Repository: jstac/quantecon_nyu_2016 Branch: master Commit: 25face5e8562 Files: 59 Total size: 19.5 MB Directory structure: gitextract_doktp_zd/ ├── .gitignore ├── LICENSE ├── README.md ├── homework_assignments/ │ ├── hw_set2/ │ │ ├── demand.m │ │ ├── main.m │ │ └── supply.m │ ├── hw_set3/ │ │ ├── company_list.csv │ │ └── company_list_corrected.csv │ └── hw_set6/ │ └── ols_via_projection/ │ ├── OLS_and_projection.ipynb │ └── trade_data.csv ├── lecture10/ │ ├── Interpolations_jl_alberto_polo.ipynb │ └── Morelli_Presentation_final.ipynb ├── lecture11/ │ ├── Scikit-Learn presentation.ipynb │ └── overfitting_noises_dcs.ipynb ├── lecture12/ │ └── mabille_julia_parallel.ipynb ├── lecture13/ │ ├── carlos_lizama_Gadfly.ipynb │ └── felipe_alves_codes/ │ ├── .ipynb_checkpoints/ │ │ ├── HANK_felipe_alves-checkpoint.ipynb │ │ └── JuliaPackages-checkpoint.ipynb │ ├── HANK_felipe_alves.ipynb │ ├── aggregate.jl │ ├── main_fig.jl │ ├── solveHJB.jl │ ├── solveKFE.jl │ ├── testing.jl │ └── twoassets.jl ├── lecture14/ │ ├── james_graham_DOLO.ipynb │ └── pre_RuixueGong.ipynb ├── lecture2/ │ ├── .pass │ ├── c_examples/ │ │ ├── ar1_sample_mean.c │ │ ├── function.c │ │ ├── function_ref.c │ │ ├── hello.c │ │ ├── hello_again.c │ │ ├── make_grid.c │ │ ├── makefile │ │ └── sin_func.c │ └── git_intro/ │ ├── Makefile │ ├── github.html │ ├── github.md │ ├── gitnotes.html │ └── gitnotes.md ├── lecture3/ │ ├── .ipynb_checkpoints/ │ │ └── command_line-checkpoint.ipynb │ └── command_line.ipynb ├── lecture5/ │ ├── .ipynb_checkpoints/ │ │ └── numpy_timing-checkpoint.ipynb │ ├── fast_loop_examples/ │ │ ├── ar1_sample_mean.c │ │ ├── ar1_sample_mean.jl │ │ ├── ar1_sample_mean.py │ │ ├── foo │ │ └── makefile │ └── numpy_timing.ipynb ├── lecture6/ │ ├── .ipynb_checkpoints/ │ │ └── IntroToJulia-checkpoint.ipynb │ ├── IntroToJulia.ipynb │ ├── JuliaPackages.ipynb │ ├── ParallelJulia.ipynb │ └── install_julia ├── lecture7/ │ └── Intro_to_pymc.ipynb ├── lecture8/ │ └── Pandas.ipynb └── lecture9/ ├── Plotly_Presentation.html └── Plotly_Presentation.ipynb ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ ================================================ FILE: LICENSE ================================================ Copyright (c) 2015, John Stachurski All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of quantecon_nyu_2016 nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ================================================ FILE: README.md ================================================ # Topics in Computational Economics [John Stachurski](http://johnstachurski.net/) This is the home page of ECON-GA 3002, a PhD level course on computational economics to be held at [NYU](http://econ.as.nyu.edu/page/home) in the spring semester of 2016. (Note: This document is preliminary and still under development) Semi-Random quote > All this technology carries risk. There is no faster way for a trading > firm to destroy itself than to deploy a piece of trading software that > makes a bad decision over and over in a tight loop. Part of Jane Street's > reaction to these technological risks was to put a very strong focus on > building software that was easily understood--software that was readable. > > -- Yaron Minsky, Jane Street Table of Contents: * [News](#news) * [References](#references) * [Prerequisites](#prerequisites) * [Syllabus](#syllabus) * [Part I: Programming](#part-i-programming) * [Part II: Comp Econ Foundations](#part-ii-comp-econ-foundations) * [Part III: Applications](#part-iii-applications) * [Assessment](#assessment) * [Additional Resources](#additional-resources) ## News Please note that the lecture room has changed to **room 5-75 in the Stern Building**. The time is unchanged: Friday 9am--11am Please be sure to bring your laptop ## References * http://quant-econ.net/ * Secondary / Useful / Related / Recommended texts * Kendall Atkinson and Weimin Han (2009). *Theoretical Numerical Analysis* (3rd ed) * Ward Cheney (2001). *Analysis for Applied Mathematics* * Nancy Stokey and Robert Lucas Jr. (1989) *Recursive Methods in Economic Dynamics* * John Stachurski (2009). *Economic Dynamics: Theory and Computation* ## Prerequisites I assume that you have * At least a bit of programming experience * E.g., some experience writing Matlab code or similar * Econ PhD level quantitative skills, including some familiarity with * Linear algebra * Basic analysis (sequences, limits, continuity, etc.) * Dynamics (diff equations, finite Markov chains, AR(1) processes, etc.) If you would like to prepare for the course before hand please consider * Installing [Linux](http://www.ubuntu.com/desktop) on a [VM](https://www.virtualbox.org/wiki/Linux_Downloads) or in a bootable partition on your laptop * Backup your data first! * Help available in the first class * Build up your [Linux skills](http://manuals.bioinformatics.ucr.edu/home/linux-basics) (and [profit](http://www.eweek.com/it-management/demand-for-linux-skills-growing-faster-than-talent-pool-report.html)) * Do some exercises in real analysis if you are rusty * [These notes](http://math.louisville.edu/~lee/ira/IntroRealAnal.pdf) look like about the right level * Read the first 3 chapters of [RMT](https://mitpress.mit.edu/books/recursive-macroeconomic-theory-1) if you don't know any Markov chain theory or dynamic programming ## Syllabus Below is a sketch of the syllabus for the course. The details are still subject to some change. ### Part I: Programming #### Introduction * Scientific programming environments --- what do we want? * Speed? * Productivity? * [Fun?](https://xkcd.com/353/) * Why [Python](https://www.python.org/)? And what is it anyway? * Background * http://quant-econ.net/py/about_py.html * http://www.galvanize.com/blog/2015/10/01/bill-and-melinda-gates-foundation-saves-lives-with-python/ * Philosophy * http://legacy.python.org/dev/peps/pep-0020/ * https://gist.github.com/sloria/7001839 * The [second best language for everything](http://blog.mikiobraun.de/2013/11/how-python-became-the-language-of-choice-for-data-science.html) * https://github.com/jstac/backup_scripts * What's Julia? * http://julialang.org/blog/2012/02/why-we-created-julia/ * http://libertystreeteconomics.newyorkfed.org/2015/12/the-frbny-dsge-model-meets-julia.html * Open Source * Examples of how contributions improve on the standard library * http://docs.python-requests.org/en/latest/ * https://python-programming.courses/general/better-date-and-time-handling-with-arrow/ * Open science * http://www.openscience.org/blog/?p=269 * https://opensource.com/resources/open-science * http://www.nature.com/news/interactive-notebooks-sharing-the-code-1.16261 * http://devblogs.nvidia.com/parallelforall/open-reproducible-computational-chemistry-python-cuda/ * How can open source produce **better** software than firms acting alone? * https://github.com/ * https://www.moore.org/newsroom/press-releases/2015/07/07/$6m-for-uc-berkeley-and-cal-poly-to-expand-and-enhance-open-source-software-for-scientific-computing-and-data-science * https://www.continuum.io/ #### Coding Foundations * UNIX and the UNIX shell * http://swcarpentry.github.io/shell-novice/ * Editing = Vim * https://danielmiessler.com/study/vim/ * https://realpython.com/blog/python/vim-and-python-a-match-made-in-heaven/ * http://vim-adventures.com/ * http://www.openvim.com/ * Tmux * http://tangosource.com/blog/a-tmux-crash-course-tips-and-tweaks/ * Version control * https://github.com/swcarpentry/git-novice * http://gitimmersion.com/ * http://luisbg.blogalia.com//historias/76017 --- Git cheatsheet * General software engineering skills * http://software-carpentry.org/ * Speed and Efficiency * Hardware * Interpreted / JIT compiled / AOT compiled * Vectorized code * C and Fortran * [GSL](http://www.gnu.org/software/gsl/) * http://computationalmodelling.bitbucket.org/tools/FORTRAN.html * Test driven development: * http://code.tutsplus.com/tutorials/beginning-test-driven-development-in-python--net-30137 #### Core Python * [Getting started](http://quant-econ.net/py/getting_started.html) * The REPLs: Python and IPython shells * Jupyter * The beauty of introspection on the fly * Basic syntax * http://quant-econ.net/py/python_by_example.html * http://quant-econ.net/py/python_essentials.html * OOP. It's like structs with lazy evaluation * http://quant-econ.net/py/python_oop.html * http://quant-econ.net/py/python_foundations.html * http://quant-econ.net/py/python_advanced_features.html * Python style * https://blog.hartleybrody.com/python-style-guide/ * https://google.github.io/styleguide/pyguide.html * https://www.python.org/dev/peps/pep-0008/ * Other general Python resources * https://leanpub.com/intermediatepython/read * http://nbviewer.ipython.org/github/rajathkumarmp/Python-Lectures/blob/master/01.ipynb * http://book.pythontips.com/en/latest/ * Debugging * http://www.scipy-lectures.org/advanced/debugging/ #### Scientific Python I: SciPy and Friends * General Resources * https://github.com/jrjohansson/scientific-python-lectures * http://bender.astro.sunysb.edu/classes/python-science/ * http://computationalmodelling.bitbucket.org/tools/ * [NumPy and SciPy](http://www.scipy.org/) * http://quant-econ.net/py/numpy.html * http://quant-econ.net/py/scipy.html * [Jupyter](http://jupyter.org/) * http://nbviewer.ipython.org/ * http://jupyter.cs.brynmawr.edu/hub/dblank/public/Jupyter%20Notebook%20Users%20Manual.ipynb * http://mindtrove.info/#nb-extensions * https://plot.ly/ipython-notebooks/ipython-notebook-tutorial/ * https://github.com/nicolaskruchten/pyconca/blob/master/jupyter_magic.ipynb * [Matplotlib](http://matplotlib.org/) * http://quant-econ.net/py/matplotlib.html * http://nbviewer.ipython.org/github/clbarnes/plotstyles/blob/master/plotstyles.ipynb #### Scientific Python II: The Ecosystem * [Pandas](http://pandas.pydata.org/) * http://geoffboeing.com/2015/11/landscape-us-rents/ * [Numba](http://numba.pydata.org/) and other JIT compilers * http://blog.pyston.org/2015/11/03/102/ * http://nbviewer.ipython.org/github/postelrich/fin_examples/blob/master/cva/cva1.ipynb * https://www.ibm.com/developerworks/community/blogs/jfp/entry/A_Comparison_Of_C_Julia_Python_Numba_Cython_Scipy_and_BLAS_on_LU_Factorization?lang=en * AOT compilers * [Cython](http://cython.org/) * Others (Nuitka?) * Visualization * [Plotly](https://plot.ly/), Bokeh * Statistics and machine learning * https://www.youtube.com/watch?v=5W715nfJNJw * PyMC, [Statsmodels](http://statsmodels.sourceforge.net/) * http://scikit-learn.org/stable/related_projects.html * https://www.youtube.com/watch?v=L7R4HUQ-eQ0&feature=youtu.be * [Seaborn](http://stanford.edu/~mwaskom/software/seaborn/) * Parallel processing * http://www.admin-magazine.com/HPC/Articles/Parallel-Python-with-Joblib * http://www.davekuhlman.org/python_multiprocessing_01.html * http://ufora.github.io/ufora/ * Blaze * Wrappers * https://github.com/wjakob/pybind11 * f2py and related solutions (https://www.euroscipy.org/2015/schedule/presentation/58/) * [NetworkX](https://networkx.github.io/) * [Sympy](http://www.sympy.org/en/index.html) * http://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/IPython%20Kernel/SymPy.ipynb * Webscraping * http://shop.oreilly.com/product/0636920034391.do * http://robertwdempsey.com/simple-python-web-scraper-get-pricing-data/ #### Julia * General, tutorials * http://julialang.org/ * http://www.slideshare.net/acidflask/an-introduction-to-julia * http://doodlingindata.com/2015/08/11/writing-good-julia-functions/ * http://computationalmodelling.bitbucket.org/tools/Julia.html * http://samuelcolvin.github.io/JuliaByExample/ * https://github.com/dpsanders/hands_on_julia * http://bogumilkaminski.pl/files/julia_express.pdf * https://en.wikibooks.org/wiki/Introducing_Julia * Libraries * [QuantEcon.jl](https://github.com/QuantEcon/QuantEcon.jl) * [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) * [Gadfly](http://dcjones.github.io/Gadfly.jl/) ### Part II: Comp Econ Foundations #### Markov Dynamics I: Finite State * Asymptotics * The Dobrushin coefficient * A simple coupling argument * Code from QuantEcon * Applications #### Functional Analysis * A dash of measure and integration * Metric / Banach / Hilbert space * Space of bounded functions (cbS is a closed subset) * The Lp spaces * Banach contraction mapping theorem * Blackwell's sufficient condition * Orthogonal projections * Neumann series lemma * Applications * The Lucas 78 asset pricing paper #### Markov Dynamics II: General State * General state spaces * Feller chains, Boundedness in prob * Monotone methods * LLN and CLT * Look ahead method * examples in lae_extension? * examples in poverty traps survey? * Applications * ARCH, AZ, STAR, MCMC, etc. #### Solving Forward Looking Models * L2 methods * Asset Pricing #### Dynamic Programming * Fundamental theory * The principle of optimality * VFI * Howard's policy iteration algorithm * Approximation * Preserving the contraction property * MC for integrals * Weighted sup norm approach ### Part III: Applications #### DP II: Applications and Extensions * The Coleman operator * [The income fluctuation problem](http://quant-econ.net/py/ifp.html) * Benhabib wealth distribution paper, heavy tails * Recursive and risk sensitive preferences * [Stochastic Optimal Growth Model with Risk Sensitive Preferences](http://arxiv.org/abs/1509.05638) * Other (see TE paper, monotone LLN) #### Optimal Stopping * Reservation rule operator * Theory * Applications #### Coase's Theory of the Firm * Theory * Implementation ## Assessment See lecture 1 slides. ### Notes on Class Presentations All students enrolled in the course must give a 20 minute presentation. The presentation can be on your class project or on a code library or algorithm in Julia or Python that you find interesting. Here are some suggestions: * Profiling (see, e.g., [this link](http://pynash.org/2013/03/06/timing-and-profiling.html) or [this one](https://zapier.com/engineering/profiling-python-boss/)) * [scikit-learn](http://scikit-learn.org/stable/) (a machine learning library) * Unit tests (see, e.g., [here](http://docs.python-guide.org/en/latest/writing/tests/) or [here](https://www.jeffknupp.com/blog/2013/12/09/improve-your-python-understanding-unit-testing/)) * Alternative plotting libraries and their strengths / weaknesses * [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) (a well-written Julia library) * Some features of vim or vim plug-in(s) that you find particularly useful * Techniques for parallel processing * Interfacing with C and Fortran code in either Python or Julia ### Notes on the Class Project You should discuss your class project at least briefly with me before you start. I am flexible about topics and mainly concerned with quality. All projects are due by midnight on June 3rd. #### Structure of the Project A completed class project is a GitHub repository containing * Code * A Jupyter notebook that pulls all the code together and runs it * A PDF document that provides analysis and reports results * like a short research paper Good projects demonstrate proficiency with * Python or Julia * Good programming style * Ideally, the techical material discussed during the course #### Random Ideas Here are some very random ideas that I'll add to over the semester. The links are to papers, code or discussions of algorithms, quantitative work, etc. that could be implemented / replicated / improved using Python or Julia. Feel free to use or ignore. (Ideally you will find your own topic according to your own interests. Please discuss your topic with me either way). * [Computing equilibria in dynamic games](https://www.andrew.cmu.edu/user/sevin/sevin/Research_files/Supergame_March_2015_KJ.pdf) * [Heterogeneous agents in continuous time](http://www.princeton.edu/~moll/HACTproject.htm) * [Computing Nash equilibria](https://en.wikipedia.org/wiki/Lemke%E2%80%93Howson_algorithm) * [The stable marriage problem](https://en.wikipedia.org/wiki/Stable_marriage_problem) * [Behavioral Macroeconomics via Sparse Dynamic Programming](http://pages.stern.nyu.edu/~xgabaix/papers/brdp.pdf) * [Krusell-Smith](https://ideas.repec.org/c/dge/qmrbcd/180.html) * [Krusell-Smith II](http://www.econ.yale.edu/smith/code.htm) * Numbafy everything in random.py (ask me) * Numbafy some of the optimization / root finding routines from SciPy (ask me) * [Assorted code / ideas from Dean Corbae](https://sites.google.com/site/deancorbae/teaching) * [Assorted code / ideas from Karen Kopecky](http://www.karenkopecky.net/) * [Assorted code / ideas from Chris Carroll](http://www.econ2.jhu.edu/people/ccarroll/) * [A paper on dynamics by Kiminori Matsuyama](http://faculty.wcas.northwestern.edu/~kmatsu/Revisiting%20the%20model%20of%20credit%20cycles%20with%20Good%20and%20Bad%20Projects-2016-2.pdf) * [An econ geography paper by Paul Krugman](https://ideas.repec.org/a/eee/eecrev/v37y1993i2-3p293-298.html) * Routines from Miranda and Fackler's [CompEcon](http://www4.ncsu.edu/~pfackler/compecon/toolbox.html) toolkit and [textbook](http://www4.ncsu.edu/~pfackler/compecon/) * [Angeletos 2007 paper](http://www.sciencedirect.com/science/article/pii/S1094202506000627) ## Additional Resources * Jupyter * https://github.com/bloomberg/bqplot * https://cloud.google.com/datalab/ * http://blog.dominodatalab.com/lesser-known-ways-of-using-notebooks/ * https://github.com/jupyter/jupyterhub * http://mybinder.org/ * Data, machine learning and prediction * www.galvanize.com/blog/how-random-forest-modeling-solves-seattles-bikesharing-problem/ * https://anaconda.org/ikkebr/brazilian-federal-payroll/notebook * Language comparisons * http://sebastianraschka.com/Articles/2014_matlab_vs_numpy.html * http://scottsievert.github.io/blog/2015/09/01/matlab-to-python/ * https://www.ibm.com/developerworks/community/blogs/jfp/entry/Python_Meets_Julia_Micro_Performance?lang=en * Python, general * https://www.reddit.com/r/Python/comments/3s4j6n/zen_of_python_verse_2/ * https://github.com/s16h/py-must-watch * https://www.reddit.com/r/Python/comments/3m3ll9/where_python_is_used_in_industry_other_than_webdev/ * http://bruceeckel.github.io/2015/08/29/what-i-do/ * http://blog.apcelent.com/python-decorator-tutorial-with-example.html * http://noeticforce.com/best-free-tutorials-to-learn-python-pdfs-ebooks-online-interactive Vectorization: * http://blog.datascience.com/straightening-loops-how-to-vectorize-data-aggregation-with-pandas-and-numpy/ Good reads * http://undsci.berkeley.edu/article/cold_fusion_01 * https://msdn.microsoft.com/en-us/library/dn568100.aspx ================================================ FILE: homework_assignments/hw_set2/demand.m ================================================ function yd = demand(price); global a epsilon; yd = a*(price^(-epsilon)); end ================================================ FILE: homework_assignments/hw_set2/main.m ================================================ global a b epsilon; a = 1; b = 0.1; epsilon = 1; mxiter = 30; toler = 1.0e-6; plow = 0.1; phigh = 10.0; niter = mxiter; for i = 1:mxiter; pcur = (plow + phigh)/2; yd = demand(pcur); ys = supply(pcur); excesssupply = ys - yd; if (excesssupply > 0); phigh = pcur; else; plow = pcur; end; diff = abs(phigh - plow); if (diff <= toler); niter = i; break; end; end; pclear = (plow + phigh)/2; yd = demand(pcur); ys = supply(pcur); excesssupply = ys - yd; [niter pclear yd ys excesssupply] ================================================ FILE: homework_assignments/hw_set2/supply.m ================================================ function ys = supply(price); global b; ys = exp(b*price) - 1; end ================================================ FILE: homework_assignments/hw_set3/company_list.csv ================================================ "Symbol", "Name", "MarketCap" "TFSC", "1347 Capital Corp.", "$58.59M" "TFSCR", "1347 Capital Corp.", "n/a" "TFSCU", "1347 Capital Corp.", "$42.01M" "TFSCW", "1347 Capital Corp.", "n/a" "PIH", "1347 Property Insurance Holdings, "6.22" "FLWS", "1-800 FLOWERS.COM, "8.07" "FCTY", "1st Century Bancshares, "8.3404" "FCCY", "1st Constitution Bancorp (NJ)", "$92M" "SRCE", "1st Source Corporation", "$781.16M" "VNET", "21Vianet Group, "18.55" "TWOU", "2U, "18.04" "JOBS", "51job, "29.95" "SIXD", "6D Global Technologies, "2.9" "CAFD", "8point3 Energy Partners LP", "$1.08B" "EGHT", "8x8 Inc", "$968.6M" "AVHI", "A V Homes, "9.58" "SHLM", "A. Schulman, "24.68" "AAON", "AAON, "20.96" "ABAX", "ABAXIS, "39.87" "ABY", "Abengoa Yield plc", "$1.59B" "ABGB", "Abengoa, "0.91" "ABEO", "Abeona Therapeutics Inc.", "$85.43M" "ABEOW", "Abeona Therapeutics Inc.", "n/a" "ABIL", "Ability Inc.", "$74.48M" "ABILW", "Ability Inc.", "n/a" "ABMD", "ABIOMED, "77.45" "AXAS", "Abraxas Petroleum Corporation", "$109.54M" "ACTG", "Acacia Research Corporation", "$196.35M" "ACHC", "Acadia Healthcare Company, "56.84" "ACAD", "ACADIA Pharmaceuticals Inc.", "$2.08B" "ACST", "Acasti Pharma, "1.53" "AXDX", "Accelerate Diagnostics, "12.31" "XLRN", "Acceleron Pharma Inc.", "$962.02M" "ANCX", "Access National Corporation", "$193.8M" "ARAY", "Accuray Incorporated", "$443.02M" "VXDN", "AccuShares Spot CBOE VIX Down Shares", "$5.06M" "VXUP", "AccuShares Spot CBOE VIX Up Shares", "$493425" "ACRX", "AcelRx Pharmaceuticals, "3.65" "ACET", "Aceto Corporation", "$602.56M" "AKAO", "Achaogen, "3.99" "ACHN", "Achillion Pharmaceuticals, "6.48" "ACIW", "ACI Worldwide, "17.5" "ACRS", "Aclaris Therapeutics, "15.65" "ACNB", "ACNB Corporation", "$127.79M" "ACOR", "Acorda Therapeutics, "35.84" "ACTS", "Actions Semiconductor Co., "1.3701" "ACPW", "Active Power, "1.06" "ATVI", "Activision Blizzard, "29.45" "ACTA", "Actua Corporation", "$311.7M" "ACUR", "Acura Pharmaceuticals, "2.16" "ACXM", "Acxiom Corporation", "$1.55B" "ADMS", "Adamas Pharmaceuticals, "15.42" "ADMP", "Adamis Pharmaceuticals Corporation", "$67.16M" "ADAP", "Adaptimmune Therapeutics plc", "$603.8M" "ADUS", "Addus HomeCare Corporation", "$260.6M" "AEY", "ADDvantage Technologies Group, "1.68" "IOTS", "Adesto Technologies Corporation", "$77.42M" "ADMA", "ADMA Biologics Inc", "$48.21M" "ADBE", "Adobe Systems Incorporated", "$40.79B" "ADTN", "ADTRAN, "18.43" "ADRO", "Aduro Biotech, "15.01" "AAAP", "Advanced Accelerator Applications S.A.", "$1.06B" "AEIS", "Advanced Energy Industries, "28.93" "AITP", "Advanced Inhalation Therapies (AIT) Ltd.", "n/a" "AITPU", "Advanced Inhalation Therapies (AIT) Ltd.", "n/a" "AMD", "Advanced Micro Devices, "1.9" "ADXS", "Advaxis, "6.3" "ADXSW", "Advaxis, "4" "MAUI", "AdvisorShares Market Adaptive Unconstrained Income ETF", "$2.27M" "YPRO", "AdvisorShares YieldPro ETF", "$32.66M" "AEGR", "Aegerion Pharmaceuticals, "6.5" "AEGN", "Aegion Corp", "$663.65M" "AEHR", "Aehr Test Systems", "$16.98M" "AMTX", "Aemetis, "1.84" "AEPI", "AEP Industries Inc.", "$381.12M" "AERI", "Aerie Pharmaceuticals, "14.84" "AVAV", "AeroVironment, "25.39" "AEZS", "AEterna Zentaris Inc.", "$31.57M" "AEMD", "Aethlon Medical, "4.88" "AFMD", "Affimed N.V.", "$101.1M" "AFFX", "Affymetrix, "14" "AGEN", "Agenus Inc.", "$266.64M" "AGRX", "Agile Therapeutics, "6.16" "AGYS", "Agilysys, "10.59" "AGIO", "Agios Pharmaceuticals, "41.93" "AGFS", "AgroFresh Solutions, "4.61" "AGFSW", "AgroFresh Solutions, "0.7" "AIMT", "Aimmune Therapeutics, "17.2" "AIRM", "Air Methods Corporation", "$1.51B" "AIRT", "Air T, "23.26" "ATSG", "Air Transport Services Group, "11.41" "AMCN", "AirMedia Group Inc", "$341.5M" "AIXG", "Aixtron SE", "$435.15M" "AKAM", "Akamai Technologies, "53.42" "AKTX", "Akari Therapeutics Plc", "$135.32M" "AKBA", "Akebia Therapeutics, "8.25" "AKER", "Akers Biosciences Inc", "$9.71M" "AKRX", "Akorn, "25.8" "ALRM", "Alarm.com Holdings, "16.04" "ALSK", "Alaska Communications Systems Group, "1.48" "AMRI", "Albany Molecular Research, "15.82" "ABDC", "Alcentra Capital Corp.", "$126.25M" "ADHD", "Alcobra Ltd.", "$146.51M" "ALDR", "Alder BioPharmaceuticals, "21.67" "ALDX", "Aldeyra Therapeutics, "4.5" "ALXN", "Alexion Pharmaceuticals, "148.6" "ALXA", "Alexza Pharmaceuticals, "0.3" "ALCO", "Alico, "21.32" "ALGN", "Align Technology, "63.1" "ALIM", "Alimera Sciences, "2.4" "ALKS", "Alkermes plc", "$5.24B" "ABTX", "Allegiance Bancshares, "18.06" "ALGT", "Allegiant Travel Company", "$2.62B" "AFOP", "Alliance Fiber Optic Products, "14.78" "AIQ", "Alliance HealthCare Services, "7.18" "AHGP", "Alliance Holdings GP, "12.94" "ARLP", "Alliance Resource Partners, "11.47" "AHPI", "Allied Healthcare Products, "0.75" "AMOT", "Allied Motion Technologies, "17.71" "ALQA", "Alliqua BioMedical, "1.46" "ALLT", "Allot Communications Ltd.", "$146.12M" "MDRX", "Allscripts Healthcare Solutions, "12.62" "AFAM", "Almost Family Inc", "$365.81M" "ALNY", "Alnylam Pharmaceuticals, "65.7" "AOSL", "Alpha and Omega Semiconductor Limited", "$250.15M" "GOOG", "Alphabet Inc.", "$487.61B" "GOOGL", "Alphabet Inc.", "$503.83B" "SMCP", "AlphaMark Actively Managed Small Cap ETF", "$20.78M" "ATEC", "Alphatec Holdings, "0.2" "ASPS", "Altisource Portfolio Solutions S.A.", "$616.97M" "AIMC", "Altra Industrial Motion Corp.", "$611.3M" "AMAG", "AMAG Pharmaceuticals, "24.09" "AMRN", "Amarin Corporation PLC", "$255.05M" "AMRK", "A-Mark Precious Metals, "19.95" "AYA", "Amaya Inc.", "$1.79B" "AMZN", "Amazon.com, "534.1" "AMBC", "Ambac Financial Group, "13.96" "AMBCW", "Ambac Financial Group, "6.439" "AMBA", "Ambarella, "42.84" "AMCX", "AMC Networks Inc.", "$4.75B" "DOX", "Amdocs Limited", "$8.73B" "AMDA", "Amedica Corporation", "$82822.74" "AMED", "Amedisys Inc", "$1.25B" "UHAL", "Amerco", "$6.75B" "ATAX", "America First Multifamily Investors, "4.91" "AMOV", "America Movil, "13.43" "AAL", "American Airlines Group, "39.34" "AGNC", "American Capital Agency Corp.", "$6.18B" "AGNCB", "American Capital Agency Corp.", "$8.27B" "AGNCP", "American Capital Agency Corp.", "n/a" "MTGE", "American Capital Mortgage Investment Corp.", "$664.64M" "MTGEP", "American Capital Mortgage Investment Corp.", "$46.5M" "ACSF", "American Capital Senior Floating, "8.36" "ACAS", "American Capital, "12.97" "GNOW", "American Caresource Holdings Inc", "$2.09M" "AETI", "American Electric Technologies, "2.32" "AMIC", "American Independence Corp.", "$153.92M" "AMNB", "American National Bankshares, "24.67" "ANAT", "American National Insurance Company", "$2.6B" "APEI", "American Public Education, "15.38" "ARII", "American Railcar Industries, "41.61" "AMRB", "American River Bankshares", "$74.68M" "ASEI", "American Science and Engineering, "23.49" "AMSWA", "American Software, "9.25" "AMSC", "American Superconductor Corporation", "$84.54M" "AMWD", "American Woodmark Corporation", "$1.02B" "CRMT", "America's Car-Mart, "25.26" "ABCB", "Ameris Bancorp", "$837.1M" "AMSF", "AMERISAFE, "51.52" "ASRV", "AmeriServ Financial Inc.", "$57.93M" "ASRVP", "AmeriServ Financial Inc.", "n/a" "ATLO", "Ames National Corporation", "$225.79M" "AMGN", "Amgen Inc.", "$113.96B" "FOLD", "Amicus Therapeutics, "6.57" "AMKR", "Amkor Technology, "4.6" "AMPH", "Amphastar Pharmaceuticals, "11.49" "AMSG", "Amsurg Corp.", "$3.3B" "AMSGP", "Amsurg Corp.", "$225.98M" "ASYS", "Amtech Systems, "5.29" "AFSI", "AmTrust Financial Services, "25.6" "AMRS", "Amyris, "1.49" "ANAC", "Anacor Pharmaceuticals, "77.79" "ANAD", "ANADIGICS, "0.6816" "ADI", "Analog Devices, "52.73" "ALOG", "Analogic Corporation", "$900.27M" "AVXL", "Anavex Life Sciences Corp.", "$132.03M" "ANCB", "Anchor Bancorp", "$57.75M" "ABCW", "Anchor BanCorp Wisconsin Inc.", "$398.72M" "ANDA", "Andina Acquisition Corp. II", "$50.71M" "ANDAR", "Andina Acquisition Corp. II", "n/a" "ANDAU", "Andina Acquisition Corp. II", "$16.24M" "ANDAW", "Andina Acquisition Corp. II", "n/a" "ANGI", "Angie's List, "9.73" "ANGO", "AngioDynamics, "10.48" "ANIP", "ANI Pharmaceuticals, "30.86" "ANIK", "Anika Therapeutics Inc.", "$566.62M" "ANSS", "ANSYS, "86.18" "ATRS", "Antares Pharma, "1.01" "ANTH", "Anthera Pharmaceuticals, "3.18" "ABAC", "Aoxin Tianli Group, "0.637" "ZLIG", "Aperion Biologics, "n/a" "ATNY", "API Technologies Corp.", "$55.98M" "APIC", "Apigee Corporation", "$176.89M" "APOG", "Apogee Enterprises, "36.78" "APOL", "Apollo Education Group, "8.82" "AINV", "Apollo Investment Corporation", "$1.09B" "AMEH", "Apollo Medical Holdings, "5.51" "APPF", "AppFolio, "14.33" "AAPL", "Apple Inc.", "$544.03B" "ARCI", "Appliance Recycling Centers of America, "0.87" "APDN", "Applied DNA Sciences Inc", "$68.85M" "APDNW", "Applied DNA Sciences Inc", "n/a" "AGTC", "Applied Genetic Technologies Corporation", "$259.75M" "AMAT", "Applied Materials, "17.14" "AMCC", "Applied Micro Circuits Corporation", "$441.91M" "AAOI", "Applied Optoelectronics, "16.43" "AREX", "Approach Resources Inc.", "$36.99M" "APRI", "Apricus Biosciences, "1.14" "APTO", "Aptose Biosciences, "2.67" "AQMS", "Aqua Metals, "4.94" "AQXP", "Aquinox Pharmaceuticals, "10.77" "AUMA", "AR Capital Acquisition Corp.", "$292.5M" "AUMAU", "AR Capital Acquisition Corp.", "n/a" "AUMAW", "AR Capital Acquisition Corp.", "n/a" "ARDM", "Aradigm Corporation", "$42.07M" "ARLZ", "Aralez Pharmaceuticals Inc.", "$367.77M" "PETX", "Aratana Therapeutics, "3.36" "ABUS", "Arbutus Biopharma Corporation", "$171.89M" "ARCW", "ARC Group Worldwide, "1.67" "ABIO", "ARCA biopharma, "3.8" "RKDA", "Arcadia Biosciences, "2.49" "ARCB", "ArcBest Corporation", "$509.37M" "ACGL", "Arch Capital Group Ltd.", "$8.35B" "APLP", "Archrock Partners, "6.92" "ACAT", "Arctic Cat Inc.", "$212.56M" "ARDX", "Ardelyx, "11.26" "ARNA", "Arena Pharmaceuticals, "1.64" "ARCC", "Ares Capital Corporation", "$4.15B" "AGII", "Argo Group International Holdings, "53.32" "AGIIL", "Argo Group International Holdings, "24.99" "ARGS", "Argos Therapeutics, "4.92" "ARIS", "ARI Network Services, "4.11" "ARIA", "ARIAD Pharmaceuticals, "5.105" "ARKR", "Ark Restaurants Corp.", "$69.76M" "ARMH", "ARM Holdings plc", "$18.68B" "ARTX", "Arotech Corporation", "$56.31M" "ARWA", "Arowana Inc.", "$107.29M" "ARWAR", "Arowana Inc.", "n/a" "ARWAU", "Arowana Inc.", "n/a" "ARWAW", "Arowana Inc.", "n/a" "ARQL", "ArQule, "1.72" "ARRY", "Array BioPharma Inc.", "$414.24M" "ARRS", "ARRIS International plc", "$3.55B" "DWAT", "Arrow DWA Tactical ETF", "n/a" "AROW", "Arrow Financial Corporation", "$344.76M" "ARWR", "Arrowhead Research Corporation", "$234.93M" "ARTNA", "Artesian Resources Corporation", "$262.17M" "ARTW", "Art's-Way Manufacturing Co., "2.7344" "PUMP", "Asante Solutions, "n/a" "ASBB", "ASB Bancorp, "24.55" "ASNA", "Ascena Retail Group, "7.55" "ASND", "Ascendis Pharma A/S", "$462.36M" "ASCMA", "Ascent Capital Group, "10.97" "ASTI", "Ascent Solar Technologies, "0.095" "APWC", "Asia Pacific Wire & Cable Corporation Limited", "$22.01M" "ASML", "ASML Holding N.V.", "$36.93B" "AZPN", "Aspen Technology, "31.96" "ASMB", "Assembly Biosciences, "6.36" "ASFI", "Asta Funding, "7.31" "ASTE", "Astec Industries, "38.18" "ALOT", "Astro-Med, "12.82" "ATRO", "Astronics Corporation", "$669.34M" "ASTC", "Astrotech Corporation", "$24.01M" "ASUR", "Asure Software Inc", "$34.22M" "ATAI", "ATA Inc.", "$115.45M" "ATRA", "Atara Biotherapeutics, "17.5" "ATHN", "athenahealth, "128.83" "ATHX", "Athersys, "1.45" "AAPC", "Atlantic Alliance Partnership Corp.", "$105.54M" "AAME", "Atlantic American Corporation", "$90M" "ACBI", "Atlantic Capital Bancshares, "11.88" "ACFC", "Atlantic Coast Financial Corporation", "$88.87M" "ATNI", "Atlantic Tele-Network, "77.06" "ATLC", "Atlanticus Holdings Corporation", "$44.46M" "AAWW", "Atlas Air Worldwide Holdings", "$939.43M" "AFH", "Atlas Financial Holdings, "16.96" "TEAM", "Atlassian Corporation Plc", "$4.96B" "ATML", "Atmel Corporation", "$3.39B" "ATOS", "Atossa Genetics Inc.", "$20.09M" "ATRC", "AtriCure, "17.95" "ATRI", "ATRION Corporation", "$744.03M" "ATTU", "Attunity Ltd.", "$99.16M" "LIFE", "aTyr Pharma, "4.52" "AUBN", "Auburn National Bancorporation, "25.49" "AUDC", "AudioCodes Ltd.", "$178.31M" "AUPH", "Aurinia Pharmaceuticals Inc", "$77.17M" "EARS", "Auris Medical Holding AG", "$151.92M" "ABTL", "Autobytel Inc.", "$191.93M" "ADSK", "Autodesk, "46.67" "AGMX", "AutoGenomics, "n/a" "ADP", "Automatic Data Processing, "85.33" "AAVL", "Avalanche Biotechnologies, "5.2" "AVNU", "Avenue Financial Holdings, "18.63" "AVEO", "AVEO Pharmaceuticals, "0.949" "AVXS", "AveXis, "18.75" "AVNW", "Aviat Networks, "0.67" "AVID", "Avid Technology, "7.28" "AVGR", "Avinger, "14.99" "CAR", "Avis Budget Group, "29.64" "AWRE", "Aware, "3.47" "ACLS", "Axcelis Technologies, "2.38" "AXGN", "AxoGen, "4.93" "AXSM", "Axsome Therapeutics, "8.21" "AXTI", "AXT Inc", "$86.89M" "BCOM", "B Communications Ltd.", "$812.98M" "RILY", "B. Riley Financial, "9.8" "BOSC", "B.O.S. Better Online Solutions", "$38.81M" "BEAV", "B/E Aerospace, "41.36" "BIDU", "Baidu, "163.53" "BCPC", "Balchem Corporation", "$1.99B" "BWINA", "Baldwin & Lyons, "23.74" "BWINB", "Baldwin & Lyons, "24.25" "BLDP", "Ballard Power Systems, "1.32" "BANF", "BancFirst Corporation", "$868.92M" "BANFP", "BancFirst Corporation", "$27.36M" "BKMU", "Bank Mutual Corporation", "$338.87M" "BOCH", "Bank of Commerce Holdings (CA)", "$79.98M" "BMRC", "Bank of Marin Bancorp", "$293.78M" "BKSC", "Bank of South Carolina Corp.", "$78.25M" "BOTJ", "Bank of the James Financial Group, "11.75" "OZRK", "Bank of the Ozarks", "$3.41B" "BFIN", "BankFinancial Corporation", "$246.24M" "BWFG", "Bankwell Financial Group, "19.31" "BANR", "Banner Corporation", "$1.34B" "BZUN", "Baozun Inc.", "$271.4M" "BHAC", "Barington/Hilco Acquisition Corp.", "$55.88M" "BHACR", "Barington/Hilco Acquisition Corp.", "n/a" "BHACU", "Barington/Hilco Acquisition Corp.", "n/a" "BHACW", "Barington/Hilco Acquisition Corp.", "n/a" "BBSI", "Barrett Business Services, "34.65" "BSET", "Bassett Furniture Industries, "29.57" "BYBK", "Bay Bancorp, "4.94" "BYLK", "Baylake Corp", "$134.77M" "BV", "Bazaarvoice, "3.04" "BBCN", "BBCN Bancorp, "14.39" "BCBP", "BCB Bancorp, "10.19" "BECN", "Beacon Roofing Supply, "34.35" "BSF", "Bear State Financial, "8.99" "BBGI", "Beasley Broadcast Group, "3.22" "BEBE", "bebe stores, "0.4649" "BBBY", "Bed Bath & Beyond Inc.", "$7.48B" "BGNE", "BeiGene, "26.06" "BELFA", "Bel Fuse Inc.", "$153.36M" "BELFB", "Bel Fuse Inc.", "$179.88M" "BLPH", "Bellerophon Therapeutics, "2.21" "BLCM", "Bellicum Pharmaceuticals, "10.58" "BNCL", "Beneficial Bancorp, "12.73" "BNFT", "Benefitfocus, "27.69" "BNTC", "Benitec Biopharma Limited", "$25.06M" "BNTCW", "Benitec Biopharma Limited", "n/a" "BGCP", "BGC Partners, "8.84" "BGFV", "Big 5 Sporting Goods Corporation", "$281.87M" "BIND", "BIND Therapeutics, "1.5" "ORPN", "Bio Blast Pharma Ltd.", "$41.27M" "BASI", "Bioanalytical Systems, "1.2" "BCDA", "BioCardia, "n/a" "BIOC", "Biocept, "1.38" "BCRX", "BioCryst Pharmaceuticals, "2.17" "BIOD", "Biodel Inc.", "$17.61M" "BDSI", "BioDelivery Sciences International, "4.135" "BIIB", "Biogen Inc.", "$58.09B" "BIOL", "Biolase, "0.84" "BLFS", "BioLife Solutions, "1.847" "BLRX", "BioLineRx Ltd.", "$53.28M" "BMRN", "BioMarin Pharmaceutical Inc.", "$12.55B" "BVXV", "BiondVax Pharmaceuticals Ltd.", "$11.99M" "BVXVW", "BiondVax Pharmaceuticals Ltd.", "n/a" "BPTH", "Bio-Path Holdings, "1.47" "BIOS", "BioScrip, "1.79" "BBC", "BioShares Biotechnology Clinical Trials Fund", "$20.91M" "BBP", "BioShares Biotechnology Products Fund", "$21.46M" "BSTC", "BioSpecifics Technologies Corp", "$263.1M" "BSPM", "Biostar Pharmaceuticals, "1.91" "BOTA", "Biota Pharmaceuticals, "1.65" "TECH", "Bio-Techne Corp", "$3.26B" "BEAT", "BioTelemetry, "9.96" "BITI", "Biotie Therapies Corp.", "$304.72M" "BDMS", "Birner Dental Management Services, "10" "BJRI", "BJ's Restaurants, "44.03" "BBOX", "Black Box Corporation", "$170.44M" "BDE", "Black Diamond, "4.2" "BLKB", "Blackbaud, "55.62" "BBRY", "BlackBerry Limited", "$3.77B" "HAWK", "Blackhawk Network Holdings, "37.89" "BKCC", "BlackRock Capital Investment Corporation", "$656.25M" "ADRA", "BLDRS Asia 50 ADR Index Fund", "$21.67M" "ADRD", "BLDRS Developed Markets 100 ADR Index Fund", "$59.62M" "ADRE", "BLDRS Emerging Markets 50 ADR Index Fund", "$133.24M" "ADRU", "BLDRS Europe 100 ADR Index Fund", "$14.14M" "BLMN", "Bloomin' Brands, "15.1" "BCOR", "Blucora, "6.27" "BLBD", "Blue Bird Corporation", "$188.88M" "BUFF", "Blue Buffalo Pet Products, "18.01" "BBLU", "Blue Earth, "0.3181" "BHBK", "Blue Hills Bancorp, "13.76" "NILE", "Blue Nile, "27.59" "BLUE", "bluebird bio, "54.24" "BKEP", "Blueknight Energy Partners L.P., "4.49" "BKEPP", "Blueknight Energy Partners L.P., "6.16" "BPMC", "Blueprint Medicines Corporation", "$481.58M" "ITEQ", "BlueStar TA-BIGITech Israel Technology ETF", "n/a" "STCK", "BMC Stock Holdings, "14.28" "BNCN", "BNC Bancorp", "$799.77M" "BOBE", "Bob Evans Farms, "41.08" "BOFI", "BofI Holding, "15.65" "WIFI", "Boingo Wireless, "5.9" "BOJA", "Bojangles', "14.59" "BOKF", "BOK Financial Corporation", "$3.44B" "BONA", "Bona Film Group Limited", "$849.85M" "BNSO", "Bonso Electronics International, "1.29" "BPFH", "Boston Private Financial Holdings, "10.11" "BPFHP", "Boston Private Financial Holdings, "24.3861" "BPFHW", "Boston Private Financial Holdings, "3.677" "EPAY", "Bottomline Technologies, "27.71" "BLVD", "Boulevard Acquisition Corp. II", "n/a" "BLVDU", "Boulevard Acquisition Corp. II", "n/a" "BLVDW", "Boulevard Acquisition Corp. II", "n/a" "BOXL", "Boxlight Corporation", "n/a" "BCLI", "Brainstorm Cell Therapeutics Inc.", "$43.84M" "BBRG", "Bravo Brio Restaurant Group, "7.78" "BBEP", "Breitburn Energy Partners LP", "$130.31M" "BBEPP", "Breitburn Energy Partners LP", "$54.56M" "BDGE", "Bridge Bancorp, "28.92" "BLIN ", "Bridgeline Digital, "0.9" "BRID", "Bridgford Foods Corporation", "$82.62M" "BCOV", "Brightcove Inc.", "$192.76M" "AVGO", "Broadcom Limited", "$36.13B" "BSFT", "BroadSoft, "29.18" "BVSN", "BroadVision, "5.9977" "BYFC", "Broadway Financial Corporation", "$43.32M" "BWEN", "Broadwind Energy, "1.8" "BRCD", "Brocade Communications Systems, "8.52" "BRKL", "Brookline Bancorp, "10.4" "BRKS", "Brooks Automation, "9.09" "BRKR", "Bruker Corporation", "$4.2B" "BMTC", "Bryn Mawr Bank Corporation", "$434.54M" "BLMT", "BSB Bancorp, "21.84" "BSQR", "BSQUARE Corporation", "$59.74M" "BWLD", "Buffalo Wild Wings, "154.55" "BLDR", "Builders FirstSource, "7.03" "BUR", "Burcon Nutrascience Corp", "$66.68M" "CFFI", "C&F Financial Corporation", "$129.15M" "CHRW", "C.H. Robinson Worldwide, "70.84" "CA", "CA Inc.", "$12.01B" "CCMP", "Cabot Microelectronics Corporation", "$888.71M" "CDNS", "Cadence Design Systems, "20.95" "CDZI", "Cadiz, "5.82" "CACQ", "Caesars Acquisition Company", "$794.12M" "CZR", "Caesars Entertainment Corporation", "$1.09B" "CSTE", "CaesarStone Sdot-Yam Ltd.", "$1.18B" "PRSS", "CafePress Inc.", "$58.29M" "CLBS", "Caladrius Biosciences, "0.5601" "CLMS", "Calamos Asset Management, "8.81" "CHY", "Calamos Convertible and High Income Fund", "$655.48M" "CHI", "Calamos Convertible Opportunities and Income Fund", "$578.46M" "CCD", "Calamos Dynamic Convertible & Income Fund", "$379.3M" "CFGE", "Calamos Focus Growth ETF", "$23.81M" "CHW", "Calamos Global Dynamic Income Fund", "$369.38M" "CGO", "Calamos Global Total Return Fund", "$84.73M" "CSQ", "Calamos Strategic Total Return Fund", "$1.34B" "CAMP", "CalAmp Corp.", "$636.32M" "CVGW", "Calavo Growers, "50.78" "CFNB", "California First National Bancorp", "$144.66M" "CALA", "Calithera Biosciences, "5.91" "CALD", "Callidus Software, "13.42" "CALM", "Cal-Maine Foods, "49.04" "CLMT", "Calumet Specialty Products Partners, "12.3" "ABCD", "Cambium Learning Group, "4.03" "CAC", "Camden National Corporation", "$397.65M" "CAMT", "Camtek Ltd.", "$63.97M" "CSIQ", "Canadian Solar Inc.", "$1.08B" "CGIX", "Cancer Genetics, "2.17" "CPHC", "Canterbury Park Holding Corporation", "$42.46M" "CBNJ", "Cape Bancorp, "12.84" "CPLA", "Capella Education Company", "$520.33M" "CBF", "Capital Bank Financial Corp.", "$1.3B" "CCBG", "Capital City Bank Group", "$247.91M" "CPLP", "Capital Product Partners L.P.", "$428.02M" "CSWC", "Capital Southwest Corporation", "$220.08M" "CPTA", "Capitala Finance Corp.", "$168.37M" "CLAC", "Capitol Acquisition Corp. III", "$387.28M" "CLACU", "Capitol Acquisition Corp. III", "$241.25M" "CLACW", "Capitol Acquisition Corp. 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Limited", "$116.02M" "HGSH", "China HGS Real Estate, "1.43" "CHLN", "China Housing & Land Development, "2.83" "CNIT", "China Information Technology, "1.26" "CJJD", "China Jo-Jo Drugstores, "1.71" "HTHT", "China Lodging Group, "27.32" "CHNR", "China Natural Resources, "0.8501" "CREG", "China Recycling Energy Corporation", "$24.58M" "CSUN", "China Sunergy Co., "0.81" "CNTF", "China TechFaith Wireless Communication Technology Limited", "$33.35M" "CXDC", "China XD Plastics Company Limited", "$135.64M" "CNYD", "China Yida Holding, "1.857" "CCIH", "ChinaCache International Holdings Ltd.", "$182.53M" "CNET", "ChinaNet Online Holdings, "0.71" "IMOS", "ChipMOS TECHNOLOGIES (Bermuda) LTD.", "$491.1M" "CHSCL", "CHS Inc", "n/a" "CHSCM", "CHS Inc", "$486.02M" "CHSCN", "CHS Inc", "$434.95M" "CHSCO", "CHS Inc", "$318.52M" "CHSCP", "CHS Inc", "$222.1M" "CHDN", "Churchill Downs, "132.02" "CHUY", "Chuy's Holdings, "30.79" "CDTX", "Cidara Therapeutics, "10.87" "CIFC", "CIFC LLC", "$154.48M" "CMCT", "CIM Commercial Trust Corporation", "$1.65B" "CMPR", "Cimpress N.V", "$2.71B" "CINF", "Cincinnati Financial Corporation", "$10.23B" "CIDM", "Cinedigm Corp", "$18.44M" "CTAS", "Cintas Corporation", "$9.04B" "CPHR", "Cipher Pharmaceuticals Inc.", "$113.09M" "CRUS", "Cirrus Logic, "32.51" "CSCO", "Cisco Systems, "26.46" "CTRN", "Citi Trends, "18.01" "CZNC", "Citizens & Northern Corp", "$246.87M" "CZWI", "Citizens Community Bancorp, "9.06" "CZFC", "Citizens First Corporation", "$26.97M" "CIZN", "Citizens Holding Company", "$109.88M" "CTXS", "Citrix Systems, "69.22" "CHCO", "City Holding Company", "$665.13M" "CIVB", "Civista Bancshares, "10.8584" "CIVBP", "Civista Bancshares, "35.8" "CDTI", "Clean Diesel Technologies, "0.53" "CLNE", "Clean Energy Fuels Corp.", "$228.24M" "CLNT", "Cleantech Solutions International, "1.43" "CLFD", "Clearfield, "14.32" "CLRO", "ClearOne, "12.2" "CLIR", "ClearSign Combustion Corporation", "$46.55M" "CBLI", "Cleveland BioLabs, "3.6" "CSBK", "Clifton Bancorp Inc.", "$364.39M" "CLVS", "Clovis Oncology, "19.86" "CMFN", "CM Finance Inc", "$99.72M" "CME", "CME Group Inc.", "$31.17B" "CCNE", "CNB Financial Corporation", "$253.71M" "CISG", "CNinsure Inc.", "$402.7M" "CNV", "Cnova N.V.", "$1.1B" "CWAY", "Coastway Bancorp, "12.4134" "COBZ", "CoBiz Financial Inc.", "$437.91M" "COKE", "Coca-Cola Bottling Co. 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Ltd.", "$161.08M" "CPSS", "Consumer Portfolio Services, "4.25" "CFRX", "ContraFect Corporation", "$95.36M" "CFRXW", "ContraFect Corporation", "n/a" "CTRV", "ContraVir Pharmaceuticals Inc", "$27.3M" "CTRL", "Control4 Corporation", "$186.17M" "CPRT", "Copart, "35.93" "COYN", "COPsync, "1.66" "COYNW", "COPsync, "0.6" "CRBP", "Corbus Pharmaceuticals Holdings, "1.28" "CORT", "Corcept Therapeutics Incorporated", "$424.23M" "BVA", "Cordia Bancorp Inc.", "$25.92M" "CORE", "Core-Mark Holding Company, "74.07" "CORI", "Corium International, "5.69" "CSOD", "Cornerstone OnDemand, "26.49" "CRVL", "CorVel Corp.", "$856.89M" "COSI", "Cosi, "0.65" "CSGP", "CoStar Group, "170.09" "COST", "Costco Wholesale Corporation", "$66.37B" "CPAH", "CounterPath Corporation", "$10.4M" "ICBK", "County Bancorp, "19.33" "CVTI", "Covenant Transportation Group, "22.43" "COVS", "Covisint Corporation", "$80.86M" "COWN", "Cowen Group, "3.32" "COWNL", "Cowen Group, "24.2805" "PMTS", "CPI Card Group Inc.", "$451.81M" "CPSH", "CPS Technologies Corp.", "$26.1M" "CRAI", "CRA International, "17.53" "CBRL", "Cracker Barrel Old Country Store, "140.67" "BREW", "Craft Brew Alliance, "8.2" "CRAY", "Cray Inc", "$1.7B" "CACC", "Credit Acceptance Corporation", "$3.96B" "GLDI", "Credit Suisse AG", "$145.8M" "CREE", "Cree, "30.89" "CRESY", "Cresud S.A.C.I.F. y A.", "$519.2M" "CRTO", "Criteo S.A.", "$2.59B" "CROX", "Crocs, "9.62" "CCRN", "Cross Country Healthcare, "11.49" "XRDC", "Crossroads Capital, "2.56" "CRDS", "Crossroads Systems, "0.2352" "CRWS", "Crown Crafts, "8.3" "CRWN", "Crown Media Holdings, "4.46" "CYRX", "CryoPort, "1.41" "CYRXW", "CryoPort, "0.44" "CSGS", "CSG Systems International, "37.98" "CCLP", "CSI Compressco LP", "$135.73M" "CSPI", "CSP Inc.", "$21.39M" "CSWI", "CSW Industrials, "29.45" "CSX", "CSX Corporation", "$23.92B" "CTCM", "CTC Media, "1.87" "CTIC", "CTI BioPharma Corp.", "$115.65M" "CTIB", "CTI Industries Corporation", "$16.67M" "CTRP", "Ctrip.com International, "41.39" "CUNB", "CU Bancorp (CA)", "$366.42M" "CUI", "CUI Global, "8.06" "CPIX", "Cumberland Pharmaceuticals Inc.", "$73.87M" "CMLS", "Cumulus Media Inc.", "$74.75M" "CRIS", "Curis, "1.55" "CUTR", "Cutera, "10.86" "CVBF", "CVB Financial Corporation", "$1.61B" "CVV", "CVD Equipment Corporation", "$51.23M" "CYAN", "Cyanotech Corporation", "$25.19M" "CYBR", "CyberArk Software Ltd.", "$1.15B" "CYBE", "CyberOptics Corporation", "$60.64M" "CYCC", "Cyclacel Pharmaceuticals, "0.34" "CYCCP", "Cyclacel Pharmaceuticals, "5.95" "CBAY", "Cymabay Therapeutics Inc.", "$25.56M" "CYNA", "Cynapsus Therapeutics Inc.", "$168.46M" "CYNO", "Cynosure, "37.1" "CY", "Cypress Semiconductor Corporation", "$2.47B" "CYRN", "CYREN Ltd.", "$43.01M" "CONE", "CyrusOne Inc", "$2.43B" "CYTK", "Cytokinetics, "6.79" "CTMX", "CytomX Therapeutics, "12.72" "CYTX", "Cytori Therapeutics Inc", "$26.63M" "CTSO", "Cytosorbents Corporation", "$100.19M" "CYTR", "CytRx Corporation", "$177.5M" "DJCO", "Daily Journal Corp. 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"Destination XL Group, "4.55" "DSWL", "Deswell Industries, "1.1999" "DTRM", "Determine, "1.72" "DXCM", "DexCom, "61.82" "DHXM", "DHX Media Ltd.", "$157.6M" "DMND", "Diamond Foods, "36.05" "DHIL", "Diamond Hill Investment Group, "192.91" "FANG", "Diamondback Energy, "70.44" "DCIX", "Diana Containerships Inc.", "$27.64M" "DRNA", "Dicerna Pharmaceuticals, "5.63" "DFBG", "Differential Brands Group Inc.", "$11.31M" "DGII", "Digi International Inc.", "$220.89M" "DMRC", "Digimarc Corporation", "$274.21M" "DRAD", "Digirad Corporation", "$86.79M" "DGLY", "Digital Ally, "5.6" "APPS", "Digital Turbine, "1.1" "DCOM", "Dime Community Bancshares, "16.67" "DMTX", "Dimension Therapeutics, "6.96" "DIOD", "Diodes Incorporated", "$846.94M" "DPRX", "Dipexium Pharmaceuticals, "6.15" "DISCA", "Discovery Communications, "26.71" "DISCB", "Discovery Communications, "24.3" "DISCK", "Discovery Communications, "26.01" "DSCO", "Discovery Laboratories, "2.43" "DISH", "DISH Network Corporation", "$21.34B" "DVCR", 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"DNKN", "Dunkin' Brands Group, "44" "DRRX", "Durect Corporation", "$136.19M" "DXPE", "DXP Enterprises, "14.48" "BOOM", "Dynamic Materials Corporation", "$91.83M" "DYSL", "Dynasil Corporation of America", "$24.93M" "DYNT", "Dynatronics Corporation", "$7.88M" "DVAX", "Dynavax Technologies Corporation", "$788.1M" "ETFC", "E*TRADE Financial Corporation", "$6.53B" "EBMT", "Eagle Bancorp Montana, "11.5" "EGBN", "Eagle Bancorp, "45.77" "EGLE", "Eagle Bulk Shipping Inc.", "$27.01M" "EGRX", "Eagle Pharmaceuticals, "63.16" "ELNK", "EarthLink Holdings Corp.", "$581.59M" "EWBC", "East West Bancorp, "29.83" "EACQ", "Easterly Acquisition Corp.", "$242M" "EACQU", "Easterly Acquisition Corp.", "n/a" "EACQW", "Easterly Acquisition Corp.", "n/a" "EML", "Eastern Company (The)", "$99.88M" "EVBS", "Eastern Virginia Bankshares, "6.85" "EBAY", "eBay Inc.", "$27.37B" "EBIX", "Ebix, "34.23" "ELON", "Echelon Corporation", "$24.07M" "ECHO", "Echo Global Logistics, "24.94" "ECTE", "Echo Therapeutics, "0.93" "SATS", "EchoStar Corporation", "$3.39B" "EEI", "Ecology and Environment, "9.3201" "ECAC", "E-compass Acquisition Corp.", "$53.05M" "ECACR", "E-compass Acquisition Corp.", "n/a" "ECACU", "E-compass Acquisition Corp.", "n/a" "ESES", "Eco-Stim Energy Solutions, "2.09" "EDAP", "EDAP TMS S.A.", "$97.09M" "EDGE", "Edge Therapeutics, "6.69" "EDGW", "Edgewater Technology, "7.02" "EDIT", "Editas Medicine, "16.7" "EDUC", "Educational Development Corporation", "$42.18M" "EFUT", "eFuture Holding Inc.", "$31.39M" "EGAN", "eGain Corporation", "$111.25M" "EGLT", "Egalet Corporation", "$215.76M" "EHTH", "eHealth, "10.47" "LOCO", "El Pollo Loco Holdings, "11.89" "EMITF", "Elbit Imaging Ltd.", "$19.69M" "ESLT", "Elbit Systems Ltd.", "$3.48B" "ERI", "Eldorado Resorts, "9.65" "ELRC", "Electro Rent Corporation", "$223.18M" "ESIO", "Electro Scientific Industries, "6.92" "EA", "Electronic Arts Inc.", "$18.51B" "EFII", "Electronics for Imaging, "39" "ELSE", "Electro-Sensors, "3.34" "ELEC", "Electrum Special Acquisition Corporation", "$240M" "ELECU", "Electrum Special Acquisition Corporation", "$245.5M" "ELECW", "Electrum Special Acquisition Corporation", "n/a" "EBIO", "Eleven Biotherapeutics, "0.2901" "RDEN", "Elizabeth Arden, "5.98" "CAPX", "Elkhorn S&P 500 Capital Expenditures Portfolio", "n/a" "ESBK", "Elmira Savings Bank NY (The)", "$50.42M" "LONG", "eLong, "17.32" "ELTK", "Eltek Ltd.", "$12.27M" "EMCI", "EMC Insurance Group Inc.", "$499.57M" "EMCF", "Emclaire Financial Corp", "$51.14M" "EMKR", "EMCORE Corporation", "$134.98M" "EMMS", "Emmis Communications Corporation", "$23.73M" "EMMSP", "Emmis Communications Corporation", "$1.62M" "NYNY", "Empire Resorts, "15.69" "ERS", "Empire Resources, "3.47" "ENTA", "Enanta Pharmaceuticals, "29.23" "ECPG", "Encore Capital Group Inc", "$571.69M" "WIRE", "Encore Wire Corporation", "$719.03M" "ENDP", "Endo International plc", "$11.72B" "ECYT", "Endocyte, "3.28" "ELGX", "Endologix, "7.17" "EIGI", "Endurance International Group Holdings, "9.37" "WATT", "Energous Corporation", "$83.05M" "EFOI", "Energy Focus, "8.99" "ERII", "Energy Recovery, "6.4" "EXXI", "Energy XXI Ltd.", "$50.99M" "ENOC", "EnerNOC, "5.31" "ENG", "ENGlobal Corporation", "$24.41M" "ENPH", "Enphase Energy, "2.24" "ESGR", "Enstar Group Limited", "$2.96B" "ENFC", "Entegra Financial Corp.", "$108.74M" "ENTG", "Entegris, "12.01" "ENTL", "Entellus Medical, "16.59" "ETRM", "EnteroMedics Inc.", "$8.16M" "EBTC", "Enterprise Bancorp Inc", "$230.74M" "EFSC", "Enterprise Financial Services Corporation", "$539.23M" "EGT", "Entertainment Gaming Asia Incorporated", "$25.73M" "ENZN", "Enzon Pharmaceuticals, "0.4984" "ENZY ", "Enzymotec Ltd.", "$180.31M" "EPIQ", "EPIQ Systems, "11.43" "EPRS", "EPIRUS Biopharmaceuticals, "3.06" "EPZM", "Epizyme, "10.22" "PLUS", "ePlus inc.", "$551.51M" "EQIX", "Equinix, "290.65" "EQFN", "Equitable Financial Corp.", "$29.56M" "EQBK", "Equity Bancshares, "20.65" "EAC ", "Erickson Incorporated", "$22.42M" "ERIC", "Ericsson", "$30.27B" "ERIE", "Erie Indemnity Company", "$4.51B" "ESCA", "Escalade, "11.66" "ESMC", "Escalon Medical Corp.", "$7.53M" "ESPR", "Esperion Therapeutics, "16.68" "ESSA", "ESSA Bancorp, "13.22" "EPIX", "ESSA Pharma Inc.", "$77.95M" "ESND", "Essendant Inc.", "$974.79M" "ESSF", "ETRE REIT, "n/a" "ETSY", "Etsy, "8.04" "CLWT", "Euro Tech Holdings Company Limited", "$7.01M" "EEFT", "Euronet Worldwide, "65.22" "ESEA", "Euroseas Ltd.", "$17.84M" "EVEP", "EV Energy Partners, "2.03" "EVK", "Ever-Glory International Group, "2.31" "EVLV", "EVINE Live Inc.", "$29.39M" "EVOK", "Evoke Pharma, "3.47" "EVOL", "Evolving Systems, "5.28" "EXA", "Exa Corporation", "$155.18M" "EXAS", "EXACT Sciences Corporation", "$636.79M" "EXAC", "Exactech, "17.36" "EXEL", "Exelixis, "4.41" "EXFO", "EXFO Inc", "$67.2M" "EXLS", "ExlService Holdings, "44.8" "EXPE", "Expedia, "110.88" "EXPD", "Expeditors International of Washington, "46.8" "EXPO", "Exponent, "47.48" "ESRX", "Express Scripts Holding Company", "$46.93B" "EXTR", "Extreme Networks, "2.63" "EYEG", "Eyegate Pharmaceuticals, "2.77" "EYEGW", "Eyegate Pharmaceuticals, "0.65" "EZCH", "EZchip Semiconductor Limited", "$762.17M" "EZPW", "EZCORP, "2.88" "FFIV", "F5 Networks, "91.52" "FB", "Facebook, "105.2" "FCS", "Fairchild Semiconductor International, "20.03" "FRP", "FairPoint Communications, "13.84" "FWM", "Fairway Group Holdings Corp.", "$17.21M" "FALC", "FalconStor Software, "1.51" "DAVE", "Famous Dave's of America, "6.18" "FARM", "Farmer Brothers Company", "$434.56M" "FFKT", "Farmers Capital Bank Corporation", "$191.34M" "FMNB", "Farmers National Banc Corp.", "$223.32M" "FARO", "FARO Technologies, "26.07" "FAST", "Fastenal Company", "$12.81B" "FATE", "Fate Therapeutics, "1.57" "FBSS", "Fauquier Bankshares, "14.98" "FBRC", "FBR & Co", "$112.27M" "FDML", "Federal-Mogul Holdings Corporation", "$765.75M" "FNHC", "Federated National Holding Company", "$339.12M" "FEIC", "FEI Company", "$3.07B" "FHCO", "Female Health Company (The)", "$47.17M" "FENX", "Fenix Parts, "4.86" "GSM", "Ferroglobe PLC", "$1.29B" "FCSC", "Fibrocell Science Inc", "$105.36M" "FGEN", "FibroGen, "19.81" "ONEQ", "Fidelity Nasdaq Composite Index Tracking Stock", "$410.37M" "LION", "Fidelity Southern Corporation", "$340.7M" "FDUS", "Fidus Investment Corporation", "$214.15M" "FRGI", "Fiesta Restaurant Group, "35.8" "FSAM", "Fifth Street Asset Management Inc.", "$88.04M" "FSC", "Fifth Street Finance Corp.", "$739.29M" "FSCFL", "Fifth Street Finance Corp.", "n/a" "FSFR", "Fifth Street Senior Floating Rate Corp.", "$212.46M" "FITB", "Fifth Third Bancorp", "$12.24B" "FITBI", "Fifth Third Bancorp", "$501.12M" "FNGN", "Financial Engines, "26.54" "FISI", "Financial Institutions, "26.86" "FNSR", "Finisar Corporation", "$1.47B" "FNJN", "Finjan Holdings, "0.99" "FNTC", "FinTech Acquisition Corp.", "$133.9M" "FNTCU", "FinTech Acquisition Corp.", "n/a" "FNTCW", "FinTech Acquisition Corp.", "n/a" "FEYE", "FireEye, "13.8" "FBNC", "First Bancorp", "$369.99M" "FNLC", "First Bancorp, "18.86" "FRBA", "First Bank", "$52.05M" "BUSE", "First Busey Corporation", "$540.57M" "FBIZ", "First Business Financial Services, "21.4" "FCAP", "First Capital, "25.45" "FCFS", "First Cash Financial Services, "40.15" "FCNCA", "First Citizens BancShares, "232.17" "FCLF", "First Clover Leaf Financial Corp.", "$66.07M" "FCBC", "First Community Bancshares, "17.82" "FCCO", "First Community Corporation", "$89.91M" "FCFP", "First Community Financial Partners, "7.23" "FBNK", "First Connecticut Bancorp, "16.21" "FDEF", "First Defiance Financial Corp.", "$351.31M" "FFBC", "First Financial Bancorp.", "$1.03B" "FFBCW", "First Financial Bancorp.", "n/a" "FFIN", "First Financial Bankshares, "27.2" "THFF", "First Financial Corporation Indiana", "$416.05M" "FFNW", "First Financial Northwest, "13.05" "FFWM", "First Foundation Inc.", "$343.52M" "FGBI", "First Guaranty Bancshares, "15.81" "INBK", "First Internet Bancorp", "$114.59M" "FIBK", "First Interstate BancSystem, "26.45" "FRME", "First Merchants Corporation", "$853.69M" "FMBH", "First Mid-Illinois Bancshares, "25.7" "FMBI", "First Midwest Bancorp, "16.61" "FNBC", "First NBC Bank Holding Company", "$475.76M" "FNFG", "First Niagara Financial Group Inc.", "$3.35B" "FNWB", "First Northwest Bancorp", "$162.58M" "FSFG", "First Savings Financial Group, "33.5" "FSLR", "First Solar, "63.47" "FSBK", "First South Bancorp Inc", "$81.89M" "FPA", "First Trust Asia Pacific Ex-Japan AlphaDEX Fund", "$39.9M" "BICK", "First Trust BICK Index Fund", "$7.59M" "FBZ", "First Trust Brazil AlphaDEX Fund", "$2.15M" "FCAN", "First Trust Canada AlphaDEX Fund", "$7.61M" "FTCS", "First Trust Capital Strength ETF", "$116.55M" "FCA", "First Trust China AlphaDEX Fund", "$15.71M" "FDT", "First Trust Developed Markets Ex-US AlphaDEX Fund", "$141.33M" "FDTS", "First Trust Developed Markets ex-US Small Cap AlphaDEX Fund", "$7.32M" "FV", "First Trust Dorsey Wright Focus 5 ETF", "$3.63B" "IFV", "First Trust Dorsey Wright International Focus 5 ETF", "$700.98M" "FEM", "First Trust Emerging Markets AlphaDEX Fund", "$134.14M" "FEMB", "First Trust Emerging Markets Local Currency Bond ETF", "n/a" "FEMS", "First Trust Emerging Markets Small Cap AlphaDEX Fund", "$30.63M" "FTSM", "First Trust Enhanced Short Maturity ETF", "$128.66M" "FEP", "First Trust Europe AlphaDEX Fund", "$364.41M" "FEUZ", "First Trust Eurozone AlphaDEX ETF", "$10.23M" "FGM", "First Trust Germany AlphaDEX Fund", "$214.18M" "FTGC", "First Trust Global Tactical Commodity Strategy Fund", "$171.39M" "FTHI", "First Trust High Income ETF", "$5.66M" "HYLS", "First Trust High Yield Long/Short ETF", "$441.12M" "FHK", "First Trust Hong Kong AlphaDEX Fund", "$160.49M" "FTAG", "First Trust Indxx Global Agriculture ETF", "$3.59M" "FTRI", "First Trust Indxx Global Natural Resources Income ETF", "$10.37M" "FPXI", "First Trust International IPO ETF", "n/a" "YDIV", "First Trust International Multi-Asset Diversified Income Index", "$12.21M" "SKYY", "First Trust ISE Cloud Computing Index Fund", "$438.54M" "FJP", "First Trust Japan AlphaDEX Fund", "$86.36M" "FLN", "First Trust Latin America AlphaDEX Fund", "$3.88M" "FTLB", "First Trust Low Beta Income ETF", "$4.73M" "LMBS", "First Trust Low Duration Mortgage Opportunities ETF", "n/a" "FMB", "First Trust Managed Municipal ETF", "$21.22M" "MDIV", "First Trust Multi-Asset Diversified Income Index Fund", "$868.75M" "QABA", "First Trust NASDAQ ABA Community Bank Index Fund", "$93.1M" "QCLN", "First Trust NASDAQ Clean Edge Green Energy Index Fund", "$64.79M" "GRID", "First Trust NASDAQ Clean Edge Smart Grid Infrastructure Index ", "$10.48M" "CIBR", "First Trust NASDAQ Cybersecurity ETF", "n/a" "CARZ", "First Trust NASDAQ Global Auto Index Fund", "$52.11M" "RDVY", "First Trust NASDAQ Rising Dividend Achievers ETF", "$22.21M" "FONE", "First Trust NASDAQ Smartphone Index Fund", "$10.27M" "TDIV", "First Trust NASDAQ Technology Dividend Index Fund", "$522.28M" "QQEW", "First Trust NASDAQ-100 Equal Weighted Index Fund", "$548.91M" "QQXT", "First Trust NASDAQ-100 Ex-Technology Sector Index Fund", "$140.55M" "QTEC", "First Trust NASDAQ-100- Technology Index Fund", "$299.19M" "AIRR", "First Trust RBA American Industrial Renaissance ETF", "$47.22M" "QINC", "First Trust RBA Quality Income ETF", "$10.04M" "FTSL", "First Trust Senior Loan Fund ETF", "$303.27M" "FKO", "First Trust South Korea AlphaDEX Fund", "$3.31M" "FCVT", "First Trust SSI Strategic Convertible Securities ETF", "n/a" "FDIV", "First Trust Strategic Income ETF", "$18.08M" "FSZ", "First Trust Switzerland AlphaDEX Fund", "$216.98M" "FTW", "First Trust Taiwan AlphaDEX Fund", "$8.41M" "TUSA", "First Trust Total US Market AlphaDEX ETF", "$5.6M" "FKU", "First Trust United Kingdom AlphaDEX Fund", "$240.07M" "FUNC", "First United Corporation", "$59.04M" "SVVC", "Firsthand Technology Value Fund, "7.25" "FMER", "FirstMerit Corporation", "$3.24B" "FSV", "FirstService Corporation", "$1.34B" "FISV", "Fiserv, "95.86" "FIVE", "Five Below, "37.54" "FPRX", "Five Prime Therapeutics, "32.77" "FIVN", "Five9, "7.07" "FLML", "Flamel Technologies S.A.", "$377.8M" "FLKS", "Flex Pharma, "7.45" "FLXN", "Flexion Therapeutics, "13" "SKOR", "FlexShares Credit-Scored US Corporate Bond Index Fund", "$7.52M" "LKOR", "FlexShares Credit-Scored US Long Corporate Bond Index Fund", "n/a" "MBSD", "FlexShares Disciplined Duration MBS Index Fund", "$21.07M" "ASET", "FlexShares Real Assets Allocation Index Fund", "n/a" "QLC", "FlexShares US Quality Large Cap Index Fund", "n/a" "FLXS", "Flexsteel Industries, "40.98" "FLEX", "Flextronics International Ltd.", "$5.82B" "FLIR", "FLIR Systems, "30.88" "FLDM", "Fluidigm Corporation", "$183.98M" "FFIC", "Flushing Financial Corporation", "$591.88M" "FOMX", "Foamix Pharmaceuticals Ltd.", "$188.23M" "FOGO", "Fogo de Chao, "15.12" "FONR", "Fonar Corporation", "$103.51M" "FES", "Forbes Energy Services Ltd", "$6.64M" "FORM", "FormFactor, "6.85" "FORTY", "Formula Systems (1985) Ltd.", "$361.44M" "FORR", "Forrester Research, "30.97" "FTNT", "Fortinet, "26.18" "FBIO", "Fortress Biotech, "3.14" "FWRD", "Forward Air Corporation", "$1.23B" "FORD", "Forward Industries, "1.4799" "FWP", "Forward Pharma A/S", "$787.96M" "FOSL", "Fossil Group, "44.3" "FMI", "Foundation Medicine, "15.01" "FXCB", "Fox Chase Bancorp, "19.1" "FOXF", "Fox Factory Holding Corp.", "$546.13M" "FRAN", "Francesca's Holdings Corporation", "$751.08M" "FELE", "Franklin Electric Co., "28.52" "FRED", "Fred's, "13.69" "FREE", "FreeSeas Inc.", "$1723.8" "RAIL", "Freightcar America, "19.63" "FEIM", "Frequency Electronics, "9" "FRPT", "Freshpet, "6.94" "FTR", "Frontier Communications Corporation", "$5.16B" "FTRPR", "Frontier Communications Corporation", "n/a" "FRPH", "FRP Holdings, "31.49" "FSBW", "FS Bancorp, "24.24" "FTD", "FTD Companies, "23.93" "FSYS", "Fuel Systems Solutions, "4.23" "FTEK", "Fuel Tech, "1.65" "FCEL", "FuelCell Energy, "5.17" "FORK", "Fuling Global Inc.", "$51.68M" "FULL", "Full Circle Capital Corporation", "$50.79M" "FULLL", "Full Circle Capital Corporation", "n/a" "FLL", "Full House Resorts, "1.41" "FULT", "Fulton Financial Corporation", "$2.25B" "FSNN", "Fusion Telecommunications International, "1.82" "FFHL", "Fuwei Films (Holdings) Co., "0.7382" "GK", "G&K Services, "66.05" "WILC", "G. Willi-Food International, "3.95" "GAIA", "Gaiam, "4.95" "GLPG", "Galapagos NV", "$1.74B" "GALT", "Galectin Therapeutics Inc.", "$27.28M" "GALTU", "Galectin Therapeutics Inc.", "n/a" "GALTW", "Galectin Therapeutics Inc.", "n/a" "GALE", "Galena Biopharma, "0.8611" "GLMD", "Galmed Pharmaceuticals Ltd.", "$56.06M" "GLPI", "Gaming and Leisure Properties, "26.25" "GPIC", "Gaming Partners International Corporation", "$79.13M" "GRMN", "Garmin Ltd.", "$7.82B" "GGAC", "Garnero Group Acquisition Company", "$182.49M" "GGACR", "Garnero Group Acquisition Company", "n/a" "GGACU", "Garnero Group Acquisition Company", "$132.34M" "GGACW", "Garnero Group Acquisition Company", "n/a" "GARS", "Garrison Capital Inc.", "$183.17M" "GCTS", "GCT Semiconductor, "n/a" "GLSS", "Gelesis, "n/a" "GENC", "Gencor Industries Inc.", "$120.94M" "GNCMA", "General Communication, "17.99" "GFN", "General Finance Corporation", "$107.69M" "GFNCP", "General Finance Corporation", "n/a" "GFNSL", "General Finance Corporation", "n/a" "GENE", "Genetic Technologies Ltd", "$25.04M" "GNMK", "GenMark Diagnostics, "4.9" "GNCA", "Genocea Biosciences, "4.07" "GHDX", "Genomic Health, "27.42" "GNST", "GenSight Biologics S.A.", "n/a" "GNTX", "Gentex Corporation", "$4.25B" "THRM", "Gentherm Inc", "$1.54B" "GNVC", "GenVec, "0.42" "GTWN", "Georgetown Bancorp, "19.4" "GEOS", "Geospace Technologies Corporation", "$134.61M" "GABC", "German American Bancorp, "31.23" "GERN", "Geron Corporation", "$457.26M" "GEVO", "Gevo, "0.41" "ROCK", "Gibraltar Industries, "20.12" "GIGM", "GigaMedia Limited", "$32.6M" "GIGA", "Giga-tronics Incorporated", "$11.75M" "GIII", "G-III Apparel Group, "49.13" "GILT", "Gilat Satellite Networks Ltd.", "$171.41M" "GILD", "Gilead Sciences, "89.36" "GBCI", "Glacier Bancorp, "23.91" "GLAD", "Gladstone Capital Corporation", "$144.81M" "GLADO", "Gladstone Capital Corporation", "$49.96M" "GOOD", "Gladstone Commercial Corporation", "$303.18M" "GOODN", "Gladstone Commercial Corporation", "n/a" "GOODO", "Gladstone Commercial Corporation", "$29.21M" "GOODP", "Gladstone Commercial Corporation", "$25.58M" "GAIN", "Gladstone Investment Corporation", "$198.58M" "GAINN", "Gladstone Investment Corporation", "n/a" "GAINO", "Gladstone Investment Corporation", "n/a" "GAINP", "Gladstone Investment Corporation", "n/a" "LAND", "Gladstone Land Corporation", "$71.41M" "GLBZ", "Glen Burnie Bancorp", "$32.54M" "GBT", "GLOBAL BLOOD THERAPEUTICS, "17.47" "ENT", "Global Eagle Entertainment Inc.", "$729.46M" "GBLI", "Global Indemnity plc", "$727.99M" "GBLIZ", "Global Indemnity plc", "n/a" "GPAC", "Global Partner Acquisition Corp.", "$190.57M" "GPACU", "Global Partner Acquisition Corp.", "n/a" "GPACW", "Global Partner Acquisition Corp.", "n/a" "SELF", "Global Self Storage, "3.8876" "GSOL", "Global Sources Ltd.", "$171.32M" "ACTX", "Global X Guru Activist ETF", "n/a" "QQQC", "Global X NASDAQ China Technology ETF", "$15.91M" "SOCL", "Global X Social Media Index ETF", "$69.89M" "ALTY", "Global X SuperDividend Alternatives ETF", "n/a" "SRET", "Global X SuperDividend REIT ETF", "$2.42M" "YLCO", "Global X Yieldco Index ETF", "$2.39M" "GAI", "Global-Tech Advanced Innovations Inc.", "$25.97M" "GBIM", "GlobeImmune, "1.44" "GLBS", "Globus Maritime Limited", "$1.74M" "GLRI", "Glori Energy Inc", "$6.05M" "GLUU", "Glu Mobile Inc.", "$469.91M" "GLYC", "GlycoMimetics, "5.15" "GOGO", "Gogo Inc.", "$825.86M" "GLNG", "Golar LNG Limited", "$1.49B" "GMLP", "Golar LNG Partners LP", "$832.39M" "GLDC", "Golden Enterprises, "4.6833" "GDEN", "Golden Entertainment, "10.05" "GOGL", "Golden Ocean Group Limited", "$139.87M" "GBDC", "Golub Capital BDC, "16.04" "GTIM", "Good Times Restaurants Inc.", "$50.39M" "GPRO", "GoPro, "12.99" "GMAN", "Gordmans Stores, "2.57" "GRSH", "Gores Holdings, "9.734" "GRSHU", "Gores Holdings, "10.09" "GRSHW", "Gores Holdings, "0.27" "GPIA", "GP Investments Acquisition Corp.", "$208.62M" "GPIAU", "GP Investments Acquisition Corp.", "n/a" "GPIAW", "GP Investments Acquisition Corp.", "n/a" "LOPE", "Grand Canyon Education, "33.63" "GRVY", "GRAVITY Co., "3.15" "GBSN", "Great Basin Scientific, "0.2289" "GLDD", "Great Lakes Dredge & Dock Corporation", "$217M" "GSBC", "Great Southern Bancorp, "37.1" "GNBC", "Green Bancorp, "7.15" "GRBK", "Green Brick Partners, "5.73" "GPP", "Green Plains Partners LP", "$424.71M" "GPRE", "Green Plains, "14.31" "GCBC", "Greene County Bancorp, "34.2646" "GLRE", "Greenlight Reinsurance, "19.94" "GRIF", "Griffin Industrial Realty, "22.54" "GRFS", "Grifols, "15.35" "GRPN", "Groupon, "3.79" "OMAB", "Grupo Aeroportuario del Centro Norte S.A.B. de C.V.", "$1.83B" "GGAL", "Grupo Financiero Galicia S.A.", "$3.72B" "GSIG", "GSI Group, "12.57" "GSIT", "GSI Technology, "3.52" "GSVC", "GSV Capital Corp", "$112.06M" "GTXI", "GTx, "0.7072" "GBNK", "Guaranty Bancorp", "$326.77M" "GFED", "Guaranty Federal Bancshares, "15.2" "GUID", "Guidance Software, "4.95" "GIFI", "Gulf Island Fabrication, "8.83" "GURE", "Gulf Resources, "1.595" "GPOR", "Gulfport Energy Corporation", "$3.1B" "GWPH", "GW Pharmaceuticals Plc", "$1.04B" "GWGH", "GWG Holdings, "5.6" "GYRO", "Gyrodyne , "28.3" "HEES", "H&E Equipment Services, "12.75" "HLG", "Hailiang Education Group Inc.", "$242.17M" "HNRG", "Hallador Energy Company", "$149.07M" "HALL", "Hallmark Financial Services, "10.11" "HALO", "Halozyme Therapeutics, "8.59" "HBK", "Hamilton Bancorp, "13.9108" "HMPR", "Hampton Roads Bankshares Inc", "$285.49M" "HBHC", "Hancock Holding Company", "$1.85B" "HBHCL", "Hancock Holding Company", "n/a" "HNH", "Handy & Harman Ltd.", "$207.42M" "HAFC", "Hanmi Financial Corporation", "$645.91M" "HNSN", "Hansen Medical, "2.45" "HQCL", "Hanwha Q CELLS Co., "15.87" "HDNG", "Hardinge, "8.76" "HLIT", "Harmonic Inc.", "$272.54M" "HRMN", "Harmony Merger Corp.", "$148.58M" "HRMNU", "Harmony Merger Corp.", "n/a" "HRMNW", "Harmony Merger Corp.", "n/a" "TINY", "Harris & Harris Group, "1.76" "HART ", "Harvard Apparatus Regenerative Technology, "1.34" "HBIO", "Harvard Bioscience, "2.86" "HCAP", "Harvest Capital Credit Corporation", "$63.5M" "HCAPL", "Harvest Capital Credit Corporation", "n/a" "HAS", "Hasbro, "72.05" "HA", "Hawaiian Holdings, "38.01" "HCOM", "Hawaiian Telcom Holdco, "22.07" "HWKN", "Hawkins, "34.11" "HWBK", "Hawthorn Bancshares, "14.935" "HAYN", "Haynes International, "31.61" "HDS", "HD Supply Holdings, "25.91" "HIIQ", "Health Insurance Innovations, "5.73" "HCSG", "Healthcare Services Group, "34.13" "HQY", "HealthEquity, "19.01" "HSTM", "HealthStream, "22.35" "HWAY", "Healthways, "10.71" "HTLD", "Heartland Express, "18.93" "HTLF", "Heartland Financial USA, "28.73" "HTWR", "Heartware International, "34.39" "HTBX", "Heat Biologics, "2.03" "HSII", "Heidrick & Struggles International, "22.28" "HELE", "Helen of Troy Limited", "$2.56B" "HMNY", "Helios and Matheson Analytics Inc", "$3.47M" "HMTV", "Hemisphere Media Group, "13.87" "HNNA", "Hennessy Advisors, "27.48" "HCAC", "Hennessy Capital Acquisition Corp. II", "$241.27M" "HCACU", "Hennessy Capital Acquisition Corp. II", "n/a" "HCACW", "Hennessy Capital Acquisition Corp. II", "n/a" "HSIC", "Henry Schein, "164.01" "HERO", "Hercules Offshore, "1.4" "HTBK", "Heritage Commerce Corp", "$298.31M" "HFWA", "Heritage Financial Corporation", "$513.04M" "HEOP", "Heritage Oaks Bancorp", "$249.08M" "HCCI", "Heritage-Crystal Clean, "8.01" "MLHR", "Herman Miller, "24.79" "HRTX", "Heron Therapeutics, "19.31" "HSKA", "Heska Corporation", "$212.89M" "HFFC", "HF Financial Corp.", "$116.03M" "HIBB", "Hibbett Sports, "34.85" "HPJ", "Highpower International Inc", "$33.22M" "HIHO", "Highway Holdings Limited", "$12.96M" "HIMX", "Himax Technologies, "8.41" "HIFS", "Hingham Institution for Savings", "$253.11M" "HSGX", "Histogenics Corporation", "$39.42M" "HMNF", "HMN Financial, "11" "HMSY", "HMS Holdings Corp", "$991.68M" "HOLI", "Hollysys Automation Technologies, "18.33" "HOLX", "Hologic, "34.47" "HBCP", "Home Bancorp, "24.84" "HOMB", "Home BancShares, "38.71" "HFBL", "Home Federal Bancorp, "22.12" "HMIN", "Homeinns Hotel Group", "$1.65B" "HMST", "HomeStreet, "19.64" "HTBI", "HomeTrust Bancshares, "17.51" "CETC", "Hongli Clean Energy Technologies Corp.", "$7.08M" "HOFT", "Hooker Furniture Corporation", "$312.24M" "HFBC", "HopFed Bancorp, "11.468" "HBNC", "Horizon Bancorp (IN)", "$285.91M" "HZNP", "Horizon Pharma plc", "$2.98B" "HRZN", "Horizon Technology Finance Corporation", "$118.08M" "HDP", "Hortonworks, "9.51" "HMHC", "Houghton Mifflin Harcourt Company", "$2.33B" "HWCC", "Houston Wire & Cable Company", "$94.89M" "HOVNP", "Hovnanian Enterprises Inc", "$16.65M" "HBMD", "Howard Bancorp, "12" "HSNI", "HSN, "45.64" "HTGM", "HTG Molecular Diagnostics, "2.3655" "HUBG", "Hub Group, "36.5" "HSON", "Hudson Global, "2.66" "HDSN", "Hudson Technologies, "3" "HBAN", "Huntington Bancshares Incorporated", "$6.99B" "HBANP", "Huntington Bancshares Incorporated", "n/a" "HURC", "Hurco Companies, "25.92" "HURN", "Huron Consulting Group Inc.", "$1.2B" "HTCH", "Hutchinson Technology Incorporated", "$125.78M" "HBP", "Huttig Building Products, "3.28" "HDRA", "Hydra Industries Acquisition Corp.", "$97.4M" "HDRAR", "Hydra Industries Acquisition Corp.", "n/a" "HDRAU", "Hydra Industries Acquisition Corp.", "n/a" "HDRAW", "Hydra Industries Acquisition Corp.", "n/a" "HYGS", "Hydrogenics Corporation", "$98.57M" "IDSY", "I.D. Systems, "4.05" "IAC", "IAC/InterActiveCorp", "$3.64B" "IKGH", "Iao Kun Group Holding Company Limited", "$82.1M" "IBKC", "IBERIABANK Corporation", "$1.95B" "IBKCP", "IBERIABANK Corporation", "n/a" "ICAD", "icad inc.", "$64.02M" "IEP", "Icahn Enterprises L.P.", "$7B" "ICFI", "ICF International, "32.94" "ICLR", "ICON plc", "$3.95B" "ICON", "Iconix Brand Group, "7.99" "ICUI", "ICU Medical, "88.55" "IPWR", "Ideal Power Inc.", "$46.81M" "INVE", "Identiv, "1.73" "IDRA", "Idera Pharmaceuticals, "1.92" "IDXX", "IDEXX Laboratories, "71.52" "DSKY", "iDreamSky Technology Limited", "$576.52M" "IROQ", "IF Bancorp, "17.27" "IRG", "Ignite Restaurant Group, "3.57" "RXDX", "Ignyta, "7.08" "IIVI", "II-VI Incorporated", "$1.25B" "KANG", "iKang Healthcare Group, "20.99" "IKNX", "Ikonics Corporation", "$23.53M" "ILMN", "Illumina, "155.63" "ISNS", "Image Sensing Systems, "2.99" "IMMR", "Immersion Corporation", "$231.94M" "ICCC", "ImmuCell Corporation", "$18.91M" "IMDZ", "Immune Design Corp.", "$222.38M" "IMNP ", "Immune Pharmaceuticals Inc.", "$18.47M" "IMGN", "ImmunoGen, "7.8" "IMMU", "Immunomedics, "2.29" "IPXL", "Impax Laboratories, "35.71" "IMMY", "Imprimis Pharmaceuticals, "4.19" "INCR", "INC Research Holdings, "39.28" "SAAS", "inContact, "8.8" "INCY", "Incyte Corporation", "$13.94B" "INDB", "Independent Bank Corp.", "$1.14B" "IBCP", "Independent Bank Corporation", "$322.2M" "IBTX", "Independent Bank Group, "28.1" "IDSA", "Industrial Services of America, "1.15" "INFN", "Infinera Corporation", "$2.09B" "INFI", "Infinity Pharmaceuticals, "6.36" "IPCC", "Infinity Property and Casualty Corporation", "$891.12M" "III", "Information Services Group, "3.23" "IFON", "InfoSonics Corp", "$25.47M" "IMKTA", "Ingles Markets, "35.38" "INWK", "InnerWorkings, "6.54" "INNL", "Innocoll AG", "$171.71M" "INOD", "Innodata Inc.", "$61.67M" "IPHS", "Innophos Holdings, "27.93" "IOSP", "Innospec Inc.", "$1.06B" "ISSC", "Innovative Solutions and Support, "2.66" "INVA", "Innoviva, "12.85" "INGN", "Inogen, "32.5" "ITEK", "Inotek Pharmaceuticals Corporation", "$201.51M" "INOV", "Inovalon Holdings, "18.92" "INO", "Inovio Pharmaceuticals, "6.92" "NSIT", "Insight Enterprises, "24.37" "ISIG", "Insignia Systems, "2.72" "INSM", "Insmed, "13.48" "IIIN", "Insteel Industries, "26.08" "PODD", "Insulet Corporation", "$1.57B" "INSY", "Insys Therapeutics, "17.8" "NTEC", "Intec Pharma Ltd.", "$40.41M" "IART", "Integra LifeSciences Holdings Corporation", "$2.12B" "IDTI", "Integrated Device Technology, "18.35" "IESC", "Integrated Electrical Services, "11.42" "INTC", "Intel Corporation", "$139.22B" "IQNT", "Inteliquent, "17.87" "IPCI", "Intellipharmaceutics International Inc.", "$54.01M" "IPAR", "Inter Parfums, "25.46" "IBKR", "Interactive Brokers Group, "33.02" "ININ", "Interactive Intelligence Group, "27.21" "ICPT", "Intercept Pharmaceuticals, "124.66" "ICLD", "InterCloud Systems, "0.58" "ICLDW", "InterCloud Systems, "0.15" "IDCC", "InterDigital, "44.86" "TILE", "Interface, "16.74" "IMI", "Intermolecular, "2.18" "INAP", "Internap Corporation", "$160.82M" "IBOC", "International Bancshares Corporation", "$1.5B" "ISCA", "International Speedway Corporation", "$1.59B" "IGLD", "Internet Gold Golden Lines Ltd.", "$249.64M" "IIJI", "Internet Initiative Japan, "9.225" "IDXG", "Interpace Diagnostics Group, "0.284" "XENT", "Intersect ENT, "18.34" "INTX", "Intersections, "2.63" "ISIL", "Intersil Corporation", "$1.65B" "IILG", "Interval Leisure Group, "11.66" "IVAC", "Intevac, "4.29" "INTL", "INTL FCStone Inc.", "$493.74M" "INTLL", "INTL FCStone Inc.", "n/a" "ITCI", "Intra-Cellular Therapies Inc.", "$1.31B" "IIN", "IntriCon Corporation", "$38.55M" "INTU", "Intuit Inc.", "$25.97B" "ISRG", "Intuitive Surgical, "541.86" "INVT", "Inventergy Global, "2.06" "SNAK", "Inventure Foods, "5.55" "ISTR", "Investar Holding Corporation", "$105.62M" "ISBC", "Investors Bancorp, "11.43" "ITIC", "Investors Title Company", "$174.6M" "NVIV", "InVivo Therapeutics Holdings Corp.", "$127.76M" "IVTY", "Invuity, "7.85" "IONS", "Ionis Pharmaceuticals, "38.74" "IPAS", "iPass Inc.", "$59.08M" "IPGP", "IPG Photonics Corporation", "$4.37B" "IRMD", "iRadimed Corporation", "$191.94M" "IRIX", "IRIDEX Corporation", "$92.2M" "IRDM", "Iridium Communications Inc", "$693.13M" "IRDMB", "Iridium Communications Inc", "$135.93M" "IRBT", "iRobot Corporation", "$876.45M" "IRWD", "Ironwood Pharmaceuticals, "8.76" "IRCP", "IRSA Propiedades Comerciales S.A.", "$1.01B" "SLQD", "iShares 0-5 Year Investment Grade Corporate Bond ETF", "$72.31M" "TLT", "iShares 20+ Year Treasury Bond ETF", "$5.2B" "AIA", "iShares Asia 50 ETF", "$329.87M" "COMT", "iShares Commodities Select Strategy ETF", "$216.4M" "IXUS", "iShares Core MSCI Total International Stock ETF", "$1.52B" "IFEU", "iShares FTSE EPRA/NAREIT Europe Index Fund", "$55.68M" "IFGL", "iShares FTSE EPRA/NAREIT Global Real Estate ex-U.S. Index Fund", "$853.05M" "IGF", "iShares Global Infrastructure ETF", "$1.06B" "GNMA", "iShares GNMA Bond ETF", "$60.73M" "JKI", "iShares Morningstar Mid-Cap ETF", "$192.07M" "ACWX", "iShares MSCI ACWI ex US Index Fund", "$1.64B" "ACWI", "iShares MSCI ACWI Index Fund", "$5.68B" "AAXJ", "iShares MSCI All Country Asia ex Japan Index Fund", "$2.8B" "EWZS", "iShares MSCI Brazil Small-Cap ETF", "$20.99M" "MCHI", "iShares MSCI China ETF", "$1.52B" "SCZ", "iShares MSCI EAFE Small-Cap ETF", "$4.62B" "EEMA", "iShares MSCI Emerging Markets Asia Index Fund", "$129.22M" "EEML", "iShares MSCI Emerging Markets Latin America Index Fund", "$7.11M" "EUFN", "iShares MSCI Europe Financials Sector Index Fund", "$334.76M" "IEUS", "iShares MSCI Europe Small-Cap ETF", "$47.56M" "ENZL", "iShares MSCI New Zealand Capped ETF", "$119.16M" "QAT", "iShares MSCI Qatar Capped ETF", "$40.5M" "UAE", "iShares MSCI UAE Capped ETF", "$25.21M" "IBB", "iShares Nasdaq Biotechnology Index Fund", "$6.64B" "SOXX", "iShares PHLX SOX Semiconductor Sector Index Fund", "$373.46M" "EMIF", "iShares S&P Emerging Markets Infrastructure Index Fund", "$51.19M" "ICLN", "iShares S&P Global Clean Energy Index Fund", "$63.83M" "WOOD", "iShares S&P Global Timber & Forestry Index Fund", "$223.31M" "INDY", "iShares S&P India Nifty 50 Index Fund", "$764.6M" "ISHG", "iShares S&P/Citigroup 1-3 Year International Treasury Bond Fun", "$148M" "IGOV", "iShares S&P/Citigroup International Treasury Bond Fund", "$434.82M" "ISLE", "Isle of Capri Casinos, "12.04" "ISRL", "Isramco, "84" "ITI", "Iteris, "2.35" "ITRI", "Itron, "36.5" "ITRN", "Ituran Location and Control Ltd.", "$421.85M" "ITUS", "ITUS Corporation", "$25.48M" "XXIA", "Ixia", "$742.39M" "IXYS", "IXYS Corporation", "$345.7M" "JJSF", "J & J Snack Foods Corp.", "$2.01B" "MAYS", "J. W. Mays, "50.5" "JBHT", "J.B. Hunt Transport Services, "77.09" "JCOM", "j2 Global, "75.43" "JASO", "JA Solar Holdings, Ltd." "JKHY", "Jack Henry & Associates, "80.02" "JACK", "Jack In The Box Inc.", "$2.75B" "JXSB", "Jacksonville Bancorp Inc.", "$43.41M" "JAXB", "Jacksonville Bancorp, "15.5" "JAGX", "Jaguar Animal Health, "1.71" "JAKK", "JAKKS Pacific, "6.71" "JMBA", "Jamba, "12.97" "JRVR", "James River Group Holdings, "31.52" "ERW", "Janus Equal Risk Weighted Large Cap ETF", "$25.21M" "JASN", "Jason Industries, "2.8" "JASNW", "Jason Industries, "0.055" "JAZZ", "Jazz Pharmaceuticals plc", "$7.55B" "JD", "JD.com, "25.72" "JBLU", "JetBlue Airways Corporation", "$6.77B" "JTPY", "JetPay Corporation", "$36.12M" "JCTCF", "Jewett-Cameron Trading Company", "$26.16M" "DATE", "Jiayuan.com International Ltd.", "$241M" "JST", "Jinpan International Limited", "$85.7M" "JIVE", "Jive Software, "3.22" "WYIG", "JM Global Holding Company", "$62.15M" "WYIGU", "JM Global Holding Company", "$63.98M" "WYIGW", "JM Global Holding Company", "n/a" "JBSS", "John B. Sanfilippo & Son, "65.3" "JOUT", "Johnson Outdoors Inc.", "$223.1M" "JNP", "Juniper Pharmaceuticals, "7.97" "JUNO", "Juno Therapeutics, "34.44" "KTWO", "K2M Group Holdings, "13.08" "KALU", "Kaiser Aluminum Corporation", "$1.35B" "KMDA", "Kamada Ltd.", "$132.2M" "KNDI", "Kandi Technologies Group, "7.42" "KPTI", "Karyopharm Therapeutics Inc.", "$230.83M" "KBSF", "KBS Fashion Group Limited", "$10.93M" "KCAP", "KCAP Financial, "2.85" "KRNY", "Kearny Financial", "$1.14B" "KELYA", "Kelly Services, "16.44" "KELYB", "Kelly Services, "16" "KMPH", "KemPharm, "15.69" "KFFB", "Kentucky First Federal Bancorp", "$75.95M" "KERX", "Keryx Biopharmaceuticals, "3.89" "GMCR", "Keurig Green Mountain, "90.4" "KEQU", "Kewaunee Scientific Corporation", "$45.07M" "KTEC", "Key Technology, "6.35" "KTCC", "Key Tronic Corporation", "$82.26M" "KFRC", "Kforce, "15.64" "KE", "Kimball Electronics, "10.95" "KBAL", "Kimball International, "10.48" "KIN", "Kindred Biosciences, "3.91" "KGJI", "Kingold Jewelry Inc.", "$58.05M" "KINS", "Kingstone Companies, "7.75" "KONE", "Kingtone Wirelessinfo Solution Holding Ltd", "$3.25M" "KIRK", "Kirkland's, "13.78" "KITE", "Kite Pharma, "48.13" "KTOV", "Kitov Pharamceuticals Holdings Ltd.", "$10.13M" "KTOVW", "Kitov Pharamceuticals Holdings Ltd.", "n/a" "KLAC", "KLA-Tencor Corporation", "$10.21B" "KLOX", "Klox Technologies Inc.", "n/a" "KLXI", "KLX Inc.", "$1.53B" "KONA", "Kona Grill, "13.66" "KZ", "KongZhong Corporation", "$331.24M" "KOPN", "Kopin Corporation", "$125.62M" "KRNT", "Kornit Digital Ltd.", "$328.33M" "KOSS", "Koss Corporation", "$15.5M" "KWEB", "KraneShares CSI China Internet ETF", "$111.66M" "KTOS", "Kratos Defense & Security Solutions, "3.28" "KUTV", "Ku6 Media Co., "0.82" "KLIC", "Kulicke and Soffa Industries, "11.33" "KURA", "Kura Oncology, "4.01" "KVHI", "KVH Industries, "8.74" "FSTR", "L.B. Foster Company", "$131.07M" "LJPC", "La Jolla Pharmaceutical Company", "$313.57M" "LSBK", "Lake Shore Bancorp, "13.15" "LSBG", "Lake Sunapee Bank Group", "$115.87M" "LBAI", "Lakeland Bancorp, "10.23" "LKFN", "Lakeland Financial Corporation", "$701.71M" "LAKE", "Lakeland Industries, "12.04" "LRCX", "Lam Research Corporation", "$11B" "LAMR", "Lamar Advertising Company", "$6.04B" "LANC", "Lancaster Colony Corporation", "$2.75B" "LNDC", "Landec Corporation", "$311.42M" "LARK", "Landmark Bancorp Inc.", "$86.48M" "LMRK", "Landmark Infrastructure Partners LP", "$188.77M" "LE", "Lands' End, "23.34" "LSTR", "Landstar System, "60.74" "LNTH", "Lantheus Holdings, "1.96" "LTRX", "Lantronix, "0.938" "LPSB", "LaPorte Bancorp, "14.4888" "LSCC", "Lattice Semiconductor Corporation", "$561.23M" "LAWS", "Lawson Products, "19.68" "LAYN", "Layne Christensen Company", "$108.89M" "LCNB", "LCNB Corporation", "$159.47M" "LDRH", "LDR Holding Corporation", "$550.7M" "LBIX", "Leading Brands Inc", "$5.6M" "LGCY", "Legacy Reserves LP", "$68.67M" "LGCYO", "Legacy Reserves LP", "$15.89M" "LGCYP", "Legacy Reserves LP", "$4.88M" "LTXB", "LegacyTexas Financial Group, "18.3" "DDBI", "Legg Mason Developed EX-US Diversified Core ETF", "n/a" "EDBI", "Legg Mason Emerging Markets Diversified Core ETF", "n/a" "LVHD", "Legg Mason Low Volatility High Dividend ETF", "n/a" "UDBI", "Legg Mason US Diversified Core ETF", "n/a" "LMAT", "LeMaitre Vascular, "13.03" "TREE", "LendingTree, "62.15" "LXRX", "Lexicon Pharmaceuticals, "9.33" "LGIH", "LGI Homes, "21.6" "LHCG", "LHC Group", "$639.92M" "LBRDA", "Liberty Broadband Corporation", "$4.86B" "LBRDK", "Liberty Broadband Corporation", "$4.81B" "LBTYA", "Liberty Global plc", "$32.1B" "LBTYB", "Liberty Global plc", "$26.96B" "LBTYK", "Liberty Global plc", "$30.99B" "LILA", "Liberty Global plc", "$29.24B" "LILAK", "Liberty Global plc", "$31.24B" "LVNTA", "Liberty Interactive Corporation", "$23.46B" "LVNTB", "Liberty Interactive Corporation", "$22.82B" "QVCA", "Liberty Interactive Corporation", "$15.9B" "QVCB", "Liberty Interactive Corporation", "$15.76B" "LMCA", "Liberty Media Corporation", "$11.24B" "LMCB", "Liberty Media Corporation", "$11.54B" "LMCK", "Liberty Media Corporation", "$11.03B" "TAX", "Liberty Tax, "18.51" "LTRPA", "Liberty TripAdvisor Holdings, "21.14" "LTRPB", "Liberty TripAdvisor Holdings, "19.9201" "LPNT", "LifePoint Health, "63.46" "LCUT", "Lifetime Brands, "11.94" "LFVN", "Lifevantage Corporation", "$114.03M" "LWAY", "Lifeway Foods, "10.3" "LGND", "Ligand Pharmaceuticals Incorporated", "$1.8B" "LTBR", "Lightbridge Corporation", "$12.92M" "LPTH", "LightPath Technologies, "2.16" "LLEX", "Lilis Energy, "0.1439" "LIME", "Lime Energy Co.", "$26.4M" "LLNW", "Limelight Networks, "1.19" "LMNR", "Limoneira Co", "$179.42M" "LINC", "Lincoln Educational Services Corporation", "$59.91M" "LECO", "Lincoln Electric Holdings, "58.14" "LIND", "Lindblad Expeditions Holdings Inc.", "$457.68M" "LINDW", "Lindblad Expeditions Holdings Inc.", "n/a" "LLTC", "Linear Technology Corporation", "$10.45B" "LNCO", "Linn Co, "0.282" "LINE", "Linn Energy, "0.68" "LBIO", "Lion Biotechnologies, "5.36" "LIOX", "Lionbridge Technologies, "4.54" "LPCN", "Lipocine Inc.", "$177.79M" "LQDT", "Liquidity Services, "4.58" "LFUS", "Littelfuse, "113.53" "LIVN", "LivaNova PLC", "$2.9B" "LOB", "Live Oak Bancshares, "13.6" "LIVE", "Live Ventures Incorporated", "$25.87M" "LPSN", "LivePerson, "4.62" "LKQ", "LKQ Corporation", "$7.95B" "LMFA", "LM Funding America, "9.01" "LMFAW", "LM Funding America, "0.6939" "LMIA", "LMI Aerospace, "9.3" "LOGI", "Logitech International S.A.", "$2.43B" "LOGM", "LogMein, "49.81" "LOJN", "LoJack Corporation", "$124.24M" "EVAR", "Lombard Medical, "0.7852" "CNCR", "Loncar Cancer Immunotherapy ETF", "n/a" "LORL", "Loral Space and Communications, "31.34" "LOXO", "Loxo Oncology, "19.13" "LPTN", "Lpath, "0.172" "LPLA", "LPL Financial Holdings Inc.", "$2B" "LRAD", "LRAD Corporation", "$50.03M" "LYTS", "LSI Industries Inc.", "$280.72M" "LULU", "lululemon athletica inc.", "$7.79B" "LITE", "Lumentum Holdings Inc.", "$1.44B" "LMNX", "Luminex Corporation", "$775.54M" "LMOS", "Lumos Networks Corp.", "$270.35M" "LUNA", "Luna Innovations Incorporated", "$24.88M" "MBTF", "M B T Financial Corp", "$180.75M" "MTSI", "M/A-COM Technology Solutions Holdings, "38.29" "MCBC", "Macatawa Bank Corporation", "$197.44M" "MFNC", "Mackinac Financial Corporation", "$62.86M" "MCUR", "MACROCURE LTD.", "$16.89M" "MGNX", "MacroGenics, "17.54" "MAGS", "Magal Security Systems Ltd.", "$68.46M" "MGLN", "Magellan Health, "55.88" "MPET", "Magellan Petroleum Corporation", "$6.11M" "MGIC", "Magic Software Enterprises Ltd.", "$269.02M" "CALL", "magicJack VocalTec Ltd", "$120.97M" "MNGA", "MagneGas Corporation", "$47.81M" "MGYR", "Magyar Bancorp, "10" "MHLD", "Maiden Holdings, "13.36" "MHLDO", "Maiden Holdings, "48.63" "MSFG", "MainSource Financial Group, "20.47" "COOL", "Majesco Entertainment Company", "$8.92M" "MMYT", "MakeMyTrip Limited", "$680.2M" "MBUU", "Malibu Boats, "14.03" "MLVF", "Malvern Bancorp, "16.46" "MAMS", "MAM Software Group, "5.97" "MANH", "Manhattan Associates, "54.02" "LOAN", "Manhattan Bridge Capital, "3.97" "MNTX", "Manitex International, "4.79" "MTEX", "Mannatech, "17.28" "MNKD", "MannKind Corporation", "$409.49M" "MANT", "ManTech International Corporation", "$1.02B" "MAPI", "Mapi - Pharma Ltd.", "n/a" "MARA", "Marathon Patent Group, "2.39" "MCHX", "Marchex, "3.86" "MARPS", "Marine Petroleum Trust", "$7.02M" "MRNS", "Marinus Pharmaceuticals, "4.65" "BBH", "Market Vectors Biotech ETF", "$632.09M" "GNRX", "Market Vectors Generic Drugs ETF", "n/a" "PPH", "Market Vectors Pharmaceutical ETF", "$332.82M" "MKTX", "MarketAxess Holdings, "114.15" "MKTO", "Marketo, "15.59" "MRKT", "Markit Ltd.", "$5.72B" "MRLN", "Marlin Business Services Corp.", "$173.93M" "MAR", "Marriott International", "$17.15B" "MBII", "Marrone Bio Innovations, "1.02" "MRTN", "Marten Transport, "17.46" "MMLP", "Martin Midstream Partners L.P.", "$553.83M" "MRVL", "Marvell Technology Group Ltd.", "$4.81B" "MASI", "Masimo Corporation", "$1.85B" "MTCH", "Match Group, "10.11" "MTLS", "Materialise NV", "$281.94M" "MTRX", "Matrix Service Company", "$467.13M" "MAT", "Mattel, "31.91" "MATR", "Mattersight Corporation", "$113.55M" "MATW", "Matthews International Corporation", "$1.59B" "MFRM", "Mattress Firm Holding Corp.", "$1.45B" "MTSN", "Mattson Technology, "3.51" "MXIM", "Maxim Integrated Products, "33.66" "MXWL", "Maxwell Technologies, "5.15" "MZOR", "Mazor Robotics Ltd.", "$205.88M" "MBFI", "MB Financial Inc.", "$2.26B" "MBFIP", "MB Financial Inc.", "$105.4M" "MCFT", "MCBC Holdings, "11.31" "MGRC", "McGrath RentCorp", "$627.46M" "MDCA", "MDC Partners Inc.", "$893.84M" "MCOX", "Mecox Lane Limited", "$49.29M" "TAXI", "Medallion Financial Corp.", "$174.57M" "MTBC", "Medical Transcription Billing, "0.878" "MTBCP", "Medical Transcription Billing, "24.37" "MNOV", "MediciNova, "4.47" "MDSO", "Medidata Solutions, "35.34" "MDGS", "Medigus Ltd.", "$9.39M" "MDVN", "Medivation, "32.28" "MDWD", "MediWound Ltd.", "$155.36M" "MDVX", "Medovex Corp.", "$16.65M" "MDVXW", "Medovex Corp.", "n/a" "MEET", "MeetMe, "3.07" "MEIP", "MEI Pharma, "1.09" "MPEL", "Melco Crown Entertainment Limited", "$8.58B" "MLNX", "Mellanox Technologies, "45.74" "MELR", "Melrose Bancorp, "14.95" "MEMP", "Memorial Production Partners LP", "$193.44M" "MRD", "Memorial Resource Development Corp.", "$2.3B" "MENT", "Mentor Graphics Corporation", "$2.12B" "MTSL", "MER Telemanagement Solutions Ltd.", "$6.03M" "MELI", "MercadoLibre, "98.24" "MBWM", "Mercantile Bank Corporation", "$368.75M" "MERC", "Mercer International Inc.", "$510.86M" "MBVT", "Merchants Bancshares, "28.55" "MRCY", "Mercury Systems Inc", "$602.56M" "EBSB", "Meridian Bancorp, "13.82" "VIVO", "Meridian Bioscience Inc.", "$838.08M" "MMSI", "Merit Medical Systems, "17.17" "MACK", "Merrimack Pharmaceuticals, "5.88" "MSLI", "Merus Labs International Inc.", "$153.9M" "MLAB", "Mesa Laboratories, "89.62" "MESO", "Mesoblast Limited", "$386.18M" "CASH", "Meta Financial Group, "39.51" "MBLX", "Metabolix, "1.42" "MEOH", "Methanex Corporation", "$2.63B" "MFRI", "MFRI, "7" "MGCD", "MGC Diagnostics Corporation", "$28.41M" "MGEE", "MGE Energy Inc.", "$1.75B" "MGPI", "MGP Ingredients, "24.41" "MCHP", "Microchip Technology Incorporated", "$8.68B" "MU", "Micron Technology, "11.43" "MICT", "Micronet Enertec Technologies, "1.9648" "MICTW", "Micronet Enertec Technologies, "0.1771" "MSCC", "Microsemi Corporation", "$3.74B" "MSFT", "Microsoft Corporation", "$414.61B" "MSTR", "MicroStrategy Incorporated", "$1.78B" "MVIS", "Microvision, "2.74" "MPB", "Mid Penn Bancorp", "$63.17M" "MTP", "Midatech Pharma PLC", "$76.81M" "MCEP", "Mid-Con Energy Partners, "0.8975" "MBRG", "Middleburg Financial Corporation", "$145.04M" "MBCN", "Middlefield Banc Corp.", "$62.28M" "MSEX", "Middlesex Water Company", "$454.08M" "MOFG", "MidWestOne Financial Group, "26.61" "MIME", "Mimecast Limited", "$513.2M" "MDXG", "MiMedx Group, "8" "MNDO", "MIND C.T.I. Ltd.", "$44.53M" "MB", "MINDBODY, "11.04" "NERV", "Minerva Neurosciences, "4.99" "MRTX", "Mirati Therapeutics, "22.53" "MIRN", "Mirna Therapeutics, "4.035" "MSON", "MISONIX, "6.0977" "MIND", "Mitcham Industries, "2.84" "MITK", "Mitek Systems, "5.05" "MITL", "Mitel Networks Corporation", "$851.51M" "MKSI", "MKS Instruments, "33.27" "MMAC", "MMA Capital Management, "14.605" "MINI", "Mobile Mini, "26.97" "MOBL", "MobileIron, "3.3" "MOCO", "MOCON, "13.64" "MDSY", "ModSys International Ltd.", "$36.77M" "MLNK", "ModusLink Global Solutions, "1.81" "MOKO", "Moko Social Media Ltd.", "$6.8M" "MOLG", "MOL Global, "0.66" "MNTA", "Momenta Pharmaceuticals, "11.51" "MOMO", "Momo Inc.", "$1.74B" "MCRI", "Monarch Casino & Resort, "19" "MNRK", "Monarch Financial Holdings, "14.82" "MDLZ", "Mondelez International, "39.81" "MGI", "Moneygram International, "5.58" "MPWR", "Monolithic Power Systems, "58.83" "TYPE", "Monotype Imaging Holdings Inc.", "$921.03M" "MNRO", "Monro Muffler Brake, "65.12" "MRCC", "Monroe Capital Corporation", "$150.89M" "MNST", "Monster Beverage Corporation", "$25.43B" "MHGC", "Morgans Hotel Group Co.", "$44.78M" "MORN", "Morningstar, "79.01" "MOSY", "MoSys, "0.6888" "MPAA", "Motorcar Parts of America, "32.09" "MDM", "Mountain Province Diamonds Inc.", "$547.7M" "MRVC", "MRV Communications, "11.14" "MSBF", "MSB Financial Corp.", "$74.63M" "MSG", "MSG Networks Inc.", "$3.69B" "MTSC", "MTS Systems Corporation", "$812.8M" "LABL", "Multi-Color Corporation", "$760.3M" "MFLX", "Multi-Fineline Electronix, "21.47" "MFSF", "MutualFirst Financial Inc.", "$175.04M" "MYL", "Mylan N.V.", "$22.75B" "MYOK", "MyoKardia, "6.94" "MYOS", "MYOS Corporation", "$4.88M" "MYRG", "MYR Group, "20.47" "MYGN", "Myriad Genetics, "35.21" "NBRV", "Nabriva Therapeutics AG", "$183.04M" "NAKD", "Naked Brand Group Inc.", "$4.21M" "NANO", "Nanometrics Incorporated", "$306.22M" "NSPH", "Nanosphere, "0.6201" "NSTG", "NanoString Technologies, "12.6" "NK", "NantKwest, "7.76" "NSSC", "NAPCO Security Technologies, "5.51" "NDAQ", "Nasdaq, "62.96" "NTRA", "Natera, "7.21" "NATH", "Nathan's Famous, "49.7" "NAUH", "National American University Holdings, "1.67" "NKSH", "National Bankshares, "33.8" "FIZZ", "National Beverage Corp.", "$1.67B" "NCMI", "National CineMedia, "15.41" "NCOM", "National Commerce Corporation", "$237.65M" "NGHC", "National General Holdings Corp", "$2.04B" "NGHCO", "National General Holdings Corp", "n/a" "NGHCP", "National General Holdings Corp", "$55.44M" "NGHCZ", "National General Holdings Corp", "n/a" "NHLD", "National Holdings Corporation", "$25.32M" "NATI", "National Instruments Corporation", "$3.63B" "NATL", "National Interstate Corporation", "$473.86M" "NPBC", "National Penn Bancshares, "11.26" "NRCIA", "National Research Corporation", "$353.37M" "NRCIB", "National Research Corporation", "$896.07M" "NSEC", "National Security Group, "16.2969" "NWLI", "National Western Life Group, "216.48" "NAII", "Natural Alternatives International, "11" "NHTC", "Natural Health Trends Corp.", "$386.4M" "NATR", "Nature's Sunshine Products, "7.92" "BABY", "Natus Medical Incorporated", "$1.1B" "NAVI", "Navient Corporation", "$3.59B" "NBCP", "NB Capital Acquisition Corp.", "n/a" "NBTB", "NBT Bancorp Inc.", "$1.12B" "NCIT", "NCI, "13.48" "NKTR", "Nektar Therapeutics", "$1.59B" "NEOG", "Neogen Corporation", "$1.84B" "NEO", "NeoGenomics, "6.22" "NEON", "Neonode Inc.", "$102.32M" "NEOS", "Neos Therapeutics, "10.17" "NEOT", "Neothetics, "0.7884" "NVCN", "Neovasc Inc.", "$219.23M" "NRX", "NephroGenex, "1.09" "NEPT", "Neptune Technologies & Bioresources Inc", "$80.28M" "UEPS", "Net 1 UEPS Technologies, "10.17" "NETE", "Net Element, "0.209" "NTAP", "NetApp, "23.55" "NTES", "NetEase, "155.96" "NFLX", "Netflix, "94.76" "NTGR", "NETGEAR, "38.42" "NLST", "Netlist, "1.2" "NTCT", "NetScout Systems, "20.34" "NTWK", "NetSol Technologies Inc.", "$71.43M" "CUR", "Neuralstem, "0.82" "NBIX", "Neurocrine Biosciences, "38.14" "NDRM", "NeuroDerm Ltd.", "$290.53M" "NURO", "NeuroMetrix, "1.48" "NUROW", "NeuroMetrix, "0.4099" "NSIG", "NeuroSigma, "n/a" "NYMT", "New York Mortgage Trust, "4.85" "NYMTO", "New York Mortgage Trust, "20.23" "NYMTP", "New York Mortgage Trust, "20.59" "NBBC", "NewBridge Bancorp", "$413.04M" "NLNK", "NewLink Genetics Corporation", "$652.85M" "NEWP", "Newport Corporation", "$583.28M" "NWS", "News Corporation", "$6.73B" "NWSA", "News Corporation", "$6.38B" "NEWS", "NewStar Financial, "6.76" "NEWT", "Newtek Business Services Corp.", "$128.94M" "NEWTZ", "Newtek Business Services Corp.", "n/a" "NXST", "Nexstar Broadcasting Group, "39.08" "NVET", "Nexvet Biopharma plc", "$33.34M" "NFEC", "NF Energy Saving Corporation", "$4.91M" "EGOV", "NIC Inc.", "$1.16B" "NICE", "NICE-Systems Limited", "$3.54B" "NICK", "Nicholas Financial, "10.63" "NIHD", "NII Holdings, "4.05" "NVLS", "Nivalis Therapeutics, "4.79" "NMIH", "NMI Holdings Inc", "$301.36M" "NNBR", "NN, "12" "NDLS", "Noodles & Company", "$349.67M" "NORT", "Nordic Realty Trust, "n/a" "NDSN", "Nordson Corporation", "$3.67B" "NSYS", "Nortech Systems Incorporated", "$10.06M" "NTK", "Nortek Inc.", "$610.4M" "NBN", "Northeast Bancorp", "$93.41M" "NTIC", "Northern Technologies International Corporation", "$48.38M" "NTRS", "Northern Trust Corporation", "$13.78B" "NTRSP", "Northern Trust Corporation", "$421.6M" "NFBK", "Northfield Bancorp, "15.36" "NRIM", "Northrim BanCorp Inc", "$158.05M" "NWBI", "Northwest Bancshares, "12.34" "NWBO", "Northwest Biotherapeutics, "2.47" "NWBOW", "Northwest Biotherapeutics, "1.62" "NWPX", "Northwest Pipe Company", "$87.52M" "NCLH", "Norwegian Cruise Line Holdings Ltd.", "$9.83B" "NWFL", "Norwood Financial Corp.", "$98.73M" "NVFY", "Nova Lifestyle, "1.29" "NVMI", "Nova Measuring Instruments Ltd.", "$266.49M" "NVDQ", "Novadaq Technologies Inc", "$522.03M" "MIFI", "Novatel Wireless, "1.4" "NVAX", "Novavax, "5.07" "NVCR", "NovoCure Limited", "$1.05B" "NVGN", "Novogen Limited", "$32.5M" "NTLS", "NTELOS Holdings Corp.", "$204.08M" "NUAN", "Nuance Communications, "18.35" "NMRX", "Numerex Corp.", "$110.14M" "NUTR", "Nutraceutical International Corporation", "$230.75M" "NTRI", "NutriSystem Inc", "$589.4M" "NUVA", "NuVasive, "39.5" "QQQX", "Nuveen NASDAQ 100 Dynamic Overwrite Fund", "$314.48M" "NVEE", "NV5 Global, "18.94" "NVEC", "NVE Corporation", "$243.02M" "NVDA", "NVIDIA Corporation", "$14.88B" "NXPI", "NXP Semiconductors N.V.", "$24.03B" "NXTM", "NxStage Medical, "14.61" "NXTD", "NXT-ID Inc.", "$17.12M" "NXTDW", "NXT-ID Inc.", "n/a" "NYMX", "Nymox Pharmaceutical Corporation", "$94.2M" "OIIM", "O2Micro International Limited", "$35.29M" "OVLY", "Oak Valley Bancorp (CA)", "$79.97M" "OASM", "Oasmia Pharmaceutical AB", "$124.8M" "OBCI", "Ocean Bio-Chem, "2.04" "OPTT", "Ocean Power Technologies, "1.46" "ORIG", "Ocean Rig UDW Inc.", "$109.64M" "OSHC", "Ocean Shore Holding Co.", "$108.86M" "OCFC", "OceanFirst Financial Corp.", "$295.32M" "OCRX", "Ocera Therapeutics, "2.83" "OCLR", "Oclaro, "4.45" "OFED", "Oconee Federal Financial Corp.", "$113M" "OCUL", "Ocular Therapeutix, "8.67" "OCLS", "Oculus Innovative Sciences, "1.02" "OCLSW", "Oculus Innovative Sciences, "0.2966" "OMEX", "Odyssey Marine Exploration, "0.2163" "ODP", "Office Depot, "5.19" "OFS", "OFS Capital Corporation", "$105.91M" "OHAI", "OHA Investment Corporation", "$60.11M" "OVBC", "Ohio Valley Banc Corp.", "$94.71M" "OHRP", "Ohr Pharmaceuticals, "3.18" "ODFL", "Old Dominion Freight Line, "62.5" "OLBK", "Old Line Bancshares, "17.43" "ONB", "Old National Bancorp", "$1.3B" "OPOF", "Old Point Financial Corporation", "$93.18M" "OSBC", "Old Second Bancorp, "6.66" "OSBCP", "Old Second Bancorp, "10.08" "OLLI", "Ollie's Bargain Outlet Holdings, "21.56" "ZEUS", "Olympic Steel, "11.14" "OFLX", "Omega Flex, "32.39" "OMER", "Omeros Corporation", "$432.47M" "OMCL", "Omnicell, "27.73" "ON", "ON Semiconductor Corporation", "$3.26B" "OTIV", "On Track Innovations Ltd", "$22.05M" "OGXI", "OncoGenex Pharmaceuticals Inc.", "$18.67M" "OMED", "OncoMed Pharmaceuticals, "9.75" "ONTX", "Onconova Therapeutics, "0.4601" "ONCS", "OncoSec Medical Incorporated", "$29.7M" "ONTY", "Oncothyreon Inc.", "$101.57M" "OHGI", "One Horizon Group, "0.85" "ONVI", "Onvia, "3.5" "OTEX", "Open Text Corporation", "$6.12B" "OPXA", "Opexa Therapeutics, "2.06" "OPXAW", "Opexa Therapeutics, "0.07" "OPGN", "OpGen, "1.96" "OPGNW", "OpGen, "0.265" "OPHT", "Ophthotech Corporation", "$1.75B" "OBAS", "Optibase Ltd.", "$36.48M" "OCC", "Optical Cable Corporation", "$16.66M" "OPHC", "OptimumBank Holdings, "4.6285" "OPB", "Opus Bank", "$876.19M" "ORMP", "Oramed Pharmaceuticals Inc.", "$89.57M" "OSUR", "OraSure Technologies, "6.38" "ORBC", "ORBCOMM Inc.", "$554.73M" "ORBK", "Orbotech Ltd.", "$839.99M" "ORLY", "O'Reilly Automotive, "254.62" "OREX", "Orexigen Therapeutics, "1.795" "SEED", "Origin Agritech Limited", "$26.69M" "OESX", "Orion Energy Systems, "1.3" "ORIT", "Oritani Financial Corp.", "$714.88M" "ORRF", "Orrstown Financial Services Inc", "$140.05M" "OFIX", "Orthofix International N.V.", "$701.95M" "OSIS", "OSI Systems, "58.2" "OSIR", "Osiris Therapeutics, "7.58" "OSN", "Ossen Innovation Co., "0.7699" "OTEL", "Otelco Inc.", "$14.8M" "OTG", "OTG EXP, "n/a" "OTIC", "Otonomy, "14.91" "OTTR", "Otter Tail Corporation", "$1.01B" "OUTR", "Outerwall Inc.", "$503.24M" "OVAS", "Ovascience Inc.", "$165.31M" "OSTK", "Overstock.com, "14.19" "OXBR", "Oxbridge Re Holdings Limited", "$30.6M" "OXBRW", "Oxbridge Re Holdings Limited", "n/a" "OXFD", "Oxford Immunotec Global PLC", "$227.86M" "OXLC", "Oxford Lane Capital Corp.", "$126.14M" "OXLCN", "Oxford Lane Capital Corp.", "$27.11M" "OXLCO", "Oxford Lane Capital Corp.", "n/a" "OXGN", "OXiGENE, "0.568" "PFIN", "P & F Industries, "9.14" "PTSI", "P.A.M. Transportation Services, "25.35" "PCAR", "PACCAR Inc.", "$18.4B" "PACE", "Pace Holdings Corp.", "$548.44M" "PACEU", "Pace Holdings Corp.", "n/a" "PACEW", "Pace Holdings Corp.", "n/a" "PACB", "Pacific Biosciences of California, "9.14" "PCBK", "Pacific Continental Corporation (Ore)", "$299.57M" "PEIX", "Pacific Ethanol, "3.68" "PMBC", "Pacific Mercantile Bancorp", "$152.82M" "PPBI", "Pacific Premier Bancorp Inc", "$433.87M" "PAAC", "Pacific Special Acquisition Corp.", "$76.42M" "PAACR", "Pacific Special Acquisition Corp.", "n/a" "PAACU", "Pacific Special Acquisition Corp.", "n/a" "PAACW", "Pacific Special Acquisition Corp.", "n/a" "PSUN", "Pacific Sunwear of California, "0.1919" "PCRX", "Pacira Pharmaceuticals, "61.94" "PACW", "PacWest Bancorp", "$3.86B" "PTIE", "Pain Therapeutics", "$82.36M" "PAAS", "Pan American Silver Corp.", "$1.35B" "PNRA", "Panera Bread Company", "$5.21B" "PANL", "Pangaea Logistics Solutions Ltd.", "$81.94M" "PZZA", "Papa John'S International, "52.88" "FRSH", "Papa Murphy's Holdings, "9.07" "PRGN", "Paragon Shipping Inc.", "$1.57M" "PRGNL", "Paragon Shipping Inc.", "n/a" "PRTK", "Paratek Pharmaceuticals, "14.12" "PRXL", "PAREXEL International Corporation", "$3.2B" "PCYG", "Park City Group, "8.44" "PSTB", "Park Sterling Corporation", "$275.9M" "PKBK", "Parke Bancorp, "12.4" "PRKR", "ParkerVision, "0.2" "PKOH", "Park-Ohio Holdings Corp.", "$351.9M" "PARN", "Parnell Pharmaceuticals Holdings Ltd", "$25.5M" "PTNR", "Partner Communications Company Ltd.", "$718M" "PBHC", "Pathfinder Bancorp, "11.98" "PATK", "Patrick Industries, "36.19" "PNBK", "Patriot National Bancorp Inc.", "$51.55M" "PATI", "Patriot Transportation Holding, "21.51" "PEGI", "Pattern Energy Group Inc.", "$1.28B" "PDCO", "Patterson Companies, "44.24" "PTEN", "Patterson-UTI Energy, "14.67" "PAYX", "Paychex, "50.61" "PCTY", "Paylocity Holding Corporation", "$1.38B" "PYDS", "Payment Data Systems, "1.9332" "PYPL", "PayPal Holdings, "36.36" "PBBI", "PB Bancorp, "8.64" "PCCC", "PC Connection, "23.11" "PCMI", "PCM, "8.67" "PCTI", "PC-Tel, "5.28" "PDCE", "PDC Energy, "47.94" "PDFS", "PDF Solutions, "10.5" "PDLI", "PDL BioPharma, "3.02" "PDVW", "pdvWireless, "23.93" "SKIS", "Peak Resorts, "4.17" "PGC", "Peapack-Gladstone Financial Corporation", "$267.17M" "PEGA", "Pegasystems Inc.", "$1.67B" "PCO", "Pendrell Corporation", "$151.35M" "PENN", "Penn National Gaming, "13.93" "PFLT", "PennantPark Floating Rate Capital Ltd.", "$289.22M" "PNNT", "PennantPark Investment Corporation", "$397.47M" "PWOD", "Penns Woods Bancorp, "40.15" "PTXP", "PennTex Midstream Partners, "10" "PEBO", "Peoples Bancorp Inc.", "$323.03M" "PEBK", "Peoples Bancorp of North Carolina, "18.6" "PFBX", "Peoples Financial Corporation", "$46.36M" "PFIS", "Peoples Financial Services Corp. ", "$274.85M" "PBCT", "People's United Financial, "14.68" "PUB", "People's Utah Bancorp", "$254.47M" "PRCP", "Perceptron, "5.07" "PPHM", "Peregrine Pharmaceuticals Inc.", "$225.11M" "PPHMP", "Peregrine Pharmaceuticals Inc.", "$14.49M" "PRFT", "Perficient, "17.51" "PFMT", "Performant Financial Corporation", "$85.6M" "PERF", "Perfumania Holdings, "2.425" "PERI", "Perion Network Ltd", "$160.33M" "PESI", "Perma-Fix Environmental Services, "3.83" "PTX", "Pernix Therapeutics Holdings, "2.25" "PERY", "Perry Ellis International Inc.", "$280.5M" "PGLC", "Pershing Gold Corporation", "$96.88M" "PETS", "PetMed Express, "16.84" "PFSW", "PFSweb, "11.9" "PGTI", "PGT, "10.31" "PHII", "PHI, "16.3769" "PHIIK", "PHI, "15.58" "PAHC", "Phibro Animal Health Corporation", "$1.12B" "PHMD", "PhotoMedex, "0.436" "PLAB", "Photronics, "9.78" "PICO", "PICO Holdings Inc.", "$197.72M" "PIRS", "Pieris Pharmaceuticals, "1.7" "PPC", "Pilgrim's Pride Corporation", "$5.96B" "PME", "Pingtan Marine Enterprise Ltd.", "$118.57M" "PNK", "Pinnacle Entertainment, "28.44" "PNFP", "Pinnacle Financial Partners, "47.55" "PPSI", "Pioneer Power Solutions, "3.39" "PXLW", "Pixelworks, "1.63" "PLPM", "Planet Payment, "2.67" "PLXS", "Plexus Corp.", "$1.21B" "PLUG", "Plug Power, "1.83" "PLBC", "Plumas Bancorp", "$42.24M" "PSTI", "Pluristem Therapeutics, "1.29" "PLXP", "PLx Pharma Inc.", "n/a" "PMV", "PMV Acquisition Corp.", "n/a" "PBSK", "Poage Bankshares, "17.09" "PNTR", "Pointer Telocation Ltd.", "$42.36M" "PCOM", "Points International, "6.88" "PLCM", "Polycom, "9.64" "POOL", "Pool Corporation", "$3.42B" "POPE", "Pope Resources", "$251.94M" "PLKI", "Popeyes Louisiana Kitchen, "61.85" "BPOP", "Popular, "26.26" "BPOPM", "Popular, "18.147" "BPOPN", "Popular, "19.98" "PBIB", "Porter Bancorp, "1.24" "PTLA", "Portola Pharmaceuticals, "31.42" "PBPB", "Potbelly Corporation", "$371.15M" "PCH", "Potlatch Corporation", "$1.08B" "POWL", "Powell Industries, "26.34" "POWI", "Power Integrations, "46.08" "PSIX", "Power Solutions International, "11.14" "PDBC", "PowerShares DB Optimum Yield Diversified Commodity Strategy Po", "n/a" "DWTR", "PowerShares DWA Tactical Sector Rotation Portfolio", "n/a" "IDLB", "PowerShares FTSE International Low Beta Equal Weight Portfolio", "n/a" "PRFZ", "PowerShares FTSE RAFI US 1500 Small-Mid Portfolio", "$992.15M" "PAGG", "PowerShares Global Agriculture Portfolio", "$28.19M" "PSAU", "PowerShares Global Gold & Precious Metals Portfolio", "$21.52M" "IPKW", "PowerShares International BuyBack Achievers Portfolio", "$52.15M" "LDRI", "PowerShares LadderRite 0-5 Year Corporate Bond Portfolio", "$4.96M" "LALT", "PowerShares Multi-Strategy Alternative Portfolio", "$15.49M" "PNQI", "PowerShares Nasdaq Internet Portfolio", "$225.89M" "QQQ", "PowerShares QQQ Trust, "102.5" "USLB", "PowerShares Russell 1000 Low Beta Equal Weight Portfolio", "n/a" "PSCD", "PowerShares S&P SmallCap Consumer Discretionary Portfolio", "$103.24M" "PSCC", "PowerShares S&P SmallCap Consumer Staples Portfolio", "$29.27M" "PSCE", "PowerShares S&P SmallCap Energy Portfolio", "$22.54M" "PSCF", "PowerShares S&P SmallCap Financials Portfolio", "$104.31M" "PSCH", "PowerShares S&P SmallCap Health Care Portfolio", "$229.28M" "PSCI", "PowerShares S&P SmallCap Industrials Portfolio", "$67.56M" "PSCT", "PowerShares S&P SmallCap Information Technology Portfolio", "$272.86M" "PSCM", "PowerShares S&P SmallCap Materials Portfolio", "$11.12M" "PSCU", "PowerShares S&P SmallCap Utilities Portfolio", "$44.59M" "PRAA", "PRA Group, "27.95" "PRAH", "PRA Health Sciences, "39.73" "PRAN", "Prana Biotechnology Ltd", "$26.69M" "PFBC", "Preferred Bank", "$387.63M" "PLPC", "Preformed Line Products Company", "$180.15M" "PFBI", "Premier Financial Bancorp, "15.11" "PINC", "Premier, "32.47" "LENS", "Presbia PLC", "$43.54M" "PRGX", "PRGX Global, "3.44" "PSMT", "PriceSmart, "77.07" "PBMD", "Prima BioMed Ltd", "$50.26M" "PNRG", "PrimeEnergy Corporation", "$114.35M" "PRMW", "Primo Water Corporation", "$230.5M" "PRIM", "Primoris Services Corporation", "$1.06B" "PRZM", "Prism Technologies Group, "0.59" "PVTB", "PrivateBancorp, "34.66" "PVTBP", "PrivateBancorp, "26.8499" "PDEX", "Pro-Dex, "2.8828" "IPDN", "Professional Diversity Network, "0.31" "PFIE", "Profire Energy, "0.81" "PGNX", "Progenics Pharmaceuticals Inc.", "$340.65M" "PRGS", "Progress Software Corporation", "$1.23B" "DNAI", "ProNAi Therapeutics, "7.07" "PFPT", "Proofpoint, "43.08" "PRPH", "ProPhase Labs, "1.23" "PRQR", "ProQR Therapeutics N.V.", "$107.86M" "BIB", "ProShares Ultra Nasdaq Biotechnology", "$488.39M" "UBIO", "Proshares UltraPro Nasdaq Biotechnology", "$12.1M" "TQQQ", "ProShares UltraPro QQQ", "$788.64M" "ZBIO", "ProShares UltraPro Short NASDAQ Biotechnology", "$3.68M" "SQQQ", "ProShares UltraPro Short QQQ", "$286.88M" "BIS", "ProShares UltraShort Nasdaq Biotechnology", "$195.35M" "PSEC", "Prospect Capital Corporation", "$2.34B" "PRTO", "Proteon Therapeutics, "7.23" "PTI", "Proteostasis Therapeutics, "5.75" "PRTA", "Prothena Corporation plc", "$1.08B" "PWX", "Providence and Worcester Railroad Company", "$63.17M" "PVBC", "Provident Bancorp, "13" "PROV", "Provident Financial Holdings, "17.14" "PBIP", "Prudential Bancorp, "15.23" "PSDV", "pSivida Corp.", "$95.4M" "PMD", "Psychemedics Corporation", "$65.61M" "PTC", "PTC Inc.", "$3.4B" "PTCT", "PTC Therapeutics, "29.77" "PULB", "Pulaski Financial Corp.", "$173.99M" "PULM", "Pulmatrix, "2.3" "PCYO", "Pure Cycle Corporation", "$105.71M" "PXS", "Pyxis Tankers Inc.", "$17.33M" "QADA", "QAD Inc.", "$343.68M" "QADB", "QAD Inc.", "$297.13M" "QCRH", "QCR Holdings, "21.93" "QGEN", "Qiagen N.V.", "$4.97B" "QIWI", "QIWI plc", "$681.97M" "QKLS", "QKL Stores, "0.55" "QLIK", "Qlik Technologies Inc.", "$1.82B" "QLGC", "QLogic Corporation", "$1.04B" "QLTI", "QLT Inc.", "$134.19M" "QRVO", "Qorvo, "41.59" "QCOM", "QUALCOMM Incorporated", "$72.37B" "QSII", "Quality Systems, "14.4" "QBAK", "Qualstar Corporation", "$7.47M" "QLYS", "Qualys, "22.94" "QTWW", "Quantum Fuel Systems Technologies Worldwide, "0.668" "QRHC", "Quest Resource Holding Corporation.", "$71.92M" "QUIK", "QuickLogic Corporation", "$78.67M" "QDEL", "Quidel Corporation", "$507.14M" "QPAC", "Quinpario Acquisition Corp. 2", "$430.94M" "QPACU", "Quinpario Acquisition Corp. 2", "n/a" "QPACW", "Quinpario Acquisition Corp. 2", "n/a" "QNST", "QuinStreet, "3.12" "QUMU", "Qumu Corporation", "$26.56M" "QUNR", "Qunar Cayman Islands Limited", "$4.95B" "QTNT", "Quotient Limited", "$207.08M" "RRD", "R.R. Donnelley & Sons Company", "$2.84B" "RADA", "Rada Electronics Industries Limited", "$5.25M" "RDCM", "Radcom Ltd.", "$116.37M" "ROIA", "Radio One, "1.44" "ROIAK", "Radio One, "1.4" "RSYS", "RadiSys Corporation", "$98.57M" "RDUS", "Radius Health, "27.21" "RDNT", "RadNet, "5.68" "RDWR", "Radware Ltd.", "$491.46M" "RMBS", "Rambus, "12.42" "RAND", "Rand Capital Corporation", "$28.98M" "RLOG", "Rand Logistics, "1" "GOLD", "Randgold Resources Limited", "$8.12B" "RPD", "Rapid7, "11.8" "RPTP", "Raptor Pharmaceutical Corp.", "$355.72M" "RAVE", "Rave Restaurant Group, "4.99" "RAVN", "Raven Industries, "15.33" "ROLL", "RBC Bearings Incorporated", "$1.5B" "RICK", "RCI Hospitality Holdings, "8.62" "RCMT", "RCM Technologies, "5.15" "RLOC", "ReachLocal, "1.7" "RDI", "Reading International Inc", "$236.73M" "RDIB", "Reading International Inc", "$275.41M" "RGSE", "Real Goods Solar, "0.443" "RELY", "Real Industry, "6.48" "RNWK", "RealNetworks, "3.51" "RP", "RealPage, "17" "UTES", "Reaves Utilities ETF", "n/a" "DAX", "Recon Capital DAX Germany ETF", "$255.6M" "UK", "Recon Capital FTSE 100 ETF", "$936500" "QYLD", "Recon Capital NASDAQ-100 Covered Call ETF", "$17.51M" "RCON", "Recon Technology, "1.23" "REPH", "Recro Pharma, "6.17" "RRGB", "Red Robin Gourmet Burgers, "63.82" "RDHL", "Redhill Biopharma Ltd.", "$116.31M" "REDF", "Rediff.com India Limited", "$14.33M" "REGN", "Regeneron Pharmaceuticals, "397.26" "RGNX", "REGENXBIO Inc.", "$389.7M" "DFVL", "region", "$2.11M" "DFVS", "region", "$1.28M" "DGLD", "region", "$8.36M" "DLBL", "region", "$4.6M" "DLBS", "region", "$13.83M" "DSLV", "region", "$28.09M" "DTUL", "region", "$4.76M" "DTUS", "region", "$10.82M" "DTYL", "region", "$5.82M" "DTYS", "region", "$46.36M" "FLAT", "region", "$5.15M" "SLVO", "region", "$8.31M" "STPP", "region", "$13.35M" "TAPR", "region", "n/a" "TVIX", "region", "$136.02M" "TVIZ", "region", "$773124.94" "UGLD", "region", "$10.58M" "USLV", "region", "$25.32M" "VIIX", "region", "$5.94M" "VIIZ", "region", "$2.27M" "XIV", "region", "$280.96M" "ZIV", "region", "$31.71M" "RGLS", "Regulus Therapeutics Inc.", "$392.05M" "REIS", "Reis, "22.52" "RELV", "Reliv' International, "0.8" "RLYP", "Relypsa, "18.9" "MARK", "Remark Media, "4.1" "RNST", "Renasant Corporation", "$1.25B" "REGI", "Renewable Energy Group, "6.85" "RNVA", "Rennova Health, "0.79" "RNVAW", "Rennova Health, "n/a" "RCII", "Rent-A-Center Inc.", "$672.37M" "RTK", "Rentech, "1.77" "RGEN", "Repligen Corporation", "$790.97M" "RPRX", "Repros Therapeutics Inc.", "$22.62M" "RJET", "Republic Airways Holdings, "2.48" "RBCAA", "Republic Bancorp, "25.6" "FRBK", "Republic First Bancorp, "4" "RSAS", "RESAAS Services Inc.", "n/a" "REFR", "Research Frontiers Incorporated", "$103.39M" "RESN", "Resonant Inc.", "$13.85M" "REXI", "Resource America, "4.5" "RECN", "Resources Connection, "14.13" "ROIC", "Retail Opportunity Investments Corp.", "$1.8B" "SALE", "RetailMeNot, "7.495" "RTRX", "Retrophin, "14.83" "RVNC", "Revance Therapeutics, "19.59" "RBIO", "rEVO Biologics, "n/a" "RVLT", "Revolution Lighting Technologies, "0.73" "RWLK", "ReWalk Robotics Ltd", "$135.42M" "REXX", "Rex Energy Corporation", "$40.01M" "RFIL", "RF Industries, "4.07" "RGCO", "RGC Resources Inc.", "$102M" "RIBT", "RiceBran Technologies", "$13.34M" "RIBTW", "RiceBran Technologies", "n/a" "RELL", "Richardson Electronics, "5.08" "RIGL", "Rigel Pharmaceuticals, "2.48" "NAME", "Rightside Group, "8.51" "RNET", "RigNet, "13.4" "RITT", "RIT Technologies Ltd.", "$8.55M" "RITTW", "RIT Technologies Ltd.", "n/a" "RTTR", "Ritter Pharmaceuticals, "1.41" "RIVR", "River Valley Bancorp.", "$85.16M" "RMI", "RiverBanc Multifamily Investors, "n/a" "RVSB", "Riverview Bancorp Inc", "$95.66M" "RLJE", "RLJ Entertainment, "0.4876" "RMGN", "RMG Networks Holding Corporation", "$27.25M" "ROBO", "Robo-Stox Global Robotics and Automation Index ETF", "$101.94M" "FUEL", "Rocket Fuel Inc.", "$122.62M" "RMTI", "Rockwell Medical, "7.91" "RCKY", "Rocky Brands, "11.7" "RMCF", "Rocky Mountain Chocolate Factory, "10.6897" "RSTI", "Rofin-Sinar Technologies, "20.8" "ROKA", "Roka Bioscience, "0.539" "ROSG", "Rosetta Genomics Ltd.", "$13.95M" "ROST", "Ross Stores, "55.07" "ROVI", "Rovi Corporation", "$1.75B" "RBPAA", "Royal Bancshares of Pennsylvania, "2.16" "RGLD", "Royal Gold, "41.53" "RPXC", "RPX Corporation", "$531.9M" "RRM", "RR Media Ltd.", "$145.4M" "RTIX", "RTI Surgical, "3.08" "RBCN", "Rubicon Technology, "0.8214" "RUSHA", "Rush Enterprises, "16.93" "RUSHB", "Rush Enterprises, "16.8" "RUTH", "Ruth's Hospitality Group, "17.09" "RXII", "RXI Pharmaceuticals Corporation", "$19.6M" "RYAAY", "Ryanair Holdings plc", "$21.82B" "STBA", "S&T Bancorp, "25.8" "SANW", "S&W Seed Company", "n/a" "SBRA", "Sabra Healthcare REIT, "16.65" "SBRAP", "Sabra Healthcare REIT, "25.3" "SABR", "Sabre Corporation", "$7.58B" "SAEX", "SAExploration Holdings, "1.63" "SAFT", "Safety Insurance Group, "56.8" "SAGE", "Sage Therapeutics, "33.82" "SGNT", "Sagent Pharmaceuticals, "15.16" "SAIA", "Saia, "26.27" "SAJA", "Sajan, 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"$2.14M" "SRSC", "Sears Canada Inc. ", "$304.61M" "SHLD", "Sears Holdings Corporation", "$1.93B" "SHLDW", "Sears Holdings Corporation", "n/a" "SHOS", "Sears Hometown and Outlet Stores, "6.36" "SPNE", "SeaSpine Holdings Corporation", "$139.01M" "SGEN", "Seattle Genetics, "31.61" "EYES", "Second Sight Medical Products, "4.71" "SNFCA", "Security National Financial Corporation", "$81.83M" "SEIC", "SEI Investments Company", "$6.14B" "SLCT", "Select Bancorp, "8.11" "SCSS", "Select Comfort Corporation", "$806.28M" "SIGI", "Selective Insurance Group, "33.76" "LEDS", "SemiLEDS Corporation", "$9.01M" "SMLR", "Semler Scientific, "2.23" "SMTC", "Semtech Corporation", "$1.13B" "SENEA", "Seneca Foods Corp.", "$287.72M" "SENEB", "Seneca Foods Corp.", "$333.14M" "SNMX", "Senomyx, "3.22" "SQNM", "Sequenom, "1.62" "SQBG", "Sequential Brands Group, "5.9" "MCRB", "Seres Therapeutics, "25.43" "SREV", "ServiceSource International, "3.9" "SFBS", "ServisFirst Bancshares, "36.61" "SEV", "Sevcon, "8.85" "SVBI", "Severn Bancorp Inc", "$52.02M" "SGOC", "SGOCO Group, "3.26" "SMED", "Sharps Compliance Corp", "$87.3M" "SHSP", "SharpSpring, "3.34" "SHEN", "Shenandoah Telecommunications Co", "$1.06B" "SHLO", "Shiloh Industries, "3.67" "SCCI", "Shimmick Construction Company, "n/a" "SHPG", "Shire plc", "$33.9B" "SCVL", "Shoe Carnival, "23.25" "SHBI", "Shore Bancshares Inc", "$141.97M" "SHOR", "ShoreTel, "7.31" "SFLY", "Shutterfly, "39.44" "SIFI", "SI Financial Group, "13.82" "SIEB", "Siebert Financial Corp.", "$25.62M" "SIEN", "Sientra, "7.89" "BSRR", "Sierra Bancorp", "$228.93M" "SWIR", "Sierra Wireless, "11.95" "SIFY", "Sify Technologies Limited", "$187.46M" "SIGM", "Sigma Designs, "6.4" "SGMA", "SigmaTron International, "6.6" "SGNL", "Signal Genetics, "0.56" "SBNY", "Signature Bank", "$6.7B" "SBNYW", "Signature Bank", "n/a" "SLGN", "Silgan Holdings Inc.", "$3.11B" "SILC", "Silicom Ltd", "$210.89M" "SGI", "Silicon Graphics International Corp", "$198.26M" "SLAB", "Silicon Laboratories, "40.34" "SIMO", "Silicon Motion Technology Corporation", "$1.12B" "SPIL", "Siliconware Precision Industries Company, "7.83" "SSRI", "Silver Standard Resources Inc.", "$445.35M" "SAMG", "Silvercrest Asset Management Group Inc.", "$135.09M" "SFNC", "Simmons First National Corporation", "$1.25B" "SLP", "Simulations Plus, "9.55" "SINA", "Sina Corporation", "$2.59B" "SBGI", "Sinclair Broadcast Group, "29.46" "SINO", "Sino-Global Shipping America, "0.5279" "SVA", "Sinovac Biotech, "6.41" "SIRI", "Sirius XM Holdings Inc.", "$18.17B" "SIRO", "Sirona Dental Systems, "101.09" "SRVA", "SIRVA, "n/a" "SITO", "SITO Mobile, "2.45" "SZMK", "Sizmek Inc.", "$100M" "SKUL", "Skullcandy, "3.45" "SKYS", "Sky Solar Holdings, "3.82" "SKLN", "Skyline Medical Inc.", "$19.72M" "SKLNU", "Skyline Medical Inc.", "n/a" "MOBI", "Sky-mobi Limited", "$52.55M" "SPU", "SkyPeople Fruit Juice, "0.51" "SKYW", "SkyWest, "15.92" "SWKS", "Skyworks Solutions, "63.71" "ISM", "SLM Corporation", "n/a" "JSM", "SLM Corporation", "n/a" "OSM", "SLM Corporation", "n/a" "SLM", "SLM Corporation", "$2.61B" "SLMAP", "SLM Corporation", "$144.51M" "SLMBP", "SLM Corporation", "$154M" "SMT", "SMART Technologies Inc.", "$33.17M" "SMBK", "SmartFinancial, "15.09" "SWHC", "Smith & Wesson Holding Corporation", "$1.27B" "SMSI", "Smith Micro Software, "0.65" "SMTX", "SMTC Corporation", "$22.33M" "LNCE", "Snyder's-Lance, "30.64" "SODA", "SodaStream International Ltd.", "$295.59M" "SOHU", "Sohu.com Inc.", "$1.78B" "SLRC", "Solar Capital Ltd.", "$699.83M" "SUNS", "Solar Senior Capital Ltd.", "$152.47M" "SLTD", "Solar3D, "2.43" "SCTY", "SolarCity Corporation", "$2.04B" "SEDG", "SolarEdge Technologies, "26.59" "SZYM", "Solazyme, "1.59" "SONC", "Sonic Corp.", "$1.44B" "SOFO", "Sonic Foundry, "5.3" "SONS", "Sonus Networks, "7.15" "SPHS", "Sophiris Bio, "1.82" "SORL", "SORL Auto Parts, "1.59" "SRNE", "Sorrento Therapeutics, "6.47" "SOHO", "Sotherly Hotels Inc.", "$75.79M" "SOHOL", "Sotherly Hotels LP", "n/a" "SOHOM", "Sotherly Hotels LP", "n/a" "SFBC", "Sound Financial Bancorp, "21.54" "SSB", "South State Corporation", "$1.5B" "SOCB", "Southcoast Financial Corporation", "$93.63M" "SFST", "Southern First Bancshares, "22.46" "SMBC", "Southern Missouri Bancorp, "23.4" "SONA", "Southern National Bancorp of Virginia, "12.61" "SBSI", "Southside Bancshares, "23.52" "OKSB", "Southwest Bancorp, "15.32" "SP", "SP Plus Corporation", "$540.45M" "SPAN", "Span-America Medical Systems, "19.42" "SBSA", "Spanish Broadcasting System, "3.2399" "SGRP", "SPAR Group, "1.02" "SPKE", "Spark Energy, "24.62" "ONCE", "Spark Therapeutics, "32.69" "SPAR", "Spartan Motors, "2.89" "SPTN", "SpartanNash Company", "$796.27M" "SPPI", "Spectrum Pharmaceuticals, "4.55" "ANY", "Sphere 3D Corp.", "$60.29M" "SPEX", "Spherix Incorporated", "$4.31M" "SPI", "SPI Energy Co., "7.87" "SAVE", "Spirit Airlines, "46.79" "SPLK", "Splunk Inc.", "$4.48B" "SPOK", "Spok Holdings, "17.44" "SPWH", "Sportsman's Warehouse Holdings, "12.25" "FUND", "Sprott Focus Trust, "5.34" "SFM", "Sprouts Farmers Market, "24.84" "SPSC", "SPS Commerce, "41.4" "SSNC", "SS&C Technologies Holdings, "57.83" "STAA", "STAAR Surgical Company", "$259.68M" "STAF", "Staffing 360 Solutions, "3" "STMP", "Stamps.com Inc.", "$1.56B" "STLY", "Stanley Furniture Company, "2.47" "SPLS", "Staples, "9.16" "SBLK", "Star Bulk Carriers Corp.", "$107.96M" "SBLKL", "Star Bulk Carriers Corp.", "n/a" "SBUX", "Starbucks Corporation", "$85.18B" "STRZA", "Starz", "$2.31B" "STRZB", "Starz", "$2.54B" "STFC", "State Auto Financial Corporation", "$899.81M" "STBZ", "State Bank Financial Corporation.", "$676.5M" "SNC", "State National Companies, "10.05" "STDY", "SteadyMed Ltd.", "$33.68M" "GASS", "StealthGas, "3.3" "STLD", "Steel Dynamics, "18.67" "SXCL", "Steel Excel Inc.", "$154.27M" "SMRT", "Stein Mart, "6.84" "SBOT", "Stellar Biotechnologies, "6.01" "STEM", "StemCells, "0.3236" "STML", "Stemline Therapeutics, "4.35" "STXS", "Stereotaxis, "0.898" "SRCL", "Stericycle, "111.41" "SRCLP", "Stericycle, "85.77" "STRL", "Sterling Construction Company Inc", "$90.89M" "SHOO", "Steven Madden, "34.02" "SSFN", "Stewardship Financial Corp", "$36.42M" "SYBT", "Stock Yards Bancorp, "37.33" "BANX", "StoneCastle Financial Corp", "$100.31M" "SGBK", "Stonegate Bank", "$364.39M" "SSKN", "Strata Skin Sciences, "1" "SSYS", "Stratasys, "17.11" "STRT", "Strattec Security Corporation", "$176.7M" "STRS", "Stratus Properties, "21.93" "STRA", "Strayer Education, "43.03" "STRM", "Streamline Health Solutions, "1.42" "SBBP", "Strongbridge Biopharma plc", "$82.91M" "STB", "Student Transportation Inc", "$394.18M" "SCMP", "Sucampo Pharmaceuticals, "12.65" "SUMR", "Summer Infant, "1.6" "SMMF", "Summit Financial Group, "12.2399" "SSBI", "Summit State Bank", "$64.29M" "SMMT", "Summit Therapeutics plc", "$92.18M" "SNBC", "Sun Bancorp, "20.72" "SNHY", "Sun Hydraulics Corporation", "$752.75M" "SNDE", "Sundance Energy Australia Limited", "n/a" "SEMI", "SunEdison Semiconductor Limited", "$154.41M" "SNSS", "Sunesis Pharmaceuticals, "0.696" "STKL", "SunOpta, "5.25" "SPWR", "SunPower Corporation", "$3.29B" "RUN", "Sunrun Inc.", "$538.77M" "SBCP", "Sunshine Bancorp, "14.23" "SSH", "Sunshine Heart Inc", "$16.87M" "SMCI", "Super Micro Computer, "32.2" "SPCB", "SuperCom, "4.53" "SCON", "Superconductor Technologies Inc.", "$6.35M" "SGC", "Superior Uniform Group, "16.99" "SUPN", "Supernus Pharmaceuticals, "13.35" "SPRT", "support.com, "0.7238" "SGRY", "Surgery Partners, "12.64" "SCAI", "Surgical Care Affiliates, "41.34" "SRDX", "SurModics, "19.44" "SBBX", "Sussex Bancorp", "$59.93M" "TOR", "Sutor Technology Group Limited", "$643906.58" "SIVB", "SVB Financial Group", "$4.54B" "SIVBO", "SVB Financial Group", "n/a" "SYKE", "Sykes Enterprises, "29.77" "SYMC", "Symantec Corporation", "$13.02B" "SSRG", "Symmetry Surgical Inc.", "$88.3M" "SYNC", "Synacor, "1.66" "SYNL", "Synalloy Corporation", "$64.69M" "SYNA", "Synaptics Incorporated", "$2.92B" "SNCR", "Synchronoss Technologies, "25.8" "SNDX", "Syndax Pharmaceuticals, "n/a" "SGYP", "Synergy Pharmaceuticals, "4" "SGYPU", "Synergy Pharmaceuticals, "9.5" "SGYPW", "Synergy Pharmaceuticals, "0.9799" "ELOS", "Syneron Medical Ltd.", "$255.33M" "SNPS", "Synopsys, "43.61" "SNTA", "Synta Pharmaceuticals Corp.", "$33.68M" "SYNT", "Syntel, "45.7" "SYMX", "Synthesis Energy Systems, "0.78" "SYUT", "Synutra International, "4.94" "SYPR", "Sypris Solutions, "0.7661" "SYRX", "Sysorex Global", "$13.4M" "TROW", "T. Rowe Price Group, "70.26" "TTOO", "T2 Biosystems, "8.28" "TAIT", "Taitron Components Incorporated", "$5.37M" "TTWO", "Take-Two Interactive Software, "34.35" "TLMR", "Talmer Bancorp, "16.36" "TNDM", "Tandem Diabetes Care, "7.25" "TLF", "Tandy Leather Factory, "6.99" "TNGO", "Tangoe, "7.92" "TANH", "Tantech Holdings Ltd.", "$113.4M" "TEDU", "Tarena International, "9.25" "TASR", "TASER International, "17.13" "TATT", "TAT Technologies Ltd.", "$59.46M" "TAYD", "Taylor Devices, "13.45" "TCPC", "TCP Capital Corp.", "$650M" "AMTD", "TD Ameritrade Holding Corporation", "$14.91B" "TEAR", "TearLab Corporation", "$28.7M" "TECD", "Tech Data Corporation", "$2.35B" "TCCO", "Technical Communications Corporation", "$4.78M" "TTGT", "TechTarget, "6.63" "TGLS", "Tecnoglass Inc.", "$274.12M" "TGEN", "Tecogen Inc.", "$69.48M" "TSYS", "TeleCommunication Systems, "4.94" "TNAV", "TeleNav, "5.91" "TTEC", "TeleTech Holdings, "26.11" "TLGT", "Teligent, "6.23" "TENX", "Tenax Therapeutics, "2.41" "GLBL", "TerraForm Global, "3.04" "TERP", "TerraForm Power, "8.54" "TRTL", "Terrapin 3 Acquisition Corporation", "$264.46M" "TRTLU", "Terrapin 3 Acquisition Corporation", "$186.3M" "TRTLW", "Terrapin 3 Acquisition Corporation", "n/a" "TBNK", "Territorial Bancorp Inc.", "$247.62M" "TSRO", "TESARO, "39.74" "TESO", "Tesco Corporation", "$252.37M" "TSLA", "Tesla Motors, "168.68" "TESS", "TESSCO Technologies Incorporated", "$130.38M" "TSRA", "Tessera Technologies, "27.93" "TTEK", "Tetra Tech, "26.66" "TLOG", "TetraLogic Pharmaceuticals Corporation", "$3.7M" "TTPH", "Tetraphase Pharmaceuticals, "4.67" "TCBI", "Texas Capital Bancshares, "33.89" "TCBIL", "Texas Capital Bancshares, "22.8" "TCBIP", "Texas Capital Bancshares, "22.6899" "TCBIW", "Texas Capital Bancshares, "16.8" "TXN", "Texas Instruments Incorporated", "$54.46B" "TXRH", "Texas Roadhouse, "37.84" "TFSL", "TFS Financial Corporation", "$4.81B" "TGTX", "TG Therapeutics, "9.02" "ABCO", "The Advisory Board Company", "$1.65B" "ANDE", "The Andersons, "26.73" "TBBK", "The Bancorp, "4.57" "BONT", "The Bon-Ton Stores, "1.71" "CG", "The Carlyle Group L.P.", "$4.71B" "CAKE", "The Cheesecake Factory Incorporated", "$2.45B" "CHEF", "The Chefs' Warehouse, "14.5" "TCFC", "The Community Financial Corporation", "$94.13M" "DSGX", "The Descartes Systems Group Inc.", "$1.25B" "DXYN", "The Dixie Group, "4.19" "ENSG", "The Ensign Group, "18.87" "XONE", "The ExOne Company", "$128.98M" "FINL", "The Finish Line, "18.69" "FBMS", "The First Bancshares, "17.7" "FLIC", "The First of Long Island Corporation", "$386.74M" "TFM", "The Fresh Market, "23.39" "GT", "The Goodyear Tire & Rubber Company", "$8.22B" "HABT", "The Habit Restaurants, "19.71" "HCKT", "The Hackett Group, "13.45" "HAIN", "The Hain Celestial Group, "37.27" "CUBA", "The Herzfeld Caribbean Basin Fund, "5.92" "INTG", "The Intergroup Corporation", "$59.59M" "JYNT", "The Joint Corp.", "$42.3M" "KEYW", "The KEYW Holding Corporation", "$169.72M" "KHC", "The Kraft Heinz Company", "$90.09B" "MDCO", "The Medicines Company", "$2.29B" "MIK", "The Michaels Companies, "22.87" "MIDD", "The Middleby Corporation", "$4.96B" "NAVG", "The Navigators Group, "80.64" "STKS", "The ONE Group Hospitality, "2.69" "PCLN", "The Priceline Group Inc. ", "$61.51B" "PRSC", "The Providence Service Corporation", "$685.66M" "BITE", "The Restaurant ETF", "n/a" "RMR", "The RMR Group Inc.", "$675.49M" "SPNC", "The Spectranetics Corporation", "$524.01M" "ULTI", "The Ultimate Software Group, "165.36" "YORW", "The York Water Company", "$357.01M" "NCTY", "The9 Limited", "$52.44M" "TBPH", "Theravance Biopharma, "16.1" "TST", "TheStreet, "1.12" "TCRD", "THL Credit, "9.12" "THLD", "Threshold Pharmaceuticals, "0.3" "TICC", "TICC Capital Corp.", "$299.34M" "TTS", "Tile Shop Hldgs, "13.18" "TIL", "Till Capital Ltd.", "$10.29M" "TSBK", "Timberland Bancorp, "12.24" "TIPT", "Tiptree Financial Inc.", "$252.57M" "TITN", "Titan Machinery Inc.", "$185.97M" "TTNP", "Titan Pharmaceuticals, "3.84" "TIVO", "TiVo Inc.", "$776.88M" "TMUS", "T-Mobile US, "36.85" "TMUSP", "T-Mobile US, "64.18" "TBRA", "Tobira Therapeutics, "7.47" "TKAI", "Tokai Pharmaceuticals, "6.85" "TNXP", "Tonix Pharmaceuticals Holding Corp.", "$52.92M" "TISA", "Top Image Systems, "2.02" "TOPS", "TOP Ships Inc.", "$4.37M" "TORM ", "TOR Minerals International Inc", "$11.6M" "TRCH", "Torchlight Energy Resources, "0.628" "TSEM", "Tower Semiconductor Ltd.", "$974.03M" "TWER", "Towerstream Corporation", "$13.36M" "CLUB", "Town Sports International Holdings, "1.09" "TOWN", "Towne Bank", "$889.25M" "TCON", "TRACON Pharmaceuticals, "7.4" "TSCO", "Tractor Supply Company", "$11.55B" "TWMC", "Trans World Entertainment Corp.", "$112.8M" "TACT", "TransAct Technologies Incorporated", "$54.83M" "TRNS", "Transcat, "9.25" "TBIO", "Transgenomic, "0.621" "TGA", "Transglobe Energy Corp", "$100.37M" "TTHI", "Transition Therapeutics, "0.89" "TZOO", "Travelzoo Inc.", "$116.08M" "TRVN", "Trevena, "8.52" "TCBK", "TriCo Bancshares", "$558.41M" "TRIL", "Trillium Therapeutics Inc.", "$63.62M" "TRS", "TriMas Corporation", "$719.08M" "TRMB", "Trimble Navigation Limited", "$5.79B" "TRIB", "Trinity Biotech plc", "$224.69M" "TRIP", "TripAdvisor, "65.35" "TSC", "TriState Capital Holdings, "11.8" "TBK", "Triumph Bancorp, "13.55" "TROV", "TrovaGene, "4.54" "TROVU", "TrovaGene, "17.09" "TROVW", "TrovaGene, "3.329" "TRUE", "TrueCar, "5.9" "THST", "Truett-Hurst, "1.27" "TRST", "TrustCo Bank Corp NY", "$534.41M" "TRMK", "Trustmark Corporation", "$1.49B" "TSRI", "TSR, "3.73" "TTMI", "TTM Technologies, "6.43" "TUBE", "TubeMogul, "11.74" "TCX", "Tucows Inc.", "$223.54M" "TUES", "Tuesday Morning Corp.", "$287.84M" "TOUR", "Tuniu Corporation", "$1.21B" "HEAR", "Turtle Beach Corporation", "$44.22M" "TUTI", "Tuttle Tactical Management Multi-Strategy Income ETF", "$39.69M" "TUTT", "Tuttle Tactical Management U.S. Core ETF", "$64.14M" "FOX", "Twenty-First Century Fox, "26.47" "FOXA", "Twenty-First Century Fox, "26.47" "TWIN", "Twin Disc, "8.98" "TRCB", "Two River Bancorp", "$71.54M" "USCR", "U S Concrete, "53.54" "PRTS", "U.S. Auto Parts Network, "2.88" "USEG", "U.S. Energy Corp.", "$12.47M" "GROW", "U.S. Global Investors, "1.61" "UREE", "U.S. Rare Earths, "0.12" "UBIC", "UBIC, "11.76" "UBNT", "Ubiquiti Networks, "33.96" "UFPT", "UFP Technologies, "21.15" "ULTA", "Ulta Salon, Inc." "UCTT", "Ultra Clean Holdings, "4.82" "RARE", "Ultragenyx Pharmaceutical Inc.", "$2.39B" "ULBI", "Ultralife Corporation", "$80.36M" "ULTR", "Ultrapetrol (Bahamas) Limited", "$14.1M" "UTEK", "Ultratech, "19.09" "UMBF", "UMB Financial Corporation", "$2.41B" "UMPQ", "Umpqua Holdings Corporation", "$3.39B" "UNAM", "Unico American Corporation", "$50.63M" "UNIS", "Unilife Corporation", "$166.59M" "UBSH", "Union Bankshares Corporation", "$993.71M" "UNB", "Union Bankshares, "27.9099" "UNXL", "Uni-Pixel, "0.528" "QURE", "uniQure N.V.", "$378.91M" "UBCP", "United Bancorp, "9.1" "UBOH", "United Bancshares, "16.7786" "UBSI", "United Bankshares, "34.55" "UCBA", "United Community Bancorp", "$54.83M" "UCBI", "United Community Banks, "16.87" "UCFC", "United Community Financial Corp.", "$284.79M" "UDF", "United Development Funding IV", "$216.24M" "UBNK", "United Financial Bancorp, "11.31" "UFCS", "United Fire Group, "36.87" "UIHC", "United Insurance Holdings Corp.", "$306.77M" "UNFI", "United Natural Foods, "35.94" "UNTD", "United Online, "10.71" "UBFO", "United Security Bancshares", "$83.49M" "USBI", "United Security Bancshares, "8.27" "USLM", "United States Lime & Minerals, "51.52" "UTHR", "United Therapeutics Corporation", "$6.01B" "UG", "United-Guardian, "21.49" "UNTY", "Unity Bancorp, "10.22" "OLED", "Universal Display Corporation", "$2.15B" "UEIC", "Universal Electronics Inc.", "$751.61M" "UFPI", "Universal Forest Products, "64.09" "USAP", "Universal Stainless & Alloy Products, "8.29" "UACL", "Universal Truckload Services, "14.63" "UVSP", "Univest Corporation of Pennsylvania", "$372.55M" "UPIP", "Unwired Planet, "9.13" "UPLD", "Upland Software, "6.95" "URRE", "Uranium Resources, "0.25" "URBN", "Urban Outfitters, "26.67" "ECOL", "US Ecology, "31.48" "USAT", "USA Technologies, "3.8" "USATP", "USA Technologies, "15.3" "USAK", "USA Truck, "16.57" "USMD", "USMD Holdings, "7.5355" "UTMD", "Utah Medical Products, "58.14" "UTSI", "UTStarcom Holdings Corp", "$79.24M" "VALX", "Validea Market Legends ETF", "$19.86M" "VALU", "Value Line, "15.75" "VNDA", "Vanda Pharmaceuticals Inc.", "$354.31M" "VWOB", "Vanguard Emerging Markets Government Bond ETF", "$399.33M" "VNQI", "Vanguard Global ex-U.S. Real Estate ETF", "$2.85B" "VGIT", "Vanguard Intermediate -Term Government Bond ETF", "$363.28M" "VCIT", "Vanguard Intermediate-Term Corporate Bond ETF", "$5.35B" "VCLT", "Vanguard Long-Term Corporate Bond ETF", "$976.83M" "VGLT", "Vanguard Long-Term Government Bond ETF", "$135.51M" "VMBS", "Vanguard Mortgage-Backed Securities ETF", "$1.51B" "VNR", "Vanguard Natural Resources LLC", "$287.02M" "VNRAP", "Vanguard Natural Resources LLC", "n/a" "VNRBP", "Vanguard Natural Resources LLC", "$35.98M" "VNRCP", "Vanguard Natural Resources LLC", "n/a" "VONE", "Vanguard Russell 1000 ETF", "$483.67M" "VONG", "Vanguard Russell 1000 Growth ETF", "$439.76M" "VONV", "Vanguard Russell 1000 Value ETF", "$387.84M" "VTWO", "Vanguard Russell 2000 ETF", "$482.82M" "VTWG", "Vanguard Russell 2000 Growth ETF", "$123.09M" "VTWV", "Vanguard Russell 2000 Value ETF", "$65.69M" "VTHR", "Vanguard Russell 3000 ETF", "$157.48M" "VCSH", "Vanguard Short-Term Corporate Bond ETF", "$10.39B" "VGSH", "Vanguard Short-Term Government ETF", "$581.12M" "VTIP", "Vanguard Short-Term Inflation-Protected Securities Index Fund", "$1.78B" "BNDX", "Vanguard Total International Bond ETF", "$3.54B" "VXUS", "Vanguard Total International Stock ETF", "$4.16B" "VRNS", "Varonis Systems, "16.87" "VDSI", "VASCO Data Security International, "13.61" "VBLT", "Vascular Biogenics Ltd.", "$65.26M" "VASC", "Vascular Solutions, "25.65" "VBIV", "VBI Vaccines Inc.", "$51.77M" "WOOF", "VCA Inc. ", "$4.13B" "VECO", "Veeco Instruments Inc.", "$784.06M" "APPY", "Venaxis, "0.2131" "VRA", "Vera Bradley, "14.8" "VCYT", "Veracyte, "6.32" "VSTM", "Verastem, "1.18" "VCEL", "Vericel Corporation", "$48.53M" "VRNT", "Verint Systems Inc.", "$1.98B" "VRSN", "VeriSign, "81.45" "VRSK", "Verisk Analytics, "68.39" "VBTX", "Veritex Holdings, "13.29" "VRML", "Vermillion, "1.38" "VSAR", "Versartis, "8.25" "VTNR", "Vertex Energy, "1.71" "VRTX", "Vertex Pharmaceuticals Incorporated", "$21.68B" "VRTB", "Vestin Realty Mortgage II, "1.314" "VIA", "Viacom Inc.", "$15.77B" "VIAB", "Viacom Inc.", "$12.46B" "VSAT", "ViaSat, "69.2" "VIAV", "Viavi Solutions Inc.", "$1.42B" "VICL", "Vical Incorporated", "$31.12M" "VICR", "Vicor Corporation", "$286.28M" "CIZ", "Victory CEMP Developed Enhanced Volatility Wtd Index ETF", "n/a" "CID", "Victory CEMP International High Div Volatility Wtd Index ETF", "n/a" "CIL", "Victory CEMP International Volatility Wtd Index ETF", "n/a" "CFO", "Victory CEMP US 500 Enhanced Volatility Wtd Index ETF", "$23.77M" "CFA", "Victory CEMP US 500 Volatility Wtd Index ETF", "$6.79M" "CSF", "Victory CEMP US Discovery Enhanced Volatility Wtd Index ETF", "$17.64M" "CDC", "Victory CEMP US EQ Income Enhanced Volatility Wtd Index ETF", "$22.91M" "CDL", "Victory CEMP US Large Cap High Div Volatility Wtd Index ETF", "n/a" "CSB", "Victory CEMP US Small Cap High Div Volatility Wtd Index ETF", "n/a" "CSA", "Victory CEMP US Small Cap Volatility Wtd Index ETF", "$1.56B" "VBND", "Vident Core U.S. Bond Strategy Fund", "$431.55M" "VUSE", "Vident Core US Equity ETF", "$354.01M" "VIDI", "Vident International Equity Fund", "n/a" "VDTH", "Videocon d2h Limited", "$563.95M" "VKTX", "Viking Therapeutics, "1.72" "VBFC", "Village Bank and Trust Financial Corp.", "$27.41M" "VLGEA", "Village Super Market, "24.66" "VIP", "VimpelCom Ltd.", "$6.66B" "VNOM", "Viper Energy Partners LP", "$1.19B" "VIRC", "Virco Manufacturing Corporation", "$50.84M" "VA", "Virgin America Inc.", "$1.31B" "VIRT", "Virtu Financial, "21.3" "VSCP", "VirtualScopics, "3.8" "VRTS", "Virtus Investment Partners, "94.47" "VRTU", "Virtusa Corporation", "$993.92M" "VISN", "VisionChina Media, "9.53" "VTAE", "Vitae Pharmaceuticals, "8.6" "VTL", "Vital Therapies, "8.05" "VVUS", "VIVUS, "1.06" "VOD", "Vodafone Group Plc", "$82.4B" "VLTC", "Voltari Corporation", "$38.5M" "VOXX", "VOXX International Corporation", "$91.08M" "VYGR", "Voyager Therapeutics, "9.93" "VRNG", "Vringo, "1.39" "VSEC", "VSE Corporation", "$312.6M" "VTVT", "vTv Therapeutics Inc.", "$211.31M" "VUZI", "Vuzix Corporation", "$91.54M" "VWR", "VWR Corporation", "$3.05B" "WGBS", "WaferGen Bio-systems, "0.64" "WBA", "Walgreens Boots Alliance, "78.16" "WRES", "Warren Resources, "0.1099" "WAFD", "Washington Federal, "21.21" "WAFDW", "Washington Federal, "4.3" "WASH", "Washington Trust Bancorp, "37.1" "WFBI", "WashingtonFirst Bankshares Inc", "$220.89M" "WSBF", "Waterstone Financial, "13.57" "WVE", "WAVE Life Sciences Ltd.", "$218.1M" "WNFM", "Wayne Farms, "n/a" "WAYN", "Wayne Savings Bancshares Inc.", "$35.93M" "WSTG", "Wayside Technology Group, "16.63" "WDFC", "WD-40 Company", "$1.52B" "FLAG", "WeatherStorm Forensic Accounting Long Short ETF", "$11.4M" "WEB", "Web.com Group, "17.47" "WBMD", "WebMD Health Corp", "$1.99B" "WB", "Weibo Corporation", "$2.92B" "WEBK", "Wellesley Bancorp, "18.46" "WEN", "Wendy's Company (The)", "$2.68B" "WERN", "Werner Enterprises, "26.86" "WSBC", "WesBanco, "28" "WTBA", "West Bancorporation", "$285.3M" "WSTC", "West Corporation", "$1.78B" "WMAR", "West Marine, "8.25" "WABC", "Westamerica Bancorporation", "$1.17B" "WBB", "Westbury Bancorp, "18.4" "WSTL", "Westell Technologies, "1.21" "WDC", "Western Digital Corporation", "$10.08B" "WFD", "Westfield Financial, "7.96" "WLB", "Westmoreland Coal Company", "$91.95M" "WPRT", "Westport Innovations Inc", "$117.73M" "WEYS", "Weyco Group, "25.83" "WHLR", "Wheeler Real Estate Investment Trust, "1.23" "WHLRP", "Wheeler Real Estate Investment Trust, "19.1568" "WHLRW", "Wheeler Real Estate Investment Trust, "0.0201" "WHF", "WhiteHorse Finance, "9.39" "WHFBL", "WhiteHorse Finance, "24.27" "WFM", "Whole Foods Market, "31.76" "WILN", "Wi-Lan Inc", "$172.8M" "WHLM", "Wilhelmina International, "6.486" "WVVI", "Willamette Valley Vineyards, "6.8201" "WVVIP", "Willamette Valley Vineyards, "n/a" "WLDN", "Willdan Group, "8.27" "WLFC", "Willis Lease Finance Corporation", "$147.75M" "WLTW", "Willis Towers Watson Public Limited Company", "$7.57B" "WIBC", "Wilshire Bancorp, "9.87" "WIN", "Windstream Holdings, "6.12" "WING", "Wingstop Inc.", "$648.51M" "WINA", "Winmark Corporation", "$417.92M" "WINS", "Wins Finance Holdings Inc.", "$243.68M" "WTFC", "Wintrust Financial Corporation", "$2B" "WTFCM", "Wintrust Financial Corporation", "n/a" "WTFCW", "Wintrust Financial Corporation", "n/a" "AGND", "WisdomTree Barclays U.S. Aggregate Bond Negative Duration Fund", "$33.93M" "AGZD", "WisdomTree Barclays U.S. Aggregate Bond Zero Duration Fund", "$65.88M" "HYND", "WisdomTree BofA Merrill Lynch High Yield Bond Negative Duratio", "$7.13M" "HYZD", "WisdomTree BofA Merrill Lynch High Yield Bond Zero Duration Fu", "$18.08M" "CXSE", "WisdomTree China ex-State-Owned Enterprises Fund", "$14.67M" "EMCG", "WisdomTree Emerging Markets Consumer Growth Fund", "$14.15M" "EMCB", "WisdomTree Emerging Markets Corporate Bond Fund", "$97.1M" "DGRE", "WisdomTree Emerging Markets Quality Dividend Growth Fund", "$11.22M" "DXGE", "WisdomTree Germany Hedged Equity Fund", "$327.46M" "WETF", "WisdomTree Investments, "11.87" "DXJS", "WisdomTree Japan Hedged SmallCap Equity Fund", "$164.81M" "JGBB", "WisdomTree Japan Interest Rate Strategy Fund", "$4.6M" "DXKW", "WisdomTree Korea Hedged Equity Fund", "$26.01M" "GULF", "WisdomTree Middle East Dividend Fund", "$38.59M" "CRDT", "WisdomTree Strategic Corporate Bond Fund", "$7.02M" "DGRW", "WisdomTree U.S. Quality Dividend Growth Fund", "$508.49M" "DGRS", "WisdomTree U.S. SmallCap Quality Dividend Growth Fund", "$23.95M" "DXPS", "WisdomTree United Kingdom Hedged Equity Fund", "$33M" "UBND", "WisdomTree Western Asset Unconstrained Bond Fund", "$4.58M" "WIX", "Wix.com Ltd.", "$778.01M" "WLRH", "WL Ross Holding Corp.", "$622.19M" "WLRHU", "WL Ross Holding Corp.", "$410.8M" "WLRHW", "WL Ross Holding Corp.", "n/a" "WMIH", "WMIH Corp.", "$482.43M" "WBKC", "Wolverine Bancorp, "25.52" "WWD", "Woodward, "47.48" "WKHS", "Workhorse Group, "5.13" "WRLD", "World Acceptance Corporation", "$295.37M" "WOWO", "Wowo Limited", "$390.48M" "WPCS", "WPCS International Incorporated", "$3M" "WPPGY", "WPP plc", "$28.04B" "WMGI", "Wright Medical Group N.V.", "$1.78B" "WMGIZ", "Wright Medical Group N.V.", "n/a" "WSFS", "WSFS Financial Corporation", "$828.43M" "WSFSL", "WSFS Financial Corporation", "n/a" "WSCI", "WSI Industries Inc.", "$10.89M" "WVFC", "WVS Financial Corp.", "$23.23M" "WYNN", "Wynn Resorts, "76.21" "XBIT", "XBiotech Inc.", "$263.27M" "XELB", "Xcel Brands, "5.2" "XCRA", "Xcerra Corporation", "$287.92M" "XNCR", "Xencor, "11.81" "XBKS", "Xenith Bankshares, "6.96" "XENE", "Xenon Pharmaceuticals Inc.", "$114.15M" "XNPT", "XenoPort, "4.14" "XGTI", "XG Technology, "0.1626" "XGTIW", "XG Technology, "0.0451" "XLNX", "Xilinx, "48.82" "XOMA", "XOMA Corporation", "$97.89M" "XPLR", "Xplore Technologies Corp", "$43.96M" "XCOM", "Xtera Communications, "3.19" "XTLB", "XTL Biopharmaceuticals Ltd.", "$16.41M" "XNET", "Xunlei Limited", "$393.57M" "MESG", "Xura, "19.12" "YHOO", "Yahoo! Inc.", "$27.74B" "YNDX", "Yandex N.V.", "$4.35B" "YOD", "You On Demand Holdings, "1.46" "YCB", "Your Community Bankshares, "31.99" "YRCW", "YRC Worldwide, "8.33" "YECO", "Yulong Eco-Materials Limited", "$40.48M" "YY", "YY Inc.", "$2.98B" "ZFGN", "Zafgen, "7.44" "ZAGG", "ZAGG Inc", "$265.29M" "ZAIS", "ZAIS Group Holdings, "6.54" "ZBRA", "Zebra Technologies Corporation", "$3.49B" "ZLTQ", "ZELTIQ Aesthetics, "20.59" "ZHNE", "Zhone Technologies, "1.28" "Z", "Zillow Group, "19.38" "ZG", "Zillow Group, "20.19" "ZN", "Zion Oil & Gas Inc", "$69.95M" "ZNWAA", "Zion Oil & Gas Inc", "n/a" "ZION", "Zions Bancorporation", "$4.41B" "ZIONW", "Zions Bancorporation", "n/a" "ZIONZ", "Zions Bancorporation", "n/a" "ZIOP", "ZIOPHARM Oncology Inc", "$841.62M" "ZIXI", "Zix Corporation", "$190.85M" "ZGNX", "Zogenix, "10.45" "ZSAN", "Zosano Pharma Corporation", "$27.52M" "ZUMZ", "Zumiez Inc.", "$531.84M" "ZYNE", "Zynerba Pharmaceuticals, "6.39" "ZNGA", "Zynga Inc.", "$1.74B" ================================================ FILE: homework_assignments/hw_set3/company_list_corrected.csv ================================================ "Symbol", "Name", "Market Cap" "TFSC","1347 Capital Corp.",18.13M "TFSCR","1347 Capital Corp.",N/A "TFSCU","1347 Capital Corp.",N/A "TFSCW","1347 Capital Corp.",N/A "PIH","1347 Property Insurance Holding",38.64M "FLWS","1-800 FLOWERS.COM, Inc.",519.58M "FCTY","1st Century Bancshares, Inc",79.02M "FCCY","1st Constitution Bancorp (NJ)",92.93M "SRCE","1st Source Corporation",792.96M "VNET","21Vianet Group, Inc.",1.66B "TWOU","2U, Inc.",858.65M "JOBS","51job, Inc.",1.67B "SIXD","6D GLOBAL TECHNOLOGI",226.92M "CAFD","8point3 Energy Partners LP",316.31M "EGHT","8x8 Inc",979.21M "AVHI","A V Homes, Inc.",206.91M "SHLM","A. Schulman, Inc.",716.82M "AAON","AAON, Inc.",1.16B "ABAX","ABAXIS, Inc.",892.32M "ABY","Abengoa Yield plc",1.60B "ABGB","Abengoa, S.A.",1.45B "ABEO","Abeona Therapeutics Inc.",81.18M "ABEOW","Abeona Therapeutics Inc.",N/A "ABIL","Ability Inc.",855220.00 "ABILW","Ability Inc.",N/A "ABMD","ABIOMED, Inc.",3.50B "AXAS","Abraxas Petroleum Corporation",100.08M "ACTG","Acacia Research Corporation",195.84M "ACHC","Acadia Healthcare Company, Inc.",3.97B "ACAD","ACADIA Pharmaceuticals Inc.",2.03B "ACST","Acasti Pharma, Inc.",16.74M "AXDX","Accelerate Diagnostics, Inc.",538.20M "XLRN","Acceleron Pharma Inc.",888.85M "ANCX","Access National Corporation",203.18M "ARAY","Accuray Incorporated",406.64M "VXDN","AccuShares Spot CBOE VIX Down S",N/A "VXUP","AccuShares Spot CBOE VIX Up Sha",N/A "ACRX","AcelRx Pharmaceuticals, Inc.",171.11M "ACET","Aceto Corporation",625.89M "AKAO","Achaogen, Inc.",69.38M "ACHN","Achillion Pharmaceuticals, Inc.",942.15M "ACIW","ACI Worldwide, Inc.",2.17B "ACRS","Aclaris Therapeutics, Inc.",337.83M "ACNB","ACNB Corporation",131.84M "ACOR","Acorda Therapeutics, Inc.",1.49B "ACTS","Actions Semiconductor Co., Ltd.",61.60M "ACPW","Active Power, Inc.",25.42M "ATVI","Activision Blizzard, Inc",23.10B "ACTA","Actua Corporation",288.38M "ACUR","Acura Pharmaceuticals, Inc.",25.25M "ACXM","Acxiom Corporation",1.57B "ADMS","Adamas Pharmaceuticals, Inc.",271.91M "ADMP","Adamis Pharmaceuticals Corporat",71.72M "ADAP","Adaptimmune Therapeutics plc",593.89M "ADUS","Addus HomeCare Corporation",245.54M "AEY","ADDvantage Technologies Group, ",16.62M "IOTS","Adesto Technologies Corporation",58.72M "ADMA","ADMA Biologics Inc",48.21M "ADBE","Adobe Systems Incorporated",41.76B "ADTN","ADTRAN, Inc.",921.15M "ADRO","Aduro Biotech, Inc.",925.60M "AAAP","Advanced Accelerator Applicatio",1.08B "AEIS","Advanced Energy Industries, Inc",1.19B "AITP",N/A,N/A "AITPU",N/A,N/A "AMD","Advanced Micro Devices, Inc.",1.59B "ADXS","Advaxis, Inc.",212.07M "ADXSW","Advaxis, Inc.",N/A "MAUI","AdvisorShares Market Adaptive U",N/A "YPRO","AdvisorShares YieldPro ETF",N/A "AEGR","Aegerion Pharmaceuticals, Inc.",186.06M "AEGN","Aegion Corp",636.51M "AEHR","Aehr Test Systems",16.06M "AMTX","Aemetis, Inc",35.83M "AEPI","AEP Industries Inc.",391.09M "AERI","Aerie Pharmaceuticals, Inc.",436.59M "AVAV","AeroVironment, Inc.",586.58M "AEZS","AEterna Zentaris Inc.",18.92M "AEMD","AETHLON MEDICAL INC",37.27M "AFMD","Affimed N.V.",98.14M "AFFX","Affymetrix, Inc.",1.13B "AGEN","Agenus Inc.",253.94M "AGRX","Agile Therapeutics, Inc.",134.20M "AGYS","Agilysys, Inc.",238.51M "AGIO","Agios Pharmaceuticals, Inc.",1.47B "AGFS","AgroFresh Solutions, Inc.",228.23M "AGFSW","AgroFresh Solutions, Inc.",N/A "AIMT","Aimmune Therapeutics, Inc.",677.67M "AIRM","Air Methods Corporation",1.55B "AIRT","Air T, Inc.",57.07M "ATSG","Air Transport Services Group, I",728.54M "AMCN","AirMedia Group Inc",334.19M "AIXG","Aixtron SE",426.23M "AKAM","Akamai Technologies, Inc.",9.64B "AKTX","Akari Therapeutics Plc",121.30M "AKBA","Akebia Therapeutics, Inc.",140.91M "AKER","Akers Biosciences Inc",7.25M "AKRX","Akorn, Inc.",2.94B "ALRM","Alarm.com Holdings, Inc.",772.02M "ALSK","Alaska Communications Systems G",77.13M "AMRI","Albany Molecular Research, Inc.",509.46M "ABDC","Alcentra Capital Corp.",N/A "ADHD","Alcobra Ltd.",119.96M "ALDR","Alder BioPharmaceuticals, Inc.",925.86M "ALDX","Aldeyra Therapeutics, Inc.",25.17M "ALXN","Alexion Pharmaceuticals, Inc.",32.74B "ALXA","Alexza Pharmaceuticals, Inc.",5.87M "ALCO","Alico, Inc.",189.57M "ALGN","Align Technology, Inc.",5.11B "ALIM","Alimera Sciences, Inc.",105.57M "ALKS","Alkermes plc",5.10B "ABTX","Allegiance Bancshares, Inc.",220.11M "ALGT","Allegiant Travel Company",2.64B "AFOP","Alliance Fiber Optic Products, ",213.70M "AIQ","Alliance HealthCare Services, I",75.04M "AHGP","Alliance Holdings GP, L.P.",775.21M "ARLP","Alliance Resource Partners, L.P",812.36M "AHPI","Allied Healthcare Products, Inc",5.46M "AMOT","Allied Motion Technologies, 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"CRMT","America's Car-Mart, Inc.",210.98M "ABCB","Ameris Bancorp",853.27M "AMSF","AMERISAFE, Inc.",1.00B "ASRV","AmeriServ Financial Inc.",58.87M "ASRVP","AmeriServ Financial Inc.",N/A "ATLO","Ames National Corporation",226.79M "AMGN","Amgen Inc.",111.73B "FOLD","Amicus Therapeutics, Inc.",894.28M "AMKR","Amkor Technology, Inc.",1.18B "AMPH","Amphastar Pharmaceuticals, Inc.",515.92M "AMSG","Amsurg Corp.",3.37B "AMSGP","Amsurg Corp.",6.42B "ASYS","Amtech Systems, Inc.",65.64M "AFSI","AmTrust Financial Services, Inc",4.20B "AMRS","Amyris, Inc.",297.87M "ANAC","Anacor Pharmaceuticals, Inc.",3.33B "ANAD","ANADIGICS, Inc.",67.74M "ADI","Analog Devices, Inc.",16.31B "ALOG","Analogic Corporation",915.09M "AVXL","ANAVEX LIFE SCIENCES",139.65M "ANCB","Anchor Bancorp",56.01M "ABCW","Anchor BanCorp Wisconsin Inc.",396.43M "ANDA","Andina Acquisition Corp. 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II",N/A "ANGI","Angie's List, Inc.",546.54M "ANGO","AngioDynamics, Inc.",376.28M "ANIP","ANI Pharmaceuticals, Inc.",360.75M "ANIK","Anika Therapeutics Inc.",569.53M "ANSS","ANSYS, Inc.",7.88B "ATRS","Antares Pharma, Inc.",154.83M "ANTH","Anthera Pharmaceuticals, Inc.",125.21M "ABAC","Aoxin Tianli Group, Inc.",21.60M "ZLIG",N/A,N/A "ATNY","API Technologies Corp.",55.98M "APIC","Apigee Corporation",177.77M "APOG","Apogee Enterprises, Inc.",1.09B "APOL","Apollo Education Group, Inc.",917.15M "AINV","Apollo Investment Corporation",N/A "AMEH","APOLLO MEDICAL HLDGS",31.56M "APPF","AppFolio, Inc.",464.70M "AAPL","Apple Inc.",537.16B "ARCI","Appliance Recycling Centers of ",5.14M "APDN","Applied DNA Sciences Inc",62.83M "APDNW","Applied DNA Sciences Inc",N/A "AGTC","Applied Genetic Technologies Co",274.16M "AMAT","Applied Materials, Inc.",21.30B "AMCC","Applied Micro Circuits Corporat",466.97M "AAOI","Applied Optoelectronics, Inc.",274.75M "AREX","Approach Resources Inc.",32.44M "APRI","Apricus Biosciences, Inc",53.44M "APTO","Aptose Biosciences, Inc.",581.43M "AQMS","Aqua Metals, Inc.",71.71M "AQXP","Aquinox Pharmaceuticals, Inc.",176.58M "AUMA","AR Capital Acquisition Corp.",71.17M "AUMAU","AR Capital Acquisition Corp.",N/A "AUMAW","AR Capital Acquisition Corp.",584649.88 "ARDM","Aradigm Corporation",39.09M "ARLZ","Aralez Pharmaceuticals Inc.",186.50M "PETX","Aratana Therapeutics, Inc.",117.62M "ABUS","Arbutus Biopharma Corporation",175.17M "ARCW","ARC Group Worldwide, Inc.",31.40M "ABIO","ARCA biopharma, Inc.",32.13M "RKDA","Arcadia Biosciences, Inc.",112.85M "ARCB","ArcBest Corporation",528.28M "ACGL","Arch Capital Group Ltd.",8.29B "APLP","Archrock Partners, L.P.",423.98M "ACAT","Arctic Cat Inc.",213.72M "ARDX","Ardelyx, Inc.",282.66M "ARNA","Arena Pharmaceuticals, Inc.",392.93M "ARCC","Ares Capital Corporation",N/A "AGII","Argo Group International Holdin",1.54B "AGIIL","Argo Group International Holdin",N/A "ARGS","Argos Therapeutics, Inc.",100.56M "ARIS","ARI Network Services, Inc.",69.72M "ARIA","ARIAD Pharmaceuticals, Inc.",961.61M "ARKR","Ark Restaurants Corp.",68.87M "ARMH","ARM Holdings plc",18.81B "ARTX","Arotech Corporation",54.12M "ARWA","Arowana Inc.",30.30M "ARWAR","Arowana Inc.",N/A "ARWAU","Arowana Inc.",96.91M "ARWAW","Arowana Inc.",N/A "ARQL","ArQule, Inc.",123.90M "ARRY","Array BioPharma Inc.",388.44M "ARRS","ARRIS International plc",3.37B "DWAT","Arrow DWA Tactical ETF",N/A "AROW","Arrow Financial Corporation",342.11M "ARWR","Arrowhead Research Corporation",243.87M "ARTNA","Artesian Resources Corporation",260.46M "ARTW","Art's-Way Manufacturing Co., In",11.49M "PUMP",N/A,N/A "ASBB","ASB Bancorp, Inc.",93.28M "ASNA","Ascena Retail Group, Inc.",1.54B "ASND","Ascendis Pharma A/S",465.62M "ASCMA","Ascent Capital Group, Inc.",149.68M "ASTI","Ascent Solar Technologies, Inc.",8.10M "APWC","Asia Pacific Wire & Cable Corpo",21.97M "ASML","ASML Holding N.V.",37.63B "AZPN","Aspen Technology, Inc.",2.81B "ASMB","Assembly Biosciences, Inc.",92.50M "ASFI","Asta Funding, Inc.",88.67M "ASTE","Astec Industries, Inc.",880.63M "ALOT","Astro-Med, Inc.",92.56M "ATRO","Astronics Corporation",716.96M "ASTC","Astrotech Corporation",28.98M "ASUR","Asure Software Inc",33.33M "ATAI","ATA Inc.",105.10M "ATRA","Atara Biotherapeutics, Inc.",469.26M "ATHN","athenahealth, Inc.",4.96B "ATHX","Athersys, Inc.",148.25M "AAPC","Atlantic Alliance Partnership C",34.31M "AAME","Atlantic American Corporation",83.63M "ACBI","Atlantic Capital Bancshares, In",292.37M "ACFC","Atlantic Coast Financial Corpor",85.77M "ATNI","Atlantic Tele-Network, Inc.",1.25B "ATLC","Atlanticus Holdings Corporation",41.75M "AAWW","Atlas Air Worldwide Holdings",908.58M "AFH","Atlas Financial Holdings, Inc.",207.51M "TEAM","Atlassian Corporation Plc",4.70B "ATML","Atmel Corporation",3.40B "ATOS","Atossa Genetics Inc.",18.27M "ATRC","AtriCure, Inc.",558.63M "ATRI","ATRION Corporation",709.15M "ATTU","Attunity Ltd.",98.97M "LIFE","aTyr Pharma, Inc.",103.58M "AUBN","Auburn National Bancorporation,",94.72M "AUDC","AudioCodes Ltd.",167.23M "AUPH","Aurinia Pharmaceuticals Inc",73.29M "EARS","Auris Medical Holding AG",144.72M "ABTL","Autobytel Inc.",178.48M "ADSK","Autodesk, Inc.",11.35B "AGMX",N/A,N/A "ADP","Automatic Data Processing, Inc.",39.52B "AAVL","Avalanche Biotechnologies, Inc.",117.69M "AVNU","Avenue Financial Holdings, Inc.",188.91M "AVEO","AVEO Pharmaceuticals, Inc.",58.05M "AVXS","AveXis, Inc.",90.45M "AVNW","Aviat Networks, Inc.",42.97M "AVID","AVID TECH INC",298.26M "AVGR","Avinger, Inc.",197.79M "CAR","Avis Budget Group, Inc.",2.98B "AWRE","Aware, Inc.",81.86M "ACLS","Axcelis Technologies, Inc.",272.56M "AXGN","AxoGen, Inc.",152.11M "AXSM","Axsome Therapeutics, Inc.",96.08M "AXTI","AXT Inc",86.51M "BCOM","B Communications Ltd.",809.46M "RILY","B. 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II",113.27M "BLVDU","Boulevard Acquisition Corp. II",N/A "BLVDW","Boulevard Acquisition Corp. 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Robinson Worldwide, Inc.",10.05B "CA","CA Inc.",12.31B "CCMP","Cabot Microelectronics Corporat",891.77M "CDNS","Cadence Design Systems, Inc.",6.49B "CDZI","Cadiz, Inc.",100.97M "CACQ","Caesars Acquisition Company",906.59M "CZR","Caesars Entertainment Corporati",1.12B "CSTE","CaesarStone Sdot-Yam Ltd.",1.26B "PRSS","CafePress Inc.",59.31M "CLBS","Caladrius Biosciences, Inc.",34.05M "CLMS","Calamos Asset Management, Inc.",173.69M "CHY","Calamos Convertible and High In",N/A "CHI","Calamos Convertible Opportuniti",N/A "CCD","Calamos Dynamic Convertible & I",N/A "CFGE","Calamos Focus Growth ETF",N/A "CHW","Calamos Global Dynamic Income F",N/A "CGO","Calamos Global Total Return Fun",N/A "CSQ","Calamos Strategic Total Return ",N/A "CAMP","CalAmp Corp.",646.84M "CVGW","Calavo Growers, Inc.",885.54M "CFNB","California First National Banco",135.97M "CALA","Calithera Biosciences, Inc.",108.27M "CALD","Callidus Software, Inc.",755.61M "CALM","Cal-Maine Foods, Inc.",2.46B "CLMT","Calumet Specialty Products Part",732.31M "ABCD","Cambium Learning Group, Inc.",193.64M "CAC","Camden National Corporation",395.31M "CAMT","Camtek Ltd.",63.98M "CSIQ","Canadian Solar Inc.",1.18B "CGIX","Cancer Genetics, Inc.",23.75M "CPHC","Canterbury Park Holding Corpora",42.45M "CBNJ","Cape Bancorp, Inc.",164.65M "CPLA","Capella Education Company",533.56M "CBF","Capital Bank Financial Corp.",1.32B "CCBG","Capital City Bank Group",248.59M "CPLP","Capital Product Partners L.P.",422.64M "CSWC","Capital Southwest Corporation",219.29M "CPTA","Capitala Finance Corp.",N/A "CLAC","Capitol Acquisition Corp. 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Limite",117.71M "HGSH","China HGS Real Estate, Inc.",57.66M "CHLN","China Housing & Land Developmen",N/A "CNIT","China Information Technology, I",42.61M "CJJD","China Jo-Jo Drugstores, Inc.",28.73M "HTHT","China Lodging Group, Limited",1.77B "CHNR","China Natural Resources, Inc.",19.82M "CREG","China Recycling Energy Corporat",22.43M "CSUN","China Sunergy Co., Ltd.",11.88M "CNTF","China TechFaith Wireless Commun",35.20M "CXDC","China XD Plastics Company Limit",141.06M "CNYD","China Yida Holding, Co.",7.17M "CCIH","ChinaCache International Holdin",191.09M "CNET","ChinaNet Online Holdings, Inc.",20.41M "IMOS","ChipMOS TECHNOLOGIES (Bermuda) ",486.64M "CHSCL","CHS Inc",N/A "CHSCM","CHS Inc",N/A "CHSCN","CHS Inc",N/A "CHSCO","CHS Inc",N/A "CHSCP","CHS Inc",N/A "CHDN","Churchill Downs, Incorporated",2.34B "CHUY","Chuy's Holdings, Inc.",504.72M "CDTX","Cidara Therapeutics, Inc.",152.42M "CIFC","CIFC LLC",151.43M "CMCT","CIM Commercial Trust Corporatio",1.57B "CMPR","Cimpress N.V",2.71B "CINF","Cincinnati Financial Corporatio",10.34B "CIDM","Cinedigm Corp",18.83M "CTAS","Cintas Corporation",9.15B "CPHR","Cipher Pharmaceuticals Inc.",124.38M "CRUS","Cirrus Logic, Inc.",2.18B "CSCO","Cisco Systems, Inc.",134.01B "CTRN","Citi Trends, Inc.",271.46M "CZNC","Citizens & Northern Corp",242.78M "CZWI","Citizens Community Bancorp, Inc",46.94M "CZFC","Citizens First Corporation",27.06M "CIZN","Citizens Holding Company",109.91M "CTXS","Citrix Systems, Inc.",10.76B "CHCO","City Holding Company",662.76M "CIVB","Civista Bancshares, Inc. 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Consolid",1.56B "CDRB","Code Rebel Corporation",23.64M "CDXS","Codexis, Inc.",166.87M "CVLY","Codorus Valley Bancorp, Inc",130.78M "JVA","Coffee Holding Co., Inc.",19.72M "CCOI","Cogent Communications Holdings,",1.53B "CGNT","Cogentix Medical, Inc.",28.40M "CGNX","Cognex Corporation",3.13B "CTSH","Cognizant Technology Solutions ",34.44B "COHR","Coherent, Inc.",2.02B "CHRS","Coherus BioSciences, Inc.",567.82M "COHU","Cohu, Inc.",313.31M "CLCT","Collectors Universe, Inc.",135.61M "COLL","Collegium Pharmaceutical, Inc.",432.17M "CIGI","Colliers International Group In",1.27B "CBAN","Colony Bankcorp, Inc.",74.69M "CLCD","CoLucid Pharmaceuticals, Inc.",88.80M "COLB","Columbia Banking System, Inc.",1.65B "COLM","Columbia Sportswear Company",4.08B "CMCO","Columbus McKinnon Corporation",275.06M "CBMX","CombiMatrix Corporation",4.17M "CMCSA","Comcast Corporation",142.03B "CBSH","Commerce Bancshares, Inc.",4.17B "CBSHP","Commerce Bancshares, Inc.",2.56B "CUBN","COMMERCE UN BANCSHA",99.72M "CVGI","Commercial Vehicle Group, Inc.",67.19M "COMM","CommScope Holding Company, Inc.",4.81B "CSAL","Communications Sales & Leasing,",2.48B "JCS","Communications Systems, Inc.",62.59M "ESXB","Community Bankers Trust Corpora",108.56M "CCFI",N/A,N/A "CYHHZ","Community Health Systems, Inc.",N/A "CTBI","Community Trust Bancorp, Inc.",586.27M "CWBC","Community West Bancshares",57.44M "COB","CommunityOne Bancorp",313.61M "CVLT","CommVault Systems, Inc.",1.68B "CGEN","Compugen Ltd.",246.06M "CPSI","Computer Programs and Systems, ",632.06M "CTG","Computer Task Group, Incorporat",107.96M "SCOR","comScore, Inc.",1.59B "CHCI","Comstock Holding Companies, Inc",5.62M "CMTL","Comtech Telecommunications Corp",315.93M "CNAT","Conatus Pharmaceuticals Inc.",37.86M "CNCE","Concert Pharmaceuticals, Inc.",300.77M "CXRX","Concordia Healthcare Corp.",1.40B "CCUR","Concurrent Computer Corporation",49.97M "CDOR","Condor Hospitality Trust, Inc.",4.59M "CDORO","Condor Hospitality Trust, Inc.",79.04M "CDORP","Condor Hospitality Trust, Inc.",N/A "CFMS","ConforMIS, Inc.",339.98M "CONG",N/A,N/A "CNFR","Conifer Holdings, Inc.",48.54M "CNMD","CONMED Corporation",1.05B "CTWS","Connecticut Water Service, Inc.",471.50M "CNOB","ConnectOne Bancorp, Inc.",461.20M "CNXR","Connecture, Inc.",57.65M "CONN","Conn's, Inc.",610.70M "CNSL","Consolidated Communications Hol",1.07B "CWCO","Consolidated Water Co. Ltd.",157.24M "CPSS","Consumer Portfolio Services, In",111.26M "CFRX","ContraFect Corporation",91.24M "CFRXW","ContraFect Corporation",N/A "CTRV","CONTRAVIR PHARMACEUT",26.20M "CTRL","Control4 Corporation",190.59M "CPRT","Copart, Inc.",4.35B "COYN","COPSYNC INC",7.12M "COYNW","COPsync, Inc.",N/A "CRBP","CORBUS PHARMACEUTICA",53.02M "CORT","Corcept Therapeutics Incorporat",430.75M "BVA","Cordia Bancorp Inc.",25.47M "CORE","Core-Mark Holding Company, Inc.",1.72B "CORI","Corium International, Inc.",129.99M "CSOD","Cornerstone OnDemand, Inc.",1.50B "CRVL","CorVel Corp.",836.13M "COSI","Cosi, Inc.",28.98M "CSGP","CoStar Group, Inc.",5.60B "COST","Costco Wholesale Corporation",65.99B "CPAH","CounterPath Corporation",10.26M "ICBK","County Bancorp, Inc.",114.27M "CVTI","Covenant Transportation Group, ",403.91M "COVS","Covisint Corporation",75.40M "COWN","Cowen Group, Inc.",360.31M "COWNL","Cowen Group, Inc.",2.56B "PMTS","CPI Card Group Inc.",435.43M "CPSH","CPS TECHNOLOGIES CP",19.66M "CRAI","CRA International,Inc.",168.47M "CBRL","Cracker Barrel Old Country Stor",3.38B "BREW","Craft Brew Alliance, Inc.",159.31M "CRAY","Cray Inc",1.67B "CACC","Credit Acceptance Corporation",3.99B "GLDI","Credit Suisse AG",N/A "CREE","Cree, Inc.",3.22B "CRESY","Cresud S.A.C.I.F. y A.",531.02M "CRTO","Criteo S.A.",2.30B "CROX","Crocs, Inc.",691.66M "CCRN","Cross Country Healthcare, Inc.",412.06M "XRDC","Crossroads Capital, Inc.",24.58M "CRDS","Crossroads Systems, Inc.",5.56M "CRWS","Crown Crafts, Inc.",81.37M "CRWN","Crown Media Holdings, Inc.",1.58B "CYRX","CRYOPORT INC",19.59M "CYRXW","CryoPort, Inc.",N/A "CSGS","CSG Systems International, Inc.",1.18B "CCLP","CSI Compressco LP",149.67M "CSPI","CSP Inc.",20.15M "CSWI","CSW Industrials, Inc.",442.57M "CSX","CSX Corporation",24.37B "CTCM","CTC Media, Inc.",288.79M "CTIC","CTI BioPharma Corp.",191.71M "CTIB","CTI Industries Corporation",16.64M "CTRP","Ctrip.com International, Ltd.",11.98B "CUNB","CU Bancorp (CA)",360.48M "CUI","CUI Global, Inc.",181.84M "CPIX","Cumberland Pharmaceuticals Inc.",74.20M "CMLS","Cumulus Media Inc.",71.55M "CRIS","Curis, Inc.",197.73M "CUTR","Cutera, Inc.",150.35M "CVBF","CVB Financial Corporation",1.64B "CVV","CVD Equipment Corporation",50.67M "CYAN","Cyanotech Corporation",23.17M "CYBR","CyberArk Software Ltd.",1.09B "CYBE","CyberOptics Corporation",65.48M "CYCC","Cyclacel Pharmaceuticals, Inc.",11.45M "CYCCP","Cyclacel Pharmaceuticals, Inc.",208.21M "CBAY","CymaBay Therapeutics Inc.",26.96M "CYNA","Cynapsus Therapeutics Inc.",163.64M "CYNO","Cynosure, Inc.",886.44M "CY","Cypress Semiconductor Corporati",2.56B "CYRN","CYREN Ltd.",54.38M "CONE","CyrusOne Inc",2.44B "CYTK","Cytokinetics, Incorporated",263.51M "CTMX","CytomX Therapeutics, Inc.",474.44M "CYTX","Cytori Therapeutics Inc",28.37M "CTSO","CYTOSORBENTS COR",110.59M "CYTR","CytRx Corporation",181.49M "DJCO","Daily Journal Corp. (S.C.)",268.92M "DAKT","Daktronics, Inc.",378.89M "DAIO","Data I/O Corporation",18.11M "DTLK","Datalink Corporation",166.03M "DRAM","Dataram Corporation",3.01M "DWCH","Datawatch Corporation",52.78M "PLAY","Dave & Buster's Entertainment, ",1.51B "DTEA","DAVIDsTEA Inc.",233.67M "DWSN","Dawson Geophysical Company",75.11M "DBVT","DBV Technologies S.A.",1.21B "DHRM","Dehaier Medical Systems Limited",10.17M "DFRG","Del Frisco's Restaurant Group, ",358.69M "TACO","Del Taco Restaurants, Inc.",412.47M "TACOW","Del Taco Restaurants, Inc.",N/A "DCTH","Delcath Systems, Inc.",6.31M "DGAS","Delta Natural Gas Company, Inc.",157.47M "DELT","Delta Technology Holdings Limit",9.09M "DELTW","Delta Technology Holdings Limit",952873.25 "DENN","Denny's Corporation",798.30M "XRAY","DENTSPLY International Inc.",7.94B "DEPO","Depomed, Inc.",1.04B "DSCI","Derma Sciences, Inc.",85.42M "DERM","Dermira, Inc.",706.36M "DEST","Destination Maternity Corporati",109.89M "DXLG","Destination XL Group, Inc.",221.52M "DSWL","Deswell Industries, Inc.",20.55M "DTRM","Determine, Inc. ",21.70M "DXCM","DexCom, Inc.",5.17B "DHXM","DHX Media Ltd.",631.66M "DMND","Diamond Foods, Inc.",1.14B "DHIL","Diamond Hill Investment Group, ",578.33M "FANG","Diamondback Energy, Inc.",4.92B "DCIX","Diana Containerships Inc.",26.38M "DRNA","Dicerna Pharmaceuticals, Inc.",114.50M "DFBG","Differential Brands Group Inc.",29.31M "DGII","Digi International Inc.",219.34M "DMRC","Digimarc Corporation",247.08M "DRAD","Digirad Corporation",91.64M "DGLY","Digital Ally, Inc.",30.51M "APPS","Digital Turbine, Inc.",73.34M "DCOM","Dime Community Bancshares, Inc.",618.77M "DMTX","Dimension Therapeutics, Inc.",191.72M "DIOD","Diodes Incorporated",848.37M "DPRX","Dipexium Pharmaceuticals, Inc.",73.65M "DISCA","Discovery Communications, Inc.",16.61B "DISCB","Discovery Communications, Inc.",16.49B "DISCK","Discovery Communications, Inc.",16.31B "DSCO","Discovery Laboratories, Inc.",18.09M "DISH","DISH Network Corporation",21.57B "DVCR","Diversicare Healthcare Services",42.56M "SAUC","Diversified Restaurant Holdings",41.56M "DLHC","DLH Holdings Corp.",31.09M "DNBF","DNB Financial Corp",82.58M "DLTR","Dollar Tree, Inc.",19.18B "DGICA","Donegal Group, Inc.",407.12M "DGICB","Donegal Group, Inc.",400.18M "DMLP","Dorchester Minerals, L.P.",305.52M "DORM","Dorman Products, Inc.",1.74B "EAGL","Double Eagle Acquisition Corp.",154.28M "EAGLU","Double Eagle Acquisition Corp.",N/A "EAGLW","Double Eagle Acquisition Corp.",N/A "DDAY","DraftDay Fantasy Sports, Inc.",8.02M "DRWI","DragonWave Inc",6.67M "DRWIW","DragonWave Inc",N/A "DWA","Dreamworks Animation SKG, Inc.",1.85B "DRYS","DryShips Inc.",71.98M "DSKX","DS Healthcare Group, Inc.",29.80M "DSPG","DSP Group, Inc.",179.35M "CADT","DT Asia Investments Limited",90.16M "CADTR","DT Asia Investments Limited",N/A "CADTU","DT Asia Investments Limited",N/A "CADTW","DT Asia Investments Limited",N/A "DTSI","DTS, Inc.",402.02M "DLTH","Duluth Holdings Inc.",546.12M "DNKN","Dunkin' Brands Group, Inc.",3.98B "DRRX","DURECT Corporation",138.60M "DXPE","DXP Enterprises, Inc.",199.99M "BOOM","Dynamic Materials Corporation",87.85M "DYSL","Dynasil Corporation of America",28.27M "DYNT","Dynatronics Corporation",7.87M "DVAX","Dynavax Technologies Corporatio",752.75M "ETFC","E*TRADE Financial Corporation",6.67B "EBMT","Eagle Bancorp Montana, Inc.",42.23M "EGBN","Eagle Bancorp, Inc.",1.55B "EGLE","Eagle Bulk Shipping Inc.",24.85M "EGRX","Eagle Pharmaceuticals, Inc.",944.85M "ELNK","EarthLink Holdings Corp.",558.87M "EWBC","East West Bancorp, Inc.",4.30B "EACQ","Easterly Acquisition Corp.",58.45M "EACQU","Easterly Acquisition Corp.",N/A "EACQW","Easterly Acquisition Corp.",N/A "EML","Eastern Company (The)",99.75M "EVBS","Eastern Virginia Bankshares, In",88.60M "EBAY","eBay Inc.",28.57B "EBIX","Ebix, Inc.",1.10B "ELON","Echelon Corporation",23.71M "ECHO","Echo Global Logistics, Inc.",706.18M "ECTE","Echo Therapeutics, Inc.",13.91M "SATS","EchoStar Corporation",3.45B "EEI","Ecology and Environment, Inc.",43.98M "ECAC","E-compass Acquisition Corp.",53.04M "ECACR","E-compass Acquisition Corp.",N/A "ECACU","E-compass Acquisition Corp.",54.15M "ESES","ECO-STIM ENERGY",35.09M "EDAP","EDAP TMS S.A.",93.59M "EDGE","Edge Therapeutics, Inc.",203.11M "EDGW","Edgewater Technology, Inc.",84.61M "EDIT","Editas Medicine, Inc.",180.51M "EDUC","Educational Development Corpora",51.28M "EFUT","eFuture Holding Inc.",33.57M "EGAN","eGain Corporation",99.61M "EGLT","Egalet Corporation",196.87M "EHTH","eHealth, Inc.",187.33M "LOCO","El Pollo Loco Holdings, Inc.",473.57M "EMITF","Elbit Imaging Ltd.",19.85M "ESLT","Elbit Systems Ltd.",3.64B "ERI","Eldorado Resorts, Inc.",452.83M "ELRC","Electro Rent Corporation",229.45M "ESIO","Electro Scientific Industries, ",219.76M "EA","Electronic Arts Inc.",19.57B "EFII","Electronics for Imaging, Inc.",1.88B "ELSE","Electro-Sensors, Inc.",11.58M "ELEC","Electrum Special Acquisition Co",58.56M "ELECU","Electrum Special Acquisition Co",59.47M "ELECW","Electrum Special Acquisition Co",N/A "EBIO","Eleven Biotherapeutics, Inc.",4.75M "RDEN","Elizabeth Arden, Inc.",173.70M "CAPX","Elkhorn S&P 500 Capital Expendi",N/A "ESBK","Elmira Savings Bank NY (The)",48.32M "LONG","eLong, Inc.",645.08M "ELTK","Eltek Ltd.",12.48M "EMCI","EMC Insurance Group Inc.",501.84M "EMCF","Emclaire Financial Corp",48.82M "EMKR","EMCORE Corporation",139.12M "EMMS","Emmis Communications Corporatio",23.87M "EMMSP","Emmis Communications Corporatio",56.01M "NYNY","Empire Resorts, Inc.",138.96M "ERS","Empire Resources, Inc.",29.73M "ENTA","Enanta Pharmaceuticals, Inc.",556.40M "ECPG","Encore Capital Group Inc",549.18M "WIRE","Encore Wire Corporation",707.26M "ENDP","Endo International plc",11.42B "ECYT","Endocyte, Inc.",134.32M "ELGX","Endologix, Inc.",510.45M "EIGI","Endurance International Group H",1.41B "WATT","Energous Corporation",98.87M "EFOI","Energy Focus, Inc.",110.21M "ERII","Energy Recovery, Inc.",350.41M "EXXI","Energy XXI Ltd.",38.66M "ENOC","EnerNOC, Inc.",148.21M "ENG","ENGlobal Corporation",24.69M "ENPH","Enphase Energy, Inc.",100.19M "ESGR","Enstar Group Limited",2.98B "ENFC","Entegra Financial Corp.",111.81M "ENTG","Entegris, Inc.",1.70B "ENTL","Entellus Medical, Inc.",301.17M "ETRM","EnteroMedics Inc.",7.67M "EBTC","Enterprise Bancorp Inc",231.93M "EFSC","Enterprise Financial Services C",539.66M "EGT","Entertainment Gaming Asia Incor",24.62M "ENZN","Enzon Pharmaceuticals, Inc.",19.66M "ENZY","Enzymotec Ltd.",187.91M "EPIQ","EPIQ Systems, Inc.",487.67M "EPRS","EPIRUS Biopharmaceuticals, Inc.",69.23M "EPZM","Epizyme, Inc.",398.26M "PLUS","ePlus inc.",563.69M "EQIX","Equinix, Inc.",18.11B "EQFN","Equitable Financial Corp.",27.23M "EQBK","Equity Bancshares, Inc.",171.61M "EAC","Erickson Incorporated",31.18M "ERIC","Ericsson",30.38B "ERIE","Erie Indemnity Company",5.12B "ESCA","Escalade, Incorporated",173.22M "ESMC","Escalon Medical Corp.",7.53M "ESPR","Esperion Therapeutics, Inc.",380.82M "ESSA","ESSA Bancorp, Inc.",138.36M "EPIX","ESSA Pharma Inc.",74.83M "ESND","Essendant Inc.",1.04B "ESSF",N/A,N/A "ETSY","Etsy, Inc.",882.45M "CLWT","Euro Tech Holdings Company Limi",7.19M "EEFT","Euronet Worldwide, Inc.",3.57B "ESEA","Euroseas Ltd.",16.39M "EVEP","EV Energy Partners, L.P.",99.21M "EVK","Ever-Glory International Group,",27.94M "EVLV","EVINE Live Inc.",28.56M "EVOK","Evoke Pharma, Inc.",25.51M "EVOL","Evolving Systems, Inc.",62.02M "EXA","Exa Corporation",159.56M "EXAS","Exact Sciences Corporation",623.30M "EXAC","Exactech, Inc.",254.08M "EXEL","Exelixis, Inc.",927.04M "EXFO","EXFO Inc",162.21M "EXLS","ExlService Holdings, Inc.",1.49B "EXPE","Expedia, Inc.",16.11B "EXPD","Expeditors International of Was",8.71B "EXPO","Exponent, Inc.",1.26B "ESRX","Express Scripts Holding Company",46.06B "EXTR","Extreme Networks, Inc.",282.06M "EYEG","Eyegate Pharmaceuticals, Inc.",22.20M "EYEGW","Eyegate Pharmaceuticals, Inc.",N/A "EZCH","EZchip Semiconductor Limited",760.44M "EZPW","EZCORP, Inc.",159.03M "FFIV","F5 Networks, Inc.",6.50B "FB","Facebook, Inc.",305.01B "FCS","Fairchild Semiconductor Interna",2.27B "FRP","FairPoint Communications, Inc.",368.66M "FWM","Fairway Group Holdings Corp.",14.70M "FALC","FalconStor Software, Inc.",57.64M "DAVE","Famous Dave's of America, Inc.",43.83M "FARM","Farmer Brothers Company",423.49M "FFKT","Farmers Capital Bank Corporatio",193.27M "FMNB","Farmers National Banc Corp.",222.83M "FARO","FARO Technologies, Inc.",459.45M "FAST","Fastenal Company",13.08B "FATE","Fate Therapeutics, Inc.",47.96M "FBSS","Fauquier Bankshares, Inc.",56.15M "FBRC","FBR & Co",117.70M "FDML","Federal-Mogul Holdings Corporat",750.54M "FNHC","Federated National Holding Comp",337.44M "FEIC","FEI Company",3.12B "FHCO","Female Health Company (The)",56.03M "FENX","Fenix Parts, Inc.",98.57M "GSM","Ferroglobe PLC",N/A "FCSC","Fibrocell Science Inc",101.40M "FGEN","FibroGen, Inc",1.19B "ONEQ","Fidelity Nasdaq Composite Index",N/A "LION","Fidelity Southern Corporation",343.71M "FDUS","Fidus Investment Corporation",N/A "FRGI","Fiesta Restaurant Group, Inc.",930.18M "FSAM","Fifth Street Asset Management I",19.31M "FSC","Fifth Street Finance Corp.",N/A "FSCFL","Fifth Street Finance Corp.",3.67B "FSFR","Fifth Street Senior Floating Ra",N/A "FITB","Fifth Third Bancorp",12.30B "FITBI","Fifth Third Bancorp",22.02B "FNGN","Financial Engines, Inc.",1.54B "FISI","Financial Institutions, Inc.",385.22M "FNSR","Finisar Corporation",1.50B "FNJN","Finjan Holdings, Inc.",21.73M "FNTC","FinTech Acquisition Corp.",46.95M "FNTCU","FinTech Acquisition Corp.",N/A "FNTCW","FinTech Acquisition Corp.",N/A "FEYE","FireEye, Inc.",2.37B "FBNC","First Bancorp",368.68M "FNLC","First Bancorp, Inc (ME)",202.91M "FRBA","First Bank",63.87M "BUSE","First Busey Corporation",539.45M "FBIZ","First Business Financial Servic",187.90M "FCAP","First Capital, Inc.",77.88M "FCFS","First Cash Financial Services, ",1.16B "FCNCA","First Citizens BancShares, Inc.",2.86B "FCLF","First Clover Leaf Financial Cor",64.38M "FCBC","First Community Bancshares, Inc",322.69M "FCCO","First Community Corporation",89.65M "FCFP","First Community Financial Partn",125.82M "FBNK","First Connecticut Bancorp, Inc.",240.40M "FDEF","First Defiance Financial Corp.",352.98M "FFBC","First Financial Bancorp.",1.05B "FFBCW","First Financial Bancorp.",N/A "FFIN","First Financial Bankshares, Inc",1.81B "THFF","First Financial Corporation Ind",422.97M "FFNW","First Financial Northwest, Inc.",179.92M "FFWM","First Foundation Inc.",340.69M "FGBI","FIRST GUARANTY BANC",125.09M "INBK","First Internet Bancorp",115.43M "FIBK","First Interstate BancSystem, In",1.21B "FRME","First Merchants Corporation",908.84M "FMBH","First Mid-Illinois Bancshares, ",214.94M "FMBI","First Midwest Bancorp, Inc.",1.31B "FNBC","First NBC Bank Holding Company",473.11M "FNFG","First Niagara Financial Group I",3.31B "FNWB","First Northwest Bancorp",164.93M "FSFG","First Savings Financial Group, ",73.17M "FSLR","First Solar, Inc.",6.48B "FSBK","First South Bancorp Inc",78.95M "FPA","First Trust Asia Pacific Ex-Jap",N/A "BICK","First Trust BICK Index Fund",N/A "FBZ","First Trust Brazil AlphaDEX Fun",N/A "FCAN","First Trust Canada AlphaDEX Fun",N/A "FTCS","First Trust Capital Strength ET",N/A "FCA","First Trust China AlphaDEX Fund",N/A "FDT","First Trust Developed Markets E",N/A "FDTS","First Trust Developed Markets e",N/A "FV","First Trust Dorsey Wright Focus",N/A "IFV","First Trust Dorsey Wright Inter",N/A "FEM","First Trust Emerging Markets Al",N/A "FEMB","First Trust Emerging Markets Lo",N/A "FEMS","First Trust Emerging Markets Sm",N/A "FTSM","First Trust Enhanced Short Matu",N/A "FEP","First Trust Europe AlphaDEX Fun",N/A "FEUZ","First Trust Eurozone AlphaDEX E",N/A "FGM","First Trust Germany AlphaDEX Fu",N/A "FTGC","First Trust Global Tactical Com",N/A "FTHI","First Trust High Income ETF",N/A "HYLS","First Trust High Yield Long/Sho",N/A "FHK","First Trust Hong Kong AlphaDEX ",N/A "FTAG","First Trust Indxx Global Agricu",N/A "FTRI","First Trust Indxx Global Natura",N/A "FPXI","First Trust International IPO E",N/A "YDIV","First Trust International Multi",N/A "SKYY","First Trust ISE Cloud Computing",N/A "FJP","First Trust Japan AlphaDEX Fund",N/A "FLN","First Trust Latin America Alpha",N/A "FTLB","First Trust Low Beta Income ETF",N/A "LMBS","First Trust Low Duration Mortga",N/A "FMB","First Trust Managed Municipal E",N/A "MDIV","First Trust Multi-Asset Diversi",N/A "QABA","First Trust NASDAQ ABA Communit",N/A "QCLN","First Trust NASDAQ Clean Edge G",N/A "GRID","First Trust NASDAQ Clean Edge S",N/A "CIBR","First Trust NASDAQ Cybersecurit",N/A "CARZ","First Trust NASDAQ Global Auto ",N/A "RDVY","First Trust NASDAQ Rising Divid",N/A "FONE","First Trust NASDAQ Smartphone I",N/A "TDIV","First Trust NASDAQ Technology D",N/A "QQEW","First Trust NASDAQ-100 Equal We",N/A "QQXT","First Trust NASDAQ-100 Ex-Techn",N/A "QTEC","First Trust NASDAQ-100- Technol",N/A "AIRR","First Trust RBA American Indust",N/A "QINC","First Trust RBA Quality Income ",N/A "FTSL","First Trust Senior Loan Fund ET",N/A "FKO","First Trust South Korea AlphaDE",N/A "FCVT","First Trust SSI Strategic Conve",N/A "FDIV","First Trust Strategic Income ET",N/A "FSZ","First Trust Switzerland AlphaDE",N/A "FTW","First Trust Taiwan AlphaDEX Fun",N/A "TUSA","First Trust Total US Market Alp",N/A "FKU","First Trust United Kingdom Alph",N/A "FUNC","First United Corporation",59.85M "SVVC","Firsthand Technology Value Fund",N/A "FMER","FirstMerit Corporation",3.27B "FSV","FirstService Corporation",1.36B "FISV","Fiserv, Inc.",21.83B "FIVE","Five Below, Inc.",2.06B "FPRX","Five Prime Therapeutics, Inc.",927.40M "FIVN","Five9, Inc.",383.83M "FLML","Flamel Technologies S.A.",390.81M "FLKS","Flex Pharma, Inc.",112.27M "FLXN","Flexion Therapeutics, Inc.",234.78M "SKOR","FlexShares Credit-Scored US Cor",N/A "LKOR","FlexShares Credit-Scored US Lon",N/A "MBSD","FlexShares Disciplined Duration",N/A "ASET","FlexShares Real Assets Allocati",N/A "QLC","FlexShares US Quality Large Cap",N/A "FLXS","Flexsteel Industries, Inc.",316.04M "FLEX","Flextronics International Ltd.",5.87B "FLIR","FLIR Systems, Inc.",4.30B "FLDM","Fluidigm Corporation",184.40M "FFIC","Flushing Financial Corporation",599.18M "FOMX","Foamix Pharmaceuticals Ltd.",173.86M "FOGO","Fogo de Chao, Inc.",441.91M "FONR","Fonar Corporation",100.58M "FES","Forbes Energy Services Ltd",6.44M "FORM","FormFactor, Inc.",406.31M "FORTY","Formula Systems (1985) Ltd.",401.51M "FORR","Forrester Research, Inc.",562.95M "FTNT","Fortinet, Inc.",4.57B "FBIO","Fortress Biotech, Inc.",138.57M "FWRD","Forward Air Corporation",1.27B "FORD","Forward Industries, Inc.",12.43M "FWP","Forward Pharma A/S",689.02M "FOSL","Fossil Group, Inc.",2.21B "FMI","Foundation Medicine, Inc.",534.34M "FXCB","Fox Chase Bancorp, Inc.",211.38M "FOXF","Fox Factory Holding Corp.",540.94M "FRAN","Francesca's Holdings Corporatio",769.40M "FELE","Franklin Electric Co., Inc.",1.33B "FRED","Fred's, Inc.",517.98M "FREE","FreeSeas Inc.",974.70 "RAIL","Freightcar America, Inc.",221.29M "FEIM","Frequency Electronics, Inc.",79.78M "FRPT","Freshpet, Inc.",223.55M "FTR","Frontier Communications Corpora",5.27B "FTRPR","Frontier Communications Corpora",102.81B "FRPH","FRP Holdings, Inc.",321.77M "FSBW","FS Bancorp, Inc.",74.80M "FTD","FTD Companies, Inc.",690.85M "FSYS","Fuel Systems Solutions, Inc.",77.80M "FTEK","Fuel Tech, Inc.",35.91M "FCEL","FuelCell Energy, Inc.",136.69M "FORK","Fuling Global Inc.",36.51M "FULL","Full Circle Capital Corporation",N/A "FULLL","Full Circle Capital Corporation",N/A "FLL","Full House Resorts, Inc.",26.37M "FULT","Fulton Financial Corporation",2.24B "FSNN","Fusion Telecommunications Inter",17.09M "FFHL","Fuwei Films (Holdings) Co., Ltd",9.14M "GK","G&K Services, Inc.",1.32B "WILC","G. Willi-Food International, L",49.15M "GAIA","Gaiam, Inc.",136.85M "GLPG","Galapagos NV",1.69B "GALT","Galectin Therapeutics Inc.",28.24M "GALTU","Galectin Therapeutics Inc.",N/A "GALTW","Galectin Therapeutics Inc.",N/A "GALE","Galena Biopharma, Inc.",137.75M "GLMD","Galmed Pharmaceuticals Ltd.",54.95M "GLPI","Gaming and Leisure Properties, ",2.94B "GPIC","Gaming Partners International C",79.12M "GRMN","Garmin Ltd.",7.77B "GGAC","Garnero Group Acquisition Compa",48.06M "GGACR","Garnero Group Acquisition Compa",N/A "GGACU","Garnero Group Acquisition Compa",49.22M "GGACW","Garnero Group Acquisition Compa",N/A "GARS","Garrison Capital Inc.",N/A "GCTS",N/A,N/A "GLSS",N/A,N/A "GENC","Gencor Industries Inc.",121.04M "GNCMA","General Communication, Inc.",708.28M "GFN","General Finance Corporation",106.90M "GFNCP","General Finance Corporation",1.64B "GFNSL","General Finance Corporation",534.52M "GENE","Genetic Technologies Ltd",26.76M "GNMK","GenMark Diagnostics, Inc.",217.30M "GNCA","Genocea Biosciences, Inc.",114.86M "GHDX","Genomic Health, Inc.",864.44M "GNST",N/A,N/A "GNTX","Gentex Corporation",4.28B "THRM","Gentherm Inc",1.53B "GNVC","GenVec, Inc.",8.98M "GTWN","Georgetown Bancorp, Inc.",34.47M "GEOS","Geospace Technologies Corporati",131.81M "GABC","German American Bancorp, Inc.",410.95M "GERN","Geron Corporation",436.69M "GEVO","Gevo, Inc.",6.39M "ROCK","Gibraltar Industries, Inc.",755.24M "GIGM","GigaMedia Limited",30.61M "GIGA","Giga-tronics Incorporated",8.39M "GIII","G-III Apparel Group, LTD.",2.24B "GILT","Gilat Satellite Networks Ltd.",169.27M "GILD","Gilead Sciences, Inc.",126.70B "GBCI","Glacier Bancorp, Inc.",1.80B "GLAD","Gladstone Capital Corporation",N/A "GLADO","Gladstone Capital Corporation",483.90M "GOOD","Gladstone Commercial Corporatio",331.03M "GOODN","Gladstone Commercial Corporatio",575.48M "GOODO","Gladstone Commercial Corporatio",575.03M "GOODP","Gladstone Commercial Corporatio",578.18M "GAIN","Gladstone Investment Corporatio",N/A "GAINN","Gladstone Investment Corporatio",687.46M "GAINO","Gladstone Investment Corporatio",722.24M "GAINP","Gladstone Investment Corporatio",N/A "LAND","Gladstone Land Corporation",76.03M "GLBZ","Glen Burnie Bancorp",30.63M "GBT","Global Blood Therapeutics, Inc.",496.45M "ENT","Global Eagle Entertainment Inc.",720.81M "GBLI","Global Indemnity plc",727.73M "GBLIZ","Global Indemnity plc",N/A "GPAC","Global Partner Acquisition Corp",46.44M "GPACU","Global Partner Acquisition Corp",46.39M "GPACW","Global Partner Acquisition Corp",N/A "SELF","SELF STORAGE GROUP",30.33M "GSOL","Global Sources Ltd.",202.46M "ACTX","Global X Guru Activist ETF",N/A "QQQC","Global X NASDAQ China Technolog",N/A "SOCL","Global X Social Media Index ETF",N/A "ALTY","Global X SuperDividend Alternat",N/A "SRET","Global X SuperDividend REIT ETF",N/A "YLCO","Global X Yieldco Index ETF",N/A "GAI","Global-Tech Advanced Innovation",25.87M "GBIM","GlobeImmune, Inc.",8.34M "GLBS","Globus Maritime Limited",1.33M "GLRI","Glori Energy Inc",5.42M "GLUU","Glu Mobile Inc.",497.45M "GLYC","GlycoMimetics, Inc.",94.62M "GOGO","Gogo Inc.",948.61M "GLNG","Golar LNG Limited",1.53B "GMLP","Golar LNG Partners LP",866.95M "GLDC","Golden Enterprises, Inc.",52.16M "GDEN","Golden Entertainment, Inc.",217.39M "GOGL","Golden Ocean Group Limited",111.86M "GBDC","Golub Capital BDC, Inc.",N/A "GTIM","Good Times Restaurants Inc.",50.54M "GPRO","GoPro, Inc.",1.76B "GMAN","Gordmans Stores, Inc.",47.21M "GRSH","Gores Holdings, Inc.",107.90M "GRSHU","Gores Holdings, Inc.",110.85M "GRSHW","Gores Holdings, Inc.",N/A "GPIA","GP Investments Acquisition Corp",51.36M "GPIAU","GP Investments Acquisition Corp",183.94M "GPIAW","GP Investments Acquisition Corp",N/A "LOPE","Grand Canyon Education, Inc.",1.78B "GRVY","GRAVITY Co., Ltd.",10.63M "GBSN","Great Basin Scientific, Inc.",682737.31 "GLDD","Great Lakes Dredge & Dock Corpo",212.94M "GSBC","Great Southern Bancorp, Inc.",524.65M "GNBC","Green Bancorp, Inc.",261.56M "GRBK","Green Brick Partners, Inc.",272.38M "GPP","Green Plains Partners LP",434.25M "GPRE","Green Plains, Inc.",575.19M "GCBC","Greene County Bancorp, Inc.",179.65M "GLRE","Greenlight Reinsurance, Ltd.",745.72M "GRIF","Griffin Industrial Realty, Inc.",118.52M "GRFS","Grifols, S.A.",20.83B "GRPN","Groupon, Inc.",2.53B "OMAB","Grupo Aeroportuario del Centro ",1.89B "GGAL","Grupo Financiero Galicia S.A.",3.76B "GSIG","GSI Group, Inc.",425.71M "GSIT","GSI Technology, Inc.",76.19M "GSVC","GSV Capital Corp",N/A "GTXI","GTx, Inc.",88.94M "GBNK","Guaranty Bancorp",319.91M "GFED","Guaranty Federal Bancshares, In",66.63M "GUID","Guidance Software, Inc.",141.82M "GIFI","Gulf Island Fabrication, Inc.",139.14M "GURE","Gulf Resources, Inc.",75.91M "GPOR","Gulfport Energy Corporation",3.09B "GWPH","GW Pharmaceuticals Plc",976.31M "GWGH","GWG Holdings, Inc",34.16M "GYRO","Gyrodyne , LLC",N/A "HEES","H&E Equipment Services, Inc.",444.47M "HLG","Hailiang Education Group Inc.",2.55B "HNRG","Hallador Energy Company",134.83M "HALL","Hallmark Financial Services, In",191.44M "HALO","Halozyme Therapeutics, Inc.",1.04B "HBK","Hamilton Bancorp, Inc.",44.10M "HMPR","Hampton Roads Bankshares Inc",294.04M "HBHC","Hancock Holding Company",1.86B "HBHCL","Hancock Holding Company",N/A "HNH","Handy & Harman Ltd.",214.37M "HAFC","Hanmi Financial Corporation",654.74M "HNSN","Hansen Medical, Inc.",52.09M "HQCL","Hanwha Q CELLS Co., Ltd. ",1.40B "HDNG","Hardinge, Inc.",114.26M "HLIT","Harmonic Inc.",242.48M "HRMN","Harmony Merger Corp.",44.31M "HRMNU","Harmony Merger Corp.",N/A "HRMNW","Harmony Merger Corp.",N/A "TINY","Harris & Harris Group, Inc.",55.01M "HART","Harvard Apparatus Regenerative ",18.73M "HBIO","Harvard Bioscience, Inc.",90.29M "HCAP","Harvest Capital Credit Corporat",N/A "HCAPL","Harvest Capital Credit Corporat",N/A "HAS","Hasbro, Inc.",9.32B "HA","Hawaiian Holdings, Inc.",2.20B "HCOM","Hawaiian Telcom Holdco, Inc.",254.09M "HWKN","Hawkins, Inc.",338.61M "HWBK","Hawthorn Bancshares, Inc.",82.19M "HAYN","Haynes International, Inc.",382.42M "HDS","HD Supply Holdings, Inc.",5.41B "HIIQ","Health Insurance Innovations, I",45.16M "HCSG","Healthcare Services Group, Inc.",2.54B "HQY","HealthEquity, Inc.",1.13B "HSTM","HealthStream, Inc.",678.47M "HWAY","Healthways, Inc.",400.75M "HTLD","Heartland Express, Inc.",1.59B "HTLF","Heartland Financial USA, Inc.",656.00M "HTWR","Heartware International, Inc.",593.04M "HTBX","Heat Biologics, Inc.",14.78M "HSII","Heidrick & Struggles Internatio",419.68M "HELE","Helen of Troy Limited",2.59B "HMNY","Helios and Matheson Analytics I",3.50M "HMTV","Hemisphere Media Group, Inc.",609.50M "HNNA","Hennessy Advisors, Inc.",130.42M "HCAC","Hennessy Capital Acquisition Co",57.88M "HCACU","Hennessy Capital Acquisition Co",N/A "HCACW","Hennessy Capital Acquisition Co",N/A "HSIC","Henry Schein, Inc.",13.68B "HERO","Hercules Offshore, Inc.",214.98M "HTBK","Heritage Commerce Corp",292.55M "HFWA","Heritage Financial Corporation",521.08M "HEOP","Heritage Oaks Bancorp",246.82M "HCCI","Heritage-Crystal Clean, Inc.",172.06M "MLHR","Herman Miller, Inc.",1.53B "HRTX","Heron Therapeutics, Inc. ",N/A "HSKA","Heska Corporation",209.33M "HFFC","HF Financial Corp.",117.43M "HIBB","Hibbett Sports, Inc.",772.45M "HPJ","Highpower International Inc",34.58M "HIHO","Highway Holdings Limited",12.83M "HIMX","Himax Technologies, Inc.",1.50B "HIFS","Hingham Institution for Savings",250.59M "HSGX","Histogenics Corporation",33.30M "HMNF","HMN Financial, Inc.",46.70M "HMSY","HMS Holdings Corp",1.02B "HOLI","Hollysys Automation Technologie",1.06B "HOLX","Hologic, Inc.",9.79B "HBCP","Home Bancorp, Inc.",179.45M "HOMB","Home BancShares, Inc.",2.82B "HFBL","Home Federal Bancorp, Inc. of L",41.14M "HMIN","Homeinns Hotel Group",1.68B "HMST","HomeStreet, Inc.",427.17M "HTBI","HomeTrust Bancshares, Inc.",321.52M "CETC","CLEAN ENVIRO TECH",7.43M "HOFT","Hooker Furniture Corporation",333.72M "HFBC","HopFed Bancorp, Inc.",73.78M "HBNC","Horizon Bancorp (IN)",288.80M "HZNP","Horizon Pharma plc",2.99B "HRZN","Horizon Technology Finance Corp",N/A "HDP","Hortonworks, Inc.",423.60M "HMHC","Houghton Mifflin Harcourt Compa",2.36B "HWCC","Houston Wire & Cable Company",91.87M "HOVNP","Hovnanian Enterprises Inc",470.30M "HBMD","Howard Bancorp, Inc.",88.07M "HSNI","HSN, Inc.",2.43B "HTGM","HTG Molecular Diagnostics, Inc.",19.70M "HUBG","Hub Group, Inc.",1.32B "HSON","Hudson Global, Inc.",90.29M "HDSN","Hudson Technologies, Inc.",105.25M "HBAN","Huntington Bancshares Incorpora",7.03B "HBANP","Huntington Bancshares Incorpora",10.79B "HURC","Hurco Companies, Inc.",179.59M "HURN","Huron Consulting Group Inc.",1.12B "HTCH","Hutchinson Technology Incorpora",126.80M "HBP","Huttig Building Products, Inc.",79.61M "HDRA","Hydra Industries Acquisition Co",28.63M "HDRAR","Hydra Industries Acquisition Co",N/A "HDRAU","Hydra Industries Acquisition Co",N/A "HDRAW","Hydra Industries Acquisition Co",N/A "HYGS","Hydrogenics Corporation",80.53M "IDSY","I.D. Systems, Inc.",51.76M "IAC","IAC/InterActiveCorp",3.76B "IKGH","Iao Kun Group Holding Company L",78.37M "IBKC","IBERIABANK Corporation",2.00B "IBKCP","IBERIABANK Corporation",1.02B "ICAD","icad inc.",72.36M "IEP","Icahn Enterprises L.P.",6.44B "ICFI","ICF International, Inc.",634.55M "ICLR","ICON plc",3.98B "ICON","Iconix Brand Group, Inc.",382.79M "ICUI","ICU Medical, Inc.",1.45B "IPWR","Ideal Power Inc.",43.95M "INVE","Identiv, Inc.",21.25M "IDRA","Idera Pharmaceuticals, Inc.",214.21M "IDXX","IDEXX Laboratories, Inc.",6.40B "DSKY","iDreamSky Technology Limited",9.16B "IROQ","IF Bancorp, Inc.",64.08M "IRG","Ignite Restaurant Group, Inc.",88.10M "RXDX","Ignyta, Inc.",215.03M "IIVI","II-VI Incorporated",1.29B "KANG","iKang Healthcare Group, Inc.",1.35B "IKNX","Ikonics Corporation",23.83M "ILMN","Illumina, Inc.",22.32B "ISNS","Image Sensing Systems, Inc.",14.45M "IMMR","Immersion Corporation",221.18M "ICCC","ImmuCell Corporation",18.56M "IMDZ","Immune Design Corp.",203.65M "IMNP","Immune Pharmaceuticals Inc.",14.23M "IMGN","ImmunoGen, Inc.",656.43M "IMMU","Immunomedics, Inc.",206.54M "IPXL","Impax Laboratories, Inc.",2.33B "IMMY","Imprimis Pharmaceuticals, Inc.",44.15M "INCR","INC Research Holdings, Inc.",2.24B "SAAS","inContact, Inc.",547.52M "INCY","Incyte Corporation",13.83B "INDB","Independent Bank Corp.",1.14B "IBCP","Independent Bank Corporation",325.53M "IBTX","Independent Bank Group, Inc",515.36M "IDSA","Industrial Services of America,",13.47M "INFN","Infinera Corporation",2.19B "INFI","Infinity Pharmaceuticals, Inc.",299.32M "IPCC","Infinity Property and Casualty ",903.44M "III","Information Services Group, Inc",115.26M "IFON","InfoSonics Corp",25.61M "IMKTA","Ingles Markets, Incorporated",697.11M "INWK","InnerWorkings, Inc.",375.89M "INNL","Innocoll AG",200.55M "INOD","Innodata Inc.",63.20M "IPHS","Innophos Holdings, Inc.",562.50M "IOSP","Innospec Inc.",1.05B "ISSC","Innovative Solutions and Suppor",42.75M "INVA","INOVA TECHNOLOGY INC",1.30B "INGN","Inogen, Inc",656.57M "ITEK","Inotek Pharmaceuticals Corporat",198.87M "INOV","Inovalon Holdings, Inc.",2.92B "INO","Inovio Pharmaceuticals, Inc.",482.18M "NSIT","Insight Enterprises, Inc.",945.26M "ISIG","Insignia Systems, Inc.",33.04M "INSM","Insmed, Inc.",824.45M "IIIN","Insteel Industries, Inc.",472.81M "PODD","Insulet Corporation",1.58B "INSY","Insys Therapeutics, Inc.",1.20B "NTEC","Intec Pharma Ltd.",30.54M "IART","Integra LifeSciences Holdings C",2.16B "IDTI","Integrated Device Technology, I",2.54B "IESC","Integrated Electrical Services,",263.31M "INTC","Intel Corporation",138.65B "IQNT","Inteliquent, Inc.",569.06M "IPCI","Intellipharmaceutics Internatio",57.78M "IPAR","Inter Parfums, Inc.",793.42M "IBKR","Interactive Brokers Group, Inc.",2.16B "ININ","Interactive Intelligence Group,",620.67M "ICPT","Intercept Pharmaceuticals, Inc.",2.96B "ICLD","InterCloud Systems, Inc",15.41M "ICLDW","InterCloud Systems, Inc",N/A "IDCC","InterDigital, Inc.",1.73B "TILE","Interface, Inc.",1.14B "IMI","Intermolecular, Inc.",105.39M "INAP","Internap Corporation",108.01M "IBOC","International Bancshares Corpor",1.52B "ISCA","International Speedway Corporat",1.62B "IGLD","Internet Gold Golden Lines Ltd.",263.27M "IIJI","Internet Initiative Japan, Inc.",896.07M "IDXG","Interpace Diagnostics Group, In",4.68M "XENT","Intersect ENT, Inc.",504.22M "INTX","Intersections, Inc.",47.48M "ISIL","Intersil Corporation",1.64B "IILG","Interval Leisure Group, Inc.",693.33M "IVAC","Intevac, Inc.",90.65M "INTL","INTL FCStone Inc.",503.09M "INTLL","INTL FCStone Inc.",484.32M "ITCI","Intra-Cellular Therapies Inc.",1.40B "IIN","IntriCon Corporation",43.24M "INTU","Intuit Inc.",25.94B "ISRG","Intuitive Surgical, Inc.",20.62B "INVT","Inventergy Global, Inc.",8.27M "SNAK","Inventure Foods, Inc.",111.77M "ISTR","Investar Holding Corporation",104.54M "ISBC","Investors Bancorp, Inc.",3.74B "ITIC","Investors Title Company",170.95M "NVIV","INVIVO THERAPEUTICS",129.13M "IVTY","Invuity, Inc.",103.25M "IONS","Ionis Pharmaceuticals, Inc.",4.42B "IPAS","iPass Inc.",58.78M "IPGP","IPG Photonics Corporation",4.33B "IRMD","iRadimed Corporation",179.06M "IRIX","IRIDEX Corporation",94.18M "IRDM","Iridium Communications Inc",721.61M "IRDMB","Iridium Communications Inc",27.82B "IRBT","iRobot Corporation",909.14M "IRWD","Ironwood Pharmaceuticals, Inc.",1.33B "IRCP","IRSA Propiedades Comerciales S.",1.01B "SLQD","iShares 0-5 Year Investment Gra",N/A "TLT","iShares 20+ Year Treasury Bond ",N/A "AIA","iShares Asia 50 ETF",N/A "COMT","iShares Commodities Select Stra",N/A "IXUS","iShares Core MSCI Total Interna",N/A "IFEU","iShares FTSE EPRA/NAREIT Europe",N/A "IFGL","iShares FTSE EPRA/NAREIT Global",N/A "IGF","iShares Global Infrastructure E",N/A "GNMA","iShares GNMA Bond ETF",N/A "JKI","iShares Morningstar Mid-Cap ETF",N/A "ACWX","iShares MSCI ACWI ex US Index F",N/A "ACWI","iShares MSCI ACWI Index Fund",N/A "AAXJ","iShares MSCI All Country Asia e",N/A "EWZS","iShares MSCI Brazil Small-Cap E",N/A "MCHI","iShares MSCI China ETF",N/A "SCZ","iShares MSCI EAFE Small-Cap ETF",N/A "EEMA","iShares MSCI Emerging Markets A",N/A "EEML","iShares MSCI Emerging Markets L",N/A "EUFN","iShares MSCI Europe Financials ",N/A "IEUS","iShares MSCI Europe Small-Cap E",N/A "ENZL","iShares MSCI New Zealand Capped",N/A "QAT","iShares MSCI Qatar Capped ETF",N/A "UAE","iShares MSCI UAE Capped ETF",N/A "IBB","iShares Nasdaq Biotechnology In",N/A "SOXX","iShares PHLX SOX Semiconductor ",N/A "EMIF","iShares S&P Emerging Markets In",N/A "ICLN","iShares S&P Global Clean Energy",N/A "WOOD","iShares S&P Global Timber & For",N/A "INDY","iShares S&P India Nifty 50 Inde",N/A "ISHG","iShares S&P/Citigroup 1-3 Year ",N/A "IGOV","iShares S&P/Citigroup Internati",N/A "ISLE","Isle of Capri Casinos, Inc.",514.80M "ISRL","Isramco, Inc.",239.39M "ITI","Iteris, Inc.",79.77M "ITRI","Itron, Inc.",1.52B "ITRN","Ituran Location and Control Ltd",380.15M "ITUS","ITUS CORPORATION",25.49M "XXIA","Ixia",752.80M "IXYS","IXYS Corporation",354.85M "JJSF","J & J Snack Foods Corp.",2.05B "MAYS","J. W. Mays, Inc.",99.90M "JBHT","J.B. Hunt Transport Services, I",8.81B "JCOM","j2 Global, Inc.",3.53B "JASO","JA Solar Holdings, Co., Ltd.",432.31M "JKHY","Jack Henry & Associates, Inc.",6.65B "JACK","Jack In The Box Inc.",2.22B "JXSB","Jacksonville Bancorp Inc.",42.04M "JAXB","Jacksonville Bancorp, Inc.",89.84M "JAGX","Jaguar Animal Health, Inc.",16.00M "JAKK","JAKKS Pacific, Inc.",136.23M "JMBA","Jamba, Inc.",199.46M "JRVR","James River Group Holdings, Ltd",879.52M "ERW","Janus Equal Risk Weighted Large",N/A "JASN","Jason Industries, Inc.",63.46M "JASNW","Jason Industries, Inc.",N/A "JAZZ","Jazz Pharmaceuticals plc",7.71B "JD","JD.com, Inc.",36.01B "JBLU","JetBlue Airways Corporation",7.19B "JTPY","JetPay Corporation",35.84M "JCTCF","Jewett-Cameron Trading Company",26.15M "DATE","Jiayuan.com International Ltd.",214.19M "JST","Jinpan International Limited",87.43M "JIVE","Jive Software, Inc.",232.08M "WYIG","JM Global Holding Company",24.26M "WYIGU","JM Global Holding Company",N/A "WYIGW","JM Global Holding Company",N/A "JBSS","John B. Sanfilippo & Son, Inc.",751.25M "JOUT","Johnson Outdoors Inc.",221.31M "JNP","Juniper Pharmaceuticals, Inc.",81.22M "JUNO","Juno Therapeutics, Inc.",3.39B "KTWO","K2M Group Holdings, Inc.",527.72M "KALU","Kaiser Aluminum Corporation",1.31B "KMDA","Kamada Ltd.",131.47M "KNDI","Kandi Technologies Group, Inc.",352.23M "KPTI","Karyopharm Therapeutics Inc.",222.97M "KBSF","KBS Fashion Group Limited",11.69M "KCAP","KCAP Financial, Inc.",101.06M "KRNY","Kearny Financial",1.08B "KELYA","Kelly Services, Inc.",638.61M "KELYB","Kelly Services, Inc.",611.64M "KMPH","KemPharm, Inc.",234.35M "KFFB","Kentucky First Federal Bancorp",75.33M "KERX","Keryx Biopharmaceuticals, Inc.",376.59M "GMCR","Keurig Green Mountain, Inc.",13.70B "KEQU","Kewaunee Scientific Corporation",43.46M "KTEC","Key Technology, Inc.",40.81M "KTCC","Key Tronic Corporation",89.43M "KFRC","Kforce, Inc.",443.54M "KE","Kimball Electronics, Inc.",316.89M "KBAL","Kimball International, Inc.",393.86M "KIN","Kindred Biosciences, Inc.",80.42M "KGJI","Kingold Jewelry Inc.",61.93M "KINS","Kingstone Companies, Inc",57.43M "KONE","Kingtone Wirelessinfo Solution ",3.45M "KIRK","Kirkland's, Inc.",236.46M "KITE","Kite Pharma, Inc.",2.06B "KTOV","Kitov Pharamceuticals Holdings ",1.69M "KTOVW","Kitov Pharamceuticals Holdings ",N/A "KLAC","KLA-Tencor Corporation",10.30B "KLOX",N/A,N/A "KLXI","KLX Inc.",1.47B "KONA","Kona Grill, Inc.",144.02M "KZ","KongZhong Corporation",341.95M "KOPN","Kopin Corporation",113.66M "KRNT","Kornit Digital Ltd.",345.60M "KOSS","Koss Corporation",15.50M "KWEB","KraneShares CSI China Internet ",N/A "KTOS","Kratos Defense & Security Solut",192.04M "KUTV","Ku6 Media Co., Ltd.",39.08M "KLIC","Kulicke and Soffa Industries, I",815.51M "KURA","Kura Oncology, Inc.",51.50M "KVHI","KVH Industries, Inc.",144.39M "FSTR","L.B. Foster Company",130.03M "LJPC","La Jolla Pharmaceutical Company",310.28M "LSBK","Lake Shore Bancorp, Inc.",78.33M "LSBG","Lake Sunapee Bank Group",119.61M "LBAI","Lakeland Bancorp, Inc.",378.68M "LKFN","Lakeland Financial Corporation",701.38M "LAKE","Lakeland Industries, Inc.",86.08M "LRCX","Lam Research Corporation",11.18B "LAMR","Lamar Advertising Company",5.51B "LANC","Lancaster Colony Corporation",2.75B "LNDC","Landec Corporation",307.91M "LARK","Landmark Bancorp Inc.",87.22M "LMRK","Landmark Infrastructure Partner",220.88M "LE","Lands' End, Inc.",727.16M "LSTR","Landstar System, Inc.",2.54B "LNTH","Lantheus Holdings, Inc.",64.20M "LTRX","Lantronix, Inc.",13.02M "LPSB","LaPorte Bancorp, Inc.",74.42M "LSCC","Lattice Semiconductor Corporati",668.42M "LAWS","Lawson Products, Inc.",144.90M "LAYN","Layne Christensen Company",117.00M "LCNB","LCNB Corporation",160.29M "LDRH","LDR Holding Corporation",629.58M "LBIX","Leading Brands Inc",6.46M "LGCY","Legacy Reserves LP",56.68M "LGCYO","Legacy Reserves LP",134.83M "LGCYP","Legacy Reserves LP",135.53M "LTXB","LegacyTexas Financial Group, In",815.88M "DDBI","Legg Mason Developed EX-US Dive",N/A "EDBI","Legg Mason Emerging Markets Div",N/A "LVHD","Legg Mason Low Volatility High ",N/A "UDBI","Legg Mason US Diversified Core ",N/A "LMAT","LeMaitre Vascular, Inc.",226.45M "TREE","LendingTree, Inc.",784.26M "LXRX","Lexicon Pharmaceuticals, Inc.",960.58M "LGIH","LGI Homes, Inc.",448.98M "LHCG","LHC Group",644.22M "LBRDA","Liberty Broadband Corporation",5.02B "LBRDK","Liberty Broadband Corporation",5.00B "LBTYA","Liberty Global plc",32.92B "LBTYB","Liberty Global plc",26.96B "LBTYK","Liberty Global plc",32.04B "LILA","Liberty Global plc",1.47B "LILAK","Liberty Global plc",1.58B "LVNTA","Liberty Interactive Corporation",5.11B "LVNTB","Liberty Interactive Corporation",5.07B "QVCA","Liberty Interactive Corporation",12.43B "QVCB","Liberty Interactive Corporation",12.26B "LMCA","Liberty Media Corporation",11.51B "LMCB","Liberty Media Corporation",11.29B "LMCK","Liberty Media Corporation",11.30B "TAX","Liberty Tax, Inc.",254.47M "LTRPA","Liberty TripAdvisor Holdings, I",1.53B "LTRPB","Liberty TripAdvisor Holdings, I",1.49B "LPNT","LifePoint Health, Inc.",2.77B "LCUT","Lifetime Brands, Inc.",164.23M "LFVN","Lifevantage Corporation",125.51M "LWAY","Lifeway Foods, Inc.",170.87M "LGND","Ligand Pharmaceuticals Incorpor",1.85B "LTBR","Lightbridge Corporation",12.85M "LPTH","LightPath Technologies, Inc.",34.79M "LLEX","Lilis Energy, Inc.",3.86M "LIME","Lime Energy Co.",25.25M "LLNW","Limelight Networks, Inc.",128.97M "LMNR","Limoneira Co",182.25M "LINC","Lincoln Educational Services Co",59.91M "LECO","Lincoln Electric Holdings, Inc.",4.22B "LIND","Lindblad Expeditions Holdings I",439.13M "LINDW","Lindblad Expeditions Holdings I",N/A "LLTC","Linear Technology Corporation",10.42B "LNCO","Linn Co, LLC",30.85M "LINE","Linn Energy, LLC",180.75M "LBIO","LION BIOTECHNOLOGIES",250.17M "LIOX","Lionbridge Technologies, Inc.",264.14M "LPCN","Lipocine Inc.",195.85M "LQDT","Liquidity Services, Inc.",137.42M "LFUS","Littelfuse, Inc.",2.53B "LIVN","LivaNova PLC",2.91B "LOB","Live Oak Bancshares, Inc.",468.47M "LIVE","Live Ventures Incorporated",24.18M "LPSN","LivePerson, Inc.",262.71M "LKQ","LKQ Corporation",7.94B "LMFA","LM Funding America, Inc.",N/A "LMFAW","LM Funding America, Inc.",N/A "LMIA","LMI Aerospace, Inc.",116.95M "LOGI","Logitech International S.A.",2.43B "LOGM","LogMein, Inc.",1.27B "LOJN","LoJack Corporation",119.23M "EVAR","Lombard Medical, Inc.",17.03M "CNCR","Loncar Cancer Immunotherapy ETF",N/A "LORL","Loral Space and Communications,",985.49M "LOXO","Loxo Oncology, Inc.",326.23M "LPTN","Lpath, Inc.",6.16M "LPLA","LPL Financial Holdings Inc.",1.87B "LRAD","LRAD Corporation",52.31M "LYTS","LSI Industries Inc.",278.74M "LULU","lululemon athletica inc.",8.38B "LITE","Lumentum Holdings Inc.",1.43B "LMNX","Luminex Corporation",774.70M "LMOS","Lumos Networks Corp.",270.80M "LUNA","Luna Innovations Incorporated",27.82M "MBTF","M B T Financial Corp",186.65M "MTSI","M/A-COM Technology Solutions Ho",2.06B "MCBC","Macatawa Bank Corporation",200.16M "MFNC","Mackinac Financial Corporation",61.61M "MCUR","Macrocure Ltd.",16.88M "MGNX","MacroGenics, Inc.",600.80M "MAGS","Magal Security Systems Ltd.",67.80M "MGLN","Magellan Health, Inc.",1.43B "MPET","Magellan Petroleum Corporation",6.45M "MGIC","Magic Software Enterprises Ltd.",296.13M "CALL","magicJack VocalTec Ltd",120.18M "MNGA","MagneGas Corporation",46.57M "MGYR","Magyar Bancorp, Inc.",58.89M "MHLD","Maiden Holdings, Ltd.",999.97M "MHLDO","Maiden Holdings, Ltd.",N/A "MSFG","MainSource Financial Group, Inc",443.88M "COOL","Majesco Entertainment Company",7.67M "MMYT","MakeMyTrip Limited",705.24M "MBUU","Malibu Boats, Inc.",264.04M "MLVF","Malvern Bancorp, Inc.",106.08M "MAMS","MAM Software Group, Inc.",71.57M "MANH","Manhattan Associates, Inc.",4.06B "LOAN","Manhattan Bridge Capital, Inc",28.56M "MNTX","Manitex International, Inc.",81.19M "MTEX","Mannatech, Incorporated",46.01M "MNKD","MannKind Corporation",409.26M "MANT","ManTech International Corporati",1.09B "MAPI",N/A,N/A "MARA","Marathon Patent Group, Inc.",35.83M "MCHX","Marchex, Inc.",172.76M "MARPS","Marine Petroleum Trust",7.93M "MRNS","Marinus Pharmaceuticals, Inc.",65.08M "BBH","Market Vectors Biotech ETF",N/A "GNRX","Market Vectors Generic Drugs ET",N/A "PPH","Market Vectors Pharmaceutical E",N/A "MKTX","MarketAxess Holdings, Inc.",4.08B "MKTO","Marketo, Inc.",649.43M "MRKT","Markit Ltd.",4.98B "MRLN","Marlin Business Services Corp.",168.92M "MAR","Marriott International",16.93B "MBII","Marrone Bio Innovations, Inc.",27.16M "MRTN","Marten Transport, Ltd.",553.63M "MMLP","Martin Midstream Partners L.P.",563.04M "MRVL","Marvell Technology Group Ltd.",4.69B "MASI","Masimo Corporation",1.78B "MTCH","Match Group, Inc.",2.60B "MTLS","Materialise NV",263.53M "MTRX","Matrix Service Company",470.61M "MAT","Mattel, Inc.",11.01B "MATR","Mattersight Corporation",106.76M "MATW","Matthews International Corporat",1.60B "MFRM","Mattress Firm Holding Corp.",1.44B "MTSN","Mattson Technology, Inc.",265.76M "MXIM","Maxim Integrated Products, Inc.",9.58B "MXWL","Maxwell Technologies, Inc.",170.62M "MZOR","Mazor Robotics Ltd.",211.32M "MBFI","MB Financial Inc.",2.29B "MBFIP","MB Financial Inc.",1.94B "MCFT","MCBC Holdings, Inc.",237.79M "MGRC","McGrath RentCorp",622.44M "MDCA","MDC Partners Inc.",1.04B "MCOX","Mecox Lane Limited",49.42M "TAXI","Medallion Financial Corp.",173.02M "MTBC","Medical Transcription Billing, ",7.92M "MTBCP","Medical Transcription Billing, ",46.20B "MNOV","MediciNova, Inc.",153.97M "MDSO","Medidata Solutions, Inc.",1.88B "MDGS","Medigus Ltd.",12.24M "MDVN","Medivation, Inc.",5.30B "MDWD","MediWound Ltd.",155.57M "MDVX","Medovex Corp.",15.68M "MDVXW","Medovex Corp.",N/A "MEET","MeetMe, Inc.",151.98M "MEIP","MEI Pharma, Inc.",38.60M "MPEL","Melco Crown Entertainment Limit",8.80B "MLNX","Mellanox Technologies, Ltd.",2.19B "MELR","Melrose Bancorp, Inc.",42.30M "MEMP","Memorial Production Partners LP",173.33M "MRD","Memorial Resource Development C",2.09B "MENT","Mentor Graphics Corporation",2.21B "MTSL","MER Telemanagement Solutions Lt",6.11M "MELI","MercadoLibre, Inc.",4.41B "MBWM","Mercantile Bank Corporation",372.47M "MERC","Mercer International Inc.",530.21M "MBVT","Merchants Bancshares, Inc.",200.28M "MRCY","Mercury Systems Inc",583.39M "EBSB","Meridian Bancorp, Inc.",714.75M "VIVO","Meridian Bioscience Inc.",841.02M "MMSI","Merit Medical Systems, Inc.",746.55M "MACK","Merrimack Pharmaceuticals, Inc.",640.28M "MSLI","Merus Labs International Inc.",144.66M "MLAB","Mesa Laboratories, Inc.",356.69M "MESO","Mesoblast Limited",430.88M "CASH","Meta Financial Group, Inc.",340.82M "MBLX","Metabolix, Inc.",37.44M "MEOH","Methanex Corporation",2.76B "MFRI","MFRI, Inc.",53.45M "MGCD","MGC Diagnostics Corporation",29.75M "MGEE","MGE Energy Inc.",1.77B "MGPI","MGP Ingredients, Inc.",372.32M "MCHP","Microchip Technology Incorporat",8.76B "MU","Micron Technology, Inc.",11.46B "MICT","Micronet Enertec Technologies, ",13.55M "MICTW","Micronet Enertec Technologies, ",N/A "MSCC","Microsemi Corporation",3.74B "MSFT","Microsoft Corporation",416.42B "MSTR","MicroStrategy Incorporated",1.76B "MVIS","Microvision, Inc.",141.22M "MPB","Mid Penn Bancorp",64.40M "MTP","Midatech Pharma PLC",64.24M "MCEP","Mid-Con Energy Partners, LP",25.27M "MBRG","Middleburg Financial Corporatio",147.37M "MBCN","Middlefield Banc Corp.",62.13M "MSEX","Middlesex Water Company",450.50M "MOFG","MidWestOne Financial Group, Inc",301.97M "MIME","Mimecast Limited",519.69M "MDXG","MiMedx Group, Inc",884.77M "MNDO","MIND C.T.I. Ltd.",47.99M "MB","MINDBODY, Inc.",506.03M "NERV","Minerva Neurosciences, Inc",123.61M "MRTX","Mirati Therapeutics, Inc.",460.43M "MIRN","Mirna Therapeutics, Inc.",82.90M "MSON","MISONIX, Inc.",46.19M "MIND","Mitcham Industries, Inc.",37.60M "MITK","Mitek Systems, Inc.",167.67M "MITL","Mitel Networks Corporation",836.15M "MKSI","MKS Instruments, Inc.",1.79B "MMAC","MMA Capital Management, LLC",94.41M "MINI","Mobile Mini, Inc.",1.23B "MOBL","MobileIron, Inc.",254.55M "MOCO","MOCON, Inc.",78.65M "MDSY","ModSys International Ltd.",37.40M "MLNK","ModusLink Global Solutions, Inc",93.69M "MOKO","MOKO Social Media Ltd.",7.97M "MOLG","MOL Global, Inc.",43.10M "MNTA","Momenta Pharmaceuticals, Inc.",702.50M "MOMO","Momo Inc.",1.77B "MCRI","Monarch Casino & Resort, Inc.",324.68M "MNRK","Monarch Financial Holdings, Inc",169.64M "MDLZ","Mondelez International, Inc.",62.28B "MGI","Moneygram International, Inc.",282.55M "MPWR","Monolithic Power Systems, Inc.",2.33B "TYPE","Monotype Imaging Holdings Inc.",946.49M "MNRO","Monro Muffler Brake, Inc.",2.15B "MRCC","Monroe Capital Corporation",N/A "MNST","Monster Beverage Corporation",26.32B "MHGC","Morgans Hotel Group Co.",47.91M "MORN","Morningstar, Inc.",3.39B "MOSY","MoSys, Inc.",39.30M "MPAA","Motorcar Parts of America, Inc.",595.96M "MDM","Mountain Province Diamonds Inc.",573.24M "MRVC","MRV Communications, Inc.",77.82M "MSBF","MSB Financial Corp.",72.12M "MSG","The Madison Square Garden Compa",3.74B "MTSC","MTS Systems Corporation",798.03M "LABL","Multi-Color Corporation",777.60M "MFLX","Multi-Fineline Electronix, Inc.",528.11M "MFSF","MutualFirst Financial Inc.",178.13M "MYL","Mylan N.V.",22.95B "MYOK","MyoKardia, Inc.",203.98M "MYOS","MYOS Corporation",5.15M "MYRG","MYR Group, Inc.",428.81M "MYGN","Myriad Genetics, Inc.",2.49B "NBRV","Nabriva Therapeutics AG",185.54M "NAKD","NAKED BRAND GRP, INC",4.33M "NANO","Nanometrics Incorporated",320.19M "NSPH","Nanosphere, Inc.",5.33M "NSTG","NanoString Technologies, Inc.",255.72M "NK","NantKwest, Inc.",636.76M "NSSC","NAPCO Security Technologies, In",114.82M "NDAQ","Nasdaq, Inc.",10.46B "NTRA","Natera, Inc.",373.97M "NATH","Nathan's Famous, Inc.",217.75M "NAUH","National American University Ho",35.46M "NKSH","National Bankshares, Inc.",234.87M "FIZZ","National Beverage Corp.",1.67B "NCMI","National CineMedia, Inc.",886.45M "NCOM","National Commerce Corporation",238.45M "NGHC","National General Holdings Corp",2.12B "NGHCO","National General Holdings Corp",N/A "NGHCP","National General Holdings Corp",2.67B "NGHCZ","National General Holdings Corp",2.53B "NHLD","NATIONAL HOLDINGS",29.86M "NATI","National Instruments Corporatio",3.72B "NATL","National Interstate Corporation",466.08M "NPBC","National Penn Bancshares, Inc.",1.58B "NRCIA","National Research Corporation",347.53M "NRCIB","National Research Corporation",871.00M "NSEC","National Security Group, Inc.",38.81M "NWLI","National Western Life Group, In",761.01M "NAII","Natural Alternatives Internatio",71.66M "NHTC","NATURAL HEALTH TREND",438.55M "NATR","Nature's Sunshine Products, Inc",139.78M "BABY","Natus Medical Incorporated",1.15B "NAVI","Navient Corporation",3.62B "NBCP",N/A,N/A "NBTB","NBT Bancorp Inc.",1.14B "NCIT","NCI, Inc.",196.26M "NKTR","Nektar Therapeutics",1.53B "NEOG","Neogen Corporation",1.83B "NEO","NeoGenomics, Inc.",372.36M "NEON","Neonode Inc.",102.75M "NEOS","Neos Therapeutics, Inc.",171.36M "NEOT","Neothetics, Inc.",10.30M "NVCN","Neovasc Inc.",216.34M "NRX","NephroGenex, Inc.",15.14M "NEPT","Neptune Technologies & Bioresou",79.50M "UEPS","Net 1 UEPS Technologies, Inc.",458.46M "NETE","Net Element, Inc.",16.02M "NTAP","NetApp, Inc.",7.32B "NTES","NetEase, Inc.",21.06B "NFLX","Netflix, Inc.",39.35B "NTGR","NETGEAR, Inc.",1.30B "NLST","Netlist, Inc.",62.44M "NTCT","NetScout Systems, Inc.",2.04B "NTWK","NetSol Technologies Inc.",70.04M "CUR","Neuralstem, Inc.",93.62M "NBIX","Neurocrine Biosciences, Inc.",3.24B "NDRM","NeuroDerm Ltd.",276.74M "NURO","NeuroMetrix, Inc.",5.99M "NUROW","NeuroMetrix, Inc.",N/A "NSIG",N/A,N/A "NYMT","New York Mortgage Trust, Inc.",543.72M "NYMTO","New York Mortgage Trust, Inc.",2.24B "NYMTP","New York Mortgage Trust, Inc.",2.25B "NBBC","NewBridge Bancorp",418.56M "NLNK","NewLink Genetics Corporation",660.04M "NEWP","Newport Corporation",580.18M "NWS","News Corporation",6.82B "NWSA","News Corporation",6.44B "NEWS","NewStar Financial, Inc.",326.90M "NEWT","Newtek Business Services Corp.",121.47M "NEWTZ","Newtek Business Services Corp.",312.53M "NXST","Nexstar Broadcasting Group, Inc",1.26B "NVET","Nexvet Biopharma plc",34.56M "NFEC","NF Energy Saving Corporation",4.72M "EGOV","NIC Inc.",1.16B "NICE","NICE-Systems Limited",3.61B "NICK","Nicholas Financial, Inc.",81.91M "NIHD","NII Holdings, Inc.",413.17M "NVLS","Nivalis Therapeutics, Inc.",72.47M "NMIH","NMI Holdings Inc",297.66M "NNBR","NN, Inc.",327.69M "NDLS","Noodles & Company",359.36M "NORT",N/A,N/A "NDSN","Nordson Corporation",3.70B "NSYS","Nortech Systems Incorporated",10.38M "NTK","Nortek Inc.",619.17M "NBN","Northeast Bancorp",97.49M "NTIC","Northern Technologies Internati",53.64M "NTRS","Northern Trust Corporation",13.96B "NTRSP","Northern Trust Corporation",6.02B "NFBK","Northfield Bancorp, Inc.",633.83M "NRIM","Northrim BanCorp Inc",160.78M "NWBI","Northwest Bancshares, Inc.",1.27B "NWBO","Northwest Biotherapeutics, Inc.",220.74M "NWBOW","Northwest Biotherapeutics, Inc.",N/A "NWPX","Northwest Pipe Company",90.38M "NCLH","Norwegian Cruise Line Holdings ",10.11B "NWFL","Norwood Financial Corp.",99.63M "NVFY","Nova Lifestyle, Inc",31.07M "NVMI","Nova Measuring Instruments Ltd.",280.47M "NVDQ","Novadaq Technologies Inc",541.15M "MIFI","Novatel Wireless, Inc.",74.01M "NVAX","Novavax, Inc.",1.36B "NVCR","NovoCure Limited",1.00B "NVGN","Novogen Limited",34.19M "NTLS","NTELOS Holdings Corp.",197.89M "NUAN","Nuance Communications, Inc.",5.86B "NMRX","Numerex Corp.",113.21M "NUTR","Nutraceutical International Cor",231.31M "NTRI","NutriSystem Inc",649.56M "NUVA","NuVasive, Inc.",1.95B "QQQX","Nuveen NASDAQ 100 Dynamic Overw",N/A "NVEE","NV5 Global, Inc.",163.86M "NVEC","NVE Corporation",247.04M "NVDA","NVIDIA Corporation",16.99B "NXPI","NXP Semiconductors N.V.",17.60B "NXTM","NxStage Medical, Inc.",907.90M "NXTD","NXT-ID Inc.",19.92M "NXTDW","NXT-ID Inc.",N/A "NYMX","Nymox Pharmaceutical Corporatio",104.86M "OIIM","O2Micro International Limited",36.14M "OVLY","Oak Valley Bancorp (CA)",76.76M "OASM","Oasmia Pharmaceutical AB",132.84M "OBCI","Ocean Bio-Chem, Inc.",18.77M "OPTT","Ocean Power Technologies, Inc.",2.69M "ORIG","Ocean Rig UDW Inc.",104.00M "OSHC","Ocean Shore Holding Co.",105.14M "OCFC","OceanFirst Financial Corp.",286.40M "OCRX","Ocera Therapeutics, Inc.",60.48M "OCLR","Oclaro, Inc.",514.14M "OFED","Oconee Federal Financial Corp.",112.20M "OCUL","Ocular Therapeutix, Inc.",208.08M "OCLS","Oculus Innovative Sciences, Inc",17.35M "OCLSW","Oculus Innovative Sciences, Inc",N/A "OMEX","Odyssey Marine Exploration, Inc",275.67M "ODP","Office Depot, Inc.",2.86B "OFS","OFS Capital Corporation",N/A "OHAI","OHA Investment Corporation",N/A "OVBC","Ohio Valley Banc Corp.",94.69M "OHRP","Ohr Pharmaceuticals, Inc.",95.33M "ODFL","Old Dominion Freight Line, Inc.",5.40B "OLBK","Old Line Bancshares, Inc.",192.17M "ONB","Old National Bancorp",1.31B "OPOF","Old Point Financial Corporation",93.28M "OSBC","Old Second Bancorp, Inc.",188.99M "OSBCP","Old Second Bancorp, Inc.",N/A "OLLI","Ollie's Bargain Outlet Holdings",1.23B "ZEUS","Olympic Steel, Inc.",119.70M "OFLX","Omega Flex, Inc.",319.88M "OMER","Omeros Corporation",397.92M "OMCL","Omnicell, Inc.",975.02M "ON","ON Semiconductor Corporation",3.29B "OTIV","On Track Innovations Ltd",29.81M "OGXI","OncoGenex Pharmaceuticals Inc.",17.29M "OMED","OncoMed Pharmaceuticals, Inc.",286.63M "ONTX","Onconova Therapeutics, Inc.",11.69M "ONCS","ONCOSEC MEDICAL",30.04M "ONTY","Oncothyreon Inc.",97.78M "OHGI","One Horizon Group, Inc.",29.80M "ONVI","Onvia, Inc.",27.12M "OTEX","Open Text Corporation",6.04B "OPXA","Opexa Therapeutics, Inc.",16.61M "OPXAW","Opexa Therapeutics, Inc.",N/A "OPGN","OpGen, Inc.",20.56M "OPGNW","OpGen, Inc.",N/A "OPHT","Ophthotech Corporation",1.70B "OBAS","Optibase Ltd.",36.11M "OCC","Optical Cable Corporation",15.94M "OPHC","OptimumBank Holdings, Inc.",4.14M "OPB","Opus Bank",1.04B "ORMP","Oramed Pharmaceuticals Inc.",96.90M "OSUR","OraSure Technologies, Inc.",351.92M "ORBC","ORBCOMM Inc.",615.42M "ORBK","Orbotech Ltd.",897.75M "ORLY","O'Reilly Automotive, Inc.",25.37B "OREX","Orexigen Therapeutics, Inc.",254.53M "SEED","Origin Agritech Limited",27.18M "OESX","Orion Energy Systems, Inc.",33.86M "ORIT","Oritani Financial Corp.",690.63M "ORRF","Orrstown Financial Services Inc",140.88M "OFIX","Orthofix International N.V.",702.48M "OSIS","OSI Systems, Inc.",1.19B "OSIR","Osiris Therapeutics, Inc.",254.26M "OSN","Ossen Innovation Co., Ltd.",16.27M "OTEL","Otelco Inc.",14.90M "OTG",N/A,N/A "OTIC","Otonomy, Inc.",359.93M "OTTR","Otter Tail Corporation",1.02B "OUTR","Outerwall Inc.",506.41M "OVAS","OvaScience Inc.",186.85M "OSTK","Overstock.com, Inc.",361.34M "OXBR","Oxbridge Re Holdings Limited",30.96M "OXBRW","Oxbridge Re Holdings Limited",N/A "OXFD","Oxford Immunotec Global PLC",220.87M "OXLC","Oxford Lane Capital Corp.",N/A "OXLCN","Oxford Lane Capital Corp.",439.37M "OXLCO","Oxford Lane Capital Corp.",433.47M "OXGN","OXiGENE, Inc.",15.92M "PFIN","P & F Industries, Inc.",35.24M "PTSI","P.A.M. Transportation Services,",208.16M "PCAR","PACCAR Inc.",18.35B "PACE","Pace Holdings Corp.",129.94M "PACEU","Pace Holdings Corp.",132.46M "PACEW","Pace Holdings Corp.",N/A "PACB","Pacific Biosciences of Californ",705.45M "PCBK","Pacific Continental Corporation",306.80M "PEIX","Pacific Ethanol, Inc.",151.79M "PMBC","Pacific Mercantile Bancorp",156.32M "PPBI","Pacific Premier Bancorp Inc",444.61M "PAAC","Pacific Special Acquisition Cor",23.79M "PAACR","Pacific Special Acquisition Cor",N/A "PAACU","Pacific Special Acquisition Cor",N/A "PAACW","Pacific Special Acquisition Cor",N/A "PSUN","Pacific Sunwear of California, ",12.92M "PCRX","Pacira Pharmaceuticals, Inc.",2.32B "PACW","PacWest Bancorp",3.89B "PTIE","Pain Therapeutics",84.65M "PAAS","Pan American Silver Corp.",1.43B "PNRA","Panera Bread Company",5.02B "PANL","Pangaea Logistics Solutions Ltd",80.14M "PZZA","Papa John's International, Inc.",2.07B "FRSH","Papa Murphy's Holdings, Inc.",161.85M "PRGN","Paragon Shipping Inc.",1.37M "PRGNL","Paragon Shipping Inc.",N/A "PRTK","Paratek Pharmaceuticals, Inc. ",237.88M "PRXL","PAREXEL International Corporati",3.14B "PCYG","Park City Group, Inc.",171.65M "PSTB","Park Sterling Corporation",265.56M "PKBK","Parke Bancorp, Inc.",74.37M "PRKR","ParkerVision, Inc.",18.88M "PKOH","Park-Ohio Holdings Corp.",344.70M "PARN","Parnell Pharmaceuticals Holding",38.65M "PTNR","Partner Communications Company ",714.87M "PBHC","Pathfinder Bancorp, Inc.",49.61M "PATK","Patrick Industries, Inc.",639.05M "PNBK","Patriot National Bancorp Inc.",51.30M "PATI","Patriot Transportation Holding,",70.86M "PEGI","Pattern Energy Group Inc.",1.26B "PDCO","Patterson Companies, Inc.",4.47B "PTEN","Patterson-UTI Energy, Inc.",2.25B "PAYX","Paychex, Inc.",18.74B "PCTY","Paylocity Holding Corporation",1.46B "PYDS","PAYMENT DATA SYSTEMS",24.80M "PYPL","PayPal Holdings, Inc.",44.02B "PBBI","PB Bancorp, Inc.",66.98M "PCCC","PC Connection, Inc.",640.70M "PCMI","PCM, Inc.",100.55M "PCTI","PC-Tel, Inc.",90.46M "PDCE","PDC Energy, Inc.",2.08B "PDFS","PDF Solutions, Inc.",320.11M "PDLI","PDL BioPharma, Inc.",490.73M "PDVW","pdvWireless, Inc.",355.13M "SKIS","Peak Resorts, Inc.",57.75M "PGC","Peapack-Gladstone Financial Cor",264.69M "PEGA","Pegasystems Inc.",1.69B "PCO","Pendrell Corporation",146.43M "PENN","Penn National Gaming, Inc.",1.09B "PFLT","PennantPark Floating Rate Capit",N/A "PNNT","PennantPark Investment Corporat",N/A "PWOD","Penns Woods Bancorp, Inc.",195.72M "PTXP","PennTex Midstream Partners, LP",400.00M "PEBO","Peoples Bancorp Inc.",325.10M "PEBK","Peoples Bancorp of North Caroli",102.97M "PFBX","Peoples Financial Corporation",46.11M "PFIS","Peoples Financial Services Corp",271.36M "PBCT","People's United Financial, Inc.",4.50B "PUB","People's Utah Bancorp",256.30M "PRCP","Perceptron, Inc.",48.61M "PPHM","Peregrine Pharmaceuticals Inc.",243.48M "PPHMP","Peregrine Pharmaceuticals Inc.",4.73B "PRFT","Perficient, Inc.",631.19M "PFMT","Performant Financial Corporatio",86.09M "PERF","Perfumania Holdings, Inc",37.95M "PERI","Perion Network Ltd",163.14M "PESI","Perma-Fix Environmental Service",42.77M "PTX","Pernix Therapeutics Holdings, I",128.22M "PERY","Perry Ellis International Inc.",276.89M "PGLC","PERSHING GOLD CORPOR",102.97M "PETS","PetMed Express, Inc.",336.42M "PFSW","PFSweb, Inc.",222.95M "PGTI","PGT, Inc.",506.96M "PHII","PHI, Inc.",257.71M "PHIIK","PHI, Inc.",257.40M "PAHC","Phibro Animal Health Corporatio",1.11B "PHMD","PhotoMedex, Inc.",13.13M "PLAB","Photronics, Inc.",665.80M "PICO","PICO Holdings Inc.",182.06M "PIRS","Pieris Pharmaceuticals, Inc.",66.80M "PPC","Pilgrim's Pride Corporation",5.85B "PME","Pingtan Marine Enterprise Ltd.",107.51M "PNK","Pinnacle Entertainment, Inc.",1.68B "PNFP","Pinnacle Financial Partners, In",1.86B "PPSI","Pioneer Power Solutions, Inc.",27.54M "PXLW","Pixelworks, Inc.",48.75M "PLPM","Planet Payment, Inc.",136.10M "PLXS","Plexus Corp.",1.20B "PLUG","Plug Power, Inc.",331.17M "PLBC","Plumas Bancorp",41.58M "PSTI","Pluristem Therapeutics, Inc.",107.11M "PLXP",N/A,N/A "PMV",N/A,N/A "PBSK","Poage Bankshares, Inc.",62.48M "PNTR","Pointer Telocation Ltd.",47.82M "PCOM","Points International, Ltd.",108.31M "PLCM","Polycom, Inc.",1.29B "POOL","Pool Corporation",3.46B "POPE","Pope Resources",230.05M "PLKI","Popeyes Louisiana Kitchen, Inc.",1.35B "BPOP","Popular, Inc.",2.76B "BPOPM","Popular, Inc.",N/A "BPOPN","Popular, Inc.",N/A "PBIB","Porter Bancorp, Inc.",29.60M "PTLA","Portola Pharmaceuticals, Inc.",1.58B "PBPB","Potbelly Corporation",372.96M "PCH","Potlatch Corporation",1.08B "POWL","Powell Industries, Inc.",299.28M "POWI","Power Integrations, Inc.",1.30B "PSIX","Power Solutions International, ",115.69M "PDBC","PowerShares DB Optimum Yield Di",N/A "DWTR","PowerShares DWA Tactical Sector",N/A "IDLB","PowerShares FTSE International ",N/A "PRFZ","PowerShares FTSE RAFI US 1500 S",N/A "PAGG","PowerShares Global Agriculture ",N/A "PSAU","PowerShares Global Gold & Preci",N/A "IPKW","PowerShares International BuyBa",N/A "LDRI","PowerShares LadderRite 0-5 Year",N/A "LALT","PowerShares Multi-Strategy Alte",N/A "PNQI","PowerShares Nasdaq Internet Por",N/A "QQQ","PowerShares QQQ Trust, Series 1",N/A "USLB","PowerShares Russell 1000 Low Be",N/A "PSCD","PowerShares S&P SmallCap Consum",N/A "PSCC","PowerShares S&P SmallCap Consum",N/A "PSCE","PowerShares S&P SmallCap Energy",N/A "PSCF","PowerShares S&P SmallCap Financ",N/A "PSCH","PowerShares S&P SmallCap Health",N/A "PSCI","PowerShares S&P SmallCap Indust",N/A "PSCT","PowerShares S&P SmallCap Inform",N/A "PSCM","PowerShares S&P SmallCap Materi",N/A "PSCU","PowerShares S&P SmallCap Utilit",N/A "PRAA","PRA Group, Inc.",1.32B "PRAH","PRA Health Sciences, Inc.",2.46B "PRAN","Prana Biotechnology Ltd",33.00M "PFBC","Preferred Bank",373.36M "PLPC","Preformed Line Products Company",172.87M "PFBI","Premier Financial Bancorp, Inc.",125.32M "PINC","Premier, Inc.",1.46B "LENS","Presbia PLC",52.94M "PRGX","PRGX Global, Inc.",82.86M "PSMT","PriceSmart, Inc.",2.33B "PBMD","Prima BioMed Ltd",56.60M "PNRG","PrimeEnergy Corporation",114.33M "PRMW","Primo Water Corporation",229.73M "PRIM","Primoris Services Corporation",1.07B "PRZM","Prism Technologies Group, Inc.",5.24M "PVTB","PrivateBancorp, Inc.",2.71B "PVTBP","PrivateBancorp, Inc.",N/A "PDEX","Pro-Dex, Inc.",12.20M "IPDN","Professional Diversity Network,",4.66M "PFIE","Profire Energy, Inc.",41.38M "PGNX","Progenics Pharmaceuticals Inc.",328.78M "PRGS","Progress Software Corporation",1.24B "DNAI","ProNAi Therapeutics, Inc.",210.71M "PFPT","Proofpoint, Inc.",1.87B "PRPH","ProPhase Labs, Inc.",20.50M "PRQR","ProQR Therapeutics N.V.",109.49M "BIB","ProShares Ultra Nasdaq Biotechn",N/A "UBIO","Proshares UltraPro Nasdaq Biote",N/A "TQQQ","ProShares UltraPro QQQ",N/A "ZBIO","ProShares UltraPro Short NASDAQ",N/A "SQQQ","ProShares UltraPro Short QQQ",N/A "BIS","ProShares UltraShort Nasdaq Bio",N/A "PSEC","Prospect Capital Corporation",2.47B "PRTO","Proteon Therapeutics, Inc.",99.07M "PTI","Proteostasis Therapeutics, Inc.",38.75M "PRTA","Prothena Corporation plc",1.20B "PWX","Providence and Worcester Railro",65.15M "PVBC","Provident Bancorp, Inc.",119.66M "PROV","Provident Financial Holdings, I",146.43M "PBIP","Prudential Bancorp, Inc.",117.96M "PSDV","pSivida Corp.",95.40M "PMD","Psychemedics Corporation",66.64M "PTC","PTC Inc.",3.50B "PTCT","PTC Therapeutics, Inc.",958.44M "PULB","Pulaski Financial Corp.",173.30M "PULM","Pulmatrix, Inc.",29.69M "PCYO","Pure Cycle Corporation",106.06M "PXS","Pyxis Tankers Inc.",9.80M "QADA","QAD Inc.",358.30M "QADB","QAD Inc.",299.37M "QCRH","QCR Holdings, Inc.",259.92M "QGEN","Qiagen N.V.",5.06B "QIWI","QIWI plc",703.96M "QKLS","QKL Stores, Inc.",835578.06 "QLIK","Qlik Technologies Inc.",1.94B "QLGC","QLogic Corporation",1.03B "QLTI","QLT Inc.",127.85M "QRVO","Qorvo, Inc.",5.75B "QCOM","QUALCOMM Incorporated",76.66B "QSII","Quality Systems, Inc.",913.90M "QBAK","Qualstar Corporation",7.35M "QLYS","Qualys, Inc.",800.40M "QTWW","Quantum Fuel Systems Technologi",20.46M "QRHC","Quest Resource Holding Corporat",64.79M "QUIK","QuickLogic Corporation",91.33M "QDEL","Quidel Corporation",500.47M "QPAC","Quinpario Acquisition Corp. 2",102.13M "QPACU","Quinpario Acquisition Corp. 2",N/A "QPACW","Quinpario Acquisition Corp. 2",N/A "QNST","QuinStreet, Inc.",138.41M "QUMU","Qumu Corporation",36.15M "QUNR","Qunar Cayman Islands Limited",5.06B "QTNT","Quotient Limited",123.63M "RRD","R.R. Donnelley & Sons Company",2.83B "RADA","Rada Electronics Industries Lim",5.48M "RDCM","Radcom Ltd.",113.68M "ROIA","Radio One, Inc.",70.96M "ROIAK","Radio One, Inc.",68.06M "RSYS","RadiSys Corporation",95.29M "RDUS","Radius Health, Inc.",1.45B "RDNT","RadNet, Inc.",265.65M "RDWR","Radware Ltd.",504.54M "RMBS","Rambus, Inc.",1.40B "RAND","Rand Capital Corporation",28.48M "RLOG","Rand Logistics, Inc.",18.28M "GOLD","Randgold Resources Limited",8.47B "RPD","Rapid7, Inc.",496.35M "RPTP","Raptor Pharmaceutical Corp.",362.53M "RAVE","Rave Restaurant Group, Inc.",50.95M "RAVN","Raven Industries, Inc.",565.10M "ROLL","RBC Bearings Incorporated",1.44B "RICK","RCI Hospitality Holdings, Inc.",85.65M "RCMT","RCM Technologies, Inc.",66.54M "RLOC","ReachLocal, Inc.",49.13M "RDI","Reading International Inc",239.05M "RDIB","Reading International Inc",285.69M "RGSE","Real Goods Solar, Inc.",5.36M "RELY","Real Industry, Inc. ",182.47M "RNWK","RealNetworks, Inc.",134.51M "RP","RealPage, Inc.",1.42B "UTES","Reaves Utilities ETF",N/A "DAX","Recon Capital DAX Germany ETF",N/A "UK","Recon Capital FTSE 100 ETF",N/A "QYLD","Recon Capital NASDAQ-100 Covere",N/A "RCON","Recon Technology, Ltd.",6.97M "REPH","Recro Pharma, Inc.",56.54M "RRGB","Red Robin Gourmet Burgers, Inc.",860.33M "RDHL","Redhill Biopharma Ltd.",114.83M "REDF","Rediff.com India Limited",12.99M "REGN","Regeneron Pharmaceuticals, Inc.",41.20B "RGNX","REGENXBIO Inc.",362.59M "DFVL","Barclays PLC",N/A "DFVS","Barclays PLC",N/A "DGLD","Credit Suisse AG",N/A "DLBL","Barclays PLC",N/A "DLBS","Barclays PLC",N/A "DSLV","Credit Suisse AG",N/A "DTUL","Barclays PLC",N/A "DTUS","Barclays PLC",N/A "DTYL","Barclays PLC",N/A "DTYS","Barclays PLC",N/A "FLAT","Barclays PLC",N/A "SLVO","Credit Suisse AG",N/A "STPP","Barclays PLC",N/A "TAPR","Barclays PLC",N/A "TVIX","Credit Suisse AG",N/A "TVIZ","Credit Suisse AG",N/A "UGLD","Credit Suisse AG",N/A "USLV","Credit Suisse AG",N/A "VIIX","Credit Suisse AG",N/A "VIIZ","Credit Suisse AG",N/A "XIV","Credit Suisse AG",N/A "ZIV","Credit Suisse AG",N/A "RGLS","Regulus Therapeutics Inc.",398.32M "REIS","Reis, Inc",249.14M "RELV","Reliv' International, Inc.",11.36M "RLYP","Relypsa, Inc.",752.94M "MARK","Remark Media, Inc.",78.91M "RNST","Renasant Corporation",1.28B "REGI","Renewable Energy Group, Inc.",302.89M "RNVA","Rennova Health, Inc.",11.15M "RNVAW","Rennova Health, Inc.",N/A "RCII","Rent-A-Center Inc.",651.69M "RTK","Rentech, Inc.",40.50M "RGEN","Repligen Corporation",862.78M "RPRX","Repros Therapeutics Inc.",21.89M "RJET","Republic Airways Holdings, Inc.",171.62M "RBCAA","Republic Bancorp, Inc.",532.64M "FRBK","Republic First Bancorp, Inc.",151.30M "RSAS",N/A,N/A "REFR","Research Frontiers Incorporated",105.55M "RESN","Resonant Inc.",13.71M "REXI","Resource America, Inc.",90.40M "RECN","Resources Connection, Inc.",515.78M "ROIC","Retail Opportunity Investments ",1.84B "SALE","RetailMeNot, Inc.",397.69M "RTRX","Retrophin, Inc.",535.35M "RVNC","Revance Therapeutics, Inc.",521.67M "RBIO",N/A,N/A "RVLT","Revolution Lighting Technologie",127.21M "RWLK","ReWalk Robotics Ltd",146.26M "REXX","Rex Energy Corporation",35.04M "RFIL","RF Industries, Ltd.",35.31M "RGCO","RGC Resources Inc.",100.65M "RIBT","RiceBran Technologies",12.68M "RIBTW","RiceBran Technologies",N/A "RELL","Richardson Electronics, Ltd.",64.09M "RIGL","Rigel Pharmaceuticals, Inc.",217.77M "NAME","Rightside Group, Ltd.",162.39M "RNET","RigNet, Inc.",231.91M "RITT","RIT Technologies Ltd.",8.77M "RITTW","RIT Technologies Ltd.",N/A "RTTR","Ritter Pharmaceuticals, Inc.",12.39M "RIVR","River Valley Bancorp.",84.72M "RMI",N/A,N/A "RVSB","Riverview Bancorp Inc",94.54M "RLJE","RLJ Entertainment, Inc.",6.24M "RMGN","RMG Networks Holding Corporatio",29.51M "ROBO","Robo-Stox Global Robotics and A",N/A "FUEL","Rocket Fuel Inc.",138.11M "RMTI","Rockwell Medical, Inc.",401.98M "RCKY","Rocky Brands, Inc.",81.72M "RMCF","Rocky Mountain Chocolate Factor",59.85M "RSTI","Rofin-Sinar Technologies, Inc.",627.44M "ROKA","Roka Bioscience, Inc.",13.01M "ROSG","Rosetta Genomics Ltd.",15.29M "ROST","Ross Stores, Inc.",22.45B "ROVI","Rovi Corporation",1.70B "RBPAA","Royal Bancshares of Pennsylvani",60.03M "RGLD","Royal Gold, Inc.",2.76B "RPXC","RPX Corporation",532.28M "RRM","RR Media Ltd.",146.58M "RTIX","RTI Surgical, Inc.",186.09M "RBCN","Rubicon Technology, Inc.",19.80M "RUSHA","Rush Enterprises, Inc.",679.48M "RUSHB","Rush Enterprises, Inc.",677.46M "RUTH","Ruth's Hospitality Group, Inc.",570.66M "RXII","RXi Pharmaceuticals Corporation",19.93M "RYAAY","Ryanair Holdings plc",21.79B "STBA","S&T Bancorp, Inc.",927.34M "SANW","S&W Seed Company",60.66M "SBRA","Sabra Healthcare REIT, Inc.",1.18B "SBRAP","Sabra Healthcare REIT, Inc.",1.63B "SABR","Sabre Corporation",7.62B "SAEX","SAExploration Holdings, Inc.",25.88M "SAFT","Safety Insurance Group, Inc.",853.81M "SAGE","Sage Therapeutics, Inc.",956.49M "SGNT","Sagent 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of",496.37M "STX","Seagate Technology PLC",9.55B "SHIP","Seanergy Maritime Holdings Corp",29.08M "SRSC","Sears Canada Inc. ",300.54M "SHLD","Sears Holdings Corporation",1.82B "SHLDW","Sears Holdings Corporation",N/A "SHOS","Sears Hometown and Outlet Store",144.51M "SPNE","SeaSpine Holdings Corporation",139.77M "SGEN","Seattle Genetics, Inc.",4.27B "EYES","Second Sight Medical Products, ",189.65M "SNFCA","Security National Financial Cor",75.26M "SEIC","SEI Investments Company",6.26B "SLCT","Select Bancorp, Inc.",93.71M "SCSS","Select Comfort Corporation",838.85M "SIGI","Selective Insurance Group, Inc.",1.95B "LEDS","SemiLEDS Corporation",8.26M "SMLR","Semler Scientific, Inc.",10.24M "SMTC","Semtech Corporation",1.16B "SENEA","Seneca Foods Corp.",290.36M "SENEB","Seneca Foods Corp.",333.12M "SNMX","Senomyx, Inc.",153.78M "SQNM","Sequenom, Inc.",182.60M "SQBG","Sequential Brands Group, Inc.",227.73M "MCRB","Seres Therapeutics, Inc.",940.05M "SREV","ServiceSource International, In",358.01M "SFBS","ServisFirst Bancshares, Inc.",955.51M "SEV","Sevcon, Inc.",33.35M "SVBI","Severn Bancorp Inc",53.87M "SGOC","SGOCO Group, Ltd",15.21M "SMED","Sharps Compliance Corp",90.09M "SHSP","SharpSpring, Inc.",21.90M "SHEN","Shenandoah Telecommunications C",1.07B "SHLO","Shiloh Industries, Inc.",70.05M "SCCI",N/A,N/A "SHPG","Shire plc",32.30B "SCVL","Shoe Carnival, Inc.",464.48M "SHBI","Shore Bancshares Inc",143.86M "SHOR","ShoreTel, Inc.",486.15M "SFLY","Shutterfly, Inc.",1.43B "SIFI","SI Financial Group, Inc.",163.92M "SIEB","Siebert Financial Corp.",26.06M "SIEN","Sientra, Inc.",127.39M "BSRR","Sierra Bancorp",235.52M "SWIR","Sierra Wireless, Inc.",390.93M "SIFY","Sify Technologies Limited",142.44M "SIGM","Sigma Designs, Inc.",240.07M "SGMA","SigmaTron International, Inc.",27.56M "SGNL","Signal Genetics, Inc.",5.32M "SBNY","Signature Bank",6.75B "SBNYW","Signature Bank",N/A "SLGN","Silgan Holdings Inc.",3.06B "SILC","Silicom Ltd",209.43M "SGI","Silicon Graphics International ",211.47M "SLAB","Silicon Laboratories, Inc.",1.68B "SIMO","Silicon Motion Technology Corpo",1.17B "SPIL","Siliconware Precision 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"STAA","STAAR Surgical Company",251.29M "STAF","STAFFING 360",14.46M "STMP","Stamps.com Inc.",1.57B "STLY","Stanley Furniture Company, Inc.",38.02M "SPLS","Staples, Inc.",6.11B "SBLK","Star Bulk Carriers Corp.",116.29M "SBLKL","Star Bulk Carriers Corp.",1.42B "SBUX","Starbucks Corporation",87.02B "STRZA","Starz",2.31B "STRZB","Starz",2.54B "STFC","State Auto Financial Corporatio",889.05M "STBZ","State Bank Financial Corporatio",668.25M "SNC","State National Companies, Inc.",496.04M "STDY","SteadyMed Ltd.",29.06M "GASS","StealthGas, Inc.",124.30M "STLD","Steel Dynamics, Inc.",4.45B "SXCL","STEEL EXCEL INC.",160.39M "SMRT","Stein Mart, Inc.",312.44M "SBOT","Stellar Biotechnologies, Inc.",49.00M "STEM","StemCells, Inc.",35.80M "STML","Stemline Therapeutics, Inc.",74.46M "STXS","Stereotaxis, Inc.",21.00M "SRCL","Stericycle, Inc.",9.56B "SRCLP","Stericycle, Inc.",7.39B "STRL","Sterling Construction Company I",91.67M "SHOO","Steven Madden, Ltd.",2.17B "SSFN","Stewardship Financial 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"SSH","Sunshine Heart Inc",12.65M "SMCI","Super Micro Computer, Inc.",1.54B "SPCB","SuperCom, Ltd.",72.71M "SCON","Superconductor Technologies Inc",3.27M "SGC","Superior Uniform Group, Inc.",235.98M "SUPN","Supernus Pharmaceuticals, Inc.",666.33M "SPRT","support.com, Inc.",44.17M "SGRY","Surgery Partners, Inc.",674.67M "SCAI","Surgical Care Affiliates, Inc.",1.68B "SRDX","SurModics, Inc.",254.98M "SBBX","Sussex Bancorp",59.80M "TOR","Sutor Technology Group Limited",2.98M "SIVB","SVB Financial Group",4.52B "SIVBO","SVB Financial Group",N/A "SYKE","Sykes Enterprises, Incorporated",1.27B "SYMC","Symantec Corporation",13.13B "SSRG","Symmetry Surgical Inc.",99.34M "SYNC","Synacor, Inc.",50.01M "SYNL","Synalloy Corporation",68.44M "SYNA","Synaptics Incorporated",2.94B "SNCR","Synchronoss Technologies, Inc.",1.11B "SNDX",N/A,N/A "SGYP","Synergy Pharmaceuticals, Inc.",436.50M "SGYPU","Synergy Pharmaceuticals, Inc.",N/A "SGYPW","Synergy Pharmaceuticals, Inc.",N/A "ELOS","Syneron Medical Ltd.",263.14M "SNPS","Synopsys, Inc.",6.78B "SNTA","Synta Pharmaceuticals Corp.",31.18M "SYNT","Syntel, Inc.",3.74B "SYMX","Synthesis Energy Systems, Inc.",72.12M "SYUT","Synutra International, Inc.",280.77M "SYPR","Sypris Solutions, Inc.",18.28M "SYRX","Sysorex Global",13.65M "TROW","T. Rowe Price Group, Inc.",17.35B "TTOO","T2 Biosystems, Inc.",215.88M "TAIT","Taitron Components Incorporated",5.37M "TTWO","Take-Two Interactive Software, ",2.94B "TLMR","Talmer Bancorp, Inc.",1.10B "TNDM","Tandem Diabetes Care, Inc.",209.32M "TLF","Tandy Leather Factory, Inc.",69.64M "TNGO","Tangoe, Inc.",302.03M "TANH","Tantech Holdings Ltd.",111.46M "TEDU","Tarena International, Inc.",526.21M "TASR","TASER International, Inc.",925.86M "TATT","TAT Technologies Ltd.",62.47M "TAYD","Taylor Devices, Inc.",46.67M "TCPC","TCP Capital Corp.",N/A "AMTD","TD Ameritrade Holding Corporati",15.01B "TEAR","TearLab Corporation",28.02M "TECD","Tech Data Corporation",2.46B "TCCO","Technical Communications Corpor",4.95M "TTGT","TechTarget, Inc.",217.22M "TGLS","Tecnoglass Inc.",285.85M "TGEN","Tecogen Inc.",60.85M "TSYS","TeleCommunication Systems, Inc.",307.00M "TNAV","Telenav, Inc.",242.87M "TTEC","TeleTech Holdings, Inc.",1.31B "TLGT","Teligent, Inc.",316.70M "TENX","Tenax Therapeutics, Inc.",65.52M "GLBL","TerraForm Global, Inc.",324.45M "TERP","TerraForm Power, Inc.",1.07B "TRTL","Terrapin 3 Acquisition Corporat",64.77M "TRTLU","Terrapin 3 Acquisition Corporat",N/A "TRTLW","Terrapin 3 Acquisition Corporat",N/A "TBNK","Territorial Bancorp Inc.",232.51M "TSRO","TESARO, Inc.",1.58B "TESO","Tesco Corporation",265.23M "TSLA","Tesla Motors, Inc.",23.30B "TESS","TESSCO Technologies Incorporate",132.37M "TSRA","Tessera Technologies, Inc.",1.43B "TTEK","Tetra Tech, Inc.",1.61B "TLOG","TetraLogic Pharmaceuticals Corp",3.34M "TTPH","Tetraphase Pharmaceuticals, Inc",171.75M "TCBI","Texas Capital Bancshares, Inc.",1.58B "TCBIL","Texas Capital Bancshares, Inc.",1.09B "TCBIP","Texas Capital Bancshares, Inc.",1.10B "TCBIW","Texas Capital Bancshares, Inc.",N/A "TXN","Texas Instruments Incorporated",53.90B "TXRH","Texas Roadhouse, Inc.",2.59B "TFSL","TFS Financial Corporation",4.78B "TGTX","TG Therapeutics, Inc.",463.72M "ABCO","The Advisory Board Company",1.54B "ANDE","The Andersons, Inc.",738.46M "TBBK","The Bancorp, Inc.",167.28M "BONT","The Bon-Ton Stores, Inc.",36.34M "CG","The Carlyle Group L.P.",1.24B "CAKE","The Cheesecake Factory Incorpor",2.36B "CHEF","The Chefs' Warehouse, Inc.",455.05M "TCFC","The Community Financial Corpora",92.39M "DSGX","The Descartes Systems Group Inc",1.24B "DXYN","The Dixie Group, Inc.",61.67M "ENSG","The Ensign Group, Inc.",980.44M "XONE","The ExOne Company",130.28M "FINL","The Finish Line, Inc.",830.36M "FBMS","The First Bancshares, Inc.",97.22M "FLIC","The First of Long Island Corpor",382.40M "TFM","The Fresh Market, Inc.",1.07B "GT","The Goodyear Tire & Rubber Comp",7.98B "HABT","The Habit Restaurants, Inc.",275.04M "HCKT","The Hackett Group, Inc.",408.41M "HAIN","The Hain Celestial Group, Inc.",3.79B "CUBA","The Herzfeld Caribbean Basin Fu",N/A "INTG","The Intergroup Corporation",61.36M "JYNT","The Joint Corp.",40.71M "KEYW","The KEYW Holding Corporation",165.34M "KHC","The Kraft Heinz Company",88.98B "MDCO","The Medicines Company",2.33B "MIK","The Michaels Companies, Inc.",4.71B "MIDD","The Middleby Corporation",5.08B "NAVG","The Navigators Group, Inc.",1.20B "STKS","THE ONE GROUP HOSPIT",66.43M "PCLN","The Priceline Group Inc. ",63.93B "PRSC","The Providence Service Corporat",712.23M "BITE","The Restaurant ETF",N/A "RMR","The RMR Group Inc.",379.84M "SPNC","The Spectranetics Corporation",546.98M "ULTI","The Ultimate Software Group, In",4.77B "YORW","The York Water Company",364.29M "NCTY","The9 Limited",49.88M "TBPH","Theravance Biopharma, Inc.",560.02M "TST","TheStreet, Inc.",39.75M "TCRD","THL Credit, Inc.",N/A "THLD","Threshold Pharmaceuticals, Inc.",18.58M "TICC","TICC Capital Corp.",296.94M "TTS","Tile Shop Hldgs, Inc.",681.54M "TIL","Till Capital Ltd.",10.91M "TSBK","Timberland Bancorp, Inc.",85.88M "TIPT","Tiptree Financial Inc.",220.36M "TITN","Titan Machinery Inc.",188.25M "TTNP","TITAN PHARMA INC",70.21M "TIVO","TiVo Inc.",786.66M "TMUS","T-Mobile US, Inc.",29.32B "TMUSP","T-Mobile US, Inc.",51.50B "TBRA","Tobira Therapeutics, Inc.",131.66M "TKAI","Tokai Pharmaceuticals, Inc.",141.15M "TNXP","Tonix Pharmaceuticals Holding C",52.54M "TISA","Top Image Systems, Ltd.",38.31M "TOPS","TOP Ships Inc.",44.09M "TORM","TOR Minerals International Inc",11.60M "TRCH","Torchlight Energy Resources, In",17.42M "TSEM","Tower Semiconductor Ltd.",983.37M "TWER","Towerstream Corporation",12.69M "CLUB","Town Sports International Holdi",30.94M "TOWN","Towne Bank",877.48M "TCON","TRACON Pharmaceuticals, Inc.",77.50M "TSCO","Tractor Supply Company",11.61B "TWMC","Trans World Entertainment Corp.",114.97M "TACT","TransAct Technologies Incorpora",54.43M "TRNS","Transcat, Inc.",63.94M "TBIO","Transgenomic, Inc.",8.84M "TGA","Transglobe Energy Corp",102.53M "TTHI","Transition Therapeutics, Inc.",36.93M "TZOO","Travelzoo Inc.",111.71M "TRVN","Trevena, Inc.",492.95M "TCBK","TriCo Bancshares",560.45M "TRIL","Trillium Therapeutics Inc.",57.60M "TRS","TriMas Corporation",723.61M "TRMB","Trimble Navigation Limited",5.85B "TRIB","Trinity Biotech plc",225.29M "TRIP","TripAdvisor, Inc.",9.17B "TSC","TriState Capital Holdings, Inc.",337.51M "TBK","Triumph Bancorp, Inc.",246.61M "TROV","TrovaGene, Inc.",136.13M "TROVU","TrovaGene, Inc.",N/A "TROVW","TrovaGene, Inc.",N/A "TRUE","TrueCar, Inc.",395.47M "THST","Truett-Hurst, Inc.",5.30M "TRST","TrustCo Bank Corp NY",536.33M "TRMK","Trustmark Corporation",1.52B "TSRI","TSR, Inc.",7.49M "TTMI","TTM Technologies, Inc.",638.42M "TUBE","TubeMogul, Inc.",407.95M "TCX","Tucows Inc.",225.99M "TUES","Tuesday Morning Corp.",283.04M "TOUR","Tuniu Corporation",1.18B "HEAR","Turtle Beach Corporation",40.82M "TUTI","Tuttle Tactical Management Mult",N/A "TUTT","Tuttle Tactical Management U.S.",N/A "FOX","Twenty-First Century Fox, Inc.",52.38B "FOXA","Twenty-First Century Fox, Inc.",52.46B "TWIN","Twin Disc, Incorporated",103.51M "TRCB","Two River Bancorp",71.91M "USCR","U S Concrete, Inc.",760.73M "PRTS","U.S. Auto Parts Network, Inc.",98.83M "USEG","U.S. Energy Corp.",10.96M "GROW","U.S. Global Investors, Inc.",23.84M "UREE","U S RARE EARTHS INC",1.36M "UBIC","UBIC, Inc.",214.47M "UBNT","Ubiquiti Networks, Inc.",2.74B "UFPT","UFP Technologies, Inc.",153.82M "ULTA","Ulta Salon, Cosmetics & Fragran",9.95B "UCTT","Ultra Clean Holdings, Inc.",154.85M "RARE","Ultragenyx Pharmaceutical Inc.",2.47B "ULBI","Ultralife Corporation",76.50M "ULTR","Ultrapetrol (Bahamas) Limited",15.41M "UTEK","Ultratech, Inc.",516.65M "UMBF","UMB Financial Corporation",2.47B "UMPQ","Umpqua Holdings Corporation",3.29B "UNAM","Unico American Corporation",49.83M "UNIS","Unilife Corporation",170.26M "UBSH","Union Bankshares Corporation",1.00B "UNB","Union Bankshares, Inc.",121.30M "UNXL","Uni-Pixel, Inc.",12.51M "QURE","uniQure N.V.",375.38M "UBCP","United Bancorp, Inc.",44.04M "UBOH","United Bancshares, Inc.",56.25M "UBSI","United Bankshares, Inc.",2.44B "UCBA","United Community Bancorp",54.73M "UCBI","United Community Banks, Inc.",1.21B "UCFC","United Community Financial Corp",277.97M "UDF","United Development Funding IV",98.15M "UBNK","United Financial Bancorp, Inc. 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",4.15B "VECO","Veeco Instruments Inc.",781.60M "APPY","Venaxis, Inc.",7.38M "VRA","Vera Bradley, Inc.",590.19M "VCYT","Veracyte, Inc.",176.50M "VSTM","Verastem, Inc.",43.95M "VCEL","Vericel Corporation",50.23M "VRNT","Verint Systems Inc.",2.13B "VRSN","VeriSign, Inc.",9.19B "VRSK","Verisk Analytics, Inc.",11.60B "VBTX","Veritex Holdings, Inc.",138.67M "VRML","Vermillion, Inc.",73.85M "VSAR","Versartis, Inc.",206.23M "VTNR","Vertex Energy, Inc",46.84M "VRTX","Vertex Pharmaceuticals Incorpor",21.93B "VRTB","Vestin Realty Mortgage II, Inc.",3.27M "VIA","Viacom Inc.",16.07B "VIAB","Viacom Inc.",14.59B "VSAT","ViaSat, Inc.",3.44B "VIAV","Viavi Solutions Inc.",1.48B "VICL","Vical Incorporated",31.86M "VICR","Vicor Corporation",294.81M "CIZ","Victory CEMP Developed Enhanced",N/A "CID","Victory CEMP International High",N/A "CIL","Victory CEMP International Vola",N/A "CFO","Victory CEMP US 500 Enhanced Vo",N/A "CFA","Victory CEMP US 500 Volatility ",N/A "CSF","Victory CEMP US Discovery Enhan",N/A "CDC","Victory CEMP US EQ Income Enhan",N/A "CDL","Victory CEMP US Large Cap High ",N/A "CSB","Victory CEMP US Small Cap High ",N/A "CSA","Victory CEMP US Small Cap Volat",N/A "VBND","Vident Core U.S. Bond Strategy ",N/A "VUSE","Vident Core US Equity ETF",N/A "VIDI","Vident International Equity Fun",N/A "VDTH","Videocon d2h Limited",623.94M "VKTX","Viking Therapeutics, Inc.",18.58M "VBFC","Village Bank and Trust Financia",27.39M "VLGEA","Village Super Market, Inc.",346.69M "VIP","VimpelCom Ltd.",N/A "VNOM","Viper Energy Partners LP",1.16B "VIRC","Virco Manufacturing Corporation",50.24M "VA","Virgin America Inc.",1.36B "VIRT","Virtu Financial, Inc.",774.00M "VSCP","VirtualScopics, Inc.",13.18M "VRTS","Virtus Investment Partners, Inc",794.13M "VRTU","Virtusa Corporation",1.01B "VISN","VisionChina Media, Inc.",47.08M "VTAE","Vitae Pharmaceuticals, Inc.",199.45M "VTL","Vital Therapies, Inc.",253.74M "VVUS","VIVUS, Inc.",109.24M "VOD","Vodafone Group Plc",81.31B "VLTC","Voltari Corporation",36.52M "VOXX","VOXX International Corporation",90.12M "VYGR","Voyager Therapeutics, Inc.",267.49M "VRNG","Vringo, Inc.",24.46M "VSEC","VSE Corporation",313.84M "VTVT","vTv Therapeutics Inc.",59.61M "VUZI","VUZIX CORP CMN STK",93.78M "VWR","VWR Corporation",3.07B "WGBS","WaferGen Bio-systems, Inc.",8.88M "WBA","Walgreens Boots Alliance, Inc.",85.01B "WRES","Warren Resources, Inc.",9.07M "WAFD","Washington Federal, Inc.",1.95B "WAFDW","Washington Federal, Inc.",N/A "WASH","Washington Trust Bancorp, Inc.",628.85M "WFBI","WashingtonFirst Bankshares Inc",259.63M "WSBF","Waterstone Financial, Inc.",376.94M "WVE","WAVE Life Sciences Ltd.",307.96M "WNFM",N/A,N/A "WAYN","Wayne Savings Bancshares Inc.",35.35M "WSTG","Wayside Technology Group, Inc.",76.87M "WDFC","WD-40 Company",1.54B "FLAG","WeatherStorm Forensic Accountin",N/A "WEB","Web.com Group, Inc.",882.44M "WBMD","WebMD Health Corp",1.85B "WB","Weibo Corporation",3.21B "WEBK","Wellesley Bancorp, Inc.",42.75M "WEN","Wendy's Company (The)",2.60B "WERN","Werner Enterprises, Inc.",1.91B "WSBC","WesBanco, Inc.",1.09B "WTBA","West Bancorporation",280.32M "WSTC","West Corporation",1.79B "WMAR","West Marine, Inc.",206.21M "WABC","Westamerica Bancorporation",1.16B "WBB","Westbury Bancorp, Inc.",72.63M "WSTL","Westell Technologies, Inc.",65.68M "WDC","Western Digital Corporation",10.73B "WFD","Westfield Financial, Inc.",142.44M "WLB","Westmoreland Coal Company",90.68M "WPRT","Westport Innovations Inc",119.48M "WEYS","Weyco Group, Inc.",285.53M "WHLR","Wheeler Real Estate Investment ",76.08M "WHLRP","Wheeler Real Estate Investment ",1.31B "WHLRW","Wheeler Real Estate Investment ",N/A "WHF","WhiteHorse Finance, Inc.",N/A "WHFBL","WhiteHorse Finance, Inc.",N/A "WFM","Whole Foods Market, Inc.",10.06B "WILN","Wi-LAN Inc",182.47M "WHLM","Wilhelmina International, Inc.",34.91M "WVVI","Willamette Valley Vineyards, In",34.50M "WVVIP","Willamette Valley Vineyards, In",991.40B "WLDN","Willdan Group, Inc.",63.93M "WLFC","Willis Lease Finance Corporatio",157.87M "WLTW","Willis Towers Watson Public Lim",7.69B "WIBC","Wilshire Bancorp, Inc.",773.06M "WIN","Windstream Holdings, Inc.",651.68M "WING","Wingstop Inc.",649.93M "WINA","Winmark Corporation",408.75M "WINS","Wins Finance Holdings Inc.",347.70M "WTFC","Wintrust Financial Corporation",2.02B "WTFCM","Wintrust Financial Corporation",1.29B "WTFCW","Wintrust Financial Corporation",N/A "AGND","WisdomTree Barclays U.S. Aggreg",N/A "AGZD","WisdomTree Barclays U.S. Aggreg",N/A "HYND","WisdomTree BofA Merrill Lynch H",N/A "HYZD","WisdomTree BofA Merrill Lynch H",N/A "CXSE","WisdomTree China ex-State-Owned",N/A "EMCG","WisdomTree Emerging Markets Con",N/A "EMCB","WisdomTree Emerging Markets Cor",N/A "DGRE","WisdomTree Emerging Markets Qua",N/A "DXGE","WisdomTree Germany Hedged Equit",N/A "WETF","WisdomTree Investments, Inc.",1.60B "DXJS","WisdomTree Japan Hedged SmallCa",N/A "JGBB","WisdomTree Japan Interest Rate ",N/A "DXKW","WisdomTree Korea Hedged Equity ",N/A "GULF","WisdomTree Middle East Dividend",N/A "CRDT","WisdomTree Strategic Corporate ",N/A "DGRW","WisdomTree U.S. Quality Dividen",N/A "DGRS","WisdomTree U.S. SmallCap Qualit",N/A "DXPS","WisdomTree United Kingdom Hedge",N/A "UBND","WisdomTree Western Asset Uncons",N/A "WIX","Wix.com Ltd.",790.69M "WLRH","WL Ross Holding Corp.",148.38M "WLRHU","WL Ross Holding Corp.",N/A "WLRHW","WL Ross Holding Corp.",N/A "WMIH","WMI HOLDINGS",492.81M "WBKC","Wolverine Bancorp, Inc.",51.08M "WWD","Woodward, Inc.",2.87B "WKHS","Workhorse Group, Inc.",96.27M "WRLD","World Acceptance Corporation",279.96M "WOWO","Wowo Limited",411.90M "WPCS","WPCS International Incorporated",3.31M "WPPGY","WPP plc",27.27B "WMGI","Wright Medical Group N.V.",878.62M "WMGIZ","Wright Medical Group N.V.",N/A "WSFS","WSFS Financial Corporation",856.58M "WSFSL","WSFS Financial Corporation",794.67M "WSCI","WSI Industries Inc.",10.77M "WVFC","WVS Financial Corp.",22.17M "WYNN","Wynn Resorts, Limited",8.05B "XBIT","XBiotech Inc.",247.82M "XELB","XCEL BRANDS, INC.",76.02M "XCRA","Xcerra Corporation",298.79M "XNCR","Xencor, Inc.",463.06M "XBKS","Xenith Bankshares, Inc.",98.77M "XENE","Xenon Pharmaceuticals Inc.",100.78M "XNPT","XenoPort, Inc.",272.42M "XGTI","XG Technology, Inc",1.76M "XGTIW","XG Technology, Inc",N/A "XLNX","Xilinx, Inc.",12.49B "XOMA","XOMA Corporation",93.52M "XPLR","Xplore Technologies Corp",45.27M "XCOM","Xtera Communications, Inc.",55.58M "XTLB","XTL Biopharmaceuticals Ltd.",14.07M "XNET","Xunlei Limited",402.34M "MESG","Xura, Inc.",450.80M "YHOO","Yahoo! Inc.",29.41B "YNDX","Yandex N.V.",4.26B "YOD","You On Demand Holdings, Inc.",36.04M "YCB","Your Community Bankshares, Inc.",167.80M "YRCW","YRC Worldwide, Inc.",271.00M "YECO","Yulong Eco-Materials Limited",46.13M "YY","YY Inc.",3.07B "ZFGN","Zafgen, Inc.",195.81M "ZAGG","ZAGG Inc",287.42M "ZAIS","ZAIS Group Holdings, Inc.",76.98M "ZBRA","Zebra Technologies Corporation",3.56B "ZLTQ","ZELTIQ Aesthetics, Inc.",853.87M "ZHNE","Zhone Technologies, Inc.",41.24M "Z","Zillow Group, Inc.",3.57B "ZG","Zillow Group, Inc.",3.72B "ZN","Zion Oil & Gas Inc",66.52M "ZNWAA","Zion Oil & Gas Inc",N/A "ZION","Zions Bancorporation",4.54B "ZIONW","Zions Bancorporation",N/A "ZIONZ","Zions Bancorporation",N/A "ZIOP","ZIOPHARM Oncology Inc",827.87M "ZIXI","Zix Corporation",202.15M "ZGNX","Zogenix, Inc.",255.03M "ZSAN","Zosano Pharma Corporation",26.09M "ZUMZ","Zumiez Inc.",545.17M "ZYNE","Zynerba Pharmaceuticals, Inc.",53.17M "ZNGA","Zynga Inc.",1.82B ================================================ FILE: homework_assignments/hw_set6/ols_via_projection/OLS_and_projection.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## OLS Through StatsModels vs Projection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this exercise we're going to run a regression using some trade data. (The regression model is a gravity model, although the details don't really matter for this exercise.) The idea is to compute the OLS coefficients and other related quantities using \n", "\n", "1. A regression package, and\n", "2. The expressions given in the lecture on orthogonal projection.\n", "\n", "Note that you need to download the data set \"trade_data.csv\" as well as this notebook.\n", "\n", "Your task is to complete the notebook, as discussed below.\n", "\n", "First let's try a standard approach, using StatsModels." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from numpy import log\n", "import statsmodels.formula.api as smf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we read in the data." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = pd.read_csv(\"trade_data.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see what it looks like." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0yeariiso3ceiso3cvaluecontigcomlang_offcolonydistdistcapdistwdistwcesellillegdpegdppcepopigdpigdppcipop
002013ABWBEL7743530107847.0707847.0707843.2557843.006004.204710e+1137599.73549811182817NaNNaN102921
112013ABWBHS47125370001588.5151588.5151634.5151628.143007.835118e+0920736.547344377841NaNNaN102921
222013ABWCHE178126260008056.3328056.3328074.218073.511104.772463e+1158996.8961428089346NaNNaN102921
332013ABWCHN2531916800014155.35014155.35014590.9214560.28004.912954e+123619.4391081357380000NaNNaN102921
442013ABWCOL221600860101036.6341036.634929.5887861.2452002.129079e+114497.19693647342363NaNNaN102921
\n", "
" ], "text/plain": [ " Unnamed: 0 year iiso3c eiso3c value contig comlang_off colony \\\n", "0 0 2013 ABW BEL 774353 0 1 0 \n", "1 1 2013 ABW BHS 4712537 0 0 0 \n", "2 2 2013 ABW CHE 17812626 0 0 0 \n", "3 3 2013 ABW CHN 25319168 0 0 0 \n", "4 4 2013 ABW COL 22160086 0 1 0 \n", "\n", " dist distcap distw distwces ell ill egdp \\\n", "0 7847.070 7847.070 7843.255 7843.006 0 0 4.204710e+11 \n", "1 1588.515 1588.515 1634.515 1628.143 0 0 7.835118e+09 \n", "2 8056.332 8056.332 8074.21 8073.511 1 0 4.772463e+11 \n", "3 14155.350 14155.350 14590.92 14560.28 0 0 4.912954e+12 \n", "4 1036.634 1036.634 929.5887 861.2452 0 0 2.129079e+11 \n", "\n", " egdppc epop igdp igdppc ipop \n", "0 37599.735498 11182817 NaN NaN 102921 \n", "1 20736.547344 377841 NaN NaN 102921 \n", "2 58996.896142 8089346 NaN NaN 102921 \n", "3 3619.439108 1357380000 NaN NaN 102921 \n", "4 4497.196936 47342363 NaN NaN 102921 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get a full list of columns." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['Unnamed: 0', 'year', 'iiso3c', 'eiso3c', 'value', 'contig',\n", " 'comlang_off', 'colony', 'dist', 'distcap', 'distw', 'distwces', 'ell',\n", " 'ill', 'egdp', 'egdppc', 'epop', 'igdp', 'igdppc', 'ipop'],\n", " dtype='object')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's regress 'value' on 'egdp', 'igdp' and 'dist', all in logs. To do this we make a `formula` object." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "formula = \"log(value) ~ log(egdp) + log(igdp) + log(dist)\"\n", "model = smf.ols(formula, data)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: log(value) R-squared: 0.615\n", "Model: OLS Adj. R-squared: 0.614\n", "Method: Least Squares F-statistic: 936.4\n", "Date: Thu, 10 Mar 2016 Prob (F-statistic): 0.00\n", "Time: 17:24:14 Log-Likelihood: -4228.1\n", "No. Observations: 1777 AIC: 8464.\n", "Df Residuals: 1773 BIC: 8486.\n", "Df Model: 3 \n", "Covariance Type: HC1 \n", "==============================================================================\n", " coef std err z P>|z| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "Intercept -27.0265 1.198 -22.563 0.000 -29.374 -24.679\n", "log(egdp) 1.2224 0.028 44.202 0.000 1.168 1.277\n", "log(igdp) 0.9679 0.031 30.963 0.000 0.907 1.029\n", "log(dist) -1.4130 0.069 -20.426 0.000 -1.549 -1.277\n", "==============================================================================\n", "Omnibus: 179.128 Durbin-Watson: 1.763\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 316.166\n", "Skew: -0.683 Prob(JB): 2.22e-69\n", "Kurtosis: 4.551 Cond. No. 652.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors are heteroscedasticity robust (HC1)\n" ] } ], "source": [ "result = model.fit(cov_type='HC1')\n", "print(result.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Replication using Projection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's reproduce the same values using the formulas from the lecture on projection. I'm going to be nice and build $\\mathbf X$ and $\\mathbf y$ for you." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data2 = data[['value', 'egdp', 'igdp', 'dist']]\n", "data2 = data2.dropna()\n", "\n", "y = np.asarray(np.log(data2.value))\n", "X = np.ones((len(y), 4))\n", "X[:, 1] = log(data2.egdp)\n", "X[:, 2] = log(data2.igdp)\n", "X[:, 3] = log(data2.dist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now reproduce the coefficients by computing $\\hat \\beta$, using the matrix expression given in the lectures." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Derive betahat using the expression from the lectures\n", "print(betahat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next replicate the value for $R^2$ produced in the table above using the formula given in the lecture slides." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'Rsq' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Derive R^2 using y, Py, etc. as defined in the lecture\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRsq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'Rsq' is not defined" ] } ], "source": [ "# Derive R^2 using y, Py, etc. as defined in the lecture\n", "print(Rsq)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: homework_assignments/hw_set6/ols_via_projection/trade_data.csv ================================================ ,year,iiso3c,eiso3c,value,contig,comlang_off,colony,dist,distcap,distw,distwces,ell,ill,egdp,egdppc,epop,igdp,igdppc,ipop 0,2013,ABW,BEL,774353.0,0,1,0,7847.07,7847.07,7843.255,7843.006,0,0,420470961323.0,37599.7354981,11182817.0,,,102921.0 1,2013,ABW,BHS,4712537.0,0,0,0,1588.515,1588.515,1634.515,1628.143,0,0,7835117785.02,20736.547344,377841.0,,,102921.0 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233,2013,ARG,GNQ,30896.0,0,1,0,8210.386,8210.386,8344.667,8333.956,0,0,9295554257.0,11661.979892899999,797082.0,331013918665.0,7781.54951041,42538304.0 234,2013,ARG,GRC,14577278.0,0,0,0,11711.41,11711.41,11772.04,11765.43,0,0,199825892302.0,18120.6079703,11027549.0,331013918665.0,7781.54951041,42538304.0 235,2013,ARG,GTM,3999809.0,0,1,0,6446.8240000000005,6446.8240000000005,6217.216,6189.426,0,0,36210068021.6,2307.72708693,15690793.0,331013918665.0,7781.54951041,42538304.0 236,2013,ARG,GUY,14979.0,0,0,0,4611.63,4611.63,4441.709,4410.981,0,0,1068555956.23,1404.08623046,761033.0,331013918665.0,7781.54951041,42538304.0 237,2013,ARG,HKG,27045667.0,0,0,0,18478.89,18478.89,18685.81,18677.96,0,0,241780080018.0,33638.9676547,7187500.0,331013918665.0,7781.54951041,42538304.0 238,2013,ARG,HND,8674886.0,0,1,0,6217.953,6217.953,6085.645,6056.114,0,0,11934602023.5,1520.51373592,7849059.0,331013918665.0,7781.54951041,42538304.0 239,2013,ARG,HRV,3458145.0,0,0,0,11656.09,11656.09,11658.35,11650.89,0,0,44921448528.3,10555.5956783,4255700.0,331013918665.0,7781.54951041,42538304.0 240,2013,ARG,HUN,146870777.0,0,0,0,11957.08,11957.08,11973.17,11965.87,1,0,113124111583.0,11434.668345299999,9893082.0,331013918665.0,7781.54951041,42538304.0 241,2013,ARG,IDN,370720708.0,0,0,0,15235.78,15235.78,15581.88,15568.05,0,0,449142287180.0,1787.5009704,251268276.0,331013918665.0,7781.54951041,42538304.0 242,2013,ARG,IRL,219337533.0,0,0,0,11012.97,11012.97,10913.81,10904.64,0,0,217273473449.0,47250.887709300005,4598294.0,331013918665.0,7781.54951041,42538304.0 243,2013,ARG,IRQ,3232.0,0,0,0,13110.86,13110.86,13251.44,13245.13,0,0,89341742737.9,2644.70336956,33781385.0,331013918665.0,7781.54951041,42538304.0 244,2013,ARG,ISL,471548.0,0,0,0,11455.87,11455.87,11375.4,11365.02,0,0,19195496469.6,59288.5449575,323764.0,331013918665.0,7781.54951041,42538304.0 245,2013,ARG,ISR,131573810.0,0,0,0,12241.62,12241.62,12350.1,12343.8,0,0,196180408875.0,24341.511120500003,8059500.0,331013918665.0,7781.54951041,42538304.0 246,2013,ARG,ITA,1634498159.0,0,0,0,11173.56,11173.56,11214.01,11205.74,0,0,1754603531900.0,29129.8111805,60233948.0,331013918665.0,7781.54951041,42538304.0 247,2013,ARG,JOR,1776206.0,0,0,0,12330.83,12330.83,12429.66,12423.32,0,0,18445245027.5,2855.30108785,6460000.0,331013918665.0,7781.54951041,42538304.0 248,2013,ARG,KAZ,232233.0,0,0,0,16110.66,15665.25,15718.94,15693.45,1,0,92422093131.4,5425.33614112,17035275.0,331013918665.0,7781.54951041,42538304.0 249,2013,ARG,KEN,303401.0,0,0,0,10423.36,10423.36,10647.45,10636.27,0,0,28056993796.4,642.141080063,43692881.0,331013918665.0,7781.54951041,42538304.0 250,2013,ARG,KGZ,256.0,0,0,0,15918.23,15918.23,15962.9,15957.5,1,0,3588617094.19,627.424486711,5719600.0,331013918665.0,7781.54951041,42538304.0 251,2013,ARG,KHM,15357821.0,0,0,0,16965.0,16965.0,17208.44,17199.24,0,0,10723297086.9,711.1616919830001,15078564.0,331013918665.0,7781.54951041,42538304.0 252,2013,ARG,KWT,132.0,0,0,0,13226.28,13226.28,13353.04,13346.94,0,0,101552063801.0,28258.445235799998,3593689.0,331013918665.0,7781.54951041,42538304.0 253,2013,ARG,LAO,41007.0,0,0,0,17399.46,17399.46,17689.23,17680.32,1,0,5093639970.87,774.1111827560001,6579985.0,331013918665.0,7781.54951041,42538304.0 254,2013,ARG,LBN,2687937.0,0,0,0,12396.9,12396.9,12494.71,12488.53,0,0,32346776994.6,7198.66992591,4493438.0,331013918665.0,7781.54951041,42538304.0 255,2013,ARG,LBY,30.0,0,0,0,10587.62,10587.62,10820.61,10805.2,0,0,38448222388.2,6136.02013349,6265987.0,331013918665.0,7781.54951041,42538304.0 256,2013,ARG,LKA,6401579.0,0,0,0,14774.54,14774.54,15078.43,15068.91,0,0,41053226583.3,2004.25848671,20483000.0,331013918665.0,7781.54951041,42538304.0 257,2013,ARG,LTU,17105486.0,0,0,0,12731.28,12731.28,12661.51,12654.49,0,0,31509484442.2,10653.4136761,2957689.0,331013918665.0,7781.54951041,42538304.0 258,2013,ARG,LUX,9579742.0,0,0,0,11333.12,11333.12,11311.68,11304.19,1,0,43203208556.1,79511.20538160001,543360.0,331013918665.0,7781.54951041,42538304.0 259,2013,ARG,LVA,591109.0,0,0,0,12773.93,12773.93,12755.68,12748.53,0,0,19392913565.3,9635.52653064,2012647.0,331013918665.0,7781.54951041,42538304.0 260,2013,ARG,MAC,120710.0,0,0,0,18451.92,18451.92,.,.,0,0,30306411098.5,53351.0976005,568056.0,331013918665.0,7781.54951041,42538304.0 261,2013,ARG,MAR,44786590.0,0,0,0,9348.923,9348.923,9321.642,9309.05,0,0,84971840153.9,2499.00830469,33452686.0,331013918665.0,7781.54951041,42538304.0 262,2013,ARG,MDA,10024.0,0,0,0,12571.03,12571.03,12601.92,12595.67,0,0,4048371113.17,1137.64114904,3558566.0,331013918665.0,7781.54951041,42538304.0 263,2013,ARG,MDG,744276.0,0,0,0,10212.05,10212.05,10439.18,10420.45,0,0,6205579787.84,270.695734179,22924557.0,331013918665.0,7781.54951041,42538304.0 264,2013,ARG,MKD,2387666.0,0,0,0,11787.62,11787.62,11816.62,11810.01,1,0,7959429439.83,3840.41703348,2072543.0,331013918665.0,7781.54951041,42538304.0 265,2013,ARG,MLT,4434251.0,0,0,0,10906.92,10906.92,10951.72,10944.78,0,0,7095691031.17,16759.864874000003,423374.0,331013918665.0,7781.54951041,42538304.0 266,2013,ARG,MMR,8182733.0,0,0,0,16823.83,16823.83,17150.68,17141.46,0,0,,,52983829.0,331013918665.0,7781.54951041,42538304.0 267,2013,ARG,MNG,,0,0,0,18125.58,18125.58,17994.31,17985.13,1,0,5080026029.69,1776.74602164,2859174.0,331013918665.0,7781.54951041,42538304.0 268,2013,ARG,MOZ,2852914.0,0,0,0,8503.743,8503.743,9263.54,9221.291,0,0,11128740364.1,420.473218683,26467180.0,331013918665.0,7781.54951041,42538304.0 269,2013,ARG,MRT,74.0,0,0,0,7401.539000000001,7401.539000000001,7513.115,7498.961,0,0,3270693687.82,844.55475526,3872684.0,331013918665.0,7781.54951041,42538304.0 270,2013,ARG,MUS,187026.0,0,0,0,10927.78,10927.78,11173.65,11160.83,0,0,8661733284.63,6881.7484125,1258653.0,331013918665.0,7781.54951041,42538304.0 271,2013,ARG,MWI,4456858.0,0,0,0,9345.976,9345.976,9601.087,9587.969,1,0,4333271640.26,267.64903746,16190126.0,331013918665.0,7781.54951041,42538304.0 272,2013,ARG,MYS,446540731.0,0,0,0,15970.66,15970.66,16333.03,16320.67,0,0,207951396117.0,7057.484158600001,29465372.0,331013918665.0,7781.54951041,42538304.0 273,2013,ARG,NAM,8224808.0,0,0,0,7342.774,7342.774,7605.196,7582.418,0,0,10513028491.2,4480.1262815,2346592.0,331013918665.0,7781.54951041,42538304.0 274,2013,ARG,NER,4738.0,0,0,0,8342.004,8342.004,8729.721,8710.78,1,0,5242200415.91,285.54058145700003,18358863.0,331013918665.0,7781.54951041,42538304.0 275,2013,ARG,NGA,258905380.0,0,0,0,7938.363,8446.601999999999,8365.153,8342.667,0,0,183309437474.0,1060.7171157999999,172816517.0,331013918665.0,7781.54951041,42538304.0 276,2013,ARG,NIC,399687.0,0,1,0,5975.677,5975.677,5753.554,5723.027,0,0,8350353262.77,1404.44844223,5945646.0,331013918665.0,7781.54951041,42538304.0 277,2013,ARG,NOR,88179320.0,0,0,0,12270.74,12270.74,12204.09,12192.73,0,0,337855180442.0,66511.861302,5079623.0,331013918665.0,7781.54951041,42538304.0 278,2013,ARG,NPL,205263.0,0,0,0,16522.17,16522.17,16648.44,16641.24,1,0,11370379663.8,408.492452852,27834981.0,331013918665.0,7781.54951041,42538304.0 279,2013,ARG,NZL,16250062.0,0,0,0,9729.112,9729.112,10156.1,10144.12,0,0,129714285714.0,29201.1178754,4442100.0,331013918665.0,7781.54951041,42538304.0 280,2013,ARG,OMN,1445054.0,0,0,0,13896.67,13896.67,13922.6,13913.1,0,0,45303761304.2,11595.797730799999,3906912.0,331013918665.0,7781.54951041,42538304.0 281,2013,ARG,PER,133022227.0,0,1,0,3133.925,3133.925,2954.814,2829.077,0,0,124791491189.0,4082.76162394,30565461.0,331013918665.0,7781.54951041,42538304.0 282,2013,ARG,PHL,104328376.0,0,0,0,17801.66,17801.66,17783.11,17771.71,0,0,155608838544.0,1594.81567728,97571676.0,331013918665.0,7781.54951041,42538304.0 283,2013,ARG,PNG,12776.0,0,0,0,14425.68,14425.68,14618.38,14607.73,0,0,8204074240.01,1122.48281539,7308864.0,331013918665.0,7781.54951041,42538304.0 284,2013,ARG,PRT,228152826.0,0,0,0,9621.854,9621.854,9636.49,9621.497,0,0,188596194503.0,18034.892819099998,10457295.0,331013918665.0,7781.54951041,42538304.0 285,2013,ARG,PRY,526187886.0,1,1,0,1051.13,1051.13,1030.452,843.2077,1,0,13122275725.6,2029.5310084000002,6465669.0,331013918665.0,7781.54951041,42538304.0 286,2013,ARG,QAT,1060504747.0,0,0,0,13337.25,13337.25,13482.19,13475.79,0,0,129889195708.0,61814.0853171,2101288.0,331013918665.0,7781.54951041,42538304.0 287,2013,ARG,RUS,291279827.0,0,0,0,13505.36,13505.36,14195.33,14105.4,0,0,993468541218.0,6922.79231916,143506911.0,331013918665.0,7781.54951041,42538304.0 288,2013,ARG,SEN,99451.0,0,0,0,6994.295,6994.295,7063.377,7051.929,0,0,11328685209.4,796.614341342,14221041.0,331013918665.0,7781.54951041,42538304.0 289,2013,ARG,SGP,111040746.0,0,0,0,15891.11,15891.11,16120.1,16110.5,0,0,202421849200.0,37491.0818639,5399200.0,331013918665.0,7781.54951041,42538304.0 290,2013,ARG,SLE,651813.0,0,0,0,6759.124,6759.124,6878.778,6867.2,0,0,3118731419.71,504.742286515,6178859.0,331013918665.0,7781.54951041,42538304.0 291,2013,ARG,SLV,609468.0,0,1,0,6280.052,6280.052,6042.198,6013.568,0,0,19420611604.6,3189.12100685,6089644.0,331013918665.0,7781.54951041,42538304.0 292,2013,ARG,SMR,268160.0,0,0,0,11313.26,11313.26,11326.95,11319.98,0,0,,,31391.0,331013918665.0,7781.54951041,42538304.0 293,2013,ARG,SVK,66905985.0,0,0,0,11879.9,11879.9,12027.78,12019.98,1,0,83210919039.9,15371.305766999998,5413393.0,331013918665.0,7781.54951041,42538304.0 294,2013,ARG,SVN,22443230.0,0,0,0,11580.75,11580.75,11616.15,11609.08,0,0,38397711727.4,18640.0911707,2059953.0,331013918665.0,7781.54951041,42538304.0 295,2013,ARG,SWE,264749443.0,0,0,0,12585.03,12585.03,12404.68,12393.87,0,0,436372281918.0,45453.651560800005,9600379.0,331013918665.0,7781.54951041,42538304.0 296,2013,ARG,SWZ,28500.0,0,0,0,8359.226,8359.226,8618.807,8603.286,1,0,3120501208.82,2495.12146877,1250641.0,331013918665.0,7781.54951041,42538304.0 297,2013,ARG,SYC,60349.0,0,0,0,11873.59,11873.59,12104.74,12094.29,0,0,1388712937.27,15447.307422299999,89900.0,331013918665.0,7781.54951041,42538304.0 298,2013,ARG,SYR,86052.0,0,0,0,12444.37,12444.37,12679.53,12671.83,0,0,,,21789415.0,331013918665.0,7781.54951041,42538304.0 299,2013,ARG,THA,936819638.0,0,0,0,16890.18,16890.18,17137.09,17126.63,0,0,230371292593.0,3415.36598877,67451422.0,331013918665.0,7781.54951041,42538304.0 300,2013,ARG,TJK,2316.0,0,0,0,15376.08,15376.08,15507.14,15501.95,1,0,3944940642.21,486.315605481,8111894.0,331013918665.0,7781.54951041,42538304.0 301,2013,ARG,TKM,700.0,0,0,0,14471.17,14471.17,14667.31,14658.22,1,0,18639001814.9,3557.00167915,5240088.0,331013918665.0,7781.54951041,42538304.0 302,2013,ARG,TUN,18147988.0,0,0,0,10668.78,10668.78,10649.98,10642.17,0,0,43322022840.0,3979.42615533,10886500.0,331013918665.0,7781.54951041,42538304.0 303,2013,ARG,TUR,381105382.0,0,0,0,12268.67,12502.27,12474.38,12462.04,0,0,654068728412.0,8719.73026299,75010202.0,331013918665.0,7781.54951041,42538304.0 304,2013,ARG,TZA,203250.0,0,0,0,10291.12,10012.61,10335.99,10318.38,0,0,27673028976.3,578.672725195,50213457.0,331013918665.0,7781.54951041,42538304.0 305,2013,ARG,UGA,256.0,0,0,0,10137.2,10137.2,10340.32,10329.29,1,0,15698319188.7,429.227929824,36573387.0,331013918665.0,7781.54951041,42538304.0 306,2013,ARG,UKR,18907709.0,0,0,0,12845.99,12845.99,12969.89,12959.39,0,0,95477307411.0,2098.88210516,45489600.0,331013918665.0,7781.54951041,42538304.0 307,2013,ARG,URY,475385424.0,1,1,0,215.0746,215.0746,529.559,308.4921,0,0,26486558038.4,7771.94805424,3407969.0,331013918665.0,7781.54951041,42538304.0 308,2013,ARG,VCT,,0,0,0,5335.351,5335.351,5169.55,5142.291,0,0,603674018.788,5521.72856465,109327.0,331013918665.0,7781.54951041,42538304.0 309,2013,ARG,VNM,197766586.0,0,0,0,17877.15,17877.15,17531.51,17513.16,0,0,92277145925.3,1028.62866366,89708900.0,331013918665.0,7781.54951041,42538304.0 310,2013,ARG,YEM,10781.0,0,0,0,12133.34,12133.34,12275.93,12267.06,0,0,18115034805.5,709.469347535,25533217.0,331013918665.0,7781.54951041,42538304.0 311,2013,ARG,ZAF,242502380.0,0,0,0,6886.516,8145.1359999999995,8071.708,8024.234,0,0,323743376883.0,6090.26831183,53157490.0,331013918665.0,7781.54951041,42538304.0 312,2013,ARG,ALB,155425.0,0,0,0,11632.19,11632.19,11662.56,11655.91,0,0,11346755234.8,3916.23123721,2897366.0,331013918665.0,7781.54951041,42538304.0 313,2013,ARG,BDI,,0,0,0,9604.157,9604.157,9834.967,9824.102,1,0,1577689946.2,150.74490032,10465959.0,331013918665.0,7781.54951041,42538304.0 314,2013,ARG,BRB,63.0,0,0,0,5317.701999999999,5317.701999999999,5164.769,5138.219,0,0,3443813562.03,12190.3610299,282503.0,331013918665.0,7781.54951041,42538304.0 315,2013,ARG,BRN,8137.0,0,0,0,16656.76,16656.76,16820.98,16812.58,0,0,10103984048.4,24554.0913791,411499.0,331013918665.0,7781.54951041,42538304.0 316,2013,ARG,DMA,7111.0,0,0,0,5569.61,5569.61,5415.011,5388.971,0,0,431857638.889,5997.60626191,72005.0,331013918665.0,7781.54951041,42538304.0 317,2013,ARG,FJI,43572.0,0,0,0,11625.82,11625.82,11587.91,11580.67,0,0,3370517356.94,3828.01490191,880487.0,331013918665.0,7781.54951041,42538304.0 318,2013,ARG,MLI,,0,0,0,7493.104,7493.104,7671.932,7656.399,1,0,7285189363.25,439.075866254,16592097.0,331013918665.0,7781.54951041,42538304.0 319,2013,ARG,RWA,,0,0,0,9763.369,9763.369,9943.114,9932.523,1,0,4724955978.85,426.51340134300006,11078095.0,331013918665.0,7781.54951041,42538304.0 320,2013,ARG,SLB,,0,0,0,13668.22,13668.22,13658,13651.09,0,0,630859261.257,1125.15808566,560685.0,331013918665.0,7781.54951041,42538304.0 321,2013,ARG,SOM,431624.0,0,0,0,11411.45,11411.45,11676.13,11660.56,0,0,,,10268157.0,331013918665.0,7781.54951041,42538304.0 322,2013,ARG,UZB,939894.0,0,0,0,15457.45,15457.45,15463.13,15453.45,1,0,27302754038.6,902.773318915,30243200.0,331013918665.0,7781.54951041,42538304.0 323,2013,ARG,ABW,,0,1,0,5396.22,5396.22,5187.788,5157.126,0,0,,,102921.0,331013918665.0,7781.54951041,42538304.0 324,2013,ARG,AGO,13018.0,0,0,0,7792.03,7792.03,7977.916,7961.882,0,0,58786650192.9,2507.08562613,23448202.0,331013918665.0,7781.54951041,42538304.0 325,2013,ARG,BEN,1116.0,0,0,0,7854.464,7884.138000000001,8050.423,8038.748,0,0,6017115759.78,582.927777615,10322232.0,331013918665.0,7781.54951041,42538304.0 326,2013,ARG,BFA,421.0,0,0,0,7952.696999999999,7952.696999999999,7990.589,7978.479,1,0,8886770898.14,520.164055681,17084554.0,331013918665.0,7781.54951041,42538304.0 327,2013,ARG,BWA,1110.0,0,0,0,8001.549,8001.549,8336.667,8318.847,1,0,15085071766.2,6930.85341498,2176510.0,331013918665.0,7781.54951041,42538304.0 328,2013,ARG,ERI,,0,0,0,11643.69,11643.69,11835.94,11827.71,0,0,1245302403.11,249.11907342700002,4998824.0,331013918665.0,7781.54951041,42538304.0 329,2013,ARG,GMB,772.0,0,0,0,6944.681,6944.681,7004.497,6993.322,0,0,832443850.134,445.90158014300005,1866878.0,331013918665.0,7781.54951041,42538304.0 330,2013,ARG,GNB,,0,0,0,6874.063,6874.063,6938.296,6927.295,0,0,737822758.1760001,419.900291369,1757138.0,331013918665.0,7781.54951041,42538304.0 331,2013,ARG,GRD,,0,0,0,5212.585,5212.585,5056.36,5028.227,0,0,678048976.06,6402.60784556,105902.0,331013918665.0,7781.54951041,42538304.0 332,2013,ARG,HTI,108239.0,0,0,0,6101.2080000000005,6101.2080000000005,5917.815,5890.566,0,0,5064322972.81,485.495358495,10431249.0,331013918665.0,7781.54951041,42538304.0 333,2013,ARG,KNA,63183.0,0,0,0,5799.189,5799.189,5921.451,5724.803,0,0,586391757.429,10798.912679899999,54301.0,331013918665.0,7781.54951041,42538304.0 334,2013,ARG,LBR,,0,0,0,6772.831,6772.831,6892.73,6880.921,0,0,975319024.053,227.15160380700001,4293692.0,331013918665.0,7781.54951041,42538304.0 335,2013,ARG,LCA,,0,0,0,5424.776,5424.776,5259.429,5232.753,0,0,1030152086.96,5650.70671108,182305.0,331013918665.0,7781.54951041,42538304.0 336,2013,ARG,LSO,314.0,0,0,0,7880.847,7880.847,8138.023,8121.328,1,0,2014152758.14,966.919719651,2083061.0,331013918665.0,7781.54951041,42538304.0 337,2013,ARG,MDV,,0,0,0,14040.08,14040.08,14227.5,14216.49,0,0,2011420213.61,5728.73026937,351111.0,331013918665.0,7781.54951041,42538304.0 338,2013,ARG,MNP,1030.0,0,0,0,16782.33,16782.33,16686.33,16681.36,0,0,,,53869.0,331013918665.0,7781.54951041,42538304.0 339,2013,ARG,SDN,238604.0,0,0,0,11085.43,11085.43,11159.57,11140.11,0,0,37130724862.7,964.0564267780001,38515095.0,331013918665.0,7781.54951041,42538304.0 340,2013,ARG,TCD,65518.0,0,0,0,9322.412,9322.412,9505.11,9492.863,1,0,9704471387.48,738.219069673,13145788.0,331013918665.0,7781.54951041,42538304.0 341,2013,ARG,TGO,2741.0,0,0,0,7739.3640000000005,7739.3640000000005,7919.379,7908.482,0,0,2892798974.63,417.508485282,6928719.0,331013918665.0,7781.54951041,42538304.0 342,2013,ARG,VUT,,0,0,0,12384.6,12384.6,12422.24,12414.3,0,0,528893109.429,2089.12412628,253165.0,331013918665.0,7781.54951041,42538304.0 343,2013,ARG,ZMB,332.0,0,0,0,8756.319,8756.319,9066.287,9049.746,1,0,15317964948.6,1004.7145837000002,15246086.0,331013918665.0,7781.54951041,42538304.0 344,2013,ARG,ZWE,2194895.0,0,0,0,8860.55,8860.55,8983.306,8966.68,1,0,6725359135.1,451.42419144,14898092.0,331013918665.0,7781.54951041,42538304.0 345,2013,ARM,BEL,70188189.0,0,0,0,3299.886,3299.886,3303.218,3301.886,0,1,420470961323.0,37599.7354981,11182817.0,6875045746.66,2297.66196376,2992192.0 346,2013,ARM,CHE,172283914.0,0,0,0,3051.8540000000003,3051.8540000000003,3033.851,3031.296,1,1,477246305814.0,58996.896141499994,8089346.0,6875045746.66,2297.66196376,2992192.0 347,2013,ARM,CHN,368606048.0,0,0,0,5947.15,5947.15,6213.067,6183.089,0,1,4912954256930.0,3619.43910837,1357380000.0,6875045746.66,2297.66196376,2992192.0 348,2013,ARM,COL,497247.0,0,0,0,12118.5,12118.5,12003.47,11998.51,0,1,212907929816.0,4497.19693577,47342363.0,6875045746.66,2297.66196376,2992192.0 349,2013,ARM,DOM,107646.0,0,0,0,10612.22,10612.22,10618.4,10618.08,0,1,50033390849.2,4866.39484098,10281408.0,6875045746.66,2297.66196376,2992192.0 350,2013,ARM,ESP,51223675.0,0,0,0,4040.452,4040.452,3994.318,3965.003,0,1,1172482153960.0,25149.743076400002,46620045.0,6875045746.66,2297.66196376,2992192.0 351,2013,ARM,FRA,61772743.0,0,0,0,3434.071,3434.071,3402.57,3391.306,0,1,2357143265760.0,35754.652407199996,65925498.0,6875045746.66,2297.66196376,2992192.0 352,2013,ARM,GBR,43742312.0,0,0,0,3619.365,3619.365,3707.216,3703.651,0,1,2577048727270.0,40199.3169439,64106779.0,6875045746.66,2297.66196376,2992192.0 353,2013,ARM,KOR,49601279.0,0,0,0,6882.943,6882.943,6954.825,6952.74,0,1,1199003686670.0,23875.1809908,50219669.0,6875045746.66,2297.66196376,2992192.0 354,2013,ARM,MEX,7179819.0,0,0,0,12406.08,12406.08,12232.21,12224.33,0,1,1045697869840.0,8450.75924284,123740109.0,6875045746.66,2297.66196376,2992192.0 355,2013,ARM,NLD,31992377.0,0,0,0,3282.178,3282.178,3263.716,3262.345,0,1,720805621191.0,42893.7807116,16804432.0,6875045746.66,2297.66196376,2992192.0 356,2013,ARM,PAN,1812532.0,0,0,0,12105.08,12105.08,12160.7,12159.72,0,1,29908913759.9,7859.01341755,3805683.0,6875045746.66,2297.66196376,2992192.0 357,2013,ARM,POL,42653158.0,0,0,0,2236.784,2236.784,2319.428,2310.361,0,1,415521978597.0,10923.2344281,38040196.0,6875045746.66,2297.66196376,2992192.0 358,2013,ARM,SUR,35.0,0,0,0,10415.96,10415.96,10375.76,10295.31,0,1,2464082868.05,4619.14493964,533450.0,6875045746.66,2297.66196376,2992192.0 359,2013,ARM,TTO,,0,0,0,10579.57,10579.57,10589.24,10588.99,0,1,19145432662.8,14200.3149757,1348240.0,6875045746.66,2297.66196376,2992192.0 360,2013,ARM,USA,117297453.0,0,0,0,9088.867,9416.021999999999,10272.02,10171.14,0,1,14451509500000.0,45660.7337641,316497531.0,6875045746.66,2297.66196376,2992192.0 361,2013,ARM,DEU,172496372.0,0,0,0,3126.0559999999996,2724.171,2934.269,2924.09,0,1,3162014177340.0,39208.7600724,80645605.0,6875045746.66,2297.66196376,2992192.0 362,2013,ARM,IND,66248836.0,0,0,0,3245.0640000000003,3245.0640000000003,3703.2,3649.393,0,1,1489775906250.0,1164.34327261,1279498874.0,6875045746.66,2297.66196376,2992192.0 363,2013,ARM,IRN,184219663.0,1,0,0,789.0012,789.0012,908.7886,752.3473,0,1,228104456699.0,2956.54216401,77152445.0,6875045746.66,2297.66196376,2992192.0 364,2013,ARM,JPN,44187726.0,0,0,0,7943.638000000001,7943.638000000001,7772.805,7768.262,0,1,4784541597490.0,37573.373733099994,127338621.0,6875045746.66,2297.66196376,2992192.0 365,2013,ARM,PAK,3791536.0,0,0,0,2635.9640000000004,2635.9640000000004,2712.69,2707.829,0,1,143816996323.0,793.724245977,181192646.0,6875045746.66,2297.66196376,2992192.0 366,2013,ARM,SAU,3294397.0,0,0,0,1741.7379999999998,1741.7379999999998,1878.971,1836.105,0,1,505813554459.0,16748.2103341,30201051.0,6875045746.66,2297.66196376,2992192.0 367,2013,ARM,AFG,5586.0,0,0,0,2266.15,2266.15,2171.302,2155.121,1,1,12679050960.3,413.23395943199995,30682500.0,6875045746.66,2297.66196376,2992192.0 368,2013,ARM,ARE,66917114.0,0,0,0,1979.161,1979.161,1965.565,1963.485,0,1,234968702615.0,25992.176376400002,9039978.0,6875045746.66,2297.66196376,2992192.0 369,2013,ARM,ARG,11603013.0,0,0,0,13417.64,13417.64,13505.4,13499.73,0,1,331013918665.0,7781.54951041,42538304.0,6875045746.66,2297.66196376,2992192.0 370,2013,ARM,ATG,120.0,0,0,0,10114.07,10114.07,10124.28,10124.02,0,1,1033152446.13,11481.385187799999,89985.0,6875045746.66,2297.66196376,2992192.0 371,2013,ARM,AUS,6015754.0,0,0,0,13666.9,13581.9,13188.62,13129.53,0,1,867152305474.0,37497.070617000005,23125868.0,6875045746.66,2297.66196376,2992192.0 372,2013,ARM,AUT,54691142.0,0,0,0,2399.4320000000002,2399.4320000000002,2481.713,2474.974,1,1,349523317995.0,41220.410466,8479375.0,6875045746.66,2297.66196376,2992192.0 373,2013,ARM,BGD,5686163.0,0,0,0,4642.789000000001,4642.789000000001,4647.69,4645.004,0,1,112095671740.0,713.2701102189999,157157394.0,6875045746.66,2297.66196376,2992192.0 374,2013,ARM,BGR,22005750.0,0,0,0,1785.844,1785.844,1659.677,1646.896,0,1,34927663768.9,4807.58580819,7265115.0,6875045746.66,2297.66196376,2992192.0 375,2013,ARM,BHR,4962.0,0,0,0,1656.715,1656.715,1661.172,1659.884,0,1,23315293065.1,17277.920973199998,1349427.0,6875045746.66,2297.66196376,2992192.0 376,2013,ARM,BIH,163841.0,0,0,0,2184.283,2184.283,2229.784,2227.134,0,1,13032998560.0,3408.62719376,3823533.0,6875045746.66,2297.66196376,2992192.0 377,2013,ARM,BLR,37663429.0,0,0,0,1983.94,1983.94,1943.57,1934.979,0,1,46593901367.6,4922.23762599,9466000.0,6875045746.66,2297.66196376,2992192.0 378,2013,ARM,BLZ,,0,0,0,12023.53,12023.53,11977.57,11977.12,0,1,1362082437.8,3957.32172879,344193.0,6875045746.66,2297.66196376,2992192.0 379,2013,ARM,BMU,82.0,0,0,0,9176.896,9176.896,9168.37,9168.106,0,1,4461518517.58,68637.69045980001,65001.0,6875045746.66,2297.66196376,2992192.0 380,2013,ARM,BOL,15532.0,0,0,0,13105.0,13028.71,12979.56,12977.33,1,1,14119217520.6,1357.62607661,10399931.0,6875045746.66,2297.66196376,2992192.0 381,2013,ARM,BRA,87299935.0,0,0,0,11769.69,11342.55,11270.88,11214.9,0,1,1204333024530.0,5896.09663075,204259377.0,6875045746.66,2297.66196376,2992192.0 382,2013,ARM,CAN,23178617.0,0,0,0,9139.136,8799.446,9157.499,9131.867,0,1,1327353688730.0,37753.632505400004,35158304.0,6875045746.66,2297.66196376,2992192.0 383,2013,ARM,CHL,2028073.0,0,0,0,14338.96,14338.96,14354.08,14347.47,0,1,171771363935.0,9773.15635254,17575833.0,6875045746.66,2297.66196376,2992192.0 384,2013,ARM,CIV,2708508.0,0,0,0,6198.215,6176.733,6199.908,6198.636,0,1,22039806841.8,1019.30012879,21622490.0,6875045746.66,2297.66196376,2992192.0 385,2013,ARM,CMR,167100.0,0,0,0,5224.684,5224.684,5057.368,5030.958,0,1,22015198848.7,991.17708853,22211166.0,6875045746.66,2297.66196376,2992192.0 386,2013,ARM,COM,4046.0,0,0,0,5772.269,5772.269,5806.193,5805.767,0,1,450313022.567,599.061886062,751697.0,6875045746.66,2297.66196376,2992192.0 387,2013,ARM,CRI,3107755.0,0,0,0,12346.34,12346.34,12352.58,12352.34,0,1,28444523960.8,6043.7541469,4706433.0,6875045746.66,2297.66196376,2992192.0 388,2013,ARM,CUB,147782.0,0,0,0,11098.03,11098.03,11019.55,11018.45,0,1,60285673300.0,5305.66748265,11362505.0,6875045746.66,2297.66196376,2992192.0 389,2013,ARM,CYP,1627807.0,0,0,0,1129.579,1129.579,1153.883,1151.781,0,1,19094028254.5,22152.4124729,1141652.0,6875045746.66,2297.66196376,2992192.0 390,2013,ARM,CZE,20179447.0,0,0,0,2582.411,2582.411,2502.72,2495.708,1,1,154008135273.0,14647.531971200002,10514272.0,6875045746.66,2297.66196376,2992192.0 391,2013,ARM,DJI,136.0,0,0,0,3184.07,3184.07,3199.006,3198.55,0,1,1032256019.99,1193.97518257,864554.0,6875045746.66,2297.66196376,2992192.0 392,2013,ARM,DNK,5373037.0,0,0,0,2897.1090000000004,2897.1090000000004,2971.627,2968.326,0,1,265136470510.0,47219.8898419,5614932.0,6875045746.66,2297.66196376,2992192.0 393,2013,ARM,ECU,8290566.0,0,0,0,12772.0,12772.0,12946.48,12945.02,0,1,58238429057.6,3718.61751159,15661312.0,6875045746.66,2297.66196376,2992192.0 394,2013,ARM,EGY,5192495.0,0,0,0,1647.892,1647.892,1673.043,1668.004,0,1,128583201068.0,1467.61173581,87613909.0,6875045746.66,2297.66196376,2992192.0 395,2013,ARM,EST,1667678.0,0,0,0,2546.553,2546.553,2486.613,2483.785,0,1,15890063424.9,12056.221239499999,1317997.0,6875045746.66,2297.66196376,2992192.0 396,2013,ARM,ETH,340659.0,0,0,0,3510.324,3510.324,3489.726,3480.32,1,1,27738863571.7,293.351740288,94558374.0,6875045746.66,2297.66196376,2992192.0 397,2013,ARM,FIN,22452097.0,0,0,0,2597.746,2597.746,2692.99,2686.226,0,1,212432657630.0,39057.5016069,5438972.0,6875045746.66,2297.66196376,2992192.0 398,2013,ARM,GAB,39928.0,0,0,0,5674.918000000001,5674.918000000001,5694.129,5692.59,0,1,11806367779.9,7153.85259251,1650351.0,6875045746.66,2297.66196376,2992192.0 399,2013,ARM,GEO,64727066.0,1,0,0,172.7219,172.7219,222.0754,190.7368,0,1,9692010303.74,2159.9238509,4487200.0,6875045746.66,2297.66196376,2992192.0 400,2013,ARM,GHA,684664.0,0,0,0,5893.944,5893.944,5870.081,5867.609,0,1,19682365238.6,752.25654578,26164432.0,6875045746.66,2297.66196376,2992192.0 401,2013,ARM,GRC,22031497.0,0,0,0,1806.0639999999999,1806.0639999999999,1829.196,1827.634,0,1,199825892302.0,18120.6079703,11027549.0,6875045746.66,2297.66196376,2992192.0 402,2013,ARM,GTM,35021.0,0,0,0,12364.26,12364.26,12370.98,12370.65,0,1,36210068021.6,2307.72708693,15690793.0,6875045746.66,2297.66196376,2992192.0 403,2013,ARM,HKG,2194856.0,0,0,0,6748.725,6748.725,6733.66,6733.441,0,1,241780080018.0,33638.9676547,7187500.0,6875045746.66,2297.66196376,2992192.0 404,2013,ARM,HND,251382.0,0,0,0,12203.89,12203.89,12142.65,12142.12,0,1,11934602023.5,1520.51373592,7849059.0,6875045746.66,2297.66196376,2992192.0 405,2013,ARM,HRV,2649188.0,0,0,0,2391.389,2391.389,2370.957,2366.491,0,1,44921448528.3,10555.5956783,4255700.0,6875045746.66,2297.66196376,2992192.0 406,2013,ARM,HUN,14810292.0,0,0,0,2186.893,2186.893,2172.696,2168.28,1,1,113124111583.0,11434.668345299999,9893082.0,6875045746.66,2297.66196376,2992192.0 407,2013,ARM,IDN,30302252.0,0,0,0,8175.6230000000005,8175.6230000000005,8202.605,8162.54,0,1,449142287180.0,1787.5009704,251268276.0,6875045746.66,2297.66196376,2992192.0 408,2013,ARM,IRL,6558459.0,0,0,0,4037.116,4037.116,4082.171,4080.422,0,1,217273473449.0,47250.887709300005,4598294.0,6875045746.66,2297.66196376,2992192.0 409,2013,ARM,IRQ,1848254.0,0,0,0,762.4228,762.4228,767.0289,707.7197,0,1,89341742737.9,2644.70336956,33781385.0,6875045746.66,2297.66196376,2992192.0 410,2013,ARM,ISL,59051.0,0,0,0,4948.93,4948.93,4924.119,4922.811,0,1,19195496469.6,59288.5449575,323764.0,6875045746.66,2297.66196376,2992192.0 411,2013,ARM,ISR,8555553.0,0,0,0,1254.7089999999998,1254.7089999999998,1263.304,1261.861,0,1,196180408875.0,24341.511120500003,8059500.0,6875045746.66,2297.66196376,2992192.0 412,2013,ARM,ITA,162046207.0,0,0,0,2677.67,2677.67,2729.921,2717.341,0,1,1754603531900.0,29129.8111805,60233948.0,6875045746.66,2297.66196376,2992192.0 413,2013,ARM,JOR,742875.0,0,0,0,1196.113,1196.113,1211.499,1208.829,0,1,18445245027.5,2855.30108785,6460000.0,6875045746.66,2297.66196376,2992192.0 414,2013,ARM,KAZ,729669.0,0,0,0,2700.2509999999997,2407.558,2377.822,2236.676,1,1,92422093131.4,5425.33614112,17035275.0,6875045746.66,2297.66196376,2992192.0 415,2013,ARM,KEN,2761292.0,0,0,0,4680.579000000001,4680.579000000001,4696.988,4692.469,0,1,28056993796.4,642.141080063,43692881.0,6875045746.66,2297.66196376,2992192.0 416,2013,ARM,KGZ,5189.0,0,0,0,2512.077,2512.077,2479.68,2475.464,1,1,3588617094.19,627.424486711,5719600.0,6875045746.66,2297.66196376,2992192.0 417,2013,ARM,KHM,1278816.0,0,0,0,6684.9619999999995,6684.9619999999995,6630.66,6628.881,0,1,10723297086.9,711.1616919830001,15078564.0,6875045746.66,2297.66196376,2992192.0 418,2013,ARM,KWT,346713.0,0,0,0,1248.981,1248.981,1263.836,1262.239,0,1,101552063801.0,28258.445235799998,3593689.0,6875045746.66,2297.66196376,2992192.0 419,2013,ARM,LAO,22308.0,0,0,0,6049.0,6049.0,6133.226,6122.847,1,1,5093639970.87,774.1111827560001,6579985.0,6875045746.66,2297.66196376,2992192.0 420,2013,ARM,LBN,1728054.0,0,0,0,1063.763,1063.763,1073.455,1071.889,0,1,32346776994.6,7198.66992591,4493438.0,6875045746.66,2297.66196376,2992192.0 421,2013,ARM,LKA,2452157.0,0,0,0,5106.343,5106.343,5074.464,5072.375,0,1,41053226583.3,2004.25848671,20483000.0,6875045746.66,2297.66196376,2992192.0 422,2013,ARM,LTU,1410293.0,0,0,0,2151.4970000000003,2151.4970000000003,2255.754,2250.669,0,1,31509484442.2,10653.4136761,2957689.0,6875045746.66,2297.66196376,2992192.0 423,2013,ARM,LUX,531449.0,0,0,0,3160.368,3160.368,3169.138,3168.397,1,1,43203208556.1,79511.20538160001,543360.0,6875045746.66,2297.66196376,2992192.0 424,2013,ARM,LVA,1709454.0,0,0,0,2376.555,2376.555,2357.776,2353.984,0,1,19392913565.3,9635.52653064,2012647.0,6875045746.66,2297.66196376,2992192.0 425,2013,ARM,MAC,3456.0,0,0,0,6698.553000000001,6698.553000000001,.,.,0,1,30306411098.5,53351.0976005,568056.0,6875045746.66,2297.66196376,2992192.0 426,2013,ARM,MAR,2027689.0,0,0,0,4545.3859999999995,4545.3859999999995,4569.928,4561.749,0,1,84971840153.9,2499.00830469,33452686.0,6875045746.66,2297.66196376,2992192.0 427,2013,ARM,MDA,1255371.0,0,0,0,1469.285,1469.285,1478.61,1475.142,0,1,4048371113.17,1137.64114904,3558566.0,6875045746.66,2297.66196376,2992192.0 428,2013,ARM,MDG,86427.0,0,0,0,6579.581999999999,6579.581999999999,6619.228,6608.028,0,1,6205579787.84,270.695734179,22924557.0,6875045746.66,2297.66196376,2992192.0 429,2013,ARM,MKD,281754.0,0,0,0,1936.723,1936.723,1940.126,1938.398,1,1,7959429439.83,3840.41703348,2072543.0,6875045746.66,2297.66196376,2992192.0 430,2013,ARM,MLT,34877.0,0,0,0,2656.562,2656.562,2670.516,2669.944,0,1,7095691031.17,16759.864874000003,423374.0,6875045746.66,2297.66196376,2992192.0 431,2013,ARM,MMR,160436.0,0,0,0,5588.3,5588.3,5495.303,5487.865,0,1,,,52983829.0,6875045746.66,2297.66196376,2992192.0 432,2013,ARM,MNG,,0,0,0,4924.905,4924.905,4834.418,4809.503,1,1,5080026029.69,1776.74602164,2859174.0,6875045746.66,2297.66196376,2992192.0 433,2013,ARM,MOZ,,0,0,0,7465.2880000000005,7465.2880000000005,6886.859,6839.933,0,1,11128740364.1,420.473218683,26467180.0,6875045746.66,2297.66196376,2992192.0 434,2013,ARM,MRT,2246.0,0,0,0,6235.411999999999,6235.411999999999,6148.526,6142.308,0,1,3270693687.82,844.55475526,3872684.0,6875045746.66,2297.66196376,2992192.0 435,2013,ARM,MUS,246624.0,0,0,0,6850.985,6850.985,6865.686,6865.393,0,1,8661733284.63,6881.7484125,1258653.0,6875045746.66,2297.66196376,2992192.0 436,2013,ARM,MWI,3052022.0,0,0,0,6127.3859999999995,6127.3859999999995,6174.998,6170.472,1,1,4333271640.26,267.64903746,16190126.0,6875045746.66,2297.66196376,2992192.0 437,2013,ARM,MYS,14453995.0,0,0,0,7051.041,7051.041,7198.772,7176.465,0,1,207951396117.0,7057.484158600001,29465372.0,6875045746.66,2297.66196376,2992192.0 438,2013,ARM,NAM,255.0,0,0,0,7539.965999999999,7539.965999999999,7489.778,7478.443,0,1,10513028491.2,4480.1262815,2346592.0,6875045746.66,2297.66196376,2992192.0 439,2013,ARM,NER,2488.0,0,0,0,5078.042,5078.042,4817.604,4800.354,1,1,5242200415.91,285.54058145700003,18358863.0,6875045746.66,2297.66196376,2992192.0 440,2013,ARM,NGA,687437.0,0,0,0,5545.438,5048.582,5264.787,5241.788,0,1,183309437474.0,1060.7171157999999,172816517.0,6875045746.66,2297.66196376,2992192.0 441,2013,ARM,NIC,27571.0,0,0,0,12316.62,12316.62,12292.08,12291.62,0,1,8350353262.77,1404.44844223,5945646.0,6875045746.66,2297.66196376,2992192.0 442,2013,ARM,NOR,1747661.0,0,0,0,3199.743,3199.743,3293.507,3287.416,0,1,337855180442.0,66511.861302,5079623.0,6875045746.66,2297.66196376,2992192.0 443,2013,ARM,NPL,77799.0,0,0,0,3975.8740000000003,3975.8740000000003,3930.73,3921.215,1,1,11370379663.8,408.492452852,27834981.0,6875045746.66,2297.66196376,2992192.0 444,2013,ARM,NZL,17603431.0,0,0,0,15974.44,15974.44,15796.94,15796.23,0,1,129714285714.0,29201.1178754,4442100.0,6875045746.66,2297.66196376,2992192.0 445,2013,ARM,OMN,1026504.0,0,0,0,2270.178,2270.178,2272.617,2261.113,0,1,45303761304.2,11595.797730799999,3906912.0,6875045746.66,2297.66196376,2992192.0 446,2013,ARM,PER,288717.0,0,0,0,13549.98,13549.98,13449.12,13446.39,0,1,124791491189.0,4082.76162394,30565461.0,6875045746.66,2297.66196376,2992192.0 447,2013,ARM,PHL,1871843.0,0,0,0,7832.967,7832.967,7977.826,7967.125,0,1,155608838544.0,1594.81567728,97571676.0,6875045746.66,2297.66196376,2992192.0 448,2013,ARM,PRK,364.0,0,0,0,6707.085,6707.085,6722.886,6721.999,0,1,,,24895705.0,6875045746.66,2297.66196376,2992192.0 449,2013,ARM,PRT,8939110.0,0,0,0,4540.028,4540.028,4543.881,4535.649,0,1,188596194503.0,18034.892819099998,10457295.0,6875045746.66,2297.66196376,2992192.0 450,2013,ARM,PRY,267297.0,0,0,0,12787.09,12787.09,12743.82,12742.99,1,1,13122275725.6,2029.5310084000002,6465669.0,6875045746.66,2297.66196376,2992192.0 451,2013,ARM,QAT,550947.0,0,0,0,1786.2160000000001,1786.2160000000001,1782.193,1780.896,0,1,129889195708.0,61814.0853171,2101288.0,6875045746.66,2297.66196376,2992192.0 452,2013,ARM,RUS,1005880593.0,0,0,1,1803.035,1803.035,2080.53,1797.612,0,1,993468541218.0,6922.79231916,143506911.0,6875045746.66,2297.66196376,2992192.0 453,2013,ARM,SEN,819.0,0,0,0,6599.536999999999,6599.536999999999,6567.278,6566.307,0,1,11328685209.4,796.614341342,14221041.0,6875045746.66,2297.66196376,2992192.0 454,2013,ARM,SGP,3197511.0,0,0,0,7366.045999999999,7366.045999999999,7360.448,7360.152,0,1,202421849200.0,37491.0818639,5399200.0,6875045746.66,2297.66196376,2992192.0 455,2013,ARM,SLE,189407.0,0,0,0,6691.235,6691.235,6650.002,6649.058,0,1,3118731419.71,504.742286515,6178859.0,6875045746.66,2297.66196376,2992192.0 456,2013,ARM,SLV,59986.0,0,0,0,12366.14,12366.14,12360.44,12360.23,0,1,19420611604.6,3189.12100685,6089644.0,6875045746.66,2297.66196376,2992192.0 457,2013,ARM,SMR,10066.0,0,0,0,2660.7209999999995,2660.7209999999995,2667.679,2666.906,0,1,,,31391.0,6875045746.66,2297.66196376,2992192.0 458,2013,ARM,SVK,7055248.0,0,0,0,2340.185,2340.185,2224.867,2218.631,1,1,83210919039.9,15371.305766999998,5413393.0,6875045746.66,2297.66196376,2992192.0 459,2013,ARM,SVN,8235609.0,0,0,0,2506.938,2506.938,2489.557,2487.926,0,1,38397711727.4,18640.0911707,2059953.0,6875045746.66,2297.66196376,2992192.0 460,2013,ARM,SWE,19274078.0,0,0,0,2817.4709999999995,2817.4709999999995,2899.191,2895.376,0,1,436372281918.0,45453.651560800005,9600379.0,6875045746.66,2297.66196376,2992192.0 461,2013,ARM,SYR,1100668.0,0,0,0,1040.517,1040.517,883.4367,849.6639,0,1,,,21789415.0,6875045746.66,2297.66196376,2992192.0 462,2013,ARM,THA,20491622.0,0,0,0,6163.356,6163.356,6167.068,6163.949,0,1,230371292593.0,3415.36598877,67451422.0,6875045746.66,2297.66196376,2992192.0 463,2013,ARM,TJK,768467.0,0,0,0,2094.471,2094.471,2108.708,2106.494,1,1,3944940642.21,486.315605481,8111894.0,6875045746.66,2297.66196376,2992192.0 464,2013,ARM,TKM,1466884.0,0,0,0,1223.83,1223.83,1277.169,1221.887,1,1,18639001814.9,3557.00167915,5240088.0,6875045746.66,2297.66196376,2992192.0 465,2013,ARM,TUN,1304135.0,0,0,0,2990.33,2990.33,3028.31,3026.769,0,1,43322022840.0,3979.42615533,10886500.0,6875045746.66,2297.66196376,2992192.0 466,2013,ARM,TUR,208608338.0,1,0,1,1315.5610000000001,992.1443,1108.182,952.4822,0,1,654068728412.0,8719.73026299,75010202.0,6875045746.66,2297.66196376,2992192.0 467,2013,ARM,TZA,40898.0,0,0,0,5261.924,5236.723,5226.198,5218.252,0,1,27673028976.3,578.672725195,50213457.0,6875045746.66,2297.66196376,2992192.0 468,2013,ARM,UGA,1925910.0,0,0,0,4596.694,4596.694,4574.078,4571.287,1,1,15698319188.7,429.227929824,36573387.0,6875045746.66,2297.66196376,2992192.0 469,2013,ARM,UKR,223347769.0,0,0,0,1575.5870000000002,1575.5870000000002,1327.038,1277.125,0,1,95477307411.0,2098.88210516,45489600.0,6875045746.66,2297.66196376,2992192.0 470,2013,ARM,URY,177749.0,0,0,0,13248.01,13248.01,13222.08,13221.48,0,1,26486558038.4,7771.94805424,3407969.0,6875045746.66,2297.66196376,2992192.0 471,2013,ARM,VNM,14410718.0,0,0,0,6124.733,6124.733,6658.508,6640.995,0,1,92277145925.3,1028.62866366,89708900.0,6875045746.66,2297.66196376,2992192.0 472,2013,ARM,YEM,52.0,0,0,0,2758.486,2758.486,2868.147,2863.788,0,1,18115034805.5,709.469347535,25533217.0,6875045746.66,2297.66196376,2992192.0 473,2013,ARM,ZAF,45338785.0,0,0,0,8671.851,7527.607,7940.606,7918.076,0,1,323743376883.0,6090.26831183,53157490.0,6875045746.66,2297.66196376,2992192.0 474,2013,ARM,ALB,449585.0,0,0,0,2077.855,2077.855,2084.606,2083.35,0,1,11346755234.8,3916.23123721,2897366.0,6875045746.66,2297.66196376,2992192.0 475,2013,ARM,BRB,640.0,0,0,0,10241.7,10241.7,10243.9,10243.7,0,1,3443813562.03,12190.3610299,282503.0,6875045746.66,2297.66196376,2992192.0 476,2013,ARM,BRN,184.0,0,0,0,8007.059,8007.059,7985.849,7985.574,0,1,10103984048.4,24554.0913791,411499.0,6875045746.66,2297.66196376,2992192.0 477,2013,ARM,MLI,151.0,0,0,0,5949.743,5949.743,5855.265,5846.068,1,1,7285189363.25,439.075866254,16592097.0,6875045746.66,2297.66196376,2992192.0 478,2013,ARM,SOM,3022.0,0,0,0,4246.931,4246.931,4075.938,4035.559,0,1,,,10268157.0,6875045746.66,2297.66196376,2992192.0 479,2013,ARM,UZB,1437463.0,0,0,0,2081.25,2081.25,2004.538,1957.607,1,1,27302754038.6,902.773318915,30243200.0,6875045746.66,2297.66196376,2992192.0 480,2013,ARM,ERI,72.0,0,0,0,2818.9840000000004,2818.9840000000004,2854.964,2853.139,0,1,1245302403.11,249.11907342700002,4998824.0,6875045746.66,2297.66196376,2992192.0 481,2013,ARM,GMB,858.0,0,0,0,6613.235,6613.235,6600.726,6599.977,0,1,832443850.134,445.90158014300005,1866878.0,6875045746.66,2297.66196376,2992192.0 482,2013,ARM,GRD,352.0,0,0,0,10489.04,10489.04,10483.95,10483.74,0,1,678048976.06,6402.60784556,105902.0,6875045746.66,2297.66196376,2992192.0 483,2013,ARM,HTI,25421.0,0,0,0,10794.76,10794.76,10790.26,10789.89,0,1,5064322972.81,485.495358495,10431249.0,6875045746.66,2297.66196376,2992192.0 484,2013,ARM,LSO,3882.0,0,0,0,7932.732,7932.732,7946.261,7945.858,1,1,2014152758.14,966.919719651,2083061.0,6875045746.66,2297.66196376,2992192.0 485,2013,ARM,MDV,120.0,0,0,0,4950.312,4950.312,4991.583,4983.179,0,1,2011420213.61,5728.73026937,351111.0,6875045746.66,2297.66196376,2992192.0 486,2013,ARM,SDN,591.0,0,0,0,2976.9709999999995,2976.9709999999995,3093.495,3062.108,0,1,37130724862.7,964.0564267780001,38515095.0,6875045746.66,2297.66196376,2992192.0 487,2013,ARM,TGO,25961.0,0,0,0,5724.177,5724.177,5662.246,5660.205,0,1,2892798974.63,417.508485282,6928719.0,6875045746.66,2297.66196376,2992192.0 488,2013,ARM,ZMB,559.0,0,0,0,6417.901,6417.901,6287.834,6281.142,1,1,15317964948.6,1004.7145837000002,15246086.0,6875045746.66,2297.66196376,2992192.0 489,2013,ARM,ZWE,9480773.0,0,0,0,6606.1,6606.1,6717.051,6714.288,1,1,6725359135.1,451.42419144,14898092.0,6875045746.66,2297.66196376,2992192.0 490,2013,ARM,FSM,4076.0,0,0,0,11482.14,11482.14,11011.75,10985.13,0,1,242459440.018,2337.67947721,103718.0,6875045746.66,2297.66196376,2992192.0 491,2013,ARM,TUV,596.0,0,0,0,14337.36,14337.36,14221.27,14219.19,0,1,26207330.1735,2653.63813016,9876.0,6875045746.66,2297.66196376,2992192.0 492,2013,ATG,BEL,902915.0,0,0,0,6881.374,6881.374,6887.061,6886.731,0,0,420470961323.0,37599.7354981,11182817.0,1033152446.13,11481.385187799999,89985.0 493,2013,ATG,BHS,132381.0,0,1,0,1834.379,1834.379,1858.855,1853.693,0,0,7835117785.02,20736.547344,377841.0,1033152446.13,11481.385187799999,89985.0 494,2013,ATG,CHE,3926303.0,0,0,0,7067.338000000001,7067.338000000001,7096.034,7095.144,1,0,477246305814.0,58996.896141499994,8089346.0,1033152446.13,11481.385187799999,89985.0 495,2013,ATG,CHN,19424780.0,0,0,0,13681.69,13681.69,14124.91,14096.9,0,0,4912954256930.0,3619.43910837,1357380000.0,1033152446.13,11481.385187799999,89985.0 496,2013,ATG,COL,2841352.0,0,0,0,2004.484,2004.484,1895.163,1868.751,0,0,212907929816.0,4497.19693577,47342363.0,1033152446.13,11481.385187799999,89985.0 497,2013,ATG,DOM,4390334.0,0,0,0,855.9273,855.9273,889.9643,885.0841,0,0,50033390849.2,4866.39484098,10281408.0,1033152446.13,11481.385187799999,89985.0 498,2013,ATG,ESP,715981.0,0,0,0,6108.097,6108.097,6185.641,6166.879,0,0,1172482153960.0,25149.743076400002,46620045.0,1033152446.13,11481.385187799999,89985.0 499,2013,ATG,FRA,5221595.0,0,0,0,6708.77,6708.77,6739.046,6733.584,0,0,2357143265760.0,35754.652407199996,65925498.0,1033152446.13,11481.385187799999,89985.0 500,2013,ATG,GBR,14026519.0,0,1,1,6582.637,6582.637,6539.859,6538.982,0,0,2577048727270.0,40199.3169439,64106779.0,1033152446.13,11481.385187799999,89985.0 501,2013,ATG,JAM,5388346.0,0,1,0,1590.519,1590.519,1608.55,1607.063,0,0,,,2714734.0,1033152446.13,11481.385187799999,89985.0 502,2013,ATG,KOR,4676842.0,0,0,0,13883.16,13883.16,13963.5,13962.76,0,0,1199003686670.0,23875.1809908,50219669.0,1033152446.13,11481.385187799999,89985.0 503,2013,ATG,MEX,3690627.0,0,0,0,3946.302,3946.302,4189.173,4131.572,0,0,1045697869840.0,8450.75924284,123740109.0,1033152446.13,11481.385187799999,89985.0 504,2013,ATG,NLD,3875890.0,0,0,0,6941.241,6941.241,6959.779,6959.381,0,0,720805621191.0,42893.7807116,16804432.0,1033152446.13,11481.385187799999,89985.0 505,2013,ATG,PAN,1883378.0,0,0,0,2119.009,2119.009,2179.71,2173.331,0,0,29908913759.9,7859.01341755,3805683.0,1033152446.13,11481.385187799999,89985.0 506,2013,ATG,POL,45035.0,0,0,0,8034.279,8034.279,7933.798,7931.619,0,0,415521978597.0,10923.2344281,38040196.0,1033152446.13,11481.385187799999,89985.0 507,2013,ATG,SUR,214910.0,0,0,0,1445.231,1445.231,1539.525,1457.058,0,0,2464082868.05,4619.14493964,533450.0,1033152446.13,11481.385187799999,89985.0 508,2013,ATG,TTO,13875389.0,0,1,0,724.2523,724.2523,741.5194,740.7512,0,0,19145432662.8,14200.3149757,1348240.0,1033152446.13,11481.385187799999,89985.0 509,2013,ATG,USA,176432542.0,0,1,0,2876.3990000000003,2831.995,3965.092,3652.015,0,0,14451509500000.0,45660.7337641,316497531.0,1033152446.13,11481.385187799999,89985.0 510,2013,ATG,VEN,243841.0,0,0,0,913.6557,913.6557,1043.747,1018.173,0,0,194650892086.0,6429.204742100001,30276045.0,1033152446.13,11481.385187799999,89985.0 511,2013,ATG,DEU,3557563.0,0,0,0,7071.959,7518.644,7277.712,7273.53,0,0,3162014177340.0,39208.7600724,80645605.0,1033152446.13,11481.385187799999,89985.0 512,2013,ATG,IND,1388328.0,0,1,0,13300.81,13300.81,13776.45,13761.96,0,0,1489775906250.0,1164.34327261,1279498874.0,1033152446.13,11481.385187799999,89985.0 513,2013,ATG,JPN,10234458.0,0,0,0,13731.89,13731.89,13814.05,13808.13,0,0,4784541597490.0,37573.373733099994,127338621.0,1033152446.13,11481.385187799999,89985.0 514,2013,ATG,PAK,44756.0,0,1,0,12632.48,12632.48,12780.52,12779.67,0,0,143816996323.0,793.724245977,181192646.0,1033152446.13,11481.385187799999,89985.0 515,2013,ATG,SAU,395.0,0,0,0,11003.94,11003.94,10722.67,10710.71,0,0,505813554459.0,16748.2103341,30201051.0,1033152446.13,11481.385187799999,89985.0 516,2013,ATG,AFG,204.0,0,0,0,12297.09,12297.09,12214.32,12212.2,1,0,12679050960.3,413.23395943199995,30682500.0,1033152446.13,11481.385187799999,89985.0 517,2013,ATG,ARE,28047.0,0,0,0,11712.02,11712.02,11764.43,11764.24,0,0,234968702615.0,25992.176376400002,9039978.0,1033152446.13,11481.385187799999,89985.0 518,2013,ATG,ARG,657141.0,0,0,0,5776.081999999999,5776.081999999999,5607.045,5581.765,0,0,331013918665.0,7781.54951041,42538304.0,1033152446.13,11481.385187799999,89985.0 519,2013,ATG,ARM,14631.0,0,0,0,10114.07,10114.07,10124.28,10124.02,1,0,6875045746.66,2297.66196376,2992192.0,1033152446.13,11481.385187799999,89985.0 520,2013,ATG,AUS,681493.0,0,1,0,16255.91,16364.74,16622.68,16600.18,0,0,867152305474.0,37497.070617000005,23125868.0,1033152446.13,11481.385187799999,89985.0 521,2013,ATG,AUT,258473.0,0,0,0,7737.4980000000005,7737.4980000000005,7659.723,7657.413,1,0,349523317995.0,41220.410466,8479375.0,1033152446.13,11481.385187799999,89985.0 522,2013,ATG,BGD,45656.0,0,0,0,14576.48,14576.48,14594.76,14594.02,0,0,112095671740.0,713.2701102189999,157157394.0,1033152446.13,11481.385187799999,89985.0 523,2013,ATG,BGR,44799.0,0,0,0,8344.458,8344.458,8480.396,8478.287,0,0,34927663768.9,4807.58580819,7265115.0,1033152446.13,11481.385187799999,89985.0 524,2013,ATG,BIH,7.0,0,0,0,7935.534000000001,7935.534000000001,7899.603,7899.01,0,0,13032998560.0,3408.62719376,3823533.0,1033152446.13,11481.385187799999,89985.0 525,2013,ATG,BLR,1485.0,0,0,0,8442.194,8442.194,8484.056,8481.706,0,0,46593901367.6,4922.23762599,9466000.0,1033152446.13,11481.385187799999,89985.0 526,2013,ATG,BLZ,8828.0,0,1,0,2865.1890000000003,2865.1890000000003,2821.493,2820.723,0,0,1362082437.8,3957.32172879,344193.0,1033152446.13,11481.385187799999,89985.0 527,2013,ATG,BMU,18980.0,0,1,0,1714.225,1714.225,1720.814,1720.753,0,0,4461518517.58,68637.69045980001,65001.0,1033152446.13,11481.385187799999,89985.0 528,2013,ATG,BOL,1311.0,0,0,0,3807.2659999999996,4048.1420000000003,3875.459,3866.398,1,0,14119217520.6,1357.62607661,10399931.0,1033152446.13,11481.385187799999,89985.0 529,2013,ATG,BRA,6419236.0,0,0,0,4818.193,3969.111,4385.591,4209.473,0,0,1204333024530.0,5896.09663075,204259377.0,1033152446.13,11481.385187799999,89985.0 530,2013,ATG,BTN,27.0,0,0,0,14193.42,14193.42,14233.3,14233.13,1,0,1490217265.02,1974.74714998,754637.0,1033152446.13,11481.385187799999,89985.0 531,2013,ATG,CAF,1112.0,0,0,0,8858.907,8858.907,8830.324,8826.415,1,0,1076987683.82,228.626894859,4710678.0,1033152446.13,11481.385187799999,89985.0 532,2013,ATG,CAN,5636390.0,0,1,0,3382.002,3402.382,4157.129,3868.477,0,0,1327353688730.0,37753.632505400004,35158304.0,1033152446.13,11481.385187799999,89985.0 533,2013,ATG,CHL,1328078.0,0,0,0,5713.025,5713.025,5690.639,5633.904,0,0,171771363935.0,9773.15635254,17575833.0,1033152446.13,11481.385187799999,89985.0 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1853,2013,BHR,NCL,27396.0,0,0,0,13570.88,13570.88,13542.61,13542.09,0,0,,,262000.0,23315293065.1,17277.920973199998,1349427.0 1854,2013,BHR,PYF,245974.0,0,0,0,17739.25,17739.25,17736.28,17736.09,0,0,,,276766.0,23315293065.1,17277.920973199998,1349427.0 1855,2013,BHR,RWA,377.0,0,0,0,3833.384,3833.384,3844.447,3844.125,1,0,4724955978.85,426.51340134300006,11078095.0,23315293065.1,17277.920973199998,1349427.0 1856,2013,BHR,SLB,416716.0,0,0,0,12402.16,12402.16,12376.84,12375.49,0,0,630859261.257,1125.15808566,560685.0,23315293065.1,17277.920973199998,1349427.0 1857,2013,BHR,SOM,18937109.0,0,1,0,2748.469,2748.469,2584.122,2516.692,0,0,,,10268157.0,23315293065.1,17277.920973199998,1349427.0 1858,2013,BHR,UZB,32930.0,0,0,0,2392.187,2392.187,2312.541,2293.896,1,0,27302754038.6,902.773318915,30243200.0,23315293065.1,17277.920973199998,1349427.0 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1865,2013,BHR,GRD,1593.0,0,0,0,11572.97,11572.97,11559.42,11559.41,0,0,678048976.06,6402.60784556,105902.0,23315293065.1,17277.920973199998,1349427.0 1866,2013,BHR,HTI,121714.0,0,0,0,12110.4,12110.4,12102.71,12102.59,0,0,5064322972.81,485.495358495,10431249.0,23315293065.1,17277.920973199998,1349427.0 1867,2013,BHR,KNA,41632.0,0,0,0,11355.69,11355.69,11020.77,9401.884,0,0,586391757.429,10798.912679899999,54301.0,23315293065.1,17277.920973199998,1349427.0 1868,2013,BHR,LBR,1258.0,0,0,0,6856.259,6856.259,6809.655,6808.475,0,0,975319024.053,227.15160380700001,4293692.0,23315293065.1,17277.920973199998,1349427.0 1869,2013,BHR,LSO,1219.0,0,0,0,6654.576,6654.576,6651.36,6651.017,1,0,2014152758.14,966.919719651,2083061.0,23315293065.1,17277.920973199998,1349427.0 1870,2013,BHR,MDV,4991.0,0,0,0,3454.322,3454.322,3491.714,3482.956,0,0,2011420213.61,5728.73026937,351111.0,23315293065.1,17277.920973199998,1349427.0 1871,2013,BHR,SDN,4083524.0,0,1,0,2220.256,2220.256,2304.977,2252.157,0,0,37130724862.7,964.0564267780001,38515095.0,23315293065.1,17277.920973199998,1349427.0 1872,2013,BHR,TCD,846678.0,0,1,0,4041.7709999999997,4041.7709999999997,3971.47,3961.889,1,0,9704471387.48,738.219069673,13145788.0,23315293065.1,17277.920973199998,1349427.0 1873,2013,BHR,TGO,1425.0,0,0,0,5676.107,5676.107,5632.614,5631.991,0,0,2892798974.63,417.508485282,6928719.0,23315293065.1,17277.920973199998,1349427.0 1874,2013,BHR,VUT,1131.0,0,0,0,13589.55,13589.55,13533.66,13532.93,0,0,528893109.429,2089.12412628,253165.0,23315293065.1,17277.920973199998,1349427.0 1875,2013,BHR,ZMB,480305.0,0,0,0,5231.338,5231.338,5097.155,5088.896,1,0,15317964948.6,1004.7145837000002,15246086.0,23315293065.1,17277.920973199998,1349427.0 1876,2013,BHR,ZWE,3122631.0,0,0,0,5338.955,5338.955,5445.5,5441.617,1,0,6725359135.1,451.42419144,14898092.0,23315293065.1,17277.920973199998,1349427.0 1877,2013,BHR,TUV,10389.0,0,0,0,14269.2,14269.2,14172.76,14170.89,0,0,26207330.1735,2653.63813016,9876.0,23315293065.1,17277.920973199998,1349427.0 1878,2013,BHR,KIR,850.0,0,0,0,13148.09,13148.09,13258.02,13243.61,0,0,120387523.426,1109.11264949,108544.0,23315293065.1,17277.920973199998,1349427.0 1879,2013,BHR,PLW,4016.0,0,0,0,9045.835,9045.835,9051.498,9051.489,0,0,182643776.185,8730.99938739,20919.0,23315293065.1,17277.920973199998,1349427.0 1880,2013,BHR,STP,967.0,0,0,0,5522.476,5522.476,5510.361,5510.268,0,0,190607748.39,1045.07883494,182386.0,23315293065.1,17277.920973199998,1349427.0 1881,2013,BHR,WSM,69.0,0,0,0,13773.63,13773.63,15412.04,15412,0,0,507952590.02199996,2667.95834877,190390.0,23315293065.1,17277.920973199998,1349427.0 1882,2013,BHR,GRL,4040.0,0,0,0,7981.751,7981.751,7853.539,7847.878,0,0,,,56483.0,23315293065.1,17277.920973199998,1349427.0 1883,2013,BHS,BEL,2307724.0,0,0,0,7313.008000000001,7313.008000000001,7300.876,7300.452,0,0,420470961323.0,37599.7354981,11182817.0,7835117785.02,20736.547344,377841.0 1884,2013,BHS,CHE,13576510.0,0,0,0,7626.146,7626.146,7637.052,7636.409,1,0,477246305814.0,58996.896141499994,8089346.0,7835117785.02,20736.547344,377841.0 1885,2013,BHS,CHN,17574353.0,0,0,0,12659.7,12659.7,13048.72,13011.79,0,0,4912954256930.0,3619.43910837,1357380000.0,7835117785.02,20736.547344,377841.0 1886,2013,BHS,COL,1653280.0,0,0,0,2288.925,2288.925,2185.913,2141.226,0,0,212907929816.0,4497.19693577,47342363.0,7835117785.02,20736.547344,377841.0 1887,2013,BHS,DOM,7869461.0,0,0,0,1069.846,1069.846,1058.642,1043.282,0,0,50033390849.2,4866.39484098,10281408.0,7835117785.02,20736.547344,377841.0 1888,2013,BHS,ESP,1453141.0,0,0,0,6901.420999999999,6901.420999999999,6982.645,6971.142,0,0,1172482153960.0,25149.743076400002,46620045.0,7835117785.02,20736.547344,377841.0 1889,2013,BHS,FRA,15848229.0,0,0,0,7209.45,7209.45,7298.163,7291.048,0,0,2357143265760.0,35754.652407199996,65925498.0,7835117785.02,20736.547344,377841.0 1890,2013,BHS,GBR,14129964.0,0,1,1,6990.2480000000005,6990.2480000000005,6890.492,6888.646,0,0,2577048727270.0,40199.3169439,64106779.0,7835117785.02,20736.547344,377841.0 1891,2013,BHS,JAM,2029250.0,0,1,0,793.998,793.998,815.7759,807.7539,0,0,,,2714734.0,7835117785.02,20736.547344,377841.0 1892,2013,BHS,KOR,5191217.0,0,0,0,12617.7,12617.7,12646.75,12645.48,0,0,1199003686670.0,23875.1809908,50219669.0,7835117785.02,20736.547344,377841.0 1893,2013,BHS,MEX,8138930.0,0,0,0,2332.616,2332.616,2511.912,2436.904,0,0,1045697869840.0,8450.75924284,123740109.0,7835117785.02,20736.547344,377841.0 1894,2013,BHS,NLD,2402017.0,0,0,0,7320.031,7320.031,7329.94,7329.513,0,0,720805621191.0,42893.7807116,16804432.0,7835117785.02,20736.547344,377841.0 1895,2013,BHS,PAN,10901654.0,0,0,0,1810.2479999999998,1810.2479999999998,1859.998,1855.989,0,0,29908913759.9,7859.01341755,3805683.0,7835117785.02,20736.547344,377841.0 1896,2013,BHS,POL,418632.0,0,0,0,8373.694,8373.694,8276.44,8273.969,0,0,415521978597.0,10923.2344281,38040196.0,7835117785.02,20736.547344,377841.0 1897,2013,BHS,TTO,5536160.0,0,1,0,2317.926,2317.926,2370.38,2365.946,0,0,19145432662.8,14200.3149757,1348240.0,7835117785.02,20736.547344,377841.0 1898,2013,BHS,USA,2397646999.0,0,1,0,1770.8929999999998,1534.43,2387.768,2021.605,0,0,14451509500000.0,45660.7337641,316497531.0,7835117785.02,20736.547344,377841.0 1899,2013,BHS,VEN,242459.0,0,0,0,1951.875,1951.875,2003.94,1982.065,0,0,194650892086.0,6429.204742100001,30276045.0,7835117785.02,20736.547344,377841.0 1900,2013,BHS,DEU,5508658.0,0,0,0,7479.404,7877.293000000001,7665.657,7661.935,0,0,3162014177340.0,39208.7600724,80645605.0,7835117785.02,20736.547344,377841.0 1901,2013,BHS,IND,1096528.0,0,1,0,13465.89,13465.89,14072.39,14054.6,0,0,1489775906250.0,1164.34327261,1279498874.0,7835117785.02,20736.547344,377841.0 1902,2013,BHS,JPN,42837130.0,0,0,0,12227.93,12227.93,12310.86,12302.27,0,0,4784541597490.0,37573.373733099994,127338621.0,7835117785.02,20736.547344,377841.0 1903,2013,BHS,PAK,56702.0,0,1,0,12786.85,12786.85,13069.85,13067.09,0,0,143816996323.0,793.724245977,181192646.0,7835117785.02,20736.547344,377841.0 1904,2013,BHS,SAU,21856.0,0,0,0,11857.8,11857.8,11627.36,11619.49,0,0,505813554459.0,16748.2103341,30201051.0,7835117785.02,20736.547344,377841.0 1905,2013,BHS,ARG,1004117.0,0,0,0,6940.775,6940.775,6760.757,6735.502,0,0,331013918665.0,7781.54951041,42538304.0,7835117785.02,20736.547344,377841.0 1906,2013,BHS,ATG,854052.0,0,1,0,1834.379,1834.379,1858.855,1853.693,0,0,1033152446.13,11481.385187799999,89985.0,7835117785.02,20736.547344,377841.0 1907,2013,BHS,AUS,1427457.0,0,1,0,15278.03,15464.67,15783.82,15732.45,0,0,867152305474.0,37497.070617000005,23125868.0,7835117785.02,20736.547344,377841.0 1908,2013,BHS,AUT,27970.0,0,0,0,8225.953000000001,8225.953000000001,8145.735,8143.937,1,0,349523317995.0,41220.410466,8479375.0,7835117785.02,20736.547344,377841.0 1909,2013,BHS,BLZ,224386.0,0,1,0,1476.3029999999999,1476.3029999999999,1436.628,1434.17,0,0,1362082437.8,3957.32172879,344193.0,7835117785.02,20736.547344,377841.0 1910,2013,BHS,BMU,1942.0,0,1,0,1461.976,1461.976,1470.896,1470.297,0,0,4461518517.58,68637.69045980001,65001.0,7835117785.02,20736.547344,377841.0 1911,2013,BHS,BOL,,0,0,0,4733.767,5086.896,4928.787,4917.556,1,0,14119217520.6,1357.62607661,10399931.0,7835117785.02,20736.547344,377841.0 1912,2013,BHS,BRA,7528164.0,0,0,0,6343.316,5556.084,6013.86,5905.033,0,0,1204333024530.0,5896.09663075,204259377.0,7835117785.02,20736.547344,377841.0 1913,2013,BHS,CAN,18128509.0,0,1,0,2074.812,2268.131,2797.16,2512.421,0,0,1327353688730.0,37753.632505400004,35158304.0,7835117785.02,20736.547344,377841.0 1914,2013,BHS,CHL,2128654.0,0,0,0,6558.368,6558.368,6560.251,6512.323,0,0,171771363935.0,9773.15635254,17575833.0,7835117785.02,20736.547344,377841.0 1915,2013,BHS,CRI,4861265.0,0,0,0,1825.164,1825.164,1850.415,1848.788,0,0,28444523960.8,6043.7541469,4706433.0,7835117785.02,20736.547344,377841.0 1916,2013,BHS,CUB,148153.0,0,0,0,560.8621,560.8621,549.6821,534.2878,0,0,60285673300.0,5305.66748265,11362505.0,7835117785.02,20736.547344,377841.0 1917,2013,BHS,CYM,354623.0,0,1,0,767.0014,767.0014,788.0254,785.5678,0,0,,,58369.0,7835117785.02,20736.547344,377841.0 1918,2013,BHS,CYP,14893.0,0,0,0,10128.48,10128.48,10127.43,10127.29,0,0,19094028254.5,22152.4124729,1141652.0,7835117785.02,20736.547344,377841.0 1919,2013,BHS,CZE,88179.0,0,0,0,8024.927,8024.927,8095.415,8093.483,1,0,154008135273.0,14647.531971200002,10514272.0,7835117785.02,20736.547344,377841.0 1920,2013,BHS,DNK,838955.0,0,0,0,7731.145,7731.145,7641.269,7640.084,0,0,265136470510.0,47219.8898419,5614932.0,7835117785.02,20736.547344,377841.0 1921,2013,BHS,ECU,466837.0,0,0,0,2820.5229999999997,2820.5229999999997,2996.789,2989.46,0,0,58238429057.6,3718.61751159,15661312.0,7835117785.02,20736.547344,377841.0 1922,2013,BHS,EGY,148408.0,0,0,0,10256.41,10256.41,10239.6,10238.17,0,0,128583201068.0,1467.61173581,87613909.0,7835117785.02,20736.547344,377841.0 1923,2013,BHS,FIN,594246.0,0,0,0,8264.933,8264.933,8192.812,8191.534,0,0,212432657630.0,39057.5016069,5438972.0,7835117785.02,20736.547344,377841.0 1924,2013,BHS,GRC,154810.0,0,0,0,9224.1,9224.1,9182.491,9181.376,0,0,199825892302.0,18120.6079703,11027549.0,7835117785.02,20736.547344,377841.0 1925,2013,BHS,GTM,430381.0,0,0,0,1802.4779999999998,1802.4779999999998,1821.662,1819.874,0,0,36210068021.6,2307.72708693,15690793.0,7835117785.02,20736.547344,377841.0 1926,2013,BHS,GUY,39396.0,0,1,0,2883.798,2883.798,2940.087,2935.679,0,0,1068555956.23,1404.08623046,761033.0,7835117785.02,20736.547344,377841.0 1927,2013,BHS,HKG,1937857.0,0,1,0,14619.86,14619.86,14580.31,14579.73,0,0,241780080018.0,33638.9676547,7187500.0,7835117785.02,20736.547344,377841.0 1928,2013,BHS,HND,1229627.0,0,0,0,1603.656,1603.656,1562.43,1559.185,0,0,11934602023.5,1520.51373592,7849059.0,7835117785.02,20736.547344,377841.0 1929,2013,BHS,HUN,234.0,0,0,0,8441.591999999999,8441.591999999999,8451.728,8450.851,1,0,113124111583.0,11434.668345299999,9893082.0,7835117785.02,20736.547344,377841.0 1930,2013,BHS,IDN,590350.0,0,0,0,17880.29,17880.29,17627.11,17617.84,0,0,449142287180.0,1787.5009704,251268276.0,7835117785.02,20736.547344,377841.0 1931,2013,BHS,IRL,1294517.0,0,1,0,6564.065,6564.065,6509.962,6509.078,0,0,217273473449.0,47250.887709300005,4598294.0,7835117785.02,20736.547344,377841.0 1932,2013,BHS,ISL,241475.0,0,0,0,5865.814,5865.814,5874.271,5872.919,0,0,19195496469.6,59288.5449575,323764.0,7835117785.02,20736.547344,377841.0 1933,2013,BHS,ISR,630988.0,0,1,0,10422.33,10422.33,10430.48,10430.34,0,0,196180408875.0,24341.511120500003,8059500.0,7835117785.02,20736.547344,377841.0 1934,2013,BHS,ITA,3517914.0,0,0,0,8174.021,8174.021,8100.11,8091.526,0,0,1754603531900.0,29129.8111805,60233948.0,7835117785.02,20736.547344,377841.0 1935,2013,BHS,KEN,32673.0,0,1,0,12503.92,12503.92,12541.7,12537.96,0,0,28056993796.4,642.141080063,43692881.0,7835117785.02,20736.547344,377841.0 1936,2013,BHS,LBN,,0,0,0,10371.56,10371.56,10369.94,10369.84,0,0,32346776994.6,7198.66992591,4493438.0,7835117785.02,20736.547344,377841.0 1937,2013,BHS,LUX,3036.0,0,0,0,7465.828,7465.828,7449.871,7449.737,1,0,43203208556.1,79511.20538160001,543360.0,7835117785.02,20736.547344,377841.0 1938,2013,BHS,MKD,37732.0,0,0,0,8858.244,8858.244,8865.577,8865.338,1,0,7959429439.83,3840.41703348,2072543.0,7835117785.02,20736.547344,377841.0 1939,2013,BHS,MYS,46967.0,0,0,0,16891.63,16891.63,16759.41,16756.37,0,0,207951396117.0,7057.484158600001,29465372.0,7835117785.02,20736.547344,377841.0 1940,2013,BHS,NGA,1310005.0,0,1,0,8788.082,9035.619,8951.357,8946.884,0,0,183309437474.0,1060.7171157999999,172816517.0,7835117785.02,20736.547344,377841.0 1941,2013,BHS,NIC,26012.0,0,0,0,1725.5520000000001,1725.5520000000001,1718.923,1716.151,0,0,8350353262.77,1404.44844223,5945646.0,7835117785.02,20736.547344,377841.0 1942,2013,BHS,NOR,8272.0,0,0,0,7517.311,7517.311,7428.404,7426.164,0,0,337855180442.0,66511.861302,5079623.0,7835117785.02,20736.547344,377841.0 1943,2013,BHS,NZL,203944.0,0,1,0,13319.88,13319.88,13266.16,13264.02,0,0,129714285714.0,29201.1178754,4442100.0,7835117785.02,20736.547344,377841.0 1944,2013,BHS,PER,4346341.0,0,0,0,4138.589,4138.589,4082.148,4049.61,0,0,124791491189.0,4082.76162394,30565461.0,7835117785.02,20736.547344,377841.0 1945,2013,BHS,PHL,9520.0,0,1,0,15189.59,15189.59,15240.6,15238.14,0,0,155608838544.0,1594.81567728,97571676.0,7835117785.02,20736.547344,377841.0 1946,2013,BHS,PRK,1274086.0,0,0,0,12506.43,12506.43,12394.13,12391.58,0,0,,,24895705.0,7835117785.02,20736.547344,377841.0 1947,2013,BHS,PRT,7253.0,0,0,0,6472.578,6472.578,6452.891,6450.123,0,0,188596194503.0,18034.892819099998,10457295.0,7835117785.02,20736.547344,377841.0 1948,2013,BHS,PRY,,0,0,0,5988.4490000000005,5988.4490000000005,6053.72,6051.072,1,0,13122275725.6,2029.5310084000002,6465669.0,7835117785.02,20736.547344,377841.0 1949,2013,BHS,RUS,39.0,0,0,0,9155.123,9155.123,9628.067,9569.862,0,0,993468541218.0,6922.79231916,143506911.0,7835117785.02,20736.547344,377841.0 1950,2013,BHS,SEN,1927.0,0,0,0,6327.6,6327.6,6397.285,6395.286,0,0,11328685209.4,796.614341342,14221041.0,7835117785.02,20736.547344,377841.0 1951,2013,BHS,SGP,1096366.0,0,1,0,17096.49,17096.49,17064.62,17064.22,0,0,202421849200.0,37491.0818639,5399200.0,7835117785.02,20736.547344,377841.0 1952,2013,BHS,SLV,217554.0,0,0,0,1775.0079999999998,1775.0079999999998,1781.467,1780.718,0,0,19420611604.6,3189.12100685,6089644.0,7835117785.02,20736.547344,377841.0 1953,2013,BHS,SWE,2130612.0,0,0,0,7932.745,7932.745,7808.63,7806.869,0,0,436372281918.0,45453.651560800005,9600379.0,7835117785.02,20736.547344,377841.0 1954,2013,BHS,TCA,2291530.0,0,1,0,750.7262,750.7262,770.8994,755.468,0,0,,,33103.0,7835117785.02,20736.547344,377841.0 1955,2013,BHS,THA,2381829.0,0,0,0,15707.2,15707.2,15675.65,15672.82,0,0,230371292593.0,3415.36598877,67451422.0,7835117785.02,20736.547344,377841.0 1956,2013,BHS,TON,5572.0,0,1,0,11754.11,11754.11,11712.65,11711.9,0,0,263100369.945,2502.4051013,105139.0,7835117785.02,20736.547344,377841.0 1957,2013,BHS,TUN,445663.0,0,0,0,8173.057,8173.057,8207.826,8207.159,0,0,43322022840.0,3979.42615533,10886500.0,7835117785.02,20736.547344,377841.0 1958,2013,BHS,TUR,474413.0,0,0,0,9462.993,9809.767,9752.447,9740.341,0,0,654068728412.0,8719.73026299,75010202.0,7835117785.02,20736.547344,377841.0 1959,2013,BHS,TZA,5610.0,0,1,0,13019.7,12630.43,12749.91,12739.89,0,0,27673028976.3,578.672725195,50213457.0,7835117785.02,20736.547344,377841.0 1960,2013,BHS,URY,105026.0,0,0,0,7041.119000000001,7041.119000000001,6994.402,6990.198,0,0,26486558038.4,7771.94805424,3407969.0,7835117785.02,20736.547344,377841.0 1961,2013,BHS,VNM,100100.0,0,0,0,14891.31,14891.31,15605.67,15588.94,0,0,92277145925.3,1028.62866366,89708900.0,7835117785.02,20736.547344,377841.0 1962,2013,BHS,ZAF,127148.0,0,1,0,12044.29,12660.75,12645.16,12636.74,0,0,323743376883.0,6090.26831183,53157490.0,7835117785.02,20736.547344,377841.0 1963,2013,BHS,BRB,2303550.0,0,1,0,2287.101,2287.101,2315.395,2311.22,0,0,3443813562.03,12190.3610299,282503.0,7835117785.02,20736.547344,377841.0 1964,2013,BHS,DMA,345948.0,0,1,0,1987.451,1987.451,2011.734,2006.976,0,0,431857638.889,5997.60626191,72005.0,7835117785.02,20736.547344,377841.0 1965,2013,BHS,FJI,534783.0,0,1,0,12255.05,12255.05,12250.52,12249.96,0,0,3370517356.94,3828.01490191,880487.0,7835117785.02,20736.547344,377841.0 1966,2013,BHS,ABW,3715.0,0,0,0,1588.515,1588.515,1634.515,1628.143,0,0,,,102921.0,7835117785.02,20736.547344,377841.0 1967,2013,BHS,GRD,2649.0,0,1,0,2188.659,2188.659,2216.592,2211.986,0,0,678048976.06,6402.60784556,105902.0,7835117785.02,20736.547344,377841.0 1968,2013,BHS,HTI,66797.0,0,0,0,891.7498,891.7498,905.81,890.3981,0,0,5064322972.81,485.495358495,10431249.0,7835117785.02,20736.547344,377841.0 1969,2013,BHS,KNA,,0,1,0,1745.8029999999999,1745.8029999999999,2171.279,1817.92,0,0,586391757.429,10798.912679899999,54301.0,7835117785.02,20736.547344,377841.0 1970,2013,BHS,LBR,385408.0,0,1,0,7359.646,7359.646,7422.239,7420.198,0,0,975319024.053,227.15160380700001,4293692.0,7835117785.02,20736.547344,377841.0 1971,2013,BHS,LCA,2741.0,0,1,0,2107.813,2107.813,2139.658,2135.076,0,0,1030152086.96,5650.70671108,182305.0,7835117785.02,20736.547344,377841.0 1972,2013,BIH,BEL,59295624.0,0,0,0,1311.355,1311.355,1261.897,1255.609,0,0,420470961323.0,37599.7354981,11182817.0,13032998560.0,3408.62719376,3823533.0 1973,2013,BIH,BHS,7928.0,0,0,0,8548.948,8548.948,8490.845,8490.14,0,0,7835117785.02,20736.547344,377841.0,13032998560.0,3408.62719376,3823533.0 1974,2013,BIH,CHE,54831615.0,0,0,0,924.4724,924.4724,859.0802,847.5732,1,0,477246305814.0,58996.896141499994,8089346.0,13032998560.0,3408.62719376,3823533.0 1975,2013,BIH,CHN,599500044.0,0,0,0,7615.6990000000005,7615.6990000000005,7976.162,7953.816,0,0,4912954256930.0,3619.43910837,1357380000.0,13032998560.0,3408.62719376,3823533.0 1976,2013,BIH,COL,18484448.0,0,0,0,9939.706,9939.706,9781.696,9775.609,0,0,212907929816.0,4497.19693577,47342363.0,13032998560.0,3408.62719376,3823533.0 1977,2013,BIH,DOM,439993.0,0,0,0,8463.724,8463.724,8424.295,8423.619,0,0,50033390849.2,4866.39484098,10281408.0,13032998560.0,3408.62719376,3823533.0 1978,2013,BIH,ESP,92453742.0,0,0,0,1861.879,1861.879,1788.526,1724.642,0,0,1172482153960.0,25149.743076400002,46620045.0,13032998560.0,3408.62719376,3823533.0 1979,2013,BIH,FRA,196298812.0,0,0,0,1352.463,1352.463,1226.467,1187.24,0,0,2357143265760.0,35754.652407199996,65925498.0,13032998560.0,3408.62719376,3823533.0 1980,2013,BIH,GBR,85846876.0,0,0,0,1625.285,1625.285,1697.175,1681.18,0,0,2577048727270.0,40199.3169439,64106779.0,13032998560.0,3408.62719376,3823533.0 1981,2013,BIH,JAM,2413.0,0,0,0,9048.972,9048.972,9010.46,9009.873,0,0,,,2714734.0,13032998560.0,3408.62719376,3823533.0 1982,2013,BIH,KOR,47573465.0,0,0,0,8473.069,8473.069,8573.107,8571.003,0,0,1199003686670.0,23875.1809908,50219669.0,13032998560.0,3408.62719376,3823533.0 1983,2013,BIH,MEX,9867244.0,0,0,0,10557.36,10557.36,10412.78,10407.25,0,0,1045697869840.0,8450.75924284,123740109.0,13032998560.0,3408.62719376,3823533.0 1984,2013,BIH,NLD,108229448.0,0,0,0,1375.3870000000002,1375.3870000000002,1293.711,1287.94,0,0,720805621191.0,42893.7807116,16804432.0,13032998560.0,3408.62719376,3823533.0 1985,2013,BIH,PAN,248188.0,0,0,0,9956.257,9956.257,9966.744,9965.239,0,0,29908913759.9,7859.01341755,3805683.0,13032998560.0,3408.62719376,3823533.0 1986,2013,BIH,POL,265228389.0,0,0,0,952.2305,952.2305,863.8704,835.217,0,0,415521978597.0,10923.2344281,38040196.0,13032998560.0,3408.62719376,3823533.0 1987,2013,BIH,SUR,3378.0,0,0,0,8256.805,8256.805,8208.081,8198.186,0,0,2464082868.05,4619.14493964,533450.0,13032998560.0,3408.62719376,3823533.0 1988,2013,BIH,TTO,238.0,0,0,0,8395.56,8395.56,8361.509,8360.982,0,0,19145432662.8,14200.3149757,1348240.0,13032998560.0,3408.62719376,3823533.0 1989,2013,BIH,USA,257593862.0,0,0,0,7188.323,7519.446999999999,8546.15,8371.563,0,0,14451509500000.0,45660.7337641,316497531.0,13032998560.0,3408.62719376,3823533.0 1990,2013,BIH,VEN,,0,0,0,8833.439,8833.439,8900.475,8895.315,0,0,194650892086.0,6429.204742100001,30276045.0,13032998560.0,3408.62719376,3823533.0 1991,2013,BIH,DEU,1164524435.0,0,0,0,1202.42,1033.222,1020.18,986.2493,0,0,3162014177340.0,39208.7600724,80645605.0,13032998560.0,3408.62719376,3823533.0 1992,2013,BIH,IND,54168700.0,0,0,0,5421.039000000001,5421.039000000001,5911.736,5877.564,0,0,1489775906250.0,1164.34327261,1279498874.0,13032998560.0,3408.62719376,3823533.0 1993,2013,BIH,IRN,1737777.0,0,0,0,2944.4509999999996,2944.4509999999996,3063.117,3022.919,0,0,228104456699.0,2956.54216401,77152445.0,13032998560.0,3408.62719376,3823533.0 1994,2013,BIH,JPN,44359691.0,0,0,0,9380.609,9380.609,9258.03,9255.133,0,0,4784541597490.0,37573.373733099994,127338621.0,13032998560.0,3408.62719376,3823533.0 1995,2013,BIH,PAK,7258211.0,0,0,0,4790.143,4790.143,4919.872,4916.875,0,0,143816996323.0,793.724245977,181192646.0,13032998560.0,3408.62719376,3823533.0 1996,2013,BIH,SAU,1916330.0,0,0,0,3342.079,3342.079,3259.765,3234.588,0,0,505813554459.0,16748.2103341,30201051.0,13032998560.0,3408.62719376,3823533.0 1997,2013,BIH,AFG,59856.0,0,0,0,4428.6140000000005,4428.6140000000005,4374.743,4367.222,1,0,12679050960.3,413.23395943199995,30682500.0,13032998560.0,3408.62719376,3823533.0 1998,2013,BIH,ARE,4438848.0,0,0,0,3908.1609999999996,3908.1609999999996,3978.999,3977.416,0,0,234968702615.0,25992.176376400002,9039978.0,13032998560.0,3408.62719376,3823533.0 1999,2013,BIH,ARG,12979206.0,0,0,0,11694.93,11694.93,11708.03,11701.19,0,0,331013918665.0,7781.54951041,42538304.0,13032998560.0,3408.62719376,3823533.0 ================================================ FILE: lecture10/Interpolations_jl_alberto_polo.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Interpolations.jl\n", "\n", "**Alberto Polo**\n", "\n", "April 8, 2016" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My opinion:\n", "\n", "* The package is easy to learn even with basic knowledge of interpolation theory\n", "* Efficient to use: only one function to create interpolation objects, with a few methods\n", "* Fast ...\n", "\n", "\n", "* ... doing a lot of things under the hood allows to achieve those results\n", "* CompEcon in turn allows to manipulate the matrixes which underlie interpolation, which can be useful in designing certain algorithms\n", "* limited types and degrees of polynomials available, mostly with the irregularly-spaced mode" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Install from Julia REPL with `Pkg.add(\"Interpolations\")`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

Plotly javascript loaded.

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "using QuantEcon: tauchen\n", "using CompEcon\n", "using Interpolations\n", "using PlotlyJS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mode 1: Equally-spaced knots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interpolation object is defined on the set of knot indexes - not the actual knot values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct interpolation object `itp` using\n", "\n", " itp = interpolate(Y, options ...)\n", " \n", "where `Y` is an `Array` of values of the function being interpolated, one for each data point (if the domain is multi-dimensional, a data point is a list of knots - one for each dimension)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Options:\n", "\n", "* `BSpline(Constant())` degree 0\n", "* `BSpline(Linear())` degree 1\n", "* `BSpline(Quadratic(x()))` degree 2, must specify the boundary condition: x can be `Flat`, `Natural`, `Free`, `Periodic`, `Reflect`\n", "* `BSpline(Cubic(x()))` degree 3, must specify the boundary condition: x can be `Flat`, `Natural`, `Free`, `Periodic`\n", "* `NoInterp()` just lookup of interpolated function value at the knot index - useful if state space is finite in one dimension (e.g. Markov chain)\n", "\n", " * If the domain is multi-dimensional, can specify different polynomial degrees in different dimensions by using a tuple, e.g. `(BSpline(Linear()), BSpline(Quadratic(Flat())))`\n", " \n", "\n", "* `OnGrid()` if data points in Y lie *on* the boundaries of the interpolation interval\n", "* `OnCell()` if lie on *half-intervals* between boundaries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More natural to define the interpolant on the domain of the interpolated function: use the `scale` function\n", "\n", "In 1 dimension,\n", "\n", " itp_scaled = scale(itp, x)\n", " \n", "where `x` is the vector of knots\n", "\n", "In n dimensions,\n", "\n", " itp_scaled = scale(itp, x1, ..., xn)\n", " \n", "where `xi` is the vector of knots in the i-th dimension" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To evaluate the intepolant at position `z` use\n", "\n", " v = itp[z]\n", " \n", "where `z` is, in general, a tuple of numbers - one for each domain dimension - and `itp` can be a standard or scaled interpolation object. If `itp` is standard interpolant, `z` is grid position; if `itp` is scaled interpolant, `z` is point in the domain" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example: 1 dimension**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "linspace(-2.0,2.0,5)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xmin, xmax = -2, 2.\n", "x = linspace(xmin, xmax, 5)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5-element Array{Float64,1}:\n", " 4.0\n", " 1.0\n", " 0.0\n", " 1.0\n", " 4.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = collect(x).^2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "5-element Interpolations.BSplineInterpolation{Float64,1,Array{Float64,1},Interpolations.BSpline{Interpolations.Linear},Interpolations.OnGrid,0}:\n", " 4.0\n", " 1.0\n", " 0.0\n", " 1.0\n", " 4.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itp1 = interpolate(y, BSpline(Linear()), OnGrid())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "5-element Interpolations.ScaledInterpolation{Float64,1,Interpolations.BSplineInterpolation{Float64,1,Array{Float64,1},Interpolations.BSpline{Interpolations.Linear},Interpolations.OnGrid,0},Interpolations.BSpline{Interpolations.Linear},Interpolations.OnGrid,Tuple{LinSpace{Float64}}}:\n", " 1.0\n", " 4.0\n", " 7.0\n", " 10.0\n", " 13.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itp1_scaled = scale(itp1, x)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "plot_itp_1d (generic function with 1 method)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function plot_itp_1d(itp, itp_label)\n", " \n", " pos = collect(linspace(xmin, xmax, 100))\n", " \n", " tr_itp = scatter(; x = pos, y = [itp[p] for p in pos], name = itp_label)\n", " tr_actual = scatter(; x = pos, y = pos.^2, name = \"Actual\")\n", " tstring = string(\"Inspect \", itp_label, \" Approximation\")\n", " plot([tr_itp, tr_actual], Layout(title = tstring, xaxis_title = \"x value\", yaxis_title = \"f(x)\"))\n", " \n", "end" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_itp_1d(itp1_scaled, \"B-Spline deg. 1\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "5-element Interpolations.BSplineInterpolation{Float64,1,Array{Float64,1},Interpolations.BSpline{Interpolations.Quadratic{Interpolations.Free}},Interpolations.OnGrid,1}:\n", " 4.0 \n", " 1.0 \n", " -2.77556e-17\n", " 1.0 \n", " 4.0 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itp2 = interpolate(y, BSpline(Quadratic(Free())), OnGrid())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itp2_scaled = scale(itp2, x)\n", "plot_itp_1d(itp2_scaled, \"B-Spline deg. 2\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itp3 = interpolate(y, BSpline(Cubic(Natural())), OnGrid())\n", "itp3_scaled = scale(itp3, x)\n", "plot_itp_1d(itp3_scaled, \"B-Spline deg. 3\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itp0 = interpolate(y, BSpline(Constant()), OnGrid())\n", "itp0_scaled = scale(itp0, x)\n", "plot_itp_1d(itp0_scaled, \"B-Spline deg. 0\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example: 2 dimensions**" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5x5 Array{Float64,2}:\n", " 4.0 4.69315 5.09861 5.38629 5.60944\n", " 1.0 1.69315 2.09861 2.38629 2.60944\n", " 0.0 0.693147 1.09861 1.38629 1.60944\n", " 1.0 1.69315 2.09861 2.38629 2.60944\n", " 4.0 4.69315 5.09861 5.38629 5.60944" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zmin, zmax = 0., 4.\n", "z = linspace(zmin, zmax, 5)\n", "\n", "f(x, z) = x.^2 + log(1 + z)\n", "\n", "Y = Array(Float64, length(x), length(z))\n", "for j in 1:length(z), i in 1:length(x)\n", " Y[i, j] = f(x[i], z[j])\n", "end\n", "\n", "Y" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5x5 Interpolations.BSplineInterpolation{Float64,2,Array{Float64,2},Interpolations.BSpline{Interpolations.Linear},Interpolations.OnGrid,0}:\n", " 4.0 4.69315 5.09861 5.38629 5.60944\n", " 1.0 1.69315 2.09861 2.38629 2.60944\n", " 0.0 0.693147 1.09861 1.38629 1.60944\n", " 1.0 1.69315 2.09861 2.38629 2.60944\n", " 4.0 4.69315 5.09861 5.38629 5.60944" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itp1 = interpolate(Y, BSpline(Linear()), OnGrid())" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5x5 Interpolations.ScaledInterpolation{Float64,2,Interpolations.BSplineInterpolation{Float64,2,Array{Float64,2},Interpolations.BSpline{Interpolations.Linear},Interpolations.OnGrid,0},Interpolations.BSpline{Interpolations.Linear},Interpolations.OnGrid,Tuple{LinSpace{Float64},LinSpace{Float64}}}:\n", " 1.69315 2.09861 2.38629 2.60944 2.83258\n", " 4.69315 5.09861 5.38629 5.60944 5.83258\n", " 7.69315 8.09861 8.38629 8.60944 8.83258\n", " 10.6931 11.0986 11.3863 11.6094 11.8326 \n", " 13.6931 14.0986 14.3863 14.6094 14.8326 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itp1_scaled = scale(itp1, x, z)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "plot_itp_2d (generic function with 1 method)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function plot_itp_2d(itp, xslice, zslice, itp_labelx, itp_labelz)\n", " \n", " posx = collect(linspace(xmin, xmax, 100))\n", " posz = collect(linspace(zmin, zmax, 100))\n", " \n", " trx_itp = scatter(; x = posx, y = [itp[px, zslice] for px in posx], name = itp_labelx)\n", " trz_itp = scatter(; x = posz, y = [itp[xslice, pz] for pz in posz], name = itp_labelz)\n", " trx_actual = scatter(; x = posx, y = f(posx, zslice), name = \"Actual\")\n", " trz_actual = scatter(; x = posz, y = f(xslice, posz), name = \"Actual\")\n", " tstringx = string(\"Inspect \", itp_labelx, \" Approximation Along x-Dimension\")\n", " tstringz = string(\"Inspect \", itp_labelz, \" Approximation Along z-Dimension\")\n", " plotx = plot([trx_itp, trx_actual], Layout(title = tstringx, xaxis_title = \"x value\",\n", " yaxis_title = \"f(x, $zslice)\"))\n", " plotz = plot([trz_itp, trz_actual], Layout(title = tstringz, xaxis_title = \"z value\",\n", " yaxis_title = \"f($xslice, z)\"))\n", " \n", " [plotx; plotz] \n", "end" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_itp_2d(itp1_scaled, 0., 0., \"B-Spline deg. 1\", \"B-Spline deg. 1\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itp2_1 = interpolate(Y, (BSpline(Quadratic(Free())), BSpline(Linear())), OnGrid())\n", "itp2_1scaled = scale(itp2_1, x, z)\n", "plot_itp_2d(itp2_1scaled, 0., 0., \"B-Spline deg. 2\", \"B-Spline deg. 1\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mode 2: irregularly-spaced knots (`Gridded` method)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Same constructor name, different method:\n", "\n", " itp = interpolate(knots, Y, options ...)\n", " \n", "where `knots` is a tuple of vectors `(knots1, knots2, ...)` specifying for each dimension the knots where the function is interpolated (if only 1 dimension, `(knots1, )`)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Options:\n", "\n", "* `Gridded(Constant())` degree 0\n", "* `Gridded(Linear())` degree 1\n", "* `NoInterp()`\n", "\n", "\n", "* Necessarily `OnGrid`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example**" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5-element Array{Float64,1}:\n", " 1.0 \n", " 1.0625 \n", " 1.35355\n", " 1.97428\n", " 3.0 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xmin, xmax = 1., 3.\n", "knotsx = linspace(0, (xmax-xmin)^.4, 5).^(1/.4) + xmin" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5-element Array{Float64,1}:\n", " 0.0 \n", " 0.0606246\n", " 0.302733 \n", " 0.680203 \n", " 1.09861 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = log(knotsx)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5-element Interpolations.GriddedInterpolation{Float64,1,Float64,Interpolations.Gridded{Interpolations.Linear},Tuple{Array{Float64,1}},0}:\n", " 0.0 \n", " 0.690695\n", " 1.09861 \n", " 1.50653 \n", " 1.91445 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "itpgd = interpolate((knotsx,), y, Gridded(Linear()))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pos = linspace(xmin, xmax, 100)\n", "\n", "tr_itp = scatter(; x = pos, y = [itpgd[p] for p in pos], name = \"B-Spline deg. 1\")\n", "tr_actual = scatter(; x = pos, y = log(pos), name = \"Actual\")\n", "tstring = string(\"Inspect Gridded B-Spline deg. 1 Approximation\")\n", "plot([tr_itp, tr_actual], Layout(title = tstring, xaxis_title = \"x value\", yaxis_title = \"f(x)\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Other" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `g = gradient(itp, x, y ...)` or `gradient!(g, itp, x, y ...)` (`g` pre-allocated vector) to compute the gradient at positions `x, y, ...`\n", "\n", "\n", "* `extrapolate(itp, x())` where `x` can be `Throw`, `Flat`, `Linear`, `Periodic`, `Reflect` to create a new interpolation object based on `itp`, with user-defined behavior outside of the interpolation interval" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparing Interpolations.jl and CompEcon.jl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example 1: high-dimensional interpolation**" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.028287 seconds (21.44 k allocations: 3.362 MB)\n" ] } ], "source": [ "# function that evaluates itp at all data points (= all possible combinations of knots)\n", "function ongrid!(dest, itp)\n", " for I in CartesianRange(size(itp))\n", " dest[I] = itp[I]\n", " end\n", "end\n", "\n", "# function that creates a collection of 20 knots with random endpoints\n", "new_k(n=20) = linspace(-rand(), rand(), 20)\n", "\n", "# create knots in 4 dimensions\n", "knots1, knots2, knots3, knots4 = [new_k() for i=1:4]\n", "\n", "function compare_to_ce(Y, k1, k2, k3, k4)\n", " # create itp object given a 4d Array Y\n", " itp = interpolate(Y, BSpline(Linear()), OnGrid())\n", " # get scaled itp object using knots in the 4 dimensions\n", " itp_scaled = scale(itp, k1, k2, k3, k4)\n", " # allocate array with same shape and content as Y, which will be replaced by evaluation of the interpolant \n", " # at all data ponts\n", " vals = similar(Y)\n", " # evaluate\n", " ongrid!(vals, itp)\n", "end\n", "\n", "A = rand(20, 20, 20, 20)\n", "@time compare_to_ce(A, knots1, knots2, knots3, knots4)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.618889 seconds (7.67 M allocations: 718.329 MB, 21.06% gc time)\n" ] } ], "source": [ "# function that creates a linear spline with 20 knots between random endpoints\n", "new_p(n=20) = LinParams(n, -rand(), rand())\n", "\n", "# create basis object with a 20-knot linear spline for each of the 4 dimensions\n", "basis = Basis([new_p() for i=1:4]...)\n", "\n", "# get all data points in a matrix (with 20 knots per dimension and 4d, it's a 20^4 by 4 matrix)\n", "knots = nodes(basis)[1]\n", "\n", "function compare_to_itp(basis, knots, Y)\n", " # get interpolation coefficients and basis matrix structure (field vals is a tuple of basis matrixes,\n", " # one for each dimension)\n", " coeffs, bmat_struct = funfitxy(basis, knots, Y)\n", " # evaluate\n", " funeval(coeffs, bmat_struct)\n", "end\n", " \n", "A = randn(size(knots, 1))\n", "@time compare_to_itp(basis, knots, A);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example 2: income fluctuation problem**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An agent is subject to a stochastic, persistent income process $y_t$ and every period can decide how much to save and consume. The current stock of savings is denoted $a_t$ and consumption is denoted $c_t$. Savings earn a net interest rate $r$, constant and exogenous.\n", "\n", "The agent enjoys utility $u(c)$ from consumption.\n", "\n", "The agent is not allowed to borrow, thus $a'\\geq 0$, and consumption cannot be negative ($c\\geq 0)$.\n", "\n", "The states are the current stock of assets $a$ and the current realization of the income process $y$. The value function for the agent's problem is therefore\n", "\n", "\\begin{equation}\n", "v(a, y) = \\max_{c\\in [0, (1+r)a + y]}\\left(u(c) + \\beta \\mathbb{E}_{y'}\\left[v(a', y')\\right]\\right)\n", "\\end{equation}\n", "\n", "where\n", "\n", "\\begin{equation}\n", "a' = y + (1+r)a - c, a' \\geq 0 \\text{ which is imposed through }c\\leq (1+r)a + y, \\beta \\text{ is the discount factor}\n", "\\end{equation}\n", "\n", "I assume CRRA utility function:\n", "\n", "\\begin{equation}\n", "u(c) = \\frac{c^{(1-\\gamma)}}{1-\\gamma}\\text{, where }\\gamma \\text{ is the risk-aversion parameter}\n", "\\end{equation}\n", "\n", "The income process follows a Markov chain constructed from an AR(1) with persistence $\\rho$ and standard deviation $\\sigma$ using the Tauchen method.\n", "\n", "The Euler equation for this problem is\n", "\n", "\\begin{equation}\n", "c^{-\\gamma} \\geq \\beta (1+r) \\sum_{y'\\in S}p(y, y') c(a', y')^{-\\gamma}\n", "\\end{equation}\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "IncomeFluct" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# type that holds all primitives and grid elements shared by the approximation methods\n", "type IncomeFluct\n", " # Model parameters\n", " beta::Float64 # discount factor\n", " risk_av::Float64 # risk-aversion parameter\n", " r::Float64 # net interest rate\n", " rho::Float64 # income process persistence\n", " sigma::Float64 # income process standard deviation\n", " \n", " # Grid parameters assets\n", " na::Int64 # number of knots for the asset space\n", " amin::Float64 # lower bound of the asset space (borrowing limit)\n", " amax::Float64 # upper bound of the asset space\n", " knotsa::Vector{Float64} # asset grid\n", " \n", " # Grid parameters income\n", " ny::Int64 # number of states for the income process\n", " knotsy::Vector{Float64} # income values corresponding to the states\n", " Py::Matrix{Float64} # stochastic matrix for the Markov chain\n", "end\n", "\n", "# type constructor which takes parameter values as keyword arguments\n", "function IncomeFluct(; beta = .95, risk_av = 2., r = .02, rho = .9, sigma = sqrt(.06), na = 100, amin = 0.,\n", " amax = 150., ny = 5)\n", " \n", " # create an unequally-spaced grid for assets\n", " knotsa = linspace(0, (amax-amin)^.4, na).^(1/.4) + amin\n", " \n", " # Tauchen discretization of the income process (using QuantEcon tauchen function)\n", " d_proc = tauchen(ny, rho, sigma)\n", " knotsy = exp(d_proc.state_values)\n", " Py = d_proc.p\n", " \n", " # return the type which holds all elements\n", " IncomeFluct(beta, risk_av, r, rho, sigma, na, amin, amax, knotsa, ny, knotsy, Py)\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Endogenous grid method using Interpolations.jl:* policy function iteration exploiting the Euler equation to avoid using non-linear solver" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "solve_agent (generic function with 1 method)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# function that computes the approximate solution to the agent's problem\n", "function solve_agent(; maxiter = 10000, tol = 1e-8)\n", " \n", " # store the model and grid objects\n", " holder = IncomeFluct()\n", " \n", " # unpack all elements needed to solve the problem\n", " beta, risk_av, r, na, ny, amin, knotsa, knotsy, Py = holder.beta, holder.risk_av, holder.r, holder.na, holder.ny,\n", " holder.amin, holder.knotsa, holder.knotsy, holder.Py\n", " \n", " # separate a-values and y-values from the Cartesian product of knotsa and knotsy\n", " mesha = repmat(knotsa'', 1, ny)\n", " meshy = repmat(knotsy', na, 1)\n", " \n", " # initial guess for the consumption policy function\n", " c = r * mesha + meshy\n", " # create array to store the consumption policy function in the next algorithm iteration\n", " cnext = similar(c)\n", " \n", " @time begin\n", " \n", " for n in 1:maxiter # count the algorithm iteration number\n", "\n", " # given the guess, invert the Euler equation to obtain c(a',y)\n", " c_Euler = (beta * (1 + r) * c.^(-risk_av) * Py').^(-1/risk_av)\n", " # use the budget constraint to obtain a(a',y) [this is the endogenous grid]\n", " grida_end = 1/(1+r) * (c_Euler + mesha - meshy)\n", "\n", " # fill in the policy function for the next iteration (i.e. interpolate c_Euler on knotsa)\n", " for j in 1:ny, i in 1:na\n", "\n", " # take the endogenous grid for each income state\n", " knotsa_end = grida_end[:, j]\n", " # pull out a(a'=amin,y) i.e. the current value of assets which induces the constraint to bind\n", " min_knotsa_end = knotsa_end[1]\n", " # create interpolant\n", " itpa_end = interpolate((knotsa_end,), c_Euler[:, j], Gridded(Linear()))\n", "\n", " # pull out knots where we want to evaluate the consumption policy function\n", " a, y = knotsa[i], knotsy[j]\n", "\n", " if a < min_knotsa_end # if the knot where we evaluate the consumption policy function is \n", " # below the minimum level which makes the constraint bind,\n", " # then the constraint necessarily binds and consumption comes\n", " # from the budget constraint\n", " cnext[i, j] = (1+r) * a + y - amin\n", " else\n", " cnext[i, j] = itpa_end[a]\n", " end\n", "\n", " end \n", "\n", " err = maxabs(cnext - c)\n", " copy!(c, cnext)\n", " if n % 50 == 0; @printf(\"Iter = %d, Dist = %1.2e\\n\", n, err); end\n", "\n", " if err < tol; break; end\n", " end \n", " \n", " end\n", " \n", " # get the asset policy function once the consumption policy function has been solved\n", " aprime_star = meshy + (1+r)*mesha - c\n", " \n", " # solve for the value function by iterating on the Bellman equation, given the policy we solved for\n", " U = c.^(1-risk_av)/(1-risk_av)\n", " \n", " V_star = copy(U)\n", " cV = similar(V_star)\n", " \n", " for n in 1:maxiter\n", " \n", " # since the asset policy function maps from the knots to asset values which are not on the knots,\n", " # we have to interpolate the value function we're solving for (which is also interpolated\n", " # on the original knots)\n", " for j in 1:ny \n", " itpcV = interpolate((knotsa, ), V_star[:,j], Gridded(Linear()))\n", " cV[:,j] = itpcV[aprime_star[:,j]]\n", " end\n", " \n", " V_next = U + beta * (cV * Py')\n", " \n", " errV = maxabs(V_next - V_star)\n", " copy!(V_star, V_next)\n", " \n", " if errV < tol; break; end\n", " end\n", " \n", " aprime_star, V_star, c\n", "end" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter = 50, Dist = 3.05e-02\n", "Iter = 100, Dist = 6.25e-03\n", "Iter = 150, Dist = 3.79e-04\n", "Iter = 200, Dist = 4.53e-06\n", "Iter = 250, Dist = 5.16e-08\n", " 0.188479 seconds (1.49 M allocations: 375.945 MB, 13.32% gc time)\n", " 0.196879 seconds (1.51 M allocations: 389.655 MB, 12.75% gc time)\n" ] } ], "source": [ "@time aprime_star, V_star, c_star = solve_agent();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Newton Value-function iteration using CompEcon.jl*" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# type that holds all the CompEcon.jl objects needed in the algorithm\n", "type CEObjects\n", " ba::Basis{1} # basis for the asset space\n", " bmaty::SparseMatrixCSC # basis matrix evaluated on the knots for income\n", " bmat_ay::SparseMatrixCSC # basis matrix evaluated on the Cartesian product of knotsa and knotsy\n", " \n", " nS::Int64 # total number of data points (na*ny)\n", " cash::Vector{Float64} # cash-on-hand value at all data points (y + (1+r)a)\n", " \n", " coeffs_V::Vector{Float64} # interpolation coefficients for the value function\n", " coeffs_cV::Vector{Float64} # interpolation coefficients for the continuation value\n", "end" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u (generic function with 1 method)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# utility function defined based on cash-on-hand\n", "function u(aprime, holder, holder_ce)\n", " risk_av = holder.risk_av\n", " cash = holder_ce.cash\n", " (cash - aprime).^(1-risk_av)/(1-risk_av)\n", "end" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "solve_aprime_ce (generic function with 1 method)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# function that solves for the optimal savings (a') at each data point using golden search\n", "function solve_aprime_ce(holder, holder_ce)\n", " \n", " # unpack the objects that we need\n", " amin, amax, r = holder.amin, holder.amax, holder.r\n", " nS, cash = holder_ce.nS, holder_ce.cash\n", " \n", " # set the lower bound on savings (in this case, the borrowing constraint)\n", " lb = fill(amin, nS)\n", " \n", " function bmat_aprimey(arg, holder_ce)\n", " # unpack\n", " ba, bmaty = holder_ce.ba, holder_ce.bmaty\n", " # compute the basis matrix on the vector of a' values (one for each data point) that we are considering\n", " bmatarg = BasisStructure(ba, Direct(), arg).vals[1]\n", " # get the basis matrix on the Cartesian product of a' values and knotsy\n", " row_kron(bmaty, bmatarg)\n", " end\n", " \n", " function obj(arg, holder, holder_ce)\n", " # unpack\n", " beta = holder.beta\n", " coeffs_cV = holder_ce.coeffs_cV # these are the interpolation coefficients for the continuation value\n", " \n", " # compute the rhs of the Bellman equation given the vector of a' values\n", " vec(u(arg, holder, holder_ce) + beta*bmat_aprimey(arg, holder_ce)*coeffs_cV)\n", " end\n", " \n", " # use vectorized golden method to solve for the optional savings\n", " obj_gm(x) = obj(x, holder, holder_ce)\n", " aprime, V = golden_method(obj_gm, lb, min(cash, amax))\n", " \n", " # return the objects we want (optimal savings, rhs of the Bellman equation, basis matrix on\n", " # optimal savings*knotsy)\n", " aprime, V, bmat_aprimey(aprime, holder_ce)\n", "end" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "solve_agent_ce (generic function with 1 method)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# function that computes the approximate solution to the agent's problem\n", "function solve_agent_ce(; ordera = 1, maxiter = 20, tol = 1e-8) # I also allow to set the B-spline order\n", " # on the asset space\n", " # store the model and grid objects\n", " holder = IncomeFluct()\n", " # unpack\n", " beta, r, knotsa, knotsy, Py = holder.beta, holder.r, holder.knotsa, holder.knotsy, holder.Py\n", " \n", " # create the 2d basis\n", " b = Basis(SplineParams(knotsa, 0, ordera), LinParams(knotsy, 0))\n", " # pull out the basis for the asset space (to be stored later)\n", " ba = b[1]\n", " # get all data points (saved in S)\n", " S, kn = nodes(b)\n", " # compute basis matrixes on all data points, separately for a and y\n", " bmata, bmaty = BasisStructure(b, Direct(), S).vals\n", " # compute the basis matrix on all data points\n", " bmat_ay = row_kron(bmaty, bmata)\n", " \n", " # get number of data points\n", " nS = size(S, 1)\n", " # get actual knots number for the asset space (if B-spline order > 1, CompEcon.jl adds knots)\n", " na = ba.n[1]\n", " # separate a-values and y-values from the data points\n", " Sa = S[:, 1]\n", " Sy = S[:, 2]\n", " # compute cash-on-hand on all data points\n", " cash = Sy + (1+r)*Sa\n", " \n", " # manipulate the stochastic matrix to fit the matrix representation of data points\n", " Pexp = kron(Py, eye(na))\n", "\n", " # store the CompEcon object that we need inside other functions\n", " holder_ce = CEObjects(ba, bmaty, bmat_ay, nS, cash, zeros(nS), zeros(nS))\n", "\n", " @time begin\n", " \n", " for i in 1:maxiter\n", "\n", " # pull out interpolation coefficients and stack them in a single vector to use Newton method\n", " coeffs_V, coeffs_cV = holder_ce.coeffs_V, holder_ce.coeffs_cV\n", " coeffs = [coeffs_V; coeffs_cV]\n", "\n", " # given the current guess of the interpolation coefficients (i.e. of the value function and continuation\n", " # value on the data points), solve for the optimal savings on all data points\n", " aprime, V, bmat_aprimey = solve_aprime_ce(holder, holder_ce)\n", " # create Jacobian for the linear system in the coefficients\n", " jacobian = [bmat_ay -beta*bmat_aprimey;-Pexp*bmat_ay bmat_ay]\n", "\n", " # compute distance between current guess of the value function at the data points and\n", " # the rhs of the Bellman equation\n", " updV = bmat_ay * coeffs_V - V\n", " # compute distance between current guess of the continuation value at the data points and\n", " # the continuation value implied by the current guess of the value function\n", " updcV = bmat_ay * coeffs_cV - Pexp * bmat_ay * coeffs_V\n", " # stack distances\n", " upd = [updV; updcV]\n", "\n", " # update coefficients using the Newton method\n", " coeffs_next = coeffs - jacobian \\ upd\n", "\n", " err = maxabs(coeffs_next - coeffs)\n", " copy!(holder_ce.coeffs_V, coeffs_next[1:nS])\n", " copy!(holder_ce.coeffs_cV, coeffs_next[nS+1:end])\n", " @printf(\"Iter = %d, Dist = %1.2e\\n\", i, err)\n", "\n", " if err < tol; break; end\n", " end\n", " \n", " end\n", " \n", " # once the value function has been solved for, get the policy functions\n", " aprime_star, V_star, bmat_aprimey = solve_aprime_ce(holder, holder_ce)\n", " c_star = cash - aprime_star\n", " \n", " aprime_star, V_star, c_star, holder_ce \n", "end" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter = 1, Dist = 5.41e+01\n", "Iter = 2, Dist = 2.35e+01\n", "Iter = 3, Dist = 1.93e+01\n", "Iter = 4, Dist = 3.07e+00\n", "Iter = 5, Dist = 5.55e-01\n", "Iter = 6, Dist = 1.77e-01\n", "Iter = 7, Dist = 4.37e-02\n", "Iter = 8, Dist = 7.21e-03\n", "Iter = 9, Dist = 6.61e-04\n", "Iter = 10, Dist = 3.01e-06\n", "Iter = 11, Dist = 2.47e-11\n", " 0.932270 seconds (2.21 M allocations: 767.083 MB, 10.89% gc time)\n", " 0.972177 seconds (2.42 M allocations: 816.660 MB, 11.51% gc time)\n" ] } ], "source": [ "@time aprime_star_ce, V_star_ce, c_star_ce, holder_ce = solve_agent_ce();" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Value Function\n", "Maximum difference = 5.7997e-01\n", "\n", "Assets Policy Function\n", "Maximum difference = 5.1406e-02\n", "\n", "Consumption Policy Function\n", "Maximum difference = 5.1406e-02\n", "\n" ] } ], "source": [ "function plots(obj, obj_ce, holder_ce, tit)\n", " \n", " # store the model and grid objects\n", " holder = IncomeFluct()\n", " # unpack\n", " ny, knotsa, knotsy = holder.ny, holder.knotsa, holder.knotsy\n", " \n", " # unpack the CompEcon objects we need\n", " ba = holder_ce.ba\n", " knotsa_ce = nodes(ba)[1] # get actual knots on the asset space (if B-spline order > 1, CompEcon.jl adds knots)\n", " na = length(knotsa_ce) # knots number\n", " \n", " # reshape the object we're plotting to have asset knots on the rows and income knots on the column\n", " obj_ce = reshape(obj_ce, na, ny)\n", "\n", " # create traces for Plotly.JS to store all lines\n", " tr = GenericTrace[]\n", " tr_ce = GenericTrace[]\n", " \n", " # set initial value to compute distance between Interpolations.jl and CompEcon.jl solutions\n", " maxdiff = 0.\n", " \n", " colors = [\"blue\", \"orange\", \"green\", \"red\", \"violet\"]\n", " \n", " for i in 1:ny\n", " itp = interpolate((knotsa,), obj[:,i], Gridded(Linear())) # if B-spline order > 1, CompEcon.jl has more knots\n", " # so have to interpolate the solution from Interpolations.jl on those points\n", " maxdiff = max(maxabs(itp[knotsa_ce] - obj_ce[:,i]), maxdiff) # iterative compute distance\n", " push!(tr, scatter(; x = knotsa_ce, y = itp[knotsa_ce], name = \"itp, y = $(round(knotsy[i], 2))\",\n", " line_color = colors[i])) # store line for Interpolations.jl\n", " push!(tr_ce, scatter(; x = knotsa_ce, y = obj_ce[:,i], name = \"ce, y = $(round(knotsy[i], 2))\", \n", " line_color = colors[i], line_dash=\"dash\")) # store line for CompEcon.jl\n", " end\n", " \n", " println(tit)\n", " @printf(\"Maximum difference = %1.4e\\n\", maxdiff)\n", " @printf(\"\\n\")\n", " \n", " plot([tr; tr_ce], Layout(title = tit, xaxis_title = \"Assets level\"))\n", "end\n", "\n", "# create plot objects and compute distances between solutions\n", "pV = plots(V_star, V_star_ce, holder_ce, \"Value Function\")\n", "paprime = plots(aprime_star, aprime_star_ce, holder_ce, \"Assets Policy Function\")\n", "pcons = plots(c_star, c_star_ce, holder_ce, \"Consumption Policy Function\")\n", "\n", ";" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pV" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "paprime" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pcons" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.3-pre", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.6" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture10/Morelli_Presentation_final.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### What is the CompEcon Toolbox?\n", "\n", "* It is a MATLAB toolbox supporting Miranda and Fackler (2005): \"Applied Computational Economics and Finance\".\n", "\n", "\n", "* The library functions include:\n", " * Rootfinding and optimization solvers\n", " * Function approximation using polynomials, splines and other functional families\n", " * Numerical integration" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* [continued...]\n", " * Solvers for Ordinary Differential Equations\n", " * Routines solving discrete and continuous time dynamic programming problems\n", " * Solvers for financial derivatives\n", "\n", "\n", "* The toolbox has been translated to **Julia** by Spencer Lyon." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Interpolations Using CompEcon\n", "\n", "* The CompEcon toolbox allows us to compute interpolations for:\n", "\n", " * Any number of dimensions\n", " * Any order of derivative and integral operators\n", " * Any order B-spline, Chebyshev polynomial, and piecewise linear basis functions\n", " * We can mix among different types of families across dimensions (i.e. Chebychev for x and splines for $\\epsilon$)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Structure of the Toolbox - Julia Version\n", "\n", "* Think on 3 main \"theoretical\" categories:\n", "\n", " 1. A functional `Basis`: Family of basis functions; Domain; Interpolation Nodes [see `basis.jl`].\n", " 2. A `BasisStructure` representation: it evaluates the basis functions at the desired interpolation nodes [see `basis_structure.jl`].\n", " 3. Coefficient vector: map from the domain of the `Basis` to the real line [obtained by solving linear system of equations]." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* This general structure is going to become clearer once we go into an example model.\n", "\n", "\n", "* This theoretical construct is mapped into **Julia** by defining groups of types\n", "\n", "\n", "* See https://github.com/spencerlyon2/CompEcon.jl" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Group of Types\n", "\n", "**Group 1:** types to represent the `Basis` in `Basis.jl`.\n", "* `BasisFamily`: abstract type that defines the interpolant families.\n", "```julia\n", "abstract BasisFamily\n", "immutable Cheb <: BasisFamily end\n", "immutable Lin <: BasisFamily end\n", "immutable Spline <: BasisFamily end\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* `BasisParams`: abstract type whose fields are all info needed to construct *univariate* basis.\n", "```julia\n", "type ChebParams <: BasisParams\n", " n::Int\n", " a::Float64\n", " b::Float64\n", "end\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "```julia\n", "type SplineParams <: BasisParams\n", " breaks::Vector{Float64}\n", " evennum::Int\n", " k::Int\n", "end\n", "```\n", "```Julia\n", "type LinParams <: BasisParams\n", " breaks::Vector{Float64}\n", " evennum::Int\n", "end\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* `Basis{N}`:\n", "```julia\n", "type Basis{N, BF<:BasisFamily, BP<:BasisParams}\n", " basistype::Vector{BF} # Basis family\n", " n::Vector{Int} # number of points and/or basis functions\n", " a::Vector{Float64} # lower bound of domain\n", " b::Vector{Float64} # upper bound of domain\n", " params::Vector{BP} # params to construct basis\n", "end\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* Finally note that inside this file there are two additional useful functions:\n", "\n", " 1. `nodes`: given the chosen `Basis`, it computes the nodes.\n", " 2. `Basis`: used to define the basis and also to create multidimensional basis.\n", " \n", "\n", "**Group 2:** type to represent the `BasisStructure` in `basis_structure.jl`.\n", "\n", "* `AbstractBasisStructureRep` [`ABSR`]: it groups the types of representation (Tensor, Direct, Expanded).\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* An important function is `BasisStructure`.\n", "\n", " * Its inputs are the `basis`, the type of representation (Tensor, etc) and the nodes.\n", " * Its output is the interpolant valued at the nodes, $\\Phi$ (Matrix).\n", "\n", "* Another interesting function is `Base.convert` which converts from a `Tensor` or `Direct` `BasisStructure` to an `Expanded` one." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* For example, the following will convert from direct to expanded.\n", "```julia\n", "Φ_direct = BasisStructure(basis, CompEcon.Direct(),snodes,0)\n", "Φ = convert(Expanded, Φ_direct, [0 0]).vals[1]\n", "```\n", "\n", "\n", "* In the end we will need the expanded version, but the other two are more efficient in terms of storage." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Quick Example (Same as Spencer's GitHub)\n", "\n", "### One Dimension:\n", "Approximate $f(x)=e^{-x}$ on $x\\in[-1,1]$ using the three types of interpolations.\n", "\n", "For a known univariate function $f(x)$ there are 3 ways to approximate it. I will do the three of them, but changing the basis type (Cheb and Splines)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "```julia\n", "using CompEcon\n", "f(x) = exp(-x)\n", "a,b = -1.0,1.0\n", "```\n", "*** Option 1: Using `funfitf`***\n", "```julia\n", "n = 10\n", "basis_c = Basis(Cheb, n, a, b)\n", "c_c = funfitf(basis_c, f)\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "*** Option 2: Using `funfitxy`***\n", "```julia\n", "xgrid = collect(linspace(a,b,n))\n", "basis_s = Basis(Spline, xgrid, 0, 1)\n", "x_s = nodes(basis_s)[1]\n", "y_s = f(x_s)\n", "c_s = funfitxy(basis_s, x_s, y_s)[1]\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "*** Option 3: Using `BasisStructure`***\n", "```julia\n", "x_c = nodes(basis_c)[1]\n", "y_c = f(x_c)\n", "phi_c = BasisStructure(basis_c).vals[1]\n", "c_c2 = phi_c\\y_c\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using CompEcon\n", "f(x) = exp(-x)\n", "\n", "# Set the endpoints of approximation interval:\n", "a = -1.0 # left endpoint\n", "b = 1.0 # right endpoint\n", "n = 10 # order of approximation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "10-element Array{Float64,1}:\n", " 2.71828 \n", " 2.17663 \n", " 1.74291 \n", " 1.39561 \n", " 1.11752 \n", " 0.894839\n", " 0.716531\n", " 0.573753\n", " 0.459426\n", " 0.367879" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Option 1: (with Cheb)\n", "basis_c = Basis(Cheb, n, a, b)\n", "c_c = funfitf(basis_c, f)\n", "\n", "# Option 2: (with Spline)\n", "xgrid = collect(linspace(a,b,n))\n", "basis_s = Basis(Spline, xgrid, 0, 1)\n", "x_s = nodes(basis_s)[1]\n", "y_s = f(x_s)\n", "c_s = funfitxy(basis_s, x_s, y_s)[1]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficients from options 1 and 3" ] }, { "data": { "text/plain": [ "10x2 Array{Float64,2}:\n", " 1.26607 1.26607 \n", " -1.13032 -1.13032 \n", " 0.271495 0.271495 \n", " -0.0443368 -0.0443368 \n", " 0.00547424 0.00547424 \n", " -0.000542926 -0.000542926\n", " 4.49773e-5 4.49773e-5 \n", " -3.19844e-6 -3.19844e-6 \n", " 1.99211e-7 1.99211e-7 \n", " -1.10118e-8 -1.10118e-8 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Option 3: (with Cheb again)\n", "x_c = nodes(basis_c)[1]\n", "y_c = f(x_c)\n", "phi_c = BasisStructure(basis_c, CompEcon.Expanded(),x_c).vals[1]\n", "c_c2 = phi_c\\y_c\n", "\n", "\n", "# Compare different approaches\n", "print(\"Coefficients from options 1 and 3\")\n", "[c_c c_c2]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/jmorelli/.julia/v0.4/Conda/deps/usr/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] }, { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Having computed the basis coefficients, one may now evaluate the\n", "# approximant at any point x using funeval:\n", "xvals = collect(linspace(-1.0,1.0,50))\n", "y_c = funeval(c_c, basis_c, xvals)\n", "y_s = funeval(c_s, basis_s, xvals)\n", "yy = f(xvals)\n", "\n", "yvals = [y_c y_s yy]\n", "\n", "Method = [\"Cheb\", \"Spline\", \"True Function\"]\n", "\n", "using PyPlot\n", "fig, ax = subplots()\n", "for i=1:3\n", " meth = Method[i]\n", " ax[:plot](xvals, yvals[:,i], linewidth=2, alpha=0.6, label=L\"$Method$ =\"\" $meth\")\n", "end\n", "ax[:legend](loc=1)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Two Dimensions:\n", "\n", "Let the \"state\" be given by $s=(a,y)$. I take the function $f(s) = \\sqrt{a}\\exp{(y)}$. To generate the interpolation, I use a linear spline for the first variable and a Chebichev polynomial for the second. Then I plot the true value and the interpolation, for different values of $s$.\n", "\n", "In this example, we get a better approximation by mixing splines and Chebychev, than by just using Chebychev in both arguments." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "100-element Array{Float64,1}:\n", " 0.0 \n", " 4261.44 \n", " 6026.58 \n", " 7381.03 \n", " 8522.88 \n", " 9528.87 \n", " 10438.3 \n", " 11274.7 \n", " 12053.2 \n", " 12784.3 \n", " 0.0 \n", " 7614.19 \n", " 10768.1 \n", " ⋮ \n", " 65.4139 \n", " 69.382 \n", " 0.0 \n", " 5.73206\n", " 8.10636\n", " 9.92822\n", " 11.4641 \n", " 12.8173 \n", " 14.0406 \n", " 15.1656 \n", " 16.2127 \n", " 17.1962 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fun2(st) = sqrt(st[:,1]).*exp(st[:,2])\n", "\n", "agrid0 = collect(linspace(0.0,10.0,10))\n", "\n", "a_basis = Basis(Spline, agrid0, 0, 1)\n", "y_basis = Basis(Cheb, 10, 0.0, 10.0)\n", "basis2 = Basis(a_basis, y_basis)\n", "\n", "c_2d = funfitf(basis2, fun2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "50x2 Array{Float64,2}:\n", " 0.0 0.0 \n", " 0.401294 0.55403 \n", " 0.789375 0.960904\n", " 0.922409 1.4433 \n", " 1.12674 2.04389 \n", " 1.81061 2.80249 \n", " 3.07046 3.76502 \n", " 4.80473 4.98737 \n", " 6.98772 6.53882 \n", " 9.51032 8.50564 \n", " 12.3315 10.9956 \n", " 15.4947 14.1431 \n", " 19.0017 18.1164 \n", " ⋮ \n", " 6501.88 6498.18 \n", " 8075.61 8073.54 \n", " 10026.4 10027.5 \n", " 12445.9 12450.5 \n", " 15447.0 15454.3 \n", " 19170.3 19177.5 \n", " 23782.5 23791.2 \n", " 29491.8 29507.1 \n", " 36569.9 36587.4 \n", " 45341.5 45355.8 \n", " 56205.0 56212.9 \n", " 69647.2 69653.8 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avals = collect(linspace(0.0,10.0,50))\n", "yvals = collect(linspace(0.0,10.0,50))\n", "svals = [avals yvals]\n", "y_val = funeval(c_2d, basis2, svals)\n", "y_true = fun2(svals)\n", "\n", "yvals2d = [y_val y_true]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Method = [\"Interpolation\", \"True Function\"]\n", "\n", "using PyPlot\n", "\n", "fig, ax = subplots()\n", "for i=1:2\n", " meth = Method[i]\n", " ax[:plot](1:1:50, yvals2d[:,i], linewidth=2, alpha=0.6, label=L\"$Method$ =\"\" $meth\")\n", "end\n", "ax[:legend](loc=1)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Baseline Model: Economic Growth\n", "\n", "* Consider an economy that produces and consumes a single composite good.\n", "* Infinite horizon. Continuous state and action.\n", "* At the begining of period $t$ it has $s\\in(0,\\infty)$ units, of which invests $x\\in[0,s)$.\n", "* State transition function: $s=g(s,x,\\epsilon)=\\gamma x+\\epsilon f(x)$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* Rewards function:\n", "\\begin{equation}\n", "F(s,x)=u(s-x)=\\frac{(s-x)^{1-\\alpha}}{1-\\alpha}\n", "\\end{equation}\n", "* Bellman Equation:\n", "\\begin{eqnarray}\n", "V\\left(\\left[x,\\epsilon\\right]\\right)=\\max_{0\\leq x'\\leq s=g\\left(x,\\epsilon\\right)}\\left\\{u\\left(g\\left(x,\\epsilon\\right)-x'\\right)+\\delta E_{\\epsilon'}V\\left(\\left[x',\\epsilon'\\right]\\right)\\right\\}\n", "\\end{eqnarray}\n", "\n", "\n", "* Assume $u'(0)=-\\infty$ and $h(0)=0$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* From FOC, we get the Euler equation:\n", "\\begin{equation}\n", "u_{t}'=\\delta E_{t}\\left[u_{t+1}'\\left(\\gamma+\\epsilon_{t+1}f_{t}'\\right)\\right]\n", "\\end{equation}\n", "* Steady State ($\\epsilon=1$):\n", " * $u'\\left(s^{*}-x^{*}\\right)=\\delta\\lambda^{*}\\left[\\gamma+f'\\left(x^{*}\\right)\\right]$\n", " * $\\lambda^{*}=u'\\left(s^{*}-x^{*}\\right)$\n", " * $s^{*}=\\gamma x^{*}+f\\left(x^{*}\\right)$\n", " \n", " \n", "* CE SS conditions imply the golden rule $1-\\gamma+r=f'(x^{*})$ where $\\delta=1/(1+r)$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "To approximate the solution, I follow Simon Mongey's notes given during Gianlucca Violante's course.\n", " \n", " * Define the set of collocation nodes $s = [\\mathbf{1}_{Nz}\\otimes{}\\mathbf{X},\\mathbf{Z}\\otimes{}\\mathbf{1}_{Nx}]$ and let $N=N_{x}\\text{x}N_{z}$\n", " \n", " * We can define the following system:\n", " \\begin{equation}\n", " V(s_{i}) = \\max_{x'\\in B(s_{i})}F(s_{i},x')+\\beta V_{e}([x',s_{i,2}]) \\\\\n", " V_{e}(s_{i}) = \\sum_{k=1}^{Nz}P(z,z_{k}')V([s_{i,1},z'_{k}])\n", " \\end{equation}" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " * If we substitute for the interpolants:\n", " \n", " \\begin{equation}\n", " \\sum_{j=1}^{N}\\phi(s_{i})c_{j}= \\max_{x'\\in B(s_{i})}F(s_{i},x')+\\beta\\sum_{j=1}^{N}\\phi([x',s_{i,2}])c_{j}^{e} \\\\\n", " \\sum_{j=1}^{N}\\phi(s_{i})c_{j}^{e}= \\sum_{k=1}^{Nz}P(z,z_{k}')\\sum_{j=1}^{N}\\phi([s_{i,1},z'_{k}])c_{j}\n", " \\end{equation}" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " * ... and write it in a stacked form, we get:\n", " \n", " \\begin{equation}\n", " \\Phi(s) = \\max_{x'\\in\\mathbf{B}(s)}F(s,x')+\\beta\\Phi([x',s_{2}])c^{e} \\\\\n", " \\Phi(s)c^{e} = (P\\otimes{}\\mathbf{I}_{Nx})\\Phi(s)c\n", " \\end{equation}\n", " \n", " \n", " * Note: recall $\\Phi([a,b,c])=\\Phi_{a}\\Phi_{b}\\Phi_{c}$. It is going to be useful later, and it is associated to the `Direct` specification.\n", " \n", " \n", " * The following scripts solves this model using Julia's version of CompEcon written by Spencer Lyon." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* In the following first block I define the Economic Growth model type EconGrowth and auxiliary functions. Some of them are going to be part of the EconGrowth model, since are intrinsic to this specific model.\n", "\n", "\n", "* In particular, these functions are the utility function, the production function and the law of motion for wealth.\n", "The auxiliary functions are for the discretization of the stochastic process and the definition of the basis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "steady_state (generic function with 1 method)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using CompEcon\n", "\n", "# Step 0: Define the Economic Growth model type EconGrowth\n", "\n", "type EconGrowth\n", " a::Float64\n", " b::Float64\n", " nx::Int64\n", " m::Int64\n", " σ::Float64\n", " δ::Float64\n", " α::Float64\n", " β::Float64\n", " γ::Float64\n", " basis::Basis\n", " bs::BasisStructure\n", " Φ::SparseMatrixCSC{Float64,Int}\n", " snodes::Matrix{Float64}\n", " ϵ::Vector{Float64}\n", " P::Matrix{Float64}\n", " tol::Float64 # Tolerance Level.\n", " maxit::Int # Maximum iterations.\n", "end\n", "\n", "# Step 1: Define the model's functions\n", "f(eg::EconGrowth, x) = x.^eg.β\n", "g(eg::EconGrowth, sn=eg.snodes) = eg.γ*sn[:,1] + sn[:,2].*f(eg, sn[:,1])\n", "u(eg::EconGrowth, sn, xprime) = (g(eg, sn)-xprime).^(1-eg.α)./(1-eg.α)\n", "\n", "\n", "# Step 2: Discretize stochastic process and build interpolation basis.\n", "# Wrap up everything in the EconGrowth function.\n", "\n", "function EconGrowth(;a::Float64=2.0,\n", " b::Float64=8.0,\n", " nx::Int64=10,\n", " m::Int64=3,\n", " σ::Float64=0.1,\n", " δ::Float64=0.9,\n", " α::Float64=2.0,\n", " β::Float64=0.5,\n", " γ::Float64=0.9,\n", " tol::Float64=1e-9,\n", " maxit::Int=10_000)\n", " \n", " # Discretize stochastic process\n", " ϵ, ω = qnwlogn(m, 0, σ^2) # quadrature for lognormal\n", " P = repmat(ω', m, 1) # Since the process is i.i.d but I wanted to keep the general structure.\n", "\n", " # Build interpolation basis\n", "# x_params = SplineParams(nx, a, b, 1)\n", " x_params = ChebParams(nx, a, b)\n", " z_params = LinParams(ϵ, 0)\n", " basis = Basis(x_params, z_params)\n", " snodes, (xnodes, znodes) = nodes(basis)\n", " bs = BasisStructure(basis, Direct(), snodes, [0 0])\n", " Φ = convert(Expanded, bs).vals[1]\n", "\n", " EconGrowth(a, b, nx, m, σ, δ, α, β, γ, basis, bs, Φ, snodes, ϵ, P, tol, maxit)\n", "end\n", "\n", "\n", "# Step 3: Compute the Steady State\n", "\n", "function steady_state(eg::EconGrowth)\n", " δ, γ, β, α = eg.δ, eg.γ, eg.β, eg.α\n", "\n", " r_ss = 1/δ-1\n", " x_ss = ((1/β)*(1/δ-γ))^(1/(β-1))\n", " s_ss = γ*x_ss+x_ss^β\n", " c_ss = s_ss-x_ss\n", " lambda_ss = (c_ss)^(-α)\n", " vals_ss = [r_ss,x_ss,s_ss,c_ss,lambda_ss]\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "* Next I write down:\n", " * the optimization problem\n", " * the one-setp update function\n", " * and the iteration function\n", " \n", " \n", "* For the optimization I choose the CompEcon Golden Method search algorithm. Alternativley I could use a Newton-Rhapson method.\n", "\n", "\n", "* For solving the Bellman Equation using the collocation nodes I just iterate. Alternatively I could use some Newton rootfinding algorithm." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "one_step (generic function with 1 method)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Step 4: Write the Optimization Problem\n", "function obj_fun(eg::EconGrowth, ce::Array{Float64,1})\n", "\n", " function obj(xp)\n", " Φ_xp = BasisStructure(eg.basis[1], Expanded(), xp, [0]).vals[1]\n", " Φ = row_kron(eg.bs.vals[2], Φ_xp)\n", " u(eg, eg.snodes, xp) + eg.δ*Φ*ce\n", " end\n", "\n", " x_sol,f_sol = golden_method(obj, zeros(size(eg.snodes, 1)), g(eg, eg.snodes))\n", "\n", "end\n", "\n", "\n", "# Step 5: Write down the iterative procedure (one-step)\n", "# Bellman Iteration\n", "\n", "function one_step(eg::EconGrowth, cc::Vector{Float64}, ce::Vector{Float64}, P_Φ)\n", " xsol,fsol = obj_fun(eg, ce)\n", " f2 = P_Φ*cc\n", "\n", " cc_out = eg.Φ\\fsol\n", " ce_out = eg.Φ\\f2\n", "\n", " return cc_out, ce_out\n", "end\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "iter (generic function with 1 method)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Step 6: Define the iteration function\n", "function iter(eg::EconGrowth)\n", " iteration = 0\n", " di = 1\n", "\n", " n_x = length(nodes(eg.basis.params[1]))\n", " P_Φ = kron(eg.P, eye(n_x))*eg.Φ\n", "\n", " cc = zeros(size(eg.snodes, 1))\n", " ce = zeros(size(eg.snodes, 1))\n", "\n", " while di>eg.tol\n", " iteration += 1\n", " if iteration > eg.maxit\n", " break\n", " else\n", " cc0 = copy(cc)\n", " ce0 = copy(ce)\n", " cc,ce = one_step(eg, cc0, ce0, P_Φ)\n", "\n", " resid1 = norm(cc0-cc)\n", " resid2 = norm(ce0-ce)\n", "\n", " di = max(resid1,resid2)\n", "# @printf(\"Iteration %d with distance %.3f\\n\", iteration, di)\n", " end\n", " end\n", "\n", " @printf(\"The total number of interations was %d.\\n\", iteration)\n", "\n", " return cc, ce\n", "end\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "* Finally I give values to the parameters and use it for input to the EconGrowth type.\n", "* With that I can solve the model, given this set of parameters.\n", "* Then I plot investment (x) in percentage to wealth (s)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The steady values are r = 0.11, x = 5.61, s = 7.42, c = 1.81.\n", "The total number of interations was 399.\n", "The vector of coefficients cc and ce are:\n", " [-6.148872886307476 -6.002342157669572\n", " 1.4293903189819739 1.365007974315387\n", " -0.30242864651737383 -0.2855586913738552\n", " 0.07582912444092763 0.07110552708156898\n", " -0.020564340751041812 -0.01916857818324296\n", " 0.0058327865049471695 0.0054283140816016135\n", " -0.0016939025683300657 -0.0015642406293160244\n", " 0.0005136389136879392 0.00047047470848094803\n", " -0.00016134840315931573 -0.0001479459366909434\n", " 3.90311738541416e-5 4.683426777777204e-5\n", " -6.004497336230142 -6.002342157669572\n", " 1.365443212024492 1.3650079743153873\n", " -0.2856095706000894 -0.28555869137385514\n", " 0.07109143135780838 0.07110552708156898\n", " -0.019172654228205393 -0.019168578183242573\n", " 0.00542009565553203 0.005428314081601409\n", " -0.0015684885484015725 -0.0015642406293160285\n", " 0.00046658723147479606 0.00047047470848118135\n", " -0.00015266865654640967 -0.0001479459366911911\n", " 4.653069839357178e-5 4.683426777800823e-5\n", " -5.847190718065338 -6.002342157669573\n", " 1.2988846788123867 1.3650079743153858\n", " -0.26848521932540076 -0.2855586913738555\n", " 0.06643831261725953 0.07110552708156882\n", " -0.01775651143559391 -0.019168578183242642\n", " 0.005056715362534851 0.005428314081601411\n", " -0.0014175870139597354 -0.0015642406293159723\n", " 0.00044286041130199507 0.000470474708481263\n", " -0.00011565259080115791 -0.0001479459366911148\n", " 5.585163923716581e-5 4.68342677780536e-5]\n" ] } ], "source": [ "# Computation of the Steady State\n", "eg = EconGrowth()\n", "vals_ss = steady_state(eg)\n", "\n", "@printf(\"The steady values are r = %.2f, x = %.2f, s = %.2f, c = %.2f.\\n\"\n", ",vals_ss[1],vals_ss[2],vals_ss[3],vals_ss[4])\n", "\n", "# Solution to the Model\n", "cc, ce = iter(eg)\n", "\n", "println(\"The vector of coefficients cc and ce are:\\n $([cc ce])\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(PyObject ,PyObject )" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using PyPlot\n", "\n", "xsol,fsol = obj_fun(eg,ce)\n", "\n", "s = g(eg,eg.snodes)\n", "\n", "yplot = (xsol)./s\n", "\n", "plot(s[2*eg.nx+1:3*eg.nx],yplot[2*eg.nx+1:3*eg.nx])\n", "xlabel(\"Wealth\"), ylabel(\"Investment in % of Wealth\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Finally, I do the same plot as in the previous step, but with a finer grid.\n", "\n", "\n", "* I choose 100 gridpoints for $x$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([2.02964,2.03239,2.03787,2.04609,2.05704,2.07069,2.08703,2.10605,2.12771,2.152 … 8.13656,8.16247,8.18568,8.20614,8.2238,8.2386,8.2505,8.25947,8.26546,8.26846],[-7.98486,-7.98102,-7.97336,-7.96194,-7.94682,-7.9281,-7.9059,-7.88036,-7.85162,-7.81986 … -4.79226,-4.78664,-4.78165,-4.77728,-4.77353,-4.77041,-4.76791,-4.76604,-4.76479,-4.76417])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "x_params = ChebParams(100, eg.a, eg.b)\n", "z_params = LinParams(eg.ϵ, 0)\n", "basis2 = Basis(x_params, z_params)\n", "snodes2, (xnodes2, znodes2) = nodes(basis2)\n", "bs2 = BasisStructure(basis2, Direct(), snodes2, [0 0])\n", "\n", "function obj2(xp)\n", " Φ_xp2 = BasisStructure(eg.basis[1], Expanded(), xp, [0]).vals[1]\n", " Φ2 = row_kron(bs2.vals[2], Φ_xp2)\n", " u(eg, snodes2, xp) + eg.δ*Φ2*ce\n", "end\n", "\n", " x_sol,f_sol = golden_method(obj2, zeros(size(snodes2, 1)), g(eg, snodes2))\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(PyObject ,PyObject )" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2 = g(eg,snodes2)\n", "\n", "yplot2 = (x_sol)./s2\n", "\n", "plot(s2[201:300],yplot2[201:300])\n", "xlabel(\"Wealth\"), ylabel(\"Investment in % of Wealth\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Julia 0.4.0", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.0" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture11/Scikit-Learn presentation.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## ILDEBRANDO MAGNANI\n", "\n", "im975@nyu.edu\n", "\n", "15/04/2016" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scikit-Learn for Linear Regression, Cross-Validation and Ridge Regression:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Scikit-Learn:\n", "\n", "* Simple and efficient tools for data analysis and data mining\n", "* Accessible to everybody, and reusable in various contexts\n", "* Built on NumPy, SciPy, and matplotlib\n", "* Open source, commercially usable \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import norm\n", "from math import pi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will begin by stating our \"true model\", defined as $y = \\cos(\\pi x)$. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def y(x):\n", " return np.cos(pi*x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now add some random \"noise\" to it in order to generate 25 data points, i.e., 25 $(x_i, y_i)$-tuples that will form our dataset. To do so, we have \n", "\n", "\\begin{equation}\n", "y = \\cos(\\pi x) + \\varepsilon, \\ where \\ x\\sim U[-1,1] \\ and \\ \\varepsilon\\sim N(0,0.5)\n", "\\end{equation}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = np.linspace(-1, 1, 100)\n", "X = np.random.uniform(-1, 1, 25)\n", "X_data = X.reshape(25, 1)\n", "\n", "y_obs_list = []\n", "\n", "for i in range(len(X)):\n", " y_obs = y(X[i]) + np.random.normal(0, 0.5)\n", " y_obs_list.append(y_obs)\n", "\n", "X_data = X.reshape(25, 1)\n", "Y_data = np.asarray(y_obs_list).reshape(25, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we want to plot the true model and the $(x_i, y_i)$-tuples we generated above." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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i2TLYZx/46lcNcOk//xN+9StDXMMz6HjIiLgOWHG6/iCH8rH9bD7QJfT2KaVH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\n0QAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,6))\n", "\n", "ax.plot(x, y(x))\n", "ax.scatter(X_data, Y_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we use Scikit for Linear Regression. The module \"LinearRegression\" fits a linear model with coefficients $\\beta = (\\beta_1, \\beta_2, \\dots, \\beta_p)$ to minimize the residual sum of squares between the observed responses in the dataset, and the responses predicted by the linear approximation. Mathematically it solves a problem of the form:\n", "\n", "\\begin{equation}\n", "\\min_{\\beta} \\lVert X\\beta - y \\rVert_{2}^2\n", "\\end{equation}\n", "\n", "The vector of coefficients $\\beta = (\\beta_1, \\beta_2, \\dots, \\beta_p)$ is designated as \"coef\\_\" and $\\beta_0$ as \"intercept\\_\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "LinearRegression will take in its \"fit\" method arrays X, y and will store the coefficients $\\beta$ of the linear model in its coef\\_ member. By default, the \"score\" method returns the coefficient of determination $R^2$ of the prediction." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import linear_model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "intercept: \n", " [ 0.01233033]\n", "coefficient: \n", " [[ 0.13812041]]\n", "Mean Squared Error: 0.85\n", "R squared: 0.01\n" ] } ], "source": [ "regr = linear_model.LinearRegression()\n", "regr.fit(X_data, Y_data)\n", "\n", "print('intercept: \\n', regr.intercept_)\n", "print('coefficient: \\n', regr.coef_)\n", "print(\"Mean Squared Error: %.2f\" % np.mean((regr.predict(X_data) - Y_data) ** 2))\n", "print('R squared: %.2f' % regr.score(X_data, Y_data))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now plot the results, which are obviously not very significant." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DWntGyfMbALv34DZjzEPAAmvtrJLn+UCf8rrTdU1cIuWss5xu9Msvd7uSfW3Z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\nL+fBBx9k0aJFbNmyhbfeeqvMh7k951y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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(8,8))\n", "\n", "ax.plot(x, y(x), label = \"True Model\")\n", "ax.scatter(X_data, Y_data, label = \"Training Points\")\n", "ax.plot(X_data, regr.predict(X_data), label = \"Regression Line\")\n", "plt.legend(loc='lower center')\n", "plt.show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make things more interesting, we are now going to use Scikit's linear models trained on nonlinear functions of the data. This approach maintains the generally fast performance of linear methods, while allowing them to fit a much wider range of data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we randomly generate a $(3\\times2)$ matrix $X = [x_1, x_2]$ of observations, and we will transform it to $X = [1, x_1, x_2, x_{1}^2, x_1 x_2, x_{2}^2]$ using Scikit's \"PolynomialFeatures\" preprocessor. This preprocessor transforms an input data matrix into a new data matrix of a given degree. It can be used as follows:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.preprocessing import PolynomialFeatures" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.82780005 0.94438455]\n", " [ 0.79859015 0.93094532]\n", " [ 0.20454604 0.37746037]]\n", "[[ 1. 0.82780005 0.94438455 0.68525291 0.78176157 0.89186217]\n", " [ 1. 0.79859015 0.93094532 0.63774623 0.74344377 0.8666592 ]\n", " [ 1. 0.20454604 0.37746037 0.04183908 0.07720802 0.14247633]]\n" ] } ], "source": [ "X_data_example = np.random.random((3, 2))\n", "\n", "print(X_data_example)\n", "\n", "poly = PolynomialFeatures(degree=2)\n", "\n", "poly_X_data_example = poly.fit_transform(X_data_example)\n", "print(poly.fit_transform(X_data_example))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This sort of preprocessing can be streamlined with the \"Pipeline\" tools. In fact, Pipeline can be used to chain multiple estimators into one. This is useful as there is often a fixed sequence of steps in processing the data, for example feature selection, normalization and classification. Pipeline serves two purposes here:\n", "\n", "* Convenience: You only have to call fit and predict once on your data to fit a whole sequence of estimators.\n", "\n", "* Joint parameter selection: You can grid search over parameters of all estimators in the pipeline at once.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we are going to fit the same dataset with polynomials of different degrees (2, 5, 14) and plot results. We will compute the MSE of the predictor to see how well the polynomials do in their approximation." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean Squared Error for Degree 2 :\n", "0.251730890466\n", "Mean Squared Error for Degree 5 :\n", "0.229458178797\n", "Mean Squared Error for Degree 14 :\n", "0.0884120296725\n" ] }, { "data": { "image/png": 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mNea3MXT9oiuf3fkZna/ofFIbW7bA7bfDmDHHD6tKyfD6687ah8cfP/m91pe0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\nEMKBDYsYZpfrqTt1Ej/410EOmA7Q7Z5ueocihBDCgT1c92E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CXEJaQsJU4uK6Ex7ujBHHx49keZkv2HVoF9NbTPd6Y4+JE53TyL79Fs45x781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\nnfH09MTb25vmzZtnrcWel4CAAJKSkgrUxokTJ2RBG2H3LHFdXN7lQhT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gtmOaHUPfY/vy5ZYvOf+E881/AyEaUOdyuknV6bUnp/fff7/f9zKzxUy5v+ry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\nHzNlynTpkYuAXDXsKvKn5zN+0Hi27tnKxNcnkv5oOrGmWN6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4pIWrJ15ZV4m71k3vXr1D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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.pipeline import make_pipeline\n", "\n", "x_plot = np.linspace(-1, 1, 100).reshape(100,1)\n", "regression = linear_model.LinearRegression()\n", "\n", "for degree in [2, 5, 14]:\n", " # chain PolynomialFeatures and LinearRegression into one \n", " # estimator (make_pipeline is just a shorthand for Pipeline)\n", " model = make_pipeline(PolynomialFeatures(degree), regression)\n", " model.fit(X_data, Y_data)\n", " \n", " #predict using the linear model\n", " y_plot = model.predict(x_plot)\n", " \n", " #plot\n", " fig, ax = plt.subplots(figsize=(8,8))\n", " ax.plot(x, y(x), label = \"True Model\")\n", " ax.scatter(X_data, Y_data, label = \"Training Points\")\n", " ax.plot(x_plot, y_plot, label=\"Degree %d\" % degree)\n", " plt.legend(loc='lower center')\n", " \n", " #compute the mean squared error (MSE)\n", " MSE = np.mean((model.predict(X_data) - Y_data) ** 2)\n", " print(\"Mean Squared Error for Degree %d\" % degree, \":\")\n", " print(MSE)\n", " plt.title(\"Degree {}\\nMSE = {:.2e}\".format(degree, MSE))\n", " \n", " plt.xlim(-1, 1)\n", " plt.ylim(-2, 2)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that a linear function (polynomial with degree 1) is not sufficient to fit the training samples. This is called underfitting. A polynomial of degree 5 approximates the true function almost perfectly. For higher degrees the model will overfit the training data, i.e. it learns the noise of the training data. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data. This situation is called overfitting. To avoid it, it is common practice when performing a (supervised) machine learning experiment to hold out part of the available data as a test set \"X_test\", \"Y_test\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In scikit-learn a random split into training and test sets can be quickly computed with the \"train_test_split\" helper function. We can quickly sample a training set while holding out 40% of the data for testing (evaluating) our classifier:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train, X_test, Y_train, Y_test = train_test_split(X_data, Y_data, test_size=0.4, random_state=0)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((15, 1), (15, 1))" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape, Y_train.shape" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((10, 1), (10, 1))" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test.shape, Y_test.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can train a linear regression model on (X-train, Y-train) data, and then compute the Mean Squared Error on the (X-test, Y-test) set." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "intercept: \n", " [ 0.15169192]\n", "coefficient: \n", " [[ 0.2190235]]\n", "Mean Squared Error: 1.34\n" ] } ], "source": [ "regr = linear_model.LinearRegression()\n", "regr.fit(X_train, Y_train)\n", "\n", "# The coefficients\n", "print('intercept: \\n', regr.intercept_)\n", "print('coefficient: \\n', regr.coef_)\n", "# The mean square error computed on validation sets\n", "print(\"Mean Squared Error: %.2f\" % np.mean((regr.predict(X_test) - Y_test) ** 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do the same with \"polynomial fitting\"." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE on VALIDATION SET for Degree 1 :\n", "1.33851766521\n", "MSE on VALIDATION SET for Degree 2 :\n", "0.506630898514\n", "MSE on VALIDATION SET for Degree 5 :\n", "0.747112367299\n", "MSE on VALIDATION SET for Degree 14 :\n", "4826715154.43\n" ] } ], "source": [ "regression = linear_model.LinearRegression()\n", "\n", "for degree in [1, 2, 5, 14]:\n", " model = make_pipeline(PolynomialFeatures(degree), regression)\n", " model.fit(X_train, Y_train) \n", " print(\"MSE on VALIDATION SET for Degree %d\" % degree, \":\")\n", " print(np.mean((model.predict(X_test) - Y_test) ** 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What we observe is a giant increase in the MSE for the polynomial of 14th degree. This is an obvious consequence of overfitting, i.e., with a 14th degree polynomal we are minimizing empirical risk, and not the \"true\" risk." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "By partitioning the available data into 2 sets, we drastically reduce the number of samples which can be used for learning the model, and the results can depend on a particular random choice for the pair of (train, validation) sets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A solution to this problem is a procedure called cross-validation (CV for short). A test set should still be held out for final evaluation, but the validation set is no longer needed when doing CV. In the basic approach, called k-fold CV, the training set is split into k smaller sets (other approaches are described below, but generally follow the same principles). The following procedure is followed for each of the k “folds”:\n", "\n", "* A model is trained using k-1 of the folds as training data;\n", "* The resulting model is validated on the remaining part of the data (i.e., it is used as a test set to compute a performance measure such as accuracy).\n", "\n", "The performance measure reported by k-fold cross-validation is then the average of the values computed in the loop. This approach can be computationally expensive, but does not waste too much data (as it is the case when fixing an arbitrary test set), which is a major advantage in problem such as inverse inference where the number of samples is very small." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.cross_validation import cross_val_score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simplest way to use cross-validation is to call the \"cross_val_score\" helper function on the estimator and the dataset. By default, the score computed at each CV iteration is the score method of the estimator. It is possible to change this by using the scoring parameter." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following example demonstrates how to estimate the \"cross-validated\" MSE of LinearRegression() on (X-data,Y-data) by splitting the data, fitting a model on k-th training set and computing the MSE 5 consecutive times on the k-th test set (with different splits each time):" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE array: [-1.14612157 -1.47367855 -0.95452968 -0.72913903 -0.77076311]\n" ] } ], "source": [ "scores = cross_val_score(linear_model.LinearRegression(), X_data, Y_data, cv = 5, scoring = 'mean_squared_error')\n", "print(\"MSE array:\", scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When the cv argument is an integer, \"cross_val_score uses\" the KFold by default." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"The Mean Square Error returned by sklearn.cross_validation.cross_val_score is always a negative. While being a designed decision so that the output of this function can be used for maximization given some hyperparameters, it's extremely confusing when using cross_val_score directly\".\n", "\n", "https://github.com/scikit-learn/scikit-learn/issues/2439\n", "\n", "From Scikit Documentation: \"Whether score_func is a score function (default), meaning high is good, \n", "or a loss function, meaning low is good. In the latter case, the scorer \n", "object will sign-flip the outcome of the score_func\".\n", "\n", "http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['recall_micro', 'mean_absolute_error', 'f1_samples', 'adjusted_rand_score', 'average_precision', 'recall', 'mean_squared_error', 'r2', 'precision_weighted', 'recall_weighted', 'accuracy', 'f1_macro', 'f1', 'precision', 'roc_auc', 'f1_micro', 'precision_samples', 'recall_samples', 'f1_weighted', 'log_loss', 'precision_micro', 'recall_macro', 'median_absolute_error', 'precision_macro'])\n" ] } ], "source": [ "from sklearn.metrics.scorer import SCORERS\n", "print(SCORERS.keys())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many built-in scoring metrics. However, we can always define our own metric:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 0., 0.])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def my_own_scoring(regr, X_data, Y_data):\n", " return np.mean(regr.predict(X_data) == Y_data)\n", "\n", "cross_val_score(regr, X_data, Y_data, cv = 5, scoring = my_own_scoring)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Scikit-Learn provides many tools to generate indices that can be used to generate dataset splits according to different cross validation strategies." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, we have the \"KFold\", which divides all the samples in k groups of samples, called folds (if k = n, this is equivalent to the Leave One Out strategy), of equal sizes (if possible). The prediction function is learned using k - 1 folds, and the fold left out is used for test." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example of 5-fold cross-validation on a dataset with 25 samples:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.cross_validation import KFold" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "len(kf): 5\n", "TRAIN: [ 0 1 2 3 4 5 6 8 9 10 11 13 14 15 16 17 20 21 23 24] TEST: [ 7 12 18 19 22]\n", "TRAIN: [ 1 2 3 4 5 7 8 9 10 12 14 15 16 17 18 19 20 21 22 24] TEST: [ 0 6 11 13 23]\n", "TRAIN: [ 0 1 2 3 5 6 7 8 10 11 12 13 14 15 17 18 19 20 22 23] TEST: [ 4 9 16 21 24]\n", "TRAIN: [ 0 1 4 6 7 8 9 10 11 12 13 15 16 18 19 20 21 22 23 24] TEST: [ 2 3 5 14 17]\n", "TRAIN: [ 0 2 3 4 5 6 7 9 11 12 13 14 16 17 18 19 21 22 23 24] TEST: [ 1 8 10 15 20]\n" ] } ], "source": [ "kf = KFold(n = len(X_data), n_folds = 5, shuffle = True)\n", "MSE_kth_list = []\n", "print(\"len(kf):\", len(kf))\n", "\n", "for train_index, test_index in kf:\n", " print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n", " X_train, X_test = X_data[train_index], X_data[test_index]\n", " Y_train, Y_test = Y_data[train_index], Y_data[test_index]\n", " regr = linear_model.LinearRegression().fit(X_train, Y_train)\n", " MSE_kth = np.mean((regr.predict(X_test) - Y_test) ** 2)\n", " MSE_kth_list.append(MSE_kth)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each fold is constituted by two arrays: the first one is related to the training set, and the second one to the test set. Thus, one can create the training/test sets using numpy indexing." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We know print the MSE that results from the 5-folds average ." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average k-fold MSE: 0.996664633076\n" ] } ], "source": [ "print(\"Average k-fold MSE:\", np.mean(MSE_kth_list))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another example of cross-validation strategy is the \"LeaveOneOut\" (or LOO, a simple cross-validation. Each learning set is created by taking all the samples except one, the test set being the sample left out. Thus, for n samples, we have n different training sets and n different tests set. This cross-validation procedure does not waste much data as only one sample is removed from the training set:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cross_validation import LeaveOneOut" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TRAIN: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [0]\n", "TRAIN: [ 0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [1]\n", "TRAIN: [ 0 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [2]\n", "TRAIN: [ 0 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [3]\n", "TRAIN: [ 0 1 2 3 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [4]\n", "TRAIN: [ 0 1 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [5]\n", "TRAIN: [ 0 1 2 3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [6]\n", "TRAIN: [ 0 1 2 3 4 5 6 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [7]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [8]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [9]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 11 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [10]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 12 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [11]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 17 18 19 20 21 22 23 24] TEST: [12]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 19 20 21 22 23 24] TEST: [13]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17 18 19 20 21 22 23 24] TEST: [14]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20 21 22 23 24] TEST: [15]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17 18 19 20 21 22 23 24] TEST: [16]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 19 20 21 22 23 24] TEST: [17]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 19 20 21 22 23 24] TEST: [18]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 21 22 23 24] TEST: [19]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 21 22 23 24] TEST: [20]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24] TEST: [21]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 23 24] TEST: [22]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 24] TEST: [23]\n", "TRAIN: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23] TEST: [24]\n" ] } ], "source": [ "loo = LeaveOneOut(len(X_data))\n", "\n", "for train_index, test_index in loo:\n", " print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n", " X_train, X_test = X_data[train_index], X_data[test_index]\n", " Y_train, Y_test = Y_data[train_index], Y_data[test_index]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---------------------------------------------------------------------------------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now turn to Ridge Regression. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Applying Tikhonov regularization argument to OLS, we can find a new (this time biased) estimator for $\\beta$ coefficients that has always lower MSE. In particular, the estimator is designed to solve the problem\n", "\n", "\\begin{equation}\n", "\\min_{b\\in\\mathbb{R}} \\left[\\sum_{n=1}^{N} (y_n - \\mathbf{x_n'b})^2 + \\lambda \\lVert\\mathbf{b}\\rVert_2^2\\right]\n", "\\end{equation}\n", "\n", "where $\\lambda \\geq 0$ is called \"regularization parameter\". In other words, we are minimizing empirical risk plus a regularization term that penalizes large values of $\\lVert\\mathbf{b}\\rVert$. It can be shown that there always exists a $\\lambda > 0$ such that the $MSE(\\beta_{\\lambda})$ < $MSE(\\beta)$, even if $\\beta_{\\lambda}$ is always biased. \"The reduction in mean squared error over the least squares estimator occurs because, for some intermediate\n", "value of $\\lambda$, the variance of $\\beta_{\\lambda}$ falls by more than enough to offset the extra bias\" (Stachurski, Econometric Theory). The outcome of Ridge regression depends a lot on the non-trivial choice of the $\\lambda$ value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with other linear models, Ridge will take in its fit method arrays $X$, $y$ and will store the coefficients $\\beta$ of the linear model in its \"coef\\_\" member:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.linear_model import Ridge" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n", " normalize=False, random_state=None, solver='auto', tol=0.001)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ridge_regr = Ridge(alpha=0.5)\n", "Ridge_regr.fit(X_data, Y_data)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intercept: \n", " [ 0.01240888]\n", "Coefficient: \n", " [[ 0.1301897]]\n", "Mean Squared Error: 0.85\n", "R^2: 0.01\n" ] } ], "source": [ "print('Intercept: \\n', Ridge_regr.intercept_)\n", "print('Coefficient: \\n', Ridge_regr.coef_)\n", "print(\"Mean Squared Error: %.2f\" % np.mean((Ridge_regr.predict(X_data) - Y_data) ** 2))\n", "print('R^2: %.2f' % Ridge_regr.score(X_data, Y_data))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "RidgeCV implements ridge regression with built-in cross-validation of the alpha parameter. It defaults to Generalized Cross-Validation (GCV), an efficient form of leave-one-out (LOO) cross-validation:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.linear_model import RidgeCV" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RidgeCV(alphas=[0.1, 1, 5, 10], cv=None, fit_intercept=True, gcv_mode=None,\n", " normalize=False, scoring='mean_squared_error', store_cv_values=False)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# a list of possible lambda values \n", "lambda_list = [0.1, 1, 5, 10]\n", "\n", "RidgeCV_regr = RidgeCV(alphas = lambda_list, scoring = 'mean_squared_error')\n", "RidgeCV_regr.fit(X_data, Y_data)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cross-Validated alpha: 10.0\n" ] } ], "source": [ "print(\"Cross-Validated alpha:\", RidgeCV_regr.alpha_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---------------------------------------------------------------------------------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this final example, we demonstrates the problems of underfitting and overfitting and how we can use Ridge regression with polynomial features to approximate nonlinear functions. The plot shows the function that we want to approximate, which is a part of the cosine function. In addition, the samples from the real function and the approximations of different models are displayed. The models have polynomial features of different degrees. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are using Ridge regression throguh \"RidgeCV\", so we choose our regularization parameter $\\lambda$ by means of cross-validation. We evaluate quantitatively overfitting / underfitting by using cross-validation. We calculate the mean squared error (MSE) on the validation set, the higher, the less likely the model generalizes correctly from the training data." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cross-Validated Lambda for degree: 1\n", "10.0\n", "Cross-Validated MSE:\n", "0.931770568646\n", "Cross-Validated Lambda for degree: 2\n", "0.1\n", "Cross-Validated MSE:\n", "0.343755078482\n", "Cross-Validated Lambda for degree: 5\n", "0.1\n", "Cross-Validated MSE:\n", "0.424128712459\n", "Cross-Validated Lambda for degree: 14\n", "0.1\n", "Cross-Validated MSE:\n", "0.463556056445\n" ] }, { "data": { "image/png": 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ceqsCPBc17kR37rntOz6RSDB27Dhqa2exZMkcamtnMXbsOLXIJRAKcQm1rk6z\nqq31K3qddZYGOOWq/HyYMcMvBHPLLW0fr/n5kkkU4hJqXZlm5Rz85jfwgx+0vxUm2WngQPjXv2DC\nhLY3S9H8fMkkuicuWaEzo9OnTvXTyF54QctwinfffX6Gwssvw4Ybtnxc4z3xgoIo9fU1uicuXaIp\nZiId9OKLfmer557zLXGRRuedB6+/7ge69erV8nGany/pohAX6YBEwu81/fe/+xHpIqlWroS994bd\ndoNLLgm6GskFGp0ua9D81ZatXAlHHgnHHqsAl+Y1DnS7/XZ46KGgqxFpnUI8y2h/6db96U+QlwcX\nXxx0JZLJBg2Cu++GsWPhww+DrkakZepOzyLaX7p1jzwCv/6132JUvw5pj+uu8+sH/Pe/0Lt30NVI\ntlJ3ugCav9qaefP83uDl5Qpwab/TToPvf19bl0rmSkuIm9m+ZjbXzN4zswnNPL+HmX1lZq8kPy5I\nx3VlTZq/2rzG++BnnQU/+1nQ1UiYmEFZmR+pfu+9QVcjsrYuh7iZ5QHXAfsA2wGjzay4mUOfcc4N\nTX78uavXlbVpf+nm/fGPsO66fiEPkY4aMMBvijNunO6PS+bp8j1xMxsOXOic2y/5/e8B55ybnHLM\nHsB459yB7Tif7ol3keavfkf3wSVd/v53v9Od7o9LugV9T3xT4JOU7+clH2vqp2b2mpnNNLMfpeG6\n0oKioiKGDRuW8wE+f76/Dz59ugJcuu700yEW0/1xySw9tWvyHGAL59w3ZrYf8ACwTUsHT5w4cfXX\nJSUllJSUdHd9kmUaGvxc8NNOg913D7oayQaN98eHDPGLwRzYZr+iSPMqKyuprKxMy7nS1Z0+0Tm3\nb/L7tbrTm3nNR8DOzrlFzTyn7nTpskmT4NFH4T//aX3pTJGOeu45OOwwf4tmk02CrkayQdDd6VXA\n1mYWNbNC4EjgwSYFbpTy9S74Nw9rBbhIOrz4IlxzDdx5pwJc0m+33fzud2PGwKpVQVcjua7LIe6c\nawBOBx4H3gJmOOfeMbNTzOzXycMON7M3zexV4BpA2/1It1i6FI46Cm64ATbfPOhqJFv94Q+wYgVc\neWXQlUiu04ptklWOOcZvKzp1atCVSLb7+GMYNgwefth/FumsrnSn99TANpFud8cd/j7lyy8HXYnk\ngi22gOuv9z0/r7wC/foFXZHkIrXEJSt89BHssgs8+STsuGPQ1UguOekkcM6PXBfpjKAHtokEqqHB\nDzI67zzoi98dAAAgAElEQVQFuPS8q6+Gp5+GBx4IuhLJRQpxCb2//MWPQv/d74KuRHJRv37+Vs6p\np8LChUFXI7lG3ekSaq++6hfeePlliEaDrkZy2R//CHPmwMyZfmEYkfZSd7rkpNpaOPpoPydcAS5B\n+9OfIJHw0xtFeopa4hJaZ54Jn3/u9whXy0cywbvv+u1un30Wipvby1GkGV1piSvEJZSefNJvbvL6\n67D++kFXI/KdG27wI9Wffx4KCoKuRsJA3emSU776Ck480f+hVIBLpjn1VNhgA79+v0h3U0tcQuf4\n4yES0b1HyVzz5sHQoX4TnqFDg65GMp1WbJOc8a9/wezZ8NprQVci0rLNNoOrrvLrF7z8MvTpE3RF\nkq3UEpfQSCRg8GC45x4/eEgkkzkHhx8OW28Nk1vcmFlEA9skBzgHv/oVfP/7fnEXkTBofON5332w\n665BVyOZSgPbJOuVl8M778AllwRdiUj7FRX5sRvHHQfLlwddjWQjtcQl4y1cCEOGwL//DTvvHHQ1\nIh137LEwcCBce23QlUgmUne6ZC3n4OCDfZekWuESVosWwQ47wPTpsMceQVcjmUbd6ZK1pk+H//0P\nLrgg6EpEOm/99eHGG/36BupWl3RSS1wylrrRJduMGQPrradudVmTutMl66gbXbKRutWlOepOl6wz\nfTp89JHf3lEkW6hbXdJNLXHJOJ9+CjvuqG50yV7qVpdU6k6XrOEcHHoobLcd/PnPQVcj0j0WLYLt\nt4eKCth996CrkaCpO12yxj33+D2Z1Y0u2Wz99eH662HsWKitDboaCTO1xCVjfPGFH/Rz//0wfHjQ\n1Yh0v1GjIBbT2uq5Tt3pkhWOPho23hj++tegKxHpGZ9/7mdgPPQQDBsWdDUSFG1FKqH34IPw4otQ\nXR10JSI9Z9Agv2XpCSfAnDnQu3fQFUnY6J64BO6rr2DcOCgrg759g65GpGeNHg1bbgmXXRZ0JRJG\n6k6XwJ10EhQWwpQpQVciEoz582GnneDJJ333uuQWdadLaD31FDz+OLz5ZtCViARn0019S3zsWHj+\necjXX2ZpJ3WnS2CWL4df/9rvt9y/f9DViARr7Fjo1w/+9regK+leiUSCqqoqEolE0KVkBYW4BOZP\nf/JTyUaODLoSkeCZwc03w6RJ8OGHQVfTPcrLK4hGixkx4lSi0WLKyyuCLin0dE9cAvHSS/DLX8Ib\nb0BRUdDViGSOv/4VZs70t5qsU3dJM1MikSAaLaa2dhYwGKgmEimlpmYuRTn+R0Artkmo1NX5rsOr\nrlKAizR11lmwbJmfrZFN4vE4hYUxfIADDKagIEo8Hg+uqCygEJced/nlEI36qTUisqb8fB/g558P\nCxYEXU36xGIx6uriQONiENXU19cQi8WCKyoLKMSlR739tt+56YYbsqurUCSdBg+GU06B004LupL0\nKSoqoqxsCpFIKf37DyUSKaWsbErOd6V3le6JS49ZtQp+9jM45hi/uIuItGzFChgyBC691O/sly0S\niQTxeJxYLKYAT9La6RIK118P06fDs89CnvqARNo0e7bfJOWtt/z+45KdFOKS8T75xK9I9eyzsO22\nQVcjEh7jxsHKlXDTTUFXIt1FIS4ZzTk/nWyXXbRPuOS2znQlL10K228P06ZBSUn31ifB0BQzyWh3\n3w0ffQQTJgRdiUhwOrvQSf/+cN11fnXD2tpuLlJCRy1x6VaLFsF228H99/vV2URyUToWOhk1Crba\nSrudZSNtgCIZa/x4OOIIBbjktsaFTmpr117opL0hfu21sMMO/t/TkCHdV6ukz8pVK/l25bdtfnSF\nQly6zVNP+Q/tUCa5bs2FTnxLvKMLnWy0kV8o6eST4YUXoFev7qk1WzjnVofoioYV7QrTdH84HJH8\nCH3y+6z+6J3fe43Heuf37tLPqe506RbffOMXrPjb37TBiQj4e+Jjx46joCBKfX0NZWVTGD16VIfO\n4RzsuScceCCcfXY3FZomzjnqV9UHEp6NoZ1nefTu1ZtIQUpopnzfu1fv5oO1yWuae6w9x+Tnta+d\nrNHpknEmTICaGpgxI+hKRDJHOhY6ef99+OlP4eWXobWGvHOOuoa6tYKtJz/y8/LXCsiWWqNrHZMM\n2FaPaeU8vfN7tztEg6YQl4zy6quwzz5+h7KNNgq6GpFgOOc6FJyJxQnmfzafdddbl/w++a0e+/Z7\n3/LFkm/ZZtuWA3rFyhUU9CpotvXZrkDsYmu0d6/e9MpTn397BD6wzcz2Ba7BT1krc85NbuaYa4H9\ngOXA8c6519JxbcksK1f6e3aTJyvAJVir3CpWrFwz3HqyNbqiYUWL3bVNH/tswedU/XcOvVx/GuqW\nsf+IfRg6eCcG9B7AxutuvNZ58of04ben9WG/H/XhwIOab432zu9NnmkWcbbrckvczPKA94A9gQVA\nFXCkc25uyjH7Aac750aa2U+Avznnmh2vrJZ4uF11ld8L+ckntcFJrmtY1ZDW0Ozoueoa6tbokm1v\nF25H73u2dEx7Q7Sz089eeskvovTmm7Dhhun77yY9L+iW+C7A+865mmQxM4CDgLkpxxwETANwzr1o\nZgPMbCPn3GdpuL5kiI8+8nNYX3hBAZ4JGlY1dEs4tvdj5aqVHb+n2SQQN+y7YbOhmXqO5u6FdiRE\ng9bZ6We77OK38z3nHLj99p6pVTJPOkJ8U+CTlO/n4YO9tWPmJx9TiGcJ5+DUU+Hcc2HrrYOuJjO0\nd45oc/cym32uoWPnaVjVsDrkUu+HNt6vbE9rtLkQbW9rtCCvANO7uTZ1ZfrZJZf4JVmfeAJGjOje\nOiUzhWPonmS8u+6Czz6D3/0u6Eq81DmiQd0TXeVWrRGgLYVd4/3LPr3WblX2792//V3BTVqjCtFw\naNxne+zY0jWmn7Vn9Pq668INN/g30G+8AX379kDBklHSEeLzgS1Svt8s+VjTYzZv45jVJk6cuPrr\nkpISSrTqf0b74gu/MttDD0FBgX+sM3NEW2yBdrIl2tYc0bUeSwnRxufW67Neiy3PtoI1Py9fISrt\nMnr0KPba6xedmn62337wk5/AxIlwxRXdV6OkT2VlJZWVlWk5VzoGtvUC3sUPbFsIvASMds69k3LM\n/sBpyYFtw4FrNLCtezWdI9qd90L/9/G3uF7fss6ANR/vlderY12xvb7r+u3sHNHUlm1Y5oiKtEdr\nc8w//9wvyfrIIzB0aEAFSqcFOrDNOddgZqcDj/PdFLN3zOwU/7S7yTn3bzPb38w+wE8xO6Gr1810\nqXNE22xhtuejg63QFStXkJ+X366u2MbQbHrMuoXrsmHfDVsN39dejnDplD48NrMP6/fvvcb0FoWo\nSHo0rvZWWOjvnzdd7W3QID+t86ST/Kj1fP3TyxlZu9hLc3NE2xV+aWqd1jXUUdirsNVW5Fqt086s\napQSmk0HLnX3QgvLl/t3/9df77v0RCT92jsFzTk/uG3fff3tLQmPoKeYpd2kZyd1uSWajjmijQst\ndOcc0TCbONHvTqYAF+k+7Z2CZgY33uj/TR56KGy5ZTD1Ss/KyBBfumIpffL7sH5k/Rbve7YVrLkQ\nokF65RWYNs2PiBWR7tORKWhbbw3nnedHqz/2mNZryAVZ250u3WflSr/QxFlnwXHHBV2NSPbryA5o\nK1fCsGF+l7MxY3q4UOkUbYAiPeovf4HHH/cfeqcv0jM6sgPanDmw//6+p2zQoB4qUDpNIS495sMP\n/ZzUl17SPTeRTDZ+PCxc6BdikszWlRDXTeMclkgkqKqqIpFItOt45+CUU+D3v1eAi2S6iy6C55/3\nc8e7S0f/hkj6KcRzVHl5BdFoMSNGnEo0Wkx5eUWbr7n9dli8GH772x4oUES6ZJ11YOpU+M1v4Ouv\n2z6+o4Hcmb8hkn7qTs9Bndn6sHFFqEcfhZ126tFyRaQLjjsOBg6Ea65p+Zi2FpNpqrPbp0rz1J0u\nHdI479T/44PUeactOfNMOP54BbhI2Pz1rzBjBrz4YvPPJxIJxo4dR23tLJYsmUNt7SzGjh3Xaou8\nM39DpHsoxHPQmvNOoa2tDx96yI92TdmXRkRCYsMN4eqr/ZKsdXVrP9+ZQO7o3xDpPgrxHNS49WEk\nUkr//kOJREpb3Ppw6VI47TS46SaIRAIoVkS67MgjIRr166s31ZlA7sjfEOleuieew9oz73TcOKiv\nh5tv7uHiRCStPv7Y73D27LOw7bZrPteRxWRSdWTuurRM88SlW8yeDaNGwVtvwXrrBV2NiHTV9dfD\n9Ok+yPOa9MMqkIOjEJe0+/ZbGDIELrvMb6YgIuG3ahX8/OcwerS/TSaZQSEuaXfBBfDOO3DffUFX\nIiLp9M47sPvufhOjLbYIuhoBhbik2euv+32JX3sNNtkk6GpEJN3+/Gf4739h5kztf5AJNE9c0mbl\nSjjxRLj8cgW4SLaaMAHmz9e66tlALXFZw+TJ8NRT2otYJNs17nRWXQ0bbRR0NblN3emSFu+9B7vu\nCi+/DFqzQST7/f738NFHUKFlzwOl7nTpslWrYOxY+NOfFOAiueLCC/3YlwceCLoS6SyFuABw440+\nyDXtRCR3RCLwj3/4f/eLFwddjXSGutOFjz+GnXeGZ55ZeyUnEcl+p50GtbVwyy1BV5KbdE9cOs05\n2GcfKCmB888PuhoRCcKyZX6r4alT/d8D6Vm6Jy6ddsstsGgRnHde0JWISFD69fP7I/z6137TIwkP\ntcRz2Lx5fn/w//zHvwsXkdx28snQq5cfIyM9R93p0mHOwciRMHy4H5EuIrJkiX9Df+utsOeeQVeT\nO9SdLh02bRosWAD/939BVyIimWLAAH9f/KST4Ouvg65G2kMt8Ry0YIHfoeyxx3x3uohIquOPh3XX\nheuuC7qS3KDudGk35+Dgg2HwYLjkkqCrEZFMtHgxbL+9X1u9pCToarKfutOl3e680y+zeMEFQVci\nLUkkElRVVZFIJIIuRXLUwIF+cNuJJ6pbPdMpxHPI/Plwzjlw++3Qu3fQ1UhzyssriEaLGTHiVKLR\nYsrLtai1BOPAA+HnP9f000yn7vQc0Tga/Sc/8eslS+ZJJBJEo8XU1s4CBgPVRCKl1NTMpaioKOjy\nJAd99dV3o9X32ivoarKXutOlTbfeCp9+qlXZMlk8HqewMIYPcIDBFBREicfjwRUlOW299fwiMCed\npEVgMpVCPAd8/DFMmOC70QsKgq5GWhKLxairiwPVyUeqqa+vIaZt5SRA++4Le+/tb8VJ5lGIZznn\n/BajZ5+tVdkyXVFREWVlU4hESunffyiRSCllZVPUlS6Bu/JKeOIJePTRoCuRpnRPPMvdcIPvSv/v\nfyE/P+hqpD0SiQTxeJxYLKYAl4zxn//AccdBdbUfvS7po3ni0qz334ddd4XZs+GHPwy6GhEJu7PO\ngkQCpk8PupLsooFtspaVK+HYY/266ApwEUmHSZPglVegQjMfM4Za4lnqz3+Gp5/2S6vm6a2aiKRJ\nVRUccIAP8003Dbqa7KDudFnDnDmw337+H9lmmwVdjYhkm4suguefh0ceAetU9EgqdafLarW1vhv9\nmmsU4CLSPc4/HxYt8gNnJVhqiWeZs8/2y6tWVOgdsoh0n7lz4Wc/8zNfttkm6GrCTd3pAvh5nCec\nAK+/DhtsEHQ1IpLtrr8ebrvNB7kWkuo8hbjwxRew445+VTatcSwiPcE5P8htyBC49NKgqwkvhXiO\ncw4OOQS23tqvrCQi0lM++8yHeEWF3/VMOk4D23LcP/4B8bjeCYtIz9toI/836Nhj/a5n0rPUEg+5\nd9+F3XaDZ56BH/0o6GpEJFedfrq/rVderkG1HRVYS9zMBprZ42b2rpk9ZmYDWjgubmavm9mrZvZS\nV64p36mrg6OPhosvVoCLSLD+8he/rvqddwZdSW7pUkvczCYDXzrnrjCzCcBA59zvmznuf8DOzrnF\n7TinWuLtdN558Pbb8NBDeucrIsF77TUYMcIvBLP11kFXEx5B3hM/CLg9+fXtwMEtHGdpuJakePRR\nvwnBrbcqwEUkMwwZ4vdrGDUKVqwIuprc0NWW+CLn3PotfZ/y+P+Ar4AG4Cbn3M2tnFMt8TYsXAhD\nh/p7TyUlQVcjIvKdxtkyW24JV10VdDXh0JWWeJs7TJvZE8BGqQ8BDrigmcNbSt/dnHMLzawIeMLM\n3nHOzW7pmhMnTlz9dUlJCSVKqtUaGuCYY+CUUxTgIpJ5zOCWW2CnneAXv/DzyGVNlZWVVFZWpuVc\nXW2JvwOUOOc+M7ONgVnOuW3beM2FwDLnXLPv0dQSb92ll8Ljj8NTT0F+m2/BRESCMXs2HH44vPyy\n9nFoS5D3xB8Ejk9+fRzwr6YHmFlfM1s3+fU6wN7Am128bk6aPRv+/ne46y4FuIhktp/9zE87O/po\n34Mo3aOrIT4ZGGFm7wJ7ApcDmNn3zOzh5DEbAbPN7FXgBeAh59zjXbxuzvniC/+P4eab9a5WRMLh\n//7PNzguuijoSrKXFnvpJolEgng8TiwWo6ioqEvnWrUK9t/fr40+eXKaChQR6QGffgo//rFf1W3f\nfYOuJjNp2dUMU15eQTRazIgRpxKNFlNeXtGl8116KXzzjZZVFZHw2XhjPx32+OPh44+Drib7qCWe\nZolEgmi0mNraWcBgoJpIpJSamrmdapE/+SSMGeMHh2yySdrLFRHpEVdcAf/8p18iurAw6Goyi1ri\nGSQej1NYGMMHOMBgCgqixOPxDp9r/ny/qcBddynARSTcxo/3m6Wce27QlWQXhXiaxWIx6uriQHXy\nkWrq62uIxWIdOk99vV/16IwzoLQ0zUWKiPSwvDy47Ta/TPQ99wRdTfZQiKdZUVERZWVTiERK6d9/\nKJFIKWVlUzrclT5+PAwYAL9fayV6EZFwGjgQ7r0Xxo3z+z5I1+meeDfpyuj0adPgkkugqgrWW6+b\nChQRCcjtt/uBui+91P6/cemc8ZNpunJPXCGeYebMgf32g1mzYLvtgq5GRKR7nHkmfPih717Pa6NP\nuLy8grFjx1FY6G9XlpVNYfToUT1TaA9QiGeJzz+HYcPg6qvh0EODrkZEpPvU1/ttS3ff3fc8tiTd\nM34ykUanZ4H6ejjiCL+5iQJcRLJdQQHcfbe/ffjPf7Z8XDpn/GQjhXiGGD8e+vaFiy8OuhIRkZ4x\naBDcd5/flfGtt5o/Jl0zfrKVQjwDTJ0Kjz3m54P36hV0NSIiPefHP/a3EA88EBKJtZ9P14yfbKV7\n4gF78knfhT57Nmy9ddDViIgE4w9/gKef9tss9+699vMand7CazMtMHMpxOfOhT328PeF9tgj6GpE\nRIKzapUfF9S3r5+CZp2KtHDSwLYQ+vJL3310+eUKcBGRvDw/yO3tt2HSpKCrCY/8oAvIRXV1cNhh\nfhT6CScEXY2ISGbo2xcefBCGD4dttoHDDw+6osyn7vQetmoVHHccfP21H5XZ1iIHIiK55tVXYZ99\n4IEHYNddg66m+6k7PUT+7//8KkV33aUAFxFpzk47+a71Qw/1Y4ekZYqRHnTttfCvf/llBvv2Dboa\nEZHMte++MHmy/7xgQdDVZC7dE+8h99wDV1zhp5JtsEHQ1YiIZL7jjoP582H//f30swEDgq4o8+ie\neA94+mn41a/g8cdhyJCgqxERCQ/n4PTTfbf6I49AYWHQFaWf5olnsFde8buSTZ8Oe+4ZdDUiIuHT\n0ODnkJvBjBmQn2V9yBrYlqHefNN3A02dqgAXEemsXr18Q2jZMjjxRD/LRzyFeDd5/30/ReLqq+Hg\ng4OuRkQk3Hr3hvvvh5oaOO00380uCvFuUVMDe+3ldyQbPTroakREskPfvvDww/425bnnKshBIZ52\nCxb4rvNzzoGxY4OuRkQku/Tr5we4PfEEXHhh0NUEL8uGBwTrk0/gF7+Ak0+GM88MuhoRkey0/vo+\nxEtL/f3xSy7JrQ1TUqklniYffQQ//zmMGwcTJgRdTfskEgmqqqpINLeJr4hIBhs0CCorfff6eefl\nbte6QjwN3n/f70Q2fjycfXbQ1bRPeXkF0WgxI0acSjRaTHl5RdAliYh0SFER/Oc/MGuW7/3MxVHr\nmifeRW+/DXvvDRMnwkknBV1N+yQSCaLRYmprZwGDgWoikVJqauZSVFQUdHkiIh2yZIlfnnWHHeDG\nG8O3L4XmiQfkpZf8ILbLLgtPgAPE43EKC2P4AAcYTEFBlHg8HlxRIiKdNGCAXxHz3Xfh6KNhxYqg\nK+o5CvFOmjkTRo6Em26CMWOCrqZjYrEYdXVxoDr5SDX19TXEYrHgihIR6YJ+/eDRR32A77efb53n\nAoV4J/zjH3762EMPwYEHBl1NxxUVFVFWNoVIpJT+/YcSiZRSVjZFXekiEmqRiN9sattt/UDjXNj9\nTPfEO8A5v4DL7bf7d3zbbBN0RV2TSCSIx+PEYjEFuIhkDef8NqY33ujnlG+7bdAVtU4boPSA2lo4\n5RR46y3flb7xxkFXJCIirZk2zc8amjbND3zLVBrY1s0++QR23x3q6uDZZxXgIiJhMGYM3Hef3zTl\niiuycy65QrwNzz4LP/mJ3wavvNyv3SsiIuGw++7w4otw991w1FHwzTdBV5ReCvEWOAc33ACHHQa3\n3OJXBMrVZf1ERMJs8819gyw/H3bbza+wmS0U4s1YtAh+9Ss/KOK55zL7XoqIiLQtEvH3xo8/HnbZ\nxfesZgOFeBNPPw1Dhvh3bi++CD/4QdAViYhIOpjBWWfBY4/5VTaPPx6WLQu6qq5RiCetXAl//KPf\n/3vqVLj6aujTJ+iqREQk3YYOhTlzfPf6TjtBVVXQFXWeQhz/H3DYMHj5ZXj1Vb/aj4iIZK911/UL\nd11+ORxwgB/3tHx50FV1XE6H+LJlvmvlwAPhnHPg3/+GjTYKuioREekphx8Ob7wB8+f7DVQefTTo\nijomJ0PcOXjgAdhuOx/kb70Fxxyj0eciIrlo0CC46y4/I+m00/xt1YULg66qfXIuxJ9/3u/9/Yc/\n+OVTb7kFNtgg6KpERCRo++zjW+WxGGy/PVxwQeZvpJIzIf7OO3DIITBqlF+9p7oaSkuDrkpERDJJ\n374waZIfHzV/vt8j4+qrM3d706wP8aoqv0rPHnv4Sf7vvuunFfTqFXRlIiKSqbbYAm69FZ56CmbN\n8mH+179mXss8K0O8oQH++U+/3N7hh8POO8P77/uF8CORoKsTEZGw2H57ePBBuPdePy3t+9+H3/4W\n/ve/oCvzuhTiZna4mb1pZg1mNrSV4/Y1s7lm9p6ZTejKNVvinO8iP/982GoruPJKOPNM+PBDP/J8\nwIDuuKqIiOSCYcNg+nR4/XXo3duv+rbvvn5s1dKlwdXVpa1IzeyHwCpgKjDeOfdKM8fkAe8BewIL\ngCrgSOfc3BbO2e6tSFet8oMQHnwQZsyAr7+GI4/0Hzvt1MkfSkREpA3Ll3+XPZWVsNdefqOsPfeE\nDTfs2LkC30/czGYB57QQ4sOBC51z+yW//z3gnHOTWzhXiyH+9dcwd65fyP7pp+GZZ6CoCPbe208J\nGD4c8rLyBoGIiGSqxYvh/vv9tqezZ/v76XvsASUl8OMf++9by6auhHh+J2vuiE2BT1K+nwfs0toL\nbrrJz99etgw+/RTee88PSFu8GLbeGnbd1be2b7gBvve9bq1dRESkVQMH+llPJ57ol/B+9VXfOr/t\nNjj7bPjyS3+bd5tt/Of11vMrxvXr5z+6os0QN7MngNR1zAxwwB+ccw917fLNu/HGifTuDYWFMGRI\nCeefX8I228Bmm6mlLSIimSs/398/HzYMzj3XP7Z8OXzwgW+MfvghvPFGJR98UMmKFVBX17Xr9VR3\n+kTn3L7J7zvdnS4iIpJtutKdns52bUsFVAFbm1nUzAqBI4EH03hdERGRnNTVKWYHm9knwHDgYTN7\nJPn498zsYQDnXANwOvA48BYwwzn3TtfKFhERkbR0p6dTpnWnJxIJ4vE4sViMoqKioMsREZEskynd\n6VmnvLyCaLSYESNOJRotpry8IuiSREREVlNLvAWJRIJotJja2lnAYKCaSKSUmpq5apGLiEjaqCXe\nDeLxOIWFMXyAAwymoCBKPB4PrigREZEUCvEWxGIx6uriQHXykWrq62uIxWLBFSUiIpJCId6CoqIi\nysqmEImU0r//UCKRUsrKpqgrXUREMobuibdBo9NFRKQ7Bb4BSjplWoiLiIh0Jw1sExERyUEKcRER\nkZBSiIuIiISUQlxERCSkFOIiIiIhpRAXEREJKYW4iIhISGVkiFdVVZFIJIIuQ0REJKNlZIhr608R\nEemKRCKREw3CjAzxJUvmUFs7i7Fjx2X9fwAREUmv8vIKotHinGgQZuSyq+Br6t9/KE8+OZVhw4YF\nXJWIiIRBIpEgGi2mtnYWfivpaiKRUmpq5mbs/hdZuuyqtv4UEZGOicfjFBbG8AEOMJiCgijxeDy4\norpRRoa4tv4UEZHOiMVi1NXFgerkI9ndIMwPuoDmPPnkVG39KSIiHVZUVERZ2RTGji2loCBKfX1N\nVjcIM/KeeKbVJCIi4ZJIJIjH46FoEGo/cRERkZDK0oFtIiIi0hqFuIiISEgpxEVEREJKIS4iIhJS\nCnEREZGQUoiLiIiElEJcREQkpBTiIiIiIaUQFxERCSmFuIiISEgpxEVEREJKIS4iIhJSCnEREZGQ\nUoiLiIh0UiKRoKqqikQiEcj1FeIiIiKdUF5eQTRazIgRpxKNFlNeXtHjNWg/cRERkQ5KJBJEo8XU\n1s4CBgPVRCKl1NTMpaioqEPn0n7iIiIiPSgej1NYGMMHOMBgCgqixOPxHq0jv0evJpJhYrEYNTU1\nQZchGSQa7fk/xBI+sViMuro4UE1jS7y+voZYLNajdSjEJafV1NSg2zeSyqxTvZqSY4qKiigrm8LY\nsaUUFESpr6+hrGxKh7vSu0r3xCWnJe9FBV2GZBD9PyEdkUgkiMfjxGKxTgd4V+6JK8Qlp+kPtjSl\n/4wgyD0AABV+SURBVCekp2lgm4iISA5SiIuIiISUQlxEetTmm2/OM8880+ZxH374IXl5+hMl0pou\n/Qsxs8PN7E0zazCzoa0cFzez183sVTN7qSvXFMkV/fr1o3///vTv359evXrRt2/f1Y+Vl5d3+/WP\nOeYY8vLyeOSRR9Z4/IwzziAvL4/p06d3ew0aKS7Suq6+zX0DOAR4uo3jVgElzrmdnHO7dPGaIjlh\n2bJlLF26lKVLlxKNRpk5c+bqx0aPHr3W8Q0NDWm9vpnxwx/+kGnTpq1+bOXKldx3331stdVWab2W\niHROl0LcOfeuc+59oK23y9bVa4nkMufcWiOm//jHP3LkkUdy1FFHMWDAAO666y6OPfZYLr744tXH\nPPXUU3z/+99f/f38+fM59NBDGTRoEFtttRVTpkxp9boHHXQQlZWVLFu2DICZM2cybNiwNabSOOe4\n+OKLicVibLzxxpx44omrjwe47bbbiMViDBo0iMmTJ6/1c1122WVsvfXWDBo0iKOOOoolS5Z0/Bck\nkqN6Klgd8ISZVZnZyT10TZGs98ADD3DMMcewZMkSjjjiiGaPaeySds5xwAEH8JOf/ISFCxfyxBNP\ncOWVVzJr1qwWz9+3b19GjhzJ3XffDcC0adMYM2bMGm8obr75ZqZPn84zzzzDhx9+yKJFizjrrLMA\neOONNzjjjDOYMWMG8+fPZ8GCBXz22WerX3vVVVfxyCOPMHv2bObNm8e6667LGWec0eXfi0iuaHPF\nNjN7Atgo9SF8KP/BOfdQO6+zm3NuoZkV4cP8Hefc7JYOnjhx4uqvS0pKKCkpaedlRNIvHbdlu2va\n8c9+9jP2339/APr06dPqsf/9739ZtmwZEyZMAGDLLbfkxBNPZMaMGZSWlrb4ujFjxnDBBRdw6KGH\n8vzzzzNjxgyuvPLK1c9Pnz6d8ePHs8UWWwBw2WWXsfPOO3PLLbdw7733csghhzB8+PDVz11//fWr\nXzt16lTKysrYeOONAd+7sM0226zRhS+SbSorK6msrEzLudoMcefciK5exDm3MPk5YWb3A7sA7Qpx\nkaBl8rofm2++ebuP/fjjj6mpqWH99dcHfMt81apVrQY4wM9//nPmzZvHpEmTOOiggygoKFjj+QUL\nFhCNRld/H41GqaurI5FIsGDBgjVqXGeddVZfv7GmAw88cPUodOcceXl5fP755+3+uUTCpmnj9KKL\nLur0udK5dnqz7RUz6wvkOee+NrN1gL2BzlcsIqs1Hb29zjrr8M0336z+fuHChau/3nzzzdlmm214\n6623Onydo48+mkmTJjF79trvvTfZZJM1NpGpqamh8P/bu//gKKt7j+Pvk5AAgWQlkkAiYSMCMjpY\nIAZlktRaxx+kKfQOYPhZRRpBdFC89AIFBFHsRYcUxKFwM5RCJYHL2MZKUKDjRdQpP2pQEAVBIUAC\nJjaEBGITTM79I9slCflJluyGfF4zDLvPc/Y8382zz373Oc95zgkMJCwsjIiIiBqTiVy8eJHCwsIa\nMaWnpxMbG3tVvdWvq4tI3Vp6i9kvjDGngXuBrcaYd13LI4wxW13FegAfGWMOAHuAd6y1O1qyXRGp\n26BBg8jKyqKoqIizZ8+ycuVK97phw4YRGBhIamoqZWVlVFRU8Pnnn5Odnd1ovTNnzmTnzp3uZvHq\nxo0bR2pqKjk5OZSUlDB//nzGjx8PwJgxY3j77bfZu3cv5eXlzJ8/v8a931OnTmXu3LmcPn0agPz8\nfN5558pVOg1/KtKwlvZOz7TWRllrO1trI6y1w13Lz1prk1yPT1hrB7luLxtorf1vTwQu0p409X7p\nxx9/nAEDBuB0OklMTKxxK5q/vz/btm1j37597t7i06ZNq/eMt/o2Q0NDazS7V1+XkpJCcnIyCQkJ\n9O3bF4fDwfLlywEYOHAgK1asYMyYMfTq1YvIyEj39W+A559/nuHDh/PAAw/gcDiIj4/nH//4R7Pf\nt0h7pQlQpF3TZBdSmz4T0to0AYqIiEg7pCQuIiLSRimJe1BBQQH79++noKDA26GIiEg7oCTuIRkZ\nm3E6B/Dgg9NwOgeQkbHZ2yGJiMgNTh3bPKCgoACncwDff/9/wF3AQTp3vp+cnCM1xpgW36NOTFKb\nPhPS2tSxzctOnjxJYGA0VQkc4C4CApw1BrkQERHxNCVxD4iOjqa8/CRw0LXkIJcv5xAdHe29oERE\n5IanJO4BYWFhrF27is6d7yckZAidO9/P2rWr1JQuIiLXla6Je1BBQQEnT54kOjpaCbyN0PVPqU2f\nCWltuibuI8LCwoiNjVUCF4+Jjo4mKCgIh8NBaGgo8fHxrFmzps0kmRdeeIG77rqLgIAAFi9e7O1w\nRG44SuIiPswYQ1ZWFhcuXCAnJ4c5c+awdOlSpky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ANYoXjyUlJSV0QYlIjiiJi0SouLg49u1LwduSHSCZtLQVxMXFhS4oEckRJXGRCBUTE0NC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BqlWsRv8r+1P/5PrhCbSYSHfpJP6QyOMz+7J1dQ0eqNmfl+6vFe6wRAKiJC5S\ngHbv9pL3JZfAG28cnsB/2/4bD05/kJV/reStFm9x7VnXasFaAdqXto+XZg7hhbkvctlx7fio94sc\nV/a4cIclkq1gkrjqiYvkwr593v7kmjUPT+B7U/fS96u+NBjWgIurXszK3itpdXYrJfACVrpUaZ69\n5j4Wd/mZpG/KcMabNRj47UDS0tPCHZpIvtCTuEiADhyAm26C9HSYMOF/+8C/+v0r7ph8B/VOrscb\nV79BtYq5PGNV8sWSJdDs5h+I63Uf6WW2MbDlQC499dJwhyVyFA2ni+Qz56BXL1izBqZO9U5i256y\nnT4z+jDzt5kMbDmQ1ue0DneYcoQ5c+CGDo6HEj5g4JqHaHN2G16+6mUqlcmHYu4ieaThdJF81rcv\nLF3qnYVepgxMWj2J8wadR0xUDD/2/jGsCdzn85GUlITP5wtbDIVVkyYwfJjx9p0d+aTZSg64A5w/\n+Hw++/mzcIcmEhJ6EhfJwcCBXjnRBQugdMVd3PfFfSxYv4BR7UaFfXj2YHnQ6Oh49u9PznV50OJi\n5EjveNZFi+Cnf+dwx+Q7qH1ibd5p+c5hZ7KLhIOG00XyyZQpcPvtXsGNTSXnc8snt3D16VfzRvM3\nKB9dPqyx+Xw+4uKqk5IyG6gFrCAmpinr1v0UdJnQoqhfP28qZM4cKBGdQr85/RizYgxDWg2hzTlt\nwh2eFGNK4iL5YOlS7yCXjz/dz9S9TzPm+zEMbT2UVme3CndoACQlJdGsWS927lxy6LWKFesya9YQ\nGjRoEMbICifnoFs32LULJk6EkiVh/rr5dPukG1eediVvNn+TCqVzUTdWJEQ0Jy4SYuvX+89CH7CO\nR1Y1ZqVvJct7LS80CRwgPt4bQocV/ldWkJq6jvj4+PAFVYiZedXldu2CPn281y6Lu4zlvZaT7tKp\nPaQ2C9cvDG+QIrmkJC5yhJ07vbPQm983mWc2XUiHGh347KbPCt3caWxsLAkJg4iJaUrFinWJiWlK\nQsIgDaVnIzraewqfPt1b5wBQsXRFEtom8MbVb3D9B9fTf35/0l16eAMVCZCG00UySEuDlq1T+avW\nE2w/6UPeb/8+DU9pGO6wsuXz+UhOTiY+Pl4JPEDJydCoEQwb5v3CdtCGnRvoNLET5aLLMfa6sYXu\nFzcpmjQnLsVeqBJZrz5beD/9BhrVrcTY68dQpWyVEEYphcmiRdC2LcydC+ee+7/X09LTeHq2twZi\n3PXjaBIP5b2iAAAgAElEQVTfJGwxSvGgOXEp1hITJxAXV51mzXoRF1edxMQJeern6SFJJJRoQK9m\nzZhy82Ql8CKuYUN45RVv7cP27f97vVSJUrx05UsktEmg08ROvLLgFTJ7sND+fCkM9CQuES1U26ye\n+mg0LyY9woBmw7jnqrb5Fq8UPg89BD/+CJ9//r+jdA/asHMD139wPacdcxoj2o44tK1Q+/MllPQk\nLsVWcnIy0dHxeAkcoBZRUXEkJycHdH1aeho9P3qA/gtfZGCDOUrgxdDBUrKPPHL0e6dUOoX53edT\nLrocDRMasmbbGnw+Hz169CYlZTY7dy4hJWU2PXr01hO5hIWSuES0YLZZ/bPvH659rw0fzl7NUyd+\ny1031MjHSKWwKlUK3n/fOwhmxIij3y9Tqgwj2ozgrvp30SihEYlJiUH94igSShpOl4h3cGgzKiqO\n1NR1AQ1tbty1kWvHX8u/vzak9h9v8/74KFQ1tHhbvRoaN4YvvoD69TNvM3/dfG744Aa2f7qb1IWL\n0El5EgpanS7FXm5Wpy/bvIw277fhQnc/P498mMXfGOXKFVCgUqhNnAgPPwzffQfHHZd5m9+2/0bj\ndxuzZdHflJ13Lmn712tOXIKiJC4SoKm/TKX7p9158OzB/Pf29ixcCGedFe6opDB59FH4/ntvoVvJ\nkpm32fHvDtq9147UfamMbTuW06ueXrBBSpGihW0iARi9fDQ9PuvBmGsmM/je9gwbpgQuR3vpJUhN\n9QqmZOWYMscw89aZ1Dy1Jm0/a8uGnRsKLD6RjJTEiyDtXz3a61+/ztNznmZWlzm8dv9FdO3qHfQh\ncqSDC91Gj4bJk7NuF1UyisHXDqbbBd24ZMQlrPxrZcEFKeKnJF7EhOrgk6LCOcejMx9lxLIRLLxt\nIeMHVKdECa+2tEhWjj8ePvgAevSAtWuzbmdm9GnUh5eufIkrxlyhAipS4DQnXoSovvTh0tLTuH3y\n7az2rWZq56l8O7cKd9zhlRgthj8OyYN33oGRI+Hrr6F06ezbTl8zna6TujK8zXDVJ5dc0Zy4AMEf\nfFKUpB5IpdPETmzatYkvb/mSlG1V6N4dEhOVwCVwd98Np532v9Kl2Wl+ZnOmdp7KnVPuJGFpQv4H\nJ0KIkriZtTCzn8zsFzN7LJP3LzezHWa21P/RNxT3lcOpvrRnX9o+bvjwBval7eOzTp9RukQ5broJ\n7r8fLr003NFJJDGDhARvpfpHH+XcvkHVBsy7dR7Pz3uet755K/8DlGIv6CRuZiWAd4DmwHlAJzOr\nnknTec65uv6PF4K9rxxN9aUhJTWFtu+3JapEFB/d+BFlSpXhqaegfHl47KhfL0VyVqkSTJgAvXtn\nPz9+0FlVzmLurXMZmDSQF+e9mP8BSrEW9Jy4mV0MPOOcu8b/9eOAc869kqHN5UAf51zrAPrTnHiQ\nimt96T3799A6sTUnVTiJ0e1GU6pEKb74As2DS0i8/TaMGhXY/DjA5n82c9XYq2h7TltevOJFTEcC\nShbCPSdeFci4SXKj/7UjNTSz5WY21cx0SHU+io2NpUGDBsUugbcc35L4Y+IZ024MpUqUYtMm6N4d\nxo9XApfg3XMPxMcHNj8OcFKFk5h761ymrZnGA9MeIN2l52t8UjyVyrlJSCwBTnXO7TWza4BPgLOz\natwvwykLTZo0oUmTJvkdn0Swval7aZ3YmtMrn87wNsMpYSU4cAC6dvUWJl12WbgjlKLg4Px47dpw\n9dXQOsdxRTiu7HF81e0rrnnvGu79/F7eafmOnsiFOXPmMGfOnJD0Farh9H7OuRb+r48aTs/kmt+B\nes65bZm8p+F0Cdi/af/SJrENJ5Q/gVFtR1GyhHdOZv/+MG0afPVV1kdniuTFwoXQvr03RXPyyYFd\ns2vfLq4eezUNTm7AgGsGKJHLYcI9nJ4EnGlmcWYWDdwEfHZEgCdk+PxCvF8ejkrgIrmxL20f10+4\nniplqzCy7chDCXzxYnjrLRg3TglcQu+SS+Cuu+CWWyA9wBHyiqUrMr3LdL7941semPYAelCRUAk6\niTvnDgD3ADOAlcD7zrnVZnanmd3hb3aDmf1oZsuAtwCV+5GgpB5IpcOHHSgbVfbQHDjArl3QuTMM\nHgynnBLmIKXI+s9/YN8+eP31wK+pVKYS07tM5+uNX/PQ9IeUyCUkdGKbRJwD6QfoMqkLu/fvZuKN\nE4kuGX3ovS5doFw5GDIkjAFKsbB+PTRoAFOmeH8GanvKdq4aexVXxF/Bq81e1dC6hH04XaTAOOe4\n5/N72LJ7Cx92+PCwBD52rDdP+d//hjFAKTZOPRUGDvRGfv75J/DrKsdUZmbXmUxfO50X5unIDAmO\nnsQlojz55ZPMWDuDr7p9RcXSFQ+9/vvvcOGFMGsWXHBBGAOUYqdnT3DOW7meG1t2b+GykZdxT4N7\nuP/i+/MnOIkIehKXYuG1ha8x6adJTOsy7bAEfuCAt8jo0UeVwKXg/fe/MHcufPJJ7q47sfyJzOo6\nizcWvcHIZSPzJzgp8gpqn7hIUIYvHc7ApIEsuG0Bx5U97rD3XnvNW4X+0ENhCk6KtQoVvKmc666D\niy6Ck04K/Nq4Y+KY2XUmTUY3oWLpirSv0T7/ApUiScPpUuhN+WUKt0++nbm3zuXsKoefEbRsmXfw\nxnffQVxcmAIUAZ56CpYsgalTvYNhcmP5luU0H9ec965/j6tOvyp/ApRCS8PpUmQt3riY7p9255OO\nnxyVwFNS4OabvT3hSuASbk8/DT6ft70xt2qfWJuPOnxE54mdWbZ5WeiDkyJLT+JSaP269Vcaj2rM\nsNbDaHV2q6Pev+8++Osvr0a4dulIYfDzz1652/nzoXpmtRxzMHHVRO6bdh8Lui/gtMqnhT5AKZSC\neRLXnLgUSn/u/pMW77XguSbPZZrAZ82CSZPg+++VwKXwOOcceO4577yCRYsgKip317ev0Z7NuzfT\n4r0WLLxt4VHrP0SOpOF0KXT27N9Dq8RWdKnZhdvr3X7U+zt2wG23eVt6jj02DAGKZKNXL6hSxTu/\nPy/uufAerqt+Ha0TW7M3dW9og5MiR8PpUqgcSD/A9R9cz7ExxzKizYhMT7O69VaIicnb3KNIQdi4\nEerW9Yrw1K2b++udc3T7pBs7/t3BpI6TDtUFkKJJC9ukyHhs1mPs2reLIa2GZJrAP/0UFizwtpWJ\nFFbVqsGbb3rnF/z7b+6vNzOGtxnO7v27eWTmI6EPUIoMJXEpNIYuGcrkXyYfdR76QT6fN1Q5ahSU\nL1/w8Ynkxs03e3PkzzyTt+ujS0Yz8caJfP7r5wxO0rCTZE7D6VIozPptFl0+7sL87vM5q8pZR73v\nHHToAKedpqdwiRw+H9SqBRMnQqNGeetjzbY1XDriUka3G03zM5uHNkApFDScLhFtlW8VnSd25oMO\nH2SawMHbRrZ6NTz/fAEHJxKE2Fhv7Ua3brBnT976OPPYM/mww4d0ndSVH//6MbQBSsTTk7iE1da9\nW7lw+IU83fhputXulmmbzZuhdm34/HOoV6+AAxQJga5doXJlGDAg732MWzGOp2Y/xTc9vuGE8ieE\nLjgJu2CexJXEJWzS0tNoMa4FdU6sw2tXZz5G7hy0a+cNSeopXCLVtm1QsyaMHw+XX573fvp+1Ze5\n6+by5S1fZrpuRCKThtMlIj08/WGiSkbx8lUvZ9lm/Hj47Tfo27cAAxMJsWOPhXff9c43yOuwOsBz\nTZ+jcpnK3P+FSpeKR0lcwmLEshFMWzuNxPaJWe6B3bzZq0w2ahSULl2w8YmEWuvWcMkl8MQTee+j\nhJVg3PXjmLtuLu9+927ogpOIpeF0KXBfb/iadu+3Y373+Zxz3DmZttEwuhRFoRpW/3Xrr1w68lI+\n6vARl8VdFroAJSw0nC4RY+OujXT4sAOj2o3KMoGD95/c77975R1FiopQDaufVeUsxrQbw40f3cj6\nnetDF6BEHD2JS4HZl7aPy0ddTttz2vLEZVmPKW7ZAhdcoNXoUnTdcgscc0xwq9UBXv/6dRJ/TGTh\nbQspU6pMaIKTAqfV6RIR7ppyF1v2bOHjGz/O9EhV8IbRr78ezjsPXnihgAMUKSDbtsH558OECXBZ\nEKPhzjlumngTFaMrMqzNsNAFKAVKw+lS6I1aPoqvkr9idLvRWSZwgA8/9GoyaxhdirJjj4WBA6FH\nD0hJyXs/Zsbw1sNZsGEBI5aNCF2AEjH0JC75bunmpTQf15y5t86lRmyNLNv9/be36GfSJLj44gIM\nUCRMOnaE+Hh45ZXg+lntW03jUY2Z0WUGdU6qE5LYpODoSVwKra17t9L+g/YMajko2wQOcP/90Lmz\nErgUH2+/DaNHQ1JScP2cG3su71zzDu0/aM/2lO2hCU4igp7EJd+ku3RavteSmsfXzPJEtoM++8zb\nE75iBZQtW0ABihQC48fDSy/BkiXBn4fwwLQHWLNtDZ91+owSpme0SKEncSmU+s/vz97UvfS/qn+2\n7XbsgN69ISFBCVyKn06d4PTTvUQerNeavcb2f7fz6sJXg+9MIoKexCVfzP59Np0/7sx3t39H1YpV\ns23bsydER8OgQQUUnEghs2kT1KkDs2Z5BxwFY8PODdQfVp9JHSfR6JQ81j+VAqUncSlUtuzeQpdJ\nXRjTbkyOCfzLL2HGDHg56+PTRYq8qlW9J/EePSAtLbi+Tql0CsNbD6fTxE5sS9kWmgCl0FISl5A6\nkH6AzhM707NOT5qd0Szbtnv2wB13ePWWK1YsoABFCqkePaBCBfi//wu+r9bntOb66tfT/dPuFLaR\nTZ/PR1JSEj6fL9yhFAlK4hJSz859FjPj6cufzrHt0097K9GvvbYAAhMp5Mxg2DDo3x/Wrg2+v1ea\nvcIf//zB29++HXxnIZKYOIG4uOo0a9aLuLjqJCZOCHdIEU9z4hIyM9fO5NZPb2XpHUs5ofwJ2bb9\n9lto0wZ++AFiYwsoQJEI8MYbMHWqN9WUzblIAVm7bS0NExry+c2fU//k+qEJMI98Ph9xcdVJSZkN\n1AJWEBPTlHXrfiK2mP8noDlxCbs/d//JrZ/eytjrxuaYwPfv94YO33xTCVzkSPffD//84+3WCNYZ\nx57BOy3f4aaPbuKfff8E32EQkpOTiY6Ox0vgALWIioojOTk5fEEVAUriErR0l063T7rRvXZ3rjjt\nihzbv/wyxMV5W2tE5HClSnkJ/Mkn4Y8/gu/vxvNupHFcYx6Y9kDwnQUhPj6e/fuTgRX+V1aQmrqO\n+Pj48AVVBCiJS9De+PoN/tn/D/2a9Mux7apVXuWmwYODHyoUKapq1YI774S77w5Nf//X4v+Yu24u\nE1dNDE2HeRAbG0tCwiBiYppSsWJdYmKakpAwqNgPpQdLc+ISlG83fUur8a1Iuj2JuGPism2bng6X\nXgpduniHu4hI1vbtg9q14cUXvcp+wVq8cTFt3m/DkjuWUK1iteA7zCOfz0dycjLx8fFK4H4qRSph\nsfPfndQdWpdXr3qV9jXa59h+4EDviMn586GExoBEcrRggVckZeVKr/54sJ6f+zxz1s1hZteZOpa1\nEFESlwLnnOPmj2+mUulKDG41OMf2GzZ4J1LNnw/nnlsAAYoUEb17ewfADB0afF9p6Wk0GdWEdtXb\n0adRn+A7lJDQ6nQpcO/98B7LtyznzeZv5tjWOe8/ovvvVwKX4i0vB528/DJMmwZz5gR//1IlSjH2\nurG8svAVlm9ZHnyHEnZK4pJryTuSeXD6g4xvP56YqJgc23/wAfz+Ozz2WAEEJ1JI5fWgk4oV4Z13\nvNMNU1KCj+O0yqfxerPX6fZJN/al7Qu+QwkrDadLruR2OG7bNjjvPJg0SXXCpfgKxUEnHTvCGWeE\nptqZc452E9pxfuz5vHjli8F3KEHRcLoUmJcXvEzpUqV5qOFDAbXv0wduvFEJXIq3UBx0MmAADB8O\ny0MwCm5mDGk1hIRlCSzeuDj4DiVslMQlYN9u+pa3v32b0e1GB7Sy9csvvY8XXiiA4EQKsVAcdHLC\nCd78+O23w4EDwcd0YvkTefuat7nlk1vYm7o3+A4lLJTEJSC79+/m5o9vZmDLgQHtMd271zusYtAg\nrzKTSHEWqoNOunf3/j0NGBCauDqc14G6J9XlP1/+JzQdSoHTnLgEpNeUXvyb9i+j2o0KqP1jj8G6\ndfD++/kbl0gkCcVBJ7/+Cg0bwnffQShOLN26dyu13q3F+OvHc3n85cF3KLmmfeKSr7749QvumnoX\n3/f6nkplKuXYftkyaN7cq1B2Qva1UETELzcJvn9/mDcPPv88NMcXT/llCvd9cR8/3PUD5aLLBd+h\n5ErYF7aZWQsz+8nMfjGzTDcSmdkAM/vVzJabWe1Q3Ffy39a9W+k5uScj244MKIGnpXlzdq+8ogQu\nEqjcbj/r08crjpKYGJr7tzq7FZecegl9v+obmg6lwAT9JG5mJYBfgCuBP4Ak4Cbn3E8Z2lwD3OOc\nu9bMLgL+zzmX6XplPYkXHs45On7UkWoVqwV0qAt45UWnToVZs1TgRCQQed1+9u230KYN/PgjHHdc\n8HFs3buV8wefz8c3fkzDUxoG36EELNxP4hcCvzrn1jnnUoH3gbZHtGkLjAFwzi0GKpmZntMKufd/\nfJ8f//qRF68IbB/p7797e1iHDFECFwlUXrefXXihV8734YdDE0eVslUY0GIAPT7roUNgIkgoknhV\nYEOGrzf6X8uuzaZM2kghsmnXJu6fdj9jrxsb0KlszkGvXvDII3DmmQUQoEgREcz2s+efh7lzYebM\n0MRyQ40bqH5cdZ6f93xoOpR8py1mchTnHD0+68E9F95DvZPrBXTNe+/Bn3/CQ4GdASMifsFsPytf\nHgYP9n6B3huCrd5mxsCWAxm6ZKjOVo8QpULQxybg1AxfV/O/dmSbU3Joc0i/fv0Ofd6kSROaNGkS\nbIySCwnLEvh77988edmTAbX/+29voc3kyRAVlc/BiRRBnTp15KqrrsjT9rNrroGLLoJ+/eDVV4OP\n5aQKJ/HKVa9w26e3sbjnYqJK6h91qM2ZM4c5oahoQ2gWtpUEfsZb2LYZ+Bbo5JxbnaFNS+Bu/8K2\ni4G3tLCtcFq/cz31htZjdrfZnH/8+QFdc8stUKUK/Pe/+RycSDGW3Ra0v/6CmjXhiy+gbt3g7+Wc\no/m45lx52pU8dqkqF+W3sC5sc84dAO4BZgArgfedc6vN7E4zu8Pf5nPgdzNbAwwBegd7Xwk95xw9\nP+vJgxc/GHACnzHD26/6vKbQRPJNTlvQjj/e29bZs6e3zTNYZsbgawfz6tev8vv234PvUPKNDnuR\nQ4YuGcqwpcNY1GMRpUrkPNOyZ4/32//Agd6QnoiEXqBb0JyDZs2gRQtveisU+s/vz7z18/i88+eY\ntpzkm3BvMZMiYN2Odfznq/8wqu2ogBI4eHNwF1+sBC6SnwLdgmYG777rFUn57bfQ3PvhRg+zYecG\nPlz1YWg6lJBTEhdvGH1yTx66+CHOO/68gK5ZuhTGjIG33srn4ESKudxsQTvzTHj0UW+1eigGNKNL\nRjOk1RAenP4gO//dGXyHEnJK4sKwpcPY8e8OHrnkkYDap6V5c2+vvurNxYlI/sntFrSHHgKfD8aO\nDc39Lzn1Elqd1Yonvwxst4oULM2JF3Mbd22kzpA6uVqN/tpr3oK2GTN0MptIQclNgZQlS6BlS68I\nUSh+0d6esp3zBp3Hxx0/5uJqmW4skiCoipnkiXOO1omtaXByA55p8kxA16xd6+1J/fZbOP30fA5Q\nRPKsTx/YvNk7iCkUEn9I5OWFL7PkjiUBr5uRwGhhm+TJkK+H8POWn+lZvWdA7Z2DO++Exx9XAhcp\n7J59FhYt8vaOh8JN59/EcWWPY1DSoEOv+Xw+kpKS8Pl8obmJ5JqSeDH17tih3PVJbzYPKclZp9fK\nsfQhwOjRsH07PPBAAQQoIkEpV84rRnTXXbB7d87tc0rIZsY717zD8/OeZ8vuLbkunyr5Q8PpxZDP\n5+Oke6txYFtnmDmSQEofHjwRato0qFOnYOMVkbzr1g0qV85+J0li4gR69OhNdLS3Ej4hYRCdOnXM\ntO2jMx9l3dZ1TL5tVq7Lp0rmNJwuuTJ68WjciQazDw6L5Vz68L774NZblcBFIs0bb8D778PixZm/\n7/P56NGjNykps9m5cwkpKbPp0aN3lk/kTzV+ijnr5lAi/jhyWz5VQk9JvJjZ+e9O3vzpTUp9Hg1p\nv/pfzb704eTJ3mrXDHVpRCRCHHecV9egZ0/Yv//o93Nbz7xC6Qq8cNkL7G26Fkos9b8aePlUCS0l\n8WLm8VmP0/qc1ox6dlhA+0537YK774ahQyEm57LiIlII3XQTxMV556sfKS/1zHs27EmNuOpENbos\n1+VTJbQ0J16MLFi/gI4fdWRl75UcU+aYgPad9u4NqakwbFgBBysiIbV+vVfhbP58OPfcw987OCce\nFRVHauq6bOfED1rlW0XjEY0Zd8k46p1TTwk8CNonLjnal7aP2kNq80LTF2hfo31A1yxYAB07wsqV\ncMwx+RygiOS7gQNh/HgvkZc4Yhw2N4fJHNRnRh+2p2wnoW1CPkRbfCiJS476zenH8i3LmdRxUkDV\niP79F2rXhpdeguuvL4AARSTfpadD48bQqZM3TRasnf/u5Jx3zmFq56nUO7le8B0WU0rikq1VvlVc\nPupylt+5nKoVqwZ0Td++sHo1TJyYz8GJSIFavRouu8wrYnTqqcH3N2zJMEZ/P5r53eerXGkeaYuZ\nZCndpXP75Nt5tsmzASfw77/3FrK9/XY+ByciBe7cc70Dm0JV6ey2OrexJ3UPH6z8IPjOJNeUxIu4\noUuG4pyjV/1eAbVPS4PbbvNqEp98cj4HJyJh8dhjsGlTaM5VL1miJP/X4v94dNaj7E3dG3yHkitK\n4kXYlt1beGr2UwxpNYQSFthf9RtvQJUq0L17PgcnImETFQUjRsDDD8OffwbfX+O4xlxU9SJe//r1\n4DuTXNGceBHWeWJnTq10Ki9f9XJA7X/5BRo1gu++A53ZIFL0Pf44/P47TAjBsefJO5KpN7Qey+9c\nzimVTgm+w2JEc+JylBlrZ/DNxm94+vKnA2qfng49esDTTyuBixQXzzwDy5fDJ58E31f8MfH0rt+b\nx798PPjOJGBK4kVQSmoKd029i4EtB1I2qmxA17z7rpfIQ7HtREQiQ0wMDB/u/bvfvj34/h679DHm\nJM8haVNS8J1JQDScXgT958v/sGb7GibcENgY2fr1UK8ezJt39ElOIlL03X03pKR48+TBGr50OGNX\njGVOtznachYgDafLIat8qxi6dChvNc+m7mAGznmFER58UAlcpLh6+WX46iuYPj34vrrX7s62lG18\n9vNnwXcmOVISL0Kcc/Sa0ot+l/fjpAonBXTNiBGwbRs8+mg+BycihVaFCl59hDvu8IoeBaNkiZK8\netWrPDbrMVIPpIYmQMmSkngRMub7MaSkpQS8J3zjRm916siRUKpUPgcnIoVas2Zw9dWh+YW+xZkt\nOKXSKQxbqspJ+U1z4kXEtpRt1BhYgymdp1D/5Po5tncOrr0WLr7YW5EuIrJzJ9Ss6f1if+WVwfW1\nfMtyWoxrwS/3/kLF0hVDE2ARpTlx4ckvn+SGGjcElMABxoyBP/6AJ57I58BEJGJUqgRDhnjrZHbv\nDq6v2ifWpsWZLXhlQSZFzCVk9CReBCzeuJh2E9qx+u7VHFMm55qhf/zhVSibPh3q1CmAAEUkotx6\nK5QvD++8E1w/G3dt5IJ3L9ABMDnQk3gxlpaexl1T7+K1Zq8FlMCdg7vugjvvVAIXkcz9978waRLM\nmRNcP9UqVuPOenfSb06/UIQlmVASj3CDkwZTqUwlbq55c0Dtx43zjlns2zefA5M88/l8JCUl4fP5\nwh2KFFOVK3sHQN12W/DD6o9e8iiTf5nMat/q0AQnh1ESj2Cb/9nMc/OeY1DLQQEdqrBpk1fwYPRo\nKF26AAKUXEtMnEBcXHWaNetFXFx1EhNDcKi1SB60bg2NGwe/Wv2YMsfwSKNH6DtbTw75QXPiEazL\nx12oVrFaQAVODq5Gv+gi77xkKXx8Ph9xcdVJSZkN1AJWEBPTlHXrfiI2Njbc4UkxtGPH/1arX3VV\n3vtJSU3hrLfP4uOOH3Nh1QtDF2ARoTnxYmjeunnMWzePpxo/FVD7kSNhyxZ48sl8DkzyLDk5mejo\neLwEDlCLqKg4kpOTwxeUFGvHHOMdAtOzZ3CHwMRExfDM5c/w+KzH0UNaaCmJR6DUA6nc/fndvNn8\nTcpFl8ux/fr18Nhj3jB6VFQBBCh5Eh8fz/79ycAK/ysrSE1dR7zKykkYtWjhHQLz8MPB9dO9Tnc2\n/bOJWb/NCk1gAiiJR6SBSQM5sfyJtD+3fY5tnfNKjD74oDcsJoVXbGwsCQmDiIlpSsWKdYmJaUpC\nwiANpUvYvf46zJwJ06blvY9SJUrxQtMXeOLLJ0h36aELrpjTnHiE2fzPZmq9W4v53edT/bjqObYf\nPNgbSv/6ax2tGil8Ph/JycnEx8crgUuh8dVX0K0brFjhrV7Pi3SXzoXDLuSxSx6jw3kdQhtgBAtm\nTlxJPMJ0ndSVqhWqBrSY7ddfoVEjWLAAzjmnAIITkSLt/vvB54Px4/Pex8y1M7nni3tY2XslpUro\nyQK0sK3YmLduHnOT59K3cc5bNdLSoGtX71x0JXARCYX+/WHpUpgQxM7Hq06/ihPLn8h7K94LXWDF\nmJJ4hEhLT+Oez+/hjavfoHx0+Rzbv/yyV17w7rsLIDgRKRbKloWxY+G++7xzJ/LCzHi+6fM8N+85\nlSoNASXxCDE4aTCx5WK5ocYNObZdsgQGDPDmwkvob1hEQqhBA+jd21swm9eZz8ZxjTm98umMWj4q\npLEVR5oTjwC+PT5qDKrBnG5zOO/487Jtm5IC9ep5x6p27lxAAYpIsZKaCpdc4hVK6d07b30s2rCI\nm7xe93kAACAASURBVCbexC/3/ELpUsX7CEktbCvibv/sdspHl+e/Lf6bY9sHH/SGuSZMgABOYhUR\nyZOffoJLL/V2vpx9dt76aPleS1qd3YreDfL4m0ARoSRehH33x3e0TmzNT3f/RKUylbJtO3MmdO8O\n338PVaoUUIAiUmwNHAijRnmJPC8HSX33x3e0e78dv977KzFRMSGPL1JodXoRle7Suefze3jpipdy\nTOB//+0NbY0apQQuIgWjd284/njo1y9v19c/uT71T67PkCVDQhpXcaIkXoiN/X4sDke32t2ybeec\nd7Zxp07BFSkQEckNMxgxwvuYNy9vfTzX9DleWfgKe/bvCW1wxYSSeCG189+dPPHlE7x9zduUsOz/\nmoYPh+RkePHFgolNROSgE07w/g/q2tWrepZbtU6oxWWnXsbg7waHPrhiQHPihVSfGX3YnrKdhLYJ\n2bb7+Wdvlei8eVCjRgEFJyJyhHvu8ab1EhNzv6j2hz9/4OpxV7P2vrWUjSqbPwEWYmGbEzezymY2\nw8x+NrPpZpbpxK2ZJZvZ92a2zMy+DeaexcHPf//M6O9H89KVL2Xbbv9+uPlmeO45JXARCa/XXvPO\nVR83LvfX1jyhJg2rNWTYkmGhD6yIC+pJ3MxeAbY65141s8eAys65xzNp9xtQzzm3PYA+i/2TeMv3\nWnLlaVfycKPsa/89+iisWgWTJ2s7mYiE3/Ll0KwZLFoEZ56Zu2uXbV5Gq8RWrL1vLWVKlcmfAAup\ncK5ObwuM9n8+GmiXRTsLwb2Kham/TOW37b9x70X3Zttu2jSvCMHIkUrgIlI41K7t1Wvo2BH27cvd\ntXVOqkPdk+oyYtmI/AmuiAr2SXybc+7YrL7O8PpvwA7gADDUOZflmElxfhL///buPK6qan38+Gcx\nKSCgFDgknuMY5aw5XTW1ckLUW85zaWY5laXftOTnUFpWmlrX9HrN9KY4ZGqKczmWqaWFs6YeHHA4\nhgIKVxD2749NJDJzRuB5v1685Oy9zlqPHA7P2WuvISkliVrzajGnwxw6Vu+YbbmrV6FBA/3eU+vW\n9otPCCFyo2nw/PNQpQrMmpW/5x68cpDuq7pzdtTZYrWKmyVX4rnuA6eU2g6UffAQoAFZbaWVXfZt\nrmnaVaVUALBdKXVS07R92bU5+YFJh61bt6Z1MclUc36eQ41HauSYwFNSoH9/GDZMErgQwvn8Ne2s\nfn145hkIDc37cxs/1piagTVZ8vsSXmn4iu2CdLBdu3axa9cuq9Rl6ZX4SaC1pmnXlVLlgJ2apj2R\ny3MmAfGapmX5Ga24Xolfu3ONWvNqsX/Ifqo/Uj3bctOmwbZt8P334CZb8QohnNS+fdC9O/zyC1Ss\nmPfn/XTpJ/qu6cvZUWdxdy3AMnCFkCPviX8HvJj2/SBg/cMFlFJeSqlSad97A+2AYxa2W+RM+H4C\ng+sPzjGB79sHn30Gy5ZJAhdCOLcWLfRpZ/366T2IefWPoH9Qzb8a/438r+2CK0IsvRL3B1YBQUAU\n0FPTtNtKqfLAQk3TQpVSlYG16F3tbsAyTdM+zKHOYnclfujKIbqs6MLpkafxLeGbZZmbN/XdyT7/\nHDp3tnOAQghRACkp0K6dvpbF1Kl5f95u026GfDeEUyNP4eZS9K9YZAMUJ2Q2mzGZTBiNRgICArIt\np2kaLRa3YHC9wQxpMCTLMqmpEBICdevCjBm2ilgIIazv2jV46il9VbcOHfL+vBZftmBk45H0rtXb\ndsE5CdkAxcmEh6/EYAimbdtXMRiCCQ9fmW3ZFcdWkJicyIv1Xsy2zLRpkJAgy6oKIQqfcuX06bAv\nvggXL+b9eRNaTODDfR9SFC7qbEmuxK3MbDZjMASTmLgTqANE4unZhqioU5muyBOSEwj+PJhlLyyj\npaFllvXt2AEDB+qDQypUsH38QghhCx99BN9+qy8R7eGRe3lN06g7vy4fPvchIdVDbB+gA8mVuBMx\nmUx4eBjREzhAHdzdDZhMpkxlP/7xY5pWbJptAr9yRd9UYNkySeBCiMJt7Fh9s5Rx4/JWXinF+Bbj\n+WDfB7YNrJCTJG5lRqORpCQTEJl2JJLk5CiMRmOGcpdiLzH34Fw+avtRlvUkJ+urHo0aBW3a2DJi\nIYSwPRcX+OorfZno1avz9pyeNXsSHR/NvovZLitS7EkSt7KAgAAWLZqHp2cbfH0b4OnZhkWL5mXq\nSh///XiGPzUcY2ljlvWMHQt+fjA+00r0QghROJUpA998A8OH6/s+5MbNxY1x/xjHh/uyndBU7Mk9\ncRvJaXT6/kv76bG6B6dGnqKUR6lMz126FN57Dw4dgtKl7RWxEELYx5Il+kDdgwdz/xv3v/v/o8qc\nKiwPWY73He9cZ/wURjLFrBBJ1VJptqgZIxqNYGDdgZnO//ordOwIO3dCzZoOCFAIIexg9Gg4d07v\nXnfJpU+477/6sWrPN5TaWoukJBOLFs2jT59e9gnUDmRgWyGy/OhyUrVU+tfpn+ncjRvwwgswf74k\ncCFE0TZzJty9C5Mm5VzObDaz9t3NpBi9iHVZRWLiToYMGY7ZbLZPoE5Okrgd3U26y4TvJzC7/Wxc\nVMYffXIy9Oypb27ywgsOClAIIezE3R1WrdJvH377bfblTCYTJagMvwyHZrPIacZPcSRJ3I4++ekT\nmgc1p3ml5pnOjR0LXl75W5pQCCEKs8BAWLNG35Xx+PGsy6TP+Dn4LNReDl67s5zxU1xJEreTy3GX\nmXtwLjOey7xu6oIFsHWrPh/c1dUBwQkhhIM89RR8+qm+J0RWPeTpM35SeuD+hytuzTpmOeOnuJKB\nbXYycO1AgnyDmPZsxrVTd+zQu9D37YNq1RwUnBBCONi778Lu3fo2yyVKZD5vNpv54egPjPplFBff\nvEhJt5L2D9JGZHS6kzt05RBdV3Tl9MjT+JTwST9+6hS0aqXfF2rVyoEBCiGEg6Wm6uOCvLz0KWgq\nm5QWujyUro93ZWjDofYN0IZkdLoT0zSNMVvH8P4z72dI4H/+qXcfffihJHAhhHBx0Qe5nTgBH+Sw\n0urYf4xl5v6ZpGqp9gvOiUkSt7FvTnxDQnJChl3KkpKgWzd9FPpLLzkuNiGEcCZeXvDdd/o022++\nybpMK0MrfEr4sPHMRvsG56QkidvQ/+7/j//b8X/MbDczfUpZaioMGaIvP5jTp00hhCiOKlSA9ev1\npVl/+inzeaUUY5uN5ZOfPrF/cE5IkrgNzT0wl7pl69Km8t87mEyYoK9StGxZ7qsUCSFEcVS/vt61\n/sIL+tihh3V7shsXYy9y4PIB+wfnZCSN2MiNuzf46MeP+Ljtx+nH5s7VP2Fu2KB3GwkhhMhahw4w\nY4b+b3R0xnNuLm6MaTqGT/bL1biMTreR1za+Rkm3knza4VNA33pvzBh9KpmsUSCEEHkzfbo+g2f3\nbn1nx7/cSbqDYbaBX1/5NdvdIAsLmWLmZI7dOMYzS57h9MjTlPEsw+7d0KMHbNsG9eo5OjohhCg8\nNA1GjtS71TdvBg+Pv8+N3TYWgE/aFe4rckniTqbD1x0IqR7C6CajOXxY35Vs+XJ49llHRyaEEIVP\nSoo+h1wpWLEC3Nz046bbJp7691OY3jBlua1zYSHzxJ3I5rObuXD7Aq899RrHjkFIiL6sqiRwIYQo\nGFdX/UIoPh4GD9Zn+QAYSxtpbWzNkt+WODZAB5IkbkX3U+8zdvtYPm77Mabz7rRvr68J/M9/Ojoy\nIYQo3EqUgLVrISoKRozQu9kBXm/yOnMOzCm2i79IErei/xz+D2W9y1KnRGeee07fkaxPH0dHJYQQ\nRYOXF2zcCIcPw7hxeiJvUakFPiV82Hx2s6PDcwhJ4lYSdy+OKbunML7+TJ57TvHWW/qiLkIIIazH\nx0cf4LZ9O0yapN9PfqPJG8w+MNvRoTmEJHEr+WDvB7Qs34ER3eozdCiMHu3oiIQQomjy99eT+Jo1\nMHEi9HiyJ8dvHOf4jWw2JS/CZHS6FZhum6j/RUNKfR3Jmy8/xpgxjo4ob8xmMyaTCaPRKHvzCiEK\nHbMZ2rbVv3xC3+Ny3CX+3fnfjg4r32R0uoONWvcOqT+PYvzwwpPAw8NXYjAE07btqxgMwYSHr3R0\nSEIIkS8BAfDDD7BzJ0StGcbqE6u5mXDT0WHZlVyJW2jF3gP0+64bc6ufZsQr3o4OJ0/MZjMGQzCJ\niTuBOkAknp5tiIo6JVfkQohCJzZWX541puVgBoVW552nJzg6pHyRK3EHOXBAY+DyN3nJ+F6hSeAA\nJpMJDw8jegIHqIO7uwGTyeS4oIQQooD8/PQVMb2Pj2TatvkkJKY4OiS7kSReQBER0Pb1NVSsnMC/\nhw9ydDj5YjQaSUoyAZFpRyJJTo7CKIu6CyEKKR8f+PGbBpRIqkCTgRuJjXV0RPYhSbwA/vMfGDz0\nHr4vvM1/evy9V3hhERAQwKJF8/D0bIOvbwM8PduwaNE86UoXQhRqnp4wp+9Ibtf4nKefzrz7WVEk\n98TzQdP0BVyWLIGen87keMIuNvTZ4OiwCkxGpwshipp79+9hmG2gb9Iuvv13MJs3wxNPODqqnMkG\nKHaQmAjDhsHx4/DfNX/SanUwe1/aS/CjwY4OTQghxAMm/jCRuHtxPGWey9ixsHSpPvDNWcnANhu7\ndAlatoSkJNi7FxacnErPJ3tKAhdCCCc0rOEwvo78mud7xbNmjb5pykcf/b3eelEiSTwXe/dCkyb6\nNnjh4XA58QzLji5jcuvJjg5NCCFEFoL8gmhtbM3XkV/TsiUcOACrVkHfvpCQ4OjorEuSeDY0Db74\nArp1gy+/hP/7P30v27d3vM24f4wjwFvuIQshhLMa2Xgk/zr0LzRNIyhIvyBzc4PmzeHCBUdHZz2S\nxLMQEwM9esD8+fDjj3/fS9kTtYcjV4/wetPXHRugEEKIHLUxtiFVS2VP1B5AH7m+dCm8+CI0bqz3\nrBYFksQfsns31KsHQUF6F0z16vrxVC2VN7e+yfRnp1PSraRjgxRCCJEjpRQjGo3g80OfP3AMXn8d\ntm6FyZP1hB4f77AQrUKSeJr79yEsTN//e8EC+PRTKPlArl5+dDkuyoXetXo7LkghhBB5NqDuAHac\n30F0fMYJ4w0awK+/6t3r9evDoUMOCtAKJImjv4CNGsEvv8CRI9CxY8bzicmJvPP9O8xqP6vQLewi\nhBDFlW8JX3o+2ZMvj3yZ6VypUvrCXR9+CKGh+rinu3cdEKSFinVGio/Xu1Y6d4a33oJNm6Bs2czl\nZv88m0aPNaJFpRb2D1IIIUSBDXtqGAsPLyQlNev11Lt3h6NH4coVqF0btmyxc4AWKpZJXNNg3Tqo\nWVNP5MePQ//++v2Sh924e4OZ+2cy47kZ9g9UCCGERRqUb0BZ77JsPbc12zKBgbBsmT4jacQI/bbq\n1at2DNICxS6J798PrVrBu+/qy6d++SU88kj25SftnMSAOgOo5l/NfkEKIYSwmmENhzH/l/m5lmvf\nXr8qNxqhVi2YOBGn30il2CTxkyfh+eehVy999Z7ISGjTJufnnDCf4JuT3xDWKsw+QQohhLC63rV6\ns+/iPi7FXsq1rJcXfPCBPj7qyhWoUUMf6Hzvnh0CLYAin8QPHdJX6WnVSp/kf/q0Pq3A1TX3547b\nPo4JLSbg7+lv8ziFEELYhreHN31r92XRkUV5fk6lSrB4MXz/PezcqSfzmTOd78q8SCbxlBT49lt9\nvfPu3aFhQzh7FsaO1Sf858WO8zs4dfMUIxqNsG2wQgghbG5Yw2H85/B/uJ96P1/Pq1ULvvsOvvlG\nn5ZWuTK88QacP2+jQPPJoiSulOqulDqmlEpRSjXIoVwHpdQppdQZpdTblrSZHU3Tu8jfeQeqVoVP\nPoHRo+HcOX3kuZ9f3utKSU3hrW1vMeO5GZRwK2GLcIUQQthR7bK1MZQ2EHEmokDPb9QIli+H33+H\nEiX0Vd86dNDHVsXFWTnYfLD0Svwo8DywO7sCSikX4HOgPVAT6KOUssr2X6mp+g/0vff0T0udO+tX\n4WvXwk8/6Uunurnlv96lvy/Fx8OHbk90s0aYQgghnMCwhsOY/2vuA9xyEhQEM2ZAVBQMGqT3+gYF\n6ftsrFwJN29aKdg8ssp+4kqpncBbmqYdzuJcU2CSpmkd0x6PBzRN07Kcs5XTfuJ37sCpU/pC9rt3\nw549EBAA7drpUwKaNgUXCz+W3E26y+OfP86anmtoUrGJZZUJIYRwGonJiQR9GsQvr/yCsbTRavXe\nuqVfPK5ZA/v26ffTW7WC1q3hqaf0xznlJkv2E7dHEu8GtNc07ZW0x/2Bxpqmjc6mLm3BAo34eH0O\n97VrcOaMPiDt1i2oVg3+8Q/9h9OqFZQvb3H4GUzZNYVTf54ivFsRWR1fCCFEuje2vEEpj1K8/8z7\nNqn//n19ZPuuXfrF5u+/w59/6rd5a9TQ/y1dWl8xzsdH/+rRo+BJPNfOZqXUduDBdcwUoAHvapq2\noSCN5mb+/MmUKAEeHlCvXmveeac1NWpAxYqWX2nnJDo+mrkH5/LrK7/arhEhhBAO83KDl+nwdQcm\nt56Mm0sB7rfmws1Nv3/eqBGMG6cfu3sX/vhDvxg9dw6OHt3FH3/s4t49SEqysL3cCmia1tayJrgC\nVHrgccW0Y9k6fHiyhU0WTNgPYbxc/2WrdrMIIYRwHrUCa1HRtyJb/9hKpxqd7NKmtzfUrat/6Vqn\nfemUmlLguq15XZtdV8AhoJpSyqCU8gB6A99ZsV2r+P3a70ScjeCdlu84OhQhhBA2NKT+kHzNGXdm\nlk4x+6dS6hLQFNiolNqcdry8UmojgKZpKcBIYBtwHFihadpJy8K2Lk3TeGvbW4Q9HYZfyXzMRRNC\nCFHo9KrVi52mnVy/c93RoVjMKgPbrCmn0em2EnEmgrHbxxL5aiTuru4ZzpnNZkwmE0ajkYCAALvG\nJYQQwjYGrx/ME48+wbjm4xwdikWj04vkim35cT/1PuO2j+Pjth9nSuDh4SsxGIJp2/ZVDIZgwsNX\nOihKIYQQ1vRXl7qzXcjmV7FP4gt/XUgFnwp0qp5xgIPZbGbIkOEkJu4kNvZXEhN3MmTIcMxms4Mi\nFUIIYS3/CPoHSil+vPSjo0OxSLFO4rH/i2XK7inMbDcT9dBm4iaTCQ8PI1An7Ugd3N0NmEwmO0cp\nhBDC2pRSRWKAW7FO4h/s+4CQ6iHULVc30zmj0UhSkgmITDsSSXJyFEaj0Y4RCiGEsJWBdQey9uRa\n4u45cPFzCxXbJG66bWLh4YXZrtoTEBDAokXz8PRsg69vAzw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FJSIBURIXKaYSExPZuzcN70h2gEVkZKwkMTExckGJSECUxEWKqfj4eJKThxAX14By5WoT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\nLaTRqEbM6jCLc08+N+D7fT6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gyqdK9tQTlzzp93U/7r34XnZu2Blw6cPu3eHOO5XARaLN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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.pipeline import Pipeline\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.linear_model import Ridge\n", "\n", "x_plot = np.linspace(-1, 1, 100).reshape(100,1)\n", "\n", "for degree in [1, 2, 5, 14]:\n", " model = make_pipeline(PolynomialFeatures(degree), RidgeCV_regr)\n", " model.fit(X_data, Y_data)\n", " print(\"Cross-Validated Lambda for degree: %d\" % degree)\n", " print(RidgeCV_regr.alpha_)\n", " \n", " # Evaluate the models using crossvalidation\n", " scores = cross_val_score(model, X_data, Y_data, scoring=\"mean_squared_error\", cv=10)\n", " \n", " y_plot = model.predict(x_plot) \n", " \n", " #plot\n", " fig, ax = plt.subplots(figsize=(8,8))\n", " ax.plot(x, y(x), label = \"True Model\")\n", " ax.scatter(X_data, Y_data, label = \"Training Points\")\n", " ax.plot(x_plot, y_plot, label=\"Degree %d\" % degree)\n", " plt.legend(loc='lower center')\n", " print(\"Cross-Validated MSE:\")\n", " print(-scores.mean())\n", " plt.title(\"Degree {}\\nMSE = {:.2e}\\n Chosen Lambda {}\".format(degree, -scores.mean(), RidgeCV_regr.alpha_))\n", " plt.xlim(-1, 1)\n", " plt.ylim(-2, 2)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---------------------------------------------------------------------------------------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References:\n", "\n", "* Econometric Theory, Stachurski\n", "* Lectures on Scikit-Learn, Andreas Mueller\n", "* Scikit-Learn Documentation" ] } ], "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture11/overfitting_noises_dcs.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Stochastic and Deterministic Noise as Catalysts for Overfitting\n", "\n", "Small exercise for showcasing the sources of overfitting. Example taken from the 4th Chapter of the book by Abu-Mostafa Yaser, [Learning from data](http://amlbook.com/)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Set-up of the problem\n", "\n", "The goal of the problem is to showcase the role stochastic and deterministic noise play in overfitting. \n", "\n", "To this end we are giong to fit polynomials to estimate a polynomial target function. We are going to use Legendre polynomials---an easy-to-handle orthonormal basis on $[-1, 1]$.\n", "\n", "The input space is $\\mathcal{X} = [-1, 1]$ with uniform probability density.\n", "\n", "The target is a degree-$Q_f$ polynomial given by $f(x) = \\sum_{q=0}^{Q_f} a_q L_q$ \n", "\n", "The coefficients are drawn from a rescaled standard normal distribution. We are rescaling the coefficients in order to keep the signal level fixed in expectation, so when we are changing the noise it has a one-to-one effect on the signal-noise ratio.\n", "\n", "The rescaling makes use of the closed form expectation we can derive in case of Legendre polynomials and uniform distribution.\n", "\n", "\\begin{equation}\n", "\\mathbb{E}_{a,x} \\big[ \\ f^2\\big ] = \\sigma_{a}^2 \\Bigg[1 + \\frac{1}{3} + \\frac{1}{5} + \\ldots + \\frac{1}{2Q_f + 1}\\Bigg] = C\n", "\\end{equation}\n", "\n", "\n", "The data set is $\\mathcal{D} = \\big\\{ (x_1,y_1), \\ldots, (x_N,y_N)\\big\\}$ where $y_n = f(x_n) + \\sigma \\epsilon_n$ and the $\\epsilon_n$ are iid standard normal random variables. $\\sigma$ defines the noise level in the problem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generating the data\n", "\n", "The following function generates a random target function of degree $Q_f$ and generates a noisy sample of size $N$ with added mean zero, $\\sigma$ standard deviation noise." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def overfit_data(Qf, N, sig):\n", " \"\"\"Generates a random sample of size N from polynomial function\n", " of degree Qf with added zero mean, sig s.d. normal noise.\"\"\"\n", "\n", " # Draw randmom coefficients from rescaled standard normal so that signal level is constant.\n", " sigma_signal = 2/np.sqrt(sum([1/(2*q + 1) for q in range(Qf)]))\n", " a = np.random.normal(0, sigma_signal, Qf+1)\n", " \n", " # Generate random input sample and noise\n", " x = np.random.uniform(-1, 1, N) # generate a sample of input vectors\n", " eps = np.random.normal(0, sig, N) # generate a sample of random noise\n", " \n", " # Generate dependent variable with Legendre poly, and noise\n", " y = np.polynomial.legendre.legval(x, a) + eps\n", " return x, y, a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Estimates for in-sample and out-of-sample errors\n", "\n", "\n", "Taking a given target function with coefficients $a$, the following function computes estimates for the in-sample and out-of-sample errors.\n", "\n", "For this end it generates 'num_exper' many experiments with training and test samples of size $N$ fits a 2nd and 10th degree polynomial. Then computes the corresponding errors." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def error(a, N, sig, num_exper):\n", " \"\"\"Generates many samples for a given target function with parameters 'a'\n", " and computes estimates for in- and out-of-sample errors.\"\"\"\n", " \n", " E_in_2 = np.zeros(num_exper) # in-sample error for 2nd degree fit\n", " E_out_2 = np.zeros(num_exper) # out-of-sample error for 2nd degree fit\n", " E_in_10 = np.zeros(num_exper) # in-sample error for 10th degree fit\n", " E_out_10 = np.zeros(num_exper) # ou-of-sample error for 10th degree fit\n", " \n", " for i in range(num_exper):\n", " \n", " # Generate sample from specified target function\n", " x = np.random.uniform(-1, 1, 2*N)\n", " eps = np.random.normal(0, sig, 2*N)\n", " y = np.polynomial.legendre.legval(x, a) + eps\n", " \n", " # Split sample to training and test set\n", " x_train = x[:N] \n", " y_train = y[:N]\n", " x_test = x[N:] \n", " y_test = y[N:]\n", " \n", " # Fit 2nd order polynomial to data\n", " fit2 = np.polynomial.legendre.legfit(x_train, y_train,2)\n", " E_in_2[i] = np.mean((y_train - np.polynomial.legendre.legval(x_train, fit2))**2)\n", " E_out_2[i] = np.mean((y_test - np.polynomial.legendre.legval(x_test, fit2))**2)\n", " \n", " # Fit 10th order polynomial to data\n", " fit10 = np.polynomial.legendre.legfit(x_train,y_train,10)\n", " E_in_10[i] = np.mean((y_train - np.polynomial.legendre.legval(x_train, fit10))**2)\n", " E_out_10[i] = np.mean((y_test - np.polynomial.legendre.legval(x_test, fit10))**2)\n", " \n", " # Average over experiments to get estimate for the errors\n", " E_2 = np.array([np.mean(E_in_2), np.mean(E_out_2)])\n", " E_10 = np.array([np.mean(E_in_10), np.mean(E_out_10)])\n", " return E_2, E_10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 10th order polynomial noisy target function\n", "\n", "Generate small sample of noisy data from 10th degree polynomial target function and try and learn it with 3nd and 10th order polynomials." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(11211)\n", "\n", "Qf = 10 # degree of target polynomial\n", "N = 15 # sample size\n", "sig = 1 # level of noise\n", "\n", "# Generate data with specified parameters\n", "x, y, a_10 = overfit_data(Qf, N, sig)\n", "model = np.polynomial.legendre.Legendre(a_10) # save instance of Legendre class\n", "\n", "# Fit 2nd order polynomial to data\n", "fit2 = np.polynomial.legendre.legfit(x,y,2)\n", "model_fit2 = np.polynomial.legendre.Legendre(fit2) # save instance of Legendre class\n", "\n", "# Fit 10th order polynomial to data\n", "fit10 = np.polynomial.legendre.legfit(x,y,10)\n", "model_fit10 = np.polynomial.legendre.Legendre(fit10) # save instance of Legendre class" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IKAAAAADIiWjUn/IQCjHlASgGBBQAAAAA5MRwDoXaPPcEQCYQUAAAAACQE8Mj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\n/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(14, 10))\n", "\n", "plt.rc('text', usetex=True)\n", "plt.rc('font', family='serif')\n", "\n", "plt.ylim(-10,10)\n", "plt.xlim(-1,1)\n", "plt.scatter(x,y)\n", "plt.plot(*model.linspace(1000), color='r', label='10th order target')\n", "plt.plot(*model_fit2.linspace(1000), color='g', label='2nd order fit')\n", "plt.plot(*model_fit10.linspace(1000), color='y', label='10th order fit')\n", "plt.legend(bbox_to_anchor=(1.01, 1), loc=2, frameon=False, fontsize = 18)\n", "plt.title('Fitting noisy 10th order target function', {'fontsize':20})\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we can see averaged in-sample and out-of-sample error in the case of fitting a 10th order noisy target function from 2nd and 10th order hypothesis spaces." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------\n", " 10th order noisy target \n", "--------------------------------------------------\n", " | 2nd order 10th order \n", "--------------------------------------------------\n", "E_in | 1.429 0.286 \n", "E_out | 2.577 4.487e+09 \n", "--------------------------------------------------\n" ] } ], "source": [ "num_exper = 100 # number oof experiments to average over the in- and out-of-sample errors\n", "\n", "E_2, E_10 = error(a_10, N, sig, num_exper)\n", "\n", "print('--------------------------------------------------')\n", "print(' 10th order noisy target ')\n", "print('--------------------------------------------------')\n", "print(' | 2nd order 10th order ')\n", "print('--------------------------------------------------')\n", "print('E_in | {:1.3f} {:1.3f} '.format(E_2[0], E_10[0]))\n", "print('E_out | {:1.3f} {:.3e} '.format(E_2[1], E_10[1]))\n", "print('--------------------------------------------------')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected the 10th order fit delivers small in-sample error, however performs terribly out-of-sample." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning Curves\n", "\n", "Since we were dealing with consistent estimation methods as the sample size increases the in- and out-of-sample errors should converge to the true risk within the corresponding hypothesis spaces. \n", "\n", "\\begin{equation*}\n", "R(\\hat{f}_n) - R(f_{\\mathcal{F}}) = \\underbrace{R(\\hat{f}_n) - R(f_{\\mathcal{H}})}_{\\text{estimation error}} \\ + \\ \\underbrace{R(f_{\\mathcal{H}}) - R(f_{\\mathcal{F}})}_{\\text{approximation error}}\n", "\\end{equation*}\n", "\n", "## Learning Curve for learning the 10th order noisy target" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N_start = 15\n", "N_stop = 150\n", "step = 2\n", "\n", "num_steps = len(range(N_start, N_stop , step))\n", "\n", "E_2 = np.zeros((2,num_steps))\n", "E_10 = np.zeros((2,num_steps))\n", "sample_size = np.zeros(num_steps)\n", "\n", "for index, n in enumerate(range(N_start, N_stop , step)):\n", " E_2[:,index], E_10[:,index] = error(a_10, n, 1, 2000)\n", " sample_size[index] = n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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o9WXuS6xzn3XLL1yWnwX3rHMPAACAzgjuAYyfujP3D4p3y0+WY3BedCk8MvcA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\nxodwD2DKG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(14, 10))\n", "\n", "plt.rc('text', usetex=True)\n", "plt.rc('font', family='serif')\n", "\n", "\n", "plt.plot(sample_size, E_2[0], color='g', label='E in -- 2nd')\n", "plt.plot(sample_size, E_2[1], color='y', label='E out -- 2nd')\n", "plt.plot(sample_size, E_10[0], color='b', label='E in -- 10th')\n", "plt.plot(sample_size, E_10[1], color='r', label='E out -- 10th')\n", "plt.ylim(0,5)\n", "plt.ylabel('Expected error', fontsize = 20)\n", "plt.xlabel('Sample size', fontsize = 20)\n", "plt.legend(bbox_to_anchor=(1.01, 1), loc=2, frameon=False, fontsize = 18)\n", "plt.title('Learning Curves with 10th Order Noisy Target', {'fontsize':24})\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see how for smaller sample sizes the restricted learner---2nd degree---outperforms the one using the true 10th degree functional form.\n", "\n", "Surprisingly, even if we know the form of the truth we are better off with restricting ourselves in finite samples in order to achieve better generalization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 50th order noiseless target\n", "\n", "Let's see how the deterministic noise affects the performance of the different model." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(11206)\n", "\n", "\n", "Qf = 50 # degree of target polynomial\n", "N = 15 # sample size\n", "sig = 0.001 # level of noise\n", "\n", "# Generate data with specified parameters\n", "x, y, a_50 = overfit_data(Qf, N, sig)\n", "model = np.polynomial.legendre.Legendre(a_50)\n", "\n", "# Fit 2nd order polynomial to data\n", "fit2 = np.polynomial.legendre.legfit(x,y,2)\n", "model_fit2 = np.polynomial.legendre.Legendre(fit2)\n", "\n", "# Fit 10th order polynomial to data\n", "fit10 = np.polynomial.legendre.legfit(x,y,10)\n", "model_fit10 = np.polynomial.legendre.Legendre(fit10)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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vqq+vT6dOnUq4vbKyUuvXr097RY5MyuseCpIVUGCVBwAAAOTO2CoPef97nONM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\nAAAAAFAzAgUAAAAAAFCz/w+c4PvnoTs0OwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(14, 10))\n", "\n", "plt.rc('text', usetex=True)\n", "plt.rc('font', family='serif')\n", "\n", "plt.scatter(x,y)\n", "plt.plot(*model.linspace(1000), color='r', label='50th order target')\n", "plt.plot(*model_fit2.linspace(1000), color='g', label='2nd order fit')\n", "plt.plot(*model_fit10.linspace(1000), color='y', label='10th order fit')\n", "plt.ylim(-10,10)\n", "plt.xlim(-1,1)\n", "plt.legend(bbox_to_anchor=(1.01, 1), loc=2, frameon=False, fontsize = 18)\n", "plt.title('Fitting noiseless 50th order target function', {'fontsize':20})\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------\n", " 5oth order noiseless target \n", "--------------------------------------------------\n", "fit | 2nd order 10th order \n", "--------------------------------------------------\n", "E_in | 0.851 0.145 \n", "E_out | 1.504 2.205e+11 \n", "--------------------------------------------------\n" ] } ], "source": [ "E_2, E_10 = error(a_50, N, sig, num_exper)\n", "\n", "print('--------------------------------------------------')\n", "print(' 5oth order noiseless target ')\n", "print('--------------------------------------------------')\n", "print('fit | 2nd order 10th order ')\n", "print('--------------------------------------------------')\n", "print('E_in | {:1.3f} {:1.3f} '.format(E_2[0], E_10[0]))\n", "print('E_out | {:1.3f} {:.3e} '.format(E_2[1], E_10[1]))\n", "print('--------------------------------------------------')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again we see that the 10th order fit delivers better in-sample error on the expense of a terrible out-of-sample performance." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Learning curve for learning the 50th order noiseless target" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "N_start = 15\n", "N_stop = 150\n", "step = 2\n", "\n", "num_steps = len(range(N_start, N_stop , step))\n", "\n", "E_2 = np.zeros((2,num_steps))\n", "E_10 = np.zeros((2,num_steps))\n", "sample_size = np.zeros(num_steps)\n", "\n", "for index, n in enumerate(range(N_start, N_stop , step)):\n", " E_2[:,index], E_10[:,index] = error(a_50, n, .001, 2000)\n", " sample_size[index] = n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0eOOWZEH69PTA90mnZ6S7BPcAAAB1R3APACWqtGJ+ll6fp+d+dqZ9UQAAAAC1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\nOcI9AAAAAABljnAPAAAAAECZ+/8ujRoeD8AiOQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(14, 10))\n", "\n", "plt.rc('text', usetex=True)\n", "plt.rc('font', family='serif')\n", "\n", "\n", "plt.plot(sample_size, E_2[0], color='g', label='E in -- 2nd')\n", "plt.plot(sample_size, E_2[1], color='y', label='E out -- 2nd')\n", "plt.plot(sample_size, E_10[0], color='b', label='E in -- 10th')\n", "plt.plot(sample_size, E_10[1], color='r', label='E out -- 10th')\n", "plt.ylim(0,5)\n", "plt.ylabel('Expected error', fontsize = 20)\n", "plt.xlabel('Sample size', fontsize = 20)\n", "plt.legend(bbox_to_anchor=(1.01, 1), loc=2, frameon=False, fontsize = 18)\n", "plt.title('Learning Curves with 50th Order Noiseless Target', {'fontsize':24})\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture12/mabille_julia_parallel.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parallel programming in Julia\n", "#### Pierre Mabille\n", "#### April 22, 2016\n", "\n", "### Adding workers\n", "\n", "We use multiple processors. I am working with an 8 cores desktop, so I can add 7 processors (\"workers\") in addition to the main one. You won't get an error message if you try to add more (unless you really add too many of them), but it will not improve performance further by magically creating cores.\n", "\n", "If you are using a PC, you can know its number of cores by typing *Ctrl+Alt+Delete*, go to the Task manager, and look at the number of windows below the label \"CPU Usage History\" (which will also show you the activity of your processors). On a Mac, open the terminal and type *sysctl -n hw.ncpu*. This count includes so-called \"multi/hyper-threaded cores\" (on Intel processors), which are multiple logical cores created out of a single physical core. \n", "\n", "Whenever you intend to use parallel programming in julia, you should start the file containing the main code by adding additional processors, called \"workers\". " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of processors:\n", "8\n" ] } ], "source": [ "addprocs(7)\n", "println(\"Number of processors:\"); println(nprocs())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remote calls and remote references\n", "\n", "Now let us look at how parallism works in julia.\n", "\n", "The function *remotecall(function, processor_nb, vararg)* executes the function *function* on processor number *processor_nb*, where *vararg* are the arguments of the function. Let us for example create a $(2\\times 2)$ matrix of uniformly distributed numbers on processor number 2. By default, processor 1 is the main processor from which messages are sent to launch and coordinate jobs on the other processors." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RemoteRef{Channel{Any}}(2,1,8)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r = remotecall(rand, 2, 2, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*remotecall* creates a remote reference *RemoteRef*, which points to the matrix we generated on processor 2. \n", "\n", "The main code returns immediately on processor 1. However, since computations run asynchronously on various processors, processor 2 may not have completed the job assigned by processor 1. The function *isready*, which takes remote references as inputs, returns *true* if it did. This is useful for conditioning statements in the main code. \n", "\n", "The type of the object *r* we created is a *RemoteRef*, so calling *r* directly in the main code in processor 1 won't work. We fetch the actual object the *RemoteRef* is pointing at by using the command *fetch*." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Did processor 2 finish the computations?\n", "true\n", "What is r's type?\n", "RemoteRef{Channel{Any}}(2,1,8)\n", "What is r?\n", "[0.6857857234780877 0.5798774701119049\n", " 0.6477623673822637 0.5684685716995346]\n" ] } ], "source": [ "println(\"Did processor 2 finish the computations?\"); println(isready(r))\n", "println(\"What is r's type?\"); println(r)\n", "println(\"What is r?\"); println(fetch(r))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Macros\n", "\n", "The syntax and going-back-and-forth between remote calls and remote references is somewhat cumbersome for large and complex codes. This is where macros come in handy. Their position is at the start of the command to be executed.\n", "\n", "*@spawn* runs the code on *some* worker, whose ID is unknow to us at the time we run the command. *@spawnat* folled by the worker's ID allows to choose the worker on which the command is run. The syntax of the former is lighter and should be preferred in general.\n", "\n", "Here we add 1 to each of the entries of our matrix *r*. Note that we still need to fetch *r*, because it is defined on processor 2 and we don't know on which processor *@spawn* will run the code. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Did the processor finish the computations?\n", "false\n", "What is s's type and on which processor was it computed?\n", "RemoteRef{Channel{Any}}(4,1,20)\n", "What is s?\n", "[1.6857857234780877 1.579877470111905\n", " 1.6477623673822637 1.5684685716995346]\n", "Did the processor finish the computations?\n", "false\n", "What is s's type and on which processor was it computed?\n", "RemoteRef{Channel{Any}}(3,1,23)\n", "What is s?\n", "[1.6857857234780877 1.579877470111905\n", " 1.6477623673822637 1.5684685716995346]\n" ] } ], "source": [ "s = @spawn 1 .+ fetch(r)\n", "println(\"Did the processor finish the computations?\"); println(isready(s))\n", "println(\"What is s's type and on which processor was it computed?\"); println(s)\n", "println(\"What is s?\"); println(fetch(s))\n", "\n", "s2 = @spawnat 3 1 .+ fetch(r)\n", "println(\"Did the processor finish the computations?\"); println(isready(s2))\n", "println(\"What is s's type and on which processor was it computed?\"); println(s2)\n", "println(\"What is s?\"); println(fetch(s2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another extremely useful macro is *@everywhere*. As we saw, an object created on some processor will not be available on others unless we *fetch* it. For instance, if we create a random matrix $B$ on processor 3, we won't be able to invert it on processor 5. Defining this matrix *@everywhere* makes it available to all processors, which can read and modify it. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "ename": "LoadError", "evalue": "LoadError: On worker 5:\nMethodError: `inv` has no method matching inv(::RemoteRef{Channel{Any}})\n in anonymous at multi.jl:910\n in run_work_thunk at multi.jl:651\n in run_work_thunk at multi.jl:660\n in anonymous at task.jl:58\nwhile loading In[7], in expression starting on line 3", "output_type": "error", "traceback": [ "LoadError: On worker 5:\nMethodError: `inv` has no method matching inv(::RemoteRef{Channel{Any}})\n in anonymous at multi.jl:910\n in run_work_thunk at multi.jl:651\n in run_work_thunk at multi.jl:660\n in anonymous at task.jl:58\nwhile loading In[7], in expression starting on line 3", "" ] } ], "source": [ "B = @spawnat 3 rand(10,10)\n", "i = remotecall(inv,5,B)\n", "fetch(i)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10x10 Array{Float64,2}:\n", " 1.31691 1.11619 0.0590151 … -0.882174 -2.03266 -0.788132\n", " 1.38902 1.58851 0.16104 1.11528 -3.83538 -1.51552 \n", " 2.73756 2.0579 -1.35635 0.218679 -4.19781 -3.49267 \n", " -2.28582 -1.22763 0.660008 0.170134 2.90604 1.35682 \n", " -2.49942 -0.968241 -0.0576566 0.0697848 3.75867 2.63068 \n", " -1.62723 -0.892431 -0.0469482 … -0.13082 3.75347 1.02696 \n", " 0.237975 -0.146719 -0.125828 0.51816 -1.75107 -0.321294\n", " 0.795711 2.01524 0.0546999 0.430407 -2.74638 -1.62258 \n", " 0.0698368 -0.60776 -0.310597 -1.00811 1.95402 1.07503 \n", " 0.485333 -2.03081 0.79794 -0.51281 1.30515 1.19486 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@everywhere B = rand(10,10)\n", "i = remotecall(inv,5,B)\n", "fetch(i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is especially true for large and complex codes, where code files, modules and types loaded and/or defined on the main processor should be defined *@everywhere*, as the growth model example below makes clear. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parallel maps and parallel loops\n", "\n", "In practice, parallel maps and parallel loops are the easiest way to do parallel programming in Julia. \n", "* *pmap* is better suited for complex computation. It has the same snytax as *map*. Its arguments are ranges of objects whose type depend on the function at hand.\n", "* *@parallel for* loops should be used when every iteration involves a small amount of computations. *@parallel* is another macro that automatically parallelizes tasks across workers. Parallel loops running on multiple processors accept outside variables (e.g. vectors, matrices defined in the main code) if they are read-only. Otherwise if each processor modifies the object at hand, parallel loops should be combined with *SharedArray*s, so that the information added by each worker is made available to other workers.\n", "\n", "An example of *@parallel for* loop is given in the growth model below.\n", "\n", "Let us give an example for *pmap*. We create a function that generates two random matrices of given sizes, inverts and sums them. We apply it to a collection of matrix sizes, and time the excution for *map* and *pmap*. You can check that they return the same collection of matrices." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.010229 seconds (9.03 k allocations: 415.130 KB)\n", " 0.009455 seconds (451 allocations: 44.234 KB)\n", " 0.274861 seconds (5.97 k allocations: 208.271 MB, 17.90% gc time)\n", " 0.144038 seconds (129.75 k allocations: 22.908 MB, 1.64% gc time)\n" ] } ], "source": [ "@everywhere function change_matrix(nA::Int64, nB::Int64)\n", " A = rand(nA, nA)\n", " B = rand(nB, nB)\n", " nmin = min(nA,nB)\n", " A = A[1:nmin,1:nmin]\n", " B = B[1:nmin,1:nmin]\n", " return inv(A) .+ inv(B)\n", "end\n", "\n", "@time map(change_matrix, 1:2, 2:3);\n", "@time pmap(change_matrix, 1:2, 2:3);\n", "\n", "@time map(change_matrix, 100:200, 200:300);\n", "@time pmap(change_matrix, 100:200, 200:300);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Neoclassical growth model \n", "\n", "Let us look at a stripped-down version of the neoclassical growth model. The environment is deterministic. Capital $k$ is the only state variable, and there is no depreciation. A representative agent solve the following recursive problem:\n", "\n", "$$ V(k) = \\max_{c \\in (0,f(k))} u(c) + \\beta V(k')$$\n", "$$ k' = f(k) - c$$\n", "$$ f(k) = k^\\alpha$$\n", "\n", "where $\\alpha<1$ and the instantaneous utility function $u$ satisfies the Inada conditions.\n", "\n", "We solve the model by value function iteration with interpolation of the value function. We compare the speed of the serial solution against the parallel one, for various grids.\n", "\n", "## Serial solution\n", "\n", "We use a single processor. \n", "\n", "We start by loading the modules on the local processor, and define the model parameters. Then we define three functions to solve the model:\n", "* *optim_step(k, Vtilde_itp)* solves for the consumption policy function given a state $k$ and an interpolant for the continuation value $Vtilde\\_itp$.\n", "* *vfi_map(grid_k, criterion)* solves the model by value function iteration given a capital grid $grid\\_k$ and a convergence criterion $criterion$. It uses the function *map* to compute the value function at the next iteration.\n", "* *vfi_loop(grid_k, criterion)* solves the model by value function iteration given a capital grid $grid\\_k$ and a convergence criterion $criterion$. It uses a *for* loop to compute the value function at the next iteration." ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "vfi_loop (generic function with 2 methods)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using Optim: optimize\n", "using Interpolations\n", "using PyPlot\n", "\n", "## Primitives and grid\n", "alpha = 0.65\n", "beta = 0.95\n", "grid_max = 2\n", "grid_size = 1500\n", "grid_k = linspace(1e-6,grid_max,grid_size)\n", "u(c) = log(c)\n", "\n", "function optim_step(k,Vtilde_itp)\n", " objective(c) = -u(c) - beta*Vtilde_itp[k^alpha - c]\n", " res = optimize(objective,1e-6,k^alpha)\n", " c_star = res.minimum\n", " V1 = -objective(c_star)\n", " return V1\n", "end\n", "\n", "function vfi_map(grid_k,criterion)\n", " knots = (grid_k,) # knots for gridded linear interpolations\n", " iter = 0\n", " V0 = 5 .* log(grid_k)\n", " distance = 1\n", " while distance > criterion\n", " Vtilde_itp = interpolate(knots, V0, Gridded(Linear()))\n", " V1 = map(k -> optim_step(k,Vtilde_itp), grid_k)\n", " distance = norm(V1-V0, Inf)\n", " V0 = deepcopy(V1)\n", " iter = iter + 1\n", " end\n", " return V0\n", "end\n", "\n", "function vfi_loop(grid_k,criterion)\n", " knots = (grid_k,) # knots for gridded linear interpolations\n", " iter = 0\n", " V0 = 5 .* log(grid_k)\n", " V1 = Array(Float64,length(grid_k))\n", " distance = 1\n", " while distance > criterion\n", " Vtilde_itp = interpolate(knots, V0, Gridded(Linear()))\n", " for (i,k) in enumerate(grid_k)\n", " objective(c) = -u(c) - beta*Vtilde_itp[k^alpha - c]\n", " res = optimize(objective,1e-6,k^alpha)\n", " c_star = res.minimum\n", " V1[i] = -objective(c_star)\n", " end\n", " distance = norm(V1-V0)\n", " V0 = deepcopy(V1)\n", " iter = iter + 1\n", " end\n", " return V0\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We time the execution of the *optim_loop* function for capital grids of various lengths. The two functions *vfi_loop* and *vfi_map* have similar performance." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1.682615 seconds (58.33 M allocations: 917.355 MB, 3.52% gc time)\n", " 4.851052 seconds (181.33 M allocations: 2.790 GB, 3.71% gc time)\n", " 9.309212 seconds (353.08 M allocations: 5.440 GB, 3.59% gc time)\n", " 13.839599 seconds (526.36 M allocations: 8.114 GB, 3.62% gc time)\n", " 99.549924 seconds (3.63 G allocations: 56.005 GB, 6.19% gc time)\n" ] } ], "source": [ "grid_size_array = [150 500 1000 1500 10000]\n", "grids = Array(Any, length(grid_size_array))\n", "for (i,g) in enumerate(grid_size_array)\n", " grids[i] = linspace(1e-6,grid_max,g)\n", "end\n", "\n", "for g in grids\n", " @time V=vfi_loop(g,1e-6);\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parallel execution\n", "\n", "Start by loading the modules on the main processor. Then define load modules *@everywhere*, i.e. on every working processor. It is often needed to load modules on the main processor first, to prevent them from overwriting each other. Also define the model parameters and functions *@everywhere*. \n", "\n", "The function *optim_step(k,Vtilde_itp)* is identical to the serial case. This is the step of the resolution of the model that we choose to parallelize. That is, we delegate the maximization of the value function for various values of $k$ to several processors.\n", "\n", "Then we gather the optimal values computed by the various processors into a *SharedArray*. This is what the function *vfi_ploop(grid_k,criterion)* does. \n", "\n", "At every iteration of the value function iteration, it delegates the various maximization problems to the various workers using a *@parallel for* loop (the macro *@sync* ensures that the program waits for all workers to complete their tasks before it goes on). \n", "\n", "The *SharedArray* makes the information contained in it readable for all workers. This is not strictly necessary here (we could have used a *DArray* -- a distributed array), but it is often more convenient to code up. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of processors:\n", "8\n" ] } ], "source": [ "## (Just for illustration, modules already loaded above)\n", "using Optim: optimize\n", "using Interpolations\n", "using PyPlot\n", "\n", "@everywhere begin\n", " using Optim: optimize\n", " using Interpolations\n", " \n", " ## Primitives and grid\n", " alpha = 0.65\n", " beta = 0.95\n", " grid_max = 2\n", " grid_size = 1500\n", " grid_k = linspace(1e-6,grid_max,grid_size)\n", " u(c) = log(c)\n", "\n", " function optim_step(k,Vtilde_itp)\n", " objective(c) = -u(c) - beta*Vtilde_itp[k^alpha - c]\n", " res = optimize(objective,1e-6,k^alpha)\n", " c_star = res.minimum\n", " V1 = -objective(c_star)\n", " return V1\n", " end\n", " \n", " function vfi_ploop(grid_k,criterion)\n", " knots = (grid_k,)\n", " iter = 0\n", " V0 = 5 .* log(grid_k)\n", " distance = 1\n", " while distance > criterion\n", " Vtilde_itp = interpolate(knots, V0, Gridded(Linear()))\n", " V1 = SharedArray(Float64,length(grid_k))\n", " @sync @parallel for i in eachindex(grid_k)\n", " V1[i] = optim_step(grid_k[i],Vtilde_itp)\n", " end\n", " distance = norm(V1-V0, Inf)\n", " V0 = deepcopy(V1)\n", " iter = iter + 1\n", " end\n", " return V0\n", " end\n", "\n", "end" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2.055595 seconds (4.95 M allocations: 363.230 MB, 2.03% gc time)\n", " 2.907471 seconds (4.64 M allocations: 353.288 MB, 1.21% gc time)\n", " 4.289460 seconds (4.64 M allocations: 371.430 MB, 0.89% gc time)\n", " 5.501156 seconds (4.65 M allocations: 389.692 MB, 0.80% gc time)\n", " 26.238703 seconds (4.67 M allocations: 400.790 MB, 0.18% gc time)\n" ] } ], "source": [ "@everywhere begin\n", " grid_size_array = [150 500 1000 1500 10000]\n", " grids = Array(Any, length(grid_size_array))\n", " for (i,g) in enumerate(grid_size_array)\n", " grids[i] = linspace(1e-6,grid_max,g)\n", " end\n", "end\n", "\n", "V = Array(Any, length(grids))\n", "for (i,g) in enumerate(grids)\n", " @time V[i] = vfi_ploop(g,1e-6);\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A few remarks:\n", "* The parallel solution for the first (smallest) grid runs longer than the next two solutions. \n", "* The serial solution does better than the parallel solution for the smallest grid, but not for larger grids. This illustrates the parallel programming trade-off between communication overhead and execution speed. \n", "* The speed of the parallel solution increases exponentially compared to the serial solution. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To complete the exercise, let us plot the various value functions obtained for various grid precisions." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject >" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fig, ax = subplots()\n", "for i in 1:length(grids)\n", " x = grids[i]\n", " y = V[i]\n", " ax[:set_ylim](-40, -30)\n", " ax[:set_xlim](minimum(grids[i]), maximum(grids[i])) \n", " ax[:plot](x, y, \"k-\", lw=1, alpha=0.5, label = \"draw $i\")\n", "end\n", "ax[:legend]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.3", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.3" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture13/carlos_lizama_Gadfly.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Gadfly\n", "\n", "Carlos Lizama" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Outline:\n", "\n", "1. Introduction\n", "2. The grammar of graphics\n", "3. Plotting arrays and functions.\n", "4. Plotting DataFrames\n", "5. Pros and Cons." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction\n", "\n", "1. *Leland Wilkinson (2005)* created the *Grammar of Graphics* to describe deep features that underlie all statistical graphics.\n", "\n", "> The grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (color, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformation of the data and is drawn on a specific coordinate system. Faceting can be used to generate that same plot for different subsets of dataset. It is the combination of these independent components that make up a graphic. \n", " \n", "2. *Hadley Wickham (2009)* builds on Wilkinson's grammar and adapts it within R. He develops the **ggplot2** package.\n", "3. Gadfly is a package that implements the Grammar of Graphics in Julia, based mainly on ggplot2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The grammar of graphics\n", "\n", "The main components of the grammar:\n", "1. The **Data** that we want to visualize and a set of asthetic **mapping**s describing how variables in the data are mapped to *aethetic attributes* which define how the data should be perceived.\n", "2. Geometric object, **geom**s for short, represent what we actually see on the plot: points, lines, polygons, etc.\n", "3. Statistical transformation, **stats** for shor, summarize data in many useful ways. For instance, the binning to create histograms. Stats are optional, but very useful.\n", "4. The **scale**s map values in the data space to values in an aesthetic space, whether it be color, size or shape. Scales also draw legend or axes.\n", "5. A coordinate system, **coor** for short, describes how data coordinates are mapped to the plane of the graphic. It also provides axes and gridlines to make it possible to read the graph. Examples of coordinate system are the Cartesian cordinate system and the polar coordinate system.\n", "6. A **facet**ing specification describes how to break up the data into subsets and how to display those subsets as small multiples.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The Grammar of Graphics in Julia\n", "\n", "In Julia, we can speficy:\n", "1. aethetics, scales, coordinates, guides, geometries, stats." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Data\n", "* The Data is supplied in form of DataFrame.\n", "* Although the DataFrame is optional.\n", "\n", "#### Statistics\n", "* Statistics are functions taking as input one or more aesthetics, operating on those values, then output to one or more aesthetics. For example, drawing of boxplots typically uses the boxplot statistic (Stat.boxplot) that takes as input the x and y aesthetic, and outputs the middle, and upper and lower hinge, and upper and lower fence aesthetics.\n", "\n", "#### Scales\n", "* Scales, similarly to statistics, apply a transformation to the original data, typically mapping one aesthetic to the same aesthetic, while retaining the original value, e.g. Scale.x_log\n", "\n", "#### Geometries\n", "* Geometries are responsible for actually doing the drawing. A geometry takes as input one or more aesthetics, and used data bound to these aesthetics to draw things.\n", "\n", "#### Guides\n", "* Very similar to geometries are guides, which draw graphics supporting the actual visualization, such as axis ticks and labels and color keys. The major distinction is that geometries always draw within the rectangular plot frame, while guides have some special layout considerations." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Gadfly\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples Gadfly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting arrays and anonymous functions" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", " \n", " x\n", " \n", " \n", " -5\n", " 0\n", " 5\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " -10\n", " -5\n", " 0\n", " 5\n", " 10\n", " \n", " \n", " y\n", " \n", "\n", "\n", " \n", " 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"output_type": "execute_result" } ], "source": [ "# line options: point, line, smooth. Use Geom.\n", "x = collect(linspace(-5,5,8))\n", "y = 5*cos(x)+x\n", "plot(x=x, y=y, Geom.step()) # optional arguments: direction :vh :hv" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", " \n", " x\n", " \n", " \n", " -5\n", " 0\n", " 5\n", " \n", " \n", " \n", " f1\n", " f2\n", " \n", " \n", " \n", " \n", " \n", " \n", " Color\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " -1.0\n", " -0.5\n", " 0.0\n", " 0.5\n", " 1.0\n", " \n", " \n", " f(x)\n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", "\n" ], "text/html": [ "\n", "\n", "\n", " \n", " x\n", " \n", " \n", " -20\n", " -15\n", " -10\n", " -5\n", " 0\n", " 5\n", " 10\n", " 15\n", " 20\n", " -15.0\n", " -14.5\n", " -14.0\n", " -13.5\n", " -13.0\n", " -12.5\n", " -12.0\n", " -11.5\n", " 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-28\n", " -26\n", " -24\n", " -22\n", " -20\n", " -18\n", " -16\n", " -14\n", " -12\n", " -10\n", " -8\n", " -6\n", " -4\n", " -2\n", " 0\n", " 2\n", " 4\n", " 6\n", " 8\n", " 10\n", " 12\n", " 14\n", " 16\n", " 18\n", " 20\n", " 22\n", " 24\n", " 26\n", " 28\n", " 30\n", " \n", " \n", " y\n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Other features: Geom.ribbon\n", "ymin = y - 1\n", "ymax = y + 1\n", "plot(x=x, y=y, ymax=ymax, ymin=ymin, Geom.line, Geom.ribbon)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting Datasets" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "using RDatasets\n", "using DataFrames" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
RankDisciplineYrsSincePhDYrsServiceSexSalary
1ProfB1918Male139750
2ProfB2016Male173200
3AsstProfB43Male79750
4ProfB4539Male115000
5ProfB4041Male141500
6AssocProfB66Male97000
7ProfB3023Male175000
8ProfB4545Male147765
9ProfB2120Male119250
10ProfB1818Female129000
11AssocProfB128Male119800
12AsstProfB72Male79800
13AsstProfB11Male77700
14AsstProfB20Male78000
15ProfB2018Male104800
16ProfB123Male117150
17ProfB1920Male101000
18ProfA3834Male103450
19ProfA3723Male124750
20ProfA3936Female137000
21ProfA3126Male89565
22ProfA3631Male102580
23ProfA3430Male93904
24ProfA2419Male113068
25AssocProfA138Female74830
26ProfA218Male106294
27ProfA3523Male134885
28AsstProfB53Male82379
29AsstProfB110Male77000
30ProfB128Male118223
⋮⋮⋮⋮⋮⋮⋮
" ], "text/plain": [ "397x6 DataFrames.DataFrame\n", "│ Row │ Rank │ Discipline │ YrsSincePhD │ YrsService │ Sex │\n", "┝━━━━━┿━━━━━━━━━━━━━┿━━━━━━━━━━━━┿━━━━━━━━━━━━━┿━━━━━━━━━━━━┿━━━━━━━━━━┥\n", "│ 1 │ \"Prof\" │ \"B\" │ 19 │ 18 │ \"Male\" │\n", "│ 2 │ \"Prof\" │ \"B\" │ 20 │ 16 │ \"Male\" │\n", "│ 3 │ \"AsstProf\" │ \"B\" │ 4 │ 3 │ \"Male\" │\n", "│ 4 │ \"Prof\" │ \"B\" │ 45 │ 39 │ \"Male\" │\n", "│ 5 │ \"Prof\" │ \"B\" │ 40 │ 41 │ \"Male\" │\n", "│ 6 │ \"AssocProf\" │ \"B\" │ 6 │ 6 │ \"Male\" │\n", "│ 7 │ \"Prof\" │ \"B\" │ 30 │ 23 │ \"Male\" │\n", "│ 8 │ \"Prof\" │ \"B\" │ 45 │ 45 │ \"Male\" │\n", "│ 9 │ \"Prof\" │ \"B\" │ 21 │ 20 │ \"Male\" │\n", "│ 10 │ \"Prof\" │ \"B\" │ 18 │ 18 │ \"Female\" │\n", "│ 11 │ \"AssocProf\" │ \"B\" │ 12 │ 8 │ \"Male\" │\n", "⋮\n", "│ 386 │ \"Prof\" │ \"A\" │ 15 │ 9 │ \"Male\" │\n", "│ 387 │ \"Prof\" │ \"A\" │ 29 │ 27 │ \"Male\" │\n", "│ 388 │ \"Prof\" │ \"A\" │ 29 │ 15 │ \"Male\" │\n", "│ 389 │ \"Prof\" │ \"A\" │ 38 │ 36 │ \"Male\" │\n", "│ 390 │ \"Prof\" │ \"A\" │ 33 │ 18 │ \"Male\" │\n", "│ 391 │ \"Prof\" │ \"A\" │ 40 │ 19 │ \"Male\" │\n", "│ 392 │ \"Prof\" │ \"A\" │ 30 │ 19 │ \"Male\" │\n", "│ 393 │ \"Prof\" │ \"A\" │ 33 │ 30 │ \"Male\" │\n", "│ 394 │ \"Prof\" │ \"A\" │ 31 │ 19 │ \"Male\" │\n", "│ 395 │ \"Prof\" │ \"A\" │ 42 │ 25 │ \"Male\" │\n", "│ 396 │ \"Prof\" │ \"A\" │ 25 │ 15 │ \"Male\" │\n", "│ 397 │ \"AsstProf\" │ \"A\" │ 8 │ 4 │ \"Male\" │\n", "\n", "│ Row │ Salary │\n", "┝━━━━━┿━━━━━━━━┥\n", "│ 1 │ 139750 │\n", "│ 2 │ 173200 │\n", "│ 3 │ 79750 │\n", "│ 4 │ 115000 │\n", "│ 5 │ 141500 │\n", "│ 6 │ 97000 │\n", "│ 7 │ 175000 │\n", "│ 8 │ 147765 │\n", "│ 9 │ 119250 │\n", "│ 10 │ 129000 │\n", "│ 11 │ 119800 │\n", "⋮\n", "│ 386 │ 114330 │\n", "│ 387 │ 139219 │\n", "│ 388 │ 109305 │\n", "│ 389 │ 119450 │\n", "│ 390 │ 186023 │\n", "│ 391 │ 166605 │\n", "│ 392 │ 151292 │\n", "│ 393 │ 103106 │\n", "│ 394 │ 150564 │\n", "│ 395 │ 101738 │\n", "│ 396 │ 95329 │\n", "│ 397 │ 81035 │" ] }, "execution_count": 19, "metadata": {}, 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y\n", " \n", "\n", "\n", " \n", " \n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# error bars\n", "using Distributions\n", "\n", "sds = [1, 1/2, 1/4, 1/8, 1/16, 1/32]\n", "n = 10\n", "ys = [mean(rand(Normal(0, sd), n)) for sd in sds]\n", "ymins = ys .- (1.96 * sds / sqrt(n))\n", "ymaxs = ys .+ (1.96 * sds / sqrt(n))\n", "\n", "plot(x=1:length(sds), y=ys, ymin=ymins, ymax=ymaxs, Geom.point, Geom.errorbar)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
StateMurderAssaultUrbanPopRape
1Alabama13.22365821.2
2Alaska10.02634844.5
3Arizona8.12948031.0
4Arkansas8.81905019.5
5California9.02769140.6
6Colorado7.92047838.7
7Connecticut3.31107711.1
8Delaware5.92387215.8
9Florida15.43358031.9
10Georgia17.42116025.8
11Hawaii5.3468320.2
12Idaho2.61205414.2
13Illinois10.42498324.0
14Indiana7.21136521.0
15Iowa2.2565711.3
16Kansas6.01156618.0
17Kentucky9.71095216.3
18Louisiana15.42496622.2
19Maine2.183517.8
20Maryland11.33006727.8
21Massachusetts4.41498516.3
22Michigan12.12557435.1
23Minnesota2.7726614.9
24Mississippi16.12594417.1
25Missouri9.01787028.2
26Montana6.01095316.4
27Nebraska4.31026216.5
28Nevada12.22528146.0
29New Hampshire2.157569.5
30New Jersey7.41598918.8
⋮⋮⋮⋮⋮⋮
" ], "text/plain": [ "50x5 DataFrames.DataFrame\n", "│ Row │ State │ Murder │ Assault │ UrbanPop │ Rape │\n", "┝━━━━━┿━━━━━━━━━━━━━━━━━━┿━━━━━━━━┿━━━━━━━━━┿━━━━━━━━━━┿━━━━━━┥\n", "│ 1 │ \"Alabama\" │ 13.2 │ 236 │ 58 │ 21.2 │\n", "│ 2 │ \"Alaska\" │ 10.0 │ 263 │ 48 │ 44.5 │\n", "│ 3 │ \"Arizona\" │ 8.1 │ 294 │ 80 │ 31.0 │\n", "│ 4 │ \"Arkansas\" │ 8.8 │ 190 │ 50 │ 19.5 │\n", "│ 5 │ \"California\" │ 9.0 │ 276 │ 91 │ 40.6 │\n", "│ 6 │ \"Colorado\" │ 7.9 │ 204 │ 78 │ 38.7 │\n", "│ 7 │ \"Connecticut\" │ 3.3 │ 110 │ 77 │ 11.1 │\n", "│ 8 │ \"Delaware\" │ 5.9 │ 238 │ 72 │ 15.8 │\n", "│ 9 │ \"Florida\" │ 15.4 │ 335 │ 80 │ 31.9 │\n", "│ 10 │ \"Georgia\" │ 17.4 │ 211 │ 60 │ 25.8 │\n", "│ 11 │ \"Hawaii\" │ 5.3 │ 46 │ 83 │ 20.2 │\n", "⋮\n", "│ 39 │ \"Rhode Island\" │ 3.4 │ 174 │ 87 │ 8.3 │\n", "│ 40 │ \"South Carolina\" │ 14.4 │ 279 │ 48 │ 22.5 │\n", "│ 41 │ \"South Dakota\" │ 3.8 │ 86 │ 45 │ 12.8 │\n", "│ 42 │ \"Tennessee\" │ 13.2 │ 188 │ 59 │ 26.9 │\n", "│ 43 │ \"Texas\" │ 12.7 │ 201 │ 80 │ 25.5 │\n", "│ 44 │ \"Utah\" │ 3.2 │ 120 │ 80 │ 22.9 │\n", "│ 45 │ \"Vermont\" │ 2.2 │ 48 │ 32 │ 11.2 │\n", "│ 46 │ \"Virginia\" │ 8.5 │ 156 │ 63 │ 20.7 │\n", "│ 47 │ \"Washington\" │ 4.0 │ 145 │ 73 │ 26.2 │\n", "│ 48 │ \"West Virginia\" │ 5.7 │ 81 │ 39 │ 9.3 │\n", "│ 49 │ \"Wisconsin\" │ 2.6 │ 53 │ 66 │ 10.8 │\n", "│ 50 │ \"Wyoming\" │ 6.8 │ 161 │ 60 │ 15.6 │" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Data2 = dataset(\"datasets\",\"USArrests\")\n", "# This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in \n", "# each of the 50 US states in 1973. 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"\n", "\n", "\n", "\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# labels\n", "plot(Data2, x=\"UrbanPop\", y=\"Murder\", label=\"State\" , Geom.label, Geom.point)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
WeightFeed
1179horsebean
2160horsebean
3136horsebean
4227horsebean
5217horsebean
6168horsebean
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14141linseed
15260linseed
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21244linseed
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" ], "text/plain": [ "71x2 DataFrames.DataFrame\n", "│ Row │ Weight │ Feed │\n", "┝━━━━━┿━━━━━━━━┿━━━━━━━━━━━━━┥\n", "│ 1 │ 179 │ \"horsebean\" │\n", "│ 2 │ 160 │ \"horsebean\" │\n", "│ 3 │ 136 │ \"horsebean\" │\n", "│ 4 │ 227 │ \"horsebean\" │\n", "│ 5 │ 217 │ \"horsebean\" │\n", "│ 6 │ 168 │ \"horsebean\" │\n", "│ 7 │ 108 │ \"horsebean\" │\n", "│ 8 │ 124 │ \"horsebean\" │\n", "│ 9 │ 143 │ \"horsebean\" │\n", "│ 10 │ 140 │ \"horsebean\" │\n", "│ 11 │ 309 │ \"linseed\" │\n", "⋮\n", "│ 60 │ 368 │ \"casein\" │\n", "│ 61 │ 390 │ \"casein\" │\n", "│ 62 │ 379 │ \"casein\" │\n", "│ 63 │ 260 │ \"casein\" │\n", "│ 64 │ 404 │ \"casein\" │\n", "│ 65 │ 318 │ \"casein\" │\n", "│ 66 │ 352 │ \"casein\" │\n", "│ 67 │ 359 │ \"casein\" │\n", "│ 68 │ 216 │ \"casein\" │\n", "│ 69 │ 222 │ \"casein\" │\n", "│ 70 │ 283 │ \"casein\" │\n", "│ 71 │ 332 │ \"casein\" │" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Data3 = dataset(\"datasets\",\"chickwts\")\n", "# An experiment was conducted to measure and compare the effectiveness of various feed supplements \n", "# on the growth rate of chickens." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", " \n", " Feed\n", " \n", " \n", " horsebean\n", " linseed\n", " soybean\n", " sunflower\n", " meatmeal\n", " casein\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " 0\n", " 100\n", " 200\n", " 300\n", " 400\n", " 500\n", " \n", " \n", " Weight\n", " \n", "\n", "\n", " \n", " \n", "\n", " \n", " \n", " \n", "\n", "\n" ], "text/html": [ "\n", "\n", "\n", " \n", " Feed\n", " \n", " \n", " horsebean\n", " linseed\n", " soybean\n", " sunflower\n", " meatmeal\n", " casein\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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" \n", " \n", " Weight\n", " \n", "\n", "\n", " \n", " \n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# boxplot\n", "plot(Data3, x=\"Feed\", y=\"Weight\", Geom.boxplot)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
ExperSexSchoolWage
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" ], "text/plain": [ "3294x4 DataFrames.DataFrame\n", "│ Row │ Exper │ Sex │ School │ Wage │\n", "┝━━━━━━┿━━━━━━━┿━━━━━━━━━━┿━━━━━━━━┿━━━━━━━━━┥\n", "│ 1 │ 9 │ \"female\" │ 13 │ 6.3153 │\n", "│ 2 │ 12 │ \"female\" │ 12 │ 5.47977 │\n", "│ 3 │ 11 │ \"female\" │ 11 │ 3.64217 │\n", "│ 4 │ 9 │ \"female\" │ 14 │ 4.59334 │\n", "│ 5 │ 8 │ \"female\" │ 14 │ 2.41816 │\n", "│ 6 │ 9 │ \"female\" │ 14 │ 2.09406 │\n", "│ 7 │ 8 │ \"female\" │ 12 │ 5.512 │\n", "│ 8 │ 10 │ \"female\" │ 12 │ 3.54843 │\n", "│ 9 │ 12 │ \"female\" │ 10 │ 5.81823 │\n", "│ 10 │ 7 │ \"female\" │ 12 │ 3.82778 │\n", "│ 11 │ 10 │ \"female\" │ 14 │ 6.73689 │\n", "⋮\n", "│ 3283 │ 9 │ \"male\" │ 9 │ 3.59347 │\n", "│ 3284 │ 6 │ \"male\" │ 8 │ 4.34279 │\n", "│ 3285 │ 4 │ \"male\" │ 10 │ 5.79039 │\n", "│ 3286 │ 6 │ \"male\" │ 8 │ 2.07699 │\n", "│ 3287 │ 9 │ \"male\" │ 10 │ 6.12445 │\n", "│ 3288 │ 4 │ \"male\" │ 10 │ 2.85625 │\n", "│ 3289 │ 5 │ \"male\" │ 8 │ 5.512 │\n", "│ 3290 │ 6 │ \"male\" │ 9 │ 4.28711 │\n", "│ 3291 │ 5 │ \"male\" │ 9 │ 7.14519 │\n", "│ 3292 │ 6 │ \"male\" │ 9 │ 4.53878 │\n", "│ 3293 │ 10 │ \"male\" │ 8 │ 2.90911 │\n", "│ 3294 │ 7 │ \"male\" │ 7 │ 4.15397 │" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Data by categories.\n", "Data4 = dataset(\"Ecdat\",\"Wages1\")\n", "# a panel of 595 observations from 1976 to 1982" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", " \n", " Exper\n", " \n", " \n", " 0\n", " 5\n", " 10\n", " 15\n", " 20\n", " \n", " \n", " \n", " female\n", " male\n", " \n", " \n", " \n", " \n", " \n", " \n", " Sex\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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" ], "text/plain": [ "65x8 DataFrames.DataFrame\n", "│ Row │ Month │ Year │ Approve │ Disapprove │ Unsure │ SeptOct2001 │ IraqWar │\n", "┝━━━━━┿━━━━━━━┿━━━━━━┿━━━━━━━━━┿━━━━━━━━━━━━┿━━━━━━━━┿━━━━━━━━━━━━━┿━━━━━━━━━┥\n", "│ 1 │ 2 │ 2001 │ 58.67 │ 23.67 │ 17.67 │ 0 │ 0 │\n", "│ 2 │ 3 │ 2001 │ 58.0 │ 26.67 │ 15.33 │ 0 │ 0 │\n", "│ 3 │ 4 │ 2001 │ 60.5 │ 29.5 │ 10.0 │ 0 │ 0 │\n", "│ 4 │ 5 │ 2001 │ 55.0 │ 33.33 │ 11.67 │ 0 │ 0 │\n", "│ 5 │ 6 │ 2001 │ 54.0 │ 34.0 │ 12.0 │ 0 │ 0 │\n", "│ 6 │ 7 │ 2001 │ 56.5 │ 34.0 │ 9.5 │ 0 │ 0 │\n", "│ 7 │ 8 │ 2001 │ 56.0 │ 35.0 │ 9.0 │ 0 │ 0 │\n", "│ 8 │ 9 │ 2001 │ 75.67 │ 18.33 │ 6.0 │ 1 │ 0 │\n", "│ 9 │ 10 │ 2001 │ 88.0 │ 9.0 │ 3.0 │ 1 │ 0 │\n", "│ 10 │ 11 │ 2001 │ 87.0 │ 8.67 │ 4.33 │ 0 │ 0 │\n", "│ 11 │ 12 │ 2001 │ 86.0 │ 10.5 │ 3.5 │ 0 │ 0 │\n", "⋮\n", "│ 54 │ 7 │ 2005 │ 47.33 │ 49.0 │ 3.67 │ 0 │ 0 │\n", "│ 55 │ 8 │ 2005 │ 43.75 │ 52.5 │ 3.75 │ 0 │ 0 │\n", "│ 56 │ 9 │ 2005 │ 44.0 │ 52.75 │ 3.25 │ 0 │ 0 │\n", "│ 57 │ 10 │ 2005 │ 40.75 │ 56.25 │ 3.0 │ 0 │ 0 │\n", "│ 58 │ 11 │ 2005 │ 38.33 │ 57.33 │ 4.33 │ 0 │ 0 │\n", "│ 59 │ 12 │ 2005 │ 42.25 │ 54.0 │ 3.75 │ 0 │ 0 │\n", "│ 60 │ 1 │ 2006 │ 43.0 │ 53.67 │ 3.67 │ 0 │ 0 │\n", "│ 61 │ 2 │ 2006 │ 39.67 │ 57.0 │ 3.33 │ 0 │ 0 │\n", "│ 62 │ 3 │ 2006 │ 36.5 │ 59.5 │ 4.5 │ 0 │ 0 │\n", "│ 63 │ 4 │ 2006 │ 35.67 │ 60.67 │ 3.67 │ 0 │ 0 │\n", "│ 64 │ 5 │ 2006 │ 37.0 │ 57.67 │ 5.0 │ 0 │ 0 │\n", "│ 65 │ 6 │ 2006 │ 40.0 │ 55.0 │ 5.0 │ 0 │ 0 │\n", "\n", "│ Row │ AvgPrice │\n", "┝━━━━━┿━━━━━━━━━━┥\n", "│ 1 │ 144.975 │\n", "│ 2 │ 140.925 │\n", "│ 3 │ 155.16 │\n", "│ 4 │ 170.175 │\n", "│ 5 │ 161.625 │\n", "│ 6 │ 142.06 │\n", "│ 7 │ 142.075 │\n", "│ 8 │ 152.15 │\n", "│ 9 │ 131.54 │\n", "│ 10 │ 117.05 │\n", "│ 11 │ 108.6 │\n", "⋮\n", "│ 54 │ 229.0 │\n", "│ 55 │ 248.62 │\n", "│ 56 │ 290.325 │\n", "│ 57 │ 271.68 │\n", "│ 58 │ 225.675 │\n", "│ 59 │ 218.5 │\n", "│ 60 │ 231.56 │\n", "│ 61 │ 228.0 │\n", "│ 62 │ 242.475 │\n", "│ 63 │ 274.2 │\n", "│ 64 │ 290.68 │\n", "│ 65 │ 288.45 │" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Data5 = dataset(\"Zelig\", \"approval\")\n", "# The (approximately) quarterly approval rating for the President of the United States from the first month of 2001\n", "# to the last month of 2005." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", " \n", " Month\n", " \n", " \n", " 0\n", " 5\n", " 10\n", " 15\n", " \n", " \n", " \n", " 2002\n", " 2001\n", " 2003\n", " 2005\n", " 2004\n", " 2006\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Year\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " 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CountryYearGDPUnemCapMobTrade
1United States19665.11114073.809.622906
2United States19672.27728293.809.983546
3United States19684.73.6010.08912
4United States19692.83.5010.43593
5United States1970-0.24.9010.49535
6United States19713.15.9011.27827
7United States19725.45.6011.21771
8United States19735.74.9011.76705
9United States1974-0.95.6013.77255
10United States1975-0.88.5017.42326
11United States19764.77.7016.52211
12United States19775.57.1017.23492
13United States19784.76.1017.54099
14United States19792.65.8018.17591
15United States1980-0.47.1019.73285
16United States19813.47.5021.51057
17United States1982-3.09.5020.53895
18United States19832.99.5018.56972
19United States19847.27.5017.81588
20United States19853.87.1018.02899
21United States19862.87.0017.20371
22United States19873.76.2017.23095
23United States19884.65.5018.29418
24United States19892.85.2700837019.413526
25United States19900.95.4145596020.638364
26Canada19666.80216763.6038.45467
27Canada19672.92364584.1040.16167
28Canada19685.64.8041.06574
29Canada19695.24.7042.76849
30Canada19702.65.9044.16533
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" ], "text/plain": [ "350x6 DataFrames.DataFrame\n", "│ Row │ Country │ Year │ GDP │ Unem │ CapMob │ Trade │\n", "┝━━━━━┿━━━━━━━━━━━━━━━━━┿━━━━━━┿━━━━━━━━━┿━━━━━━━━━┿━━━━━━━━┿━━━━━━━━━┥\n", "│ 1 │ \"United States\" │ 1966 │ 5.11114 │ 3.8 │ 0 │ 9.62291 │\n", "│ 2 │ \"United States\" │ 1967 │ 2.27728 │ 3.8 │ 0 │ 9.98355 │\n", "│ 3 │ \"United States\" │ 1968 │ 4.7 │ 3.6 │ 0 │ 10.0891 │\n", "│ 4 │ \"United States\" │ 1969 │ 2.8 │ 3.5 │ 0 │ 10.4359 │\n", "│ 5 │ \"United States\" │ 1970 │ -0.2 │ 4.9 │ 0 │ 10.4954 │\n", "│ 6 │ \"United States\" │ 1971 │ 3.1 │ 5.9 │ 0 │ 11.2783 │\n", "│ 7 │ \"United States\" │ 1972 │ 5.4 │ 5.6 │ 0 │ 11.2177 │\n", "│ 8 │ \"United States\" │ 1973 │ 5.7 │ 4.9 │ 0 │ 11.767 │\n", "│ 9 │ \"United States\" │ 1974 │ -0.9 │ 5.6 │ 0 │ 13.7726 │\n", "│ 10 │ \"United States\" │ 1975 │ -0.8 │ 8.5 │ 0 │ 17.4233 │\n", "│ 11 │ \"United States\" │ 1976 │ 4.7 │ 7.7 │ 0 │ 16.5221 │\n", "⋮\n", "│ 339 │ \"Japan\" │ 1979 │ 5.2 │ 2.1 │ -1 │ 22.0022 │\n", "│ 340 │ \"Japan\" │ 1980 │ 4.4 │ 2.0 │ 0 │ 26.3161 │\n", "│ 341 │ \"Japan\" │ 1981 │ 3.9 │ 2.2 │ 0 │ 31.2153 │\n", "│ 342 │ \"Japan\" │ 1982 │ 2.8 │ 2.4 │ 0 │ 32.5188 │\n", "│ 343 │ \"Japan\" │ 1983 │ 3.2 │ 2.6 │ 0 │ 32.8061 │\n", "│ 344 │ \"Japan\" │ 1984 │ 5.0 │ 2.7 │ 0 │ 29.7358 │\n", "│ 345 │ \"Japan\" │ 1985 │ 4.7 │ 2.6 │ 0 │ 30.6541 │\n", "│ 346 │ \"Japan\" │ 1986 │ 2.5 │ 2.8 │ 0 │ 29.1616 │\n", "│ 347 │ \"Japan\" │ 1987 │ 4.4 │ 2.9 │ 0 │ 21.9099 │\n", "│ 348 │ \"Japan\" │ 1988 │ 5.7 │ 2.5 │ 0 │ 21.7124 │\n", "│ 349 │ \"Japan\" │ 1989 │ 4.7 │ 2.26475 │ 0 │ 23.1288 │\n", "│ 350 │ \"Japan\" │ 1990 │ 5.2 │ 2.099 │ 0 │ 20.0003 │" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Data6 = dataset(\"Zelig\",\"macro\")\n", "# Selected macroeconomic indicators for many countries." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", " \n", " Year\n", " \n", " \n", " 1965\n", " 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false }, "outputs": [ { "data": { "text/html": [ "
FreqSexMethodAgeAgeGroupMethod2
14malepoison1010-20poison
20malecookgas1010-20gas
30maletoxicgas1010-20gas
4247malehang1010-20hang
51maledrown1010-20drown
617malegun1010-20gun
71maleknife1010-20knife
86malejump1010-20jump
90maleother1010-20other
10348malepoison1510-20poison
117malecookgas1510-20gas
1267maletoxicgas1510-20gas
13578malehang1510-20hang
1422maledrown1510-20drown
15179malegun1510-20gun
1611maleknife1510-20knife
1774malejump1510-20jump
18175maleother1510-20other
19808malepoison2010-20poison
2032malecookgas2010-20gas
21229maletoxicgas2010-20gas
22699malehang2010-20hang
2344maledrown2010-20drown
24316malegun2010-20gun
2535maleknife2010-20knife
26109malejump2010-20jump
27289maleother2010-20other
28789malepoison2525-35poison
2926malecookgas2525-35gas
30243maletoxicgas2525-35gas
⋮⋮⋮⋮⋮⋮⋮
" ], "text/plain": [ "306x6 DataFrames.DataFrame\n", "│ Row │ Freq │ Sex │ Method │ Age │ AgeGroup │ Method2 │\n", "┝━━━━━┿━━━━━━┿━━━━━━━━━━┿━━━━━━━━━━━━┿━━━━━┿━━━━━━━━━━┿━━━━━━━━━━┥\n", "│ 1 │ 4 │ \"male\" │ \"poison\" │ 10 │ \"10-20\" │ \"poison\" │\n", "│ 2 │ 0 │ \"male\" │ \"cookgas\" │ 10 │ \"10-20\" │ \"gas\" │\n", "│ 3 │ 0 │ \"male\" │ \"toxicgas\" │ 10 │ \"10-20\" │ \"gas\" │\n", "│ 4 │ 247 │ \"male\" │ \"hang\" │ 10 │ \"10-20\" │ \"hang\" │\n", "│ 5 │ 1 │ \"male\" │ \"drown\" │ 10 │ \"10-20\" │ \"drown\" │\n", "│ 6 │ 17 │ \"male\" │ \"gun\" │ 10 │ \"10-20\" │ \"gun\" │\n", "│ 7 │ 1 │ \"male\" │ \"knife\" │ 10 │ \"10-20\" │ \"knife\" │\n", "│ 8 │ 6 │ \"male\" │ \"jump\" │ 10 │ \"10-20\" │ \"jump\" │\n", "│ 9 │ 0 │ \"male\" │ \"other\" │ 10 │ \"10-20\" │ \"other\" │\n", "│ 10 │ 348 │ \"male\" │ \"poison\" │ 15 │ \"10-20\" │ \"poison\" │\n", "│ 11 │ 7 │ \"male\" │ \"cookgas\" │ 15 │ \"10-20\" │ \"gas\" │\n", "⋮\n", "│ 295 │ 6 │ \"female\" │ \"knife\" │ 85 │ \"70-90\" │ \"knife\" │\n", "│ 296 │ 34 │ \"female\" │ \"jump\" │ 85 │ \"70-90\" │ \"jump\" │\n", "│ 297 │ 2 │ \"female\" │ \"other\" │ 85 │ \"70-90\" │ \"other\" │\n", "│ 298 │ 24 │ \"female\" │ \"poison\" │ 90 │ \"70-90\" │ \"poison\" │\n", "│ 299 │ 1 │ \"female\" │ \"cookgas\" │ 90 │ \"70-90\" │ \"gas\" │\n", "│ 300 │ 0 │ \"female\" │ \"toxicgas\" │ 90 │ \"70-90\" │ \"gas\" │\n", "│ 301 │ 19 │ \"female\" │ \"hang\" │ 90 │ \"70-90\" │ \"hang\" │\n", "│ 302 │ 4 │ \"female\" │ \"drown\" │ 90 │ \"70-90\" │ \"drown\" │\n", "│ 303 │ 0 │ \"female\" │ \"gun\" │ 90 │ \"70-90\" │ \"gun\" │\n", "│ 304 │ 2 │ \"female\" │ \"knife\" │ 90 │ \"70-90\" │ \"knife\" │\n", "│ 305 │ 7 │ \"female\" │ \"jump\" │ 90 │ \"70-90\" │ \"jump\" │\n", "│ 306 │ 0 │ \"female\" │ \"other\" │ 90 │ \"70-90\" │ \"other\" │" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Data7 = dataset(\"vcd\",\"Suicide\")\n", "# Data from Heuer (1979) on suicide rates in West Germany classified by age, sex, and method of suicide." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", " \n", " Age by Sex\n", " \n", "\n", " \n", " \n", " \n", " female\n", " \n", " \n", " male\n", " \n", " \n", " 0\n", " 50\n", " 100\n", " \n", " \n", " 0\n", " 50\n", " 100\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " 0\n", " 500\n", " 1000\n", " 1500\n", " \n", " \n", " other\n", 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-1.8\n", " -1.6\n", " -1.4\n", " -1.2\n", " -1.0\n", " -0.8\n", " -0.6\n", " -0.4\n", " -0.2\n", " 0.0\n", " 0.2\n", " 0.4\n", " 0.6\n", " 0.8\n", " 1.0\n", " 1.2\n", " 1.4\n", " 1.6\n", " 1.8\n", " 2.0\n", " 2.2\n", " 2.4\n", " 2.6\n", " 2.8\n", " 3.0\n", " 3.2\n", " 3.4\n", " 3.6\n", " 3.8\n", " 4.0\n", " 4.2\n", " 4.4\n", " 4.6\n", " 4.8\n", " 5.0\n", " 5.2\n", " 5.4\n", " 5.6\n", " 5.8\n", " 6.0\n", " -6\n", " -3\n", " 0\n", " 3\n", " 6\n", " -6.0\n", " -5.5\n", " -5.0\n", " -4.5\n", " -4.0\n", " -3.5\n", " -3.0\n", " -2.5\n", " -2.0\n", " -1.5\n", " -1.0\n", " -0.5\n", " 0.0\n", " 0.5\n", " 1.0\n", " 1.5\n", " 2.0\n", " 2.5\n", " 3.0\n", " 3.5\n", " 4.0\n", " 4.5\n", " 5.0\n", " 5.5\n", " 6.0\n", " \n", " \n", " y\n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "Plot(...)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# contour also works for functions!!\n", "plot(z=(x,y) -> x*exp(-(x-round(Int, x))^2-y^2), x=linspace(-8,8,150), y=linspace(-2,2,150), Geom.contour)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Advantages and Disadvantages\n", "\n", "#### Advantages\n", "1. Nice plots.\n", "2. Great to display data.\n", "\n", "#### Disadvantages\n", "1. A bit slow.\n", "2. 3D graphs?\n", "3. ggplot2 is not completely implemented, e.g. there is no polar plots in Gadfly" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### References\n", "\n", "[1] [Gadfly Github page](http://dcjones.github.io/Gadfly.jl/) and [this](https://github.com/dcjones/Gadfly.jl) \n", "[2] https://en.wikibooks.org/wiki/Introducing_Julia/Plotting \n", "[3] *The Grammar of Graphics (2005)*, Leland Wilkinson. \n", "[4] *ggplot2: Elegant Graphics for Data Analysis (2009)*, Hadley Wickham. \n", "[5] *ggplo2 Essentials (2015)*, Donato Teutonico. \n", "[6] *R graphics cookbook (2013)*, Winston Chang " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.3", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.3" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture13/felipe_alves_codes/.ipynb_checkpoints/HANK_felipe_alves-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# HANK Model Application \n", "\n", "#### Felipe Alves" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Type examples\n", "\n", "Let's take a look on some of the types defined in the project" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "Consumption-saving problem with two assets and non-convex adjustment costs\n", "\n", "## Fields\n", "\n", "#### Parameters\n", "- `γ::Float64` : parameter on CRRA utility\n", "- `ρ::Float64` : discount factor\n", "- `ξ::Float64` : automatic deposit\n", "- `σ::Float64` : frisch elasticity of labor supply\n", "- `ψ::Float64` : disutility of labor\n", "\n", "#### Prices\n", "- `rᴬ::Float64` : interest rate on illiquid asset\n", "- `rᴮ::Float64` : interest rate for savings in liquid asset\n", "- `wedge::Float64` : interest rate for borrowing in liquid asset\n", "- `w::Float64` : wage rate\n", "\n", "#### Tax\n", "- `τ::Float64` : tax on income\n", "- `T::Float64` : Lump-sum transfer\n", "\n", "#### Cost Function\n", "- `χ₀::Float64` :\n", "- `χ₁::Float64` :\n", "\"\"\"\n", "type TwoAssetsProb\n", " γ::Float64 # CRRA utility with parameter γ\n", " ρ::Float64 # discount rate\n", " σ::Float64\n", " ψ::Float64\n", " ξ::Float64 # automatic deposit on illiquid asset\n", "\n", " rᴬ ::Float64 # ret on illiquid asset\n", " rᴮ::Float64 # ret on liq asset\n", " wedge::Float64\n", " w ::Float64 # wage rate\n", "\n", " τ::Float64\n", " T::Float64\n", "\n", " χ₀::Float64 # parameters on adjustment cost\n", " χ₁::Float64\n", "end" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type SolutionExp\n", "\n", " ## Solution\n", " V::Vector{Vector{Float64}} # Value function\n", " g::Vector{Vector{Float64}} # Density over state space\n", " c::Array{Float64,3} # Optimal consumption\n", " sc::Array{Float64,3} # Savings without deposit\n", " d::Array{Float64,3} # Optimal deposit flow\n", "\n", " ## Matrices to be filled\n", " A ::Vector{Base.SparseMatrix.SparseMatrixCSC{Float64, Int}} # used on HJB to compute vⁿ⁺¹\n", " Au::Vector{Base.SparseMatrix.SparseMatrixCSC{Float64, Int}} # used for KFE to compute transition dynamics\n", "\n", "end\n", "\n", "\"\"\"\n", "Specification for the Implicit-Explicit Finite Difference method\n", "\"\"\"\n", "type FDExp\n", "\n", " ## GRID INFO\n", " b ::Vector{Float64}\n", " Δbgrid ::Vector{Float64}\n", " ΔTbgrid::Vector{Float64}\n", " rbdrift::Vector{Float64}\n", " netbinc::Matrix{Float64}\n", "\n", " a ::Vector{Float64}\n", " Δagrid ::Vector{Float64}\n", " ΔTagrid::Vector{Float64}\n", " radrift::Vector{Float64}\n", " netainc::Matrix{Float64}\n", "\n", " ΔTab::Vector{Float64}\n", "\n", " ## LaborSupply decisions\n", " ℓsupply::Vector{Float64}\n", " ℓutilgrid::Vector{Float64}\n", "\n", " ## Δ in finite difference scheme\n", " invΔ::Float64\n", " invΔᴷ::Float64\n", "\n", " ## Stochastic Information\n", " z::Vector{Float64}\n", " λ::Matrix{Float64}\n", " λdiag::Vector{Float64}\n", " λoff::Matrix{Float64}\n", "\n", " ## Solution\n", " sol::SolutionExp\n", "\n", " ## Storage\n", " btilde ::Vector{Vector{Float64}} # RHS of hjb\n", " B ::Vector{Base.SparseMatrix.SparseMatrixCSC{Float64, Int}} # storage matrix\n", "\n", " \n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is useful for bigger projects" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "function Base.show(io::IO, fd::FDExp)\n", " fd.Δbgrid[2]-fd.Δbgrid[1] == 0 ? (binfo = \"uniform \") : (binfo = \"non-uniform \")\n", " fd.Δagrid[2]-fd.Δagrid[1] == 0 ? (ainfo = \"uniform \") : (ainfo = \"non-uniform \")\n", " @printf io \"\\n\"\n", " @printf io \" Explicit-Implicit Finite Difference Method\\n\"\n", " @printf io \"\\n\"\n", " @printf io \" Grids \\n\"\n", " @printf io \" ------- \\n\"\n", " @printf io \" %12sb: %3d points in [% .0f, %.0f]\\n\" binfo length(fd.b) fd.b[1] fd.b[end]\n", " @printf io \" %12sa: %3d poitns in [% .0f, %.0f]\\n\" ainfo length(fd.a) fd.a[1] fd.a[end]\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Include files" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "solve_fp! (generic function with 2 methods)" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#== INCLUDE files ==#\n", "include(\"aggregate.jl\")\n", "include(\"twoassets.jl\")\n", "\n", "include(\"solveHJB.jl\")\n", "include(\"solveKFE.jl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create an `TwoAssetProblem` instance" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " HOUSEHOLD PROBLEM\n", "\n", " Parameters \n", " ------------ \n", " γ: 2.000\n", " ρ: 0.060\n", " ξ: 0.100\n", "\n", " Prices \n", " -------- \n", " rᴬ : 0.040 \n", " rᴮ : 0.030 \n", " wage : 8.000 \n", " τ : 0.000 \n", " T : 0.000 \n", "\n", " Cost Function \n", " --------------- \n", " χ₀ + χ₁ * (d/a)² * a\n", "\n" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#== Instance of prices ==#\n", "pr = Prices(0.0, 0.04, 0.03, 0.09, 8.0)\n", "\n", "#== Instance of TwoAssetProblem ==#\n", "twoap = TwoAssetsProb2(pr; γ = 2.0, ρ = 0.06, ξ = 0.10, χ₀ = 0.08, χ₁= 3.0, τ = 0.0, T = 0.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With that I will create the Finite Difference specification" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "\n", " Explicit-Implicit Finite Difference Method\n", "\n", " Grids \n", " ------- \n", " non-uniform b: 100 points in [-2, 40]\n", " uniform a: 70 poitns in [ 0, 70]\n" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#== Create and FiniteDifference Structure ==#\n", "fde = FDExp(twoap; z = [.8, 1.3], λ = [-1/3 1/3; 1/3 -1/3],\n", "fixedΔa = true, fixedΔb = false, bn = 100, bmin = -2.0, bmax = 40.0, an =70, amax = 70.0, invΔᴷ = 0.05)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "The solve_hjb function will use information from \n", "```julia\n", "twoap::TwoAssetProblem\n", "fde::FDExpl\n", "```\n", "and save solution on \n", "```julia\n", "fde.sol::SolutionExp\n", "```\n", "\n", "Why is useful to separate these? Further in the code, when we are doing transitional dynamics, I will maintain grid structure while solution will change for each period. Moreover I will need to save all `SolutionExp` before computing the dynamics. \n" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value function iteration 10, distance 0.1684 \n", " value function iteration 20, distance 0.0264 \n", " value function iteration 30, distance 0.0051 \n", " value function iteration 40, distance 0.0010 \n", " value function iteration 50, distance 0.0002 \n", " value function iteration 60, distance 0.0000 \n", " value function iteration 70, distance 0.0000 \n", " value function iteration 80, distance 0.0000 \n", " value function iteration 90, distance 0.0000 \n", " value function iteration 100, distance 0.0000 \n", " value function iteration 110, distance 0.0000 \n", "hjb solved : 115 iterations\n" ] } ], "source": [ "#== Solve the HJB equation ==#\n", "solve_hjb!(twoap, fde, fde.sol)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " density iteration 50, distance 0.0016 \n", " sum 1.0000 --> 1.0000\n", " density iteration 100, distance 0.0000 \n", " sum 1.0000 --> 1.0000\n", " density iteration 150, distance 0.0000 \n", " sum 1.0000 --> 1.0000\n", " density iteration 200, distance 0.0000 \n", " sum 1.0000 --> 1.0000\n", "kfe solved : 240 iterations\n" ] }, { "data": { "text/plain": [ "Void" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#== Solve the KF equation ==#\n", "solve_fp!(fde, fde.sol, maxit = 4000, tolFK = 1e-9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graphs" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/MbedTLS.ji for module MbedTLS.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/HttpServer.ji for module HttpServer.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/HttpParser.ji for module HttpParser.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/Blink.ji for module Blink.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/Mux.ji for module Mux.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/WebSockets.ji for module WebSockets.\n" ] }, { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

Plotly javascript loaded.

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "using PlotlyJS" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "layout with fields legend, margin, title, xaxis, and yaxis\n" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sol = fde.sol\n", "c = sol.c; d = sol.d; sc = sol.sc\n", "b = fde.b; a = fde.a; z = fde.z;\n", "\n", "an = length(a)\n", "bn = length(b)\n", "zn = length(z)\n", "\n", "netainc = fde.netainc\n", "\n", "# agrid, bgrid = meshgrid(a,b)\n", "\n", "#== Consumption 2D ==#\n", "tcons = Any[]\n", "for ai in 1:10:an\n", " push!(tcons,scatter(;x=fde.b, y=c[:,ai,1], name = \"Illiquid level $(round(a[ai],1))\") )\n", "end\n", "\n", "#== Deposits 2D ==#\n", "tdep = Any[]\n", "for ai in 1:10:an\n", " push!(tdep,scatter(;x=fde.b, y=d[:,ai,2], name = \"Illiquid level $(round(a[ai],1))\") )\n", "end\n", "\n", "\n", "lcon = Layout(;title=\"Consumption Low state\", \n", " xaxis_range=-2.0, xaxis_title=\"Liquid asset\",\n", " yaxis_title=\"Consumption\",\n", " xaxis_showgrid=true, yaxis_showgrid=true,\n", " legend_y=0.0, legend_x=1.01)\n", "\n" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lcons = Layout(;title=\"Consumption Low state\", \n", " xaxis_range=-2.0, xaxis_title=\"Liquid asset\",\n", " yaxis_title=\"Consumption\",\n", " xaxis_showgrid=true, yaxis_showgrid=true,\n", " legend_y=0.0, legend_x=1.01)\n", "\n", "\n", "plot([tcons...], lcons)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ldep = Layout(;title=\"Deposit High state\", \n", " xaxis_range=-2.0, xaxis_title=\"Liquid asset\",\n", "yaxis_title=\"Deposit flow\",\n", " xaxis_showgrid=true, yaxis_showgrid=true,\n", " legend_y=0.0, legend_x=1.01)\n", "\n", "plot([tdep...], l)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Check SUM \n", "Sum to 1.00 \n", "Check SUM \n", "Sum to 1.00 \n" ] }, { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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69KhefvllnTx5Ui6XS88991xJlwYApR6hFABs8vTTTysxMdHzvWEYeuyxxxQXF1eCVQFA2UAoBQCb5K7gDg0NVdOmTfXwww/r8ccfL+mySgwr2gFYUSZW32dkZGjVqlVq1KhRsV14GgAAAMHLysrSwYMHddddd6l27dqWjy8TI6WrVq3S8OHDS7oMAAAAFGLhwoW67777LB9XJkJpo0aNJLnf5A033FCyxVzDxo8fr5kzZ5Z0Gdec3/3O/eeOHePVqpX78335ZSnAnSlxlfgZdhafr/P4jJ3F5+usnTt3avjw4Z7cZlWZCKW5LfsbbrhB8fHxJVzNtSsiIoLP1wEREe4/Q0IiFBHh/nzbtJG4QpD9+Bl2Fp+v8/iMncXnWzyKOtWS65QCDspvxjY3uAEAwBehFCgBOTklXQEAAKULoRRwUH7hk1AKAICvMjGnFMVj6NChJV3CNce7fV+/ft7nS/veGfwMO4vP13kFfcaHDh1SRkZGMVZz7encubNSU1NLuowyrXbt2oqJiXHk3IRSePALx37eoTQ6Ou/zZaTUGfwMO4vP13n5fcaHDh3SDTfcoHPnzhVzRdeeF154oaRLKNPCw8O1c+dOR4IpoRRwUH4LnQilAKzIyMjQuXPnuDQiSlTuJZ8yMjIIpUBZw5xSAHbi0oi4lrHQCSgBhFIAAHwRSgEH5Rc+WegEAIAvQingIOaUAgAQHEIp4CBCKQAAwSGUAg4ilAKAr0mTJqlq1apXfZ7u3burX79+NlRkr3nz5snlcunkyZPF8nqNGzfWk08+afm42bNnq2/fvoqMjJTL5dKHH37oQHXWEEoBB7H6HgB8GYYhwzBsOU9pZNf7c9qCBQt04sQJ9enTp9TUyyWhAAflN1LKQicAKN0uXLig0NDQki7DMRs3bpQkff/995o/f34JV+PGSCngoPzCJyOlAJC/iRMnqm3btqpataoaNGigYcOGKS0tLeC+CxYs0HXXXafw8HD16NFDe/bs8Xk+OztbEyZMUHR0tMLCwtSuXTv985//9NnnwQcfVJs2bbRy5UrFxcWpcuXKWr58ecDXy8zM1OjRo9WgQQOFhYUpJiZGw4YN89vv0KFD6t27t6pUqaJmzZppwYIFfvv89a9/VYsWLVS5cmU1btxYr732mswrRjOOHj2qBx54QPXq1VN4eLhuuOEGzZo1K9/P7sSJE+rYsaM6duxYbFMI7MJIKeAg2vcAYF16eromTpyo6OhoZWRkaMaMGerevbu+/fZbuVx542kpKSn67rvvNH36dJmmqRdeeEE9e/bU7t27FRISIkkaNmyYVq9erT/+8Y9q3ry5EhMTdc899+ijjz7S3XffLcndcj969KjGjRunP/zhD4qJicn3jkXjx4/XqlWrNG3aNMXGxurYsWNauXKlzz6maWr48OEaPXq0nnrqKb377rsaOXKkOnXqpObNm0uSZs2apXHjxmncuHHq06ePNmzYoEmTJikzM1PTp0+XJJ08eVKdO3eWy+XSn/70JzVu3Fh79+7Vd999F7C2tLQ03XHHHapZs6ZWrFihKlWqXN1fRDEjlAIOIpQCKG7jx0unTjlz7ho1pJkznTm3t/fee8/zOCcnRzfddJMaNmyodevW6fbbb/c8d/z4cX322Wdq0qSJJCkuLk7NmzfXvHnzNHr0aG3btk3JycmaM2eORo0aJUm68847deDAAU2ePNkTSiXp9OnTWrVqlTp06FBgbVu2bNGwYcM0fPhwz7bBgwf77ffEE09ozJgxkqSbb75Zy5cvV1JSkp5//nnl5ORoypQpGjZsmGb+9wO9/fbblZ2drYSEBE2cOFE1atTQjBkzlJGRod27d6thw4aS3Au8Ajl8+LB+9atfqUmTJkpOTlZYWFiB76M0on0POIhQCqC4nTolnTjhzJdTYfdKK1euVJcuXVS9enVVrFhRDRs2lGEYfq351q1bewKpJDVt2lQ33nijNm/eLEn67LPPZBiGBg4c6HPckCFDtHXrVmVlZXm21apVq9BAKknx8fGaN2+eZsyYoR07dgTcxzAM3XHHHZ7vw8PDFRsbqyNHjkiSdu3apYyMjIB1ZWdn64svvpAkrVu3TrfddpsnkOZn37596tatm1q3bq1ly5aVyUAqEUoBR+U3p5SFTgCcUqOGVKuWM181ajhf/5dffqn+/furQYMGWrhwoTZt2qTNmzfLNE2dP3/eZ9/IyEi/4+vWratjx45Jco9+hoSEqHr16n77mKap06dP+2wLxptvvqn7779fCQkJatOmjWJjY/XOO+/47Xfla4aGhnrqP3XqlAzD8HvN3O9z54KeOHFC9evXL7SmLVu26PDhwxo5cqRn2kJZRPsecBAjpQCKW3G0152UnJys6tWra/HixZ5thw4dCrjv8ePH/balp6erXbt2kqSaNWvq4sWLyszMVEREhGeftLQ0GYbhExyDvSxS1apVlZCQoISEBO3YsUNvvPGGxo4dqzZt2qhLly5BnaNmzZoyTdOv/vT0dEnuUdvcP48ePVro+YYOHaqKFStqyJAhWrFihXr06BFUHaUNI6WAg7zDp/e/d4RSAAgsKyvLb7Rv4cKFAUPj9u3btX//fs/3+/bt09dff63OnTtLkrp27SrTNLVkyRKf45YsWaJ27dpddZu7VatWSkhIkCTt3Lkz6OOaN2+uOnXq+NW1ePFiVapUSR07dpTknme6bt06T9u/IAkJCRoxYoT69evnudxTWcNIKeAg7zZ9SIh04YL/dgAoby5duqSkpCS/7Z06ddIdd9yhN954Q0888YQGDBigjRs3BryckuRud/ft21eTJ0+WaZp66aWX1LBhQ40YMUKS1KZNG/3mN7/RhAkTdO7cOTVv3lwLFizQpk2btHTp0iLV3rVrVw0YMECtW7dWhQoVNH/+fFWqVEndunUL+hwul0svvviixo0bpzp16qh3797auHGjpk+frgkTJqjGf+dJjB8/XomJierWrZtefPFFNWnSRPv379eePXs0depUv/O+/fbbysrKUq9evfTJJ58UOEc2JSVFBw8e9IzWbty4UaZpqk6dOrrlllssfir2IJQCDvIeEa1YMS+UMlIKoDzLzs4OuGJ9wYIFGjZsmKZNm6ZZs2Zp3rx56tq1q1asWKFmzZr57R8fH6977rlHzzzzjNLS0tS5c2e9/fbbPiOt77//vp5//nlNmzZNJ0+eVIsWLZSUlKTevXv7nCvY9n3Xrl21YMECHThwQC6XS23atNHy5cs9l3rKz5V3enr88ccVGhqqhIQEvf3224qKitIrr7yiiRMnevapWbOmNmzYoIkTJ+rZZ5/VuXPn1KhRI40dOzbf87733nvKzs5Wz549tX79erVu3TpgPW+++aYSExM958gd8b311lu1bt26oD4LuxnmlVdpLYVSU1PVvn17paSkKD4+vqTLAYK2e7f0+9+7H0dESJmZ7scPPyz17VtydQEoW/g9iNKgsJ/Dq/05ZU4p4KAr2/e5GCkFAMAXoRRw0JXt+0DbAQAAoRRwFKEUAIDgEEoBB9G+BwAgOIRSwEHe4dM7lHJJKAAAfBFKAQfRvgcAIDiWQumXX36pxx9/XK1bt1aVKlUUGxurIUOGaO/evUEdn5mZqYcffliRkZGqUqWKbrvtNm3durVIhQNlgfeIKKEUAID8Wbp4/rRp07RhwwYNGjRIbdu2VVpammbNmqX4+Hht3rxZLVu2zPdY0zTVu3dvffPNN3rmmWdUq1YtzZ49W927d1dqaqqaNm161W8GKG3ya98TSgEA8GUplD711FNatGiRKnoN+QwePFht2rTR1KlTPXcGCGTJkiXauHGjkpKSNGDAAEnSoEGD1KxZM7388stauHBhEd8CUHrRvgcAIDiW2vedO3f2CaSSdN1116lVq1bauXNngccmJSWpXr16nkAqSbVr19bgwYP10Ucf6eLFi1ZKAcqE/Fbfs9AJQHk1adIkVa1a9arP0717d/Xr18+Giuw1b948uVwunTx5slher3HjxnryySctHXPs2DH9/ve/V5s2bVSlShU1bNhQ9913nw4dOuRQlcGxZaFTenq6ateuXeA+W7duDXjLqU6dOuncuXPas2ePHaUApQrtewDwdeW92q/mPKWRXe/PSampqVq6dKmGDx+u5cuXa+bMmfrmm2/UqVMnnThxosTqstS+D2ThwoX64Ycf9Oqrrxa437Fjx3Trrbf6bY+KipIkHT16VK1atbracoBShfY9AJRNFy5cUGhoaEmX4Yhu3bpp165dcrnyxiZvvvlmxcTEKDExUePHjy+Ruq5qpHTXrl16/PHH1aVLFz3wwAMF7puVlaVKlSr5ba9cubJM01RWVtbVlAKUSoyUAoB1EydOVNu2bVW1alU1aNBAw4YNU1paWsB9FyxYoOuuu07h4eHq0aOHX+c1OztbEyZMUHR0tMLCwtSuXTv985//9NnnwQcfVJs2bbRy5UrFxcWpcuXKWr58ecDXy8zM1OjRo9WgQQOFhYUpJiZGw4YN89vv0KFD6t27t6pUqaJmzZppwYIFfvv89a9/VYsWLVS5cmU1btxYr732mkzT9Nnn6NGjeuCBB1SvXj2Fh4frhhtu0KxZs/L97E6cOKGOHTuqY8eO+U4hqFatmk8glaTo6GjVqVNHR48ezffcTivySGl6err69OmjGjVqaMmSJYUOVYeFhSk7O9tv+/nz52UYhsLCwopaClBqcUcnALAuPT1dEydOVHR0tDIyMjRjxgx1795d3377rU+YSklJ0Xfffafp06fLNE298MIL6tmzp3bv3q2Q//6jO2zYMK1evVp//OMf1bx5cyUmJuqee+7RRx99pLvvvluSu+V+9OhRjRs3Tn/4wx8UExOjmJiYgLWNHz9eq1at0rRp0xQbG6tjx45p5cqVPvuYpqnhw4dr9OjReuqpp/Tuu+9q5MiR6tSpk5o3by5JmjVrlsaNG6dx48apT58+2rBhgyZNmqTMzExNnz5dknTy5El17txZLpdLf/rTn9S4cWPt3btX3333XcDa0tLSdMcdd6hmzZpasWKFqlSpEvRnvmfPHh0/frzAKyk5rUih9KefflLPnj31008/6fPPP1e9evUKPSYqKkrHjh3z2567rX79+oWeY/z48YqIiPDZNnToUA0dOjTIyoHixR2dAMC69957z/M4JydHN910kxo2bKh169bp9ttv9zx3/PhxffbZZ2rSpIkkKS4uTs2bN9e8efM0evRobdu2TcnJyZozZ45GjRolSbrzzjt14MABTZ482RNKJen06dNatWqVOnToUGBtW7Zs0bBhwzR8+HDPtsGDB/vt98QTT2jMmDGS3K3x5cuXKykpSc8//7xycnI0ZcoUDRs2TDNnzpQk3X777crOzlZCQoImTpyoGjVqaMaMGcrIyNDu3bvVsGFDSe4FXoEcPnxYv/rVr9SkSRMlJydbHux78sknFR0drXvvvTeo/RctWqRFixb5bMvMzLT0mleyHEqzs7N19913a9++fVq7dq0n8RcmLi5On3/+ud/2TZs2KTw8XM2aNSv0HDNnzgy4WAoorbh4PoDiNv7/xuvU+VOOnLtG5Rqa2XOmI+f2tnLlSr366qvasWOHfvrpJ0nu0cw9e/b4hNLWrVt7AqkkNW3aVDfeeKM2b96s0aNH67PPPpNhGBo4cKDP+YcMGaIJEyYoKyvLE95q1apVaCCVpPj4eM2bN0/16tVTz549A66HMQxDd9xxh+f78PBwxcbG6siRI5Lc0x8zMjIC1vWnP/1JX3zxhe666y6tW7dOt912myeQ5mffvn3q1q2b4uPjtXjxYs8ocbBefvll/etf/9KqVauCDrOBBgVTU1PVvn17S6/tzdKc0pycHA0ePFibN2/WBx98oE6dOgXcLy0tTbt379Zlr9/IAwcOVHp6uj788EPPtoyMDH3wwQfq16+f5Q8QKAuYUwqguJ06f0onsk448uVU2PX25Zdfqn///mrQoIEWLlyoTZs2afPmzTJNU+fPn/fZNzIy0u/4unXrerqwp0+fVkhIiKqPXxMiAAAgAElEQVRXr+63j2maOn36tM+2YLz55pu6//77lZCQoDZt2ig2NlbvvPOO335XvmZoaKin/lOnTskwDL/XzP0+dy7oiRMnguokb9myRYcPH9bIkSMt56l3331Xr776qubMmZPvKGxxsTRSOmHCBC1btkz9+vVTRkaG3n//fZ/n77vvPknSc889p8TERB08eNAzJ2PgwIH685//rJEjR2rHjh2qXbu2Zs+erZycHE2aNMmedwOUMvmtvqd9D8ApNSrXKJPnzpWcnKzq1atr8eLFnm35XT/z+PHjftvS09PVrl07SVLNmjV18eJFZWZm+kz/S0tLk2EYPsEx2Ms4Va1aVQkJCUpISNCOHTv0xhtvaOzYsWrTpo26dOkS1Dlq1qwp0zT96k9PT5fkHrXN/TOYhUdDhw5VxYoVNWTIEK1YsUI9evQIqo7k5GSNHTtWU6ZM0YgRI4I6xkmWQunXX38twzC0bNkyLVu2zO/53FBqGIbfqi6Xy6WVK1fq6aef1qxZs5SVlaVOnTopMTFR119//VW8BaD0YqETgOJWHO11J2VlZfmN9i1cuDBgaNy+fbv279/vaeHv27dPX3/9tR599FFJUteuXWWappYsWeKZUyq57zLZrl27q15k3apVKyUkJOh//ud/tHPnzqBDafPmzVWnTh0tWbJE/fv392xfvHixKlWqpI4dO0pyzzOdMWOGjhw5ogYNGhR4zoSEBGVlZalfv35avXq1br755gL3X79+vYYNG6YxY8bo+eefD6pup1kKpf/617+C2m/u3LmaO3eu3/aIiAjNmTNHc+bMsfKyQJlF+x4A/F26dElJSUl+2zt16qQ77rhDb7zxhp544gkNGDBAGzduDHg5Jcnd7u7bt68mT54s0zT10ksvqWHDhp5RvzZt2ug3v/mNJkyYoHPnzql58+ZasGCBNm3apKVLlxap9q5du2rAgAFq3bq1KlSooPnz56tSpUrq1q1b0OdwuVx68cUXNW7cONWpU0e9e/fWxo0bNX36dE2YMEE1arhHpMePH6/ExER169ZNL774opo0aaL9+/drz549mjp1qt953377bWVlZalXr1765JNP8p0ju2vXLv36179Ws2bNdN9992nz5s2e5+rUqeMzT7c4XfXF8wHkj/Y9APjLzs4OuGJ9wYIFGjZsmKZNm6ZZs2Zp3rx56tq1q1asWBFwQXR8fLzuuecePfPMM0pLS1Pnzp319ttv+4y0vv/++3r++ec1bdo0nTx5Ui1atFBSUpJ69+7tc65g2/ddu3bVggULdODAAblcLrVp00bLly8vdOH3lXd6evzxxxUaGqqEhAS9/fbbioqK0iuvvKKJEyd69qlZs6Y2bNigiRMn6tlnn9W5c+fUqFEjjR07Nt/zvvfee8rOzlbPnj21fv16tW7d2q+WzZs368yZM9q+fbu6du3q89yIESN8rn5QnAzzyqu0lkK5q7lSUlJYfY8y5f33pb//3f34qaekGTPcjzt2lF56qeTqAlC28HsQpUFhP4dX+3N6VXd0AlAw2vcAAASHUAo4KL/2PaEUAABfhFLAQay+BwAgOIRSwEEsdAIAIDiEUsBBzCkFACA4hFLAQd4joswpBQAgf4RSwEFXtu9zLyVHKAUAwBcXzwcc5D1S6nK5vy5fJpQCKJqdO3eWdAkox5z++SOUAg7yDp/eoZSFTgCsqF27tsLDwzV8+PCSLgXlXHh4uGrXru3IuQmlgIO8Q2mFCu5QeuV2AChMTEyMdu7cqYyMjJIuBeVc7dq1FRMT48i5CaWAgwK17yVCKQDrYmJiHAsDQGnAQifAQVe27ytU8N8OAAAIpYCjaN8DABAcQingoPza9yx0AgDAF6EUcFCg1fdXbgcAAIRSwFG07wEACA6hFHDQle373IVOtO8BAPBFKAUcRPseAIDgEEoBB9G+BwAgOIRSwEGsvgcAIDiEUsBBXDwfAIDgEEoBB+WGT8Nwf9G+BwAgMEIp4KDcNn3uCCkjpQAABEYoBRyUGz5zR0i9R0pNs2RqAgCgNCKUAg7KL5RKhFIAALwRSgEHXdm+9w6ltPABAMhDKAUcdOVIaW449X4OAAAQSgFHFdS+J5QCAJCHUAo4KDd4BmrfcwF9AADyEEoBB+UGT0ZKAQAoGKEUcBDtewAAgkMoBRx0Zfvee6ET7XsAAPIQSgEH0b4HACA4hFLAQbTvAQAIDqEUcBCr7wEACA6hFHAQ7XsAAIJDKAUcRPseAIDgEEoBh5hmwavvCaUAAOQhlAIOMc28x4FGSplTCgBAHkIp4BDvkVDa9wAAFIxQCjgkUCilfQ8AQGCEUsAh3u35QJeEIpQCAJCHUAo4hPY9AADBI5QCDimsfc9CJwAA8hBKAYfQvgcAIHiEUsAhtO8BAAgeoRRwCKEUAIDgEUoBh3iHTu7oBABAwQilgEO855RyRycAAApGKAUcQvseAIDgEUoBhwRq3xNKAQAIjFAKOKSw9j2hFACAPIRSwCFcPB8AgOARSgGHcPF8AACCRygFHMJCJwAAgkcoBRxSWCilfQ8AQB5CKeAQ2vcAAASPUAo4pLCFToRSAADyEEoBhzCnFACA4BFKAYdwm1EAAIJHKAUcwh2dAAAIHqEUcAhzSgEACB6hFHAItxkFACB4hFLAIbTvAQAIHqEUcEhh7XsWOgEAkIdQCjiE9j0AAMEjlAIOoX0PAEDwCKWAQ1h9DwBA8AilgENo3wMAEDxCKeCQwtr3LHQCACAPoRRwCCOlAAAEj1AKOCTQnFJCKQAAgRFKAYcEat9znVIAAAIjlAIOoX0PAEDwCKWAQ2jfAwAQPEIp4BBW3wMAEDxCKeAQ2vcAAASPUAo4hDs6AQAQPEIp4BDmlAIAEDxCKeAQ7/Y9c0oBACgYoRRwCCOlAAAEj1AKOIRQCgBA8AilgEMCte9Z6AQAQGCEUsAhjJQCABA8QingkMJCKQudAADIQygFHBLojk607wEACIxQCjgk0B2dDCNvG6EUAIA8hFLAIYHa91LeaCmhFACAPIRSwCGB2vdSXkAllAIAkIdQCjgkUPve+zELnQAAyEMoBRySX/uekVIAAPwRSgGH0L4HACB4hFLAIfm171noBACAP0Ip4JDC2vfMKQUAII/lUPrzzz/r5ZdfVq9evVSrVi25XC4lJiYGdez8+fPlcrn8vipUqKDjx49bLh4ozWjfAwAQvIpWD8jIyNCUKVMUGxuruLg4rV+/3tLxhmFoypQpatSokc/26tWrWy0FKNUKW31PKAUAII/lUFq/fn2lpaUpMjJSKSkp6tixo+UX7dmzp+Lj4y0fB5QltO8BAAie5fZ9SEiIIiMjr/qFz549qxyGinANy699z0InAAD8FftCJ9M01b17d1WrVk3h4eHq37+/9u3bV9xlAI7LHQk1DN973tO+BwDAn+X2/dUIDw/XyJEj1aNHD1WrVk0pKSmaMWOGunTpotTUVEVHRxdnOYCjckOn64r/+tG+BwDAX7GG0kGDBmnQoEGe7/v166c777xTt9xyi1577TXNnj27OMsBHJVfKKV9DwCAv2INpYF06dJFN910kz755JNC9x0/frwiIiJ8tg0dOlRDhw51qjygyHJHQr3nk0q07wEAZd+iRYu0aNEin22ZmZlXdc4SD6WS1LBhQ+3Zs6fQ/WbOnMmqfZQZjJQCAK5VgQYFU1NT1b59+yKfs1Tc0Wn//v2qU6dOSZcB2KqwOaWEUgAA8jgWStPS0rR7925d9lrNkZGR4bffxx9/rJSUFPXq1cupUoASkRs682vfS5JpFl89AACUZkVq37/11ls6ffq0fvjhB0nS0qVLdfjwYUnSk08+qapVq+q5555TYmKiDh48qJiYGEnSL3/5S7Vr104dOnRQRESEUlJSNHfuXMXGxmrixIk2vSWgdMj9/1h+I6W5+1QsFZNoAAAoWUX6dfj666/r0KFDkty3DU1OTlZycrIk6f7771fVqlVlGIZcV/w2vvfee7VixQqtWbNG586dU1RUlMaMGaOXXnqJ9j2uOYW17733AQCgvCtSKD1w4ECh+8ydO1dz58712fbKK6/olVdeKcpLAmVOfu177+8JpQAAuJWKhU7AtSjY9j0AACCUAo6hfQ8AQPAIpYBDgll9TygFAMCNUAo4hPY9AADBI5QCDinsjk7e+wAAUN4RSgGH0L4HACB4hFLAIcG07wmlAAC4EUoBB3jfPpQ5pQAAFI5QCjjAO2wyUgoAQOEIpYADvMMmd3QCAKBwhFLAAd5hk5FSAAAKRygFHED7HgAAawilgAOCbd+z0AkAADdCKeAA2vcAAFhDKAUcQPseAABrCKWAA1h9DwCANYRSwAG07wEAsIZQCjgg2PY9C50AAHAjlAIOKKh9z0gpAAD+CKWAA2jfAwBgDaEUcEBB7XsWOgEA4I9QCjgg2PY9c0oBAHAjlAIO4DqlAABYQygFHMCcUgAArCGUAg6gfQ8AgDWEUsABLHQCAMAaQingANr3AABYQygFHBBsKKV9DwCAG6EUcIB32OSOTgAAFI5QCjiA9j0AANYQSgEHFBRKWegEAIA/QingANr3AABYQygFHMBCJwAArCGUAg6gfQ8AgDWEUsABwd7RiVAKAIAboRRwQEF3dCKUAgDgj1AKOID2PQAA1hBKAQcE275noRMAAG6EUsABtO8BALCGUAo4gDs6AQBgDaEUcEBB7XvmlAIA4I9QCjiA9j0AANYQSgEHcEcnAACsIZQCDuDi+QAAWEMoBRwQbPuekVIAANwIpYADuHg+AADWEEoBB9C+BwDAGkIp4ABW3wMAYA2hFHAAq+8BALCGUAo4gPY9AADWEEoBBxTUvmehEwAA/gilgAOCbd8TSgEAcCOUAg6gfQ8AgDWEUsABLHQCAMAaQingAO+weeVIKXNKAQDwRygFHMCcUgAArCGUAg4glAIAYA2hFHAA7XsAAKwhlAIOKGik1DDyHrPQCQAAN0Ip4ICCQqn3NkZKAQBwI5QCDiiofe+9jVAKAIAboRRwACOlAABYQygFHBBsKGVOKQAAboRSwAGFte8ZKQUAwBehFHAA7XsAAKwhlAIOKCyU5o6e0r4HAMCNUAo4gPY9AADWEEoBB3iHTe+L5ecilAIA4ItQCjggN2wGat17b6d9DwCAG6EUcEBu2AzUuvfezkgpAABuhFLAAcGOlBJKAQBwI5QCDiCUAgBgDaEUcEBu2KR9DwBAcAilgANy55Sy0AkAgOAQSgEH0L4HAMAaQingANr3AABYQygFHBBs+16STNP5egAAKO0IpYADgm3fS8wrBQBAIpQCjiisfe8dSmnhAwBAKAUcYaV9TygFAIBQCjiisPa99wgq7XsAAAilgCNo3wMAYA2hFHAA7XsAAKwhlAIOYPU9AADWEEoBm5lm3rVHGSkFACA4hFLAZt4jn4Xd0UkilAIAIBFKAdt5h0xGSgEACA6hFLAZoRQAAOsIpYDNgmnfs9AJAABfhFLAZoyUAgBgHaEUsFkwoZSFTgAA+CKUAjbzDpnc0QkAgOAQSgGbec8RZaQUAIDgEEoBm1mdU8pCJwAACKWA7WjfAwBgHaEUsBntewAArLMcSn/++We9/PLL6tWrl2rVqiWXy6XExMSgj8/MzNTDDz+syMhIValSRbfddpu2bt1qtQyg1OKSUAAAWGc5lGZkZGjKlCnatWuX4uLiZBhG0MeapqnevXvr73//u5588kn9v//3//Tjjz+qe/fu+u6776yWApRKtO8BALCuotUD6tevr7S0NEVGRiolJUUdO3YM+tglS5Zo48aNSkpK0oABAyRJgwYNUrNmzfTyyy9r4cKFVssBSp1g2vcsdAIAwJflkdKQkBBFRkYW6cWSkpJUr149TyCVpNq1a2vw4MH66KOPdPHixSKdFyhNaN8DAGBdsS502rp1q+Lj4/22d+rUSefOndOePXuKsxzAEbTvAQCwrlhD6bFjxxQVFeW3PXfb0aNHi7McwBFWV9/TvgcAoJhDaVZWlipVquS3vXLlyjJNU1lZWcVZDuAI2vcAAFhneaHT1QgLC1N2drbf9vPnz8swDIWFhRV4/Pjx4xUREeGzbejQoRo6dKitdQJXg/Y9AOBat2jRIi1atMhnW2Zm5lWds1hDaVRUlI4dO+a3PXdb/fr1Czx+5syZAeekAqUJq+8BANe6QIOCqampat++fZHPWazt+7i4OKWmpvpt37Rpk8LDw9WsWbPiLAdwBO17AACscyyUpqWlaffu3brsNQw0cOBApaen68MPP/Rsy8jI0AcffKB+/fopJCTEqXKAYhNM+57bjAIA4KtI7fu33npLp0+f1g8//CBJWrp0qQ4fPixJevLJJ1W1alU999xzSkxM1MGDBxUTEyPJHUr//Oc/a+TIkdqxY4dq166t2bNnKycnR5MmTbLnHQElzGr7nlAKAEARQ+nrr7+uQ4cOSZIMw1BycrKSk5MlSffff7+qVq0qwzDkuuI3ssvl0sqVK/X0009r1qxZysrKUqdOnZSYmKjrr7/+Kt8KUDrQvgcAwLoihdIDBw4Uus/cuXM1d+5cv+0RERGaM2eO5syZU5SXBko9q6vvWegEAEAxL3QCyoNgRkqZUwoAgC9CKWAz5pQCAGAdoRSwGRfPBwDAOkIpYDPa9wAAWEcoBWzm3b5noRMAAMEhlAI245JQAABYRygFbEb7HgAA6wilgM2stu8JpQAAEEoB29G+BwDAOkIpYDOroZSFTgAAEEoB29G+BwDAOkIpYDMWOgEAYB2hFLAZ7XsAAKwjlAI2o30PAIB1hFLAZoyUAgBgHaEUsBmXhAIAwDpCKWCzYNr3LHQCAMAXoRSwGSOlAABYRygFbEYoBQDAOkIpYDPvkBnM6nsWOgEAQCgFbOcdMhkpBQAgOIRSwGbc0QkAAOsIpYDNrLbvCaUAABBKAdvRvgcAwDpCKWAzq+17FjoBAEAoBWxH+x4AAOsIpYDNaN8DAGAdoRSwGavvAQCwjlAK2CyY9r1hBN4fAIDyilAK2CyY9r33cyx0AgCAUArYLpj2vfdzjJQCAEAoBWwXbCjNbe0TSgEAIJQCtvNux+c3p1SifQ8AgDdCKWAz2vcAAFhHKAVs5h0yvVfZX4lQCgBAHkIpYLPcdrzLFVwopX0PAAChFLBd7shnQa17iYVOAAB4I5QCNgs2lNK+BwAgD6EUsFluO76glfcSoRQAAG+EUsBmtO8BALCOUArYzGr7noVOAAAQSgHb0b4HAMA6QilgM9r3AABYRygFbMbqewAArCOUAjajfQ8AgHWEUsBmLHQCAMA6QilgM6tzSiXJNJ2rBwCAsoBQCtjMavve+xgAAMorQilgM6vte+9jAAAorwilgM0IpQAAWEcoBWxG+x4AAOsIpYCNTDNv0ZKVhU6MlAIAyjtCKWAj73BJ+x4AgOARSgEbeYdLK+17QikAoLwjlAI28p4bykgpAADBI5QCNipq+56FTgCA8o5QCtjISvuehU4AAOQhlAI2YqETAABFQygFbMScUgAAioZQCtiI1fcAABQNoRSwkZX2vXdoZaETAKC8I5QCNqJ9DwBA0RBKARux+h4AgKIhlAI2YvU9AABFQygFbET7HgCAoiGUAjYq6up7FjoBAMo7Qilgo6KuvmekFABQ3hFKARt5j3hynVIAAIJHKAVsVNSFTrTvAQDlHaEUsBGr7wEAKBpCKWAj2vcAABQNoRSwEbcZBQCgaAilgI1o3wMAUDSEUsBGtO8BACgaQilgI0ZKAQAoGkIpYCMuCQUAQNEQSgEbFfU2o4yUAgDKO0IpYCPvEU9W3wMAEDxCKWAjK+37atXyHmdkOFMPAABlBaEUsJGV9n1MTN7jw4edqQcAgLKCUArYyEr7vm5dKTTU/fjQIedqAgCgLCCUAjayuvq+QQP346NHpYsXnasLAIDSjlAK2MhK+16SGjbMO+7oUWdqAgCgLCCUAjay0r6XmFcKAEAuQilgIyvteylvpFRiXikAoHwjlAI2stq+Z6QUAAA3QilgI6vt+3r1pIoV3Y+//96ZmgAAKAsIpYCNrLbvK1TwXYF/6ZIzdQEAUNoRSgEbWQ2lUt680suXpWPH7K8JAICygFAK2Mi7fR/MnFKJeaUAAEiEUsBWRRkp9Q6lrMAHAJRXhFLARlfTvpcIpQCA8otQCtioKO37qKi8fWnfAwDKK0IpYKOijJRWrCjVr+9+fOSIb7AFAKC8IJQCNipKKJWk2Fj3n5cuSWlp9tYEAEBZYDmUXrhwQc8++6yio6MVHh6uzp0765NPPin0uPnz58vlcvl9VahQQcePHy9S8UBpY/WOTrmYVwoAKO8qWj1gxIgR+vDDDzV+/Hhdd911mjdvnnr37q3169frl7/8ZYHHGoahKVOmqFGjRj7bq1evbrUMoFSyekenXFdeFurmm+2rCQCAssBSKP3iiy+0ePFizZgxQ+PHj5ck3X///WrdurWeeeYZff7554Weo2fPnoqPjy9atUApV9T2PSOlAIDyzlL7/oMPPlDFihU1evRoz7ZKlSrpoYce0saNG/XDDz8EdZ6zZ88qx/u3N3CNKMrqe8m90Ck3xLICHwBQHlkKpV999ZWaNWumKlWq+Gzv1KmT5/mCmKap7t27q1q1agoPD1f//v21b98+iyUDpVdRR0pDQnxX4PN/NgBAeWOpfX/s2DFFRUX5bY+KipJpmjp69Gi+x4aHh2vkyJHq0aOHqlWrppSUFM2YMUNdunRRamqqoqOjrVcPlDJFDaWSe17pkSPShQtSerr7+qUAAJQXlkJpVlaWKlWq5Le9cuXKnufzM2jQIA0aNMjzfb9+/XTnnXfqlltu0WuvvabZs2dbKQUolYravpf855USSgEA5YmlsZywsDBlZ2f7bT9//rzneSu6dOmim266KahLSgFlwdWOlOZiXikAoLyxNFIaFRUVsEV/7NgxSVL93ElxFjRs2FB79uwJat/x48crIiLCZ9vQoUM1dOhQy68LOMGuUMoKfABAabZo0SItWrTIZ1tmZuZVndNSKI2Li9P69et19uxZn8VOmzZtkmEYiouLs1zA/v37VadOnaD2nTlzJpeTQql2Ne37+vUlw5BMk1AKACjdAg0Kpqamqn379kU+p6WxnIEDB+rSpUuaM2eOZ9uFCxc0b948de7c2bNYKS0tTbt379Zlr9/QGRkZfuf7+OOPlZKSol69ehW1fqBUuZqR0tDQvHmkhw+7wykAAOWFpZHSTp06adCgQZo4caLS09M9d3T6/vvvNXfuXM9+zz33nBITE3Xw4EHF/Lcn+ctf/lLt2rVThw4dFBERoZSUFM2dO1exsbGaOHGive8KKCFXE0ol92Kno0fdK/CPH5fq1rWvNgAASjPLtxldsGCBXnzxRS1cuFCnTp1S27ZttWLFCnXp0sWzj2EYcl3xG/nee+/VihUrtGbNGp07d05RUVEaM2aMXnrppaDb90BpV9TbjOaKiZE2b3Y/PnSIUAoAKD8sh9LQ0FBNmzZN06ZNy3efuXPn+oycStIrr7yiV155xXqFQBniPVJqdU6p5L8Cv2PHq68JAICyoAhjOQDyc7Xte1bgAwDKK0IpYKOrbd9HR7tX4EuEUgBA+UIoBWx0tSOllSrlzSNlBT4AoDwhlAI2yg2lhpE34mlVbgv//HkpwJXUAAC4JhFKARvltu+LssgpF/NKAQDlEaEUsFHuSGlRWve5GjbMe0woBQCUF4RSwEa5I6V2hdLDh6+uHgAAygpCKWCj3JHSq2nfM1IKACiPCKWAjexo31euLEVGuh+zAh8AUF4QSgEb2dG+l/IWO507J504cXXnAgCgLCCUAjayo30vMa8UAFD+EEoBG9nRvpe4LBQAoPwhlAI2srt9LzFSCgAoHwilgI2caN8zUgoAKA8IpYCN7Grfh4VJtWu7Hx86xAp8AMC1j1AK2Miu9r2U18L/+Wfp1KmrPx8AAKUZoRSwkV3te4kV+ACA8oVQCtjIrva9JMXG5j1mXikA4FpHKAVslNu+t3uklFAKALjWEUoBm5hm3oIkO0ZKad8DAMoTQilgE+8V8naE0l/8QqpZ0/2YFfgAgGsdoRSwSW7rXrKnfS/lrcA/c0b66Sd7zgkAQGlEKAVskrvISbJnpFTidqMAgPKDUArYxIlQymInAEB5QSgFbOIdSu1u30uEUgDAtY1QCtjEe06pEyOlrMAHAFzLCKWATZxo31etKtWo4X7MSCkA4FpGKAVs4kT7XsobLc3MZAU+AODaRSgFbOJE+17yvd1ocrJ95wUAoDQhlAI2caJ9L0m/+lXeyOsHH0hbtth3bgAASgtCKWATp9r3TZtKI0bkfZ+QIB0/bt/5AQAoDQilgE2cat9L0q9/LXXu7H589qw0fbp06ZK9r3EtyTiXodXfrVbCxgTNSZmj705+V9IlAQAKUbGkCwCuFU617yXJMKTf/U4aN05KT5d275bmzpVGj7b3dcqqC5cvaPvx7Uo9lqrUY6k6/JPv9bOW7Vmm62ter17X9VK32G6qXLFyCVUKAMgPoRSwiVPt+1y/+IX03HPS00+7R0mXLpVatpS6dLH/tUo70zR15KcjnhC6/cftunD5QoHH7D25V3u/2Kv/2fo/uq3Rbep5XU/FVo8t8BgAQPEhlAI2cbJ9n+u666SHH5Zmz3Z//8YbUuPGUv36zrxeafLzhZ/1VdpX7iCalqqMcxkB9zNkqFmtZoqPile7eu30feb3Wrl3pfaf3i9JOnfxnJbvXa7le5erZe2W6nldT3WJ6aLQCqHF+XYAAFcglAI2cbJ9761nT2n7dunTT6WsLGnqVOn116XQayxT5Zg52ntir7ambVXqsVTtytglU2bAfWuF1VJ8VLzio+J1Y90bVbVSVc9zN9S5QXc1vUt7T+7Vyr0r9emhTz2jqt9mfKtvM77Vu6nv6vYmt+uupncpulp0sbw/AIAvQilgk+IKpT4Ln5QAACAASURBVIYhPfGEtH+/dOSIdOCA9Ne/ureVpLMXzirp2yTtObFHLsMlwzDkMlw+X4Z8t+W3z0/ZP+nr9K915sKZgK8V4gpRqzqt1L5+e8VHxathtYYyDCPf2gzDPXrarFYzjYofpXUH1mnlvpWeuadnLpxR8q5kJe9KVtvItup1fS91btBZFV38EwkAxYV/cQGbeLfvnZhT6q1yZff80gkTpAsXpNWrpVatpNtuc/Z1AzFNU2sPrNXcr+bqp2znbjnVoGoDz2ho68jWqlSxUpHO84vQX6hv8766u9nd2pmxUyv3rtTnhz/XpRz35Qy2Hd+mbce3KaJShO5seqfuanqX6lapa+dbAQAEQCgFbFJcI6W5YmOlsWOlP//Z/f3s2e45pzExzr92roOnD2r2ltnambHT9nOHh4Qrrm6c2kW1U3xUvCJ/EWnr+Q3DUMs6LdWyTkuNzh6ttfvXauW+lTp29pgkKTM7U0u+XaIPvv1A8VHx6nVdL3Wo30EVXA7/jwMAyilCKWCT4g6lkvtuTzt2SGvWSNnZ0p/+JM2c6R5JddK5i+f0v9/8r5btWaYcM++Nd23YVSPbjVS1StV0OeeycswcmTKVY+b4fJlmgG3/3e9yzmVVdFVUTERMsQXAapWqacANA/TrFr/WtvRtWrlvpTYd2aTL5mWZMpVyLEUpx1JUK6yWfnPDb9Tn+j6EUwCwGaEUsElxtu+9PfKItG+fe27pkSPSm29KTz3lnntqN9M09dmhz/S3rX/TyayTnu3RVaP1SIdHFFcvzv4XLUaGYejGejfqxno36lTWKa3Zv0ar9q3S8XPuW2idyDqhd1Pf1dr9a/V4p8d1fa3rS7hiALh2EEoBm5TESKnkXnX/3HPui+tnZUn//rfUurV7lb6dDmce1jtfvqNtx7flvXaFUA1pNUQDWgxQSIUQe1+whNUIq6HBrQZrYMuB2npsq1buW6kvfvhCpkztP71fT61+Sn2u76P7b7xf4SHhJV0uAJR5hFLAJiUVSiX3dUrHjXNfHkpyr8a//nqpadOrP/f5S+e1ePti/XP3Pz2LgSTppuib9HD7h22f61nauAyX2tdvr/b122t3xm69+cWbOph5UKZMLd+7XBuObNDD8Q/rlw1/WeAVAAAABSvmX53AtcvpOzoVpksX/f/27jysqnJ74Ph3H+ZBRAQEZwZxQsQ0c5acp5xyyNuAQ9bVzLRMMzO1LK3rcG+Wpl5valj3Z06ZQ+VVUStnxVlDEQcUFJF5Eti/P3YcIEABoQ2H9Xme/bDPns46O+Ss3v2+6+WZZ7T1jAytf2lcXMmvp6oqB28cZPz28Wy4sMGYkLrauvJ+p/d5r9N7Jp+Q/llD54Ys7rWYUf6jsDLTRv/HpMQw/9f5fLj/Q+4k3dE5QiGEqLgkKRWilPwVMzo9yujR4OOjrUdFwcSJcPr0w88pyO2E28zZN4ePf/mYu8l3ATA3mPNc0+dY1m8ZT9Z6shSjrljMDeYMbjyYpX2X8mTNnPtw9NZRxm8fz8bzG/O0KAshhCgaSUqFKCV6Pr7PZm4O06ZBtWra65gYeO89CArKmzQXJj0znW/OfMNrO17j+O3jxu1PuD3BF32+4Hm/52U6zj+42rkys9NMpneYjpONEwBpmWmsPrWayT9O5mL0RZ0jFEKIikX6lApRSvR+fJ/N1VWrXbpoEZw6BaoK//d/WovplCna/j9TVZV91/bx9amvjSPNAZxtnRn7xFja1m4r/SULoCgK7eq0w9/Nn6DTQWz7fRsqKuFx4UzdNZWeXj0J9A/E3tJe71CFEKLck5ZSIUpJeXh8n83JCT78EAIDc2K5cEF7nP/LL3mPPRV5ikk/TmLhwYXGhNRMMWNwo8Es67tMBvAUga2FLa+0fIWFPRbi6egJgIrKj1d+ZNz2cey/th9VVXWOUgghyjdpKRWilJSHx/e5KQoMGQLNmsGnn8KdO5CUBJ98AiEh0H1oON9eWJ3nMT1oj+rHPDGGulX/wqmhTESD6g1Y1HMR237fRtCZIFIzUolNjeUfv/2D/4X9j3GtxuFexV3vMIUQolwqB1+dQpiG8vL4/s8aNoTPPoOOHbXX6ebRLDv5T3r+ayIHruQkpJ6Onnz49IfMeXqOJKSPwcxgxoBGA1jWdxltarUxbj8ZeZIJOyew/tx6GQglhBAFkJZSIUpJeXp8/2d2djD+jSTu1vqOb45vJUN9AClw/hw083Jlep8XCajfWR7TlyJnW2dmdJrB4ZuH+fL4l0QnR5Oemc7Xp78mODyY1558jaauTfUOUwghyo1y9tUpRMVV3h7fZ3uQ+YDvL37PK9vGctGwkSa+D7C1BfNMe2rfHYPFT8v4dV0AiYmSkJaFp2o/xdI+SxnQcAAK2j2+EX+Dd3a/w2eHPyMhLUHnCIUQonwoR1+dQlRsuVtKy8Pje1VV2X9tP3/f9nf+ffLfJKRryU8VOwveHTSYaU1W4hY7EINqyeHD2iCos2d1DtpE2VjY8PITL7O452IaODUwbt8Vtou/b/87RyKO6BidEEKUD/L4XohS8rCW0l1XdnE19ioDGw0s01mQ0jPTuZVwi+tx19l8YTOX71827lNQCKgfwAt+L2gxtII2T2jloxISIDoa3n0Xhg+H554rH4m1qfFy8mJBjwXsCN3B2lNrSclIIT4tnrn75zL2ibE80/AZvUMUQgjdSFIqRCkpLCmNTo5myZElqGi1QN/t8O5j9SVUVZV7KfeIiI8gIiGCm/E3iYjXft5NvotK/tJDLdxaMNJ/JJ7VPPNsb90aliyBhQvhzBmtpul//6uVjQoMhKee0kbxi9JjUAz08+lH29ptWXZsGYcjDqOisuLECqKSohjdYjQGRR5iCSEqH0lKhSglhQ10ikmJMSaK8WnxvLf3Pca1GkcPrx6PvmZWJqExoZyOOs212GvcjL/JrcRbpGakFikmD0cPRvmPooV7i0KPqV4d5s6FDRtg3Totub55Ez76CBo3hlGjtJ+idFW3rc6MjjMIOh3E+vPrAfj+0vdEJUYxpd0UrMytdI5QCCH+WpKUClFKCisJlZmVd37PjKwMlhxZQnhsOGNajMHMkHOwqqpEJEQQEhlCSGQIZ+6cIflBcpHe39bCllpValHboTa1qtTCy8mLlu4tizSi3mCAYcOgRQtYuVIrtA/az6lTtRbTwECoU6dIoYgiUhSFF5u/SA37Gnxx9Auy1CwORRxi+u7pvN/5fRytHfUOUQgh/jKSlApRSgp7fJ+7JqWTjRMxKTEA/PD7D9yIu8GrrV4l9F4op6JOERIZwr2Ue4W+h4KCm70btarUopZDTgJa26E2jtaOj13SqUEDrbj+kSOwerXWYgpw+LC2rXt3eP55bcYoUXp6ePXAxdaFeb/MIyUjhdCYUN766S1mB8ymTlX5PwEhROUgSakQpaSwx/eZas6O7p7dqWFXg6XHlpKRlUFIVAjjto8r9JoOVg40r9Ecfzd/Gjk3wt3eHQszi7II30hRtJbRVq1g927tkX5MjNbf9OefITgYBg6EwYO1+qeidLRwb8Gn3T9lzr45RCdHcyf5Dm/vept3O76LXw0/vcMTQogyJ0mpEKWksMf3uVtKzQ3mdPfqTi2HWnx84GPi0uLyXMPSzBJfF1/83fxp7tYcD0cP3Qram5lBjx7QuTN8/z1s3AjJyZCeDuvXw86d2kj9Pn3Aomzz5EqjvmN9FvZYyJzgOYTFhpH0IIlZwbOY2HoiT3s8rXd4QghRpiQpFaKUFOXxvZmiZatNXJqwqOcilh1dRtKDJJq5NqO5W3MaOzcu85bQ4rKy0vqb9uqlJaPbt0NGhlZG6t//hq1b4cUXteRVRuo/PicbJ+Z3m8+nv37KsdvHyMjKYNGhRUQlRTG86XCZdUsIYbIkKRWilBT2+P7PLaXZXO1cmRUw668IrVQ4OMDLL8Mzz8DXX8O+fdr2O3e0klKbN2sj9f399Y3TFNhY2PBep/dYfnw5Oy/vBGDdmXVEJkYyofWEPL9HQghhKqQYnhClpCij73OPtK+oatSAKVO0ovu5E9CwMJg5U1uyR++LkjMzmDGu1ThG+482btt9dTez9s4iKT1Jx8iEEKJsSFIqRCkpyuN7U2rh8vKCDz/UFs9cNflDQrQyUu++q62r+Wv5iyJSFIVBjQfxTvt3sDSzBOD0ndO8vett7iTd0Tk6IYQoXZKUClFKijL6PrtPqSnx99daTd96C1xzzaB65ozWavr221o5KUlOS6593fZ81OUjHKwcALgRf4O3fn6L0HuhOkcmhBClR5JSIUpJUUffmyJFgYAAWL4c3ngDatbM2Xfpktaa+sYb2vSlue+TKLpGzo1Y0H0BNe21mxubGsv03dM5fPOwzpEJIUTpkKRUiFJSaEupifUpfRhzc+jWDZYt01pI69XL2Xf1qlaYf/x42LNHG8Evise9ijsLeiygiXMTANIy0/jowEf8cOkHnSMTQojHJ0mpEKWksvUpfRiDATp1giVL4L33tJmiskVEwOLF8Oqr8OOP8OCBfnFWRFWsqvBhlw/pVLcTACoqK06sYOXxlWSp0gwthKi4JCkVopQUOvo+V5/SypKUZsueHWrhQvjgA/D1zdl35w588YVWZur77yE1Vb84KxpLM0umtJvCsCbDjNu2/r6VeQfmkZaRpmNkQghRcpKUClFKilKn1BQHOhWFokCLFjBvHsyfD088kbMvJkYrwj9mDHz3nTZrlHg0RVF4sfmLvN76dQyK9gt3KOIQ03dPJzY1VufohBCi+CQpFaIUpKbCyZPausEAVark7Mvdp7SytZQWpGlTmDMHFi2CNm1ytsfHw9q1MHo0rFsHsZJXFUkPrx7M7jwbG3MbAEJjQnnrp7e4EXdD58iEEKJ4JCkVohT89JM27SZofSnt7HL25WkpNfGBTsXRoAHMmAGff67ds+zZM5OS4L//hZEjtVZVqXX6aC3cW/Bp909xtnUG4E7yHd7e9TZ7r+5FlZsnhKggJCkV4jFlZMCWLTmvhwzJu78y9yktinr1tJH6X34J3bvn9MfNzIRff9Vqnb76KmzcCHFx+sZantV3rM/CHgvxdNRmMkh6kMSiQ4uY9r9phN0P0zk6IYR4NElKhXhMe/dCdLS23rp13jJIIH1Ki6pmTZg4EVauhKFDwdExZ9/t27B6tdZ6+umncPq0tJ4WxMnGifnd5tOhTgfjtgvRF5j04ySWHV1GQlqCjtEJIcTDSVIqxGPIytJa8LING5b/GOlTWjwuLvDSS/DVV/DOO9qMUdkyMuDAAe2x/7hxWgt1guRZedhY2DCtwzRmd55tLLSvorLj8g5e3fYqP13+SUpHCSHKJUlKhXgMBw9qdTcBmjWDhg3zHyN9SkvG3Bzat9dmg1qxAp59FqpWzdkfEQGrVmkJ7IIFcPastJ7m1rJmSz7v8zmBzQOxNrcGICE9gc+Pfs6Un6dwKfqSzhEKIURekpQKUUKqCuvX57weOrTg4ypj8fzS5u6uPbr/6iuYOhX8/HL2ZWTAvn0wfTq89ppW81RaTzUWZhYMaTKEZX2X0bFuR+P20JhQpuyawmeHPyMuVTrqCiHKB0lKhSihkych7I/xI15eeR8z5yZ9SkuPhQV07AgffaQNjBo4MG/5rRs3tJqnI0dqJacuXJDWUwBnW2emtp/KR10+oq5DXeP2XWG7eHXbq/xw6Yc83UyEEEIPkpQKUULffZezPnRoTkmjP5PR92WjVi2t4P7q1fDWW1r902zp6doAtKlTYcIErfU0Pl63UMsNvxp+/Kv3v3i5xcvYWtgC2ij9FSdWMOnHSZy9c1bnCIUQlZkkpUKUwIULWh9G0JKjtm0LP1b6lJYtS0sICNBqmi5dCv37560Te/261noaGKgdc/x43ilhKxtzgzkDGg3gy75f0tWjq3F7eFw403dPZ+FvC4lJidExQiFEZSVJqRAlkLuVdMiQvNOK/lnux6IWBosyjErUqQNjx2ozQ02eDI0b5+zLyNDqns6erbWwBgVBZKRuoequmk01JrWZxKfdPjXWNgUIvhbMq9teZfOFzXn+h0oIIcqaJKVCFFN4OBw9qq07O2utdA8jLaV/PUtL6NJFq2m6dCkMHpx35H50NPzf/2kJ7IwZEBysPfKvjBq7NGZxr8WMbzUee0t7AFIzUvlPyH94fcfrhESG6ByhEKKykKRUiGLasCFnfdAgrXTRw0ifUn3VqQOjRml9T2fM0CY4yN2yffo0LFyolZZatgwuX658g6MMioHeDXqzvN9yenn1QkHrIH0z4SYz985k3oF53Em6o3OUQghTJ9+QQhRDZCTs36+tV6kCPXo8+hwZfV8+mJtDmzbaEhMDe/bArl1w65a2PykJduzQFg8PbcrTgIC8o/tNnYOVA6+1fo2e3j358tiXXLqn1TL97eZvHLt9jGFNhjGo8SAszSx1jlQIYYqkpVSIYti0KacVrX9/sLZ+9Dm5+5QaFPknVx44OWl9gb/8Uhv81LUrWFnl7L96VSvY/9JLWheAkJDK1Xrq7eTNP7r/g0lPTaKqldbvIT0znaAzQby2/TWORBxBrUw3RAjxl5CWUiGKKCZGa1kDLRnt169o52W3lJobzFEKqxsldKEoWimppk3hlVe0KUx37YJLf0x2lD2t6YED2vSn3bpB585axQVTpygKXT270qZ2G7458w3bQreRpWYRmRTJh/s/xNfFl5H+I2noXMA0ZkIIUQKSlApRRN9/ryUpAH36gL190c7L7lMqj+7LN1tb6NlTW65f15LTPXty6pvevQvffqstderAU09pXQF8fAqvUWsK7CztGNtyLN29urP82HLO3tVqoZ29e5Ypu6bQrnY7Xmr+ErUcKkGmLoQoU5KUClEEiYlaX0PQZhUaMKDo5+ZuKRUVQ926WtmowEA4ckRLUI8fz3mEf+OGtmzYoHUFaN1aS1D9/LTfD1NU37E+H3f9mF9v/MraU2u5nXgb0PqbHoo4RA/PHoxoNgInGyedIxVCVFTyLSlEEWzfDqmp2nrXrloiUlTZfUqlHFTFY24O7dppS3S09hj/0KG805fGxMCPP2qLjQ20bKklqK1a5S3ibwoURaFD3Q60qd2Gn6/8zLdnvyU2NZYsNYsfr/zInvA9DGw4kMGNB2NnaWIfXghR5iQpFeIRUlNh61ZtXVG0mpfFkaFKS6kpcHbWSoANGgRxcVoL6qFDcPIkPHigHZOSAr/8oi1mZtCsmZagPvWUdr6pMDeY06dBH7p4dOH7i9+z8cJGUjJSSM9MZ/359ey8vJNhTYfRp0EfGakvhCgy+ZYU4hF+/jmnX2HHjuDuXrzzjS2l0qfUZFStqpWM6t5d+5+Wkye1BPXIEa2rB0BmpjZqPyREG+Xv7Z2ToNarZxr9UK3NrRnuO5xe3r1Yf249Oy7vICMrg4T0BFadXMXWS1t5we8FAuoHSOUJIcQjSVIqxENkZMDmzTmvhw4twTWkT6lJs7aGtm21JTMTzp/XEtRDh+BOrnrzly9rS1AQuLnlJKhNmjx8mtqKoKp1Vca2HEv/hv0JOh3Evmv7UFG5m3yXxYcWs/nCZgL9A2np3lIqUAghCiXfkkI8RHCw1pcQ4MknoX794l9DktLKI/uRfbNm8PLL2pS02QlqWFjOcZGRsGWLtlSpopWkatxYW7y8tGlSK6Ia9jV4q91bDGo8iLWn1nL89nEAwuPCmbNvjpSREkI8lHxLClGIrKy8U4qWpJUUpCRUZaUo2sxQHh4wYoTWanr4sJagnj2r/X4BJCTkJK6gDa7y9tYS1EaNtJ/Vqun3OUrCs5onswNmczrqNKtDVhMaEwpIGSkhxMNJUipEIQ4dgogIbd3XV0sOSkJaSgWAqys884y2JCTAsWPa79ipU9oUp9kyMuDiRW3J5uaWN0mtV69iPPL3q+HHwh4L+fXGr3x96mtuJWpzukoZKSFEQeRbUogCqCqsX5/zesiQkl5HJUvVmsSkJJTIVqUKPP20tqiqVvP0woWc5datvMdHRmrL3r3aaxsbaNgw55G/j0/5LT+Vu4zUriu7+ObsN1JGSghRoGInpenp6cycOZOgoCDu37+Pn58fc+fOpVu3bo88Ny4ujrfffpstW7aQnJxM69atWbhwIS1atChR8EKUlZAQuHJFW/f0hCeeKNl1sh/dg7SUioIpilasv25dbTYp0EpOZbeWXrgAoaGQnp5zTkpKzsj+7GvUq6e1pDZpov10cytfI/zNDeb0btCbpz2eZuulrWw4v0HKSAkh8ij2t2RgYCCbNm1i8uTJeHt7s3r1avr06UNwcDDt2rUr9DxVVenTpw9nzpxh6tSpVK9enaVLlxIQEMCJEyfw8vJ6rA8iRGn67ruc9aFDS/7lnv3oHqRPqSi6qlW1kflPPaW9zsjQBkrlbk2Nick5XlW1QVXh4VoRf9BaY93ctKVGjbw/nZ21vqt6sDa3ZljTYfT06sl3579je+j2fGWkAuoH4FnNE69qXrjZu8mIfSEqCUVVs+clebQjR47Qpk0bFi5cyOTJkwFIS0vD19eXGjVq8MsvvxR67vr163nuuefYuHEjgwYNAiA6OhofHx/69OlDUFBQoeeeOHGCli1bcvz4cZ4oaZOVeKRvv/2WESNG6B2GbpKTtZH2ly7BZ59p22rVgqVLS95/72b8TcZtHweA5TlLNs7dWErRioJUlt9hVYW7d3NaUi9c0JLWov41VxRwcSk4Ya1RQ0uKC8oDy+L+RiVGse7MOoLDg1HJ/wFszG3wcPTAy8kLz2qeeFbzpG7Vuib75KGy/A7rRe5v2XrcfK1Y/6o3bNiAubk5Y8eONW6zsrJizJgxzJgxg4iICGrVKng05caNG3FzczMmpADOzs4MGzaMdevW8eDBAyxMddLoCsKU/7FmZGgtS3fv5izR0Xlf5x5sku3ZZ4ufkKqqytk7Z9n2+zYO3jxo3H5+7/nH/BTiUUz5dzg3RdEGTrm6QqdO2rbUVPj995wk9do1uHev4ERVVbVqALnrqOZmZVVwsvqf/3zLoEEjsLYuvc9Sw74Gb7Z9k0GNBrHm1BpjGalsKRkpnI8+z/nonH8/5gZz6jrUNSapntU88ajmga2FbekFppPK8jusF7m/5VuxktKQkBB8fHywt7fPs71169bG/YUlpSdPniwwa27dujUrV67k999/p2nTpsUJRwhA+4JNTMxJLu/cyZ90xsQUvRUpm7u7NhDlYdIz04lLjSMuLY641DhuJdzipys/cS3uWp7jzA3mONua0DyTotyxtgY/P23J9uCB9vsfGQlRUfl/Zs8+9WdpaXD9urbkduaM1p3Fzg7s7bWlSpW8639+nXvd0rLwrjAe1TyYHTCb6ORorsRc4cr9K4TdDyPsfhh3k+/mOTYjK4Ow2DDCYsPgas52d3t3Y5LqVc0Lj2oeMrJfiAqkWEnp7du3cS9gjkV3d3dUVeXWn4eM/unczp07F3guwK1btyQpLUdUVaujmJVV9HVV1Wa0yV4v6LjHvV5aWt5k885dlbt3IS09ExQVlSxABSXLuK4qWaiGLCDLeIyaa93MPItqTlk4Oak4Vc/CsVoWjk6ZeDVOYN/1OOLT4olLiyM2NVZbz05C0+JIzUh96H10snGit3dvenr1JHBd4F/xn04IIwsLqFlTWwqSlFRwspr9MyOj8POyzy0Oc/OcRPXPP3PWnbGwcMbL7Cl8bMHcAVIyE4hMu0pEyhUiksO4mXSFyOSboKgoCsblRuxtbsbd5kD4r8ZtjjaOWoKaqwuAu7279FMVohwqVlKakpKClZVVvu3WfzzLSUlJKdG5qqo+9NxsAxbPwqpG9WK3eBWs+Bd57LdVC1wt+NBifMg8hyoFv0mBV8uzXyX67AlqT3yhgMPUgk8q7GJK4bEXfq0inqOof2zJ0hJPGxXqFnoqABbmYGkFVpbaT0tLbbH646eFBSQpkATcyD4pBTjx8Os+TGPnxvTz6Ue7Ou1Mtu+bqPjs7LTqEp6e+fepqvaEIXeievWqNro/Lk6rtZqYmDMJQFFkZMD9+9pSPFUAvz8WjaWSTrJlOMlWYSRbXSHZ6irJVlfJUtLznKkosfxPOY7CcRQDKADKHz/JTkwVFBRytuZaVw3GY3KOy3md+5ic83Jfp3hunzuJ95SRxT5PFI3c37KVFnXvsc4v1reljY0NaWlp+banpqYa95fkXEVRHnpudsIaFx2BeVZccUIWxZCZnkLyveuPPrAcMShaUmlh+UfyaaG9Nv8j2bQw/1Of0Ey0hDMFMtCWkrC1sMXewh57K3vtp2XOuqeTJ7UdasM9OH3vtPGcuLg4Tpx4jExXPJLc47Lh6Kgt1avHMXx4zv1VVa0va0qK1nKa+2dysrbkfp17PT39IW9YDFa4YoUr1WiLSiZp5ndJtYggxfImqZY3SbWIINMsuXTe7C/wIC2ZmNthjz5QlIjc37KVEaP1CSpKQ2NBipWUuru7F/iI/vbt2wDULOwZ0R/nZh9X3HPDw8MBSPjxZHHCFSVw/5sDeodg0lq2bKl3CCZP7nHZkvtb9uTvcNmS+1v2wsPDad++fbHPK1ZS6u/vT3BwMImJiXkGOx06dAhFUfD393/ouQWVjDp06BC2trb4+PgUem7Pnj0JCgqifv36D21RFUIIIYQQ+khJSSE8PJye2TOBFFOJ6pQuWLCAN998E9BmePL19cXFxYVff/0VgMjISOLi4vD29sbMTCsYvn79ekaMGMF3333H4MGDgZw6pb1792bdunUl+gBCCCGEEKLiK1ZSCjB8+HC2bNnCpEmTjDM6HTt2jD179hibakeOHMnatWsJDw+nbl1tFEpWVhYdOnTg3LlzTJkyBWdnZ5YuXcqNGzc4evQoDRo0KP1PJ4QQQgghKoRiDwv++uuvmTlzJkFBQdy/fx8/Pz+2b9+ep++AoigY/lRx3GAwsHPnTt5++22WLFlCSkoKrVu3Zu3aX++oUAAAEK5JREFUtZKQCiGEEEJUcsVuKRVCCCGEEKK0lXBGb33t2bOHMWPG0LBhQ+zs7PDy8mLs2LFERkbqHZpJiIyM5J133qFLly44ODhgMBjYv3+/3mFVSOnp6UybNo1atWpha2tLmzZt+N///qd3WCYjKSmJWbNm0bt3b6pXr47BYGDt2rV6h2Uyjh07xoQJE/D19cXe3p569eoxfPhwQkND9Q7NZJw/f55hw4bh5eWFnZ0dLi4udO7cmW3btukdmsn66KOPMBgM+OWe/kyU2L59+zAYDPkWMzMzjhw5UqxrVciq3tOmTeP+/fsMHTqUBg0aEBYWxpIlS9i+fTshISG4urrqHWKFdunSJf7xj3/QoEED/Pz8OHjw4KNPEgUKDAxk06ZNTJ482dgHu0+fPgQHB9OuXTu9w6vwoqOj+fDDD6lXr56xOogoPZ988gm//fYbQ4cOxc/Pj8jISJYsWcITTzzB4cOHadKkid4hVnjXrl0jMTGRkSNHUrNmTZKTk9m4cSP9+/dnxYoVvPzyy3qHaFIiIiKYN29evunSxeObNGkSrVq1yrPN29u7eBdRK6ADBw7k27Z//35VURR15syZOkRkWhITE9X79++rqqqqGzZsUA0Gg7pv3z6do6p4Dh8+rCqKoi5atMi4LTU1VfX29lbbt2+vY2SmIz09XY2KilJVVVWPHTumKoqirlmzRueoTMfBgwfVBw8e5NkWGhqqWltbqy+++KJOUZm+rKws1d/fX23cuLHeoZic4cOHq926dVMDAgLUZs2a6R2OSQgODlYVRVE3btz42NeqkI/vO3TokG9bx44dcXJy4sKFCzpEZFrs7OxwdHTUO4wKb8OGDZibmzN27FjjNisrK8aMGcPBgweJiIjQMTrTYGFhIU9GylCbNm0wN8/7QM3b25umTZvK39oypCgKderUITY2Vu9QTMr+/fvZtGkT//znP/UOxWQlJiaSmZlZ4vMrZFJakKSkJBITE3F2dtY7FCEACAkJwcfHJ99jotatWxv3C1ERRUVFyd/aUpacnMy9e/cICwtj8eLF7Ny5k27duukdlsnIyspi4sSJjB07lqZNm+odjkkaNWoUDg4OWFtb06VLF44fP17sa1TIPqUFWbx4MQ8ePOC5557TOxQhAG0KXXd393zb3d3dUVW1wCl7hSjvgoKCiIiIYO7cuXqHYlLeeustli9fDmglFJ999lmWLFmic1SmY9myZVy/fp09e/boHYrJsbS0ZMiQIfTp0wdnZ2fOnz/PggUL6NSpE7/99hvNmzcv8rV0T0pVVSU9Pb1Ix1pZWRW4ff/+/XzwwQcMHz6czp07l2Z4FV5p3F9RMikpKQXeU2tra+N+ISqSixcvMmHCBNq3b89LL72kdzgmZfLkyQwdOpRbt26xfv16MjMzSUtL0zsskxATE8OsWbN4//33cXJy0jsck9O2bVvatm1rfN2vXz+effZZ/Pz8mD59Ojt27CjytXR/fL9//35sbGweudja2vL777/nO//ixYsMHjwYPz8/Vq5cqcMnKN8e9/6KkrOxsSnwSyU1NdW4X4iKIioqir59+1KtWjW+++47FEXROyST4uPjQ5cuXXjhhRfYunUrCQkJ9O/fX++wTMKMGTOoXr06EyZM0DuUSsPLy4sBAwawd+9e1GKUw9e9pbRRo0asXr26SMf++VHojRs36NGjB9WqVWP79u3Y2dmVQYQV2+PcX/F43N3dC3xEf/v2bQBq1qz5V4ckRInEx8fTq1cv4uPj+eWXX3Bzc9M7JJM3ZMgQ/v73vxMaGiqzHj6Gy5cvs3LlSv71r38ZB5eqqkpqaioPHjzg2rVrODg4UK1aNZ0jNT116tQhPT2dpKSkIpfg0j0prVGjRokeA8XExNCjRw8yMjIIDg6mRo0aZRBdxVfS+yseX3bdzMTExDz/IA8dOoSiKPj7++sYnRBFk5aWRr9+/bh8+TK7d++mYcOGeodUKWR374mLi9M5kootIiICVVWZOHEir7/+er79np6evPHGGyxatEiH6EzblStXsLa2LlZNWN2T0pJITk6md+/e3L59m+DgYDw9PfUOSYh8hgwZwoIFC1ixYgVvvvkmoM3wtHr1atq0aUOtWrV0jlCIh8vKymLYsGEcPnyYrVu3GitHiNJz9+5dXFxc8mzLyMhgzZo12NjYyAQFj8nX15fNmzfn2z5jxgwSExP57LPPJId4TNHR0fmqcZw6dYoffviBvn37FutaFTIp/dvf/sbRo0cZM2YM586d49y5c8Z99vb2DBgwQMfoTMPcuXNRFIVz586hqipr167lwIEDgPaPWTxa69atGTp0KNOnTycqKso4o9O1a9f46quv9A7PZHzxxRfExsYaH81t3bqVGzduADBx4kSqVKmiZ3gV2ptvvskPP/xA//79iY6OZt26dXn2P//88zpFZjpeffVV4uPj6dSpE7Vq1SIyMpJ169Zx6dIlFi1ahK2trd4hVmjVq1cvsG/u4sWLURSFZ555RoeoTMvw4cOxsbGhXbt2uLq6cu7cOVauXIm9vT3z5s0r1rUUtTg9UMsJDw8Prl+/XuC+evXqERYW9hdHZHoMBkOBAxkURSEjI0OHiCqm9PR0Zs6cSVBQEPfv38fPz4+5c+dK/cFS9LC/B1evXqVu3bp/cUSm4+mnn2b//v2F7n+cItlCs379elatWsWZM2e4d+8eVapUoWXLlkycOLHYrUyi6J5++mliYmI4deqU3qFUeJ9//jnr1q3j8uXLxMfH4+LiQrdu3Xj//feL3QpdIZNSIYQQQghhWnQvCSWEEEIIIYQkpUIIIYQQQneSlAohhBBCCN1JUiqEEEIIIXQnSakQQgghhNCdJKVCCCGEEEJ3kpQKIYQQQgjdSVIqhBBCCCF0J0mpEEIIIYTQnSSlQgghhBBCd5KUCiFM1rVr1zAYDKxdu7ZM32f27NkYDEX7c2owGPjggw/KNB4hhKiIJCkVQlRIa9aswWAwcOLEiYcepyhKmceiKEqRk9KK7MKFC8yZM4fr16/rHYoQwgSZ/l9RIYTJelTCWa9ePVJSUnjxxRfLNI6ZM2eSnJxcpu9RHpw/f545c+YQHh6udyhCCBNkrncAQghRliwtLcv8PQwGw1/yPnpTVfUvaXkWQlRO0lIqhDBZhfUp3bJlC76+vtjY2ODn58eWLVsYOXIkHh4exmP27duHwWBg//79j7xmQX1K09PTmTx5Mq6urjg4ODBw4EAiIiKKFPeDBw94//33adWqFY6Ojtjb29OpUyeCg4PzHfvf//6XVq1a4eDgQNWqVfHz8+Ozzz4z7s/IyGDOnDn4+PhgY2ODs7MzHTt2ZPfu3Xmuc+nSJYYMGUL16tWxsbHhySef5IcffjDuX7NmDcOGDQMgICAAg8GAmZlZvvsjhBAlJS2lQohK5eeff2bIkCH4+voyf/587t27x6hRo6hdu3a+VsCitgoqipLv2DFjxvDNN9/w/PPP07ZtW/bs2UPfvn2LdM34+Hj+85//MGLECF555RUSEhJYtWoVvXr14siRI/j5+QGwa9cu/va3v9G9e3c+/fRTQOv3+dtvvzFx4kQAZs2axfz583nllVd48skniY+P59ixY5w4cYKuXbsCcO7cOTp06EDt2rWZPn06dnZ2rF+/noEDB7Jp0yYGDBhAp06dmDhxIkuWLOG9996jUaNGADRu3LhI90gIIR5FklIhRKUybdo03Nzc+OWXX7C3twegc+fOdO/enfr165fKe5w+fZp169YxYcIEY6vluHHjeOGFFzhz5swjz3dyciI8PBxz85w/0WPHjqVhw4YsWbKElStXArBjxw6qVq3KTz/9VOi1duzYQd++fVm2bFmhx7zxxhvUr1+fo0ePGt9z3LhxdOjQgWnTpjFgwAA8PDzo2LEjS5YsoVu3bnTq1KlI90IIIYpKHt8LISqNyMhITp06xciRI40JKUDXrl1p0qRJqb3Pjh07UBSF119/Pc/2SZMmoarqI89XFMWYHKqqyv3790lPT6dVq1Z5qg04OjqSlJT00KTU0dGRc+fOcfny5QL3379/n7179zJ06FDi4uK4d++ecenRowehoaHcvn27KB9bCCEeiySlQohK49q1awB4e3vn29ewYcNSfR+DwYCXl1eJ32PNmjU0b94ca2trqlevjqurK9u3bycuLs54zPjx4/Hx8aFPnz7UqVOHMWPG5EtQP/jgA2JjY/Hx8cHPz4+pU6fmaa29fPkyqqoyc+ZMXFxc8iyzZ88G4M6dOyW4C0IIUTzy+F4IIQpQWN/PzMzMMn/voKAgRo0axeDBg5k6dSqurq6YmZnx8ccfExYWZjzOxcWFkJAQfvrpJ3bu3MnOnTv56quvCAwM5KuvvgKgY8eOXLlyhe+//56ff/6ZVatWsXjxYpYvX87o0aPJysoCYMqUKfTs2bPAeApK4oUQorRJUiqEqDTq1asHQGhoaL59ly5dyvO6WrVqqKpKbGxsnu1FqdFZr149srKyuHLlCg0aNDBuv3jxYpHi3LhxI15eXmzYsCHP9vfffz/fsebm5vTt25e+ffsCWl/QFStWMHPmTDw9PQHtEX5gYCCBgYEkJyfTsWNHZs+ezejRo43HWFhY0KVLl4fGJeWghBBlSR7fCyEqDTc3N/z9/VmzZg0JCQnG7bt27eL8+fN5jq1Xr16BJY+WLl36yOSsd+/eqKqapzQTwD//+c8iJXZmZmb5th0+fJiDBw/m2RYTE5PvuGbNmgGQlpZW4DG2trZ4e3sb97u4uBAQEMDy5cuJjIzMd73o6Gjjup2dXYGJuhBClAZpKRVCVFiqqrJq1Sp27tyZb9+kSZMKPGfevHn069eP9u3bM3r0aO7du8fnn3+Or68viYmJxuMcHBwYOnSoMbH08vJi27Zt3L1795FxNW/enBEjRrB06VJiY2Np164du3fv5sqVK0Ua6NSvXz82bdrEwIED6du3L2FhYSxfvpymTZvmifHll18mJiaGLl26ULt2bcLDw/n8889p0aKFsVRTkyZNCAgIoGXLljg5OXH06FE2bNhgLBkF8MUXX9CxY0eaNWvG2LFj8fT0JCoqioMHDxIREcHJkycB8Pf3x8zMjE8++YTY2FisrKzo2rUrzs7Oj/xMQgjxSKoQQlRAq1evVg0GQ6FLRESEGh4erhoMBnXNmjV5zt28ebPatGlT1cbGRvX19VW3bNmijhw5UvXw8MhzXHR0tDp06FDV3t5erV69ujp+/Hj1/Pnz+a45e/Zs1czMLM+5aWlp6qRJk1QXFxe1SpUq6sCBA9WIiAjVYDCoH3zwwSM/3/z581UPDw/VxsZGbdmypbpjxw515MiRqqenp/GYTZs2qb169VLd3NxUa2trtX79+ur48ePVqKgo4zEff/yx2qZNG9XJyUm1s7NTmzRpos6fP1/NyMjI835Xr15VR44cqdasWVO1srJS69Spo/bv31/dvHlznuNWrVqlent7qxYWFqrBYFD37dv3yM8ihBBFoahqEf63XQghTNyoUaPYt29fnoFEQggh/jrSp1QIIYQQQuhOklIhhBBCCKE7SUqFEOIPUvJICCH0I31KhRBCCCGE7qSlVAghhBBC6E6SUiGEEEIIoTtJSoUQQgghhO4kKRVCCCGEELqTpFQIIYQQQuhOklIhhBBCCKE7SUqFEEIIIYTuJCkVQgghhBC6k6RUCCGEEELo7v8BWyo+EtmjA8gAAAAASUVORK5CYII=", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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zFgNzFkNZOScnR2LLlgCEh4ejZcuWZT6eavbgPohGo4GHhwfS0tKUHoUqAb5BioE5i4NZi4E5i0HunM1kfbRKICcnB7m5uUhPT8dvv/2GXbt2Yfjw4UqPRUREREQmorqCO3XqVPz4448AAK1Wi2effRaLFi1SeCoiIiIiMhXVHaIwefJk7Nu3D6tWrULfvn1hMBiQn5+v9FhUCYSFzVJ6BDIB5iwOZi0G5iwGuXNWXcFt2LAhunXrhsDAQGzfvh2ZmZkYMGBAmffbtWs4goICi/3Ztq034uN3FlsvIeEAgoICS9w/NPQdREevKbYsOTkSQUGByMtLKbb85Mk5iIhYWGxZVlYCgoICkZYWU2z5uXM/lQhdr89BUFBgiVNqxMZueeC1nPftG8Pt+IsatkMteVTUdlhbu6piO9SSR0Vux+3bJ1WxHWrJo6K2IzPzqiq2Qy15VNR23H+M+/e738XWrWuJzZu74MiRGSUe52FUfRYFAPjpp5/w+uuvIzo6Gg0aNChxO8+iQERERFS58SwK/5CbmwsASE9PV3gSIiIiIjIF1RTcO3fulFim1+uxcuVKWFtbo0mTJgpMRURERESmppqzKIwdOxYZGRno1KkTatWqhcTERKxduxYXL17E/PnzYWPD8+iJLi0tBg4OJQ9TIXVhzuJg1mJgzmKQO2fV7MF94YUXoNPp8MMPP+DNN9/EggUL4OHhge3bt2PixIlKj0eVQFjYR0qPQCbAnMXBrMXAnMUgd86q2YM7bNgwDBs2TOkxqBLz9/9C6RHIBJizOJi1GJizGOTOWTV7cInKYmfnrvQIZALMWRzMWgzMWQxy58yCS0RERESqwoJLRERERKrCgkvC+OdVWkidmLM4mLUYmLMY5M6ZBZeEodfnKj0CmQBzFgezFgNzFoPcObPgkjD8/KYrPQKZAHMWB7MWA3MWg9w5s+ASERERkaqw4BIRERGRqrDgkjDy8lKUHoFMgDmLg1mLgTmLQe6cWXBJGMHBvGSzCJizOJi1GJizGOTOmQWXhOHnN03pEcgEmLM4mLUYmLMY5M6ZBZeE4eLSXOkRyASYsziYtRiYsxjkzpkFl4iIiIhUhQWXiIiIiFSFBZeEER29RukRyASYsziYtRiYsxjkzpkFl4SRnHxG6RHIBJizOJi1GJizGOTOmQWXhOHv/6XSI5AJMGdxMGsxMGcxyJ0zCy4RERERqQoLLhERERGpCgsuEREREakKCy4JIygoUOkRyASYsziYtRiYsxjkzpkFl4Th4zNa6RHIBJizOJi1GJizGOTOmQWXhOHu3lXpEcgEmLM4mLUYmLMY5M6ZBZeIiIiIVIUFl4iIiIhUhQWXhBEfv1PpEcgEmLM4mLUYmLMY5M6ZBZeEERu7RekRyASYsziYtRiYsxjkzpkFl4QRELBU6RHIBJizOJi1GJizGOTOmQWXiIiIiFSFBZeIiIiIVIUFl4iIiIhUhQWXhBEcPF7pEcgEmLM4mLUYmLMY5M6ZBZeEwavhiIE5i4NZi4E5i4FXMiP6l7y8hig9ApkAcxYHsxYDcxaD3Dmz4BIRERGRqrDgEhEREZGqsOCSMBITw5QegUyAOYuDWYuBOYtB7pxZcEkYERGLlR6BTIA5i4NZi4E5i0HunFlwSRgBAUuUHoFMgDmLg1mLgTmLQe6cWXBJGGZmNkqPQCbAnMXBrMXAnMUgd84suERERESkKiy4RERERKQqLLgkjLCwWUqPQCbAnMXBrMXAnMUgd84suCQMO7taSo9AJsCcxcGsxcCcxSB3ziy4JAwfn1eVHoFMgDmLg1mLgTmLQe6cWXCJiIiISFVYcImIiIhIVVhwSRhpaTFKj0AmwJzFwazFwJzFIHfOLLgkjLCwj5QegUyAOYuDWYuBOYtB7pxZcEkY/v5fKD0CmQBzFgezFgNzFoPcOZvJ+mhElZidnbvSI5AJMGdxmDrrrKwbOHVqPgoLM2Fj4wZbWzfY2NSArW31ou95WVn58TUtBrlzZsElIiJ6CL0+D2fOfIfwyG+gtbCDpZM39KnnUJh1E1JhdrF1tZYOMLOrATO7Gqhl4QJb2xp/K8Nuf31fDVqtuUJbQyQGFlwiIqJSXL26B/uPzUBhxjU0GzABrV6YCQvbqkW3F+RkICflJrJTbyIn9SayU27c+5p6Ewnx11CYFAZ91i3AWPi3R9VAZ+MKMzs3mNnVhIeFa1EJvvenJhwdG0Or1Zl+g4lUQjUF9+TJk/j5558RHByM+Ph4ODs7o23btpg9ezYaNGig9HhUCURELESLFhOUHoMqGHMWR0VmnZ4eh99PfIjsy7thU6cbBn32Oxw9GpdYz8KmCixsqsDBw7vUx5IkCfkZKcj+WwG+X4KvX7qG+LvnoL++F/qc24BkBADobKvDu+4z8PQciOrVW0OjEfcjM3xNi0HunFVTcOfMmYMjR47gueeeg6+vLxITE7Fo0SK0bNkSx44dQ5MmTZQekRSm1+cqPQKZAHMWR0VkXViYhdOnFyDy7A/Q2bqhx3u/om67wdBoNP/6MTUaDayqusCqqguc6/mWup7RoEduWhLSb8YiPmwbovdvRFTUUpjZ10Ljus/A03MQXF1blmuWJxFf02KQO2eNJEmSrI+okLCwMPj5+cHM7P+dPTY2Fs2aNcNzzz2HVatWPfB+p06dQqtWrTBkyD64uDQ31bhERFSJSJKEuLhtOHR8Fgx5qWg57F00H/IOzCytlZvJaETShSOIC92A6AMbYci+DbMqtdGk7gB4eg6Ci4uvcGWXxJWcHIktWwIQHh6Oli1blrm+avbgtm3btsQyLy8vNG3aFBcuXFBgIiIiehKkpERh5/EZyL0eAruGA/HMO1/DvnpdpceCRquFW1N/uDX1R7sxXyMx6hDiQjciKng9zpxZDHOH+mj6155dJ6emLLtEf6OagluapKQk+Pj4KD0GERFVMvn5aTh58gtEnV8BC8cG6PvxHrg/1UPpsR5Iq9Ohpm9X1PTtig5jF+HmmQO4HLoRZw6tRkTEN7Bwagifus+gfv1BcHIq/XhgIlGouuCuWbMGN27cwOzZs5UehSqBvLwUWFk5Kz0GVTDmLI5/m3Vu7h3Exm7B8YivIRny0faVr+DTfzy0Zk/Gqbu0OjO4P9UD7k/1gP8b3yEhYh+O/roOEVFLcerUPFi4NIFfw0A0bTpGFXt1+ZoWg9w5q/ZjmdHR0Rg3bhw6dOiAkSNHKj0OVQLBwROVHoFMgDmL43GyzstLxYULq7Fm9zCsXuODo2EfwtazN/6z9BJ8B015YsrtP2nNzFHbrw+e/3w1Xv4lCZ0mLENB8nkcP/cDAFV8xIavaUHInbMqC25SUhL69esHR0dHbNq06ZF+gt21aziCggKL/dm2rTfi43cWWy8h4QCCggJL3D809B1ER68ptiw5ORJBQYHIy0sptvzkyTmIiFhYbFlWVgKCggKRlhZTbPm5cz8hLGxWsWV6fQ6CggKRmBhWbHls7BYEB48vMdu+fWO4HQA8PQepYjvUkkdFbUeLFuNVsR1qyaMit8PMzPKh25Gfn4aLF9dh7d7hWLXKGyEhUwEAHd/6ASNXJ6L/5E9xaPFrSLseXXw7dixC2PJpxbcjLwdBnwxAYlRo8e04+AuCv3655HbMeR7xR7cV345TexD0yYAS64Z+/xai9ywrvh2xpxD0yQDkpScXW35y7YeI2Dyn2LKs29cQ9MkA3IkNx7F186GzrY4hvTcgKmqZKv67cnT0UsV28HX+8O3w8OhatB2xsVuKuti6dS2xeXMXHDkyo8TjPIxqzqJwX0ZGBjp37oyEhASEhoaiUaNGD12fZ1EgIlKPgoJMXL0ahOPXdiD7yj7AqIe1hz9a9R+Ouu2GwMaxutIjVoi89GRsmNwV+pzbGNJ3KxwdGyo9EpGshD2LAgDk5+ejf//+iI2NxZ9//llmuSUiInVISYnC3vPfIfPSb5AM+bCu1RbtR89FvQ5DYetcU+nxKlReRgo2TOkGffZtDOm3heWWCCoquEajEcOGDcOxY8ewfft2tG7dWumRiIiogiUlncS+cwuRHbcLZlVqo/XI2fDs+DzsXD2UHs0k8jJTsWFyN+izEjG4769wdOSOHSJARQV3ypQp2LFjBwYMGIDk5GSsXbu22O0jRoxQaDKqLKKj18Dbu+RxSqQuzFn9JEnCzZsh2Hv0PRSkXoKFUyN0mbwSXp2GP7EfFvs38rPuYsOkbtBn3sTgfr/CyankpYTVgK9pMcids2oKbmRkJDQaDXbs2IEdO3aUuJ0Fl5KTzyg9ApkAc1YvSTLi6tXdOHR2IfJunYTW2gUB725GvXaDodGq8jPTpcrPSsOGSd2hz0zAoL6/wslJvZej52taDHLnrJqCe+DAAaVHoErO3/9LpUcgE2DO6mM06nH58m84fGYR8pOjYO3eAX1m7YJ7y16qOM/r48rPSsOGyQEoSL+KQX03w9m5qdIjVSi+psUgd86qKbhERKQuBkM+Ll3aiLCzi1GYdhm29Xqg59vfo0bTjkqPppiC7HRsmNITBWmXMajvZri4NFN6JKJKiQWXiIgqlcLCbERHr8aJs99Dn3UL9o0G4ZkPN8HFq+xTA0mSpNq9ugU5GVg/pScKUmMwsO9muLj4Kj0SUaXFgktERJVCfn46oqKW4XTUTzDk3UWVpsPR683/wtHj4R+eMhoMiD24Fsd+/QDG/Hw07TIW3j3HwK5abRNNXvEKcjKxYUovFKRcxMC+m+HqyvO2Ez2MWEflk9AedKUXUh/m/OTJzb2D48c/wepfnkL46flo1HUYhv8Uixc+X/3Qchv0yQBcPvwr1k/zRvCCl2DfqCGaD+qKyN/nYd2r9bD16264evx3GA0GE26N/O6X2/w75zGgzya4urZQeiST4mtaDHLn/Fh7cD/++OPHfgKNRoMZMx7v8mpEFcHHZ7TSI5AJMOcnR1ZWAiIjv8X5i2ug0ZjB55k34DtoCmwc3R56P0mSkHBqN5KTzuDaF0Ph3LEDXlzzOTxa3/uVfZ+v3kHkL39g/6IN2P3JM7CqWQNNu7wO756jYetcyxSbJpvC3CxsmNoH+XfOYUDfTahW7SmlRzI5vqbFIHfOj3WpXu0DTsNy/1infz6MRqMpOhbKUIl/eualeomITCstLQ67L3yH9Kh10FpUwVNDJqJp/3Gwsncq8763zh1C6JYpuHsiHA6tnsLQuZNRv0ubUtdPOHkW2+dtwK3ffoexoACu3bqgZfupcH+qJ7Q6nYxbJb/CvGysn9IH+Ymn8Uzfjahe/WmlRyJSTIVeqtdoNBb7/saNG+jXrx98fHwwadKkokvjRkdH4+uvv8b58+fxxx9/PM5TEBGRAoxGA4D7Oyo0f/uglka2D22lpJzDnqhvkXlxC3Q2rmg76gs07j0W5tZ2Zd73TsxJhGybjORDoajStAle+mMJGvbpVOZs7n7N8OYvzZCX8S4i1m7HgUUbEPRRX1jXqoWmXd+Ad49XYONUQ5btk5M+LwcbpvZDfuJp9O+znuWW6DE91h7cfxo0aBDMzc2xadOmB94+dOhQGAwGbN269V8PWNG4B5eIRGYw5OPEic9x5uwPgPSov23TAH8rwPe+aIr+/vdy/Pf1JH0uzKvWQZsR76Jh91Ews7Aq85lSr0YhZMdkJO3eC1vP+hjwxUQ0HdLzgb9RfBSSJOH6sUjsmLcBt37fCUmvR+eDe9Ewtcu/eryKoM/Lwfqp/ZB76wSe6bMebm5tlR6JSHEVugf3n/bv3485c+aUenv37t0xffr08jwFkWzi43eibt2+So9BFYw5P7rU1AvYcfAt5KdEo+Ww/8LerT7u78WVJAm4v//jr68S/v/9//eN/GMdSSr+GH+7zcapBuq2HQStruz/9WTcisOhXVNwc9sOWNeqiaE/f4EWgQOKHVZwfts+NBkUUPR92rWbMBTq4exZ+tkTNBoNardtgbc2tUBu2nu4tOsQMmrVxPValwAAHmcbljlbRdLn52L9288g99Zx9O/9C8st+JoWhdw5l6vgWllZ4ejRo3jjjTceePuO9C1/AAAgAElEQVSRI0dgZVX2T+hEphAbu4VvkgJgzg8nSRJu3TqMg5dWIjNmByycG+HZr0/AuV7l+A1W1u1rCNk3DQmbtsDCyREDFs+A35jnYGZhUWLdyF9+Lyq4hsJCfFmnKwDgv0lHYFfNucznsnaogubD+xd9H3IVuN5M2aIb/sss5CYcRr8+G1CjRntFZqhs+JoWg9w5l6vgjhgxAgsXLoSDgwPGjx8PT09PAEBcXBwWLlyIdevWYcKECbIMSlReAQFLlR6BTIA5P1hBQSZiYjbiRPRKFKRcgIWzN9q/9jW8e455pEMFKlp2yg2c3vgZLuz9CWb2duj9xVS0eWM4LGysS73P8A1fF/1dZ26Okb//iPSERNi6PvzDarej4wAA9tVdYO1YtcTtSu7FvXTsMOrV6YOaNTsoNkNlw9e0GOTOuVwFd86cOUhOTsbixYvx7bffFh0TZTQaIUkShg8f/tBDGIiIqGKlpkbj/PnliI7ZBKM+F/YNnkHPKd+iRrMu5f7wWHbKTWTejkcVN0/YOFb/V4+RczcJEZu/wPmg76CztkbPjyeg7bgRsLSzfezH8u7Xpcx1tr3xIS7vD0NBVg7cfBuhw5SX0aBHB4RcvXe7kuVWkiTk34mCs++bis1ApBblKrgWFhZYvXo1pk2bhp07d+Lq1XvvEHXq1EGfPn3QvHnl+JUXEZFIjMZCxMfvxOGLK5F7PQQ622poMWQyvHu/BjsX93I9dk7qLUTt/A6xwWuRn3UX1g7VYefqgca9x6J+h6GPfKncvPRkRG79Cud2LoLGTIeu749Fh0mjYFWl7DMq/Bv5WdnYMHwqMm4k4bnVXyE9IRFhi9cg/tAJ3Gpw71CA2ucaVchzP6rslBsw5qfB0fHhV24jorLJcqleX19f+PrymthERErKyUnEhQurEXlxNfRZt2Dt3gHd31mPum0HQ2de8hjW+zISL+Py4c3QmVnAzMoWBdlpSL8VB0lfiNptBsC5jg+05lawsnPE3esXcCNiH9q8/CVqNQ9AbvpthK/7EGHLpqJms66wqvLwY1/zs+7izNZ5OPPHAgBAxykvoePUVx54qICcbkfFIvXydby4/Xs4e9aGR2tfRG/fjxv5VvDUaIr23D5qQa8IqfFnAQBOTiy4ROUlS8ElehIEB49Hly6LlB6DKphoOd/70NgRHIxZhcyY7dDoLOHdPRBN+r4J53oP3/GQfiMGZ35bgOh9S6E1M4MkSTDm5RVb5+K+5cXvpNVCZ2WFkJ/HwczWFmZ2ttBaWCDrzjUEf/MKbJ1rwtzaHhbW9jC3toe5lR3Mbe79/U7MSUTumAtjYSH8JwSi47TRsHUp++IOpdn88nsYuuLzh65zv7BmJt5BQXYu0q7egLNnbew5egsXDpyEa/VCxJz4GNk+XeDda4xi5RYAUq+ehdbcDvb2HorNUBmJ9poWldw5P1bB1Wq10Gq1yMnJgYWFBbRabZlvBhqNBnq9vlxDEsnB3b2r0iOQCYiSc0FBFmJjN+FE9ErkJ0fBwqkh2o+Zj4bdRsLCtvS9oWkJF3H4h7dwKyoERkMhtJaWCJj1FjpMGgUzSwvE7Q/DnQtxcGlUD1tGv49WrzyL+l1aoyArB/mZ2UV//v79nejLSDMzQ3rBNaRdi4U+OxuG7Jx7X3Nyip5ba2GBdm8OR6d3X4N9dZdy/xs06Fn6B7HuF9v7/49yqF0TDnVqYtvrs2DX3h/X1vyCum0GwaNVHyRFH8HJdTOh0enQKOBlxfbipsafhYVrE2g0/+4cv2olymtadHLn/FgFd+bMmdBoNDAzMyv2PdGTwMtriNIjkAmoPee7dy8hKmoZomM3wViYDTuv/giY9A1q+nYr8/04524S9nw6COmJsdBotWjYoyPi/jwKrVZXdLaCBj06oEGPe8XRyqEKIEmo29EPOnPzose5XwDvf/2hwwto99Z/0P/r90s8p9FgQEF2LgqysmFuYw1rhyqy/Vv8/RRffxe+4lfcPh8LjU6HlqMGw9mrDmo+1QS9PpuMYxFJSNr7J5r0eQPtX1sIjUYDr87/Qci3Y3Ht5E54dhqu2Fklrp+PRD2nFoo8d2Wm9tc03SN3zo9VcGfNmvXQ74mISH5Gox7x8btw5NJK5Fw7CJ1NNTQfNAGNe4+FztwSF/cux+mNn8GqqiuaD5kGV69Wxe5vKCxA9J6fcGz9B9CnpaHZsD7o/837sHdzRci85Ti6cDV8hvaCs1ede4cp6PXQmZvDqb47Ui9fR35GFmycHYseT6PRwKDXQ2dmhtAFP6MgKwdt3xrxwNm1Oh2sqthV2IfH/mlj4Nu4tDsUTQYF4NLOg4jdcxgN+3ZCz9mTcc29FWq4AynLd8O2mXvRDwRmltYoyMmApb2TYuXWqC9EQcpFOHkNV+T5idSGvwchIqrErl7dg583tsa+fa9AMhSg29vrMGrNdTz94mzYuXogfP3HiD+2DW5NO0KrM8f26f5IiNgH4N7e00v7V2PthPo4/ON42LjXQo0W3ugzdzrs3VwBAI2f6QpoNIjddwTAX3tn/zrlY40WjZF29Sayk+8W3Xb/cXVmZrh1JhrHvluHNm/+By4N6sJoeNRL/VaMmxEXkHQuBi/vXoYhP83G1Ni9aNS/CyLW7MDqWasAAG7Ha6IgNxOFuZnITb8DALgVFYKMW7Fw81buwgrpN2MgGQrg5NREsRmI1KRcHzJbtWrVI603cuTI8jwNkSwSE8N42UsBqCXn/Px0bAufhfRza2BbvycGzt5R4mpjiReOIO7QenR4fTG8Or0AANhbkIuIzV8g/cYlRO7/GlmXYlCtR3eMDfoR0TsO4OLOg8hLywA8agAAqtSqjrqNLXDn4AZ4vp4Lo2QENBpooUFqy1s480sKspJS4NqoftEeT61OB0NhIXa/Ow81WzZBm7EvFC035fGr8aEnUdffr+j71LhruH0hDg61722bubUVNM/8B04pBlye/wO8HQbD3MsODbuNxPFV/8W1E7/DwaMJrp34Hc0GTIJ3rzEmmftBUq/yDAqlUctrmh5O7pzLVXBHjRpV6m1/f4NjwaXKICJiMXr35puk2qkh5+vX/8TekCkwFmSh88TlaNh91ANL483IP+Hg7o0aTTsVLXPxaoVTm2fjZuSfcG7fFiNWfAKPts2h1Wpx5+IVZK34tWiPrCeWQ29hQK0mLoj7MwEtUBUGGKH7aw/u7RbVIeWmwPrmBnjibLHn3vHNMSSePInXl/VByNxluHY0AjdPRaHze2Px9KvDTFJyD325tFjBtXF2QLXGnrgRHoWGvToi5CpgWc0VrXwmQu99A+G/fIRe729D496vwdzaHmkJ0SjMy0Lfj/egurey/82kXj0HM1s3WFn9+7NKqJUaXtNUNrlzLlfBvXLlSollBoMB8fHx+O6773Dt2jWsXLmyPE9BJJuAgCVKj0Am8CTnXFCQga3hHyP97ErY1g3AwJnLYeda+imjzKzskJd+GzoLK9yJOYlDWychJeQwzOzt0bB3R4zatRRGo7GobNZo7g1JAjQX18Kz63kAgJ+5E47Y2SPur8fU6f5/5FrPurUx384K+vNZ0BzLQHJSFjr08IKVtTlen3McmSm5+PGlnXD2rIpGHWqh+9CWeKpvKmw0Kx44bxxekecfCsCNU1HwHd4f8aEn4dasEayq2qNaEy8YCvWI3nEA192awcLB4d75bT0A9xY9EHNgNTIS41C1ZgN4da5cx7rGhEfA0tVH6TEqpSf5NU2PTu6cy1Vw69Sp88Dl9evXR7du3dCvX7+iy/gSKc3MzEbpEcgEntScExKCsSdkMoz56eg4bgm8e5Z9TtaqNb2QnXoLO5cMRnLwIdh6eWLYunm4/GcYMm4m3VtJkoC/Hqe15z4c8rHAyd9iMO31LkWPc3hPDFp3qQe93gAzMx2MRiM2LDmBkyHxiL+Ugu9nB+P72cHw8auFhj7V4VHfCWsOvopadR1hbVP6BST+LgLp8MS9c+puXu2Eup2ehmOdWo//DwVgzwcLcHjBSrg1a4hbkdFo0MsfXf77Ojxa+6Lzu69i8yvvw6deMzxddwJgee8+NZp1xpGlk1CYm/XXP4tyF3R4kPzkKDSu3UfpMSqlJ/U1TY9H7pwr9ENm/fv3x4YNGyryKYiInmgFBVnYeOxd7Nz5HCycGuD578+ica9XyyxfmbevIiL8O+jzspBxLgrPrvgc70XvQIvh/QENYGZpgYKcXGh1OnhpVqC+tAwAMHFSZ1wOu4VZb/6GuAu3sWnpCdy+mYku/RrBzOzeMbRarRYXIm4hOzMfX/z8LP44PxHRxk+x+fib8KjvBEmS4NWk+iOXWwBogapogapolGONfdM/wXyvAOx++VkkXyr5m8CHuRJyEpHrfkfg1sV49eAaDN+wAJLRiLWD30Lu3XRkdRqEmgOfQfzcJbhy5Nei++XeTYKrZytYO1QDgEpVbgtzs1CYdoXH3xLJqEKvZBYXF4f8/PyKfAoioifWjRsh2B0yCYbcFPi/+T0a9x5bZvHKTbuN0xs/RdTu72FepQqsnari6VeG4KmRg6D969jZ5ItXUN2nASxsrGE8NhtLV57D2FfaoZmfO9CpKj5eMhhbfz6Foa2/g5WNORq4AhMHrURNVwvUq2kFt7ZN0cjXDT2HNEUdL2fUqF38whHlKYfWNhbYH/M2Ni45jh/mHsKhlb3Rdpg3/P47BzV8vcu8//Wjp2FmZYkGPf0BAI0HdIeTVx1sHPE2Fvd+E8/NOAGPUVuw5+YQnNk6FxeCfkTNZl0RtfM71O8wFLbO/26vcUW6ey0KAD9gRiSnchXcQ4cOPXB5WloaDh06hIULF2LQoEHleQoi2YSFzULbtrOUHoMq2JOQc05OEn6PWoi000tg49EJA+YFo4pbvYfeJz/rLs5sm48zO+ZDo9MiYOZbaD9xJI4uWoPTq39DlVrV0H7CSMTsCUXypXj0HuUKTyxHUoIBZ3dcxoWn6qKZnzskSUKf55rh6c518cPsYGz8IQyx+TqMH+yLrFw9Lty4jtBN4biamI9C/b3TgpnpNKhT4175rdGmCRr4VMfgUS1hYfHv/hdiY2uBUZP98Z8322LLz+FY/EUwjjYfiFbPeKHr1z/AqX7pxx071KmFwuxcZCbeQei85ejz1XRUb+IFj+nv4fQrb+HMtvnwHTQFncYvxZ1Lx3Fp/ypkJMah9cjP0Lj3a/9q3oqWGn8W0Gjh4NBQ6VEqpSfhNU3lJ3fO5Sq4Xbp0eeBP8pIkQafT4bnnnsOiRbx+NFUOdnaVb88Nya+y5ixJEhITwxAcsxqZl7ZBo7NA+7GL0LTvm0XnnX2Q7JSbOL/zO5zd9Q2MBYXoMCEQnae/WnThhXbjA5GfmY3jSzbi6KI1yEm6hYDXW2D8yPYwgw5ZPSyxLvQ11Kpzb/3779lZ6flY++1RONiZIWLJMLi7Fr8Qg95gxPELt7FmXyw2BMchLiEfcQn5wPGjsLHSon2AFzzql+8T/xaWZnhhbBt06NEAz3f5EeE7YlGt8150nFr6h9FsnB1g4+KIc5t3o4p7DYRcvbe8iXYQcntF4MrhzWjYdSSsqrrAo1VveLTqDaNBD62uQn9hWS4ZSVdgZl8LZmbWSo9SKVXW1zTJS+6cy/WKP3DgQIllGo0Gjo6OqFOnDqpUke+SjETl5ePzqtIjkAlUtpwLCrIQG7sJJ6JXIj85CuaOXmj3ypdo2P0lWNo5PvA+kiQh8XwoToR8jqTde6G1sED7N1+A/9RXii7QcJ+lnS16fjoZ3ftnIeN2Dvo+5VVUZgHArooV7KqUvDpX3QYuWLJzFKYOXY1ub2/Blln9kZVbiLDzt3HowkWcPJ+F60kFAICaruYY2MkR3gPaonnb2mjasiasrM1LPOZj/9vk67Fifii+nX0A1lUsMGzt3FIvv3ufV0B7ODeog9DVu9BgykQ4A/fOlGADOHo0QfzRrZAgFbtPZS63AGDUF0CrU+YKak+Cyvaapoohd87letV37txZrjmIiFQlNTUa58+vQHTsJhgLs2Hn1R/dJ36NWr7dSt1jW5iXjdiD6xCxbx4yoy/Ctn5d9Js3HS1fGgyrqvYPvI8nlgMaoEX7po89Y8deDbEhfCLGB3yHZqM3AwCsLDRo3tAWPQJbo3lbD7RoWxtu7lXLeKTHF7L7EmaM/w1Jl9PQfuIodPtwXNHlfFMvX4e5rTXsq7sUu4/RYIBWp4PzpOm4MnI0Un8IQq2b3kCDe7db2jvBzMpW9lkrmtFQCOjK/wMDEf1f5f6xlojoCWI0FuLKlT9w5NIq5F4Pgc62GpoPnojGvV576Pls02/G4vzO73DhwDLoMzNRrXtXDF04HZ7d2xV9cOyf7p9yqwXKVz7rNXTBL2ffwZVLydBogEa+bv/62NpHcfNaGqZP3objWy6hSWcPPLdlFdx87h17eufiZfwybBIko4SMG0lo+9Z/4PNcb9Tw9YYkSTicoINkMMC6hhs6jliEU+s/Ruh3b6BRzzGwcaqBE6vfR3XvdqXuGa+sjIUF0Gj5v2MiOZXrFVWvXr3H/jStRqNBXFxc2SsSySwtLQYODg2UHoMqmBI5Z2XdRHT0akReXA1DdhKs3f3R/Z31qNt2MHTmDz6VlmQ04vqpIIQfnIM7wYdg7uCA9m88jzavvwDHuu6lPpdcxfbv7Ktawffp0p9TDpnpeVj7bRi+nb0ftg5WeH7dPPi+0K/o/yF3r97AmsHjUL9rGzz14kDcOHkOYd+uxc3TF1Bt8juw8/KEZDDA41yje3vAWzVEXkYKki4cxtlt86DRmaGmbzf4v764QrejIhgNhdDoHv2Ua6Lhe7cY5M653IcohIeHIyoqCk2aNEGjRo0AABcvXsT58+fh4+ODVq1ayTIoUXmFhX2E3r3XKD0GVbC/52wwFCAnJxEGQwGMxgIYDAUP/LvRWAiDIR8GQyGMxntfDYb8ouXFv/7/fne0BhgLspB7IwxaM2t4B7yIJn3fgFPdZqXOl5eZikv7ViBy3zfIvXYdVXya4tkVn8P3+b4wty55HObt6Dicmfcu+k9tjVrezrIW24piMBgRE5WEM8cSEBF2DcfC4nHjQgq0Oi3aTxqFbjPfgqV98Q+1JV+8goKsHHSb8Sbs3VxRu20LONSugZ1z1yL1sy8xcMLee8fS/m2HdlzoBvSesR0F2enQF+TCxtHNxFsqD6O+EBotD1EoDd+7xSB3zuUquIMGDcJvv/2GvXv3onv37sVu27t3L4YNG4ZPPvkEAwcOLNeQRHLw9/9C6RHIBO7nfO3aPuw78g70Gdcf/c4aLTQ6S2h0Fn99Nf/b9xZ/W37vNtfqNtCaOaBG/2/QoOuLsLAp/YO1yZcjcPzoZ7i5bQckgwE1+vfBSxvmwqNN8xK/CZMkCZeDj+H0vI9x6o84VK1uixcGP4UW3pWz3N5JzETkseuIDLuO0LAruHwyEXlZBdDqNKjdzBU1O/VAm2nNUb9rm1KvXmY0GGBmaYHcuxmwd3O9d3aE5t1Rc0A6bv2wCZFbvsJTz70HALgcugkWNlXhP3YxJEmChW1VWNhWzn+bR2E0sOA+DN+7xSB3zuUquDNnzsT48eNLlFsA6NGjB8aNG4cPPviABZcqBTu7iv0VLFUOOp0V1oeMQ8aFDbCp0w0Bk3+EuZUttGYW0JlbFn3VmVlAa24JnbkFdGaW0JpbQqvTyTqLobAAV45uwekDX+HuyVOwdKuO7h+8Dr8xz5X4ABUA6AsKcHbDTpyYPw/xEbdRu5krPl/xLPoPbw4Ly8pxjGZ+XiHOn76FyLBrCD52GbFhN3HnagYAwLGGHRq0q4kuMybAo20L1GrVFBa2j3b5TVtXJ+SlZ+LA3vOoYV0fGo0GHmcbopr76zjmdQWJ5w4hp/soGPWFiPp9MQz6fPT9eE+luiLZv2XU80NmD8P3bjHInXO53jFjYmLg7Oxc6u3Ozs483paITEKSJMTEbETIsQ8hSRK6TF6JBl1fVKQAZafcxIXdSxC17zvk37kDp3Zt8J/NC9F4YHfozEq+7eakpuHEko04vuhH3L2ZhRZ96mPFV/3RrrunogVOkiRcv5yKiLDrOHPsOo6ExSM+IgmGQiPMrczg6eeGRkOHIqBtc3i0aY6q7m7/el53v2ao2rEjYr5ejKb2w2Dn6gFJkmBp54D6/s9hz6eDYDQaYFetNpoNnAy76nUfusf8SXJvD27l+AGGSC3K9Yry9PTEihUrMHr0aNjZFT+eKjMzE8uXL0f9+vXLNSARUVkyMq5ie9h05MT/iSqNn8fA/y6EtUM1k87woHPX+r00EG3fGoHqTR/8wYmU2Ks4/PVKnF6xEUaDER1fbIopk7qiQdPqJp39vsz0PJw5fh2Rx64jJOwKYo7dRGZyLgCgRkMnuLXpiH6j7pVZN99G0JnLs9ex6GINs2YgrPsQhK2Yhg6v/T9DG8casLCpgty7ibBzcUfdduq6Quad27lwMZR+sQ8ienzlKrizZ8/G0KFD4e3tjVGjRsHLywvAvT27K1euRFJSEjZt2iTLoETlFRGxEC1aTFB6DJKR0ajHuXNLcCx8DnTWzuj94R9IjT9r0nJbkJOJuJD1xc9dO/9dtBw56IHnrpUkCfGh4YiYPwsnf4uBvYsNXnunE4a/0QbO1exKrBseGo+gTefQpb83OvTwqpA9ukf3x2HGxN+QEJUMSQJsHSzh1aYmnn5rNDza+MKjtW/RldPkdL/YAn9drAGA3fSN2PFeZ1jaOqBeuyGo5t0O0XuWwsaxBuzdiu8widg8By2GTpd9LlOTjHrodE/e+XtNhe/dYpA753J/yGznzp2YPn06Pvvss2K3tWjRAsuWLUOvXr3KNSCRXPT6XKVHIBklJ5/FzsNTkZcUAZ8BE/B04GyYW9vh9sVjpd5HkiQYCvJQmJeFwtzMv/78/+8FuZnQ52ahoOi2zKJ1M3VJMGTnQJ+dDX12NgxZ2dDn5EAqLAS02jLPXWsoLMS5X/fgxLwvcflkImo1dsYnSwbjmREtSlwVrLDQgN2bz2HNRztw+mIOHKvosHrRUXRsYY8JPwbCt3Xp59R9XDvWRWD6qM1o1MEdz674Ah5tm8O5Qd1Sz79bXkajEVqttqjc3i+291Vr1Abdpv2CC0E/YvenA+Hg7o3ctCT0fH8brOyLXxpYn59TITOanKEQWjMeg1savneLQe6cNZIkSWWvVrbExERcvXrvHatOnTpwc3syTtdy6tQptGrVCkOG7IOLS3OlxyGiMuj1uQgP/wqRZ76DpbM3+ry7DNUatSmxXl5GCg78/hZST5yEIScH+qxsGLKzIRkMD318rZUVzOxsYWZrC52NTdHf3arZwsLOBpb2trC0L/73uh39Sj13bV56Jk78tBHHF36PlOuZ8O1RF+OndIF/T68SJTIjLReblp7E2q/+xI3bBejSqgreGdoevZ72wO9Hr2L6shBEx+diQCdHvLFkFOo3cn3gcz4KSZKwbG4IvnonCJ1f8kHAT+tlO+SgNFcOncD6sR/De8Z/4dKhXYly+3f5WXeRfjMG+Vl34d6iR6lXf1OD1a+3h4dtbXTp8uSdw5fIVJKTI7FlSwDCw8PRsmXLMteX7ah2Nze3J6bUEtGT6caNEOw5/Db0mTfgN+IjNB8yrcSFFCRJQlzIBoQufwvGgkI8PWoQrBzsi8roP8uphV3x5XKdSSH1ynUcXbga4Ut/QWG+Hv4jmmDK5K7w9q1RYt3rV1Kx6psj2LIkDPmFEoZ2d8L0oV3Q3PP/H+Id0KEu+rWtjTX7YvHBiiPo32QBRvR2wegfRz/2pXQNBiOmTN6KoEXh6PrBGwj4eKLshz4YjUbcib6MhGORuBYWieiQSGReiEbVFs1RO6UFnB9SbgHA0s4R1Rq2lnWmykoyFkLL04QRyeqxCu6qVasAAC++eO+Tyfe/L8vIkSMffzIior/k5d3Fb6dnI/3sKli7+2Pw5zvh4N6oxHpZt69h79qXcGd/MNz69sKopR+gSg3Tftjs2tHTOD3/Qxzfcgm2DpYYNbE9RrzVFtVqlPzE/+mj1/DLuxuwI/QuqtrpMHFIc7w1qClqOD/41Fo6nRYv9WqIF7p64vvt5zF77Qls9PwSIyb647V3O8PBqexTcuXnFeLVwLU4vjUGA3/4CG3GvlDubQaAzKRkJByLxPVjZ3D2YCTSz5yFPjML0Ghg5+WJmnU64un+H8LTf5iq98b+G5KBBZdIbo91iIJWq4VGo0Fubi4sLCwe6RgtjUYDQxm/ElQSD1EQR15eCqysSj+tHVU+kmREbOyvOHR8FiR9PjqM+RLePceUKEiGwnyc3/k9jv/yPnQ2Nhj200doMjDApLNeCTmJg9On4dLRm6jR0AljJ3fEoJFPwdqm5CVYzxy/ji9fXoUT57Ph5W6FqUP9MLJnQ9hYldzncPzCbQRM3I5tX/RBt5bFL5KQkV2AeZvOYN7GCJiZaTBtwQA8N+bpUmdMTEjH2OFrcTk8Ec+vX4jGA0qew/xxRG3di/3LdyItIhK5CTcAABYuLnBo4Ys6NbqjWqM2cG3wdIWdzisvPRlWVUueT/hJ8/PLTdHQrQPat/+s7JUFxPduMZSVc4UeonDlyhUAgIWFRbHvS5OZmYmMjIzHeQqiChMcPJGXe3xCSJKE+PidOHT6K+QnR8G+4SAM+O+3sHWuWWw9o0GPS3+uxPEtM5F3KxG1//M8rJITTF5ur4VFYFXvUfDwccH3219El36NHroD4NSRazhxPhu2Vlr8d3gbvNizAcx0D17/6PkkZOqN2Bt+o0TBrWJrgYlDfHD2ejy2HkjF/h//fGDBLSjQ4+cFh7H4k/2wsrPAK/vXonbbFuXa5iMLV+H3iZ+iavNm8PJ7HtX+0wbVGrWBnWttk527N3jhK+g9Y7tJnqsiGfPSYG5uV/aKguJ7txjkzvmxCm6dOnUe+v0/ffrpp/jwww+h1+sffzIimfn5TSuBHXkAACAASURBVFN6BCqDJElISDiAA6fnIC/xFGzqdMXA6UtR3btt8fWMRsQdWo+wX/+LnPircOvXBy/sXYpqjT1x41SUSWe+fSEOa/q9hHotq2PDnldLnBHhQUZN6oDWneth6fg1eOWrg/hkbRhmjGiLwB4NYG5WvOhOfLYZxg1qCq22eGk0GiUs2xmNd5ceRaFBwnsL+mHEW8X/nQAgZPclzJiwHUlxd9Fuwkvo/uG4B56+7HGEzF2GXdO+hO+QaWgzao5iF6PwGz5LkeeVk74gD/qsW7C3f/j/T0XG924xyJ1zhV86RaaTNBCVGw9Bqdxu3TqCfae/RG7CYVjXbIP+n+5HTd+uxdaRJAlXw37DkV+nI+viJbh274pXtn6Dmi0aF61Tq2XTR35OSZJQkJ2DvPQs5KVlIC89E3lpmchLz0RuWgby07OQ+7fl2vQLyEnLQ3ZaPnLS85GTlo/8nEJ4+Lhg1faXH6nc3tfkqZqYH/oOXo24iWUT1uKVrw7i4zVh+GBEG4zs2bBY0dX9Y+/u8Qu38cbCvTh1MRsv9HTGuJWvwdWteGlNiL+Ldydvw4ltMWjSpTaGbVlV6gUnHseBT7/H3g++xlPD3odf4CeKXmnNxavsX1NWdlm3rwKQUKVKXaVHqbT43i0GuXPmtQGJSFG3b5/G3ogvkR2/D5bVmqP3h3/Ao1WfYsVJkiQknN6DI7++jfQz5+DcoR0Cf97w0F+z6wsKcHbjLtwMj0LuX6VVk3axqJjmpOcjOy0PRsODfwjX6jSwdbCCTVVL2DhYwtbBCtWr2sLe2xH2Va1QxcEKdlWtUNXRGl36e6Oqo/W/2v7GLWpi7qFpGHPmFpaNX4Mxcw/h4zX39uiO7NkAFub/P6vDnbRcvL10F1bvSoaPpw3WhY5Fqw7F9/zl5RZi6VeH8MPnB2HvbIUX1i9As2F9yl1EJUnCn7MWYf/H38JvxMdo+cKMcj0e3ZOReO9y9iy4RPJiwSUiRdy9ewm7Tn+OrNjfYeHcGAHvbka99kNKFLFbUSEI3TIFd4+fhEPLpzB6/0p4di35q/j78jOzcOKnTTi24FukJGSiZiMn2DlZwcbBCrWqO8C+kdVfBdUadlUtUcXBGvZVrWDvcK+03vu7NaxtzE26d9Lbtwa+OjgNo8/cwvLxa/DqvENFe3Rf7NEAy3ddxPvLwwAAMxYPwAtjWxfbsytJEvbviMasSduRkpAJ/6lj0OX912FpV/4rZEmShN3vzcOhOT+h9UtfqOLqYZVFRuJlaHQWsLHhaTaJ5MSCS8KIjl4Db+9ApccQXmFhFk6dmo/Is9/DvIoHuk5ZDc9Ow0ucf/b2pRMI3TYFySGhqNK0CV76Ywka9ulUaunMTLyDo4vW4MiCpTAUGuE/ognefrsbGvo8WcXB27cGvjw4DWPOJWL5+NV4fUEIxs4PgUYDjOzritdXvAon1+IfSIqPSca7k7bh9M7L8O1ZFyN2rYdro/qlPMPjkSQJO6d+gcMLfkbb0fPhO2iyLI8rh+g9y+Ddc7TSY5RLZuJlmFepA61WnvMvqxHfu8Ugd86PXXBPnTr1yOvevHnzcR+eqMIkJ59RegShSZKEK1d+R/CxmTDkJsNv+Ez4DpkGMwurYuulxp9FyI4pSNqzD7Zenhi+6Rs0HdKz1LMSJF+6gsh503Fo5TnozHWo39AVP+x4ETU8HEyxWRWmoY8bvjgwDaOjknDl4h24eTjA9+niV0vLyS7Aj58FY+ncEDjWsEXg1m/ReGB32fY8G41GLHl5Nq6tWosOr3+Lpv3elOVx5ZIcdwrAk11wr0TF4H/s3Xd8FMX7wPHP9cvl0js9kFADhN6LFEVAQJqiKPZeUBTxq9jwZ+9ib4ig0hUQAqL0FlpCS4AASSAhvedydff3x4VAjAiBI0eSeb9e593Nze4+mzHLk7nZGY1vM3eHcU0T1+76wdXtXO0Et2vXrpd88ZRl2a03IAjC+fr2fcfdIdRbhYXHWRn7IqUn1+HZ4kZumvEZ3qHhleukH2PTH09zZsUfeDRuxIS5b9PxtpsuuLLYqZ3x7HnnRXYtO4p3sCdPvDKEWx/qjrfv5Y2FvVZFtgshsl1IpTJZllmz5CCvPb2SoiwT/Z9/mP7T70NrcN25S5LEl7e/xOkFi+n/2De0vuE+l+3bVfo+/Jm7Q7hitsJkWgRdeO5iQVy76wtXt3O1E9wffvjBpQEIglB32e1lxMV9zL74T1Ebw7hh5nKadr+pUp3irBQ2r5lG2pLf0AYFMubLV+ly91hUmqozEsiyzNHVm9j1ziwObzxFWKQfr311M6PviEanrx8rQR1PyGL648s4+FcKXUdFMPDDL/Fv3tilx5AcDr645QXSlv3OwKlzaDlIrEZ5NciyjLXgJN4tJrg7FEGoc6qd4E6ZMuVqxCEIQh2TkrKWv3e8gL0knejx0+k0/nnU+nNLyZYVZLF3wSwOr/0KjZcXw9+bTveHJqHR66rsy261sv/XVex8911OHcwhokcYs5fezqBRbapMoVVXlRRb+Oy1v/nxo60ENfNhyh9f02r4AJcfx2G38/m458j4YzWDnp5HxIBJLj+G4GQuzEa2lYoZFAThKhA3mQmC4FLFxams2PUSJUl/4NlsCDe/vQbfhi0rPpccDhJivmTn/OdBoWDoK4/T6/HJ/3q3/z9nROg8sgVvfX4zXfo2qzfDn2w2B3/8up+3nltNaYGZwa8+Sd9p96DWVV0C+Eo5bDZmj55G1p9/MfjZBTTvM87lxxDOOTtFmFjkQRBcTyS4Qr0REzNZLPd4FTkcFuLjP2NP3Eeo9H4MmbGI8N7jKiWimYk7WD/nXooOJ9D41glM+fRpDAF+VfZVnJHNtk9+Ytfnc7CU2qo1I8JDo+by5XLnV+qlJRYKck0Ehhhr3RCG4wlZLP5+D0vm7qEwy0TPCa3o/96X+DZpcPGNL4PdYmX2yCfJ3rSZoTOW0KzHqKtyHFeKmTWqVi/VW5RxAhAJ7sWIa3f94Op2rlMJbmlpKe+88w6xsbHExsaSn5/PnDlzuPNOMX5MgKio2n239bXs9OkNrNv+P6yFJ+kw5im63PoSGo9zU1mZC3P4e+UjnP51Ed5R7Xh4+wIa96i6as0/Z0S47cHu3Plk72rNiDD5sV5kphXy48fbWPjZVopNEgBeniqC/dQE+WnwadmUgBAjASFGAkOMBIZ4VbwOCDFi8HR97+ilKCm2sHrhAeZ+t4Oj29PxCvCgwx0T6XLPOELbt7pqxzUXFvPFmKfJ3baDYf9bTuMuw67asVwpasRj7g7hihRnnEBlCEKrNV68cj0mrt31g6vbuU4luDk5OcyaNYumTZsSHR3Nhg0b3B2ScA1p1Oi6i1cSqqWkJJ0Ve16l+MhSPBr3Y/ysZfg3PbdUrixJJP75HTvmTUeWJEZ99hLdH7y1yswIrpoRIelwJn9/uIKH1uWi1yl5ZFR7BnQMI6vATGa+icz8MjLzy0hLPkXcPhvZ+TZyC+1IUuX9eOqVBPlpCPJT49OyKYGh55Jf57NXxXtPo/aKhkvIssyerSl8/d02diw8grXMRscbwpm08CPajBp8VYYinGUtNbF99jz+fvNbJKuVYTP/oFH0kKt2PFdr1Pl6d4dwRYoyTqDxaebuMK554tpdP7i6netUgtugQQMyMjIIDg5mz549dOsmpl4RhKvB4bBw8OA3xO59D6XWk+umzSNiwG2VEr2so7tY/9P9FMbF03DczUz5/FmMwQGV9pOybS+bnp9OwqYrmxEh+VgOH075npjtBYQFaHjj3u48MLIN3pfQE+twSOQUmiuS33MPZ0KclnaaQwftZOc7E2KHVHUf903vz7NvV6/Xs6igjAVf72L+d7GcOZpHcLgP/WY8TOe7xuLbOKxa+6ouWZaJ/XoBq1/8FFthIW2G3EenW17EM+DqDH8Q/t2pI0k0NLh2BgxBEJzqVIKr0WgIDg52dxiCUGfJssTx48vYvOdNbEWniRr5KF1vfw2tp09FnaKME2xc/gRnVvyBV+uWPLB5Ps36dv3HfmQO/7aORbdPpWGbgCueEWHr2mPEbC8AoGd7I1Hh/hj0l3Z5UyoVeOjUeHtqsTkkZBnUKgV6rQqjhwY/o45cHzOZAdnkFdo5nmamoNhRaR9r5+5g2psXXozifDmZxcz5cCvzPt+B3eqgx/hWDP/iA8IH9rik7a+ULMusmfEem975loZjx9Dvxg+qzEksXH12qxlLVjx+HWr3MAtBuFbVqQRXEP5LcvIqmjUb7u4waq20tM2s2/0alsw4jBEjGfPmavwat6n43FyUy94Fr3No9Wdo/f0Y+93/0XnKzVWGIyT9tZ1NLzxP0s4zdB7Rgh8W3Yne48puALv90V70v7EVK36OY9HHG1m2YTWBvmrG9YukeZgXDkkmr8hCbpGZjKIs8ovt5BfZySuyk1/kwO6Qq+xTo1bg763Gz1uNn5cKf2814X3aEO3vgW+AAV9/A74BztdRXRtdNDlNS8nnu3c3s/C73ag1Sro9fCd9nroLr9CgKzr36pBlmdXPvM2WD36g9/0fEzXqiRo79tWQvP03mvUa4+4wLsvpPTFI1mLCw0e6O5Rrnrh21w+ubmeR4Ar1RlLSUnGRvAx5eYdZvff/KD2xFn1YN256axNh7fpVfG63lHFwxSfsXfp/yLLE0Fceo/fUKVVW1Urdvo/NL0zn0PpUIrqH8cOf99BrcAuXTffVuLk/j7w4iKMHMvlsxgBW/BzPlqW7+WpFGf7e6vJkVYWft5rQTpG0Oi9BdSarBnzOJq8BBgyeVza29qzjCVm8/fY6tsw/jMFHx4AXHqXXo7fj4edz8Y1dSJZl/njqDbZ9PJc+D82m3YhHa/T4V0PSpl9qbYK7fenP6IKi8PNrefHK9Zy4dtcPrm5nkeAK9caQId+6O4RapaQknT8OvEfhgXlofJtXmfZLcjhI2jCPHQtmYMnOoefDk7hu5iMYg/wr7Sc9LoFtM6exd+VxmrQP4vPf72DQTa2v2jy2Hy1wLkzQtlMDePdGJEmqka/+/+ngnjTeefNPYpcexa+BkWHvPke3+yei9TRcfGMXk2WZr+6aRerc+fR95Ava3vhQjcdwNQx5boG7Q7gsdrOJkuOr6NKxdveg1xRx7a4fXN3O9WMJoEuwevUkYmImV3r89tswkpNXVap3+vR6YmImV9l+y5bpJCZWnr8tJyeemJjJmM25lcp3736buLhPKpWVlJwmJmYyBQXHKpUfPPgNO3a8UqnMbjcREzOZjIwdlcqTkpayYcPjVWJbt+4+cR7iPC75PE6dWs+uXW/wy8KelCStJnLwnTTr1IfmfcZXJKWn9q5hzh1+bPjoLnw7R/N0wipu+uRFzuw7zNxRzuQp+8gJVtwymtmdxpC4+TQTH+jGmrgnGTyqDQqFgkN703ho1FzyckorxfHJy+v4+u2NlcrSUwt4aNRcjidmVSr/6dNtvP3s6kplZSYrD42ay+4tyQAVye3KX+J5/u7FVX7GU2/5hXW/Ha5UtmXtMR4aNbdK3Vcf/Z1F3+2uVHb+eciyzK5NJ5k47BvGdf2MQ+tTGfP160w9vp0+U+/ClFvA3FEPkZV4vNI+tn36E6uffbtSmdVUxtxRD5G8pfLx4n9ZyeK7n68S2y+3TOXwb+sqlR1bu4W5Nz3El3e8Qurc+fR77Gva3vgQW754lMS131Wqm5O0l5hZozAX5lQq3z3/ZeIWV46tJCuVmFmjKDiVWKn84IpP2fH9s5XK7GYTMbNGkXFoS6XypI2/sOGju6ucx7q3byF5+2+Vyk7vXUvMrKrz8tbW80jd/QeyrZQWLZy9z+J6Jc5DnEfl80hKWlqRi/38c2cWLx7Itm0zq+znvyhkWa46+KwOODuLwsXmwd27dy9dunRh7Nh1BAZWnZdTEOoLSbKTmDiP7XvfRbIW0fHmp4gePwOtwbuiTs7xfWxc8ji5m7fi160Lt3wynSY9oyvtJz/5NLGvPsmmuYfwb2jkqZeHcvOUTqjVqn8ess6QZZlNq4/y/hvrOLI1jSbtg+j1v+eJGn8DKrX7viiTJImvJr/MqV8X0f/xb2k99B63xSKc88u0m7EWJjNl9J/uDkUQao2cnHiWLh3Cnj176Ny580XriyEKglDPybLMqVN/8veuWVhzE/Fudzsjp72JMejc9EXFWSls/ONJ0pctxzO8GZN/+4w2owZXGmZQdCaL9a9/we5vfsXo78ELH43glge6o9XV/suMLMuUmWwUF5opKTRTUmShuNBMcaGZvKwSfvxmJynxWbTs1YA7V3xJqxED3b6UsORw8MWkF0lbvIyBT/5Ay8FT3BqP4GQ1FVNyIoZunae7OxRBqNNq/788gnCJNmx4nIEDP3V3GNeUnJwDxOx+DVPqBgxNBjDyxfkERpz7y9hSks++RW9ycOXHqL29Gf3FK3S9d3ylXklTbj4b3/6GnbN/RKNXM/W1oUx+vJfbVgN7/u7FvPnD+Ir3kiRhKrFWJKRnk9OS8vfFhZbzXptJKyyhrMiCqfDco6zIisP+LxPgAgoFtB/SjPs2/ER4/25uT2yhPLmd+D/SflvOdU/NJfK6ql9f1gUbPrqbgVN/cHcY1ZIauwLZbqZFi9HuDqXWENfu+sHV7VznEtzPPvuMgoIC0tLSAFi+fDmnTp0C4IknnsDLy8ud4QluJFbDOaekJJ2V+9+l6OB8tP4tuWHmCpp0G1GRnDlsFg798Rl7Fr+GZLNx3f8epO+0u9EZPSv2YS4qYcsHP7D9g6+RZbjvmX7c/XTfaq8+5mp9ro+kqKCMFfPj+PGbnaTuz+JCA7EUSgUGby0GHx0ePjoM5Q9to2i82hrR+3ih9/FC53Putd7HiM77vM+8PKtMheZODrudLybM4MyKVQyaNp+I/re6O6SrplGn2reSWezvP6MP64aXVxN3h1JriGt3/eDqdq5zY3DDw8NJTU39189OnjxJkyaVLypiDK5Qn1itJcTHf0LcgS9Rao30mvIqrW+4H6XK+beuLMuc2LKQbT9Pw3wmg0a3TmDye49VmqvVaipjx2fz2fLWp1hKbdzxWC/uf64//kFGd50W4Iw9fucpvvh6K9t+TcBuddBlVCQNbrgdD19v9OVJqu5sourtidboeU30uLqKw27n87HTyVgVw+BnfqF53wnuDkk4j7W0kB9vD6ZHt5l06FA3ZrIQhJpS78fgnjx50t0hCMI1x3kD2Xy2732n/Aayp4ke/1ylG8gyDm9l4y+PUhgXT/CQ63hw7TcEt2lR8bndamXXN4vY/Pr7FOeUMei+Dsx88QZCGtbsfK7/VFxoZvn8OOZ8tZ3U/dkENfVmwAuP0eWecXiH1Z+VDR02G5/d/AyZa9Yx+NkFNO8zzt0hCf+QvPN3ZIeV5s2rzgghCIJr1bkEVxCEc5w3kK0rv4EsAe92tzHy6TcxBp/7JqMwPYkNvz1G5uo1eEe1496/f6TFdT0rPnfY7cT99DubXn2LnFPF9JvcjpdeHkbj5v7/dsgaIcsyB3ad5vOvtrDt10RsFjtdbopg0NtvEDG0zzU1ZKAm2K1WPhs9jay/1jN0+qJau/hBXbfr95/xaNQbo7GBu0MRhDpPJLhCvZGRsYPQ0J4Xr1hH5OQcIGbPLEwp6zE07s/IF+dVuoHMXJzH3l9ncWj1Z+gCA5gw92063j6qYt5YSZI4uCiGTS/PIv1IHj3GtWTOqruJaBvirlOiuNDMip/jmPPVDlLiswhs4k2/5x+h6z3j8G7gjCt5y26a9e3qthhrmt1qZfbIqWRv3MT1M5bQtPtN7g6pxmQc2kJou77uDuOSmIvzKE3+i949Z7k7lFqnvl276ytXt7NIcIV6Iy5uNsOG1f2LZGnpGVbGv0Phwflo/SO5YeZymnQbWeUGst2LXkWWHAx99XH6TJ2CxkNfsY/jf29n3dPTSYnPIvrG5sz+eRLtOjd01ylxcE8as7/YzLZfErBZ7HQeGcHAN18n8vq+VXprN73zrcsSXFuZmZStezEXlWAtMWEtNZU/l2ErNWEpMWErLQOFApVWg1qnRa3TotJqUOm0qMufL/SZh78PTXp1uuxxwHaLldkjnyB70xZueP43mnStX8uZxi19h2G1JMFN3r4MZInmzevPHyCuUl+u3fWdq9tZJLhCvTFkyNfuDuGqstvNHDjwBbvjPkKp8aTvw5/R+vr7UKo1FXVSd69i09xHKUs9ReNJE5n8/uMYgwOq7OunkfchyzB/8wN07dusBs/iHEmS2LjqKB+/u56ETacIaOxFvxkP0+We8fg0vHAv8q2/fnjFxzbl5rPj85/Z9PE8rLl5FeUKlQqVwYDK4IHa04DKw/kaQLJakSxWJJvN+br8WbbZnOVW678eq8/UKQz/4PnLSnL/nPkR2Zu2MOx/y2ncZdjlnWwtNuTZX90dwiWRZZnYRZ9jaDIAg8F934DUVnX92i04ubqdRYIr1BtqtcHdIVwVsiyTkrKG9bEvYSs6RYdRT9L51ploPc/d/FWQdpT1Cx4ge/1GAvr04v4VswlpF3nBfbboFobBR+eW5NZitrF8Xhyfv7+R9MQ8Ins24LYln9J29OBLGlurNVz+NGV5J06x5cM57PpuCbIk0XrQPbQd/jAG/wZoPIwo1drL7m2VZRnZYcdhs+CwW5FsFo5vWcjWj6ai8/FiyCtVl7P8L+lxCWz5YA5db59VL5NbALW+dvxOn9y2FHPGXkaO/O3ilYUq6uq1W6jM1e0sElxBqMXy84+yatfLlCavwzN8CDe/sQrfxq0rPreaitm38HX2L/8QfUgwty+dTdsxQy6apDW5cSLrX/uYlx5cRkCIkcAQ43nPXgSFGvH00rl0iq38XBO/frmTHz7dRlFWKV1HR3LTt7Np2qeLy45xIad37Wfpq9+RsXotGh9vOo6ZTrsRj+LhE3TxjS+RQqFAodagVGs426feftST2C0m/n71f+i9jfR9+u5L2pfkcPDTlBfxjGhBx5ufcVmMgutJDjubvv4fnuFDadCgj7vDEYR6QyS4glALWa1F7NnzLgcOfYvGu3GVcbayJHFswzy2z3sGW3Exg196hH7T7qk0zva/dLz9JnK2/c7OXakUZpZSlGWqspKXRq/GN8SAT4gnPiGeNA/x/Zdk2EhgiBfevvoLJsOnTuQx58MtLPp+D5Ik02nKOPo+fTeBLcOv7Id0EZIkcXT1JlbO+o68nbEYmjahzwOf0mrwXTXaM9hpwvPYTEWsmvYWOi9Put0/8aLbbJ89j8IDhxj9ztZKQ1CEa8/Rv37EmneUkQO/cHcoglCviARXqDd27HiFnj1fcXcYV6SsLIeEhB/Zd/g7JFsp3Sa/RvvRT6HWnktcs47uYsO8ByjYF0fYyOHc8dmz+Dap3rREvo3DGLf8j4r3kiRhzi+kODOXkswcSv7xLGfGsSfuNIWZpRRmmrBbHZX2p9aq8Al2JsO+oZ6Eh/gSGGIkPimDnUuOYvTT0+fZB+n5yG3/Oia4OlY/+zY3vvvcBT+3W6zEzV/O2rd+oORYEj7RHRkyYzHNeo5x2/Ri3e58gzyPVJY9+BJaL0863jrignULUtOJ+d9HNJk8iZDWvWowymvPju+fpec977o7jAuyW8rYNudlvFpPIDCwg7vDqbXqwrVbuDhXt7NIcIV6w2h03ywAV6qg4Bj793/JkaSFgILWQ6fQaeILGAMbVdQpK8hi/cpHOb1wCV6tWnLfhp9oPqC7S46vVCoxBPhhCPAjpG3Ef9aVZRlzYfG/JsIlmbnImXuJP5RO4d8mdAY1N81+ic5Tbr6isbPn87lAMl9WUETsl7+y4cO5WLKyCR5yHdfd+z2hbfu6fTUzhULBDeN/4o/SUhbeMR2d0UDrkVWXrZRlmTn3vobay8igEZ+7IdJrizHo2l7u9vCqz7GXZjK8oxhGciVq87VbuHSubmeR4Ar1RlTU/e4OoVpkWebMma2sP/wVpSdiUHkG0+XWF2k77EH0PoEV9SS7jYMrZ7N74UsolCpGzZ5JtwduQaV2z6+3QqHAw9cbD19vglo1r/Hj9378jkrvC1LT2frRj+z4eiGSzUbDm0fTa8Br+DVuU+Ox/ReFUsnwOxezonQ488Y9wd0x31RacAPg0NK1ZK1bz9Dnl1S6ibC+irqpejfm1SRraSGxP7+Bb/sp+PjU/O9BXVLbrt3C5XF1O4sEVxCuMQ6HlePHf2P74a+wZO1HFxTFgCd/IGLAJFQaXaW6p/f9yca5D1N64iRNbr+VOz98AkOAn5sidz+H3U7O0WTOxCWQEZ/Ioe0J5G3bgcrTk/bDpxJ10+MY/ELdHeYFKVVqRj64gt9Mg5kz8mHu/+sHmvSMBsBcWMySR2YRPHQwzXrd7OZIhYuJX/Yest3EiKip7g5FEOolkeAKwn+QZRmrtQizOZfS0jPY7WVoNAa0Wh98fMJdOq2JxVJAQsKP7D38HfaSM3iGD2Xwa2tpGF111oOijBOsX/QgmWvX4de9G/cs/oCwjq0vsOe6qaygiIz9RzgTn8iZuASO7Uqk5GgSksUCgL5BGN5tW9Prng9oNfQeNB5GN0d8aVQaHaOeWMNvb/fn2xvu5+HNPxHWoTVzHnsfe2kpQyb94PYhFcJ/M+VnErf0Azq0ux9Pz2v3DypBqMtEgivUGwUFx/D1vfDcr//m2LEFHDjwNfn5iSgUapRKNSBhMITQoEF/Ond+Gk/PsCuKq6joJAcOfMXho7+AZMe77SSGPvgs/k3bVambe3I/Oza9SvrvK9AFBnDrrx/SfuKNdTrhkSSJguQ00st7ZQ9uT6T4cCJlaWkAKLQavCIjCQ3tTkDP+9F5+tGk+0j0Xv5ujvzyafSejJr2F8te78NXg+9l1HvPkDrvF3o/8AnGoMbuDu+aUXAqsdK0eNeKfQtfR6HUEh39hLtDqRMu59ot1D6ubmeR4Ar1xo4drzJs2LxLrp+YOI9tuA0YnQAAIABJREFU216gbdu76dPnLYzGBiiVaqzWYrKy9rJnzzvY7Sb69/8QlUpb7Xgkyc6CTQ9TfPR3VB4BdBr3DG2HP4LBr/JKR7Isc3rvGmLXzSJ3yzb0YaHc8H9T6fnIbWg969YE6FZTGZkHj5ERn0h6XAJHYxMpSjyCo6QUAG2AP15t2xDZfRIB4R0JCO+Ib6PWlabKipk1ipaD73TXKbiMzujL6Oc3sPTlniy+awY+HTvQdvgj7g7rmrJjznSGzVzu7jAqKco4waFVX9G963PodL7uDqdOqO61W6idXN3OIsEV6o2+fd+qVv39+z+nZ8/XaNt2SqVygyEUX99IQkK6smzZ9VgsL2MwBFdr35t8PQFQezWg36NfEnndHah1lWcRsFvNJG2Yz96Ytyg5loR3+3bc8vP7RI2/AZWm9s99WpKdR9quA5yJT+TAtkSKDidQmpwCkgRKJZ7Nwwlt0I3IsRMIaB5NQHhHPPxCL9pb3ffB2TV0Blefh08QY2Zu4q+F99J3+Ptum8bsWnUttvWqD19E7REgboxyoepeu4XaydXtLBJcod4wGhtdvNJ5LJYCjMYLDz9QKJTIsowkWS95n2cTW4D+/Tyg32dV6pQVZnN41eccWPMJ1rx8gocMYtJ3r9Ksb5daOxRBlmVyk1JI2bKHlK17SVi/h9ITJwFQexnxat2KZm1HEDC8I/7hHfFvGlUl4b9UxuBre+qo6vIMaMioh2PcHcY16Vpr69yT+yk6/Ct9+74jlpd1oepeu4XaydXtLBJcQbiABg36cuDAN3h7h+PpGYYsS8iyhN1ehsVSyM6dLxMW1huNxuui+6qS2P6HZa/1oeToMbrcPZYBzz9IYGSzKz0Vt5BlmfR9hzm4KIZdv67BlJwCCgVerVrStNUwQm7uQ0jrXniFhNfaxF0QzpIcDla/+zga33Bat77d3eEIQr0nElxBuIBevWaxdu1dLFs2lODgrnh4BCHLEjZbCXl5h9FqvRk48FN0uv+ej/RscnuxxPasnjf/H39/fCfHYhMZpK1dQxFkWSZt9wEOLl7Drl/XUJZ6Co2vL827jSP8zpsJadMHnVGMSxTqnvil72A6tZmRI5eiVNau31tBqIuU7g5AEGpKXNwn1apvMIQwZsxqBg36Gl/fSBwOM5Jkw2hsRNeuzzF27F8XXX6zusktQPO+E7j5nR3Yior4qNNYjv+9vVpx1zRZljm1M57Vz77N600G83n3CWz/dglN29zI8NfWMuWHLAY88S1Nuo2okeQ2bvHbV/0YwrXhWmnrzMTt7PppJp06TaVBg77uDqfOqe61W6idXN3OogdXqDfs9rJq1ZdlCYfDStOm19O06fUXqCNf8Ov1y0luzwoI78iEt+L448vRfDf0Hoa/O50+T911zXyVL0kSp3fGc3DxGnYvWIM5LR1tYAAtuk2g+UPjCYsagFLlnsuL3WJyy3GFmncttLWlpIA/Zt2KR1hXunR51t3h1EnVvXYLtZOr21khy7Ls0j3WMnv37qVLly6MHbuOwMCO7g5HuIbs2/ch2dlx9Oz5Kt7ezZAkx3kJpuI/k80rSW7PJznsxM79H/uXvkuDMTfxwPxZaA1Xts/LjkWSOLV9HwcWxbB34VrMZzLQBgYS0X0izftOILRtP3GXv1CvyLLMr9MnUnpiLbeMW4+X17V105sg1CU5OfEsXTqEPXv20Llz54vWFz24gnABDocVq7UYSbIDoFAoUCguPqrHVcktOJdu7Xn3OwRFdGHDp3fzfrckHvpjNn7NauauYsnhIGXrXg4uXsPehWuwZGahCw4iovutNO8znpA2fURSK9RbR9b9QHHiYgYP/lokt4JwjREJriBcQNeuz1V6f7HktjozJVRXi3634NuoDaveHcFHncdxx6KPaDGoJ5LDgWSz47DacNhszmerDUdFmfNZstmwW23n1f3HNhX1zpUXpWcRv+QvLNnZ6EJDiOwxiea9xxPSpjcKpRi+L9RvBacS2fzF4/i0n0KLFje7OxxBEP5BJLhCvWE256LXB1R7O0lyYLMVY7OV4nCYkWUJpVKL0dgIpVJ1VRPb8wWEd2DC2/Gs+mI03w+5CxQKcNEII4VKhUKjQalRO5/VatRGIy17T6Z53wkEt+xRa5Jac2EOep9Ad4ch1AB3tbXdaub3Vyai8WrM2K6v1vjx65vLvXYLtYur21kkuEK9sWHDk9VeBtBszufYsQUkJ8dgMp3Bbreg0RjQ6fwIDe1BUdd70NHqqia259N7+TNm2gZO9FiIrawYpVqLUq1BpdaiVGlQarTOMpUGlaa87Pw65a//Wae2JK+XYsMn91xzy7cKV4e72jr2xxlYc48wdswaNBrPi28gXJHLuXYLtY+r21kkuEK90bVr9e5wNpvzWb/+ETIzY4mMnEhk5AQ0Gk8kycZ+cyqHDs4jVHWUHg9/DjS/OkH/C6VKRcSASTV2vNqm66RX3B2CUEPc0dYpsSs5uPxjevd+g4CAqBo/fn1U3Wu3UDu5up1FgivUG9WdJePo0V8wm/O4+eY/8fFxJrBnhyMEAcMfeYo1r4/i+OYFdJrwvKvDFS5TYMTF764V6oaabmtT3hnWvXc3ni1upF27+2r02PWZmOGofnB1O4sEVxAuoLQ0A0/PMHx8ml9gnK0HxsAmlOVnuCdAQRBqjCxJLHv5TlCqGNv7g2tmTmpBEP5d3Rl4Jwgu5u3djNLSdGJK4gHo21tDv756JLsNu9nEsQ3zKTyTREDzaDdHKgjC1Ra3+C1Kk9dxw4BP8fAQNzEKwrVOJLhCvZGYWL3B682bj8LLqymZS8aT8edUfv/wHQ789gF7F7zOptn3E/vj8wRFdKF5nwlXKWLhciSu/c7dIQg1pKba+sSWRez66QU6d55Go0bX1cgxhXOqe+0WaidXt7MYoiDUGzk5+6tV38MjkAEDPuL48WUcOfILucdWsGu3A4OXEZ+wCLrd8ToRA25z25K0wr/LOb4XuNfdYQg1oCbaOjNxB3+9fyfebSbSpctzF99AcLnqXruF2snV7SyW6hVL9QrV8G+rlMmyLMbjCUIdVJyZzMIneqD1a8GkGxaiVuvdHZIg1FtiqV5BcDFJcmCx5GG1FtEut4zdBiVrFipQG8MYPDxCJLeCUAdZSgpY8txwlFpPxg/6XiS3glDLiARXEP6D1VrM0aO/cPToAoqLU7HZTKhUOvT6AKSg1qw8dSsjHrhTJLmCUIdIdhuLZozDXnKG8aNXiZvKBKEWEgmuIFyA3W5i1643SEycT8eOjxAc3BW93g9wTiF2/PgyUjf8jz+UKkbeP9nN0QqC4AqyLLPo5QcxndrEyOGL8PWNdHdIgiBcBjGLglBvxMRULwnNzz9GaupaRoxYTNeuM2jSZAjBwV0IDu5CePgIhgz5lg4tbydv1yds2lx2laIWqitm1ih3hyDUkKvR1vuXvUfh/h8Y0O8DGjTo6/L9C9VX3Wu3UDu5up1FD65Qb0RFVe9ua1mWsNvLCAzscME6np6h6MsK6F9QyqbNzrLzb0ATal7UiMfcHYJQQ1zd1ie3LWXnD88RHT2VVq3EctjXiupeu4XaydXtLHpwhXqjuvNXengEoNP5cujQtzgcFmy2Uux2MzZbCWZzHqdPrycxcR5Nmw4DoH9BKYDozXWzRp2vd3cIQg1xZVtnHd3Funcn49V6HN26iaW3ryVi7uH6wdXtLHpwBeECjMbGREdPZdOmqSQnryIgoD0qlQ6Hw4LJlElu7gF8fSPo2PHRim36F5Syydfzoknu+b28RRknsVtM+DZqJebUFQQ3KM5KYcVLI9EFt2dC7w9QKETfjyDUduJfU0G4AIVCQcuWEwkO7kxCwlzy8g5hNuejVuvx8mpG164ziIysuorZ2Z7c/3J2OANAxto3KIj7FkPj/tz26V8iyRWEGmTKz2TJszegVBuYMHgOarUYYiQIdYH4l1SoN5KTV9Gs2fBqb+frG0GvXq+5NJbzk2BT1FNkBPZk3V8PsuunF+hx19suPVZ9k7z9N5r1GuPuMIQacKVtbS7MYdHTg5EshYwb+RseHkEujE5wlcu9dgu1i6vbWXwPI9QbSUlLL3vbsrJsDhz4krS0TdjtZgAkye6SuAyGYJo3H03PHi8Rv+Qdkncud8l+66ukTb+4OwShhlxJW5uL81jw9CDspmzGjliKj08LF0YmuNKVXLuF2sPV7SyW6hVL9QqXIDs7nmXLhuLj05yWLW8hMnICRmMjlx5DlmV+2XAvptTNTJy9F+/QcJfuXxAEJ0tJAQumDsJWdIqbRyzF37+Nu0MSBOEiqrtUr+jBFYRLoFJpUKl0jB79B2fObGfLlumkpq5DliWXHUOhUDCu94eoPPz47cVx2K1ml+1bEAQnq6mIBU9fj7UwmTHDF4nkVhDqKJHgCsIlUKsNSJIdvT6A4cMX0qLFWOLjP+XQoe8pLc1w2XF0Oh9uGvQNlpzDLJ31hMv2KwgC2MpKWPD0MKx5Rxl94yICAqLcHZIgCFeJSHAF4T84578twWotAqCgIImiopOEhfUmOvoJ9u37gPXrH0GSbC47ZmBgR/r2foOCuG9I2vCzy/YrCPWZzVzKr0/fiCX7IKNuXEBQkBiSJgh1mUhwhXpjw4bHq73NmjV3sHLlWDZufBKQ+euv+1i37n5iYm5j16438fNrRUbGzoobz1yldes78G47ifWfPED+qQSX7ruu2/DR3e4OQaghl9rWdksZC565CXPmPkYO+4Xg4C5XOTLBlS7n2i3UPq5uZzFNmFBvXM4qKWFhvbDbzUiSjdzcQzRteiMGQzCgQKlUoVRqaN36DtRqvUtjVSgUjOvxJvMz4/h95jhu/3IXGr2nS49RVzXqJFYyqy8upa3tVjMLnh1FWdoORt74K6GhPWogMsGVxEpm9YNYyUwQLlNExNhqb9Op01MA2O0mUlJiiIp6AL3ez9Wh/SuNxpPRg75l8W/Xs+DZ0Qx56BVC2vZBoVDUyPFrq4gBk9wdglBDLqWtF790H6Un19G8uXO+XIulAJ3O92qHJrjQ5Vy7hdrH1e0sElxBuARqtYGJE7cBlM+ccH6SKV+1pT39/Fpy/aCv+XvHCyyf0Q+tf0uiR91Ly0FTMPiFXJVjCkJd0rpLFPFZ7TmR/AcnTvwGgNqrIbqgKCK8WuLv35aAgLb4+ESgUmndHK0gCK4i5sEV8+AKl0iWJbetUS/LEunpW9h8YgHFR39Hlh14txrLoPueIbhlN7fEJAi1iWS3UZB2hLzkA+Ql7+fY3jgsOYewF51yVlCq0fm3orFva/z92+Dv3xZ//7Z4ejYQ35oIwjWguvPgih5cod7IyNhBaGjPy97eXcnt2WM3bNifWxv2x9ztdY4dW8juhO/4bVp3PBr1pu8d02jaYzRKlcptMV4rMg5tIbRdX3eHUaNKc9PYv+x9UnetRO8TRIcx0wjv/d9f95mL8zAX5aDWGfDwCUKl0dVQtK5TnbZWqjX4N43Cv2kUDJhE9ynOcktJAfkpB8lLOUBu8n6S98dzIu5PpPKZU5Q6X3RB7Qj3blmR9AYEtEWjMV6t0xL+4Uqv3ULt4Op2FgmuUG/Exc1m2LDaf5HU6/1o3/5B2rW7j5SUGLYkfMOfb45D49OMrhOepNXQe9AavN0dptvELX2HYfUowS0rzGbT7AcwF2bTedLL5CTtIfbHGUgOOy36TUSW5YoeyLOvc47vI27Rm6Tt/wvJbqVh9PV0n/Imvg1bIksSCqUSq6mIhJivObF1EVqDD22GPUDzPuMr7cdhs5KXvB+rqRCtwQfPwMY1OnTGFW2tM/oS2q5vRaLcD+f5lWSnVvT25qUcIOnQDqwJc0F2gEKJLqANTf07EBzciaCgzvj7txFDHK6SunLtFv6bq9tZDFEQQxTqDbvdhFptcHcYV0V2dhx/JX5L0ZElKNUG2g2/j6iRj+MV0szdodU4u9mEWu/+dj4/mdy7YBYZh7dgDGxMtzvfoHHnG/5z29Nx68g6sgON3kjD6CHOXsfz9msuzMZmLkGhUHJs4y+c3LqIHlPeolFn56wCMa+OBGDw9F/ReFTuabSZS1kz6yYcVjMDn5qLSq1lzf+NxjssgoFT56DReyI5HGz69D6yj8YSOehOTHnpnNq7hs63vEjkdZMB5+wEB37/kBObF2Azl+KwmTEGNaXbHa/ToP3ASon11VLTbe2wWcg/lUBO0h6yjsZyct9OLDmHQLKjUOnQBbenhX9HgoI6ERTUCV/fCLd+81NX1OVrt3DOxdq5ukMU6lSCa7VamTlzJvPmzSM/P58OHTrw+uuvM2TIkAtuIxJcoS4pKUnn8OHv2J/4E5KlEK+WYxh03zOEtO7l7tCuabIkVSRkCuXFExK7pQzJYUOhVKHWGSolcrIkYbeYcNgsFGUcZ/2HdxLQtAOthz3Aqd2rOLF1Edc9NZcGHa7DXJyHrawYWZJQqjUY/EI5uOITjq3/Ca3BB4fNjEKppuc97xHS2tmzUVaYzd5fXyM9/m/yTyfg4RNMUGQ3Bk//FZVGj1KlYsuXj3F84y+otHoUShWBzTvR+daZBEV25ejfPxG/5G36PPQZDdoPACBp4y/ELX6Tnne/R6PO15O04Wf2LXqDjuOm03LQnQD8+eZ4bGXFDJw6B4N/GEmbfmX3vBdpO/xROox5CrvZxLp3b8VSnMegafPqzR9XdksZuSfjyT4aS9axXaTuj8WadxQApdYbfWgnIv06EBTUieDgTnh6NhRjegXhMtTrMbhTpkxh6dKlPPXUU0RERDBnzhyGDx/Ohg0b6N27t7vDE4SrzmhsQPfuM+nU6WmOHl1A7OFv+P3Z3ujDutHnjmk07ztR/ON6HlmSOPD7hxxY/hHmohwCW3Sm9/0fExTZ9YLbJO/4ndgfZ1CceRLPwMZ0HPssbYY9UPF5zol97Jr7P/JSDmLKS8c7LIKuk2fh26gVjaKHkHF4C0mbfkGhUBG3+E2yk3YjOWx4h7Ygevzz7F/2Hm2GPUiXSS9jN5tYPqMfB1d8jG+j1uiMvsiSRECzjkQMuI3d818iO2kPxuBm5T2vdgCKM06AUkn7UU8S1LI7CTFf89d7tzHkuYXkHN+LMagJxsBGFTEHNI/GUlJAWVE2AKf3rcUY3JRG0UMr6jTvO5F9C1+n4HQiBv8wzAVZaPRGGrQfCIBab6Bhx8Gc2LIQc1FOvUlw1ToPQlr3rPgDBJzjenOO7yH76C4Ob9/OoRNLscd/CoDKEIw+tDOt/ToSFBRNw4b9UKlq3/hnQbjW1ZkENzY2lgULFvD+++/z1FPOuUvvuOMOoqKimD59Olu2bHFzhIJQczQaT9q1u4e2be8iJSWGP9c/wl/v3IqHb2hFr11tJcsykt2G5LAh2a1IdhsOu7XitWS3lr8v/9xhB2RkSQJZRpadvbXIEqfj1nF41edEjXoSY1BTkncsY+ULg+j9wCeodR7OepJUsU1Jdip7fn6Zpj1G02rofeSe3Mfmzx8mL+Ugfk3aAVCclYJK60GzXmNJWP0FSpWGtPh1pB9YD4DeJ4jT+/7k2Pp5NOw4mI5jp6PS6igryOTQH7NRafSoNDoOr/qCRp1voOO454j9cQYl2SnojL4Y/EJofcN9AKg0emSHHZ3ROTezUuW8pLfoP4mizJM06DCIoMiuBEV2Z8GDkaTv/xtTXjoavRHNeeO0NXojDmsZivLp7wrPHMOnYatKdQx+odjKSpAkBwBNe4wiI3EbiWu/Jbz3OABObFlEaNu++DZucxX/D7j26Yy+hLTujWdAIwJadKI4M5msIztI2fk7lpIsSk/EsIcYALp2fZ7OnZ92c8SCUPfUmQR38eLFqNVq7r///ooynU7HvffeywsvvEBaWhoNGzZ0Y4SCu+3Y8Qo9e77i7jBqVFFRMpsOzEa2m+ly2yuEtu1T4zHYzKWY8s5gyj+DKS+d0tz0iteFnEKy2ZwPqw3Zbkey2ZDPltnKy6zlZXY7ss3m8hj3L3230vuNH//3ErDJ25eSvH1pxftDKz+Ffw5tkGWQZQpOJ7DtmyfPFTscFa9T96wide9qZ12FAiQJgNh5L6JQKOj02cd4DGyE6fMs0hsfxdTemXTLdjtKjYZSbS6ySqYwMIvUtgkoymfRyNx9BNlPRW6nPMqaJDj/KNDLFDbMpTStAJWHnvR2J9D65QNQmpKKTSqjsF0Jp9ofxSTnoG8cQXrHkzQ9cm78r91SilrrAYBXSDMCmnUgIeYrTm5bgqUkj6Y9RtNhzLQaW3Vvx/fP0vOedy9e8SqwlZVQnJVCSXYKxZnJFGclU5KVQvrxE9iKUnGUZp2rrFCi9mqExrcdkWEN8PJqgtHYCC+vxoSEdHdL/LVJfbx210eubuc6k+DGxcXRsmVLjMbKN1R079694nOR4NZvRmP9aX9ZljlyZB6bt89E7RnKmHe3EtzKtUuU2s0mSvPOJaumvDPO93np5FuPY87MwpKVhb24pNJ2Sr0efUgwuuAgwhr6odJ6otJqnA+NGpVWe95rZ7lSrUKhUCADSDKSJCE7JCSHA8luR7I7nx02O2l7DhHUKhyH1YbdYsVhtTnH2Epy+bPzobGkkrDpNI2jAjH46pEdEg6HRGZSAZIkE9TUB1mSkRwSkiTjsEvknSpGrVdj8NYy7LHOjJrek7+/i2feM+v5Pn9q5Z+PzcFk/fv0vb0Nj829qaJ89ae7+Wnaenrf2obCzFJS92fjHWyg54TWHFh7kkbtApn83nXoPbVAKmmJ+4i1mRkRtoLIpns4zj1IkgqlEhKUNhyBPnjlnaZ3QwdqnTPBTT2wi0YRDejbxhNjiIrN73+P2mFj6Pie7Mw+SX5yGn3C1eh9nDEdP55GrEpJ786hhDWFA956GvkokB0OTrV3jifNSDuAbFCR1zEPW+ujHP/ia87sWknLN58nsF9fLFlZ7L/9Cfb8/Ao973mvRm7+MgY1uerHALCaisg4vJUzBzdydMdGrHnHcJTlnqugVKPxbozGuwlNvCLxajAYL69GGI2N8fJqjKdnGEqlpkZirYvq07W7PnN1O9eZBPfMmTOEhYVVKQ8LC0OWZdLT090QlXAtiYq6/+KV6oCyshyWbX+WkqSV+HS4m7EvflLlTvqLsVvKyDm+l9Lc0+clrmfItyaVJ67Z2IuLK21zfuLaKDwYr24t8W4QjFeDYLzCgjEE+oEsYS4soSgtk6LTGZRk5WEtKcVaWkZZXiHq0iTMpTYspTbMJVYsZ1+X2pCli98Pq9Gr0XtqKDudhM5Tg85Ti0anQqlSoFQrnc8qJX4aLRb0ADQK9cE/0BOlSolSCYpSifwcEz17NkOlUqBSKVGqlCgUsPSHPbTr0oDoXk3p0SWcaHzIDQri22Ib7R1eqFTnenHtCgcKwGhR0UHyQlnew7sx24HDJrFz0RGmvzOMod+1Y9+2FJ6/ewlhTXwJQEt72RtPnOMyVSXFIEFn7wCKZJlw6VtQKFCiQG1Jo2VnI3G/ryFrkYI+k9oQu+woh5etZeqC0XQMWcGiVzaz/v1d3Pv59YS2b0XkDX1Zeu8L5CenEdaxNQAHF68huF0EXmFBAIRERZJzLIUbwlWoy4eHxpyII6hJCH3b++LdALbNn0+fJ+6g/72DANisa0T4i49w6KXXCAodj6HxuTG+52t8oCUlWanofYNRa/WX+H/kv4u66fEr2v5CzMV5ZBzazJlDmzi2fT3mrHiQJVSeIRga9aVd2EC8vJrg5dUYo7ExBkMoSqWYg/pqqS/X7vrO1e1cZ2ZRiIiIoHXr1qxcubJS+cmTJ2nRogUfffQRTzzxRJXtxCwKdZck2bHZSnA4rEiSDUmynvfagSzbkSTnQ5YdlcpttlJstrLybcw4HP/c3npemRmbzVxRfnY/4Oz5A6l8eV8ZODcGVJal8mOfX4fy95W3O1v2b892Bcg4vw4HkG0mkGW8Qpuh9fQ5V1+qfHw4O75UxqazOuc/LR/faj6TWfF1uUKtRm30RGUw4OVjQGPQozXoUev1qPU61DpnLysKBZLN2ZvqsNnQ21Nw2ByU5JnJO11MYZbpgm2lVCnQGTQYPDToPTToKp7V6D006A3O92fLPAzac595aNB7qNkUc4y/lydU7DOyid75YzyPfN5/bTaZlAwrjYI16LTO5FOWZHKLHJitEg2Dzs1p6hxtIHM6y4rRoMLbUwWyc09lZomcQjsNg9ScXcLZ+eOWyci1o1GDv7caBc4e6NxCG3YHaNTg66Wu2E9pmQOzVUatVODleS5RtlglTBYZb4MSqx3KLFKlc/Lw1GD01lGQW4YkySgVCow+OvwCPSkqKCMnswSlUkHDZn7YFBIOh0zBmRIUCgV6owaHTaK0wIKnvx6NXkVBemnFvkM7tsa/RWNMuQWkbN5DUJsW+DYJBRSkbtuHWq8lsFU4SrUa2SFRkJJGcUYOIe1bolSpymencPaeI8kUm53vSxKdvcL6sFC6jJ7p/P/87PhoSQJZqnhducxZ59y46HN1Kpc5kB0O5+/z2cfZ8n9+Jkvk5lhBdiBLEg5TFpbsQ4CM2rsxhsb9iA7oSlhYb7y9m4ubNAXBTertNGHt27cnNDSUP//8s1J5QkIC7dq146uvvqo0Pvesswmuh0cQQUGVf2Bmcw7R0U/QrNnwirLTp9dz8OB3DBs2r1LdLVumExjYgdatJ1eU5eTEs3v3uwwc+DF6fUBF+e7db6NWexAdfS7hLik5zZYtM+jZ82V8fSMryg8e/IaSkrRK41LsdhPr1j1AdPRjlVb9SEpayunT6xk48NNKsa1bdx8REWPr9HkMGPAhubmHyctzPpJOrUMyF4BsR7hCCqokioLgMkqNc67Y8ofssKBQalGotKBQolAokSUbkr0MtUeAsx4KUCiRzPkoVDqUet/y+WYVSA4LUlkuau/GKFU6UKpQKFTYitNAoUbr2xQUKhRKJZLDCtkJ+Pm1Rqv1RqlUodX6YLePwQjaAAAgAElEQVSXoVZ70L//BxVhiuuuOA9xHjV3HklJS0lKct7nkJd3GK3WG63Wm4yM7fUvwb3++utJT0/n4MGDlcr//vtvhgwZwooVKxgxYkSV7UQPbu1it5spKDhKXl6CM5EtOoIl+0DFDR0KtQe6wDY0bR+Nf7MOeAY0RKXRoVLrMOVn4N0gAqVKg1KtQalSo1SqUajUKFXnnpFkkmOXU5h+jMySfZSeTMaUkopsP5csqzw8CGsfSWj7lqBQkLJlDyWZuZTlF17R+VXMCS9dnZxSAaBUOHOJ8p6oyj1SMmc7d2VJLu+B/o/9KUDjoUarV6PRq1HrVGi0KlQaJXZ1kHPsrMqZpCgUClBQMdSgyCwh2WzYS0qxl5RgLyq+4A1kSg8PNN5eqL280Ph446VsgM7TD53RH53RD523P1q9F7ICTDnp6H2DkSU7ssOGrawUS2kB1pJ8LKX5mIvzyM3IwWHKw1GaDrJU5XgKjSdqr0aoPAIIUfvi4RGIh0cAmZl7yM9PpFOnp1Gp1MgyJCTMQalU067dfYCi/OepRKlUsnXr83h5NaW4OAWbrRSdzofw8JEUFBynuDgZD49giotT6NPnXQ4d+gabrRSDIYiCgiS8vZthtZZQWppOt27P4+3dHKVShcNh5fTpDWg0Bvbt+4jQ0B40azYcvd4fb+9wFAolhw9/T1LSErp3n0lYWB/ONfHZ+BT/eP/PzxTIsoOiohOUlmaiUmkJCuqERuNZqU5q6lqOH/+d4uIUdDofmjUbQdu2d1Wqc/b1uWeVy3pBCwqOVfqHV6ibRDvXDxdr53o7D250dDQbNmygpKSk0o1mO3bsQKFQEB0d7cbohOqSZZnS0jTy8g5X9MyeKkh0TqAuO+9C1/iGowtsR8eRD+If3gH/Zh3wDm2BUvXvY+FiZo1i2Mzll3T8tjc+WOm9ZLdRlHGCgtOJFY+svDjiFq6pGIuqUKvxbB6OZ4vmNAgxIkvOm6Bkh6P8BiYHst3hvGnJbnfeIOVwlN8g5ai4Uers1/xKewEOmwOHXcJhk7Bbna/tNsfZHwEKFSiVChTlD5VK6XytUJR3dClw5pUKJMAqO8c8KioSnPKEtvx7chkZhVQ+3EGSUNrtyHZ7pTv/K7cTWE12rKbKPeUKlQqlzoRSqy1/aFBqtSg0GpQaNQqNBg+bD0q1FlWQHlUDXfkfHRoUyvJeO0Ahy8iSA4fNQuaZYmRrGeZ0E2X2FCRbArLNhGQvQ7KZQPrv2RWUen/UniGoPUNo4h2JR0gwZWVZnDixnKioBwgJ6cKJEytJT9/MxInb0el82L79JUymTPr3fx9w9mgsWXIdRUXHiYiYQFraRgoKjjNixGJCQ503tNrtZjIytld8XR4Q0I4OHR5CqdQSFNQRo7ER+flHWL78JkJCuhMePpLCwiRycw8xePDXhIZ2IzFxPjk5+/HxiaBfv3fx9z837ZbJlMWxYwux201oNEYyM2NJS9tIcHBnRoxYhlKp4tCh7wHYtu0FFAolyvJe0j593iIiYux//pzO5+XV+D8/b9nyFlq2vOWS9+dqO3a8WqXXSqh7RDvXD65u5zrTgxsbG0vPnj157733ePpp55yCVquVqKgogoKC2Lp1679uJ3pw3c9qLSrvkU0kL+8wJ4uOYMk+iGRx9oYqdT7ogqII79AR/2YdCAjvgF+TKLQGr2odpyQrFWOwa++6lmWZsoLMSolvRuFeHCZT+ZjC8sRRco7/w3HuLn7Z4XCOGyx/UF4mS845Ws+Vnx136Ew8z5YrlErKs9dzPWYKBRW/0Wd7yRQKsKiQFZyX1p5Lcp2vnf8tH80LsrPM+bp8/CMySI7ysZAO52uq9n5eCYXGgFJtQKHxOPdcXhaEFrXao9JDpfKoUmazmfDyalzxXqv1xmAIvuBk+ocP/0B8/GxMpmyCgjrQu/ebBAa2B2DTpqcoLU3nxhsXVNTPytrDtm0vkp+fgNHYhG7dZlT6mq24+BQLF/ZBoVCgVhsqxnqHhfVi2LCfK+plZu4mNnYWBQXH8PFpTnT0kzRpcm5hhStht5uQJDtWawl2exkORxk2mwlv7yYYDKEuOcb/t3f/wVXU9/7HX3tyQn6QQIGECEQBiehQihhjjQaVXzMipVpRoQ6UH3L5Dsxghc4U6FdtvZaOUpk6I9Y6oBew6DgWqS3+CFPECLnhR4OiVyUCClgCCQ2IkpAQQvb+geR6TDAhbnaTfT8fM2cm+Zw9Oe8zr1l4ZbNnT3tQWXlQKSlNv5kN4UHONjSXs9lzcCVp4sSJeuWVVzR37tyGTzIrLi7Wxo0blZfX9PU/KbjB2b37RRXuWKy6EwfPLjhx6tT9MiWkD9YV11x1tsz2G6LO6Rd79ifNrf/1SzmRiK6dttiTnyd9dcWBvTtUVlKkjwo3q/b4p3JPV6m+rkbuuduZUxf8c51oopy4REWiiWe//uoWiSbKiUuQItGv/uwfkXT2qGePOrfhdADna+vn/mx+bv3c7ev3N7UWiUTlOFFFIvGKROJivm5qLfb+6FdHDpv++uw2nb5WVhN5Aw8AoElmT1GQpD//+c968MEHtXr1an3++ecaMmSIXnvttfOWWwQrKamn6mvPXiN16F3/X1ff/WvFxbftR1ZeNGiYnO94OZ/KioM6UrJFZbuKtPefhaopf0+qPy0nvrOSel2jyzKuV3x8iqLRRMXFJSouLkHRaILi4pJa/HVcXAJlDwCAVgrVEdzW4AhusE6eLNPftj+oE7tfUUrWjzRuwVKlZvRrN+Wuvu60Kj7dqSMlW/Q/mzerunRrwxHn+K791Dc9RxkZ1ygjI0fduw9SJBKq3xkBAGgXTB/BRceTnHyR7h6+XPv63qaNRQv14sxL5UQTFU3po/jUPuo94BKlpGWqc9rF6pyWqZQemeqcfrESu6RdcAneuWaxht654Fu3qf7i3yov2aLyXUXave2/VVO2Q25dtZy4BCVedJUG9b+todAmJ2d8l5eONrJz5xMxl7pBeJG1DeRsg9c5U3DRLvTvP04/63OjSks3q6qqVJWVh1VVdUile/ep7t3NOn3iUMy75J24Toqm9lE0pY/6ZPVV5x6ZMQW4c49MJXVNP/tGrK/UnYr9oIH6M2d0/F8fqWxXkcpLirT/3SKd/nyvJCna+SIlZV6na3N+pYyMa5SW9oPzvkkJ7UtdXXXQI8AnZG0DOdvgdc6cosApCh2C69arurpCVVWHvyrAh1RVdfZWWlOuuspS1Z0olXum9v8eFIlXfGpvRVMz1fvSSxoKcE3lMZVsKVTNoX+qvvZLyYlTYs8hGpB2tTIyfqiMjBylpGS2m9MkAACwjlMUEEqOE1Fyck8lJ/dUenrTv4i4rquamgpVVR1qOAJ87mjwoX2lOv3+NtWdKD37ZrA+1+rqK+9VRsY1Sk8fqvj4zj6/IgAA0FYouAgNx3GUlJSupKT08x6NP/cHC47OAgAQXpHmNwHCoabm6FfXe6XchllNzdGgR4BPyNoGcrbB65wpuDCjoOC+oEeAD8jZDrK2gZxt8DpnCi7MyMn5ZdAjwAfkbAdZ20DONnidMwUXZnCVDBvI2Q6ytoGcbfA6ZwouAAAAQoWCCwAAgFCh4MKMkpLVQY8AH5CzHWRtAznb4HXOFFyYUVHxftAjwAfkbAdZ20DONnidMwUXZgwb9vugR4APyNkOsraBnG3wOmcKLgAAAEKFggsAAIBQoeACAAAgVCi4MCM/f3LQI8AH5GwHWdtAzjZ4nTMFF2YMHjwj6BHgA3K2g6xtIGcbvM6ZggszMjNHBD0CfEDOdpC1DeRsg9c5U3ABAAAQKhRcAAAAhAoFF2bs3/960CPAB+RsB1nbQM42eJ0zBRdm7N27NugR4ANytoOsbSBnG7zOmYILM0aPfiboEeADcraDrG0gZxu8zpmCCwAAgFCh4AIAACBUKLgAAAAIFQouzCgouDfoEeADcraDrG0gZxu8zpmCCzP4NBwbyNkOsraBnG3gk8yAVsrKGh/0CPABOdtB1jaQsw1e50zBBQAAQKhQcAEAABAqFFyYUVa2NegR4ANytoOsbSBnG7zOmYILM3bufDLoEeADcraDrG0gZxu8zpmCCzNGj14W9AjwATnbQdY2kLMNXudMwYUZ0Why0CPAB+RsB1nbQM42eJ0zBRcAAAChQsEFAABAqFBwYcbWrQ8FPQJ8QM52kLUN5GyD1zlTcGFGSkqfoEeAD8jZDrK2gZxt8DpnCi7MGDx4ZtAjwAfkbAdZ20DONnidMwUXAAAAoULBBQAAQKhQcGHG8eN7gh4BPiBnO8jaBnK2weucKbgwY+vW/wx6BPiAnO0gaxvI2Qavc6bgwoxhwx4NegT4gJztIGsbyNkGr3Om4MKMlJTMoEeAD8jZDrK2gZxt8DpnCi4AAABChYILAACAUKHgwoydO58IegT4gJztIGsbyNkGr3Om4MKMurrqoEeAD8jZDrK2gZxt8DpnCi7MyMlZEPQI8AE520HWNpCzDV7nTMEFAABAqISm4JaVlWnhwoUaOXKkunTpokgkok2bNgU9FgAAAHwWmoL78ccf67HHHtOhQ4c0ZMgQOY4T9EhoZ2pqjgY9AnxAznaQtQ3kbIPXOYem4Obk5Ojo0aMqKSnRvHnzgh4H7VBBwX1BjwAfkLMdZG0DOdvgdc5RT39agDp37hz0CGjncnJ+GfQI8AE520HWNpCzDV7nHJojuEBz0tKuDHoE+ICc7SBrG8jZBq9zpuACAAAgVNrlKQqu66q2trZF2yYkJLTxNAAAAOhI2uUR3E2bNikpKanZW3Jysnbv3u3Jc77xxt3Kz58cc3vllTHav//1mO0OHnxL+fmTGz2+sHC+SkpWx6xVVLyn/PzJjd4ZWFy8uNFH0lVWHlR+/mQdP74nZv2DD5Zr69aHYtbq6k4qP3+yysq2xqzv3btWBQX3Npptw4b/4HVI+uc/HwnF6whLHm31Oj788L9C8TrCkkdbvo51634SitcRljza6nW8+eb/C8XrCEsebfU6iooeaHgde/eubehiL7yQrTVrhquo6MFGP+fbOK7ruhf0CB+Ul5dr/fr1Ldr29ttvV2pqaszayy+/rAkTJuitt97SjTfe+K2Pf+edd3T11Vdr/PgNnOcTcoWF8zVs2O+DHgNtjJztIGsbyNmG5nKuqHhPa9eO1o4dO5Sdnd3sz2uXpyhkZGRoypQpQY+BkOEfSBvI2Q6ytoGcbfA653Z5igIAAADQWu3yCG5rLVq0SI7j6MMPP5Trunruuee0efNmSdL9998f8HQAAADwQ6gK7q9//euGj+h1HEcrVqxo+JqCCwAAYEOoTlGor6/XmTNnGt3q6uqCHg3tQFPvMkX4kLMdZG0DOdvgdc6hKrjAtxk8eEbQI8AH5GwHWdtAzjZ4nTMFF2ZkZo4IegT4gJztIGsbyNkGr3Om4AIAACBUKLgAAAAIFQouzPjmxwIinMjZDrK2gZxt8DpnCi7M2Lt3bdAjwAfkbAdZ20DONnidMwUXZowe/UzQI8AH5GwHWdtAzjZ4nTMFFwAAAKFCwQUAAECoUHABAAAQKhRcmFFQcG/QI8AH5GwHWdtAzjZ4nTMFF2bwaTg2kLMdZG0DOdvAJ5kBrZSVNT7oEeADcraDrG0gZxu8zpmCCwAAgFCh4AIAACBUKLgwo6xsa9AjwAfkbAdZ20DONnidMwUXZuzc+WTQI8AH5GwHWdtAzjZ4nTMFF2aMHr0s6BHgA3K2g6xtIGcbvM6ZggszotHkoEeAD8jZDrK2gZxt8DpnCi4AAABChYILAACAUKHgwoytWx8KegT4gJztIGsbyNkGr3Om4MKMlJQ+QY8AH5CzHWRtAznb4HXOFFyYMXjwzKBHgA/I2Q6ytoGcbfA6ZwouAAAAQoWCCwAAgFCh4MKM48f3BD0CfEDOdpC1DeRsg9c5U3Bhxtat/xn0CPABOdtB1jaQsw1e50zBhRnDhj0a9AjwATnbQdY2kLMNXudMwYUZKSmZQY8AH5CzHWRtAznb4HXOFFwAAACECgUXAAAAoULBhRk7dz4R9AjwATnbQdY2kLMNXudMwYUZdXXVQY8AH5CzHWRtAznb4HXOFFyYkZOzIOgR4ANytoOsbSBnG7zOmYILAACAUKHgAgAAIFQouDCjpuZo0CPAB+RsB1nbQM42eJ0zBRdmFBTcF/QI8AE520HWNpCzDV7nTMGFGTk5vwx6BPiAnO0gaxvI2Qavc6bgwoy0tCuDHgE+IGc7yNoGcrbB65wpuAAAAAgVCi4AAABChYILM0pKVgc9AnxAznaQtQ3kbIPXOVNwYUZFxftBjwAfkLMdZG0DOdvgdc4UXJgxbNjvgx4BPiBnO8jaBnK2weucKbgAAAAIFQouAAAAQoWCCwAAgFCh4MKM/PzJQY8AH5CzHWRtAznb4HXOFFyYMXjwjKBHgA/I2Q6ytoGcbfA6ZwouzMjMHBH0CPABOdtB1jaQsw1e50zBBQAAQKiEpuBu3LhRM2bM0OWXX67OnTtrwIABmjlzpsrKyoIeDQAAAD4KTcFdsGCB3n77bY0fP15Lly7V3XffrZdeeknZ2dk6cuRI0OOhHdi///WgR4APyNkOsraBnG3wOufQFNzHH39ce/fu1SOPPKJ77rlHixYt0quvvqqysjI9+eSTQY+HdmDnzieCHgE+IGc7yNoGcrbB65xDU3CHDRvWaO2GG25Q9+7dtWvXrgAmQnuTmJgW9AjwATnbQdY2kLMNXuccmoLblKqqKlVWViotjZ0DAADAilAX3Mcff1ynT5/WT3/606BHAQAAgE+iQQ/QFNd1VVtb26JtExISmlzftGmTHn74YU2cOFE33XSTl+MBAACgHWuXBXfTpk0aMaL5C/46jqNdu3Zp4MCBMeslJSUaP368hgwZouXLl3/rz6iurpYkff75ntYPjA7h3/9+RxUV7wU9BtoYOdtB1jaQsw3N5Xyup53rbc1xXNd1PZnMQ+Xl5Vq/fn2Ltr399tuVmpra8P2//vUv5eXlKSEhQYWFhcrIyPjWxz///POaPJnPuQYAAGjvVq9erUmTJjW7XbssuK117Ngx5eXl6YsvvlBhYaEuvfTSZh9TUVGh9evXq1+/fkpKSvJhSgAAAFyI6upq7d+/XzfffHOLLh4QmoJ78uRJjRgxQh9//LEKCgo0dOjQoEcCAABAAEJTcH/yk5/o73//u2bMmKHhw4fH3JeSkqLbbrstmMEAAADgq9AU3P79++uzzz5r8r6+ffvq008/9XkiAAAABCE0BRcAAACQQv5BDwAAALDHbMGtra3VggUL1KdPHyUnJys3N1cbNmwIeix46O2331YkEml0i4uL0/bt24MeD61UVVWl3/zmN7rlllvUo0cPRSIRPffcc01uW1JSojFjxig1NVU9evTQlClTVFFR4fPEaI2W5jx9+vQm9/NBgwYFMDUuVHFxsebMmaPBgwcrJSVFffv21cSJE7VnT+Nr07M/d1wtzdnL/bldftCDH6ZOnaq1a9dq3rx5ysrK0sqVKzV27FgVFBTo+uuvD3o8eGju3LnKycmJWcvKygpoGnxXFRUV+u1vf6u+fftq6NChKigoaHK70tJS3XDDDerWrZseffRRnThxQo899pg++OADbd++XdGo2X/+OoSW5ixJiYmJevbZZ/X1M+66du3qw5T4rhYvXqyioiLdddddGjJkiMrKyrR06VJlZ2dr27ZtDcWG/blja2nOkof7s2vQtm3bXMdx3D/84Q8NazU1NW5WVpabl5cX4GTwUkFBges4jvvyyy8HPQo8VFtb65aXl7uu67rFxcWu4zjuqlWrGm03e/Zst3Pnzu7Bgwcb1jZs2OA6juMuX77ct3nROi3Nedq0aW5qaqrf48EjW7ZscU+fPh2ztmfPHjcxMdH92c9+1rDG/tyxtTRnL/dnk6corFmzRtFoVDNnzmxYS0hI0IwZM7RlyxaVlpYGOB3aQmVlpc6cORP0GPBAfHy8evbs2ex2a9eu1bhx49SnT5+GtVGjRmngwIF66aWX2nJEeKClOZ9TX1+vEydOtOFEaAu5ubmNjr5mZWXp+9//vnbt2tWwxv7csbU053O82J9NFtydO3dq4MCBSklJiVn/4Q9/2HA/wmP69Onq0qWLEhMTNXLkSO3YsSPokdDGDh06pCNHjjQ6NUU6u5+/++67AUyFtnLy5El16dJFXbt2VY8ePTRnzhxVVVUFPRa+g/Ly8oZPq2J/Dq+v53yOV/uzyZNWDh8+rF69ejVa79Wrl1zX1aFDhwKYCl7r1KmT7rzzTo0dO1ZpaWn66KOPtGTJEt14440qKirSlVdeGfSIaCOHDx+WpPPu58eOHdPp06cVHx/v92jwWO/evTV//nxlZ2ervr5e+fn5euqpp/T++++roKBAkYjJ4zgd2urVq1VaWqpFixZJYn8Oq2/mLHm7P5ssuNXV1UpISGi0npiY2HA/Or7rrrtO1113XcP348aN0x133KEhQ4boV7/6lV5//fUAp0NbOrcPN7ef8x9ix/e73/0u5vsJEybosssu0wMPPKA1a9ZowoQJAU2G1igpKdGcOXOUl5enKVOmSGJ/DqOmcpa83Z9N/mqblJSkU6dONVqvqalpuB/hNGDAAN1222166623Yt6hiXA5tw+zn9s0b948OY7DpR87mPLycv3oRz9St27d9Je//EWO40hifw6b8+V8Pq3dn00ewe3Vq1eTpyGc+zNI7969/R4JPrr44otVW1urqqqqRudhIxzO/Snz3D79dYcPH1b37t052hNiiYmJ6tGjh44dOxb0KGihL7/8UmPGjNGXX36pwsJCXXTRRQ33sT+Hx7flfD6t3Z9NHsEdOnSodu/ercrKypj1rVu3ynEcDR06NKDJ4IdPPvlEiYmJlNsQ6927t9LT01VcXNzovu3bt7OPh1xlZaUqKiqUnp4e9ChogVOnTmncuHHau3evXnvtNV1++eUx97M/h0NzOZ9Pa/dnkwX3zjvvVF1dnZYtW9awVltbq5UrVyo3NzfmMiTouJr6hJv33ntP69at08033xzARPDTHXfcoVdffTXmsn9vvvmmdu/ezXmZIXHq1KlGByok6eGHH5Yk3XLLLX6PhAtUX1+vCRMmaNu2bVqzZk3D1Yy+if25Y2tJzl7vz45r9ETEiRMn6pVXXtHcuXMbPsmsuLhYGzduVF5eXtDjwQOjRo1SUlKSrr/+evXs2VMffvihli9froSEBBUVFbX4t0e0P3/84x91/PhxlZaW6umnn9b48eN11VVXSZJ+/vOfKzU1VQcPHlR2dra6du2q++67TydOnNCSJUt0ySWXaPv27fxJswNoLudjx47pqquu0t13360rrrhCkpSfn6833nhDY8eO1auvvhrk+GiBuXPn6oknntCtt96qu+66q9H9kyZNkiT25w6uJTkfOHDA2/3Zk4+L6IBOnTrlzp8/3+3du7eblJTkXnvtte4//vGPoMeCh5YuXerm5ua6aWlpbqdOndw+ffq4U6dOdT/55JOgR8N31K9fPzcSiTR5O3DgQMN2H330kTtmzBg3JSXF7d69uztlyhT3yJEjAU6OC9FczsePH3enTJniDhw40E1JSXGTkpLcH/zgB+7ixYvdurq6oMdHCwwfPvy8GUcikZht2Z87rpbk7PX+bPYILgAAAMLJ5Dm4AAAACC8KLgAAAEKFggsAAIBQoeACAAAgVCi4AAAACBUKLgAAAEKFggsAAIBQoeACAAAgVCi4AAAACBUKLgAAAEKFggsAAXrooYcUicT+U9yvXz/dc889Dd+//fbbikQi2rRpU5vOMm3aNPXv37/Z7Q4cOKBIJKLnnnuuTecBgNai4AKAj1atWqVIJKJ33nlHkuQ4jhzHidnmm9+fb81rjuM0KtsA0BFFgx4AAKy50LJ60003qbq6Wp06dWqjic565plnVF9f36bPAQB+oOACQAfQ1uVWkuLi4hQXF9fmzwMAbY2/RQFAO3e+c3CXLVumrKwsJScnKzc3V4WFhRo+fLhGjhzZsM3KlSsViUT02WefNfszmzoH94svvtC0adP0ve99T926ddP06dN1/PjxNniVAOAdjuACQAfwzdMann32Wc2aNUvDhg3TvHnz9Omnn+rWW29V9+7ddckll8Q87nynRDR17u8312699VYVFRVp9uzZuuKKK/TXv/5VU6dO9eWcYABoLQouAHQwdXV1uv/++5Wdna2NGzcqGj37T/mgQYM0c+bMmIL7Xfztb3/T5s2btWTJEv3iF7+QJM2ePVvDhw/35OcDQFvhFAUA6GCKi4t15MgRzZo1q6HcStLUqVPVtWtXz57njTfeUHx8vGbNmtWw5jiO7r33Xrmu69nzAIDXKLgA0MEcOHBAjuMoKysrZj0ajerSSy/19Hl69eql5OTkmPXLL7/cs+cAgLZAwQWAEDvfubJnzpzxeRIA8A8FFwA6mL59+8p1Xe3Zsydmva6uTvv27YtZ69atmyQ1uvLB/v37W/Q8hw8f1smTJ2PWS0pKWjE1APiHggsAHUxOTo7S09P19NNPq66urmF9xYoVjYrsgAED5LpuzOXA6uvrtWzZsmafZ+zYsTp9+rT+9Kc/xTx26dKlXEUBQLvGVRQAwGeteYPW1x8TjUa1aNEizZo1SyNGjNDEiRO1b98+rVixQgMGDIh53KBBg5Sbm6uFCxfq6NGj6t69u1588cUWfWLZj3/8Y+Xl5WnhwoXat2+fBg0apLVr1+rEiRMXPD8A+IkjuADgs6auP/vN75vbZubMmXrqqad0+PBhzZ8/X4WFhVq3bp0yMzMbPd8LL7ygvLw8LV68WI888ohGjRqlRx99tNnZHMfRunXrNGnSJD3//PN64IEHdPHFF2vVqlUX9HoBwG+Oy7VeACA0RowYIcdxtHHjxqBHAYDAcAQXAAAAoULBBQAAQKhQcAEgZLjCAQDrOAcXAAAAocIRXEHbv+kAAAA6SURBVAAAAIQKBRcAAAChQsEFAABAqFBwAQAAECoUXAAAAIQKBRcAAAChQsEFAABAqFBwAQAAECr/C28RHuJSyhQFAAAAAElFTkSuQmCC", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "include(\"main_fig.jl\");\n", "\n", "#== Policies ==#\n", "# fig_pol(fde)\n", "#== Distributions ==#\n", "fig_dens(fde)" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.3", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.3" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture13/felipe_alves_codes/.ipynb_checkpoints/JuliaPackages-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Julia Packages and Packaging System\n", "\n", "**Chase Coleman & Spencer Lyon**\n", "\n", "3-4-16" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Managing Packages\n", "\n", "One of the tools that Julia provides is a built in package manager.\n", "\n", "All of the package manager commands are within the `Pkg` module, and are called by `Pkg.command(arg)`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Two types of packages\n", "\n", "* Registered: Mature package that has some sense of approval from the community\n", "* Unregistered: Less mature package that maybe is still developing basic functionality" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Adding and Removing Registered Packages\n", "\n", "Registered packages can be added and removed by using `Pkg.add(\"PackageName\")` and `Pkg.rm(\"PackageName\")`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Pkg.add(\"Distributions\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# Pkg.rm(\"Distributions\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Adding and Removing Unregistered Packages\n", "\n", "Unregistered packages can be added by using `Pkg.clone(\"git_repo_url\")` and are removed with `Pkg.rm(\"PackageName\")`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "Pkg.rm(\"PlotlyJS\")" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Cloning PlotlyJS from https://github.com/spencerlyon2/PlotlyJS.jl.git\n", "INFO: Computing changes...\n", "INFO: No packages to install, update or remove\n", "INFO: Package database updated\n" ] } ], "source": [ "Pkg.clone(\"https://github.com/spencerlyon2/PlotlyJS.jl.git\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Updating Packages\n", "\n", "Packages can be updated to their most recent version by using `Pkg.update()`\n", "\n", "Running this command will update:\n", "\n", "- Your local `METADATA`, which tracks all versions of registered packages\n", "- Registered packages to latest version\n", "- Unregistered packages to most recent commit on active branch\n", "\n", "Doesn't update \"dirty\" packages (`git status` $\\neq$ clean)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Demo of Recommended Packages" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Distributions.jl\n", "\n", "This is _THE_ package for dealing with distributions and random variables.\n", "\n", "It is also an excellent example of how to properly leverage multiple dispatch over different types.\n", "\n", "We will demonstrated how to use this packge." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Distributions" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## What Distributions Are Included?\n", "\n", "There are ~70 different distributions that are included in the package.\n", "\n", "Hard to find distributions that are not included" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ncud = length(subtypes(Distributions.ContinuousUnivariateDistribution))\n", "ntd = length(subtypes(Distributions.Distribution))\n", "\n", "println(\"There are $(ncud) Continuous Univariate Distributions\")\n", "println(\"There are $(ntd) Total Distributions\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Using Distributions\n", "\n", "Our example will not demonstrate everything that `Distributions.jl` can do.\n", "\n", "It does much much more, but it will give you an idea of the types of things that you can do with it -- you can [read the docs](http://distributionsjl.readthedocs.org/en/latest/) for more information." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Example Methods\n", "\n", "You can find a more complete list of possible methods in the [documentation](http://distributionsjl.readthedocs.org/en/latest/univariate.html), but we list a few of the methods below to give you an idea of what is included: \n", "\n", "* Parameter Retrieval: `params`, `scale`, `shape`, `dof`\n", "* Standard Statistics: `mean`, `median`, `std`, `skewness`, `kurtosis`, `entropy`, `mgf`\n", "* Probability Evaluation: `insupport`, `pdf`, `cdf`, `likelihood`, `quantile`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Below we create two distributions that we will play with" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nrv = Normal(0.0, 1.0)\n", "tdist = TDist(5)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Evaluating Statistics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for f in (:mean, :median, :std, :skewness, :kurtosis, :entropy)\n", " ftup = (eval(f)(nrv), eval(f)(tdist))\n", " println(\"Normal and T \", f, \" are \", ftup)\n", "end" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for f in (mean, median, std, skewness, kurtosis, entropy)\n", " ftup = (f(nrv), f(tdist))\n", " println(\"Normal and T \", f, \" are \", ftup)\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Evaluating Probabilities" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for f in (:pdf, :logpdf, :cdf, :logcdf, :quantile)\n", " ftup = (eval(f)(nrv, 0.25), eval(f)(tdist, 0.25))\n", " println(\"Normal and T \", f, \" are \", ftup)\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## PlotlyJS.jl\n", "\n", "Many usable plotting packages in Julia, but no standard package (yet).\n", "\n", "One that seems promising (and feels natural) in Julia is `PlotlyJS.jl`.\n", "\n", "> Disclaimer: Spencer is involved in developing PlotlyJS.jl -- Chase is writing this so don't worry, this is an unbiased opinion." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Flexibility\n", "\n", "We will only cover a fraction of what this library can do.\n", "\n", "For more information see: [The examples page](https://github.com/spencerlyon2/PlotlyJS.jl/tree/master/examples) and the [plot attribute page](https://plot.ly/javascript/reference/#)\n", "\n", "Other good plotting options include: `PyPlot.jl`, `Gadfly`, and `Plots.jl`" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/Blink.ji for module Blink.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/Mux.ji for module Mux.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/HttpServer.ji for module HttpServer.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/WebSockets.ji for module WebSockets.\n" ] }, { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

Plotly javascript loaded.

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "using PlotlyJS" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Making a simple plot\n", "\n", "We will first make a line plot because that will be required for your homework\n", "\n", "In our next slide we will create a short function which takes a distribution and plots its pdf." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "plot_distribution (generic function with 1 method)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using Distributions\n", "function plot_distribution(d::Distribution)\n", " p_001, p_999 = quantile(d, 1e-3), quantile(d, 1-1e-3)\n", " x = collect(linspace(p_001, p_999, 100))\n", " y = pdf(d, x)\n", " t1 = scatter(;x=x, y=y, showlegend=false)\n", " \n", " return t1\n", "end" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(plot_distribution(Normal(0, 1)))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Making a Histogram\n", "\n", "`PlotlyJS` also supports making histograms.\n", "\n", "Below we will create a short function which takes a distribution and plots a histogram of random draws." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "hist_distribution (generic function with 2 methods)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function hist_distribution(d::Distribution, N=10_000)\n", " y = rand(d, N)\n", "\n", " t2 = histogram(;x=y, histnorm=\"probability density\",\n", " showlegend=false, nbinsx=250, opacity=0.6)\n", " return t2\n", "end" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(hist_distribution(Normal(0, 1)))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "multiple_surface (generic function with 1 method)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function multiple_surface()\n", " z1 = Vector[[8.83, 8.89, 8.81, 8.87, 8.9, 8.87],\n", " [8.89, 8.94, 8.85, 8.94, 8.96, 8.92],\n", " [8.84, 8.9, 8.82, 8.92, 8.93, 8.91],\n", " [8.79, 8.85, 8.79, 8.9, 8.94, 8.92],\n", " [8.79, 8.88, 8.81, 8.9, 8.95, 8.92],\n", " [8.8, 8.82, 8.78, 8.91, 8.94, 8.92],\n", " [8.75, 8.78, 8.77, 8.91, 8.95, 8.92],\n", " [8.8, 8.8, 8.77, 8.91, 8.95, 8.94],\n", " [8.74, 8.81, 8.76, 8.93, 8.98, 8.99],\n", " [8.89, 8.99, 8.92, 9.1, 9.13, 9.11],\n", " [8.97, 8.97, 8.91, 9.09, 9.11, 9.11],\n", " [9.04, 9.08, 9.05, 9.25, 9.28, 9.27],\n", " [9, 9.01, 9, 9.2, 9.23, 9.2],\n", " [8.99, 8.99, 8.98, 9.18, 9.2, 9.19],\n", " [8.93, 8.97, 8.97, 9.18, 9.2, 9.18]]\n", " z2 = map(x->x+1, z1)\n", " z3 = map(x->x-1, z1)\n", " trace1 = surface(z=z1)\n", " trace2 = surface(z=z2, showscale=false, opacity=0.9)\n", " trace3 = surface(z=z3, showscale=false, opacity=0.9)\n", " plot([trace1, trace2, trace3])\n", "end" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "multiple_surface()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Setting Plot Attributes\n", "\n", "We often want to add titles, labels, or other information to a plot.\n", "\n", "Here it makes sense to mention that `PlotlyJS` constructs figures in two parts.\n", "\n", "* `trace`s: Stores plot data and how it should be displayed\n", "* `Layout`: Figure wide settings\n", "\n", "Let's write another function that combines two traces from the previous functions and adds layout information." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "full_plot_distribution (generic function with 2 methods)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function full_plot_distribution(d::Distribution, N=10000;\n", " xlim=(quantile(d, 1e-3), quantile(d, 1-1e-3)))\n", " # Create multiple traces which will go on plot\n", " t1 = plot_distribution(d)\n", " t2 = hist_distribution(d, N)\n", "\n", " # Create layout\n", " l = Layout(;title=\"$(typeof(d))\", \n", " xaxis_range=xlim, xaxis_title=\"x\",\n", " yaxis_title=\"Probability Density of x\",\n", " xaxis_showgrid=true, yaxis_showgrid=true,\n", " legend_y=1.15, legend_x=0.7)\n", " \n", " return plot([t1, t2], l)\n", "end" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "full_plot_distribution(Normal(0, 1))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Subplots\n", "\n", "Combine plots in the same way you would build an array." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p1 = full_plot_distribution(Normal(0, 1), xlim=(-3, 3))\n", "p2 = full_plot_distribution(TDist(5), xlim=(-3, 3))\n", "[p1 p2]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## Interpolations\n", "\n", "It is important to interpolate. `Interpolations.jl` is an _extremely_ fast interpolation package that is based around using splines.\n", "\n", "Have a look at their [benchmarks](https://github.com/tlycken/Interpolations.jl)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Interpolations" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Create Interpolator\n", "\n", "There are multiple types of interpolators. We will focus on `BSplines()`.\n", "\n", "See the [docs](https://github.com/tlycken/Interpolations.jl#general-usage) for information on the other types." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "### Create Interpolator\n", "\n", "Interpolators by default are only defined on `[1, Npts]`\n", "\n", "`BSpline(Linear())` specifies the type of interpolation you want\n", "\n", "`OnGrid()` specifies where the points lie" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = linspace(-1.0, 1.0, 50)\n", "y = sin(collect(x))\n", "itp = interpolate(y, BSpline(Linear()), OnGrid())\n", "diff = maxabs([itp[i] for i in 1:50] - y)\n", "println(\"The max absolute difference is: \", diff)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Change Interpolator Scale\n", "\n", "Since interpolators are defined by default on `[1, Npts]` we need to change it to our domain\n", "\n", "We will use the `scale` function to do that." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "itp_scaled = scale(itp, x)\n", "diff_scaled = maxabs([itp_scaled[el] for el in x] - y)\n", "println(\"The max absolute difference is: \", diff_scaled)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Evaluate Derivatives\n", "\n", "We can evaluate the derivatives of splines" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "gradient(itp_scaled, 0.0)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "### Interpolations.jl Speed\n", "\n", "Evaluate a linear spline on 1,000,000 points\n", "\n", "* `scipy.InterpolatedUnivariateSpline` : 12.4 ms\n", "* `Interpolations.jl` : 1.2 $\\mu s$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Other Recommended Packages\n", "\n", "Here are some other recommended packages\n", "\n", "Some are useful, others fun\n", "\n", "They appear in no particular order\n", "\n", "
    \n", "
  • [DataFrames.jl](https://github.com/JuliaStats/DataFrames.jl)
    • \n", "
  • [NLopt.jl](https://github.com/JuliaOpt/NLopt.jl)
    • \n", "
  • [NLsolve.jl](https://github.com/EconForge/NLsolve.jl)
    • \n", "
  • [Optim.jl](https://github.com/JuliaOpt/Optim.jl)
    • \n", "
  • [HDF5.jl](https://github.com/JuliaLang/HDF5.jl)
    • \n", "
  • [JLD.jl](https://github.com/JuliaLang/JLD.jl)
    • \n", "
  • [QuantEcon.jl](https://github.com/QuantEcon/QuantEcon.jl)
    • \n", "
  • [Gadfly.jl](https://github.com/dcjones/Gadfly.jl)
    • \n", "
  • [PyPlot.jl](https://github.com/stevengj/PyPlot.jl)
    • \n", "
  • [Distances.jl](https://github.com/JuliaStats/Distances.jl)
    • \n", "
  • [IJulia.jl](https://github.com/JuliaLang/IJulia.jl)
    • \n", "
  • [Interact.jl](https://github.com/JuliaLang/Interact.jl)
    • \n", "
  • [DistributedArrays.jl](https://github.com/JuliaParallel/DistributedArrays.jl)
    • \n", "
" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.3", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.3" }, "livereveal": { "scroll": true, "start_slideshow_at": "selected", "theme": "white", "transition": "fade" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture13/felipe_alves_codes/HANK_felipe_alves.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# HANK Model Application \n", "\n", "#### Felipe Alves" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Type examples\n", "\n", "Let's take a look on some of the types defined in the project" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "Consumption-saving problem with two assets and non-convex adjustment costs\n", "\n", "## Fields\n", "\n", "#### Parameters\n", "- `γ::Float64` : parameter on CRRA utility\n", "- `ρ::Float64` : discount factor\n", "- `ξ::Float64` : automatic deposit\n", "- `σ::Float64` : frisch elasticity of labor supply\n", "- `ψ::Float64` : disutility of labor\n", "\n", "#### Prices\n", "- `rᴬ::Float64` : interest rate on illiquid asset\n", "- `rᴮ::Float64` : interest rate for savings in liquid asset\n", "- `wedge::Float64` : interest rate for borrowing in liquid asset\n", "- `w::Float64` : wage rate\n", "\n", "#### Tax\n", "- `τ::Float64` : tax on income\n", "- `T::Float64` : Lump-sum transfer\n", "\n", "#### Cost Function\n", "- `χ₀::Float64` :\n", "- `χ₁::Float64` :\n", "\"\"\"\n", "type TwoAssetsProb\n", " γ::Float64 # CRRA utility with parameter γ\n", " ρ::Float64 # discount rate\n", " σ::Float64\n", " ψ::Float64\n", " ξ::Float64 # automatic deposit on illiquid asset\n", "\n", " rᴬ ::Float64 # ret on illiquid asset\n", " rᴮ::Float64 # ret on liq asset\n", " wedge::Float64\n", " w ::Float64 # wage rate\n", "\n", " τ::Float64\n", " T::Float64\n", "\n", " χ₀::Float64 # parameters on adjustment cost\n", " χ₁::Float64\n", "end" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type SolutionExp\n", "\n", " ## Solution\n", " V::Vector{Vector{Float64}} # Value function\n", " g::Vector{Vector{Float64}} # Density over state space\n", " c::Array{Float64,3} # Optimal consumption\n", " sc::Array{Float64,3} # Savings without deposit\n", " d::Array{Float64,3} # Optimal deposit flow\n", "\n", " ## Matrices to be filled\n", " A ::Vector{Base.SparseMatrix.SparseMatrixCSC{Float64, Int}} # used on HJB to compute vⁿ⁺¹\n", " Au::Vector{Base.SparseMatrix.SparseMatrixCSC{Float64, Int}} # used for KFE to compute transition dynamics\n", "\n", "end\n", "\n", "\"\"\"\n", "Specification for the Implicit-Explicit Finite Difference method\n", "\"\"\"\n", "type FDExp\n", "\n", " ## GRID INFO\n", " b ::Vector{Float64}\n", " Δbgrid ::Vector{Float64}\n", " ΔTbgrid::Vector{Float64}\n", " rbdrift::Vector{Float64}\n", " netbinc::Matrix{Float64}\n", "\n", " a ::Vector{Float64}\n", " Δagrid ::Vector{Float64}\n", " ΔTagrid::Vector{Float64}\n", " radrift::Vector{Float64}\n", " netainc::Matrix{Float64}\n", "\n", " ΔTab::Vector{Float64}\n", "\n", " ## LaborSupply decisions\n", " ℓsupply::Vector{Float64}\n", " ℓutilgrid::Vector{Float64}\n", "\n", " ## Δ in finite difference scheme\n", " invΔ::Float64\n", " invΔᴷ::Float64\n", "\n", " ## Stochastic Information\n", " z::Vector{Float64}\n", " λ::Matrix{Float64}\n", " λdiag::Vector{Float64}\n", " λoff::Matrix{Float64}\n", "\n", " ## Solution\n", " sol::SolutionExp\n", "\n", " ## Storage\n", " btilde ::Vector{Vector{Float64}} # RHS of hjb\n", " B ::Vector{Base.SparseMatrix.SparseMatrixCSC{Float64, Int}} # storage matrix\n", "\n", " \n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is useful for bigger projects" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "function Base.show(io::IO, fd::FDExp)\n", " fd.Δbgrid[2]-fd.Δbgrid[1] == 0 ? (binfo = \"uniform \") : (binfo = \"non-uniform \")\n", " fd.Δagrid[2]-fd.Δagrid[1] == 0 ? (ainfo = \"uniform \") : (ainfo = \"non-uniform \")\n", " @printf io \"\\n\"\n", " @printf io \" Explicit-Implicit Finite Difference Method\\n\"\n", " @printf io \"\\n\"\n", " @printf io \" Grids \\n\"\n", " @printf io \" ------- \\n\"\n", " @printf io \" %12sb: %3d points in [% .0f, %.0f]\\n\" binfo length(fd.b) fd.b[1] fd.b[end]\n", " @printf io \" %12sa: %3d poitns in [% .0f, %.0f]\\n\" ainfo length(fd.a) fd.a[1] fd.a[end]\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Include files" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "solve_fp! (generic function with 2 methods)" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#== INCLUDE files ==#\n", "include(\"aggregate.jl\")\n", "include(\"twoassets.jl\")\n", "\n", "include(\"solveHJB.jl\")\n", "include(\"solveKFE.jl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create an `TwoAssetProblem` instance" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " HOUSEHOLD PROBLEM\n", "\n", " Parameters \n", " ------------ \n", " γ: 2.000\n", " ρ: 0.060\n", " ξ: 0.100\n", "\n", " Prices \n", " -------- \n", " rᴬ : 0.040 \n", " rᴮ : 0.030 \n", " wage : 8.000 \n", " τ : 0.000 \n", " T : 0.000 \n", "\n", " Cost Function \n", " --------------- \n", " χ₀ + χ₁ * (d/a)² * a\n", "\n" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#== Instance of prices ==#\n", "pr = Prices(0.0, 0.04, 0.03, 0.09, 8.0)\n", "\n", "#== Instance of TwoAssetProblem ==#\n", "twoap = TwoAssetsProb2(pr; γ = 2.0, ρ = 0.06, ξ = 0.10, χ₀ = 0.08, χ₁= 3.0, τ = 0.0, T = 0.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With that I will create the Finite Difference specification" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "\n", " Explicit-Implicit Finite Difference Method\n", "\n", " Grids \n", " ------- \n", " non-uniform b: 100 points in [-2, 40]\n", " uniform a: 70 poitns in [ 0, 70]\n" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#== Create and FiniteDifference Structure ==#\n", "fde = FDExp(twoap; z = [.8, 1.3], λ = [-1/3 1/3; 1/3 -1/3],\n", "fixedΔa = true, fixedΔb = false, bn = 100, bmin = -2.0, bmax = 40.0, an =70, amax = 70.0, invΔᴷ = 0.05)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "The solve_hjb function will use information from \n", "```julia\n", "twoap::TwoAssetProblem\n", "fde::FDExpl\n", "```\n", "and save solution on \n", "```julia\n", "fde.sol::SolutionExp\n", "```\n", "\n", "Why is useful to separate these? Further in the code, when we are doing transitional dynamics, I will maintain grid structure while solution will change for each period. Moreover I will need to save all `SolutionExp` before computing the dynamics. \n" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " value function iteration 10, distance 0.1684 \n", " value function iteration 20, distance 0.0264 \n", " value function iteration 30, distance 0.0051 \n", " value function iteration 40, distance 0.0010 \n", " value function iteration 50, distance 0.0002 \n", " value function iteration 60, distance 0.0000 \n", " value function iteration 70, distance 0.0000 \n", " value function iteration 80, distance 0.0000 \n", " value function iteration 90, distance 0.0000 \n", " value function iteration 100, distance 0.0000 \n", " value function iteration 110, distance 0.0000 \n", "hjb solved : 115 iterations\n" ] } ], "source": [ "#== Solve the HJB equation ==#\n", "solve_hjb!(twoap, fde, fde.sol)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " density iteration 50, distance 0.0016 \n", " sum 1.0000 --> 1.0000\n", " density iteration 100, distance 0.0000 \n", " sum 1.0000 --> 1.0000\n", " density iteration 150, distance 0.0000 \n", " sum 1.0000 --> 1.0000\n", " density iteration 200, distance 0.0000 \n", " sum 1.0000 --> 1.0000\n", "kfe solved : 240 iterations\n" ] }, { "data": { "text/plain": [ "Void" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#== Solve the KF equation ==#\n", "solve_fp!(fde, fde.sol, maxit = 4000, tolFK = 1e-9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graphs" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/MbedTLS.ji for module MbedTLS.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/HttpServer.ji for module HttpServer.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/HttpParser.ji for module HttpParser.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/Blink.ji for module Blink.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/Mux.ji for module Mux.\n", "INFO: Recompiling stale cache file /Users/Felipe/.julia/lib/v0.4/WebSockets.ji for module WebSockets.\n" ] }, { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

Plotly javascript loaded.

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "using PlotlyJS" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "layout with fields legend, margin, title, xaxis, and yaxis\n" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sol = fde.sol\n", "c = sol.c; d = sol.d; sc = sol.sc\n", "b = fde.b; a = fde.a; z = fde.z;\n", "\n", "an = length(a)\n", "bn = length(b)\n", "zn = length(z)\n", "\n", "netainc = fde.netainc\n", "\n", "# agrid, bgrid = meshgrid(a,b)\n", "\n", "#== Consumption 2D ==#\n", "tcons = Any[]\n", "for ai in 1:10:an\n", " push!(tcons,scatter(;x=fde.b, y=c[:,ai,1], name = \"Illiquid level $(round(a[ai],1))\") )\n", "end\n", "\n", "#== Deposits 2D ==#\n", "tdep = Any[]\n", "for ai in 1:10:an\n", " push!(tdep,scatter(;x=fde.b, y=d[:,ai,2], name = \"Illiquid level $(round(a[ai],1))\") )\n", "end\n", "\n", "\n", "lcon = Layout(;title=\"Consumption Low state\", \n", " xaxis_range=-2.0, xaxis_title=\"Liquid asset\",\n", " yaxis_title=\"Consumption\",\n", " xaxis_showgrid=true, yaxis_showgrid=true,\n", " legend_y=0.0, legend_x=1.01)\n", "\n" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lcons = Layout(;title=\"Consumption Low state\", \n", " xaxis_range=-2.0, xaxis_title=\"Liquid asset\",\n", " yaxis_title=\"Consumption\",\n", " xaxis_showgrid=true, yaxis_showgrid=true,\n", " legend_y=0.0, legend_x=1.01)\n", "\n", "\n", "plot([tcons...], lcons)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ldep = Layout(;title=\"Deposit High state\", \n", " xaxis_range=-2.0, xaxis_title=\"Liquid asset\",\n", "yaxis_title=\"Deposit flow\",\n", " xaxis_showgrid=true, yaxis_showgrid=true,\n", " legend_y=0.0, legend_x=1.01)\n", "\n", "plot([tdep...], l)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Check SUM \n", "Sum to 1.00 \n", "Check SUM \n", "Sum to 1.00 \n" ] }, { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "PyPlot.Figure(PyObject )" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "include(\"main_fig.jl\");\n", "\n", "#== Policies ==#\n", "# fig_pol(fde)\n", "#== Distributions ==#\n", "fig_dens(fde)" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.3", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.3" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture13/felipe_alves_codes/aggregate.jl ================================================ ################################## ## ## Prices ## ################################## """ Type that contains information of prices ## Prices - `rᴷ` : rental rate of capital - `rᴬ` : return on the illiquid asset - `rᴮ` : return on the liquid asset - `wedge` : - `w` : wage rate """ type Prices rᴷ ::Float64 rᴬ ::Float64 rᴮ ::Float64 wedge::Float64 w ::Float64 end function update_prices!(pc::Prices, P::Vector{Float64}) pc.rᴷ, pc.rᴬ, pc.rᴮ, pc.w = P end price_partial() = Prices(0.0, 0.04, 0.03, 0.09,4.0) function Base.show(io::IO, pr::Prices) @printf io "\n" @printf io " Prices \n" @printf io " rᴷ : %.3f \n" pr.rᴷ @printf io " rᴬ : %.3f \n" pr.rᴬ @printf io " rᴮ : %.3f \n" pr.rᴮ @printf io " wedge : %.3f \n" pr.wedge @printf io " wage : %.3f \n" pr.w end ################################## ## ## SteadyState and Transition ## ################################## """ Description of the SteadyState # Endogenous ### Prices - `rcapital` - `ra` - `rb` - `rborr` - `wage ` ### Interest rate - `rnom` ### Aggregates to clear - `Eb` : aggregate liq saving from households - `bond` : total amount of bonds in the market - `Ea` : aggregate illiq saving from households - `capital` : capital demanded by firms - `labor` : ### Firm Stats - `KYratio` - `KNratio` - `π` - `mc` - `priceadjust` - `output` - `profit` - `dividend` ### Fund - `divrate` - `investment` - `deprec` - `caputil` ### Government - `G` - `lumptransfer` - `τ` - `taxrev` - `govbond` """ function initializeSS(KYratio = 8., rb = 0.015) ssvar = [:rcapital, :ra, :rb, :wedge, :wage, # prices :rnom, # :Eb, :bond, :Ea, :capital, :labor, # aggregates :KYratio, :KNratio, :π, :mc, :priceadjust, # :output, :profit, :dividend, # :divrate, :investment, :deprec, :caputil, # :G, :lumptransfer, :τ, :taxrev, :govbond, # :tfp] # equmSS = (Symbol => Float64)[var => 0.0 for var in ssvar] equmSS[:tfp] = 1. equmSS[:KYratio] = KYratio equmSS[:τ] = 0.25 equmSS[:rb] = rb #having problem with 0.02/4 equmSS[:wedge] = 0.10 #0.0161134 equmSS[:KNratio] = ( equmSS[:tfp] * equmSS[:KYratio] )^(1./(1.-α)) equmSS[:mc] = (ϵ-1) / ϵ #== Prices ==# equmSS[:rcapital] = equmSS[:mc] * α / equmSS[:KYratio] equmSS[:wage] = equmSS[:mc] * equmSS[:tfp] * (1. - α) * equmSS[:KNratio]^α #== Firm ==# equmSS[:priceadjust] = 0 equmSS[:labor] = ( (1 - equmSS[:τ])*equmSS[:wage] /ψ )^σ equmSS[:capital] = equmSS[:KNratio] * equmSS[:labor] equmSS[:investment] = δbar * equmSS[:capital] equmSS[:profit] = (1. - equmSS[:mc])* equmSS[:capital] / equmSS[:KYratio] - equmSS[:priceadjust] equmSS[:divrate] = equmSS[:profit] / equmSS[:capital] #== Illiquid return ==# equmSS[:ra] = equmSS[:rcapital] - δbar + equmSS[:divrate] #== Government ==# equmSS[:lumptransfer] = 0.20 * equmSS[:wage] * equmSS[:labor] ### Solution ### return equmSS end _unpackprices(equmSS::Dict{Symbol, Float64}) = Prices(equmSS[:rcapital], equmSS[:ra], equmSS[:rb], equmSS[:wedge] , equmSS[:wage]) ################################## ## OLD AGGREGATE vars ################################## """ Holds the aggregate variables of the econmy ### Prices - `prices` ### Government - `Rev::Float64` - `τ::Float64` - `T::Float64` ### Firm - `tfp::Float64` - `KY::Float64` - `KN::Float64` - `output::Float64` - `profit::Float64` - `capital::Float64` - `investment::Float64` """ type AggVar prices::Prices ##Gov Rev::Float64 τ ::Float64 T ::Float64 G ::Float64 ## Firm tfp::Float64 KY ::Float64 KN ::Float64 output::Float64 profit::Float64 capital::Float64 investment::Float64 end function AggVar(;tfp::Float64 = 1., KY::Float64 = 5., τ::Float64 = 0.25, rᴮ::Float64 = 0.03, wedge::Float64 = 0.09) KN = (tfp * KY)^(1./(1.-α)) #== Prices ==# rᴷ = mc * α / KY w = mc * tfp * (1. - α) * KN^α priceadjust = 0 ℓ = 1.0 capital = KN * ℓ investment = δbar * capital profit = (1. - mc)* capital / KY - priceadjust divr = profit / capital rᴬ = rᴷ - δbar + divr # Define this so that a fixed fraction receives more transfer than pays T = 0.8 * w output = 0. Rev = 0.0 G = 0.0 AggVar(Prices(rᴷ,rᴬ,rᴮ,wedge,w), Rev, τ, T, G, tfp, KY, KN, output, profit, capital, investment) end ================================================ FILE: lecture13/felipe_alves_codes/main_fig.jl ================================================ using PyPlot using QuantEcon: meshgrid #== Value Function ==# function fig_vf(fd::FDSpec) b = fd.b; a = fd.a; z = fd.z; an = length(a) bn = length(b) zn = length(z) V = vcat(fd.V...) V = reshape(V,bn,an,zn) p_args = Dict{Symbol,Float64}(:lw => 2, :alpha =>0.7) fig, ax = subplots() for ai in 1:7:an ax[:plot](fd.b[end-10:end], V[end-10:end,ai,1] ; p_args...) end # ax[:legend](loc= "upper right", fontsize = 11) ax[:set_xlabel]("Liquid asset") fig[:show]() fig, ax = subplots() for bi in 1:7:an ax[:plot](fd.a, squeeze(V[bi,:,1],1) ; p_args...) end # ax[:legend](loc= "upper right", fontsize = 11) ax[:set_xlabel]("Iliquid asset") fig[:show]() end """ Compute the figures... """ function fig_pol(fd::FDSpec) sol = fd.sol c = sol.c; d = sol.d; sc = sol.sc b = fd.b; a = fd.a; z = fd.z; an = length(a) bn = length(b) zn = length(z) netainc = fd.netainc p_args = Dict{Symbol,Any}(:rstride=>2, :cstride=>2, :cmap=>ColorMap("Oranges"), :alpha=>0.7, :linewidth=>0.5) pp_args = Dict{Symbol,Float64}(:alpha=>0.7, :linewidth=>2) agrid, bgrid = meshgrid(a,b) #== Consumption 3D ==# cons_low = c[:,:,1] fig = figure(figsize = (8,6)) ax = fig[:gca](projection="3d") ax[:plot_surface](agrid, bgrid, cons_low; p_args...) ax[:set_xlabel]("illiquid") ax[:set_ylabel]("liquid") ax[:legend]() ax[:set_title]("Consumption") fig[:show]() #== Consumption 2D ==# fig, ax = subplots() for ai in 1:7:an ax[:plot](fd.b, c[:,ai,1] ; pp_args...) end # ax[:legend](loc= "upper right", fontsize = 11) ax[:set_xlabel]("Liquid asset") ax[:set_title]("Consumption Low Shock ") fig[:show]() fig, ax = subplots() for ai in 1:7:an ax[:plot](fd.b, c[:,ai,2] ; pp_args...) end # ax[:legend](loc= "upper right", fontsize = 11) ax[:set_xlabel]("Liquid asset") ax[:set_title]("Consumption High Shock") fig[:show]() #== Deposit ==# fig = figure(figsize = (8,6)) ax = fig[:gca](projection="3d") dep_high = d[:,:,end] ax[:plot_surface](agrid, bgrid, dep_high; p_args...) ax[:set_xlabel]("illiquid") ax[:set_ylabel]("liquid") ax[:legend]() ax[:set_title]("Deposit High") fig[:show]() #== Illiquid Savings ==# # adrift = zeros(d) # for zi in 1:zn, ai in 1:an, bi in 1:bn # adrift[bi,ai,zi] = d[bi, ai, zi] + netainc[ai,zi] # end # fig = figure(figsize = (8,6)) # ax = fig[:gca](projection="3d") # adrift_low = adrift[:,:,1] # ax[:plot_surface](agrid, bgrid, adrift_low; p_args...) # ax[:set_xlabel]("illiquid") # ax[:set_ylabel]("liquid") # ax[:legend]() # ax[:set_title]("Illiquid Saving") # fig[:show]() end function fig_dens(fd::FDSpec) a,b = fd.a, fd.b an = length(a) bn = length(b) zn = length(fd.z) gmat = reshape(vcat(fd.sol.g...),bn,an,zn) ΔTbgrid = fd.ΔTbgrid ΔTagrid = fd.ΔTagrid p_args = Dict{Symbol,Float64}(:lw => 2, :alpha =>0.7) #== Marginal wrt to a ==# adens = zeros(an,zn) for zi in 1:zn, ai in 1:an adens[ai,zi] = sum(gmat[:,ai,zi].*ΔTbgrid) end #= Check summation =# @printf("Check SUM \n") @printf("Sum to %.2f \n",sum(adens.*repmat(fd.ΔTagrid,1,zn))) Ea = sum( a.* sum(adens,2).* ΔTagrid ) fig, ax = subplots() for zi in 1:zn ax[:plot](fd.a, adens[:,zi], label="Labor shock $zi";p_args...) end ax[:legend](loc= "upper right", fontsize = 11) ax[:set_xlim]([0.0,2*Ea]) ax[:set_title]("Density Illiquid") ax[:set_xlabel]("Illiquid asset") fig[:show]() #== Marginal wrt to b ==# bdens = zeros(bn,zn) for zi in 1:zn, bi in 1:bn bdens[bi,zi] = sum(squeeze(gmat[bi,:,zi],1).*ΔTagrid) end #= Check summation =# @printf("Check SUM \n") @printf("Sum to %.2f \n",sum(bdens.*repmat(fd.ΔTbgrid,1,zn))) fig, ax = subplots() for zi in 1:zn ax[:plot](b, bdens[:,zi], label="Labor shock $zi";p_args...) end ax[:set_xlabel]("Liquid asset") ax[:set_ylim]([0,0.8*maximum(bdens[:,1])]) ax[:set_xlim]([b[1],5.0]) ax[:set_title]("Density Illiquid", fontsize = 20) ax[:legend](loc= "upper right", fontsize = 11) fig[:show]() bran_max = searchsortedfirst(b,5.0) # aran_max = searchsortedfirst(a,Ea) aran_max = searchsortedfirst(a,25.0) #== Joint density ==# arange, brange = 1:min(round(Int,aran_max),an), 1:bran_max # dens_low = gmat[brange,arange,1] # dens_high = gmat[brange,arange,2] #= Construct probabilities =# gvec = vcat(gmat) ΔTab = zeros(gvec) bvec = zeros(gvec) avec = zeros(gvec) ijk = zero(Int) for zi = 1:zn, abi = 1:bn*an ijk += 1 ΔTab[ijk] = ΔTagrid[afromab(abi)] * ΔTbgrid[bfromab(abi)] bvec[ijk] = b[bfromab(abi)] avec[ijk] = a[afromab(abi)] end prob = gvec .* ΔTab ggmat = reshape(prob,bn,an,zn) dens = sum(ggmat[brange,arange,:],3) dens = squeeze(dens,3) dens2 = ggmat[brange,arange,2] #== Construct 3D figure ==# fig = figure(figsize = (8,6)) ax = fig[:gca](projection="3d") agrid, bgrid = meshgrid(a[arange],b[brange]) ax[:plot_surface](agrid, bgrid, dens, rstride=2,cstride=2, cmap=ColorMap("jet"), alpha=0.5, linewidth=0.5) ax[:set_xlabel]("Illiquid") ax[:set_ylabel]("Liquid") # ax[:set_xlim]([0,) # ax[:set_ylim]([-2,10]) ax[:legend]() fig[:show]() fig, ax = subplots(figsize = (8,6)) ax[:xaxis][:grid](true, zorder=0) ax[:yaxis][:grid](true, zorder=0) ax[:contourf](agrid, bgrid, dens, 5, alpha=0.6, cmap=ColorMap("jet")) cs1 = ax[:contour](agrid, bgrid, dens, 5, colors="black",lw=2) ax[:clabel](cs1, inline=1, fontsize=10) ax[:set_xlabel]("Illiquid") ax[:set_ylabel]("Liquid") # ax[:set_xlim]([0,) # ax[:set_ylim]([-2,10]) ax[:legend]() fig[:show]() # [0,0.005,0.01,0.02,0.04] end # function fig_pol(fd::TwoAssetsFD) # # c = fd.c; d = fd.d; sc = fd.sc # b = fd.b; a = fd.a; z = fd.z; # # an = length(a) # bn = length(b) # zn = length(z) # # p_args = Dict{Symbol,Any}(:rstride=>2, :cstride=>2, :cmap=>ColorMap("Oranges"), :alpha=>0.7, :linewidth=>0.5) # agrid, bgrid = meshgrid(a,b) # # #== Consumption ==# # cons_low = c[:,:,1] # fig = figure(figsize = (8,6)) # ax = fig[:gca](projection="3d") # ax[:plot_surface](agrid, bgrid, cons_low; p_args...) # ax[:set_xlabel]("illiquid") # ax[:set_ylabel]("liquid") # ax[:legend]() # fig[:show]() # # #== Deposit ==# # fig = figure(figsize = (8,6)) # ax = fig[:gca](projection="3d") # dep_low = d[:,:,1] # ax[:plot_surface](agrid, bgrid, dep_low; p_args...) # ax[:set_xlabel]("illiquid") # ax[:set_ylabel]("liquid") # ax[:legend]() # ax[:set_title]("Deposit") # fig[:show]() # end ================================================ FILE: lecture13/felipe_alves_codes/solveHJB.jl ================================================ ############################################################################## ## ## Solve hamilton-jacobi-bellman ## ############################################################################## ############################## ## ## FDSpec ## ############################## """ Use finite difference method to solve for HJB equation. ### INPUTS - `fd` : Finite Difference scheme - `sol` : holds solution information - `twoap` : household problem description """ function solve_hjb!(twoap::TwoAssetsProb, fd::FDSpec, sol::SolutionExp; maxit::Int = 400, crit::Float64 = 1e-8, monotonicity = true, verbose::Bool = true) bn, an, zn = length(fd.b), length(fd.a), length(fd.z) #== Initialize Vold ==# Vold = zeros(bn,an,zn) #== Construct functions ==# compute_pol!(solut, V_old) = comp_pol!(twoap, fd, solut, V_old) update_V!(solut, V_old) = updateV!(twoap, fd, solut, V_old, test_mon = monotonicity) for it in 1:maxit copy!(Vold, vcat(sol.V...)) # vcat is important in the FDExp case where fd.V is vector of vectors #== Compute the Optimal Policy ==# compute_pol!(sol, Vold) #== Update vⁿ ==# update_V!(sol, Vold) # check convergence distance = chebyshev(vcat(sol.V...), vec(Vold)) # distance = norm(vec(fd.V)-vec(Vold), Inf) if distance < crit println("hjb solved : $(it) iterations") # println(out_text,"hjb solved : $(it) iterations") break else # update V using newV verbose && (it % 10 == 0) && @printf(" value function iteration %d, distance %.4f \n", it,distance) # verbose && (it % 10 == 0) && @printf(out_text," value function iteration %d, distance %.4f \n", it,distance) end end end function optconsump(twoap::TwoAssetsProb, ∂Vb::Float64, liqinc::Float64, ℓutil::Float64) invγ = 1.0/twoap.γ # ∂Vb>=0 ? c = ∂Vb^(-invγ) : error("negative value not allowed") # REVIEW:110 trick to not get stuck in case of non-monotonicity ∂Vb>=0 ? c = ∂Vb^(-invγ) + ℓutil : c = liqinc sc = liqinc - c dV = utilfn(twoap, c, ℓutil) + sc*∂Vb return c, sc, dV end function comp_pol!(twoap::TwoAssetsProb, fd::FDSpec, sol::SolutionExp, V::Array{Float64,3}) #== Construct fns ==# dχinv1(∂ratio, a) = dχinv(twoap, ∂ratio, a) χ1(d,a) = χ(twoap, d, a) utilfn1(c, ℓutil) = utilfn(twoap, c, ℓutil) #== Check if function is changing ==# # println(dχinv1(1.1, 1.0)) # println(χ1(1e-5, 100.0)) #== Unpack from FD ==# z = fd.z; a = fd.a ; b = fd.b; Δbgrid = fd.Δbgrid Δagrid = fd.Δagrid netbinc = fd.netbinc ℓutilgrid = fd.ℓutilgrid # precompute the lengths bn = length(b); an = length(a); zn = length(z) invdb = 1.0./Δbgrid invda = 1.0./Δagrid for zi in 1:zn, ai in 1:an, bi in 1:bn # == Derivative w.r.t B == # bi1 && ( ∂Vbᴮ = ( V[bi, ai, zi] - V[bi-1, ai, zi] ) * invdb[bi-1] ) # == Derivative w.r.t A == # ai1 && ( ∂Vaᴮ = ( V[bi, ai, zi] - V[bi, ai-1, zi] ) * invda[ai-1] ) ### CONSUMPTION decision ### liqinc = netbinc[bi,zi] ℓutil = ℓutilgrid[zi] # == Foward == # if bi0.0 ? (validᶠ = 1 ) : (validᶠ = 0 ) # == Backward == # if bi>1 cᴮ, scᴮ, Hcᴮ = optconsump(twoap, ∂Vbᴮ, liqinc, ℓutil) else scᴮ, Hcᴮ = 0.0, -1.0e12 end scᴮ<0.0 ? (validᴮ = 1) : (validᴮ = 0) #== Catch Other cases ==# c⁰ = liqinc sc⁰ = 0.0 Hc⁰ = utilfn1(c⁰, ℓutil) if validᶠ==1 && ( validᴮ==0 || Hcᶠ>=Hcᴮ ) && ( Hcᶠ>=Hc⁰ ) sol.c[bi, ai, zi] = cᶠ sol.sc[bi, ai, zi] = scᶠ elseif validᴮ==1 && ( validᶠ==0 || Hcᴮ>=Hcᶠ ) && ( Hcᴮ>=Hc⁰ ) sol.c[bi, ai, zi] = cᴮ sol.sc[bi, ai, zi] = scᴮ else sol.c[bi, ai, zi] = c⁰ sol.sc[bi, ai, zi] = sc⁰ end #== DEPOSIT decision ==# #== b backward, a forward ==# if bi>1 && ai0.0 && Hdᴮᶠ>=0.0) ? ( validᴮᶠ=1 ) : ( validᴮᶠ=0 ) else validᴮᶠ = 0; Hdᴮᶠ = -1.0e-12 end #== b forward, a backward ==# if bi < bn && ai > 1 dᶠᴮ = dχinv1(∂Vaᴮ/∂Vbᶠ, a[ai]) # change in utility Hdᶠᴮ = ∂Vaᴮ * dᶠᴮ - ∂Vbᶠ*( dᶠᴮ + χ1(dᶠᴮ,a[ai]) ) (dᶠᴮ<-χ1(dᶠᴮ,a[ai]) && Hdᶠᴮ>=0.0) ? ( validᶠᴮ=1 ) : ( validᶠᴮ=0 ) else validᶠᴮ = 0; Hdᶠᴮ = -1.0e-12 end #== b backward, a backward ==# if bi>1 && ai>1 dᴮᴮ = dχinv1(∂Vaᴮ/∂Vbᴮ, a[ai]) # change in utility Hdᴮᴮ = ∂Vaᴮ * dᴮᴮ - ∂Vbᴮ*( dᴮᴮ + χ1(dᴮᴮ,a[ai]) ) (dᴮᴮ>-χ1(dᴮᴮ, a[ai]) && dᴮᴮ<=0 && Hdᴮᴮ>=0.0) ? ( validᴮᴮ=1 ) : ( validᴮᴮ=0 ) # REVIEW:80 BOUNDARY Adjustment 01 # force the use of ∂Vbᴮ in case bi==bn # bi == bn && (dᴮᴮ<=0 && Hdᴮᴮ>=0.0) && (validᴮᴮ = 1) else validᴮᴮ = 0; Hdᴮᴮ = -1.0e-12 end #== Check and assign deposit ==# if validᴮᶠ==1 && (validᶠᴮ==0 || Hdᴮᶠ>=Hdᶠᴮ) && (validᴮᴮ==0 || Hdᴮᶠ>=Hdᴮᴮ) sol.d[bi, ai, zi] = dᴮᶠ # ------------------------------------------------------------------------------------------ # elseif (validᴮᶠ==0 || Hdᶠᴮ>=Hdᴮᶠ) && validᶠᴮ==1 && (validᴮᴮ==0 || Hdᶠᴮ>=Hdᴮᴮ) sol.d[bi, ai, zi] = dᶠᴮ # ------------------------------------------------------------------------------------------ # elseif (validᴮᶠ==0 || Hdᴮᴮ>=Hdᴮᶠ) && (validᶠᴮ==0 || Hdᴮᴮ>=Hdᶠᴮ) && validᴮᴮ==1 sol.d[bi, ai, zi] = dᴮᴮ # ------------------------------------------------------------------------------------------ # elseif validᴮᶠ==0 && validᶠᴮ==0 && validᴮᴮ==0 sol.d[bi, ai, zi] = 0.0 end # u[bi, ai, zi] = utilfn1( c[bi, ai, zi] ) # bdot[bi, ai, zi] = sc[bi, ai, zi] - d[bi, ai, zi] - χ1(d, a[ai]) end Void end function updateV!(twoap::TwoAssetsProb, fd::FDExp, sol::SolutionExp, V::Array{Float64,3}; test_mon = true) #== Construct function ==# χ1(d,a) = χ(twoap, d, a) utilfn1(c, ℓutil) = utilfn(twoap, c, ℓutil) #== Idiosyncratic component ==# λdiag = fd.λdiag λtoff = fd.λoff' #== HOUSEHOLD Parameters ==# γ, ρ, _, σ, ψ = _unpackparams(twoap) #== Optimal policies ==# c = sol.c; sc = sol.sc; d = sol.d #== Extract GRIDS ==# b = fd.b; a = fd.a; z = fd.z Δbgrid, Δagrid = fd.Δbgrid, fd.Δagrid netainc = fd.netainc; ℓutilgrid = fd.ℓutilgrid invΔ = fd.invΔ #== SOLUTION Matrices to fill ==# Vnew = sol.V A = sol.A Au = sol.Au #== Storage matrices ==# B = fd.B b̃ = fd.b̃ # precompute the lengths bn = length(b); an = length(a); zn = length(z); abn = bn*an invdb = 1.0./Δbgrid invda = 1.0./Δagrid inv1γ = 1.0/(1.0-γ) for zi in 1:zn #== set values to zero ==# fill!(nonzeros(A[zi]), zero(Float64)) fill!(nonzeros(Au[zi]), zero(Float64)) for abi in 1:abn # NOTE:90 Positions of A that I need to fill up # A[zi][abi-bn, abi] - v(i ,j-1,k) --> - invda*[ d⁻ ] (SHOULD BE POSITIVE) # A[zi][abi-1 , abi] - v(i+1,j ,k) --> - invdb*[ (scᴮ)⁻ + (sdᴮ)⁻ ] (SHOULD BE POSITIVE) # A[zi][abi , abi] - v(i ,j ,k) --> + invdb*[ (scᴮ)⁻ + (sdᴮ)⁻ - ( (scᶠ)⁺ - (sdᶠ)⁺ ) ] # + invda*[ d⁻ - (d⁺ + ξwz + rᵃa) ] # A[zi][abi+1 , abi] - v(i-1,j ,k) --> + invdb*[ (scᶠ)⁺ + (sdᶠ)⁺ ] (SHOULD BE POSITIVE) # A[zi][abi+bn, abi] - v(i .j+1,k) --> + invda*[ d⁺ + ξwx + rᵃa ] (SHOULD BE POSITIVE) ai = afromab(abi) bi = bfromab(abi) ℓutil = ℓutilgrid[zi] #== RHS of the Bellman equation==# b̃[zi][abi] = utilfn1(c[bi, ai, zi], ℓutil) + V[bi, ai, zi] * invΔ + dot(λtoff[:,zi] ,V[bi,ai,:][:] ) ## COMPUTE THE ENTRIES ## # ================================================================== #== bdrift ==# bdriftᶠ = max( sc[bi, ai, zi], 0.0 ) + max( -d[bi, ai, zi] -χ1(d[bi, ai, zi], a[ai]) , 0.0 ) bdriftᴮ = min( sc[bi, ai, zi], 0.0 ) + min( -d[bi, ai, zi] -χ1(d[bi, ai, zi], a[ai]) , 0.0 ) bi < bn && ( budriftᶠ = max( sc[bi, ai, zi] - d[bi, ai, zi] - χ1(d[bi, ai, zi], a[ai]) , 0.0 ) ) bi < bn && ( budriftᴮ = min( sc[bi, ai, zi] - d[bi, ai, zi] - χ1(d[bi, ai, zi], a[ai]) , 0.0 ) ) bi == bn && ( budriftᶠ = max( sc[bi, ai, zi] - d[bi-1, ai, zi] - χ1(d[bi-1, ai, zi], a[ai]) , 0.0 ) ) bi == bn && ( budriftᴮ = min( sc[bi, ai, zi] - d[bi-1, ai, zi] - χ1(d[bi-1, ai, zi], a[ai]) , 0.0 ) ) #== adrift ==# adriftᶠ = max( d[bi, ai, zi], 0.0 ) + netainc[ai,zi] adriftᴮ = min( d[bi, ai, zi], 0.0 ) bi < bn && ( audriftᶠ = max( d[bi, ai, zi] + netainc[ai,zi], 0.0 ) ) bi < bn && ( audriftᴮ = min( d[bi, ai, zi] + netainc[ai,zi], 0.0 ) ) bi == bn && ( audriftᶠ = max( d[bi-1, ai, zi] + netainc[ai,zi], 0.0 ) ) bi == bn && ( audriftᴮ = min( d[bi-1, ai, zi] + netainc[ai,zi], 0.0 ) ) #== BOUNDARY Adjustment 02 ==# # bi < bn && ( bdriftᴮ = min( sc[bi, ai, zi], 0.0 ) + min( -d[bi, ai, zi] -χ1(d[bi, ai, zi], a[ai]) , 0.0 ) ) # bi == bn && ( bdriftᴮ = min( sc[bi, ai, zi], 0.0 ) -d[bi, ai, zi] -χ1(d[bi, ai, zi], a[ai]) ) #== BOUNDARY Adjustment 03 ==# # ai < an && ( adriftᴮ = min( d[bi, ai, zi], 0.0 ) ) # ai == an && ( adriftᴮ = d[bi, ai, zi] + netainc[ai,zi] ) # v[i, j- 1, k] if adriftᴮ != 0.0 && ai>1 row = abfromab(ai-1,bi) A[zi][row, abi] = -adriftᴮ * invda[ai-1] end # v[i, j- 1, k] KF if audriftᴮ != 0.0 && ai>1 row = abfromab(ai-1,bi) Au[zi][row, abi] = -audriftᴮ * invda[ai-1] end # v[i-1, j, k] if bdriftᴮ != 0.0 && bi>1 row = abfromab(ai,bi-1) A[zi][row, abi] = -bdriftᴮ * invdb[bi-1] end # v[i-1, j, k] KF if budriftᴮ != 0.0 && bi>1 row = abfromab(ai,bi-1) Au[zi][row, abi] = -budriftᴮ * invdb[bi-1] end # v[i, j, k] val = 0.0 ( bdriftᴮ != 0.0 && bi > 1 ) && ( val += bdriftᴮ * invdb[bi-1] ) ( bdriftᶠ != 0.0 && bi < bn) && ( val -= bdriftᶠ * invdb[bi] ) ( adriftᴮ != 0.0 && ai > 1) && ( val += adriftᴮ * invda[ai-1] ) ( adriftᶠ != 0.0 && ai < an) && ( val -= adriftᶠ * invda[ai] ) val != 0.0 && ( A[zi][abi, abi] = val ) # v[i, j, k] KF val = 0.0 ( budriftᴮ != 0.0 && bi > 1 ) && ( val += budriftᴮ * invdb[bi-1] ) ( budriftᶠ != 0.0 && bi < bn) && ( val -= budriftᶠ * invdb[bi] ) ( audriftᴮ != 0.0 && ai > 1) && ( val += audriftᴮ * invda[ai-1] ) ( audriftᶠ != 0.0 && ai < an) && ( val -= audriftᶠ * invda[ai] ) val != 0.0 && ( Au[zi][abi, abi] = val ) # v[i-1, j, k] if bdriftᶠ != 0.0 && bi1 Vnew[zi][abi]1 Vnew[zi][abi] - invda*[ d⁻ ] # A[ijk- 1, ijk] - v(i+1,j ,k) --> - invdb*[ (scᴮ)⁻ + (sdᴮ)⁻ ] # A[ijk , ijk] - v(i ,j ,k) --> invdb*[ (scᴮ)⁻ + (sdᴮ)⁻ -(scᶠ)⁺ -(sdᶠ)⁺] + invda*[ d⁻ -(d⁺ + ξwz + rᵃa) ] # A[ijk+ 1, ijk] - v(i-1,j ,k) --> invdb*[ (scᶠ)⁺ + (sdᶠ)⁺ ] # A[ijk+bn, ijk] - v(i .j+1,k) --> invda*[ d⁺ + ξwx + rᵃa ] ijk +=1 ai = afromab(abi) bi = bfromab(abi) ℓutil = ℓutilgrid[zi] #== RHS of the Bellman equation==# b̃[ijk] = utilfn1(c[bi, ai, zi], ℓutil) + V[bi, ai, zi] * invΔ # ================================================================== ### COMPUTE THE ENTRIES ### #== bdrift ==# bdriftᶠ = max( sc[bi, ai, zi], 0.0 ) + max( -d[bi, ai, zi] -χ1(d[bi, ai, zi], a[ai]) , 0.0 ) bdriftᴮ = min( sc[bi, ai, zi], 0.0 ) + min( -d[bi, ai, zi] -χ1(d[bi, ai, zi], a[ai]) , 0.0 ) #== bdrift for KF ==# bi < bn && ( budriftᶠ = max( sc[bi, ai, zi] - d[bi, ai, zi] - χ1(d[bi, ai, zi], a[ai]) , 0.0 ) ) bi < bn && ( budriftᴮ = min( sc[bi, ai, zi] - d[bi, ai, zi] - χ1(d[bi, ai, zi], a[ai]) , 0.0 ) ) bi == bn && ( budriftᶠ = max( sc[bi, ai, zi] - d[bi-1, ai, zi] - χ1(d[bi-1, ai, zi], a[ai]) , 0.0 ) ) bi == bn && ( budriftᴮ = min( sc[bi, ai, zi] - d[bi-1, ai, zi] - χ1(d[bi-1, ai, zi], a[ai]) , 0.0 ) ) #== adrift ==# adriftᶠ = max( d[bi, ai, zi], 0.0 ) + netainc[ai,zi] adriftᴮ = min( d[bi, ai, zi], 0.0 ) # for KF bi < bn && ( audriftᶠ = max( d[bi, ai, zi] + netainc[ai,zi], 0.0 ) ) bi < bn && ( audriftᴮ = min( d[bi, ai, zi] + netainc[ai,zi], 0.0 ) ) bi == bn && ( audriftᶠ = max( d[bi-1, ai, zi] + netainc[ai,zi], 0.0 ) ) bi == bn && ( audriftᴮ = min( d[bi-1, ai, zi] + netainc[ai,zi], 0.0 ) ) #== REVIEW:100 BOUNDARY ADJUSTMENT 02 ==# # bi < bn && ( bdriftᴮ = min( sc[bi, ai, zi], 0.0 ) + min( -d[bi, ai, zi] -χ1(d[bi, ai, zi], a[ai]) , 0.0 ) ) # bi == bn && ( bdriftᴮ = min( sc[bi, ai, zi], 0.0 ) -d[bi, ai, zi] -χ1(d[bi, ai, zi], a[ai]) ) #== REVIEW:100 BOUNDARY ADJUSTMENT 03 ==# # ai < an && ( adriftᴮ = min( d[bi, ai, zi], 0.0 ) ) # ai == an && ( adriftᴮ = d[bi, ai, zi] + netainc[ai,zi] ) # v[i, j- 1, k] if adriftᴮ != 0.0 && ai>1 row = abzfromabz(ai-1,bi,zi) A[row, ijk] = -adriftᴮ * invda[ai-1] end # v[i, j- 1, k] KF if audriftᴮ != 0.0 && ai>1 row = abzfromabz(ai-1,bi,zi) Au[row, ijk] = -audriftᴮ * invda[ai-1] end # v[i-1, j, k] if bdriftᴮ != 0.0 && bi>1 row = abzfromabz(ai,bi-1,zi) A[row,ijk] = -bdriftᴮ*invdb[bi-1] end # v[i-1, j, k] KF if budriftᴮ != 0.0 && bi>1 row = abzfromabz(ai,bi-1,zi) Au[row,ijk] = -budriftᴮ*invdb[bi-1] end # v[i, j, k] val = 0.0 ( bdriftᴮ != 0.0 && bi > 1 ) && ( val += bdriftᴮ * invdb[bi-1] ) ( bdriftᶠ != 0.0 && bi < bn) && ( val -= bdriftᶠ * invdb[bi] ) ( adriftᴮ != 0.0 && ai > 1) && ( val += adriftᴮ * invda[ai-1] ) ( adriftᶠ != 0.0 && ai < an) && ( val -= adriftᶠ * invda[ai] ) val != 0.0 && ( A[ijk, ijk] = val ) # v[i, j, k] KF val = 0.0 ( budriftᴮ != 0.0 && bi > 1 ) && ( val += budriftᴮ * invdb[bi-1] ) ( budriftᶠ != 0.0 && bi < bn) && ( val -= budriftᶠ * invdb[bi] ) ( audriftᴮ != 0.0 && ai > 1) && ( val += audriftᴮ * invda[ai-1] ) ( audriftᶠ != 0.0 && ai < an) && ( val -= audriftᶠ * invda[ai] ) val != 0.0 && ( Au[ijk, ijk] = val ) # v[i-1, j, k] if bdriftᶠ != 0.0 && bi0)*rᴮ⁺ + ( 1-(b[bi]>0) )*rᴮ⁻ # set the interest rate # # ## == Derivative w.r.t B == ## # bi1 ? ( ∂Vbᴮ = ( V[bi, ai, zi] - V[bi-1, ai, zi] ) * invdb ) : ( ∂Vbᴮ = ( ( 1-ξ ) * w * z[zi] + rᴮ⁻ * b[1] )^(-γ) ) # # ## == Derivative w.r.t A == ## # ai1 ? ( ∂Vaᴮ = ( V[bi, ai, zi] - V[bi, ai-1, zi] ) * invda ) : ∂Vaᴮ = zero(Float64) # # ## CONSUMPTION decision ## # cᶠ = ∂Vbᶠ^(-invγ); cᴮ = ∂Vbᴮ^(-invγ) # scᴮ = (1-ξ) * z[zi] * w + b[bi] * rᴮ - cᴮ # scᶠ = (1-ξ) * z[zi] * w + b[bi] * rᴮ - cᶠ # # ## DEPOSIT decision ## # dᴮᴮ = foc_dep( ∂Vbᴮ, ∂Vaᴮ, a[ai] ) # dᴮᶠ = foc_dep( ∂Vbᴮ, ∂Vaᶠ, a[ai] ) # dᶠᴮ = foc_dep( ∂Vbᶠ, ∂Vaᴮ, a[ai] ) # dᶠᶠ = foc_dep( ∂Vbᶠ, ∂Vaᶠ, a[ai] ) # # # DOUBT: having a hard time to understand these constraints... # # Although the deposits are computed for bi=1,bn, later on # # these are set to zero when doing upwind for b # # #== eq (13),(14) + set the boundaries in a ==# # dᴮ = min( dᴮᴮ, 0.0 ) + max(dᴮᶠ, 0.0) # eq (13)/ note dᴮᴮ > dᴮᶠ # ai==1 && ( dᴮ = (dᴮᶠ>0)*dᴮᶠ ) # Moll: make sure d>=0 at amin, don't use VaB(:,1,:) # ai==an && ( dᴮ = (dᴮᴮ<0)*dᴮᴮ ) # Moll: make sure d<=0 at amax, don't use VaF(:,J,:) # # dᶠ = min( dᶠᴮ,0.0 ) + max(dᶠᶠ, 0.0) # eq (14) # ai==1 && ( dᶠ = (dᶠᶠ>0)*dᶠᶠ ) # ai==an && ( dᶠ = (dᶠᴮ<0)*dᶠᴮ ) # # sdᴮ = - dᴮ - χ(dᴮ,a[ai]) # sdᶠ = - dᶠ - χ(dᶠ,a[ai]) # bi==bn && ( sdᶠ = min(sdᶠ,0.0) ) # # # == consumption UPWIND SCHEME == # # c₀ = (1-ξ) * z[zi] * w + b[bi] * rᴮ # Icᴮ = (scᴮ<0) ; Icᶠ = (scᶠ>0 && scᴮ>=0); Ic⁰ = 1 - Icᴮ - Icᶠ # # c = Icᴮ*cᴮ + Icᶠ*cᶠ + Ic⁰*c₀ # u[ij] = inv1γ * c^(1-γ) + invΔ * V[ij] # notice that V[ij] still valid # # # == dep UPWIND SCHEME for Vb == # # Idᶠ = (sdᶠ>0); Idᴮ = (sdᴮ<0 && sdᶠ<=0) # # # NOTE:30 Boundary constraints of b # bi==1 && ( Idᴮ =0 ) # make sure don't use ∂Vbᴮ if ib=1 for deposit decision # bi==bn && ((Idᴮ,Idᶠ)=(1,0)) # make sure don't use ∂Vbᶠ if ib=bn for deposit decision # # and FORCE ∂Vbᴮ is used at bn for deposit decision # Id⁰ = 1 - Idᶠ - Idᴮ # INACTION Region # # #== dep UPWINDE SCHEMEq for Va- eq (17) ==# # d⁻ = min(Idᴮ * dᴮᴮ + Idᶠ * dᶠᴮ,0.0) # use backward for ∂Va # d⁺ = max(Idᴮ * dᴮᶠ + Idᶠ * dᶠᶠ,0.0) # use forward for ∂Va # mf = d⁺ + ξ*w*z[zi] + rᴬ*a[ai] # # # NOTE:40 Boundary constraints of a # # Aggregate all terms in ∂Vaᴮ at an # ai==an && (mf = 0; d⁻ = ξ*w*z[zi] + rᴬ*a[an] + d⁻) # # # NOTE:50 Positions of A that I need to fill # # A[ij-bn, ij] - v(i ,j-1,k) --> - invda*[ d⁻ ] # # A[ij- 1, ij] - v(i+1,j ,k) --> - invdb*[(scᴮ)⁻ + (sdᴮ)⁻ ] # # A[ij , ij] - v(i ,j ,k) --> invdb*[ (scᴮ)⁻ - (scᶠ)⁺ + (sdᴮ)⁻ - (sdᶠ)⁺ ] + invda*[ d⁻ - (d⁺ + ξwz + rᵃa) ] # # A[ij+ 1, ij] - v(i-1,j ,k) --> invdb*[ (scᶠ)⁺ + (sdᶠ)⁺ ] # # A[ij+bn, ij] - v(i .j+1,k) --> invda*[ d⁺ + ξwx + rᵃa ] # # # # the matrix a is changing its entrances... Note that code from aigary only changed when # # it was nonzero... # # current = Array(Float64,0) # zi == 2 && push!(current, 0.0) # # ij>bn && ( ai>1 ? push!(current, - invda * d⁻ ) : push!(current, 0.0) ) # ij>1 && ( bi>1 ? push!(current, - invdb *( Icᴮ*scᴮ + Idᴮ*sdᴮ ) ) : push!(current, 0.0) ) # push!(current, invdb *( Icᴮ*scᴮ + Idᴮ*sdᴮ - Icᶠ*scᶠ - Idᶠ*sdᶠ ) + invda *( d⁻ - mf ) ) # ijtolFK && it<=maxit g̃ = gold * C updateg!(gout, g̃, B) ## Aggregating household decisions ## # copy!(gvec_old, gold) # copy!(gvec_out, gout) # Eaold = sum(avec .* gvec_out .* ΔTab) # Eaout = sum(avec .* gvec_out .* ΔTab) # Eb = sum(bvec .* gvec .* ΔTab) distance = chebyshev(vec(gout), vec(gold)) # distance = abs(Eaold - Eaout) if distance < tolFK println("kfe solved : $(it) iterations") # println(out_text,"kfe solved : $(it) iterations") break else (it % 50 == 0) && @printf(" density iteration %d, distance %.4f \n", it, distance) (it % 50 == 0) && @printf(" sum %.4f --> %.4f\n", sum(gout'*ΔTab), sum(gold'*ΔTab) ) # (it % 50 == 0) && @printf(out_text," density iteration %d, distance %.4f \n", it, distance) # (it % 50 == 0) && @printf(out_text," sum %.4f --> %.4f\n", sum(gout'*ΔTab), sum(gold'*ΔTab) ) end copy!(gold, gout) it += 1 end #== Update the converged density on solution ==# for zi=1:zn sol.g[zi] = gout[:,zi] end return Void end function updateg!(gout, g̃, B) zn = size(gout,2) for zi=1:zn #== Solve the system B(k) gⁿ⁺¹ (k) = C[] ==# gout[:,zi] = B[zi] \ g̃[:,zi] end return Void end function fillB!(fd::FDExp, sol::SolutionExp) abn = length(fd.a)*length(fd.b) zn = length(fd.z) #= Construct a vector nb*na with the terms Δb̃[bi] * Δã[ai] =# ΔTab = fd.ΔTab #= D matrix =# D = ΔTab Dinv = 1./ΔTab # ========================================================================= ### Allocate LHS/RHS matrices ### ## ........................................................................ #== Back out information from fd ==# λdiag = fd.λdiag invΔᴷ = fd.invΔᴷ #== matrices to be filled ==# Au = sol.Au # intensity matrix Au A = sol.A # use A from HJB as storage B = fd.B # storage matrix for zi = 1:zn Avals = nonzeros(A[zi]) fill!(Avals, zero(Float64)) Auvals = nonzeros(Au[zi]) Arows = rowvals(Au[zi]) for abi in 1:abn for k in nzrange(Au[zi], abi) row = Arows[k] current = Dinv[row]*Auvals[k]*D[abi] Avals[k] = current end end end @inbounds for zi in 1:zn Avals = nonzeros(A[zi]) Bvals = nonzeros(B[zi]) #== erase Bvals ==# fill!(Bvals, zero(Float64)) Brows = rowvals(B[zi]) for abi in 1:abn for k in nzrange(B[zi], abi) # loop over elements in the column ij row = Brows[k] Bvals[k] = -Avals[k] row == abi && (Bvals[k] += invΔᴷ - λdiag[zi]) #NOTE:110 `λdiag` is entering only in this part of the code end end end # ========================================================================= return Void end function solve_fp!(fd::FDImp; maxit::Int64 = 400, tolFK::Float64 = 1e-5) #== Back out information ==# invΔᴷ = fd.invΔᴷ Au = fd.Au # use Au B = fd.B # storage matrix ΔTbgrid = fd.ΔTbgrid ΔTagrid = fd.ΔTagrid abn = length(ΔTbgrid) * length(ΔTagrid) zn = length(fd.z) #== Construct a vector nb*na with the terms Δb̃[bi] * Δã[ai] ==# ΔTab = zeros(fd.g⁰) ijk = zero(Int) for zi = 1:zn for abi = 1:abn ijk += 1 ΔTab[ijk] = ΔTagrid[afromab(abi)] * ΔTbgrid[bfromab(abi)] end end D = ΔTab Dinv = 1./ΔTab Avals = nonzeros(Au) Arows = rowvals(Au) ijk = zero(Int) for zi in 1:zn, abi in 1:abn ijk += 1 for k in nzrange(Au, ijk) row = Arows[k] current = Dinv[row]*Avals[k]*D[ijk] Avals[k] = current end end Bvals = nonzeros(B); fill!(Bvals, zero(Float64)) Brows = rowvals(B) ijk = zero(Int) @inbounds for zi in 1:zn, abi in 1:abn ijk += 1 for k in nzrange(B, ijk) # loop over elements in the column ij row = Brows[k] Bvals[k] = - Avals[k] row == ijk && (Bvals[k] += invΔᴷ) end end gold = copy(fd.g⁰) gout = zeros(gold) it = 1 distance = 1 while distance>tolFK && it<=maxit g̃ = gold * invΔᴷ gout = B \ g̃ distance = chebyshev(vec(gout), vec(gold)) if it % 5 == 0 @printf("Density iteration %d, distance %.4f \n", it, distance) #== Check if the summation is held constant across iterations ==# @printf("sum gold %.4f \n", sum(gold'*ΔTab)) @printf("sum gout %.4f \n", sum(gout'*ΔTab)) end copy!(gold, gout) it += 1 end fd.g = gout return Void end # function solve_fp!(fd::TwoAssetsFD) # # g⁰ = fd.g⁰ # Δa = fd.Δa; Δb = fd.Δb # # # == A' in the notes == # # A = fd.A # # i_fix = 1 # Aurows = rowvals(A) # Auvals = nonzeros(A) # @inbounds for ij in 1:size(A, 2) # for k in nzrange(A, ij) # Arows[k] == i_fix && ( Avals[k] = zero(Float64) ) # end # end # A[i_fix,i_fix] = one(Float64) # # # == solve system Ag = b == # # gg = A \ g⁰ # # total = 1/( Δb*Δa*sum(gg) ) # fd.gg = total * gg # # return Void # end ================================================ FILE: lecture13/felipe_alves_codes/testing.jl ================================================ #== INCLUDE files ==# include("aggregate.jl") include("twoassets.jl") include("solveHJB.jl") include("solveKFE.jl") #== Instance of prices ==# pr = Prices(0.0, 0.04, 0.03, 0.09, 8.0) #== Instance of TwoAssetProblem ==# twoap = TwoAssetsProb2(pr; γ = 2.0, ρ = 0.06, ξ = 0.10, χ₀ = 0.08, χ₁= 3.0, τ = 0.0, T = 0.0) #== Create and FiniteDifference Structure ==# fde = FDExp(twoap; z = [.8, 1.3], λ = [-1/3 1/3; 1/3 -1/3], fixedΔa = true, fixedΔb = false, bn = 100, bmin = -2.0, bmax = 40.0, an =70, amax = 70.0, invΔᴷ = 0.05) #== Solve the HJB equation ==# solve_hjb!(twoap, fde, fde.sol) #== Solve the KF equation ==# solve_fp!(fde, fde.sol, maxit = 4000, tolFK = 1e-9) include("main_fig.jl"); #== Policies ==# # fig_pol(fde) #== Distributions ==# fig_dens(fde) ================================================ FILE: lecture13/felipe_alves_codes/twoassets.jl ================================================ import Distances: chebyshev ############################################################################## ## ## Types and constructors ## ############################################################################## ################################## ## ## Household PROBLEM ## ################################## """ Consumption-saving problem with two assets and non-convex adjustment costs ## Fields #### Parameters - `γ::Float64` : parameter on CRRA utility - `ρ::Float64` : discount factor - `ξ::Float64` : automatic deposit - `σ::Float64` : frisch elasticity of labor supply - `ψ::Float64` : disutility of labor #### Prices - `rᴬ::Float64` : interest rate on illiquid asset - `rᴮ::Float64` : interest rate for savings in liquid asset - `wedge::Float64` : interest rate for borrowing in liquid asset - `w::Float64` : wage rate #### Tax - `τ::Float64` : tax on income - `T::Float64` : Lump-sum transfer #### Cost Function - `χ₀::Float64` : - `χ₁::Float64` : """ type TwoAssetsProb γ::Float64 # CRRA utility with parameter γ ρ::Float64 # discount rate ξ::Float64 # automatic deposit on illiquid asset σ::Float64 ψ::Float64 rᴬ ::Float64 # ret on illiquid asset rᴮ::Float64 # ret on liq asset wedge::Float64 w ::Float64 # wage rate τ::Float64 T::Float64 χ₀::Float64 # parameters on adjustment cost χ₁::Float64 end function Base.show(io::IO, twoap::TwoAssetsProb) @printf io " HOUSEHOLD PROBLEM\n" @printf io "\n" @printf io " Parameters \n" @printf io " ------------ \n" @printf io " γ: %.3f\n" twoap.γ @printf io " ρ: %.3f\n" twoap.ρ @printf io " ξ: %.3f\n" twoap.ξ @printf io "\n" @printf io " Prices \n" @printf io " -------- \n" @printf io " rᴬ : %.3f \n" twoap.rᴬ @printf io " rᴮ : %.3f \n" twoap.rᴮ @printf io " wage : %.3f \n" twoap.w @printf io " τ : %.3f \n" twoap.τ @printf io " T : %.3f \n" twoap.T @printf io "\n" @printf io " Cost Function \n" @printf io " --------------- \n" m =""" χ₀ + χ₁ * (d/a)² * a """ @printf io " %s\n" m end function TwoAssetsProb2(pc::Prices; γ::Float64 = 2.0, ρ::Float64 = 0.06, ξ::Float64 = 0.1, σ = 0.5, ψ = 27.0, τ::Float64 = 0.0, T::Float64 = 0., χ₀::Float64 = 0.08, χ₁::Float64 = 3.0) # REVIEW: define some global functions that will now change on the go #== Substitute the functions to be used afterward ==# # global χ(d,a) = χ(d,a, [χ₀,χ₁]) # global dχinv(pVb::Float64, pVa::Float64, a::Float64) = dχinv(pVb, pVa, a, χ₁) # global TwoAssetsProb(γ, ρ, ξ, σ, ψ, pc.rᴬ, pc.rᴮ, pc.wedge, pc.w, τ, T, χ₀, χ₁) end _unpackparams(twoap::TwoAssetsProb) = twoap.γ, twoap.ρ, twoap.ξ, twoap.σ, twoap.ψ _unpackprices(twoap::TwoAssetsProb) = twoap.rᴬ, twoap.rᴮ, twoap.rᴮ+twoap.wedge, twoap.w _unpacktax(twoap::TwoAssetsProb) = twoap.τ, twoap.T function utilfn(twoap::TwoAssetsProb, c::Float64, ℓutil::Float64) return (c-ℓutil)^(1.- twoap.γ)/(1.-twoap.γ) end function χ(twoap::TwoAssetsProb, d::Float64, a::Float64, abar::Float64 = 2.0) χ₀, χ₁ = twoap.χ₀, twoap.χ₁ return χ₀*(abs(d)>0) + 0.5*χ₁*d^2.0/max(a,abar) end # χ(d::Float64,a::Float64, χval::Array{Float64,1}) = χval[1]*(abs(d)>0) + 0.5*χval[2]*d^2.0/max(a,1e-5) function dχinv(twoap::TwoAssetsProb, ∂ratio::Float64, a::Float64, abar::Float64 = 2.0) χ₁ = twoap.χ₁ return 1.0/χ₁ * ( ∂ratio-1.0 ) * max(a,abar) end function change_prices!(twoap::TwoAssetsProb, pc::Prices) twoap.rᴬ, twoap.rᴮ, twoap.w = pc.rᴬ, pc.rᴮ, pc.w return Void end function change_transfer!(twoap::TwoAssetsProb, τ::Float64, T::Float64) twoap.τ, twoap.T = τ, T return Void end # OLD function # function foc_dep(∂Vb::Float64, ∂Va::Float64, a::Float64, χ₁::Float64) # # d = ( ∂Va/∂Vb-1 ) * a/χ₁ # optimal d in case of adjustment # # # NOTE:340 compute the change in utility and use that to decide whether to act or # # not... # # ∂V = ∂Va*d - ∂Vb*( d + χ(d,a) ) # change in utility # d = d * (∂V > 0) # end ############################################################################## ## ## Helping functions ## ############################################################################## function powerspacegrid(init::Real, eend::Real, n::Int64, k::Real = 1) if n<=2 println("n has to be larger than 2") return end x = collect(linspace(0,1,n)) z = x.^(1.0/float(k)) return init +(eend - init)*z end ################################## ## ## FDSpec ## ################################## abstract FDSol abstract FDSpec type SolutionExp <: FDSol ## Solution V::Vector{Vector{Float64}} # Value function g::Vector{Vector{Float64}} # Density over state space c::Array{Float64,3} # Optimal consumption sc::Array{Float64,3} # Savings without deposit d::Array{Float64,3} # Optimal deposit flow ## Matrices to be filled A ::Vector{Base.SparseMatrix.SparseMatrixCSC{Float64, Int}} # used on HJB to compute vⁿ⁺¹ Au::Vector{Base.SparseMatrix.SparseMatrixCSC{Float64, Int}} # used for KFE to compute transition dynamics end unpackcopy_fdsol(fdsol::SolutionExp) = deepcopy(fdsol.V), deepcopy(fdsol.g), deepcopy(fdsol.c), deepcopy(fdsol.sc), deepcopy(fdsol.d), deepcopy(fdsol.A), deepcopy(fdsol.Au) """ Specification for the Implicit-Explicit Finite Difference method """ type FDExp <: FDSpec ## GRID INFO b ::Vector{Float64} Δbgrid ::Vector{Float64} ΔTbgrid::Vector{Float64} rbdrift::Vector{Float64} netbinc::Matrix{Float64} a ::Vector{Float64} Δagrid ::Vector{Float64} ΔTagrid::Vector{Float64} radrift::Vector{Float64} netainc::Matrix{Float64} ΔTab::Vector{Float64} ## LaborSupply decisions ℓsupply::Vector{Float64} ℓutilgrid::Vector{Float64} ## Δ in finite difference scheme invΔ::Float64 invΔᴷ::Float64 ## Stochastic Information z::Vector{Float64} λ::Matrix{Float64} λdiag::Vector{Float64} λoff::Matrix{Float64} ## Solution sol::SolutionExp ## Storage b̃ ::Vector{Vector{Float64}} # RHS of hjb B ::Vector{Base.SparseMatrix.SparseMatrixCSC{Float64, Int}} # storage matrix end function Base.show(io::IO, fd::FDExp) fd.Δbgrid[2]-fd.Δbgrid[1] == 0 ? (binfo = "uniform ") : (binfo = "non-uniform ") fd.Δagrid[2]-fd.Δagrid[1] == 0 ? (ainfo = "uniform ") : (ainfo = "non-uniform ") @printf io "\n" @printf io " Explicit-Implicit Finite Difference Method\n" @printf io "\n" @printf io " Grids \n" @printf io " ------- \n" @printf io " %12sb: %3d points in [% .0f, %.0f]\n" binfo length(fd.b) fd.b[1] fd.b[end] @printf io " %12sa: %3d poitns in [% .0f, %.0f]\n" ainfo length(fd.a) fd.a[1] fd.a[end] end function FDExp(twoap::TwoAssetsProb; z::Vector{Float64} = [.5,1.5], λ::Matrix{Float64} = [-1/3 1/3; 1/3 -1/3], fixedΔb = true, bn::Int =100, bmin::Float64 =-1.0, bmax::Float64 = 40.0, bparam::Vector{Float64} = [0.35, 0.7], fixedΔa = true, an::Int = 50, amin::Float64 = 1e-8, amax::Float64 = 70.0, aparam::Float64 = 0.7, invΔ::Float64 = .025, invΔᴷ::Float64 = 0.025) # z = [.8,1.3]; # λ = [-1/3 1/3;1/3 -1/3]; # # fixedΔb = true; # bn =100; # bmin =-2.0; # bmax = 40.0; # bparam = [0.35;0.7]; # # fixedΔa = true; # an = 50; # amin = 0.0; # amax = 70.0; # aparam = 0.7; # # invΔ = .025; # invΔᴷ = 0.075 #== Household parameters and prices ==# γ, ρ, ξ, σ, ψ = _unpackparams(twoap) rᴬ, rᴮ, rᴮ⁻, w = _unpackprices(twoap) τ, T = _unpacktax(twoap) #== Construct the grids ==# if fixedΔb b = powerspacegrid(bmin,bmax,bn) else ngpbPOS = Int(0.9*bn) ngpbNEG = bn - ngpbPOS bpos = powerspacegrid(1e-8, bmax, ngpbPOS, bparam[2]) bneg = zeros(ngpbNEG) n = Int(ngpbNEG/2+1) bneg[1:n] = powerspacegrid(bmin, (bpos[1]+bmin)/2, n , bparam[1]) b = [bneg;bpos] for i in n+1:ngpbNEG b[i] = bpos[1] - (b[ngpbNEG+2-i]-b[1]) end end fixedΔa ? (a = powerspacegrid(amin,amax,an)) : (a = powerspacegrid(amin,amax,an,aparam[1])) zn = length(z); abn = an*bn #== Grid counting functions ==# global afromab(abi) = div(abi-1,bn)+1 global bfromab(abi) = rem(abi-1,bn)+1 global abfromab(ai,bi) = (ai-1)*bn + bi global abzfromabz(ai,bi,zi) = (zi-1)*an*bn + ( ai-1 )*bn +bi #== Grid info ==# Δbgrid = b[2:end]-b[1:end-1] Δagrid = a[2:end]-a[1:end-1] ΔTbgrid = zeros(bn); ΔTagrid = zeros(an) ΔTbgrid[1] = 0.5*Δbgrid[1] ΔTbgrid[2:end-1] = 0.5*( Δbgrid[2:end] + Δbgrid[1:end-1] ) ΔTbgrid[end] = 0.5*Δbgrid[end] ΔTagrid[1] = 0.5*Δagrid[1] ΔTagrid[2:end-1] = 0.5*( Δagrid[2:end] + Δagrid[1:end-1] ) ΔTagrid[end] = 0.5*Δagrid[end] #== Construct a vector nb*na with the terms Δb̃[bi] * Δã[ai] =# ΔTab = zeros(abn) for abi = 1:abn ΔTab[abi] = ΔTagrid[afromab(abi)] * ΔTbgrid[bfromab(abi)] end #== Labor grid info ==# ℓsupply = zeros(zn) ℓsupply[:] = (1/ψ * (1-τ) * w)^σ ℓutilgrid = ψ * (z .* ℓsupply.^( 1 + 1.0/σ ) ) / ( 1+1./σ ) #== return drifts ==# rbdrift = (b.>= 0.0)*rᴮ + (b.<0.0)*rᴮ⁻ rbdrift = b .* rbdrift # NOTE: trick on the upper part of grid #== impose tax on the very top part of grid ==# τa = 15 τc = rᴬ * ( a[end] * 0.999 )^(1 - τa) radrift = rᴬ * a - τc * a .^ τa #== net?drifts ==# netbinc = zeros(bn,zn) netainc = zeros(an,zn) for zi in 1:zn netℓinc = (1-τ)* w * z[zi] * ℓsupply[zi] + T netbinc[:,zi] = (1-ξ) * netℓinc + rbdrift netainc[:,zi] = radrift + ξ * netℓinc end #== Idiosyncratic shock ==# λdiag = diag(λ) λoff = λ - diagm(λdiag) ### STORAGE ### A = SparseMatrixCSC{Float64, Int}[spdiagm( (ones(abn), ones(abn-1), ones(abn-1), ones( abn -bn ) , ones( abn -bn ) ), (0, 1, -1, bn, -bn) ) for zi =1:zn] # B = SparseMatrixCSC{Float64, Int}[spdiagm( # (ones(abn), ones(abn-1), ones(abn-1), ones( abn -bn ) , ones( abn -bn ) ), # (0, 1, -1, bn, -bn) ) for zi =1:zn] B = Array(SparseMatrixCSC{Float64, Int},zn) Au = Array(SparseMatrixCSC{Float64, Int},zn) for zi=1:zn fill!(nonzeros(A[zi]), zero(Float64)) B[zi] = deepcopy(A[zi]) Au[zi] = deepcopy(A[zi]) end #== Initial Distribution ==# g = zeros(abn,zn) bpos_ind = findfirst(b.>0) #REVIEW:70 change ydist assumption ydist = ones(zn)./zn for zi=1:zn for abi =1:abn ai = afromab(abi) bi = bfromab(abi) ai==1 && bi==bpos_ind && ( g[abi,zi] = ydist[zi]/(ΔTagrid[ai]*ΔTbgrid[bi]) ) end end g = Vector{Float64}[g[:,zi] for zi=1:zn] #---------------------------------------------------------------- ### SOLUTION ### V = Vector{Float64}[Array(Float64, abn) for zi=1:zn] ij = zero(Int) for zi in 1:zn ij = Int(0) for ai in 1:an, bi in 1:bn ij += 1 V[zi][ij] = ( (1- ξ)*w*z[zi] + rᴬ*a[ai] + rᴮ⁻*b[bi] ).^( 1- γ )/( 1- γ )/ρ end end # == storage arrays == # b̃ = Vector{Float64}[Array(Float64, abn) for zi=1:zn] # RHS of the Bellman equation #== policies ==# c = Array(Float64, bn, an, zn) # consumption policy sc = copy(c); d = copy(c) #== Create solution type ==# sol = SolutionExp(V, g, c, sc, d, A, Au) FDExp( b, Δbgrid, ΔTbgrid, rbdrift, netbinc, a, Δagrid, ΔTagrid, radrift, netainc, ΔTab, ℓsupply, ℓutilgrid, invΔ, invΔᴷ, z, λ, λdiag, λoff, sol, b̃, B) end function initialize_solexp(twoap::TwoAssetsProb, fd::FDSpec) #= precompute the lengths =# b = fd.b a = fd.a z = fd.z bn = length(b); an = length(a); zn = length(z); abn = bn*an #= extract some other from fd =# ΔTagrid = fd.ΔTagrid ΔTbgrid = fd.ΔTbgrid netbinc = fd.netbinc #== Create the A matrix ==# A = SparseMatrixCSC{Float64, Int}[spdiagm( (ones(abn), ones(abn-1), ones(abn-1), ones( abn -bn ) , ones( abn -bn ) ), (0, 1, -1, bn, -bn) ) for zi =1:zn] Au = Array(SparseMatrixCSC{Float64, Int},zn) for zi=1:zn fill!(nonzeros(A[zi]), zero(Float64)) Au[zi] = deepcopy(A[zi]) end #== Initial Distribution ==# g = zeros(abn,zn) bpos_ind = findfirst(b.>0) #REVIEW:70 change ydist assumption ydist = ones(zn)./zn for zi=1:zn for abi =1:abn ai = afromab(abi) bi = bfromab(abi) ai==1 && bi==bpos_ind && ( g[abi,zi] = ydist[zi]/(ΔTagrid[ai]*ΔTbgrid[bi]) ) end end g = Vector{Float64}[g[:,zi] for zi=1:zn] #---------------------------------------------------------------- ### SOLUTION ### V = Vector{Float64}[Array(Float64, abn) for zi=1:zn] ij = zero(Int) for zi in 1:zn ij = Int(0) for ai in 1:an, bi in 1:bn ij += 1 V[zi][ij] = ( netbinc[bi,zi] ).^( 1- γ )/( 1- γ )/ρ end end #== policies ==# c = Array(Float64, bn, an, zn) # consumption policy sc = copy(c); d = copy(c) #== Create solution type ==# SolutionExp(V, g, c, sc, d, A, Au) end """ Update the grid for a different specification of HOUSEHOLD PROBLEM """ function change_grid!(fd::FDSpec, twoap::TwoAssetsProb) #== Parameters ==# _, _, ξ, σ, ψ = _unpackparams(twoap) rᴬ, rᴮ, rᴮ⁻, w = _unpackprices(twoap) τ, T = _unpacktax(twoap) b, a, z = fd.b, fd.a, fd.z bn = length(b) an = length(a) abn = bn * an zn = length(z) #== Labor grid info ==# ℓsupply = zeros(zn) ℓsupply[:] = (1/ψ * (1-τ) * w)^σ fd.ℓsupply = ℓsupply fd.ℓutilgrid = ψ * (z .* ℓsupply.^( 1 + 1.0/σ ) ) / ( 1+1./σ ) #== Update the drifts ==# rbdrift = ( b.>= 0.0)*rᴮ + (b.<0.0)*rᴮ⁻ fd.rbdrift = b .* rbdrift τa = 15 τc = rᴬ * ( a[end] * 0.999 )^(1 - τa) fd.radrift = rᴬ * a - τc * a .^ τa for zi in 1:zn netℓinc = (1-τ)* w *z[zi] * ℓsupply[zi] + T fd.netbinc[:,zi] = (1-ξ) * netℓinc + fd.rbdrift fd.netainc[:,zi] = fd.radrift + ξ * netℓinc end end ############################################################################## ## ## Implicit scheme ## ############################################################################## type SolutionImp <: FDSol ## Household Solution V::Vector{Float64} # Value function g::Vector{Float64} # Density over state space c::Array{Float64,3} # Optimal consumption sc::Array{Float64,3} # Savings without deposit d::Array{Float64,3} # Optimal deposit flow ## matrices to be filled A ::Base.SparseMatrix.SparseMatrixCSC{Float64, Int} # intensity matrix A on vⁿ⁺¹ Au ::Base.SparseMatrix.SparseMatrixCSC{Float64, Int} # intensity matrix A for kfe end """ Specification for the Implicit Finite Difference method """ type FDImp <: FDSpec ## GRID INFO b ::Vector{Float64} Δbgrid ::Vector{Float64} ΔTbgrid::Vector{Float64} rbdrift::Vector{Float64} netbinc::Matrix{Float64} a ::Vector{Float64} Δagrid ::Vector{Float64} ΔTagrid::Vector{Float64} radrift::Vector{Float64} netainc::Matrix{Float64} ## LaborSupply decisions ℓsupply ::Vector{Float64} ℓutilgrid::Vector{Float64} ## Δ in finite difference scheme invΔ ::Float64 invΔᴷ::Float64 ## Stochastic Information z::Vector{Float64} λ::Matrix{Float64} ## OUTPUT sol::SolutionImp ## Storage b̃ ::Vector{Float64} # RHS of hjb B ::Base.SparseMatrix.SparseMatrixCSC{Float64, Int} # B storage matrix Λ ::Base.SparseMatrix.SparseMatrixCSC{Float64, Int} # stochastic process terms (useful bc can fill before) end function Base.show(io::IO, fd::FDImp) fd.Δbgrid[2]-fd.Δbgrid[1] == 0 ? (binfo = "uniform ") : (binfo = "non-uniform ") fd.Δagrid[2]-fd.Δagrid[1] == 0 ? (ainfo = "uniform ") : (ainfo = "non-uniform ") @printf io "\n" @printf io " Implicit Finite Difference Method\n" @printf io "\n" @printf io " Grids \n" @printf io " ------- \n" @printf io " %12sb: %3d points in [% .0f, %.0f]\n" binfo length(fd.b) fd.b[1] fd.b[end] @printf io " %12sa: %3d poitns in [% .0f, %.0f]\n" ainfo length(fd.a) fd.a[1] fd.a[end] # @printf io " z: %.2f\n" twoap.ξ end function FDImp(twoap::TwoAssetsProb; z::Vector{Float64} = [.8,1.3], λ::Matrix{Float64} = [-1/3 1/3; 1/3 -1/3], fixedΔb = true, bn::Int =100, bmin::Float64 =-2.0, bmax::Float64 = 40.0, bparam::Vector{Float64} = [0.35, 0.7], fixedΔa = true, an::Int = 50, amin::Float64 = 0.0, amax::Float64 = 70.0, aparam::Float64 = 0.7, invΔ::Float64 = .01, invΔᴷ::Float64 = .05) #== Parameters ==# γ, ρ, ξ, σ, ψ = _unpackparams(twoap) rᴬ, rᴮ, rᴮ⁻, w = _unpackprices(twoap) τ, T = _unpacktax(twoap) #== construct the grids ==# if fixedΔb b = powerspacegrid(bmin,bmax,bn) else ngpbPOS = Int(0.8*bn) ngpbNEG = bn - ngpbPOS bpos = powerspacegrid(1e-8, bmax, ngpbPOS, bparam[2]) bneg = zeros(ngpbNEG) n = Int(ngpbNEG/2+1) bneg[1:n] = powerspacegrid(bmin, (bpos[1]+bmin)/2, n , bparam[1]) b = [bneg;bpos] for i in n+1:ngpbNEG b[i] = bpos[1] - (b[ngpbNEG+2-i]-b[1]) end end fixedΔa ? (a = powerspacegrid(amin,amax,an)) : (a = powerspacegrid(amin,amax,an,aparam[1])) zn = length(z) abn = an*bn #== Grid counting functions ==# global afromab(abi) = div(abi-1,bn)+1 global bfromab(abi) = rem(abi-1,bn)+1 global abfromab(ai,bi) = (ai-1)*bn + bi global abzfromabz(ai,bi,zi) = (zi-1)*an*bn + ( ai-1 )*bn +bi #== Grid info ==# Δbgrid = b[2:end]-b[1:end-1] Δagrid = a[2:end]-a[1:end-1] ΔTbgrid = zeros(bn); ΔTagrid = zeros(an) ΔTbgrid[1] = 0.5*Δbgrid[1] ΔTbgrid[2:end-1] = 0.5*( Δbgrid[2:end] + Δbgrid[1:end-1] ) ΔTbgrid[end] = 0.5*Δbgrid[end] ΔTagrid[1] = 0.5*Δagrid[1] ΔTagrid[2:end-1] = 0.5*( Δagrid[2:end] + Δagrid[1:end-1] ) ΔTagrid[end] = 0.5*Δagrid[end] #== Labor grid info ==# ℓsupply = zeros(zn) ℓsupply[:] = (1/ψ * (1-τ) * w)^σ ℓutilgrid = ψ * (z .* ℓsupply.^( 1 + 1.0/σ ) ) / ( 1+1./σ ) rbdrift = (b.>= 0.0)*rᴮ + (b.<0.0)*rᴮ⁻ rbdrift = b .* rbdrift #== impose tax on the very top part of grid ==# τa = 15 τc = rᴬ * ( a[end] * 0.999 )^(1 - τa) radrift = rᴬ * a - τc * a .^ τa #== net_?drifts ==# netbinc = zeros(bn,zn) netainc = zeros(an,zn) for zi in 1:zn netℓinc = (1-τ)* w * z[zi] * ℓsupply[zi] + T netbinc[:,zi] = (1-ξ) * netℓinc + rbdrift netainc[:,zi] = radrift + ξ * netℓinc end ## STORAGE # NOTE:250 create a sparse matrix of the RIGHT format that later will be filled with zeros # here we have to be careful bc prob has 3 state variables (bi, aj, zk) onesλ = Array{Float64,1}[ones( (zn-i)*abn ) for i=1:zn-1] colλ = Int64[(i)*abn for i=1:zn-1] A = spdiagm( (ones(abn*zn), ones(abn*zn-1), ones(abn*zn-1), ones( abn*zn -bn ) , ones( abn*zn -bn ), onesλ..., onesλ...), (0, 1, -1, bn, -bn, colλ..., -colλ...) ) fill!(nonzeros(A), zero(Float64)) Au = deepcopy(A) B = deepcopy(A) Λ = deepcopy(A) # == fill up matrix Λ == # ij = zero(Int) λt = λ' @inbounds for zi in 1:zn, abi in 1:abn ij += 1 Λ[ abi:abn:(zn-1)*abn+abi , ij] = λt[:,zi] end # == storage arrays == # b̃ = Array(Float64, abn*zn) # RHS of the Bellman equation c = Array(Float64, bn, an, zn) # consumption policy sc = copy(c); d = copy(c) #== Initial value function guess ==# V = Array(Float64, abn*zn) ij = zero(Int) for zi in 1:zn, ai in 1:an, bi in 1:bn ij += 1 V[ij] = ( (1- ξ)*w*z[zi] + rᴬ*a[ai] + rᴮ⁻*b[bi] ).^( 1- γ )/( 1- γ )/ρ end #== Initial Distribution ==# g = zeros(abn,zn) bpos_ind = findfirst(b.>0) #REVIEW:60 change ydist assumption ydist = ones(zn)./zn for zi=1:zn for abi =1:abn ai = afromab(abi) bi = bfromab(abi) ai==1 && bi==bpos_ind && ( g[abi,zi] = ydist[zi]/(ΔTagrid[ai]*ΔTbgrid[bi]) ) end end g = g[:] sol = SolutionImp(V, g, c, sc, d, A, Au) FDImp( b, Δbgrid, ΔTbgrid, rbdrift, netbinc, a, Δagrid, ΔTagrid, radrift, netainc, ℓsupply, ℓutilgrid, invΔ, invΔᴷ, z, λ, sol, b̃, B, Λ) end ################################## ## ## TwoAssetsFD ## ################################## # # type TwoAssetsFD # ## Parameters of the problem # twoap::TwoAssetsProb # # ## Discretization # b::Vector{Float64} # Δb::Float64 # rbdrift::Vector{Float64} # a::Vector{Float64} # Δa::Float64 # radrift::Vector{Float64} # invΔ::Float64 # # ## Stochastic Information # λ::Matrix{Float64} # z::Vector{Float64} # # ## Solution # V::Vector{Float64} # Value function # gg::Vector{Float64} # distribution # # ## Storage # newV::Vector{Float64} # New Value function # u::Vector{Float64} # utility term # g⁰::Vector{Float64} # for KolmogorovForward # A::Base.SparseMatrix.SparseMatrixCSC{Float64, Int} # rhs matrix on vⁿ⁺¹ # B::Base.SparseMatrix.SparseMatrixCSC{Float64, Int} # B = (indΔ + ρ)I - A # Λ::Base.SparseMatrix.SparseMatrixCSC{Float64, Int} # stochastic process terms (useful bc can fill before) # end # # # function TwoAssetsFD(twoap::TwoAssetsProb, # z::Vector{Float64} = [.8,1.3], λ::Matrix{Float64} = [-1/3 1/3; 1/3 -1/3], # bn::Int =100, bmin::Float64 =-2.0, bmax::Float64 = 40.0, # an::Int = 50, amin::Float64 = 0.0, amax::Float64 = 70.0, # invΔ::Float64 = .01) # # #== Parameters ==# # γ = twoap.γ; ρ = twoap.ρ; ξ = twoap.ξ; rᴬ = twoap.rᴬ; # rᴮ = twoap.rᴮ; ; w = twoap.w # rᴮ⁻ = twoap.rᴮ + twoap.wedge # b = collect(linspace(bmin,bmax,bn)) # a = collect(linspace(amin,amax,an)) # Δb = b[2]-b[1]; Δa = a[2]-a[1]; # # rbdrift = ( b.>= 0.0)*rᴮ + (b.<0.0)*rᴮ⁻ # rbdrift = b .* rbdrift # radrift = rᴬ * a # # zn = length(z) # abn = an*bn # # # NOTE:380 create a sparse matrix of the RIGHT format that later will be filled with zeros # # here we have to be careful bc prob has 3 state variables (bi, aj, zk) # onesλ = Array{Float64,1}[ones( (zn-i)*abn ) for i=1:zn-1] # colλ = Int64[(i)*abn for i=1:zn-1] # # A = spdiagm( # (ones(abn*zn), ones(abn*zn-1), ones(abn*zn-1), ones( abn*zn -bn ) , ones( abn*zn -bn ), # onesλ..., onesλ...), # (0, 1, -1, bn, -bn, colλ..., -colλ...) # ) # # fill!(nonzeros(A), zero(Float64)) # B = deepcopy(A) # Λ = deepcopy(A) # # # == fill up matrix Λ == # # ij = zero(Int) # λt = λ' # @inbounds for zi in 1:zn, abi in 1:abn # ij += 1 # Λ[ abi:abn:(zn-1)*abn+abi , ij] = λt[:,zi] # end # # V = Array(Float64, abn*zn) # ij = zero(Int) # for zi in 1:zn, ai in 1:an, bi in 1:bn # ij += 1 # V[ij] = ( (1- ξ)*w*z[zi] + rᴬ*a[ai] + rᴮ⁻*b[bi] ).^( 1- γ )/( 1- γ )/ρ # end # # # == b such that Ag = b in plank # g⁰ = fill(zero(Float64), abn*zn) # i_fix = 1 # g⁰[i_fix] = .1 # # # == storage arrays == # # gg = Array(Float64, abn*zn) # u = Array(Float64, abn*zn) # newV = deepcopy(V) # # TwoAssetsFD(twoap, # b, Δb, rbdrift, a, Δa, radrift, invΔ, # λ, z, # V, gg, # newV, u, g⁰, A, B, Λ ) # end ================================================ FILE: lecture14/james_graham_DOLO.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introducing DOLO\n", "\n", "### What is DOLO? \n", "\n", "A Python-based language to write and solve a variety of economic models (often dynamic, often stochastic). It uses a simple classification syntax written in YAML (Yet Another Markup Language) to express model objects, which can then be solved in a Python console or Jupyter Notebook.\n", "\n", "Up front, one of the biggest advantages of DOLO is that it can serve as a straightforward replacement of Dynare for solving DSGE models. We'll talk about this advantage a lot as we go on.\n", "\n", "### Jumping right in...\n", "\n", "DOLO can be installed very easily with instructions from the website (http://dolo.readthedocs.io/en/doc/)\n", "\n", "If you have Anaconda, you can quickly install Dolo with:\n", "* ``pip install dolo``\n", "\n", "Website provides a good intro to the Dolo package, but for further information you'll often have to go looking through the code yourself. Most of the algorithms we'll be using can be found in, for example: \n", "* ``C:\\Users\\James\\Anaconda3\\Lib\\site-packages\\dolo\\algos``\n", "* For example, ``dtcscc`` holds scripts that solve \"Discrete Transition Continuous State Continuous Controls\" models\n", "\n", "### An RBC model example\n", "\n", "Let's begin by importing DOLO and an example RBC model file from EconForge. We then print the model to see what's inside. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " 
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name: Real Business Cycle\n",
       "\n",
       "symbols:\n",
       "\n",
       "   states:  [z, k]\n",
       "   controls: [i, n]\n",
       "   auxiliaries: [y, c, rk, w]\n",
       "   values: [V]\n",
       "   shocks: [e_z]\n",
       "\n",
       "   parameters: [beta, sigma, eta, chi, delta, alpha, rho, zbar, sig_z ]\n",
       "\n",
       "\n",
       "equations:\n",
       "\n",
       "\n",
       "    arbitrage:\n",
       "        - 1 - beta*(c/c(1))^(sigma)*(1-delta+rk(1))   | 0 <= i <= inf\n",
       "        - chi*n^eta*c^sigma - w                       | 0 <= n <= inf\n",
       "\n",
       "    transition:\n",
       "        - z = (1-rho)*zbar + rho*z(-1) + e_z\n",
       "        - k = (1-delta)*k(-1) + i(-1)\n",
       "\n",
       "    auxiliary:\n",
       "        - y = z*k^alpha*n^(1-alpha)\n",
       "        - c = y - i\n",
       "        - rk = alpha*y/k\n",
       "        - w = (1-alpha)*y/n\n",
       "\n",
       "    value:\n",
       "        - V = log(c) + beta*V(1)\n",
       "\n",
       "calibration:\n",
       "\n",
       "    # parameters\n",
       "    beta : 0.99\n",
       "    phi: 1\n",
       "    delta : 0.025\n",
       "    alpha : 0.33\n",
       "    rho : 0.8\n",
       "    sigma: 1\n",
       "    eta: 1\n",
       "    sig_z: 0.016\n",
       "    zbar: 1\n",
       "    chi : w/c^sigma/n^eta\n",
       "\n",
       "    # endogenous variables\n",
       "    n: 0.33\n",
       "    k: n/(rk/alpha)^(1/(1-alpha))\n",
       "    w: (1-alpha)*z*(k/n)^(alpha)\n",
       "    i: delta*k\n",
       "    y: z*k^alpha*n^(1-alpha)\n",
       "    c: y - i\n",
       "    z: zbar\n",
       "    rk: 1/beta-1+delta\n",
       "    V: log(c)/(1-beta)\n",
       "\n",
       "\n",
       "distribution:\n",
       "\n",
       "    Normal:\n",
       "\n",
       "        [ [ sig_z**2] ]\n",
       "\n",
       "\n",
       "options:\n",
       "\n",
       "    Approximation:\n",
       "        a: [ 1-2*sig_z, k*0.9 ]\n",
       "        b: [ 1+2*sig_z, k*1.1 ]\n",
       "        orders: [10, 50]\n",
       "
\n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dolo import *\n", "import numpy as np \n", "import matplotlib.pyplot as plt\n", "\n", "filename = ('https://raw.githubusercontent.com/EconForge/dolo/master/examples/models/rbc.yaml')\n", "\n", "pcat(filename) # Print the model file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first thing we'll want to do is read, import, and check the steady state of the model object. Doing this with ``yaml_import``, we'll be able to see what \"kind\" of model has been imported. In this case we have a Discrete Transiton Continuous State Continuous Controls model. \n", "\n", "Note that we do not need to include the steady state equations in the model file, these can be computed later. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model type detected as 'dtcscc'\n" ] } ], "source": [ "model = yaml_import(filename)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the residuals from the solution for the steady state of the model:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Model object:\n", "------------\n", "\n", "- name: \"Real Business Cycle\"\n", "- type: \"dtcscc\"\n", "- file: \"https://raw.githubusercontent.com/EconForge/dolo/master/examples/models/rbc.yaml\n", "\n", "- residuals:\n", "\n", " transition\n", " 1 : 0.0000 : z = (1-rho)*zbar + rho*z(-1) + e_z\n", " 2 : 0.0000 : k = (1-delta)*k(-1) + i(-1)\n", "\n", " arbitrage\n", " 1 : 0.0000 : 1 - beta*(c/c(1))**(sigma)*(1-delta+rk(1)) | 0 <= i <= inf\n", " 2 : 0.0000 : chi*n**eta*c**sigma - w | 0 <= n <= inf\n", "\n", " auxiliary\n", " 1 : 0.0000 : y = z*k**alpha*n**(1-alpha)\n", " 2 : 0.0000 : c = y - i\n", " 3 : 0.0000 : rk = alpha*y/k\n", " 4 : 0.0000 : w = (1-alpha)*y/n\n", "\n", " value\n", " 1 : 0.0000 : V = log(c) + beta*V(1)\n", "\n", "\n" ] } ], "source": [ "print(model) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Approximate model solutions\n", "\n", "We can solve models with several DOLO methods.\n", "* Pertubation methods: \n", " * Only first order for me; I seem to be missing a package for higher orders\n", "* Global solutions: \n", " * Works very well \n", "* Dynare: \n", " * Looks like it is still under development\n", "\n", "Let's first use a first order pertubation method (i.e. a linear approximation) to solve the model using the ``approximate_controls`` function. This will use a Schur/QZ decomposition method, which is the same as in (Matlab's) Dynare. We will use the ``dr_`` prefix to denote a solved decision rule object. \n", "\n", "One nice thing about DOLO is that the solved decision rules are much nicer to work with than in (Matlab's) Dynare." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dr_pert = approximate_controls(model, order=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's find a global solution to the model. The elements of the code are as follows:\n", "* ``time_iteration``: is a function that finds a global solution using backward time-iteration. The algorithm iterates on the residuals of the arbitrage equations, in this case the Euler equation and labor supply equation. \n", "\n", "* ``interp_type`` lets us use either ``smolyak`` interpolation or polynomial ``spline`` interpolation for the policy functions. \n", "\n", " * For splines: ``interp_type`` sets the order of the polynomial interpolation in each state/dimension.\n", " * For Smolyak: ``smolyak_order`` sets the order of Smolyak interpolation.\n", "\n", "* ``pert_order``: determines the pertubuation order of the model solution which is given as an initial policy. For example, setting equal to one begins with a policy that comes from linear approximation. (Not clear what happens if the model has inequality constraints...)\n", "\n", "* ``verbose``: setting to true makes the number of iterations visible.\n", "\n", "Let's solve using both spline and Smolyak methods. In each case, we'll use 3rd order polynomials. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr_global_spl = time_iteration(model, pert_order=1, verbose=False, interp_type=\"spline\", interp_orders=[3,3])\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dr_global_smol = time_iteration(model, pert_order=1, verbose=False, interp_type=\"smolyak\", smolyak_order=3)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting decision rules\n", "\n", "Next, let's plot the three kinds of solution: linear approximation, cubic spline approximation, and third order Smolyak approximation. We look at the decision rules for investment and labor supply. \n", "\n", "Note how much easier it is to observe decision rules than in Dynare.\n", "\n", "The key command is ``plot_decision_rule``, which takes the decision rule object as an input (and other standard plot inputs)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5evQo/fr145133jH/iq6U4vnnn8fR0ZE777wTFxcXoqOj8fHxAWDYsGEcPHiQ\nYcOGsWLFCp544glCQkLMn6lv374sXbr0omM6OjoydepUli9fzoIFC0hMTCQ1NZV7770XgNtu+7nh\n0bdvX6ZNm8a2bdvMhZLWmn/+85+89957bNu2jQ4dOlzhb0cIcakRnUZQXFHMwayDAGxP3Y5bt3sZ\nFdOOs+8UkFzgBWlprPuvE95/DsDf38oBC3GNpCaQmsDWSetLNKjBsUoODhAdjXuoDzPD4+hW2YnQ\n4x2pKSnj8GHYm5rIl8e/lGeINsAWx4DZAlvMq4+PD7m5uRfdrXXnzp0UFBTg4+NT711cMzIyzCc8\nABcXF3x8fEhPTzfPCwoKMr8PCQkhIyMDgJycHKKjowkKCsLT05OYmBhyc3OvK3YPDw9eeukl4uPj\nyc7OJjw83NwV7gI/Pz/zeycnp4vG3zk5OVFSUlLvZwoJCaG6uprs7OzLjjtz5kxWrFgBGH+hnzJl\nirmr4p49e7jjjjvw8/PD09OTt99++7LP949//IP/+7//azEncSGayu23345Sivu630c3727m+V8c\n/4JjA4KYPK4Kb6dyAKqOHGflf/IoLbVWtLbDFs9dtsLWcis1gdQEjUEa9+L6tG0LMTF4B7sSGxZH\n95JeBJ70paq4nLg4+D55L9tTt1s7SiGatSFDhtCmTRs+/fTTq94mICCA1NRU83RpaSl5eXkXnbzr\ndolLTU0lICAAgD/96U8YDAYSExMpLCxk+fLljfIjnLe3N0899RQZGRkUFFz7kzMu/Uypqak4ODjU\nezOem2++GUdHR77//ntWrFhBbGysedn06dMZN24c6enpFBYW8uijj170+ZRSbNq0iQULFvDJJ59c\nc5xCCLAz2DG5z2QC3QIB0Gg+PraOnHsiiL4jmzZ21aA153YnsfrdEmpqrBywEDZCagIjqQlujHTL\nFw3aunXrlX/1dHaGmTNpv2QJMTXxvB83gKpTu8jpXMbhw20wGLbg4ujCwICBDe+jFfrFvIrrcr15\nbewueNfCw8OD559/nscee4za2lpGjRqFi4sLcXFxlJWV1btNdHQ006dPZ/r06fTo0YO5c+cyePBg\nOnbsaF7nlVdeYdCgQRQXF/Paa6/x1FNPAVBSUoKnpydubm6kp6fzyiuvXHfszz77LLGxsfTs2ZOy\nsjLeeOMNunbtipeXF0VFRddUIERHR7Nw4UJGjx6Nr68vzz33HNOmTTN3Tbx0X7GxscyZMwdHR0du\nueUW8/xot71hAAAgAElEQVSSkhK8vLxwcHBgz549rFixwnxDnwv76dOnD1999RWjR4/GwcGBqKio\n686BEK1J3e9YRztHpvebzpKDS8grz6O6tpqVxz5mdswkJuZtZ+XuUHR1Nae/TGRjh/5ETWpDC33q\n1A2TmsByrie3UhNcH6kJmg+LX7lXSo1WSh1VSv2klHqmnuXTlVJxptcOpVRYnWUppvkHlVJ7LB2r\nuA5ubjBzJh2DNNF9E+l9djBeqXaUFVZy+DB8emQjSWeTrB2lEM3WH//4R1599VUWLlyIv78//v7+\n/OY3v2HhwoUXnaQuGDlyJAsWLGDChAkEBgZy6tQpVq1aZV6ulGLs2LFERkYyYMAAoqKimD17NgDz\n5s1j//79eHp6EhUVxcSJEy/ad91nvu7YsQN3d/cG4y4rK2P8+PF4eXnRtWtX0tLSLrpRzaXPj73S\n9OzZs4mNjeW2226jS5cuODs789prrzW4bWxsLAkJCRf9Qg/wxhtv8Oc//xkPDw9efPFFpk6dWu8x\nw8LC+Pzzz3nkkUf4+uuvG/yMQoiGuTi6EBMWg6ujKwDnq8+zPOUz/H99K3d2P21cqbycAx8msWeX\nXL4X4mpITSA1wY1SlhwXrZQyAD8BI4EMYC8wTWt9tM46g4EkrfU5pdRo4AWt9WDTsmQgUmt9xT4d\nSikt47ut7OxZeO89jqS6sPJIVw4FbaY42B4PXwci+tsxKyKWTp6drB2laKWUUnIPiBbk/PnztG/f\nngMHDphvBtScNfTvzzRfrmc2MqkJmlZmcSbvHXqPyppKAPxc/JjVZjBf/fU4h7ON3WgNAe2JWdCT\nzl3kn7uwPqkJWhapCS5m6Sv3g4DjWutUrXUVsAoYW3cFrfVurfU50+RuILDOYtUEMYrG0K4dxMbS\nO6iI8d1T6Jd+O05nyjmXV83h+Bo+PLySrJIsa0cphGgB3njjDW666SabOIkL0dJ1cOvA1D5TzU/I\nySnNYXVNHPc86kegm/HZ1LUZ2az5Ryp5edaMVAjREklNcDFLN5wDgboPOzzDxY33Sz0EfFlnWgPf\nKKX2KqUetkB84gqu+fmgHTrAjBkMCM7lvtAMwtPvwDGjiPyzNcQlVLAsbjkF5dd+Y42Wxtaeu2or\nJK+tQ2hoKK+//jqLFi2ydihCtCpX+o7t4t2FcT3HmadTz6XymV8OU6LBzbECgPKkFFb+M4vz5y0d\nqW2Rc5flSG5bPqkJLtdsbqinlBoBPADUfYDjrVrrTKVUO4yN/CSt9Q7rRCiuSnAwTJvGLStWcL7a\nnuq02zlk2EyO8uKgfQkf2C3jwQGzzWP0hBDiWpw6dcraIQgh6hHWPoySyhI2ndwEwJHcJFwjI5ma\nWcDSjX5U1xrI/eEnPv5fG6If88Ig/TKFEDdIaoLLWbpxnw4E15kOMs27iOkmeu8Ao+uOr9daZ5r+\nPKuUWoexm3+9jftZs2bRqVMnADw9Penfv7/5DpkXfrmT6SaaTkuD4GBG1KZSXtWFlDhvTmbFAeHs\nt88nNe55Rncdza9G/qp5xNvE0xfmNZd4Wsu0ENa2detWli5dCmA+XwlhK67mruNDgoZQVFHE7jO7\nAdiTuR+3qFsZezaTj3cHQm0txz9L4rugcO4a52LhiG3D1eRVXB/JrWiNLH1DPTvgGMYb6mUCe4Bo\nrXVSnXWCge+AWK317jrznQGD1rpEKeUCbALma6031XMcuXlOcxQXh/5kHZ8k9WJLSQUJwbugvR+d\nuyiGh4UyI2wG9oZm03lEtGBy8xxhTXJDvaYlNYF1aa35OOljEnISzPPGtR9O7qIsdpzoYJzh6sr4\nF8IJH+hgpShFayY1gbAmm76hnta6BpiDsWGeCKzSWicppR5VSj1iWu3PgDfwxiWPvGsP7FBKHcR4\no73P62vYC8u54Sue4eGo++5lXM+j3OLkQo8zEXA2h+STmh+STvFJ0ifU6tpGidWWyJVky5C8CiGE\n5Vztd6xSinE9xxHqGWqe91nO9wTPaU+PdqY76pWU8PnCJM6cbn01wKXk3GU5klvRGll8xJPW+iut\ndQ+tdTet9d9M897WWr9jev+w1tpHaz1Aax2htR5kmn9Ka93fNK/fhW2FjbnpJux+NZLJvRMZYu9H\naHpPyM3l2DHNtiNH+PL4l/LrqRBCCNGC2Bvsmdp3Ku1djI/Cq9W1rCnaxc3/54qfSykA1Tn5rHrx\nBEVF1oxUCCFaFot2y28q0gXPBnz3Hee37GLpoTC+d0wiPTAN5etDWJhiQsQIhncabu0IRQsmXfCE\nNUm3/KYlNUHzUVxRzLsH36XwfCEALg4uTEjrxtr/uVBebeySHzA0lAfmheAgPfRFE5GaQFiTTXfL\nF8Lsjjtoe8sAYsPiubm8L35Z7dD5BSTEaz49vIV9GfusHaEQzcqOHTvo1auXtcNotlJTUzEYDNTW\nNk63Xsm3EI3PrY0bMWExONk7AVBaVcqGkBTuvS8fgzIWtxk7U/j0nWykrSVEw+QcdWVSE/xMGvei\nQY06VkkpuPtuXG7qzf3hhxlUdBNeWS7UFJzj8GH46OBGjpw90njHa8ZkDJhl2GpeQ0ND2bx582Xz\nhw4dSlJSUj1b2L758+czc+bMG96PUtf/A7fBYCA5Odk83ZLzLURjuN7vWF9nX6b3m46DwXhpvqCi\nkB+G5DHy5mzjClqTsO442z4710iR2hZbPXfZAlvMrdQE109qAiNp3IumoxSMHYtH/1DuD0vgpoKh\nuGXZUZ1fxKE4zYcHPialMMXaUQrRqtXU1DT7/TfGPm6kCBBCXJuOHh2Z1HsSCuP/u4zyHE6OK2JA\nl7PGFaqr2frWURL2llsxSiHEpaQmsD3SuBcNssjzQQ0GmDSJdv38mdXvKJG5I3DKrqYyv4QDh2pY\nun8lWSVZjX/cZkSeu2oZLS2v27Zto2PHjubp0NBQFi1aRHh4OF5eXkRHR1NZWWlevmHDBiIiIvDy\n8mLo0KHEx8ebl/3973+na9euuLu707dvX9avX29e9v777zN06FCeeOIJfH19mT9//mWxzJ8/n8mT\nJzNt2jTc3d0ZOHAghw8fNi/PzMxk0qRJ+Pn50aVLF15//fXLto2NjcXT05O33nqLl156idWrV+Pm\n5kZERIT589W9WjF//nxiY2OBn7vbLVmyhJCQEEaOHAkYH7n17rvvEhgYSGBgIIsWLTJvv3fvXm65\n5Ra8vLwIDAzkt7/9LdXV1QAMHz4crTVhYWG4u7uzZs2ay/J99OhRRowYgZeXF/369ePzzz83L3vg\ngQeYM2cO9913H+7u7gwZMoRTp0794t+pELbsRr9je/j24L7u95mnk89nUDWriFBf0xX78nLWv3SE\nMynVN3QcW9PSzl3NSUvKrdQEUhNcLWnci6Znbw/TphHQ25P7e58gIudOHLPLKM8rZd/BCt7bv5yC\n8gJrRymE1V36S/KaNWvYtGkTp06dIi4ujqVLlwJw8OBBHnzwQRYvXkx+fj6PPvooY8aMoaqqCoCu\nXbuyc+dOioqKmDdvHjExMWRnZ5v3++OPP9K1a1dycnJ47rnn6o3ls88+Y+rUqRQUFBAdHc24ceOo\nqalBa01UVBQRERFkZmby3Xff8e9//5tvvvnmom2nTJlCYWEhDz30EHPnzmXq1KkUFxdz8ODBq/78\n27dv5+jRo3z99dfmeVu3buXkyZN8/fXX/P3vfzcXA3Z2dvzrX/8iPz+fXbt2sXnzZt544w3AWCQB\nxMfHU1RUxOTJky86XnV1NVFRUYwePZqzZ8/y2muvMWPGDI4fP24+7urVq5k/fz6FhYV06dKlwbwJ\nIX4WGRDJ7Z1uN0/H16Th+2gevs5lAFQXFLNqXhLnCmUAvhCXkppAaoKrYW+1I4tmb+vWrZb71dPR\nEWbMILRqKTNrTlN1bCQHDV9TogzsPgBtHZbx8MDZuDq6Wub4VmTRvLZi15vXF7a+0KhxvHB74+6v\nrt///ve0b298tFRUVBSHDh0CYPHixfz6179m4MCBAMTGxvLXv/6V3bt3M2zYMCZOnGjex+TJk3np\npZfYs2cPUVFRAAQGBvLYY48B0KZNm3qPHRkZyfjx4wF44oknePXVV9m9ezcODg7k5uaaT2SdOnXi\noYceYtWqVdx1110ADBkyxHyshvb/S5RSzJ8/Hycnp4vmv/DCC7Rt25a+ffvywAMPsHLlSu644w4G\nDBhgXic4OJhHHnmEbdu28bvf/c48v6G7Je/atYvS0lKeeeYZAEaMGMF9993HypUref755wEYP348\nkZGRAMyYMYMnn3zyuj6XELaisc5dw0OGU1xRzP7M/QDsdTjNLbOh9K2OlFc7UJKax8q/HGf237rj\n6HjDh2v2pCawnOvJrdQEUhNcytZqArlyL6ynbVuIiaFnD830zmfpm3UHhuw8zuWcZ8f+fJYd+pCK\n6gprRylEs3HhJA7g7OxMSUkJYOyitmjRIry9vfH29sbLy4szZ86QkZEBwAcffGDunufl5UViYiK5\nubnmfdXtetaQuusopQgMDCQjI4PU1FTS09MvOvbLL79MTk7ONe3/agQFBV00rZS6aF5ISIj5Mx8/\nfpyoqCg6dOiAp6cnzz333EWf+UoyMzMvizkkJIT09HTztL+/v/l93b8LIcSVKaW4t/u99PDpYZ63\ny/c0ERNTsVPGO11nHcjg43+eppFufC1EiyQ1gdQE9ZHGvWhQk/yS7OoKM2cS0bOcKUGl9M65DbJy\nyMusYOv+TFYmrKK6tmWNv5Nf6C2jNee1Y8eOPPfcc+Tn55Ofn09BQQElJSVMnTqV06dP88gjj/DG\nG29QUFBAQUEBffr0uegX6qu5kUxaWpr5vdaaM2fOEBAQQMeOHencufNFxz537txF49Eu3X99x3Nx\ncaGsrMw8nZV1+b036tuublynT58mICAAgN/85jf06tWLkydPUlhYyF//+terfq5xQEDARfu9sO/A\nwMCr2l6Ilqgxv2MNysCk3pPo6G4smDXwY690Boz4eZzqsa9T+G5lTgN7aDla87nL0lprbqUmMGqt\nNYF0yxfW5+EBM2dyy5IllFf5UXV2MMcMu8lWHfhm7ymc7D9hcp9JGJT8FiUanyW7zF2tyspKKip+\n7qVib39tX80PP/wwEyZMYOTIkQwaNIjS0lK2bdvG8OHDKS0txWAw4OvrS21tLe+//z4JCQnXHOP+\n/ftZv349UVFR/Pvf/6Zt27YMHjwYpRRubm4sXLiQ3/3udzg4OHD06FHKy8vNXQIv1b59e7799lu0\n1uaTc//+/Vm1ahWjR4/m0KFDrF27lrvvvtu8TX0nYa01CxYs4J133iE5OZn33nuPFStWAFBcXIy7\nuzvOzs4cPXqUN998Ez8/P/O2/v7+JCcn07lz58v2e/PNN+Ps7MzChQt54okn2LFjBxs2bOCFF164\n5rwJIernYOdAdL9olhxcQm5ZLjVoDg/LoFeOPUkJIVBby86lx/ENakvEcHdrhytaCakJro7UBM23\nJpDWkmhQkz4f1McHZs7kjl6ZRLm7EprbH7KzSE+t5st9R/jy+JdX/Qtbc2eLz121Bbac13vvvRdn\nZ2ecnJxwdnau9+60V/olPTIyksWLFzNnzhy8vb3p3r0777//PgC9evXiySefZPDgwfj7+5OYmMjQ\noUOvOcaxY8eyevVqvLy8+PDDD1m3bh12dnYYDAY2bNjAoUOHCA0Nxc/Pj4cffpiioqIG9zV58mS0\n1vj4+JhP9gsWLODEiRN4e3szf/58ZsyY8YufXynF8OHD6dq1K3fddRdPP/20+a65//jHP/jwww9x\nd3fn0UcfZdq0aRdt+8ILLzBz5ky8vb1Zu3btRcscHBz4/PPP+eKLL/D19WXOnDksW7aMbt26NRiL\nEC2dJb5jnR2ciQmLwc3RDYAKVUPauNN0DDJdpauq4vNXkkg51nKH6Nnyuau5s9XcSk0gNcGNUC2h\nwaSU0i3hczQ3VrnJy5kz1C79gLVxXfi0JoV0n+Pg34GuPeyIHTqC4Z2GN208FiA3z7GMK+VVKdVi\nfhyyhvnz53Py5Ek++OADa4dikxr692ea37yqghZAagLLsOS5K6ski/cOvkdFjbER71VmD2/3oeCc\nFwBO7d15+D/heLezs8jxrUlqAstpKLdSE9wYqQlujKVrArlyLxpklZNNUBCG6dOY2Pc4o3RX/Ao7\nQnYWJ36qYdWPW9iXsa/pY2pkchK3DMmrEM2HUmq0UuqoUuonpdQz9Swfo5SKU0odVErtUUrdaprf\nRin1o2l+vFJqXj3bPqmUqlVKeTfFZxFGlvyO9Xf1Z1rfadgpY+O9wLkaQ0w8Tm2MY27Ls4tY8eck\nzpe3vAaZnLssR3IrWiNp3Ivmp3Nn7KZOYlq/JO6sCMPrXDvIzubokVo+2LWRI2ePWDtCIYQQDVBK\nGYD/AKOAPkC0UqrnJat9q7UO11pHAA8C/wPQWlcAI0zz+wN3K6UG1dl3EHAXkGr5TyKaUqhXKON7\njTdP57WrxXXyPgwG4011c4/msublE3IHfSGEuAJp3IsGWXWsUs+eOE4aQ2y/RG4vHYRbkRs6O4eE\nhBre3fUxKYUp1ovtBtnqGLDmTvJqOfPmzZPud+JaDAKOa61TtdZVwCpgbN0VtNZldSZdgdp6lrXB\neOPfupdr/wn80RJBiytriu/Yvn59Gd11tHn6bJcafO7ejTb9Ezi5PZ0v30mjJfWolnOX5UhuLUNq\nguZNGvei+QoLw2ncKGb3O8Kwc0NxKnakNvsshw5V884PK8kqufyxGEIIIawuEKj73KAzpnkXUUqN\nU0olAZ8Ds+vMNyilDgJZwDda672m+WOANK11vCWDF9Y1OGgwt3S8xTihFGcjK/EavMe8fO9Hp9jz\nxdU9n1oIIVobuaGeaP527CD/853893A3vvf9lkp3hUOAL0MHufHbobPxdpJhl+LK5OY5wppa2w31\nlFITgVFa60dM0zHAIK317xpYfygwT2t91yXz3YH1wBzgFLAFuEtrXayUOgUM1Frn1bM/qQlsnNaa\ndUfXcTj7sHG6sgqXj/0oOxYOgGrjyIy/9aNrhJs1wxQ2SmoCYU2WrgnkOfei+Rs6FO/z53mk5iDn\nE0fwo/qGqqx8du0DJ8flPDZkNq6OrtaOUgghhFE6EFxnOsg0r15a6x1Kqc5KKW+tdX6d+UVKqS3A\naGAT0AmIU8bnDgUB+5VSg7TWOZfuc9asWXTq1AkAT09P+vfvb7651oWuujLdvKfH3jaWksoSNm/Z\nDEBIVA0O55w5cbQUgDXz7XjovxEkHv+hWcQr07Y1LYS1bd26laVLlwKYz1eNQa7ciwZtbU6PZ9Ea\nvviC09/+xGtH23PA7ztqPV1wDvTi7qEBPDJoFm3s21g7yqvSrPLaglwpr/IrvbCmVnjl3g44BowE\nMoE9QLTWOqnOOl201idN7wcAn2qtOyqlfIEqrfU5pZQT8DXwN631F5cc4xQwQGtdUM/xpSawAGuc\nuyqqK1h6aCmZJZkA6NwS+KA/qigIAK9gNx76bwQubrY7ylRqAstpKLdSEwhrkiv3QgAoBffcQ3BF\nBb+uOcO/T95GvNpKmcHApl0KJ8dVzBowA3uD/JMWlwsJCcF4sU+IphcSEmLtEJqU1rpGKTUH49V2\nA/Cu1jpJKfWocbF+B5iolJoJVALlwBTT5h2A90133DcAqy9t2F84DCD/qVu4NvZtmBE2g3cPvEvB\n+QKUrys14/dTu8oZhwpvCk4Xs3reEWYu7Iu9nP7FVZKaQFiTpWsCuXIvbEtNDaxZQ8L2fF4/bccx\n313g441XsDtTRvRmar9JGJTt/oIvhGg9WuqVe2uTmqDlySvL492D71JWZXyQQmVcAXz2KxxrXADo\nf28gY5/qhrTXhBC2qrFqAmkFCdtiZweTJtF3sCsPdVCEFgyAvHwK0kr45PsjbPzpS+lqJYQQQrQg\nPs4+TO83HQeDAwCOYZ5U3fYN1aoSgENfZLBz9RlrhiiEEM2CNO5Fg5rtTUfs7WHaNG662cAsb1cC\ni3pBbi5nU0tZsXUv21K2WzvCK2q2ebVxklfLkdwKIaz9PRDkHsSUPlOMvfOUwnmoGyUR31BLDWjN\nt4tPkbTjsocnNHvWzmtLJrm1DMlr8yaNe2GbHB1hxgxuu6mCGOcA/EpD4exZMpPLeXfzFvZl7LN2\nhEIIIYRoRN18uhHVPQoAZWeH+6/syeu2FY2Gmho+efkomSdKrRylEEJYj4y5F7atpAT97hI+2eXL\ncp1AgXMm+LenSy8nnrx7Mr3b9bZ2hEIIUS8Zc28ZUhO0fNtTt7P5lPEReVUFJRSs8cEvYwgA7n5t\nefitSNy8HawZohBCXBMZcy8EgKsr6v6ZjL8pm/E1/XA77w3ZOZw8WsHr335MSmGKtSMUQgghRCMa\nFjyMmwJuAsDByxXXezLJ9joMQFHOeVb+6TBVlfIDjxCi9ZHGvWiQzYyp8fTEcH8s0wemc1/FTThV\nuEJ2FkmJ5fz7u5VklWRZO8KL2ExebYzk1XIkt0KI5vQ9oJTi7m5308u3FwDOQd443HWEs64nAcg4\nVsy6FxOxhQ4czSmvLY3k1jIkr82bNO5Fy+Dri/2sGGYNSONXJUNwrGwLWdkciivh31uWk1+eb+0I\nhRBCCNFIDMrAhF4TCPYIBsC7tz81Q3dT0DYDgCPbc9nyv5PWDFEIIZqcjLkXLUtaGqX/W8m/Dgay\nxXMb1W1qsQv0Z/gQP54YMRtXR1drRyiEEICMubcUqQlal/KqcpYcXMLZsrOgNSnfZOC7725cK71B\nKSY8042w0QHWDlMIIa5IxtwLUZ+OHXG5fxJz+qUzuHAohkpNTUY22388y1s7P6SiusLaEQohhBCi\nkTg5OBETFoN7G3dQipAR7Unv+TXn7UtAaz579QRphwusHaYQQjQJadyLBtnsmJrOnfGIieL3vXKI\nLLgVKqupTs9h0850luxZRXVttVXDs9m8NnOSV8uR3AohmvP3gEdbD2LCYmhr3xblYE+XX3lxMvQr\nqgznqa6sZdXziRRmlls7zHo157zaOsmtZUhemzdp3IuWqVcv2k2/i8e7FdCn4GaoqKDyTA6fbTvJ\nioOfUKtrrR2hEEIIIRqJn4sf0/pOw07ZYe/als53teFY4DfUqGpKC6tZ8cwhKspqrB2mEEJYlIy5\nFy3bnj2c+vAH/noaTngcAmcnXEP9ePCeQYzrfQ9KyXBXIYR1yJh7y5CaoHU7cvYIaxLXoNEUJueR\n/rU7vbNHojDQfYAr016JxGAn/+2EEM2LjLkX4moMGkTopEieDLQjqKgnlJVTkprL0q/3sPnkdmtH\nJ4QQQohG1Ltdb+7udjcAnp198Bmcy08+u9BofjpQwjf/SrByhEIIYTnSuBcNajFjaoYOpdfYnjzp\n54FfaSiUlHLuVD5vfLGZH9P2NXk4LSavzYzk1XIkt0IIW/oeGBQ4iKHBQwHw7++PQ1gyKZ6HANj1\neR77P2o+j8izpbzaGsmtZUhemzdp3IuWTym4804i7uvI79w74FUeAMXF5J0s5NUNG0nMOWLtCIUQ\nQgjRiEaGjiS8fTgoReiwQEq7xZHhdgyAjW+f4dSuLCtHKIQQjU/G3IvWQ2v45BO++rSQ/1YlUdwm\nF7y86NjHm/kTYujsHWrtCIUQrYiMubcMqQnEBTW1NaxMWMmJ/BPUnK/i0IZ0QpLvwLcsGCcXAw+9\nHo5PZw9rhymEEDLmXohrphSMG8eo0c7cb+iNU5U7FBSQllTAyxtWkVmcae0IhRBCCNFI7Ax2TOkz\nhQC3AOzaOtD3zvacDNzKuTY5lJfWsuLZw5QXVlg7TCGEaDTSuBcNapFjauzsUFMmM+FOO6bVROBY\n4wx5+RyPz+PljR+SX55v8RBaZF6bAcmr5UhuhRC2+j3gaOfIjH4z8Hbypo2nE72Ge5Lo/y2lDoXk\n5dTw0TP7qamy3uNxbTWvtkByaxmS1+ZNGvei9bG3xzB9GjF31DLm/EDsax0hN5fDB3NY+NVySipL\nrB2hEEIIIRqJi6MLMWExuDi44B7kTtebHTnc/hsq7Mo4daySLxbsR9fKUA4hhO2TMfei9Sovp3Lx\n+/xzmwNfu/5AraEW5e/HHbd15um7ZtHGvo21IxRCtGAy5t4ypCYQDckozmDpoaVU1lSSsiuTs/HO\nRGTdjX2tI6Nj2zH4wT7WDlEI0UrJmHshbpSTE44PzOC3g88ztHgQaNDZOWzZeYr/bl9FdW21tSMU\nQgghRCMJcAtgSp8pGJSBkMH+uHQqIsFvM7Wqhq+Xn+X4plPWDlEIIW6INO5Fg1rFmBo3N5wfms5T\nkZVEFkVCraY2I4cvth3jvd2fUKsbfxxeq8irFUheLUdyK4RoKd8DXb27MrbHWJRS9BwRQK1/Nkm+\n31OrNWsXpZCTkNOk8bSUvDZHklvLkLw2b9K4F8LLC/eHpzK3bw29isOgtpbqM9ms+SaONYe+RLp3\nCiGEEC1HuH84d3a+E4ODHX3v7ECRTwonvfdyvgJW/L9ESjKLrR2iEEJcFxlzL8QF6emkv76W506U\nkeJ6DBzscerkz+8m3MndvW63dnRCiBZGxtxbhtQE4mporfnqxFf8mP4jJTmlHNx0lpCzkQSf60dQ\nR8X9bw3BwcXR2mEKIVoJGXMvRGMLDCTw0TE8H+KKf2knqKqmPDWb/372HT+k7LN2dEIIIYRoJEop\nRnUdRe92vXH1c6H3LV4ke+8n2+UkZ9I06+fu+f/s3Xd0VFXbxuHfnlQSEnrvvRcRUAEFRBHEgoIF\nRMWGXVG6CIgU6YiIn6IoigUVe1cUUFQEkQ4CAtJ7SQghbWZ/f0zMy+tLlDInZ2ZyX2u5zN6ZcvOs\nkzNnz5znDNbr3lfkiYicCS3uJVf5sqemShWq9mrP46WLU/R4GcjIJGXzPsa9/wkrd68NyFPky7rm\nAdXVOaqtiITjfsBjPFxb51oqF65MsaqFqN4ont9LLORQ7E7WrMji23FLHc8QjnUNFqqtM1TX4KbF\nvTf3DFMAACAASURBVMjf1apF/bsuYkDh8iSkF4f0dI5s3MvIOe+y6aCupCsiIhIuIj2R3Fj/RkrG\nl6R84xKUrRrNmlLzOBp9kIVfHeO3V1e5HVFE5JSp514kN4sX880LS5mUto7jUckQF0eFRuUZ2+02\nyiaWcTudiIQ49dw7Q8cEciaS05OZ8dsMjhw/wuqvdpK8K5ImuzsR7y3ITY9VotqlVd2OKCJhTD33\nIk5r3pxLbqnHXZF1ifbGQWoq21ft4on3X+fQ8UNupxMREZEASYxJpEfDHsRFx1G3XRliimawstTX\npJk03hm/lX2r9rodUUTkX2lxL7lSTw2Yiy7kmm5V6O5rRKQvGlJS2PDbdp78cBYpGSln9JiqqzNU\nV+eotiKSH/YDJeJL0K1BN2JiY2hwSWl8CUdZVepbUjOzePPxtaTsTAr4c+aHurpFtXWG6hrctLgX\n+SfGYC5rzy1dS3N1xjl4bAQkJ7P8ly089dkbpGelu51QREREAqRioYp0rduV2IQYGlxcktT4A6wp\nOZ9DST7e6r+MzBS97otI8FLPvcip8PnInP0e4z/cxzdxv2KxUKwoHS5pRN/LbiLSE+l2QhEJMeq5\nd4aOCSQQft31K59u+JSDW5JY9f1hSh+tTq0DLalbP5Lrp7TEROjzMREJHPXci+Qlj4eo66/h0csK\n0yK1sX/u4CG+nreS579/H5/Vd+GKiIiEi6Zlm3JRpYsoVqUQNc4pyJ6Cf/Bn4WWsW+1l7ujFoDeQ\nRCQIaXEvuVJPzd9ERhJ783UMal2YRqn1AfDtP8gHXy3hjcVfcKqfFKmuzlBdnaPaikh+3A+0rdyW\nc0qfQ7mGxSlfPZathVeyM+F3fvw2jV9fXhmQ58iPdc0rqq0zVNfgpsW9yOmIjqbg7dczrHlhaqTW\nAAvePft57ZMFfLxygdvpREREJECMMVxZ60pqFqtJtRalKFYmko3FfmF/3FY+f/0Qf3z5h9sRRUT+\ni3ruRc5ESgp7Jr9J39U72VFgK3gMsRVLMbB7V9rUbOp2OhEJAeq5d4aOCSTQMrwZvLr8VbYd2s6y\nT3eQesTQcM+llLKluH1cbUo1LuN2RBEJceq5F3FTwYKUfvB6RtYsQ4m0MuCzpG3bx/h33+e37Wvd\nTiciIiIBEh0RTfcG3SmZWIIGl5YhKs7H6lLfccge4c0h6zi6/YjbEUVEAC3u5R+op+ZfFC5M5Ye7\n8kTFiiRmFAOfj2Ob9/Lkm2+xft+WXO+mujpDdXWOaisi+X0/EB8dT4+GPShWtAgNLi6Jjc5gZelv\n2Juaylv9l5GRnHZGj5vf6+ok1dYZqmtw0+Je5GwUL0693lfzeImqxGUWAq+XIxt3M+T1Wew4stvt\ndCIiIhIgRQoU4aYGN1GsdGHqtSpKRmQqq0rNZeveDN4fuBhfptftiCKSz6nnXiQQtm5l7lOfMO7Y\nWjIiUiEqioqNqzH5tl4UiyvqdjoRCULquXeGjgnEaZsPb+aNlW+wfdV+NvyaTKG0UjTa256WrQtw\n2bALwOjPWkROj3ruRYJJpUpc8mh77omuS6QvGjIz2bZyM4+9OZOj6SlupxMREZEAqVqkKp1rd6Zs\n/aJUqFmApNi9rC2xgJ/mp7HkxeVuxxORfEyLe8mVempOU82aXPNwa24y9fHYCEjPYP2vG3hizmuk\nZ6Xn3Ex1dYbq6hzVVkS0H/hvDUo1oH219lS9oCTFy0ZxIG4bfxRdzGdvHmbjZxtO+XFUV+eots5Q\nXYObFvciAWQaNuDW+1twtbcBBgNpaSz9cRWjP3qTLF+W2/FEREQkQFpUaEGLCi2oc3EZEop42Jn4\nO1sLrebdp3ey59cdbscTkXxIPfciDvAuWMjoFxbxbfRq/0TBeK7odBGPdrgOj9F7aiKinnun6JhA\n8pK1lvfWvcdvW5ax9NNdpB+31D7QippU5a5pjUmopOvuiMi/U8+9SBCLaN2KAT0ac156bf9EyjE+\n+2IhLy34DB10ioiIhAdjDJ1rd6ZWuZo0vKQUEVGG9cV/ZIt3F28OWE5G0nG3I4pIPqLFveRKPTVn\nJ7pjO4Ze24B66VUBsMlHmf3xXIa/MMXlZOFJ26tzVFsR0X4gd5GeSG6ofwPVKlWm3oVFwWNZU3I+\n6w8d5L3+v+DLyL0tT3V1jmrrDNU1uGlxL+IUY4jv0pHRHRpQOb08AL5DSXwybyFfr/7V5XAiIiIS\nKLGRsdzU4Caq1qxAjXMT8ZksVpX6luWbkvl6xC+gs/ZEJA+o517EaV4v+6bP4aEffmNP9F4AYsoU\nY/idd3B+tbouhxMRt6jn3hk6JhA3HUg9wIzfZrB64Va2/55KbFZBmuzuxNVdSnLefee6HU9EgpR6\n7kVCRUQEJe+8ljHnNqBIpv/COum7DzJi5kzW7NzicjgREREJlOJxxeneoDu1LihLifLRpEWmsLLU\nN3z67gE2fLzO7XgiEua0uJdcqacmgKKiqHx/V0bVaUD6zmQAju3Yx+MzZvDnwd0uhwsP2l6do9qK\niPYDp65CoQpcV+866rQpQ2LRCFKiD7G65ALeeWYHuxdv/6/bqq7OUW2doboGNy3uRfJKbCx1H7me\nm0tXIcYbBxYOb9nJgBens+/oIbfTiYiISIDUKl6LzvWupv4lZYiN83C4wC5WFf6ZN4ZvIHnLQbfj\niUiYUs+9SF5LSmLuiDcYs285WZ4M8HioVL8mU+97gMTYgm6nE5E8op57Z+iYQILJ/D/n89lvX7Hs\niz1kZVoqJNWnZVQTbnv+PGKKxrsdT0SChHruRUJVoUJcMugG7k2sj8dGgM/H1jUbGDDjRdIy091O\nJyIiIgHSulJrLqrXgnoXFcN4YHuh1fya9jtzBiz5x6/IExE5E1rcS67UU+OM+fPnQ7FiXDv4em6K\nqYfBgNfHumWrGfr6q2T59GJ/JrS9Oke1FRHtB86MMYZONTtxfuNzqdmsEAB/FF3Cjzu38OUTi5j3\n3TyXE4YvbbPOUF2Dmxb3Ii4xZUpz++CuXOmp45/I8rL4pyU89c5sfNbnbjgREREJCI/x0LVuV5qf\n15CKdeIBy7oSP/D1bztY9+F6t+OJSBhRz72Iy3wb/mD4qLdZYDb6J2KiufbKjjzYqTPGqB1XJFyp\n594ZOiaQYJWamcqM32bww6er2bc9nQhfFE12d+COe2pTu0s9t+OJiItCpufeGNPBGPO7MWaDMWbA\nSX7f3RizIvu/hcaYBn/7vccY85sx5mOns4q4wVOzOoN7X00TbyX/RHoGH3z2JbPmf+duMBGRM3QK\nr/1XZb/uLzPGLDbGtMyejzHG/JI9v8oYM+yE+4wzxqwzxiw3xrxnjEnMy3+TyNmKi4rj5kY30/TS\nGiQWi8TryWRF6W9544VN7Pp5q9vxRCQMOLq4N8Z4gGeBy4B6QDdjTO2/3WwzcJG1thEwEnjxb79/\nGFjrZE45OfXUOONkdY0+pz6j7r2aWt6yANjj6cycM4dPf12cx+lCl7ZX56i2cjpO8bV/rrW2kbX2\nHOAO4CUAa2060DZ7vjHQ0RjTPPs+XwP1rLWNgY3AIOf/NfIX7QcCo3BsYW5tcjPnXlaZ2HgP+/Zv\nZmmJb3lt5BqS/tjvdrywom3WGaprcHP6k/vmwEZr7VZrbSYwG7j6xBtYaxdZa5Oyh4uAcn/9zhhT\nHric7Bd9kXBWoEUTxvXoSIWsEgD4jh3n6dde5ft1a1xOJiJyWk7ltT/1hGFBwHeS38UAkYDNnp9r\nbc4FSRYB5Z2JL+KsUgVLcUuzmzinfVkiIiE1KomfEucxa9Ay0g+muB1PREKY04v7csD2E8Y7OGHx\nfhJ3Al+cMJ4M9CP7hV3yVps2bdyOEJb+qa6FLruQ8VdfSglvYQCyko8x+qUXWL5lcx6lC13aXp2j\n2sppOqXXfmNMZ2PMOuAT4PYT5j3GmGXAHuAba+2SkzzH7fz38YI4TPuBwKpSpAo3XXAjF3ZrgPFA\ncsx+5puFvN3/F3zpmW7HCwvaZp2huga3oLlavjGmLXAbMCB73AnYa61dDpjs/0TCXumulzL24rYk\negsCkHYwmSHPP8emvbtdTiYiEjjW2g+ttXWAzvjb8v6a92Wfll8eOM8YU/fE+xljBgOZ1to38zSw\nSIDVL1mfG1t3oVZz/xv6B+K28eWRxXw25CesV9+aIyKnL9Lhx98JVDxhXD577r8YYxoC04EO1trD\n2dMtgauMMZcDBYAEY8xr1tpbTvZEPXv2pHLlygAULlyYxo0b57yz9FdviManN/5rLljyhMv46aef\n/uftc8ECqJTAyKYtGbD0e3bv28KRPXvo/+xUnu3Tl/XLVwbVvydYxn/NBUuecBovX76c3r17B02e\nUB3Pnz+fmTNnAuS8XoWpU3rt/4u1dqExpqoxpqi19tAJ88nGmHlAB7KvvWOM6Ym/Xe/ifwqgYwLt\nY0NlvGjOIqqXKM/xellsXZPC2mMLmPbTTopPjeWC3ue5ni+Ux3/fdt3OEy5jHRME9zGBo1+FZ4yJ\nANYD7YDdwGKgm7V23Qm3qQh8C9xsrV2Uy+O0BvpYa6/K5ff62hsHzJ8/P2djlMA55bpmZfHjuFkM\n+/0nsjz+U/TKVavCtD59KBxX0NmQIUjbq3NUW2eE61fhneJrfzVr7absn5sAH1lrKxhjiuP/VD7J\nGFMA+AoYY6393BjTAZiI/yK8B//h+XVM4ADtB5wxf/58WrduzfvrPuDtt79m39Y0AGoduIBHbm1B\n7esa/MsjSG60zTpDdXVGoI4JHP+e++wX4yn4WwBmWGvHGGPuBqy1drox5kXgWmAr/lPvM621zf/2\nGFrcS/6Uns5nI2YycftifMYLBqrXqcMzvR8mLjrW7XQichbCdXEPp/Ta3x+4BcgAjgN9rbU/Z38d\n7qvZ9/MAb1trR2U/5kYgGvhrYb/IWnvfSZ5bxwQScrw+L7OWvc4Hb/1A0oEsDIbGB9rQb+CFlG1V\n1e14IuKwkFnc5wW9kEtYS03lzaEzePHAb1gsGEPjJk0Yf9+9REU43VkjIk4J58W9m3RMIKEqPSud\n6T+/xOdvL+V4ig+PjaTFkUvoO64thWuVcjueiDgoUMcEnkCEkfB0Yq+SBM5p1zUujm5DbqVrfD3/\n2FqWL1vGiFdfw2d1wZ2/aHt1jmorItoPOOPEusZExnDbebdyYafaREYbfCaLXwrN44XHf+T4nqTc\nH0ROStusM1TX4KbFvUgIMIUSuW/4bVwaXdM/4fPx/Y8/MvmdOegTKhERkfBQMLogd7e+g2btKmIi\nDJmedObFfMfM/t+TlZLmdjwRCXI6LV8khGRt38XgYdP5xfenfyIqkluu6crtl3dwNZeInD6dlu8M\nHRNIONiZvJOx705h5cJ9YKFgRlFuKtaBGye3w0RGuB1PRAJMp+WL5EORFcry5IBbqUtZ/0RmFrM+\neo/3f/jR3WAiIiISMOUSy/Fg57uo3qgQACnRh3hn3zy+HrkQ9OaViORCi3vJlXpqnHG2dY2pVYVx\nvW+mkq84ADY9k2lvvsZ3y1YEIF3o0vbqHNVWRLQfcMY/1bVGsRrcf8NtlK0eB8DhAruZsWY+i//v\n1zxKF9q0zTpDdQ1uWtyLhKCCjesw8a7ulPT639H3Hk9nzIwX+HXDHy4nExERkUA5p0xjenW/gWJl\nowHYF7+FqXPnsf79NS4nE5FgpJ57kRC27bOFPPT2bI54UgCIL1SISf0HUKtcWZeTici/Uc+9M3RM\nIOHGWstHaz5hxssfc/RQFgC1jpzL0L5XUK5VFZfTiUggqOdeRKjYqRWjO3QizlcAgGNJSQyYNJEd\nBw66nExEREQCwRjDVfWuoOv1bYiN9x+6ry+8lImT5nL4970upxORYKLFveRKPTXOCHRd63brwBMt\nLyHKRgFw5OBB+k6cxKGUlIA+T7DT9uoc1VZEtB9wxqnW1WM8dG92Ax07NyUy2v/h3rKEn3l62Dek\n7k5yMGHo0jbrDNU1uGlxLxLqjKH5XdcwoGFrPNb/J71n9076TJjMsXR9J66IiEg4iIqIoleb22h9\nWW08EQZrfCyMWcALA78hK0Wv9yKinnuR8OH1Mmfky0zb8hMW/99Dvdr1ebpfb6IiIl0OJyJ/p557\nZ+iYQMJdUloSo2ZPZPGCbWAh2luAboUup+fTl2MiI9yOJyJnQD33IvLfIiLoOuhWupc6J2dqzfrV\nDJn2Il6fz8VgIiIiEiiFYgvR97oHqNOkBAAZEcd55/A3fDJiPuiNLZF8TYt7yZV6apzhaF2jo7lz\n2F1cXqiuf2xh0bLFjH/lDcL9kyxtr85RbUVE+wFnnGldS8aXZECPB6hYIwGA41HJvLThSxY+90sA\n04U2bbPOUF2Dmxb3ImHGxBWgz/B7aFmgqn/CWr788Tumv/uxu8FEREQkYCoXqUT/O+6hRNkYAJJj\n9jN5waesnbPK5WQi4hb13IuEqcy9B+nz+CRWZu70T0R4uLdrD27oeLG7wUQEUM+9U3RMIPnNT5t/\nZsyzM0g+lAVAuWPVGfNgdypcVNXlZCJyqtRzLyL/KKpUMcYMvp+qHn9PHl4fL7z/Jl8tXOxuMBER\nEQmYFlUvoNetXSgQ7z+s3xn/ByOf/YBD6/a6nExE8poW95Ir9dQ4Iy/rGle5LBP73EMZCgPgy8xi\n/GsvsWjFujzLkFe0vTpHtRUR7QecEai6dmrYge43XEpUjP+Dv/Vxqxj95Psc25UUkMcPRdpmnaG6\nBjct7kXCXJF61Zh4350UsfEAZGVkMPz5qazZtNXlZCIiIhIIxhi6t7qeK69ojifCv8D/NWYxkx77\nkMyjaS6nE5G8op57kXzij7m/0HvWK6QY/4t8YsFEpg5+nEplSrqcTCR/Us+9M3RMIPlZhjeDp96c\nzLzv1oEFj42gS8Jl3Du5C56oCLfjiUgu1HMvIqel+iXnMaLzDUTbSACSU5LpO3Yc+w8nu5xMRERE\nAiE6Ipq+NzxAk3MrAOAzXj5I/pZ3nvwS69ObXiLhTot7yZV6apzhZl3PuaYtg9teRUT2n/7+Iwfo\nM3osR1OPu5YpULS9Oke1FRHtB5zhRF3jo+N5/I5HqF6rKABZnnRe2fI53z67IODPFcy0zTpDdQ1u\nWtyL5DOtb72Sh5tcisF/5s+2/TvpO3oi6ZlZLicTERGRQChaoCjD7+tDmXL+6+2kRx5j8qKP+O3t\nZS4nExEnqedeJD+yllefmsErGxbmTJ1bvSHjHutNhEfv+YnkBfXcO0PHBCL/8fueDQwcN54jhzIB\nKJpRigm97qJqm+ouJxORE6nnXkTOnDHcOuA2Opc/J2dq6R8rGTXlJXRQLCIiEh5ql67JwHt6UaCg\n/5D/UPRehkyfxf5Vu11OJiJO0OJecqWeGmcETV0jInhoyL20KVYrZ+q7FT/x7EuzXQx15oKmrmFI\ntRUR7QeckRd1Pb9mMx669UaiYvwfCu6M3srj42dydMcRx5/bTdpmnaG6Bjct7kXyMU9MNEOe7E2T\nhEo5c+/99BWvz/7UxVQiIiISSB2aXsotXS7D4//CHNZ71jNy2CwyktPcDSYiAaWeexEh/XAyDz42\nig3H9wJgPB76XHcrV3Rs7XIykfClnntn6JhA5OR81sfUd6bzwZeLIPtPpEPCRfSfdCueqAh3w4nk\nc+q5F5GAiSmSyITH+1A+yv+1OdbnY/KcWSxc+JvLyURERCQQPMbD/dfdSevz6uTMfXX0B2Y88R7W\npzfERMKBFveSK/XUOCNY65pYriST+j9McU9BALzeLEbMfJ4Vy9e7nOzUBGtdw4FqKyLaDzgjr+sa\n6Ylk0J0P06hOeQAsltk7vubjZ77K0xx5QdusM1TX4KbFvYjkKFmjEhMefIAEUwCA9KwMBj83hc2b\ntrucTERERAIhNjKWJx/qR+Vy/rP1vCaLZ5d+yMI3f3I5mYicLfXci8j/WP39Uvq+/DxpZH8vboHC\n/N8TQylVqqjLyUTCh3runaFjApFTs/vIXh56cjj7D6UCEO9NYHzPe6nbrq7LyUTyH/Xci4hj6l90\nLkO69iAyexdx6PgRHh01hqTkYy4nExERkUAoU7gUo3o/QkLBKACORRzl8VdnsHOlztYTCVVa3Euu\n1FPjjFCpa8srWtOn3TUYsr8XN3kffZ8cS1p6psvJTi5U6hqKVFsR0X7AGW7XtWbFGgy5+x6iY7Lf\nzPccZMDk/+PItkOu5goEt2sbrlTX4KbFvYjkquPNV3JXs0tzxhsPbGPQiIl4vV4XU4mIiEigNG9w\nLo/26E5EpH+8w+5i0IjnSE867m4wETlt6rkXkX9mLc+Omc6c9T/nTLWudS7DBjyAx6N2YZEzpZ57\nZ+iYQOTMzPrgHV7++HP++vO5IKERoyY9hCcqwt1gIvmAeu5FJG8Yw3397qRduQY5UwvWL+WZaa+5\nGEpEREQCqUfn67iy5fk545+PrmDSsJlYn94sEwkVWtxLrtRT44xQrKsnMoLHhj5E06LVcuY+XDqP\n11770MVU/y0U6xoqVFsR0X7AGcFUV2MMve/oRcu6tXPmPt21kFmT33Mx1ZkLptqGE9U1uGlxLyKn\nJCImilHD+1CrYNmcuVfmfcTHH33rYioREREJFI/xMPSRR6hbvlz2jGXmyi/4ctZcV3OJyKlRz72I\nnJaj+w9z35CRbE87CECEJ4Jht9zNRW2au5xMJLSo594ZOiYQOXtHU1O49/Eh7Dh0GIAoG8Xobr1o\n1qGZy8lEwpN67kXEFQklijBpUB+KRyYC4PV5GTXrRVYsXetyMhEREQmEhLiCjB84kKLxcQBkmkye\nmP0KG5dudDmZiPwTLe4lV+qpcUY41LVEpbJM7P0wCZ4CAKR7Mxn8/FQ2b9zqWqZwqGuwUm1FRPsB\nZwRzXcuULMVTDz9CXHQ0AMdIZcC0Z9m7ebfLyU5NMNc2lKmuwU2LexE5I5XqV+OpO+4m1vhf9FMy\nj9N34iT27N7vcjIREREJhFo1azDs9l5ERfiXDId8SfQZM5mj+5NcTiYiJ6OeexE5Kz99sZChb79C\nFl4AyhUswXMjh1KocILLyUSCm3runaFjApHA+/yzbxg/5w3++tOqE1+ZKeMHER0X424wkTChnnsR\nCQotOraiT4euGPz7o50p++n75FjS0jJcTiYiIiKBcHmnS7nt4stzxuuO/cnQoVPwZnpdTCUif6fF\nveRKPTXOCMe6dryxI3ed3yFnvPHQDgYNn4DXm3cv+uFY12Ch2oqI9gPOCKW63nLz9Vx1bouc8aID\na5k48iWsLzjPlAml2oYS1TW4aXEvIgHR/Z4buK5uq5zxst0beHL0VHxB+qIvIiIip6f3/XfRsnrd\nnPHnW3/mlSmzXUwkIidSz72IBIzP62PU8Cl8u21FzlznxhfRu/ftLqYSCU7quXeGjglEnJWZlUnv\nx0ewZs82AAyGR9pez1W3dnQ5mUjoUs+9iAQdT4SHwUMfomnx6jlzHy7/nldfmeNiKhEREQmUqMgo\nxg0ZSKXCJQCwWKZ8N4eFn/zkcjIR0eJecqWeGmeEe109kRGMGt6XWonlc+ZmLviMj9/72tHnDfe6\nukm1FRHtB5wRqnWNj49jwuABlIj3fzOO13gZ+d6rrP5xtcvJ/iNUaxvsVNfgpsW9iARcTHwsE4b1\no2KBE97V/3Q238/92eVkIiIiEgglShRnXJ8+JMTEApBGOoNfep5ta7e6nEwk/1LPvYg4Zv/2vdz3\n5Cj2ZyYDEBMRxdh7HqZxs/ouJxNxn3runaFjApG8tWLZKvpPm0J6VhYApSKL8n/DH6NoueIuJxMJ\nHeq5F5GgV6JCKSY8+igJEXEApHszefyFaWxav8XlZCIiIhIIjc5pwKCbeuLx+Ncle7MO0XfEZI4f\nOeZyMpH8R4t7yZV6apyR3+paqU5lnrr7PmI90QCkZB2n36TJ7Nm5N6DPk9/qmpdUWxHRfsAZ4VLX\nNm1bcX+nLpD9uePmtJ0MHDaZrLRM1zKFS22Djeoa3LS4FxHH1W9en2HdbieSCAAOpSfTZ/R4kg4n\nu5xMREREAqFLlyvo1rJdznhF0h+MeGIa1qc2GZG8op57EckzX7z7NeM+ewuL/++1RuFyTH1qKLEF\nYlxOJpL31HPvDB0TiLjHWsvoiVP5ZvVvOXPX1riIhwbf7mIqkeCnnnsRCTkdr2tPr1ZX5Iw3HtnJ\noCfGk5XldTGViIiIBIIxhoGP3E/TStVz5t7f+ANvPveei6lE8g8t7iVX6qlxRn6va7c7u3Bdg9Y5\n42V7/2DEqCn4vL6zetz8XlcnqbZyuowxHYwxvxtjNhhjBpzk91cZY1YYY5YZYxYbY1pmz8cYY37J\nnl9ljBl2wn2KGGO+NsasN8Z8ZYwplJf/pvxO+wFnhGNdIyIiGPVYP2oWL5s9Y3npl8/46u1v8zRH\nONY2GKiuwU2LexHJc/f2vpVLqjTJGS/YspJnprzsYiIRCRRjjAd4FrgMqAd0M8bU/tvN5lprG1lr\nzwHuAF4CsNamA22z5xsDHY0xzbPvMzD7frWA74BBzv9rRORMxMTEMGHIQMomFAHAZ3xM+GI2S775\n1eVkIuFNPfci4gpflpcBg8eyZO+GnLnbWnXk1jtvcDGVSN4J1557Y8z5wDBrbcfs8UDAWmvH5nL7\nC4CXrLX1/jYfB3wP3GutXWKM+R1oba3da4wpDcy31v79TQMdE4gEkV27dnP/kyM5nOb/Wrx4U4DJ\nDzxKzXNruJxMJLio515EQponMoKRw/pQu1DFnLmZC7/k43e/cDGViARAOWD7CeMd2XP/xRjT2Riz\nDvgEuP2EeY8xZhmwB/jGWrsk+1clrbV7Aay1e4CSDuUXkQApW7YMY3v3Ji7K/3W4x+xxBk6byu5N\nO11OJhKetLiXXKmnxhmq63/ExMUw/ol+VIzzH6NbLFM+f5fvv1542o+lujpHtc2fsvvfuxtjHjPG\nDP3rv0A9vrX2Q2ttHaAzMPKEeV/2afnlgfOMMXVze4hAZZF/p/2AM/JDXWvWrsETd95NVIR/AY9w\nqQAAIABJREFU2XHIl0zfsU+TvC/J0efND7V1g+oa3LS4FxFXJRRJYOLgfpSI8V8by2t9jJo9k+WL\nVricTCTf+wi4GsgCjp3w37/ZCVQ8YVw+e+6krLULgarGmKJ/m08G5gEdsqf2GmNKAWSflr/v1P4Z\nIuK25uedS5+uPfAY/1nHOzP202/4RNJT0lxOJhJe1HMvIkFh68ZtPDh2DMlZqQAUjIxlSp9+VKtT\nzeVkIs4I9p57Y8xqa239M7hfBLAeaAfsBhYD3ay16064TTVr7absn5sAH1lrKxhjigOZ1tokY0wB\n4CtgjLX2c2PMWOCQtXZs9hX4i1hrB57k+e2tt95K5cqVAShcuDCNGzemTZs2wH8+ddJYY43zfjz4\n8eF89uvPFC5dGoBSqdH06nUD7S5pFxT5NNY4r8bz589n5syZAFSuXJnhw4cH5JjgHxf3xpiF1tpW\nxpij/Pfpbwb/xXESzzZAIGhxLxIe1ixdR99pT3Pclw5A0eiCPPf4Y5SuWPZf7ikSekJgcT8dmGqt\nXXUG9+0ATMF/huAMa+0YY8zd+I8dphtj+gO3ABnAcaCvtfZnY0wD4NXs+3mAt621o7IfsyjwDlAB\n2Apcb609cpLn1jGBSBCbMu0lPljyn/a7yyo2Z+AT92I8Qbs7FHFcnlxQz1rbKvv/CdbaxBP+SwiW\nhb045693lySwVNfc1Tu3DkN73EmkiQTgUEYKj46ZQNLB/zl+/x+qq3NU23yrFbA0+3vlV2Z/7/zK\nU7mjtfZLa20ta20Na+2Y7LkXrLXTs38eZ62tb61tYq1taa39OXt+VfZcY2ttw78W9tm/O2StvST7\ncdufbGEvztF+wBn5sa4P3XcHF9b4z0lBX21bzEuT3wz48+TH2uYF1TW4qedeRILKBRc3o8+V3TD4\n37zclXqIPk+OIS1VfXkieawjUANoD1wJXJH9fxGRM2aMYdiA3jQsWzln7o1Vc/nglU/dCyUSJtRz\nLyJB6a2ZH/HC/A9yxueUqMr40Y8RGRXpYiqRwAn20/JDlY4JREJDamoq9z8+jC2H9gMQYSMYek1P\nWne+0OVkInlP33MvImGtW8+ruf6ci3PGy/ZvZsSIyfi8PhdTiYiISCDExcUxYfAgSsb7O329xsvo\nD2ex8ofTvsyHiGTT4l5ypZ4aZ6iup+7eh27h0mrNcsYLtq3hmUnTT3p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DAAAg\nAElEQVQdYjswZ9gc2ndoy/CbUqmOKyOj8zucqqxm0b/toGD7cb9DFJ+ouZd6aabGG8qrN5TXWsxI\nmXE9353Ul95FgwEoPVbI4lf+xlu73r3ol1NuRUT7AW8or95p6bnt0q4LM4bMoF1SG9JuTKasbSGZ\nKasoKDEWfmcLxfvzPHnflp7XcKfmXkSkJTKj1yNT+faIvqQW9wOg6EA+f/zzCj44uM7n4ERERKSh\n+iX1Y/rA6SSkxDJ0YieK2uaQ1WkNJwojWPytTZQdL/A7RGlimrkXEWnJSktZ9++v8/MDmZyIOwwR\nEaSmpfD9mXNISx3md3StmmbuvaGaQERam/cPvs87e98hb38xW9ecoGvBAPrnj6Vn1yD3PjmW6KR4\nv0OU89DMvYiInF9sLOP+ZQoPJw0hoTwZgkGOb8nll6+/wJ58XVVXREQk3F3d42rGdR9Hp97xDBjX\ngSPtt3Oo/RYOHQ2w5OsbqCpq2C1xJXyouZd6aabGG8qrN5TXz5CYyE0/uJ45USNoW5EIVdXs+/g4\n//3msxw5deS8T1duRUT7AW8or95pTbk1M6b0m8LQlKGkXp7AZaPbszfpE47H72bfgQhe+MY6qkvK\nG+W9WlNew5GaexGRVsC6pHLXv45jetUVxFS1hYpKtq09wi/fWUReiTcX3REREZGmYWZMHzidPol9\n6Da0A33SEtjR6X3yY4+wc5fxyrc/JFhR5XeY4jHN3IuItCIVH2fw5I8zWN7uPSojyoloF8f4m/vz\nrRu/QEJMgt/htSqaufeGagIRac3KqspYkL6A7NPZ7N2Qx5GtZQw/NoWEimRGjjKm/dd4LDLgd5hS\nh2buRUTkokVfkcYjj/bh2sLxRLhIgkUlfLByH797fzGllZrJExERCWdtItswN20uiW0S6XNlJ1IH\nRJPZeRWlkadI3+hY8YMPcdVBv8MUj6i5l3pppsYbyqs3lNcL13by1XxldgpX5l+DYVTlF/Hu2zv5\n00dLqKyu/IftlVsR0X7AG8qrd1pzbtvFtGNu2lziomLpf1UKHftDRud3qAiUsv79Kt79z3VwiWc4\ntea8hgPPm3szm2pm281sp5l95xyPzzazzaF/a81sWGh9dzN718y2mlmmmX3V61hFRFoFMzrcPZmv\n3dyBYflXA1B+vIA339rKc5tfIuj0jb6IiEg46xTXidnDZhMViGTAtZ1p17eCzJSVVFsl762q4L3/\n2XDJDb40X57O3JtZBLATuBE4CnwEzHTOba+1zTggyzlXaGZTgR8658aZWSqQ6pzbZGbxwCfA7bWf\nW+s1NF8nInKxKivZ8/M/89P1uezq8DGY0b5/MvOmX8Mdg6dhpnFwL2nm3huqCUREPrUjbwdLtyyl\nOhhk6ztHcXtTGJp9AxEEuHlGAmMfHeV3iEL4zNyPAXY55w445yqBpcDttTdwzq1zzhWGFtcB3ULr\njzvnNoV+LgayzjwmIiKNICqKfo/dxlcGJdGzcBg4R+GePJa8tY6Ve9/1OzoRERFpoAGdBnDr5bcS\nEWEMmdSVYM9sdnT6AIdj+fOnSH96s98hSiPyurnvBhyqtXyYz27QvwAsr7vSzHoDI4D1jRibnIdm\naryhvHpDeb1EcXGkfedmHu6aSmpxfwgGyd2Ww9NvvcuHh9YByq2IaD/gFeXVO8rtp0Z3Hc3E3hOJ\nCBjDbupKac/97OuwEYDXn85n64vbLvi1lNfmrdlcUM/MrgceAL5TZ3088BLwtdBf8EVEpDF16MA1\n37+e++L70rGkO1RVc2RTLn9c+RcyszP9jk5EREQaaEKvCYzuMppAVARpU7qS33MrhxO24TBe/m02\nO9/Y6XeI0gi8nrkfR80M/dTQ8ncB55z7WZ3t0oCXganOuT211kcCbwDLnXO//oz3cfPmzaN3794A\nJCYmMmLECCZOnAh8+g2TlrWsZS1ruf5lt3MX//7IQt61XUT1bAOxbUjoVMaMKyczZ9oc3+ML9+XV\nq1fz9NNPA9C7d28ef/xxzdx7QDP3IiLnFnRBnt/yPDtO7KCipIrNbx6j197xpJT0ITLgmPP9XvS5\nsa/fYbZKjTVz73VzHwB2UHNBvWPABmCWcy6r1jY9gVXAvc65dXWevxDIc8594zzvowO5iEgjqPoo\nnT/9505ei/2Q09EFWLu2jLqxK9+44X66JeiyJ41JF9TzhmoCEZH6VVZXsnDzQg6dOkR5UQWbl+dw\n+d5JJJalEh3luPfx/vS4uoffYbY6YXFBPedcNfBl4G1gK7DUOZdlZo+Y2cOhzf4NSAJ+a2bpZrYB\nwMyuAeYAN4TWbwxdTV+ayJm/OEnjUl69obw2jsgrR3Lfo124sehqYqra4opO89dnP+G3a58lryTP\n7/BExCfax3pDefWOcntuUYEoZg2bRae4TsS0iyZtSjK7ev2V4qh8KiqNZx/fzbGNx+p9vvLavHk+\nc++cW+GcG+Ccu8w599PQuj84554K/fxF51xH59wo59xI59yY0Pr3nXMB59yI0PpRzrkVXscrItLa\ntZl8LV+cG8/4k9cRFYwhWFTChjXH+MOHizhVfsrv8ERERKQB4qLimJs2l/joeNq0j2HIlA5s77mS\nskAxZeXGon/dTu7WHL/DlEvg6Wn5TUWn4ImINLJgkNynXuGXr5ezLvldglZFmx6dmDKlL/809gFi\no2L9jjDs6bR8b6gmEBG5MMeLj7MgfQHl1eUU55Sw680Khh68mahgDO3awYO/SqNDvyS/w2wVwuK0\nfBERCVMRESQ/eBuPXucYnjsBwyg7fIJ3Vx9gYfoSKqsr/Y5QREREGiA1PpWZQ2cSsADxKXH0uymS\nrG6rqLYqiorgmW9mcOpQod9hykVQcy/10kyNN5RXbyivHoiOpsdXpjOyw1YG5l4DzlG0L5cVf9vF\nC1teIuiCfkcoIk1E+1hvKK/eUW4vTJ8Ofbhj0B0AJHSNp+fkKnakrsERpKAAFn5jE6ezP70bufLa\nvKm5FxGR+rVtS897ruIL/aLol38lVAc5uSOX19ds4fUdy9DpzyIiIuFtaMpQpvSbAkBizwQ6Tz7F\nnuT1OBx5uY5Fj31CaX6pz1HKhdDMvYiInN/hw6z+wV/5U34OB9tnQkw0Pa/ozH03TmBS30l+RxeW\nNHPvDdUEIiKX5u09b/PBoQ8AyN2RT8nyfvQqGA5A914B7n1yLDHtov0MscXSzL2IiDSd7t2Z8M9j\nuCu2C6nF/aG8goMb81j6/hrWHV7nd3QiIiLSQJP7TiatcxoAyQOSiJm0i2PxuwA4fKCaJV/fQGVp\nlZ8hynmouZd6aabGG8qrN5RX75zJrQ0cwK2PXc5t7nI6lnSH0lJ2b8hn4QfLyczO9DdIEfGU9rHe\nUF69o9xePDPj9gG307dDXwBSh3aCSZs5EXsIgP27q/jhrN9TXVHtZ5jyGdTci4jIBYsYcwX3PJLM\nTWUjaV+WAkXFZH1YwIJ1r7A7f7ff4YmIiEgDBCICzBgyg9T4VAC6jOhE+Q3rORWTC8CR/VW8/L2P\nCFbporrNkWbuRUTk4jhHyQtv8PuFVaxq/zdORxcQSEli7PWd+NI18+iW0M3vCMOCZu69oZpARKTh\niiuKmb9xPifLToJzHPvwJO3fvZG4qvYADL+6LdOfuAKL0GGsMWjmXkRE/GFG3F238OBt5Vx98jpi\nqtpSnZvPx++fZP5Hz5JXkud3hCIiItIA8dHxzE2bS1xUHJiRelUHCq9dTXmgBIDNH5zmzR+n44L6\nMrU5UXMv9dKskjeUV28or945Z24DARIfuIOHri/mytwbiKqOoeJIHuvez2f+x4s4VX6qyeMUEe9o\nH+sN5dU7ym3DdYzryOxhs4mKiMLMSL0uga1dF1JlFQB8tOoUK3+ZiU6Waj7U3IuIyKWJiSH10Tt4\naEwhw3NuICIYoORADu9/mMsz6YsprdQ9cUVERMJZ94Tu3D3kbiIsAjOjw8gYCq9cQ5Cai+q9vyyf\n936/1eco5QzN3IuISMPk5bHlx6/x1PY2ZHZeiYsK0GlIKlOv68u84fcSFYjyO8JmSTP33lBNICLS\n+NKPpfPajtcACFYFKXozioSN4zFqDmNTH+jCuHkD/AwxrGnmXkREmodOnRj69cnM7lnBwNzxUFlF\nXlYu727Yz4vbXiLodEXd1sbMpprZdjPbaWbfOcfj08xss5mlm9kGM7smtL67mb1rZlvNLNPMvlrn\neV8xs6zQYz9tqt9HRKS1G9llJDf0uQGAiMgI2t1cQemQT84+vmLBMTa+uMev8CREzb3US7NK3lBe\nvaG8eueCctuzJ+MeG8cdSdH0zx8D5eUczcjl7Y+3s2zHMvSX1NbDzCKAJ4EpwBBglpkNrLPZSufc\ncOfcSOAh4H9D66uAbzjnhgBXAV8681wzmwjcBgxzzg0DfuH5LyNnaR/rDeXVO8pt47u257XEHIoB\nICIqQMy0Aiou23L28WW/O0zmGwf8Ck9Qcy8iIo1l0CAmf3kAt8R2pGfhMCgpZf/GfN5M38iqfav8\njk6azhhgl3PugHOuElgK3F57A+dcSa3FeCAYWn/cObcp9HMxkAWcubfio8BPnXNVocd1WwYRkSZk\nZoztPpaBnWq+rw3ERBJxx1Fcr70AuKDjlV/uZ/uqI36G2app5l5ERBpV9Yp3WPyHIpYHtnE8fjeW\nlMjQaxKZM3Yq47qP8zu8ZqOlztyb2eeBKc65h0PLc4Exzrm6p9hPB34CJAOfc86tr/N4b2A1MNQ5\nV2xm6cBrwFSgFPhn59zH53h/1QQiIh6qrK5kUcYiDhYeBCB4uorIxYMIHqv5LjYQFcHsH15Ov2tS\n/QwzrGjmXkREmqXAlEnMnGFcV5ZGx5IeuPwCtm4o5oWNK8jIzvA7PGkmnHOvOucGAdOBJ2o/Zmbx\nwEvA10J/wQeIBDo458YB3wZeaMp4RUSkRlQgillDZ5EclwxARNtIgrO3E9mp5oSq6sogS/9jJwc3\n6gSrphbpdwDSfK1evZqJEyf6HUaLo7x6Q3n1zkXn1oyYu27j3oLnKX1tLGsiyinMySFzQwRLol8l\nbkwc/ZP6exav+O4I0LPWcvfQunNyzq01s75mluScyzezSGoa+0XOuddqbXoI+HPoOR+ZWdDMOjrn\nTtR9zfvvv5/evXsDkJiYyIgRI85+hs/M4Wr54pbPrGsu8bSU5V/96lf6fHq0XPez63c8LWV506ZN\nPPbYY8RGxdKroBdbd20lZUgKtAuwd+QbRLzZn+5R46ksD/KjR59lyj/15a4Hbms28TeX5dWrV/P0\n008DnD1eNQadli/1Wq1myRPKqzeUV+9ccm7Lysj7zXP8fmUKH3Zayek2hbTplcrY8e14+Mp5dEvo\ndv7XaMFa8Gn5AWAHcCNwDNgAzHLOZdXapp9zbk/o51HAa865HqHlhUCec+4bdV73YaCbc+4HZnY5\n8I5zrtc53l81gQe0j/WG8uod5dYbdfOaXZzNgk0LKKsqAyC2IIrq/x1BRXFbAOLaBbj/VyNI6dfO\nj3DDRmPVBGruRUTEO4WFHPrF8/xhXRc+6ryC8pgy2l2WylVXt+fhKx6kU1wnvyP0TUtt7qHmVnjA\nr6kZ/5vvnPupmT0COOfcU2b2beA+oIKa+flvOec+DN0Sbw2QCbjQv+8751aYWRTwJ2AEUA580zn3\nt3O8t2oCEZEmtL9gP4s2L6LaVQOQkB9H2R+GU1Fec2X9+A5RPPibEST1aOtnmM2amvtadCAXEWnG\nsrPZ8bNXmZ/ZmfQuy6lsEyRpUCrXjE3ii6MfIiEmwe8IfdGSm3s/qSYQEWl623K38eLWF3HU7H+T\nTyRS8PuhVFbWTIG3T47mwf87kvapsX6G2WzpgnriudqzStJ4lFdvKK/eaXBuO3dmwFdu4p5+eQw7\nPomIckf+jlw+2nSSRZsXU1pZ2ihxioh3tI/1hvLqHeXWG/XldXDyYKb2n3p2ObdjAZ2/sJvIQM1f\n8wtzK3jm65soOlHRFGG2WmruRUTEe336cMWXxnJbl9MMzbkeKyvn+NYTbNiSzZItS6isrvQ7QhER\nEWmAsd3HMr7n+LPLhzsfp89DhwhYEID8Y+U881g6pwur/AqxxdNp+SIi0mTce2t5/f8dYkVRCVnJ\n70H7BC4bk8T1aZczc+hMIqz1fOes0/K9oZpARMQ/zjle3f4qm7M3n103dP/lbHumC8HQIa9z3zjm\n/Xo0ce0CfoXZ7Oi0fBERCTs2/hpunZ3A+JhE+uePgcJT7Np4ig927GTZjmWoKRMREQlfZsa0AdP+\n7pa3W3vvYuScfCw0j5+9t4RF39pEWUnQrzBbLDX3Ui/NKnlDefWG8uqdRs2tGYFbb+buO6sZ67rT\ns3AYnMhn28enWb0jnVX7VjXee4lIo9E+1hvKq3eUW29cSF4DEQHuGXIPXdt1BcDh2Hx5FlfNLDrb\n4B/bUcTib2+mvExf6jcmNfciItK0IiKInnEHs6fmM7psEKnF/Qlm55H5cRlvZa1l3eF1fkcoIiIi\nDRAdiGb2sNkkxSYBUBWsIn3oNq698/TZbQ5vKeS572VSUa4Gv7Fo5l5ERPxRUkL+bxbzx1U92dDh\nfU7EHyGmdyqjxkYza8SdpHVO8ztCT2nm3huqCUREmo/80nzmb5zP6cqapr59TAIj3xvI6jfjzm7T\nZ2wKs58YTFSUX1H6TzP3IiIS3uLiSHr4Lu4du5cR+eNpX9KJ8oM5ZKRX8dKWV9mdv9vvCEVERKQB\nkmKTmJM2h+hANACF5afImriPiZM+vQ3uvvU5PP8fO6jSRfQbTM291EuzSt5QXr2hvHrH09wmJdH1\n0duZlbaHtNwbaFsSz+l9OWRsrmJp5gscOXXEu/cWkQumfaw3lFfvKLfeuJS8dm3XlXuG3HP2jjjZ\nJbnsv+UYEyeUnd1m93vHePEnu6mubqxIWyc19yIi4q9u3ej/T5P4/GX7ScueTMzpKAp25ZKxpZzF\nGc+SV5Lnd4QiIiLSAP2T+jNtwLSzy/tPHST38yeZMO7Tv+DvWHWYP//PPoK6iP4l08y9iIg0D598\nwntPbmbZ4Y6kd3mTyvZR9BjekVFDEnlo1EMkxCT4HWGj0sy9N1QTiIg0X2sPrmXl3pVnl8d2Hk3k\nHyN4f2Pbs+vSpvdl+ld7EtGK/gytmXsREWlZRo9m/NzeTEguZtjxSQROlXFoSyFb9xSyOGMxpZWl\n538NERERabau6XENY7qNObu8PvsTYv8plnHDPr2KfsZr+3jjqaPoe9qLp+Ze6qVZJW8or95QXr3T\nlLm1G67n5s/HMSbBGJJ9PVZQyO5NRWzZl8OSLUuorK5sslhE5FPax3pDefWOcuuNhubVzJjafyqD\nkwefXbfy6Bq6fKMTVwwsrlnhHBtf2M2bfzquBv8iqbkXEZHmw4yI6dP4/OfKGB4dz8Dca+HECbI2\nlpBx4CAvbnuRoNMwnoiISLiKsAjuHHQnvdr3OrvutYNvMfB73RnRr6hmRTDIR8/t4u1nc9XgXwTN\n3IuISPNTXk7pHxYyf0U3NkXuZ3enj4jslsqIsTFc238k0wZMwyy8x9U1c+8N1QQiIuGhrKqMP6X/\niZzTOQBEB6KZ1+sO1v3bLjIPhK6zExnJtY8M5oa7kgjzw/5n0sy9iIi0XDExxD4wk7nj9zOorB89\nTw6l6mgOGRsr+XB/Oqv2rfI7QhEREWmANpFtmJs29+wFcyuqK3ju0BtM/NEgBncrrNmoqor3nspi\nzesFPkYaPtTcS700q+QN5dUbyqt3fMttu3YkfvFu5ly5k4Gn0kgt7EPFoRwy0qv56561rDu8zp+4\nRFoh7WO9obx6R7n1RmPnNSEmgblpc2kT2QaA05WnefbYcqY+MZTLU0/VbFRZyV9/m8X7y0816nu3\nRGruRUSk+UpOJvXhacwavp1BJ8bRsTCVkgM5bMkI8ubOFWRkZ/gdoYiIiDRAStsUZg+bTWREJAD5\npfk8n/8u058YQr/kUENfXs47v97GulWnP+OVRDP3IiLS/G3dSuaTf+OF7ZeT0fltCjsWkzwomaFD\nA8xJm03/pP5+R3jRNHPvDdUEIiLhKSs3ixe2voCjZh9+WdJlfD56LM//yzb2nQjN4MfFcev3hnHF\ntbE+Rtr4NHMvIiKtx5AhDLtvJDf3PcDQnBtpezKa3J0n2bmzmqVbnufwqcN+RygiIiINMCh5ELdc\ndsvZ5V35u1jBFmb+cAA9E0N/wS8p4Y2fbSV9XblPUTZvau6lXppV8oby6g3l1TvNJrdXXcVVd3Xj\n2q65pGVPpk1+kCPbT7F3fyXPZT5HXkme3xGKtFjNZj/Qwiiv3lFuveF1Xq/sdiXX9bru7PKm45t4\nP/4Ic37Qn24JodvkFRfz+k+2krmx0tNYwpGaexERCRs2dQpTbotmVNJp0rInE5VXwt7M0+w9VMKi\nzYs4Va6L7YiIiISz63tfz8jUkWeX1xxYQ0bKaeZ+rwep8cUAuMJTvPLEFrZlVPkVZrOkmXsREQkv\nVVVULVjE4r90IKOkks1d3ibYtSPDrmzDwB4pPDDiAWKjmv8snmbuvaGaQEQk/FUHq1m6ZSm78ncB\nYBj3DLmHXtuKefq/csg53RaAiOQkZjw+hAGDA36G22CauRcRkdYpMpLIuTOZOeEYl0XFMiR7ImTn\nsjW9gj3Hc1iyZQmV1TpVT0REJFwFIgLcPeRuurXrBoDD8XLWy+QOT+G+rybSKa4EgGBuPi/8x3b2\n7Ar6GW6zoeZe6qVZJW8or95QXr3TLHMbG0ubB2Yx56q99HEdGZh9DdXHsslMr2LH8YO8uO1Fgk4H\nepHG0iz3Ay2A8uod5dYbTZnX6EA0s4fNpmNsRwCqglUs2bKEkvGXcd8jsXRoUwpA9fFcljy+g/37\ndNaWmnsREQlPiYkkPHQ3c0Zvp1d5T/rnjKbicA4Zm6rZenwny3YsQ6dni4iIhK+20W2ZmzaX+Oh4\nAMqqylicsRimjGLegwHax5QBUHU4m+ce38XBA637uK+ZexERCW+7d3Pgt39h0aZh7GyfzsHUXST0\n78zwEcaEPuOZ1HeS3xGek2buvaGaQESk5TlWdIwFmxZQUV0BQErbFB4Yfj+lz/+VBYsiKaqIASCm\nfw/ue7wf3br5Ge3F08y9iIgIQP/+9LpvAncOyqLvyZGk5nbn1L48tm1zvHdgLesOr/M7QhEREWmA\nLu26MGPIDCKspn3NOZ3D0q3PkzBrMvNmlBEfXdP0l+8+xKIf7ef4cT+j9Y+ae6mXZpW8obx6Q3n1\nTljkdsQIBt8zlFsu28OAvKvpmJPEiT0F7NwJy3etICM7w+8IRcJaWOwHwpDy6h3l1ht+5rVfUj+m\nD5x+dvlA4QH+vONVkubdwn13FBEXVXMx3bLt+1n444Pk5PgVqX/U3IuISMtw3XWMua0z1/Y8zODc\nCbQ/HsOxnUUcOACvbn+V3fm7/Y5QREREGiCtcxo39bvp7PK23G2s2PcOyQ/dxr23nqRNZM1970sy\n9/LMT46Ql+dXpP7QzL2IiLQcwSDuuSW8uizAx7kd2JS6nNM9ohkwIo6e3aKYN2Ie3RO6+x0loJl7\nr6gmEBFp2ZxzvLXnrb8bu7uxz41cmzqGI795mYUrUiivjgSg3ZiBPPDdVJKS/Ir2wmjmXkREpK6I\nCOyeu5l2fRED258mLXsybY6WsjOznOy8agrLCv2OUERERBrAzJjSbwpDU4aeXbdq3yo2ncyi25fv\nYM6Nx4kOVANQ9NEOnvlFLgUFfkXbtNTcS700q+QN5dUbyqt3wi630dEE5s7invFH6R0bJO34JGKO\nlBD5yZ20rxjid3QiYSns9gNhQnn1jnLrjeaSVzNj+sDp9Ensc3bd6zteZ3fpEXp+7Q5mX3eYyIgg\nOEfhuiye+eUJTp3yMeAmouZeRERanvh4Yu6fxZyxu+kWiGbMgWl02FRE4bESvyMTERGRRhAZEcmM\noTPo3LYzAEEX5IWtL3CkuoDeX7+DWVcfIGBBCAY5uXYbz/z6JMXFPgftMc3ci4hIy3XoECd+9wKL\nNw7mtgE76Xv3aBg/3u+oAM3ce0U1gYhI61JUXsT89PkUlNWce982qi0PjXqIpDJj589eYen6PgSd\nQWQkKZOGcf9X2xMX53PQdTRWTaDmXkREWrasLKqXvkjg+utgwgSw5tFPq7n3hmoCEZHWJ68kj/kb\n51NaVQpAhzYdeGjUQ8QXlrLtp6/z0sa+NQ1+VBSpU4Yz70vxxMb6HHQtuqCeeK65zNS0NMqrN5RX\n74R9bgcNIvDlR2HixGbT2IuEm7DfDzRTyqt3lFtvNNe8dorrxOxhs4mKiALgZNlJnst8joqk9gz+\n1i3cMXwvhoPKSo6/k8nip0ooL/c5aA+ouRcRkZYvOdnvCERERMRDPdr34K7Bd2HUfJF/tOgoz295\nnurOKQz75k3cPnRPzYbl5RxZnsHi/y2jqsrHgD2g0/JFRER8oNPyvaGaQESkdfvk6Ccs27ns7PLw\nzsOZPnA6tn8/H//Xu7yR1Q+AiSMKmPDzW7GoSL9CPauxagL/fxMRERERERGRRjC662iKKopYvX81\nAJuzN9Muph2T+k7iiq9fS/Uv3qOyyhh/3whoBo19Y9Jp+VKv5jpTE+6UV28or95RbkVE+wFvKK/e\nUW69ES55ndBrAqO7jD67vPbgWtYfXg+XX87Yr45l/NevhFGjfIzQGy3rqwoRERERERFp1cyMz13+\nOYoritlxYgcAK3avID46niFDh/ocnXc0cy8iIuIDzdx7QzWBiIicUVldycLNCzl06hAAAQtw7/B7\n6Z3Y29/A6tCt8ERERERERETqERWIYtawWXSK6wRAtatm6ZalZBdn+xyZN9TcS73CZaYm3Civ3lBe\nvaPcioj2A95QXr2j3HojHPMaFxXH3LS5tItuB0BZVRmLMxZTWFboc2SNT829iIiIiIiItFiJbRKZ\nkzaHmEAMAEUVRSzOWExVsGXd6F4z9yIiIj7QzL03VBOIiEh99p3cx+KMxTgc0xQcYI8AAAr+SURB\nVAZMY0TqCL9DAhqvJlBzLyIi4gM1995QTSAiIp9la85WogPRXNbxMr9DOUsX1BPPheNMTThQXr2h\nvHpHuRUR7Qe8obx6R7n1RkvI65CUIc2qsW9Mau5FREREREREwpxOyxcREfGBTsv3hmoCEREJNzot\nX0REREREREQANffyGVrCTE1zpLx6Q3n1jnIrItoPeEN59Y5y6w3ltXlTcy8iIiIiIiIS5jRzLyIi\n4gPN3HtDNYGIiIQbzdyLiIiIiIiICNAEzb2ZTTWz7Wa208y+c47HZ5vZ5tC/tWaWVuux+WaWbWYZ\nXscp/0gzNd5QXr2hvHpHuZWLdQHH/mmh4366mW0ws2tC67ub2btmttXMMs3sq+d47jfNLGhmSU3x\nu0gN7Qe8obx6R7n1hvLavHna3JtZBPAkMAUYAswys4F1NtsLXOecGw48ATxV67EFoeeKDzZt2uR3\nCC2S8uoN5dU7yq1cjAs89q90zg13zo0EHgL+N7S+CviGc24IcBXwpdrPNbPuwGTggMe/htSh/YA3\nlFfvKLfeUF6bN6//cj8G2OWcO+CcqwSWArfX3sA5t845VxhaXAd0q/XYWuCkxzFKPQoKCvwOoUVS\nXr2hvHpHuZWLdCHH/pJai/FAMLT+uHNuU+jnYiCLWnUB8Evgnz2MXeqh/YA3lFfvKLfeUF6bN6+b\n+27AoVrLh/n7g3RdXwCWexqRiIiIeOmCjv1mNt3MsoBlwIPneLw3MAJYH1qeBhxyzmU2fsgiIiLh\nL9LvAM4ws+uBB4DxfsciNfbv3+93CC2S8uoN5dU7yq14wTn3KvCqmY2nZixv8pnHzCweeAn4mnOu\n2Mxige/X3gbQnQaakPYD3lBevaPcekN5bd48vRWemY0Dfuicmxpa/i7gnHM/q7NdGvAyMNU5t6fO\nY72AZc65NOphZrrnjYiIhJ2WeCu8Cz3213nOHuBK51y+mUUCbwDLnXO/Dj0+FFgJlFDT1HcHjgBj\nnHM5dV5LNYGIiISdxqgJvP7L/UdA/1CDfgyYCcyqvYGZ9aSmsb+3bmN/ZhPO8+18SyyOREREwtSF\nHPv7nTnmm9koINo5lx96+E/AtjONPYBzbguQWuv5+4BRzrl/uC6PagIREWmtPG3unXPVZvZl4G1q\n5vvnO+eyzOyRmofdU8C/AUnAb83MgErn3BgAM3sOmAh0NLODwA+ccwu8jFlEREQu3QUe+z9vZvcB\nFUApcA9A6JZ4c4BMM0sHHPB959yKum+DTssXERH5O56eli8iIiIiIiIi3vP6avmNxsy+bmZbzCzD\nzJ41s+hzbPMbM9tlZpvMbIQfcYab8+XVzCaYWYGZbQz9+1e/Yg03ZvY1M8sM/ftqPdvoM3uRzpdX\nfWYvjJnNN7NsM8uota6Dmb1tZjvM7C0za1/Pc6ea2XYz22lm32m6qMNDA3O738w2m1m6mW1ouqjD\nj+oCb6gu8IZqAm+oJmg8qgu80dQ1QVg092bWFfgKNfN1adSME8yss83NQD/n3GXAI8DvmzzQMHMh\neQ1Z45wbFfr3RJMGGabMbAjwEHAFNbdyutXM+tbZRp/Zi3QheQ3RZ/b8FgBT6qz7LrDSOTcAeBf4\nXt0nmVkE8GTouUOAWWY20ONYw80l5TYkCEx0zo08M6Im/0h1gTdUF3hDNYE3VBM0OtUF3mjSmiAs\nmvuQANDWaq6iGwccrfP47cBCAOfceqC9mXVu2hDD0vnyCpprvBSDgPXOuXLnXDWwBrizzjb6zF68\nC8kr6DN7Xs65tUDdi5HdDjwT+vkZYPo5njoG2OWcO+CcqwSWhp4nIQ3ILdR8dsPp2Own1QXeUF3Q\n+FQTeEM1QSNSXeCNpq4JwqKAcM4dBf4bOEjNrW8KnHMr62zWDThUa/lIaJ3U4wLzCnBV6BSxv5jZ\n4CYNMnxtAa4NnXYTB9wC9KizjT6zF+9C8gr6zF6qFOdcNoBz7jiQco5t6n5uD6PP7YW4kNxCzYXi\n3jGzj8zsi00WXZhRXeAN1QWeUU3gDdUE3lNd4A3PaoKwaO7NLJGabzh6AV2BeDOb7W9U4e8C8/oJ\n0NM5N4KaU25ebdoow5NzbjvwM+Ad4E0gHaj2NagW4ALzqs9s49EVV71TX26vcc6NoqZI/ZKZjW/C\nmMKG6gJvqC7whmoCb6gm8IXqAm80Wk0QFs09MAnY65zLD51282fg6jrbHOHvv63rHlon9TtvXp1z\nxc65ktDPy4EoM0tq+lDDj3NugXPuCufcRKAA2FlnE31mL8H58qrPbINknzkN1MxSgZxzbHME6Flr\nWZ/bC3MhucU5dyz031zgFWpOd5R/pLrAG6oLPKKawBuqCTynusAbntUE4dLcHwTGmVkbMzPgRiCr\nzjavA/cBmNk4ak4ly27aMMPOefNae97LzMZQc/vE/KYNMzyZWXLovz2BO4Dn6myiz+wlOF9e9Zm9\nKMbfzyK+Dtwf+nke8No5nvMR0N/MelnNVbRnhp4nf++ic2tmcWYWH/q5LXATNaedyj9SXeAN1QUe\nUU3gDdUEjU51gTearCaIbGikTcE5t8HMXqLmdJtKYCPwlJk9UvOwe8o596aZ3WJmu4HTwAM+hhwW\nLiSvwF1m9mjo8VJghm8Bh5+XQ98OVwL/xzl3Sp/ZRvGZeUWf2QtiZs8BE4GOZnYQ+AHwU+BFM3sQ\nOADcE9q2C/BH59ytzrlqM/sy8DY1XxDPd87VbapatUvNLdAZeMXMHDXH52edc2/78Cs0e6oLvKG6\nwFOqCbyhmqCRqC7wRlPXBOacRidEREREREREwlm4nJYvIiIiIiIiIvVQcy8iIiIiIiIS5tTci4iI\niIiIiIQ5NfciIiIiIiIiYU7NvYiIiIiIiEiYU3MvIiIiIiIiEubU3Iu0UGb2NTNr01jbNSCOCWa2\nzKvXFxERkfNTXSDS8qm5F2m5HgPiGnG7hnAev76IiIh8NtUFIi2cmnuRMGdmcWb2hpmlm1mGmd1t\nZl8BugJ/NbNVoe1+a2YbzCzTzH4QWneu7W4ysw/M7GMze97MGu0Ab2ZXmtlGM+vTWK8pIiIin1Jd\nINJ6mXP64kwknJnZncAU59wjoeV2zrkiM9sLjHbOnQytT3TOFZhZBLAK+Ipzbkvt7cysI/BnYKpz\nrtTMvg3EOOd+VOc9vwXMPkc4a5xzj9XZdgLwTeAnwG+A6c65I42ZAxEREamhukCk9Yr0OwARabBM\n4Bdm9hPgL865taH1Fvp3xkwz+yI1/9+nAoOBLXW2Gxda/76ZGRAFfFj3DZ1zvwB+cRExDgb+ANzk\nnDt+Ec8TERGRi6O6QKSVUnMvEuacc7vMbBRwC/CEma10zj1Rexsz603Nt+SjnXOnzGwBcK6L5Rjw\ntnNuzme9Z+gb+nNt87e639CHHANigFHAm+f5lUREROQSqS4Qab3U3IuEOTPrAuQ7554zs0LgodBD\np4AEID/032KgyMw6AzcDfz3HduuAJ82sn3NuT2iurptzblft97yEb+hPhuJaaWannXN/u5TfVURE\nRD6b6gKR1kvNvUj4Gwb83MyCQAXwaGj9H4EVZnbEOXejmW0CsoBDwNpaz6+73QPAEjOLoeZqtv8K\n/N1B/FI453LN7FbgTTN70Dn3UUNfU0RERP6B6gKRVkoX1BMREREREREJc7oVnoiIiIiIiEiYU3Mv\nIiIiIiIiEubU3IuIiIiIiIiEOTX3IiIiIiIiImFOzb2IiIiIiIhImFNzLyIiIiIiIhLm1NyLiIiI\niIiIhDk19yIiIiIiIiJh7v8DfUw1n9T9LckAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "# Get bounds for the approximations\n", "spl_bounds = [numpy.min(dr_global_spl.grid[:,1]), numpy.max(dr_global_spl.grid[:,1])]\n", "smol_bounds = [numpy.min(dr_global_smol.grid[:,1]), numpy.max(dr_global_smol.grid[:,1])]\n", "\n", "plt.figure(figsize=(17, 7))\n", "\n", "plt.subplot(121)\n", "plot_decision_rule(model, dr_global_spl, 'k', 'i', label='Global: spline', bounds=spl_bounds, linewidth=3, alpha=0.5,color='r')\n", "plot_decision_rule(model, dr_global_smol, 'k', 'i', label='Global: Smolyak', bounds=spl_bounds, linewidth=3, alpha=0.5,color='b')\n", "plot_decision_rule(model, dr_pert, 'k', 'i', label='Linear perturbation', bounds=spl_bounds, linewidth=3, alpha=0.5,color='g')\n", "plt.ylabel('i')\n", "plt.title('Investment')\n", "plt.legend()\n", "plt.grid()\n", "\n", "plt.subplot(122)\n", "plot_decision_rule(model, dr_global_spl, 'k', 'n', label='Global: spline', bounds=spl_bounds, linewidth=3, alpha=0.5,color='r')\n", "plot_decision_rule(model, dr_global_smol, 'k', 'n', label='Global: Smolyak', bounds=spl_bounds, linewidth=3, alpha=0.5,color='b')\n", "plot_decision_rule(model, dr_pert, 'k', 'n', label='Linear perturbation', bounds=spl_bounds, linewidth=3, alpha=0.5,color='g')\n", "plt.ylabel('n')\n", "plt.title('Labour')\n", "plt.legend()\n", "plt.grid()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparative statics\n", "\n", "Next, let's carry out a comparative statics exercise by changing the value of the depreciation rate $\\delta$. We'll just use the linearized model for this. \n", "\n", "First, we'll create a set of linearized models, each solved with a different value of delta. We put these models into a list object, and then call each of them when plotting the associated decision rules. \n", "\n", "The ``model.set_calibration`` command lets us change the calibration of the model object. We then append our chosen model approximation to a list of decision rules, ``drs``. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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gypQpAGzfvh1rLY2NjUyZMiX42UPBdVHffgv49ej/n8Dv/UV9B/xaFStyw6c1\nok4orm4oru4otm6kW1Hf4aIxgRu6DrihuLqj2LqhuLqRrkV96+rqePrpp3n++ef5xCc+wZIlS2J+\nj6VLl9LU1MSjj3ornr/97W/T2dnJww8/POj+f/jDH/jlL3/J2rVrAXjttdeCs3emTZvGSy+9xOjR\no2M6zmg/Y6dLlqy1nUCggN9e4MlAAT9jzJcivaS/17o8XhEREREREZG0YMzQ3AahtbWVxYsX89Wv\nfpUHHniADRs2DOp9CgoKCE06tbe3U1xcPOj+zc3N/OpXv2LVqlXBx6ZOnRq8P2bMmOAM6KHgeslS\nvwX8ejz+hf5eK8NH2W83FFc3FFd3FFsR0XXADcXVHcXWDcU1xcRx9syaNWuYO3cuWVlZlJSUhBXh\nDV2yFCrSkqXq6upgrReAxsZGZsyYEfVz++v/6KOPsnLlSvLz86mtrWXLli2sX7+e1atXA14iaShr\nyThdsjRcEn0qloiISE9asuSGxgQiIpJs0nHJ0ooVK6ipqWHevHn8+Mc/pqamhgULFsT8Pm1tbcya\nNYs9e/YA3pKijRs3UlpayoEDB5g8eXJYYqev/itWrODqq6+moqKCQ4cO0d7eTmZmJp2dnVx77bWc\nOXOGD3zgA+zdu5e8vLyYjjPaz1gJGYlKa0TdUFzdUFzdUWzdUELGDY0J3NB1wA3F1R3F1g3F1Y10\nTMi0tLSwYsUKqqurqa2t5b777hv0e61atYqDBw9iraWqqoqFCxcCMGPGDB5//PFeuzdF6r9161au\nvfZaoHsmzqFDhygvL2f16tUcP36cgwcPctttt3HVVVfFfIzRfsbOlyyJiIiIiIiIiAQUFBTw4IMP\nDsl73XHHHREf37Vr14D7X3PNNXR2dkbsH0jwuKAZMiIiInGgGTJuaEwgIiLJJh1nyKSbuOyyJCIi\nIiIiIiIivSkhI1EN5XZe0k1xdUNxdUexFRFdB9xQXN1RbN1QXEWGlhIyIiIiIiIiIiLDTDVkRERE\n4kA1ZNzQmEBERJKNasikPtWQERERERERERFJEErISFRaI+qG4uqG4uqOYisiug64obi6o9i6obiK\nDC0lZEREREREREREhplqyIiIiMSBasi4oTGBiIgkG9WQSX2qISMiIiIiIiIiMgRefvllzp49y7lz\n59i8efOg3kMJGYlKa0TdUFzdUFzdUWxFRNcBNxRXdxRbNxRXSUTr1q3jBz/4AcuWLePXv/71oPv/\n/ve/Z/WtGOBXAAAgAElEQVTq1Tz00EP89Kc/DT7+m9/8hh//+Md89rOf5cknnww+/vnPf568vDwu\nvfRSTp48OahjHzGoV4mIiIiIiIiIXITNmzezZ88e1q1bx7Jly5g+fXpMr29ubuahhx5i586dAFx9\n9dXcdNNNlJSUxNR/xIgR3HLLLZw6dYrs7GzGjh3LzTffzIULF2hsbGTJkiWcOHGCKVOmMGvWLCor\nK/nmN7/JvHnzKCsrIzMzc1DnrxkyEtWcOXPifQgpSXF1Q3F1R7EVEV0H3FBc3VFs3VBcZaht27aN\nhQsXsnHjxpiTMeAtG7ryyiuD7alTp7Jp06aY+48ePZodO3YwcuRIjDF0dnZirWXv3r388Ic/BGDs\n2LHU1NSwY8cOALKysqioqBh0MgY0Q0ZERERERERE4uDGG2/klltuYcOGDcHExoEDB1i5cmWgEC4Q\nLIqLMYZZs2Yxf/58AOrr6ykqKgq+X1FREfv374/6eX31DyRqNm/ezLXXXktlZSXl5eWsX78+2L+h\noYEpU6YAsH37dqy1NDY2MmXKlOAxxUIJGYnK5/MpC+6A4uqG4uqOYisiug64obi6o9i6objKUKqr\nq2PTpk0YY1i+fDlLliwBoKqqiqVLlw7oPZqamsjJyQm2s7OzaW1tHXT/3/72tzz33HP85Cc/AbxZ\nMB/4wAcA+MMf/sBHPvIRpk6dCsAXv/jF4KyeadOmMXv2bEaPHj2g4w5QQkZEREREREQkzZghKtJs\nB5Gka21tZfHixTzzzDNMnz6dhx9+OJiQiUVBQUFYQd329nYmTJgw6P633XYb//iP/8j06dP505/+\nxKWXXgp4tWd+9atfsWrVqmDfQGIGYMyYMfh8PhYsWBDT8SshI1Ep++2G4uqG4uqOYisiug64obi6\no9i6obimlsEkUobKmjVrmDt3LllZWZSUlIQV4Q1dshQq0pKl6urqYE0XgMbGRmbMmBH1c6P1X79+\nPQ8//DBbt26loKCA8ePH88wzzwSTRI8++igrV64kPz+f2tpatmzZwvr161m9ejXgJZgGU0tGCRkR\nERERERERGTbt7e3U1NQA8MILL3D77bcHn4tlydLs2bO5//77g+1du3bxyCOPAF5iZ/LkyWGJnWj9\nd+3axXXXXQd4iZ+6ujo+9KEPAbBixQo+9alPce7cOV599VXa29uprKzk7rvvBuDMmTOcOHGC66+/\nPuY4mECRnGRmjLGpcB6JRmtE3VBc3VBc3VFs3fAXpzP995RYaEzghq4Dbiiu7ii2biiubrgaEyTy\n38SWlhZWrFhBdXU1tbW13HfffYN+r1WrVnHw4EGstVRVVbFw4UIAZsyYweOPP95r96Zo/X/2s59x\n4cIFamtrmTJlCnfffTdbt27l2muvBbpn6Bw6dIjy8nJWr17N8ePHOXjwILfddhtXXXVV1GOM9jPW\nDBkRERERERERGTYFBQU8+OCDQ/Jed9xxR8THd+3aFVP/L3/5y70eu+aaa+js7IzYP5DIuRiaISMi\nIhIHmiHjhsYEIiKSbNJxhky6ifYzzojHwYiIiIiIiIiIpDMlZCQq3xBtgybhFFc3FFd3FFsR0XXA\nDcXVHcXWDcVVZGgpISMiIiIiIiIiMsxUQ0ZERCQOVEPGDY0JojvZfpKVO1dSUVhBeWG5929BOblZ\nufE+NBGRtKYaMqlPuyyJiIiIpLGOzg5OtJ1g97Hd1DfXc7j5MIdbDlOQXRBM0FQUdCdrAgmbisIK\nCkcWYozyhyIiIkNJM2QkKp/Px5w5c+J9GClHcXVDcXVHsXUjHWfIGGPmAcvxlkw/bq1d1uP5+cD3\ngC6gA7jHWrvV/9xB4HTgOWvtzCifoTFBDLpsFyfaTnC4+TD1zfVeoqYl/H7d6To63+2kclplMEET\nmqwJzLgZmzeWDKPV8LHQ9dUdxdYNxdUNzZBJfZohIyIiInFjjMkAVgA3AEeAV40x66y1fwvp9kdr\n7e/8/T8IPAVc4X+uC5hjrW0axsNOeRkmg9JRpZSOKmV62fSIfay1/PcL/03V9KruRE3zYXYf3c0f\n9v8hmMxpPd/KxIKJ3UuiCirClkdVFFYwIX8CIzI0/BQREQHNkBEREYmLdJshY4yZBXzHWvsP/vYD\ngO05Syak/9XAL621V/rb7wIfsdY29vM5GhPESXtHO0dajkScaRNoHz9znHGjxkWeZeO/X15YTs6I\nnHifjojIsNEMmdSnGTIiIiIST+VAXUi7Hui17MgY80lgKTAOuDnkKQu8YIzpBB6z1q50eKwyCLlZ\nuVQXV1NdXB21T0dnB0dbj4Yvi2o+zGtHX+tV1yaQqIm0PCpQ10ZERGTdunXs3buXzMxMJk6cyOc+\n97mL6v/aa6/x/PPP88ADD7g8bEAJGemD1oi6obi6obi6o9jKcLLWrgXWGmM+BnwfmOt/6hprbYMx\nZhxeYuZNa+2WuB1omhmq60BWZhaTRk9i0uhJUftEqmtT31zPS7UvhbUzTEbYcqhIBYnH5o1N6GLE\nur66o9i6objKUNu8eTN79uxh3bp1LFu2jOnTIy+fjaa5uZmHHnqInTt3AnD11Vdz0003UVJSMqj+\n1lq+9a1vMXNmxFJ1Q04JGRERERkOh4FLQtoV/scistZuMcZUGWOKrbUnrbUN/sePG2Oew5tdEzEh\ns2jRIiorKwEoKipi2rRpwf+B8Pl8AGrH2A4Yzs8vHVXK6bdOcwVX8OUbvhz2/OzZs2k+18yzG57l\nRNsJSi4pob65nv/e+N8cbztOe0U79c31tLzVwti8sdTMqKGisIKud7sYmzeW6+ZcR0VhBYdeP0Rx\nbjE3XH/DsJ8fwO7du4f189RW+2Lbu3fvTqjjSda2z+fjiSeeAAj+vUpX27Zt46677uIrX/nKoF7/\n8ssvc+WVVwbbU6dOZdOmTXzmM58ZVP//+q//4rrrruPMmTODOp5YqYaMiIhIHKRhDZlM4C28or4N\nwHbgNmvtmyF9qq217/jvzwDWWWsnGWPygAxrbasxZhSwEfiutXZjhM/RmECC2jvag8ujou0kdaLt\nBKWjSvssRjyxYKLq2oiIM+lcQ+a1117j/vvvZ8OGDWRmZgJw4MABVq5cGYgLEIwRxhhmzZrF/Pnz\nAfj5z3/OG2+8wb/+678C8MADDzB69GgefPDBiJ/XV//GxkZeeuklWlpaqK2t5dvf/vaQnadqyIiI\niEjcWGs7jTGL8ZIpgW2v3zTG3O09bR8DPm2MuRM4D7QDt/hfPh54zhhj8cYuqyMlY0R6ys3Kpaa4\nhprimqh9AnVteiZrdjbsDN4/0nKEwpGFfRYjriisoGBkwTCenYhIcqurq2PTpk0YY1i+fDlLliwB\noKqqiqVLlw7oPZqamsjJ6U6YZ2dn09raOqj+zz77LHfddRe/+tWvBnM6g6KEjETl0xpRJxRXNxRX\ndxRbGSrW2v8LXN7jsV+E3H8UeDTC694Fpjk/QIkqla8DsdS1CS1EXN9cz6aDm8LaGSaj32LEJbkl\nwbo2qRzXeFNs3VBcU4vP+IbkfebYOTG/prW1lcWLF/PMM88wffp0Hn744WBCJhYFBQWcPHky2G5v\nb2fChAkx99++fTtXXXVVzJ9/sZSQERERERHpQ4bJoHRUKaWjSplRNiNiH2stp8+d7rU0amfDTta9\ntS7YbutoY2LBRCoKK8g6lMX6jvW9kjgT8ieQmZE5zGcpIulmMImUobJmzRrmzp1LVlYWJSUlYUV4\nQ5cshYq0ZKm6upodO3YE+zQ2NjJjRuTrdF/9t2/fTltbGxs2bGDr1q2cPXuW3/3ud8HPcUU1ZERE\nROIg3WrIDBeNCSTRtXW0Bbf3Dptx09J9/0TbCcbnj4+6NKq8sJzygnJGjhgZ79MRkSGQjjVkVqxY\nQU1NDfPmzePHP/4xNTU1LFiwIOb3aWtrY9asWezZsweAadOmsXHjRkpLSzlw4ACTJ08OS+z01T/g\nu9/9LsaYYakh4zwhY4yZByyne734sh7Pzwe+B3QBHcA91tqt/ucOAqcDz1lrI+49lci/aCIiIpEo\nIeOGxgSSCjo6OzjScoTDLYejFiNuaG1g9MjR3cWHI2z7rbo2IskhHRMyLS0trFixgurqampra7nv\nvvsG/V6rVq3i4MGDWGupqqpi4cKFAMyYMYPHH3+811ba0foDPP300yxduhRjDA8++GDU3ZpiFZeE\njDEmA9iHt6PCEeBV4FZr7d9C+uRZa9v89z8IPGWtvcLfPgB82Frb1M/nJOwvWjLTGlE3FFc3FFd3\nFFs3lJBxQ2MCN3QdcONi4tpluzh+5njEZE3o/REZI/qcadOzrk2q0O+sG4qrG+mYkEk38dplaSaw\n31pb6z+IJ4EFQDAhE0jG+OXjzYYJMHgza0RERERExC/DZDA+fzzj88fz4YkfjtgnUNcmkJwJLIna\ncWQHa99aG5x9036hPVjXJtouUqprIyIy9FzPkPk08PfW2i/523cAM621X+vR75PAUmAccLO19i/+\nxw8Ap4BO4DFr7coon6PMn4iIJBXNkHFDYwKR2AXq2oQlbnrMuGlsa6R0VGnEpI3q2ohcHM2QSX3x\nmiEzINbatcBaY8zHgO8Dc/1PXWOtbTDGjANeMMa8aa3dErcDFRERERFJMXlZeUwpmcKUkilR+5zv\nPE9DS0OvYsQ7GnYE7x9pOUJRTpHq2oiIDJDrhMxh4JKQdoX/sYistVuMMVXGmGJr7UlrbYP/8ePG\nmOfwlkBFTMgsWrSIyspKAIqKipg2bVpwfaPP5wNQO8Z24LFEOZ5UaS9fvly/nw7agccS5XhSqb17\n926+/vWvJ8zxJGvb5/PxxBNPAAT/XokkC5/qRjiRTHHNzszm0qJLubTo0qh9umwX7515r3vnKH/i\n5sV3XwybfZOVmRV1aVRgts3F1rVJptgmE8VVZGi5XrKUCbyFV9S3AdgO3GatfTOkT7W19h3//RnA\nOmvtJGNMHpBhrW01xowCNgLftdZujPA5morlgC64biiubiiu7ii2bmjJkhsaE7ih64Ab6RhXay2n\nzp7qtxjx2QtnKS8oD5ttE1qIuKKwgvGjxketa5OOsR0OiqsbWrKU+uK97fW/0L3t9SPGmLsBa619\nzBjzz8CdwHmgHfiGtfYVY8xk4DnA4s3kWW2tfSTKZ+gXTUREkooSMm5oTCCSGs6cP9NreVR9cz31\nLd79uuY6mtqbGJ8/vs/ZNhMLJqqujSQ8JWRSX9wSMsNBv2giIpJslJBxQ2MCkfQRqGsTaYZNoN3Q\n0kBRTlGf235XFFaQn50f79ORNKaETOpTQkZipimJbiiubiiu7ii2bigh44bGBG7oOuCG4upOILad\nXZ28d+a93rNtWsJn3mRnZvdbjLg4t/ii6tqkAv3OuuFqTJCbm3v07Nmz44f6fSV2OTk5x9rb2yf0\nfDwhdlkSEREREREZapkZmZQVlFFWUMZHJn4kYh9rLU1nm3oVI95+eDvPvvlscLZNoK5N2HbfPWbc\n9FXXRmS4RUoASGLRDBkREZE40AwZNzQmEBFXWs+3crj5cMSlUXWn6zjccpim9iYm5E+IOtsmUNcm\nOzM73qcjCURjgvSlhIyIiEgcaPDlhsYEIhJP5y6co6G1oddsm0Ax4vrmeo62HmVM7ph+t/5WXZv0\noTFB+lJCRqLSGlE3FFc3FFd3FFs3NPhyQ2MCN3QdcENxdSeRYxuoa9Pf1t8jM0f2W4x4TM6YYa1r\nk8hxTWYaE6Qv1ZAREREREREZJqF1bT5a/tGIfay1nGw/2asY8bb6bWGzbc53no9YgDj0fumoUtW1\nEUlQmiEjIiISB/o2zA2NCUQknQTq2vSqadNcF6x3E6hr01cxYtW1iS+NCdKXEjIiIiJxoMGXGxoT\niIiEO3fhHEdajkQsRhy4f6z1GMW5xf1u/T0qe1S8TyclaUyQvpSQkai0RtQNxdUNxdUdxdYNDb7c\n0JjADV0H3FBc3VFsY9PZ1cmxM8d6FSPumcQZcWgEk6dP7rMY8XDXtUkFGhOkL9WQERERERERSWOZ\nGZlMLJjIxIKJUB65j7WW3z3/OyqnVYYla16pfyUscdPR2RFegLigdzHi0lGlZJiM4T1JkQSkGTIi\nIiJxoG/D3NCYQEQkvlrOtfQqRlzXXMfhlsPB2Tenzp6irKCsz2LEZQVlaVPXRmOC9KWEjIiISBxo\n8OWGxgQiIokvUNemr62/A3Vt+ipGXF5QnhJ1bTQmSF9KyEhUWnvrhuLqhuLqjmLrhgZfbmhM4Iau\nA24oru4otm4MZ1xD69qE1bZpCa9zk5eVF1aMuOfyqPKCcopyihK6ro3GBOlLNWREREREREQkoYTW\ntZlZPjNiH2stJ9tP9ppd8+e6P4e1L3RdCJth8+krPs2C9y0Y5jMS6U0zZFLcYzsf4891fw6b6he4\nleSWJHSmWEQklenbMDc0JhARkZ561rWZUjyFay65Jt6HFaQxQfpSQibFvX70dXY27Azbri5wa7/Q\nHrYOs6KwgkmFk8La40aNUwV0EREHNPhyQ2MCERFJNhoTpC8lZNLYmfNnwjLFdafrgusy65vrOfDa\nAc5WnGViwcTuJE1B75k2E/InkJmRGe/TSRpa0+yG4uqOYuuGBl9uaEzghq4Dbiiu7ii2biiubmhM\nkL5UQyaNjcoexWUll3FZyWURn/f5fMz62Kywoln1zfW8ffJtfLW+YLuxrZHx+eP7TNpMLJhIVmbW\nMJ+hiIiIiIiISGLSDBm5aOc7z9PQ0tB7WVRL9/1jrccoySuJmLSZNHpSMGmTMyIn3qcjIjIs9G2Y\nGxoTiIhIstGYIH0pISPD4kLXBY61HqOuuS58xk1L93KpIy1HKMopiliAOPSWl5UX79MREbloGny5\noTGBiIgkG40J0pcSMhLVcK8R7bJdvHfmvYgFiENveVl5ERM1oQWJC0YWDNtxx0prb91QXN1RbN3Q\n4MsNjQnc0HXADcXVHcXWDcXVDY0J0pdqyEjCyDAZTMifwIT8CXxk4kci9rHW0tje2CtJ83Lty93F\niZvrGJExos+aNhWFFRTlFGnbbxEREREREYkLzZCRlGOt5fS50/3OtOno6ghP0kRI3IzNG6ukjYg4\noW/D3NCYQEREko3GBOlLCRlJWy3nWqhvrg9u/d1z2+/65nraOtooLyjvs6ZN6ahSMkxGvE9HRJJM\nOg6+jDHzgOVABvC4tXZZj+fnA98DuoAO4B5r7daQ5zOAHUC9tXZ+lM/QmEBERJJKOo4JxKOEjESl\nNaLQ1tHWa9vvnkmbU2dPUZZf1mfSZkL+BEZkeCsEFVc3FFd3FFs30m3w5U+m7ANuAI4ArwK3Wmv/\nFtInz1rb5r//QeApa+0VIc/fA3wYKFRCZnjpOuCG4uqOYuuG4upGuo0JpJtqyIj0IS8rjyklU5hS\nMiVqn3MXznGk5Uiwfk19cz0Hmg6E1bU50XaC0lGlVBRWMLJuJNPPTu+VtJlYMJHszOxhPDsRkWE1\nE9hvra0FMMY8CSwAggmZQDLGLx9vpgz+/hXATcDDwL3DccAiIiIiLmmGjMgw6OjsoKG1oc+aNkdb\nj1KcWxx156iKwgrKC8vJGZET79MRkSGQbt+GGWM+Dfy9tfZL/vYdwExr7dd69PsksBQYB9xsrf2L\n//Gn8ZIxo4ElmiEjIiKpIt3GBNJNM2REhkFWZhaXjL6ES0ZfErVPZ1cnR1uP9qpps/vY7mDS5kjL\nEQpHFva7g9So7FHDeHYiIkPHWrsWWGuM+RjwfWCuMeZm4Ji1drcxZg6gQauIiIgkPSVkJCqtEXUj\nWlwzMzIpLyynvLCcmeUzI762y3Zx/MzxXrNr/vjuH8PaOSNyos6yCdwKRxY6PtPhpd9XdxRbGSKH\ngdCsdIX/sYistVuMMVXGmGLgGmC+MeYmIBcoMMb8H2vtnZFeu2jRIiorKwEoKipi2rRpwd9hn88H\noHaM7cBjiXI8qdJevny5fj8dtXv+7sb7eFKlvXv3br7+9a8nzPEka9vn8/HEE08ABP9eSXrSkiWJ\nyqf/CXPCdVyttZxsP9lnIeK603VkmIw+CxFXFFYwJmdM0mz7rd9XdxRbN9JterIxJhN4C6+obwOw\nHbjNWvtmSJ9qa+07/vszgHXW2kk93mc2WrI07HQdcENxdUexdUNxdSPdxgTSTQkZkTRkraX5XHPk\nejYhiZtzF85FTNSEzroZmzc2aZI2IokkHQdf/m2v/4Xuba8fMcbcDVhr7WPGmH8G7gTOA+3AN6y1\nr/R4DyVkREQkpaTjmEA8SsiISFSt51v7LERc31xP6/lWygvL+6xrUzqqlMyMzHifjkhC0eDLDY0J\nREQk2WhMkL6UkJGoNCXRjVSLa3tHe7AQcaRbXXMdTe1NlBWU9Zm0KSsoY0TG4MtapVpcE4li64YG\nX25oTOCGrgNuKK7uKLZuKK5uaEyQvlTUV0QuSm5WLjXFNdQU10Ttc+7COY60HAlL1Bw8dZAtdVuC\n7eNnjjNu1Lg+kzblheVkZ2YP49mJiIiIiIi4oRkyIpIQOjo7ONp6tM+aNg0tDYzJHRN156iKwgrK\nC8rJzcqN9+mI9EvfhrmhMYGIiCQbjQnSlxIyIpI0Ors6OXbmWJ81bQ63HKYgu6DfHaTys/PjfTqS\n5jT4ckNjAhERSTYaE6QvJWQkKq0RdUNxdSMQ1y7bxYm2E/0WIx45YmSfy6MqCisoHFmoHaTQ76wr\nGny5oTGBG7oOuKG4uqPYuqG4uqExQfpSDRkRSSkZJoPSUaWUjiplRtmMiH2stTSdbeqVpNlatzWs\nGDEQnqSJkLgpzi1W0kZERERERGKmGTIiIlE0n2vuc/eo+uZ6zl041+/yqLF5Y8kwGfE+HUkw+jbM\nDY0JREQk2WhMkL6UkBERuQit51s53Bxh2++QYsQt51qYWDCxz6TN+FHjyczIjPfpyDDS4MsNjQlE\nRCTZaEyQvpwnZIwx84DlQAbwuLV2WY/n5wPfA7qADuAea+3Wgbw25D00+HJAa0TdUFzdSOS4tne0\n99r2O3SWTX1zPSfbTzIhf0KfSZuy/DKyMrOG/fgTObbJTIMvNzQmcEPXATcUV3cUWzcUVzc0Jkhf\nTmvIGGMygBXADcAR4FVjzDpr7d9Cuv3RWvs7f/8PAk8BVwzwtSIiCS83K5fq4mqqi6uj9jnfeb5X\n0ubQ6UP8ue7PwfZ7Z95jbN7YPpM25QXljBwxchjPTkREREREBsPpDBljzCzgO9baf/C3HwBsHzNd\nrgZ+aa29MpbX6tswEUkHF7oucLT1aMSZNnWn6zjccpiGlgbG5I7psxBxeWE5eVl58T6dtKdvw9zQ\nmEBERJKNxgTpy/UuS+VAXUi7HpjZs5Mx5pPAUmAccHMsrxURSRcjMkYEkyrRdHZ18t6Z93olbf56\n/K/B+4ebDzMqe1TEpM2k0ZOC9/Oz84fx7ERERERE0ktCbHttrV0LrDXGfAz4PjA3zockaI2oK4qr\nG4qrJzMjk7KCMsoKyvho+Ucj9rHWcqLtRK+kja/W1z3r5nQd2ZnZVBRWkHc4jw9d9aGIS6RGjxyt\nbb9F0oCusW4oru4otm4oriJDy3VC5jBwSUi7wv9YRNbaLcaYKmNMcayvXbRoEZWVlQAUFRUxbdq0\n4MXC5/MBqB1jOyBRjidV2rt3706o40mVdkCiHE8ytMeNGsfpt05zBVfw5Ru+HPb87NmzOXX2FM9t\neI5th7fx0fKPUt9cz7Prn+V4+3HaytuoO11HxzsdjBs1jss+fBkVhRV0Huhk3KhxXH/d9UwqnMTB\n3QcpHFnIddddF/fzjXfb5/PxxBNPAAT/XomIiIhI+nJdQyYTeAuvMG8DsB24zVr7ZkifamvtO/77\nM4B11tpJA3ltyHtovbiISBw0n2vuveW3v65NYDvw9gvt4TNrItS1GTdqHBkmI96nM6y0XtwNjQlE\nRCTZaEyQvoZr2+t/oXvr6keMMXfjFeh9zBjzz8CdwHmgHfiGtfaVaK+N8hkafImIJKgz589wuOVw\nxMRN4Hb63GkmFkzsM2kzIX8CmRmZ8T6dIaPBlxsaE/Shvh7uvhsuvxwuu6z7NnEiZKRXQlREJJFo\nTJC+nCdkhoMGX274tEbUCcXVDcXVneGI7dkLZ4PbftedrutO1rR0J20a2xoZnz++z6TNxIKJZGVm\nOT3WoaLBlxsaE/ShtRVefBH27fNub73l/dvcDFOmhCdpAkmbMWMAXWNdUVzdUWzdUFzd0JggfSVE\nUV8REUlvOSNyqBpTRdWYqqh9zneep6Glodfsmm2HtwXvH2s9xti8sRELEM+/fL52jpL0lp8P8+f3\nfry5Gfbv707UPP88/Nu/eQmbkSO9xExBAbzySnfCpqYGcnOH/xxERERSiGbIiIhIyrjQdYFjrceo\na67rlbj595v+nZK8kngfYpC+DXNDY4IhZC0cO9adqAm9vfsujB8fPqsmcLv0UshMneWFIiKuaUyQ\nvpSQERERiQMNvtzQmGCYXLgAhw71Xv60bx+89x5Mnhy+9ClwKy0Fo197EZFQGhOkLyVkJCqtEXVD\ncXVDcXVHsXVDgy83NCZwI6brQHs7vP125Jk1589HnlUTWBaVZnR9dUexdUNxdUNjgvSlGjIiIiIi\nMnRyc+GDH/RuPZ08GZ6gefZZr37N/v0wenTkRE1VFWRnD/95iIiIOKYZMiIiInGgb8Pc0JggSXV1\nwZEjvZc/7dsHdXUwaVLkZE15ubbsFpGkpzFB+lJCRkREJA40+HJDY4IUdP68V0Q4Ur2a06e9HZ96\n1qq57DIoLo73kYuIDIjGBOlLCRmJSmtE3VBc3VBc3VFs3dDgyw2NCdxI2OtAS0v4lt2hCZusrO7k\nTGjCJoG27E7YuKYAxdYNxdUNjQnSl2rIiIiIiEhyKiiAGTO8Wyhrvd2eQhM1q1Z5/x444O321HNG\nzeWXa8tuEREZVpohIyIiEgf6NswNjQmkX52dUFvbeweot94K37K75238eG3ZLSJOaEyQvpSQERER\niS+ufHQAACAASURBVAMNvtzQmEAuymC27J4yBQoL433kIpLENCZIX0rISFRaI+qG4uqG4uqOYuuG\nBl9uaEzghq4DeFt279/fexeo/fu9hEykejX9bNmtuLqj2LqhuLqhMUH6Ug0ZEREREZH+FBfDVVd5\nt1CRtuzetKl7y+6Kisgzayoq4nMeIiKSMDRDRkREJA70bZgbGhNIQulry+5Tp6Jv2V1SEu8jF5Fh\npDFB+lJCRkREJA40+HJDYwJJGn1t2T1iRO8doAJbduflxfvIRWSIaUyQvjLifQCSuHw+X7wPISUp\nrm4oru4otiKi64ADBQX4mpvh1lvh29/2tuV+9VVv5swbb8CyZXDttdDcDKtXw223ecumLrkEbrwR\n/umfYPlyWL8e3nkHLlyI9xklFP3OuqG4igwt1ZAREREREUkUxnhbbI8f7yVkQkXasnv9eu/fo0ej\nb9k9YYK27BYRSUBasiQiIhIHmp7shsYEkrba272ZMpHq1Zw7FzlRc9ll2rJbJAFoTJC+lJARERGJ\ng3QcfBlj5gHL8ZZMP26tXdbj+fnA94AuoAO4x1q71RgzEngZyMab3fuMtfa7UT5DYwKRngJbdofO\nrAncCgoiFxauqoKRI+N95CJpIR3HBOJRQkai8vl8zJkzJ96HkXIUVzcUV3cUWzfSbfBljMkA9gE3\nAEeAV4FbrbV/C+mTZ61t89//IPCUtfaK0OeMMZnAVuBr1trtET5HYwIHdB1wI+5xtRYOH46cqDl0\nCMrLw4sKh27ZnZHYpSjjHtsUpbi6kW5jAummGjIiIiIyHGYC+621tQDGmCeBBUAwIRNIxvjl482U\n6fncSLzxi7IuIhfLGC+5UlEB118f/lxHR/iW3a+/Dk8/7d1vavJ2fOo5q+byy7Vlt4hIDDRDRkRE\nJA7S7dswY8yngb+31n7J374DmGmt/VqPfp8ElgLjgJuttX/xP54B7ASqgX+31j4Y5XM0JhBxrbU1\n+pbdmZmRa9XU1MCoUfE+cpGElG5jAummGTIiIiKSMKy1a4G1xpiPAd8H5vof7wKmG2MK/c+/31r7\nRhwPVSR95efD9OneLZS1cPx4eKLmt7/1/n3nHRg7NvISqMpKGKH/LRGR9KMrn0SlNaJuKK5uKK7u\nKLYyRA4Dl4S0K/yPRWSt3WKMqTLGFFtrT4Y83myM2QTMAyImZBYtWkRlZSUARUVFTJs2Lfg77PP5\nANSOsR14LFGOJ1Xay5cvT63fz5de6m5/7GPe8/Pmee3OTnxPPQV1dczJzYV9+/CtXu21T52Cykp8\nxcUwaRJzbrgBLrsMX2MjFBcz57rrYj6enr+7CRGfFGjv3r2br3/96wlzPMna9vl8PPHEEwDBv1eS\nnrRkSaLy6X/CnFBc3VBc3VFs3Ui36cn+Yrxv4RX1bQC2A7dZa98M6VNtrX3Hf38GsM5aO8kYMxbo\nsNaeNsbkAs8Dj1hr10f4HI0JHNB1wA3F1e/sWW8GTehW3YFbe3v0LbtHj476loqtG4qrG+k2JpBu\nSsiIiIjEQToOvvzbXv8L3dteP2KMuRuw1trHjDH/DNwJnAfagW9Ya1/x77j0K//rMoA11tqHo3yG\nxgQiqaSpqbteTc+ETX5+5CVQ1dXasluSSjqOCcSjhIyIiEgcaPDlhsYEImnCWjhypPeMmrfegtra\n6Ft2T5qU8Ft2S/rRmCB9KSEjUWlKohuKqxuKqzuKrRsafLmhMYEbug64obg60tGB78knmTNmTO+E\nzcmT3gya0K26A/dLSrytwCUq/c66oTFB+lJRXxERERERSR1ZWd5MmEiJg9ZWePvt7uVPf/oT/Oxn\nXjsjo3edmssv15bdIuKMZsiIiIjEgb4Nc0NjAhEZFGvhxIneM2r27fMSOIEtu3veJk/Wlt1y0TQm\nSF9KyIiIiMSBBl9uaEwgIkOusxPq6iIna44cgcrKyMmasjItgZIB0ZggfSkhI1Fpjagbiqsbiqs7\niq0bGny5oTGBG7oOuKG4ujNssT17Fg4ciLxld1vboLbsTmT6nXVDY4L0pfl1IiIiIiIig5GTA+9/\nv3fr6dSp8C27f//77mTNqFHasltENENGREQkHvRtmBsaE4hIwrMWGhp6b9e9b5+3ZffEiZF3gdKW\n3SlLY4L0pYSMiIhIHGjw5YbGBCKS1Do64ODB3suf3nqr95bdoQkbbdmd1DQmSF9KyEhUWiPqhuLq\nhuLqjmLrhgZfbmhM4IauA24oru6kZGwDW3ZHStYYE7lWzZQpQ7pld0rGNQFoTJC+VENGREREREQk\n0eXnw7Rp3i1UpC27n3zSq1/z9tve7JmetWouu8zbHSorKy6nIiIezZARERGJA30b5obGBCIiIbq6\nwrfsDt0N6sgRuPTSyEugtGX3sNKYIH0pISMiIhIHGny5oTGBiMgABbbs7rkEat8+OHPGW+4UaRlU\nUVG8jzzlaEyQvpwnZIwx84DlQAbwuLV2WY/nbwfu9zdbgH+y1u7xP/f/s3fv8XGf5Z33P/dIc9RI\nI5/PlhzHdg7YsV3iOInjOrCQUHgKLd1CKNCUFuirSzm03YXysC/K0m1L96EPZfuUbliKS5c2Ld1S\nCkshnITthJyIHSfgQ3w+yWdrpPEcNIf7+eM3Gs1ImlhOdGtm9Pu+Xy+9NL85WD9dDKMr1+++rvsY\nkARKQN5au6nOz1DyVc/RozA87FW/I5Hreql6RN1QXN1QXN1RbN1Q8uWGcgI39DnghuLqjmL7MlVv\n2V311ffTn7Ktq2viQs3Kldf93xviUU7gX05nyBhjAsBfAK8GzgBPGWO+Zq3dX/W0I8BWa22yXLx5\nCNhcfqwEbLPWXnF5njPaP/8zfO5zcOoUzJrlFWZ6e72vsbdjscaeq4iIiIiINF53N9x+u/dV7Qc/\ngJtuqm2BevRR7/axY16r00QtUMuWQVtbQ34VkWbmdIWMMWYz8HFr7evKxx8B7NhVMlXP7waes9Yu\nKx8fBV5prb10jZ+jq2HXUipBf7/3QXn8uPe9+vbx49DVNXGhZuR7Z2fjzl9EZIbR1TA3lBOIiDRI\noTDxlt0HD8KFC+O37B4p2Myd6/t5NcoJ/Mt1QebNwH3W2veUj98ObLLWvr/O838PWF31/CPAAFAE\nHrLWfr7O65R8vVylEpw/P75QU307Gq2/uqa3FxKJRp29iEjLUfLlhnICEZEmdPVq/S27ra2/ZXc8\n3ugznxbKCfyraba9NsbcC/wasKXq7ruttf3GmHnAd4wx+6y1uxpzhjNcIAALF3pfm72OsZreW2u9\nynZ1oebgQXjkkdHjYPDFW6JmzfJ99RvU0+yK4uqOYivVjDFh4M1AL1V5hLX2vzTqnMQ9fQ64obi6\no9i68ZLj2tEBt93mfY01dsvur3zF+37oEMyePXGxZsUKbdktM4LrgsxpYHnV8dLyfTWMMevwZsfc\nXz0vxlrbX/5+wRjzVWAToIJMIxgD8+d7X2N7ScEr2Fy+XFuwOXYM+vpGb1tbf3VNby/MmaOCjYhI\n8/sa3sD9HwO5Bp+LiIi0urlzva+77qq9v1Ty5mBWb9X9ne9430+fhuXLR2fUVH8tXqz/ppCW4bpl\nqQ04gDfUtx94EnjAWruv6jnLge8B77DWPl51fwwIWGtTxpgO4BHgE9baRyb4OfZXf/VX6e3tBaC7\nu5v169dXqrd9fX0AOm708fr1cOwYfd/4Bpw9y7ZgEI4fp++557zjUgl6e+nr7IQFC9h2993e8aVL\n3vEv/AIY0zy/j451rGMdX8dxX18f27dvB6C3t5dPfOITLbk82RjzvLX2FY0+j3rUsiQi4gO53OiW\n3dUFm4MHIZXy2p0mKtY06ZbdalnyrxctyBhjdllrtxhjhoDqJxq84bxd1/wB3s5Jf87ottd/Yox5\nb/n1DxljPg/8InC8/O/mrbWbjDErgK+Wf2478GVr7Z/U+RlKvmaCwcGJZ9eM3L561VtRU2+FzYIF\nXuuViEgLaNXkyxjzEPDfrbXPNfpcJqKcQETE55LJ0S27xxZrolGvMPOe98A739noM61o1ZxAXj6n\nK2Smi5IvN/qarfc2lfIKM/WKNsmkt3SxXlvUokVNsd1e08V1hlBc3VFs3WjV5MsY81PgRuAoXsvS\nyEWadQ09sTLlBG7oc8ANxdUdxdaNlo6rtXD2rFeYmT0b1q5t9BlVtGpOIC9f0wz1FbmmeBxuvdX7\nmkg6DSdO1BZrvvGN0duXLsHSpROvrunt9fpN2/V/CRGRa3hdo09ARETkuhnjXaBdtKjRZyJSoRUy\n4h/ZrFewGTt4eOT4wgWvKFNvhc3SpZrmLiJTRlfD3FBOICIirUY5gX+pICMyYngYTp4c3wo1cnz2\nrDenpt4Km2XLIBRq2OmLSGtR8uWGcgIREWk1ygn8SwUZqaule0RdyOe9rffqzbA5cwbmzau/wmb5\ncohEFFdHFFd3FFs3lHy5oZzADX0OuKG4uqPYuqG4uqGcwL80MENksoJBWLHC+5pIoeAVZaoLNY8/\nDv/wD97tkye9AWKzZsFtt40v2vT0QCw2bb+OiIiIiIiINI5WyIhMl2IR+vvrr7A5cQISifrbevf0\neIONRWRG0NUwN5QTiIhIq1FO4F8qyIg0i1LJm1NTb2vv48e9FTT1WqJ6eryCjoi0BCVfbignEBGR\nVqOcwL9UkJG61CPqxkuOq7XeTlBjV9ZU3w4G66+u6e2F7m5vy78ZSO9XdxRbN5R8uaGcwA19Drih\nuLqj2LqhuLqhnMC/NENGpFUYA/Pne1+bNo1/3Fq4fLm2UHPkCHz/+97to0e9f6N6Rc3Ygs2cOTO2\nYCMiIiIiItJMtEJGxC+shYGB+qtrjh/3tv5+sRk28+erYCMyRXQ1zA3lBCIi0mqUE/iXCjIiMiqZ\nrD/D5tgxSKdHd4SaqGizYAEEAo07f5EWouTLDeUEIiLSapQT+JcKMlKXekTdaOm4plK1Q4bHFm0G\nB2HZsvorbBYtgrY2J6fW0nFtcoqtG0q+3FBO4IY+B9xQXN1RbN1QXN1QTuBfmiEjIpMXj8Ott3pf\nE0mnx6+w+cY3Rm9fvuwVbOqtsFm8GNr1sSQiIiIiIjOfVsiIyPTJZuHEiforbC5c8IoyYws1I8dL\nl3o7SYnMAH68GmaMuR/4DBAAvmCt/dSYx38e+CRQAvLAh6y1jxpjlgJfAhaUH/u8tfazdX6GcgIR\nEWkpfswJxKOCjIg0j+FhOHmy/tDh/n6v7aneCptlyyAUatz5i1wHvyVfxpgAcBB4NXAGeAp4q7V2\nf9VzYtbadPn2WuAfrbU3G2MWAguttXuMMXHgx8Abq19b9W8oJxARkZbit5xARqk3QOpSj6gbiuuL\nCIVg5UrvayL5PJw6VVus2bkT/tf/om/fPrZdvuztBFVvhc3y5RCJTNdvM2PoPStTZBPwgrX2OIAx\n5mHgjUClqDJSjCmL462GwVp7Fjhbvp0yxuwDllS/VtzS54Abiqs7iq0biqvI1FJBRkRaRzAIK1Z4\nX2P19cGWLXDmTO2qmscfh4cf9m6fPAlz5tRfYbN8OcRi0/gLifjKEuBk1fEpvCJNDWPMm4A/BuYB\nr5/g8V5gPfCEi5MUERERmS5qWRIR/ygWvbanejNsTpyARGLiHaJGvsfjjTt/mVH8tjzZGPNm4D5r\n7XvKx28HNllr31/n+VuAj1trX1N1XxzoAz5prf1andcpJxARkZbit5xARmmFjIj4R1ubNxh46VJv\nNc1YpRKcO1dbqNm7F/71X0d3j4rF6m/r3dsLXV3T+AuJtJTTwPKq46Xl+yZkrd1ljLnBGDPbWnvZ\nGNMO/BPwt/WKMSMefPBBent7Aeju7mb9+vWVJfZ9fX0AOtaxjnWsYx037Livr4/t27cDVP5eiT9p\nhYzU1aceUScUVzemJa7WwvnzXmHm6NHxW3wfP+7NwanXEtXTA93dYFrrAojes2747WqYMaYNOIA3\n1LcfeBJ4wFq7r+o5K621h8u3NwJfs9YuKx9/Cbhorf2da/wc5QQO6HPADcXVHcXWDcXVDb/lBDJK\nK2RERCbLGFiwwPvaNG70hVewuXSptg3q8GH4/vdHizbG1F9d09sLs2e3XMFGZDKstUVjzPuARxjd\n9nqfMea93sP2IeDNxph3AsNABvhlAGPM3cCvAM8ZY3YDFviotfZbjfhdRERERKaCVsiIiEwXa2Fg\noP623seOeTtJvVhL1Lx5KtjMELoa5oZygvpOZrO868ABNsTjbIjH2djZyapolIA+U0REGko5gX+p\nICMi0kySydHizEQFm3S6dsjw2ILNggUQCDTu/GXSlHy5oZygvqvFIj8cGOCZoSF2p1LsTqW4kM+z\nrqODjZ2dlULNrR0dhPQ5IiIybZQT+JcKMlKXekTdUFzd8E1cU6mJCzUjtwcHYdmy0ULN2MLNokXX\nXbDxTWynmZIvN5QTXJ8r+Tx7UimeSaXYXS7UHM1muSkWq6yi2RCPk3z6aV736lc3+nRnHH2+uqPY\nuqG4uqGcwL80Q0ZEpJXE43Drrd7XRNLp8cOGv/710ePLl0cLNhOtsFmyxNuNSkR8YVYwyL2zZnHv\nrFmV+9LFInvLK2ieSaX4Yn8/zz3/PDdUraIZKdTMDgYbePYiIiKtTStkRET8JJuFEyfqr7C5cAEW\nL66/wmbJEtB/gE0JXQ1zQzmBG/lSiX3pdKXd6ZlUimdTKWa1t9e0O23s7GRxKITRXBoRkUlTTuBf\nKsiIiMioXA5OnqzfFnX2rNf2VG+FzbJl3tbfck1KvtxQTjB9StZyOJOpaXd6JpXCQM0qmg3xOCs1\nPFhEpC7lBP6lgozUpR5RNxRXNxRXd2pim8/DqVP1V9icOePtBDXR6preXli+HMLhRvwaTUfJlxvK\nCdyY7GestZbTuVylODNSqLlSKLB+TLvTzbEYQZ8PD9bfLncUWzcUVzeUE/iXZsiIiMjkBYOwYoX3\nNZFCAU6fri3WPP44PPywd/vUKZgzp/4Km54eiEan6ZcRkalmjGFpJMLSSIT/a+7cyv2X8vlKceZb\nly/zx8ePcyKX45ZYrGYlzbp4nJjmWImIiE9ohYyIiEyfYhH6++uvsDlxAhKJ+jNsenqgo6Nhpz+V\ndDXMDeUErSNVKPDs1as17U7702luiETGtTx1a3aViMxgygn8SwUZERFpHqWSN6fmxbb2jsfrr7Dp\n7YXOzoad/vVQ8uWGcoLWliuV+OnVqzXtTs+mUswPhcYVaRap/VFEZgjlBP6lgozUpR5RNxRXNxRX\nd5oqttbC+fP1izXHjkEkMnGx5jWvgViscec+hpIvN5QTuNHIz4GitbyQTo+bSxMKBMZtw70iEmmp\nHZ6a6vN1hlFs3VBc3VBO4F+aISMiIq3DGFiwwPu6447xj1sLFy/WFmoOHYLvfhfuvrupCjIiMjlt\nxnBTRwc3dXTwwIIFgDc8+EQuVynObD97lg8MDZEqFtkwZhvuNdEo7T4fHiwiIs1JK2REREQaQFfD\n3FBO4G/nh4fZPWYb7jO5HK/o6Khpd1rb0UFEw4NFpEkoJ/AvFWREREQaQMmXG8oJZKzBQoFnx7Q7\nHcxkWBWN1rQ7rY/H6WrX4nERmX7KCfxL6zelrr6+vkafwoykuLqhuLqj2IpIK38OdLW3c093Nx9Y\nupTtN9/Ms7ffzpW77+av16xhSyLB/nSa/3T4MIsee4xVTzzBL//kJ/zx8eN8+/Jlzg8POz23Vo5r\ns1Ns3VBcRaaWLgOIiIiIiK9E2tp4ZVcXr+zqqtxXKJU4kMlUVtH8yYkT7EmliAUCNe1OGzs7WR4O\nt9TwYBERaU5qWRIREWkALU92QzmBTCVrLceyWW8eTblQszuVIlsqjduGe3UsRpuKNCLyEign8C8V\nZERERBpAyZcbyglkOpzN5SrFmZFCzbnhYdaN2Yb71o4OwtrhSUSuQTmBf+kvhNSlHlE3FFc3FFd3\nFFsR0edArYXhMK+bM4eP9vTwT694BYc3b+bUnXfyRytWcGM0yg8HBnjnvn3M2rWLDU8/zbv27+e/\nnzrFo8kkqUKh8u8oru4otm4oriJTy/kMGWPM/cBn8Io/X7DWfmrM428DPlw+HAJ+y1q7dzKvFRER\nERFpBt3BINtmzWLbrFmV+zLFIs9dvVpZSfO3587xk6tXWRoOs7Gzk65z58hfvsyGeJy5oVADz15E\nRBrBacuSMSYAHAReDZwBngLeaq3dX/WczcA+a22yXID5A2vt5sm8turf0PJkERFpKVqe7IZyAml2\n+VKJ/el0TcvTnlSKRHv7uLk0SzU8WMQXlBP4l+sVMpuAF6y1xwGMMQ8DbwQqRRVr7eNVz38cWDLZ\n14qIiIiItJJgIMDaeJy18TjvLN9Xspaj2WxlHs3nzpzhmaEhSlCzu9OGeJwbo1ECKtKIiMwIrmfI\nLAFOVh2fYrTgMpHfAP7tJb5Wpph6RN1QXN1QXN1RbEVEnwNujMQ1YAwro1H+/fz5/NENN/Bv69Zx\n9q67ePaVr+QDS5cSb2vjH86f5769e+netYt7du/m/S+8wPb+fp5NpciXSo39RZqQ3rNuKK4iU8v5\nDJnJMsbcC/wasKXR5yIiIiIi0kjGGBaHwywOh3n9nDmV+y/n8+wptzt958oV/vTkSY5ls9wSi7Gh\nqt1pXTxOR1tbA38DERG5FtcFmdPA8qrjpeX7ahhj1gEPAfdba69cz2tHPPjgg/T29gLQ3d3N+vXr\n2bZtGzBaydWxjpvheOS+ZjkfHet4MscjmuV8WvG4r6+P7du3A1T+Xom0iuq/YTJ1XkpcZweDvGrW\nLF5VNTz4arHI3nKRZncqxRf6+9mXTtMbibAxHq8p1MwKBqfwN2hees+6obiKTC3XQ33bgAN4g3n7\ngSeBB6y1+6qesxz4HvCO6nkyk3lt1XM1wE9ERFqKBvi5oZxAxDNcKrEvna7MpdmdSrEnlWJuMOjN\npKkq1CwKhTQ8WKSBlBP4l9MZMtbaIvA+4BHgJ8DD1tp9xpj3GmPeU37afwZmA39pjNltjHnyxV7r\n8nyl1tgr4zI1FFc3FFd3FFsR0eeAGy7jGgoEuC0e59cWLeKzq1axc8MGklu28O1163jL/PkMFYv8\n+alT3Pb00yx67DFet3cvHz1yhH86f57DmQytXtjUe9YNxVVkajmfIWOt/RawZsx9/6Pq9ruBd0/2\ntSIiIiIicv0CxrA6FmN1LMZb5s8HwFrLqVyusormb8+d43cOH2awUGB91SqajfE4N8VitAdc7wki\nIuIfTluWpouWJ4uISKvR8mQ3lBOITI2Lw8OVIs3uVIpnhoY4lctxa0dHTbvT2o4OohoeLPKyKCfw\nLxVkREREGkDJlxvKCUTcGSoU2Hv1as1cmgPpNCuj0Zq5NOvjcRLtTbOZq0jTU07gXyrIzHCfPHaM\n7165wspolBujUVZGo6yMRFgZjV5zyn5f1U5AMnUUVzcUV3cUWzeUfLmhnMANfQ64MRPimiuV+MnV\nq5VVNLtTKfamUiwMhdjQ2ekVacqFmgWh0LSd10yIbTNSXN1QTuBfKl3PcL+xaBF3JRIczmQ4nMnw\nlfPnOZzNciiTIWhMpUBTKdaUvxZN4x9MEREREWlN4UCAjZ2dbOzs5NcXLQKgaC0H0+nKKpr/dvIk\nu1MpooFApTgzsqKmJxLRDk8i4ltaIeNT1lou5POVQs3hbHb0dibDYLHIivJKmsrqmvJxTyRCSAPd\nREReFl0Nc0M5gUhzstZyIperaXd6ZmiITKnE+qp2p43xOKtjMdpUpBEfUU7gXyrIyIRShQJHyitp\nxhZtTudyLA6HKwWamtU1kQhx9QyLiFyTki83lBOItJbz5eHB1YWa/lyOtSNFmnKh5hUdHYR1QVBm\nKOUE/qWCjNRVr0c0XypxPJutWVUzUrg5ks3S2dZWU6CpLtjMCwZ9vyxVvbduKK7uKLZuKPlyQzmB\nG/occENxnViyUODZqlU0u1MpDmUyrI5Ga9qdbovH6axzIVCxdUNxdUM5gX9pKYNct2AgwI2xGDfG\nYuMes9bSPzxcs6rmm5cvV46HreWGqlao6naoZZGIlqeKiIiI+FyivZ2t3d1s7e6u3JcpFnm+PDx4\ndyrFl8+d4/mrV1kSDtdsw70hHmeeZiGKSIvQChmZVgP5/Lh5NSPH54eHWT5SrBlTtLkhEiHa1tbo\n0xcRmTJ+vBpmjLkf+AwQAL5grf3UmMd/HvgkUALywIestY+WH/sC8AbgnLV23Yv8DOUEIj5RKJU4\nkMnUtDvtHhqis729ZhvuDfE4y8Jh36/Slublx5xAPCrISNPIFoscHSnWjCnaHMtmmRsM1hRpqos2\ns6+xhbeISLPxW/JljAkAB4FXA2eAp4C3Wmv3Vz0nZq1Nl2+vBf7RWntz+XgLkAK+pIKMiNRjreVo\nNlvT7vTM0BAFa2vanTZ0drIqGiWgIo00Ab/lBDJKLUtS13T3iEba2ri5o4ObOzrGPVa0ltO5XM28\nmv998WKlYBMwpmYnqOqizeJwuKn+2Kr31g3F1R3FVqbIJuAFa+1xAGPMw8AbgUpBZqQYUxbHWykz\n8tguY0zPNJ2rjKHPATcU16lnjOGGaJQTTzzBf62KbX8uVynOfOXCBT569CgX8nnWdXSwsard6daO\nDu0m+iL0nhWZWirISEtoM4blkQjLIxHunTWr5jFrLZfGtEL9cGCAv+7v53A2y0ChUNnCe2zRpldb\neIuITJclwMmq41N4RZoaxpg3AX8MzANePz2nJiIz3aJwmEXhMD83Z07lviv5PHvKrU4/GBjg0ydP\ncjSb5aZYzFtJUy7U3BaP06HWeRFxQC1LMuNdLRY5MmZezchKm1O5HItCoQmHDK+MRutO7hcRebn8\ntjzZGPNm4D5r7XvKx28HNllr31/n+VuAj1trX1N1Xw/wdbUsiYgr6WKRvSPzaMoran6aTtMTiVRW\n0YwUatQyL1PFbzmBjNJ/bcqM19HWxtp4nLXx+LjH8qUSJ8qtUCNfjw8OVm53jGzhPcHOUPO1YRCp\nyQAAIABJREFUhbeIyPU4DSyvOl5avm9C5RalG4wxs621l6/nBz344IP09vYC0N3dzfr16ytL7Pv6\n+gB0rGMd6/hFjzcnEvT19fE24O6tW9mXTvN33/42j2cyfP3WW9mTShHdu5dV0SivfdWr2BCPk33m\nGeYGg9x7770NP38dN/dxX18f27dvB6j8vRJ/0goZqavP5z2i1lrOjmzhPWbI8KFMhtzYLbyrbi8P\nh2kPTNwK5fe4uqK4uqPYuuG3q2HGmDbgAN5Q337gSeABa+2+questNYeLt/eCHzNWrus6vFevBUy\na1/k5ygncECfA24oru64jm3JWg5nMjUraXanUgDjVtKsnEHDg/WedcNvOYGM0goZkTqMMZV+4y0T\nPJ4sFGqKND9OpfjHCxc4VN7Ce1kkMuGQ4WyxOO2/i4hIo1lri8aY9wGPMLrt9T5jzHu9h+1DwJuN\nMe8EhoEM8MsjrzfG/B2wDZhjjDmB1870xen+PUREAALGsCoWY1Usxi/Pnw94F/NOl4cH706l+Ltz\n5/iPhw9zpVBgfblIM1KouTkWI1jn4p2I+IdWyIg4kC0WOZbNTriy5lg2y+zyFt4T7Qw1u71drVAi\nPqCrYW4oJxCRZnMpn2d3eQXNyGqaE7kct8RiNTs8rYvHiWl4sC8pJ/AvFWREpln1Ft7V7VAj23kb\nqDtkeEmTbeEtIi+dki83lBOISCtIFQrsvXq10uq0O5VifzrNivLw4JFCzfp4nFkaHjzjKSfwLxVk\npC71iLrxYnGdaAvvkaLNoUyGgUKB3jGtUDdWbeEd9vHSV71f3VFs3VDy5YZyAjf0OeCG4upOK8Z2\nuFTiJ1ev1sykeTaVYn4oNG4uzaJwuCHn2IpxbQXKCfxLM2REmogxhrmhEHNDIe7o6hr3+NgtvPen\n0/yfS5c4nMlwMpdjYSg04ZDhldEoCW3hLSIiItK0QoEAGzo72dDZybsWLQK8ldUvpNOVVTR/dvIk\nu1MpgsbUtDtt7OxkRSSitneRFqMVMiIzxERbeFevtIm1tU04ZHhlNMrCUEh/wEWmma6GuaGcQERm\nOmstJ3O5mnanZ4aGSBWLrK9aRbMhHuemWKzuzp/SPJQT+JcKMiI+YK3l3PBwpfVpbNEmXSzWXVnT\n8yJbeIvIS6fkyw3lBCLiVxeGh2vanXanUpzO5XhFR0dNu9Pajg4iGh7cVJQT+JcKMlKXekTdaMa4\nJguFmlao6qLN2eFhlobDlVk11UWbG6JROprkD3ozxnWmUGzdUPLlhnICN/Q54Ibi6o5i6xksFHi2\nahXN7lSKg5kMq6LRccODuybR3q64uqGcwL80VEJESLS3V3qWx8qVSt4W3lVFmh9cucLhbJZj2Szd\n7e2VAs3Yos2cYFCtUCIiIiIN0tXezj3d3dzT3V25L1ss8pN0ulKgefj8efamUiwOhyutTiPFmvmh\nUAPPXmTm0woZEXnJStVbeI8p2hzKZLAwblXNSNFmSThMm4o14mO6GuaGcgIRketXKJU4mMmMa3mK\nBQI1K2k2dnayPBzWBbcpppzAv1SQEREnrLVcLhTqDhm+lM97W3iPWVVzYzTKimjU11t4iz8o+XJD\nOYGIyNSw1nIsm60ZHLw7lSJbKtUUaTbE46yOxXSh7WVQTuBfKshIXeoRdUNx9aRHtvAes7LmcDbL\niWyWBSNbeE9QtOkOBsf9e4qrO4qtG0q+3FBO4IY+B9xQXN1RbN3o6+vj5rvuYvfQEM+UCzW7h4Y4\nOzzMuqpWpw3xOLd2dOgC2yQpJ/AvzZARkYaItbXxinicV8Tj4x4rlEqcrGqFOpTJ8NT585WiTSQQ\nGFekGUylWJ3LsUhbeIuIiIg4syAU4v45c7h/zpzKfclCgT3l4swPBwb4zKlTHM5kWB2N1rQ73dbR\nQXwSw4NF/EIrZESkpVhrOZ/Pj5tXM7LS5mqxyIpy69PYok1PJEJQV2qkSehqmBvKCUREmkOmWOS5\nq1crq2h2p1I8f/UqS8PhmnanDfE4c30+PFg5gX+pICMiM8rgyBbeEwwZ7h8eZkk4XDOvZqRgc0Mk\nois2Mq2UfLmhnEBEpHkVSiX2p9M17U67Uylvx88xc2mW+mh4sHIC/1JBRupS760biqsbk4nr8Ngt\nvKtuH8lmSbS11ayqubFqhs1cH2/hrfesG0q+3FBO4IY+B9xQXN1RbN1wEdeStRzNZsfNpSnCuG24\nb4xGCczAfEw5gX/pcrCI+EYoEGB1LMbqWGzcYyVrOZPL1RRpvn7xYuW4YO2EQ4ZvjEZZqi28RURE\nRF6SgDGVvOqX5s+v3N+fy3kFmqEh/vHCBX7/6FEu5fOs6+iomUtzSyymlnRpWVohIyIyCVfKc2sO\nTdAOdTGfp6e6UFN1+4ZIhEhbW6NPX5qQroa5oZxARGTmupzPe8ODq9qdjmaz3ByL1bQ8rYvH6Wih\n/Es5gX+pICMi8jJlikWOlos0h8a0Qx3PZpkfCk24fffKaJRZE2zhLf6g5MsN5QQiIv6SLhbZWy7S\njKyo+Wk6TW8kUtPutCEeb9q8SzmBf6kgI3Wp99YNxdWNZo1r0VpOZrOVAs2hqpU1h7NZguVlujdO\n0A61KBRqij7pZo1tq1Py5YZyAjf0OeCG4uqOYutGq8Q1Xyrx03S6Zi7Ns6kUs9vb2djZyYMLF/Lz\nc+c2+jQrlBP4l2bIiIg41GYMvdEovdEor541q+Yxay0Xqrfwzmb5wcAAn+/v53Amw1B5C++Jhgz3\nRCKE1C8tIiIiMk4wEOC2eJzb4nEeLN9XspZDmQy7y4UZkWagFTIiIk1qqFDgyJh5NSMrbU7nciwO\nhyccMrxSW3i3BF0Nc0M5gYiItBrlBP6lgoyISAsaLpU4PlKsGVO0OZLN0lm9hfeYVTbzfLyFdzNR\n8uWGcgIREWk1ygn8SwUZqatVekRbjeLqhuI6qmQt/cPDE66sOZzJMGxt3SHDy8Jh2se0Qim2bij5\nckM5gRv6HHBDcXVHsXVDcXVDOYF/aU27iMgMEzCGJeEwS8JhtnZ3j3t8IJ/ncDZbGTD85OAgf3/u\nHIezWc4PD49u4V3+nk4mmXf1KjdEIkRbaAtJEREREZFm5nyFjDHmfuAzQAD4grX2U2MeXwN8EdgI\nfNRa+2dVjx0DkkAJyFtrN9X5GboaJiIyBbJVW3hXF21GtvCeEwyOzqoZ0w41u0m3kmxWuhrmhnIC\nERFpNcoJ/MtpQcYYEwAOAq8GzgBPAW+11u6ves5coAd4E3BlTEHmCPAz1tor1/g5Sr5ERBwrWsup\nXG5cK9RI0abNmHHzakaKNovD4abYwruZKPlyQzmBiIi0GuUE/uW6ZWkT8IK19jiAMeZh4I1ApSBj\nrb0IXDTGvGGC1xu8lTXSAOoRdUNxdUNxdWcktm3G0BOJ0BOJ8KoJtvC+OLKFd3mFzQ8HBvjr/n4O\nZ7MMFAqjW3hHIqPFmmiUXm3hLdL09BnrhuLqjmLrhuIqMrVcF2SWACerjk/hFWkmywLfMcYUgYes\ntZ+fypMTEZGpYYxhXijEvFCIzYnEuMdTY7bw/kk6zb9eusThTIZTuRyLQqEJt+9eGY3SqS28RURE\nRGQGct2y9GbgPmvte8rHbwc2WWvfP8FzPw4MjWlZWmSt7TfGzAO+A7zPWrtrgtdqebKISIvKj2zh\nPWb77sPZLEcyGTpGtvCeoB1qfgtv4a3lyW4oJxARkVajnMC/XF92PA0srzpeWr5vUqy1/eXvF4wx\nX8VbXTOuIAPw4IMP0tvbC0B3dzfr16+vLKfr6+sD0LGOdaxjHTfx8Y2xGH19fdxS9fgPfvADLmWz\nLLj1Vg5nMnyvr49duRyptWs5nMlw9ZlnWBwKcduWLayMRik+8wyLw2He/JrXsCwcZteOHU3z+/X1\n9bF9+3aAyt8rkelUzBS58t0rJLYkCM7SEG4REZFGc71Cpg04gDfUtx94EnjAWrtvgud+HEhZaz9d\nPo4BAWttyhjTATwCfMJa+8gEr9XVMAf61CPqhOLqhuLqTjPHdmQL77Eraw5nMpwbHmZ5JFJZWfOJ\n3l7mhkKNPuUKXQ1zQzlBfdkTWfa/az9DTwwRWREhcU+C7q3dJO5JEF4cftHXNvPnQCtTXN1RbN1Q\nXN1QTuBfTlfIWGuLxpj34RVTRra93meMea/3sH3IGLMAeBroBErGmA8AtwDzgK8aY2z5PL88UTFG\nRET8qzsY5GeCQX6ms3PcY2O38I62tTXgDEWaR2R5hPXfXU8pXyK1O0VyZ5Jzf3eOg791kPbudq9A\nc083ia0JojdGW7YdUEREpFU4XSEzXXQ1TEREWo2uhrmhnOD62ZIlvS/NwM4BkjuTJHckKeVLXnHm\nngSJrQnia+OYNr1dRURcUE7gXyrIiIiINICSLzeUE7x81lqyx7NecWZnkoEdAwyfHSZxV8Ir0NyT\noOv2LgJhbVcvIjIVlBP4l/6SSl0jwyhlaimubiiu7ii2Iv5ijCHaG2XhOxay5qE13LH/DrJfzLLo\nNxaRP5/n0AcPsWvOLnb/7G6OfOwIl799mcJQodGn3ZL0+eqOYuuG4ioytVzvsiQNdvUnVylmisRW\nx2jv0v/cIiLSOMaY+4HPMDpX7lNjHv954JNACcgDH7LWPjqZ14pbwVlB5m2bx7xfnAdAYbDA4I8G\nGdg5wPE/Os7Qj4eI3RQbbXO6J0FoXvMM0RYREWlGalma4U7/5Wn6P99P+oU0bfE2YmtixFbHiK6J\net9XR4neECUQ0mIpEZHp5LflycaYAHAQb+fFM8BTwFuttfurnhOz1qbLt9cC/2itvXkyr636N5QT\nNEApV2LwqcFKm1PysSThReFKcSZxT4JIT0SDgkVEJuC3nEBGqSDjE9Zahs8Mkz6QJn0wTeZAxvt+\nMEP2ZJbIsgjR1VFia2KV77HVMUKLQ0qeREQc8FvyZYzZDHzcWvu68vFH8HZcnHClizHmTuB/Wmtv\nvZ7XKidoDrZoSe1Njc6h2TlAIBSoFGe67+kmdnMME/DN/wVEROryW04go9TD4hPGGMJLwoSXhJn1\nqlk1j5WGS2SOZCpFmtSPU5z/u/Psen4Xtw3fVllJE1sdGy3YrI7RntDb56Xo6+tj27ZtjT6NGUdx\ndUexlSmyBDhZdXwK2DT2ScaYNwF/DMwDXn89rxV3rvdzwLQZOjd00rmhk6XvX4q1lsyhDMkdXnHm\n5H87SSFZILElUWlzim+IEwj6a8WuPl/dUWzdUFxFppb+i1oIhAJ03NRBx00dNfcn+5LcteGuykqa\n9ME0F79+sXK7Ld42rkgTXaMWKBEReemstf8C/IsxZgvwh8BrGnxKMgWMMcRWxYitirHo1xcBkDud\nq2y1ffZvzpI9lqXrjq7RnZzu6KIt1tbgMxcREXFHBRmpa6T63XV7F123d9U8VmmBOpgmfcAr2Az0\nDYxvgRozrya8JOz7FihdVXBDcXVHsZUpchpYXnW8tHzfhKy1u4wxNxhjZl/vax988EF6e3sB6O7u\nZv369ZX38cgOITpu/HF4SZh9C/fBv4dt/9828lfyfPOvvknq2RQ3/9vNpPam2N+zn47bOrjvV+4j\ncXeCR599tGnOfyqOR+5rlvOZScfbtm1rqvOZSccjmuV8WvG4r6+P7du3A1T+Xok/aYaMTLlKC1R5\nJc1IK1T6QJpiqkhsVW2RZmRejVqgRMRP/NYvboxpAw7gDebtB54EHrDW7qt6zkpr7eHy7Y3A16y1\nyybz2qp/QznBDFFMFxl8whsUPLBjgKEnhoisiJDYOtrmFF4cbvRpioi8bH7LCWSUCjJSV5+DHtFC\nskD6hdqhwiODhts62mran0ZuR2+IEgjPnBYoF3EVxdUlxdYNPyZf5a2r/5zRrav/xBjzXrwBvQ8Z\nY/4T8E5gGMgAv2et/VG919b5GcoJHGiGz4FSvkRqd6oyJDi5K0l7ot0bErzVK9BEb4y21ErcZojr\nTKXYuqG4uuHHnEA8WpIg06o90U7XK7voeuUELVD9w5X2p/TBNAM7yi1QJ7KEl4bHraiJrokSXhzW\nDg0iIi3CWvstYM2Y+/5H1e0/Bf50sq8VfwkEA3Rt6qJrUxfLfncZtmRJ70szsHOAK9+7wrE/OEZp\nuOStntnqzaGJr41j2pQniIhIc9IKGWl6pXyJ7JHsuBU1mQMZCoMFoquqijRVW3cHu4ONPnURkbp0\nNcwN5QT+lj2eZWDHQGW77Vx/jsRdicoqms5Xds6oVbciMjMoJ/AvFWSkpRUGC2ReqCrSlAs2mYMZ\nAtHAuB2gYqtjRFfOrBYoEWlNSr7cUE4g1YYvDJPclaxst53en6bzZzorBZquO7to79SCcRFpLOUE\n/qWCjNTVyj2i1lqGz9a2QI3MrckezxJeEh63oia2JubtAuW4BaqV49rMFFd3FFs3lHy5oZzAjZny\nOVAYKjD4o6pBwT8eInZTrDIkOHFPgtC80LSdz0yJazNSbN1QXN1QTuBfuiQgM5IxhvCiMOFFYWZt\nm1XzWClfIns0WynSpJ5NceErF0gfSFNIFojeGJ1wuHBwllqgREREWll7ZzuzXzub2a+dDUApV2Lo\n6SEGdg7Q/4V+Dvz6AUILQ5XiTOKeBJGeSEsNChYRkdahFTIiVQpDoy1QNfNqDmYIRALjijSx1TGi\nN6oFSkSun66GuaGcQF4OW7SknktVZtAkdyYx7aYyJLj7nm5iN8e0oYCITCnlBP6lgozIJIy0QI20\nP1UXbLLHs4QXhyecVxNeql2gRGRiSr7cUE4gU8laS+ZwhuSOZGW77cJAgcTdo1ttxzfECQR1YUZE\nXjrlBP6lgozUpR7RySnlS2SPZSecV1MYKLdAVRVpnk49zX0P3KcWqCmm96s7iq0bSr7cUE7ghj4H\nRuXO5CrFmeTOJNmjWTo3dVa22+66o4u2WNuk/i3F1R3F1g3F1Q3lBP6lGTIiL1MgGCC2KkZsVWzc\nY5UWqHKR5vK3L3P66dM8/pHHCYQD44YKx1bHiKyM0BaZXCInIiIi0yu8OMz8t8xn/lvmA5C/kmfw\nsUEGdgxw9P8+Smpvivja+Ogcmi0JXYQREZEJaYWMSANYaxk+N1yzTXf6QLqmBWqieTXhZWqBEpkp\ndDXMDeUE0mjFdJHBJwcrbU6DTwwS6Y1UCjTd93QTXhJu9GmKSBNRTuBfKsiINJlSwdsFaty8moNp\nCpfH7wI10goVnK2rbyKtRMmXG8oJpNmU8iVSe1KVrbaTu5K0d7WT2JqobLcdXRXVTk4iPqacwL9U\nkJG61CPqxsuJayE1Zheoqnk1JmgqbU/VrVDRG6O+aIHS+9UdxdYNJV9uKCdwQ58DU8eWLOn9aZI7\nkzzylUdYc3ANpeESiS1Vg4LXxTFt+nh4OfSedUNxdUM5gX9phoxIC2mPt9O5oZPODZ0191tryZ/P\n12zTffZvzpI5mCFzNEN4UXjcipro6iiR5RG1QImIiEwjEzB03NJBxy0d9K7p5c5td5I9nq0MCT7z\nuTPk+nMk7izPoNmaoOv2LgJh7eQkIjLTaIWMyAxXKni7QI1dUZM+UG6BWhmtKdKMrLIJzlELlIhL\nuhrmhnICmQmGLwyT3OXNoEnuTHJ131U6N3Z6M2i2dtN1ZxftXbquKjJTKCfwLxVkRHyskCqQOZSp\nFGmqhwubdlMzq6amBSo681ugRFxT8uWGcgKZiQpDBQZ/NFjZbnvo6SFia2KVIcGJexKE5ocafZoi\n8hIpJ/AvFWSkLvWIutEKcbXWkr+Qr5lVM3I7cyRDaGFo3A5Q0TVRIssiDet5b4W4tirF1g0lX24o\nJ3BDnwNuvNS4lnIlhp4eqrQ5JR9NEloYqhRnElsTRHoivh4UrPesG4qrG8oJ/EtrHUVkHGMMofkh\nQvO95K5aqVAidzxXKdKk96W5+LWLZA5kyF/ME1kZGR0uXNUKFZqrK3ciIiJTIRAOkLg7QeLuBHwE\nbNGSes7byenSNy5x5MNHMO2mstV24p4EHbd0aG6ciEiT0QoZEZkyxatFMocyo8OFq+bVmDYzbgeo\n2JqYWqDEt3Q1zA3lBCLeStfMoUxlBs3AzgEKVwoktiQqbU7xjXECQQ0KFmkGygn8SwUZEXGu0gJV\nNaem8v1IhtCCUE2RpmYXKG37KTOUki83lBOITCx3JlcpziR3JskezdK5qbPS5tS1uYu2mC6QiDSC\ncgL/UkFG6lKPqBuKa61SoUTuRK6mSDNSuMlfKLdATTCvJjgnWNMbr7i6o9i6oeTLDeUEbuhzwI1G\nxjV/JU/y0dGdnFLPpuhY20H31vIcmrsTBGe37o6Les+6obi6oZzAvzRDRkQaKtAeIHpDlOgNUXhd\n7WOVFqhy29PA9wc481dnvBaoQO0uUFcKV0jNTnktULrCJyIi8qKCs4LMfcNc5r5hLgDFdJHBJ7yd\nnE5/9jT7fmUfkZ5IZUhw9z3dhJeEG3zWIiIzi1bIiEjLsdaSv1i7C9TIvJrskSzB+cHx82pWx7wd\nJ9QCJU1CV8PcUE4gMjVK+RKpPSmSO8ptTruStHe1ezNoyqtooquivt7JSWSqKCfwLxVkRGRGsUVL\n9nh2/Lyag2ny5/NEboiM2wEqtiZGcG5QSaVMKyVfbignEHHDlizp/WkGdgxU2pxKwyUSW0YLNPF1\ncV34EHkJlBP4lwoyUpd6RN1QXN2YTFyL6dEWqOodoDIHMgDjdoCKrY4RXaUWKL1n3VDy5YZyAjf0\nOeBGq8c1ezzrrZ7Z4RVocv05EncmKm1OXbd3EQg3ZienVo9ts1Jc3VBO4F+aISMivtEWayO+Lk58\nXbzmfmst+Uv5SpEmczDD+b8/77VAHc4SnBestD3VtED1qgVKRET8K9ITYWHPQha+fSEAwxeGSe5K\nktyR5NAHD5Hen6ZzY2elzanrzi7au/SfHyIiI7RCRkTkRdiiJXsiW2l7qt4NKn8+T2RFpHZFTbkV\nKjhPLVDy4nQ1zA3lBCLNozBUYPBH3qDggR0DDP14iNiamFegKW+3HZofavRpijSccgL/UkFGROQl\nKma8Fqia7boPeLdtyY4WaapboVbFaOvwdwuUeJR8uaGcQKR5lXIlhp4eGm1zeixJaGGoUpxJbE14\nA/h1QUN8RjmBf6kgI3WpR9QNxdWNZotr/lJ+tEhTNVw4cyhDcG5w3A5QsTUxwj1hAu2N6bV/Mc0W\n25lCyZcbygnc0OeAG36Pqy1aUs+lKkOCB3YMEAgGvOJM+avjlg5M4Po/Kv0eW1cUVzeUE/iXmjhF\nRBwIzgmSuCtB4q5Ezf22aMmezNYUaS5/87K3C9S5qhaoMfNqgvPVAiUiIjOLaTN0ru+kc30nS397\nKdZaMocylQLNyU+fpHClQGJLotLmFN8YJxBsvosXIiIvhVbIiIg0iZoWqDHzamzRjivSRNeoBaqV\n6WqYG8oJRGaW3Jmct3pmp7fddvZIls47OittTl2bu3y/G6K0PuUE/uW8IGOMuR/4DBAAvmCt/dSY\nx9cAXwQ2Ah+11v7ZZF9b9TwlXyIyo+Uv5WuLNOV5NZlDGdrntE84rybSG2nKFijxKPlyQzmByMyW\nv5In+Wiysoom9WyK+Lr4aJvT3QmCs4ONPk2R66KcwL+cFmSMMQHgIPBq4AzwFPBWa+3+qufMBXqA\nNwFXRgoyk3lt1b+h5MsB9Yi6obi64de42pIldzJXO1S4PLcm158juiI64bya62mB8mtsXVPy5YZy\nAjf0OeCG4vryFdNFBp8YrBRoBp8YJNITYf/K/bz2ra+l+55uwkvCjT7NGUPvWTeUE/iX6xkym4AX\nrLXHAYwxDwNvBCpFFWvtReCiMeYN1/taERG/MwFDpCdCpCfC7NfOrnmsmCmSOTy6C9Tgo4Oc/eJZ\nrwUqb2u26a4UbVZFaY9rvJiIiLSGtlgbs+6dxax7ZwFQypdI7Ulx9K+Pcv7h87zwvhdo72onsXV0\nq+3oqqjmsolIU3C9QubNwH3W2veUj98ObLLWvn+C534cGKpaIXM9r9XVMBGR65C/lCf9Qu2KmvSB\ntNcCNat9dEVNVStUZIVaoKaSroa5oZxARKrZkiW9P83AjoHKKprScInElgTdW70CTXxdHNOmj2Np\nHOUE/qXLoCIiPhScEyQxJ0Fi85hdoEZaoKqKNJe/fdlrgTqTI9IbmXC4cGhBSFcbRUSk6ZiAoeOW\nDjpu6WDJby4BIHs86w0J3pHkzOfOkOvPkbizPINma4Ku27sIhHUBQkTcc12QOQ0srzpeWr5vyl/7\n4IMP0tvbC0B3dzfr16+v9Df29fUB6Pg6j0fua5bzmSnHn/nMZ/T+dHA8cl+znE+rHv9wxw+949ds\ng9d4j+/Zs4cPfuuDFLNFHvn7R+g/1c+G4AYGfzTINz77DXIncqy364mujvJ89/NElka49757ia2J\n8eTZJ2mLtTXN79fI476+PrZv3w5Q+XvlN5MY9P824MPlwyHgt6y1e8uPfQD4jfJjn7fWfnZ6zlpA\ncyNcUVzdebHYRnoiLOxZyMK3LwRg+MIwyV1JkjuSHPrgIdL703Ru7PS22t7aTdedXbR36To26D0r\nMtVctyy1AQfwBvP2A08CD1hr903w3I8DKWvtp1/Ca7U82QF94LqhuLqhuLozmdjmL+fJvJAZP1z4\nhQzt3bUtUCMrayIrIgSC/r0C6bflyZMc9L8Z2GetTZaLN39grd1sjLkV+HvgdqAA/Bvwm9baIxP8\nHOUEDugz1g3F1Z2XE9vCUIHBH3mDggd2DDD04yFia2JegaY8hyY0PzS1J9wi9J51w285gYyarm2v\n/5zRq2F/Yox5L2CttQ8ZYxYATwOdQAlIAbdYa1MTvbbOz1DyJSLShGzJkjtV2wI18j13JkekJzJu\nB6jo6iihhTO/BcpvyVe52PJxa+3ryscfwcsFPlXn+d3Ac9baZcaYX8KbK/fu8mMfA7LW2v9ngtcp\nJxCRKVXKlRh6eqjS5pR8LEloYahSnElsTRDpicz4v1vijt9yAhnlvCAzHZR8iYi0nmIc1v8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "original_delta=model.calibration_dict['delta'] \n", "\n", "drs = []\n", "delta_values = np.linspace(0.01, 0.04,5)\n", "for val in delta_values:\n", " model.set_calibration(delta=val) # Change calibration\n", " drs.append(approximate_controls(model, order=1)) # appending another model object to the list\n", "\n", " \n", "plt.figure(figsize=(17, 7))\n", "\n", "# Plot investment decision rules\n", "plt.subplot(121)\n", "for i,dr in enumerate(drs):\n", " plot_decision_rule(model, dr, 'k', 'i',\n", " label='$\\delta={}$'.format(delta_values[i]), \n", " bounds=spl_bounds)\n", "plt.ylabel('i')\n", "plt.title('Investment')\n", "plt.grid()\n", "\n", "# Plot labor decision rules\n", "plt.subplot(122)\n", "for i,dr in enumerate(drs):\n", " plot_decision_rule(model, dr, 'k', 'n',\n", " label='$\\delta={}$'.format(delta_values[i]), \n", " bounds=spl_bounds)\n", "plt.ylabel('n')\n", "plt.title('Labour')\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", "plt.grid()\n", "plt.show()\n", "\n", "# Reset model back to the original calibration\n", "model.set_calibration(delta=original_delta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulations/Impulse response functions\n", "\n", "Now we turn to simulating the model. From now on we'll just deal with the linear and spline approximations. \n", "\n", "First, reload the model with the original calibration." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model type detected as 'dtcscc'\n" ] } ], "source": [ "# Reload the model with the original calibration\n", "model = yaml_import(filename)\n", "dr_pert = approximate_controls(model, order=1)\n", "dr_global_spl = time_iteration(model, pert_order=1, verbose=False, interp_type=\"spline\", interp_orders=[3,3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we start the simulation at the model steady state, and then get the model shocks. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['z', 'k']=[ 1. 9.35497829]\n" ] } ], "source": [ "s0 = model.calibration['states']\n", "sigma2_ez = model.covariances\n", "\n", "print(str(model.symbols['states'])+'='+str(s0)) # Print the steady state values of each state variable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can simulate functions easily using the ``simulate`` function. This is very similar to Dynare's ``stoch_simul`` command: it both simulates and creates IRFs. It's inputs are:\n", "* ``model``: our RBC model object\n", "* ``dr_``: the solved decision rule of choice\n", "* ``s1``: the position from where all simulations start, in this case from the date of the shock\n", "* ``n_exp``: the number of simulations. Set to 0 to produce IRFs\n", "* ``horizon``: the number of simulated periods\n", "\n", "The ``simulate`` command then returns a Pandas table of size $horizon×numvar$." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s1 = s0.copy() # Copy steady states \n", "s1[0] *= 1.05 # Set size of shock to 5% larger than steady state value\n", "\n", "irf_glob = simulate(model, dr_global_spl, s1, n_exp=0, horizon=40 ) # Simulate spline model \n", "irf_pert = simulate(model, dr_pert, s1, n_exp=0, horizon=40 ) # Simulate linear model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's plot the impulse responses to a 5% shock to productivity: " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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add8cH3eASgg5O4SdP+TsEHb+kLND+PkzyFRgbzNrbWa1iCZTGVt8BzPbk6iR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oY1YWZ0vo76uYupNvBCWo64Pm5ZjDGmMrPSzdKJd/9ojFN+/BEef5zwkmWsXg1fhLrwXqMbCTaq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\n9ENa73OdcH6YzUs2s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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15, 7))\n", "\n", "plt.subplot(221)\n", "plt.plot(irf_glob['z'],linewidth=2, alpha=0.75,color='b')\n", "plt.title('Productivity')\n", "plt.grid()\n", "\n", "plt.subplot(222)\n", "plt.plot(irf_glob['i'],linewidth=2, alpha=0.75,color='b',label='Spline')\n", "plt.plot(irf_pert['i'],linewidth=2, alpha=0.75,color='r',label='Linear')\n", "plt.title('Investment')\n", "plt.legend()\n", "plt.grid()\n", "\n", "plt.subplot(223)\n", "plt.plot(irf_glob['n'],linewidth=2, alpha=0.75,color='b',label='Spline')\n", "plt.plot(irf_pert['n'],linewidth=2, alpha=0.75,color='r',label='Linear')\n", "plt.title('Labour')\n", "plt.legend()\n", "plt.grid()\n", "\n", "plt.subplot(224)\n", "plt.plot(irf_glob['c'],linewidth=2, alpha=0.75,color='b',label='Spline')\n", "plt.plot(irf_pert['c'],linewidth=2, alpha=0.75,color='r',label='Linear')\n", "plt.title('Consumption')\n", "plt.legend()\n", "plt.grid()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Now let's try again with a 20% shock. We can see how much different the spline and linear approximations are in the face of large shocks: " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GVImNoYux+vXh+uu96SefhLVrg81jjDHGxIWUFOT222jbOZ0eOz4i\n7f2pvPhi0KGMMSYxWUG3D8ccAyeeCPn5XtfL0r4kdb2vrsv5Xc4Obud3OTu4nd/l7KYWad2aOjde\nT7sD4OzVjzDlkeUsW/br1Vw+X13ODm7ndzk7uJ3f5ezgfv7KsIIuCiNHQqNGsGABTJ0adBpjjDEm\nTpx4Ig3PO5lWTfO58Pu7GHNnPvn5QYcyxpjEYmPoojRjBtx1F6Snw8SJ0Lx50ImMMcZNNoauYuK9\nfWTXLkKXjmDpjFV8sN9ZFI4cxahRQYcyxhg32Ri6apSdDcceCzt3woMPlt710hhjjEk49eqRfOft\ntOuQQp9Nb/L9pNl89lnQoYwxJnFYQRclEbjuOthvP5g7F95/f88y1/vqupzf5ezgdn6Xs4Pb+V3O\nbmqpgw4ifdQIWreCwSv/zvi717N5s7fI5fPV5ezgdn6Xs4Pb+V3ODu7nrwwr6CqgaVO46ipvetw4\nihsrY4wxJuH97nc0O70XLept44yv/srfx4StN4sxxtQAG0NXQarwf/8Hn38Oxx8Pd9wRdCJjjHFL\nbRpDJyIDgLF4X5BOUNX7Siw/E7gbCAMFwHWq+kk020bsw4n2EYC8PPJ/fynfztnEO82GcfDfhjJw\nYNChjDHGHTaGrgaIwA03QN268NFHMGtW0ImMMcYEQUSSgEeAk4EuwBARObjEau+r6hGq2h24FHiq\nAtu6JyODtDtv4YADhP4/TeK9v39Fbm7QoYwxpnazgq4SWraEyy7zpseOhXfemRlonqpyua+xy9nB\n7fwuZwe387ucvZbpCSxT1eWqWgBMBs6KXEFVd0bM1se7UhfVts7q0YOMK4aQ2UQZ9N1fuerid8jL\nCzpU5bj+Z83l/C5nB7fzu5wd3M9fGVbQVdJZZ0HXrt44utdfDzqNMcaYALQBVkbMr/Lf24uIDBSR\nb4ApwCUV2dZZF19M6xMOplXKenosnsStNxbyyy9BhzLGmNrJxtBVwapVcOmlsHs33HQTnHxy0ImM\nMSb+1ZYxdCJyLnCyqo7w538P9FTVa8pYvw8wWlVPqsi2IqJDhw4lKysLgIyMDLp160Z2djaw59vo\nuJvv3Jndf7yS/3z6I0vSupM0dAL33JvExx/HST6bt3mbt/k4mM/JySHP78aQm5vLpEmTKtxGWkFX\nRW+/DQ88ACkp8NBDcOihQScyxpj4VosKut7AHao6wJ+/CdCybm7ir/M9cBTQKdptXW0fAVi6lF1X\nXMd3C3fyUcZZ7Bx+LX/+P0Gc/79vjDHVw26KEoDTT4cuXWZSWAi33QYbNgSdqOKKvi1wkcvZwe38\nLmcHt/O7nL2WmQ90FJF2IpIKDAbeilxBRA6MmP4NkKqqm6LZtlbo1Im5Q86mfec69N30JjrxGZ55\nJuhQ0XP9z5rL+V3ODm7ndzk7uJ+/Mqygi4Gzz4Zu3WDTJq+oy88POpExxpjqpqohYCQwDVgMTFbV\nb0TkMhEZ4a92rogsEpEvgXHA+eVtW+MfoiZ07Ej9+24nq73Qf92z/PjAa0yZEnQoY4ypPazLZYxs\n2QKXXw4//QQnnQQ334x1KTHGmFLUli6XNcX19rHYu++y4ca/s3Il/KfdrZz96G859tigQxljTHyx\nLpcBatQI/vpX7/l006fDiy8GncgYY4yJI6ecQuatl9OyJQxZPoYXr5/L4sVBhzLGGPdZQRcDRX11\nO3SAW27x3nviCZg7N7hMFeFyX2OXs4Pb+V3ODm7ndzm7STx7na+DBtFy1BCaNQ3x+2Wj+ddVi1ix\nIrBo++T6nzWX87ucHdzO73J2cD9/ZVhBF2N9+8KwYaAKd98NK1fucxNjjDEmYciI4ew//FQyG+Rz\nwaKb+ceVP7BxY9CpjDHGXTaGrhqowh13wKxZsP/+8K9/Qf36Qacyxpj4YGPoKqY2tY/FQiEKb7uT\nHyZ9zNrdTXnrpHHc9lgrMjKCDmaMMcGyMXRxQsR70HiHDt7Dx+++G8LhoFMZY4wxcSI5mZQ7/sIB\nA7vTLHkjp35wA3+5dC1r1gQdzBhj3LPPgk5EJojIOhH5qpx1HhaRZSKSIyLdI97PFZGFIrJARObF\nKnS8Ka2vbr16cM890LAhzJvnjamLVy73NXY5O7id3+Xs4HZ+l7ObxFPm+ZqaSt3776HDgM60SV7L\noFlXMuaixSxZUqPxyuX6nzWX87ucHdzO73J2cD9/ZURzhW4icHJZC0XkFOBAVT0IuAz4V8TiMJCt\nqt1VtWeVkjqoVSu4805ITvbuevnOO0EnMsYYY+JIejp1H3uQAwf3pFW9PC5acB1PXzTDmZuKGWNM\nPIhqDJ2ItAOmqGrXUpY9DsxQ1Rf9+W/wirh1IvIjcKSq7nO4c60cI+B76y345z+9rph/+hOcemrQ\niYwxJjg2hq5ianP7WCwUIjR2HKsefZNNm+G91pfS9b4LOe10O02MMYklqDF0bYDIezmu9t8DUGC6\niMwXkeExOJaTzjwTRozwbpZy//1egWeMMcYYX3IyyddfywFjrqRFS2HAmgmsvuY+nnmqkNpeyxpj\nTFWlVPP+j1XVtSLSDK+w+0ZVZ5e18rBhw8jKygIgIyODbt26kZ2dDezpDxuP85F9dctav1WrmRx/\nPHz0UTb//Cfk5MzkuOPcyR+v8yU/Q9B5Eil/Tk4Oo0aNips8iZR/7Nixzvz9WJQ3Jyen+O93k1hm\nzpxZfC6USwQ5/zxat2lNytV3w4//5bs71/HQ2rsYeXMDUqr7XyyliDp7nHI5v8vZwe38LmcH9/NX\nRnV0uVwCHK+q60qsNxrYpqoPlnEMZ7uUVOTEef11ePhhb/rKK+G886ovV7RcPvFdzg5u53c5O7id\n3+XsYF0uK8rl9hEqeb4uXcrGy25h1cKNrEvdn3nn3McND7SmXr1qiVgm1/+suZzf5ezgdn6Xs4P7\n+SvTRkZb0GXhFXSHl7LsVOAqVT1NRHoDY1W1t4ikA0mqul1E9gOmAXeq6rQyjuF0g1URU6bAg35Z\nO3w4XHBBsHmMMaYmWUFXMYnUPu5l/XryrriZlbN+YKs2ZHr2X7l6/GE0bRp0MGOMqT7VUtCJyPNA\nNtAUWAeMBlIBVdUn/HUeAQYAO4CLVfVLEWkPvI43ji4F+I+qjinnOAnVYL37rjeeThUuvhj+8Ieg\nExljTM2wgq5iEq193MvOnWz9812semUuOwtSeK/zKE74x6kcc6ydPsaY2qlaboqiqheoamtVTVPV\nA1R1oqqOLyrm/HVGqmpHVT1CVb/03/tRVbv5jyw4vLxiznVF40Uq4pRTvIePi8DEifD00wQ28Lsy\n+eOFy9nB7fwuZwe387uc3SSeKp2v6ek0fPivZF03kIz9CjltyT/IHXIzj921gV27YhaxTK7/WXM5\nv8vZwe38LmcH9/NXRizucmkqqX9/uPVWSEqC556DJ58Mrqgzxhhj4lJyMuk3XsOBE26hRYf6HLp9\nLt0evpiHTpvG4kXWaBpjTFRj6GpCIncpmTkT7rkHQiHvJilXXOFduTPGmNrIulxWTCK3j7+ycSN5\nt97PT2/OZdcvsLjRMXD9DQy+skkgd8E0xphYq7abotSERG+wZs+GO++EwkLo3RtuuQUaNAg6lTHG\nxJ4VdBWT6O3jr6hSOPW/rL55HJtW7WRncgPm9b6W3z12Am0PsNPKGOO2oB4snvBi0Ve3Tx/429+8\nIm7OHLjsMvj++6pni4bLfY1dzg5u53c5O7id3+XsJvHE/HwVIeX0AbT7YCJtzjySjKRtZH9yD7P6\njWbqf/JiOnTB9T9rLud3OTu4nd/l7OB+/sqwgi6OHHkkjB8PBx0Ea9fCVVfB9OlBpzLGGGPiUPPm\nNH/m7xz42A00bF6PQzd9TKNrh/H44JmsWG5XNI0xicO6XMah/HwYOxbee8+bHzjQK+5sfIAxpjaw\nLpcVY+1jFNatY9XV97HpwwWEQrB8vy5sGzyc0245giZNgg5njDHRszF0tYgqvP02PPywN66uSxe4\n4w7IzAw6mTHGVE1tKuhEZAAwFq/HywRVva/E8guAG/3ZbcCVqvqVvywX2AKEgQJV7VnGMax9jIYq\n256fwrq/Pc321VtQ4LvGR5F82XBOueYg6tULOqAxxuybjaELSHX01RWBM87wCrpmzWDxYhg+HBYu\njPmhnO5r7HJ2cDu/y9nB7fwuZ69NRCQJeAQ4GegCDBGRg0us9gNwnKoeAdwDPBGxLAxk+89rLbWY\nqw1q7HwVocGFZ9Jx3vPsf9vFpDdNp+Pm+bQfM4I3f3Mn0yaspLCwYrt0/c+ay/ldzg5u53c5O7if\nvzKsoItzhxwCTzwB3btDXh5cfz28/LI9r84YY+JAT2CZqi5X1QJgMnBW5AqqOkdVt/izc4A2EYsF\na4djLz2dzOv/QOcvnqfZVedTt34dOv80k8w/DeOVXvczd8p6a0ONMbWKdbl0RCgETz0Fkyd78716\nwXXXQYsWweYyxpiKqi1dLkXkXOBkVR3hz/8e6Kmq15Sx/p+AThHr/wDkASHgCVV9soztrH2sAl3/\nMz/c8Sy/vPYOu/PDFEodfjh8IJ1HD+aIfk3sua/GmLhiY+gSwMcfw333wY4dkJYGl1wC554LyclB\nJzPGmOgkYkEnIv3wumf2UdXN/nutVHWtiDQDpgMjVXV2Kdvq0KFDycrKAiAjI4Nu3bqRnZ0N7Ole\nZPPlz/fJ6sjSmycy551XCYXgN3UyWdGuL7l92nDo6Qdy8oB+cZXX5m3e5hNjPicnh7y8PAByc3OZ\nNGmSFXRBmDlzZvH/mJqwcSM88ggUdRHu2BFuuAEOLjlyI0o1nT+WXM4Obud3OTu4nd/l7FCrCrre\nwB2qOsCfvwnQUm6M0hV4FRigqqU+YVRERgPbVPXBUpY52z5C/J2vu/73Hctuexb5dDaFBd7vdeN+\n7Sg45UyO+L+TaX3QfsXrxlv2inI5v8vZwe38LmcH9/PbTVESRNOmMHo0jBnjdbn87ju48koYNw52\n7gw6nTHGJIz5QEcRaSciqcBg4K3IFUTkALxi7qLIYk5E0kWkvj+9H9AfWFRjyRNYvcM70vWNuzgk\nZzJ1h/8Bmjal6Y7ltHxlHKt6n8vUfv9g4SvLbJydMcYZdoXOcb/8ApMmwUsvQTjsPdbgmmugb9+g\nkxljTOlqyxU6KH5swUPseWzBGBG5DO9K3RMi8iRwDrAc7yYoBaraU0TaA68DCqQA/1HVMWUcw9rH\n6lRYyKoXP2Ht429SZ/GC4kJuU7ODSTvvTA4Z3oemWQ2CzWiMSRg2hi6Bff89/OMfsGSJN3/ssV5h\n17x5sLmMMaak2lTQ1QRrH2vO1sUr+fb+t0ia9h5Ju7YDEJZk8rK6Ue+kvnS6pA+ZnZsGnNIYU5tZ\nl8uAFA1wDNKBB8Kjj8K110J6OnzyCfzhD/D4497jDsoTD/kry+Xs4HZ+l7OD2/ldzm4Sj0vna8Mu\nbTnqmavo9t0r1Lnl/5jdqjlJojT58QvqPTGWlb1/x6zDr2LOqMn8vGBV0HH3yaXffUkuZwe387uc\nHdzPXxlW0NUiSUkwcKDXBTM7G/Lz4cUXYfBgGD9+34WdMcYYYyA5PY3D/nwKhz92BZ2/fgO56Sa2\nHnYs4eRUGqz6mrRJ41l1wkV8evAlzL18IqunLUYLKvjUcmOMiRHrclmLLV3qFXeffurNp6XB2WfD\noEGQkRFsNmNM4rIulxVj7WP8+CXvF76ZNI/Nb31M/a8+pU7hnjuRaWoa+Qd2Ia3nEbQacAQt+x2C\npKUGmNYY4yIbQ2dKZYWdMSaeWEFXMdY+xqf8HYV8/Z8FbHhzNnUW5dBo64q9lielpvBLh0Op2/MI\nWp58BM37dUHq1Q0orTHGFTaGLiDx3le3Uyf461+98XRHH+11xZw8GYYMgSeegLfemhl0xEqL99/9\nvric3+Xs4HZ+l7ObxOPy+Vpe9rT9Uug+4ihOmnodx+dOotms19j1f3ew7uiz2dioA+HdhaQu+Yrw\ns8+x5sI/sajdqXxx+DC+OPtulox+gQ3vzkc3bgosf7xzOTu4nd/l7OB+/spICTqAqTmdO8O993p3\nwpw0CebMgRdegK1b4Ysv4IwzoEcPEPve3BhjjImaCOx/eGP2P/x4uPl4VGHl19vIfft/bJuVQ8rX\nC2mW9x1Jq5bDquXsmvkhKx+GNclARmNC7TuSduiBNOnZkcwe7Ug5oLV3hzNjjImCdblMYEuWwPPP\ne3fEDIe991q1gtNPh1NOgcaNg81njKmdrMtlxVj76D5VWP3jblZ/ksvm+d/xy9ffk/Ljd2TmfUda\neOde6wqQmgqhRo0Jt2xD8gFtqNexDQ0PbUPjQ1uT0q4NNLDn4hkT11QhFCp+aWGIcEGIpMaNkKTy\nmz8bQ2cqZeNGeOcdePttWL/eey85Gfr08a7a/eY3dtXOGBM7VtBVjLWPtZMqbNqo/PjZT2yY8x27\n/vcd/PA99Tauomn+GlK04FfbCJCaBtSvT6hxJmRmktIik9TWmaQfkEn9rEzqt2uKNG/mfSubZCNr\nTJwqKngKC4tfWhgitDtEKL+QUH4hhfmFhPJDhHcXFr8X2h0iXBginF9Y/H5RsRTeXUi4IIQWFKIF\n3nTxfKF3rKLpoldRBm+6ECJ/hkNIRFEmoUJvOhwiKVSIhP11/FdSqBBRbxpVVL2PiULR3+Cdl00l\nPbP8q+9W0AVk5syZZGdnBx2j0oryh8Mwfz5MmeLdQKXof0fr1nDqqd6jENq0CTTqr9SW372LXM4O\nbud3OTtYQVdRLreP4Pb5GkT23bth7Rpl/eKf2bxoNTuWraYwdzWydg31Nq8mM381qeFfytxeBOrU\ngTp1hE+TdnNU8wOhYUOkUUOSmzQipUlD0po1pG7zhtRr2Yj0lg2R/dKhXj3Ybz+vq2dqauDf5Lp8\n3kA15lf1ulUVFv6qICIUIpxfQMEur5gp/MUreMK7C/eaDu32i5/8AkK7vYInlO8XQbsLmffDInq0\n6FhcGBUXQbsLiosfLSxECgoiCqFCxM8gRfOhQr8o8qaLiiAJez9VQcMUFz6x+lvu84LNHFkn+G5m\nYZIJSzIh8X6qJHN4znM0bFP+FfbKtJE2hs4US0qCXr2814YN3lW7qVNhzRp46inv1aEDHHcc9O0L\n7dsH/ve9McYYU6ukpkK7LKFdVnM4rTnQvXhZfr5f7C3bwrbcjexcsYH81RsIrduA/ryB5M0bqLdz\nI40Kfqb+jjwKCraze9uKsg/mS0ryeuYU/ZTkJMJp6YTrpUPdemh6OlK3LqSlIXVTSaqbhtRNI7le\nKkl1U0mql0Zyuj+fmkJyajJJqSnedJr3Kp5OTSapjn+wyJfI3vPr1sHKlXtClvcPjuKKQPcUPEXv\nF0wEYkcAACAASURBVE2HQmhYCRWE0VCYcKH30lCYcEi9qzz+e+GCUPHPoqs/Gir9/XBh0RWh0F5X\nfn5csYQmz3wBhaGIq0L+lZ/CQjQUirgatKcYIlQ0XYj4xZGEC/2f3nzJjxv5ioVQwWbCEQWR+K+q\nCpeYV5IISYr3SkomJCmEk1LQpBQ0KZlwsj+dkgJJyWhyCpqcAsnJxT9J2fNTU1KQ5GQ25f3AupaH\nQEoKkuItS6qTDMnJSKq3TlJqClLHW55UJ7n4Z1Kq/15qMsmp3nZSx/uZVCe5+PxNTvVfaSlIchIp\ndVP2vJ+WQnKKeC8/YtEpXl3sCp0pVzgM8+bBBx94V+12RnT1b9PGK+yOOw4OPtiKO2NMdOwKXcVY\n+2gqIj8ffv4ZNvxUyI61W9n501byf97K7p+3ENq0hcLNW9G8rci2rSRv30LKrm2khXZSN7yT1PAu\n6oZ2kqK7aySr+P+RiDekeEF0RYSWmNCS0/5EbfoTpMieQkhS/KtAKYSkDmGJKISS936RnIym1Nmr\nCCr5kjopkJJMUp0UqFMH8QsaqVNnT8FTJ6W4YC+aTklLLp4uKtwjC/mUtOTi4j45LYU6dZNJqSO/\nimH/lrQul6aaFRbCl1/CrFkwezZs2bJnWWamN+aud284/HC7OZcxpmxW0FWMtY+mOoXDsGuX94Vt\n8WtrIfl5u8jfvJPdeTsp2LKT0M58QjvzCe/KJ/zLbvQX7yf5+egv+eju3bB7N1LU1c7/WfQq6oqX\nFC5ENIxomCT2/AQlScMI4f9n777jo6rSP45/noQaCASpSlfqsgJ2VFyDBRCkrAoCiiAi2EVZXXR1\nEVd/6u6quGAXBVTADiKuYouKBRsRZAEbCdWghN4Dz++PcweGmJBJMsnMSZ7363VfmdtmvnMd53Dm\nnrL/b14k7P8FQVEEFQnWJN91JYF9kgCSgIq7C6jibptoYiIS3CHUhEQ0IXH/LUtNTAQJbl0mhv4m\nIsFfKrjHoTtBUiERSUwIKkeuYnTQnaDQ4+DuT+iOplQMu5MZ3OHcXwEKKkcVqgSVoUqyv05WMax+\nVrGidZssC6xCFyPlsZ33vn2waJGr3H38sfs1MCQhAdq2hY4d3YAq7dq5ycxLQnm89vHC5+zgd36f\ns4NV6ArL5/IR/P68+pwd4jd/eMvI3H9Djz/8MI3TTkst8HlCLTVFDizh66EKTmJi6d79iddrHwmf\ns4P/+a0PnSk1CQnQoYNbrrkGli1zd+2++cY9XrzYLc8/7345+uMf4Zhj3NLWNWs2xhhjTDmUu7KV\nl+rVoWbN0stkjM/sDp2Juu3bYeFCV7lLT4cffzy4o26lStCihet317q1W5o0sXbTxpQXdoeucKx8\nNMaY8sOaXJq4tHkzfPstLFjgKnmZmb8/pmpVaNXqQCWvTRto0MAqecaURVahKxwrH40xpvwoShlp\nXSejIC0tLdYRiqWk89eo4UbDvO46mDwZXn8d/vUvGD7cba9Xz3XI/vZbeOEFuPNOGDQIevaEK6+E\ne++FGTPgs89g7dqD7/bZtY8dn7OD3/l9zm7KH58/rz5nB7/z+5wd/M7vc3bwP39RWE8mU+qSk+H4\n490Skp0N338PS5e6Zdky2LjxwHq4ypVdE82mTWHbNjdty+GHuwnQa9a0u3rGGGOMMab8sCaXJm5t\n2uSaZ2ZmQkbGgb/r1+d/TpUqrnIXWo44wv2tV89NrVCjhlX4jIk1a3JZOFY+GmNM+WF96Ey5sHXr\ngYreihWuGebatbBmjbtjdygVKriKXWipW/fg9ZQUtyQnW8XPmJJiFbrCsfLRGGPKD6vQxYjv8134\nnD939i1bDlTwwit6v/4Kv/1WcIUvJCHhQOUufKlVy1X28lqqVy/8hJ5l6dr7xuf8PmcHq9AVls/l\nI/j9efU5O/id3+fs4Hd+n7OD//lLZB46EZkEnAtkqWr7fI75D3AOsA0YqqrpwfbuwHjc4CuTVPW+\nwoTzRXp6utcfHJ/z584eqly1apX38Tt3uopd+BKq7K1f75p5btjgKn7Z2W4pjKSkAxmSkqBatYP/\n5n78+uvp1K6dSpUqbqTPKlUOLPE+V5/PnxvwO7/P2cuagso5ERkE/DVY3QJcpaoLIzm3rPD58+pz\ndvA7v8/Zwe/8PmcH//MXRST/ZHwGmABMzWuniJwDHKWqLUXkJOAxoJOIJAATgTOBNcCXIjJLVZfm\n9Tw+27hxY6wjFIvP+QubvUoVaNTILYeyZ4+r3G3ceGDZsMH93bzZNfvcsuXgZds2Nwff9u2QlRVZ\nnuXLN7JgQd77KlRwlbzKlQ8slSr9/nH434oVD/wNLbnXK1Z0zx2+5LUtMdEtoccJCQc3Q/X5cwN+\n5/c5e1kSYTn3M/AnVd0UVOCewMpIb/icHfzO73N28Du/z9nB//xFUWCFTlXniUjTQxzSh6Cyp6rz\nRaSmiNQHmgM/qGomgIjMCI4tc4WVKXsqVjzQry5Sqq6iF6rshSp3oYpeeIVv2za37NoF7dq5aRt2\n7jyw7NgBOTkHKovxIryC9+OPbqqJUGUvVAEMPc69LbTkXs9vEcn/cWg9r7+hx3DwtvAlIQEWLoRp\n0w5sCz8eDj4+kvXwbaHHuf/mPvZQ2/N6jpCMDPjww/yPj2S9oD6iee2PZNsRR0Djxod+7jLkRAoo\n51T187DjPwcaRnquMcYYE4loNOpqCKwMW18VbMtr+4lReL24k5GREesIxeJz/njKLnKgueXhh0d2\nztChGUyc+Pvtqu4uYaiCt3u3q/yF/uZ+vGuXOz607N6d/3pOjltyP9679+Bte/ce/FfVPd6712Xc\ntCkDn38EW7Ikgw0bYp2iaJYsySAzM9Yp8nbRRW6OyXKisOXccOC/RTzXW/H0PV1YPmcHv/P7nB38\nzu9zdvA/f1FENChKcIdudl596ERkNnCPqn4arL8L3Iy7Q9dNVUcE2y8GTlTV6/J5DX97fBtjjCmU\nsjAoioicT4TlnIh0wTWx7KyqGwp5rpWPxhhTjkR9UJQIrAbCG9g0CrZVAprksT1PZaFwN8YYU66s\nJoJyTkTa4/rOdVfVDYU5F6x8NMYYc2iRDrIuwZKX14FLAESkE7BRVbOAL4EWItJURCoBA4JjjTHG\nmLKgwHJORJoArwCDVfWnwpxrjDHGRCKSaQumAalAbRFZAYzF3X1TVX1CVd8UkR4i8iNu2oJLcTv3\nisg1wFwODMm8pITehzHGGFOq8ivnRGQkQRkJ3A4cBjwiIgLsUdUTrYw0xhgTLXEzsbgxxhhjjDHG\nmMKJtMlliRGR7iKyVES+F5G/FnxGfBGRDBH5VkQWiMgXsc5zKCIySUSyRGRh2LZaIjJXRJaJyNsi\nUjOWGQ8ln/xjRWSViHwTLN1jmTE/ItJIRN4XkcUiskhErgu2e3H988h/bbA97q+/iFQWkfnB/6OL\nRGRssN2Xa59f/ri/9iEikhBkfD1Y9+LaxwOfy0ifykewMjKWfC4jfS4fwe8ysiyUjxCdMjKmd+jE\nTaz6PWETqwIDfJpYVUR+Bo4L6+get0SkM7AVmBoasVRE7gPWq+o/g38s1FLVMbHMmZ988o8Ftqjq\nAzENVwARaQA0UNV0EakOfI2bc+pSPLj+h8h/IX5c/yRV3S4iicAnwHXA+Xhw7SHf/OfgwbUHEJEb\ngOOAGqra26fvnVjyvYz0qXwEKyNjyecy0vfyEfwuI30vHyE6ZWSs79Dtn1hVVfcAoYlVfSLE/jpG\nRFXnAbkL1j7AlODxFKBvqYYqhHzyQ/4D9sQNVf1FVdODx1uBJbhR7by4/vnkD02Q7MP13x48rIzr\nO6x4cu0h3/zgwbUXkUZAD+CpsM3eXPsY872M9KZ8BCsjY8nnMtL38hH8LiN9Lh8hemVkrL9o85uU\n3CcKvCMiX4rI5bEOUwT1glFJUdVfgHoxzlMU14hIuog8FY9NAnITkWZAR+BzoL5v1z8s//xgU9xf\n/6A5wwLgF+AdVf0Sj659PvnBg2sPPAjcxIFCFjy69jHmexnpe/kIVkaWOp/LSB/LR/C7jPS8fIQo\nlZGxrtCVBaeq6rG42vXVQZMHn/k2Ss4jwJGq2hH3P3Nc314PmmO8DFwf/JKX+3rH9fXPI78X119V\n96nqMbhffE8UkXZ4dO3zyP8HPLj2ItITyAp+vT7Ur6Vxe+1NsZS18hH8+6zG/fdEOJ/LSF/LR/C7\njPS1fITolpGxrtBFPLFqvFLVtcHfX4HXcE1kfJIlIvVhfzvwdTHOUyiq+qse6Aj6JHBCLPMciohU\nwH3ZP6uqs4LN3lz/vPL7dP0BVHUzkAZ0x6NrHxKe35NrfyrQO+hLNR04Q0SeBX7x7drHiNdlZBko\nH8HD74lwnnxPAH6XkWWhfAS/y0gPy0eIYhkZ6wqd1xOrikhS8IsMIlIN6Ap8F9tUBco9SfzrwNDg\n8RBgVu4T4sxB+YMPesh5xPf1fxr4n6o+FLbNp+v/u/w+XH8RqRNqbiEiVYGzcX0cvLj2+eRf6sO1\nV9VbVbWJqh6J+35/X1UHA7Px4NrHAW/LSE/LR7AyMpZ8LiO9LB/B7zLS5/IRoltGxnweOnFDiT7E\ngYlV741poEIQkea4Xx0V1xHz+XjOL2GTxANZuEniZwIvAY2BTKC/qm6MVcZDySd/F1x79X1ABjAy\n1O44nojIqcBHwCLc50WBW4EvgBeJ8+t/iPyDiPPrLyJH4zoVJwTLC6p6t4gchh/XPr/8U4nzax9O\nRE4HRgcjeHlx7eOBr2Wkb+UjWBkZSz6XkT6Xj+B3GVlWykcofhkZ8wqdMcYYY4wxxpiiiXWTS2OM\nMcYYY4wxRWQVOmOMMcYYY4zxlFXojDHGGGOMMcZTVqEzxhhjjDHGGE9Zhc4YY4wxxhhjPGUVOmOM\nMcYYY4zxlFXojDHGGGOMMcZTVqEzpoSJyAciMizWOYwxxpjyTkRuEZEnYp3DmGiyCp0xhSAiy0Xk\njFjnMMYYY6JFRAaJyJciskVEVovIHBE5Nda5iktETheRleHbVPUeVR0Rq0zGlASr0BlTRohIYqwz\nGGOM8YuI3Ag8ANwF1AOaAA8DvWKZK0oE0FiHMKakWYXOmGISkRQRmS0i60RkffC4Ya7DWojIfBHZ\nJCKviUhK2Pm9ReQ7EckWkfdFpE3Yvn0icmTY+jMicmfw+HQRWSkiN4vIWuDpkn6vxhhjyg4RqQGM\nA65S1VmqukNV96rqm6o6RkQqicj44K7dKhF5UEQqBueGyqAbRSQrOGZo2HP3EJHFIrI5dFywfYiI\nfJwrx/6yLijnHhaRN4M7hh+LSP3gtbNF5H8i0iHs3OUiMiZ4rfUiMinInQS8CRwRPM9mEWkgImNF\n5Nmw8w9VBi8XkdEi8q2IbBCR6SJSqUT+YxhTDFahM6b4EnCVqca4Xza3AxNzHTMYGAo0APYCEwBE\npBUwDbgOqAv8F5gtIhWC8wr6ZbEBkBK8rjUhMcYYUxgnA5WBmfnsvw04EWgPdAge3xa2vwGQDBwB\nDAceFpGawb6ngMtVtQbwR+D9sPNyl2251/sBtwK1gd3AZ8BXwforwIO5jh8EnA0cBbQGblPV7cA5\nwBpVTVbVGqr6S/jrRVAGh7J0BZoH12Bo7otkTKxZhc6YYlLVbFV9TVV3qeo24B7gT7kOe1ZVl6jq\nDuB2oJ+ICNAfeENV31fVvcC/garAKcF5UsDL7wXGquoeVd0VtTdljDGmPKgN/Kaq+/LZPwgYp6rr\nVXU97m7e4LD9u4F/BHf1/gtsxVWoQvvaiUiyqm5S1fRD5Mhd1r2mqumquht4Ddihqs+rqgIvAB1z\nHT9BVdeo6kbgbmDgod/2fgWVwQAPqWpW8Nyz83htY2LOKnTGFJOIVBWRx0UkQ0Q2Ah8CKUGFLSS8\nU3YmUBGog/tVMzO0IyisVgK5m2zm51dV3VOsN2CMMaa8Wg/UEZH8/j14BLAibD0z2Lb//FyVwe1A\n9eDx+UBPIDMY7blTIXJlhT3ekcd69YMPZ9UhMh5KJGVw+GuHvz9j4oZV6IwpvtFAS+AEVU3hwN25\n8Apd47DHTYE9wG/AmmCdXMeGCqftQFLYvga5jrXO3sYYY4rqM2AX0Def/as5uIxqiiu3CqSqX6tq\nX1xTxlnAi8GubYSVayKSu1writxlbChjQWVkQWWwMV6wCp0xhVdJRCoHSxWgFu4Xw80ichhwRx7n\nXCwibYJO2uOAl4JfAl8EeopIFxGpICJ/AXbiClmABcAgEUkQke7A6SX83owxxpQTqroZGIvr+9Yn\naHFSQUS6i8h9wHTgNhGpIyJ1cF0Gnj3UcwKISMVgKoQaQVPGLbguAgDf4ppitheRysHrF/bHydxN\nNK8WkYZBGXwrMCPYngXUDgZ/yUtBZbAxXrAKnTGFNwd352wH7pfGmrg2978Bn+JG1QqnuAJwCu7X\nwErA9QCq+j1wMW4QlV9xzVN6qWpOcO4ooDewAdcn4LWSelPGGGPKH1V9ALgRN9jJOlwTy6tx5c1d\nwNfAQlxF7CtcH7V8ny7s8WBgedAVYQRwUfB6PwB3Au8B3wMf536SSGLnWp8GzAV+BH4IZVTVZbhK\n6c/BKJYH3Q2MoAy2VjDGC+JuEhRwkLszMB5XAZykqvfl2t8b+AewD9eU7AZV/STYdwNwWbBvEXBp\n0MnVGGOM8UIE5WAN4DnciLOJwP2qOjnYNwk4F8hS1fZh59TCDfDQFMgA+qvqphJ/M8aUISKyHLhM\nVd8v8GBjyqgC79AFHWUnAt2AdsDA8Dk6Au+qagdVPQZXeXsqOPcI4Frg2KAQqwAMiGJ+Y4wxpkRF\nWA5eDSxW1Y5AF+D+sKHPnwnOzW0MrvxsjRvS/ZaSyG+MMaZsi6TJ5YnAD6qaGYymNwPoE35AMNdH\nSHXc3biQRKBaULAlEWFnWmOMMSZOFFgO4ppmJQePk3Gj/+UAqOo8XLPp3PrgmmIT/M1vYApjTP6s\nWaQp9yKp0DXk4CHXV5HHkOoi0ldEluDm6BgGoKprgPtx7bFXAxtV9d3ihjbGGGNKUSTl4ETgDyKy\nBtfX6PoInreeqmYBBBMe14tCVmPKFVU90ppbmvKuQsGHREZVZwIzRaQzrhPt2SKSgvsFsimwCXhZ\nRAap6rTc54uI/cJijDHlhKrmHqXOd92ABap6hogcBbwjIu1VdWshniPPctDKR2OMKV8KW0ZGcodu\nNa6Td0ijYFt+AeYBRwZDx54F/Kyq2cGwta8CpxziXC+XIUOGxDxDec3vc3bf8/uc3ff8PmdX9bJu\nEkk5eCmujENVfwKWA7n72eWWJSL1Yf9cXOvyOzDW/83K6+fV5+y+5/c5u+/5fc5eFvIXRSQVui+B\nFiLSVEQq4QY1eT38gODXyNDjY4FKqpqNa2rZSUSqiIgAZwJLipTUGGOMiY0Cy0EgE/cjJkElrRXw\nc9h+4fdzZ70ODA0eD8FNvmyMMcYUSoFNLlV1r4hcg5vfIzRc8xIRGel26xPA+SJyCbAbNzdX/+Dc\nL0TkZdzkyHuCv0+UzFuJnWbNmsU6QrH4nN/n7OB3fp+zg9/5fc7uowjLwbuAySKyMDjt5uCHTURk\nGpCKm+B4BTBWVZ8B7gNeFJFhuAph/1J9Y6XE58+rz9nB7/w+Zwe/8/ucHfzPXxQR9aFT1beA1rm2\nPR72+J/AP/M5dxwwrhgZ415qamqsIxSLz/l9zg5+5/c5O/id3+fsvoqgHFxL3lMToKqD8tmeTXBX\nryzz+fPqc3bwO7/P2cHv/D5nB//zF0UkTS6NMcYYY4wxxsShqI1yaYwxZUmzZs3IzMyMdQyvNW3a\nlIyMjFjHMMYYE2VWRhZfNMtIKepoKtEmIhovWYwxRkSKPNqUcfK7hsH2sjZtQYmx8tEYE2+sjCy+\naJaRdofOGGOMKe9UITsbfvkFfv0VqlaFww6DWrUgJQUq2D8XjDEmXtk3dBSkpaV53QHT5/w+Zwe/\n8/ucHfzPb0yhbd8OP/7oKm2//AJZWQf+ZmVBTk7+5yYnH6jg1arlHjdoAB06wFFHQUL+XfJ9/n/N\n5+zgd36fs4Pf+X3OXl5Zhc4YY4wpyzIyYOZMmDsXduzI/7iUFKhfH+rWhZ07YcMGd9du40bYssUt\nefWZqVbNVew6dnRLARU8Y4wx0WV96IwxJg/WP6D4rA9ddBSpfMzJgU8+cRW59PQD21u2hMaNXcWt\nQYOD/1apkvdz7dsHmze7yl12tqvobdgAy5fDt9/C2rUHH1+tGrRvf6CC17IliP3nNqYssTKy+KJZ\nRlqFzhhj8lBWC6suXbowePBghg0bxrRp05g6dSpvvfVWibyWVeiio1DlY3Y2vPEGvP46rF/vtlWt\nCl27Qp8+0Lx59ANmZbmKXXq6W3JX8OrVc6/frRs0ahT91zfGlDorI4svmmWktYmIgrS0tFhHKBaf\n8/ucHfzO73N28D//vHnzOPXUU0lJSaFOnTqcdtppfP3114V6jkGDBpVYQWVK2eLF8I9/wIUXwjPP\nuMpckyZw3XXw8sswalTJVObA3d3r2hVuvhmmTYMZM+CWW+Ccc6BePdKWLYPnnoPBg+Hqq2HWLNd8\n0wO+f0/4nN/n7OB3fp+zh5S3MtL60BljjGe2bNlCr169ePzxx+nXrx+7d+/m448/pnLlyrGOZkrb\nvn3w2GPw0ktuXQROOw3+/GfX3DEWTR1DFbyuXd3omZMmuTuHaWnwv/+5ZeJEOOUUd9fuxBNtFE1j\nTNSUyzJSVeNicVGMMSY+xPN30ldffaW1atXKc9/kyZP11FNP1WuuuUZr1qypbdu21ffee2///tTU\nVJ00adL+Yzt37rx/n4joY489pi1bttRatWrp1VdffdBzT5o0Sdu2bauHHXaYdu/eXTMzMw+ZM79r\nGGyPebnjy5LvZ3H7dtVbb1VNTVU96yzVJ59UzcrK+9h4sGOH6jvvqP7lL6pdurjcqamqffuqPvyw\n6tq1sU5ojImQlZHxVUbaT2LGGFMEXbpE77k++KBwx7dq1YrExESGDh3KgAED6NSpEykpKfv3z58/\nn/79+7N+/XpeeeUVzjvvPDIyMg46JkRy3cGZM2cOX3/9NRs3buS4446jd+/edO3alVmzZnHvvffy\nxhtv0KJFC+69914GDhzIJ598UqT3bIrp11/h1lvdNATJya65ZYcOsU51aFWqwFlnueXXX+Hdd+Ht\nt93ImS+9BK+84v7HuvBCN5CKMcZLsSwfoXyWkdaHLgp8b2vsc36fs4Pf+X3ODn7nT05OZt68eSQk\nJDBixAjq1q1L3759WbduHQD169fnuuuuIzExkf79+9O6dWvmzJkT0XPfcsstJCcn07hxY7p06UJ6\nMELi448/zi233EKrVq1ISEhgzJgxpKens3LlyhJ7nyYfP/wAV17pKnONGsEjj8R1ZS7P/9fq1oWB\nA11/v0cfhbPPds1D33sPRoyAm26Cr75yTTZjyOfvCfA7v8/Zwe/8PmeH8llG2h06Y4wpgqL8ahhN\nrVu35umnnwbg+++/56KLLmLUqFF069aNhg0bHnRs06ZNWbNmTUTPW79+/f2Pk5KS2Lp1KwCZmZlc\nf/31jB49GnDN9UWE1atX07hx42i8pbgmIt2B8bgfQiep6n259tcAngOaAInA/ao6+VDnishY4HJg\nXfA0t6rqoXvgf/KJuxu3a5ebGuAf/4AaNaL0LmNABNq0cXcbhw93A7jMnu0qc199BS1awIABkJoK\niYmxTmuMiUCsy0cof2Wk3aGLgtTU1FhHKBaf8/ucHfzO73N28D9/uFatWjF06FAWL14MwOrVqw/a\nv2LFCo444ohivUbjxo15/PHHyc7OJjs7mw0bNrB161Y6depUrOf1gYgkABOBbkA7YKCItMl12NXA\nYlXtCHQB7heRChGc+4CqHhss+VfmVOHFF+H2211lrls3uP/+uKvM7doFK1e6utibb8LUqbBqVSqv\nvAL//a8bF2X+fFi40N1gXL3aTWmXk4Ob3uCqq9z7HD4catVyB911F1x8Mbz6qpvwvBT5/j3hc36f\ns4Pf+X3OnpfyUEbaHTpjjPHMsmXLmDNnDhdeeCENGzZk5cqVTJ8+fX/BkZWVxYQJE7jyyit57bXX\nWLp0KT179izWa15xxRXcfvvtdOjQgT/84Q9s2rSJd955hwsuuCAabynenQj8oKqZACIyA+gDLA07\nRoHk4HEysF5Vc0SkUwHnRjYM5QMPuPnlwFV2Bg2K2WTdOTmwaBF8/z2sW+emoVu3zi2bNhXtORMS\noFkzd0OuVatkWh59ES169iNp3lx44QVYtQomTIDnn3fTH5x7ro2MaYzJU3ksI+3bMArS0tK8/jXD\n5/w+Zwe/8/ucHfzOn5yczPz583nggQfYtGkTKSkp9OrVi3/+85+88sordOrUiR9++IE6derQoEED\nXnnllf2dvXN38A6Xe1/4et++fdm2bRsDBgxgxYoV1KxZk7PPPru8VOgaAuEdIVbhKnnhJgKvi8ga\noDpwYYTnXiMig4GvgNGqmneV6I03oFIlN79bDD63GzfCF1/AZ5+5v9u3531chQruRltoqVsXli5N\no0mTVLZvhx072P83tGzfDps3w88/u2Xu3NCzVaJhw3NpeVQPTmn+CR2+e57avy0j8aGH3F28Sy+F\nM890tcES4vP3BPid3+fs4Hd+n7ND+SwjRWPc4ThERDReshSW7x98n/P7nB38zu9zdig4v4jg43fS\nlClTmDRpEh999FGso+R7DYPtsbm9VAQicj7QTVVHBOsXAyeq6nW5jjlFVUeLyFHAO0B7XFPLPM8V\nkbrAb8Fw2HcBh6vqZXm8vg5p3JhmvXpB3bqkpKTQsWPH/Z/f0AAG0VxXhcaNU/n8c3j55TQyMyEl\nxe3fsCGNBg2gR49UDj8cVq9O47DD4NxzU6lVCz788ODnGz9+fIF5d++Ghg1T+fFHeOutNFauhF27\nUsnJca8HUCvldI7ZNo/Wv91NA/mFHg1rUbFlc9KOPRbatSM1GFovmtcjfHCIkrzeJbXuc/7cFeO9\nWQAAIABJREFU7yHWecpT/vT0dEaNGnXI47t06WJlZDGJCB988AHp6els3LgRgIyMDKZMmVLoMtIq\ndMYYkwer0BVfGarQdQLuUNXuwfoY3DxB94Ud8wZwj6p+Eqy/B/wV1xLmkOcG25sCs1W1fR6vr7p2\nLTRoUDJvMMzy5TBzprsT9+uvB7ZXqADHHAMnnwydOsHhh5d4FHJy3IwG33/vutItWQJLlwL79nF8\n9lzOyXqGhhXXkVITqp3wB6qNutxNpm6MKXFWRhZfNMtIa3JpjDHGHNqXQIug0rUWGAAMzHVMJnAW\n8ImI1AdaAT8Dm/I7V0QaqOovwfnnAd/lm6CEK3OLF8O0afDppwe21a59oAJ37LFQtWqJRvidChXg\nqKPcEpKdDZ9+msC8ed2578szOTFrNmdnPUv1Wf+jyts3kNPheGqMvpymZ7cq3bDGGBNDdocuCgpq\nuhXvfM7vc3bwO7/P2aHsNrmMJ2XlDh3sn3rgIQ5MPXCviIzE3W17QkQOByYDoXtX96jq9PzODbZP\nBToC+4AMYKSqZuXx2iVSPqq6/nDTprlRJ8F10+vRwy0tWkRn3JWS+q7Yvt3l//z97VSc9TKnrnqB\nyvtc576MVt1IvvFyTutbm8qVi/4aZf17Lp75nB38zh9Jdisji6/U79BFMP9Ob+AfuEJpD3CDqn4i\nIq2AF3CjfwlwJHC7qv6nMCGNMcaYWAqmFGida9vjYY/X4vrLRXRusP2SKMeMyN69bvqAadPcICQA\n1atD375w/vkQjA0Q95KS3PgwqalJ5Pz9EhbO60v2w9OonfYKzb5/m91XfciEey8maUg/zj2vEk2a\nxDqxMcaUjALv0AVz6HwPnAmswTU9GaCqS8OOSVLV7cHjo4EXVbVtHs+zCjhJVX83bbrPd+iMMWWP\n/fpYfGXpDl0sRat83LUL3noLZsyAX4KGnrVrQ79+0KuXqyCVBbuWr2HV3x5l74fz2LYdsisdzqwj\nrqRCamf69BVOPdVmPDCmuKyMLL5olpGRVOg6AWNV9ZxgPc8O3WHHnww8partcm3virs7d1o+51mF\nzhgTN6ywKj6r0EVHcctHVfj4Y3j4YTdXHECjRjBgAHTtChUrRilovPnmGzb930Q2pS9nQzZ8X+0Y\nXmt4DbsbHUnPntC7t6vQGmMKz8rI4otmGRnJ5C15zaHTMI8X7ysiS4DZwLA8nudCYHphwvkifIha\nH/mc3+fs4Hd+n7OD//mNicSKFXDzzTB2rKvMHXUU3HEHTJkCPXuWTmUuZv+vHXssNV94kib3j6Ld\nycmk1lzAbZnD6ZL+IK88vYmBA2H8+AOV3Lz4/j3hc36fs4Pf+X3OXl5FrdGBqs4EZopIZ+Au4OzQ\nPhGpCPQGxhzqOYYOGUKz5s0BSmWeHVv3fz0kXvKUp/zp6elxlack8pvoGD9+POnp6TRr1izWUcqN\nHTtg6lR4+WU3/H9yMlx2mWtamRDJT7llRWIi9OlDhTPOoO7kydSZOZMmm1/nrKz3eb7Spbw+sy9z\n5iTQvTsMGlQ60zEYY0y0RdrkssA5dHKd8xNwgqpmB+u9gatCz5HPObov/Vukw++m4DHGmFLnY3OS\nefPmcfnll7NkyZJYRwGsyWW0FKbJpaob8OSRR+C339wolT16wOWXQ82aJZvTC5mZMHEifPUVO3bC\nkpyWPJRwIyuS2pCQAGefDRdf7JqkGmPy51sZGW/lI5R+H7pEYBluUJS1wBfAQFVdEnbMUar6U/D4\nWGCWqjYO2z8deEtVpxzidXTtmPE0uOf6wuQ3xpgSEe+FVfPmzZk0aRJnnHFGrKPkyyp00RFphS4z\nEx56CBYscOutW8OoUdCmTQkH9I0qfPIJTJgA69axc7fwSa1ePLhlONsSkhGBM85wFTu7qWxM3uK5\njPShfIRS7kOnqnuBa4C5wGJghqouEZGRIjIiOOx8EflORL4BJgD9w0Il4SZbfbWg19r25oewb19h\n8scF35tn+Zzf5+zgd36fs4P/+ePJ3r17Yx2hXNu+HR57zDWpXLDANa8cPdrdpYuHylzc/b8mAp07\nu46EAwdSpWoCZ257nZeTLuGqFnNJTFDeew8uvRSGDk1j5e/G5fZH3F37QvA5O/id3+fs8aa0yseI\nWtKr6luq2lpVW4YmRFXVx1X1ieDxP1X1j6p6rKqeqqqfhZ27XVXrquqWgl5nx9oNaPq3RX0vxhhT\nrn344Yc0bry/cQTNmzfn/vvvp0OHDtSqVYuBAweye/fu/fvfeOMNjjnmGGrVqkXnzp1ZtGjR/n33\n3XcfLVq0oEaNGvzxj39k5syZ+/dNmTKFzp07c+ONN1KnTh3GjRtXOm/QHCTUvPKSS+CFF9zvob16\nwXPPwbnnlrO+ckVRpQqMGAFPPQXt21Nl50b6/XgPs5qPYvBpGVSoAOnpMHSoGzxlw4ZYBzbGFFVZ\nLx/jaiaWPXtg7bQPOOLYY2IdpVBCAyn4yuf8PmcHv/P7nB2ikL9Ll6jkAOCDD6L2VCIHt9J46aWX\nmDt3LpUrV+aUU05h8uTJjBgxggULFnDZZZcxZ84cjjvuOJ577jl69+7N999/T8WKFWnRogWffPIJ\n9evX56WXXuLiiy/mp59+on79+gDMnz+fQYMGsW7dOvbs2RO1/CYyK1e65pVff+3W27RxzStb/276\n8tiL+++KZs1cje2dd+DRR0n6cSHDlg+nX4/+TNp9Ca/PhVmzYO5cN9VD//6uLuiDuL/2h+BzdvA7\nv5WP/pWPcff73Y63PwJrvmOMMVFx/fXXU79+fVJSUujVqxfp6ekAPPnkk1xxxRUcf/zxiAiDBw+m\ncuXKfP755wCcf/75+wunfv360bJlS7744ov9z9uwYUOuuuoqEhISqFy5cum/sXJq1y53Q2nYMFeZ\nC29eGY+VOW+IuEn5pk51E9Tt20fy7OmMWjCE56/9nJNPdiOHPvOM61s3Z479U8UY35Wl8jGu7tD9\nWrkxFX5Zyd6vFpB40vGxjhOxtLQ0r3+J8Tm/z9nB7/w+Z4co5I/ir4YlKVToACQlJbF27VoAMjMz\nmTp1KhMmTABAVdmzZw9r1qwBYOrUqTz44INkZGQAsG3bNn777bf9zxXedMWUPFX49FM3jkdWltvW\no4drMRjvo1d69V2RnAw33ADnnAMPPEDaF1+Q+p9b+L/UVBbdcS0PTz+MZcvg3/92U0KMHAknneTq\ng/HIq2ufi8/Zwe/8Vj76Vz7G1R26jOZdyMmBtdP9+CAYY4yvGjduzN/+9jeys7PJzs5mw4YNbN26\nlQsvvJAVK1YwYsQIHnnkETZs2MCGDRto167dQaNx5W66YkrWrbfCbbe5ytxRR7mR92+6Kf4rc95q\n0wYefRT69oXKlSEtjaP/PYRHz53D7bcpDRpARgbccgvceCP88EOsAxtjosXH8jGuKnQ1+7o2t7ve\n+djNhOoJX3+BCfE5v8/Zwe/8PmcH//MD7N69m127du1fCtNW//LLL+exxx7b30xk27ZtvPnmm2zb\nto1t27aRkJBAnTp12LdvH8888wzfffddSb0NE4HPP4ekJLj2Wnj8cWjXLtaJIuft/2uJiaTecQdM\nnuxuw23ditz/b854fRRT71rBVVe5G3rp6e5O3QMPwObNsQ59MG+vPX5nB7/z+5w9pLyVj3FVoTuh\nXzN+qdKMbVlbyJn/dazjGGNMXOvZsydJSUn7lzvvvPOgXwYP9Svhcccdx5NPPsk111zDYYcdRqtW\nrZgyxU0V2rZtW0aPHk2nTp1o0KABixcvpnPnziX+fkz+zjrLde867zxITIx1mnKmQQO45x64/XZI\nSYGFC6l4xWX02zGV56fk0K+fG1F09mzXv27WLC9nYDKmTClv5WOBE4uXFhHRffuUJ/80lRO+e4Y6\ng7rR+OExsY4VEZ/bSYPf+X3ODn7n9zk7FJw/nidN9UVZmlhcRLoD43E/hE5S1fty7a8BPAc0ARKB\n+1V18qHOFZFawAtAUyAD6K+qm/J47YgmFo9XPn9X/C77li2uKeZ//+vWmzaFm24is3o7/vMf+OYb\nt7lFC7juOjj66FKPfJAyde0943P+SLJbGVl8pTqxeGkSgVp/Dppdvj/PzWNgjDHGxJCIJAATgW5A\nO2CgiOSesvtqYLGqdgS6APeLSIUCzh0DvKuqrYH3gVtK/t2YYklOhptvdu0rGzWCzEy45hqavjae\nf9+5nXHjoF49+PFHV6H7v/+D9etjHdoYU9bF1R06VWXFClh00nAa7/6JP7x2NxX+dEqsoxljyiH7\n9bH4ysodOhHpBIxV1XOC9TGAht+lC7Y1UtVrRKQ58LaqtjrUuSKyFDhdVbNEpAGQpqq5K4re36Er\ns3btcrO4T5/u5jCoWxduvJFdx3Ri2jS3ec8eqFoVhgyB88+HCnE1trgxRWdlZPGV2Tt0AE2awJrW\nXdi7D1Y/b6NdGmOMibmGwMqw9VXBtnATgT+IyBrgW+D6CM6tr6pZAKr6C1AvyrlNSapcGS67DJ54\nwk0C+OuvcMstVP7XXVz6541MmQKnnOLmr3vsMXfoggWxDm2MKYvi8rei2hd0gW+fYk/aJ+4XsDif\ntNbndtLgd36fs4Pf+X3ODv7nN3GnG7BAVc8QkaOAd0SkfSGfI9+fu4cOHUqzZs0ASElJoWPHjvs/\nv2lpaQBxuz5+/Hiv8oavhx4f8vgVK6BfP1LXr4ennybt5ZfhrbdIHTeOu+86g0cf+5BXX4UVK1K5\n8UZo0SKNPn3g3HPjJH+crud+D7HOU57yp6enM2rUqALfnym+0PXeuHEjwP657Qor7ppcAqxdC18d\nN5Imu76n7Yt3UunM02Kc7tDSPP+Hoc/5fc4Ofuf3OTvYoCiloYw1ubxDVbsH63k1uXwDuEdVPwnW\n3wP+ivvhNM9zRWQJkBrW5PIDVW2bx+t73eTS5++KQmdfs8bNOh66FdepE9xwA3tq1WPGDHj2WdcM\nMznZTQrfs2fJTkperq59nPE5fyTZrYwsvmiWkXFZoQN46qwZHPf14xx2XheaTvp7DJMZY8ojK6yK\nrwxV6BKBZcCZwFrgC2Cgqi4JO+ZhYJ2qjhOR+sBXQAdgU37nish9QHZQufsrUEtVfze8s+8VunJH\n1Y2C+cgjsG2bm0BwxAjo3ZvVa4Tx4+Grr9yh7dq5icmPPDK2kY0pLCsji69cVOhmPf4LTcYMJLl2\nZVosnhX3zS6NMWVLs2bNyMzMjHUMrzVt2jTP5iO+Vehg/9QDD3Fg6oF7RWQk7m7bEyJyODAZODw4\n5R5VnZ7fucH2w4AXgcZAJm7ago15vLZV6Hy0fj2MHw/z5rn19u3hppvQho1IS4OJEyE7280r2K+f\nGzilSpWYJjYmYlZGFl80y8i4GxQl5OQ/N2BFUlu2Ze9i94efxTrOIfnentjn/D5nB7/z+5wdCs6f\nkZGBqsbl8sEHH8Q8QyRLUfsCxCNVfUtVW6tqy1CFTFUfV9UngsdrVbWbqrYPlumHOjfYnq2qZwX7\nuuZVmSsLfP6uKFb22rXhzjvhjjv2T0jOZZchL8ygy5/2MmUK9OnjJiGfMQOGDoXPovzPnXJ77eOA\nz/kjyW5lZHyVkXFboatXD347ugv71Ea7NMYYY4yHROD002HqVOjaFXbvhscfh6uvpvq6nxk1yrXM\nbNECsrLg1lvh73+3ueuMMYUTt00uAd54eh0NR19I8mGVaPHdTDeZizHGGK/52OQylqzJZRkyf76b\nlHzdOtfW8qKLYPBg9koFXnsNnn7aTXOQlAQjR0KvXiU7aIoxJv6UqT504H6h+qjDtTTf/h1tnr2N\nKj3PjFE6Y4wx0WIVusKxCl0Zs327m7tu1iy33qwZ/PWv0KYN69a5bnehppdHHw2jR0PTpjFLa4wp\nZWWqDx245ufZHbqgCmviuNmlz+2kwe/8PmcHv/P7nB38zu9zdlP++Px5LZHsSUkwahQ89BA0bAgZ\nGXDVVfDoo9SruYu774axY6FWLVi0CIYPh8mT3XQHhWXXPnZ8zu9zdvA/f1HEdYUO4PABp6MIez+b\n737VMsYYY4zxXfv2MGkSDBjg1l98EYYNQ75NJzUVpkyBc8+FnBz3ePhwV8Ezxpj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BeUr1q/93Hf++r6nN/n7OB3fp+zg9/5fc5uGh+f368+Zwe/86dU9rZtYdIkV9Sdfz6kp8P775N1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\n//AcmfNfo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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s1 = s0.copy() # Copy steady states \n", "s1[0] *= 1.2 # Set size of shock to 5% larger than steady state value\n", "\n", "irf_glob = simulate(model, dr_global_spl, s1, n_exp=0, horizon=40 ) # Simulate spline model \n", "irf_pert = simulate(model, dr_pert, s1, n_exp=0, horizon=40 ) # Simulate linear model\n", "\n", "plt.figure(figsize=(15, 7))\n", "\n", "plt.subplot(221)\n", "plt.plot(irf_glob['z'],linewidth=2, alpha=0.75,color='b')\n", "plt.title('Productivity')\n", "plt.grid()\n", "\n", "plt.subplot(222)\n", "plt.plot(irf_glob['i'],linewidth=2, alpha=0.75,color='b',label='Spline')\n", "plt.plot(irf_pert['i'],linewidth=2, alpha=0.75,color='r',label='Linear')\n", "plt.title('Investment')\n", "plt.legend()\n", "plt.grid()\n", "\n", "plt.subplot(223)\n", "plt.plot(irf_glob['n'],linewidth=2, alpha=0.75,color='b',label='Spline')\n", "plt.plot(irf_pert['n'],linewidth=2, alpha=0.75,color='r',label='Linear')\n", "plt.title('Labour')\n", "plt.legend()\n", "plt.grid()\n", "\n", "plt.subplot(224)\n", "plt.plot(irf_glob['c'],linewidth=2, alpha=0.75,color='b',label='Spline')\n", "plt.plot(irf_pert['c'],linewidth=2, alpha=0.75,color='r',label='Linear')\n", "plt.title('Consumption')\n", "plt.legend()\n", "plt.grid()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Approximation errors\n", "\n", "We can compute the approximation errors for the optimality conditions: \n", "\n", "* $$EulerError = 1 - \\beta E \\left[ \\left( \\frac{C_{t+1}}{C_t} \\right)^{\\sigma}( 1- \\delta + r_{k,t+1} ) \\right] $$\n", "* $$LaborSupplyError = w_t - \\chi n_t^{\\eta} C_t^{\\sigma} $$ \n", "\n", "First, let's look at the maximum and mean errors (i.e. using the ergodic distribution of the model) over the state space for each of the approximations. We use the ``omega`` function to do this. We then print the errors for each equation." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model type detected as 'dtcscc'\n" ] } ], "source": [ "# Reload the model with the original calibration\n", "model = yaml_import(filename)\n", "# dr_pert = approximate_controls(model, order=1)\n", "dr_global_spl = time_iteration(model, pert_order=1, verbose=False, interp_type=\"spline\", interp_orders=[4,4])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Linear approximation\n", "\n", "Euler Errors:\n", "- max_errors : [ 0.00082703 0.00646158]\n", "- ergodic : [ 0.00018773 0.00084238]\n", "\n", "--------------\n", "\n", "Cubic spline approximation\n", "\n", "Euler Errors:\n", "- max_errors : [ 0.00020388 0.00025505]\n", "- ergodic : [ 1.48554280e-04 8.03166294e-05]\n", "\n", "--------------\n", "\n", "Smolyak approximation\n", "\n", "Euler Errors:\n", "- max_errors : [ 1.38008613e-04 2.28991817e-06]\n", "- ergodic : [ 1.32367122e-04 6.62075500e-07]\n", "\n" ] } ], "source": [ "from dolo.algos.dtcscc.accuracy import omega\n", "\n", "err_pert = omega(model, dr_pert)\n", "print(\"Linear approximation\\n\")\n", "print(err_pert)\n", "print(\"--------------\\n\")\n", "\n", "err_spl = omega(model, dr_global_spl)\n", "print(\"Cubic spline approximation\\n\")\n", "print(err_spl)\n", "print(\"--------------\\n\")\n", "\n", "err_smol = omega(model, dr_global_smol)\n", "print(\"Smolyak approximation\\n\")\n", "print(err_smol)\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also visualize the errors over the state space:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QQRUiywencnyGauLDYbwj0sSJ1OTotkSqksqWgnwopeNBpO7oky5AVHLfSNs3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\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Xfg3KVVBvJFbDdY7FfXVdF2dXpgy/rqB+DUNQgr8FBooOfJEHMOpnFIM+o0FB1q/d2qVX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\n+zhvtKf9+dWF4wkzM2u0lsQUk6rpcDJwW0TsjYh9wGeBXyw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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a = err_pert['domain'].a\n", "b = err_pert['domain'].b\n", "orders = err_pert['domain'].orders\n", "errors = np.concatenate((err_pert['errors'].reshape( orders.tolist()+[-1] ),\n", " err_spl['errors'].reshape( orders.tolist()+[-1] ),\n", " err_smol['errors'].reshape( orders.tolist()+[-1] )),\n", " 2)\n", "\n", "plt.figure(figsize=(15,8))\n", "\n", "titles=[\"Investment demand pertubation errors\",\n", " \"Labor supply pertubation errors\",\n", " \"Investment demand spline errors\",\n", " \"Labor supply spline errors\",\n", " \"Investment demand Smolyak errors\",\n", " \"Labor supply Smolyak errors\"]\n", "\n", "for i in range(6):\n", "\n", " plt.subplot(3,2,i+1)\n", " imgplot = plt.imshow(errors[:,:,i], origin='lower',\n", " extent=( a[0], b[0], a[1], b[1]), aspect='auto')\n", " imgplot.set_clim(0,3e-4)\n", " plt.colorbar()\n", " plt.xlabel('z')\n", " plt.ylabel('k')\n", " plt.title(titles[i])\n", " \n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "#### Pros\n", "* DOLO is fairly easy to use \n", " * Much easier to access decision rule objects than Matlab's Dynare\n", "* Implements a range of approximation methods\n", "* At least as fast as Matlab's Dynare\n", "\n", "#### Cons\n", "* Still very much \"In Development\"\n", "* Some functions not working (higher order pertubations, Dynare)\n", "* Not yet well-documented \n", "* Not yet well-used (far less online help than for Matlab's Dynare)\n", "* Does not have Dynare's Ramsey Policy functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture14/pre_RuixueGong.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Statsmodels v.s. Scikit-learn\n", "\n", "** Ruixue Gong, NYU**\n", "\n", "This notebook helps economists better understand the differences and similarities between machine learning package Scikit-learn and traditional statistical package Statsmodels. \n", "\n", "First of all, we provide an overview of Scikit-learn and Statsmodels. Secondly, we make a brief contrast of these two packages. At last, we talk about the applications on several topics such as linear regression models, logistic models and time series analysis with the use of Statsmodels and Scikit-learn respectively.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Overview of Scikit-learn" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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rvLoMAxXHY8dLosQmq+chnkiMjpoMoysieQSelnsG+nYcVeSzZhhLyIK3rfA9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NdeC8pjtp0IQTpq8mr2XfzPebJ6JYjFV5Kv5NODabfGz4fE3ve7ypK/FYxD0aeG1ntiLrym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\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Dqln6w7CPwsqurhp/xKXsJVOvDzJ+0mTBN83VIMvDXUj4aKSMD27jlFw5vsK5SixPqe0s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vRWqbe4NGyFzmtsbt/1AKQFpm8n9A7vZ/rCyR1PN49/7NU/pZgd14+eC6z2cPQb3Bcmbr/04XWO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\n8rp/FfMK5W2yitUy/tXXo9hW6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IiKpIQlUJVFNiLhERSQERFDJQREUEhERAEREAREQBER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\nNnqt5cMpe8jPDcXdH13atsw/aJrhxWmV1A+I2e21u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"text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='scikit-learn-flow-chart.jpg') #source: web" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Overview of Statsmodels\n", " * Linear regression models\n", " * Generalized linear models\n", " * Discrete choice models\n", " * Robust linear models\n", " * Many models and functions for time series analysis\n", " * Nonparametric estimators\n", " * A collection of datasets for examples\n", " * A wide range of statistical tests\n", " * Input-output tools for producing tables in a number of formats (Text, LaTex, HTML) and for reading Stata files into NumPy and Pandas.\n", " * Plotting functions\n", " * Extensive unit tests to ensure correctness of results\n", " \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Contrast**\n", "\n", "* Statsmodels\n", " * traditional model where we want to know how well a given model fits the data, and what variables \"explain\" or affect the outcome, or what the size of the effect is\n", " * analyzing the training data including hypothesis tests and goodness-of-fit measures\n", " \n", " \n", "* Scikit-learn\n", " * machine learning algorithms where the main supported task is choosing the \"best\" model for prediction\n", " * model selection for out-of-sample prediction and hence cross-validation on \"test data\"." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Contents\n", "\n", "* Generalized Linear Models\n", " * OLS\n", " * Testing\n", "* Logistic Regression\n", "* Time Series Analysis\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", "from scipy import stats\n", "import pandas as pd\n", "from sklearn import svm\n", "from sklearn.linear_model import LogisticRegression\n", "from statsmodels.base.model import GenericLikelihoodModel" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## OLS and Testing\n", "\n", "Linear regression is in its basic form the same in statsmodels and in scikit-learn. However, Statsmodels can generate a summary of OLS result like Stata and R. Lecture 11 had already given an introduction to regression analysis in Sciki-learn, so we only present the different features owned by Statsmodels here.\n", "\n", "\n", "Now consider the real model as follows: \n", "$$y = \\beta_1 x + \\beta_2 sin(x) + \\beta_3 (x-3)^2 + \\beta_0, \\quad \\text{ where } \\epsilon \\sim N(0,1)$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "n = 20\n", "x = np.linspace(0, 5, n)\n", "\n", "sigma = 0.3\n", "beta = np.array([1, 0.5, -0.02,5]) # real coefficient\n", "e = np.random.normal(size=n)\n", "X = np.column_stack((x, np.sin(x), (x-3)**2, np.ones(n))) \n", "y_true = np.dot(X,beta)\n", "y = y_true + e" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#do regression\n", "model = sm.OLS(y, X) #Pick a class. GLS, WLS...\n", "results = model.fit()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We can get the summary of the regression result just like R.\n", "\n", "* Summary\n", "```\n", "results.summary()\n", "```\n", "\n", "We can also extract the values that we are interested in.\n", "\n", "* Coefficients: ```results.params```\n", "* R square: ```results.rsquared```\n", "* Fitted values: ```results.fittedvalues```\n", "* Predicted values: ```results.predict()```\n", "* Standard errors of each coefficient: ```results.bse```" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: y R-squared: 0.750\n", "Model: OLS Adj. R-squared: 0.704\n", "Method: Least Squares F-statistic: 16.03\n", "Date: Tue, 03 May 2016 Prob (F-statistic): 4.44e-05\n", "Time: 14:53:46 Log-Likelihood: -21.592\n", "No. Observations: 20 AIC: 51.18\n", "Df Residuals: 16 BIC: 55.17\n", "Df Model: 3 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "x1 1.7893 0.411 4.355 0.000 0.918 2.660\n", "x2 1.9644 0.702 2.797 0.013 0.475 3.453\n", "x3 0.3210 0.152 2.110 0.051 -0.001 0.644\n", "const 2.0976 1.467 1.430 0.172 -1.013 5.208\n", "==============================================================================\n", "Omnibus: 0.532 Durbin-Watson: 2.338\n", "Prob(Omnibus): 0.766 Jarque-Bera (JB): 0.619\n", "Skew: -0.214 Prob(JB): 0.734\n", "Kurtosis: 2.252 Cond. No. 37.7\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "print(results.summary())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficients: [ 1.7893301 1.96435659 0.32104257 2.0975902 ]\n", "R2: 0.750366491542\n", "Standard errors: [ 0.41085449 0.70239255 0.15213774 1.46709401]\n", "Fitted values: [ 4.98697334 5.48416331 5.99063564 6.47362943 6.905062\n", " 7.26346243 7.53544993 7.71665512 7.81202093 7.83546222\n", " 7.80890771 7.76079011 7.72408843 7.73405729 7.82579947\n", " 8.03184902 8.37993116 8.8910533 9.57805861 10.44474174]\n", "Predicted values: [ 4.98697334 5.48416331 5.99063564 6.47362943 6.905062\n", " 7.26346243 7.53544993 7.71665512 7.81202093 7.83546222\n", " 7.80890771 7.76079011 7.72408843 7.73405729 7.82579947\n", " 8.03184902 8.37993116 8.8910533 9.57805861 10.44474174]\n" ] } ], "source": [ "print('Coefficients: ', results.params)\n", "print('R2: ', results.rsquared)\n", "print('Standard errors: ', results.bse)\n", "print('Fitted values: ', results.fittedvalues)\n", "print('Predicted values: ', results.predict())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TEZHz/f47EBICxMYa3RLDMAATEZHzpKQAzzwD3H8/MGYMEBBgdIvsxmq1Ijo6utznMwAT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\nJauaQkMZfIk8yoULMjo6aBA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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "prstd, iv_l, iv_u = wls_prediction_std(results)\n", "#wls_prediction_std returns standard deviation and \n", "#confidence interval of my fitted model data\n", "\n", "fig, ax = plt.subplots(figsize=(8,6))\n", "\n", "ax.plot(x, y, 'o', label=\"data\")\n", "ax.plot(x, y_true, 'b-', label=\"Real\")\n", "ax.plot(x, results.fittedvalues, 'r--.', label=\"OLS\")\n", "ax.plot(x, iv_u, 'r--')\n", "ax.plot(x, iv_l, 'r--')\n", "ax.legend(loc='best')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Joint Test**\n", "\n", "We can test $$R\\beta = 0$$ or formula-like $$\\beta_2 = \\beta_3 = 0$$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "#test beta_2 = beta_3 = 0\n", "print(results.f_test(\"x2 = x3 = 0\"))\n", "\n", "#test R beta = 0\n", "R = [[0, 1, 0, 0], [0, 0, 1, 0]]\n", "print(results.f_test(R))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Maximum Likelihood Estimation\n", "\n", "Statsmodel has many built-in regression models using maximum likelihood estimation. However, Scikit-learn doesn't. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "::\n", "\n", " Number of Observations - 32\n", "\n", " Number of Variables - 4\n", "\n", " Variable name definitions::\n", "\n", " Grade - binary variable indicating whether or not a student's grade\n", " improved. 1 indicates an improvement.\n", " TUCE - Test score on economics test\n", " PSI - participation in program\n", " GPA - Student's grade point average\n", "\n", " GPA TUCE PSI\n", "0 2.66 20 0\n", "1 2.89 22 0\n", "2 3.28 24 0\n", "3 2.92 12 0\n", "4 4.00 21 0\n" ] } ], "source": [ "data = sm.datasets.spector.load_pandas() #use the dataset spector\n", "exog = data.exog\n", "endog = data.endog\n", "print(sm.datasets.spector.NOTE)\n", "print(data.exog.head())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.400588\n", " Iterations: 292\n", " Function evaluations: 494\n", " MyProbit Results \n", "==============================================================================\n", "Dep. Variable: GRADE Log-Likelihood: -12.819\n", "Model: MyProbit AIC: 33.64\n", "Method: Maximum Likelihood BIC: 39.50\n", "Date: Tue, 03 May 2016 \n", "Time: 14:53:54 \n", "No. Observations: 32 \n", "Df Residuals: 28 \n", "Df Model: 3 \n", "==============================================================================\n", " coef std err z P>|z| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "const -7.4523 2.542 -2.931 0.003 -12.435 -2.469\n", "GPA 1.6258 0.694 2.343 0.019 0.266 2.986\n", "TUCE 0.0517 0.084 0.617 0.537 -0.113 0.216\n", "PSI 1.4263 0.595 2.397 0.017 0.260 2.593\n", "==============================================================================\n" ] } ], "source": [ "exog1 = sm.add_constant(exog, prepend=True) #combine X matrix with constant\n", "\n", "#plug in the log-likelihood function of my own model\n", "class MyProbit(GenericLikelihoodModel):\n", " def loglike(self, params):\n", " exog = self.exog\n", " endog = self.endog\n", " q = 2 * endog - 1\n", " y = stats.norm.logcdf(q*np.dot(exog, params)).sum() \n", " return y\n", "\n", "sm_probit_manual = MyProbit(endog, exog1).fit()\n", "print(sm_probit_manual.summary())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now I want to compare binary predictions.\n", "\n", "* Logistic Regression using Statsmodels.\n", "\n", "* Supporting Vector Machine (SVM) with linear kernal using Scikit-learn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I use the spector dataset which contained in Statsmodels package. To visualize the results, I set two exogenous variables, x1 and x2.\n", "\n", "We all have familiarized with the mathematical formation of logistic regression. So I put a brief introduction graph about supporting vector machine algorithm as follows." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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"text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='svm.jpg' ) #source : web" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization terminated successfully.\n", " Current function value: 0.499734\n", " Iterations 6\n" ] } ], "source": [ "X = np.delete(data.exog.as_matrix(),2,1)\n", "y = np.asarray(data.endog.tolist())\n", "\n", "#classifiers\n", "clf = svm.SVC(kernel='linear', C = 1) \n", "\n", "#fitting by Scikit-learn\n", "model_svm = clf.fit(X,y)\n", "\n", "#fitting by Statsmodels\n", "XX = np.insert(X,0,1,axis = 1) #add a column with contant 1\n", "model_logit_stats = sm.Logit(y,XX)\n", "res = model_logit_stats.fit()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4p887zHhkhtHhKIpSBJX4lTJ38dZFhi4fSn/f/nw26DOsLMq6H4CiKKWhEr9S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\n0masrKwsvnj3Xfr6+OR9djgqir5TppT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "w = clf.coef_[0]\n", "a = -w[0] / w[1]\n", "\n", "c = -res.params[1] / res.params[2]\n", "\n", "x1 = np.linspace(2,4)\n", "x2 = a * x1 - clf.intercept_[0] / w[1] #svm \n", "x3 = c * x1 - res.params[0] / res.params[2] #logistic\n", "\n", "h1 = plt.plot(x1,x2,'k-', color = 'green', label = 'SVM')\n", "h2 = plt.plot(x1,x3,'k-', color = 'blue', label = 'Logit Statsmodels')\n", "plt.scatter(X[:, 0], X[:,1], c = y, alpha = 0.4)\n", "plt.legend()\n", "plt.title('Comparision: LogisticRegression and SVM')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Time Series Analysis\n", "\n", "As I mentioned in the beginning, Scikit-learn focus on prediction of time series by machine learning algorithm. It doesn't contain traditional time series analysis such as ARMA, VAR, and relevant statictical tests. But you can find these in Statsmodels package." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "::\n", "\n", " Number of Observations - 309 (Annual 1700 - 2008)\n", " Number of Variables - 1\n", " Variable name definitions::\n", "\n", " SUNACTIVITY - Number of sunspots for each year\n", "\n", " The data file contains a 'YEAR' variable that is not returned by load.\n", "\n" ] } ], "source": [ "dta = sm.datasets.sunspots.load_pandas().data #use the dataset sunspot\n", "print(sm.datasets.sunspots.NOTE)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008')) #set time series\n", "del dta[\"YEAR\"]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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qgC72mIRtNwnfBXTKMHjmmepjFUEx7Bk6w3FShxhmbjhM6oxJAHG6w2ofqXM6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\n/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ATikNAR8AbgLuBq5LKe2OiPdHxGV5ma8BD0bET4EtwO9We96psGTJEt761lfR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\nZEmSJKnAgCxJkiQVGJAlSZKkAgOyJEm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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,8))\n", "ax1 = fig.add_subplot(211)\n", "fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40, ax=ax1)\n", "ax2 = fig.add_subplot(212)\n", "fig = sm.graphics.tsa.plot_pacf(dta, lags=40, ax=ax2)" ] } ], "metadata": { "celltoolbar": "Slideshow", "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture2/.pass ================================================ ================================================ FILE: lecture2/c_examples/ar1_sample_mean.c ================================================ #include #include #include #include #include double ar1_ts (double * params, int n, unsigned long int seed) { double beta = params[0]; double alpha = params[1]; double s = params[2]; /* create a generator chosen by the environment variable GSL_RNG_TYPE */ const gsl_rng_type * T; gsl_rng * r; gsl_rng_env_setup(); T = gsl_rng_default; r = gsl_rng_alloc(T); gsl_rng_set(r, seed); int i; double x = beta / (1 - alpha); // Start at mean of stationary dist double sum = 0; for (i = 1; i <= n; i++) { sum += x; x = beta + alpha * x + gsl_ran_gaussian(r, s); } gsl_rng_free (r); return sum / n; } int main(void) { clock_t start, end; double cpu_time_used; int N = 10000000; double beta = 1.0; double alpha = 0.9; double s = 1; double params[3] = {beta, alpha, s}; start = clock(); double sample_mean = ar1_ts(params, N, 1); end = clock(); cpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC; printf("mean = %g\n", sample_mean); printf("time elapsed = %g\n", cpu_time_used); return 0; } ================================================ FILE: lecture2/c_examples/function.c ================================================ #include double f(double x) { return 1.0 / x; } int main(void) { double x = 4.0; double y = f(x); printf("y = %f\n", y); return 0; } ================================================ FILE: lecture2/c_examples/function_ref.c ================================================ #include void f(double x, double *y_pointer) { *y_pointer = 1.0 / x; } int main(void) { double x = 4.0; double y; f(x, &y); printf("y = %f\n", y); return 0; } ================================================ FILE: lecture2/c_examples/hello.c ================================================ #include int main(void) { printf("hello world\n"); return 0; } ================================================ FILE: lecture2/c_examples/hello_again.c ================================================ #include int main() { printf("hello world\n"); return 0; } ================================================ FILE: lecture2/c_examples/make_grid.c ================================================ #include /* Creates a linear array. */ void linspace(double *ls, double a, double b, int n) { double step = (b - a) / (n - 1); int i; for (i = 0; i < n; i++) { ls[i] = a; a += step; } } int main(void) { int n = 10; double grid[n]; linspace(grid, 1, 2, n); int i; for (i = 0; i < n; i++) { printf("%g\n", grid[i]); } return 0; } ================================================ FILE: lecture2/c_examples/makefile ================================================ maketest: gcc -Wall ar1_sample_mean.c -lgsl -lgslcblas -lm -o ar1_sample_mean ================================================ FILE: lecture2/c_examples/sin_func.c ================================================ #include #include int main() { double x; int i = 3; x = 1.0 / (double) i; /* cast i to double */ double y = sin(x); printf("i = %d, x = %f, y = %f\n", i, x, y); return 0; } ================================================ FILE: lecture2/git_intro/Makefile ================================================ all: reveal beamer pdf reveal: pandoc --self-contained --variable theme="simple" -t revealjs -s github.md -o github.html beamer: pandoc -t beamer -s github.md -o github_slides.pdf pdf: pandoc -t beamer -s github.md -o github.pdf ================================================ FILE: lecture2/git_intro/github.html ================================================ Intro to GitHub

Intro to GitHub

Spencer Lyon

2016-02-05

Git remotes

What's a remote?

  • A git repo can be on your hard drive: called local
  • A remote is a copy of the repository on someone else's hard drive or server
  • You git push commits from local to remote
  • git pull commits from remote to local

Collaboration

  • Working with remotes enables many workflows
  • One common workflow (image taken from here)
Centralized workflow
Centralized workflow

GitHub

Facts

  • GitHub is a common place to have a remote
    • Over 2.2 million active repositories
    • Unlimited free public repositories
    • 5 free private repositories for academics

GitHub Collaboration

  • Permission management: only certain users can push
  • Forking: anyone can create a copy of any (visible) repository under their account
  • Pull requests: system for project maintainers to review proposed changes before accepting them
  • Automated testing (CI) and coverage

Example

quantecon_nyu_2016

  • Add this lecture to course repo
  • Steps:
    1. Login to github (online)

    2. Clone repository

      git clone https://github.com/jstac/quantecon_nyu_2016.git
    3. Fork repository (online)

    4. Add remote that points to fork

      git remote add fork https://github.com/spencerlyon2/quantecon_nyu_2016.git

Steps (continued)

  • More steps

    1. add + commit files

      git add files
git commit -m "Adding github intro slides"

    2. push to fork

      git push fork master

    3. Open pull request (online)

Exercise

Pushing homework

  • We will let you practice hw submission process
  • Steps
    • Create account on github
    • Clone homework repository
    • Fork homework repo
    • Add your fork as remote
    • Create file firstname_lastname in folder hw_github_intro
    • Leave short message in file
    • Add and commit the file
    • Push to fork
    • Submit pull request

Extras

SSH keys

  • Two modes of authenticating to github: https, SSH
  • https will require you to enter password on every push
  • Adding ssh-key removes this requirement
  • Follow these steps:
    • ssh-keygen: follow prompts
    • Copy the output of cat ~/.ssh/id_rsa.pub (use shift-c)
      • NOTE: path ~/.ssh/id_rsa.pub might be different depending on your answers to previous step
      • Important part is to get the .pub version
    • Go to github account settings
    • Click "ssh keys" link on left, then "New ssh key"
    • Give it a title (name) and paste clipboard contents in key
    • Click big green "add SSH key button"
================================================ FILE: lecture2/git_intro/github.md ================================================ --- title: 'Intro to GitHub' author: "Spencer Lyon" date : "2016-02-05" --- # Git `remote`s ## What's a `remote`? - A git repo can be on your hard drive: called local - A `remote` is a copy of the repository on someone else's hard drive or server - You `git push` commits from local to remote - `git pull` commits from remote to local ## Collaboration - Working with remotes enables many [workflows](https://git-scm.com/book/en/v2/Distributed-Git-Distributed-Workflows) - One common workflow (image taken from [here](https://git-scm.com/book/en/v2/Distributed-Git-Distributed-Workflows)) ![Centralized workflow](images/git_centralized_workflow.png) # [GitHub](https://github.com) ## Facts - GitHub is a common place to have a `remote` - [Over 2.2 million](http://githut.info) active repositories - Unlimited free public repositories - 5 free private repositories for academics ## GitHub Collaboration - Permission management: only certain users can `push` - Forking: anyone can create a copy of any (visible) repository under their account - Pull requests: system for project maintainers to review proposed changes before accepting them - Automated testing (CI) and coverage # Example ## quantecon_nyu_2016 - Add this lecture to course repo - Steps: 1. Login to github (online) 2. Clone repository ```sh git clone https://github.com/jstac/quantecon_nyu_2016.git ``` 3. Fork repository (online) 4. Add `remote` that points to fork ```sh git remote add fork https://github.com/spencerlyon2/quantecon_nyu_2016.git ``` ## Steps (continued) - More steps 5. `add` + `commit` files ``` git add files git commit -m "Adding github intro slides" ``` 6. `push` to fork ``` git push fork master ``` 7. Open pull request (online) # Exercise ## Pushing homework - We will let you practice hw submission process - Steps - Create account on github - Clone [homework repository](https://github.com/jstac/quantecon_nyu_2016_homework) - Fork homework repo - Add your fork as remote - Create file `firstname_lastname` in folder `hw_github_intro` - Leave short message in file - Add and commit the file - Push to fork - Submit pull request # Extras ## SSH keys - Two modes of authenticating to github: https, SSH - https will require you to enter password on every push - Adding ssh-key removes this requirement - Follow these steps: - `ssh-keygen`: follow prompts - Copy the output of `cat ~/.ssh/id_rsa.pub` (use shift-c) - NOTE: path `~/.ssh/id_rsa.pub` might be different depending on your answers to previous step - Important part is to get the `.pub` version - Go to github account [settings](https://github.com/settings/profile) - Click "ssh keys" link on left, then "New ssh key" - Give it a title (name) and paste clipboard contents in key - Click big green "add SSH key button" ================================================ FILE: lecture2/git_intro/gitnotes.html ================================================

Version Control with Git

I suspect that the most of us have participated in some sort of "home-rolled" version of version control. For example, below are three forms of "collaboration" that we might have engaged in

  • Tediously "careful" approach: This is when a person (or people) maintain a type of version control by placing the date edited at the end of the file name and then maintaining a full folder of these types of files.
  • Stake your claim approach: This appraoch takes the form of calling "dibs" on the use of a specific file for the time being by shouting across the office or sending emails to your collaborators and informing others that the file is "currently under your control" and that others should stay away!
  • Where's that email approach: Perhaps worst of all is the approach in which collaborators each maintain their own file and email it back and forth with changes as they make them.

All of these are dangerous workflows and will result in mistakes and unwanted material showing up in your files (or wanted material being erased). Luckily for us, a variety of version control products have evolved in order to help us eliminate the need for these types of behaviors. Dropbox, Google Drives, SVN, Mercurial, and git are all examples of version control systems.

Version control allows us to keep track of what changes have been made over time. Careful maintenance of code and data is vital to reproducibility which "is the hallmark of good science." While Dropbox and Google Drives may be useful for sharing certain types of files (I am not an advocate of their complete abandonment), they are not suitable for the fine tuned version control that we need to maintain good science (or software).

What is git?

Git is a distributed version control system. Distributed version control means that the entire history of every file is kept on your computer. It was originally written by Linus Torvalds (creator of Linux) to help maintain the Linux project (Fun Fact: Git was originally written because Torvalds found all other alternatives of version control to be too slow to manage a project as large as Linux, so he decided to write his own version which he began on 3 April 2005 and started being used on 7 April 2005. Read more about the history).

Using git

Many people associate git with the cloud based repository service Github, but git can be run independently either just on your own computer or on a self-hosted server. In this short tutorial, we are going to create a git repository hosted on our computer. We will then talk about some of the day to day commads that will be used in git.

WARNING: Until you know what you're doing and exactly how they work, NEVER use the -f or --force flags no matter what the internet tells you.

We are now going to walk through some of the basic settings you should set and an example of some commands.

Configuration

Here we deal with configuration details such as our default editor, user name, email, colors, etc...

  • Set name: git config --global user.name FirstName LastName
  • Set email: git config --global user.email email@email.com
  • Sets git colors: git config --global color.ui "auto"
  • Sets editor to vim: git config --global core.editor "vim"

Creating a folder

We will create a folder called <MyFirstGitRepo> using mkdir <MyFirstGitRepo>

Initializing a git repository

Now enter that directory using cd <MyFirstGitRepo>. To initialize this directory as a git repository (which in the background will create a series of directories and files) we will use the command git init.

We can see the things that were created by typing ls .git, but don't worry too much about what is being kept inside yet.

Four Stages of Files

Files in a git repository can be in four different stages: untracked, unstaged, staged, and committed.

  • Untracked: This is a new file that your repository hasn't seen before.
  • Unstaged: A file that has previously been tracked and saved, but has changed since its last version.
  • Staged: A file that has been changed and is prepared to be committed. Nothing is set in stone yet though and new changes can be made.
  • Committed: When a file is committed it becomes a piece of the history of the repository. This moment of time in the file will be able to be referenced or referred to in the future.

The picture below illustrates this "life cycle"

Let's illustrate this through an example and to expose ourselves to the commands that will be helpful: git add, git commit, git diff, and git status.

First let's check what is new in our repository. Type git status. What does it say? It should say that there is nothing to commit because we haven't done anything yet.

Now let's create a file called README.md. Open this file and type your name in it (could also use the command echo "FirstName LastName" >> README.md). If you type git status now, what do you see? It should list README.md as an untracked file.

We can move this file from untracked to staged by using git add README.md. Type this command and then once again type git status. Our README.md file now shows up in green as a change to be committed. This means it is staged.

We can take our "snapshot" of the file by using the command git commit -m "Type a meaningful msg here". Commit the file and then once again type git status. What do you see now? The repository should be clean again.

Add something new to the README.md file. Once again, we can check the status of our git repository by typing git status. It will tell us that README.md is unstaged because we have made changes to it.

Imagine we wanted to double check the things that we changed. We could type git diff README.md which will show us the changes that have been made to that file since its previous commit.

These commands will be the core of your git workflow so I suggest familiarizing yourself with what they do.

Commit History

Remember how we can leave ourselves commit messages. It is useful to leave meaningful commit messages because they are left as a guide for yourself. We can see our history of commits by typing git log. We can do this in several formats: Try git log --pretty=oneline, git log --stat, git log --since=2.weeks, etc... See the Git Pro book for more options.

If we want to return to a previous commit in our history we can use the information from git log to get back. There is a sequence of numbers and letters, which we will call commit hash, at the top of each entry in your history. If you copy this and type git reset <commit hash> then it will return us to that moment of our history. All of our more recent changes will be there, but they will be as if they had been just staged. Can use git reset <commit hash> --hard to reset to a previous point in time and delete all changes, but be very careful with that command as it can erase your history (in fact, I suggest not using it until you really know enough that you know it is what you want).

Branching

A branch is essentially an specific version of your folder. You start with a main branch which is called master. When you create another branch, it is an exact replica of the branch it is being created from (typically master) and includes the full history of the repository. After creating a new branch you can make changes and this new branch will develop its own history (without changing the history of the original branch -- such as master). If you decide you like the changes that you made then you can bring them into the original branch.

The command git checkout is used for a few different things, but we will mostly use it for switching between branches. When used with the -b flag, it creates a new branch and switches to it.

Let's create a new branch in our repository. git checkout -b test. What has changed? Let's look at the output of git branch. Now let's make some changes in our branch.

> echo "New line" >> README.md
> git add README.md
> git commit -m "Branch update"
> git log

We can see the history has changed and we have a new commit. Let's return to the master branch by typing git checkout master. Now check git log, notice that we no longer have the changes from our test branch.

Imagine that we decided to bring those changes into our master branch. We could use the git merge command by typing git merge test which will merge the branch test into the current branch.

Ignore Files

Sometimes there are files that get created via an intermediate process (such as .aux, .synctex, etc... in latex). We often don't care about keeping track of these files. Another wonderful thing about git is that it allows us to ignore files we don't care about!

We do this by creating a file called .gitignore at the initial directory of our git repository.

For example, echo *.garbage >> .gitignore.

Then create a file called foo.garbage with touch foo.garbage.

Now type git status. Notice that this file doesn't show up! It is because it recognizes that anything with an ending of .garbage should be ignored (this is because * is treated as a wildcard. Read more about regular expressions or wild cards for more information on how to use them).

Resources and References

Below is a sequence of references to things that I have found useful. I suggest reading a few of them and at least skimming the majority of them. In particular, the software carpentry lectures on git are very useful for learning the basics -- The Pro Git book is the biblical reference for git and can likely answer any question you will have for at least a few years to come. I have organized both sections by how relevant/convincing I found the documents.

Git Technical References

Software Carpentry: Git Lectures Pro Git Git Tower

Why Git References

Difference between git and Dropbox Version control for scientific research Why Physicists Should Use Git Or Why Everyone Should Try Git Git can facilitate greater reproducibility and increased transparency in science

================================================ FILE: lecture2/git_intro/gitnotes.md ================================================ # Version Control with Git
I suspect that the most of us have participated in some sort of "home-rolled" version of version control. For example, below are three forms of "collaboration" that we might have engaged in * Tediously "careful" approach: This is when a person (or people) maintain a type of version control by placing the date edited at the end of the file name and then maintaining a full folder of these types of files. * Stake your claim approach: This appraoch takes the form of calling "dibs" on the use of a specific file for the time being by shouting across the office or sending emails to your collaborators and informing others that the file is "currently under your control" and that others should stay away! * Where's that email approach: Perhaps worst of all is the approach in which collaborators each maintain their own file and email it back and forth with changes as they make them. All of these are dangerous workflows and will result in mistakes and unwanted material showing up in your files (or wanted material being erased). Luckily for us, a variety of version control products have evolved in order to help us eliminate the need for these types of behaviors. Dropbox, Google Drives, SVN, Mercurial, and git are all examples of version control systems. Version control allows us to keep track of what changes have been made over time. Careful maintenance of code and data is vital to reproducibility which "is the hallmark of good science." While Dropbox and Google Drives may be useful for sharing certain types of files (I am not an advocate of their complete abandonment), they are not suitable for the fine tuned version control that we need to maintain good science (or software). ## What is `git`? Git is a _distributed_ version control system. _Distributed version control_ means that the entire history of every file is kept on your computer. It was originally written by Linus Torvalds (creator of Linux) to help maintain the Linux project (Fun Fact: Git was originally written because Torvalds found all other alternatives of version control to be too slow to manage a project as large as Linux, so he decided to write his own version which he began on 3 April 2005 and started being used on 7 April 2005. Read more about the [history](https://en.wikipedia.org/wiki/Git_(software))). ## Using `git` Many people associate `git` with the cloud based repository service Github, but `git` can be run independently either just on your own computer or on a self-hosted server. In this short tutorial, we are going to create a `git` repository hosted on our computer. We will then talk about some of the day to day commads that will be used in `git`. WARNING: Until you know what you're doing and exactly how they work, NEVER use the `-f` or `--force` flags no matter what the internet tells you. We are now going to walk through some of the basic settings you should set and an example of some commands. ### Configuration Here we deal with configuration details such as our default editor, user name, email, colors, etc... * Set name: `git config --global user.name FirstName LastName` * Set email: `git config --global user.email email@email.com` * Sets git colors: `git config --global color.ui "auto"` * Sets editor to vim: `git config --global core.editor "vim"` ### Creating a folder We will create a folder called `` using `mkdir ` ### Initializing a `git` repository Now enter that directory using `cd `. To initialize this directory as a `git` repository (which in the background will create a series of directories and files) we will use the command `git init`. We can see the things that were created by typing `ls .git`, but don't worry too much about what is being kept inside yet. ### Four Stages of Files Files in a git repository can be in four different stages: untracked, unstaged, staged, and committed. * Untracked: This is a new file that your repository hasn't seen before. * Unstaged: A file that has previously been tracked and saved, but has changed since its last version. * Staged: A file that has been changed and is prepared to be committed. Nothing is set in stone yet though and new changes can be made. * Committed: When a file is committed it becomes a piece of the history of the repository. This moment of time in the file will be able to be referenced or referred to in the future. The picture below illustrates this "life cycle"
Let's illustrate this through an example and to expose ourselves to the commands that will be helpful: `git add`, `git commit`, `git diff`, and `git status`. First let's check what is new in our repository. Type `git status`. What does it say? It should say that there is nothing to commit because we haven't done anything yet. Now let's create a file called `README.md`. Open this file and type your name in it (could also use the command `echo "FirstName LastName" >> README.md`). If you type `git status` now, what do you see? It should list `README.md` as an untracked file. We can move this file from untracked to staged by using `git add README.md`. Type this command and then once again type `git status`. Our `README.md` file now shows up in green as a change to be committed. This means it is staged. We can take our "snapshot" of the file by using the command `git commit -m "Type a meaningful msg here"`. Commit the file and then once again type `git status`. What do you see now? The repository should be clean again. Add something new to the `README.md` file. Once again, we can check the status of our git repository by typing `git status`. It will tell us that `README.md` is unstaged because we have made changes to it. Imagine we wanted to double check the things that we changed. We could type `git diff README.md` which will show us the changes that have been made to that file since its previous commit. These commands will be the core of your `git` workflow so I suggest familiarizing yourself with what they do. ### Commit History Remember how we can leave ourselves commit messages. It is useful to leave meaningful commit messages because they are left as a guide for yourself. We can see our history of commits by typing `git log`. We can do this in several formats: Try `git log --pretty=oneline`, `git log --stat`, `git log --since=2.weeks`, etc... See the Git Pro book for more options. If we want to return to a previous commit in our history we can use the information from `git log` to get back. There is a sequence of numbers and letters, which we will call commit hash, at the top of each entry in your history. If you copy this and type `git reset ` then it will return us to that moment of our history. All of our more recent changes will be there, but they will be as if they had been just staged. Can use `git reset --hard` to reset to a previous point in time and delete all changes, but be very careful with that command as it can erase your history (in fact, I suggest not using it until you really know enough that you know it is what you want). ### Branching A branch is essentially an specific version of your folder. You start with a main branch which is called master. When you create another branch, it is an exact replica of the branch it is being created from (typically master) and includes the full history of the repository. After creating a new branch you can make changes and this new branch will develop its own history (without changing the history of the original branch -- such as master). If you decide you like the changes that you made then you can bring them into the original branch. The command `git checkout` is used for a few different things, but we will mostly use it for switching between branches. When used with the `-b` flag, it creates a new branch and switches to it. Let's create a new branch in our repository. `git checkout -b test`. What has changed? Let's look at the output of `git branch`. Now let's make some changes in our branch. ``` > echo "New line" >> README.md > git add README.md > git commit -m "Branch update" > git log ``` We can see the history has changed and we have a new commit. Let's return to the master branch by typing `git checkout master`. Now check `git log`, notice that we no longer have the changes from our test branch. Imagine that we decided to bring those changes into our master branch. We could use the `git merge` command by typing `git merge test` which will merge the branch `test` into the current branch. ### Ignore Files Sometimes there are files that get created via an intermediate process (such as `.aux`, `.synctex`, etc... in latex). We often don't care about keeping track of these files. Another wonderful thing about `git` is that it allows us to ignore files we don't care about! We do this by creating a file called `.gitignore` at the initial directory of our git repository. For example, `echo *.garbage >> .gitignore`. Then create a file called `foo.garbage` with `touch foo.garbage`. Now type `git status`. Notice that this file doesn't show up! It is because it recognizes that anything with an ending of `.garbage` should be ignored (this is because `*` is treated as a wildcard. Read more about regular expressions or [wild cards](http://tldp.org/LDP/GNU-Linux-Tools-Summary/html/x11655.htm) for more information on how to use them). ## Resources and References Below is a sequence of references to things that I have found useful. I suggest reading a few of them and at least skimming the majority of them. In particular, the software carpentry lectures on git are very useful for learning the basics -- The Pro Git book is the biblical reference for git and can likely answer any question you will have for at least a few years to come. I have organized both sections by how relevant/convincing I found the documents. ### Git Technical References [Software Carpentry: Git Lectures](http://swcarpentry.github.io/git-novice/02-setup.html) [Pro Git](http://git-scm.com/) [Git Tower](https://www.git-tower.com/learn) ### Why Git References [Difference between git and Dropbox](https://gist.github.com/magicseth/1951984) [Version control for scientific research](http://blogs.biomedcentral.com/bmcblog/2013/02/28/version-control-for-scientific-research/) [Why Physicists Should Use Git Or Why Everyone Should Try Git](http://openmetric.org/assets/slides/whygit/#/) [Git can facilitate greater reproducibility and increased transparency in science](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3639880/) ================================================ FILE: lecture3/.ipynb_checkpoints/command_line-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Intermediate UNIX Shell\n", "\n", "### The command line interface in a Linux environment\n", "\n", "#### John Stachurski " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue Jan 12 14:06:51 EST 2016\r\n" ] } ], "source": [ "!date" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is to familiarize you with the UNIX shell in a Linux environment. \n", "\n", "I'm using the [Z Shell](https://en.wikipedia.org/wiki/Z_shell) but most of the following will work on any standard UNIX shell (bash, etc.)\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BwwfhTjzw1DHM2rcA39mRgxQAI9qNq2+sxqoZHagstNDTXIS9Oydh49Ec9Eo2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gUTplT5I6tteex2/Xnp9oKIHBUWvYXnsevzW6cVJ54zpyOooNtWfx8dqzE8qAoTRnZdp8x3Af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\njUaj0Xgx/B+KwpPzAj0BHgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "execution_count": 2, "metadata": { "image/png": { "width": 600 } }, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(\"terminal.png\", width=600)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This lesson is written up in a Jupyter notebook because it's an easy way to store a list of commands and their output. In Jupyter, input is treated as a shell command whenever it starts with ``!``. For example" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/zsh\r\n" ] } ], "source": [ "!echo $SHELL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If I forget the ``!`` I get an error because my command is interpreted as a Python command:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (, line 1)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m echo $SHELL\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "echo $SHELL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, there are some [IPython magics](http://ipython.readthedocs.org/en/stable/interactive/magics.html) that mimic shell commands and work without any quanlifier. The next command moves us to my home directory." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john\n" ] } ], "source": [ "cd ~ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To show the present working dir use `pwd`" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/home/john'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pwd " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To list its contents use `ls`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34manaconda3\u001b[0m/ \u001b[01;34mDesktop\u001b[0m/ \u001b[01;34msync_dir\u001b[0m/ \u001b[01;35mterminator.xcf\u001b[0m\r\n", "\u001b[01;34mbackups\u001b[0m/ \u001b[01;34mDownloads\u001b[0m/ \u001b[01;34mtemp\u001b[0m/ \u001b[01;34mversioned_dotfiles\u001b[0m/\r\n", "\u001b[01;34mbin\u001b[0m/ \u001b[01;34mMusic\u001b[0m/ \u001b[01;35mterminator.png\u001b[0m\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could add the ``!`` but it wouldn't make any difference." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john\r\n" ] } ], "source": [ "!pwd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're working directly in the shell then of course you should omit the ``!``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### File System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The top of the directory tree looks like this on my machine:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34mbin\u001b[0m/ \u001b[01;34mdev\u001b[0m/ \u001b[01;36minitrd.img\u001b[0m@ \u001b[01;34mlost+found\u001b[0m/ \u001b[01;34mopt\u001b[0m/ \u001b[01;34mrun\u001b[0m/ \u001b[01;34msys\u001b[0m/ \u001b[01;34mvar\u001b[0m/\r\n", "\u001b[01;34mboot\u001b[0m/ \u001b[01;34metc\u001b[0m/ \u001b[01;34mlib\u001b[0m/ \u001b[01;34mmedia\u001b[0m/ \u001b[01;34mproc\u001b[0m/ \u001b[01;34msbin\u001b[0m/ \u001b[30;42mtmp\u001b[0m/ \u001b[01;36mvmlinuz\u001b[0m@\r\n", "\u001b[01;34mcdrom\u001b[0m/ \u001b[01;34mhome\u001b[0m/ \u001b[01;34mlib64\u001b[0m/ \u001b[01;34mmnt\u001b[0m/ \u001b[01;34mroot\u001b[0m/ \u001b[01;34msrv\u001b[0m/ \u001b[01;34musr\u001b[0m/\r\n" ] } ], "source": [ "ls /" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some comments\n", "\n", "* ``bin`` and ``usr/bin`` directories are where most executables (applications) live\n", "* many shared libraries in ``usr/lib``\n", "* The ``home`` directory is where users store personal files\n", "* ``etc`` is home to system wide configuration files\n", "* ``var`` is where logs are written to\n", "* ``media`` is where you'll find your USB stick after you plug it in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Searching" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I have a paper by Lars Hansen on asset pricing somewhere in my file system but I can't remember where. One quick way to find files is to use the ``locate`` command." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/Desktop/PDFs/Econometrics_bruce_hansen.pdf\n", "/home/john/Desktop/PDFs/hansen_averaging_ecma.pdf\n", "/home/john/Desktop/PDFs/hansen_shrinkage_qe.pdf\n", "/home/john/backups/sync_dir_backup/papers/asset_prices/hansen_nobel.pdf\n", "/home/john/backups/sync_dir_backup/papers/lae_stats/references/chen_hansen_carrasco.pdf\n", "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_2012.pdf\n", "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_knightian.pdf\n", "/home/john/backups/sync_dir_backup/to_read/financial_econometrics/scheinkman_aitsahalia_hansen.pdf\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.aux\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.log\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.pdf\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.tex\n", "/home/john/sync_dir/papers/asset_prices/hansen_nobel.pdf\n", "/home/john/sync_dir/papers/lae_stats/references/chen_hansen_carrasco.pdf\n", "/home/john/sync_dir/to_read/sargent_hansen_2012.pdf\n", "/home/john/sync_dir/to_read/sargent_hansen_knightian.pdf\n", "/home/john/sync_dir/to_read/financial_econometrics/scheinkman_aitsahalia_hansen.pdf\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.aux\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.log\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.pdf\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.tex\n" ] } ], "source": [ "!locate hansen" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more sophisticated searches I use ``find``. \n", "\n", "For example, let's find all Julia files in ``/home/john/sync_dir`` and below that contain the phrase ``cauchy``." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/sync_dir/books/quant-econ/QuantEcon.applications/lln_clt/cauchy_samples.jl\r\n", "/home/john/sync_dir/books/quant-econ/private/_backup/html/_static/QuantEcon.applications/lln_clt/cauchy_samples.jl\r\n", "/home/john/sync_dir/books/quant-econ/private/_backup/html/_static/QuantEcon.jl/examples/cauchy_samples.jl\r\n", "/home/john/sync_dir/books/quant-econ/private/_build/html/_static/QuantEcon.applications/lln_clt/cauchy_samples.jl\r\n", "/home/john/sync_dir/books/quant-econ/private/_build/html/_static/QuantEcon.jl/examples/cauchy_samples.jl\r\n" ] } ], "source": [ "!find ~/sync_dir/ -name \"*cauchy*.jl\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next command finds all files ending in \"tex\" modified within the last week:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/sync_dir/papers/fwd_looking/fwdlook.tex\r\n", "/home/john/sync_dir/teaching/nyu/functional_analysis_intro/fa.tex\r\n", "/home/john/sync_dir/teaching/nyu/functional_analysis_intro/fa_homework.tex\r\n", "/home/john/sync_dir/teaching/nyu/advanced_python/adv_py.tex\r\n", "/home/john/sync_dir/teaching/nyu/coding_foundations/hw_coding_foundations.tex\r\n", "/home/john/sync_dir/teaching/nyu/coding_foundations/foundations.tex\r\n", "/home/john/sync_dir/teaching/nyu/core_python/core_py.tex\r\n" ] } ], "source": [ "!find /home/john/sync_dir/ -mtime -7 -name \"*.tex\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Working with Text Files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When working with specific text files we often use a text editor. However, it's also possible to do a significant amount of work directly from the command line. Here are some examples." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/temp_dir\n" ] } ], "source": [ "cd ~/temp_dir" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to make a text file using the shell redirection operator ``>``" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 44\r\n", "drwxrwxr-x 16 john john 20480 Dec 31 10:48 econometric_theory\r\n", "drwxrwxr-x 5 john john 4096 Jul 3 2015 kn\r\n", "drwxrwxr-x 12 john john 4096 Jan 8 13:07 quant-econ\r\n", "drwxrwxr-x 11 john john 12288 Dec 21 09:55 sed\r\n", "drwxrwxr-x 10 john john 4096 Jul 3 2015 sed2\r\n" ] } ], "source": [ "!ls -l ~/sync_dir/books" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!ls -l ~/sync_dir/books > list_books.txt" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "list_books.txt\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can read the contents of this text file with ``cat``" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 44\r\n", "drwxrwxr-x 16 john john 20480 Dec 31 10:48 econometric_theory\r\n", "drwxrwxr-x 5 john john 4096 Jul 3 2015 kn\r\n", "drwxrwxr-x 12 john john 4096 Jan 8 13:07 quant-econ\r\n", "drwxrwxr-x 11 john john 12288 Dec 21 09:55 sed\r\n", "drwxrwxr-x 10 john john 4096 Jul 3 2015 sed2\r\n" ] } ], "source": [ "!cat list_books.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can search within this file using ``grep``" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "drwxrwxr-x 16 john john 20480 Dec 31 10:48 econometric_theory\r\n", "drwxrwxr-x 11 john john 12288 Dec 21 09:55 sed\r\n" ] } ], "source": [ "!grep Dec list_books.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can show just the top of the file using ``head\"" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 44\r\n", "drwxrwxr-x 16 john john 20480 Dec 31 10:48 econometric_theory\r\n" ] } ], "source": [ "!head -2 list_books.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also append to files using the ``>>`` operator" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!date > new_file.txt" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "list_books.txt new_file.txt\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue Jan 12 14:10:55 EST 2016\r\n" ] } ], "source": [ "!cat new_file.txt" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!date >> new_file.txt" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue Jan 12 14:10:55 EST 2016\r\n", "Tue Jan 12 14:11:03 EST 2016\r\n" ] } ], "source": [ "!cat new_file.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can change \"Jan\" to \"January\" using the ``sed`` line editor" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue January 12 14:10:55 EST 2016\r\n", "Tue January 12 14:11:03 EST 2016\r\n" ] } ], "source": [ "!sed 's/Jan/January/' new_file.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Putting Commands Together" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The command line becomes very powerful when we start linking the commands shown above into compound commands. Most often this is done with a pipe. The symbol for a pipe is ``|``." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at some examples.\n", "\n", "The first command searches for files with the phrase ``hansen`` and pipes the output to ``grep``, which filters for lines containing ``sargent``" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_2012.pdf\r\n", "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_knightian.pdf\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.aux\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.log\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.pdf\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.tex\r\n", "/home/john/sync_dir/to_read/sargent_hansen_2012.pdf\r\n", "/home/john/sync_dir/to_read/sargent_hansen_knightian.pdf\r\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent\r\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.aux\r\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.log\r\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.pdf\r\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.tex\r\n" ] } ], "source": [ "!locate hansen | grep sargent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's do the same but print only the first 5 hits" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_2012.pdf\r\n", "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_knightian.pdf\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.aux\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.log\r\n" ] } ], "source": [ "!locate hansen | grep sargent | head -5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an exercise, let's see how many Python files I have in ``/home/john/``. " ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "33067\r\n" ] } ], "source": [ "!find ~ -name \"*.py\" | wc -l" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here ``wc`` is a program that counts words, lines or characters, and ``-l`` requests the number of lines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see if any Python files in my papers directory contain the phrase ``bellman_operator``. To do this we'll use ``xargs``, which sends a list of files or similar consecutively to the filter on its right." ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/sync_dir/papers/policy_iteration/programs/Python/consumer_prob.py:def bellman_operator(m, V, use_bisection=False):\r\n", "/home/john/sync_dir/papers/policy_iteration/programs/Python/consumer_prob.py: V, c = bellman_operator(m, V) \r\n", "/home/john/sync_dir/papers/policy_iteration/programs/Python/consumer_prob.py: V, c = bellman_operator(m, V) \r\n", "/home/john/sync_dir/papers/policy_iteration/programs/Python/consumer_prob.py: V, c = bellman_operator(m, V) \r\n" ] } ], "source": [ "!find /home/john/sync_dir/papers -name \"*.py\" | xargs grep bellman_operator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final Comments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One useful trick with bash and zsh is that CTRL-R implements backwards search through command history. Thus we can recall an earlier command like" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "mupdf sync_dir/work/referee_reports/JET/JET-D-15-xxx/JET-D-15-xxR1.pdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "by typing CTRL-R and then `JET` or similar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another comment is that file names starting with ``.`` are hidden by default. To view them use ``ls -a``" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john\n" ] } ], "source": [ "cd ~" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34manaconda3\u001b[0m/ \u001b[01;34mDesktop\u001b[0m/ \u001b[01;34msync_dir\u001b[0m/ \u001b[01;35mterminator.png\u001b[0m\r\n", "\u001b[01;34mbackups\u001b[0m/ \u001b[01;34mDownloads\u001b[0m/ \u001b[01;34mtemp\u001b[0m/ \u001b[01;35mterminator.xcf\u001b[0m\r\n", "\u001b[01;34mbin\u001b[0m/ \u001b[01;34mMusic\u001b[0m/ \u001b[01;34mtemp_dir\u001b[0m/ \u001b[01;34mversioned_dotfiles\u001b[0m/\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34m.\u001b[0m/ \u001b[01;34m.ipynb_checkpoints\u001b[0m/ \u001b[01;36m.tmux.conf\u001b[0m@\r\n", "\u001b[01;34m..\u001b[0m/ \u001b[01;34m.ipython\u001b[0m/ \u001b[01;34mversioned_dotfiles\u001b[0m/\r\n", "\u001b[01;34m.adobe\u001b[0m/ \u001b[01;34m.julia\u001b[0m/ \u001b[01;34m.vim\u001b[0m/\r\n", "\u001b[01;34manaconda3\u001b[0m/ .julia_history .viminfo\r\n", "\u001b[01;34m.aws\u001b[0m/ \u001b[01;34m.jupyter\u001b[0m/ \u001b[01;36m.vimrc\u001b[0m@\r\n", "\u001b[01;34mbackups\u001b[0m/ \u001b[01;36m.latexmkrc\u001b[0m@ .Xauthority\r\n", ".bash_history \u001b[01;34m.local\u001b[0m/ .Xdefaults\r\n", ".bash_logout \u001b[01;34m.macromedia\u001b[0m/ .xfigrc\r\n", "\u001b[01;36m.bashrc\u001b[0m@ \u001b[01;34m.mozilla\u001b[0m/ .xscreensaver\r\n", ".bashrc-anaconda3.bak \u001b[01;34mMusic\u001b[0m/ .xsession-errors\r\n", "\u001b[01;34mbin\u001b[0m/ \u001b[01;34m.nano\u001b[0m/ .xsession-errors.old\r\n", "\u001b[01;34m.cache\u001b[0m/ \u001b[01;34m.pki\u001b[0m/ \u001b[01;34m.zcompcache\u001b[0m/\r\n", "\u001b[01;34m.config\u001b[0m/ \u001b[01;36m.profile\u001b[0m@ .zcompdump\r\n", "\u001b[01;34m.continuum\u001b[0m/ \u001b[01;34m.ptpython\u001b[0m/ .zcompdump.zwc\r\n", "\u001b[01;34mDesktop\u001b[0m/ .python_history .zhistory\r\n", ".dmrc \u001b[01;34m.ssh\u001b[0m/ \u001b[01;36m.zlogin\u001b[0m@\r\n", "\u001b[01;34mDownloads\u001b[0m/ .sudo_as_admin_successful \u001b[01;36m.zlogout\u001b[0m@\r\n", "\u001b[01;34m.gconf\u001b[0m/ \u001b[01;34msync_dir\u001b[0m/ \u001b[01;34m.zprezto\u001b[0m/\r\n", "\u001b[01;34m.gimp-2.8\u001b[0m/ \u001b[01;34mtemp\u001b[0m/ \u001b[01;36m.zpreztorc\u001b[0m@\r\n", ".gitconfig \u001b[01;34mtemp_dir\u001b[0m/ \u001b[01;36m.zprofile\u001b[0m@\r\n", "\u001b[01;34m.gnome\u001b[0m/ \u001b[01;35mterminator.png\u001b[0m \u001b[01;36m.zshenv\u001b[0m@\r\n", "\u001b[01;34m.hplip\u001b[0m/ \u001b[01;35mterminator.xcf\u001b[0m \u001b[01;36m.zshrc\u001b[0m@\r\n", ".ICEauthority \u001b[01;34m.texmf-var\u001b[0m/\r\n", "\u001b[01;36m.inputrc\u001b[0m@ \u001b[01;34m.thumbnails\u001b[0m/\r\n" ] } ], "source": [ "ls -a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another thing to note is that files have permissions associated with them, so your system can keep track of whether they are executable, who is allowed to read / write to them and so on. To view permissions use ``ls -l``" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls -l ~/bin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The permissions are the characters on the far left. Here ``x`` means executable, ``r`` is readable and ``w`` is writable, ``d`` is directory and ``l`` is link. To learn more about permissions try googling ``linux file permissions``. To learn more about links google ``linux file links``." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture3/command_line.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Intermediate UNIX Shell\n", "\n", "### The command line interface in a Linux environment\n", "\n", "#### John Stachurski " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue Jan 12 14:06:51 EST 2016\r\n" ] } ], "source": [ "!date" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is to familiarize you with the UNIX shell in a Linux environment. \n", "\n", "I'm using the [Z Shell](https://en.wikipedia.org/wiki/Z_shell) but most of the following will work on any standard UNIX shell (bash, etc.)\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BwwfhTjzw1DHM2rcA39mRgxQAI9qNq2+sxqoZHagstNDTXIS9Oydh49Ec9Eo2\nQp8d2eiu2oUXPp2Cr119DeYc+QCHu/lvjVIJvjkha99uwSbjholIOOQwMgPhUN+1nUqC5sA6B7ZZ\nPBWz89pw8EQr3JwIscNTUDB9HFJHf46atlzEZt+P0lv+HqUAkDiDtq3dUG+DJGoP7Mf5JauwcnIO\nDh/rhm0YiITDfO/ZUXdYKSRoHgDRPqw06bJGUprRVK+BBcXZ7/19OiT65Ehj2BZSMBAKhQZHvE0T\n4VAIsG3YqSR6U0MXFMM0EQmHYRrGEPsZWGwBhEwTsG1YyQTiydTQCaNfH9MwBupFThA2gGgkMmic\nOiPxaX3SfWolE+hNWUMXfUr7yLTzkAWbM28M6A5w5ZB9Qet3wzAAwxhSd6eMtJx0f1nJxEAask+d\ndXf2Kat9qAE9F/MGqQ+rnVlyaLahok/aNtJySH2cbejUh2Y/rDRu6uV1TkjbWHqcupFj2/YQ25At\nawjxdTF2WDYv0pk2lsldO9nxTtrqkAAGQB1fLAITKpmKOfntOHC8RZknGIYBo2IKom3n0NUJhBev\nwZRbTDS/8TqqTrUivGItpk2PALaFwrln8OVbEtjz3hy80pTEvBvO4OG7wtjx9gw8ezGGDgC5k6vw\ntUfqgKMT8erLubArmnDH6lP45rip+PdXy9Fi2ShdeAp/uiaO4x9Ow48umCid2oA1d15CuRHGaQOA\nHcKp9+fix7HL9TULm/HQvfUIHazAhfS58ngRfvfsQkSj7Xj48fOB7tSHihvw2dtb0LJrEp57Lwar\nqA2rbzuPrxSZ+ParZWizADNsIcwgQcmkifQMGB5Xgz+5K4SD26fhF8lO3HxHDR6/Kwf/+ocytPUU\n4o/HInh6djPGfpSHi8l0+W2YlZODvSdiSAEw81rxwGOnsNwsxqYd0/Buh40xM+pwx93HUBRfiP84\nHR5wSuQCDElc2r8P55dcj5WTc3DkeE+frVJ4nW3bsE2zf1yEEA4ZA+vgoHU5lUCiv9KsAIzfwQKm\nDYSKFuIrj1+Dsf3XZulN+PrnF6IMQPPBF/CDLY3g+bFdF5qQY7XggoK364SROImW9/9toMJd1TvR\nkjcesVwLiZZapFLuGsLqqsb+ZhN3TStF9Fg3Evnz8OUvrUQlJ4+z7r11b+HbL54D+V0NnUan0Wl0\nGp1Gp9Fp+Gmc6DjXgDyrBefbUrBt1e9cGDAL8mD0tCFl5yB/6Tgkd/9cKdr9AAAgAElEQVQB9ceb\nYSOESG6fY45QL5Zc14qu3Quw8VhfZLZ+wxTM+XIVzNZcNLSbQKgLN91ei+Jz0/DtjeVoswBUFeF8\nexJ/8Zlq3FJZildqe3DDqnZ07JqHl3bnIQHgQm0Bas3D+OYqa0CnrsZ8nEurGO7GzTc3oLR2Ar6/\nrRA9AyzTRFtLDIjGEbeD/fJeqqUCP/9pBRK9Rh/JvZSHOnTgb+5twLScMuzvTmLhI4xoPvLxwo/m\nYl9P+tLER7+che3NBoBCNOa14K9WN6MyUoa2uInqfeVoWtyAFeMq8YdqE4CN8llNKGkpx8FmA4CF\niSvOY3lOCZ7/xXQc6urr7xPninHmYiusC+EhuwMyO5epzirsazJw97RSRI/XIJE3F09+cQWX10Wm\nfBZ/fn8FYhhqq2ff/jV+diqesZ1PQMIGus4dwb4WC3a0CXHzOMpCFpoP7cDui73ULZU+5W2kwpNx\nfaIH3e64OAUW7K6L6OnyKiaO+sZeRMsLkWsCvYkG7NlzEGW8twHFLte98+zH+Oh4x1BHZFjS2P1p\n2rNEH1mdd0rJ+eOxofUyctTrTsoxDIOqc4o8KpEzOM32E52wieMpqfDgNDtPdQ/Rh0yz60x8SJqE\nObh9dpzsGtBnYDKi6GyTXjqjL5zHaqzI4DbcfqIvzaBJT9BfhmEMmhPS7UPWa3jS9NVrx8mhaci6\nk/ZDq9eOk12wiSMZl+VcTsMuS8Y2LutskwsNkWbnqa4h/aVWlrw+H58emiYZYqdJ6zRYzk5B+8j0\nVzD2k9aXZ8/0cTF0DLLKSs8tgxZ2yvhiyxlqqwPRuSh9LDv7gtTZ2T70OWGnlG2k7dBZlmh8wZHe\nMIBUeDJuTPSgxzYGzlyLjtcMSmP1RTiBJJItSURmTEPOx62IF8xC+eI8GPUADAu5ISARNy9HXFMh\n9No2ov3rfqi4FYtKTBz7oLiPiPej89x4HOo5hlnT4oh1dmJ6XgRHT+YgafRFVG0baL5QgK5VbUN3\n140UZqw+hTvLC/GHX4/DpWF7aYSB3t7Bd3rbctCJdhRGbaBraDR/AHYY9T2Oy+qx2NecrqmBrpYo\nkuEkcvvbMdlYju31l7Dmqna8c7EY3WYPlizoRe2npWiyAIR7sHBGAm1HxuN4l6PFbBOXjpZK12hI\nxNyKo6EpgWh5IXKMi4j31mPv3kNDeN2gY2Mnu5Hsmo4C8zJPsGNjcc2CccKyg4Cxfv16+1s/+vWQ\nB6HixXj68WsQ3/A8fn66l5KVImxgwTeQN/te/MXqdvz211twsj97dpwbDGPSzevwlfH78e8vHuoz\nkAwgW84/+o3RWi8S5Jcu0/d458FVr8mzbM4z5OQRGvKLU848JNJ6m6Y5pGzWeWpZvUWQOasvku/G\nxnhfKuO1lbNd0+3l1It1BtHrWXrV7wXIwE3fyX4PgqUXy45o184yZM/Eq4KXR6bNZc5ss+YAXlt4\naWdW+c5yaZ9JXXnnxUXPSfnO/6x6D71noGDuZ/Bfb+nCc7/4QMgTaPU0p9+BWXc24/xPdyOeOwVj\n161B2bgQ0NuA9poCFJgf4sTzp1B582F8fW4uXnt5Kva2pjD3xhP43PQi/OK5KTgTByKVZ/FfH23D\np88vxJu1l4/0IdSDW754CDfUzMG397bj619owr7nFuL9xsu6hMddwF883oyD/Xn7tUPx3DP4xr2d\nOPrqfLx8Okw/SRBtwxNfP4n8DzhnxmOt+MqfnoLBe4c4L42ZxJQFdbhuVjsmjYmjMMdCOGwhbOTg\nnWfnY1uLhP0NnBmfhX9+rQjpo+axKafxd48k8NqP52Jvf6C0cP4J/NUaA7//2Swczr+IP3+sHZuf\nnYPdHQYQacdjT5/AuJ0L8d3dfcdW3GKwPYQx5dZH8dS4fVj/0mEqr+PZZBqh4sX40y+tQNfbvx7E\ne2Xmnk9++7OBdDt27EBlJTsuX1NTg5UrVw7U48MPPwxqd8RGoqMdPeFijMs1cbI3Q4xXCiHkF0Zh\nxzv1DwBpKME5mJ1fWiI/8/KS5IO2KNO+COdc5JyEkfclOmfZTh1Z5FGUxg0Rp7WDaHFX26oenJem\nA4us0NrZqTerDViEnFa287lK+/nh5NLaMpPbrsMNURvKOIrkmCDli9qXZy88O+PVg0e+WfdpZbDq\nJaO/CLR2Gzq/oJ8nlDF5Aq9tDMOAVX8W8YKrUVT+CerqzqP22WdRnxOFHe8FonkIh+NI2Saqt0/D\njlnH8MAXWvAAALu1FG+8OgFnemzYAFLxKLqRRHmBBaPW8ZI5I4niGNDTGUayN4IeI4kxeRbQeJnw\nGuEkogCcaobL6vDYXS3o3D0Xr59hEPFMwExi/p1H8MWZEezZOQ5v7Y6gKx5Gb14Dnni4LZAiO86M\nxTHrNK6f1YPm0ibk10zAsc70tkcYzT3A7HFxROCNjA9GCHkFEdjxTvRYNtImS9ovj4izkKmAY2Cv\nNky2VuGiVYL5lbEg35+ojnAJZo0x0VLdcvn8loaGAKxotWVZgyLXJKljRctoESQn0Xa+MpH8TJJx\nMkquGl2m5fVKxHmg1Z+mK+sZK58sRGSGF9Fk6SrzjFVOkJO9THSa9Zxmq37rGlT9ZWWq7BKx5Mss\n8KwxqVIOLZ8MZGyU1dcyfc9yFnhR+PT/RMsFXLRKsHBi7hCeIDMGja4LaDgeRdndi5AT7Tsia/XE\n+/SNdyLRmQRgo2BKHeZ3TMT6Hy7B//7xMvzDz6djR31oIBKdai3G4XYbc65rGnS0IW9KPRbnRXDy\nTAyJ7gKcak9h9uJ25A+c1Eihck47cpw6xTpw54PVmFg7Cc9tzx/WoJ+R04bV85I4vWEW/rC7FMer\nClBVH0NvQTeKAvLL7Z5C/PFoGJOWX8SaeSmc/rQIAy/US8Zw6FQEOXOqsbLC6aLYyC1KIKKg0yC7\nCJdgdkVoCK8j7Zv8zMJwBC2C+95ATw32VNl4dOksVJw4gNphfc94GgZilfOwKLcNH59q89Er07gS\nIIok8aI4ogWRtW1NfvYS3XRGqEiZpHxa2qDAitrxrp1gRfR5OxW0/CIdZe6l77OinpmKsqQhcszc\n7l7w8juf+VVflfb3KldFPs3uaOOZN3ewZKjorpKPJcdP2+TNg4Pq2lOD3ResPp5wbN8gniAzdm2r\nB53vb0Lzl+7G9K+MRf2Ww+io7YJROgnFs5NoevcIelM2iid3oDAWxqTKOC61G8jLM9DdHkNLT/85\n8mQetr03BosfrsLXHkhiw4F8xItacOvqJhhnpmPLpRBsKwc7dxZh5ZrTeCo1AVtOR1AyrR4rp6Pv\nezkAYCSwcM1p3FCci21b8xEt78Ll70XasDrzUNNhAKaFvJwUQrEkIiYQiiVQlA8k4hF0pd9CEk0i\nL2ID0SRCAIycBIryLdi2ia6uEFIyaRIx1HTauHpZPWY3laPejGParDqsua4doUEuhJ8wUb2/HM1L\nLmFWbyl+OehLmSYufjwFu2eewh2fP4pxuypwvBkondSEGxeksO25+djUqLrmGMiZMB+Lctuwk8Hr\nVOeiTM/TQJBk3O7BqV1H0bD2Kjy4+Dx+/mkr91vVmYARHYMbbpiBSNVW7GnJpqMzGiMFJHGlPUuD\nRzCdclgLN5nfNM2BKLwMWRI5BzzC4JWEq0bnWeRfJcJJEnGvkHWuZIi8rD5uIjas4wcq5YlkeO0f\nL/BKxP3S3QuZFpEBkaMvgmw+1hhnEXK3c4F0HrsHJ3YeRuPnrsZDS87hZ5+0SPOEAV27qnDp2T+g\nZ/U1KF19OypKYrDaG9Cxby9sAwBM1B0uR+uSS3jowfpBX7TsrhqP375ZiVNdBjrPTsF3f5uLe2+q\nw933XUKkOxcnd83Gr3cVo8WyARhoOTQTP8QFPLSyBp+dHcaFE2Pw8psG7l1Xi7AJGPmtuGlOEkAS\nqx84htWEzun3iZvFdXj6yYuoSD+46Sj++ibn+8YtTFh9BF9f4miNNYfxNwDQMRbff3YSqpMSaRL5\nePflqQjdcRGff6IGZncMF85U4NVXY3jwwWCOqQBAsqkMe5sv4fqLFThHvMvc6irGK8/NQ/Xqaqxa\nXIUluSYaLxVh2yuTsFWZiPfxuhtvnInwhS2DeJ3bsehEJkl5IF/gHGgEhDHhuvvx1NIUdr7+FjZU\nD32LhAi+NYaZi7m3PITHZzXj7d9txM7WzMbFh8PTygRGa71YYEWXnUdInP9p27zpz2QElzyG4izP\ntoceiXGmSf+RR2bSaXhfPnWW45Tntj3I6yAib84yeASVRsBofZJ+xjr6w4IoYk+750c7+Ol00OS6\nTUf2tYrTwoLKLokor2iXQrQjJLJlns3IjANVJ1Z2B8OZllcGGW0XjQFSJmvXLS2bah9GBBNXPICv\nXmVj+6tv4N2qHiZPYBEsXn+YeS14eN1FdH4wBxsuhJGCDcOwEStpxsOPnsXYXUO/TOhmXF1payEP\nobJaPPOlOpz73UK8finAg8pmLubf9gg+P7sFb/72XXzc/yoc1rzvvKbq7fgC589Oyf0iUrqsvc//\ndECumy9wBnuc206g+uMN+P2JMFbc/yAeXVyG2DB8f8iIVWDFnQ/g8bm92P3OZuzKMBHXGH7IHllg\ngTaYvWwJO2XwyCEJGRJGS0OTT8vDumbpzNNVpHOQixdt0ee1Z/ozi4Cw7IfVTipp3UDWwRHZGEuu\nTPksQiTb9rLluNGPlZbXBqKx6aVcXlov5bL6wcvcpCKPlYfm9JOfLwtJoGrnu3jpeAirHnwYjy0p\n5/IEVZsIFbVjVmkvxk9sw7TxPSgrSKKoqBvTprViYiSKMzXev2CpiTgQqajDAytaMHVSE+659yLG\n1o3DR/XBUUwjVoFVdz+Ez89LYNfbm7CbQcQH0vscuPBbJvuYSjgHMQCIeWxMqx2HNr2K+pqVuKs8\nB7zXeQeDECqX34Y7Si/ijZd2YE99IpifnNW44uB2ILIW0HQk3bKsQa/VUymXPG7C00EkzymDR7y9\nOjl+k1Rn9IzXDqK2YkX70rJlwXJ0ZIgWT5ZTd1W9eBFgWp+w0vvdf2R5ontu0rjNI1s+7RiIbD63\nCIoMytoWWWeWnTrTDvpstePA+39A3cVVuHtMLpMnuHHOErUT8Ov3DNy2tAqfX55ATgiweiNoqivC\njlfmYnt1yDUn0CQ8DRvR/ATGLanG06tMNJwZj19urEBjYHHPECauWIM7y2rw+ovbB3idys4edX6N\n5CIGwMrJPFNlknEzpwT5AHJLfFDKTqDuyDb8algMN4Xave/gOzva0E79zWoNDXV4JSC8qB3ruawc\nHgGlkar0YilD5Fl6uyEgXiDanlclp6rHPkhCQZYvoycPKv3HS69SDs0myDSiPmZFx1XLVoFfUXfW\nPZm2ZjkwqoRcpcxMjTUWWI6v8/cMlBxGO4Haw1vxCx+do76EIVw8OAnPHZxEf0yh4uQ406RbBAOd\nZyfiJz+h/oyn/6UZFi7teRvf3t6GjlT6HvtYoOzxwDTvjUnwXr/HH5OM21YCKQCphD9fdBxOY051\ntaJ92ErXyAZ4IYoykWA/olo0Ms5bDFh60SJQ6fu0NCx5NP3IdKrkVXZSFMni6Ul+JvPQ9GfViRXp\nS98LgiT5FYEVyRFFv3n2wCLuznwqjqWbXQI/o+AyTgOrv531pBFPN/ag0maq8ln9xUrHswuRDqxx\nnwmS63YcsnYENbILqa5WdAiCKLR1gDvW+3lv0ifeqwI2Ge9qRCumId6kz1drjA54mVh5BDwdVXb+\n8fLKEinZRUEmuklLKyMvaNCILwna1rRqJFWWNLgFzzFiHQUJAipy3ZI5r+Vmk2wVqLZttkVVZXWS\n3SViOfe0+zLlOWXKtBkvnRci7vyfLX2nwYaMnZKfWf1qdTagFTPR1Zj0T0FJBPZqw1VGEuONvkrH\nbQPv2AYGVS9UgOkzxqO4/2hsqr0ahy92D35HZKgAM4g0h6q79PvBNTIGXuQz/Z/8IR4yDWuhYsmj\nlc2KNoois7TIOCtazqq7l0gRrf6sdDwZpE5eiKQKkWf1l2wk1w3Jl8nj17EOVuTTazTX2U9u5ZEy\nZe6ryPKLaIki5ry+8uoEyoxd8r6f2+uknYvmKZbT6tRNpT2C3oXSpDz7QF8vDYSLJmJ+Zfp7iUk0\nnT+Ls530X3XlyZXVwW+bCIaMG0CZkcT/MvobwjbQa0exwam7baFg7vV4cFI/0+49h+Tzm3CkR5zm\ncHf2DAwjfx6e/tIy1LzyIl67pN2E0QQREZeNjJP50rJ5g9/NtjxLNo0kieDngk2TTYvmu4kgq06s\nZNvQyKgqeNv2bmX7HfUTkVrRTo0bqBxnEOX3E16IPG9nhZQtS+T8XthF7e6ns0XKZDn8tDyiIzDk\ntd9zkibYowhmAZatWYPPjOu3kfhJ/Or0GQBy/RzkeieLYMi4DbxnR1Adivf9+pRh41tmCptTIQy8\nsdzqwpGPj6Jl0gKUAEB0Km5fVIwTu1svR9AZaY7vakHmNxF4SKE7oQf2aINqhFLmSAiPeMoe0xBB\nZpFL35OdhNwcCxG133BNgF6i1jTIRP95/SEieKrwo04yzqIfToFfNh803NiKigPidgdFRRfWGPY6\nDsixziPitF0m0ZgJAkHsVqnBQI45AX+WMx+PhAuQZ7dja+9B/FO8FrVKaUKYFlmIv4tNxHVmBO1W\nPV7o2YefJLsg95bsy8gZ24B7b7uEJZW9sFuKsH3rFLx/Kqp8EsGIdmHFrRdw05wumE3F2LltMrac\njwx+faSRxKSFNbjnhnqMPTcH//udAsj8mg3d5g1Exy/FreMuP7u051Oc7rGU35IjO6aCsM1AXgJp\n2zbiRgjfcYifbqZwM1HPZMMhfFB9uYvKl1yFWcQvtCbqDw5JMzt3+L0YEtmnkUamobq9yvrzC16j\nkCydeBFsN1F4LzrSEMSESh674JUpIrFe2yUtgxZFdCub1X/kHy2PF2SahIkge1xKBdmwG8XTw+19\n2rEU5/30D5CR6UhHgDf/iYIAqnOnlzGi3j82ojlJ6tuhTWMM/jbvWtyDM/iLjg/w5Z4GTIqtxA9i\nRX2vlJZMEzan4L/nlOJS4gC+2bUL30tG8Z/ybsQ3Q2ElTmIWNeALj17AtNYKvPTKDLx9NoXrHziO\neyak1LhNuAfXP3gcd4/Jw6Y3Z2BTTS9ufuQkbq24zOFCJQ149MkDeObWFpTn+sCdjAIsXTUb+enr\n+ElsONwm7URkQ1QcCIiMA4Bt2XjPCuNi+oZh4y9MC1FnIqsTR3YeQ2v6OjoNaxYWDw7Xy6TJAmRJ\nf2r4DJloNwnZbTHnH3nu3EvUkVe+aHFM68bT13lNk8VapL3Aa+SORyply5bZJfHzCIlsXj9IuKw+\nQRFy1Xp4WUBFeYNwjGXr6LVMUfuJdCCfqYwdJwlPE3HnZ9o8w5v/3BxbI8m7im3J7mBIz9PhLtz9\npUP43JShtDAvNBOfMevxz92nscNqw97EQfxDbw9mRSoxSSFN0jqDr7ZvxT/Gq7AtWYPf9ezE/0rl\n4JFoCXKFtUnDwsRrazC9fTye2zAOB8+U4OPNs/D8MQvLb2hBoTRLtFE0+wLWlJXgty9Pxq7Txdi5\naSZeudCDm29pHvjun9VdgJ1vL8T/87152FjLl+gEfV0yEa1cipvHXr53ac+nOBPPrp01GQT6C5w9\ndgjfsS834HQziZuGRMeJyPdSWnT8wJA00tHx8Dh85sk/wTeWV2LutbfiqS98Ef/4tcfwzJ2LMC3X\nABDFrLufwH+/Z0K/t2kgp6gIheE++dGJt+FvnrweU8MAjAjGzF6J//T5L+Afn3kSf/u5m7FyXBSG\njotrEJBdcIOKisvAjdMgs0j6GbFVlSfrPMks0jJRfpHjotKnftmBn8TcS7psiTjJQuXomOiZCEES\nchUdWOPA7dhw5iMj4yJCrjpugmynoMdtyo6jCxFUDGQxEIGJXrsbbQppABDvSbeRgo2ETXt7OqsC\nKYwZm0T3xUK0pP0GO4RzBwuRGNuCygiZ3kJ+QWroDzOZScxZ2on2A+NwqrvvVmRMI66fbCM0qQGz\n8vqDGvEcnL0UhQpfZu6YGAVYsnI2CtIPfIyKZ3pNFpJxMxxGLBJBNBpFXm4u8nNzkR+LIhqJUP8i\n4XDff9MAbGBDKoyatDDDxl8OU3S8bOENuDG/Djs3v4ff/vE8ElOX48mHr8akcAKNNR0wCouRawAI\nl2H1Aw/gkalRGADMSAyhrma0pUwUz7sdT98+GV2HtuBnL72D108auPbWpRhvQLjXMtIWJg0+aIsM\nK+pMW4hoA51H+GiRaZZePPh1NMbNYuXMJ6oHIG4PEVScDRnw+tprflnnQLZOsvq4eUbqwiJZQcLt\n7oYoDS0dbZxk43zuxy6FW7mqOya03SZy1401T/Hszi+n340clTzd1in8MlWMv85bgBvNMMaE5+Hv\nI0m81FODBoU0TphGPlZEr8Zfmx34TaIF3dKKm+jqNhAr7kXMGHzfjvSiLOasl4WJqw/jv311P76y\nND6YkId6MKvMRNX5GJIAjJx23PPAJYT3j8e5ZA9mlbo/xslIhej4Jbhl3OU7A1Fxw0CEwVEH/qIx\n5OXmID83B/mx9P3QECons0b4MR8I+ezE2z6HvwVglt6Er39+IcoANB98AT/Y0gjea9E7D76O9R81\nowchfMdK4l/Mvo6YbiZxYyqCDxxVTjQcxAfVc/HwxD7foHzpMsw8tAXHHG9W6a0/MCTNrENbcFTm\nzSo2gNZP8MK2U2i3AFTX4lxzCN98YCFumXIQL9Q1oOfacSiPHEFbbCxmFEZQPrMcsVOXkFucg1Rb\nK7pC5bjl2kp07n0Fr+xrRgJAVX096syH8J+XD+1AjZEJcptTFl4jXSoLQfp4iJ+QjQqSC6ITts1+\nbWP6czoNLa1f8LNt0rJkdeW1D+9+kHBrLzI2LepHWnt47Xuv41KGKLL0Y80PXtrXbVtk0pacuvqx\ng8KaC1gOjxfHMWg4yw9XVONbX6zte+FEGg/vwz/1f2zeNR//58NcJO02PNu1C7MLVuAHBTNhwcau\nng/wr6nE5Yi2TJq+UnFj3j34QTgEIIkPuzfh2ZTK1y5DOLe3DF2frcL9S3Lw2pEo8iY24J67G5Fn\nxJA7iCUa6GnNQXuvgYYOc5AeRiSJ4rCJ03EDMHqx7M4zWNYyCd/9Yy5umFmLyjwLBkxhxF52vTTM\nQiymRMWTtg0zfx6++uQNqFRoBSc6GXoFaWtMMm7F67Dnk0Mo6Y+d29EmxM3jKAtZaD60A7sv9lIb\nta8hLXRd7EGyX/G9tgmkNw4M4GYD+GCQs9WNC+fbgYnFfdfR8ZhTHMKxnpQwzdFuufeqtF5sRPeA\n92AjXncSJ+KzMHtSEbCnFs3mQkwuCKG6aCIKOlphjZuGseE6oDyKzsZOWLkTMT2vE8fOtCIxIDWF\nluo6dC2X6/KgO1PDPVQWROfiwVukaGlo5XlZ1MlyVfK72ZZ3a7/Oxda5CMuUy5PnN2R3HFTbTkQm\nvDooNNIrq4+bckm7l4kckdH0IKPLXufZoPUjywKyM9pOQpUU0+rEsx3e7gNtnnO7Q6gCli3Qykg1\nj8UvnytFxAQQ6sbND15A4Y5ZeONiH5FKdMSQAmAYpfhq3jVYbR3HX8fbMD2yEM/kXI+/sbbin5Nx\nWJJp+kvF7u4P8KCZj6sic/BXuTfgr61N+OdUghswdaKnaiJ+vTmJx287ir+7DUA8Hzv2lKF9VRch\nw0Djvln4l33UlgIAhM0Uxl1zBg+MK8bvflOOplQ3Erbc9+qUxkC0FHPGXE6fqr+Ai/E+be1EA3bv\nPoAyzq/am/mTsGpOad9F8ylsP5uuawr1zfLv6/Nr3LJ/gbOnFh/vHHy6fuOW16SUGlgY0NcBT5mX\nSbVhm3jWJpSPjMX1y4ovp2k4gI/qB3t2RnTckDR/rJNvMNsizlCletDUDURzwjDiTbjQk48pxTEc\nn1iIxk/2oeOaZZhbkoO6kgg6z3XDjuQgZqQQJxxOO9X386nZP41qsODHYCIJFY0UqSzwKgRHliTL\npCMdjSDIOS2frBw/CYuXYwdBtQ2NZIgcAT+PT8gQD1I3mr3L2KUXJ0D2vkw6GTKsumPmZoctkw5A\npsALXLBshBYld8rjXQcF6T5MRlBX33/IOmyjI2kg1JyL6lonKwxhYXQ5/tQ4iy91HsF+2waSddiH\n2/DD3AXY0PEpPrZNLBCmSetko8fuxOlUJ06nmnDWuB0/yZmEn3WdQY1s89ghVH8yA/+6L4XCfBvd\nHWGYk0/jaoTQ3iu5ZiXDaE8lMHVeFRYstvDHlybjWJcBRFIoipjo7DE8v2pwkEPWU43Nhzoxd2nf\ne1RCk67C8tKz2NyUgh2vx8c76gXO2k68tUG+bF4aP8ZtoF/gBIBKI4F1ji8gvJ4K4dygFAaK51yD\nawa++hvHgZ0n0WTx0+zfcYJIw4YNIBQxBxNmM4yCKNDTEUcq1YbzrQbGTKjAjLFJnKyqwaHGXMyZ\nNhbjY5240JJEqrcLPUYuxuQObjIjHEOkT0UpjLbJ9kqEKBrD+i+Cm3zkZCB31m5webTPNB1ohMyL\nPWc6uu187geB5REDmUg66xntWpagpPvOC0kh+59WT15deXUXRetVj6DI5pFJx6sXb0yqOEt+6DnS\n4WxL0Zc8vbRl9iIfN0fyUJ+sxrGBuvZiV+95VBtjcZNpSKahIYWzyW6YZgnGuzAj2wqhrT2MhG2j\nuLIb0c581PaI8wEAkjk42Wxj8tUtaNoyA5su9R1JCRV0YkIkitPNfLqp3rdJ1Hy6B6cHeGAJblwx\nCXkZMhFyzfUK38g4fdsWeNpMDRRi2Aa+axHeUWQcbry2YoDLGo37sLl68BEYIzpemIavHFA8YQzy\nzcs3cirnYkFeL86da0PSTqL+YifypszEgvAlnO7oRtXJVhTNno6Z0Q5Ud1iwe+pxqiOCWQsrkT+g\nSATjZ1YiR/F9KqNjQrkyQSO/Tngl5CRoC5VKJCl9n6cvSz6LkHYJlJQAACAASURBVLHIYhB27bbd\nSKeEpiNPZxWSp5LPrT7p5zw9WLp4sT3RczeOo0hHN+3O00+UVuY4BO+zDIZjzs+WdYbl1KT/WK8/\npAUYnPczrbt/sNADYEyoCKWOQGW+UYhSpNBiy6YBDBTgKtPxTnEjiuWRIoSsdjQq6WQjHLYvc6xY\nJ1YuiaP1eClqyQMIhoXCwuTQt6lYERzbX4CUnYNTFyN9B5ONFCYva0BZUzmOtXvftRtSZMcZvHfg\n8gnv2MzluK6UczaFIcdtOj9tMdDXdQ+NiofFUfEdElHx7fJR8TTs0mvw2K152HWmGYmiKbj+utnI\nq92JzZf6LK2trhX28qkoP/kOGpM2kjXn0HrrNRjbuheNCQBWE3btrsGKW27Hk8ld2Ho2jpKp87Bi\nCvrOd6mp43obW8NfBEUgaechZfPKgkYi3NgVmY88ZhDEcQyeLryyWNeyOvh9HEUFrLYaLqLm7G8R\nZCL9fraViqzhnEezdQ53Etds0NHZn27GeDbUQRnJfLz2k6WUB514PV6Np3KX4cc5Mfww0YqkOR5P\nxSqRSOzCq7YlmcbEnNhK/Cqawuu9p/G+lcLU8Fz8l3AKr3edR5WCqjkTq/HVO+M4tqcMF60ezF9W\ni2VGGZ7blef4jhwAWJh002E8c3UvqjYvxI8/iTleI2ig9ehkvL/4KO56+Ayi28vQVlGHe5aY2PFS\nOZez0YO5Mju7CVR/sgdnFt+E6SaQjo5//M45dPlgMjJ2m1WRcboi9uCoOAx8TyIqvkUiKk6m4aMv\nZfOBLdjUNgbX3XQr1i6vROr4h/jRG0fQ0G9JydZLaISNS2ea0Qsg1VmNY+1AqqUBbVafnNaj7+Mn\nm08iPvEaPHL7Mswxz+EP7x5BI0yEmdtGGtkM1gDjkSa3BJsVvaSVwZIjmhB4USSVow403Whlyujs\n1J1XBy+OiCxk9HAjUxa8yHhQZdLykv3Ni6qLjmbIRIz9OBakUp4qZKPjbsvMNJn028b9AMuO0tFx\n1i4dK1qeKR0DKAX1iU/w+e4TqArNxP+btwr/HBuD+t6d+EL3RdTbsmksnOjdhb9P9GBWdDH+T+5V\n+ILZiZ92b8H/TPYyv7xJm4PiDWXYWZXC4hvP4PHb61HZOg6/en4qjnSRzpGJ7pZctPXmoLY1BJBz\nWDIXW1+eizerE7jm9rO4c0oYW38/B+9Uh5TPi/PmIOe11XEaGxnRcT/6kzWO/N4NNtavX29/60e/\n9iaE4s1WGr3YGLpMxt9MRvFXg8i4geKF9+K/rE4T7TgOvfV7/P58LzfNwTdfItIIEB6L+754H2Yd\nehnf29UK+a98Bo9smijdYjTUQYS0d8wjuM505DNaXt7xCOd/8rNTNo9U0/6Txwpo0UeWnipl8fKp\nkB7VIxtebJGnl8yEKxfF8Q+syDHLVoIYpyLbIfUh7Y+mr4i002T4CZV+l3E8eOmuVLDsxvkrnE7w\nyHkm7Ho09Z+XeSroOc7LTphZMBdPPrG6PzoOxE9txL8R0XE/d1loDs3Hv/rhgOwdO3agspL9lr2a\nmhqsXLlyQNaHH34YzBc4bdvCE6Y1ILzXNoecFbfDFVixZMxAxDtVtw+bq3qV08jp46YWGhrqA1cU\n9RURCZLQ8KJFaRkk2WdFXkULnYx+PF1JEibzx5KrSsRVwVvUVftclqyL/lR0T/+XaU+3i4+qXJbj\n54TKgi4jT6Srqq05r3l5Zdt0NBE5v8DqI/LsuFsb0qDDS7v5EZAQQWassOyhLzreNsANY1OX4eoS\n/tlxtwjK/gI6M27gX5Ix/Ev/FbUBk3XY8NtfgvNmGRjJOrz7/C/wbiA6amjwQVs0VCKy6fSi/yTc\nRjKdJMIpQzYiwNJHVCZZHimTpgOr3kFDtk/dygy6DioLlhddgmgn0uZF40aUhqerrD6kbiy4kTna\n4TXSyNoV8duBYe3UqOTXTtXwQtz+CVzY9gL+YRtfhh9zmPOzn0dVPJPxICcfXwZAsg5v/fJZ73I0\nhmC0T1AigimTnwXWQsTLK0NeaDJEkxCNvImOC6jC60ToZYtU1gkJyp5pdfdiV6KyeOWqyiDv0+pB\nkp2sXxMyiNFM5LzUS8b+ZUh0ELYmmptHKryMTdm8Xo/4yM6VbuFFFm0HOC3TDxsJ9G0qgH9e82jC\naK7baAO5mIr6jhbxZi0qtIVahgTTCJeTUNOi4izwiKibCYZHPpw60SYw1fJkSDbv2svCpCpHJnrL\nq4+bMtL3solMsPrebXQ7KLgh0ax2DiriO1rBmpN0+3kHbV1wc+RKdk5xY/tuCLwqVOpPW5ODcBh8\nI+N6wuFjtLXLaKuPCKIjKrJRaFZeGoGXiUSy7tGOrNAmID8id7IEnJdfFX5M7m6IqlfCq3q8gjav\nqi4EMn3gNcqpEh1nOaFuSIFKGUFCJWroNToncgZH8twsu0tFpvF6FOVKg9c2EgVTvIzvTIOnG4uI\nB8F3A4+Ma/RhNE2YVyp4A5ZHQHjE15mXJE3kM3ICEEWaWfKdZfh9NIJVT7/AiyirEGuZ8agiT+T0\nyH5myWXdCzLiJKsP7b7KtrZfzqDXnRYnMmm/XvO7tYdsBW88O+sq2olwynI+y0Tk9UoCa90Bsq/t\nvO5eycztbiF8m4pZOBN3r5qJsWEDMPKw9JE/wf94ZAYKHHWS2VrPJEJFsy7rPAQRTL7hYfztF1Zj\nfq6CrkYelq39Mv7n2pmD6u4WTu9KY/TBtvmv4SIJtcokIFOuGxlutoVlo5Ru4GxDWr1U2jeth8q4\nozkvqgR9NEIlgs1znlRkieB2d4VmJ6o6yR4hCtIegpafCbDGNzn+R3o9ryRkE8ehOesiB561JgZh\ngwIyHkbl0muxcnohTGfhyQSSAY4HbxUNY/ySa4bq7IBlJZFMppgvxeci4LqPBOjJkA7WQBUtKrz2\nJCcMGqFkEQraAu3MzyKmpI4s3d0QcRqpldFbRMxpupN1FtVfJD8oIq7qJJF5h2PBE5Fs8r6IUPk5\np4hsnFe2qlMsS8Q15KDbSiMI0NZQ3jUNrDnPL5vlHlMx8iZj9bwozm07hroUQP7mO1kBPyZYz1uW\neZNx0/wYzm873qfzECRQvf11/P/bPRXjG7L5LJWGN8j2q8y2f5DbfizZtK1/lg5BRch5cmmRa15e\nliPAGoOs+ovGrNv28YLhmEdo7cNqMz/BqiuvXDJP+rPKuHKzOyIDfYRxMMi+8Rt6zfUO1ljI9rbl\nramy6wBvzfACTmTcROmcpZiTOI2tZ3vcRZEzDhNlc5dhTuI0tpzpHnadaREama0RjeyB6oBTiRyz\nonasqLQzH+2/LHjRVFrUPhNb4KxouJe0vLOMInluoBoNlz3ex9M5U/3Dgl+Ohx9zoNvgj2w+WpuL\n8qpG2tP39Jog74iznmczKcxmjDb7E60DKrtbQe7ssSPjkQqsXFKM5kObcC5OFmQgd/xC3Lt6CRaW\nm2ipOo5N2z7F4fbUwPNo+WysWbUA8yuLEetpxukje/DW3iq0poDY1LvwV2ss/PGwiaULx8A6vwev\nHsvD6uvnY0ZuC3a8uxEbq+KwYSCnYi7uuHERFowtRK7ViQsn9uOd7cdQPUQnns5RzHvwi3hsguNe\n1xH89Dc7UJW8XKdo+WzcecOiAZ1PHd49oDNZ9/tuWoqF5SaaLxzDe5v34EiHBZj5uPqRR3Ff57v4\n3v4xuP3GRVhQaqPhwjF8sPVTHOsc/KuJJMHSk8fIBMtzTn9OQ2UCoEWHaPYiI5MVeRNFxGn1kgXN\nvv2A6hhRcVj8iOqqRk2caWltPRIWRS/tRtbfi83xdAlybg3CztOyrvQ1wY1teXWQNPowGuxPlXTL\nIMORcQP5067CsmgNth5uRZJ4apcuxhfumIzOA9vwm40H0TBmIR65ZRqKTcC2gZyJ1+Era1dieudR\nvPXmO3jpk0aUXLUGf3rXTJT0l2hGJmKRcQgvbDgBzFiJJ28pxakt7+G9lgqsunYi8g0DORNW4CsP\nLkDkxEd49jcv4rtvfIJLY1fgybvnoGyI5jydEzj9/sv4/n+8gu//x2t4/mDnkLw5E6/D0+uux/TO\no3jzjbfx0ieNKL36Dvznu2cN6Oyse8f+rXhuwwE0VizCuttnotiRJlxxDb5023i07tuK5947hvbx\nS/DZW6eh0Bx6fnWQFiNg4dWgg0ZoyeiM6NgELarOi8AFMdGQugYReXV7zlaF4LqFHxEPNw4DbS4Y\nSfOBl3bj2TFrN1GkCyt6nckdHtWoOK2uI80OgoBoh0xmp4J8PlrbVLQLL5Ofdy2C210xLzrz5Mre\nl9ltDnoeoUfGzWJcdc14JE++jaNdlOc5wCcvbMRHLRaAi6iPTMG3bpqGyvBJtKAUK26aj5ILm/Hv\nm8+g3QJQXYuqrhx8445rcdO4s3gXgG3XYPPeKlxMJHC2ZwGMQ7ux62IHxtR0AZNzETULMG/VbKR2\nvYJXD7YjBQAdJ/Du+4WY+dmFWFp8EpuaU0N0Tpx4G0c6yYay0dvRgjoAQAhWN3GAJVSClTcvQMmF\nLfjOptMDOl/ojOGbd16Lm8adwWuXLtd9739s6K97NerCk/GXt0zHhMgJtCb6S8tL4uMX3sfO/vZp\nzJuCP18xDZWRM2iP96UZDV6nhjuwHDJRBJsly60OzjJk4Efk2At4OrMmU9VIqxsi5NcC76ZPshkq\n9qIyH46E9hkJOl4JEM0VV0I/BVlP0RgfrvZVdSpU5p6gdt6oZDw6fhFWFrdgx7v1iFOeG/VHsK8l\n/Y1OG/H2dsSNAuRHDITypmJBUS9ObrvQR2r7VEXnuYM4HL8bMyYXIlwL2IkudCRtADYs20ZnS98Z\nb9uyYRuAGRuDBWPCGDf2s/j760kNEqjIM4DmwTqvKmnFzg0NVJ15CBVNxcLiBE5sO0/R+R7MnFKE\n8KX4QN33t1oDafrqXoS8MIB+Mo6aQzjQcjlNT1sXkuEYcs2+9hrUllfIhDBawCNMrMi385pFvmUn\nNDeEj3acxUkgZWzQTxIuQ9J4Ex7v6A5Njuy2tWwfyHym6cG7R6sHL8rHk5tN8MuBk90p8YMYZPqY\ni0wZep1Qg5tjYiMdoqCNn/VUJeGZbGMZ4i0ToGG1WVB1GUrGzXzMvWYGYlVb8UkL4yuQyQRSzler\n2BYsGDABmLEC5KMXp3uIvFYPmrqB+fkR+tkYon5GNA95ZgLH3nsD75CvRbFtxLucUfF8zLt2JmJV\nW7G3mfoKFS7SOp/i6txP8clXGzrqnkaqN4FBWlgWbDsCM7vXTQ0B3GzZsUiY7HYYWbZbciPKp0Jw\nZeGczGgToJctVPKcMS2d24mUtkNBk+llV4EVuSHbXrSojYRt90wSIy/5ZcmMyk4LD6pjYDQRx6Cg\n24cNkf24nUNU56xMQ8W5pq0lmdJ/CBkPlczBTRPjOPBKFdpd6JCKd6IbORhTEIZR3zvAsW0jiuIY\nEO9MSL3lxE72IG5HUBxNoLW1k5snXDoXN02MY/8fLviqM8yYks4aowt+RqpkjqWIdJEh5Cp6sdKS\n91Wi2LyyVY8tsGTQdBTlS+cV5WF9ll1gvDpSvP4TtS2ps98Q2UQmjjE5nbtMw+3OCw9uCLkz75UO\n3QZqCJpc+nGu2k+H2o/5KFPHBwcHqY0oJi+bjzFNB/FRHfm1TTlY7RdwtDOMmVfNdHzJ0kDupAVY\nlNuNU+fbh3whlAa7pwFHmwyMXzwb45wugxFGfix0OS7vk85HOkKYdfUsTzoP0l9ya1xPJqMXfm93\ny0x0MoTNqVs6Su/8c95XhcrZPLfODOnYqJzrlimDJ5NF6L2WzdJDFTSngfanKtOLM6JSjt8yvYAc\nE7TnZDqZvlNNz5NzpcAvG7iS2ixojAbuojIvBm07gyLjZv503DLLxKkPTqHRRSjYtm0g2YiPtpzA\nwntX4Mv35OL9w/WIF07B6pXTYJzdiq21KWCKhDCrDZ9+dAwr7r8KT94fwdsfn0GtVYDpC6/FrWNO\n4qcvfora1GWdT75/0pXOAAZ0XnTfCjx1by7eO1SH3qKpAzpvuZQEEFUWSxJussOzbTtHwz+okFIy\nvWzU1+9oaLpcHtllHf1g6S+KxNAig6xrFbmssnjtzrqn6jT4OY7d1tOPNG7hJkIuc6QoU/OjjE2Q\n92mOroytOdO5dX71uiEHTcT7wLIZ2riVCSp6DTqxjgEGCbflBDneHGQ8hDELlmBazwn8/GwPeYRb\nATa6zv8RP3q5EXetWoA1dyxBpLsJpz/ZiOf3VKPNBmKScrqrPsIPf9+ANasW4Y77FiE/1YGamvN4\n671D/b+u2afz1O7jSjobBgAr5Ujfp/MPf9+Iu69fiDvu7NP51N4N+E2/zuSvj3qFH9s5GpmD261k\n3nY+bRISRXt551bdknXWMQq/tuVFxzREx2545fMWFlnwyicdar2gy4F3pEXGQVO5JuHmKIfKsS9Z\nR9uLQ6LtbCi8HIMaSe3p9igSK7Dh11zIy+MVQRxx8+IkDMdxMGP9+vX2t370a8CIYNz8a7E8tR9v\nH+sE62uQMtGyILYq6cpEMH7BcixP7cdbRzsYOhuI5OUj1NOBHguAkYuF930On8vdgW+/dAwtGWhn\nntc3Eon4SNTZDUSLI6sdaISWRXJFz0Vl8vKJSEH6uWVZ3D4lJ3SZevPS8coh5dCi4mnZLKLMSsuK\njNOOvpB1cEvIZOomyputjrsXB5VVR1Fd3Wwly7SZ6jhMP2fpKzNGeP08HIRoNENlTh1O+DWHOq9V\nx1Gmo9QykB3XMush7Vq0O+tMz2qXT377s4FnO3bsQGVlJTUdANTU1GDlypUDZX344YeOyLidQO3h\n7XiDmV0OGSPiAGAncOnQR3idlyZcgdsevgszL+7GppOdyJuyFPdMTuHIhvN9Ee8MIJuMWkMeoqiC\nStRBdruPjJh7KVNmQZc59kFOQkFH8MgdA1lngPecJFsyuwmyRJx3VMFveN0m9VNH1Z0X52feUQ6R\nHbrRUVY/kSxSf9quD2v8qkYtVYhZkNvnox3Z0nZ+7ESSthfUmHED2eCDbD4Rgoi2Bwn6j/4IENQC\nHAiSDfho22GUr1qGz87LgdVxCfs2v4kNp7uH/Q0p2TABaAQL2oRAEksnCQfEZI+cbN2MR5KU0Uia\nTESFJK9uI/XpqLWI3HgZMzwHh6aPqkxVHd3Wy42+stExt7Ykk49HNoaDEMlEw9KQJQwiR5onX9ap\n0evG6IOfnCqbSKjsvODGplk7bDLzo2y+TMIVGQf8nRiCnVwstJ/fg+fP7wmwjKHQE6ZGGiwiRCPV\nKgSBBpmJTWYLnZbHa2TczdZ7UCRcVq7bRUI2v2zkVoUMuilDJq0KWZUhpDK7MjJ6iXSTSc+7pjnP\nIrm0dM5oupeIpZtdBY3sRaZJYKZsZLjJrSqGm5C7JuOA2NvQ0BjtcLMAOkktGVGmRaxlFn9Stp9g\nEXE/kalJkLYLEHTdnHA74Wein0WQPZaSTuunIykaZ6xdI5Ys3rUseBE4HhFP/yfHv14/hw9ej3Zk\nG/wmlm6PmIhkZGrO9aPMoMeqEhkfbQY7nBguw9TwHyrRW1Yar33vpz2RkUCSrA53BMGpiyxYx2eG\ngwipkFoWgozsi6CqP+vIEavdWYue3xF03o6JjCyefDd9rAl5cFCZszLNc1QDLmRet3JlwDtaRn7m\n5Ruu9cItEWe1HS1A5peNUH+ZXgV68lDHcBMZDXmoLqZujmKw7suOLbdEXHSkIa2DbGQ8iLby0ga0\nZ2S5Xo83eJn/yPZV6XOWPplEJuZ+Pxd1Vr+nbcLLAkuzK5WjMLx7NLg5analw834ynaI+puss5dI\nttOm3RxPpI0zN/LSGK6+9LqGsCBNxkUT1Wgz8qAw0idL3c/ykJ38WYSMR9JoE5tTnt/g1cULoRXl\ndSOb1TYisiQjmwa/dztG0hiTOTpCXtNsmnaMg4VMbL+r5He7MKuso6okaqSvM0HC69GKIKASdHDj\noHqZU0gSrjJWZWSK4Nd8qLJrJgO/CbmvZ8Y1Rjd0f6vBbeSL3OImz5Y675F5WNe0smnbjSpb5bwI\nh8r2KamHnyDbiKVfmhyyyqe1tZtIalD1pOmQqfHKazfSdllpeTsvNJsM+kgHb/udF+FT6QNWnf1y\n7mTGokYf/NzxcguV4yqkfQY9HoIsXyZvkPOmjFzWnOXnGJMi49rLVoObjtETpoYTtImOtmCQExnt\nM23CcBtN4W1dqoBGyFXAImisZ85yyWuViIloy1d2UVftCxZGwi4ljZCz0tE+A8NznlpEjGSOCMhc\nB92Hw9F2IwmsoEm2EHJegMCNgyaThxYJd94ngzhuQQvEqOy2sfQOEm6CMDJw9QVOcgHTA/0yeG2R\nqSMFfmMk6JjtkD3iwJqYZCLionHJi9TziJIoQqhSP15amTSykzGtLm7sWHXideuIe11EeMQuU2CV\nzVuwVSJLXqJxfi3SLHIC8Ek1y5l2a6N+OK8afGQTIU+DttOnSoxlxiPvs/Na1V5l5lO37c4bE6q8\njPZc1klyC2kyHpTHMZomCJW6jKZ6a7AhIqss0siaYFVJvQxEBFw0CbmNLPEWHRWCOhxbl26dDFm4\n3ZYdzggy76iGjDOpiiDq6cfuEXmfdJRUnF5euSPBNrIVMu033I6tyvynGjGW3cGj7QC5mdtFu2JB\ngxXcEqUXyfKzTkIyHmTY/0qbGK60+o4GuO0zcpuPRcqdC3X6zznAWQOetcvCSkcrlyyHlOE8WsCr\n2/9l773D47rOc9/f3tPQeyfYwd5JkRRFUr13ybJabB/HkmtOboqdk3tPkpM855bk5N4kduzYThwX\n2ZZlualZhaqkqMJOESwgQIIESDSid2Da3vePwQz2bKzdZgZgm/d58GD2nlW+1b71rnetvUdkk5ES\nKLJZD7uqi5ldonqzssfuBJUKIp4sERCVNRpvurZRzewQ3dMTUicTolEaqYDVAtkqP1VVkWXZVO0W\njSszpdEpRGNdD7O20dp2teNSrwfRIk5EkkVxUgX9XOFkJ84OcZ8pOCXhdnxFqjhyUg9wQuIVeakP\ngDTS0CIRpVZLxI1IuaIocf+jeVnlbcceu2RD5Fij6dvZLjVTxkXbqFbqo50tVLu2mBFskW2i7xK1\ny66tZoTTaMGgbX/RPSsVNtUwWjAaEXLRtd10o3ETtVP034rc6r9TFMWw/5iNKyN7jPIxi+dUnUwk\njSsZdhaFM1U/dgQII2HESHgwK59dwUMbVjSeRZ/195zkpc0z2bq3O/9ZzStG80uqx4+jBzhTlfnV\n7ADSuDxg5FxEypLVhCfLctx/bRz9JK4oSkKTuBlExENbBivyYObMjSYH0XeJkiojRdVMtTDyVaI6\n0KvLidhoBasFg13ib5aGHjOliovawWpXwUjJt4NUK+R20hcp3KLFj9lno7yt5tX0fDm9mKnFqlFe\nRgt0o8Wtlf9zIhqY9T29eGQ1po2Q6DyWCInXxzXzi07EHTthk4UjZdzphHE1IV0XVxbsDDKt0ms2\nsUf7RlRN0xNwJ1t++rStVB0j5cQsPyvVW5+2NqxTEmmUtujaruNz4qfM6sfsO20YpzBa1DntB0Zk\ncKag71927bDTv/Th9f3MKYwIjIjIaElI9F6iaenjG5EDp34gkT6ShjOkcoFrNUeYLa5F35upzkZ+\ny45SLspDf20m1qR6DtOmn0jdO1Xjja6TWXDYhSEZN+oMadKZRhpToXV0ekeod4jayT6RidipXXZV\nDaMxbzT2ndpq14eY5aevX7M07drnZMHhJD0naqdTciWqA5EdqepPVv3Ebv0nQwyN6tUuobfaSTH7\nTq9UasNr//RxtPaKiFEiC7FUEJ70PC6GESEzI792Yccf2CHKifolO3kZhTX7rEUi9WN3wZhKf6ZP\nV/vfCsnMNWZI+Mx4ejBPIl0XaURhRhSNyKOekJula3RtZZNTiByUmbruRL20UvOdwsg5WiniZvf0\nE56Rcm6WhtGCzIwY2SVjdgm5ke3JwMlkrrdD9NluXDv52iHaRiqgvi7tEAs9GRep6to+IOqXiRBy\np0jPUdYwIuIihXS6iKFdiAh5tO+JVHLttT4NIwXYDum/nERaMx/sZGEyXWUVknEzpSiNNK5WJLId\npyfiImXcCRIlQk5gNf6tVF1ROc3CpxqpSF9ExFNV53Z2KeykIdqFmUkku3OQCpjtCuivrciVPq4T\nxT36PIgRGTdTxfWf9emn593phxURN1qMz0TbmO082RUVop+tRB0nu1yJ2DxTEJXTCnZEEqM5MBU+\nzVQZT4R8pJHGlQa7qqOR8iciLUb3rCZlu6pgouPVyqmYEXK7DklUbqN8RDYlo6ybtZGTdBLNP9E4\noj5mRxm/2IRda4f2fzJpOA3nZNfAiLSbxU+mTKIxn4hqF01LGyc9Z9tDou1n1G4zubASkWk74ex8\n50QtFsHJfJRKscNunpCYIj6jyjikfiv5SkW6Lq5cJELCjeCUcDqF1fZksrBS9vSkz4zUWKWlhZ2J\nxii+XdXUKC2r9kmmfpMl+9qJP1WE/HImcKkm+VYkW9u3RYq4CGaLbTt22VFmRePlcm3TmYAZCTVT\nxRPNx6pN7cDOvCOy3Swvrf8wqpNkBAs7dZgsIZ8OQq9Ne7oxhYzb2bJII40rHamY3KPjR+sM7ZJ4\nJ2NPn6bosxO79dDab7TlbpSOyMFH6yH6bnUrddCM6DipIztwsjiwCm+nPp3YJkpDtAgysnu61Cen\naaZyXrHK26nKrCfZ2vtGbWxExvXjxi6MCLUVSbJq+zTiYUcNtut3zPpKqvy8Pl/tZ+0xKW2eRuHt\n+h475Z9OEuwUiajhducXu3NConD8AGd6UF9dSLe3M4hUFZh6dlz/2SwtkSJhtgWYyOQrclB2lIxE\nISIq0+HUzZR1I/udEGszOCXbySiZTgh5IrBSYZ0g1Ts2dnZMzMouWigmogImEseofzodj0Ztk/bf\n9mFW/2Zhjb5PREwRwUnfM1tMRH811iwNO3WQSh9yqcBse+q86gAAIABJREFU59BsMZ3K+cqUjE9H\nxV2qjZFGGk5hpEqZrbT1MHOARo7BicopCmOHVOnVW70qbneCsEsKzFRHJ3H08bX/rdIRlS+RSVe0\nWHLitJ0QKdHiTnvPyI5E/PB0qF+pmOScKotGizK7i2Sr/PVqmmjMRPt2NC+rvmpkk5Xaqe8P6fnX\nOaz6ix52F2FOF7dO/YH2v97HaX9czsg+Ub6pEioSRbKCguhaT8SdquHTooxbda5kB3LaEVxeuFrb\ny+ngMiLiZo7DzoRvRLr1ziMZe+3AieJnl9gYqQ2i8HYnKZGDFN0zytfoc6LquhER1rZrKsaYk4WZ\nmVKcjC121D0nxFBPXO3CKWFJBGYLZlE/MpvQRcTJyF4n4y5Nvs2RqvZP1oZEfbgIdvxg9GigLMtx\nxwT1dtnJKxGbU+Hz7Ppgozh2BCrtPaP6SfUiJEbGU70lMV3pXEq4EsuUhjMYDXKRqmuloOrTMSKI\nl6LaZUY+9QTQTHlIxsEZEWe76rrd9nFij1X+ZjY6mbj0ZNdO39Kn73SiFC2a7JJhs3vJKpGiPmUU\nRkSsjRaSov6sj2umromIt/6z0dg3I+RmC0ajMZhGPMzGilkYI9hd0KdqUaztH9HPosUeTBJyvRps\nV/TQ/59JZVyERObBRAi51RhLFm6zhO3I9bYhufC5FPyhtCNI49JGIiTMqTLpJE8jkmjkDC+FyTY6\nKVgtGqwmQauyGJE4OyqiHeVSFNeKOFuV16r9jGwR5W1FzuzGEZUj1RAt2OyEs0rT7NronlEb6QmN\n1bg0+s6qXxup4kb1Y7XAslLG9WEuBR8hxAzxBDPxwGhBb9UvnS6ctXkksygWLcS1oofedlH6RgtN\nu4trJ/OlUfkuJqm36zfsxk0Eto6pJAs5q4qtN25jc+gjvvvmeUZm1A9I+CpXsy23mQ9O9eNPUd6X\nrDNLEa708lnBTKkzg1XYVE2EViRXFM4JErFTX2eKothSabX52VUsnNhjRvr09hgpkKL6tlJgUz3p\n2FHvRDab9WVRH0qmj9qtOys4UepE14kSJ5GtUZJstLAxIm96GJFvszgioiVqQyvVzmgBeCn5eVf2\nLLbffD2bQx/xnTeaZ5gnGMOsfQCknE6e/tw5On67hhfbZGEYo/4o5/bw2KfPUVi/mB98kEVgMgZF\n60/y9RvGYneafr+GH5xyaVOdEqb51bX8sNETl6eIiJst/ETXVkiGkCfmDyUyqtawLbeZ3Q19+FV7\n/lYkUljNE9p407lgmHJMxciZJpxB4TIee2gz8/uP8vwbrRdlgHmzC1l7y3rW1+zjuXdO0JIqRp7G\nVQErp2E2QJ2oknZtMVIxRGFFedpZMFh9b0d51t8zIgzR60R8jx3nmCrCYURq7ZAcKyJtps6bKexm\ntorIm918Uq2eOlloOSXRRn0mkb6TTJmNJmw7JNxqDNvJQ6TuitTR6WznROEpWs6Tj1zHvL5annst\nAcEuo4yi266lqKYcry+A/0wdnTsOMjQQTqmdxuNYZjxkTXCnxpcIh2VCYUCSIBZHov9oDf/YoIJv\nkCc+d05kzZQwelU8ETHGaF5x4vft5JkssfVmF7Lu1g2sr9nLL94+nhSvS0Z4ShUM36aSksw8Zdx0\n52YW9O7l+6/U0XlRjqioDJ3exb8N9/LovZv4wzsVvv9qHV2hi2DKZYJLwTlfKTAjG3qHaVfNS1Tt\nNrJpOtR8kRJnpOqJrq1IuZWKaBbXCE7q1EoVTyRNp3bZVTftKlCJqNf6dJ30Eyf90IhkWqVplb9V\nf7SCFQE3CmumiKvq5NsutMTKqAza/0Y2ihR2/fcXC5K3nFvvuY75PXv47kvHHfMEKX8Rsz63DV/j\nHtp/9g7+UDZZazdT9ZnttP54F8OjqVF5LRVyg/BmUIaK+NUzRZE2IL6NlKCHgSAQ9BASpA8Iw2j7\nTHR3Uit2GPUpq3Ib7QyJkOwcZQ2VwVM7+c5wL4/dt5kv3KXwvd+foCtkT6SIfjZCoj4tGUzZU0md\nKu6ieOVWtmS38urbJy8SEY9CZbzjKL96vY7Bqmv59JoC5y9Y16aWJqtpYK5ogZhsGKnDdvrUdDk3\nu07JjEA4TdcsbTvp2CFj2onH7E8fV+QD7RJeUdnM7iUCUT8yWpyI6sMsrURgRRqN8tCSBLN+ZlWX\nRp+tICLQdv9kWY790Ir+v9WiwqytFEWJ+wuHw7H/0T/t93b6pt2yXxy4KFm1jS3Zrbzy5gnnPEHK\nJPeO68g8uYOm1+sY6R4h1N/J4K43aWufTdnKXCGJFcGOTzFvW+LCmfV50XUq20Q7JvV9Sr/Qs/L/\nRn9mNs5Mf1IZa6/l+ddOMDRriyGvs+PPjXxR9Dt9XLv15wRCTpqKxFVXIRtX5jN0bCdHhhTnCUhe\nPAsepWT1JrLyvYR7DjN4dAdDXUE8828m2/8OPafbUGybqjLeeoBffTKbL6/fwJK6dznucMWcRhpg\nvY2uV4ETUQvtqIRW4UTpGqVnpWSI1DM7ipod1d8JCTdKXxTPjmqot8PuNuxMwqre9H3GTFDRtoWo\nHznZXUj1hOu0X6dK0bVDLEQExMxeM9uM6l1PnqKfo9C3mz6uUV5GqrjexlTVpyO4i9i8upCB2gR5\nQu48imf10vV6O2F8ZK7fTPmmBWQVegFQXflI+wZRpTCVy1u5f0svc3IkeppL2HVSZtG6Hub4K/jB\nC6X0q+At6ebO6y+wfNYYvvFMzhyv5rUDRQxETZMUimtauGtjB/PyQwR6inn/QE6M8EtymOUPHeKx\nKkE9hgr46Q9qOBUAMvp56sunmTfxVds7a/j3415UmNpGzmsl1ickSQJJoWRRO3df08PckgDSUCbN\nTWW89UEJrf7J8NH/QjIqB1n/yGHuHV7Ivx72c/sNHSwvVuhqLmXHW5WcHJaQMgZ44qlGqo8u5V92\nZxM7fOAe4b4vnGRR7Qq+tSeDMCB5x1i3tYUtC4apzFUY78vj0N5q3q7PJOCowCpjLft5/vBsvnLN\nRpbUvc3xUWsfqL1nNf6NfGKqMYWMxzKUZNwueXJVKUm4Xa7INkg4RNCCBbsK5rE4e4hjpwZI6ESI\nZy4588sJn/wRbQMZZCy+n8Ib/4ZCgOBZBnaN4rxuQnTUHuHc6uu4dnYGJ+rHUCUJj9sdt3qeUuma\nMIZlvwTDRNsLJTwljKqql6TNFzOMWbujhImKNnGDVpDOlEGtCaOEggTCgh9cmAgjS1KsvaaQyYk2\njYYJqQIFZiIdVHVKubTpaMultTn2X5Zj/UcJBWPpxE38dnyCJOH1eKbkFWe3rg4D4cn338ZsB1Ob\ntf05WnZtOkbtZbdNReVyOgaFop8mTLSep0yGsizsY3Gqt6Z+jMquTUfbf7SIpoOqxuyxqkOrPm+n\nDrU+yqgtUMKxcsXZpOuH2jB2bI71RU1f1docF05Qh3qbtXWoTUeroItsjpJwWZJQJAlFVQ3tEaWj\nhIJxbaodF0b1o/cJepuTaVMnPOFoQ39CPEEunYN3sJnREfCsvo25N8n0vvIy5xsHcG/+FPMXeJBQ\nyVvSxFM3hTjw1iJe6A2xbFsTD9/p5uPX5vPj9gyGgczZLXz54U6k+mpeeTELtbSXW7ae4mtl8/je\naxUMqCp5Sxt4+oZhzu6t4SetbvJnd3LjzS0USS7OyxKoMmffW8YPfZO+S87t4/47L+A+XkZLSEKS\ngEA+v35mFT7fMPc/ehZvrPqSJ3363TBXQScP39RL//5Z/Py8DyVvkO03N/NUrsS/vFTEoAKyW8Ed\nc8eT/SfyPvJIfwRwl7fz+dtdHN8zn5+GRth+SytP3JHBP71QxOB4Lh/We/jSoj7KPs6mbaJBXfmD\n1GRkcOiUjzAgZw1w32On2Sjn896eeewYVilZ0Mltd9aT51/B82c9DhcgE7xuzVZTXjcFkguvx8A/\nKyGCipiI29kFTQRxrzbUZiLnLufpJzZQFr0uvJ6vPLmCIqDv2K/43q4ezNaxI+d6yFT6OTeY4AMU\ngVP0vf1Pscuxtn30Z1XgzQgTGugkHE6sw6pjbdT2ydwxrxBv/RiBrCV84XNbqDSJoy17oPM1/uXX\nzQTTYdJh0mHSYdJh0mHSYRyF0WK4uZushHmChJyThTQ+SFjNIGdNOaEDL9DV0IuKC0/mhAjgCrBm\n8yAj+5fxVn1Eme3aMZvFT7UiD2TSPeQC1xg33HKB/OZ5/MubxQypElJLHueGQvzp3S1cX17Cy10B\nNm/sY/zwCl44nEcQaOnMpUMa4WsbAzGbxvtyORcVEVyjbL++i6LOWXz/w3zGo7RFlRkayGDIG8Cv\ngk+Kf4A9lUpsuL+UH/1nKcFARGWXLmTTyTB/eXc38zOLqR0LseLhIzw+SxQ7m9/9cAW1Ew9HqlkS\ne59dwke9AHl0Z/bx9W19VHmLGfK7aD1STO+qbjaVVfJimwtQKVrYQ0F/Mcf6JEChalMzGzMKeO6Z\nBRwfjZTzVHM+Z9sGUM67E9oJUEZbOdIrccf8Irz1rQSyl/LFz2815XWeOY/wp/eV4mNqXz3/5i/4\n0ZlIm1rtcFkdTbMLy6PTo811HOlXUL29+OUGilwKfcf3cKAtYFJpKmH3bLYGxxlLYOdJDAV1tA3/\naLLJ+OnqCeAtziVThkCoh4MHj1HkMonjmyz7SNM+PmoYnroQSYdxFObD+qEpYaQMJ2HUiTCDSaZj\nP4ydcn18aiQWJjpAFc/UMGHdSttZXupEOpEwWkegz2vP6dG4vIC4sRzNS9VtnU+mozLStDdWrjjH\nlECbauvHyB47eUXrJ05IcGSP/f5j3RZ7UzAu1IkwU222SkeSJGGb6utQVC5VN4no05mZsZN42UVh\nUjWWtfUzdSyrE+Nrsq8ajfd9Z8bj+qokSQSk+DB7G8dQIP5XEU3KFRvzSdZhjPxlxPfn6fS9k4jw\nhO3J8ARFBckFhAj1h/AsmEfG3n78OTWUrM5G6gIkhUwXBP1yjLOoYRcBVcU7Me+78gdYWSDT8F4B\ngwpIUqRuRporOD7ex8I5Y3hGRpmX6abhTBZBTd31t+QyurEH7VZ9ZKclzLxtp7mtKIdXnqviQnhC\nFdfEnZmz1RKBgMYuIDiUyQiD5HpVGHVx5t2l/MAnUHxVNz3joEZrrq2cI30Skf0fibEBHyF3gMyJ\negz1FPNxVwe3rhvijbZ8xuQxVi8PcOGTQnoVwD3OigVBBusqqB/RpKvKdJwsnMg5gSNxAl534MBR\nU14nnx4jNDqfHHlyrlS8paxbWhqxwkQN19qVKjIuffvb31a//oNnp2Qo563kS09swK9ZIThIlqxF\nd/ONG0Z49pn3OO00+rTCTfUNn+bpilr+9dfHIx3EAS7WudGZxNVQRiPYWQVr/+u/twoTnWy1k27c\n+T6DeFYPnEzZmtaVJ+7Ih8H5QNFKX5+W/qiKk/qyOpunt80pRHGT7ctmtibqgK3OuJv1LScwesBI\n1G9Ettg5N65HqsiFVToiW6zsS+RMOjDlwUyjsaQPq/2sjS/LsvCMuN2H6+yMIzMk0q7TA4mcJffy\n324a46c/ejshniDPv41Fd/TT/IP9+DPnUP7obRSVuyDQzVB7DjnyBzQ8e5rKG07w1aVZvPTbORwa\nCLN0+2kenZ/Hj382m6aAhKeyib94bJAjz63k1QuR91pIkoTk9nP9k0fZfmEp3zw8zBcf7+LE82t5\nt3fy3ReusnP88SOdnPzVGl7rjLI/lbzFZ/jq7QOc+v1KXmzyThUYALyDPPn0SfJ2reXfj3tRRP7a\n28/TX2tEmvKecQ18A8Zh5BBzlneyqWaI6hI/uRkKbreCW8rgzWdWsLvfvB9Fz4xvePQI9w7X8L9e\nLWR8wjbv7Eb+8kE/r/xgKYcmzmrnLjvFN26V+O0PaziR3cafPj7Erp8s4cCwBJ4hHvtiA+V7V/Cd\nA5FjK04h7vduZt/4KE9X1PKtXx2L43VmY2jKw9e5K/ji4+vxv/kLftjoj8XXzyn6cS3LMnuf+V4s\n3J49e6isNNbl29vbufbaa2O2fPDBB5PKeConMFAJDg8x7i6kPFPmdCBl8ngK4CI714vqH0nZDwCl\ncfVB9KALWG9dRcmy3W1IO+TfKI6Z3XbHuIiUR+/bTcuIBJktHBJFav2YcTtbfWeVJtgnU1YLHlFa\ndsvuhNQa9YXpgLZunfZVURynRFxrg6i9jBakIru119H0tERcT76NSLjIPrswGqcXV3hRCQwNMu4u\nSZgnKF1N+HM2kFd8iM7Oc3T86Ed0ZnhR/QHwZuF2+wmrMq0fz2PPogYe+Ew/DwDqQCG/f6mKJn9E\nnQ37vYwRoig7jIRGQSdIvg/Ghl2EAh7GpRDFmWGiL6JTVRVcQbwQU70lScJVeIFHbulm/PByXjvn\nQ5UAh743JZBDLLu9js8s9HBwbzmvH/Qy6ncTyOrmsw8O2EpCVdU41R+m9qdIH49cD58to145w3U1\n4/QV9pLdXsXJ4WhibvrGYVG5Hw+JkXGx37XP64yEIv2cbdRWsiybxksUlq82TASqqhLsP0+bUsDy\nqoypmVxMuAuoKZHpa+mbPL9lE1eDYnw1lDEZiJRiPUTE2ehPlHYiE7Edwqa/duJAjFT4RCFKwyzd\ny6VfJuOY9cqrVZmdbqMakUmjNM3stNOfk4WIBJvlbRZOe1+Uh5kNegXbaKchqq6J0tC+HUX7ekIj\nQm7HNie4VMdPlCesmJWZGE8YOUd3g5fiu1aR4Y0QQmXcH6lH/wjBkRCgkjOnk2XDs/jX763iH/99\nDf/jR/PZ0+WKke7wQD4nhlQWb+qNO9qQNaeLVVkeGpsyCI7lcGZYYeGKAbInjrEghSmvGSBDa5N3\niJvvaaa6czbP7cubFtHP7riTMgbZvjTEmTdrePFgEQ0tObR0+QjkjJEniftFsr5eHc/lw5Nuqje2\ncevSMGc+yWM4mk3Ix/FGDxmLW7m2VLv4UsnMC+KxkY3QFncBi0pdU3id1bxo5ENEyrfZXyp8n9vI\n4EQHb8yRjLVx4LzCY2tqKG2o5UJqfwgrQUj4KpeyMnOQfWeGbK/KLlVHlmpcLeU0gln5zRSvKERk\nwApmipVWfbayWaxUTP1spTg6IVZGeRuFMYJIORSlYTetVPdjo3q1umfWrkbQx3OyeyK6Z6YQJ5O+\n3bipgpENojHodDFr1CZOF7yiSd2oLY3U8GSIuFVbW4W/GFDH2th/Lszj6xZTWn/YOU9Q/Qy//S59\nn7uLBV8so2vXCYYvjCIVVpO/KETPGycIhFUK5oyQ6/NQXennwrBMdrbE2JCP/vEJFTyUxe63S1j1\nUAtfui/EW8dy8Of1c+O2HqSz89nV4QIy2b+/gM03nebzip9dZzzkz+1k01wFBZAlCUkOsfSmU1yX\nl8HHH+bhKRqhKmqqqqCMZNE+LIGskOkL4fIF8cggewPkZUsExt2MRt9C4g2R5VFRPSFcgJQRJC9b\nAVyMjroIa8LgDceFUVU5Eiboo31EZf3aLmp6i+l2BZi78AK3bhrENbGE0PpV0aLd7tibhExrbTF9\nqzuoCRTyTPShTFUFJNr2zeHAwkZue/Ik5ftLaeiDwupeti0Ps/vny3ivxymxneR1exoHCTnYSRON\nXX35jOb1VIhSWsQ9wJksKY8Lq45zel8dPZ9ez4Orz/HDw4m9uiiVkLwlbN26AE/L+xzsv5SOzlx8\nXAqO+VKH0bazFnaUbxFx1k7CiQxuo3jatM1IuBmsFgWJpCeyR2+rGcFPtQprF4nkbVU/dgik2Q6M\n1cLNycLACaGz25/s7B6ZxbWzADIbk3o7zOw1WgyajXXtZzNSb0XG9eHtIpmF8EWHOk7DnuP0PL6e\nh9Y08Z+H+pzzhNEWOn78AuPbN1C4/RZKC3woQ90MHzkUOR6CzIXjRQysvsBDD3bFve5urKWC516t\npHFUYqRpDt95LpO7r+/kjrs78Ixlcnr/In5+oIChiVhDJxfzA5q4f1MrDy1w0dpYxktvFHLHw224\nZSCrn201kcPvW+4+zhadqe3vRs6GS3kXeOqz5ymNfrHtBH++Tfu+cYWq7XV8eZXmIP2tJ/hLgOEy\nvveT2bSGzMN898fVtAaz2fG7ubhua+PJz7Ujj/k4f7aUl17y8cADgxN9JPnjlHqEeos41NfBdW2l\nNPvjv1NG83nx50tp3d7KllUtrM6U6enIY/eL1bzvmIgDnmK2b6/BfX4XB/rsreas1H8zIm5XrHIK\n6dvf/rb65//xcyB+UMt5K/nyk9cwvuNZfnzW7OVEk5jSaJKH6msf4Om1CntefpU3W/2mr0OcVsiZ\nLLnxQZ6o6eO1X77JvkF7llwtJPVqKacREiWU0c9Gf6J8RH/aH/fQpm828I1W6CJHoiUERuTAyHYt\n0YnGEf1ctxGRE5EWvX16G0X/jcpvJ6wREu33dtUREbHT162VAmtXkRWlmYzSqs/DyKaZ8B2i/qj9\nbLYwcNKfRHUnCmN3rBvlKxo3elKeKpjZdklA8jB7y4N8aT189OIr7GgZTylPkLP6efjRdobfWcSb\n590oEkiSiq+gj4cea6Js/+TDhGYqqL6trRZ1UTgZi0b9QpSuUXwre7RhzeYxq/yNFpcArqILfPWz\nnTQ/v4JXOlJ/UDlmh5zJ0pse4slF/bz63A729Jsv5fRlFD3A+YVPryH0zvNTeK+ZyKaqKh/96Dux\n8ifyAGeslqZlcKpBWvbu4DcNLq69/0EeW1WE7yIs0iVfKZtvv58nlgQ48MZODtgk4mlcHZiOvq93\nqkZKmJWqZaSeOSHedspnd6VvNjmZkWurukhkArJbl2bkSeRc7SggTgizEZE0i+cUZnWYLBF3+t1M\nwUq90odLBEbjJzqBW50djS60rd6YckmR45mGGuT8njf4db3MdQ8+zOOri1PKE1x5Q9QUBqisHmJe\nxTiF2UHy8saYN2+AWR4vZ9vdqBYLKhGcqKR6fzbdKqsWVmKOFmb90coPe0o7uX9zP3Ore7nr7jbK\nOsv5qMs5EbdbJ5KvlGvveIAnlwbZ//p77B8Im5bVaKGlLYMWRuN7utrM8My45MnEB5DhAtPX9ltA\nGeLYuy/S1b6FO4ozMHud9/TAReXGm7mtsI3f/2YPB7uCCb1UPo0rE6meBKPpGaknIkVRq1CI1Gp9\nmlHYIYuidKNp6p2RU0Jup+70ZUgUViTWavLQEreo49XXbSI26tvOauIzU5TtqOR2bEgERn3M6F6i\naSUKkYon+q8Pb2f8GcUXXWsVtegfTL6uUPvq0ui19r4VUqViXwqLJUdQhqh9+3d0tl3HnSWZKeUJ\nwQtV/PQtiVvWtvAHG4NkuEAJeOjtzGPPi0vY02b+QzN2fKUdH6D9zsofJLuQNoJdMUBkqyjeZHwV\nb3aQ8tUtfGmLTPfZCp55q5Qeh88AiMa4yEZVlZm16RZuL2rn97/Zw4HOgK3dFCNlOwZ3Bj7AneFC\nkhTDBT8Q9zB3KtrJ8Ed/5IwCsoHMAnvDwtQYNciFE+/zU6fWpQRhLhx6g2/tGWRI+HvUaVyNmC41\n3A4pcqKEOSU2VosBI1g5XiOSZqfMqSLkRmlb2epEnU6ExJgR8mRIspM4yZJw7bXZgsIqn5lUeM36\nrJWtZn3VKoyWkOsVTv0OhZaQOymTk7JcMVCDdBzfxU9Snq6LtmPV/OxYddztRBaZVnFsKboCVTZ6\nP1WihV1l28xeu34xEk5ipGkWP/hBlWE4KzhrjzAdB1/nm3uGGA4T+QkiC5HJlnjlyycbkPLjf1vA\nqvzTSsZVJUgYCAet1xuXuqMIjw4wlEC8S71caVw6MFNarRyAU+i3yvQkQJS2XVKoLYdowhDZkoh6\naxbPyilbESmnhDJVqrg+Lad5GbXZTMKOGqb/bHYvlTCrbzOl0WpsWEE71kRb1tp0tQTcCQk3Qyrr\nNT2nTcIOuQbjo4BG/lHvl/X3jEhhKtpG5LuNwok+i5ReI5+Vqr6UqJ8Ljw4wpFsIG+1Y2BVIlHAg\n8pa9oGIYJnpPu+tld+fLDIZkXBnpZoB5jPeYH4hPD+40rnaInIAVQUgVnCrmTmDm3IzStCKbVosC\nq0WMEdkyghUpN8vP6SJDf201KSajhDlR3O3Y4SS+0zZINews7ERE3MlCVAstgRKdG9XXjfZMeDJl\nSxXSc7QxkqlvJ7sd0Xys1Olk/YH2s9XOjj6ekQ1WCnkq/Fgq4jlV/kFQ1tEeBphDqDcsFFW0/0Uv\nMUgGhmTcDswM2OpSqJj47Fcl3lCk+FcWuXJYsLCS/Inz/aHBFk60jcW/+9tOmDTSuESgV5WNHH0i\nypwTG5JNQ5+eXZXZjlM2IvCi/KaDmETzM1PnnbaPFSGP5qnPX3vfTp5mk00iqrXZtq2d+DOhhIN1\n/zBb+IomUTt5RqFXNI1UcW1+2onaTp52yu0UaQKeHMzIqhPyabToN5oXkiG2WntFPkl/T0/KRQtY\nUVms8k+GmDuJG1+HEp78apZVRp5LlKQwveeaODc2NZ6ofNG89f5Db5PepySz8NYjYTJumrkExXKY\n/0eeCKNKBIIe3tRGURVyllzHA9UTTDvQTPgX73FiTHUWxhIyBasf4E+3FsTunNvxLD86EzCJc3U5\ns6uprCI4IURRGBEwKyIezc+JM4/+N9vitGOvPk2rsHbyEak6Tp2qiJjatdGJ2ilyrKmCGSEXwelC\nxqo+9JN9smlZYbrq0SovIyJupuYlPsETR8BFRNxMKUu2Xq52vzxTcLroN/M9It+vV8b1Plz7nYi4\niyDqp6L7RunoibidOdDunJbIfGqWpz4f/bUkSSDnsvbWW7mnbOIL/2l+0dSMNriZj9DfE41h0Xep\nGuuQIBm3zFiFt8JuWuUgswAklT93KewMycQosDJK3b6T9FcvpwDAO5dbVubTsF/z40DKKCf21nFz\n9QrjMJZQGKx7g39qlJC81Tz6+FZHZb3ScbU6/FQork5IpIi0GqXnxMZE2i+RycdpfSWjclipzHbT\ndULYzO4lCjuE3G6edtrAziRuFd+OHRcbZnVhpGJeg5ZMAAAgAElEQVTpvxNBT5Kin81ea6adjFN5\nTjyNyxNGfXMqWZbwSZV8ybWQB6RMshnmg9AJ/j7YRXssjUiYP/It5WF3DlnqELuDx/n7YBcXBOlL\nkouF7ht43tPLV8Y+Yb9W19Sp8iI7faVd3Lb9PKsq/Kj9eezZPZd3Gr2GJxEM/YVnhE03nuP6xaPI\nvfns3T2bXec88W87kUJUr2jnrq1dlDUv5v/bkUvAxvg0qktvxRpuLJsM23m4lqaAePEugn5u0S6u\njcKJPicDxy+BtJvpmCrxTXWysebLYW7QtV2w6xjvtk42UfHqdSzKjA8U6j5uGcYKSnCMoZFRBkf9\ntl7SeLU406ulnHaRKEE3IwDadEUqtygNkV1GRMFO/ERh16ZkFPtoWlZ2JELEtXGt/lIBO+mYtWUq\nFohO2sJJ24nqyaruZkoxF/UNOwuvKIzGp14Rd7lcce8S15LwcDhMOBxOk/ErAMakbxJmCqomFB5f\nEJ88dazJUgl/Kq/kLs7y30Mf8aVQP5XujfybJ4cMTZi/zNzA3VIT3xjdydOBXmZ5N/Ndb24sjPbP\nLc/jrzxZyGp8H7YzvqXcLj79UCPzB8t44ZVFvNGssOW+eu6qCgt+n3PqWIl99vi57oEG7izJ4r1X\nF/Bee4AbHj7NTaWTHM5V0M1jnz/KV2/qpzgTYfpGdSqcd+RcVl9bQ3Y0kP8079SPoOhsM8pDNBdE\nx7R2bGuv9a8uTQUckXFHmarwVthFW/RaUvm6S8WrDaOMcGLPSQai19553LoiP16utxMmDcdITxip\nhV1Sl4jCrCduqSDiTuPYDZ9I+RKNb2WTaJFkNnlOx7aqGZwuYIzSNkrH6X2jPJ2ScH3Y6YQZEXey\ngBMtlPVkxugNKvoJ2mxBnsblBT2ZMxMGpqilrhFufuwwj1QHY2lE/zKk+dwjdfJPSiv7GKZWPcn/\nVEIscJUzeyKvLNdC7pW7+Ad/E3vVIT4Jn+DvQgEWuiuYPYWQZnGrZxHz1fCUvKyhUL72PAuGK/nl\nu7M43lTIgfcX81yDyjXX9ZFrwBKnjhnIrTnHLYX5PP/iXA6cLWDvewt58fw4N9zYF3v2TxnLYe/r\nK/g/v7uMt/QSv6BezfOW8Vau4QaBKq4PaycPvTKu/dMSctGPdyWL1P9OqQZjisy34tTxENfr6iXU\nrVPH14jUceswyUMip3o1n3rkUf7qa1/gb596hC/dtYGleTP/M0VppA7TpYImk7+VLUbqqR2nkkoy\nKwqTCMFJJezUiRZ22z0V/SIVuwPToVAnu3ORStgdg9M1Ts12c/QkXH9OXK+Eh0IhSxKexuUBO+TM\nDqL9RBtH39ckSUKVgozioST2c0MSblUiyDhDE/0vGqZMjvZHFx7kWBhNrhTIy/kzqYV/DI/G3rkt\nWmBq7Yqpu2qQopIA4+159AYn+rIi03wsl2BZP1Ve3QLVBdk54YkHJTULUynIotXDDB+r4PRoJA9P\nSQ/XzVZxVXdTkzVRL/4Mmjq8+B0MFdECWZZlZFceqzbXkBMNOKGKa4/WmI1LuwLNTIxrSzIuu914\n3W48Hg/ZWVnkZGWR7fPi9XhM/zyyBCrsCLlojyYmqXzjElXHXYWr+cy9q8lr3stPn32ef315P/Wu\npTx+/zqq0jL8FYWZnjTNiLgVmTYjUWZk3oltqYbd/M1UcSPC5CR9PawWRckQaTtxjcIkkm+iffhi\nEHLRZDcTthuREVE4fVjRT9zr1TInRDxN0i9/6H2G2cJOBG0YP2f5mVrIn7gWslVyUSQv5q9cAV4I\ndtI9EdbPWX6qFPB13xK2uzwUu5bEwvTE5VvMF92F1IZPc8ggT9GOTrQ/q4rE6JiEN8+PF00/VmVU\nT4Ain7asKpVbj/GXXzjMU6vH438p1TXOwkKJlnO+yDN9vkHuur8Dd20FzaFxagoTe2ZHPy8AscWJ\nt2I1N5RNxun65GhEFZckPB4PHrcbj9sd4aQTn6PXXq+P7KxMcrIyyfZN8Fa3HDs2I5qjRW2eKqHD\nkmZW3/IY/wcgF17PV55cQRHQd+xXfG9Xj+nPjw4ffZlvfdDDmCrzLUXiHyberDJfDrFd8vCOps6D\n3cd4t3UpD82KrA2K16yl5vguTmremmInjBWMHaKEt3g2FdIwb586z/mBMDDCrte6OFUYpsP+06Jp\nXEaIOtTpStvonhkZN4Jd5VQf3k4edslotL5SpSInEl5rq97uS43w2CXf+jKJ1DWjdERtl6r6mO76\ndJL+dO6yiJRLSZJwuVxxinjUZhERj36XxtUNV0kLf/x4KwXam3fv468mPo4cWcP39hcQYohfqseo\nkVbxz675KKh8Et7NN1FAkiYI4RDPho6wyLuOb3kXxMJ8S1KBSBhVlVnsXsVd6gn+IBxC0bBj0Rwg\n8i2qKtF8qIiRT53nnpUZvFznJWtWN3fe0U2W5CPLEz82/IOZDAVUekZcIEkQTdsTIt8tc9YvgRRg\n7e1nWdtfzXc+zGTrwgtUZinIkgvFYG4UjR6RQBM7LibnsmLTQnImYkqBRt5tGEWRJOTspXzuiWuo\ntNdsU9B0EXa5jH/0x9/JwcPHKZjQzlVvL365gSKXQt/xPRxoCwgrbyI2I60T7wNX4ZAqQ3TjQILr\nUXlHe2xfGeP8uSGYlR+59lawKE/m5FjYWZiEoTLedoyjYzdx66fvZ9bxeo7Un6Whe5S2nhQkn8Zl\ngZkYfHYn71Suuu0SaKeLE5GqP5MObKbzM7LBaRhRPRuV5WItNOwuAmYC00nEtddWY05/hlT7q3sX\nux+mMX2wUmu1UPrLefaXhXhkkNzjXHf3GYoPLef1Cx4kSSI8mjXBhAr4jLScGzjN/1DGqGYxX3Jt\n5C/UPfyDEkQBJKmQz7tXc716ir9RxpgtLYmEYS9/P/FLkW5pHv/d5eeZQDvtKpQYlEHvR/T/x1tm\n8bOdQZ64qY7//SbAn83eQ8UMXTsSE12j46L/2BL++ZiwpgBwy2HK1p/h/vJ8nn+2mN7wGEE1wtuj\n6SSyEJ8yRn1FLCrW5N7TSsfEGzrUYDeHD5+g2WV81FLKmsWWxYWRm32NfNw0OlHWMF19M6/AGivj\n/k727e2MXaqqyps7X3KcgSTBU/IkYZZUmR+rOkfoLee6tfmT191H+agr7DhMMlBHm/jdcy/RsHYt\n1y2/lsfXXEugt4nduz/ig9Zx012ANC5fpEodt6Mqi/6L0hClNVPHC0T1ob1npo5rv7NyuCJFV5+P\n/vvphlNCZWab3f6gvzaqP7u7FqK07SCRsjvZ1UmUrCbS/kY7BkZpT1HcdPWofVhL9PrCS2kRlUby\n0Psx0XeGcUMeuro9kQs3jIRlMvsyaev0IcvRU8Eyi6Q1fE1q4ovhM9SqKqraRa20nX9zL+LtYB37\nVJnF8jq+ylm+GD7DUQC1h1ppG//mquEtpY69agZ3eWsoUD7ieTWilsftviLeMROVE9VF2ycL+efa\nMLnZKmPDbuTZZ1iHi6GAzYe9Q26GwkHmLDnPslUKH/5mNvWjEnjC5HlkRv2yiYA7FXryPeUVo4F2\nPqj3s3j5xMHnytVsyG9hZ6+COt7Jwf2dU46Q6XenX3vTgUHTDFsPcCbjUKrkEJ+OtaXEyyGZ5rgQ\nEvmLN7AhM3rtp3bPKXoVp2GSg6qqKOM91O55h+//+Kf8ywsfUqvO5ZZ7trM85Q+LpnGlQ7RF6FQN\nSJUybkScjc652j3WYiesU9tE15fKMYBk2ySRI0nTeUbRrA84sVN0PxV22o2bTL8QTfh6Qm5ExO3U\n3UwuJtNIPbRjRN/uTvxkPLLZKnnpVTtoiN0LcCjcSgulbJdAknLYJvvo40JcmMNKWyyMLFXyB5KL\n2a7tfJhxNwcz7uJNTx6SVM33fXfzvCsD2YGooaoqquJicMhNUFXJrxzDO5JNp9/mc0uhDE73qcxe\n30/vrgW81xEh366cEao8Xs702X9fiNm4nHyOI0TXsSOcifHAAq7dUEGWpClPAu12sWBZO8k5Oviy\nS4llIqkS31GkuNWR5K1g2zWlsUMrUs8RdrYGHIdJBqoK2VULmBtrxRD97fXs+LAZv7uAyuxpfelM\nGhcZyQ5QM/VBn4dVXmYExwhOnYwVAbYTx268VNlzsR3oTJMqOwRcf89JHaWqPkU22QnnNF0r6Pu/\nVXz9mBXVs3ZciV5nlirb07g0oBcZzIQBJ2lNhcI4UCTlUKB5m0oWORQSZgApLkyRHO2XcixMvwqK\n2sJf+HfzmP/9ib/d/NfQGNDJ/+XfxTfC/ri3ipgvklXcbnWSY/lG2LxqnMFTRVzQndiQZJWcnCBT\n3jOneGg4mktYzeBM+8SPBUlhZq/tpqi3mPoh5887acm38Me3RpvZdXLyPYbueeu5Jl+aMifq51+z\n3SwzmAlbycL0Ac5kM5iqirusVfGPbajiU8KYQUL2RB5CwOvDDbgyMsnJcqEqQcbGgoSlXJZtvoH7\nCpaw92AdDT1BfIUVrFgzF0/3IWr7U3cc5lLAxSY2MwnR1tyVACO1OFG1xmr7VRs3mWMHZmmLiOXF\nJDapyjvZIyRa2Dn+4/SIzKWGZOpdW35ROnrCbkRQRCRcfzzFzji41Os6DXNo/ZCTfilJEoRzeP0n\n10wQSC1hG+YNpZM/lFfxbcnHfzJCUC7ns1IxinKIVySQGGWH2sUXpFV8W8rgPxkhQBmflYomw0hB\nWtSAhgxKDEohUAOcV4c5h/lD/9r7vqoWnr5tnIZDxbQp4yxd08FaqYhfHMgmoKqaJ/wUKrYc5ctr\nx2nfvZr/PJKpIfwSg/VzeGflCW5/8Ayej4sYLO3krtUye35TbMrZJCnyQKrR0bHoEZ+pu7IBOmo/\n4ezSTcyXIaqO732rhREHu6oin2E1V6Z6bBuS8eQnDZ0qjsS/2VDFd9lQxfVhzOGicstDfHFFxuSt\nGx7mGzcAw8f5j1/spTU0xMFXfsfIhg1sXbGFDYWZSP5BWs/u5Scf101ZGV7OSE8O0wf9gE7lgJ3p\ndjMjdUa2JFteUZ5mCob+u2TzTxX5TsURlulahNjddZiOIyKpHg+J1JHRESDt96JjKWZ9XmRbGlcu\nkj8mptKnHuUPlQX8ubyAv8YDDHNAOcTfKT10TQkzn7+SJsP8bbh7IoyePJr3R7Md3GBvCftaz7Ft\n6xmu97i5cKacn71UScOoBMQT4LH+TAYDcGEg8maUuN4eyuSDF5cRvLGJ7bc04enP4/3fLmRXq4vo\no4IiXydJErKmXs1U/CmK91Aj7xxfxdOrIoqtd8E1XJPfys4J9p/oUTIzfzUdY1xIxlORUbwqDq8E\nXTTFhZiqeB/dc4oeC1V8ahgrhGh9/zn+7v2p38StskID1O19l7q9TtK+vJCeJC59OFE2nbZnIuFn\nWpm2s/0/Hf1YVM5ECKnTBYxZmokSPbuLGiuFPYpUlckKiRxNsYpnRU7MJnyrX9mbzv6YxqUJSUcY\n7YYFfT8J0qLW8+dKfVx4JJBi0mOINk7xDeVUXHw1LowWKh2hXawPTV5r7dTvbMbZH8jm4LvLOPCO\nvq9PnWu6P1nA/3skOm6mlln1Z7Fnx3I+fkM3ZvTjRPVw6JcbOCzYoYoq4UbHxuIJeZi2Tw5zdsV1\nMXX8uo1V7N1xjlFdlokcl5up8T3lMHRqMlb5nDypigdUecpZcTxlbF5dEutS4c4j7GwJOA+TRhoa\npOJc2KWAVK/Ik4lnJ66Z6pIqOLUjGSSjuqYKye4ypNIO/fZwqvtnskdTzNIUqd9WqpvoeIq23KL0\n0ri8YZdc22nvREifKB+zsPrvnR6jMRofIoiIsN4P2F2kiOzQknDt0RSj8akfp+GhRt4+NhTjhr65\na1mXL5tyASc2zgSm6bclJf4+6OXvzYIEL7DjFz9hR7Jh0kjDAS4nFSsVhGemjwXolYSZVA8vVtum\n4niONi2j7+zcd7Ll6tTuZOs3WSXeCUTEQPRZZFOiP2+vVRwvJz+Thn3Y9YH6sGbH0JIRMvR5JBNf\nDzt+XLQg0I4Bqx1Hq4Wx9rOxMq4CQc7v/hV/uzu556KMkOgupRNctT/0nnaWVwcuxlGLVCORCd5p\n/7ZTR6K61DppkQOeqXF2OY5no6Mjdgl5KjAdbZXsYszO8RNRHCOFz8497cQuIuJmu2yXY99LIx5O\n1VxIbqcu2XnJSPTQpj9TMCKqWhtFCw69Mm/ki7RjU/QMx3T7lmj4ZMUMK1y1ZDyNNC4H6J2aHWdi\n10HYWe2LnLxZ3kYOKhWOaybUCW26F+vYyXQpyFYkYjoU3VTsGCSjZhkp5EaTffS/E2X8YhKhNJKH\nkborCmfWj8zCwtT+rPUxdo6JmNlv5K/s7qA5mTOsxqVZ+YyIuJlN+mMp+vGaCpgtts3sS6XPdBsZ\nciXjaisvXJ1lvliYrq1qO+ml6riE9t6lsLNwKdhghEQnBzPVzM59IxXdqq7sbJ9fqf7CDgnRv8LQ\nLL5ZPldqHV4NMGq/RP2hlsCa7eKYxTWzUXQ8RGur1SLeTjhtXnaP64jSsyLf2s96Qi4KmwgulXlN\ni6tOGb8aHeTVWOaLAb3zizqjROvfKakXkTWwt01vxw6j6+lGIkq8U5gdD0nFLoQeVmWym3aidhvZ\noZ/c7WI6yaeT/uY0bPS/fqI32yGyu4hKE/IrC053SRLZVXGapx1C7gRmxzHs+iWneYqIt53vjGC3\n3HbKYbbr4DQ/K1xVZFxbYXLOMr782WvIk8KcfOFXvNR+Cb1M3F3OfZ+7h9VuCBx/mW9+2E3QJLiU\nXcPjj1zHPK9K/9HX+eGebqK/SZWeDKYfIhIe/TwJmaK1D/Jn2wrj4ja9/jN+2OiPi5sKsqt3aJIk\nIWUvjfX5+hd/zcsdYWH4RHApEA+nSrMonNFWb0zNyV7KVz63kTwpTN3vno/5DTlnUWwM9tW+FjcG\nndifSB3aId92FxSJkPBE45pBzlnGV//LJvKkMCd++8tYXzWr50TGjXYLXHsd/ayHnWMFIoKUxuUP\nrX+WMzZx++ZPUekKM3L+e7x4usV0jrZSpq3i2EUqCLk2Lf3nRMaYKB3RosVo7BmJTFZ5TTdSPbav\nGjIurjQFRQ0xFrRZoZKb4kWbeODaGubkuAGV0HAHe97fzc6mYVJN5xXA7w9i/Vp1hVBYQQkrhBUl\n9nqfq3oS8M3n6S/ewlztPVVlvLeJ3bs/5sOWUabrd1XF25th+o+/xj81Z5GdPYsHHtxE0TTkadTm\nqhr9FTWHfV4b34So2tlKnS7YOZaRCkIOGNahqobjxqD+xzCszlfasdWqHJO2S2SVL2HbijI8o+3s\nO3Da9LcZ9ItHuxO50Y5L6tpb1FfFvk4E0SJFP/GL/vSwM8Ff1b522iGRUbCF9bPn4fGfpvb0Pvps\n/NaIU5XaDPrxoKohwkoYVQpH/gvys6skm/kbkPDmbWZVxSy8gUZOnvtkStntjF8n41Lkk7RlSkYs\nEu2yihbDiS5EtJ9TOSbtqOPJ4qoh40aQQPj6fBFceYu477pqilyjnDlxjp6suWyYV87Wu+/C/cKL\n7GgPXpR3oKsjZ/j1z89chJwvPcQNQjXEGCodB/dSO5bN3EVLWVsxj9seLKfk1Rd48eyYjYWOPZgN\n1qg9SnCM/r4x+kdzGIcpfcXqTF2qIOrzdpxXsor9xVQJU0HI49Jjsg5VVUUdbuT5nzY6TtMsL7Nz\nnOak0UXR0vVsW+iGsSEO7Ld/ltxoi9oJUtnO0XqO2q8MN/KrnxnXM5iTcO11MhO/1aIojVTCQ8Gs\ne1lf4YFAH0dPW8eYLt8Z8+XjB3lr96G4vPQ5Jupv4uN5yC67gzWlMgS6qT+XnN2i/KKwOweYpaFN\nx2xs6e2xEpPMYGVHMkh2znOCq56MA9it6/BAHc88cxKY6DTSMVoefJT7CrNZv6qYd9s7HG9NpzGN\nUBXCKPQ0neZAR4gDR+s4efsjPD7Px7qb1nOw9UOaU9hgiQ5cq/OoyZ4rFOfpOMqUPJ2cbU41Et0u\nTSkhd1iH+joz2vrV22lEkk0sw5flRVUkGBua8it0lxv0RXWy5W9GBuyQcX36IqKfxnRDwuvzgSpD\noIdxi/483URcrzibhTUKY/9IXbTsKlKw17DsomMdTpVw0W6YyMfbIeJ62+J3FcRzRyKL/pnEdBPz\neDIuuSmuuYb7NtcwJ9eDDKjBEVobj/D6hw20hrJY/6nHuCdXReo7xems+SzKcyGpCoNtx3nt3cOc\nHFas0wlMdGRvMWu3bOHGRSXkeyRAJTTayeGPP+KtU/0EVAlv8WLuunEdq8oycQFqcJim2o956WAL\nA2q2qT2vvnOI+hEVJA+lizfy4HWLqM6UUVEJDfUxrtW3JDclizYKbX7tg/qYzfEdJrIVrSgQDqZK\nY42Hp3orf7S2nGKPBKqfC6cP8Ltd9XQEQfXO46mnb447itH23nP8R91YRHWVs9nwyOPcmwf0NtCY\nvSBWPwOtx3j1nUOR9rrCIEmSeIdCGaV+3zF6a9aQ7V7AhtJ9NLeG8JUs4e6b1rO6PCvWx84e+YgX\n9ndQfseTPFEhoV54i//1aityfj45bhgfGCBQcTPfuKsUSbnA/pGFbMlFWM+/f/ugg3qW8JUsmdLn\nm4/uifT5MCC5KVq4wbSvqrgpXbyJh7YuNu7zSORUr+Ke7atYnD+RjuKnr7WBHbsOUT/kvG8YO1SJ\n3Nmruff61Swp8Mby6m2p542dB6n3z+KJ/3I7NS4YrX2Bb37YEzn25S7l3s89wBoPDB9+jY/m383d\nNvqz5C1m/datU3zLwQ8/4M2GPgI2/H60Dh/etkRYh6qqonrnTTkO1fruL/j3E6OxPuguvoY/eWIV\nWYQ5X99NycIq8t2AqjB04SQ73jnAsYFQHOs02no2hZTJ6k89ySPlmnsVN/AXX70BgK4Pf833a4cJ\nI5FRuoQ7b1rPymJfZBJQ/Fw4fZiXdtfRU3kH/+2OIiQJAnjxSirDHR0M5VVQlSVDqIcP3jhI5m13\nsNINgQvtBErKKfHKqATpazrMi+8d4+yozf4jeUzrGQDffJ6yqGdPyUb+t8dXkkWYloYeihdUxup5\nuLOeN989yLGBEIrFlrjTYympPA6Rhha5LNnyf3NbgeZWwWf4wp2fAaDn5P/k+bPdKMj48rawfdXd\nLMrLxQWgjtHd/irv1X1IZ1DBlXsPf7DtJjIJ0dHeQkH5YnJlgBCj/R/z0fkyblpRjSxBkEzcKIwO\nNDKauZBSrwtJaeHwoe/y0cBiHrz1D6nSmNRz4u/4bdtQ5EIqZPE1f831mSqM7KPFt445mW4gzEjf\nbj468QbNARXwklt2D9vmr6M8I+IPUQbo7nyXPY376QrnMGftX3NLXmQMqUioeU/y2PZINkNn/oHf\ntQygIOHO3symRTezMC83MpbDvbS3vsD7Z+sZDKtI2Xfx2OZtZBKi80IbeaU1sbKPDexhT93rNI4F\nzY+gyAu4/vo/psYFoYFTBHMXUOBxgepnqOdtdp7YSXuoIK7s571rmZvlAcIM9exi99FXOTseBimD\nosqHuXXpRko8csSOoWaG3FUUeVT8gy/zm30fMTTNQ8mumCSqk1ScydfDdffdd//dGweORC7yl/Hk\n3Uso9YZpO3GEvWeH8FZUMqtkNmtLB/ikcYSiZStZlg1STh7ZDFJ/soNwcTHFeeUsLx3k8Kk+wnlW\n6fQRzJjNvZ++i5tmZZMR6uboiTOcG8+kqrSIygULKe1s4EzOBr788DrmZrkZbz3JgXN+CspLqKha\nyDVlwxw9M0yhpT0DZC69na/eNJt8d4Cmo7Uc7pIoryohV3bjlYK01J3grHspf3DPpM17zgziq6yK\n2Fw2yCen+/Br6lz25rJg7fU8tCgTl3uAj3ceoTFVEpScw5I1i5ntAV+2h9G2RmrbQ+SVFVBcPIcN\ns/wcq+9iLDzC6YaznGwZoXxhFT5gtOkYB7tDE8q9l6rlkfqRp9RPBctLBzl0qtcWMbns4Cpg/fq5\nZKHSXXeM+gmSpgQVylcspQw3mcONHJfX8JVHNjA328NYSx37z41TUF5KRdVCNpYNcritgHUVgNzP\nwfow2x59gNuWL2HewEnqpEVsneVG8rdzWq2kRlPPJ+vaUbT13KCpZ3cB6zYsJBcYaTzK4b7I6XVJ\nksms3izs8+WVC7imdIijjX0Ecpcajq81pQMcPt1P5rI7+NrNc0z6fB1NvtX84UNrqPJJ9Bzfy6sH\nmxnKm82i8jJWLnRx6kQ7ej5upeIbwV20lqc+tY6qDJmuox9H8sqtZnFFOatq3NQfOUVrzhI2FkFG\nXpgTx1sZVsBVsJQ71xbico2wf3cdgQUr4vrzlHo+1UsoYzb3P3ZPnG9pHsugqrSIqoU1lHSe5ES/\n1RMDMgXL7+CPbplLvjvA2dojU+rw/InjnB4aiozB8yNU1ETH4FEOdk0+ziVnVXHtqgoycFNUmIU3\n2M2RumZ6fblkuHPI6G6I2GPjGEn0T3hPDdHXfJraMxcIVc5nlgvC7Xt59r1a9h75hH2tw4yHIaPq\nWr50/wpmZYZoOvAxO451Q1k1c8tmsW7WOHWdBVyzuBBZcjPc1EywsIi83Dxy6aJ+KJsSTxazi4bo\nzqhklgd82V7GWk9z6Nwo2WVFFBfOYt1CF6dOtDJoycdTV8+urFlsXlkeqeeCSD3XnjxHnzeXDNdk\nPZsph4kqX0aTcpqcJ4Mgg137aehoJFy0jjIXKP2/49XatzjatIOjPb0EFPAWPcijm2+h1Bui5dTz\nfNjcjJS/nFkFy1haOMTptvMEvItYPaeGDHzkZ+fhDZ2n4fxRBtzF+OQifP5xCosrkSQv413HCeZU\nkJNRQjbnaBovoMCVR0V2G8fOH6ex7RPO9gxQXLEIrwTjne9RNxyIjF8pk+Kq65mfAVJGMRlqN+fa\nGwnlzCI/cz4LcjtpuHCBUOZWbl+9iUJXiN72dznR04c7r4aSnKUszu3gVFcr/b2HONPdTCBvNeUu\nCWnwJd6qf5+Tre9yvH+AgCrjLniA+9bdQgLhIkgAACAASURBVKXPS7D/Y+p6x8jOq6aoYD3Lcns4\n3dlOwLOIFdXzycBHXlYu3tB5GttO0O8uwicXkTG8jzNjk0+8iZRtpELmzt1MpQe8vgz8/fs52TNI\nRl4V+dmLWVrmoqW9hYzKybJn0k1T6ynCudUUZC1gYV4nJ9ra8FR+nkdXLiVb7qG+4S3aXDVU55eQ\n7Rmmoe7nvH+mgaGQ9ZHfZI9MOk1H63tFcb78qftjn1taWsjNzTVMa3h4mOrq6lha586di1fGwwMn\neOan9UioBAJhVCSODOTz9dsKkWcvYV5GZySgDNDLzhd3sKdPIb+/kD+7LgdX+TwqPY00WKZznu71\nW9mQFYJgC79+bifHx1SQjnBg0VyKJIWejmw2f2o5BSFQ23fx76+dYUiR+KjtFv7k9lm45mzk+rLf\n06Kx570X3mBvvxpnT4VvgAXXVKAEYfDoDp7d20uQY9SNPcQfbcwBJKSJsv/kmak2f+P2ogmbz1A7\nqgIeFt3zJJ+Z65qotSCnd77LB93T8DigCnTu4ZnXGxlUJHa33smf3VqOq2It28rqeakjyNBAL4Oj\nWQxD7IHAKRPARP28+7vX2dOnUNBXwJ9vzcVdMZ9Kz2mG/Kk3/WLCdAJUxugZU5GzITO3mC1LV1IQ\nAqVtJ9/7/WmGFIkPW2/lz+6cjWvuRtY1teD3ViB7KijJVViY4yYfyKwpI78jg3BQhsFBAjnE6vmd\n374Wq+evb8ubqOdThvUcG9iuAq69wbzPby87y8vtU8fXJ/15fOP2IlxzljIve4hZGytjff7ne3oI\nSfo+L+Etnk25pALDHDt2ipP9CifPt3G0yIMUHqXD4olk87OO2vLJU/I60RfmRHMLtcVeZGWMC+EQ\n0tF6+lesJMuzmM2lh3mxXaF4YQ2FAVDHGjgxqESUUV1/zo+r5yZGrtkW8y3PP/vuhG85zP4J39Ld\nErB+tsNdzHWbqlCCMFD7Rpzf+K+bcon6DdQgg/09U8bgVMgRxS7UzC+fe4+6MZDcB/FIoIYCqEjg\ngLiJt74hODpE17jMiKoSVCDY20FTy+TbmCQ5j5VbFuEeU+mvfY3na4cJA439bqofXUpGxUpWNAyj\n4kdRe3nr/RMsrJzHCjcMf/IROz23s2SZB7w+ZBVQQb2whx+/Hhk77zddz588sJCM/BXcNLeWnzeO\nm9e1rp5/vqeHIEepG3vYcT1H+t9kPf/q+V0T9XwoVs+KARE3m5DthBUSmDSShErQ301vUGYMlZAC\nwaGztPY2T74sQSpmydKtuAMwdPZfeaO5GwVoGfVRtnU7vqKbWZy9l0OSBLgmFOijvP7BMzT6VSTX\n7/HIEp6izzNfHQHa+LhuJ9WFK1nogrGmX3PQ/TTzqnzgycUrBRgYbWU0kM+YClrRPuIPo3YBtHPw\nwPc4NgY5o5U8WVMAeWspcR/h/PiH7Ph4L5EHksOARMNYGU8sK4fCa6nyHONUoIf+kItxCUKKijrS\nTMdA+2TZ5UqWL95CvqLCwLO8fPQow4pEbd/neWzFQuTie1mff4T3g8SV/Z19z3EmAJLrDdwSoIzH\n7Dc87qKqkbGugDT4Iq8cOcQoMp90P87j69bhybyBjcVHOCVJIKlAOwf2f5dPhkLkjVTw2SXFuArW\nU+JponjhcpQQDDb/mJ1nOlBahym98dPk4yI40Ej3uNl7aRz0nhQutrVpTtezT7oz4yqBQCjuOjjc\nzxgFePCR4yHy4FkQpO7j1E4oemMDI4TkLCTZR6YsCdMJDPVNppORT9n8TAJhCNbX0jAWfeAuyIWG\n01wA5II1rMhXCKJw5si5CYVOZaT5GMf9s1nuzmDh7BzaNfYc7Vem2JOVU878rABhKcTJswMTk1KY\n/tZOxjZmaSrAqOyFsbJHEKLpvd/x/dIKFi9exfaaXObfcBd3Db7E71v8KX2AM6jAeEcfo9Gynz/J\nebWSajKYU5WNq2PQ1htB1CDQdUzXXtlx7XWlwHKQqCqKGpngXb7KWB87/UlzrI8NNx3luH8uy90Z\nLCgYpE+aRa5cxPxZLnLGg+BTkSrnMSfsw6/KhHpGUXOM6jlnSj0bOQRX3lwbfT4Xd3t/XF9VVWVi\nfEX6am5eWazP153pJwhIhOlruaDp8yrjbcc4Nn4jKzMKuOWxR1hyqo6Pj5ziZFd/St40M+m0onnd\nzMqMAm574lGWNpzgo08a4vPqO8VH3Wu5Pc/NyrUV7OgaZtXSHEZVGDp2hl4F5jK1nscHRyfr2VvA\nvAVZBMIQOHlE6FvsOFI5a2odqmooUoebshN62CYM9Bw8SP3oxJtWQgHNMybiicPs7KkhKZTdZHhA\nlSAUCMen7C1mZambfBnyt36av9mqT8FFacYwhMKoyijDYQWFCLEf6R9HKVYjh/PkCN8IhmG0rYex\nib46fqGBU/4aVrpdVM3Ox904bvrKN1E9E+2rmnqObSmbpBVFGOg9dChWz2rQj3YdbJeIm503d0Le\n00gSkhefK9L24WAwsuqM1rF7DgvzvORIkLPsb/jqMn1kN8U+F9GBpgB9ja9yNrrNrYwTVCL8UVVC\noA4yFo68DSmkqIyPjhDOVSeeJHZNHvCzInVhkIZ2cToyMPCPDRKW85HIIkOSAIVgKKBJSyU83olf\nLcFNJlkuKULsZd+UssdsyFzBwowgQcJ0ttQzokZKON6zizOhpSyQ3cwqKsJ1YbLsg01vRMouSajh\nscjc4ICghlTw97XiR0KWJcLDh2gOXcNCWaakuIhGTdlPjYaRJJXx0QHCclGk7LIPnxzJLxyMqN+q\nEiCIjKq68MjJk2UjTNsLEVKUbvycImdSvXQN16+Yw9yCLLxu7VPC48gTmSoqEAhOTqKKMuElZWTJ\nOh1J8pHvBcUF48PiV/e5MnLJQcFFgIFxTQhlnN4xcGWBL8eDZGGP2/P/s/fewXFl953v59zO3Wjk\nHBhABAYQzGk4keQETZY0Mwq2JduSbMn283p37V2/V1vrra19ZVe93ar1ymtLXku2lTXSBEkTpQkk\nh3GYI0AEEgCRiAx0o3Pf+/7ogEbjdvftRgMEOPhWodDh3BN/53t+5/f7ndMmzEJBRwBvjIahBP0E\nUdBH6hWu80NNq9XbTkSZUvC7HAx0Oxjo6aLL91l+d62RbXvXcPTVG0xmMQRbAeTATIaK38W4B6oN\nYDTro7cMpIKsgJJsvD5J0JnJM0FAKLi8JqypZMzsos1jpEFvpHaNwuSoH1EqcOsqaSg24NcJnKNu\n5NXz62chxCyZn/LGLPiKN0bmjUiSharG5sSyqleXeeTALJlXXF289pNf0b5jF49sKKe6YQcvNuwg\n6LjN0Q+P8VGfZ9bcnI8Cori6+PkPX6dt1x4ObKygpnEnn2vcSdBxm8PvHw2VJTu4emGAxw8Uol/T\nRGP5CFty3EhMc7rTiYwt1Ixk/SwZk3KLVkVJMphn9WHUShTPGwkwt68CBPDT3T8drXcqy02ig1Dx\n6eYcwEJCR+jcRFBWotcsCiEQRisWKYgXmd7jH3BkJBgjZwo+1zSugv3UqzVKpetCZ2ZirnEMhuaO\nsIU4SlLLJwbx/RxtS4J+Tr3wzfRzIO6Q3WIoySuK+EIgJM8Iop6NyJhKxjwsIoAPmTst3+HMlD/m\nexm/bwKH2wdWAC9BfAyMTSa9RUtJ8i4VoretKCACXuRwXQRyOC8pVDdhp7j8IFsrN1Bhs6MXghk1\n2xl+raAoItr2+DA2yVCEhQA63Ez7gzOWbcXJpB+EAQwmc3gOhtp+Z8IRqlNcnZNtSEN5RmLXQ9Dp\ndMhCIElupgICyQA6Y/jcEQIlpu0owZm2KyPcHJpiR40NS/0LNDnexVP5GVYpMgQu0Dod0OhtnV3f\nVPMum4r4Qh3knFHGJQuNBz7NF9YZQHHS2XqB9mEnTl0Vjz9QhUGrTEoWGg88nzwfxcuED/QmyCmx\nYWCuhTfoncaFDjtminP0iOGQa1kRRvLMEJTA6/SjpLisWfa78Ag9ZiSKLRKESxJ6E3pESFAlC+sP\nPjerzm1DDpy6Kp54sDpx25UAIwMuqLchcovJk7KrjAtA6GeWM0UYsBlAlsDt9GXtWr5PEiRbGbU2\nP4oI0NU3wZqNq2dkbChsRZRMMTI2QfekQlOeoLhY4rbLzfBwLmttBqptPsb9Pm5PBDRTtpp1M/KZ\n7HNFZb7IpoNIfWJlflpQd+DTvJREVpUEMo/OOCPz4WrInlEuH3+Hyyf05JXVcfDAPjbba3jkyfsZ\n/cH7XPPMt8dnoHjHuHTsbS4dD5V16OB9NOfWcPDpBxn93m+46laY7r7CjeCjrNVV8sCOAnLcEkxc\nCx3ETqXZhQqJ45bJWdyi1cUo+zTwRhI+VhRFZVFX8MvaFpdUSHUjgU6EuksnidkLb8CDT9Eh0GEN\nTHC7b3oOj5hytUlzSMEHnUGaMQwIHTnGkBXO4/Sm9LDE97OiqPdzIvfw3M+S93M2kK4lfQXzhxCh\nP0maragFAw58hAxTJnmQgbFx1bMXoaBSGYVgdJOWWT2E6rQPyb5a6JhKapFDZeO/44lSEyhj9A+8\nR49jDLfUyK66ehWvmxJtfyyCvnE8GLCiJ8+kh3AAi6KYsekUZAF+b8SgkrztKW8oirkUQdLpkGUZ\nRZKQFQMWKVRW0ONFNs5uu4jLTwg/dzq+x5WKP2WzaRMP7tkUKt9ziY/O/ZI+v3oYWbL6akG2FeiF\nUMijy5swV3J/nRGPEHS9/yt+cPQSp1pu0hPIwYoeo8ZyhakidT6BSVpuutEbZEx1u9ldFIm/Fphz\nLNjtZnTO27Q4BQZZom5HHYVhVrbWbKLJHESvd9PR45xD9nOsSZ4hOqZ16AMG6psqsAlAGCivq8SM\nDl1cnW+990u+f+SiatuFPo/VJUWsLjCErelm1tbnI2FEmp4g2xeTGCTILS/CGm57Xu1GavUB9JKT\n9j7XijKeLoSRNdubKQ8IrI5OznR3RWWsfme9qoy1d40z2OfCpJcxmQV50gCXb4xjNgSxmAQ5eie9\nWRh4IQTBqR5VmbdUb5ypz2AO+2Jk9XuHL3DyeifdfltUVhXP8ByZV9BTUV81I/MIrBW1NKypZbVV\ngBJgcrCVXx3pwisCBPUFVNi0aL8z9U/ybbSsVRaiZf3y8K05ZSneQY63eTAHZUqqzPiNOm5e7gu7\nYDUgMMX1Thd6g4y5fg97iqOBDljsVux2M0YN7iDZPcMbDZsro7wxuw+zg3RIPTauM/42kCj3KTLe\nYEghN9vNobpKYYucZ4Tro2CUoWJLA2XR7tFjM5vIMemiHtCU9RRglCCvshirUACBpXI9m6wywuCj\nq2sy5Q+hpdPPyaxk2V4YE3ktEoUGrSjiCwgliF8OKdQmSw6SErIYgwS+Hm46wCBD6Zp9FEUoSxix\nGK1YDIkDyjK9hjaZHGgqx9jAljIzPiEYavkm77R/wPXBywzKRViFcZauJZAJKAIdYDBZw5sKCZBQ\nvNfp8ukxKDoqV28jP2RZxFj4IHVGPzqdg97RMdUNcUJ5FUYs5nzsZlvIWi8EUoQ7wvPdll+NGQVZ\nBr19D+uMAZDcDI6OR3WS2H6a3X6BrfgRaoMDuKbe4RdH/yvf++Av+Nbh73LF4V3QuaQl3/h6a70p\nKRv8E5VUxeekf1pQZYGabVvZqoxirl7PAxtL0SXLIQ6KX0s+AQYvHOd87SG25xRy4MUXaegaxGEq\npaHCDDg4+vIvOXGknean6rGX7OEPXyjj8rCR+sZKTAr4u89wdEhmXaoKBcb5+ON+9j5SiaH2IN94\n7hY9Sgn1ZaEtnAIIlTpbajbE1VlH6fbH+P3tVsBDV2cfk9Zqmqt0KPi4cqpD06+CpQUJKN3L156r\n4qbLTn1tCXoF/N1nOT0ug6THajaTk5uLOWwJM9oLKM0Dx7QbrTeL3csQCIw5eZRX5rO+eSeP1JmQ\ncXPmzQv0e3xMfHiD5mcasZfu5RsvhWSsYX0VJgV8XR9zZNCLxziObM5Fh4Hcsdv09bqYemQ3JUCe\nb4hRP+EAiiT1EBJ6kxm71YzFasdEaLxMeQWUF3nwuN043WOcPNpB85N1iWW+z8H+GFndFp5fD24q\nm5HVwBinT/ex90AVxnWH+MZzN+lRSmgon5F5FBvrdz/AM2VAcDM3bvQwpOSypm4VxqCAiTauZMvN\nI3LYsOdBni0XENxCa2s3Q0oua+vXYAwKlPEbXI7ebuJn4NoNJpq2UgBYRRenI2cxNJF0gIHzxzi/\n7jG25xRy8KWXaLg1gMNUSmOlBXBw+CevcWRMTk7OcX34R89H+tAEhHkDQnPQYkk8BwOZu0+ThbHE\n33M8yzoedHB7LIh+jQ594yN82TiCvrQQnc/JuTff5sLxVvY9t57c/G185bO5HGsdx7K6id2lAtl1\nnX89lbTKsyEBJXv56nOVdDjM1DVUYVMgOHSeDwc0HMSadz+LaD8v5B3Ai32n8QpiII8z6PCjKzWg\nq/wyz+p70eeWIwXHuHb272lpOU7z7v3k2J7ghfuKOd/bj7HkYZrz9Sjeo/zs+NtMxGSX7uZtVlph\nxGy0Y7UWR5VmvaWcAovA5XPiifPIqJYTGGPEK1FmUChedYB6BjAU3seWilWznH+KokBwjKHpIFKh\ngNLf4jH9IFJOKVJwjM6r3+Vq2znWNW3DlvM8z+9YS9uUlZryeoyAPPIy56cC4RCduZjLLwaKGv4f\nPluTAyiMtv81r3aPhhTsSDsEKPbneWZbPb0eO9Xl9ZgBMfUG5yb9FMW1Pb71QhixF67BajSBVEll\ncR1WvxevZ4RxxwBTfu1e5kygecOU4vnYqw2zHzMeGOL9t05iOLidrSWbeO7xkEtzpG8QpSpfk4c4\nlM8w7715AsOhHUnzkV23+dVPf0X//vt4qK6Q6rVrARn3+G0+Pn6SY2NBgmPH+ftXRnn6oW1sKFrD\njiLA5+DmlZP84mwfU3EqkHpHK0y2vs+3xT5e2LeOsopaNipeRvoGMdSUYkZCr4zwVqTOpU08/4Ra\nnYMMXfqAX1j28nBDCWvqGwAF39QAHx87xoc92T28SdDJoEthos9Nde1atkoCZDe3r53klVNdTMkg\n5W/kK1/cSUnMY8U7n+CPd4buG/92azYrtLShOvaSERvQ/MSnaQ6lwjXSyZHDJzk1GBqv6e5j/N3P\nRnnm4e1sLF7LzmLA56Dz8gle+/g2UwpIEwOMsppCFAZujuGe9tDqhCIzyOPDGq5vA9BRse95/qDJ\nMuvTsr1P8vW9wPQ1/unHZ+jrOc4/vDrKUw9unSPzr58J3TP+mzeOY3h05yxZHe4dQKkuCMuqwkTL\ne3xL3MeL99VRVrmOTYqX4d4BjKvKQjIvOTn/xmu4d+3lwPoyGpq20Agosoehzou8feIGd1KZNbVC\ncUTLOrihnMbNW1kfLutOxwXePNYyq6zAWCfnJ7byoBUCnS30pLh7Mz5MI+jq4fUfvU7fA/fzcH0R\nNbW1RLjl1EfHOTauIElSlIvmyE44NjV1Hwp0eZv46m/tmjUHS3Z9ij/ZNXMPdjaR6Aab2eEaXjqO\nfMBJ6wPsKbVQs7aGoHeSjuuXuTEt4548wf/+2ShPPLydzcXreGQ/IPuZvNPJ4Y+uMpxTNrfgBPAG\nwDc8jlJay84qAYqHwRtn+PnRNkbk0CGvhGtHFvr5j8P9/I8tbvUiEsTWJ0Im7u8VLDRcdF/7Zy6Z\nvkhzXg7lpeuR/cP09L5PlyeAx/UyPznRy/1NT1Jv38nuDYDiwTFxlrMtHzIma4twS4TYMdZZH+LT\n9z056xYVY+03eKkWRq79Fa8MaMgw2M25S69i2PgEDfaHeGATIDuZmOhCyS+JM366GGj/IVc3fZZN\nOTkUF9ahBEboHzhMtzeIz/1zXrvQx776A6y1bWGDDZDH6e/+OUe62nAqJPR0qbQUn7sfl7wao+Jg\nzO0LedQUJeqe8gUh6BiA3G1sKBCgTDM28Cs+bDvHuFKoqozHh6mM9Z/Dueph7Ppmdm1snlUD/8Sv\neev8m9z2Lu15tRBhKuKb3/ym8mff+l5WM71b+KQS4ye13bFYan2QaKLGH8DTEn+a6F5krW63dF7H\nfpbuoZhUddY6RrqCLfzh55vJJcCVV17mjQS7Aq2HeOLdpNb6p/nLx0qTV2LwMH/zSof28JgMkYrQ\ntV7Plc4hxUwWkTn56kt55svP0KwH14VX+F+nx+NCUkL9/H8/nkKxz6CftdRfS5p05tNKnPjSRrYU\no1Q3FiULW4jl9GThLKnyTja/YxHPsYlCPNJdN1TLEqu5b/+f0KADT/ff8JOOOwRj0kZCWSQp5oyb\nWjidsYmHdj5HieSj5/LfcnLUjSL0GG07OLjv81QLcLb/N35ya3hRw3AzuZwgdnyFEJz9wT9G0546\ndYqKioqEeQ4MDLB3797os8eOHcvohq4VLDFoPZS2gsXBfBeGbCm1861HuifW5wNDcRNPbirEqvMh\nV26gVAZl7DwnhxOb5zNTPBU83Uf59usFFNn1qqEvis/F6Ogo7iUwpRKFpaRKvxhQQmclE32Lp/so\n33ot1M+qh94y7OdkVqlUylTk+WTvU+W5gqWDVEqUFnmIpI39r2Xexa67WjZranyqpoDH1zud+HQ1\npNqop2pDKF5cgMp8V9sQRJ5Ry08yr2eVNRcTMnLFHuqkbqYCJgpKtlGh84Hioi9840smSMcokQ1k\ns5wVZfwewYpCfveR7qKdFQtlms/GkpVWhSb284WRMYHRaqOkro5VZkCRmey9xGvvXWd0Acwjsm+S\nvr5J+mI+W+pzZyEUwkSWvWxBrZ8XEwstx0tdZlYwG8lCluJlQuvmNx2kk2emYRDJFPfYOGctByVT\nrQ+JjEax5cTnIzvf4s1rsL92O+XVz/NYTSQDPy7nVc61vs7F8dlXeGVyiHIxlPJsh6rcM8r4CjF+\ncrFUxj7dyamWXkt4ihZodXWq1SOVop5OO7UpPgrTPaf5p++c1pTnvYRsEPpibOrmjGNgiF9955/4\nVdolZwfpymDkmWyVtVQ455OMTEILEj2XyXjGc3Uiy3e8dTzTOZ+pzCXiYC0hclGFWunmxLE/53gk\n5CQu3/i2xirisz5TPIwN/Jxf9L2MLMtptyudjfRibrizUdY9oYyvEONKHywVzEchTxWKspi7/HQt\n52pW1kSW18WUVS3jcbfnTqZWuMUOn0ikWKT73FKDlvot5fqvIDNkYuxQC3FKxGtqchVvOU6kxCYr\nMxES5ZOKA1OF0qT6LpK/LMuzYsZjn1HjDi0W71ThQfH1yRYnpquEZ6Pc+RwyXhJYIcmVPlhqSHc8\nksWw3s2x1eLK1AK1uM14l2KiGMNPUrxupL+1KrjzKWexsJDjl6kSpbaAfpLkbAXqSGf+RdLHPpco\nr/h8tfJ9Okp47GstcfJa+TdReYmUaVmWU/ah2vPZMkSkO4aJnr8bWNaW8U+6EvpJbz8s3T7IxEIe\n+z/Z60ywkHGKmVpH07F6zAfJ2rUc5We5KI7xi+3d6uulOsYrWDzMxzumNURJq9co1pKeyBKula/S\nDRdMVnaq9SaZNTv2dayFPFl9tcaCLwbmW352w1T0ZTz9O0/SrAfftV/ytydG0fCTDZlB2Nn70os8\nlqMg9/+a//52H+n+6vbdHry7jay1X9jZ97mXeNwOwb53+f/e6k17LFYwAymnni+8uJ81RoXxy2/x\nnVMj+OaZ590O68jUhZqpArYYitty5o9sukbnC61jJeXU8/kX7ovOi386OTzveQHq4VXx9Uq26Ash\nELb1fP1Lu8gVQa6/8hN+MZDZBftSzga+8eXd884nIYSJ6qZ9PLVzLdU2CRTwTtzmo8NH+agv9LPn\n8fwz088ShVs/zb+9v2BWll1vf5/vdHqzW88lAK1ymUjpTJW3MO3i8b0vUKmXcfb8Pa+29czSl7Qo\n1Inix9MxHGTKY2qhNan6TEu4S7LwmDmhKpKN0qrn2b9mM6VmA6Dgn77G+as/5tyYM3R5S4pNw2Jh\nMdakOZZxGfD5Aotyx2NQgaDXn3ZZy3kh1QTTWn7/K4+wOvYzRcEz1sWxY6c43utS/Ynb+SCoQCDl\nWAisZY3s31iCwTXAx2c7GMl2RbIEY9XD/PmzVeiUCd74wVtccIZlRlhoeuolni+GYO+7/I/3BrOi\nFMxAJhCUkYMyQVlGVpTQtVBhxMcKxmIxw1O0xBAmOpCUbjkz+UlYShs0yU+8MpUty8VSj1+ORSor\nWDbbsfCK/ex5kc0RSLWB1NZPMrISwBOYr4yE8nH703k+xKv3byrF4Brg9Jl2lXkhUdj0KF99qAQJ\nH61nLtKXu4mH1pby0KGtdPzwFH2BUPnq/Swzce0t/ke3FZutiuee301hhi1cfAjM+fvYXrMGg7eD\nyx0fMy6n3pTGxivH/tdqBU8+J4IE5SCKLIf+q6RIxzIeeR1fVxAYc/fQVFaJyX+Tlu4LjAXVeThZ\n+1LVRctzyWLc4z27auEisy3rOuxVX+X5xioELro632DI8gA7S9eys/kxej56laFg8rovFtKRn/lg\n2YWp3O2BWQwoigJKADcKd85/zGW3jVV1jWwtX8Oh58oofut1Xr/lXtRL8UPQUbh+Ow/U6cHt5OyZ\nRa9AWhA6CwYsHNpazNVjw/gBfdFGDq0xYIQF8QDIzk5++r3O2R8mcUeqYTGV8mTIlgV2hrxD8nP/\nOh24HWnJT7aU6HTyWGqhLdm2iGeSTzrjEKtshOZFR9rlaUWyDaTa57GILrbhv/ki/Xx0FG3YEeXV\nMx+rJclj29Yy/F7wXnuXl0+P4Jc6aSu3IileBsNGeFX+CUP2u5kYdzPhysEDC/qz49mFgfyqp9le\nbgDfOFc7BbG11xLSla7xIWVYi/c8v/nofGxBoBJqqJafmiKbuDwDttLH2VoqgW+Uli5FwzMx9dQQ\n1pLIOq4WOpmOxyHyp2r5l8qpr1mD3y8T6Ps/vNfZi59zdOflIuFidIkY+RbT+7i0DnAKPUaDAZNe\nZIUUlzUUmaASZLSrg7OXzvHqa6/z279cOQAAIABJREFUkw4/BExsfXgbNcYFLl91LAQmqxFF1qG4\nHbjuEpur7boTQQC5m3ey0SZAWGjc00wR2Vl000E6CszdQGyfxvetltepECK19ORHq/s2VR7xf1qR\nzJp0N0NEtMr+UsBi1zMRN2jljMiwzn8Dmlbq1PNCZ6XEBrIexu84QiERsof+/jF6B6az7ildShBC\nwmgygaIH3yieeYpUKl5R29BpWmuS8IJWa/zc9wKD0QiKHuEfm3fbk20U0pH5RLwav4ZEDnTGPgOA\nZKfArCDrwDk1TlAIBC5GJga4Mz6xpOV5obhf1TJuqN7PH20ppcggQPFyp+Msr3/UzoBPAaGnqG4n\nz+ypY5XdgAQo/mn6Oi/x9vE2+nzhjjcWsXXfPh6uLybPENrJBlxDXDh5gt90qHS1vpD7nnqaQyUC\nnNf4/hu3WPeZZ9llBO+11/lfJ0M/t2ysfJB/9/wqdLg48tOTFDz3KZr04B8axFtUSrFRQsHPRPdF\nXv/wKl1uBRDYa5p56oHNNOYbQ3WWvYz3tfHukfO0Ti3loQ/vRmUXbWeuMVbXjE1fy46SM3T3BTCX\nNPKpAzvYXGwODabsZbD9PL/4qIU+70zbn36weVbbx3pvhNruUCnPUMR9Tz3No6USOK/xvdevkvPk\nF3gh9petyx/iP/zRQwAMHXuZf7jkIIjAVNzIpx7eRnOZFR2g+J3cunSC18/eZlKU8eyXn6ZJD747\nA/iKy6LjNd51IWa8EiOdxV0hgIweWa7g0JZC2trW8Gi1D59iQecHRC6Hfu9pthvBc/U1/ufx0aiM\n/fln1qDDxYfff5Vjkwr2mmaeeWjLnD585/C5kPyY1vLVrx2cFVrU98GP+PZ11ywLlDAWsX3//jnz\n4vyJ4/ymfQJvTOLFUmSSWegTuVTTgrDQ9OnP89nSmOfKH+IvvhGSn+HjP4vKj7mkkSce2a4qzyPl\nj/GXnypGCPBhxCgUnIODOHLLqbRKEBjl2DvnsDz6eAoZS39zlMwtrJZ+MRBvdZJyN/EHv7OXYhQG\nfv0jvtvuCcmerohDX3iWrUaFqZM/4/+0uDCl4A190U7+9PNNWAnS2zZKUW0FeXpAkXHcaeWd985w\ndTJITgJuiZ0XX0k5L0K88eQj2+fwxmtnephUbOx88Qs8nQuMtdFpq6U+V4dQZCb7rvLGe+dodcoQ\nlp8nD+5MzIfCQHHDbj69v4Fqi4SCQsAxjidde7YwUNKwm8/c35gknxTtki1seeG35vDqf/zjGV79\n+4tTYcVEwiAAoRAIqsiYRv7R0LCEffj60euz1pSkfJgV2Gnc9//yaH7MR/m/ze89/tsAjLb+V356\nawRF6DDl7uP+pk9Rn2tHB6C4GR18iw9bjjMcUJBynuQL9z2EhQB3BvvIK63HLgEEcE2c5GRvGQ9v\nrEIS4MeCHhnXZCcuyzpKjDqE3MvFC//AKcd6nn3kS1TGVGnk2l/xSr8j/AOV+Wzc+1c8aFFQnKfp\nNW1jtdUABHGOHeXY1bfo8sqAkdyyZ3iobidl5lAfIk8yMvQBpzrPMBzMYdXW/8TB3PBd3ICS+0W+\n8NAXAZjq/Gte7Z1ERqC37WFPw0HW5dpD4xUcY7D/dY7euoFDAWH7FC/sug8LAYaHBrAXr4u23TN1\nmlMtb9PpTnFCUCph087/wH6bgOnXefn8CSZlKxXr/4JnyowoviO8cvJt7vgVhL6KjetfYmfFKnJ0\n4YOcvi7ab73Kx3fCZxiEHj0yQijILA6X3m2+TgXdk08++V/eOXsJpBwattRTYwCj1YB74CaXB/zk\nluZTVLSKHVVert4Yxpe7gS8+2UiJMUj/9UucvuXAWF5BVXENW0smudg5jt9cw9MvfopHqmyYAyNc\nuX6THo+FypJCKmrXUTJ8k6nq9aw1gjLRzsleM/uefZqDxWFF/PWz3PJZqd3cSJUOgsOtnO4NHVDR\n2Vezb30eEn66r/ViWR+us82Ap7+TCz0urKWFFOVXsa1WR0dLP678rfz+Z7ZSaZYYuXqKN8514bBX\nU19WRtM6HW3X+3EsfsxHYujy2b5tFVYURluvcWM6fI+nX6Z003pK0WOZ7uQqW/mD5zdTZQlw68wJ\n3r06jCitZk1pNVur3FxtG8FfsJWvfHbbnLY3lJexuU5P2/UR8jduotYI8kQbp/os7Hv2aQ6VhBXx\n185w0x1gvLuDyzfvEKhYGxqTgdP88MPLnL50kdN9TjxBgaV6D1//7HZW2wy4e1s40+Mhv6yE8sp1\n7Cx1cvmml9VbGqgxgMlmxN3XwfkeF7bSQooKqti2Tkf79X6msjAWutw13NdoAyUIBDCUFmIsWEON\nXoccEAhFQZnqYTB3NRU6CAy1cuq2Oypj923IR8JP1+UW+m1b+OoL26k0SwxfOckbZ7tw5NZE+/DG\ntT4cgWk62m7Renua8rpKTICr6wrnhmdITrLU8Oznnp41L7rdZipLCqmsraN4qJXrE6GFbL4kodX6\no9XikygfbQgy0dPJlVt38JevoUoH8uDH/PDDS3x8+VJUfkwVe/jD55oSynPLUAE7GwqRhB5nVzf+\ngkJy7bnYGeaGw0axwUpNoYMRcwVVMTJ24bY7xAkxMuZQMrOya0lzN6zmQggUv4tAzSY2GiHXOs65\ntjF8gGRfy2M7ijHqXJw/eY2hoj38YQre8Foq2dNUhhk9hflWjP4RLrf2MGa0Y9bnYBltp01q4vfD\n3JL5vAjxxjde2KHKG7tKnVzudFC4cTMbbCDl5GJjitaWAeSiIopyy9lYMsX5tnGkir18/dPNVFkC\n3Pz4OO9cGUaU1cS0awzThsf5owOryNP76LpymQvDgrLKYuySHqPwc/v6dTqmZ2/A5loAJfI3Ps4f\nH1xNnt7HrcuXVPPpK9idvF3tQwx0dXD55iDBytoor/7gg0ucuniR070OPKKIQ1/8LX5vfx1FksAg\nBIUNWziwezsHdtQw3dZGnxeQU/NPFPp8tu1Yhx1wdlzmwngwOhbmysR9uK3Kw5UboTUlJR9mZT31\nMzV8hvY7NwkWbKNUB/LEq7x15T2udP2aK2Pj+BSBsfB5Xtx9gBJjgL6Olzne043I3Uhl3gbWFzro\nGLiN39RAU00tZkzkWnMxBm7T3nuVCX0hJqkQs9dDQVE5QhjxDF/Dn1NOjrkYGz10efLJ1+VSYRvg\nWu91bg1e4tbYFIVldRgF+EaO0OL0hX8Ix0Jx1UOsNYNkKcbCCN39HQTtVeRb1rIud4jWwQGC1gd4\nYuteCvQBRvvf59roGIa8Oopz1tNgH6RjpJ/x0fPcHOnGl9tMmU4gpn7Br1uP0NL7AdcmJvEpEvr8\n53h2+yEqTEb8EydpGXNjy62mIG8bG3JG6RwexGeoZ2PVGsyYsFvsmIK9dA60MKErxCgVYHGG1vhY\nLolFKGx2mrFJP9U1DRjNjVT5L9Kle5QnG6oRummuX/ghNzxBMGzg4f3/ht2F+RiDPbT3nmMwkEOR\nvYKi4u0UuXrI2fgfeWrdVvKFQC8k7GUH2Fn7ODvXrsc78DF3/NkPx0sWxpSqHK31+PoLz0Vf9/b2\nYrfbE6Z1Op1UV1dH8+/p6VGxjCsghk/zr293MiULjvU/wZ8dLEVXtoX7S2/wi8Hr/Ov3biBQ8PmC\nKAguTebx7x8tQKppZI35NiPb97PDGgB/Lz/78WGuuRUQlzhbv5pCITPaF6A2Up6Uz/3P7uOBAlAi\nirgH7dHsSvhv6DT//HYHDllwtOtB/vTZWsx5m3hk9TVelWooEwrg5OrVdlrGg7T09HGlyIgku6Px\ndksdQvEw6laQbGDJyWfzfQ3o3QoTl9/iJ2ELSueEnurPbcBcsZkteR2cLppp+5UrbVwfD3K9u5fL\n0bYbZ6wp4bF4sFCgRBXx0MTwTU8x5BZMKwp+Gfxjg3T1jsycINcVsO/hTeQHQO4/zLfe7MQhC070\nH+LPHq9Gt3o3D5V9GAqrU0C5c2rWeP2b59aFx+sKP+j0ZCmeUQFlihZnCY3WSvZWe3D7x7k0Vs7O\nXM29jjFBH16aJT9+piZGmXJZcYLK4Sg9lbseiM6Ll3/0YXheXIzOi5Fe36LFcWo97Z8qbSpEYgx9\n01MMx8hPYGyQ7r4Y+RH2WfL800uOOfK8qc2BghdZGePXR65RV7GGTXpwXjzBYcNjNG4wgNGEFMMJ\n//JOSMaO3HpgjoyRZhx6Ov2w2AdFFUWB4DRtl/qQHytFqtrIhpxOPnZA7up6ijwKwtPODaeVzY+m\n4o12PgJAClkZA928/NMjXHcpCP05DAKUgB/DuizMC10+9z3SFOWNf3gjNF7H+w7xb5+oCfPGL+kL\nVQcY4/1X3uLUuEz+eD7//v5c9OVrqTAOkr+/Eb1bYfzSm7Pb9fmNoXYVjWPeVYHsh6kr7/LD02P4\nuUqL+9P88a4cYG54pKpHxFDMfbsrkf0wefkdfnBqFD9XaHF/hj/ZbQ/lI2loV/lNftGvxqvDMfNi\nghO/eoXWgtU889QOihCMffwevxn04HI6GI0cTFdS8Y8GiByaU/VhXnvCNeVS1tdThYBvlLGADjcK\nARn8jlv0jfUQLUIU0dB4H3ofOLu+yTvdI8hAr8tE6X33Yyp4hAbbx4Siu3VhC/QVfn3yB3R6FZDe\nRC9AX/Bl1ijTQD8nWw5TXdDEOh24u37GWd1XWFNlRtHnYMDL5HQvLl8ebgVijfZCiJg4J4ABznz8\nv7k8LWN3lvE7jUWIvG0U68/T4/qIt46dQgiFQDCIJOlo95TxhQ1lULCXSsM12ryjTMS0XXZ2MTg5\nMNN2qYKNDfvIk4HJH/LLK1eYViSuTPwuL22sRSp6mu15Vzjqn2m7UK7y4dmf0ulVkPS/Ri9ADmg7\nPRWcPsaHndv5XF0JRXVf5oC/ApMMwYHvcM7hB4yU1n2BTSY/BK/zzkf/TKdPQad7j5aqLeTrZKbH\nepl2/E/6rU3s2XSQfGC663ucmpzG4x5lwjv3gGi6PHo3Qwjngzkqr18Gz8AYLhlAYfp2K7eVcqox\ns6rShm5wCp8vdrYp+BzjuMnHgAmbKZfStRZ8QfDfuEybe4Ys7rR1cAdA2KklJLf6Vft40CggeJtX\nfhlWxFWgKEpCRcUfBHf/KO5wnT132mj3rqNJr6Oi2k7g7FWueh6hyZzPoc+/yPr2Fk5cbKN1eGnG\nJiUUJkVBVkILhmTIZ1OJnjwJ8va/yF/tj0+so9Qm8PRf5arnAE3mfB79wkusb7s+u+3CGC4TDKvv\n46HwWPz8FzOKeBSSHrMBFAEB3+wT5Lrc1WzKk/Ej03mpJ2wZUXB2XeGadxUb9WbWVVu5SWi8XHPG\nqy48XrnoOz1ZvFYzQNvZ22w4tBqBBe/l9+mp+BQ7NbuklXAfHqTJnM9jX/wcG9quc/zCjfTkR5/H\nplorviD4Wi/NmheDN9oZjC0xy1bxREh1el4tTGU+inky+RGmYppi5Pk/q8hzidkBgSCK7MIZlJEJ\nKTCuSS9KESgoIIXWwQgneBSBEOAdao/KWGVNXlTG0iX7TA9OLQ4UXD3XaAtUUyuVsKfOzrlLgqZN\n+bgRTF/rYtxQzDMpeUMCd+hdEBg7f54b4UBmJeCL3j4UHLgW5dVM50Usb3Rc7I7jjdVs1JupW2Vn\nEFD8wPBVLoctue7JaQJSDkIyYbWUzMjP/S/xX+5XaVdBKUVWH0ERoOXmRJhjgoz33sG9y6rZ/iMs\nJaxNlM9uG3pA2FZpaFcu+v4xAknmBUoQ1+Q4LncOTlkhX8hMD/fTfju7d0BB3BxM1IfRNWWefKgR\noflmxKQL7a+Dfv/s/tGvYl2ukRwBOev/E19fH5+DnkKjRGRBkYHJW29zyxuOAw+6Q18poMgBUKZw\nB0O3YAVkBfe0EzmX8KlcXfiQpvova4ZnSOiDIAjHEdpdwVBYnWeKoFSIwIpFkhBCwR/wRS2ziiIT\ncN/BqxSjx4JZROLUDdG2y4EAiND6rygKkmUTdZYAfoLcud1KyKEj4x07ys1AI7WSnsqCAvTDItp2\nZ/e73IoovJG2M3MgVu3GlAiECDLV+2NOlf8H9tkrqDKB8J/hnVs9IU7QlbKuLA+/DL6+9+j2RZ73\nMjZwlkmdLtTe4B28gSLcikyuUPA6Ougd84RizBVm1SMTaDm0Oh8s2m0qoUGf8TEpfhfjHqg2gMGk\nQ0gWqtdv4cFNq1idb8WonzmJDh4kyUyeEWQduB2+pDd+6I0AUsi4ra/h/vUF3Dg/PlcRi+1MFfey\nAshyjLIe9DDmBmEDo1kP07d45UdTtO3czYGNFVQ37OClhh0EHbc58sFH0Ttalzx0ZvJMEBAKbpce\nkxTEi0zv8Q84EnsXlqLgc00z4QggB27x8x++TtuuPRzYWEFN404+17iToOM2h98/ykf9oUfix+KB\nDYW0no2/a15ChwiRojx7c6Qz28lBRoePSU9Mb8qhsdBZwWQzRKd9svHK9qliT89Z3mv3USJ5uX55\nHF2FSqIkMiZP3+JnP3iNG7v2cHBTJTWNO/l8uA8/fO+INvkRpui88DhnrpDMxsTOFtkkUs7V3JaZ\nlKkoIqH8YLBgSSHP7sL7aQh/NKt8lS5UiKybYWun7J0lYzoh8GdofVlKcYbxULyDnOr0sXGdDnNT\nPRUDevbkeTAok5y8OQ2m1Sn7ecIRgDyAAAH89Ay4kMVcq7Hi6uKVH/0iyquZzAtNvJET4g1ZAcXn\nn1H4ZDk89hKScUZ+bh97X7VdTkMjXxMKOgJ4Y7VGOUAQJaREx7dRZax1BjNmlXyUoD+aj86Uo6Fd\n4VjhJLy6mBBGa8o+jKwp8+bDNBDhDQShq2Jj66y3YxEBfMgMtX6XM1OxK5ZMwD+Jw+1DsSqAlyA+\nBsYmCSY5iJ7sXTR93PPxyrisAH7PTDy0Ioe/k8IW9BxKKx9le/UmKmw56IUU45dxhjYHigIxbZ9l\nvBACoS/EQgAdblxhnS3Ei9NM+kEyCoxmS1jGQm0fmnSiJDj/ouWQqVCGaetqZdfmRnSA984JBvyR\n0bZi14MigdftIhjuk8hBzsjGQ5KkWfNsZkOS2PgzH87V4gVOJ6+FwhxlXABCH6MOSUZsBpAl8Lgk\n6g58ms+vM4DipLP1Au3DTpy6Kh5/oAqDAiheJnygN4G9NAcDjoS7ZSUAjJ3h5/2NvLg1h/I9j/L4\nndd5s88X2ikGQBhAp5sZPGE0YkBEFbbweovOEDPAkp4cY2gn6HF6CQKyZ5RLx97m0nE9+eX1HDyw\nj+bcGg489QCj33+PqykODi4FSLYyam1+FBHgVt8EJc06BDqsgQlu902rEqAQAsU7xqVjb3PxmI78\n8noOHbyP5twaDj79IKPfPwmExkIZ/ZhXBtZHx+KJwdd4o9c7W+kWIW+xTpq9OAe907jQYcdMcY4e\nMRS28Egm8swQlMA77Q+TU+rxyiqC45x490j4jZEZ44miScYgJD8XP3qLi8dC8vPoof0059Zw6JmH\nGP3XX3Mt4tFJFJuGLzovckps6JVJgkv8QEk81A5xpquUq8mPoigQ8OBTZuS5p9c5R57Nedr6Ro0T\nhM6wsDK2ZOCn90oHjg0NmAyNPLhLYHJLKINXaHMrKJbZ/RzhjXi5Cy0MoXgfv5y43yPccum4nryy\nuii3ROZFKl7VxBtOP0qquAsN8iPZp/EIPWYkii0SihIMya/OiD4y3zWIs+xzzcqHsDQJvSmaj+zX\n0q4ZY1UiXl1MKBr6MIJUfJjt9TQSASJJs/lHCTrxoUcAJnmQgbHxOUorEAq3QkYhSEBFEU90S5T6\nzSokVcbjn4+PSxaSnZqNf86nysygjNHX/x49jjHcUiO76xvQx9Ur0vZ4qpX9E3gwYEVPnkmPcIYP\neypmcvQgCwh4PWHle27btWLWtYxSNc1169EpCkLImKpfoLn/7zgz5QNcOAKgM0BOXhF6Qoc1I22J\n/DKnoiiqMp5M6c522F+2jS/ZMIjNMUIaJMirKMYaZqbctRuo1QfQS07aRwvYX2fEIwRd7/+KHxy9\nxKmWm/QEcrCixyiAwCQtN93oDTKmut3sLtJFqos5x4Ldbg6dCgcCQfCOD9F+5n3evSNAtrLziQfY\nbANkL+MegVECW3kxOQIQZtY2lqEnxnoqwChBbkURtnCdLZXr2WSVEQYf3d0OTJXraFy7jtVWAUqA\niYEWfnWkC68IENQXUGFbWjc8xiI6yMLI6m2bKQ8IrI5OzvUPc30UjDJUbGmgLLKtEnpsZhM5Jh0C\ngbWiloY1tayygCDIxEALvzx8a6btoYGOjsWN07/hnUFAtrLrUw/SnBNrgZTxBkMLh9luDhFcmGjk\nqR5anAKDLFG3o47C8FhYazbRZA6i17vpuO1CDo9XXmVxgvGaIlHIYdYVVcXHuDtUn5yKkqiM1a4v\nj5ExgU1Ffn7x4c1oH1bm6BIWESXiwCTXO13oDTLm+j3sKdaH2yOw2K3k5lqi80IrsnVYMNXVXam+\nS3Zt3KyFCSWh/OAZ0SDPGhHDCTk6gRAS5orGOTI2/4OpSxP+0RtcmNIjBSw01Ojwm31cvHQHjwJK\ngn4WkiHNfp7hlth5MYtbNPBqMIY36nfWq/JGe3dig04EinemXZVbG1XlR3EP0TGtQx8wUN9UgU2A\ngp6K+irM6NChSRdHjsmnYXMlNgEIw6x8mO7V0K4w1yXh1cWUyljZSNSHQgMfZn09VYL45ZBCbbLk\nICkhCzNI4OvhpgMMMpSs3ktRpGhhxGK0YtYn5mY1RVxTdeK4LhUHznneUM/Wcgs+IRi89re8deM9\nrvZfZCBYiFUYMYqZPAVytO0GkzW8qQi1XfFe55ZXh0HRUbl6G3ki1C+GggdYZ/AhSVP0q1wTGF/f\nWfUWRizmfHJMVvQx60torTFRtOa32GbyIMlt3HAICFawfdODFEkKBIe4eWcKnS6IseJ5mu3GcHkC\nozkXi8mGFL7qUG3DEvta7brF+XB0umOUDrK1dswNk5NAKdnD156ronM6h/raEvQK+LvPcvqOC/20\noMoCNdu2slUZxVy9ngc2ljIj8gEGLxznfO0htucUcuDFF2noGsRhKqWhwgw4OPqz95n1A7yBCU69\n+xFrvvAgDYZVPP9EE4OvX6Xj8m18j1ZjKN7Llx/LpU2sYusqw9xWSEDJXr7ybBUdDjPr6iuxKRAc\nOs+HgyY2PPMgz5QBwWZaW7sZUnJZW78aY1CgjN/g8sTStJMJBAZbLmUV+axv3snD64zIuDn71kX6\nfX5Gj7ey77n15OZv4yufzeVY6zjmVZvYUyYhu67zjy93UL3nQZ4tFxDcEtP2NTNtn5RnDtNCdCzW\nfvEhGgyr+PQTTQy+foU7AUB2cnssiH6NDn3jI3zJOIK+tBCdz8m5N9/m+OE2mp9uwF66l6+/WMbl\nYSMN66swKeDr+pgjd4I8CDHjVUmHw0xdQ9XMeA1kL1o8NXx0XOrB9/gqDMV7+d3H7bSJVWxbHXOJ\nu8hh496Hwn24lZaWLoaUXGob1s6WH0mP1WIhJzcXc9jKZcotpDRvHMe0G1cgQP+5jzi/7nG25xRy\n8KWXaLg1gMNUSmOlBXBw+CevcXiRf+0gk4UoGfkk/V520jsuo8+V0Dc8zJeMo+hKCqLycyFGnr/6\nwlx5/peT6tmqlieBUryH332qkk6nhdq6CmwKyMMXODwYUHWBzrIALWEPRUoEJrh4dZIH9+UBBuzO\ni5wdDhDy80/N6mc13vj2T84zGpflnHEVOWzYM8OrM/NiTcp5YbQXUJpHeF6McuzDGzQ/04i9dC/f\neEmdN+pTtVl2cP5YC/ue30Bu/ja+9mIuH7WMYVndFG3Xt358ntOn+9h7oArjukN847mb9CglNJSH\nlQa0KeMExmbl80fPR/IxzeQT1NCumV/qmcWrX47h1bNvvMnJSQ1BHyn7GXRGC3arGYvVjokwR+UV\nUF7kxu1y4XRPpe7Dn7SxSgsfZhPyOIMOP7pSA7rKL/Osvhd9bjlScIzr5/6BlpbjNO/eT47tCT67\nr5jzvf0YSx6mOV+P4j3Kz0+8w0SS7FPx2ezERixmO1ZrScj4CBisFRRaBS6fE4+sYkmPzycwxohX\nosygULL6IA30Yyzaz9bK1XOto/I4Q9NBJLOEVPbbPG4YRMopRQRG6bz6Xa62naNu83ZsOc/z3Pa1\ntE1ZqSmvxwgooz/j3KQ/HKIzuz3q1n8DBev+ks9U2wCF0fa/5rWeMZBCtdLZD/HImnz8yIy2/JQT\nk83k7HqOUssTPLr2Gi+39zF08ydcL/sDNpor2Xv/f2bNnQ6mDWtYU2gHxrhw4r9zwaOuEGflGt15\nIFPre7bqOPtqw6YKpICX3h4HudVVrC60YZTd9F79iO8f7WIy4KK714W9spSK4go21K1mXaGOyYER\nsOsJEqDnSis9zknaWntxWoupKLBTUlRAiV2Pd6KXk+8f5qM7ElWbQtfpKRPtnLw1TdA/QeeIja0N\nRRhtVTQaB/j4UittvgJqK/IoKC6lOk9hbHACXa4BmSD9LT0YG+spBtxDowRLqqgtzcWMhzttp/j+\nb24w7Pcy0HGLO7o8youKqKmqZE1ZAbk6L0M3z/Kz967Rl/2zMPODvoBtO+ooEXrK6taza8Ma1hYa\ncI/c5IO33+PwQOjWjYCjl/NdLnJKSygrKmXdqkpq7DA51MmvP7hA57SH/vabDOnz57T9TucZXv7N\nVfp8RmqaZq42PHlrmoBvnM4RG9saizDmVNFgHODibScBJcjEwBimyiqqbBbyC/KwKm76W85zqmeS\nqfEeLvR4yC8poay4hKqSXEx+B50XD/O9o11MCRuNWxuj4yWXVLOuLDRegzdOhsZLxSyeibDrctdw\n33o7Eh6uXeiI+UUvHcXrm9lkEShT7Rw510qHv4B1lfkUFpdSna8wOjCOPteITJC+K5c4caUzJD/F\nxayqqmRNeWFcH4IufzN/+DuP8siGavJEyMplraxjd/Nmah2tnBv2I/snuXG9B6e1mMqCXEqKCinJ\nDc2L47/5gKOD6d2mkulVe9mRDaviAAAgAElEQVQ4nZ7ZdVAz8lNpM5OXn4tVcTPQeoFT3RM4Jm5z\noTuxPHcbatlfZwHFyaXLI5RtbqBYgKf7Gu22RnaUSBAYZ1hfQj7gGR4jWFxFbak9ygk/fL89KmPx\n1pj4+qu5mJcHFDzTgsYtNZhQ6D1xlBPD/qgbPTDVm7SfO6dlJGsle5pKMCAzcO0aHfG/RKN4Z3FL\n/Lz46a+vROfF17/0GI9srInOC1tVPbu3bKZ2KjQvfJO3udDtoaB0Lm/865FbTGGkctNmGkyAo4Nj\n7aFbPvR5tdzfaEfg5cbFG9wevT2LD+tWV0Xb9e775+mcDuIZ6aZ12saaykKK8gspscNY3xD+PCsC\nhdEb12mbBp0QSEKgkySk8GtJiFCYhAD3cFfyfFqvcqW3O3m7Zg7NMN6fgFe7J0MH3XV5bNm+jlwB\njvYrXIpTeFP3s0LVAy/wfx1qZteGauyErK32mgZ2b97EfY0GWq/eZmyiN3kfOt30t99MyIeRcc8u\n/EyN92MsbKTMnIPdVoxZcTDU+zaXh+8w7bpO6/AU1rxVFNnXUlPSSLlFwTl1jpNX36XHE0AY69m8\najV6goz0fkSPV541r3XWnWyvsCMY40Z3D0Wr91AgwDt8mC7jHpryDBC8yfXRtTz70NfYVb0Re7if\nLYW7aFr9CNWu47Q4JIqrH2aNAfCc5cLACAFFQWfdzrbKAgQuum/9mqtDk9gK11Jsr2dt2WZqbHqc\nE30oFiMyPu70nuJOAIQI4JwcxFDQQInRis1SiFlxMDb4G66NjeBytdA27sZmr6bAVkOpvQijMs5g\nzw95u/06Thkw1LGxqho9Qcb6j9HjSbRZEuisdawrtCMpk/T3n6Tb7Q95afTVbNn6RdbpBdLkq7zd\neQu3r48+Xy2bS3MwFaxHN3yK29MDdN2+ittcQ6mthAJ7JYUWIz5nK5cu/wvnJ0O3WAldKetWbSFH\ngHf4KO3uoKo1PBYLqZgnssyng/lebSi++c1vKn/2re+lXfDCQKA36NEL8Pv8qeOb9KU8/aWnadaD\n++KrfPPjiYQhDp9kZCbEAoPRoH0stEBfyjNffoZmPbguvJJyvJaydVJNeUsELe7QbF/hlE1C06r0\na/ksUWxmOnWLd13OsmjrSnjqd56iWQ+eS6/xd2cmU4Y9aa3HUpZHrdDi9l1Kh5oWCuptF1jrn+Yv\nHytN/vDgYf7mlY7w7RUruBvQKrvpKHip5Fht7iTjOLW6JAvDUMtbLYQjthytIYbpzNFEfRVbj8gv\na8b+B6L/kz2r0+mQJAmdTjfne1mW5+Qdm/9CIRtceOHH34mmP3XqFBUVajdFhDAwMMDevXujZR87\ndkzzbU6LBAW/z5fWtXahwxQLVZ/lj8wXyvTHQlOuGsdrqS/wmd4okiy/dJCKMLNRRjpQ649UfRS/\nAUlFiFo3QIlkTO3waaJDqYncqEtdLlNBi9ym287l1i+J26/g6T7Kt18voMiun3VIL5rC52J0dJRl\ncN7/E4l0wxvUwtTS4fZUBpZkz8XzT/x3mZarliadORpbfqrNRuxmINWmIFKHWMVaiNDtKvHvI4c9\nI2kXKoRwKXk9l5QyvpwIfSljpR8/mVgIYkknz0wUPS2Lp9aFJFOrv5oSfq8q5FrHMxOlJhbLtY9k\n3yR9fZOhHxli+bZjBYmRSrFLx+uZbnlqSMabWjYKieaqFmOHFvlWyz/hAdAkiFXII0p55HaVSDmR\nsmI/j1yLmKiN6bQl0bPJ0iwWBywZZTyjBgeGePOfv8ub2a/OksWyXhwCQ7zx3e/wxt2uxzJHJjHc\ni4X4xULNqq1GcKkWilQLZDRtYIi3/uWfeTtJerVFKpFCHl+PTxoyVbIXypK1ghVA5p7JWItvqoOC\n8dZhrSEt8c9pqacW3kwFrbyaqPxk9YnNP94anq4hRc06Hh+CE1tWxHIe3/+J6qcFidbQu8lVS+JO\nvxWyTg2tu8/linR32StYeGSq4Gu1tsQTsNbys7EZUSP/RHktlY2OFiTr02y0I3bc4v+WA9L19Kxg\n+UBtDs9HOUuUZywSrVla5oWakpoMauEg8+UqLfwb245EYSmJ+iC+DDWLutpzkiTN+p8OX6fb5qXC\nX3fdMr5CeNpwt3dtK8gM2R6zu20VT9Se+dQhmYs2mXs2/tlUC5PaAq01TGWx5l+2yrkbfLEcPAnp\nWFRXOHd5IZnlOtW4zycMTgt/aUGq+sVb85Plo1VuU7UpXgmPDTGJV6ZTKfZqZcXmF0kXvwmIWMbj\n48jV8k/W9vko8IuBu2oZXyG6FdwrSEQ02cR8CCSTuqRjudHitk0X8cSsNV81K0qq96leayl3vshG\nvukoDPOVz0SW8aW40K3g3kM2OG2+5WbLi6ZloxAfGnK3vMhaLPRqSGUhjyj4kbSxf5IkzbGSx3OP\n1rUi2eZJy2cLhbumjK8o4uljZZFbPkjXBZkpFlom1Ig3VVqt6UFb2IOaGzGRqzGd5xKlvVfmWSau\n8nTyTvbZvdKHK7i3kU1eXogNr5oivtBIpqxq3QQk4p5EfRSriMemTaSEx5ehdVOUan2Ir9di4q6E\nqawo4iu4FxDvMkzkJlsKivh8XO6ZPJcNxTY+7EGrpVrL+/g81RaCVG7PhUC2LXcRaFl05mvVuxv9\nlQnSae9yaM8nGemGgsQ+p/Y6Uf6plOH4OmgJsUv2Xbbma7pGFLV2xL9PpZCnMx6x+URuTVHj69jr\nD9XqFflMS5hKuvISv8YvFBZdGV8htvkhkfKwgqWBhVLEl4PVMdNFMdVzyWRe67NqnyfK727NrYUs\nV4tLWUv5S1X2MoFWRW5FIV/aUDOKRKCmgGndNKYTg5zo+Ui6VHnEtkGr0pjuRkSLNVvtdeRZNUU8\n3dCOVOkjVvJEXs6IYq5Wh0SbpmTe0ETtSLThWUguWFRlfIXQUmNF2V5eSGX1yAaypYgvllKxUOEe\nWhWndPLTsmDGY7nPzflawmORaHFbLlhRyO8NpDs2WsM+5mP9VfO2xX6nVRHXGkqRah1KpKjGh35E\n/sem1xKakgrJFPFIH0T+4jlKTRGPr1OyMU0UzpKqfouJRVPG73ZDlwPSWdhWFoelA7UFfSEU8WTf\nay1voTZ7iRYTtfqrWSFShZaoPae1TqnSZKKQ3wvQspBrxXLux3QU8kj6FSxvaOGg+HSpwkkShb4k\nsrAns74mKi/Zc6kMQ4kU6mTcraaQp6uYp7Phic8/3iKudpOKljKWAxZFGb8XOiobSFfBSBWrtFix\nTAuB5VbfVIi3dCwEkllLsq2Up+sCnQ+0KOKxn6dyg6qlS8d7kcilHf//k4hk/aDV2riCFdxtxMtu\nIkNYorm+GDwQz8GplHm1NsRzodY6CyGiiq9aSEg6bUg3faTcSJx47I/+LJTRaylgwW9TuZc6a6GR\nSjFP5mZZLMVpBYmRjsVgvuEmiwGtVpB0rOKZIF7uU+WrVmc1y04qRVzNfbsCdaV7ufP8cq//CtJH\nMkt3NjxzmSKZpTgVEvGkVv5UU3a11CFZCIqWctXKi73LPD4frbwcH+aSDSzUWrCglvEVgptBMiVa\nzaWVKq+Vvl1+ULMAa/WMaLUWp+stSeY+jf8slZs2WT1TlRn/fKKy0rHwZLIYxKaLtyxp9T4sZ49V\nIsTLV7xl8F7gpMX0Bq1g4aFFYUv1Xbycq8n7UpOZRBsJtTmqhdPjlfFk3tRUdUrGoZHPIkp47JWG\nan2tpmireTsSeT3S4fHFwIJZxpc7MWcT87WCqlnFlxoBpEKm8WYrmI10rB2LKSPplpWOxTlbFup0\nZS+T+ZbJ5mQ5IZE1XItXbzkg3Q3eCpYusjlWmfJbsmfVeC1WUVzItTLZnFSzzKt9l05ZWj6Lr4Oa\nhVytPoms51qQrrV/IbEglvFPGmEl24lmgmTPLkcL1HKr7ycN8TKVqeIJ6cl9IitHorzjLVVaLdXz\nqWNsuZliIeZstvNMp0/U+j7ZgrvcrOfLoY4r0I5M12I1BU+rJzNTHlQrN9EcS+Sl0lKGGuLnabxC\nnAm0eCUStVet3Nj8ZFmOKuqJfjQo0XqixdIf+W6xDAlZV8Y/aUSWKnwg0XdLbXFfKCyXet4NZNI3\nC0UO6cqUVnLXavlW+x//Ol4RVwud0Ip06hafNtnitxjkHdtP2VbIY/NPlVar8nGvhbOsYPlB67zM\nVDbj52QqTppPXeKV8NjXmSrkicLO5mOhT2czEqsYx7YpXiGPV8bjLeeRNPHRBKm8AFot5Nnw0CZC\nVpXxTxrJZjIg6Vrk4t/HC8ZS7vOlXLe7hYXsk8Xa5GWTiBIp4lot44ks+lrasZhWj2whFSdkA+kq\nLmrKQTyWOlet4N6EVi9OKiVWy0ZVLeRETSmPTZfMKp5qvmTiIUxVXrxinMjqHJ9fpuuG2iY9Nq/I\nDwCp9X98+Ep8ObEKuZr1XYsirlbfhVozshYzvkK02pDIJZNJ2pU+X164F8YrXYvOQrc5U2JUWwi1\nlqHF2jJfq9JyQqrxXm4bnhV88pDKw62FLxLNg2TrtlpoRezrVMaKVCEu8VCzGKe7OUmUbyok48VU\nelAqTo23hqdrGV8KyIoyvpQatJjItN3z7a/l0N/LoY73Ghaqz+OJLV2ko5BqTTcfBS8byuFCuCvV\nFpF43I15pdXLsIIVLEUk2iAm2mTHv1dTkBMpwcm4TqsxLT7/dLyHsXVIZDFWKydRm9IJe5nP+pDo\n8/h+TaaUZ6qQp4NEHtts5D3vMJUVEl5YLKewlBVkB3fTkpitstXceWquxviwk2TPR57V6hJNthDF\nfjZf9242MN/6LVWsWMVXsJShxhOx3JNM8V7o9TgZJ6iFxCQLu1BT7mORLW+mWn3mCy11S6aIq4Xf\nZIJsbT4SYV6W8eW8SGjFQi4m6fbfijv4k4lsW0m1uP2yAa0WonSfTxfJFrVE32vJcyGs41qQzLW9\nUGVkAyv8tIK7hUwsvInmuBaLdHy56cwnrZbWVMphumWmg8Way8ks4lpCVeZT12SeyoVof8aW8Xtd\nEU9nJ7mQZatBC7Hc6+Nzr2IxJn0iqFmu5/usls+TWTtSKfRaFrtEVvZUZSWzzMdaW7JleUkH90oZ\nywVLKWxoBekjWyEKauOdyOOX6HW89T2ZZT5RWbHp4+uWLP/YOiTKUwvms1YkQzr1UlO+JUlSzSeZ\nAp+OcWMh1+OMlPEVAkrPRZVIcLW40uPzWMHSxHzJLT6PRLIwXzJYaDlKtJAkIjy1BSReuVVbVNJ1\nq2pVyLV+Hj934+u8ghC09PtSh5bFeWXcly4S8VG25TJTpVZNAc8kr1hZVOMnrfVJB9nox2S8m05Y\nSuQXO2PrFvmfyoCZTEdbLKStjH8SSCeRe2O+bU8muPOxvKgJYCrFYEVx+P/Ze9MoOY7rXPDLqt4b\n+w4CpAgSAPdVoriBlCiRkmlrtxZasi3LshaPn8carzPvvPNm3ptzvM2MbdmWZclaLMmSrcWmKVkS\nKYoUKdEkuK8ACRIgCBD72kADaHR3VeX8aGQj+va9ETciI6uqu/M7p09VZUbcuLF/94us6jjQEDVf\nOzGJeIgveRdYjWrCqUEcbJuKRomW+scF26LsUux91f6pTlYppgMBz6CtR4xgvERzEaOffE7EOD5h\nEx9cfkqquJSHEw9iQCsocgg5sTTvmSTcJOOcEk4/c6cJkm95nlbQQv3MuCu6cCKportjeizQgP7b\n09wRkYlsQGnL9JnwoUpgieYiFhFvp/4sYqGPQcRdeWyg/WRuBnRDsM1rnzk/HTDdgw+K6V6/QtHG\nPCE26cy7PlHCmZujTUGYa630OArXb5LgStPZ0sSGShnP28HV/hVYd9MNuLr2ID5z13Ycb+p4SdC9\n/FKsm70ND7w0gGFL2Xk6INYk0CiZ3JFWyFFPdj+zURRK1UiG5gjc157tuTjN+Mqz+MQk4drHU0Ie\nQdGcGrnGLaeem/+kIkY7T9X54uqTqfJ4x3RS+dsd1f4VuOFNN+Lq2oP42zu3ReMJPvsll1dCNs+r\ncw7gEx/ZiV3fvgS377STbkoCK7MP4gPv2475m9biiw/Owuh4uQ3Mu3wjPrXu2HjeHXe+Fl/c0ml6\nhzmXPoffuX5w/MrOu15H0kz2wYY8j8f4pAt9bOZ0+1XQc8blWDd7Gx56eRDD5HSBI+WmDc1jhc2e\n904ynnex7FxwIW57zzU4+/Az+OZPdjSZiI+hq38+rrj5tXjtmkfwjR9vwA6GkfuQ2dgbCLep+xJy\nzp6Pn0U9thJSt5mKWNH4VG5jjoS7SDnNZ+ah72keCo6I20CPiiuVitVPCVO1v/JiKozVEEI+FerV\nTuhccCE++N7rcPbhZ/DPP3jVnyf0LMGCW67BgtVL0dU9guGXn8e+ux7H4JE6APlLi7Z+9QvyKxga\nDen3BPV6BbU6AJL3yHNr8BcvpUD3Ubzvgy8jK8lcD49uWIu/fKkBdB/Fe39pCxKDgOYN9jX1CN2n\nYsyPzr55uOxNV+DK1Y/iW/dtmsDr6ImBeRppE6uyz5y/XN6YmPSYiiT1hyDpWoo33XoNVh1cj8/d\n8ThePN7IZS8MKQY334/P3P449p7xevz6rRdgsSMEcR1hjFtWEGbtsUkIig4KSjQPoUTc9TxdXvs+\nCCEsXB6OaLvUcS6fzSdOQeHKsa2H5mMq9LEV16MqMwFSm01FlMS6OCRdS3HzL1yHVQfX47O3P+rN\nE5K5a7DyY7+A+fWXsPtr38CLf/9D7N+3FGf88g2Y1WdXY108x4cHmWSZ4xDcIxLpsYX4zteuwBce\nmT2uimdojHbi6PFuHD3ehRqxkaE+0oGBwU4MDHagZvFXux75cD+fuRx/3qc4tuWn+OwdT2LP8qvw\nq7eswaLq5LXatj9q/W3WmjWBjMddcKpYePH1uLZ/J77/4xewr9bKxSzF0O5n8M0fbMTRM67B+y6b\np3o+h+vUGG3kOq5pxsJfdLlaojXVwRE29eJtWbB9ffC5bvPHpgpo8sUgoaELJXcKw0HqM/Ov0Wio\nrmf2svK4DXeqEtBYcBHymd4+MxtVLLpkHa7t34nv/WijP09IejH7rdeh94W78MoPn8fxA8dRG9iH\no/f/CLt2n4klF8+GObpsa2V+ldjPdQ1c/vqQZm5NsnERF7F3lemynRdp2sDQ7mfwnbs24cjyq/GL\nl8xBVdkmIaeXRWOck0Z3qGMBXn/JPBx99j48PRigiCdd6Dzn/Vh82dXom9uF2oEncPTZuzC4fxSd\nq96E/uF7cHDzLjTUbqc4ufMxfOups/CJ174O5z1/Dzac8DtC9mkj3/a0RXDmZh+zn0KOX/OUVeI0\nuAWSu59Bcwrj80ye62hO+hySh/oUe8z5kDuqiGthmyvSo1jauVvOjemJ8lEVBToW4OpL5+PIM4E8\nYfbZWLjiEPb/cDfq6EbvlVdj6evPQd/8LgBAWp2L5JGjSJM6ll+4E++49hDOmpXg4LZFuP+FCtZc\ncRBnDS/DP9y+GAMp0LXoAN56wx5ctPIkuk/2YstzK/Afj8zDQP3UGlBJsWjNTrztmv1YNa+O4QML\ncN8jfacJf1LDxe99GretYHytzcNX/2E1No8C6BnARz7+Es4+dWv3vZfj8xu7kY0Wc72hxNEmcrGB\nb9LAgtU78dYr9uGshcOoHOvD9m1L8ZP1y7BzeLKb7JitjOKq257F246di79+chhveeMeXLQwxf5t\ni3H3PSvwwrEESc8RfOAjL+HM5y7AX/y0b1zNR8dxvOOjm7D66Qvx6fU9qANIuoZw5bqduPacY1g+\nu4GTh+fgiYdX4sebejGimDKnfWxgaMej+PbTK/HxK67E2ud/Ms7rXEGDdg+Q9rzYc7tDNJpU0NlR\nNQZZgs6ODiQA0noNow4WXJ13Ntb2D+K5l46c7hQfdL4Gs89ZhtrzX8Suo73oWfsOzL/pv2MBAIxu\nxcB9J+DfFjXseeZpbL/0OlxzZg82bhpCeqpeFRtBSBJ0VKsT6j6pg07ZQZqK7ZNUKuNtiEadb0Om\nnSdFcUaazM6kQWf43KiNqsvySTPuF5Nm0kKSs6zplMYUf8bJuGNs0H739WfCHDfGBleWOZ5D68WV\nxdnhypLsuOYXnRfUTtaG1UoFSFNxXiSVCro6OyfNwQnKkpEmSRtsfzXSFNVKZcIcnKQQAW09VkPS\nSONn0kZmpClqjYqdxpynLjvasTrj0hjIeMKzLw4E8YTK4rPQdXQbThwHOi+9Ba+5qYJD3/suXt1y\nBB1X/yJWndOJBCnmnPcKPnpTDY/dvQa3H6rhgnWv4D0/14GHfrAKX97dg2MAes/cgU+8Zx/wwgp8\n9/Y+pIsP4eZ1m/FflrwGf/u9xRhopJh34WZ88k1D2PzgOfjCjirmnbUfb7p5FxYmHXilkiBBB16+\n93z8Q/fpeldmH8Y7b92Hjg1L8Gr2LMrwHHznq5eiq2sQ73j/VnQbddIIL7ZHMShhr849gHfeuB/H\nnzob3/lpL+qzj+Dqda/gw7MTfOaHyzBQS5FU6+jAZKEiTVPUahU0TtnrWLobv/aWKjasX4Wv1I7j\nxpt34ba39OIv/n0hjp6cjQdf7MLHVh/Ckgf7sKs2thZU5h7F6p4ePPFSN+oAKn1H8I7btuCqylz8\nZP3ZuOtYikXn7MMtP7cJc4Yvwr+83AF7C0xspzQdwe6nn8L2S67D1Su7sWHTEFCpoIPhdRNOOlFB\nZ8fE9XmcwDdqGFXGhrFIufi0RnXORfj4h67CklOfK/NvxCc/eBEWADj83Lfw2fsPwubr8e0H0dsY\nwPaj9TDPRl7Cobv/3/GPJ3Y+jMN9y9Dd28DowF7U62EN0DixE08fSvBzqxaga9NOjPafj4/+6rVY\nbslj1n1k3w/wl9/eNun5rjJNmaZMU6Yp05RpyjT2NCaObTuAvmCekKAyqw/JyaOopz2YddlS1B67\nHftfPIQUVXT2jgXLqI7gsquP4vijF+DuTWPK7P67zsTaj+5E5UgvDgxWgeoQ3vDmvZi77Wz85d0L\ncbQB4NXZ2D5Yw//2CzvxhmULcMe+YVx79REcf+wC/OvjY7988ureWdhbOYH/cvX4U90YOjQL2zKC\n1jGEG2/cj/l7z8BnfzYbJ1MASJGggsEjPUDXCIZToBtuUp2RSN+TxcaRJfjGPy1BvVZFCiDdNwv7\ncAyfevM+vKZ7KY406jjvHY/hA2dwnKof3/78+Xj6ZPaxgvVfW4uHDicA5uBg3wB+b90hLO9ciKPD\nFex6ZhEOXbwPVy89A7fvrABIsXD1IcwbWIjnDicAGlhx9XZc1TMP3/jHVdhwYqwuL22bi627jqDx\nqkzEpROBJElO87qz56Nr0xBG+87DR37lGiuv6zzrvfjU2xejG5PH6va7vo4vvTwilkf9ikHInY9O\nn9j2PJ46XAe6D2G48iIWVBs4vGE9Ht05bIleUtQ7zsS60ZMYivadzQbSE7tw8kReM8M4cGgUnQtm\nobcCjIwewOOPP4cFVcujAUbdj7/yCB588djkQKTN0/znpsFJaZKeyXbq9JhrPE16ys5Rpx1NWTM5\nDe2vJEmQdk1M89BLxyekSdO0pePH9Gf8EYyuiWPjoZcm23HVS0qTkrnY6DTLethhZ8yf9Zsnpzlt\nJ7y/uPZ5eMvQpP6a6PMjePDFQcv80vgTaw42fy7HWqPaJ83pPjXnRVhZjzr6opn9NTa/YrUP5/Np\njPGEG/LwhEYKJFUANdQGaug852z0PDyA4VmrsejSfiT7ASQN9FaB0eHK6cdA6lWMpCm6qmP9Vp0z\ngIvnVbDp3rljRPwUjr2yFBtODmD12cPoPDaIVX2d2LSlFyPjpCzB4Vdn4cTVA+NPEZw+Da5j1brN\neMvC2fj3f1qGPbWxOk8gb45nqdM0RdposGnMx+GyB9b5RzMSjI4mgMHYRgd7cRzHMLsrBU5Use3+\ni/Clrsnfh0Hagf0nT1tKdy7B04eTcbtDR7pR6xhBb3XsSu3gQjy0fxduvvwo7tw1D0OVk7j0whHs\nfWo+DjUAdJzEReeM4ujzy/DiCcPPtII9L8yHDybU8xSv61o4G33VBEdqB/HEExuwoDoxz4QnGjYP\noXZiFWZVTq9RafcSXHnBkgn2Nc+7x3j0Mvmbv/mb9Hc++5VJN6pzL8EnP3QVTt71dXxxC/Ngkd0s\n+tf+Av7gjSfwT/94LzZPDjBaiA6c+cb346NLn8Zff3vD2ADB5EEc8py4CVuHFf2cdhFfltCWORO+\ntOkL2zfq6XsKn77UfFNem9f25UdbGmkBcyHkS322L+HYvqBEv4jp8kWym/1VKpP/d1pmu3FqI+XW\nk5jzoh2fT9b0aTt+kUqCz7rd7nVpDySYdd7b8Ic3DeGrX/pxEE+orLoFa946gG3/8CiGe8/C0vff\nggVLq8DIAQzunoVZlQfw4tc3Y/kbNuI3z+/DHf96Fp44Usf5N2zG+1fNwZe/dia2DgNdZ2zDH3zg\nKJ76xkX4wT6DwVVP4o2/vAHX716Lv3xiEJ/80CE8/U8X4d5DY3M+SRJ0LH0Vn/rAQWz8l0vw/b3Z\nT5ummLP2Zfz2zx/HC3dcgH97uQMNTF6Xku5BfOhjmzD7vsvw+Y3daHDrRNcAPvqbm1H9wRX44pZO\nfhx2H8GHP/YCOo3fIh/nNJUaVp6/B69dNYAzFgxhdk8D1WodHUkPfvKNy/Gzgcn/MGfS2lgZxes+\n8AzefnwN/uR7c5Exwu6zXsYfvWsY//GFC/DEKaF01vkv4vfeDPzbl9ZgQ99OfOq2Qdz35bV47FgC\ndA7ito+/hKUPX4S/fWzssRUNbGvJ2PsOrHzD+/Aby57B33xnIw41Ju5Fru8KZWk75l2KT3zwdRj+\n0TfGlXHtXH7snz4/nnb9+vVYvlzW5Xfv3o1rrrlmvOwHHnhA909//JFi9NggTnYswNLeCjaPtOIn\nDSVU0TerE+nIifF/AKQhHnnRii9KlhtC65GHiFP4fomT80VDPjVktEhoy3B9KTL0yzZSWvrFTFvg\nwT3TWeR8bMe5HnKkXku70m0AACAASURBVGImIcXI4FGc7FgUzBMa+1/B8KzXYs7CJ7Bv33bs+dKX\nsK+nC+nwCNDVh46OYdTTCnY+dDbWr3kR7/zlAbwTQHpkPv7jjjPwyilWWR/uwhBqWDirAeytjCvN\nSGqY2w2cPN6B2kgnTiY1LOprAKfIeJqmSDpq6MLpLADQsWAfbnvrAI4/dh6+t7Vj8ilemk5av6T1\ngiOSk/YSYU9Jk1GsuelZfHBVFU8/vgI/fqobQyOdGO7djw/8/H62TfOuJce2LsGmxhZce+4QDs0/\nhP7dZ2DT8VM+pR04fBJYs3QYndCTcRP8/llF/+wupMPHMYIE3LKTCTBuW6c/c/1UFCZLOpEwOvAq\ndjXm4cIzeoorJAQd87BmcRUDOw6fen5rIkKJUrNgKnK2bwubk1abJwbakRS0ArSdfUm3JprP41ee\nsWDzJcTXvMFJiD+h7aA53eLUpVacVrUDfOrZjuttCGZK38ZAxhMuWtEbxhOOb8eBF7uw8NZL0NM1\n9ihG4+Tw2NwbPo7R4zUAKWadtQ8XHFuBv/7sJfjzz12G//6lVVi/vzr+4Eb9yFxsHEyx9vWHsLDj\n9DjsO2s/LunrxOat3Rg50Y8tg3WsuWQQ/eNPatSxbM1R9BguVXqO45Z37MCKvSvx9fWzMAK/cT1J\nPXec8pmv2fvxtaf7CK5bM4zt956P7z65CJt3zcHOAz0Y7j+BOeM8Ph4/SNMU6cnZ+M8XOrDyql24\n+fw6Xn5qDo6NP7nTgw1bOtGzdieuXWL+M54UvXNG0ckULe0PE04pO+dj9aIKBnYOYKhhV/m1nIju\nv66/vChIGQfSoV147NUGPnD5Gix+8WnsDfweZ1wk6F5+Pi7uPYpHXh60RmXNPPbVKEguwuIaEFTF\nC32cwBczaWNyKcy+j/DkfdSjSMRSPX1txCK1tFxXfVyLuS0wKE+rxjDVlfKp7n87Ih3ahUe313Hb\nFWuxeNOT/jwhHcaxH9+Lw796K8752BLsv38jju09gWT+SsxdU8PBOzdipJ5i3lnHMbu7EyuXD2Pv\nsQr6+xMMDXZj4OSp58hrffjZjxfhknfvwMffXsPdz83C8JwBvHHdISRbV+H+PVUkaR8efngOrrn5\nZXy0sQL3benA/FUHcPXZ6dgjKACQjOLCN2/B9XN78cDPZqFr4Qlkv3KYpg2kJ/qx+1gCVBro7amj\no6eGzgpQ7R7FnP4Eo8OdOJH9CknnKPo6U6SdNVQBVHtGMac/BVDFiRNVNABUu2ro60yBrrE0le4R\nzO5LkaYVnDhRRX2kC3tOAK+9ZA/OObQEB6ojOOucPXjjlQdRRQ9LRH0U4YmENbtawc5nFuLwpXuw\nemQ+vvJqB5Akp0KSBLsffQ0eO3czbv6l57Hk0cV4aSDBvBUHse7COh74+oX4ycFkEk+hmBignOZ1\nj57idVKwMoHAO4SVIsQwGwoj40hP4qWHN+Lg+6/Euy7dhi8+GfbTRTGRdC3CunXnouPV+/HY4Ymz\nnpJds/NidIRmsw8h5K7NX8on5Y2BmbZpcXV1BU9S/2n6U7IZAy67ErH09cfVZq6yuc9ceqkc7p4r\nmLWpKZSQ51FNmjFHm4mZtB5Mh/5qKtKTeHH9Bhy87Uq8+7JX8IUnDvvzhBM7sOfLt+PkDa/F/Bve\njMXzutEYPIBjTz+BNAGACvZuWIAjl+7Fu9+1f4JOPbRjGf75+8ux5USC46+chc/8Sx9uvWEv3vrz\ne9A51IvNj67B1x6diyMpACQ4snE1/h7b8a5rduG9qzuwY/Ni3P6DBLe+dw86KkDSfwQ3rBkFMIp1\nb38B64irO+6+BJ/b0IXK3H342Id3YnF2Y91G/N46YNc9l+FzG7qQooHl6zbiE5cYD9K/6Tn8PoDk\n+FJ87quvwa56A0uvew4fv9j4Tt9Nz+J3jTQ7R/rw4zvOQfVNr+J9v7QTlaFu7Nq+DD/8YS9uvfXA\neDatOKe9Xzu0AE8c3oPrdi3GtmHAbPTGibm44+sXYNcNO3HNJTtwaW8FB/fMwQN3nImfHpQJsiRu\nJV2LcP3156Bzx/14fOD0A0HaU1FbPZpFxAEU9QXOzHonVl7zTnzsihTrv/t93LXjpPXnEAtFpRfn\n3/Ru/NLqw/j+P9+FR4yvTHNRku2IWQMbgXJtttJAlAgA/TKapOJR//LUj0Kqx3TfnGyLho2AcG3j\no6D6PNrhWnA0NrR+uWy5lA5N2Vy7+T5mIh7rOvJyC3nWl3mPL31PTqYSbHNiqp0c+IzVEgoknTjz\n2nfh41cCD/7796LzhErfAN7z/t04ds8a/OjVDjQSIElSdM87jHd/4BUsefT0lwmlxyCk/tSeprny\n+65fvqCKMC03SZJxHlGv1618ggNNW12wF7/5K/uw7ZsX4T/2Vp35aTmuOoy/Vnpx3hvfhdvOPYQ7\nv/VjPHykbl17XaealTkX4xMffN0E3qudy0984wvjaUO+wFns49zpKHY8fBe+82IV17zjXfjAJQvQ\n3QKBJOlejGve+k780nkjePSHP8FjR/2melHH8VInNyMas0WFvrDVY6YhhNTS/o5BxF2RvzZNCGI8\nXuPbjtKJAte2schfzGB2psBnbLcrtAGp6zSlxCmko3h1/Z349qYKrnvXe3DbpQuj8oTqnEGsnj+C\n5SsHcfayk5jfP4o5c4Zw9tlHsKKzC1t3T/6CpQY+fRlzr/UJ+qUxR5Vf269LaevQuXgf3nH1AF6z\n8hBu/fldWLJvKR7cf5pi+hBx2wnx+L2uRXj9LW/HbWuH8fhd9+ORgZq4vrvmIm1HichXKhVUq9VJ\nf9wvavlCfEwl6ewd+yH67pyFNAbx7D23Y9+ua/Fzi3pRdeeIjCrOeP2bccv8Xfjetx/C4/tHVf/d\nCQhXdjV5QjdwTgXUHjFxyJN3pkPqB+6eBF9i6CqjyM3eZ4zEfjSBLpLa41LN5uKrYpuPuUgKe4yA\nZDrBHA+usTEV1iRzk/fFVKhf09EYxDM//jfs23VddJ4wuvcMfPXuBG++fAc+dNUoeqpAY6QTh/bN\nwfp/Pw8P7axaOUFoP9F+tp2I2/Z1Wx5buRoizhFS0w5NI9lLEqCrfxRLL92Bj19bwYGty/CVuxfj\nUMPvdJjzky+zgjOuehNumb8L3//Xh/HYvpGxf2hkWfcrlYpVFU/T9DTv7amKRF76Kdu8EMl4pWce\n+gH0zIswLdJR7N34U0x+GKYZqGPP4z/EXz10FIPs/9n2B2142yRrJiSSwKXLYA64ogKPEjzyKLQh\nxL/ZJEBDyEMCCttJjDQXuTmbNyCmBDOUiNvKma4w207qizx9NBUw3evnjXQUezbcj3+MbreKXc+t\nxNeeWxnbsgpcP8uk1k1eNUJDRjxpeRyh166TtlPYNAVObFuJL3xhhZiGe68BH8iP8bpPrx8c53U2\nwUZSxieR8e656AfQPXeyop8Rcdq2sdZ8kYynjVHUAdRG2+k3wv3x6T/6bWeaT/3534oTQZpIPkRK\nE+FqJiclAdwg4KJZasuWp9wcmoMQIh5DPc1DQEPzcWNY+ixdM2Ebpzb1SKPG2uBSmVy+2GALJKYr\ntMHaVGsLn2ByKtZvOkN7gtOKkyyNwEbVcM0puk2kKWJsak816TXaL/UTR3AUE+eQS2nnCPiEYCOt\noQ6gXksnkO6MiJtEPkOsOSyS8cbxAziCVRg62OrfQGkubEc/IQ0e8riCbdM3P2uPwWxReR4iPtM3\nEdtiLbV5aJvZxpFGdfEtW6vkaG1JpzHcfZttKdikebhAleZzHQtzdm0KE/fqg9hjo93mp4Z8c5hK\nhNUnqDPnwVSp30yAa28NQcj67yPymaRR44NmP/ERDV2Q2jGGyKNZe7Nr2X9E5pAkCTB0CEewCiMD\nKTo6JtJjiYyP582Jwn7a8PpqA8tOvR9BBT+sY+JPFlVn4ZxzlyM7Dagd3YGNu4Ym/va3Jk0EaJU4\nemTjq4hLZfv4YyMpXF5KUDgyUxLxOAjpN43aEeJHnvxa+Ci5UsCo3SB85h435m2wLay2QFUKeH0D\n6zwIPUGYapgKdbEFehJKQt6e0KriviQ7Tz+79n5JHTfv0fdSGT5CCy1Huzb73refHibonHsmLlje\nc+r7BjUc3LYV2064H8Mx32efsy9qmnlMIt5UZTwXEmBRtYE/qZxyMG1guNGBH5n+pg3MPv96vGvl\nKaY9sg3f/Pq92DiUTkgz67zr8K4zq3KaiAhVbjjEJkJa0iYp3RIR53yVVL8Sk2FfIMbgQ1ppem25\nUhqtPZrHtzxtHW2quM/88xmz2o3V/Gz6ZFvMbfdsvseYU1oVtp3mLzdfOPJg3p8K0JAd6XSoJOTt\nCdovscaobz/7kHDz1ZZHKkdDpl1p8hJyG6T9NU36ccUtt+BtS0/Vb3gzvvLyVms+c+3JVO/sy5lj\nZPx0OrP/p5YyngI/qlXxqa7a2H+fSlL8brWB+2oVjP+EfeMENj78PN608iLMA4Cu1+DNF8/Fi48a\n/xzoVJo3n3mxnCaWyxayCoSr47F8k65rVD8z2qU+F62elvBbfHyJc+jpSwi040YzL1xkxDeAsdmi\n5VI1S7upaTYiF1pBttqd5NmCxqlMVrUBIFAq5O0MKYDM3ocgSSroTpbjt7rPx7ur/ehLB/Gz0Q34\n45F92DduG+ipnIHf6pmcZu8pO5XkLHyl7zJcMsnprfjI8PN4Vix/8vravXg/brnhVVyybBjpwBys\n/9lrcO/L3ZOeRHDVOek6gavfuB03rDmOyqG5eOSBs3D/9s7xn49MkgRIalhx4S7cev1+LNm2Fv/P\nnbMwYrUqI02B7jMux01LT9dp92NPYuuwfb/ifl1FEjY5O7HnaWG/Mz6UJvir9LTj51QbeANpj9H9\nz+Henae7etFlV2JN78REtQMbcM8Oe5rYkEgHt0kXFSVJfpl/9Br1ybboa5X2Em7EmpTcuLKphlnZ\nrr8iUATBp+9t/tsWUakcOme4cjlCri3Dtg4U0Rc+/azxfypgKtaBG6vcxs+NuRLtA/9TsBSd3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gUTplT5I6tteex2/Xnp9oKIHBUWvYXnsevzW6cVJ54zpyOooNtWfx8dqzE8qAoTRnZdp8x3Af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\njUaj0Xgx/B+KwpPzAj0BHgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "execution_count": 2, "metadata": { "image/png": { "width": 600 } }, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(\"terminal.png\", width=600)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This lesson is written up in a Jupyter notebook because it's an easy way to store a list of commands and their output. In Jupyter, input is treated as a shell command whenever it starts with ``!``. For example" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/bin/zsh\r\n" ] } ], "source": [ "!echo $SHELL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If I forget the ``!`` I get an error because my command is interpreted as a Python command:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (, line 1)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m echo $SHELL\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "echo $SHELL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, there are some [IPython magics](http://ipython.readthedocs.org/en/stable/interactive/magics.html) that mimic shell commands and work without any quanlifier. The next command moves us to my home directory." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john\n" ] } ], "source": [ "cd ~ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To show the present working dir use `pwd`" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'/home/john'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pwd " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To list its contents use `ls`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34manaconda3\u001b[0m/ \u001b[01;34mDesktop\u001b[0m/ \u001b[01;34msync_dir\u001b[0m/ \u001b[01;35mterminator.xcf\u001b[0m\r\n", "\u001b[01;34mbackups\u001b[0m/ \u001b[01;34mDownloads\u001b[0m/ \u001b[01;34mtemp\u001b[0m/ \u001b[01;34mversioned_dotfiles\u001b[0m/\r\n", "\u001b[01;34mbin\u001b[0m/ \u001b[01;34mMusic\u001b[0m/ \u001b[01;35mterminator.png\u001b[0m\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could add the ``!`` but it wouldn't make any difference." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john\r\n" ] } ], "source": [ "!pwd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're working directly in the shell then of course you should omit the ``!``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### File System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The top of the directory tree looks like this on my machine:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34mbin\u001b[0m/ \u001b[01;34mdev\u001b[0m/ \u001b[01;36minitrd.img\u001b[0m@ \u001b[01;34mlost+found\u001b[0m/ \u001b[01;34mopt\u001b[0m/ \u001b[01;34mrun\u001b[0m/ \u001b[01;34msys\u001b[0m/ \u001b[01;34mvar\u001b[0m/\r\n", "\u001b[01;34mboot\u001b[0m/ \u001b[01;34metc\u001b[0m/ \u001b[01;34mlib\u001b[0m/ \u001b[01;34mmedia\u001b[0m/ \u001b[01;34mproc\u001b[0m/ \u001b[01;34msbin\u001b[0m/ \u001b[30;42mtmp\u001b[0m/ \u001b[01;36mvmlinuz\u001b[0m@\r\n", "\u001b[01;34mcdrom\u001b[0m/ \u001b[01;34mhome\u001b[0m/ \u001b[01;34mlib64\u001b[0m/ \u001b[01;34mmnt\u001b[0m/ \u001b[01;34mroot\u001b[0m/ \u001b[01;34msrv\u001b[0m/ \u001b[01;34musr\u001b[0m/\r\n" ] } ], "source": [ "ls /" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some comments\n", "\n", "* ``bin`` and ``usr/bin`` directories are where most executables (applications) live\n", "* many shared libraries in ``usr/lib``\n", "* The ``home`` directory is where users store personal files\n", "* ``etc`` is home to system wide configuration files\n", "* ``var`` is where logs are written to\n", "* ``media`` is where you'll find your USB stick after you plug it in" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Searching" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I have a paper by Lars Hansen on asset pricing somewhere in my file system but I can't remember where. One quick way to find files is to use the ``locate`` command." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/Desktop/PDFs/Econometrics_bruce_hansen.pdf\n", "/home/john/Desktop/PDFs/hansen_averaging_ecma.pdf\n", "/home/john/Desktop/PDFs/hansen_shrinkage_qe.pdf\n", "/home/john/backups/sync_dir_backup/papers/asset_prices/hansen_nobel.pdf\n", "/home/john/backups/sync_dir_backup/papers/lae_stats/references/chen_hansen_carrasco.pdf\n", "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_2012.pdf\n", "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_knightian.pdf\n", "/home/john/backups/sync_dir_backup/to_read/financial_econometrics/scheinkman_aitsahalia_hansen.pdf\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.aux\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.log\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.pdf\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.tex\n", "/home/john/sync_dir/papers/asset_prices/hansen_nobel.pdf\n", "/home/john/sync_dir/papers/lae_stats/references/chen_hansen_carrasco.pdf\n", "/home/john/sync_dir/to_read/sargent_hansen_2012.pdf\n", "/home/john/sync_dir/to_read/sargent_hansen_knightian.pdf\n", "/home/john/sync_dir/to_read/financial_econometrics/scheinkman_aitsahalia_hansen.pdf\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.aux\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.log\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.pdf\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.tex\n" ] } ], "source": [ "!locate hansen" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more sophisticated searches I use ``find``. \n", "\n", "For example, let's find all Julia files in ``/home/john/sync_dir`` and below that contain the phrase ``cauchy``." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/sync_dir/books/quant-econ/QuantEcon.applications/lln_clt/cauchy_samples.jl\r\n", "/home/john/sync_dir/books/quant-econ/private/_backup/html/_static/QuantEcon.applications/lln_clt/cauchy_samples.jl\r\n", "/home/john/sync_dir/books/quant-econ/private/_backup/html/_static/QuantEcon.jl/examples/cauchy_samples.jl\r\n", "/home/john/sync_dir/books/quant-econ/private/_build/html/_static/QuantEcon.applications/lln_clt/cauchy_samples.jl\r\n", "/home/john/sync_dir/books/quant-econ/private/_build/html/_static/QuantEcon.jl/examples/cauchy_samples.jl\r\n" ] } ], "source": [ "!find ~/sync_dir/ -name \"*cauchy*.jl\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next command finds all files ending in \"tex\" modified within the last week:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/sync_dir/papers/fwd_looking/fwdlook.tex\r\n", "/home/john/sync_dir/teaching/nyu/functional_analysis_intro/fa.tex\r\n", "/home/john/sync_dir/teaching/nyu/functional_analysis_intro/fa_homework.tex\r\n", "/home/john/sync_dir/teaching/nyu/advanced_python/adv_py.tex\r\n", "/home/john/sync_dir/teaching/nyu/coding_foundations/hw_coding_foundations.tex\r\n", "/home/john/sync_dir/teaching/nyu/coding_foundations/foundations.tex\r\n", "/home/john/sync_dir/teaching/nyu/core_python/core_py.tex\r\n" ] } ], "source": [ "!find /home/john/sync_dir/ -mtime -7 -name \"*.tex\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Working with Text Files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When working with specific text files we often use a text editor. However, it's also possible to do a significant amount of work directly from the command line. Here are some examples." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/temp_dir\n" ] } ], "source": [ "cd ~/temp_dir" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're going to make a text file using the shell redirection operator ``>``" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 44\r\n", "drwxrwxr-x 16 john john 20480 Dec 31 10:48 econometric_theory\r\n", "drwxrwxr-x 5 john john 4096 Jul 3 2015 kn\r\n", "drwxrwxr-x 12 john john 4096 Jan 8 13:07 quant-econ\r\n", "drwxrwxr-x 11 john john 12288 Dec 21 09:55 sed\r\n", "drwxrwxr-x 10 john john 4096 Jul 3 2015 sed2\r\n" ] } ], "source": [ "!ls -l ~/sync_dir/books" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!ls -l ~/sync_dir/books > list_books.txt" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "list_books.txt\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can read the contents of this text file with ``cat``" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 44\r\n", "drwxrwxr-x 16 john john 20480 Dec 31 10:48 econometric_theory\r\n", "drwxrwxr-x 5 john john 4096 Jul 3 2015 kn\r\n", "drwxrwxr-x 12 john john 4096 Jan 8 13:07 quant-econ\r\n", "drwxrwxr-x 11 john john 12288 Dec 21 09:55 sed\r\n", "drwxrwxr-x 10 john john 4096 Jul 3 2015 sed2\r\n" ] } ], "source": [ "!cat list_books.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can search within this file using ``grep``" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "drwxrwxr-x 16 john john 20480 Dec 31 10:48 econometric_theory\r\n", "drwxrwxr-x 11 john john 12288 Dec 21 09:55 sed\r\n" ] } ], "source": [ "!grep Dec list_books.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can show just the top of the file using ``head\"" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 44\r\n", "drwxrwxr-x 16 john john 20480 Dec 31 10:48 econometric_theory\r\n" ] } ], "source": [ "!head -2 list_books.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also append to files using the ``>>`` operator" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!date > new_file.txt" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "list_books.txt new_file.txt\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue Jan 12 14:10:55 EST 2016\r\n" ] } ], "source": [ "!cat new_file.txt" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!date >> new_file.txt" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue Jan 12 14:10:55 EST 2016\r\n", "Tue Jan 12 14:11:03 EST 2016\r\n" ] } ], "source": [ "!cat new_file.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can change \"Jan\" to \"January\" using the ``sed`` line editor" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tue January 12 14:10:55 EST 2016\r\n", "Tue January 12 14:11:03 EST 2016\r\n" ] } ], "source": [ "!sed 's/Jan/January/' new_file.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Putting Commands Together" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The command line becomes very powerful when we start linking the commands shown above into compound commands. Most often this is done with a pipe. The symbol for a pipe is ``|``." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at some examples.\n", "\n", "The first command searches for files with the phrase ``hansen`` and pipes the output to ``grep``, which filters for lines containing ``sargent``" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_2012.pdf\r\n", "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_knightian.pdf\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.aux\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.log\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.pdf\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.tex\r\n", "/home/john/sync_dir/to_read/sargent_hansen_2012.pdf\r\n", "/home/john/sync_dir/to_read/sargent_hansen_knightian.pdf\r\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent\r\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.aux\r\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.log\r\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.pdf\r\n", "/home/john/sync_dir/work/referee_reports/hansen_sargent/report.tex\r\n" ] } ], "source": [ "!locate hansen | grep sargent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's do the same but print only the first 5 hits" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_2012.pdf\r\n", "/home/john/backups/sync_dir_backup/to_read/sargent_hansen_knightian.pdf\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.aux\r\n", "/home/john/backups/sync_dir_backup/work/referee_reports/hansen_sargent/report.log\r\n" ] } ], "source": [ "!locate hansen | grep sargent | head -5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an exercise, let's see how many Python files I have in ``/home/john/``. " ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "33067\r\n" ] } ], "source": [ "!find ~ -name \"*.py\" | wc -l" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here ``wc`` is a program that counts words, lines or characters, and ``-l`` requests the number of lines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see if any Python files in my papers directory contain the phrase ``bellman_operator``. To do this we'll use ``xargs``, which sends a list of files or similar consecutively to the filter on its right." ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john/sync_dir/papers/policy_iteration/programs/Python/consumer_prob.py:def bellman_operator(m, V, use_bisection=False):\r\n", "/home/john/sync_dir/papers/policy_iteration/programs/Python/consumer_prob.py: V, c = bellman_operator(m, V) \r\n", "/home/john/sync_dir/papers/policy_iteration/programs/Python/consumer_prob.py: V, c = bellman_operator(m, V) \r\n", "/home/john/sync_dir/papers/policy_iteration/programs/Python/consumer_prob.py: V, c = bellman_operator(m, V) \r\n" ] } ], "source": [ "!find /home/john/sync_dir/papers -name \"*.py\" | xargs grep bellman_operator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final Comments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One useful trick with bash and zsh is that CTRL-R implements backwards search through command history. Thus we can recall an earlier command like" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "mupdf sync_dir/work/referee_reports/JET/JET-D-15-xxx/JET-D-15-xxR1.pdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "by typing CTRL-R and then `JET` or similar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another comment is that file names starting with ``.`` are hidden by default. To view them use ``ls -a``" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/john\n" ] } ], "source": [ "cd ~" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34manaconda3\u001b[0m/ \u001b[01;34mDesktop\u001b[0m/ \u001b[01;34msync_dir\u001b[0m/ \u001b[01;35mterminator.png\u001b[0m\r\n", "\u001b[01;34mbackups\u001b[0m/ \u001b[01;34mDownloads\u001b[0m/ \u001b[01;34mtemp\u001b[0m/ \u001b[01;35mterminator.xcf\u001b[0m\r\n", "\u001b[01;34mbin\u001b[0m/ \u001b[01;34mMusic\u001b[0m/ \u001b[01;34mtemp_dir\u001b[0m/ \u001b[01;34mversioned_dotfiles\u001b[0m/\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34m.\u001b[0m/ \u001b[01;34m.ipynb_checkpoints\u001b[0m/ \u001b[01;36m.tmux.conf\u001b[0m@\r\n", "\u001b[01;34m..\u001b[0m/ \u001b[01;34m.ipython\u001b[0m/ \u001b[01;34mversioned_dotfiles\u001b[0m/\r\n", "\u001b[01;34m.adobe\u001b[0m/ \u001b[01;34m.julia\u001b[0m/ \u001b[01;34m.vim\u001b[0m/\r\n", "\u001b[01;34manaconda3\u001b[0m/ .julia_history .viminfo\r\n", "\u001b[01;34m.aws\u001b[0m/ \u001b[01;34m.jupyter\u001b[0m/ \u001b[01;36m.vimrc\u001b[0m@\r\n", "\u001b[01;34mbackups\u001b[0m/ \u001b[01;36m.latexmkrc\u001b[0m@ .Xauthority\r\n", ".bash_history \u001b[01;34m.local\u001b[0m/ .Xdefaults\r\n", ".bash_logout \u001b[01;34m.macromedia\u001b[0m/ .xfigrc\r\n", "\u001b[01;36m.bashrc\u001b[0m@ \u001b[01;34m.mozilla\u001b[0m/ .xscreensaver\r\n", ".bashrc-anaconda3.bak \u001b[01;34mMusic\u001b[0m/ .xsession-errors\r\n", "\u001b[01;34mbin\u001b[0m/ \u001b[01;34m.nano\u001b[0m/ .xsession-errors.old\r\n", "\u001b[01;34m.cache\u001b[0m/ \u001b[01;34m.pki\u001b[0m/ \u001b[01;34m.zcompcache\u001b[0m/\r\n", "\u001b[01;34m.config\u001b[0m/ \u001b[01;36m.profile\u001b[0m@ .zcompdump\r\n", "\u001b[01;34m.continuum\u001b[0m/ \u001b[01;34m.ptpython\u001b[0m/ .zcompdump.zwc\r\n", "\u001b[01;34mDesktop\u001b[0m/ .python_history .zhistory\r\n", ".dmrc \u001b[01;34m.ssh\u001b[0m/ \u001b[01;36m.zlogin\u001b[0m@\r\n", "\u001b[01;34mDownloads\u001b[0m/ .sudo_as_admin_successful \u001b[01;36m.zlogout\u001b[0m@\r\n", "\u001b[01;34m.gconf\u001b[0m/ \u001b[01;34msync_dir\u001b[0m/ \u001b[01;34m.zprezto\u001b[0m/\r\n", "\u001b[01;34m.gimp-2.8\u001b[0m/ \u001b[01;34mtemp\u001b[0m/ \u001b[01;36m.zpreztorc\u001b[0m@\r\n", ".gitconfig \u001b[01;34mtemp_dir\u001b[0m/ \u001b[01;36m.zprofile\u001b[0m@\r\n", "\u001b[01;34m.gnome\u001b[0m/ \u001b[01;35mterminator.png\u001b[0m \u001b[01;36m.zshenv\u001b[0m@\r\n", "\u001b[01;34m.hplip\u001b[0m/ \u001b[01;35mterminator.xcf\u001b[0m \u001b[01;36m.zshrc\u001b[0m@\r\n", ".ICEauthority \u001b[01;34m.texmf-var\u001b[0m/\r\n", "\u001b[01;36m.inputrc\u001b[0m@ \u001b[01;34m.thumbnails\u001b[0m/\r\n" ] } ], "source": [ "ls -a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another thing to note is that files have permissions associated with them, so your system can keep track of whether they are executable, who is allowed to read / write to them and so on. To view permissions use ``ls -l``" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls -l ~/bin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The permissions are the characters on the far left. Here ``x`` means executable, ``r`` is readable and ``w`` is writable, ``d`` is directory and ``l`` is link. To learn more about permissions try googling ``linux file permissions``. To learn more about links google ``linux file links``." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture5/.ipynb_checkpoints/numpy_timing-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Speed Comparisons: NumPy vs Pure Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Speed comparisons for two different routines. \n", "\n", "First, some imports:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import random" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fix data size" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n = int(10**6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make an evenly spaced grid of n points between 0 and 1." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 117 ms per loop\n" ] } ], "source": [ "%%timeit\n", "x = []\n", "a = 0\n", "step = 1 / (n - 1)\n", "for i in range(n):\n", " x.append(a)\n", " a += step" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do the same using `np.linspace`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 3.92 ms per loop\n" ] } ], "source": [ "%%timeit\n", "x = np.linspace(0, 1, n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take the maximum of n standard normals." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 1 s per loop\n" ] } ], "source": [ "%%timeit\n", "running_max = 0\n", "for i in range(n):\n", " x = random.normalvariate(0, 1)\n", " if x > running_max:\n", " running_max = x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do the same in NumPy." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 38.4 ms per loop\n" ] } ], "source": [ "%%timeit\n", "all_max = np.max(np.random.randn(n))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture5/fast_loop_examples/ar1_sample_mean.c ================================================ #include #include #include #include #include double ar1_ts (double * params, int n, unsigned long int seed) { double beta = params[0]; double alpha = params[1]; double s = params[2]; /* create a generator chosen by the environment variable GSL_RNG_TYPE */ const gsl_rng_type * T; gsl_rng * r; gsl_rng_env_setup(); T = gsl_rng_default; r = gsl_rng_alloc(T); gsl_rng_set(r, seed); int i; double x = beta / (1 - alpha); // Start at mean of stationary dist double sum = 0; for (i = 1; i <= n; i++) { sum += x; x = beta + alpha * x + gsl_ran_gaussian(r, s); } gsl_rng_free (r); return sum / n; } int main(void) { clock_t start, end; double cpu_time_used; int N = 10000000; double beta = 1.0; double alpha = 0.9; double s = 1; double params[3] = {beta, alpha, s}; start = clock(); double sample_mean = ar1_ts(params, N, 1); end = clock(); cpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC; printf("mean = %g\n", sample_mean); printf("time elapsed = %g\n", cpu_time_used); return 0; } ================================================ FILE: lecture5/fast_loop_examples/ar1_sample_mean.jl ================================================ function ar1_sample_mean(N, beta, alpha, s) sm = 0.0 x = beta / (1 - alpha) for i in 1:N sm += x x = beta + alpha * x + s * randn() end return sm / N end N = 10000000 beta = 1.0 alpha = 0.9 s = 1.0 tic() result = ar1_sample_mean(N, beta, alpha, s) println("mean = $result") toc() ================================================ FILE: lecture5/fast_loop_examples/ar1_sample_mean.py ================================================ import numpy as np import time from numba import jit @jit def ar1_sample_mean(N, alpha, beta, s): x = beta / (1 - alpha) sm = 0.0 for i in range(N): x = beta + alpha * x + s * np.random.randn() sm += x return sm / N N = 10000000 alpha = 0.9 beta = 1.0 s = 1.0 t = time.time() result = ar1_sample_mean(N, alpha, beta, s) elapsed = time.time() - t print("mean = {}".format(result)) print("elapsed time = {}".format(elapsed)) ================================================ FILE: lecture5/fast_loop_examples/makefile ================================================ maketest: gcc -Wall ar1_sample_mean.c -lgsl -lgslcblas -lm -o foo ================================================ FILE: lecture5/numpy_timing.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Speed Comparisons: NumPy vs Pure Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Speed comparisons for two different routines. \n", "\n", "First, some imports:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import random" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fix data size" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n = int(10**6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make an evenly spaced grid of n points between 0 and 1." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 117 ms per loop\n" ] } ], "source": [ "%%timeit\n", "x = []\n", "a = 0\n", "step = 1 / (n - 1)\n", "for i in range(n):\n", " x.append(a)\n", " a += step" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do the same using `np.linspace`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 3.92 ms per loop\n" ] } ], "source": [ "%%timeit\n", "x = np.linspace(0, 1, n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take the maximum of n standard normals." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 1 s per loop\n" ] } ], "source": [ "%%timeit\n", "running_max = 0\n", "for i in range(n):\n", " x = random.normalvariate(0, 1)\n", " if x > running_max:\n", " running_max = x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do the same in NumPy." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 38.4 ms per loop\n" ] } ], "source": [ "%%timeit\n", "all_max = np.max(np.random.randn(n))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture6/.ipynb_checkpoints/IntroToJulia-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Introduction to Julia\n", "\n", "**Chase Coleman & Spencer Lyon**\n", "\n", "3-4-16" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Opening Example" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "bisect (generic function with 3 methods)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# *1 *2 *3\n", "function bisect(f, a, b, maxit=100, tol::Float64=1e-9)\n", " fa, fb = f(a), f(b)\n", " # *4 *5\n", " for it in 1:maxit \n", " mid = (a + b)/2\n", " fmid = f(mid)\n", " \n", " # *6\n", " if abs(fmid) < tol \n", " # *7\n", " return mid\n", " end\n", "\n", " if fa*fmid > 0\n", " fa, a = fmid, mid \n", " else\n", " fb, b = fmid, mid\n", " end\n", " end\n", " \n", " # *8\n", " error(\"maximum iterations exceeded\")\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "1. Define new functions with `function ... end`\n", "2. Default arugments `(..., arg=default_value)`\n", "3. Typed arguments `(..., arg::Type)`\n", "4. For loop `for X (in|=) SOMETHING ... end`\n", "5. Create ranges `A:B` (not dense like Matlab)\n", "6. If statement `if CONDITION BLOCK end`\n", "7. Return statement `return STUFF` (optional, see next example)\n", "8. Throwing error `error(MESSAGE)`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "f3 (generic function with 1 method)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# shorthand function syntax\n", "f(x) = x^2-2\n", "\n", "# longer syntax -- equivalent to above\n", "function f2(x)\n", " x^2-2\n", "end\n", "\n", "# much longer syntax -- still equivalent to above\n", "function f3(x)\n", " return x^2-2\n", "end" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.4142135623842478\n", "1.4142135623842478\n", "1.4142135623842478\n" ] } ], "source": [ "println(bisect(f, 0, 4))\n", "println(bisect(f2, 0, 4))\n", "println(bisect(f3, 0, 4))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Types\n", "\n", "Everything in Julia has a `type`.\n", "\n", "You can inspect the `type` of an object using the `typeof` function:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0 is a Float64\n", "1 is a Int64\n", "foo is a ASCIIString\n", "φ is a UTF8String\n", "bisect is a Function\n", "4 is a Int8\n", "Float64 is a DataType\n", "true is a Bool\n" ] } ], "source": [ "for obj in [1.0, 1, \"foo\", \"φ\", bisect, Int8(4), Float64, true]\n", " # Notice string interpolation syntax `$`\n", " println(\"$obj is a $(typeof(obj))\")\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Type Parameters\n", "\n", "Types can have parameters. \n", "\n", "This concept is best understood by example" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "Array{Int64,1}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = [1, 2, 3]\n", "typeof(x) # whats the `{` and `}` stuff all about?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "`Int` and `1` are type parameters. In this case it tells us that the array\n", "is filled with `Int`s and has `1` dimension (it is a vector, yes Julia has\n", "vectors).\n", "\n", "Allow you to do all sorts of magic that will become clear later. For now just\n", "recognize the `{` `}` syntax." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "### User defined types\n", "\n", "You can define your own types (you _should_ do this a lot).\n", "\n", "Two forms of types:\n", "\n", "- abstract: you can't create these, but they help you group related types together\n", "- composite: you do create these, they are the actual data of your program" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "abstract Exog\n", "\n", "type AR1 <: Exog\n", " rho::Float64 # you should put types on the fields of your types\n", " sigma::Float64\n", "end\n", "\n", "\"\"\"\n", "I'm docstring. I describe the `MarkovChain` type.\n", "\n", "`x0` is the initial distribution\n", "\"\"\"\n", "type MarkovChain{T} <: Exog\n", " Π::Matrix{Float64}\n", " vals::AbstractVector{T}\n", " x0::Vector{Float64}\n", "end\n", "\n", "# functions to give `vals` and `x0` default arguments\n", "MarkovChain(Π, v) = MarkovChain(Π, v, fill(1/length(v), length(v)))\n", "MarkovChain(Π) = MarkovChain(Π, 1:size(Π, 1))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "AR1(0.9,0.1)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AR1(0.9, 0.1)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "search: " ] }, { "data": { "text/latex": [ "I'm docstring. I describe the \\texttt{MarkovChain} type.\n", "\\texttt{x0} is the initial distribution\n" ], "text/markdown": [ "I'm docstring. I describe the `MarkovChain` type.\n", "\n", "`x0` is the initial distribution\n" ], "text/plain": [ "I'm docstring. I describe the `MarkovChain` type.\n", "\n", "`x0` is the initial distribution\n" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "MarkovChain\n", "\n" ] } ], "source": [ "?MarkovChain" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Multiple Dispatch\n", "\n", "Function behavior can be specialized based on the type (and number) of all function arguments\n", "\n", "Let's see some examples" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "g(hello) I have something\n", "g(1) I have an integer\n", "g(1.0) I have a float\n", "g(1//2) I have some kind of number\n", "g([1,2,3]) I have an array\n" ] } ], "source": [ "g(x) = \"I have something\"\n", "g(x::Int) = \"I have an integer\"\n", "g(x::Float64) = \"I have a float\"\n", "g(x::Number) = \"I have some kind of number\"\n", "g(x::Array) = \"I have an array\"\n", "\n", "for x in (\"hello\", 1, 1.0, 1//2, [1, 2, 3])\n", " @printf \"%-12s%s\\n\" \"g($x)\" g(x)\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We can add methods to the `g` function that take multiple arguments\n", "\n", "Notice how the return value depends on the types of both arguments" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "g (generic function with 13 methods)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g(x, y) = \"I have two things\"\n", "g(x::Int, y) = \"I have an integer and something else\"\n", "g(x::Int, y::Number) = \"I have an integer and a number\"\n", "g(x::Int, y::Int) = \"I have two integers\"\n", "g(x::Array, y::Array) = \"I have two arrays\"\n", "g(x::Array{Float64}, y::Array{Float64}) = \"I have two arrays that have floats\"" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "g(x, y) I have two things\n", "g(1, x) I have an integer and something else\n", "g(1, 1//2) I have an integer and a number\n", "g(1, 2) I have two integers\n", "g(1, 2.0) I have an integer and a number\n", "g([1], [2]) I have two arrays\n", "g([1.0], [2.0]) I have two arrays that have floats\n" ] } ], "source": [ "stuff = ((\"x\", \"y\"), (1, \"x\"), (1, 1//2), \n", " (1, 2), (1, 2.0), ([1], [2]), \n", " ([1.0], [2.0]))\n", "\n", "for (x1, x2) in stuff\n", " @printf \"%-18s%s\\n\" \"g($x1, $x2)\" g(x1, x2)\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Example\n", "\n", "Let's construct routines that will allow us to simulate any subtype of `Exog`\n", "\n", "To do this we will need each subtype of `Exog` to implement a `iter` method\n", "\n", "This method should take two arguments:\n", "\n", "- The `Exog` subtype\n", "- The current state\n", "\n", "It should return the state on the next `iter`ation" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "iter (generic function with 4 methods)" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iter(ar1::AR1, x) = ar1.rho*x + ar1.sigma*randn()\n", "\n", "function iter(mc::MarkovChain, s::Int, v)\n", " ind = searchsortedfirst(cumsum(vec(mc.Π[s, :])), rand())\n", " return mc.vals[ind]\n", "end\n", "\n", "iter{T}(mc::MarkovChain{T}, v::T) = iter(mc, findfirst(mc.vals, v), v)\n", "iter(::Exog, x) = error(\"iter should be implemented by each Exog subtype\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Now we will define single `simulate` function for all `Exog` subtypes" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "simulate (generic function with 2 methods)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# NOTE `;` for keyword argument\n", "function simulate(ex::Exog, x0; capT::Int=10)\n", " out = Array(typeof(x0), capT)\n", " out[1] = x0\n", "\n", " for t = 2:capT\n", " out[t] = iter(ex, out[t-1])\n", " end\n", " out\n", "end\n", "\n", "# for MarkovChain we have more info, so we don't need to give it an x0\n", "# define another method that hands off to the method above\n", "function simulate(mc::MarkovChain; capT::Int=100)\n", " v = mc.vals[searchsortedfirst(cumsum(mc.x0), rand())]\n", " simulate(mc, v; capT=capT)\n", "end" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "10-element Array{Float64,1}:\n", " 0.5 \n", " 0.415777\n", " 0.364054\n", " 0.320674\n", " 0.300484\n", " 0.456054\n", " 0.316475\n", " 0.303914\n", " 0.35384 \n", " 0.282838" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar1 = AR1(0.9, 0.1)\n", "simulate(ar1, 0.5)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "5-element Array{Float64,1}:\n", " 0.5\n", " 0.5\n", " 0.5\n", " 0.9\n", " 0.5" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc = MarkovChain([0.7 0.3; 0.4 0.6], [0.5, 0.9], [0.2, 0.8])\n", "simulate(mc; capT=5)" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.2", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.2" }, "livereveal": { "scroll": true, "start_slideshow_at": "selected", "theme": "white", "transition": "fade" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture6/IntroToJulia.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Introduction to Julia\n", "\n", "**Chase Coleman & Spencer Lyon**\n", "\n", "3-4-16" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Opening Example" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "bisect (generic function with 3 methods)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# *1 *2 *3\n", "function bisect(f, a, b, maxit=100, tol::Float64=1e-9)\n", " fa, fb = f(a), f(b)\n", " # *4 *5\n", " for it in 1:maxit \n", " mid = (a + b)/2\n", " fmid = f(mid)\n", " \n", " # *6\n", " if abs(fmid) < tol \n", " # *7\n", " return mid\n", " end\n", "\n", " if fa*fmid > 0\n", " fa, a = fmid, mid \n", " else\n", " fb, b = fmid, mid\n", " end\n", " end\n", " \n", " # *8\n", " error(\"maximum iterations exceeded\")\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "1. Define new functions with `function ... end`\n", "2. Default arugments `(..., arg=default_value)`\n", "3. Typed arguments `(..., arg::Type)`\n", "4. For loop `for X (in|=) SOMETHING ... end`\n", "5. Create ranges `A:B` (not dense like Matlab)\n", "6. If statement `if CONDITION BLOCK end`\n", "7. Return statement `return STUFF` (optional, see next example)\n", "8. Throwing error `error(MESSAGE)`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "f3 (generic function with 1 method)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# shorthand function syntax\n", "f(x) = x^2-2\n", "\n", "# longer syntax -- equivalent to above\n", "function f2(x)\n", " x^2-2\n", "end\n", "\n", "# much longer syntax -- still equivalent to above\n", "function f3(x)\n", " return x^2-2\n", "end" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.4142135623842478\n", "1.4142135623842478\n", "1.4142135623842478\n" ] } ], "source": [ "println(bisect(f, 0, 4))\n", "println(bisect(f2, 0, 4))\n", "println(bisect(f3, 0, 4))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Types\n", "\n", "Everything in Julia has a `type`.\n", "\n", "You can inspect the `type` of an object using the `typeof` function:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0 is a Float64\n", "1 is a Int64\n", "foo is a ASCIIString\n", "φ is a UTF8String\n", "bisect is a Function\n", "4 is a Int8\n", "Float64 is a DataType\n", "true is a Bool\n" ] } ], "source": [ "for obj in [1.0, 1, \"foo\", \"φ\", bisect, Int8(4), Float64, true]\n", " # Notice string interpolation syntax `$`\n", " println(\"$obj is a $(typeof(obj))\")\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Type Parameters\n", "\n", "Types can have parameters. \n", "\n", "This concept is best understood by example" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "Array{Int64,1}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = [1, 2, 3]\n", "typeof(x) # whats the `{` and `}` stuff all about?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "`Int` and `1` are type parameters. In this case it tells us that the array\n", "is filled with `Int`s and has `1` dimension (it is a vector, yes Julia has\n", "vectors).\n", "\n", "Allow you to do all sorts of magic that will become clear later. For now just\n", "recognize the `{` `}` syntax." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "### User defined types\n", "\n", "You can define your own types (you _should_ do this a lot).\n", "\n", "Two forms of types:\n", "\n", "- abstract: you can't create these, but they help you group related types together\n", "- composite: you do create these, they are the actual data of your program" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "abstract Exog\n", "\n", "type AR1 <: Exog\n", " rho::Float64 # you should put types on the fields of your types\n", " sigma::Float64\n", "end\n", "\n", "\"\"\"\n", "I'm docstring. I describe the `MarkovChain` type.\n", "\n", "`x0` is the initial distribution\n", "\"\"\"\n", "type MarkovChain{T} <: Exog\n", " Π::Matrix{Float64}\n", " vals::AbstractVector{T}\n", " x0::Vector{Float64}\n", "end\n", "\n", "# functions to give `vals` and `x0` default arguments\n", "MarkovChain(Π, v) = MarkovChain(Π, v, fill(1/length(v), length(v)))\n", "MarkovChain(Π) = MarkovChain(Π, 1:size(Π, 1))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "AR1(0.9,0.1)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AR1(0.9, 0.1)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "search: " ] }, { "data": { "text/latex": [ "I'm docstring. I describe the \\texttt{MarkovChain} type.\n", "\\texttt{x0} is the initial distribution\n" ], "text/markdown": [ "I'm docstring. I describe the `MarkovChain` type.\n", "\n", "`x0` is the initial distribution\n" ], "text/plain": [ "I'm docstring. I describe the `MarkovChain` type.\n", "\n", "`x0` is the initial distribution\n" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "MarkovChain\n", "\n" ] } ], "source": [ "?MarkovChain" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Multiple Dispatch\n", "\n", "Function behavior can be specialized based on the type (and number) of all function arguments\n", "\n", "Let's see some examples" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "g(hello) I have something\n", "g(1) I have an integer\n", "g(1.0) I have a float\n", "g(1//2) I have some kind of number\n", "g([1,2,3]) I have an array\n" ] } ], "source": [ "g(x) = \"I have something\"\n", "g(x::Int) = \"I have an integer\"\n", "g(x::Float64) = \"I have a float\"\n", "g(x::Number) = \"I have some kind of number\"\n", "g(x::Array) = \"I have an array\"\n", "\n", "for x in (\"hello\", 1, 1.0, 1//2, [1, 2, 3])\n", " @printf \"%-12s%s\\n\" \"g($x)\" g(x)\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We can add methods to the `g` function that take multiple arguments\n", "\n", "Notice how the return value depends on the types of both arguments" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "g (generic function with 13 methods)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g(x, y) = \"I have two things\"\n", "g(x::Int, y) = \"I have an integer and something else\"\n", "g(x::Int, y::Number) = \"I have an integer and a number\"\n", "g(x::Int, y::Int) = \"I have two integers\"\n", "g(x::Array, y::Array) = \"I have two arrays\"\n", "g(x::Array{Float64}, y::Array{Float64}) = \"I have two arrays that have floats\"" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "g(x, y) I have two things\n", "g(1, x) I have an integer and something else\n", "g(1, 1//2) I have an integer and a number\n", "g(1, 2) I have two integers\n", "g(1, 2.0) I have an integer and a number\n", "g([1], [2]) I have two arrays\n", "g([1.0], [2.0]) I have two arrays that have floats\n" ] } ], "source": [ "stuff = ((\"x\", \"y\"), (1, \"x\"), (1, 1//2), \n", " (1, 2), (1, 2.0), ([1], [2]), \n", " ([1.0], [2.0]))\n", "\n", "for (x1, x2) in stuff\n", " @printf \"%-18s%s\\n\" \"g($x1, $x2)\" g(x1, x2)\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Example\n", "\n", "Let's construct routines that will allow us to simulate any subtype of `Exog`\n", "\n", "To do this we will need each subtype of `Exog` to implement a `iter` method\n", "\n", "This method should take two arguments:\n", "\n", "- The `Exog` subtype\n", "- The current state\n", "\n", "It should return the state on the next `iter`ation" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "iter (generic function with 4 methods)" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iter(ar1::AR1, x) = ar1.rho*x + ar1.sigma*randn()\n", "\n", "function iter(mc::MarkovChain, s::Int, v)\n", " ind = searchsortedfirst(cumsum(vec(mc.Π[s, :])), rand())\n", " return mc.vals[ind]\n", "end\n", "\n", "iter{T}(mc::MarkovChain{T}, v::T) = iter(mc, findfirst(mc.vals, v), v)\n", "iter(::Exog, x) = error(\"iter should be implemented by each Exog subtype\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Now we will define single `simulate` function for all `Exog` subtypes" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "simulate (generic function with 2 methods)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# NOTE `;` for keyword argument\n", "function simulate(ex::Exog, x0; capT::Int=10)\n", " out = Array(typeof(x0), capT)\n", " out[1] = x0\n", "\n", " for t = 2:capT\n", " out[t] = iter(ex, out[t-1])\n", " end\n", " out\n", "end\n", "\n", "# for MarkovChain we have more info, so we don't need to give it an x0\n", "# define another method that hands off to the method above\n", "function simulate(mc::MarkovChain; capT::Int=100)\n", " v = mc.vals[searchsortedfirst(cumsum(mc.x0), rand())]\n", " simulate(mc, v; capT=capT)\n", "end" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "10-element Array{Float64,1}:\n", " 0.5 \n", " 0.415777\n", " 0.364054\n", " 0.320674\n", " 0.300484\n", " 0.456054\n", " 0.316475\n", " 0.303914\n", " 0.35384 \n", " 0.282838" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar1 = AR1(0.9, 0.1)\n", "simulate(ar1, 0.5)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "5-element Array{Float64,1}:\n", " 0.5\n", " 0.5\n", " 0.5\n", " 0.9\n", " 0.5" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mc = MarkovChain([0.7 0.3; 0.4 0.6], [0.5, 0.9], [0.2, 0.8])\n", "simulate(mc; capT=5)" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.2", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.2" }, "livereveal": { "scroll": true, "start_slideshow_at": "selected", "theme": "white", "transition": "fade" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture6/JuliaPackages.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Julia Packages and Packaging System\n", "\n", "**Chase Coleman & Spencer Lyon**\n", "\n", "3-4-16" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Managing Packages\n", "\n", "One of the tools that Julia provides is a built in package manager.\n", "\n", "All of the package manager commands are within the `Pkg` module, and are called by `Pkg.command(arg)`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "Two types of packages\n", "\n", "* Registered: Mature package that has some sense of approval from the community\n", "* Unregistered: Less mature package that maybe is still developing basic functionality" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Adding and Removing Registered Packages\n", "\n", "Registered packages can be added and removed by using `Pkg.add(\"PackageName\")` and `Pkg.rm(\"PackageName\")`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Pkg.add(\"Distributions\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# Pkg.rm(\"Distributions\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Adding and Removing Unregistered Packages\n", "\n", "Unregistered packages can be added by using `Pkg.clone(\"git_repo_url\")` and are removed with `Pkg.rm(\"PackageName\")`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Pkg.rm(\"PlotlyJS\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Pkg.clone(\"https://github.com/spencerlyon2/PlotlyJS.jl.git\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Updating Packages\n", "\n", "Packages can be updated to their most recent version by using `Pkg.update()`\n", "\n", "Running this command will update:\n", "\n", "- Your local `METADATA`, which tracks all versions of registered packages\n", "- Registered packages to latest version\n", "- Unregistered packages to most recent commit on active branch\n", "\n", "Doesn't update \"dirty\" packages (`git status` $\\neq$ clean)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Demo of Recommended Packages" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Distributions.jl\n", "\n", "This is _THE_ package for dealing with distributions and random variables.\n", "\n", "It is also an excellent example of how to properly leverage multiple dispatch over different types.\n", "\n", "We will demonstrated how to use this packge." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Distributions" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## What Distributions Are Included?\n", "\n", "There are ~70 different distributions that are included in the package.\n", "\n", "Hard to find distributions that are not included" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 46 Continuous Univariate Distributions\n", "There are 66 Total Distributions\n" ] } ], "source": [ "ncud = length(subtypes(Distributions.ContinuousUnivariateDistribution))\n", "ntd = length(subtypes(Distributions.Distribution))\n", "\n", "println(\"There are $(ncud) Continuous Univariate Distributions\")\n", "println(\"There are $(ntd) Total Distributions\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Using Distributions\n", "\n", "Our example will not demonstrate everything that `Distributions.jl` can do.\n", "\n", "It does much much more, but it will give you an idea of the types of things that you can do with it -- you can [read the docs](http://distributionsjl.readthedocs.org/en/latest/) for more information." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Example Methods\n", "\n", "You can find a more complete list of possible methods in the [documentation](http://distributionsjl.readthedocs.org/en/latest/univariate.html), but we list a few of the methods below to give you an idea of what is included: \n", "\n", "* Parameter Retrieval: `params`, `scale`, `shape`, `dof`\n", "* Standard Statistics: `mean`, `median`, `std`, `skewness`, `kurtosis`, `entropy`, `mgf`\n", "* Probability Evaluation: `insupport`, `pdf`, `cdf`, `likelihood`, `quantile`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Below we create two distributions that we will play with" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Distributions.TDist(ν=5.0)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nrv = Normal(0.0, 1.0)\n", "tdist = TDist(5)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Evaluating Statistics" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normal and T mean are (0.0,0.0)\n", "Normal and T median are (0.0,0.0)\n", "Normal and T std are (1.0,1.2909944487358056)\n", "Normal and T skewness are (0.0,0.0)\n", "Normal and T kurtosis are (0.0,6.0)\n", "Normal and T entropy are (1.4189385332046727,1.6275026724143997)\n" ] } ], "source": [ "for f in (:mean, :median, :std, :skewness, :kurtosis, :entropy)\n", " ftup = (eval(f)(nrv), eval(f)(tdist))\n", " println(\"Normal and T \", f, \" are \", ftup)\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Evaluating Probabilities" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normal and T pdf are (0.38666811680284924,0.36572004265594327)\n", "Normal and T logpdf are (-0.9501885332046728,-1.0058871490503956)\n", "Normal and T cdf are (0.5987063256829237,0.5937329346279383)\n", "Normal and T logcdf are (-0.5129840754094305,-0.521325665725598)\n", "Normal and T quantile are (-0.6744897501960817,-0.7266868438004229)\n" ] } ], "source": [ "for f in (:pdf, :logpdf, :cdf, :logcdf, :quantile)\n", " ftup = (eval(f)(nrv, 0.25), eval(f)(tdist, 0.25))\n", " println(\"Normal and T \", f, \" are \", ftup)\n", "end" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## PlotlyJS.jl\n", "\n", "Many usable plotting packages in Julia, but no standard package (yet).\n", "\n", "One that seems promising (and feels natural) in Julia is `PlotlyJS.jl`.\n", "\n", "> Disclaimer: Spencer is involved in developing PlotlyJS.jl -- Chase is writing this so don't worry, this is an unbiased opinion." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Flexibility\n", "\n", "We will only cover a fraction of what this library can do.\n", "\n", "For more information see: [The examples page](https://github.com/spencerlyon2/PlotlyJS.jl/tree/master/examples) and the [plot attribute page](https://plot.ly/javascript/reference/#)\n", "\n", "Other good plotting options include: `PyPlot.jl`, `Gadfly`, and `Plots.jl`" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ " \n", " \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

Plotly javascript loaded.

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "using PlotlyJS" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Making a simple plot\n", "\n", "We will first make a line plot because that will be required for your homework\n", "\n", "In our next slide we will create a short function which takes a distribution and plots its pdf." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "plot_distribution (generic function with 1 method)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function plot_distribution(d::Distribution)\n", " p_001, p_999 = quantile(d, 1e-3), quantile(d, 1-1e-3)\n", " x = collect(linspace(p_001, p_999, 100))\n", " y = pdf(d, x)\n", " t1 = scatter(;x=x, y=y, showlegend=false)\n", " \n", " return t1\n", "end" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(plot_distribution(Normal(0, 1)))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Making a Histogram\n", "\n", "`PlotlyJS` also supports making histograms.\n", "\n", "Below we will create a short function which takes a distribution and plots a histogram of random draws." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "hist_distribution (generic function with 2 methods)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function hist_distribution(d::Distribution, N=10_000)\n", " y = rand(d, N)\n", "\n", " t2 = histogram(;x=y, histnorm=\"probability density\",\n", " showlegend=false, nbinsx=250, opacity=0.6)\n", " return t2\n", "end" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot(hist_distribution(Normal(0, 1)))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Setting Plot Attributes\n", "\n", "We often want to add titles, labels, or other information to a plot.\n", "\n", "Here it makes sense to mention that `PlotlyJS` constructs figures in two parts.\n", "\n", "* `trace`s: Stores plot data and how it should be displayed\n", "* `Layout`: Figure wide settings\n", "\n", "Let's write another function that combines two traces from the previous functions and adds layout information." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "full_plot_distribution (generic function with 2 methods)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function full_plot_distribution(d::Distribution, N=10000;\n", " xlim=(quantile(d, 1e-3), quantile(d, 1-1e-3)))\n", " # Create multiple traces which will go on plot\n", " t1 = plot_distribution(d)\n", " t2 = hist_distribution(d, N)\n", "\n", " # Create layout\n", " l = Layout(;title=\"$(typeof(d))\", \n", " xaxis_range=xlim, xaxis_title=\"x\",\n", " yaxis_title=\"Probability Density of x\",\n", " xaxis_showgrid=true, yaxis_showgrid=true,\n", " legend_y=1.15, legend_x=0.7)\n", " \n", " return plot([t1, t2], l)\n", "end" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "full_plot_distribution(Normal(0, 1))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Subplots\n", "\n", "Combine plots in the same way you would build an array." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p1 = full_plot_distribution(Normal(0, 1), xlim=(-3, 3))\n", "p2 = full_plot_distribution(TDist(5), xlim=(-3, 3))\n", "[p1 p2]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## Interpolations\n", "\n", "It is important to interpolate. `Interpolations.jl` is an _extremely_ fast interpolation package that is based around using splines.\n", "\n", "Have a look at their [benchmarks](https://github.com/tlycken/Interpolations.jl)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "using Interpolations" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Create Interpolator\n", "\n", "There are multiple types of interpolators. We will focus on `BSplines()`.\n", "\n", "See the [docs](https://github.com/tlycken/Interpolations.jl#general-usage) for information on the other types." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "### Create Interpolator\n", "\n", "Interpolators by default are only defined on `[1, Npts]`\n", "\n", "`BSpline(Linear())` specifies the type of interpolation you want\n", "\n", "`OnGrid()` specifies where the points lie" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The max absolute difference is: 0.0\n" ] } ], "source": [ "x = linspace(-1.0, 1.0, 50)\n", "y = sin(collect(x))\n", "itp = interpolate(y, BSpline(Linear()), OnGrid())\n", "diff = maxabs([itp[i] for i in 1:50] - y)\n", "println(\"The max absolute difference is: \", diff)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Change Interpolator Scale\n", "\n", "Since interpolators are defined by default on `[1, Npts]` we need to change it to our domain\n", "\n", "We will use the `scale` function to do that." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The max absolute difference is: 0.0\n" ] } ], "source": [ "itp_scaled = scale(itp, x)\n", "diff_scaled = maxabs([itp_scaled[el] for el in x] - y)\n", "println(\"The max absolute difference is: \", diff_scaled)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Evaluate Derivatives\n", "\n", "We can evaluate the derivatives of splines" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1-element Array{Float64,1}:\n", " 0.999931" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gradient(itp_scaled, 0.0)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "### Interpolations.jl Speed\n", "\n", "Evaluate a linear spline on 1,000,000 points\n", "\n", "* `scipy.InterpolatedUnivariateSpline` : 12.4 ms\n", "* `Interpolations.jl` : 1.2 $\\mu s$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Other Recommended Packages\n", "\n", "Here are some other recommended packages\n", "\n", "Some are useful, others fun\n", "\n", "They appear in no particular order\n", "\n", "
    \n", "
  • [DataFrames.jl](https://github.com/JuliaStats/DataFrames.jl)
    • \n", "
  • [NLopt.jl](https://github.com/JuliaOpt/NLopt.jl)
    • \n", "
  • [NLsolve.jl](https://github.com/EconForge/NLsolve.jl)
    • \n", "
  • [Optim.jl](https://github.com/JuliaOpt/Optim.jl)
    • \n", "
  • [HDF5.jl](https://github.com/JuliaLang/HDF5.jl)
    • \n", "
  • [JLD.jl](https://github.com/JuliaLang/JLD.jl)
    • \n", "
  • [QuantEcon.jl](https://github.com/QuantEcon/QuantEcon.jl)
    • \n", "
  • [Gadfly.jl](https://github.com/dcjones/Gadfly.jl)
    • \n", "
  • [PyPlot.jl](https://github.com/stevengj/PyPlot.jl)
    • \n", "
  • [Distances.jl](https://github.com/JuliaStats/Distances.jl)
    • \n", "
  • [IJulia.jl](https://github.com/JuliaLang/IJulia.jl)
    • \n", "
  • [Interact.jl](https://github.com/JuliaLang/Interact.jl)
    • \n", "
  • [DistributedArrays.jl](https://github.com/JuliaParallel/DistributedArrays.jl)
    • \n", "
" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Julia 0.4.3-pre", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.3" }, "livereveal": { "scroll": true, "start_slideshow_at": "selected", "theme": "white", "transition": "fade" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture6/ParallelJulia.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Parallel programming in Julia\n", "\n", "**Chase Coleman & Spencer Lyon**\n", "\n", "3-4-16" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Basics\n", "\n", "Julia has built in support for parallel programming\n", "\n", "To add more computing processes use the `addprocs` function" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "4-element Array{Int64,1}:\n", " 2\n", " 3\n", " 4\n", " 5" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "addprocs(4)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Each process has a unique `id` (integer)\n", "\n", "You can see that we added processes with id from 2 to 5" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "You can also add processes on remote machines.\n", "\n", "See the docstring for `addprocs` for more info" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "To get the number of active processes use the `nprocs` function " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nprocs()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "When you have `n` active processes, typically `n-1` will be used for computation\n", "\n", "The first process (with id 1) is used to direct the computation\n", "\n", "Other processes are called workers:\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "4-element Array{Int64,1}:\n", " 2\n", " 3\n", " 4\n", " 5" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "workers()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## `pmap`\n", "\n", "\n", "One of the easiest ways to get started parallel programming in Julia is the `pmap` function\n", "\n", "In its simplest form `pmap` takes two arguments: a function and a collection (array or tuple)\n", "\n", "The function is applied in parallel to each item in the collection" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.201885 seconds (98 allocations: 21.513 MB, 1.02% gc time)\n" ] } ], "source": [ "# TODO find a more compelling econ example\n", "\n", "args = (rand(200, 200), rand(400, 400), rand(200, 200), rand(400, 400))\n", "\n", "# first a serial version\n", "@time for X in args\n", " svd(X)\n", "end" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.105647 seconds (1.28 k allocations: 6.189 MB)\n" ] } ], "source": [ "# now in parallel\n", "@time pmap(svd, args);" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We have 4 workers and had 4 arrays, why didn't get get a 4x speedup?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Notice that 2 arrays were `200x200` and two were `400x400` (computational load is _unbalanced_)\n", "\n", "Julia gave each worker one array, but some had less work than others so they finished first" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This means some processes were inactive during the total computation time\n", "\n", "Also, there is (small) overhead in passing data to the worker and passing the result back to process 1" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## `@parallel` loops\n", "\n", "Julia also has the ability to make a for loop run in parallel\n", "\n", "To do this use the `@parallel` macro" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "> There are subtleties to using `@parallel` \n", ">\n", "> We will cover only basic examples here\n", ">\n", "> Consult the [documentation](http://docs.julialang.org/en/latest/manual/parallel-computing/#parallel-map-and-loops) for more information" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "There are two possible syntaxes for `@parallel`:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The first is\n", "\n", "```julia\n", "@parallel for ...\n", "end\n", "```\n", "\n", "and simply executes the body of the for loop in parallel" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The second is\n", "\n", "```julia\n", "@parallel (f) for ...\n", "end\n", "```\n", "\n", "It the same, but also applies a reduction function `f` to the result of each iteration\n", "\n", "The result of each iteration is the result of the last statement in the loop's body\n", "\n", "The function `f` should take two elements and output one" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Let's see this in practice" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\tFrom worker 3:\trandn() = -1.4823226833572487\n", "\tFrom worker 3:\trandn() = 0.008485042894568029\n", "\tFrom worker 2:\trandn() = -1.4372949820146432\n", "\tFrom worker 2:\trandn() = 0.14678523736405727\n", "\tFrom worker 2:\trandn() = -1.293961883026427\n", "\tFrom worker 5:\trandn() = -0.39493431470157886\n", "\tFrom worker 5:\trandn() = 0.2494670221840125\n", "\tFrom worker 4:\trandn() = -0.6202425665423612\n", "\tFrom worker 4:\trandn() = -1.853569956218026\n", "\tFrom worker 3:\trandn() = 1.5581361796835485\n" ] } ], "source": [ "@parallel for i = 1:10\n", " @show randn()\n", "end;" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\tFrom worker 2:\trandn() = -1.1271781195591901\n", "\tFrom worker 2:\trandn() = 0.9025559215992444\n", "\tFrom worker 3:\trandn() = -0.912358216201542\n", "\tFrom worker 2:\trandn() = 0.009225014074008112\n", "\tFrom worker 3:\trandn() = 0.8497725800879736\n", "\tFrom worker 3:\trandn() = -1.005123841699878\n", "\tFrom worker 5:\trandn() = -0.3685509736298961\n", "\tFrom worker 5:\trandn() = -1.637375058113114\n", "\tFrom worker 4:\trandn() = -1.8505401231390373\n", "\tFrom worker 4:\trandn() = -0.7754764372191437\n", "-5.915049253800575\n" ] } ], "source": [ "# now with a (+)\n", "total = @parallel (+) for i=1:10\n", " @show randn()\n", "end\n", "println(total)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "One issue with `@parallel` is that all variables used in the loop are copied to the working process, but not copied back to process 1\n", "\n", "That means code like this will not work as you might expect:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "10-element Array{Float64,1}:\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = zeros(10)\n", "@parallel for i=1:10\n", " a[i] = i\n", "end\n", "a" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "For that to work we need an array that provides shared memory access across processes..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## `SharedArray`\n", "\n", "A `SharedArray` is an array whose memory can be shared across all processes on the same machine" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This comes with a number of benefits over `Array` for parallel computing.\n", "\n", "Some of them are:\n", "\n", "- Save on the overhead of passing arrays to worker processes\n", "- Update arrays in a predictable way from `@parallel` loops" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Let's see an example" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0,0,0,0,0,0,0,0,0,0,0]\n" ] } ], "source": [ "a = SharedArray(Int, 1000)\n", "@parallel for i in eachindex(a)\n", " a[i] = i\n", "end\n", "println(a[end-10:end])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "I know what you're thinking \"wait, you told me that this example should work\"" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Well it did..." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[990,991,992,993,994,995,996,997,998,999,1000]\n" ] } ], "source": [ "println(a[end-10:end])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "... but there's a caveat" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "An `@parallel` loop with a `SharedArray` will run _asynchronously_\n", "\n", "This means the instructions will be sent to the workers, and then the main process will just continue without waiting for workers to finish" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "To fix this problem we need to tell Julia to `@sync` all computations that happen in the loop:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[990,991,992,993,994,995,996,997,998,999,1000]\n" ] } ], "source": [ "b = SharedArray(Int, 1000)\n", "@sync @parallel for i in eachindex(b)\n", " b[i] = i\n", "end\n", "println(b[end-10:end])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Because `SharedArray` data is available to multiple processes, you need to be careful about how and when it is accessed\n", "\n", "For more details see the [`SharedArray` docs](http://docs.julialang.org/en/latest/manual/parallel-computing/#id2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## More info\n", "\n", "We've only scratched the surface of Julia's parallel computing capabilities. \n", "\n", "For more information see these references:\n", "\n", "- The offical [documentation](http://docs.julialang.org/en/latest/manual/parallel-computing/) on parallel computing\n", "- [Parallel Julia](https://github.com/JuliaParallel) github organization\n", "- Other packages:\n", " - [MPI.jl](https://github.com/JuliaParallel/MPI.jl)\n", " - [ParallelAccelerator.jl](https://github.com/IntelLabs/ParallelAccelerator.jl)\n", " - [DistributedArrays.jl](https://github.com/JuliaParallel/DistributedArrays.jl)" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Julia 0.4.3-pre", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.3" }, "livereveal": { "scroll": true, "start_slideshow_at": "selected", "theme": "white", "transition": "fade" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture6/install_julia ================================================ #!/usr/bin/env sh wget -O julia_binary.tar.gz https://julialang.s3.amazonaws.com/bin/linux/x64/0.4/julia-0.4-latest-linux-x86_64.tar.gz mkdir -p $HOME/src rm -rf $HOME/src/julia* mkdir -p $HOME/src/julia tar -C $HOME/src/julia -zxf julia_binary.tar.gz --strip-components=1 rm julia_binary.tar.gz # prepend julia to path echo "Adding julia to path" echo "export PATH=$HOME/src/julia/bin:$PATH" >> $HOME/.bashrc ================================================ FILE: lecture7/Intro_to_pymc.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "-------------------\n", "# Intorduction to `PyMC2`\n", "\n", "#### Balint Szoke\n", "-----------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Installation:\n", "\n", "`>> conda install pymc`" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import scipy as sp \n", "import pymc as pm\n", "import seaborn as sb\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probabilistic model\n", "\n", "Suppose you have a sample $\\{y_t\\}_{t=0}^{T}$ and want to characeterize it by the following probabilistic model; for $t\\geq 0$ \n", "\n", "$$ y_{t+1} = \\rho y_t + \\sigma_x \\varepsilon_{t+1}, \\quad \\varepsilon_{t+1}\\stackrel{iid}{\\sim}\\cal{N}(0,1) $$\n", "\n", "with the initial value $y_0 \\sim {\\cal N}\\left(0, \\frac{\\sigma_x^2}{1-\\rho^2}\\right)$ and suppose the following (independent) prior beliefs for the parameters $\\theta \\equiv (\\rho, \\sigma_x)$\n", " - $\\rho \\sim \\text{U}(-1, 1)$\n", " - $\\sigma_x \\sim \\text{IG}(a, b)$\n", " \n", " \n", "**Aim:** given the statistical model and the prior $\\pi(\\theta)$ we want to ''compute'' the posterior distribution $p\\left( \\theta \\hspace{1mm} | \\hspace{1mm} y^T \\right)$ associated with the sample $y^T$.\n", "\n", "**How:** if no conjugate form available, sample from $p\\left( \\theta \\hspace{1mm} | \\hspace{1mm} y^T \\right)$ and learn about the posterior's properties from that sample \n", "\n", "> **Remark:** We go from the prior $\\pi$ to the posterior $p$ by using Bayes rule:\n", "\\begin{equation}\n", "p\\left( \\theta \\hspace{1mm} | \\hspace{1mm} y^T \\right) = \\frac{f( y^T \\hspace{1mm}| \\hspace{1mm}\\theta) \\pi(\\theta) }{f( y^T)}\n", "\\end{equation}\n", "The first-order autoregression implies that the likelihood function of $y^T$ can be factored as follows:\n", "\n", ">$$ f(y^T \\hspace{1mm}|\\hspace{1mm} \\theta) = f(y_T| y_{T-1}; \\theta)\\cdot f(y_{T-1}| y_{T-2}; \\theta) \\cdots f(y_1 | y_0;\\theta )\\cdot f(y_0 |\\theta) $$\n", "where for all $t\\geq 1$\n", "$$ f(y_t | y_{t-1}; \\theta) = {\\mathcal N}(\\rho y_{t-1}, \\sigma_x^2) = {\\mathcal N}(\\mu_t, \\sigma_x^2)$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate a sample with $T=100$ for known parameter values:\n", "$$\\rho = 0.5\\quad \\sigma_x = 1.0$$" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def sample_path(rho, sigma, T, y0=None):\n", " '''\n", " Simulates the sample path for y of length T+1 starting from a specified initial value OR if y0 \n", " is None, it initializes the path with a draw from the stationary distribution of y. \n", " \n", " Arguments\n", " -----------------\n", " rho (Float) : AR coefficient \n", " sigma (Float) : standard deviation of the error\n", " T (Int) : length of the sample path without x0\n", " y0 (Float) : initial value of X\n", " \n", " Return:\n", " -----------------\n", " y_path (Numpy Array) : simulated path\n", " \n", " '''\n", " if y0 == None:\n", " stdev_erg = sigma / np.sqrt(1 - rho**2) \n", " y0 = np.random.normal(0, stdev_erg)\n", " \n", " y_path = np.empty(T+1)\n", " y_path[0] = y0\n", " eps_path = np.random.normal(0, 1, T)\n", " \n", " for t in range(T):\n", " y_path[t + 1] = rho * y_path[t] + sigma * eps_path[t]\n", " \n", " return y_path \n", "\n", "#-------------------------------------------------------\n", "\n", "# Pick true values:\n", "rho_true, sigma_x_true, T = 0.5, 1.0, 20\n", "\n", "#np.random.seed(1453534)\n", "sample = sample_path(rho_true, sigma_x_true, T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Probabilistic models in `pymc`\n", "\n", "*Model instance* $\\approx$ collection of random variables linked together according to some rules\n", "\n", "### Linkages (hierarchical structure):\n", " * **parent**: variables that influence another variable\n", " - e.g. $\\rho$ and $\\sigma_x$ are parents of $y_0$, $a$ and $b$ are parents of $sigma_x$\n", " \n", " * **child**: variables that are affected by other variables (subjects of parent variables)\n", " - e.g. $y_t$ is a child of $y_{t-1}$, $\\rho$ and $\\sigma_x$\n", "\n", ">*Why are they useful?*\n", "\n", "> child variable's current value automatically changes whenever its parents' values change\n", "\n", "### Random variables: \n", "- have a `value` attribute producing the current internal value (given the values of the parents)\n", " - computed on-demand and cached for efficiency.\n", "- other important attributes: `parents` (gives dictionary), `children` (gives a set) \n", "\n", "Two main classes of random variables in `pymc`:\n", "\n", "#### 1) Stochastic variable:\n", "- variable whose value is not completely determined by its parents\n", "- *Examples:*\n", " * parameters with a given distribution \n", " * observable variables (data) = particular realizations of a random variable (see below) \n", "- treated by the back end as random number generators (see built-in `random()` method)\n", "- `logp` attribute: evaluate the logprob (mass or density) at the current value; for vector-valued variables it returns the sum of the (joint) logprob\n", "- *Initialization:*\n", " * define the distribution (built-in or your own) with `name` + params of the distribution (can be `pymc` variable)\n", " * optional flags: \n", " - `value`: for a default initial value; if not specified, initialized by a draw from the given distribution \n", " - `size`: for multivariate array of independent stochastic variables. (Alternatively: use array as a distribution parameter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initialize `stochastic variables`" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Priors:\n", "rho = pm.Uniform('rho', lower = -1, upper = 1) # note the capitalized distribution name (rule for pymc distributions) \n", "sigma_x = pm.InverseGamma('sigma_x', alpha = 3, beta = 1)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialization:\n", "Current value of rho = -0.615898\n", "Current logprob of rho = -0.693147\n", "\n", "After redrawing:\n", "Current value of rho = 0.630793\n", "Current logprob of rho = -0.693147\n" ] } ], "source": [ "# random() method\n", "print('Initialization:')\n", "print(\"Current value of rho = {: f}\".format(rho.value.reshape(1,)[0]))\n", "print(\"Current logprob of rho = {: f}\".format(rho.logp))\n", "\n", "rho.random()\n", "print('\\nAfter redrawing:')\n", "print(\"Current value of rho = {: f}\".format(rho.value.reshape(1,)[0]))\n", "print(\"Current logprob of rho = {: f}\".format(rho.logp))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "------------------\n", "#### 2) Determinsitic variable:\n", "- variable that is entirely determined by its parents \n", "- ''exact functions'' of stochastic variables, however, we can treat them as a variable and not a Python function.\n", "- *Examples:*\n", " * model implied restrictions on how the parameters and the observable variables are related\n", " - $\\text{var}(y_0)$ is a function of $\\rho$ and $\\sigma_x$\n", " - $\\mu_{t}$ is an exact function of $\\rho$ and $y_{t-1}$\n", " * sample statistics, i.e. deterministic functions of the sample\n", "- *Initialization:*\n", " * decorator form: \n", " - Python function of stochastic variables AND default values + the decorator `pm.deterministic` \n", " * elementary operations (no need to write a function or decorate): $+$, $-$, $*$, $/$ \n", " * `pymc.Lambda`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initialize `deterministic variables`:\n", "\n", "(a) Standard deviation of $y_0$ is a deterministic function of $\\rho$ and $\\sigma$" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@pm.deterministic(trace = False)\n", "def y0_stdev(rho = rho, sigma = sigma_x):\n", " return sigma / np.sqrt(1 - rho**2)\n", "\n", "# Alternatively:\n", "#y0_stdev = pm.Lambda('y0_stdev', lambda r = rho, s = sigma_x: s / np.sqrt(1 - r**2) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(b) Conditional mean of $y_t$, $\\mu_y$, is a deterministic function of $\\rho$ and $y_{t-1}$" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# For elementary operators simply write\n", "mu_y = rho * sample[:-1]\n", "print(type(mu_y))\n", "\n", "# You could also write, to generate a list of Determinisitc functions\n", "#MU_y = [rho * sample[j] for j in range(T)] \n", "#print(type(MU_y))\n", "#print(type(MU_y[1]))\n", "#MU_y = pm.Container(MU_y)\n", "#print(type(MU_y))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see the parents of `y0_stdev`..." ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'rho': .new_class 'rho' at 0x7f75561a3048>,\n", " 'sigma': .new_class 'sigma_x' at 0x7f75561a39b0>}" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y0_stdev.parents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that this is a dictionary, so for example..." ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(0.6307927826852653)" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y0_stdev.parents['rho'].value" ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(-0.8067244934281792)" ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rho.random()\n", "y0_stdev.parents['rho'].value # if the parent is a pymc variable, the current value will be always 'updated'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and as we alter the parent's value, the child's value changes accordingly" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Current value of y0_stdev = 0.951137\n", "\n", "After redrawing rho:\n", "Current value of y0_stdev = 0.564751\n" ] } ], "source": [ "print(\"Current value of y0_stdev = {: f}\".format(y0_stdev.value))\n", "\n", "rho.random()\n", "print('\\nAfter redrawing rho:')\n", "print(\"Current value of y0_stdev = {: f}\".format(y0_stdev.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and similarly for `mu_y`" ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Current value of mu_y:\n", "[ 0.04004946 0.04786201 0.02833744 0.1285417 ]\n", "\n", "After redrawing rho:\n", "Current value of mu_y:\n", "[ 0.38506324 0.4601785 0.27245577 1.23588879]\n" ] } ], "source": [ "print(\"Current value of mu_y:\")\n", "print(mu_y.value[:4])\n", "rho.random()\n", "print('\\nAfter redrawing rho:')\n", "print(\"Current value of mu_y:\")\n", "print(mu_y.value[:4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How to tell `pymc` what you 'know' about the data? \n", "\n", "We define the data as a stochastic variable with fixed values and set the `observed` flag equal to `True`\n", "\n", "For the sample $y^T$, depending on the question at hand, we might want to define \n", " - either $T + 1$ scalar random variables \n", " - or a scalar $y_0$ and a $T$-vector valued $Y$ \n", "\n", "In the current setup, as we fix the value of $y$ (observed), it doesn't really matter (approach A is easier). However, if we have an array-valued stochastic variable with mutable value, the restriction that we cannot update the values of stochastic variables' in-place becomes onerous in the sampling step (where the step method should propose array-valued variable). Straight from the pymc documentation: \n", ">''In this case, it may be preferable to partition the variable into several scalar-valued variables stored in an array or list.''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (A) $y_0$ as a scalar and $Y$ as a vector valued random variable" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y0 = pm.Normal('y0', mu = 0.0, tau = 1 / y0_stdev, observed = True, value = sample[0])\n", "Y = pm.Normal('Y', mu = mu_y, tau = 1 / sigma_x, observed=True, value = sample[1:])" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-0.49029625, -0.29028745, -1.31677523, -0.2966883 , -0.5337306 ,\n", " -3.36540298, -2.02043236, -1.0834821 , 0.01594337, -0.57355078,\n", " 0.92050047, -0.09611809, -0.21923504, 0.80092806, 0.0518965 ,\n", " 0.60521111, 2.05571842, 0.5691191 , 0.84095268, 0.4107615 ])" ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y.value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the value of this variable is fixed (even if the parent's value changes)" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.7791903326271949" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y.parents['tau'].value" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.20473853378\n" ] }, { "data": { "text/plain": [ "array([-0.49029625, -0.29028745, -1.31677523, -0.2966883 , -0.5337306 ,\n", " -3.36540298, -2.02043236, -1.0834821 , 0.01594337, -0.57355078,\n", " 0.92050047, -0.09611809, -0.21923504, 0.80092806, 0.0518965 ,\n", " 0.60521111, 2.05571842, 0.5691191 , 0.84095268, 0.4107615 ])" ] }, "execution_count": 156, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sigma_x.random()\n", "print(Y.parents['tau'].value)\n", "Y.value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### (B) $T+1$ scalar random variables\n", "\n", "Define an array with `dtype=object`, fill it with scalar variables (use loops) and define it as a `pymc.Container` (this latter step is not necessary, but based on my experience Container types work much more smoothly in the blocking step when we are sampling)." ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "array([ .new_class 'y0' at 0x7f755613bf60>,\n", " .new_class 'y_1' at 0x7f7556220c50>,\n", " .new_class 'y_2' at 0x7f75562200f0>,\n", " .new_class 'y_3' at 0x7f7556220160>,\n", " .new_class 'y_4' at 0x7f7556220b70>,\n", " .new_class 'y_5' at 0x7f7556220b38>,\n", " .new_class 'y_6' at 0x7f75562204a8>,\n", " .new_class 'y_7' at 0x7f7556220390>,\n", " .new_class 'y_8' at 0x7f75562202b0>,\n", " .new_class 'y_9' at 0x7f75562207f0>,\n", " .new_class 'y_10' at 0x7f75560d4d68>,\n", " .new_class 'y_11' at 0x7f7556080400>,\n", " .new_class 'y_12' at 0x7f75560807f0>,\n", " .new_class 'y_13' at 0x7f75561a32e8>,\n", " .new_class 'y_14' at 0x7f75561a3780>,\n", " .new_class 'y_15' at 0x7f75561a3f98>,\n", " .new_class 'y_16' at 0x7f75561a37b8>,\n", " .new_class 'y_17' at 0x7f75561a3ef0>,\n", " .new_class 'y_18' at 0x7f75561a30b8>,\n", " .new_class 'y_19' at 0x7f75561a3fd0>,\n", " .new_class 'y_20' at 0x7f75561a3208>], dtype=object)" ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y_alt = np.empty(T + 1, dtype = object)\n", "Y_alt[0] = y0 # definition of y0 is the same as above\n", "\n", "for i in range(1, T + 1):\n", " Y_alt[i] = pm.Normal('y_{:d}'.format(i), mu = mu_y[i-1], tau = 1 / sigma_x)\n", "\n", "print(type(Y_alt))\n", "Y_alt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Currently, this is just a numpy array of `pymc.Deterministic` functions. We can make it a `pymc` object by using the `pymc.Container` type." ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pymc.Container.ArrayContainer" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y_alt = pm.Container(Y_alt)\n", "type(Y_alt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the pymc methods are applied element-wise. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a `pymc.Model` instance\n", "Remember that it is just a collection of random variables (`Stochastic` and `Deterministic`), hence" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ar1_model = pm.Model([rho, sigma_x, y0, Y, y0_stdev, mu_y])" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{.new_class 'rho' at 0x7f75561a3048>,\n", " .new_class 'sigma_x' at 0x7f75561a39b0>}" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar1_model.stochastics # notice that this is an unordered set (!)" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{,\n", " }" ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar1_model.deterministics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This object have very limited awareness of the structure of the probabilistic model that it describes and does not itslef possess methods for updating the values in the sampling methods." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----------------\n", "# Fitting the model to the data (MCMC algorithm)\n", "\n", "### MCMC algorithms\n", "\n", "The joint prior distribution is sitting on an $N$-dimensional space, where $N$ is the number of parameters we are about to make inference on (see the figure below). Looking at the data through the probabilistic model deform the prior surface into the posterior surface, that we need to explore. In principle, we could naively search this space by picking random points in $\\mathbb{R}^N$ and calculate the corresponding posterior value (Monte Carlo methods), but a more efficient (especially in higher dimensions) way is to do Markov Chain Monte Carlo (MCMC), which is basically an intelligent way of discovering the posterior surface. \n", "\n", "MCMC is an iterative procedure: at every iteration, it proposes a nearby point in the space, then ask 'how likely that this point is close to the maximizer of the posterior surface?', it accepts the proposed point if the likelihood exceeds a particular level and rejects it otherwise (by going back to the old position). The key feature of MCMC is that it produces proposals by simulating a Markov chain for which the posterior is the unique, invariant limiting distribution. In other words, after a possible 'trasition period' (i.e. post converegence), it starts producing draws from the posterior.\n", "\n", "\n", "### MCMC algorithm in `pymc`\n", "\n", "By default it uses the *Metropolis-within-Gibbs* algorithm (in my oppinion), which is based on two simple principles:\n", "1. **Blocking and conditioning:**\n", " - Divide the $N$ variables of $\\theta$ into $K\\leq N$ blocks and update every block by sampling from the conditional density, i.e. from the distribuition of the block parameters conditioned on all parameters in the other $K-1$ blocks being at their current values.\n", " * At scan $t$, cycle through the $K$ blocks\n", " $$\\theta^{(t)} = [\\theta^{(t)}_1, \\theta^{(t)}_2, \\theta^{(t)}_3, \\dots, \\theta^{(t)}_K] $$\n", " * Sample from the conditionals\n", " \\begin{align}\n", " \\theta_1^{(t+1)} &\\sim f(\\theta_1\\hspace{1mm} | \\hspace{1mm} \\theta^{(t)}_2, \\theta^{(t)}_3, \\dots, \\theta^{(t)}_K; \\text{data}) \\\\\n", " \\theta_2^{(t+1)} &\\sim f(\\theta_2\\hspace{1mm} | \\hspace{1mm} \\theta^{(t+1)}_1, \\theta^{(t)}_3, \\dots, \\theta^{(t)}_K; \\text{data}) \\\\\n", " \\theta_3^{(t+1)} &\\sim f(\\theta_3\\hspace{1mm} | \\hspace{1mm} \\theta^{(t+1)}_1, \\theta^{(t+1)}_2, \\dots, \\theta^{(t)}_K; \\text{data}) \\\\\n", " \\dots & \\\\\n", " \\theta_K^{(t+1)} &\\sim f(\\theta_3\\hspace{1mm} | \\hspace{1mm} \\theta^{(t+1)}_1, \\theta^{(t+1)}_2, \\dots, \\theta^{(t+1)}_{K-1}; \\text{data})\n", " \\end{align}\n", "\n", "2. **Sampling (choose/construct `pymc.StepMethod`):** if for a given block the conditional density $f$ can be expressed in (semi-)analytic form, use it, if not, use Metrololis-Hastings\n", " \n", " * Semi-closed form example: Foreward-backward sampler (Carter and Kohn, 1994): \n", " * Metropolis(-Hastings) algorithm:\n", " 1. Start at $\\theta$\n", " 2. Propose a new point in the parameterspace according to some proposal density $J(\\theta' | \\theta)$ (e.g. random walk)\n", " 3. Accept the proposed point with probability\n", " $$\\alpha = \\min\\left( 1, \\frac{p(\\theta'\\hspace{1mm} |\\hspace{1mm} \\text{data})\\hspace{1mm} J(\\theta \\hspace{1mm}|\\hspace{1mm} \\theta')}{ p(\\theta\\hspace{1mm} |\\hspace{1mm} \\text{data})\\hspace{1mm} J(\\theta' \\hspace{1mm}| \\hspace{1mm}\\theta)} \\right) $$\n", " - If accept: Move to the proposed point $\\theta'$ and return to Step 1.\n", " - If reject: Don't move, keep the point $\\theta$ and return to Step 1.\n", " 4. After a large number of iterations (once the Markov Chain convereged), return all accepted $\\theta$ as a sample from the posterior\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, a `pymc.Model` instance is not much more than a collection, for example, the model variables (blocks) are not matched with step methods determining how to update values in the sampling step. In order to do that, first we need to construct an MCMC instance, which is then ready to be sampled from. \n", "\n", "MCMC‘s primary job is to create and coordinate a collection of **step methods**, each of which is responsible for updating one or more variables (blocks) at each step of the MCMC algorithm. By default, step methods are automatically assigned to variables by PyMC (after we call the sample method).\n", "\n", "#### Main built-in `pymc.StepMethod`s \n", " * Metropolis\n", " * AdaptiveMetropolis\n", " * Slicer\n", " * Gibbs\n", "\n", "you can assign step methods manually by calling the method `use_step_method(method, *args, **kwargs)`: " ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": true }, "outputs": [], "source": [ "M = pm.MCMC(ar1_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the step_methods are not assigned yet" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{.new_class 'y_12' at 0x7f75560807f0>: [],\n", " .new_class 'y_19' at 0x7f75561a3fd0>: [],\n", " .new_class 'y_16' at 0x7f75561a37b8>: [],\n", " .new_class 'y_15' at 0x7f75561a3f98>: [],\n", " .new_class 'y_14' at 0x7f75561a3780>: [],\n", " .new_class 'y_4' at 0x7f7556220b70>: [],\n", " .new_class 'y_5' at 0x7f7556220b38>: [],\n", " .new_class 'y_17' at 0x7f75561a3ef0>: [],\n", " .new_class 'y_13' at 0x7f75561a32e8>: [],\n", " .new_class 'y_8' at 0x7f75562202b0>: [],\n", " .new_class 'y_9' at 0x7f75562207f0>: [],\n", " .new_class 'y_20' at 0x7f75561a3208>: [],\n", " .new_class 'sigma_x' at 0x7f75561a39b0>: [],\n", " .new_class 'y_3' at 0x7f7556220160>: [],\n", " .new_class 'y_7' at 0x7f7556220390>: [],\n", " .new_class 'y_10' at 0x7f75560d4d68>: [],\n", " .new_class 'y_2' at 0x7f75562200f0>: [],\n", " .new_class 'y_18' at 0x7f75561a30b8>: [],\n", " .new_class 'y_6' at 0x7f75562204a8>: [],\n", " .new_class 'y_1' at 0x7f7556220c50>: [],\n", " .new_class 'rho' at 0x7f75561a3048>: [],\n", " .new_class 'y_11' at 0x7f7556080400>: []}" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M.step_method_dict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can specify them now, or if you call the `sample` method, pymc will assign the step_methods automatically according to some rule" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [-----------------100%-----------------] 50000 of 50000 complete in 35.7 sec" ] } ], "source": [ "# draw a sample of size 20,000, drop the first 1,000 and keep only every 5th draw \n", "M.sample(iter = 50000, burn = 1000, thin = 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... and you can check what kind of step methods have been assigned (the default in most cases is the Metropolis step method for non-observed stochastic variables, while in case of observed stochastics, we simply draw from the prior)" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{.new_class 'y_12' at 0x7f75560807f0>: [],\n", " .new_class 'y_19' at 0x7f75561a3fd0>: [],\n", " .new_class 'y_16' at 0x7f75561a37b8>: [],\n", " .new_class 'y_15' at 0x7f75561a3f98>: [],\n", " .new_class 'y_14' at 0x7f75561a3780>: [],\n", " .new_class 'y_4' at 0x7f7556220b70>: [],\n", " .new_class 'y_5' at 0x7f7556220b38>: [],\n", " .new_class 'y_17' at 0x7f75561a3ef0>: [],\n", " .new_class 'y_13' at 0x7f75561a32e8>: [],\n", " .new_class 'y_8' at 0x7f75562202b0>: [],\n", " .new_class 'y_9' at 0x7f75562207f0>: [],\n", " .new_class 'y_20' at 0x7f75561a3208>: [],\n", " .new_class 'sigma_x' at 0x7f75561a39b0>: [],\n", " .new_class 'y_3' at 0x7f7556220160>: [],\n", " .new_class 'y_7' at 0x7f7556220390>: [],\n", " .new_class 'y_10' at 0x7f75560d4d68>: [],\n", " .new_class 'y_2' at 0x7f75562200f0>: [],\n", " .new_class 'y_18' at 0x7f75561a30b8>: [],\n", " .new_class 'y_6' at 0x7f75562204a8>: [],\n", " .new_class 'y_1' at 0x7f7556220c50>: [],\n", " .new_class 'rho' at 0x7f75561a3048>: [],\n", " .new_class 'y_11' at 0x7f7556080400>: []}" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M.step_method_dict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sample can be reached by the trace method (use the names you used at the initialization not the python name -- useful if the two coincide)" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.38520033, 0.38572793, 0.24201839, 0.24201839, 0.25300562,\n", " 0.49019388, 0.49019388, 0.58805794, 0.56849525, 0.56849525,\n", " 0.56849525, 0.56849525, 0.72880629, 0.72880629, 0.51403447,\n", " 0.66472121, 0.40543629, 0.40543629, 0.40543629, 0.40543629])" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M.trace('rho')[:20]" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(9800,)" ] }, "execution_count": 167, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M.trace('sigma_x')[:].shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then this is just a numpy array, so you can do different sort of things with it. For example plot" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 1.24900000e+03, 4.48600000e+03, 2.78500000e+03,\n", " 9.30000000e+02, 2.47000000e+02, 7.10000000e+01,\n", " 1.80000000e+01, 8.00000000e+00, 3.00000000e+00,\n", " 3.00000000e+00]),\n", " array([ 0.33314016, 0.60141877, 0.86969738, 1.13797599, 1.4062546 ,\n", " 1.67453322, 1.94281183, 2.21109044, 2.47936905, 2.74764766,\n", " 3.01592627]),\n", " )" ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FPz8eaAcut9ZuzXI1RESkiKjlTQSUbVLw642NdHcxsNRaO43AtDl/Am4C7rLWTgU2AVcY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\nZK2tgevi8/k7y718vfMWUm9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LwJ+stfcYYw4HHiXw8nMP8C1rbZsxZgZwFdAB3G+tnWmM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\npDYeLtTCfcwnhnDDdz7Dmyt2MmfRtqT3Fc05J49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yjPWAfM9DM1sK3EFQTj9w99t7JpUd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\nIiIiIiIR8P+PH1bREtk2JgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sigma_sample = M.trace('sigma_x')[:]\n", "rho_sample = M.trace('rho')[:]\n", "\n", "fig, ax = plt. subplots(1, 2, figsize = (15, 5))\n", "ax[0].plot(sigma_sample)\n", "ax[1].hist(sigma_sample)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Acutally, you don't have to waste your time on construction different subplots. `pymc`'s built-in plotting functionality creates pretty informative plots for you (baed on `matplotlib`). On the figure below\n", "- Upper left subplot: trace, \n", "- Lower left subplot: autocorrelation (try to resample the model with `thin=1`), \n", "- Right subplot: histogram with the mean" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Plotting rho\n" ] }, { "data": { "image/png": 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QypTp8tx0001N3me6PLGSLc+uXfnkpDAycV5ebpvnzMbPKLaeg8Zz07lz7oaY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\nZvYGUAtcbmYXAFucc08AVwO/jyTaL3bOPelfkUV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M7ovTgNcCd0naImkbcAdwQhvjbNYp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\nAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pymc.Matplot import plot as fancy_plot\n", "fancy_plot(M.trace('rho'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a non-graphical summary of the posterior use the `stats()` method" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'rho': {'95% HPD interval': array([ 0.1894716, 0.8630899]),\n", " 'mc error': 0.0029385342710042278,\n", " 'mean': 0.5298227172415475,\n", " 'n': 9800,\n", " 'quantiles': {2.5: 0.1767998875856974,\n", " 25: 0.41720379051147455,\n", " 50: 0.53323473221261153,\n", " 75: 0.64675882923717398,\n", " 97.5: 0.85677266144745734},\n", " 'standard deviation': 0.17113578794137885}}" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "M.stats('rho')\n", "\n", "# Try also: \n", "#M.summary()" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N = len(rho_sample)\n", "rho_pr = [rho.random() for i in range(N)]\n", "sigma_pr = [sigma_x.random() for i in range(N)]\n", "\n", "Prior = np.vstack([rho_pr, sigma_pr]).T\n", "Posterior = np.vstack([rho_sample, sigma_sample]).T" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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iBKNLAjAAAABgMOsEW3MOwRiWAAwAAAAaN5eKIYHWx8xlm06FAAwAAAAatvSgZM6h2dK3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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, bx = plt.subplots(1, 2, figsize = (17, 10), sharey = True)\n", "sb.kdeplot(Prior, shade = True, cmap = 'PuBu', ax = bx[0])\n", "bx[0].patch.set_facecolor('white')\n", "bx[0].collections[0].set_alpha(0)\n", "bx[0].axhline(y = sigma_x_true, color = 'DarkRed', lw =2)\n", "bx[0].axvline(x = rho_true, color = 'DarkRed', lw =2)\n", "bx[0].set_xlabel(r'$\\rho$', fontsize = 18)\n", "bx[0].set_ylabel(r'$\\sigma_x$', fontsize = 18)\n", "bx[0].set_title('Prior', fontsize = 20)\n", "\n", "sb.kdeplot(Posterior, shade = True, cmap = 'PuBu', ax = bx[1])\n", "bx[1].patch.set_facecolor('white')\n", "bx[1].collections[0].set_alpha(0)\n", "bx[1].axhline(y = sigma_x_true, color = 'DarkRed', lw =2)\n", "bx[1].axvline(x = rho_true, color = 'DarkRed', lw =2)\n", "bx[1].set_xlabel(r'$\\rho$', fontsize = 18)\n", "bx[1].set_ylabel(r'$\\sigma_x$', fontsize = 18)\n", "bx[1].set_title('Posterior', fontsize = 20)\n", "plt.xlim(-1, 1)\n", "plt.ylim(0, 1.5)\n", "plt.tight_layout()\n", "plt.savefig('beamer/prior_post.pdf')" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AAAAArcp683ABbNfYs1xM0jmKRo9XXVa2JKnbzk+Vv2+Xy4nQURTA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Piep+K/c2Zx/c3i+pvDtTdrKPdYWZ0CmSaj4mLbdr1t27+zbTvY99AVkoruWJdSb4Iv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\nY/g7r1O1ZAG+VFun9n57GX57GZEH7wMgNWw4LcccS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rho_grid = np.linspace(-1, 1, 100)\n", "sigmay_grid = np.linspace(0, 1.5, 100)\n", "U = sp.stats.uniform(-1, 2)\n", "IG = sp.stats.invgamma(3)\n", "\n", "fig2, cx = plt.subplots(2, 2, figsize = (17, 12), sharey = True)\n", "\n", "cx[0, 0].plot(rho_grid, U.pdf(rho_grid), 'r-', lw = 3, alpha = 0.6, label = r'$\\rho$ prior')\n", "cx[0, 0].set_title(r\"Marginal prior for $\\rho$\", fontsize = 18)\n", "cx[0, 0].axvline(x = rho_true, color = 'DarkRed', lw = 2, linestyle = '--', label = r'True $\\rho$')\n", "cx[0, 0].legend(loc='best', fontsize = 16)\n", "cx[0, 0].set_xlim(-1, 1)\n", "\n", "sb.distplot(rho_sample, ax = cx[0,1], kde_kws={\"color\": \"r\", \"lw\": 3, \"label\": r\"$\\rho$ posterior\"})\n", "cx[0, 1].set_title(r\"Marginal posterior for $\\rho$\", fontsize = 18)\n", "cx[0, 1].axvline(x = rho_true, color = 'DarkRed', lw = 2, linestyle = '--', label = r'True $\\rho$')\n", "cx[0, 1].legend(loc='best', fontsize = 16)\n", "cx[0, 1].set_xlim(-1, 1)\n", "\n", "cx[1, 0].plot(sigmay_grid, IG.pdf(sigmay_grid), 'r-', lw=3, alpha=0.6, label=r'$\\sigma_y$ prior')\n", "cx[1, 0].set_title(r\"Marginal prior for $\\sigma_y$\", fontsize = 18)\n", "cx[1, 0].axvline(x = sigma_x_true, color = 'DarkRed', lw = 2, linestyle = '--', label = r'True $\\sigma_y$')\n", "cx[1, 0].legend(loc = 'best', fontsize = 16)\n", "cx[1, 0].set_xlim(0, 3)\n", "\n", "sb.distplot(sigma_sample, ax = cx[1,1], kde_kws={\"color\": \"r\", \"lw\": 3, \"label\": r\"$\\sigma_y$ posterior\"})\n", "cx[1, 1].set_title(r\"Marginal posterior for $\\sigma_y$\", fontsize = 18)\n", "cx[1, 1].axvline(x = sigma_x_true, color = 'DarkRed', lw = 2, linestyle = '--', label = r'True $\\sigma_y$')\n", "cx[1, 1].legend(loc = 'best', fontsize = 16)\n", "cx[1, 1].set_xlim(0, 3)\n", "plt.tight_layout()\n", "plt.savefig('beamer/marginal_prior_post.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sources and further reading:\n", "\n", "`pymc` official documentation: https://pymc-devs.github.io/pymc/index.html\n", "\n", "Rich set of fun examples (very easy read) -- **Probabilistic Programming & Bayesian Methods for Hackers**\n", "http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/\n", "\n", "Nice example about `potential`: http://healthyalgorithms.com/2008/11/05/mcmc-in-python-pymc-to-sample-uniformly-from-a-convex-body/\n", "\n", "Non-trivial example comparing the Gibbs and Metropolis algorithms:\n", "https://github.com/aflaxman/pymc-examples/blob/master/gibbs_for_uniform_ball.ipynb\n", "\n", "Another example: https://users.obs.carnegiescience.edu/cburns/ipynbs/PyMC.html" ] } ], "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture8/Pandas.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Intorduction to Pandas - Pan(el)-da(ta)-s\n", "\n", "#### Laszlo Tetenyi" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import requests, zipfile, io # So that we can download and unzip files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Survey of Consumer Finances (SCF) 2013\n", "\n", "Load and explore data from the SCF website - note that this data cannot be loaded by 2007- Excel due to its size" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r = requests.get('http://www.federalreserve.gov/econresdata/scf/files/scfp2013excel.zip')\n", "z = zipfile.ZipFile(io.BytesIO(r.content))\n", "f = z.open('SCFP2013.xlsx')\n", "table = pd.read_excel(f, sheetname='SCFP2013')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets have a quick look at the table" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " YY1 Y1 WGT HHSEX AGE AGECL EDUC EDCL MARRIED KIDS \\\n", "0 1 11 3100.802441 1 54 3 11 2 2 1 \n", "1 1 12 3090.352195 1 54 3 11 2 2 1 \n", "2 1 13 3094.100275 1 54 3 11 2 2 1 \n", "3 1 14 3098.507516 1 54 3 11 2 2 1 \n", "4 1 15 3104.670102 1 54 3 11 2 2 1 \n", "\n", " ... LLOAN11 LLOAN12 NWCAT INCCAT ASSETCAT NINCCAT NINC2CAT \\\n", "0 ... 0 0 1 1 1 2 1 \n", "1 ... 0 0 1 1 1 2 1 \n", "2 ... 0 0 1 1 1 2 1 \n", "3 ... 0 0 1 1 1 2 1 \n", "4 ... 0 0 1 1 1 2 1 \n", "\n", " NW10CAT INC10CAT NINC10CAT \n", "0 2 2 4 \n", "1 2 2 4 \n", "2 2 2 4 \n", "3 2 2 4 \n", "4 2 2 4 \n", "\n", "[5 rows x 324 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can select particular rows of the table using standard Python array slicing notation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " YY1 Y1 WGT HHSEX AGE AGECL EDUC EDCL MARRIED KIDS \\\n", "0 1 11 3100.802441 1 54 3 11 2 2 1 \n", "1 1 12 3090.352195 1 54 3 11 2 2 1 \n", "2 1 13 3094.100275 1 54 3 11 2 2 1 \n", "3 1 14 3098.507516 1 54 3 11 2 2 1 \n", "4 1 15 3104.670102 1 54 3 11 2 2 1 \n", "\n", " ... LLOAN11 LLOAN12 NWCAT INCCAT ASSETCAT NINCCAT NINC2CAT \\\n", "0 ... 0 0 1 1 1 2 1 \n", "1 ... 0 0 1 1 1 2 1 \n", "2 ... 0 0 1 1 1 2 1 \n", "3 ... 0 0 1 1 1 2 1 \n", "4 ... 0 0 1 1 1 2 1 \n", "\n", " NW10CAT INC10CAT NINC10CAT \n", "0 2 2 4 \n", "1 2 2 4 \n", "2 2 2 4 \n", "3 2 2 4 \n", "4 2 2 4 \n", "\n", "[5 rows x 324 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "table is a DataFrame - a multi-dimensional equivalent of Series (another Pandas object), as it has multiple columns, so you can think of it as a matrix, where columns can be accessed by their 'names'. In fact, many operations can be performed on them (coming from numpy):" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1324540600.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table.max().max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But they know more than that - they have several built-in statistics :" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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YY1Y1WGTHHSEXAGEAGECLEDUCEDCLMARRIEDKIDS...LLOAN11LLOAN12NWCATINCCATASSETCATNINCCATNINC2CATNW10CATINC10CATNINC10CAT
count30075.00000030075.00000030075.00000030075.00000030075.00000030075.00000030075.00000030075.00000030075.00000030075.000000...30075.00000030075.00000030075.00000030075.00000030075.00000030075.00000030075.00000030075.00000030075.00000030075.000000
mean3013.27331730135.7331674074.1503021.23657551.7532833.23225313.9635913.0254031.3734000.840067...539.6369084231.8916042.9961433.5747633.5728353.5979051.8320866.2290616.2502086.291837
std1739.20518717392.0519242199.7353380.42498616.1706211.5320902.6994561.0487330.4837151.174573...12572.614911124661.1214411.4935471.7819431.8403131.7768780.7849933.1700653.0963573.081563
min1.00000011.0000001.6200621.00000018.0000001.000000-1.0000001.0000001.0000000.000000...0.0000000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
25%1508.00000015084.5000003037.7393091.00000039.0000002.00000012.0000002.0000001.0000000.000000...0.0000000.0000002.0000002.0000002.0000002.0000001.0000003.0000004.0000004.000000
50%3013.00000030133.0000004303.4268031.00000052.0000003.00000014.0000003.0000001.0000000.000000...0.0000000.0000003.0000004.0000004.0000004.0000002.0000007.0000007.0000007.000000
75%4519.00000045191.5000005469.2287581.00000063.0000004.00000016.0000004.0000002.0000002.000000...0.0000000.0000005.0000005.0000006.0000005.0000002.00000010.0000009.0000009.000000
max6026.00000060265.00000010617.3663412.00000095.0000006.00000017.0000004.0000002.0000008.000000...720000.0000008000000.0000005.0000006.0000006.0000006.0000003.00000010.00000010.00000010.000000
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8 rows × 324 columns

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" ], "text/plain": [ " YY1 Y1 WGT HHSEX AGE \\\n", "count 30075.000000 30075.000000 30075.000000 30075.000000 30075.000000 \n", "mean 3013.273317 30135.733167 4074.150302 1.236575 51.753283 \n", "std 1739.205187 17392.051924 2199.735338 0.424986 16.170621 \n", "min 1.000000 11.000000 1.620062 1.000000 18.000000 \n", "25% 1508.000000 15084.500000 3037.739309 1.000000 39.000000 \n", "50% 3013.000000 30133.000000 4303.426803 1.000000 52.000000 \n", "75% 4519.000000 45191.500000 5469.228758 1.000000 63.000000 \n", "max 6026.000000 60265.000000 10617.366341 2.000000 95.000000 \n", "\n", " AGECL EDUC EDCL MARRIED KIDS \\\n", "count 30075.000000 30075.000000 30075.000000 30075.000000 30075.000000 \n", "mean 3.232253 13.963591 3.025403 1.373400 0.840067 \n", "std 1.532090 2.699456 1.048733 0.483715 1.174573 \n", "min 1.000000 -1.000000 1.000000 1.000000 0.000000 \n", "25% 2.000000 12.000000 2.000000 1.000000 0.000000 \n", "50% 3.000000 14.000000 3.000000 1.000000 0.000000 \n", "75% 4.000000 16.000000 4.000000 2.000000 2.000000 \n", "max 6.000000 17.000000 4.000000 2.000000 8.000000 \n", "\n", " ... LLOAN11 LLOAN12 NWCAT \\\n", "count ... 30075.000000 30075.000000 30075.000000 \n", "mean ... 539.636908 4231.891604 2.996143 \n", "std ... 12572.614911 124661.121441 1.493547 \n", "min ... 0.000000 0.000000 1.000000 \n", "25% ... 0.000000 0.000000 2.000000 \n", "50% ... 0.000000 0.000000 3.000000 \n", "75% ... 0.000000 0.000000 5.000000 \n", "max ... 720000.000000 8000000.000000 5.000000 \n", "\n", " INCCAT ASSETCAT NINCCAT NINC2CAT NW10CAT \\\n", "count 30075.000000 30075.000000 30075.000000 30075.000000 30075.000000 \n", "mean 3.574763 3.572835 3.597905 1.832086 6.229061 \n", "std 1.781943 1.840313 1.776878 0.784993 3.170065 \n", "min 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "25% 2.000000 2.000000 2.000000 1.000000 3.000000 \n", "50% 4.000000 4.000000 4.000000 2.000000 7.000000 \n", "75% 5.000000 6.000000 5.000000 2.000000 10.000000 \n", "max 6.000000 6.000000 6.000000 3.000000 10.000000 \n", "\n", " INC10CAT NINC10CAT \n", "count 30075.000000 30075.000000 \n", "mean 6.250208 6.291837 \n", "std 3.096357 3.081563 \n", "min 1.000000 1.000000 \n", "25% 4.000000 4.000000 \n", "50% 7.000000 7.000000 \n", "75% 9.000000 9.000000 \n", "max 10.000000 10.000000 \n", "\n", "[8 rows x 324 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example try to access normalized income and net-worth variables." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(324,)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table.dtypes[0:5]\n", "table.dtypes.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are way too many variables in there - try to search for the proper column names" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['NETWORTH']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[col for col in table.columns if 'NETWOR' in col]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['INCOME',\n", " 'WAGEINC',\n", " 'BUSSEFARMINC',\n", " 'INTDIVINC',\n", " 'KGINC',\n", " 'SSRETINC',\n", " 'TRANSFOTHINC',\n", " 'NORMINC',\n", " 'EQUITINC',\n", " 'DEBT2INC',\n", " 'INCCAT',\n", " 'NINCCAT',\n", " 'NINC2CAT',\n", " 'INC10CAT',\n", " 'NINC10CAT']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[col for col in table.columns if 'INC' in col]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "income = table['NORMINC']\n", "net_worth = table['NETWORTH']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the \"mean\" (non -weighted) income and minimal net worth" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "713563.8921620903" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "income.mean()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-227019000.0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net_worth.min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That is, there is one person with a net worth of -227 million \\$ ! Suppose we do not want our analysis to depend on these extremely low values and we would like to trim our dataframe. As a first step, create a new dataframe that only contains the variables of interest (and the id of each observation)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "keep = ['YY1','Y1', 'NORMINC', 'NETWORTH']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = table[keep]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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YY1Y1NORMINCNETWORTH
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111239566.725979-400
211335508.600237-400
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" ], "text/plain": [ " YY1 Y1 NORMINC NETWORTH\n", "0 1 11 37537.663108 -400\n", "1 1 12 39566.725979 -400\n", "2 1 13 35508.600237 -400\n", "3 1 14 38552.194543 -400\n", "4 1 15 35508.600237 -400" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rename the columns:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.columns ='Household', 'Observation' , 'Income', 'Net Worth'" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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HouseholdObservationIncomeNet Worth
011137537.663108-400
111239566.725979-400
211335508.600237-400
311438552.194543-400
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" ], "text/plain": [ " Household Observation Income Net Worth\n", "0 1 11 37537.663108 -400\n", "1 1 12 39566.725979 -400\n", "2 1 13 35508.600237 -400\n", "3 1 14 38552.194543 -400\n", "4 1 15 35508.600237 -400" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try to get a general picture of what would be a \"good\" trimming value of net worth by plotting an estimated kernel density " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['Net Worth'].plot(kind='density')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how many observations we eliminate if we exclude everyone below -1 million \\$" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_trimmed = data[data['Net Worth'] > -1000000]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape[0] - data_trimmed.shape[0] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But how many households are in this category?" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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HouseholdObservationIncomeNet Worth
2770555555156813.760380-1692510
2771555555254784.697509-1760510
2772555555353770.166074-1673500
2773555555449712.040332-1464610
2774555555549712.040332-1962990
142302852285210.000000-215289000
142312852285220.000000-220820200
142322852285230.000000-227019000
142332852285240.000000-226697000
142342852285250.000000-216780000
187853764376410.000000-2037500
187863764376420.000000-1390100
187873764376430.000000-2130900
187883764376440.000000-1293000
187893764376450.000000-1521200
\n", "
" ], "text/plain": [ " Household Observation Income Net Worth\n", "2770 555 5551 56813.760380 -1692510\n", "2771 555 5552 54784.697509 -1760510\n", "2772 555 5553 53770.166074 -1673500\n", "2773 555 5554 49712.040332 -1464610\n", "2774 555 5555 49712.040332 -1962990\n", "14230 2852 28521 0.000000 -215289000\n", "14231 2852 28522 0.000000 -220820200\n", "14232 2852 28523 0.000000 -227019000\n", "14233 2852 28524 0.000000 -226697000\n", "14234 2852 28525 0.000000 -216780000\n", "18785 3764 37641 0.000000 -2037500\n", "18786 3764 37642 0.000000 -1390100\n", "18787 3764 37643 0.000000 -2130900\n", "18788 3764 37644 0.000000 -1293000\n", "18789 3764 37645 0.000000 -1521200" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data['Net Worth'] < -1000000]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_trimmed['Net Worth'].plot(kind='density')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Pivoting\n", "\n", "Note that the data has a nice panel structure which we so far did not exploit - each household has multiple observations. Lets use pivoting so that our dataframe reflects that. First get the index of our dataframe: " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8,\n", " 9,\n", " ...\n", " 30065, 30066, 30067, 30068, 30069, 30070, 30071, 30072, 30073,\n", " 30074],\n", " dtype='int64', length=30075)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We simply have the index of each observation. As a first step, replace these indeces by the household identifier and group income levels in each observation:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\Programs\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:426: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self.obj[item] = s\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " Household Observation Income Net Worth\n", "0 1 1 37537.663108 -400\n", "1 1 2 39566.725979 -400\n", "2 1 3 35508.600237 -400\n", "3 1 4 38552.194543 -400\n", "4 1 5 35508.600237 -400\n", "5 2 1 22319.691578 -8700\n", "6 2 2 22319.691578 -8600\n", "7 2 3 22319.691578 -8700\n", "8 2 4 22319.691578 -8600\n", "9 2 5 22319.691578 -8600" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_observations = data.loc[:,'Observation'] - 10 * data.loc[:,'Household']\n", "data.loc[:,'Observation'] = new_observations\n", "data[0:10]\n", "# Normally, you should not do this - instead use assign" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Reload the data\n", "data = table[keep]\n", "data.columns ='Household', 'Observation' , 'Income', 'Net Worth'" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Household Observation Income Net Worth\n", "0 1 1 37537.663108 -400\n", "1 1 2 39566.725979 -400\n", "2 1 3 35508.600237 -400\n", "3 1 4 38552.194543 -400\n", "4 1 5 35508.600237 -400" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = data.assign(Observations = (data['Observation'] - 10.0 * data['Household']).astype(int))\n", "del data['Observation'] # delete the old column\n", "data = data.rename(columns = {'Observations':'Observation'}) # rename the column\n", "data = data[['Household', 'Observation' , 'Income', 'Net Worth']] # reinsert the column\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p = data.pivot(index = 'Household' , columns = 'Observation' , values = 'Income' )" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "Observation 1 2 3 4 \\\n", "Household \n", "1 37537.663108 39566.725979 35508.600237 38552.194543 \n", "2 22319.691578 22319.691578 22319.691578 22319.691578 \n", "3 52755.634638 52755.634638 52755.634638 52755.634638 \n", "4 125801.897980 125801.897980 125801.897980 125801.897980 \n", "5 99424.080664 107540.332150 108554.863580 108554.863580 \n", "\n", "Observation 5 \n", "Household \n", "1 35508.600237 \n", "2 22319.691578 \n", "3 52755.634638 \n", "4 125801.897980 \n", "5 92322.360617 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If instead we are interested in both income and net worth grouped by observations then:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p = data.pivot(index='Household', columns='Observation')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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IncomeNet Worth
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Household
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599424.080664107540.332150108554.863580108554.86358092322.360617676600675300677100677400677300
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" ], "text/plain": [ " Income \\\n", "Observation 1 2 3 4 \n", "Household \n", "1 37537.663108 39566.725979 35508.600237 38552.194543 \n", "2 22319.691578 22319.691578 22319.691578 22319.691578 \n", "3 52755.634638 52755.634638 52755.634638 52755.634638 \n", "4 125801.897980 125801.897980 125801.897980 125801.897980 \n", "5 99424.080664 107540.332150 108554.863580 108554.863580 \n", "\n", " Net Worth \n", "Observation 5 1 2 3 4 5 \n", "Household \n", "1 35508.600237 -400 -400 -400 -400 -400 \n", "2 22319.691578 -8700 -8600 -8700 -8600 -8600 \n", "3 52755.634638 -500 -600 -500 -500 -500 \n", "4 125801.897980 2111950 2169750 2149850 2216950 2289850 \n", "5 92322.360617 676600 675300 677100 677400 677300 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p.head()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Use stacking to transform the data into a panel structure we are familiar with (and unstacking to go back to cross-section):" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "panel_data = p.stack()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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IncomeNet Worth
HouseholdObservation
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" ], "text/plain": [ " Income Net Worth\n", "Household Observation \n", "555 1 56813.760380 -1692510\n", " 2 54784.697509 -1760510\n", " 3 53770.166074 -1673500\n", " 4 49712.040332 -1464610\n", " 5 49712.040332 -1962990\n", "2852 1 0.000000 -215289000\n", " 2 0.000000 -220820200\n", " 3 0.000000 -227019000\n", " 4 0.000000 -226697000\n", " 5 0.000000 -216780000\n", "3764 1 0.000000 -2037500\n", " 2 0.000000 -1390100\n", " 3 0.000000 -2130900\n", " 4 0.000000 -1293000\n", " 5 0.000000 -1521200" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "panel_data[panel_data['Net Worth'] < -1000000]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Pandas even have their own data-structure for panel data - for it, we need to create dataframes as inputs" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p = data.pivot(index='Observation', columns='Household')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pdata = {'Observation 1' : pd.DataFrame(p.ix[1,:]),\n", " 'Observation 2' : pd.DataFrame(p.ix[2,:]),\n", " 'Observation 3' : pd.DataFrame(p.ix[3,:]),\n", " 'Observation 4' : pd.DataFrame(p.ix[4,:]),\n", " 'Observation 5' : pd.DataFrame(p.ix[5,:])}\n", "pdata = pd.Panel(pdata)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\n", "Dimensions: 5 (items) x 12030 (major_axis) x 5 (minor_axis)\n", "Items axis: Observation 1 to Observation 5\n", "Major_axis axis: (Income, 1) to (Net Worth, 6026)\n", "Minor_axis axis: 1 to 5" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "but this is not as useful - unfortunately the panel part of the package has been neglected. Very few functions are available. Now as a last exercise, save our dataFrame to a csv file" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.to_excel('SCF_2013_inc_netw.xlsx')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: lecture9/Plotly_Presentation.html ================================================ Plotly_Presentation

Overview of Plotly for Python

Victoria Gregory

4/1/2016

What is Plotly?

  • plotly.js: online JavaScript graphing library
  • Today I'll talk about its Python client
  • Both plotly.js and the Python library are free and open-source
  • Similar libraries for Julia, R, and Matlab

What can I do with Plotly?

  • Useful for data visualization and fully interactive graphics
  • Standard graphics interface across languages
  • Easily shareable online
  • 20 types of charts, including statistical plots, 3D charts, and maps
  • Complete list here

Just a few examples...

In [13]:
import plotly.tools as tls
tls.embed('https://plot.ly/~AnnaG/1/nfl-defensive-player-size-2013-season/')
Out[13]:
In [20]:
tls.embed('https://plot.ly/~chris/7378/relative-number-of-311-complaints-by-city/')
Out[20]:
In [5]:
tls.embed('https://plot.ly/~empet/2922/a-scoreboard-for-republican-candidates-as-of-august-17-2015-annotated-heatmap/')
Out[5]:
In [6]:
tls.embed('https://plot.ly/~vgregory757/2/_2014-us-city-populations-click-legend-to-toggle-traces/')
Out[6]:

Getting started

  • Easy to install: pip install plotly
  • How to save and view files?
    • Can work offline and save as .html files to open on web browser
    • Jupyter notebook
    • Upload to online account for easy sharing: import statement automatically signs you in

How It Works

  • Graph objects
    • Same structure as native Python dictionaries and lists
    • Defined as new classes
    • Every Plotly plot type has its own graph object, i.e., Scatter, Bar, Histogram
  • All information in a Plotly plot is contained in a Figure object, which contains
    • a Data object: stores data and style options, i.e., setting the line color
    • a Layout object: for aesthetic features outside the plotting area, i.e., setting the title
  • trace: refers to a set of data meant to be plotted as a whole (like an $x$ and $y$ pairing)
  • Interactivity is automatic!

Line/Scatter Plots

The following import statements load the three main modules:

In [15]:
# (*) Tools to communicate with Plotly's server
import plotly.plotly as py

 # (*) Useful Python/Plotly tools 
import plotly.tools as tls

# (*) Graph objects to piece together your Plotly plots
import plotly.graph_objs as go

The following code will make a simple line and scatter plot:

In [16]:
# Create random data with numpy
import numpy as np

N = 100 
random_x = np.linspace(0, 1, N)
random_y0 = np.random.randn(N)+5
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N)-5

# (1.1) Make a 1st Scatter object
trace0 = go.Scatter(
    x = random_x,
    y = random_y0,
    mode = 'markers',
    name = '$\mu = 5$',
    hoverinfo='x+y'     # choosing what to show on hover
)
In [3]:
# (1.2) Make a 2nd Scatter object
trace1 = go.Scatter(
    x = random_x,
    y = random_y1,
    mode = 'lines+markers',
    name = '$\mu = 0$',
    hoverinfo='x+y'
)
# (1.3) Make a 3rd Scatter object
trace2 = go.Scatter(
    x = random_x,
    y = random_y2,
    mode = 'lines',
    name = '$\mu = -5$',
    hoverinfo='x+y'
)
In [4]:
# (2) Make Data object 
# Data is list-like, must use [ ]
data = go.Data([trace0, trace1, trace2])

# (3) Make Layout object (Layout is dict-like)
layout = go.Layout(title='$\\text{Some scatter objects distributed as } \
\mathcal{N}(\mu,1)$',
        xaxis=dict(title='x-axis label'),
        yaxis=dict(title='y-axis label'),
        showlegend=True)

# (4) Make Figure object (Figure is dict-like)
fig = go.Figure(data=data, layout=layout)
In [5]:
print(fig)  # print the figure object in notebook

{'layout': {'showlegend': True, 'yaxis': {'title': 'y-axis label'}, 'xaxis': {'title': 'x-axis label'}, 'title': '$\\text{Some scatter objects distributed as } \\mathcal{N}(\\mu,1)$'}, 'data': [{'name': '$\\mu = 5$', 'mode': 'markers', 'hoverinfo': 'x+y', 'y': array([ 4.04668017,  6.19854098,  4.62444061,  5.09242471,  3.66649515,
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        6.6377433 ,  3.50985828,  5.91740602,  3.42162452,  5.58354415,
        4.42149207,  5.12235742,  4.68431865,  3.85567028,  6.45240545,
        4.39855931,  4.34472981,  4.29497064,  5.50473226,  5.21625372,
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        3.07037985,  5.43110144,  4.39505671,  6.45377374,  4.79077288,
        4.52728717,  3.82026289,  4.53460106,  4.49427596,  4.43350851,
        3.52965903,  4.87801779,  3.97288821,  5.3209264 ,  5.17132472,
        5.38980652,  4.67934665,  5.90043308,  4.47941995,  4.04320281,
        4.05764549,  5.87833999,  5.42252719,  4.78779398,  4.80676704,
        5.21730358,  4.3471276 ,  6.28070553,  5.28354741,  2.30351284,
        5.10559463,  4.18524983,  6.19731501,  5.62587121,  4.37582245]), 'x': array([ 0.        ,  0.01010101,  0.02020202,  0.03030303,  0.04040404,
        0.05050505,  0.06060606,  0.07070707,  0.08080808,  0.09090909,
        0.1010101 ,  0.11111111,  0.12121212,  0.13131313,  0.14141414,
        0.15151515,  0.16161616,  0.17171717,  0.18181818,  0.19191919,
        0.2020202 ,  0.21212121,  0.22222222,  0.23232323,  0.24242424,
        0.25252525,  0.26262626,  0.27272727,  0.28282828,  0.29292929,
        0.3030303 ,  0.31313131,  0.32323232,  0.33333333,  0.34343434,
        0.35353535,  0.36363636,  0.37373737,  0.38383838,  0.39393939,
        0.4040404 ,  0.41414141,  0.42424242,  0.43434343,  0.44444444,
        0.45454545,  0.46464646,  0.47474747,  0.48484848,  0.49494949,
        0.50505051,  0.51515152,  0.52525253,  0.53535354,  0.54545455,
        0.55555556,  0.56565657,  0.57575758,  0.58585859,  0.5959596 ,
        0.60606061,  0.61616162,  0.62626263,  0.63636364,  0.64646465,
        0.65656566,  0.66666667,  0.67676768,  0.68686869,  0.6969697 ,
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        0.95959596,  0.96969697,  0.97979798,  0.98989899,  1.        ]), 'type': 'scatter'}, {'name': '$\\mu = 0$', 'mode': 'lines+markers', 'hoverinfo': 'x+y', 'y': array([-0.34691493,  2.17938364,  0.51809962, -0.5959291 ,  0.23412758,
       -2.07896695,  1.40091551,  0.24549119, -0.56185707, -0.34058195,
       -0.61737475,  0.45612955, -0.52261175, -0.48896567, -1.04546019,
        0.26031148,  0.8714819 ,  0.66574976, -0.01393808,  1.33190518,
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       -0.21764378, -1.42432799,  0.51234302,  0.12061286,  1.94768532]), 'x': array([ 0.        ,  0.01010101,  0.02020202,  0.03030303,  0.04040404,
        0.05050505,  0.06060606,  0.07070707,  0.08080808,  0.09090909,
        0.1010101 ,  0.11111111,  0.12121212,  0.13131313,  0.14141414,
        0.15151515,  0.16161616,  0.17171717,  0.18181818,  0.19191919,
        0.2020202 ,  0.21212121,  0.22222222,  0.23232323,  0.24242424,
        0.25252525,  0.26262626,  0.27272727,  0.28282828,  0.29292929,
        0.3030303 ,  0.31313131,  0.32323232,  0.33333333,  0.34343434,
        0.35353535,  0.36363636,  0.37373737,  0.38383838,  0.39393939,
        0.4040404 ,  0.41414141,  0.42424242,  0.43434343,  0.44444444,
        0.45454545,  0.46464646,  0.47474747,  0.48484848,  0.49494949,
        0.50505051,  0.51515152,  0.52525253,  0.53535354,  0.54545455,
        0.55555556,  0.56565657,  0.57575758,  0.58585859,  0.5959596 ,
        0.60606061,  0.61616162,  0.62626263,  0.63636364,  0.64646465,
        0.65656566,  0.66666667,  0.67676768,  0.68686869,  0.6969697 ,
        0.70707071,  0.71717172,  0.72727273,  0.73737374,  0.74747475,
        0.75757576,  0.76767677,  0.77777778,  0.78787879,  0.7979798 ,
        0.80808081,  0.81818182,  0.82828283,  0.83838384,  0.84848485,
        0.85858586,  0.86868687,  0.87878788,  0.88888889,  0.8989899 ,
        0.90909091,  0.91919192,  0.92929293,  0.93939394,  0.94949495,
        0.95959596,  0.96969697,  0.97979798,  0.98989899,  1.        ]), 'type': 'scatter'}, {'name': '$\\mu = -5$', 'mode': 'lines', 'hoverinfo': 'x+y', 'y': array([-4.48987402, -5.00299715, -5.27569342, -3.8223728 , -3.6502064 ,
       -4.93187342, -3.5115968 , -7.6986876 , -3.76826013, -6.4241302 ,
       -4.73376471, -5.04163874, -5.77634254, -6.26209807, -4.60243579,
       -4.67358739, -5.14992072, -5.29748225, -5.06602165, -6.13815511,
       -5.564722  , -5.63855557, -4.82780904, -6.33164629, -5.56346489,
       -5.70394686, -4.330434  , -5.49693236, -4.84009953, -4.6317476 ,
       -4.010567  , -7.48336095, -6.09522473, -4.89513925, -4.83467357,
       -4.70691006, -6.33809177, -6.29332726, -2.96358237, -4.7275947 ,
       -5.83294186, -5.9606109 , -5.58472397, -5.62344515, -4.02465416,
       -3.02183737, -4.86414021, -4.39816038, -6.61453443, -5.61632089,
       -5.61164201, -3.60435107, -4.93173037, -5.2246923 , -4.56053351,
       -5.71917183, -5.71984894, -5.60812793, -5.16043173, -4.15756347,
       -3.7194692 , -5.52792439, -3.62796425, -4.86906244, -3.40373272,
       -5.84820953, -3.46684876, -5.16100917, -5.74868657, -5.0236031 ,
       -3.56692692, -2.02221892, -6.80212881, -3.8346147 , -5.91834124,
       -2.31426304, -6.77714362, -4.95939023, -5.32436323, -4.86277363,
       -5.28515788, -4.56678228, -3.81648371, -4.407105  , -4.70197081,
       -6.01753656, -6.82434989, -5.93431004, -4.24573797, -4.33361792,
       -5.97240983, -4.21877573, -4.80431133, -4.50803704, -4.76269954,
       -4.79829357, -4.33052356, -4.4148409 , -5.98019236, -3.94674547]), 'x': array([ 0.        ,  0.01010101,  0.02020202,  0.03030303,  0.04040404,
        0.05050505,  0.06060606,  0.07070707,  0.08080808,  0.09090909,
        0.1010101 ,  0.11111111,  0.12121212,  0.13131313,  0.14141414,
        0.15151515,  0.16161616,  0.17171717,  0.18181818,  0.19191919,
        0.2020202 ,  0.21212121,  0.22222222,  0.23232323,  0.24242424,
        0.25252525,  0.26262626,  0.27272727,  0.28282828,  0.29292929,
        0.3030303 ,  0.31313131,  0.32323232,  0.33333333,  0.34343434,
        0.35353535,  0.36363636,  0.37373737,  0.38383838,  0.39393939,
        0.4040404 ,  0.41414141,  0.42424242,  0.43434343,  0.44444444,
        0.45454545,  0.46464646,  0.47474747,  0.48484848,  0.49494949,
        0.50505051,  0.51515152,  0.52525253,  0.53535354,  0.54545455,
        0.55555556,  0.56565657,  0.57575758,  0.58585859,  0.5959596 ,
        0.60606061,  0.61616162,  0.62626263,  0.63636364,  0.64646465,
        0.65656566,  0.66666667,  0.67676768,  0.68686869,  0.6969697 ,
        0.70707071,  0.71717172,  0.72727273,  0.73737374,  0.74747475,
        0.75757576,  0.76767677,  0.77777778,  0.78787879,  0.7979798 ,
        0.80808081,  0.81818182,  0.82828283,  0.83838384,  0.84848485,
        0.85858586,  0.86868687,  0.87878788,  0.88888889,  0.8989899 ,
        0.90909091,  0.91919192,  0.92929293,  0.93939394,  0.94949495,
        0.95959596,  0.96969697,  0.97979798,  0.98989899,  1.        ]), 'type': 'scatter'}]}

Figure objects store data like a Python dictionary.

In [6]:
# (5) Send Figure object to Plotly and show plot in notebook
py.iplot(fig, filename='scatter-mode')
Out[6]:

Can save a static image as well:

In [59]:
py.image.save_as(fig, filename='scatter-mode.png')

Histograms

In [ ]:
# (1) Generate some random numbers
x0 = np.random.randn(500)
x1 = np.random.randn(500)+1

# (2.1) Create the first Histogram object
trace1 = go.Histogram(
    x=x0,
    histnorm='count',
    name='control',
    autobinx=False,
    xbins=dict(
        start=-3.2,
        end=2.8,
        size=0.2
    ),
    marker=dict(
        color='fuchsia',
        line=dict(
            color='grey',
            width=0
        )
    ),
    opacity=0.75
)
In [7]:
# (2.2) Create the second Histogram object
trace2 = go.Histogram(
    x=x1,
    name='experimental',
    autobinx=False,
    xbins=dict(
        start=-1.8,
        end=4.2,
        size=0.2
    ),
    marker=dict(
        color='rgb(255, 217, 102)'
    ),
    opacity=0.75
)
In [8]:
# (3) Create Data object
data = [trace1, trace2]

# (4) Create Layout object
layout = go.Layout(
    title='Sampled Results',
    xaxis=dict(
        title='Value'
    ),
    yaxis=dict(
        title='Count'
    ),
    barmode='overlay',
    bargap=0.25,
    bargroupgap=0.3,
    showlegend=True
)
fig = go.Figure(data=data, layout=layout)
In [9]:
# (5) Send Figure object to Plotly and show plot in notebook
py.iplot(fig, filename='histogram_example')
Out[9]:

Distplots

Similar to seaborn.distplot. Plot a histogram, kernel density or normal curve, and a rug plot all together.

In [ ]:
from plotly.tools import FigureFactory as FF

# Add histogram data
x1 = np.random.randn(200)-2  
x2 = np.random.randn(200)  
x3 = np.random.randn(200)+2  
x4 = np.random.randn(200)+4  

# Group data together
hist_data = [x1, x2, x3, x4]

group_labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4']

# Create distplot with custom bin_size
fig = FF.create_distplot(hist_data, group_labels, bin_size=.2)
In [63]:
# Plot!
py.iplot(fig, filename='Distplot with Multiple Datasets', \
         validate=False)
Out[63]:

2D Contour Plot

In [21]:
x = np.random.randn(1000)
y = np.random.randn(1000)
py.iplot([go.Histogram2dContour(x=x, y=y, \
        contours=go.Contours(coloring='fill')), \
       go.Scatter(x=x, y=y, mode='markers', \
        marker=go.Marker(color='white', size=3, opacity=0.3))])
Out[21]:

3D Surface Plot

Plot the function: $f(x,y) = A \cos(\pi x y) e^{-(x^2+y^2)/2}$

In [5]:
# Define the function to be plotted
def fxy(x, y):
    A = 1  # choose a maximum amplitude 
    return A*(np.cos(np.pi*x*y))**2 * np.exp(-(x**2+y**2)/2.)

# Choose length of square domain, make row and column vectors
L = 4
x = y = np.arange(-L/2., L/2., 0.1)  # use a mesh spacing of 0.1
yt = y[:, np.newaxis]  # (!) make column vector

# Get surface coordinates!
z = fxy(x, yt)
In [6]:
trace1 = go.Surface(
    z=z,  # link the fxy 2d numpy array
    x=x,  # link 1d numpy array of x coords
    y=y   # link 1d numpy array of y coords
)

# Package the trace dictionary into a data object
data = go.Data([trace1])

# Dictionary of style options for all axes
axis = dict(
    showbackground=True, # (!) show axis background
    backgroundcolor="rgb(204, 204, 204)", # set background color to grey
    gridcolor="rgb(255, 255, 255)",       # set grid line color
    zerolinecolor="rgb(255, 255, 255)",   # set zero grid line color
)

# Make a layout object
layout = go.Layout(
    title='$f(x,y) = A \cos(\pi x y) e^{-(x^2+y^2)/2}$', # set plot title
    scene=go.Scene(  # (!) axes are part of a 'scene' in 3d plots
        xaxis=go.XAxis(axis), # set x-axis style
        yaxis=go.YAxis(axis), # set y-axis style
        zaxis=go.ZAxis(axis)  # set z-axis style
    )
)
In [8]:
# Make a figure object
fig = go.Figure(data=data, layout=layout)

# (@) Send to Plotly and show in notebook
py.iplot(fig, filename='surface')
Out[8]:

Matplotlib Conversion

In [ ]:
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab

n = 50
x, y, z, s, ew = np.random.rand(5, n)
c, ec = np.random.rand(2, n, 4)
area_scale, width_scale = 500, 5

fig, ax = plt.subplots()
sc = ax.scatter(x, y, c=c,
                s=np.square(s)*area_scale,
                edgecolor=ec,
                linewidth=ew*width_scale)
ax.grid()
In [6]:
py.iplot_mpl(fig)
Out[6]:

Pros/Cons

Pros:

  • Beautiful, interactive plots
  • Great documentation: easy to figure out how to customize
  • Easily shareable and modifiable by collaborators using the online GUI
  • Can convert matplotlib graphs

Cons:

  • Static export only supports .png or .jpg. No .eps for free accounts
  • Although not required for use, your data is hosted on their cloud
  • Limited number of private figures for free accounts
  • Limited usage opportunities for economists

Extra Resources

================================================ FILE: lecture9/Plotly_Presentation.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Overview of Plotly for Python\n", "\n", "**Victoria Gregory**\n", "\n", "4/1/2016" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## What is Plotly?\n", "\n", "* `plotly.js`: online JavaScript graphing library\n", "* Today I'll talk about its Python client\n", "* Both `plotly.js` and the Python library are free and open-source\n", "* Similar libraries for Julia, R, and Matlab\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## What can I do with Plotly?\n", "\n", "* Useful for data visualization and fully interactive graphics\n", "* Standard graphics interface across languages\n", "* Easily shareable online\n", "* 20 types of charts, including statistical plots, 3D charts, and maps\n", "* [Complete list here](https://plot.ly/python/)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Just a few examples..." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import plotly.tools as tls\n", "tls.embed('https://plot.ly/~AnnaG/1/nfl-defensive-player-size-2013-season/')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tls.embed('https://plot.ly/~chris/7378/relative-number-of-311-complaints-by-city/')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tls.embed('https://plot.ly/~empet/2922/a-scoreboard-for-republican-candidates-as-of-august-17-2015-annotated-heatmap/')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tls.embed('https://plot.ly/~vgregory757/2/_2014-us-city-populations-click-legend-to-toggle-traces/')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Getting started\n", "\n", "* Easy to install: `pip install plotly`\n", "* How to save and view files?\n", " * Can work offline and save as `.html` files to open on web browser\n", " * Jupyter notebook\n", " * Upload to online account for easy sharing: import statement automatically signs you in" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## How It Works\n", "\n", "* Graph objects\n", " * Same structure as native Python dictionaries and lists\n", " * Defined as new classes\n", " * Every Plotly plot type has its own graph object, i.e., `Scatter`, `Bar`, `Histogram`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "* All information in a Plotly plot is contained in a `Figure` object, which contains\n", " * a `Data` object: stores data and style options, i.e., setting the line color\n", " * a `Layout` object: for aesthetic features outside the plotting area, i.e., setting the title\n", "* *trace*: refers to a set of data meant to be plotted as a whole (like an $x$ and $y$ pairing)\n", "* Interactivity is automatic!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Line/Scatter Plots\n", "\n", "The following `import` statements load the three main modules:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# (*) Tools to communicate with Plotly's server\n", "import plotly.plotly as py\n", "\n", " # (*) Useful Python/Plotly tools \n", "import plotly.tools as tls\n", "\n", "# (*) Graph objects to piece together your Plotly plots\n", "import plotly.graph_objs as go" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The following code will make a simple line and scatter plot:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Create random data with numpy\n", "import numpy as np\n", "\n", "N = 100 \n", "random_x = np.linspace(0, 1, N)\n", "random_y0 = np.random.randn(N)+5\n", "random_y1 = np.random.randn(N)\n", "random_y2 = np.random.randn(N)-5\n", "\n", "# (1.1) Make a 1st Scatter object\n", "trace0 = go.Scatter(\n", " x = random_x,\n", " y = random_y0,\n", " mode = 'markers',\n", " name = '$\\mu = 5$',\n", " hoverinfo='x+y' # choosing what to show on hover\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# (1.2) Make a 2nd Scatter object\n", "trace1 = go.Scatter(\n", " x = random_x,\n", " y = random_y1,\n", " mode = 'lines+markers',\n", " name = '$\\mu = 0$',\n", " hoverinfo='x+y'\n", ")\n", "# (1.3) Make a 3rd Scatter object\n", "trace2 = go.Scatter(\n", " x = random_x,\n", " y = random_y2,\n", " mode = 'lines',\n", " name = '$\\mu = -5$',\n", " hoverinfo='x+y'\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# (2) Make Data object \n", "# Data is list-like, must use [ ]\n", "data = go.Data([trace0, trace1, trace2])\n", "\n", "# (3) Make Layout object (Layout is dict-like)\n", "layout = go.Layout(title='$\\\\text{Some scatter objects distributed as } \\\n", "\\mathcal{N}(\\mu,1)$',\n", " xaxis=dict(title='x-axis label'),\n", " yaxis=dict(title='y-axis label'),\n", " showlegend=True)\n", "\n", "# (4) Make Figure object (Figure is dict-like)\n", "fig = go.Figure(data=data, layout=layout) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'layout': {'showlegend': True, 'yaxis': {'title': 'y-axis label'}, 'xaxis': {'title': 'x-axis label'}, 'title': '$\\\\text{Some scatter objects distributed as } \\\\mathcal{N}(\\\\mu,1)$'}, 'data': [{'name': '$\\\\mu = 5$', 'mode': 'markers', 'hoverinfo': 'x+y', 'y': array([ 4.04668017, 6.19854098, 4.62444061, 5.09242471, 3.66649515,\n", " 6.58469017, 5.19130891, 6.6651075 , 4.69078908, 5.8217442 ,\n", " 6.6377433 , 3.50985828, 5.91740602, 3.42162452, 5.58354415,\n", " 4.42149207, 5.12235742, 4.68431865, 3.85567028, 6.45240545,\n", " 4.39855931, 4.34472981, 4.29497064, 5.50473226, 5.21625372,\n", " 4.46215315, 5.00053252, 5.90014207, 5.41637191, 5.51115194,\n", " 4.56673328, 6.03843503, 5.56792862, 5.7704772 , 3.71154776,\n", " 4.7388194 , 6.08732718, 5.42687078, 6.58736437, 5.67774481,\n", " 5.74155225, 5.91060711, 4.88168997, 5.26141665, 4.70980663,\n", " 4.54812936, 4.90691582, 4.81522669, 4.71825569, 5.55335487,\n", " 4.08928611, 5.63225122, 5.94667951, 4.39225355, 4.74958193,\n", " 3.92407449, 7.07111905, 5.495516 , 4.09448008, 5.77320071,\n", " 4.64677053, 3.10583353, 4.98918102, 4.48544076, 4.76270418,\n", " 3.07037985, 5.43110144, 4.39505671, 6.45377374, 4.79077288,\n", " 4.52728717, 3.82026289, 4.53460106, 4.49427596, 4.43350851,\n", " 3.52965903, 4.87801779, 3.97288821, 5.3209264 , 5.17132472,\n", " 5.38980652, 4.67934665, 5.90043308, 4.47941995, 4.04320281,\n", " 4.05764549, 5.87833999, 5.42252719, 4.78779398, 4.80676704,\n", " 5.21730358, 4.3471276 , 6.28070553, 5.28354741, 2.30351284,\n", " 5.10559463, 4.18524983, 6.19731501, 5.62587121, 4.37582245]), 'x': array([ 0. , 0.01010101, 0.02020202, 0.03030303, 0.04040404,\n", " 0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909,\n", " 0.1010101 , 0.11111111, 0.12121212, 0.13131313, 0.14141414,\n", " 0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919,\n", " 0.2020202 , 0.21212121, 0.22222222, 0.23232323, 0.24242424,\n", " 0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929,\n", " 0.3030303 , 0.31313131, 0.32323232, 0.33333333, 0.34343434,\n", " 0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939,\n", " 0.4040404 , 0.41414141, 0.42424242, 0.43434343, 0.44444444,\n", " 0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949,\n", " 0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455,\n", " 0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596 ,\n", " 0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465,\n", " 0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697 ,\n", " 0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475,\n", " 0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798 ,\n", " 0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485,\n", " 0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899 ,\n", " 0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495,\n", " 0.95959596, 0.96969697, 0.97979798, 0.98989899, 1. ]), 'type': 'scatter'}, {'name': '$\\\\mu = 0$', 'mode': 'lines+markers', 'hoverinfo': 'x+y', 'y': array([-0.34691493, 2.17938364, 0.51809962, -0.5959291 , 0.23412758,\n", " -2.07896695, 1.40091551, 0.24549119, -0.56185707, -0.34058195,\n", " -0.61737475, 0.45612955, -0.52261175, -0.48896567, -1.04546019,\n", " 0.26031148, 0.8714819 , 0.66574976, -0.01393808, 1.33190518,\n", " -0.93344882, 1.47167414, -0.44745963, -0.34761853, -0.8235109 ,\n", " 1.23946937, -0.57356471, 0.12733162, 1.18614807, 0.84700632,\n", " 0.24209963, 1.23406421, 0.16085798, -0.21386201, -0.4344829 ,\n", " 0.28582313, 0.96303331, 0.64243359, 0.80443922, -0.73621594,\n", " -0.63861189, 0.19156248, 0.13184313, 0.00497728, 0.99412137,\n", " -0.92522068, 0.42878841, -2.02951441, -1.28557997, -2.74433002,\n", " -2.60336845, 0.20100076, 0.18442098, -0.04819198, 1.55876483,\n", " 0.2357085 , 0.43286067, -0.07853408, -1.10796824, 0.73222129,\n", " -0.18911711, -0.46665695, 0.22134336, -0.34721588, -0.31997409,\n", " 0.22769666, -0.12279111, 0.39043892, -0.40059278, -1.47428438,\n", " -0.25698252, -1.15126189, 0.98357977, 0.84970328, -1.98720117,\n", " 1.11064262, 1.44028829, -1.63808531, 0.98075371, -0.58109039,\n", " -0.38653214, -0.82579092, -0.08508776, 0.15513385, 0.1818524 ,\n", " -0.92253578, 2.0418283 , -1.02322954, 3.76191565, -0.89168133,\n", " 0.28982609, 0.12799418, 1.01013012, -0.24028744, -0.5506389 ,\n", " -0.21764378, -1.42432799, 0.51234302, 0.12061286, 1.94768532]), 'x': array([ 0. , 0.01010101, 0.02020202, 0.03030303, 0.04040404,\n", " 0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909,\n", " 0.1010101 , 0.11111111, 0.12121212, 0.13131313, 0.14141414,\n", " 0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919,\n", " 0.2020202 , 0.21212121, 0.22222222, 0.23232323, 0.24242424,\n", " 0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929,\n", " 0.3030303 , 0.31313131, 0.32323232, 0.33333333, 0.34343434,\n", " 0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939,\n", " 0.4040404 , 0.41414141, 0.42424242, 0.43434343, 0.44444444,\n", " 0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949,\n", " 0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455,\n", " 0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596 ,\n", " 0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465,\n", " 0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697 ,\n", " 0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475,\n", " 0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798 ,\n", " 0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485,\n", " 0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899 ,\n", " 0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495,\n", " 0.95959596, 0.96969697, 0.97979798, 0.98989899, 1. ]), 'type': 'scatter'}, {'name': '$\\\\mu = -5$', 'mode': 'lines', 'hoverinfo': 'x+y', 'y': array([-4.48987402, -5.00299715, -5.27569342, -3.8223728 , -3.6502064 ,\n", " -4.93187342, -3.5115968 , -7.6986876 , -3.76826013, -6.4241302 ,\n", " -4.73376471, -5.04163874, -5.77634254, -6.26209807, -4.60243579,\n", " -4.67358739, -5.14992072, -5.29748225, -5.06602165, -6.13815511,\n", " -5.564722 , -5.63855557, -4.82780904, -6.33164629, -5.56346489,\n", " -5.70394686, -4.330434 , -5.49693236, -4.84009953, -4.6317476 ,\n", " -4.010567 , -7.48336095, -6.09522473, -4.89513925, -4.83467357,\n", " -4.70691006, -6.33809177, -6.29332726, -2.96358237, -4.7275947 ,\n", " -5.83294186, -5.9606109 , -5.58472397, -5.62344515, -4.02465416,\n", " -3.02183737, -4.86414021, -4.39816038, -6.61453443, -5.61632089,\n", " -5.61164201, -3.60435107, -4.93173037, -5.2246923 , -4.56053351,\n", " -5.71917183, -5.71984894, -5.60812793, -5.16043173, -4.15756347,\n", " -3.7194692 , -5.52792439, -3.62796425, -4.86906244, -3.40373272,\n", " -5.84820953, -3.46684876, -5.16100917, -5.74868657, -5.0236031 ,\n", " -3.56692692, -2.02221892, -6.80212881, -3.8346147 , -5.91834124,\n", " -2.31426304, -6.77714362, -4.95939023, -5.32436323, -4.86277363,\n", " -5.28515788, -4.56678228, -3.81648371, -4.407105 , -4.70197081,\n", " -6.01753656, -6.82434989, -5.93431004, -4.24573797, -4.33361792,\n", " -5.97240983, -4.21877573, -4.80431133, -4.50803704, -4.76269954,\n", " -4.79829357, -4.33052356, -4.4148409 , -5.98019236, -3.94674547]), 'x': array([ 0. , 0.01010101, 0.02020202, 0.03030303, 0.04040404,\n", " 0.05050505, 0.06060606, 0.07070707, 0.08080808, 0.09090909,\n", " 0.1010101 , 0.11111111, 0.12121212, 0.13131313, 0.14141414,\n", " 0.15151515, 0.16161616, 0.17171717, 0.18181818, 0.19191919,\n", " 0.2020202 , 0.21212121, 0.22222222, 0.23232323, 0.24242424,\n", " 0.25252525, 0.26262626, 0.27272727, 0.28282828, 0.29292929,\n", " 0.3030303 , 0.31313131, 0.32323232, 0.33333333, 0.34343434,\n", " 0.35353535, 0.36363636, 0.37373737, 0.38383838, 0.39393939,\n", " 0.4040404 , 0.41414141, 0.42424242, 0.43434343, 0.44444444,\n", " 0.45454545, 0.46464646, 0.47474747, 0.48484848, 0.49494949,\n", " 0.50505051, 0.51515152, 0.52525253, 0.53535354, 0.54545455,\n", " 0.55555556, 0.56565657, 0.57575758, 0.58585859, 0.5959596 ,\n", " 0.60606061, 0.61616162, 0.62626263, 0.63636364, 0.64646465,\n", " 0.65656566, 0.66666667, 0.67676768, 0.68686869, 0.6969697 ,\n", " 0.70707071, 0.71717172, 0.72727273, 0.73737374, 0.74747475,\n", " 0.75757576, 0.76767677, 0.77777778, 0.78787879, 0.7979798 ,\n", " 0.80808081, 0.81818182, 0.82828283, 0.83838384, 0.84848485,\n", " 0.85858586, 0.86868687, 0.87878788, 0.88888889, 0.8989899 ,\n", " 0.90909091, 0.91919192, 0.92929293, 0.93939394, 0.94949495,\n", " 0.95959596, 0.96969697, 0.97979798, 0.98989899, 1. ]), 'type': 'scatter'}]}\n" ] } ], "source": [ "print(fig) # print the figure object in notebook" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Figure objects store data like a Python dictionary. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# (5) Send Figure object to Plotly and show plot in notebook\n", "py.iplot(fig, filename='scatter-mode')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Can save a static image as well:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "py.image.save_as(fig, filename='scatter-mode.png')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Histograms" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# (1) Generate some random numbers\n", "x0 = np.random.randn(500)\n", "x1 = np.random.randn(500)+1\n", "\n", "# (2.1) Create the first Histogram object\n", "trace1 = go.Histogram(\n", " x=x0,\n", " histnorm='count',\n", " name='control',\n", " autobinx=False,\n", " xbins=dict(\n", " start=-3.2,\n", " end=2.8,\n", " size=0.2\n", " ),\n", " marker=dict(\n", " color='fuchsia',\n", " line=dict(\n", " color='grey',\n", " width=0\n", " )\n", " ),\n", " opacity=0.75\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# (2.2) Create the second Histogram object\n", "trace2 = go.Histogram(\n", " x=x1,\n", " name='experimental',\n", " autobinx=False,\n", " xbins=dict(\n", " start=-1.8,\n", " end=4.2,\n", " size=0.2\n", " ),\n", " marker=dict(\n", " color='rgb(255, 217, 102)'\n", " ),\n", " opacity=0.75\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "# (3) Create Data object\n", "data = [trace1, trace2]\n", "\n", "# (4) Create Layout object\n", "layout = go.Layout(\n", " title='Sampled Results',\n", " xaxis=dict(\n", " title='Value'\n", " ),\n", " yaxis=dict(\n", " title='Count'\n", " ),\n", " barmode='overlay',\n", " bargap=0.25,\n", " bargroupgap=0.3,\n", " showlegend=True\n", ")\n", "fig = go.Figure(data=data, layout=layout)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# (5) Send Figure object to Plotly and show plot in notebook\n", "py.iplot(fig, filename='histogram_example')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Distplots\n", "\n", "Similar to `seaborn.distplot`. Plot a histogram, kernel density or normal curve, and a rug plot all together." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "from plotly.tools import FigureFactory as FF\n", "\n", "# Add histogram data\n", "x1 = np.random.randn(200)-2 \n", "x2 = np.random.randn(200) \n", "x3 = np.random.randn(200)+2 \n", "x4 = np.random.randn(200)+4 \n", "\n", "# Group data together\n", "hist_data = [x1, x2, x3, x4]\n", "\n", "group_labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4']\n", "\n", "# Create distplot with custom bin_size\n", "fig = FF.create_distplot(hist_data, group_labels, bin_size=.2)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Plot!\n", "py.iplot(fig, filename='Distplot with Multiple Datasets', \\\n", " validate=False)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 2D Contour Plot" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.random.randn(1000)\n", "y = np.random.randn(1000)\n", "py.iplot([go.Histogram2dContour(x=x, y=y, \\\n", " contours=go.Contours(coloring='fill')), \\\n", " go.Scatter(x=x, y=y, mode='markers', \\\n", " marker=go.Marker(color='white', size=3, opacity=0.3))]) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## 3D Surface Plot\n", "\n", "Plot the function: $f(x,y) = A \\cos(\\pi x y) e^{-(x^2+y^2)/2}$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# Define the function to be plotted\n", "def fxy(x, y):\n", " A = 1 # choose a maximum amplitude \n", " return A*(np.cos(np.pi*x*y))**2 * np.exp(-(x**2+y**2)/2.)\n", "\n", "# Choose length of square domain, make row and column vectors\n", "L = 4\n", "x = y = np.arange(-L/2., L/2., 0.1) # use a mesh spacing of 0.1\n", "yt = y[:, np.newaxis] # (!) make column vector\n", "\n", "# Get surface coordinates!\n", "z = fxy(x, yt)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "trace1 = go.Surface(\n", " z=z, # link the fxy 2d numpy array\n", " x=x, # link 1d numpy array of x coords\n", " y=y # link 1d numpy array of y coords\n", ")\n", "\n", "# Package the trace dictionary into a data object\n", "data = go.Data([trace1])\n", "\n", "# Dictionary of style options for all axes\n", "axis = dict(\n", " showbackground=True, # (!) show axis background\n", " backgroundcolor=\"rgb(204, 204, 204)\", # set background color to grey\n", " gridcolor=\"rgb(255, 255, 255)\", # set grid line color\n", " zerolinecolor=\"rgb(255, 255, 255)\", # set zero grid line color\n", ")\n", "\n", "# Make a layout object\n", "layout = go.Layout(\n", " title='$f(x,y) = A \\cos(\\pi x y) e^{-(x^2+y^2)/2}$', # set plot title\n", " scene=go.Scene( # (!) axes are part of a 'scene' in 3d plots\n", " xaxis=go.XAxis(axis), # set x-axis style\n", " yaxis=go.YAxis(axis), # set y-axis style\n", " zaxis=go.ZAxis(axis) # set z-axis style\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Make a figure object\n", "fig = go.Figure(data=data, layout=layout)\n", "\n", "# (@) Send to Plotly and show in notebook\n", "py.iplot(fig, filename='surface')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Matplotlib Conversion" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.mlab as mlab\n", "\n", "n = 50\n", "x, y, z, s, ew = np.random.rand(5, n)\n", "c, ec = np.random.rand(2, n, 4)\n", "area_scale, width_scale = 500, 5\n", "\n", "fig, ax = plt.subplots()\n", "sc = ax.scatter(x, y, c=c,\n", " s=np.square(s)*area_scale,\n", " edgecolor=ec,\n", " linewidth=ew*width_scale)\n", "ax.grid()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "py.iplot_mpl(fig)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Pros/Cons" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Pros:\n", "* Beautiful, interactive plots\n", "* Great documentation: easy to figure out how to customize\n", "* Easily shareable and modifiable by collaborators using the online GUI\n", "* Can convert `matplotlib` graphs" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Cons:\n", "* Static export only supports `.png` or `.jpg`. No `.eps` for free accounts\n", "* Although not required for use, your data is hosted on their cloud\n", "* Limited number of private figures for free accounts\n", "* Limited usage opportunities for economists" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Extra Resources\n", "\n", "* [Extensive user guide](https://plot.ly/python/user-guide/): can also fork GitHub repo and use their IPython Notebook files\n", "* [GitHub](https://github.com/plotly/plotly.py)\n", "* [Reference guide](https://plot.ly/python/reference/)" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }