGkTzcKssLi8soAcumOeJLN5smKhZ3LLznvaaYrpoaTKwT2WRw8BBISAvYn+jZGTDgAuRqkFlntSNBOenwoI8+p+zZ9OXDpP7/2RJ1jv777yyOEp/uGg+PXnpItvXuiGI8auhvNCyXfCmKvEj892tlT5I5RyvN+atFJV2+jf2NEShQwvNfheuY1jEFrdy7JaeOo8Qr3PsKF/N8ts9OSMLHMyjg3EHJbp2CefUjRKCstl7lG0m5gvmgF5gqbjllBfAso2I/LFY9eJWrlgxhurLYrbGL6BORihu//nPf9JJJ51Ew4YNo6KiIurs7KTLL7+c9u3bZ30xEKJqReCka/i3z3fTi19ZquQLkOWOjdNp4ah6ZSffPPycDKpMWswD7OjGcpraXq18/LggLzI4uRSnETt3VyqCyhhXBLKjpsYy1fIWkaK4lexEm0ViJ5MyS0pVuhrL6E9fXEQ/PHGq67x4GJV0skWb8behNe4tX1QCgNy01p9AHmbLHfa4fLHAhcisEbXUWVdCy8e7P55PNGDt9fvz59DckXVE5K3yxhBszeYMR0WpFf/+MumEx54SwHcEz8GvoH+id+Dk+Fy+xFWCW2SWNtblGZX8BgW1wF1D2PDrGJx5sbv/gFhx++r7n/gigwwvqlG7QwvIlOBk7kWhfonnhi8dNoZKY/l0weKRBoV6/BnctHYS9bRWSgOXyThbMSaCHey7SrB/zffWTaE1U1vpjo0zBvMQZ6Da77z7scW8ZzAbJWsuTs1orS6mujJvAgiKMCtjnJwQc5PGDb/aNJPOXjCcrjtqQrJMj/s4JyfBrH3c2vvNSk9qZ5g9yHQeThVCXinF7FqoxrGrN5YpSOMfv7h8NFUVF9CXV45Vzte8Yf3N1T0UzYsMBKVVwHz3XhkPxLEKnqiWr9r8usAQnCy9bdCL8lRyCLFNhiUddaX0+CULLQ33VOJKgFQywsftiy++SP39/XTLLbfQ8OHD6S9/+QudcsoptGfPHrruuuuCFi9jkSnORKhPtDTLYAo8prRX05T13gQx8QPR/bO7wim+XCMa/WzjdMu8Lzl0lOFv2aNmFa9G+dReEOtw/7094gWBl5Nhs2WUcSfamNa8AGUtOT/bf9ATeWo9inLOQ9XyzKxory2N0T3nzaHiqHP/UamypAqwzCMlKcvoIeVUZJI7lp9H318/hfYf7KeKIv5Rwlh+Hv3+/LnWCxDms2xyVViQR8Prk4otbrYOjZOMMji/VpxmINX6mcPoit+8wE8jqVtB+4xiSzdESnYpltkCNQ7bLzhylRARu3PwUma7vxvfsWa450hEoy8uH0NF0TxaNUktkJ/frmHipKP22fFxe9+L73hadk1JlN7bs8/gg92MVxsAVgGxeDgOTubQx+3IhjJ65vLFlBfR6IBhfB8oeNn4Ia7GmvMXd9G6Ge20/PqH6a1dnxl+c/qU7faRhQV5wrFJ9Hxbq4tp66oJhu9aqoro/z74NCVtnqbRQYX2+fI7e6S/Z/Oi166iw4/ebmhNCZ2/uIveYxTo6bb247F4bIO0n7Bb362CSDq1uHV6msyuqwIRIgtVq9uxK7dUcTv4+eTZHXTSrGH802ACeVjFs6YNrJVf+PISg7sne3IZ/37m8sXCa2XxSW47YTJd/uvn6VvH9NDRNz+qJIsIVjkte+/GDXdXRaZgqagfLM//OCn+5u8FUpduCjeQAbcYSjJCcbt06VJaunRp4u+Ojg7auXMn3XTTTVDcusBwXF7S97NNM1SOuQX4KaFo19aLPrys0KjYkj3rw3ua6fcvvE3TTUf92StULXY/+nS/OJ2i5ShXABNyi1vjheYj91Pbk5NJryZyfqK+EZKaUsWa7ti+Nvrx46/SsnEOjyp5BfMuSmN8ZfP8UfWW2fjZr3h5msBo7WnX5Jb9aF3y0rGNdNfzb8my8VzZ6CUVxQX01SPGkUaa0NpaFaPiNvmZ7Qvs+pE0X5NqraieH98dh/OXIevH8zSNKooL6Msr5T4DWdLVZTpxq2QX8wLUvFnEMmloladl//iUafSN7TvpvENGepovj3Uz2ulAv05fuZO/gaOCN/7g5cQX2+yimxdkyCk1Hm+w2n0kxSYDBKdP9P4L5tFlv3qefvLEq4bvIxEiUtiLtopub+fotBdtU7Z5oY53nUS61iaqVoFOcJLdkAp5W5PGsDA9/zvPmpWyDjFjx3KVncc7trh1eJ0qlm4zbOZnPi0hWkPZra8G13KD+agqbQfKM/49o7OG7nnh7cTfIsMKIrkbKSfBR0V3zpYja1vsWMPbDHODeX2ZahzhAZpGtaVRevfjfTSGcYloF7825MsK82n3Zwd8yZslTOuVTCIjXCXw+Oijj6i6Wr4zuHfvXtq1a5fhH0hiPMIhbkHsgjbX25lot9kLZaLVsz1s0NXC8dOGUjQ/QreeMJk2zBxmzMPjF2TIz53elk6Z02HKW7zINz9PdtJiFfU2bPgxOF2+Ygxt2zCFvvH5Hu8ztwH7JvwOBOTlwiwu69RhA2NIgaq1JjuxtFmmzMKc9/11n+8WpGEn8ELxQsHavqF0bF+bZbqxTeqTV9ba4gDTFzjyccvkZTVht4uVOKobaxFNM6T9XwfH/4PY6/KqLprzYd9zaSyftm2YIrzWzlFUFboay+iW4yfTqEZxfQ1iMVI0qFzsbqn0PG/VuY2mafTMZYvpqS8ukirTncBboHpZxvyuOuFvsnLsjHkFeRHuSZrpg0F+a0vlfoCtlDSa6X+/+Omp0+jHJ/dZKvh4OO2H3FhvTWn3dvPGzfPtbqkgIqKFgs1sP2Ic8AN+8ctRaVN2+jd2fHaqYwraTkP1nfz4lD46blobnb3Q6N5F9fRdIo3ge7uuHqw2Vq5hXH7YnauI+r3De5poVGMZTetwFjvEYBgh9XGb7AsPeLwe7Bo0nBGtCbwa3+/YOINOmD6U/n3dZH45Ac3mdZ0MwRu/dsSAm6Nvre5JTeuyrKBPCGYqGWFxa+all16iG264wdLaduvWrXTllVemSarMRtYZsb5rSj3ZZfcXP3feRVYNXkwuZDt7GhFdd3Q3HTOlLaFs4udh00KWSLn3dfpYRzaU0u2nTqfqEuPCRDZZkI3FXg/UvmCwEPS+Psby82hel7Ulq1f8atNMWnnjI9zfLl8xhm68/2XLqMFe4ra9xd/IpcvHUEtVMR2qeJyXfZV2ZbCrV4wH5kuRQSBPyo9pgvU3lufAXQER0eIxjfT8G2obq3nMpJ11UePIxy2bl+l9um231r4H1fKPaMa0f33T/ga0FxHPVdCEf3gHu4C957w5KVG846yf0W5pjeYHXvb3qjn95qxZ9JMnXqXT5nZYJ7aLjX4uXfXsRydNdWzFz3umXz1C7INXrrh1JIKBf/t8D2175BU6slfu9sRKgeT3xmmcPodKGSI1//thx43M31s/hf7n2Tfp8J5m7u89PgQ+k4nrxJrQzvqKdZ/i1DrQ7xN21q4S1PKZ0VlLMzprU/N3IBMPUVBWET8+ZRoddsPDib/N/UNlcVT4mxnzWlKU/FvHTCRd1x3PfQzBySTPXWYd7JZ/P2EyfXP73+hUwVgaf1ZurF01GogHYufUlBvs9lls8mP72mjVpGZHri8tyzHJlWlGWUERqMXtli1bSNM06b8XX3zRcM3rr79OS5cupaOPPppOOeUUaf4XX3wxffTRR4l/r732mp+3kzHEd5TMfvRExPLz6AcnTqX/WD/Z4LcwrPg5FxRN1L3obqwGx8KCPJo1otYTJ/DKMtnt8JkLbj5uEnU1lNF3jp2UorQlkk8WZJFkM6FzV7Gu9Au7EwoV+USRlLtbKmnDzGH05KULDX5l/cDLxxive6WxfNo0f3hKUDWr65zglc+nsC12q0qitGl+J521YLjj8aGnrVL6O9ueWMVdS1Vx4rMTJQprceumX+EVbSWOdEFt+OzeLmHy0Co6ZfYwutphkChV0lE3eUGwgpKFh9c+91QYXl9KXzpsDNWXFQrTOJUrbK6J2qqLafYIsYWsFfF+eOPczsR3JYJNMqJUVwkGlzc2KxkvdXVJlM5f3GUZmNTS9aINUcIyhHgph5tNVTOyOZSb3ri2NEbrZrQLNziG1pTQ3efOoScuXego/zkj62jLMlOsDDv1QpB4RXeTI3kMrhJMj1T1iLjvvkQt3qdrIwGbFrcijK4SrBnXXGE4jeJmrExxlSBzP+biJlWD/zo5XaVKa3UxfWN1j/BUTdjm3yoUODBqYBEpbb0yoImzVxJoFiQJ1Hxy8+bNtH79emmajo7krscbb7xB8+fPpxkzZtCtt95qmX8sFqNYzN8oqZnI7adOIyLzcUz5NfEI7ZlGZ52z6MwihIobT0xuTX8ajo0Ej93xaum4IbR0nNiSUTYAyhaLmWBxq+qGJNNZOHrA6jcTfF+zOBXXeJlNBbmzIjn5iK3qg6prFy4ZZZlm8yEj6d+2/43725wRtXTT2kk0stFa+c9O2ouiebTjskMoPy/iqA7mm47ds7it0lbXqy6kNM0LWTS6dLla9GlX5dj1ie6AdFkWOiWsfeHVR06wTsQhbMOt2zV7/PLVU1rp5gdfFuZ5wvSh9NDf/kVHTW4V5uU0Ur0jLN6DE9cFmYLShma6xj5WQezDzLxLYQwUEc3TaOPcTrr6d0mDJzv9kahtjWsqp98884ZteUYPKSd6+nUiMvYjX1g6io6cxLc6NuPlxtEXlo6ir9/1Im2a30k33j/Q9i0tbj18x27qaL5Ni1si41gpGzct60ia+jlR/IIg8aNfUXKZ4WGxNRZueJzyqctA4eZ79CrweLYTqOK2rq6O6urUFIKvv/46zZ8/n3p7e+n73/8+RVzuIOQq9WUxqhq0fpQFh8pk2M7grAUjPM1bNLHxYnGT1kWAIkY/tN7KJ8tP9jxl1rhhwWAxF77X6hlpVVIYFkwus3Iot6pFAL9M79OkHHUMcV2TBdfRNE05+ny+yR0De+TPLpqm0Q1rJtLHew9QQ3mh6Tc7+fDzll4jGXPNFvuZOD77VRcNR0YlzyUoBW9bdTH3+1uO76XTfvRUmqVJInsaMncAqqcR0oVX75V1PcDL88srx3GP/RqC+toUZW5XHd328CuOAimKFEj/fsJk+rd7dtI3OT4IRfhpsWYHL5uoKK95XfX05D8/sJeXrG8Ox6PzBdF9O51vxQMszhlZSzte+zDx/enzOsUXeVQ2j9PnddLqKa10sF9PKG4ty3ctgD2FqyiNsY9Sq4Sq1r6WPm6VSnMP29daLfPyIlpaTl+OH/RLHSfhKsFF0W4CkhlQkGFcczldcyQ/XgY/S/UbkwU2V8NYs2Bxq0b4HZbSgNJ23rx5NHToULruuuvoX//6V+K3xsaAo6pnMKx/v2ydjHh9X6LFuJtd2eOmtdHj/3hf2c+m18gkt/v47KSXRRAtiYkXkmHZiZURVqsrHk4fZ5EPPo/ShdO3YziOabtM50pfcZ7yv7MF9rl7rXSIHwN99OX3jGV6+TS5il1JcuY3c3CyMGM8aeAN5qbCLmClz9Cj8lX58cl99MjL79IxU/gWmkvGNlJbdTG96iC4nBfIupyNczrpiVfep8N7Uo9Eb5zbSXv2HqDFY4Obaxv6S48s4VUCa1qN43aVyLNH1NEdG6c7UoaLxoxDxjTQIYKo7mWCmBTHTG2j2598jXqHVtmWww3mW0jHhtSpczro2rt3epafm83bsOP1gcJofiShpH361Q8d5eF1wLbqkii9+/HexN9WNdCtbtArVwkWsQm5GMZKxXQ8PF9HK6WSP/g8TaODaTiL2llXSr8+cyZ97jv8GB92+J+zZ9FvnnmTNs233rjw6pnfedZsbzLyAVjcOiMjFLfbt2+nl156iV566SVqaTE68PcjCmeuwPpKzQS/oaoYLSK8HXH6BIHB3Dy+qw73x++gF0d8jH7DrPOz87h5EURvWDORbnno5UQkSx7p9PHrlAzRszhmWkc1XXqo/0evWbxc5Dk9sOHKj54flSLF4jbba55/JxPcWC9bJuXUFTvHHbP/raqTp+jrL91NYcbwWpoxPDU4DYsfR6u9oKK4gH5++gzub0XRPPriYent6814+dR4faRT63ondWxKuzi4rAwnS50fnDiV+31PayXdf8E8qvQxyI+nuGjLBXkR6m6poGf+7yPla+Q+bjOfeF1Srb9e9Fs1nDgXKvixNLV3Is6dAEY7WesHLmrndoOTmdPJ5obWwcmSyIJje4nVe49EiChNer4JLZWJz27m2GObKmhsU4V1wpBz/ZqJdM7tT9O3bJzy4GF+krC4VSMjFLfr16+39IUL7BNltvD2H8yeBuPVDifLvZvn0u9feJtOmN7O/T2M+wdey+T1LfIsF1Z0N1kGQsgIxW2As3v2vV++wnrB7UTW20+dbv8iL3HtFN/ZCzJazdoTgp14i6zGlQKYGawaNeFvocMj4dJ1zNfrUoqjefTJvuRKQ/U4rqb5IIxPqC4UbeVp+juiSX40pAvfQwtynhC+p+EMzyy5mXfhtKpE0uhywInyLB6ImEcQLjCaKozuaLzsy43v0PisvFT+hbBbSWDbfZOpNflZn5eMbaR104fSxLb0WnnzsDM2ube4ta9w5aFqPSu8xsXpFPYepqVJcWtVlwdODKdfbxHi5k+rJjbT5PZq+uPL79Kdz77pOJ88C8uWz3U30ZKxDRTLd3fq0twORUGwgZHwa0CAb7AKsGxS3LJ4tXjrrCul0+Z2Cn3BhdHy24vJqux4d/yofHNlkbO8HR47L3ByZihAghzoN8wcFmDp3pIOf3hWRBzWWSJjcAmh4lYhn1ywqjXD3rHZx61vZdp4ztbBPYge3bKQLj10NHONat7Gccys/AgrXlXT1YOuB7oHfc0ZF7Ah8pWgQJDThPDNUJzhR//ndJ6YzrgETupO2IaK6Z01NKmtMvG3p2O6pMF76V4rzOcfXPv+F+XrweOLRDS6cuU4OnyiWlAyP7HzBt2u7RT3GZPlCb43Wtyq3YFq2QrTF09REd+qzQblpzvhasf3AVXh/kxJmquK6Ni+NncKVZ1oZU8TddaV0PoZ7cJkbpW2REbxh9YU03VHOwugmmtklgYEeArb8e0/mC3TeiPp6tvD+PS8UCYbBlhTdr/YNIMOmzCEtm2Ywk9vJ28bRDNMcRvieX7O4nTxz15nt3mx/e0BF/2t7Jgf+2dtacxxGX6waHQ9ERGNqC91lU/aLG49LqaiuIBGDUlawMkXUkblJJv2qxI3MkFjCKrmUZ4LRzfQ78+fSz/bOGDlr/r+w6xgUSVsircw4F3zT/bBdrJk06bX4tY+XrWBHo8soTRNo9PmJv07Wim+Zw26Hjlh2lCFvMW/ealkyeY2mc33xmLH5ZX7QLj8z3ZxsrnE9k+y6636seJY+g9nWz13JwEevSBMTaSuNEZzRtZ5nm9JLJ/u3TyPrvjcWM/zZmGr5MXLRlF9WWYYJQRNhmlAgF9kq8VturpZvy1unWTvhUilzIAdKzB2F6May+k7x06ijjpnihijqwR1YYdkiMWZKunyGZXpGPcQ3FVup3M+o8WtPRnYiaYbn+KyAFDsbz86ie/fMCiGVBTRM5ctpt+eYz9YAvu08p06KLYg5VnaudZBfZJtHhiUQ5oxbZhdxfi18B9eX5qw8LDy9Rf3pSgK2BQkYTyZkwkY3Bp4NKerLE763HRqcZtO3UGQFrfD60vpN2fOoscuXug6L1ZZa7UJc9u6yfTrM2fSOonllwqZENA2nczorOF+nw2bXSrYuc/DJgy4bmurLvagLOtyRVWVPdmo7iqBKVlykaj/u+7obhrZUEpXrRynWKIaar5+5W02nZtmhnIHn5Xfmxwq+WuaRj8U+DHPBIz3mBt9jxdkhI9b4D9uLMDChjEid3rKDOO80IvJakksn3500lSKaBoVFvCPRhinJTaOFzOfVSS9+bhe+vETr9Kly4MNlJItlETzaM++g77s2IYdp92CQdlm158cc+1BU9ucPaKW/vD3d+nzgoj0SvIMfJP4FAuhgq+i2FkwHLYvS+fxZD+R3YUs4n2m3L1fr0m2cUFEdP+F8+jNDz+jrkaxf8+gCDQGbAjnKOp4L3xtaYy+t24yFRbkOVYCpNePcrAvcHyLN0F17Bz5LizIMwQGkmGYT5oeld2psGxz0FWA0oBZOraReodW0Zq+NiLinNgRvI6s23Cy0WxXTWym1qoiGjWk3FlRHlncsj6p1d0syS1u8yIaHezXaVwzv20f1dtCR/W2cH/zG2sft9nuKsE+8SeSKVNkr/w/5xpQ3AIiItqXRRa3rPIwXRPrQBdkArySafYIuWLP6cSEncA3lltb0S4d10hLxzWqF5CjqL72ezfPo8f+8R4tnzDEV3nCiBd+Et00rwOmxvmDDVNp994DVGEzyrfM4jabfOGyw1NeGH3cCr6P5kdo34F+GjskdWHkJDK0tLAQkA4Vs9WYXl5YQOWNzjYI/MaL6Oy5jpfd2sLR7qyy06m4zQYft0RGSzkvvV7J+ms3p2NSyglzB2xBS1URnTKnI/F3dUnU8HsY60vQRCIa9XXwLZTt4ubxsr79VeugVf/027Nn00+eeJU2zR/uQjL7qNQzqzYblI9bc5vJNtI5QwlK+Z7pQHGbY4gapYriLBPx6VRtCmFckKVrl9yNv9DHL1lI+w/2U0kAPpTCQlBDV2NFoaNAEU6PjIUJL+YLbprXQdNGWSSi2VbaEsl93GbTlOhgf/J5pWuyZ6cUkcXejssOob37+7mWxrLFF9t3a5pmGLjDrDhIhwUFuxgN44apjDBa6WQCYXpuhhNdaTzUEKJH4Ip8Rb+bdpGd4LLbT8gCYIZZuWl3zt9QXkiLRjfQ7//6NhGJx5YwtT8vSOc7tLuZLlpLslbgypu+AjnidDWW+e7H1C5FBXn06f6DNHWYXFmebqXfTWsn0e1PvkZfWDoqLeW5ubsQd1EG2EDjmSJzGAjfWUqQVrZtmEKbDxlJCwcDx2QDxqOU6ekOwjixUXWVEOQxqIbyQmqpynxFYC5R5fC4u1s8jUAd8OrLK880sv4tzAtMu7A+gf2ytIiZXMGoPL+TZg2jEfWldIRgA6Q4mk9VjIWGoauV5G/w6akZ04b5vaZj48DgZzqMA6+EzJI2PMhchwRJei1u1WpP0GObFewz81RxK8nqykHl1FkL1CwL8yWmwIbYDBnWonnSruhOnrgSukrwRxwlNsxsJyKioz08sp/OFmIMNGqNqJnLNhNE+NHW/K4Lf/riIvrjlgUGn7480u3jdtn4IfSDE6dSzWDQ31C2/DT53/UKg+I2U4QOAblr5gaIiGheVz3N68oepS2Rdz6F7BDGBWT4JMod7FQHr+voCdOH0n899X80r8sn37VZMMB6cQduFm2sBamXGBcJmf+e4hgUtz7Vv+6WClrR3US/eeYNIlJ7fl86zJ6/baMCSu2aiKZRP3NlprxVvybibL6wuM0NjBboAQpiIq2KW8V0BWlyJeOUPIOrhPTIOnN4Lf31y0upKMqP02CmQOoqIUk2tOeIgmIxyPu89NDRtHz8EGVfxyoEpSRyU6wTC9Mg1sJuKYnlK53AzPZj9pnyvtwQ9dJXTg4BxS3IarLFx62T2whiUYtdswHYepfuYEoTWirpz186hCodHL1XIag3zCrS3C4kvHglbmQw+7h1jMxVQhY1xY66UiqO5lNlcYFvlhaaptENayYmFbc+Pz/VvtJkcBvqPtYqcJgXGCxuM257Mjh5M+9ZhRN2HEpnU1Qdb1qqiunYvjYqieZRLF9NUZlO2LW6l125Vb+oqrQlIspTDE4WNqZ3yo+X8xTlxmDOAlcJAfYd+XkRmtxe7WmeabW4NXx2XrIxqJ/aNZEs3cgnkrdR4Jx0GqEV5NuzRgcDQHELso4gJtZ+T2yc9KXKrhLsZw0sqCguoM9PbqF+nRJHa9KJnw70w7xwUcWLDR037eagR74SVKNCZzoRjejOs2al2Tedv4XJcme7bnNdDfM7TodSmV3AZprF203H9dKJ255UttQO8asOjDDV/3Ruytqp6l87YrxvcrjFcHzbp004t/2CzGo5jBtnf7hoPj32j/eEMQvOWTiCfvH063QaE5iMh+jWhtZklzuz4HzcWqcXVV2jj1u1G4j4tEniFi/aUNAWt2E8Zasl/g/Ry5YQzbPvtxlAcQuynHRZ3IawD4dGNmCuOao7aBGyijYPFw+eKG5dNPqDPnUYQVmD+Y2up8+n2ZyRdfTQ3/5F62cM9bUc2fthNwI1LTODzvlV/4qj+XTExGb6bP9BGlKRWUFVp7RX0zOXLU67fz6icCqcVAmrC5gw+rgNO3k+BSfzEif+RIOktbqYWiVBY887ZCSdd8hI7m+GOYOgba3sbqZX3/uUprRXuRM0JKSzD/GqLCd10lB2ZlVpS4IYQ9NJmMY5vyiAqwRHQHELso4g/Pqo+ORJN6oWt16S/UON92TaAB2UtCfNGkbX3r2TiNzvSXjiKsHFtf0euUow34bdCMYgldtOmEz/ePdj6moo8zxvg69OxZakaUSannkKeT/7tW+u7vEtb79J94Jz3fSh9Og/3qMVE5rSWq6XsE8sTOv17taKwMpeNLqeFo9tDKx8pwTpRkqVfMVj2GFXpZcXWq9LDK9A8DoiEY3OWTTCG6FCQJgtbkVWQKyFqar4bF8Zpk0SLyTJsL2VtBKiVy0lmg+LWyeET9sEgEu88ilkhzPmddLTr35AR0z0LvIpixMlbBCKW2CfTBuwglIIFhaEzF9fCHzcynLJsGolJZ09WTQ/QqMay30vR2pxa7phY9rMeLOZ1q9lK1euHBe0CN4ScMXSNKK7z51Dv37mdTptbmfaymX7hJqSKN22bkrayvYSJ746003Qx7C9YuuqCZZpwropki78nlsY1qMuKryTdpMOn/NBobq5kqk4qSph7U9FsBa3UFeoA8UtyGrSNRGpLI7SHRtn+Jb/voP2o9AH0RFm2sAB7JMNrzgo64PSWD59vPcAzR1Z53tZ2dQWs+WYsCrs3WqkpbhOyAQyRU4A7NLVWEYXNo5Ka5lsH7CiO3Otp1kFFC9Ylhe4jTnRXlvikSTB8JWVY+mp//2Alo6zZ5GdK6d00qrQ9KgsVsmlbpDEKnvD8269EMWvviOTiU+TQ/SqpbAWt/sd6DhyFShuQdZhiJKaJZ373v32OzWvAtfbIdOO/QP7hGFS4FaR5+YejpjYTH99cxfNHF5r+9qHvzCfXnv/Uxrf4vyIrayNGRckIXhRQIisDrZWFVFrdRGVRPOpIE+jfQdZFwvh5oiJzfT2rs9odBqslkHuEfb67xfskLdlWXqVxl5icJUQsvn5j06aSg+/9C4dM6U1aFFccfz0djp+ertSWislZjTD/FDetHYSnf6ff1ZO7/d81m7cAWFwsjx7+ZgJWVNzTdj6Dq8J6u7SaSNRzJyi/NfuvekrOMOB4jZHqCmJ0nt79tGMzpqgRUkr2dK37z1w0PY1FUUFPkgCcp1sUAi6sbj95uoe0nXdkQVDZXGUKoujjsu2ImuDkwUtgA/I2lF+XoQeuGA+aTRgKaOF1HKGRyb7nw0b2VjvnRBE3IKwwS6oQ+c2yAYGX50he5mzR9TR7BHWp2EOHd9Ir3/wKY1vDs7HsXfw5wzfWt1Dl/3qL3Tz8b0ByOScZeOH0KMXL6AX39pNNz3wMj3xyvspaVQCsnmF3c10keLMiaIyrBv52WBx67eC00nf6MVzrS2Luc9EkUiIx4IwA8VtjvDLTTPpzmffpLXT2oIWJQ0oeNvPMPYeULe4vWHNRPrJE69mtFUGCDFZ0KTc3kJYJxnZ7NMsG2Dn+lbrDpFPO7zX3AHvOpWgg+wEVbzb4/9hweAqIaTjqBXfXdvrePM2bIiUe4dPbKaVPU0ZeY9DKopoSEURtVUX0yk//BNtmjfc8Hs6N4KMPm6d58P2e06y0TLLcNqSbPFD7Q/2n82Nx06i3//1bVo/o917cRTKPXKSP/GBshEobnOE1upiOn1e+gIpBAk7OAa9K+cVdhS3K7qbMtoHWq6RafPiMIjrdgmbiYuROAXMkTmzEkMlQjQIEKbi2qmDxgAyeLG5wpG9LfTvf3iF5nX57xM7U8jV2p8tbr5ZKyu/pufpeFaZPIdgkSkWM/0eO+tK6b7N81K+T+ddefUMNeEfDq53wbiQWJlPaKmke198J7Dyw9g04hsvTmRbPmEILZ8wxGOJwltuJgPFLchqMnVH38ze/fZdJQRBljzutBKmI0wqNFUWBS2Ca2pK/HNX4Dc1pTE6bW4H5Uc0Ko2Jh/BMq1cyskVpwWLn7bCLP/SxuUNZYQE9/IX5Ga9AAe7Jli6QnZNnSwyKTCYXxxbjPafvplVKElnWG+RU7AyMSnl397n9vDn0/Bu7aPGYBlf5EHkzN904r4Py87TANjWzcU4KMgMobkHWYbBOyvDjIZo2MEBMbq/yrQwvB6AcmfflJD86aSrd/sRr9MXlo4MWxTGLRtfT3gP9dNHSrqBFccXFy/jvIFt93GYjdt4P9Bu5C5S2RoJ/HIH5SsgK2Dm5X4YVWfKo0oLxkE7gjSstGE+wpK8sFUTrMUfuEdhNEpf3OaKhjEY0lLnLxENi+Xm0af5w64Q5SG604twFiluQ1WT6sdL7N8+j+3e+Q2umeu+buKwwn3Z/diDnAtYBZ6gG7kgHTjcbDh0/hFZlsS+lbPWFmo0LcVuuEjJ8HAPAHekLJhRWssbHbQ5aeIaZXAz8l855klfP1G0+Yeo3c6WeucHJM8JzzQ2guAVZB7vIzXQft+21JbShdpgvef/27Nn0u7+8Scf2DfUsTwwcIMxE8zPcBN8G2aTs03EuLUFRNHMjygPgBK+US5ncJWZLF5jpc/Jsg20TmW7ooko6XSV4pTB1m09YXu2WZaPot8+9GbQYAGQsubOKBTlDJgRyOWMwUNxpczoCk6G1uphOndMp9ZMJ/CekVTSUrJrYTERkO9DiuYtG0OwRtbRkbKMfYoWSbKhW0zsGTgMcP827zaUgcWM1d8HikXTizGHUWVfqoUQAZBa5Ol5mid4Wfm1DTC6+mbD1J+Z2/sctC+jZKxa7zjcM99k7tIo2zu3Minrm9wkIN4r6MLxr4B/Q2ICsJqy7+xcu6aJVk5qxCAfABl8/agIdP30oTWiptHXduYtG+iNQyGCtsrJh8vbDk6bS6x98Su21JUGLEjhnLhgRtAgAZDRedIlB9avZcuqAdZXg1wmYbHlW6QB+8cNNPBjwR5/sT3ynWrvD9jrDJk+2oSX+x5POZqC4BVkHO/nwK/iBWzRNo+H14XH07h3hfN4gOyjIi9DENv8C9WUT2TB5K8iLQGlrk5AOeQB4gjtLpMxtHNmiimSNKaJ5OPQZOAY3JJnbPpySMXfsQFD2xCn2MgDIDqC4BVlNBPPCtJKD8z4AwgnaYujA4gkA+xjcX+XonC5b+g5WcVuQQz7nw0rOTxNCtmjxsp23VhfRzOE1VBzNp8KCEPnGD9kzd4Lf/XGu+nIH1kBxC7IO1iIjrK4SAADATzB5AwBkG64sbj0pPxg66kpox2sfBlS6d7Cn4GKwuAUBk83TJE3T6D9Pnha0GAkwJ/UXPN/cAIpbkHXkYpTUsICjbwCEA/R84SMdVnN47yCbyVVLpMsOG0Ox/Dw6qrclaFFcwQYn88vHLVAnF90jsKTz9lWGf5WgV5lqfT++uZKIsmOOUlMapb+/41/+bp4R9B7ZDUZNkNWgA0sPG+d20uwRtTR7RG3QomQcuT5xBv6AepWb4L2DbKbbZmDKbKGyOEpbV42n3qHZ4+O9obwwaBFynlwfLTIlFkAmD+t3nzuHzl00gi5Ykj1Bgq89qjtoEYScMa+T6stitGl+Z9CiAB+AxS3IauAqIT1sWTYqaBEyFtRQ4BWstQbqVW6C9w6ymTMXDHd8baYoabKdW4/vpY8+3U+t1cVBi5Lz5LpxS47fflroaiyjrsZkMO5seOZh7Lvim/b15YX0+CULsYmfpUBxC7KOg/1J5UUeOi4AQA6Crg8AkA2wfZmrIDse9InoV92zeGxj0CKAQXK9Pvtt21NWmE+ddSV0sF+nutKY9QUCNwgFTFTGGFyMZD1u2yWUttkLFLcg6+hnHADlagRikDl8su9A0CKALATWZbnFhUu66Nq7d9LXj5wQtCgAhJLywoKgRQBpIFN9gIL04/c8KRLR6J7z5iY+WyGqukXRPLrq8HF0sF+nqpKohxKmH8x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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14, 4), tight_layout=True)\n", + "plt.plot(t, w_t)\n", + "plt.xlabel(\"$t$\"); plt.ylabel(\"$w_t$\")\n", + "plt.title(\"Examplary AR(1) time series\");" + ] + }, + { + "cell_type": "markdown", + "id": "441eac8f-7b1a-4fdb-a2b5-3d9504caf03f", + "metadata": {}, + "source": [ + "#### Visibility Relations" + ] + }, + { + "cell_type": "markdown", + "id": "b0970273", + "metadata": {}, + "source": [ + "Now we construct a `VisibilityGraph` instance. Note the keyword `horizontal`, which is `False` by default." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d481e772", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating visibility relations...\n", + "VisibilityGraph: time series shape (5000,).\n", + "InteractingNetworks:\n", + "Network: undirected, 5000 nodes, 16703 links, link density 0.001.\n" + ] + } + ], + "source": [ + "VG1 = VisibilityGraph(w_t, horizontal=False)\n", + "print(VG1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "367aaa50", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating horizontal visibility relations...\n", + "VisibilityGraph: time series shape (5000,).\n", + "InteractingNetworks:\n", + "Network: undirected, 5000 nodes, 9986 links, link density 0.001.\n" + ] + } + ], + "source": [ + "HVG1 = VisibilityGraph(w_t, horizontal=True)\n", + "print(HVG1)" + ] + }, + { + "cell_type": "markdown", + "id": "0a3d1527", + "metadata": {}, + "source": [ + "It can be illustrative to plot the visibility relations via the method `VisibilityGraph.visibility_relations()`. Note that here we zoom in along the time axis." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8d8b5cdb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating visibility relations...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# obtain the visibilty relations as a Boolean mask\n", + "visibility_relations = VG1.visibility_relations()\n", + "# zoom\n", + "cutoff = int(N/10)\n", + "\n", + "# plotting the visibilty relations as grey arrows\n", + "plt.figure(figsize=(14, 4), tight_layout=True)\n", + "plt.plot(t[:cutoff], w_t[:cutoff])\n", + "plt.xlabel(\"$t$\"); plt.ylabel(\"$w_t$\")\n", + "plt.title(\"Time series with visibility relations\")\n", + "for i in range(cutoff):\n", + " for k in range(cutoff):\n", + " if visibility_relations[i][k]:\n", + " plt.arrow(i, w_t[i], (k-i), (w_t[k]-w_t[i]), color='lightgrey')" + ] + }, + { + "cell_type": "markdown", + "id": "e24eb9d5", + "metadata": {}, + "source": [ + "Of course, we can also plot the visibility relations for the HVG using `VisibilityGraph.visibility_relations_horizontal()`. In this case, only the visibility lines for one specific node are plotted as an example, albeit not horizontally." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3c3f7f27", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating horizontal visibility relations...\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visibility_relations_horizontal = HVG1.visibility_relations_horizontal()\n", + "\n", + "i = 209 # only plot the nodes visible from here\n", + "plt.figure(figsize=(14, 4), tight_layout=True)\n", + "plt.plot(t[:cutoff], w_t[:cutoff]) # zooming in a bit\n", + "plt.xlabel(\"$t$\"); plt.ylabel(\"$w_t$\")\n", + "plt.title(f\"Time series with horizontal visibility relations for node {i}\")\n", + "for k in range(cutoff):\n", + " if visibility_relations_horizontal[i][k]:\n", + " ax = plt.arrow(i, w_t[i], k-i, w_t[k]-w_t[i], color='darkgray')" + ] + }, + { + "cell_type": "markdown", + "id": "445f785f", + "metadata": {}, + "source": [ + "### Irreversibility Test" + ] + }, + { + "cell_type": "markdown", + "id": "eaea9cb7", + "metadata": {}, + "source": [ + "#### Linear Time Series" + ] + }, + { + "cell_type": "markdown", + "id": "fd5fd60a", + "metadata": {}, + "source": [ + "We start by analysing the linear time series via its time directed degrees:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c8abc84c", + "metadata": {}, + "outputs": [], + "source": [ + "k_r1 = VG1.retarded_degree()\n", + "k_a1 = VG1.advanced_degree()" + ] + }, + { + "cell_type": "markdown", + "id": "8b81f887", + "metadata": {}, + "source": [ + "Applied to a VG, both methods return an array with the corresponding degree for each node. For such an array, we can obtain the probability for each degree with a kernel-density estimate using Gaussian kernels. [Kernel density estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation) is a way to estimate the probability density function (PDF) of a random variable in a non-parametric way. We use `scipy.stats.gaussian_kde` for that." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ab06fcb7", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import gaussian_kde \n", + "gkde_r1 = gaussian_kde(k_r1)\n", + "gkde_a1 = gaussian_kde(k_a1)" + ] + }, + { + "cell_type": "markdown", + "id": "19cb5e7a", + "metadata": {}, + "source": [ + "Now we plot $p(k_v^r)$ against $k_v^r$ in red, and $p(k_v^a)$ against $k_v^a$ in black, and compare both visually. The y-scale is linear in the first figure and logartihmic in the second. Both plots are cut off at a degree of 12, at which the empirical frequencies become too low to be comparable." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "20c8f530-2060-4c78-a467-39321a0ae403", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_time_directed(r, a, ylog=False):\n", + " plt.figure(figsize=(14, 3), tight_layout=True)\n", + " x = np.linspace(0, 12, 1000)\n", + " plt.plot(x, r.evaluate(x), color=\"red\", label=\"retarded\")\n", + " plt.plot(x, a.evaluate(x), color=\"black\", label=\"advanced\")\n", + " plt.xlabel(\"$k$\"); plt.ylabel(\"$p(k)$\")\n", + " if ylog:\n", + " plt.yscale(\"log\")\n", + " plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cedb7d7c-e784-4451-81b2-da08de0d493a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_time_directed(gkde_r1, gkde_a1)\n", + "plot_time_directed(gkde_r1, gkde_a1, ylog=True)" + ] + }, + { + "cell_type": "markdown", + "id": "adebe395", + "metadata": {}, + "source": [ + "By visual investigation we already find strong similarities between the distributions. Now we also apply the KS test for this time series, using `scipy.stats.ks_2samp` to obtain the $p$-value of the two input data sets." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0c5cead0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KstestResult(statistic=0.0072, pvalue=0.9994903068538411, statistic_location=3.0, statistic_sign=1)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from scipy.stats import ks_2samp\n", + "ks_2samp(k_r1, k_a1)" + ] + }, + { + "cell_type": "markdown", + "id": "ec953d2d", + "metadata": {}, + "source": [ + "The result of `pvalue=0.999... > 0.05` indicates that the time series has likely been generated by a reversible process." + ] + }, + { + "cell_type": "markdown", + "id": "da798677", + "metadata": {}, + "source": [ + "#### Non-Linear Time Series" + ] + }, + { + "cell_type": "markdown", + "id": "daa011e0", + "metadata": {}, + "source": [ + "Now we will perform the same procedure for a non-linear time series. We can construct one using the logistic map for instance, which we take from the tutorial on recurrence networks. We only change the bifurcation parameter ($r = 4$) to find ourselves deeply in the chaotic regime." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "76ce744f-94b4-494c-99b6-6fcecbcc0eee", + "metadata": {}, + "outputs": [], + "source": [ + "def logistic_map(x0: float, r: float, T: int, spinup: int = 100):\n", + " \"\"\"\n", + " Returns a time series of length T using the logistic map\n", + " x_(n+1) = r*x_n(1-x_n)\n", + " at parameter r and using the initial condition x0.\n", + "\n", + " INPUT: x0 - Initial condition, 0 <= x0 <= 1\n", + " r - Bifurcation parameter, 0 <= r <= 4\n", + " T - length of the desired time series\n", + " spinup - number of spinup-timesteps before storing results to output\n", + " OUTPUT: numpy array of timeseries under given parameters\n", + " \"\"\"\n", + " # spinup\n", + " for n in range(spinup):\n", + " x0 = r * x0 * (1 - x0)\n", + "\n", + " timeseries = np.zeros(T + 1)\n", + " timeseries[0] = x0\n", + " for n in range(T):\n", + " # get current timestep value\n", + " xn = timeseries[n]\n", + " # calculate next timestep value\n", + " xstep = r * xn * (1 - xn)\n", + " timeseries[n + 1] = xstep\n", + " return timeseries" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7051fb29-c0f0-4d8d-b0e2-2a5b8e7bd353", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "r = 4\n", + "x0 = 0.8\n", + "T = 5000\n", + "time_series = logistic_map(x0, r, T)\n", + "\n", + "cutoff = 200\n", + "plt.figure(figsize=(14, 4))\n", + "plt.plot(time_series[:cutoff])\n", + "plt.xlabel(\"$n$\"); plt.ylabel(\"$x_n$\");" + ] + }, + { + "cell_type": "markdown", + "id": "3fa8ae3b", + "metadata": {}, + "source": [ + "We again compare the time-directed degree distributions visually:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b72858bd-3bf4-4aa7-91c7-8acc95a864f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating visibility relations...\n" + ] + } + ], + "source": [ + "VG2 = VisibilityGraph(time_series)\n", + "k_r2 = VG2.retarded_degree()\n", + "k_a2 = VG2.advanced_degree()\n", + "gkde_r2 = gaussian_kde(k_r2)\n", + "gkde_a2 = gaussian_kde(k_a2)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "95c9298a-1147-4fb2-b650-0decaf64a2a0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_time_directed(gkde_r2, gkde_a2)\n", + "plot_time_directed(gkde_r2, gkde_a2, ylog=True)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "bdf697a7-6733-481c-b907-bb40c6064161", + "metadata": {}, + "source": [ + "Here we find a stronger divergence between the retarded and advanced degree distributions, suggesting irreversibility of the underlying time series. And indeed, the KS test clearly yields `pvalue < 0.050`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "12f6159a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KstestResult(statistic=0.0593881223755249, pvalue=4.3359432807261227e-08, statistic_location=1.0, statistic_sign=-1)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ks_2samp(k_r2, k_a2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyunicorn", + "language": "python", + "name": "pyunicorn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/tutorials/climate_network.ipynb b/examples/tutorials/climate_network.ipynb deleted file mode 100644 index 2c2e7de8..00000000 --- a/examples/tutorials/climate_network.ipynb +++ /dev/null @@ -1,241 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Climate Network Tutorial\n", - "\n", - "Tutorial on analyzing climate networks using Python.\n", - "\n", - "Uses the Python packages ``core`` and ``climate`` providing all kinds of tools\n", - "related to climate networks. Written as part of a diploma / phd thesis in\n", - "Physics by Jonathan F. Donges (donges@pik-potsdam.de) at University of Potsdam\n", - "/ Humboldt University Berlin and Potsdam Institute of Climate Impact Research\n", - "(PIK),\n", - "\n", - "Copyright 2008-2023." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download the data set from the link below and copy it to the directory of this script or change the path to the location of the data set\n", - "\n", - "https://www.esrl.noaa.gov/psd/repository/entry/show?entryid=0def76a0-9b32-47a4-8bc3-c4977c67ed95" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "DATA_FILENAME = \"air.mon.mean.nc\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create ClimateData object containing the data and print information" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyunicorn import climate\n", - "\n", - "# Settings\n", - "\n", - "# Type of data file (\"NetCDF\" indicates a NetCDF file with data on a regular\n", - "# lat-lon grid, \"iNetCDF\" allows for arbitrary grids - > see documentation).\n", - "# For example, the \"NetCDF\" FILE_TYPE is compatible with data from the IPCC\n", - "# AR4 model ensemble or the reanalysis data provided by NCEP/NCAR.\n", - "FILE_TYPE = \"NetCDF\"\n", - "\n", - "# Indicate data source (optional)\n", - "DATA_SOURCE = \"ncep_ncar_reanalysis\"\n", - "\n", - "# Name of observable in NetCDF file (\"air\" indicates surface air temperature\n", - "# in NCEP/NCAR reanalysis data)\n", - "OBSERVABLE_NAME = \"air\"\n", - "\n", - "# Select a subset in time and space from the data (e.g., a particular region\n", - "# or a particular time window, or both)\n", - "WINDOW = {\"time_min\": 0., \"time_max\": 0., \"lat_min\": 0, \"lon_min\": 0,\n", - " \"lat_max\": 30, \"lon_max\": 0} # selects the whole data set\n", - "\n", - "# Indicate the length of the annual cycle in the data (e.g., 12 for monthly\n", - "# data). This is used for calculating climatological anomaly values\n", - "# correctly.\n", - "TIME_CYCLE = 12\n", - "\n", - "data = climate.ClimateData.Load(file_name=DATA_FILENAME,\n", - " observable_name=OBSERVABLE_NAME,\n", - " data_source=DATA_SOURCE,\n", - " file_type=FILE_TYPE,\n", - " window=WINDOW,\n", - " time_cycle=TIME_CYCLE)\n", - "\n", - "print(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate climate network using various procedures" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Indicates whether to use only data from winter months (DJF) for calculating\n", - "# correlations\n", - "WINTER_ONLY = False\n", - "\n", - "# fixed threshold\n", - "THRESHOLD = 0.5\n", - "\n", - "# fixed link density\n", - "LINK_DENSITY = 0.005\n", - "\n", - "# Create a climate network based on Pearson correlation without lag and with\n", - "# fixed threshold\n", - "net = climate.TsonisClimateNetwork(data, threshold=THRESHOLD, winter_only=WINTER_ONLY)\n", - "\n", - "# Create a climate network based on Pearson correlation without lag and with\n", - "# fixed link density\n", - "# net = climate.TsonisClimateNetwork(data, link_density=LINK_DENSITY, winter_only=WINTER_ONLY)\n", - "\n", - "# Create a climate network based on Spearman's rank order correlation without\n", - "# lag and with fixed threshold\n", - "# net = climate.SpearmanClimateNetwork(data, threshold=THRESHOLD, winter_only=WINTER_ONLY)\n", - "\n", - "# Create a climate network based on mutual information without lag and with\n", - "# fixed threshold\n", - "# net = climate.MutualInfoClimateNetwork(data, threshold=THRESHOLD, winter_only=WINTER_ONLY)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculate some measures" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Link density:\", net.link_density)\n", - "\n", - "# Get degree\n", - "degree = net.degree()\n", - "# Get closeness\n", - "closeness = net.closeness()\n", - "# Get betweenness\n", - "betweenness = net.betweenness()\n", - "# Get local clustering coefficient\n", - "clustering = net.local_clustering()\n", - "# Get average link distance\n", - "ald = net.average_link_distance()\n", - "# Get maximum link distance\n", - "mld = net.max_link_distance()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save results to text files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# Save the grid (mainly vertex coordinates) to text files\n", - "data.grid.save_txt(filename=\"grid.txt\")\n", - "\n", - "# Save the degree sequence. Other measures may be saved similarly.\n", - "np.savetxt(\"degree.txt\", degree)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot the results into the file \"climate_network_measures\". This only works if you have the pyngl package installed!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "map_plots = climate.MapPlots(data.grid, DATA_SOURCE)\n", - "\n", - "# Add network measures to the plotting queue\n", - "map_plots.add_dataset(\"Degree\", degree)\n", - "map_plots.add_dataset(\"Closeness\", closeness)\n", - "map_plots.add_dataset(\"Betweenness (log10)\", np.log10(betweenness + 1))\n", - "map_plots.add_dataset(\"Clustering\", clustering)\n", - "map_plots.add_dataset(\"Average link distance\", ald)\n", - "map_plots.add_dataset(\"Maximum link distance\", mld)\n", - "\n", - "# Change the map projection\n", - "map_plots.resources.mpProjection = \"Robinson\"\n", - "map_plots.resources.mpCenterLonF = 0\n", - "\n", - "# Change the levels of contouring\n", - "map_plots.resources.cnLevelSelectionMode = \"EqualSpacedLevels\"\n", - "map_plots.resources.cnMaxLevelCount = 20\n", - "\n", - "# map_plots.resources.cnRasterSmoothingOn = True\n", - "# map_plots.resources.cnFillMode = \"AreaFill\"\n", - "\n", - "map_plots.generate_map_plots(file_name=\"climate_network_measures\",\n", - " title_on=False, labels_on=True)" - ] - } - ], - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/tutorials/climate_network.py b/examples/tutorials/climate_network.py deleted file mode 100644 index 141bb5c9..00000000 --- a/examples/tutorials/climate_network.py +++ /dev/null @@ -1,183 +0,0 @@ -#!/usr/bin/python - -""" -Tutorial on analyzing climate networks using Python. - -Uses the Python packages ``core`` and ``climate`` providing all kinds of tools -related to climate networks. Written as part of a diploma / phd thesis in -Physics by Jonathan F. Donges (donges@pik-potsdam.de) at University of Potsdam -/ Humboldt University Berlin and Potsdam Institute of Climate Impact Research -(PIK), - -Copyright 2008-2023. -""" - -import numpy as np - -from pyunicorn import climate - -# Test if Ngl package is installed -try: - import Ngl - ngl_flag = True -except ImportError: - ngl_flag = False -# -# Settings -# - -# Related to data - -# Download the data set from the link that is printed below and copy it to the -# directory of this script or change the path to the location of the data set -LINK = "\nData available at: https://www.esrl.noaa.gov/psd/repository/" + \ - "entry/show?entryid=0def76a0-9b32-47a4-8bc3-c4977c67ed95" -print(LINK) - -DATA_FILENAME = "./air.mon.mean.nc" - -# Type of data file ("NetCDF" indicates a NetCDF file with data on a regular -# lat-lon grid, "iNetCDF" allows for arbitrary grids - > see documentation). -# For example, the "NetCDF" FILE_TYPE is compatible with data from the IPCC -# AR4 model ensemble or the reanalysis data provided by NCEP/NCAR. -FILE_TYPE = "NetCDF" - -# Indicate data source (optional) -DATA_SOURCE = "ncep_ncar_reanalysis" - -# Name of observable in NetCDF file ("air" indicates surface air temperature -# in NCEP/NCAR reanalysis data) -OBSERVABLE_NAME = "air" - -# Select a subset in time and space from the data (e.g., a particular region -# or a particular time window, or both) -WINDOW = {"time_min": 0., "time_max": 0., "lat_min": 0, "lon_min": 0, - "lat_max": 30, "lon_max": 0} # selects the whole data set - -# Indicate the length of the annual cycle in the data (e.g., 12 for monthly -# data). This is used for calculating climatological anomaly values -# correctly. -TIME_CYCLE = 12 - -# Related to climate network construction - -# For setting fixed threshold -THRESHOLD = 0.5 - -# For setting fixed link density -LINK_DENSITY = 0.005 - -# Indicates whether to use only data from winter months (DJF) for calculating -# correlations -WINTER_ONLY = False - -# -# Print script title -# - -print("\n") -print("Tutorial on how to use climate") -print("-------------------------------") -print("\n") - -# -# Create a ClimateData object containing the data and print information -# - -data = climate.ClimateData.Load( - file_name=DATA_FILENAME, observable_name=OBSERVABLE_NAME, - data_source=DATA_SOURCE, file_type=FILE_TYPE, - window=WINDOW, time_cycle=TIME_CYCLE) - -# Print some information on the data set -print(data) - -# -# Create a MapPlots object to manage 2D-plotting on the sphere -# -if ngl_flag: - map_plots = climate.MapPlots(data.grid, DATA_SOURCE) -# -# Generate climate network using various procedures -# - -# One of several alternative similarity measures and construction mechanisms -# may be chosen here - -# Create a climate network based on Pearson correlation without lag and with -# fixed threshold -net = climate.TsonisClimateNetwork( - data, threshold=THRESHOLD, winter_only=WINTER_ONLY) - -# Create a climate network based on Pearson correlation without lag and with -# fixed link density -# net = climate.TsonisClimateNetwork( -# data, link_density=LINK_DENSITY, winter_only=WINTER_ONLY) - -# Create a climate network based on Spearman's rank order correlation without -# lag and with fixed threshold -# net = climate.SpearmanClimateNetwork( -# data, threshold=THRESHOLD, winter_only=WINTER_ONLY) - -# Create a climate network based on mutual information without lag and with -# fixed threshold -# net = climate.MutualInfoClimateNetwork( -# data, threshold=THRESHOLD, winter_only=WINTER_ONLY) - -# -# Some calculations -# - -print("Link density:", net.link_density) - -# Get degree -degree = net.degree() -# Get closeness -closeness = net.closeness() -# Get betweenness -betweenness = net.betweenness() -# Get local clustering coefficient -clustering = net.local_clustering() -# Get average link distance -ald = net.average_link_distance() -# Get maximum link distance -mld = net.max_link_distance() - -# -# Save results to text file -# - -# Save the grid (mainly vertex coordinates) to text files -data.grid.save_txt(filename="grid.txt") - -# Save the degree sequence. Other measures may be saved similarly. -np.savetxt("degree.txt", degree) - -# -# Plotting -# - -# Add network measures to the plotting queue -if ngl_flag: - map_plots.add_dataset("Degree", degree) - map_plots.add_dataset("Closeness", closeness) - map_plots.add_dataset("Betweenness (log10)", np.log10(betweenness + 1)) - map_plots.add_dataset("Clustering", clustering) - map_plots.add_dataset("Average link distance", ald) - map_plots.add_dataset("Maximum link distance", mld) - - # Change the map projection - map_plots.resources.mpProjection = "Robinson" - map_plots.resources.mpCenterLonF = 0 - - # Change the levels of contouring - map_plots.resources.cnLevelSelectionMode = "EqualSpacedLevels" - map_plots.resources.cnMaxLevelCount = 20 - - # map_plots.resources.cnRasterSmoothingOn = True - # map_plots.resources.cnFillMode = "AreaFill" - - map_plots.generate_map_plots(file_name="climate_network_measures", - title_on=False, labels_on=True) -else: - print("\nPlots can only be created when Ngl package is installed!") diff --git a/examples/tutorials/climate_network_cartopy_plotting.py b/examples/tutorials/climate_network_cartopy_plotting.py deleted file mode 100644 index b19a0219..00000000 --- a/examples/tutorials/climate_network_cartopy_plotting.py +++ /dev/null @@ -1,177 +0,0 @@ -#!/usr/bin/python - -""" -Tutorial on analyzing climate networks using Python. - -Uses the Python packages ``core`` and ``climate`` providing all kinds of tools -related to climate networks. Written as part of a diploma / phd thesis in -Physics by Jonathan F. Donges (donges@pik-potsdam.de) at University of Potsdam -/ Humboldt University Berlin and Potsdam Institute of Climate Impact Research -(PIK), - -Copyright 2008-2023. -""" - -import numpy as np -from matplotlib import pyplot as plt - -from pyunicorn import climate - -import cartopy.crs as ccrs -import cartopy.feature as cf - -# -# Settings -# - -# Related to data - -# Download the data set from the link that is printed below and copy it to the -# directory of this script or change the path to the location of the data set -LINK = "\nData available at: https://www.esrl.noaa.gov/psd/repository/" + \ - "entry/show?entryid=0def76a0-9b32-47a4-8bc3-c4977c67ed95" -print(LINK) - -DATA_FILENAME = "./air.mon.mean.nc" - -# Type of data file ("NetCDF" indicates a NetCDF file with data on a regular -# lat-lon grid, "iNetCDF" allows for arbitrary grids - > see documentation). -# For example, the "NetCDF" FILE_TYPE is compatible with data from the IPCC -# AR4 model ensemble or the reanalysis data provided by NCEP/NCAR. -FILE_TYPE = "NetCDF" - -# Indicate data source (optional) -DATA_SOURCE = "ncep_ncar_reanalysis" - -# Name of observable in NetCDF file ("air" indicates surface air temperature -# in NCEP/NCAR reanalysis data) -OBSERVABLE_NAME = "air" - -# Select a subset in time and space from the data (e.g., a particular region -# or a particular time window, or both) -# Plase note that "time_min" and "time_max" have to be in the time format of -# NetCDF files, e.g. 1918248. to 1921128. for the NetCDF file used here. -# In particular, they are not indices, e.g. "time_max"=10. will not work. -WINDOW = {"time_min": 0., "time_max": 0., "lat_min": 0, "lon_min": 0, - "lat_max": 30, "lon_max": 0} # selects the whole data set - -# Indicate the length of the annual cycle in the data (e.g., 12 for monthly -# data). This is used for calculating climatological anomaly values -# correctly. -TIME_CYCLE = 12 - -# Related to climate network construction - -# For setting fixed threshold -THRESHOLD = 0.5 - -# For setting fixed link density -LINK_DENSITY = 0.005 - -# Indicates whether to use only data from winter months (DJF) for calculating -# correlations -WINTER_ONLY = False - -# -# Print script title -# - -print("\n") -print("Tutorial on how to use climate") -print("-------------------------------") -print("\n") - -# -# Create a ClimateData object containing the data and print information -# - -data = climate.ClimateData.Load( - file_name=DATA_FILENAME, observable_name=OBSERVABLE_NAME, - data_source=DATA_SOURCE, file_type=FILE_TYPE, - window=WINDOW, time_cycle=TIME_CYCLE) - -# Print some information on the data set - -data.grid.print_grid_size() - - -# -# Generate climate network using various procedures -# - -# One of several alternative similarity measures and construction mechanisms -# may be chosen here - -# Create a climate network based on Pearson correlation without lag and with -# fixed threshold -net = climate.TsonisClimateNetwork( - data, threshold=THRESHOLD, winter_only=WINTER_ONLY) - -print("net created") - -# Create a climate network based on Pearson correlation without lag and with -# fixed link density -# net = climate.TsonisClimateNetwork( -# data, link_density=LINK_DENSITY, winter_only=WINTER_ONLY) - -# Create a climate network based on Spearman's rank order correlation without -# lag and with fixed threshold -# net = climate.SpearmanClimateNetwork( -# data, threshold=THRESHOLD, winter_only=WINTER_ONLY) - -# Create a climate network based on mutual information without lag and with -# fixed threshold -# net = climate.MutualInfoClimateNetwork( -# data, threshold=THRESHOLD, winter_only=WINTER_ONLY) - -# -# Some calculations -# - -print("Link density:", net.link_density) - -# Get degree -degree = net.degree() -# Get closeness -closeness = net.closeness() -# Get betweenness -betweenness = net.betweenness() -# Get local clustering coefficient -clustering = net.local_clustering() -# Get average link distance -ald = net.average_link_distance() -# Get maximum link distance -mld = net.max_link_distance() - -# -# Save results to text file -# - -# Save the grid (mainly vertex coordinates) to text files -data.grid.save_txt(filename="grid.txt") - -# Save the degree sequence. Other measures may be saved similarly. -np.savetxt("degree.txt", degree) - -# -# Plotting -# - -# create a Cartopy plot instance called cn_plot (cn for climate network) -# from the data with tite DATA_SOURCE -cn_plot = climate.CartopyPlots(data.grid, DATA_SOURCE) - -# Add network measures to the plotting queue -cn_plot.add_dataset("Degree", degree) -cn_plot.add_dataset("Closeness", closeness) -cn_plot.add_dataset("Betweenness (log10)", np.log10(betweenness + 1)) -cn_plot.add_dataset("Clustering", clustering) -cn_plot.add_dataset("Average link distance", ald) -cn_plot.add_dataset("Maximum link distance", mld) - -# before plotting, we can change some things, like cmap -ax = plt.set_cmap('plasma') - -# Plot with cartopy and matplotlib -cn_plot.generate_plots(file_name="climate_network_measures", - title_on=False, labels_on=True) diff --git a/notebooks/img/Characteristics_rP.png b/examples/tutorials/images/Characteristics_rP.png similarity index 100% rename from notebooks/img/Characteristics_rP.png rename to examples/tutorials/images/Characteristics_rP.png diff --git a/notebooks/img/ECA.png b/examples/tutorials/images/ECA.png similarity index 100% rename from notebooks/img/ECA.png rename to examples/tutorials/images/ECA.png diff --git a/notebooks/img/EventSynchronisation.png b/examples/tutorials/images/EventSynchronisation.png similarity index 100% rename from notebooks/img/EventSynchronisation.png rename to examples/tutorials/images/EventSynchronisation.png diff --git a/notebooks/images/HVG.PNG b/examples/tutorials/images/HVG.png similarity index 100% rename from notebooks/images/HVG.PNG rename to examples/tutorials/images/HVG.png diff --git a/notebooks/img/REcurrencePlot_v2.png b/examples/tutorials/images/REcurrencePlot_v2.png similarity index 100% rename from notebooks/img/REcurrencePlot_v2.png rename to examples/tutorials/images/REcurrencePlot_v2.png diff --git a/notebooks/images/SimpleVG.PNG b/examples/tutorials/images/SimpleVG.png similarity index 100% rename from notebooks/images/SimpleVG.PNG rename to examples/tutorials/images/SimpleVG.png diff --git a/examples/tutorials/recurrence_network.ipynb b/examples/tutorials/recurrence_network.ipynb deleted file mode 100644 index 8c91f97f..00000000 --- a/examples/tutorials/recurrence_network.ipynb +++ /dev/null @@ -1,228 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Recurrence Network Tutorial\n", - "Tutorial on how to handle recurrence plots and recurrence networks using\n", - "Python, based on the timeseries package.\n", - "\n", - "Written as part of a PhD thesis in Physics by Jonathan F. Donges\n", - "(donges@pik-potsdam.de) at the Potsdam Institute of Climate Impact Research\n", - "(PIK) and Humboldt University Berlin,\n", - "\n", - "Copyright 2008-2023." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create, print and plot a time series using the logistic map" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import pylab\n", - "import numpy as np\n", - "\n", - "def logistic_map(x0, r, T):\n", - " \"\"\"\n", - " Returns a time series of length T using the logistic map\n", - " x_(n+1) = r*x_n(1-x_n) at parameter r and using the initial condition x0.\n", - "\n", - " INPUT: x0 - Initial condition, 0 <= x0 <= 1\n", - " r - Bifurcation parameter, 0 <= r <= 4\n", - " T - length of the desired time series\n", - " TODO: Cythonize\n", - " \"\"\"\n", - " # Initialize the time series array\n", - " timeSeries = np.empty(T)\n", - "\n", - " timeSeries[0] = x0\n", - " for i in range(1, len(timeSeries)):\n", - " xn = timeSeries[i-1]\n", - " timeSeries[i] = r * xn * (1 - xn)\n", - "\n", - " return timeSeries\n", - "\n", - "# Parameters of logistic map\n", - "r = 3.679 # Bifurcation parameter\n", - "x0 = 0.7 # Initial value\n", - "\n", - "# Length of the time series\n", - "T = 150\n", - "\n", - "time_series = logistic_map(x0, r, T)\n", - "# Print the time series\n", - "print(time_series)\n", - "# Plot the time series\n", - "pylab.plot(time_series, \"r\")\n", - "# You can include LaTex labels...\n", - "pylab.xlabel(\"$n$\")\n", - "pylab.ylabel(\"$x_n$\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate and plot a recurrence plot object with fixed recurrence threshold EPS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyunicorn.timeseries import RecurrencePlot\n", - "\n", - "# Settings for the embedding\n", - "DIM = 1 # Embedding dimension\n", - "TAU = 0 # Embedding delay\n", - "\n", - "# Distance metric in phase space ->\n", - "# Possible choices (\"manhattan\",\"euclidean\",\"supremum\")\n", - "METRIC = \"supremum\"\n", - "\n", - "EPS = 0.05 # Fixed recurrence threshold\n", - "\n", - "rp = RecurrencePlot(time_series, dim=DIM, tau=TAU, metric=METRIC,\n", - " normalize=False, threshold=EPS)\n", - "\n", - "pylab.matshow(rp.recurrence_matrix())\n", - "pylab.xlabel(\"$n$\")\n", - "pylab.ylabel(\"$n$\")\n", - "pylab.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculate and print the recurrence rate and some standard RQA measures" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RR = rp.recurrence_rate()\n", - "DET = rp.determinism(l_min=2)\n", - "LAM = rp.laminarity(v_min=2)\n", - "\n", - "print(\"Recurrence rate:\", RR)\n", - "print(\"Determinism:\", DET)\n", - "print(\"Laminarity:\", LAM)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate and plot another recurrence plot object with fixed recurrence rate RR" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "RR = 0.05 # Fixed recurrence rate\n", - "\n", - "rp = RecurrencePlot(time_series, dim=DIM, tau=TAU, metric=METRIC,\n", - " normalize=False, recurrence_rate=RR)\n", - "\n", - "pylab.matshow(rp.recurrence_matrix())\n", - "pylab.xlabel(\"$n$\")\n", - "pylab.ylabel(\"$n$\")\n", - "pylab.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check the recurrence rate and calculate some standard RQA measures" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "RR = rp.recurrence_rate()\n", - "DET = rp.determinism(l_min=2)\n", - "LAM = rp.laminarity(v_min=2)\n", - "\n", - "print(\"Recurrence rate:\", RR)\n", - "print(\"Determinism:\", DET)\n", - "print(\"Laminarity:\", LAM)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate a recurrence network and calculate some measures of the network" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyunicorn.timeseries import RecurrenceNetwork\n", - "\n", - "rn = RecurrenceNetwork(time_series, dim=DIM, tau=TAU, metric=METRIC,\n", - " normalize=False, recurrence_rate=RR)\n", - "\n", - "L = rn.average_path_length()\n", - "T = rn.transitivity()\n", - "C = rn.global_clustering()\n", - "R = rn.assortativity()\n", - "\n", - "print(\"Average path length:\", L)\n", - "print(\"Transitivity:\", T)\n", - "print(\"Global clustering:\", C)\n", - "print(\"Assortativity:\", R)" - ] - } - ], - "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": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/tutorials/recurrence_network.py b/examples/tutorials/recurrence_network.py deleted file mode 100644 index 8f137a68..00000000 --- a/examples/tutorials/recurrence_network.py +++ /dev/null @@ -1,142 +0,0 @@ -#!/usr/bin/python - -""" -Tutorial on how to handle recurrence plots and recurrence networks using -Python, based on the timeseries package. - -Written as part of a PhD thesis in Physics by Jonathan F. Donges -(donges@pik-potsdam.de) at the Potsdam Institute of Climate Impact Research -(PIK) and Humboldt University Berlin, - -Copyright 2008-2023. -""" - -# array object and fast numerics -import numpy as np - -# plotting facilities -import pylab - -from pyunicorn.timeseries import RecurrencePlot, RecurrenceNetwork - - -# -# Functions -# -def logistic_map(x0, r, T): - """ - Returns a time series of length T using the logistic map - x_(n+1) = r*x_n(1-x_n) at parameter r and using the initial condition x0. - - INPUT: x0 - Initial condition, 0 <= x0 <= 1 - r - Bifurcation parameter, 0 <= r <= 4 - T - length of the desired time series - TODO: Cythonize - """ - # Initialize the time series array - timeSeries = np.empty(T) - - timeSeries[0] = x0 - for i in range(1, len(timeSeries)): - xn = timeSeries[i-1] - timeSeries[i] = r * xn * (1 - xn) - - return timeSeries - - -def logistic_map_lyapunov_exponent(timeSeries, r): - """ - Returns the Lyapunov exponent of the logistic map for different r. - - INPUT: timeSeries - The time series generated by a logistic map - r - the bifurcation parameter - """ - lyap = np.log(r) + (np.log(np.abs(1 - 2 * timeSeries))).mean() - - return lyap - -# -# Settings -# - -# Parameters of logistic map -r = 3.679 # Bifurcation parameter -x0 = 0.7 # Initial value - -# Length of the time series -T = 150 - -# Settings for the embedding -DIM = 1 # Embedding dimension -TAU = 0 # Embedding delay - -# Settings for the recurrence plot -EPS = 0.05 # Fixed threshold -RR = 0.05 # Fixed recurrence rate -# Distance metric in phase space -> -# Possible choices ("manhattan","euclidean","supremum") -METRIC = "supremum" - -# -# Main script -# -# Create a time series using the logistic map -time_series = logistic_map(x0, r, T) - -# Print the time series -print(time_series) -# Plot the time series -pylab.plot(time_series, "r") -# You can include LaTex labels... -pylab.xlabel("$n$") -pylab.ylabel("$x_n$") - -# Generate a recurrence plot object with fixed recurrence threshold EPS -rp = RecurrencePlot(time_series, dim=DIM, tau=TAU, metric=METRIC, - normalize=False, threshold=EPS) - -# Show the recurrence plot -pylab.matshow(rp.recurrence_matrix()) -pylab.xlabel("$n$") -pylab.ylabel("$n$") -pylab.show() - -# Calculate and print the recurrence rate -print("Recurrence rate:", rp.recurrence_rate()) - -# Calculate some standard RQA measures -DET = rp.determinism(l_min=2) -LAM = rp.laminarity(v_min=2) - -print("Determinism:", DET) -print("Laminarity:", LAM) - -# Generate a recurrence plot object with fixed recurrence rate RR -rp = RecurrencePlot(time_series, dim=DIM, tau=TAU, metric=METRIC, - normalize=False, recurrence_rate=RR) - -# Calculate and print the recurrence rate again to check if it worked... -RR = rp.recurrence_rate() -print("Recurrence rate:", RR) - -# Calculate some standard RQA measures -DET = rp.determinism(l_min=2) -LAM = rp.laminarity(v_min=2) - -print("Determinism:", DET) -print("Laminarity:", LAM) - -# Generate a recurrence network at fixed recurrence rate -rn = RecurrenceNetwork(time_series, dim=DIM, tau=TAU, metric=METRIC, - normalize=False, recurrence_rate=RR) - -# Calculate average path length, transitivity and assortativity -L = rn.average_path_length() -T = rn.transitivity() -C = rn.global_clustering() -R = rn.assortativity() - -print("Average path length:", L) -print("Transitivity:", T) -print("Global clustering:", C) -print("Assortativity:", R) diff --git a/notebooks/air.mon.mean.nc b/notebooks/air.mon.mean.nc deleted file mode 100644 index 25076d86..00000000 Binary files a/notebooks/air.mon.mean.nc and /dev/null differ diff --git a/notebooks/tutorial_ClimateNetworks.ipynb b/notebooks/tutorial_ClimateNetworks.ipynb deleted file mode 100644 index d0650e42..00000000 --- a/notebooks/tutorial_ClimateNetworks.ipynb +++ /dev/null @@ -1,460 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c79b8e2b", - "metadata": {}, - "source": [ - "# Tutorial: Climate Networks" - ] - }, - { - "cell_type": "markdown", - "id": "677ae7d7", - "metadata": {}, - "source": [ - "The objective of this tutorial is to introduce climate networks, and to explain and illustrate their application with the `pyunicorn` package. First some theoretical background for understanding general climate networks will be given, and then some methods provided by `pyunicorn.climate.ClimateNetwork` will be illustrated. An introduction and application of coupled climate networks will follow. For a detailed discussion and further references, please consult __[Donges et al., 2015](https://aip.scitation.org/doi/10.1063/1.4934554)__, on which this tutorial is based. " - ] - }, - { - "cell_type": "markdown", - "id": "76c98668", - "metadata": {}, - "source": [ - "## Introduction" - ] - }, - { - "cell_type": "markdown", - "id": "a56c11e0", - "metadata": {}, - "source": [ - "_Climate networks (CN)_ are a way to apply complex network theory to the climate system, by assuming that each node represents a varying dynamical system. Of interest is then the collective behaviour of these interacting dynamical systems and the structure of the resulting network. This approach was first introduced by __[Tsonis and Roebber, 2004](https://www.sciencedirect.com/science/article/abs/pii/S0378437103009646)__.\n", - "\n", - "CN analysis is a versatile approach for investigating climatological data, and it can be used as a complementary method to classical techniques from multivariate statistics. The approach allows for the analysis of single fields of climatological time series, e.g., surface air temperature observed on a grid, or even two or more fields. It has been successfully applied in many cases, for example to dynamics and predictability of the El Niño Phenomenon (__[Radebach et al., 2013](https://arxiv.org/abs/1310.5494)__)." - ] - }, - { - "cell_type": "markdown", - "id": "05e76cc7", - "metadata": {}, - "source": [ - "## Theory of Climate Networks (CNs)" - ] - }, - { - "cell_type": "markdown", - "id": "fcc79d2d", - "metadata": {}, - "source": [ - "CNs are a typical application of _functional networks_, which allow to study the dynamical relationships between subsystems of a high-dimensional complex system by constructing networks from it. `pyunicorn` provides classes for the construction and analysis of such networks, representing the statistical interdependency structure within and between fields of time series using various similarity measures." - ] - }, - { - "cell_type": "markdown", - "id": "1860c9d0", - "metadata": {}, - "source": [ - "### Coupling Analysis" - ] - }, - { - "cell_type": "markdown", - "id": "30cd9555", - "metadata": {}, - "source": [ - "CNs represent strong statistical interrelationships between time series of climatological fields. These statistical interrelationships can be estimated with methods from the `funcnet.CouplingAnalysis` class in terms of matrices of _statistical similarities_ $\\textbf{S}$, such as the _(lagged) classical linear Pearson product-moment correlation coefficient_ (CC). The CC of two zero-mean time series variables $X,Y$, as implemented in `funcnet.CouplingAnalysis.cross_correlation()`, is given by \n", - "$$\\rho_{XY}(\\tau)=\\frac{\\langle X_{t-\\tau}, Y_t \\rangle}{\\sigma_X \\sigma_Y}\\,,$$\n", - "which depends on the covariance $\\langle X_{t-\\tau}, Y_t \\rangle$ and the standard deviations $\\sigma_X, \\sigma_Y$. Lags $\\tau > 0$ correspond to the linear association of past values of $X$ with $Y$, and vice versa for $\\tau < 0$. " - ] - }, - { - "cell_type": "markdown", - "id": "70377c40", - "metadata": {}, - "source": [ - "### Similarity Measures for CNs" - ] - }, - { - "cell_type": "markdown", - "id": "fadb2909", - "metadata": {}, - "source": [ - "By thresholding the matrix of a statistical similarity measure $\\textbf{S}$, the interrelationships between time series of climate networks can be reconstructed:\n", - "$$A_{pq} = \\Theta(S_{pq}-\\beta)\\quad \\text{ if } p \\neq q; \\qquad 0\\quad\\text{otherwise}\\,,$$\n", - "where $\\Theta$ is the Heaviside function, $\\beta$ denotes a threshold parameter, and $A_{pp} = 0$ for all nodes $p$ to exclude self-loops. A CN that is reconstructed using the Pearson CC from above is called a _Pearson correlation CN_." - ] - }, - { - "cell_type": "markdown", - "id": "9c64c013", - "metadata": {}, - "source": [ - "## Constructing CNs" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ff7f5d81-129e-4966-a7fc-a0d25aea87f3", - "metadata": {}, - "source": [ - "Having established some basic theoretic background, we will now use `pyunicorn` to construct a CN. We start with some imports and some specifications regarding an example __[NOAA dataset](https://psl.noaa.gov/repository/entry/show?entryid=synth%3Ae570c8f9-ec09-4e89-93b4-babd5651e7a9%3AL25jZXAucmVhbmFseXNpcy5kZXJpdmVkL3N1cmZhY2UvYWlyLm1vbi5tZWFuLm5j)__, which is already contained in this notebook's directory." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "e793f1a2", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from pyunicorn import climate" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "6f1a55f9-8560-484d-ab6a-17ba8b8cd67c", - "metadata": {}, - "outputs": [], - "source": [ - "DATA_FILENAME = \"./air.mon.mean.nc\"\n", - "# Indicate data source (optional)\n", - "DATA_SOURCE = \"ncep_ncar_reanalysis\"\n", - "# Type of data file (\"NetCDF\" indicates a NetCDF file with data on a regular\n", - "# lat-lon grid, \"iNetCDF\" allows for arbitrary grids - > see documentation).\n", - "FILE_TYPE = \"NetCDF\"\n", - "# Name of observable in NetCDF file (\"air\" indicates surface air temperature\n", - "# in NCEP/NCAR reanalysis data)\n", - "OBSERVABLE_NAME = \"air\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "12e44ccb-ba25-4bf9-8170-ed12310b739b", - "metadata": {}, - "outputs": [], - "source": [ - "# Select a region in time and space from the data (here the whole dataset)\n", - "WINDOW = {\"time_min\": 0., \"time_max\": 0., \"lat_min\": 0, \"lon_min\": 0,\n", - " \"lat_max\": 30, \"lon_max\": 0}\n", - "# Indicate the length of the annual cycle in the data (e.g., 12 for monthly\n", - "# data). This is used for calculating climatological anomaly values.\n", - "TIME_CYCLE = 12" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9f54ffe5-02a5-47e4-a459-870e1e8afef6", - "metadata": {}, - "source": [ - "Now we set some parameters for the CN construction, the first being the threshold $\\beta$ from above, and create a `ClimateData` object containing our data." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "baf245bd-2f3e-401d-bc1d-9943fee4bbf2", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading NetCDF File and converting data to NumPy array...\n", - "Global attributes:\n", - "description: Data from NCEP initialized reanalysis (4x/day). These are the 0.9950 sigma level values\n", - "platform: Model\n", - "Conventions: COARDS\n", - "NCO: 20121012\n", - "history: Thu May 4 20:11:16 2000: ncrcat -d time,0,623 /Datasets/ncep.reanalysis.derived/surface/air.mon.mean.nc air.mon.mean.nc\n", - "Thu May 4 18:11:50 2000: ncrcat -d time,0,622 /Datasets/ncep.reanalysis.derived/surface/air.mon.mean.nc ./surface/air.mon.mean.nc\n", - "Mon Jul 5 23:47:18 1999: ncrcat ./air.mon.mean.nc /Datasets/ncep.reanalysis.derived/surface/air.mon.mean.nc /dm/dmwork/nmc.rean.ingest/combinedMMs/surface/air.mon.mean.nc\n", - "/home/hoop/crdc/cpreanjuke2farm/cpreanjuke2farm Mon Oct 23 21:04:20 1995 from air.sfc.gauss.85.nc\n", - "created 95/03/13 by Hoop (netCDF2.3)\n", - "Converted to chunked, deflated non-packed NetCDF4 2014/09\n", - "title: monthly mean air.sig995 from the NCEP Reanalysis\n", - "dataset_title: NCEP-NCAR Reanalysis 1\n", - "References: http://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis.derived.html\n", - "Variables (size):\n", - "lat (73)\n", - "lon (144)\n", - "time (900)\n", - "air (900)\n", - "ClimateData:\n", - "Data: 10512 grid points, 9460800 measurements.\n", - "Geographical boundaries:\n", - " time lat lon\n", - " min 1297320.0 -90.00 0.00\n", - " max 1954032.0 90.00 357.50\n" - ] - } - ], - "source": [ - "# For setting fixed threshold\n", - "THRESHOLD = 0.5\n", - "# For setting fixed link density\n", - "LINK_DENSITY = 0.005\n", - "# Indicates whether to use only data from winter months (DJF) for calculating\n", - "# correlations\n", - "WINTER_ONLY = False\n", - "\n", - "data = climate.ClimateData.Load(\n", - " file_name=DATA_FILENAME, observable_name=OBSERVABLE_NAME,\n", - " data_source=DATA_SOURCE, file_type=FILE_TYPE,\n", - " window=WINDOW, time_cycle=TIME_CYCLE)\n", - "print(data)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2fade6f6-8457-436f-a52a-77464e92fd54", - "metadata": {}, - "source": [ - "Next, we construct a CN based on the Pearson CC, without lag and with fixed threshold. Alternatively, several other similarity measures and construction mechanisms may be used as well." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c5326b90", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating a Tsonis climate network...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n" - ] - } - ], - "source": [ - "net = climate.TsonisClimateNetwork(\n", - " data, threshold=THRESHOLD, winter_only=WINTER_ONLY)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2cdee7ef-7d2f-46d9-83ed-c9253e0100c0", - "metadata": {}, - "outputs": [], - "source": [ - "# Create a climate network based on Pearson correlation without lag and with\n", - "# fixed link density\n", - "# net = climate.TsonisClimateNetwork(\n", - "# data, link_density=LINK_DENSITY, winter_only=WINTER_ONLY)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e6bf3b44-193b-48c0-b7be-f056bd35d72c", - "metadata": {}, - "outputs": [], - "source": [ - "# Create a climate network based on Spearman's rank order correlation without\n", - "# lag and with fixed threshold\n", - "# net = climate.SpearmanClimateNetwork(\n", - "# data, threshold=THRESHOLD, winter_only=WINTER_ONLY)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5cdcfc8a-6448-4df3-b49f-f3e5d220f0f2", - "metadata": {}, - "outputs": [], - "source": [ - "# Create a climate network based on mutual information without lag and with\n", - "# fixed threshold\n", - "# net = climate.MutualInfoClimateNetwork(\n", - "# data, threshold=THRESHOLD, winter_only=WINTER_ONLY)" - ] - }, - { - "cell_type": "markdown", - "id": "b443476e", - "metadata": {}, - "source": [ - "We finish by calculating some basic network measures for the resulting CN, optionally saving them to text files." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "4f568b0f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Link density: 0.025814135861437906\n", - "Calculating closeness...\n", - "Calculating node betweenness...\n", - "Calculating local clustering coefficients...\n", - "Calculating average link distance...\n", - "Calculating angular great circle distance using Cython...\n", - "Calculating maximum link distance...\n" - ] - } - ], - "source": [ - "print(\"Link density:\", net.link_density)\n", - "\n", - "# Get degree\n", - "degree = net.degree()\n", - "# Get closeness\n", - "closeness = net.closeness()\n", - "# Get betweenness\n", - "betweenness = net.betweenness()\n", - "# Get local clustering coefficient\n", - "clustering = net.local_clustering()\n", - "# Get average link distance\n", - "ald = net.average_link_distance()\n", - "# Get maximum link distance\n", - "mld = net.max_link_distance()\n", - "\n", - "# Save the grid (mainly vertex coordinates) to text files\n", - "#data.grid.save_txt(filename=\"grid.txt\")\n", - "# Save the degree sequence. Other measures may be saved similarly.\n", - "#np.savetxt(\"degree.txt\", degree)" - ] - }, - { - "cell_type": "markdown", - "id": "15af9941", - "metadata": {}, - "source": [ - "## Plotting CNs" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "b26e5953-53c6-418a-b08b-509ad415081f", - "metadata": {}, - "source": [ - "`pyunicorn` provides a basic plotting feature based on the __[`cartopy`](https://scitools.org.uk/cartopy/docs/latest/)__ and `matplotlib` packages, which can be used to have a first look at the generated data. We start by initializing a `MapPlot` object:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "b823297c", - "metadata": {}, - "outputs": [], - "source": [ - "# create a Cartopy plot instance called map_plot\n", - "# from the data with title DATA_SOURCE\n", - "map_plot = climate.MapPlot(data.grid, DATA_SOURCE)" - ] - }, - { - "cell_type": "markdown", - "id": "422af668", - "metadata": {}, - "source": [ - "With `MapPlot.plot()`, we can now plot some of our previously calculated measures on the given grid." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "056f3a92", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot degree\n", - "map_plot.plot(degree, \"Degree\")\n", - "\n", - "# add matplotlib.pyplot or cartopy commands to customize figure\n", - "plt.set_cmap('plasma')\n", - "# optionally save figure\n", - "#plt.savefig('degree.png')" - ] - }, - { - "cell_type": "markdown", - "id": "b8feb1e0", - "metadata": {}, - "source": [ - "Try plotting more measures if you like." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8b67424d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot betwenness\n", - "map_plot.plot(np.log10(betweenness + 1), \"Betweenness (log10)\")\n", - "\n", - "# add matplotlib.pyplot or cartopy commands to customize figure\n", - "plt.set_cmap('plasma')\n", - "# optionally save figure\n", - "#plt.savefig('degree.png')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pyunicorn", - "language": "python", - "name": "pyunicorn" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/tutorial_CoupledClimateNetworks.ipynb b/notebooks/tutorial_CoupledClimateNetworks.ipynb deleted file mode 100644 index 59b45764..00000000 --- a/notebooks/tutorial_CoupledClimateNetworks.ipynb +++ /dev/null @@ -1,1241 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9b3f935f", - "metadata": {}, - "source": [ - "# Tutorial: Coupled Climate Networks" - ] - }, - { - "cell_type": "markdown", - "id": "41e36ae2", - "metadata": {}, - "source": [ - "The objective of this tutorial is to introduce *coupled climate subnetwork analysis*, which uses *interacting networks* in order to study the statistical relationships between several fields of climatological observables or between a climatolical obervable at different vertical levels. First, some theoretical background on *interacting networks* is given and the method of *coupled climate network analysis* is explained. Then, some methods provided by **pyunicorn** are illustrated by the example of the Earth's atmosphere’s vertical dynamical structure. An introduction to (single layer) *climate networks* and their application with the pyunicorn package can be found in the tutorial [Climate Networks](https://github.com/pik-copan/pyunicorn/blob/master/notebooks/tutorial_ClimateNetworks.ipynb). For a detailed discussion and further references, please consult [Donges et al., 2015](https://aip.scitation.org/doi/10.1063/1.4934554) and [Donges et al., 2011](https://link.springer.com/article/10.1140/epjb/e2011-10795-8), on which this tutorial is based." - ] - }, - { - "cell_type": "markdown", - "id": "122ca935", - "metadata": {}, - "source": [ - "## Introduction" - ] - }, - { - "cell_type": "markdown", - "id": "64969606", - "metadata": {}, - "source": [ - "*Coupled Climate Networks* is a very useful tool for representing and studying the statictical relationship between different climatological variables or between a single variable at different physically separable levels. This can be useful for finding spatial as well as temporal patterns accounting for a large fraction of the fields' variance. The method can also be applied to study the complex interactions between different domains of the Earth system, e.g. the atmosphere, hydrosphere, cryosphere and biosphere, which still remains a great challenge for modern science. The urge to make progress in this field is particularly pressing as substantial and mutually interacting components of the Earth system (tipping elements), may soon pass a bifurcation point (tipping point) due to global climate change.\n", - "\n", - "Mapping the complex interdependency structure of subsystems, components or processes of the Earth system to a network of interacting networks provides a natural, simplified and condensed mathematical representation." - ] - }, - { - "cell_type": "markdown", - "id": "5d0b07ef", - "metadata": {}, - "source": [ - "## Theory of Interacting Networks" - ] - }, - { - "cell_type": "markdown", - "id": "6fd973d9", - "metadata": {}, - "source": [ - "The structure of many complex systems can be described as a *network of interacting or interdependent networks*. Notable examples are representations of the mammalian cortex, systems of interacting populations of heterogeneous oscillators or mutually interdependent infrastructure networks. \n", - "\n", - "**Pyunicorn** provides the class `core.InteractingNetworks`for constructing and analysing all kinds of interacting networks. *Coupled climate networks* applies *interacting networks* to *climate networks*. The class `climate.CoupledClimateNetworks`of pyunicorn inherits from `core.InteractingNetworks` and `climate.ClimateNetworks`." - ] - }, - { - "cell_type": "markdown", - "id": "0eab5909", - "metadata": {}, - "source": [ - "*Interacting networks* can be represented by decomposing a network $G=(V,E)$ into a collection of *M subnetworks* \n", - "$G_i=(V_i,E_{ii})$. Here, $V_i$ denote the disjunct sets of nodes corresponding to each subnetwork and the internal links \n", - "sets $E_{ii}$ contain information on the connections within a subnetwork such that $\\cup^M_{i=1}V_i=V$.\n", - "Additionally, disjunct sets of cross-links $E_{ij}$ connect nodes in different subnetworks with $\\cup^M_{i,j=1}E_{ij}=E$. \n", - "Alternatively, a network of networks of this type can be represented by a standard adjacency matrix $A$ with with block structure." - ] - }, - { - "attachments": { - "Systems-of-interacting-networks-or-networks-of-networks-are-a-natural-representation-of_W640.jpg": { - "image/jpeg": 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- } - }, - "cell_type": "markdown", - "id": "43bc2905", - "metadata": {}, - "source": [ - "![Systems-of-interacting-networks-or-networks-of-networks-are-a-natural-representation-of_W640.jpg](attachment:Systems-of-interacting-networks-or-networks-of-networks-are-a-natural-representation-of_W640.jpg)" - ] - }, - { - "cell_type": "markdown", - "id": "9a3e271c", - "metadata": {}, - "source": [ - "In the following, we introduce some local and global measures for interacting networks. \n", - "\n", - "The indices $i,j,k,l$ always denote subnetworks while $v,w,p,q$ designate single vertices and we always assume $v\\in V_i$. The formulae explicitely account\n", - "for the general case $i \\ne j$ but can be, nevertheless, easily modified to suit the special case $i = j$. Furthermore, the term *cross* refers to the interaction between subnetworks $G_i,G_j$, whereas *internal* refers to the structure within a singular subnetwork." - ] - }, - { - "cell_type": "markdown", - "id": "cc54fec3", - "metadata": {}, - "source": [ - "### Local Measures\n", - "\n", - "The **cross-degree centrality** $k_v^{ij}$ gives the number of neighbours of the vertex $v$ within subnetwork $G_j$,\n", - "$$k_v^{ij}=\\sum_{q\\in V_j}A_{vq},v \\in V_i.$$\n", - "\n", - "The **cross-closeness centrality** $c^{ij}_v$ measures the topological closeness of $v$ to subnetwork $G_j$ along shortest paths,\n", - "$$ c^{ij}_v = \\frac{N_j}{\\sum_{q\\in V_j}d_{vq}},$$\n", - "where $d_{vq}$ is the shortest path length between vertices $v$ and $q$. \n", - "\n", - "For any vertex $w \\in V$, the **cross-betweenness centrality** $b^{ij}_w$ indicates its role for mediating interactions between two subnetworks $G_i$ and $G_j$,\n", - "$$ b^{ij}_w= \\sum_{p\\in V_i,q \\in V_j;p,q\\ne w}\\frac{\\sigma_{pq}(w)}{\\sigma_{pq}}= b^{ji}_w,$$\n", - "where $\\sigma_{pq}$ denotes the total number of shortest paths from $p \\in V_i $ to $q \\in V_j$ that include $w$.\n", - "\n", - "### Global Measures\n", - "\n", - "The **cross-edge density** $\\rho_{ij}$ measures the density of connections between distinct subnetworks $G_i$ and $G_j$,\n", - "$$ \\rho_{ij}=\\frac{|E_{ij}|}{N_i N_j}= \\rho_{ji},$$\n", - "where $N_i$ denotes the number of vertices in the subnetwork $G_i$.\n", - "\n", - "The **cross-average path length** $\\mathcal{L}_{ij}$ measures the average length of existing shortest paths between two subnetworks $G_i$ and $G_j$,\n", - "$$ \\mathcal{L}_{ij}= \\frac{1}{N_i N_j - M_{ij}}\\sum_{v\\in V_i,q\\in V_j}d_{vq}= \\mathcal{L}_{ji}, $$\n", - "where $M_{ij}$ is the nnumber of pairs ($v\\in V_i$, $q\\in V_j$) which are not mutually reachable.\n", - "\n", - "The **global cross-clustering coefficient** $\\mathcal{C}_{ij}$ is an estimate of the probability of vertices from subnetwork $G_i$ to have mutually connected neghbors within subnetwork $G_j$,\n", - "$$ \\mathcal{C}_{ij} = \\langle \\mathcal{C}^{ij}_v \\rangle_{v\\in V_i} = \\frac{1}{N_i}\\sum_{v\\in V_i, k_v^j>1}\\frac{\\sum_{p\\ne q \\in V_j}A_{vp}A_{pq}A_{qv}}{\\sum_{p \\ne q \\in V_j}A_{vp}A_{vq}}.$$\n", - "\n", - "The **cross-transitivity** $\\mathcal{T}_{ij}$ is the probability that two vertices in subnetwork $G_j$ are connected if they have a common neighbour in subnetwork $G_i$,\n", - "$$ \\mathcal{T}_{ij}= \\frac{\\sum_{v\\in V_i;p\\ne q \\in V_j}A_{vp}A_{pq}A_{qv}}{\\sum_{v\\in V_i;p\\ne q \\in V_j}A_{vp}A_{vq}}.$$" - ] - }, - { - "cell_type": "markdown", - "id": "75c19d62", - "metadata": {}, - "source": [ - "## Application: Analysing the vertical dynamical structure of the Earth’s atmosphere" - ] - }, - { - "cell_type": "markdown", - "id": "0dec789d", - "metadata": {}, - "source": [ - "In the following, coupled climate network analysis is illustrated by the example of the dynamical structure of the Earth's atmosphere. \n", - "\n", - "In order to treat a climate network as a network of networks, an ab initio physical separation of the climatological fields is necessary regarding processes responsible for internal coupling within a single field and those mediating interactions between both fields.\n", - "\n", - "For the Earth system, there are distinct physical processes behind vertical and quasi-horizontal atmospheric dynamics: we have a stable isobaric quasi-horizontal stratification, while local heating of the Earth’s surface and atmosphere induces minor disturbances of the system. Therefore, we can treat the considered climatological field variable at different isobaric quasi-horizontal surfaces of the Earth's atmosphere as separated subnetworks of an interconnected network." - ] - }, - { - "cell_type": "markdown", - "id": "54ae354e", - "metadata": {}, - "source": [ - "The small vertical disturbances of the system due to convection processes lead to vertical movement resulting in pressure gradients which are balanced by quasi-horizontal geostrophic winds along isobares." - ] - }, - { - "cell_type": "markdown", - "id": "61de5550", - "metadata": {}, - "source": [ - "We consider the discretised and vertically resolved geopotential height field $Z^i_v(t)$ sampled at predefined points $v$ on isobaric surfaces $i$ as the climatological field variable to construct coupled climate networks as it reflects global weather and climate dynamics to a good approximation: it captures the dynamics of both the geostrophic wind field as well as convection processes.\n", - "\n", - "We specifically focus on the interaction structure between near ground and upper level atmospheric dynamics, which is particularly interesting as a large portion of the solar forcing driving atmospheric dynamics takes place on the Earth’s surface." - ] - }, - { - "attachments": { - "Illustration-of-a-coupled-climate-subnetwork-as-it-is-constructed-in-this-work-where-V1_W640.jpg": { - "image/jpeg": 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" - } - }, - "cell_type": "markdown", - "id": "58970dbf", - "metadata": {}, - "source": [ - "![Illustration-of-a-coupled-climate-subnetwork-as-it-is-constructed-in-this-work-where-V1_W640.jpg](attachment:Illustration-of-a-coupled-climate-subnetwork-as-it-is-constructed-in-this-work-where-V1_W640.jpg)" - ] - }, - { - "cell_type": "markdown", - "id": "668d5926", - "metadata": {}, - "source": [ - "## Loading the Climate Data" - ] - }, - { - "cell_type": "markdown", - "id": "0fe0dac8", - "metadata": {}, - "source": [ - "For this tutorial, we use Reanalysis 1 data provided by the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/-NCAR). You can download the data set following this [link](https://downloads.psl.noaa.gov//Datasets/ncep.reanalysis/Monthlies/pressure/hgt.mon.mean.nc) and copy it to the directory \"notebooks\" of this script or change the path below. Some information on the data can be found following this [link](https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html). The Reanalysis 1 data set contains the monthly averaged geographical height potential for 17 isobaric surfaces $P_i$ of the atmosphere on an equally spaced spherical grid with latitude and longitude resolution of 2.5° x 2.5°." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f197742a", - "metadata": {}, - "outputs": [], - "source": [ - "DATA_FILENAME = \"./hgt.mon.mean.nc\"" - ] - }, - { - "cell_type": "markdown", - "id": "120a2f88", - "metadata": {}, - "source": [ - "Now we will start with some imports and some specifications regarding the data set." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "695c5769", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "climate: Package Ngl could not be loaded. Some functionality in class MapPlots might not be available!\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from pyunicorn import climate\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2091a999", - "metadata": {}, - "outputs": [], - "source": [ - "FILE_TYPE = \"NetCDF\"\n", - "# Type of data file (\"NetCDF\" indicates a NetCDF file with data on a regular\n", - "# lat-lon grid, \"iNetCDF\" allows for arbitrary grids - > see documentation).\n", - "# For example, the \"NetCDF\" FILE_TYPE is compatible with data from the IPCC\n", - "# AR4 model ensemble or the reanalysis data provided by NCEP/NCAR." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d1100a46", - "metadata": {}, - "outputs": [], - "source": [ - "# Indicate data source (optional)\n", - "DATA_SOURCE = \"NCEP-NCAR Reanalysis 1\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a00e52f1", - "metadata": {}, - "outputs": [], - "source": [ - "# Name of observable in NetCDF file (\"hgt\" indicates the monthly mean of the geopotential height\n", - "# in the NCEP/NCAR reanalysis data)\n", - "OBSERVABLE_NAME = \"hgt\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "016b0415", - "metadata": {}, - "outputs": [], - "source": [ - "# Select a subset in time and space from the data (e.g., a particular region\n", - "# or a particular time window, or both)\n", - "# If the boundaries are equal, the data's full range is selected.\n", - "# for this tutorial, we choose a window for lattitude and longitude corresponding to North America\n", - "WINDOW = {\"time_min\": 0., \"time_max\": 0., \"lat_min\": 45, \"lon_min\": 80,\n", - " \"lat_max\": 60, \"lon_max\": 120} " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f0233e5a", - "metadata": {}, - "outputs": [], - "source": [ - "# Indicate the length of the annual cycle in the data (e.g., 12 for monthly\n", - "# data). This is used for calculating climatological anomaly values\n", - "# correctly.\n", - "TIME_CYCLE = 12" - ] - }, - { - "cell_type": "markdown", - "id": "67822736", - "metadata": {}, - "source": [ - "We will generate a separate `pyunicorn.climate.ClimateData` instance from our data file for each of the 17 vertical levels in order to construct a coupled climate network for each vertical level coupled to the near ground level in the following steps. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "43c938d9", - "metadata": {}, - "outputs": [], - "source": [ - "# Vertical_level indicates the vertical level to be extracted from the\n", - "# data file. Is ignored for horizontal data sets. If None, the first\n", - "# level in the data file is chosen.\n", - "# in the following, we will loop over all 17 levels in order to generate a ClimateData object for each level.\n", - "VERTICAL_LEVELS = 17 # number of total vertical levels in the data file" - ] - }, - { - "cell_type": "markdown", - "id": "76f6ef6a", - "metadata": {}, - "source": [ - "Now we create an numpy array containing 17 Climate Data objects from our data file (one Climate Data object for each vertical level)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "82ac45fb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n", - "Reading NetCDF File and converting data to NumPy array...\n" - ] - } - ], - "source": [ - "# loop over all levels to create a climate data object for each level, needed for constructing coupled climate networks\n", - "data = np.array([climate.ClimateData.Load(\n", - " file_name=DATA_FILENAME, observable_name=OBSERVABLE_NAME,\n", - " data_source=DATA_SOURCE, file_type=FILE_TYPE, vertical_level = l,\n", - " window=WINDOW, time_cycle=TIME_CYCLE) for l in range(VERTICAL_LEVELS)])" - ] - }, - { - "cell_type": "markdown", - "id": "a7b51a1a", - "metadata": {}, - "source": [ - "One can use the print function on a Climate Data object in order to show some information about the data." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "0afb1744", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "File format: NETCDF4_CLASSIC\n", - "Global attributes:\n", - "description: Data from NCEP initialized reanalysis (4x/day). These are interpolated to pressure surfaces from model (sigma) surfaces.\n", - "platform: Model\n", - "Conventions: COARDS\n", - "NCO: 20121012\n", - "history: Created by NOAA-CIRES Climate Diagnostics Center (SAC) from the NCEP\n", - "reanalysis data set on 07/07/97 by calc.mon.mean.year.f using\n", - "/Datasets/nmc.reanalysis.derived/pressure/hgt.mon.mean.nc\n", - "from /Datasets/nmc.reanalysis/pressure/hgt.79.nc to hgt.95.nc\n", - "Converted to chunked, deflated non-packed NetCDF4 2014/09\n", - "title: monthly mean hgt from the NCEP Reanalysis\n", - "dataset_title: NCEP-NCAR Reanalysis 1\n", - "References: http://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis.derived.html\n", - "Variables (size):\n", - "level (17)\n", - "lat (73)\n", - "lon (144)\n", - "time (905)\n", - "hgt (905)\n", - "ClimateData:\n", - "Data: 119 grid points, 107695 measurements.\n", - "Geographical boundaries:\n", - " time lat lon\n", - " min 1297320.0 45.00 80.00\n", - " max 1957656.0 60.00 120.00\n" - ] - } - ], - "source": [ - "# Print some information on the data set\n", - "data_level0 = data[0]\n", - "print(data_level0)" - ] - }, - { - "cell_type": "markdown", - "id": "9eea2cec", - "metadata": {}, - "source": [ - "## Network construction" - ] - }, - { - "cell_type": "markdown", - "id": "fb13be4b", - "metadata": {}, - "source": [ - "The `pyunicorn.climate.CoupledClimateNetwork` class provides the functionality to generate a complex network from the matrix of a similarity measure of time series from two different observables or one observable at two different vertical levels. The idea of coupled climate networks is based on the concept of coupled patterns, for a review refer to [Bretherton1992](https://journals.ametsoc.org/view/journals/clim/5/6/1520-0442_1992_005_0541_aiomff_2_0_co_2.xml). The two observables (layers) need to have the same time grid (temporal sampling points). Some information on the construction of a Climate Network based on different similarity measures is provided in the pyunicorn tutorial on [Climate Networks](https://github.com/pik-copan/pyunicorn/blob/master/notebooks/tutorial_ClimateNetworks.ipynb)." - ] - }, - { - "cell_type": "markdown", - "id": "e5e230be", - "metadata": {}, - "source": [ - "For our example, we contruct 17 coupled climate networks (coupling the lowest level with each other level) from the data based on Pearson correlation without lag and with a fixed threshold. For the construction of a coupled climate network, one needs to set either the threshold $\\beta$ or the link denisty. In this example, we set the threshold $\\beta = 0.5$." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "68fdaf51", - "metadata": {}, - "outputs": [], - "source": [ - "# For setting fixed threshold\n", - "THRESHOLD = 0.5" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "8872d035", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\hotz\\AppData\\Roaming\\Python\\Python39\\site-packages\\pyunicorn\\core\\interacting_networks.py:965: RuntimeWarning: invalid value encountered in scalar divide\n", - " average_path_length = path_lengths.sum() / norm\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n", - "Calculating daily (monthly) anomaly values...\n", - "Calculating correlation matrix at zero lag from anomaly values...\n", - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "Calculating all shortest path lengths...\n" - ] - } - ], - "source": [ - "# generate a coupled climate network for each level and the lowest level and calculate some cross network measures.\n", - "\n", - "cross_link_density = []\n", - "cross_average_path_length = []\n", - "cross_global_clustering = []\n", - "cross_transitivity = []\n", - "\n", - "cross_degree = []\n", - "cross_closeness = []\n", - "cross_betweenness = []\n", - "\n", - "for l in range(VERTICAL_LEVELS): # loop over all vertical levels\n", - " \n", - " # generate a coupled climate network between the ground level and the level l\n", - " coupled_network = climate.CoupledTsonisClimateNetwork(data[0], data[l], threshold = THRESHOLD)\n", - " \n", - " # calculate global measures\n", - " cross_link_density.append(coupled_network.cross_link_density()) # 1 output\n", - " cross_average_path_length.append(coupled_network.cross_average_path_length()) # 1 output\n", - " cross_global_clustering.append(coupled_network.cross_global_clustering()) # 2 outputs\n", - " cross_transitivity.append(coupled_network.cross_transitivity()) # 2 outputs\n", - " \n", - " # calculate local measures\n", - " cross_degree.append(coupled_network.cross_degree()) # pointing upwards and pointing downwards\n", - " cross_closeness.append(coupled_network.cross_closeness()) # pointing upwards, pointing downwards\n", - " cross_betweenness.append(coupled_network.cross_betweenness()) # near ground and upper level component" - ] - }, - { - "cell_type": "markdown", - "id": "583b18dd", - "metadata": {}, - "source": [ - "## Plotting some global measures for the coupled networks" - ] - }, - { - "cell_type": "markdown", - "id": "3b27db00", - "metadata": {}, - "source": [ - "Now we plot the global meaures that we calculated for the 17 coupled climate networks in dependence of the average geopotential height of each level. Therefore, we first calculate the average geopotential height for each vertical level in km." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "c1ead3e6", - "metadata": {}, - "outputs": [], - "source": [ - "# read out the observable hgt (geopotentential height) and averaging it for each level\n", - "hgt_averaged = []\n", - "for l in range (VERTICAL_LEVELS):\n", - " hgt_averaged.append(np.round(data[l].observable().flatten().mean()/1000,1))" - ] - }, - { - "cell_type": "markdown", - "id": "cfea75df", - "metadata": {}, - "source": [ - "Cross-link density $ \\rho_{ij}=\\rho_{ji}$ and cross-average path length $ \\mathcal{L}_{ij} =\\mathcal{L}_{ji}$ are symmertrical, but global cross-clustering coefficient $\\mathcal{C}_{ij} \\neq \\mathcal{C}_{ji} $ and cross transitivity $\\mathcal{T}_{ij} \\neq \\mathcal{T}_{ij}$ are not." - ] - }, - { - "cell_type": "markdown", - "id": "05c2ddbb", - "metadata": {}, - "source": [ - "### Cross link density" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "cde73cbf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the geopotential height over the cross link density (rho_1i = rho_i1)\n", - "plt.plot(cross_link_density, hgt_averaged, 'o', color='blue'); \n", - "plt.xlabel(r\"$\\rho_{1i}$\");\n", - "plt.ylabel(r\"$Z_i$(km)\");\n", - "plt.title('cross link density');" - ] - }, - { - "cell_type": "markdown", - "id": "a20a0dc7", - "metadata": {}, - "source": [ - "### Cross average path length" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "c754459b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the geopotential height over the cross average path length (L_1i = L_i1)\n", - "plt.plot(cross_average_path_length, hgt_averaged, 'o', color='blue'); \n", - "plt.xlabel(r\"$\\mathcal{L}_{1i}$\");\n", - "plt.ylabel(r\"$Z_i$(km)\");\n", - "plt.title('cross average path length');" - ] - }, - { - "cell_type": "markdown", - "id": "e18b3e2b", - "metadata": {}, - "source": [ - "### Global cross clustering coefficient\n", - "\n", - "The global cross clustering coefficient $\\mathcal{C}_{ij}$ gives the average probability of vertices from subnetwork $G_i$ to have mutually connected neghbors within subnetwork $G_j$. In general, it is not symmetric. The method `climate.CoupledClimateNetwork.cross_global_clustering()` of two coupled climate subnetworks $G_i$ and $G_j$ returns the tuple $(\\mathcal{C}_{ij},\\mathcal{C}_{ji})$." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "525617d2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the geopotential height over the cross global clustering pointing upwards C_1i\n", - "plt.plot(np.array(cross_global_clustering)[:,0], hgt_averaged, 'o', color='blue'); # C_1i\n", - "plt.xlabel(r\"$\\mathcal{C}_{1i}$\");\n", - "plt.ylabel(r\"$Z_i$(km)\");\n", - "plt.title('cross global clustering pointing upwards');" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d37f9aad", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the geopotential height over the cross global clustering pointing downwards C_i1\n", - "plt.plot(np.array(cross_global_clustering)[:,1], hgt_averaged, 'o', color='blue'); \n", - "plt.xlabel(r\"$\\mathcal{C}_{i1}$\");\n", - "plt.ylabel(r\"$Z_i$(km)\");\n", - "plt.title('cross global clustering pointing downwards');" - ] - }, - { - "cell_type": "markdown", - "id": "d94df103", - "metadata": {}, - "source": [ - "### Cross transitivity\n", - "\n", - "The global cross transitivity $\\mathcal{T}_{ij}$ gives the probability that two vertices in subnetwork $G_j$ are connected if they have a common neighbour in subnetwork $G_i$ . In general, it is not symmetric. The method `climate.CoupledClimateNetwork.cross_global_transitivity()` of two coupled climate subnetworks $G_i$ and $G_j$ returns the tuple $(\\mathcal{T}_{ij},\\mathcal{T}_{ji})$." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "506b3642", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the geopotential height over the cross transitivity pointing upwards T_1i\n", - "plt.plot(np.array(cross_transitivity)[:,0], hgt_averaged, 'o', color='blue'); \n", - "plt.xlabel(r\"$\\mathcal{T}_{1i}$\");\n", - "plt.ylabel(r\"$Z_i$(km)\");\n", - "plt.title('cross transitivity');" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "f4ddc31a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the geopotential height over the cross transitivity pointing downwards T_i1\n", - "plt.plot(np.array(cross_transitivity)[:,1], hgt_averaged, 'o', color='blue', label=r\"$\\mathcal{T}_{i1}$\");\n", - "plt.xlabel(r\"$\\mathcal{T}_{i1}$\");\n", - "plt.ylabel(r\"$Z_i$(km)\");\n", - "plt.title('cross transitivity');" - ] - }, - { - "cell_type": "markdown", - "id": "711d6aef", - "metadata": {}, - "source": [ - "## Plotting some local measures for the coupled networks" - ] - }, - { - "cell_type": "markdown", - "id": "5898b11d", - "metadata": {}, - "source": [ - "Local cross-network measures of `climate.CoupledClimateNetwork` give values for all vertices $v$ of the coupled network in form of a tuple of two numpy arrays where the first entry of the tuple corresponds to the measure for all nodes of the first subnetwork and the second entry for the nodes of the second subnetwork respectively.\n", - "\n", - "For visualising the inherently three-dimensional fields of local cross-network measures $m^{ij}_{v(\\vartheta,\\phi)}$, where $\\vartheta$ and $\\phi$ denote latitude and longitude, we choose to focus on their variation with height and latitude. Therefore, we consider zonal averages\n", - "$$ m^{ij}(\\vartheta)=\\langle m^{ij}_{v(\\vartheta,\\phi)} \\rangle _\\phi $$\n", - "along circles of constant latitude." - ] - }, - { - "cell_type": "markdown", - "id": "3d049591", - "metadata": {}, - "source": [ - "The local measures of vertices of a subnetwork of a coupled climate networks instance are ordered by latitude and longitude in one single numpy array. In order to select the measures of the nodes by latitude and longitude, we need to reshape the single output array into a $N_{\\vartheta} \\times N_{\\phi}$ matrix, where $N_{\\vartheta}$ corresponds to the number of lattitude values and $N_{\\phi}$ to the number of longitude values." - ] - }, - { - "cell_type": "markdown", - "id": "d203bb28", - "metadata": {}, - "source": [ - "First, we determine the number of lattitude and longitude values for our example. It is the same for all 17 generated coupled climate networks." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "14e75a44", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "number of lat values: 7\n", - "number of lon values: 17\n" - ] - } - ], - "source": [ - "# calculate the number of lon and lat values\n", - "print(f'number of lat values: {len(np.unique(coupled_network.grid_1.lat_sequence()))}') \n", - "print(f'number of lon values: {len(np.unique(coupled_network.grid_1.lon_sequence()))}') " - ] - }, - { - "cell_type": "markdown", - "id": "6bc0c10e", - "metadata": {}, - "source": [ - "### Cross degree\n", - "\n", - "The method `climate.CoupledClimateNetwork.cross_degree()` of two coupled climate subnetworks $G_i$ and $G_j$ returns the tuple $(\\mathbf{k_v^{ij}},\\mathbf{k_v^{ji}})$, with $\\mathbf{k_v^{ij}}$ being a numpy array containing the non-symmetric cross degree of the vertices." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "aca9b7f0", - "metadata": {}, - "outputs": [], - "source": [ - "# cross degree\n", - "\n", - "cross_degree_upwards_zonial = np.zeros((17,7)) # 17 levels and 7 lat values\n", - "cross_degree_downwards_zonial = np.zeros((17,7)) # 17 levels and 7 lat values\n", - "\n", - "for l in range(VERTICAL_LEVELS): # loop over all vertical levels\n", - " \n", - " cd_upwards = np.array(cross_degree[l][0]).reshape(7,17) # k_1l, 7 lat values and 17 lon values\n", - " cd_downwards = np.array(cross_degree[l][1]).reshape(7,17) # k_l1, 7 lat values and 17 lon values\n", - " \n", - " cd_upwards_zonial = cd_upwards.mean(axis=1) # average over lon with same lat\n", - " cd_downwards_zonial = cd_downwards.mean(axis=1) # average over lon with same lat\n", - " \n", - " cross_degree_upwards_zonial[l] = cd_upwards_zonial\n", - " cross_degree_downwards_zonial[l] = cd_downwards_zonial " - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "71cceb19", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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6JwgzszrcB2FmZsOFm5jMzKyA+yDMzKwqJwgzMxsmEAPupDYzsyLupDYzs2HCndRmZlZNOEGYmdlwzR2sr5c4QZiZ1eEahJmZDRMBA4NOEGZmVsBXMZmZ2TCBm5jMzKyQO6nNzKyKiE5H0BlOEGZmdbiJyczMhsmuYvJYTGZmVsBNTGZmVshNTGZmNkygUZsg2tawJmknSddIWiHpDkkfTfOnSbpa0t3p77btisnMrIwoOfWbdva8bAI+HhF7AvsDH5K0F3AKsDgidgMWp/dmZt0hIAZVauo3bUsQEbE6Im5JrzcAK4AdgcOBBWm1BcAR7YrJzKyMCJWa+k1H+iAk7QzsA9wEzIiI1ZAlEUnbdyImM7NqfBVTm0jaCrgEOCkinpTKZV1J84B5AJPYsnUBmpnljOaxmNp694ek8WTJ4cKIuDTNXiNpZlo+E1hbtG1EzI+IORExZzwT2xOwmVkAoXJTn2nnVUwCvgOsiIiv5RYtAuam13OBy9sVk5lZGRHlpn7TziamA4D3AbdLui3N+xRwOrBQ0vHAA8CRbYzJzKyO/rxCqYy2JYiIuAGqPnXjkHbFYWbWsCbWDiRNBc4BXpVKPg74FfADYGdgJfDuiFjfvL1untE5ApWZWVnR9Mtcvwn8OCL2APYmu+S/K+8HazhBSJosaWwrgjEz60pNupVa0tbAm8j6Y4mI5yLiCbr0frC6CULSGEnvkfSfktYCdwGr03AZX5G0W+vDNDPrJJWc6noF8CjwXUm3SjpH0mQq7gcDuuJ+sDI1iGuAXYFTgZdGxE4RsT1wIHAjcLqkY1oYo5lZZw2WnGC6pCW5aV5FSeOA1wBnRcQ+wNN0SXNSkTKd1IdGxMbKmRHxONk9DZek+xvMzPrP0H0Q5ayLiDk1lq8CVkXETen9xWQJYo2kmWk0iar3g7Vb3RrEUHKQNEfSZZJukbRM0u2SluXXMTPrR826DyIiHgEelLR7mnUIcCctuh9spH3GjVzmeiHw98DtDFWmzMxGg+beBPdh4EJJE4B7gfeT/Vgf8f1gksYARwHvBfYFngUmSnoUuBKYHxF3ly2vkQTxaEQsaiRYM7O+0MRhNCLiNqCoGaoZ94NdA/yUrM94eUQMQvbcHeBgsj7jyyLigjKFNZIgPifpHLJrdJ8dmpkbU8nMrC+pd4bRaGqfcSMJ4v3AHsB4XmhiCsAJwsz6Vwh6Z6iNP5L0YbJn7TwM3AxcHhEPDa3QSJ9xIwli74j4owbWNzPrD71Tg/g+cALwW7If708DH5V0NdkTPZ+ttXGlRu6kvjE9ItTMbHTpnYdSbwBuILuh+fGI+BCwF3A/ML/RwhqpQbwRmCvpPrI+CAEREa9udKdmZj2lO778y/gasBSYQNbnQEQMAF+RdGejhTWSIA5rtHAzs57X2I1yHRURF0r6CTATOFzSXGAj2SWvDd9810iCmB4RS/MzJL2DrOpiZta3eugqJiJiHbBO0mvImpcmkg0n/plGy2okQZwtaW5E3A4g6WjgJOBHje7UzKyn9EiCkKSI7J7uiFhQb516Gumk/ktggaQ9Jf0N8LfAWxvY3sysJynKTV3gGkkflvSy/ExJEyS9WdICXhjSo67SNYiIuFfSUcAPgQeBt0bEM2W3NzPrWT3SB0HWV3wccJGkXYAngC3IKgNXAV9Pd3KXUjdBSLqdF1ewpgFjgZsk4auYzKyvdc8lrHVFxO+BbwHfSndMTweeSQ8laliZGsTbN6dgM7O+0QMJQtIXyH683wbclgblWz2SMsskiAfqdWg00ulhZtZr1APjV0fEZyXNAPYB/kLSrhHxNyMps0yCuEbSJWTjeTwwNDMNVftGsg6Pa4DzRhKImVnX6pGfvxGxBvhxmkaszFVMhwEDZJ0eD0u6U9K9wN3A0WSdHuc1Ixgzs25T9gqmLrmK6XmSTh5pGXVrEM3u9DAz6zk9cBWTpIX5t8Bs4MsjKbOR+yCIiI0RsXpzkoOkcyWtlbQ8N+80SQ9Jui1Nb2u0XDOzluuNwfqejIh3p+lIsgcHjUhDCWKEzqN4PKevR8TsNF3ZxnjMzErpkSamL1W8//RIC2xbgoiI64HH27U/M7OmiOwqpjJTR8OMuG/otaQvpqfIkZs3ttEyG04Qkr4g6UuSjpS0W6PbFzhR0rLUBLVtjf3Ok7RE0pKNNPTMCzOzkemNJqa8HdN4eQBI2p7NaHJqOEFExGeBfyF7MMVfSDq70TJyzgJ2JetMWQ2cUWO/8yNiTkTMGc/EEezSzKxBvZcgTgDmSdpP0r7Az4CvNlpImaE21gFzI+I/h+Y161rbVM7Qfs4GrhhJeWZmrdAF/QulSDofuAW4FfgQ8D1gE3BERNzTaHllahAbgbMkfaAgmIsa3WHF9jNzb98JLK+2rpmZ1bWA7Hv9OLLksDOwHjhG0l82WliZO6lXA+8CfixpVkScllu2R9kdpWRyEDBd0irgc8BBkmaTVc5WklWLzMy6S4/UICJiMbB46L2kcWQPDdob2B+4uJHySg33HRErJR0A/EjSLGBeRAzSwGmLiKMLZn+n7PZmZh0Rnb9CaXNFxCZgWZr+vdHtyzQxKe3oMeDNwPbAIklbDi0zM+trvddJ3RRlEsStQy/SsBtHAA8B1wJbtyQqM7MuIXrmRrmmq5sgIuK4iveDEXEC8J/ALq0KzMysa7gG0ZiI+DxOEGbW73p0NNdmKP1M6iIRcX+zAjEz61o92kk9UiNKEGZmo0E/1g7KcIIwM6vHCcLMzIbp0w7oMtr5PAgzs57UzE5qSWMl3SrpivR+mqSrJd2d/lYd1brdnCDMzOpp7mWuHwVW5N6fAiyOiN3Ihsk4pSkxN4EThJlZHc16YFAaqujPgHNysw8nG2SP9PeIJoe/2dwHYWZWS2O1g+mSluTez4+I+bn33wA+CUzJzZsREasBImJ1erhPV3CCMDOrQTQ06Ny6iJhTWI70dmBtRCyVdFAzYms1Jwgzs3qacxXTAcCfS3obMAnYWtIFwBpJM1PtYSawtil7awL3QZiZ1dGMq5gi4tSImBUROwNHAT+LiGOARcDctNpc4PIWHkpDXIMwM6untfdBnA4slHQ88ABwZEv31gAnCDOzWlrwwKCIuJbskQlDz9o5pLl7aA4nCDOzekbpndROEGZmdXiwPjMzK+YEYWZmRVyD6CUSGje+01EMEwMDnQ6hWIzSp52YNUPgBwaZmdlwYvTWINp2o5ykcyWtlbQ8N69rh7k1M3tec0dz7RntvJP6POCwinldO8ytmdkQRZSa+k3bEkREXA88XjG7a4e5NTMDytce+i8/dLwPovQwt5LmAfMAJrFlm8IzMxu9fRCdThClpTHV5wNsPeYlo/Sfy8w6odlDbfSKTo/muiYNb0u3DXNrZvY8NzF1xNAwt6fTwDC3ktCE7rsPguc6HUCx6NLbM3x/hvWEEkN596t2XuZ6EfBzYHdJq9LQtqcDb5F0N/CW9N7MrLu4BtFaEXF0lUVdOcytmRmM7hvlOt3EZGbW9TQ4OjOEE4SZWS192nxUhhOEmVkdo/UyVycIM7N6XIMwM7Mi7qQ2M7PhAujDgfjKcIIwM6vDfRA9Jboyo2uMOh1CFWM7HUAhjenej1906WWNsWljp0MYdXwfhJmZFYvu/EHaDk4QZmZ1uAZhZmbFnCDMzKyIaxBmZjZcAAOjM0M4QZiZ1eEahJmZFfNVTGZmVsQ1CDMzG24UD/fdtkeOmpn1IgEaiFJT3bKknSRdI2mFpDskfTTNnybpakl3p7/btvq4yujRGoTQ2C4cPmLChE5HUEjqziFANGF8p0OorkvbnAfW/7bTIRTq9yFA1LzPwybg4xFxi6QpwFJJVwPHAosj4nRJpwCnACc3a6ebyzUIM7NaooGpXlERqyPilvR6A7AC2BE4HFiQVlsAHNHEI9hsPVqDMDNrl4bGYpouaUnu/fyImF+0oqSdgX2Am4AZEbEasiQiafsRBNw0ThBmZnU0cBXTuoiYU7c8aSvgEuCkiHiyW5uB3cRkZlbP0Iiu9aYSJI0nSw4XRsSlafYaSTPT8pnA2pYcR4OcIMzMaommXsUk4DvAioj4Wm7RImBuej0XuLzpx7EZuqKJSdJKYAMwAGwqU0UzM2ub5l3UdgDwPuB2SbeleZ8CTgcWSjoeeAA4sml7HIGuSBDJwRGxrtNBmJlVatZlrhFxA9mtFUUOacpOmqibEoSZWXfq0vtiWq1b+iACuErSUknzilaQNE/SEklLnovftzk8Mxu1AhgsOfWZbqlBHBARD6drf6+WdFdEXJ9fIV1LPB9gmwkzQtts3Yk4a5syudMRFBqcPLHTIRR6bmp3xgXwuxndeVf876d26eWQmzodQRX/dvGIixDRzDupe0pX1CAi4uH0dy1wGbBfZyMyM8sZHCw39ZmOJwhJk9OYJEiaDLwVWN7ZqMzMEjcxddQM4LJ0J+E44HsR8ePOhmRm9oLR2sTU8QQREfcCe3c6DjOzqpwgzMxsuIYG6+srThBmZrUEUGIYjX7kBGFmVof7IHpITBzHxl26Yrj0F3lmxqROh1Do6Zd2/GK1Qht26d7/dNP27M5RX8ZEd/5bPruxS79K/q1J5ThBmJnZMAEMOkGYmdkw7qQ2M7NqnCDMzGyYAAb68DbpEpwgzMxqCggnCDMzK+ImJjMzG8ZXMZmZWVWuQfSO57Yew4OHbtnpMIYZ3PPpTodQaPaOD3U6hEIn7rC40yFUtdv4pzodQqEHN3XnQ5Z+/dyMTodQ6K+bVZAThJmZDRMBAwOdjqIjnCDMzOpxDcLMzAo5QZiZ2XDhq5jMzKxAQPhGOTMzK+ShNnpHTBpk0+6/63QYwxyyy92dDqHQW7Zd3ukQCu094ZlOh1DV1mO26nQIhR4d6M5ztjF68quknAgYdIIwM7Mi7qQ2M7Mi4RqEmZkN5wcGdZSkw4BvAmOBcyLi9FrrbzF+I3vt8EhbYmvE7CkPdDqEQgdMWt3pEAptNab7hksZsnagO4fa+MXvd+10CIVuf3pWp0NoHQ/W1zmSxgJnAm8BVgG/kLQoIu7sbGRmZll+iFE61MaYTgcA7AfcExH3RsRzwPeBwzsck5lZJtIDg8pMJUg6TNKvJN0j6ZQWRz8i3ZAgdgQezL1flea9iKR5kpZIWvLcE915qZ+Z9acYjFJTPbkWkz8F9gKOlrRXi8PfbN2QIFQwb9iZjoj5ETEnIuZMmLpFG8IyM0uaV4PoqRaTjvdBkNUYdsq9nwU8XGuD3/7q0XVXvOlf72/S/qcD65pR0BXNKOQFTYvrQ80o5AVNi6vJujUuaGpsTb3goFvPWTPjevlIC9jA+p/8NC6eXnL1SZKW5N7Pj4j5ufdFLSavG2mMrdINCeIXwG6SdgEeAo4C3lNrg4jYrlk7l7QkIuY0q7xmcVyN6da4oHtjc1zlRMRhTSyuVItJt+h4goiITZJOBH5CdpnruRFxR4fDMjNrhYZbTDqp4wkCICKuBK7sdBxmZi3WcItJJ3VFguiw+fVX6QjH1ZhujQu6NzbH1Wa91mKiGKW3kJuZWW3dcJmrmZl1IScIMzMr1NcJQtJYSbdKuiK9P03SQ5JuS9PbqmzX0lvhC+L6QS6mlZJuq7LdSkm3p/WWFK0zwriGlS9pmqSrJd2d/m5bZduWnbMqcX1F0l2Slkm6TNLUstu2OK5u+YwVxdbxz5mkqZIuTv92KyS9vhs+Y1ZFRPTtBHwM+B5wRXp/GvCJOtuMBX4DvAKYAPwS2KuVcVUsOwP4bJXtVgLTW3i+hpUP/DNwSnp9CvDldp+zKnG9FRiXXn+5KK5Wn7MqcXXLZ6zmcXfqcwYsAD6QXk8ApnbDZ8xT8dS3NQhJs4A/A85pcNOW3gpfKy5JAt4NXNSs/TXB4WT/qUl/jyhYp+3DB0TEVRGxKb29kex68l7R0eEWOvU5k7Q18CbgOwAR8VxEPEGXfsasv5uYvgF8EqgcIOXE1CxxbpWqbKnBA1sQF8CBwJqIqPZw6wCukrRU0rwmxlSr/BkRsRog/d2+YLtWn7N6x30c8F+buW0r4ur0Z6xWbNC5z9krgEeB76Ym1nMkTaY7PmNWoC8ThKS3A2sjYmnForOAXYHZZIPanFG0ecG8plwLXCOuIUdT+1fdARHxGrKRID8k6U3NiKsJ5bd6+ICqcUn6NLAJuLDRbVsUV0c/Y3ViG9Kpz9k44DXAWRGxD/A0WZNSGT01REW/6MsEARwA/LmklWRV0TdLuiAi1kTEQEQMAmeTVVsrtfJW+MK4ACSNA94F/KDaxhHxcPq7FriM4vg3W5Xy10iamWKcCawt2LSlwwdUO25Jc4G3A++N1FBddttWxdUFn7GqsUHHP2ergFURcVN6fzFZwuj4Z8yK9WWCiIhTI2JWROxMdiv7zyLimKEPYfJOYHnB5s/fCi9pQtp+USvjSosPBe6KiFVF20qaLGnK0GuyTtqi+DdLjfIXAXPTanOByws2b9k5qxaXssfUngz8eUT8rsFjamVcHf2M1YotLe7Y5ywiHgEelLR7mnUIcCcd/oxZDZ3uJW/1BBzEC1cx/TtwO7CM7MM1M83fAbgyt83bgF+TXTXx6VbHld6fB3ywYp3n4yJrv/1lmu5odlzVygdeAiwG7k5/p7XznNWI6x6yNunb0vTtdp6zGnF1/DNW67i74HM2G1iSzs8PgW07/RnzVH3yUBtmZlaoL5uYzMxs5JwgzMyskBOEmZkVcoIwM7NCThBmZlbICcLMzAo5QZiZWSEnCNtskp4qud5USX9bMe9/qy0rWeZpkj6xGdsdI+mW9EyBOQXLt5B0naSx6f1Raf2TcuuEpDNy7z8h6bT0eoKk69OQFmY9zQnC2mEq8KIkEBFvqLasVSRtRzY8xxuAE4ATC1Y7Drg0IgbS+6OAfYH9JW2V5j0LvEvS9MqNIxuKejHwV00O36ztnCCsqST9MA0TfUduqOjTgV2VPaHsK2m9p4qWSdpZ0vJceflf559W9kSxnwK7k5NqBjencv5tqAZQYSYwkezhM+spfobEe3nxWEBDo4hG7vUmYD7wd1VOww9TOWY9zdVga7bjIuJxSVsAv5B0CdmQzq+KiNkF679omaSdiwqV9FqyX/P7kH1ubwGWpmV7kv1iPyAiNkr6FtkX9Pm57Y8lGw30EuAGYBpwXcU+JgCviIiVudmXko0ddEFEbMjNPxNYJumfC8JdTlbrMOtpThDWbB+R9M70eidgN+CRJpR7IHBZpJFbJeVH8jwEeC1ZQgLYguFDRu8FrIiI7wKnpiRyZ8U604En8jMiYgEvPO0sP/9JSecDHwGeqVg2IOk5SVMqkopZT3GCsKaRdBDZcNKvj4jfSboWmNRgMZt4cdNnfvtqI0sKWBARp9YodyKwMcU5NsX5TxXrPENj8X6DrCbz3Sr7+30DZZl1HfdBWDNtA6xPyWEPYP80fwMwpco2lcvWANtLeomkiWQPBAK4HnhnuspoCvCO3DaLgb+UtD2ApGmSXl6xn1/xQrPPx4CfRkT+EZZExHpgrKRSSSIiHgcWAsfn50t6CfBoRGwsU45Zt3KCsJHYUtKqoQnYAxgnaRnwj8CNABHxGPA/kpYPdVIPqVyWvlS/ANwEXAHclda7hewpaLeR9SP8d66MO4HPkD1HeRlwNVmHdN75wGxJdwGvBj5e5ZiuAt7YwDk4g6xpKu9g4MoGyjDrSn4ehFmOpH2Aj0XE+0ZQxqXAqRHxq+ZFZtZ+rkGY5UTErcA1VS6TrStdCfVDJwfrB65BmJlZIdcgzMyskBOEmZkVcoIwM7NCThBmZlbICcLMzAo5QZiZWaH/A1BhMkm2dFW5AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# heat plot for cross degree pointing upwards\n", - "\n", - "lat = np.unique(coupled_network.grid_1.lat_sequence())\n", - "hgt = hgt_averaged\n", - "X,Y = np.meshgrid(lat,hgt)\n", - "Z = cross_degree_upwards_zonial\n", - "\n", - "plt.pcolormesh(X,Y,Z)\n", - "plt.colorbar(label=r\"$k^{1i}(\\vartheta)$\")\n", - "plt.title('cross-degree pointing upwards')\n", - "plt.xlabel(r\"Latitude $\\vartheta$ (°N)\");\n", - "plt.ylabel(r\"$Z_i$ (km)\");\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "id": "0c33d55e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# heat plot for cross degree pointing downwards\n", - "\n", - "lat = np.unique(coupled_network.grid_1.lat_sequence())\n", - "hgt = hgt_averaged\n", - "X,Y = np.meshgrid(lat,hgt)\n", - "Z = cross_degree_downwards_zonial\n", - "\n", - "plt.pcolormesh(X,Y,Z)\n", - "plt.colorbar(label=r\"$k^{i1}(\\vartheta)$\")\n", - "plt.title('cross-degree pointing downwards')\n", - "plt.xlabel(r\"Latitude $\\vartheta$ (°N)\");\n", - "plt.ylabel(r\"$Z_i$ (km)\");\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a9035fe5", - "metadata": {}, - "source": [ - "### Cross closeness\n", - "\n", - "The method `climate.CoupledClimateNetwork.cross_closeness()` of two coupled climate subnetworks $G_i$ and $G_j$ returns the tuple $(\\mathbf{c_v^{ij}},\\mathbf{c_v^{ji}})$, with $\\mathbf{c_v^{ij}}$ being a numpy array containing the non-symmetric cross degree of the vertices." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "fbd46ed8", - "metadata": {}, - "outputs": [], - "source": [ - "# cross-closeness\n", - "\n", - "cross_closeness_upwards_zonial = np.zeros((17,7)) # 17 levels and 7 lat values\n", - "cross_closeness_downwards_zonial = np.zeros((17,7)) # 17 levels and 7 lat values\n", - "\n", - "for l in range(VERTICAL_LEVELS): # loop over all vertical levels\n", - " \n", - " cc_upwards = np.array(cross_closeness[l][0]).reshape(7,17) # c_1l, 7 lat values and 17 lon values\n", - " cc_downwards = np.array(cross_closeness[l][1]).reshape(7,17) # c_l1, 7 lat values and 17 lon values\n", - " \n", - " cc_upwards_zonial = cc_upwards.mean(axis=1) # average over lon with same lat\n", - " cc_downwards_zonial = cc_downwards.mean(axis=1)\n", - " \n", - " cross_closeness_upwards_zonial[l] = cc_upwards_zonial\n", - " cross_closeness_downwards_zonial[l] = cc_downwards_zonial" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "id": "19039e2a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# heat plot for cross closeness pointing upwards\n", - "lat = np.unique(coupled_network.grid_1.lat_sequence())\n", - "hgt = hgt_averaged\n", - "X,Y = np.meshgrid(lat,hgt)\n", - "Z = cross_closeness_upwards_zonial\n", - "\n", - "plt.pcolormesh(X,Y,Z)\n", - "plt.colorbar(label=r\"$c^{1i}(\\vartheta)$\")\n", - "plt.title('cross-closeness pointing upwards')\n", - "plt.xlabel(r\"Latitude $\\vartheta$ (°N)\");\n", - "plt.ylabel(r\"$Z_i$ (km)\");\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "id": "5d454d12", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# heat plot for cross closeness pointing downwards\n", - "lat = np.unique(coupled_network.grid_1.lat_sequence())\n", - "hgt = hgt_averaged\n", - "X,Y = np.meshgrid(lat,hgt)\n", - "Z = cross_closeness_downwards_zonial\n", - "\n", - "plt.pcolormesh(X,Y,Z)\n", - "plt.colorbar(label=r\"$c^{i1}(\\vartheta)$\")\n", - "plt.title('cross-closeness pointing downwards')\n", - "plt.xlabel(r\"Latitude $\\vartheta$ (°N)\");\n", - "plt.ylabel(r\"$Z_i$ (km)\");\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c72290fc", - "metadata": {}, - "source": [ - "### Cross-betweenness\n", - "\n", - "In contrast to the latter two local measures, cross-betweenness $b^{ij}_w$ with $w\\in V_i \\cup V_j$ is symmetric with respect\n", - "to exchanging the involved subnetworks, but assigns a value to vertices of both subnetworks. Therefore, we will in the following analyse zonally averaged fields of cross-betweenness\n", - "$$ b^{ij}_i(\\vartheta)=\\langle b^{ij}_{w(\\vartheta,\\phi)}\\rangle_{\\phi,w\\in V_i}$$\n", - "for vertices taken from a specific isobaric subnetwork $i$." - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "c2094391", - "metadata": {}, - "outputs": [], - "source": [ - "# cross betweenness\n", - "\n", - "cross_betweenness_nearground_zonial = np.zeros((17,7)) # 17 levels and 7 lat values\n", - "cross_betweenness_upperlevel_zonial = np.zeros((17,7)) # 17 levels and 7 lat values\n", - "\n", - "for l in range(VERTICAL_LEVELS): # loop over all vertical levels\n", - " \n", - " cb_nearground = np.array(cross_betweenness[l][0]).reshape(7,17) # near ground level, 7 lat values and 17 lon values\n", - " cb_upperlevel = np.array(cross_betweenness[l][1]).reshape(7,17) # upper level, 7 lat values and 17 lon values\n", - " \n", - " cb_nearground_zonial = cb_nearground.mean(axis=1) # average over lon with same lat\n", - " cb_upperlevel_zonial = cb_upperlevel.mean(axis=1) # average over lon with same lat\n", - " \n", - " cross_betweenness_nearground_zonial[l] = cb_nearground_zonial\n", - " cross_betweenness_upperlevel_zonial[l] = cb_upperlevel_zonial" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "id": "ed47746e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# heat plot for cross betweenness near ground\n", - "\n", - "lat = np.unique(coupled_network.grid_1.lat_sequence())\n", - "hgt = hgt_averaged\n", - "X,Y = np.meshgrid(lat,hgt)\n", - "Z = np.copy(cross_betweenness_nearground_zonial)\n", - "Z[Z<1] = 0\n", - "Z[Z>1] = np.log10(Z, where = np.greater(Z,np.ones(np.shape(Z))))[Z>1]\n", - "\n", - "plt.pcolormesh(X,Y,Z)\n", - "plt.colorbar(label=r\"$log_{10}(b_1^{1i})$\")\n", - "plt.title('cross-betweenness near ground')\n", - "plt.xlabel(r\"Latitude $\\vartheta$ (°N)\");\n", - "plt.ylabel(r\"$Z_i$ (km)\");\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "id": "7802ea9d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# heat plot for cross betweenness upper level component\n", - "\n", - "lat = np.unique(coupled_network.grid_1.lat_sequence())\n", - "hgt = hgt_averaged\n", - "X,Y = np.meshgrid(lat,hgt)\n", - "z = np.copy(cross_betweenness_upperlevel_zonial)\n", - "z[z<1] = 0\n", - "z[z>1] = np.log10(z, where = np.greater(z,np.ones(np.shape(z))))[z>1]\n", - "\n", - "plt.pcolormesh(X,Y,z)\n", - "plt.colorbar(label=r\"$log_{10}(b_i^{1i})$\")\n", - "plt.title('cross-betweenness upper level')\n", - "plt.xlabel(r\"Latitude $\\vartheta$ (°N)\");\n", - "plt.ylabel(r\"$Z_i$ (km)\");\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "my_env", - "language": "python", - "name": "my_env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/tutorial_EventSynchronisation_EventCoincidence.ipynb b/notebooks/tutorial_EventSynchronisation_EventCoincidence.ipynb deleted file mode 100644 index 43be803b..00000000 --- a/notebooks/tutorial_EventSynchronisation_EventCoincidence.ipynb +++ /dev/null @@ -1,640 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "12877375", - "metadata": {}, - "source": [ - "# Tutorial for performing event synchronization (ES) andevent coincidence analysis (ECA) as part of event series analysis" - ] - }, - { - "cell_type": "markdown", - "id": "efc240da", - "metadata": {}, - "source": [ - "Written as part of a PhD thesis in Physics by Jonathan F. Donges (donges@pik-potsdam.de) at the Potsdam Institute of Climate Impact Research (PIK) and Humboldt University Berlin,\n", - "\n", - "Copyright 2008-2019." - ] - }, - { - "cell_type": "markdown", - "id": "f2f2eb59", - "metadata": {}, - "source": [ - "Synchronisation measures of time series have been attracting attention in several research areas, including climatology or neuroscience.\n", - "\n", - "Synchronisation can be understood as a measure of interdependence or strong correlation between time series. \n", - "The main use cases of synchrinisation are:\n", - "- Quantification of similarities in phase space between two time series\n", - "- Quantification of differences in phase space between two time series\n", - "\n", - "A research example of synchronisation phenomena is the analysis electroencephalographic (EEG) signals as a major infleuncing factor to understand the communication within the brain. See [Quiroga et al., 2001](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.65.041903)\n", - "\n", - "Two widely accepted measurement methods of synchronisation are **Event Synchronisation (ES)** and **Event Coincidence Analysis (ECA)**. The non-linear nature of these two methods makes them widely applicable for a wide range of utilizations. \n", - "While ES does not include the difference in timescales when measuring synchrony, when using ECA a certain timescale has to be selected for analysis purposes.\n", - "\n", - "For more background information consult [Odenweller et al., 2020](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.052213) or [Quiroga et al., 2001](https://www.researchgate.net/publication/11025829_Event_Synchronization_A_simple_and_fast_method_to_measure_synchronicity_and_time_delay_patterns)." - ] - }, - { - "cell_type": "markdown", - "id": "5424b801", - "metadata": {}, - "source": [ - "### Event Synchronisation (ES)" - ] - }, - { - "cell_type": "markdown", - "id": "a0721685", - "metadata": {}, - "source": [ - "As mentioned before, the paramter free method ES offers a fast and reliable method to measure synchronizations between time series.\n", - "The fundamental idea of the method is illustrated by the picture below (from [Odenweller et al., 2020](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.052213)):\n", - "\n", - "![Event Synchronisation](img/EventSynchronisation.png)\n", - "\n", - "Two events *l* and *m*, from timeseries *i* and *j* are considered synchroneous if they occur within a certain time interal $\\tau$ retrieved from the data properties. The time interval $\\tau$ is defined as:\n", - "\n", - "![](https://latex.codecogs.com/svg.image?\\tau_{lm}^{ij}&space;=&space;\\frac{1}{2}min[{&space;t_{l+1}^{i}&space;-&space;t_{l}^{i},&space;t_{l}^{i}&space;-&space;t_{l-1}^{i},&space;t_{m+1}^{j}&space;-&space;t_{m}^{j},&space;t_{m}^{j}&space;-&space;t_{m-1}^{j}&space;])\n", - "\n", - "From here the occurences of synchronised events in timeseries *i* when given an event in *j* gives:\n", - "\n", - "![](https://latex.codecogs.com/svg.image?c(i|j|)&space;=&space;\\sum_{l=2}^{s_i&space;-&space;1}\\sum_{m=2}^{s_j-1}J_{lm}^{im})\n", - "\n", - "whereby $J_{lm}^{im}$ counts the events that match the synchronization condition. For more detail on this, see [Odenweller et al., 2020](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.052213).\n", - "\n", - "Finally, we can define the strength of event synchronisation between the timeseries *i* and *j* by:\n", - "\n", - "![](https://latex.codecogs.com/svg.image?Q_{ij}^{ES}&space;=&space;\\frac{c(i|j|)&space;+&space;c(j|i)}{\\sqrt{(s_i&space;-2)(s_j&space;-2)}})\n", - "\n", - "In the usual case for which the timeseries are not fully synchronized, $0 <= Q_{ij}^{ES} <= 1$. For total or abscent synchronisation $Q_{ij}^{ES} = 1$ or $Q_{ij}^{ES} = 0$, respectively. \n", - "\n", - "To generate a network from a set of timeseries, we can consider the the $Q_{ij}^{ES}$ values, as the coefficients of a square symmetric matrix $Q^{ES}$, from which an unidrected network from multivariate data can be constructed. \n", - "It is to be noted that fully synchronized time series will adapt a value of $Q_{ii}^{ES} = Q_{jj}^{ES} = 1$. \n", - "\n", - "The advatage of ES is that no parameters, specially a delay specification of the two timeseries, has to selected a priori as the algorithm classifies two events as snychronized automatically. " - ] - }, - { - "cell_type": "markdown", - "id": "1efab43e", - "metadata": {}, - "source": [ - "### Event coincidence analysis (ECA)" - ] - }, - { - "cell_type": "markdown", - "id": "fae1001e", - "metadata": {}, - "source": [ - "In contrary to ES, ECA is characterized by the incorporation of a *static (global) coincidence intervals*. Hereby a tolerance interval $\\Delta$T is defined so that for events in *j* at $t_{m}^{i}$ preceding events in *i* at $t_{l}^{i}$ obey to $0 <= t_{l}^{i} - t_{m}^{j} <= $ $\\Delta$T. The fundamental idea of the method is illustrated by the picture below (from [Odenweller et al., 2020](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.052213)):\n", - "\n", - "![ECA](img/ECA.png)\n", - "\n", - "It is to be noted, When determining the coincidence rate while performing ECA, the differentiation between *precursor* and *trigger* event coincidence rates applies. The former refers to the number of events in *i* that precede the events in *j*. The trigger rates, on the other hand, quantify the events in *j* that precede at least one event in *i*. \n", - "\n", - "Thus, the definition of the precursor event coincidence rate is defined as:\n", - "\n", - "![](https://latex.codecogs.com/svg.image?r_p(i|j;&space;\\Delta&space;T,&space;\\tau)&space;=&space;\\frac{1}{s_i&space;-&space;s_{i}^{'}}&space;\\sum_{l=1+s_{i}^{'}}^{s_i}\\Theta&space;[{&space;\\sum_{m=1}^{s_j}1_{[0,\\Delta&space;T]\\left&space;[&space;(t_{l}^{i}&space;-&space;\\tau)&space;-t_m^j&space;\\right&space;]}]})\n", - "\n", - "The trigger event cincidence rate on the other hand is defined as:\n", - "\n", - "![](https://latex.codecogs.com/svg.image?r_p(i|j;&space;\\Delta&space;T,&space;\\tau)&space;=&space;\\frac{1}{s_i&space;-&space;s_{i}^{''}}\\sum_{m=1}^{s_j&space;-&space;s_j^{''}}\\Theta[\\sum_{l=1}^{s_i}1__{[0,&space;\\Delta&space;T]}((t_l^i&space;-&space;\\tau)&space;-&space;t_m^j)])\n", - "\n", - "For detailed information on the calculation of e.g. $s_i^{''}$ or $s_j^{''}$, consult [Odenweller et al., 2020](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.052213)). \n", - "\n", - "By changing the indices of the precursor and trigger rate, one gets the rate in the other directions e.g. for $r_t(i|j; \\Delta T, \\tau)$.\n", - "\n", - "Subsequently the ECA analysis gives out four coincidence rates: the precursor and trigger rates evaluated in a bi-directional manner. \n", - "\n", - "To construct the event matrix composed by a single association measure, one can take the mean or the maximum value of the two tigger coincidence rates $r_p(i|j; \\Delta T,\\tau)$ and $r_p(j|i;\\Delta;T,\\tau)$ to compute the *degree of event synchrony* $Q_{ij}^{ECA, mean}$ or $Q_{ij}^{ECA, max}$." - ] - }, - { - "cell_type": "markdown", - "id": "8a82f258", - "metadata": {}, - "source": [ - "## ES and ECA analysis of simple random event series" - ] - }, - { - "cell_type": "markdown", - "id": "4dbe6d18", - "metadata": {}, - "source": [ - "`pyunicorn` provides a class for event synchronization (ES) and\n", - "event coincidence analysis (ECA). In addition, a method for the generation of\n", - "binary event series from continuous time series data (method `make_event_matrix`, is included." - ] - }, - { - "cell_type": "markdown", - "id": "fe16ce2c", - "metadata": {}, - "source": [ - "First we import all necessary packages" - ] - }, - { - "cell_type": "code", - "execution_count": 185, - "id": "82ab6709", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pylab\n", - "import math\n", - "import random\n", - "import matplotlib.pyplot as plt\n", - "from pyunicorn.timeseries import RecurrenceNetwork\n", - "from pyunicorn.timeseries import RecurrencePlot\n", - "from pyunicorn.core import Network\n", - "from pyunicorn.eventseries import EventSeries as EV" - ] - }, - { - "cell_type": "markdown", - "id": "7f2e09e3", - "metadata": {}, - "source": [ - "Next we create a toy data example.\n", - "The data structure has to follow an event matrix style, whereby the first axis represents the timesteps and the second axis covers the variables. Each variable at a specific timestep is either '1' if an event occured or '0' if it did not, e.g. for 3 variables with 10 timesteps the\n", - "eventmatrix could look like:" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "id": "a2c1ca5e", - "metadata": {}, - "outputs": [], - "source": [ - "series = np.array([[0, 1, 0],\n", - " [0, 0, 0],\n", - " [0, 0, 0],\n", - " [1, 0, 1],\n", - " [0, 1, 0],\n", - " [0, 0, 0],\n", - " [0, 0, 0],\n", - " [0, 0, 0],\n", - " [0, 1, 0],\n", - " [0, 0, 0]])" - ] - }, - { - "cell_type": "markdown", - "id": "f1991e38", - "metadata": {}, - "source": [ - "Now we apply the class `EventSeries` to our data to initialize the process of synchronization analyis." - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "id": "eeddb65f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The initialized Event Series of the examples is EventSeries: 3 variables, 10 timesteps, taumax: 1.0, lag: 0.0\n" - ] - } - ], - "source": [ - "ev = EV(series, taumax=1)\n", - "print(f\"The initialized Event Series of the examples is\", ev)" - ] - }, - { - "cell_type": "markdown", - "id": "82290301", - "metadata": {}, - "source": [ - "From here, the initialized data variables can be analyzed in terms of synchrony by applying the methods of `pyunicorn` to calculate ES and ECA" - ] - }, - { - "cell_type": "markdown", - "id": "c350db9f", - "metadata": {}, - "source": [ - "Note: the argument `tau_max` which is defined automatically as infinite when not specified when creating the EventSeries, represents the maximum time difference between two events to be considered synchronous. Caution: For ES, the default is np.inf because of the intrinsic dynamic coincidence interval in ES. For ECA, taumax is a parameter of the method that needs to be defined!" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "id": "9295b844", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.42857142857142855, 0.42857142857142855)\n", - "(1.0, 1.0, 1.0, 1.0)\n" - ] - } - ], - "source": [ - "#Check for ES and ECA synchronization between two variables x and y of the generated dataset\n", - "\n", - "print(ev.event_synchronization(series[:,0], series[:, 2]))\n", - "print(ev.event_coincidence_analysis(series[:,0], series[:, 2], taumax=1)) #Taumax has to be selected when calculating ECA; taumax=1 was selected for simplicity" - ] - }, - { - "cell_type": "markdown", - "id": "5c68ceac", - "metadata": {}, - "source": [ - "- The return of the ES method is [Event synchronization XY, Event synchronization YX]\n", - "- The return of the ECA methos is[ Precursor coincidence rate XY, Trigger coincidence rate XY, Precursor coincidence rate YX, Trigger coincidence rate YX]" - ] - }, - { - "cell_type": "markdown", - "id": "80260d7e", - "metadata": {}, - "source": [ - "If input data is not provided as an eventmatrix, the constructor tries to generate one from continuous time series data using the `make_event_matrix` method. Hereby the argument `threshold_method` needs to be specified along with the argument `threshold_values`. `threshold_method` can be 'quantile', 'value' or 1D numpy array with entries 'quantile' or 'value' for each variable. If 'value' is selected one has to specify a number lying in the range of the array; for quantile a number between 0 and 1 has to be selected as it specifies the percentage of the array's values which should be included in the event matrix. Additionally one can specify by the argument `threshold_type` if the threshold should be applied 'above' or 'below' the specified `threshold_method`. A simple example of finding the synchrony between variables x and y of a dataset reflecting continuous time series, would be:" - ] - }, - { - "cell_type": "code", - "execution_count": 178, - "id": "56cec935", - "metadata": {}, - "outputs": [], - "source": [ - "series = 10*np.random.rand(10,3)\n", - "series = series.astype(int)\n", - "\n", - "#Initialize dataset as EventSeries \n", - "ev = EV(series[:, :2], threshold_method = 'quantile', threshold_values = 0.5, taumax=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "id": "08e046a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.42857142857142855, 0.42857142857142855)\n", - "(1.0, 1.0, 1.0, 1.0)\n" - ] - } - ], - "source": [ - "#Applying ES and ECA to find synchronity between variables x and y\n", - "print(ev.event_synchronization(series[:,0], series[:, 1]))\n", - "print(ev.event_coincidence_analysis(series[:,0], series[:, 1], taumax=1))" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "id": "f6caefe2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.0, 1.0)" - ] - }, - "execution_count": 180, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#When one just wants to get the precursorevent coincidence rates; _eca_coincidence_rate method can be used\n", - "ev._eca_coincidence_rate(series[:, 0], series[:, 1], window_type = 'advanced')" - ] - }, - { - "cell_type": "markdown", - "id": "cd2a4196", - "metadata": {}, - "source": [ - "To get the matrix to construct the functional unidirectional network described in the Introduction to ES and ECA at the beginning of the tutorial, the method `event_series_analysis` can be used. Hereby the method to anylize synchrony has to be specified by the argument `method` as *ES* or *ECA*.\n", - "\n", - "For detailed information on the calculation of the matrix and the required arguments when selecting *ES* or *ECA*, consult [pyunicorn Github](https://github.com/pik-copan/pyunicorn/blob/master/src/pyunicorn/eventseries/event_series.py)/from line 704.\n", - "\n", - "The return is a NxN matrix of the chosen event series where N is the number of variables. " - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "id": "0f5cf4b9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Matrix of Es strenghts is [[0. 0.16666667]\n", - " [0.16666667 0. ]]\n", - "Matrix of ECA strengths is [[0. 0.75]\n", - " [0.75 0. ]]\n" - ] - } - ], - "source": [ - "matrix_ES = ev.event_series_analysis(method='ES')\n", - "print(f\"Matrix of Es strenghts is\", matrix_ES)\n", - "\n", - "matrix_ECA = ev.event_series_analysis(method='ECA', symmetrization = 'mean', window_type = 'advanced')\n", - "print(f\"Matrix of ECA strengths is\", matrix_ECA) #Symmetrization was selected according to Odenweller et al., 2020" - ] - }, - { - "cell_type": "markdown", - "id": "3cf206ad", - "metadata": {}, - "source": [ - "Approaches of complex network theory can be now applied to the matrices calculated, as part of non-linear timeseries analysis. See jupyter notebook `tutorial_recurrenceNetwork`." - ] - }, - { - "cell_type": "markdown", - "id": "6376d465", - "metadata": {}, - "source": [ - "### Significance level calculations" - ] - }, - { - "cell_type": "markdown", - "id": "d28b190a", - "metadata": {}, - "source": [ - "The signifcance levels of event synchronisation can be calculated using pyunicorn too. The package provides to methods `_empirical_percentiles` and `event_analysis_significance`, to either calcuate the p-values through a Monte Carlo approach or the signifcance levels ( 1 - p-values) through a Poisson process from an event matrix, repectively.\n", - "\n", - "For detailed information on the calculation of the significance levels consult [pyunicorn Github](https://github.com/pik-copan/pyunicorn/blob/master/src/pyunicorn/eventseries/event_series.py)/from line 830." - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "id": "0bf34e66", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0. 0.13]\n", - " [0.12 0. ]]\n", - "[[0. 0.388]\n", - " [0.388 0. ]]\n" - ] - } - ], - "source": [ - "MonteCarlo_ES = ev._empirical_percentiles(method='ES', n_surr=1000)\n", - "print(MonteCarlo_ES)\n", - "MonteCarlo_ECA = ev._empirical_percentiles(method='ECA', n_surr=1000, symmetrization = 'mean', window_type = 'advanced')\n", - "print(MonteCarlo_ECA)" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "id": "726c572a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0. 0.128]\n", - " [0.115 0. ]]\n", - "[[0. 0.364]\n", - " [0.364 0. ]]\n" - ] - } - ], - "source": [ - "Poisson_ES = ev.event_analysis_significance(method='ES', n_surr=1000)\n", - "print(Poisson_ES)\n", - "Poisson_ECA = ev.event_analysis_significance(method='ECA', n_surr=1000, symmetrization = 'mean', window_type = 'advanced')\n", - "print(Poisson_ECA)" - ] - }, - { - "cell_type": "markdown", - "id": "cb3c47de", - "metadata": {}, - "source": [ - "## ES and ECA analysis to generate a climate network" - ] - }, - { - "cell_type": "markdown", - "id": "8671e524", - "metadata": {}, - "source": [ - "Further application of the ES and ECA analysis is the possible generation of a climate network using the resulting ES and ECA matrices from the calculations shown above. This can be done by using the pyunicorn Class `EventSeriesClimateNetwork`. An example will be shown next. " - ] - }, - { - "cell_type": "markdown", - "id": "4befd006", - "metadata": {}, - "source": [ - "Note: If other applications of event series networks are desired, use the `EventSeries` Class together with `Network` Class" - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "id": "ea03f3ea", - "metadata": {}, - "outputs": [], - "source": [ - "#import additonal packages to generate Climate Networks \n", - "from pyunicorn.climate.eventseries_climatenetwork import EventSeriesClimateNetwork\n", - "from pyunicorn.climate.climate_network import ClimateNetwork \n", - "from pyunicorn.climate.climate_data import ClimateData\n", - "from pyunicorn.core import Data #Encapsulates general spatio-temporal data" - ] - }, - { - "cell_type": "markdown", - "id": "c33df5f1", - "metadata": {}, - "source": [ - "We shall use the small test climate dataset provided by the Class `EventSeriesClimateNetwork`. See [pyunicorn Github repository describing Class EventSeriesClimateNetwork](https://github.com/pik-copan/pyunicorn/blob/master/src/pyunicorn/climate/eventseries_climatenetwork.py) for more specifications." - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "id": "9b8c2e94", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Information of dataset generated Data: 6 grid points, 60 measurements.\n", - "Geographical boundaries:\n", - " time lat lon\n", - " min 0.0 0.00 2.50\n", - " max 9.0 25.00 15.00\n", - "Observables of dataset generated [[ 0.00000000e+00 1.00000000e+00 1.22464680e-16 -1.00000000e+00\n", - " -2.44929360e-16 1.00000000e+00]\n", - " [ 3.09016994e-01 9.51056516e-01 -3.09016994e-01 -9.51056516e-01\n", - " 3.09016994e-01 9.51056516e-01]\n", - " [ 5.87785252e-01 8.09016994e-01 -5.87785252e-01 -8.09016994e-01\n", - " 5.87785252e-01 8.09016994e-01]\n", - " [ 8.09016994e-01 5.87785252e-01 -8.09016994e-01 -5.87785252e-01\n", - " 8.09016994e-01 5.87785252e-01]\n", - " [ 9.51056516e-01 3.09016994e-01 -9.51056516e-01 -3.09016994e-01\n", - " 9.51056516e-01 3.09016994e-01]\n", - " [ 1.00000000e+00 1.22464680e-16 -1.00000000e+00 -2.44929360e-16\n", - " 1.00000000e+00 3.67394040e-16]\n", - " [ 9.51056516e-01 -3.09016994e-01 -9.51056516e-01 3.09016994e-01\n", - " 9.51056516e-01 -3.09016994e-01]\n", - " [ 8.09016994e-01 -5.87785252e-01 -8.09016994e-01 5.87785252e-01\n", - " 8.09016994e-01 -5.87785252e-01]\n", - " [ 5.87785252e-01 -8.09016994e-01 -5.87785252e-01 8.09016994e-01\n", - " 5.87785252e-01 -8.09016994e-01]\n", - " [ 3.09016994e-01 -9.51056516e-01 -3.09016994e-01 9.51056516e-01\n", - " 3.09016994e-01 -9.51056516e-01]]\n" - ] - } - ], - "source": [ - "#Generate Test dataset and returns ClimateData instance for testing purposes\n", - "data = Data.SmallTestData() #Return test data set of 6 time series with 10 sampling points each\n", - "print(f\"Information of dataset generated\", data)\n", - "\n", - "#To actually print out the \"observables\" of the toy climate dataset\n", - "print(f\"Observables of dataset generated\", data.observable())" - ] - }, - { - "cell_type": "markdown", - "id": "1e90b671", - "metadata": {}, - "source": [ - "The Class `ClimateData` is applied by definition on the Test dataset for generating and analyzing complex climate networks. See [Github repository describing Class ClimateData](https://github.com/pik-copan/pyunicorn/blob/1e0eb4d8e6e4ec07254d425975b154ee1519dfe1/src/pyunicorn/climate/climate_data.py#L36). \\\n", - "**Note**: `ClimateData` Class shall be applied to a newly generated dataset of observables if not `SmallTestData()`" - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "id": "e591bcf2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "The output of event series analysis is EventSeriesClimateNetwork:\n", - "EventSeries: 6 variables, 10 timesteps, taumax: 16.0, lag: 0.0\n", - "ClimateNetwork:\n", - "GeoNetwork:\n", - "SpatialNetwork:\n", - "Network: directed, 6 nodes, 0 links, link density 0.000.\n", - "Geographical boundaries:\n", - " time lat lon\n", - " min 0.0 0.00 2.50\n", - " max 9.0 25.00 15.00\n", - "Threshold: 0\n", - "Local connections filtered out: False\n", - "Type of event series measure to construct the network: directedES\n" - ] - } - ], - "source": [ - "#Apply the EventSeriesClimateNetwork to the toy climate data generated\n", - "climate_ES = EventSeriesClimateNetwork(data, taumax=16.0,\n", - " threshold_method='quantile', threshold_values=0.8,\n", - " threshold_types='above') #'ES' method is default\n", - "\n", - "print(f\"The output of event series analysis is\", climate_ES)" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "id": "bbb03017", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting network adjacency matrix by thresholding...\n", - "Setting area weights according to type surface ...\n", - "Setting area weights according to type surface ...\n", - "The output of event series analysis is EventSeriesClimateNetwork:\n", - "EventSeries: 6 variables, 10 timesteps, taumax: 16.0, lag: 0.0\n", - "ClimateNetwork:\n", - "GeoNetwork:\n", - "SpatialNetwork:\n", - "Network: directed, 6 nodes, 0 links, link density 0.000.\n", - "Geographical boundaries:\n", - " time lat lon\n", - " min 0.0 0.00 2.50\n", - " max 9.0 25.00 15.00\n", - "Threshold: 0\n", - "Local connections filtered out: False\n", - "Type of event series measure to construct the network: directedECA\n" - ] - } - ], - "source": [ - "#Try out for ECA analysis\n", - "climate_ECA = EventSeriesClimateNetwork(data, method = 'ECA', taumax=16.0,\n", - " threshold_method='quantile', threshold_values=0.8,\n", - " threshold_types='above') #'ES' method is default\n", - "\n", - "print(f\"The output of event series analysis is\", climate_ECA)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/tutorial_VisibilityGraphs.ipynb b/notebooks/tutorial_VisibilityGraphs.ipynb deleted file mode 100644 index e682f60e..00000000 --- a/notebooks/tutorial_VisibilityGraphs.ipynb +++ /dev/null @@ -1,768 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6154b988", - "metadata": {}, - "source": [ - "# Tutorial: Network-Based Time Series Analysis via Visibility Graphs" - ] - }, - { - "cell_type": "markdown", - "id": "86362e1e", - "metadata": {}, - "source": [ - "The objective of this tutorial is to introduce the network-based method of visbility graphs for nonlinear time series analysis and explain and illustrate their application with the __pyunicorn__ package. First some theoretical background for understanding visibility graphs will be given and then some methods provided by `pyunicorn.timeseries.VisibilityGraph` illustrated. For a detailed discussion and further references, please consult __[Donges et al., 2015](https://aip.scitation.org/doi/10.1063/1.4934554)__, on which this tutorial is based. " - ] - }, - { - "cell_type": "markdown", - "id": "f5ecd3c6", - "metadata": {}, - "source": [ - "## Introduction" - ] - }, - { - "cell_type": "markdown", - "id": "82630840", - "metadata": {}, - "source": [ - "_Visibility Graphs (VG)_ encode visibility relations between data points in the one-dimensional time domain, by drawing upon analogies between height profiles in physical space and the profile of a time series graph. VGs are based on the existence or non-existence of lines of sight between well-defined objects \\[__[Donges et al., 2015](https://aip.scitation.org/doi/10.1063/1.4934554)__\\].\n", - "\n", - "They can be applied to investigate purely temporal features such as long-range correlations \\[__[Lacasa et al., 2009](https://iopscience.iop.org/article/10.1209/0295-5075/86/30001)__\\] or time reversal asymmetry \\[__[Donges et al., 2013](https://iopscience.iop.org/article/10.1209/0295-5075/102/10004)__\\]." - ] - }, - { - "cell_type": "markdown", - "id": "e02236af", - "metadata": {}, - "source": [ - "## Theory of Time Series Visibility Graphs" - ] - }, - { - "cell_type": "markdown", - "id": "7083e755", - "metadata": {}, - "source": [ - "### Standard Visibility Graph" - ] - }, - { - "cell_type": "markdown", - "id": "4d45f2a5", - "metadata": {}, - "source": [ - "In a time series context, well-defined objects between which lines of sight can be established are the sampling points of a (univariate) time series graph. These sampling points are uniquely characterised by pairs $(t_v, x_v)$ with $x_v = x(t_v)$. From a practical perspective, we can identify each node $v$ of a standard visibility graph with a given time point $t_v$. For $t_v < t_p$ (and, hence, $v < p$) a link between the nodes $v$ and $p$ exists iff\n", - "\n", - "$$x_q < x_v + \\frac{x_p-x_v}{t_p-t_v}(t_q - t_v)\\;\\forall\\; v < q < p$$.\n", - "\n", - "Put differently, the topological properties of VGs take into account the time-ordering of observations explicitly and are thus closely related to the roughness of the underlying time series profile. An illustration of a timeseries with respective visibility relations (grey lines) can be seen in the figure below, taken from __[Donner and Donges, 2012](https://link.springer.com/article/10.2478/s11600-012-0032-x)__, that illustrates a timeseries . The VG of a time series stays invariant under arbitrary affine transformations.\n" - ] - }, - { - "cell_type": "markdown", - "id": "dbec9eef", - "metadata": {}, - "source": [ - "![visibilitygraph](images/SimpleVG.PNG)" - ] - }, - { - "cell_type": "markdown", - "id": "a0c4fc91", - "metadata": {}, - "source": [ - "### Horizontal Visibility Graph" - ] - }, - { - "cell_type": "markdown", - "id": "1b60e2a4", - "metadata": {}, - "source": [ - "A notable algorithmic variant are _horizontal visibility graphs (HVGs)_ that facilitate analytical investigations of graph profiles \\[__[Luque et al., 2009](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.80.046103)__\\]. For HVGs the more restrictive condition\n", - "\n", - "$$x_q<\\text{min}\\{x_v,x_p\\}\\;\\forall\\;vv}A_{vp}\\text{,}$$ with $k_v^r$ and $k_v^a$ being the time-retarded and the time-advanced degree respectively.\n", - "\n", - "\n", - "- In a similar manner, the local clustering coefficient $C_v$, which characterises the likelihood that the neighbours of $v$ are also connected, also can be expressed in terms of past and future contributions: $$C_v^r = \\left( _{2}^{k_v^r} \\right)^{-1} \\sum_{p,q\\in V:p,qv}A_{vp}A_{pq}A_{qv}\\text{,}$$ with $C_v^r$ and $C_v^a$ being the time-retarded and the time-advanced local clustering coefficient." - ] - }, - { - "cell_type": "markdown", - "id": "71ebb8e0", - "metadata": {}, - "source": [ - "#### Irreversibility-Test via Kolmogorov-Smirnov (KS) " - ] - }, - { - "cell_type": "markdown", - "id": "d82fb93b", - "metadata": {}, - "source": [ - "Looking at the frequency distributions $p(k_v^r)$, $p(k_v^a)$, $p(C_v^r)$ and $p(C_v^a)$, it is expected that the retarded and advanced sequences for one vertex characteristic, e.g. $\\{k_v^r\\}$ and $\\{k_v^a\\}$ should be drawn from the same probability distributions, $p(k_v^r)$ and $p(k_v^a)$, in case of time reversibility. Then, rejecting the null hypothesis that $\\{k_v^r\\}$ and $\\{k_v^a\\}$ ($\\{C_v^r\\}$ and $\\{C_v^a\\}$) are drawn from the same probability distribution, respectively, is equivalent to rejecting the null hypothesis that the TS is reversible. For sufficiently long time series the samples of individual vertex properties approximate the underlying distributions sufficiently well and the Kolmogorov-Smirnov (KS) test can be used to check this hypothesis. Specifically, a small $p$-value of the KS test static (e.g., $p<0.05$) implies that the time series has likely been generated by an irreversible process.\n", - "\n", - "For illustration purposes, the distributions of time-directed vertex properties of a linear and a non-linear timeseries will be analysed in the following section and evaluated." - ] - }, - { - "cell_type": "markdown", - "id": "41f104c7", - "metadata": {}, - "source": [ - "## Application of Visibility Graphs" - ] - }, - { - "cell_type": "markdown", - "id": "0ddeee77", - "metadata": {}, - "source": [ - "As an example for the application of VGs and HVGs and the class `VisibilityGraph` a linear and a non-linear time series are visually tested and the KS test is applied for irreversibility with the distributions of time-directed vertex properties, after going through some basic concepts." - ] - }, - { - "cell_type": "markdown", - "id": "90a819f1", - "metadata": {}, - "source": [ - "### First Steps" - ] - }, - { - "cell_type": "markdown", - "id": "185accd3", - "metadata": {}, - "source": [ - "Import all needed packages." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "745b1e50", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import math\n", - "from matplotlib import pyplot as plt\n", - "from pyunicorn.timeseries import VisibilityGraph" - ] - }, - { - "cell_type": "markdown", - "id": "757844b5", - "metadata": {}, - "source": [ - "Then we create and plot linear a time series (TS) based on a linear-stochastic first-order autoregressive (AR(1)) with an additive gaußian noise term $\\xi$:\n", - "$$x_t = \\alpha x_{t-1} + \\xi_t \\text{,}$$ \n", - "where $\\alpha = 0.5$ \n", - "For more background on the construction of time series, consult __[Shumway and Stoffer, 2017](https://link.springer.com/book/10.1007/978-3-319-52452-8)__. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1a5e5042", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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1r3/Fm2++icWLF/v3uU+fPvjll19Ef1M2u0VZ0LZq1SpUqlQJAMoNjrxs2TJER0ejV69eYffJLq+88gp69uyJSy+9FA888ADq16+PU6dOYefOnfjxxx8xd+5cyd/OmzcPY8aMwbBhw9C9e3dUq1YNR44cwRdffIGZM2fijjvuQHp6OgDg6aefxpw5c9C9e3c89NBDuOSSS5Cfn489e/bg559/xttvv+1f1oz0X3nllWjZsiU6duyIGjVqYO/evXj55ZdRr149yR63RI5hd+8Nokgh1ysWgPDee+8JgiAIf/nLX4TExER/z78y//3vfwUAwn/+8x//Zx988IFwySWXCPHx8UKDBg2EyZMnC1OnTi3XG7JevXrCFVdcUS5NAISRI0cGfVbWm/CFF17wf3bnnXcKFStWFDZs2CD06dNHSEhIEKpWrSo88MADwunTp4N+L9Yr9rnnnhPq168vxMfHC82aNRPee+89f+/TcOkJNXHiRAGAsGzZMtnlBEEQ1q9fLwAQxo4dK7nM1q1bBQDC6NGjBUGQHqBYEATh8OHDQqVKlYJ64v72228CAGHFihXllpc736EuvfRS4corrwy7T4G09Ipt0aJFuWXVXh933XWXUKdOHSE2NlaoUaOG0L17d2HSpEmyad2/f7/wxBNPCD169BBSU1OFmJgYISkpSejSpYvw2muvlevxffToUeGhhx4SMjMzhdjYWKFq1apChw4dhMcff9x/zYldq4HphEhPZCXpf/HFF4Xu3bsL1atXF+Li4oS6desKd999t7Bnzx7ZfSRyAp8gGDQaJhF51vDhw/H111/j9OnTdicFHTt2hM/nw8qVK+1Oil/r1q3Ro0cPvPXWW5p+v2vXLjRu3BizZs0KGqeQiEgt9oolIsfLy8vDkiVL8Nhjj2H16tX+nr9O8fzzz+PDDz/EgQMHNP1+0qRJ6NevH4M6ItKNbeyIyPHWrFmDvn37olq1apgwYQKuueYau5MUZNCgQXjhhReQlZWluu1XUVERGjZsiPHjx5uUOiKKJKyKJSIiIvIIVsUSEREReQQDOyIiIiKPYGBHRERE5BER1XmipKQEhw4dQlJSkqIpaIiIiIjsJggCTp06hbS0NERFyZfJRVRgd+jQIWRkZNidDCIiIiLV9u/fH7bnfUQFdklJSQBKD0xycrLNqSEiIiIKLy8vDxkZGf44Rk5EBXZl1a/JyckM7IiIiMhVlDQjY+cJIiIiIo9gYEdERETkEQzsiIiIiDyCgR0RERGRRzCwIyIiIvIIBnZEREREHsHAjoiIiMgjGNgREREReQQDOyIiIiKPYGBHRERE5BEM7IiIiIg8goEdERERkUcwsCMiIiLyCAZ2REREDlZQVIz8wmK7k0EuwcCOiIjIoUpKBHSa9CvaPj0bhcUldieHXICBnUvsP3EWOWfP250MIiKy0NnCYuTlFyG/sARHTxXYnRxyAQZ2LnDkVD4ufX4e2j49x+6kEJFLnTxzHr9uOYwilvoQeRoDOxfYfCjP7iQQkctd8+bvuOfjVZi6OMvupBCRiRjYERFFgL3HzwIAft6UbXNKiMhMDOxcwGd3AoiIyBaCINidBHIZBnYu4PMxtCMiIqLwGNi5AMM6IuMVFZfgUM45u5NBJIsv9qSWawO7yZMnw+fzYezYsXYnhYhcaNj7y9H9ublYsuuY3UkhksSqWFLLlYHdypUr8e6776J169Z2J4WIXGp51gkAwBcr9tucEiIi47gusDt9+jSGDRuG9957D1WqVLE7OZZgSTyReVgiQkRe4rrAbuTIkbjiiivQv3//sMsWFBQgLy8v6A8RUSCGdeQWfMknJWLsToAa06dPx5o1a7By5UpFy0+ePBlPPfWUyakyn4/dJ4iIiEgB15TY7d+/H2PGjMGnn36KChUqKPrN+PHjkZub6/+zf78729LwLY2IiIiUcE2J3erVq3HkyBF06NDB/1lxcTEWLlyI119/HQUFBYiOjg76TXx8POLj461OKhERkeHYHJSUcE1g169fP2zcuDHosxEjRqBp06b4v//7v3JBHREREVGkcU1gl5SUhJYtWwZ9VrFiRVSrVq3c517DmlgiE7EUhDTIPVuIopISVKtkXa0Qm+WQEq4J7CIab2YiIscQBAFtnp4NAPjj6UFIiGONETmHqwO7+fPn250ES7BXLBGRcxSXXCzmPZR7Dg1rVLIxNUTBXNMrlojIDALrYkkHvnaT0zCwIyIicii+dpBaDOxcgA1micwTaUNIMDvRL8IuGXIZBnZEREQqBL4M+Ex+8460Fw/Sj4GdCwRmG5ywnIj0YA6iH9tlkpMxsHOBwDdCxnVExuI9RWoFldiZvjGzN0Bew8COiIiIyCMY2LkMX96IiIhICgM7Fwhsm8s2dkTGirT2UuwVq19w5wmTtxVh1yfpx8DOBZgRExE5B4MtcjIGdi7D7ISIyF7BnSc43Ak5CwM7FwiuirUvHUReFGn3VITtrinsOoacN5yUYGDnMqwCoJdmb8Nny/fanQwighVt7AL/zfyfwouxOwGkBN/SqNQff+bh1bk7AQDDutSzOTVEkYmd2MjJWGLnMlbkJwVFxcg6dsb8DZFqp/KL7E6C53jtEX3sdAGmzNqG/SfOin7P10T9WBVLTsbAzgXMLuoPdcNbS9F3ynws2H7U2g1TRDtTUISr3/gdb8zbaXdSXG3052vx+ryduP6tJXYnxbOsLLBj6SCpxcDOBYLnijV/exsP5gIA/rtqv/kbI1X0Bvm7jp7GuK/WYdfR08YkyECfLtuL9ftz8MKsbXYnxdWWZR0HABw5VWBzSjyMsRZdsOFADp76cTNyzxXanRQ/trEjScy7vGfYe8uRnZeP33cew/LH+tudnCAFRSV2JyEi8L7WL7ATg5WdJ8h5rnr9dwDA2YJi/PuG1janphRL7FzG0l5RzFEcR+8zJDsvHwBwOI+lOWVY00VuwV6xzrXt8Cm7k+DHwM4FfAGvhJa27WAmQkRUDl8GyMkY2LkA+0ERkVGYn+gXGNf5TK6LDQwiGVA6l5NODQM7l7Hy4mEm4jxW95C2kn275q0Lnfet+dhTlZyMgZ0LBE8pZl2GwrzL2fhwIbKHpS/YHnvxIPMxsHMZZiiRJzs3XzSIY1xHZA+77j3e8g7moAyZgZ0LcLTxyPXRkj3oOvk3PDdz64VPAjrS2JMkz3FQfkxUHq9PV3DSaWJg5zLWjnhu3bZI3FM/bgYAvLNgd7nvWBVrDB5FUiuwNoP3IQHOel4ysHOBoAbzHMYOAJBfWIxDOefsToblgtpb2pcMoshmV1WsSPSw9/gZFJcwN6CLGNiRJCe9gYQa9PJCdH9uLv74M8/upNjGyedHCy/3+CVvCbz1zL4P5Vb/3doD6P3CfDz42WpzE0GuwsDOZdihodSe42cBAL9syrY5JfbhtUBkD6e8VJU10Zi1+bDNKXG/BduPYssh7QUFTsqPGdi5jLUZinMu1EgVOvhpUK28wtOzcPtRzIzgAJicKS/fOZOmq2XlQ5wDFJtv19HTuPODFRjy6iK7k2IIBnYuYFe7KmYi9tNbOykIAu74YAXu/3Q1jp7i/LBi2Pjdej+uP4TWE2fjpdnbNP1+9d4T6Db5N8zc9KfBKXMXs2e9iBRZR8/YnQRDMbBzASOGOxEEAct2H0fO2fPKf6N7q/qdPHMei3ccQwkbB5ejNh7JPaf83NuBD6nI8fh3GwEAr87dqen3w6etxJ+5+bj/0zVGJosilBFZj5PeDxnYOdjW7DwcOx1cyqK1dOG7tQdxy7vLMPgV5UXNTijJGPjyQvxl6nJ8vfqA3UlxhMDgR211kANOJ5EhzheV2Lp9K6tHndR2i6Q5KX9lYOdQO4+cwqCXF6HjpF8NqYr9eWNpG6s/c/MDtnEaf//veuw5Jl4M7YTr9MiF6sNZm9lGLJSTMhI342EkimxeqyyIsTsBJG7lnpOin2t/mJf/4U3vLMWJM+exPOsEFv6jr4HbIqOEZjh6hjTk6SQyntklanKlgx6LR1zNSfkrS+wihFiQduJMaZurfSfOiv/GzASRbkqqyhmch8dj5D52l7DwkiEnY2DnUFL5lrXd7Jl9ORnPjv3emLcTN769BOfOF9udFPIo3uekFgM7h5J8I+VdHtGC2luqvBYYpxvvhVnbsHLPSXy5cp/dSSGN1u47iednbkV+obbg3NL5u0MeAHaXXHqFUSNPOAXb2LmAEdeLcy45MozDT+r8bUewfn8uHurXyNFDmRhxGAts7qVJ2l375hIAQEx0FMZd3kTRb5z0ECcKxRI7l+EAxZFF7k1S9XAnGq6e33cew4GT4m0wwxk+bSX+8+t2TnekUWFxCT5dthe7j562Oym6hQZCTsxaXv1tB05pmA3D7H1hEGkB5753asLAzqGkHuh2FvuT/QKvCyXXgp4zuGz3cQx7fzl6/nuejrUAB3PO6fp9pPpoyR488f0mXPbiAruTosvqvSfQ7pk5ho5FaUTVmZgXZ283Zb1GYYxHSjCwcxmtwRbf+rzH7DO6as8Jk7cQzK7aWqfeG6v3ig955Db3fbIGOWcL8ch/19udlLD2Hlc2tVTwECTWDXcSysEtHFzrkMYXUSdlIwzsIoSWa07LW3HO2fOeqDpyjNBx7II6T9j3QFG3HgfleCYx4wHrnYd2+fPv1F1zcltQMWaVXEaawKPY/bm5+GKF+s5QTqrhYmDnVBL3q9OrYts+PQeXvbgAWRKzWehxMOccThcU6VpHcYmABz5djTfmyc9ROW/rEfzz+00oKHLuMBaqByh2Tr7jOR8t2Wv4NFdmPbSdELvwUtSGx80aE37YbHcSdGFg51DBMwwIAf92h5UGV+PtP3kWPZ6biw7PzNG1nnlbj+CXTdl4YdY22eVGfLgSnyzbi4+X7NW1PTNxuBPnOJhzDu8v3m3sSk0KwOy8Dk4XFGkeVsSpeFu5X2hJrd1zEevF4U4ihNWZeZTBxQLbD5dW7+odVuKcyofKoVx7G/6HHsWgtj0ueaRESkC5Zm8OAGD/ibNYs+8krmydhqgo7feB0l+WlAh4e+EudK5fFR3rV9W8PSu0nDALSRX42NEqEpo1uJWTTg1L7FzGLTe2A2p7RDmhGkrOmn0nccu7S7H5UK7o90HBnACcO1+M3LPSQzTouV7ccaU5SekRu/T5eRgzfR2+WrVf19qUtvf6bu1BPD9zG254e6mu7VnlVL6+5hSAefexltWanSWz80R5WcfOYNTna7DlUJ4h6/PaYWRg51BSmbqlbex0bCuSMpzTBUW4/5PV+HH9Id3ruu7NJVi2+wRue2952GUFAO2emY02T89G7jn1429ZRWnJol0Nwc26p5Zn6WuOoPRo7D7mzc5Kx08XWL5NpfmWS96vPWv4tBWYseFPXPPm76Zva9PBXPzl/eXYcCBHdjknXRIM7BzK6Eec1RedUwM7tcGDkgz8nQW7MHNzNkZ/sdb/2bnzxfhl45+aO3tIBWrBwywA+YWlVdN//Bn+zdWuqtvIeQgGX1t6S9edeg9Z4YPFWegw6dewnZwigRvbWJtt7/HSQdOtaAt3y7vLsHjnMVz/1hLTt2UUBnYuYMiUYhY/Xb3YDX/H4VN4Y97OchO+Hzt9vtyyj3+3EQ98tgajPl+ja5tyD3e3tLGLVLuO6usZrvQOUnuvuSFgfHrGFgAI28mpzLr9OXh/0W6UlOi9JzRVxurcpnYuOJWuV/ZyXlhc/jwHPled1EyKrVhdxohr5+z5IiTGmXvqnfrw0JOuy/+zEEDpjf5/g5oGfHPxpBzJy0fN5Ar4du1BAMD8bUe1b1BE4Ok3u1esg/IpU5kVIG88mIvzRSWIi9H2/mzWmGpePK/XvFFaJVclMQ7Xd0g3fXt8qfIWrbfam/N3GZsQg7DEzqGkLjQjMpRlu4/rXkeZ6Sv24ds1xk0V5Abr9uVIfvfgZ/pK6MIJekNUsrx5STGcU18G9MjXMQ6iVw6HGYGk1LHZfviU8RsLw8rOE14Myt0ssETZSaeGgZ1DnZTo6eikzhPHTxfg0W83YtxX68u1dVA73MnLv27H4FcWaWqTtvFALr5bGz64zDp2xvTAa5WF00AFBnlODgIKi0uwfPdxRw/27EgKT6oXA2KtStiukTTwWtMhVsU60OzN2XjmQhsTJzsb0NasuERAcUD7FrUZ5Mu/7gAAfLpsL+7v3VDVb698fTEAIDU5Ad0aVpNc7tFvNqhLFMTbTdhZDaOnKtYuUy5MrH59+3S8eFMbm1NjJmNPSODDJr+wGNFRPsRGu+9d3E3BktKkBo8naS7Z9bvp4JJl3JdLRAC5BsNOfZb/83+b0D5gVgitb0BFxdp7Oe08Il8Nk29QDyq59tlG57Ohx9HpU8rJ+cahVfZODZADr6XmT85Ez3/PNXy9TnDg5Fnsu9DLUSm54aAEQXBUQ3ZjeW+/luw6hv+tO2jY+s4UFKk+/4bcEw46NQzsXEZrhmV2O42vVx8IGqJD642iJG2BvVLPqKm6dWFmL9srlp0hDGHccTE2YgpcW4kAHM6zflw3K/T89zz0emFeud7mWhQLAq5/awnu+GCFaF558sx5TPs9y5Yx8rSSy/MdFqNrctt7yzFm+jrskGgfKQiC4mtj55HTaDFhFkZ9vjb8wh7GwM5ltD6DrK4+NDPDaffMbP+/py7OMnFLEsfb1gApsPNE+IQwmLOXnvtAaTtVLzzcAeD4Gf3BVul0bjlYtOOY6PSDIz9fg6d+3IL7PlmtaztubBLhdHskSm1Hfr4GzZ6ciaxj4YcP+njpHgDATxv/VLVtr9xDZRjYuYwRVQyKR1cPEzjIrcfM6p6yQXlVc1od1AUlJQKembEFP6vMjAIvBbOGxrCS+/fAHbwciITbtyW7SkcEkOrk5PTbyMvnTmoe7583ZgMAPlm6N+w6zB7Gyy0Y2LlMWfuur1btx01vL8WJM+UHxw3HiszBkYGGQTtudOnnL5uyMXVxlqIeu5Y22vbwQySQceez/HpKBBhSxSjL4Hvt/k9WS1ZlaiE2gLdZ7Lhkt2YbM1+pFLl9cmI2q1W+RGBXRsm+xkVrPCAeOo4AAzvXKctr//H1BqzYcwIvzlY2MrvVD2mt94nTYgmx4ybbeULDNo6cyle8Pj3HR20A47Rz4QahD6erXl+MZk/ORM5ZY4Kb/MJivD53h2GTn4utf+bmbCzcfhQHTp4zZRtmMrvTRHGJUK5zxpjp60zdZiAv35MFYQI7RTRGumqH53I6BnYuEJhXhY7TpGXyd6OuYaeVynk50xNj9EMsV2LsRLc7d744aCges+ScLUTTf84M+qxsTstPl+1F/Ud/wl/eX65rG2/O34Ups7djyKuLNK/DERPd61y3WTmPXG/+s+eL0G3yb7j341UmbV1cpJScS1XFljHzaWNEYOek08TAzmVCAzstA3IalVHI3QpOC/qMFBpQ2TVotLKZJ5QlbvIvf6DN07MxY8MhbQkzgBmXTO7ZQjR7ciauujDWIVA6JdyIaSv8/zfq/MkNTl02lt/incewdJf2mV82Hcwt95naw1ZULGBb9inHDQliRJ6hN36XS8KCbUdx5FQBfv3jiL6NRKCSEgFTF2dh3f4c6WXCnDsll4fWKyjKY48rBnYOsGTXMYz+Yi2OKeiCH5oXKy2JcFgeDqA07R8szhJ9WDmFFb2JZ27Klvwu9GEXPOm0cWl4Z8FuAAgeGNuEiya/sNiAidqVW7ijdK7ezQFVl1MXZWGewXP4qrH5kL3X+8aDuRj48kJ8unyf7HKmXvsuf5DalZ2G3pJ6DqMVpdhlflh/CM/M2OKf09csWt8NjHipcNIlzcDOZt+vPYjb3luOH9cfwsQfNoddXnNg58DhTr5ZfQBPz9iCoa9dLE1xYgAaSi6JWvLK5VknFC+7bHfgsupm+nDCsW05YRZufnepoet89bcd+GKFfJAS6FS+e6ucjXx4TF20u/z6AzbghOvFanb19Jdn/Ik4ciofbZ+ajce+22j4usXsCDN4PBD+2lYSfNk5NZiTbhf2DbbZ2C/X+f99MCd8Y+XQqtciE9+6wmXsejO6rdnWT9htBDsfeP/5dbvmdDgh4ykqEbByj3Hz6e44fAovzSk9Jrd2rlvue7F9jra53kXpeRO7v5xwDg0hsyNyvUyf+nFzuXmpA+mdK1aek8pk9PloyR6cKijC58v34dlrW5m+Pa/NxXoo5xz+9dMfdidDEkvsHERJnhSacVlZnK7WpoO5+GLFPsm2PDFau6ZbqCzpGw7kXPzMnqSUY0Y6iktKmwa4pVQrT0M6o1zcoGbh9vJVyGaVJNl1nQ96WbxjyNnzRZj2+x58tnwfTqmZcUbCPR+tku2RLse+drXBG3ZLW2YjbrmyVUjNUAHouRfUndCHv1ynehBkK7HEzgUCb+blWSfQrm6Vi98pvB61ZEThfhLuLaysirVKYhwGtUwt932MwQ9YMzLbslVe9XpA2xCH1FEpKZ1Qm9Rjpwtw23vL0ax2Mvo3q6kxZc4WHdpu0eLt62kWYWYJfSinda5Qu+vhkv/rH4cR/2Nw2YbXSpakWL2fiqpRFSZpmEzPcqv2at8JdXMbW40ldi7z3C9bg0op3PDCtk2iytXuKjE3kDtCep+7X68+gL5T5mPX0dPlvvvjT5MHXRVJvFUPGzeX2JnNYbFcECWBplzplpgjecpL7ILzWgsDbMu2ZB5DnlMX1nHklPHz/Dr5uteCgZ0L5QWMXWfmBannXlRyI4sFdnpKM6wKcpWk0Iq0BJbYKdlc6IPxkf+uR9axM/i/rzcYkp5NB3Nx67vLsF5mSIOLaTFkk5rWE1piZ/WT0ykPETur8cw6BFZ3ErOaU64dtQLHqCss1jYlpJIXPzcUdFiBgV2E0JIfSP0mv7AYT/+4BUt3H1O4HvE1GT3atxmZXpFIJuSUzNWodJw2oL0SANz8zlIs3X0c17+1JOyyVh1CsVKeaAW5XnZuPj5assewY2Mms0o6TR2fWMPFqzYQ1XR/yPWK1bA6szkhTaEzcYjJOXOxMKKoWNuVpWgcO43PFIdk6YZhYEeqTV2chQ9+z8LDX65XtLzUPW91gBS6OSUPl69WHRBZT/gBiq3IcAO3e/JsYdj9kfpWqpeh2vNz5sKcqFa2A9NCSeZ/3Zu/Y8IPmzHhf+GHIFJL6dFR+ozySimFEbthVZ5ixnbOnS/Gc79sxZp9wb3GzdiW3mvm5JnzGPfVOizZdQzXv7UE17zxu+z4lIG1C5skxnGMlPaNVnBVYDd58mR06tQJSUlJqFmzJq655hps26ZsrlQ3c0oJUZkDJ8M3HF1/wLmDDpfRelwt7REn811gZnnvx6vw1I9bZJaWViAzfIRZrGqYrySI23AwB7M3Bw8SfSi3tO3Vgu3GzzIQuut2dVI4XVCE3/44rDqwFwQBWcfOQBAE7Dl2Bg9/uU6yHa3VQmdmOX66AF+t2o8zCktelYYWZtwzb87fibcX7MJ1b0qXeGu9VJ6fuRWXTZmvaQrKUPuOn8XgVxbh2zWlY7Cu2ZeD9QdyZQfYD4z5bnxb2ziWZoZ9TnvG6uWqwG7BggUYOXIkli1bhjlz5qCoqAgDBgzAmTNn7E6aIYy4tvLyC/HW/F3YH9prx8ALV0kVauBNrmbTVrYZfPz7Tdh73L3XTmhG+uGSPaZv89s1B7Byj/IBlaUoOc2CIGD/ibOygU+49SgJmvILS/DXT1bLjp9mlvzCYvR/aQEeDhjP0ipHTxXg7o9W4fmZWyWWED92z/78B/pOmY/X5+7EiA9X4ru1BxVVv1shtDT9jg9W4B9fb8A//7dJ97oDXxICB1U3yo7D5TsxAfLtBpWWvL05fxd2HzuDT5buUZWmpbuOY/QXa3E0oMNCrxfmIVuk04ncnaak7aMRJc9eKb3Wy1WB3cyZMzF8+HC0aNECbdq0wbRp07Bv3z6sXr3a7qQ5xj+/34R/z9waNDcmEP7G+khFUKCkN2uxgnYURjd0DvcQLw75/osV+/CXqeonZVcSfBrVMF2ujdfdH6mbjFxv0LzhQA7GfbVe8xt3uLSEHrJnf/4Dlz4/D28vKD9Dghn2HBMvid555BQe/WZD+ZcljQKv+wXbj2LX0TP4bu1BQ9atxVer9vv/reQaeW9RFgDgxTnbkXWs9MVIbVtE0zpPBJbYCYJ/KrkZG/SPOeaEmEGAgOISASOmrcCUWeprq9T2W7j1vWX4cf0hTPghfGAse+0YcMKVzRWrsY2dASUKThoeyFWBXajc3NLqvqpVq4p+X1BQgLy8vKA/XpV/odfR7ztLJxg/eVZdkfuEHzbj5JnzwR9KXKdKSuyKg3NYVWkxy6aD5c///hPhZ/vQwgkPAb1CA++9x40bu0lJUF8WQPxbskTJCj5c++YSTF+5H3d/tNLwtRtxawRea0bOxemQ2zas/IAel4FJDvy30vmJ3TDg78LtRzFv21G8Pm+n6t+W3Xdq9/LASX35pLkzglxk5OmTG6Td6VeJawM7QRAwbtw49OzZEy1bthRdZvLkyUhJSfH/ycjIsDiV5e05dsbwMcJ+XH8ITf85U3Uxeyil7UaUlNhZOdF7oPzC0gbIRlQXSlG7Z0a0azHC52EmfXcjueeF3NRTSvl8wKn80tKo7RJVZWqZ+Yxbp2CoGaXUJlPNPadpwHSJHwXts8R6lQYWdj6wpYKS0KQb0b5v1d6T4RcyUONaSbrXYWbnCrGr48HP1pi2PbO5NrAbNWoUNmzYgC+++EJymfHjxyM3N9f/Z//+/ZLLWqXPlPkY/MoinAgtHdNh9BdrAQD/lOnBp6z6UNn2FFXFBmxQatNiaVq667iyREiYujgLby/YZUh1oZTQB0y40qc2T81W3HjbTN+sKd/DVw0j34bNfoH/8PcsNHniF8zdanznB7NoHd9Lr7Pni7HlQpWlnuYRN769FJslejzq8aTG9nGB15gR75lOKMwLvW/UBjuCAOSeLcQSnfms6Lplrp3M6hUN354YradILD9atEPZcF5O5MrAbvTo0fjhhx8wb948pKenSy4XHx+P5OTkoD9OcShHedG2ujzJ/JIyJVWxgUNe5J4rxHVv/q6oRHGFzpI2sVkU9CprR6RG6CHadfQ08vILcSq/UNW518st1WlGePbnP/yloxMv9BD+37pDin9vz4P74gl6fa76qjUjFJUIGPLqIizaUX4eWrU2mNAb/uOle7Hn2BlF1aReHKBY9h5Wec0KAE6cDS5UEAQB/5mzHb9uOQwA+O2Pw5hn8AuRIU0OFI1jp387XuCquWIFQcDo0aPx3XffYf78+cjMzLQ7ScaSuPrVNMpUsmh+obKSAQGlwxpkVElATMCorkpmZApM88dL9wIA1uzLwe3d6ges31xLdh5Do5qVUDO5gq719J0yP+j/St6aSz+7uOCNby8NqkJZOv4y1E5J0JUuswXu58QfNqNj/SrSC+tYt1HeXbgbR08ViM5L7AbfrT2Ihy9vovp3Rj3Mft6YjQ711M9DrYXaAOx8cYlkPhjYiSBoEZfFeFLnsdjIZi2CUC63+u2PI3jltx0AgBs7pOO/q0tL9rc+M8i47Uomx7yTtPvoaTz23UaM6tsYPRtXl0+H2y6WMFxVYjdy5Eh8+umn+Pzzz5GUlITs7GxkZ2fj3DnrSkCM4vMBr/y6Q/K7QEZccoHrGPn5GtF2X6E3/Mo9J9F3ynzXtDXYE9K4/7b3l6Pzs78pbuN2pqBIdKYJI4S2i1m1x9o2Lnp9uGSPoW1cjMpIQx8M3609iPs+Ma6XvBkFAFIPM63HxMgG/04t4ZXbQ6n2YlqOp9yhNLs0SOz+2n74FK58Xd/QKqE1DqH78WfA0CVlQR0Q3ClFCTXXzo7Dp/DVyv1BVeThrmMlhz/wGI76fC2W7T6haeQDt3NVYPfWW28hNzcXffr0Qe3atf1/vvzyS7uTpsl/ft0u+nm4G0TuBgj86VGZyZIXq2g/MPtCEb0adowiLjWO284j8tWz3609gJyz59FiwiwMeHlh2O0YEZRsUdCBxqpOF6y+UCe/sNiwNrJyE5rb3elGy3Wu9OGuZLgbLfTemXJJsCNP+/cv+nuED/zPxTxN65iiRu/55f9ZiH98swHfBwzzE3YbCi6QwEWOygyYXI5DX2i0clVgVzYnXeif4cOH25001fRkEkp7nN5jwvAMgIGBgEOKB56Z8Ye/08buo+Hb05WbOUAsVwhzjN6avyvsdq7W+aY+7fcs3Pb+Ml3rMEvZMVRbKuAEPf89F+2fmYMjIoO0KjFldukL3dyth/FkQIen0Ovq543Bs2FYzSG3pyqBpaFuTL9aSrLi8xprIYys1ZZ6Zmw4kKN8HTrTEElcFdi5QXGJgOvfWoKx09cats7QDOrS5+cp+l3gtF5OGjyxjHRvWevTqqY6y6rUhVYtq/XUj1uwdl+O5t+buZ8CgPcW7kbTf870N9rWuh611LyYiC177HRpad3S3fp6FioJ7q00b+sRxxZcSNVuhDIj/eeLSpCrclxQvWZuEh9QWW/WaG67SXu2q4ZUoYhDkmcYBnYGW7c/B6v3nsT3Knrjhaem84SyC1d0DkyHvBLpaSu8UaJXXrjhRpR0CCkTeIxL56IsP4yIQw6lInZMNi8IAv718x8AgL/9dz0AoFDBbCV2237YuDlRnTbpeeg0UVYHAXLb+3ljNrYqmI82dK5YtcRe8PpOmY82T8+WnQvVEAGbvv9Tc9o1CxAUX3dqX7C1vJCH+8XZ8xfzbbX5T+ji2w+fQvtJc/D+Iv0z2YhdJ07KvRjYGUx2XkuNOaUZGaxYMOIUekYp3yHank7A+G83yv5OawN0pSUJTmbW7BtyAs9wiSCgqLjE5hkmxIPx0IdgjoklN04p1SjjtJ6Cf/tqfdhlgqoPNRzQn0SmHjt4YXgivSW0APDB4izc+/EqRYNni5UuCRCCAhy12ZYgKP9N4NbNegUJascnshG1g6rL5eNPfL8JOWcLMemnP2TT4QUM7CwU7iI2uverWRS98SkaEkX8c0O791/ww3r5ElQ1GVdg6s4UuK+NmNMIgnwnAsvSYfUGnVVgB8DCZhAim7nytcWybS4VDeCsM/2y7dEMODRPz9iCOVsOh82PAGDIq4vwm8h4cpadIp2ln3rN33YkKAhT8twx+5Ya/cVa3PTOUtHCByfdzq4ax85L1LxprdmnfGgMqZte0cwTDrk07Xh7UvXmq+RYOuNQ6lI2gXoZI3cpdLJ2I9bjNh64RAx1/Mx5zNwk3WFEyQtf4CKHcrR1bpFet3EX27nz4WeiUVL1rJap7WZlVi45XZpMikJrWfTmqXI/V1o6/aNMQO6krIgldhbSmjE8+7O9VVRWs2rC6EBKZtMooyR1SoNkNfOZmjXGnpSF26VnInji+42y6QlbuhJwEIsFwRGBsGhVrAPSFY6RaQwcb9GOoFkueFOSLwQu87NEBwStDD0cGk+aEZ0nlFfFXtyY3ktMWcFCMC1T7OmppvYSBnYWmr9N/5Q94Tix96sUqbekYluK7JQvatQxPpRzTlXmNeA/4cfYs8qny/bh6wuDmZ7KL8TxkIbl4a71wHOvdCYUozilZHp5lr7p88zQcdKvlmxH6t6Xu7OUlNgFLmP0WTYyWxJvz6llPSrnilUTnhq4v1LzVMsd09DOVMoGKJb5TuZLFz02FWFgZ7DAiyc0ADC9V5UMRaVMznje2cKOXX/g09WIVtEdd7eGOWuNFHp9lF3PrSbORoeQgCBc6aLeycz963FUBQgpPR9aHqRKAju5dsznzjunPaxYXmvkDCLy21bYK9bAbc5VOPfslFnb8MT3pVWwamozxHgtWFODgZ3BAi+m0Aaywb2MLA4lFF7l+0/oGzutjJ69s+OGVFMVq2R8OSWrC23DRs4Tehqd+PJjVl5ix30oP6qAgt8HVR8GH5fX54lP4aiUkU1EtL/IhKzHgdejGkLIv1+ftxOfLtuHvcfPlDveZ84XY8aGMJ3gtFZxa/qVczGwM9Gy3eqqWgQBOJyXLzFkh8J1iHz21cr9QYMVy9l0MPxyRmUmUvnk0l3HcSrf2gFB1e5TfmExXp+7A38omBrMqG0qYXU7PK28kJEaXcJyMOccTho0VZkXKWmiIbfI1j/1dUYw65oVBAGv/rZDclDi0GV1XXaC8pduLXFsfmExpi7Owq6j6p9hgU0ySkTaAr69YBdGfb5Wdh1BNWaqU+AdDOwMFnhhnZXp+SR1c97z0SqDUwT845sNipZzysvf/Z+uxk3vWDsVVuAbtJJSyzfn7cSU2dsle66pnbDaKB8v3Wv4OqUonbNY9HsdpR9aGlUDwL4wJa1WVYXJuVxDO0qzku20au4iRb1inZVmKecKi7HhQA4EQcDyrBN4ac52RQN0P/rNxqCAa8ku+bH1QoePEaCt84Si5YXSfPGZGVvQ78UF/s/lXtID9yVwHMuYKJ+mwDJw19Q093BT23QlGNiZSO1wbD4fsFFBiZmskG2qnc/SAc82ANBVEqZFYFO3XxS8OYc7T4rbsRicn8zbpqwti9207vZvfxxG48d/uThwqYoV9XphnnyaVJ6Mh76QLz3Qws52uKHsmH5KdpOKqmIvCr0FxcaEU/VyFWb7woWBtpV4ZsYWXPX675ix4U+8s0D51HLbZGY+EZtdJ/R6Er3GpWYrUnn+BQhYuaf80FzhBocXExutPjTJyy/EPwPmXpYrWPE6BnYmCr2Jwt0oRmSkoau4feoK/SsNMGfLYazbnxN2OSWBjaPekYI6vVi/TXcwLsHl3qYVrvq+T1YDAB77Tv3DQgsnlOJ5kZZByBWVIKlcrVgJn1RwtjxLvnTsjg9WoPOzv6kKKL5YsQ/zVI6WIHVJhvaan7U5G3//r7LamnCUNuURO0dzNMwFrWaKR6D0nLWeODvos7MhnWVke8Wq25zjMbCzSH5hMQ7lGD91U7jMTu4NL1S4h9juo6dx78ersGjHMcXrdAK1gzMreeYYkRGozbzcRG1wrOVQ7NHYS1jqMmcQF8yWh53MRpVcU4E9x5WczVV7y5cwfSTRnOHYafn2j4t2HMOJM+fx+07lU4+dMbCn7sGQ58t9n6wuNw2a2DHcdVT8PgpddMlOM/J96WFvlF5/i3YcxfOztpmRDNdiYGeiwGtlyCuL8Pq8nf7/mzf3nvYrtPS30inbf9LYwNRJzRoCgywj2ukobWPntHZMVtG634GxV58p81W9uBhhvYLSaqsxHDXWrxpKmAKpyYPFqk/NFlr9/OGSPaLLhe5HuHtNEOzJ02+fugLvLtxt/YZDOOp5ZncCPC3gRIeOQWZU4UDoTWrkG2AoNRmWmxqj+hBcWqMk7WFL9Tz4tA29ZvX2zjPCUz9uUbX8hP9t0tVzWGwC8Uiy84i5gbRcwK/2knl17s7wC0mkwareyWdNCOyO5OXjk6V7RL9TcwxDs0FF4wiqWL/YNgI/tzIL9doLNueKNZHqXkUeu7jCCbe/Ry2aGD60p5iSqli56baUiuSavz9zQzr1KDwWpS8y2u+Tj5buRUbVBM2/t4oTXozE0vDpsn0mb1PuO2uOybLdJ9DumTkY2bch/j6wqerfq0mlGbPsdH72N8nv1GwuNJBT1C5SbYcLqcDOhKeh2fmtk/JzltiZSBCADQdyXDM2VdghKixJxUX3fGzM0C9KbrjAqthyQYdJfHBW8b2Vrn7jd9nvQx/i/lkDDMg8D+cpf2FwUmYtxbzhTqzblhJW3ypvzFPeWzWQnUH5kl3y7eDUhEufLQ9uaxhuuBlB5fpl12XxITSk46KD8nKW2BnuYs63IusEftmUjQqx5sXPRr/XOOlBZlR7JmU33MUd/2KF/lKJU/nhq1jc1ljfztRuPpSLjvWrGrIuNxx1Jz0kyvy04U98u+ag7DK6J6k3cd1OZPRYlqtFhhsJpfSZEdpJTktP5tLt6U+LEcSOdXGJUG6sv1B1KieU65jidCyxM9HxCyV1YpOcD5+20urkWErR7eqgjNrIHqpqxw50K7mH0sjP1+hat9RD3OqgzK7Y2wm3RuA5KCouwcjP1yD3nLUzwriRnQHoi3O2y36vJm2hncjUztV78TMtQ9tY46rXF6PFhFk4LtPjuXJirEWpMQ5L7GxywOAepkYIe/854WmjwdIwo7OHdp7Q69mflTWwNyNmWLTj2IVph9xQLnXRa79pa+iuheRwJ5alIDwntLELpGTWByM4bLc1sehQaab0GKvtPCEIxpW/mX39nzxzHqfyi/zzdcsN7O6yrBQAAzsKYdg1bNFYcEqEe4sFjH2of79OfqLqwI2aMSbgzE3ZGNyqtuHrNTNY/GRZcHue0Guj7P9uzGTNZsbUdKX036E5Z93RvjhSqAmYypXYhflt6LcvzNqK9CqJYdKjODm6BeYd7Z6ZI/mdFzCwc5DCIg1F1hbeGF7utfu2iml9jHT/p6sNX+fKPSdNCexCHwqfLN2jabJvu6kJhMwLmuQ54U7Tkrecyi/C+aISxMWUtvK56Z2lBqfK+byST2oa7iTgR2WdT2I0tHP53ZTBkCMH29g5SNaxM6rbeoWbBFoNr2RIWvyyKdvybZpXzmLNeTyUm4+vVx8wZd1mVsW44e1cze47aX/+PXMrej0/z///7YfVBf5uzYNyz15seygW/5w8cx5zt+ob+NgIamZ0CB17tahY2zh2Ws7o/32z0RHNEQRBwKaD1s5bbgSW2FEQo6rblGTQTrhx7eS2dnBOOFtGlKDJrWFF1glHVB86IcDROo5kdoR0Hgq0/+RZ2e+vfuN37Dshv4zThZuRR2rmCbmSPqe0R/x5o/iL/Xdr5XuBOxUDOwc5r2NEfNLOrrzFZXGdpaTOiZnHzOcrX3VoW69YBzzwHvistGfz1mcGWbZNJ+y3XmIvrE4J6vQc36KS8M8nI0+fE66FCT9stjsJmrAqli4KcyOpudGccFOSse77xPj2gGpM/uUPnDVgyjypklKxcRP3HjfngbzxQK4p6w1VXCLgx/WHcEjHOFz/W3fQsvvZrdlG4CUlCKWdRr5efQCnbZgLVo4AQXNNSSSWOygZj9SJWGJHlnpj3k6M7NsIgHOCP7sKzlhgJy302thyKA/vLDBmom+p4651blEtrnx9sSXb+WLFPjzx/SZE6xiosajEjAmeJDglU1CpoOhi1CNAwF8/WY0VWScwd+thvDmsg+Tv3FVqL2BPSLu70O/Vnr9zhdYFTmY3fXFC84kyDOwoiFGXvtT9/cKsbUivkoAps7chLcUZc3baVQVuVkbj0mejLKeVfJjNqHO4+MJwOlpnDSBlnp+5Nej/K7JOAJBuu2UXqXZwSv396/WS3xUUlWC9ypJouePDK1Y7BnbkF3Z8YoPutDHT1wEA9p9wxiDNbi1u9zInvf3awaj9N2qSeateFlYomBLLiZbtPuH/98NfSgc/oayal9ooeeek88qhrxlbCm1k57olu45h4fajhq1PjF1DI4lhGzuDuatovbzIfpxay+WXint57MDLlfzO2WLMEBtW5Qs/rlc4uDdpYuZ5NDr4N7KQ+bb3lhu3MglOehllYGcwN1eDzdjwJ+79eJXk92p2zUkXuVO5/SXATErGzNLKDYfdaflIpA9N5CR6rt/Pl+9DYST2gogwrIolv2dmbDFsXQd19MKLHG4IMexx90cr7U6CrZSEUQVFxZix/k8csWDcOIZ1zqH3XHy5cr8h6SDnYmBnMJbClHLjaN1W47UiLbDNEmBsiZEbBoZWsr9vzNuFV3/bYUFqyEsOuaxdH6nHqlgim5gVXpzKL8KHv2eZtHZ7TJm93bB1uSCuU2T+tiOWbYs1sd6h9SVJELxz73gdAztSjO1sjGVWJvnNmgOY+KNx1epe44Znk5I7zarb8Yd1h1BQpH9gaHIG5uLex8COyCZO6h5PzuKkd6jlWSfwnzms8vUMB11bZA4GdqQY8wMiizjsZvtixT67k0AG4YgF3sfAzmAsgyGl2F7FHm7oPKFEXn6h3UkgG3jj6iUzMbAzGN+FSClm0PZww3FXUqqy9/hZC1JCTqP3GaOnmp9j4LkDAztSzEntfoi8jPeadxg9T6/eTmxaf36+qAS7jp7RtW0vc9I9y8COyCZeqRJ0m+NnztudhLAc9IyIaDM26J/irMTgJ/7oL9bq+v3CHdrmTF29z53z+EYiBnYGc1LUbjxP75zlODsHiTldUITNh3LtToYu+YXeGB5l1Of6gigzFOqcbu/seW+cG5LGwI6IyEFenL0Nt09dYXcydHlt7g6cKSiyOxlkoGzOWOEanFLMcCzVIiLtdh45bXcSdHtj3i68MW+X3ckgAxUUseOEW7DEzmDeroolIrOx7SUR6cHAjhRj0EpkPk7dR0R6MLAzmJez5M85+jyR6YzuRUn24ukkqzGwM5hXb+L/rTuIRTuO2Z0MIs8zetwzsteEHzbZnQSKMAzsSJEx09fZnQSiiLBs9wm7k0AG+mLFfruTQBGGgZ3B2D6GiIgosjjp0c/AzmAOOrdEREQUYRjYEREREengpFGKGNgZzEnFsURERGQ+BnZEREREZDgGdgYT2MqOiIiIbMLAzmiM64iIiCKKD86pi2VgR0REROQRDOwMxgI7IiKiyMLOEx7GXrFERESRJS0lwe4k+DGwIyIiItJhZN9GdifBj4GdwdgrloiIiOzCwM5grIolIiKKLGxjR0REROQRTirUYWBnMAedWyIiIoowDOwMJjgpbCciIiLTsSqWiIiIiAzHwM5gLK8jIiIiu2gO7AoKCoxMh3cwsiMiIiKbaA7sunfvXu6z7du360oMEREREWkXo/YHM2bMwNatW3HmzBkcOnQIaWlp/u9uvPFGrF+/3tAEug0HKCYiIiK7qC6xa9GiBRITE3HkyBHceuutaNCgAXr16oVbbrkF0dHRZqQxyJtvvonMzExUqFABHTp0wKJFi0zfphrsFEtERER2UVxit23bNlxyySXIzMzEgw8+iJYtW6JXr14AgIMHDyIrKwstW7Y0LaEA8OWXX2Ls2LF488030aNHD7zzzjsYPHgwtmzZgrp165q6bSIiIiKnU1xi17p1awwZMgSzZ88GAH9QBwB16tRBz549UblyZcMTGOill17C3XffjXvuuQfNmjXDyy+/jIyMDLz11lumblcNltgRERGRXRQHdllZWejcuTNGjBiBZs2a4e2338bZs2fNTFuQ8+fPY/Xq1RgwYEDQ5wMGDMCSJUssS0c4Gw7k2J0EIiIispCDxidWHtilpaVh4sSJ2Lt3L5566ilMnz4d6enp+Mc//oG9e/eamUYAwLFjx1BcXIxatWoFfV6rVi1kZ2eL/qagoAB5eXlBf8y2dn+O6dsgIiIiEqM4sDt37hwOHTqEbdu2IS0tDePGjcM999yDt956C40bNzYzjUF8IfN2CIJQ7rMykydPRkpKiv9PRkaG6emLjnJS3E5ERESRRHHniYoVKyI5ORk1atRAUlISkpOTkZycjKuvvhrJyclmphEAUL16dURHR5crnTty5Ei5Urwy48ePx7hx4/z/z8vLMz24i2FgR0RERDZRHNjdeOONmD17NgYNGoQxY8agUaNGZqarnLi4OHTo0AFz5szBtdde6/98zpw5uPrqq0V/Ex8fj/j4eKuSCIAldkRERGQfxVWxX375JTZu3IiKFSuia9euuOqqqzBv3jwz01bOuHHj8P777+ODDz7AH3/8gYcffhj79u3D/fffb2k65MREcfpdIiIisoeqKCQ9PR3PPfcc9u3bh8GDB+OBBx5AmzZtMG3aNLPSF+Tmm2/Gyy+/jKeffhpt27bFwoUL8fPPP6NevXqWbF8JltgRERGRXXyCoGzktVdeeQWnTp3C6dOn/X/n5uZi7ty5OHPmDIqLi81Oq255eXlISUlBbm6uae0CF+04itunrjBl3UREROQ8n9/TBd0bVTdt/WriF8Vt7KZPn47KlSujSpUqqFy5MtLT09GyZUsMHToUVapU0Z1or7i0cQ1UToxFztlCu5NCREREEUZxYLd06VIz0+EpDWtUwuq9J+1OBhEREVnBQa2w2NKfiIiISA8HTSfKwI5crUM9NgMgIiJ7SU2UYAcGduRqd3RzTo9oIiKKTDHRDOw8zTmnl4iIiMzmpKHOGNgRERER6eCk6UQZ2BERERHpwBI7IiIiIo9IrhBrdxL8GNgREdmobUZlu5NARDplVE20Owl+DOxM4KBez0TkcMO61LU7CUTkIQzsiIiIiDyCgR0RkY2cNLApEbkfAzsiIhsxrCMiIzGwMwHfwIlIKWYXRGQkBnYmeOiyxnYngYiIiCIQAzsTpKZUsDsJROQCV7SuzRI7IjIUAzuKKL2b1LA7CUR+D/RuCB9b2RGRgRjYmYBv4NYRBHXLfzC8kzkJIdKAeQURGY2BHUWUKB+QFB9jdzKI/BjcEZGRGNiZgPk0ESnBalgiMhoDOw+qGBdtdxIci0PRkNPwmiQiIzGwM4GdGXVShRj0aVrTtu2TNSrE8taNBDFR5uUlf+nKOWqJvIhPB49pk16ZlTvhqDhAz1zdwrx06BATpf3W7dqgqoEpIT18Pvuabui5hojIuXhnm8DOwKpEbTdRlxNg3v5WrxSP27vVN239ejB49w65An7W0hKRWgzsPCbSAjtSj5eIs8h1oOhQr4qFKSEiL2BgZwI737JLPPbQHtW3kY1bd/DBdHhJzuCWqXYnwRXC5RVtMipbkg5SrlWdFLuTQA6TVMFZQ2gxsPMamVjEjb1lHxl4iW3bNrJkq5/BHVr0xHVWhKsMSJSTrYp1egQfgapUjLM7CeQws8b2sjsJQRjYeYwg0+qMwyrYJ8HgoJrn0jvkzmTgaf7XtS1NTwuFlxjrvhdkMlda5QS7kxCEgZ0J7HzLlqqKvbx5LTbEVsnIki2tpX8f39XZwFRcYEGRHS81ZdTkFRlVEk1MCSk1iM0MyOEY2HmMVOeJB/o05MNWJcEBvQx6NamBOga/DZrZk5jUk6+KJaeY/XAvvH9HR1zdNs3upBDJclaLP49wYucJPiDs5aRgyop4laXDakgfLB5H56iVVAFNaiXZnQxPqhgXjTPni+1OhmewxM5jHujdUPI754QW9rLjWVlSov23bny4u6XRf63keFu37/MpP79GXwduvK5sxeNFLsHAziOqVYzD6MsaSbb/YGP7i5QGuIa2sWNY7Thv/6UDOtV39iwcbgmQifTg88lYDOw8YtUT/fG3AReGBhGJIXwSn7udmdWKatadUVW+HZyTxhcUS8qjg5tang67DWqZ6ohbQmmvWC+7xAVVnJFyLuzAQ2ssBnYmsCMD4BuPckqPlJrOEz+M7BlmXYpXZTqx/TJxrnmS4YPye9erpXfVK8WjYjyHEIlo3ry0bcPALkL4fJ4ssDOVmkF2wz2bC4u1N7JjzG4eJxxa2RI7y1JhH7fkTZFwLsgbGNiZwImlZ1592zfTSze1VbxsuOO7YPtRzenguTOPEQHFlBvbGLAWCQF5iQOzFcN4eNdIgWhWGRiKgV2E8PJDwSw1kuztMWkWsWDGSVXFbtO/mfbp4tT0iiV7OfGF3Ssa1qhkdxI8hYGdCey+/aV6YDphwF09Wqc7ePJtE0+62PPE7eeSLlI6QLHd+YpZnLRfy8b3szsJEaka5981FAO7COGWdixyqlcqX4LWvWF1AECVxFirk2Mrp5ceODx5hrmkVpLuqvLzRdLtL6tX8t4DL6mCc8fFT02pIPmdky/pWztn2J0EXaJ8Pvw6rpfdyfAMBnYmsPqhlhQfPqP0ajut1JQKWPl4fywVedPu1qCa6G/sCIoa1Kio+bdGF851byh+XEi9B/pIDwiujA+nC6RH3B/a2nvTV1VJDA5W3fIS4OR09mhU3e4k6BIVBTSq6fwhb9yCgV2E8Pnsb0fVrHay7Pc9GgUHHKEZqVT1Y42keFSILT9cwpVtxB+KbqvGvLVzXQBAk1r62qGs/eflmDn2UjRNlT8PpFxMtHlP+4cua4SowEblRs88YcPL3ujLGiFB5F6lyFb2su3k0lw3YWBnAjMyzEY19T3UnfC2Ga7j072XNsBjQy4OlKs3yVZ2tCoxcQTiey7NxEd3dca7t3f0f6YlOK1SMU40qOtcvypaObn9ooMJAnRfqJLn0udzxH1rpPrVxEuund60APBurYcTRF04/9+P7IHh3evbmxgPYGDnEj8/dKmu3zshUwrXpT0mKgqXNq7h/39oZq82lImSeFiY8RApDhdo6Yj7YqOj0LtJDVQMqHI3ah/2PHcFvrq/m+SxInPZedjtmOZObH+dkDeRvcoeDQ1rVMLEq1rYmxgPYGBnAjMy69B1yjWW/Wuv0nY/DQPadUX5zMvIBzSvpWi5cMGI2urisMfZwudFcZgSO3dV/pJSRpxXqfvC7MvXjhYJbn5/cHLaK8a5uwqzQoy7queHdalrdxJkMbBzoXrVEvHsta0kv2+bURnrJwzA67e1939mZqaktDG+kqrRwOBTf1WsdTlxzTBj3rmtXR8pIwiCafeWzxc63ImDIwsVxEvtSI/eTWqEX8ih4mKi8LcBTexOhqzA9uFDW9fG/b31dpoyFwM7lwjM+KokxoUt/UpJiA3JQH22d57QG2ipTb+VbezCnQ9jSnYMWAnMKT18qF9jE9bqfT5IB/1mB3J2vGyI7ZOTS8Lc4J6emcGdbFxmzsO9UDNZepgZp3n9tvaopGAkCjsxsDOBEbfYde3rBAUmgYGDlnvYCZlnuHSHfi322Pn4rs4qtueAnb7A7qDabJfU4lAFZjP6crbjkpTaB4/fHqZScuySPdrbdNI1LZGSEFljmCrBwM6hXrqpbVAvyMD8UGnAEvh27IP9maeSBv/hAqBeAVUOYZvYOSeus6WhuhSxw+K1wHPTUwMVLdewuvbxBYHS42bmZWZWb9EmtSp57pybzUn5iZfoKZn+S9d6ePmWtsYlRiGnXwsM7Mxg8knXUhKlp/SqVrJ8+zEjHz41ZbZl1HPI6TellMBk66lGM+N57rRjqrSq5IE+jXQNr6A2YA8dp0vu3jHzmLZOr2zby4bYPjvs8nEVJcfOycPJODhpfqH5rdPbuzKwc7DASymwDYXSGyFwOTNvHsPW7QNqJl1saxEueAmXWUkFs3aUVDi9dETvOQwdysbJD5JACXHRePyKZpp/r/a8/mPgJeXXEfDvv3S92Nsu9AgafUSdfE06scmY0x/mclxyO2rj4OvYLgzsTGB2BqC8Kjbw3w6YLFbn9tWWUjkpM1OT9A71qoh+bmawpPchHzpNlJsoPapiw/oIgrrzkiAyLEXgrwPnQy7XK1bl+a+RFI93b+8g+b09bezK545iezWkVW0rkqOKk/ITtVycdF3qVE4wZ8UOP6AM7BxMKpCpH9ouSMFF5tMxjp1T3+zD7bb0AMXGp8VNzNr9l25qY9KazaU0YHpjWHs8pXPw1PJVOiFpMfDsrHisH7rLzCFqyzh2Up+HfCGXtC6ZVY1KTsRQ+1JwdVvr5ig2Mz++vVs981buYAzsTGDWsBRf/rUrbumUgUcHN5Vf0OB02EXtcyd0LDln9Yo19imqZ20lJjzRfT73X2/hxEZHlZvvWM2R/GWMutlj9JbQhv+9Q9/Ywnjmmpa404YHthGXt5L2nIlx7hqsNxI5Pa9jYOdgoc/fLg2q4bnrW6vo3h0wREqUeePYKb3Gw5UYqi2tSAspZk8IyRArxNp3eX84olPQ/9UceslSDa0rDOHUElgr9b3kYu/qKB9QvZKyquRyJUuCEHReRvaVHrg0NCj0r0PF9sJJVTEeWEmJunUbQewFQDQAlTkoUT7gqatbGpswiyiZLuv5G1obvl0ntlksI3b+qyQaM4RJuN2+Q+MLgoMPJwAGdqYw7qQb9wTWkyanxQHv3N4BTVOT8N4dHWWXi4ux7/KOjQ7e9tDWytsMmf02aEqJnYJlrm1Xx/DtipkxumfYZZoGBFk+nw9LHu1nyLbDtTUMPfI+n4o2s1rGr5T4vGlqko29YsMv46ThgcpY1SHIjDbaamsvumQqm03ILF/d182S6mCnB2haMbDzAomrU65XrNNHzgakS5YGtkjFzLG9cElqmEFxHfRseESkN6RaRj1Xwkxrq4mStIUO9WGWsNeFCK0vAfGx6qrNxKrkO9UX7ywj5/N7uoh+ruQ8jO3fGHd2ry95f9VOMW8WAKVBS41K8kMshWqTURnfPNBdS5Icx4z4UW1gd3Mn6bnIjSaWssa1kvDKLe10r/umjhmoLFP6V1tj5wqn9/pnYGcCo0660oIVJcv54AuKc9S0+Qq7qEH7a+S9ckXr2jh6usC4FaoUuivxKia5NrtXdYkZkZ3LGFFo2feSGhjcMlXVdSt26OtVEx8kubRXrLprQcnSD/RpiNjoKMn3nkEtU1VtUw2lxyqjaqL/378/epnsskNapeJ/I3soflkd0kp8/+7pmSn7O6se5UZsZ+Xj/dEgoJOd2qrY0OGLzKQ335cr3a1SMQ6rn7gcneuLd7gZ3r2+pnl2nR3WMbDzhNMFRaKfBw+VoGcLxgQCVrbteuO29tZtzAB1KifgX9e2xFf3dVO0vNM6T4RmdXZmfNEWvU1PG9G5XJV7OGqOvZYAX8lLZdl6pZJix3htctssP2SFvvQNbS1exfe3AfpL1Y1gxOVbIykejWpW8v/fSXPJjg/t/GcyuSC1Qmw0ptwo35v/xg7pRifJdAzsTGDULaT38RuYyft89kz6baRwyXf57mFYl3roLDOUg1EPXLUFdm3SU8Iu44SaiQf7NMTGiQMUPcS0tuEK2880zGpDj73c8uU7agT8O0w6pNYR+JltbewUXMf9mpWOGVgjSXmVrNz+XNa0Zvh0hUmW3mu83GgGJgu81pw0QsB9vRvi6asvdiKxe+DncNdYR5GmEg46nKIY2DmYkYFKaFWsXemQU3avPH99aa8wucFVvSYo6FbSuFzlSWmbUdn/b7UldlUrKusxGu7hYWZeeGvnuhjTvzGSKijsTafxmhbrka7mwWTUy5URq7mmrXhnFqsfWmLby6xeEcvG98Oif/QN/3sFx/+D4Z3CLmN2PlcWXK56or8/jxN3cX+uahO+A4GS8+WgAjsA1pfoG/0SY3cwGo7zW9C7kHHj2Om7GI2rilW+HTlKA4qbOmXg2vZ1ylVzhfu9omEUYFGm4qD7/v07OqJbw4u93MzqFTuoZSoGtUhFj8bVUVikfSyNGzqk4+vVBxQvX71SHCZf10rz9tRoXCsJ/zeoqaqSpECh7RvNLiUSXeeFv3s1qYFO9atg5Z6Txm8kzLZDxcaU/ybVxE4cYsIOx6R3XMELf1evFI+WdcKXggPAnd3r4dnrWqHlhFmSy0jfzhe/cFKJXSgHJw2AO2uCWGLnYMaW2Nlvzb4cxcuqbbsEOOsG1JORSv4y4As1u9qpflVUDGhYbkbnCZ/Ph/iYaLx9ewfc3lXf4LEJKnuaSpEr7dBzBB7o0xA3KGh3M2tsr3KfqaqKDfl/YGmfVBDSv1lpqVBg+yo5jWuV70FsZl4hNZB1ZuhsOqpWWvpXRZHp2tQIPReX1Eoy9IVB6dzdwd/5NI9gEHitWRk8qZ3GywnPpnBCrw2nB6MM7EzglGLawGtRT6AR7iFo1TAWZgts96FXJ4leWE6QmlI+45UrrdBSUiH2kyskGq2Hul51Y2Xx9N0pM8p/6CwlRgk8jmLDrqgaqFrmsEsOVVI5AeufHOCf5UJsuXDnU+rrZ69thdho/Xlb6BrU9v6VSl9gT1o5UscutCTb5wOaiAS+2inbR7Galpdvbhv2d4NDejMHvghY2ctVEQMio3CdHgLpeel3UHmBYgzsHObqtmn438geAPRfUIE3dmnnCf3rGT+4KbY8PTDo+ysMmrDb7rGBaosEPFqJZaQxOjNXow7PgOa18NBljfDBcPkBnv3bNWiZzplV8dvfeoddrm1GZfz3fvnewUp6qnWoVwWv3NLWfz+VubljhqFzSKppMqGmjZ3WF8SUxFh/ibfY1rReRglxRjwuHBZgBDB7QHMtQ72U6aVgSI7QSysxoKQv9FpyVKCnMSlls8dYUVPj9BK6UN4oanGplnWS0b1hdZw8cx7/vdCmKHBQRiN7sRpVipgYH4PEgCqPCrFRiNFQbaqF2ptL7R4b1fasfjXxkoO6VROx+9iZsL83PBMJWV9UlA/jQoZ2MLqUWWptDWsoqyIMNzWW0mN0tUgHgX+bMGWTUnquMUHi33qsU9E8wgg+Hwy/wNWuLXTzTVOT8Nz1rVWNNWmGL+7tijMFRaqrpaUOZ1pAG8Vy7Y/VJs5gQaWSGlNjZUEAq2JJ8q6pGDKX6YzRl+KxIc3QpYEF07fouBDdWBQNKEt3yzoXp5YyKo6uYFAbMTlGv6XKV8WG/33oMkrHzWqtYCgV0e3Z/mjSRk3zRvmqWGUrElsucL1b/swT+V6q05E5x9zsc/nQZY2C/h96SNrXqxLUa9wsgXspdvra1a2M/s1rhf2tUmqG0vE6tz7DtGJgZ6HV/7wcbSzIQMoEXsxm3ch6A4wH+1ycNN3+zEbdzqhtJKyU1IMu6MFgaVYV/sSEplnJqRzYopYhvfXsvm7U3ANKhq8IFLhvSsaxCz0UVk4y0qNRNdzaOQPt6laWXEbsVKlt21q+jZ78BXBVmDlHLRvOKSCdSktu9Vzacltw0ouR3fdvOIIgVuLp7EQzsDOB1IVaITYaiSKlOX0utBVorLAnm1KBeYfSB6hcpmwGJcnq0ai0RPP2rvXVrVtlWozK4KXWc0tn6fkX1W7aiMbmWtenbJvOzvjskmbSy4AkDZ0n5FROlB7TsEH1Sph8XWvUDdOJIXDrfzw9CH0uCT94sD7OuBbDpUJ8MGmf5HehQl/2ZPMzmw+JEdmDmlXobdY05cY2qJIYi0nXtCzdtjMuKUkM7CwmVtJSvVI8Nk682JOtzIDmqahfLRHXt9c6pUlA54mQb8b2byL6C7HrNeieMPj1tiigSEFq1R+O6Ix5j/TRPYdldZGJxbWM5l8mIaRqvcuFWSNulQjg7u7ZAF/+tSsuMaCnndEldrrHTAx9o1WY8XmhisTI2yPwPIQGYIHf1ZMInq4MKRGMj1WfxcudumnDOwU1Xwj63YUfHso5J71uny/o2gi9h6zg8wHPXHhAyy/ng5FXqFTpq/97KA/ixKi59v56aQPJ78JtP72KsS8nDo+RIEBAyzopWPPPy/EXnUM5WcUVgd2ePXtw9913IzMzEwkJCWjYsCEmTJiA8+fP2500UXIXqtTNl1QhtlwnhIS4aMx7pA9evEl5t27JNIUk6p5LMxX1UASM68QR2sYQAPYEdCYoKhEf1DY2OkrfOFcA4mOi8OV9XQFIl1gIgvIevrWS4/HqLe1Qv1oiXr21tMPLR3d1xncPdscd3eqL/iY6yocuDaqJPswU9TwNSLeVY/ZpedAoKSF2enWGWTKqSj8YA4+JD9LHqFqleMwce/FFsFWdFGx5emC5EsEKsdEYd7n4S5wWLeukYMboS1E5UWwGjlJyAx6bcca1lMzrHWtRi8BzKVYVK1piJ/Jbvb55oBselrgmWqQlY8Vj/WV//+ndXQxLC+Ce0v2gKTptTIcSrgjstm7dipKSErzzzjvYvHkz/vOf/+Dtt9/GY489ZnfSTGfURV+uDZTPp7iHotz8pWqItfcJqrYxMVh5/obWYfdXgKD4jq1TOQHN05Ix/+99/e2mKsRGo13dKpom3DazPaTa1fVvFtyAW0ty9I6mIBa4bpw4QHRZuzPZwLHdlExp9s393SW/U3Pum6ZeLDnz+RDUWz1Qz8bVla9URmDa/nuf/HA0TuLEuMHIYWiktyFeG9KhXlXJ4U461a8adlYVsRK7bx6QvqZF02ZAXq/mvOrZnJaxIO3misBu0KBBmDZtGgYMGIAGDRrgqquuwiOPPIJvv/3W7qSJcspJD5p+VGOSbutSF8/foL/EEACKRe6QwGDLqobe916ohhjYolbY6hEzaJ1Gyqqr6rVb26FVwJRHFRWMfB9aQqekhEHtNal4HliLxcdEY9qITnj39g5ITgh/rCrIVD/K3rMi393Xu/RaHj+4mdLkahaYNrEZK5RQcs7FmkwEr0PdhaOrA4JEnqClOUVgssVL7C5UxYqlWMNOpBk4LmdgMCd2/DvUq6JqfYE1NXrzNSfNOOQUrgjsxOTm5qJqVeeO7i/FymvQiFKgx4c0UzwJfDj39SrfriMwINDbzkvp7+/r1QA/juqJ125tH5QpqBljzOg3wJphxm0zcvvhJMRFo1eTi6U8V7YJXz2tpY1daRMm/Xti1ntUYHu1mzrKt3Pte0lNDGiRqmh35JIbIzOzQ9D9fOHv8YOb4Y+nBwXNBVzudyoPsVSJTbjjrDTwDne6Qtsah11f2BclbReIVDtGwIC8SiywE1lOT6nUHd3r4fau9TBteCdVaRMz4cqLPZelklQ2zd6IHvXDru/9xVm60yTm3ksz0V3mXjCKM4pupLkysNu1axdee+013H///bLLFRQUIC8vL+iPFeROul0DfittoxGYCb5zewdFpTVKiXbYsKHELCrKh1bpKYpHmh/YovzYUlcqnB5LiV5NauC1gIGpFVE1Hpq+iy4uWn0Dd73btHY4F3H39Mz0/9vsKeLu7pmJHo2qoUejiwF1adN96Q5QZYzugHBNu/KDOstJTa6AHo2q+UsP5ZTOFSt/bYSrCjTbV/d1w9Vt0/DMNS0lq7f1Eq/eE/lMRYeK0HXGx0TjmWtaom/Tmrrvp4rxF68xqbS8cENrbJs0CPWqhW8PHVgjILdv9/duGDTQciCxZ9rjVzQXrWZW+2xZ+8/LZb93SKWcJFsDu4kTJ17oJSX9Z9WqVUG/OXToEAYNGoQbb7wR99xzj+z6J0+ejJSUFP+fjAzp4SasMumalqheKR4Tr2xu+rb0VsUObFG+F6rW7CExLlr0hgv8xP5HubghIR0q/nVtS9l5SNX6+K7OqCsxW0UgMzOT0PaH0So3Fjpdmhlt7AL5fBcD7ntlevjpSoMpaxX3z6HN8dk9XYOOuxk9Roe2Dl/6KjX1nVRAdmvnuvjsnq6KSuzc0GGmc2ZVvHJLO9RIikez2km4q0cmHh8SXNWtdz9E29ipGNrECjcETtsX9CyRvj7iY6IVHZmg8UtlfvHo4Kb4/dHLFKzRWFUCaqmc+lySY+uUYqNGjcItt9wiu0z9+vX9/z506BD69u2Lbt264d133w27/vHjx2PcuHH+/+fl5VkS3MndmI1qJmHl4/0sb4endGuqe5iF+T4pPgbjBjQp1yC/TFBVrM2NJaSqYpdnnQj6//Xt03XNtRi26kjJWTD48qmVXAG/jLkUlS6U0N7VMxOvzt1ZuimZbf2la12cOHMejULGYFQ6bqKeM/76be2x4/BpNKtt5ETtF2m5HpX8Qu7eD/xqaEipcImCh6uebXuVnj32+Xx40oSX8BKFDYrLTpcpvYl98i9QsQGjNBidM6uZnceJ16wT0xTI1sCuevXqqF5dWY+tgwcPom/fvujQoQOmTZuGqKjwhY3x8fGIj7e3WF+MVReF3JhYWqldS4W4aIzokSn5fVDnBW1JMoxUJhf6ud4pw5SURoVfia4kiGpW+2Ivy9CBaDc9NRDFxQLaPD076PNJ17QSXZeS6jS9pR6x0VFoniY+pprRlN4/Rr6cxEZHSVahmZmD+ODDrZ0z8MWK/cZv06d/PaG/D7c+Jz6DQ9stX9bUiEGazctBDX/n9kn8W+s6wtCVfBf2znBFG7tDhw6hT58+yMjIwJQpU3D06FFkZ2cjOzvb7qSJsrq6QWqasuCZJ4zZltGXuJkldmozdKnNO+XBEHhdqWkzY0TyK8XHIEVk7DIp3RtWKzdHpxpOyEq1pCG0pDK0JNNuiq4FHzD5utYY2bdh8McSP66noBkBBR+/xrWS8OTQiyWBGYG9TkV/65BMyCBBcZ3OXdNae9KklrPuTSO5IrCbPXs2du7ciblz5yI9PR21a9f2/yFlmbWSjKGBzkGAtbBjuBEpUps3usOLx/JoUT6fD+MGXGJ3MnTRcj1e3rwWmqYm4dbOdQGIj/Cv5vTHBtRMtKtbGZ3qV8GljasjUWP7Oz3XXugL6+f3dMHD/ZuEnf/23ds7BKzD+M4RYnlb4NibTm3Xd1dA5xwj2J1/qmFkoNqzcXW0SU+RnPEHgOjBmXilsjmKXXRY/WytilVq+PDhGD58uN3JUMzqB7fU9tTe6F8/0B1//XhV+AVVCHcogsdHMnTTqkmVGBr9YNCaAWs9PnYf10hSITYaM8f28v9fT29En6+0B/ey8f1QVFKCivEx+OrCwMCa29hpTk153RtVR/dG4ZvSBJZiZlRNxMSrWuDM+WLcYeLsD6kBwwe54fqX7pBw4W+d6zdyqrvwy4antia2W4NqWLr7ePA6LvwwNjoK/xvVU2nyVKtoUs9oM7kvxaRYrWR1b8ZVK8ZZlgmmJMTikQFN0KFewJu1zTmw1FhygSV2P4zqYVFq5KnJqOOiHVowL3O6w1XLW3OpBI7cb86coUqkpgQGKfp2XM3vQ3dZ8xymAf8u63398V2dta1MwzbdIHDKRNUDMOsI8u06TqqbydiQ0olXNsey3SdwVVvjhrayCgM7D5C6R6pVisc3D3RHgpoeSBZVW/RsVB23h8ypqrQnpVl6Na6Or1btL/d5YMapd85ao6jJ5vT04DWTD9pLEqyodrK7astpZ80p6Sk3GLbIMnb3sFfqq/u6YfGOoxjWpa7o96rGsTMyYaHrVjNupqJlLi6lJDBVs32xZbUcm+E9MjFcpuOfkzGw8zi1U73Yyeixz9QGiuGqQwBjMk+tI+UHt0dU0XnCDXVRDmRHaGD2qdI39If12yzzYJ+GeHP+rgvrc+f1LNUWMHQubqv2Ts31bXin2ICdtKBTrKmUDMxtNYfW0bibl5+jSuKJwCld1BwLtYPihtO7SY2wyyibAiqw566eFOlbR3CvWOtUMnD2kUByAWe4/TPjHvthVA/836Cmhq5TdIYBOx9JDs2bYsM0Fwic+SP03CfGl6+RMGI6RbeRe9kTHRDZvKSEZfW2ezUO/ywAgOQKMdj01EBV6+6aaf4UZmoxsPMAp5XIfHJ3F02/099+KPj/8QqnDFOzXjureKKC0iG/bG2JaXjUeOaalvhrrwZonZ4SfmGNnDB1WJnW6ZXxQJ+G4Re0iBn3tZKgUnqz5uUzI/s2ROOalfDoYInAWmTTz17bCm0zKuNhsakKZQZztuua03L0LnaeMOFaUNXe0uBjFlhiZ0E18+h+yoZeqpEUb9qLrJUY2HmAlWFduZvwwh137YX5Jfs3q6W5TZfeKZTKN/Y25sgErsWI/C38zBNSv1O+P0bs+e1d6+GxIc0c9+JgFUG4OA5dn0uMGEDWHaReiMy8DKpViseccb1xf2/xwFps3LPbutTF9yN7oFol5w1Cr4fW43zHhWkOezRSVoKkJ1jT2/lFdaAqOreu+DrEAvf4GOOn53My94emDuTWNiB6PHttKwxskYpLGwcPfyB1LAJvvjH9GmP/ybNoY2LJkFKNapQftDIqIFCVmnbMCoHx8h3d6uHjpXvRq0kNLNx+1LY0mcUJbd8FQcDMMZfiXGGxonlQRdch8pnSB7dxuUjgDDTSS337YHdEX5jv02mMfLkwuslHoMEtU/HLJuMGzveX2ClIct9LamLRP/oqLq33hZtTLEBoVXkvmWYuyqbVC/i3g56XDsh2DMHAjoJpvMcS4qIxqGWq4uXrVr3Yu/Thy0WqUjQwIr9+oE9DnC8uCZrbNnC4kBgFU9mFo3VKscCH25BWtTGiRybSqySg8eO/6E6THXwA2tetgk0H8+xOiqSY6CgkmThcTOilYPZDTm7t7evKd7RySpssRb0oA4PZ0HWZuCNtMyobG9ipPOoZVc2ZBaRrg2roc0kNNKllzLzMYiWwclSNo+eV6EwHBnYmcMoAxZrWFeZ7vTfNF/d2xc8b/8RoHdNNSSlXFathHRVio8s1oK9U4eJtomZaLa2UpFsQnDP0ih7/N6gpalSKx5er9uPAyXMB39ifOxuRAqc9ZHTNPGFjlbzaXpRCUBu7cmszIEXirmqbhsm/bNW1Dic2fYiO8uHDEcqqX+VSX1bNr3Yfr2yThpV7TireTqRjGzuTlc2jeI2Jgxw6qSg7nG4Nq+GZa1qiogcaqGplRL7tlnG65FROjEXF+BiM7tfYP3CtcuZf81YcYrm9sPv5Xr400T7B457pW5eZwzrK5Wt2nk+784sv7u2K5rWTMf2vXQGoPxZ/6VIPn92jrVOe2ZzUAaxM5D5dTRR4zU65sQ3yzhWih4Kpd7zIzszMqG07MYZyYJIUe+WWtvh69QHx3owOYkWGXS54MuV+CQiKXPQSGEhtm6zkCrHo2ag6CotLgqYXA+wfCF2NOhemXDQjyVYehW4Nq+HnMZeKblvJvkVF+SL2GaoFAzuTJcRGB43BZAr35FOOc0OHdDw9YwtapCVbtk07x8Jzgqvb1sHVbevILuPm/QskFhy6Ja6wsm1aOGoDAQD45O7OF5YP/oEBzWRNt/qJ/jhfXOKJoTfEGHEtueU+soM3rxqbObGNhFmcWAytxvDu9dE8LRkt60j3yLV6H9U2DrdL30vCD/o5sEUtzNp8WPW67d87WJII+apYM8YuU76sUaWJZbPfVNHTPlXDtiVncDHgTdjsLD50CBe3lrRKM68U2eqXQie+hLrg3YXC8dotr1XZTBPVKsYBUPZgjIryoWuDapa+GRvxUChxQGby6q3twi7jxExPKSOSXnYtmrmN8JQNdxKO1gdwlYpxWP/kACx7rJ8h21aSCtn9NOD+e/GmNqLnNtahxYFW3oZqhzsh47HEzgRuvmbD3XBqG+FaeSwev6IZLklNChqqxInCDneiaB32RkyNalbSPLabGCfGf0bMXDKgeSqGd6+PthmV/Z/ZW/qiK7LTTG9v8qB8SXfnCf3Hv0VaClY90R+Z43/2fzb5ulayg6wbeb8EcuPzRkvVevl16Ntz0TzHiRmRBgzsTGbFm4mxw524MZsolRgXgzu61bc7GZZwS/5zfYd0zN5yGJeoHP/Kzrh1bP/GyDp2xl+FqEdUlA8Tr2qheHmrSzJqJcfjcF6BomWdEo4qyaPkrh+j9iO0RuDWznUll/3i3q6Oay9n1rWmrERVXQksqeOsK80jvFzMHEntB8toDTLu6pGJD37PKve5IYcwTJrMjouUlhgOaF4Lv4y5FPWruWfMvbEm99aVO//1LgzcbdpDN2S9dSonKA7stJR0jehRX/VvxAQFAg4osVOrW0PnTRRvJzPPgNL2x15+kjGwM5mTSsDKpqG6uWOG5nXYXQXoJmXzjKpl9EjsdvL5fGhW27oex24gdwslxEVjw8QBprXVKt/TVVn+1CItWVNwMuFK5aWVctQOUCy3W2aMYydXWmc2d+QEwYLOp8ZA25T43DmPa10Y2JnA6lItpcHjP4c2x1Vt0tA6vbK5CQoQiSV8ZaR23YjYuKQkzLb1b8JW4QJXL19WySa1xQLKHzelL2ozRvd0zL2sOx0m7Eb7upWNX2kAhxx6wzipwCOIG6NkEQzsTOakNnax0VHoaPaYeg7lpoJGuQdXepUEHDh5LuzYiC7a3Yjjxoe03UGd2q3L3e81k+Klv1Rp7t96Y82+HFzXTn5cRr10d/IVOR6lwZU9OYXaElgzeDmPZGDnAW58UESycOfr8SHNsHZfDu65NLPcd/Me6YOCIu8OXFqmQfVKqBQfg9MFRYas77N7uuDRbzfguetaG7I+Pex8yXBsSYkKWvdg6p0dcfRUARrVNGYiewBoUKMSGqieDk+9mGjjq+bNas6hdq1uf345sdDAmYPueIjbLlq3pVcpO/ZL6ybrV6+IlY/3w/29G5b7LjY6SlFQ5/bTGBcThdX/7I9eTcIPgqxEj0bVsegfl0X8tESqBih24AMLULYPYsv0a1YLtwS0hcuoWjpd19DWtY1Kmqg+CgbyVmK5jnEAxdgZ5JuZHzv1urWSt1/7bXR581o4droATQx8O7RCuBuuQz13VuW67WbXW/Vleq9Yo9cncoLiY6IRLXEY3By4OmVqLsCd1VFGBSSzxvbCgZPn0ETlUDxK3dmtHq5skyY7q40atZIroE1GZazfn4MrWtXGTxv/LP1CUWcrETYOdxK0vANKEwa3TMUvm7Lx114NVP+2dYYx59dIDOxM8t4dHSEIgiUXrRVvXsvG98PBnHNole68i9hs7AnsPG4+I4F3a4wZXTS9zqBDlhgXY1pQB5QGLEa3aZ42vBNmb87GFa0DAjutbLyJjBhyxsg+NK/d2g77TpxVVa2+ceIAnC4oQs2kCvoSYgIGdiZywpuIUVJTKiA1pfwF7JaYx5aqWO+cfjJQTHQUhnevj9MFRciommjptkPzJDfev5F8X1WtGBdUnWyn3k1qYMH2o7iilfqq7CqJ8lPtWSHw0o+JjlLdVjKpQqxps4noxcDOAyI5o1PKLQ8wPVrVSUHlxFiM6FEfj3+3ydyNWXQ8pTbj9ktezWwUFMzt594xdB7I129rh3nbjqJf05qqf5uaUgEv39wWFU3oBBYBWX1YDOwoiBd6zRnt5k518ca8XRjcMtXwdT/Ur7Fh60pNqYD37uh44X8mB3bkSqEvge55KVSX0Eh4kVPDjOYkSRVicVWbNM2/v0bnEDF8VkljYEdBJlzZHNe9tQQP9mlkd1Ico0ZSPDZOHGDKkAPjLjdn+io+2KhM4LUQ+jB043XipSYuRmCAE8KF17TRGNhRkMa1krD+yQGICtOoe0irVPy8MRt39yw/1logr+TBWoI6Zrj6uTHwIHPpnVKMyOsY2HmA0W+w4YI6AHjpprYY0SMX7TIqG7pts9iS0ds4VIdXHmyVKjCLMpIXrgsv7INTuWkEAF4H0jhAsQfoub4vbVw6YGtPlQO3VoiNRqf6VU2pnjSDHflVXYkej89e1wpVK8ZhwpXNTdu2WfubmlzaM7qvhgbTWjxxRTO0yaiMF29sY8n2vE5NXmHWzAR6KSkJd1F8opvWAIdxkXfxdTjCvX5re/y08U8MaWV8x4BI17VBNfzr2pZoGNKNvmlqMlY/0d+VbYX+N6oHFmw/qqvRtBq1UxLwv5E9yn3uwkNnG7lj5Zb4J7AkKcod75JEtmFgF+FSEmNxWxfzxkWK9AfwsC71RD83O6gza/W1kivgpo4Z5qycSEJJQAQareDijvR8J5RbAng1pE6xU0uarcR3Hw9gJkahIqkqipRza15REnBBR3O2jiBaj0a4ayE+huGBW7HEzgOYzRGRl5UEFNm5sQmDG426rBEW7zyGmzqm250UUbwOpDGwIyIiRythCbTlqleKx6/jetudDNKAZa0e4OQ3F47lRmQv2Wp5E+rsJ1/XCgAM7fVdzLYFkpRk/5F0+CJpX6WwxI5M1bx2st1JIKILrHgJvLVzXVzZJg2VDJwHtIRPa8OZ9dLdKj3FlPWG0pv6CrHeLdfy7p6RrX4Zcynu6pGJZy+8vROR/VqkWfOiZWRQBwB1KicYuj4ruGmwXyPVSq6AhX/vi34mjHX5r2tb6l5HWkoFXN8+He3rVjEgRc7EEjsPcGJlZ7PayXjSxAF4iUi9K1urGH/QQXFJk1pJeOWWtqid4r4Az2xObO5St1oiKifGGb7eWkkVdK9j5GWNJIeh8gqW2HmAg5vYESmitnDDiQ8zNwidLtBBsVtYV7etg86ZVe1Ohitd0bo2AKBRzUphlnQPqeeem65ps7DEjohcp2pF40sDKARjZ88Y278xWtVJQdcG1US/b1yzEnYcOY2bO5k3WL0RWIihDAM7igjMEJxN6fl5/bZ2+GVTNu7r3cDcBEWIjCqJ2HAgV/xLFn24gpJ7Jz4mGkNa1Zb8/pO7u6BCbJQp1adGitBmi6oxsPMERi3heLmhrBcozbCHtk7DUDXtxEjWxKtaIDrKh1s7O7ukhswV5YPhQZ3ZU3tJ9fC+u2cmVu89ictM6LzhFgzsPIClUdLmPdIHp/ILkVE10e6kEDlOjaR4vHprO7uT4UmXpDp7qKe/DWiCST/9UfoflzxDlDzrhrSqjYV/74u0yuIdLSKhfS47T5CnZVaviNbple1OhuU4ETZFMjur7H4c1ROPD2mGmztl2JcIBW7oEDBVmMeyi7rVEhETHbnhDUvsiIiIDNIqPcWyQXqdrHKCs9vreRkDOw/wfsEyKeEL+jevCopckdQ8xZBdNeF4jenXGDuPnsZ17eoYv3KSxcDOAyIpEyNpQtC/3VW3khAXbXcSPCt07DoiK6QkxuLjuzrbnYyIxMCOiGw34crmOHDyHEb0qG93UjynbXpl9GxUHRlVOWsDUSRgYOcBnGaHAHdXxaZXScQvYy61OxmeFBXlw6f3dLE7GWQWjVU2bhwTjrVTykRutxEPmDa8E65qk4aHL29id1LIAdxcFUvOwyuIvCgSgkOW2LlY36Y10TeCB2EkIiJtIiHAiVQssSPyCObTRKUa10qyOwmW0Xrfu7Eq1gid6le1OwmmY4kdkUdEaD5N5Pf9yB5YvOMo7uhWz+6kkMOs/eflOH6mAI1qVrI7KaZjYEdEROW4sQS4bUZltM2obHcyXCHSqmKrVIxDlYqRMWgyq2KJPCLC8mky2b29GqBWcjzu793Q7qSQCSK1KjYSMLAj8qCx/Ut7Sl/XnqO+kzbVK8Vj2fh+eHRwU7uTQjIireSNwmNVLJEH3dq5Lno0rI70KhzjkLTzMWpwvJZpnJeWgjGwI/KoutUS7U4CEZlkzsO9sOXPPPRrFjlDXrlt4HW7MLAjIiJymca1kiJqWBdSjm3siDyiY/0qdieBiIhsxhI7Ipeb90gfLNl1DDd1zLA7KUTkQqzi9BYGdkQul1m9IjKrV7Q7GUTkUpxb2ltYFUtERETkEQzsiIiIIhirYr2FgR0REVGE4RCF3sU2dkRERBEmJSEWg1qkolgQUL1SZMyhGikY2BEREUUYn8+Ht2/vYHcy1GEpoyKsiiUiIiLyCAZ2RERERB7husCuoKAAbdu2hc/nw7p16+xODhEREZFjuC6w+8c//oG0tDS7k0FERETkOK4K7H755RfMnj0bU6ZMsTspRERERI7jml6xhw8fxr333ovvv/8eiYmJdieHiIiILMROscq4IrATBAHDhw/H/fffj44dO2LPnj2KfldQUICCggL///Py8kxKIREREZmJM9oqY2tV7MSJE+Hz+WT/rFq1Cq+99hry8vIwfvx4VeufPHkyUlJS/H8yMjJM2hMiIiIi+/kEQbAtCD527BiOHTsmu0z9+vVxyy234Mcff4QvYA6U4uJiREdHY9iwYfjoo49EfytWYpeRkYHc3FwkJycbsxNERERkuvnbjmD4tJUAgD3PXWFzaqyVl5eHlJQURfGLrVWx1atXR/Xq1cMu9+qrr2LSpEn+/x86dAgDBw7El19+iS5dukj+Lj4+HvHx8YaklYiIiMjpXNHGrm7dukH/r1SpEgCgYcOGSE9PtyNJRERERI7jquFOiIiIiEiaK0rsQtWvXx82Ng0kIiIiciSW2BERERF5BAM7IiIicrzAkTFIGgM7IiIiIo9gYEdERETkEQzsiIiIiDyCgR0RERE5Xtv0ygCApAquHNDDMjw6RERE5HgpibFY/+QAxMeyTEoOAzsiIiJyhZTEWLuT4HgMe4mIiIg8goEdERERkUcwsCMiIiLyCAZ2RERERB7BwI6IiIjIIxjYEREREXkEAzsiIiIij2BgR0REROQRDOyIiIiIPIKBHREREZFHMLAjIiIi8ggGdkREREQewcCOiIiIyCMY2BERERF5RIzdCbCSIAgAgLy8PJtTQkRERKRMWdxSFsfIiajA7tSpUwCAjIwMm1NCREREpM6pU6eQkpIiu4xPUBL+eURJSQkOHTqEpKQk+Hw+07aTl5eHjIwM7N+/H8nJyaZth5TjOXEmnhdn4nlxJp4XZ7LivAiCgFOnTiEtLQ1RUfKt6CKqxC4qKgrp6emWbS85OZk3n8PwnDgTz4sz8bw4E8+LM5l9XsKV1JVh5wkiIiIij2BgR0REROQRDOxMEB8fjwkTJiA+Pt7upNAFPCfOxPPiTDwvzsTz4kxOOy8R1XmCiIiIyMtYYkdERETkEQzsiIiIiDyCgR0RERGRRzCwM9ibb76JzMxMVKhQAR06dMCiRYvsTpJnLFy4EFdeeSXS0tLg8/nw/fffB30vCAImTpyItLQ0JCQkoE+fPti8eXPQMgUFBRg9ejSqV6+OihUr4qqrrsKBAweCljl58iRuv/12pKSkICUlBbfffjtycnJM3jt3mjx5Mjp16oSkpCTUrFkT11xzDbZt2xa0DM+L9d566y20bt3aP65Wt27d8Msvv/i/5zlxhsmTJ8Pn82Hs2LH+z3hurDdx4kT4fL6gP6mpqf7vXXdOBDLM9OnThdjYWOG9994TtmzZIowZM0aoWLGisHfvXruT5gk///yz8PjjjwvffPONAED47rvvgr5/7rnnhKSkJOGbb74RNm7cKNx8881C7dq1hby8PP8y999/v1CnTh1hzpw5wpo1a4S+ffsKbdq0EYqKivzLDBo0SGjZsqWwZMkSYcmSJULLli2FoUOHWrWbrjJw4EBh2rRpwqZNm4R169YJV1xxhVC3bl3h9OnT/mV4Xqz3ww8/CD/99JOwbds2Ydu2bcJjjz0mxMbGCps2bRIEgefECVasWCHUr19faN26tTBmzBj/5zw31pswYYLQokUL4c8///T/OXLkiP97t50TBnYG6ty5s3D//fcHfda0aVPh0UcftSlF3hUa2JWUlAipqanCc8895/8sPz9fSElJEd5++21BEAQhJydHiI2NFaZPn+5f5uDBg0JUVJQwc+ZMQRAEYcuWLQIAYdmyZf5lli5dKgAQtm7davJeud+RI0cEAMKCBQsEQeB5cZIqVaoI77//Ps+JA5w6dUpo3LixMGfOHKF3797+wI7nxh4TJkwQ2rRpI/qdG88Jq2INcv78eaxevRoDBgwI+nzAgAFYsmSJTamKHFlZWcjOzg46/vHx8ejdu7f/+K9evRqFhYVBy6SlpaFly5b+ZZYuXYqUlBR06dLFv0zXrl2RkpLC86hAbm4uAKBq1aoAeF6coLi4GNOnT8eZM2fQrVs3nhMHGDlyJK644gr0798/6HOeG/vs2LEDaWlpyMzMxC233ILdu3cDcOc5iai5Ys107NgxFBcXo1atWkGf16pVC9nZ2TalKnKUHWOx4793717/MnFxcahSpUq5Zcp+n52djZo1a5Zbf82aNXkewxAEAePGjUPPnj3RsmVLADwvdtq4cSO6deuG/Px8VKpUCd999x2aN2/uf4jwnNhj+vTpWLNmDVauXFnuO94v9ujSpQs+/vhjNGnSBIcPH8akSZPQvXt3bN682ZXnhIGdwXw+X9D/BUEo9xmZR8vxD11GbHmex/BGjRqFDRs2YPHixeW+43mx3iWXXIJ169YhJycH33zzDe68804sWLDA/z3PifX279+PMWPGYPbs2ahQoYLkcjw31ho8eLD/361atUK3bt3QsGFDfPTRR+jatSsAd50TVsUapHr16oiOji4XeR85cqRcpE/GK+vBJHf8U1NTcf78eZw8eVJ2mcOHD5db/9GjR3keZYwePRo//PAD5s2bh/T0dP/nPC/2iYuLQ6NGjdCxY0dMnjwZbdq0wSuvvMJzYqPVq1fjyJEj6NChA2JiYhATE4MFCxbg1VdfRUxMjP+48dzYq2LFimjVqhV27NjhyvuFgZ1B4uLi0KFDB8yZMyfo8zlz5qB79+42pSpyZGZmIjU1Nej4nz9/HgsWLPAf/w4dOiA2NjZomT///BObNm3yL9OtWzfk5uZixYoV/mWWL1+O3NxcnkcRgiBg1KhR+PbbbzF37lxkZmYGfc/z4hyCIKCgoIDnxEb9+vXDxo0bsW7dOv+fjh07YtiwYVi3bh0aNGjAc+MABQUF+OOPP1C7dm133i+GdsWIcGXDnUydOlXYsmWLMHbsWKFixYrCnj177E6aJ5w6dUpYu3atsHbtWgGA8NJLLwlr1671Dyfz3HPPCSkpKcK3334rbNy4Ubj11ltFu6Snp6cLv/76q7BmzRrhsssuE+2S3rp1a2Hp0qXC0qVLhVatWnGYAAkPPPCAkJKSIsyfPz9oqICzZ8/6l+F5sd748eOFhQsXCllZWcKGDRuExx57TIiKihJmz54tCALPiZME9ooVBJ4bO/ztb38T5s+fL+zevVtYtmyZMHToUCEpKcn/7HbbOWFgZ7A33nhDqFevnhAXFye0b9/eP+wD6Tdv3jwBQLk/d955pyAIpd3SJ0yYIKSmpgrx8fFCr169hI0bNwat49y5c8KoUaOEqlWrCgkJCcLQoUOFffv2BS1z/PhxYdiwYUJSUpKQlJQkDBs2TDh58qRFe+kuYucDgDBt2jT/Mjwv1rvrrrv8+VCNGjWEfv36+YM6QeA5cZLQwI7nxnpl49LFxsYKaWlpwnXXXSds3rzZ/73bzolPEATB2DJAIiIiIrID29gREREReQQDOyIiIiKPYGBHRERE5BEM7IiIiIg8goEdERERkUcwsCMiIiLyCAZ2RERERB7BwI6IiIjIIxjYEREREXkEAzsiIgOMHTsW11xzjd3JIKIIx8COiMgAK1euROfOne1OBhFFOM4VS0SkQ2FhISpWrIjCwkL/Z507d8by5cttTBURRaoYuxNARORm0dHRWLx4Mbp06YJ169ahVq1aqFChgt3JIqIIxcCOiEiHqKgoHDp0CNWqVUObNm3sTg4RRTi2sSMi0mnt2rUM6ojIERjYERHptG7dOgZ2ROQIDOyIiHTauHEjWrdubXcyiIgY2BER6VVSUoINGzbg0KFDyM3NtTs5RBTBGNgREek0adIkfPnll6hTpw6efvppu5NDRBGM49gREREReQRL7IiIiIg8goEdERERkUcwsCMiIiLyCAZ2RERERB7BwI6IiIjIIxjYEREREXkEAzsiIiIij2BgR0REROQRDOyIiIiIPIKBHREREZFHMLAjIiIi8ggGdkREREQe8f/yFxMg3unXjgAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "np.random.seed(42) # we set a seed to reproduce results later\n", - "mu, sigma = 0, 1 # mean and standard deviation\n", - "N = 5000\n", - "xi = np.random.normal(mu, sigma, N) # gaussian noise time series\n", - "a = 0.5\n", - "w_t = [0]\n", - "t = np.arange(0, N, 1) # construct time array\n", - "for i in t[1:]:\n", - " w_t.append(a*w_t[i-1]+xi[i])\n", - "w_t = np.asarray(w_t)\n", - " \n", - "# plot the timeseries\n", - "fig = plt.figure()\n", - "ax = plt.plot(t, w_t)\n", - "ax = plt.xlabel(\"$t$\")\n", - "ax = plt.ylabel(\"$w_t$\")\n", - "ax = plt.title(\"Examplary AR(1) Time Series\")\n", - "fig.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b0970273", - "metadata": {}, - "source": [ - "Now we construct an object of class VisibilityGraph. Note the keyword `horizontal`, that can be set to be `False` or `True`, and is `False` by default. We denote the object with \"1\" to distinguish it from the second timeseries that will be analysed later on." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "d481e772", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating visibility relations...\n", - "VisibilityGraph: time series shape (5000,).\n", - "InteractingNetworks:\n", - "Network: undirected, 5000 nodes, 16703 links, link density 0.001.\n" - ] - } - ], - "source": [ - "VG1 = VisibilityGraph(w_t, horizontal = False) # creating object\n", - "print(VG1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "367aaa50", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating horizontal visibility relations...\n", - "VisibilityGraph: time series shape (5000,).\n", - "InteractingNetworks:\n", - "Network: undirected, 5000 nodes, 9986 links, link density 0.001.\n" - ] - } - ], - "source": [ - "HVG1 = VisibilityGraph(w_t, horizontal = True)\n", - "print(HVG1)" - ] - }, - { - "cell_type": "markdown", - "id": "0a3d1527", - "metadata": {}, - "source": [ - "It can be illustrative to plot the visibility relations for the VG for better understanding of the concept. They can be obtained with the method `VisibilityGraph.visibility_relations()`. Note that we zoom in a bit here, by only plotting the first 200 data points." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8d8b5cdb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating visibility relations...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# obtain the visibilty relations as an narray with boolean values\n", - "# i.e. Trure: two data points are \"visible\" to each other\n", - "visibility_relations = VG1.visibility_relations()\n", - "cutoff = int(N/10)\n", - "fig = plt.figure()\n", - "ax = plt.plot(t[:cutoff], w_t[:cutoff]) # zooming in a bit\n", - "ax = plt.xlabel(\"$t$\")\n", - "ax = plt.ylabel(\"$w_t$\")\n", - "ax = plt.title(\"TS with visibility relations\")\n", - "fig.tight_layout()\n", - "# plotting the visibilty relations as grey arrows\n", - "for i in range(cutoff):\n", - " for k in range(cutoff):\n", - " if visibility_relations[i][k]:\n", - " ax = plt.arrow(i, w_t[i], (k-i), (w_t[k]-w_t[i]), color='grey')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e24eb9d5", - "metadata": {}, - "source": [ - "Of course, we can also plot the visibility relations for the HVG after obtaining them with `VisibilityGraph.visibility_relations_horizontal()`. In this case only the relations for one specific node are plotted as an example, so that one can easily convince themselves that there is only a link between data points for which a horizontal line could be drawn. (Note though that the visibility lines are not plotted in a horizontal manner here). " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "3c3f7f27", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating horizontal visibility relations...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visibility_relations_horizontal = HVG1.visibility_relations_horizontal()\n", - "cutoff = int(N/10)\n", - "fig = plt.figure()\n", - "ax = plt.plot(t[:cutoff], w_t[:cutoff]) # zooming in a bit\n", - "ax = plt.xlabel(\"$t$\")\n", - "ax = plt.ylabel(\"$w_t$\")\n", - "ax = plt.title(\"TS with horizontal visibility relations for node 55\")\n", - "fig.tight_layout()\n", - "\n", - "i = 209 # only plot the nodes visible to node 209\n", - "for k in range(cutoff):\n", - " if visibility_relations_horizontal[i][k]:\n", - " ax = plt.arrow(i, w_t[i], (k-i), (w_t[k]-w_t[i]), color='red')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "445f785f", - "metadata": {}, - "source": [ - "### Irreversibility Test" - ] - }, - { - "cell_type": "markdown", - "id": "eaea9cb7", - "metadata": {}, - "source": [ - "#### Linear Time Series" - ] - }, - { - "cell_type": "markdown", - "id": "fd5fd60a", - "metadata": {}, - "source": [ - "We start by analysing the linear time series on irreversibility. For this we use the time directed degrees via:\n", - "- `VisibilityGraph.retarded_degree()`\n", - "- `VisibilityGraph.advanced_degree()`" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c8abc84c", - "metadata": {}, - "outputs": [], - "source": [ - "k_r1 = VG1.retarded_degree() # obtain retarded degree\n", - "k_a1 = VG1.advanced_degree() # obtain advaned degee" - ] - }, - { - "cell_type": "markdown", - "id": "8b81f887", - "metadata": {}, - "source": [ - "Applied to VG both methods return an array with the corresponding degree for each node. For such an array we can obtain the probability for each degree with a kernel-density estimate using Gaussian kernels. [Kernel density estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation) is a way to estimate the probability density function (PDF) of a random variable in a non-parametric way. We use `scipy.stats.gaussian_kde` for that." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ab06fcb7", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.stats import gaussian_kde \n", - "gkde_r1 = gaussian_kde(k_r1)\n", - "gkde_a1 = gaussian_kde(k_a1)" - ] - }, - { - "cell_type": "markdown", - "id": "19cb5e7a", - "metadata": {}, - "source": [ - "Now we plot $p(k_v^r)$ against $k_v^r$ in red and $p(k_v^a)$ against $k_v^a$ in black and compare both visiually. The first figure is plotted on a non-logartihmic y-scale and the second one on a logartihmic scale. Both plots are cutoff at a degree of 12, for which probabilities get very low too be comparable. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "07800115", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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ZyYmzTAwePNj0ceueVMGCBdmwYwe5M2XiENDku++4MWgQGIbZ0URE5B9atGjB1atX+fbbb9m5cyc7d+4E4O7duxiP+e/sTp06MXfuXMB6GrZRo0Zky5YNgLNnz9K0aVP8/PxYuHAhoaGhBAcHAxAbG5u0DVdX13u2abFYkvbv4eHxyP0nJCSQO3duwsLC7vk5evQoQ4cOfazPkJJ0xE5Y/fbbnAeyODvT9h/XGNiTwoULs3brVmpWqsSu6GieGzeOlV5epEu8blBExFGlT5/+npsDUnvfj+vq1ascOXKEyZMnU6NGDQC2bt2a9Lqvry+zZs0iJiYmqVzt2LHjvu107NiRd955h9DQUBYsWMDEiROTXtuzZw9xcXF88cUXODlZj13Nnz//iT6Tp6cnhQoVYv369dSpU+e+1ytUqMDly5dxcXGhUKFCD9xGqVKl2LFjB126dEla9qDPkhJU7NI6w+DbxP/Td6lTh3Tp0pkc6OmVLl2aVRs3Uq9GDTbeuUP7Tz5hoacnrv+6aFZExJFYLJYnOh1qlsyZM5M1a1amTJlC7ty5OXfuHMOGDUt6vWPHjgwfPpyePXvyzjvvcObMGT5PHILrn3x8fKhatSo9e/YkLi6O5557Lum1IkWKEBcXxzfffEOLFi3Ytm3bfXfRPo4PPviAvn37kiNHDpo0acKNGzfYtm0br7zyCvXr16dKlSq0atWKUaNGUaJECS5evMjKlStp1aoVgYGBDBw4kK5duxIYGEj16tWZM2cOhw8fTrrBIyXpVGwad2nBApYlDnHSa+RIk9M8u6CgIJatXk06FxeWAd3efJOExMPwIiJiHicnJ3788UdCQ0Px8/Nj8ODBjBkzJun1jBkzsmzZMsLDwylfvjzDhw9n1KhRD9xWp06d2L9/P88///w9p079/f0ZO3Yso0aNws/Pjzlz5jDyKf62de3ala+++ooJEyZQunRpmjdvzvHjxwFrkV65ciU1a9akR48eFC9enBdffJEzZ86QM2dOAF544QXee+893nzzTQICAjh79iz/+9//njjH07AYj3tSW4iKisLb25vIyEi8vLzMjpMsRpYrx9sHDlA1Z062Xb5sdpxks3LFCp5r2ZK4hAT6AhNmzMDStavZsUREntnt27c5ffo0Pj4+dn2WRe71qN/rk/QPHbFLw4yoKGYm3jTxcu/eJqdJXk2bNWP2nDlYgEnAsG7dMBYsMDuWiIhIilKxS8PCvv6a3wyDdBYLbV57zew4ye6FF19kcuK1FaOBz154AX75xdxQIiIiKUjFLg2bM20aAC18ffHy9jY5Tcro1acPn48eDcDbCQlMaNkSQkJMTiUiIpIyVOzSqPgLF/jh7FkAOv1j/jtH9NrQobz79tsADIiLY27jxrB7t8mpREREkp+KXRq1ecQILgKZnJ1p3L272XFS3IeffMKAvn0xgK63b7Oibl1IvL5QRETEUajYPYbg4GB8fX0JCgoyO0qy+SHxRoK2FSvi7u5ucpqUZ7FY+Do4mE7t2xMHtL15k821akHi7esiIvZGg1o4luT6farYPYb+/fsTHh7Obgc5fRd/9iyLE+fEe2HgQJPTpB4nJye+mz2b5o0acRtocf06e2vWhHPnzI4mIvLY/p4O69atWyYnkeT09+/z39OdPSnNPJEGbf3iC64AmV1cqPX882bHSVWurq7MX7yYJvXqEfLrrzS+fJktNWtSYudOSBxYUkTEljk7O5MpU6akSevTp09vd3N8y/8zDINbt24RERFBpkyZcHZ2fqbtqdilQYsXLQKgpb//M/+XgT3y8PBg6apV1KlWjb2HDtHg7Fm21qpFge3bIUsWs+OJiPynXLlyASSVO7F/mTJlSvq9PgsVuzTGiIhg8YULALTu1cvkNObx8vJi1YYN1Kxcmd9OnaLB0aNsqV+fHCEh4OlpdjwRkUeyWCzkzp2bHDlyEBsba3YceUaurq7PfKTubyp2aczeceM4B6R3cqLhSy+ZHcdU2bNnZ82mTVSvVIljly7ReN8+NjZtiveaNfCPuQdFRGyVs7NzshUCcQy6eSKNWfTjjwA09fW9Z+LktCp//vys3bSJ7Jkzsw9osXUrMa1bw927ZkcTERF5Yip2aUlMDCtOnQLguY4dTQ5jO4oXL87q9evxypCBLUC71auJ7dgR4uLMjiYiIvJEVOzSkAsLFrDfMLAAjV9+2ew4NqV8+fKsWLUKD3d3VgBdFy4k4eWXISHB7GgiIiKPTcUuDVn13XcABOXIQbbs2U1OY3uqV6/OgkWLcHF25gfgle+/xxg4EDQIqIiI2AkVu7TCMFi5cycATevVMzmM7WratCmzZs/GYrEwAXh3/Hh4912zY4mIiDwWFbs0IvbgQdYljmrdpE8fk9PYthdffJEJEyYA8CnwxaefwqhR5oYSERF5DCp2acT2SZOIArK7uhJYo4bZcWxe3759GTFiBACvA9OHDYPEsiciImKrVOzSiJUrVgDQqFw5nJz0a38cw4YN4/XXXwegF7Cwf3+YOdPcUCIiIo+gv/BpwY0b/JI40X2TDh1MDmM/LBYLo0ePpmePHiQAHYG13bpB4pRsIiIitkbFLg24sGgRBwEL0LBLF7Pj2BWLxcLkKVNo26YNd4HWhsGO9u3hl1/MjiYiInIfFbs0YOMPPwAQkD072bJlMzmN/XF2dmb2nDk0bNCAaKBpfDwHW7aExNPbIiIitkLFLg3YsGsXAHWrVDE5if1yd3dn0eLFVKlcmetAw7g4TrVqBUuXmh1NREQkiYqdgzP++IP1168DUFfTiD2TDBkysGLlSsr4+XEZqB8Xx8U2bWDxYrOjiYiIACp2Du/0jz9yDnABqjdvbnYcu5c5c2ZWr1lDkSJFOA00iIsjol07WLjQ7GgiIiIqdo5uw4IFAFTOm5cMGTKYnMYx5M6dm7Vr15InTx7CgTrx8Vxu3x7mzzc7moiIpHEqdg5uw759ANStWdPkJI7Fx8eHTZs2kTdvXsKB2gkJXHzxRUi8UUVERMQMKnYOzDh1ig3R0QDU1TAnya5YsWKEhISQP39+jgK1DYPfO3aEqVPNjiYiImmUip0DOzJ7Nn8A6ZycqFynjtlxHFKRIkUICQmhYMGCHAdqAed69YIvvzQ7moiIpEEqdg5sw7JlAFQvWBB3d3eT0zguHx8fQkJC8PHx4RTWcnd6yBD44AMwDJPTiYhIWqJi58BCDh8GoHbt2uYGSQMKFixISEgIRYsW5QxQHQj/8EMYMkTlTkREUo2KnYMyzp5la0wMADXatzc5TdqQP39+QkJC8PX15SJQA9j91Vfw8ssQH29yOhERSQtU7BzU6UWLuAy4WiwE1apldpw0I0+ePGzevJmKFStyDagLbJw+HTp0gLt3zY4nIiIOTsXOQW1NvL4uMFcuPDw8TE6TtmTNmpV169ZRt25dbgJNgJ9/+glatYJbt0xOJyIijkzFzkFtDQsDoFrFiuYGSaM8PT1ZsWIFrVu35g7QBpj5yy/QuDFERZkdT0REHJSKnSO6epVtifPDVn/+eZPDpF3p0qVj/vz5dOvWjXigK/DVli1Qrx5cvWp2PBERcUAqdg7o6i+/EJ74uGqTJqZmSetcXFyYNm0agwcPBmAw8NaePRg1a8KlS+aGExERh6Ni54C2L1oEQMlMmciePbvJacTJyYkvvviCTz/9FIDPgG7h4cRWqwanT5sbTkREHIqKnQPaumMHANXKljU5ifzNYrHw9ttvM336dJydnZkJPHf6NNHVqsFvv5kdT0REHISKnaOJiWHb5csAVG/e3OQw8m/du3fn559/xiNdOn4B6l66xJVq1WDfPrOjiYiIA1CxczB3duxgd+JMB9VbtTI3jDxQs2bN2LBxI1kyZ2YXUO3aNc7UqgXbtpkdTURE7JyKnYMJW7yYu0A2NzeKFC1qdhx5iMqVK7Nt+3YK5MvHMaDKjRvsr18f1q41O5qIiNgxFTsHs3PLFgAq+vhgsVhMTiOPUrJkSX7duZMypUtzGah5+zYbmzaFJUvMjiYiInZKxc7B7Dp2DIBKVaqYnEQeR548edi8dSs1a9QgCmgcF8dPbdrA7NlmRxMRETukYudILl5kZ+KUVRVbtjQ5jDyuTJkysXrNGto8/zx3gRcSEhj/0kswdarZ0URExM6o2DmQa+vWcSLxccVatUzNIk8mXbp0zJs/n37/+x8G8AowvFcvjMmTzY4mIiJ2RMXOgexavhyAYl5eZMmSxeQ08qScnZ0ZHxzMJx9/DMAIoGffvsQFB5sbTERE7IaKnQPZtXs3ABVLlTI5iTwti8XC8HfeYeq33+JksfAd0GrAAKK//NLsaCIiYgdU7BxFfDw7z58HoFLduiaHkWfV8+WXWbJkCelcXFgB1BsyhCuffWZ2LBERsXEqdg7COHSIXfHxAFTUjBMOoUXLlqzftInM6dKxE6j+1luc/eADs2OJiIgNU7FzEKeXL+cK4Gqx4B8QYHYcSSZVq1VjW2go+T09OQpU+fBDDrzxhtmxRETERqnYOYid69cD4J8rF+7u7iankeRUyteXX8PD8cuenUtAzTFjCOnf3+xYIiJig1TsHMTuQ4cAqFiunMlJJCXkzZePzb/9Ro38+YkEGk6YwIJu3cyOJSIiNkbFzhHcucPeK1cACKxXz+QwklIyZ8nC6t9+o3WJEtwF2n//PV+3bm12LBERsSEqdg4gYf9+9hkGABUaNDA5jaQkj/Tp+enQIfoFBGAAg5Ys4bU6dUhISDA7moiI2AAVOwdwavVqogB3JydK+fqaHUdSmLOLC+N37eKzOnUAGLtpEx0CArh9+7bJyURExGwqdg5g76ZNAJTNmRNXV1dzw0iqsDg58eb69cxu2RJXYH5YGI38/Lh+/brZ0URExEQqdg5g7+HDAFTw8zM5iaQqi4VOS5bwS4cOeAGbT56kuq8v586dMzuZiIiYRMXO3t25Q2hEBAABiafmJA2xWKg3Zw5bevYkLxB++TKVy5Rh//79ZicTERETqNjZOePgQfbqxom0zWKh7Lff8mu/fpQGLkVFUaNyZdatW2d2MhERSWUqdnbu3Nq1XANcLBb8ypQxO46YxWIh//jxbB04kNrAjdu3adKoETNmzDA5mIiIpCYVOzu3d+NGAPyyZ9eME2mdxUKmL79k1Wuv8SIQl5BA9+7deeuttzQciohIGqFiZ+f2HjwIQIXSpU1OIjbBYsF9zBjmDB3KO4mLPvvsM9q2bUt0dLSp0UREJOWp2Nmz2Fj2/vEHAAG1a5ubRWyHxYLTqFF8PHQoswA3YPHixdSoUYMLFy6YnU5ERFKQip09++033TghD2axwKhRdB46lA1AdmDfvn0EBQWxZ88es9OJiEgKUbGzY39s3sxlwAKUKVvW7DhiaxLLXbXXX2cXWO+YvXSJmjVrsnDhQrPTiYhIClCxs2MHEmecKJYpExkyZDA3jNgmiwVGj6bQa6+xHWgCxMTE0LZtW0aMGIGReMRXREQcg4qdHTtw4AAAZQsXNjmJ2DSLBcaMweu111gKvJq4ePjw4XTp0oU7d+6YmU5ERJKRip0dO5A4dVTZwECTk4jNSyx3LkOG8DUwAXB2cmL27NnUrVuXiMTZS0RExL6lyWLXunVrMmfOTNu2bc2O8vT++IMDt28DULZuXZPDiF2wWODzz2HwYP4H/JKQgLeHB9u3b6dSpUocOnTI7IQiIvKM0mSxe/XVV5k5c6bZMZ5JbGgo4YmPy1asaGoWsSMWC3zxBQweTANgR0wMRbJn58yZM1StWpWVK1eanVBERJ5Bmix2derUwdPT0+wYz+TYhg3cBTxdXChYsKDZccSe/F3uhgyhJLDzzz+pXbQoN27coEWLFnz11Ve6qUJExE7ZXLHbvHkzLVq0IE+ePFgsFpYsWXLfOhMmTMDHx4d06dIREBDAli1bUj+oyQ7s2AFAmTx5cHKyuV+j2Lq/T8u++SZZgdUnTvBypUokJCQwePBg+vbtS2xsrNkpRUTkCdlcI4iOjqZcuXKMHz/+ga/PmzePQYMGMXz4cPbt20eNGjVo0qQJ5xJvJAAICAjAz8/vvp+LFy+m1sdIcQeOHQOgrK+vyUnEblksMHIkvPMObsCUnTv5olkzLBYLU6ZMoXHjxly7ds3slCIi8gRczA7wb02aNKFJkyYPfX3s2LH07NmTl19+GYCvvvqK1atXM3HiREaOHAlAaGhosmS5c+fOPUNBREVFJct2n9mdOxy4cgWAstWrmxxG7JrFAh9/DK6uWN5/nyErVlC8c2c6LFnChg0bqFy5MsuXL6d48eJmJxURkcdgc0fsHuXu3buEhobSsGHDe5Y3bNiQ7du3J/v+Ro4cibe3d9JP/vz5k30fT+W33ziQeA1U2Vq1TA4jDuG992DECACaz57Ntk6dKFCgAMePH6dy5cps2LDB5IAiIvI47KrYXblyhfj4eHLmzHnP8pw5c3L58uXH3k6jRo1o164dK1euJF++fOzevfuB67311ltERkYm/Zw/f/6Z8ieXa9u28XviY78yZUzNIg7krbdgzBgAyk6ezK5WrahcuTLXr1+ncePG/PDDDyYHFBGR/2Jzp2Ifh8Viuee5YRj3LXuU1atXP9Z67u7uuLu7P1G21HAwcSqxQl5eeHt7mxtGHMvrr4OLCwweTM5x49g4aBDdChZk3rx5dOzYkcuXLzN48GCzU4qIyEPY1RG7bNmy4ezsfN/RuYiIiPuO4jmyA/v3A5pKTFLIoEGQePNSuq++Ym7OnAx81ToR2ZAhQ3jjjTdISEgwMaCIiDyMXRU7Nzc3AgICWLt27T3L165dS9WqVU1KlfqSphKrUMHkJOKw+veHyZMBcBo3ji9jY/ks8eakMWPG0K1bNw2HIiJig2yu2N28eZOwsDDCwsIAOH36NGFhYUnDmQwZMoSpU6cyffp0jhw5wuDBgzl37hx9+/Y1MXUqiojg4N9TidWpY3IYcWi9e8P06WCxYJk4kTdPnWLG9Ok4Ozsza9YsWrRoQXR0tNkpRUTkH2zuGrs9e/ZQ5x+FZciQIQB07dqVGTNm8MILL3D16lU++ugjLl26hJ+fHytXrkwzsy8kHDzI3zN6lgkMNDWLpAHdu1uvuevWDb79lq6xseRYsoS2L7zA6tWradSoEStXrsTLy8vspCIiAlgMzR302KKiovD29iYyMtK0P2RnPvwQnw8+wM3Jieg7d3BxsbluLo7ohx/gpZcgPh46d2ZHnz40adGCv/76i8DAQFavXk2WLFnMTiki4pCepH/Y3KlYebTDO3cCUDJbNpU6ST0dOljLnYsLzJ5N5eBgNqxZQ9asWZOOskdERJidUkQkzVOxszPhv/0GgK/uiJXU1q4d/PQTuLrCjz9SftQoQtatI2fOnBw4cIBatWo51LR9IiL2SMXOnhgG4RcuAOBbvrzJYSRNatUKFi0CNzdYuJDSH3zA5nXryJcvH7/99hs1a9bk7NmzZqcUEUmzVOweQ3BwML6+vgQFBZkb5I8/CL97FwDfGjXMzSJpV/Pm8PPP4O4OP/9M8WHD2LJuHT4+Ppw8eZJatWqp3ImImETF7jH079+f8PDwh049llqMQ4cIT3zs6+9vZhRJ6xo3huXLwcMDVqyg0KBBbFm7lmLFinH27Fnq1KmTNESRiIikHhU7O3J+61ZuAq4WC0WLFjU7jqR19evDypWQPj2sWkXeV19l46pVFClShNOnT1OnTh1+//33/96OiIgkGxU7OxK+axcAxbNmxdXV1eQ0IkDt2rBihfXI3cqVSeXOx8eHU6dOUadOHS4kXhcqIiIpT8XOjoQfOQKAb5EiJicR+Yfata2nZdOlgxUryD9kCBtXr6ZQoUKcOHGCunXrcunSJbNTioikCSp29sIwOJw4lISurxObU7cuLFtmLXfLllFw6FA2rl5NgQIFOHbsGHXr1uXy5ctmpxQRcXgqdvbi0qX/vyO2enWTw4g8QP3699wtW2jYMDauWUP+/Pn57bffqFevngYxFhFJYSp2dkJ3xIpdaNjw/8vd4sUUHj6cjWvWkDdvXsLDw6lbty5//vmn2SlFRByWip2duLh9O1GAs8VCsWLFzI4j8nCNGsHixUmDGBd57z02rl1Lnjx5OHz4MPXq1ePKlStmpxQRcUgqdnYiPHGO2GJZsuDu7m5yGpH/0KSJdYYKV1f46SeKffghG9asIVeuXBw8eJB69epx9epVs1OKiDgcFTs7cVhzxIq9adYMFi60lrt58yjx6adsXLs2aW7Z+vXrc+3aNbNTiog4FBU7e/DPOWJ1fZ3YkxYtYMECa7n74QdKfvYZG9etI0eOHISFhanciYgkMxW7x2D6XLEREYTHxgLgW62aORlEnlbLljB/Pri4wJw5lBo9mg1r15I9e3b27dtHw4YNuX79utkpRUQcgordYzB7rljjyBHdESv2rVUrmDcPnJ1h1ixKjx3LhrVryZYtG6GhoTRq1Ii//vrL7JQiInZPxc4OXNmzh+uABShevLjZcUSezvPPw48/Wsvd99/j9/XXrF+7lqxZs7J7924aNWpEZGSk2SlFROyaip0dOJp4pLCAlxceHh4mpxF5Bm3bwty51nL33XeUHT+e9WvXkiVLFnbt2kXjxo2JiooyO6WIiN1SsbMDR8OtJ2JL5MtnchKRZNC+PcyeDU5OMG0a5SZOZP3atWTOnJkdO3bQuHFjbty4YXZKERG7pGJnB46ePw9AiZIlTU4ikkxefBFmzbKWu2+/xX/KFNatWUOmTJn49ddfadiwoa65ExF5Cip2tu72bY4mXndUPCDA5DAiyahjR/j+e7BYYPJkKkyfzrp/HLmrU6eOph8TEXlCKna27vhxjiY+LBEYaGoUkWTXuTPMmGEtdxMnEjBjBps2bkwa56527dpcvHjR7JQiInZDxc7GxR4+zMnExzoVKw6pSxeYPt1a7oKDKTt9OiGbNpE3b17Cw8OpWbMmZ8+eNTuliIhdULGzcad37CAO8HB2Jp9unhBH1a0bTJ1qfTxuHCWnTGHL5s34+Phw8uRJatSowfHjx02NKCJiD1TsbNzR/fsBKJ4jB05O+nWJA+vRA7791vr4q6/wCQ5mc0gIJUqU4Pz589SsWZPDhw+bm1FExMapKdi4oydOAFDCx8fkJCKp4OWXYfJk6+OxY8n3+eeEbNxI2bJluXz5MjVr1mTnzp3mZhQRsWEqdrbMMDh6+TIAJcqWNTmMSCrp3RsmTrQ+HjeOnEOHsnHNGipWrMi1a9eoW7cuq1atMjejiIiNUrGzZZcucTQuDoASlSqZHEYkFfXtax3E2MUF5swhS/furF+6lIYNG3Lr1i1atGjBnDlzzE4pImJzVOweQ3BwML6+vgQFBaXujn/77f+HOvHzS919i5itUydYuhQ8POCXX8jYujXLvv+eDh06EBcXR+fOnfnyyy/NTikiYlNU7B5D//79CQ8PZ3finK2p5a99+4hIfFy8ePFU3beITWjSBNavh8yZ4ddfcatfn9mjRjFw4EAAhgwZwrBhwzAMw+SgIiK2QcXOhh3btQuA3Bky4OXlZXIaEZNUqQJbtkCePHD4ME7Vq/Nlnz6MHDkSgFGjRtGzZ0/iEi9bEBFJy1TsbNjR8HAASmj8OknrSpeG7duheHE4dw5LtWoMq1qVadOm4eTkxHfffUfr1q2Jjo42O6mIiKlU7GzY0XPnAChRooTJSURsQMGCsHUrVK4M169Dgwb08PBg8eLFpEuXjuXLl1OnTh3++OMPs5OKiJhGxc5W3b7N0agoAIpXqGByGBEbkT07bNgAzz8Pd+9Cx460PHyY9evWkTVrVnbv3k2VKlU4evTof29LRMQBqdjZqlOn/v+O2IAAU6OI2BQPD/jpJxgyxPr87bep+v33bA8JoXDhwpw+fZqqVauybds2c3OKiJhAxc5GJRw9yonEx8V1KlbkXk5O8MUX8M031sfffkvx117j17VrkwYyrlevHgsXLjQ7qYhIqlKxs1GX9u4lBnC2WChUqJDZcURs04ABsHgxpE8Pq1eT4/nn2TBrFi1btuTOnTu0a9dOY92JSJqiYmejToSFAVAoUyZcXV3NDSNiy1q2hJAQyJkT9u8nQ61aLBo2jH79+mEYBkOGDGHQoEHEx8ebnVREJMWp2Nmo4yesJ2KL5s1rchIROxAYCDt2gJ8fXL6Mc926jK9endGjRwPw9ddf0759e2JiYkwOKiKSslTsbNSJCxcAKKbr60QeT6FC1rHumjeH27exdOzI0Js3+WHOHNzc3Fi0aBH16tXjypUrZicVEUkxKna2KCaGEzduAFC0fHmTw4jYEU9PWLIEXn/d+vyjj3jx559Zs3QpmTJl4tdff6Vq1aqcOHHikZsREbFXKna26OTJpDtii5YrZ2oUEbvj7AxjxsC0aeDqCvPnU+udd9i2aBEFChTg+PHjVKpUic2bN5udVEQk2anY2SDj+PH/L3bFipmaRcRu9egBa9dC1qywZw++nTuzc8oUgoKCuHbtGvXr12fmzJlmpxQRSVYqdo8hODgYX19fgoKCUmV/f+zbRzTgpKFORJ5NrVqwcyeUKgUXL5KrdWs2DRxI27ZtiY2NpWvXrgwfPpyEhASzk4qIJAsVu8fQv39/wsPD2b17d6rs7++hTgp4e+Pu7p4q+xRxWEWKwK+/QuPGEBND+s6dmVeqFG+/9RYAI0aM4IUXXuDWrVsmBxUReXYqdjboxLFjgIY6EUk23t6wbBkMGgSA08cf8+mhQ8yYMAFXV1cWLFhA7dq1uXz5srk5RUSekYqdDTquoU5Ekp+LC3z5JXz3Hbi7w7JldP36a9ZNn06WLFnYvXs3FStW5MCBA2YnFRF5aip2tubWLU7cvAlAUX9/c7OIOKJu3WDLFsibF44epWb//uwcMYLixYtz/vx5qlWrxvLly81OKSLyVFTsbM0/hzopW9bUKCIOKygIQkOhenWIiqJo377saNOGunXrcvPmTVq2bMmIESMwDMPspCIiT0TFzsYYx45pqBOR1JAzJ6xfD/36AZB55EhWeXrSp0cPDMNg+PDhvPDCC0RHR5scVETk8anY2ZgrYWFEARagcOHCZscRcWxubhAcDFOngpsbrj//zKQdO5j0wQe4urry008/UbVqVU6fPm12UhGRx6JiZ2NO7N8PQD4vL9KlS2dyGpE0omdPCAmB3LkhPJw+n3/OhnfeIUeOHBw4cICgoCA2bNhgdkoRkf+kYmdjThw/DmioE5FUV7ky7N0LtWvDzZtUf/999jRtSkCFCly9epWGDRsybtw4XXcnIjZNxc7GnPj9d0BDnYiYIlcu6zRkiYMX558xgy3OznRu3Zr4+HgGDhxIjx49uH37tslBRUQeTMXOlhgGx8uUAaBohQomhxFJo1xcYMQI64DGmTLhsXs3Mzdt4otevXBycmLGjBnUqlWLC4njTYqI2BIVO1tisXAiLg6Aon5+JocRSeOaN7eemg0IwHL9OkOmTmXViy+SOXNmdu3aRWBgINu3bzc7pYjIPVTsbMyJE9bBTooWLWpyEhHBxwe2bbMOiWIYNJg7l90lSuBXsiSXL1+mdu3aTJ061eyUIiJJVOxszJEjR9i6dSvFixc3O4qIgHX6seBgmDMHMmSgyI4d/Hr9Os/XrElsbCy9evWif//+xMbGmp1URASLoVu8HltUVBTe3t5ERkbi5eVldhwRSW1HjkDbthAeToKTEyPq1+fdNWsAqFmzJj/99BM5cuQwOaSIOJon6R86Yici8rhKlYJdu6BzZ5wSEnhnzRqWBgXhmTEjmzdvJjAwkL1795qdUkTSMBU7EZEnkSEDzJwJkyeDuzstdu9mp5cXxQoU4Pz581SrVo25c+eanVJE0qhnKnaxsbGcP3+eo0ePcu3ateTKJCJi2ywW6N0btm+HwoUpdfEiuy5domnp0ty+fZtOnToxdOhQ4uPjzU4qImnMExe7mzdvMnnyZGrXro23tzeFChXC19eX7NmzU7BgQXr16sXu3btTIqtpgoOD8fX1JSgoyOwoImJLKlSA0FBo1YpMsbEsPXyYt0qVAuDzzz+nVatWREdHmxxSRNKSJ7p54ssvv+TTTz+lUKFCtGzZkooVK5I3b148PDy4du0ahw4dYsuWLSxevJjKlSvzzTffUKxYsZTMn6p084SIPJBhwJdfwptvQlwc83Pnpuu1a9y+c4fAwECWL19Ozpw5zU4pInbqSfrHExW7du3a8d5771EmcXaEh7lz5w7Tpk3Dzc2Nl19++XE3b/NU7ETkkbZtgxdegAsX+NXNjRZubly9eRMfHx9++eUXSmiqQBF5CilW7P7pjz/+SHP/BapiJyL/6c8/oXNnWLOG40CTTJk4+ddfZMmShaVLl1KtWjWzE4qInUmV4U7atGlDXOL0V//2sOUiIg4ve3ZYuRLefZdiwPa//qJSpkxcu3aN+vXrs2rVKrMTiogDe+pilzlzZl555ZX7ll+9epX69es/UygREbvm7AwffQQ//kiOdOnY8NdfNM+Ykdu3b9OyZUsWL15sdkIRcVBPXexmzZrF+vXr75kn8ciRI1SsWFGnKUVEwHq93ebNpM+dm0U3b9LezY3Y2FjatWvHnDlzzE4nIg7oqYtdpkyZWLhwIW+88QY7d+5k1apVVKlShTZt2vDzzz8nZ0YREfsVFAS7d+MaEMDcu3fp5uxMfHw8L730Et9++63Z6UTEwbg8ycrPPfcc/v7+lC9fHn9/f8qUKUNwcDDNmjXj9u3bBAcH07Vr15TKKiJin/Lmhc2bcX7hBaYtX056i4UJhkHv3r1xcXGhe/fuZicUEQfxREfsihUrxrZt2+jVqxeFCxcmS5YsTJkyBcMw6NSpE/7+/sTGxqZUVhER+5U+PSxejFP37ow3DAYmLu7Zsyc//PCDqdFExHE89XAnv//+O2FhYff8nD59GhcXF0qWLMn+/fuTO6vpNNyJiDwzw4DhwzFGjuR/wGTA2dmZn376idatW5udTkRsUKqMY/cgN2/eZN++fRw4cID+/fsn12ZthoqdiCSbceNIGDiQ7sBMwNXVlSVLltC0aVOzk4mIjTGt2Dk6FTsRSVZTphDXpw+dgPmAh4cH69evp0qVKmYnExEbkmIDFJ87d+6Jgly4cOGJ1hcRSVN698Zl+nRmA02BmJgYmjdvztGjR81OJiJ26omKXVBQEL169WLXrl0PXScyMpJvv/0WPz8/Fi1a9MwBRUQcWvfuuM6cyXyLhSDg2rVrNGrUiEuXLpmdTETs0BMNd3LkyBFGjBhB48aNcXV1JTAwkDx58pAuXTquX79OeHg4hw8fJjAwkDFjxtCkSZOUyi0i4jheeokMzs4s79SJasCJs2dp2rQpISEhuuxDRJ7IU11jd/v2bVauXMmWLVs4c+YMMTExZMuWjfLly9OoUSP8/PxSIqvpdI2diKSoqVM52asXVYEIoEGDBqxYsQJXV1ezk4mIiVL15om/r6PLmzfvs2zGLqjYiUiK+/JLQocMoRYQDfTr14/g4GCzU4mIiVLs5ol/2rZtGz4+PhQoUIACBQqQM2dO3nzzTaKiop52kyIiMngwAR98wBzAAkyYMEHFTkQe21MXuz59+lC6dGl2797NgQMHGDNmDOvXrycgIIArV64kZ0YRkbTlvfd4bsgQPkt8OvDVV1mzZo2pkUTEPjz1qVgPDw8OHDhAsWLFkpYZhkH79u1xdXVl7ty5yRbSVuhUrIikGsPA6NaN7jNn8j3gnTEjv+7aRalSpcxOJiKpLFVOxZYqVYrLly/fs8xisfDRRx+xbNmyp92siIgAWCxYpk5lcv36VAcib96kRePGXL161exkImLDnrrYdevWjd69e983aHFkZCTe3t7PHExEJM1zdcV90SIW+fnhA5w8d462zz1HbGys2clExEY99alYJydrJ3Rzc+P555/H39+f+Ph4Zs+ezTvvvEOHDh2SNaiZgoODCQ4OJj4+nmPHjulUrIikrsuXORwQQJWLF7kB9O3Zk4lTp5qdSkRSSaoMdxIREcG+ffvYv38/YWFhhIWFcfz4cSwWC6VKlaJMmTKULVuWsmXL0rhx46f6ILZG19iJiGmOHmVFYCAtbt7EAILHjaPfK6+YnUpEUkGqjmP3T7dv3+bgwYOEhYUlFb5Dhw7x119/JdcuTKViJyKm2r6d0bVq8WZcHM4WC2vWrqVuvXpmpxKRFGZasXN0KnYiYjZj0SK6tGnDbCBL+vTsOnCAIkWKmB1LRFJQqtwVKyIiqc/y/PN8O2YMFYFrt27RsnZtDQwvIklU7ERE7Ey6119nycsvkwcI//13OjVpQnx8vNmxRMQGqNiJiNih3JMmsaR2bdIBy7dv553//c/sSCJiA1TsRETskbMzQStWMC3x+rrPvv2WORMmmBxKRMymYiciYq/Sp6fj9u28lSkTAD0HDGDX5s3mZhIRU6nYiYjYsxw5+OTXX2np6sodw6BVw4Zc+NeMQCKSdqjYiYjYOaeSJZm9YgWlLRYu3blDq6AgYm7dMjuWiJhAxU5ExAF4NmjA0nHjyArsiYjg5Ro10DClImmPip2IiIMoPGAAC/r2xQWYu3cvo156yexIIpLKVOxERBxI7QkT+KZmTQDenjOHZWPGmJxIRFKTip2IiCOxWOi7fj39ChXCADq+8QaHVqwwO5WIpBIVOxERR+Piwlf79lHH05ObQMtWrbh67JjZqUQkFajYiYg4INdMmfhpxw4Ku7hwOi6OVoGBxFy7ZnYsEUlhKnYiIg4qq68vSxcuxBvYeuMGL/r5EXf3rtmxRCQFqdiJiDiw0i1bsvTrr3EHll66xP8CAjQMiogDU7ETEXFwNV99lR8HD8YJmHroEO81aWJ2JBFJISp2IiJpQKuxY5nYqhUAn6xezbiXXzY3kIikCBU7EZE0oveiRXwYEADAwGnTmDRwoMmJRCS5qdiJiKQVFgvv/vorrxcrBsD/xo1j6rBhJocSkeSkYicikoZYXF0ZfeAAgwoUAKD3qFF8/9FHJqcSkeSiYiciksZY0qVj7OHDDMidGwPo/v77fP/ZZ2bHEpFkoGInIpIGWTJmZFx4OH2zZcMAur31Fl+//77ZsUTkGanYiYikUZZMmQg+fJhBWbIAMOijj/hg8GCNcydix1TsRETSMKccORh74AAfZ84MwIdffcWrPXoQHx9vcjIReRoqdiIiaZwlb17eCQtjfNasAIyfMYPWjRtz8+ZNk5OJyJNSsXsMwcHB+Pr6EhQUZHYUEZGUUaAA/fftY16OHKQDlq1bR/VKlTh//rzZyUTkCVgMXUzx2KKiovD29iYyMhIvLy+z44iIJL/z59lZuTLPXbzIH0Cu7NlZ9PPPVKlSxexkImnWk/QPHbETEZH/lz8/lXbsYGeBApQBLv/5JzVr1uSrr77STRUidkDFTkRE7pU/PwW3bmWbjw/tgbi4OAYPHkzbtm2JjIw0O52IPIKKnYiI3C9/fjw3b+bHEiUYD7gCixYtwt/fn02bNpkcTkQeRsVOREQeLF8+LFu20L98ebYBhSwWzpw5Q506dRg4cCC3bt0yO6GI/IuKnYiIPFz27LBxI0HVq3PAMOjl7AzAuHHj8Pf3Z8OGDSYHFJF/UrETEZFH8/aG1avxbNSIKfHx/OLiQt4sWTh+/Dj16tXjhRde4Pfffzc7pYigYiciIo8jfXpYuhTatqVxXByHrl9nQM2aODk5MX/+fEqWLMmnn35KdHS02UlF0jQVOxEReTxubvDDD9CrF5kMg282bya0c2eqVatGdHQ077zzDoULF+abb77hzp07ZqcVSZNU7ERE5PG5uMDkyfDppwD4z5zJlrx5mTtjBkWKFCEiIoJXX32V4sWLM27cOB3BE0llKnYiIvJkLBZ4+22YNQtcXbHMn0+HadM4sm0bkydPJk+ePJw7d46BAwdSoEAB3n33XSIiIsxOLZImqNiJiMjT6dwZVq0CLy/YsgXXmjXpXasWJ06cYOLEiRQpUoRr167xySefUKBAAbp06cL27ds1g4VIClKxExGRp1e3LmzbBvnzw7FjULEiHhs30rdvX44ePcqCBQuoVKkSd+7cYdasWVSrVg1/f38mTpzIjRs3zE4v4nBU7ERE5Nn4+cHu3VC9OkRFQfPmMHo0zk5OtGnThl9//ZVdu3bRvXt3PDw8OHDgAP369SNPnjz07duXAwcOmP0JRByGxdAx8ccWFRWFt7c3kZGReHl5mR1HRMS23L0Lr7wCU6ZYn3fsCN9+ax0qJdH169eZOXMmkyZN4rfffktaXq1aNfr370+bNm1wc3NL7eQiNu1J+oeK3RNQsRMR+Q+GAZMmwauvQlwclC4NP/0EpUr9azWDkJAQJk6cyKJFi4iLiwMgR44cvPzyy/Tp04cCBQqY8QlEbI6KXQpRsRMReUwhIfDii3D5svWI3aRJ8NJLD1z14sWLTJ06lcmTJ3Px4kUAnJ2d6dSpE8OGDaPUv0qhSFrzJP1D19iJiEjyq1ULwsKgXj24dQu6dIGXX4aYmPtWzZMnD++99x5nzpxhwYIF1KlTh/j4eGbOnEnp0qVp27YtBw8eTP3PIGKHVOxERCRl5MwJq1fDhx9ax76bNg0CA2Hv3geu7urqSps2bdiwYQO7du2idevWGIbBwoUL8ff3p2fPnly4cCGVP4SIfVGxExGRlOPsDO+9B+vWQa5cEB4OlSrBJ59Yr8F7iKCgIBYtWsShQ4do164dCQkJTJ8+nWLFijFixAju3r2bih9CxH6o2ImISMqrWxcOHoS2ba2F7t13oUYNOH78kW8rXbo08+fPZ/v27VStWpWYmBiGDx9OQEAAO3bsSKXwIvZDxU5ERFJHtmwwf751KjJvb9ixA/z9Yfx4SEh45FurVKnC1q1bmTVrFtmyZePQoUNUrVqV119/XUfvRP5BxU5ERFKPxWKdiuzgQetRvFu3rGPf1ahhPU37yLda6Ny5M7/99htdu3bFMAy++OILqlatyvH/OPInklao2ImISOrLnx/WroXgYMiYEbZvtx69+/BDuHPnkW/NmjUrM2bMYMmSJWTJkoXQ0FDKly/Pjz/+mDrZRWyYip2IiJjDyQn69bMeqWveHGJj4YMPoEIF+PXX/3z7c889x4EDB6hduzbR0dF06NCBt99+m4T/OK0r4shU7ERExFz588PSpTBvHuTIYS161arBgAHWuWcfIW/evKxbt4433ngDgJEjR/Lcc88R9R/vE3FUKnYiImI+iwXat4cjR6BbN+vUZMHB4OsLS5Y88q3Ozs6MGjWKWbNm4e7uzvLly6lduzZ//PFHqkQXsSUqdiIiYjuyZIHvvrOOe1e0KFy4AK1bW39+//2Rb+3cuTObN28me/bs7Nu3j2rVqnHq1KlUCi5iG1TsRETE9tSrBwcOwNtvg4uL9aidr6/1KF58/EPfVrFiRbZt20ahQoU4efIkVatW5cCBA6mXW8RkKnYiImKbPDzg009h3z6oXBlu3LBed1etmrX0PUSxYsXYvn07ZcuW5Y8//qBu3brs378/FYOLmEfFTkREbJufH2zbZj1a5+UFO3dCQAC89RbExDzwLblz5yYkJISgoCCuXr1KvXr1VO4kTVCxExER2/fPoVGef946Ldlnn0GZMtbr8R4gU6ZMrFmzhooVK3L16lUduZM0QcVORETsR968sHCh9Zq7vHnh5Elo0AC6dIE//7xv9X+Wu2vXrlGvXj2OHDmS+rlFUomKnYiI2J/nnrMOjfLqq9ahUmbNglKl4PvvrUOl/IO3tzdr1qxJOi3bsGFDzp07Z1JwkZSlYiciIvbJ0xO+/hp27ICyZeHqVesYePXrw4kT96zq7e3NypUrKVWqFL///jsNGjQgIiLCnNwiKUjFTkRE7FvFirBnD4waZb2TdsMGa9H74ot7hkbJli0ba9asoUCBAhw7dowmTZpohgpxOCp2IiJi/1xd4Y034NAh6xh4MTHw+utQpYp1WaJ8+fKxdu1asmfPzt69e2nZsiV37twxMbhI8lKxExERx1G4MKxdC9Omgbc37N4NFSrABx/A3bsAFC9enFWrVuHp6UlISAg9evQgISHB3NwiyUTFTkREHIvFAj16WIdGee45iI2FDz+0FrxduwCoUKECCxcuxMXFhblz5/Luu++aHFokeajYiYiIY8qTBxYvhnnzIHt2OHzYemr2tdfg1i0aNGjAlClTABgxYkTSYxF7pmL3GIKDg/H19SUoKMjsKCIi8iQsFmjf3jo0SufOkJAAY8daBzbeuJHu3bvz3nvvAdCvXz9++eUXkwOLPBuLYfxrwB95qKioKLy9vYmMjMTLy8vsOCIi8qRWroQ+feD3363Pe/fGGDWKbgMHMnPmTDJmzMjmzZspX768uTlF/uFJ+oeO2ImISNrRtKn1lOz//md9PmUKltKl+fa556hbty43b96kWbNmGsBY7JaKnYiIpC1eXjBhAmzaBEWLwsWLuLVpw6LMmfErVYpLly7RvHlzjXEndknFTkRE0qZateDAARg6FJyc8F64kBWXLpErUyYOHjxI+/btiYuLMzulyBNRsRMRkbTLwwNGj4adO6FMGQr89RfL//qL9M7OrF69mgEDBqBL0cWeqNiJiIgEBlqnJfvoIwJcXZkbH48FmDx5MmNHjzY7nchjU7ETEREBcHODd9+Ffft4rmpVxiYuHjpsGIveftvUaCKPS8VORETkn0qXhq1bGThzJv3Tp8cAOo8cya5q1eDYMbPTiTySip2IiMi/WSxYXnqJr37/naaFCxMDtNi+nTOlS8Prr8P162YnFHkgFTsREZGHcMmcmR/DwvAvVYoIoFlcHH998QUUKWKdweLOHbMjitxDxU5EROQRPD09Wb52LXnz5iUcaJshA7HXr1vnnC1ZEn74wTpVmYgNULETERH5D3nz5mX58uVkyJCB9dHR/K9GDYzcueHMGejYESpVsg54LGIyFTsREZHH4O/vz7x583BycmLali2M6tsXPvkEMma0DpVSpw60aGGdskzEJCp2IiIij6lZs2aMGzcOgLfef5/5xYrByZPQrx84O8Py5VCmDHTrBmfPmhtW0iQVOxERkSfQv39/Bg0aBECXLl349eRJCA62Hqlr0wYMA77/HooXh8GD4c8/zQ0saYqKnYiIyBP6/PPPadmyJXfu3KFly5acOnUKSpSABQus05PVqQN378JXX1nvoP3oI7hxw+zYkgao2ImIiDwhZ2dn5s6dS4UKFbhy5QpNmzbl2rVr1hcrVoT162H1aihf3lro3n/fWvC++cZa+ERSiIqdiIjIU8iQIQPLli0jf/78HD16lOeff567f5c2iwUaNrTeVDFvHhQtaj0l++qr1iN7s2dDfLy5H0AckoqdiIjIU8qTJw/Lly/H09OTkJAQevXqhWEY/7+CkxO0bw/h4TBpEvw9RMpLL1mP5q1YYb0mTySZqNiJiIg8g7Jly/LTTz/h7OzMzJkz+eSTT+5fydUV+vSBEydg5Ejw9oaDB6F5c6hZE7ZtS/3g4pBU7ERERJ5Ro0aNCA4OBuC9995jzpw5D14xfXoYNgxOnYI33oB06WDrVqheHVq2tJY9kWegYiciIpIM+vTpw+uvvw5Ajx492LJly8NXzpIFRo2yHsHr3ds6Bt6yZVCuHHTtaj1dK/IUVOxERESSyahRo2jTpg13796lVatWHD9+/NFvyJsXJk+2joHXrp31eruZM603WAwaBBERqZJbHIeKnYiISDJxcnJi5syZVKxYkWvXrtG0aVOuXr36328sUQLmz4ddu6BePeuQKF9/bR0i5YMPNAaePDYVOxERkWSUPn16li5dSsGCBTlx4gStWrXizp07j/fmoCBYtw7WroWAALh5Ez78EAoXtha9x92OpFkqdiIiIsksZ86crFy5Em9vb7Zu3UqPHj3uHQblv9SvD7t3W4/iFSsGV65YT80WKwbffguxsSmWXeybip2IiEgK8PX1ZcGCBbi4uDB37lzef//9J9uAxWK97u7wYet1eHnzwvnz1pstSpXSIMfyQCp2IiIiKaR+/fpMmjQJgI8//pgZM2Y8+UZcXa1l7sQJ+PJLyJ4dTp60DnJctiwsXAgJCckbXOyWip2IiEgK6tmzJ2+99RYAL7/8Mr/88svTbShdOuvp2FOnYMQIyJTJOqNF27YQGAgrV2oWC1GxExERSWmffPIJnTt3Jj4+nrZt27J79+6n31jGjPDWW3D6NLz7rvX5vn3QrJl1oOONG5MvuNgdFTsREZEU5uTkxLRp02jYsCG3bt2iWbNmnDhx4tk2mikTfPSRteANHQoeHrB9O9Sta735YseOZMku9kXFTkREJBW4ubmxYMECKlSowJ9//kmjRo34448/nn3D2bLB6NHW6+7697dek7d+PVSpYp2Ldt++Z9+H2A0VOxERkVTi6enJypUrKVy4MKdOnaJp06bcSK7Bh3PnhvHj4fhx6NnTOk3ZihVQoYL17trw8OTZj9g0FTsREZFUlDNnTlatWkW2bNnYu3cvbdu25e7du8m3g4IFYepUa5Hr2NE6bMqCBVCmDLzyCvz1V/LtS2yOip2IiEgqK1asGCtXriR9+vSsWbOGl19++ckGMH4cxYvDnDlw4AC0amUdEmX8eOvy6dM1RIqDUrETERExQVBQEAsWLMDZ2ZlZs2YlDYmS7Pz8YPFi61RlpUrBn39aT9VWq2Yd/FgcioqdiIiISZo0acK0adMAGDVqFF9//XXK7axePQgLgzFjrEOk7Nhhvf5uxAiIi0u5/UqqUrETERExUdeuXRkxYgQAgwYNYubMmSm3Mzc3eP11+O036x2zd+/C8OFQuTIcPJhy+5VUo2InIiJismHDhjFw4EAAunfvzqJFi1J2h3nzwtKlMGsWZM4MoaEQEABff63ZK+ycip2IiIjJLBYLY8eOpXv37iQkJPDiiy+yevXqlN4pdO5svc6uRQuIjbVOWdaqFVy9mrL7lhSjYiciImIDnJyc+Pbbb2nXrh2xsbG0bt2arVu3pvyOc+eGn3+Gb76xnqpduhT8/WHLlpTftyQ7FTsREREb4ezszOzZs2nSpAkxMTE0a9aM0NDQlN+xxQIDBlhvqChWDH7/HerU0alZO6RiJyIiYkPc3NxYuHAhtWrVIioqioYNG7J3797U2Xn58tbr7Tp1gvh466nZ7t3h9u3U2b88MxU7ERERG+Ph4cHSpUupVKkS165do169eqlz5A7A09N6U8WXX1qnJfv+e6hZ03oUT2yeip2IiIgN8vLyYvXq1VSpUoW//vqLevXqsWvXrtTZucViPVq3ejVkyQK7d0NgIOzZkzr7l6emYiciImKjvL29Wb16NdWrVycyMpIGDRqwY8eO1AtQr561zJUpA3/8AbVqwbJlqbd/eWIqdiIiIjbM09OTX375hZo1axIVFUX9+vVZs2ZN6gXw8YGtW6FRI7h1yzocSnBw6u1fnoiKnYiIiI3LmDEjK1eupH79+kRHR9OsWTPmzJmTegG8vKxH6nr1goQE6x20r71mfSw2RcVORETEDmTIkIEVK1bQoUMH4uLi6Ny5M2PHjk29AK6uMHkyjBxpfT52LLRvrztmbYyKnYiIiJ1wc3Nj9uzZDBo0CIDXXnuNfv36ERsbmzoBLBYYNgzmzrUOZrxwITRuDJGRqbN/+U8qdiIiInbEycmJsWPHMnr0aCwWCxMnTqR+/fpERESkXogOHWDNGusp2pAQ600Vly+n3v7loVTsRERE7IzFYmHo0KEsXboUT09PNm/eTFBQUOoNZAzWMhcSAjlzwv79UK0anDyZevuXB1KxExERsVPNmzdn586dFCtWjHPnzlGlShW+/vprjNSaBszfH7Ztg8KF4dQpa7nbty919i0PpGInIiJix0qVKsWuXbto0aIFd+/eZdCgQTRr1ow//vgjdQIUKWItd/7+/z/W3aZNqbNvuY+KnYiIiJ3LlCkTP//8M+PHj8fd3Z1ffvkFX19fvvvuu9Q5epcrl7XM1aoFN25Yx7xbtCjl9yv3UbETERFxABaLhf79+7Nnzx7KlSvHtWvX6NGjB/Xq1eO3335L+QDe3rBqFbRuDXfvQrt2MGVKyu9X7qFiJyIi4kD8/PzYvXs3o0aNwsPDg40bN+Ln50efPn24ePFiyu48XTr46af/H8i4Tx/4+GNIrWv+RMVORETE0bi6uvLGG29w+PBhWrZsSXx8PFOmTKFo0aK89tprnDt3LuV27uxsHcj4nXesz997zzpTRXx8yu1TkqjYiYiIOCgfHx9+/vlntmzZQtWqVYmJiWHs2LEULlyYDh06sHnzZhJSYlowi8V6pG7cOOvjCROsp2ZjYpJ/X3IPi5Fq90Tbv6ioKLy9vYmMjMTLy8vsOCIiIo/NMAxWrVrF559/zoYNG5KWFyxYkE6dOtG6dWsqVKiAk1MyH/P56Sfo3Nl63V21arB0KWTJkrz7cHBP0j9U7J6Aip2IiDiCffv2MX78eH766Sdu3LiRtDxr1qw0aNCAqlWrUqFCBcqVK0fGjBmfeX/Gpk3EPPcc0VFRRBcpAt9/j5uPD66urqRPn54MGTI88z4cmYpdClGxExERRxITE8OyZcv48ccfWbdu3T0l72+5c+emYMGC5M+fn8yZM5MxY0Y8PT1xcXEhLi6OuLg4YmNjuXXrFtevX+evv/665ycyMpLo6OhHDrvi6elJ3rx5KVCgAOXKlaNChQpUr16dfPnypeTHtxsqdilExU5ERBxVbGwsO3fuZP369YSGhrJ3714uXLiQ7PvxsFiwGAZ3gbj/WNfPz4+mTZvSpUsXSpcunexZ7IWKXQpRsRMRkbTkypUrnDlzhrNnz/L7778TFRXFjRs3uHnzJrGxsbi6uuLi4oKrqyseHh5kzpyZTJky3fPj7e1NxowZyZAhA+nTp8f5xg1o1QpCQjBcXLg5eTIXq1XjwoULnDx5kn379rFnzx727Nlzz1G+oKAgBgwYQIcOHXB1dTXvSzGBit0jnD9/npdeeomIiAhcXFx49913adeu3WO9V8VOREQkGdy+DV26WG+sAPjwQ3j3XesdtImuXr3K2rVrmTdvHsuXLycuznp8r1ChQrz55pv07NkzzRQ8FbtHuHTpEn/88Qf+/v5ERERQoUIFjh49+lgXbqrYiYiIJJOEBHjjDfjiC+vzzp1h6lRwd79v1YiICKZPn86XX35JREQEAL6+vnz11Vc0aNAgNVOb4kn6R5obxy537tz4+/sDkCNHDrJkycK1a9fMDSUiIpLWODnB559bBzN2dobZs6F+fbhy5b5Vc+TIwbBhwzhz5gzjxo0ja9ashIeH07BhQ9q1a8eff/5pwgewTTZX7DZv3kyLFi3IkycPFouFJUuW3LfOhAkT8PHxIV26dAQEBLBly5an2teePXtISEggf/78z5haREREnkrv3tY5Zr29YetWqFQJHjK3rYeHB6+88grHjx9n0KBBODs7s2DBAnx9fVmwYEEqB7dNNlfsoqOjKVeuHOPHj3/g6/PmzWPQoEEMHz6cffv2UaNGDZo0aXLP9CgBAQH4+fnd9/PPOfKuXr1Kly5dmKIJikVERMxVvz78+iv4+MCpU1C5Mqxf/9DVM2fOzJdffsnu3bspU6YMV65coV27dvTr1487d+6kYnDbY9PX2FksFhYvXkyrVq2SllWqVIkKFSowceLEpGWlSpWiVatWjBw58rG2e+fOHRo0aECvXr146aWXHrneP/8PEhUVRf78+XWNnYiISEr4809o3Rq2bQMXFwgOth7Re4S7d+/y4YcfMnLkSAzDICAggJ9++gkfH59UCp3yHPYau7t37xIaGkrDhg3vWd6wYUO2b9/+WNswDINu3bpRt27dR5Y6gJEjR+Lt7Z30o1O2IiIiKSh7dli3Djp2hLg46NMHBgyA2NiHvsXNzY1PP/2UlStXkjVrVkJDQwkICGDTpk2pl9uG2FWxu3LlCvHx8eTMmfOe5Tlz5uTy5cuPtY1t27Yxb948lixZgr+/P/7+/hw8ePCB67711ltERkYm/Zw/f/6ZP4OIiIg8Qrp01hspPvnEOvxJcDA0aGA9mvcIjRs3Zt++fVSsWJHr16/TsGFDvv/++1QKbTtczA7wNCz/GOcGrEfh/r3sYapXr05CQsJjrevu7o77A267FhERkRRkscDw4VC2LHTqBCEhEBQES5ZA4sgWD5I/f342bdpE9+7dmTdvHt26deP48eN89NFHODnZ1bGsp2ZXnzJbtmw4Ozvfd3QuIiLivqN4IiIiYudatIAdO6BoUTh7FqpV+/9BjR/Cw8ODuXPn8vbbbwPw6aef0q1bt6QBjh2dXRU7Nzc3AgICWLt27T3L165dS9WqVU1KJSIiIinG1xd27YKGDeHWLWjfHt55xzrA8UM4OTnx6aefMn36dJydnZk1axbt2rXj9u3bqRjcHDZX7G7evElYWBhhYWEAnD59mrCwsKThTIYMGcLUqVOZPn06R44cYfDgwZw7d46+ffuamFpERERSTObMsGIFvPaa9fmnn1rnm42KeuTbunfvzuLFi3F3d2fJkiU0b96cmzdvpnxeE9nccCebNm2iTp069y3v2rUrM2bMAKwDFI8ePZpLly7h5+fHl19+Sc2aNVM8m6YUExERMdmsWdCrF9y5AyVLwuLF1v99hI0bN9KyZUtu3rxJ5cqVWblyJZkzZ06lwM9Oc8WmEBU7ERERG7B7t/WI3cWLkDEjzJgBbdo88i07d+6kSZMmXL9+nQoVKrBu3Tq7KXcOO46diIiICEFBEBoKNWvCzZvQti0MHWod++4hKlWqREhICNmzZ2fv3r3Ur1+f69evp2Lo1KFiJyIiIvYnVy7rYMZ/X3f3+efW8e7++OOhbylTpgwbNmxIKncNGjRwuHKnYiciIiL2ydXVWujmz7eekt20CSpUsM47+xB+fn5J5S40NNThyp2K3WMIDg7G19eXoKAgs6OIiIjIv7VrZx0SpWRJ63V3tWrB+PHwkNsI/i532bJlS5qq1FHKnW6eeAK6eUJERMSG3bgBPXv+/yDGHTvCpEng6fnA1Q8ePEjdunW5cuUKgYGBrFu3Dm9v71QM/Hh084SIiIikPZ6eMG8efPEFODvD3LkQGAiJY+P+29/X3GXLlo09e/bQtGlTux/nTsVOREREHIfFAkOGwMaNkC8fHDsGlStDcPADT82WKVOGtWvXkilTJrZv385zzz1HTEyMCcGTh4qdiIiIOJ4aNaxH6po3tw5mPGCAdViUB1xL5+/vz+rVq/H09GTDhg20adOGO3fupH7mZKBiJyIiIo4pa1ZYuhS+/NJ6B+2iRVC+POzYcd+qFStWZMWKFXh4ePDLL7/QoUMH4h4xLp6tUrETERERx2WxwKBBsH07FC4MZ89aj+aNGAHx8fesWqNGDZYuXYq7uzuLFy+ma9euxP9rHVunYiciIiKOLzAQ9u2DF1+0zlAxfLh1WJTTp+9ZrX79+ixYsAAXFxfmzp1Lnz59SEhIMCn0k1OxExERkbTBy8t6p+z331vvoN22DcqVsz7/x40VzZs354cffsDJyYlp06YxcOBA7GV0OBU7ERERSTssFujSBfbvh2rVrGPfdetmHeT46tWk1dq2bcv333+PxWJh/PjxvPnmm3ZR7lTsREREJO3x8YGQEOu1di4usHAhlCkDa9YkrdK5c2cmTZoEwJgxY/joo4/MSvvYVOxEREQkbXJ2hrfest4lW7IkXLoEjRrBq69C4lh2vXv35quvvgLggw8+YPTo0SYG/m8qdo9Bc8WKiIg4sIAACA2F/v2tz7/5BoKCrKdrgYEDBzJy5EgA3nzzTcaPH29W0v+kuWKfgOaKFRERcXCrVkH37nD5Mri5wciR1uFSnJx47733+PjjjwGYPn063bt3T5VImitWRERE5Gk0bgwHDkDLlnD3Lrz2mvX07IULfPjhhwwePBiAl19+mfnz55sc9n4qdiIiIiL/lD07LFkCkyaBhwesWwdly2JZvJgvvviC3r17k5CQQKdOnVi+fLnZae+hYiciIiLybxYL9OljHdQ4IACuXYM2bbD06sWE0aPp2LEjcXFxtG3blg0bNpidNomKnYiIiMjDlChhnY7srbesZW/aNJwDA5nx6qs899xz3Llzh5YtW/Lrr7+anRRQsRMRERF5NDc363h3GzdC/vxw4gSutWoxr0EDGjZsSExMDMePHzc7JaBiJyIiIvJ4atWyDoHSsiXcuYP7gAEszpKFXxYvpkuXLmanA1TsRERERB5f5szWGytGjwZnZ9L/+CMN334bjhwxOxmgYiciIiLyZCwWGDoUNmyA3Lnh8GFYu9bsVAC4mB1ARERExC7VrGm9a3bqVHjlFbPTACp2IiIiIk8vZ04YPtzsFEl0KlZERETEQajYiYiIiDgIFbvHEBwcjK+vL0FBQWZHEREREXkoi2EYhtkh7EVUVBTe3t5ERkbi5eVldhwRERFJA56kf+iInYiIiIiDULETERERcRAqdiIiIiIOQsVORERExEGo2ImIiIg4CBU7EREREQehYiciIiLiIFTsRERERByEip2IiIiIg1CxExEREXEQKnYiIiIiDsLF7AD25O9pdaOiokxOIiIiImnF373j7x7yKCp2T+DGjRsA5M+f3+QkIiIiktbcuHEDb2/vR65jMR6n/gkACQkJXLx4EU9PTywWS4rsIyoqivz583P+/Hm8vLxSZB9phb7L5KHvMXnoe0w++i6Th77H5JEa36NhGNy4cYM8efLg5PToq+h0xO4JODk5kS9fvlTZl5eXl/5BSyb6LpOHvsfkoe8x+ei7TB76HpNHSn+P/3Wk7m+6eUJERETEQajYiYiIiDgIFTsb4+7uzvvvv4+7u7vZUeyevsvkoe8xeeh7TD76LpOHvsfkYWvfo26eEBEREXEQOmInIiIi4iBU7EREREQchIqdiIiIiINQsbMxEyZMwMfHh3Tp0hEQEMCWLVvMjmRXRo4cSVBQEJ6enuTIkYNWrVpx9OhRs2PZvZEjR2KxWBg0aJDZUezShQsX6Ny5M1mzZiV9+vT4+/sTGhpqdiy7EhcXxzvvvIOPjw8eHh4ULlyYjz76iISEBLOj2bzNmzfTokUL8uTJg8ViYcmSJfe8bhgGH3zwAXny5MHDw4PatWtz+PBhc8LasEd9j7Gxsbz55puUKVOGDBkykCdPHrp06cLFixdTPaeKnQ2ZN28egwYNYvjw4ezbt48aNWrQpEkTzp07Z3Y0uxESEkL//v3ZsWMHa9euJS4ujoYNGxIdHW12NLu1e/dupkyZQtmyZc2OYpeuX79OtWrVcHV15ZdffiE8PJwvvviCTJkymR3NrowaNYpJkyYxfvx4jhw5wujRoxkzZgzffPON2dFsXnR0NOXKlWP8+PEPfH306NGMHTuW8ePHs3v3bnLlykWDBg2SptEUq0d9j7du3WLv3r28++677N27l0WLFnHs2DFatmyZ+kENsRkVK1Y0+vbte8+ykiVLGsOGDTMpkf2LiIgwACMkJMTsKHbpxo0bRrFixYy1a9catWrVMgYOHGh2JLvz5ptvGtWrVzc7ht1r1qyZ0aNHj3uWPf/880bnzp1NSmSfAGPx4sVJzxMSEoxcuXIZn332WdKy27dvG97e3sakSZNMSGgf/v09PsiuXbsMwDh79mzqhEqkI3Y24u7du4SGhtKwYcN7ljds2JDt27eblMr+RUZGApAlSxaTk9in/v3706xZM+rXr292FLu1dOlSAgMDadeuHTly5KB8+fJ8++23ZseyO9WrV2f9+vUcO3YMgP3797N161aaNm1qcjL7dvr0aS5fvnzP3x53d3dq1aqlvz3PKDIyEovFkupH5zVXrI24cuUK8fHx5MyZ857lOXPm5PLlyyalsm+GYTBkyBCqV6+On5+f2XHszo8//sjevXvZvXu32VHs2qlTp5g4cSJDhgzh7bffZteuXbz66qu4u7vTpUsXs+PZjTfffJPIyEhKliyJs7Mz8fHxfPrpp3To0MHsaHbt778vD/rbc/bsWTMiOYTbt28zbNgwOnbsmOrz8KrY2RiLxXLPc8Mw7lsmj2fAgAEcOHCArVu3mh3F7pw/f56BAweyZs0a0qVLZ3Ycu5aQkEBgYCAjRowAoHz58hw+fJiJEyeq2D2BefPmMXv2bObOnUvp0qUJCwtj0KBB5MmTh65du5odz+7pb0/yiY2N5cUXXyQhIYEJEyak+v5V7GxEtmzZcHZ2vu/oXERExH3/JSX/7ZVXXmHp0qVs3ryZfPnymR3H7oSGhhIREUFAQEDSsvj4eDZv3sz48eO5c+cOzs7OJia0H7lz58bX1/eeZaVKlWLhwoUmJbJPQ4cOZdiwYbz44osAlClThrNnzzJy5EgVu2eQK1cuwHrkLnfu3EnL9bfn6cTGxtK+fXtOnz7Nhg0bUv1oHeiuWJvh5uZGQEAAa9euvWf52rVrqVq1qkmp7I9hGAwYMIBFixaxYcMGfHx8zI5kl+rVq8fBgwcJCwtL+gkMDKRTp06EhYWp1D2BatWq3TfkzrFjxyhYsKBJiezTrVu3cHK690+Ws7Ozhjt5Rj4+PuTKleuevz13794lJCREf3ue0N+l7vjx46xbt46sWbOakkNH7GzIkCFDeOmllwgMDKRKlSpMmTKFc+fO0bdvX7Oj2Y3+/fszd+5cfv75Zzw9PZOOgHp7e+Ph4WFyOvvh6el533WJGTJkIGvWrLpe8QkNHjyYqlWrMmLECNq3b8+uXbuYMmUKU6ZMMTuaXWnRogWffvopBQoUoHTp0uzbt4+xY8fSo0cPs6PZvJs3b3LixImk56dPnyYsLIwsWbJQoEABBg0axIgRIyhWrBjFihVjxIgRpE+fno4dO5qY2vY86nvMkycPbdu2Ze/evSxfvpz4+Pikvz9ZsmTBzc0t9YKm6j248p+Cg4ONggULGm5ubkaFChU0TMcTAh74891335kdze5puJOnt2zZMsPPz89wd3c3SpYsaUyZMsXsSHYnKirKGDhwoFGgQAEjXbp0RuHChY3hw4cbd+7cMTuazdu4ceMD/73YtWtXwzCsQ568//77Rq5cuQx3d3ejZs2axsGDB80NbYMe9T2ePn36oX9/Nm7cmKo5LYZhGKlXI0VEREQkpegaOxEREREHoWInIiIi4iBU7EREREQchIqdiIiIiINQsRMRERFxECp2IiIiIg5CxU5ERETEQajYiYiIiDgIFTsRERERB6FiJyKSwl577TVatGhhdgwRSQNU7EREUlhYWBj+/v5mxxCRNEDFTkQkhe3fv5/y5cubHUNE0gAVOxGRFHT+/HmuXr2adMTur7/+okWLFlStWpVLly6ZG05EHI6KnYhICgoLC8Pb2xsfHx8OHjxIUFAQuXPnZtOmTeTOndvseCLiYFTsRERSUFhYGOXKleOHH36gZs2avP7660yZMgU3Nzezo4mIA7IYhmGYHUJExFG1adOGjRs3ArB8+XKqVq1qciIRcWQ6YicikoLCwsJo06YNt2/f5q+//jI7jog4OB2xExFJITdu3MDb25vQ0FD279/PwIED2b59O6VLlzY7mog4KBezA4iIOKqwsDCcnZ3x9fWlfPnyHD58mBYtWrBr1y6yZctmdjwRcUA6FSsikkL2799PyZIlcXd3B2DUqFH4+vry/PPPc/fuXZPTiYgj0qlYEREREQehI3YiIiIiDkLFTkRERMRBqNiJiIiIOAgVOxEREREHoWInIiIi4iBU7EREREQchIqdiIiIiINQsRMRERFxECp2IiIiIg5CxU5ERETEQajYiYiIiDgIFTsRERERB/F/ROFEClHjHo8AAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure()\n", - "x = np.linspace(0, 12, 1000)\n", - "ax = plt.plot(x, gkde_r1.evaluate(x), color=\"red\", label=\"retarded\")\n", - "ax = plt.plot(x, gkde_a1.evaluate(x), color=\"black\", label=\"advanced\")\n", - "ax = plt.xlabel(\"$k$\")\n", - "ax = plt.ylabel(\"$p(k)$\")\n", - "ax = plt.legend()\n", - "fig.tight_layout()\n", - "\n", - "fig = plt.figure()\n", - "x = np.linspace(0, 12, 1000)\n", - "ax = plt.plot(x, gkde_r1.evaluate(x), color=\"red\", label=\"retarded\")\n", - "ax = plt.plot(x, gkde_a1.evaluate(x), color=\"black\", label=\"advanced\")\n", - "ax = plt.xlabel(\"$k$\")\n", - "ax = plt.ylabel(\"$p(k)$\")\n", - "ax = plt.legend()\n", - "ax = plt.yscale(\"log\")\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "adebe395", - "metadata": {}, - "source": [ - "By visual investigation we already find strong similarities in the distributions. Now we also apply the KS test for this time series. For this we can use `scipy.stats.ks_2samp`, which gives us the $p$-value of two input data sets, therefore telling us if they are likely to have been drwawn from the same distribution, which would be the case for an reversible process. E.g. $p>0.05$ would imply then that the time series has likely been generated by an reversible process." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0c5cead0", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "KstestResult(statistic=0.0072, pvalue=0.9994903068538411)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from scipy.stats import ks_2samp\n", - "ks_2samp(k_r1, k_a1)" - ] - }, - { - "cell_type": "markdown", - "id": "ec953d2d", - "metadata": {}, - "source": [ - "The result of `pvalue=0.999... > 0.050` implies reversibility of the time series." - ] - }, - { - "cell_type": "markdown", - "id": "da798677", - "metadata": {}, - "source": [ - "#### Non-Linear Time Series" - ] - }, - { - "cell_type": "markdown", - "id": "daa011e0", - "metadata": {}, - "source": [ - "Now we do the same for a non-linear time series. We can construct one using the logistic map for instance, which we take from the tutorial for the recurrence plots. We only change the bifurcation parameter to find ourselves deeply in the chaotic regime to $r = 4$." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "7c13c46c", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def logistic_map(x0, r, T):\n", - " \"\"\"\n", - " Returns a time series of length T using the logistic map,\n", - " x_(n+1) = r*x_n(1-x_n) at parameter r and using the initial condition x0.\n", - " INPUT: x0 - Initial condition, 0 <= x0 <= 1\n", - " r - Bifurcation parameter, 0 <= r <= 4\n", - " T - length of the desired time series\n", - " \"\"\"\n", - " # Initialize the time series array\n", - " timeSeries = np.empty(T)\n", - "\n", - " timeSeries[0] = x0\n", - " for i in range(1, len(timeSeries)):\n", - " xn = timeSeries[i-1]\n", - " timeSeries[i] = r * xn * (1 - xn)\n", - "\n", - " return timeSeries\n", - "\n", - "r = 3.6\n", - "x0 = 0.8\n", - "T = 5000\n", - "\n", - "time_series = logistic_map(x0, r, T)\n", - "fig = plt.figure()\n", - "ax = plt.plot(time_series[:200]) # also zooming in a bit here\n", - "ax = plt.xlabel(\"$n$\")\n", - "ax = plt.ylabel(\"$x_n$\")" - ] - }, - { - "cell_type": "markdown", - "id": "3fa8ae3b", - "metadata": {}, - "source": [ - "We apply the same procedure as before for the linear TS." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "9284ffe6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating visibility relations...\n" - ] - }, - { - "data": { - "image/png": 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/PVOmTKGmpsZxDv/fTPFlsVgc1z/ZAgdWq5W4uDiWL19+1GsREREN+gwtScGurbK3wHXubE5QfDy6x05ExK2FhITUGxzQ2tduqPz8fDIyMnjttdcYO3YsAN9//73j9f79+/POO+9QUVHhCFdr1qw56jyTJk3ioYceIjU1lQ8++IAFCxY4Xlu3bh21tbU8++yz+PiYwwjef//9Rn2m9u3b0717d5YuXcrZZ5991OvDhw8nJycHPz8/uh9nurB+/fqxZs2aeqtlHeuztAQFu7bK3g1rD27HY3993z6oqzNb+ERExG1YLJZGdYe6SocOHYiMjGThwoXExcWxd+9eHnjgAcfrkyZNYubMmUyZMoWHHnqI3bt388wzzxx1nsTERMaMGcOUKVOora3lkksucbzWo0cPamtreemll5g4cSKrVq06ahRtQzz22GNMnTqV6Ohoxo8fz6FDh1i1ahV333035557LqNHj+bSSy/l6aefpk+fPmRlZbFkyRIuvfRSkpOTueeee7jhhhtITk7m9NNP51//+hebNm1yDPBoSRoV21ZlZZnP8fEnPi4+3gxzNTVm962IiEgT+Pj48O9//5vU1FQGDhzI9OnTmTt3ruP10NBQPvvsMzZv3sywYcOYOXMmTz/99DHPde2117JhwwYuu+yyel2nQ4cO5bnnnuPpp59m4MCB/Otf/2L27NmNrvWGG25g3rx5zJ8/nwEDBnDRRRexzbYKk8ViYcmSJZxxxhncdNNN9O7dm6uvvprdu3cTExMDmEukPvLII9x///0kJSWxZ88ebr/99kbX0RQWo6Gd2l6qpKSE8PBwiouLCQsLc3U5ref++2HOHLjnHpg378THdu9udt2uXg2/uZFVRERaV2VlJbt27SIxMZGgoCBXlyNOcqI/18ZkFbXYtVX2iRjj4urtLigocIwMcrAPrrC38omIiIhbUrBrq+zB7oiu2LfeeovY2FhiY2PrNY87jlGwExERcWsKdm3Vb1rsdu7cya233kpNTQ2GYXDfffexdu3aesfwm+VWRERExL0o2LVVvwl2c+bMoaamhnPPPZdrr70WgP/7v/+rd4xa7ERERNybgl1bVFkJtvXsiIujqqqKxYsXA/Dggw/y8MMPA/DFF1+Y99vZu2LVYiciIuLWFOzaIvu0JYGB0KEDKSkpFBUVER8fz5lnnkmfPn1ITk6mrq6ODz74QF2xIiJuqI1PauF1nPXnqWDXFtkDWmwsWCykpKQAcPHFF+Nrm4D4iiuuAOCrr75SV6yIiBuxL4dVXl7u4krEmex/nr9d7qyxtPJEW/SbEbH29e5+97vfOQ6xL368fPlyaqOjzb8o+flQVWW29ImIiEv4+voSERHhmJoqJCQEi8Xi4qqkqQzDoLy8nNzcXCIiIhwNLE2lYNcWHTFwIj8/n59//hmAM88803HIsGHDiIiIoKioiPW7dzMyIACqq81u3G7dXFG1iIjYxMbGAhw976h4rIiICMefa3Mo2LVFRwS7H3/8EYA+ffoQHR3tOMTX15exY8fy2Wef8cOaNYyMizNXn8jOVrATEXExi8VCXFwc0dHR1NTUuLocaSZ/f/9mt9TZKdi1RUcEu7S0NACGDx9+1GEjRozgs88+Y926deZ9dvZgJyIibsHX19dpgUC8gwZPtEX2pvvo6BMGu+TkZIDDwQ40gEJERMSNKdi1RQcPms+dOjmC3bBhw446LCkpCYAtW7ZQEhVl7lSwExERcVsKdm2RrcWuOCSEnTt3AscOdtHR0XTt2hXDMEi3Wuu9V0RERNyPgl1bZGux25ifD0BCQgIdO3Y85qGDBg0CYHNFhbnjwIGWr09ERESaRMGuramshNJSALbk5QHQr1+/4x7ev39/ADYVFZk71GInIiLithTs2hr7/XX+/mzNzASgd+/exz3cHuw221vqFOxERETcloJdW2MPdlFRbNm6FTDnsDseR7Dbu9fcoWAnIiLithTs2pojRsRu2bIFOHGws3fT5hw8SAFAWZn5EBEREbejYNfW2FrcaiMj2bFjB3Dirtj27dvTtWtXADbbFya2h0MRERFxKwp2bY0tlO1u146amhqCg4NJSEg44VvsrXa/hoaaO9QdKyIi4pYU7NoaW7Db6mP+0ffq1QsfnxP/NejZsycAOwIDzR0KdiIiIm5Jwa6tsQW7XbYJh0855ZSTvqVHjx4AbLfvULATERFxSwp2bY0t2O2pqgKge/fuJ32LPdjtqK42dyjYiYiIuCUFu7bGfo+dbZLihgQ7R1dsaSkGKNiJiIi4KQW7tsYe7AoLAejWrdtJ35KYmAhASXU1+aBgJyIi4qYU7NoaWyjbYwt4DWmxCw4OpnPnzoDtPjsFOxEREbekYNeWVFVBSQnlQG5+PtCwFjs4ojsWFOxERETclIJdW5KXB8Ae2/QmYWFhRERENOitjgEUoGAnIiLiphTs2hJbK92e9u0Bs7XOYrE06K32aVF2gXmfnm26FBEREXEfCnZtiS3Y7Q4KAhp2f52dvct2L0BtLdgGX4iIiIj7ULBrSwoKANjj5wc0LtjZ14vdY2/hs4VEERERcR8Kdm2JPdjZfrSHtYawt9hlGgZWcNyvJyIiIu5Dwa4tsQW7rNpaALp06dLgt8bHx+Pj40M1cADUYiciIuKGFOzaElsY229bTiw+Pr7Bb/X393fMZbcX1GInIiLihhTs2pKCAgwgq6wMaFywgyPuswMFOxERETekYNeWFBRQApTX1ACND3b1RsaqK1ZERMTtKNi1Jfn57LdtRkREEBIS0qi3q8VORETEvbldsJs/fz6JiYkEBQWRlJTEypUrj3vs8uXLsVgsRz1+/fXXVqzYgxQUkGXbtN8v1xj1WuwU7ERERNyOWwW7xYsXM23aNGbOnElaWhpjx45l/Pjx7N2794Tv27JlC9nZ2Y5Hr169WqliD1NQ4Gixa2w3LPymxU5dsSIiIm7HrYLdc889x5QpU7j55pvp168f8+bNIyEhgQULFpzwfdHR0cTGxjoevr6+rVSxh3FSi526YkVERNyT2wS76upqUlNTGTduXL3948aNY/Xq1Sd877Bhw4iLi+Occ85h2bJlLVmm5yovh8pKp7TYFQElublOK01EREScw8/VBdjl5eVRV1dHTExMvf0xMTHk5OQc8z1xcXEsXLiQpKQkqqqqeOeddzjnnHNYvnw5Z5xxxjHfU1VVRZVtHjeAkpIS530Id2afnNhiAcNoUrBr37494WFhFJeUsL+wkDCrFXzc5v8NRERE2jy3CXZ2FvtapDaGYRy1z65Pnz706dPH8fPo0aPJzMzkmWeeOW6wmz17No8//rjzCvYU9mDn6wu1tU3qigWzC7e4pIT9hkG/oiLo2NGJRYqIiEhzuE1zS1RUFL6+vke1zuXm5h7Vincio0aNYtu2bcd9/cEHH6S4uNjxyMzMbHLNHsW+6oTtx6a02AF0ti1Dth90n52IiIibcZtgFxAQQFJSEikpKfX2p6SkMGbMmAafJy0tjbi4uOO+HhgYSFhYWL1Hm1BQgBXItq0T25wWO7AFO42MFRERcStu1RU7Y8YMrr/+epKTkxk9ejQLFy5k7969TJ06FTBb2/bv38/bb78NwLx58+jevTsDBgygurqaf/7zn3z44Yd8+OGHrvwY7qmggFygDrO7uzGtoEeqF+zUYiciIuJW3CrYXXXVVeTn5zNr1iyys7MZOHAgS5YscUyzkZ2dXW9Ou+rqau699172799PcHAwAwYM4H//+x8TJkxw1UdwX/n52Du5o6Oj8fNr2h+9gp2IiIj7cqtgB3DHHXdwxx13HPO1t956q97P9913H/fdd18rVOUFCgo4YNtsamsdqCtWRETEnbnNPXbSwpwc7PaBWuxERETcjIJdW3FEsIuOjm7yabrYRsUeAGo0SbGIiIhbUbBrK/Lzscew5rTYderUCX9fXwwgZ//+kx4vIiIirUfBrq1wUlesj48PcR06ALD/OCuCiIiIiGso2LUVTgp2AJ1tXbn7dY+diIiIW1GwawsMA/LznRfs7CNji4ubWZiIiIg4k4JdW1BRAVVVzgt2XbsCsL+sDKzWZhYnIiIizqJg1xYUFmIFDtp+bM6oWIDOPXoAtrnsioqadS4RERFxHgW7tqCoiALM5cTACcHOthKIJikWERFxLwp2bUFRkaMbtmPHjvj7+zfrdFpWTERExD0p2LUFRwS75t5fB4cnKd4PGAp2IiIibkPBri0oLHRqsIuPjwegHCjOzGz2+URERMQ5FOzaAie32AUHB9MhIACArF27mn0+ERERcQ4Fu7agqMixnFhzB07YxYeGApC1b59TziciIiLNp2DXFji5KxYgPiICgCwtKyYiIuI2FOzaAid3xQLEd+oEQJYGT4iIiLgNBbu2oAWCXee4OAD2FxQ45XwiIiLSfAp2bUFLtNjZpjzJKi11yvlERESk+RTs2gCjoMBpy4nZxdtWn8iqqHDK+URERKT5FOzagPLCQipt21FRUU45Z3yvXgBk1dSAYTjlnCIiItI8CnZtQH5REQAB/v60a9fOKeeM79sXgGzAWlLilHOKiIhI8yjYeTurlXxb8Irs2BGLxeKU08YmJmIBaoC8bducck4RERFpHgU7b3foEPm2rtJIJ3XDAvgHBBDtY/71ydq61WnnFRERkaZTsPN2RUXYZ5qLss095yzx9mXFdu506nlFRESkaRTsvF1REfm2zcjISKeeOt52v17Wnj1OPa+IiIg0jYKdtyssbLlgFxYGQNb+/U49r4iIiDSNgp23a8kWu44dAdh/4MBJjhQREZHWoGDn7Vow2HW2TXacpWXFRERE3IKCnbc7cvCEE0fFAsR37gxAVnGxU88rIiIiTaNg5+1a8h67rl0ByCorc+p5RUREpGkU7LxdS95jl5gIwIHqampra516bhEREWk8BTtv14LBrtMpp+AHGEBOTo5Tzy0iIiKNp2Dn5WoKCrCv5OrsYOcTHU2cbTsrK8up5xYREZHGU7DzcvkHDwLgY7EQERHh3JNHRhJv29QkxSIiIq6nYOfl8m1TkXRo3x5fX1/nnjw8/HCw27bNuecWERGRRlOw83L5RUUARDq7tQ7Ax4f4oCAAsnbvdv75RUREpFEU7Lxc/qFDgPPvr7OLDw0FYH9mZoucX0RERBpOwc6b1daSX1kJQGSnTi1yic4dOgCQlZ3dIucXERGRhlOw82bFxYdXnYiNbZFLxNtaArNsgzRERETEdRTsvNmRc9i1UItdvC0wZhUWtsj5RUREpOEU7LxZCy4nZhefkABAQUUFlbZuXxEREXENBTtv1oKrTthFxMcTZNvWJMUiIiKupWDnzYqKHPfYtVSws0RF0dm2rWAnIiLiWgp23qwVWuzqrT6hYCciIuJSCnbe7Ih77KKiolrmGlFRCnYiIiJuQsHOi1kLCymwbavFTkRExPsp2Hmx4gMHsNq2WyPY7d+3r2WuISIiIg2iYOfF8nJzAQgNDCQgIKBlLtKx4+HBE3v3tsw1REREpEEU7LxYfp45JjbStp5ri/D3Jz4kBICs/ftb7joiIiJyUm4X7ObPn09iYiJBQUEkJSWxcuXKBr1v1apV+Pn5MXTo0JYt0IPkFxUBEBUR0aLXcSwrduBAi15HRERETsytgt3ixYuZNm0aM2fOJC0tjbFjxzJ+/Hj2nqSLr7i4mMmTJ3POOee0UqWeIb+4GIDIDh1a9DpxtuXKSisqOHToUIteS0RERI7PrYLdc889x5QpU7j55pvp168f8+bNIyEhgQULFpzwfbfddhuTJk1i9OjRrVSpZ8gvLQUgsqWmOrEJjY4mzLa9X92xIiIiLuM2wa66uprU1FTGjRtXb/+4ceNYvXr1cd/35ptvsmPHDh599NGWLtHj5JWXAxAZHd2yF4qM1OoTIiIibsDP1QXY5eXlUVdXR0xMTL39MTEx5OTkHPM927Zt44EHHmDlypX4+TXso1RVVVFVVeX4uaSkpOlFu7PKSvLr6gCIjItr2WvZJinOQMFORETEldymxc7OYrHU+9kwjKP2AdTV1TFp0iQef/xxevfu3eDzz549m/DwcMcjISGh2TW7peLiw6tOdO58wkObTZMUi4iIuAW3CXZRUVH4+voe1TqXm5t7VCsewKFDh1i3bh133XUXfn5++Pn5MWvWLDZs2ICfnx/ffvvtMa/z4IMPUlxc7HhkZma2yOdxuSOWE2vpe+wU7ERERNyD23TFBgQEkJSUREpKCr///e8d+1NSUrjkkkuOOj4sLIyNGzfW2zd//ny+/fZbPvjgAxITE495ncDAQAIDA51bvDsqKjoc7Fpq1Qm7I1ef0OAJERERl3GbYAcwY8YMrr/+epKTkxk9ejQLFy5k7969TJ06FTBb2/bv38/bb7+Nj48PAwcOrPf+6OhogoKCjtrfJhUXk2fbbPFgZ7vHDtRiJyIi4kpuFeyuuuoq8vPzmTVrFtnZ2QwcOJAlS5bQrVs3ALKzs086p52YjCO7YluhxU6jYkVERFzPYhiG4eoiXKmkpITw8HCKi4sJCws7+Rs8RNmLLxJ6zz2AeT9iaEsuK5aZyZ6uXemO2aVeWVl5zAEvIiIi0niNySpuM3hCnCs/OxuAAB8f2rVr17IXi4wk1rZZXV1NQUFBy15PREREjknBzkvl20YXRwYHt3zrWUgIgcHB2MfeagCFiIiIayjYeam8gwcBiGzp1jo7TXkiIiLicgp2Xiq/sBCAyPbtW+eCGkAhIiLicgp2Xiq/qAiAqA4dWueCarETERFxOQU7L5VvWwM3smPH1rmg5rITERFxOQU7L5VfVga0wnJidlp9QkRExOUU7LxUXkUFAJHHWGe3RagrVkRExOUU7LxUfnU1AJFxca1zQQ2eEBERcTkFO29UXU2+1QpAVEJC61zziHvscnJyqKura53rioiIiIOCnTcqLj68TmyXLq1zzchIojH/QlmtVnJzc1vnuiIiIuKgYOeNiooOB7vo6Na5ZmQkvkCsry+gARQiIiKuoGDnhaoPHqTEth0ZGdk6F7VdJ94wAN1nJyIi4goKdl6oIDMTAAsQERHROhe1BbvOtnv7FOxERERan4KdF8q3haqOfn742rpGW1x4OPj6asoTERERF1Kw80L52dkARAYGtt5FLRbNZSciIuJiCnZeKP/AAQAiQ0Ja98JafUJERMSlFOy8UF5eHgCR7dq17oXVYiciIuJSCnZeKL+gAIDIsLDWvXBUlFafEBERcSEFOy+UX1wMQGRrjYi1O6LFLi8vj6qqqta9voiISBunYOeF8ktLAYiKimrdC0dG0hEIsI3EzcnJad3ri4iItHEKdl4ov6wMgMhOnVr3wpGRWID4oCBAAyhERERam4KdF8qrrAQgMiamdS9sayGM9/cHdJ+diIhIa1Ow80L51dUARMbHn+RIJ7OvPuFj/rVSi52IiEjrUrDzQvm1tQBEdunSuhe2BbuEujoA9uzZ07rXFxERaeMU7LyMtbqaAtt2VLdurXtxW7BLtLUY7t69u3WvLyIi0sYp2HmZ4n37sNq2IxMTW/fitnvsuldUALBr167Wvb6IiEgbp2DnZfJsYSoUCGjtlSc6dgSLBXucVLATERFpXQp2XiZ/3z4AIm1zybUqX1+IiqK77cfi4mKKiopavw4REZE2SsHOy+TbRqJG2qYcaXUxMbQDOoWHA2q1ExERaU0Kdl4m37baQ5RtkuBWFx0NQKJtIIWCnYiISOtRsPMy+QcPAhAZHOyaAmyTIieGhQEKdiIiIq1Jwc7L5OXlARDZvr1rCrAFu+6BgYCmPBEREWlNCnZeJr+wEIBIW4tZq7O32FksgFrsREREWpOCnZfJLy4GILJDB9cUYL/Hzrb6hYKdiIhI61Gw8zL5paUARNkmC2519q7YsjLA7Io1DMM1tYiIiLQxzQp2NTU1ZGZmsmXLFgoKCk7+Bmlx+eXlAETaWs5anS3YdSsuxmKxUF5ezoEDB1xTi4iISBvT6GBXWlrKa6+9xllnnUV4eDjdu3enf//+dOrUiW7dunHLLbfw008/tUSt0gB5lZUARMbGuqYAW7ALPHiQbra1ardt2+aaWkRERNqYRgW7559/nu7du/P3v/+d3/3ud3z00Uekp6ezZcsWfvjhBx599FFqa2s577zzuOCCC/QLvZUZhkF+TQ0AkfHxrimiUyfzuaaG3ra1ardu3eqaWkRERNoYv8YcvHr1apYtW8agQYOO+frIkSO56aabePXVV3n99ddZsWIFvXr1ckqhcnLl5eVU2e5ni0xIcE0RQUEQHg7FxfSJj+drFOxERERaS6OC3X/+8x/H9oEDB4ixdbv9VmBgIHfccUfzKpNGy8/PByAACHVVix2Y3bHFxfS2rT6xZcsW19UiIiLShjR58MTll19OrW1Ki9863n5pWY5VJwBLRITrCrEF/t6hoYBa7ERERFpLk4Ndhw4duPvuu4/an5+fz7nnntusoqRp8jIzATPYER7uukJswa5PQAAA27dvp66uznX1iIiItBFNDnbvvPMOS5cuZdGiRY59GRkZjBw5kjBXrXrQxuXbg53FYt7r5iq2qVYSqqsJDAykpqaGPXv2uK4eERGRNqLJwS4iIoIPP/yQ++67jx9//JEvv/yS0aNHc/nll/PJJ584s0ZpoPysLAAibS1lLmNrsfM5eNAxeEb32YmIiLS8Rg2euOSSSxg6dCjDhg1j6NChDBo0iFdeeYULL7yQyspKXnnlFW644YaWqlVOIt82EXBUcLBrC7EPqjlwgD59+vDLL7+wZcsWxo8f79q6REREvFyjgl2vXr1YtWoV8+fPJz8/n4iICIYMGYJhGFx77bUMHTqUmpoa/P39W6peOQHH4ImQENcWYp8cOSeHfuPGAbBp0yYXFiQiItI2NCrYPfPMM47tffv2kZ6eTnp6OpGRkY777fz8/Ojbty8bNmxwerFyYnl5eQBEtm/v2kLsU61kZTF48GAAfv75ZxcWJCIi0jY0KtgdqUuXLnTp0oWLLrrIsa+0tJS0tDT9EneR/KIiACJdOSIWDge77GwGDxgAwC+//EJdXR2+vr4uLExERMS7NXnwxLGEhoYyduxY7rzzziafY/78+SQmJhIUFERSUhIrV6487rHff/89p512GpGRkQQHB9O3b1+ef/75Jl/b0+WXlAAQ2aGDawuJiQEfH6iro2d4OEFBQZSXl7Nz507X1iUiIuLlGhXs9u7d26iT79+/v1HHL168mGnTpjFz5kzS0tIYO3Ys48ePP+5127Vrx1133cV3331HRkYGDz30EA899BALFy5s1HW9RX5pKQBR9vVaXcXPzzGAwjcnh4EDBwLqjhUREWlpjQp2I0aM4JZbbmHt2rXHPaa4uJi///3vDBw4kI8++qhRxTz33HNMmTKFm2++mX79+jFv3jwSEhJYsGDBMY8fNmwY11xzDQMGDKB79+5cd911nH/++Sds5fNm+RUVAETa5pFzqc6dzef9+3WfnYiISCtp1D12GRkZPPnkk1xwwQX4+/uTnJxMfHw8QUFBFBYWsnnzZjZt2kRycjJz585t1PQW1dXVpKam8sADD9TbP27cOFavXt2gc6SlpbF69Wr+9re/NeZjeYXq6mpKamoAiLSPSnUlDaAQERFpdY0Kdh07duSZZ57hb3/7G0uWLGHlypXs3r2biooKoqKiuPbaazn//PMdXW+NkZeXR11dHTH2OdBsYmJiyMnJOeF7u3TpwsGDB6mtreWxxx7j5ptvPu6xVVVVVFVVOX4usd2X5ukKCgoAsAAR9lDlSke02A21LTG3bt06FxYkIiLi/Zo0KjYoKIjLLruMyy67zHEfXWf7L/Jmslgs9X42DOOofb+1cuVKSktLWbNmDQ888AA9e/bkmmuuOeaxs2fP5vHHH3dKre4kPz8fgA6Ab8eOri0G6rXYJSUl4ePjw759+9i3bx9dunRxbW0iIiJeqsmjYletWkViYiJdu3ala9euxMTEcP/99ze5BSwqKgpfX9+jWudyc3OPasX7rcTERAYNGsQtt9zC9OnTeeyxx4577IMPPkhxcbHjkWlbX9XT2YNdJEBEhCtLMR3RYhcaGurojl2zZo0LixIREfFuTQ52t912GwMGDOCnn37i559/Zu7cuSxdupSkpCTHRLmNERAQQFJSEikpKfX2p6SkMGbMmAafxzCMel2tvxUYGEhYWFi9hzewB7soAFfPYwf1WuwARo8eDcAPP/zQ5FNmZmby8ccfs2bNGgzDaHaJIiIi3qbJwW7Hjh08//zzDB8+nAEDBjB58mR++uknhg4dyp/+9KcmnXPGjBksWrSIN954g4yMDKZPn87evXuZOnUqYLa2TZ482XH8K6+8wmeffca2bdvYtm0bb775Js888wzXXXddUz+Wx8rLzQVsLXaunscO6rXYAYwaNQpoWrCrrKzkzjvvpHv37vz+979n9OjRjBw5kj179jitXBEREW/Q5JUn+vXrR05ODr169XLss1gszJo1i5EjRzbpnFdddRX5+fnMmjWL7OxsBg4cyJIlS+jWrRsA2dnZ9ea0s1qtPPjgg+zatQs/Pz969OjBU089xW233dbUj+Wx8m0tY24X7AoKoLLS0eq6bt06SktLCQ0NbdBpKisrmTBhAsuWLQNg8ODB7Nixg3Xr1nHWWWexbt06IiMjW+QjiIiIeJomt9jdeOON3HrrrUdNHlxcXEx4M7oC77jjDnbv3k1VVRWpqamcccYZjtfeeustli9f7vj57rvv5pdffqGsrIzi4mLWr1/P7bffjo+PUxfU8Aj52dkARPn6QlCQi6vBvM/PXkdWFj169KB79+7U1NSwYsWKBp3CMAwmT57MsmXLCA0N5csvv2TDhg1kZGRwyimnsHv37jYZ4kVERI6nyQlo2rRpbNmyhd69ezNp0iTmzJnD7NmzmTJlCnPnznVmjdIAebZBJ1EhIS6uxMZiqdcda7FYOP/88wH4+uuvG3SK1157jf/85z8EBATwySefON6fkJDAhx9+iK+vLx9++CFLly5tkY8gIiLiaZoc7HJycvjiiy+YNWsWYLamPfLII2zbto2nnnqK6667jjlz5vDll186rVg5vryDBwGIbGAXZ6tISDCfba2648aNA+DLL7886eCHX3/9lRkzZgDw1FNP8bvf/a7e60OHDuX2228H8Mrpa0RERJrCYjhxeGFlZSUbN24kPT2dDRs2kJ6ezi+//EJRUZGzLuF0JSUlhIeHU1xc7NEjZMf06cMPW7fyUd++/D4jw9XlmP74R3jrLXjiCfjrXykpKSE6OpqqqirS0tIYOnToMd9WXV3NqFGjSEtL47zzzuPLL788Zvf6/v376d69O7W1taxdu5YRI0a07OcRERFxgcZkFafejBYUFORYT/bll1/m+++/d+tQ503ybN9zpDsMnLCzDXph924AwsLCuOiiiwB49913j/u2hx9+mLS0NCIjI3nrrbeOe89k586dmTRpEgAvv/yy8+oWERHxUG1vlIGXyi8tBcyJnt1G9+7msy3YAVx77bUA/OMf/6CiouKotyxfvtxxj+aiRYuIP8nyaPbBEx999BFlZWXNr1lERMSDKdh5gbq6OgrLywGIPMkqHa3KHuyOmG/uoosuolu3buTm5vLmm2/WO/zAgQNcf/31GIbBzTffzKWXXnrSS4wePZrExERKS0v55JNPnFi8iIiI51Gw8wKFhYXYb5TsGBvr0lrqOTLYWa0A+Pv7c++99wLwyCOPcODAAcC8f+DSSy9l37599O7dm+eff75Bl7BYLI4Jqd977z3n1i8iIuJhFOy8gH0JtwjA3526Yjt3Bh8fqKoCW4ADuPXWWxk6dCj5+fmcc845vPjii4wePZo1a9bQoUMHPvvsswZPYAxwxRVXAPDNN99Qbmu5FBERaYsU7LyAfZ1Yt1l1ws7fH7p0MbeP6I4NCAjgvffeIzY2lk2bNnHPPfewefNmYmJiSElJoXfv3o26zKBBg+jatSuVlZV8++23zvwEIiIiHkXBzgvYW+yiwL2CHRxzAAVA3759WbduHdOmTWP8+PHMnDmTjRs3kpSUdOLzrVoFf/kLPPUU2Obus1gsjtG2n332mZM/gIiIiOdo8lqx4j7swc7tWuzADHbffXdUsANzupKG3ksHwJNPwsyZh39+8UVYvhx692bixInMnz+f//3vfxiGgcViaW7lIiIiHkctdl7A3hXr1i12O3c27zzvv3841F15JfTpA9nZcPHFUFXFmWeeSUBAAPv372fbtm3Nu5aIiIiHUrDzAvblxNwy2PXqZT43J2wVFoJt+TDuvx8WL4YVKyAuDrZsgeeeIzg4mFGjRgGwbNmyZhYtIiLimRTsvEB+Tg7gpl2x9oEQW7Y0/Rz/939QUAADBsDf/mbui4mBOXPM7WeegfJyzj77bEDBTkRE2i4FOy+QZwt2UX5+EBzs4mp+o08f8zk7G0pKGv/+ggKYP9/cfvZZ8DvittBrroFTTjGP+cc/HMFu+fLlOHEJZBEREY+hYOcF8u2DJxox91urCQ83W9cAtm5t/Pv/+U9zHryhQ2HcuPqv+frCn/5kbi9axKhRowgKCuLAgQP8+uuvzSpbRETEEynYeYE8++CJsDAXV3Ic9la7xgY7w4C//93cvvlmONZI10mTzIC3fj2Be/YwevRoAL777rtmFCwiIuKZFOy8QH5xMQCR7nZ/nZ092DX2PruffoJffoGgIDPAHUunTnDeeeb2e+8xZswYANasWdPEYkVERDyXgp2Hq6uro+DQIQCi3Gk5sSM1dQDFokXm8xVXnHhQyNVXm88ff+xosVOwExGRtkjBzsMVFRVhtQ0UiLTfy+Zu+vY1nzMyGv6e0lJ47z1z+5ZbTnzs+PHmc3o6p3brBsCvv/5KQUFBIwsVERHxbAp2Hs4+OXEY4B8Z6dpijmfwYPN582ZzIERDvP++Ge569YKxY098bHQ0JCcDELVuHb1sc+etXbu2qRWLiIh4JAU7D+fW68TaJSRAx45QWwubNjXsPScbNPFb9la7L75wTFT8ww8/NKFYERERz6Vg5+HsLXZuOTmxncViTlcCkJZ28uN/+QXWrDHnrJs8uWHXOP988/nbbxltC3a6z05ERNoaBTsP5xEtdgDDhpnP6eknP/b1183niRMhNrZh5x8xwhw9m5fHKNu9hj/++CNWq7XxtYqIiHgoBTsP5xEtdnC4xW79+hMfV1UFb79tbt98c8PPHxAAtpa6gbm5BAYGUlxczM6dOxtfq4iIiIdSsPNwHtNid+qp5vO6dVBZefzjPv7YXCKsc+fD3asNZRtk4b9qFYNtAzbWnyxIioiIeBEFOw9nb7Fz+2DXs6e5tFh1NZxotKp97rqbbjJXlGiMM84wn1euJCkpCYDU1NQmFCsiIuKZFOw8nL3Fzu27Yi2WesHrmLZtg2++MY+96abGX2PUKDMM7t1LUvfugIKdiIi0LQp2Hi7vwAHA1mIXEeHKUk7OHuyWLj3266++aj6PHw+2YNYooaEwaBAAw33Mv9rr16/HsE3gLCIi4u0U7Dxc/sGDAET6+kJIiIurOYkJE8zn774DW0ujQ0UFvPmmuX3HHU2/xogRgDmAIiAggMLCQnbv3t3084mIiHgQBTsPl2e/xy48vGET+brSKaeYo2Pr6uCzz+q/tmgRFBaaLXUXXND0a9iCXcD69Qyytd6pO1ZERNoKBTsPZrVaKSguBiDSne+vO9Lll5vP9rnqwGytmz3b3L7//sYPmjjSyJHm87p1DLfNnaeRsSIi0lYo2HmwoqIi6mwT8EZ26uTiahroppvMOedWrTo8iOLxxyE721x67I9/bN75BwyA4GAoKSGpSxdALXYiItJ2KNh5sIO2++vCgUBPCXbx8XDjjeb2jTfCI4/AnDnmzy+9BIGBzTu/n59jlYsk267U1FQNoBARkTZBwc6D2YNdJ4DISJfW0ihPPQVdu8LOnfB//weGAXffDZdc4pzz2+6zG5Sbi5+fH/n5+WRmZjrn3CIiIm5Mwc6DeWyw69DB7Iq97jo4/XSzpW7ePOed3xbsAtPTGTBgAABpaWnOO7+IiIibUrDzYLm5uYAt2HXs6NJaGq1LF3jnHfM+u7vuAh8n/lW0dcXy888Ms61Rq2AnIiJtgYKdB7O32EWDZ7XYtbTevc179UpLGZaQAGhkrIiItA0Kdh7MY7tiW5qfn2MFimEBAYBa7EREpG1QsPNg9bpiFezqGzIEgKGHDgGwb98+x7q6IiIi3krBzoPV64r1tHvsWprt3rr2GRn06tULUKudiIh4PwU7D6au2BOwtdixYQPDbIMpFOxERMTbKdh5sNwDBwAFu2MaPNh8zsxkWJ8+gAZQiIiI91Ow81CGYZCXnw9AdGCguYyWHBYeDomJAAxr1w5Qi52IiHg/BTsPVVRURG1tLQBRaq07Ntt9dsOqqgDYtm0bpaWlLixIRESkZSnYeSj7iNgwIDAqyrXFuCvbfXbRO3bQuXNnDMNgw4YNLi5KRESk5SjYeSgNnGgAW4sd6emOARS6z05ERLyZgp2H0lQnDWAfGZuRwTDbYArdZyciIt5Mwc5DaXLiBujWDcLCoKaGYbbuagU7ERHxZgp2HkpdsQ1gsTimPRlu27Vp0yaqbIMpREREvI3bBbv58+eTmJhIUFAQSUlJrFy58rjHfvTRR5x33nl06tSJsLAwRo8ezVdffdWK1bpOva5YBbvjs3XHdt23jw4dOlBTU8OmTZtcXJSIiEjLcKtgt3jxYqZNm8bMmTNJS0tj7NixjB8/nr179x7z+O+++47zzjuPJUuWkJqaytlnn83EiRPbRHdbva5Y3WN3fLZgZ/n5Z61AISIiXs+tgt1zzz3HlClTuPnmm+nXrx/z5s0jISGBBQsWHPP4efPmcd999zFixAh69erFk08+Sa9evfjss89aufLWp67YBrIPoFCwExGRNsBtgl11dTWpqamMGzeu3v5x48axevXqBp3DarVy6NAhOp6gBauqqoqSkpJ6D0+krtgGGjgQfHwgN5fhp5wCKNiJiIj3cptgl5eXR11dHTExMfX2x8TEkJOT06BzPPvss5SVlXHllVce95jZs2cTHh7ueCQkJDSrblep12KnrtjjCwmBXr0AGObvD0B6ejp1dXWurEpERKRFuE2ws7NYLPV+NgzjqH3H8t577/HYY4+xePFioqOjj3vcgw8+SHFxseORmZnZ7Jpbm2EY6optDNvI2N75+YSEhFBeXs62bdtcXJSIiIjzuU2wi4qKwtfX96jWudzc3KNa8X5r8eLFTJkyhffff59zzz33hMcGBgYSFhZW7+FpjlwnVi12DWC7z873l18YrImKRUTEi7lNsAsICCApKYmUlJR6+1NSUhgzZsxx3/fee+9x44038u6773LhhRe2dJluwd5aFwYEhoWBn59rC3J39gEUGzZoAIWIiHg1twl2ADNmzGDRokW88cYbZGRkMH36dPbu3cvUqVMBsxt18uTJjuPfe+89Jk+ezLPPPsuoUaPIyckhJyeH4uJiV32EVmFv1YwB6NTJpbV4BHuw+/VXhg8aBGjNWBER8U5u1dRz1VVXkZ+fz6xZs8jOzmbgwIEsWbKEbt26AZCdnV1vTrvXXnuN2tpa7rzzTu68807H/htuuIG33nqrtctvNQcOHAAgFhTsGqJLF+jQAQoLGWbrek9LS2vw/ZsiIiKewq2CHcAdd9zBHXfccczXfhvWli9f3vIFuSG12DWSfWmxFSsYWF6On58fBQUFZGZm0rVrV1dXJyIi4jRu1RUrDWMPdmqxawRbd2xgRgb9+/cHdJ+diIh4HwU7D6Rg1wRHDKAYPnw4oGAnIiLeR8HOA9nvsVNXbCMcOTJ26FBAAyhERMT7KNh5ILXYNcGAAebSYvn5DLOtNqIWOxER8TYKdh5Iwa4JgoKgTx8AhlitAOzbt4+8vDxXViUiIuJUCnYexmq1kpubC6grttFs3bFh27fTs2dPQK12IiLiXRTsPExhYSE1NTUARIOCXWMcYwCF7rMTERFvomDnYezdsB2BQFCwawwtLSYiIl5Owc7D1BsRGxJiPqRh7MFuyxaGaS47ERHxQgp2HkYDJ5ohLg6iosBqZVhwMADbtm3j0KFDLi5MRETEORTsPIyCXTPYlxYDojMziY+PxzAMNmzY4OLCREREnEPBzsNocuJm0goUIiLixRTsPEy9FruoKJfW4pGOCHbJyckA/Pjjjy4sSERExHkU7DyMumKb6Yhgd9qYMQB8//33LixIRETEeRTsPIy6YpupXz/w84OiIk6Ni8PX15c9e/aQmZnp6spERESaTcHOw6jFrpkCAx2tdu03b2bo0KEArFq1yrnXMQzYsQN+/hlqa517bhERkeNQsPMgdXV1HDx4EFCwa5ZRo8znNWs4/fTTASd3x6amQnIy9OxphsjOneGf/3Te+UVERI5Dwc6DHDx4EKvVig8QBQp2TXXqqebzjz86P9gtXQqnnw7r14O/P4SFQW4uXH89PPOMc64hIiJyHAp2HmT//v2AeX+dH0BsrCvL8Vz2YLd+PaeNGAHAzz//THFxcfPOu3UrXHopVFbCBRfA/v2QlwcPPmi+ft998NVXzbuGiIjICSjYeZCsrCwA4u07YmJcVotH69ULOnSAykriDh6kZ8+eGIbBypUrm37O6mq4+mooLYUzzoCPPzZbVP394ckn4bbbzPvubr7ZPEZERKQFKNh5EHuLXWcwu/i0TmzTWCz1umPPOeccAFJSUpp+zhdfhLQ0iIyEd981B2kc6fnnITER9u0zg56IiEgLULDzIPYWu86g1rrmOiLYjRs3DoCvv/66aefKyYFZs8ztuXPNwRK/FRxshjuAF14wu2hFREScTMHOg9hb7OJB99c1l31k7KpV/O53v8PHx4dff/21afPZPfYYHDoEI0bADTcc/7iLL4bhw6G83Ax3IiIiTqZg50Hqtdgp2DXPmDHg6ws7dxJRUsLIkSOBJnTH7tsHb75pbj/zDPic4D8piwVmzjS3X3kFKiqaULiIiMjxKdh5kHotduqKbZ6wMEhKMreXL+e8884D4KvGjlp95hlz4MQZZ5iPk7nkEujaFQoL4T//aWTRIiIiJ6Zg50HqDZ5Qi13znX22+bxsGRMmTADgiy++oKqqqmHvz82FhQvN7Yceath7fH3h1lvN7VdfbUSxIiIiJ6dg5yEqKyspKCgAdI+d0xwR7EaOHEl8fDyHDh3im2++adj7n3/e7E4dMQLOPbfh173pJnO92h9+gE2bGl+3iIjIcSjYeQj7/XVBFgsdQMHOGU47zQxYe/bgs3s3v//97wH48MMPT/7ewkLzPjkwW+ssloZfNy4Oxo83t999t5FFi4iIHJ+CnYdwTE7s44MFdI+dM4SGwujR5vaSJVx++eUAfPLJJ9TU1Jz4vS+9ZI6EHTwYLrqo8deeNMl8/ve/zYmLRUREnEDBzkM47q+rqzN3qMXOOS6+2Hz+9FPGjh1Lp06dKCgoOPEgikOHYN48c/uvfz3xSNjjmTjRnGB6505Yu7bx7xcRETkGBTsPcdRyYtHRLqvFq9iD3fLl+JWVcf311wOwaNGi479nwQKzK7Z3b7jiiqZdt107c11ZgPfea9o5REREfkPBzkPUGxEbGQkBAS6tx2v07g19+kBNDXzxBTfffDMAn3/+OdnZ2UcfX14Ozz5rbv/1r+Yo16a65hrz+YMP1B0rIiJOoWDnIbScWAuy3VvHP/9Jv379OO2006irq+PVY01H8ve/m9OcdO9++D65pjr3XLPlbv9+SE1t3rlERERQsPMYWk6sBdmXAfviC8jO5p577gHghRdeoLi4+PBxpaXw1FPm9oMPgr9/864bFHR4dOzHHzfvXCIiIijYeQz7GqZdQC12zta7t7nEmNUKb77J5ZdfTv/+/SkuLmaefZAEmKtM5OTAKaeceE3YxrDfZ/fJJ845n4iItGkKdh6grq6Offv2AdANID7+hMdLE0ydaj7Pm4dPRQWPPvooAE899RQ7duyAPXtg7lxsOyEw0DnXnTDBvE/vl19g+3bnnFNERNosBTsPcODAAWpqavC1WIgD6NzZ1SV5n2uuMVviDh6EF1/kD3/4A+eccw6VlZXccMMNVN94ozlwYuzYE46EzczMZPny5Xz11VdkZGRgnGxQRIcOcNZZ5rZa7UREpJkU7DzA3r17AegcEIAfQJcuLq3HK/n5wWOPmduPP45l40Zee+01wsLCWLVqFdcvX05VUBC8/vpRq0xkZGTwxBNPMHz4cLp27crZZ5/NBRdcQP/+/enWrRvPPfcclZWVx7/2JZeYz7rPTkREmknBzgPYg11X+w612LWM666DCy6Aqio47zx6fP45i4cNww94H0iOiuKtVav48ccf+fzzz7n//vvp378//fv356GHHiItLQ0fHx/69OnDoEGDCAkJITMzkz//+c+MGjWK7cfrarUHu9WrzRZDERGRJvJzdQFyco5gZ1/mSsGuZVgs8K9/wTnnQHo6TJvGBcAXwFUhIfyybx9//OMfj3qbv78/5557LpdffjmXXHIJUVFRAFRWVvLuu+/ywAMPsGHDBk4//XSWLl3KgAED6p+ga1cYOtS85pIlzhuYISIibY6CnQdwBDur1dwRF+fCarxcx46wahW8+CIsXw4xMZx7++1s692bl156iS+++ILs7Gw6dOjA4MGDGT9+PBMmTCA8PPyoUwUFBXHTTTdxwQUXcOGFF5Kens65557LunXr6PzbcH7xxWaw+/RTBTsREWkyi3HSu7u9W0lJCeHh4RQXFxMWFubqco7p0ksv5ZNPPmE+cHt0NBw44OqSpJEKCgo488wz+eWXXxgxYgQrV64k8MiRtampkJxsTlicl2fOcSciIkLjsorusfMAe/bsAWxTnWjghEfq2LEjH3/8MR06dOCnn37i8ccfr3/A8OFmF3tZGXz7rWuKFBERj6dg5wHqDZ7Q/XUeq0ePHrz++usAPP3006xdu/bwixaL2R0LZnesiIhIEyjYubnS0lIKCgoABTtv8Pvf/55JkyZhtVq55ZZbqKurO/yiPdh99pm5CoaIiEgjKdi5OftSYuEBAYSBgp0XePHFF+nQoQM///yzowUPgLPPhtBQyMqC9etdV6CIiHgsBTs35+iGDQgwd+geO48XGRnJY7bJkB966CGKi4vNFwID4fzzzW11x4qISBMo2Lk5+8AJTU7sXW6//Xb69evHwYMHeeqppw6/oPvsRESkGRTs3NzOnTsBSKyuNnco2HkFf39/R6B76aWXyM3NNV+YMAF8fGDDBrCFehERkYZyu2A3f/58EhMTCQoKIikpiZUrVx732OzsbCZNmkSfPn3w8fFh2rRprVdoK7EHux72YKeuWK8xceJEkpOTKSsrY86cOebOqCg47TRzW612IiLSSG4V7BYvXsy0adOYOXMmaWlpjB07lvHjxzvuM/utqqoqOnXqxMyZMxkyZEgrV9s67MHuFIAOHcBNJ1GWxrNYLMyaNQuAV155hezsbPMFdceKiEgTuVWwe+6555gyZQo333wz/fr1Y968eSQkJLBgwYJjHt+9e3deeOEFJk+efMwlnbzBjh07AFuw69bNpbWI811wwQWMGjWKysrKw/faXXKJ+bx8OdgHVoiIiDSA2wS76upqUlNTGTduXL3948aNY/Xq1U67TlVVFSUlJfUe7qqwsJCioiLAFuy6d3dhNdISjmy1W7hwodlq16sX9O0LtbXw5ZcurlBERDyJ2wS7vLw86urqiImJqbc/JiaGnJwcp11n9uzZhIeHOx4JCQlOO7ez2bthY9u1IwTUYuelzj33XEaPHk1lZeXhe+3UHSsiIk3gNsHOzmKx1PvZMIyj9jXHgw8+SHFxseNhnwDYHTm6Ye0LwivYeSWLxeKY1+7VV181W+3swW7JEqipcV1xIiLiUdwm2EVFReHr63tU61xubu5RrXjNERgYSFhYWL2Hu3KMiLXvUFes1zrvvPMc99rNnTsXRo0yR8gWFcF337m6PBER8RBuE+wCAgJISkoiJSWl3v6UlBTGjBnjoqpcy9FiV1lp7lCLndeyWCw8+uijgNlqdyAv7/Agivfec2FlIiLiSdwm2AHMmDGDRYsW8cYbb5CRkcH06dPZu3cvU6dOBcxu1MmTJ9d7T3p6Ounp6ZSWlnLw4EHS09PZvHmzK8p3OsdUJ2Vl5g4FO692/vnnM3LkSCoqKsxWu+uuM1/4z3/AHu5FREROwGIYhuHqIo40f/585syZQ3Z2NgMHDuT555/njDPOAODGG29k9+7dLF++3HH8se6/69atG7t3727Q9UpKSggPD6e4uNjtumUTExPZvXs33wOnhYZCSQk48X5DcT9ffPEFEyZMIDg4mF07dhBz6qmQmQnvvw9/+IPzL7htG6SlgWHAkCHQp4/+jomIuJnGZBW3C3atzV2DXWVlJSEhIRiGQQ4QM2AA/PKLq8uSFmYYBqeeeio//fQT9957L3P9/OCpp8zBFJ984rwLrV4N990Hq1bV3z9yJDz/PLTR2x9ERNxRY7KKW3XFymHbtm3DMAwiQkKIBnXDthFH3ms3f/58cidMMF9YsgScMe2P1Qp//SucfroZ6vz8zIEaY8ZAQACsXQtnnAHz5jX/WiIi0uoU7NzUli1bAOgTHo4FoGdPl9YjrWfChAkkJydTXl7Os59/bgav2lpYuLB5J66qgmuugdmzza7XP/4R9u6FH34wQ97evXDttVBXB9Ong31OPRER8RgKdm7q119/BaCvn5+5Q8GuzTiy1e6VV17h4A03mC+8+ipUVzftpHV15mCM998Hf3/45z/hjTcgLu7wMTEx8M47YFsJg/vvhw8/bMYnERGR1qZg56YcLXb2X+S9ermwGmltF154IUlJSZSVlfHsjh0QGwvZ2fDRR40/mWHAHXfABx+Yoe6zz8yWuWOxWODhh80WO4AbbgDb30UREXF/CnZuytFiV1Bg7lCLXZtisVh45JFHAHh5wQIO2Kc+eeIJs/WtMR56yOzGtVjg3Xfh/PNP/p45c+Dss6GszOyybew1RUTEJRTs3JBhGI5g16emxrzBXatOtDkTJ05kxIgRlJWVcd++fRAebo6MXry44Sd5/nl48klz+9VX4YorGvY+Pz946y1o3968B+/FFxtdv4iItD4FOzeUnZ1NaWkpvj4+5nJi3bubv2ilTbFYLLz88stYLBbe/ve/WWmfx+6hh8yWtJN5+22YMcPcfuIJuPXWxhXQtSs8+6y5/fDDkJXVuPeLiEirU7BzQ/bWusSoKAJB99e1YSNHjuSWW24B4PZVq6js0gV27TKD1ol8+incdJO5PX06PPhg0wq4+WZzVG5ZmTlNioiIuDUFOzfk6IYNDTV36P66Nu3JJ58kOjqaTRkZ3Dd8uLnz+eeP3yX7zjtw2WXmfXGTJ8Mzzxy1moTVamXTpk189tlnLF68mJSUFPLy8o4+l8UCL7xgbv/jH/DTT078ZCIi4mwKdm7oF9sKE4Ps3a9qsWvTIiMjeeuttwB46dNP+eiii8wXrr/eHBRhtZo/5+fD7bebYc4+vcmiReBz+D/zbdu2MXXqVOLi4hg4cCAXX3wxV199NePGjaNTp06MHTuW999/n3oL0owcaV4L4C9/MUfZioiIW1Kwc0M///wzAIPLy80darFr88aPH8902xQk137zDT+cey7U1MBtt0GPHuZKEgkJ5gAJMOeg+8c/zOlNgA0bNnD11VfTt29fXnvtNXJzc2nXrh3Dhw/nzDPPpJftfx6+//57rrrqKk477TTHlDuAeY9eQACsWAFLl7bqZxcRkYZTsHMzhmEcDnb2JaT693dhReIu5syZw4UXXkhlZSXn//gjKVOmmKNWd+82V46oqIChQyElxVxf1seH1atXc9FFFzF06FAWL16M1WrloosuIiUlhYKCAlJTU1m+fDlbt25l3759PPzww7Rr144ffviB5ORk/vOf/5gXT0iAqVPN7YceUqudiIibshhG2/4XujEL67aG3bt3k5iYiL+fH2W1tfiHhkJJyVH3SEnbVFZWxoUXXsiKFSvw8fFh+p13cv9pp9HJxwf69oWBAykrL+d///sf8+bN44cffgDAx8eHK6+8kgceeIAhQ4ac8Br79u3j+uuvZ/ny5QC8/PLL3HnnneZatT16QHm5OThj4sSW/rgiIkLjsoqCnZsFu08//ZRLLrmEId26kb5nD4wYYS7MLmJTVVXF1KlTHffdBQQEkJycTHR0NLm5uaSlpVFRUeF47frrr+f+++93dLc2RG1tLTNmzOCll14C4JlnnuHPf/4zPPAAPP00DBkC69fXu39PRERaRmOyiv5VdjOObtjwcHPHgAEurEbcUWBgIG+++Saff/45ycnJVFdXs3r1aj7++GNWr15NRUUFiYmJPPTQQ+zZs4dFixY1KtQB+Pn58cILLzhWv7j33nv5xz/+AffdB2FhsGGDuUSZiIi4Fc1662Ycwc6+Q8FOjuPCCy/kwgsvZMuWLaSmpnLo0CHCw8MZNGgQ/fv3x9LM7nuLxcLjjz9ORUUFc+fOZcqUKXT69FMm/PnP8Oij8Mgj5rQqmjxbRMRt6F9kN7NhwwYABhcVmTsU7OQk+vTpQ58+fVrs/E899RQHDhzg7bff5uqrr2bNN9/Q/8UXYcsW+Oc/4cYbW+zaIiLSOOqKdSNFRUVs3boVgOH795s7FezExXx8fFi0aBFnnHEGhw4d4pJrr6Xw7rvNFx9/HKqrXVugiIg4KNi5kXXr1gFwSpcuRNXVmVNZJCS4uCoR8Pf354MPPqBbt25s376dq1eupDYmxpxq5fXXXV2eiIjYKNi5kZ9syzWN7NzZ3DFsmKY5EbfRqVMnPv74Y0JCQvh66VLus8+v+Le/mXPoiYiIyynYuZG1tmlNRthWC8C+LqiImxg6dKhjmpXnly3jrchIyMqC+fNdW5iIiAAKdm7FHuxGFhebO5KSXFiNyLH94Q9/4OGHHwbgtuJiVoO50sWhQy6tS0REFOzcxv79+8nKysLHx4dhO3aYO9ViJ27qscce4/e//z3VtbVc5utLZl6eGe5ERMSlFOzchH3pp4G9etGuvBxCQqAFp7AQaQ4fHx/efvttBg8ezIG6Oi4FyufOhe3bXV2aiEibpmDnJlasWAHAGV27mjuGDgVfX9cVJHISoaGhfPLJJ0RFRbEeuLamhto//cnVZYmItGkKdm7CHuzOtK+9mZzswmpEGqZ79+589NFHBPj78zFw0xdfYP3wQ1eXJSLSZinYuYH8/Hw2btwIwBmZmebO0093YUUiDTd27Fj+88EH+FosvAPcdt111GZlubosEZE2ScHODXz//fcA9OvTh+hffzV3nnaaCysSaZyLL76Yd956CwuwqLKSy4YPp6y01NVliYi0OQp2bmDZsmUAnNGjB1itcMopEB/v4qpEGueayZP54NlnCQI+O3CAkaec4lj7uDnq6uooKyujtLQUwzCaX6iIiBdTsHMDX3zxBQDjQkLMHWqtEw912YwZLP3LX4gFNh88yIikJKZNm0ZOTk6D3l9UVMSKFSt48cUXuemmm0gaNox2ISGEhobSvn17ggMC6N25M9dddhmvv/46+fn5LfuBREQ8jMVo4/8LXFJSQnh4OMXFxYSFhbX69bdv306vXr3w8/Mjf/hwwtauhYUL4ZZbWr0WEWc5eNtt3LJwIZ/Yfvb19eX888/n7LPPpnfv3kRERFBRUcGBAwf49ddf2bRpExs2bGDPnj2Nuk6Ary+XTpjAg7NmMXToUKd/DhERd9CYrKJg5+Jg99JLL/GnP/2Js8eO5dvVq6GuzlxYvVu3Vq9FxGnq6uCOO0hZuJBHgR8a8dZuwBBgqO15SEQEsf37Q4cO5FZUsG3XLr7ftYtPgSM7ei+78EKeeeklEhMTnfYxRETcQWOyil8r1STHsWTJEgAmdO8OK1eakxIr1Imn8/WFV1/lvPh4zps1i81WK5/7+7MmLo59Pj4UV1cT4u9PR4uFPlVV9M3JYYhhMBjoANC3L/zhD3DFFTBoEFgsACTaHuOKi5n1+eekzZ3L3A0b+Dfw0f/+xxcpKTz86KP8+d57CQgIcNnHFxFxFbXYubDFrrCwkJiYGGpqath8xRX0++AD+NOf4IUXWrUOkRa1ciXMmAHr1p34uMGD4fLLzTDXv3/Dzm0Y8M03bLrrLv60dSvf2nYPHzKEf/373/Tt27dZpYuIuAO12HmI//73v9TU1DBo0CD6rVlj7jz/fNcWJeJsY8fC2rVmwPv0U/jlFygoMJfN69kTkpJgwoSmtVRbLHDeeQz45Re+efpp/vXYY0yrq2P9hg0MHzqU5+bN47bbbsNia/ETEfF2arFzYYvduHHjSElJ4YnbbuOvr70G7dtDbi4EBbVqHSJeIz2drMsu48Zdu0ix7browgt5/Y03iI6OdmlpIiJN1ZisoulOXOTAgQMsXboUgKtqasydEycq1Ik0x9ChxKen8+WVV/I8EAB8/r//MWjAAMe0QiIi3kzBzkXeeOMNrFYro0aNosc335g7r7jCtUWJeIOwMHz+/W+mvfoqPwUEMBDIzctjwoQJ3H333VRUVLi6QhGRFqOuWBd0xdbV1dGjRw/27NnDP+6/n8lPP212wx44AMHBrVKDSJuwcSOVf/gDD2zZgn1IUv9+/fjXu+86Z947wzDvGfzmG/j1V9i+HUpKoKoKwsIgJgYGDDDvIzz7bHOfiEgjafCEm/viiy/Ys2cPHTt25ModO8yd116rUCfibIMGEZSayrx77mH8669zI7A5I4MRycn86Z57ePTRRxv/P3SlpfDtt/C//8GSJbBv34mP/+gj89nf3wx3111nTuWi2y5EpAWoxa6VW+wMw+D0009n9erV/OXOO5mzcCHU1EBaGmjmfJGW88EHHLzzTm7PzeVD267YDh14ZNYs/njzzQQdL2gZhtka9/XXZpBbvhyqqw+/HhxsBrakJHOUb2QkBARAcTHs3w8bNsCqVbB16+H3dOwIf/wjTJsGXbq00AcWEW+hlScaobWDXUpKCuPGjSMoKIhdt95K7Isvwqmngn26ExFpOSUl8OijfPnyy/yptpZttt3xwcHceeaZTJ4wgS4JCeZxe/fCxo1mKNu/v/55EhPhwgvNaVrOOqthre1btsB//gOLFoF96TR/f7j+erjvPnNychGRY1Cwa4TWDHb2wRI//fQT06ZO5fl//QsOHYL//hcuvbRFry0iR8jKomrOHBa+/jpzSkuxd6ZagNOAc23P/YE4wBIYaM7Hd8EFZqDr08exGkaj1dXBl1/CM8+YrX9gnuuyy+Dee2HUqOZ9NhHxOgp2jdCawW7hwoXcdttttG/fnq033EDsyy+byyWlp4OPBiiLtLraWqq+/Zb3Fi7kze++47uDB486JCQoiMioKCKjoo5apsxqtWIYhuPZvh0YGEhkZCSRkZFER0fTo0cPevbsSa9evUhMTMTH/t/7mjUwe7Y5cbPdqFEwfboZ9PyceBt0XR3s2mV2CRcWmv9TWVNjThTdrh107my2RMbH698jETejYNcIrRXs9u3bx+DBgyksLGTeX//KPXPnmv+ofvIJXHxxi11XRBpu9+7dfP311yxdupS0tDR27NiB1Wp16jVCQ0MZOnQoSUlJDB8+nOHDh9O3tha/efPgvfcO378XG2sOsrjqKjPs+fo27AJWq9mNvGmTOWJ30ybzkZFBZUUF+4AcoA6wYrZShmOu0RsJtG/XDoYPh5EjYfRo8/7Bjh2d+h2ISOMo2DVCawS72tpazj77bL7//nuGDxvGj8HB+K1ebXbpfPZZ07t0RKRFVVVVsW/fPgoKCigoKKDGPpk45kAoHx8fLBaL49n+qKysJD8/n4KCArKystixYwfbt29n+/btVFZWHnWdoKAgBg8ezLA+fRheWMiw77+nf1ER7ewHtG9vhqyBA81WtY4dITDQ/J/DggLIzaX211/J2rSJzO3byaysJBPIBPbanjOBo9sjj9YROAXogdkVPQQYOngwXSdMwDJunFlHS43oNQxz2qdt2yAvz2xZLC42X/PzMz9zp05m6O3c2Rx4otZFaQMU7BqhNYLdzJkzefLJJ2nfvj3rr7mGngsXmv9Qb9hg/iMtIm1CbW0tW7ZsYf369axfv57U1FTS09M5dOjQMY+PCgyka00N8VYr7YF2mHNUVdoexUC27XEQswXuZEJCQoiLi8Pf3x8fHx/q6uooLi6msLCQqqqq474vAjPkDfH1ZVCPHgwaO5YBl19OaEMHjxypqgp27DBHG2/ZUv/ZHuQaol076NsX+vc3b2sZPNh8xMa27P8wG4a5/OP+/VBUZD5KS82Q6ednjoqOijLnMYyL0/yF0mwKdo3QGsHuhx9+4Oqrr2bu737HlW+9Ze5cvBiuvLJFricinsNqtbJjxw7S0tJYv3694zkvL6/R5/Lz9aVLbCwJiYkkdO1K165dSUhIICEhwbHdoUMHLMcJPYcOHWLXrl3s3LmT7du3s3HjRjasW8emLVuoras75nt6AAPataNbTAxd4uNJ6NyZyNhYQoOCaBcYSGBtLTV5eeZj/34O7dlD4YEDFBoGhUAhUIQZUkvsz/7+FFssVIIZ0CwWLECQxUK4xUJEXR3h1dXEGAZd4KhHeFTU4ZBnf/Tv37gAWlMDu3ebk05v3w47d9Z/lJc3/FzR0dCvnxlCBw+G5GTzWXMZSgMp2DVCq9xjZ7VSPnMmIU89Zf786KPw2GMtcy0R8QrFxcXs2bOHvXv3kpOTQ1lZGaWlpdTW1hIUFERQUBChoaHExcURGxtLXFwc0dHR+Db0XrxGqKqqIiMjg/S0NDZ+9x0bf/yRn3ft4sAxupXdQXvMgJdw5LPFQkLnzkT16EH7yEjaR0YSGh6OP2DU1GAtLcWam0tlbi4lWVmU7N9PidVKCXAIM3Qe9QgMpMTPj1KLBXx9sQAYBhbDIKS2lrCaGtrX1hIGhGG2enawPSJ8fenQqxcdhg6lw6hRhI0Zg8+gQU0Ke4ZhUFZWRmFhIUVFRcd9rqmpcQzyMQwDf39/2rdv73hEREQQGxvr+PvUsWPH4/5PgLQujw528+fPZ+7cuWRnZzNgwADmzZvH2LFjj3v8ihUrmDFjBps2bSI+Pp777ruPqVOnNvh6rRLspk2DF2wLGj34IDzxhO6rExGPdzA3l43ffcev331HZkYG+7KyyCwooLC8nLLaWsrq6qiyWvH388Pfzw+/gADah4XRISaGDtHRdOjQgQ4dOhAREUF4eDjh4eGEhYU5noODg7FYLI4gUllZSVFREcXFxRQVFZGTk8O+ffvYt28fmZmZZGZmUlRU5OqvpUkcg1j8/ekQGkqHyEgiOnWifadO1AUHU+vjQ21tLTU1NZSUlNT7HoqKiqg7Totqc/j7+xMXF0dcXBzx8fGO5yO34+LiiIyMPDzSW1qExwa7xYsXc/311zN//nxOO+00XnvtNRYtWsTmzZvp2rXrUcfv2rWLgQMHcsstt3DbbbexatUq7rjjDt577z0uv/zyBl2zVYLdzz+bI8ueecacbV5ERFpEWVnZUWFvX2YmmTt2kLlzJ4VFRRyqqKC0uhrrcX79hQYH0z4sjLCICMLCwmjfvr0jbB7rERISgo+PD/Zfp1arlYqKCkpKSjh06BAlJSWUlJRQWFjoeBQdPEhhXh6FJSVU1NY65bP7WSx0CAigg78/Ef7+h5/9/Aj38yPQxweLYWABLIZBldVKaW0th2prOWQYFBoGOdXVZFdUUNCI1lh/f39iYmIICwsjNDSU0NBQ2rVrR0hICL6+vuYAI4sFi9XqeBh1dVBXh1Fba25brRi1teY+2wPAAPDxwbB1yQcGBBAcFERwSIj5aNeOkNBQgtu1Izg0lODQUELCwggICcE/OBi/gAD8/f0dDz8/PwzDcITkmpqaettVVVVUVVZSVVZGVWmp+Wx/lJdTWV5OVUWF+aisNI+vqaFzbCzzPvnEKX+Ox+Kxwe7UU09l+PDhLFiwwLGvX79+XHrppcyePfuo4++//34+/fRTMjIyHPumTp3Khg0b+OGHHxp0zVabx66kRDfQioi4CcMwKC8vp66uDh8fH8fI5oCAgBbpzj6RqqoqCgsKKNy6laJ16yjcuJHCLVsozM6mNC8Pv7Iy/DEHzvhhdjVHHPEItz2HYLb8OaUm4ACQxeEBOllHPNu3GzLSui3oFxDA5hMMPmquxmQVJ85+2TzV1dWkpqbywAMP1Ns/btw4Vq9efcz3/PDDD4wbN67evvPPP5/XX3+dmpoa/P39j3pPVVVVvZFfJSUlTqi+ARTqRETchsVioV27dic/sBUEBgYSGxdHbFwcnHnm0QeUl8O+feb0L0eOwrW1dGG1mtu2Vi18fA5vN+Rni8WcP7GkxPEILC6ma0kJXYuLzcmsf/uw/R6txgyAOUDpbx5lmC1uVtuz42GxYPH1xeLnB35+WGwPfvNssVjA1sKHYUBdHVV1dVTU1lJRU0NFbS3ltbVU1NWZD6uVcsOgwlZXDVBrez5y2wKOoHzksz8QeKyHr6/58PMj0N//8CMgwNzn60un+Hhn/pVoFrcJdnl5edTV1RETE1Nvf0xMDDk5Ocd8T05OzjGPr62tJS8vj7i4uKPeM3v2bB5//HHnFS4iItKSQkKgd29XV1FfTQ0cOkTAoUMkVFSQYBiHQ6Y9iAUEmINBAgPrPztzRZXfslrNkFpdbYbP3z7/dp+/vzlaOjjYrM2+bX/4+3vcPfFuE+zsfjsCxzCME47KOdbxx9pv9+CDDzJjxgzHzyUlJSQkJDS1XBERkbbH39+cKNvdViXx8TEDWhueSsZtgl1UVBS+vr5Htc7l5uYe1SpnFxsbe8zj/fz8iIyMPOZ7AgMDCQwMdE7RIiIiIm7EbcYnBwQEkJSUREpKSr39KSkpjBkz5pjvGT169FHHf/311yQnJx/z/joRERERb+Y2wQ5gxowZLFq0iDfeeIOMjAymT5/O3r17HfPSPfjgg0yePNlx/NSpU9mzZw8zZswgIyODN954g9dff517773XVR9BRERExGXcpisW4KqrriI/P59Zs2aRnZ3NwIEDWbJkCd26dQMgOzubvXv3Oo5PTExkyZIlTJ8+nVdeeYX4+HhefPHFBs9hJyIiIuJN3GoeO1dotXnsRERERJqgMVnFrbpiRURERKTpFOxEREREvISCnYiIiIiXULATERER8RIKdiIiIiJeQsFORERExEso2ImIiIh4CQU7ERERES+hYCciIiLiJRTsRERERLyEW60V6wr2FdVKSkpcXImIiIjI0ewZpSGrwLb5YHfo0CEAEhISXFyJiIiIyPEdOnSI8PDwEx5jMRoS/7yY1WolKyuL9u3bY7FYWuw6JSUlJCQkkJmZedIFfOXk9H06l75P59L36Vz6Pp1L36dztcb3aRgGhw4dIj4+Hh+fE99F1+Zb7Hx8fOjSpUurXS8sLEz/ITmRvk/n0vfpXPo+nUvfp3Pp+3Sulv4+T9ZSZ6fBEyIiIiJeQsFORERExEso2LWSwMBAHn30UQIDA11dilfQ9+lc+j6dS9+nc+n7dC59n87lbt9nmx88ISIiIuIt1GInIiIi4iUU7ERERES8hIKdiIiIiJdQsGsF8+fPJzExkaCgIJKSkli5cqWrS/JIs2fPZsSIEbRv357o6GguvfRStmzZ4uqyvMbs2bOxWCxMmzbN1aV4rP3793PdddcRGRlJSEgIQ4cOJTU11dVleaTa2loeeughEhMTCQ4O5pRTTmHWrFlYrVZXl+YRvvvuOyZOnEh8fDwWi4WPP/643uuGYfDYY48RHx9PcHAwZ511Fps2bXJNsR7gRN9nTU0N999/P4MGDaJdu3bEx8czefJksrKyXFKrgl0LW7x4MdOmTWPmzJmkpaUxduxYxo8fz969e11dmsdZsWIFd955J2vWrCElJYXa2lrGjRtHWVmZq0vzeD/99BMLFy5k8ODBri7FYxUWFnLaaafh7+/PF198webNm3n22WeJiIhwdWke6emnn+bVV1/l5ZdfJiMjgzlz5jB37lxeeuklV5fmEcrKyhgyZAgvv/zyMV+fM2cOzz33HC+//DI//fQTsbGxnHfeeY5lNqW+E32f5eXlrF+/nocffpj169fz0UcfsXXrVi6++GIXVAoY0qJGjhxpTJ06td6+vn37Gg888ICLKvIeubm5BmCsWLHC1aV4tEOHDhm9evUyUlJSjDPPPNO45557XF2SR7r//vuN008/3dVleI0LL7zQuOmmm+rtu+yyy4zrrrvORRV5LsD473//6/jZarUasbGxxlNPPeXYV1lZaYSHhxuvvvqqCyr0LL/9Po9l7dq1BmDs2bOndYo6glrsWlB1dTWpqamMGzeu3v5x48axevVqF1XlPYqLiwHo2LGjiyvxbHfeeScXXngh5557rqtL8WiffvopycnJ/OEPfyA6Opphw4bx97//3dVleazTTz+dpUuXsnXrVgA2bNjA999/z4QJE1xcmefbtWsXOTk59X43BQYGcuaZZ+p3k5MUFxdjsVhc0mLf5teKbUl5eXnU1dURExNTb39MTAw5OTkuqso7GIbBjBkzOP300xk4cKCry/FY//73v1m/fj0//fSTq0vxeDt37mTBggXMmDGDv/71r6xdu5Y//elPBAYGMnnyZFeX53Huv/9+iouL6du3L76+vtTV1fHEE09wzTXXuLo0j2f//XOs30179uxxRUlepbKykgceeIBJkya5ZC1eBbtWYLFY6v1sGMZR+6Rx7rrrLn7++We+//57V5fisTIzM7nnnnv4+uuvCQoKcnU5Hs9qtZKcnMyTTz4JwLBhw9i0aRMLFixQsGuCxYsX889//pN3332XAQMGkJ6ezrRp04iPj+eGG25wdXleQb+bnK+mpoarr74aq9XK/PnzXVKDgl0LioqKwtfX96jWudzc3KP+T0ka7u677+bTTz/lu+++o0uXLq4ux2OlpqaSm5tLUlKSY19dXR3fffcdL7/8MlVVVfj6+rqwQs8SFxdH//796+3r168fH374oYsq8mx/+ctfeOCBB7j66qsBGDRoEHv27GH27NkKds0UGxsLmC13cXFxjv363dQ8NTU1XHnllezatYtvv/3WJa11oFGxLSogIICkpCRSUlLq7U9JSWHMmDEuqspzGYbBXXfdxUcffcS3335LYmKiq0vyaOeccw4bN24kPT3d8UhOTubaa68lPT1doa6RTjvttKOm39m6dSvdunVzUUWerby8HB+f+r+ifH19Nd2JEyQmJhIbG1vvd1N1dTUrVqzQ76Ymsoe6bdu28c033xAZGemyWtRi18JmzJjB9ddfT3JyMqNHj2bhwoXs3buXqVOnuro0j3PnnXfy7rvv8sknn9C+fXtHS2h4eDjBwcEurs7ztG/f/qj7E9u1a0dkZKTuW2yC6dOnM2bMGJ588kmuvPJK1q5dy8KFC1m4cKGrS/NIEydO5IknnqBr164MGDCAtLQ0nnvuOW666SZXl+YRSktL2b59u+PnXbt2kZ6eTseOHenatSvTpk3jySefpFevXvTq1Ysnn3ySkJAQJk2a5MKq3deJvs/4+HiuuOIK1q9fz+eff05dXZ3j91PHjh0JCAho3WJbfRxuG/TKK68Y3bp1MwICAozhw4dreo4mAo75ePPNN11dmtfQdCfN89lnnxkDBw40AgMDjb59+xoLFy50dUkeq6SkxLjnnnuMrl27GkFBQcYpp5xizJw506iqqnJ1aR5h2bJlx/z38oYbbjAMw5zy5NFHHzViY2ONwMBA44wzzjA2btzo2qLd2Im+z127dh3399OyZctavVaLYRhGawZJEREREWkZusdORERExEso2ImIiIh4CQU7ERERES+hYCciIiLiJRTsRERERLyEgp2IiIiIl1CwExEREfESCnYiIiIiXkLBTkRERMRLKNiJiDjJn//8ZyZOnOjqMkSkDVOwExFxkvT0dIYOHerqMkSkDVOwExFxkg0bNjBs2DBXlyEibZiCnYiIE2RmZpKfn+9osSsqKmLixImMGTOG7Oxs1xYnIm2Ggp2IiBOkp6cTHh5OYmIiGzduZMSIEcTFxbF8+XLi4uJcXZ6ItBEKdiIiTpCens6QIUN47733OOOMM7j33ntZuHAhAQEBri5NRNoQi2EYhquLEBHxdJdffjnLli0D4PPPP2fMmDEurkhE2iK12ImIOEF6ejqXX345lZWVFBUVubocEWmj1GInItJMhw4dIjw8nNTUVDZs2MA999zD6tWrGTBggKtLE5E2xs/VBYiIeLr09HR8fX3p378/w4YNY9OmTUycOJG1a9cSFRXl6vJEpA1RV6yISDNt2LCBvn37EhgYCMDTTz9N//79ueyyy6iurnZxdSLSlqgrVkRERMRLqMVORERExEso2ImIiIh4CQU7ERERES+hYCciIiLiJRTsRERERLyEgp2IiIiIl1CwExEREfESCnYiIiIiXkLBTkRERMRLKNiJiIiIeAkFOxEREREvoWAnIiIi4iX+H0N1e/jBzg5qAAAAAElFTkSuQmCC\n", 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R0dHWNlxcXO5q02KxWK+fNWvWh14/Li4OX19fgoOD7/o6ePAgQ4YMSdRnSE3qsRPYto1vY2MBeL5jR5s3X6t3bx6fP58DsbEsWrCA3gEBNr+GiEhm5eHhcdfkgLS+dmJdvnyZAwcO8Pnnn1OnTh0Afv31V+vrfn5+zJ8/n1u3blnD1Y4dO+5pp1OnTgwfPpxdu3axfPlypk+fbn3tjz/+ICYmhk8++QQnJ7PvaunSpUn6TJ6enhQrVoyNGzfed0vNypUrc+7cObJkyUKxYsXu28bjjz/Ojh076Nq1q/XY/T5LalCwE8599x2/xz9v+fzzNm/f8uSTvJotG2/cuMHsyZMV7EREbMhisSRpONRecuTIQa5cuZg5cya+vr6cPHmSoUOHWl/v1KkTw4YNo0ePHgwfPpzjx48zYcKEe9opXrw4tWrVokePHsTExPD8HX9vlSxZkpiYGKZMmUKLFi3Ytm3bPbNoE2PkyJH07t2bvHnz0qRJE65fv862bdvo168fDRo0oGbNmrRq1Ypx48bx2GOPcebMGdasWUOrVq2oWrUqAwYMoFu3blStWpXatWuzcOFC9u/fb53gkZo0FCt8/+OPGED14sUpUKCA7S/g7EzXZs1wBXYdOnTX9HUREckcnJycWLx4Mbt27aJ8+fIMGjTIurc4QPbs2fn+++8JCQnB39+fYcOGMW7cuPu21blzZ/bu3Uvr1q3vGjqtVKkSEydOZNy4cZQvX56FCxcyduzYJNfarVs3Jk2axLRp0yhXrhzNmzfn8OHDgBmk16xZQ926dXnllVcoU6YMHTp04Pjx4+TLlw+A9u3b8/777/P2229TpUoVTpw4QZ8+fZJcR3JYjMQOagthYWF4e3sTGhqKl5eXvcuxjchIWnp48H1cHKMHDeLdiRNT5zrLltGhXTuWAK+//rr1ngcREUmaiIgIjh07RvHixXF3d7d3OWIjD/tzTUr+UI9dIgQGBuLn50e1atXsXYrNRe/cyeb4WUlNbLAo8QM1bsyr8fc7LJw/n5s3b6betURERDIpBbtECAgIICQkhKCgIHuXYnM7Fi0iHMjj6krF1Nyc2MuL+nXqUBwIvX6dZcuWpd61REREMikFu0xuw6ZNADz7+OPWGUSpxalZM3rEP581a1aqXktERCQzUrDLzAyD9fGLKTZs0iT1r9e0KS8DzsC2bdsICQlJ/WuKiIhkIgp2mVjonj3sjIkBoMHLL6f+Bf38KFCkCM3jv509e3bqX1NERCQTUbDLxDZ/9RWxQBl3d4qUKZP6F7RYoEkTesZ/O2/ePCIiIlL/uiIiGZAWtchYbPXnqWCXiW35+WcAGpQunXYXbdqU54BCzs5cuXKFb775Ju2uLSKSASRsh6XVBTKWhD/P/253llTaeSIT+yV+scW68Vu7pIn69XF2daVHVBT/w5xE0TEVtjETEcmonJ2d8fHxsW5a7+HhgcVisXNVklyGYXDz5k0uXLiAj48Pzs7OKWpPwS6Tun7tGsE3bgDwVJs2aXfh7NmhXj1eWb+eURYLmzdv5vDhw5ROy15DEZF0Ln/+/ADWcCfpn4+Pj/XPNSUU7DKpHcuXEwcUs1goVK9e2l68aVOKrF9Pk5w5WXP5MrNnz37gtjEiInIvi8WCr68vefPmJTo62t7lSAq5uLikuKcugYJdJvXL6tUA1M6dG2z0H1OiNWkCgwbR89o11gBffvklo0aNws3NLW3rEBFJ55ydnW0WCCRj0OSJTOrXXbsAqPPEE2l/8TJloEQJmsXGUihXLi5cuMDChQvTvg4REZEMRsEuE4qOjmbHmTMA1G7UKO0LsFigaVNcgAHx99ZNmDCBuPg9a0VERCR5FOwyod07d3IrLo6cQNkWLexTRNOmAPQ8dQpPT08OHDjAjz/+aJ9aREREMggFu0zot++/B6CWszNOZcvap4innwZ3d7xPn6ZX69YAjB07VgtuioiIpICCXSYU9MsvADxZoAA42ek/gaxZoX59AAYWKICbmxu//vora9eutU89IiIiGYCCXSa088ABAKrZY+LEnZo0AaDQ9u3069cPgKFDhxIbG2vPqkRERNItBbtM5urVq/xz9SoAVevWtW8x8ffZsW0b7wQE4O3tzb59+5gxY0aymouLi+P48ePs2LGDAwcOaG0nERHJdBTsEiEwMBA/Pz+qVatm71JS7I8//gCgJJCrVi37FlOiBDz2GMTEkHPXLkaPHg3A22+/zbFjxxLdzD///EPv3r3x9fWlePHi1KxZEz8/P3Lnzs1rr73GiRMnUusTiIiIOBQFu0QICAggJCSEoKAge5eSYgn311UDKF/errUAt3vt1qyhT58+1KtXjxs3btC6dWvCw8Mf+tZ//vmH7t27U7ZsWT7//HMuXLiAq6srRYoUwdPTk7CwMGbOnEn58uWZP39+GnwYERER+1Kwy2R2bt4MQDUfH8iRw77FwF3BzsliYd68eeTNm5fg4GCaNWvG5cuX73nLgQMH6NatG2XLlmXevHnExsbStGlT1q1bx/Xr1zlx4gRXr15l8+bNPPXUU4SHh9O1a1fGjx+fxh9OREQkbVkMrS+RaGFhYXh7exMaGoqXl5e9y0mWgj4+nAkN5ZcaNaj922/2LgciIyFXLrhxA3bvBn9/du7cSYMGDbh+/Tp58+alb9++lCtXjrNnz7J69Wp++ukn67IozZo14/3336d69er3bT42NpZhw4ZZ96KdO3cuL7/8cpp9PBERkZRKSv7QXrGZyOnTpzkTGooT4G/v++sSuLlBgwbw7bewYgX4+1O9enW2b99O27ZtOXjwIO+///49b3vhhRd49913qVq16t0vHDkCy5bBP/+AtzfODRrw0ZgxZMmShdGjR9O7d28ef/xxatSokUYfUEREJO0o2GUiCRMnygHZqlSxbzF36tjRDHYLFsCoUeDkRPny5dm3bx8LFy5k9erVnD59mly5clGjRg3atWtHmTJl7m7j6lV46y2YPfvu4xMnQrVqjJo3jwMHDrBy5Uq6du1KcHAwHh4eafcZRURE0oCCXSYSvGcPAFUAKlSway13adkSvLzgxAn45ReoVw8AFxcXunfvTvfu3R/+/n/+Me/VO3zY/L5RI3jqKTh7FhYuhKAgnGrVYs7y5ezYsYPDhw8zfPhwJk6cmLqfS0REJI1p8kQmEhx/T11FJydzmRFHkTUrtGtnPv/qq6S9d/9+qFnTDHVFipjB8Kef4P33Yfp0OHjQfP3aNXw6dGD2Bx8AMHnyZA7EL9QsIiKSUSjYZSJ7//wTgEpFioCLi52r+Y+uXc3HxYvhypXEvef4cbN37tIlqFwZfv8date++xxfX1i3znz90iWafP45z7dsSWxsLEOGDLHpRxAREbE3BbtM4tq1axw7dw6AivbeSux+ateGihXh5k1IzM4T589Dw4Zw5gz4+ZnhLX/++5+bPTusWgXe3rBzJx+XLEmWLFn44Ycf2By//IuIiEhGoGCXSfwZ31tXBMhRsaJ9i7kfiwXefNN8PmWKGfAeJCzM3Gf2n3+gWDEz1OXK9fD2Cxc22wXKfP45r3XuDMAH8UOzIiIiGYGCXSaxd+9eACoClC1r11oeqH17KFoUzp2D+HXn7hEZCS+8AHv2QJ48ZqgrWDBx7XfpYk6quHmTt2/cIEuWLGzevJnfHGE9PxERERtQsMskgoODAagE8PjjdqzkIVxcYMIE8/nHH8Nff939+q1b5iSLTZvM4dW1a6F06cS3b7GYy58AhVesoOvzzwNY96gVERFJ7xTsMom9u3YB8T12jjQj9r/atIHGjSEiwlwGJWEJkyNHzIWMv/vOXNT4m28gOWvxVa8OzZuDYTDUYsFisfDDDz9w6NAh234OERERO1CwywRiYmL4K35pj0q+vpAtm50regiLxVyouEQJOHbMnBhRvrwZRrdvN9e7++knM+Ql17vvAlD6229p9uyzAEybNs0W1YuIiNiVgl0mcPDgQSKjovAEipcvb+9yHi13bvj1V3PWa0yMuVZdbKy5tMmePdYFjJOtZk2oWxeio+mbOzcAX375JeHh4TYoXkRExH4U7DKBhPvrKgBOfn52rSXREtafO3AA1qwxh2J/+snsybOFfv0AaLhpE6VKlSI0NJSFCxfapm0RERE7UbDLBP6Kn4TwBDjujNgHKVvWXNrEVoEuwfPPQ/78OF24QED8osbTpk3DMAzbXkdERCQNKdhlAiEhIQCUA8edEZvWXFygRw8Auh09iqurK3/++ae1d1NERCQ9UrDLBPbH99iVg/TXY5eaevYEi4UcW7fSqlEjAL744gs7FyUiIpJ8CnYZ3M2bNzl67BgA5by8IG9eO1fkQIoWtU7EeDlPHgAWLlxIZGSkPasSERFJNgW7DO7gwYMYhkEuIE/ZsuZyInJb/NZiDXfupECBAly5coUffvjBzkWJiIgkj4JdBrd//37AHIa1JGWXhsyibVtwdcV5/35eatwY0HCsiIikXwp2GVxCsPODpG2/lVn4+Jg7UQDd43sz165dy/nz5+1YlIiISPIo2CVCYGAgfn5+VKtWzd6lJNldM2JLlbJrLQ6rSxcAyq5fz5NPPklsbCyLFi2yc1EiIiJJp2CXCAEBAYSEhBAUFGTvUpLszqFY9dg9wHPPmdusnTpF1/jJFF999ZWdixIREUk6BbsM7ObNmxw9ehSIH4pVj939Zc0KzZoB0D4iAhcXF/bs2WNd2FlERCS9ULDLwO6cEZvXxwdy5rR3SY6rTRsAcq1ZQ7P4kDd//nx7ViQiIpJkCnYZ2J0TJyxlyti3GEfXtCm4u8M//9C1bl0AFixYQGxsrJ0LExERSTwFuwzsrvvrNAz7cNmzQ/xyJ00vXSJHjhycOXOGTZs22bkwERGRxFOwy8ASZsRqqZNEih+Odfv2Wzp06ABoOFZERNIXBbsM7NChQwCUBfXYJUaLFuDiAvv3W2fHrlixgvDwcDsXJiIikjgKdhlUTEwMR44cAeAxULBLDB8fePZZAJ785x9Kly7NzZs3WblypX3rEhERSSQFuwzq+PHjREdH4w4UAg3FJlb8cKxl5UpeeuklQGvaiYhI+qFgl0ElDMOWBpy01EniPf88ODnB7t10iZ8du2nTJv799187FyYiIvJoCnYZ1MGDB4H4YdjSpSF+H1R5hDx54OmnASgeFETdunUxDIOFCxfaty4REZFEULDLoBJ67MoAlCxp11rSnfjhWFasuGs41jAM217n+nW4cAHi4mzbroiIZFoKdhlUQrB7DKB4cbvWku688ILZw7ljBy/WqoWbmxshISHs2bMn5W0bBnzzDTz5JHh7Q7584OsLw4eDZt+KiEgKKdhlUAlDsWVAwS6pfH3hqacA8N6wgVatWgE2mEQREQHdukHr1rBzpxnywOy1Gz3aDHvHjqXsGiIikqkp2GVA4eHhnD59GlCwS7aE4djly63DsV9//TXR0dHJay8y0mxz/nxzcsY778CZM+bxFSvMMBkSYi63cuaMjT6EiIhkNgp2GdDhw4cByA3kBChWzI7VpFOtW5uPv/5KowoVyJs3LxcvXmTdunVJb8swoGdPWLMGsmaFn36CMWPMMOfqal7rjz/MeyGPHYOOHUF71IqISDIo2GVAd02csFigSBG71pMuFSkC1auDYeDyww906tQJSOZwbGCg2VPn7AzffgsNGtx7ToEC8OOP4OkJW7fCxx+n8AOIiEhmpGCXAd01caJQIbNXSJLuPsOx3377LdeuXUt8G3/9BYMHm8/Hj4eGDR98bqlSMHWq+fyDD+DEiaTXLCIimZqCXQakiRM20rat+bh5M/758lGuXDkiIyNZtmxZ4t4fGws9ekB0NLRsCQMHPvo9L70E9erBrVvw1lvJLl1ERDInBbsMSEud2EiJElC7NsTFYZk/n27dugEwZcqUxK1pN3myOfvV2xumT0/cItEWi/k+gKVLzR4/ERGRRFKwy2AMw7i7x04TJ1Lm5ZfNxy++4NUePciePTv79u1j7dq1D3/f0aMwbJj5fMIE8x66xKpQ4XZv4YcfJr1mERHJtBTsMpiLFy8SFhaGBSgJ6rFLqRdfBA8POHSIHH//zWuvvQbAuHHjHvyehFmwt27BM8+Yw7FJNXy4+bh0KcT3wIqIiDyKgl0Gc+TIEQAKOTvjDgp2KeXpCe3amc+nTWPQoEG4uLiwdetWNm/efP/3zJkDmzaZS5vMmpW8fXorVoRmzcyQOG1a8usXEZFMRcEug0kIdiUT9h9VsEu5fv3Mx8WLKRgTY+21Gzx4MLH/XW/u9Gl44w3z+QcfpGyf3r59zccvv4QbN5LfjoiIZBoKdhmMNdgZBri4JO3eLrm/ypWhfn1zluunnzJixAi8vb0JDg5m9uzZt88zDOjdG8LCzO3BEjML9mEaNTKXQAkNhQULUtaWiIhkCgp2GczRo0cBKAHmIrvOznatJ8NIWHpkxgxyh4UxYsQIAN58801rmOaLL2D1ajNQz5mT8p+9kxP06WM+nzMnZW2JiEimoGCXwVh77EDDsLbUqJG5Y0RkJAwYQP9+/ahbty7h4eG0bt2aa1u3QkCAee6oUVCu3AObun79OhcuXCAmJubR1+3SBbJkgaAgOHDARh9GREQyKgW7DEbBLpVYLPDZZ2Zv3OrVOH/8MfPnzydfvnz8+eefPPvss5yIiDAnPPxnYeETJ04wc+ZM2rRpQ86cOfHy8iJfvny4u7vz1FNPMWPGDCIiIu5/3bx54bnnzOfz56fyhxQRkfTOYiRqpVUBCAsLw9vbm9DQULy8vOxdzj1u3LhB9uzZAbgC5BgzBt55x75FZTSBgbcnNTz/PHtdXHh2+XIuA1ktFrq89BJVatUiOjqa/fv3s3HjRg4fPvzIZkuWLMm8efN46qmn7n1x2TJzZm7hwnD8uDlEKyIimUZS8oeCXRI4erDbt28fFSpUwCdLFq7GxMDChRC/eb3YiGHA2LHmOnPx/+scB7r4+LDtAXvIOjs7U6NGDRo3bkyjRo0oV64cWbNm5eTJk6xatYpPPvmE06dPkyVLFubOnWvdl9YqIgLy5zcnUWzaZK6NJyIimUZS8keWNKpJ0kDCxImSCT06RYrYsZoMymKBd9+FFi1g0SK4dYtiDRvyS+PGbNy8mdWrV3P06FFcXFwoXrw4devWpW7duvj4+NzTVPHixRk0aBCvvPIKvXr1YunSpXTt2pUsWbLQsWPH2ye6u5s7UcyZY/beKdiJiMgDqMcuCRy9x27ixIm88cYbtLNYWGIYcOKEwl06ERcXR79+/Zg2bRqurq5s3LiR2rVr3z7hxx+hSRPIl89cK0+znUVEMo2k5A/drJOB3LWGnZOT1rBLR5ycnJgyZQpt2rQhKiqKTp06cfXq1dsn1K8PPj5w/jxs3263OkVExLEp2GUgd82ILVDAXCZD0g0nJye+/PJLSpcuzalTp+jdu/ftF11d4fnnzefLl9unQBERcXgKdhnIPYsTS7qTPXt2vv76a5ydnVm6dClr1669/WLbtubjihWQsGWciIjIHRTsMojY2FiOHz8OxPfYKdilW1WrVmVg/HZkffv25datW+YLDRuCp6d5j93vv9uvQBERcViZMti98MIL5MiRg7YJPSAZwKlTp4iOjsbV2ZmCoGCXzo0YMYICBQpw9OhRPvvsM/Ogm5s5GxfMXjsREZH/yJTBrn///nz11Vf2LsOmEu6vK541K85gLmYr6ZanpycfffQRAB9//DHXEtbIa9PGfFy50rqOnoiISIJMGeyeeeYZPD097V2GTVnvr9MadhlGp06d8PPz4+rVq0yYMME82LgxZM0Kx47Bn3/at0AREXE4Dhfstm7dSosWLShQoAAWi4VVq1bdc860adMoXrw47u7uVKlShV9++SXtC3Uw1hmx0dHmAQW7dM/Z2ZkPP/wQgEmTJnHp0iXIlu323rErV9qxOhERcUQOF+xu3LhBxYoVmTp16n1fX7JkCQMHDmTYsGHs2bOHOnXq0KRJE06ePGk9p0qVKpQvX/6erzNnzqTVx0hzCRMniifcaK9glyG0atUKf39/bty4QWBgoHnwhRfMRwU7ERH5D4feecJisfDNN9/QqlUr67Enn3ySypUrM336dOuxxx9/nFatWjF27NhEt71lyxamTp3K8oesCRYZGUlkZKT1+7CwMAoXLuyQO0/UrFmTHTt2sBxo4+EB4eHm9leS7i1evJiOHTuSK1cuTp48iUdkJOTNCzExcPAglClj7xJFRCQVZdidJ6Kioti1axeNGjW663ijRo3Yngqr8Y8dOxZvb2/rV2EHnpCQ0GNXDMzeOoW6DKNt27YUL16cy5cvM3fuXMiRw9yJAuCbb+xbnIiIOJR0FewuXbpEbGws+fLlu+t4vnz5OHfuXKLbady4MS+++CJr1qyhUKFCBAUF3fe8d955h9DQUOvXqVOnUlR/aomIiLB+/mKgYdgMJkuWLLz55psAfPLJJ8TExEDr1uaLCnYiInKHdBXsElj+0xtlGMY9xx7mp59+4uLFi9y8eZN///2XatWq3fc8Nzc3vLy87vpyRAn3F2Z3dSUnKNhlQN27dyd37twcP36cb7/91txezGIxFyr+9197lyciIg4iXQW73Llz4+zsfE/v3IULF+7pxctMEoZhi3p4YAGtYZcBeXh40KtXLwBzYlH+/FCrlvnifWaOi4hI5pSugp2rqytVqlRh/fr1dx1fv349tRL+ksuErPfXZcliHlCPXYbUp08fnJ2d2bJlC/v27bs9HKvZsSIiEs/hgl14eDjBwcEEBwcDcOzYMYKDg63DjYMHD2b27NnMnTuXAwcOMGjQIE6ePEnv3r3tWLV9nThxAoBiMTHmAQW7DKlQoUK8EL/UydSpU28ve/Lzz3DpUupc1DAgKip12hYREZtzuGD3xx9/4O/vj7+/P2AGOX9/f95//30A2rdvz6RJkxg1ahSVKlVi69atrFmzhqJFi6ZaTYGBgfj5+T3wXjx7sw7F3rhhHlCwy7D69u0LwIIFC7jq4wP+/hAXB99/b9sL7dxp3seXLZu5R23JkjB6NERE2PY6IiJiUw69jp2jSco6MmnpqaeeYvv27SwFXgS4dQvc3e1claQGwzCoWLEi+/bt45NPPmHwjRvw/vvQvLltwl1cHAwfDg9aE7J8efjhB/3jQUQkDWXYdezk/qxDsQB58ijUZWAWi8XaaxcYGEjs88+bL6xbB9evp6zxuDjo3v12qOvaFYKD4cIF+OoryJcP/voLnn4akrC8kIiIpB0Fu3QuMjLSulVaMYCCBe1ZjqSBzp074+Pjw9GjR1l74oS580RUFKxZk7KG33wT5s+HLFnMIDdvHlSsaP5j4aWX4I8/oFQpOHYM2rc3d74QERGHomCXzp06dQrDMMjq4kJuULDLBLJly8arr74KwGeTJ9tmduzSpfDpp+bzL780g9x/FSoEq1eDpyds3QqffJL864mISKpQsEvnrMOw3t7mGnaFCtm1Hkkbffv2xcnJiQ0bNvBXxYrmwTVrkje54fRpSJhVPnw4dO784HMfewwmTzaf/+9/ED9xR0REHIOCXTpnXcMu4b469dhlCkWLFrUufTJ540ZzUerwcLNHLSkS7qu7ehWqVTMnYjxKt25Qr545SWfYsKQXLyIiqUbBLp2zLnWSsKWagl2mMWDAAADmL1jA5TZtzIMzZyatkSlTYMMGyJrVvL/OxeXR77FYbg/bLloEBw4k7ZoiIpJqFOwSwZHXsbMOxUZHmwcU7DKN2rVr4+/vT0REBDOzZDED1/r1cORI4hrYvx/eftt8PmGCOcyaWP7+5gLJhgGjRiW9eBERSRUKdokQEBBASEgIQUFB9i7lHtah2ISlLhTsMg2LxcLAgQMBCFy0iOgGDcwXZs169JujoqBLF4iMhCZNoE+fpBcwYoT5uGwZnDqV9PeLiIjNKdilc/fsOqFgl6m0b9+efPnycfr0aZb4+ZkHZ8yAa9ce/sb33zfXqMuVC+bMMXv7kqpiRXNNu9hYmD496e8XERGbU7BLx6Kjozl9+jQQv4Zd1qzg42PHiiStubm50b9/fwA+/PFHYv38IDT09szV+9mwAT7+2Hw+axb4+ia/gH79zMeZM83JFCIiYlcKdunY6dOniYuLw83Fhbxg9tYlp+dF0rW+ffuSM2dODh48yOL69c2DEybAv//ee/LZs+YadYYBr71m3id3Hzdv3uS3335j1apVbNmyhfDw8PtfvGVLc0bu5cvw3Xc2+kQiIpJcCnbp2MmTJwEolDOn+QepYdhMycvLizfeeAOAUevWEfPkk+b2Yj17msOkCcLCoFkzczswPz+YOPGetv755x+6d+9Onjx5qFWrFi+88ALPPPMMuXLl4pVXXuHf/4bFLFnM5U/A3KlCRETsSsEuHTsVf8N6kezZzQNanDjT6tevH7ly5eLQoUPMePZZcHODH38016i7dg327IE6dczHPHnM3jUPD+v7o6Ojeffdd3n88ceZN28eN2/eJF++fDz55JMUKVKEqKgovvjiC8qXL8/q/66Vl7BLxU8/aQ9ZERE7U7BLxxKCXWFXV/OAeuwyLU9PTz744AMAhgcGcn7KFHNYfsECyJEDKleGP/+EfPnMwFeypPW9R48epXbt2owdO5aYmBiaNGnCtm3bOHv2LDt27ODEiRP89ttvVK9endDQUFq1asXChQtvX7xMGahZ01zs+Ouv0/qji4jIHRTsEsFR17GzBjvDMA8o2GVqvXr1onLlyoSGhtJ7zRqMdeugfHnzRRcXaN8egoLMkBdvxYoV+Pv7s3PnTnx8fFixYgVr1qyhVq1aWO64X7NGjRr8+uuvdO/endjYWLp3787GjRtvX7xrV/PxzsAnIiJpTsEuERx1HTtrsIuKMg8o2GVqzs7OzJo1CxcXF1atWsXEvXvNXrrLl82ZsosXmxMdMIde33zzTdq2bUtYWBhPPfUUe/fupXXr1g9s38XFhblz59KxY0diYmJ48cUXb99z16YNODnB7t1w7FhafFwREbkPBbt0zBrstDixxKtcuTITJkwA4M0332TK1KkYOXKYS+HE+/PPP3nqqaf45JNPrOdt3ryZIkWKPLJ9i8XC3LlzqVq1KlevXqV79+7ExcWZ9+3Vq2eetHKl7T+YiIgkioJdOmYNdleumAcU7ARzIsWgQYMA6N+/P40aNeKLL75g7ty5tG3bFn9/f4KCgvD29mbFihWMHz8el8TsERvP3d2dBQsWkDVrVjZu3Mjs2bPNFxJ6+xTsRETsxmIYCTdoyaOEhYXh7e1NaGgoXl5edq3l5s2bZMuWDYCrgI/FYm4PlYS/oCXjMgyDCRMm8O677xITE3PP623atGHy5MkUKFAg2deYNGkSgwYNIleuXBw+fJgcN2/enpl9+jSkoG0REbktKflDPXbpVMK9Tdk9PPAGc7ajQp3Es1gsDBkyhIMHD/LWW2/x7LPP0qBBA4YMGcLevXtZvnx5ikIdmPee+vn5cfnyZUaOHGn2GNeoYb74zTcp/xAiIpJk6rFLAkfqsdu4cSMNGjTg8UKFCPn3X6hSBf74w641SeazYcMGGjZsSJYsWfjnn38ounQpvPUWPPccrF1r7/JERDIE9dhlAtb76zw9zQNanFjsoEGDBjRo0ICYmBjGjBlj7mwBsHkz3Lxp3+JERDIhBbt0SosTi6MYMWIEAHPnzuV41qxQtKh5v+emTXauTEQk81GwSwRHXKDYGuwSFpFVsBM7qV27trXXbtzHH0PTpuYLa9bYtzARkUxIwS4RHHGB4nsWJ9YMRLGj4cOHAzBv3jwu16ljHlyzBnQLr4hImlKwS6eswS7hPiYFO7GjunXrUrlyZW7dusXnhw6BuzucOAEHDti7NBGRTEXBLp2yBrtr18wDvr72K0YyPYvFYl0UecqMGUTWrWu+8MMPdqxKRCTzUbBLh8LCwggLCwPuCHb589uvIBGgXbt2FChQgHPnzrEkb17zoO6zExFJUwp26VBCb10Ob2+yAWTJArly2bUmEVdXV/r27QvAtH37zIO//goJexmLiEiqU7BLh6zDsHnymAfy5wcn/VGK/b3yyiu4uLjw+969BBcpAjExsHGjvcsSEck0lAbSoZMnTwJQ2MfHPKD768RB5MuXj9atWwPwecLq6D/+aMeKREQyFwW7dMjaY+fhYR5QsBMH8tprrwGw4MgRroO5tZiWPRERSRMKdumQNdhlyWIe0MQJcSBPP/00ZcqUIfzWLb7OkgVOnoS//7Z3WSIimYKCXTpkDXZxceYB9diJA7FYLPTu3RuAz7NmxQCz105ERFKdgl0iONqWYqdPnwagUGSkeUDBThxMt27dcHNzY8/16+wE3WcnIpJGFOwSwdG2FEsIdgUTlpFQsBMHkzNnTtq1awfADICff4YbN+xak4hIZqBgl85cv36d8PBwAApcuWIeVLATB5QwHLvYYuFqVBRs2WLfgkREMgEFu3QmobfOy8uL7BcumAc1eUIcUM2aNalQoQIRhsE80H12IiJpQMEunbEOw+bPby7+CpAvnx0rErm/OydRzAAMBTsRkVSnYJfOnDlzBoCCOXOaB3LnBldXO1Yk8mBdunQhe/bsHAS2HD0Khw/buyQRkQxNwS6dSeixK5A9u3lA99eJA/P09KRLly4ATAfNjhURSWUKdumMdSjW3d08oGAnDi5hOPYb4NyqVXatRUQko1OwS2esQ7FO8X90mjghDq5ixYrUrFiRGGDO1q1w65a9SxIRybAU7NIZ61BsbKx5QD12kg70HjQIgJkxMcRs3mznakREMi4Fu3TGOhQbEWEeULCTdKBd+/bkcnPjJLB86lR7lyMikmEp2KUjcXFxnD17FoCCYWHmQQU7SQfc3d3p36oVAB9t2oRhGPYtSEQkg1KwS0cuXLhAbGwsTk5O5NOuE5LO9P3oI7IBeyMj+WnePHuXIyKSISnYpSMJw7D58uUjy7lz5kFNnpB0ImexYrxWsCAAH374oXrtRERSgYJdIgQGBuLn50e1atXsWsddu04kbKiuHjtJRwZ36YI7sO3IEb799lt7lyMikuEo2CVCQEAAISEhBAUF2bWOhKVOCuTIYR7Int38EkknCnbowOD4528NGUJUVFSS2zAMg99++40hQ4bQqFEjatSowQsvvMCUKVMIDQ21bcEiIumMgl06Yu2x8/Q0D6i3TtKbihUZWrw4eYHD//zDuHHjkvT2ffv2Ub9+fWrVqsWECRNYv349v//+O6tWraJ///4ULVqUOXPmaJhXRDItBbt0RLtOSLpnseDZpQsT478dNWpUonrCr1y5Qr9+/ahUqRJbtmzB3d2dzrVqMadkSb4BxgF+WbIQGhrKq6++yuuvv05cXFxqfhIREYeUomAXHR3NqVOnOHjwIFcSZmlKqrEOxSbsOpEvnx2rEUmmTp3oBLxosRATE8Pzzz/P8ePH73tqbGwsM2bMoEyZMkydOpW4uDjaNm/O3088wYLt23nlyBFaAW8Bf8bEMBawADNmzOD1119Xz52IZDpJDnbh4eF8/vnnPP3003h7e1OsWDH8/PzIkycPRYsWpWfPnna/Fy2jsvbYJfREKNhJelS2LJbKlZlpGJTLn5+zZ89Sq1YtNm7caD0lJiaGlStXUqFCBfr06cPly5cpX748m2bPZtnu3RQNCoJs2eCDD+D4cbh+HecVKxhasiRfY/5i+/zzz5k8ebK9PqWIiF0kKdh9+umnFCtWjFmzZlG/fn1WrlxJcHAwBw8e5LfffmPEiBHExMTQsGFDnnvuOQ4fPpxadWdK1mAXHW0eULCT9KpHD3yAH93d8fPz4+zZszRo0ICyZctSr1498ufPT5s2bQgJCSFnzpxMmTKFPQsX8sw778CZM+DnB3v2wPDhULSoOYmodWsICqJDjRpMiL/MkCFD2Lt3rx0/qIhI2rIYSRirePHFF3n//fd54oknHnpeZGQkc+bMwdXVlVdffTXFRTqKsLAwvL29CQ0NxcvLK02vfevWLTw8PAC40qgROdatg5kzoWfPNK1DxCbCw6FQIQgN5fqyZbyzZQuff/45MTEx1lPy5MlDz549GTJkCD4XLkC9enDuHFSuDBs2QMLs8P+6dAmjenVaHzvGKqBChQr88ccfuLi4pMlHExGxtaTkjyQFuzudP3+efJmsx8iewe7IkSOUKlWKrFmzcqNcOSx//AHffgstW6ZpHSI28+ab8MknUKMGbN/O1WvX2L59O2FhYRQvXpwqVaqYYez4cahTB/79FypUgE2bIFeuh7f9229ceOopyhkGl4BJkyYxYMCAtPhUIiI2l5T8kezJE23atLnrX9d3etBxST7rMGzBglguXDAPZrJgLRnMG2+Y98nt2AHLl5MjRw6aNWtGx44dqVGjhhnqzp6FBg3MUPf442ZP3aNCHUDNmuQdPJgx8d+OHDmSy5cvp+rHERFxBMkOdjly5KBfv373HL98+TINGjRIUVFyL+uM2AIF4Px586CCnaRnvr5mrx1Av363/7tOcPIkPPssHDkCxYvD+vWQJ0/i2x8+nFdy5qQCcO3aNcaMGfPIt4iIpHfJDnbz589n48aNzJ4923rswIEDVK9ePc2HKTMDa49d3rwQGWkezJvXjhWJ2MBbb0H58maoa9rU7JkzDPj+e3OI9sABKFjQ7KmL32c20Xx8cB45ko/iv50xYwYXL160+UcQEXEkyQ52Pj4+rFixgrfeeovff/+dH3/8kZo1a9KmTRvtAZkKrMHO29s8kD07xE+mEEm3PDxg2TLInRt274ZixcxeuZYtzWHYcuXgt9+gRInktd+zJ8/lzUtV4ObNm0ycOPGRbxERSc+SFOyef/55RowYwapVqzh+/DhPPPEEgYGBNGvWjLZt2/LZZ5/x8ccfY7FYUqveTMs6FJs1q3lAw7CSUZQtC9u2Qe3aEBsLly+b/3B58034/XcoXDj5bbu7Yxk0iOHx306dOpVr167ZomoREYeUpGBXunRptm3bRs+ePSlRogQ5c+Zk5syZGIZB586dqVSpEtEJa6yJTZ09exYA3yxZzAMKdpKRlCkDv/xizoDdvdtc1mT8eHNyRUr16UPLbNkoh7nA+rx581LepoiIg0pSsJswYQIbNmzg4sWLnDx5kq+++opnn32WZ555ho0bN1K5cmWyZ89OxYoVU6veTOvcuXMA5E9YnUbBTjKiokXB3982gS6BtzeWl16ib/y3gYGB2kdWRDKsLMl9Y6FChShUqBDNmze3HgsPD2fPnj38+eefNilObrP22GnXCZGk692bLjNmMBQ4fPgw69evp3HjxvauSkTE5pI9eeJ+smfPTp06dQgICLBls5nejRs3uH79OgC+ERHmQc2IFUm8ihXJXqMGL8d/O2XKFLuWIyKSWpIU7E6ePJmkxhNmcqZ3gYGB+Pn5Ua1aNbtcP2EYNmvWrHheuWIeVI+dSNL06sXr8U/Xrl1rnZAkIpKRJCnYVatWjZ49e7Jz584HnhMaGsqsWbMoX748K1euTHGBjiAgIICQkBCCgoLscn3rMKyvr3adEEmuNm0o7e5ObSAuLo4FCxbYuyIREZtL0j12Bw4cYMyYMTz33HO4uLhQtWpVChQogLu7O1evXiUkJIT9+/dTtWpVxo8fT5MmTVKr7kwlocfO19fXnC0ICnYiSeXlBc8/T/clS/gV+PLLLxkyZIiWZxKRDCVJPXY5c+ZkwoQJnDlzhunTp1OmTBkuXbrE4cOHAejcuTO7du1i27ZtCnU2lNBjlz9/fm0nJpISXbrwIpAV8x+q9uqFFxFJLcmaFevu7k7r1q1p3br1XZvTS+qwDsXmyQPh4eZBBTuRpGvcGK9cuWhz+TILMHvtqlevbu+qRERsJtmzYrdt20bx4sUpUqQIRYoUIV++fLz99tuEhYXZsj7hjqHY7NnNA25u4Olpx4pE0ikXF2jfnu7x3y5atIioqCh7ViQiYlPJDnavvfYa5cqVIygoiD///JPx48ezceNGqlSpwqVLl2xZY6ZnHYp1czMP5MsHui9IJHnateNpwNdi4dq1a6xbt87eFYmI2Eyyg92RI0f49NNPqVy5MuXKlaNr164EBQVRqVIl+vfvb8saMz3rUKyzs3lAw7AiyVe7Ns558/Ji/C4uS5YssXNBIiK2k+xg9/jjj1uHCBNYLBZGjRrF999/n+LC5DbrUKy2ExNJOWdnaN2a9vHffvvtt9y6dcuuJYmI2Eqyg1337t3p1avXPYsWh4aG4u3tneLCxBQTE8OF+LXr8kdGmgcV7ERSpm1bagBFnJy4fv06a9eutXdFIiI2key9YgcOHAhAmTJlaN26NZUqVSI2NpYFCxYwfvx4W9WX6V24cAHDMHByciLPjRvmQW0nJpIy9erhlCsX7S5fZgLmcGzr1q3tXZWISIolO9idO3eOPXv2sHfvXoKDg/nyyy85fPgwFouFjz76iB9++IEKFSpQoUIFnnvuOVvWnKkkDMPmy5cP54sXif/GjhWJZABZssALL9B+9mwmAKtXr+bGjRtky5bN3pWJiKRIsoNd3rx5ady4MY0bN7Yei4iIYN++fQQHB7N3716+++47xowZw7Vr12xRa6akxYlFUknbtlSZPZsSTk4cvXmT77//ng4dOti7KhGRFEl2sLsfd3d3qlWrRrVq1WzZbKZ213ZiR4+aBxXsRFKufn0sOXLQ4epVxmAOxyrYiUh6l+zJE5I21GMnkkpcXKBVK+vs2LVr16bOAusnT8LYsdCpE/TsCYsXQ0yM7a8jIoKCncOzrmGXNy9cvWoeVLATsY127XgCeMzZmcjISL777jvbtR0XBx9+CKVLw7vvwqJFMHs2dOwIFSvCX3/Z7loiIvEU7BzcPduJOTtDjhx2rEgkA3n2WSw5c9I+Nhaw4WLFMTHQvj289x5ERUHdujBuHLz1FuTODSEhUKsWBAXZ5noiIvEU7BycdSjW1dU8kDcvOOmPTcQmXFzuWqz4p59+4mpCz3hyGQb06AHLl4OrK8ydC1u2mKFu3Dgz1NWpA9evQ5Mm8O+/Kf0UIiJWSggO7p7txLSGnYhttWuHH1De2Zno6GhWrVqVsvYmTYKvvjJ715cvh5dfvntv5zx5YM0a8PeHy5ehQweI7zEUEUkpBTsHZhjG7aHYhIMKdiK29cwzkDu3bYZjd+6Et982n0+eDC1a3P+87NnN0OflBdu2weefJ/+aIiJ3ULBzYKGhoURERACQPzraPJgnjx0rEsmAsmSBNm1oF//thg0buHz5ctLbiYyEl16C6Gho2xb69Hn4+SVKmLNlwZxcEb91oIhISijYObCE3jpvb2+yJtz3o2AnYnvt2lEGqOTsTGxsLCtXrkx6Gx9/DIcOQf78MGvW3cOvD/Laa1C5MoSGmu8XEUkhBTsHZr2/ztcXErYTU7ATsb169SBv3uQPxx4+DKNHm88nTQIfn8S9z9n59vumTYP4f8yJiCSXgp0Du2txYgU7kdTj7AydOlmHYzdv3sz5hAXBH8UwICDAHIpt1AjatXv0e+7UuDHUqAG3bsH48Ul7r4jIfyjYObC7thNTsBNJXa++SgmgGhAXF5f44dglS2D9enBzg8DAxA3B3sligfffN5/Png3h4Ul7v4jIHRTsEiEwMBA/P7803wNXQ7EiaahcOahZ09prl6jh2GvXYNAg8/mwYVCqVPKu3bgxlCkDYWHmUikiIsmkYJcIAQEBhISEEJTGq8Qn9Njly5dPwU4kLfTsaQ12W7du5cyZMw8/f/hw8764MmXMBYiTy8kJ+vc3n0+ebG5HJiKSDAp2DizhHp/8uXKZ/5IHBTuR1NSuHUVy56Ym5jqSixYtevC5QUHmhAeA6dPNodiU6NoVPD3h4EH45ZeUtSUimZaCnQNLCHZ5E/7CcHZO/Gw7EUm6bNlg4EC6x387Z84cDMO497yoKOjVy5w40aUL1K+f8mt7epq7UAB8+WXK2xORTEnBzoElBLt8WbKYB/Lk0T6xIqktIIAOnp54AAcOHOC3336795z334fgYMiZEyZMsN21u3c3H5ct0yQKEUkWpQQHFRcXx8X4++ryJfQYaBhWJPX5+OA1ciTt47+dPXny3a+vXHl7MeHZsyFfPttdu2ZNKF0abtwwtxwTEUkiBTsHdfnyZeLib6DOExVlHlSwE0kb/fvzaunSACxZtoywI0fM40uXQqdO5hBs377wwgu2va7FcrvX7osvbNu2iGQKCnYOKmEYNleuXLhoOzGRtJUlCzXXrsXP2ZmbcXHM9fODsmWhfXtzIeIXXjB3mEgNL71kPm7dCqdPp841RCTDUrBzUNb767TUiYhdWEqWZED8wsGfRkURffAguLrC0KFmz52zc+pcuHBheOop87mGY0UkiRTsHJSCnYj9dX3rLfLmzctJYNmQIXDyJIwdCwkTmlJLwrZkS5em7nVEJMNRsHNQ1qVO8uZVsBOxE3d3d/r27QvAh6tXE5MrV9pcuG1b83677dvh1Km0uaaIZAgKdg5KPXYijqFfv37kzJmTAwcOMHfu3LS5aIECUKeO+VzDsSKSBAp2DurChQuAgp2Ivfn4+DBixAgA3n//fa5du5Y2F9ZwrIgkg4Kdg1KPnYjj6N27N2XKlOH8+fPWodlU16aNORy7YwecOJE21xSRdE/BzkFZg12uXKDlTkTsytXVlXnz5uHk5MTChQuZMWNG6l80f36oV898ruFYEUkkBTsHZQ12CfvEOjmZ2xeJiF3UqFGDUaNGARAQEMCsWbNS/6Ivvmg+LluW+tcSkQzBYtx3h2u5n7CwMLy9vQkNDcXLyyvVrmMYBu7u7kRFRXHixx8p8txzZm9d/H13ImIfhmHQu3dvZs6cCUCLFi3o168flSpVIkuWLFy8eJG///6bv//+mxMnTnDmzBmioqLw9vamUqVKNGvWjHLlyiX+gufOmRMpDMMcji1SJJU+mYg4sqTkDwW7JEirYHf16lVyxvfO3VqzBvemTcHPD/bvT7VrikjiGIbB6NGjGTlyJLGxsUl+/9NPP82kSZOoWLFi4t5Qr565C8Unn8DgwUm+noikf0nJH6m8yqYkR8IwrJeXF+6hoeZB3V8n4hAsFgvDhw+nbdu2TJw4kbVr1/Lvv/8C4OHhwWOPPUbZsmUpXrw4BQsWxN3dnQsXLrBt2zbWrl3Lli1bqFy5MuPGjeONN97AYrE8/IIvvmgGu2XLFOxE5JEU7ByQljoRcXxly5a1DslGRUUB5iSLhzl16hSDBw9m+fLlDBkyhCNHjjBt2rSHh7s2baB/f3N27KlT5pZjtnb+PMyZY17DMKBaNXj1VXMYWETSFU2ecEBa6kQkfXF1dX1kqAMoXLgwS5cuZfLkyVgsFmbMmMHgwYN56B0xvr5Qu7b5fMUKG1V8h1mzoGRJGDYMvv8eVq+GESOgVCmYPdv21xORVKVg54AU7EQyLovFQr9+/ay7WEyaNMna8/dAqTE71jDgzTehVy+4cQOqV4fPPoOpU6FGDbh1C3r2hDFjbHdNEUl1CnYOSMFOJOPr3r07Y8eOBcxty3bs2PHgkxMWK96+HeLv50uxUaPMCRlghrcdO8wh34AA2LbN7LUDsydvwQLbXFNEUp2CnQO6K9glLHGiYCeS4bz99tu0adOG6OhoXnrpJW7evHn/EwsUgKeeMp/bYjj2u+9g5Ejz+dSp8M47ZnBM4ORkvv7uu+b3r70Gf/+d8uuKSKpTsHNA6rETyRwsFguzZ8+mYMGC/PPPP7ybEKTux1bDscePQ7du5vOBA80eugcZNQoaNICbN81wp9WxRByegp0DSgh2efPmVbATyeB8fHyYHT9JYfLkyfz222/3P7FNG/Nx2zY4fTp5FzMM6NEDrl0z76kbN+7h5zs7mxMoPDzMJVe++ip51xWRNKNg54Csy53kzg1XrpgH8+a1Y0Uikpqee+45unXrhmEY9OvXj7i4uHtPKlgw5cOx8+fDpk3g7g5ffw2JmMlL0aLw/vvm8+HDISIiedcWkTShYOeA7tonNmHoI1cuO1YkIqlt3LhxeHl5sWvXLr744ov7n5QwHLt4cdIvcOnS7QWOR4wwlzhJrAEDzGD577/m8igi4rAU7BxMeHi49QbqfAk3M+fMCVm0lrRIRpYvXz5Gxk9oeOedd7h27dq9J7VrZw6P/vYbhIQk7QJvvAGXL8MTT5jP48XFxfH999/TvXt3qlevTs2aNXn99dfZuXPn7fe6u5u9dWDOoFWvnYjDUrBzMAm9dR4eHmS/ccM8qPvrRDKFvn374ufnx8WLF/nggw/uPcHXF1q0MJ8/au27O23aZN4fZ7GY73NxAWDv3r1UqVKFli1bMm/ePIKCgtixYwfTp0/nySefpEOHDly+fNls45VXzF0vzp0zh3FFxCEp2DmY+86IzZ3bjhWJSFpxcXFh4sSJAEyZMoXDhw/fe1KvXubjV1+Ziwg/SmQk9OljPu/Tx1x8GFi8eDHVq1cnODgYLy8vBg0axMqVK1m8eDEvvfQSzs7OLFmyhFq1anHs2DHzfrx+/cx2Pv1UM2RFHJSCnYO5K9gl/EtZPXYimUbjxo1p2rQp0dHRDBky5N4TGjWCIkXg6lVYuPDRDY4bB4cOQf781l0kZs+eTadOnYiKiqJ58+YcOnSIiRMn8sILL9C+fXu++uorfv/9d4oUKcKhQ4d49tlnzd9NPXtCtmzw11+wYYONP7mI2IKCnYO5a6mTS5fMg5o4IZKpfPLJJzg7O/Ptt9+ycePGu190djYnMwB89BHExDy4ocOHb28JNmkSeHvz888/07t3bwzDICAggFWrVpn/kPyPKlWq8Ntvv1GiRAmOHTtGy5YtifLwgJdfNk+YMiXlH1REbE7BzsFYlzq5s8dOQ7EimUrZsmV5/fXXARg8eDCxsbF3n/Daa+Y/+I4ceXCvXXQ0dO1qDsU2agTt2vHvv//Srl07YmNj6dSpE1OmTMHZ2fmBdRQoUIAff/yRnDlzsnPnToYNGwbxdbFmDZw5Y4uPKyI2pGDnYO4ailWPnUimNWLECHLkyMGff/7J3Llz734xWzZ46y3z+VtvmcOy//W//5n7v3p7w8yZREZF0aZNGy5cuEDFihWZNWsWlju3EXuA0qVLM2fOHAAmTJjAlvPnzfX0YmNh3ryUfkwRsTEFOwdz32CnHjuRTCdXrlzW5U+GDRtGaGjo3ScMGABly5r7SffqBXcuavzllzB6tPl8xgwoWpR+/fqxc+dOcuTIwcqVK/Hw8Eh0La1ataJnz54A9OnTh6ju3c0X5sy5+7qpyTBg9Wrzs7ZqZS7Zsnt32lxbJB3JdMHu1KlTPP300/j5+VGhQgWWpXTfRRu77+QJBTuRTKlPnz489thjXLx4kTEJ98olcHMzg5WLCyxfDh06wObNMGTI7fvghgyBDh2YNWuWtYdu0aJFlChRIsm1jBs3jrx58/L3338z8cwZ8PQ0h4J//tkGn/QRTp2COnXMpV5mzYJvv4WJE6FKFXNYOjGzg0UyCyOTOXPmjLFnzx7DMAzj/PnzRsGCBY3w8PBEvTc0NNQAjNDQ0FSrr3Tp0gZg/Pzzz4ZRvLhhgGFs25Zq1xMRx7Z69WoDMFxdXY0jR47ce8KCBYaRJYv5u+LOrwEDDCM21tixY4fh6upqAMbo0aNTVMtXX31lAIaHh4dx7qWXzOt06pSiNh/pwAHD8PU1r5Utm2H0728YgYGG0aGDYVgs5vH69Q3jxo3UrUPEjpKSPzJdj52vry+VKlUCzJmnOXPm5ErCfqwOoEKFClSsWJGCBQtqKFZEaNq0KQ0bNiQqKoq3Eu6ru1PnzvDrr2ZvVrFiUL8+fPcdTJrE+YsXadOmDVFRUbzwwgu88847KaqlS5cuVK9enZs3bzI6MtI8uHIl3G+XDFs4fx6aNIGzZ6FcOXOZlc8+MydwLFoEP/0E2bObCzD36KG19UTA8Xrsfv75Z6N58+aGr6+vARjffPPNPecEBgYaxYoVM9zc3IzKlSsbW7duTda1goKCjHLlyiX6/LTosbOKjLz9L+/Ll1P/eiLisPbt22c4OTkZgLFly5ZEvScyMtKoW7euARhly5a12e+tjRs3GoDh4uJiHC1d2vwdNWOGTdq+S2ysYTzzjNl+yZKGceHC/c/bsuV2j2VgoO3rEHEA6brH7saNG1SsWJGpU6fe9/UlS5YwcOBAhg0bxp49e6hTpw5NmjTh5MmT1nOqVKlC+fLl7/k6c8fU/MuXL9O1a1dmJmVbnrSUcH+dkxP4+Ni1FBGxr/Lly/Paa68B8NprrxEeHv7Q8w3DoG/fvmzduhVPT0+++eYbvLy8bFJL/fr1adiwIdHR0YxIaPOLL2zS9l0mTzbvGfTwgB9+ePBC7fXqwccfm8/fftu8H08kM0v9nJl83KfHrnr16kbv3r3vOla2bFlj6NChiW43IiLCqFOnjvHVV1898rzQ0FDr16lTp9Kux27fPvNfoLlzp/61RMThXbp0yShYsKABGJ07dzbi4uIeeO6ECRMMwLBYLMbq1attXktQUJABGE5OTsYRJyfzd9X+/ba7wPHjhuHubrY7ffqjz4+NNYyaNc3zW7e2XR0iDiJd99g9TFRUFLt27aJRo0Z3HW/UqBHbt29PVBuGYdC9e3fq16/PSy+99NBzx44di7e3t/WrcOHCya49ybSGnYjcIVeuXCxevBhnZ2cWLlzIO++8g/Gfe8oMw2DSpEm8+eabgDmTtVmzZjavpWrVqjz33HPExcXxccLvRVv22o0YARER8PTT5qzXR3FygpkzzceVK831+0QyqXQV7C5dukRsbOw929/ky5ePc+fOJaqNbdu2sWTJElatWkWlSpWoVKkS+/btu++577zzDqGhodavU2nZxa+JEyLyH7Vr12batGmAGdo6d+7M2bNnATh9+jRdu3Zl0KBBgPn7KyHgpYZ3330XgC9On+YMwPz55m4XKRUSYrYF5j63iVhEGYDy5SFhfb2hQzWRQjKtLPYuIDn+u1q6YRiJWkEdzF+McYlcUNPNzQ03N7ck12cTWsNORO6jV69exMTE0L9/fxYtWsSSJUvInz8/Z8+exTAMnJycGDt2LEOGDEn078XkqFOnDrVr1+bXX39lYtasTDh/Hn780ZydmxLDh5uLHr/wAlSvnrT3jhhhbrH288/m19NPp6wWkXQoXfXY5c6dG2dn53t65y5cuHDfTazTNQ3FisgDvP766/zyyy/Wf6ieOXMGwzCoW7cuv/zyC2+99VaqhroECb12M2JiuAwpH479/Xf45htzSPXDD5P+/iJF4JVXzOcTJqSsFpF0Kl0FO1dXV6pUqcL69evvOr5+/Xpq1aplp6pSiXrsROQhatasyS+//MKZM2cICgri7Nmz/Pzzz2n6u/C5557D39+fG9HRTAb4/nu4eDF5jRkGJKyz17Ur+Pklr53Bg83h2x9+MId101JcHCxbBh07wjPPmGvrbdmStjVIpudwwS48PJzg4GCCg4MBOHbsGMHBwdblTAYPHszs2bOZO3cuBw4cYNCgQZw8eZLevXvbsepUoHvsRCQRfH19qVq1Kvnz50/za1ssFmuv3WRnZ67HxMCCBclrbMMGc3kTV1eI3yM3WUqVModxAT75JPntJNXp0+a2Z+3aweLFZqCbO9cMeN26mZNBRNJCqs7PTYbNmzcbwD1f3bp1s54TGBhoFC1a1HB1dTUqV65sbr+VBtJ0geImTcyp+3PmpP61RESSKSYmxnjssccMwPgYDOOJJwzjIUux3FdcnGFUrWr+zuvfP+VFbdtmtuXubhhXrqS8vUc5ccIwChc2r+npaRhDhxrGwoWG0auXYTg7m8cbNTKMqKjUr0UypKTkD4thaOrQowQGBhIYGEhsbCyHDh0iNDTUZot9PtCTT8LOneZm1y1bpu61RERS4Msvv+Tll18mH3AccP/jD6hSJfENrFgBbdtCtmxw9CjkzZuyggwDKlWCP/80tyDr3z9l7T1MWJj5+/rvv6FMGVizBkqWvP36hg3w/PNw86a5FVpgYOrVIhlWWFgY3t7eicofDjcU64gCAgIICQkhKCgo7S6qyRMikk507tyZIkWKcB6YC0mbRBETA8OGmc8HD055qAPzHruE9e9mzEjdpU/69jVDXaFCZoi7M9QBNGgAS5aYz6dNg7VrU68WEUA9dkmQlMScYt7e5r8E//4bHnssda8lIpJCgYGB9O3bl6LAYR8fXM6eBXf3R79x7lxzkkHOnGZvnbf3fU87duwYP/74I0ePHsXNzY3KlSvz3HPP4eHhcf92w8KgQAG4cQO2bjXvf7O1pUuhfXtzFu/WrfDUUw8+d+BAs/ewSBE4eDBxPxuReOqxS++io81fSqDJEyKSLrzyyivkzZuXE8Cia9fgu+8e/aZbt8y158CcEXufUHfo0CFatmxJiRIleP3115kwYQKjR4+mTZs2FClShMmTJ99/bVIvL3N2Kpi9drYWHm72MAK8++7DQx3AmDFQuDCcPGnugyuSShTsHFHCUicWC/j42LUUEZHEyJo1K4Pjg85YIG7SpEcPgU6aBP/+awaegIC7XjIMgxkzZvDEE0/w/fffY7FYePrppxk4cCA9e/akaNGiXL58mQEDBtC0aVOuX79+b/sJw7ErVsCVKyn+jHcZO9acCVu8+O2h5Ifx8IDRo83nY8ZAaKht6xFJkLrzODKWNJsV+9df5iyqXLlS9zoiIjYUGhpq+Hh7G4AxHwxj/foHn3zhgmF4eZm/67766q6XYmJijFdffdW6KkLjxo2Nv//++55zpk2bZnh4eBiAUa1atXt/N8fFGUalSuY1Jk2y1cc0jGPHDMPV1Wx31arEvy821jD8/Mz3jRtnu3okw0tK/lCPnSPSxAkRSYe8vLx46+23AXgXuDVsmLlo7/288YZ5y4m/P3TubD0cHR1N586dmT17Nk5OTnz88cesXbuWx/5zr7GzszN9+vRhy5Yt5M6dm6CgINq1a0f0nfvVWizQs6f5fNYs202iGDMGoqKgfv2krVrg5ARvvWU+nzQJIiNtU4/IHRTsEiEwMBA/Pz+qVauWNhfU4sQikk4NHDiQIgULcgr4dOdOmDPn3pNWr4b5882gM326+QhERkbSrl07lixZgouLC8uWLXvknrfVqlVjzZo1eHh48NNPP/Hee+/dfULnzpA1K+zfDzt2pPwDnjhxe9bv//5nhsek6NgRChaEs2fNyRciNqZglwhpvtyJthMTkXQqa9asjBk3DoDRwJFBg+Cvv26fcOAAdOliPh840FwDDoiIiKB169asWrUKNzc3vvnmG1q3bp2oa1arVo158+YB8PHHH7Np06bbL3p7m7tBgNlrl1JjxphLtDz7LNSunfT3u7revvdv9uyU1yPyHwp2jkhDsSKSjnXs2JF6detyE3jlxg3i6tc3e+jmzDGXHQkNhVq1zJAE3Lp1i+eff541a9aQNWtWVq9eTbNmzZJ0zbZt29KzZ08Mw6Bbt26Eh4fffjFhOHbJktsrDiTHyZO3e+sSZvMmx8sv314i5dCh5Lcjch8Kdo5IPXYiko45OTkx94svyJYtG1uB4RcvQteu8Oqr5u+3atVg1Spwc+PatWs0b96cdevWkS1bNtauXUuDBg2Sdd1PP/2U4sWL8++//zLyzv1ma9WCxx83d3/4+uvkf7CxY83lqOrXT9m6eIUKQZMm5nP12omNKdg5It1jJyLpXIkSJZg+fTpgLn8y3tcXw9/f7KX7+WfIk4f9+/dTo0YNNm3aRPbs2Vm7di316tVL9jWzZcvG1KlTAZg0aRJ//vmn+cJ/J1Ekx5kz5mLKAO+/n+warV591XxcuPDBE0xEkkHBzhFpKFZEMoCXXnqJ9+ND0Ftnz9KyYEG+L1+edb/8Qt++ffH39+fgwYMUKlSIrVu3UscGu0M0bdqUtm3bEhsby+DBgzESZsK+9JJ5f9vu3eZXUn3yiTkTtnZtSEH4tGrSxLz/78wZ2LYt5e2JxFOwc0QaihWRDGLkyJF8+umnZMmShdWrV9OyZUsaN25MYGAg0dHRNG/enKCgIPz9/W12zfHjx+Pq6srGjRtZt26deTB3bkiYjJHUXrvLl2/vXpGYxYgTw80NWrUynyfsJStiAwp2jkg9diKSQVgsFgYOHMhff/3Fa6+9Rvny5SlbtiwdO3Zk/fr1fPfdd+TPn9+m1yxWrBh9+/YF4K233iI2NtZ8IWE4duFCcw/ZxJo82bw/z98fGje2XaHt25uPy5dDQo0iKWQxDFut2JhxBQYGEhgYSGxsLIcOHUrUJrwp4uNjzho7cADKlk2964iIZFCXL1+mZMmShIaGMm/ePLp27Wrey1amDBw5Yt4v9/LLj24oLAyKFoVr12DZMmjb1nZFRkdD/vzmdmebNsEzz9iubclQwsLC8Pb2TlT+UI9dIqTpOnbR0bf3ENRQrIhIsuTKlYt33nkHgPfee4/IyEhziZGESQszZyauoalTzVBXtuztoVxbcXGB5583n3/7rW3blkxLwc7RJGxUbbFAjhz2rUVEJB3r378/BQoU4OTJk8xIuEeue3czUO3YYc7OfZjLlyF+sWWGD7fukPEoERERnD9/npiYmEef3KKF+fj997bb8kwyNQU7R5MwcSJHDnB2tm8tIiLpWNasWRkRv5Dw6NGjuX79ujn0mdBr97//PbyBsWPNodiKFc2twB4iMjKS6dOnU716dTw8PMifPz9Zs2alYcOGfPvttzzwrqeGDc3ZukePwsGDSf2IIvdQsHM0WsNORMRmXn75ZUqXLs3Fixf59NNPzYPvvGP22m3eDBs33v+NISHmpAkwA95Deut27dpFhQoVeP311wkKCrKGuJiYGDZs2ECrVq1o1qwZlxJ+v98pe/bb99Z9/31yP6aIlYKdo9GMWBERm3FxceGDDz4AYMKECWa4KlwYevc2T3j9dYiIuPtNMTHmDNroaGjeHJ577oHtL168mJo1a3Lo0CHy58/PpEmTOHXqFDExMRw8eJC3334bd3d31q5dS7Vq1Th27Ni9jTRvbj6uXm2Lj5wo0dHRXL16lTgtjpzhKNg5Gq1hJyJiUy+++CL+/v5cv36dsWPHmgc/+AB8fc29Wvv3v31/m2HAkCGwfbvZmzZtmnnP8318/vnndOrUiejoaJ5//nn279/PgAEDKFSoEM7OzpQpU4aPPvqInTt3UrJkSY4fP069evU4efLk3Q0lBLtt227fZ50KDMPg+++/p379+mTLlo2cOXPi5eVFu3btCA4OTrXrShozJNFCQ0MNwAgNDU29i4wZYxhgGN27p941REQymR9//NEADDc3N+PEiRPmwTVrDMPJyfyd26OHYfz6q/loxjvDWLbsge0tXrzYAAzACAgIMGJjYx96/TNnzhhly5Y1AKNSpUpGeHj43SeUK2dec/HilH7U+7p06ZLRvHlza83//XJ2djZGjhxpxMXFpcr1JWWSkj/UY+do1GMnImJzjRo1ol69ekRGRvK/hEkTTZrAlClmj9ycOeZ2YXPmmK99+ukD16zbunWruS4e0K9fP6ZMmYLTI2bM+vr68uOPP5I3b16Cg4Pp0aPH3RMqGjUyH9evT9HnvJ9///2XGjVqsHr1atzc3Hj77bc5dOgQERER/PHHH7z44ovExsYycuRIXnvttQdP9JD0IdVjZgYwdepU4/HHHzfKlCmT+j123bqZ/2r76KPUu4aISCa0fft2AzCcnJyMAwcO3H5hwwbDePZZwyhQwHzctOmBbYSEhBg+Pj4GYLRu3dqIiYlJUg2//vqrkSVLFgMw5s2bd/uFNWvM3/1FihiGDXvNzpw5Y5QoUcIAjKJFixp79uy573mzZs0ynJ2dDcB47733bHZ9sY2k9Ngp2CVBmgzFNmtm/s89a1bqXUNEJJN6/vnnDcBo06ZNkt976dIlo2TJkgZg1KxZ07h582ayavjwww8NwPD09DSOHj1qHgwPNwxXV/P3/8GDyWr3v27dumU8+eSTBmCUKFHi9hD0A8yaNcs6NLtmzRqb1CC2oaHY9ExDsSIiqebDDz/EYrGwYsUKfv3110S/LyoqirZt23LkyBGKFSvGt99+S9asWZNVw9ChQ3nqqae4fv06vXr1Moc+s2WDWrXMEzZsSFa7/9WnTx9+//13cuTIwU8//USRIkUeev6rr75Kv379AHOZmIsXL9qkDklbCnaORsudiIikmvLly9OjRw8AevTowa1btx75HsMw6Nu3L1u2bMHT05Pvv/+ePHnyJLsGZ2dnvvzyS9zd3dmwYQNff/21+ULDhuajDe6zW7JkCV9++SVOTk4sXbqUUqVKJep9H3/8MeXLl+f8+fMMHTo0xXVI2lOwczRPPmn+q61AAXtXIiKSIY0fP54CBQpw6NAh636yDzN27FhmzZqFxWJh0aJFlC9fPsU1lCpVivfeew+AQYMGceXKldvBbtMmcy29ZDp79iyvv/46AMOGDaNBgwaJfq+7uzuff/45AHPnzuX3339Pdh1iJ6k+MJyBpMk9diIikupWr15tvZ/srkkM//Hpp59az5s0aZJNa4iMjDT8/PwMwOjRo4dhxMQYRo4c5n1227cnq824uDjrsib+/v5GZGRkstrp1q2b9V5CLYFif7rHTkRE5CGaNWvG8OHDAXNIduHChXe9HhUVxRtvvMGgQYMAGDlyJAMGDLBpDa6urtbesTlz5vDL9u3w7LPmi8kcjv3iiy9YvXo1rq6ufPXVV7i6uiarnbFjx+Lu7s5vv/3GTz/9lKw2xD4U7EREJFP63//+R5cuXYiJiaFLly60bt2a+fPnM3HiRCpWrMjEiRMBGDVqFO+//36q1FC7dm169uwJQK9evYh8+mnzhWRMoDhx4gQDBw4E4IMPPkjRkLGvr691OPf999/X2nbpiMXQn1aihYWF4e3tTWhoKF5eXvYuR0REUiguLo7hw4czbty4e/ZNzZMnD9OmTaPtAxYqtpWrV6/y+OOPc/78eUb278+IyZPBxcXcXix79kS1ERcXR8OGDdm0aRO1atVi69atODs7p6iuCxcuULx4cW7evMnatWt57iF75krqSkr+UI+diIhkWk5OTowZM4bg4GAGDBhA/fr1admyJZ9++ikHDx5M9VAHkCNHDj777DMAxsyYwd8FCkB0NPzyS6LbmDJlCps2bcLDw4N58+alONQB5M2bl9deew3A2nspjk89dkmgHjsREUkNhmHQrFkz1q5dS738+dl87hyWN96ACRMe+d4DBw5QuXJlIiIiCAwMtA6h2sLx48cpWbIkcXFx/PnnnzzxxBM2a1sSTz12IiIi6YjFYmHatGl4eHjw87lzfA6Jus8uOjqarl27EhERQePGjenTp49N6ypWrBht2rQBYNKkSTZtW1KHgl0iBAYG4ufnR7Vq1exdioiIZFDFihXjww8/BGAgsHfvXnjE7g+DBg3ijz/+IEeOHMydOxeLxWLzuhJmBi9YsIALFy7YvH2xLQW7RAgICCAkJISgoCB7lyIiIhnYgAEDaNq0KZHAi8ClVaseeO7s2bMJDAwEYN68eRRIpYXta9asSfXq1YmKimLevHmpcg2xHQU7ERERB+Hk5MS8efMo7OnJYeC5d981d6X4j6+++opevXoB5hp7LVq0SNW6Eq41c+ZMLX3i4BTsREREHEju3LlZN348uYFdly5RpUoVvvvuO6Kjozl16hR9+vShW7duGIbBa6+9Zt2aLDW1b98eT09P/vnnH7Zs2ZLq15PkU7ATERFxMGU7d2azszMlMGemPv/887i5uVGkSBFmzJgBwNtvv820adNwckr9v8qzZ89O586dAbPXThyXgp2IiIijyZ6d8jVrsgt4q3FjcubMiWEYWCwW6tSpw4YNG/joo4/SJNQlSBiOXblyJZcuXUqz60rSKNiJiIg4omefxQcY5+PDxYsXOXv2LKGhoWzdupVnE/aUTUP+/v5UrVpVkygcnIKdiIiII2rQwHzcuBEnIH/+/Hh6etq1pIR9befMmaNJFA5KwU5ERMQRVa8O2bLBpUuwb5+9qwGgQ4cOeHh4cODAAX777Td7lyP3oWAnIiLiiFxdoV4983kidqFIC15eXrRr1w4w19ETx6NgJyIi4qgS7qXbuNG+ddzh1VdfBWDJkiWEhYXZuRr5LwU7ERERR5Vwn93PP0NUlH1riVerVi3Kli3LzZs3WbJkib3Lkf9QsBMREXFU5ctDnjxw8yb8/ru9qwHAYrHQo0cPQMOxjkjBLhECAwPx8/OjWrVq9i5FREQyEyen28OxDnKfHUDXrl3JkiULO3fuZJ+DTOwQk4JdIgQEBBASEkJQUJC9SxERkczGAe+zy5s3Ly1btgTMpU/EcSjYiYiIOLKE++x+/x2uX7dvLXdImEQxf/58IiMj7VyNJFCwExERcWTFikGJEhATA1u32rsaq0aNGlGoUCGuXLnCqlWr7F2OxFOwExERcXQJvXZr19q3jjs4Ozvz8ssvA5pE4UgU7ERERBxdixbm4zffQFycfWu5wyuvvILFYmHDhg0cO3bM3uUICnYiIiKOr2FD8PSEM2ccZtkTgGLFitEgvjfxiy++sHM1Agp2IiIijs/NDZo3N5+vWGHfWv4jYU27uXPnEhsba+dqRMFOREQkPWjb1nxcsQIMw7613KFVq1bkzJmT06dP89NPP9m7nExPwU5ERCQ9eO458PCA48fht9/sXY2Vm5sbXbt2BeCzzz6zczWiYCciIpIeeHhAu3bmcwebhdqvXz+cnZ1Zt24du3btsnc5mZqCnYiISHoRvygwS5ZAWFjS3hsTY86q7dQJqlSBWrVg0CA4cCDFZZUoUYKOHTsCMHbs2BS3J8lnMQwHGqh3cGFhYXh7exMaGoqXl5e9yxERkczGMKBcOTOMTZoEAwYk7n3btsHrr8Off977mrMzDB0Ko0aZe9Mm0/79+ylfvjwWi4V9+/ZRrly5ZLcld0tK/lCPnYiISHphsdwOcx9/DBERDz/fMGDCBKhXzwx1OXLAkCHw3XewaJG5Pl5sLIweDZ07m8+TqVy5crRp0wbDMBg8eDDqN7IPBTsREZH0pHt3KFzYXNNu6tQHnxceDu3bm0EuNha6dIHDh81A2KIFdOhgBryFC8HFBRYvhjffTFFp48aNw8XFhXXr1vHDDz8ku53Y2FguXbrEzZs3U1RPZqRgJyIikp64ucHIkebz99+Hf/6595x//oEaNWDZMjO0TZsGX30FuXLde26nTrBggfl80iRIwb6vJUuWZODAgQD07t2by5cvJ/q958+fZ+zYsTz55JN4eHiQJ08esmXLRpkyZRg6dCinTp1Kdl2Zie6xSwLdYyciIg7BMODZZ2HzZihTBjZsMHvx4uJg3jwYPBiuXQNfX3Pdu5o1H93mW2/B+PFm+Dt48P4hMBFu3LhBlSpVOHjwIM2aNWPVqlVkyZLlgeefPn2ajz/+mJkzZxLxkKFlDw8PRo0axeDBg7FYLMmqzZYMwyAoKIh9+/YRGRlJw4YNKV26dKpcK0n5w5BECw0NNQAjNDTU3qWIiEhm9++/hlGkiGGAYWTPbhhNmxpGsWLm92AYNWsaxunTiW8vMtIwnnjCfG/v3ikqbdeuXYabm5sBGB07djRu3bp1zzlHjhwxXnvtNcPV1dUADMCoXr26MXPmTOPo0aNGdHS0cfHiRWP58uXGU089ZT3nxRdfNG7evJmi+lIiLi7OWL58uVGqVClrTYCxZMmSVLtmUvKHgl0iTJ061Xj88ceNMmXKKNiJiIjjOHrUMKpXvx3mwDC8vQ3j448NIzo66e1t2WK2YbEYxt69KSrt22+/NZydnQ3AeOyxx4zAwEBj3bp1xpw5c4xWrVpZXwOMOnXqGOvWrTPi4uLu21ZcXJwxffp0w8XFxQCMpk2bGpGRkSmqLzkiIiKM7t27W+vOnj270bhxY6Nt27bGb7/9lmrXTUqw01BsEmgoVkREHI5hmMuZhISYQ6/160O2bMlvr317WLoUWrdO8b60GzdupGPHjly8ePG+rzdu3Jh33nmHevXqJaq9zZs306xZM27dukWnTp1YsGBBmg3LRkRE0LJlS9avX4+TkxPvvvsuQ4cOJVtKftaJlJT8oWCXBAp2IiKS4YWEQPnyZmDcuxcqVEhRc2FhYcycOZMff/yR8+fPkzdvXmrUqEGHDh144oknktzeunXraNasGTExMUyYMIE33ngjRfUlRmxsLG3btmXVqlVky5aNlStX0qhRo1S/bgIFu1SiYCciIplCQq/diy+ajw5m2rRpBAQE4OTkxObNm6lbt26qXm/48OGMHj0aNzc31qxZQ/369VP1ev+lBYpFREQk+YYPNx9XrICTJ+1by3306dOHbt26ERcXR7du3QhL6vZqSbB69WpGjx4NwBdffJHmoS6pFOxERETkbk88Yd6rFxdnroHnYCwWC1OmTKFYsWIcP36cwYMHp8p1zp07R7du3QDo16+fdT9cR6ZgJyIiIvfq3998nDULbt2yby334enpyVdffYXFYmHOnDn8+OOPNr9G3759uXLlCv7+/kyYMMHm7acGBTsRERG5V/PmUKwYXLli7ivrgOrUqcOA+L1zX3/9dZtuQbZ8+XJWrFhBlixZ+OKLL3B1dbVZ26lJwU5ERETu5ewMvXubz+fMsW8tD/HBBx9QuHBhjh07xqhRo2zS5qVLlwgICADg3XffpWLFijZpNy0o2ImIiMj9de1qBrzt2+Hvv+1dzX1lz56dwMBAACZMmMCff/6Z4jYHDhzIhQsXKF++PMOGDUtxe2lJwU5ERETuz9cXnnvOfP7ll2l33R07oGNHKFECSpaELl3gjz8eeHqLFi1o06YNsbGx9OrVi7i4uGRfevXq1SxcuBAnJyfmzp378CHY06dh+XKYPx+OH0/2NW1JwU5EREQe7JVXzMd58yAmJnWvFR0NgwdDzZqweDEcOwZHj8LChVCtGgwbZs7UvY/PPvsMT09Pfv/9d6ZPn56sy4eGhtI7fvj5jTfeoFq1avc/8coV6N4dChc21/rr2hV++y1Z17Q1BTsRERF5sObNIXduOHcOfvop9a4TG2v2zH36qfl9t26waRNs2ACdO5vHxoyB1183d8X4j4IFCzJ27FgAhg4dyvFk9KANGTKE06dPU6pUKf73v//d/6RTp6BWLTPoGgZUqQING0KRIkm+XmpQsBMREZEHc3W9Hay++CL1rvPGG+YuFy4u5vDml1/CM8/As8/CggXmtS0W+Pxz+Pjj+zbRp08f6tSpQ3h4OD169EjSkOymTZuYNWsWAHPmzCFr1qz3nhQaCo0awcGDZm/d9u3mEPG6dfDUU8n51DanYCciIiIP9/LL5uP338Ply7Zvf+VK+Owz8/nXX0ObNvee0707xE+SYNgw2LbtnlMS7ovz8PBg06ZNiR6SvXz5snUh4j59+tx/izLDMHsR//4bChaEX381h4wdjIKdiIiIPFzFiuZXVJR575stnT8PPXqYz996C9q2ffC5vXubvYexsfDqq2Y9/1GqVCk++ugjAN58801279790MsbhsErr7zCv//+S+nSpRk3btz9T/z6a/j2W7MH85tvHGbo9b8U7EREROTRunc3H+fNs227b74J165B5crw4YcPP9digalTIW9es+ds/Pj7nhYQEECTJk2IiIjghRde4Pz58w9s8r333uO7777D1dWVpUuX4unpee9Jly5B/ELIjBhhTuRwUAp2IiIi8midOkGWLBAUBCEhtmlzyxbz/jmLBWbMMO+vexQfH5g40Xw+dixcuHDPKU5OTnz99deUKlWKkydPUr9+fc6ePXvXOYZh8NFHHzF69GgApk+fTqVKle5/zQ8+MIegn3gChgxJ/OezAwU7ERERebS8eaFpU/O5LXrt4uJg0CDzee/eSesF69QJqlaFGzfMcHcfPj4+/PDDDxQsWJCQkBD8/f356quvuHjxInv27KFt27a88847AIwaNYpXEpZ1+a/jxyHhXr1PP01c+LQji2HcZ86w3FdYWBje3t6Ehobi5eVl73JERETS1sqV5sQGX184edLswUuuZcugXTvw9DTXq8uVK2nvX7/enKHq6gr//GPOUr2PI0eO0LJlS0Lu08vo7OzMxIkT6d+//4Ov0727GWSffdZcesUOkpI/1GMnIiIiidO8uRnAzp5NWciJiYH33zefv/FG0kMdQIMGUK+eOYEiYWj2PkqWLMmuXbsYPXo0JUqUAMDDw4PWrVuze/fuh4e6I0fMXSXAXEMvHVCwExERkcRxdTWHQSFlW4wtWGBOfsiV6/ZwbFJZLBA/lMqsWXD16gNPdXd359133+XIkSNEREQQHh7OihUrqFChwsOv8emn5pDxc89B9erJqzONKdiJiIhI4sWv98aqVeZs1qSKioKEXR3efhtScmtTo0ZQoYJ5r10i16xzc3PDYrE8+sTLl2HuXPO5g0+YuJOCnYiIiCRe5cpQvjxERsKSJUl//+zZ5oSE/PkhICBltVgs5nIpAJMnQ0REytq70/TpcOsW+PubO2CkEwp2IiIikngWy+1eu6TOjr116/ZadcOHg4dHyuvp0AEKFTIXOv7665S3B2ZAnDLFfP7mm+ZnTicU7BIhMDAQPz8/qjnwgoQiIiJppnNncHaG334z901NrOnTzYkXRYqYO0fYgosLJEyAmDjR3PorpRYvNtfHK1wYXnwx5e2lIQW7RAgICCAkJISgoCB7lyIiImJ/vr7QuLH5/IsvEvee8PDba86NGAFubrarp2dPyJYN9u9P+ZIkhnG7ty4gwOHXrfsvBTsRERFJup49zcfPP4fr1x99/uTJ5tZcpUpB1662rcXHBxIWGH7I0ieJ8ttvsHs3uLvbrlcxDSnYiYiISNK1aAFlypgzY2fPfvi5Fy/e3tf1f/9L2cLGDzJggHkv3I8/pmzLs4Teuk6dkre+np0p2ImIiEjSOTvfnpE6caI5MeJBhg0zA2ClStC+ferUU7IkPP+8+XzSpOS1ceYMLF9uPu/XzyZlpTUFOxEREUmel14yZ6T++++Dh0B37rzdozdlihkIU8vgwebj/PlmL2FSff65uStG7dpmCE2HFOxEREQkedzdYdw48/nYsXD06N2vX79uzqA1DOjSxQxMqal2bahSxVyuZMaMpL03KsoMdgB9+9q+tjSiYCciIiLJ17Ej1K1r7v7Qti2EhprHIyPNYdd//jGXN5k8OfVrsVhu99oFBpo1JNayZeZaeAUKQOvWqVNfGlCwExERkeSzWMy9X3Pnhj17zD1VR42CJ5+EtWsha1Zzh4ocOdKmnhdfhIIFzZC2aFHi3mMYMGGC+bx373S3xMmdFOxEREQkZQoXhvXrzW3CDh0y16nbuxdy5oTVq6FGjbSrxcXl9sSHTz9N3ILF69dDcLC5E8brr6dqealNwU5ERERSrlIlc5mRjz4y16n78EPz+/r1076WXr3MkPbnn7B586PP/+ij2+9Lh0uc3MliGLbYeyNzCAsLw9vbm9DQULy8vOxdjoiIiDxI377mfXbNmpm9hg/yyy/mPYJZspiTPwoXTrsaEykp+UM9diIiIpLxJCxY/MMP8KAtQQ3j9lp8PXo4ZKhLKgU7ERERyXhKlzaXWgHzvrnY2HvPmTfPXGcvWzYYOTJNy0stCnYiIiKSMY0fD15e8Mcft9fbS3D8OAwcaD4fPtyc+JEBKNiJiIhIxpQ//+0dMd57D+bONYdfT56EJk3MNfeqV789HJsBKNiJiIhIxtWjB/TpA3Fx5vPHHjO//v7bXO9u+XJz4kQGoWAnIiIiGdvUqeY9dK6ucPiwueVYzZrw668ZYsLEnbTcSRJouRMREZF07Px52L3bHKKtVMmcNZsOJCV/ZJy+RxEREZGHyZfPvLcuA9NQrIiIiEgGoWAnIiIikkEo2ImIiIhkEAp2IiIiIhmEgp2IiIhIBqFgJyIiIpJBKNiJiIiIZBAKdiIiIiIZhIKdiIiISAahYCciIiKSQSjYiYiIiGQQCnYiIiIiGYSCnYiIiEgGoWAnIiIikkEo2ImIiIhkEFnsXUB6YhgGAGFhYXauRERERDKLhNyRkEMeRsEuCa5fvw5A4cKF7VyJiIiIZDbXr1/H29v7oedYjMTEPwEgLi6OM2fO4OnpicViSZVrhIWFUbhwYU6dOoWXl1eqXCOz0M/SNvRztA39HG1HP0vb0M/RNtLi52gYBtevX6dAgQI4OT38Ljr12CWBk5MThQoVSpNreXl56X80G9HP0jb0c7QN/RxtRz9L29DP0TZS++f4qJ66BJo8ISIiIpJBKNiJiIiIZBAKdg7Gzc2NESNG4ObmZu9S0j39LG1DP0fb0M/RdvSztA39HG3D0X6OmjwhIiIikkGox05EREQkg1CwExEREckgFOxEREREMggFOwczbdo0ihcvjru7O1WqVOGXX36xd0npytixY6lWrRqenp7kzZuXVq1acfDgQXuXle6NHTsWi8XCwIED7V1KunT69Gm6dOlCrly58PDwoFKlSuzatcveZaUrMTExDB8+nOLFi5M1a1ZKlCjBqFGjiIuLs3dpDm/r1q20aNGCAgUKYLFYWLVq1V2vG4bByJEjKVCgAFmzZuXpp59m//799inWgT3s5xgdHc3bb7/NE088QbZs2ShQoABdu3blzJkzaV6ngp0DWbJkCQMHDmTYsGHs2bOHOnXq0KRJE06ePGnv0tKNn3/+mYCAAHbs2MH69euJiYmhUaNG3Lhxw96lpVtBQUHMnDmTChUq2LuUdOnq1as89dRTuLi4sHbtWkJCQvjkk0/w8fGxd2npyrhx45gxYwZTp07lwIEDfPzxx4wfP54pU6bYuzSHd+PGDSpWrMjUqVPv+/rHH3/MxIkTmTp1KkFBQeTPn5+GDRtat9EU08N+jjdv3mT37t2899577N69m5UrV3Lo0CFatmyZ9oUa4jCqV69u9O7d+65jZcuWNYYOHWqnitK/CxcuGIDx888/27uUdOn69etG6dKljfXr1xv16tUzBgwYYO+S0p23337bqF27tr3LSPeaNWtmvPLKK3cda926tdGlSxc7VZQ+AcY333xj/T4uLs7Inz+/8dFHH1mPRUREGN7e3saMGTPsUGH68N+f4/3s3LnTAIwTJ06kTVHx1GPnIKKioti1axeNGjW663ijRo3Yvn27napK/0JDQwHImTOnnStJnwICAmjWrBkNGjSwdynp1nfffUfVqlV58cUXyZs3L/7+/syaNcveZaU7tWvXZuPGjRw6dAiAvXv38uuvv9K0aVM7V5a+HTt2jHPnzt31d4+bmxv16tXT3z0pFBoaisViSfPeee0V6yAuXbpEbGws+fLlu+t4vnz5OHfunJ2qSt8Mw2Dw4MHUrl2b8uXL27ucdGfx4sXs3r2boKAge5eSrh09epTp06czePBg3n33XXbu3En//v1xc3Oja9eu9i4v3Xj77bcJDQ2lbNmyODs7Exsby+jRo+nYsaO9S0vXEv5+ud/fPSdOnLBHSRlCREQEQ4cOpVOnTmm+D6+CnYOxWCx3fW8Yxj3HJHH69u3Ln3/+ya+//mrvUtKdU6dOMWDAANatW4e7u7u9y0nX4uLiqFq1KmPGjAHA39+f/fv3M336dAW7JFiyZAkLFizg66+/ply5cgQHBzNw4EAKFChAt27d7F1euqe/e2wnOjqaDh06EBcXx7Rp09L8+gp2DiJ37tw4Ozvf0zt34cKFe/4lJY/Wr18/vvvuO7Zu3UqhQoXsXU66s2vXLi5cuECVKlWsx2JjY9m6dStTp04lMjISZ2dnO1aYfvj6+uLn53fXsccff5wVK1bYqaL0aciQIQwdOpQOHToA8MQTT3DixAnGjh2rYJcC+fPnB8yeO19fX+tx/d2TPNHR0bRr145jx46xadOmNO+tA82KdRiurq5UqVKF9evX33V8/fr11KpVy05VpT+GYdC3b19WrlzJpk2bKF68uL1LSpeeffZZ9u3bR3BwsPWratWqdO7cmeDgYIW6JHjqqafuWXLn0KFDFC1a1E4VpU83b97Eyenuv7KcnZ213EkKFS9enPz589/1d09UVBQ///yz/u5JooRQd/jwYTZs2ECuXLnsUod67BzI4MGDeemll6hatSo1a9Zk5syZnDx5kt69e9u7tHQjICCAr7/+mm+//RZPT09rD6i3tzdZs2a1c3Xph6en5z33JWbLlo1cuXLpfsUkGjRoELVq1WLMmDG0a9eOnTt3MnPmTGbOnGnv0tKVFi1aMHr0aIoUKUK5cuXYs2cPEydO5JVXXrF3aQ4vPDycf/75x/r9sWPHCA4OJmfOnBQpUoSBAwcyZswYSpcuTenSpRkzZgweHh506tTJjlU7nof9HAsUKEDbtm3ZvXs3q1evJjY21vr3T86cOXF1dU27QtN0Dq48UmBgoFG0aFHD1dXVqFy5spbpSCLgvl9ffPGFvUtL97TcSfJ9//33Rvny5Q03NzejbNmyxsyZM+1dUroTFhZmDBgwwChSpIjh7u5ulChRwhg2bJgRGRlp79Ic3ubNm+/7e7Fbt26GYZhLnowYMcLInz+/4ebmZtStW9fYt2+ffYt2QA/7OR47duyBf/9s3rw5Teu0GIZhpF2MFBEREZHUonvsRERERDIIBTsRERGRDELBTkRERCSDULATERERySAU7EREREQyCAU7ERERkQxCwU5EREQkg1CwExEREckgFOxEREREMggFOxGRVPbGG2/QokULe5chIpmAgp2ISCoLDg6mUqVK9i5DRDIBBTsRkVS2d+9e/P397V2GiGQCCnYiIqno1KlTXL582dpjd+3aNVq0aEGtWrU4e/asfYsTkQxHwU5EJBUFBwfj7e1N8eLF2bdvH9WqVcPX15ctW7bg6+tr7/JEJINRsBMRSUXBwcFUrFiRRYsWUbduXd58801mzpyJq6urvUsTkQzIYhiGYe8iREQyqjZt2rB582YAVq9eTa1atexckYhkZOqxExFJRcHBwbRp04aIiAiuXbtm73JEJINTj52ISCq5fv063t7e7Nq1i7179zJgwAC2b99OuXLl7F2aiGRQWexdgIhIRhUcHIyzszN+fn74+/uzf/9+WrRowc6dO8mdO7e9yxORDEhDsSIiqWTv3r2ULVsWNzc3AMaNG4efnx+tW7cmKirKztWJSEakoVgRERGRDEI9diIiIiIZhIKdiIiISAahYCciIiKSQSjYiYiIiGQQCnYiIiIiGYSCnYiIiEgGoWAnIiIikkEo2ImIiIhkEAp2IiIiIhmEgp2IiIhIBqFgJyIiIpJBKNiJiIiIZBD/B/WKb58nbW2iAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "VG2 = VisibilityGraph(time_series)\n", - "k_r2 = VG2.retarded_degree()\n", - "k_a2 = VG2.advanced_degree()\n", - "gkde_r2 = gaussian_kde(k_r2)\n", - "gkde_a2 = gaussian_kde(k_a2)\n", - "\n", - "fig = plt.figure()\n", - "x = np.linspace(0, 12, 5000)\n", - "ax = plt.plot(x, gkde_r2.evaluate(x), color=\"red\", label=\"retarded\")\n", - "ax = plt.plot(x, gkde_a2.evaluate(x), color=\"black\", label=\"advanced\")\n", - "ax = plt.xlabel(\"$k$\")\n", - "ax = plt.ylabel(\"$p(k)$\")\n", - "ax = plt.legend()\n", - "fig.tight_layout()\n", - "\n", - "fig = plt.figure()\n", - "x = np.linspace(0, 12, 5000)\n", - "ax = plt.plot(x, gkde_r2.evaluate(x), color=\"red\", label=\"retarded\")\n", - "ax = plt.plot(x, gkde_a2.evaluate(x), color=\"black\", label=\"advanced\")\n", - "ax = plt.xlabel(\"$k$\")\n", - "ax = plt.ylabel(\"$p(k)$\")\n", - "ax = plt.legend()\n", - "ax = plt.yscale(\"log\")\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "6f4688a4", - "metadata": {}, - "source": [ - "Here we already visually find a stronger divergence between retarded and advanced degree distribution, suggesting non-linearity of the underlying time series." - ] - }, - { - "cell_type": "markdown", - "id": "de88641b", - "metadata": {}, - "source": [ - "We appply the KS test again." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "12f6159a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "KstestResult(statistic=0.032, pvalue=0.011947718089521279)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ks_2samp(k_r2, k_a2)" - ] - }, - { - "cell_type": "markdown", - "id": "6a1d0870", - "metadata": {}, - "source": [ - "The result `pvalue=... < 0.050` suggests that the TS is time-irreversible." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/tutorial_recurrenceNetwork.ipynb b/notebooks/tutorial_recurrenceNetwork.ipynb deleted file mode 100644 index 0d823d11..00000000 --- a/notebooks/tutorial_recurrenceNetwork.ipynb +++ /dev/null @@ -1,1082 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "d35172b4", - "metadata": {}, - "source": [ - "# Tutorial: Recurrence Networks\n", - "\n", - "This tutorial aims to introduce the analysis of non-linear timeseries with a complex network approach. Its application in the __pyunicorn__ package is illustrated in two examples to help understand the basic use of the `pyunicorn.timeseries.RecurrencePlot` as well as `pyunicorn.timeseries.RecurrenceNetwork` classes." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "bcbb7d28", - "metadata": {}, - "source": [ - "## 1. Introduction\n", - "\n", - "### 1.1 Recurrence plots as an approach to analyze complex systems\n", - "\n", - "So far, analysis of complex networks in different scientific fields, has been performed by the study of the adjacency matrix $A_{i,j}$. Recent work has been focussing on studying time series using a similar approach by tranforming the time series in a complex network and analyze the phase space and corresponding properties from the **Recurrence plot (RP)**. \n", - "\n", - "The recurrence matrix can be considered as the adjacency matrix of an undirected, unweighted network. With this approach it is possible to characterise the local and global properties of a network. \n", - "In particular, this approach can be applied (i) to both univariate and multivariate time series (phase space trajectories), (ii) with and without pronounced oscillatory components and (iii) with as well as without embedding. Moreover, similar to traditional Recurrence Quantitative Analysis (RQA), studying network properties for sliding windows in time also allows for coping with non-stationary time series. Consequently, unlike for most of the existing techniques, there are no fundamental restrictions with respect to its practical applicability to arbitrary time series.\n", - "\n", - "For more detailed background information consult [Marwan et al. (2009)](https://arxiv.org/abs/0907.3368).\n", - " \n", - "\n", - "### 1.2 Visualization of a timeseries and its recurrence network\n", - "\n", - "Before we get started, we shall visualize a timeseries based on a three dimensional chaotic oscillator example described by the Lorenz system defined by:\n", - "\n", - "$$\\frac{d}{dt}(x,y,z)=(10(y-x),x(28-z)-y,xy-\\frac{8}{3}z)$$\n", - "\n", - "The following figures graphically represents the timeseries in phase space and what its respective recurrent plot looks like. \n", - "\n", - "![Recurrence Plot](img/REcurrencePlot_v2.png)\n", - "\n", - "__(A)__ A state at time *i* (red dot) is recurrent at another time *j* (black dot) when the phase space trajectory visits its close neighborhood (gray circle). This is marked by value 1 in the recurrence matrix at *(i, j)*. States outside of this neighborhood (small red circle) are marked with 0 in the recurrence matrix. __(B)__ Graphical representation of the corresponding recurrence matrix (recurrence plot) and adjacency matrix (modulo main diagonal).\n", - "\n", - "For literature review and background information see [Donner et al. (2010b)](https://www.researchgate.net/publication/47557940_Recurrence-based_time_series_analysis_by_means_of_complex_networkmethods) .\n", - "\n", - "\n", - "### 1.3 Correspondence between recurrence network and phase space measurements\n", - "\n", - "The following table shows how some of the correponding characteristics measured from a recurrence network translate into phase space features of the time series.\n", - "\n", - "For more background information consult [Donges et al. (2012)](https://www.researchgate.net/publication/225288426_Analytical_framework_for_recurrence_network_analysis_of_time_series)\n", - "\n", - "\n", - "| Scale | Recurrence Network | Phase space interpretation |\n", - "| :--- | :--- | :--- |\n", - "| Local | Continuous local $\\epsilon$-clustering coefficient *C*
Continuous $\\epsilon$-matching index | Local dimension
Local density gradient | \n", - "| Global | Continuous $\\epsilon$-transitivity
Continuous global $\\epsilon$-clustering
| Global dimension
Average local dimension | \n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ec852748", - "metadata": {}, - "source": [ - "## 2. Example: Logistic Map\n", - "\n", - "_Calculating properties of a time series through recurrence network analysis using a toy example._\n", - "\n", - "First we import the necessary packages:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b8e8e60f", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from pyunicorn.timeseries import RecurrenceNetwork\n", - "from pyunicorn.timeseries import RecurrencePlot" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e4994e9a", - "metadata": {}, - "source": [ - "We create a function to calculate a logistic map timeseries:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "39494417", - "metadata": {}, - "outputs": [], - "source": [ - "def logistic_map(x0, r, T, spinup=100):\n", - " \"\"\"\n", - " Returns a time series of length T using the logistic map\n", - " x_(n+1) = r*x_n(1-x_n) at parameter r and using the initial condition x0.\n", - "\n", - " INPUT: x0 - Initial condition, 0 <= x0 <= 1\n", - " r - Bifurcation parameter, 0 <= r <= 4\n", - " T - length of the desired time series\n", - " spinup - number of spinup-timesteps before storing results to output\n", - " OUTPUT: numpy array of timeseries under given parameters\n", - " \"\"\"\n", - " # spinup\n", - " for n in range(spinup):\n", - " x0 = r * x0 * (1 - x0)\n", - "\n", - " # Initialize timeSeries as python list with initial condition as first entry\n", - " timeseries = [x0]\n", - "\n", - " for n in range(T):\n", - " # get current timestep value\n", - " xn = timeseries[n]\n", - " # calculate next timestep value\n", - " xstep = r * xn * (1 - xn)\n", - " # append new timestep value to timeSeries\n", - " timeseries.append(xstep)\n", - "\n", - " return np.array(timeseries)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9c841c22", - "metadata": {}, - "source": [ - "After choosing the bifurcation parameter $r$, the initial value $x_0$ and the length of the timeseries $T$, we can now generate a timeseries. Play around with the parameters if you like! For an interesting regime, we should stick with $r >3.6$. \n", - "\n", - "(Note: Our logistic_map function includes a spinup of 100 timesteps by default, so the resulting timeseries will not start at $x_0$.)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4e4391fc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$x_n$')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Parameters of logistic map\n", - "r = 3.679 # Bifurcation parameter\n", - "x0 = 0.7 # Initial value\n", - "\n", - "# Length of the time series\n", - "T = 200\n", - "\n", - "#Generate the timeseries\n", - "time_series = logistic_map(x0, r, T)\n", - "\n", - "# Plot the time series\n", - "plt.figure(figsize=(14, 4), dpi=80)\n", - "plt.plot(time_series, \"r\")\n", - "plt.xlabel(\"$n$\")\n", - "plt.ylabel(\"$x_n$\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ca7ab08c", - "metadata": {}, - "source": [ - "### 2.1 Recurrence properties under fixed recurrence threshold $\\epsilon$\n", - "\n", - "Now we can create a recurrence plot from the created time series using the RecurrenePlot Class from pyunicorn. Following [Marwan et al,. 2009](https://arxiv.org/abs/0907.3368), we choose a fixed recurrence threshold $\\epsilon$ that is 5% of the time series' standard deviation. For that purpose we set the parameter 'threshold_std'. (Note: For a one-dimensional timeseries we do not use embedding.)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ad52fdac", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating recurrence plot at fixed threshold in units of time series STD...\n", - "Calculating recurrence plot at fixed threshold...\n", - "Calculating the supremum distance matrix...\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "EPS_std = 0.05 # Fixed recurrence threshold in units of the time series' standard deviation\n", - "\n", - "# Default distance metric in phase space: \"supremum\"\n", - "# Can also be set to \"euclidean\" or \"manhattan\".\n", - "METRIC = \"supremum\"\n", - "\n", - "rp = RecurrencePlot(time_series, metric=METRIC,\n", - " normalize=False, threshold_std=EPS_std)\n", - "\n", - "plt.matshow(rp.recurrence_matrix())\n", - "plt.xlabel(\"$n$\")\n", - "plt.ylabel(\"$n$\")\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "61d57bf6", - "metadata": {}, - "source": [ - "Some of the main properties of a recurrence plot can be easily extracted by applying the `rqa_summary()` method:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4d22a95e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'RR': 0.05061755897131259, 'DET': 0.7494577006466949, 'L': 3.971264367701975, 'LAM': 0.10958904109535426}\n" - ] - } - ], - "source": [ - "print(rp.rqa_summary())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8ef17b28", - "metadata": {}, - "source": [ - "#### Network calculation and properties\n", - "\n", - "Now we can use the recurrence properties, which mimic the phase space properties of the time series to calculate quantitative characteristics of the time series implicitly. We shall distinghuish here between local, intermediate and global properties. To construct the complex network we use the RecurrenceNetwork Class combining the RecurrencePlot and Network (see Class Network from pyunicorn/core) charactistics. For more information on the individual properties see [Donner et al. (2010a)](https://iopscience.iop.org/article/10.1088/1367-2630/12/3/033025/meta). \n", - "\n", - "The main focus will lie on determining the local and global clustering coefficient as well as the network transitivity. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "6b5a9d37", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating recurrence plot at fixed threshold in units of time series STD...\n", - "Calculating recurrence plot at fixed threshold...\n", - "Calculating the supremum distance matrix...\n" - ] - } - ], - "source": [ - "rn = RecurrenceNetwork(time_series, metric=METRIC,\n", - " normalize=False, threshold_std=EPS_std)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8c74cd38", - "metadata": {}, - "source": [ - "##### Local scale network properties: Local Clustering Coefficient\n", - "\n", - "The local scale network properties consider only the direct neighbourhood within a defined $\\epsilon$-ball of a vertex *v*.\n", - "\n", - "The __local clustering coefficient__ of a vertex $v$, $C_v$, characterizes the density of connections in the direct neighbourhood of this vertex in terms of the density of connections between all vertices that are incident with $v$.\n", - "\n", - "We consider the clustering coefficient by Watts and Strogatz:\n", - "\n", - "$$C_v=\\frac{2}{k_v(k_v-1)}N^\\Delta_v$$\n", - "\n", - "whereby $N^\\Delta_v$ represents the number of closed triangles including vertex $v$ and $k_v$ stands for the local recurrence rate around a vertex $v$." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "72890ded", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating local clustering coefficients...\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, '$C_v$')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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d5D7xQufEww4T5xMIf22yIj7jOp95EJ/ldD6tc2+QsXrVKu/303jt7rvj79Xeu1fmWopPEohqrHgb95gVKwsXyiChE4sKUXVhjjlGjqVw2+AdBeuKVbWJTxVwdXXe4vPYY+XRKuDUYZ85U+673t5k024GB8VlOvLI4rY6td3aVmVoSNqjAYGT+JwwQRxzIPk+o/f1KafIYxris7VVzl8DojtkSQUMO3bIuYu6b3vyZDPlOA6GAfz+9/L/gYFgTl2a4lN//9veJivkaYnPtjYpkhbH+aw28Zmk87l9u//v27gRmDix+CiBNIPNOOg8M3KkjMdJz2NWnntOFnNnz3Z3Bq3Pa/+sJpISnzqvaNotEL7PVvKolaDO58SJ7u+RJ/Fpdz4nTZK+kLbzGWSs9ku91Rhox474qbdVfsYnQPFZXvSsz2oSn3GPWbGyYIEEq7pya3U+AVNEJbnXqZrF55YtIt5mzAgmPq3Op3XSTeO+275dBKReO7vzqV/PnFncVmX3brkX2tuBKVNKxafVtQXScT5Hjzbbn3TRoZ07Jd3p8MNF5EV1yHp7k6louWNH8f6QQkGC17jibMUKudatrfJ1EJclzYJDOl5NnSpuR1p7PseMkX735pvOxbbcCJrKdfLJ8pgl8VnugkMbNxan3ALZFZ8qlkaONEVOWnt2n3tOFv2GDXMfG6ziUzMTqgn9PIMsUnihc+LMmVKsasaM6nU+u7tLs5d0Hmtudn8PHffzuOczqXnMiaTHamsMFLdPUnySUKQVSKdJks6nBvraSZ99VlKqxo2Tr6OmxXhR7Wm3kyaJA+g0cXg5n1bxqQFckved3hcdHTKpu4lPdUbt7devx44VkWBt+7598vMzZ5rFRtJwPtvaJN27vT1551OLAdTXi7O/YkXwtOeDB4tLxyfhoKjzaWX2bLkn4uyF1klUq1UHqf6qf8/Bg85putpP+/rCF2qyunNz5iR/3IpVfEYZr4Kspjc2yr3Z2Zm8gHn1VSnkontjw6Cf7YQJ7t+TlPgcGJDtKdUiPjUQHTHCHI/TKDrU0yP/jj5a7hM359MqSu+/P7tnozphGObnmaTzCUifXb3aFJRByIL4HD9exkv7mBGkHkeenU8gmXnMiSBjdWurzDVTp/qP1Tp3tLQAd94Zr236XhSfJBDVmHabpPNprXh78KBU3tTngOhpMV5Uu/PZ3i6iwcv5PPJICUTcxGcazqf1vmhv9xef9vbr12PHShu3bjUDJC1cM3OmeURRGs6n3tPz5onzmeSeWD0AGpD7eseO4AV57EFCEivWO3eWis85c0SYBW2XE3fdJe/7sY/J10HEp7VPOp3PZu2nYYJEoHhf4uzZEpAkuefNuuIcZbyyBjFuq+kjR8qK/oIFcl8mGVT94Q8S+PzhD+F/trtbgmCvc7cbG6XPeonPujp/UfHGG3Jv6uKTMm6cCP+sis+RI83ieWns+9SsoaOPliDcz/mcPFnm2nvvTb4taXHggLkoFdf5XLdOBIIKtGOPlfvKb3+elSyITy2sY0+9DXISQZ7Ep935BGScjzuPORHE+Rw5Uv6/YIEsangVyNKY55xzxKl9+eX4baP4JIEYN07y02vV+Wxvl+Bl5UoJqvr7TTcUMF0wOp8ihN54wxSfvb2lA5sOZuPHi0tiFZ/WQgtpOp9tbab4tIq3oOJzzBhzVVrbrH/HYYfJY9LVegcHpZKiVXzu3ZvsmZCadguYTkjQ+9oapFu/jooWfHJyPoHogfzrr8vf9L73yf0HhHM+Aec+aX0ubOqt9b5MoxKi1fmcP1+c7aSdz1Gj5P8LFkiwtXp11NaWosF8mOBbCVr1fPRob/E5fbq/+HSqdAukm2YXB2vabRrzmGIVn17Opz7/oQ/JY1ynpZxY5+wkxOdhh5l73aNkK2RBfHZ1yaN1T/3AgMxjQZ3PPKTdujmfQPILUmHH6oMHvbfv7Nghc7pmCcXpkxSfJBSFQnrHXqRFks6nruY/95w5+FvFJyCTw0svRT9g3k61Op/bt8tAO2WKKRrcUldHjxYBt369KQDXrZMiBCNGSLBXKKTnfE6ZIhO09bN+4w0JxLRSpZ/zqW22PurzM2aImE3qQPVt2+Rz0kl77lx5TGrf58GDcq+p+AzrkOnEomdJxg0arJ+1lbiTtrpnZ58taUdAeOczafHZ3S33fGurKT6TFCrWPtfcLIsrYZzPMKvpmhWSZOptHPEZ9LxnL/HZ1CTjRVTxCch9u2VLcnNEEljTbgFZcEpyHlOCOp/6/OzZskjyhz8Eq0SdBaziM07a7eCgzHk6jwDRshWyID6dnM/t22Ue8+uTI0aI6Mmz8wmkIz51ATiI8wl4j9Vad+Ed7xATKs6+T72WPOeTBGbGDJlYkz72Ii26u0W4eO3zCcPChTIh33GH+bUVTYtJqtBGtTqf6hyq8wk4C7jRo8V9mTlT/j6dnPRgbUAKU0yZkq7zaW2z/t+v7YCz+LQWiQDMI4qScibtCyp6/EhS+z7tE8MRR4hQCbrargJH06XjBg1pic+77pIA+D3vkVTL8eOTdz7D7lWzCiT9+5IWnyNGmKvvxx4rf3PQ8zh37TLbF2Q1HUi26JCKz2efDTcH9feLEIjrfI4eLRlAfqJCU6XtabeAGYjbi5RVEmvaLSAiJ8l5THnuObl/Jk4M5nw2Nsri0NatwOOPJ9uWtEjK+Xz9dfkcrOJT9+eFcT51AeHAgWT3jwfBy/kMcvQRIAJq1Kh8iM/+/vKKzylT5P9+Y7V1S5kbWnehuRl497ul4m3U+5vOJwlNR4fsc4pS8KES9PRIUGlPdYiKBlT33COrP7NmFb+edNGhanU+g4pPfc0q4Pr6pAqnddJN2nG37/m0tln/395uCjAv8anptVbns6HBdE2TThu2p5Kr85mU+LSmZgLyt8yfH7zokE4sKj7Tcj7b22UijDJp9/UB990nZeb175w+3V98WouJAOmk3ep1nTEj+eNWrOnUgDlePfNMsJ/ftcu8r/1W0zs75f9JChgVfdu2OR/d4IbOV3GdTxWf+/d7j8d+zieQrX2fKpiszieQbP2CwUHg+efF9QSC7flsaBDxCVRP1Vtrv4jjfNozaJRjjpHP0Wm/uRPWfedBfyYpvJzPIEcfKW59stoYGCiNRePMY24Yhoz1uvhlH6t1HtOxevZsWYANIj4B6ZNDQ3LmZxQoPkloqq3ibdBUq6Co+BwYkEm0znYLJl10qBbFp905BOS+e/PN5CbPnh5xVEePLhWf+/bJwB3U+Wxvl/eyik8VDtp2IDnxbHc+p0yRFcyk0m6dKtEde6wE8EGcQZ1YNPBOyvm0p+jU1cniTxRxdv/9ci9pYAuIqNq82dsdOHCg2K1JUnwODclnrNe1oUH6QJJBiQooJYzIGBqS1XJNUbavpg8MSKCrq+l1dbJosXJlcpky1pX2MKm3QV0WQD6fPXtK0zyt4hPwFhYbN4rDMXly6WtZFJ9OzieQ7L7Pdeukb6j4bGyU+8IpnVZFaWOjnHk7aVL1iM+knE838XnssfL5PP98sPexis9yxxC7dskWAh0znJzPIPHZmDH52fNpdz7r6pLfB75/v/wuXSi0j9U6j+lYXV8v/dJrrLbGa+9/v2QURu2TFJ8kNOWqeLtjRzJCI2iRiaDMm2cOHvaUW0DSiaZPT27Srta0Wz07cMoUb/dQBzOre2gv2AOY912SqattbTKAqvjUNutje7u42w0N3uKzrs48bsUwzCIR9ran5XwWCuJ+puV8AuGC0XI5n4BM2hs2hN9Pq5OmXXz293tndWiQrgVAkky7festCcStwZget5LUfje787lggdy/Qa7r3r1yf48bJwGlfTVd/1YNaAAZI3fu9K7Yu3lzcHG6fbu54Kf7B4MQ1mUBSv++MOJz0yYJuO2Lk0Cy4tPJfY6CteAQINtUOjqSFZ96vebPl0ddnHNyP61pt3V1wFlnSXX5rKQqHzjgfv3TFp9hFwYqLT5Hj5bss7o6Z+czTip8teHkfALR5zE39LOaNEn6kH2c0K/tY/W2bc7nPg8MyH2tc3BbG3DiicAf/yg1IqK2j+KTBKZczuf8+cD/8//Ee48DByTwSdL5HDZM9sABpcWGlGOPlYky7DELTuzZI6tSQHWJTz/nc2hIBqAwzieQ3KKHdVFC90Vom/VxyhQRGU5HxdgF0cyZMnn09EiQnmbbnVaM582T552OtAmLk/hUh+ypp/x/3u58pi0+tTBHGB54QH5WhQBgrhJ7LXDoCrLuIU/S+XRy52bPlsk9qeNW7OKztVXunTDXdfRoCVrsq+n6tQoYwH/f56pV0j9+9rMgrZdgXo8CSdP5BIqD3aEhGYvDOJ9OKbeALFCOHBlffD74oLTnb3+L9z5AacEhQD7nF16IFlw6YS02BJiLuE4BtzXtFgA+8AF5zIr7+a1vAYcf7vzZJFVwSMWnVuJWVHwGSWfv7/fP1EgTFZ/19TJmRnU+VXxWS60RN5ycTyD6POZG0mO19Vxz5QMfkHEjyvizc6d8Ds3N4X82I1B8lptyOJ9DQxIAxjlHCDAdjCSdT8B0PJ2cT0BWhAYHgwV0fvT2ege6WWXLFplwJk50Fp86kehrGtStX++84puke2gYxenYmhpnF5/qiLqJz+HDzYnksMPEqf/730vbrk5RksIZKJ60k6x461SJ7qij5G949NHgP59UwSGniU+JehzJG2+UOgpBxKcG6TqmJCk+na5rksetHDgg96g9ffnEE+XetJ91a8ca0IwcGWw1XQMatz2l994r4/2f/uTf/oMH5XfMni3XKoz4jOJ82iv7GkYw8bl3r7zmJj71uJW41/Tmm6VNQffremFPuwVkTBscdHZCovDcc+J+6eKtl/NpTbsFgCVLzPfIAhs2iEvktNin4rO5OZ7zuX69zE2trcXPT54s91CQImH28alS4hOQMTOO89nfX10xkBNezieQXCp+nLHaqeKt09aXE06Qxygxh94XmkFUhVB8lhtNJUrT+dSVurhBa5LHrFj57GeBf/on0w2ys3ixPD7ySLzfYxgykXkFulllyxaZJOvrnY9acXKzZs40026tBXuAZN3D3l4JwvVzHTZMRLKX+HQ6JsbedkCK2Fi/BmSA1SrRSdDdLUc+WANFrXibhPh0cj4bG4Hjjwcee8y9SIii/VbHirSdTyDcpL13r/yzBz1hnE8dU9zEp7Y1rvOZ5HErbuXtdbzShRO/nw+zmj5/vtyrbu+tzz/yiL+roWJv/Hh5X79D0a3EdT6tf7uf+PSqdKvMni3fF3VriWFI0TsgGXHY2yvjlFXo6D6911+P//6ACEctbAKEcz7HjRNXNivHvHnFKBrYd3TIWBB13rZWfLdSXy99OIiwtWdfVVJ8TppU6nw2NRULIDd0zKr21Fsv5xNIR3wGHau1DU5ZNk5zsM6BYQq/WdtXxSm3AMVn+WlokEkpzUkgKfEZZmUtDMcfD/zoR86DCAAcd5y85hfM+dHXJ0HGxInydTWJzzfeKBZvQPEqsZv4fP11CSo7O810YyBZ59NpUaK9PbzzGVR8AtL+pI4o6umRtltXDZOseOtUcAgQkbJ3r7/7sGuXtG3UqGRK5CctPt0yIlQsBHE+3cSnYYig0GyFMHs+ndy5JI9b8bquQHDxqdc1yGp6c7OskD/ySKnIMAxzgW7LFv++bRefBw8Cr7zi/TNKXOfTGsyNH1/cHjtelW6V2bPl79ctBmF5+WVx3wApxBaXPXskk8O6RzVJ8WkY8rlYx0WdP/32fALmAp7+zZVG2+d0hIX2eb3+UVJve3vlnnUSn0Cw434Ac/Fr+HB5LGcMMTAgv1+FY1ub9CNdcLHWXfDDqU9WG4ZRGecz6Fg9fry7o+40B+v8GfSYLnv7qviMT4DiszLMmFEd4jMt59OPlhbZl/H3v8c7VytIil8WGRoS8al7KUeNkkEtiPg0DCmwYp90x4yR90nivnMKRKdMkTYbRnGxJG1jX1/x/h438anBsL39M2bI9du2LX77ddK2MmuWBGpJik/75KCpb34iZedOuVZ1dclUKdyxQxYirPvRlGnTxLkOM2m7LUppsO21v1KDTbc+qYGVis+4zmdHh1zXJMWn/boefrgEs36ZGvZUrt27ixdTnFbTAeC00ySYthdIWb+++F72u6/U6VHxCQRPve3uFldPg3Av4jqfQcUnED3YVNcTSMb5tB67oGh/8EvHDsLevdI3dCEVMINwJ+fTnnYLyILkxo3lP6vSCa8YRcWnZutESb11qntgZfz4cM6nLpiUM4bQ8cDqfALm4p8uogZB36OaK95q0Tgn0yLKPOaF01htxWmsbmiQ+ySo+Bw3TuZlOp+kbMyYIQNf2DPsgpK0+Eza+QzC4sUSnARdmXdCxWe1OZ/btknwoM6hihAn8WkNhK0VYq3/V9Q9jIvTfdHeLp/vrl0SbI0ebaag2Z1bw5D/u7V99OhSly6pvdL2/apKQ0P0Y0fsuKVnnniiPAYRKTqxJFGlUD9rpxXy+vrwx5G4ic/hw+W6xXE+9Wvts1HEp/XaJnncipv4LBRkvFqxwnuMsQaTo0ZJHz9wwHzdaTUdAE49VR4ffLD4eb2P/ut/Lf7ajbjiM2ygm2babdx06nvukZTF9vbk0m7tiztJOp+66GYVn0GcT6tLNGOGLAAm4fTGJW3n06niu5Wgzmclxae9oqmOt93d5jwWNDbLQ9qt3udOzmeUecwLu/O5b19xP3Mbq9vagovPujr37/fCMKTfUHyS0CR9dIQdHdj3749XaS9MqlXSqEsUZ9+nTmLVJj7taatAaeqqm/Pp9H9F903GXfl2S7sFpO1btpS23drm3l5ZxbS2fdQoc4J3azsQv8/s3i19wmnSnjTJ+5iQoOzcKYGtvRLduHGytzRIeqZOLEk5n04pt8rs2RKs+e1FVbzS8fWsTzf8nE/9Oorz2dMjAbldHOrfF/e4FTfxCYj4HBgAnnzS/eftq+lAcfDt5nyeeKL8XXbxqffR+efLvRvG+ezqEqcgqPgMc+SWn/jUQhmVcj737pXP8tRTRZwklXZrv246BiYhPnVcCup82tNuAbPqaxb2fWbB+ezr898znCXxqfNtT485j8VZEKo2nO5pK2HnMS+sWyy0X1tTb93Gajcx6Vb0z15EKgi9vRLDUXyS0KR93Ip1Mooz2FTS+TzpJHmMs+9TB4vRoyXQoviUCStKmocVJ/HhJT7t55S67UHUNnuJz7iBk1cq+cSJ0ra4k9fOne4Tw5Il8jd4BaROzmecva5BxGd/f/DjSIKIT7f2BnU+NdgLs+fTbQ/UnDly38cdb73OVguSUm1fTQeKAxq31fTWVtkn//DDxQL673+XfjZjhojfZ5/1/rw0iB83TgK4I44IJj6HhqKl+LmJTy344iU+R43yDq60imkU8fnAA3I/vPe9sjVg69b45wM6ic+WFul3SYpPXZQBvJ1Pp7RbHUOzsO/Ty/ncs0fma922Ecf59NrzGeS9VXxWomK+m/js7g6/JSoP4tPL+QTCz2NeRB2r29pkvrWbPm4xz6RJ4eOxHJzxCVB8Voa0j1tJSnz29Egqkb1UeTlob5eV2jjiUwOxESMkEKgW8WnfMwkEE5/Tp5tFhpwm3aQcdy/nc+1aCSi8nM8o4jOptnsJpwkTzJTgONjPgrQSpDiN3fkcGIh35q29srCdsMeReB3BNG2apJK6uRVBnc/hw6XPhnU+nYIxdXziBiVezudxx0lQFFR8hnE+AXHpdu82y/jv2iWFq5YsEbG9ZImIxMcfd//9VucTkPMiN2/2v9937pR7MAnnUz87r7THTZu8U26BeMet6H5PFZ9A/AU5p7RbQFJv00q7DeJ8WgP1anI+R4wwBWIU53PdOhGw1nnIivYBv/fOkvNpTbsNWwxS+1017/n0cz6TPFbLOl6FGav1ethrU7jFPG1tZvX4oLgVvqsyKD4rQTU5n5VIuVWWLJGjL6Ke9WU9e62axKeb87l7t+l8OKVxNDaaIi1N97CnR4I/6yq8BnFPP138tbWNccSnHjuSpvOpf0/c1FuvSnR+DtmBA/LP6nzqe0ahv1+COT/nEwg+aWvgYw2EFb+Kt35HrejXLS0iQINOyl57oPS5uNfVS3y2tppF0txcX3u1W8A5oHE6OsG+7/Pxx+X36GJGkEUNu/jUfZ9+1ZeTcFnsgbSb+NSqrl4pt8rs2TIehNlaokesHHaYpB7rGcVxUm/7+6XPOi0aqPiMW6XbKe02TLVboHqcTxWfQQUiIPutb7jB/Pfss3KN61xC3KDOp44/WRCf1rRbOp+lJFnxdtcuWcgfPjzcWO1WwVZjHvv3W69pmLYBFJ8kAuV0PuOsdIXZ55MGGlA9+mi0n1fnMy/iEzCvp1PBIQBYuFCCDKcAOUnnc/z44klA2/rUU8VfA8HF58KF8qiHNVvRI4qScj7d0m6B+BV1vZzPOXPks3Pby2yfWOKuWLvtNbESRXy6ZUT4nfXpVwTMLj6Dpt3u3Ss/63Vdo5S0t+IlPgEZr7Zvdy+SpgFNa6vzPiLrYpnTe9fXA3/9q3yt948uZhxzjOwz9toj/9ZbEozrvRW06FBYl2XYMNnvHEV8bt0qQi6o+BwaCiemXnlF9gO+972ygKaLZHGKDlnnGTtTp5qF2OLglHYbttptW5tcl2pzPoOk3Z59NvDpT5v/Nm8GjjzS/ftrzfnMg/gMsucTSE586ikDXmO1PdvBS3zqlgMrFJ+krOjAmmXnc2hIJrxKO59A9KJD1gGi2sRnQ0NxoOEk4EaOLF0FXL7c3f1I0vm0T3p6bubzz8vXUcTnRz8KvPCCud/XThJHFHntY9bPO474HBiQ+85NoGhl1GeecU6ltacnxg0avM74VDo65D4KIz6dXE/AFJ9uKa67d5sptYVCcs6n13XVtibhaAPuk74ulrmNV1qhUM9w1eesrw8bJiLSzogRwNveBjz0kGQ//P3v8hnpgk1Tk6T+Pvqoe0Gx7dtl3lE3KKj4jHLklr1Ks5P47O0tdS2DVLpVogSb1pRbwBSfcZxPt0AUSK7ibRLVbrN01mcQ8TlqlLTfTyDu2SNz5rveBfzxj+a/G25w/5mwez6zID6bm+UzieN8VnParZ/zGXYe88K69cVtrB4+vFRMeolPpznYuqAQpm0Az/kkEenoyPaez7fekiCnks7nUUfJJBR132e1pt3qGZ/WlCGnoj1Og9nYse77XCZPlsE5CefTPuk1Nsq9ohNEFPFZKEgRFDc6OiQIi7P/0cv5TEJ82s9mc2LJEvfKqPb9HHGdTzeH3EpDg6SohRGfbuNCEOdTV5Sbm73F54gRwcWn13VNMu22udlZHAL+KdXWgMZtNd3JPVNOPVXasHIl8NhjIkatLsDixXL/rV7t/PPbt5tBNCCf1cSJwZ3PuOKzqcn87DT4t+83DVLpVokqPocNA04/Xb7WtNs4zqeXY51UxdutW81CTUrYareA7Pt87bX4acBx8Uu7HTlSxoggR6JoZduTTgLe8x7zn9eCWzU6n4BZHTVsMciWFrkXgsSDr7zifF0qjZ/zGXYe8yLqWB1WfNL5JGWno0MCtDQOfE5CfEZZ7U6a+no5ZuCJJ6IdGVOtBYe2bCneMwk4p916Ta5O1NfLfffyy9HbdvCgtMFp0rO22WnPp7Y9SCqoE1owI87Zr93dIuqtQbiSxJ5Pv9RMwHt/nn1iKYfzCcgZp6++6h+UGkY88bl7tzlpO/XJNJxPva5JpN16XVetPOuVUu23mu6031PRfZ/XXy9jm4pdxS9TRJ1PpVAQ9/O557znoShVz0ePLl4wsVeAdnOewojPWbPk8dVXg7Wpr888YmX4cHkuybTbNJ3PrVvlPrZWcg5b7RaQ+3PfvmSOlIqDm/NpGBLY62c5bpy/QPSrbOtENTqfgMRjPT3OdRe8KBSCnRnd1ycp/F//evB2Pvlk/GrRQfBzPoHg85gfUcfqcjqfFJ8kEqNHi7Pod85UFPzE5+AgcMEFkpriRiWPWbGyZIl8RlrlMQzV6HwODkoKmN29dHIPw4o3AHjHO0R8vvhitPZ5uSDWNlvF58iRInz9nE8/3vlOefy//zfcz1np7pYJ254uAySz5zNISsxxx0lQGER8JuV8+n3WWqU2buXTkSNlUvZzPoFg4jPonk+v+7KxUf7+JJxPv1QnryJpcZ3PJUtk4eTXv5avdRFD8TqeyjBKnU9AxGdfn7dbkFTabRDxGSbtVl3LoIsKjzwi9/iZZ5rP6ViQRNqt255PIJm0W3uqe9hqt0B2Kt66OZ8HDsgcqOJz/Hh/8anO52GHBf/9YZ3PSh61YhU5bW0yjr3xhvs85oZ9QciJN96QBb+gC7yrVskxUF/4QvB2RMXP+QRk3PCqth4EwyheLIvrfA4NyX3u5XxSfJKyocU64qQQuuEnPjdsAG68EfjsZ93PNIySapUGfvuovKhG53PrVpl8vcTn0JD/8RlufOxj8njrrdHa51XoQNs8dqx83kqhIEG7XXyG3bNwyinye2+5JfrKppdrpwFJHPEZxPlsaTEro9odp0o5n7pYoMWu3AhS6GL69GSczxEjxGkPcu6q32LZxInpO5+Ae5E0wzCLWADRnM9Ro+S+0c/Dvje6rU0KWjmNlb29Mi/Yxaemny5f7v57e3okyA0z3oweLQGaVucOKj43bpTxQkWbF8OGyfUIel2feUYejz/efK6+Xj63tNJuk3Q+7eIzbLVbIDsVb92cT7uLrGm3XuN9FOdz1ChZyAnqfOr9Ws4YYudOiROt13DSJJkzXnwxfGw2Zoz/PKLjaNDFmGeflcef/MSsdJ8WQZxP63njUdm3T35XFOdz1CjZWmAdk/ScbqfxM0oxPIpPEotKik9dFVq/HrjpJuf3yIrzeeKJEoxE2fe5Z49MMC0t8nkPDAQLZCuJBkFe4nPPHpmAomw4P/10CUBvuSVa+7xcEG2z055T6zmlO3bI3rnm5nC/u6EBOPdcmXhfeCHczypexwe1tsq/JNJu/SaGxYsl8LGvMFei2i1gXjO/IDyI+Jw2TcSnPWDUlLowzicQLPXWz51TxyAOdgHlhFtKtT2gcVtN9xKfgJl6O3euc+r44sWSdmZfSdcg2/4z73+/VAX90Y/cA3E9wsbt2Aon9O/Uvy+M+Jw82X1frR3dAxcEzZ7RQkvKlCnppd1OnCjiIU4w3N8v/dieYhm22i2QfefT/lmOH28eF+WG35meTtTVBUvp1bGntdV5j3qaOI03Ou66bX3xIkjarfaloP1Bt/AYBnDllelsI1OCOJ9JLPbYs5fsY7V9HrNSKMh1sYpJr8X2YcNkbg7jfPKcTxKLLIhPAPiXf3EeMLLifI4aJYehRxGfWjWvUDCduKy7nxqkuO353LEjetoqYAq4F16IJuCCpN0GEZ9R2g6Yzm0U8bx/vwQ7XpP2xInpO5+Ae3GaSjmfQVeMVcD5ic++vtIU3v37JSgOs+cTCCY+vc4e1ee3bYseHA0MyHjid12PPtq5SJr9umpwrcH3wID87V5ptwBw2mnyaE+5VdycV/sZn0pdHfCVr8jf9sMfOr9nlCO3rPft0JAEa0HFZ5CUW8Ue6HmxcqXsCbMHjVOmiNMTNZvCy/msq5P3jxMM67WL4nzaXaKsOZ+6kKrYj63R+9XLoVy3zvtMTzeCFDPq65PPubGx/NlTTuLTOu+Gjc1Gj5bxJsj+7q1bgy3Uv/yyxFef+YycPfyLX4RrUxiCOJ9Jik/7QqGO1fZ5zI6b+HSbg3Ufb5j26XFWVQzFZ6WopPjU4PrII8VF+u1vS78nCwWHlPnzJSgOuv9LseblV5v49HI+44hPAPj4x+UxSuqtlyOugtkunIHStNuobX/728UZiZJ6G2RBZcKE9Pd8AuZZpvbKpG7iM+09n0mm3boVHdIgPazzGaTfO509a2XiRAm6gpwZ6ETQ69rQIFW6X3qp+Hl7FWQ9wFw/E/tn48YZZwCXXgp87nPOry9aJI/2+8pNfAIyHsycCfzgB8VOrOKVLeCGVXzu2SN91U989vSI4zJ3bvDfM2mS9FdN73Wjr08CZT2axsrkyZLe7bff2Q0v8QnIWB4nGNYFnyh7Pu0u0ZQp8lxWnE/DKO7f9mNr9D5xcyiHhiSDK0zKrRJkP2lfnxmrVbv4HDNGPi+v8VTnd8MIlinyyivipl93nbTny1+O3o/8KLfzqZ+9VukOOlar+NT4xG8ODpO9oe2rctcToPisHGmKT+uKlZfzee21MqB++9ulgXxPj3TyLJwlpJUPwx4RoiXbgeoRn5ruYhdwOtgkIT7j7J30EnA68Dvt1xo71tx3Fkd81tcDH/mIBJLPPRfuZ8shPoM6nzNmyEq97ldS7CKnqUlWOOM4n9ZzJd1IOu0WKBWfKsDC7PkEgjufXtc17nErQa8rIMFYd3fx2O60T2fUKPMzsX82bjQ3y/mFKjLtuFWA1THfWu1WaWgQ93PHDuDHPy5+be9e+RfH+XT6251EhR49dMIJwX9PW5tZTMmL55+XwNtJfMateOuVdgvIeNjdHb0iqN6z9rTbKNVu6+pkPq208+kWozil3QLu1/eNN6TATBTxGWQ/adbEp7UfxumTblhFkN++z6EhYM0aoKtL3vt735O5M0yl3DCUa8+n03g1cmTwsbqtTe4TnbeCOJ/btwffErZrVzbi8phQfFaKLKTdzpsHfPrTUojBXvlW9/lYS7tXiqji01qyvVrEpw5Y9oGtvl4GwyTEZ5y9k17O56JFwHe/Kyk4dqzHrcQRn0D01Nsg+5gnTpRrEPU+CSpSGhvlvraLz507JUC0BrJjxsRzPkeP9k9JmzRJ+nolnE9r8Bdnz6ffdbW2PyxhxKdW3bS6S06VK0eNCu98+jFunLTRTXw6OZ8AcOGFct2WLi2+96Nuv/ATn9r/rc7nE0/Io7UgkB9uRxvY0f2ebs4nEL3irZ/zOXWq3OP297/5Zvm8/dDFsCjOp1O/r/RZn4ODxb/buu/TqeAQ4J6xEKXSrTJ+vAhXrxisUuJzYEDGvaTTbgFv8Wm9R/36w6ZN8nkcfrh8fcEFsp3kJz8xCxElSRDnc8IESUlN0vkEwo3V9jEpiPMJBF8YpfNJYqEDWhqDWVDxOX48cPXV0pn/5/8snhCi7PNJC92nEkV8Vpvz6TXA6r7JuOITiC7genokMND710pdHXDNNc5n9GlbX39d/sY4bV+yRNyKsM5tUOcTiO5+hikGMHOmiE/r36AVUa2LPkEKRbgRVOg3NASr+qmfodf5crpnL4jzaRjFZ/hGSbvVlMk0nc8wFQY1ENbA2O3nw6ymh2HWrNKjU/zE57BhMhd0dxdXvg2y2OCEn/hsbJS/1S4+hw0rLQjkRRLisxzOJ1AaEH/lK5KmeOCA9/u7pd3qHOFWcKix0XnxuLNT5sa00iP9sLc3jvMZpdKtEuSsz0qJT3uavpKE8+m1kGl1Pv36gxbL6+qSx0IB+M53xBGNcxyaG0Gcz0Ihfpp7Es4nYI5JfkX/wh63QvEZjTVr1mDx4sXo6urC8ccfj9X2vSkADMPA1VdfjSOPPBLz58/H6aefjrVe55BVI+VwPltbvcXnuHGy2n3RRVIgQ4WIYUTb55MWcdJuq8351EB82LDS11R8Bq1g6sXb3y7XN6yA83OY3NC2arAQp+11dcBHPyopP2FWWIM4n3HF565d5n4+P2bNkiDQ+rucJpa4zmfQz3rKlGDOp9feSiCc8wkU90n9f3NzcOczSBGkSjiffuIzDecTkPvq9deLP1e3ardWLrtMPqfvfc+8dlH3/vuJT6C44IthiPhctMh57HMjjPgcP955S0Bc8Wnfp2hHf6e1b73xhtwfAwP+2SduabfaB90KDrk5RLqYa9/3uW1bedxQjU9UGDs5n0ELDsURn0HO+uzrM8ehcopPtz4zerTZP6Ls+bS+txPd3aZb7ud8aqVbdT4ByVqorzePNUqSIM4nIOIzaNqtU5ZTuZ1PvY72McxpO4GeGUrxGZ4rrrgCl19+OV555RVcc801uPTSS0u+584778Tf/vY3rFy5EqtWrcI73/lOfPWrXy13U9PFT3z+9rfAT38a7b21k06YIJW5rM4CIDf06NFmJ/5v/00G2E98Qg7gfuQR6ZBZcT7VSQkjPg8ckM8hb86npq3q11Gx7p1ctSr4z0VdlNC2akAep+2A6dzefHPwnwkSSGuAF2dv4JgxwdLVNWCypt46ic9yOJ+ATNpvvOEdgAbJiBg1SgJxe391cj6BUvHZ1FSceuwnPoM42uXc8xnW+TSMZJ3P2bNLf7+f8wnInPT1r0s63Zw54sppgJm08wkUi8916+T/YfZ7Wtvl5RoMDsoYt2CBc79MIu22pcV9QUb3oVndGGs1ZL9A3S3t1sv57O93b48et2Ld9/nII+ZiZBDWrAGuuCJ8EUBtG2COS9axLWzBIR07o6TdBnE+9+6tjPPp1mf0KA8gvT2fWvDLrz/YnU9AFg2POCId8RnE+QRksaenpzTudeKMM4APf7j4OafspTjOZ9C0W/sYdscd8pp1gb23t7R4W5VSVvHZ09ODFStW4PzzzwcAnHvuuVi/fj02OGx+P3DgAPbv3w/DMLB7925M09X0vOAnPv/lXySFMQpW8QmUDjbbthUHIbNmSUGGCy4A/vxnccWA7DifI0dKxw1Toc+evlMt4tPP+dy505wskxJwv/lNsO8fGpLgPY7zmZT4POkkmWRuuSX48RlBUgg1wIuTdhu0GEBQ8TlmjNzPYc+oHRyUiTKM83nggHc6Xk+P+3EmSqFg7iuzEtT51OeDOp9B9/IC5XE+p08X8WwVf05pdKNGyTXavz955xMo3ve5fbt8rvrZuvFf/6vs/z/8cHFAdQ5K2/mMst8TCOZ8vvqq3ENOKbdAMmm3bq4n4Jx2+8gj5v/9AvUozqem3Trh5HwuXSrjaJAjzQYHgfPOA/73/wb++lf/77ej8YnGIF57PoOk3U6YEK3fBHU+7eKzHO6wV5q/9sWkxWdfn3z+Rx8tY3gQ57Olxcx0URYtkoVHvyJgYQnqfGp/8+vPfX1yPMxDDxXHEG7O5/790oaozqebYHRzPv/619I+mZMzPoEyi89Nmzahvb0dDf8YNAuFAjo6OrDRtkJ+9tln4/TTT8fkyZMxZcoU3H///fjWt77l+J5Lly7FtGnTDv3rjbISVwn8xGdvr9xoQVZv7NgHd3vK3vbtpSvgnZ3Ar34lE+GZZ8pzRx0V/nenxYwZ4ZxPexGIahGfXgPsmDEy8WnQEFfALVkin+v3vy9n/PlNqlu3SuChTkEYkky7BSS4P+88eb8PfjCYM7hliwzaXudjJbHnM2nxqV/bD2T3Qz+TMM4n4D5pDwzI2BEk6JkxQ+5T66Qe1Pm0i0+/MV2DHK99qDrexd3zGeTaDhsmAZB1UdXN+QTkc0l6zydQKj69XE+lUADe8x5gxQrg17+W6zh8uOmWBcWa4uclPnftkvsqqvh0C9ysqHPgJj6bm6W9cdJuva6bm/gcP14+gyDi05qppPg5n25But353LjR3KNnPyLIieuvNysTR+lPXovjTovGTU3eabdRUm4Bf+dzcFAW46zic2goetXiMHiJz6OOkj7ut5BkR/uk2xYOXcSbOlWuTRDx2dVVWtRKK3HrPuukCON8Av6pty+8IDHP3r2S7aE4jfXav/fsieZ8jhjh3h/dnE/NSLOm5YepPZBxyp52W7ClvRgOAe+KFSvw0ksv4fXXX8eWLVvwzne+E59zOdfsqquuwubNmw/9G+G1Apkl/MSnrvZHCYL9nE+vQGTBAuCee6Tj/sOhzgQdHbIPye88N6XanU+3tFtAJtzhw/1XAP2orxeXY9Ys4POfl/MDvYpfxKksmLT4BOSooIsuAn7/e+DEE800IDfWr/dvexJpt0EnBrtI0BR5J+dT3zsMuuIaVAz7lalXkRdEfHZ2yjhkDeh10vZzPnVsDJp2q32mqcn9exobJdiM63wGvbaHHeacdmuvdgsUi88knU9rnYTt252PWXGjrk7G/5dflr8jzM8CwZ1PQD7bJ56Q+1RThoMyZowEo17X1avYkDJ5cry0Wy/xOXy4/O0qPvv6RNwvXixtWrnSe17bts0528Bvz6dXGnB9vbmI+ZOfiKhqapIK6F5s3Ah89atm343Sn8I4n4WC+3mc+/bJ+BJlPrL+fjeHTsclq/i0Pp8mXiLjRz8Cnnoq/Hv6OZ/WbSmTJ3svxuzbJ/eCNeVWUfGZdOptmD2fgH/RIet2I7vAq68vLqoYZqy2Z9n4bX1xKjhkGBSfSTJ9+nRs3rwZA/8YLA3DwKZNm9Bhq475i1/8AqeffjrGjBmDuro6XHTRRXjggQfK2dT0CSo+4wzuTuJz3z7557cKPmWK//EM5aSjQybZoAFCNTuf9fXOn701dTWpc57mzpXUk/e+F/j5z4FTT3WfdDSYjrLSbE+7TaL9zc3S5n/7NxGexx8vCydO9PfL6qZf2+Ok3WoxgKB/29ix8r0qyN1SO4MUinAi7N5gv/TDMJVP1V2xpva5pd1ax8C+vvBpt16p6lYmTizPnk9AAuIdO8xrtmuXBNJWkWJdTfc7riMM7e0iJKI4n3aamvzTrJ0YNsw8n9ZPfHZ3ixg7/vjwR3vpHjg/8TlsmLmXzYkpU9JLuwXEjdFg+MknZS5bvBg45hi559escf/ZrVudr0GQardONDRIaviGDbLg9dOfyrj4oQ/JGOmWaWAYwJVXSn/8t3+T55IQn17Op36fkzupzm1azqeOS5UUn07jTWtrtPkzrPj0irXWrpX7wVpsSNFFnqTFZ1jnM4z4tBY+1ewj61gUZqxuapKfDyo+9fQAa1/avNmcc+xtA3jOZ1ja2tqwaNEi3HjjjQCA22+/HZ2dnei0pfTMnDkT999/P/r/MUjdddddOCpLKaBJUCnxGSRFLYvoAkXQfZ/VKj4PHnQPonUAO3gwGedQGTMGuOsuKTz1+OOy78uJOJUFdbBUoZBU+wsF4ItfBP70JxHsH/6wc3rqxo0iDv3aPnasvGcU8dnbK78jzMSgx60A3hUOgejOZ9i0WzfnM4z41H1l1tRTe7qS03FTUfZ8BhWfbW3xxGdjY/BUN3vRoV275O+2Liql5XzW1Yn7qeJzcFDaH0V8xkELZfmJzwcfFBEUNuVWCSI+jzrK2zGZMkXaGWV+8HM+AQmItV/pfs8lS/xdIsOQschpvo5a7RYw0+Jvvlne/8orpVAM4J5BctttkmXyyU9KfQggHfE5bFhxXx43ztmdjDMfWX+/m/OZBfGZpMPlN4/YxWdvr/tChFOxIevvmTlTFpSSJOyezyDiU7Nl7O6i/XMPO1Zbx6SdO/3n4La2YudThXFzs7yPxiN0PqOzbNkyLFu2DF1dXbjuuuuw/B9nil122WW48847AQBXXnklOjo6cPTRR2P+/Pl44IEHcP3115e7qeniJT61CAWQvPjUm7jcgUhcwp71Wa1pt15Bg3UAS1J8AuK2/su/SJDmtlcjzmRvX0lMuv1nnAF86UvSb5yCp6Btb2iQtkURn2HdMUBEwubNku7sNrHkzfn06pNR9nwGDUomTpTrGjR130qYKsaAKT5VfDsFNGk5n4DcVxs2iDDR+6CS4lOdUCsqPv/4R3lMQ3z29Ijo80q5BaJXvB0clDncz/lsb5f7ePduKSDS2Agcd5y/+NQ9sWGdT6+0W0D6544dwHe/K7HIxRcD8+bJa06ptzt3yqLkuHHiera2yt8cJz4ZM0YWSqyLhXv2lH6W48dLW+2F5eKKzxEj5DNycz510ct61ApQveJz2DD5G4I4nzoXuFWRdjpmxcqiRfI9fguHYQjqfPotogJmWuuxx0rf9xOfTmO1V5/XMckwgonPSZOK+5KKz/e/Xx7V/cyR+PS5islz+OGH49FHHy15/oYbbjj0/6amJvw06jEj1YJTyplifS4t57PaxGfYsz7dnM80zlVNkiDOp/3/SdLVJcUnnNqxbp0EDFF+d12dDJhJnFHqhq7CvvKKBHZWwpTkj5qeGaUS3cyZMkFt2FB553PSJBFXaTqfI0aY7p+f+GxpkfYk6XwahgSbYVNJwx7sreJbnU+ns9nsq+lNTeHOuPRi1iwz1Vw/n0qIzzffdP/sVHz+5S/y+La3Rfs9bW0i7KyVSRUtNrRggfd7WBdewuwh1HsziPMJyELT3/8uQW9Li4xZra3u4lPHobB7PgcGvM8a1v754ovA5ZfLGKFpyU5Fh26+WYTIsmXFR33EiU+GDZM+YHc+7UH9uHEiPHftKh7L4opPr/2kQP6cT32/oGm3gPRf3UNuRcWnk/MJSDr57beLiDrppHhtVoIuMmpaspfzuWWLzAPz58ui2OOPy9xQKMjno8f7Kfax2jqPOdHWBjz6qLzX4GAw53PFCrMNKj4/9jH5HF94ATjllFyJzwxt6qsx6usl2HASQ9ZgK87grhNWLYpPOp/RmDNHJnprBVZl3broxR0As826Aps0c+bIYxznE5BFmyjOZ5T9GNaKt5V2PhsbZcxIQnxOnCjX2O58WlOV7H3SMIrFZ6EgAbSf+LQGs35tsv4dYQhTxRhwTrv1W01PyvUEiotZVWrMtzqfXuJz714Z36NU0Qa8j1sJUmwIMMVnWOczqGOt4vP++6VfLlkiX9fXSwD8zDPO1cbdjlkBole7BYqrF2sxxzlzJKB2cj4fe0weP/hB87mJE+PFJ42NMh7YCw45OZ9AqUhcv14EeJxj+KzH/djJgvhMIg3finUB2I6Kz4kTzb7olgXzyivS79zGxDSKDgV1PoHiPdZOqLibPx848kgZgzZuNJ3KuGN1W5vEUbr1IYjz2d9vXptVqyQ2OPFE+TqHzifFZyVpbU1XfObJ+Zw8WSaruM5n1sXnwYOVFZ9W99DeriAFe7zQNuu+yqTRSplu4rNQMFf8vVDxGfY8tyhpt0HEZ1TnM4rL3N6eTNqtftZ259M6adv7ZH+/TNjWhYnhw/3Tbr0qRFtR8RnV1Q5zXdvbpT1W8WkPJO2r6UkGmtoXrOIzbMXauIweLePwjh3e4hMATjgh+u8JIj79nE+/YNsNpwI5Tqj4vOUWeVy82Hxt0SK5RtbjHhRdBEuy2i1gjoOnnirnOgLiAB12mLPz+fjj8jPW816tqYVhsIpPuxPnJT7tInHdOlm0CCJG3Miy89naGr+ivZ0xY7ydz/Hj5Xd6paEbhjifbim3QDriM6jzCZh7rN3uTav41L3Oq1fLNR8c9M9S8RurdUxSh9hv7rBWvN2/X35u/nxxYEeMMNOCec4nSYRKis9qKzhUVycdMWjBoWp2PiuddguUCjhdFUxKfKbBiBEy6TiJz/XrZYXc6zgOZeJEs0iLG6+/LvtM//3fzeei7vkERCSk5XyGac+UKe6Tdk+PBHpB36+zU/qrvpef86mPdvGZZNot4D2mDg0BX/mKnHWp7xu2ijEgjtaMGXLfHTwoAUUtOp+GIYLO6bOzis+o+z0B77M+V64UQeUXrPntdwbkLOQFC4pFUFjn8+GH5VGdT8A7UPdKu41a7RYQsf/BD8o+fytz50rlXaug3bVLBKm6MEpbm3xf2EUxP+fT/lnqfWIViYYR74xP63u/9ZbzeFdp8ZmGwPBLu9W+5CU+t2+XucUt5VZ/fvLkyjqfe/e6n42t4vOoo8T5BETguc3BUZxPwBSfQdJuAbkGL74o8cf8+bKIe8QRdD5JwriJT+tKf5xDnNVhykPBIUBWOfPufHqlS1kDuLTFp730f9z9NUD64hOQ9q9ZUxpMhAlUdGHGLfV27Vrg5JMlhe5//A/zbNQo4nP6dBEqaTifUcRne7v8PU6/q6dHJsmgrvWMGSK6VBT4OZ9O4nPEiOBpt3Gdz4EB4FOfAq67Dvjzn4G77zbbbRjhy9sfdpj3Xt40nc8ZM2TBrtLiE3B2EoDicSCO+HRbVNi3T0STX8ot4J12axjAN78pZyGvWiVnkipBxacWQQFkYcDqIAYRn0lXux0+XPb22/fjzZ0riyXWM2qffFI+A7s7rZ972BjFzfk0DPeCQ0Cx+Ny6VcaFuOJz/Hj5/PQ6Wsmr+Oztdb5nrOLTqz/4FRtSFi0CnnvOeXEkCmGcT7+zPletksXR0aNN59MqPu1jfVznM0jaLSBj2HPPyf/nz5fHI4+Ua7N9u7SvqSnYInrGofisJGk7n01NMinmIe0WEPG5c2fpatZbb5VOgPagoLFRgvysi0+vgkMNDebfk5aAmzhRBmS7e1hN4nP37uJ+s2OH/Au6X9VLfK5aJcJz40bgne+Ue+8Pf5DXoqxKNjSIUFi3zl28anEDL+ezv790n+6OHXK/hElL86oUuHVrsJRbRfeVbdhgBpZZcD6dguX9+4GPfAT49a/F0a6rA375S3kt6mrzYYdJ23Xfj99qepLic9gwGS/XrjWdukqJT/v/lZYW+VdXJwVKouImPl94QVxrv5RbQPpcU1Op8zk0BHzhC8C3vmUGiNaxMWja7aRJMv8Axa4nIO5Lfb2z+AySdhul2q0bTkWHHn9cHt3EZ9gYxe587t8vffjAAVmocCo4BBQ7zknMR27vrbiJz3IULUxLfOrcYo+h9u+X36n3+OjR0h/iis+DB533EEchrPMJOM9jBw/K/a3ibvx4+btXrw62UBhkrNa+oWNFGOfTmhIMFKcF79qVizM+AYrPypKm+KyvF4fCnmaxfbvs7bBXBawGtOiQfW/M+98vaXJWNCiwVvxracm++PRbsU5bwBUKIuCqWXwCxe3XVfy4zudjj8keqR07gFtvBX71q2KREsX51HZ5OZ91dTLZeTmf//t/S8EQ69lqfodbO+GVfqjOZ1Csx63s2yeBZVjnM8yez6gFh/bskTHkd78Dzj9fHM8zzpBFhW3bol9X/ft136H9umqQvX27BH9Jpt0C5lmflXY+7f+3MnWqiEM/8eaFmwh6/nl51D2NXhQKkiZove8HBuQIkh/+UPq9VuW1ji1Bnc/6ejOV0S4+m5vF3QibdltXJ//cqt1G2S+ox63YxWdDg+nQKkmIT70vdu92F/JOzmdS4tPrrM9KH7WSlvOp729Fr6GKT6f+oHid8Wkl6X2fYfd8As7O50svSf9QcQdI/1u92n1Ppfbvnp7SecyJOM7nqlUSn+u9bU8LzkHKLUDxWVn8xOfIkfJ62LOSrALGSXxWo+sJmEUSrPs+u7tFFDz7rHk2KiBBQWurudoMVIf49HI+gfIJuC1bioN+LdijCwBRqJT4DBuoOKVnDg0B55wjfesPfwA+/GFxCc84Q8TK1q3RRcqsWdLH16yR+9XpiASvQhGApOoMDYkgVqKITzfnc98+6VNhjiixHreiQXpY51PTbr2KmgQNSsaPl3vY7nx+61siLK68UhYSGhuBCy+U9/3Nb6JfV3XaVXzaV8vr6mSM18866cqWs2fLZ6fOQ5r9zokg4vOOO+QYjzi4LSpokQ51DvyYPNl0egYHgQsukP501lnAPfeIK9ja6iw+g4hnDYjt4hOQQH3TptIFr61bxYFyOzalsTFatVs31PnUe8YwRHwuWFBaoTwp5xMIJj6t7mTYBUU3wjif+ph2DDE4KJ9FOcWn9ZgVxdofrLz8ssxTfp+9ZjNYF0TjEMb59Eq7tTuLgIwRvb3mgpX9s29slEUifb+gzqfeQ0HFpzqfmg2hbQNM55Pik8TGT3xq8BJlcPcSn9VWbEhxOm7l/vvlcWioNB3KPolVg/j0Cxo0AE4z9cJp3+e6dbI/Mc45hOVsexzx6eR8vvyyTMRXXCGCU7noIpkUb7pJREqhEN7B0natXCmTmtOeSq8S+YDZJ37zGwlegGSdTxVsUZ1PTfOK4nwODpruphNBnc+GBgk27ePpAw+IgPnhD82z2845R8aPX/4yvvjUsyadgoaRI82AJg3nEwCeekrabl2IKwdBxOfRR5tHJEWlpUU+Ow2glRdekHtCK//6MWWKvEd/P3DppdKXzjlHBLKmB8+Z45x2G+TanXCCjE/qLlpxc4m2bZN7022fdUNDqfNpGNJnoqTdjh8v4586n6+9Jv3FqRpxks7nrl3un6VTwaEw5zZ74eV8VmrPp46VaYpP+1ziJj67uyW2svLKK/K5+423Wugra86nk/hUd/HRR+Ux7lg9blzxOaB+8/DYsTI+P/ecfObWtnV0mBVvKT5JIrS2SuBknzziik9ryo1dfG7bVr3Op5P4vPde8//WvQVOFcmqQXxmwfl0Oi9z/fr4q8zlaPthh8kgnnTarU5K9gIdH/qQ3Ge//KV5nIbX4dNOaLv6+twnFj/nU/tEd7eIqaEhCTCScj7DHLOiTJok93Ic51MdH6/sj6BHrQASyFudz74+EYcnnVQc4Le2Ah/9qAg3r4DECx2/Ndhx+vlRo0x3IWnnU8VnX19lxvwg4jMp9NgPK6tXy760oCJsyhTpN5/8pPTn979fBKj1vurqkr6m92rQtFsA+MEPpE1O44Ob+Ny61TvbwMn51Hgi6jEd8+aJ+FTXE0hWfFrbZ99LB5QuGg8bJs+pQNy2TRbqRo+OP5dE2fOZdgyRZkVTt8rpKj6tZ+1OmSKLGFZhPjgo+8j99nsCMp4uXCjXyi5goxDG+WxrkzjAac/nqlXiYloXpdRd/Pvf5THuWF1XV9xv/RYu6+qkzVrMzLpVQCvePvec9BGKTxIbtzSOpJ3P/ftlM//AgAw61So+p0+XRw20DUPEp04KWo4ayK/zuWCBTAppXkO7e7hjhwiZuOLzqKNkkLWu6iVNY6P0G7vz2doaXDg5pd2qALGezwfI+37sY5Ja9PTT0VxdFQmA+8SizqdT+qlhiEOhTuN//qdMUlEqtE6aJJOd3fmMIj7r6iT11s35rKuTwDKI+PTa96nFVYJU4bWLlKeeknHRfl0BcbUB4Kc/lcewn+XEicXZLW6r6XpN03I+gfyLz0mTiq9rb6/cd+poBEED79tuA971Lnm0LwR2dcn10iJSYdJuCwV391kr8jqJT69MJSfnM4xD5MTcuTLm9/R4i09tV9LOp9NnOW6cOD/vfa9cpxUrRLDHPS86i85nmuIzbNotUJx6++qrstgXRHwCco327CkthheFMPd1fb3ESW7O55FHFvdFHSf0b01irNbPsqUlWHXatjYza8keIx1xhIwFhkHxSRJABzV76m3S4hOQwaZSVQ+TYvhwmfBUfL78sgwu550nk1AtOJ/f+IY4eXHSX/2wO59JFXc48ki5Lta01TTo6pLVWR3I162TvhQ0UBkxQj5fq/P597+L8249MkG58EJ53LEjmvi0fq5ezufAgPP9u3OnBG7veIfss7n9dlM8hnUGGhtFNCXhfAIiPjdsMMWnfcXY2ifd9nwC/s5n0P4wcaKMgxqw60q33dEGgLe/XQS9jpthr22hYC4IAO6r6U7/T4JaEp9tbRKcqcOiC5FhxKcubp56qhxD0txc+j32LQlh0m69GDVKnJjHHjMD3H375L4P63zq11HSboHiirePPy73vVNqdEOD3Fdp7/kERAi9/jpw333AmWdKVeq77gr3e52oNeczbNotUCw+H3hAHp3GSyd036cu3sYh7H3d3l4qPrdulbnRLu7Gjy+e25IYq/X9gs7B1s/eXiTNOo5RfJLYlFt8VvMxK0pHh1lwSFNuP/AB+azs4rPanM+hIRFMXit79fXpn/E0apRMPBpgJSU+gfJUWe7qEkGyaZN8nhs2hGt7oSCLHCo+d+6UYNZtwj35ZLOvRhGfo0ebQZCX86ltsaOLMR0dshCzezfwf/6PPBclLa29PRnnExDxtXevXAPAe0Eoatptf39w8dnWJsG9joWPPirBzHHHlX5vXZ0UnVGiXFvrnjS31XSn/yfBiBFmQFML4nNw0DzbVosNhRGfH/kI8JOfAL//vfs4Zc8KCeN8+vHBD0o/eegh+drrmBXFyflMIu0WkDS/FSvkDFa3rQRO6c5+RHE+r78e+MUvRCT9/vdSlTqJz9zP+dTsDCAf4tMv7dY6vqv4tM4F994rn8k73hHs9733vXKdrYXwojIwYFZ4DsLUqSKcdREaKD1D04qfwAs7VocVn/r9U6eWjtfWomkUnyQ2budG6SCsAXPS4rNaCw4BEmC//roMRPfdJ2Ls1FNlwnz5ZXl+cFAmiGpzPnVSTtPVDEpXl3yehpFccYdyYQ0QN2+WeyKscLbuDdTUMzfxWVdnup9Riylp+9x+3i1oAIrF58c/LuJZU0WjiM8pU8T5tKb4xnE+AXPSD+t8Bt3zGTTQtqZUG4aIzwUL3MWGXlcgvvh0Wi1P0/kETPezFsQnYN6nUcTnyJFSUMxL1NjFZ2+v3HtJLAheeqk8Ll8ujzr+eM3XXs5nnLRbQKoQ79/vnHKrxBWfTs6nU2B/3HGSBq+LdEmhKZFOzufevTL+aMZMHsSnV9rt2LHFsYfd+RwclAKPxx0X/DpMmCCLKvfdZ9ZeiErYCs5Tp8qCvrUQmVOxIUUFXkOD83xQLufTTxjznE8SGz/nU1O2khKfupJa7c7n4KAE3A88AJx4ogwE8+bJ371unfsk1tIie1+T2PyeBnGDhiTp6hKXbfv25MralwtrgBi17Vbn022/p5ULLjDPRouCioS4zufUqcBpp5kTblTnc//+4t/ldd6gFzqG6aQf1fn02vMZJu1WA4KtW2Ws2LrVO4Vs9mxxtocPj+a0+InPNJ1PwLyvkg7ag9DUZIqycovP1avlnkh6zBo3TuZPq/OZ1HWbN0/uxVtvLZ6vwzqfcdNuOzok5fjhh+VrP/G5fbvzWaNuuDmfSbrIQSkU5Hq6OZ9WEVJfL23Oq/i0pn0CZuVzFZ9PPSVzwrvfHe536qLKz38e7ufsDAyEu6edKt7qPOR09q8KvNGjnbfopO18eolPrXir7csBFJ+VxEt86tleI0cy7daKVry97bbi/YO6avXii+7pOxrUWs8DzRJhqnamjVXArVsn92JY4VEp7G0Hwru2EyZIn+nvl32Bzc3ikLkxa5aky33zm9HarEGy155PwEwrtGIVn4BU61SiOp9AcbpVT4/cA27nDbqh4lOdqDT2fIZZEbeeCelWwdjOTTfJin+U4iZ637W2Orcxz84nYN7P5RKfuujywgvi4EUVYF5Yj1tx2t4Rh0svlX5w003BFnzSqHZbX2+OoYCk3bqhn7v9fFIvnJxPv7TbNBk3zn3Pp90BK0f2VJriUz9vpz2fdvGpX6v4/POf5fFd7wr3O9/1LtlP/fOfF6fAhiWs82mv3L53r4zj7e3OfUpjSLfPPW3nc9o0edTK11YKBTMdnuKTxMZLfGqQFzWtRSfdvIlPTeP72c/kUQdC7ZirV7uXvy9X2kxUspZ2C5gCbubM+JUFy8XUqXKtreIzivMJSAD4+OOSauR3XZYsMYVbWPzEp4q4tWtLX1PxqQVTzj3XnKSjOp+AOWnv2ydps1FcXe2vKh7T2PMZtuAQINc1iKMNSFDg5f54oeLT7bqm7XxqQKUuQLkZPVqujVPxniSxOp979kifCJNyG4auLvk9KpiSvG4f+5jc88uXB0u7TaPaLWDOpzNneotf62JOUJycT7+CQ2kS1PkEql981tfLQubmzeZzBw+K+LaLz6YmmT9UfN57r1ybE08M/zsvvlh+p/VovLDEdT6vvlrGhS99yfn7rc6nE2k7n+eeK3tjzz03WvuqDIrPSpKm+My78/nyyzIA6KqsTpZBnM+si88sOJ9a3XD1ainwVC0pt4DswZw9O57zqUHV3/4mgVHQ6n5ROfZYEfdOVSWB4vvbzsaNEjhogD92LPC+98n/o6Rb2p3P666TCfwznwn/Xu3txQFD1D2fSafd9vSIoz15simQ00AXDZJaTQ/Lhz8M3HEHcPbZyb93EMaOTfdcX8V6XaNUug2DteJtkmm3gLzXJz4hKY5/+Ys8V+5qt4C579NPaEQ569M6zzU1Sd+ttPO5Y0fpdpw8ik9A0mYfe8ws3KjXzi4+ARkf33hD7vNHH5UtHVEWxy++WOY33c8chSh7PgGZu+65B/jxj4HTTwe++EXn758wQY6QcZuDw47V+nkGnYOHDZPtO2799uSTRchXS+0NHyg+K0lQ8WktIR+EvBccUk47rfjvbG+vbudT026z4HzOmiWTxV/+Eq1gT6Xp6pLKkS++KBNo2Cq72kfuvFMe/dyxuCxaJCvDH/yg8+tTpsg9bj3LVtm4sbhfAMD3vw9897vuE6kXVudzzRoRn0cdBXzhC+Hfq77edGSB0rTdpKrdhk27Xb9e9v+cdFK6jv6YMbLY57bgl7bzWV8PnHOO+/mSafMv/wL86Efp/x4N9Hp6ohUbCoM1KyTptFvA3COnR4mUu9otYDrmaYpPa3ZWpZ3PoaHSfZCVEp/6/mlVhtciajfeKI9Ox6wokyeL8/nXv8p9FXa/p9LZCbzzncDvfld8fnYYojqfq1YBl1wi99kvfuFdLffRR82sOjvW8TnI9pPjjpO5U/tzXC6+WOZ6ik8Sm6Dic3DQudCIG9ZgzFolc9s2CUKq2bZvazPFmf28yHnz5GwyPVOQzmd0mppkwnj6afm62gY8PQx+5cpowlnF5z33yGPazicgos9NCOmeD7vz2d8vItEuPufMAa65JpqwUudzyxbgyitlUeTHP45+X6r7N3Jk6cTf0iJBhfUM0zTP+Rw/Xj6TP/5RAs5yXNebbwb+/d+dX9MV9ObmbPT7pHnHO8R9TZtx4+TeKqfz+fLLyafdAiL4NNOhUPB2jtOodgvIgsX/+l/+gbO1gFdQ7O0bNcp0PocNK//iq9tZn3v3VkZ8pr0I/e53y4LGr34lc6Sf+Ny501yEDbvf08qll8q1//Wvo/18WOdz5EiZP37/exHQ119fOk/aGTvWXVjqWO00jzlRVwd8+cvJbXmoq3M+Z7xKofisJEHFJxB+ZdHN+Rw3rnr27jlRV2cOIPaB8Igj5LPTAJ3OZzysRSeq0flUoghndRt27pSfd5qYy828eRLkWYt7bNkiIspvUg2D7u286SbZo3PxxZLyExVNa3VKVbL2yXKc81lfLwJUP8NyiM93vtP5HFHAHKPScD1ribo66bPqfDY1pTdmzZ4tjytXSvCetFNXKJiib/x4b9c6jWq3gMQPV17p777FTbsFTOczDRc5CE5nfQ4NyXjklamRFgcPyv2cRrEsQD73T35SnPsnn/QWn7oQeeutksFy+OHRf++HPiTibvny4mO8ghLW+QRM4fexjxUX4osCx+pEofisJG7is7fXHITjis+RI2UyU/FZzfs9leOPl1Vt3Zei6GrxE0/IY7WJzyw5n0B+xGcc5xMoj0AJgrWis2KvdJsEjY1muv/YsZK+Gwer82nHLj4bGooDjKB7PsP0GR1TGxtlr20lUUGexn7PWkPrI2il27RSjYcPlyJUmhWSRjCqe7/8KoynUe02DEmIT6vzWQnx6eR8akX8SjifBw6kvwCtqbe/+pW/8wnI9XnXu+IZF83Ncl+vXi17msMS1vkEgIULZfHzxz+Ob7pwrE4Uis9K4iQ+Bwdl4IvqfBpGcSetq5PJMU/i85e/lMHLPpjYxWe1pd1m2flUAVEtJCk+097vGRSnokNpiE/AXPH+7nfjH7ETxvm0up5A8mm3gPn3LFpU+vvKDQOa5Ghrk/6waVN6KbdKV5dZRTMN8dnWJgGz39FNaVW7DcqYMdKGvDmfGpNVKu027Rhg0SJZzLzpJukvgLf4BKLv97Ryzjny+OCD4X82ivN5440idpM455hjdaJQfFYSJ/Gp/48qPvUcJevEM3q0pA++9VZ1FxtSGhqcS/erM+QWFGRdfGbV+Wxvr3yQHpbx4829UnlxPsspPj/5SeC885IplhDG+bTfZ8OGSX9PKu0WMMVnFq4rU7mSo61NXCOgPOJTSUswXXYZ8PGPe39PWtVug1IohK/I7+R8Dg7Ke1SiHzg5n17is78/3nmVfhw4IGnjaVIoiPv51luyJx3wFp+FgmwfiMtxx4kh8vjj4X82ivPZ0JBc4SaO1YlC8VlJtFNYxZAGWVHFp5OAGT1aVrcGBvLhfLoxcWLxChedz3hogFVtxYYAmSzjtH/YMAmKWluB+fOTbVtUZsyQe9ha8VbL5SctPq+5RlaNgxRW8EPFZxTnE5Cx0E18Gkb0tNssiE+upieHXlegvOKzksGoOp/WPXTlTLsFootP+1nkO3ZUh/MJpBtDlMP5BGRxsVCQv3vUKOcFfRWfixYlY1yMGCF987HHwv9sFOczSThWJwrFZyVxcj7TEp+a159n8VkomO4nQOczLtOnS5B1+umVbkk0zjhD2h+1QtyJJ8rRJ5Wc8KzU10vBB7vz2dQUPzU2TaZNkyItxxxT+lpQ8em251MD7TDB2gknyDh42mnBfyYthg+XMavSe0/zgNW5SVt8Wo8wqqT41LnC6sSVex6JIj4bGsxtM9ZgvhLiU+8bzZgCKis+y+F8AjIuq5vpVlBv5kx57fzzk/u9J5wgx4pt2RLu56I4n0kyapTMvxyrEyEjUVWNoitNXuJTjwaIKz6VPItPQFITH35Y/l+tzmdWxGd9vRwnUK38z/8p/6Lypz9Fq8qXJvPmyT4dLc6hZ3xmuYJ1Q4OcF+qEXXzq0VBWRoxwdz51vAsjPi+8UApfZOEzKxTMcylJPHShtrk5/WyNcqTdBkEXxqyuUDnTbgH53Ht7nc/FdMIuIqzxSSU+ywkTZIvGSy+Zz+l4k2fnE5Bx8L773MXn8OHAG28k+ztPOAG44QZJvdU9oEGotPNZX198j5BY0PmsJIWCDG5e4rO+XgZHis9g6L64xsbS1cOsi88ogTRJlywIFCvq7L/0kgjj115LPuW2nMRNu426YJO160rio+IzzUq3ymGHmb8jC86ndd9nJdJugeBnfWZNfOoZylZhYa+9oeTJ+QTkDN4JE4ozxuwUCsmOlyecII9h931W2vkkiULxWWn8xCcQLq3FT3zmoeCQFzqIOk1iWRefWXM+SfbQxZXVq83jCWpBfLql3WZtnzSpHCqC0k65BWSM1kJmlXQ+ncRnJdJugXAxirVt1rTbSgn5uXPl7F89/7cW9nwCcu++9BLwgx+U5/cBEqONGBFefFba+SSJQvFZadzEp3VCCyM+nVY9a9H59KusmUXofBI/rBVv06p0W07iOp9Z2ydNKsfcubIX84MfLM/v09TbShccAoqPW6lE2i0QLkbJkvMJmOOqup+VFp/lcj4BiQmdig2lRX29VL196qlwVYPpfOYKis9KYxefusJvdz537DBX+b2o9bTb6dPls6tm8ckBlrgxe7YElVbxqedoViPaJ/fulfHNSXyOGCFjpNP+WzqfRBk9GnjlFeCjHy3P7zvqKElHrGSxryyl3SbhfFZafGoxt0oXHMr7eHbiiRLrWiu3e2EYIlTpfOYGis9KEyTtVic3TQnxotbFZ6EAfPnLwKc/Xfpa1sUnA2nix7BhIkBXr86X87ljR/HXVoYPl+DDqd8yW4BUiv/234D77wemTq1cG7ycz2oRn1lwPufOlcesOJ95H8/C7vss94IKSR2Kz0oTdM8nEGxw9xOf1nMw88o3vgF84Qulz2ddfNL5JEGYNw949VWzgmwexKce8O4mPgHnfZ/cJ00qxZgxlT+GymvPZ7lcIl0cr2bns7NTUl3V+axktdtyFhyqFFHFJ53P3EDxWWnKKT5Hj67tIE33NWRVfNL5JEE44ghgaEhcF0DOa6tWgohPDUid9n2yz5Baxsn5LLdLFFd8ZsH5rK+XPbyVTrsdGpLrl/fxbMoU2SL12GPBvp8L87mD4rPSqPjU/Uxpis+8p9z6USiIAM2q+OQAS4Kg+5Oee07GBifBVi2EcT6dxCfTbkktk4Vqt8OHy7+o4tNan6GSxZvmzZOjq/r6KnfUii6m5d35BMT9fOEFYM8e/++l85k7KD4rTWurbKTWCcNLfAY5R4vi05uWluyKT7o4JAgqPoHqTrkF4otPpt2SWiYL1W4BiVGinvNZX286npU8tmbuXDEBXnmlcs5nLcUAJ5wgn/dTT/l/LxfmcwfFZ6XRwU0Hu6ScT+vEQ/FpkmXxyQGWBGHuXPPQ71oSn157PmshWCPEThaq3QLhzyK3t033fVZSfFqPW6mU+DxwQB5rxfkEgu37pPOZO8ouPtesWYPFixejq6sLxx9/PFa7lFp+7rnncNppp2HevHk4/PDDcccdd5S5pWUiLfFpHdzHjZNgdfLkeG3NA1kWnwykSRBaW83jVWpBfHrt+WTaLallslDtFjDFp9NxSHb6+0tFhC6QV9r5BGTfp8Zj9vMv6Xwmx7HHiusdRHxyYT53lH0Z4YorrsDll1+OT33qU7jttttw6aWX4tFHHy36nr6+PnzoQx/CL3/5S5x88skYGBjADi3FnzecxGdTU/HgPGqUDEZRxefIkcDttwPz5yfT5momy+KTAywJyrx5wIYN1S8+GxuBujpg+3b5mmm3hAQnC9VuARGf/f3Arl1SBdiLrDqfhx8ui/TqfLa2mhkmCvd8JkdrK3D00SI+DaP0s7ZC5zN3lNX57OnpwYoVK3D++ecDAM4991ysX78eGzZsKPq+//zP/8RJJ52Ek08+GQDQ0NCAiZU8yDlN7OKzt7d0k3uhIIP75s3+K4tuAuacc4BZs+K3t9ppba2s+OzpAb7yFXOSscJAmgTliCPksdrFZ6EgfVLvfXuaG8C0W0LcyEK1W8DMztq82f97ncRnFpzPlhY5cuXFF2Why2ksKlfaba2MZyecALzxhv99w4X53FFW8blp0ya0t7ej4R8DZqFQQEdHBzbqYen/YPXq1WhubsZZZ52FhQsX4sILL8RWl83sS5cuxbRp0w7963UKULKMk/NpF58AcPzxwMqVwD//s7cAZSf1ptLO5223AdddBzz0UOlrTCEkQXn/++Vwe903U81Y3U4n59M+RlrheEdqmSxUuwUkPgGAj39cxIQXTuLztNOAU06p/Nw3d64UHNqzpzLis9YW0xYtkscXXvD+PjqfuaPsez4LNmvdcBBS/f39+NOf/oRly5bhmWeewfTp03HllVc6vt9VV12FzZs3H/o3opIrZ1EIKj5//nMZnH/wA+DTn5YKuU4wGPOm0uJTf7dTG+h8kqCcfrqsFlfzGZ+Kn/jUfVfqCliptWCNECtZqXb7oQ8B/9//B6xeDbz97bIlwAnDkLba57ivfAV48MG0W+nPvHkyzrz8snMcxoJDyaLz15Yt3t/HuDZ3lFV8Tp8+HZs3b8bAPwZKwzCwadMmdNhSx2bMmIHTTz8dU6dORaFQwHnnnYcnnniinE0tH0HF56hRwD33AGeeCSxfDnzyk86pm+yk3qj4DFIYIQ10cnEKpOl8klrET3xqIMY+Q0gxWal2CwBXXQUsWwasWwecfLLsnbRTqbYFRYsO6Z5PO8OGyVYBOp/J0N4uj37ik85n7iir+Gxra8OiRYtw4403AgBuv/12dHZ2orOzs+j7Pvaxj+HJJ5/E7t27AQB//OMfsWDBgnI2tXwEFZ/6vf/3/wLnngvccgvwxS+Wfg/FpzctLcDQUPFkXU727y9+tMJrR2qRoM6nU59htgCpZbJS7Va5/HLgxhuBN9+UVFr7Nqisz3HWM5SdxGehkG72VK05n0HFZ9bvGxKasqfdLlu2DMuWLUNXVxeuu+46LF++HABw2WWX4c477wQAdHR04Ctf+QpOOukkLFiwAPfddx+uv/76cje1PIQRn4AMSr/5jez3cipRzU7qTdppM354OZ8HD0ow4VX1jZC8wbRbQqLh5XxWyiX65CelNkV3N/Daa8WvZT0+UecTcBafQLris9bGs4kT5T59/XXv76v0PU0Sp+xX8vDDDy85WgUAbrjhhqKvL7zwQlx44YXlalblsIrPoSEZ1Pz2rTY0SHU4umfhsYpPrbBXTvycT143UmsETbv1Gu9qJVgjxIqb81koyBmKlWL8eHm099msxycTJsi/bdsqIz5rzfmsqwOmTKHzWYOU3fkkNqziU91PL+dTaW6m+IxC1p1PBtGk1mDaLSHRcKt2W+n+4NZnqyE+UfeTzmd5aG/nns8ahOKz0miw1ddnHqIeRHw2NVF8RqHS4pPOJyHFxCk4VIvBGiGK2zmflQ7S3bIVqiE+0X2flRSfteJ8AiI+33yz+B62Uw33DQkFxWelsTqfujk/qPPpVf2RndSZSotPvWZuLg6DaFJrMO2WkGhk3fm0xyjVEJ+o+HSLw8qRdltL49nUqbLlrKfH/XvofOYOis9KYxWfYZxPt7TbrJcyrzSVFp96zdwWDnjdSK2hfbJQcA669Hkv55P9htQibns+K90f3NJuqyE+YdpteQlS8bYaFi1IKCg+K01U8cm022hkRXzS+SRE0D7Z0uJe6dlvjzv7DalF3KrdVtohqua02xNPBBYskLNKnWDBoWQJIj7pfOYOXslKE8f5HBwsnWiqYXCvJJUWn14Fh7KwYk1IubGKTzeamuh8EmIn685nNabdjh0LrFzp/rqKT8NI/li0WnQ+p06VR6/jVqrhviGhoPNZaRobpSR6FPEJVOfgXkkqLT79nE9eN1JrBBGfbs5nLQZrhChZ3/NZjc6nHzpOOS2GxYXOpzN0PnMHxWelKRTE/bSKT79zPoF8D+5pYq0uXAn8nE8G0aTWiON8Mu2W1DJZrXab5/gkzQXsWlxM457PmoTiMwvYxWfQPZ9APgf3NKHzSUi2SML5ZL8htUhWnc88xye6VSqNGKIWnc/Ro+Uz9Uq7pfOZOyg+s0AU8ZnnlcU0qbT4pPNJSDFx93zW1cnWBUJqDe75LD9+McTAAPC3v8me0LDUovNZKIj7SeezpqD4zAIqPsOe8wm4D+5cIXKm0uLTy/nMQtBASLmJ43xywYbUMlmtdpvnxXG/GOKOO4BTTwUeeyz8e6v4rCXnE/AXn9VwRA8JBcVnFmhtlYEsSeez0pNPVqm0+PRyPnnUCqlF4jqfDEhIrZJ15zOP8YlfDNHdXfwYBh3jai0OaG8Htm93XmAE8nHfkCIii8/ly5cn2Y7aJuk9nw0NyZcAzwuVFp90PgkpJu6ez1oL1AhRuOez/PjFEF5zvB+1mHYLmMetvPGG8+t0PnNHZPE5fPhwfPSjH8UzzzyTZHtqkzh7Pp3SbtlB3am0+HRzPoeG5NzWWpt0CAnjfNr3UTHtltQyWa92W4t7PvX5KDFGLTufgHvqLZ3P3BFZfH7iE5/AL37xC9x666340pe+lGSbao84ez6dVhareWBPm0qKz6EhcxDN44owIVEI6nwODRUH2QDTbkltk1Xns5b3fOrzUZ3P+vraK6Cm4tOt4i2dz9wReRnh5z//OVatWoV169ZhcHAwyTbVHlq6e/t2WfEKsrrjldbCDupOEuLztdckTSTsKpx1Fdi+Ilyr6TaEBHU+Aek31vGNziepZbK657OhQapQ16L41L85qvNZa8WGADPtls5nzRDoSt51113YtWsXzj///EPPtba24rOf/SzmzJmTWuNqBhWfW7cCI0YE+xk6n9Gor5fPJ6r4fO01YNYs4Cc/AS67LNzPWq9VHidlQqIwbpzsUZ8wwf17rOOddYyk80lqGQ3Gs1btFnDep52HeS7NtNta3cPul3ZL5zN3BEq7/fa3v42TTjqp6LmPf/zjWL16NR555JFUGlZTWMVnkJRbgHs+49DSEl18vv667M187bXwP0vnk5BSpk4FHngA+Pzn3b/H6nxaqdVgjRBAFm3q683g3DBkfspCDNDcXJt7PuMWHKpF55N7PmuOQOJz165dmDVrVsnzXV1d+O///b8n3qiaI474pPMZnjjiM86qJp1PQpw59VRgzBj3173GO4pPUss0NprzR5bmkbw7n319zq/HLThUi+NZSwswdiz3fNYQgcRng8tqw7x587DF62BYEgwVnwMDwcUn93xGp1Li07oKfPBgceVOdT557Qgpxcv5ZJ8htUxDgxmc62MWHKKmpnyLz7Scz1oUn4C4n3Q+a4ZA4nPOnDm45557HF9rqsUUgaRR8QnQ+SwHWXA+geJAWgfXWp14CPHCbbyr5WCNECDbzmctpt3GXaCu1Zg6iPis5vuGFBFIfF577bW4/PLLcdNNN8GwuDVPPvkkRgQtkEPciSM+ueczPFlwPu1f0/kkxB0355Npt6TWaWjIrvik8xmOWl5Ma2+X4wZ37y59LUuOPkmEQFfy6KOPxm233Ybzzz8fX/7yl3HsscfCMAw89NBDuP3229NuY/6h81lesuJ87t8PjB4t/6fzSYg7Xs4nxztSyzQ2ZjPttrkZ6O4ufq4WxGfcBWqvqt95xnrcyqhRxa8x7TZ3BL6SJ5xwAl566SXcfffdePbZZzFy5Ej867/+K2bPnp1m+2qDKOLTbc/nwEB1D+zloNLO58iRwJ49xdcuD5MyIWnhNt7VslNACJBd59Npz2ceCscEFZ90PsNhrXg7d27xawMDUtW5UCh/u0gqhFpGqK+vx9lnn42zzz47rfbUJlbxyXM+06fSzufo0SI+ndJua3XiIcQLp20Gg4PA0BD7DKltrM5nlsRnXvd86ljkl3bLPZ/hUPHpVPGWcW3uCLTnk6QM93yWl5YW+ZwGB8P/bBLOp6ba0vkkJBhOzif7DCHFzmfW0m7zuOezrk7GozTSbmvZ+bSm3doZGMjGPU0Sg+IzCySZdkvx6Y9f2owXSTmfAJ1PQoLitNjGfdKEZNf5bGqSBV5tG5Ct9sXBK3sqbsGhWnc+ncQn49rcQfGZBaKIz4YGWYGj+AxPpcQnnU9CouG02MYK0YRkd8+n09agLLUvDl7iM2qMMDgo/2p1MW3SJNnT6ZR2S+czd1B8ZoEo4rNQKE1rMQwWHAoCnU9Cqgsn55N9hpBsV7sFnLMVqj1GcROfhhHd+az18ayxUQQonc+agOIzC0QRn0Dphv48VJIrB3Q+CakuvPZ81mqwRghA57MSuInPgwdFgALhYwSND2o17RaQ1Fvu+awJKD6zQFTxaS9lzrOQgkHnk5Dqwsv5rPZAlpA4ZHnPJ1AaoxQKcmxGNeMmPq1/K53P8Kj4HBoqfp7OZ+6g+MwCKoaA8M5nHlcV0yYJ8XngQOkA6YddfPLaERIMJxeFwRoh2a52C5Sm3WahbXFxE5/W5+h8hqe9Xe6R7duLn6fzmTsoPrNAfb054AQ95xOg+IxKEuITCL+yaU+7pYtDSDB0fGS1W0KKyarz6ZZ2m4W2xSWI+BwYKK706wcX08yKt2+8Ufx8Xu4bcgiKz6ygqbdx9nxmaeLJMkmJz7A/H8T5rOWJhxA3vJxPjneklqm2PZ9ZaFtcgqTdOn3tBZ1PYORIeeztLX6ezmfuoPjMClHEp9uezzwM7mlSKfFJ55OQaDg5n3QKCMlutVu3PZ95mONaWuRYFI25FHtMEEZ8cjwz49+9e4ufz8t9Qw5B8ZkVVBBxz2f60PkkpLrwqpzJPkNqmYYGEUKGka0YwG3PZxbaFhe3GELHp0LB+XUvVHzWsvOpJkxfX/HzdD5zR9nF55o1a7B48WJ0dXXh+OOPx+rVq12/d//+/TjiiCNw3HHHlbGFFYJpt+Uji84nrx0h7qjAZLYAIcXo/T8wkK15JO9pt0BpDKBfjxnj/LoXOrbV8mIanc+aoezi84orrsDll1+OV155Bddccw0uvfRS1+/92te+hpNOOqmMrasgTLstH5V0PuvqzGvMyp2EBKOuTvoGnU9CitH5vr+fabflws/5HDu2+OsgMAag81lDlFV89vT0YMWKFTj//PMBAOeeey7Wr1+PDRs2lHzvQw89hDVr1uCCCy4oZxMrR2urDDphOlhzc3FFNYrPYFTS+Wxudk9HAnjtCHGjqYnOJyF2NGag81k+/JxPFZ9RnM9aTrul81kzlFV8btq0Ce3t7Wj4x2BZKBTQ0dGBjRs3Fn3f3r178cUvfhE//vGPfd9z6dKlmDZt2qF/vfYqWdXCJZcAX/pSuJ+xi5gsTTxZJqr4NIziiTSK89nU5LwizFVPQryx73FnnyGk2PnMUgxQy3s+x40r/joIHM/ofNYQZU+7LehG7H9gGEbJ91x99dW48sorMXXqVN/3u+qqq7B58+ZD/0aEOSczS5x3HvCd74T7GYrPaEQVnzqR1NdH+3k6n4REx+58Mu2WkGLnM0tpt3Q+6XyGhc5nzVDWEWr69OnYvHkzBgYG0NDQAMMwsGnTJnR0dBR938MPP4y7774b3/rWt7B//37s2LEDRx55JF544YVyNjf72B00CphgRBWf+v3jxgFbt9L5JKScuDmfHO9ILZNV57MW93zaxSedz3Co+LQ6n0ND8i8LCyokMcrqfLa1tWHRokW48cYbAQC33347Ojs70dnZWfR9q1atwoYNG7Bhwwb85je/wdFHH03h6YR9ZTFLE0+WSUJ8Rvl5dT4bG6UUO51PQoLjtuezloM1QrK+57MW027pfEZD026tzqe6+Xm4b8ghyp52u2zZMixbtgxdXV247rrrsHz5cgDAZZddhjvvvLPczalu7OKTnTQYlRKf+/fLNSsUuH+NkLC4nWvMPkNqmaxWu3VKux0YyEd8QuczHZyczyzd0yQxyn41Dz/8cDz66KMlz99www2O33/aaafhqaeeSrtZ1Qn3fEZj2DARgJVwPidMkP+7HZPDAZYQZ1jtlpBSsu581lLabRznU8ezWnY+m5okNrM6n1m6p0lilN35JAnCPZ/RKBRk8qiU8wnIoz2QbmiQthFCSqHzSUgp3PNZftIsOFTL41mhIO4nnc/cQ/FZzXDPZ3RaWyvjfOqE7OR81vKkQ4gf3PNJSClZr3Zby3s+mXYbntZWOp81AMVnNUPxGZ0sOp+8boS4o86nHs/FtFtCsut82uMTwwAGB7PRtrjwqJX0oPNZE1B8VjM6SHHPZ3jKLT6HhuT60PkkJBpNTdKPNBhh2i0h2d3zqdtI8rg4TuczPeh81gQUn9UMnc/olFt86gKBm/OZl3QkQtLCnsZH55OQ7Fa7tVd1z1N84uV8DhtmVm2l8xkeOp81AcVnNeMmPtlJ/YkjPseMKf46CPaJxe58HjzIFU9CvLAXMKFTQEh2nU+geJE1a22Lg5f4bG52rvTrB8czgc5nTUDxWc3Q+YxOHPHZ2iqffZif12tE55OQaLgdLVXrwRqpbZycz6zMJU7OZx4Wx73Sbltaop0lTudToPNZE1B8VjPc8xmduOIzbLVcOp+ExMPN+eR4R2oZJ+czK4G6dZ7LU3zi53w2NAB1dXQ+o9DaKp/b4KB8naf7hhyC4rOaofMZHRWfWjkzCDrR6Mqmk/jctAk49ljgxReLn3dyPg8eNH8/nU9CvHHb81nrwRqpbbJa7RbIb9ptYyNQX+/ufEY5S5zjmWDfL0vnM5dQfFYzFJ/R0ZVLa+qrH0HE52OPAStWAA89VPy8k/NpfZ7OJyHe2J3PrLk8hFQC+zmfdXXyLwvkteAQ4BwDqPMJFP/tQThwQD6bQiG5NlYjra3yqPs+83bfEAAUn9WNWzDGTupPlD0ZQcTn7t3Fj4qT8wkUrwrzuhHijpPzyWCN1Dp25zNLizF5TbsFnLfeqPMJRHM+uQBtOp+67zNr+5hJIlB8VjNuBTjYSf1JQnxaN8Ure/YUPypuzqd1/xonHkLccVpsY58htY59z2eW5v9adz7D1oWo9WJDgLvzmaVFFRIbis9qhmm30aHzSUh14eR8UnySWsde7TZL80he93wCzjGA3fkMW3CI4xmdzxqB4rOaofiMThzx2dzsLj7jOJ+8boS4wz5DSCnqCGUx7baWnE/DkK+jpt3S+RTofNYEFJ/VDI9aiU5U8dncXFzJzl4tN47zyVVPQtxhnyGkFJ3vs5h229RkFkLKW3xiF5c6LkUtOETnU6DzWRNQfFYzWmwjryuLaaLi02nfphv2VU3DMMujK27Op14jJ+dzcBAYGuJ1I8QLOp+ElGJ1PrOYdguIMMtbfGIXnzou0fmMB53PmoDis5opFPKd1pImUZ1P68Ti9PNuzqfTqqg+r9eNq56EuMM9n4SUYnc+sxSk15L4tG7L0Uc6n+Gh81kTUHxWO9YBjp00OGmJT3U83dJunZzPvE3KhKQBq90SUop9z2eW5hFrXYq8xSctLSKqh4bkayfnU1OOg0DxKaj4pPOZayg+q52mpuI9UIUCUF9f2TZVA1HEZ19fcOfTreCQk/OpqbuceAhxx+2cT0JqGavzmbW02zwvsmoMoKLTyfm0vu4H024FTbul85lrKD6rHXvaLTtoMMqddkvnk5B42IM5OgWEZL/aLZDftFvAjAGcnE/r635wPBPofNYEFJ/VDsVnNNJOu92zp7gSrpvzuX8/nU9CguBU3Zt9htQ6Wa52a53n8i4+3ZzPoDEGnU+BzmdNQPFZ7TQ1UXxGIW3nc2CgON3GzfnM44owIWng5Hyyz5BaJ8vVbvOc4RPU+QySdquV87mYRuezRqD4rHaam4udgLwM7GkTVnw6HSBt//mDB81rARTv+6TzSUg86HwSUko1VLutBfGpj1HSbgcGJMag80nns0ag+Kx2mHYbjbDis79fqtp5TSz2IkPWfZ90PgmJB/d8ElJKNVS7zeM8FzTtNojzyQVoE57zWRNQfFY7FJ/RCCs+g6xq2sUnnU9CksO6YGMYHO8IAbJd7baWnM84BYc0BqDzCdTVyX1D5zPXUHxWO/Y9n1wdCkYa4lOdzokTi78G6HwSEhfr/jENSLhgQ2qdLFe7ddrzmaX2xSFJ51MXpzmeCcOH0/nMORSf1U5zswRig4N0AsKQpvM5dWrx14BMLnV15gDq5Hzy2hHiTl2d9BH2GUJMWO22MqThfFJ8Cq2tdD5zDsVntWPfU8EOGoywZdDDOJ8qPu3OZ3MzUCgU/36r88mJhxBvtMAagzVCBF3QPHhQ6hJkKQbgns9wzifTbgU6n7mH4rPasa8s5mVgT5u6Ohnoy+l8WicWazoSXRxCgqHbDLhgQ4hQXy+POhdlKUin80nnMwp0PnMPxWe1Y99TwQ4anJaW8jufCp1PQsJjdz453pFap1AQwalzUZb6RC2d8+nmfAaJMeh8FkPnM/dQfFY7TLuNTtLiM6rzmbdJmZC0oPNJSCmNjdkUn3Q+edRKFOh85h6Kz2qHabfRqaTz2dgoK9bcv0ZIcLjnk5BSrM5nlhyiWtzzGSXtls5nMep86pFaQLbuaxIbis9qh2m30amk81kolLo4vHaEeKN9hmm3hJg0NppOUZb6RJ4zfDQG0M89TsEhLqYV09oqwjOPixYEAMVn9UPnMzppOZ9TphR/DZQ6nwBdHELCon2GabeEmGR1z2ctpt3q3xyl4BCdT2H4cHncu9dMu6XzmSt4Nasd7vmMThzxWV9fvM8GMJ3O0aOBESO8nU+AzichYbE7nxSfhBTPRVkK0q3xyeCg/D8v85xT2u2wYVJJH4h21ArHM6G1VR77+sz4SKs6k1xQdudzzZo1WLx4Mbq6unD88cdj9erVJd/zl7/8BSeccAKOOOIIHHXUUfja174GwzDK3dTqQAe4vr7snfGVdeKIT6ef371brkdjIzByJJ1PQpKG1W4JKYXOZ/lxcj7t8YH1dS8YAxRjdz4bGswz0kkuKLv4vOKKK3D55ZfjlVdewTXXXINLL7205HvGjh2Lm266CatXr8ZTTz2FBx98EDfddFO5m1odqJvW2yuPeRnYy0HS4nPPHmDUKPn/qFF0PglJGla7JaSUrDqfOqflcZ5rahJBZHU+nY5TC+N8Mu1WsDufeblnyCHKKj57enqwYsUKnH/++QCAc889F+vXr8eGDRuKvm/RokWYOXMmAKC5uRkLFy7EunXrytnU6kEHOIrP8LS0SCqQTopeBHU+R46U/1udz6EhWdmk80lIPJqbpT9lsbgKIZWioSGbR1IUCtJn9++X9tXVmWmp1Y7+bW7OZ2OjpIrS+QyPk/NJckVZR4FNmzahvb0dDf+4kQqFAjo6OrBx40bXn3nzzTdx22234X3ve1+5mlldqKBRly1LE0/WCZMWE8X5VPHpVkyAzich4dA+pOMdgzVCiueOrM0j1iJhWWtbXKwxgN35BEzh7Qedz2LU+dy7N5/3DSl/2m3BlrfttZdz9+7dOPvss3HNNdfgmGOOcfyepUuXYtq0aYf+9aoDWCtQfEYnafFpdz737JFy4fYqeAr3rxESDvt4R/FJSLEzlDWXSAVYHkWEXXxa4wP7617Q+SxGnc++PjqfOaWs4nP69OnYvHkzBv6RHmIYBjZt2oSOjo6S792zZw/OPPNMfOADH8BVV13l+p5XXXUVNm/efOjfiBEjUmt/JuGez+ik7XwODsrrbqua3L9GSDjszifHO0Ky7Xxa57mstS0u1hjAnnZrf90LOp/FWNNu83jfkPKKz7a2NixatAg33ngjAOD2229HZ2cnOjs7i76vt7cXZ555Jt7znvfgG9/4RjmbWH3Q+YxOkuLz4EGZQKzOJyDXxcv5tB4bwWtHiDd0PgkpxeoMZW0eqSXnM2raLZ3PYqwFh+h85pKyp90uW7YMy5YtQ1dXF6677josX74cAHDZZZfhzjvvBAD84Ac/wBNPPIHf/va3WLhwIRYuXIhvf/vb5W5qdUDxGZ2w4rOhoXgQtE48+vlbnU9AUnF18nFyPg8e5MRDSFC0D+l+avYZQorn/awF6rWy5zOO8+lWF6JWofOZe8o+Sh1++OF49NFHS56/4YYbDv3/a1/7Gr72ta+Vs1nVC9PQohNWfDpNLP39kl6rwbBdfO7ZYwYDTquiAFOmCQkKF9sIKSXLzmdTE7B9ez5FhIpLre3gNMfv2OH/Ppp2y8U0gc5n7slJzesahgImOkmIT31Nxac97dbP+QSYQkhIUNhnCCkly3s+ayHtVsUjCw4lA53P3MPlhGrH7gRwhSg4SYpPt7TbPXvkrC/A3fmki0NIMLjnk5BSsl7tVtNus9a2uKi41BiCR60kA53P3EPns9qhgIlO1pxPDrCEeMNtBoSUQuezMrS0SMrtrl3m1/bX6XyGh85n7qH4rHYYjEWnXM6nrmp6OZ+NjYDtDFxCiA06n4SUkvU9n/39Mg9mrW1x0RhA93U6zfGDg+LeeUHnsxg6n7mH4rPa0eCLez7DkyXnk0E0If7Yq91yvCMk+9VuAYlR8tZfNQZ4663ir+2v+8UY6nxm7dpVisZG+UfnM7dQfFY7hYIM7nQ+w5M155MQ4g2dT0JKyXraLZDPec4uPt3meL8Y48ABWVhj9pNJayudzxxD8ZkHmpvNtI68De5pkhXns7eXQTQhQWC1W0JKyXLarQqwgYHstS0u9rRbtxjBr+jQwYMcy+wMH07nM8dQfOYBq6hhJw1OUPE5OCiTQ1rO59AQrxshQdA+o32K/YaQbKfd5jk+STLtlvs9i6HzmWsoPvOAVdTkbXBPk6ATg65auk0sfX2lzqdWawvifAK8boQEwb6AQ7eAkOpwPoHstS0uQQoOAf7O54EDHMvs0PnMNRSfeSDPg3uaBBWf+noQ51PFZ12d/D+I8wlw4iEkCPYFHPYbQqpjzyeQvbbFJUnnk2NZMa2tsiXJMOh85hCKzzxABy0aWs47CfG5e7d8bR0kR46k80lIktgXcNhvCCmed7IWqOd5nkvS+WTabTHDh5vnp+btviEUn7kgzyuLaZK086n7PJVRo+h8EpIk1gCtvl4yDAipdeh8VgY6n+nR2mp+bnm7bwjFZy7I8+CeJg0N8i+o+FSnVLE6p7t3mym3it359BKfvG6E+MMFG0JK4Z7PykDnMz20bgaQPTefxIbiMw/keXBPm5aWdJ3P3btN59Mr7ZaBNCH+5DmFj5CoZLnabZ7jE12ApvOZPNbF/rzdN4TiMxcwIItOUuLTzfncs8f8eTqfhMSDzichpWTZ+cxzfGJPu6XzmRx0PnMNxWceoIiJTtrO59CQmZJD55OQeOQ5kCUkKtzzWRk0BujtLf7a/jqdz/DQ+cw1FJ95IM+De9okIT537ZLJw8n5BICtW6Uwin31jteNkHDU1Zl9hcEaIUKWq93meZ5ziwnsX1N8hofOZ66h+MwDeR7c0yYJ8dndLY9Ozicg4rOpCSgUil+n80lIeLTfsM8QItD5rAz2mCBK2q1hiPhk2m0xVvGZt/uGUHzmAqaiRSeO+GxsFCemp0e+9hKf9kkJyPekTEhaaL9hnyFE4J7PypCE89nfL49cTCvGmnZL5zN3UHzmAYqY6MQRn4WCPKfOp1va7a5dzquaeZ6UCUkLOp+EFMNqt5XBHhPY5/kgzqdbNfxah85nrqH4zAN5HtzTJo741Oe2bpX/uzmfgL/zyUCakGBov2GfIURQwVlXJ/+yRJ7jE2tM0NRU+tkHcT4PHpRHjmfF0PnMNRkbpUgkrCtm7KThaGmRlcehIffv8ROfg4PyfzfnE3Be1czyPh1Csor2JfYZQgTtC1nsE3mOT+rqTNHotcBM5zM8dD5zDcVnHsjzymLaqKD0mhz8xKcS1vksFOjiEBIW9hlCilFRl0Vxl/f4RGMAr/iAzmd46HzmGorPPJD3wT1NgkwOQcVnWOfT+jyvGyHB4J5PQorJsvOZ9/hEYwCnBeaGBqC+ns5nFOh85hqKzzyQ98E9TXTi6Otz/560nE/r8wykCQkGq90SUow6Q1nsE3mPT7ycT32ezmd46HzmGorPPMCqqdEJ4nyqMKXzSUjlofNJSDE6f2QxSLf20zzOcxSf6UDnM9dQfOYBXVnMYqW7rBM07bZQcJ4c6HwSUl7ofBJSTJadz0Ih34usXmm3+jzTbsND5zPXUKnkAQZj0QkqPltaZBJ1+3mg1PkcPtz8GTqfhCQDnU9Cisnynk8g3zEKnc90oPOZayg+80CeB/a0CSM+vX4eKBWfhYL5HJ1PQpKBfYaQYrJc7RbId4xC5zMdmprMxfus3tckMhSfeYDuWXSSEp+trVLVzo6KTzqfhCQD+wwhxWTd+cxzn6XzmQ6Fgul+5vG+qXEoPvNAnlcV0yYp8Wnf76no83Q+CUkG9hlCisnynk8g3zEKnc/00H2fdD5zB8VnHsjzwJ42SYlPe8qtQueTkGThnk9CislytVsg3zEKnc/0oPOZWyg+8wAFTHSy4nzy2hESDPYZQorJuvOZ5xglrvOp4pPOZyl0PnMLxWceYDAWnaw4n1z1JCQY7DOEFJP1PZ95jlGCOJ+Dg0B/v/PrmnbL8awUOp+5heIzD+R5YE8bOp+EVBfc80lIMax2WzmCiE/A3f1k2q07dD5zC8VnHsjzwJ42fuLTMJIRn3Q+CUmGPKfwERIFOp+VI0jaLeAeY7DgkDt0PnNL2cXnmjVrsHjxYnR1deH444/H6tWrHb9v+fLlmDNnDmbNmoXLL78cAwMDZW5pFcFgLDp+4lMnhrhpt3Q+CUkGOp+EFMM9n5UjqPPpFmPQ+XSHzmduKbv4vOKKK3D55ZfjlVdewTXXXINLL7205HvWr1+Pb3zjG3j44Yexdu1avPnmm1i+fHm5m1o95HlgTxu/iUGfp/NJSDbgeEdIMax2WzmCOp9uabd0Pt2h85lbyjpS9fT0YMWKFfjzn/8MADj33HPxuc99Dhs2bEBnZ+eh77vttttwzjnnYNKkSQCAz3zmM/je976HK664opzNrR4KBRm42EHDoxPHunXAAw+Uvv7WW8Xf5/bzdD4JKQ90PgkpJuvOZ57nuaDO50MPAVu2lL6+dq08cjwrRcVnVhdVSGTKekU3bdqE9vZ2NPzjRioUCujo6MDGjRuLxOfGjRsxY8aMQ193dnZi48aN5Wxq9TF+PDB2bKVbUX0MHy4T4h/+IP/ccPtsJ06Ux8mTnV/X58eNc359/Hjv9yeEFMM+Q0gxuvjstghaacaPB+rrgREjKt2S5NEYQMclO2PGyOOnP+39Pm7ZU7XMxIlirvCzyR1lX04oFApFXxuG4ft9bt8DAEuXLsXSpUsPfd3b2xuzhVXK3XebgxwJzrBhwB//CLz8svv3NDQAH/6w82vHHis/f9ppzq9/8IPA738PnHGG8+sXXwzMnQsccUSoZhNSs5x+uiwUvfvdlW4JIdmgsRH485+BOXMq3RJnrr4aOOss90XYaua975U5/r3vdX79wgtl76LXWZ8dHUBbWzrtq2a+8AUZ76dMqXRLSMIUDC9llzA9PT2YM2cOtm/fjoaGBhiGgSlTpuCxxx4rcj6///3vY8OGDbj++usBAHfffTe+973v4a9//avv75g2bRo2b96c0l9ACCGEEEIIIcQNLz1W1oJDbW1tWLRoEW688UYAwO23347Ozs4i4QnIXtDf/va36O7uhmEY+MlPfoJPfOIT5WwqIYQQQgghhJAEKXu122XLlmHZsmXo6urCddddd6iK7WWXXYY777wTADBz5kxce+21WLJkCWbNmoW2tjbHqriEEEIIIYQQQqqDsqbdlgOm3RJCCCGEEEJIZchM2i0hhBBCCCGEkNqE4pMQQgghhBBCSOpQfBJCCCGEEEIISR2KT0IIIYQQQgghqUPxSQghhBBCCCEkdSg+CSGEEEIIIYSkTu6OWmlqasLEiRMr3QxHent7MWLEiEo3g4DXIivwOmQHXovswGuRDXgdsgOvRXbgtcgGWb8OW7duxYEDBxxfy534zDI8gzQ78FpkA16H7MBrkR14LbIBr0N24LXIDrwW2aCarwPTbgkhhBBCCCGEpA7FJyGEEEIIIYSQ1KH4LCNXXXVVpZtA/gGvRTbgdcgOvBbZgdciG/A6ZAdei+zAa5ENqvk6cM8nIYQQQgghhJDUofNJCCGEEEIIISR1KD4JIYQQQgghhKQOxWcZWLNmDRYvXoyuri4cf/zxWL16daWbVDPs378fH/rQh9DV1YWFCxfizDPPxIYNGwAAp512GmbOnImFCxdi4cKF+Ld/+7fKNjbndHZ2Yu7cuYc+75tvvhkA0NPTgzPPPBNz5szBUUcdhYcffrjCLc03O3fuPHQNFi5ciK6uLjQ0NOCtt95in0iZz3/+8+js7EShUMDzzz9/6HmvPtDX14f/8l/+C2bPno2uri7ccccdlWh67nC7FpdccgkOP/xwLFy4EKeccgpWrlx56LVPfepTmDZt2qH+cfXVV1eg5fnD7Vp4jUfsF8njdh0WL1586BocddRRKBQKWLVqFQD2ibTwil1zMV8YJHVOP/104+c//7lhGIZx6623GieeeGJlG1RD7Nu3z/jDH/5gDA0NGYZhGD/84Q+Nd73rXYZhGMapp55q3HXXXZVsXk0xY8YM47nnnit5/uKLLza++c1vGoZhGE888YTR0dFh9Pf3l7l1tcv3v/9946yzzjIMg30ibR588EFj06ZNJX3Bqw9ce+21xkUXXWQYhmGsW7fOmDRpkvHWW2+Vu+m5w+1a/O53vzv02d91113GnDlzDr120UUXGT/84Q/L3ta843YtvMYj9ovkcbsOVm699VbjqKOOOvQ1+0Q6eMWueZgv6HymTE9PD1asWIHzzz8fAHDuuedi/fr1h1YwSLo0Nzfjfe97HwqFAgDgxBNPxLp16yrcKmLllltuwZVXXgkAeNvb3oZJkybR/SwjP//5z3HppZdWuhk1wSmnnIJp06aVPO/VB26++eZDrx122GE45ZRT8Lvf/a58jc4pbtfiAx/4ABoaGgDIfPHaa69haGio3M2rKdyuhRfsF8kT5Dr87Gc/43xRBrxi1zzMFxSfKbNp0ya0t7cfmswKhQI6OjqwcePGCresNvmP//gPnH322Ye+vvrqq3H00Ufj4x//OEVpGTjvvPNw9NFH47LLLsPWrVuxfft2DA0NYeLEiYe+p7Ozk/2jTDz66KPYvn07zjrrrEPPsU+UF78+sHHjRsyYMcPxNZIuP/jBD/C+970PdXVmqLR06VLMnz8fZ511VlFKLkkHt/GI/aL8vP766/jrX/96yExR2CfSR2PXvMwXFJ9lQFcuFIOn21SE73znO1izZg2+/e1vAwB+/etf48UXX8SqVavw9re/vSgAJ8nzt7/9Dc8++yxWrFiB8ePH46KLLgLA/lFJfvazn+HCCy88tDjGPlEZ/PqA9XX2j/Jw44034pZbbsGyZcsOPfftb38ba9euxapVq3DppZfive99L3p7eyvYynzjNx6xX5SXX/ziFzjrrLMwYcKEQ8+xT6SPPXbNw3xB8Zky06dPx+bNmzEwMABAboRNmzaho6Ojwi2rLf71X/8Vd9xxB+655x60trYCkGsDSEf93Oc+h3Xr1mH79u2VbGau0Xu+sbERX/ziF/HQQw9h/PjxAICtW7ce+r7XXnuN/aMM7N27FzfffDMuueSSQ8+xT5Qfvz7Q0dFRtE2D/SN9br75Zlx77bW499570dbWduj5qVOnHnJBzznnHIwaNQovv/xypZqZe7zGI/aL8mIYhuMWDfaJdLHHrnmZLyg+U6atrQ2LFi3CjTfeCAC4/fbb0dnZic7Ozso2rIZYunQpbrrpJtx7770YM2YMAGBgYADd3d2Hvuf222/HpEmTDnVskix79+7Fzp07D3190003YdGiRQCAj370o7j++usBAE8++STefPNNnHzyyZVoZk1x6623Yv78+Zg7dy4A9olK4tUHrK+tX78eDz74ID7wgQ9UrK1555ZbbsHXv/513HfffSVB2+bNmw/9/7HHHsP27dsxe/bscjexJvAbj9gvysuDDz6IgwcP4l3velfR8+wT6eEUuwL5mC8KRlY92Rzx8ssv41Of+hS2b9+OUaNG4Ze//CWOPPLISjerJti8eTOmT5+OmTNnYuTIkQCApqYm/OUvf8Gpp56KAwcOoK6uDhMmTMDSpUuxYMGCCrc4n6xbtw7nnnsuBgcHYRgGZs6ciR/84Afo7OxEd3c3LrjgAqxfvx7Dhg3Dj370I5x66qmVbnLuefvb345LLrkEF198MQBZIGCfSJcrr7wSv/vd7/Dmm29iwoQJGDFiBNauXevZB/bu3YtLLrkETz/9NOrq6vCd73wHH/nIRyr8l1Q/bteisbERkydPLlp0uf/++zF+/HicccYZ6O7uRn19PVpaWvCd73wHp59+egX/inzgdC2effZZz/GI/SJ53PoEAFxwwQWYOXMmrr322qKfYZ9IB7fY9fHHH8/FfEHxSQghhBBCCCEkdZh2SwghhBBCCCEkdSg+CSGEEEIIIYSkDsUnIYQQQgghhJDUofgkhBBCCCGEEJI6FJ+EEEIIIYQQQlKH4pMQQgghhBBCSOpQfBJCCCGEEEIISR2KT0IIIYQQQgghqdNQ6QYQQgghBLjmmmuwa9cudHd3Y+XKlejs7MTdd9+N1tbWSjeNEEIISQQ6n4QQQkgGWLFiBXp6enDTTTdh/fr12Lt3Lx577LFKN4sQQghJDDqfhBBCSAZ45pln8Nhjj6GlpQUAcPDgQUycOLHCrSKEEEKSg84nIYQQUmE2bNiAYcOGYc6cOQCA/fv3Y/369Zg3b16FW0YIIYQkB8UnIYQQUmFWrFiBt73tbYe+fvbZZ3HkkUeioYEJSoQQQvIDxSchhBBSYZ555hkcd9xxh75++umnccwxx1SwRYQQQkjyFAzDMCrdCEIIIYQQQggh+YbOJyGEEEIIIYSQ1KH4JIQQQgghhBCSOhSfhBBCCCGEEEJSh+KTEEIIIYQQQkjqUHwSQgghhBBCCEkdik9CCCGEEEIIIalD8UkIIYQQQgghJHUoPgkhhBBCCCGEpM7/D+1a/zuz2CY5AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "C_v = rn.local_clustering() #rn being the recurrence network\n", - "\n", - "plt.figure(figsize=(14, 4), dpi=80)\n", - "plt.plot(C_v, \"r\")\n", - "plt.xlabel(\"$n$\")\n", - "plt.ylabel(\"$C_v$\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2e0821fe", - "metadata": {}, - "source": [ - "##### Global scale network properties: Global clustering coefficient and network transitivity\n", - "\n", - "Global scale network properties take all vertices into account.\n", - "\n", - "The __global clustering coefficient__ is considered as the average value of the clustering coefficient taken over all vertices of a network. It is calculated as\n", - "\n", - "$$C=\\frac{1}{N}\\sum_{v=1}^{N}C_v$$" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "85ed81cf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating global clustering coefficient (C_2)...\n", - "C = 0.7532331807249787\n" - ] - } - ], - "source": [ - "C = rn.global_clustering()\n", - "print(f\"C = {C}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6c9458f8", - "metadata": {}, - "source": [ - "The __transitivity__ $T$ of a network measures the probability that two neighbors (i.e. recurrences) of any state are also neighbors. It can be calculated from the link matrix $A_{i,j}$ of the network as\n", - "\n", - "$$T = \\frac{\\sum_{i,j,k=1}^N A_{i,j}A_{j,k}A_{k,i}}{\\sum_{i,j,k=1}^N A_{i,j}A_{k,i}}$$\n", - "\n", - "where $A_{i,j} = R_{i,j} - \\delta_{i,j}$, with the recurrence matrix $R_{i,j}$ and the Kronecker-delta $\\delta_{i,j}$." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "02a5687e", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating transitivity coefficient (C_1)...\n", - "T = 0.8021460350693536\n" - ] - } - ], - "source": [ - "T = rn.transitivity()\n", - "print(f\"T = {T}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d88f8295", - "metadata": {}, - "source": [ - "### 2.2 Recurrence properties under fixed recurrence rate $RR$\n", - "\n", - "For comparison, we now calculate the same recurrence properties using a fixed recurrence rate." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e5b29970", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating recurrence plot at fixed recurrence rate...\n", - "Calculating the supremum distance matrix...\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating recurrence plot at fixed recurrence rate...\n", - "Calculating the supremum distance matrix...\n", - "Calculating local clustering coefficients...\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating global clustering coefficient (C_2)...\n", - "C = 0.7544336150477271\n", - "\n", - "Calculating transitivity coefficient (C_1)...\n", - "T = 0.8005944339367739\n" - ] - } - ], - "source": [ - "RR = 0.05 # choose fixed recurrence rate\n", - "\n", - "# Default distance metric in phase space: \"supremum\"\n", - "# Can also be set to \"euclidean\" or \"manhattan\".\n", - "METRIC = \"supremum\"\n", - "\n", - "rp = RecurrencePlot(time_series, metric=METRIC,\n", - " normalize=False, recurrence_rate=RR)\n", - "\n", - "plt.matshow(rp.recurrence_matrix())\n", - "plt.xlabel(\"$n$\")\n", - "plt.ylabel(\"$n$\")\n", - "plt.show()\n", - "\n", - "# calculate recurrence network\n", - "rn = RecurrenceNetwork(time_series, metric=METRIC,\n", - " normalize=False, recurrence_rate=RR)\n", - "\n", - "## Local measures: \n", - "# Local Clustering Coefficients\n", - "C_v = rn.local_clustering()\n", - "\n", - "# plot\n", - "plt.figure(figsize=(14, 4), dpi=80)\n", - "plt.plot(C_v, \"r\")\n", - "plt.xlabel(\"$n$\")\n", - "plt.ylabel(\"$C_v$\")\n", - "plt.show()\n", - "\n", - "## Global measures: \n", - "# Global Clustering Coefficient\n", - "C = rn.global_clustering()\n", - "print(f\"C = {C}\")\n", - "print()\n", - "\n", - "# Transitivity\n", - "T = rn.transitivity()\n", - "print(f\"T = {T}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "479d003e", - "metadata": {}, - "source": [ - "## 3. Example: Lorenz Attractor \n", - "\n", - "_Calculating properties of a time series by recurrence network analysis using Lorenz attractor example._\n", - "\n", - "In this example we will be using a complex chaotic system to calculate recurrence network properties from it: The Lorenz attractor. It is defined by\n", - "$$\\frac{d}{dt}(x,y,z)=\\left(10(y-x)\\right(28-z),xy-\\frac{8}{3}z)$$" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6178bd02", - "metadata": {}, - "outputs": [], - "source": [ - "def Lorenz_timeseries(dt=0.01, num_steps=10000, x0=0., y0=1., z0=1.05, s=10., r=28., b=2.667, spinup=100):\n", - " \"\"\"\n", - " Given:\n", - " dt: length of timestep\n", - " num_steps: number of timesteps\n", - " x0, y0, z0: initial values for timeseries\n", - " s, r, b: parameters defining the lorenz attractor\n", - " spinup: number of spinup-timesteps before storing results to output\n", - " Returns:\n", - " timeseries: numpy array of three dimensional timeseries on Lorenz attractor with length num_steps\n", - " \"\"\"\n", - " \n", - " # spinup\n", - " for n in range(spinup):\n", - " x0 += (s*(y0 - x0)) * dt\n", - " y0 += (r*x0 - y0 - x0*z0) * dt\n", - " z0 += (x0*y0 - b*z0) * dt\n", - "\n", - " # initialize timeseries with spun-up initial values\n", - " x = [x0]\n", - " y = [y0]\n", - " z = [z0]\n", - "\n", - " # calculate timeseries\n", - " for n in range(num_steps-1):\n", - " # get current values\n", - " xn = x[n]\n", - " yn = y[n]\n", - " zn = z[n]\n", - " # calculate next timestep values\n", - " xstep = xn + (s*(yn - xn)) * dt\n", - " ystep = yn + (r*xn - yn - xn*zn) * dt\n", - " zstep = zn + (xn*yn - b*zn) * dt\n", - " # append to timeseries\n", - " x.append(xstep)\n", - " y.append(ystep)\n", - " z.append(zstep)\n", - "\n", - " timeseries = np.transpose([x,y,z])\n", - " \n", - " return timeseries" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "cdb6dfe6", - "metadata": {}, - "source": [ - "Now calculate a timeseries on the Lorenz attractor with the defined function. Try different timesteps, initial values and Lorenz parameters, or just use the default ones defined above." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "05eb6b57", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# calculate timeseries on Lorenz attractor \n", - "Lorenz = Lorenz_timeseries(spinup=100)\n", - "\n", - "# Plot\n", - "ax = plt.figure().add_subplot(projection='3d')\n", - "\n", - "ax.plot(Lorenz[:,0], Lorenz[:,1], Lorenz[:,2], lw=0.5)\n", - "ax.set_xlabel(\"X Axis\")\n", - "ax.set_ylabel(\"Y Axis\")\n", - "ax.set_zlabel(\"Z Axis\")\n", - "ax.set_title(\"Lorenz Attractor\")\n", - "\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "4bea44c2", - "metadata": {}, - "source": [ - "Calculate the recurrence plot for this three dimensional timeseries while using a fixed recurrence rate." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d1276409", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating recurrence plot at fixed recurrence rate...\n", - "Calculating the supremum distance matrix...\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# choose fixed recurrence rate\n", - "RR = 0.05\n", - "\n", - "#For the obtained timeseries, calculate recurrence plot for a fixed global recurrence rate\n", - "Lorenz_rp = RecurrencePlot(Lorenz, recurrence_rate=RR) #supremum metric is used by default\n", - "\n", - "#Show plot\n", - "plt.matshow(Lorenz_rp.recurrence_matrix())\n", - "plt.xlabel(\"$n$\")\n", - "plt.ylabel(\"$n$\")\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "588bd7b5", - "metadata": {}, - "source": [ - "#### Local clustering coefficient\n", - "\n", - "Calculate local clustering coefficient and dimension of generated Lorenz timeseries and visualize result on plot." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "126f01df", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating recurrence plot at fixed recurrence rate...\n", - "Calculating the supremum distance matrix...\n", - "Calculating local clustering coefficients...\n", - "C_v = [0.65332227 0.65165906 0.64570268 ... 0.65980106 0.65624366 0.6570686 ]\n" - ] - } - ], - "source": [ - "# Calculate RecurrenceNetwork of generated Lorenz-timeseries\n", - "Lorenz_rn = RecurrenceNetwork(Lorenz, metric = \"supremum\", recurrence_rate=0.05)\n", - "\n", - "# From obtained RecurrenceNetwork calculate local clustering coefficients\n", - "Lorenz_C_v = Lorenz_rn.local_clustering() \n", - "\n", - "print(f\"C_v = {Lorenz_C_v}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "bf75a0ee", - "metadata": {}, - "source": [ - "From the local clustering coefficient we can calculate the local clustering dimension $CD_v$ by taking the log of the local clustering coefficient $C_v$ and dividing it by $log(3/4)$:\n", - "\n", - "$$CD_v=\\frac{log(C_v)}{log(\\frac{3}{4})}$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "a1a156c2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CD_v = [1.47970552 1.48856605 1.52048446 ... 1.44540435 1.46419666 1.45982974]\n" - ] - } - ], - "source": [ - "# Calculate CD_v\n", - "CD_v = np.log(Lorenz_C_v)/np.log(3/4)\n", - "\n", - "print(f\"CD_v = {CD_v}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "12015724", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot local clustering dimension on Lorenz attractor\n", - "# get colormap\n", - "cm = plt.cm.get_cmap('jet')\n", - "ax = plt.figure().add_subplot(projection='3d')\n", - "\n", - "# plot underlying trajectory\n", - "ax.plot(Lorenz[:, 0], Lorenz[:, 1], Lorenz[:, 2], lw=0.5, color=\"grey\")\n", - "# plot LCD \n", - "scat_plot = ax.scatter(Lorenz[:, 0], Lorenz[:, 1], Lorenz[:, 2], lw=0.5, c=CD_v, s=1, cmap=cm)\n", - "cb = plt.colorbar(scat_plot, pad=0.2)\n", - "\n", - "ax.set_xlabel(\"X Axis\")\n", - "ax.set_ylabel(\"Y Axis\")\n", - "ax.set_zlabel(\"Z Axis\")\n", - "\n", - "ax.set_title(\"$CD_v$ for fixed $RR = 0.05$\")\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "e3d9bb11", - "metadata": {}, - "source": [ - "#### Global clustering coefficient" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "84b44f07", - "metadata": {}, - "source": [ - "Here, we can calculate the global clustering coefficient and dimension of the generated Lorenz timeseries and visualize the results on plot" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a75b1826", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating global clustering coefficient (C_2)...\n", - "C = 0.6179359105713065\n" - ] - } - ], - "source": [ - "# Apply global_clustering method to get global clustering coefficient (C)\n", - "C = Lorenz_rn.global_clustering()\n", - "\n", - "print(f\"C = {C}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "02dc6c56", - "metadata": {}, - "source": [ - "From the global clustering coefficient we can calculate the global clustering dimension (CD) by taking the log of the global clustering coefficient and dividing it by log (3/4)\n", - "\n", - "$$CD=\\frac{log(C)}{log(\\frac{3}{4})}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "5eaa8851", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CD = 1.67327260741976\n" - ] - } - ], - "source": [ - "CD = np.log10(C)/np.log10(3/4)\n", - "print(f\"CD = {CD}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c192e4cc", - "metadata": {}, - "source": [ - "#### Transitivity" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0a7c3def", - "metadata": {}, - "source": [ - "Finally we will calculate the transitivity $T$ and its corresponding dimension $TD$ of the generated Lorenz timeseries and visualize the results on a plot." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "aae67f5a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating transitivity coefficient (C_1)...\n", - "T = 0.6405742697542526\n" - ] - } - ], - "source": [ - "# Calculate transitivity from RecurrenceNetwork of our timeseries\n", - "Lorenz_T = Lorenz_rn.transitivity() \n", - "print(f\"T = {Lorenz_T}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ebca8960", - "metadata": {}, - "source": [ - "From the transitivity we can calculate the transitivity dimension $TD$ by taking the log of the transitivity value and dividing it by log (3/4)\n", - "\n", - "$$TD=\\frac{log(T)}{log(\\frac{3}{4})}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "bb257ba4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TD = 1.5482028639163619\n" - ] - } - ], - "source": [ - "TD = np.log(Lorenz_T)/np.log(3/4)\n", - "print(f\"TD = {TD}\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "2fea4b6b", - "metadata": {}, - "source": [ - "### 3.1 Bonus exercise: Transitivity dimension for sweeping recurrence rate\n", - "\n", - "We can also calculate the transitivity dimension for an array of different recurrence rates. (Note: this will be somewhat computationally expensive and might take some time to run through). We first create a function to do this:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "e8c2c271", - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import clear_output\n", - "\n", - "def TD_RR_sweep(timeseries, rr):\n", - " \"\"\"\n", - " given:\n", - " timeseries: timeseries to calculate transitivity for, given different values of epsilon\n", - " eps: array of values of epsilon to calculate transitivity for\n", - " returns:\n", - " transitivity: numpy array of calculated transitivity for each value of EPS\n", - " \"\"\"\n", - "\n", - " TD = []\n", - " it = 0\n", - " its = len(rr)\n", - " for r in rr:\n", - " ## display progress\n", - " # clear previous output\n", - " clear_output(wait=True)\n", - " # print iteration\n", - " it += 1\n", - " print(f\"Iteration {it}/{its}: Calculating TD for rr = {r}\")\n", - " \n", - " ## do calculations\n", - " # calculate transitivity from RecurrenceNetwork\n", - " T_r = RecurrenceNetwork(timeseries, recurrence_rate=r).transitivity() # ~30s runtime\n", - " # calculate transitivity dimension\n", - " TD_r = (np.log10(T_r))/np.log10(3/4)\n", - " # append to list\n", - " TD.append(TD_r)\n", - "\n", - " print(\"Finished calculating transitivity dimensions.\")\n", - " return np.array(TD)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f0b7b886", - "metadata": {}, - "source": [ - "Now we can choose an array of recurrence rates to calculate the transitivity dimensions for:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "524378fe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 5/5: Calculating TD for rr = 0.17999999999999997\n", - "Calculating recurrence plot at fixed recurrence rate...\n", - "Calculating the supremum distance matrix...\n", - "Calculating transitivity coefficient (C_1)...\n", - "Finished calculating transitivity dimensions.\n" - ] - } - ], - "source": [ - "# define array of epsilon to calculate transitivity dimensions for\n", - "RR = np.arange(0.02, .22, 0.04) # for quick test (~5 min)\n", - "#RR = np.arange(0.05, 1.05, 0.05) # for a more extensive calculation (~ 20 min)\n", - "\n", - "# calculate transitivity for different values of epsilon using above function\n", - "transitivity_dimension = TD_RR_sweep(timeseries=Lorenz, rr=RR) " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "0a009840", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot series\n", - "plt.figure(figsize=(14, 4), dpi=80)\n", - "plt.plot(RR, transitivity_dimension, label= \"Lorenz attractor (10000 timesteps)\") \n", - "plt.xlabel('Recurrence Rate $RR$')\n", - "plt.ylabel('Transitivity Dimension $TD$')\n", - "plt.legend(loc = \"upper right\")\n", - "plt.show()" - ] - } - ], - "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": "3.7.16" - }, - "vscode": { - "interpreter": { - "hash": "45ca354fb3f6330470f28203103c66b355f6bc9fd4170de504cc6983f9e7e887" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/pyproject.toml b/pyproject.toml index ba54e37a..c3ce5e52 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # package ====================================================================== @@ -36,7 +36,8 @@ omit = ["*/utils/navigator.py"] [tool.pylint.main] ignore = [ - "CVS", ".cache", ".tox", ".ropeproject", "build", "mpi.py" + ".cache", "__pycache__", ".pytest_cache", ".tox", ".venv", ".ropeproject", + "build", "mpi.py" ] ignore-patterns = ["navigator", "numerics"] persistent = false diff --git a/setup.cfg b/setup.cfg index 11af02c7..a628608b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # package ====================================================================== @@ -18,7 +18,7 @@ keywords = nonlinear climate recurrence plot surrogates spatial model license = BSD license_files = LICENSE.txt -url = http://www.pik-potsdam.de/~donges/pyunicorn/ +url = https://www.pik-potsdam.de/members/donges/software-2/software project_urls = Documentation = http://www.pik-potsdam.de/~donges/pyunicorn/ Source Code = https://github.com/pik-copan/pyunicorn @@ -47,7 +47,8 @@ install_requires = numpy >= 1.24 scipy >= 1.10 igraph >= 0.11 - h5netcdf >= 1.1 + h5netcdf >= 1.1 ; python_version >= "3.9" + h5netcdf == 1.1.* ; python_version < "3.9" tqdm >= 4.66 python_requires = >=3.8 packages = find: @@ -63,25 +64,29 @@ include = pyunicorn* [options.extras_require] dev = Cython >= 3.0 -docs = - sphinx >= 7.0 - matplotlib tests = - tox >= 4.3 - flake8 >= 6.0 - pylint >= 2.17 - pytest >= 7.3 - pytest-xdist >= 3.3 + matplotlib >= 3.6 + cartopy >= 0.21 ; python_version >= "3.9" + cartopy == 0.21.1 ; python_version < "3.9" + networkx >= 3.1 ; python_version >= "3.9" + networkx == 3.1.* ; python_version < "3.9" + tox >= 4.11 + flake8 >= 7.0 + pylint >= 3.0 + pytest >= 8.0 + pytest-xdist >= 3.5 pytest-cov >= 4.1 - networkx >= 3.1 ; python_version >= "3.9" - networkx <= 3.1 ; python_version < "3.9" - cartopy >= 0.21 - matplotlib +docs = + sphinx >= 7.0 + nbsphinx >= 0.9.3 + nbsphinx-link >= 1.3.0 + ipython >= 8.4 + pandoc >= 2.3 + matplotlib >= 3.6 # test suite =================================================================== [tox:tox] -minversion = 4.3 requires = setuptools >= 65 isolated_build = false @@ -105,6 +110,7 @@ allowlist_externals = pylint pytest sphinx-build + pandoc [testenv:style] commands = diff --git a/setup.py b/setup.py index 430e5ed3..d667174f 100644 --- a/setup.py +++ b/setup.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/__init__.py b/src/pyunicorn/__init__.py index 93a9215f..aa3436d4 100644 --- a/src/pyunicorn/__init__.py +++ b/src/pyunicorn/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/__init__.py b/src/pyunicorn/climate/__init__.py index 3ff47560..67d27c1c 100644 --- a/src/pyunicorn/climate/__init__.py +++ b/src/pyunicorn/climate/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/_ext/__init__.py b/src/pyunicorn/climate/_ext/__init__.py index a8b29d54..6fb1b624 100644 --- a/src/pyunicorn/climate/_ext/__init__.py +++ b/src/pyunicorn/climate/_ext/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/_ext/numerics.pyx b/src/pyunicorn/climate/_ext/numerics.pyx index 4c493ecf..fb538cc8 100644 --- a/src/pyunicorn/climate/_ext/numerics.pyx +++ b/src/pyunicorn/climate/_ext/numerics.pyx @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/climate_data.py b/src/pyunicorn/climate/climate_data.py index a832e981..cba9e550 100644 --- a/src/pyunicorn/climate/climate_data.py +++ b/src/pyunicorn/climate/climate_data.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/climate_network.py b/src/pyunicorn/climate/climate_network.py index 2bf271f9..7650e924 100644 --- a/src/pyunicorn/climate/climate_network.py +++ b/src/pyunicorn/climate/climate_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/coupled_climate_network.py b/src/pyunicorn/climate/coupled_climate_network.py index 49a02b7d..6bae9fd7 100644 --- a/src/pyunicorn/climate/coupled_climate_network.py +++ b/src/pyunicorn/climate/coupled_climate_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/coupled_tsonis.py b/src/pyunicorn/climate/coupled_tsonis.py index 959a7090..22d4e294 100644 --- a/src/pyunicorn/climate/coupled_tsonis.py +++ b/src/pyunicorn/climate/coupled_tsonis.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/eventseries_climatenetwork.py b/src/pyunicorn/climate/eventseries_climatenetwork.py index 96926ca7..8c5605b4 100644 --- a/src/pyunicorn/climate/eventseries_climatenetwork.py +++ b/src/pyunicorn/climate/eventseries_climatenetwork.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/havlin.py b/src/pyunicorn/climate/havlin.py index b4fc70c4..8aec3472 100644 --- a/src/pyunicorn/climate/havlin.py +++ b/src/pyunicorn/climate/havlin.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/hilbert.py b/src/pyunicorn/climate/hilbert.py index 9b721b96..3f72da76 100644 --- a/src/pyunicorn/climate/hilbert.py +++ b/src/pyunicorn/climate/hilbert.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/map_plot.py b/src/pyunicorn/climate/map_plot.py index 1611f1aa..3982da6d 100644 --- a/src/pyunicorn/climate/map_plot.py +++ b/src/pyunicorn/climate/map_plot.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/mutual_info.py b/src/pyunicorn/climate/mutual_info.py index 6a47d09d..781aa2b1 100644 --- a/src/pyunicorn/climate/mutual_info.py +++ b/src/pyunicorn/climate/mutual_info.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/partial_correlation.py b/src/pyunicorn/climate/partial_correlation.py index 7980d72a..f9fc8422 100644 --- a/src/pyunicorn/climate/partial_correlation.py +++ b/src/pyunicorn/climate/partial_correlation.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/rainfall.py b/src/pyunicorn/climate/rainfall.py index ed11da2a..7b75c094 100644 --- a/src/pyunicorn/climate/rainfall.py +++ b/src/pyunicorn/climate/rainfall.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/spearman.py b/src/pyunicorn/climate/spearman.py index fcd894a2..2a7af179 100644 --- a/src/pyunicorn/climate/spearman.py +++ b/src/pyunicorn/climate/spearman.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/climate/tsonis.py b/src/pyunicorn/climate/tsonis.py index f95a9e8d..a6398851 100644 --- a/src/pyunicorn/climate/tsonis.py +++ b/src/pyunicorn/climate/tsonis.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/__init__.py b/src/pyunicorn/core/__init__.py index e9c16302..6d224e11 100644 --- a/src/pyunicorn/core/__init__.py +++ b/src/pyunicorn/core/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/_ext/__init__.py b/src/pyunicorn/core/_ext/__init__.py index a8b29d54..6fb1b624 100644 --- a/src/pyunicorn/core/_ext/__init__.py +++ b/src/pyunicorn/core/_ext/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/_ext/numerics.pyx b/src/pyunicorn/core/_ext/numerics.pyx index 829c5b01..dda6c0d3 100644 --- a/src/pyunicorn/core/_ext/numerics.pyx +++ b/src/pyunicorn/core/_ext/numerics.pyx @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/_ext/types.pxd b/src/pyunicorn/core/_ext/types.pxd index b7bf50da..ba8c7343 100644 --- a/src/pyunicorn/core/_ext/types.pxd +++ b/src/pyunicorn/core/_ext/types.pxd @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/_ext/types.py b/src/pyunicorn/core/_ext/types.py index 3031b6d8..5efb4937 100644 --- a/src/pyunicorn/core/_ext/types.py +++ b/src/pyunicorn/core/_ext/types.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/data.py b/src/pyunicorn/core/data.py index 6007febd..421df850 100644 --- a/src/pyunicorn/core/data.py +++ b/src/pyunicorn/core/data.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/geo_grid.py b/src/pyunicorn/core/geo_grid.py index ed88d7e6..37f166b1 100644 --- a/src/pyunicorn/core/geo_grid.py +++ b/src/pyunicorn/core/geo_grid.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/geo_network.py b/src/pyunicorn/core/geo_network.py index 24a0e0c5..5358d2d4 100644 --- a/src/pyunicorn/core/geo_network.py +++ b/src/pyunicorn/core/geo_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/grid.py b/src/pyunicorn/core/grid.py index 29aeb359..4548084d 100644 --- a/src/pyunicorn/core/grid.py +++ b/src/pyunicorn/core/grid.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/interacting_networks.py b/src/pyunicorn/core/interacting_networks.py index 5f96dfcf..a5e82ba5 100644 --- a/src/pyunicorn/core/interacting_networks.py +++ b/src/pyunicorn/core/interacting_networks.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/netcdf_dictionary.py b/src/pyunicorn/core/netcdf_dictionary.py index d2c71a0a..80ce6854 100644 --- a/src/pyunicorn/core/netcdf_dictionary.py +++ b/src/pyunicorn/core/netcdf_dictionary.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/network.py b/src/pyunicorn/core/network.py index c1a1813b..69f09415 100644 --- a/src/pyunicorn/core/network.py +++ b/src/pyunicorn/core/network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/core/spatial_network.py b/src/pyunicorn/core/spatial_network.py index 91944fc3..ca8fc6f3 100644 --- a/src/pyunicorn/core/spatial_network.py +++ b/src/pyunicorn/core/spatial_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/eventseries/__init__.py b/src/pyunicorn/eventseries/__init__.py index 852fd1bf..2462ccf7 100644 --- a/src/pyunicorn/eventseries/__init__.py +++ b/src/pyunicorn/eventseries/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/eventseries/event_series.py b/src/pyunicorn/eventseries/event_series.py index 212d3cda..e13dca7b 100644 --- a/src/pyunicorn/eventseries/event_series.py +++ b/src/pyunicorn/eventseries/event_series.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors @@ -55,13 +55,13 @@ def __init__(self, data, timestamps=None, taumax=np.inf, lag=0.0, eventmatrix could look like array([[0, 1, 0], - [0, 0, 0], + [1, 0, 1], [0, 0, 0], [1, 0, 1], [0, 1, 0], [0, 0, 0], - [0, 0, 0], - [0, 0, 0], + [1, 0, 0], + [0, 0, 1], [0, 1, 0], [0, 0, 0]]) diff --git a/src/pyunicorn/funcnet/__init__.py b/src/pyunicorn/funcnet/__init__.py index e7e69dd8..33bcced8 100644 --- a/src/pyunicorn/funcnet/__init__.py +++ b/src/pyunicorn/funcnet/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/funcnet/_ext/__init__.py b/src/pyunicorn/funcnet/_ext/__init__.py index a8b29d54..6fb1b624 100644 --- a/src/pyunicorn/funcnet/_ext/__init__.py +++ b/src/pyunicorn/funcnet/_ext/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/funcnet/_ext/numerics.pyx b/src/pyunicorn/funcnet/_ext/numerics.pyx index e4fe09d0..75b59764 100644 --- a/src/pyunicorn/funcnet/_ext/numerics.pyx +++ b/src/pyunicorn/funcnet/_ext/numerics.pyx @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/funcnet/coupling_analysis.py b/src/pyunicorn/funcnet/coupling_analysis.py index 3e428477..ca1f6148 100644 --- a/src/pyunicorn/funcnet/coupling_analysis.py +++ b/src/pyunicorn/funcnet/coupling_analysis.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/funcnet/coupling_analysis_pure_python.py b/src/pyunicorn/funcnet/coupling_analysis_pure_python.py index 59a9453c..84025713 100644 --- a/src/pyunicorn/funcnet/coupling_analysis_pure_python.py +++ b/src/pyunicorn/funcnet/coupling_analysis_pure_python.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/__init__.py b/src/pyunicorn/timeseries/__init__.py index a80eada1..6fe190a0 100644 --- a/src/pyunicorn/timeseries/__init__.py +++ b/src/pyunicorn/timeseries/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/_ext/__init__.py b/src/pyunicorn/timeseries/_ext/__init__.py index a8b29d54..6fb1b624 100644 --- a/src/pyunicorn/timeseries/_ext/__init__.py +++ b/src/pyunicorn/timeseries/_ext/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/_ext/numerics.pyx b/src/pyunicorn/timeseries/_ext/numerics.pyx index 30e82b35..83004406 100644 --- a/src/pyunicorn/timeseries/_ext/numerics.pyx +++ b/src/pyunicorn/timeseries/_ext/numerics.pyx @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/cross_recurrence_plot.py b/src/pyunicorn/timeseries/cross_recurrence_plot.py index 709643a6..de1bc29d 100644 --- a/src/pyunicorn/timeseries/cross_recurrence_plot.py +++ b/src/pyunicorn/timeseries/cross_recurrence_plot.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/inter_system_recurrence_network.py b/src/pyunicorn/timeseries/inter_system_recurrence_network.py index e47d8840..dd35f740 100644 --- a/src/pyunicorn/timeseries/inter_system_recurrence_network.py +++ b/src/pyunicorn/timeseries/inter_system_recurrence_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/joint_recurrence_network.py b/src/pyunicorn/timeseries/joint_recurrence_network.py index 206da754..c0f1da41 100644 --- a/src/pyunicorn/timeseries/joint_recurrence_network.py +++ b/src/pyunicorn/timeseries/joint_recurrence_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/joint_recurrence_plot.py b/src/pyunicorn/timeseries/joint_recurrence_plot.py index 809d557a..5909e5a4 100644 --- a/src/pyunicorn/timeseries/joint_recurrence_plot.py +++ b/src/pyunicorn/timeseries/joint_recurrence_plot.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/recurrence_network.py b/src/pyunicorn/timeseries/recurrence_network.py index 337d6602..9eeb5f6b 100644 --- a/src/pyunicorn/timeseries/recurrence_network.py +++ b/src/pyunicorn/timeseries/recurrence_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/recurrence_plot.py b/src/pyunicorn/timeseries/recurrence_plot.py index 03ea0ff9..b9f96680 100644 --- a/src/pyunicorn/timeseries/recurrence_plot.py +++ b/src/pyunicorn/timeseries/recurrence_plot.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/surrogates.py b/src/pyunicorn/timeseries/surrogates.py index 1beafa54..d98a0602 100644 --- a/src/pyunicorn/timeseries/surrogates.py +++ b/src/pyunicorn/timeseries/surrogates.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/timeseries/visibility_graph.py b/src/pyunicorn/timeseries/visibility_graph.py index a3c709fd..f11429ba 100644 --- a/src/pyunicorn/timeseries/visibility_graph.py +++ b/src/pyunicorn/timeseries/visibility_graph.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/utils/__init__.py b/src/pyunicorn/utils/__init__.py index 9f22ed4d..8fb3c1c3 100644 --- a/src/pyunicorn/utils/__init__.py +++ b/src/pyunicorn/utils/__init__.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/utils/mpi.py b/src/pyunicorn/utils/mpi.py index e2cdc760..a117ff8d 100644 --- a/src/pyunicorn/utils/mpi.py +++ b/src/pyunicorn/utils/mpi.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/utils/navigator.py b/src/pyunicorn/utils/navigator.py index bb114f4c..d0920b66 100644 --- a/src/pyunicorn/utils/navigator.py +++ b/src/pyunicorn/utils/navigator.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/src/pyunicorn/version.py b/src/pyunicorn/version.py index 514a1619..d777547e 100644 --- a/src/pyunicorn/version.py +++ b/src/pyunicorn/version.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/conftest.py b/tests/conftest.py index 1639c9cb..111f8b3b 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors @@ -12,14 +12,46 @@ # and J. Kurths, "Unified functional network and nonlinear time series analysis # for complex systems science: The pyunicorn package" +from pathlib import Path + +import requests import pytest @pytest.fixture(scope="session", params=["supremum", "euclidean", "manhattan"]) -def metric(request): +def metric(request) -> str: ''' A fixture for creating parametrized fixtures of classes that have a `metric` argument, as in `RecurrencePlot` and its child classes. ''' return request.param + + +@pytest.fixture(scope="session") +def reanalysis_data() -> Path: + """ + Locate, and potentially download, a small NOAA dataset. Currently used in: + - `tests/test_climate/test_map_plot.py` + - `examples/tutorials/ClimateNetworks.ipynb` + """ + service = "https://psl.noaa.gov/repository/entry/get" + data_name = "air.mon.mean.nc" + query = ( + "entryid=synth%3Ae570c8f9-ec09-4e89-93b4-" + "babd5651e7a9%3AL25jZXAucmVhbmFseXNpcy5kZXJpdm" + "VkL3N1cmZhY2UvYWlyLm1vbi5tZWFuLm5j") + url = f"{service}/{data_name}?{query}" + + data_dir = Path("./examples/tutorials/data") + data_file = data_dir / data_name + + if not data_dir.is_dir(): + data_dir.mkdir(parents=True) + if not data_file.exists(): + res = requests.get(url, timeout=(30, 30)) + res.raise_for_status() + with open(data_file, 'wb') as f: + f.write(res.content) + + return data_file diff --git a/tests/test_climate/test_climate_data.py b/tests/test_climate/test_climate_data.py index c8abb430..4f29a5c2 100644 --- a/tests/test_climate/test_climate_data.py +++ b/tests/test_climate/test_climate_data.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_climate/test_climate_network.py b/tests/test_climate/test_climate_network.py index c4f60dab..fcd7755c 100644 --- a/tests/test_climate/test_climate_network.py +++ b/tests/test_climate/test_climate_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_climate/test_coupled_climate_network.py b/tests/test_climate/test_coupled_climate_network.py index b79306f7..b44841e7 100644 --- a/tests/test_climate/test_coupled_climate_network.py +++ b/tests/test_climate/test_coupled_climate_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_climate/test_eventseries_climatenetwork.py b/tests/test_climate/test_eventseries_climatenetwork.py index aae703c6..ac6b8b8c 100644 --- a/tests/test_climate/test_eventseries_climatenetwork.py +++ b/tests/test_climate/test_eventseries_climatenetwork.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_climate/test_map_plot.py b/tests/test_climate/test_map_plot.py index cd6f5577..6101340e 100644 --- a/tests/test_climate/test_map_plot.py +++ b/tests/test_climate/test_map_plot.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors @@ -12,6 +12,8 @@ # and J. Kurths, "Unified functional network and nonlinear time series analysis # for complex systems science: The pyunicorn package" +from pathlib import Path + import matplotlib.pyplot as plt from pyunicorn.climate.climate_data import ClimateData @@ -26,20 +28,19 @@ class TestMapPlot: """ @staticmethod - def test_plot(): + def test_plot(reanalysis_data: Path): """ Check error-free execution. """ # prepare ClimateNetwork fixture # (select subset of data to speed up loading and calculation) title = "ncep_ncar_reanalysis" - file = 'notebooks/air.mon.mean.nc' window = { "time_min": 0., "time_max": 0., "lat_min": 30, "lon_min": 0, "lat_max": 50, "lon_max": 30} data = ClimateData.Load( - file_name=file, observable_name="air", + file_name=str(reanalysis_data), observable_name="air", file_type="NetCDF", window=window, time_cycle=12) net = TsonisClimateNetwork(data, threshold=.05, winter_only=False) diff --git a/tests/test_climate/test_tsonis.py b/tests/test_climate/test_tsonis.py index 9c7dab8c..38115046 100644 --- a/tests/test_climate/test_tsonis.py +++ b/tests/test_climate/test_tsonis.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_core/test_data.py b/tests/test_core/test_data.py index 72c8078d..ded4a718 100644 --- a/tests/test_core/test_data.py +++ b/tests/test_core/test_data.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_core/test_geo_grid.py b/tests/test_core/test_geo_grid.py index f4cef889..d360c5d3 100644 --- a/tests/test_core/test_geo_grid.py +++ b/tests/test_core/test_geo_grid.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_core/test_geo_network.py b/tests/test_core/test_geo_network.py index 33a01b78..8ae5bd4c 100644 --- a/tests/test_core/test_geo_network.py +++ b/tests/test_core/test_geo_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_core/test_grid.py b/tests/test_core/test_grid.py index 189c5c92..7afb6ca9 100644 --- a/tests/test_core/test_grid.py +++ b/tests/test_core/test_grid.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_core/test_interacting_networks.py b/tests/test_core/test_interacting_networks.py index eafa249e..283b082d 100644 --- a/tests/test_core/test_interacting_networks.py +++ b/tests/test_core/test_interacting_networks.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_core/test_network.py b/tests/test_core/test_network.py index 4a4e1bbb..3f320559 100644 --- a/tests/test_core/test_network.py +++ b/tests/test_core/test_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_core/test_resistive_networks.py b/tests/test_core/test_resistive_networks.py index df063a57..3274b8c5 100644 --- a/tests/test_core/test_resistive_networks.py +++ b/tests/test_core/test_resistive_networks.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_core/test_spatial_network.py b/tests/test_core/test_spatial_network.py index 02471e34..8bf74a61 100644 --- a/tests/test_core/test_spatial_network.py +++ b/tests/test_core/test_spatial_network.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_eventseries/test_event_series.py b/tests/test_eventseries/test_event_series.py index 30a0017f..933a1299 100644 --- a/tests/test_eventseries/test_event_series.py +++ b/tests/test_eventseries/test_event_series.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_funcnet/test_coupling_analysis.py b/tests/test_funcnet/test_coupling_analysis.py index 4a21038c..89b74aa7 100644 --- a/tests/test_funcnet/test_coupling_analysis.py +++ b/tests/test_funcnet/test_coupling_analysis.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_generic.py b/tests/test_generic.py index e0874e1d..2cf32776 100644 --- a/tests/test_generic.py +++ b/tests/test_generic.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_timeseries/test_joint_recurrence_plot.py b/tests/test_timeseries/test_joint_recurrence_plot.py index a90917d3..5cc4c486 100644 --- a/tests/test_timeseries/test_joint_recurrence_plot.py +++ b/tests/test_timeseries/test_joint_recurrence_plot.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_timeseries/test_recurrence_plot.py b/tests/test_timeseries/test_recurrence_plot.py index a693a840..79c78100 100755 --- a/tests/test_timeseries/test_recurrence_plot.py +++ b/tests/test_timeseries/test_recurrence_plot.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tests/test_timeseries/test_timeseries.py b/tests/test_timeseries/test_timeseries.py index 3236e03c..542a8c39 100644 --- a/tests/test_timeseries/test_timeseries.py +++ b/tests/test_timeseries/test_timeseries.py @@ -1,6 +1,6 @@ # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors diff --git a/tools/update-copyright b/tools/update-copyright index 2b747be5..1f9a01f1 100755 --- a/tools/update-copyright +++ b/tools/update-copyright @@ -2,7 +2,7 @@ # # This file is part of pyunicorn. # Copyright (C) 2008--2023 Jonathan F. Donges and pyunicorn authors -# URL: +# URL: # License: BSD (3-clause) # # Please acknowledge and cite the use of this software and its authors