From e83f55bee197a213ea65ad746a5b25ed24c55ae1 Mon Sep 17 00:00:00 2001 From: Etienne Roesch Date: Fri, 23 Jul 2021 16:08:56 +0100 Subject: [PATCH] ENH: Initial commit. --- Appendix_-_Glossary/Glossary.ipynb | 73 + .../Ch0.1_Beginning.ipynb | 222 +++ .../Ch0_Introduction.ipynb | 223 +++ Chapter_0_-_Introduction/README.md | 4 + Chapter_1_-_Z-test/Ch1_Bayesian Z-test.ipynb | 986 ++++++++++ .../4.5.0/models/23wo2jct/model_23wo2jct.o | Bin 0 -> 260568 bytes .../Ch2_One-sample_t-test.ipynb | 691 +++++++ Chapter_2_-_One-sample_t-test/test.ipynb | 65 + LICENSE.txt | 88 + README.md | 10 + environment.yml | 13 + requirements_conda.txt | 153 ++ requirements_pip.txt | 106 + ...tegorical regression (one-way ANOVA).ipynb | 785 ++++++++ ...n estimation of logistic regression.ipynb | 1262 ++++++++++++ wip/Bayesian Factor analysis.ipynb | 289 +++ wip/Bayesian Multiple regression .ipynb | 902 +++++++++ wip/Bayesian Poisson estimation.ipynb | 1304 +++++++++++++ wip/Bayesian Robust Regression.ipynb | 531 +++++ ...an between subject t-test estimation.ipynb | 1044 ++++++++++ ...mation of 2x2 Between subjects ANOVA.ipynb | 299 +++ ...ian estimation of Linear mixed model.ipynb | 924 +++++++++ ...yesian estimation of Welch's t-test .ipynb | 946 +++++++++ ...timation of repeated measures t-test.ipynb | 990 ++++++++++ ...timation of simple linear regression.ipynb | 1228 ++++++++++++ ...yesian estimation ordinal regression.ipynb | 126 ++ wip/Bayesian fisher test.ipynb | 331 ++++ ...sian multiple correlation estimation.ipynb | 665 +++++++ ...ian one-way ANOVA (between subjects).ipynb | 1730 +++++++++++++++++ wip/Data/.Rhistory | 512 +++++ wip/Data/Atir Rosenzweig Dunning 2015.csv | 203 ++ wip/Data/Awards.csv | 201 ++ wip/Data/Birthweight_reduced_kg.csv | 43 + wip/Data/Cholesterol_R.csv | 19 + wip/Data/Crime.csv | 48 + wip/Data/Dawtry Sutton and Sibley 2015.csv | 306 +++ wip/Data/Diet.csv | 79 + wip/Data/Harvie et al. 2015.csv | 49 + ...James et al 2015 Experiment 2 Data Set.csv | 73 + wip/Data/Maglio and Polman 2014.csv | 203 ++ ...Mehr Song and Spelke 2016 Experiment 1.csv | 97 + wip/Data/Schroeder and Epley 2015.csv | 40 + wip/Data/Sleepstudy_data | 181 ++ wip/Data/Turning_Hands_Data_Final.csv | 103 + wip/Data/Tworek and Cimpian 2016 Study 1.csv | 132 ++ wip/Paths to Bayes.drawio | 13 + wip/Patsy contrast analysis tutorial.ipynb | 412 ++++ wip/Practicing Bayesian Statistics.md | 160 ++ wip/README.md | 2 + wip/Within subject ANOVA.ipynb | 1427 ++++++++++++++ .../4.5.0/models/ceykbmxk/model_ceykbmxk.o | Bin 0 -> 283176 bytes wip/debug.log | 5 + 52 files changed, 20298 insertions(+) create mode 100644 Appendix_-_Glossary/Glossary.ipynb create mode 100644 Chapter_0_-_Introduction/Ch0.1_Beginning.ipynb create mode 100644 Chapter_0_-_Introduction/Ch0_Introduction.ipynb create mode 100644 Chapter_0_-_Introduction/README.md create mode 100644 Chapter_1_-_Z-test/Ch1_Bayesian Z-test.ipynb create mode 100644 Chapter_1_-_Z-test/build/temp.macosx-10.9-x86_64-3.9/Users/eroesch/Library/Caches/httpstan/4.5.0/models/23wo2jct/model_23wo2jct.o create mode 100644 Chapter_2_-_One-sample_t-test/Ch2_One-sample_t-test.ipynb create mode 100644 Chapter_2_-_One-sample_t-test/test.ipynb create mode 100644 LICENSE.txt create mode 100644 README.md create mode 100644 environment.yml create mode 100644 requirements_conda.txt create mode 100644 requirements_pip.txt create mode 100644 wip/Bayesian categorical regression (one-way ANOVA).ipynb create mode 100644 wip/Bayesian estimation of logistic regression.ipynb create mode 100644 wip/Bayesian Factor analysis.ipynb create mode 100644 wip/Bayesian Multiple regression .ipynb create mode 100644 wip/Bayesian Poisson estimation.ipynb create mode 100644 wip/Bayesian Robust Regression.ipynb create mode 100644 wip/Bayesian between subject t-test estimation.ipynb create mode 100644 wip/Bayesian estimation of 2x2 Between subjects ANOVA.ipynb create mode 100644 wip/Bayesian estimation of Linear mixed model.ipynb create mode 100644 wip/Bayesian estimation of Welch's t-test .ipynb create mode 100644 wip/Bayesian estimation of repeated measures t-test.ipynb create mode 100644 wip/Bayesian estimation of simple linear regression.ipynb create mode 100644 wip/Bayesian estimation ordinal regression.ipynb create mode 100644 wip/Bayesian fisher test.ipynb create mode 100644 wip/Bayesian multiple correlation estimation.ipynb create mode 100644 wip/Bayesian one-way ANOVA (between subjects).ipynb create mode 100644 wip/Data/.Rhistory create mode 100644 wip/Data/Atir Rosenzweig Dunning 2015.csv create mode 100644 wip/Data/Awards.csv create mode 100644 wip/Data/Birthweight_reduced_kg.csv create mode 100644 wip/Data/Cholesterol_R.csv create mode 100644 wip/Data/Crime.csv create mode 100644 wip/Data/Dawtry Sutton and Sibley 2015.csv create mode 100644 wip/Data/Diet.csv create mode 100644 wip/Data/Harvie et al. 2015.csv create mode 100644 wip/Data/James et al 2015 Experiment 2 Data Set.csv create mode 100644 wip/Data/Maglio and Polman 2014.csv create mode 100644 wip/Data/Mehr Song and Spelke 2016 Experiment 1.csv create mode 100644 wip/Data/Schroeder and Epley 2015.csv create mode 100644 wip/Data/Sleepstudy_data create mode 100644 wip/Data/Turning_Hands_Data_Final.csv create mode 100644 wip/Data/Tworek and Cimpian 2016 Study 1.csv create mode 100644 wip/Paths to Bayes.drawio create mode 100644 wip/Patsy contrast analysis tutorial.ipynb create mode 100644 wip/Practicing Bayesian Statistics.md create mode 100644 wip/README.md create mode 100644 wip/Within subject ANOVA.ipynb create mode 100644 wip/build/temp.macosx-10.9-x86_64-3.9/Users/eroesch/Library/Caches/httpstan/4.5.0/models/ceykbmxk/model_ceykbmxk.o create mode 100644 wip/debug.log diff --git a/Appendix_-_Glossary/Glossary.ipynb b/Appendix_-_Glossary/Glossary.ipynb new file mode 100644 index 0000000..3a30687 --- /dev/null +++ b/Appendix_-_Glossary/Glossary.ipynb @@ -0,0 +1,73 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ca7f37e5", + "metadata": {}, + "source": [ + "# Glossary" + ] + }, + { + "cell_type": "markdown", + "id": "a81e1836", + "metadata": {}, + "source": [ + "## M" + ] + }, + { + "cell_type": "markdown", + "id": "e1d11961", + "metadata": {}, + "source": [ + "* Model - A model is an expression about the state of the world, as we hypothesise it to be. It is always formulated in the form of a mathematical expression, even if your favourite statistical software hides it from you, and it is always based on" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Stan", + "language": "python", + "name": "stan" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + }, + "toc": { + "colors": { + "hover_highlight": "#DAA520", + "navigate_num": "#000000", + "navigate_text": "#333333", + "running_highlight": "#FF0000", + "selected_highlight": "#FFD700", + "sidebar_border": "#EEEEEE", + "wrapper_background": "#FFFFFF" + }, + "moveMenuLeft": true, + "nav_menu": { + "height": "48px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 4, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": true, + "widenNotebook": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Chapter_0_-_Introduction/Ch0.1_Beginning.ipynb b/Chapter_0_-_Introduction/Ch0.1_Beginning.ipynb new file mode 100644 index 0000000..02b2775 --- /dev/null +++ b/Chapter_0_-_Introduction/Ch0.1_Beginning.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##
Practicing Bayesian statistics
\n", + "\n", + "## Contents\n", + "- Introduction\n", + "- The two statistical philosophies\n", + " - Frequentist Statistics\n", + " - Bayesian Statistics\n", + "- Bayes Theorem\n", + " - prior\n", + " - Likelihood\n", + " - Posterior\n", + "- Steps of a Bayesian analysis \n", + "- Effect indices/ hypothesis testing\n", + "\n", + " - Maximum a posteriori estimates (posterior point estimates )\n", + " - Credible intervals\n", + "- Markov Chain Monte Carlo (MCMC)\n", + "\n", + "## Introduction\n", + "When Efron and Hastie (2016) identify that “Statistical inference is an unusually wide-ranging discipline, located as it is at the triple-point of mathematics, empirical science, and philosophy” (pp. xv) they make its multidisciplinary nature and complexity clear. This complexity has led to misinterpretation of the standardly taught analysis (frequentist) methods by applied by researchers (Hoekstra, Morey, Rouder, & Wagenmakers, 2014; Lyu, Xu, Zhao, Zuo, & Hu, 2020), seemingly result primarily due to the “philosophy” section of Efron and Hastie’s quote above. As demonstrated dominant use of frequentist methods, whilst seemingly wanting to interpret the results of their analyses under the Bayesian framework (Etz, Bartlema, Vanpaemel, Wagenmakers, & Morey, 2019). The question then is what is Bayesian statistics?\n", + "\n", + "## The two statistical philosophies\n", + "“The numbers have no way of speaking for themselves. We speak for them. We Imbue them with meaning.”\n", + "\n", + "Silver (2012).\n", + "\n", + "“Technical mathematical arguments and formula, though valid and of interest, must always assume, tacitly or explicitly, a philosophy.”\n", + "\n", + "Briggs (2019).\n", + "\n", + "“An important insight that would seem desirable for any statistical philosophy: Conclusions are only as plausible as the subjective foundations on which they are based.”\n", + "\n", + "Western (1999).\n", + "\n", + "As the quotes above point out there is no free lunch when analysing data and conducting statistical inference, but this is often forgotten or ritualised to make it seem as such (Gigerenzeer, 2004).\n", + "\n", + " Crucially, statistical inference is only possible at all because of the application of probability theory and its interpretation when the data are analysed. However, heated debates about what interpretation of probability to apply have occurred since the inception of statistics and have resulted in what has been termed “The statistics Wars” within the statistical literature (Gigerenzer et al., 1983; Mayo, 2018; Salsburg, 2002).\n", + "\n", + "Despite these worthwhile discussions, it is more important to remember it is the act of applying interpreting probability, that makes statistical inference possible at all; and crucially understand what separates the Frequentist and Bayesian frameworks is their differing interpretations of probability.\n", + "\n", + " The two interpretations of probability are objective and subjective probability. Lambert (2018), describes it as a difference in world view by the analyst. As a result, by applying either framework, the analyst is making assumptions about how to model the world through these different views of probability.\n", + "\n", + "Frequentist Statistics (objective probability).\n", + "\n", + "The philosophical grounding of the Frequentist statistics framework is objective probability. The meaning of objective probability in terms of data is expressed by the view that any set of observed data is a sample that has been generated from an infinite replication of the experiment from the data generating process. Therefore, any inference that is justifiable within this framework by analyst is that the observed sample is one with a long-run view of repeated sampling; meaning the data is treated as random but the statistical models that are fit to data are an attempt to estimate fixed population parameters.\n", + "through the attempts to estimate sampling error and this variation is expressed in varying estimates between experiments or observations and stipulates the necessity for replication and long run error control to understand the phenomena under from a statistical analysis view.\n", + "\n", + "Frequentist statistics dominates research with the use of by Null hypothesis significance testing (NHST). NHST tools for inference are based on repeated on tis sampling paradigm such as the p-value which is the $P(D|H_0)$ and confidence intervals (CI) which are not probability statements but an interval estimate of the parameter estimates generated from the data. As such a CI is again based on repeated sampling. As CI is a such that 95% of the confidence calculated will contain the true population parameter within their estimated intervals.\n", + "\n", + "Bayesian statistics (subjective probability).\n", + "\n", + "Bayesian statistics applies probability as an expression of the analyst’s belief and as an expression of their uncertainty around what they are analysing. This is expressed in the reversal of what the Frequentists do by treating the parameters of the statistical models that they fit as random and the data as fixed. In terms of parameters then a Bayesian does not have to deny the existence of a population parameter but can accept the uncertainty generated by each experiment/acquisition of a sample, that the estimates will vary due sample variation. However, a Bayesian analysis can also specify that differences in parameters is due to our uncertainty based on our knowledge.\n", + "\n", + " Under this framework, the use of Bayes theorem and data can be used to update beliefs about phenomena being studied through data analysis. Bayesian statistics allows for inference statements that $P(H_0|D)$, which is of course the opposite of that above from frequentist methods. This type of inference is achieved by the application of Bayes rule.\n", + "\n", + "## Bayes Rule\n", + "$$P(A,B) = P(B,A)\\: \\:\\: \\: \\: \\:(1)$$\n", + " $$P(B|A)P(A) = P(A|B)P(B)\\: \\:\\: \\: \\: \\:(2) $$\n", + " $$P(A|B) = \\frac{P(B|A)P(A)}{P(B)}\\: \\:\\: \\: (3)$$\n", + "\n", + "Hopefully by the outlining Bayes rule above it becomes explicit why we would want to use the statistical practices on which it is based. In case it is not explicit, it is because the posterior allow sus to answer a question in which we are interested in that what is the probability of our hypothesis we are testing base on assumptions of the statistical models of which we use to test the data.\n", + "\n", + "## Steps of a Bayesian analysis\n", + "Kruschke (2015) outlines general Bayesian analysis steps which include:\n", + "\n", + "1. Identify data relevant to the research question including the variables of interest.\n", + "2. Identify a descriptive model for the data that you are going to analyse. Meaning the mathematical model and the parameter's should be appropriate to the data.\n", + "3. Specify priors for the parameter of the model, priors should be reasonable and will have to pass an audience of reviewers.\n", + "4. Use Bayes theorem and calculate the posterior.\n", + "5. Conduct posterior predictive checks.\n", + "\n", + "## Testing indices/hypothesis testing in the Bayesian framework\n", + "\n", + " Due to the standard training of researchers to use NHST methods in analysing their data and the ingrained practice of using p-values, which has created an illusionary sense of the ease to understand data analysis and statistical inference with decision-making rules such as standard use of thresholds (i.e. ≤ .05) and concluding a result or body of work is meaningful or significant in the standard sense of the word when it is only significant in the statistical sense of the word. With this culminating and resulting in publication.\n", + "\n", + " A result of the standard training in NHST is likely to lead to the first question any researcher curious about Bayesian methods to probably ask, what is its p-value equivalent? A more general and helpful question though would be what are the outputs that are used for making inferences from the data within this framework? Answering that question is likely to result in trepidation by interested parties in applying Bayesian tools in analyzing data, due to the flexibility of the framework and the variety of the analysts’ options. This can either be seen as complicating or liberating.\n", + "\n", + " To simplify Bayesian methods of inference is to separate out Bayes factor analysis and Bayesian estimation. There is plenty of discussion in the literature of the advantages and disadvantages of each method (Dienes, 2020; Makowski, Ben-Shachar, Chen, & Lüdecke, 2019), with no general consensus.\n", + " The notebooks here focus on Bayesian estimation. For clarity, this is not to suggest a preference because when it comes to statistical tools more options are an advantage but to go over those methods is a project on its own.\n", + " Bayesian estimation test indices\n", + " Point estimates (Mean, median)/Maximum a posteriori estimation (MAP - the mode)\n", + " Posterior mean - minimises the expected square error\n", + " Posterior median - minimises expected absolute error\n", + " MAP - most probable value on the posterior distribution.\n", + "\n", + " Expression of the uncertainty in parameter estimates.\n", + " Credible intervals Krushcke (2015) (plausibility intervals, McElreath, 2020; uncertainty intervals, Gelman et al., 2013).\n", + "\n", + " 95% credible interval - Krushcke (2015) 10000 effective samples for stable estimates\n", + "\n", + " 50% credible interval - (Gelman, 2016) suggest using the 50% credibility interval, which gives the quantile interval between 25% and 75% of the posterior distribution.\n", + " Gelman argues usin this interval: Increases the computational stability, gives the credibility interpretation that true value contained in this 50% interval and finally this interval helps avoid certainty.\n", + "\n", + " ROPE (95%)\n", + "\n", + " ROPE (Full)\n", + "\n", + " Hopefully, the different recommendations of the interval size to use when summarising the posterior makes the reader think this is arbitrary like a p-value threshold of .05 because it is and it is down to the analysis to decide and make the argument for their choice. If that choice is due to expert suggestions or for general research practices.\n", + "\n", + "## Markov chain Monte Carlo (MCMC)\n", + "In laymen terms MCMC are mathematical tools/algorithms for sampling form posterior distributions. MCMC underlies all modern applications of Bayesian statistics. this is because many of the statically models to answer complex question in which statisticians and researchers are concerned do not have analytical solutions for calculating the posterior. To overcome this numerical methods such as MCMC must be applied.\n", + "\n", + "## Why Stan?\n", + "\n", + "\n", + "###
References
\n", + "\n", + "Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint\t \tarXiv:1701.02434.\n", + "\n", + "Briggs, W. M., 2019. Everything Wrong with P-Values Under One Roof. In Beyond Traditional Probabilistic Methods in Economics, Kreinovich, V., Thach, N. N., Trung, N. D., Thanh, D. V. (eds.), pp 22–44. DOI 978-3-030-04200-4_2\n", + "\n", + "Briggs, W. M. (2012). It is time to stop teaching frequentism to non-statisticians. arXiv preprint\t\t arXiv:1201.2590.\n", + "\n", + "Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal\t\t of statistical software, 80(1), 1-28.\n", + "\n", + "Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell,\t\t A. (2017). Stan: A probabilistic programming language. Journal of statistical software,\t\t 76(1).\n", + "\n", + "Dienes, Z. (2020). How to use and report Bayesian hypothesis tests.\n", + "\n", + "Efron, B., & Hastie. (2016). Computer age Statistical Inference: Algorithms, evidence, and data science. New York, NY: Cambridge University Press.\n", + "\n", + "Etz, A., Bartlema, A., Vanpaemel, W., Wagenmakers, E. J., & Morey, R. D. (2019). An exploratory survey of student and researcher intuitions about statistical evidence. In\t\t Poster presented at the annual meeting of the Association for Psychological Science, Washington, DC.\n", + "\n", + "Gabry, J., & Goodrich, B. (2016). rstanarm: Bayesian applied regression modeling via stan\t[computer software manual]. Retrieved from http://CRAN.R‐project.org/\t\t\t\t package=rstanarm (R package version 2.9.0‐1)\n", + "\n", + "Gelman, A. (2016, November 5) Why I prefer 50% rather than 95% intervals [blog post]. Retrieved from https://statmodeling.stat.columbia.edu/2016/11/05/why-i-prefer-50-to-95-intervals/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+MyDataScienceBlogs+%28My+Data+Science+Blogs%29\n", + "\n", + "Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., Kruger, L. (1989). The empire \t \tof chance: How probability changed science and everyday life. New York, NY:\tCambridge University Press.\n", + "\n", + "Hoekstra, R., Morey, R. D., Rouder, J. N., & Wagenmakers, E. J. (2014). Robust misinterpretation of confidence intervals. Psychonomic bulletin & review, 21(5), 1157-1164.\n", + "\n", + "Krushcke, J. K. (2015). Doing Bayesian analysis: A tutorial with R, JAGS and Stan. London, England: Academic Press.\n", + "\n", + "Lambert, B. (2018). A student guide to Bayesian statistics. London, England: SAGE.\n", + "\n", + "Lynch, S. M., & Bartlett, B. (2019). Bayesian Statistics in Sociology: Past, Present, and Future.Annual Review of Sociology, 45, 47-68.\n", + "\n", + "Lyu, X. K., Xu, Y., Zhao, X. F., Zuo, X. N., & Hu, C. P. (2020). Beyond psychology: prevalence of\tp values and confidence interval misinterpretation across different fields. Journal of Pacific Rim Psychology, 14.\n", + "\n", + "Makowski, D., Ben-Shachar, M. S., Chen, S. H., & Lüdecke, D. (2019). Indices of effect existence and\t\t significance in the bayesian framework. Frontiers in psychology, 10, 2767.\n", + "\n", + "Mayo, D.G. (2018). Statistical inference as severe testing: How to get beyond the statistics\t\t wars. New York: NY. Cambridge University Press.\n", + "\n", + "McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and\t \tStan. London, England: CRC Press.\n", + "\n", + "Morey, R. D., Hoekstra, R., Rouder, J. N., Lee, M. D., & Wagenmakers, E. J. (2016). The fallacy of placing confidence in confidence intervals. Psychonomic bulletin & review, 23(1), 103-123.\n", + "\n", + "Morey, R. D., Rouder, J. N., Jamil, T., & Morey, M. R. D. (2015). Package ‘bayesfactor’. URLh\t\t http://cran/r-projectorg/web/packages/BayesFactor/BayesFactor pdf i (accessed 1006\t\t 15).\n", + "\n", + "Muth, C., Oravecz, Z., & Gabry, J. (2018). User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. Quantitative Methods for Psychology, 14(2),\t\t 99-119.\n", + "\n", + "Salzburg, D. (2002). The lady Tasting tea: How statistics revolutionised science in the twentieth\t\t century. New York, NY. First Holts.\n", + "\n", + "Stan Development Team. (2017). Stan modeling language users guide and reference manual,\tversion 2.17.0. Retrieved from http://mc-stan.org/\n", + "\n", + "Vasishth, S., & Nicenboim, B. (2016). Statistical methods for linguistic research: Foundational ideas–Part I. Language and Linguistics Compass, 10(8), 349-369.\n", + "\n", + "Vasishth, S., & Nicenboim, B. (2016). Statistical methods for linguistic research: Foundational\t\t ideas–Part I. Language and Linguistics Compass, 10(8), 349-369.\n", + "\n", + "Western, B. (1999). Bayesian analysis for sociologists: An introduction. Sociological Methods &\t Research, 28(1), 7-34.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Stan", + "language": "python", + "name": "stan" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + }, + "toc": { + "colors": { + "hover_highlight": "#DAA520", + "navigate_num": "#000000", + "navigate_text": "#333333", + "running_highlight": "#FF0000", + "selected_highlight": "#FFD700", + "sidebar_border": "#EEEEEE", + "wrapper_background": "#FFFFFF" + }, + "moveMenuLeft": true, + "nav_menu": { + "height": "243px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 4, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": true, + "widenNotebook": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Chapter_0_-_Introduction/Ch0_Introduction.ipynb b/Chapter_0_-_Introduction/Ch0_Introduction.ipynb new file mode 100644 index 0000000..2f16aba --- /dev/null +++ b/Chapter_0_-_Introduction/Ch0_Introduction.ipynb @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Statistical Methods for Research Workers: Bayes for Psychologists & Neuroscientists\n", + "\n", + "##### Version 0.1 (EBRLab)\n", + "\n", + "The full Github repository is available at [github/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists](https://github.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists). We hope you enjoy this book, and encourage contributions!\n", + "\n", + "This work is shared under a [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/), which means:\n", + "* You CAN share — copy and redistribute the material in any medium or format\n", + "* You CAN adapt — remix, transform, and build upon the material\n", + "* You MUST give proper appropriate credit - provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n", + "\n", + "EBRLab (in preparation). _Statistical methods for research workers: Bayes for psychologists and neuroscientists_. [https://github.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists](https://github.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists). doi: https://doi.org/10.5281/zenodo.8475.\n", + "\n", + "* You CANNOT use the material for commercial purposes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**tl;dr**: In these pages, you will be shown how Bayesian statistics can work for the kinds of things that researchers in psychology and neuroscience might want to do, using Python notebooks. You can dive in the notebooks and copy-paste what's most relevant to you, or you can take your time and follow the table of content. We aim to explain what it means to run a Bayesian analysis, and introduce concepts as we need them. Therefore, it may be useful to review the earlier notebooks even if you are not interested in these simpler statistical tests." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_\"[...] we may say that a phenomenon is experimentally demonstrable when we know how to conduct an experiment which will rarely fail to give us statistically significant results.\"_\n", + "[Ronald A. Fisher](https://en.wikipedia.org/wiki/Ronald_Fisher) (17 February 1890 - 29 July 1962). The design of experiments (1951). p. 14.\n", + "\n", + "_\"All models are wrong but some models are useful.\"_\n", + "[George E. P. Box](https://en.wikipedia.org/wiki/George_E._P._Box) (18 October 1919 – 28 March 2013). Robustness in the Strategy of Scientific Model Building (1979). p. 2.\n", + "\n", + "_\"The big problem in science is not cheaters or opportunists, but sincere researchers who have unfortunately been trained to think that every statistically 'significant' result is notable.\"_\n", + "[Andrew Gelman](http://www.stat.columbia.edu/~gelman/) (11 February 1965 – very much alive). Essay: The Experiments Are Fascinating. But Nobody Can Repeat Them. [The New York Times (19/10/2018)](https://www.nytimes.com/2018/11/19/science/science-research-fraud-reproducibility.html).\n", + "\n", + "_\"The purpose of models is not to fit the data but to sharpen the question.\"_\n", + "[Samuel Karlin](https://en.wikipedia.org/wiki/Samuel_Karlin) (8 June 1924 - very much alive). 11th R. A. Fisher Memorial Lecture (20 April 1983) Royal Society.\n", + "\n", + "_“But this long history of learning how to not fool ourselves of having utter scientific integrity is, I'm sorry to say, something that we haven't specifically included in any particular course that I know of. We just hope you've caught on by osmosis.\n", + "The first principle is that you must not fool yourself and you are the easiest person to fool. So you have to be very careful about that. After you've not fooled yourself, it's easy not to fool other scientists. You just have to be honest in a conventional way after that.”_\n", + "[Richard P Feynman](https://en.wikipedia.org/wiki/Richard_Feynman) (11 May 1918 - 15 February 1988). Surely You're Joking, Mr. Feynman! (1997), p. 198.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "[Statistical Methods for Research Workers](https://en.wikipedia.org/wiki/Statistical_Methods_for_Research_Workers), written by [Ronald A. Fisher](https://en.wikipedia.org/wiki/Ronald_Fisher) in 1925, is a landmark publication that ended up not only defining how psychologists and neuroscientists analyse their data, but also influencing how they actually perceive the world.\n", + "Almost a hundred years ago, the field of statistics, i.e. mathematics applied to data, was not at all structured in the way it is today, and \"research workers\" had to refine, and often invent the methods that would best suit their needs (e.g., computing numbers without a computer) and constraints (e.g., not having a lot of data).\n", + "\n", + "In this seminal book, Fisher codified what it meant to analyse data for the purpose of creating knowledge about the world, paving the way for what came to be statistics as we now know it. Later, the advent of computers transformed the tools available to analyse data, widening the use of statistics to researchers who did not necessarily need or want to deepen their mathematical understanding of the methods.\n", + "\n", + "It is important to note that we, \"research workers\", do not all have the same experience of statistics. Most psychologists and neuroscientists, for instance, would barely have heard of the debate between Frequentist and Bayesian methods, which can be rocking the core of other scientific fields.\n", + "Indeed, in psychology and neuroscience, the teaching of statistics typically follows almost to the letter the table of content from Fisher's book, and it is not unusual for students to only \"know\" statistics through the menus of a Graphic User Interface on a proprietary piece of software, instead of having acquired enough intuition to understand how distributions of data are manipulated and evaluated.\n", + "\n", + "In this book, we are proposing an opinionated exploration of how Bayesian methods can be applied to the fields of psychology and neuroscience, using [Python](https://www.anaconda.com/products/individual), with the explicit intent of providing research workers with an opportunity to see and understand what they are doing to their data.\n", + "Our motivation stems from our experience of the so-called [Reproducibility Crisis](https://osf.io/qky8t), which has hit both fields.\n", + "The Bayesian approach requires commitment to all parts of a given [model](https://nbviewer.jupyter.org/github/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/blob/master/Chapter_0_-_Introduction/Glossary.ipynb#M-11), and relies on explicit assumptions formulated by the researcher.\n", + "Doing good research is thus about justifying one's choices, and not about finding a test that will yield the required number of stars in a table.\n", + "\n", + "Importantly, we do not seek to engage in the debate opposing Frequentists and Bayesians.\n", + "Although our preference might show at time, we favour the more oecumenical view that frequentist shortcuts can be justified in precise contexts; a point, we posit even Fisher would concede.\n", + "We will do our best to only expect entry-level [Python](https://www.anaconda.com/products/individual) coding skills and statistics, and to favour explaining over writing optimised code, showing over assuming." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "The ultimate prerequisite to be able to follow these pages is the willingness to question your knowledge, and to deepen your understanding of the methods you may have heard about for many years.\n", + "\n", + "Although you might learn a thing or two, these pages are not about teaching and learning [Python](https://www.anaconda.com/products/individual). In fact, even though the code is documented extensively, we ambition for the notebooks to be understandable by anyone who has passing command of English and no Python; just skip the code and focus on the rest.\n", + "If you did want to brush up your Parseltongue skills, we recommend you start [here](https://swcarpentry.github.io/python-novice-inflammation/), and hopefully we will even meet you [there](https://aspp.school/).\n", + "\n", + "Perhaps the most difficult prerequisite, however, for a psychologist or a neuroscientist, may be the ability to see the world differently, to ask questions beyond evaluating differences between groups.\n", + "The Bayesian method affords many more questions to be asked than does the Frequentist method.\n", + "In these pages, we apply the Bayesian framework to the subset of questions that would feature in your typical frequentist textbook, like [this one](https://www.amazon.co.uk/Discovering-Statistics-Using-Andy-Field/dp/1446200469), which followed suite from Fisher's seminal work.\n", + "In so doing, we also point out a range of other questions that someone may be interested in.\n", + "\n", + "We are forever in debt to the authors who compiled academic books and articles about Bayesian methods, which we learned from and inspired many, many of our examples and explanations. If you want to go beyond the material we present, we recommend the following textbooks:\n", + "\n", + "* Kruschke, J.K. (2011). [Doing Bayesian Data Analysis: A tutorial with R, JAGS, and Stan (Second edition)](https://www.amazon.co.uk/Doing-Bayesian-Data-Analysis-Tutorial/dp/0124058884). Academic Press. ISBN: 978-0-12-405888-0.\n", + "* Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., & Rubin, D.B. (2014). [Bayesian data analysis (Third edition)](https://www.amazon.co.uk/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954). CRC Press. ISBN: 978-1-4398-4095-5.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running our notebooks\n", + "\n", + "The notebooks should be self-explicit, as we think that explanation is more important than code. Therefore, there is no need to run the notebooks yourself. We provide you with [everything you need to do so](https://github.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists), if you want to, but you don't have to.\n", + "\n", + "Each notebook is also meant to be self-contained, and will have been compiled to show the output of each cell. We aim for the notebooks to be interactive as well, using [Binder](https://mybinder.org), but please use this feature sparingly: running any model is always done at a cost (e.g. [MIT Tech Review | Training a single AI model can emit as much carbon as five cars in their lifetimes](https://www.technologyreview.com/s/613630/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/), 6/6/2019), so think before you click.\n", + "\n", + "If you want to run these models at home or adapt the way of doing things to your own purposes, it helps to understand the workflow. Designing a Bayesian analysis is done iteratively, looping through the following stages:\n", + "1. Formulating a question: writing down a model/assumptions on paper.\n", + "2. Implementing the model in code.\n", + "3. Answering the question: Evaluating specific parts of the model, and going back to 1.\n", + "\n", + "Writing down a model means that you must make explicit every little assumption you have about the state of the work your model is describing, that includes information about the kind of data you intend to collect (collectively known as priors of the model), about the kind of apparatus you will be using that will have its own intrinsic biases (known as the likelihood of the model) and everything else you can think of, like how the variables relate to each other, etc.\n", + "\n", + "In your typical Frequentist framework, these decisions are made for you, in the form of the (infamous) \"assumptions of the test\", which you may remember from your UG Stats 101 module. These include assumptions about the sample of data you are collecting, like constancy of variance of residuals (homoscedasticity) and their independence and normality, but they also include assumptions about how the data is expected to relate to the \"population\" of all possible data.\n", + "\n", + "Implementing the model and running it will typically be done with dedicated tools. Because of the big number and wide range of questions that could be asked of any given data, there is no such a thing as a complete piece of software with a nice graphic user interface. In fact, because the aim is to be explicit about everything, it may not even be desirable to have such a magic tool, which may be incomplete and opaque. The closest approximations of such tools may currently be [JASP](https://jasp-stats.org), or the R packages [BRMS](https://cran.r-project.org/web/packages/brms/index.html) or [rethinking](https://github.com/rmcelreath/rethinking), but what you can do with these packages will be limited in some ways.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Content\n", + "\n", + "### Bayesian analysis in practice\n", + "\n", + "* The beginning\n", + "* Diagrams, Distributions\n", + "\n", + "\n", + "### Tests of Goodness of Fit, independence and homogeneity\n", + "\n", + "* [One-sample Z-test](https://nbviewer.jupyter.org/github/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/blob/master/Chapter_1_-_Z-test/Ch1_Bayesian%20Z-test.ipynb)\n", + "\n", + "* Simple linear regression (work in progress)\n", + "* Logistic regression (work in progress)\n", + "* Ordinal regression (work in progress)\n", + "* Multiple correlation estimates (work in progress)\n", + "* Poisson regression (work in progress)\n", + "* Robust regression (work in progress)\n", + "\n", + "### Tests of significance of means, differences of means, and regression coefficients\n", + "\n", + "* [One-sample t-test](https://nbviewer.jupyter.org/github/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/blob/master/Chapter_2_-_One-sample_t-test/Ch2_One-sample_t-test.ipynb)\n", + "\n", + "* Between-subjects t-test (work in progress)\n", + "* Repeated measures t-test (work in progress)\n", + "\n", + "* Welch's unequal variance t-test (work in progress)\n", + "\n", + "* Categorical regression (one-way ANOVA) (work in progress)\n", + "* Within-subjects ANOVA (work in progress)\n", + "* One-way Between-subjects ANOVA (work in progress)\n", + "* 2x2 Between-subjects ANOVA (work in progress)\n", + "* Linear mixed model (work in progress)\n", + "* Patsy contrast analysis (categorical dummy variable) (work in progress)\n", + "\n", + "### Intraclass correlations and the analysis of variance\n", + "\n", + "* Fisher's exact test (work in progress)\n", + "* Factor analysis (work in progress)\n", + "\n", + "### Further applications fo the Analysis of variance\n", + "\n", + "### Appendices\n", + "\n", + "### Prologue" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "colors": { + "hover_highlight": "#DAA520", + "navigate_num": "#000000", + "navigate_text": "#333333", + "running_highlight": "#FF0000", + "selected_highlight": "#FFD700", + "sidebar_border": "#EEEEEE", + "wrapper_background": "#FFFFFF" + }, + "moveMenuLeft": true, + "nav_menu": { + "height": "243px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 4, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": true, + "widenNotebook": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Chapter_0_-_Introduction/README.md b/Chapter_0_-_Introduction/README.md new file mode 100644 index 0000000..5757761 --- /dev/null +++ b/Chapter_0_-_Introduction/README.md @@ -0,0 +1,4 @@ +# Chapter 0: Introduction + +### [Read it online here](https://nbviewer.jupyter.org/github/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/blob/master/Chapter_0_-_Introduction/Ch0_Introduction.ipynb) + diff --git a/Chapter_1_-_Z-test/Ch1_Bayesian Z-test.ipynb b/Chapter_1_-_Z-test/Ch1_Bayesian Z-test.ipynb new file mode 100644 index 0000000..5f30079 --- /dev/null +++ b/Chapter_1_-_Z-test/Ch1_Bayesian Z-test.ipynb @@ -0,0 +1,986 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": "true" + }, + "source": [ + "# One-sample Z-test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook will be slightly different from the other notebooks in this repository: here, we will simulate data, whereas in the others, we use data from the real world.\n", + "\n", + "Z-tests are a family of statistical tests that allow us to ask questions like \"Is this sample of data representative of the population, which we assume follows a normal distribution?\" Such tests will calculate a statistic (a number) to estimate whether the distribution of the data mimicks what we could be expecting to see in the [normal distribution](https://en.wikipedia.org/wiki/Normal_distribution) of the population. For instance, assuming that [the IQ of the general population peaks at 100, with a standard deviation of 15](https://en.wikipedia.org/wiki/IQ_classification), you measure the IQ of your closest friends. Are you and your friends unusually intelligent?\n", + "\n", + "Demonstrating the technique is therefore more easily done when you can create as much or as little data as you want. Particularly, we can specify the mean and standard deviation of the grand population, and we can also draw as much sample data as we want and create all kinds of situations. Because we are the Masters of our \"data generation process\", we also remove the uncertainty on the fact that there may not be any statistical relationship in the data to begin with." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The classic one sample z-test\n", + "\n", + "The one-sample Z-test calculates a Z statistic by comparing the mean of the data $\\bar{X}$ against that of a population with mean $\\mu_0$ and standard deviation $\\sigma$, and assumes that there is no difference between the two distributions (the \"Null hypothesis\").\n", + "\n", + "$$Z = \\frac{\\bar{X}-\\mu_0}{\\sigma}$$\n", + "\n", + "This Z-score, as it is sometimes called, is a quantity that represents how the mean should be in relation to a standard deviation, and we can then compare the score we obtained from the distribution of our data to that of what we would obtain in a normal distribution of data. If the scores are different, it means they are drawn from the same population. Alongside tables of such numbers calculated for a normal distribution, you will also find measures of probability that a given score can be found, which would be used to determine a p-value, against an acceptable $\\alpha$ level, as it is frequently used in a frequentist null hypothesis significance test.\n", + "\n", + "$$H_0:\\bar{X} = \\mu_0$$\n", + "$$H_1:\\bar{X}\\neq \\mu_0$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + "Following on from the quick description of the classic Z-test above its important to keep in mind that Bayesian analysis inference are all derived from the application of Bayes rule\n", + "\n", + "$$P(\\theta \\mid y) = \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$$\n", + "\n", + "and as such while the following description of the Bayesian model is an equivalent to the Z-test, it is fundamentally different, because its uses fully probabilistic modelling and the infernce is not based on sampling distributions.\n", + " \n", + "For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. If scientific research publication is the goal, the priors must be accepted by a skeptical audience. Much of this can be achieved using prior predcitve checks to ascertain if the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then use the posterior for inference.\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation\n", + "\n", + "The example below is based on simulating and analysing IQ scores. A highly understood phenemomena in the general population, in terms of the standard deviation; due the standardisations of the test with an average IQ set at 100 and standard deviation of 15 (Warne, 2020). In additon to this using IQ data has become standard in Bayesian data analysis teaching (Kruschke, 2015) for pedagogical reasons and as such is used here." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulate the IQ scores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import data analysis and visualisation packages.\n", + "import numpy\n", + "import pandas\n", + "import stan\n", + "import matplotlib.pyplot as plt\n", + "import seaborn\n", + "import scipy.stats as stats\n", + "import arviz\n", + "\n", + "# Importing nest_asyncio is only necessary to run pystan in Jupyter Notebooks.\n", + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#from IPython.core.display import HTML as Center\n", + "\n", + "#Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set random seed so the analysis to follow is reproducible.\n", + "np.random.seed(1)\n", + "\n", + "# Generating 100 random samples from a normal distribution with mean of 100 and standard deviation of 15, representing \n", + "# IQ scores, that are then analysed below.\n", + "IQ = np.round(np.random.normal(loc = 100, scale = 15, size = 100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualise and explore the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Set Seaborn theme for data visualisations.\n", + "sns.set_style(\"white\");\n", + "\n", + "# Plot a histogram of the data with a normal distribution imposed.\n", + "sns.distplot(IQ, fit = stats.norm, kde= False);\n", + "plt.xlabel(\"IQ Scores\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From eyeballing the data its looks normally distributed. This of course is unsurprising seeing as it has been randomly generated from a normal distribution. This point is brought up because even though the data visually looks normally distributed it is not a justification for selecting a normal likelihood to model the data. When deciding on how to model the data the likelihood should be based on attempting to model the Data Generating Process (DGP) this of course is unknown, but there is a level of pragmatic choice. As as analyst being open to critisism to your modelling choices can helpful oneto improve your modelling and two ultimately improve the inferences and understanding of the phenomena you are studying.\n", + " \n", + "However as this notebook is demonstrating a Bayesian Z-test equivalent a normal likelihood will be used as that is an assumption of the Z-test, and is an assumption of the Bayesian model specified below in section 5, however using full probability models and Bayes rule for our inference allows much greater flexibilty, which will be demonstrated in the other repository notebooks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "\\large y_i &\\sim Normal(\\mu,15)\n", + "\\\\ \\large \\mu &\\sim Normal(100, 20)\n", + "\\end{align*}\n", + "\n", + "The formulation for presenting statistical models here follows that used by McElreath (2020) for its intuitive nature. In plain english the model specifies that the dependent variable $y_i$ (IQ scores) is distributed normally in terms of the Likelihood with a known standard deviation of 15 but an unkown $\\mu$ that is to be estimated with a normally distributed prior probability on $\\mu$ that has a $ \\mu = 100$ and $ \\sigma = 20$." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3 - Specifying priors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prior predictive checks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualising priors\n", + "The first step in prior predictive check is to visualise the priors within the model for the parameters being modelled.\n", + "As the statistical model specified above shows their is only one prior in the analysis and the standard deviation\n", + "is assumed to be known at 15 and as such is not estimated and does not need a prior.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# PLot the prior of the model for describing possible mean values of the DGP for IQ scores. This is quite a broad prior\n", + "# and shouldnt be too controversialto a skeptical audience of IQ researchersas it assigns 95% probability of the mean for IQ to be estimated ranging from \n", + "# from 60 and 140.\n", + "\n", + "#Creat range of points to plot the pdf function on\n", + "Range_of_X_axis = np.arange(60, 140, 0.001)\n", + "\n", + "#Plot the normal pdf that shows our prior on the means of the probable mean values for IQ\n", + "plt.plot(Range_of_X_axis, stats.norm.pdf(Range_of_X_axis, loc = 100, scale = 20));\n", + "plt.xlabel(\"95% prior probability on the mean value of IQ\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that that the pdf is cut of at 2 standard deviations above as this is where 95% of the probability mass lies for the probable values of $mu$ and shows how broad the prior is in the case of probable IQ scores, follwing the 68–95–99.7 rule of normal distributions.<\\font>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating data based on the priors\n", + "\n", + "Following the visualisation of the priors for the parameters of the model to \n", + "check how they interact it is important to run prior predcitive check by \n", + "simulating data based on the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Following visualisng the prior for the only parameter that will be estimated within the model \n", + "# we can generate simulated data based on this prior and assumed known standard deviation of 15 for IQ scores.\n", + "\n", + "# Set seed to allow for the reprodcuiblity of notebook.\n", + "np.random.seed(1)\n", + "\n", + "# Simulate data from the prior for the mean of the model specified above.\n", + "sample_mu = np.random.normal(loc= 100, scale = 20, size = 1000)\n", + "\n", + "# Generate a simulated data set of IQ scores based on the prior for the means and the assumed known Standard deviation (15)\n", + "# and the normal likelihood.\n", + "prior_PC = np.random.normal(loc = sample_mu, scale = 15, size = 1000)\n", + "\n", + "# Plot the simulated data density\n", + "sns.distplot(prior_PC, hist=False);\n", + "plt.xlabel(\"Prior Predictive simulated IQ scores\");\n", + "\n", + "# Plot vertical line of the 2 standard deviatons either side of the simulated data.\n", + "plt.vlines((np.mean(prior_PC) + 2 * np.std(prior_PC)), ymin=0, ymax=0.018);\n", + "plt.vlines((np.mean(prior_PC) - 2 * np.std(prior_PC)), ymin=0, ymax=0.018);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As the prior predictive check of our priors shows that the simulated data is reasonable within the understanding of IQ, whilst sldo incoprortaing s reasonble uncertainty for a skeptical audience in that our priors enocde that it is 95% probable to observe individuals with IQ's as low as 50 and as high as 150." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4 - Use Bayes rule.\n", + "\n", + "The software of choice to conduct Bayesian inference on the data here is Stan and the model is specified below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stan model of a Bayesian Z-test\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below the statistical model defined above in section 5 of this notebook is coded in Stan code for compilation below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Stan model to repliciate a bayesain estiamtion equivalnet of the classical one sample z-test.\n", + "\n", + "One_z_test_model = \"\"\"\n", + "\n", + "data{\n", + "// Number of IQ data points\n", + "int N;\n", + "\n", + "// Define a vector of dependent variable (IQ scores) values\n", + "vector[N] y;\n", + "real sigma;\n", + "\n", + "}\n", + "\n", + "parameters{\n", + "// Because in the traditional Z-test we assume we know the standard deviation of the DGP\n", + "// we do not estimate it and the only unknown is the mu (mean) parameter in the normal \n", + "// model specified below.\n", + "real mu;\n", + "}\n", + "\n", + "model{\n", + "// The priors\n", + "mu ~ normal(100, 20);\n", + "\n", + "// The likelihood\n", + " y ~ normal(mu, sigma);\n", + "}\n", + "\n", + "generated quantities{\n", + "\n", + "// Generated a real value for difference between the MCMC samples of mu and 100.\n", + "real diff = mu - 100;\n", + "\n", + "// Cohen D calculation of the MCMC samples to get the standardised effect size\n", + "// for the diffence between the mean estimates and 100.\n", + "real Cohen_D = (mu - 100) / sigma;\n", + "\n", + "// Generate posterior p-value variable \n", + "int mean_pv;\n", + "real yrep[N];\n", + "\n", + "{\n", + " // Generate data for posterior samples\n", + " for (i in 1:N) {\n", + " yrep[i] = normal_rng(mu, sigma); \n", + " }\n", + "}\n", + "\n", + "mean_pv = mean(yrep) > mean(y);\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Stan model above takes the data in the model block, the only parameter in this Z-test equivalent to be estimated is $\\large \\mu$ and as such is specified in the parameters block. The model itself is then defined in the model block, this is where most of the action happens, and the postersior is estimated using Stan NUTS HMC sampler by taking the prior on $\\large \\mu$ and the likelihood for the IQ scores data to estimate the posterior." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The Stan model is complied into C++ code\n", + "#sm = ps.StanModel(model_code = One_z_test_model) # 2.19 (windows)\n", + "\n", + "# To use pystan the data must be stored in a python dictionary that coincides with what was declared in the data\n", + "# block of the stan model above, in order, that it can be passed to the compiled stan model for fitting below.\n", + "\n", + "data = {'N': len(IQ),\n", + " 'y': IQ,\n", + " 'sigma': 15\n", + " }\n", + "\n", + "sm = ps.build(One_z_test_model, data=data, random_seed=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fit the One_z_test_model specified above.\n", + "#fit = sm.sampling(data = data, iter = 2000, chains = 4, seed = 1, warmup = 1000) # 2.19\n", + "\n", + "\n", + "\n", + "# 2000 iterations of MCMC samples with half being used as warmup, within 4 indepedent chains, \n", + "# seed is set for reprodcubiltiy, however due to stochastic nature of sampling there may \n", + "# be slight variation across machines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python print statement a large number of model outputs do not scale and pRICULAR outputs cannot be selected,\n", + "# so it is easier to put outputs into a pandas dataframe.\n", + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns=summary['summary_colnames'], \n", + " index=summary['summary_rownames'])\n", + "\n", + "#Output model results.\n", + "fit_df.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit-visualisations - Bayesian one sample Z-test\n", + "The arviz package offers many useful functions for plotting MCMC samples of the posteriors produced by Bayesian data analysis with Stan." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior distributions plots\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using the arviz package the posteriors can be plotted from the MCMC samples\n", + "az.plot_posterior(fit, var_names=(\"mu\", \"diff\",\n", + " \"Cohen_D\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The posterior above shows that the simulated $\\large \\mu$ of 100 is captured witin the posterior with a posterior mean of 101 and there no statistical difference between the estimate $\\large \\mu$ and 100 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autocorrelation plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using the arviz package the autocorrelation of the 4 MCMC chains can be plotted.\n", + "az.plot_autocorr(fit, var_names=(\"mu\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The autocorrelation plots do not show any serious autocorrelation problems, as the values quickly decrease to 0." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MCMC traceplots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using the arviz package the traceplots of the 4 MCMC chains can be plotted.\n", + "az.plot_trace(fit, var_names=(\"mu\", \"diff\", \"Cohen_D\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The traceplot show good mixing of chains and show a \"hairy catepillar\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 5 - Posterior predictive checks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a pandas dataframe of simulated datasets genreted from the posterior check. #\n", + "# the dataframe is a subet of the first 202 simualted data sets. (Stan generates as many datasets as Iterations)\n", + "yrep_df = pd.DataFrame(fit['yrep']).T.iloc[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the pystan object into Arviz inference object for use in plotting functions\n", + "data = az.from_pystan(\n", + " posterior=fit,\n", + " posterior_predictive='yrep',\n", + " observed_data=[\"y\"])\n", + " \n", + "az.plot_ppc(data, data_pairs = {\"y\" : \"yrep\"}, num_pp_samples= 100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " figure 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The posterior samples show that the simulated datasets can capture the orignal data well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the Bayesian one sample Z-test equivalent\n", + "\n", + "As Kruscke (2015) correctly points out there is not standard formula or presentation method for presenting the results from Bayesian data analyis in journal article like the APA guide for reporting frequentist analysis. It is likely there never will be, because as McElreath (2020) explains Bayesian data analysis is more like a engineering approach to the problem and the resulting model that is fit will be analysis specific. In addition, as Gabry et al, (2017) argue visualisations maybe even more key so the all the visualistions above would have to be included with any write up. Anyway, the write up below generally follows the advice of Kruscke (2015) chapter 25. In any application though it comes down to the problem to be described and satidying the audience of the work.


\n", + "\n", + "

Write up of One sample Z-test


\n", + "\n", + " Four chains were ran with 2000 MCMC samples each disaring the first 1000 warm up samples. The data was analsyed using the model described above with the $\\mu$ being the only model parameter estimated with a normal prior with a $\\mu = 100$ and $\\sigma =20$. The standard deviation of the normal likelihood was modelled as known at a value of 15. Prior predcitve checks revealed that these values were uninfomative giving high porbaility to a large range of values for the IQ scores.\n", + " \n", + "Convergence of the MCMC chains was examined using autocorrelation and traceplots (in a paper referncing appropriate figures here will be of value). The posteriors showed that the most credible value of $\\mu$ = 100.88 with a 95% CrI [97.94, 103.78]) for the IQ scores under the model used in the analysis. Posterior predctive checks revelaed that the model could also reasonable recover the orignal data of the two groups. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "==========================================================================================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of the two sample Z-test\n", + "Below the model will be extended to estimate two groups in a classical z-test Bayesian estimation equivalent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for the question under investigation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simulate a second group of data\n", + "For the analsyis below we need to simulate a second group of IQ scores." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set seed for reproducible notebook\n", + "np.random.seed(1)\n", + "\n", + "# Simulation seocnd group of 100 Independent IQ scores from a normal distribtuion but this\n", + "#time with a mean of 110 and Standard deviation of 15\n", + "IQ_2 = np.round(np.random.normal(loc = 110, scale = 15, size = 100))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualise and explore the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set Seaborn theme for data visualisations.\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "# Plot a histogram of the first group IQ data with a normal distribution imposed.\n", + "sns.distplot(IQ, fit = stats.norm, kde= False);\n", + "plt.xlabel(\"IQ Scores\");\n", + "\n", + "# Plot a histogram of the first group IQ data with a normal distribution imposed.\n", + "sns.distplot(IQ_2, fit = stats.norm, kde= False);\n", + "plt.xlabel(\"IQ Scores\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "y_i{_k} &\\sim Normal(\\mu{_k},15)\n", + "\\\\\n", + "\\mu{_k} &\\sim Normal(100, 20)\n", + "\\\\\n", + "\\label{eq:1}\n", + "\\end{align*}\n", + "\n", + "The model above in plain english states that the depedent variable of (IQ) for the k groups (two here) are distributed normally, with the prior specifying the mu for IQ are distributed normally with a mean of 100 being the most probable $\\mu$ value for being IQ 100 but with a standard deviation of 20. \n", + "\n", + " Note to reader in this model the prior is build so that they are the same for both the $mu$ estimations for the separtae groups, but this is not a neccesity and separate priors can be set<\\font>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3 - Prior predictive checks\n", + "as the model priors are the same as above for both groups for PPC see section 6 above" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4 - Use Bayes rule.\n", + "Below the statistical model defined above in section of this notebook is coded in Stan code for compilation below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Two_z_test_model = \"\"\"\n", + "\n", + "data{\n", + "\n", + "// Number of IQ data points for the two groups which are the same.\n", + "int N;\n", + "\n", + "// Vector of dependent variable (IQ) values defined for the two groups\n", + "vector[N] y_1; \n", + "vector[N] y_2;\n", + "\n", + "real sigma;\n", + "}\n", + "\n", + "parameters{\n", + "\n", + "// Because in the traditional Z-test we assume we know the standard deviation of the data generating process\n", + "// we do not estimate it and the only unknown to be modelled is the mu parameter in the normal likelihood\n", + "// specified below in the model block.\n", + "real mu_1;\n", + "real mu_2;\n", + "\n", + "}\n", + "\n", + "model{\n", + "\n", + "//Priors\n", + "mu_1 ~ normal(100, 20);\n", + "mu_2 ~ normal(100, 20);\n", + "\n", + "//Likelihood\n", + "y_1 ~ normal(mu_1, sigma);\n", + "y_2 ~ normal(mu_2, sigma);\n", + "\n", + "}\n", + "\n", + "generated quantities{\n", + "\n", + "// Unstandardised difference between the MCMC samples of the two posteriors\n", + "// for the two groups modelled above\n", + "real diff = mu_1 - mu_2;\n", + "\n", + "\n", + "// Calculating a standardised Cohen D measure between the two groups\n", + "real Cohen_D = (mu_1 - mu_2) / sigma;\n", + "\n", + "\n", + "//Posterior predictive check code\n", + "real y1rep[N];\n", + "real y2rep[N];\n", + " \n", + " {\n", + " // Generate simulated data from posterior samples\n", + " for (i in 1:N) {\n", + " y1rep[i] = normal_rng(mu_1, sigma);\n", + " }\n", + "}\n", + " {\n", + " // Generate simulated data for posterior samples\n", + " for (i in 1:N) {\n", + " y2rep[i] = normal_rng(mu_2, sigma);\n", + " } \n", + " }\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compile stan model into C++ code\n", + "sm_2 = ps.StanModel(model_code = Two_z_test_model);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a python dictionary for passing to pass Stan for fitting the model above.\n", + "data_2 = {'N': len(IQ),\n", + " 'y_1': IQ,\n", + " 'y_2': IQ_2,\n", + " 'sigma': 15}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fit two sample z-test model to the data.\n", + "fit_2 = sm_2.sampling(data = data_2, iter = 2000, chains = 4, seed = 1, warmup = 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python print statement arge number of model outputs do not scale and cannot outputs cannot be selcted,\n", + "# so it is easier to put outputs into a pandas dataframe.\n", + "summary_2 = fit_2.summary()\n", + "fit_2_df = pd.DataFrame(summary_2['summary'], \n", + " columns=summary_2['summary_colnames'], \n", + " index=summary_2['summary_rownames'])\n", + "\n", + "#Output model results.\n", + "fit_2_df.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit visualisations - Bayesian two sample Z-test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior distribution plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using the arviz package the posteriors can be plotted from the MCMC samples \n", + "az.plot_posterior(fit_2, var_names=(\"mu_1\", \"mu_2\" ));\n", + "az.plot_posterior(fit_2, var_names=(\"diff\", \"Cohen_D\" ));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autocorrelation plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "az.plot_autocorr(fit_2, var_names=(\"mu_1\", \"mu_2\", \"diff\", \"Cohen_D\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Autocorrelation plots show little issue in terms of autocorreltion with the values centered around 0." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MCMC Traceplots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "az.plot_trace(fit_2, var_names=(\"mu_1\", \"mu_2\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Traceplots show good mixing of the Markow chains and show a hairy catepillar." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 5 - Posterior predictive check - two groups" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = az.from_pystan(\n", + " posterior=fit_2,\n", + " posterior_predictive='y1rep',\n", + " observed_data=[\"y_1\"])\n", + " \n", + "az.plot_ppc(data, data_pairs = {\"y_1\" : \"y1rep\"}, num_pp_samples=100);\n", + "plt.xlabel(\"IQ Scores\");\n", + "\n", + "data = az.from_pystan(\n", + " posterior=fit_2,\n", + " posterior_predictive='y2rep',\n", + " observed_data=[\"y_2\"])\n", + " \n", + "az.plot_ppc(data, data_pairs = {\"y_2\" : \"y2rep\"}, num_pp_samples=100);\n", + "plt.xlabel(\"IQ Scores\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the two sample Z-test\n", + "see section (9) of this notebook for brief discussion on the lack of clarity in reporting Bayesian analyses.\n", + "\n", + "## Write up of two sample Z-test\n", + "\n", + " The IQ data for the two groups was analsyed using the model defined above with the $\\mu$ being the only model parameter to be estimated for each of the two groups, each with a normal prior with a $\\mu = 100$ and $\\sigma =20$. The standard deviation of the normal likelihood of each group was modelled as known at a value of 15. Prior predcitve checks revealed that these values were uninformative give high probability to a large range of values for the IQ scores.\n", + " \n", + "The MCMC chains were ran to acquire 2000 samples wiht the first 1000 samples being for warm up. Convergence of the MCMC chains was examined using autocorrelation and traceplots (in a paper referncing appropriate figures here will be of value). Both sets of plots showed no issues of autocorrelation or lack of mixing for the chains. The posteriors showed that the most credible values for group one was $\\mu$ = 101 with a 95% CrI [98, 104]) and $\\mu$ = 111 with a 95% CrI [108, 114]) for group two of for the IQ scores under the model used in the analysis. Anasyis of the unstandarsised differnce between the two groups showed credidle values of $\\mu$ = -10 with a 95% CrI [-14, -6]). For the standardised Cohen D scores credible values of $\\mu$ = -.67 with a 95% CrI [-.95, -.38]) were found.\n", + "\n", + "Finally, the posterior predctive checks revealed that the model could also reasonable recover the orignal data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
References
\n", + "\n", + "Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389-402.\n", + " \n", + "Kruschke, J. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS and Stan. Oxford, England: Academic Press. \n", + " \n", + "McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. Boca Raton: CRC Press.\n", + "\n", + "Warne, R., T. (2020). In the know: Debunking 35 myths about human intelligence. New york, NY: Cambridge University Press." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Stan", + "language": "python", + "name": "stan" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + }, + "toc": { + "base_numbering": 1, + "colors": { + "hover_highlight": "#DAA520", + "navigate_num": "#000000", + "navigate_text": "#333333", + "running_highlight": "#FF0000", + "selected_highlight": "#FFD700", + "sidebar_border": "#EEEEEE", + "wrapper_background": "#FFFFFF" + }, + "moveMenuLeft": true, + "nav_menu": {}, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "threshold": 4, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "383.917px" + }, + "toc_section_display": true, + "toc_window_display": false, + "widenNotebook": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Chapter_1_-_Z-test/build/temp.macosx-10.9-x86_64-3.9/Users/eroesch/Library/Caches/httpstan/4.5.0/models/23wo2jct/model_23wo2jct.o b/Chapter_1_-_Z-test/build/temp.macosx-10.9-x86_64-3.9/Users/eroesch/Library/Caches/httpstan/4.5.0/models/23wo2jct/model_23wo2jct.o new file mode 100644 index 0000000000000000000000000000000000000000..ade60cc162bc593424f95420645154d0f1a3b0bc GIT binary patch literal 260568 zcmeEv4SZD9nRkXHGAeKf1&xXlT5J;)ny6T^#hOS4&XqfW6*ROUPz0eBq+~{M`3g=V z++MF`yRW-+TX(a&wVQ2UTi)$zgNi1ANdU!w7K2zx{mgW%4WJ=_GVlL+&bf2%OeTQV zRof-MU*?{B?)g5?dCqg5uk-xTKOXO!mF3IM!Vms)@t=PN{oo&e7vO(AMfURN|8^h# zQc8L7S3gU>qs%P&A4rk-n>jN)cWGFL<@=Oi#T5753H4b)t;ouv@^bL(p4TU9-~a@2 zva0Z#{F^!R_BnHA&RsfZ?&9#w@a%8ToS9l)jw)x!`7%p+)R#pqt9q$V)}Ul5ZeZrj zo5Hih3+CvH7H})m%gg^ivRw~Tr|>uHpM2cD{I)(>ODUp9`|el}p1WlB9aqyw>VInc zT7E3c8zP4*%l~h(eKK`c7R`tEZ{|!@Uhy>?onBt&L$bW8e3`50r?P!c88n36zhrr% zWo&wRi=UF^6{+@Z*y>AZ6U~kHZ|2NzS5|r=X3i`xn^NXRIB-!Qi{%gAK6;}0+sv7B z?p(O=&P7fM>E*T0mJQ2u65x5_@@|_Qp6w=1FR!*iwxCKiq`gV@w|!rqth3x~$q=<~ zQ6+;4$EBB-zgZR(=qIzxYLVp?z0xOZw5lUn7y>=ZONE!Bohs86GS}i4Q|H%P7KKq> zWq8SgMYpRD>GNB3RF>DN$pnjeb*RL6sA*3|VExWtGI#cE&NQd@w_D9eAekd|KF;yP zQ!rWHU9*=E+@zP6f0k@Pk;*qKAm2OR?8EEsTovy;X3kuA=WR0=VD-+N7g;o?M|qP~ zd9^CTI+RE75_n7EgEO6i)RWm)&CDr!DOU)m}5WM&s1T) zIU`1S=4nGQLi8@IyZtI|9le*`Qg(CMl$&&OPnmgGH$QlK7Ls>R9${_~X0u_wRc;tPgrrcTDP+6bN8`O*~>Rr_*%wwW? zyq^-{$%BO_BMf124iRk#2(zzY-=2>z`6^|BdNSxCo-t(e_)=^h*BSOIfhh4Gzxer; zc=AZ%S)sDbQqTG7xmZ1y%IBIYdg70fD|bgkm-(|I14O<2kX0qjv1rqNH-&dF3^O5f zS<8woS8IbbI>nd|^j6I1?;t8nl{2Sd+q>(O{Sh zTMR3*!7w{TV@Lk=cE9(zchN?_C~g#wFBma8?*GI7hlKX4?ZVtC8sGg2#TOr-2;F>6 zZ~Ws|^y23X?N@&YS<8nPgfLj&HOy|Yt3zy@s{X{VMv5wI5gV^kf5O}>hHXcceZ;U9 z;cFFUqiBrf2w$V9*VG?<+2~V$sILVnMvLeX-`xXr`<8?;><{t5v425x4f7A7k`I^o z4YQ+ow|@Aru!0T3LjR_=3M;1-9S$!OR-UNY7p{yR>7zyWVIk`F_=GTn^`V-s$XRhA ztc&RTO~M-MH%dA%C0`8Ii?0P+Bc;Mx7NF^V#W3X*qP*6afrX684rFQ7WfYY8&vo*@ z;p7j${B%CrsODpfVJ;4c=F&VGWy75B7tIvP(yj90qeY@&Y2QOayR0ETmN&}*L89&GeCYL7Kew69)zNV~O>TYN|h^f&*} zm!%b;yy?48@D-cIMs6~eDL{c@I<=9FLK}NPFX_^?v0YTY1I7##G}L-cb0k+E_DnoL zQ+2j8RcMZG4YbEiAt<&yG>mx?MiJNVld8FB0atx;cG#(G4b2S=j`?)!UFl zSfzfa?KS^O5yM(yKf@DKs?`uZ8v8bWB)*jMqgCH|Jdtqb=zOE3dx>9yjM++)t<_B8 z?{dO-xD&2bKaofzVz=}tZ|F(NOMHm>2uvc+#fPNU*Vvw|XP+U=JUxNQe;P0;p+r{; zzie$^!>Y_P%w>7#kzp-t313%M_4TZ9v9PX1;;h8^hBca2T)@Fs&NG=nftNEK4t5aH ztp$YdX1M6zT9zl8$1+@caGSY5p64JU=NZRzG&wcJ&y42~5}_T$TG7Hl#<+83FqTVL0kaSEa3wAGKgwQ3^w+~)DF*Rw|oLnpVbc&O%&;r}wM z%MDLgXmnmFtGX^LyfayXVdaNv-UvTpm~X4*%4ht?Zk=0rKfU=T+S^k0FcDql&kB!p z5w`bp27LalvRh|t@uo4eWz>nVyV~EK{WbbdKH6c}cLTf2&6(Q`?A0QoTL`(gc#wN| zrLb2Z;#J^Sna-NgBASCOJjB3pw^pra%L-3UA11&ShRF+CJWPPC)L}}FV-mK~XF|3S zvzedVQ$c_@ZFidLLWefAlCHh`UCZQMm|}_N3L%&&m;dN>D45? z0@%CLkL9XWV{6IM%`Ra94-y5C52Ofc5yVGJ3h}X70KYKt(ISdFK@4mLmed>n1mdF^ z#0QAlj-Dh($0tZ~oEfs`CPKqLh@TrWKM0lliMFtA+B)9X%@>8$7h706?@Z4SB4c7h ztEP>T36Ad~peLS-U4o~a4`Wz|QPQr}`~xQ^+C!`UK8K0^=#K2yt!pIV(G@1S$X->5R=ta|UWBaL&?@|9 zf##80;rqgDMa#m!F5V~1HiiXvqj2ECd8V#MnqS84Pk4;} zkhKzhJ9gK_vCA>-Lg6x?2K7N(b1vew=mF%`t#KgOPC^fXyt#E;KKF?Tl{X$Z38B(0 z36&#E_}u#-b^Fvv)y6+CoeGN82S{uM-3n5*Rcv%gRnV;-QWc=wF*@4qyZdTEWTv*M zC3@hxuD$iN$BP%*MKsYzXxBH3k}Y_&VN8|`kt5BgG47E2t?~_^PQvGe5qZOw=RnW# zOyW8GP@5=h6_2~ep(ZhGk30^w3STE3j~f2~7~AYU4z-G>JTQg>+q*ap z9rxXRrm)8*n6}nSey3<_arh9^);Ys(F#B~F)^(tSwCcSW+30bcfL`Wi?}!nV%=j7f zoJ?exhqUVdhjeJ8Zq4))m<-!ul*GfY=oV%wI;IUr9t-)1>=WjSR^8G<=7*wYTlfY% zW5j_Xd~`VSH5?FybtZiQCBF)X;_vZ6>U9E)R~Z0+&j#yO2z!7^snzTTW}1ib4FX_S z;9%I7tF?04aHP}CAm#|Iz^h@7CYm@FM0#P4!FxhCe;2!kUuU$$hEO|_YXo0I=KIb# z?7}x{ddPe?gc(X7wou9YTJ?4mM0MQ|9n*mxuR#{w>T7@?qyvb7SA=7O@Fe<#`%7#V zoIBAe)nA@ zrqz6fqN7{$JQOUCuY;(aXL3C_^sRG}s2ZIWKF=lXqIfMWZcx;#I&Cy%`JT=PWT=w2 z81`cE6h%0tKSi<)ht7+CHCfz8%P5+oc_@_r5Dd6MzZ*n7PJ(V3qO_fEklTn?#J0P! zZBEeXHcnp~eB4Vuz%aKbf^ooMs3s9964uw~IWflT#TtwC2HGUr&@D=uBf!baM$55~ z>BC?*jLlC^lCP3bNm}*aQJcVHNJ$lscxnd=LDoJ()jmEx6FE*&^$RDfI(%V|YSDhb zn#*+FU+Q^H@|V!xW6q{c?QI4&rvV=8S5HQvPt%(!HCxyM=x+kf5IDt80JB3KFq_Bb zt2_nt^yV3h^Jb$U{*yVOfS}%-kdJ&tlrH2ujMMOF!>lYtl%Oa)C#z?Ldd^bM`Rch? zJ(tR7w2Gei18A(3A>tT>HLM@+q1do-_O_Sh8}=OGHc&U8aiCqKY^%=EI(9!54b~H- zw3aO;1duN*t~F)Sh51Akl6FuNgdN-}Ol)5W0H*@Mnft-5HOzxx_k>K~?@%iz1aGA! zp3Trg@N}X{b=IMFwsU@R_vh8`2rM93HBb={R&LjpzLCMSFRk86-#my!*qiczUJTjY zkiDqXFdwNSzK8wj24;Uqj;1Xzb@qCSi8ho9vxahs=9*f>P$y+|&B0oFX%5!mYeKg{ z=7CFz)Ra+=HqAH8N4Ywuj!jgD>ingYRgZ23Kt0S=H`jEk(os>j8_Yu4loqjm6>P2v zAe2Xu_9ofH-flnQDHM(6qxJ1NTn`l7g7})NFI8BT; z0dP0b1UgA*2Gx+QkdT`}o*>T)9u^BjkY?DwsiH2~V!kkcz(Izm@d3kLl_$J3==i*@ z@fFO=PIo$;<_1mb zH0-+<%i)={Sgy6c!k+1OQn9pUyuWL_z|es3l0)N+gJ;s5(E_SS8=55k$9UK?PoaLa zGIRaFfu}Mxy$nSvy~5{99Jayv?J(>yc=iXN08(xQ5*Y;f(tr15aItR$=p{!h`wDYqg>D`# zH@E1`jldX*=<9h}^%hJiq&$%`qsQ>}DBeOfyTe-7xKdwah_G)h6^SkJ^Ml3vYPLl# zEZ!!3yY3OO2M7+VTPKIi7xY4W3BLu#OZWmR=dQA@27d$)aztD{v4E0a5dXGe?xm0x zuMpP6JbVb?b?F4XWXJMxbYRJ&Okpt9R_0}euf|ItoJ$*SA;)XNx)~WD61y4v`J40k z``Kd7E|Gn60V0cZa~q^QisC3$6iDfOG!k1<=`8hB6icb1U`mCGFICUc>RF_o1?riv zp0t@!c>(qG%P0MU3}0J?2M*|-`7Zan07=p!b#NNiU>`Ct0*jWLs z`XwHov6xDbW7b%791=x)88FF;R>fd_>O4-fqP45957I*Gt0MZ=ago?ryt@M*Fu1`M zy$}>&q(KxyG!kmXsMPr)eqt8v>WBkbjupb1Fj@Foh|+~PdbI9)&M?1QBz!xCSy`dm zA)hcqpb~Dv3>;wUK)e2+u+9_aO%+{ZF$G`f8jCf7<3H%(L=!f3t8%jL1L08#0ttr* z^QL_2>m{h5D7ZF~71SPs1_0PkDTHpB_81tjIDVGSm+Rj{1t;1aYL|NzZ*=%6e10T- z16TgMM<+JbF%TIb=Oh_bwPFK~&cEdGpbCJshvT_CyP({YsyAzQcFtixr z6)hlp)d0#J98v1sPqbD((u3Ao8+?fp^B3)@;7es@^>7K(D;h$+<7KPg+<=5-(GT+t z^Qf+s?eK)y?O1^TTp8 z^rSFbL}90HiM(KOmndvBv`GhW_^1=+3!?B4xXLTfBTfnqC&GL=F?p6KY13*pV`xZ5 z`T&iXVb0y4M~|OhIdH6tFyX-4ZiRh;8?WAvw^M+3zazScoiwBDdrYVMHH(#+>{)O z6k^5H>4k^Eu3CAH%7Q6Gdg6+oO*}rvdhzUF@gIrnAKg0IdsgXDsMGjf=ud`Ul1EzO zX4M5E`E~xtxAo?60gOx*o__gc@nj4tYFO9fuz$_N{Yf!r$r9 z4?DH!Gf0Qfd3WDD)-E5BRkrpFQm;rf7$vVqz8m`?mvSpugY)vexqhH>X={kp)FY=> zL&z#N1*t;zihv${yVD`xbaNcupZA5#uZPSDb z$qwz_Qi?HSBKrGwVSf)R{IF>JQ?5ufn@6Lsfz5h+R^?sAT`^*kok~@WC=T64RsYeW zss~S6l@@J6J*e&rNGhUle;^J+Rc4(Q$hivo3|9rTfjLFmK%*$21O?rMSryH;dvHQ`{_y1Cji0m54UyMCwG%Zms$<)F`4&0de?c2cJ={PA$;y zD$3NU4eVEh1o`SYm!O^Xi#23=4FGo{!>%J;# z5eCIA-fh2bK1H9oNys-VIXIAz&F4%^UnSe4n=9pc3yB3Mrv4)FWR+OTL%iuF?T_@t z^pxX+^!dG}m*WGy4Ng8$7S9|_w*#ttXqthlLYw3mxo2D`E`aU6U#lkmjbwC&FurFzeyl-IJG)ne| zF9ogXFH?nlBO!*h#`_y3ty0W-kj1Pm;Zef!5&sQ|OHZ2EKS4>bgmnRFtBnL0%S?Vf zvE9g60Z3C2jAJ%a7IUa*2KWKk5)&by^M}j|e_2y$z-O4m>L*Q52Om1)o!lQ&m~HbP@)9MK=v51a^ntYM#Q?tyIzer$?1CeUDn;o8{b#zeOZ*?bZ8FbDLy!(} z3|hv)l@7guD0g)1m2%CbCvw+I%6N;_?+_l;O(+*Ys}r0RS8Fy8A8_VN>e>4F-5py9 z%<#2Hr5epod%OUHl^CJ;B-E$L^eo=D@fV52s;5Ff0kzEHX$8*&=ZCv+?!MG)Hg}nw zcso#rh2cA<{c1~@ufg0?vpe!~Sv}=0D{Lz_8^nSZDn&g0=HjfZpQCJ|O%K1u6e=%L z2+SKhE)feFC=X~=oXxfBF-W-j2xZjPP^0J7hkjDLd*0y#+HYE(qJ&O+xCLm>0CSIQ zo4E@u=s*YhQU?wJl4J+u{(P$Kr_9OrQz7E93_Dnh zGXeO#0&j`QU|OxH1vV#nLa>eye{z4oxvY-yau?U-(`x=5)G$%*TFtL0#2sKJ(uyih zSOyxs099AH?Ub$9M6EDv(hhx>&j3XvBQFpZe*oLev`dk~^a-nGDFSFij)RX7{(yyU zkUbD{*)TT+D2cg=QpyI>nZrFz2s`)I9X`w=9cgmcage*vTr(e82oGYAYVpW2tcR)M zpmoLYAdtonZlXAQ6D1S&L)7YZsE?kI&(vZsVN3Bb){Y0=juD$AQPm6zTzwYE5vw zKIa%%K4moiLf9n#YOzqQGJ* zfcki1GC0f(FAfBoHThgJuSzZ*Kv0EwlP6>sTFb06pjPm}-{T7yQigr)_H7WQ+mBGg z;!~kdVFQd!S%L#}Dd6D99~BTlS7Oey!Wy(gSdaQqhJZSmIcR5DbZeiolC44Wy;YY7 z?eK6cHJf0~(kB~Wz?h63%FPJCuxq82Xe=)|uMVed1Ak4yhO?T3jg<>-Ux*=oX0}3Z9|a71Lv||&*@et*7tv|e7h|Ly=puT; zJujr=yTLe0OMEwsLOcgveCODdbg$AO!Y%F5k6w^pUdX;X<*F}cP@=)AC?J}E6qGFdkp2T<(Gl4+1`(MQE3IQqiKlTi@mSGM!qo(1W z%QIp;Kqj!=Kx%#}whK^`n^_kGVrwFqJcn0|>@0jwZVg)XjYnu5C}xwIN3EtDu`$7) z(lcy0mMO!(BR2LQ{3$)de$w{uQNwP;#`fTcc7xb)RHd6Qfr}VBhTT908NR9?!+SD& ztS=ZbCVK7Y{>hYO+XKA(04FVeH=!kS$RF$?3~B zk=0^sN-~#fNaR9A3`BxdPUJ>sQOqH6%1H=_@UwAEb((?G7k(mrs=~iYj)GG%VM;|_ z$VNS#Y%w*#v+tK}A{ieW201J2(>G?itW0;n`T!g-@{qZ8VYR5Y22 zCFyLa)m(znb`}c2?$_TQ5?S(+HiXG8E^*Q1gB71g)s-WW;D|1(dTgLdxUZc^PwWjOf1~vU=z!! zZ^{{h<@P8St2A}HxDu%kKxJ^kG_8hFWiZH19*cTWVW4g*seJrmyf_6zD6=V9Ff-G+ z3Ple=3Rl!hUQg z)P&L{%cC9&Mfvq|15D8Njl#Do0n^d(I2iA`iEqxu#WBg8JIrV7nXaP(i6M zLx)K8i+UmKQOCf+ef(@h$5(Fhs5Ln&y=ErNF@p4G-*@DE+# z$XXbPpP?t75xf3?$p?Kei?UbL0fwlJYT~>qEvTV*n~aAjrZ0!$6Q0iZGz5F;IimjC zFo&Lf#~pWKm>NMEY@36&gOf|g8ArvY7eK=jNy83Z!8)8e8!=@-C{n8@_G@}m))L$b zhYL{W3#(@RcY3n|vRQLxSK@#cY-|c{JHNvrf;&&O4X5CnsjrYpz-e*@0^$SS%XI8b2s%i#( z=(I2$kW^Lk|NN>hrK+Z%ys8gB__KTUIEsiJ04h95uYP#qs?t`ygzsCaY?2S1r0i=> zTy}CRH~}0SB8u&@lUKC^>HbVOSW8tAZE})c&HD4Jx)?>oZ#hXl-A7N@rHnJP6?vGW z`L>*?7Jiqz0LP@Ggv&&IWYKpQ-Ff$-e2JFsT(aQy1&e0ifwyg<9)Wy?E_OBr_na!C zxE9sM{zkSXR*q-5C z`6U0!P`=XzPd&tMhB{VL-yvcnJbKK%W9F4tLC3}?HePwv-FII3ojKUnCiQI^uVs-; zz@v{ag}e=v>L%zcx%6`_0UVfW-7q*t{rm>e~Jza{iR?QE#pv)X9b0AFfH^S*k zc*LB=i-jq!5gQlG$saLd@e;64MUmLJXvE^Vx6NLJK8OObaZVWD?w-8_Z~0;)^^|&u zw>+^CjzaRO|9A_Cjki~#AtOd$JB8l*MsDZ4J7$A})l~X*r~^o*JDz?50lm2|J)SPY zk7^!CjWtv+q9=z5=og6A-a%CZjpR{%(U+My9F!B}8kbW)L z7J;IBH>RwV4z(rF>-y~s)N(}!Juj{!v&iVUc#8jym8a!Y4CpfIl zM~EX+>kF^rNI)0^>Lu}c2txWDxyCutav#dL4>xchZgToC-RZ+z2d?OxhNH)EF4J2; z_-QoCvmhQz?ZhKJI`PvUo%pf06K;jBKDg9xTm8=ZOz^AS(m?nP|LgEyr4=&Z;z5ij zJeVUhXJ!FDZosjx0cCh>UD%WaALy(KA%$+vAWT}H3aXivI~@-qc;-`~cowK9VIz7U zt)8XoDb#badRFk$u9$_ivsB!C^;}HPr~UZpppXJ>;5I-z+ve!zq{TS*p}jaEPMWV< zmk_gKB>_EYm{pTbfD$gn$U0&kD0^;DM#^eyYDU>7Cfts7b}5f~6HtasQUn0%fhSKox!Pb&Ao z+(husO-hAwD5tL8N^s#>Rh*2FE*p-8FVwB^^HYk`O{I?S0J5^?!H%YQ7SP!2t(BxD zaN-uGXdrr3-qPjE)NXSJYiu%ySAjC1bOXMCL>ncZ03!#iMAdU_k7lSyRt+oo>G+MD;qzt-bV6x>_K_5L?ODgAZyjg*kL*hrA)&9 z2=E&8%cM~`gVs71wAKQ&s(J;j6;)4-L{SPXiQoqgwSrYx^OUg;@3nFwQ3mJ({DHht z3d!1I4^l6{xKjFXmXxqs57Jf^3m~zQw}6plKRO$cF0%M2O90E;VfgV1_(~UV3cko_ zo{sXaV3jWt>f-T1TQ=dK5xWt!C*|GA$&vZOEkIU87ID8BZl6rf7!?-&HD2Yun=JKq zl&WwXXN%oT5j~Jr{H*wp_!;rMc%S$n5Jk)}8<2z!!o7&AMJwykK&mt*)jK$4GLNE6W1p^5ky*EWMkN3*uko zv={?3{(CN#U`!tP2=FARD7uYKaOLv+=H>UE93PH`bmkMnK#@NmA3pNvC)S#ef)Bs; z*;M=Ne}7`V{k-DCoc}zH4^QJm+MImh&j=r~A4%xf%H6Xa0+x10od>~iX#?7I-U4_d z6C(04g>7HOI|pIc-ki$AdAdh=skx}Eyc3jFjr~L7>(lL%Gw|0se(ru8eFt_mJIR3! zJF=AkiPJ`~m0&?w--MsQec=%#TyN}vW1;6M*6UbE*!|i-2sYrK4l1p(gj)A_GqqOO zp#~6!U@U~pPDvuL-_Q^&s^Gur2*{ow5tLB9ia@(h3eW-Vq=}(bU&8HRRR`!OmXiLE z)K5(6ry*$rC;g&K+Cm3B5SJ33@#5GgC>92mK}GsWl2{+K!l>Ka>@4l3x0}zYA9=$gKGB@V*0A zJzwVWhixh`1l;i_enSiK8%X}%Ec=$K|DKuuPA+|%`IpRgHUG|hF3WQWPE4ue!!Z5) zJsKw@Vxrrd@`3VGd~4hZhNq5OCWzB*K9hkIIFv-DBn9~V3P>w31bL+CAp$(2F zjaCRf$l-?aq``%ok8GgQ)a)A8r5F(QqJetoi($_3qfh}Lo->(#D(GicR`b~Tc}R)F z+K-^O`kgO-(D0X=2$rH88VETTa+qa!^;Q~u&r$956LF5$Ca zzSM)2E}@4;0HaSmb--ULqO7MFsffa99C%Sj;^yFbF=a> zn1arPIFsQ*VHXd)Tm>}Pvdi|SWJ8^XNPwepc9J9)O(uSK3eIClf7 z$)O0Cp(zeU{BS6uoUX{xa3<6<8_oa`(DgPNuKWSxaRPrL-u93Kk>1(^9l2APonA)A znHxtIB6#m2#kf31Aq?>_7RR{ETnD=e-fh%g3=o2ZfcGxQ59aI9+dHxTr7TB8=hkBH zs`Kt$1gIV_P_Q${8hEIV(S(FxM-eaD4Dl*EaxzRvrs44)i$v0e53&$9qh>ea~hS3YaED5b?QloW^0^& zoLpQQ?Q9KPzTDZi#zT~DCZ&@&MIvJMyu}2fStG}sC-akV1m(!Qz5F-wmkc+R$3@BA z{_xch_sAiD*MfXS+DE>eO4hT#Nb`(%U*$5KWJ2t|?`c1F0cQlAiOoDl%C`wt`6NtW z7Usm76tJLQ+kXN0*2oKuX+`R$`a|*V$3#M4KL(`|YAJ(B8oEKmT!0pYExn_;D*!s~ zyg|$DnA12KlGumQaJWOOMNVwTk~V|yd)kj1lEmCr?8gtwjPfAjY}`=r#pXbBbev`CZ7-%K%DotCm`aqxX^*-IUYglUT5??NQQxLG7&y^*sbOy}H`Nsv_NW`^ zGb=%>&rnI8f^~E~;xSBA)#C-o%~`P-3i45&TzuZ$SboGf@8`6sTXlQ|$~@mwCLPP1 z=D1gTAkjc-5RucK_Bzz+$F4q|QmAtFy8hZvFod5{ z&JKJ~?b|ip&*(EFPsTlcoWQ@`|DyaZeG`3WG+Q#g2Y+6S<5G688THW4OykMI9%RVy zt}WPZbPG<~L)LA>VG$yB#q|Uf_s5<^HcVQy0I<$BO9RPfDcAsQf=04dx-?!4ce@wr z(YIi#B-IP0c2uh#-3dFT2mpOX3z)EcqIWEm%AtdRt$j7_RRcNQ@HlB2acj|-^ObGN zEojOrwoPd!+Y}^o_Gl;{1wg$)cK}o%tF&Oh4uM6CQc!LWm6lP%Wj(cOK;W#E&emKC zyazg^-0Trqb<*Jwo%(C_KS(V@#5c856a8^Ge90_@YFWl}* z@qeqt=!k`{3uXD(BfF#DdrIp^_CCc=q>~@|#I)YvkdD~P3ApAXTdXwwVdXFGF9^Pd z*`E%4ZM~oCISpS#q7Do))7e`u>~LqEZk+*Vb+B1z)~d2Zyq>Fc`8wRfQNLXY-01M%Qv}aW0ep6JUgGDY-&`bVcgrGq0NiOf zu?sk7e5h2(hDy#)Dt}ns|3;Ue8P?tKocVs`Ab1EGh|9a8&tM(gG29>f0Ha5Dui^4$ zQ3!vUFX@emY+UCH4i_$ZCZ#S~5~7~OBKRAs7j~f^f)X_z@1vLO1mZX#Hp;IF4Yor- z9G!HL&nU7^A;h1P4>w5%dueW==&bMmA{Q|24OY(>&v*IffvX7hE+p&eNvk{(fjOOK$&C ze2Q*JX3PCo&K5+`xjSUA!wSAfuF9T7B3TMb3iRK1oe#_LRt)@QQ#%ncW5Yph`pXBk zX?yX%5C0c8a*7IWJ9;#uj{AzFp*kdOfg3j1mSMSqZD6CxFonaj9Z%qTn0SzSiX8-k zh0x8tfG67ub;d3L6lBN)gq=B0Y-r{TV0UH6fd0nLMTQgL*VXUTVKjJGm10Hd)C%G| zK{b)@wMXZ3s1K^BPnkgUs(&64Q)FIc_b-*f1_wvOK^xgH;7$syn!uQLKJvT1q7`~n z8w}f?g|EOn;H5o6l^OPmDzc}u9;7#nZq8;5HXB+HO#JNtFlNw(4M3zzUXH`ZpL%rK5r3x{LpCj zpFk9x?A&S@#4be8`HLK~wwaQ<_+lTPBXjGnCE$Ayu`*}wa86G1ojXwm3DiRMaw8kr zqTTlsBv!6u{{}&P&HWBa0U!pEO4?&8_d#$#NCOFrLTaKc0!EwYRTZ<~9loGZvSKaS z>83|pE7oH9SJD0Jjj`(t-x2svKMo(keU^`$r)!J09@Nf3>&{`nTg6@Tw7{T4^R(%$ z^KkPzS~3WRj!k_;$!bb29@KJ!ef2R> za5Z{IIxd(XC+A~e zM`zg6cN7VGN=uPp?&J$94!>HA>jq1+=D`8n^kc2`xb0Xw8u~K+UqfT};{W$>fjIpr znm+KhqwO+fCq0IPgvgMv6A6bOg@1KuVKnuL{IsmA(HAZhzUMIrLy!eE1TthC(oGd~ zV|&<~9E|E8qko34vD`dfh+8nWi^IDOABuh}Tl%F=?l+j}eme+l0JC5e2KH;=TR3+{ z-V)KH*-LKe`5iIF4!>8^gAG%u$xm;DN`Q zv6fVkzl2Q$ptKCP)9(|Sj!Q5mfLy_SrRSlzbI``!u?uOMwOe+_2GRsWyQJ~m>*#VZ zTpNP!_rb6?i0H8#?fyAbHXPw2_6CgGv~7s?Yxk4U+eW}ROvK8mq=G>NL({iYlHK#P z3Jl`3eR%Gm#)^_7%U{9gR{R6WS8Ns~$5-~pOTVL3B4Q9rUk3wxDq2O!A?<;CP`0_Z z5cfwM7KdNLy%hj(3_D#BI&U4ik#i9J0Ei=bqDdsSMdG-}He0*53<;d?0plPBl~V!j zOoKbX3d~TT{2+#vV;k`{+Jv?qiZoz}JXl2Q5{LsY-_HH+1%wo)=T`Ew z0NcZ0lvIIdAulnM6O>$VbcC~_%)yZo$|e1I;vKb3V+Aeb1eDMwBXfiPsp9PiiP>ly zR&j*FJzAl~34&v-=4x`cQ-L4sFrZ`!W02Yhbi-{%b_lLRR0E#t32Sh%K!&le!aqyT zJiz{$G7vtJh^&V4#qPjZfLcqo(@ORz&(sk)Q;Qv8JqVR`y7?aNG70dxqFd44gRj;l3HVhxobU9FKoi0cM9j;VQwD^0q%AdL~C zFEf0mDBeH0vzhlA@{ar`p)S>=B;il-Uk^{I* z0$!jU3|^)$D1lZ+t%AZCb(xc)fDOHVL1!}+?VdgeFl{y)hhUkehihY{mq(W};L7~Y ztnv7rOgr|=fYA7I{KPB_7oJ^guq?G-XpbClA~PSYY&>!#fD8Mx8PurK~U%W0vEvcAXbWeMI4 zyZ>T}G@HwADob3+!OYN=2z04$9PLA7V85FJ8M%{=0sr9uNrC8(tW#|fLb6qBImA+9vzQlUBFww!Ez zpgnD>JyMliEfrbxM_MYqEC@h4aE*jVIzYN=op`e%FUl#lPOwmVSq>;TFLS|Z3uOg% z44xUTN~(rcIXRu#I;ABtoR$dbj>pzwwrGiv;WzOI)^XVx*M~EQYEkg~$%{W}kP+)l zRu|(kxjqPg$&s-)(L~B{%K9LpYrH3!QELTcr zMNYPTAreTP?c6=FakuH+Aj zFl7pncDhq2af(~wcv<2|S>hEvN_6CNL~oJ|_yr~50P~Hn$$`gG+SBm2s(80Jd<;zZ z<%SN?gkEy1KYW!}?|i7EcYcLvd>!ny{Srk$?ffEKMw3)K52ajawB>0((6LMj%IJh- zxMP2qy5C3NnXfeAC0T{T02ha+*LRcwuR_+h{B+6y_~)*>BIz<9SOo&Lj#$s2*OP2} z+^7+(YYx`lMtghkr;2S~2PRG}*!DkFEhhd786!j#x8GhVV1ZGJ2p~Fnryt5mO(b(L zu_sG$u0WJ$mJQ6IzYx{(u0!fT9{#*Mw$VsOx6b#RxF9NFS2OW*C`7`8I5@1$PY|P? zdGu%TAwupn*!_g$(Hqv7HiupH`Qy1@Kpttf6Rt|=NzHU;>8Ej<$F8`kKMb$fouy1O7$@?PaYVw}TE9z+1Q z=f^HVNjh%yHmtse`I=FDUPUz}Zt(vYYz_nUNVY^2j5UOb~EVS%PhBKiyM-hX1XEs zr1`)wV@~9JG;3@AUCzgcc*y=I=K~W*Vi~&SbAoMr2#`8sf{A&VWQXOHT)-S2R6t&bYa}ZD5MraJmsLMZjqx~d~tO$W(p-cgp_QiKW|I(P(;qvli++f0?gcF7Hgo)$h3 zo*jIFn`RP%Qp&=zn#dWPo3iBo=u5;dhfqdk%YA<#eI#j5?ociv*lq>i;cI0mkquo} z+$U^USCK-kEEMd#z~BlL1dchlfrV4S$h$1mf!G;f(@ZDX4P-b2AEHw|SY_b2PNV<+ zBRETi;;jt~Rq+r}-`65pa2?oLxqEY|*lB#GMA1PM4f;8W8&dEMw!c(-a}Kqb@XZ$y zAPiH5=8!)IwIiosAWU~2SBo0P<^u~AU{0XIJGMwYN2_P4dJ6TNtezF>Ig6gL|H6-A z@?l#{ilXW;whef1#}jf5rE-gzwL%@S>#<-nEQ6`EgW$A~_WA&$NmEii3LrJs|S87W=VS*9(he zR=B_EQe=V17KV~M4*lnRm%t4`W%svaupUCIT1o}+5En*5E0Pnk&0s^aGEreF3 zwC3!o@P_*cRi`f7a+laT$gk)FW?ss#CYM2X5SKPH2aGLa~&KU@Km zDC_+`Qj|3qV5>zB!$3|BR_-)TPC~TYs0`KqioA=G|BuPLzMKm*{}&|h;=%a*%e&ywwWgtrZ7B^c z*olKPgebS{X59Q$Lej)`wQo{_uNRVnul@i~h!G(8lJc&-BsCSjy(BdSuEuIfm3O^H z@-7h|>69z)dMXDHmy~yPBE|GhN|Gk;A{o-2ba~ey($}T`eAY7s=*!NO@N$@HQg>#^$uJ zc0%&izwtOLHr3=Y%A3yb=sBdwgLk1s{09K)gl`HTB!gbm#3KHDgfT5h`XIp;VHU0W z>Jv}VG>m4!_SgVQWqPLwLS+&!(lkLU<5%Us)Gsis}aKLsff@N)81iSrRI-q947k zkzM%<=y}EJ`r3HERt?b&&VkUBRj1h$inSn}V!hd!bR76WCPA@uqMvqO5GvtwK+gap zR9sJd5a=*AM&Mr}6D8}d3ktQmb(dch?h%$Gj&0mZZ~kB{x+%W)gDO0(EIzd?y`zZfHKp^ZPh5v>BUJ_jzl5#L9CjoSa5IG!-U%u&{Jh^25uga@*(Ot<_VDvircFlVQv}%YgzUCQeod+wbkdqc zO_uj(khZW@iSh?+r+X<`d7~u~i+=@lN(T)a!x46yjyTEz)mwnPGEPywKT^;zqeF6% z0+Q@ST2AJUUhEa5bbz5Z{u}Jkpyr2D+<3pZoP^;;qOm*Muy4;p(@3AnoKS>$tHhKH zf*kyNKBMGnwEV!5w_;OZ?TdRi^2wpU@Fk3r!@71oE>cUv=Q5y>WdWlkQF$);B_ll< za~U{Wi5MMx4GT=2qedZ0WA+EG4~NAg;bQ1OR-kOX9^g%XV1GPM%0rDup#z!d6Drvs z(yl+|9<}w7?ICR}9kn5(0GDD#efX=O;Bo4kT8E|95`voZ8Xrmo3ncBxjpQTw6>8-p_cC;ALVz4C870_xh!ceC(nVnl(TC1? zdt11lVLs0@ghBD^(N*19T6GzK6R-J(^<8jJ{uCZWR0ZzQfZP$>iDmh*P81dbb}(q9 zGBS#;q2=cpsb{FI%qaR0Oo`{fl!&<_CACF6=%xl@618B2AloD+(KFhb{iq8g^hZ1- z#KB97ViG}fFTzZTH=uU@j%;Mkd%9izp2H@3I|Pmr^CW=&JX)g|mWGho0agnZ3t+{Z zfQ~kY2QXuz8}~yv{D?3*?C5a-bG6^uuDuB4UUD^VDuw! zq18f?7|2mRa8F-K|8&L{H;TFT>H?G*TbR3)u7mQG8FV;u+MdY`7V&Hq+H?4wK*TQo z1^-_97-Ie5q$HbVqof3jwdq`Z?%-s@DhOF~VdNVDvj-f;kU2(i80Y$9-3&EBfMIX! z#W#_{3pJ<6UQ9v_j!BBO_%KRJVJ%|YQBc#sP@^=A4Rg#PxN17Y0CNF`!{sZ=S&{ri zV*Q286~Gcf$;Il8pBO%`7v5Eh$}#kdr7*n*>(Ij=0A(^hAEXFuL9Fp0i7HC5XALZc zR*$rgr|(71_ky0^1&Lru^^o0zlm9<^NutbbEmNCIWlnKd{8rvzx~MEvlXJi=Mn#n$ z@Mnml-BUFuna1v{&$5FS&1w>-AUeX$QlQb~W z^e;}^OQ;Kn{<`cX9uD<}pc~TLgf>4KKBwXA=nyS@nn|v080}fHBTNabbI-5NV&Hr zjnp2Uh311!kV`wM#_P?46K54jzO_j^wZYtUobb#e>#(h)*r$cDKMqXZ)x-2%(mqWp zGh+PY_nudCCAEps6;d^_I!Sh zMS8oGIg>RV8{Rl5y^DDqQpb(T;OTZ%ez@_YR*paUlJ#yA7t=PO3Z^TplLe<3*Ht;2 z#aiY8c=RD8JU9dBN_Ac8+w}+P_~4}V>u7D8wj2Xqr)+E#!7+}cHXC|a8vXJ{UV^Py zf_r4J0ZYFgrJ*=k4he0DnoY4Zf~1D;!BUn+yjq~zh9Q*nJLEMK2cVYnnhO5n%4_PS zyk;EaHIxa5(<+e8YYlS+Bs6o^LsC=g%n?LH^?+|Q5eGBU;H178%`0l9?YvqDx5Q-_P`ARbHpC#1_s$GS}Qc${-L8^vZxISSoYZ z4TLekTB70hvXZ`+Zty+ca0P9IOKy%|ZK43Mc=fUATbDnsOJN+5x z(~mdwZ8!IHlxw0bsA5T8jvXJPJeD%LkHMuuNQEEW1=ag$t;1(o?t$ z0m2I$Tq41`3OYXhEbcmn6LbuH+xo+=KpyE13!U0vZjlp!M< ze-FMC?~_d1St+^-F2+mx<97DD7A#zcMeCT@7V$=srmp}Jjds4ZJ@plSeFJX~=x_gh zGD!Lgbtss&J}!*5zHN9<(O0O;sINfeuBW{x(^r6Z1G2Zh4f`ME6!KO{=qpINlXqUj zWQ$Pv^QE05v*H4XGuG8?z|M$GUN)O|Myf%(H;kN)@!8G-MFlA#Fgc$46Hd;`3r#XuPoTcm*(SC7nsSjiy37w=?r{p*lC*8+ z2amAqDeFNose}S@$D<0zvU$T8OK{20c%5eT|_Q(mZ|@%!A_>tZmfA3(#{ShvfOq8q-0Bt;Kji0S>*?Q?fV z{u5`k7NWC9gXIdSxdmzo3E1PlL`+AxCxl}O=pb~9X!~))9`Dnm-Px62LR0ZS?TuI- z%`I;cf&TvugD8NW9RMiChC*fnG42_VS2sX-1{+;@YAu);Kos;95*h7tA%6_rFBqDpmC*P!`QGt^2+}BavqwwId%llfOSR2+yl^w78*2)rw95qp{XS0$KW)G#CFo>@QzRNJJAZ|O*BqP z4S^;apQQFc6O9jl!%+@%+06O^^87Op^d(;}4u1oDEITzi;vz{$pkp-za#}TX`+FfD zJ~35*9T*r#72r6j0({{ErV1KxOyNyRsshka4m819#8MX#tVfXhX0uC$4wR#?e@&mG zlp;+VAc8!Z3#~eFp|q35?j{9;43tE2tZH6V6h&`}0Yp4_Pj8Np3XQX&l*Y}2J(O{5x8Q6yL4FPK*0*W z#6%%(w1nPKNq1$5!SpJWfc_1nK#I!F4R*h7Hr_FD2a#^#Vj)0g*za%~nu7H?^O}Pj z7e<k#?=tP|(NHl^%{wRm7N9`Tz(fWB%zTM!X zqcwav_OtcS+F8dYUH^!Z()BJtq~bI=b-ncJ*6$Gd)_Q}P6Q5|`5of&%_+*oQ-8_~< z7#Zo;4c_T^O!bn(;Q{Q^EvcVF{@gIk(o`?adXWdo(WQxz)dR;!YZ9_aZHf&0!TUj>Wp{j`u){kR3wwFXVmUq~AikMzl@e1x3Kua&&vj?tp%HAM%__dCo`!F47 zL)OxOQF5#@V3=Q|T6D9*ANwt;)Ga{h%f0MX6$8*F&t3(`7pG+IO8XQT?2%fX^ltM} zN(!ivtQnpz0Aa19Z`(D=9&>z>IsDy8_HFO-CI#J4^!|N?W8Vclp`%5HIm>UDOX0Wq z5Oprx4?7f54Xi(!vOiU=2!I{?J=kc1pTSNf@ytE;4!x62#6Z3T4t`m04VKay@g16L z%JGX4sS3?mQK0y*^4RYhqS0Eq2@If6Z4h9)b~0+1XkYyyawWGg-nAr;KynQx+QYjs z3yWzMu!&xRO~{NB2<&%!48;2v@59w-u?xU#(@k<-7px80D@u`TG;-;MuK)pOm*HwW zpIK9cn6jsk5?s?qm@A#{nWmChHVijZExmA2U|O{zB7~oc?Wp&-SdYa0zyt}dFaO%&0__L? zfaW?aElV`TekP?p#P3O~7M`G}K4CkxFO{{+T4|tSIsBcOMQNFlYchS5ZO&YVNKR<% zcC-TAG=c)dzH|);ey9vUAz{R74lV@B<&S#deh`SY5!eu9sO~Z=FsKcg z$H~Use7W%t+0oaa*?&;iChh6k(pT4ld$2=^LSzwOB{zVT3?9p3X!;z_t{jb#x(E(H z&(!Wen_w2U6fl!#TM8)oAL*+_{|*o(?Ji*bP_rFxvESoIHyd;?qIDlQsqHX^1E~VP z6*7)r#%BO_X#le*=@- zihqD#1vFY)h)){m2j`;|jk?_rNejSM9o&XVZzJv82UgNB*w-h_J<+$&(C)q6ly%YYv znHc#QGNhCTOZ1BAZs2e)qrw+~bY2oBV`yd^#dOyTZga!h%OSvh4X z6+pHu&D18igBb$-;vFga#T7$#L8BOaYV?o5(_Ba-!+OIKlS4JzBG&?8&cN3H5g=1$ z#{A8wB95M}U4|V)z(00lM|QLwaM}VmZR~0!I0ZhxwGEaPqOe)mcPp{SVMss#t4Fgd z#{gC#?l{s1dq#_P|3wV1_W)kYj+;lLuQ3>C(SHSSN`oQ5NzKayuLl@jyCuAK6TIs7 zAcDqz5IrPvWN}k5Gnly#gs%x-${Pd1UJADOVa*dKE0 ze3Ty{_$WU@@KJt*;G_Hq!AJQKf{*ee3Vx5J!0(tN#W1FH;WuZ$NWgCKRf6B`4*c#c zY-0FrHGC~bVS^Wba~K|AHwyUe1~Huuzd59fUcQUlw~O1ii`%!0+qa9`w~O1i3+>ws z_}wny_wDXt=+9S3Mfvgrob>=_J-}HHaMlBy^}uLO3HZfbWhI9Rew%;=2!3-86hqMD z=^qpSfbm1(DWF>V8wx!rf;%jiTN{h9A&qR){t^2V5eXq{1qk z?`*Y0whO?kEELkMsW?h~s2Bb=WWFoCiHyUQ5S`kZFF{tU7rtnizo%Ov^u`Zhsp@#p5f1>w z$Lq-!l85(06YcOJoC*H8atJ687+%2(z06K-e3SVJx}_mT1f7`+Pwy`gz136lhJ{SltbnzNfvM!p%1fo#qS?JEfYYIHx(g8d zbc~H1sZJ{pGXJC(zNB_;-B$3Fo&8ba>FZJvgNLpUa-YJu(V$tZ3`wx18qFZ)1+9b& zT1PX0R&wYT^U&!fztyU7LI5BGx;h+tFsxL9LF#-MmYeBthC;{~d^{>A#~h z{K_FjJJWhi4r;5n96BNuP}w-y5lBKqhB^dsyH+i)qi$t`L~K{`eumvNwpOT0tKNH@ zDAHbrIQ9g6CEm+0o}@N$c@5E{D5jhDH&vcv)Vq2s9dJFzZ-*-o8-uNvM<8}3LO6>{ zCSYuMGX6q}_nc*8XCfkr<=h+xF{a7rWcjanW8K8hc%xYAviody87KuU@>LIzbP|Hw{9VfHC&Fpgp)5_QBTh&7#aVBM_Mkm|XIZ;AsOGWJyQB-Qgg%$MwWJrzzQ zL-+ZW=hIE3>}vt4=X{aGNmS1ZXn{nZgN^PZR3cQ*^YO^6dQJ(P)^%|uQuUn5;HIQA zX0xGB=Url%Xg4bnMBG8(nUF9lVbQ>((6o$u}2gW&Ipuu5Ws#B|D4Y_F%adxOnX=GH~e{ z3TT0A>8bQCDM9Wkm7xDh1Ppl~oEL=mk$LON;pb~X_r#yj?04&j-yoJAj%m9(gs&A^ zK9`6E4ZncDN4>BaZjlzWz4y;F<8AukH%ad>En#0I6op$*WMNzG&+$@j9x2?bjcZA4 zTMgZV#C3Q7-MY|%#Hmy!7VP_|RUI#URU6lt*tr@QCUKp%=7)I9`^4)zTKMwn&9ZOk zr8aJJqUBod+CP}v8~@;6{Tk}V99*^;yYI9utKXt;c!kOk9A+TuSsXa<-j4U*gOBib zx%SwBa`V8h80IbKdHfj9gOHup4ifQMZOuYVG3io%4^J|d|K$7QP8E8ljcZM8*KXN& zP-vIEOs#F3SFVlRao6_|4qQR)4_w^|o!|G-!b9l6`#4v$;vEF|v=`{)f$+2H{W-+p zz2Z5)jC~A=N7S&)o^m5EPN? zAOZoWWo|uwB2s*U5u01ju&m&k0KWe}AU#8V4QD)TSdltf7>iW=r889EVgmt;H3UQV z9U!e8Fq_SaoN{w?{1&)QbHoPdblVw?k7KKEKG2dpS?cJ{mR zXcBr3GQR+$G3{BD-*^|spyJX2NM06w%Qx08za}9mo(Ae;ngzNm;!M-ug}+IO4_bf^ zsMig>A%(%ez{ZI#k5JE1^i+mqG!j3RpD(7T)YSt&4g7-B_<)h!DdV4ygby~S;R9{j zbA%4cwaZ%YQ?U&{({^f)ZKH+OZzuhhL(LG8+n}~OvQb+@tX0AX?4;pS!v}8=K6njq z`Zgf>mAmc-Tw}$v=KqUmF>pFIkwEC*+N11;~<=FQZX0vZQ4FPI9tZk^5_;iJif%4YtT7k23MCj1(KADb7OW z9R=Q<9(u9GPy_fN`UTq0#}48Bd>kI!RAlEXJj#3CM<6hR5VbTwtpP{PdZ;$Qe+h^x zKQ;LH3(;92{$pDWs~et8z?bGCN=Tf@-t)BWMa5Q!O*KyG^jSv!VL~h z2NKca96gODgmBP)lse0$%IgDaot(~?pVIXK!-(*{oqp&Jfix9?jCTh(6XHw;xyqFz z-^CYgNNSb_TaC!3WJ4Y5mH19_oSh^ztsHhJ;|%xEq{8WsK^24U{AWC5A(H4!D(`#s z4b=Ve71(ke^MP8Jd2C>4fN?V}aw<`e0Q@!z0x24N$_r^;{ zs3lJA#>9kfD1+5C|nu$qAb$Y+}-0v5~-6sdW&tSiR_z-L2quHNWaNqA?9kWS=QPTNm zTcD63!3ix;=6xa-C?o%8EKsC_4QYkq3V@Zej@Mo?vpvd1PmoaT^I(s1q@F+m;&6HA z95@e#C?NOF-FoQkHKmLfs03&m?$B7K^*rQy;IoKX$DF;*SQQihr_GXVVR+ae# zrHP)hJ<6XTJ_BKM+8)JGtP@HAX=SC^qZ|f7tn5(^aPYJ}%4vI))AlH*?NLtKqm-Yv zN5Q$utDk<_9)%UYlQ-jdN9VLX3hbLs+oQmq?6f_~X?ql$iL+v-?NK1t`Yi2HD&Ojj zd*f&zeggI=&VB0pzOuKWE%ys+vaao4sP#E(63+Hdk{V|^V zMqEqjccQ+nuO4}IvwDAS@7A{dt0>(wZMn-hJ$7njlDYSd z7|3lyuiBoYg0Jy>Izu)a)ect~^F3d?O>`CC0*FIXskz91h9|{aNP%u6&3iSn*`4%M zooZA0+Y#sH-{VQ~|Ji#FfT)fwZv5UYG{FTcc0?0fP_V^*v7lhZUZNr@cF<6a(V!?c zL=-gkh9!y>yI4>VMT!Oct_z5Y3Sz;AUH-qBduMl9@tM5uz4yKE{X??OoHH|L&YU@O zrq4~(r9UI3k2463k<#xil76c$hB`GfLF=%I#7b2wmylTJGgDE=@eo_Q;n(NH`$euZ zA4kF}XzOZnw7IHa^85!B<^}3flboJ}x@@3M)=soTeNMDir)VcSX2#R+n0GhBc0rC4 zzF8cnT6y}mcMi^yo`}MY2DKtwo$4Ce&)PL)*%+Kk#k1eI_`Wjk_HF_pI0zI%!pK7x zBq*5%5r_n-&*Lm9PA$`CeVmdh@%Y>H>23ti&xEoLEGv41#MTc)mV(zC=x;#HeZ~4uj%NoJ5m2B1yPDNjOb{cRe~bL%>RX zz)Fc0IMUIH4uVWG&uF8=iAxeWBT=M2Q6%Lrh4M$(cSUfNJ~&FEX;4~1TN@oo>1m_M z(OX(>cO=}aPq>#RBqbhzfEay1j6~6(#MeatM%+68C?Dj4lJJtZ?WSBd+P8EWXSl#;kvgok*M7bq|-tBxW)aIGV{JBuEW zP`>&D?G@^iLZ}mE?hgLL$Vqsj@6g!Ow|5f=s!m!%cNBttjaVN_PH&Tj0CnyTp@ot2 z^7>E$(X>ooM5)CQP80NX+a0w*zhJCSNlx1%rCzbSqng9W;ctB$0*TZw&{nKYC51qv zNky_$Nrg`9RkJ%(h(5`!Cqb^P6B4)~-W{45My_1yLkT2iP=s!f$Ua&RNd|onBX>CU z$p}Pvry59fw|g_hsa+%vfke_0yNq?xGS5nFy}KjQITlGrAd(Kc*1a}Dn-vKqkVq8* z&{K3KZQYTku|pc?B54RjxV=DZ4K0x^;vO0 z-u>%m1}iy$L@0ojbXKiQyuF)%YQcC<_Ob-tjIqY+fjW|bv3cIznt${k)3u&gnp^P4LjR#|A(^-p0;b88JiIax zgXiO-DA}%ZFIqNIF7k0)eR*=pqXD@8KuSelV=yimrc+`zD$kXo#v#64GYXFbgzxeidp1suR3_35u&Bfj0R?r%i`tnrK&=A zyV-I6R=A3h1&%rTz`vA^ zIP=({U}pG?hGH;#7Azif6Yv6C9xi<(kW9?=v~|ydvm^%J zOC=u*$K5OClt@&3Sm0Y)Rj0@+e$+?*2@QN3e2(3_w6^MosJJ|an_T26t^s9T0OMjA zxo{c>94?y;z(q6qExcesT$# z{W;E_qnFNR?$>~6X$&bY|`O%Qc-jtqRl3f7*BPP4dFktd~8hNO)By|TWt5SD>^5S}t z$@+*)B$)`f2$SLsIi?H7u73OTi~Fyx(Ce;Yt!v+hDyv*DsVnFj)yLI5^<7vq-uXvcLfZBPDyN2NF1qpZ~>|Q>biBXf|f=9ekiS3KS z15|hn;~{RhF%#52QSk$p=n_@aHO9r)ccQP|SRYuz?XEF)qXK+J_>Y|kXdihv*vF1M z9E=M$PqRsVlx+(>bzG?q_l+w!&HG#62|K*n(Kjj2)b;WsJU$zbD5AMS%B9#~H-L$m zmMkPr?I5xgvB<4H;H48i;yaZL%2VcHM1?Q0M)3jG1uk)X0Qqh*6%ita2F6e?3Gjai zFpYxx7&`j~>xElkr<~Cuq$s#R03$CkR^%`nOHNMENO`gDLRA9xme|MM?$i(}L65vA zf9&5FEm3Eo9wxg>L@uM+>vo*Rk8uukM%!|;*zu*rB#MY`f=7~0;xvEn$!_YsY_5YNr8>l$Uoi1;ksnctSZapLmty=4Cr}ehhU{UORdM;Vo zO3!=;CzebLuy_`y$M5fBAQC9KC%Io)-%xY4! zk1}or)SCL;7pZz?N;XuZe1tJ`?i=u-iPzl0`WiqizU5|UCRfN%tK+q;YGP_fNn_Zb znVU-<(#X(glsgddb}pF$YqBO=WhSew))Gp)5gg;(PQ_`S9>oF=#4vD`NJJTWwH(V=24O+CE$97CPU*Cm#2WMK1wLL;sxfJ?}*R0msIvq zt|}i4u!bx9{DR_#HXEfHtQ-h0#%6eQuq+vm;b+q=F2ecChw%UhAFCiUm1bMp4Ne-=PGk6Sp*6vf@~Yz$sdX8WhG%FNORh7%PhPjPMi& zzL620!oasQ!c!Rd_C|OL1K-66PhsGD8{sJo{7*W(*1?1Mu~esx?L*|@FdjHkK0g{J z(pRUXD^r(=?gcE*m(=e^WLjr@xXDlxKFAe-}4E!Gq{DlU7`4oLUnHu;@82D8N{?g1( z`IsOgr51||e?0?#0|S2}1OJZ({+0%Q!NA|1`APnPv$YQGfFPN7 zrU6=qUIa#lgIGlPhZ^{Y8~8^V_{SOeeVCtS60UXd27+V~Km)W6fdmHOIV>XlAqM_1 z1Al~pf0==Qm4Sbqfqx_O)6Cau9U_4snMct8t;1ddBf}UL5&jbf{x}2w83TWkf&V=7 z(@dhY4yizpOs>)Zt%H`pAbgKSg#V#|KhMDbjQJ_CgIb5DKoDXDG(hX{nZO`m@~d9i zESR4par34$e{xSBlL< z-0sAOrPv%8>-2ZnUrI^8h^4DUtU6*f3jZc+y$_PLD&mP^p0&(_rBpF!w&bC!{>)F> zCi4@!0)H}A#dZ@rnRz-h72TjFD-(NL=#s^aD!}L_#Xi#EG-Ly|usrsjY)`$y(lU0t zymQdGw5o}xK*Y|7cG#!TNsCh%bfV}8wGKp|G|99>tl%2f`r=K}V>KBL2boEoFbd5& zDZ&c6U#Xu$7hGWisSqs1Wr4Ec41|uK4Zd$P1)nmZQ`+zP-k?+3hjdEY+DC*i=agPS zVN+Uh!be8wn9^c?I^VBU`!Ge*`7Zdps`qxRx`?w{yp?M_tHooExF{&jYB7xr*oz~X zSRiBPblWF(XhPP9r1=TEU@K0cbNi4*dFiP?8}ihjIEyBms+dK)g=FBZ8GKsfK2C9q zpKXw4(c%k#-f`eGshGF@5?#WJ&Y~lV%%WFe7QK|Qd!HkWTl+5JmlkyazWYl@)jN%2%>m#KQO!Kk-BTe%O5&z@5MF2fD z{a{%?|JUj7%*V~BUcAJ2=<&9@__Vw@|L+#&Ma znLM5yiUS6%1u7`6^I@4Wj=<+!k`SS=TiA3=NMlJly#70;Ysevzj%6V=6ZW-YJZyvS z9nf6Q(GM~ZLde4wNhGhG-dkJpo}*s~uq9E$VhQK2p>ssXImh+fY^55gvIy1IV`hfD z#0go!oRGYzU?m=n`J>DfP+r8>en|c_0rJu%F8bOJDJzRY3M=~Y@Z!ufMD7O9qN0BD zpaKbb{qfJgpM%?!6Rc~C>vMmv2Z_tyf3F8g8v3tZ5Bz&Q=|fj;522eL*aDEw6VUw=$Q*G|#A2QUlwfTz?vZBhWr1)}zUe*im^(Hegh9YGbaYT40ZVGt{}uaq)&OGdUIJ zEJkWgmXs2C)X^xL(bT`x$vfl=1#6xNrO z!r-co4zG1^V18P@22#o&DOT$s5E$j_q>BY=3^q`Jn_0PL?37@16C5v6@Y3WHd%5uU=phl===%w!z5 zOa^gWd_V(m??;!0>C!UWriGEQLs4_^BUXUAp@BFereCG@PKOBYl4JLp)9O_99ZhO_ z99=pGMqJZAW3Z+jn5OkMguT7DU!zP^yyzGT%F3Xl{_S$#{K;4|*x)Pwr;=b6#H z+u@FrvL-knrtc!!S9+4#sxd7TY2>4bp@-{34Z7B0GJ(<9r;7$&XZk3NMm^UEPhsE} z8R01m{Bk2ag@Iphgr_j@TlIJt4haT+j}8yJPCHc;)sBopy7nSvBii&zHFv*(0l}Re zocgAFZAcIGB;`Cz)D)KUB!N-RL<;LICxwyUMI$_gfxlsdr!erDMtBMX|3JjkvOE&~ zF!sb?;ln>YVd9UFUR@aqFR38toz4Ij;lHHRMU|7fsHy}8|KC$s&p(BcZ+#;?g@JEs zgr_j@ZH({~2EL;nPs>1I;Jb@>U0H6qqbzygUZYHamz0Ifg_-3jQkI6p#kyz#m()c& z0;4RQDXcFGg^^b;BRqwHA7q55Fz~~T@Dv7qoDrVF!225EDGdBn5l_oKUG(e9`=AGS zPazwoq8_TP(ts(pxHX1}#jOXI6t@w9QQVdk))$w;$hN%^p2EO)F~U z5uU=pk1)bh82IseJS{SXf%g;ftmx?dNwG7N#ex?(@5th3g&Iwp3GsRBe11CLuR7lh zoo|lL7pn6`=zJ@5zI8g^W}R=l&KIro9n$%Z>wNJ#-#MM{g3fo9`fz^0K?7gzK2nFu zlC;RGxre?iEgA7>9oC_N5Ci)MZ75oY-2?{y5QX*CN@0jS)(B5w;LjT2DGdAtBRqwH zzb@iw^=U-EsHvpVsHU<8+N0|i6(zDh+*4$I99*K2z61vAQz@)xox;d+mL5;Dr7-Xd zMEqA83O&D#3#B=bAf0p5Gn5!F;z%J;wj7+9Jg=pnGgJka<1s;0+DwF=p4j6i zrNokq;R2QJym4FU`!v#h5ZeQEb4I!kk}5*~z4t*fF`~mxNtdm}`7>J=QMAUPdck56 z2LE^52O;;_U;~nfntD4*=cwu8S-|&GYtqBg8rGR;9kvgHK}yErn(n{6s?h#i2|lm6 zhzpGuG`NEaH#wm4)QTAy8Cr4*nI4~>d4}X8T~tGeCL=xL7ABTBWUZQW4ne8~@%R^U zaei4GFOVZ*XQXz1Xbf5*^gZ0DsXN^DpbaT-m%+KO8|Wq=^l@--9Gunx=G$tkryVH* z_8y5aC;tX*Z$p~1M4II&O{(LBf=0O-G>HvKM~9q&gCAJ`ARLhu$Y6Y10KL_3h;chl z72FR{41Pd)ODGqDl2O!93DuCG@SBvc$hnN!qj7m-zZ3f+RD9!RLwBvuKEx+$UN5tJoGB~g^$lo%@t z#){zBn34eVhNDt%p|a@_y>>&18c|bOv`)3sktS*%z0(Y;({wMb&Nb0S)maU>CsjcywQQCt#uR~3~EP8YkL&y zxBwMmWb>nA0pgq2$*w%Ek}Cmlsw0(Mbza4Q!lY5bZBMBNQ`T`J}=uvRA4(1183;=&-SUDuYrSK1 zjnW&?E-luRNH-rJQ}_lss5Yxr!F};jOsFSKnI<&=M8ug-2sSg7s$qvZZ#6lij>@3N zk+cz$CVT;Rl1US$HETVO< zcTuhT4sC&{CZwmf+TM=P!5bl(r)t7!j2)_3qkKTnI87&3JEW7LM%~~aI*t%W5#-dY z`YzC-!8i|k=?U5id_KwziuB10^?eL>f8gnAA~XOsmY#`sG{_c2oSkcH4%UKL*nk6Q zQn0P!U`1^;f@IGQ(NYTGu)h^Wb1)cW5{AXx`<7Mf%53kOp zN;CUbz-*!;7-(I{Ol%&H_{madk?MOT_X@rzP2KIK-bexeAt*YjK&Tv7%2 zxh*a6R}_Hk)6k-#zM&uvso1CZE4BY-2d({DJCRH>OtkiQIukll?T>PqQ=ppN0J5NR z83n08CWhljs3V#HZB$ea^EIiry3JSTVfdV?PJxW@YC*!N=1H0hzueA!37KZ3qeny= zz(3%+EZJQ-4J{yBHmF`4SS->HGS%L z|AuH|?lJ0+Y^8w&8zhjLL?^up)byH+Y!Nj#m?RS1QjBF zmOV^qg_;92i3}Ug(et~`=vA2j^`~xeI9hkQSceVwM9c9*&*BHelmc7Y7w)g<-P!vG z>^wNVKZj3&o`PD4i?le%NpIESh>`e^GCLa@S{6~j(rsE9#a^t{519#ltX~YnW+=vG zv{KN3Q8(#gm+@O0u^P{49ok}V2I@hV=+m&4hn7MP%`;ozK=y8k5ML3Cg-RxGM>zmT zst4#am>tl?@}xK%5>>^al9Vb(JcKVMEBDQ0isF(Ll1-L>nA;P?Aj}lWE$5rbZWNd7 z@;8&6DlXZvZzhW>F4^X9CW|O8*}`uon_675fNv%nQCza2-%QrExMZEanXE~1$sE6# ztZH$|Dtt4UX>rM9-%OU@w;1Kh1M(m1q^7uJ*S?wTbaBaIznN@jamluRkqqO#BF*%7 zDH+{N#G7t(_=7d3Va3hA$@#JyUtIPzJmY0YO_X%=q4*2wxY}!tTkJ2}swSF@Qca5M z{!%rgeQnI^40ELC%jtPDc6&g)zHR$|_rCsr-v#|5{mH-h{NG=d|KI1i$P+#K^KD-} zekR=)py%2C3-9L_r{(|V3;FCz>yxCzTUbZV!Vva~w8Dei@BtQ6&2aTT_{^js3FP|w zsU~E~O-CN8;6@lhYm^5mN)97>{*4V?GbAL=PClb41| zmdU#i7^BU5?i412oG}v0PK<)Fse_AzdLqS2Cz}LuB7g@bk$$NJq>+O%Hzyy(N@VL^*^J7}QDrgj*Vp49bcyverFF zDBj>qCpqKp=0W0OHUA@V&t#s(`agYSEO#Ewa9^Fk$T(Vp&k-0>zGN7FMwc;rxzOPh2}gz`K4E~yN5(g4I*6RSaENF5 zuqZ0&rG0V#4?pAJj3CFc-~1W@05RAdD-Ka=P@lsxlzPPsBRxBfk}D3OahaL z1Rx{CArheFo1P95U zOyj|tIO&g4TOFt`_I+B1eORO>R*x)JgPcP=fnoKMDXiZhrtoRCGLWQ1w?X2bmEvBI z(q1PpEM$h5_AE>SES#EjGt|tgmF%I7*RNK!Z1bs4l!P6 z9lnE*2`{@P`ZPb#XE4nVmh}nA_X76I#eB)LwOC2kI(QNIEcWk3Jh;w{C3=j9Z`4+C zSi6V1K`x>+1HlbZLDCz1OX%L$u;f|~@_kQ_iii>>oWAS~PLT5lxI{v7!Wr--C(?V&0-fr$Cv)dw)^gnpISMDa;wViRGbCc%OkgcH9uItbjrc1h1AYxhw2w02K8 z!HZUroWqQc6yxn7ZZ6Rj$-L44NL(a@*M3PtNF25}UQ1&`wjx!)G!O=tuwp!50K4Lw z(vk^xH!`@iNY124)E+yNA~_aQB-rdTIHY*NnhWJ!c9-+N3Em7{e1+ zBRqwHcQ?XQ82AB3cnSkQjNz$C7!HW!GLB+#-h{x=?y0(1;DW(OJp zZ3qT$$4HeW8dXwV^z>B&Y%CMS{t+(WQ6MmQ>?FcuBGy$x^^u|sCNNkXNnyQBrEmgf z57>FTpgBv&;21cg6=E4oFRhjg2X&vG)3Zmi4=*H2VYxY?>sw@eQ**_MZ!;$^S!SzSGXb=P0BPU`=lVCN0QRXZO&LJ@7LHQz#W(Du;JppK}$B5xPBqMH~v0uu5P?}EC9 zHqCiJHcLYN@b2H6oLlf0w6~*=t4wpufsrPA7i66;m-veu@6q2sYug9pK6DMO(KXc1 z)-`y9Ek_;{Zq+uZ4d-898Woc_br12!<<}0@mi$T-@L z%N-hk711HqZlQr3jp`Pvs97YcTL`;=;&QER2;P-$iAuzC&x6|z5rtsz9y8^)Pw#Fa zm%4={f>&IDFEHWp=5(kFp3R(MO|vYJqek6AXNXYS0!2*hoVlujodY zG$zJ#NPLC3;``GgFVg-UzuIil@bDrePGZpg!T+Dhhqmtm*D#Nqf=)TqF3k|%97m&e zYn~Z!-?;sf_4L0GUs^ApLCz-9cKwxBi)Y#$S(Ii)S}U4Ks?yrVZB+ZsD5m_{v-PAGADog&)Fk(K3riGXA2> z;;xKW%f92t2RX=J;Tku-4QI!3cFL{he0|G>=G-fpS)LicQ*QCWj5{QMfQW09mKV&p z`zB^D&G^fv77NU|OjG42Gwz;gDncbngq96&R?T7z-^OAXFJTMURN+f=IA>K=na%U{ zEHClgkB1Br6`zLE84Nmo^mMFvMFms)B5+lUrbXZ);bnQt(=z2nIX_RXOqA>55jmCN zxH4u9xyl%P7p|$!m*@5?IQ*0wO!-YF$`z*kHj^Mj9t-&jTwk;+tHp9|_zxVnh_{N6 zaclV5O<(pQt zTp{CoT8GGZ&*~p|elK4NUJb8&$MczdcaGa7Q{n*OKA8o_9hWInWPF^glwo}p^48o9 z5WT@$9+7c(`JY93l&fYMFXP8s?2++{c#EwvekFg=AZzfnlIQ30>53ZH%gOI5%N!~v zKWl2Xv%EaU%<^ez`AKt&r)A}N=9MGM${$%+2A7o|DPi-dj6B}b>|A+ya>;5d%E)i4 z%DgKr-)CcXw7h(4DUxb`seC2JZL;m~dqwVnE!Iw!RZzXJz-_5u&vA(rR8J~!*DI7m zSY8EGz>12>*AfME+2T;BBpfk5CLZ+o|H#^JZIB+&kW8uZmkBvjufo zBY3aNthcDRS#o8N0=n@d$DNdyL)~1HV-(wRK8L25lRg3ZYs;YP;yU{h)}2`6lR+sLb!?Yr3t^x1jjM1nv{EL z!sVI}3MM2%kSU>Hs?0RyBTRwaY-*`7?If=Hz9EcO75^a|SqQzJ!KUnaw1bfNCvcQ7VS`cc_2_m=zi8j9kqWxY% z`Je>9ssyk*OIY41!6lX;(b5UxDUJ4$L@O+zyllzOwgh&$rR8}`Zl5KIc8(x4mV{cS zC89mDR35eBpAqa#E6XER+*&IVZNHU@WX8RaSw@<1JLT5v&A42-yRW&9C5t6x;_HZ03BqH4N$+c_Y}&i}FZRv>3mEJBc}$H$&(tei@I%*JRcg zkyus<1Z5<@W%6>n<*1G=Bo)b{hgrUY+-qR`XHwo{n_*n|>6XAV<-1>@~TPJUHN6w|`cwghd*AC&$G;%(I$CNdOFAMJj z-Vz;Iq|ADqoL?e02WE}jljCO4;=<hUP2&{YE(ICX;E(8$uh~uo zD$Z=H0vf!Jzoy{MllU6B^+pBvSl$S9HYoP%f{(DiGBvOqUd#nx6yW8M2mOZzU1ZNx9`} z1(!mE^gyl(Q*f{409@JmTR??0eF|IvEz z;`_LnUsuRy$*fWo^7S%X_z%mhPAj-eavxw~6|NjNN7>K4(NWMrGHuy~>{s5_!Fi-2FGsGV)i z9adzN;JBsM7U|afI%`a1m|m>;UzCX#-(#mr72f!1{Qw((<;FTdYL8-l*q1|CvOTo-wY_q7 z4dr`#qhg8n>aUIyh@EgD{mcl`&xF!`CX)6uyLmE=`&aZc|4}>fzg8Z!@h-&f&8BuQ zlG?qP|Htj#|6-Y_z3__W=iAADm)%3lc_TNwR83w;?ZrCSi$iwuwXheOwX7wyLE;^;A_$(9=|%h?q&#UW8ZgFslZ4 zjo6D|dsU7-w}jY>gZ3(|J(pl#4q+N%FXq%x{$4}5phmIwVoejiUS-ovo@-!&VP#wS zR-XU4Iu^W^@g_HTK9-kX(={oq zb=8!+uuc(U$}-$2yy_=E!E@Vr(dwV z=7Z%Mc+LZ3HT;2(#>kn>7FzDbdI;|hV=wFe2E%Q+>JJ4MEpK#Ofnm7yW(7A}VYx)X zg)259I?|ONVTSqK3f^pmoSP@Bv{8<=n8yerVgKu8|9_=?-_lWQ?yU`mmNu%8Qrx^! zSWcsLhb5OPCEIfKzNWXh^V46xo99;Z%F{fz;XkFf-Q?js=Pv54xF3SmMT|0bMrF19-H$x_!)ln2pTasl0OmJ6RrEpjHVYgj-Qu5L-&P2_7~tx0rg^U5%U zZBQc4IXO{uY|UAVdl2GzXz?D!SNV~yMtn6K`w==;{qbof|?st>={PX;g2T+U44@*_W;(dz68}XrggO=zY;RmeuP(xiv?D%p zk5&2{kLFH>G8|JGav%~%G;|LN&J-&>YNGDxyGK|%5NTu+AEhBmAG%47cj(f(h-oLV zw0fQiHwN!ve_ejkrn_OwrgHzHykC+3e=L2m?U`N$)g7ubHet9Tkfpb7Uum?gK8r1% zaulY0#pzP7V)1(UQyJ>)!B_QHS+ZC<8s&Rf*)Q|`8hv7K!l*VHo%+D1hl$OgUniID z=*HMObm;S;dIe|K5ZZ~j#>8%X=$hvKLeL>jP{dt5;zG+Aqt-_j=n>l* zS*xJhQY(d?=y{stfj)-!ns)9ttqh3vY41BYyni z2<##K;19yj89!^J4#a8f49A8^FwK$nDbg2m0vtrnjSX#Kcn45^YfF;(ZZ z0pD#_uc|=!7gn#*2r*U5IsSuYBdis0ZIG_bs8OSY0fjRpx`cO1A8y4s z_>q6m2`+Lk{6Wr~bAX5o$|7)^pis+`lOiGnZW4K_M&!%A?0&+F4wts&D50(UgGST8 z&oe|+bt$B(iUzUD$7P}hzwMQITn`EztJPAB5IAm)A<{@3Ukr#FUK zKFl=^H-=|Q5d$UHIGoX8u5tLc;6M7t_P1^qd_o>4QfNQX%>CG=oBNc`BK&AWQsi$B z2ZWf!iSV@5O*I>W%4D%{<@Vn1lSaP0q>#e<4yoAPV|qlo%vp@TYWI?DJnqI-j~A9+Vy&@ z7*jL)subRLru~}2p5s1WlEMdgWX!fIT=MRs6rPZFY0F!mpv?19`0s6x?K~H{YDcmZ zE^_tH*MGeGjyzSf`+GGK{is&eBb)bW7L*~upHlWa-EGnNhE~e&_D=)eP1a7x(n{eC z-LuCnY3lbOO9~&_EAaBY)bQo^r0~FutV(+xb=jLOg>Qa0;q6Zkr#`qR!Ts(voK@~Z zbZm|k|N9PRgI#p*ew|P>ySBdG%8pdp2@>qgR1`ankV@Qh4Cn=7;N6c^30hqUX_Wn)#FO4z74D!6VnzQ@P$a`r@q=f9DSI?}HT1 z;7?L`^?JUEM`rap!Q>O@U2(lf#ar38rT8sw9lzW&v&U*CAJ3r+^5mckFqZdeScH*9x-6u#(Q zGx~pl6wc(s;zyk|ckCR0_xK$te0Sc|8dX+TdXX)`kN7pEqBoh`VEiX~^p5n)`R-1p z6kd2}{hlo6eydn{mxLt6)E-c7`y(m5^uc7OE^a@>kmq5@U;VtOq@Vw=So%PMUzAxs z@64=nH=auIeOJENcC+gH3|9ZiOWmxlzo~QQxfGtbtMSwhO3mEIQu;db9!DO`b4hu{ z=(XN@eRAWY(X6~x8~&1%Ufplr8!7xr(^Ut@Jgl>YwO_}8H*Y>YEO(gcYpJF_!>4w7 zw)>$Jzho1oMfasPuUY-i@SUH$o#RjG%EQgv(E8W$C!d@W%NMcX7n#lGXGd9mCS6#% zIKh2;5UanW2aj)+sj_T6E6=TJ=a=8UHFzP@=Xt#QNtcD6UcZ#+?MrF*siE)sd{*Db z4wT)dNp7=)mG@J|_LaXLZdAbdANoG`^cMM|drZGKTI)Q)7~S3$@4`5 zf3dBs$dt~KI8Yg z>(SDms&Bk{S1QlhIL9rcr^c^h?bXlJXQl1Op?OSyw;j@SuG^X0$?|{fGRW)dBD-rW zzu^0W3&ut%7BPN@w7Ql0WL?WOOkNe+EOJv%=@iQ3d3VvNp(%NdPCt?AL)mxT{5kO_ za-K=>@6~Oq)e3E_H8!Mi*_;S$QYdIM#Zhrp!(zkNUhz zC(rOeUH!&%jxUwoEp;D@zpCCGpZF!0wz2r%ci)ht{b;*_m3O?VYnKOaC$44T=R!Mg z4m&k4;eBb`f?`Fv2M0%f>->epFUMrfHV6NVd#fncO-hY<(E9m4EmZ2;R z|0u!gl_RDtc$atooy6~>?+$ufv<*1T_}{fU`t_-{`wHaFB7M_iH-?u~&p2!%!Dnrn zK5H#Ip2o&+muskltADDypM|H6m@wF;&Y55aTMljQW!+eouaMG5sCJFcnl&nx(XWW~(Z|etXwhI@Izr*V9 z6JLGVves{QzLmI7(6g4)MHH3;CL2)=1=o`l=0mX#q3kg?69jHx}Cko zi#wdOW&+efhYLvPKJXM}ieXXDRxgQu8ySx#L2S;}w5g}K36_p>VtCAdkI8i#K> zjnuWr>w_*v``P!^GI{&huS|Ma+wv(Z-?8J3rdjoQa)sreP-Sl7^QOv6?pAkp4e~e1_K%(W>^$-qy>U<0SPV;D^M;M5>Ug)4FFWWzlhMCk zz3pSy#Ja(Z|F9ON2FB)G*vR<5!nMn-qKVNm`0@w)O6j3x@86gBdDG;3>+z9~FR=1t z*(nd5zc}zAvo~GN-l=^;rAi%|n0DEJkHIs(AAhoR{cD>U zylv~9+zB!Fl0Hb~JC@j_SC;3e!>m5e9(XkL*o60=Sp5D2<{g@P?3Ya}zew-s9)0>b zWwP>J?ceX$g5WOMtUR@{vR^OoE;WnJWFfzcE1a*i?`|5!`UiFJscJcM+Wr1k%0EKB zI&)NXk054`w#$C0(>msO+IuP7Y;To8UCLG3{Yipv&Oh9(dQ`>^CjT0LlvMeg|FoXj z|3!IC`>45YQA|JUo^b7-R&kzgykNRug~I;T!Q(7_hl&IGuDd%d_me=Yo6D|v`EVUY zKn9bC>6ysJT`P^rWBD7~8=GO(TRv`7Y0hVfzt9#-c4#{tTgc#rhYzmrvfz0bgV!B8 z>2vwY^f&f#Vt&tCb~@J6?xd!g1Yf?u?V)(w5trlc2@9?Oy1RreF*>8dD z(Dn*a{Lr%pFCHG!HoB}7-Z?5ZA$nHgY#S-OR=oSAsohGgu$01k*BEjm@K(fY6Dho+ z<29AcZ}u~0FWv`OAJ^=hvX0g7>!)Jz=s}X@;h67=g09|+8<&1 z@x$#yFOJS1@{F~IW1SvMz2cLivyVf|?BBS)PP_9g{@B?f%;cYxp87GkoTiC589%h+a~CW>%EB6z=gfD(P_RHYeD8dSb7; zO9q!&d4}ox{ga7rN@Nb5$>ed@^Vs%JQ_PRD@?Fj^zjP^kOYx^>xyD(SbI&3xW8_bH;&sU?$#+k%^7lDhvR|ccvmUeZ z_u28V-@x5n(v@*EI(PDQ+qxbVH7l9E3~EwiafK!Qo-%tfZ_GY(k15AKv-)o!92jo$ zTLoQxx6Smc*)MwZV%DEuI=j8pm3XV1QqCfM*Uzo(KmD2$QCfnB=jLB7dt-)<|8ozN zza4GcsgUV+PTO10>)L1{Sb4nCWBGHXZr^9+v8ZjLDLa2hPzi}%!qtv@dLQO5nZ}9v z{dB)lVvDoop0WOOO6|Jptyf)R3#InlEa5)CCuhq>R=*35HLvsACbJ|~zvV|dcX@I= zIhmEG{>H_=@vmx!Y!gKKt+t=F9dz{W#={c4d-C_%ns=i&?2+K#A{4(yYs$QgmEc8> zs=Qu0>uTg#2_Cy>OSsd&6^M_nf>?8|Ov z>ldSs+aG>kz3g^2-}F1x$z4(3e_ndO<`k4cpB3OSN<xDOc|@{diq!aQ~TSYKAa-`XRjk>ggXg z4AvGJF&{kUvjCOiF3`z0mg#Qf*1 zcg^kdviAXV30}8i!nM5NuhuYoxOJ6PmrE8sbmOlc_0uc;-0!Or^f%-t?uQ_`S3I}6Sh z7?$V3%YD>7GWqTa;Jdmsy*{&nvsj*TH=p!qvt(;_1Bt)U4^2it?+|r@g)eKo?QH{_ zP4V@m@DhbTS}ZI(YJr24eq4r0@{R(BCH16m{}s7)LP~dhTw4mS6#T+@>e$Z9zL&!5 zY<$uE`7gf9YDnR}`*Pa!+`RQEv%giB*(NWz)@UW`pL+7GrUm+Yt!DPBuQqvG_Kvy* ztUV7+Ykd5{rrFC?68$RcHA4#a{W#Y`qAy=`{#dC~dsZ=f+p1IZtsnEv{a`B?*3JR(?1CI_~-n39eRYuKmPaU6yA^aI^eb6+WPSvh}6s0b|b| z4}7sSR|@Z+KF8O@rT%HQ9?;{=uo8#y|4S*{Lt5Wr<3+Z<$L!VYk(uury;L0fEXCKg z*BKoBGlq@h*Dc$jyHD`DO{~4?V2R!sqyAf${**{RBE;rw`d zd^V`V`>1@2hOZcWc=C`}b-n6cW$<35GP1R8nW*Xaz`F+KfmgDDb zV&%PmX#XRVRW;AE@Q&+}Hm`Sj70mRfOS7`;x%{PxEPU@P|In_Hv2iT?Q`(e-PjC00 zV&RQG)tLLX1s}}x$I7PTzJQ7Ijy~ewn?5Fvgq?U#Mn7X5`I^cRF3xC;W!FlJc$8uQsV)^)8u6B+= zEd0~#uvs~CTwk*A{xzEKD$&t1mW{9L2*DkKOcUR;@M*z)14=(^|AOhu%>3Jty|;3O zEZl#J`asL>$I{q*?9L}mjQ{qWRcyY|;T zr^Rf(v9I6R>-INe?=kv{gqpY2!xm_mJ(^$hM&_Q<39H!r`?sZAmcA|F^@o*Yuc{|3 z+?f0B$t@Ot`_Ws|k498p$Kuc3az_2zfs?xU6OWy~^nUovjU}b@*=u|EuV24lAEP(@ zW5@2R#;(?dKQRhlU>4gi|7OeZw^I9R(@yor@@J#=v-OOvCmvVW8sBCE8^0)@Z+OOLB{ASM^%NMfomEX-nV@CXv zw2qCR#-2^N^vdq(DOO&Ea>tqxKQ0bq?f>3P3wbB^8?mfE`2L3a^r7E0FWCB7uhJHK z%DH#Cz}BCixcWC9e;{)2J1PHW;jbe%1$jSV_VBjDnMJkN-o4A{rJi?OVYmDI9wyI= zONGiiwz%zJ{4T4PFm|)q(Hz!(ch#w!KYZJX3^soJxOYI?B-8HMOum(R_|6*}-)|F> z#|;0BNhYa3?Pu~3cc1z^Ve^^lGFf^&Yx->yDNzLIO)m!Qc31c`@@x-uoT z!M>GDKBoJt_9?S>_c}Iyt9rUwRjb{fR>W5A!`p8cO`qxb8WGJ>Epd$ zr<%8%^5B@MvzY(!krlQL>=SmyOoE^7G)-xAWy1{ydt1z%Rd-&C%`AQ!hYRy8qif7% z?Z@fdMAP2~gq&iqgCc$2nA1;Ru=aeuSBI8e5^m3A>yhJDx0x)9iBJA0$>&3h{ae;F zxw(@T^m!`!zhH^R@7=;lD^H<@*Kx8+>p<#q-JN42&zvzX;)6N*mE< zExB2ODHmq}oCLGhn02acdGAiYEnwF3+v$RjPp_j%uIen(XM8Z1$>VGA*Z2+c5b4Q8 zy3S?d$}h0CbbRvbsUJqp3mmnX%`WqWqV*}Qs#rISyV~+&=fI=gPj>1R@WFM(9Z9az zszi9pxS>0T`N(ei#;&;G=jL?sjxeD0n@ipM*_=EcdsB1!H*M>mTjO-iB(^Fla`X4U z-^d9a89YY9e+>?j!oTJGtIP3c^JVg2?$`Ka{CzE);lm|4Gne6w-Tzy9 zWz2^${NKu#F`xf#c=t!1W^qw+yM7YkRLSl9S%i}%*R@cD&q;0%PP9umBzFie!s(Lx zlT3tfN$vo-2w#`n-U<=EEV;(@Vhl5R{8c=i082{@0 z{;E9w-SKsT_#gKe%h4GAv&H{!NoUN5G5lxC^Jn7~M_+D`ShNm$gv?CuCqD8N)<5(2 z_8a2}Mlp z^&IKp?>WiKW8!c>FW<>tzAjw%M*hQxJ2{T^@%Qo_uN8W}Lb z%hxz=GaA*$b3((WUy0~3xe@60_HX3j;pEh8xToLP5gva2zGHnx<6kdNZ?_TMdn07T z7*Aghe_zkB{(f#FTwH)_?m1z?#1WqU6MYH7MNg!OA(7z$qy7SghK;=^j`W(~(Xi1k z6B~{j;qT$&>Fwn=2_ zj?2bhkB#~p=`q67&;QRvc4|ao>oWUONgPQM|6eD0jUD-C;x{jbI@N8*$)3JrJtlij z2=Hoq?lE#??Z_?EckUBl{L_#OW5-_&D$~t-M{do7IV0zlDyV;hH!t5d4 z;$pl$3RQY1UQyKkDE#`fW#YX@AA}`A(_Oml`5-Lq`+7)o-w(pdx^4lHO+N^`f)}@& z_TjxS?DL1j({b;G52igkO`7{&_+$Sim%y&?g~*f@zuhbKUWj@+PQ6z1PB?!x(Qk9) zJ7IXqu1lVeekXj29ldsDgLlHn{y$dg{^qSPbJqHUJ+TWrcm2Ft^X9!3YH_Zvxjo(r zV@Fm0Dbwb7~!Nb2{pYmH@2^+hF{nFg$mEc=$mU+#luY?cH zB9^6ndMWh&Y5K@!XI}~(pRe}sgIf(DvW`Dj>HSjprSYuop%q>V9ybG(D{j9K0!G|@ z-}H|cLX{dvXAB?rLdc!4{e!B(3*r4wZa+48`COQqS?bvtUJ+Vz{z~U$M;utr|J2E^^0)7?yd8M$Bv(??JsyHgj^ms_m4@> zguyX=a$R$t3N<>sG#@+UsSxB}@$C6aPlWLk)K-m!C&C|%#_a01^ReJ|xzygfr5_7h zI`sH$>XbZT&ehTG`_+$x2>I?xR*fGCbCP`rzYWP1Mqf(Xo~X$YQikpt5M`euj4W?C zY~zTB!bqW2^=WG!2tLmqYon9y3sbtqnsv?178;BRyL0mWJ)yR$7VkL7i9{vmNy6s`#XfFki?Y@72l7da>|Zr#smf&6f%dPT$xr zY+5cfTs(AB{~oIax3>eTEK{u);+iElzn>o|Y#Q?2ky&>)31)+Cr3&d=gtIYr8Oa%2 z1=X%ruG$At!p82?;+wtMAuJf*VnlSw-GcCLVbjtM(Zc;(w&vBg?-dsGu0GiG)IK4# ze`A}*>ivSF_lhYiUmXyFy=qRYQ|6G6xH5NaM$^MWU1!yeor8}Ev!C>x7&a$H=->R| z!)g1D3a#HwOaD3Rm@sQWskm{aP6)PxR;3K@6f3wcw0YET#z~=^ZQ`L0$4&`v`)_tC z`&*pQ#M5_ZaOHTR{h*TzC!CBIYEF*ck>+_?c=D{mgJ_ckVTeoSc8lE!LLcppHFx@) z5eD=tv%1OWGs3;?AC8%BKP$Mqx9B{uZ=&$N=c1%~@^iwQ#JER6`_Bo}hR9Y88j&Pq zI81$dtZcGyH$~ojOLDR>&Z^=&+Zids=+%$!RBD%tZPsqyV5T^H6=?CV%6^SV&- zmyqN4y510e@7V3a@e?)b+bzt~n_tL0N(-d~n_06SIW)rm{n(CGQAsV?PFs z?RQ7mG(4dB=?!;;#`iZkRC;trI33u2`1B@sg@|TX|0tMwSCB27yKV8MyTa}vrltB- zy(b(E8MmXq&pqM7vOfNsPu&xee)URsE0ryr`=RHf!K1Q;*&V%q%RiniXciSNs#yBI zFjC!l$;G-aT11yO_r6>NBs7xyEDf=~Hcv6xQ_mbiUGS*TR~V zf=-hkr4-Vv-q3u@huMWR%Vu`nUCptOX4uy=Q86zIYOWvM5tCzCK}}VsFrPu43ToEO zYMf(T!GfBoW<3Yl#F=T@tUh<)2)?EIX8MOI1v{B(Y9#mG@vxMc=JCmojnmH+(5(G5 zb6!C20-6>tTzrnE<<}e;^S}{K^E{XXx$@P;1=S;|_IZ?M# z|7kY)G$Yr)OD=HFRFiu8bfeU%rkb^hM;jJxV5%ATrsX}?w|OuCwk-0R-1{{3o&@-2&SbUF*!>i`f zao>W@9=d|f;tl7R{(}2&_j5Q|z?K5d>V63UM zt<+HK8%CN|%X_Y!zu8D*JLk&$F=LH1)z6e1_o};*=5wQiXX0xbX*R$9@Z8GONE3H& z^HhV!IW%LYOdH~KD2JwTJG;dn=H<|IS~vA`!QdPk?=$sx%x#rJ6SuZ%-cglM?pm?y zMZ+ALAj`S=hut&Ow7c!RbHaW@O|=efnu|6VzA(^qt2$_n$7utNdeDls`PLd}mTn2WYc$?KbHwQQIA?DI zO*Q+#LVpt<-aZ=bD32AVyW-nbY)R%rs~sMaq&q0$sTUGr1O8kMHN zX8TK%Ca5&GD;B(Tr>{!0(qh)q{w-9RlJyF{v8%4qTrK!zNS*vDO+xOwd6E);h{q4! z>^QmlhluOdZoYZ{A7W%oRKPjwAENKf?rG0(T{$ScMUnmo)5Xa0sSDeVO&9G~6&*IG zWx818QgLUU!lo1~bj#IaW$9xf~2Q>AjPyQk%kFK>nq01NXAY{5xk4j%e z;Uekpi+=np=D444W^&@Qa4oRvQjIyE#ZwcHu9G}Ji>goV?=M>OvltkB@%%Nz&*IZR z!~36z|0MEQYzt3X@kv-;9(dU%`|CZ^$l590H#>c@Q=e-PhPH%D#3_2>)dbE)RfQpKub{Yx+0pDK=zIi2P(Emf@b z8!#u$Emg$%HF7RP-&EJYXRzuXoo~4L71_N?zj!6;0s+I>HMW%?);lZW;^-dAJx8?iTvtEk0>9DZi zvV19G`FbPoqsQKeUFn^xHSv2Rmd!DIc+&ih=w0&R zT6B(V)GuuJD>1Eq!rOzrUWso{Ql`Jl`AXdGcC?z=)?~3PK4@X#&dFjyde_-s@K)TU z8DhtwH7~`Yd94P!wRtJdSiEye`0zq_#Lw+eVaW^8Vxsw>Mourp;T%80s=s_L>K5MI z=-$ld!fD0#gB|KV7gbNhxlFwGOe8<3W;Jl^GqK=WdWVu#pNZg6BZdWDdMZK&=PDl% z`cxc<96Y#S@u$LR{ny?d4n7fyeNFu>J)Ve5HSV68Z}dcbKeX3z`r5~$b>N^XIa@py zv-4i)|M_K-DD+EGEKQBx`n(Q)Th`zQG3|i)+-$Eib-PXjiGLL#POAP@2(zx zTjXfp^U&#ew?urMavdW!-xPuFS6sUuN)W~4{<(GG46fU&txeHfx-O2jFkJHa!ZlGg zJ@3gPC$5UK0gmYtcU%!QE8dDUoF6ZOK3=!D9dubZ^f=i(rGA{4f6#wVLh=Q1e$U`H zm+PJr5kroCi*z|HLaMfS{G{9ov42|k^|fXn5!G6EZ@&Hdeo?$(&nZi~L}z(_&@%5b z+fv6I#lEy@pYm*PD=IWFb=_61tFT!4U|s(?9^yy(^7j)H`iO!pFO1sJ#ZNTWboO=3 zH9&kX_vqrhuOUKRJGss>qk*D!vw2sammeZd^bGyluJds5<%V08cWolXfbbo!e7}tn zy#w1F*&97ZoGY4?cXq%y@ga1>lihX`#KE;Q`o1unBrMM~>i9KbvglkXzW{X296E3!EI7hTI+_=WE z`F~=dW<~H6*STVD+Jsv+p7Vsyg5(aB!{&=-RSOsJnY=(OYBTG}lNAd^P?xVR%MUFQ zrX_2-M&4a4`i8a}vgOAT5jlVBsgD-R#Ots5x^Ax+CE8sdW;!G$N-W<|d|6zF<>G3g z_ij6rmkZO_PG`zbSRuBUixH*EuM|Iu{b(8$yHXVIU(tM{+bZ$$#N2lds@39b{E5OH zSF9EdqbiFuu(iOo69Sv#U{~WaG4xqzHbs{U2K|P+_qUf z-dw z?iT9Z)_10j+bwn%9osB#%5G7{q4@aD-S>$1Yi>k@oZ2JyAFp5C-EOa_-=W)>GAs6q zeW8yhZZ+E{g8J8NY96sqR8DOY*E(gN*i-kWV^5F$!rRq*TjMMHMKkYOu7*txh#3_J zbY8gcfY@qQBh|DsEKPcwU-duEMo)4ZT}OoXS;wt+KcfEGfuSes9u=Ae?!i@u z9u;<%jK<|TbW{WkKeWNe;F#EI`t^Pz=VRi`wK)}+Ogbiv*MA;2@ZvGCuwUK0R|+2& zC(m1+iSBw_%(b4MP%!Ga_`2Jn%cm#DMf0h-{Cn3pAwCSM(a1IYgxG!5HTT6sCxlJE z=&y0PPKr&JtvmK=cT!ZowWim=MJL6Jv9CQ#KRGGtRW7lwoy{qc-elRnBEwIKEhWdD z?sw*tc=B|POZ%dy#g`IOk6*xj?6ux5%kFJEEe0Q`;5A-#Ml7H8Wa-JaXGHm;4U(dk zoe_mjBwa3=az@1TxSzjj!?U8fLu|`Cv(AdV-Cs7cdVE$icynj31&&9nlY)mGnsQFm za;oQ(e*c_!y>(yDCwAwBVX=#C2TeXNmc@OnyF2l`_-?aPy`s(qvG?_(5)Ect5cS%Y zYc}=i1+jLF)%EF`i{hQ8tE$<;i(*mYvjY25FNzTj=Jt5n_L6X&{i9X<#!L9zY4*kj zMscG0u)z@@Y~w_HWY3iRz2ijT2G83}of#);c9`)w;7FYCTXVDh-nVgL|FZq(eal@I zZ)QHcH@3rNF}iqh)4by@i*7LsAAH|^S=`P&^i!|rm&K0}dEZ)=ki0F0JzFPFKX6;oF+-nR-Qx+rH7d z*|964&2nqQZeOp6hYgFpd~I`8yg3w6EqA}G;>xWlMK-RuDoTH;nE%G@tK!3?d(&nV zxh9^jJXrQd+iT*@dV9a<$=8J6YnzH?PF@q?UEcg#-tf9es?@B}(FWJWvV13Jqz}C= z7C%p_aC_f%v1F-NgYKWNix<9 z?0UVWP~AJX#hBIk<8oHLBiz4@xp`;M9Wg6WbLYtEJ0fq15zh}6zbl5k+Bf`W|GUDp z?AnQ?58oAObsmJxE__e4yE44Cv+q3-xnyoj$Jl$Kb)%P;#}~UVzMeQd-7@&T2zM@< z?s(z8uz2sj?V))hjsx*8pEXVt!|T7c*%y*1s>Zoa?7k{dR4Y?$RKm4HG3{8X7WGUX zh{Z)~#!RUHK)epT=zlTrfv8Zw%&B6lABagqCln94{Xm>$BsS`o-NmRKT!6m7;!cCqJVKvMMWD6%gL{w zioGXt4tmz}nXrn#629{4Gf{6t$Ky7x&&4ADjz#9(doG@S?Y(+`_zO{WLAE;Ehbie(Yhac)``@C0{jpi+`x~MMOudclIw&{q7HMP0Cd06z1+9e*3vE%ZJb}2*WR++WT zhRa3=#?D)_*kf)DE>FqZvF(StvJdW^?lvwZ8| zLVJ(Rx^MJuSmEzDtA6uC#dZEWoPGL_sIt(U%b!OVIJ!S2X|wKqlI};pv}zTnO4WV; zdq?%U+kVXVi&C*(rQ5m;9B55nt$Od&;vF8KM|kv_x5yaJ*JA?w78t2in)|Em!|`Q^ zW;-hsc?sFx)t$!D#TJ&uUi$TfiQ%=MlY82dc69)i*)IH}DA+0JBw?J zszDMe`E4#P0AruOa(?x#$kNIhS@+s)NT_kbmpENHta9k|Hd{kR>B?Kqr(O?OJML6P z)_*nq5ns%#_;m{|Kf4_>$k1ZJ`f^+@Jjdccm+@8?bnlagEG!%8GNjj$Qf#k%W?9mR zDPc2o<9oAy|E1Tys@L&1l~r5bjf%IlDZPll*{mw{EccEjW!~Zw7nOFq``T$+e7h|f z#PM; z^3TuMG!irhCQ#;66-Ha;^e<8H1IF)pP8B-)CI9u+>DRRC{7e5=q5UI0d$rqDSFeOXXLsoR{`%8* zKmF&7y1rN}{Asry(%5SK!0#FHJGR>rrC#_h!~fXDWc8#e$207=KD99~d{UiZpH=JZ zjJg|Zeb@Ex!qXQWM-}Y+S=au8VZqTyf^NOi+4IeK^wGm%^DUj-Zq>6Lvs%22(b?B` zJpKNC%H{ub_O`bgj{ax;_p7@1#nw&ucxR67*s;3zneN>!;I}^E)>Pf`rupjbwJ#1d z`tp^>@6zV*dllJ$2cv1JhR=RclQmd*~ z{;YdnZ0@=?V+$`eo}xQ{+;kkhXm-9T$93&3GkQMbc5AnBCAj@{6)Huay|?F{?s)&+ zt>H2MFD5Tc*shYBa(#0(etq!wgj|;kr6=ir-+-!Fw@JBO1|2qym6E9Et7e)VJNzgw zmxJEe*pFN_Z@KP#^6#_c51v+bdvxW|<5;hhN{$)V+Ud z-KSjg$(&{O=3#r>*s}KXyH8r9yPin2Pl;|a8UNTS+f{PQ7?0&48^UAWoE>Yy_MXj3 z6#h5K_>}JaYhLdB;nXrs?i6Qx>lWLR*1Gt4)oc+0^XHqW87?z1LljUc4RO^I)fdg_dkL+4*4D z$`?@)y7pInxchCk$;O4c>z~gqecI(aK53Hf_cQnu_3LNVaqi}<#>3C}hV6>(y(Sdl z{wHoZ-fGm7o%4!v`Egium65l`p3&Kz-1FDH)Z*(t-S3~NXi#`)v0M??b?YzT&g;af z2SUzd>@TWcxBiOu(vHW*dshE1qdohbjhBA-`02E+Ju6l1x%o_|?$xz7%Xvv;!Pg@% z>)Nw$+cKtO>%?7(_A=&s$kIXf*Pb6ArMsSv%-`2==A2e*3TR}%LOXXZR-}K!QMz&t zgFWr$jUN`N+kfi0o!C1jN2_bP@w2rGYbZwI-wkB{U$etM2UGcD>r&HlZq}{GOP!3z zb}}n*Pd6X`n%j2mUZKfmUHOY;??&$~gdQu%@oE?ZFO9P|oTKZnO=`mX*l8bb=#CHE zUPgYEET@m^e0|{T>g=Dh=gdK~|EagE3YR~1@>=yMso+-K@j3AO@_mJp4Q}Z6U-z7o zul@M4;X$!j896S&KBd6J-U+(n%Rbu^R-3|SEzQMt>j3i)P3NDQV#V>>cCXUtz(|J_ z-Tta-TD()*NA)G$@%VkHeetKW*Wt;njK6tt{(n}@t-W8D@8qu6BPv1nsky8Wl~(prV$9g6SJ%}3?$N52~+-QSaw+naDd`Bmz&VW)M+ zyS*6hUE;%xBiqRomd-bpGe*VTU z=Ve{~wUYO2_i(8CL$}_p&Mxz~@7+_;dAL7Ib0&J882osmu6)+GdlAcaMIPwt#wB0#xvtPwGOTg?CrVaT{*;eh__p)I-rWW zQa~j&UG;l=g{b{K2KoAj`Kv?1f`S5rL%qG!q*M6@;3D4FON}2s0sh_rq3X~;bzODu z!J*zE)zv|v!H%k|zGZ7~|Eopi(cT!S+MtC{)nJuZU|4TIZ&iq| zkH3d%aIklfs%q7$>gG+ zb%3`Ix)JWJ_VPt;A-Ge~W{pm&>D!k1_t03F92C0CvX3t(7|eaRaNcj5fBil9R#hPx0ebXLBrI5jD%sp z5cT#B9q8>HfVkwPa@KuI7=uIK%GE9w?cXx)DYYf}ZCzDZK>vWifi$+hKEC)uxSHx! z>h7|P7(G?$%6+s$+}pcPV6e9u-$W1AriE`k<2%p(RUp79SF*x5TlER_^YRW3v8vn0 zs#YIs>+o8B_3VP{+4zU{@$;(}YCXs{#NQg5Pk@!HRo~FiAo^OnTlm01bq4o#t8Mk` zDyu=pO4ZeV7#p=so$6}OK)*2m0JSyEKmWb%|EBetHkr0=Yx6&9zE-Bq*R=Z|HDCAt zw)uLQHg8k&f0MnPT}JbEdMmGH?f+5pH8YyGsYC5+cL%$lE8>5&2<(3L!}kBO8;U(q zjz;aDWAs0ol`M9N|Iq=#);1$y{iEUk>Ud=PbMQLPOZM35e!X%~(UpUz8$SBhUI;qUHA{eJ zO?9|!4cj_3tfje+pNCI~+EHz3NpF*>e~p0JfgZsDmOt0uhC-knkdsM{o!oC2{%ch5BHB4?j1*Ag?~E z_P9t3^v}5XRJEda<>+b^ZziD|xYkv5fV;O|nXUo@JOWg7d8oKNR+Uxtq04Y@RZw6E zuExW?RozuRwZfm*sMTxKsF87<%6$nA@X2sda|lX9<*(}N5h5?0_1o&}flF%-bwFT% zh;qUL^P{F1`gUa|(Mb(y{JgA-N$%CpRUugCT3&1rj z64t(#rM;R(fv!5$a8ot*2n+F6^Hr(6TKiEZE`?F)gOtxTYD=6Y={Cv-OJ@+)IJ%)|EN~$1lH5WwVpZ3UMF{R(B^sz|$|x%iBtuuoW#Ss}S7n z;QrE0H=Q9?-02#9u@koU_V9D1RrAXb$){!Z_N;XC^2KYSp`Lv!qgS}2)H%`F^KZ&7 zfquT8gVhyvdTxu}p2e+?pPM!c4iFBgsjjYV*Fasdin_b56%4%IGQlmcjOO@jmxHJl z>&M<+`?tXk4SLECCH_DBnMdYipiIq-EAnSySF^nXSTL_qLoTX}hRaY9T#--mxA^Kg zvZ`bjb`9HcACE(-;cx!Y+4#q0V^3kbHR9uWr2*QXi2(?J`3TjO#U}FZa<3cfoMg)|8Z%*Hp0?Q3)V+P(w(yA+ZQ{fl*?AKzUU-(1?yEBYV7c1=-f z9|M2={G!+%S4`T+0-5FK&fgcBnuR@u?IW|W2bGZVCuCt)@quM_7WOE%FUrEMp@oah z4*zF1z7cG9$ig1W_MsTR%=|TFD7s2j0sm)a4`O>mtqKyw7R&bTS=iP8N<&}8{N3UC ziBwB_dxbrf?I~rYy@SGDcO)vXxs;c7OS!&erL5BajHoCJRqAvV z+NuAU*@I}|W9tLE{`zah{Y$JS?Za_`*0%?--MyZ)SHW>g-)4%O1m47S^rbm z?w*C+rIGZnE+K=;ziWTezotuDdF(&>$It4cr8L?}+7BuAFITn)(SZTmNrl~y?V1+S zp4a%-{3f#9rETW+M7F!TN_#5+`E#}^RoyY%Kejhh*qzv}>X^B|3)@}TuJrH0_NXlU z`?5VH3;$5IYv{y_P1*klwnwsE+5S|vC$fDy($!xd^VuHMMaJjDyc`r+rTrO^jXg3O zdsH^|=xpq<+1TTiXyLG0VoTW;@Lfw%^7#i0v*~qrq?XShia#_K&?h ze<^(63{cn;+3wy;+pa1c$n5x&$o3Ig*xl*Cfi0`^Ukcl^I{yuG*Sf3PD*9(OR;C)) zTiS;LIDh|^e=ARE=fmi4_6WA)D}lePZzql)(MS3#$1jxa(S4;oNYTH=Y*+V}_N>mI z?%CL*vau&-V^{lS(>~kj*Hh~6-{-mi?sQ{@Evxf;6x;bU|J(S+vRxTpHBL%ngZ>;} zMK@m9vWl-}`#VMd?Kr+VMEYmde|NTL6+eRQ%KrD|_z_`$jvveRtl}rKUD^M|96xd3 zpW~|sNqbiDHEe%`@xl4^xB0OiBK>_7>o<yUs=ASZL+GoR(+4Vu>NNFE} z8yx-hWj9`i$}?Wt>HMUB{oRV~o!H)9%6@A;R6dujh|{3GW{`+AI@{`^I-zh=8hWR$+ zxA+m+*rV8fP=@)<-+iMrDECjZiBcN3S=yEJ8_IS~w6yP3tiMFIt9DBJdzs&F?W=ZY z9>3WnDGk~y?O}@f-^uor{n9?fVS5{ey%^gy6{W6&!d{u} zQEaFD_2bvo#b>*+{bp=;sU+jmVOBrCE^IfeEbV=8eAG6f#m^rR*3!NW^QS*Qec4}A zQ`++@>`}V*Ye~Cu{$ki3#rCY`&#d-OfBpQ=v%fprmGgH;*M6O!{`&Kms%xL^%JDOr zCH+)&C6)7M&UP2JE9cLG?a^#k&Yztwemxmq*}fCo-Px|3KUcP^?WMnRe!SS8=pgON z@n6h#Ye#9%Bd1&zeonti!*;s9$N2wdcV~N6^B2c*6eI>G8(?`Zv_CG}zpY6)_ zRs38zh3#~kqMx4`+oOCqeirke(ofox#)tRlIhyZE# z%3^(`1WEgH3Hej6{itABwK0qJ;WAL#8^I^D^jIRzrelnz_2Las#&iA|ZxeK?K5|^>`!?E5H_nZJ=6e;ox~%(C|I(n-u!ghA!?ebmo}B zXRM>^2JNOBXT5wKpi}pid35`O&I`IO+32dne=5bhCijnMWxN(+a!t)S&3L-e45;L~ zj=|r|Zb&`ttJ+Q1PrY@R1f2=%bo*^)`?0B!N|kb5#`}}b4!SP4q>fLIKlkG%W3+wL zu7BuMcchMvU)n8`{*8sM?h~mStJLYm+XP({>!$uecNIF5r!pRY{GMt5(x7u^-Go2H zv%t0SY1YmDgRT{HnrE`zMSswRLbsQ7c1oSzye)<e^F8SD6T@-yFtKaU^D z)V~+9e--`^&m5EAjCBo_Ixbs)4$uX$u6q_bTGw9CCA^mH()UEPn{FLsvaaWl{;ky0 z_eOrv>y1wgbcyezuBlR|w{DW4Gf9=YzDk|*&-H4W3;CGRZx{LTgCEQO97nx&?Vwx7 zI(P0~#{Q(I>j7ON>u4Obn=WrI(m0NVE}@)k_fO~9P0+>uh3+c#?=N&|&_(`*&H|s! z4EhUQE9l(+LKg~Mv%kPshPk(5?Fm-FfI@|3a4v zUBX}JO6A4*@Go>u(3#YddHm@(?FXHjb-$gj^~Plmbj9jqA1?+vYu5cKUJ`Vb>t!F$ z)D-6j*8M4-9dvc;XCJQzbS|v>Q@pXzHFL~9-X`eWS(jD3>KMnX&~;(mBjxeF>6n~T zGp?dkD$*O_W@KVR**-nz*KWG~srB#IU3So~Yb5pjG0D&Kgx>XG59pq>kUIMOrQ4cNIGGc2ei9jHh?JNrSGhYxe!Kz)jt(ztFXUE|zuc zl-f3%Z}BMa#n8Eg%XZ%@$I0c_bxHd3 z(8o=edV1cg-E{dV*1a}==p9x`eOBv};#!ztov@Cc6?*);O4{ovdanB? zJ@sQO^h-BNJwNCDZ9GV~3A!>{ve#XO?(;RNi3j{L;T> zKkv7CKo?P3>iGFWrt!u?x2~+z{dT<8yRO;~@P8cp4d?yWq;d9sbKqC( zfNW3x)@qDjO@*K0Jg(iJ;HQa|e!BZzlcowk9!Kh58vc)DeKP(|n@Kf=zUA-tAM`w7 z;t@qVg%y5!#}RsDoWS}(*^J8M6E85``045CQEaiJGQMsdnA}$AIS%E0Tu6y`mi&hwA84^m*w= z?@wym`N1a$-8?F;^T|*9Jb|AR=swRh$&r0~i_u;rf4<*d*Uv|a_A=?`Rru}Y=La2h zes8k(qvr{#M42CdO+aOGPT{ARXUfkBesv#6KYDMEHj_OHzf8tu82lpmb7p#*hhR-%>x5;phHE`qBG&TEF*-{Wg>K=y}4kZ_pOUqHD)iCH_&!3Kq&GQRTsqSyaq z=u?#O|IIpnDSX~;mRpWz#`t_wtWS#G&?)~x5R^Wr(4LZ8Y0s(qf2SGGg%ZP8|O zSmBq+x{N`4arvYleV5jeRQOpGl794_n$~Y^_I}l|kEWPQKb7wIyfS+~FZk{K zN0Hy6?EUC@LUeKIN8g9m_H#=1eplgFxwQ17XXaYJF$%v-=D`%7zq|aa@Ee)EpA-BB z@$-Ury7q=7v7QRNlw4b0w;o+q5HApJ7h3(4Lu8GeaXq#u2MUOO&<+51`G!vqs6 z=|}HHY5lU9KRUkjfL{y#{5<1$q#PH$^YT>aV_8q{M`_#XuIOJT`@wPe?X4^ONAE>x z{W@gtmj*vG2kA%eJ8At|X76W*&*$rEq+iCmY^?ChWPQ=|gdqML`iX9xWHaA1j;r7| z(OI@f?=5Nj>7!_`h5SAV9fz($Z_-Ta={+W`zO6#97)NS{o+qU6=he@2{Syj5{+hNd#ftI%gMZmZzerHk~-Sht@P*B6=i-GN_Z zH|du#Pvx_3&m7kWDoSeiOZzh>he|(s|4G|k0fk>C`5EDJb(8<3 zU&eWKM)rOV@Qauy{W8|&1chHF{iNp!dlyK*jCHvqd%sQaOIf67?`igaN${(@RMB2x z#r1L~{Vavg+uft2U&emWRN*d`8`iS$2{!~)v_4+dv`Y6`7*Tv7J(ChVwo+reyzKu@* zR59;*`ef)+SfBAZ)~T%ZrSN&X`hueWYqQq3f?mV=jQ&r^T0ac>AlC2H_1{OK=kcWD zDLqdJx-R=e@9)rN(o~^WtPAo>g5T!^=|}Hf(e|5PChNSEImY*vBL8ZN_VnhDo+r59 zR_OC8^m_9#4EhMxXRN1}isPBydfEiN=8hu1@_tpXKX<4oeBH4uxKC zUC{G{XUh1>`xCwXq*8w#%Kl`GtMa}?uRj*}T)r;rGuDOjzC=&&3Vj#WXRM3*ivH{A z$3j1m^%=+Ql3DBNdBQqn|Gz1&hxOtoL7%|-jCFBaq1Rg%=6G(@#;eo-H#AHCm2o5@OrUn}|jP0|m8KKh5$chah%pP z7ek-O`iymSDQo?C=*{@~ddB!{&03!Zy*2AI`adIUef1JJp0a+mZaf1OdLGYoyvK6d zP-U6IyvW?hoXBj>EY5t4FGOF1-#u`Lp~~?FxC`9H^;^I#D6i%Eaa=zFT!;FBT;GxF z+khKU-<0bsaD5qYJL>=8`VV;Th}wM(ZbJQ2klH=L^@qUCDDUC=|G0iSh9`@rG1&1FNbOz&sXrINttg-3`W+yR>q_Q-OiyM9 zrZe+9-ZvwCJTsc)LnM;`eF~>27GsBqvOm}7%rVFzIh{pj% zN-{r($^JcKCNdM4am*9UgUnsb&CD4f#U0FLf2IesBeOZvk!i!M!Ys=y&dkgF5i0xn zp81q{n|XM#9YgSDW41U7>1 z4%h(P2;Rf^Edk$ylR#>3C`kGNpfhy+z=ohZ*S80qP;LooKzoqdtI90IjKO=*6n_gy z@t1=XKaAj89$Yo%uHgQXC7z9Fn2OHF;_7cGbb=d zfs|)BmxDl>2XD|3zK%OPQUZ~Fj8lZd+57P0B&GI@(=h4$3`7HrSKZ)(5xV|;lTXB6Qko=0X z{S|&c0QublDQ-NdjmPyPxIP>tzrJjDV0$Hy`u{J8XC;>A;1$pmEQdUd!82ewzObXNd7S( z=>tLXcL&MO1th;>Agz}|U?S>2_Lk)b%-bNT;G7pRhZ?uJ{OnYxyyEvL25US%Lkad zxPAeb$1+E7eQz$eW;W&eYFsYLG~@bIH`(5O=1q{=J;>#C%oSWep34K6{kgs^m+LcY zaeYZHn}M`HCF1vY&^k&0Y5iRQY5ko5%VGWxfwcbifd67$tpREM`GVx<0lI-5!0NEu zf~JUT!7L4SgS{Y^-}jXHyacKJ10c=gX0Bht^}*n6_%-LU2E>1sb@7AZ+b~Ol9Z>(N zhm11`q_`0v=?8(NAHeJmlD;8G?bY?;-ONGlsbdq;?l_c_v8xoCH!oM>G9E>StS! z^i4qOrz1%ES|I6bFiU`>GXu%*ZAV!i!yFFMJUD~2A2@<^9InAE&-EstKkC1_8mMX@ z&U5e}=Hnsgi~4I|FnER;3sOJ!fgz}$3u4!?oW}LznJ(aK*sVZ1j;p|q;9GPS->0*D z3{spM%*kLw#OVrB-ffu9Am#4}lD;;xG>HE!=?CdQwU@d#AnBelFM~8rr$Ew2gVatZ zko;PM6yF&nzXr?-AnA=k(xu_|7?Ca&B;70Kb&%qp2T4DL%VR)%^`VF@m#Z>MFbgpa znO|LGoOjF@%$p$XSC>KR#}2Mv%Us5s!JNPx$P8qkosAMnV;>3%&)CvKVL8(GUJ(NnfsaB znJbtJnUk5LnZe9{%-+my%;q5VuO3MKtHCV8_M*(3%=DJBpRbuunAe#XnTMFWnQNF! znRA)bnZrTqhd)UD=*?`;_9n~*%o@y!%%aTv%=8wrpC6e|nD?01nHQM{L27>;NbOJO z`ti(gW&pDnvlFukvjMXPvm&!7Ge0xEIroqGn)!rz2c&+SXKn-O__!9N<6{(a97xB< zFp&E1&1?aZe|_*7>Mg-XUjYB#b|Ce?1=9+o`2T_wzW_6}sq}jcQv4er#lOVmqaelK22%Wu zAjMw;Qv7AiDImok#Ow=FJ6)KLAoM^S@%Q6cxb2AN?U!0}iGv*!URpxPK409866>~9j4s#?({qg2< zPiAYTGt-e-i&=?Tl3AFUgZa%#_VYdSCG!DD?OkS`WJa?+iaDPd$(+a>!VG5iW_Dw? zXSQJ00ja&xTrR>iW`1uh`}u+Sig}-Tg?Wy7fVqRYk-3t&0HpRNF^6z{D3`rJx=!iL zWkLclzE?diFt~7n7N0!8KnNrXHH_eGs`iHFw=1HM*52& zUDuuj{lEp_clta4%z^9IcAz2H0_+ESO^`kx$Pd!z2|2;j;Af5WzW|coK9KC&xPCa- zyK#MIko=mny)@hNu-%aBF9;cL3rO+SfaEtHr2hJ`y)#I8v<2z@)(NEhTL-WV@~H)$ z25W$&aQsk%bbo6AlHbb)GVUXg;+_R;Hihz_~evs~4((B9nliOe~*yBMu?rsNh zYihX?r2CqMTt6G6`x!rw^qySbgIN)z`x#@9#_Ni`yq`G^(*4YCknI0~WcOsdE8Cla z)V@74KS+7x1U5ehDDO-2_tHc_8I61El#L3(|ZK z11ln*Fz_rG09s&N-9eh~h9LP>11WBKkmBY6Y5l&gEyrU6NbM{FX*}kDG#!kL2bXm^3-pT`rQm9KMlAL zv;!xC7T_!7SsI*)azT*tI%Fqx+duq(yY>z7r(-I*{5~$n*i}`0ot%M0<{0Uz=G8r0bZHURG21X3G3}YQ%&N@d%zR7( z=7;LC-_MzMnU|R-m022rZcl1(~?<%S(;gdX~6t|lM;>d zbLL&WR_(XVdiFjuPXcbmid@@lX-!8 zgt-r->&0zcUe8?4oW>l*9L)4(dNA8Dn=|V%ZJ1@5#hJO82F$lrczl^RnU|Som`9k) zK{{^C2D{<7FczfaiYME;)oqOBX7`ZUd5ENs!_e1}W}WTrA+!V0jOu@mL5_UQ<9C zkFg+)hd)T;;R(|Dr8!9bYzR^uQ;_`PaT2FEn?Ukg43ggjP}^@%+iy_YZ&2HBP}^@1 zLu~0nW%$3lkz>%*BE@HOoJf|PGcS$V#E0MhyL8c1Bs1FBee+~fYc-4o? zy_lVtZJ15K`|xWBlD;h07i8uJpTeH5Hc&kTzkpPJ13m_yfYe?BNbO(d@)?lg9pUmm z<~HVf=5mnQUks9d7}xtTy+CTOD@g6Ta=8Ua?KcFe{hA=PU!BVqAjK=iWpkz}(};lrfmtp2-7Aqm!arsB`VNrP;y9H9e&vH42xs&VHaXE@Pm+L2Uc^EU4 z>-%!qo!N=&TX5NdX~*@IxvXXu<@!8a{#Hcx?+r-D->1yGTz`Riit7(Ew=vf<32I4}mJC zRI1X^;6;>|g5>YWGy=;Z&f7wg_n7BESLk*!mog_X!$I=%<8m)9yK>ncq;|d*lw1i? zKYM`G&v|B?2T1jKK&o$BK-QasR3D#T)?0xT?|nX5zY`?=h&(usf=RgyR94_>kj85v zb1YaI<2f9x1baOY@5Wmm%Zbk&z#x$H-k>K;4MExu>w&aRN^?0kNV;@m$yXrh9)qMi z&-F)`J3wkT08BwUo*?P!gVgVvM$-Q(`Agv!;kaVUXtt&&2 z*3)N0S)T;bdb$D9db$MCdO8i#dO8BqdfEq49$P_LPpd(SI~Sz&G!CSGc!AUpTafO% z%7e6?{sCz{2#DX0W7&tx9l>;zt-#jsFAicCvNQ*2 zJm0BgIToZmcY~Dw7LfWm1*E)3aoGo?yt{#vH-7h!Ht&`oFYa~eH6$X-jACShYBS_3etGp1!=sNg5)0x(mLr6(mLtRYyl#KWo<5305L?CKk)D1(K>k!(mF{3X`GI7 zITEBi$Agsr2$0(84pQFjxLgOMysLtgcR7&qE(ucJ<{;&53R2#NAm#n}i;VXOq`WVI zl=o_o^7aQQ@9rS2leQq`?F>@h#X(vp&pyj_vJ<3r5&%*^%Yw8{%$a#XOrhl~R8oCB zeo%i-F?WNM{|1oO$s(?gWCk;Rm|a0yC)Qvaw08@GPU930Qu~`g8ow1Fjn{mx9}CiW z4FqYt0zn$Dz95a42T0@96{P;P1!=sTL5f=&r17c%(s&txG+yx^g%KI`%@s5I&_coC7jsz+1ULdWL zwjkx*7^J-Gfs}XpJL$h3#8g=>X3hq&ge(VhIS9nAVc7y~1HZ3tWj^mfYX3Y)c^(BR zuf1Hq3Z%T|ft1$_kmhqDNO_F{DX+mG(sn$PYa&1XxdJxKFek;_Fun$I_HcS(@)HU}y1d?4j*3{u|TUdz1Sf)wu|NO@ldDeq{I@*V-w zdcLgc$G9dY%c_rsFhPeZz`CP!|NRZ~UAJ_)^++c3xYXnk%z9&m2fRtw( zNS`ks2SU zr5uN+;5L+Rf|Tc3a4Zo|Dx7UEXk*5bp!D9X?@iOzJoTrD?%wEskawEr{%Y5%DW z(z>b&(*C0cY5&O&lK^ zVHO6dpXcw%e&zzTdEAxtLqO{P;XAT^EJ*rBAk{CsE$cl&sGQSv~vX{-A0i5-5jKLtUwxvZ_xIX=o)ZGB_k%Z+Ia0uuLQrtWstv5q33jOLqvyOaj+~w4RrNw4UdIw4SGd)UUxHt!F=w;(LMp5w|Bu>$ww1>$weRioBgbYR4WV z|7swu=aL}JZ+_4ex*Q;l+lO)}5`!AWdz%-13V+#NY`B}jSS2PyAsAmzOtr0e}bU}ew=r0f0k z<1+46kgo4mFc*Myy+4%8?jZH2Bhv}YiE>@g7_7qeWtb}Fhhx(3DM;7*hrl*y*9)X^ z>H*UJ;R4e5S#dcx_yg^~J1UPmdqEoaC15q^RNz-S{~b}OaLH{M1LC8YC1Yb{zk&`+ zmOF^wwFJMs%xf{Sej-Tu3}^ary%$LNwBh>3%#R0Dsw!x=4M={CnRZ}#^`?Ehhq z`n?|1jyI?sZ;;0O$Uai2e##xWG6{&xeZ{d!z)3Q~Ip%#^)Sp9GTb8c6oD z%x%my%=usi^sgUCsljc-w-sUsmRNbCA|&E|Avek3DjIegb3j_3y+PV9JAkx4n}W1H9YI>3>AR)>Rgm_}b0A%hA7E|( z(@>tv<#8bGmu?{Km(4+1A5I{RXE`pv+9lV^KClvW9w7C*6SFx;`i3Blrxn**FrV&} z^WFg@zh+E(&;sL;vO|vJ6OhLD9!TSG0Hkr<#^re+jq7BP#&tAE`~6Um#x)eAarFbK zo!%ggYZs8>H3n&1tw0)AW01!6!ge{X2S6Ivtssr-YLLctFi7jL8CV%K2WcH%+$Q6W z1V15eFw+;L{k|oatwHKf1*SPj<7EQUczubM_Seif<_YE=koNoOU>me+57IccdH4dcl@&akR zY(ZLw*5yazY3&vxCs1+I8**#d*=ZkM{&0C0~})v2+@LQL3C5> z+?_gAE@v!PVHu+k!l|9DvsDfiTc#e}5CQ@Mm@c|Qv*@CWZoq&j0Vg3KO2E_zLNSDV z`JQLyIq7ciPCD7fwA|13-|ss!@4QoXc6N4lS3C+_33-;}L%?q!{~BaJ+!|y*+yZ1h zeRi{z9|c*?dq9@|R*-(41+v^HOAdi7cO}Sj{}yDqXMimC6p-cifh_mFAj|CmX>TWx z<=zxzxqEK1<$e%kxtD+}_thZFeG$lV9|1D``XKwmH#b^-3VsdwRmsnR|AxF6oQn8| z++fQ!3ET<#ao`N-M}oBXk3U&IZUI^Un}HnXzFLfW#5nje$og0YvfK}YEcZPi%Y7Tj za^DED+*gAeq1+dNEVmzIeus+FK>9Heq@6J!=V|+bcYxU-=V^O@O#jd8t)Cqr>#GH% zpU+=s_2+`r&jhKz>soxbnBxq{_8bXvydNrlc8zWC53aW5d=2~x<@_`F1>zki{Ung( z9t-mPmj}|`FD0+M%G!Ms+#UKGK=!M%!Mz}Vaiu-~eFD<%6CnHB=^*QOlQx&@2(<6N zudv7IhvMtt`$!+S)Sefoid%wA|I8(}emlh_AobUSzW~pX{#0>)a1YqK{9>E#Jn>|3 z2h?{C$a1BFtfy}-vg6TH;0Wl?2RR;X2J*bPA;|J9TV%^|JIH*m1nKvMAj@?&NWTsj ze+{x<>>_S1{rVu&ue{KPe=NQ%J|*490 zwh_}orrQ8yy0_1_<$Vcc`g1_0F9F$)+W%n$LU*Q-JLzX1FY^n*NZHUqhyFdSq#*Au^OweqK8C&+jml5Z7n5U&uA1X=$< zkn35$0~dl*z>m>R3YCZpnnIXy;r1vTJrsp+a+Hm z`C`duO3nv|z+Vs83VR>^!LD~b0^W{#_!IaGgwF(Vi+AJ%ko7-W+(*n3_ZI(krptv} zyCdHP8UGD&x%h&3Bgl5R8e}_M2(lec1KAE?knPX}vff95JRWwG{O{k}cK8>_cK8Tn zJNyk~JG>0C9UceS4!419hs7Z4?P8GaaF*n-9+o!05?MYJtTIBw~D_9S$`*itiSmn z>u)y5`YQuje=|VVUmD2zTSxNpg|_~>K-S+=AnUIKWc}R=vi`0BS%0U4tiR(y*58pJ z>n|YrP|1@d?=RT{vLEjv{V$~-F8SNj)UQF>eM|CRB`=fQ04gWq**zbq+We`HTWN&h8F!oZSJWy)8l3!w`_^ zzdgl{Q?G%0LB0{>`Fw`-`+_{5?G6q_`mI2o=e|DKp0_>~k3GSjuV;ySf{gD1neP{2 z%XdKPUju&uJ|X>mViULr?7x4!jsLpX1!kf=^FdxOj0Z=6n}Iw(|M@uU*E1mfeH3Iq zSBfWr^rJbzs@ zdGU0R^?U-zct?O;;0(!yAloksWc%#}vi)`e*?!xDY`-l*`oAH__FD&}z0Z!e=gBug z=HCf2|Fb}zCyxi&&l^Dx>Lm#BJUI$v`d5##^XR9=hrzFLKD=J?<>0>|w}4X-e+Q8B zh;2ZYe|?a4-dte00m%F_TC6-AybAKVl0TY{^D+ASbKvvPKLIlQ0g!eUiDxVPG=@HgO9lFtM`gP$!R z{R}C5w!+7V1q$~ld@GQC{F3oT+p!L$pGD%n(vJe^=f)uYysFXqaS2!o zxfGW7eMMC1L^P8ApPA3q#r9oHs24x%bI zy+P)`4ao6qGmz))q2LMNmvi7R!v6{Ky!|$4@&P%%T_O1x@FmDiU06~gUGg!Knv?e1EyzhP_ zh%J9310eH12xR|15ae}aK6nS{0eKy{8_0TE7yJ->ufopH9tYW84}fg1TS4}>D?#?3 zi$S*6Ss>eMoR|Z$-F60<@3vqF`E3E-1`Y)u0{>f%??oW|9gz9I2D06jfvlegK(^a$ zAj@&1ko|T#$bLHsH2oH2ygfnM9R~6^?)KYs&w)($2*`BzflPNh$aEKjOxFT3-C@#C z0GV$g$n@DD(~kt1epis`(?F*0Il`uY5oEefkm>FKneJwg>8=Ht?kte$LLl=k1=;S! zAk&WnO}?PX7i9V!L8kxUcecI%4pP1sWV_!6a=g6}yb-(@ycawV$ndu$cY(Bf63Fr%1G3!@16l5I zAdjQnKpsc${npmQ8zAf9C2%45D9HYQKgfD$2U+ep;&_np_W>DyPmu9<1{r@_kn!I= z%<@%mLzMePkpBJ&WW8Jfvizrmte4{yJ_V%z<3akr1xUZY`i}@B%Oho(j$Z=Yg}p zYLNY?45T0Xi#tmH*`an`|2k;=6i))V&!7yXKYM|kKfO59+It#&2l5@_Vz3eMPF47E z($5l0!6t-z6~3qRTZqHJdW64wh_(MZNc&HS9bgFI=PLXR>6^rAa4y2fEBpZIcM~(f z8iao~!`lA8Lk=^*2MHqF|5TYMa3yx)V2cOuAmK9K!ye~|fa zBD%zPr&|4^;yiJ*I6~Z794>w_#iqL(WWL9Ntgo>k>uWQR_4UeR>(2`y{h1BYk5bVC zGW}PRY`jlE##;_D-c#beAmfEWmTx}Dc;yP8E{+8mZv&9=Tp;7UHPPC8NxTzeyrV(J zYXBL~4YIy=2ATg?MV5aC$07c+k{5%^5xx-o3&xofq(4|3CVn!(rf&gx{PrAV`Mh`r z$n@2q87DyIJ4q}M_Y}7kHxh?{W8l|UC=Am-12TO(NdK+^Y5x-GPZgWQfH+h1f{gzU zpSAOWNSJ6C|yAYTa5ud^hFB_AmUz(SN`s=~*M2Pk|L z$ns=@w7a$R!^IV2t^Ng&em*R=f%In>_zSe}e^6PJ?*J)ZC;5Dkb|-`M^8j&kkmrky zK#o`6j&`~J3Hcq6@oonheiO*>OBH?+$o4oIo(D@oo(Cs`wDSoH&-uc; z;HNIv{%?Q}BHnWFpQxAfK+X?N2fM(N!RNsF;0xevkm>#4T$G~}Tml{pRzp7qWcn=d zS#VGA0NC9Lq`mDy+DijzXDC>Y^#45&=UuP|d=Pve3?lrmAniQ^(%vKB{;+p1NPBmJ zw0AW~dzXOaNdHIh2JjT{9`HDj_Ub{}D+6ioP;fukn+DR}M3DCKLE3YJwMf4Q*bZ(3 zJ^*eG(%uk|_P#(NY40O&U)XyGq`fyl+ItqHy~ja6(mx3P3A`KpE%b{)+PMg%omOxk z*jWhD&PgEc90}4+5ae}P1$Z-90`j_SGRXPf{vhp*0%>noumJY92Wc+@q`jda?foAr zwF>FK0&fCWfcJrKg0%M{NPCZhw0AF<4|}(Pw0AQ|dzXN;cODo*`ZK}Xz+*vPhs*SC``R$J2jo@Y==eHYzwD&CegeBYf)Bv%HsD7{|4(!-+I<_O-M@f;2A>Ccy}A^<6MPut{P2p8gid`t1Ul{&>)h^oL2_O<Ij!wsT{}3M%j|5qs z(crI2Fr}wcwl3Uo80;@Kf}s zMv(c;1vi5KzX8**Pi1fEcM&(2eh7Fl;;-1#rhgNp{5aSHdk=!NcL~V+&sF%z;MWL$ zcZ994Wgz37B_1J;64w#0+1 z4#@nwce1<={3qlv$o6`DM_aEigFFtGfjr(G5g!0AgnSFgw^UqGgR7G(M-K&F2XWcuqtrau8>`ne#}{RU*Z zDIn902bt~wkm+^@nQk4B=~m!ip#Bw*>7N6c{t=Mr?*p0sc98Kd1x@{dOkWH#-2oue zc|oSz3uL;TL8jw3yqK;#!=`%xWWTusWctM*(=P&<{v43$PXU>JBFKKTFZcpDO8Oxn z$MbKuvg7#|Ajk6$!D{GV19={L3gmeH5O@rDH+VF7BS^dFfb4GzLH4&3K=!w};3;4k z$n)4tkmJ+(Aj{(dTM^E0-SK>NImq+XDIn|T5b#6jb3vAO6v*AE)qYkaiA}JQn16dS5XM#LzMF(M>Tf zLjO{_)ZU^0O|kBk~=~Ae+Nka zFO_`09-Kqlm6SGHvYXJ%h?XHoQpx0^N$KYP2tCj4ImEv zkrP428v`<556JxXl)Mv2KhmZDCobwYhWsALe!c+w6ucds@oMOg2H7tg@elju!|UmI z02y!my0+dtAnOf#UQE4h0Mh?;K-Sxr>sa|CkmY#?WW6l|O}~+Ro8-$t*53sn%Mk`K z8FCPBK-Fiude5?$gSF>X-r5=Y?prre3+nVccsgyWWSp}2fQ zE7yp{IL;WpH~K$01occ_iT<$+ezSk!xBXcl%Xyj@7MsO6VkO9Q?0*j;{=wn|%VS3? zJO_LL;lCDlvpjYOg|nZwU>w^BydUv~ivK|cLO%9O@IL7O0UiOq4BiVq3ZiYczYXjF zuLSP~Tfw_P_TM|fI`9tgci`>dWbig{Uy%O0!I#%{ZNCS&KH8c67_V9xxea(L;(dvJ zyD#K-LH5_zB|j(me(*(HKVAUtgLtP(f1Knx$%lh4Af6Y@LA>3;gHY~F5Pg0Bt-zVk ze~V0BM!XlnLm=M|z6AMPkm1LGU67|Md_VAc$Qj__2;Uq;7ua$maV6S$2J|0+P;L2^ z_!`LguY!!rA&8WV*}6Gr?uZuL3MU_;l$fO5R7ZSMpAhw~_oUj@##u z{;wcj2|E(!U}OIg$+tuIoypawBjh#*e4) z$;}nubjaP{<{-zHEx;~?cY-@2UI(}n*bZI_E(R|F+d$e`2tEh4fPX^zY7k>}S}BOJ zJFOV(fPMmqHcTr7hlBaxQiP8HOCV>0n}QkOW?;I)UEubJw-V#sufQJgVsHg`5!emV z&Qg$mbb!x-i$VIa2z(oC1?k5^a0wU&S3uta-VHef(vND;3w;@w1(t%eHv!xj;e}ut zm=B__rHuetkD1_>UPHP{BC52h_rc&oS&q`emKeJ}*x1v}Ls zj_tHE$)#W?v^wr=i(3eSH3f>NVG59L<6QnN$?|?oZTn@ck`Vru@&}V`lL7yRgI`|m$ z!@$Qu7sz-kFjyZCc7tJXIY@mchnQ_#9CAANCOAxb7szxgaZ>39dqBot0pc8% z)(x`XEC)I6cZo~IPO(F57Z;0d;v%tCTquUc7BM7Ni)CV|SS(He7s9_nZ~>SPo(Hl)AvE&xY>m~*9NNH)ISPU}$1dw(L z!L7l3a2wDKb|E|iTpw~e2;b9&NsqDBbu+>-Ho9&ByFtcZ3ci8%=mfh#Tw}QY4z`1@ zgNwm?5N{FqSIDj4-@t_+k0IEVh8_B0L1XigqfKzEt{R=?kULm)38QJEiZCzFqn@=@&`gDt%b`7U@IMmq}kJW{Mdg%b5<+50~^S zx0ipQ@eefqN#7-Xr}S-NSS%9@#Y`~+H2HxhKj~NEJ_hwYpvez3`AOd;eW&!iufq84 z(zi*!NcvXk!_v1%ACkUI`cmnOr7x5|UwXInnbK!SpDw*i`fl7mG5tww6BmK>vlV2y z!qT@$ACkUI`cmnOr7x5|UwXInnbK!SpDw*i`juGdGW7|X`UFjVO5Y`Yr}Q1tw@cq9 z{UYgGr4LKrB7I2uGU-dDFP6Sg`b;rH;pqx@NxyPy`3o9z`b_CFq)(ULCH+ba;HG{-<1c9ZmA*^*PU$D#1VBz>#&Vd-0>4@qApeW~=t z(icjfFTGp(OzBmOfK@m$(uqcatAz z@&ir%NZ%!Wr}Q1tw@cq9{UYgGr4LKrB7I2uGU-dDFP6Sg`b;rH;pqx@Nxu@45aTas z`~{7_(sxPUDSew57R$s^(9{=b>Pz~3>A4g(;i|6NIUHyC2=qmA+W|Lh19RcT1lsy-V!IQg-;_^m6T@PeSSV(SF0mUI2PVDPCWgf_k?W`Q zs~DtTh0=5V)c7lXru1AtrJn1jjPH`3>!;Ln{gnD{(A1aoUD9_--ywaw^lj2FlD<{? zu=Fj`homo)zEt{R=?kULm)4`g^3$aJ03w~31s-m37h^exhdq%V`cRQh7+ z3#HGO-YtEm^cm8pOYf3?B`#{|R}VA^Amf)wUn)J<$EYuqK2zlS zm#H6xyQE(UrO6-vnEXMLKZqzp+r+R~CKigBqD$;XVy5d9+r+R~CKigBqD$;{DZSVx zhQ%@vQHPd-$Z}{gXv!;nrU;mLm3F>7C3M6(m@*G>?T+iZG#J3?(EOf!95M_IITlL% ze)}L~6d)6er~KV^zm*FmZ+oAW+a*tW(aK$t|Gdn~J(9nD(#n}w6y@*Vk6F1;@&S)p zxl{7_4=X+vPx<@kAuAV4-dg^JB%dt*(y@rk-^cgb_-&FmkblLCZ20x^uT}C}@~=zs zz49;pA{&2G`4^Hr`(bOpOY+~Ig^W+=v|%xqzn7UF^|^)WbJTM-JhR1y??rnE-%sJs zt35g~UhsD-%Zu=Z3jgc@D_<%(O#cynzvTPqKjbdS$1DGLB!BRT4gXy74Em4wL$Dal z-y5>Oh2$ywAsj)RXYltI$sx(Nv3`+$Z^ie?pZz2+mOo{Z*H`&NDu0dq3oHCl`PU}- zggl!+*K_#mkbkc!d`Gr7^8Z|NrTodqB0GNzWpA?Nnn?XGu;F(uHTH*uB+s7*8Rza0 zZkF)(X{WUxQu;j=zw2llzFwrgB?s8Q(EcIGMLa$r7fSw|{z6Wdd>Q*McdtJOa3GK3&M*fAI#$wa;9XyCz*1O%JYfJ+bMaf`qM^Xo8MXTpEn)&oAUzF zvpsRsj=!qstv$EGzkJ4q&rtZ#E*oB^@DtSDH45(GiW97gcFPU*8xwf6t>h_ydi@@2Ajxa7m-@0pTo zBIAwZ!iR18k0hV2^rg6I#@_^`4@f8CNxRo)4>NypzMvVVu|spOfmHw-rs`Fl(KwMKHS`s;GZ9+j`~EE~UE<-1Ap z)19_`w!lqJ{x+9CVaelVFTd4>50|}=y&(~>ZeQci;?3)@(7i0rR3Mt zpNHWlDt||D{8<-!J|ths@fGq2$$RMd%9kA0@iIa3qYvBorIMR<{8UR`ruJ%+oFo74 zm;6t)*D}e!RQ|6?-ctF0F1b|s55dh}{+1~JEhJ}jT7PztoF#v+fP zv+Acx^274)QpqPMzndgKruMvF@-LL%%aTLN?;Xj1QG0wY`Azk=A-GA--+9V!3(3!^ zf9xXp+{pML`El7BC;3^~tCCzQdo7Y*QGZ$}d2f|xk>qBTr(NR+9bvsIoh$!944 zZpqgw{~pO6<wTPM5q` zaH6Qeq+3QRdSDHKD5c-?)bY2sv=AQzYVUna;A}xwhr`>@1;#`cv$j{?N%-;u;H~+6y9j% zk{u;CTlui9tlT}<$}N+voQdO#zjrewEB(j6v~ru`ucP>#=x6+Wqw;s6U-7pN?kljo zWs3iv{3$~};O}(#Q>OH-@+VXAzmWZI`MZPScjGwWZ?{N%+538k{JX)1WABE^KfKt= z+x^1IA<4fUZ{=>uC1b7ZzQD%ct=kiQv9&&}ri`ISD@VdWuS8^0LGHGf;=Sef@v_*<54<@VjJeC>2Ahbyhz ztp3!qzLmc@z=o&W;ls7{W>#*s!LBpZe%-^YVv72&f?Z!n`tP1rbMnD9edq%#e>vC;=T@_ZI zd7ScJX61?!E0^uRj!^{FKAEGfoWGO8547@u8M3GN*WtKhc?&CTxL5XiW?A{(OdIYF zSovYp1LL-_V8`eW-3HhwmaBZg<{e6{N&n_tg( z8-C#3RxUoo%6r$__&u`sPSDD3rJwpcD|aeswWiNe%l_#LzGyg8dFZNqG zUGXR4xMp~p!WUuOrd+1*d9$rtDEXU!m5U|sqVsP^@->Ir@UY~LSypbByr0r{N`4UI z9P?|HT!`b)_#^p9wO99OTb_yPFU1F2`7n)7ZL_WHj*M3|R-RuBd+0CSTUhxT`P;7Y z-s`jBg;_Ryt3#}uxsR2fj`UBrl~2?8ptHove6N!^UU#(ebrY@Jvz?W*XF}ev)7AE( zm7k)0$nCFKdA7nk|7zuL72frhl@Bho_S)Vu&Fe}VZRPfNt=y7k<>E0`p7;kVr$2Ay zZ_luDm&Ut^ApLb8Wy3T6WaZX>TKU?Wtep9=l|NXh^fz01*4b7ryV1&fUvK5m=T`m@ z<2vng(>;Ix!nkc@v?G6|I8jk9Y`5}Gi>=)Djg^_!K(G%FXN9@Xlr{^X3KZmo-}X zsdcUFI?~EZ6y8>6<^3gRwpjU`p-O+Xm49)jl|$(F{OxzHmD5$;A?I7U2mOw}63mO3 zf1702wMUxL7fSxS z^6ydn{jRd{yCt89c^d7t<2dE->`Sd&Ci$v1*;o7*lz&+AzB+y~53uda(+usU>wG?3 z;rv)9f5R2tEqVFI%1`IpR}hEki*-J}M&ZRepSCHyQ0LqC)PBV~BLKex&`+7ZbGVg{ z%d&FYMpi!W6f3*-uyU?f;qa5cb@G&cJ1f5@nfpfho13q2oM-vFTjk+?PX5l@)rN9e}-H-8f2l&^sqm?HpKKH@#H%;-oGOS#>i%s8sgq8iw6%8y%ZK!xPg%LC z!OAVle@58KZpGiilrQXBsrXmQUc2O3lG9bbOHa1xM@U|0o|XA<34e#P{~-VNWmdlJ zXe)=FwX$1s`!iO)LFMg{KU+52_+iP%t9}>B-Z+d~wBM@mDva}{z8<&s&Rk;UF11(X zEmqD~eZ7F=ev=Kuu2Fc%fxk=GUXZ6fXXR?P7v!TQm$1Dc-_m8n`Mm%WF8PB6R-P)E z*KrKbl-!~CuS?#L^@;GKbiO{8?FD(G7p(nawijf#@;~!k1WMsu{{!+=OnBx!r zDE`N$KX$q%s6YPJ9Dm4P;d`6ouhW&O{2w#NGsb_#-;V8x{FW>JlcxW7x=K~PPt5Vw z=~}7&f4KbZ(DAg~96t!x@ibL(zT^kkKCs`W{Bq3kg!Lua+d}?#Df|_-2jZ{L@pFsn zzeoO`sQPb_f7fXI<$h}ZZUlLL>Dk)K-|T4R&O+PX>tt9tBza+`m0NYadiF1^TqyY~ zoL6X%4~6she_L9)J>SaZ3h&v?%GC;Qll;JrHh#MDdu%H!ck29mXr`6B12+8;%D+|e z3SH0KwwINcOYYv=%H5J58ENG%$qhKq)BZ5o|3BGV zsqk4!&xcj{J51?&6u!?!NDqHnTCG1%U|vD_+|#XGxq+3xJ=4mgH?;E1Q?2}f@3kF2ij4*FUoI63MwASoz(35S|uxWomus9M#t`Wj6dp$>CX6-cI85b|F2+pRSd*yyr;{e{JQzJkD^;zk00vV!oC6(op^m+uh1;$rbbm@iP_wD!h5X{B#d`ngIUR{HIpvG#f-ZzzAtR9|2G+J<+3VDmfOt@s~Wxj=IG zBP%~Ix%Fc!Z_4!Oj~VK38>qd`!gU;fPYp#l`q%JNt^9a_oiDZzxANa1TT52{p&s<*UZW z%Ij?rD=*n3R{n7FSosQve2gQ0ha>z>hy7nU%JaFyUw&UUy8gCt$fr2`f5DO8`3`@^ zIKs;v;lFc)f99zF$FgJnX>|CzqbD}}_I+dJ|8vOYj`kZ>92@^)jO)?me;(Ie(K6o? z7cKAUus3&ONBHzunePvaPX9ZH{VIq3*_+13=X*<|)AI#G(Q+=vvuOEwNBw-TQ*8LB zIG;zy_c-#~+|gd69mmfxNF1GhXl|^$G$&Rb=Lo;cas173)X$|3f0j7%FLBg&fkXbg z!`>B+<6$<2nP~qfIpjcIZ1_43f9`YC_b(jzO>xxU2iwQmJH!$HBZq$ZhbUv%`R^GC$m3p?b^9P$AUc~ghIqCH~m&2YrOZTHylw;gin zZn5FpJLD@J$48kXzmFSY(=T$!k2vJH4*$M&_%p&0{%^taXzrycSE4*x@r<8!{_ z_&nD!UR>lDFESnByF0>fa@fD!VSlWnKYjA+*!oI$)aTX?xxrDMjUDo7j{2G6Xs?SM z;aLv*CppHC4IKV{>_|V#k^c>j_BysJwtVf5@#-N*|K++`^zrwp!=ED@>DwLtWIMvU z9R5sqgn#X@f4d_--)kK0&+ZO?A92JVfWk2u0Fa+v7}^;dexalMcRTzkb@=nHqrbZRwSn5o&;pm=@2_kr_cv8H)X#@-SV>bsQ=p+X zSXbT@m`8=zGds}aZ)_^B^M@MhX9a5l{;Kk(auZfnQ*1c3q$$hq_v8ob8~qjKje+c7 zU9icQI4lb>8)31cys9GDkZ^H3q8!4gQ9J@xy10PRgmMuWxMfxaT*NhXO5? zflyOXNs}kLeqNwqR!#kUf1shEzQLE5QYvp%eQkNLZcu4FHTAQDm8mD0TH?>HEpMv! zt-iS3UdYJH8u5>z)v~5;Hb6r!gHel;Kdwz9!B+E$%jc$*aFzZiEdEIPJo+)df z%5OUiS^#|}ADzu#S>D*>8&on+7Sr__8eHOnRZQn`Gt;I8p+K-|kU27;t=O1iu4aeH zLhp1|^U6pS-3P>C*S zhKoU@_h#4DR|RVP-mLlc-Z_;`{<`woKx3%9GT?E9c=Byb_!i=z6|9?8AJ=_7?#AX? z|2%a0hM?`838KUv6iK4xIZ&jc%(15B9V%RFG!Vp$j z9Y9kw;P6K;2{tY8)%vDRI;3Wv&tF+HQ2NM#&VekdLQ}LFT^f0T5h~y-o>W=l^#_CT ze)OgA=3x{L;zS$p*Wn~m7nqH+$vpPrsz6J`LVpJOnlN`ZPA=%1ejIIqBb&=>imHN> zE6`ui_^29VEXR*dGJR6NE_w28A>fZWB{ZUni(-4PH`U&YhzWYH&o_^CGaAmIcgD3+ zU+pAz#STunc3>&0nutX1H|T#KVT01RbMb+^#x1v`u906P!p&P)HTfyHU|8r zDvQj}fC0mF(Ykt$W;k=&0m0)P-Oy0J;84tcFme>(z|Qh(kjP54nh`N!HLIe1wi*t> znBXrN>!0YGTs#j$P02(*N0WIa6Z{x8@CVJ|CdY;FKhG+bGTW9iRNok6Pc90|iR>sR zf^vbG=lL-h;*ZTdd-XE++RSHhUb>dEcC;?Kq3Pt&#jg6UitViPwVHWsov+QTy_i6A z(2w*rzuJ8b`Jk(voZ9B5Kud0O9cG+!bqcjZkPXR>({nz_Sraa5INwh;*`HO@+*mDx zDP9BRRMynj1^Q1Ddj*tAis<<^&K3h?5jj~Vy84MtJcvo0+fL#*%ZsVvRIi*08msH) z*Ou2UsEkyOZKB>w)m&WNqM=sFcy{B0y1taX7a8Ncz1Ex;tIyEvm#6?2+>_Bfma4YHpsaM^Xy~l&=SseW4LZ~+!KKEW7 zMJahPf&dQR&=VztTS0kHHW2&x~7H&<2=2`9<#I~ag@@N8>}-k zYkzse>}F2KdIM7*m3ot)4jijd(Lp)AD=qdKxHrtMYb9AK>Wu~Cu|q^((LCPhYwPF~ zjVv6=*6C)_C0JBdLwH( z&-I*|K$O=`JE=DvdXLu!#nrM^e9HI*~6WuM#Io;#1@>Uzi{L&>;?FlrvHqs|UQN_-#Mq{uCn0*t z7)L=oXi9d#MZHwalR-*&c+b&YYwR zq>?&O?WdA1_9UL-B1)vVt2XhARPf-Yi%IfKZ4T$ia!`q!vl1m9U=Ei!$J7!zb6l;- zn!}MbVJo)Jtj^RbNu#a-lWSt)=cu|>p0h>lq&`KkDa;-)E|7AQ40nCah~72BO=49G zum+E1(%uL&aR!K6Op7E<7-BNPo#>XJ$>OloW?qBXM&$Ek2ODvV95)!v27szvnri0l zLYaQ8x$Fvpb=)&m8Swj}a=?Z%uz*>EEitABS+|ab#on4tq-T8RYGZGLgaJ$ubF%M^9Kp9F#cFU?L!c$(^4DNP1YrL0A2fN|n9-B5 z!LYf)U*wP7Dm1Dxgm~QcSPS~mHT>9L*n|tna?1V+?rPwFxF6EoJ>c$(-lfV&2(pdU znq$Q=i$G+f?wcb}6Ws+p9$co>me=@eLRGWO&Z#xJl_J58nJRO4-&}m4Edou=4RvOl zS%U~Y-6jv-!F#UHQa@gYCp|oMdk4yTxbubpMFm{dwf-cEFpJ_VC4h< z6)Tlz?Y^Tp}dm2G8?fF7{FXI@%k>d z^y1o@vy14BL+a8P_lnsv(HS_SCVrrxrqO2;*+mYsU=Y1;f}5wY+q9`3J5O8itcKaj z6qAkD@8^0ZHjl7>)5kn%%;N)gW=xSIL{aOwF$tqqb9o;3`SSgEyoFV!n%A1d|LW0J ze#-Tjo1_*^M-4UL;gPtm-M1qqClH)njsCmX-rB5X-P@2{+H ztkGo%$?Ff$T!4U9Y7Exl|9C)(z(uDv^X3%S;L(?96?1Tc8dKhAj+h*?!8iFve;U zmPQPI$a|8>++1meni~TCdBKJz>`Zszj!ZSSM&h9xZd4zr0tb9lqOS&RlXzhg%~b12 zQuM*yA8xZ`7{%gsIQ9-uA~JaK2O7LALB#;&QF-(z?M}f zT6gi}M%G=bf_P4-sj@ohj4liFHs|Cl$B(IZ|9^OX!4eMktD8S&zdE18!5NX8GO-P* zhBIES7&R3ICkJ!0nIgLu&pPt3=4wMl0Fz`at--s93Uh~u0tY(AoR^Rb~#Ue;Z z&6`+=SX1NgeQXo@L+a+^NfH0_0H$yO#;aB6ZxGbb$iTZ}a+0ErfWfx+i zc89W1(Q7XK+}~ZDRYrQlRy3{9jPpX2&W?^<{E6@t)FOf zt2QIm*`(GOk9f99rtrvZs0MqDeYLe1%A-7+oaE`h*jTH!bs%a+tsG+>2*oM7FPGR2 zlDOp^6%@NX85|af(^Jn1<=ZmhBRV!^l2oC{ZLnIz8u*N2(e!t#}C0{e* z;0a>RaPYKYd98g?l9v;vOGX}(;(m&F5215Dgv^LhlUi=|!HFl=AKBqhQDItTFm7gj zUrt6{^~CKr@Z{rpyS~lyW>>M#mM!JGD*f2#H|xCEtZ}1c&^fQZi99_opsb@Dj(H}t z%E!eXJ|^y)H^Xn*-YzX@Ehuu|BO7~d@L-!=?8Z45=j)1Qt|jB?%*q;EIpX2iewK|8 zy6VeD=$!*C8&ys3&v`s759cwWp7**L1P*y!_lyn$LX z(wmW}Xrk558yA|UTI=f_y%$+o?&t>{%?lm;(Jx;pnux*JKHG0ra!LsN)9zmEjMju0-99wU_cb_;lbKLn$F;&d{yj0TgY9iSV zk|;+tv-#ip#UR%&vhgq?+MSC7%;CKe`wEr3s(Mp$R^0^W0QzIP)$ADiov#0o?=KJF`VZ%(@&Jym z;W}Q8JITh;r~}$Lu)Aqh1CkxBs zW|L^depIUb1Q*zmSL+NmU8Fn3rBmCdwEn3F@!8}NckQtf^BzZ3aMFuR+gntXQALfp zHNh3#V34=rz0q|ZlSzVWW^23OB(WFFDI|!#MDYz2f8c`m>zw0CMA>=ztAa%3iA@mq zJigamSsz-!?Xitq{o_ktJ@^qKyyS&+E?zB0Nw*iOHnCj>o zidVrHXMG7?c9Xznq8nie#txtN;1ZT6<>k5M zrCMA9xAZM^4kBHWtXftvE1Pb{%&Dp0@h~~fC>eB$qCuya8NIhZLF==2;Yif7Vq9|f>gGSNf$02u zCkq9U=|NO+aUX&Au_E&c?ql=~TqA45WVDZwgD<~kc6n`Wc}xZUBvzwR?PQ*xQw=2Q z0R>gTpL-YhSye%zK9K*j?*c!k8c5g!5}XIV@72Q98p3~o&L^z*1V`Qv(Dj7#GkiuN z{>p5$f8GaI+j{?_+Una+;Qf#9c4&$R+)trk ziQ0>+e6?{$dBSI0al8BnaRWQm7W+vQEm4~lL>`)mUh?W^K`Cx~(LfKe;O5-G`v_8P zh99c8iE8%exoIrbmi%As*oE{&Jtx6n)6Wt4)7fyCYTx-^{osW{_QurAsVTn`Gjb|3IW;CGc1_Q#O(`m0R*=M^y(t%U zn>*%?o>L#WiRaL3yhyl>KW4jt-B53zddz98Z*IU##rVd1bH6?64xn>iLyQr-=fICyQ* z!y}5MTJ@yS1>3BA0DkV{2$C zK5faz3#BV)#?~=@$fb$T>f!Mxyd&9k1RE=-lE(nP9zA31gyiWxy^a+Z z#as7Go_3IRJ3(lk@j5|(C#TmaO6yM)qhPAH%Y2onpK{WK54f$ zIGz?U#l;qhzIJjxPhw+4AN>hq==j4PKy2%?@1VicNU^VcE}Exy?S}jXf@tH+dXi*-zEA1c?TAE|lNd|V!nM5x<8tBzL0G*7xM+HxYsiB zv)&q)dUL_}7Yp;Y)IWXnjNVz4nKbAc-@<&%&tKWlgwNXdbNQAX`;uDsYrdJnl&@=0;6$>cP35bt90WZ^Ayek`R_@NH~0=GDo( zK6Omzq6UN57d!YS2kSiehkx<@=7_*6;w(SkdY3pGxR}qmO(fe|M$OQlA+9Q546|v^d+#b*T z20T}5S8lO*ZBLvjyc#}tb>8unKhQhA;D|3cJ5U$tu~o?iqmqOC6&3xAFB8_t_!3=7 zky%0tb@W}oK7St8$WyN=U;i~VevQ;rYNw>6&6(1fC)b{N%t6r4xu;;DbI*5F;)Lg( zpLB^afxu@JQaxmUDsBAZEAh|Zv|%Tjkr$<6Z_l{s3v_9|bbrGYtlk4;qZP&!}xWG~>RPFjQgIH&g#3fEDgrg~0+ zVrCwZ>;Dulo%)%Itd#QW-qmOAbKf=s2&rFc8{$UPsjDdJ5t@B=yQGP!MYiuEs z%zdWf!IP-FUbr{PSu_fVO;IQ}$1N&QIDGXhzHm@}_l4{CaQ?}ZGVuZblPO;Gknoc! zlqbO@M{MD61r>9EMXp-y;e`?8M|F7p2uhR6Ve}&?M)JeuyK+ADQz_q$o8*dNPCr)y zNzWApb%FWzTj8r>7SG5gI|Zd)B(K|XQKW^PxP#dneYVu(o;O%|@6;a4n%NgIa9rLwK{uUW2MgJ^bDybHk)P zpE!cSbYG- zZ$1d(DJY23<;AN!33NFLq9xFIvlGNipv#Jn=1rvY7NE?0n~+~GHsTHwzD92pLY&3( zdXmOQNL*|;V;{B_XQ!ho>uah;#d}haALpDWfi5pWv_!hx_;@+-DsOhY&XY)&6(7&* z%}?4a31WMa#zshdv&8E>ZdzFTO%?A+L0+80o&>tQ1krNibvf}WZ+5&cD_-SIpn@*h zv63LRCtl})E{TN%vAymD7NGk+8Y+P&`MGf|l^3t_B+%t1h?YR-%}x+6kuEDfo;QIi zfzAV6k_KT&ZJ@T%Tvj#V^V4;n1U3`sJZ{=t`^QqecbJbt*XnB*yMo3(2PXe8r7S?{V#Czf?$d2>alOM0ki&y2wt2~Kx zIq~tb<5k`Sx~%wU9_W%BBne`BlSW2Jd_%?SJf3)+*G;QyzwzQd@f2jml_Ec0B+ zOAswLUY8xO%8FNc6X-n9C2Oce7UBxx@g_BZkod++pmWFTJkWg~jTi5UmwqIg-$yNi zWDRt|y=pIyHj65Ge(Xxbp8>C+&DEXHTbb11#GgkTIC|grRej!r2aewN;Z~owc;M*K z))C#=VLS!#y8L*RCxI?6KAJZtUY8xO%8FNcp-Xn0Bn|N9DD z>U(O|1hKtI zVEQBOW=yzx-y6xPM&=G(j>lIG+3h-V$ZWtC}lYzRXh6TQg=+ximupM0`YvUGjbh!u9s%!5K=PG)* zx;Hxevh~l#dTc1r(8&9l!KMZNdG2JVEo@E=G&Tlm{DZ%Z)5o$&Xig z66o?0L`$U0jgRNeiPw1&=)BqS@w{2_IuCToM%eh+UT?e(Z|6^D!Gx^+3tCUSlU}#m F_5ZsFLu>#5 literal 0 HcmV?d00001 diff --git a/Chapter_2_-_One-sample_t-test/Ch2_One-sample_t-test.ipynb b/Chapter_2_-_One-sample_t-test/Ch2_One-sample_t-test.ipynb new file mode 100644 index 0000000..cb370c1 --- /dev/null +++ b/Chapter_2_-_One-sample_t-test/Ch2_One-sample_t-test.ipynb @@ -0,0 +1,691 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# One-sample t-test" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import analysis packages\n", + "%matplotlib inline\n", + "import stan as ps\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of the one-sample t-test\n", + "\n", + "This notebook follows nicely on from the Bayesian equivalent of the classical Z-test and provides an example equivalent of a Bayesian one sample t-test. However, the analyis below is conducted on a real dataset and demonstrates for a simple case the realities of fully probabalistic statistical modelling and dealing with uncertainty under the Bayesian framework.\n", + "\n", + "\n", + "## The classic one sample t-test\n", + "\n", + "Before going any further, a brief review of the one sample t-test is given. The one-sample t-test calculates a t-statistic from calculating the difference between the sample mean and theorised known null hypothesis population mean and then finding the quotient of this difference over the estimated standard error from the sample data.\n", + "\n", + "\n", + "$$ \\large t = \\frac{\\bar{X}-\\mu_0}{\\frac{S}{\\sqrt{N}}}$$ \n", + "\n", + "where\n", + "\n", + "$\\mu_0 = $ is a theorised constant for the population mean\n", + "\n", + "$\\bar{X} = $ sample mean\n", + "\n", + "$N = $ Sample size\n", + "\n", + "$S = $ sample standard deviation\n", + "\n", + "$\\frac{S}{\\sqrt{N}} = $ estimated standard error\n", + "\n", + "\n", + "This t statistic is then used to calulate a p-value under a null sampling distribution with a certain degrees of freedom which is determiend by the smaple size ($df = n - 1$). If this p-value is $\\leq$ to the 𝛼 level pre-determined before the analysis under the Null hypothesis significance test the resuts is determined to be statistically signicant and the null hypothesis is rejected.\n", + "\n", + "$$ \\large H_0:\\bar{X} = \\mu_0$$\n", + "$$ \\large H_1:\\bar{X}\\neq \\mu_0$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following on from that quick description of the classic one sample t-test above its important to keep in mind that Bayesian inference is all derived from the application of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to the one sample t-test, it is fundamentally different, because it uses fully probabilistic modelling and the infernce is not based on sampling distributions.\n", + " \n", + " For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for the question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. If scientific research publication is the goal the priors will need to be accepted by a skeptical audience. This should be achievable using prior predictive checks to ascertain if the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then use the posterior for conducting your inferences.\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for the question under investigation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Study/data description\n", + "\n", + "The data analysed here were produced and analysed originally by Mehr, S. A., Song. L. A., & Spelke, E. S. (2016). For 5-month-old infants, melodies are social. Psychological Science, 27, 486-501. The data analysed below were downloaded from https://sites.google.com/view/openstatslab/home and stored in the same Github repository where these notebooks are stored.\n", + "\n", + "The research focused on the seemingly universal act of parents singing to their children and that this singing appears to focus their childs attention towards the parent that is singing. Mehr et al. (2016), aim was to study the function of this singing between mother and child. in particular Mehr et al. hypothesised that these specific melodies convey social information to infants.\n", + "\n", + "The resarchers argued that social groups share melodies and that different melodies exist between social groups. These shared melodies might signal to infants that novel individuals who sing these songs are from the same social group. Therefore, if a novel person sings a familiar melody this may signal to the infant group membership of this novel individual.\n", + "\n", + "All together, 32 parent and infant pairs were recuited with the aim of testing the hypothesis that melodies signal group membership to infants. The methodology they used to test this hypothesis involved taking each infant and mother pair in and teaching them a new lullaby during theit first visit to the lab. The parents were then asked to sing the new lullaby to the infant every day for a 1-2 weeks before the return to the lab for the experimental session. \n", + "\n", + "The Experimental session consisted of showing the infants side by side videos of two unfamilliar individuals, on seperate screens. As with many infant studies the infant gaze is the standard behavioural measure for assessing infants attention. During the initial period of the experiment the two unfamiliar faces were presented with them both smiling at the infant silently. This allowed expert raters to produce baseline gaze proportions. This was followed by the each of the unique indivduals singing their lullaby (either the one the mother had been taught or one that had the sme rhythm, and lyrics but the melody was different). Folwwing this singing of the luublabies the infants were then left to view the two individuals when silent and smiling, to record another gaze proportion score.\n", + "\n", + "Of particular concern for this notebook is the Baseline proprtion scores, which were analysed in the orignal study using a one sample t-test with its goal to test if there was any bias in the infants basline gaze proportions as a group by comparing the $\\bar{x}$ with a hypothsied proportion of $\\mu = .5$, to test for any statistical differnce. Mehr et al. did noto find a statistically significant differecne at the $\\alpha \\leq .05$ and proceeded to claim that this showed tere was no bias in baseline proprotion for either of the indivudals presented on the two screens. There are statistical reasoning faults of this approach due to the limits of the NHST that will not be discussed further here. However, for the interested parties Dienes (2014) explains the problem nicely and how Bayesian methods can offer a solution, and it will not be discussed further here, because the methods below offer a Bayesian estimation solution to the problem of determining any bias is baseline gaze probabilistically in the fashion of a Bayesian estiamtion equivalent of the one sample t-test." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Call github repository were the data is stored\n", + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Mehr%20Song%20and%20Spelke%202016%20Experiment%201.csv\"\n", + "\n", + "# Import data .csv file into a pandas dataframe\n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Output data frame for evaluation\n", + "df.head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clean the data\n", + "\n", + "The analysis below is conducted on the experiment 1 data of Mehr et al. (2016), as such the data needs to be cleaned and reduced for clarity before condcuting any analyses." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The data for the specific experiment is in the first 32 rows of the dataframe\n", + "red_df = df.iloc[0:32,]\n", + "\n", + "# unmark code below to output dataset for any checks that see fit (i.e. that extracting only experiment one)\n", + "red_df.tail(5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data visualisation and exploratory data analysis " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set Seaborn theme for data visualisations.\n", + "sns.set_style(\"white\");\n", + "\n", + "# PLotting a histogram and Kernel density estimate of the baseline gaze proportion scores\n", + "sns.distplot(red_df[\"Baseline_Proportion_Gaze_to_Singer\"], fit = stats.norm, kde= False);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just Eyeballing the data visualistaiton above might suggest that the data is not normally distributed. However, there are only 32 data points, so we have a high level of uncertainty around determining this. As such the use of a normal likelihood may still be appropriate and as the intial authors used a one sample t-test, which assumes normal distributed data, the analysis below will assume a normal likelihood for the Bayesain equivalent of the one-sample t-test." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "y_i &\\sim Normal(\\mu, \\sigma) \n", + "\\\\ \\mu &\\sim Normal(0.5, 0.2) \n", + "\\\\ \\sigma &\\sim Exponential(0.1) \n", + "\\end{align*} \n", + "\n", + "The formulation for presenting statistical models here follows that used by McElreath (2020) for its intuitive nature. In plain english the model specifies that the dependent variable $y_i$ is distributed normally in terms of the Likelihood. The $\\mu$ and $\\sigma$ parameters are to be estimated. With the priors for the $\\mu$ parameter being $Normal(0.5, 0.2)$ and the $\\sigma$ being expoentially distributed $Exponential(0.01)$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3 - Specifying priors\n", + "\n", + "Following the description of the statistical model above the readers should rightly review and criticise the model freely, especialliy in asking why i selected these priors. The priors were selected using the prior predictive checks i ran in the code below to determine if my priors can generate data that falls reasonable on the dependent variable outcome space for the data generating process i am trying to model which in this case is expert rating of baseline gaze proportion scores of infants." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prior predictive checks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualising priors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Visualise prior on mean parameter.\n", + "x = np.arange(-0.5, 1.5, 0.001)\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(x, stats.norm.pdf(x,loc=0.5, scale=0.2));\n", + "plt.xlabel(\"prior on mu\");\n", + "\n", + "# Visualise prior on Standard deviation parameter.\n", + "x = np.arange(-.01, .1, 0.001)\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(x, stats.expon.pdf(x, scale = 0.01));\n", + "plt.xlabel(\"prior on sigma\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating data based on priors\n", + "\n", + "Following the visualisation of the priors for the parameters of the model to \n", + "check how they interact it is important to run prior predcitive check by \n", + "simulating data based on the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set seed to allow for the reproduciblity of notebook.\n", + "np.random.seed(1)\n", + "\n", + "# Set the number of data point to sample\n", + "n = 1000\n", + "#Specify prior values for mu\n", + "mu_loc = 0.5\n", + "mu_scale =.2\n", + "\n", + "#Specify prior values for sigma\n", + "sd_scale = .1\n", + "\n", + "\n", + "# Simulate data from the priors for the mean and SD for the normal model specified above.\n", + "sample_mu = np.random.normal(loc= mu_loc, scale = mu_scale, size = n )\n", + "sample_sigma = np.random.exponential(scale = sd_scale, size = n )\n", + "prior_PC = np.random.normal(loc = sample_mu, scale = sample_sigma, size = n)\n", + "\n", + "# Plot the simulated data\n", + "sns.distplot(prior_PC, hist = False);\n", + "\n", + "# Plot vertical line of the 2 standard deviatons either side of the simulated data.\n", + "plt.vlines((np.mean(prior_PC) + 2 * np.std(prior_PC)), ymin = 0, ymax = 1.8);\n", + "plt.vlines((np.mean(prior_PC) - 2 * np.std(prior_PC)), ymin = 0, ymax = 1.8);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The prior predictive check show that generate reasonable data for baseline child gaze rated by expert rates on a proprotion scale between 0 and 1 follwing a normal model. Rember it doesnt matter if it looks like the data this is all in reltion to prior so everything is done in terms of assumptions of the unobserved data generating process (which in this case we assume is can be described by a normal pdf) not the data we actually observed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4 - Use Bayes rule\n", + "The software of choice to conduct Bayesian inference on the data here is Stan (Carpenter et al., 2017) and the model is specified below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stan model of Bayesian One sample t-test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Stan model to replciate the frequentist one sample t-test.\n", + "\n", + "One_sample_t_test_model = \"\"\"\n", + "\n", + "data{\n", + "\n", + "int N;\n", + "vector[N] y;\n", + "\n", + "}\n", + "\n", + "parameters{\n", + "\n", + "// Model paramaters to be estimated\n", + "\n", + " real mu;// Bounded between 0 and 1 because as proportion of gaze scores cannot exceed those bounds\n", + " real sigma; //Standard deviation bounded at 0\n", + " \n", + "}\n", + "\n", + "model{\n", + "\n", + "//priors\n", + "// Informed prior based on the use of a normal likelihood to estimate mean and standard deviation parameters\n", + "// of proportion score that ranges from 0 to 1\n", + "\n", + "mu ~ normal(0.5, 0.2);\n", + "sigma ~ exponential(0.1);\n", + "\n", + "// Likliehood\n", + "y ~ normal(mu, sigma);\n", + "\n", + "}\n", + "\n", + "generated quantities {\n", + "\n", + "//Generated a real value for difference between the MCMC sample of mu and 0.5.\n", + "real diff = mu - 0.5;\n", + "\n", + "// Generating a quantity of a effect size similar to cohen D of a standarised diffence comparing\n", + "// a reference mean (here is proportion of 0.5) as would be specified in a one sample t-test\n", + "real Cohen_D = diff / sigma; \n", + " \n", + "vector[N] yrep;\n", + " \n", + "// Generate data for posterior samples\n", + " for (i in 1:N) {\n", + " yrep[i] = normal_rng(mu, sigma);\n", + " }\n", + "}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# StanModel function can be called and be passed the model string specified above to compile into C++ code.\n", + "sm = ps.StanModel(model_code = One_sample_t_test_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate python dictionary to pass to Stan model to sample and run Bayesian One sample.\n", + "data = {'N': len(red_df),\n", + " 'y': red_df[\"Baseline_Proportion_Gaze_to_Singer\"].values}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fit and sample posterior for the model for the data, taking 2000 samples per 4 chains.\n", + "fit = sm.sampling(data = data, iter = 2000, chains = 4, seed = 302675)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python pritn stament it is easier to extract the resut sint a panda data frame for data expression\n", + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "fit_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.shape(fit[\"yrep\"].T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit-visualisations - Bayesian one sample Z-test\n", + "\n", + "## Posterior distributions plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using arviz built in Bayesian anlysis plot_posterior funcion to display the posterior probabilty distributions\n", + "# for the fitted model parameter estimates.\n", + "az.plot_posterior(fit, var_names=(\"mu\", \"sigma\"));\n", + "az.plot_posterior(fit, var_names=(\"diff\", \"Cohen_D\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autocorrelation plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Using the arviz package the autocorrelation of the 4 MCMC chains can be plotted.\n", + "az.plot_autocorr(fit, var_names=(\"mu\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The autocorrelation plots do not show any serious autocorrelation problems, as the values quickly decrease to 0." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MCMC traceplots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "az.plot_trace(fit,var_names=(\"mu\", \"sigma\", \"diff\", \"Cohen_D\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The traceplot show good mixing of chains and show a hairy catepillar." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 5 - Posterior predictive checks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "yrep_df = pd.DataFrame(fit['yrep']).T.iloc[:,:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Convert pystan fit object to IO for Arviz functions.\n", + "data = az.from_pystan(\n", + " posterior=fit,\n", + " posterior_predictive='yrep',\n", + " observed_data=[\"y\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot posterior simulated data sets for posterior predictive check\n", + "az.plot_ppc(data, data_pairs = {\"y\" : \"yrep\"}, num_pp_samples= 100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The posterior predictive check shows that the simulated data sets condtioning the model on the data vary from the original data sets. This could mean many things; the first being that an alternative better fitting model may need to be fit or secondly that the small amount of data from a highly variable measure of infant gaze which has to go through a noisy expert rater) is presenting a simple reality that our model and its resulting inferences are more uncertain. Of course, this is not neccesarily a problem it just needs to be communicated to and appreciated by the audience that is viewing the results of the analysis, whom may even recommend improvements for the model and the modellers choices." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the Bayesian one sample t-test equivalent\n", + "\n", + "As Kruscke correctly points out there is not standard formula or presentation method for results like the APA guide for reporting frequentist analyses using the Bayesian framework. It is likely there never will be, because as McElreath (2020) explains, Bayesian data analysis is more like a engineering approach to the problem and the resulting model that is fit will be analysis specific. In addition, as Gabry et al, (2019) have argued visualisations maybe even more key, so all the visualtions above would have to be included with any write up. Anyway, the write up below generally follows the advice of Kruscke (2015) chapter 25. In any application though it comes down to the problem to be described and the audience that needs to be convinced.


\n", + "\n", + "

Write up


" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: a probabilistic programming language. Grantee Submission, 76(1), 1-32.\n", + "\n", + "Dienes, Z. (2014). Using Bayes to get the most out of non-significant results. Frontiers in psychology, 5, 781.\n", + "\n", + "Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389-402.\n", + " \n", + "Kruschke, J. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS and Stan. Oxford, England: Academic Press. \n", + " \n", + "McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan.Boca Raton: CRC Press.\n", + "\n", + "Mehr, S. A., Song. L. A., & Spelke, E. S. (2016). For 5-month-old infants, melodies are social. Psychological Science, 27, 486-501." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Stan", + "language": "python", + "name": "stan" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + }, + "toc": { + "base_numbering": 1, + "colors": { + "hover_highlight": "#DAA520", + "navigate_num": "#000000", + "navigate_text": "#333333", + "running_highlight": "#FF0000", + "selected_highlight": "#FFD700", + "sidebar_border": "#EEEEEE", + "wrapper_background": "#FFFFFF" + }, + "moveMenuLeft": true, + "nav_menu": { + "height": "319px", + "width": "873px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "threshold": 4, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "315.806px" + }, + "toc_section_display": true, + "toc_window_display": false, + "widenNotebook": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Chapter_2_-_One-sample_t-test/test.ipynb b/Chapter_2_-_One-sample_t-test/test.ipynb new file mode 100644 index 0000000..6dde6b6 --- /dev/null +++ b/Chapter_2_-_One-sample_t-test/test.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f6b71e32", + "metadata": {}, + "source": [ + "test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54a75853", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Stan", + "language": "python", + "name": "stan" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + }, + "toc": { + "colors": { + "hover_highlight": "#DAA520", + "navigate_num": "#000000", + "navigate_text": "#333333", + "running_highlight": "#FF0000", + "selected_highlight": "#FFD700", + "sidebar_border": "#EEEEEE", + "wrapper_background": "#FFFFFF" + }, + "moveMenuLeft": true, + "nav_menu": { + "height": "12px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 4, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": true, + "widenNotebook": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..f594c5c --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,88 @@ +Creative Commons Attribution-NonCommercial 4.0 International Public License + +By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions. + +Section 1 – Definitions. + +Adapted Material means material subject to Copyright and Similar Rights that is derived from or based upon the Licensed Material and in which the Licensed Material is translated, altered, arranged, transformed, or otherwise modified in a manner requiring permission under the Copyright and Similar Rights held by the Licensor. For purposes of this Public License, where the Licensed Material is a musical work, performance, or sound recording, Adapted Material is always produced where the Licensed Material is synched in timed relation with a moving image. +Adapter's License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License. +Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights. +Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements. +Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material. +Licensed Material means the artistic or literary work, database, or other material to which the Licensor applied this Public License. +Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license. +Licensor means the individual(s) or entity(ies) granting rights under this Public License. +NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange. +Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the public may access the material from a place and at a time individually chosen by them. +Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world. +You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning. +Section 2 – Scope. + +License grant. +Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to: +reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and +produce, reproduce, and Share Adapted Material for NonCommercial purposes only. +Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions. +Term. The term of this Public License is specified in Section 6(a). +Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, and to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, simply making modifications authorized by this Section 2(a)(4) never produces Adapted Material. +Downstream recipients. +Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License. +No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material. +No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, the Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i). +Other rights. + +Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise. +Patent and trademark rights are not licensed under this Public License. +To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes. +Section 3 – License Conditions. + +Your exercise of the Licensed Rights is expressly made subject to the following conditions. + +Attribution. + +If You Share the Licensed Material (including in modified form), You must: + +retain the following if it is supplied by the Licensor with the Licensed Material: +identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated); +a copyright notice; +a notice that refers to this Public License; +a notice that refers to the disclaimer of warranties; +a URI or hyperlink to the Licensed Material to the extent reasonably practicable; +indicate if You modified the Licensed Material and retain an indication of any previous modifications; and +indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License. +You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information. +If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable. +If You Share Adapted Material You produce, the Adapter's License You apply must not prevent recipients of the Adapted Material from complying with this Public License. +Section 4 – Sui Generis Database Rights. + +Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material: + +for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only; +if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material; and +You must comply with the conditions in Section 3(a) if You Share all or a substantial portion of the contents of the database. +For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights. +Section 5 – Disclaimer of Warranties and Limitation of Liability. + +Unless otherwise separately undertaken by the Licensor, to the extent possible, the Licensor offers the Licensed Material as-is and as-available, and makes no representations or warranties of any kind concerning the Licensed Material, whether express, implied, statutory, or other. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, absence of latent or other defects, accuracy, or the presence or absence of errors, whether or not known or discoverable. Where disclaimers of warranties are not allowed in full or in part, this disclaimer may not apply to You. +To the extent possible, in no event will the Licensor be liable to You on any legal theory (including, without limitation, negligence) or otherwise for any direct, special, indirect, incidental, consequential, punitive, exemplary, or other losses, costs, expenses, or damages arising out of this Public License or use of the Licensed Material, even if the Licensor has been advised of the possibility of such losses, costs, expenses, or damages. Where a limitation of liability is not allowed in full or in part, this limitation may not apply to You. +The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability. +Section 6 – Term and Termination. + +This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically. +Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates: + +automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or +upon express reinstatement by the Licensor. +For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License. +For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License. +Sections 1, 5, 6, 7, and 8 survive termination of this Public License. +Section 7 – Other Terms and Conditions. + +The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed. +Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License. +Section 8 – Interpretation. + +For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License. +To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions. +No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor. +Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..ab04042 --- /dev/null +++ b/README.md @@ -0,0 +1,10 @@ +# Chapter 0: Introduction + +[![DOI](https://zenodo.org/badge/302400422.svg)](https://zenodo.org/badge/latestdoi/302400422) + +You can cite this book + +EBRLab members (in preparation). _Statistical methods for research workers: Bayes for psychologists and neuroscientists._ https://github.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists. doi: https://doi.org/10.5281/zenodo.8475. + +### [Read it online here](https://nbviewer.jupyter.org/github/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/blob/master/Chapter_0_-_Introduction/Ch0_Introduction.ipynb) + diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..4a77718 --- /dev/null +++ b/environment.yml @@ -0,0 +1,13 @@ +name: stan +channels: + - conda-forge +dependencies: + - arviz=0.11.2 + - numpy=1.21.0 + - matplotlib=3.3.4 + - pip=21.1.3 + - pandas=1.2.5 + - seaborn=0.11.1 + - scipy=1.6.2 + - pip: + - pystan==3.2.0 diff --git a/requirements_conda.txt b/requirements_conda.txt new file mode 100644 index 0000000..0e07f21 --- /dev/null +++ b/requirements_conda.txt @@ -0,0 +1,153 @@ +# This file may be used to create an environment using: +# $ conda create --name --file +# platform: osx-64 +aiohttp=3.7.4.post0=pypi_0 +anyio=3.2.1=py39h6e9494a_0 +appdirs=1.4.4=pypi_0 +appnope=0.1.2=py39h6e9494a_1 +argon2-cffi=20.1.0=py39h89e85a6_2 +arviz=0.11.2=pyhd3eb1b0_0 +async-timeout=3.0.1=pypi_0 +async_generator=1.10=py_0 +attrs=21.2.0=pyhd8ed1ab_0 +babel=2.9.1=pyh44b312d_0 +backcall=0.2.0=pyh9f0ad1d_0 +backports=1.0=py_2 +backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0 +blas=1.0=mkl +bleach=3.3.0=pyh44b312d_0 +bottleneck=1.3.2=py39he3068b8_1 +brotlipy=0.7.0=py39h89e85a6_1001 +ca-certificates=2021.7.5=hecd8cb5_1 +certifi=2021.5.30=py39hecd8cb5_0 +cffi=1.14.6=py39hb71fe58_0 +cftime=1.5.0=py39he3068b8_0 +chardet=4.0.0=py39h6e9494a_1 +clikit=0.6.2=pypi_0 +crashtest=0.3.1=pypi_0 +cryptography=3.4.7=py39ha2c9959_0 +curl=7.71.1=hb0a8c7a_1 +cycler=0.10.0=py39hecd8cb5_0 +decorator=5.0.9=pyhd8ed1ab_0 +defusedxml=0.7.1=pyhd8ed1ab_0 +entrypoints=0.3=pyhd8ed1ab_1003 +freetype=2.10.4=ha233b18_0 +hdf4=4.2.13=h39711bb_2 +hdf5=1.10.6=hdbbcd12_0 +httpstan=4.5.0=pypi_0 +idna=3.2=pypi_0 +importlib-metadata=4.6.1=py39h6e9494a_0 +intel-openmp=2021.2.0=hecd8cb5_564 +ipykernel=5.5.5=py39h71a6800_0 +ipython=7.25.0=py39h71a6800_1 +ipython_genutils=0.2.0=py_1 +jedi=0.18.0=py39h6e9494a_2 +jinja2=3.0.1=pyhd8ed1ab_0 +jpeg=9b=he5867d9_2 +json5=0.9.5=pyh9f0ad1d_0 +jsonschema=3.2.0=pyhd8ed1ab_3 +jupyter_client=6.1.12=pyhd8ed1ab_0 +jupyter_core=4.7.1=py39h6e9494a_0 +jupyter_server=1.9.0=pyhd8ed1ab_0 +jupyterlab=3.0.16=pyhd8ed1ab_0 +jupyterlab_pygments=0.1.2=pyh9f0ad1d_0 +jupyterlab_server=2.6.1=pyhd8ed1ab_0 +kiwisolver=1.3.1=py39h23ab428_0 +krb5=1.18.2=h75d18d8_0 +lcms2=2.12=hf1fd2bf_0 +libcurl=7.71.1=h8a08a2b_1 +libcxx=10.0.0=1 +libedit=3.1.20210216=h9ed2024_1 +libffi=3.3=hb1e8313_2 +libgfortran=3.0.1=h93005f0_2 +libnetcdf=4.6.1=hfd9a460_3 +libpng=1.6.37=ha441bb4_0 +libsodium=1.0.18=hbcb3906_1 +libssh2=1.9.0=ha12b0ac_1 +libtiff=4.2.0=h87d7836_0 +libwebp-base=1.2.0=h9ed2024_0 +lz4=3.1.3=pypi_0 +lz4-c=1.9.3=h23ab428_0 +markupsafe=2.0.1=py39h89e85a6_0 +marshmallow=3.12.2=pypi_0 +matplotlib=3.3.4=py39hecd8cb5_0 +matplotlib-base=3.3.4=py39h8b3ea08_0 +matplotlib-inline=0.1.2=pyhd8ed1ab_2 +mistune=0.8.4=py39h89e85a6_1004 +mkl=2021.2.0=hecd8cb5_269 +mkl-service=2.3.0=py39h9ed2024_1 +mkl_fft=1.3.0=py39h4a7008c_2 +mkl_random=1.2.1=py39hb2f4e1b_2 +multidict=5.1.0=pypi_0 +nbclassic=0.3.1=pyhd8ed1ab_1 +nbclient=0.5.3=pyhd8ed1ab_0 +nbconvert=6.1.0=py39h6e9494a_0 +nbformat=5.1.3=pyhd8ed1ab_0 +ncurses=6.2=h0a44026_1 +nest-asyncio=1.5.1=pyhd8ed1ab_0 +netcdf4=1.5.7=py39h93ad9c5_0 +notebook=6.4.0=pyha770c72_0 +numexpr=2.7.3=py39h5873af2_1 +numpy=1.21.0=pypi_0 +numpy-base=1.20.2=py39he0bd621_0 +olefile=0.46=py_0 +openjpeg=2.3.0=hb95cd4c_1 +openssl=1.1.1k=h0d85af4_0 +packaging=21.0=pyhd8ed1ab_0 +pandas=1.2.5=py39h23ab428_0 +pandoc=2.14.0.3=h0d85af4_0 +pandocfilters=1.4.2=py_1 +parso=0.8.2=pyhd8ed1ab_0 +pastel=0.2.1=pypi_0 +pexpect=4.8.0=pyh9f0ad1d_2 +pickleshare=0.7.5=py_1003 +pillow=8.3.1=py39ha4cf6ea_0 +pip=21.1.3=py39hecd8cb5_0 +prometheus_client=0.11.0=pyhd8ed1ab_0 +prompt-toolkit=3.0.19=pyha770c72_0 +ptyprocess=0.7.0=pyhd3deb0d_0 +pycparser=2.20=pyh9f0ad1d_2 +pygments=2.9.0=pyhd8ed1ab_0 +pylev=1.4.0=pypi_0 +pyopenssl=20.0.1=pyhd8ed1ab_0 +pyparsing=2.4.7=pyh9f0ad1d_0 +pyrsistent=0.17.3=py39h89e85a6_2 +pysimdjson=3.2.0=pypi_0 +pysocks=1.7.1=py39h6e9494a_3 +pystan=3.2.0=pypi_0 +python=3.9.5=h88f2d9e_3 +python-dateutil=2.8.1=pyhd3eb1b0_0 +python_abi=3.9=2_cp39 +pytz=2021.1=pyhd3eb1b0_0 +pyzmq=20.0.0=py39h23ab428_1 +readline=8.1=h9ed2024_0 +requests=2.25.1=pyhd3deb0d_0 +requests-unixsocket=0.2.0=py_0 +scipy=1.6.2=py39hd5f7400_1 +seaborn=0.11.1=pyhd3eb1b0_0 +send2trash=1.7.1=pyhd8ed1ab_0 +setuptools=52.0.0=py39hecd8cb5_0 +six=1.16.0=pyhd3eb1b0_0 +sniffio=1.2.0=py39h6e9494a_1 +sqlite=3.36.0=hce871da_0 +terminado=0.10.1=py39h6e9494a_0 +testpath=0.5.0=pyhd8ed1ab_0 +tk=8.6.10=hb0a8c7a_0 +tornado=6.1=py39h89e85a6_1 +traitlets=5.0.5=py_0 +typing-extensions=3.10.0.0=hd3eb1b0_0 +typing_extensions=3.10.0.0=pyh06a4308_0 +tzdata=2021a=h52ac0ba_0 +urllib3=1.26.6=pyhd8ed1ab_0 +wcwidth=0.2.5=pyh9f0ad1d_2 +webargs=7.0.1=pypi_0 +webencodings=0.5.1=py_1 +websocket-client=0.57.0=py39h6e9494a_4 +wheel=0.36.2=pyhd3eb1b0_0 +xarray=0.18.2=pyhd3eb1b0_1 +xz=5.2.5=h1de35cc_0 +yarl=1.6.3=pypi_0 +zeromq=4.3.4=h23ab428_0 +zipp=3.5.0=pyhd8ed1ab_0 +zlib=1.2.11=h1de35cc_3 +zstd=1.4.9=h322a384_0 diff --git a/requirements_pip.txt b/requirements_pip.txt new file mode 100644 index 0000000..feed008 --- /dev/null +++ b/requirements_pip.txt @@ -0,0 +1,106 @@ +aiohttp==3.7.4.post0 +anyio @ file:///Users/runner/miniforge3/conda-bld/anyio_1624539530706/work/dist +appdirs==1.4.4 +appnope @ file:///Users/runner/miniforge3/conda-bld/appnope_1610094673755/work +argon2-cffi @ file:///Users/runner/miniforge3/conda-bld/argon2-cffi_1625821234041/work +arviz @ file:///tmp/build/80754af9/arviz_1614019183254/work +async-generator==1.10 +async-timeout==3.0.1 +attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1620387926260/work +Babel @ file:///home/conda/feedstock_root/build_artifacts/babel_1619719576210/work +backcall @ file:///home/conda/feedstock_root/build_artifacts/backcall_1592338393461/work +backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1618230623929/work +bleach @ file:///home/conda/feedstock_root/build_artifacts/bleach_1612213472466/work +Bottleneck @ file:///opt/concourse/worker/volumes/live/ac8c8ef3-2ed0-42e9-6ec0-5bb05ad938f6/volume/bottleneck_1607575111469/work +brotlipy==0.7.0 +certifi==2021.5.30 +cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1625835329396/work +cftime @ file:///opt/concourse/worker/volumes/live/d87b7f0e-8878-4641-6f66-5fae48d1a989/volume/cftime_1621828176900/work +chardet @ file:///Users/runner/miniforge3/conda-bld/chardet_1610093613037/work +clikit==0.6.2 +crashtest==0.3.1 +cryptography @ file:///Users/runner/miniforge3/conda-bld/cryptography_1616851523607/work +cycler==0.10.0 +decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1621187651333/work +defusedxml @ file:///home/conda/feedstock_root/build_artifacts/defusedxml_1615232257335/work +entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1605121927639/work/dist/entrypoints-0.3-py2.py3-none-any.whl +httpstan==4.5.0 +idna==3.2 +importlib-metadata @ file:///Users/runner/miniforge3/conda-bld/importlib-metadata_1625463744025/work +ipykernel @ file:///Users/runner/miniforge3/conda-bld/ipykernel_1620913000384/work/dist/ipykernel-5.5.5-py3-none-any.whl +ipython @ file:///Users/runner/miniforge3/conda-bld/ipython_1625027511604/work +ipython-genutils==0.2.0 +jedi @ file:///Users/runner/miniforge3/conda-bld/jedi_1610146808808/work +Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1621419064915/work +json5 @ file:///home/conda/feedstock_root/build_artifacts/json5_1600692310011/work +jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema_1614815863336/work +jupyter-client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1615693636836/work +jupyter-core @ file:///Users/runner/miniforge3/conda-bld/jupyter_core_1612125283939/work +jupyter-server @ file:///home/conda/feedstock_root/build_artifacts/jupyter_server_1624558637735/work +jupyterlab @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_1621368934875/work +jupyterlab-pygments @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_pygments_1601375948261/work +jupyterlab-server @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_server_1625800044174/work +kiwisolver @ file:///opt/concourse/worker/volumes/live/f8867fdb-2fa2-4145-73d9-4d6f6dad5f7c/volume/kiwisolver_1612282424136/work +lz4==3.1.3 +MarkupSafe @ file:///Users/runner/miniforge3/conda-bld/markupsafe_1621455730541/work +marshmallow==3.12.2 +matplotlib @ file:///opt/concourse/worker/volumes/live/41e8cd50-031f-4dda-5787-dd3c4f4e0f08/volume/matplotlib-suite_1613407855571/work +matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1618935594181/work +mistune @ file:///Users/runner/miniforge3/conda-bld/mistune_1624941339085/work +mkl-fft==1.3.0 +mkl-random @ file:///opt/concourse/worker/volumes/live/ee9c6651-7474-4ea3-40dc-9000610cf5d2/volume/mkl_random_1618853976708/work +mkl-service==2.3.0 +multidict==5.1.0 +nbclassic @ file:///home/conda/feedstock_root/build_artifacts/nbclassic_1621947328608/work +nbclient @ file:///home/conda/feedstock_root/build_artifacts/nbclient_1614336084111/work +nbconvert @ file:///Users/runner/miniforge3/conda-bld/nbconvert_1624472871251/work +nbformat @ file:///home/conda/feedstock_root/build_artifacts/nbformat_1617383142101/work +nest-asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1617163391303/work +netCDF4 @ file:///opt/concourse/worker/volumes/live/2cff32d2-6330-4fce-41b3-549a7868536f/volume/netcdf4_1624439348539/work +notebook @ file:///home/conda/feedstock_root/build_artifacts/notebook_1621259862661/work +numexpr @ file:///opt/concourse/worker/volumes/live/8a490d79-0e07-4fed-40fc-a78f896e4811/volume/numexpr_1618856522733/work +numpy==1.21.0 +olefile==0.46 +packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1625323647219/work +pandas==1.2.5 +pandocfilters==1.4.2 +parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1617148930513/work +pastel==0.2.1 +pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1602535608087/work +pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work +Pillow @ file:///opt/concourse/worker/volumes/live/140f8506-0993-42b0-466b-ccbbcb5df83e/volume/pillow_1625655825783/work +prometheus-client @ file:///home/conda/feedstock_root/build_artifacts/prometheus_client_1622586138406/work +prompt-toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1623977816122/work +ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl +pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1593275161868/work +Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1620245170812/work +pylev==1.4.0 +pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1608055815057/work +pyparsing==2.4.7 +pyrsistent @ file:///Users/runner/miniforge3/conda-bld/pyrsistent_1624984762901/work +pysimdjson==3.2.0 +PySocks @ file:///Users/runner/miniforge3/conda-bld/pysocks_1610291470465/work +pystan==3.2.0 +python-dateutil @ file:///home/ktietz/src/ci/python-dateutil_1611928101742/work +pytz @ file:///tmp/build/80754af9/pytz_1612215392582/work +pyzmq==20.0.0 +requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1608156231189/work +requests-unixsocket==0.2.0 +scipy @ file:///opt/concourse/worker/volumes/live/e6cafc01-ffb4-49e0-5767-9e6700679b1a/volume/scipy_1618855952637/work +seaborn @ file:///tmp/build/80754af9/seaborn_1608578541026/work +Send2Trash @ file:///home/conda/feedstock_root/build_artifacts/send2trash_1624366715919/work +six @ file:///tmp/build/80754af9/six_1623709665295/work +sniffio @ file:///Users/runner/miniforge3/conda-bld/sniffio_1610318427209/work +terminado @ file:///Users/runner/miniforge3/conda-bld/terminado_1623365368681/work +testpath @ file:///home/conda/feedstock_root/build_artifacts/testpath_1621261527237/work +tornado @ file:///Users/runner/miniforge3/conda-bld/tornado_1625488906146/work +traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1602771532708/work +typing-extensions @ file:///tmp/build/80754af9/typing_extensions_1624965014186/work +urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1624634538755/work +wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1600965781394/work +webargs==7.0.1 +webencodings==0.5.1 +websocket-client @ file:///Users/runner/miniforge3/conda-bld/websocket-client_1610127894508/work +xarray @ file:///tmp/build/80754af9/xarray_1625000640608/work +yarl==1.6.3 +zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1625284368454/work diff --git a/wip/Bayesian categorical regression (one-way ANOVA).ipynb b/wip/Bayesian categorical regression (one-way ANOVA).ipynb new file mode 100644 index 0000000..37b1d2d --- /dev/null +++ b/wip/Bayesian categorical regression (one-way ANOVA).ipynb @@ -0,0 +1,785 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import relevant packages for analysis\n", + "import pystan as ps\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "from patsy import dmatrix, dmatrices\n", + "from patsy.contrasts import Treatment, Sum\n", + "import arviz as az" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation categorical regression (equivalent of One-way ANOVA)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following the quick description of the classic one sample t-test above its important to keep in mind that Bayesian analysis inference are all derived from the applciation of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to the Z-test, it is fundamentally different, becuase it uses fully probabilistic modelling and the infernce is not based on sampling distributions\n", + " \n", + " For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. In the case of scientific research publication is the goal, as such the priors must be accepted by a skeptical audience. Much of this can be achieved using prior predcitve checks to acsetain os the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then interprete and the posterior\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation\n", + "## Data overview and study description\n", + "\n", + "The data and the analysis has been taken from https://sites.trinity.edu/osl/data-sets-and-activities/one-way-anova-activities, with the oringla dataset being from James et al. (2015).\n", + "\n", + "See orignal paper here https://journals.sagepub.com/doi/pdf/10.1177/0956797615583071?referrer=&priority=true&module=meter-Links&pgtype=Blogs&contentId=&action=click&contentCollection=meter-links-click&version=meter+at+null&mediaId=\n", + "\n", + "A reality of trauma is that indivudals can experience flasbacks which have been termed \"Intrusive memories\". A form of treatment that has been argued to be effective for suffers of intrusive memories is to use reconsolidtion methods. As such, James et al. (2015) wanted to inestigate if a video game treament (tetris code improve the number of intrusive meories nd indivduak experienced.\n", + "\n", + "The partipants with the study were split into four codntions (n=72, with 18 per condition).\n", + "\n", + "1. No-task control: These participants completed a 10-minute music filler task.\n", + "2. Reactivation + Tetris: These partipants underwent a reactivation task to (trama film) to reactivate teir traumati memories, ehich was then followe dby 10 minute filler music task. this was follwoed by playing tetris for 12 minutes\n", + "3. Tetris: this group played tetris for 12 minutes\n", + "4. Reactivtion only: Partipats only watch the trauma film\n", + "\n", + "## Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ConditionTime_of_DayBDI_IISTAI_Tpre_film_VAS_Sadpre_film_VAS_Hopelesspre_film_VAS_Depressedpre_film_VAS_Fearpre_film_VAS_Horrorpre_film_VAS_Anxious...Day_Zero_Number_of_IntrusionsDays_One_to_Seven_Number_of_IntrusionsVisual_Recognition_Memory_TestVerbal_Recognition_Memory_TestNumber_of_Provocation_Task_IntrusionsDiary_ComplianceIES_R_Intrusion_subscaleTetris_Total_ScoreSelf_Rated_Tetris_PerformanceTetris_Demand_Rating
0121330.00.00.00.40.30.8...241518590.6299999999.00
\n", + "

1 rows × 28 columns

\n", + "
" + ], + "text/plain": [ + " Condition Time_of_Day BDI_II STAI_T pre_film_VAS_Sad \\\n", + "0 1 2 1 33 0.0 \n", + "\n", + " pre_film_VAS_Hopeless pre_film_VAS_Depressed pre_film_VAS_Fear \\\n", + "0 0.0 0.0 0.4 \n", + "\n", + " pre_film_VAS_Horror pre_film_VAS_Anxious ... \\\n", + "0 0.3 0.8 ... \n", + "\n", + " Day_Zero_Number_of_Intrusions Days_One_to_Seven_Number_of_Intrusions \\\n", + "0 2 4 \n", + "\n", + " Visual_Recognition_Memory_Test Verbal_Recognition_Memory_Test \\\n", + "0 15 18 \n", + "\n", + " Number_of_Provocation_Task_Intrusions Diary_Compliance \\\n", + "0 5 9 \n", + "\n", + " IES_R_Intrusion_subscale Tetris_Total_Score \\\n", + "0 0.62 9999 \n", + "\n", + " Self_Rated_Tetris_Performance Tetris_Demand_Rating \n", + "0 9999.0 0 \n", + "\n", + "[1 rows x 28 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/James%20et%20al%202015%20Experiment%202%20Data%20Set.csv\"\n", + "\n", + "#Generare apndas data frame with the study data\n", + "df = pd.read_csv(url)\n", + "df.head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exploratory data analysis \n", + "### Plotting histograms for the data depednent variable of the number of intrusive thoughts" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Set them of seaborn plots for nicer display (not APA, work that one out soon)\n", + "sns.set()\n", + "\n", + "#Genrate grd to dspaly th four separate condtions\n", + "g= sns.FacetGrid(df, col=\"Condition\")\n", + "\n", + "#Generate the four hisogram plots for the 4 condtions\n", + "g.map(sns.distplot, \"Day_Zero_Number_of_Intrusions\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g= sns.FacetGrid(df, col=\"Condition\")\n", + "g.map(sns.distplot, \"Days_One_to_Seven_Number_of_Intrusions\")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(df[\"Days_One_to_Seven_Number_of_Intrusions\"]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Treament coding \n", + " Generating a design (contrast) matrix for categorical regression \n", + " analysis with treatment coding" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "df.Condition = df.Condition.astype(\"category\")\n", + "df.Condition = df['Condition'].cat.reorder_categories([2, 1,3,4])\n", + "Reordered = dmatrix(\"Condition\", df)\n", + "Reordered = np.array(Reordered)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "One_wayANOVA = \"\"\"\n", + "data{\n", + "int N; // Number of rows in the contrast matix\n", + "int K; // Number columns\n", + "vector[N] y; // Dependent variable data points\n", + "matrix[N,K] x; //Contrast matrix\n", + "}\n", + "\n", + "parameters{\n", + "\n", + "// Beta coefficients\n", + "vector[K] beta; \n", + "// Pooled sigma estimate\n", + "real sigma;\n", + "\n", + "}\n", + "model{\n", + "\n", + "//likelihood\n", + "y ~ normal(x * beta , sigma);\n", + "\n", + "}\n", + "\n", + "generated quantities {\n", + "\n", + "real yrep[N];\n", + "yrep = normal_rng(x * beta, sigma);\n", + "\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_ef5cc59eef1be6fe800180e9cf7fe3d0 NOW.\n" + ] + } + ], + "source": [ + "sm = ps.StanModel(model_code = One_wayANOVA)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "#Generate python dictionary for passing the relevant data for the analysis to stan\n", + "data = {'N': len(df),\n", + " 'K': Reordered.shape[1],\n", + " 'y': df[\"Days_One_to_Seven_Number_of_Intrusions\"].values,\n", + " 'x': Reordered}" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "fit = sm.sampling(data = data, iter = 10000, chains=4, seed= 302675)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python pritn stament it is easier to extract the resut sint a panda data frame for data expression\n", + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
meanse_meansd2.5%25%50%75%97.5%n_effRhat
beta[1]1.8865340.0091650.7630840.3933391.3745431.8844672.4021273.3723326932.0723781.000122
beta[2]3.2153280.0115421.0775341.1195182.4793813.2237783.9310915.3354808714.9876770.999992
beta[3]2.0037690.0118881.079119-0.1455791.2905001.9988762.7141394.0972838240.3755111.000013
beta[4]2.9539860.0115091.0815580.8112822.2250912.9599853.6796215.0687208831.0474791.000091
sigma3.2368150.0023760.2804442.7440553.0396083.2169953.4164943.83836513936.3454221.000110
\n", + "
" + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% 75% \\\n", + "beta[1] 1.886534 0.009165 0.763084 0.393339 1.374543 1.884467 2.402127 \n", + "beta[2] 3.215328 0.011542 1.077534 1.119518 2.479381 3.223778 3.931091 \n", + "beta[3] 2.003769 0.011888 1.079119 -0.145579 1.290500 1.998876 2.714139 \n", + "beta[4] 2.953986 0.011509 1.081558 0.811282 2.225091 2.959985 3.679621 \n", + "sigma 3.236815 0.002376 0.280444 2.744055 3.039608 3.216995 3.416494 \n", + "\n", + " 97.5% n_eff Rhat \n", + "beta[1] 3.372332 6932.072378 1.000122 \n", + "beta[2] 5.335480 8714.987677 0.999992 \n", + "beta[3] 4.097283 8240.375511 1.000013 \n", + "beta[4] 5.068720 8831.047479 1.000091 \n", + "sigma 3.838365 13936.345422 1.000110 " + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit-visualisations - Bayesian simple regression\n", + "\n", + "## Posterior distributions plots" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Posterior plots showing the defaults 94% Credible interval of the arviz package\n", + "az.plot_posterior(fit, var_names=(\"beta\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior autocorrelation plot" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Using the arviz package the autocorrelation of the 4 MCMC chains can be plotted.\n", + "az.plot_autocorr(fit, var_names=(\"beta\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior trace plots " + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(fit, var_names=(\"beta\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Posterior predcitive check " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "#Convert pystan fit object to IO for Arviz functions.\n", + "data = az.from_pystan(\n", + " posterior=fit,\n", + " posterior_predictive='yrep',\n", + " observed_data=[\"y\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot posterior simulated data sets for posterior predictive check\n", + "az.plot_ppc(data, data_pairs = {\"y\" : \"yrep\"}, num_pp_samples= 100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the Bayesian categorical regression\n", + "\n", + "As Kruscke correctly points out there is not standard formula or presentation method for reuslts in journal article like the APA guide for reporting frequentist analysis. It is likely there never will be, because as McElreath (2020) explains, Bayesian data analysis is more like a engineering approach to the problem and the resulting model that is fit will be analysis specific. In addition, as Gabry et al, (2019) argue visualisations maybe even more key so the all the visualtions above would have to be included with any write up. Anyway below the write up as below general follows the advice of Kruscke (2015) chapter 25. In any application though it comes down to the problem to be described an the audience that needs to be convinced.


\n", + "\n", + "

Write up


" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References \n", + " \n", + "Hays, W.L. (1973) Statistics for the social sciences. Winston New York: Holt, Rinehart.\n", + " \n", + "James, E. L., Bonsall, M. B., Hoppitt, L., Tunbridge, E. M., Geddes, J. R., Milton, A. L., & Holmes, E. A. (2015). Computer game play reduces intrusive memories of experimental trauma via reconsolidation-update mechanisms. Psychological Science, 26, 1201-1215." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian estimation of logistic regression.ipynb b/wip/Bayesian estimation of logistic regression.ipynb new file mode 100644 index 0000000..8d68ae7 --- /dev/null +++ b/wip/Bayesian estimation of logistic regression.ipynb @@ -0,0 +1,1262 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import data analysis and visualisation packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import patsy as pt\n", + "import pystan as ps\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import scipy.stats as stats\n", + "import arviz as az" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation of a logistic regression model\n", + "\n", + "Before discussion of Bayesain logistic regression a short of the review of classical logistic regression is reguired. Logistic regression is part of the generalised linear model framework and like all regression models within this framework its primary application within the proccess of modelling data is for parameter estimation for the purpose of prediction and for logistic regression this is done specfically in terms of the modelling of binary outcome data ($y$ that take on values of either 0 or 1). \n", + "\n", + "In order to model this type of data two additonal features are required on top of the standard regression model $y = \\alpha + \\beta x$. These featues being:\n", + " 1. a nonlinear transformation which bounds outputs between 0 and 1.\n", + " 2. modelling the resulting outcomes as probabilities.\n", + " \n", + "The features above are achieved through the generalised linear model framewrok through the application of a link function which for logistic regression is the the logistic function $logit(x) = log(\\frac{x}{1-x})$ that maps the outome space of (0,1) to $(-\\infty,\\infty )$.\n", + "\n", + "In additon to the logit(x) function the inverse logit function $logit^{-1} = \\frac{e^x}{1+e^x}$ is alos of critcal importance as this allows the coeffients oestiamted form the model to be converted into probabilities for ease of interpretation.\n", + "\n", + "Of course classical logistic regression as regularly applied takes a Null hypothesis singificance testing for which the p-values are calculated for each regression coefficient using the Wald test (unlike regular regression which uses t-tests).\n", + "\n", + "$$Z = \\frac{\\hat{\\beta}}{s.e.(\\hat{\\beta})}$$\n", + "\n", + "$\\hat{\\beta}$ = maximum likelihood estimate.\n", + "\n", + "$s.e.$ = standard error\n", + "\n", + "With this commonly testing the null hypothesis of $H_0: \\hat{\\beta} = 0$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# logit and inverse logit functions" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Generate numbers from logistic function \n", + "y = np.linspace(-4, 4, num = 1000)\n", + "x = stats.logistic.cdf(y)\n", + "\n", + "# plot logit function\n", + "plt.subplot(1, 2, 1);\n", + "\n", + "plt.plot(x,y);\n", + "plt.title(\"Logit link funtion\");\n", + "plt.xlabel('Probability');\n", + "plt.ylabel('log-odds');\n", + "\n", + "# plot inverse logit function\n", + "plt.subplot(1, 2, 2);\n", + "plt.plot(y,x);\n", + "plt.title(\"Inverse logit link (logistic) function\");\n", + "plt.xlabel('Log odds');\n", + "plt.ylabel('Probaility');\n", + "\n", + "# Add space between subplots\n", + "plt.tight_layout(pad=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For clarity, the plots above show how the logit funtion maps probability scale onto the log odds scale and how the inverse logit log odds to probability scale." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. In the case of scientific research publication is the goal, as such the priors must be accepted by a skeptical audience. Much of this can be achieved using prior predcitve checks to acsetain os the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then interprete and the posterior\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation\n", + "\n", + "The following data has been downloaded from https://www.sheffield.ac.uk/mash/statistics/datasets and contains data about 42 mothers and their newborns. The change in the source of data here from https://sites.google.com/view/openstatslab used within the other notebooks is simply due to know datasets specifcally aimed at demonstrating logistic regression at https://sites.google.com/view/openstatslab.\n", + "\n", + "As stated above the data used below is from mothers and their newborns. The point of the logistic regression analysis below is estimate the probability of low birth weight (low being anthing < 6$lbs$) using relevant predictor variables. within the first analysis below only a dictomous predictor varaible will be modelled - smoking status of the mother - in order to estimate the differnce in the probability of the child being born < 6$lbs$ in weight. of the 42, 20 were non-smokers and 22 were smokers." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IDLengthBirthweightHeadcircGestationsmokermagemnocigmheightmppwtfagefedyrsfnocigfheightlowbwtmage35
01360564.55344402001625723103517900
11016534.3236400190171621912018300
2462584.10394103501725831162518501
31187534.07384402001746826142518900
4553543.9437420240175663012018400
\n", + "
" + ], + "text/plain": [ + " ID Length Birthweight Headcirc Gestation smoker mage mnocig \\\n", + "0 1360 56 4.55 34 44 0 20 0 \n", + "1 1016 53 4.32 36 40 0 19 0 \n", + "2 462 58 4.10 39 41 0 35 0 \n", + "3 1187 53 4.07 38 44 0 20 0 \n", + "4 553 54 3.94 37 42 0 24 0 \n", + "\n", + " mheight mppwt fage fedyrs fnocig fheight lowbwt mage35 \n", + "0 162 57 23 10 35 179 0 0 \n", + "1 171 62 19 12 0 183 0 0 \n", + "2 172 58 31 16 25 185 0 1 \n", + "3 174 68 26 14 25 189 0 0 \n", + "4 175 66 30 12 0 184 0 0 " + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Change working directorty to import data for analysis.\n", + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Birthweight_reduced_kg.csv\"\n", + "#Import data .csv file into pandas dataframe.\n", + "df = pd.read_csv(url)\n", + "\n", + "# Output data frame for evaluation\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exploratory analysis and daata visualisation" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the numner of smokers vs non smokers.\n", + "sns.countplot(x=\"smoker\", data=df);" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the numner of low birth weight vs non low-birthweight births.\n", + "sns.countplot(x=\"lowbwt\", data=df);" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the count of low birthweight vs non births vs non low-birthweight births split by smoking status.\n", + "sns.countplot(x=\"lowbwt\", hue=\"smoker\", data=df).set_yticks(list(range(0, 21)));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "\\\\y_n &\\sim Bernoulli(logistic(\\alpha + \\beta x))\n", + "\\\\\n", + "\\alpha &\\sim Normal(0, 1.5)\n", + "\\\\\n", + "\\beta &\\sim Normal(0,0.3)\n", + "\\end{align*}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3 - Specifying priors\n", + "\n", + "Of course the analysis below is a Bayesian data analysis and therefore we need to specify priors. The diffculty of specifying priors in the logistic regression case extends beyond expressing our ignoranne of model parameters in terms of probabilities, because priors withinin a logistic regression are expressed in terms of log-odds. The difficulty of this arises as the relationship between log-odds and probabilities is non-linear (see, plots above). Therefore, using prior predictive checks is a good safeguard from specfiying ridiculous priors and can show why the ones selected above have been selected essentially for being reasonble in the face of this non-linearity. \n", + "\n", + "The prior predcitive checks within this analyis will be conducted using Stan instead of directly codin git up using python." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "logisticRegression = \"\"\"\n", + "\n", + "data {\n", + "\n", + " int N;\n", + " int K;\n", + " matrix[N,K] x;\n", + " int y[N];\n", + " \n", + " // logically evaluates below to determine if it running Prior predictive checks.\n", + " int onlyprior; \n", + " \n", + " // values for setting priors in the model block\n", + " real intercept_mu;\n", + " real intercept_sd;\n", + " \n", + " real slope_mu;\n", + " real slope_sd;\n", + " \n", + "}\n", + "\n", + "parameters {\n", + "// First beta parameter is the alpha parameter of the model\n", + "// This format is simple shorter and allows vectorisation\n", + " vector[K] beta;\n", + "}\n", + "\n", + "model {\n", + "// Priors\n", + "beta[1] ~ normal(intercept_mu, intercept_sd);\n", + "\n", + "\n", + "// Set up to genralise to greater number of predictors\n", + "for (i in 2:K){\n", + "beta[i] ~ normal(slope_mu, slope_sd);\n", + "}\n", + "\n", + "// Likelihood\n", + " if(!onlyprior)\n", + " y ~ bernoulli_logit(x * beta);\n", + "}\n", + "\n", + "generated quantities{\n", + "\n", + "//Vectorised prior/posterior checks\n", + "int yrep[N] = bernoulli_logit_rng(x * beta);\n", + "\n", + "// Ouput the converted log-odds of the estimated parameters to the probability scale (0,1) \n", + "// using the inverse logit function\n", + "vector[K] P_beta = inv_logit(beta);\n", + "\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_a99eac55034565df8ec4565b5c717439 NOW.\n" + ] + } + ], + "source": [ + "sm = ps.StanModel(model_code = logisticRegression)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [], + "source": [ + "x = pt.dmatrix(\" ~ smoker\", data = df)\n", + "x = np.asarray(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "Prior_data = {'N': len(df),\n", + " 'x': x,\n", + " 'K': 2,\n", + " 'y': df[\"lowbwt\"].values,\n", + " # set to 1 to run prior predcitive check\n", + " 'onlyprior': 1,\n", + " 'intercept_mu': 0,\n", + " 'intercept_sd': 1.5,\n", + " 'slope_mu': 0,\n", + " 'slope_sd': .3}" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "prior_PC = sm.sampling(data = Prior_data, iter = 2000, chains = 4, seed = 1, warmup = 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python print stament it is easier to extract the results of the prior predictive\n", + "# checks into a panda data frame.\n", + "summary = prior_PC.summary()\n", + "prior_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Plot the probablities generated by the prior predcitive checks\n", + "plt.subplot(1, 2, 1);\n", + "sns.distplot(prior_PC['P_beta[1]']);\n", + "plt.subplot(1, 2, 2);\n", + "sns.distplot(prior_PC['P_beta[2]']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can now see the priors that were selected result in relatively uniform prior distributions over probability scale of (0,1) for the intercept term and general balcance between potential for no effect at .5 probability and potetnial for larger effects on the slope terms before seeing the data.\n", + "\n", + "An activity left to the curious reader if they are interested is to test the code above by altering the priors parameter values to test their impacts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fitting the model\n", + "\n", + "Now that reasonable priors have been specified above it is time to fit the model." + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "data = {'N': len(df),\n", + " 'x': x,\n", + " 'K': x.shape[1],\n", + " 'y': df[\"lowbwt\"].values,\n", + " # set to 1 to run prior predcitive check\n", + " 'onlyprior': 0,\n", + " 'intercept_mu': 0,\n", + " 'intercept_sd': 1.5,\n", + " 'slope_mu': 0,\n", + " 'slope_sd': .3}" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [], + "source": [ + "fit = sm.sampling(data = data, iter = 2000, chains = 4, seed = 1, warmup = 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python print stament it is easier to extract the results of the prior predictive\n", + "# checks into a panda data frame.\n", + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
meanse_meansd2.5%25%50%75%97.5%n_effRhat
yrep[41]0.1652500.0060440.3714520.0000000.0000000.0000000.0000001.0000003777.6552110.999902
yrep[42]0.1712500.0060760.3767740.0000000.0000000.0000000.0000001.0000003844.9353790.999551
P_beta[1]0.1714490.0011100.0596630.0759040.1286960.1649330.2060950.3038282887.6501720.999149
P_beta[2]0.4576200.0012950.0687920.3227260.4111760.4574250.5050070.5906792822.9651310.999473
lp__-18.7049970.0235981.047727-21.550484-19.098093-18.402748-17.961831-17.7089241971.3389971.001833
\n", + "
" + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% \\\n", + "yrep[41] 0.165250 0.006044 0.371452 0.000000 0.000000 0.000000 \n", + "yrep[42] 0.171250 0.006076 0.376774 0.000000 0.000000 0.000000 \n", + "P_beta[1] 0.171449 0.001110 0.059663 0.075904 0.128696 0.164933 \n", + "P_beta[2] 0.457620 0.001295 0.068792 0.322726 0.411176 0.457425 \n", + "lp__ -18.704997 0.023598 1.047727 -21.550484 -19.098093 -18.402748 \n", + "\n", + " 75% 97.5% n_eff Rhat \n", + "yrep[41] 0.000000 1.000000 3777.655211 0.999902 \n", + "yrep[42] 0.000000 1.000000 3844.935379 0.999551 \n", + "P_beta[1] 0.206095 0.303828 2887.650172 0.999149 \n", + "P_beta[2] 0.505007 0.590679 2822.965131 0.999473 \n", + "lp__ -17.961831 -17.708924 1971.338997 1.001833 " + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Outputs from the fitted model.\n", + "fit_df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(42, 4000)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit['yrep'].T.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit-visualisations - Bayesian logisitc regression\n", + "\n", + "## Posterior distribution plots" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(fit,var_names = ('P_beta'));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior autocorrelation plots" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_autocorr(fit,var_names = ('beta'));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Posterior trace plots " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(fit,var_names = ('beta'));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 5 - Posterior predictive checks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the Bayesian logistic regression\n", + "\n", + "As Kruscke correctly points out there is not standard formula or presentation method for results in journal articles like the APA guide for reporting frequentist analysis using the Bayesian framework. It is likely there never will be, because as McElreath (2020) explains, Bayesian data analysis is more like a engineering approach to the problem and the resulting model that is fit will be analysis specific. In addition, as Gabry et al, (2019) argue visualisations maybe even more key for decribing and analysis (show dont tell); as such the all the visualisations used above would likely be included with any write up. Anyway, the write up below generally follows the advice of Kruscke (2015) chapter 25. In any application though it comes down to the problem to be described and the audience that needs to be convinced.


\n", + "\n", + "

Write up


\n", + "\n", + "The data on newborn weights were analysed in order to estimate the probability of infants being underweight (dichotomised as $y < 6 lbs = $ underweight, by fitting the logistic regression model defined above using mother smoking status during pregnancy as as predictor. As the model is a logistic regression regression the likelihood used was the bernoulli lielihood using the logit link function, with $\\alpha$ and $\\beta$ parameters. The prior slected for the $\\alpha$ parameter was $Normal(0, 1.5)$ and for the $\\beta$ parameter $Normal(0, 0.3$. These priors were determined by conducting prior predictive checks and observing that model parameters in the case of $\\alpha$ parameter has uniform prior probaiblity for the probability scores before seeing the data. In the case of $\\beta$ parameter prior probability is given for a range of probability scores estimates but not overly extreme probabilites such as 0/100%.\n", + " \n", + "Four MCMC chains were ran to acquire 2000 samples with the first 1000 samples being for warm up. Resulting in 4000 saved samples for use in condcuting our inferences Convergence of the MCMC chains was examined using autocorrelation and traceplots (in a paper referncing appropriate figures here will be of value). Both sets of plots showed no issues of autocorrelation or lack of mixing for the chains.\n", + "\n", + "Using dummy coding for the contrasts of interest the model parameter posteriors showed that the most credible value for the $\\alpha$ parameter (the average probabilty for birthweight to be < 6lbs for non-smoking mothers) to be $\\mu$ = .16 with a 95% CrI [.06, .26]). The $\\beta$ parameter (under dummy coding the difference in probaability between non-smoker & smokers) \n", + "most credible value to be $\\mu$ = .53 with a 95% CrI [.40, .66]). For this analysis, the crucial parameter of interest is the $\\beta$ parameter for the contrast between non-smokers and smokers mother and predicting probability of low infant birth weight. As the credible values for $beta$ showed the 95% CrI [.40, .66] was consistent with the data and thus we have can predict ubased on this estimation that smoking during pregnancy increases the probabilty of an infants birthweight being $< 6lbs$ between .40 and .66 percent with a 95% probability.\n", + "\n", + "Finally posterior predcitive checks showed that the orignal data could be reasonble generated by the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multiple logistic regression\n", + "\n", + "The simple example above was equivalnet to a comparison of proportions, but logistic regression like other types of regression can be expanded to model multiple predictors at once. This will be demonstrated in the following section below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adding predcitor variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the example below ad additonal predcitor varaible will be added to the analysis to estimate the $p(\\theta|y = 1)$. The predictor that will be added to the analysis is mothers age a contious predcictor that has been found to seemingly affect many postnatal outocomes including birthweight (Richards, Hardy, Kuh, & Wadsworth, 2002)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting multiple logistic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the design matrix for the logistic regression\n", + "x_2 = pt.dmatrix(\" ~ smoker + mage \", data = df)\n", + "x_2 = np.asarray(x_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "data_2 = {'N': len(df),\n", + " 'x': x_2,\n", + " 'K': x_2.shape[1],\n", + " 'y': df[\"lowbwt\"].values,\n", + " # set to 1 to run prior predcitive check\n", + " 'onlyprior': 0,\n", + " 'intercept_mu': 0,\n", + " 'intercept_sd': 1.5,\n", + " 'slope_mu': 0,\n", + " 'slope_sd': .3}" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "# Fit the multiple logsitic regression to the data.\n", + "fit_2 = sm.sampling(data = data_2, iter = 100000, chains = 4, seed = 1, warmup = 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python print stament it is easier to extract the results of the prior predictive\n", + "# checks into a panda data frame.\n", + "summary = fit_2.summary()\n", + "fit2_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
meanse_meansd2.5%25%50%75%97.5%n_effRhat
beta[1]-0.2458450.0030621.224551-2.627721-1.073698-0.2531440.5778792.170942159980.1571881.000031
beta[2]0.1576840.0006190.285639-0.401304-0.0351250.1575520.3505810.717703213017.4541370.999997
beta[3]-0.0686840.0001310.051943-0.173891-0.102998-0.067395-0.0330670.029473158256.9619971.000026
yrep[1]0.1755300.0006050.3804210.0000000.0000000.0000000.0000001.000000395148.7521360.999998
yrep[2]0.1857120.0006250.3888750.0000000.0000000.0000000.0000001.000000387031.1911780.999999
\n", + "
" + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% 75% \\\n", + "beta[1] -0.245845 0.003062 1.224551 -2.627721 -1.073698 -0.253144 0.577879 \n", + "beta[2] 0.157684 0.000619 0.285639 -0.401304 -0.035125 0.157552 0.350581 \n", + "beta[3] -0.068684 0.000131 0.051943 -0.173891 -0.102998 -0.067395 -0.033067 \n", + "yrep[1] 0.175530 0.000605 0.380421 0.000000 0.000000 0.000000 0.000000 \n", + "yrep[2] 0.185712 0.000625 0.388875 0.000000 0.000000 0.000000 0.000000 \n", + "\n", + " 97.5% n_eff Rhat \n", + "beta[1] 2.170942 159980.157188 1.000031 \n", + "beta[2] 0.717703 213017.454137 0.999997 \n", + "beta[3] 0.029473 158256.961997 1.000026 \n", + "yrep[1] 1.000000 395148.752136 0.999998 \n", + "yrep[2] 1.000000 387031.191178 0.999999 " + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit2_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(fit_2,var_names = ('P_beta'));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389-402.\n", + "\n", + "Gelman, A., J, Hill., A, Vehtari (2020). Regression and other stories. New york, NY: Cambridge university Press.\n", + " \n", + "Kruschke, J. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS and Stan. Oxford, England: Academic Press. \n", + " \n", + "McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. Boca Raton: CRC Press.\n", + "\n", + "Richards, M., Hardy, R., Kuh, D., & Wadsworth, M. E. (2002). Birthweight, postnatal growth and cognitive function in a national UK birth cohort. International Journal of Epidemiology, 31(2), 342-348." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "341.333px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian Factor analysis.ipynb b/wip/Bayesian Factor analysis.ipynb new file mode 100644 index 0000000..d2604a2 --- /dev/null +++ b/wip/Bayesian Factor analysis.ipynb @@ -0,0 +1,289 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import analysis packages\n", + "%matplotlib inline\n", + "import pystan as ps\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as ss" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent ofFactor analyis\n", + "\n", + "## Classic Factor analysis\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following the quick description of the classic one sample t-test above its important to keep in mind that Bayesian analysis inference are all derived from the applciation of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to Factor analysis it is fundamentally different, because it uses fully probabilistic modelling and the infernce is not based on sampling distributions\n", + " \n", + " For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. In the case of scientific research publication is the goal, as such the priors must be accepted by a skeptical audience. Much of this can be achieved using prior predcitve checks to acsetain os the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then interprete and the posterior\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Study/data description" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Call github repository\n", + "url = 'https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Birthweight_reduced_kg.csv'\n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clean the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4 - Use Bayes rule" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stan model of Bayesian Factor analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "factorAnalysis = '''\n", + "\n", + "data {\n", + " int N; // number of observations\n", + " int P; // number of \n", + " matrix[N,P] Y; // data matrix of order [N,P]\n", + " int D; // number of latent dimensions \n", + "}\n", + "transformed data {\n", + " int M;\n", + " vector[P] mu;\n", + " M <- D*(P-D)+ D*(D-1)/2; // number of non-zero loadings\n", + " mu <- rep_vector(0.0,P);\n", + "}\n", + "parameters { \n", + " vector[M] L_t; // lower diagonal elements of L\n", + " vector[D] L_d; // lower diagonal elements of L\n", + " vector[P] psi; // vector of variances\n", + " real mu_psi;\n", + " real sigma_psi;\n", + " real mu_lt;\n", + " real sigma_lt;\n", + "}\n", + "transformed parameters{\n", + " cholesky_factor_cov[P,D] L; //lower triangular factor loadings Matrix \n", + " cov_matrix[P] Q; //Covariance mat\n", + "{\n", + " int idx1;\n", + " int idx2;\n", + " real zero; \n", + " zero <- 0;\n", + " for(i in 1:P){\n", + " for(j in (i+1):D){\n", + " idx1 <- idx1 + 1;\n", + " L[i,j] <- zero; //constrain the upper triangular elements to zero \n", + " }\n", + " }\n", + " for (j in 1:D) {\n", + " L[j,j] <- L_d[j];\n", + " for (i in (j+1):P) {\n", + " idx2 <- idx2 + 1;\n", + " L[i,j] <- L_t[idx2];\n", + " } \n", + " }\n", + "} \n", + "Q<-L*L'+diag_matrix(psi); \n", + "}\n", + "model {\n", + "// the hyperpriors \n", + " mu_psi ~ cauchy(0, 1);\n", + " sigma_psi ~ cauchy(0,1);\n", + " mu_lt ~ cauchy(0, 1);\n", + " sigma_lt ~ cauchy(0,1);\n", + "// the priors \n", + " L_d ~ cauchy(0,3);\n", + " L_t ~ cauchy(mu_lt,sigma_lt);\n", + " psi ~ cauchy(mu_psi,sigma_psi);\n", + "//The likelihood\n", + "for( j in 1:N)\n", + " Y[j] ~ multi_normal(mu,Q); \n", + "}\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_b416f8d5e3e0c0d71640c639266fc9df NOW.\n" + ] + } + ], + "source": [ + "sm = ps.StanModel(model_code = factorAnalysis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian Multiple regression .ipynb b/wip/Bayesian Multiple regression .ipynb new file mode 100644 index 0000000..efc8146 --- /dev/null +++ b/wip/Bayesian Multiple regression .ipynb @@ -0,0 +1,902 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Import relevant packages for analysis below.\n", + "%matplotlib inline\n", + "import pystan as ps\n", + "import patsy as pt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of multiple regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data above is taken from the multiple regression tutorial from Trinity open stats lab https://sites.trinity.edu/osl/data-sets-and-activities/regression-activities, for the original article from Atir, S., Rosenzweig, E., & Dunning, D. (2015). When knowledge knows no bounds: Self-perceived expertise predicts claims of impossible knowledge. Psychological Science, 26, 1295-1303." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The classic simple regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following the quick description of the classic Z-test above its important to keep in mind that Bayesian analysis inference are all derived from the application of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to the classic simple regression, it is fundamentally different, becuase its uses fully probabilistic modelling and the infernce is not based on sampling distributions\n", + " \n", + " For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. In the case of scientific research publication is the goal, as such the priors must be accepted by a skeptical audience. Much of this can be achieved using prior predcitve checks to acsetain os the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then interprete and the posterior\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Study Description\n", + "\n", + "The background for the data analysed below, is that experts play an ever increasing role in a ever increasing complex society with focused specialisation of said experts. A supposed phenomenen is that experts may be over convinced of their capabilities and no more than they actually do and are vulnerable to illsuionary thoughts. All resulting in overclaiming.(e.g. claiming impossible things.\n", + "\n", + "Atir, Rosenzweig and Dunning (2015) study took 202 people and exposed them to a overclaiming task. Partipants completed a overclaiming task and self percieved knowledge questionaire." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idorder_of_tasksself_perceived_knowledgeoverclaiming_proportionaccuracyFINRA_score
0115.50.4444440.2500004
1714.50.5555560.1944444
21013.50.1666670.3472225
31216.00.722222-0.0555564
41412.50.3888890.1666673
\n", + "
" + ], + "text/plain": [ + " id order_of_tasks self_perceived_knowledge overclaiming_proportion \\\n", + "0 1 1 5.5 0.444444 \n", + "1 7 1 4.5 0.555556 \n", + "2 10 1 3.5 0.166667 \n", + "3 12 1 6.0 0.722222 \n", + "4 14 1 2.5 0.388889 \n", + "\n", + " accuracy FINRA_score \n", + "0 0.250000 4 \n", + "1 0.194444 4 \n", + "2 0.347222 5 \n", + "3 -0.055556 4 \n", + "4 0.166667 3 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Atir%20Rosenzweig%20Dunning%202015.csv\"\n", + "\n", + "#Read in csv of the data from Atir, S., Rosenzweig, E., & Dunning, D. (2015).\n", + "df = pd.read_csv(url)\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plots for Exploratory Data analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Histogram of the dependent and independent variables\n", + "\n", + "# Overclaiming_proportion\n", + "sns.distplot(df[\"overclaiming_proportion\"])\n", + "plt.show()\n", + "\n", + "# Self percieved knowledge\n", + "sns.distplot(df[\"self_perceived_knowledge\"])\n", + "plt.show()\n", + "\n", + "# Accuracy\n", + "sns.distplot(df[\"accuracy\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Scatterplots\n", + "sns.scatterplot(x = \"self_perceived_knowledge\" , y = \"overclaiming_proportion\" , data=df).set(xlabel='Self Percieved Knowledge', ylabel='Overclaiming')\n", + "\n", + "plt.show()\n", + "\n", + "sns.scatterplot(x = \"accuracy\" , y = \"overclaiming_proportion\" , data=df).set(xlabel='Accuracy', ylabel='Overclaiming')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model below is an example of a model that uses data informed default priors that are weakly infomaative and provide regularised inferences (as sepcified by https://cran.r-project.org/web/packages/rstanarm/vignettes/priors.html#default-priors-and-scale-adjustments). The choice to use these defualts here is that it demonstrates this analystical option, as well as, demonstrating my own ignorance for how to set priors for this analysis problem, whilst at the same time attempting to avoid the the use of uniform flat priors.\n", + "\n", + "This use of defaults makes prior predictive checks mute as priors here are not aiming at placing resonable prior probabiltiy to the model parameters to the DGP outcome space, but they simple reguralise the inference. Finally it is important to point out despite the use of priors here the nature of them being defaults informed by data means the analysis is no longer truely Bayesian (to fullest extent) and falls more towards an empirical Bayesian analysis as the priors are informed by the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "y_i &\\sim Normal(\\mu_i, \\sigma) \n", + "\\\\ \\mu_i &= \\beta_0 + \\beta_1(x_1-\\bar{x}_{1}) + \\beta_2(x_2-\\bar{x}_2)\n", + "\\\\ \\beta_0 &\\sim normal(m_y, 2.5 \\cdot s_y)\n", + "\\\\ \\beta_1 &\\sim normal(0, 2.5\\cdot s_y/s_x{_1})\n", + "\\\\ \\beta_2 &\\sim normal(0, 2.5\\cdot s_y/s_x{_2})\n", + "\\\\ \\sigma &\\sim exponential(1/s_y) \n", + "\\end{align*} " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " As the model above shows the priors are informedede by the data as the sample means and standard deviations are used to set the hyperparmeters of the priors. What is also showed is that these defaults are based on centering the predictor variables; therefore, the estimated parameter for the intercept is to be interpreted as expectation values of the dependent varaible when all of the predictors are equal to zero, whIch because of the centering is expected value of the predictors.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Stan model for conducting multiple regression\n", + "\n", + "Multiple_Regression = \"\"\"\n", + "data {\n", + " int N; // Sample size\n", + " int K; // Number of predictor variables\n", + " matrix[N, K] x; // Predictor variables (IV) matrix\n", + " vector[N] y; // Vector of Dependent variable (DV) values\n", + " \n", + " // prior settings for beta coeffiecents\n", + " vector[K] mu_PS_beta;\n", + " vector[K] sd_PS_beta;\n", + " \n", + " // prior settings for sigma coeffiecents\n", + " real lamda_PS_sigma;\n", + "}\n", + "\n", + "transformed data{\n", + "// Creating a new design matrix with the Independent variable of Inherence bias\n", + "// centered. This may potentially be simpler to do outside of Stan but it demonstrates\n", + "// some of the options of Stan\n", + "matrix[N, K] x_transformed = x;\n", + "\n", + "for (i in 1:K){\n", + "x_transformed[,i] = x[,i] - mean(x[,i]);\n", + "}\n", + "}\n", + "\n", + "parameters { \n", + " vector[K] beta; // Coefficients for predictors\n", + " real sigma; // Standard deviation\n", + "}\n", + "\n", + "model {\n", + "\n", + "// Default priors\n", + "\n", + "for (i in 1:K){\n", + "beta[i] ~ normal(mu_PS_beta[i],sd_PS_beta[i]);\n", + "}\n", + "\n", + "sigma ~ exponential(lamda_PS_sigma);\n", + "\n", + "// likelihood\n", + " y ~ normal(x_transformed * beta, sigma); \n", + "}\n", + "\n", + "generated quantities {\n", + "\n", + "real yrep[N];\n", + "yrep = normal_rng(x_transformed * beta, sigma);\n", + "\n", + "}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_ab1e064979fe37d6294c0ff24fb0b6aa NOW.\n" + ] + } + ], + "source": [ + "#Code below compiled the Stan mdoel specified above.\n", + "sm = ps.StanModel(model_code = Multiple_Regression)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "#Generate design matrix for the regression analysis for use in the stan model above\n", + "x = pt.dmatrix(\" ~ self_perceived_knowledge + accuracy \", data = df)\n", + "x = np.asarray(x)\n", + "\n", + "# Generating a python dictionary for passing to the stan model specifiesd above.\n", + "data = {\n", + " # Specifies the number of data points.\n", + " 'N': len(df), \n", + " \n", + " # Specifies the number of columns in the array (matrix) of the predictor values generated above.\n", + " 'K': x.shape[1],\n", + " \n", + " # Specifies the predictor (IV) values for use in the Stan model above.\n", + " 'x': x,\n", + " \n", + " # Dv values for the regression model \n", + " 'y': df[\"overclaiming_proportion\"],\n", + " \n", + " # Data informed priors for the beta coefficents\n", + " 'mu_PS_beta': [np.mean(df[\"overclaiming_proportion\"]), 0, 0],\n", + " \n", + " 'sd_PS_beta': [2.5 * np.std(df[\"overclaiming_proportion\"]), \n", + " 2.5 * np.std(df[\"overclaiming_proportion\"])/np.std(x[0]), \n", + " 2.5 * np.std(df[\"overclaiming_proportion\"])/np.std(x[1])],\n", + " \n", + " # Data informed priors for the sigma coefficents\n", + " 'lamda_PS_sigma': 1/np.std(df[\"overclaiming_proportion\"])\n", + " \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "#Fiting model to the complied stan model above with 4 chains \n", + "fit = sm.sampling(data= data, iter=2000, chains=4 , seed= 302675)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python print statement it is easier to extract the results and to use panda data frame for result presentation\n", + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
meanse_meansd2.5%25%50%75%97.5%n_effRhat
beta[1]0.2928880.0096570.575321-0.821182-0.0949500.2953990.6840491.4306383549.4574861.000346
beta[2]0.0987110.0003340.0201180.0591650.0849080.0986090.1122470.1383453630.5034120.999659
beta[3]-0.6675680.0017020.107301-0.874201-0.737847-0.667735-0.597308-0.4546553976.1363341.001443
sigma0.3355310.0002580.0166550.3052330.3239220.3349000.3463360.3698554177.8011700.999517
yrep[1]0.1321210.0053030.334346-0.527250-0.0913210.1311050.3548620.7996733975.5941920.999471
\n", + "
" + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% 75% \\\n", + "beta[1] 0.292888 0.009657 0.575321 -0.821182 -0.094950 0.295399 0.684049 \n", + "beta[2] 0.098711 0.000334 0.020118 0.059165 0.084908 0.098609 0.112247 \n", + "beta[3] -0.667568 0.001702 0.107301 -0.874201 -0.737847 -0.667735 -0.597308 \n", + "sigma 0.335531 0.000258 0.016655 0.305233 0.323922 0.334900 0.346336 \n", + "yrep[1] 0.132121 0.005303 0.334346 -0.527250 -0.091321 0.131105 0.354862 \n", + "\n", + " 97.5% n_eff Rhat \n", + "beta[1] 1.430638 3549.457486 1.000346 \n", + "beta[2] 0.138345 3630.503412 0.999659 \n", + "beta[3] -0.454655 3976.136334 1.001443 \n", + "sigma 0.369855 4177.801170 0.999517 \n", + "yrep[1] 0.799673 3975.594192 0.999471 " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit-visualisations - Bayesian estimation of multiple regression\n", + "\n", + "## Posterior distribution plots" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(fit, var_names=(\"beta\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior autocorrelation plots" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_autocorr(fit,var_names=(\"beta\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Posterior trace plots " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(fit, var_names=(\"beta\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 5 - Posterior predictive checks" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "#First take a random sample of the posterior parameter estimates for plotting\n", + "posterior_samples = pd.DataFrame(fit.extract(pars = ['beta[1]','beta[2]', 'beta[3]', 'sigma']))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "#Convert pystan fit object to IO for Arviz functions.\n", + "data = az.from_pystan(\n", + " posterior=fit,\n", + " posterior_predictive='yrep',\n", + " observed_data=[\"y\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot posterior simulated data sets for posterior predictive check\n", + "az.plot_ppc(data, data_pairs = {\"y\" : \"yrep\"}, num_pp_samples= 100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the Bayesian simple regression\n", + "\n", + "As Kruschke correctly points out there is not standard formula or presentation method for results in journal article like the APA guide for reporting frequentist analysis. It is likely there never will be, because as McElreath (2020) explains, Bayesian data analysis is more of an engineering approach to the problem and the resulting model that is fit will likely be analysis specific. In addition, as Gabr}y et al, (2019) argue visualisations maybe even more key so the all the visualtions above would have to be included with any write up. Anyway the write up below generally follows the advice of Krushcke (2015) chapter 25. In any application though it comes down to the problem to be described and the audience that needs to be convinced.


\n", + "\n", + "

Write up


\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "Atir, S., Rosenzweig, E., & Dunning, D. (2015). When knowledge knows no bounds: Self-perceived expertise predicts claims of impossible knowledge. Psychological Science, 26, 1295-1303.\n", + "\n", + "Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389-402.\n", + " \n", + "Kruschke, J. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS and Stan. Oxford, England: Academic Press. \n", + " \n", + "McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan.Boca Raton: CRC Press." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "341.333px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian Poisson estimation.ipynb b/wip/Bayesian Poisson estimation.ipynb new file mode 100644 index 0000000..fe844e8 --- /dev/null +++ b/wip/Bayesian Poisson estimation.ipynb @@ -0,0 +1,1304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import data analysis and visualisation packages\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pystan as ps\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import scipy.stats as stats\n", + "import arviz as az" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of Poisson regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following notebook will provide an example of how to conduct a Bayesian poisson regression " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classic Poison regression\n", + "\n", + "Before proceeding it will be useful to quickly decribe poisson regression under the classical statistical framework. Poisson regression is a special case of generalised linear model within the general regression analysis framework. Its primary application is to model count data (dependent variables that can only take positive interger values). \n", + "\n", + "Poison regression is a generlised linear just as simple linear regression is a generalised linear model. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bayesian inference\n", + " Following the quick description of the classic Poisson regression above its important to keep in mind that Bayesian inference is derived from the application of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to Poisson regression it is fundamentally different, because it uses fully probabilistic modelling and the inferences are not based on sampling distributions.\n", + " \n", + " For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for the question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. If a scientific research publication is the goal, the priors must be accepted by a skeptical audience. This can be achieved by using prior predcitve checks to ascertain if the priors are reasonable.\n", + "\n", + "4. Using Bayes rule, estimate the posterior for the model parameters using the likelihood and priors. Then use the posterior to conduct your inference.\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Additonal information about the Poisson distribution\n", + "\n", + " The poisson distrubtuion takes the form of the $P(\\lambda) = \\frac{e^{-\\lambda}\\lambda^x}{x!}$ where $ x = \\mathbb{Z^+}$\n", + "\n", + "It is most important to identify that there is only one parameter associated with the Poisson distribution $\\lambda$\n", + "and this parameter is both the mean and varaince of the distribution and therefore are equal to each other. What is of critical importance though is that the value of $\\lambda$ gives the $\\mathbf{E}(x)$ number of counts of the random variable." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD4CAYAAADlwTGnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3de7wdZX3v8c8vV24hkAsYCTFgAhjRpiWgLYItHCxYBfoSFcpROC9ejbSlp+dQW6GnXurBI/S0pq1aBbkYUW6FqvEQSJUAFQTMDgkkIUAuhNyTnezck51k7/07f8ysnVlrz9pr1n2tme/79dqvrD3zzKxnsmfm98zzPPM85u6IiEj2DGl2BkREpDkUAEREMkoBQEQkoxQAREQySgFARCSjhjU7A+UYN26cT548udnZkJRauHDhNncf3+jv1Xkt9Vbs3G6rADB58mQ6OjqanQ1JKTN7uxnfq/Na6q3Yua0qIBGRjFIAEBHJKAUAEZGMUgAQEckoBQCRGGZ2qZm9YWYrzeyWmPUjzezhcP1LZjY5XD7czGab2RIzW25mtzY67yJJKQCIFDCzocC3gcuAacA1ZjatINkNwA53nwLMAu4Il38SGOnu7wPOAT6XCw4irSZRAEhQGrrQzF42sx4zuyqy/PfMbHHkp9vMrgzXfd/M3oqsm167wxKpynnASndf7e6HgIeAKwrSXAHMDj8/ClxsZgY4cKyZDQOOBg4BuxuTbZHylAwACUtDa4HrgQeiC939aXef7u7TgYuA/cB/RJL8VW69uy+u/DCSe+aNrazr2o+78+jC9XQf7m3E10p7OQVYF/l9fbgsNo279wC7gLEEwWAfsInguvgHd+8q/AIzm2lmHWbW0dnZWfsjSJmnlm9h064D9PU5j3Ss43BvX7OzlApJXgTrLw0BmFmuNPRaLoG7rwnXDfZXuQp4wt33V5zbGrj+vgWMGDaEO//rOXz+317htY27+dLHC+OZZJzFLCucOKNYmvOAXuCdwInAL83sF7nrpz+h+13AXQAzZszQpBwl3DC7g5NGjeSWy87irx99lS27uvnzi6c2O1ttL0kVUJLSUBJXAw8WLPuamb1qZrPMbGTcRvUoKR3q6WN392EAOvcerMk+JVXWA6dGfp8IbCyWJqzuGQ10AX8EPOnuh919K/A8MKPuOc6ArXsOsnN/cN1u33eoyblJhyQBIElpaPAdmE0A3gfMiyy+FTgLOBcYA3whblt3v8vdZ7j7jPHjGz5Mi2TTAmCqmZ1mZiMICi9zCtLMAa4LP18FzPdger21wEUWOBb4IPB6g/ItUpYkASBJaaiUTwE/dvfDuQXuvskDB4H7CB6dRZourNO/iaDAshx4xN2XmdlXzezyMNk9wFgzWwncDOQ6R3wbOA5YShBI7nP3Vxt6ACIJJWkD6C8NARsISkN/VOb3XENQ4u9nZhPcfVPYc+JKggtGpCW4+1xgbsGyL0U+dxN0+Szcbm/ccpFWVPIJIElpyMzONbP1BCf+nWa2LLd92Af6VODZgl3/yMyWAEuAccBt1R+OiIgklWg46ASloQUEVUNx264hptHY3S8qJ6MiIlJbmX8TOGi3E5FW8/zKbTy/clvR9f+xbDMX/P18vRNQhcwGgKDpQURa1bV3v8S1d7+Utyx62f7tT5ayrusAXeoSWrHMBgARkaxTABARySgFABGRjFIAEBHJKAUAEZGMynwAUCdQkfYT7b6tntyVy2wAUCdQkfYTvW7Vk7t6mQ0AIiJZpwAgIpJRCgAiIhmlACAiklEKACIiGaUAIBLDzC41szfMbKWZ3RKzfqSZPRyufymc9wIzu9bMFkd++sxseqPzn3ae99mZ/as1fP2J5U3LT7tSAFAfYilgZkMJpna8DJgGXGNm0wqS3QDscPcpwCzgDgB3/5G7T3f36cBngDXuvrhxuW9vO/YdYuHbO4quj47ia5FOoV+es4w7n11d17ylUWYDgPoQyyDOA1a6+2p3PwQ8BFxRkOYKYHb4+VHgYhs4xvg1wIN1zWnKfOrOF/jEd37V7GxkRmYDgMggTgHWRX5fz8BZ7frThNOm7gLGFqT5NEUCgJnNNLMOM+vo7OysSabTYMXWvc3OQqYoAIgMFPd8WFhZOGgaM/sAsN/dl8Z9gbvf5e4z3H3G+PHjK8+pSBUSBYAEDWIXmtnLZtZjZlcVrOuNNIjNiSw/LWw8WxE2po2o/nBEamI9cGrk94nAxmJpzGwYMBroiqy/GlX/SIsrGQASNoitBa4HHojZxYFco5i7Xx5Zfgcwy92nAjsIGtVEWsECYGpYSBlBcDOfU5BmDnBd+PkqYL6HI5SZ2RDgkwRtByItK8kTQMkGMXdf4+6vAolmZw4byy4iaDyDoDHtysS5FqmjsE7/JmAesBx4xN2XmdlXzSxXiLkHGGtmK4GbgeiT8YXAendXt5Q6iY4AqtFAKzcsQZq4BrEPlPEdR5lZB9AD3O7uPyFoLNsZXmi5fRY2sgFBYxkwE2DSpEllfG0yrn6gEsPd5wJzC5Z9KfK5m6CUH7ftM8AH65m/rIr2s1JPvuolCQBJGsQGM8ndN5rZ6cB8M1sC7E66T3e/C7gLYMaMGTW7W5sGhBaRjEtSBZSkQawod98Y/rsaeAb4TWAbcELYeFb2PkVEpHpJAkCSBrFYZnaimY0MP48DzgdeCxvLniZoPIOgMe2n5Wa+Vrr2HeLBX69t1teLiDRFyQCQpEHMzM41s/UEdaJ3mtmycPP3AB1m9grBDf92d38tXPcF4OawEW0sQaNaU/z3Bxdx678vYeXWPc3KgohIwyVpA0jSILaAoBqncLtfAe8rss/VBD2Mmm7b3oMAHOpRg7BIu9FVWzm9CSwibUe992oj8wFAfYhF2ocV+SyVyWwAUB9iEcm6zAYAEZGsy0QA+PMHF3H1XS+UTKd6RRHJkkwEgJ+9spEXV3cVXT9wHg8RaVfn3z6ff5j3RrOz0RYyEQBEJL28oCfHhp0H+NbTK5uUm/aiACAibcddT+61oAAgIk3V09vHz17ZOKAkH0s3/ZpK9CZwmmlccZHm+t4v3+KOJ1+nTxdgw2X2CUAvlIi0hi27uwHYvvdQk3OSPZkNACKDSTAP9shwLuuV4dzWkyPr3m9mL5jZMjNbYmZHNTLvIkkpAIgUSDgP9g3ADnefAswimOM6N0H8D4Eb3f29wO8ChxuUdZGyKACIDFRyHuzw99nh50eBi8O5rj8CvOrurwC4+3Z3721QvjNJTQeVUwAQGShuHuzCOav704RzZuwimNfiDMDNbJ6ZvWxmfx33BWY208w6zKyjs7Oz5gfQjsq5j+ueXxsKACIDJZkHu1iaYcCHgGvDf//QzC4ekND9Lnef4e4zxo8fX21+21o5PTvVYaO2Mh8AHFfXYimUZB7s/jRhvf9ooCtc/qy7b3P3/QQTKf1W3XMsUoHMBgDd9GUQSebBnkMwlzUEc1vPD+e6nge838yOCQPDh4HXEGlBmX8RTKSQu/eYWW4e7KHAvbl5sIEOd59DMIf1/eGc1l0EQQJ332Fm3yAIIg7MdffHm3IgIiUkCgBmdinwzwQXw93ufnvB+guBfwLeD1zt7o+Gy6cD3wGOB3qBr7n7w+G67xOUjnaFu7ne3RdXe0AitZBgHuxu4JNFtv0hQVdQqVCiYSGkaiUDQKRP9CUE9ZsLzGyOu0cfa9cC1wOfL9h8P/BZd19hZu8EFprZPHffGa7/q1ywEJFs002/8ZI8AfT3iQYws1yf6P4A4O5rwnV90Q3d/c3I541mthUYD+ykBen8E2k8q6BvTzAaaB0ykzFJGoGT9IkuyczOA0YAqyKLv2Zmr5rZLDMbWWS7uveX1okk0h50rdZWkgCQpE/04DswmwDcD/w3d889JdwKnAWcC4wBvhC3bb37S6vULyJZlSQAJOkTXZSZHQ88Dvytu7+YW+7umzxwELiPoKqpgVSUEJFsSxIAkvSJjhWm/zHwA3f/t4J1E8J/DbgSWFpOxkUkvfRk3hglA0A4zkmuT/Ry4JFcn2gzuxzAzM41s/UE3eLuNLNl4eafAi4ErjezxeHP9HDdj8xsCbAEGAfcVtMjE5FMGCxYrNiyh74+RZNiEr0HkKBP9AKCqqHC7Yr2h3b3i8rKaQO4hpgSabh6Ney+tnE3H/2XX/KXl5zBn188tT5f0uYyOxREVCXd0ESkmUqP4bVp1wEAFq1ryV7nLUEBQETahgprtZX5AKBKHxHJqswHABFpDdHGXBXMGiOzAUBvFIpkg8YYKi6zASCOzhORxjjc28cl33iW+a9vqbpWv1jvPRXySlMAQCeKSKN17TvEiq17ueWxJRXvQw3C1VMAEIlhZpea2RtmttLMbolZP9LMHg7Xv2Rmk8Plk83sQOTFx+82Ou9ZoKf12tCMYCIFEs6BcQOww92nmNnVwB3Ap8N1q9x9OlJzelqvLT0BiAzUPweGux8CcnNgRF0BzA4/PwpcHI5rJRWK1uXXsuFWDwvFZT4A6FFSYiSZA6M/TThe1i5gbLjuNDNbZGbPmtkF9c5sO3PqV6pXG0Fpma0CqnqSA0mzJKdHsTSbgEnuvt3MzgF+YmbvdffdeRubzQRmAkyaNKkGWW4vujW3hsw/AYBORhkgyRwY/WnMbBgwGuhy94Puvh3A3RcSzIB3RuEX1HuioyzRU3zlFABEBkoyB8Yc4Lrw81XAfHd3MxsfNiJjZqcDU4HVDcp3piStOlKAKC6zVUAixbh7j5nl5sAYCtybmwMD6HD3OcA9wP1mthLoIggSEMx/8VUz6wF6gRvdvavxR5FuiW7qerQvSQFAJEaCOTC6CSZAKtzuMeCxumcwJfLG/0lwU9c9vbZUBVRgVedefv2WCmwidRW5k8f1nlWtTWPoCaCg7/HF//gsAGtu/4NmZUhEpCEy+wSQV+rQ+zsiqaWnieISBYAE46JcaGYvm1mPmV1VsO46M1sR/lwXWX6OmS0J9/kveotSJIuqvz0X24NuKKWVDACRcVEuA6YB15jZtIJka4HrgQcKth0DfBn4AMHr9V82sxPD1d8heBFmavhzacVHISJtJe4t3XJDgW7w1UvyBFByXBR3X+PurwJ9Bdv+PvBzd+9y9x3Az4FLzWwCcLy7v+DBoB8/AK6s9mBEJBuKzQFQzIadB/i7ny2jt08VQlFJAkCScVHK3faU8HPJfZrZTDPrMLOOzs7OhF9bGZ0aIo0XO6ZGkYux0orimx9ezH3Pr2Hh2zsq20FKJQkA1QybU2zbxPvUK/MiUg13p0+vA8dKEgCSjItS7rbrw8+V7LOm3FWXKNIs9bwvq19JaUkCQJJxUYqZB3zEzE4MG38/Asxz903AHjP7YNj757PATyvIv4i0oVremzXpe+VKBoBwrPPcuCjLgUdy46KY2eUAZnauma0neDX+TjNbFm7bBfxvgiCyAPhqZFyUPwHuBlYSjJj4RE2PrASVDURaSyPu4woW+RK9CZxgXJQF5FfpRNPdC9wbs7wDOLuczIpIClVYGiunikeTw8TL7JvAcVQ4EGmsYpdcqW6eulZrQwFARBquWHm8lm0DKvOXpgCAhgISaRfVVuXowSGfAoCINMz7vjyPT3znV43/YhXyYmU+AKhEIHESDIA40sweDte/ZGaTC9ZPMrO9Zvb5RuW5Hew52FP0bdxyh3c4sp1UKrMBQNU+UkzCARBvAHa4+xRgFnBHwfpZNLhrczty97r30FGDcXGZDQDxdKYIkGAAxPD32eHnR4GLc0Oam9mVBBPBL2tQfttOqS6ctZgeMu4rFAzyKQCg6kEZIMkAiP1pwpcldwFjzexY4AvA3w32BY0c5DCNNHR0bSgAiAyUZLDCYmn+Dpjl7nsH+wINchhPN+rG0pzAIgMlGQAxl2a9mQ0DRgNdBJMfXWVmfw+cAPSZWbe7f6v+2W4/0aiaqHqmgghRaeNyFigAiAzUPwAisIFgAMQ/KkgzB7gOeAG4CpgfTm50QS6BmX0F2Kubf+tQMMiX2iqgNzbv4R/mvVFy8CcNDiWFkgyACNxDUOe/ErgZGNBVVIqLFuSr7ZFXdPKYyLeo11+81D4BfPK7v2J3dw+f+/DpsevVQ0AGk2AAxG6C0W8H28dX6pI5CeimXrXUPgHk5v5MMmKgJo4QkSxKbQAQkdZX+NSdtDBWztN6Xlo95edJfQBQHb9I62nEQ3f0O6LtAd9//i3+9EcL65+BNpDaNgBV64i0h3Ku1Fpc1V/52Ws12Es6pP4JQERE4mU+AKh6UKTdJb9ydY3nSxQAKh0a18yuNbPFkZ8+M5sernsm3Gdu3Um1PLCcOr1cKCI1UKyNrhaDwZWzr6wqGQCqGRrX3X/k7tPdfTrwGWCNuy+ObHdtbr27b63B8RzJd8n1uu2LNEux66+WV2UtXzZLqyRPAFUNjRtxDfBgNZkVkfQqp8eehnSojSQBoOKhcQvSfJqBAeC+sPrnizEBA6h+2Fw9/om0NpXOmydJAKhmaNxgpdkHgP3uvjSy/lp3fx/B4FkXEFQRDdxJpcPm6qQSaXllj+tfZbRQgTBfkgBQztC4FAyNm3M1BaV/d98Q/rsHeICgqqmpdHKINEgDC2iO6ymjiCQBoH9oXDMbQXAzn1OQJjc0LuQPjYuZDSEYNOuhXGIzG2Zm48LPw4GPAUuphxI3dXc9goq0mnLq+IsW3HRdl1TyTWB37zGz3NC4Q4F7c0PjAh3uPodgaNz7w6FxuwiCRM6FwHp3Xx1ZNhKYF978hwK/AL5XkyMSkbZUbmFMb/tXL9FQENUMjevuzwAfLFi2DzinzLyWpeSpoXNHpCH2dB9m14HDTDzxmGZnRb2HCmT+TWARqa8rvv08H7rj6fiVXuE7OWXex/XeT7zUBwBFfKlEFW+/nxd5u/0VM/vDRue91azu3DdgWSNrb9S5o7jUBoBK6gc1dLRAdW+/E3RmmBG+/X4pcGfYM05qoLyRQ1XqLyW1AaAcOlGkQMVvv7v7/vBlSICj0PhjFSlrwpc67TcLUh8AqvmDr9y6h617umuXGWkXVb39bmYfMLNlwBLgxkhA6FftG+5p0ajReNVhKF7qA0Apg510/+Ub/8nvfH1+w/IiLaOqt9/d/SV3fy9wLnCrmR01IGGlb7inULGZu0pulzCdCv3FpTYAlIr4SU+enj6dPhlUi7ffcfflwD7g7LrltE2pQN4aUhsAKqFbvYQqfvs93GYYgJm9CzgTWNOYbGdHonk+YqJM4XZ/+K/P8+Wf1mcQgnaQ+gCgm7qUK6yzz739vhx4JPf2u5ldHia7Bxgbvv1+M5DrKvoh4BUzWwz8GPhTd9/W2COQpBat3cnsF95udjaaJrXd08p6xNTzqBSo9O13d78fuL/uGUyJojOCFUmvxtzaSv0TgIi0nrj3dCrtsVdyO1UDFJX6AKCXu0RamxX9pfZ0P8iX+gBQik4IkfZUq55+WZbaAFDZUBB1yIiINJ2Gjo6X2gBQik4IkeartMylJ/faSH0ASNRfuO65EJFS6jVyb3S/Chv5UhsAdFMXaV/FSvh6cK+t1AYAEWkTkbt6Jff3Yk8OVuV+syD1AUBVhSKtq5rrU8O4Vy9RAKhidqTJZnYgMkPSdyPbnGNmS8Jt/sVaoFVWs4eJSJaUDABVzo4EsMrdp4c/N0aWfweYCUwNfy6t/DDi8l3LvYlIu8p7ylAZL0+SJ4CKZ0cqtkMzmwAc7+4veNDa8wPgyrJzX4Vo5hQsRGqrt89ZumFXWdvUehawvHkGdI3HShIAqpodCTjNzBaZ2bNmdkEk/foS+6wJVeuINN4/P7WCj33zubKDQI6u2sZIMhpoNbMjbQImuft2MzsH+ImZvTfhPoMdm80kqCpi0qRJCbI7WJZEpBFyN/4tuwefUtXxsp7G1fBbW0meACqeHcndD7r7dgB3XwisAs4I008ssU/C7TR1nkibakQvvPKqjooMP+3O1+cu5+3t+2qUq/aQJABUMzvS+LARGTM7naCxd7W7bwL2mNkHw7aCzwI/rcHxDFTOCajnTpGaaEQ5PWm9vlM6P6s693Hnf67mj3/QUW222krJAFDl7EgXAq+a2SsEjcM3untu3tQ/Ae4GVhI8GTxRo2Mqmx4rpVAVXZ8vMbOFYRfnhWZ2UaPz3kqaWaYq76oOctqbsTnAE80IVsXsSI8BjxXZZwd1nCw7cekgW39vSSDS9fkSgurKBWY2x91fiyTr7/psZlcTdH3+NLAN+Li7bzSzswkKTnXp4NDKKrn+mnspWgvkofFS/yZwMeoWJoOouOuzuy9y91x71jLgKDMb2ZBct6Ako3bGTt5eYjPNHlYbqQ8AGft7Sm1U2/U55xPAInc/WPgFZjbTzDrMrKOzs7NmGW8drVXCKjXQQFYLhKkNABn9e0ptVNP1OVgZdHe+A/hc3BdkpXdbuQWwUu1xlT0tJM9F1gqMqQ0AlcjaH1+Kqrjrc/j7RODHwGfdfVXdc9uCWqFEXU4eWiC7TZH6AJAk+LfCySotpZquzycAjwO3uvvzDctxiypZOi8ncQ3U8mkhDVIfAETKVWXX55uAKcAXI6PgntTgQ2i6VitTlcpPCwxG3BSJuoG2o+QviWQr4ksyVXR9vg24re4ZbBsJegHF3Z5reFnqXdDiMvsEoJe/ROqn3gXqWt+os3o3SH0AUAlfpHnK7Xdf28AxcGcls5Ox20VqA0AlJfxif/y+PueOJ19n484DVeZKJBsa+YRdi0JeRpsA0hsAylHqj79kwy6+88wq/uKhRY3JkEhKtEr9ezltgod7+3hy6aZM9AhKfQCoxd8wt4tDPX3V70wkxTbsPEBfnzekRJ20506iruCRJ5ZvPrWCG3/4MvNf31pp1tpGqgLAzv2HuOH7C+jad6im+83o06FIWdZu38/5t8/nn59a0b+s0vdw6tV2V6pU7w4bdgaT2Gyv8X2kFaUqANz/wts89fpW7n3urZqOBprbV/ofCEUqtzmc/ev5ldtaoht2WW8CZ3T+4FQFgJz6lR7qsluRVKhFSb5+994KqosycL2nKgD0l9QTjDFezsmqdwZEkgtm4KrvNVPPBtpczrPQhTxlAeDISVfO6Zd0BMIsnBAileq/cUZuzmXN15uoOjamb38Z4w0lyY6qgNpcvW7TqgISKS7vxtmAkThreZ/OawPIzQ6Wges9nQEgWgVUw79iFk4IkcoNnFaxmW/e5sWjxJ1CIk8vtc1OS0pVAKhXVU2WHglFquVefem80YWtvOrjmLbEtEoUAMzsUjN7w8xWmtktMetHmtnD4fqXzGxyuPwSM1toZkvCfy+KbPNMuM+aDZkbrcuv5CWRssYuF5E8cd2ly30Cb/awzE62Cnwlh4M2s6HAt4FLCGZBWmBmc9z9tUiyG4Ad7j7FzK4mmArv08A24OPuvtHMziYYXz06t+q17t5Ro2M5osZ36iN1ggoBIsXkV7lUMBZXvYpYCRql4+f3dPr6nF53hg9NVWVJvyRHdR6w0t1Xu/sh4CHgioI0VwCzw8+PAhebmbn7InfPTaW3DDjKzEbWIuNx4ksgRdLGbFdqvyKSQAPm4E3Wm6f8XoFB1o80Av/PRxYz9X89UW722kaSAHAKsC7y+3ryS/F5acLZlHYBYwvSfAJY5O4HI8vuC6t/vmhFigxmNtPMOsyso7Ozc9CMxnVDE6lEFdWeY83saTPba2bfanS+myl3CQfvAZSzXRlpy8pRZXmIfv7p4sKpoNMlSQCIfzoqI42ZvZegWuhzkfXXuvv7gAvCn8/Efbm73+XuM9x9xvjx4wfPaJ3Ojiw1CkletedlwDTgGjObVpCsv9oTmEVwfgN0A18EPt+g7LaM2JtAPXv51PB676/mRb2ACq0HTo38PhEoDIv9acxsGDAa6Ap/nwj8GPisu6/KbeDuG8J/9wAPEFQ11UStTzq9CZw51VR77nP35wgCQSYs3bCLTbsO5BWUqr051+/mm2SKylzS9IeAJAFgATDVzE4zsxHA1cCcgjRzgOvCz1cB893dzewE4HHgVnd/PpfYzIaZ2bjw83DgY8DS6g4lGsUrU3ryovSfEALUrtqzqHKqNlvdx775HL/99fn9v+eXops/FlCSnj21DF7tpGQACE/umwh68CwHHnH3ZWb2VTO7PEx2DzDWzFYCNwO5OtObgCnAFwu6e44E5pnZq8BiYAPwvWoPpl4j+qkKKHOqrvYspZyqzXYRfYM2eaNrZcNGJMtPpdtVV5BsJyW7gQK4+1xgbsGyL0U+dwOfjNnuNuC2Irs9J3k2y1PW+CMJmqwyVCCQQDnVnusLqz2zKnaAxTa5ix4ZAO6Idsl7NVLZuTXvEbTocKCV7Dcosazdvr+ifEnbqLjas4F5bFlBNUryC6yil7/KeIEzb7MKJ6hJq1QFgP5uaDWuxztSBeTc89xbXPh/n2bphl21+wJpKVVWe2Jma4BvANeb2fqYHkSpVs9SdEWxItFLA0fSFutOvmT9LpZv2l1+BlpYoiqgdtGIySQ61uwAYG3Xfs4+ZXTdvlGaq9Jqz3Dd5LpmrkXlj6iZTLF7c5KHqfLeH6jkzeR8H//WcwCsuf0Pyt5Xq0rZE8DAZeX0Qih20kXfMB4S/o/16WlfJE/ckCnNvEoqCxCeV5OQdqkKADl1q4r1bI0VLlKxSqv161z/rss2X6oCQP58FLVrhLKYX3QiieSLnZK1TUpKce8BtEfOq5OuABAZiySpcqahc2CIDXzMFcmaV9fv5BevbclbFjerVlSzrhnHK2w89tjPaZKqRuCccsb4L2+/HukhULv9irSby78VvNgfbRCtZDydSq+jat42jhN9DyA+eOX/3tcXBJVmz19QrZQ9AQT/Jon4ZVURFfkOERmonmMBlXfdlj+BPBTrTHJEb59z+t/M5WuPL0+cl1aVrgBQ5fZFT7poL6AM9RAQKUfsTb+cGffKvKbqMUhjsaEpostzPQC//6s1Nf/+RktVACDm5lzL+3T0JZE+BQCRPPnVKNXto9aqnXPAY9anoSt4ugJAqNw/S6lzI6+kEXkr2N3pVSQQAfLfmM9JUlVa72r08sYGi6/mje4jV++fhks/VQEg2kBby3MqekIMifQIuumBRbz7b+YW31Akg4oNv9wK98vyXgwtvd3h3j7u/pqGJF8AAAoHSURBVOVqDvX0VZu1pmj7ANDX53z/+bdYvG5nZV29yklbME7I40s2lf+FIm3qnufeYvItj7Nr/+EiKSprdK2XSscNiutOXuw4fvji29z2+HLuee6t8r+sBbR9AAD4ys9e49k3OvNe586pdf9dzQ0gWfXQr9cCsHl3/GRnFrn8qm2gLafBOFHPngp7/ZWy/1AvALu7iwXF1tb2AWDIEMMMevuOPIIlGY62VI+FuLTu5FUBiWTJ0CHBuV+q3cuLfS5xp26N9gKPtPNFl8enz90P+vqcnfsP8cKq7fXNYI21fQAAGDbE6Omr7G2/cuW+Iw09AESSONgTlHJzAaDYuR+tHq30WixvHoFk6er5stnQ8A7a2+fcMLuDa773IgfCp4J2kIoAMMSM3r74t3QTDQVecr7Q/FGGCr8D4LGF6we8Gi/S7t7csocz//ZJ5i7Z1B8Aeoo8AcTdvGtdPVOtklVLlPcCWe4JoNedN7fsAeBQTx9bd3ezYeeBarLaEKkYCmLYkDAA1Okt3WjJZkiRgaL+8t9eAdI1VrhIbgKUJ5duLlkFlPceQKv1AirnPYAy2vmi/yfDw8eBw319nPd/ngJa/36Q6AnAzC41szfMbKWZ3RKzfqSZPRyuf8nMJkfW3Rouf8PMfj/pPssxNKwCyqlfN9D4vs59BRfEm1v20H24fR4DZaB6nPPt5FN3vsCDv1575KbW28dQG7wKKCdJ98mWVSS7xY5iWCQA5D739OanPv/2+XzzqRXAwHtFs5UMAGY2FPg2cBkwDbgmZoq7G4Ad7j4FmAXcEW47jWA+1fcClwL/amZDE+4zsWFDh4RVQPV/lIybD6A38kv34V4+Mus/uemBRbg7L67entqRBNOqHud8o/Jerp7ePnbsOwTAnFc29vf0+fVbXdz670vyAsCQIje4nPxSf/sOkhY79leRa3hIpF0k+n8VtWHnAf7x52+yZts+Tv+buTz+6iY69xzk/hffxt3Ze7CHzj0H+7+nkfcLK/VlZvbbwFfc/ffD328FcPevR9LMC9O8YGbDgM3AeMJ5UnNpc+nCzQbdZ5wZM2Z4R0fHgOXnfu0XHDzcy+7ungHrTjnh6P66uKknHceKrXsBGH30cHYdyO+6dfTwoRwIS+7RtKeNO5a3tu0rmq93jz+WVZ37BnyOM/Wk4wY7RKmzSWOO4Z7rz41dZ2YL3X1GPc55d3+hWJ6Knddzl2xi1s/frOxAB7Fj/2G27T3IO0cfxcZdQZfO08cdy+rwHI+e+8eMGNrf1TFn1Mhh7DnYMyDtpDHHsLZrf9HvnXLScawM054+/lhWD3KdFMvP8KHG4YIANO64EWzbe2hA2pNGjWRreGPNGWJH3uCNpo1+X2x+IvmNHkeciScezfodA+85I4YNGfSFsWjayWOPYc32/YwfNZITjh5edJuoP77gdD517qmx63LnduHyJG0ApwDrIr+vBz5QLI2795jZLmBsuPzFgm1PCT+X2mcu4zOBmQCTJk2KzeCNH343C9/uoqfX+Y/XtnDB1HHs6e5h8bqd/MapoznY08u2vYeYenLwHzxh9FFMP/UEnli6mXMnn8jQIcaLq7v43TPHs2TDLtbvONCf9oRjhvOeCaN4a9s+3j9xNCceM4Jn3+zkkmkns2bbPlZs3cuZ7xjFuq4DYHDmO0axqnMfU086jkljjuGp17fy4TPGs6f7MC+v3cnUkxUAmunk449Kkqxe53y/JOf16KOH1+V86euDJ5dt5t0nHceY40awdMNuzpowiq79h9i5/3D/uX/KCUfz/omjeWLpZs47bQwQPBl8aOo4Xl2/iw07j1wnY44dwdmnHM/arv38xsTRHH/0cH65YhsfmXYyqzr3sqpzH2ecfBxvb9/H0CHGWe8YxerOfZx58ihOHXM0v1i+ld87czydew/256dz70H2dPf0f8e7xh7De95xPE8u28zvvHssB3v6WPj2Ds6dPIaX1+5gy+6D/WlPGjWSc951Ik8s3cxvTTqBo0cM5fmV27lk2sm8sXkPa7bv7087auQwzpowitXb9jFtwvGcfPxInn6jk4vPOokNOw/w+uY9nPWOUWze1c3Bnj7OODkIAKePP5Yp44/rv+fs7u7hlXU7ef/E0Rw41Mv2fUfuOe8cfRS/Ed5zzps8Biz4v7zs7HeweN1ONu3q7k974jHDmfbO41mzfT8TRh/FxBOPTvR3PeGYZIEiKkkASNJjvliaYsvjqp5iH0Xc/S7gLghKSnFpbvjQadzwodPiVolUoh7nfP6CBOf1+VPGcf6UcYPnVKQKSRqB1wPR54qJwMZiacLH4dFA1yDbJtmnSLPU45wXaTlJAsACYKqZnWZmIwgauOYUpJkDXBd+vgqY70Hjwhzg6rDHxGnAVODXCfcp0iz1OOdFWk7JKqCwfvMmYB4wFLjX3ZeZ2VeBDnefA9wD3G9mKwlKQVeH2y4zs0eA14Ae4M/cvRcgbp+1PzyR8tXrnBdpNSV7AbWSYr0lRGqhWE+JetN5LfVW7NxOxVAQIiJSPgUAEZGMUgAQEckoBQARkYxqq0ZgM+sE3i6yehywrYHZaSQdW2O8y93HN/pLM3xeQ7qPr5WOLfbcbqsAMBgz62hGD45G0LFlV9r/f9J8fO1wbKoCEhHJKAUAEZGMSlMAuKvZGagjHVt2pf3/J83H1/LHlpo2ABERKU+angBERKQMCgAiIhnV9gGglpPLtwIzO9XMnjaz5Wa2zMz+Ilw+xsx+bmYrwn9PbHZeKxXOC73IzP5f+Ptp4cTqK8KJ1kc0O4+tIE3nts7r1jyv2zoA1Hpy+RbRA/ylu78H+CDwZ+Ex3QI85e5TgafC39vVXwDLI7/fAcwKj20HwYTrmZbCc1vndQue120dAIDzgJXuvtrdDwEPAVc0OU9VcfdN7v5y+HkPwQl1CsFxzQ6TzQaubE4Oq2NmE4E/AO4OfzfgIuDRMEnbHluNperc1nndmsfW7gEgbvLuARNwtyszmwz8JvAScLK7b4LgYgJOal7OqvJPwF8DfeHvY4Gd7t4T/p6qv2EVUntu67xuHe0eABJNwN2OzOw44DHgf7j77mbnpxbM7GPAVndfGF0ckzQVf8MqpfL/Red1ayk5JWSLS+UE3GY2nOAi+ZG7/3u4eIuZTXD3TWY2AdjavBxW7HzgcjP7KHAUcDxByekEMxsWlpZS8TesgdSd2zqvW+/v1+5PAKmbXD6sO7wHWO7u34isik5Cfh3w00bnrVrufqu7T3T3yQR/q/nufi3wNMHE6tCmx1YHqTq3dV635rG1dQAII2tu8u7lwCMpmFz+fOAzwEVmtjj8+ShwO3CJma0ALgl/T4svADeHE6yPJbhRZFoKz22d1y14XmsoCBGRjGrrJwAREamcAoCISEYpAIiIZJQCgIhIRikAiIhklAKAiEhGKQCIiGTU/we/pwpVtKEr2gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visaulitng Poisson distribtuion with different Lamda values\n", + "x = np.arange(0, 50, 0.001)\n", + "\n", + "# Plot of a poisson distribtuon with a lambda value of 5\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(x, stats.poisson.pmf(x, 5));\n", + "\n", + "# Plot of a poisson distribtuon with a lambda value of 5\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(x, stats.poisson.pmf(x, 25));\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data overview and study description\n", + "\n", + "The data and the analysis has been taken from https://drive.google.com/file/d/0Bz-rhZ21ShvOM1cxWUpUNlQ0UlE/view, and stored in the Github repository for these notebooks for ease of import. The dataset is orignally from James et al. (2015). See the original paper here https://journals.sagepub.com/doi/pdf/10.1177/0956797615583071?referrer=&priority=true&module=meter-Links&pgtype=Blogs&contentId=&action=click&contentCollection=meter-links-click&version=meter+at+null&mediaId=\n", + "\n", + "A reality of trauma is that individuals can experience flasbacks which have been termed \"Intrusive memories\". A form of treatment that has been argued to be effective for suffers of intrusive memories is to use reconsolidation methods. As such, James et al. (2015) wanted to investigate if a video game treament (tetris) could reduce the number of intrusive memories a traumatised indivdual experienced.\n", + "\n", + "The participants with the study were split into four conditions (n=72, with 18 particpants per condition).\n", + "\n", + "1. No-task control: These participants completed a 10-minute music filler task.\n", + "2. Reactivation + Tetris: These partipants underwent a reactivation task to (trauma film) to reactivate their traumatic memories, which was then followed by 10 minute filler music task. This was followed by playing tetris for 12 minutes\n", + "3. Tetris: this group played tetris for 12 minutes\n", + "4. Reactivation only: Participants only watch the trauma film" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ConditionTime_of_DayBDI_IISTAI_Tpre_film_VAS_Sadpre_film_VAS_Hopelesspre_film_VAS_Depressedpre_film_VAS_Fearpre_film_VAS_Horrorpre_film_VAS_Anxious...Day_Zero_Number_of_IntrusionsDays_One_to_Seven_Number_of_IntrusionsVisual_Recognition_Memory_TestVerbal_Recognition_Memory_TestNumber_of_Provocation_Task_IntrusionsDiary_ComplianceIES_R_Intrusion_subscaleTetris_Total_ScoreSelf_Rated_Tetris_PerformanceTetris_Demand_Rating
0121330.00.00.00.40.30.8...241518590.6299999999.00
1123271.90.70.50.80.20.2...231719490.6299999999.00
21110422.21.20.90.20.10.4...5612210100.5099999999.00
3111411.21.00.65.10.40.5...021619080.5099999999.03
4121270.20.10.02.90.00.7...5314221081.0099999999.0-7
..................................................................
67422340.50.01.02.11.53.4...211520470.5099999999.00
68422280.80.91.00.00.00.0...241421681.5099999999.0-5
69420231.60.30.50.00.01.3...341824790.5099999999.00
70424422.25.02.30.00.00.0...1271317370.5099999999.0-1
71414540.90.00.00.70.53.3...341721290.6399999999.00
\n", + "

72 rows × 28 columns

\n", + "
" + ], + "text/plain": [ + " Condition Time_of_Day BDI_II STAI_T pre_film_VAS_Sad \\\n", + "0 1 2 1 33 0.0 \n", + "1 1 2 3 27 1.9 \n", + "2 1 1 10 42 2.2 \n", + "3 1 1 1 41 1.2 \n", + "4 1 2 1 27 0.2 \n", + ".. ... ... ... ... ... \n", + "67 4 2 2 34 0.5 \n", + "68 4 2 2 28 0.8 \n", + "69 4 2 0 23 1.6 \n", + "70 4 2 4 42 2.2 \n", + "71 4 1 4 54 0.9 \n", + "\n", + " pre_film_VAS_Hopeless pre_film_VAS_Depressed pre_film_VAS_Fear \\\n", + "0 0.0 0.0 0.4 \n", + "1 0.7 0.5 0.8 \n", + "2 1.2 0.9 0.2 \n", + "3 1.0 0.6 5.1 \n", + "4 0.1 0.0 2.9 \n", + ".. ... ... ... \n", + "67 0.0 1.0 2.1 \n", + "68 0.9 1.0 0.0 \n", + "69 0.3 0.5 0.0 \n", + "70 5.0 2.3 0.0 \n", + "71 0.0 0.0 0.7 \n", + "\n", + " pre_film_VAS_Horror pre_film_VAS_Anxious ... \\\n", + "0 0.3 0.8 ... \n", + "1 0.2 0.2 ... \n", + "2 0.1 0.4 ... \n", + "3 0.4 0.5 ... \n", + "4 0.0 0.7 ... \n", + ".. ... ... ... \n", + "67 1.5 3.4 ... \n", + "68 0.0 0.0 ... \n", + "69 0.0 1.3 ... \n", + "70 0.0 0.0 ... \n", + "71 0.5 3.3 ... \n", + "\n", + " Day_Zero_Number_of_Intrusions Days_One_to_Seven_Number_of_Intrusions \\\n", + "0 2 4 \n", + "1 2 3 \n", + "2 5 6 \n", + "3 0 2 \n", + "4 5 3 \n", + ".. ... ... \n", + "67 2 1 \n", + "68 2 4 \n", + "69 3 4 \n", + "70 12 7 \n", + "71 3 4 \n", + "\n", + " Visual_Recognition_Memory_Test Verbal_Recognition_Memory_Test \\\n", + "0 15 18 \n", + "1 17 19 \n", + "2 12 21 \n", + "3 16 19 \n", + "4 14 22 \n", + ".. ... ... \n", + "67 15 20 \n", + "68 14 21 \n", + "69 18 24 \n", + "70 13 17 \n", + "71 17 21 \n", + "\n", + " Number_of_Provocation_Task_Intrusions Diary_Compliance \\\n", + "0 5 9 \n", + "1 4 9 \n", + "2 0 10 \n", + "3 0 8 \n", + "4 10 8 \n", + ".. ... ... \n", + "67 4 7 \n", + "68 6 8 \n", + "69 7 9 \n", + "70 3 7 \n", + "71 2 9 \n", + "\n", + " IES_R_Intrusion_subscale Tetris_Total_Score \\\n", + "0 0.62 9999 \n", + "1 0.62 9999 \n", + "2 0.50 9999 \n", + "3 0.50 9999 \n", + "4 1.00 9999 \n", + ".. ... ... \n", + "67 0.50 9999 \n", + "68 1.50 9999 \n", + "69 0.50 9999 \n", + "70 0.50 9999 \n", + "71 0.63 9999 \n", + "\n", + " Self_Rated_Tetris_Performance Tetris_Demand_Rating \n", + "0 9999.0 0 \n", + "1 9999.0 0 \n", + "2 9999.0 0 \n", + "3 9999.0 3 \n", + "4 9999.0 -7 \n", + ".. ... ... \n", + "67 9999.0 0 \n", + "68 9999.0 -5 \n", + "69 9999.0 0 \n", + "70 9999.0 -1 \n", + "71 9999.0 0 \n", + "\n", + "[72 rows x 28 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/James%20et%20al%202015%20Experiment%202%20Data%20Set.csv\"\n", + "\n", + "#Generare apndas data frame with the study data\n", + "df = pd.read_csv(url)\n", + "df.head(72)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot data " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Genrate set of subplots of the data for each Experimental condition for Day zero count if number of intrusions\n", + "\n", + "plt.subplot(2,2,1)\n", + "sns.distplot( df[df['Condition'] == 1]['Day_Zero_Number_of_Intrusions'] );\n", + "\n", + "plt.subplot(2,2,2)\n", + "sns.distplot( df[df['Condition'] == 2]['Day_Zero_Number_of_Intrusions'] );\n", + "\n", + "plt.subplot(2,2,3)\n", + "sns.distplot( df[df['Condition'] == 3]['Day_Zero_Number_of_Intrusions'] );\n", + "\n", + "plt.subplot(2,2,4)\n", + "sns.distplot( df[df['Condition'] == 4]['Day_Zero_Number_of_Intrusions'] );\n", + "\n", + "#Correct for layout formatting\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Defining the descriptive model \\begin{align*}\n", + "y_{ik} &\\sim Normal(\\lambda_k) \n", + "\\\\ \\lambda_k &\\sim halfnormal(0, 100) \n", + "\\end{align*} \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3 - Specifying priors\n", + "To get to the model described above prior preditive checks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stan model" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "Poisson = \"\"\"\n", + "\n", + "data{\n", + "\n", + "int N; // Number od data points\n", + "\n", + "int y[N]; // Dependent varaible data\n", + "\n", + "int K; // number of groups\n", + "\n", + "// array of integer vlaues for the indicator variables\n", + "int x[N];\n", + "\n", + "real prior_mu;\n", + "real prior_sd;\n", + "\n", + "// logically evaluates below to determine if it running Prior predictive checks.\n", + "int onlyprior; \n", + "\n", + "\n", + "\n", + "}\n", + "\n", + "parameters{\n", + "\n", + "vector[K] lamda;\n", + "\n", + "}\n", + "\n", + "model{\n", + "\n", + "//priors \n", + "lamda ~ normal(prior_mu, prior_sd); // prior on each group\n", + "\n", + "//Likelihood\n", + " if(!onlyprior)\n", + "y ~ poisson(lamda[x]);\n", + "\n", + "}\n", + "\n", + "generated quantities{\n", + "\n", + "vector[N] yrep;\n", + "\n", + "for(i in 1:N)\n", + "yrep[i] = poisson_rng(lamda[x[i]]);\n", + "\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_08d10891ca2b974673b7a5d3d4ed6e3e NOW.\n" + ] + } + ], + "source": [ + "sm = ps.StanModel(model_code=Poisson)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "Prior_data = {'N': len(df),\n", + " 'y': df[\"Day_Zero_Number_of_Intrusions\"].values,\n", + " 'K': max(df[\"Condition\"].values),\n", + " 'x':df[\"Condition\"].values,\n", + " 'prior_mu':0,\n", + " 'prior_sd': 100,\n", + " 'onlyprior': 1}" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "prior_PC = sm.sampling(data = Prior_data, iter = 2000, chains = 4, seed = 1, warmup = 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python print stament it is easier to extract the results of the prior predictive\n", + "# checks into a panda data frame.\n", + "summary = prior_PC.summary()\n", + "prior_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
meanse_meansd2.5%25%50%75%97.5%n_effRhat
lamda[1]78.5565121.11217060.6868702.62174430.15587165.611608113.018389224.0284532977.4690570.999808
lamda[2]82.0102841.15178260.5149194.85878533.50329969.104278117.382946222.6406262760.4764941.000959
lamda[3]81.5636561.27296361.0571494.63153933.22279169.047608117.648328224.7564882300.6013781.000438
lamda[4]80.8219841.18489558.0195304.11896934.05337769.801435116.065759215.4692332397.6657161.001173
yrep[1]78.7557501.12330561.4538952.00000030.00000066.000000114.000000227.0000002992.9774210.999801
\n", + "
" + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% \\\n", + "lamda[1] 78.556512 1.112170 60.686870 2.621744 30.155871 65.611608 \n", + "lamda[2] 82.010284 1.151782 60.514919 4.858785 33.503299 69.104278 \n", + "lamda[3] 81.563656 1.272963 61.057149 4.631539 33.222791 69.047608 \n", + "lamda[4] 80.821984 1.184895 58.019530 4.118969 34.053377 69.801435 \n", + "yrep[1] 78.755750 1.123305 61.453895 2.000000 30.000000 66.000000 \n", + "\n", + " 75% 97.5% n_eff Rhat \n", + "lamda[1] 113.018389 224.028453 2977.469057 0.999808 \n", + "lamda[2] 117.382946 222.640626 2760.476494 1.000959 \n", + "lamda[3] 117.648328 224.756488 2300.601378 1.000438 \n", + "lamda[4] 116.065759 215.469233 2397.665716 1.001173 \n", + "yrep[1] 114.000000 227.000000 2992.977421 0.999801 " + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prior_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "yrep1 = prior_PC['yrep[1]'].T\n", + "yrep1" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(yrep1[0:18], kde=False);\n", + "sns.distplot(yrep1[19:36], kde=False);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fitting the model" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "data = {'N': len(df),\n", + " 'y': df[\"Day_Zero_Number_of_Intrusions\"].values,\n", + " 'K': max(df[\"Condition\"].values),\n", + " 'x':df[\"Condition\"].values,\n", + " 'prior_mu':0,\n", + " 'prior_sd': 100,\n", + " 'onlyprior': 0}" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "fit = sm.sampling(data = data, iter = 2000, chains = 4, seed = 1, warmup = 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python print stament it is easier to extract the results of the prior predictive\n", + "# checks into a panda data frame.\n", + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
meanse_meansd2.5%25%50%75%97.5%n_effRhat
lamda[1]3.6108770.0072590.4601552.7494773.2951993.5963963.9164884.5464264018.1250960.999216
lamda[2]3.1537230.0062670.4017472.4250822.8739073.1374903.4140433.9936344109.5147700.999902
lamda[3]3.2274040.0064850.4302862.4349312.9283833.2133273.5146574.1007854402.8551790.999848
lamda[4]3.4958260.0066070.4387832.7184043.1938963.4755423.7730704.4049374411.1060861.000440
yrep[1]3.5465000.0302191.9295770.0000002.0000003.0000005.0000008.0000004077.3418531.000800
.................................
yrep[69]3.5260000.0295151.8913540.0000002.0000003.0000005.0000008.0000004106.5077551.002371
yrep[70]3.5385000.0309401.9409940.0000002.0000003.0000005.0000008.0000003935.6006381.000488
yrep[71]3.5107500.0314091.9428920.0000002.0000003.0000005.0000008.0000003826.2883371.000034
yrep[72]3.4660000.0314101.9281530.0000002.0000003.0000005.0000008.0000003768.3475811.000197
lp__50.9413580.0322471.46150647.30870450.28821951.29056851.99123952.6936802054.1302521.004072
\n", + "

77 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% \\\n", + "lamda[1] 3.610877 0.007259 0.460155 2.749477 3.295199 3.596396 \n", + "lamda[2] 3.153723 0.006267 0.401747 2.425082 2.873907 3.137490 \n", + "lamda[3] 3.227404 0.006485 0.430286 2.434931 2.928383 3.213327 \n", + "lamda[4] 3.495826 0.006607 0.438783 2.718404 3.193896 3.475542 \n", + "yrep[1] 3.546500 0.030219 1.929577 0.000000 2.000000 3.000000 \n", + "... ... ... ... ... ... ... \n", + "yrep[69] 3.526000 0.029515 1.891354 0.000000 2.000000 3.000000 \n", + "yrep[70] 3.538500 0.030940 1.940994 0.000000 2.000000 3.000000 \n", + "yrep[71] 3.510750 0.031409 1.942892 0.000000 2.000000 3.000000 \n", + "yrep[72] 3.466000 0.031410 1.928153 0.000000 2.000000 3.000000 \n", + "lp__ 50.941358 0.032247 1.461506 47.308704 50.288219 51.290568 \n", + "\n", + " 75% 97.5% n_eff Rhat \n", + "lamda[1] 3.916488 4.546426 4018.125096 0.999216 \n", + "lamda[2] 3.414043 3.993634 4109.514770 0.999902 \n", + "lamda[3] 3.514657 4.100785 4402.855179 0.999848 \n", + "lamda[4] 3.773070 4.404937 4411.106086 1.000440 \n", + "yrep[1] 5.000000 8.000000 4077.341853 1.000800 \n", + "... ... ... ... ... \n", + "yrep[69] 5.000000 8.000000 4106.507755 1.002371 \n", + "yrep[70] 5.000000 8.000000 3935.600638 1.000488 \n", + "yrep[71] 5.000000 8.000000 3826.288337 1.000034 \n", + "yrep[72] 5.000000 8.000000 3768.347581 1.000197 \n", + "lp__ 51.991239 52.693680 2054.130252 1.004072 \n", + "\n", + "[77 rows x 10 columns]" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian Robust Regression.ipynb b/wip/Bayesian Robust Regression.ipynb new file mode 100644 index 0000000..1d8742f --- /dev/null +++ b/wip/Bayesian Robust Regression.ipynb @@ -0,0 +1,531 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Import relveant libraries/packages.\n", + "import pystan as ps\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import arviz as az\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as ss\n", + "plt.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Robust Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data example below is from https://www.sheffield.ac.uk/mash/statistics/datasets. The code below is for conducting Robust regression which differs based on the use of student-t distrtibution as the likelihood function to model the data and accomadate outliers." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CrimeRateYouthSouthernEducationExpenditureYear0LabourForceMalesMoreMalesStateSizeYouthUnemployment...ExpenditureYear10LabourForce10Males10MoreMales10StateSize10YouthUnemploy10MatureUnemploy10HighYouthUnemploy10Wage10BelowWage10
045.5135012.4695409650680...715649740682201632142
152.3140010.955535104516135...54540103917138391521210
256.6157111.24751296202297...4452995902498330359256
360.3139111.946480968019135...41497983020131500510235
464.2126012.210659998904078...9760298904279241660162
\n", + "

5 rows × 27 columns

\n", + "
" + ], + "text/plain": [ + " CrimeRate Youth Southern Education ExpenditureYear0 LabourForce \\\n", + "0 45.5 135 0 12.4 69 540 \n", + "1 52.3 140 0 10.9 55 535 \n", + "2 56.6 157 1 11.2 47 512 \n", + "3 60.3 139 1 11.9 46 480 \n", + "4 64.2 126 0 12.2 106 599 \n", + "\n", + " Males MoreMales StateSize YouthUnemployment ... ExpenditureYear10 \\\n", + "0 965 0 6 80 ... 71 \n", + "1 1045 1 6 135 ... 54 \n", + "2 962 0 22 97 ... 44 \n", + "3 968 0 19 135 ... 41 \n", + "4 989 0 40 78 ... 97 \n", + "\n", + " LabourForce10 Males10 MoreMales10 StateSize10 YouthUnemploy10 \\\n", + "0 564 974 0 6 82 \n", + "1 540 1039 1 7 138 \n", + "2 529 959 0 24 98 \n", + "3 497 983 0 20 131 \n", + "4 602 989 0 42 79 \n", + "\n", + " MatureUnemploy10 HighYouthUnemploy10 Wage10 BelowWage10 \n", + "0 20 1 632 142 \n", + "1 39 1 521 210 \n", + "2 33 0 359 256 \n", + "3 50 0 510 235 \n", + "4 24 1 660 162 \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Crime.csv\"\n", + "df = pd.read_csv(url)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(df[\"CrimeRate\"]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "y_i &\\sim Student(\\nu, \\mu, \\sigma) \n", + "\\\\ \\nu &\\sim Gamma(2, 1)\n", + "\\\\ \\mu_i &= \\beta_0 + \\beta_1x + \\beta_2x\n", + "\\\\ \\beta_0 &\\sim normal(0, 1)\n", + "\\\\ \\beta_1 &\\sim normal(0, 1)\n", + "\\\\ \\beta_2 &\\sim normal(0, 1) \n", + "\\\\ \\sigma &\\sim Exponential(1)\n", + "\\end{align*} \n", + "\n", + "The formulation for presenting statistical models here follows that used by McElreath (2020) for its intuitive nature. In plain english the model specifies that the dependent variable $y_i$ (diffence scores of gaze proportion is distributed normally in terms of the likelihood with the prior on $\\mu$ being Normally distributed with a $\\mu$ of 0 and $\\sigma$ of 0.2. In addito the prior on the $\\sigma$ parameter of likelihood being exponetial distributed with a $\\lambda$ of 0.1. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4 - Use Bayes rule.\n", + "\n", + "The software of choice to conduct Bayesian inference on the data here is Stan and the model is specified below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stan model of a Bayesian robust regression" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "Robust_Regression = \"\"\"\n", + "data {\n", + " int N; // Sample size\n", + " int K; // Number of predictor variables\n", + " matrix[N, K] x; // Predictor variable (IV) matrix\n", + " vector[N] y; // Vector of Dependent variable (DV) values\n", + "}\n", + "\n", + "transformed data{\n", + " vector[N] y_std = (y - mean(y)) / sd(y);\n", + " matrix[N, K] x_std;\n", + " for (i in 2:K){\n", + " x_std[,i] = (x[,i] - mean(x[,i])) / sd(x[,i]);\n", + " \n", + " }\n", + "}\n", + "\n", + "parameters{\n", + " real nu; // degrees of freedom \n", + " vector[K] beta; // Coefficients for interpectp and predictors\n", + " real sigma; // Standard deviation\n", + "}\n", + "\n", + "model {\n", + "\n", + "//priors\n", + "nu ~ gamma(2, .1);\n", + "beta ~ normal(0, 1);\n", + "sigma ~ normal(0, 1);\n", + "\n", + "//Likelihood\n", + " y ~ student_t(nu, x * beta, sigma); // likelihood\n", + "}\n", + "\n", + "generated quantities{\n", + "//Transform data back to the original scale\n", + "\n", + "\n", + "// Generate posterior p-value variable \n", + "int mean_pv;\n", + "int sd_pv;\n", + "\n", + "real ypred[N] = student_t_rng(nu, x*beta, sigma);\n", + "}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_44a54ca7c95a0fc2f2f53e2fd9632139 NOW.\n" + ] + } + ], + "source": [ + "# StanModel function can be called and be passed the model string specified above to compile into C++ code.\n", + "sm = ps.StanModel(model_code = Robust_Regression)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = pt.dmatrix(\" ~ Inherence_Bias\", data = df)\n", + "x = np.asarray(x)\n", + "\n", + "data = {'N': len(filtered),\n", + " 'y': filtered[\"Ought_Score\"].values,\n", + " 'x': x,\n", + " 'K': x.shape[1]\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 5 - Posterior predictive checks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## posterior p-values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the Bayesian robust regression\n", + "\n", + "As Kruscke correctly points out there is not standard formula or presentation method for reuslts in journal article like the APA guide for reporting frequentist analysis. It is likely there never will be, because as McElreath (2020) explains Bayesian data analysis is more like a engineering approach to the problem and the resulting model that is fit will be analysis specific. In addition, as Gabry et al, (2017) argue visualisations maybe even more key so the all the visualtions above would have to be included with any write up. Anyway below the write up as below genral follows the advice of Kruscke (2015) chapter 25. In any application though it comes down to the problem to be described an the audience that needs to be convinced.


\n", + "\n", + "

Write up


\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian between subject t-test estimation.ipynb b/wip/Bayesian between subject t-test estimation.ipynb new file mode 100644 index 0000000..12640a6 --- /dev/null +++ b/wip/Bayesian between subject t-test estimation.ipynb @@ -0,0 +1,1044 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Import the neccesary python libraries for Bayesian analysis\n", + "# and data visualisation.\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('ggplot')\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "import pystan as ps\n", + "import arviz as az\n", + "\n", + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of the between subjects (student) t-test\n", + "\n", + "This notebook is the natural follow on from the one-sample t-test notebook (so if anything is unclear that notebooks may help.\n", + "\n", + "## The classic between subject (student t-test)\n", + "\n", + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for the question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. If scientific research publication is the goal the priors will need to be accepted by a skeptical audience. This should be achievable using prior predictive checks to ascertain if the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then use the posterior for conducting your inferences.\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following on from that quick description of the classic between subjects t-test above its important to keep in mind that Bayesian inference is all derived from the application of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to the one sample t-test, it is fundamentally different, because it uses fully probabilistic modelling and the infernce is not based on sampling distributions.\n", + " \n", + " For a fuller description see the practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for the question under investigation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Study/data description\n", + "\n", + "The following notebook describes a Bayesain estimation analysis of the data from a replication study by Wagenmaker et al. (2015) of Topolisnki and Sparenberg (2012). The replication group interestingly are advocates of Bayesian analysis and generally advocate for the general application of Bayes Factor and for the use of their own analysis software JASP which interested readers can download for free https://jasp-stats.org/. //Their replication demonstrated a Bayes factor analysis too demonstrate the neccesity of replication and the power of Bayes to approach evidence of absence question that NHST cannot conduct, for replication.//\n", + "\n", + "The replication was based on Topolinski and Sparenbergs's second experiment. The original study and thus the replication study were premised on the conjecture that clockwise and counterclockwise motion are abstract symbols for progression/regression in time. Topolinski and Sparenberg predicted and aimed to show that clockwise movements would induce psychological states of temporal progression and oritation to the future.\n", + "\n", + "In order to test the orignal finding and this conjecture Wagenmaker et al recruited 102 participants to take part in their replication of the original experiment. Participants were split into two conditions were they would either rotate a pair of kitchen rolls clockwise or counterclockwise. The shortened 12-item version of the openness to experience subscale of the Neuroticism–Extroversion–Openness Personality Inventory (NEO PI-R; Costa and McCrae, 1992)\n", + "\n", + "However, following this description of the data this notebook is concerned with Bayesian estimation methods and demonstrates an example for analysing the replciated data more in the vain of Kruschke (2013)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clean the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
mean_NEORotation
00.666667counter
11.166667clock
20.833333counter
30.000000clock
4-0.250000counter
\n", + "
" + ], + "text/plain": [ + " mean_NEO Rotation\n", + "0 0.666667 counter\n", + "1 1.166667 clock\n", + "2 0.833333 counter\n", + "3 0.000000 clock\n", + "4 -0.250000 counter" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Use os to change working directory to import data into jupyter notebook for analysis\n", + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Turning_Hands_Data_Final.csv\"\n", + "#Read in csv file to dataframe\n", + "df = pd.read_csv(url)\n", + "\n", + "#Extrract neo mean (openess to experience) scores and Rotation conditions for comparison below\n", + "df_Mu_neo_condt = df[[\"mean_NEO\", \"Rotation\"]]\n", + "df_Mu_neo_condt.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#Extract counter clockwise groups neo-score means and convert into numpy array for vectorisation using python dictionary below.\n", + "df_counter = df_Mu_neo_condt[df_Mu_neo_condt['Rotation'].str.contains('co')]\n", + "\n", + "# Extract counter clockwie groups neo means and convert into numpy array for vectorisation using python dictionary below.\n", + "df_clock = df_Mu_neo_condt[df_Mu_neo_condt['Rotation'].str.contains('cl')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data visualisation and exploratory data analysis " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(df_clock['mean_NEO'],hist=True,kde=False);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(df_clock['mean_NEO'], hist=True,kde=False);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "y_\\scaleto{1}{1pt} &\\sim Normal(\\mu_1,\\sigma\n", + "\\\\ y_{2} &\\sim Normal(\\mu_2,\\sigma)\n", + "\\\\ \\mu_{1,2} &\\sim normal(0,1) \n", + "\\\\ \\sigma &\\sim exponential(1)\n", + "\\end{align*} " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#Stan model put into string to be called when the model is compiled\n", + "Independent_t = \"\"\"\n", + "\n", + "data {\n", + " int N1; // # of observations (counter clockwise)\n", + " int N2; // # of observations (clockwise)\n", + " vector[N1] Y1; // Neo scores (counter clockwise)\n", + " vector[N2] Y2; // Neo scores (clockwise);\n", + "}\n", + "\n", + "transformed data{\n", + "vector[N1] y1_std = (Y1 - mean(Y1)) / sd(Y1);\n", + "vector[N2] y2_std = (Y2 - mean(Y2)) / sd(Y2);\n", + "}\n", + "\n", + "parameters {\n", + " real mu_1_std; // mean of (counter clockwise) group bounded to lower -2 and upper 2 in line with the likert\n", + " //scale on the neo personality measure.\n", + " real mu_2_std; // mean of (clockwise) group \n", + " real sigma_std; // pooled standard deviation\n", + "}\n", + "\n", + "model {\n", + "\n", + "// likelihood\n", + " Y1 ~ normal(mu_1_std, sigma_std);\n", + " Y2 ~ normal(mu_2_std, sigma_std);\n", + "}\n", + "\n", + "generated quantities {\n", + " real cohen_d = (mu_2 - mu_1);// / sigma; //effect size;\n", + "}\n", + "\n", + "\"\"\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#Create python dictionary to specify the data to be modelled using th stan coded model above,\n", + "#(Should provalbly use length funtion for the N vector.\n", + "Model_t_dat = {'N1': len(df_counter) ,\n", + " 'N2': len(df_clock), \n", + " 'Y1': df_counter['mean_NEO'].values,\n", + " 'Y2': df_clock['mean_NEO'].values}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_e1c7384001b69df3d4a66b6f8d5916b0 NOW.\n" + ] + } + ], + "source": [ + "#Fit the model using 2000 sample iterations and 4 chains with the 4 cores on this machine.\n", + "#If you have more you can use more if less then use less.\n", + "\n", + "sm = ps.StanModel(model_code = Independent_t)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fit Stan model " + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "fit = sm.sampling(data= Model_t_dat, iter=10000, chains=4, seed= 302675)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Print model output" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inference for Stan model: anon_model_35f1fbb6789109324c5876037799ebc7.\n", + "4 chains, each with iter=10000; warmup=5000; thin=1; \n", + "post-warmup draws per chain=5000, total post-warmup draws=20000.\n", + "\n", + " mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n", + "mu_1 0.71 5.0e-4 0.07 0.58 0.67 0.71 0.76 0.84 17756 1.0\n", + "mu_2 0.64 5.4e-4 0.07 0.5 0.59 0.64 0.69 0.78 17313 1.0\n", + "sigma 0.49 2.8e-4 0.04 0.43 0.47 0.49 0.51 0.57 16661 1.0\n", + "cohen_d -0.07 7.2e-4 0.1 -0.26 -0.14 -0.07-7.7e-3 0.12 18023 1.0\n", + "lp__ 21.54 0.01 1.26 18.29 20.97 21.86 22.46 22.96 9917 1.0\n", + "\n", + "Samples were drawn using NUTS at Wed Nov 18 21:16:26 2020.\n", + "For each parameter, n_eff is a crude measure of effective sample size,\n", + "and Rhat is the potential scale reduction factor on split chains (at \n", + "convergence, Rhat=1).\n" + ] + } + ], + "source": [ + "#Print the outputs STAN model for comparison of independent means. \n", + "print(fit)\n", + "\n", + "# EXtract samples generated from the posterior\n", + "samples = fit.extract()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot posteriors and Traceplots from the MCMC sampling of the Stan model." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([,\n", + " ,\n", + " ,\n", + " ],\n", + " dtype=object)" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ],\n", + " [,\n", + " ],\n", + " [,\n", + " ],\n", + " [,\n", + " ]],\n", + " dtype=object)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The posterior plots from the arviz package show the 94% credible interval of the estimated posterior disitributions\n", + "\n", + "### for the means, pooled standard deviations(estimated on only for the cohen d calcuation). The cohen d standardised mean difference \n", + "\n", + "\n", + "### Kruskche (2013) as while the point estimate suggests a diffenece, the credible interval spans across zero \n", + "### so the diffence could reasonalbe be credible in either direction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ============" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Two group categorical Regression." + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "Two_group_categorical_Regression = \"\"\"\n", + "data{\n", + "int N;\n", + "vector[N] y;\n", + "vector[N] x;\n", + "\n", + "}\n", + "\n", + "parameters{\n", + "real alpha;\n", + "real beta;\n", + "real sigma;\n", + "}\n", + "\n", + "model{\n", + "// Priors \n", + "alpha ~ normal(0,1);\n", + "beta ~ normal(0,1);\n", + "sigma ~ normal(0, 10);\n", + "//Likelihood\n", + "y ~ normal(alpha + x * beta, sigma); // Estimation all through one likelihood function\n", + "\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_4d1ec45e7f8ca5ad8aff3c29b11001e6 NOW.\n" + ] + } + ], + "source": [ + "sm_2 = ps.StanModel(model_code = Two_group_categorical_Regression)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "from patsy import dmatrix, dmatrices" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5,\n", + " -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, 0.5,\n", + " -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5,\n", + " 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, -0.5,\n", + " 0.5, -0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5,\n", + " 0.5, -0.5, 0.5, -0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5,\n", + " 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5,\n", + " 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5, -0.5, 0.5,\n", + " 0.5, -0.5, 0.5, 0.5, -0.5, 0.5, 0.5, -0.5, 0.5, 0.5, -0.5,\n", + " 0.5, 0.5, 0.5])" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Using the patsy library allows for easy design matrix generation and then manipualtion for use in Stan\n", + "DM = dmatrix(\"mean_NEO + Rotation \", df_Mu_neo_condt)\n", + "\n", + "DM = pd.DataFrame(DM, columns=DM.design_info.column_names)\n", + "T_DM = DM['Rotation[T.counter]'].astype(int).values\n", + "Eff_DM = (T_DM-0.5)\n", + "Eff_DM" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "Two_group__Treat_categorical_Regression_dat = {'N': len(df_Mu_neo_condt),\n", + " 'y': df_Mu_neo_condt[\"mean_NEO\"].values,\n", + " 'x': T_DM}\n", + "\n", + "Two_group__Eff_categorical_Regression_dat = {'N': len(df_Mu_neo_condt),\n", + " 'y': df_Mu_neo_condt[\"mean_NEO\"].values,\n", + " 'x': Eff_DM}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "fit_T = sm_2.sampling(data = Two_group__Treat_categorical_Regression_dat, iter = 2000, chains=4, seed= 302675)\n", + "fit_Eff = sm_2.sampling(data = Two_group__Eff_categorical_Regression_dat, iter = 2000, chains=4, seed= 302675)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inference for Stan model: anon_model_4d1ec45e7f8ca5ad8aff3c29b11001e6.\n", + "4 chains, each with iter=2000; warmup=1000; thin=1; \n", + "post-warmup draws per chain=1000, total post-warmup draws=4000.\n", + "\n", + " mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n", + "alpha 0.64 1.5e-3 0.07 0.5 0.59 0.64 0.68 0.78 2181 1.0\n", + "beta 0.08 2.1e-3 0.1 -0.11 0.01 0.08 0.14 0.27 2219 1.0\n", + "sigma 0.49 6.8e-4 0.03 0.43 0.47 0.49 0.51 0.57 2661 1.0\n", + "lp__ 21.48 0.03 1.24 18.33 20.89 21.81 22.39 22.89 1570 1.0\n", + "\n", + "Samples were drawn using NUTS at Wed Nov 18 22:09:13 2020.\n", + "For each parameter, n_eff is a crude measure of effective sample size,\n", + "and Rhat is the potential scale reduction factor on split chains (at \n", + "convergence, Rhat=1).\n" + ] + } + ], + "source": [ + "print(fit_T)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([,\n", + " ,\n", + " ],\n", + " dtype=object)" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(fit_T)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ],\n", + " [,\n", + " ],\n", + " [,\n", + " ]],\n", + " dtype=object)" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2gAAAG4CAYAAAAwt8Q7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydd5wcZ33/38/sXlGXLPeOjSgmNiUQSADTHKoDoYVASGg2AYKTAKEFkoBDT4CQxECAFAjgACa/mOJeZFm2XGT1etKdrvfd2z6zM/M8z++PZ2Z2dm8lneSTTpaf9+sl3d3u7Mwzzzwz+/083/IIrTUWi8VisVgsFovFYll4nIVugMVisVgsFovFYrFYDFagWSwWi8VisVgsFssJghVoFovFYrFYLBaLxXKCYAWaxWKxWCwWi8VisZwgWIFmsVgsFovFYrFYLCcIVqBZLBaLxWKxWCwWywmCFWgWyxwRQvQLIZ49h+0+I4T41+PRJovFYrFYFgr7vWixHBusQLNYLBaLxWKxWCyWEwQr0CwWi8VisVgsFovlBMEKNIulBSHEN4UQY0KIghDiZiHEOW22WSuE+KwQYocQIieE+LoQIn0/LRVC/J8Qohxte3r0udVCiFujz0wKIa4TQnQct5OzWCwWi+UIsd+LFsvxxQo0i2U264EnA2cBeeAfDrLd24ArgUuAlwF/knrvDcCXgdVAGfhQ9LoD/AtwNvBM4HLgXfPbfIvFYrFY5hX7vWixHEesQLNYWtBa/1hrXdJau8BXgOcfZNPvaq37tdYTwDeAN6beu0VrvUFr7QM/Ay6L9j2ltf6V1rqutR4BvneI/VssFovFsuDY70WL5fiSXegGWCwnGkKITwPvBE6PXzrIpsMtv5+V+nsy9XsNWBLtexlwHWZmcRnmHrzzUTfaYrFYLJZjhP1etFiOL9aDZrGkEEK8CLgKeAWwAnghB/8iOrfl9/E5HOLD0X4v01ovBz55iP1bLBaLxbKg2O9Fi+X4YwWaxdLMMiDAxNgvAz51iG2vEkJcECU6/znw8znu3wWKQoiLgD99lO21WCwWi+VYYr8XLZbjjBVoFksztwAPAYPAFuDeQ2x7PfBrYA+wFvjBHPb/DeACoAD8D/D/HkVbLRaLxWI51tjvRYvlOCO01gvdBovlMYcQYi3wr1rrGxa6LRaLxWKxLDT2e9FimT+sB81isVgsFovFYrFYThCsQLNYLBaLxWKxWCyWEwQb4mixWCwWi8VisVgsJwjWg2axWCwWi8VisVgsJwhWoFksFovFYrFYLBbLCUL2SDY+9dRT9YUXXniMmmKxWCyWk5VHHnlkWmt92kK3Y76x34sWi8ViORoO9b14RALtwgsvZOPGjfPTKovFYrE8bhBCDCx0G44F9nvRYrFYLEfDob4XbYijxWKxWCwWi8VisZwgHJEHzWKxHHu01vjKRypJZ6aTrGNvU4vFYrFYLJbHC9bys1gWCK0149Vxtk5tZevUVgZKA4xVxxirjlENqgAIBKu6V7Fm5RouPe1SXnTui7j01EvJOJkFbr3FYrFYTji8InSvWOhWPGa4ccsIVzz1DJZ0WXPYcmJhR6TF8mgJ6zD0IIxvh1wveAUIPHAysGgVrDwPVpxPednp7M4KdpT62D61na1TWyn7ZS5ZfQmXnXYZr7jwFZy15CzOWnIWqxetJutkqQU1Jt1JemZ62DSxiY+s/QgdmQ7e9pS38aYnvYnFHYsX+uwtFotl7mgNQix0K05OKpNwYB1c+qaFbsljCjeQVqBZTjjsiLRYjgatof9eeOT7sPcmWHYmnPNsWH0xnPYUakKwuzbGztIBdo7cyq4DeQZ1wIVBwCV08Zyl53LV6c/nSRe8mI7zfgu6lrU9TGemk5XdK3nSqidx5UVXop6nWD+ynu/v/D7/vuPfef/T38+bn/Rm61GzWCyPDfbdBp1L4MIXLHRLTj5ksNAteEyi9UK34HFOYdCM3dUXL3RLTiisQLNYjpS+e1C3fpZwfITw/FfjPvVLjIQew7kBRvt2MlAbYTSYYvmyUzl39UU857Qr+IMLnszFZ17Cko4sTrEXZ2YPTO5A7P4E5Pvg9Evg/OfBec81/1ae1/bQjnC4/NzLufzcy9kwuoGvPPwVftX3K659/rVctOKi49wRFovFcoTUy+afZf6xnsmjQmMV2oIy9JD5aQVaE1agWSxzIJyZoXr7zVR+/m3KBybQJYews4PisruYXnQrTmcnizqXcknXMp6bWcUidSqOH6LrE2hvAFX/NROeh6rXIYhmOTMZnK4uRPeTcbIS4azD0bchdJWO5R10XnAenU/9Tbpf8Fo6n/48RMuX72+f/dv89Mqf8p3t3+GPfv1HfPK5n+S1F792AXrHYrFYjgArJCzHmaHSEOcsOwdHtClebvWZ5QTECjSL5SBorak9/DC5//5vKmvXMn16yIY1WXY9fRnLnnYZTz3/WVx22tO5/NSncUr3KXPfbxiivDq67qFcD+255u/opyoXCXZvxN+7mdovb8X7zv8inAyLnnIeS176Gpa97i10nHEGAB2ZDv7sGX/Gc898Lh9f93F253bz0ed8tP2XkMVisSwQoQptRVrLgrF9ejuLOhZx6qJTF7opJzX7J8vsHC3xumecs9BNecxjn5YWSxvcrVsZ+dIXqfb2cPMzFZveI3nOmt/mFc++hr867bJHlfMlslkyS7OwdMnBN3rVq5NftVemvvYn1G6/gepPvsHkN77N4ksuYuXV17DsZVcgMhmefeaz+fFrfsz773w/H1v3MT7/gs/Tlek66jZaLJYFZPsN8ORXQ+fJUwTotv7beMn5L2ERAI/eg6bqdQCcLvucaxD16/EoxCJDUxBryWNH8NRlve3rynrQ5o2psn90H7STN7Ow0+wWSwpZKjHwiY+y711/wg9P2cMPrzmVlz/V5Sdv/iEffc1/8IwznnncC3KI7mV0v/IqTvnqLZz3q0dY860/Z+nKIab+5kP0/e6LKP7iRrTWnLHkDL7/yu+T9/K87/b3UfJLx7WdFotlHghc81OdfAUffHmUxlsbKnevpXLXXfO2v5MKrdq+7IXe/B1jcif0rZ2//R1DdFQF5GDnb3PQ5g91tBVXMh3z25CTACvQLJaIygMPsOtVL+fentv54d88l7e/6oX8Q36SZ73jNsQ5z1ro5hkWrSRz+fs45d82cdF3/55TL/OY+twnGHjT7+H19LCscxnfvuLbnLroVN59y7speIWFbrHFYjkSvOJCt+CYoQ4iHI4GHYZoOX/7O6k4SD/fNXgXOTc3P8dQ4fzs5zgQC7CDedBsFcf5Qx6tO9KxAq0VK9Asj3u01ox+6zp63381P3yJ4Nyvf42vLFrNE/vWwzt/DasuWOgmzsbJIC57Iyu+/jAXXfcplnT2MPCm1zPx939Lth7ypRd+iSef8mSuvv1qivWT1+CzWOaKEOKVQoi9Qoj9QohPtHn/fCHE3UKIzUKIbUKIV7fbzzEnLpV+ElmNsQcjEWgHC78rT5h1JS1Hh2iEOCrXbXor7vuif/TfB9r3Kd1+e/THUe/muBOPv4N5yh5DpzIvVOsh9/RMHZN9y6N9bjmz5YgOQyr33vsoW9Rgqlw/egG5AFiBZnlco4OA/X/1F+z/0b/x8796Nn/96Zt58e7boedWI85WnBiJrlpran6IF8jkywYAIXCe83ZO+48HecKHX0D9rus58HuvINjXy7W/cy1rVq7h6tusSLM8vhFCZIDrgFcBlwBvFUJc0rLZp4Gfaq2fCfwh8M3j28qIefQyzSsyAPXo2nbYULL+e2F8x5Ht8yQSso+aaOyEuWnKd9zR9FYcXvpowkz94WF0Pf78idvvXuiR9/IA3NR3E24YidWWJifC7RiOobJ34oUqT5XrFGrzF26cRsqj7cvZkzaqVkMW5y9V4/7eafqmKvO2v2ONzcqzPG6RlSp7PvAeDoztZPQL7+CzL/wI4p4vQ88tRpwtP/u4tMMPFaMFl+EZl+GZWtPPqUqdkhtQ8sJk5kcI6M5mWNqd5fxTFnPxaUu49JwVPPdV/8gTL99C/jPvZeAP3sDpH/84177lWj59/6e5+rar+d4rvsfyzuXH5ZwslhOM3wL2a637AIQQ/wO8DtiV2kYD8Q2yAhidy46V1iilcZx5KsqQCLQTzADe/Qv04tO4VdT43Qt+94hycWNhJpWMXjlUX839vCdqE9x34GZefdHCODuPBK015TvuYNlLX4rIHKM85lhwlKdg+GHg95K3giin8dGIEW/X7tSxTtCJBGDr1FZybi4ZF540uWcHC7E9lhr/rj2TPO+i1ZyxvPvYHeQIOeo8sTlw1B60Q6C1nrXM0NHiP4bCoq1AszwuCaem2PueP2GrGGHx1z7Ney99C2y5Hh76Drzn9oOKM6010xWfiZJHPZQEUhNKTagUWhtDRCljYiit0drEZFfrIeV6SMULman5jBVdxkt1xosuk+U6SzqznLtqEeeuWsy5qxbxG+es4JW/cRanL+9ixaIOlnd3sKw7i9IaL1C4gaTsBQzkauybKLNu3zRfuXUvq5d08vtv/E/e9sTPMP31L+I+sJ6//8o/8ckH/4Zr7ryGf/vdf6M7e+J8UVgsx4lzgKHU38PAc1u2+QxwmxDiGmAJcMVcdlxyQw7kqlx82tL5aCeJQJmjobN5cIYzV3Rz1opF83R8w1BpqLksudaoyjhq6VJu7b/1qETR4TxoN+W3c2n3Ys6by84EuEHtoG+X/TLLOpfNrWHlCRjdDE9+5dy2P1LqFXSo0HUfHQTHTqDF/dumGEbsOZu3ghgnsOdSaglBiLe3B1KpTa3nfrxO4ViE1YXbfoq88AV0HcVE8jGL8tt+A4TPAnEUlVXbCbD4AikF83TPzOe1qPgVFncsPmbLGlmBZnncEU5Ps/eP/pB1p05zwWe/wKvXXAkH7oWbPwZ/dEOymr1Smq3DBTb05dg0UODAdIWRgksgNWcs66K7I0M2I+jIOGQcgRACATgChBDmJwLHgaVdWZZ1d7C0K8uqxR2sOeM0zlzezRnLuzlv1WKWL8rOeYZocWf82yKecuZyXvG0MwHjiXugL8etO8d5jfMR3n/ljbzkzlsZedsb+dx3f8BfbPoUH73no3ztJV+jwybkWh5ftLu5Wr+p3wr8l9b6q0KI3wb+WwjxG1rPnnYXQrwXeC/A6edeSOYIZ3elkvSX+rl45cVtWnVkBsRgvkbZC+ddoG2f3k5XpouXXfCy5DV1lMb9kYSSFfzKnATawZ6XD/fnCaVmRjzIs854FmcuOfPwOyuPgn8MQ596bkGrTiADUh5286MmHqrJz0a5/WAeq4JqrREnkId3KF9jvOTxnAvNeqQCQTZfop7bB5eI2TmQLTQNS78G+26Fp71+Tseu1kOWdB3alD4WKx5sqgwwPVDj1Ze+84g/e7QetHpfH8Jx6LzwQnQQoDyPzLLmSRDhl2G+lr44wQXauuF1rFm1hjWr1szbPtNYgWZ5XBHOzLD/HW/n3tPynP2ZzxpxNrUXfvrH8Np/hvOfS99UhesfGuQXW0dRGl74xFN58ZNP4+oXPoFzT1nMGcu6yGZOvPTNzqzD5U86jcufdBqfee3T+NW2p/Ldrify3rv/hd7feyWf/4/v8xe9X+Dv7vs7PveCz9nFrC2PJ4ahye4/l9khjO8BXgmgtd4ghOgGTgUmW3emtf4O8B2AJ17ydH2kBljOy7E3v/cgAi1lXM+RY5X4Psuo14DWdPaOUO67g2VXNJyMYT5P9f4NrLjyNbP2k4Q46kicHKLD5tyXB9lwtGDyjTqWGy/anATaEfS19n20UjjdRxaJoGsVYAX6GAg0d9s2ui+5BKFbvK9agTCGbSxO5s+DNlvslLyAIFSsXnpkBnrPRJmZqs9zL1p9VE3pm64eMqcqPvf452TJY8XiDjqiwhRNfeIVQc39Gt2xe4Lfvng1py87+Hg40gmcwxK4uCo46sqHTQItfwCKw/CEFx72c3GIa+eFF+Lt3Ik/PNJ0v9f8R1PZ8xB9dLSuzrFtUJmENann1Dw9K2PRnzzTjgHWQrM8bpCFAn3v+BM2rJgm+7EP8Lo1v29u3h+9CZ7/l+xY+VLe+4ONXPkv6ym5Ide97Vk8+MmX8bW3PIO3P+8CnnvRas5ZueiEFGetdGQcXv/Mc/n8x/+Cic9fT9c5NSbe8ge8M/hDduV28Q8P/4NNrrc8nngYWCOEeIIQohNTBOQXLdsMAi8DEEI8FegG5lTq7HBf+uW770b7c0zKT+7Lud2fTqVMfecOKvX5L3ve5HHIdBhDdmQjmVwR5TVXW1QV44HShygkMpdnjpirWTKHCaZGcQg9b9Uhqw8+RPmOOyndetsRfU4H5vocC4HmDw4hy2UaYyb2PDTGxKMVZk3t1rrJaN4/s58tk1vYNDDD+v3TR7zvnoky46V5XKMNTEhuiyiN/97Ql2P3WLmpR6SSHCge4GhyP/2w/ZhXSd74PAs0vxrJmUO3tVgvclPfTW3alfoj3weViSNugg5nP292jZWOPr+tloPBB5qPcQjP+76JMpPlw4yZ0gh4hWRRe4DwqIuYNBM/W1rbJitVVO3goddHwolvaVos84AsFjnwrnfxyNJpRj/4Ot5z2VUmlOH6P8S74MV8bOzFvPU7D/DkM5dx/ydeypffdBnPvvCU+Uv8XyCEEDz32b/FRd+/nyXPW8U5f/sxLtvxIm7rv4Pvbf/eQjfPYjkuaK1D4IPArcBuTLXGnUKIa4UQr402+whwtRBiK3A98E49x1mMw33pq2oNWa3OsbEH96C1a07HxAid4yNUvPYCTQcBSit2Tu+cw7E1DD3UfsbayRpzUCtEu0T7qGhI6aabZ+82NpA5fIL+nJ+4jjis6Eg8gFN7YPcvD7Hl3I025RrjSwdzDxms1ENcNzIS2xi280Y8duKCIDIl0KJrmnfzR1XJsXTzLanj6CYP2kB5gNHKKMsXHZ1HZ749wBoNGkIdieI2IY7pcaY1TLvT7B5/BKZ7jvx4B2l+XDBj3s0IvwII3Kiy88GoBu2fOc0i6ij7/ljEbRaHm/9WB38W7horsWPkMNWpo0mc8v/+kGBsDLTilL4bj8hDejDiZ0vr2nqVtWuprJufpQGsQLOc9MhymcGrrmLnkhkees9v8Ynn/TVCK/jfq5mSi3nh9tfgS82dH3kRH3n5k1nZSPI6aXAWr+TCb67l9Csv4W0//TfW3PcMvrftB/xkz08XumkWy3FBa32T1vpJWuuLtdafj177W631L6Lfd2mtn6+1frrW+hla6zm7SOZiYKZn0bXWENRANhvrvvTxky/82fv8xdZRirVIGLgFGNuKiDbryLQpU+15lG69jVpQY6A0cHgPVliHwiCoEKd1XSIn2xBEbfYjUlboRHWCnbnZgrBhIB8ixPHQLUxt2Niy4lcI2yycrLVmQ2+Oeq086716eJRG2lHYs3vGSxyYNG1wdxzZMgJHRHxd8gfMz7hP/Co6KqhS9sttr81RHCz5rStjQhqTkMFDjDPl+/iDgwd9392+A39g4NG1LDq+L30IQlRo7pn0BIEjGrlpmkjU+TWoHsYDGPrmX4qDeY2ORnjOaVxGHrR9E2U29EYLj4d1qDeP885MZ9SO5n3Ohz6bN4F2iLGSeG0P2r+H2bfImGtcGACtEVqaayLnLx+z7XNnniZhrECznNTISpWhq9/L0OI6P3jDSj5/+RfJOBmCWz7NaP8e3jD5Xr705mfyT3/4TE4/gcrgHhMyWU79/A2c+faX8qG7fskrt1zKFx/4KtfvbI30slgsc8dUcU0oDEHQCL1JjNVWg2Z8O4xtbXpp3fA67pl8hLoK8dpU4oNUmejcfpjelxgv7cKodGTsCRUbooexxlILHXc5LXlEItP4dLvdpARdb7GXgWLKyI4j7uZQml3MUaKlz3fd8Dr2zuxtu91k2aNcjwyy7Tcks/K37Bhnqnz8FsWOQz9VdX7Cn5jcA177NaKSnoknAPbeDAfuabSljcHbtohIYciEwLXSEuLY6RghkHhKq1VksUh57doo9LKBf6Afd9v2WbvsiFIH/IEBvD3tr+XBaC8INYH0WbJhB8EDD5t2pe7T9O3S+PwcSrDvvx16mr3EB9NhRxPud8uO8SSPMmmfUoQzM40X/CoCgUARxM+D/vVm/dYUcZ55q5fnaDxosmK8cTk3x7rhdQcVaE65RFffEXghD9VH6SIh7dp0OAHsOGjfjGvRGY/Rxv/zwbzldLbBCjTLSYuq1Rh6359SXCL41BVT/OPLvs7ijsWM3f7PFB7+CV8+5Vp+/qGX87KnnrHQTT1+CMHKj32TM9//Ft522118cOxSvvDQ3/NP9/16oVtmsTwm0ejmHLShB2Ey5aGIjYuUQZOIi5Yy8b70CVTA3YU9rB9/sOm92BBLPGUtuWrtjMHkkFEb5p53qpPlOBJR5WSSz4tYbKTz6lI5YVnRXH8sNmIqweErJc45X6dlu3bCLj5uU0GkmvE4bJ25m+laPt5w7hxljs28m3ETO2C6p/matgrgtIe2Tbt3j5XYPVai5Je4vf/22cfwiuA2hIFUIaEKZoU4xp6a+BCV++6ncu96VKWKzOVadtq+J5y0hzl8dB4OjabT6TTjTSlUJC7SBR0c0ahD2Zh4SPflQa5Y4M7ywBzOg3ak177ektPm9/dTve/+pjYIAUKn5IE/O5yx7AXsGC0eWqDNYTz7AwNU1q4FTIGjil+ZfZ9G++kYH6NzrCVUsQ2jlVFuPnAzh+ydwzy3DivQhIOqN1+rI719y16A68/2ah6uMq3y/bnnHR8EK9AsJyXKdRl63/sJFnXwly8Z4m8vv5YnrHgCm+/4H5as/yL3Puc6vn7Vqw9ZeelkZsUHPsvp738Xl/9kPZ8tX8S/93yWD97wy0PGs1sslnboNoZCynhpM/ubfKm3CY9BKxR6lrcpznNrGEa6+UcbOyEp5heFCh1utlel8t+yjhFZSaEN4bSEOGpqDz6UPlry28EWsZ52TfjYUD3f2G8Lc/WgtdKdmf0sTwRlWqClyunXZfs2tKMW1CjWD5PzEiMDmIk8iImR2XhbKT0/3ruZfhjZ3Pi7dRCkBEm78dEzUaZnokwtnijYdWOLod/sKeuZ6WHH9I7k+sdkokqRsQDSSjWKshzEkG+lKU/rUMO0OHKINxuctvg08t5M45gqbBr/6Qhe4xA0eWv+WA5/LHdIS766vY9g1BSBVVqyd2Z2yGjxV78mLBkP52R1gtv6j6yoTJpZIXOBC9lumjqqzbMkV63jBXJWpcHmx9XhFUs42Shkq1oqsQYj0fU4lPKZ3G0iBlLkq+NmnBzqc9G9E46Pty0+dNhFsYXT2L9niuioFu/v4bhrzyT39DTO/8YtI3iBTMbSwZ6plTvvpLJ+/ZyP0w4r0CwnHapeZ/jP/gw6O7j2SpfXPPl1XHH+FfzfTTdx8foP0f/if+YNr37VY74AyKNl1fs/zuqrruLS72/nb6uncF/li/zet2+kb+oYrgdksZxkaKCWmmGVVRd/rFH88aAhjtA+WT3avDvTHGKYhDYm0/7m7xXd2ebjNO3LbONHIWOHE2i39N/GuF9q8o7Es+9ayqQqHVqj0eggNUOs2xjsSlKX9ZSwMz+2V4fpK7QJneMIctBaaCcKZWTUtc72x+fR+Ao4vMG2O7+b+0bua9pWFgqztivddhth/3YYfrh531rhLF0CQL7mc3/v9PxMiHmNNrihyy35HY2xli4SEisSrSEITehhGNLdkWnk0cjAeM0G7ofeuyNh02hjkycmNUZiD2UiBKQPY1vMubeO+xZDO57cyKS+j/PVOtuH24hhGcLghqYQ4nZorVmSXQxpI3rkEVRKfJoctMb2Zowq3P3DuL0j5rOB23aCJSxW8IeMl8iVZcaq7T1GsmTCO6e9qba5SgAPjD3QEMhzJXQR2W6TS5+c9Ox2xvmp6cmeGW8Gma7uqVVbD+BUuZ5Up0w/W5J8tui61jZvOXx7J3aa5YxSOP33mQmGdss13H47slJNRJm3Z++se62n9DDj7oFZn1VaNSZShAMrzjfn0Lc2Ohc4Up+mruagMJT0QyDVrMqgsz4jFarWMgEUuFAen/NxrUCznFQo32f4g9cA8IurnorKZvizZ1zDV392Jy946APULv8bLnvJmxe4lScOqz/4V6x82zt45n+P8IGSwl11Ha/71i3ctnPuDxGL5fGMAEpukBj97v4R3D29jQ1a16aiIRh0FCpV80P6i/3Ru+YLv6tljaOJqAx5a6GORoW6No2LDBw5Nk5nz6CxS8a3H8ILoSmGLmiV7Le+cTP13l5K9z5Cfe8gI3mP6YrPTLVOpd5Sej0qt5B1spTrAfcPb+LOgTtTe0818iBK7LAetN6725bMl2HQ8BRGbY/b1yzQdDLzLoSYcyGHRVmzEHg6j6my/j5UrYYsNsSE9gNkXDJeBo3rLlWyqG8czlfyWkL5wnojfK6ag6k55PJ0NBYK9qSHQiO1RGW7qbipXMhIRHXuHyZzz0NU7llH94GeZoEGZt200mgUCqrbGtCtNm6yzp0KEXWvEdpVr4AQ5Cr15P6Ir8224QJD+VoSupu+RGNFj77p1ESh1rDz/yCuSthmsmNVfmvDW4nGEU4qDM1sI1Pjpu38bDpkUCvY82uYmJ0vN+tjqd/roUyE975Jcw6Hql6ad/OJZzlGAOPV8ajsfwtKgpJkZuosHzlMQRMxW0RsGN3AcLXftFtrBqarbBqcmfXR+3un2bN/xHgCZcBMzaVvqkKYeNBa5UPcC206NjO78Jqo+XTuH4A2YknXfSPI0jmDLUWLXFmh4E+yfbjYlLM3UBqIJlIwfdW5NNqpRuhorGptxP4hlgRJc9rU/TD0ILpuxl9218/RUcXJZMSEvll37VCMbDK5gnPECjTLSYPyfUau+XN0vc7E376b6/t+zrW/83k+9eMHePPeD7PoOX/EmS/9wEI384Tj1A9/jGWvfQsv/ZnPlbkpzn/yf/FXP3+Ib9yxrzFjbrFY2iIiIyj2cJnCHIKSX2Jwzy+aQ9AiYsPx4WIPD409xO27JhgsthpbjXsvX/XZPWbCpRr5Kc0Gb1vvWEoUdoxOm22m9sL4QQwJrfYfSUwAACAASURBVMmFFdzQbcwQT07hDwyC1siyi1JQDyT7pyr0TNeaPstMPww9REZk2Dte5qEBM9FTu28DTqGMRuMfxJMQc9gUtFrOVLCk2bSTDzxC5Z510evmne0js7dDN4ek/mrbKDPVIwg3bPE23HPjdYzdaYpGbOzPM1X2QEBYrqG9UnJ0jU7i6mS+OS8r8Urs/mWjmIdXgGrDE6vchhF6YLrKdKFmrn1KaWQik85VARPVkDv3Ngz8WKBlpxqeCBFKOjIiKhASe3pbY/9avHztitFEfRJqScf4SGMsTu6ir3iAX+/azdBMNFZ04xz2jhSYLo6zo3BvI1Uz48w28aMQRbwitTZLBGhgcW0Y5ZUSweekzFsdC6RKNbn2QjSWaTDTCrpFoEW/BS0FO2JPdtxI3+eMO26hVhwCYF3PNLdGE5z5ihlXaVE/1zzQndM72Z3bPfuNSGxl+iZYNj7TNlqvWC82TYy0Eo+34q9+TbVmRPxQvtaoCBkfKhpzWya3sH1qDzM1Hx2LvZZxoA9Vuj47e+FyZ7xIx/D0QcMNhaAprPFghWD6piv0TDQK0TRVrFQhOvLmNwoVRX/s+j8YuO/gbY7oSK17q1Neel3Ls3Egz0QxmgQpjx1+iYYjrHxpBZrlpEDV6wxfcw3KdVn+jS/zyYc/w4d/82N8/n+HeffIpzn7Sc9iyav+fqGbeUIihOD0v/4Ui174Kv7gpuU8dbKXZ/zGD/jFtkHe/6NHjskCuBbLyYLGeESU1qCUyRcRgqJXZDy/F3JRKF8bA2O6XmzMnqvm4h9aa2SphCwUmqpEPtCXa97fITxoOrWNPGV5w3COvDRKaR7sy6G1Tgp+FMIaD0880mS8xiJArzwfIURUYEEnnqmb+n7N1K7/A69EZtniJNwwnrmXhRk6xvNorfEigda6FleSL5Yyz8N8Hndnu5LwqTwSJY0hVq6kFojVrVsDUNszQDAxlSreEHvawuR4lXvXN/Yr25TQbtm3UBo3NMcdKbhMVeqgNdWt+/EPHEhd90Z/+Q8+2NSwW/tvbYQP1tOeI2NshlNTlO+8K3m57AWUH9yJPzxF2mMRG8lu6DMe5NlXa/RdbFwKP0gMRR3XqlFBw5vQFCqqKdbqs0MxW8Zy7CGSSrKisAsqDWE5WB5k3DvQGJ/p6o+7t5G/9SakDhthdplMkxPGCyQ7YqHtu6wt7p0dEhh9dsdYiZu2j0FpDGe0MTGigY6CR+fGPUilqQQFZLRGmiM9dBCJXVTq+rYXDnFVQCJvbUc5h+N5bB17GHrvgpmGKF4WhR+nPVi/2DrKdGUOEwJRHyilkZP7ICloE90nh5g8zbk5E1qcPBuaPUVKy4YYjdbzG8jVZi38LDKp3EJh+suUqffNv4jyXXcfOq2rnQdNiJTXPd04ya6xogkbP0wxk/iVdIGZpoJAWkGmo+kT5pDRJ73ZIcqtNAm0Nh63ih8wUZ3AD+aSz2oFmuVxRizOtFfn3G99k89t/Qd+Y/XT+eldp3JV7qs89YyldLzx35qzgi1NCCE469rP0nHRZXxw3RPITOzkGRd/j1Ap3vDN+xjIzXGRXYvlcYZQIVlCY4CqMPGgSS1RWqPjioZtQhydlBcsKWSR2q6y7l4TRtc2Esds110aYHFtuP0Mc/qDoUTH6zdFM9puIJmYnKG2bTtbf/btxOjUWlLzQ/ZPVvBDFYUXach0IABjs2hEbMzLkEq9CFohHJF4LzRxcRIQQbRocNTu1pyc2IhMCzS/vx//QH+bU0+d69hWGN6ISpWc162/RUokmC7iD49R9cOmt6VUuEFIsG9zI1yxfz3snr0EySxPpWi0efXUQyx2x/F69kUnJVNtAOXmIKg17SE+7yAObRSCohtQqzfCI2PDcKpc58YtI0mVe+X5DWVer6DjBarRRlOrRvXPtuZeNA4f6J0ikOHsMaQ1O4fzbBqcwVnUjf+Es5PXm3o5+pzUIV1+HlEem32sxtbJb06tluQJdpWNB0o4zR60oXyN3ijcUUVCuD5rCYpIaMcho+VxnJTQ1UqRlcaTprwivZXN9JfMNTpjfB1LDtweFQnRtIYQt6IigRZfEyFDk72mJNTyLKo1zr0rG+ditlRlzA3A9hsSb0+7sN74tY0DM+zdvRWKg8hK45zKnuJgywK0FgVpHbNKY8SL1ocMKU7ub6WSrQKlYGwLYmhDY3+1WmNCp93uYoGmNVO1KTaMbkiE1CzRoxWuLym4ZtIgV6nzyECemheY3C+tmyaeoFmgNZ2OkjTGfewnTY3dOXgzU/qs7fIEWmsemXiEgeowQ7kqNT80XvTWtoR1KI0QSsUjA/nDHhesQLM8xlG1GsMfvAbtB5z37W/xy7Hb2Tq1jX27XsF7/B/y24tHyLztx21d7JZmRDbLOV/7KmRX8ndbf5MDk1tYs/xrvOKSM3jddfdx776pw+/EYnmc4YRVlpX2RR60MPreVqioGiNRiE3a+DWhaRk6RQa0YlthbbIOVVKjMVWiWbouot6ytlq0v+7KEN3edHtbI53DIRU6mfU2R5Fa0zk2TDA4SKneCMfL13w2D85QcH2zcK4QgEb6AUJjCixpENnIhFABrgzYXTW5bRpNpjVHJQhRRH0SCdimpjI7F0l0NOfhNUidbOjTMVYCNz/r/YNVWotDucI4ik0qdo6WyPenCh64M6Tzr5J9tPVcmEZ316dY5I43DD9B9LsAHIZ3PWgq2qVaOV4db9p/1Ves3TvJxv5ccnyRNSJ/aLplwW1BI+Rs/12oaM0yHXlHBLpR6CGddCV9KA5z6tQD5lRDiVsP2mUD4WufnDsOmQxy5VJEZrbZ2MhBawjy5mamZEB6oEal7jvrLivyW4wRKwRpy1ZqjYg9QdEEg69awhxbcs20lk3CQ6NxEEYM9NyKo/zI0AeBRCvVyE1KfQqgvzaRyg8FHYt7pZkseQhpwkOTwhuxdzLljZPRRAXVaRzp4bjx+GtMWsjAN+v0tdzIhZpv9qI1lbX3JO3K1wK0EMz4k7NCIZPJjjY5aBDda8rciXGeWjUsMumZBcRv3GLu43jxeZEq+x+EUT/F9+/YVhN2fCixE3uxZMBodZQZbyYRVaHfIrbjMaSNeCu45rm4rmeKzYMFbj5wM7vzu1lW6qXTm0CooCmf0GmSNRrZEuJofm8OE08OXasR5pvFU3ocib03RR9XjU/mTQREN1mmdg+wd7zMYL4RzjvQu4nRwgBUp8lXfap+yHhxbiHVVqBZHrOEMzMMvuvd4AjO+9Y3GQ6m+NKDX4Kpt/LOzP28PLgL8fYbYNGqhW7qYwZn0SLO+9Y3kcMFvjrwcu6a2cnK4if48u8/lQ/8aBPfWdc75xh6i+XxgDH+NFpBxStSrZvkc6klUqtEoMX4wyP49z0ImU66nQ6IQttij1JscHfcv6PxmfvXs3RjYx2kdCUy431rvxaTToXyiek+9PiOpveV0ogwnOVxGsq1lFoXDmjzblfew+gz1TBeVMhMUKWug2g7ncw8h8oYdCKQxosUe1siQ0y5LqreCMfSuRLhlJkMiheXbTnx5Lzj9jlugK8CtkxuJhO6OEGrxz9lZKWsuTBUEIaozaZf3EixBVJRdFsKeCTGfzRrv2QxwhFoIZpEpdl9FIaWzNqb7WqBuba5KOdNa5PfAw2RcyBXi/onZQRH0R+BZ4SJisPThIDqdFJiPekRpSPhU0j1U8rcmxmE0ggideVF5D1KVx0NpvKMemP0lncmfS9olC7vcSfYNrkV3Xs3BFVTQCK1vlggFbUofLSxOsTscXrGvqGG7hXNHjSzvETc+aY/WsNjRSTuTxmN8veUbDLatVZkhIDQI9Qq2l80SZFZhNIaGdSgMt7okeg+3FUeYFduV7IvWanhdHWAVmzoyxnPNDopUBPvd+vkVipBKTp+tNe+tayaadyD8T0Q6pCbt/ZTdH0IfbTnJfeW05q3FN8nQoAjGKv1ziom0hB+8Wead4HWiGiMxPsbqR1gzO1tDmdNFbhJrmnruo6hB+5MSgQKgkgAzyLl+YyjCMr33IM/OWOWNoCk37WZE8IP4/ZowsgbXChN0l2dodsd5+zR25ITDPN5hBvfWxqlFPf0TFEO6iitEqGP1oSlKsV1jTDYsl/mwZv/k+r9xjNYqPnNfRAdRbg1alv2U7x9A04QsnRHD07FNWMzCJt8mjPeDPvX/xdbdv4ItGKy7FGph22f1e2wAs3ymCQYG2Pg7X9M54UXct6//iuyK8tH7/k4mcoLeGtnjTcV/h3xtp/CyvMXuqmPOTIrV3L+d79LuH4b/1p6M9+v9eFueRf/++5L+eEDg3zshm3JrKzF8nhHReFkUmt+0Xs3Q8UKYNYx0+ikOAPaeDM2btyDLhtPSIfIJAaAbJnVTXt9kryX+Jhmejn52wFUsYS7raX4R7xNJgt1l7VDd5ELqskxZCTQGvlc5ocb1qiGxaRdsahRWuPIlNxxhBGW0jfeCSHwPJ/1eyaSJmwZmsGXKhKCjRAyWTFrC5XvvIvqvfcmr6udfVSj9dViD1q8DtRkyWUoX+NA4knSifFZjzyQp02s54yJe5LjCxWg0iZ/6tdQK4bzm+jL9xqjNVKVd/Zu5aGxxjmYc2++PiLbgVbG0BUI45ERGWNQ63h9p8jbIMxV0mikUuQqfrSndoIz8sAoiVKqyVsqZMiY28eGiTvIh1U2VQZNWfziYLQ3DYtWoVddgNbQEZTQUaERpWQSwtqUZ5Y4+8z5lW65HW9gHI1DbdsestUafqjYMVQAxzGhaVF79ruTDJeHzXj0q4QybOrfsaJL72SZFf1DyPtNbp+WEurlxqlqk0+V5AY1O9CifMFobEQ5gcH+O8xPGVDxK5TrMwyHOapx+KEOkxLzYDxGGQTIwIx5TSIwlNOJryTaKybXoeIFVOMFjrUmk2+Ez/oTM2RXLkv6wFEBUoFUAd6BUTL9g6AkUoMXhWSmvcXK6UiEcahDUBK19xaEBjdQLNmxCbV+bSJgGvos8px5eYbqM4CDzrRWKI2OkV7gWSlki5eq6fkRnUeHMPdaO4EmpGx9qQXR2BYYdfczXUsVHIk/lKqiGe9maM8Wprb3RUsbgFYhjqxHuZgaXzZEVbw0UnY8x5LJMtmahyhVk1DxmbX3oG75AaCRoRedJ+yojDKeWsNQKY0sN+cxTrvTeJGAlErzv9u34vohguZQbCFDdBAymHPpiJdX0RqpAjORkuqgmXreRDGEddOmaJwrrQl8D/bc1K4zE6xAszzmqPf20v/Wt7H0RS/irC9+AdHRwdcfvo79k2Xe0vEU3j35JcSb/xPOfsZCN/UxS8dZZ3H+d78D19/EdeJdfFlPMXbz73PjnzyBA9NV3vEfD1GsBYffkcVykmOKZThJDlEcKhXqkMyeMdw9A9GGGrdWpnNkA2XP5BcpNNn6mSzOLk+FbemWn1CXLeGAsTEbbyZADvbjDw41Ny4y1DrWXIyIDJ2p3gPs7jceAak1hKnQtnj2OiX+lCIqqabNcYXJR6uvXg0dHUagKUmolfESFWss3tnwOEBjrSutzUKxQgj8iR1GXADKqycCKDn7IEgEhfI8Kveu54G9o0yUPXrGU6F+LcIm1M3PpbNHb0O4KWPRr3BWtgIaQgmlIEcldE2nRgbgwMwehoPmPCotfQhTa7o5gv2TZeP1wHgKNQ6OMEU8+nLVqCy8Jus4LOnuhJay/mljN23E91d2MDazl7unt7JlaktyHZ0woByYoi5l5eIq33SY0onnkq4VqFUXoBGmrHg8THr7WHrb2qjt0cSAEOajOvICx22puGhfglaoDHihTMRPXNwh5zdEsvYDhOtHHuOUtxJBV32GJdMTUI28moUhmNyVXOs4X85471SUg5bycirVWM9LBbiBpBblM26f3s664XUMFzcxLHP0923DqVXMuWkgFy13oTRCC4RW+HE+X6nOeNGjomqsr+5Fpdo9Wamzd9yIMqfkoTbu5f7908lFy65cilYK4fsE5RyhVCgZUh+ZxsnNkJ2eZLpcTzwl8U+tNTLTlYgqqSSoqN+0JF/1cTw36TszzBLXIwBbp7axvTQEwonqCs0WaFIbr9720QKn9g4T3rEWUt7z9GLNsTDviJb1uHuwMbnhhVW2Tm1N8kej0wdg3C+yZXIz00HZeE1THm2Akt8QtcldHXq4vmSi7CW5al61QMFteIb98TGWF/dC/zr86LhSS0LlJ2vliUjUr9w/hdM7RCYaIDuHc1RrAcgAtetGM8mhFUpDRjgUgymkVqztmUjCthstbPxe8+sM1/ZSl/VorTmRXD+0Jlf0TJRA9MyfLLtU6l6jzkG7ao1aoWQs0CS/2ryXWqU0e7sUVqBZHlO4W7Yw8Edv55Q/fjtnfOyjCMfh7v4H+OHuH/F72ddwzeS1iNf9CzzxioVu6mOerjVrOPdb36TzX37M15f9KR9fFDD4k1fxo9d0cfryLl7/LVs8xGLRSpPNjSML/TjKhE9pJU3oYcHFrzXCsYRbwBHKhP1hxMpEsYZDhu6Ht7GqkPJmpaz3sULzDLhG80DfNGPFqHKYcBIv06y2nXYamXMbxR3Kw5NM7txNNaiiVDQj3JKoHxtby0v7mS4UE1GmwVQD1BrZvQglAyPQ4hl5k5pGR7l1sXtFPZBsHMij0HQg6J2qsGuskoQwJYUmlEJqTfnOu/D2mrLVqlhEFotkSqbqmkDTJFBpGFiBk0le1Ci6s5mm0Cqqk6wcu49TZraYAhpa42iTC6gjQ6yr1FgnbqTgMl702PPIWoLhTYzMuCilCBQU3YCSF+KGLrWghqia6xEbltUN9zAwOELGyXD68kWAOGh404w3YzwjAorBFGL7FtTwNNWgmvLWyKjiIYjIwh3y8rjSjwS0jmbxU+UjYnFQ9cCvQpynFr0ntSYblhH1YpORqqJcK5Hq56QAhFegEETXWCsy24dYsulAElqXeOUELKqNsshL5S9HRu3q6YfNldTgROJUqZTAS8ZD46NhscjYjMuBlu+d7IypVimmC3SMjZjDp4SwQuFUYxGgQCvKXsjW4QKuquMFisFoGYA4mC+pKqqh6ktTnTPeryPwp0ZY+vB63PG9RiMnFT8FIpSRbpYU/EkqfskUEAK0yCJkSNkLkrDmEIWjQ2p+mHTeLA9aMi8gopwwB6F1G3kWn6P5UFe5inILMLUHMGGnUoV0uVOmi+N7XyscFVBMFVep+EUzWdM6ZoUgH4URD9cLTbmQy0r7yEiXWrqqYTzho0IG8zWGpquo8XgJDE2PP05F1nmg3Mcjd/wPAkm+FtDbbyacJr1+ekoPJWLViR800TVeNB1HDmgCABUg0aBCAgK0NktQHKjtYjzIE05NRf0724OttaZUr5ENa6AlQoWoJI/Y3A9udH+fkt9Fpl5gpOCxfXKIGWrRs3D2Pa5USHnjJsLRKcbcXnaXHkyWZjkYVqBZHjNU1q1j8KqrOf3jH2f1e94DwMBMjg/d/XGe57yav5v4R8TvXgtPe/0Ct/TkYfGznsXZX/kKq77wn3xm9bv54OqljPzkDfzTpQNcednZvP6b97Oxf24ViSyWkxHHD+nesYn8nnsZmCihMLkPoQzwpGT/dGRMRjk+OCIJvVGYvIgOFZIZ3EzHZMEYM20qm6XXo9o/WUFJSXG6BNp48HQwe30oKX02T21JGb2ADkFp6rKO1JpFxRHEyMbUsTQdUR5HNqwQugXClPGCcHCkoiRn6J9aRzCwHuJQw1SeUUe1YaBJrZKwaKUVWQQKRV+uyni0jlDsQRuZKjC+454mwekWcpT9MiI6x1PymxsiINom/rzvZMxpapNP5TiCVjM2FknGO6NNYQGlUfEaWrJR4GFjf54HD+QIghrlesh4yWXzUIGHB8zivp3eFNOVcTaObyTT04+mUZ2z4Poc2LGDeOFkISD7xHORS5cnlyNmb34v26vDjbZqTagUS7KLG2tMaY3UIVJrk4PUsYhdtVH6aiORNwpwMmitEgGmdVy4Q+BEQk7gUJEeKl/A6TEFF5zyaDQ+omsd535p40kT0hSlELUcuncti+LKfCpEB5KscEzOY8rwdYSgq1Q3+YfRNRmtmdDWbnciyVdbXDWL/kqlIk9kw8hVUaio1praIztZNlYy74c+XSXj5ZRBiBOFmy0v9kR92Gx8Z6p16kFILQhNFGUUeiyRSK3pnWperFmkjOy44mD/dNV4zB2Hwb2bou1AZR1UmBJoMojyQhVDtT1RP0mU0iwv7SVbGmLvRJnBmRITJY9ASoSWjIR5vNB4JutBapF1nVxdI8qk6WdHK9qVTTQetKjtUiUhj2UvYOtwAb84EQnkRjctmtnJysLOpoqTYbs157S50R0EcmW8SLogkAE1VQcd0uGXqKc/m4h8E2zcXaqkcvaIxrWkENaa8guXTm1BAIE2xYmyaQ+a1knhm47iYPS6Mt2hJFJJavWAUAbRsyDyFObKdO/e2XTuvYVe7jmwBak0m4cK3Nc7xvJSD+HEDuNBS+cRp9ZZcxQI5RvRplwGdZ7mu9pQ80Pu6tvPqJxBVV0C5WMKSc3atAkr0CyPCYq//CUjH/4IZ//jP7Dy9b8PQK5S5803fISzM+fx7emfIX7nGvjNdy5sQ09Clr30JZzxyU/whM9dz/tO/QPef96F5G76EB/uvJG/ec1TeNd/PpxUfbJYHndEX7KVDTvpKlVM7TalUGEctme+Zkdmajx4IA9CkPOmAY0SAgdNhwqQWuO4ppKi66loNt0coNubNNUBI/ZPVhDFEnp3P9qtI4CO8e1MlL0kkR7ADV3q2k/ZcJowY4xX33fJVSfJ+uVG1TetyE5WOGPngeZzjAxUFf3u1kN8EdBdnKFeGDGCMsofGgtmqAYTZPygEbaYMnYlmqxOeSqi1z0Zebmkj2wxXPZO76a3sB8nDBkJcwyF09B/H4R+w5Cvl6CWIxAmWM9E/anIiE2fiki8S3XPw5Eh2aBCIMMkj80JZZOZVQpy5FU58UqYHUVhT24Rr1aMcghFItC01uyuj+KUp1FagBAIIZCOA236BaDshVR9RXdtikxYZbjsIXK9Sa6KlsaDZj6ewReduH5oFqjWmk2VAaq+KaYQ29kqtgLTSVlass+b4kA4gTO0nyDTnYQbloMKO2tjEBcD0ZpVhV0sK/YYzx3G4I+r5e0cLlDyfJxsBqklvg7ZWh2iLD0E0JVaWytfnmBouj+ZoCDKkQIj5qSUPDJkCu34QdpY16hQgpZkfJPLSGmYrhkTPtw9XeG8fUb4OdIzfZ0SLlorhC8pVv1oweyGZA90vL5XlBeYdJNOfmaia711aMa8HIWyaa2R3RixnK6YGoYmL1Sbfmr4c5vxpc9owaXkBTgqYEhOU6ib+3zzUIHCZAHHjz13EoYeRAxsREgFZMwx24TT+dKPLp05shuYJRTu2mP6SGAK9hjdp8js6sOpTNBZqRMqnYTbKj07jUEDU9WAQrWOWr4EgFw1YFd+D9uCATxtnkdSpj4bh74qiRCCTCqn1oS2mlbFB3BVnU31PjKCqOCIuYcdJene0oNQkYCOLpFS5t4QWqEEaBWya7zIvokSSstGaLWO8x0VrpTJRM1weYThgkvfVBmtNdVonb1KbojOe+8yoYlKmXzcZMIjHkOtV1Wj42VJIoq1gMFirsm5JqRsFEY5CFagWU54cv/1X0x87vOc953vsOzFLzavVeq8/gf/DJ29XF/chXPpm+AFf7mwDT2JWfnGN3LKO9/Jb335Zl62/Hn8xZOehbftel7f9xn+4+2X8plf7OSf79w3y+CwWB4fmHyyzqpLsLgTqRUyqDcZeyMzNVOeWQiqfsV4bDCz4E68Vlr+AK4fMDVVZbLssWVyM1OVCkLLKBfCIAKfjt4oty2UOCh0eZrhXLWp+qCK1mRL25zeyrNxfUlf361s6/s1QsnExNAyqrYYz9ZHFRs2DxepB9FabyJDRtZRAoR22N63A2LjVgjyYZVF1WG6vJxZEBcijwigzfIDWeFEfzY8VTPeTNxEstmGiAHwfGMwidBnOJxmsjRmnjVhHOIpCNHgFkwlQYznZfFkDjbvbZnTFolxtWRwLVlpypjv9QfZ5po+zVaMqAiN5ceyvl8iQlN9LZOqEBk/74JQ4fphot9Ma0xlTynrIIwnxNjxGg7ymIzD6Lq8Eo4OQGhGS/1sLQ9B6OKOm/BEGR03XwuZKNXN6IsEyb7pKgemykBUDCOpime8HtHASNpQi3KIYmb8Iv3FYhJ+pbVE6NAYukJQjbww8Q4W6278UCMyRqDtCkxYWk36qaVHBf25KjP33oNTdRMP2eJyL9ncLgSajIBQSrL1GZzQZW9xAw+PGy+P0JrQ9Uyhk8jrpjVQ840Yj8SvIArZjbxOjehAjQgVjtbs9AcRWpIN4pBGc56JQOsyoqMclChG48sRTqQCoomMjMmT6+50cM/CtCkuDy+EKaohjCfLScq8pwRFdP0m3bGoeiYIHYeUxnMdgiUb91DbtpZR2YhSEdKci+rsQKim8jcAVPwKxVRBDICRkks9VdhLEQvj6GB1H+0HrOqdxim7qKgto+UxpNL0TVcJ4uUaNCx6oJ9apeHpmih59BZGAegLjQgMZ2Yai6+jKauQHbn9AGSCRuXYhm8w6kcUI9J4ohwhGKnta1xF3yVTqBAtw5jcf0orwiBMrma9XqcWhCitqCsXpTHrCmJk4JQ/xs+GdyTe+1ZkNFHTPzxG4LmmQu/GPdR39iNSHrSM1EYsRiHrAliKWZLCCyR7x0vJ+YXR+IqvwqJiheDAaNvjx1iBZjlh0Voz+Y//SP4//4sLfvjfLH7WMwEjzt7yH7+isuRnXFcPWXHB8+GKzy5wa09+Vr/3apZdcQVv/NYuTtEr+OtLno+qTvGctW/nxnc8kf+3eYSP3bCtaQbfYnk8oIU24YpK4y9fZBwDoZ/kZCmlkbFbyIkEk5JUVYijNRmRRaPJaBjOVcnUJWhNiwE4YAAAIABJREFUPVTsnZwxhqdWZEoFMsUZiGaLNeB3nQKA6wWAShLpAbM2kyNQaAKlCKUmFyhKXoAMAwhCtA4phS5T+QI7R4uRsRE1VWscWSfwPar1AI2mo16iyy+iRCpwMPGgNcSgkPVEoKmo0MOKySGC3jGyiUcuTERsXdZxqnW0cDBOvtTaS74xlDumJjln+ySn7J+OOlYmeSjx2nJR7UyU1nQEJtSsKcfOEXFTCZSiO1qjyleNhZqzNVN1LZSajKyzaGScxfkaUmmWVAejExSJZ9KXisn7G4VRtDRFUxQKpBd50IxRL7VOJHN6Pms8zidE4ISp/C+tyQUVGNtGtm8zHVWXxc7SRhCf1oRKRwUjzOdHq4NUZA0nkCgv3q8JTTMdn15aQRCX1zdCAXylcf3QCD0lybrm2muBMfQ1SciY45vCKRlE5GGIDqFTiyBHAjAomZDceLHgbFBOdYIgV/ZYXuxhSW0ItKYaVPFClzNH70S6PnF1zFApymM55NZ+ZGQgxwLYUSFSN/IATRdFwgoTbru0MsC5W28DKRu5Ryoy1jOdKKeLHYVN3Ffab65HHLYZj7WoqIk6/TRUR8aMg+R7T4AMEQiUlibENlLuce5SqDSu9qmGJWKT3Yny0ZykyxyUhgl3iJwss6UyREXWcaQEP0B1dJJRPpnQi0cCAOuG11GPPMDpnMJ0cZpY4cbjByCepgm1QkYCbbpSZSRXpKueI1QKR4ho4XNBlyYZ/zXtJevAxa85j+zgzoE7jcjWit56nlt6d1Byg8hD3SzR4uyy+F7NugFM5SmdfRre8iVR5U2JG0hUEBhPWDzmpaK89h5QkrGgQBiacvc94SiT9SG8JWczWEh57QKT6xnGz4XcfjKyUWEy9hz62UVkZQ2Ng6oHyFINoSQV5SGROFKb66ejtfqUJqNMwSEvWlIjSC1REHe3RuNEVW0PhRVolhMSHYaMferTlO+8iwuv/zFda9YAMF2p89bv3o+/8gf8iXR4zqqnwJX/1L5qjmVeEUJwxic/QfeaNfzlT10G8wP8y9NeDGc/k/N/fiU3vmEpPZMV3vfDTc2lei2Wk5jYUFaYL2klNNpRqIFNRhygCBWU3HhdnUjLSG3KlmMM2XqgWFJTLK9pnCDO1zJf4E50hMXbN7F4x2ay+elUqI2IQoq0EW1pO0xJ8Mvo3b9kpuozXXLRWTOjrwIfEfjkgzw7ixPo0ghLij0NYwPIhopOP0+3P52ETIWdqxDAysJWHKTJA9Eysf/jw3d6ebJ+DkeF6JLJrVo2PoOaLOLM1MiGChmHUEX5Op0jpaRP+yeLDOSqpsR/VEjE8WotHgOdfN78dJCRcFUatGPeUq7fJNIa5UBgcdV4UhxlDCyRcRCBjGa9o5PyJULFYVISpSWVsJgyEAOy1Uri5czXZxiUURGGwCOpAycw4Zttkk+GC0ZI6TCko+rFFxCkwvFjw96jw+kgjPJ3Fk8UOWWggCREyoAlDw0ifG1yhzSsHMhTf9DkF+rIw0SUoxMvVKdib6bWVOtBY8wJhwFvgnByitU9k9GaegIjKBrLJWS8amRwysZizUQGf8vXchjlUupVF4D+/+y9ebRtWVXm+Ztr7b1Pd/vm9e9FHwQQQVcgIGiamvaiqakwaEQ6TSiFIjFHlaipllmKpllVJmomEDRCZdqUombaVGKX9gR9E0RARLyI17f33ne70+1mrVl/rLX3OTeAwHQkBuCdY0C82+2z9lrr7DO/9X3zm/Cp/CwDnwNC6T2fvBiYOW9aiFcqX/HRa3+NOXeZ0X3n8FPM38Wd4KTnnQt1WfHmjK+4sj3i1NqgMdrwGsBFLW4VrUg3djGDfnMf7X5gJ31nARC6NUOkijWmtk4FYNOP2fFDNDYQD9RouPe03EWqqqlBs9QMmueBq8GAw3ll7IMMcaXs4baHSPx78dKsR2bb+HTSiuPk6CpSKeI8Ls0QhZmdB2D3ahyqol65+8I26/0pS3vVve6h0DB30wcczrbxVWC5H+p/LDwLqjFpuQu+wkg4jFAkzEmzIyZzIxq+l7scu7bJ8A/eQ9nvUw0LbDVCipKZ9WvNOlatJIzHCFOUK92NYfgVI01dY7+ouLQ9xldFlHnG9R2MceMc70uceqqyAJSxlqCCV4kgXVmZbYX7iROhqrB7mSzfjHtFm+eSbOyGdRHbAF5XjbnsNhn6Ee3tEeJykiqHeDihVgIgXQtrvbabh0Prh8majas+rdT44bEP0PbjCy78aMT5V7+G/ORJrvuV/0R6JDiQrfdzXnDnXXQP/D5H9QLf3zoB3/GWvT1d9uPzGmIMh3/mZ0jbHd7wJwf4nft/h9++9dnw7H/B3K9/O7/+levklePFb38/O+N9G/79+IcR6y6Yg6CKE49pgW5uRVDjG1t9AHwAUVJWgVVRxRASHxTmNgahfkZ9U5BuJIAvH/9nxqMA0KJZxMAXuClWDaCfV5w9dxXVaA0vEhpTxw6wxXCAek+peSO7MUpz2g8weykApmTtSiMry9uriCQMdYxUORjDoszsIWggMH69wXmyYgsGVyOzEupwzH2Xmb86wrmCnWILVPFb23gbAYAqm4NxSDJtAs5jxeJ9qHGCwISo7K0vgyhy0sCa2aoCYnLW1GJJw/YBLFwNdSD1p4j0L5AOwvec94h6pKwwkTlCHTvlBpfHp5o1tcUu7Xwt1GepcmkceqiVzuOqHFXBGBPq31TZLTe4OHxgUh82FclD51g4F15//vwW7ZNXyc5u4b0yLAtSaaGuZqqEVh4aho99AYvXIU6m0mXwxZSLqAiahcbMjUm+wPJHHmBwYZv7P3B/0wTboOQ+x0dKpzYeqSupVD0PrQ/orF+hnG1jnSfZuNYk/h7lQd1rI+6i46dKQm18MfZVc74qWjH2BeeGD7CweU9TeyTj2MhcpOb78CZcw4/G2ChnrNRRumFct8l29BrmSJkcPtSvWb9jKnXQXYLWHAqs3nsOKsflnVFYP1cwv3kvCLyvf5qrbhtNYi2akWBqo4rxOa3+lXBw4x12qg/iZB6UMQXHNk5x+L7zFB89i/UBUJka7qijl8yjSbK3p1tZIZVHsxZ4pcLgNk8BMPIlVXxmlJWH2jhItJHFzl24yvLH7p8w0DWDFg98nCpeK3bLvYYXqpMJVZOEfxb9eBiyhyMCArhOLwT3zr/5k9+j+PCDpNUuc6fuY2b3fFjDThpkzhoYQ8TiphQ44bllyIprZPlOY2DiihzjalZU0MGIvHK0imvhIGGnv6fcQpEGIHXTwNBrswE8suckQdFxaNWQnr+CaIVGqORUGVex/QJCOgr99uZ2HqB+bnmZ7DETWTmnk/dNPVe2rCYM3meJfYC2H19Q4ba3OfvyV6BlyXXveDvJ4iIAV3fGPP8td3H0yH1sVH/M/6UrpM99J9j0UR7xP7wwWcbRN/4Cyfo2v/j+x/Fz7/9Z3nf8CfDcd9L+/dfwjpv/itWZjOe9+S6u7n5mjfd+7MeXUuz4IYWWiHpK9WiaomV92j9JFOrkUgm1XvbaCONdUxtUVD5Kdwy26mMGl+PfBcBzeXyKi6OTobk0ircWVeFUfpWhz5FYEM+1hzj1qY9w6dR5qtX5hu8IFWUCiUHOnUcqx4iCmGeSGEG9YnxBlm/S3hoz8jmVHwfjk3g7qopREF+RieXC1THDvIy1P+Fa2daYdDckKKqKGYRTfhfHYDGM/C6XhucZV2PKuz4YT5/D3xutEBFKN0Su3EtiUrbz881cfnD7bHCNY9L8tZbP1Q2h09pZz/tPY9DqSPChwbQS7r3aCgkwivfQ2T0HziNTUiXrRohWrBcXw6k54OPpfyk0hhFe4VJ+iSvVZsN0Fl7ZGJ9jLT8P+daefWQQmKoBBDBR+rjWH1NUHitJ4zZZowyDUFQViJAUJTuDSQ8uSQPLoxLz67TFsKjo1+Yc0TW0e3YTu7HTgMYruzlWDBqZtlriqKpU3pG7EgGywQ75yjxmZR5Gob+cAn7xBvL42nW4aHFfN2/XelZq7KyeoeaoFlhfodHIQUWCuUdqJ9d3sX/faIyZwgfXqt0gIa1dAjXY3ddGH/VJgzaMrzJ7YZt0d0TNVVs/pju8hBmW9McVRaWkxTYzu6cQkVDzBmj9wgIUIzzhsIbBNgjYfIeFrU/uOfSAIK2t1JN437QYqKWzQjhIwVVsDx3Y2NaiBhyVQypP1WpFJ0PLmfUB5WDEyJehTi/uveUPfrC55ZpB62z3MWU4MKrKsqnNqms31YPHIZUjG059fqsypZ7GqIIPe0AFOg+FeipbugmhXUwY1VI9Nq/QcRFrGiPDGoFTqYq97wpXdiburz4eLLWKTVr5enNvu8OcxGnwsRGFouL+K7uYKhj5zHz8DHZ9O4xDA29ay4WJ35u8yN5DHkVZ+cjd9Nb68e8cisGr8onheTaKi83v1WF9mHBRcDYcAG3ml5H+yakLR3CpCuoxrmJQfvoBzXTsA7T9+IKJ8spVzrzou0mPHOH4v/8lTC8U617YGvHcN7+XO67LuW/873lDOcPh5/8GpJ1HecT/cMPO9Dh+51tonzzPz3/sCfzgn72OUys3wMv/iORj/5Ff6LyZLzve5Tv/w3v3e6Xtx5d4hA/ZXCtStVQSjEB8NMDw6rnn2sfJXXgfePWoBoDWPrlG2u83H8Tr/Rwpy9BU14eaG4ln/6CUPsdphSlGkKYUWcrQl8x1UkSV3IUTbS58GHP546irqMSwMyobLaYArtvClJ6DVbtJMlNryLZHUHrScpf5jZOICNuMuVxcnBYxgcQzZe+oKsWqZ2tQxgQ+3Etre8TSQxvUhfum6EeAGgGasWyW6wyLipObpxmWA8QmxPNuZndP0h6v4fw46gKV3mjSPHp7VFKpQxR28ipKujRI65zDbu5CEQwp1PtJjZAqp8u15oQ9LbbwJg0MmoK0WthYw1P2h8xf/jigzYn9ObdOr3+G7uACI9ef7AAJEqpKLFaEshMOD413bPjdAI5FQq+wmCzK6feEvmDjLeZ27g+1ebo3aZMINFx0VDdqIhMVWIDQHlsoqgIQlh+6wML5qxNJm5lO8wSSNMjURBgtdXEz3QB44rXGZTC4uNzvT0xFCMmyF4PHcbK8xJ9dejDsTVcy8gZjDN3N+8JYZ9s4m+CyVhiCG+HdOPbM82wU25yrNphugKAxcW3YIlWcD8Y0Ndvx4PawOSDorN0dxp3n9Jgc1BoFfDXVXNnj1BEsUsKboH4NEwoU6a73mT1bG3FMgeMy1DgaEVbW3tcw1hrfSA2bKML2zi5r/THbOiBJSlCwxTapGyDja8hULz4XWwkEEnFvjZLRULMqrsKIbZxQ89JRes/axS2kdPgsiyua0N4csfOh+4Mj5cUPx7tQjBvTG5xDpZbr0tTP4T3bw3FzuwH8aON4uHT6IrOXJy6Dqn4PQKsNaHz8WfviBsmoJNvNMRJAkRlPZKLqleUH1ki2AncbFLP1sw0qFHttNDX/Gu99UhtZRanz+fWd8BYSxeNgfZPNg/MURw6EwxnnApBteNPwFKrfg0wxqdPMYJjPCOjHoY7NeIcijH1c042rk/mNf2jrfaOhFvTs8FPsVA9vPzR5/kh8/WRmlUeKfYC2H18QUZw7x5kXvpDu05/OkX/zs0h8+JzZGPDcN72Xr3nsIudGP8ELCsuzX/BfoDX7Oa64H5/vSBYXOfG2t7P0kdP8q7tv5gf+5AfYmj0Ar/gTzM5FfmLz9bzg8R2+803v5YEru5/7gvuxH1+kIQi5liyMrlASnebqmgM86/2ccVX3Q4sGCjERBqZ6LgnW1U1ufWRihGvVJuvVJFlK8j5DqXBlQakVxghrfocr43NNfj82XeY372Zr5xon13eZVMuBphYpKubPXAtJaMxRehd3sJsDRKDnlYXVGXaPzkdZpm9YOETJl7qoEcTHfkzQGHTE20QRbGS1RB3Wl5FBgwTLoApuc9dG/SADtZOG2ADtwTnuPn0RVzkqV5JguHbTytTEB/allj7l0XzAbA/o3X2S9u6QLT8ISfBUQ+yrbhtVSK5GBiupov02iLXkhaO9MWD88QeRtc0AqN3ekn6j5eQgXhXrQy3bthPyCjqRlhSvuOGQzXNn2fRDtlyOaGjOPHRFqF3avUxSDZt+bNOOnXi3p4ecQSYOfM1MQxnlnOE1J6YYJFGiiFJ0DpBj2chHqAhlN2NUbDHyo+Zqa1H1cF9+Jsjzmr3pscbi1JMPdijXdgPkUUeBZWdUYfs7ZP0cP3887rM2nTSh0IrLux8L66Seq+MNLrotiCl0nUQvX/vwZGlVQ62dKp90F9j1o+BcGEdaH4AcSw9wUzqd6IbNLEXZSNdKV2ISG5noSZ+61at/ha0mIFtdOSXXExhXsRm0sNhOJ1MeHTFr8ITAYFyx4TqgTa9wXEzdZf0kXDsNwD3FWYZVHmtWwRDZ8Clg7hXEeYwYhuMSial6Xnp0Y5fhlT5lGnKkoR83xj6FdxOSSBXjghTauGJi3iUTkKquwnuJtWueQ3aZxClGPOI93cF5stiQXL0y/Q4Y5o61i6FlQi3vrl88EYOWOS3JmjXx6hDnm/v0gKZm6jkZxtZIUBdvCKBSw3PSAGXdDNyV4R5MKDIt1bGuZ6gyExgz54KRUrNb6p3gG/Zs/mzse+cntWwAxgTQl1YBeIs61Au5hkOR1sa0NX4EaGqCFFqVQnxzT9NRHwgFJtSTuDweSH322Ado+/GoR37yJGde+CLmv/U5HPyRHw5vOuDk1V2e++b38l1POcpo+MPM531e9bzfDTrx/fiCiPTgAU684+3c+NdneP6HOrzuz19H2ZqF7/5tZPU2Xnn/K/ihJ1c8/867uO/yPkjbjy/BULAmpJgtEkoJvXi8nyTVAFWU46E+9KkaFYDBiDQn0wYJgAnBRy9pxbPpttmp+vho8LHVP8lp3eVyvt6AplIdNUQC8CaNJiGD5sS/OS1PLGZYRoOSeEI9pQAzCIe6KWkrxVgDhMTP++A0GYCMoZhrM1NZZquyYRf26IWMkESDAu/BVsOm3m3e9kglxYplvZ+ztlvE5sHKqWKNQXTV6+SXGY8rTq+F2g+1BmcDM+OLErs9agwft4ah95zDYSVhPpunTEJy5CNAy30VndQgvbgeErxuF6tBYpd7R+WV2fPXOJdvsDm8hrOhOfekriUmomZvCpW3DzD0AmIwUeEhTsl2Rzw0vMzp6ioPlVcDhlDHA/kVuPxxcBMpWKCMJnM4yAv646qxSU9FSIfjZiw+KL0YjEef1pEJgb7mlD5Y5F/YdWyMg+OoGkFF6Bex1qh24GxeOjgz1gnzpWoDQwJ45i5sc+DMtcBAeYe3CaNSSa71yfqhLtGrx6UtxEwaDAcZnYa6QoWJoyN0swhmtAaZSlV51Acjml0/4nSyFQ440CjFVDQvSFSm6tgCcyPORUMc5fTGgKGYJk2XCCqSatRIX1UEXwyaCfC9Flo5WvkWOjhPRQCLngAuDIaKyBCZeFBgMsp2GptLh2XsSEZ/7LjWL1BVdnXMB8YPUR931GY/JRXOdjAEKaiNvebEK6mGdXYS/jsqK1yWkncWsVVcS1UKrVCviHO0rlyDaHZh/JhBHlxKwzveY1HwDnP2Mg7FeJgxbU6cvZvZ7QcxvsBoBe0pievU/lcVxl4pKhcPb7R5iKRaINvnaCUtblu6jdQkVHEdQanaaQQxQtlOUbFBGhgPBEQgsYatcRHAFuG9WbkKUUeabzeAzhDkzJIYZtvg1YRx+omLqBKAoEQX0D3xsDowifeauEn7g/PFRU6NNyIrGWXb3lF3O5O4l1HITcVnjvr9Gn4vKfsUrfnP8rtxWz3iT/djPz7PMfrEPZz57hez9JKXsPqa1zSuS/dc3OZ5b76Llz/7BubHb+Aj/bP82+f8KnbuyKM84v14eGTHjnHi7W/jaX96kdv/8gI/9b6fQk0Cz/l38Mzv55997Pt4w+Mv8Pw77+Leizuf+4L7sR9fRJFJwko6A0CitvaJw/nAdjTF97EPTmqFQZKwuz6AKLwyCmoM5Y1H6BgQL7Hhqufq+GyUgikuCT3Ihv0LOJsxnmtRdTsIsDyTkYV8NNQJrW+y60fUhupKkGwJYNMMBZJ8J9at0aQyKRYRkMJBGv/tAzjxhKa9Gk+0D6YLHFnzLH/yAlbimKfyH0VICPVTeeUx0dFSgVnT4fbZp5JMnbJbD5oYcl80AM0bCY6X1Q4jLVhN59FoDOWKCjm7CTvRYEGVoRtwMr+Ea2WkK0corILzoSYQyN10k9wQW0uPwdRmBfHaqrDlRmyMr1FlZk/7gbjEcdIm9UxeEkpjsCbBzRxm2DkSLuRp+nQNfI6oMr/9SQbVmPTCFsn5y8yZDmMt434Jr2UQBnmFZ1K92DGWpQdOxXmeOOmd3thlc1h+2hDv232QS4MNdH0HMCSRedlauYUym2ss/VHlbLVGpQXtrRG2qCVZxLFoBCJxhwukxoJ6XJKEH9WyMWPweFwr7M16T3hfURyZQ5MExeBtsJLHCLcdnqMVAVW4iISkXoMcs8RRZcEYZOGjp/GbocfU1skPMd3Z3HjItsfo/CykEwZwbG1T79aYQmhwn6y/LnpH+NC5K0EqaEytWoVik1P5FSpfcm64E5gegXPDU+G+IpB1acpwuYeIJ905TWdwDhFl1QbjkQrHrBEoAli61i8agDbY3kDF0hqvoVWJUYeIQbynvTMK7z8jTd82by0uSUiadh5K4SpSSVgtenTPX8XUe10r+nmYSwRUPXZtC6lGeJuF3oQa5qq1tkvv3Gl6o9DPTtoZa487xKUn3EQrmUCGWpy6kVoqLQMjHXsw6lyXspWA97STDpWvKFtzkZFSRkvdKO9Uxu0uefdgrEucOG0iwS7FENbfaIW/9gnaxRbpMDYlFwmHAfEg64HyCrUhiLrwrDMIIhazFSzyubJVv3HDWP1EZisE9jMpdklLH1tXKKMobwSC6RGQVI3NDtYpyx8+jS0qijjemgVf7oX3mx32414JDJpBOTV8iEeKfYC2H49aDD/wAc6+7GWsvva1LL/spc33P3J2kxe+9X289p/cwmPKO7lz44P84lf/IgsHHv8ojnY/HilaN93EibfeyTf94Sb+d/+Yd937rvAJ/oxXwXe+ja+978d4y01/wwvvfC+fuLD9uS+4H/vxRRIGw1waGJ0WFjXgUIgn4FkSPtl3ymtUviIzgu0I7XHRJPgGxUtCb24Wi5AN8ua0mSidAY9PEzqjS2T5Dt6mXDnWY+O2E2EcIlj1/PGZ/w8/zhk8+EkKrSh9AFZqhM4FSAYjbmgf4XHdw4EFaNVW4SEHmZceiZEgwUxqb8OKMvYWSjWqhERYNXMIQtukdEgCjNyDYwQbX7+OS7tj+uMqys20kW+183VsbSoydQW1gnillW8y1BwRi4/GFZX3eK+kuWvWotIAgKqZHvLEJ1FVFeUnH2wkjoWfPuEOyZkaG2uXNM57TfAoRhWXTJgkgE0f5apiUBFcbaQhhkIMqU2jK10Yu9QITkIyX09SttYnu7BD68wm7aPPBeC8W2uEWS1JuS5ZRSdVLrTjIWbFpJeZKLgp+dj0/OMc4weuMB4WoTbIJJilLsOlOVQMptrbEqXUsAbpIDgnBiOK0Aj6vis5RSMV9aSR8XDGkvpyIlczBu8Vn6S4zpQkdeOBMCZjGmnj6Mhc2OYou35E349Ra1BNcJULEjdgvDKDy2xTP1ZtD/GqGO/3OP/ZUUHaz/HdNsPtSY1VeWypOYQ4bBaA0Ei8rvP00uaenRZbxRpnqrVQaxbXTuOhx/lqg3v7a1HW50GEBdOlQaHqUBtYHaJzaOVDrdK4qhhqwcKD6yzfczmwvP0cG+/PnL+CNxbxBZ31j2Oiu6ApXTjAAdo6qbVzicGlCaaMjbk9FGXBsXSJo9kNSDECrcG3Q4cj8OVE4pgXpP3zgFBS0RI7ASqxjhbgoF3gYHuFyhoyKehm0XQGoWMyto4uM4z1tfPngmww6WSUvQy0wrRbqHq8yXBZJzBkXtlNKqqFDtf8IMKyMH9puQORFfYoVh2Py27AeMdo+5Ok1XBySGCCw61HUecwxlI7GYXWC3UDc4MZFqzcfy7a80+HNqxtuHfIinVspRO5qGmx96kU9mstoVy8PABVumNloPWze1qmrCzdG8B87iusGyOqjI/t16DtxxdgDN77Xs696n/m0I/9GIvPe27z/fc9tMGL3/5+fuSbHssz+B1ef/b3+Jmn/q/cdP1XPXqD3Y+/VXQe/3iue+tbef4f59zzy2/kz879WfjBLV8LL/tDnrr2W/zmkV/hJW/9Kz5+fusRr7Uf+/HFE0onSVjsZbTUBrlOyDgxIk0D2NLnXM7PoXjm7Tqo4FRCf6GYrqaJQQRsERKpysWmxuG4GZdMahZ8NKNYc7EmwlhEK85t7LI2HJNF6U6ocAnyI29aCIYkyUjFInM9rt2yGlOPyCI4h429wCSp2STPp/ILXBpcIjFJYNAEqsozKh1GhMeYA/i6CXcTQuIhby1RpPNk/SLmT6GOSkbDAGRU8a2KZGfMOv29s2saqircphjURgbNTZgmACtJcJbzSo6SmQR1jrLyrG3sxvsrmzE2/7UWG6VHzofE28dzeYOhMsp1ZimqmPYmc6Lgsrp2TCnEkpoExHB4oRsSxYjPahlhfbou1VQSJ5ZB7zirC+2p2QvJpTMTmVnLeypfsjNVN2ZV8N6xO94rrxJCA/OL67tcGwQTES+W3ZtX8FmCRtfHySEADXiUuOfQwGoK4cBhTibmXNZKcPc0CYkvJ0DcCB6Hiw6E9Tgr9RReOb81DuyUV5w05xTcX1xk4MeBNdVoXOE9S50U7aQNEFICOHexNtCXjuPZcjMuvztGWymDvIqrrPjEsJTOc9QsNYmvPXOR+U9dCjJjN5G/5a0VsBa7OwrtFYDa0ESnVLypTbktPRYZE4JcrmEZJw25BWFceR79D3t1AAAgAElEQVTSi7SKCPiKkt7JS0gtbx3nqMZ19or1tTzRIQaGK0cZHTyBiQ6YLjH41IZnBQEOeOeiTLqivXuOpNyJ7KDH3nea7PwZFDi20G7eUkW2QImjrZG99OFeTZrhbIsZ0+G6ZBWjSmvjHqQKRh6q8b1obPx68s63HhxB/lrOLTTf7xrD5u3LDFZn2LjjINXB2SBTNMG5snb3DBMu+FpdYDMO2jlyV5AgDZstwOVqiwtug872A2BNsNnXidTbRIDmgfbuYFrpHTfL3poxI7VsePKbVbrIZ4oa8HdNG9TTImXk83BP0XEyTQxLXoNxDXBx/TQLG58Il+60PuN1m7E84k/3Yz8+DzH88Ic5/+rXcPinf4r5b/nm5vt/+qkrvOKdH+Snv/0O/nH657z63jv53lufx1fe8eJHcbT78d8TnSc8gevf/na+58/gD974g9x3Lbh6ceA2eMWfcpO9yh8s/lte89Y/4qPn9kHafnzxhwBtk2IxIVEWwUkAVU6Va9EIoz7RHfsi2IWjXN4eo+owsTaklSSAYNwYNcKg9ME8IsriXDYF0EyQlZUxkcMkiDpKV3J+Z5ckSipdZCfq5HNQVNQkTs1GBPwQ62h8kDGacYVkyR7mqPIhGakt26vK0c/D9WzpokPcJNQYrHcUEbBAYBcNgmztcuCvfgMjBsHhWkkYg0h0aItl/QZQwUu4dxHBmwlwpB47YCRBI0BbG+TsDENfut1RwdWrO0hiKTUyT14pTQc/cxhvLTaC4avFzgS4aWi43E/bofYOmvto6u1Uw5q7wJCU4thdeSrGWFpJkMnVPcsgykMji9d1Uz08xaAYrJHGxGLKd5BdP0RRkmoigazHuTsqmubVdQyXZ+ifWGCcF2g0ngHBAaOVO6iSbpNcy/TFptdPPasyi28ljSTNMAUish4oVMaSGBNZBYLbo/doYidsYL6B19BseFR5lk6u0RpWeBvqzaYtKELzc4k9sjxGlapBuZOhBrfDmj2Jck8h9Ilrkv0IvPF0TIujdpmZT14I14iy1xrgZ4Ng8e5MFljacUXv6m59PoLRuD/jgYexCV4Shr2jtK/loa+YFUQjGI41a/X+7GSWrmmhgPNBWlnvBY/S2ymjAA5sdOU0lUeM4fITn8j2jU+it9ThyhOPBuY2STCxUXflPXmZI2owzgVZL4Ck1KtvijwyaJ7Cefp5Rd5epVRHW6cMK8SRWBP61cX3YVPN5YoIkgWDCeUMMLV6IF/2VYwrh4w2ueua0m8cEZVDxxZIEzNZa6lraCt8d4ptjfMgEhjuFEvuq7j/lJlWglGhkvAMGC50sOInNZyRuTQEZt3H+75QbFEwJQX2nuwDp2hv9uNe8M2eqLRk7AtM3MVWLN5ke/bgY7IjZIQ5MCJI5bA2xcTnrxVh2RpQ15TwJKM8yCntI/fw3Qdo+/H3GqNP3MO5V76KQz/+Y8x93dc13/+195/lf/nVj/LGFzyZf5x9mFd94Kd49vGv4iVf/qOP4mj34+8SnTvu4Ma3/zLf/d88/+nfvJT10Xr4QW8ZXvyfOXDicfxe9yf4ybe9e1/uuB9fEtEyKbfHeiOVmASPKsZ+b0+rtfwSZ/K1xupaRRAP1jtWZtucWOqBCGU2T95eBgTfmjQapt1t/qnGxuQl1G6QtDBujHVFY4TRkpS2SZoRFB6MzoAIO6MSoxJ7l2ljsuDiv82oDCe88Y+tCJVXOrZFR1pYsQ3oc16RsgxNjacAnZgEWzn6rk9B1ZxJC7InORbv8emk6S8w6b8lJl4ygjBr8HVi8zCAlpgUNAA0b+Bxh+bD/eCRcY6kCWVsfmxcNFdIu6gJNWYG4d7hxSYprW/FJSaMM580rYVYVxbXfNC9jp1Wl/UjiyTpbASboRG5eK2VfUH2qA6rCcvSnbqWxZYlgjZ1K7XHIcAVF56ViZswW/WcjIsq9BibDgGXWoyrGPqSnNBqwSlg23FuLSYC/KbGTn1kBgNkSjFolnDpCUdDvVRdvoXSvpJRaS/sRWuDQ11MuD0BoKF7+2c555trWDGBQdOaawq7oz93PeIVu9WntbUZmEn1zNgOR8wi9ab0xlKkKxzupRAPL6RmWIzsSXAzk8Y6wZBu169X70c1huXTl+rFYI9ELc503jkaTQDjqnS7IJYymyFPDgUjCkB8OIZAA7MkCIsPrmPLiiwCGl8DtMjoqiqSpChKXjlMVXFHb4vu4BLW2nDQMrvA4PYj8X3kw2FB7OP1YH6F7XwQQEJZUMV9IiadgCcX5JBefexJF+dRPUmkBiW2HjB2L6MlKHrdQcQXkciMhwcSGbSp6Zo9cDS0eRhvo1kW3TsJLq3TmwEYz3UwIlzIt5rnVNkKjbArIyRaoWLJJPQiM4QDHGuC2kBsikttrIut2TUfnw0S5kNsfNZqPGAJY9hwfTRKSdNxDagcXhQ/06Ff7XDebQTpM5CKRaPZTD2ngpDfcWvzb28NVtLmfVXPnfiiMctBw5639pEh2D5A24+/txjffz/nXvEKDvzLH2T+Oc8BwkPp5//4fn7uPffxH1/xdJ6V3str//Jfcvzgk3n9P3ljc+KwH19c0bnjDm56x7v4jj8a8I6ffiHjugdMksG3/SIzz34lv5r8OG9625u4f9+Cfz++FEJikmtC0tkfVexWSVS2xTN69ST9HImgw4hlfqPPzAc/RmotWZqCGMp0lnF7CRCmD7a1NZHEeJNyKJmS3tgMn3SY3X2QYVGghISlZSNLErOf5dZREGFzVCDOU2QLzfgVyHTiQkc7bZI4a4LksiWWg8kC/2j2NjJjqJKZYAuuHjXBF7JBe0YwzkW2Kcg9S52YMkBkFnG4xDZNiX2YnPALqQ05aH2SL6Zxs9SY2LskmLSkkY0Qr2DhwEwXiP2UygqTJcGq23sOd1OOLc2EJNOGWizrfUiE6zwqzplPknAS/snLjUEJ0EjbasanoMSYBJO0mikINTcTK++aeXO2xbL0JvPg4dDdp5HS0YpskJGaQYuXwtO6st58r5Zbmng6n0wlvwqUCtblbPs8tGPA4PwEMBWteWw1bNYh3i2pMY1Vv9HIYqinNzw3YdAU2oMxRbaAMxIAexyRa2WMbz1OO5vlyOLjme9mzWu6sIgQGRgf5y64b4Z5cjZl7vI28/eeZv785VCPJHE+ItsmEmRxUnpa1kRWmmZ9ulmCiWyXAEftUlwDs2f/1f2svBHslNlIcEZkEl6Di6UIosK1pxxDur2wDjXbK4JPE/BFqGGLUjsjkPVzsmFJVrO/PphY1OA01FtF6a7tYMqCzG1zq++ymPbwCj27FIwx6iHFfSuq7EqGqGJUkKoGUQq2zdiXwXTHu8iee05VV9jSYVwTDSYhEa5iAohe7LVBlYVOhlHlo8lawwzVh1HepM081pEkKcP2UUjaqLEECw8aV82wn8LvjxdnGN3+mDgP4d6qbhYBWovUjVFjSPsFSw+uT/UeCwZLalvxXsHPrLDILB1asdFaXHEJz6XagKQe65YfNA6qtaW+ITCEbr7X7BKtDZ4ICgchuGb22mmYsVYb4j4aLXej7b6L4LXeZ5BFk5Uq9vub6+5LHPfjCyCK06c5+/KXs/KqV7L43FBzVlSe1//W3fz2Ry7w7ld9OXfISX74D/85bvlm3vBN78CaR6Z/9+MLO7p33MFNv/wuvub3L/Jr//uLJhp1EfjyV9P6rrfyf5o38ntv/lc8dHUfpO3HF2/MPvW2ANCiocJuUZG3D1Oms4goHdOjJRlZf4xxgaGorCEzHcxuH+JpuMU00qxepx2SPztJJyXJmtf0xpBGWVz9C96ED/zShQoqQUIDalu72U0deLVb2KUFqjQ6qsVEqy0Zt3UPhtfLkgBAREiMYavXpW0siJAlhuuXu4w6B/GtDEFwdY4HlOkMGBMbPAvTAGU6QU6i/bVPDBYLJrB4ag25LyEN7FUtLTRi2T66ys6xaPQQpaMQGJnwIiGJD3LBifOdpAlunHPg7ot01/rYtBVYFWtIBgXt3TyAhEbWVbN4QcI3zVoBdAZnm0Q1HeVoMcakHTTNwpjqMVduz9+Jek6kR0m80pqfwXfTxrwg9MaSwH4he67j1Dc1SNDkoCRYjBjms2XmklCLlY7KyBq4qBMN4NaJNENpZQcQlKodPmsza+hkkZmoAriQWHAoKO3x1ck6AqlJotzPYIxtOAVEqA6vBEfMpIVN0iiBjP2vRDC+xBLqqAS4ku80TdyNC++HYHrigrunEa6tPB2J0r2T5UWqBGzuEWtiDz3ARrliYugfnIsgKyb/Emo34yah8FVsYGzotJLITkbAFa3r63sNbqYuspABdBkEFRtZbBi1VylmV2CujYolb62G13dKf+Z63PzRsH4qWOpGx0LamWXUWsKni1Q2Q8UirsSmKYkafGsOjzCbrPCMuZtJJLQx8NbEWifDVRZh9ggGQ1LlzR5R20ZUueQ2Y73bZB/220FS6PEkvt574TsCoeWRKq3U0BueC8y6rxutg4ribFZPZxOpTRv2UI2hqBzb4wpNE/ai3vBly6ShbUJ81nRti5Zt40xC4gsUQzaujYAm73WHx4g0Lpm6cIJ5MxfWxTuibycGQ9+PcampPVIaUD38m78IYy53WO1fpF+G0otqea65KR/ryZLIoJlYS2qAweJtuHSGqp3iZ7tB+urhxvQwPrKltBdZnWtj4ynFUMfh2p+DgNgHaPvxeY/ywgXOvPRlLL3gBSx9z/cAsN7PedFb38cDV/u8+1Vfzgl3mp/83RdxavEob3zOr9Kyj3yysB9fHDF7xxM58a53ctt7PsV7Xv/iPYXEPOYbaH3vH/KK9L/y8Te9lHNr+zVp+/H5CxH5BhG5T0ROisgPfZbfea6I3Csi94jIr/xtrqsIpp0Rsj8wxjCqgvNaTUtl1S4dM4OJp7WmcFS1Q2Lt1iiCtTbq4ODAfI/EGnxsNFzccYRkKjm/fnWOZPqENot1XqqM2gea7ydGOLHSYalTO8CFRNU99gbs0QM8efbptCRlJgnmDyH5jMxMTathQ3JiDD2bgJn0GlrstWjd8uVo0o7gK0rcTAsxgn0YOGnGEMOqQ9TzpN7NoaYl1qCNTMUZv0VpPZOrxtqQTovRci8mgeBtGHvRDQ2STWM+4bEYnPpwap0m+HGBwZCuD9CDR+jfcTMuNVSzxxBjcZNetnENAzioq6mcbdGRLAJy1/wcYOwHjFaXyW96TJNIIlNzENVVop7FZBl1iu+2KZxO7O7xjQPkzh3X4e64mROtE0EOV25j6wyT+j9KJglGgzlJDeCMcxNWN5pPJMaE9UU40rk5rCMGl4WxznVTFjqWrrQwzjP2OUZhoZs19zrN4GQ2vJ6KpfYAFJqXC3IwAdoLGBvkskEFGd0kRXDtsC/HEVQINM6S3nsMod9UQEQ2uN9pgapnx5RUrQUksZNcNwLBTmeGvNdicOUUoi72Iwz7qHbfO+vWAugFsJbZtKarA6NWhxJaOGiUCFY+AINEAhhtGDSgas2C5igwaq9Q2VZjNGKMIRFLlc7GWqcIdpM2u4uHmT+zQb97jJPFecQVjUlPOXcimGgAi2mPmldVYxHnmI2lAlerWXaGFWmVQ7xHTEaeBtDe3X2IVr4Ooy3KdJ4yW4rj9nH9wiGO1zIyWgYqF/dcZMajljHP5hivfgXL2TLtxOxh0LII2lSV9vjKBJTZiSz60wCdUw51j7P2uCMkJoymikDx2FKPhdnINtfPptqYBWElmSMlHKI0wlYXZJVHutchhDYNwZzHRPl0nPuLH49gqySTFBdZeCuWqh0t8kfBpdKmEwatmaukiyBs33aA8Y2HAOgML5OW/QmD1luF5Zua++2uD0jGVbNvPlvsA7T9+LxGefUqZ176Mua/5ZtZfuUrAfjEhW2+9Rf+ihtWevzK9z6d5fw8P/3b38VHFw7wlm/9DWaz2Ud51PvxPzJWbn8KB9/1Ntp/8WE+8C9ehk4Xsx98PHOv/kue1rvC2n/4Zi5fvvDoDXQ/vmRDRCzwS8A3Ao8Dni8ij3vY79wCvB54lqo+Hnjt3+rijTlB7H1jLd6EEnqNSXyrXEf8CFOFnmMkhp0jcxDrYTImxfh14mCTjOWZFi428F3uZU0SrMbQSgNoQiARE06nJbA8noTaBloIdRh79QhTLJaxnEhW6NmJe2ANBENtTgShEnz8OkUR5F8mgdmDnDh8iCMHV7HG4KJcrZbaiTHMnFtDkaaexZuMfPGG5rVaRTRRSZJgqhCZkMJ4Rt0j7CZlkw4BWAzXrcyy1EubZLHIFikXbkQjUAt2+ZEREOjrmNPVFcbGM4wATdMOfnYetzjD2Dh87E1UxaQbwn2LQCeZaUwonO1QzdxAS5IJzJTpZH5SmwIB7JlREXFxfW0NyZtz9NwSs6O5AKiQJoEV5zmw2OFJ1y+z2OuFJFk9yVRtS+2amEoS13nCphKll2bKHc+YaP1fZ34muvCZvbV8bZNG6WkAxGmyN1WsbzdJDIlkAcxMKV52hxXXhgUicY+25ynnrkfF4hXa0mbUPoTFBHArIB88FQ0earmlwXmP1dgF7mHMqy09xcLxkJC3Zpr94Y0w105ZefI3gxFGpaM9vkpN5Gq0ky914ngZmLDAztYxcj7UUQE+taCQpysMZw9FmWgN2mVq/T1qMzQKJ/GercXHszn3lPA6EkBy0VrEqjIXwWlo1SBYSai0DIxk5TARMC50VznQvj5Y9kt41iiRQSsnn6dXtkaUDpIySBx3dYSIpUoCuKnyy7SKHawIRxY7DfPs1GM17D2DgAsmFoggZdUwP3W9athcYEzG4fbR8P6cOnxNbYJLE7TKWdz+RL1rmsOo+A6oiTisNQyLiktbJS5JaJuwnwd+xNCPme9kJLEvn42wxdYsd2T0ITS3DjSfwZQV8584hx0Hs5X6WQuwczQ0iK5wPDC62uyqcJjjSIyEg7bOAYbdI6Q7O2yOK9xjrg/sb9iRlAs3UD7pqQ0TOu4GgNYudsIayaT5essGlUGaLpHtFthyUs/52WIfoO3H5y2qzU3OvuxlzHzFV7D6utcB8O4Pnef5b7mLV37VTfzMP7uDrH+Jn/vNf8pdswvc+W3vZrmz/Dmuuh9fjHHDbU9n5m1vZONj7+cTr3oJPp9IMJhZ5chr/gi7eBz35q/m2ulPfPYL7cd+/N3iy4CTqvqQqhbArwHf9rDf+V7gl1R1E0BVr/53vUJEEZUKVSNfAtEKtYa502dZGYzwB2YpDs4y7maUszPgHYlYnrR4W7xO+FhOk1CH4bIGljUAzSXBIj2NH+FtSScADaJjGVRz7VCrEzIoagbBGqGdmgb1+CnpXiLSsCQiMF7qsX18MSTr3iHGBJlZ0qH7Dc+n96xnIdYiIlRAlXaZ76QcWeiCMdiq2gtgxDCaP9F8nbhQn5pYi/OKRrMQ7z0qhs2sxBGc7K488SgdyWhlKakEuVQt85vNZmhHgFYbdxiZJF1eEtZHYzaHxeQM/MSRME9WaJk2VoRcpGHQwqwIC8ki5WOfRj7fQRGcneGW9Ci9rE6wP8N2qAk0JVTgiHAiq13qQgKv3pPYlI6kDWuk6ihuO8T8006w3GuRWEuWZU3tl7GTXlhXbzkWpIZYRJMAkBE6iQ1rJBL/GxJFa0xMMKEdk38vtjFkkTCAPUwtmAlrgTTS2frrlZkOxJpKtYEx273W5/T6ADGGNLreWTE4Y7k9PcFq+zoQWH/mMyMRYTBzR5i5sI2grN90jCQxOOdJ1AUDlySAwPp95dMePukAPro2CmU6z7gTeksladqMe/OG5QBu6hpJaNpfhLUyIalv7jswaOMIflxskE1RUrR7ePUYQzj0ENOsv1dFkxZ1jZ33jlGVY/PwWskgJ5HA6IpCN5nqa2aFlZk2C724Ft4jsa4uS7ustK6Pct7oYxjlg9MMtahixGKrcXNQ4NUz107DYUs0MTHzvSilFnI3xqPRJCREJzF0ssAWUVXQXQK0kY8KsYmzmPA8CBsHgJlWghXL7rGDVE++bo8piMZDBp900Nbc5CBIJI49wZuEzKSUPrCAFR4/HjfPpBqYzZlOwF225v7CaUzLj9EkyG3FedK1tQnLqUGpMF6cmPMMXI4gzF7cxkiK14rlmXYDsLzJ8GXBDC2Go7qZdoB6mnTwMzPN16PI4Nf3XCU9aM+BTYOBUZyz69MjYY/II0OwfYC2H5+XcDs7nH35y+k84Ykc/JEfZmdU8epf/Qg/9577uPN7nsqLn3k99Nf4v3/jW/hvvS5v/bZ3s9p95KZ9+/HFHU997Ncgv/B/8MC5j3Lfy74bt7PT/EzSNk/4/l/h7kP/lPSdX8/gnvc8iiPdjy/BOAqcm/r6fPzedNwK3Coify0id4nIN/ytrtwLH/ZGTGBsrOBFEZ18vKo1qHccrip6i50p5sUgZRWYt5hEF1Ems9RdBe8aBi1QOR3Gcx2GM0dBCI2CMczaFreu3Mxctoyo4klw7Ywrdzwt9PWJyVh9xm1EarKiMSkg1rclU85iJp56l93oKhilXUcXOxya72A6HZLFRZBgD1/GBNGYINcUY5oatu1jB2mnhoVuhmYTts6qR7xreiGZ2r2xnTBYnkcSS6EVx7PDADz5a19BmqYsZPMce8YzG4B2rHcjSS2Nj9I/A7QTi0cps1nuH3VRgmTSLczhZ4MRgNpQQ2VFUGvotZImITyWLDOfLeMWj9X6PCQmYE+ZO8HB1kTxYaeS0RrECHWPKGmSyyU3BiTIsGqnw6pqkkidb+N72UQFZicAzU6BwWK22zSRFpNQJRF6ClxvD0+Ag53YgIetJBxd6CBiI0CbRpiKHl1tmJK6SfZCJ+XwQjswL5MNQnCuC9Jcbw2bt66ydWw11D2KIYl1OJ2kR9U5zvDoPyKzKUe7t5DPB/c7n/XQ7jKdjQEoPP7GA3SzBFvEdhFeme9mZDaCBgAxXN4ehzpFAkjOW4vk7aVmjawJc/4VB28jjwA4MSnGCMUeBo3YYLpuCB5cHauaQYvsi5QFLk3p+uA8mERmWJqFUg4uznK52g5A3hUsfPI+Zk6fwhph5vxGBCWhwfby3CJH5zuMThwCV3F4vstcOwmuos5h4rolWdYwVNZAaMQQJI6mKhGtsBKs6A0Jphg3eye1NrDvEg4EjHck88ehPc+o2uXy+KFwXKDxCSEpYjztcejFqEvz+JnDVKYVau3EIOoZVX0QsDZBfEU6vEpiJJqzGMTYwDyaKdCr0ryHFleewPFWkFiq1dAXT0Btxmqvg2ptLkRTbLloZlgwPU7YVVbT2bATTNK8H8UICSWaWJyp3z+T44S6KTkw6UUYli3Mc+HwGg6hFjsZ3VZC23QxzpGqCf0hjWkOCUzNMU8x7gDu2ApFtoSzbZgLz43QNDuMxsaWIQ93tHx47AO0/fgfHn4w4Nw/fyWt66/n8L/+Se46tck3/ru/QBX+62u/gmfcuIwfXuOn/t9v5E/aGW/7tt/i0MzhR3vY+/H3EN9w+7czfMPr+FD+ACef91yKc5OcWYzha7/3Z/i1Iz8Ev/lS8r/4+U8rKN6P/fg7xmf6JHz45kqAW4CvAp4PvFVEFh7+RwAi8n0i8kER+WC/39/zCoszLRbnWhiFo+0bgJj0mw5tm+J62UQiZlqgnurIIWae8VQA3OIcAIdvfBLDJx1FE8vpr/56/PwClTi2blwODatFGBx+FkU6j4iwPHuQpewQJzYtZjhGRSizBUp1nMmvxfEFNsRYQ1652Mg3nj5nQQZlDcF0gUlqgwjztsPx9EZEhG6aNGADwu8bMeQiU3VxgrWCxP5VuwcWaSWWmbSHtOabvz00u0AHg00MpfeTflbWMLjlVqzAzvEF2sev48bkINYmPOumb+Hmm76dOdOORhKTaG2NolmLQURJjWnYEueCu+Vg5gaYOcj53fPNwtXgxVthpp2SGGHcXmXWtsCmWGOjhbewFN3XalOSnflb4npO5uT4zAlSm01O9ydbhEN2iWx8Da1cU5cj3tOzs2Rim425Et0ptbMMWMpuFl9z4ninRsjbB5hrH6Rz5FZ8lmHEkOqE+WrFhrgmMgkicNPqLE84sYgzabPecTNgmvtQqjjwpZk2vSzBWJmwg/H/g5tfMOrozC7g0+huF+fnpoWbOdw9wlJ2M91OBytCImlooRBfyU/5tId9I4hWIYf1ymwn5fbji5NaJ5MgKDb2oJqP7GnZmsEt3hiAhDV4lNV2l4OzbQ4vdHh84vfU0YU7CMxiaiZ1mtOsqLNZcHEscpZmruM6OURqDdGjtXG8rFppACzh24hXWoMhIFy31MVaiSy44NsHYP4YqTX4TgupygmDp9DaGWHShGOLHQ4vzTRjMXH9FOXoSo+lxLDYCzWwN/eejMEEMxPiwciNx7l18ZYAtNQHbn9mNbI3k/eO8aDdNou3HaW6dA27mzM6cQB/9EDTr0skyFrrORIsVizG5RxZPt5cK7MZSJj7iVw4OniiGFVEEm5ur/KUzu2oVg27NdObYalTH+AYDpjZ0NQeWLaztJOEzCSk9TPKTqTGglAs3MTuUgT+iTRgqpVaLLrnk0AVCje17+K71RjhxNIMS7MtrJnUJRoJYMwgLB65nlZiGhB5NFmiE9+vN95+nOKWm8M1N3exm/1guBIugyLckH5uQmIfoO3H/9Dwec657/8B7MIC3R//1/zof7mX7/t/Psjrvu4x/OILnsxCN6MabfGjv/71fDi1vPM7fpcjsw8/yN6PL+V40ZNeytX/7UX84Q27nHru8xh+6EPNz6wRXvKy7+dnj7yRzT9/E+7d3wfl6FEc7X58icR54PjU18eAi5/hd/6zqpaqegq4jwDYPi1U9S2q+lRVfersTEyeTHQMM4YxJYKQStYwNHOmy21z16GxIfP/lN3IUutQOMltp9j5AMwk1vIYI8H4A5jtHuIrl+/gMeNhAB821F1oexkkGofHpLh1aZP2hatRTmMpcWDCOJp/b4YAACAASURBVOrkpLFZdx5Qtucfh+3WxiKCnetBZw6J7FCWzDBrO7TIkNrdblq26DzGChVCUidzRiIbJw0jAZCmPap0Iu3qtWc4cvMTERMs4GvvFAu0Ta+RWZobb+CAnQ9gsb1AGq3Ha4Cms3MgFluEZO+WmcchhKTLESRlg8Uu44UOYrJm/PVdjJ9yezACFEFWb21kW1LXtJjQ08yKsDrb4rE3HKPVakWJm6GVGHpZsBEHmMvmWO2sxFcIxh/1vIsRdro3h4VIk/An3jNjZ2NtjvLMuZs4EOuxTbtNvrTEtZtXJk6VNblghGH3KEdmr6Pbmqeam8OIUD721iZ57HWyuKdCjVrTX80rTiyHW9OlBTpJJlGkeyM6cwCOPS2s31SdVs0ceJOxc/gObrn1y1gwnbDmqhDlb7PZLEliKJ1nsZc1c25NC1AS7yfAK7KNB9vLnEhWuT07yon2ATBCYpKm7q7K5kGVmXQBshlaNv3/2TvvMEmqcv9/zjlV1dW5J/TkPLM55102wMKSERVQuYiK6SrqFQwX/ek1473q1YsJxERURARFFBBEEAQFEQxEyQILLGwOE7urfn+cSt3TMzu77LIs9Pd59pmd7lNV55w6VfN+z/u+35eUkcWR3uiEYCTZoMtZSEF7bYJEzKCweXsJqR9KNiOFzus0vHBMBEF+2PrpTQwmGliXbWd7/zqwYlhSEjcV6Y0P6vBRV0vHr587W9csS1heIXjHK6chgrEZeB66WB6RSPsLAlEseqUKwvsgDYWlFMqIfu6F57oO3S2dKCkxpV5bwtGFmn05eaSgP5ciaaW0p3akgLXdCddQcEbtSUfoYtpBD4JyD766jaA+kSIbNz2vu/QIjItTp5/NvngDTckmTfIdwnA/28Zu74X+DYCLkiZSSC3GYdqMGFmEUAynWjHaF+l3qVC0qXoYKQbPa9xUTF8+DUNJapMmlml5pEcHITh2DcOJHNumtLN5arP+znWJWyZNWYty2rOlvxDMQZDX5pGw/mndbJ4+nXwqRjZuajIqJK6hcGZN93IB9THdVkOQwxjdABDDI6hNW4O59OfRFiaTPCI5FqoErYo9Bnd4mLUfPA2AP538YQ755h/ZPDDC9R9axQkL2hBCMDy4lY9edjhPKDj/hGuoTzbu415XsS9w+sIPse6k1Vx2RJIn3/MeNl/x8+A7U0k+ccrr+VTDN3nwkUdxzjsStlTFQ6p4UbgTmCSE6BZCWMCJwFVlba4EVgMIIerRIY+PTfgKLfO0cQ88626hMOKFsSF0GJpTCIQbao0kpjAwvByUqJQ6JcZYiJSZJF0cZGlsMkWlyNjZwCAQAmQsRlHZDCR0MV1X4hVVBZSBW9MTnEtKydbMlOB3R8WotevpUHmEAHtaBzuWz0V6Ra5rm45GNC7ASWUqGw1OUXtnJMSUn/sjqElaur2QSM9DIo0YjjJwunT+l23bLJ3bgpRaFCLItfLPLbxaVmYiGCugd/Vdl22Nmtg6yRSFfKO+By4kVAI8QuWgi/IOJEy2dNYiRLjzHXiDpMI2FZ0NKXprNC93ve9daWAYismyiUzcxFQKcp3a++WdpCUbLwkPje7UOzieCEW4118sWmB5c+W6gTCGX9tNRk5grjqIZ2YtBSFQMnoH3EBBMhkzAll0IQRuMkF9OqaJhvJVHFXgQUNA3DBIJ0yEUXpOy1uXSSwcIwnpMMKlycrQY3plGAJvlmBrdiqbWpeUuqUjhMPvd13KCjx0ebuVFblFCDOO47rk7BgJQ9KQaqTBrtPqlIkmDM/DpZS+l46hQAotbAFQ002xaxW+N09497Pg2RclHjNpBGUXAIrKxlf+i0Xy+2pS3v9NU4c4CgM1UqRgmoFnUAqJI42gyLmLo0N9G9Js6awL6yB497LRrtNlCxAYRQeVyeo+Sy0oIssJmpfjSPSeCz8HyqUpXR8QIMcj+a5T6iUKJESUJmgFI4XhjbOrPun1TuJu7SdLkVTMKrmWvryXeyhgarIdX+zGlQZShsG8nVYjqaa5eqzxNjpidcE58kumcORBJ0JhCFkcRkkzEAlxRJHhWE7fOykxE7Wa4HoeTZlKhptLEmJ5Xf/RVBIhdd3GorcJIoTAVQbFRAxH6bnqysdI2KGo0ChI/UxI15tZ4eXh2jGKmSwxU4dwS6nIWGkMr7SJECLcIHLBl2KKPrvhNfSnBioU7VGV3/XBIeN+W0UVE4RbLLL2jDPYsG4DH55zMufe/jTfPHEeZ580n+asDj3oH9rKBy47nK1uke+fcC3ZRP1OzlrFKxVSSD53wOfYvno+PzilmXXf+DrPfuazOMO6IKRtKr5+ysF8Ov1Zbh7sw/3eQfDkHfu201Xst3BdtwB8ALgOeAC4zHXd+4QQnxdCHOs1uw7YIIS4H7gJ+E/XdTdM9Bo+gZBSCw64TiEwFmszSdpjA+AUWJqbxJykduYpZXg5YG6ogueFGkYT7wUSzHhwLUcpGlNNGEIbU8WOWRjdM3Xei6lrJnXWJRGoCAGJ5JYJgvfyiJkOPrOk4XkBFZhGELrlGjbCsBjsnazJZKTwMmgPmr8rbwjF4KR6+mf3ko0rLMPwrpPCTeRJmFo8xM153gNpQmEwyCOxlKBoGRSyYZ5aERdpeQQtsNT0Lr5P0GxDghXzZOX9UgHacC56SoBFiiXhUP7/gJAsK0GtXePxJm1wu1KRtC1NCLx7ipD40oD9NRlvpz6yHoQOiXJNGZCvjG3SnLX1OYYGwNJ12HStgCKu5SsAloZtKkNRtBJazj4ITfNCx5SgMWNTk7BAKY8s6HuRTpjYpsL1CJilYno1Cf0ONqQgl4iFmwPovljSotHIUS8zo8rnCSGDMFYhQmXFouviGokg384l4qkjNDbjplFivm5pPxg3Xo+DQ9IyydiG5wHzchGTTd4OhEBJk6Sw6Zozg5Jy0wKkqY9zPS8hQiC98LQgZNMFlEkRlwFfKMIFV8YAm5ZcnJ7UDD2eiKPSEQrhONTHJAUvxFMitAqlMALvk+M6ul6e0DlxsS3bS9aaRAbkWI0UkUnf+y55bnIHsdkzcRK2vkeFYaQXmirKDPmgALe0SkIx5fAgamSAeGFTED5a9GpFuEoxYqYYsTLYSR25rcoYgAXEPfImCEm18OcUQBmR/5sYrq6t5jguplWrRTGAzvQUslLXBUsJm9ZUPWayRr/fBChp8vgLen6sQEFWn9iUFqa0SSld6zAxe9qo5xUCZytCCEwU+UReP07KwEGwwx3yyKSXe+Y6JWs5gPDukgC143mtvou/7JQXHaGf/c50hxZk8teG99BvHRwJSWQl8Q8pmBZvps7IkbBMvXklqgStir2MQqHIne//CPff9SAfnX8Kr13ay7WnrWTFpJCAbdzxPO/+6WGYxRHOecNvSFbJ2aseSiq+uOKLiFlT+dqpTQw88hD/OvktjDz7LKB3hM97+zK+7L6Vy2vejfuj4+Hui/Zxr6vYX+G67jWu6052XbfXdd0vep992nXdq7z/u67rfth13emu685yXffSXTm/TzDwcrhcb7d284wmsqksGbMIO16g1kpieYajMiTSGWRadigwkp1EnX8a3UZAbdKCTBgK7iit2GgqA1NaxA8+DJlK4RM6V+kqS6ZhEDP9sBtJTGpS5nqemLVtRzPsh7f5RC5gdAKaZrIx1YcQvpR1mH9UOrmO12G9A+5YBk5NqsTb4wrJ9Nbl1MXrPSM6YI56TB4nMZVg/bRGqNNEdSTbi5lbyEiqia3pSaEHzYh5WiBFuupTdNclEIaFKOpwRqGE50GTnkcKim4RP8U/atwDqHSKwZktgUfL0BkrmFIyHMuRilseMSX0IDjaOB1OJcArExA9bX28ntmLF2JMa8InRnFTl0aQw0P0d67QBb1dTSoLdXk2zGti0BmJ0o9Ivp9AlYhSQE1rlpacp16pFANtbQx2tHo2p+fbkJKhtE3MK8wdEJZkAied0Mqa8SbwQlpjyiQj7GCdlt/0wURTQOyzcQtL6fBUX80RvCFFiIUvuOGp6ofrwju/64a5YSLiKZSe3D9SYBgSJSSz69p86zg4ie15mxzfGyJl4OEM+JnrQryWwVxnoFwJsD0zGQoWSmr1QoGgmImxfkoDUkiKykANDZNL2YwoL+xVCF0wWpoMZlI4SuGi1R2FCGujhatBd9TviyoUESnP+ysERdvC7uhkcGaPvs/F/qAOWqnan/buuK4Dmabg3vTU5ega3oIYKZCxvXpfUgReTKH8ymuCxnwXOw6cR9F7R+1oSgeUP2lqUmgISdI0mN2aC0McAeI5/YgIkIaJ6XplMLw5CXopwtpoTUYNc/NzvIHrfDklY2wdKuAKxZyGBbQmJnshg3rjaknrTOa3den7p8KNpkJ+BuUQwEK7h7kN83Q7JVnnbKboXd9xnXAOK7zA/LH78vm+lxI0QRNS4iqbQbsRcfgJPLPwUO9UonRTpsyDFoiBAEhJTJoUW1pQSmKbqmTTrBKqBK2K3Uah6PDzu57iB298P1v/fg/bPv81rvnEkbx1WRexSJjOk5sf5y1XHEVPocDX33QddrJKzqrQMKTB/6z8H1LNbXz6TS5q9nQef/1xbLvhBgCyCZOL37mE72xezIV9Z+He+EW45j+hOLKTM1dRxUsMv36V0n/gi54V7ZoS2y4tH+L/4TU9AzIT97wYM4/HMX0jWXuAOuuSzGpuAyuUhi5aJsmEFtqYnl1Oe7ot6IIQgq3OFtaObGZZb56Ykl6uTpKmeLdux2g7JQxU8uugCWQsRWtDA3Pba/xGmtxYZomV7RZ1iKP+XrAi00dLrg+pSsmEFEpfJ1Izy7dYhwKZef2x5Xl97Fyeya0HgzTZlg1zNnzVR9cF0w+zMgxkwUEWnMADGVVcK1IM6ZkMxws6V81NhOFdvbFGWqw26FrGiJXTIVKOS43ZqsvBSAmFQujrEpCyTRrStverNvvaGhqItWRAihK/mLFpA65h6XpynkiIZSQ9lZbKRLjMmaXRXktiqg5XFVJSSKWxp3YxaNcH8+sqwdaeelRTcxBqKRAIy2RgwZTSxeA6GIbhFaYWWFHviZ0Fw2Io0cJQrA7XdYlN66GvQXuCRET0o+iWKpnWp2KaSJYNwnVh68LpbBrcRFAAWAgsz4YQTjEgRH6JAWFoH6GIWMfK82oEHiUhEJ5XtMTQFZLsMQeTz8a8OXWRQjKcTSJrstozJQQF12FFdw/T4804UiKHCjgxk7hKBca8LqyuGKjNsHnpTHrzicBn6EQWvkuoGCiEDnlWBQeZTHpzoNsaUov/1Kdt4t31IcEuc3XFlMKvc4gQNJppZrTNIBkzYKSAlIoNM9vZ3F1HX2aePsgwcFxdjsA2bNozHTiemMjWbC8DNVNAgGGZ9MUbiEkTXP0cRssTEK+NzHkCadraeyj8EWpIaQT82Wmqx6jzN4I8T5W0kEKwsK+ZmGFjCANDxTig6UD8EgBK6fpzQka2VLxIBZRFcdIihruaEW0NGEIivdBEVxk4EbGirmwXjV6ObfT5GYhrxVwsM/CKame38M6l31muEBRq+thYN59YMsFwMu+dS5SJFPnETGAI6MtOY26Dnn83oefJtcyQ/MkIgauAKkGrYpcxVChyyR1PsvqrN/HMV77Kso2PsPLKn3D86hnBH1Uff1/3N95y1fEcMwyfP/F6zES1zlkVpTCkwZdXfZmOmm4+OPOvxD9zBs9++jM8++nP4PT3k0/HuPhdS/j+Y3WcP/18WHs3XPx62DHh6LMqqtjrEH4Bds+WiBtpXATL01Noq+vROUs+vNdk3PYIgRSaoAkRqIqZqWTAVnqyPWCEIX8z5pxI3cz5RGzRaE9whVahE1EiVLKhL0bt3mrT2KuXJSRHdRyCFJJ8OkZ7nacmKATpQw4hOb2r9FgVytJLKUgoCykVIp4ovaz3041c2zdWBq16XAFW4xQaanPebrqLbShWT20Ijo56WXzTyFSe9IJp0rxFkCWu+dcTfwg8ggpB0dXG/qbpUxmaGrkfEOTlgDaYDSFRUpFuqGXhpCaElNQlTPrqfDEAgVsMiwQjBEpAo2f4q7Qn+qJCZcCAfQ6PIEZGvCLR3qw4RYRh0hmro9w0C0IehVbFjEmbLe21oay/N58FoQP/GjNxlixcDFLpfEQlWWj1IT31T+kRaW3g+9X1BKZUzM30eCF1mknNas2GC6xpNnQsKRXX7W4L7o3nfNTXjIiE6Hskg7DaQJbc1Ru9jm2E43Q10ZNSgmlSKLrBOjaVBDsGMYukmSZthGRB99n3XAmEDMU1ysnu1MZpWEoFYYAdtUlSS6ZhLVrASNENDO98LI0pFA6ghoZZ1HEA+VirJhhCYEiFE3gZIZ8xSZgJalUiWJtJyygR3tDeNV0fUNo2QioGR/r1+KTOSYwZCvIJPU8N00cNIJcwSZj+dfU6tVIZlBSsz81HKEkhHsOp7cYwUySXLsG1LYbcYdyaPFZXl/YMunr9FqwkBU8FU1omKb9chd9nr0acQIARw0m34AoTw4jTXzNFi6w4pfldUskwP6u5HmGEaogCqI0347qgTDvw6LpCkLUTwYvCf0cJIRHe+08IwEyQWTGHwqQORrqaEbk0nttUr0HDQHjeKyHAkhbNySbSKgwTB3C8dVXoa6Jw2HvD0EQlgudDoqMH4pbOUW3J2XTWhWHoTvRZ8AWekMxtzzG1KSzBQTpFdvksEIKRZCMk8+STDYyHKkGrYsIYGC5y3q2Pc+BXfs+ldz7J//b/hcOfv4fpl1xErHH0QvvdE7/lvb85hQ8PKk79t+sQidoKZ62iCv2H6cwVZ7KsZRnv2nEu1o++zcgzz/D4ccczcM+9tObi/OhdSzjn7n4unHw25Drg+wfBc/fs665X8SqHqyxdyNU31r2woQa7HYFLTBnE5y3G6JgchCn6Xp10PEZPfapEJKTouDy1aAYqlSq9kGc0bahbwGEzmir3xfXMH19lMdhFdikRShCUyOSDJlbb0z3IA5YHjXzpc78+lHBdjEQcESmKDZCYP4/UotmeAEUo0W/U5xhsCTfldPqai2tFhAg8o2b2lF7yvfMRyiKVSARGfKBOXe7xEzAwovsX98mrUtSrDHlVg6CoQxzTXm4ggsasSY1tMZJJQ8wsOa9lxaFplvYQuC7DmU6GrRoSS5fR3FwLQpGNGSSC47wctAqKeADWvHlkDj8MkesAQ+d+1RspWjxvp9/fgNwUHXIJm7hnpAsENM6A1gW6Zp3voXVd6tsXMlKXIRUzvPxF3Yf2+hQ9+TRSSNK2GbhKXam9C0pp1TlfLtwnaH4ykvRqRSVti7RtEo+ZXn6VTwR1e78vbnAO/2s/rM0nYBXcgJE5AhgsOFiGYnB2H8WefNAgs3oFHYsmU5eyg/EpKSlO60YqSVuyi1orIjImJVOaMp6KozduJIO5OGailHDk7JwO//PGUZuwsAyJUIrhooNAUsDx91p0QejhYWRMq04CLMtNIyEtXGHQUZukrSahPVQIhBQUDX3NhnSMvnzSu7LrKUaCKIIwDC1y4Rn5yg8lFpSFAUefXV1rbHpLxvNG64/jc+ZgKolb0B434boMpvtwkRj19QjhjSnbUJpThlZYdBDEFkzV3nH/Ut78mEohonTBy4c1DDtY/66rSu6rENKfKjJ26ClKKps+qwkpTe0FNOygtIZtKpqz8SDnLm6atLfVgpAklsz3zitg0qGIyYcF3lLfc0bEg+a/+/xQcyIeNR9+SKNrKpxEMvhcF3P3QmWFgRCCmKk4fEYTMUMxqSGDk7C1SExJWHO4iWRIWSro43vFhaBopaG2h9IZG40qQatip+gfLnDuzY+y4ss38pt7n+MrJ8zmh/ydupt/Q+eFF2C2tJS0d12Xi++7iE/dcgZf61e89qRrIFn1nFUxPqSQnLHoDF4/6fW87c7T2XDme6k56SSePOUU1p/7XbpyMS5+52LOuvlJLmv9OCx9P5x/FNx35b7uehWvYrjKgt6Dg9+FtyuNUjjSwk02IOw0IhaHrDbObVMxvSVDrRcOR4SgHda3kBNnrR59Ie+PfTGyC+yTmNAj4UYIWvj5aIwOoTOkpGCmKOb7gj75BE15sviuCIuyRhX6hGVhpCK700LQk+uhLV5PWA5Yw2prxbUjbT2ClrAMTCXAhUNmnMTi7tfoa/tjDHruHSYEjrdLnrYyesxKsS3dy4iVDQo6GzM6GahNeMaQFjPJmQ3hLr13PiUlddlOqJ+EMEyKiTyuMkNlRlHWAyECogIwNLmD4Z7W4HupJMI0ES1zoXkuUihsaTB3xr+xqWaWN4jQCBfFAjO6vNAy36vVMA1quwMD1/XIkFEY4Pie1bTVeB4pb2201qWY09JIre1vhrp4cVpBnxBR0qt/0V4n1yPXDrlUnHwqRnNOG63tmY7IvQ3sblC+ZyWE9ijoT4puuAbtGdPDU3gHDBcdNvcPYypJsTZDMeN7SXTZiJq0jaFkkN+phGJmSxZLSa0eGOSguQilAvITzoliS2etDpWj9HmYXTsFqZJs7mjGmj0z+D5lGdimCsiWo0xNRAsOIhZDuAUKRZdE6wI9XmHQkI6RMFXgoXXalrAt1eqti7C6h0CgTAND2SSMtA7JnbaUoTodjloiLuHfN494BH2v7UV6kUhBrpvrIpNJ7LoaRKGAX45ZiZBsBSTa1vdURsx/VdPEyMpDtNCKkiSm+Pc7zOGL3mUBpI2aYEzT4y0khFXyTtEq+14EQH242XRg/RxazBqKrsu2dC8y1xqEcE5uSmtvo7dAY1Om0NCglS6tSYsZiOuSCCjT2/QonSq9sQEoI5AD8sNL8TzyUbTUxPWmkfK8g75X0gtx9IV+9IlKiZiTSSIPPbjkHRDNQVOZsg02/x5G8zSdMVQl/XGN+20Vr2oMjhT5wR8eY+WXb+KWh17gnDfP57L3LmP67y5n889+RueFF2B1dJQcM1wc5tO3fYqL7/om529XHHDy1ZDaeUG+KqoA/QfnvXPey0cWfoRTb3w/NyyJ0fmTS9h63XU88aYT6d7yDOefsogvXP0AVydeC2+6GH79IbjxzCCBv4oq9hW0P8HBSTZQMJO40mC4Y6X+MhoXlsyTqO8OC8D6IY5AOhZncn3l2pBrW49kxMpW/A4ihjMK8lNwXZe0soNvHMs3akSJsbpj3mIsw/dYBfFFFByvRpBSiNWHghFNei9VIFNSkpFxMl4OXdJMYiubTCYe1JQSArLZJLapKBhJCkYqUsDVy19yXWKpJhKNc4l6/kIy6l1e6GOWxiZjGzYqm0XEbApmxpP/1kTZUJJtzRn6p08lZesCtymzBuX13/+JcJiTn8Py1uUIT2p8+7KDQk9jwEu99tL3HOgvOnrnMtLROJpIembW5Jo+fRukAhElXJ4BXhhBWAmvzphvYmukbZMDeuvw9+iNwg5S2RpUohZqu4O+CCnJ2Tkm13i5eq6jDXjfQ2YaWiBGhCFcei1oZUgpJVZDBmLJ4J4A9OR6gr5EQ0tROqcwuCdKhGUChAgSwlQ6hZHPI2IWRl0tZXYyrSlPxt9UuEqEobnecxEt8mubpVL0ATwZc1fIiDFcunZonh00z006jGKqm20NdVjtIYma0pSidWqHdxwMdBwFQpCPNyIsixEzzcZkT7D+XSFZ3rqc1nQrjqPvmzTiYbhwzCYbyzIwb7K+46ZBW3IaWaseISXWkiWkc0nP46IFZDzXTeQZFWER9IapiKZZQX+H+hoQk/QmuTBM5OCALhqOiyEj3rAgr9TzKHv3NmXW0JmcgRMhh2Y+5x3khZYWd3jPp0ty+QH0Nc6gNtYczJHphdKGBc79HNGg+yE8EuU4LluzU1FmTG8cRJqF4dfee1EIzESWgWRLaXht5Jz+z2zcxIqZDMYyFGUM0wsxNhsby5edzksVimKiTod8TuvWdE4IEl7tSFOUKmVGO2oIoyzE0d9IkEjbIrn8gPAQz4vtJmxcF1IrlmPU1FQYTLR/VVRRhqLj8pM/P8mqr9zEdfc9x7dPms8l717Kkp461p/7XTb95FJNzjpLY/if73+et1/7Np5+7AYuHUwy5ZTrIV05FKeKKsbDsb3H8sPDfsj3/vE9vrT+Elp/cjHpww7jXye9mdZfXMR3T5zFx674BzeNTId33wgPXg0/fTMMbt3XXa/iVYyG9kkYdT0Usp2BcVOiIOfDiEHHkpDkRMKtolie6WN5pi/8oDycrsxwcPHyLxxXC1CgCx4DjCyby8A0bdi5oVMFACeRAnQoXCbuF+uVgQdN/1pmLlSQiJ5utRMzkpE2kuaueqxDF5S0s5RkXeOBbEv3hYRHSILCSITy1f5lVTlRI1TKyx5zNLG+PkgkvHmQSBdQFqprFRvyC1CTFiKAjKdS15nuDM4DUHRHUFKRjWWRsRjDBS+80xcTKcv3ooy4+SQmCM0sJ3YVQqxc1/MxGAoxPKxrQEkFsSyipqt0coPDXARF7bkVEpIN4b2pVFdJRnL+OtrZ3lgXegb8tenl1NHVTLyrAWLe7r+/3qL33jP+Qee2RSIcEUBSeDLxQnufAJKrVqFSKTKHHqqJdNm6ndfor0tB/4J2TdBCDfWweHtk/UvbppjOhdOjFLq+HSXEZtCOpF/EsyXt9WyWh6gJ3N6O4PeYEWdybikNiUaEYZCOx6BxZjA3rjTIxrLYyg48aFISbBhm0rX6nDELOaOXxNROXCkpFN1gwyNjm8xtD8ein8XIrAqdd1baTw0nZSPq9aaIMA2mJlzqGjL6XhjhOaXUipMiIjYEMK2hCSVKQ2UjB4EZR8RrkehwPqOmhowdmUfAzXZoEjV3OqlVekNKinBuZfkNF3D/s/pvtRACqULZeiBSXxCQOpxaScFwawc0hjalf37h3XOBFupZOqmRye1NkF9FykwjU0kSC+aXdwEhBNtTHQzU9AWfGh45tqTB0T1HETfSo7xv/vwbSpWEOObT8fD7WBphRmvKeX22bQqrSLsSQgAAIABJREFUD0Xlove7MqoErYoS/OWJjRz77Vs579bH+coJs7nsPctY1qvd6eu/9302/fjHdFx4AVZXV8lxd6+7mxN/9UZmrP8X33PrqX3br3VeRhVV7CZm1M/g0mMu5YmtT/Dm69/Gpjeupuvyn9F/+x00fvTfOWeeyQcv+St3bM7AO3+r/2D+8FDY8Oi+7noVryKUOMbq2yEZ5tEAgZCDURt5HwZiSeHOb6UiqlkjTtaIj/p8LIw0tjBS653b0GFaCgHSwo3HcJJphjp7vUuWbwvDop48dSnPy6bMQESgIkbV+tETUYjVIScfoj+KpUpDh7xmMhK6NTrPxvP2OS5ZM0/a0sankqONPLeMJEpbh8i5QmoPmlNAGlpa3pDaWGqMafGOaXXT9HW8G1h0Q2VYs62NlvaG4G8fAEYcUnmE6RvKsuRnaDSXGqWBOqbwhDfK5t1Fh2aK4SGEobQHLZ5DtJYalD4sJXGTiTIlzNCDFkWLlcMXTwAQsRhFy0QFBq3uywvNKzHMHHlTG94irueoUg6UiNnhmjcNMmYmMkZBnUozJbNYh44ZJrHDjxi11qK/TWlKByQ4qnzoTaJuLUvnEgRy5YEMTJ0ZNDVbWxFzpuHIkEy7rkuxRPAicmXfA0qE9AtB9pijEcmyZ87P2TMMDpzcwKrJ+ZCg+TmXXkiwQJPfkXiMjV0tYakBKVD1NaikXqNKjhbqAZ9Mu6Pmfkl3HUfO9L1WJS6pkKgYBpm4hZlOcEByBjWRHL31jStZN2kKwquBJoQmjfkOHXbtuJQIEY3MbGW4qwmmHg2ZZkZi9RS9emVmmS/KOuS1kEpgZpKoTCbo1/bm5UwEyvegee/AUBxEeOtcj3e4owdjztxRx+s5D/2pwjSImwolFDKVwmxsDDyAPhpUju6MJsvDThEpYU5HTtcN9NoEmzOiVIHVX4emMEs8aP5dUe2LoX5yWGhcn0z/dN3KXsAKqBK0KgB4YdsQp1/6V95xwZ0cP7+Na05byUFTGvTCdF3Wf+c7bLz4IjouvJBYd3dwnOM6nH/v+bz/hlP54OZtfCLei3nS5UE9lSqqeDGoj9fzw8N+yBHdR3DyNSfzk/5baLv4QmredCKNZ36M727/Ax847zb+8UIB3ngxzHg9/OAQeOR3+7rrVbxKUJKD4P1BNnwCQmiox/r6yB5zNMw6Aeon6QNkJDembGd7InCc0r/0Q31TgzDGQm1GG4zTjoHaHq8WkGC4rZO0bVYspqpzQXxPkVniQRuFshDHQJjkkMMROW+Xu20xdB9Y4q0TpkFTJjQES0McQw9a2jZpTrayqm0VMJqgFR03lP72zwUMdvVplTx3BC0Zr6/lF/Wut+OsmpQvOcpWCRqT4WdGXR2Na1YHkvn6QwNqekKC5vWnYHpqjeV98afRL+BdSVLbF9swdCFkKSXmrBMiBD5Ea7KVRW3N1B25BqeuBhfB/Mb5LGxaGBp/Ruk15qbaqY+lg3pa/lIV5eTRSNEU76HJ8oz3hGdkB+GfEYImtRT581O6KMzsoz3TzmFdhwOgPI+Zn5cmXGPUvJTDiNxXV0hPnKHMW+rNYXTNKu8Zc6wYqrZGe9Aa88xsrwkk+tuTk2mLdYUXi66hIETVLcnHAk+swx+vRxCV1ARISeFdezRB9z1ovvz9jnxNEB7n5/kBzO/I0VmbCEJESydEsWP5tLCvXrimkiJQyQ5LYghI1pP01653/4WhEGUkypUmO/I1QUF2iaR/yXRSS5YA3kZFdDMoGSvxyI4kWuiv9WqQOeHGjRACK69z6IwI4ZRCP6P6/6VjLH/zBOPxF6iKrDuv9ADAqkl5WiIF7ImsZwFIyyI+dy7SiAj5LF2OPW2afr9EBG8Ugpp4DVIZjBQLCGTQfzeyTl4zW9cti7Iqfx0qqUojI/DmX1n6vkXFkCIYKU4sHaNK0F7lcF2XK+56mkPPuhlDSW766EG8Y0W3lrNFJzE+/6UvselnP6PzoouI9YTkbMvQFk678TR+fv+Puei59bxu0nHwhgvBtMe6XBVV7DKUVLxr1ru48MgL+eWjv+Sdv30X649ZTPeVV9K26Vm+f/NZfO3MC3nohR1w0Mfh2G/Bz94Of/x2xbCxKqrYk4iuMH/zKmObdHhSzEa55yeCINemrhus5OgGjTO1UMQYGCmWrm/HdQNj1rU8Y92MQ8SQmNWapb02MSqaCdAGuU/KlEnRiXrQyp6lCgRPfx4ZrzIQVjzgfPbyFcT6+pjSlGaeV1vNr1UF4NbmKDToz+OW4gjPYwBBGlWAuKlGedCUFDiJJK6Q2IPPe0PSRlLSI64xaVCTDA2nuKE4pm8N3dluxoU/LiOUNwcoGn5IqP6RKruPUkiO6jkKqcxRr6Pgd39wSmmpdUpD7wDaM+0ckJuKEC4CrR7ZlGyiIdEQ9K2iR0YI4rbJ7NYcuXi8xLMnA+LjETjPoBZ+DppHKqPnlWm9+TqUSeJ6uXq+Wp0ZT7Ku8SAMpXS+DRJRcflHCJ/XIGWlyFu5Es9eeRhpFP44dixYRmLRouBctm0EIZ0JM03eah59sDemwONSRlh9ghbk1/khoVEC7BdYF2H4peM6SK8e1+gOhzIblhJIGRHciaAj00FTSd1EQfkkRo87fM47aZ7xBv25T0zUaH3A6c21KGFgCv0cODggJbbvNXPBNca23doTU5mRW6ibRspLFB0X2/TmooTE6FIFowhoqmHMd4f0lUpL1A9V0L4maVWcs6gj3mprDZ4hQeReSEUxbZfNpUAo5RG00fMMpXl1kaMAHeJY2g+PuEXeS7ZfozAy5oHhcSIToteeUKsqXpFYu3mAU86/k7NueIhv/ds8vvqGOWF4C+AWCjz7iU+y/bbb6LrkkhLP2Z+e+RPHXXUcia3P8tNHH2TSYV+Ggz85Ooa5iir2EKbWTuXSYy5lactS3nLNWzjrmR+R+85ZdH/0dD7yl0v4wynv44lHnoJpr4F3XAt//h784r0wMrCvu17FKxg+8ckceQSxSZOCz5OBHPs4BM33xozVpmGqllofAyNlwjg6bSfMG4oa+eUGf3m/Yj3d2FMmgycMglQlIY6pmFnSfqwQx0rWdCA/ncmEOWce/N1uhKQ4bxpOTeXoi3KjN24papKl8umGEp4nTgXGjZIGc9pyzG7VRm9MlHqZlNSKmjtDYMSbYX+BIM9KIFjTuYZ5eV8OvPwEEjoPKFGfc1s8yX2lAitTeqIT8UqhrQLPM+XiRs23gOiNzkFz2mop1CSxDEljqpY3zF4YjMf36k3KZ8mnY2GpCE9GPcgzjJzXam+n44TXBjXNAF23C22sF8wkhvQkypGVuFVFrGpbxfS2A5BWyltCbhjaGCnd4KPOv/cRoqMlegiXoFdmwEfU8Fe5HI5pBMdFf/rz4gVY4kqtECkqEDQiZK7gFpARUpC32+nOdHn9jMxE1OvutZ1Rr5/zmfUzicvSdT2KoEVCSlXEky388URqzvmL44DOyczMrQyuN+LokF4lFZaSDBUcrY5Y1i8fUxprmdXcGpwyn9Z9HBgpEDMUHbWJEvtRCK9YefkCqOlic++xVELQNCrEI2X0m8rHCVHyfsjZnieYyOdCMtJTx47FOlc0kOg3DIadgt4Y8KMeBsq0Z8vfPX6ZgVF5uJ4nL/JuNOrDgvE+BgtVglbFOPjl39Zy5Ndvobs+yXWnr2LlpFKlRWdwkKc/eBpDjz1G58UXYzbpkJXBwiBf+vOX+MjvP8yHhyy+/K+HSLz1lzpsp4oq9jJiKsapc07limOv4OntT3Pslcdy3dQhJl/3KzrzaZ4//vU88eOf4jZMh3//PWx7Vkvxb31mX3e9ilcogrCxMuPYl2cflTsVhZdEXsnzUQmLu2uZ3xEqf40USgna0p66ILwLIUpCFP3/CyGgfQnD6VDkyZASe/p0zNbWirlwAE1Zm2PnREqqVAhx7M2n6GusTLAmNaQDNccooiS1nERGYVSYI1HWh2nNGbq6mhipzeud746lgC5wnDBtcMEqDzWcKIMoJ2jega4MDXRLWSUGczlkOsxDdIXgXzuKWoxEeSGB3hrKxsZW6gQQbhHLjM5lBc+Dj0wc7NCrlzASQX99T8OM1ho6apO4w0O6kemF2/pjKztvT0Oa1txoAhl4iAyljWbGnoug59Fb3jQL2Ty3hIBAGPoWNXyzCZMjZzZz8NRQBEQgcGMmblwTBUPJMAy2a0WJoS0TCZ6fP7vkvP71DGmAshCmrsfnmqYmnSUhpN79j+agOU5QYw5gZcdc8ikvD0wIRLoZkvUVN2RsFXqvSmsWyjE9aGOGjxqKfKpyeJ1/zEgxzLmMmZKhQnFsrzgwszVLNiJU0lGb5JBpjSzp1hsfDWmbRERhUwrBkJmr2MNi+caS/9yP8qCVhjiWIxQJKVWlTZla5MahGL5/haTDrqPBe7a0/L6LlAbDxZGgnwPTGhjuHh1iHF2m9fF65jfOJ26GYelTGrWYSJ1MY0XzHoO/CwJaF5DtmktXXYVoiQqoEBRdxSsZ24cKfPqX93Lrw+v5zskLWN5XP6pNcft2nj71fWAoOs47D5XSi+nudXfz2T99lgZX8fNn19PUOw/ecFk136yKlxxt6Ta+dfC3uG3tbXzj7m9wwX0X8L7PvI9Hr1zBtK9/m+Grf03Xf38B6+Sfw28/Bd87CN70I2hfvK+7XsUrDEZ57J3/ueGHu4zjQfOTyCcYihv1WgAUynLQ8ukY+XSCTQCytE6Xb8zETQXZdoae3xYZQ6SPTrh7vLBpIcPF4bC//lhSDZANpcl91CQsMMqUJj3TJxsvJWfBtJSFDI6FStwjHbfYHvk9YRm0N2Z5dM4ByM03gjQCb0jCTHBU01LY9HjpeSe6T+0LFxghoTTrsuyw9Oamb+SbStCSi2OVrYv0mkMQsYjhVmKEewZ3JRXGKIQE12F5by1YUeGVMdbPlCNxX/g7DG2GKUeN+tonaIYwGAYYGQRsMG2EqXCdsKZfJeRioRLdQZMbiJmSScNFhBjhvs0ghVGRAI/zSFT2ONf1Qe3WUd9ZhgzysvxjXdtiaP5UAOa25xhM5OH5J8DOYTY14QxEIypCAz8KJRQ0zUa0LIV1UMzWIPufRtiREEChJd1NI/Sggp9/5xNMkMkk6TWHwDM3QrwGGmcj1/0JZ/uOUcf5iKnSZ0WVybHvLK9PePlu3fVJUjGj7Dv90/egAdiGYhuFCW8UqUwapBp17vLrOEIxnGwBSqNYhgul69UvneDnSpast3EIWngtWfY46V8KzggJK1SJ7bbr6U7muZ0HPO+qQhkGhaKDnyLopG1cYQQ1K4PBlD1jTckmdgzpd6UpvcLwQJ/ZXJJv6j/zAhdqu5mzC9p5VYL2KsLfntrMaZf+lUkNKX5z+ipqk6N3WIafXsvTp56K1d1Ny1f/F2lZbBnawll3ncX1T1zHB0Udb3zyH8hjvg4zXrcPRlFFFSGWty5nWcsyrv/X9Zz917OxJlk8dMY7afrp3Rzz+uPJv+ffqXvH5xCNM+Hi42DNZ2DRu3b6wq+iiokiMADK4BO38XiHMF7cn+DapBUYCcFniRo6k9MZLAtxdFyHI2c2BwZtR22ShGVw5xMbNbHykWqExFoA4ka8cqhd96oKvdm9fE/h53AhyhLuS1HJE9lak6JFlRmv2hXFYMM8SOZJSsWazjX6y3gOzV5DVBJLqdxRz+i2whDHxLROtm5LwNDGEmGLRV2jrTBpl+X3+EakimNJiSgyJhEqgetqL2ek3yXELworqcMVhzZHruv1R8gSsQOztQVzaEfQLLNkBv1P9TPiVPbMHdl9ZMnvvnfFNhVDRV2/TooKIhiEHjFgFJENCE66yYt82I4wk5CM7ZSYgN6I8NtZhsSKh/fL6uoapUANo0m6kgqkQiiLolMAIag54ohR+Uh1yRiHT9dhf2F5hTIhEyL33nfmRNZCubcQoDPeRENW5y5ljz6qJCwWIjly4/0dc11mt5VKuS/orAmef9+TCrB1UJO1TDwGg2Of0kdyxYqd/g0VCDZsHyYjTcoJWqHMg2abigWdtcH7MFr3jqbZo8ZfDl1IezTmdqZor/XG6ffXddia6WNLzSAIiTSM0blyygi876A3MtLG6DDoirmG5W18oZDB4fEbVkCVoL0K4Dgu597yKOfc9CgfO2IKJy/trPhg99/9V57+j/8gd9xx5D90Og4uVz5yJV+/6+vMV2mufGotDdMXwwcuhORoF3AVVewLSCE5ousI1nSs4ZeP/JLv/P07/PX4NLf+43D+59rr2Hr11TR/4fPE3/ZLLR7y+C1w7Df1bmYVVewl+ARtPOeYUVdHrDXP7pKbJd2jiUBXTYLalgx3lYU4Fp1iibfBMiQtuThHzmwuJT/xHPQevFv9qYSd7vb75EJIGpON7BjZUbFdY9ouCe8EIN2E2PZsxfZOrisI0bM8oRDqeqG2p/T6E92s8Q3wpFcjzDMyh8YrRTD+CQHYVDuXHTsy2GvXTsCDERqZUYJm1NSQOfywikeMT3pDgYvEvHlw/5Pgh75l22DLNtg8XJE4jjdvWjFUE4nxpvfgqQ2jvDBBiGAsDT0Hwv1Xh6FvO/WkiNHKo6F05aj2wQZG2VdRD0igRDhWqLI/h4RKk2PW//JgT54ckaOvPI6ECjcuyuGXniiHkc+j0k+hCdHo+95WE5KyaXXTmFyrC5ov6KxFCjC3bxp1TMX+TcDTJiUMFYqsT02BSaVEcUwVQ3/DSkUIqF+TrwIyVoaNgxupTcYo2qPnKRurFEroUjBSCDHsXcrwxE2iOYKlRyw77G0427ZRDv/2DsUbQW0Y/YWHgQVTyeQri9WMhypBe4XjuS2DfPiyv7Fh+zBXnHoAU5oqP9hbrrqK5z7/BRo/+Ulyr38dtz97O1+9838Z7l/P59dvZFVMwclXwhi1WaqoYl/DkAbHTz6e1/S+hqsevYpvuN/lxC6Hzz4/i5F3vZvssceSf99vUDd9As5dBcf/QBcLrqKKvQEhmNKYHhXaV9LENLG7d/0Pd3iJ0UaJFGCbBshSYZCCWxjVFighbS8KYxAB39gt97gA7Ji1AHtyPTz5LBgxOpOtdGY6R7UDbSAHu+E+6nr1vwiCyEk1hkE9huDCziAMA5XLYTY3MfA3XQwbw2aYyvO6M7he/qErDRwzobu10xBHPw7LobxQuDDHXmcl7SIem1q7lul108Mv4zUw3K//37EUNvwVNj8z4dA3H7ayaUv2IIuVx+NH5qYr5SQiRt8TPwdtAuGoLm5lIjeOp7T8vEZESKY4FsEtJ34Rz6RPEqMEbUb9DJqT+lmXySSx3tJ6hKVjjhbXGt3vsQiaSqdJHXgg3HP5TsOmox5UX/CDHd61rBTY4+dB7gz+2F1pjDqXrz47qaF0HMIvWO15nZyh8b1OC5sW4rgOxgO/DhRHfRzadWiJmmIA12FSeiFp8y+6nzGboutqAj7jBLj5nlFK5CqTGZdQ92d7Yfpy1GMb2Fg7b9Tmr5NOlKjVThRVgvYKxvX3PcfHrvgHr5nTwnmnTAvifKNwh4dZ95X/Zeu119L+3XN5uMPg49e/mwee/wfv39rPcTKLsebLMOXIalhYFfsFLGVxwuQTeF3f6/jSLT/lM/ICet6Z4hN//Atbj7+B5v/6L9I9q+HHJ+hwx4M+vtMQiiqqmCiUVBSdIjKVJmVvfMmv70YMx6j3pFQyf69cueKnTckmlrcur2g0O5lsoDy3pz3a45U3iGKiHjQhJakVkcK7LjDtGOY+/DjPbN085nGVsH3+Uh1G5UOKEpGQcXpRMcRxPMyon0FHpiM8Q4QQSCHpynaFjbtWVlQYnFDoZbSXQtCR6uXZLQNj0N+xyUNFGXV37O9K2nlhshXJToX5ytvtmCIWEBX/eVGR+oTOWEQnnivxxkaFRoTrhzuGzcfaeAj6XpJEpaJfjGqbMCdSM3E3vPL+HE06jN7BjWQH1+/6Ofyrj3N534MWVU816uu0SBGhh84d6B/3GgHJrDBHfn5lpY4ljLQ+Nm4z3NFLYdvacMW0zgc1MXvAv2zM2+Sa25FjsGnuHrOVqwTtFYiB4SJfvOZ+rrnnOf73hDmsmd5Ysd3Ic8+x9vQPgRDs+P7n+dBjX+Pe+x7grVu28dV4J6nVn9JV5KvErIr9EIY0+K+D3szhXUfw75dfxMePuJn5Dw3z1k/9P7ILFtP6vsswb/8sfG81vP5caJ69r7tcxSsAhjAoUsSeMZ3YlMkvfQc8w6g310td/CUMRR/DIBNCjK9KmKiFvjWjVSF3E76hqybo9ZmwSEg5PMJ70KTuccMIK8GNj2FgT9SD5hUdnwhiKkYsPtrgrOg59KT+g36WqepZsrIyYCU0Z21N0HbRfoiqIAbw67MF3qrK5xSMowRa4ZjaWD1pozY4r3+s70ETiLF5jlQlEUWB2AeSjmwH+djGCeUolR8PeIXsXVh3X0Vi6Ss+RgV8StB5AIxT02xM+M+glNQn6qlPjBaRGxeR8Y5JbNG5e4MjpRtGyaVLS35PrViOiFfIf32x8Ne09+vAkEv/cCHMHZTmKG/cWPDvWdxzfsQMFZSc2BOoErRXGP765CY+ctnfaa2Jc+1pK2nMVH5Id9x+O2s/8hE2HjCVs+c/yRN/Pp23bRvga93HkDr8XePW3qmiiv0Ji7rquOod7+HtF8xjbe9TnHPGb5n9iz+y6s23knvrybTOVcjzj4Tlp8OKD5XubFdRxS5CSQVFbdT6oTo7xR4tqK7PNaV2yh48515GPLfzNhNEEOI4AQ+aIY1dN0I9uJE8ml0lIRUhJpLbs+setNFnGC1KMSb8dakUB7UfhKkmHqbVmotz95ObKuYbjbfaKxFHrSS9PfzOrCxT7uegTTRstZxA+QQtpnzlvXE8aGP0WwpJ2krTkugbv8RGpM/R4/VJlC5QP0aR+kqCJCXItFT+fKed2XOVt4rO2PO2tGfnUoYqtyvvhF14/mIp6A8FXGa11fDUg0/u8iYLhHy0UnTankDVEnmFYKTo8K3fPcx5tz3BGUdM4eQlnRWTWl3H4dGzv8bAeRdz8eGSh/v+xMn9NRw998PYs94I1kRc51VUsX+hvTbBz9+3nI9d/g/ufaSVNWdYXP63HzLnxxfxwhU2De85g9YHrkA8+Gs45qxqrmUVuw1jgruvew17lOztAsw4DO5aqN/exESMpsO6KgtrTAhj1IvbfUzAyAw8aMXd9jgWvDIKsYmEcXlCKEIpEmrXbAPf/qh0H0amzmJg09bKx0Vk6gHShx3KiFuAtb/Xn089ZkwiUZGYjfM8GFIwVNJUtw1EZcY/vPTaZaQpZkhqKihl7+z4ieKwrsP2/LtmD0ZLjTdve2RDI4qOZVAY2Hm7eA7qp8CmQUwvRzVj6+dgKFJTcrx6jFH4o4iZe6ekdJWgvQLwj6c38/Er7sE0JL/8wHJ686NVb7YOb+XG23+E/T/fQ24f4u7jHY6fcQCLl34Y0TxrH/S6iipeWmRsk3PePJ8f3/Ekn7v8QT5w8H8x7Ucj3Hjxmcw6+7s81lhP52vn0nnhsTD7DXDIp6tKj1XsMiZk+O5NVLCM6uJ1bB/eXqHxHkTbor1AWnYd/ujT9l42b5wXN1YlRYmXYYKZcC/ag+arZE7EuHfHUtubIF47t7Xi5042S6FS+QZGEzRpWchixOtnjh26JxD0j/SPVnIcA1EPV328noylc6J8gubg7JYHDeCImRMT/5mot68ce2UjaA960CZKcvYIUvmJtevzym2wNijv4AuJbBvUGxcpK7XTQvE+wsLm489bbbyW1lTlZ2E8VAnafowdQwX+77cP8dM7n+K0Qybx9uVdJUVT+0f6uWXtLVz/4OUM/f523nltkYGZMeZ86nQOW3iyrpFSRRWvIgghOHlpJ4u6avnAJXfzuwdMzvy372C/+Tn+9I3/Iv6t63mor4eu+MNMun8hHPo5mHNS5Sq5VVRRAXPzcxkoTmA3d2+hgkG5qGnRboXw7BKMiXsL9iaMcTw3exIvlrxk4ybN2Tj3PbMFx5ogqRdCFxJ3ijpXZjfQk+uhPj7BsM69tGbG9a4gRntYRPjdeLCUVYGcjX2xaJTR4ubFwf+j5GecSL3ScwlZ8nNXsce9SruDTBs0j+y83QQw0XnbV0jEFLguSigmN6aZltObsavaKtV4rAz/jpljKcZ6WNq8dNzvx0KVoO2HcF2X6+57ji/8+gH6GlJce9rKQH64f6SfP6z9A9c//hv+8PTNTO13eec1O2h8Jk7bFz5G6ti37OPeV1HFvseUpjS/+o8VnHvzoxx3zm28eWknp336x2z5jydY+3+fZNv5/+CqqXkaRr7CkrsvRBz239C+aF93u4r9AKYydylXZ0+jEhHTSmf7oDP7ALapxvTc7FG8CFXMI2Y2oYTg2S26KnCxtp4dSyZiGArY9pwO1dpNQhxTMfKJCXoc9pJHdDzbvdyDBhMXcsnZOm9pWcuy8MMxPHUwfp7i6o7VXpH2ynX5yrHTvLCxjvNzAl8OD6gyPIGSF4/xctD2NY6c2czA09oTq4QiY5u01uy6IInPqU1VDXGsAvj7U5s58+r7eWbzIB8/cirHzG5mw+AGLn/oGn7/1O+5/Zk/0SPjHL7peT7wTI7iHySxmUtoPv+/MWp3nphZRRWvFtim4vQ1k3nd3FY+fdV9HPK1mzltzSSO/++fsO30f7Hp/z6FfendXNb3PPHHTmLN7MUk1nx+VN2lKqp48dhzxszOpdpfHthbRs1LBfdFGKC+0ltJ+NxE6pgJofP8dlcEYlex1zxo43i1XsRmQkzFmN84n1wsIjCRysO0Yyu2b83FGRiuTLTjHrHb1du8q6qgu0vsXu7Ixg2enljd65ccliEZAFAqKKmwOwhCHHfiQdtdVAnafoKH123jWzc+wk3/fJ5TD+zlwJlF/vTcNZx8zU08sPEBFiTbWb11E594bh0NnUfz/D+nsf3Oe2n8fx8nc+xSRhA+AAAgAElEQVSxLw/3eRVVvAzRVZ/kwrcv4ncPPM9ZNzzEt298hA8c3MfxX7oI54znGTznv7F+eSPX3fk3tt15BAcfcBBtqz8JNV37uutVVDEKsb4+jMbKpVVeTmjK2hw8tWFfd2P3sYfryk2sbJuAwhDExi6auyfxYkjouOcd5ztTmZiilKyGpRN2bkw3JZtGfziGt7GrPklX/fipHhPNQQtCHHczHP5l4UHbg+hrSHPfM5WFYF4OSK1aiTBN+h3dx6gwzK7CqnrQXp245+ktnH3TI/zh4edYPbefEw59hF8993V+9NstrGiYx8lFmxXrtpJOvIC74BS2prM89tVvkFy5gp5rrq56zaqoYgIQQrBmeiOHTGvgdw88z9d/9xDfuOFh3rionRNP/zJN/+kQu+hs+i/+CQ/cfhu/uOFwupdO5+DDv0Cicea+7n4V+zt2M5+oEoRhYNTsH+I2aXvfhYK+GFhdXRi1L36O22sSxAzFHY9v2LVN1Jcqf3xvib6Mw3mm104fNRdSSA5qP2ifCPBMWMXRI1hK7GJR71cYMdtfoDJ6k8PdsQWApLF7z9SS7jpyib2Tf1slaC9DjBQdbrh/HT+8407+ueUuOlqfJj31fh52MrSpVXymZQ0LHroF846fw/TXwht/zNBwHev+50sMP/44LV/5CqmVK/b1MKqoYr9DlKj96bENXHLHkxz8td+zvLeeE1a8hQPfdhpbb/g1uQvOxfzqg1xw9RvoX1zPiiPfz6Kpb3hR4RJVvErRt6Yq2LSfIT5zz9QJlVLQlNW5MBNzoHmtXiqisrdCHMdhaGMR1YS5b0oATXQGfA/a7qorVqOc9g0CYr2bf7v953dvoErQXka499nn+N6fr+OWp2+D+MMIaxvLZi1iVdtqliXfQs8/b0T84XxI5mHBKfDGCygMwvqzz2HLz39Ozckn0/atbyIT1VpmVVTxYiCE4IDeeg7orWfD9iEuv+tpvn3TI3zkZzs4eGo3R3/uIpYZ20hc9G0GL/4da3/9OT4950ySKxdw0JJ3sahlKeYe9IpU8QrGHizSXMX+iwkRAT9P6SUSoXFfZCmBMc/78tWPGIVdVT/dVQ+aj/3ek2bYkK4QXvoyR2OykTWda3becB+gStD2IUacEW58/C9cdu8N/H39nQyqJ8nIDo6Ytpzjpr2LuZkerId+A3++FNbeBTNeDydeAm2LKO7oZ9PFl7Dhhz8kecAyuq+6CqvtJVCuqqKKVxnqUjHec2Av7zmwl8fX7+Cae57l2zc9wmnPb2dJ35tZ83+nsuKJ2+m88gKcG/7MPb13ctEck/yKlaycfCzLWpaRttL7ehhVVFHFKwXGfu5B248I2kQR1sTaNbPaP26/96BNO2Zf92C38WLyz/YmqgTtJYTrujy+5QmueOBGbvrXrTw9eC9OwabRnM1xff/G2+cfSnMiC4/eCLedC/+8FprnwOw3wZt+BPEcxe072PT9H7DxvPOwp0+n/TvfITF/3r4eWhVVvCrQXZ/k/av7eP/qPp7dMsBND77AjQ8+z5ceb6b5wM9x1PEuh933C+b87haGf3UD90z5Pe+fIjDmz+KAnoNZ2bqSSTWTXnGKXVVUUcWLw4Q8NY4uprsncxbHxV4LcXzlwVdvfNV60KrY46gStL2MDQMbuObhW/nNY7fwzy13MeRsRw5NYmp2AWfMej+vmzGHlBzRpOyGT8FD10K8FuacCId8OlCKG37qKTb96Fw2//znxGfNpO2cs0nMn79vB1dFFa9iNGfjnLSkg5OWdDA4UuSOxzdyy0Mv8J/NJ/D4gUdwnPkcxz39WxZddy+FX9zNE5Pu5ZypZ/NAn83M7mUsalrEosZF9OZ69//d0yqqqOJFYUKkpTisf+6mUuAuYy+FOO5PFRaUFBOq6bW7HrTg+CpBq6IMVYK2h7F9aAfXPPJHbnj8Nu7d+Be2OU/jDrXREpvNa1o+yuumL2NWSy1y61Pw2M1w5VnwyO+grg+mHg1vvQqaZoEQOAMDbL/2WrZc9St23H47mSOPpPOiC7GnTdvXw6yiiioisE3FgZPzHDhZF4Bdt3WQWx9ez08emc+t3S8waf2DvGPzrbzvln/i/GIT25uu44HJf+TM9mHWtieZ3bGYRU2LmFU/i8k1k/dpoeNXKoQQRwDfABTwA9d1vzRGuxOAnwGLXNf9y0vYxSqqGB+FoZf0cruafzVRLOupZ7iwlxQi9zAOmTqxkhWOp3i5ux60Kj+rohxVgvYisWVgkKseuIMb/3UrD275K9t5BEbqyRszWZY/iddOXcnSrlaswfXwxB/g7s/BFbfAtmehYylMOhwO/2LgKSts3MiOq69h+003se2mm4h1d5M56iiav3hmVTK/iir2EzRmbI5f0MbxC9pwXZd/rlvCrQ8fzcUPr+eJBx7mpM23sOy5+1l691YK2/rpz1/HE+0387MGh4fqisQnTaa3cy4z6mfSm+ulO9tN0qwq/e0uhBAKOBs4FHgauFMIcZXruveXtUsDHwTueOl7WcWrGRPiQu1LQqGQlwJ7qQ5a3FLErf1D8Xai/QwI2i6qAfokeFcLXFfxykeVoO0Ctg2OcM/aTdz0+N3cte4unuq/lwH1MIo4zdYsDm49hmMnH8ii2hRy3d/hmb/BXz4OV/0Ntj8HrQug+0A49lvQvhhXWYysfYbB2//BwN9/yo4/38HQw48QnzWL1MoV5P/jA1hdXft62FVUUcWLgBCCqU0Z/j97bx4mR1kt/n/e6p4t+55AEkgiYQnIDkHZUS6LCqiobFdREFxwA0Su158L6FfFBbxeLgooeAFBRK8iW0hCEkhIyEbWIckkk9n3pfe1qt7fH1W9zfRMepJZOsn5PM880139dtWp029VnfOe8573+BnjuPX8ecTNM9hQexmvVXWwcncHob07+Ux8I2f5d3H6zlaSHTGs4Fasku34JnjYMU6xYpQJY0ZRNnEyYyZMY+yYyVQcdxwTzljIpPJJjCkdw7jScUU72bkIOBvYrbWuBlBKPQdcDVT2aHc/8ABw9/CKJwgFOEPlw7NAdZpDsZrHEDGudBynTDtlv78vae5CT8RBy4M/kmR3e4jdbUG2tdRT2VVJXXgXYXbjGVVHmRrNvPL38ZlZx3HFhAtZkAiiuqqh9hnY+EOIBWDaCXDkqTDvIqzTv4xpTSTR2Eyiuob40tdI1PyeRHU1dixG+YkLqHj/yUz9ylcYtXAhnrFS8U0QDlXKvJ50Cf97gFD8HDbVXc2btV2sr+lma107JxoNXOap4aRYDQv99Sh/F4FAOwF/kKjdTEJ72Nq4hiW+Z+i2E4SsOBY2ZUYpY0pGU+GtoNRbxgMX/ILjJh03cCG1zqy5dGgwE6jPet8ALMxuoJQ6DZittX5JKdWvg6aUug24DeCoo44aZFEFoUgYqoWqD0GUUswcs/+VtGUOmtCTQ89Bs0zo3uvcWLL+LMsiEk8SjCYJxROEonFC0QT+YBBfIEhzpJWWWBtdVichT4DoqCBdpSFiymSuLuOyMg9nWDZndQaZGdwDnp1YJTMwjalEPNMwmYRlnY6ZWIgZNTHXd5BsrcVsWYsdCuGZOJHSo4+mdO5cSufMYcwFF1I6dw5l8+ahvIfezyAIQmGMKfNy3vwpnDd/CgCmZbOrNURlc4CXmvxUNgWobA7gseJ8YGKAk0d1Mb+knQ97A1xnBhhr+ihJdJCIdBE0OwjaTcRsk6RSzNx6LuTMiXCNgJTzpXXuvRL3/ZRj4Y51w6qHISaf9ZMODyilDOBB4OZCdqa1fhR4FODMM8+UMINwwBRlsKoohTq02N+iIgcDZ86ZNGTzGA8Hhr1nrKzqoKotiNZga53+b2tndXmtwbY1/mQrzfFKNE6Orq1tNE6er9agsbFsjWnbJC3nz7RtvMkQVwX+jA3un8JGYWkwlSLkMQh4FAGPwm8ofB5Fq0dT5oF5RglzzBKOjpYxN1DBbHsqk5Kj0FGNFTYxg3ESwclU+aZhdftAm3gmxfFOCuOdUo5n8ii8k8dSdsxkRp97Lt7p0ymZMQPv9OkYZcO0bokgCAc1Xo/BgiPHseDIcVx7xizAuQc2dEep7ghT1xVhbVeEus4ItV0RGrsjBGImJR7FxFGlTBpdytTRHiaXWozzJhlVoij3GlSUGJR6DTwKDKXwGKCUB6UUhseDYRgoZWAYHrThIbneCTjNmlDBB4+ZMpIqGQwagNlZ72cBTVnvxwInAcvdVKMZwItKqaukUIhwuCLG9dBT6inlirlXHJIpjjMnVIy0CAc1w+6g1XSG2VTvw1AK5RoKlz79AIZtg6HQhgFKESfEPN2CVsr5MxRagVYK2/li+r8BGDj/lQKDo53X7jav1pSamlLTptxUlJqKElPjTdp4wzFUMAKmiSrz4hlXjjF+HJ7xE/CMG4c5YQLeWZMpmzyZ0ZMn4508Gc/kKXinTMYzYQLKc3BMdBUE4eBFKcXsSaOYPWlU3s8Tpk13JEFnKEFXOEFnOE44bhFNWkQTJtGkRSBhE4tZ2LbGsjWWO0CWea2xbBMrtdYSEQBOP3rCoeCgrQPmK6XmAo3AdcANqQ+11n4gfZJKqeXA3eKcCcNFMbpCYy64ACxrpMU45DkUnTPhwBl2B+2mc47mpnOOztnmH3UDWBbatsFyUm20bYOd+q/d15bzWttoy05vU0q5KTvuf6Xcl85r5fWiyspR5WUY5eWostT/cjxjx2CMG49n/DiM8vLhVocgCMIBU+o1mD6unOnj5B6WD621qZS6A1iEU2b/j1rr7Uqp+4D1WusXR1ZC4XDmxCPHF2VVQ5kPLwgjR1Ekv47/yEdGWgRBEAThEEZr/QrwSo9t3++j7UXDIZMgABwzbcxIiyAIQpEhCy8IgiAIgiAIgiAUCeKgCYIgCIIgCIIgFAnioAmCIAiCIAiCIBQJaiBlVJVS7UDt0IlTEFOAjhGW4WBBdDUwRF+FI7oqHNGVw9Fa66kjLcRgo5QKAjtHWo6DGLk+DgzR34Eh+jswRH8HRp/PxQE5aMWAUmq91vrMkZbjYEB0NTBEX4Ujuioc0dWhjfy+B4bo78AQ/R0Yor8DQ/Q3dEiKoyAIgiAIgiAIQpEgDpogCIIgCIIgCEKRcDA6aI+OtAAHEaKrgSH6KhzRVeGIrg5t5Pc9MER/B4bo78AQ/R0Yor8h4qCbgyYIgiAIgiAIgnCocjBG0ARBEARBEARBEA5JitZBU0pdrpTaqZTarZS6t5921yqltFLqsK0isy9dKaVuVkq1K6U2uX+3joScxUAh/Uop9WmlVKVSartS6s/DLWMxUUDfejCrX+1SSvlGQs5ioABdHaWUWqaUelcptUUpdeVIyCkMDoU+ow5nlFKz3T7/nns//Ya7fZJSarFSqsr9P9HdrpRS/+XqdItS6vSRPYPiQCnlce8bL7nv5yql3nH19xelVKm7vcx9v9v9fM5Iyl0MKKUmKKVeUErtcPvhB6T/DQyl1Lfc63ebUupZpVS59MGhpygdNKWUB3gYuAJYAFyvlFqQp91Y4OvAO8MrYfFQqK6Av2itT3X/Hh9WIYuEQnSllJoP/Adwrtb6ROCbwy5okVCIvrTW30r1K+C3wN+HX9KRp8Dr8HvA81rr04DrgP8ZXimFwWIA993DHRO4S2t9AnAO8FVXT/cCS7XW84Gl7ntw9Dnf/bsNeGT4RS5KvgG8l/X+58CDrv66gVvc7bcA3VrrY4AH3XaHO78BXtNaHw+cgqNH6X8FopSaiWNnn6m1Pgnw4Dy/pA8OMUXpoAFnA7u11tVa6wTwHHB1nnb3Aw8AseEUrsgoVFdCYbr6IvCw1robQGvdNswyFhMD7VvXA88Oi2TFRyG60sA49/V4oGkY5RMGF7nvFoDWullrvdF9HcQxjmfi6OpPbrM/Ade4r68G/lc7rAEmKKWOGGaxiwql1CzgI8Dj7nsFXAK84Dbpqb+UXl8APuS2PyxRSo0DLgD+AKC1TmitfUj/GyheoEIp5QVGAc1IHxxyitVBmwnUZ71vcLelUUqdBszWWr80nIIVIfvUlcsn3ZD9C0qp2cMjWtFRiK6OBY5VSq1SSq1RSl0+bNIVH4X2LZRSRwNzgTeGQa5ipBBd/RC4SSnVALwCfG14RBOGgIKvDcHBTXU6DSfjZbrWuhkcJw6Y5jYTvfbmIeAewHbfTwZ8WmvTfZ+to7T+3M/9bvvDlXlAO/CEmyL6uFJqNNL/CkZr3Qj8EqjDccz8wAakDw45xeqg5fO20+UmlVIGTuj0rmGTqHjpV1cu/wLmaK1PBpaQGd043ChEV16c9IaLcCJCjyulJgyxXMVKIfpKcR3wgtbaGkJ5iplCdHU98KTWehZwJfCUey8TDj4Gcm0c9iilxgB/A76ptQ701zTPtsNWr0qpjwJtWusN2ZvzNNUFfHY44gVOBx5xU8vDZNIZ8yH664E7P+9qnAHYI4HROKmgPZE+OMgUq3HQAGRHeWaRmw40FjgJWK6UqsHJbX9RHZ6FQvalK7TWnVrruPv2MeCMYZKt2Ninrtw2/9RaJ7XWe4GdOA7b4Ugh+kpxHYdveiMUpqtbgOcBtNargXJgyrBIJww2A7k2DmuUUiU4ztkzWuvUHNXWVOqY+z+VSi56zeVc4CrXznkOJ63sIZzUO6/bJltHaf25n48HuoZT4CKjAWjQWqfqFLyA47BJ/yucDwN7tdbtWuskzjzzDyJ9cMgpVgdtHTDfrRJTimP8vZj6UGvt11pP0VrP0VrPAdYAV2mt14+MuCNKv7qC9A0oxVXkTjY+nNinroB/ABcDKKWm4KQ8Vg+rlMVDIfpCKXUcMBFYPczyFROF6KoO+BCAUuoEHAetfVilFAaLgq6Nwx137skfgPe01r/O+uhF4HPu688B/8za/lm3mt45gD+VinY4orX+D631LNfOuQ54Q2t9I7AMuNZt1lN/Kb1e67Y/bKMXWusWoN59RoFz/61E+t9AqAPOUUqNcq/nlA6lDw4x3n03GX601qZS6g5gEU7FmD9qrbcrpe4D1mut5UHoUqCuvq6UugqnolYXcPOICTyCFKirRcC/KaUqAQv4tta6c+SkHjkGcB1eDzx3ON+EC9TVXcBjSqlv4aR83Hw46+xgpq/fe4TFKkbOBf4d2KqU2uRu+y7wM+B5pdQtOAbgp9zPXsFJ/90NRIDPD6+4Bw3fAZ5TSv0YeBe3CIb7/yml1G6cZ/11IyRfMfE14Bl3IKUap08ZSP8rCK31O0qpF4CNODbku8CjwMtIHxxSlNgHgiAIgiAIgiAIxUGxpjgKgiAIgiAIgiAcdoiDJgiCIAiCIAiCUCSIgyYIgiAIgiAIglAkiIMmCIIgCIIgCIJQJIiDJgiCIAiCIAiCUCSIgyYIgiAIgiAIglAkiIMmCIIgCIIgCIJQJIiDJgiCIAiCIAiCUCSIgyYIgiAIgiAIglAkiIMmCIIgCIIgCIJQJIiDJgh9oJTSSqkpIy2HIAiCIBQD8lwUhOFBHDRBGGSUUk8qpe4eaTkEQRAEoRiQ56IgDAxx0ARBEARBEARBEIoEcdAEoX+uV0o1KqUalFKfS21USo1SSj2ilGpWStUppe50t98I3Aj8RCkVUkr9SillKKX+TynVppTqUko9q5QaP1InJAiCIAgHgDwXBWGI8Y60AIJQ5FwCHAssAJYopVZqrfcAvwbKgWOAicAbSqnNWutnlFKXAtu01r8EUEoZwN9wHlBl7uvvAN8d9rMRBEEQhANDnouCMMRIBE0Q+uenWuuw1nod8CJwjftg+Sxwl/tZA/Ao8Il8O9Ba21rrp7XWEa11N/Ab4NzhOgFBEARBGETkuSgIQ4xE0AShfxp6vD4CmAJUAHuUUqnPPMBr+XaglPICvwQ+jjOqaAB7h0heQRAEQRhK5LkoCEOMRNAEoX9m9XjdAnQAUWCW1nqC+zdWa/0pt53usY8bgQ8C52itx7nvFYIgCIJw8CHPRUEYYsRBE4T++Y5SarRS6gzgY8A/tNY28L/AL5VS493JzicqpU5zv9MGzMnax1ggDviUUtOAO4dRfkEQBEEYTOS5KAhDjDhogtA/y4AqnDz7b2mtd7vb78R5uGwDuoA/AOPcz54ELlJKdSulHsB5aIWBdmA58OpwCS8IgiAIg4w8FwVhiFFa94w6C4IgCIIgCIIgCCOBRNAEQRAEQRAEQRCKBHHQBEEQBEEQBEEQigRx0ARBEARBEARBEIoEcdAEQRAEQRAEQRCKBHHQBEEQBEEQBEEQigTvQBpPmTJFz5kzZ4hEEQRBEA5VNmzY0KG1njrScgw28lwUBEEQ9of+nosDctDmzJnD+vXrB0cqQRAE4bBBKVU70jIMBfJcFARBEPaH/p6LkuIoCIIgCIIgCIJQJAwogiYIhx22DTEf2CYYXiifAIaMawiCIAiDh9YapdRIiyEIQpEgDppw2GJrm7gVx9Y2lrYoUyWUtW6H6uXQsA5at0OgCbQFypP5P34WTDsB5pwPx14Gk48BebAKgiAI+0FbMMbqPZ1cferMkRZFEIQiQRw04ZAnaSfZ0bmDrR1b2d65nRp/DW3RNjoiHZjazGlbojVjjVKml01k1vFnMXvifI6begonTX0/s0cfiYp2Q/deaNkK1ctg+U9h9FRYcBWccTNMmjcyJykIgiAclASi5r4bCYJwWCEOmnBIorVmW8c2Xqp+iddqXsNreDl5ysmcNOUkLj36UqaNmsY0XzNj1j+JZ/cSjAXXED/50wSnH08wGaYl0kJDsIHaQC3PV/2VH675EWWeMi6afRFXzLmCs8/4HN6zbgEzAXVvw5bn4ZFz4X2XwIXfgSNOHmkVCIIgCAcBlq1HWgRBEIoMcdCEQ44NrRv4zcbfUBuo5Yq5V/Dwhx7mxMknZvL7O6pg0feg/h0461a48pcwdjqjgFHAdOCYicfk7NOyLap8Vbxe8zo/eecnhJIhrj32Wm44/gYmz7sI5l0El94Pax6GJz8CJ3wMPvR9GDtjOE9dEARBOMgwbXukRRAEocgQB004ZKgN1PLztT9na8dWbn3/rXzmuM9Q7i3PNIh2w4oHYMOf4Oxb4ROPQvn4gvbtMTy8LzmRzzXO4zN7L6G9ejtNf3meN2KPMmPaPN53wgcYffwJjDrzFkrOvh2W/ggeXgiX/xROuV7mqAmCIAh5kQiaIAg9EQdNOOjRWvP8zud5cOODfPq4T/PzC37O2NKx2Q1g+//Bq/fA7IXw5ZUFzRXTpklk7VoCixcTWb2GZFMTFaecQvmCEzjynEuYPeHjNAQbWLz9n2zZ9XfO3TiD8u//gNL3zWPCNdcw/qNX41lyN+x6Da76bygfN4RaEAShP5RSlwO/ATzA41rrn/XR7lrgr8BZWuuhX+Bs6wtwxCkwZf6QH0ooTpKWOGiCIOQiDppwUBO34vzw7R+ysXUjj176KCdP7TH3y98AL98NLVscJ+m4y/vdn7YsIus3EHj5ZYKLF2OMHs3Yy/6NGT/4PhWnnYZRXp7TfjywQH+JRTWL+Mban3LJtI9wR+Qcgn/9O+3/Xcmkm77A5PhajMcuhuufEyNMEEYApZQHeBi4FGgA1imlXtRaV/ZoNxb4OvDOsAoY6QTk3nC4Ymtx0ARByEUcNOGgJZAI8LWlX6PEKOEvH/0LE8onZD7UGt59Ghb9J5zyGfjkY1A2Nu9+tNbEKisJvPQygVdeAY/B+CuvZPZjj1F+4oJ9rk2jlOLyuZdz1oyz+M6b3+EO/Wceevghpu1ppe1Xv8ZX08wR153PmD/8G3zmaZhz7mCqQRCEfXM2sFtrXQ2glHoOuBqo7NHufuAB4O7hFU84nDElgiYcgvxzUyMfPmE6o8vE1dgfRGvCQUlruJUvLfkS8yfM58fn/ZhST2nmw1A7/OsbTin86/8Mc87r9X07Hieyfj3ht1YSWr4cq7ubsVdczsxf/4qK005D7cdi1JMrJvO7S3/HL9f/kutfvp7fXPwbTnryCYKvvkrjj+5jwnkXM/Xp6zBueMopKiIIwnAxE6jPet8ALMxuoJQ6DZittX5JKSUOmjBsSARNOFQJxU1x0PYT0Zpw0NEQbOALi77Ah476EN8+69sYKsuZ2vEyvPh1OPZy+PIqdNlYrK4uko2NxPfsIba9ktj27cQqKymZMYPRF5zP9P/8LqMXLkSVlvZ90ALxGl7uPfte5k+Yz22Lb+Ohix9i4ZVXUnHaaTTd8x1qth3DrPBnKb3tGZh7/gEfTxCEgsgXBk9bxUopA3gQuLmgnSl1G3AbwFFHHTUI4gmHM+KfCYLQE3HQhIOKlnALt75+Kx+d91G+dtrX0umHVlsDsae+TWzTehKTLyS5O0nysRtINjWhEwlKZsyg9OijKT/xRCZ99t8pP+n9lM6ambPvpGWzpz1EVWuIJl+UrnCCuGljKMXoMg/TxpUze2IFC44Yx7Rx5fnES/PJYz/J+LLxfHPZN/nFhb/gvJnncdSTT9DxyO+o+d8nmBW7iVHffBaO/uCQ6UoQhDQNwOys97OApqz3Y4GTgOXuPWUG8KJS6qp8hUK01o8CjwKceeaZYl4LB4RE0IRDjcOlMmksaWEoRal34FlX+0IcNOGgoSPawRdf/yKXHHUJd5zyVSKrVxNY9DrhFUtJtnZQOrmM8jMvp3T+8VTMnEnpzJmUzJyJd9o0lDd/V6/tDLNsRxvLdrazprqT0WVejps+lpkTK5g8upRxFSXYtiYYM9nd1kFtZ4SqtiBTx5Rx4XFTufi4aZw3fwqjSnvv/8NHf5gSo4S7V9zNT8/7KRcfdTFT7/gqJbNmUv+jH3JE9EbG/X9/g5mnD7XqBOFwZx0wXyk1F2gErgNuSH2otfYDU1LvlVLLgbuHpYqjcNijAZVIjLQYhwTrW9Zz6rRT8Rpi3o4kh4uDtuS9Vsq8Hi5dMH3Q9y09WDgoCCaC3Lb4Ns6ZfAa37p5N9X9eiU4kGH/SeI44pZ6Kz3wP4wO3FLTeWCA9S/YAACAASURBVHc4wd82NvD8+noauqOce8wULjtxBj/5+EnMnFCxz6IgCdNma6OPZTvaeWhJFXc8+y4XHjuVa8+YxSXHT6PEkxlJuXD2hfzqwl9x14q7+O0lv+WsGWcx4ZprKJk+nYavfhnr+zcw8cE3YNyRB6wjQRDyo7U2lVJ3AItwyuz/UWu9XSl1H7Bea/3iyEooHM6o9lbGbHoXzp6778bJGHjLZG3NPGitaYu04Yv7mFIxZd9fEIaMlIN2qDtqlq2JJMwh2bc4aELRk7ST3LX8Li6sruDKf76Jf0ol07/yWcY0PYIqj8E1y2BS/w82rTUbarv50+paFle2sHDuZO689FguPn4aZV7PgOQp9RqccfQkzjh6EndfdhzN/igvbmriF4t28t2/b+Wa02Zy48KjmDd1DADnzjyXH3zgB3xz2Td54vInOHbisYz+wAc4+plnqbvpOviPq5n44AooHbXfOhIEoX+01q8Ar/TY9v0+2l40HDIJAgDxAUTPdrwEE+fArDMLah41o/jjfmaMnrF/sh1EWNoCIGbGCmtvaxKmTUXpwGyAAyWWtCjxGHiMQ9fJtvTh4aAppdBDlKIsDppQ1Git+fmyH3LpE9s4pd7D9O/+B2NnhVEv3wULb4cLvwOevruxadm8tr2Fx9/aS31XhBsXHsWSOy9k1sTBc4aOGF/B7Re+j9sumMfmBj9/WVfHR3+7koVzJ3HzuXM5/5gpXDH3CjqiHXx58Zd56sqnOHLMkZSfcAKzn/hf6j57A+r7n2TC/3sZ9qN6pCAIBzkyB+mwRjPA37+7BqafBCX9z4UGqOyspDXcypXzrtw/4Q4ibG0DhTtoWxp81HVFuPrUmftuPIgs2t7CzAkVnDln0rAed7BJmDZKkZM1lCLlmB3q8ytLPYq4KQ6acBjy7Ou/4vz7XmTWSecw5x/341n9M1j0Mnz6T/C+i/v8Xixp8cKGBn63Yg9lXoNbz5/Hx0+bSXnJ0I2UKaU4dfYETp09ge9cfjzPravnu3/fSnmJwc0fnMMnTr+e9kg7d7xxB09f8TSjSkZRcfIpHPXYY9Tdegtqwu2Mv/exIZNPEARBKA601ry4uYkrFkxFmfHCv2h4wLbAjRYJGVIOmlWgbqKJkdNhZASPPVi8uq2ZKWPKOPeY3umk9kGS4hg3Leq7ohwzbcxIi9ILGa4XipYV//of5t/7R4649nrm/fgePH/9JHTtgS+t7NM5iyRMHn+rmgseWMZfNzTwg4+dyOJvXcj1Zx81pM5ZTyaMKuVLF76PFd++iG9fdhz/2tLMOT9dSqj535hQMo3vrfpeOixecdYHmf2bX9DyzFuEX3hk2GQUBEEQBk6ypeWA92G6hmukejUTW98u/IteN2rmOiP7QuVdYeLQJOWgFRqRLG7XYYSwkgNq3tdUSPMgcdBqOyNsb/Lv9/fNITw/cdCEomTL4ucY873fYtx1O8defjLq8Q/BCR+Df/8HjO1dLScQS/Lwst2c9/NlLK5s5defPpV/fOWDXLpgOsYI5nl7PQaXn3QEz9/+AZ677RwCMYvVay5jVd02frr6t+l2oy76KEd84wYa7vsNsY0DeFgLglBUmPbQTBgXigOdSBBZvwH7AKoudoUT+CKOIRwLB9B2Yc4W4ETP4KBOi/VHk7T4C0tDHAipyFmhEbTBVuFA5iIVZY2XUBtU/nNAX8mX3giZ1EaryPtpwhzAtZeHoXRAxUETio6ala+R/PZ9dH/9M5xxRABevAOu/QNcdK+T3pGFP5Lk16/v5LyfvcE7e7v43U1n8JfbP8B586fssxrjcHPikeP55adO4Y07L+fC8ffw5x1P88k/PcbGum4Axt36faZefgL1t99GsqlxhKUVBGF/eL3mdar91X03qFkJwdbcbUV2rxL2jY4PIC2xB29VtfP2ng7AWX8zZeJprWkKNRG3+tl3KnJWaAStCPvWpnof7+ztHPT9piNoBToFA5771w8xM8are19NyzCURM0oe/17B3/HifCg7WqkqzhqrQvqB0lr6H+v/UUcNKGoaF+3is477mLPzRfw4bJtsOs1+OIbMP/SnHaxpMXvV+zhgl8sY3tTgKduWcj/fuFszp5b/JNujxhfwQNXf5hfXPBzatTjfP6Zf3H9o2vYWNfNpJ/8mXHHeKn/7KexI5GRFlUQhP0gEA/0/WGwBbp7GFdFPsos9EbHBicCZNk6J9duj29P//0n7QAMsM+4xnckGWFn184hqzxXCGPKhqb8QSpyVqiTNJi+Q8qp9sf3P12uULZ1bOO9zvcGf8eD6FymImgj1c1e3trMpnpf3s+iCQtfxImAp1IUR/J66Atx0ISiIVK7l/ovfYkN1x7PtWVboKQCbnkdJs1Lt7FszfPr67n4l8tZXNnKHz53Jn+4+SxOmT1hBCXfP6445mK+evrtHHnscyw8poLPP7GO257dTuR7f6SENpq+cXtR3jQEQeiffY7MD/N1nbASRM3osB4TANse8JyWYif1y9kHEEHLxsoawbdtja3t/h0MbTkR14H0oXgIdr4KOA7EHt8eImY/A4DJGGx9ATsaxQ4PXlQlxSi3rP1AoitvN71NZWdlv21SeivUQRvM52uqcmQoGRq0ffZFoSmcA2Y/9NHXV1LbRyqCZtmazlD+NOTV1R2sf/ZfRLduzWkPsLl987BEQQtBHDShKDDDId695TrePXsCnxu1A2PO+fCpJ6F0dLrNsh1tXP7Qmzz+VjX3X30Sf/3SBw76MrWfP/HznDhlAXv04yy/+0LmTR3DR//SxsZP3Eh863o6H/6vkRZREIQBYtn7MqCG12h5u+ltltUtG9ZjAtC0ccBzWiBTAW6o0Vrvt+Oqk4PjeFpapx16rTWWtvp38LUGo6RgY1qhQGfmRWYfq0+SjvMWeustgsuWF3ScfPxzU2M6UnGg+GI+msPN/bZJXXcFO2gHLFWGhJUY0E4PpHhLiVECFHKfGSAH6pjEQ84fmQhaMZbZT5gaIxbBbG1N/wqW1pi2SWOwsf8U42FEHDRhxLFtm+Vf+RTtFQk+eVQdZed8CS7/WXq+WZMvyu1PrefbL2zhixfM49VvXMCHF0wvytz6gaKU4r4P3kd9sJ7ndz/JvVccz5K7LuSVMR+h+dxptD/+GIGlS0daTEEQBsA+DcR+jBbL1gRjB2b8ayvXcIskRyhdOpo/xWhf/GtLE3vahz4Ssdu3e+CO62AYnLaFt6MNcNbqzJqE1n/fSRUTMTwDNKYzz8q0g9afJ5F6tib3v+BNygGMJns7ESkVDtR496r+UyMHGkHrSwVJK4k/5nPSkQskZdTbHHj0xdY2vljf146hHNM9YQ+O85vmQB20Xa85f2TSR4c7gBbdtIlEXR2Q/+eNmTGSdub+amuoC1fijweHLjK5n4iDJow4i3/yFUqq6jn7rE4mfOSX8MGvgVKYls3jb1Vz2YNvMn1cOUvvupBPnzkbzwhWZRwKRpWM4sGLH+RPlX9iZeNKjhhfwe8/exaJGx9h/Nlhar51Jy1b+k/tEARhZEm2tBDdvBnIGGmb6335U3z6MYQqmwK8saNtv+VoeG89b/7ppwW390eSxPIY0SONP7p/TmqjL8obO3oUYenDEQgnDyB97wAMz5KWJip2bsNjKCw74yppy8LSVl4HoyXcwqZtzzhvDG8vAaxQqJdjno++StHnRNRcB+BAitekHLPSPqr8FUQid2Ah5Zj0Req6K7T4R18OYrW/mlXVrzoFfQpkoPPf+mNv+zbe3v7MPtulo3Yu2rYxu7v3/8ADkP2lLU39fp6ZgzbM6dwNjcSrq7G1RSDR1evzN+reoCqwOf3e1pruRCvNodZ0Bd6Ci8y47QwrAdED0HsfiIMmjChL/vILprywgpkXhTjy2t/CyZ8CYG9HmGt/t5q/b2zk6VsXct/VJzG+omSEpR065o6fy/3n3s+9b91LQ7ABgEtOX8CEbzzM1AUhdt3yRdZvrRlZIQVB6JNEdTWJeufaTRlptZ1hYkkLKxTCCoVJtra6D/W+DYBEAUZ2f4S6W/fdKIvlu9pYU913Rb0DK3pQgKETy18QY39cg3DcZH1NF8GYmWtkbfsbtGxzDNqskvb7ZUwPgsGpXBlKPQbhuJljzNrazutg1ARqaOre7bzJE0ELLV9BbMeOfR/c3XVKP3ErjhUKE3j5lWwJ3X/7f647WoL7FqW/3XfXws5XsHy+9Dw4j+p/LVPbHthC1dnH3965PW2gl3pKQdvE3EhLJFF4JDGlVzsaxf/SywV/LxszEe7zusg+Rs8lPZJ1dYRXDXyZnqRlOxHrVJ+Kh3o5xz2x9lFcI62HEcpw7Ig3UBV4N+9nCStT4Cd17Rl40vocaP+Z2L0Fdg9+ppM4aMKIsfKdFxj/0z9Scn6cYz77MCy4Gq01T6+p5arfruSC+VP45x3nHpQFQPaHDx31IT517Ke4c/md6QnHo068khk3XsWRR8Sp+to3eXxFlRQOEYQiRGudLtlsa5vojh2MWfUGttaEV64ktHw5kXXrsYKR9JPdDoXwr9zSY0+q1357jpRnE03kGhOpKENyAMU5TMuRpy0Yo64zY5glrSSrGlflpAQNOlWv502F3J+7nC8r6hbvsb5RLNTCkm3PYG57Ib3twFKaHAmTdjJ9v+6P8OrVWH7X2XUN4UmjS933rsFtWWknrV+UkT/aYe7bkch2/gKJAEtrl6KTPfpXKnJ2ABG0pJmKZvUm7ZD29yu7fT60chWhVasAMIz+TVaNxlBGb/2ZCYj1HmjIPnqtvzY9GJEaRLG0jS+SYHHlvgc9UueSjlD2s06eEQpAYh9LKfTTB/rSm+7x+7cFYwVFotfXdLOt0Z855q7XnOvyALA16Qhxn22G0Hvrr29VhBsY69/ZQwbDuR9YNsnm/uc6Zo7hoPTQ3B/FQRNGhJVVS4je/QPUApOTv/4IHP8RusIJvvDkOh5/q5o/3XI2d/7bcX0ugniocsepdzC+bDw/XvPjtCOmLvsJ8y7SnG504H/kEb727LvEzeJLSRKEwxpbs6XBR8x0UtSSHZ2pzajSskw7pdKGkNXd2zHpaRNX+apYUrsk7yGDsSSvV+bOk0lFGRbXLs4ZzOlvzq5n9w4sn4/1Nd28W59J1Uk7MAOwo/wvvZwpoFHoYJI5WBURNSoWZeyqN3qtb2RjQzKCL6t6YT4HbaBV55buXcOz217ZZzuzsytj+LmRHqUUY8u8aT1ZqYjIPkXoo4pjvt84GIIsXWQbrmknvkdUrb/dFUohCxQX2j10wpFznxE0beMxPL0HMRvXQ9XiPO3zC2C6RVU0kChwnax0xMhNs6zvjrK3I4RpWr361OjN6ymp7DkwkyNZQemGvdJUeyx4vnpPJ6vd9fb+uamRhu78UbG2YCy1g/S2uBnrcx5cIY6VrTVew2Bs82rw917XdU97iH9lpUkGlywhXlW1z/0OBA0Ely8nVpk7RaQ03oHXcnThd9MgtTYwbRNvSyexjZnIm9nV1WckdKgLoBxe1q9QFKysf5Oau7/F1FFJzvjuL+DYy9hU7+Njv13JxFGlvPz18zn9qIkjLeaI4DE8PHDBA6xtWctfd/3V2Vg6CuMzjzF3YS0f2fMmkzat4fNPrCMU3/8J3IIgDA66x6tUqXTLziyaa4yqSLdyHCW3bbK3Y9LTJu5V4CMr9Slp9TYQDCNjxGY7IP0Zt7q+jvCmzb2cmoIKSqRIRNB1awGw4wmIdEF/63lBVrWIwbmXWVpjxB1jM9EjgpZSVTirQlu+KngvbWmiPdhfdEPn/K9q81Hfh+Gb+z0bdi8BM5FOccx85vwzQwH3bW9995ojls+A7+FRxa04es1Gyqqb0zKn9pO9P0u5+7Jt6NgNuxYRWLMdO7yP368f6oN7CSY78zphBdm1eRrtaw5aKoLWy/HuYwCg5yFSgximbUIq1bQvWSNdThGRHtHAlF63NQXoCidYs7uDxZUtbGrblBNpVbG+q4dqW/cfQcujGysUwmztf+6qL7KPSE/WMbeEG3i7KZMu2eKPpeeq9oxOA9DjWrJt8BoKb7QNfLX7lMWOxUk09nbkDhQ7FCZevZdkS2YgS2UtaP6e712UUpimcz9QWuc4Xv0tM5FqdiAVOftDHDRhWHmz4U1W/ORrnNKa5P3f/w6ceDVPranlpsff4asXH8OvPn0Ko4doEcuDhYnlE3nwogf59YZfs7ndncw683RKr/g6My8xuHHZk8wKd3D9o2voCBVHOVhBOGxJPct7GN3aSkVFNDluV9YaVvnSoPqtThv15aQe5RvB9RTooCUtOyedMTWKHjZ9aaewr4ISefHV0V6/M+Pk9TH6vrRuaSZlMmUQRjp6GXgZOQtPH8rWR08HLfVZzHUGa/w1+ON+LFvz+nv11AXq0m37KppidndjR13DWmvwNzKtfUNhwmnt/vZWVgQNQKc/i65xHdy+jPPy8TBqsvvFfL9Jbt9ZVr/MWfQ6lXGRXdKfzGsrVXmwcy80b3I+Ny1o3d7rCHYi0StSA87ARHb10YbIHlpjtfmdzbQzk/X9WIzYzp101u7KGZRQWUXBPMqDHQ73uQadRuNV3n6nAdjhMHY0Sty0+sxEsWwLOvegs/yzXvvc84ZTRKRlS8452drGH/eTdCssdofjxE2bplAT7dH2zHllpSPWBeroiHbknMlAUxwj69dnUmjzEDZ9NIR6O0q5O85yTHoco6otSFfYOadEOMyYtStTkjps/7+c9rbWeDzK2WVeBzlPv4gNnj0zrqqKMc0ZnUbWZ12nrm5rOiNEEiYlhiJp245jbveYI9zP/TirvM+gyZ2NOGjCsPFS9Uv8+bFvcs3qOMd+5yashZ/nnhe28Lvle/jzFxdyw8KjDonS+YPBiVNO5J6z7uHO5Xdmbtzn3cnYYycy6bzZ3L70UU6ZUsqnfreaRt8ILEArCALQtwGXWZMp35dyHbScVMS+DhRsdSIwWeRz0LL3Zdpm2tgt8eQWWeoKJ3LSGVOjwDXdq1jX4Bhf2ZXpEqZNbWc/VQ+tBHWdEdqDMYIJf17ZLNsikohR1eYeN2WEdlRB23t5d7u4djGBRGGRHMvWad3GE0no2ouO+dFao10nJOGmr+3odgpqJCybPd172NqRWbSWRJxEQ+/R/PCqt4msW5/ZEO2i1Og/7S5DesYKKpW+ntqiNYq+qxBqnXHimDSv7whaD+xejlRWBA2d6aOpaG80v4GfPZAQfH0xgVde7VUxclODL6f6qNZg9BG17dk1fDEf8fo64lW72bb0edbVZRW6yHJGDWUQXLacyJo1uTtIRiHc6USr881Bc7+fqK0luGw54VWr8IUTeLo6XHlyBcqkOOqsJQHyngrEgzSFmggnnGvDxmZV4yoaI7vcQ2fJkr2PrN9mW8c21rdk+pVOfWcf65z1lcKcr5pnc7SamuAuYtHc4i3Z+9D9zDXNrjZqh8OoZCItX2VTgEZfbhRZa/DizKkk0gnh3EJE+RaR7jmHbr/QzrIVZZ1djKvva+6go99gLEkFHsZG60lalnv95c4B7c8m1brnizxtTLOg6qr5EAdNGBaernyaJ166n6//I8pRn7+Q6GX38Nk/vkNNZ5h/fe08Tp51eBQCGQifmP8Jzp95Pve8eY8zsmN44BO/Z8rUdZRMKOdL6/7CpSdM48bH1vSfkiMIwhCS34LT6YIhuscobNYIrWX2+m6f9oCvrtemPIGMjHHvjginnJsxJWNy2pk95FVK4TGjTAjugrZKEnV1mAHHmNNaU9sZZlN9P+uaWUnnvBSsblrDa02r2BnJGEhaa5J2En80wfamYGpjjuR9EU32HoSybd1rLoy1t5pR2zdha4v2YCc0bqByw0oqmwNYpCJojhGaWlPL0bc70h/pwrDiULOX6KZNeWVJza97t7abSDxJ6f5UVc+agwYa1e44rLqPiGVoxQo8W3ahInH8S1YwkDlolq2JxjMGYs9FsZ02yRy5ehLdnhtJS1o2L6/MdaibfFECyc6stcg0HuXN+7OmDX2tWVnVwd/eW0pjKDMfKWUUW8EI2spUtUwNIvRaJLz+HahehtYaj+HpvRZZak22rduct7aN0dXBqPdyo1+p/WdXR0xHGfvy0JTBprZNdEQ7MJSRVb3Q0a3K+p3SDniJUxwmO30udw23wlJ/c/qJW0DFsu28i6h7VAkl3UHeefY3ufvIjmKafTtozhiB23dch8OIRdHaia41+2O5qYFaM6VlZUbCVKEj2yLg78q7Pl42ZmcnZnt73mhtT6pag2yodUvqN6xjb90KfIl2V/e9f7eSYIyIHSdhRSlP+CmPd5G0bOf6a9qCztZ71jXV0B0Z8JIkoRUrWPfPx2kOuWnGde+498p9Iw6aMKRorfmvjf/Fn9c/xo+fizDtgqPouulXfOJ/3mb6uHKevnVhppKV0IvvLvwu0WSUhzY85GyYOAd1xU+ZeeJ2Yls2c3v7Os6bP4V//8M7+PeVXy4IhzFKqcuVUjuVUruVUvfm+fxOpVSlUmqLUmqpUurogew/M7fHeW9bqehTDwNBZzVKPfv7qSaYN43GznL+so6d08zWmLaZSREM5qaGWT3mrxkGTOl4BwVoM0l0y1aSLc1pGfaZxGMlXP/MdR4U7Im10eo6iIu2txBNJjAMhdYafzDCos31me97cp8DCdNm1W4nwhHLY3Cu2NXOil3tvFL9CqGEs6i15TqUvkQbG1pWA9AY6SacMNNnkHR1nZrPpAClDLQGe/dSxgV2FlTjvzuSoGN3HRO25i6t0Cc5kZSsoh1u1E/R99pRdiiM0eWnrrrdWdi6RwQt2eZGrvLI3RFO0JSKbmg7d+6ZGwGx3L6q9xGxSR3D7K4H1XNfmr2hLTQGncijbbvphnl2oU0zXXGyMxynM5zA0hmj2OtGe0ObnWUF0pdLylg2PLkpgVlRQUMZuTa5ZfaKRKmSEgzILGvQM/qdihyjaeyKgGWRaG3FCuWLIGeUnh29Szlm5ZWbGf3uO66YrpxeZ3DAjjlpxbbWBKJJQnGTl/a8SnvMdTb6+D3yzSNMybGp3kfNP3oXrfEqb3ogKOVghZNhVjZm1nrrz0GzbI0dixFasQIdd9qpWCznztDYnRlIsbWm1Ao711X2IFXrNkr25K8QqdyicFYoRHj1GsLvrCXZtO+KinvawzR0R3ml+hWCVoxgtBvb43Hl6H1djmvw0WR10RypwbaTlHs8JCybaCzu9Insqqyu3FprNtR2s6s1E4Hsby5lMObjlepXsKMxor529vj3OB/4652IYgGIgyYMGZZtcd+a+1i85zUe+muMsVNLqf3GE3z8kdV87JQjeegzp1LmLTQ95PCk1FPKry/6NS/ueZFFNYucjafegGfu6cz6+DQ6/vu/uffIGMfNGMvNT64lLIVDBKEXSikP8DBwBbAAuF4ptaBHs3eBM7XWJwMvAA8Usu90zYgeI722G0HrNfKePaqrnXk7W7ZXphfFLWzCeWqUvvdcnnT1V9vG0lZ6vpfxziaiG5x5GKt2d/Qqv62UwrBNZ88pBzBrXo3Wznb/Sy+j69fDey/limQl0nI5y2c555HQJpatiZs2MTPhbte0vLwI7+YNJDvd9EUjd+5xNGHRFozijyZZurO3kRaIJQm4c55SUcL0PCBsku78oqrEXhqtznRVwZTOsufkKcC2knQnIuwymzGUgo5dBIP+9Dyl3vOaNHY4guHOkSpkTbXUnLNUkRDnbVbUIauwjD+SzO07CpStSVoqY+wmo7B7KZG169z95+k72QMCPeagpVL5zFQEzbKdOW495S7NOM/eKVNRgUYqIm150/60m0apgVJPSf5AX+VWxr7zZnpOk5GKYJoxMON4epimumdETCnWNq/NzFVTmd/AwMhxGvyP/z/sUO6Cxaqk1JlCmGxnq29Fr4ibbdt0hOIkbQu78p+U1+5k2+I/41uRZ62rLJ17lCfTD9wTN8IhjIib/tjc4kS3tAbDSEe6mv1xdrUFWfpeK7vbgkRSlUb7iKDlLd6TJUfcstyQV+a8PMqLdtVqRx0HpC3SlpM+vKWuHbOPiJWlNToQwAqG0imvyrZynOFoMiOv7fpkttZsrOvO3G+sZJ+OTVfUYm9HuFdkPb5nTzr6mY+saYq0RyOYlp0513xp4CkZ0VhYlBpebG3z9p5WwnETnT1nLqVXVy/ZhZmy993z/hDY9oLTn11CyVDmQ3HQhJEkYSX49pvfZnvHdn671EOJL8TWux/lc09t5XsfOYFvXXqszDcrkCPGHMEDFz7Aj97+EXt8e5wbxsd+Q3l0AzM+fxlNd97Jzy48gsmjy7jtqfW9JscLgsDZwG6tdbXWOgE8B1yd3UBrvUxrnZpIsQaYNaAj9Ix6pEarbRuy5nak5xM5jYgkLKJmko3NToGIvm6LWmvMtPGXMuTztHNH3cveq8WyHQctNf8sNcejIxSn2Z+bNpguWOHut8a/N5OSlXRGypWbkqm7GnKMD/fIQJax5J6IgUGDG1VJWEls20lZSyZMPOEgkfdqsEK9UxiVcgzQQCyZThfri5QTamlI2nFAo937oMdQhHQsE3UwLfwvvYzXlzGYFAbatqmJd9Ft+hy9Rrt5Z/N2NtS66YeJRG5EQ2uUnUxHDK08hm1NRzirhHnGSUq9VjV70r+lUrlz0JbvassZrQeFsixHwW4ELeCrIRTKWmbB1XlbMMYbO7Lm3+QEWPNE0FLnpS1n3z0wshw0pxKks6dYTQ06mUzr1ms4zpFpm9g2lHi8jsPWs4COW2hlY1136tSc9LL2neBvwINB3qhw6q3byTIpZ1lRrC4/hi9Lb7aZjvikUCUlJO0kCTvqeBK9xlAsAjGTSNzE0CZeK0YoGSZq5ZtKkBtBS6dLak3YDGDazndUPIm1pZJ4dbVj7JeWomNRCLXnOPexpI2h3X320e+zf0PdXUtkyV9zoqeeSDts+xszG19LdztDeTHcAQ3tVpBMWImc7mzKAAAAIABJREFU6KSZTJBI5rcfOmNNxNzz18k4cSuCZSUdSdKDH9k61HgNld5/3Mx2ZjLtLDszL3JvV4QtDb5eN7Z4VRWJ2lr8L72M2dUFO1/NWTuxLN5FRcRJke0Mx+gOJ0EpN5k8cz5WKITRHcz6vW1s26RUebBsjWFb2LaNbSYy/c+9plJLZGQPmmgygyK21ullDHQ8jmlbeDoyMqb2l7Rsks3b+w+/uYiDJgw64WSYryz5Cr64j/+qnE5ybRVvf+nHfHdZJ3/43Jl84vSB2T0CnHPEOdzy/lv45rJvOuk8oybBNf/D+OBTjLvoXFq/9S1++6mTSJg2//H3rf1WsRKEw5CZQFY+HQ3utr64BXi10J374342tGxIRw6A9Hwib9tWVN3qTGNNZmTbvU4jdpSWRB8FGlLGS7iJ17u3k7AyJfwzC/66bZNJ7PpG0OBprHVSHO0kpUZpjiGV/d3McTKfJ8wovriPpOkWMdn1Gh5fLaQmu5t9LMKbipxl7bsx0c3GVmddobiVSBtxSct2UyENpzphjwiUI46NbYNNfkO1ItwITZvSC3lroCm6h5gVTuvNYzhpZzHTdgxo9xw8scx3cJ3BhG0yKtIE8azfor0dbVnoUMBZTyvbmncdXsO0MG2b1nBrTkRic4OPd+tSRlr6V8KTdCIqau8e16p1jbz072oz3leJnVXQQaMwbNtpqwzQmpVtG3jLn7V2lGss7m0PE4yZEGrFkwyiso6dnR6XTuVzHTSdSp/siSc7uunuw7aJbdtGormZQDzs6tpJX7WxsdGkljENvPIqyawS8Kk5WNlia22nI0Zew5vpa5DrP1kJtBv521zvZ3dbKC2zRuPdtIPSd3emzxHA0jZNviipBahrI/WsbnYj1trOcXiy9ZEqSGqVlTnGuNdDPBqCrS/QlQzTmQznRtDcNdhS5fk74g10xV0H2j2fRG0tRiKOLilFt1fB3hXp74/r3o4R68zssp/5V+lqqQ2bSVZtTfdFbzJEqT9VrTHbY7KZUe/HY3jT82PX1rRR2+X8doYC1c+ct73BHdSFaxyxIiFaYjV0xhucS9j9PbKdFyeCpjIOq3tOu0NNdCfDmXmGtiYQihJJmBhuh8lXVMOybYKxJHYgAIkwBDNR9cntq5nU5dxjUlF/7R47u1BOZO06KjZXpa8H7bpwJYbHuR9qEzRO/0p/T2Fpk+imzYxZvTwnwugUpXFed0eS6cGcwKJXSW6vpXx7TbqtQoFts63JT2VzgETN7n3aaeKgCYOKL+bjlkW3MKZ0DL8KnIr/6cW89ekv8ljTBP7+lXNZOK93+oRQGF846QscM+EYvrfqe86FfcyH4LSbmD7rHUDj+8XP+f2/n8n62i4eXrZ7pMUVhGIiX1wq79NRKXUTcCbwiz53ptRtSqn1Sqn1HZ2dTinzHju0LU3SThAOd/V4ELupR5bpTkbXzjyk9L7zHzPkRqy2NPh4t7bdiVKkgzLOi9d2vEg4GQY7geUNo7XtRNCMErKNc+hdJEQHg3gqd6eEh0Qwp8y+MiPp0uC6LwfNmYSWOhPnONpOy5mwTCdVStvu8R0HLbS1tvd8G52JFNjaxk5EoSq3imVFtBmsOEk7iR2LpQ1qT7wLIx5yDChDYWHzbkMXsYSVjuZ47NTcEsd4sm1NPDUPyq3Ip5WiZPtmko2N2LEIqiSzqDTaicYBeJKmM4IeaqAj2Azx3Ep5vkiCTe2b6TYjYJtM7N6SE31DuWmWWamvY0J78UYyRqhWTtqqUm5UIrugSCIMPdbLU8kERv1OSqJOsYScyK37vZR+o8mkE82yLacYFdAY9xG0Yu5xtLPmV7pSoeOgaQ31oQaW1y93xXYdItup3qfIChZHMvO3chw0bafb2a6DEI5bWPFUH3OvF9tN22t6F7s1t7hHdoqjcp1XZ4OjI3/UpMkfdZya1kqiLVtRwRbQNlO7AiRbcxd7TxWJsLJ0rDUYEyfwxpYaQnGTNcFq3glW5zi0CpVOB07NqUvPdbQ1TaEmdONWDCsBZWW9inlUhPZQEWlxU4TJSXF8eUszne6yOq3+KJsbfGitaYl2OXK6OhgVricfWmuCUZPuiBNhBuiORjNppkphaCfFOdHW3SvC4zg77qBHzVsYdgKPKkGjMUgNLGTa224EzbKBaAztyrcrVE9VrI1x5V48hsKyNSt3tvFecyCzhmOOg6YARSBmsrpuByvr1jrXsBmjM9pJU6gp5+auU/Pd3OtkV1sAyzZJWHEnjTwRTt+nPUmTUd1hSgwP2rapiDSgtDsH0e074USQre1biZlRJ208nsCyLSf66OoNeqSy2xY63CPDQAHaxrI1ScsmumEdVldu6m1PxEETBo32SDufX/R53jfhffzYuIi2+/+HtZd8mNcmfJD/+8q5zJ0yeqRFPKhRSnH/ufdT7a/mD9v+4Gz88A9RpWXMvHoKoWXL4R9/5Ymbz+LxlXt5cXNTf7sThMOJBmB21vtZQK8LRCn1YeA/gau01n2WRtVaP6q1PlNrfebkyZOzHujuaDEKW2uaorvZtfw1zO4gtG4lnAw5TcwYVP4DLLv3sjuuuRFNWDkP/WzHbXXzGlY2ruwVQVNJk5gVIz5jEpbXwNYWlm1huBXeso2unnPjbFujYolMpC0eziqzr9nk20pb2JnorvNUMksNLKeMioTrLRm7Wind24TWmqSVwHbLddsph8E1pnpH0DKK0dpm8frtJMI95hK5n5u2SXDJUrS7DlRpopsx3buw0SgcB81GY5qKuGWxobYrZ5S+OVoNkRhRt6BKOrtJaww7AVE/Oh7HKHccC3/cx67AWtep1CjLwrJtElYCu3Et7FqU3nc8abJiZwu21vjMWDr6qLTtLotmp43JVCqn7UbYbO+oHGdPpSJckS5ozZqT07oNWrZmChoAZdVVVFS2ppSZjhmknW6deR2KOilddjiKFXG6fXsy6BR4SfXtmpVQvxa0Rts4USCticZzC2dotFPIBDeiRm9SRTKwEsxsfBVD29jaSkedt9T7aehwU1C1RndVQ+P6TGXUeColVsO7G4hW1RK3TWr8NbT4YnSG3Uu311xKDckQRjyEEWiiJBlgXH0L8cr3wLbTkTO73inqkRrEaG9fnOW0Gz3SWd1U3mAEbyCSduZsN+02M7/PjVJGfXjNALq0NJ2Ga8RDGHYyvR5dZh5bpo+atk1rwDmvWGpeJJpNgb10ZM1v0qUlqNEV6ffJ1HVumShl0B1JpPVixNrTDoahUnuE6K56VChOWzBGfVfE7SvOwAraJp50CgKVKGeOoSdP5M3W2v39NZ4dNeiuTDqroRSGUniUchaXd3+fuDuHLfU7Z2MoCFtB6nydBN7ehtnWxsa2jWxqc9ftc+88NtoZkHDPK5a0qPZXU9lZ6ewo5s8ZZDmiajcepTC6uhjVWYfSGtMdYHhl199oanOus7gbpbcDAXZ272RJ7ZLU2ApA7mL1tuVWjc3cw5xBIAssG9XcAXZSHDRheGgKNfG51z7HmdPP5P8bfxVN3/gPKk8/iQ0fuIWnbj2biVKpcVAYUzqGhy56iD9u/SOrm1aDpwQ+9SQlDYuY/a1raH/wIabt2szvbjqD7/59a6b0rCAc3qwD5iul5iqlSoHrgBezGyilTgN+j+OcteXZR59Y2kKjKU10pR/+KSPLSj3CExGqOt6jK5kxaEPxpFNuOstxSjli79Z30+yPZs1pyRwvkAgSSUay0tXc7yZM7Alj2R216QjG0bbljnCnHLS+ipa430+Z8bYTlbGT8dTXCCSDBKOtzrt8CxWnGuIYWDuag5i2xu4MEN/TjJEMone9RlltE9p2Ik7O8ZwR8mwHrTvRQtyMZc3N0RjaSpe4tuNxPL4ulDsiHXcje+MaV1GS9KfPwTHMbSxtUxZvp8MfodWde1euStPnBjBm/U5K19WQsxEY7dsJtW+j4wnHQdN2pjhFVsU+WzvLCNg9oosTO99l5o6/pR0kUvOYstNcDUXTBJP6qFs103UUS8LNaWfPcTUdw1OPmtZL//8/e+8ebVtW13d+5pxr7cfZ53XPfVXdelMUz5aCCKKYDhEFGoxAoAlIG6NEBZWoAzSkldE+YjRRI9oKI6a1jRCGhrYDxg4IgoWARQH1rvt+nnvPvee9zzn7vV5z/vqPOdfa+9wqQaOOQtm/MaDuPXfvtedaa659ft/f9/v7/nworLOkFfNVXr7Altm82k8lg6aSlDzzaxqduUj/C4+S5gWbvYRBFqRe+/rBfFePylIc4LJkMt/FS0UtWimcWO65+rH9FxSq5FkHBkmJd9msGETnkCJH1yIQh4TrXY0hCM+bIKjdNvl6m63cA9leYitDil4AntXyG01qt9zqnyUnaJv4HbiwQP3UMskn7vEfH9i/EoglbgjOUhQ5cr0EVMEot9TvP03twVOeQQNyGxwaA+hSdgwWlcCQpGLOo9E6tWp2oNBNUy7vDB/HKp/b7JEVbh9TxXWzw2RpEbTiarrL+XzN91ZuD8BZ/z0g/h4iQqtzkvn1dRqPPTB+dkt3jcJxZWfIg1d2q89zzsJgizzPfFEhFFs0JVs4XodzEOvxtZosiOTWg7dapElzOwFEhE629Xg2fYI1LhlwN5joIVUK0fH4+jKxx3RgxMrnQWnGJ1tg0NTPrtE4cZLmxXUiC3mQOGYb5+islvPppLreFXMrjzfmubo79OsMVZHUpljnn94/eHgZ88gZovW2B3G9/Uz79TEFaNP4K8dyZ5nv/Oh38rLbXsaPHft2rrz5zVy96wZWvuPnePfUqfGvPZ6y+BR+6kU/xTs//U5W+6swdwO8/j/ROPlL3PjOH+Da29/B8+jwU696Nm95/wNV4+o0pvHVGuKbVt4GfAw4BXxQRE4opX5GKfWq8LJfBGaB/0cp9bBS6r/9OYe77tilJAZm+pchH9JNcpY3+49/sU3JJ6rNeagYT85KYn2V2fs+TWFlP4MmY0Zt4coqrbPXKklf8uijDO67D5UXpFrT0odYVDNVQlvNZxK3b2ai0eN+sYqxkrF0rJR6+WTDM0VOnrjCPdnTVkz8++4gpZMWiEuQL56jdfEyZtjHOTB2SFI4Tq73IO1C5r+rrgxOcW14EULSLoz7XAB6jzyEfvTPEHFknzrPA2e8RM0pRVSMqvvixBtUWBxOhEbeI8ktucvY7K9x+8LtY+wgjiwvwMk+u/tSviXWoq7/XRaScQ98HbnNcUXBqSs7nFrzstfacBtzfmUMkrb9AGMd2JH5hsFozc7BJhKOXw0qnnCTE1XuE0XvgQv7XC9X0h1W0z1QcLJ9kofbf7aPcvW+mcC5j4+Ty3B+M/edgHMXq/NBaZLgBrwxHHB29+yENNZLJT3b6o1X7HVW6wBpYTFakUkyPu/JyxaOpwJAm7u2gWyPpcAiBe7RE2AMZu+kX70IV7srPDa4inMOc/4a8VoYJq7UPvdTJ8IgLfjU6U12BuO+R+sEVW+gnC94VHsqjsAYpLAUOztkzmHQFE6IjaYewODZ9mmOdz9fzdIrr+6J1Q7b/bSy2Z9kfyuDG+eZ0jwwheeHp+mE8RA455/vLEFbS3fok/pPnFz1roYT8dHja2x2R+XdQE0AfSeCBHv55aTNtuuhnbfvV0Va7YTOqEM/7WJxzG9tY7p7mF5guMpHd8KR0TphYe8UargNu8vV91a5H8p5jgr4fO8SW0PvBqnN+J708zEY2eym7AwymjXDKLdEpSlpMWJ5cBwXQKcT4aGVckaggBS40kXxcVLwsYRblZrh8HNQY7noRIusEstsLSbup1VxoJ55k5truwMGnz0He6NqLQDKWVqRV4MldrQPlAK+D01sBbYf2jjO5Z0hCji/8nHabkBeP+Dv1+CJxjaMYwrQpvFXiot7F/muP/ou3viMN/KDN76eS294Dd0bmvR//Hf4oW+ZOjX+TcXLbn8Zr73rtfzAJ36ATtqB278RXvwvmV/9FQ5+xxtZeev385rbGrzua2/me9/3wNR+fxpf9SEiHxGRp4nInSLyb8LP/g8R+W/hz98iIkdF5Lnhf6/60kesjuyZnqpfBdrDHt18O/xd2B2krPS77A4SRhOzhsqqskyyHZ0Oynpr+odX9ji5GkwnxHG149mZuY1dGpu+4l64jOzaCsV2G5UVnNoZgvYJq7jcW4+HOV87g5TPX2qjbYYpRszWfU+VVmO3NQAVpFeVeUTgN4z1M6OeCKBNvj8vvNxTAQUOUQrnct+P5gQKi073aA1WGOUO0NBdhXMf22diIYGti7odlAQucussJ7fPspFcBvHASw0eX4RSNhhV5D0EsDh0SFB7eRuxLljtV9wWubUoJ4goOsWQ3XQTUJUphtK6uho48X1ueOBUjjRwp6+SPHia1ZDYVcYLImTWUfS2w8Xy13Dx4mVum68jSsZAvfzvyJ/XxmDd23SLNwlJbMof744lju18wGbeQylFWjJ0k0zPJEiaYD9LhrKY7PdSfl86FSHKeMt3GR+n3A96EGZcWd+3FGcdRGB3rc2DH/4TYq0RZ6mttx9vhhAQkwpM4cLyZVi/so9BAxDnaD3nKd6ZtARZIrg8J7q8TuPKst9PE1KzEqid2/TXq5PkrIYipXPOUyri+z7LcQf3XWyzlwVQMByx3O6jg/zOKFUNFMcJheR07Wj8WeE6J7klK8pePzCo4EQ6ZtDEaE4N1xnJCNG6GiegxKLEEZ24yOKlberXtql3RuAKRtnj5cS5df75Fhk7mYhw5ZF7/HKcEK13OHBhm530Kid3HuLg1heo5x0cwqPHP8HnT30cGYyY277MgfZDtB6+L1zfcB0nZMxOBONSTHAMLfLQxyaOeLjBwa3xex/Z3GRrtIUTqQBG6jIebj+27w45EWZqEaPMg3mAw7O1cPwMJ4690YDtXgoK9nbP0ArgXERwCmhf9EOfRdgu9iaYRTVB2goXNvt0hwlXdob79ootCg406qEHUhAFcS7YYZt00MOkBWq37LsMxa5J05E8CRLR8f6upTuQdKvPLr/a9TDl4MWr5IQ+T3G4KYM2jb+pWOmu8L0f/17+2bP/Gd8x91LOvuZbsYcd0S9/iFc979Yne3l/5+OH/t4P8ayDz+IHP/mDjIoRfMPb4La/z8GZj9J64QtYectb+dG/fwvHFhq8/YMPVw2+05jGNP56w4pFOVdJvKy1uHJAtAirgwGnehv0k5ytwQg3Sul89tGxa93mHrl1XN7sQhxRyHgGVruf+Lk5yR6d5q04XfefIlDYghOdP6MdBtv2lzewkQGl0AI7wyQYJyjag5QLK22aJx6mNbjCfPcsO9kKK/2T+6rAtW5S9S6VUjsX2DclIc2/3jbdWmzmEyVvABJ6kEKKIVrh8H1a1lpm9trVsTM77he5NmrzkUt+yK7DIeIw3SFLJ8/4mUtAsvIQp9f2mO0vE5VV+TKvFypGoeyxMnhGYSQpNRWPq+x2/6gCcf6aKudT6ktJm8uji/STnGt7I6TIQSuGSR6ST0vZI6RE+OzqZz17MvIASWvFsOh6gBY+ZG0w4J5t38ennd1PAkyA5KoX7eGT2FHK2mBtLHFE0csHyMYeqrSPL/ttzpxFP3LagzE17v9SIlzY8myN5CXDKNVIhqJ0z3NesJZX1vXKz4ETx2c651gZbXN5Z5WRzVFpCjMtrPNzow5vfMYDogsrRHttjFaYzh7x6SvVZ1f7pWR2Q7EiTneR7iqDLGeYW7CWQTHggcEVPED2MuASwLq8YJhbVtMLfr7dJIgyHnTbSWlm+T4nqCgCK+S5RYu3ic+Lgu4gIyssOIvDy4IfHa2wZTtewqpUAINjPrD8TJ0XFE643B6GfTvxmWEQdrSxgwQCtj3rzUzK7wov6fOfW++nNFfaLKzsoUIBopw7Vl0/qN6vwvfEvZeW2Sw6KLx8ONroUeuntJOrDK9+EkSIAvNn2h2ix86iR6k/PuKNSwAJ5jkUbuK5Cn8IkmdbFBXTXh9tYh46jcuFoc0onDATzVBYD25LSex+FtX/vBFrknx8n44tNKhlHYpzn2RjuMG5vTMYm/DF5R2u9K6G9TmW0zb37p6E/gYkewhwoRi3FHt2dMz61vIevcHwcSOItBQ0TYRCURusI8qgtFce6LUHw8n79zx2NbixOluBtaQYPc7U6cDmZ3Hd1Up1gBQoKdCDFG2DADP03MqXycmmAG0a/0OxPljnez7+Pbzuaa/jteobOPvaVzNz65D5X/9DnnvXLV/+ANP4K4dWmp/+xp9mvjbPOz71Dq+b/kfvRumIG56zSXTDUdZ++Id59+uezaXtAe/+xNkne8nTmMbfuRDgcucypYU3zpskCF62pJM9dFXXF7KiwAWJ0KC/i2AwnQG7g4yNz/8ua/0zXBueo8gLonzA4e37GK2f8em5Ulh8D4toX5mejM5ODxtpUAprHY9eabPVS1GoypY72tvxYMvlbPYvsJttgFLXjwIGwLXPU1y5n2y3hzgX2CX2z7XqXGPwuU/Tuzc069sC11kHhCg4xInybF57mJJax+z2RsXe+EP5q5Pee5Jo1Q9x3U03SdzQW+xLgc5GnNnocXy1M07wpexhmnA0nGiI8om2v/Y7bsCSmsWkvg9LnIS5VYT3+BTSiMN1fNLXpOHXMkjBWpTWXLi6Sq+9FlR3ggvJZpblDLNx8gaOc70HsEXme7VE2LNDNvJu+Lz9zIgohcpz4t2eP78ABgiGEAQXR1DkLqd+sU28skX1j8CJ1Q6yuROuSUHhHGnhE+DdYTY2egA+e36L9U3fr1cO8RYgKRzpxBDztY6XKfZswtXhHp+9fJI1u+f3YK3Gbj/h1FovsIrWu5MiREZhel16acHOcL/fTrl/VD52uhMRdgYZvbQAcWTl3tYT/YlOQCukUaOzt+v3eRhToMJ9lFhTS9sUWVExZGOA5nDasDvI6I9ySgSy2H6YrL/LmY0uX1gem8sMM8uu9P25+qa0wMH695lw7xevboS7oCqJowa0KzzIwmJ2up69A/Jm7IsWtqBz72PEuz2MdWHvhN4661g88xmyNKX78T9mpnu9QU65zQQrjo3+Gntu4A03nLDb89fPuhHYbJ/dPGkXZ0CHRyWV3D9DAkU5Q84G0yMZS61L11IbnDcRx0jHFKpApQW5La0xvNzSbu1Ws8CcEyRPxvcKh1aKQVZQjzQLzRgNHqCl3oEWm3N4615AyAtf0BBr2UsTsiKndmGL+rmNcHvHpjdl1HUTFDSSLeK07XOkie8Ig6vAvRl5plcpjRiFDUWv9c4wPB+lxNEFhQAkNq1MVsp4OLvEuVEw53EWlVwjUm3MuUepZR0GbkDBWHHxpWIK0Kbxl46dZIfv/fj38rLbX8arhy9g+U1v4NAzuhz+lQ9x8613PNnL+6qKWMf80ot/iU7W4afu/SmcieAN70et3c9N//hmXJrS++mf5P/6jq/lA5+/wh88fO3JXvI0pvF3Kyo8UDaGO2xI1I1Nx3VjRVURL7U4FzZPMKLYd7A88f4k5tJ5TJBSdUY5mSu8gxxjpqCah6bGCzHSRymohcS2KAp0Ntg3v0fhaCYbzLUf8U6F1dv317klG7B9epW1z58L1XoX5H7+VUW7TXrfH3Lmc/fQS3K/rNEWuncNEJqnNnyFWSnE5hV4qRlVmZ4IfgB3xWx1vaSolmzSz9vEgw3idAedTvRjVe6S/niN0iGOsdHJ/NU9krO+6l4L9t01pZm/sutP1rpggS4BePpzjzKH63YonKMehvb23MjLUY2mkWyiBushofWJWy3dY+fsvZxc64z7W/odmjtdcpdzsVivkkJxkNYPoZ3bNxzcowBonrjMTrrrXyjjEQyl6QA6oij7msrkMMjxvMzOr3muf7FSTSh8Ej5MC7Igqx1kOaubuyxv9RiNSkmW4+L2iI2dfsXAyUSfYlN7k5QSWkoUkbY76DwPUs8CCm/gYbTC2pSZ3iWirLufdQ3rOrzxueo0xiJM5RPbsM2X0za7WZ9LW32sdYgCMT7pBnudrM1Sy3aI8x6bk1bx4T969xLH1/qMMt9rmEmKdhlRMSTOejgRvnjVS/E0yq8ztAeIFlwALXlgHvXCLf4elbchDNgWcZgiYWZ4lUa6WzGiYlQ4T//30WBE7hzz5ze46eHlce9jOCm9ukVRyuCGkzJeQaGhyFBOvOmG8nu8iDzYTYNE0Vo/jNsMEqTRYHhgAfpbSL5HrHwf46rdQeGgM6C4eNl/9maXRq/PTdc+grr2AADdoocVx8Cl/pydcCld4Zptc2RGeYmqgB3tUjghu7CKubYJCPHODp3f+2Van3kMhfLFK6XY6CZs9xIOzNSCpNqRFDmbw01ELDu2x/zeGaKs5w1f84LeyKJOrGDaA0zX2+ar3HnWL8TB+Ea0MoAL4yxg1YUh2GE/xOkeNeV5focjk6wqhBVF6nsGbRGcZ8Ob1h7jgYe+iEpzcptXSHmmFpG7jFwJQ5szKhwMtrBzDVxNQWeLODD+HqA98WzHyZgCtGn8pWKYD3nbJ9/G8448j29ZvZutt/xzjj13jyP/9veYvfnZT/byvipjJp7hPS95D8e3j/Pv7//3SPMAvOmD6Af+I7d8/zeRnjtH/Ms/x3vfeDfv+tBxHlnZ+/IHncY0pvEXC5fTXb/AMCkqoJFnBU4RpHsTqWfZyG8tuXW+16f8NayCA1lwAcytr/bqJGNmvcvqsA1orPJJojMaWw7UDRVr4zIa6VWefnSemgkSqO5l9NX7KVzByPZBLLrXQ3V8nw3iZ4VVAGmiX6j8WeE8o6NdCbH8zz/zif/ExtnzOGVCQig4Z6sEsyiEtHCBLSjdCyHS476N3aLLleyK7y3By9UWr6wyt3mJPN1CWUst3WH20sMT69rPjDTaHqA5giOginwCvrmLRlPTBq0gElhoxh6ItTtw+oJP7krgKYJxjkJyssKxdGaFnIKLxQZFkXFms8/FYpORyyaklBAVKSbJqXf61b2unXoishZZAAAgAElEQVSMQxdWGNkJlijshpVsHSSfoEH8XVztjHDbZxiuP8rIZRQup90fsbY3Qg2G1DuJB+mVI53/T45UM7LG/TcTYFuE4/kV7llf5gsXN5FgPlLfepjZwVX63WskdugxoVITBhHebKEcWF4ms5WFeFxDjxIOXfBAWDmLhH0baQXWOzzO9i/A3mWcOApXeAlZPtwH2ixjkwesrazXTw3XWM26fj6VtYHtLUHt+AKKCN1RjgmsnEpGzO+dmrjyoPvrZALKQTzKSRZncDMdz5yIY5isEicZh0+sY5RmYWUXc/wCWoHTcDlY/6fWkhYOpcseRhWulnfRVO1zzHX9TMFILP077vSrqBl/2YoOje55Ll3eZGeQBjGpVAzaviHyjzzA9VGZ+GR9b2qjDKS79I4tIBpGrVuqXql4OCDqJsys7lLPd4kLDxJcpIllDAGUWMyla17KG6LR8a9Vu8vVfUkRBiI0ohni3llGO48gwInRCm0X+j3b54kHG2ijKoapcW2dR9bPgStQhfWFofL2iaC0QsShEJIgt8Q5LtttIjek5EmlKNBoFJbCCqPw7B49sUrzoePV3Vbl60VC7ykotd/k56ZoiYPB8KPjRgxdAkqTY9meGDifHVyk7CVsDlaZ6S9T3PMgrt+vmFuj4Gz3CzjdYNe12BiNCxs2itCjYrxdtaafD+ln0x60afw1ReEKfuzTP8ZifZEXPvZM3E+8g1u/ocPhd/0m0a1f92Qv76s6FhuL/MZLf4NPXvkkv/LgryAH74Q3fRDzmZ/i1p94E8mpU9z6W+/mx1/xNL7v/fez3km+/EGnMY1pfPnIE7bXLrM7SLlm24g48rzAaSiTZRs1Qu+Yg8JS7PVZ2R3SGWYUc7P7jxcYjgs9n5jpJGdmvUfrwWuI0qznQeoU6WpOGZ0VxHrQUBiFMb4HDZsheYJSitxlbI2WObD7KDMnHkJfvIrCD6fVrqiSeTNhKFTNjwJ01oOSaXJC4Ry9Uc7Qlb1nsGf7nO5dQILTQKcobegdOumMU1ktqLD2jTy4tNnwaZsnWFxdZWarx9zeY6FPSiAvsGVPjhv394U/+P8vmYzgzifi0GnBkUeuEWvF847OU4sMEtwX5YrvWzl6+lJ1/toJF3dPeabLDbmSe2OELMvJgxnIyOUVg+ad9IQDF9scOXu5uifVHKYSmFYzroShZMgEYAWY6ZxFBHLn5U8X01XOp6vs9VJGeehjC5LMogSH4XzXxV/Xue5ZbDnLTY2NEnRwxcud74cZXr4fnQ9QrvDzqLqrTPhlolbGg5vjvMOw7yVbNgztswiFFBTlnKusQCnxPZWFd7DTWlUulAKQD/nEmQ9x/8b92KJgtfcA6uoGMvAssZXx54tYgs0NAEW4z5l1pNbyZ1tXq/vuWUJHL8k5v9lDS4Ez3qBkb+8hLuRrY7bWgdIKkxXEw4z+zYvYmgdZUT5AkmVq/SG6sMSDjEYwevHLUL7fyzmudgdc2xt5hmZSqqa0vzeJT7xFe4Gti4M7ZxRSbpeiigTyDKV0BdBsyZKW+xmQfjesfZLbDj1oznozHJtTG/lr4hRYVfMSXjQ3nLiEU561Mbmlnvh76ZyjTjw+npQG9eNwYV7d2MHQcS7ZQpTQGq0SjzYonUz3mgts2k7YZ5bzqx9gW4Ye3CKVIYy4Ap1bnnfz/FgeKN6AR8Shw8xC/+P9LJNSBIDmoz3IWOsm4++u1cvheBYl0L/9NgrJK9GxVjWYOMtImSBxDNb6yt9D0YqraSkfvq54ENi07X6GvXhlbIQXCjuIxUazXvrs3UeQSIF1vicPiIzhfP8K5/fO86ViCtCm8RcKEeFn7/tZtodt7v700zn2nn/LU17cZekH/x3c9dIne3nTAG5o3cBvv/y3+djyxzxIu/n58PrfJvrkO7j1Z95CcuYML/7Qf+CVzzrC973//mqm0DSmMY3/8ZDBiEakq2RyfW9AnlucGidZWTSLi2Y8UNkbsHbOAwPnHPnhA1Uy73Mz37s221tGXO6TwpAc6DSHBx+lZNAGeZ94MCIfbpEPt9F4SZpSYIBD9z+M2gs9cEpRgoTIermUUqCLjFb3HJJ6dmBubVw5rpISwc9RskWFiXaHORvdIYOsIMr6mLzHuWIdKqknKK2xtYha0WZh5REEXzXXQDPM/PIgR1ef5ZSiOdpgTppeyjcxluCL2Xm+kJ2rhgBPugN+9uoXSQb7K9JWhPreiEhrtFKorBSI+gZ9P7g4JUqyCmRq6xiOejjES/PwxiFZkYP2yXSSW5RYL33SCuUcpnS+LByZK7jSewxjR2wWe+VpVv9zGIzkVc+SFYeyI7TLqOGT4qFLUTgiCfPinEXwDoRFeU00JJk30LC1iKgYsL4broFSVWJtihKwapq2R3eUs7T7iGcz8PLIsU19KcPzxzB2xGOXPsEwKzi/1UPbHAu082028h2SwqKsDcmoDRbpPgnWEyC0f/449Y98DG0tViwuAlECg9G4d6tEUrbgUt/3+dV15J0FRbi40Wd3VGDHS61kdUrjAWeeIUbTPHOcftoNz1TYW86B9kvNZusQ+jUlGJAorSvTjTi4J9YijUZwWoiynJsfPONBNB4keeBc3g4VpMRewlvMxCjABrt5MXgGVJUiR0FpgwBaPAhQ4XzyeB6A2d3TsHOR+nBtYmeX98uhh9bPnRPfU+kUOF3z/ZfKSwmxQiIZunCeXQaSjuKYWayOqEI/VnbwGdXPXPC/d5PgRHtHWFUaXQi4qIXEc+R4YJuHAk1f5SgTQGA5MkIEnVuOzsU0Nx/i4Pb9IGAi4++PCEXpblsBtDAxUSlU6HPzV0FopRn5NV9QGOb+u0dZ64tOUcxussVQfEFaq9r4IQzH49jzcLccpH3XYZQTFMb3OYb76E9bjfloa8cupOubxJcvoDfbyENnqGd+XqPDUHJ4/jqOZ681Yu036368/YQxBWjT+AvFbzz6G3xu9fPc/d+fytf9we9w18szFt70drj7jU/20qYxETfO3liBtF9+4JeRO78Zvu1XiD7yfdz2828nPX+B7/7j/8jBSPix3390X4IzjWlM4y8foqC11aeWeSaoKBzKOayuCqiA8wNuxSFFQSfJwz84xEQhaw+yMWvRLsfY1Ffa3bjiu4RPqJqjDdzONv2P/RduOHmRi3s73HPlBPXRBk4rLx0SoTHaRA/6lcTn+tDOz+PSYr1d9XXhk+YAaIC8GHKhf8lLerRGibA3yGFCIojz4EchaFEki00i54jW/JyrmXg8v0tCUoqCjdT3AJXV81mp+V6NkJDt2QknwAo4SnWc3fYmI1f4a64VRTyLbSxickcjNhydq6MnxgOsdkac2+hjNh7B2FEFSlzhz3n7uTd5Fih8TpFlbEkPEArnJXEnkmvsuQG1QYKNNBqNs5aBJETDVZqjDcQFZiJcH5/baQggcyVp89jgGhYhznscjVo0tkeUvTK66kuySEjmd4ajClAVnRHKOS+plbHcDsY5YC3bRecWjWLWFGSFQ9tk3/Dzxs6DrHaWcXFMgaN344L/B6XAOd/nlPeZGVwlc4KVgu28T5JbtLjAxlrc0A9Q10qhwnwsi2V9+5o/5yv3IkXiWS4chxeboBSFOHZsH1CIKxhlBTbIFktjnNLooixoKFxwa3TY+86w0D6PdhmiFVor1PVDnEWgpv07A5sl5UMqQGQqaaAqhCNzDWZqEQg0TXP8XJRX1klgkMa9gEUAFQrBRcGipnq2BGdqFMFZFXEUUonxKhBgsi7bd92ElJ812GKmf4U463Bk/U+hsDR3Oh5EDHM20x415YsQCy3jZX8Eg5yJnlGFwgT54M27OfH2uKDhXVIVu4njsaTPtX6PQxdW0Fc8+OnN3Rm0g5HfYzpIqKWUFkPcT1k6v0UWegiV0dRrMQ7PuJfnt5vs0c86mM4yjWTDwy+tkaRDPe1yqd315jbOUVdx9QxqhS8STUA0RLgQZgsW1cA7y+LeSSSKqtcqIJGcyZEm1jQ9Y3Zozu+ZYDjjjWnG+8YxAer2ejQvhbmL4uDSObiywtlsDVNYlC1w+Oe0JENLUCuBiXXKgN1v8PREMQVo0/iy8aFzH+J9Jz7AN/zXp/DKL36cZ7xamH3Z6+BF/+LJXto0niBKkPaplU/xk/f+JMWzXg3f8tOYP/in3PoLPwpJwk/c816WL1zjPfd8aYp9GtOYxpcLRWu1iwm9RhqoJ7s4JeOeAydeShOkehByHRFcHNGbuxMlXr4mIVHyiKi/r0JviKrklHRQSXfSSFfDoUVrjAZtBeVShAKlVCXFm4yFR64ys9sFHMj+ZBbGCb8LBhraOXp2WIENHUxM5OCiN5PwJ1u5qpVgSTtVCZeWZuq+lyhEyTjcu3OJnWHGdj9YgZdtVtabTqwXYyOQkgmsZqYB9XQX3yeicZEhr81TtA6jg4QsSgsYZZRc5fLdT/fJ9D4jiZDMqiDnM+WcKiiyjMuuTZW5iaMQPyJAFwUuNiitWd71s6bGckvhsFngttbN4bKJ3yWBBUtLNlAR+toUg2HqwZM4z04hwUDBH3SzNwLRDHcHtE5tEg8zUIod18NLBPGJZ2l+aAwms0RWiG2KFc8+TBbors0XXLx7kRzLUFJunF/0+1FBmqXVHigvlpWCrk08C2c9M4UIvWSHdrbqE/SwgGt2h828x5yuU4hjduMLgf8RMGWfpjcyEVSwnYf+Uw+z3kt84h1YLn9gv4pIe9AmoUBw5ORKBUaMGu8PAkPoXDl4PLBuZZ9oYK6UNhWDpifAnRK4q3Uzt8w8kwPRQV+gSAv0Zx/DTjC8MYJtr3tbivIeGE3eaFDccJDn3/VCREVkJkaURonDhvKJH+8guJoHJJ69kmq7OQyNZJO46HPg6jkOXriCiKPTSRkqhcE7O5/OV7m8ezIANA1iq1lpbuJ4CiAfF2U0FlGwlwiX3B7dVt2zb7tdRkWBKBOejfBMBOaxTuR7whTVtetnObHREBkOztbJo5Qkd3TUIiCYwtJPu9zTO0XfeZdQbQyyc4GDpzawzpI7hxNHTY0LOkpBLe1WoKtkR0uAHEbiVW6nKqpVe1ah/YDxia9AJVB/2tOgHiHlfDxtEKWIuuM2ECeKzCWsDE+T4ZBgZOQcXBmc4VrhjVAOP3acxb4mMg0E6IwK/x1hVGVmo4A8blEcajxhUWwypgBtGl8yPn310/zs536eb/t/b+e1y4/wzNc4mi/8Znjpv36ylzaNLxE3zt7I77zidzi7e5Z3fOodpM97E7zkXZjf/3Zu+cnvZ+b2W/nFz7yHD3/0fv7o+PqXP+A0pjGNJ47AHgCgFBpo9VcQKdAlK6M8XDE2qayqS4AmSuNMRK3jraWjXc/SKBGU7Y0BBHDk3CpN5a3fmRjeq5yQtRp+FUFCSG4ZuIS8HNAsEE8OPMO/bn5nmY1iB7FFldCmYY2OAFYQTJITpTmFgiTz/WDNZJMojrGtWXC+mo5z40HWqoYzEZGoykBcK01a9kSZJmn9IKIU2/2EwjpmqVNXcXBYpOrJK9me2bUuxvoepbHdf9nfBaOlFulcHRFY7Qww4pmF5vF10ssbZSMLreEKzdEWcbt0xxMsEcf0IiUo1dpW17co0krOCKCdlyiK9gDNS0u9lGkgCa40JnDCvJmteK0S9HbsHutX/mjMd5WARml2UxuSXcdeL0gknUWUJrOZB8tK097ukdown05RAXYJ1u6lpYtpzmKygtbFTRYfPocqLLGM56CJ8gl25oSVZJlt22WhMRt0g4p2d1T1n43PwdEveswMllFhRp7OvbNdPTYsterIBMhp91P6I4d65ApJAAaFWN/7s69uIIgtKOoRRT3C4fBYOUBrRSXjPUYRhsSHXkC8BM9FOrjz+/M/kV0GZbFOuJRe5toBGB1qhU8L5yPgzMT9LcaLMoVDGeNZwfBc6MJyeafPp66drQDjbLrF7D2fKm8nt8dH6D3rMHW7wfCpN7KgFVrHJI0jKBS5G5EGEKsCTx2VhQID3fmnktQPVuetBgPMiQvMdlb8fi8yXOHlrUZpRMUkR58TZLTliAkPbpOFJt3nTIxAKvuhSsDlLKARZXxvFtBINtlQPT7dPxMAJVUfpNLa95aJ0NItNONn1D1ygUPLbah5tm3ovLyxqM17GXFmsS6rer/idBetNU4ptPWurt5Z1oO0SQHATLc9ZqfCdSuLITb8QUS8nDOu073pqL+fKlzEiYEiCqp5cuMCkwataG70xuYiOkLJHjfkK1wptuiLL0xVI+NCcUu7nLmkRinxLgrL3jDHmagaVaBCAcXUG/vW8kQxBWjT+HPjwfVH+ZFPvIM3f/gWXrO7yjNfnVN/7jfCK36BcWl4Gl+psdRY4rde/lsMigFv/eO30rv7n8BL3oX63f+VG3/421l6xcv51U/9Ku/7tQ9yYrXzZC93GtP42xnOjk0IFGhxHKgbclVURFjSOIxoXxkX60hdxlXxdu+iNaObjzF3eZf17hqj3m6VkGTZHv3AUt1UX2Q2PjDOVWTci6GtoCVIZpQKrJnG4khMA43CacWBmQhhv5MZImhxiC2C3fZYKlQCNovjwOlN6lnGblpwcauPcwQJoCZvHvBEX7AhFxGa7S7G+mQ6cjpIi7yl/5G5GkfnGnQX7qIILmrKeelgLJpbokOYJOfoI9eoOcGZIBF0QmuzRzxMqoZ7GNvtCw4X18LvJ8XuKK0GVZehgSjvUct3qWdtmss7FdPjdEzUuA1RfoA2cZDBAec3jlN5vwONpE3uhMQKJgA0pzzgMeixDM8JUehdKwk6UHTdkJFLGdiU4d3HEEK/kIrIrWXp/BZJ7ji9uYwSYafnHRwfXNgL56FR1o7nWylFxw0YFF10mge3RP9PtUYDk9kgmSw4dGaTQxdWcSKkNQ9IlVJsdNOKjYjL4dVaoa3DumDhIR74KD3BYiCIEmppH4fiQDMi0grlClRgQEQ8YZP2hn5kgRLW7B6PDJb33Z8boyWcKyoWU/Cy08OR5tlH56vcw+k6SgQrnoUt+6SKucZYpRYKDf1olmvFNtYJbbdH7+ZFotZs6MscM1XWKOajg9wZ3YAuxiyvLhlCPHPkuWCFoGinvTHYEV/kKIsvc6bpZ2xlu2yduY/Ly2eJTIM8ngMds1fsci2Y/njBpyMq/PtFOzp3v4gdU8eKI8oGmN1dyHIa2z3PCA6HuMgESScoZdCxdyn1klsvo8R6FkfraF/BBxQ36iUiZYiyPbRLsTg6s8coDR7TSFi4f4UoD6xrHCSHWnv7/k6CVibMVAu7YbdDo+/7OtFRZdY5lnBa0lJxoDSHtu/HKeVl4BDGSCiO1Jb2Y/cQbl8Pl2exFf7Zcnq8f28/NFf9XQXwOcm4VTQqTAA0KEdMmHDTY2e87Nwoz0qGXeOMoT64RklVK7EQNcIeELQSnKkjytFJfGEF5S39tYmvuxePjylAm8YTxhevnePNH3kLb/vIMV6WjLjrlSPiZzwfvvXd1bDFaXzlRytu8d5vfi8HGgd488fezMaz/pEHae//xxx5wzdxy8/+DP/7F97Hf/3Rn2OzO3V2nMY0/rLhf92Xv9wVRhxNYzi62GSxEeN03RsD6BrgK9srts3I2Ordo6O+ytvOO1CaJQgspxcf90u8Si0mZvMoJ8RFN0i2DI68el2vfswn3tqvMm75OWT1yPgkX8TLiJwllZxmHI1Vf6VdvrPkTogVZOLZpQ9vPoJRkDmQOPYVficMk5zCCUvLGyilcUZhHJhIUcQGozU1A61GHNJcf1aHzmySuQIdzDZMyI/rhQT5H5hgbFRPtyjEcS67FngT/x4PWTWi4NhiE5RQ7wz39eBZETLJg+Q0fLpINTPsQKNWJW2mFmFNA0eT3e5euIbj+5HmfoRACdBa0TwaxUiyqrrebA8gsA+e2eJx97Scj7V+9020Z2aZvzYehWLC+AJFAWh2whBpqw1mQioqSrHQ9KYUJskoxAO0Wr1J09RY3EmDwYHjaLNGhPWuebUah+YaY+lsiNhE3FG7mUXTotbLibcHLDSj6nrVtEGk/LtD7XS48fg5wPfHaTwrUwI0rRSR0h6EIwwPzfgPmhhUDlAjwuQDjE0q90axjtkLbeJgtjFLDac0GkfhLOdkLGUrE22tvNTt0GydpHGIQufeDCUcMwq7QpRHzdo6rPFAVSlV7UMAVYSuMymIimTC5j9IA8ProvEqUAH0ahRR3mWUFbSHGXPxYjjtCOVy/OQtvxOtMhQH5xkstVA4pDXLWrHNtk0w6xu0dpYn7pDCXbxCEZmwDsVCq04cG6xA79hC1YNmkoLFaI4jemEfW9mZfzqlFX0Za6MLDBcOVu6ELuxNnXtrfCLPipVAdW5lB+dg5uomUbk3nUVpQ9rp8bDtMT9TY64RkeX+ysWjnCTrV9LrQ7N1Zpu1CqBZKWj3HcnIVc+2CIgxHJppInGz+u6b1GxKuJ9lIcEYU3FU/l4HTUPYbhoFxvgXBwZcoehkBdZJBZAiZ6gn7Wrf+ANqnNFom6En5eEm2jcwW/CukNUlVrA5ukahlJ8r+CVimmlP43Hx0VNn+ecf+R5+9E+WeHE/5Y5/uEF01/Pg2/7PKTj7Wxg1U+MX/8Ev8vyjz+fb//u3c/yOF8JL3gXvew3zzznKXR/8XV565Yt89o3fzXBz68sfcBrTmMYTRh61MEFyNdeMiFRlGA1KUUQzldTlyNIMNe0Ta6cNhfKVenEuyN3EMxBOcFGT4cLTACqA4ETIcj9sWZVGFOEVhcsR0yCpH2aUONqDDJxj8PSb2FFdbj4ww9EFbzldugbifPO9L4wL2/3UuwsqhQ3DXiOtyJUfxJs7CQmWRozxEignKGyomHtppWjNjbMN4ljjaia0KpUOaRJ6WvzarTiiJIfIMFp4JuDd9KKar3zHA88SmtC7EV/y8sSy8d+JQ4kCpTDaGzZEmTzu91YFsipQIvTuPEbWqnPrAc/oxUajAoNW76WYwobkf5wYapeHHrTcgzeticP1FKCpatxWzId5T+Gz9lXwywUFUCGWYbZf9uQizcxwlTjv47SmF4ZnW6VDf1p1UoHRFCgKtoqQUEYRdxyZ5UhUQ2c5QkGkFJtFj0RyiCIvHyxVukEmFmvPwIhSWNMg3hsy1/QMjHJCXdWwUvbouaq30urxcZS11ewprTwjsZekvi9KOdL5hh8SPiH1isQy17uMUhZrhSKaqS6RkuCaKQpREUoc6ZEj2NuOVQnwgp4J+bbQaHsQoCQ4LFqpgGjZm+hEoQYjoiSniBT1wBwWZr5ak7YOEcfcaI1Fu0MqZe+QQmedaqC8EevXYYMJUAAxK9lVxhFArY58aUGHv+PHCxSHFujdvEh/9lZU7IFnv3bIy/Z2dyZut0I2dzwgyA+gNBxZmMEEQ4oiCmATR609oKZjhrO3+b3fOjTed8oz5G3n+6qsFPSPLE1u14neSZAo9vBT6eA66Wed6cIxu+FNR6xzKK1ImkchqqO1oRFHFCIk8zdjozrJzpoHeSIszGge3HqA8luhCN8PrnRaDffMNpeYiQ2qOb43iDCnW5jmMf/XiStttAnSaoUzrWDUNC4LufD9IyI0I02zZhAUvcz6QkLoD6258fdHKRtWgNX6OjGZw2Bw4mhEvhgh2gM0E76DioU7PFurFXfN3cqXimm2PY0qRIT3/OljvPPTb+PH753hG9oFt/39q0TPeTm86tdBmy9/kGl8RYbRhnd+3Tv5/ud+P9/z8e/ho4eOVSCtMTvkuf/fh0kaLU698tvofepTT/ZypzGNvzVRiMPpcp6Qxojzznv9NfRom8olX8CaBsPQe0GsOTRbw2mFKEhQKAdJ7ueS+UKvVPLJS7ul65fPCHpJhhUZS/jKhEoprFhExwyWDqNCL0p9sI7UY0CR2pzjg9XQ86JxSjBJuzpGmRg5EQxeKmkwfqC1GVfctYId1yVTwuli1QM0cbQ2e2HdBrTDJP3KGt0EN0t0meBRARaLw4wyDh1ZwMWz3BbfwKGsjjNC3Wi+tntg37XP41nGA34hIQMJ9pkh8fRMxvh31231JZRYL3fSXqamgcX5G8ln6p7pCoxEObfK1mJKKdWkqYgKuZ4Si9MGpcaAySkvh2rqOqpR93OmgsSxBEFVHLjd30Kxj/u3ojE5q2rMEDmt98nw5k1gpBDEwcAOvPGB1sSRIdK+d8iKG89/EkGi0hK8Oi2glDiqak/hhERyDrbqfsm2z0zvCgC1bAdnFLGKuE3ViToXqZ1f8eczIXFsRTUKXadjU5RynrUIBYv86CyUXFIwX9k6cDd5vDC+GOW+E3A68sePhHq6g8Mxr5rMaN+jaSinqsGh5U0au4Mx8wUYPDiZvNzWjBPpXBYYPesodr4eRig4lMvQ2jOk/rponDhm+1cwdkRNe4ZWgFrfwXyLTK53ktQ0YhP6FcHqeOLfLCbsVafrVV3h3PY6F4p1RISeG1VyTisOFwo8CvxcNq2xIsFF1lD2ORm0t+4XmCzmlOzytu37WXTXt69MskEiEMX+umlVSS3n6jGRmWCpXeh7LZ+78JzXjCanhkQR6fZFD5BxjGyK4KWNBj+n0IunvQTXKc+2N2oLXvJq4uoxVDiaOg4um/6LtrzzjeFG9bq0edTvhnIPoYnCHkApblxsopXyw9q1wqkYg+aAbtFSM5U3TcmOKRRWX6dukOBqKQ7XCCMMAkhzQeLtxxMKJqqhZX8x5vqYArRpADDKLD/0e1/gN8+8ix97WPOC1ZzbXnSJ6EXfAS//N3D9QzuNv5Xx+qe9nl97ya/x85//eX4tGuK+6Sfgfa+mMbzA//L+9/L+F7yei2//l6z/zM/gRqMne7nTmMZXfFhxuKjFoHUzXuLoDR7E+SSuNMcIYkRvPoHCtmrexc34qq2LNDr3dvzaFZTN/c2dQRhyXVIcYeht+N1uQgLsIk3SPIqgiELiWdRilAvJTjECClCKU30/h80GlksrKGwJulTFZ5FHHC0AACAASURBVFjxDF0mBbqUhIUKvX+tT+I3ZA+nxsm2yS1KhblMkUErXbEkGoUEB8RSCtaKfEXcJ74KGh5sxGgvPdNwaKYOxeOdJmGcNPnKezAxqHpF1HggLoR+sACqlfaFRwl27dpb5ROkacWCt1bPS4AVJI63R0eq44lWzNU0M3OzaCYAGhOwp1avkjcvlbuu90QpblkKAOsJANrkT0oZ2MAqtPMJ9Ww9IjL1MAdPexZWfGFOKYMKphngDRjiuBVsDqER+0Hp5Ypm6j6xrBnvZudNMzRLao6tokdsFEfmGszHDWLnZfFqwuGxXu6B1W2UdRUQEaAVxyzoeRIpiJUJ0lXPKJqbbmLOLAVppJeh9dPgHljG6sNhTYILDNrV7HzV91VdThRxiQ1C75XphdEE4WJG5awqNT53F+kKoAXEg0ReBizi2W0dkvwjczVqccx2z1+D5mjDAzTrgfgt8c2oRn1838rPEM/UlD2U1mnSwhcSBMHWD5LVFsdrABJtsLrGQFI27B4uFB5E3Ji9RqGVKU0Wg5GKGju3LsxVbOTV3VG1l9TE7tpxvepZUaKwpkmhZ/zzZL0ywM14AFyaaxydq3Mk9P2V4cLg6XH4NcRGkxcWRUFaZNRWO+h2h+GZZVCKVGkvPZXw3IqQmxk6i89Ao1hsHiN95WsYHfHGKbYWeXMaZdAqIiqGILB352Hy+iKNwUYwwb2+10uxZFoBm6kxcFeazPl5cM54+eFBM0/NVmRj9T0211/GSbEvNVYiGKcpsCThu8BGTdATLo6hvy2K6vvA7xPFFKBNg6u7Q173Hz7Dg8mv831nU77+eJdbv+4c0UvfDv/wX03B2d+xeMENL+AD3/oB/uTKn/DDvUfofNO/gve9mqX2g/zIv34rP/qKd3Lp4dNceu3rGD322JO93GlM4ys6BKFw+AGwKvTdBIv24DyOEy8fLJNrrRVuts7ghXdg44irows+kSwcgkFJTr2XceBim4X5W8jrSxOfWHZt+V/uNe2TwP7Rea4+66ncXb+Lrzn0VIrnPxPRpewwyLrSXf/+uRsgrpNHTSKBONYTw3fHvWfOSdUon1F44wWtsOF4RkP32BJFbPY5HIJne5TS2NhzfOJKNstLEo8PrpK5lJrRHKrfBMDwYAtn6iS3feP4OFawSnzvR15gQ+Z9cLbO7Qc9uGhErnKeGx6YrX5n+X6vfbkjBoNyFqdiUCoYBzisCBiFCrI9jSJfbLL+3FtZrB30Mr7AXkUTzIAoP3Or1pr38+TKZE8YSytNmX7DUbNATV3Xe6JVZboxeQ3vrB0jn4kDywVZPIeUTIUxgHBooeElZgHEi1bk1ieZWgWmppQrimcsIh1XDNrizBFmTaPCjPaw74esBwe+w8xzpHYjkpfDqIVb6kssRc19qYFYP4POLc1N3DtbcmII4mWT2qCUoqX9/LPGWtdvydESzZk7vDx1guURpRgtzSD1CJzj0GwNJXCodsz3TZKzand8D2G5IJHK5MQ/h14i60lB/5qGqvGimbsQNR5C7CJdWcoI4JoxYsIwa5sRFX1m4oj5ekQzNsRxvG90RWw0hUCdiFa8gDKPz50cJoCp8DnlS4JCKZ1/GgtHXhqYNKH9lJvoHLuBUfMYJrbM1UvGTdFPMgpg7Tl3+aHzWlfup6LDeQOjAzOYuVnSxmH6s3dMsGRjeCaRwYqjFpXfL6EY4BxJZtFOEBUjzRk6I1/oceLnIeqJfs7y6uknSBsjDXnhEAOuKKivdqitbJJLGJLtcorGEZR1NEcbRMUguKF6EKWjGrbVxMW+kNK7cR7lcpRSLNaWOKjnPIg0CqfrREomOgT9ccpCmQhYXaccVF7eCy+DDaxxuQfznBuihWpflFcu7k6OKfK7vOa0l/mOLzFJ4wjpfL2ag9Y7dgQTxdU4iT8vpgDtqzzuPb/Nt/36Z4iP/j6vurjBS/50k1u/8Srx//Ze+Pq3PtnLm8bfUNwydwv/+ZX/mUbU4A0rH+bki98OH3g9t3e+wK/9i5fyg3d/J8v/8yu48l3fzeav/iqSffmhitOYxldj2JDIAiAKgxvPa0KB0uzlWz4hMHVERT55CQmY/52tKrZCtEE5z1jV+imteJYijqoEKNJ1n7Rmnk2Kyp4nrcibDe48tESsFEcOtji80AwMmqJQDVQxRLRArQUqQqIGWiJUpLhatEO1eVxRduLTyTJKgCZ4Z0olQjo3Q05glwKwm1dNannHs1Gh/8KFcQQKkKQDIhSS0n/eM7nzSEjqlUJUjK6P5Xo430NmrGcUlXj2hMVbKyOD1pEmM5FPXPsH5nhW7ZaxfbYas2kAsTb+DHXsz8XUifMOduhNTQgSR6NKzhBqUYvrLbHLxFy0Cs4rDbSy1CLNQjM4tAWApkywKA9gOQ+2409Y+2yMwZt77tNJDszQv3XRAxalK5AvUQTiUJEO28xUkj0VGoe0NqA0KiTRBc6bdWgTbqaAiTyYDvcuueUWNp99IzowkZHAjJlDFaX1ORx49lNp/b2n75Nj6q098sUWsrRQXW1VeJEpCm7US8w8/VYvLFOKWTPjzW20GV/LcG7GqQoMK20YHZhBhf66ODZoBzPxARDLzQdbdI0fSKwAFAyPzjOrRgFk+F3nRz+M16sUjBafHhi08vMVM6HvMGvNgFJ8zcKt3N64oUriZ2qGQzOxB/7KYApLa6tPlc6L0JAIRRQktP59i83YS+gw181B9n1vEgYyKxPxjIN3VmsdHlzEGX9vmw3NUr1RrX+zl3j5nDHcUFvwfX+RZuOZd+BqY+mqaFMVWib7H2UCWLk48ox6yaAFHlo5X7ww1rF4w4spGmFgt4ylxWoCzABBghmOO+efZbvQII4Cg6Z94UWbup+vF74vM5ejVeydT4uBLyowEaaOFU9nKfz3qbZ+ZqDRhjnTCEUTQBm/z8t1lZujlDwuPofB/FMgjAvw176Gc6Xl/vhbwxUZh+I5mqY+uRqSNAdlGLRuonR4NaKxE9/XShxp6yb2nnLUS4xnm/RuOYqJaiBTgDaNJwgR4Tc/c5G3vP+L3P3cj/E/XTrLq/9wjVtfMqD+Q38Az/jWJ3uJ0/gbjlbc4hf+wS/wnc/6Tt586ff4/Rd9N/Jf/inP6PwZ//ebv54fyZ7G5Z97L8PP3celf/IGktOnn+wlT2MaX3HhAYCq/qxtwdMGM9hWbR91Ex+8mYNLX8OgdTP54p1I3MA7No7tzJUNSRHipWkKYhOTHJhj7+7nAtBq3MQdsWc5WoMr1BCeEt0QknNNI478EGSlqNcj5te22Yu+hlw3fA5hQ7FlZglXaxHj2a+U/5+98w6Q5Cqv/e/eW6lzmu7JcWdnZ3PU7mpX0rLKIBBJJAHGPAw4gQHzjHg8gsGYYJMFmCBsAwKBLGyCAZtHFAgM2MhgMgKBEgqbd2J3131/3FvV1TO9QQFWEn3+0I56qqtu3QrznXu+73zm87iOHlD7ZsjeFDWIFkzg0DNvGvEu+D3IEEb7CizSjFPPtJAoa8AQhTiSVr2ZBMKjd0DYpB4u0sxmyaf9eP4QAqlaNvWy2QQlTO2ao2xtmwDlEU2wXDiE9NJ2H8IEcFEsFkLBSRFYMwZXmH2E0hCF0C+ghcQL6wSuby+oPZ6FUv7ytERMCqa2dWza9fEA1zpkCojTvISKSIgGm0p655o+mon6sgjNweqyz4QyoWrGKbRq0FyPcsZDKzPXUppUSC0ladt3TWJqkoSQCCGoZzxL2gR4hphJZcwqImVSSY+BnowJ1G3NXSglotkK3mXaw80Gtpmy1Q4W60bFiajK3AFE07QnF0Ba+vgD/ehCESElSijWOkNkpZ9gK1aZtamgjpSM5QfNs7HQILjBGJ+EgWn0PJ6qsrVvDbMsxsoIwGIuIJc6TDUXYDJWJfLogum51ppV6pVpmirFnU3bZkYIerI2CLfXv1wok3XSNDU0N65C2oUIswgDasH0p8tN12LC7WcmEMoolxpNw8lQ6h8ncCRS+KwqbjDukdF9pGTchkNJz7pxirjWLOr7tqAbBKqloGkdEkqJVoKKm0WGZv4Xs+n4fABC5eFHBFC36lqJlyAMQWsQooRgdeF0Fv0yi27etEQAAgElr4eGsGm39n6ZCRfjWkyzSzOu6Nj1oRoz20eZX1XDkdK0TpAC0WiilGSgmIr7LYY6RAoHd3aRUNjm4jpkRjaZ88ooYRwiTaqxIaZam3eK0g08JSmmXZoStAqsq2ZESS3ZEzDuDeC7BbNIFimo2T7CwmBi3iTzfdZxs75oFjaUop5pLaCIZghCoYVLM+USesrc845KLL7Yd3r0OvEcQ+6l2yVoXSzH3GKT53/kev7x679gzxlfJHvTD3nqh29h6MI0qRf/PxjYfKqH2MVvCUIILl19Ke8+7928686v85JN53H0n5/FpkOf511P2cqfX3eA/7ns9RQe8XB+eemTufMd70DX6yfecRdd3M8ghLhQCPFjIcTPhBCXdfi9L4T4iP39fwghxk5mv66SzPcUOForAdL0QdMezZxv/0ibv9QzwyP46QpKOEiJSdlqLNCI+qMJQTDfxBGuqY6RDq6UBI5nHPJsYF4PqswVVgGmqW0hNDVWJlXLqCyETULRIgjB7XfE6Xhx7pHyzCpzaHqkSUtgJNBwTOpgqHW7sYINjjSghSEdrutSyBiSV/QUfpACTEApRRT0SxpehoZbMGlwhCwO5DmkZuPgqT9j+6EhjBrkWHc7W7QfSBddytt4R5CYWoRukk6l7Dyq9v5WQO+aVWzbdVZ8HqNe1bjnCUHDUTRVmlFZZLNTtav50U1h/km5+ba1/IXAkChHm/OueiU29O+gLP34mMKcuPnZaaW76voioVKErkI7Vo1IlyE/kDwkAH15o5Z41qwk2dNNe44pI7PHUNKJsrhQWLdFqZBSWgtxwZHRHmNggzF7EVrjWKvvesrch4508R1pU+8EOpOiqdI03HxCXTGq6IxqcvvGQWb6CybIVdYyXkBq/rCpQUvUIqnyKAv5SUCilLJVYK2Znd24BdIB/tFFELBxsMaq4hpzvXU8tfx6/VnMT05TkD7VdJm6Do35Dq17Q8eHjUz/Nb9s3EESgeOYdLZoSwGelGQ8hbLPpcymENIx5i5SIpUiLigCZrJDRhULTU2m0g756+8AYe6vUGtyveOIoMhsvcmhUi+5gYlYWTs4VLPPkxNfx/bgvnVXLIQNAulSywXM9dZAh5RzAdFSiGqJtmb7QVMrWfH64rTcfeOD7J/opzk5zNjWdTTXriQlPEJXUdcNlDQLKvWgh1B5CGtE40sdC9p3TfebXmQYs5UoxXHc6WUoM2CUNREtWpnn8LziGtb1rCGlchzNTyAWjRJ2e/0w+xszcPBX9hpY8xQRiV6SW+d/Qd3NIbUhbMqaooSuY41mQLsplE0VnndhfuUaPMcBoW27A6OYh46MF46S6Y6AXZQAtdhAI1joN6ZEYWMRIQSecDjU1x/Pb9NvXavFQpr5nHElraUnEgaxtgJZN1kopMhNriNE88PbZ2nUO9fURugStN8x3Hxglkv+7jrumplnx/bPMXPLd3nO+26kdv4E2Vd9CYrDJ9xHFw8+bKhu4KMP/yhHgxyXjK/gO//2QnYd+Szv/r1tvPhffsDnN57P2Ec/wtEvfJEbn/gkFn7601M95C66OGkIY+P3duChwBrgSUKINUs2ewZwQGs9CbwJeN1J7psjo30cHjZ/uIWN708rTJjgMw72Be7cDK7wOXTmJnTKFIk3bGCGEOh6naJTNMG1cCilPTJ+wP7eHkgoOtW8cSzzhINqGqt50/hJGjOQmTvRzbop+sCEqHU3Z1S2RF2MEMI0kxYQesaooz5YZC7Tw3xQI7TjACg7BaTrEwoIw5DyDbeYFWTl0KAJUpDaP4vjtFaYJXHkg3YzaOmghHGZqw8VuW3DFA1dByFZm+unlg9Mqpo09vwRQkeRK02gHSdOhxNAmEkxt2kAlzppz5gVhHFakwkMI8IQEVOvt4SzfhVRv7Q7Fk0Q2dCgRB20bEsD61EFpPIQmDQ1gEKpSlOlcIVEK4mvPDLpKsJp8d/0oTmEkJRSLvRWIGPIWKNep+kq9pc3kc702vlxoWD+9iodcmSgwKHhEhnPzH02cJkPqjHhHc6NseiloLEAyjT4FcqoLlqKOJ3NUcYIRQhT8xRKTVjOQq2ECGFK9aMcD71+mIVCgELymA2rgCjtT6BLeY5uP4O51IDN4jIBryqPMe7UmHB6jVEFtO5RASUnzbTqo9ftZVD2cDi/Cq9vFbl0maF0lYxMsegWISKpgE5nwDYh11KS9hRC+eZSKtOfTiw0QHlo17SJcFyXQAbMNkzvv7BcYEU1G/ehMw6CMlajWtCkvXZn6r5cYKmcIO/XqLl5ZOCBVOiGMYQQQuLsmyH4sW1JE2oEITRDhIBhp4fZRYF0HKI+Xym/dWPMT04j/QBt648O9fUxOzYaK2hCuEQ91OL+ZAnW7gnF4lCBI0MDIDT5tMem4TLSXvdkLVijt9K6v6LPUgELpRzkMqipVWgpGHQqhMU8M9Us+9atNKmbkfpmVZ7s4qJpNm4LO0PdZC7Vj4ifM/vUSZO63ZZaqE0qa8YLcIXLouMh6s14m1BrWJwBa/IDdhHH76GhAiIqFZkmSqVMKrIUHBnII0NNmOlBB+V4n4u1IZxE6mXWK9BbWWcWRhLvFqO0m5e2sLmnkbtjrC82FpECfKnitggAMwMFDm4a4M6Vw8z15hGEiGaIcnxz2kLRtMYsEs3BsTJTK3bZOjeHucXjL3Z3CdrvEL5+wz4uvvxr7J4sM7zyE9x68zd5yXt/TmHHBsqv/wS4qRPvpIsHLUpBibfsfQvP3Pwc/ri3ytuueyXb7vwo7/9f2/nrT/+QD9+uGPvwh8idew43PuGJ7Hvve+P0hy66uJ9jO/AzrfXPtdaLwFXAI5ds80jgH+3P/wScI4QQnAgCNg8VcS0ZktasIOsGsToENrDr62OuWrF9p8z2a0ZMTyItRWxsoKQgkzLmDynPZ9ZvNU8GQ9CE1gTCJai3mhpL6SEbxlUuPX/YGkmYlX8tJGRrpv4sGroALVKECpp+nkWvaNQnq8gBZFL9jKgqJaeARKOFRjQapA4etUqFoK5DnLm6SUnycyZoEyIOqFTPFNqLbNSNFXlkZtC0zpKRf4NGI5UE1eoPFQYugZdt1U1FUJKeShYpBEoqXKEA1eJnEUGTMq5XE76HdBWhMOpX0zGtBxASaa3/o7jsrNwUK7wBmkGaZB2b9guI3GrWrTvXNqEVCM8DT7U0j7COKxzSuQKzjZBfNU19G81Fmq4JmFdkx5fdTloIZqtZ5svpeF+ukgzWqnjKJVSKSjrLfLpsiIEduxZFo9g4ColChBqZSrUImp8hlILFlVVEtYiJcE1Kqsgaw47V7hC+Y1Q7R8jYWTJKw6VnFdjrq6SkIDPUVCFOTzNUqFXXpAEpHHzp0nTSOH6G7St6GC/UcJXDbHqEZmlFq3OBPc5CZoijWbtYHBSMOpgqgZcFbVNmhVEJlSORwuHmiTJH1wxD2jw3sTmEIxksZUjn/ZaqY9MHSxkf2QxbhhZ2IBnfJeen2ZYbNXMnFYuziyhhjDi8Ww4Tn2CoEbpp6i03nRdd/ZgUHSltAi8X3ZBmvgU0PMXRvjwoyUKmFitHUrVUmbhJMy07fBlvB3PhDKJJ7DwZ5Ewt51Rvjloucb6iRSrWDxZaqapCxL87WpvitlVb8MtZrN4bf6c4WMCfXwStaUTqodY0VaQYmweu5ZKZeEZFkmYYtbQpJWKxSY9XiJ9L2dTM7ByP50ELzBwmlFNlV7ua0hSKaiEIEaiGRiiFY993i14BhIOLMAoaMP2QDTTXbCOXLyKFYqCYMve3lKxO98fzATA/WuTX68biUctmHSkErlDM+S0H11AAImS+mEe7phUHTY2QDlIKZjLDZmGM1uvbkZJGGOKrPPOzRzgeugTtdwBaa/7ha7/gWe//Ni95+Apudd7Kjb/4PK/+8K/xRjfQ+5Yr2/74d/G7CyEEj516LFddfDVfH1zD7/3P5eR/+iY+/MydXP7FG3jLl39B5Q//kNErP8ihT36KXz75KSz84henethddHEiDAI3Jf7/ZvtZx2201g3gEFA5mZ1LYZ3ekLFFuBLtf14FIFatYdU5mylnPAhM+kw6a9IJ0dr8QRaSXDZFKZdmMuglGxGqxP6cSoUJt5+RtRtwbHQ5l+pDCBWTi5HVj2a6ui5xdNClMXR5NDEmwVxmmEZlEqEc6m4OIWDf1CjzhRSH5+rM+jUWatutoYBJk9T1hGmQTZ9r+oowPwg2wI+w05/Cc4P4HARmdTwyHxFAbu9e0usmrKmCJZRSsuiVaRLS9B085cf5W1EtSUQi6305lDDqXGiNMcx2puk32RpYJ0QhTEPoppPmSH5ly+RFmpokEgpdIF1cqVjsH2Lf6l6b+mcC7L70JGkvQ2hJtXRctOeSV+ZcM9KnqDLgZWgCQodILRhJZQht/Y4oJBruAocK0+zr2R7/f9ycQRrS7kxN0kj5OEKy6KZoVLN4/RVAcnR4M42hXkJHUkqZOirlBxw5Y7111DSptY6QJvVRY/uqteploityZmElFTdDJnIMlDZg9/LGiEK2bOpmMqMgpGknIa0hR7alotbdIqlUllzgmhQ0Za+PTUVMtkCIXU6dFHV730spafRsR/gZFlb0MremFy1kXIsnZ+5ACMVioMCLjito2kKrxU07KYz2I52E3Y2AWj4g7SnTZ85V7J/sIcz5SA25wGXLaMs5NTXcR7OYM0RoaVpaMzKPcQi2nmY+UgHCDbhr5EImiqfHx13Vm7MmQYJFv8DhfrM4I4SO71kpW6nRoQ4ZLWfiZ9oVxmCkWUjhNOdxm/OIegMEFM7YwMjABLsGdpEPTJpqTHakZCRq4wBkVctpM3qmXD9F3c3Rk/XZtaKnbR2kWUwhF+uIet24ndJS1jRg3YXMO84eM/qXDqFl03WQc3U86TARmDlwtLCuhlZBEzZdGd2mmgMI10m8AzCptK5CKQcBzKUGDEETgonsRmpOnt7yKHge61ZtIhwfo7+QopQ2C189ttWEjFxaPZem77VIcVi3i0BO23tYax2TR3PvamQY2ibk7b6WMREVkvXFPVRTQxydmV0+OQl0CdqDHAuNJi+65ru859pf8J6nr+ZjNz6f5i+v4w3XZQnDUQbf+T6E45x4R138TmE0P8o/Xnw1Z615Epf+6mN8+RvP4sPP3Monrr+VP7vqepicYvzqj5I+fSc3XvI4Dl5zTSsdo4su7n/otAK19IY9mW3MhkI8SwjxbSHEt+fm5xHRwrHAFOELEa90t1J9THDlKpuGlKvB4BbaklyEqY9YHCkjpENKuW2/A6j31PA3rIfRfmQ5F6tkc6k+VFR/VZ6ATA++Nc6QcdBEW5GKK12kcJm3fbJyTgmEcRqbqVSpOxmQDk0NYblI5DuphMKVHquCPopBDoSgMrWOlGPqYWanBpivZFhYP2VPvaWGSQQLK6vI4e1xDzeZyeBuPD8el5QSrRxmsqMsFtbSH/QjHB+kYK6SYaGQsvNtU6SyPlI6NtiX8VzdtXLYKDqVCcT4LnNBmyHSmrPU+gZYzKRo2FTCRmHUqlGR6gnNoEQoFPgeGd9hpJijlg9YWc3GCqJGIFwPlKLfLbIxM8SYVzEkRNp6MBEy7Q6y0qkSKmUVlzOY274pPu+mCmg6KQ4W13E0O95eHAWQNlkuSgoabsDCeAWdscQ1V2BxvIoWpseXihLepCQVpBCEDPfX8FJ52/tNG+t0qWLTCM8aiORUQHxTi6gVAYAG5cb3+lRvjsP5SUusQ9Key7rBAnOreyFv7r2Gm6F/egdTvS2nToRgqJxhtJQy6Yf2NLVQ9OYChHRNPzc0jhL0FbczlBsmTAXgKqNAWzVFzt2OtHYQTiJ4jhS0MF/E2bSeZsptCatxIqOpSTq8cZB6xpiVtEhc63VQWDfF/vGzUFK0/Z0bz7cIPa6LknCgvJmmkzb3ovJwpW9Vafts2kWKI7kJDhVXE7iKdMpHI7l94yDSpqrWwwVunbuBjK+Yzu/AlS59Xh5/7ThhxsNtzCNpIuZmWmclJEXbIDnZA20hqBIMmIzuMwf3MJZebeZbE/eZG+/Js36wEH+37c3nmnpTfegQizbFMWrFYdID7cyKhM4cs6n216qQ0PA94woqEum4lqC1FDSTuhuNo+INWCdPUJ5DGEbbgNSa0WqeYiZyuIwUPU0gAspOzi4SCVCSZjmx7haPr6X7mlYDhmI1V43RnBrFdeSyRbcwXkKx5ytsiqZy7ZS0FpTmCxMczY6h7N8GHbdSPza6BO1BjNsPz/PEd3+DX+6b5d2/P8qrr3sSY7f9kFfPXcDR784z/K53ofL5E++oi99JuNLlj3b9X648++1cu//7/MW/P5RXPz7L/plFnvjub3DnQkjtz/6M4fe8mzsvfzu3/sWLaB6dOdXD7qKLTrgZSBbYDgG3HmsbYQqvCsD+TjvTWr9ba71Na70tlUolUnuEcemTMmFPH/3Zt6vMiYAA6XK4bhzkhDUhMOqAjGu/4mamie9JpRDFlP2/1h95gWJx1SNjoyffM9ukIsVGSKrZvjgImq5tNKqb1gQqIG1X1pWUNJXHfFAFBMNrV9K/cRJneoT5oMJcZghHuATKpZg3dvembsaufqcDjg4VEF5kHd7Sy4QOWSwGJg1IJFSN/ACRzb2QEpQJaAdKGTbUBsAxROTIcJHDg/klc2LMFRxsKmdCOThQ2ohIl2xtjCFoZp4FzvRKjlbL3LFuBADpSFvr1LrWs5X1aKHi1fTxzCCOlPTlfYQWtqbPqkpKxoG461i3RLuyjjZNjpUQxnlRSISXIhxeaU8lniFC6aCVZwiBDaDH8mPxOcgzzmIhYxvtSnu+GlZmjCicUmkmvCETwOqQdC4HO6eRgxsR/ZsQV6UWeQAAIABJREFUUqFm68YMQao4EHRUi8LE9zMY0ho5RARFxNT5gCBvXSiFNCmDfYU0qS1PNMTUaa/vasEE8r2FFOW+Kp7yme3NmnBaKGZ7t5paOzsfSgikclBCof3I6bBlNSqkjA1uCiIymoEwXh+xKYrjFcTOKda7Y0CrlYSgleIoBYYuStl2D0jl4HnGhCIZUgsBIgwJlWRhvLctgLc8tDWVjk9z5cNa19qy4nUDBeR4P/tWj+M7EkmixqlxKH6mBjNTxqkxSoNuzjHfm2NxenX73CaOL3VIoZxmoacP3bsegJSTZjEzyO29e4xZkV2w8RwH314zKWAhqRS6yqjToaYRtbqIHCbtf4UQ4Fo1OmGzvxSOEDRsKwlBi1SrEAibrO4v2NTLiDyaVNzAyaA8Hykkg4Uh0/eOVvqnH3goFdXzGvIm7TxH5h9I4/qoZdI9NbpIwhjqQJyV4AhpUmZd85nrpUlizivEKaIao/YhRGJBqoVmUGLRK8bpqK63pBdiB3QJ2oMU3/nVAR7xtq+yYbDAy/Ye5LmffQznzs7zkunXc+f7v8zQ296KNzJyqofZxQMAk6N7eP/jP8cli/CCLz6DtWs/x/SAx6Mu/xrf+dUB0lu2MP6xawiPHuXGxz6W+R/84FQPuYsuluJbwEohxLgQwgOeCHxiyTafAJ5mf74E+II+SVm4ldpjVq2FTfur17LMlUzQ6KqgLZ0rwkw9WtQwQY2M8nawSphvSJPvJGrHIrIRHx1SKkfZ7yNKHwPIpgv0pyZI2d5OQgpcP2il7vi+sWIPQwIVxPtSNkDJ+SbQqGQ9hitZ3HKOpnKtCidpDm7HyfUCshUE0QpM4rawVgkUgNCahg5R0rXNeFsBkraZc8YkxJxjb86nkvVButw2eJ5xjBPLa1+lVBScNEKlmLNERVuLeWn3D0AzZHaxgUbh5dJxw2ew5gPSYdEzKkSQKTBYTttUKwHKR1SnWtcBCJW0TXSVIZV27tzIjCVO0zJ9xEQYkvWr1ALz93e6PG1PP1JBogDdoZj2eNzuZ0BtmsAJYldIN51ppW9GJFBrctZwQyFwm4JQhCgp8YRCSqMqSalwogDy8Gxs0w+Q9V1DhM3sxfOSNFUAkF629bOQOMJlLigiE9k4QhnSaMw8EhCRUiJIbdlCz0MvYd3Ytjitz3db9YIabWztlUJuXY+2CmJaZSDR0Dv6KYis5C0Bi1RWJR20FKTzaSarGVN/aM9tizfBGdlp9g6O4CqJbDZAem3ukwiBl68yO3xmu+ahJSLEpkm6uKpFSqVoaTKxgu2n4inoyXrkrekMSnIkrUi5prF461bVcUAfXSNpnwsVNpkZqtAcHOTMoTPtOFtjlgik1ixsHKSRL+A77dew4WYZLKbiBYC8axY9Ase8p5xkM3VlbOKbWtMIDal1hGC8konbZwCElaLVnXTrGiY6VgvXRVoFbX1mkIqbj90lRQjoJqkzdjCf7o1bYRRvvgNfpnCEi1QuF45fyPq+DfF10fZc49RZInOP6L4OTWqtiEam43eLmbJ2UlvLBXiOahE8QIaLUFuDqq5lrmjex03pczg7wVxto73GimrOI3BVW8ZAvG/7c1Sr7KVa7/NjoUvQHoT46Ldv4qlXfJMXnjvOWd67eObXXsgfVLbwx+dfw80v/Vt6X/IS0tu2nephdvEAgsr18eSn/D/+SY7yqx/9M/9Zv4wLt+/nqVd8k3d9+QZkocjQO95O6dIn8cun/h77P/ShbspjF/cb2JqyPwX+Dfgh8FGt9feFEK8UQlxsN7sCqAghfga8AFhmxX8sRAqaFoKVQqPrhkCoiT4ODpui8qxbQslk6mM8NvuDramywbYQgtTpZ5PatImNpb0ETisojoOfxCM2ld9Gjz/UFkBKpfBkQKBMzyAAespkvSyudBHKBCGyqenJpuktmABSSknWd8kGtuFqaMwopI7D3pgbOMIxfa2QDGcHWVUZo5YLkj5uONUqoWfqN2RjjkZsfd2qmYvmUaNNcK8U9UqN9OlnwaqHArB7wxg9GS9O3UIAPYYwSTdDxc2wtrSHI1WjFmgl44AxVtUiYyMhUFIwmdvKVGBUrCgYrnt5VLqEv/YipnpzLQXNSyOUF183AbiuJPAUwnEhQXZiS3rraifQqP2HaPx6H325VdSCUYSAieJEfP67+s4hcHxSKkfOmhH0FfvBSaOkopnO2HEqeoNRplI1hBKEqYq5gWwNopqZQR3cT9XPcEbfljhlLdQhQohYaRDYGjRgtJxmuJSOCVpyjrWQKCVbtUCyJQ9dtGGAQ7XTmfXyyJEd5juVSVDGwzMMlpiPCSDXD+N7DPFyXQ4MnIMQZl/5wDOqrr0fTEoYlIdW4CqPopOm5PcQplvPQ4OQuptnJjceH8L0ujaultIqgKElhlo65poI8LXAkyp+pgjrsWNka8yStO8gMz1kpde6tsISC5vO5qmEg6nTMvuIjT1iEi4YKaeYqrVqwQpuD1UnZ/sqQsUfQAnPuCkmSFBEHKU26YBrKmvIeYn00ej4QrB5eiUqlefc1b2GOAgRt2wwmwvSVs2arkxz3uh5nD1yNqHWjGbWUHSrRlW116ARhiw2QkQY0icKCYVQMrNnE7q/J57HnJNhIDtg2zyYlgPZPWdxdPtmEALX98z7IJ53Q7NlOoO2bSJCS7hjtdQuAKR81zSxtjKlBFAOjnQouxlGMytNrai9NJEyZpwxiQ2IzK6ThiaCXMpBCUHOKTDgJzLMHB/luGhHcah/DXOZQbQI4xTMQPoErjErauZNqnjcAF3rOLM85Sp2TlQYHB9mprqS46FL0B5EqDdDXvGJ7/M3//ZjPnJxikPfewIvu/3L/O32l/D4vZdz0588l+JjHkPx0Y861UPt4oEIP8vgpdfwjsI2/nz/Ab5w51vZefqnuOq//oen/8O32DezSPlpT2PkH/6B/Ve8j9suu4xwbu5Uj7qLLgDQWn9aaz2ltV6htX61/exlWutP2J/ntdaP01pPaq23a61/frL7bmtUfecR80c/KLK3uIq6G5BzSgQihRSwrmcdo4XRtu9LbPArbC+z0AQT3sgI3rDNzFwSMLaO3Y42O3EZqWu275UQiL4q44UJ1vSsMaoPoJUglcvSX8pB/waUYwJ9majlQEjr4hitkJuVfiVNM2QHRdbL0ZupUhuxaXsaerI+hTXT1M/ciARk2CQMm0gh6UtNMJBOZHIYHoEUktD1CDNZ3P5+8DIUHn4RpUqedM8Q1aHx1pzYeUkFRRg2BKGZsc5pzZC4WD8KklM+C74xJvAdSdrJkbepndF87FixljN2PCkeVjLFMV4V1yYwHahkTHNwm9IYpyoKga6W8DesQ+gQFc6b+RfC1IDRItoRzl3dz/axMlP5bYxk1sWfnz/9CPxs0TrFmeuScYpMpXoRyiPl+2wZrcREwJ2fwWnOIoWgmrZGFFobgoYJIqNzMQ6X2MWDNrYco5zxmezNmhTHZelrAkf51MPF2IiFdCVWJkK/3TRGCGGMZLKthtwNLeLZBRgpZVnRU2Jn/854XHkvz3kDu9mVX8FwOUeqXKJwhlFSmoS2pmsth/NTiGwtVlbA3KPGcDFM6CrtKY4xsW4sguMikuN2M4xVLIEVgkGrsJr+FFF/QYWTsGBXtd7451hBS0xtSEsBBKgFI2zMDJurIiDvVmhacwo7cWZfkTOrboB0GMwlvI6WGhN5aaitie+zizcOxE6xEY6WplkIakghca3SHbgK7Qc40qcn66OFIcqNpmYhDIEQUTEmKhroO30HO/p3tp1jetMaaulecm4O+jbAqouQQUCQM2mCuSf9KVRaixMpS8aUckg7OQIcS6RBhiGLfYPIteaZUK5rMhaE6dsnkAjXLACN+GUq/qCZRwlCh6Y3fJQ2mUhxFJ6LTLenLaZVgBCwe3yYzQOJpvE2JXvbaJn+9DAFt0qoW9cw35izSRACnSvG83Co3+wjWZHcmw9IZ1Ls23kxx0OXoD1IcNfRBX7vim/yP7+6g89s+iLv/+Yf8PG0zwcv/hinr3o8tzz/+Xhjo1Sf/7xTPdQuHshQLuLRf8eFkxfz8VvuYNiBhd7XMZ+6lgve/CU+9d1bCdatZeyfrqZx1z5ufNKlLN5004n320UXD2DEPEHYfkulcVixF4DQtQFA2EQKga98KkFl+Q6AUAY0eqtRYlTHbRAC6WdZ9EpgPMcBOH3C7DPJz9KZFP3FAGduFtE0q/BKOuTPP88ENXYleSQ7wvjDnxAdCCUEyhKwJHlUOqThKjQhi9Ua/pq1ONJBYyzuo1Xi+bIhUK5QjFUyONKkb42XraU9JmjNuWXG86uWnSJSsDg8xsLg8jR8URwlWxtPfsKqVB9pN5EypBQLK4dZzAQtEiQk+dPX4Y/2UXdzuEqSsn2wwsisxJ7AVGUlg7lWQ9q4+bVVoMyH1gJdSZw92+LaoCgyk2bpHndgAFcKxGK9dZ/o9uC87fzs/ndN9dkdOchUitzZe2N1NHKbE8Kom3PbVzM4XLNuohgFEgjdEmQighYagiIkMpFyqoWITULaU3AjhzpzL0jVqvtSouXiiBA4yvTlk66f+L5NJ/PNZ/kLzo8G336+QLiERNcyJU4bWEPaTSOlaNngR9enluPcNS0ClFbZmDgfya9EVFfZFEcBUqCEMoYOXgaJpKBy9LvFxBhajdozm1aS3rKZYLVJPSXXD9UpimmPUsaoTRU3y6byajPXUVsGodrUcX/DhoSaulxBW1tZG6e3AlbxiTQfoy6HhDStW2K0r0jl1c0Fc391qqeK9mLfPe1+gu3+H/V0L7PpdkPbjO9Q2rQBpk9jIChBgrw7Spp7qZChWbDuh55DT9q8f+qWvHpl8+90eRWraxvAOrmO92RYO1Awzdnt2Ddlh0lFjcGlQy0YZFRVKWbTpD0H2WhS7x9C54yipbIZ6hP9IGA6GEZIYzTUcsKMVD/JeCVlaiVFpKRCqIyqm92zB2VddAPlU06VKVg10kukq5pJNGrflt4tFG1PQq1D0r7ZLi2i3m6A4xCoFEr6NK1CGb3cWq/xpUtry9ElaA8C/Ocv93PRW6/lzODnvJ0X8tw7P8P+ke1c+dhPM1Zawa9f9Vc0Dx1i4PWvb8+r7qKLewIh4NxXUNhzGS//1r9w+cQlzKe+wsDq9/HKz36BZ77/P7lLphh+97vI7tnDjZc8jqPXXnuqR91FF78xSLsur6U07nGOb1SS6YfTcFLMT68nzOSW/VHeM7zH/KA1t02cxpGhcebHxgjTAXr9ZLzd7skeThuzK9aOsZJuqgA01HMmkG/V7STG5fus2H2aCXKbTUsQROz8GP016At68GwzZqya4lgDDymdOFAXAuqegxCaZpDCHx9Da81iCAqFYxWeeduLLa7timo+LEkFWJrqaU8C0CYtULZq6Tpt50qXlC3aX5GqLlMPGoPVFhmI6gOVqW/StALOvnzAkfwKbq+d2WYysHxctBOrqAZKEKelAUjXxRvoIXPaanQuDcplw1AR7Xvge0ZFazQ6HsbMS2IIax/d1p80zjqMxjN5DmJgM9o2wbarBMjQBOXZRHphpKBJIZG48fZat5QmseLsxDnbYwq7XWIAIjI+sRu6jk8ucJAJ44PAksCFoTHS27Yi3NYxk7AZfG1qaOAEFH0T4KuEKkmkUC3Zx4A/wvrinrbPQsJYAZZCoge3oSuTNFZdxJQ/TtnWdN5R283s6ENwV14AysULJG7/gKlp6nCsGL1rza9Dbfpv2XvcsypipEKbc2i1eIj+HcgOtKW3rh8sUcl6hjAL4jpBo+7p2N4+iuG0XlwezyWfgcGtiOKoPd4Sgtah/CC+PhaFbIDwU9S8oiGiiV3E1EUAro8otRZSovou16Z75twc44XWgooSIjYtaju+nSxXuYTK50D/ViaqOVxl6npDz+fwfMu4RJYNsRJpq377fkKZlvG/4z1psxAjDQHWWhMG2WXzsre6lfHE9YhOOOM5pi0KgOPQlzEmSwJBNqWoZMwChCcUYrFVG7uyOIXS7TWByWOeyMERSNjFdPGAg9aav//ajbzzc9/lyol/p3HHv3Jpfx/nrHgEf7H9RTjSYd8VVzDz1a8y9pGrTNPKLrq4r7Dt6VCZZMtHf4+rtz2d91Wq/P3829ivzuW8N/+aPz93LU957nMJ1q3llhf8OZX/9XQqz352d5Ggiwcd+oIScDuhEjR1aBrqArgBT1n3KG4/vMAdR+bj7aNgJFCtNKrRyQkCv05wu4MXuIhqqw9TT9YEAT9TkgUbSHmOIu94zAeRtbTZdlnwJWUiPc8cOwo+hYC5lWtIbTCBib9uLfXmz1CzdRyhjCEHCqFDoloPLRWaBgpjnBHqkJTnkg9ALJhAPl7V19q4M1qjE53tQxz+lRkW7WlfAMNBmX1yYZnKsgxCsLZnHTeog/GJpzdvMud+V2uzktfLzPp+nFoV5g4kvi9NbQ+wY6LCgZkcX/npncbFETq+oywFN7/Txg2SxBlEgVf6tDWkXI1eMA24hXLidEc9MkSQLVKXA/H8x0OKlcXEv3J5INuGVAm8NMwv2P1JtAAVNhjvyeANJ9RJwjjF0S2XCdMu4aLLYtBaOBCpUvv+lQOYoFgLQ0aWzY8QnL9mhB/d4Jhm3cAFYxdQ//4N3KUBx8Ht60vsdAlBQxAuSXEEcMrm/lcyof/EKYRLCIdw4vTR6P6PDXQs441aJ0jlx7swCw5FtJ9HSWPYKhvz4CbT3o6tdAwU0ogjthbK1u6NDZT4ya/uMsRySYplJ5OgCMOVDOyXEJpvTVbz3FFv1bs1miGZjRM0bCptGC4iZHt6XptJiOPH12mpgrYUR05/yLL4cFVfjgnVx+xNabQ8xMrzH8ePPv5NGho84cbnp4pj1oHVHCOUZh4CJwBm2yV92omJ01NB7wuQmYDmjCE3KTfLzO5z4MafAwcppT1k2mftdB8ZL5FCKoGGNsY0K0dx+voQ0TxLAU2rSmpTJCgcB6Sikeqh6RfJnXN2fL9CgqxlqlBNxSRvdb9R7Zqn7Ynfm327tjPUmECHPyPtptlQ3IBcvJ7GzK1w1GyfT3mMVtL8FOjxB5FyHzm39U5PeyemX12C9gDF0YUGL7rmu7i//Apfy7+XL4p+XtHfy/O3vYDHr3o8AIc/8xnuevd7GLvygzg9Pad4xF08KDF+Jjzz83gffhJ/WBrjwnPfzauufyu16ct5//WP5UPfnOJlD9/MaR+5ipv/9DnMfe9/GHjda1G53In33UUXDwAoIenxi4xk8+xXPyRMp9sC64lqlolqlo9ff8uy70Yq0taxEheOnc9nfvEZtk1UuOmXfhwgJLGimuW/HdNjZ8dEFQ7OcvtEmdCZjUMwf6m9eRjGKXEI2laNBdCo9eEUbVrS6CjcdCOeauJE24qon5JJfQyBud4y9ZKprci4GSq5FNN+isM3SQhhTWUNzWCSBR3C6K74eFq1LN2lkOwYr1DKtFbuJzO9HJG3HS8mbsNYYYxq/wAc3Y87aNO07mrNc0plCPMFov5T8TiEbOOAkbOaco+nmph0wijYbR46hCoVoWHmpJWFJ4FmIg2xdT0ODJ3Nqsle9PduO+Y5JQ0oliIKbr1EHVGbOmKKbpBagxMQrGnVscU1aEJQCEqkS0UOsY6GSse9qNpd54D8EHdkBihGn7QkvLZ5yfjW3t43Aa+SCl3Ox66LSXijI8uubyNy/ROCzO5dCNdDZTPxucZ1U0sVNCFBhzQcQ1SUEMYGHuPiKO0+VeIaOFKx4FcQiS4bQghTP9a0XQmdRKpm+ti96j1H0uMOMFsYBdt/zN+1m6O1O1vPHOYd0aCljnZEVD+ojCrTVyxyez1qYm+HVcjSDBuAoKnrIJam4S39X9H2b8fDCnHMBREBUBqFoSEcx2VFNcv+qqBfDrCmOMrsnR6h01okuXXoIop3/DuDqkw1KHOY4zdiTu/YAembDLGeC2DudoSXAWZYHBpF3DGDFJBPpeNFqghKJe6BfMaQp6hlgJJQByE03Pwts5nrgpDMDO5G7p9dLljIRFuMoLDsAYxSIQEmhiocmJeIW29ACqPKB/272Roc5fsNoyamt21lcHaQ4OafM5ie4k79DWp+S2nM+g6P3NSeWroU3aXsByB+/OsjXPq2f+Pxt/0tbxBv5oq1Z/JX3jxvPvutMTk78qUvcdtLX8bQW9+KPzl5gj120cW9QHkC/uDz4OcY+9BTeO/aP+I5W/+QeuX9VMau4bkfvZY/uXY/jbdfgXAcbrzkcSz89KenetRddHGfYjAzhnYkTTcgtX7dib/AErIUpb5YJaATQQPjiieEgOoU1FYjPB9dzCGF4GHr+6nm2gMZ3WziOdKqYWJJEN6+uh39bmP/BNW8aV69dbSMtP19lK1xumU4jU5nUVJQSVV42NiF0UkAJkCXWrWcAKOxlwtIxyqAyqOvELQRSulGAfjJhSaBm2K4OA5DWzv+XiYD2MRcrx8qMtaTsav8LUt8qY59fGnT+pLzp/Kmsa+26Z/2y2b7iKAlalkc5bapKM2wQ6pZXMrWkaEBUM35XLC2b9l2WkNv3mcoG6BymSVpa2F8Hp6bZmPOmM/opDlIG+8ywWqkiCjlQBhSTSeME+x2vm+VnOS55jPktq2Oe6VFkEFAMNVqVSBFyxETwCmVYnIGUEi77Jq0JGmpgrb20QAselGD5WhIlqAtUTfB1HAdya9kfvrRbafqq8Rz45v9sf4S85wlT3cJyyroHOXUELsHd5v9BwHaD6y5SeuY5lyPw9C8LPRv4mEbBiik3djIJUL0XU8oxlNVO5alCmv7+0Qdw4zmZKEjQxjHwZEuxbTHzqHNbN78+/hT5+IpD0e6MTGKGnKnpB+31liq6CcVtNg0B5gIetidn2wtbgmB8D1WV1YzVp5gKTK2x2LDSbXO2j7vfrTQEjZNKrIO42fBtC5YMh8TD7FusEsWO46Dpc+nEAJPOmZxBHD7+hid2MSqvDEuqgY1Us7dW5juErQHELTWvP/rN/KGd76TD9Wfz2n9IS/a9nA+M38LVz7sSnb0mxth5rrruPWF/5vBN72RzI7tp3bQXfxuwM/CY94DZzwP8f5H8ai7buPjF/8zoz1pMivehJv/Lo/5++t5w67fR190MTc+6VIOffJTp3rUXXRxn2HLaJGxnjy+8ozzYAdEwcrS4Ks9iDG90DoRtOCsPTRLNlgNCtC7lvnsMPNBL0KwzKENDEFTQjBWsYYBiWDNV5LefLBMrVlTXkPYbNVb6chm3zhfoDVcsHagZdktlxObRtjqoxWhOT5I8XRDXtvNDexubEF9Olj+u3uCtjlM/FzNZ3jUyosoB2U7FmuqEalX7vLkooPVbVCdbie4kQtmkG+pFVFQHpEFp5XG6joSJUVcTxh28AoRy35oIeMU42biQYc6Hi00Oebw9EIbWQJiIxCBgGwv9K0xY1BOqy9eMiSMj2++2J8dYGffdgazQ0tyMwWun2LmIVtoLnEmHK8W2DtdW34iHXA8DhGTeLlE4RQC1l9CaFOFo8D78MJhbls8tNx5M/Gzo1qLFQLoSfWwpzBl0huPk2Kb3bKK3NZW6uhIwWNFb5GCJXVR6qwSIibpyprQHJcnCQE9rcV0V7rk/Tw7B3aybiDPqj7rTCoEqzNDZrHkODVovvI7PmN3C/a9tGNgJ4W0SX/ty/aT9rPtx06ovnH6a9RUPd3e7+tYY1JCUrB9/OK3RqjxVYCSy5/HrZUBHju+gfmgxnzD1n5FClri2dAN6zqpFEKY95JaeiEyPR3SiY9Palt9C9uvQcbr/L31PZvj++Bk0U1xfIBg39EFXvbRb3DBrZfzjuCb3Hbe/+UFt/47FRpc+bAr4z4Ys9/+Njc/988YeO1ryJ555ikedRe/UxACTnsGDG6Fa55B+Sef5bWPfAdfHnsor/z6K9lz5jq8o4/j4rsmeNYTXsCFr3ktc9/5L2qXXdZWXN5FFw8kaIz1uO8odg5uIzxwYFlwvBSVoMLOgZ3LPt/au5XK4jy3I5anL4G1hJ5p+6yRKrOvZ9uxV8lttDN72mpq6Rq1dCtgFsDOiVYKV8vxkHZupcHY7Auw6WPJtLGIoInE+rix+1+yeq413uA2UPWOQxW+y3RfAV8dPziKD3uCumqnTUFrJ2vJwCoKpMMoSOsQoIdSgvJME+2mPS+lYHgHTrbWxhlY/QjUDz/JptPOwnE9KAwB+3EsgR4omnE3E8R8aQ1ap8s5Ui7iO7vbPutJ9bB/3tRPaYDmHBy4AVFqL2toa5osJRSGgVtpCNWqB1ySupiEVIpApU3S2jHutUbYaP/APYm6d2FqyJpuNnb1Oybi3y8//mAxxcpajoNzi3z/UDxo80+HxY6kshedd0b5J1ROokWEzO5dLPzoR9TYT2Z6rO18wEyxigna3VeypJCcMXiGOVZuH/OeA9jnRkimnAH6+za3f8nuf8/wHhzp4FhCcLyepMcdkf1eJVUhnJlpO0b7YVtEWNdq6AMZkA6Fh1+0bNtVpVUMW/W2DUFxuRIeWgfLDs+jKxR9fp7/CieZTQ+1jc13HGARAYTNZvwaEhgFTR4r1zT5cXQv5/rBP7byJZfoXGPlAD/dquWc7ssb06V7UHvfVdAeALj2p3fy0je9nVfd9iweNia57pK38qSf/SNnDp3J289+e0zOjnzxi9z0h39E31++gty5557iUXfxO4uBTfDsa6E6De/cxZ4Dt/PPj/wYPekC1y28mMsumeXm0dU8Y/dz+PHXr+dnT7yU+i3L63O66OKBBkcqpFBtaW2dIISI1ZskejO9CNs0t5OC5jrLP1uq3CyFv2KCYMd2dCbF5tpmUk4yaD5e4KZbK9exSYgxRAhD2no+IZUprk806r2jtps7a7va9qnRiJ5JSFeW9YECE4hlNq9pq/dYijOHzuSsobMAUJVj1wcB+MkUv2Twf4zJStngu1NAuDQdFEA4DnuG9zBrqHZkAAAgAElEQVSUHWodRhDXMDlRIGjNN5wlgWGnFMeYoHUY32Qty+7JduK1oriCh46bRt6xOtFsLrPyF/G/9qfITEMpZG0tFIbb07Zka7u0bxoNt1wcO89fZAkPwMrzYHR3x+3ax2VSKfcP7j2xOcxxTFP6iykKaZfRSob11fVQm0ZWVlDwC2TdRJN3ewjTuLnTgE4uLHZKpbi2XxWLy36vhIivt7yvjbGEZMdoD6PlnmWfg6kL9ZUfK/X1sPOCyAnR1lOxE4lvhxRApUw4OXzMeVRStRprA6x+hP2FEz8n8eFjp9TOx3SlZGVvnkrJfC8y/WjVLEpULo1wI6IujIJ2TIKWUFqje3lsN/Rv6HCu7fMRp+imCgyXW+Ytq/pyTNayJ7y1O6GroN2PMV9v8pZPf4ex77yON7v/gbrotfyd3s+V334dr979avaO7I23PXjNNdz+mtcy+MY3kD3rrFM46i66wDiLPfR1MHUhfPxPyP/oU7zyojdx3diFvOLrr2BVZRVvfu7zueqrk1SuuoKHPfxRFP/q1YxddP6pHnkXXdx9BLZm5R7WerRB+ThCxnbVSWR9h4eu65w+uSxtx0I4DqpShtkTr+AnTQUmi5O4eVuXE4YQNhFC0BuMc1gfalfQhICJPXDjF+OPMsUaWf/YAXXGyXT83CkaF7ShUqrjqnMU3IVn713WZDaJHf07+MFNIftnF+2OPVNT9L1/6qhORgX7h6AzURBNEgbj5iOlyNj+ay0FLfHdOMAV9BcCStX2cw47KBvJOqq7i5Jf5PT8JM39twMJsjR1IRy5CQ7/Ig4stbX614CqTEDjYMeayL3TNVK9BWZ/LjrnZCbQDBPHjJ6JEyA65HHrsyLE7qDt41hqtpByUka9U4txbRgYopQPXC7eONA+hrb/OUEkPbSt1QMvapeQXAiIxNWEauKrexdqL1PArDHFMsJ6jLF3ImithurHPq4qlYwRTnLD43zhrKkqP/zhHeZ/OqQldkSHMeuWDB9t1Pm7fo41A/n4fzM7dqALBzgUtSKYvhj3x58gv2NtPPRmGJ7cvZbphUSfuqWI3n9t6uy6xx5zfgqpu59u2iVo91N868b9XHnVlfyf+uXkxtZy5KLP8pL/fit3zN7Bhy/6MKN5099ChyF3vfOdHPjglYy87wpSG5Yz/S66OGVYsRf+6Dr4zIvgHTvYdf6r+dgjruHN33kLf/bVp/C8Lc9j23mv5TPvvJrtl13G1z/5JXb+5WWM9+ZPvO8uurg/wMtAZYX5+QR/+E/c+QZwfCaCKiR6B7UdbomKFtc8HcciLko8XFrYLjPHVqoG0n3MeyZXTGsNMybwanoFmuHBZXV09gAx9kxVl/06CjTPGzsvTr86FraOLlcY28Z+DHK2dqBAqDWVVA7P2df5y0vt5JehfZ6GSikaMwFxilmEROC9zNSjPGFTG4HyOINrXVhire07keucF9cwHU9BOyEcH085zLGE4PlZxEImGqhBuDy9shM8KU1KmJSGoB1nYG0K2t3ESfFRKY0rqHdsYg7gCIfQd5cNNe2012HGqmJywxNJHaWx+MeIoCURuQsKIeJdra6sYrjet2zbk4XKLFnMiEiBSgT9A5tb99sSZNzliyHHdZSMtgkCsrstwY17Ch77ixnfsQ23OXGLiBhiyb8JRES802BXX7yMBMp0BjKplsPjkt8LAY2mxneP4VqZPDcpIXfsaxYraHbcyjbHPhZygXtC18al6BK0+xlmFhq8+dPXM/7ff8Prna/hPvy1XFcd4yVf+GPOHDqTN+55I2nbo6Oxbx+3XvZi6jffzOiHrsQf7/wHvYsuTilSRXjMu+An/w6ffiHZ73yA/3vRG7lg7AL++j/+mqvl1bz4WS+m+uirWHzu8/ivxz6R9z3l+TztMTuZrHXt+Lu4fyNJuvyVK3Fqvfduh06AI+RJr0C39c06BjxpU/cS2+QvOH9ZrVyUtiiFJHQSxw81jJ0Fjoe8/geEoe5MsE5IUK3D2fFqje6lCjlZa5HOfMrl14fn2zdYf8kJ97HUqW/raJkjN7nM1uvtaVBLWxqQmOPBLa0PlQvl9r/PF67ri+3yzx45O1EjZn5/UmR+KZIBu2jfQ7z/qLIloXZ1JNsxWiqGbjaPqext798eE6C7A3US928b8gMn3ERJRVjMwd52B8Zj3XdtBPUkUxwB/ImJZS1jsr4TO2xGgmPGC8h4LbOY45SEdYQ7OEi+rw/mD8DPv5SQHRPPYLRItAQPm3jYss9OGyvHCz1jlUxHc6GliK/7CQavlW8J2skqaMuve/SeyOzezdGvXNv5nnM61K17aRg5HUVnUxYhBIfm6vQfoweZPz1N8+BB+jIz9Gc7ZypESNYV7pmqkvXvezrVJWj3I3ztZ3dx5Uev4hXh5eRHp+GRX+Fvb7iGf7n2nbz09JdyYWRljHFqvOVFLyJ71lkMveXNx0316KKL+wWmzoexb8C1b4D37OW0Hc/m6gv+kY/84pM85wvP4YzBM3j+Ne9i4O0fZPJdL+b133gM4d7zefaeCbaNlu6xVXAXXfy24FQqOMeoi1rdn6evEHT8XRuiILu5fHW+E05GQXOVuyxQa7dgN3CkE2/njo6ienpoHjxo/r5kjKIlbIDfKag/0TOq7xntuMeY7ssx3tM5lfK4cJbPTSd1SCRIrOYE6VhL0NZeIGlYcm/ec0IdWxqxH8ckPKHKnJTbnxSEs7OIoLPxR0/qnvVadSLF6R59uzPi4DlxffaO7F1mFtL5fr0bRh6ZDN5SdYuWw2anFFZonXM0huOZeMTbKWXcBnvXmXfErd85sanKMRCZ1AAMl9NtNVPHRAdlsdO4Q+WbN0SHNOLOOHbqpMrnj/m7Y6IwiHN0oeOvlBAcrjdpHCNV1+3txe3tZUvH37YjeS8V078Zk7MuQbsf4I4j87zhU9ez7sdv5c3OV3Af+mpumNjF//nqZaScFFc/4moGsmbVqHHgAHe+8U0c/uxn6Xv5yzu65HTRxf0WXhrOeSlseAL86wtw/m43Tz7n5Tz0UZ/gbddfzsWffiyPPf+xPGX3q/jzl72Gm5s38fyfPZRqf4Vnn7WC89b0HjcQ7aKL+yumek9SDRYCSuNGeT6Zze2/9/VjIYRAZbPLDDt2jlf48cFft9egtb50/J3+dvmZaSDbwY7+eMjs3tXR9KE/08/BhYPA4db+EwTAd5RVI+7dhWgJFfdgsoRszXFzqUmI2bFnjVy8FStY2G36dRX8Ag8ZfkjnfSaMQcKjR9uI3X2BWL25D+/fiIQmCVi7OU472s0r7ztDj06X8OzpGrmkg2Sb9+lJoDYNh22Tbcc//rb3JSKCljgpXV9e2xa1PDhpnAz5upuLFpE5S/wt+/3IlGehcfxaypPB0hTH3wS6Lo6nEI1myPu++gsue8Pb+YufP50njB5B/PFXeI+7wJM//RTOGTmHKy64goHsAM3Dh7nzbZdzw/kXEM4cZeJTn+qSsy4euKhOwdM+Cee9Er7wKsofuISX95/LVQ+/igMLB3j0rS/nk6/Yy1Bmkb//2lv4ff8OXvuZH3LuG7/Mlf/xS+br97zWoYsu7vcY2tqernYc3BtTiXuCbODgO6ojQfMnJ/FGR34r4/hNwSl1VuvXVNawa2AXSSYhgvZgtCd77wNmIQSbh0v3LGXKz8U3RLjQriJE55RMd927tp9zV5uU3Kh04jgDo3n48H2erROleZ7Af+RuIbo3T0RyOz4xJ107dWKM9aRZuSRNP3df9PiLarOcu0mG7gWi+6dtTjvU4MVp0yf5/uqE5Zft7r3b4ibxS1JB054Z231B0I7VB+2+RFdBOwXQWvPln9zJW/71v3jm/N/zbu86nAv/mu8Nb+RVX7sMKSQfeNgHmCpNUf/1r7nz6n9i/wc+QHrzZkbe9z5S69ed6lPooot7DyFg7aNh1UXw7SvgI09mYmQXrz3vL/n5hmfxgR98gKee8V0uHRrgvHf+FR+44AJ+cPHTeee3b+ZNn/sJTzt9jKeePvobSy/ooouTQdLC+9Tgt6sox4YjHUiMOzCAO3DsGqHfdorjbxrC70TI7v31GKncQxKUKuKd+0zmfvoydLN9EWupggadm10Dpllzw9bu2Wi5sc/0WhP+ffu+jYLpY6Wd3RNE6bcna1rSdivfh6pULnBZM3ACoiK4+8py0zqT3kt3yHuLTgoawC1DF7EtOEmjr3jyOz833vAQTnW54dDxsLSdRZRuuWW0xKe/dxul9H1AkqNd/wYXxroE7beM7996iNd8+kdUbvsSH3SvID2+lbvO+Qxv/smH+eLn3smzNjyLS0cfw/y1X+VXH/sbZr/1LbIPeQgj731P16GxiwcnHA92/hFsfJKpT3vXHiZWP4KXn/VCnrflefzLz/6F145czYUf+xTTn/8sL/nfz6R+4WN431d/xTtecwMPW9/PpTtG2DJS7NapdfFbx29yBfX+iFDf80D65AjaA+cZvj+/b3SjnZxETn7/n73zjo+juhr2c3dXXbJkuTdcwBXTjAMEDITQIQRCJ6GTEN4kpEHypsEXAqmkJ6S9QBJCSyAQIJiOwTbu3bjIvah3abV9Z+73x8xWraRVXUk+j3/y7s7cmTl3dnbmnHvKzUrHs3HMeYCGA2+3W+VIaZT2nlRzwvUWX9jX6fp23ueZ50OKiof9SX+GyPU1KjsrIcRVGynmMuzj/uSdcEL3t8mywo0dCigcZ+XuYYXTdreSYlf05/cnBtoAsae2jd+9u5vtO7bxyJgXmZmzieBFP+Qx2nj8rc9yletU/um/Gf2r5exb+ytyZs6k+PLLmfjwz3CN7KoksCAMA/JK4IIHLWPtg9/An8+mePZF3HLmvdx8083svHQnG579PTN/9HvKpv+R8TedyTeu+hiHKkZy5xPrGFOUw6dPPYorTprEiL4IIxGEAeAj4z/SK8/SQNsIXZXHP1LImXlMpkXonCQPWnFOMQvGLaAoK41cSLtCXu7cOe28F6mKy/QFfRF2low/7O+6EXFDAmnO3daX5Dpz8Zie7m00cjrkdT4NRX8w4oLEeUqjc6T1BXE3ssIcF+5AekWSUu9KccIUW7bpZ/ZWsi6P1V/InbafiRhmS7cf5teT3+dX2c/gn3g1L/vmsetPv2RupeLR8jAu52pYaFB04QVM+NEPyRrXy1LNgjBUGTHRmuR60ddh5e/gsfNRR5/D3NO/zNwv/J7QDU0U//j7LHxgKUsv2Murc2qYs+BYxmWdyIvbqvjJa0WcO3cCnzxhImfNGp1QLU048lBKlQL/BKYBB4BrtdZNSW1OBP4IjMCa4feHWut/DoR8Y/K7F76TzECPv08umkxJbs8Usx4Vvhik5M6enXrFIPaqjS/o3lxcOUfHSrePuPgiWl97vfs14tMgy+kglMIb0xtG542mIHtgvWE94bSJpyVO8J0OSqVdRKg/KTj99H7Z76KZozusgDnYEA/aEENrzbqDTfzf0n2s3lPNT8es5IGGF2jeW8T6+unkPfUWpaNzOW/ByUy78SLyFywge/r0QR0uIQgDTtE4uOAhOONrsPpP8MwNUDKFrFPvYvaPH8b34Q4Kf/AgF2yZQMUtJ/HWxAYai15h1FxNhesEHnh3Gi3Pz+DCuTM4d85YFs0c3TcJ2sJQ41vAO1rrnyilvmV//t+kNl7gZq31bqXURGC9UuoNrXXzQAvbXQb6uaGUoii7Z/MTDosctK7Odw+N176k8MzTe1yCvSOU00nOrJndzgdKh3NmjyXYxwbaKRNO6bJNdMLqDKpeOc4cGKJjiKnuPT0/l7EN05mXbbAgRUKGCIGwwetbq3hp8RpG7t7Cnb4NfPPAboJu2DylgLUT/TguPppFFz3IotnniEEmCOlQMAo+/l048x7Y9gKsfARe/zZ5x1/LtN/eR8vqvfDb33HnlCl8555fc2BKNssqlrG8YDkfjniSDaGjWb1iFrUvTmfBhGM5a+Y4TpleyvGTi4fUg0DoMZcDH7Pf/x14jyQDTWu9K+59pVKqFhgDDHoDbSjNOpGWgTaUn4vzrujTMu09xTn/oq4b9YDcWbO6btQD8rKd5GXQShlKeWCDnZ7bZ0PzOyjO6b+wWDHQeok2DPas2sTaV5YQ2LCeY+v3cm/IS3BcmN1Ts3njkwXUzhjFebMv5TOzrulydnJBEDogKxdO/LRVTKRyI2x6CvXkJykpPooR911G4zYo//xdFJ1wAjff9XnuuuQumv3NrKxayfKK5SwtfpL9YZOGw/N5fOMM2pqPZsGUiRw/uYTjJhUzf2IxU0rzZOBk+DFOa10FoLWuUkqN7ayxUuoUIBvYOxDC9ZY540cwfsTAldvuKTNHziS3u3MkDTUyXFVP6Blyyx8EOIdeNeZLZlzSr/uXu0k3MQMB/Fu3Ur18FVXLVpG3exshpRg1tYDGY3z8c1Eb703J4ugRR/PRqefxtekXMK90nih9gtBXKAWTFlh/F/wQdr2OY/tLjG57g5GfnkpTRSvlX7iLnJmzKb39Di46+wIunn4xpjbZ0biD5eXLWVaxjG31T9OcN5tNrcez/OAx7K0owuVwMH9SMXMnjGDm2EJmjivimLGFFOdJaORgRin1NpAqwea73dzPBOAfwC1ad1yuUCl1J3AnwFFHZXber2yXg7FDxEBLi/xR/SuIICQh2tkgoBfzpg1XxEDrBK01zQ2V1K1ZRtvatQTWf0jhvnKaCrLZOdHFvqkhNp4exldiMsehmDfqWC6adRn3TTu/x0nUgiB0g6xcOPYK6y/kw7nnHUbvfpPS0Vto/nAFNd/7kGqyGPnJ8yi+6S6OnXQsx446ls+f8Hma/E18UPkBS8uX8oHxGyaV5nPCqNMY7TyBUNsM3t7h4Y/v7+VQo5exRTnMHFvEzHGFHDO2kBmjCzl6TAFjinJk8GUQoLU+r6N1SqkapdQE23s2AajtoN0I4FXge1rrVV0c7y/AXwAWLlw4DBKrBgnHXZ1pCQRB6AU9fh4OQQ9af3NEGWgbajbQFmojaAStPzOIN+SlJdhCa6CV1mArodpaindWMGZXPUft8zCp3qRytJPdE7OoPDoL46xCJuW2MCMc4PQxx/PtaR+j+NiroCSzo6iCcMSTlQdzPwFzP4HjMk1p/S5G7n0fz/tv0/Tey9Q/8SJ5E7MYccoxFJ19BiNnnMAnRs/nE9MuJoxma/1WlpYvZVn5s+xt3sucUXP45LELOX7UAorUMVQ1KXbXtPFeWR1//eAABxs85LiczBhTwIzRBcwYU8j00QX250Lysodo5vfw42XgFuAn9utLyQ2UUtnAi8ATWuvnBlY8QRAyioyxZR4x0NpxRBloj259lOZAM1mOLLKd2eRqF5Oqw0w90MaMPQ2U7msgr9lHy5g8PKMdFM11MHl0E/Oy/ZxZMpPcqSfjmHAiTD4Zxp8g8eaCMFhRCsbMRo2ZTeFpd1L4vxCu3I/7hX/gfud9ql96nNxRTgrGeMgfFyBv5mROGjOFk0ZM4CtFJ9M87lzWay/r3NX8ufw37G7dz5i8Mcwunc1x82fzqZJjGJs/HjNUTEtbHgfr/eyt8/DBnnr21XuobwswYUQuM8YUJhhwU0rzmVCcS26WGG8DyE+Afyml7gAOAdcAKKUWAndprT8LXAucBYxSSt1qb3er1npTBuQVBjOizA87pEjIIMCeTFqIMTwsDCMMYR+EfBDyQtAL/hbwN4O/Be1rwtdQywP7g/gPtRI81IhR6caoD+HMMckfFcQsdRJaUEh48iTGjJnOtIlzGD9tHs4xM6HkKPIlPlYQhjSuidMZ+aX7GfklMNxuvGvW4FmxgtqVKwi8d5CssVXkHeUju/QgOQUhzshr5WzqcYZr8SvN3sJWdtXXUpazlhecimplUqODBNGMduUzwlVAwZgCFkwqJNtVSNjMwxN28qHfZNU+k9ZtBh6/ibdlFiXMZ0JJLhOL85hYksfEklzGFuVSWpCd8CeGXO/RWjcA56ZYvg74rP3+SeDJARZNEIRBgESp9x09OpcS2pySjBpoB+o9vPZhNQDnbfsWOeFWlDZR2kBpE4c2UBjRZQ57udMM4jL9ZJl+XGYAhxHGDCuCgSz8gRz83hx8vhwC3ixCbgUtBi5PmGBeFr6SIjzjxhI44zT0/JMYMft4ph41lSmji3EMpXrFgiD0GGdREUXnnkvRuZbebrR58G/fhn/bdoL79+M9cIDgAS/hOic4J+MqHUnByGIWFhdwao4D5dQ4nAbKESaIH4/hwW+0ETQaCJpBgjpESIcxHQrTmYXpctEyKZeWKfksGF3NPNduWnQBjWY+9c3Z1Fc7KQ8otgScNAYUtT6F28jClZVFUW42RXlZ5Ga5yMvOIi/bRW52llWaOttFfraTbJci2+Egy6XIcjoIjziKrOwcsl0OnErhcCi7HLv16lAKFfeqUDjiqoPHzxFalOti8sj8Af1+BEEY5GQN/kmg00U0v75jzvgiRhVIuGJfMOAGWv1f/g/P0qVoNN5AmLktPhTQHGpGoYn+VDSAw/rTkc9xaHAGgrj8PlzBAADh3DyCI0djjBqDHj0Gx8xx5E6ZRMnc2Yw5dja5I6VwhyAI7XEWFlBwyikUnJI4uakOhzGamgjX19t/DZheD9rnw/T6MH0+XH4fxaam2DTR2gRTg2mCGUaHAhAKQDhA0cR5jFgwA3xN4G+m2NfMUf56CLjBFYAcH4T9EPajQ350yIcZDqG1iRnU4Lf3r7X1irbfg0ah0far4t7S31NNKcGwiWFas0+ZkbZaWyKm+Bw/+hkJ+zlnzhh+fOXxA/dlCIIw+Bk9c1jk3mc7HRTmDo9gssFAUW4WRbkScdYXDPhVWfDRj5I9bSooxSilmOJwAMqyy5SyKsBE/rBelSP1MkdhIY6iIpyFhTgKC1Eu+ZEJgtB3KJcL15gxuMaMGdjj2n89nfL2L30oiyAIMfJOOBHt82ZajMyjlFVFd4hz8XEyN60wOBlwiybvuPnkHTd/oA8rCIIgCILQK7LGdTrPuSAIQp/Q0wFaQRAEQRAEQRAEoY8RA00QBEEQBEEQBGGQIAaaIAiCIAiCIAjCIEFpnVwesZPGStUBB/tPnH5jNFCfaSH6iOHSl+HSDxg+fRku/YDh05fh0g+A2VrrokwL0dcopdxAWablGMIMp2s8E8j56x1y/nqHnL/eMVVrnbIKWbeKhHS0k8GOUmqd1nphpuXoC4ZLX4ZLP2D49GW49AOGT1+GSz/A6kumZegnyobLd5QJhtM1ngnk/PUOOX+9Q85f/yEhjoIgCIIgCIIgCIMEMdAEQRAEQRAEQRAGCUeKgTac5m0dLn0ZLv2A4dOX4dIPGD59GS79gOHVl3iGa78GCjl/vUPOX++Q89c75Pz1E90qEiIIgiAIgiAIgiD0H0eKB00QBEEQBEEQBGHQMywNNKVUqVLqLaXUbvt1ZIo2JyqlViqltimltiilrsuErF2RTl/sdq8rpZqVUv8daBk7Qyl1kVKqTCm1Ryn1rRTrc5RS/7TXr1ZKTRt4KbsmjX6cpZTaoJQKK6WuzoSM6ZJGX76ulNpu/y7eUUpNzYScXZFGP+5SSm1VSm1SSi1XSs3LhJzp0FVf4tpdrZTSSqlBWTUrje/kVqVUnf2dbFJKfTYTcvYF6X5nRzJKqSlKqSVKqR32s/Yr9vKUzzVl8Vv7nG5RSi3IbA8GB0opp1JqY+T5rpSabj8vd9vPz2x7+ZB4ng4kSqkSpdTzSqmd9nX4Ubn+uodS6mv27/dDpdQzSqlcuQb7n2FpoAHfAt7RWs8E3rE/J+MFbtZaHwtcBPxaKVUygDKmSzp9AXgYuGnApEoDpZQTeAS4GJgH3JBCSb4DaNJaHwP8CvjpwErZNWn24xBwK/D0wErXPdLsy0Zgodb6eOB54GcDK2XXpNmPp7XWx2mtT8Tqwy8HWMy0SLMvKKWKgC8DqwdWwvRItx/AP7XWJ9p/jw6okH1EN/p6pBMG7tFazwVOA75on6eOnmsXAzPtvzuBPw68yIOSrwA74j7/FPiVff6asJ6jMASepxngN8DrWus5wAlY51GuvzRRSk3Ceu4s1FrPB5zA9cg12O8MVwPtcuDv9vu/A1ckN9Ba79Ja77bfVwK1wGCc563LvgBord8B3AMlVJqcAuzRWu/TWgeBZ7H6E098/54HzlVKqQGUMR267IfW+oDWegtgZkLAbpBOX5Zorb32x1XA5AGWMR3S6Udr3McCYLAm3KbzOwF4EMvQ9A+kcN0g3X4MB46kvvYYrXWV1nqD/d6NpRxPouPn2uXAE9piFVCilJowwGIPKpRSk4FLgUftzwr4ONbzEtqfv8H+PB0wlFIjgLOAxwC01kGtdTNy/XUXF5CnlHIB+UAVcg32O8PVQBunta4C6wEBjO2ssVLqFCAb2DsAsnWXbvVlkDEJOBz3udxelrKN1joMtACjBkS69EmnH0OF7vblDuC1fpWoZ6TVD6XUF5VSe7EMmy8PkGzdpcu+KKVOAqZorQdVCHMS6V5bV9nhQ88rpaYMjGh9znC6JwwIdqjTSVge4I6ea3Je2/Nr4JvEBv9GAc328xISz9FQeJ4OJDOAOuCvdojoo0qpAuT6SxutdQXwc6wooSqsa2o9cg32O0PWQFNKvW3Hwyb/dWsU0x4d+Qdwm9Y6I96PvurLICTVqEmyFyOdNplmKMiYLmn3RSl1I7AQK3x2sJFWP7TWj2itjwb+F/hev0vVMzrti1LKgRUqcs+ASdQz0vlOXgGm2eGzbxMbaR1qDKd7Qr+jlCoE/g18Ncmz3a5pimVH7HlVSn0CqNVar49fnKKpTmPdkYgLWAD8UWt9EuCh4zQRkPPXDjs/73JgOjARKxrl4hRN5RrsY1yZFqCnaK3P62idUqpGKTVBa11lG2C1HbQbAbwKfM92Z2eEvujLIKUciB8hnwxUdtCm3HafFwONAyNe2qTTj6FCWn1RSp0HfBc4W2sdGFnMc20AACAASURBVCDZukN3v5NnGbz5BF31pQiYD7xnR4qMB15WSn1Sa71uwKTsmi6/E611Q9zH/2Po5icMp3tCv6KUysIyzp7SWr9gL+7ouSbnNZEzgE8qpS4BcoERWB61EqWUy/ZQxJ+jofA8HUjKgXKtdSRv93ksA02uv/Q5D9ivta4DUEq9AJyOXIP9zpD1oHXBy8At9vtbgJeSG9gVZ17Eijd+bgBl6y5d9mUQsxaYaVf7ycZKLH05qU18/64G3tWDb3K+dPoxVOiyL3Y43Z+BT2qtB+uAQDr9mBn38VJg9wDK1x067YvWukVrPVprPU1rPQ0rL3CwGWeQ3ncSn8/xSRILHwwlhtM9od+wc08eA3ZoreOL9HT0XHsZuFlZnAa0RELRjkS01t/WWk+2f/fXYz0fPwMswXpeQvvzN9ifpwOG1roaOKyUmm0vOhfYjlx/3eEQcJpSKt/+PUfOoVyD/Y3Wetj9YcW7voOlkL0DlNrLFwKP2u9vBELApri/EzMte0/6Yn9ehhVr7cMawbgw07Lbcl0C7MLK7/uuvewHWAomWKOCzwF7gDXAjEzL3MN+fMQ+7x6gAdiWaZl70Ze3gZq438XLmZa5h/34DbDN7sMS4NhMy9zTviS1fQ+rolbG5e7Bd/Jj+zvZbH8nczItc1/2Vf7anaNFWOFNW+LuJ5d08lxTWNUx9wJbB+t1nqFz+THgv/b7Gfbzco/9/Myxlw+J5+kAn7cTgXX2NfgfYKRcf90+hw8AO4EPsVKCcuQa7P8/ZZ9QQRAEQRAEQRAEIcMM1xBHQRAEQRAEQRCEIYcYaIIgCIIgCIIgCIMEMdAEQRAEQRAEQRAGCWKgCYIgCIIgCIIgDBLEQBMEQRAEQRAEQRgkiIEmCIIgCIIgCIIwSBADTRAEQRAEQRAEYZAgBpogCIIgCIIgCMIgQQw0QRAEQRAEQRCEQYIYaIIgCIIgCIIgCIMEMdAEoROUUm1KqVGZlkMQBEEQBgPyXBSE/kdprTMtgyAIgiAIgiAIgoB40ARBEARBEARBEAYNYqAJAqCUciilHlFK1SulWpRSq5RSWUoprZQabbeZo5Rao5RyK6X+rpR6TSl1q73ub0qpXyilltnhH39VSk1WSi239/enuGNdrpT60N5PmVLqigx1WxAEQRBSIs9FQcgcYqAJgsUFwCnAdKAU+CpgJrV5CnjRXv8OcF7S+muBO+x9XAg8B9wKHAN8Uil1ht2uBbgKKAa+CTyhlCrt2+4IgiAIQq+Q56IgZAgx0ATBIgSMAGYDptZ6ldbaiKxUSk3DeqA8rLUOaa2fAPYk7eMZrfUurXUdsAxYqrXeE/f5eACt9Xta6zKttam1fgnYC5zQz/0TBEEQhO4gz0VByBBioAkCoLV+B/gL8DegSin1QFKT8UCN1joct6w8qU1t3HsfUJf0uQBAKbVIKbVCKdWolGoGjsMafRQEQRCEQYE8FwUhc4iBJgg2WutfaK3nA6cDNyulzo9bXQ2MU0q54pZN7uGh/gH8GRintS4BtgKqh/sSBEEQhH5BnouCkBnEQBMEQCm1UCl1slLKCbgBw/4DQGt9ACt04x47SfpGrNCOnlAENACGUuoGYH6vhBcEQRCEPkaei4KQOcRAEwSLYuDvWInKW4F/aa3fTWrzGeBqoBE4H3gfCPTgWHdjhY00AGcCK3sosyAIgiD0F/JcFIQMIRNVC0IPUUrtAj6rtV6aaVkEQRAEIdPIc1EQ+gbxoAlCmiilPqqUmqqUciml/gcrJGNNpuUSBEEQhEwgz0VB6B9cXTcRBMFmCvAvYCRQBlyltfZnViRBEARByBjyXBSEfkBCHAVBEARBEARBEAYJEuIoCIIgCIIgCIIwSBADTRAEQRAEQRAEYZDQrRy00aNH62nTpvWTKIIgCMJwZf369fVa6zGZlqOvkeeiIAiC0BM6ey52y0CbNm0a69at6xupBEEQhCMGpdTBTMvQH8hzURAEQegJnT0XJcRREARBEARBEARhkCBl9oUjB63B2wghL2QXQN5IUCrTUgmCIAjCoEMbBsrpzLQYgnBEIgaaMLxpPgzbXoRdb0D1Fgi0gisPwn5w5cKkBTDjHDj+Ghg5LdPSCoIgCELGCezbj3/7doo/cWmmRRGEIxIx0IThSfVWWPow7HoTZp4PC26CSQuhdDo4nGCEobUcDq+FXa/BI6fB0efAOd+B8cdlWnpBEIThhxGCcAByCjMtidAFZps70yL0HVufhymnQMlRmZZEENJGDDRheOGugTe/B2Wvwal3wlcehqJx7ds5XZbHbOQ0y3vmbYSVv4fHL4YTrodz74Pc4oGWXhAEYfhSvg5aK+C4qzMtiXCk4W/JtASC0C2kSIgwPNAaNj4Fj3wEXDnw5Y1w7v2pjbNU5Jda7b+wElor4Q+nw8GV/SuzIAjCkYQRyLQEwpGKMyfTEghCtxAPmjD08bfAf78Gh1bDdU/B9DN7vq+SKXD9U7DpKXj6WvjYt+C0L0gxEUEQhF4j91FhgDFN69WZlVk5BKGbiAdNGNocXgt/WmTlNvzP8t4ZZxGUgpNuhNtfh1V/tIw/I9T7/QqCkDGUUhcppcqUUnuUUt/qoM21SqntSqltSqmnB1rGYY8MdA0dhst3FfHaOsQfIQwt5IoVhi7r/wZvfA8u+AGcfFu3HyieQJhdNW4ONXpp9YUIhE1yXA7GF+dxVGk+U0pnk//Zd+DZG+Cpa+Cav0FeSb90RRCE/kMp5QQeAc4HyoG1SqmXtdbb49rMBL4NnKG1blJKjc2MtMOZYaL0C0MHI5hpCQShR4iBJgw9jBC8/m3Y8Qrc/BJMPjntTffUtvHa1ire2F7Njio3E4pzmToqn5L8bHKcDgJhk8oWH4cbvTR4gswdP4Izp/+c2+t+xpjHL8Jx0wswYmI/dk4QhH7gFGCP1nofgFLqWeByYHtcm88Bj2itmwC01rUDLuVwR0nQjjDAaDPTEghCjxADTRhaeBvhXzdDyAd3vgcjJnS5STBs8tKmCh7/4ACHG72cO3csXzpnJqfNKKUkP7vD7Vp8Idbub2TlvgZuabmLTzf/iUt++zE2nvUYp5/6UQpy5OcjCEOEScDhuM/lwKlJbWYBKKU+AJzA97XWrw+MeEcIwyVsrp/Qpoly9N6I9QUN1hxo5OxZY/pAqiGO1pE3GRVDELqLaJjC0KH5EDx5NUw4Hj75e8jK7bR5IGzwxIqDPLp8H6MLc7jr7BlceOx4crOcaR2uOC+L8+aN47x544B5HKxfyL7FP+aUJZ/htre/yZT5Z3LVyZM4bfooHA5RPARhEJPqB5qssbmAmcDHgMnAMqXUfK11c7udKXUncCfAUUfJ3EppIx60DvGsXoMO+Ck866xe76uqxUezt7ehfcPsmabFQOsNgX37CezezYgLL8i0KEcMYqAJQ4OqzfDUtXDiDfDx+6GTUUatNe/sqOXBV7czqiCbX1xzImccMwrVjdFbbZqE6+oxGuoxAwGU08mEkhKmXP//cGydxTNvfI9/6yLu+Vc9DqW46uTJXLVgElNHFfRFbwVB6FvKgSlxnycDlSnarNJah4D9SqkyLINtbfLOtNZ/Af4CsHDhQtH80ibDSr+vCfJGZlaGDgjX1aGcfWPAegJGn+xnqKMNA+3zSzW8PiBcX4cOSbG0gUQMNGHws+cdeO42a/LoUz7XadO9dW384JXt7Khq5TuXzOXi40ZT5aliQ+1BGnwNtIXaCBpBgkYQl8NFflY++a58xhoFjN1Vj2P9VnwbNhLYtw9tGLhGjcKRm4s2DIyGBky/n5xZs8ifcSGXVt/PVbc/zMqCj/P8+sNc9OtlHDepmKtOnsQlx02gKFfK+grCIGEtMFMpNR2oAK4HPp3U5j/ADcDflFKjsUIe9w2olEMJIwzuKmtqknTJZIijr9l6lgzmSbKd6UV3dIUvJAYagG/zZkL7d1E8y8VQCnE82HqQouwiSnNL+/1Y1Z5qar21HD9qfqcD330Reit0DzHQhMHNpqfhtf+FT/0J5lzaYTO3P8TP39rMc9uWseAYL2dMb+SvB3/H97ceJD8rn9F5oxmVO4rC7EJynDlkO7LJbnQzefVBpq+vwnW4jY1jFWUzsgmdOZMpd9/KScdfwDGlc6KeN601RnMz/i1b8KxaTeUr2+GD+5h76Vv84p6f8+AV81m8tYrn15fzwCvbueKkSdx6+jRmjSsaqLMlCEIKtNZhpdSXgDew8sse11pvU0r9AFintX7ZXneBUmo7YADf0Fo3ZE7qQU7TfqjaTENOPqurVnPJjEvS2KhzA80f9pPr6jx0vccMgWIRg0YJ3vo8BCZlWopeYzQ1xUIbh1CI47b6bRRlF3Hm5D6YNqgLDrZag9fHV++CSQugdEbqhoPl2jyCEANNGLys+iO8/zO48QWY8pF2q70hL6sqV/H01iWsqVoHWVUcPWs6M8Ydx6yRJ3H9nGuZOXJmwihUuKkJ9xtv0PrfV/Ft2ULBokWM+NKdFJ65iNlFBcx1H2Z97XpWV63mj2/dyajcUVw16youm3EZJbkluEaOpPDssyk8+2zGfuNePK8+TeNvHqL+lbMY/cW7ueamm7juI0ext66NJ1Yc4FOPfMCJR5Vw6+nT+ficsTglV00QMoLWejGwOGnZ/XHvNfB1+++IxWhuxrNiBSMu6cLgshXe1mBr+jvvIAdtf72HjRX7yS06lKahN0hpq7Nyo3N6OCg3mJTgsDfTEvQa0+9nSHjO2uqgfC3MiV373UnJ6A2O+N9kwN1xQynw0yEr9tYza1wRowtz+nS/YqAJg5OlP4fVf4ZbX4Vx86KLG/2NvH/4fd499C4fVK5AhcbiDMzkjhPv5NYFH6c4p7jdrky/n7b33qPllf/iWbaMvJMXUPypK5j8yO9xFsfaO4EZJTOYUTKDa2ZdQ8gI8V75e/x717/53cbfce2sa7njuDsYmWvlMCiHg8LLbqTwjDPw/fwKap57jObnnmf8D37A0aeewgOXz+eeC2fz/LpyHnp1Oz99fSffuHA2F8wbN2A3X0EQhChaW56vjkbJATMYRJsao60NZ2FhZztL/5iR+10Ht72qZh81nnqmxts1pu3x6qXREjbDbK3fykmFU3u1n7TY/75lnM26sGfb9/FzoevvsFNh+lSWjKA7/DC48NRBKNEgdgxQ5pwrfgLvTibzHjTe3UFInTtAkTPE6OrtcMy5fbZfMdCEwYXW8O5DsPkZuO01GH0M9b56Xt//Om8fepstdVuYV3o8gZZ5hA7ey5fPOpVbTp9Gtivx5mH6/XhWrMT99tu433yTrClTKP7EJxh//31kjRuXlihZzizOn3o+5089n30t+3hk4yNc8sIl3Hzszdwx/w6ynXaJ/tLp5H3nLab+40pa9kP53XdTfMXljLv3XkbkZnP7ounccvo0XthQzgMvb+PP7+/lvk/M46SjBmeyuiAIA4N/1y6UUuTMnDkwBwx6oGIDlEwFR+p8p2hIdyAAnSn36YSMVW6Chj1xeV+plX4NBExf0rYbLWOyk5wxo82Ds7DzwkzuoJuqtipOKuh9tU2tNToUwpHd8fQs3alUqbVGKYW2z2VfK8Ft771P4aIzcJaU9HgfH+yp56jSfKaU5vehZP3A1udh7mXgSvZiDIEQR2f7fPWMeNBUJzmQw8hA01qzcl9Dn1bgVkYQgm19sq8Iw+eMC0MfreHt78PWfxG65SXe8Rzk7nfv5qJ/X8SKyhVcNuMKbpn8GJvX3sDUrAt55yuf4nNnzSDb5UBrTfDAAZqee47DX/oSuz56OnW/+iWuMWOY+vRTzHjxBUbdcXvaxlkyM4pn8IuP/YLHLnyMVZWruOaVa9hStyXWoHAs6rbFlBybxYzr8/Bv3siBG28iWF4BgNOhuGbhFN6992NcPH8CNz+2hh8v3oFfkrkF4cgh5IdwIPoxsGs3/rJdA3f8iNJndFyNTaeds6Pt/ztp56lPffyUe0vaT6jzELtwUxNt773XaZv4/ZqRHLS4foVqarrcPkGkQ4dwv/lW543SNNC27a9h8Yoy64NhPwf6Ugm2PZC6l4ZJfVuAw439HO6odcxjauML+wh1cp2mJO63lbDvwU7EcxUn60AZaC4V70HrxECzr+u+quR4sPUg7mAnIZX9SNjU1LkDBI2O81IDRqBb15/Sus/zXMVAEwYP7/2Emm3/5tenXMO5b93Bbzf+lgVjF/Dala9xzZTv88jLJby7zcMTt5/CT86ZRP7m9TQ89hjlX/0au888i/1XXoX79TfIP3khM176DzNeeYWxX/squbNm9ZmI80bN468X/ZXrZl/HnW/dyS/X/zL2I84dATe+QNbcU5m6YBMFsyex/6qrcL/zTnT73CwnnztrBq9++Uw2HW7m0t8u48OKlj6TTxCEQUzZq7Dn7cwd37QNAbMTxaMrA621KqldN47fifGik5WbLgwdHUxPeYoYKEbS/g23G+/addYH0wR/17l0ps/XZZtOldw4fOs3kLdpLYGwgY4YaH0YVqiMsPXaG0XfW09B28H029ftgvL13T/O4TWw/T8Ji5YcWsKa6jXpbR+5FodK6oC7OvFzxIMWZxCoAQoxdShH3PnrrIqjJU/rG2+mve+lu+o6HITeVr+Nvc170xd0gAg3NdHy31fZ3bSbw+7D0eWGqSlv6nigQkGfG2gS4igMCva/cz9/3vNv3hmVyznhNn537u84fvTxHKh187M/vItvZxnfL/ExtbmCwLM72d3cTM6MGeTMmU3eiScw6rOfJXfObJSr/y9ph3Lw6bmf5szJZ/KdZd/h5tdu5uGzH2Zy0WTrRnvxT1ETT2Lsq/eSd/uNVH3nuwTu2Meoz302+rA8alQ+z3zuNP624gA3/GUVD14xnytOGvpVswRB6AStIZSGkt9fmJbS3pkHLaKsaTOF5WUacPAD631eT8LmbKXTNBO8RVp34YlLLWiarSIeNCN2MKViHgHDQNVsTQrF7AERD1CaHrQcZcm14WAzp07Ii0rbZ4St71qbPVQalYKGvZTkBwhOSHOQs26HdW1NPrl7x/LWp1Ru/YY/4bOpTQxtkOVICgnsTDHuRw+aJxCmzh1g2uhuzH8acMOB5TD/qvYGpREElxU+O1AGmtPhBG3fFzq7dnvg3W3yBmn1h8jNSj1okeeyrvvgoUPoUIico49OWH+40cvEkrw+L67WmT1vNDdbr9qIed2xKoVvq2xl8siOQn112tdaeZOXUQU55GV3PpgjBpqQUeq8dfzhzS/xavM2rp57OS+f+EVK9tfT+Oxylr/+A4oO7ObG7ByKjp1Lwai55JxxKbn3fJ3so4/uPA9gAJhSNIXHL3qcRzY+wrX/vZYHTn+A86eeb6084XoYM4eif91E9s3HcvifzxI6fIjx99+PyrIeLg6H4vZF05k/qZj/eXI9O6pa+eZFc6TSoyAMZ9L0sPQLEYUjYqilbBNRMhKVDdPnI7CrjLyInuZrtlt1ppQkrYtqRJ1s01Zr5cJ05Q1J0/AI2301k/oV8QhgGFaRht6iuxeuHsmbznKqmAetp8ZUCiIeNNPrhdIezKfVTcNmQ80GxgabmezshrHSzWNtqdtCZVtl+0qfnW7ffzloO6vdlDd5u2egRT3PZiznK7LMCEabDVSIo1M5wTQxtEmnd6Y4402Hw10Ohhv2AI8zRT+Cdj8jU2r4tmwFaGegba9qpSjXRUn+wOt6yaHBhqk7vYQUkXur2aUxu/5gE5NK8lg4rfPfpYQ4ChnB1CZP73iay/59McHKTTw/7uvc9HYWredfye7Pf4HFr61h5ZxFFDzzb47fsIYZTz7BuG9/m5JPXUHu3LkZN84iZDmy+OrJX+VnZ/2Mh1Y9xEOrHiJg2HHwE0+Ezy8lp9TBtAua8H+4mcN3/Q9GW2Ii6SnTS3n57kUs31PPF55aTyAseWmCMGzpLBG/v4mEOKbhQUs2Fkyfj3BtdYoNOidU1xxTdiJKXpLHQ6NjIY77l8K+JV3vOE2Fe0PNBoCE0fCE3ZgmhP0p13WL6Hmzz7GnAZoOdNjcYSuuWU5HNActrXwxrWH7S116YlXYgGAbvjUfdL3PPqDaU82hQFMPt07vu+w4ZymyfWdGTd8YaGWNZWxv2N7rfepQMP6T/RKXgzagIY6GHQLccX9U3MCxDofxBQ3MVF52m4MNHiaVv2oVJrLxBMK0BcLRQZN0+tjJIXpMOt56bf+Ll6PT32e84d1HiIEmDDgVbRV87s3P8cL6x3j0rSZuf3Ei/ocep9F08ptLvsbdVz7IjF/8jG/8/KvMPH7mkChJv2jSIp677Dn2Nu/lpsU3cbjVjl3OGwnXP4XrzNuYetIGHKF6Dn7mRkI1tQnbTyrJ45+f/yjN3hCfe2I9vqAYaYIw1NDBYMJnz+o1eDdsTGzUSSnrfkf3IgdN65jxEb84xbJ4vGWHMN1JinUKJSb+aBXBZnxGsF2blHKmiREf4hj3qvtsQCxioNneyfI1UL4uZctwYyPaHqgLm2bMg5ZOn4Iey8Burey0mTLCULPN+usljlQeV2+jVfQm/pjxIXJp5PRF6eZ3GX2+Jm+fUlfoWw1/b/NeDrQcSDhsdwlWN9L6xhuxBSmUe0c3qoH2Gm1ixIXovbSpol3uWGvIQ7XHyj/VoRBvbq9mZ3XHRT622rn1Om5uteV76nlnR6wwT1eGktbxnu++I506SKZpoFtismudWlrDvv8pumegpdMrMdCEAeXdQ+9y/YtXc+kyNz98uIIi72xG3PNtnrn3Ea5znsqxZy3kza+fzblzh95cYWPzx/J/F/wfZ04+k+tevY63DtrVvpSC0+/GcevzTDqujPzSNg7ecD2BffsTti/McfG3205BAbc8vga3v2+qJQmC0L8cdh+m2lNN65tvYXpiI8bhujpClUmKdEKIo+6eIttbTAOtNV5PomLVFghHR4ejrymGrmPFLBKWJmyXuEqn3i6lgRZbttlbyW5PZcI+2u+6e4pbNActKm9khUE6xTlMn6+9oZkoUNz+iO4z3NjYrv9Gs10YSilChu6egea3t+3IgxYOkH1wGxjhXoX1xT9/Sw++Bu6kipd734XDqxMWRebu+u+63bR9uLgbx++enDubdqbevpNrsCNZ3t9VRzDcM69Ht6Q2QrD9ZdAmZiCY5KFur9wPlP6j0Xb6VGIOVbKBdrjNuseB5UED8KVRhdrUsX64bC9cxJvd9W+487DC/kRX1WKu3hD9bHZgLL5x4A2CZtyE6GkaaJXNvi6jpcRAEwYEU5v8afOfePLJb/GHJ3I4aemHTPnypex84HEu2+DkYEuQ1796Jl885xhyXBkMAeolLoeLu0+6m4fPepgHVz7IT9b8JFbl8ajTUF9YxrhzCimZ1sTBG67DuzFxdD0v28lfbj6ZkQVZ3PG3deJJE4QhwNa6rdFpN1o9XYTLxRtofrdVWCGJLeXNhDopAd1jzDA17gDryxIHh97ZUUNFs63wd5CDhtaxcvAJi21lK4W6GlXAkg2Q5BBHHTue1jqxWEFS23B9vWX0pjQWO8ZIUWYfOjI62+NZsaJd5EMiSX2zFWzPipUE9+9P2d7lgLChwTStnJ50tNFoOGYHbSs2kL/6FVyN9anX95RAioGEpLy7iNfHYYbwBrthIHbQrqMQuHaKfVrHad+mLRCm2Rukxdf1YGjYDBNK8jx3FuLXjpDPyjHTpj3fXdy2KX4X/RniuLJyJVvrtsYtMTHRCTIld80ZZy50p9R+vFETKYoRuVek40Hr7TQRHe23wxW73rAGWQwzoaGpdbtzErketNbd9qABtPo6yQVGDDRhAAgYAe575cuM+NFjfOM/MPl4J+PvOpmvuK7nvv98yP2XzeOxWxZ2Uh1n6HHGpDP412X/YkfDDm5+7WYq2qz50CgYjbrx34y+5QbGzq/n8B234V6SmG+R43LyuxsWkJPl4ItPb+gfRU0QhD7F5XDR4g2ybFcHBSciD/uEEMfUmsL+eg9Nni5C/HqCNglqJ65w+3LRtR7b+OggB42O5vlJ8rylXJeWBy322uI32FrZkiiPjWfVarwbNuLfWdb+eJ3QPgctzniMeCs6UQZ1qHNlqr2SHZez084ItDwDTuUgZJrosIHKStNAiwz4deRZtOcCc/h8fRbcpxSp5xhLMiKUnV+ptGX4aG2yp9bdqYeq3ldPm5FeDmCHCn30nGvK3eWJ12IH56nmYFU03M5I0rwD+/bT8t9XE5Ytr1jO0vKlsQXu6vbXfNlr0NBB6fjIoIMZBodK+g0MrAetyd9EZdRDDWhtDWDE9Se5b874QRP7ek4lYqiqCtPnI8cugmPE7SayzN/VbykiA/2bgxa91QUCBMKBuAUhTBKvWVPrduckfp40Fb2BpT9w1NVXLAaa0K/4wj5+/JebufyHSzljypnMvO9iXJNa+fjuaxhdlMvb95zNJcdNGHLhjOkwvmA8j134GKdMOIXr/nsdSw7ZhpjDCWd/k5Lv/pVJizxUfu3LNP/z2YRts10O/nzTyTR7g9z73ObujdQJgjDgZDmyCHf2O414PpzxBY66cd+zczla/aEOB23213vYWd1JyKRpYLp9ZG3YmrA4bAbZ3rSRgBGg2d/M5rrNoDWL9y2mzmsZnOXucgi3HzlP9qA1+ZtYvG9xZK19gHDi52QvVtJ+/GHbsxS/TfJx40bx69sCrD+YukCFUoqCrIJorkiyDDr+XHaaT9fFPTjqoUv0oAGpPY+Ay6kIhEzruE5nYkn8YAdzLkW9OB3IY4dNOUIpDKqgt+P9doWRykBLpC7UYlUDNAK0+sMEDYNtla1UNqcOx9Ras6ZqDXt83ZswPMWeIjtkS90W3CF3ynXx+FavwtFm/VYMrfGsWoVn1Srrc2NDuyN4Q15LiY9wYDlZ3iS5gx5orcBobU3IR91S3kyl20dYm2CEUE4naDP6fbuDduEwbbKtwcoZdPShem60ttL62msJyxI9PRwX8wAAIABJREFUdDpaqKfJ32QvScQZX9yok4EE7/oN+HfsINflwOlQCQZW5H0kfDKdYh3dyUFbvG8xDb7Yd7evZR8baze2axebwtF60/rWm7y/6p/R6pJWmyQDLVqkMSZP1INGXIGVbnjQurr7i4Em9BttwTb++p0ruOLP25j+je8x7rZzCW56jJs9d/PQtafy4yuPY0RuVtc7GsK4HC6+dvLX+NGiH3H/ivv54aof4g3ZD8gZZ1P4w2Ucdc1Yan/yIPW/+UXCCE1+tou/3noKZdVuHnq1fRiUIAiDh6yk4h8NbQGMVAp3vMJhK/G+kBd/Z5UEg17YY014v2RnLTu2fwh7l7RrtrWihbJOEvdX1W3E525DxSkZVvK7xuFw4Al5aPU3W8qJLWdrsJWQEaKssYxQysIdiR601mBr8iq0YbB432KCEeOiIyVGW4FWTqcrzqsVa2t6E42LRn8DnpCHgw0eypu87SrkRuRyOpyJ86DFv8YbZZ2NfqeRL5Mob5wHrYOQvLwsJyHDxO8LolxZMa24tRLKFidsYpqaQDAI4WCn8kS8dSpVYY+yxXB4VRf9iIivol1QAOEgu5p2JXgN2h1bKcoDTZQ2WkpxJI8xWcne17yPTbWbooVbCh056cnU4YETr8HUYXFx13ySwWyamnB9A+H69oZZ54c1yQq2JC1VtC1dllAcqC0QZm31epY077QMbDsXS4fDLN63mGW16wlpA9A0+hqtvaQ7aK017Hu/0ykaWqpq+fBw+wEMbZp2DprJita9bGnaxYrKFTiMII6KxEnCncoJ/mZrku2ufgtKobXGoVSCdzLy3hOMhQZihCDgTqgSGcEKK+ze4LQnFMsB3t+yn6q2qnZtokM/0VuBRgWCcXmq7Y3HiBzx8sQMNB0rs98dA62L71gMNKFfaGup5+1Pn89Jq+o5+pl/4j9pPqHn7+RPpd/kL1+9jnPnjsu0iAPKWZPP4rnLnqOirYIrX76SFZUrrBVF48n71utM/cJHaH7yMaq/c2/Cw6M4P4u/3vYRXt1ayT9WHsiI7IIgdE2Wsg00pTBNzfI99bG8LoDIwEz8A9x+QC859C4bamMJ6fGEzBAt3voE40H7GsHbEA13M9o8KbelpTzBAGn0N9FMkHhlNWRoTG3iUInKTcg2BCIGHFqjtNlO+Y1UcUw5Gm731bQ9b1Hlvl0Omn0MI4hG43TaBTbMmKFo+v2437WMUn/Yj3I6ONR6iLIGq2CEw9NG23vvt9svWLlRZrLxFR9+GTlHXVSk7JSotmfvI173Svas2m2VgqJcF22+AMrlYkPNeuscpQgn3FzezCuv/5Yt+1eAM4t2Pg6twTRZtbsaTzBMUcveOEU0rq2zh1PUaJM9TXto8HdmxCirGqCN0YHRsKd5D5VtlXHXTG8jRCI5TWlW0AuHE85J2NQoZ/fU4VB9C5iasbXLobWKwJ49+A/EjAHDHRuoME1oCbZYRphp2LF7JkS8wBp8Rgi0GZ0bLMHDFQ5AIDb40NAWoC1gG+CmPY9fJx7OJl+4XdEPgNbFr6H91nauWjeta9YDkBOow9mSWCnToRR4Ggg37Yueu47MC+V0orWJy6Fo9MYM+oj3KeIdb2gLoKs/hNrtdOgpT/PS0N5GMII4jGCXlUvb3cO0RunE/mw81BTLWyXeQIu1iXjcEnPQ0r+WxYMmDDj++hrWXnsJ2drJgv++zWqjmKZ/3MzOSVfy1f+5mzFFvRwtG6KMLxjPI+c+whdP/CLfXPpNvrn0m9bojjOLnM/+lWnfvQrf0lep+PytmP7YaPqE4jweu+Uj/OyNMpaUdZakLghCpnApF1mNZZQ0beWVLVZ+R+Sh3vrWWzHFPVX+la+ZFk9q5XJX4y4+qFqVmB8SyQcJeUFr2t59h1BNTXvPwaFV0Hwo7kAGOBPvv4apo4pt2AwTto0pXzhmXEYqvdl1rxNl14kGWmL+j/UStud8UkDADBMygrT6Q7y0qYKWgF2OW5t2EQVbcdGaYJMb33ZrzqlI5bjAtHHsbNyRUGwl4ZBxoWURmZzK2bExYJpx4YlxRrBhJBZD6EDxCuzdi3fduth+o+3i1C9tWgq2J1a4wxp1V+S4nPj8frTLgQobtDanDvdrC4SpDlWzta2SFh1mcdUKy0C3J6Sm5kPY9gL+YCumIwtXsBVnSWFMpojx2dE8fKaJb9s2fJs3p1wd1tZxXJ1OE6EScv1MMxK2mrSvZO9eN4uEtCPJc5aQb5iiiqMOhdEaHLZH1jA1Kqt70TzenQeh2fZKmSH8O8sIlNdFB110IHYdGvGeICMEaFp9QYxQLPTXawZAm+RnWbn4Cd6VHa/ArtehpQJMg+V76lm+O6kIjG2gd0b8bzOyfx2yDENXvQen20ekomNHdoapNWYnefH+sB+UA22a+EIGNa0xXcZI+n42lTfR4LYHhSraT0lh3W7SM3hCu9+E+l04Wqug1o440ljnrKUicb+pXpsOQOP+aB+1aSaErEfexg9EGdHBKbPzIiEHV0Bb93U3MdCEPsV/+BCbr7yU6nHZnPWPV3l0fQP7nr+faaW5fOS2X+JI4cY+klBKcdnRl/HyFS8zInsEV7x0Bb/d8FvcoTZcVzzI1J98HWPvag7deB1GSyx0Yv6kYn593Yl85ZmN7KgawLLcgiCkhUMrHIFW8nzt56fSgSDtcpTiCNaUceBwRbtiBWDP35UUnqci+wj5oWEvlfveZmtV+1wLe6PYW9PEdDkTloVNM5b/pXU0jNEIx8J3LMXOUlrQOsHLH1XCI0pyqmqOkbmClGJJSxnLqlYR2vgvtOHng4oP8IXboh60PXVu6lp9KCDsD2K2tEb7qlr3YxYVWJ8jz5IkBdyMN9CiI/2qvaKeYNW196B5V6+m9c032+0rmeCBA4SqazC9vlgBGK3jctA0lG+AA8th33v2+TDt82HlG6+rXklQWcu8K1KHIMbEVtSEbaW2bHF0n3jq8BgBdnn34LaPq+KLn8SFJobMULRkOmBN9bDtBYL7DxA8XI7HCOAxfMSMTJWQn9MhStnVACM913gDNayqXBIrlJXQqa532Y44r2psP9a5a3njbZz1zUnXoCYQNhIVffvaztu9nYDhY1nl2wkGWnKVwsYOCvZEf64Jv+mYjrPk0BL2teyzPEfRkNowmJoD9W6qmiMhuxq/aVW9zHJ0YShue5Ecf31cHmqc53bbCx0Uc7EI7NpFsNz+HhKuf5O4qxVTm+2+mvhpN4wOKp96Q152N+8Gh4reF+LJaj3I+LqVsQIcOm6/duTBhxUxvceSpcPuJBDSBmgTM27gJnfJWpyHd1uDF3FUuCso95bF7lmmYf0Gai3P26b6LfjNWDj1y5sro3mUieNPcQMDHdzfTW1SUbc9caAsrn+dIQaa0Gf4d+xgx9VXsHZ+Duf+8SUeXHyQnate447sNxnxmb/bYRkCQGluKd877Xs8ecmT7GjcwYXPX8iv1/+apoVXctTP/x9Znu0cvO5qQlWxkIlz547ja+fP4o6/raW2Nb2qV4IgDAyp0gniPQBhI2xXcuvAW6DDKec+tIyLJAMtsjLkgUArTWEv9d4uqkcCaAPtzAZiRpZhakxMfEEDXyiEYXs34pXcmEfCCnFsXbmNYK1dTMCWLTq3EYmKH8DqilV2WxNTm/jtPDXTsJQeAzsXxghRH8qKbqdNjY4UJgkFMLxN6GTDLCqkvU0gpqBGFEGHcrRrHzW4Igq/w5WgXBmtrSTp+amxvZne9RtiXr34udW0tsKvgnH5caYZ3V+WE8ygQVBZ5zHcwSTi8Ydv03aYnBGKlb83whha4zDDGBEPa+SiNM1YcZFgG2X1O3hjz0pe2mQr6wnGl+b9ll1sbEgMEwvY2yd4v5IveuWIGkIaB9rQZFUtRjfuYHPt5rhCLZEjRdp2jTvoxh/2k79qG9m7kpXdiHcqjLPJTXmjNyGkb2t5MwcbYudfh2N98BlthA0j+t0ZO5cTrov9lurbAqxYs5P8FYmFdSDukkoKW3YH3YR0CF/YR42nxh54iVxvIYJhA7SOjjHEqiiaKQc6Wv0h1h1stHLA7LVm8kBD5NymymW12xjNLWifZXiEwkHaQm2x42jrXnCo0Wv9lttbaFG5zGhuYWIT085dVU5rEKg0PzGc1uWrJzfUHLNl0NFLSDkUbn+YvXVx31OKyokdEYzch1Tcb9DbiMMbSpy2A9jfspeGQGwgLRJBEAnlbp8yqmnyBu0+Jg5ARN5FfwnaJFRdjXeDFbJe661ls+cwqQIau+qbGGhCn+Dbto3dN32alxdlc+GDz3DPs3sor6rk19l/wHnxT2HU0ZkWcVAyc+RM/njeH3n0wkcpbyvnkhcu4Yeh/Rj33U1BwX4OXH0lvk2bou1vO2M6588bxx1/X2fNMyMIQkaJeml0RKeJKw5hL/GE2niz8gNLCUyRg2bairUnxbyHDuWIC11KCqMJ+UEpshwO0CQoHSkxDbTDKkYRrqmh9c03rYmSteZwk5e1h6ppsZVAIxwz1GKKixnVykx7vrfO8n6i6kdMIyNLOcGuMBgyLaUnbIbQ2qShxU3AkWMn3GtrM9PA1CY1ra3sr/egSksw83Nj5yQyAJ/CQIuscygHS3fX2nk7OiF8M7qfuNC9cFNTewMwuW8R5TGqZJux8EFtJhQ5CTe5aVm+JbatYcT0ahXAVd+MLi22zontSdtW2UKdO87YjBrnmpBScZ2z5TZDaDQOw4yFwCpFiy9khdwaIVrCPlraqjHqy3AHQgkhgR2GktnLg+5q8DUmhg966sHbiGmauKrqMTQEDAOtnKCsuefC2qChuQVv0rXtjA9R7WjQIs4AXFa+zDIOvW34GpIK4cQbNRq2VDSyuyai6FvL4kv9xxto1hUcNxhRu9uqxhj2w9bnCRkmjmAAFQyhvInGj47zUPpDVtVKgL3NezjQdjgqkxFvaJgGZTVuFCaumFZvVXjsoMBEa9ZYAPxet2UsVP0nziMauyeETIMDzftj52T7y/acXnYoXjCYYBTsadoda6s13mCI+rYABmGSfWjxxrRhe+/M5sTCI2ZkPjXlQGuT0YU5CTa8aWqrsmNCcGHMgxbvyY8eN9DJ5PCVG63iSREDFwhH6t2HA+igO7rvBOyPkcJMyXm08cVDwKpyG+1D/PhT9LqL8+qaBr4tWwhVVtmHilXaafEmVuAVD5rQ7/i3b2fvbTfz9MecnP+VR7n7HwcozHHxxITncR51Kpz46UyLOOiZN2oePz/75zx32XOEzTBX73+SH33mONqOrefQ7bfT/OJ/om3v+8Q8Rhdm89VnN0n5fUHIMFonGgqpsLwOES9NihBArcltbCa42vI05XkqqHfbyqCC5Pw1BVZEQsgKQ3NhjVgHjBTFQiKj5y0t5JRVoB1OS8mvrEQHQ3aBDmv/tb5KgpHiIHGGlxXCEzm+pskTIGR7KCKhTGZk9D/VeTBiikyOIys6yh/WAfv8WMVBvD4fm8MHURHDwTTRhsHOxp0sq/wApTT+kIFRXBirVphU7TG+VH1E4VJKoTHxhwzagm3WlCdaY+gwTb4GtGli4GB7ZQumx4PngxVdGmgbDzfz+ofVmGg21W60DJyIYaRNQNHis89lOMnwjpNxU90KCIUxC/Ostkphen0E126npiXOkIwLcTTiPYJRr104aqCYthxaKcuTZFrl3T9o3cMHrXsg6LEUbTuvbEXNOmu5dYA4QRVoWO7bSYvhA3cNm2pjA4aBw7X4Nqwh3NxMTtkhDjX6KKttRWkDjQPTNDDs68jtDxIwwgTsc6FQ7RTiZBzVDQkhq81tPnx1B6iuST2lgrYV/sQctPYH0GEjzuCwPSbx4aBKYQQ9rHMftHI07XPsbPXE7RSi36rWbKtspbwpFhZqxIXxmWZSDpodMuyMiys0sD1ocR6tCJF26/ZWE9BhGs02tLaNrTjDoCLYzMbaDy3DXmvLMxoOxAy0hJxK+6Vud7SAUTg+XLmdgzqWT2hqE8JhHOvXWJVTtz4PAXe0jXJag0oOh7J+y6YJtTsxTasYkRHNW42bPyxu0MHqlsZhBMne/wZmR1NDNB8GXxMcXh2dx81w24a5EYgNmCR50NDWCX2/4r24/upouGXM8ILqtmq2tXwQ2zTuWjIxIdiGI+SOXj6h2jp0MG6uQtPA0Jrd7lqWlNUkpKh05RwUA03oFf6dO9l76y08tcjkjM/9hnueqmHRMaN5ZGEtzn1L4JJfdD0bnxBl6oipfP/07/Pala8x9+gL+fpZpfz5igCHf/IQlT/6ITocxuV08LtP/3/23jxukqys8/2eE5GZ71pLVzXdTcuqgKKIXh1lHGbmgyMKKipuiFeHFhSZ63ZhnDuKjjoqel2u4+5lgOswjgIKqKBOK1sDdjdNN9XVtW9vVb37/uaesZ1znvvHORGRWb3I3Kv0CPl8Pt1VlRkZGXHiROTzO7/f83v+F1YORvzSnRce70OexjQ+s+NhDZ0fbUPxCcN4M1oAEZzAzP4Brn2AiHBT+yQrq8thm7GmtuEX/f7eSUY69hJHoKljEGhF85OHlhd03+edDYuNDeLdjmdXRHCBafIGBuMgs2TIDmD1PkS8BFIFBk2csDEcstsbTdSv7QxS3vPQRrUaPTE2YwmPRgW2B2yQzeXBgU45g1Mx5cq6iJc4JiYJ2ZzirotbAbRS7Vvn/YpBS9KcYXC48+6UuqpBcyKVhBNgc7DFQ7sn6d93jt6lVZbHmBmZMAhwpGayl1cvKcitqy3DkWBNrz1rJnB5J8jnbgznOQQF6AAQTHkNdKijEyHd3eNDF3bq/YfTdkpDAFfoGJIO2KKWF4bf3PKn9+j+g9Vc0SjEGQ8cAsT4yNVV9vNRNagqM6h0spZvI+uADs2HnWCdI13ZIl9dw6Z+36kZT2C91boNbGh7VPDfT29y55m6wXO97SPfNM1zV0nP1PVDV7e7DHODNo9sMlIxHxP7k4ohql9yEyxJ9ZbNPUWiFIlN2Sl6Xp4YNtCBNSawINaWCwl+HKNgLw/gyr7UlGYTJZDy80W7cb/LMYljYZi/64S/j7I+mIwooBjtcpwIjUihxIXauHDuRU6kFNd2utyztDfxnCmNZFyRjw11+Et3tTKwMOE8bOnFefqdnqUfi0FmOHH9APrtMAx+kq0cXObeqx/yG2kvdVWEBay87+vAXIHWqlpY1nmGjBmijbdl8MPo+MjgImfu/pPa/Cdp18dUTvD+lr/mxmLve8gviFhTLYglxj9LjDOPWEvpmJR0ln91IqTFJDh0N06trbMc2/1YPd7jvSL3LsGlO9nupvz50imGpjtRZzyVOE7jHyyyy5e5escr+KN/Zvm8V/w8P/MnI77reU/hJ//VE9F/+Vr42l+B+WOP92H+o4xjs8d4zXNfw19/x0d4wRd8Mb/xnTkPvf/tfOzbXsT28gUWWjFvueOf8O4H13nbxx9efDqNaUzjUxN1sunYNl22bKd6r5S3VEXkOnrYsmlqnHeRw+GkZoBckK4pW0DojVQmAYXL6UrhV+OV8qCHGxPTkrkJ5h5Zx68wBxfHMuFxAi7IpZxItaLtsvGV3jKb9avU63afy709lnYGyI53WewnHlTItbvGzDZCwu8cOIfZ2Z84DwLLkBZ+1TuCSp6nCGNhbRjH8c+omuFLuhxZ+xA4x5n1Lndf2uX957fr8agURnJDciUYMSjnEAdmc4/ZUycD4SITEvJr3WtcOJhcDGvG/jivHYzCWBvsKBuTsY5d+xC5daz112ppGTXWLCglpVSsz6Dfp5cWpEUtiUTARQGUgf++K++vPqtkXGLre5nNJlswaoPSNHRUMRgTfZ8c1dycO7VBvNer9gIwcjllynjP2XVOL+1SsqqjQafaNIvnPMhWCnFCs5f5sXf1/FR7XaLVrQnQ+WjhxnrbKfHui+rGRZE6pfbjI27Mo6UER4/2LV7C63Cw8WB1/5WNogsb7qFxJq5kpcfufYBYqxq8ld83GEG/hxNhkBnuubwNTtBSTIAlI74uTazxrHCawaW/huV7iDQIGm1zjFgW9kcgtgKPxX6X3vs+gEZjbmwKDxWD5gpTvz52Klf3hlgrFKaeF9WzJACUch73M0sv63N5/29DGwx/vrvpPrMPXPQ7VgpxriaUw72gsz5aKUQ82zuztY1dr+vsB6HBuMtzenfeWR3m4OLF2l30ygdg7f7wiZo9r2zwnQ2tM0IrhdYiDy7vs3Iw4sGdB3n/8vvHL331+fEYXziQG1RKHz6/RrF+qny3+rN0cbw6WGF3NOnYmJr6eaDV+P352DEFaNP4/xTF5ibXXvVK3vnljmMv+xF+7c8b/OALPocfeMHnwPv+A9z+JfD5L328D/MffTTjFi/5prfypid8AU/4rieyfrPm2jd/M7/1pldj9S5vecWX8gt/ef7hlrvTmMY0PiUhY7Kg3aLPmtl92A++34CQvE8yKuudEfvDsp8OlWGDoBER1NZF9L4HNqZ9ULMFFdirpVmNjq+dqlzjxmRbCoFGC9EzXlwWEjnnhJvu+xizB73yhLwt/Q0MEsD+IPUGB2GfuZWqFqZwninBFuCM7+dVL0UT7XXJzlwCQO/uevAojkbUYPbuD7G4dYCXHtX0mHPezEQrjdhQ1yV2wiWudLRUznr53NjY+/5ECjZ2OHL/gxNueuKcByhBmigo9LCPc8JWL52QIqX24cYLrdjLpxpBf+ZGIwYPnKsYtDHbgDAEwqm1DpcOrrAz3EHnQ+L0gDjzUjVTAs7w+V3X4/yOl1YdDPNJgDae2o3Vzol/s66NUiE5HmO1GioKNVFgxV+7Y0vrLI7/hoztPjp/rT6TsN/01Enk7FLY1tLpl6YajrR5hM3bvwZQ2GTE4au74CTI5/yOm5dXaF5ZmzzuG2KQFf4Egrxw9hMXOHZ9vTytCSlrmaBvDTq0hxkTNUHhfCaEPBLgvRgcNhBsk2xvKZ3Nrakkt7Wc0LE3yCpJoBtjjfOsvB/8n/HHTzP/0P0gMMoNynmbfWWziXusrEGTAO6lrHcLtvyiY7TLMXnO/FoHyVPuvbrP0u4Al3iLfh3uDyeT90ElcRx3wKxY7VC7ZS25rc0y6vk26U7oRLA2BcHXU40t3ihrK3mkINXCEYEN1zbxNWhOKqCtxq7TvZt/y6DoYJIEsRblHJ1hTj9Naa9scPflMM9uNMFBPCAWD7WlKAGahaiBdobcOIb5AA6uEm/vc2R1i/o5c0Mvx3BM+8k+5/f9AlRkRmibMZtsM7j7fdheb2LxpTyP6/212rF0oiVFzWq38y1yl04ljtP4+w/b6XDtVa/kQ88qSL7x2/n9O2/l9S/+PO74Z0+DpQ/5nh1fN5U2/r2FjlDf+ha+xFm+9eufxS2v/3H+5e99jD/80Zfw4e238oZveRY/8EcnuLz9GMW005jGNP5BIglyujLBU8Bif+kRtgwmEhO/ynVqqsQDCgmAp2J8zi8ze24bK47hPfcSt/erfYkxDO57sGJdDp0/T7y7xXtPbZDmYyYC1obncSnBkqpJbm4NI9vn2LV1Zve7IKC1xoX0YKmz5GvoRNjpJeyH2rjICfnsE5CW77VVGOcbWQOI433L72NimV4rzvdWOLm1S/P8NubaBuCYiWZw4milnmmRqhbFJ3oE9q1KnMWzYmWNTyVZDFLN8YRPELTS6E6PUuJYObaJC66CbqJGRaR2qQP/eyfdhz9bW4FBazbG686g2O0gecZGqB8rgUQpbSoNV1pzA5rdqxw6swQCeQBLMtNEnJBKweKedw48GOY1hFE1c+P/HXmA1x1U+5aorL1RY3jXo5RYRVhfWsaV/oOICLPtPq1eqI26MWvMPDNqnVTj5Obm6vedq66BErBjTKAz5XwQjJN6tgdF2Xitz3hYJ1zY7DHMDR9f6eCcoPsj5vZLC3bHXWdq235Je3yoe4Gzeyts99IbAJpMzB/C8ShdcLR9mvXRZQ9ISvMGJ3STAmNLttpUU7FmXIReWlT7HORe4plZ4eTV3TAWIfm3NWPlBLQYeoAOxjjl/kqJI9Z5GepoxFbe4+xwjXs75xAVocVg8lIi6cd5uxvkv+JQ+JovyZLJ86+A18MjLZvdj8swcWMfD+cxBtBEhFgprBPPnAPqyhrSqAHJpd4JdoqeP56xGrxIeVAo5b0dXs8Dq6YC84oIOIs2jl6W0xnl7PWDzDhqsp/sc+feaZLCP5skSCqtc37xxxk/nuPOueKbejeurrK4tR++wtb3TXXKY/8Oxze/8Ve+KTlgtg/Il1ew4p9CfjmmlokrNflM8C/XDNrK8DzbyfVHXJgYjylAm8b/ULg0Zfl/+zecPNLlxDc+n7/48HP4hZc+h2//J0/yjTjf+8PwNb8Ai7c+3of66RWNWfiOt8HVu3jabV2e9a4/56UbT+QZ//FtvPXEq3jJlw945VvvZ2/w6D1QpjGNafz9h3Wu+oEHn6RGdrJeScAnIzdYuY8XxpdOemLy6lNOBNUdhvfL5Das9iqNHY6ww8QnmBf8D74KwGuY5iHhDgmHggmHyQAY2nsrJNbXcB1dWgURIh3XLI2zoRcbgKsSTiWgopiSEbSBIfFJR7DjT3KS3JIFNkQQtPFJ2Whvj+bOHlppZnrXaeZl964yuZFabibheJXyq+JK45zDCnSSDBQUJmEjuTKZFA12xlodyMPqP4yzpGmw4VYaJYK13txgL1tndzDgw3/4XuTcpYdd96j0SA91WaWccnRxhfzyGVYOEnZst0rCKoBW5sFxkLAqjSCsDdbJnvlkTyJW9SzCxc0/Zuuev8QZg9VNhoeeMem4qGNsd8jw9FVMZ+DFoBOmCGMFe61FzJEn0y5qwOkPS4X11PEkvQZPpnAsH4yqRVcZ62ea2pTlXpDZiyOTMH+V8nKzcAROhOMLzXqsxuLGfzsRlHWMcoMRPLgTUKU0U2uKh06SLS1hV86x9fY3o85veJZYYH10mWFacHK17S3f3Vgqlw6cAAAgAElEQVQj4XARVKTISlAMuAB4evmIi9t7bHb9QoQ3zQlHmPVQNq9ZW2DoUj6yc8JfWAVFmiNxPLY2UZvoOCekZsSq2w7HWo91xaA5i1IKs7PDicEyK+keQ5PgVEQcC+c73h1ShX5n860AQCTce86xeOJeJM+qc3W2XEQCe3BAvFa3ERiG+k8n4Z42gVEsT6C3DmsPVOfjwjmVxiUX1ry8Va9uoQJ4dNYysn12TJ/zg5OsDTcqebE7fQW1sgy7F/35BmAzylLi7TZlTaB/3FiUdZW62YT2HEQNunmXqwcDzm6Elh3lGIQ6PkrAq+OxhSP/8RI8C6GthfN94EY2p21GFQsIUm17tlglKpn0AIiv7AxY3egHd88a3Po/iuo+VqUwgOoWItbNv7PH2xSgTeOTDrGWtR/9Ua6MVnj3S5/Jvfd/Jb/2si/mJc99ot/ggz8Hx58Jz33543ugn66xcDO8/B3wkV+mlV/gme/8U77wC1/Iz7yxz8YnfpHjn/VBvu+/3jfR/2Ua05jGP3QIp9e6pIWhsI/xi2ttMOh4BPkjMHBDzu+uMBh5cKdEMGnmk8fZuHJ5Q/x7onUFjpwTNjo+eVAhGbPV6rRUCQjA+uDa5GF97F4ausl8fLg8HXQUs1nWUYgdMxFxlU20doBuIGkPds5RuDqZwTl2BhkXN3ts9hJW94c0lzexLgA04HK6TWvpIgqFE0fD+lX/204toQpLlBbY1LvRKRvOQ8HNu/cgrsDhOBhmHNx5H5JkWDPCiqG5fBWVpZD1keW7UVkfUDix3DU8X1lrX97ucWWnx/X9AWgdcmWf4ClgaLrsDXscZJvk+STghjq3nuxH5a+v2r+EuIyrZrtyxTQ3uAuIBlBIHI2xY8qbQ4RtZ3cHzA3XKK6fgDz3rKpqVuzhg4MVzg5WsP0A4jMvK5WoKv6p/jyz7hPpgRpL5vHAUTufpPe7+1W/p7EjhSBrbWrvNHnk4CGIY3KxrI/V2ygnOMoaqHAuSAVoZhoRzUh784vRfg0CDgaTzc9F0M6N9dvyaXy5H4k0ca9DdvkKNhuyW/SJumkFKZ90/1n611d8bWekw3gqbK9HcvqMBwEIq9ZLh8XVMrcH+8tsZsvVdU2KERWz2FmB9jKIkDjffD6jIMAWUApVGGYWDgU05Bh/JDgRVJGhjTDe8wwZAxjWM2Gk/pqUYEl0g0RlZLmfv/MnfZ+tmbhu7eBEUE7AGdxWWTNZy5lFBNvtErd71b2aBEl1Oda3XLjGhMRx/wq0r48BTX8dFHDLoRkWWrr6HgCJo/C8EBpKk7oRO8PdQKIryAr01hqkHbrZNkvbnhUt8oy5CysIFmcMAmjnwvPC7/ti52NsBZDW0A2sCw2qCSyWeCA8waCNtza5QcYK4KypQOKJwTKnh2t+oas0knnYIz14hVpLPy1Y3Owze2kNBIph4p0qUbB+AtIuSkcV0OoVe5xvPwhArBoTEslHiilAm8YnFSLC1s/9PGuXPsHvfPNxzp79Zv7v7/oyXvjsW/wGy/fCybfB1//6VNr4Dxm3PBu+5S3w7u9Hdy7zxJ//OZ78Ez/Nv/tT4Wvu/ihbrV/jR/7ko3/njT+NaUzj7ynEmz9c3RmQFvYR2pH6UGV/qjJJSLsw3K1+/xWQu4S8GGPQSpmiUh5wOV8zNcoK9kd+5ZcJbkBQIdEtrGe91gZrbG0+GJbHNdjaeRH8CvKMnqelZ0GgsbGL1hFFyeQ5G6SAwk6xy8dGXr6pnENFsU9Xsn4wnhCS3IAzpEldt6UcRIPUS9XGLOdFLHV7Ku9WqY0QF5bD565j93pgUvS1j6JMhqhwrq6U4wV2MC8wtm4PoIcDMKFuq0gZJAYTgMNSzwPUq7sDz+6IkFmfYi8MrsHOhYn+W33Tpp922WgPSYvaOKQCODVVQuYKz4RoVTXJLsOOgQ0ELqSrpJKjGrqSP1GaTDiHKP8dsbH0Tcru8LpPGlVtT7+Zd1kebuGCoYk3RwCjHVacP4/A0u71Szc6r2986sLn0tQzuKRDKzsAgd3rZ7i6N5g4bsHPXYAjjcNeMmoTZKbJ5dEWfTOs2Nhbz6wGltPbJlRufeEiRzrUdo32YXRQfUd6ZpVkva6BEwFlvT15agcMiyHtYR7krWqCwdtOD2ib2mlPie9711gPLqhxhLIOK4Zrlz9GvnQJMyybMYdrgq91zJ1hmNaGOQDn2ic9Ix5AmtKRl0G6IV942wIFwYyCUCNZGFSzxSFmmP/oybqBe5CJNi6vMLM/RAHdtOtzq17ipb3iQmN2qd1QvRUioiJyZ1ClbDQ4cFYulOJ7kGnnAYdsX8S0+/6zpfkHcHK5zSjNA57UVYNnq3wD+6gwwMP74pX/KqRAnKWR99Aa+sOM7d5YjWYUVZ9tat/+Q1m/Px0cG0UENciIs4KBHWKcsNb1LqJOXF1f6CxRYasnnHLWA7KwaCRQsaBOHDEah8UawwfPbYT7RI09k8N+AuhWWY61Zqxurt5Mufq+vjEy49jsJP6ahmeg299n9aNnuLLdr54fWZYi2hvmKBH2sjX208l5/lgxBWjT+KRi/41vZPODf8UbvnWOq9e/m9+/4/k8/xnH/ZtFAu/5Qfiqn4YjT3p8D/QzIZ7xQvjKn4A/+g7ob3H467+Oz37Xu3jh5jF+8c/2ubT7U/zEX9z1eB/lNKbxGRGNyCedVQPSR/zR9SyW6LF6iOV7YFhLjVTwctBVYi++D1iob3HOwc658Hlhu18CtDrpR0AVKU4cNhTsb7eXOXfpv1ffWyUe4d+FK9AqYlz+GLm8dmKUWuJYuAznQrG7gNJxlWQkRYoSx9LegGjpIW46/VGfUItnC7TSwRhgnNgRrPf8QEFgylRoUxRgSDGCoiDurtbHrcKKeSkRdcG4ADww0prs0kWyS6toBUt7A3Swye+GFXgJPaeUc5warbCX+5V819tma+gbfivKHmNC3L3O8KBmi8rztk7Y7KQ4HOeHa6ykeyhdA3HnLMPMJ4HqoOd7rjlHz43ouBESeUBT2AC+glFEvjCDizQNJzil2Rle93NiUqxXXUfjfCKuBA5ch3W7jyih7KDuma0asM0Mt1BmhBu1x/ZShwrf4+uzAiSQcKGcg2YjyPLgwmaXpFJueADZdgM+uv0RBOH4hS1mD3pEyl/rcq7JcJ/UWK73e6z3Rt4Qo0h8Mm8d1sHG8BL3bX6sqvkSYHasIfu4QUeYGv4o0rJeSZOYlMsHJ7i6dCdsnuRvz66zXTYBD/fN2d0znBtt0hnkARMFRkiE47v3wWgvqIR1YKQVczanwPrGza68vxzSiKokXQSOtM/QGHoGWaU5DoXGcXr/BKlxtIc5qQPEIttn0QdXa9baheunItpZRqOo5yTA9cESJwerEBZIEKFX7OHyguHZa7jR0LNEUYSxgrFB1gvEOqoUeXnD15K6MHfLx1nqCoZ5wYllzzZumwOa2T6z/RUUitGpU6y2R9UNIXGEsQYlLgA0iD96gqI3JLpwPawPOObObdPqDxGE3FhSa/w9II6PXLmTUTGqAJqfhY5GMQxtOhzF2Qs0O6NKiu0QGmgymzNMc7SYIIuu78WSNYx66zRMj/nTSxibI8UIRBFv92mudIKb7Q3OneW8EuFgmLO01fP3RtRABKLhAaPconCVE+791/ZZzxeoRdA1Mym4yhDq0WIK0Kbxd0bnXe9m8/ffzE9/i2Zj/1X80StfyJc+9aZ6g7v+T1i8Db7kex6/g/xMiy97NTzrRfC2l0OR0HzKU3ja29/O5z7vxfz6H/TZuPff8TN/fefjfZTTmManfcR2FJLCsl5KKte0iXBC99omn3ggGIhETc8GhLcVQuqGJNd8w2Alwt2nLiHGy/zKFfUSYLmQKNZ1bA7lcm7evIfTBx/iYLAHTtgf5mz0hmFbPIMGVeJhsy439S5XCTmA7q+hhrUlfrmqXZUphfPZTx3bK3s0VcyoGFRvNlc7zG0FIIRnQ7RSYWX9xi5VvkBDo9HGMeYST1ZYLm33MCbIwcJ4SWDOorzHTLKJ6w6YWfN1UJkdMrJ9is0NzG4n7F0R56UVvFTjHU4Ep1TFJIxMxn62G45tchU9z9us9T1QjFLftiC3jsTY4Cqp/Hq9VlWCnduC7X6KcYJe3kBMxtJ2H4uQ33KIqLtENNzi1OoeaG9PjhNE+WQ5Chg3ktKNUU+YmiiEUWZYORjirGdYXRQF3KE4sEOGLvGSrzC2R+I5butcZ2a0ig09pUqZV9U4OIyQQ8A48ubRIN/SvhF1s1HND0Rqea8IKhkydGkFIkCI8gKthYZLq5GX6x8hNw6HYzPvItfv5v33/iq5MWhb2sgHo46xazbOoN1YyKPQ1f4jMwIlrJld2nm7StBVkdctBsShVWggr5VfePDd6ULinWCc9UBQmEj4swuXaMa+Jx7YAF4txB4MjXITjGsKFs8tceziDlorvL+hVPMYhP4HLrC/uYekfYy1GGOI9wbM33/Nz0TdIMdghxFeNOfQJmW/e4W+STyoshblhEHeYXXfz/ePL+0xGGZIFHMx3JNRMOGIVMxa27OPqRFiM2Q+WUVwGGsR5/hg5wLnBtvU8kCYHW1W/jMq80C4bEthlGK7N0IpiJVn0DRgc1PeHdVzQIUFEmsFK5bcOJob67QurdDLe4gp0HlZ42pZ6C9XTpVmZZWFnV51j3rjEs8ynh9s1HVn4fnoL315DgrtHMoY7rn0XkznOgg0dsbMgERQaQe2zoWP1HNOh+vcCa1F2m5Au71H4Rw6tGUo52uSQ948DgiLrUZl8HMsTpm58hc8VkwB2jQeM/p33cX6G36On30pbGTfxzte+RKe81mH6w3WPwH3vwW+4TerYulpfApCKXjRL8HMYfjT14Bz6GaTW3/i9Tz9l/8vXvc3joU/+D/4lfe95/E+0mlM49M+FIoyZ1zY6NE68dDDNxJhcPEaenULcY5RljOOeMrEeONM2ePHM08StDeVJXfoPeRt+C2g2BuWq/6WYjTgCReXcXffyyCwSvOXd0lOnQY02oaajsBISTZA41nAMtmJoohKeyg+eXJFKISvtE4FMx9/AM5vMuc0mUv8qnP4THXaeImcsXhnvHwea0vTB89+KUCJClK6MkkW0sJSWEua1Q15QXBhFV4FVsykeWUZv5dtcG1wCt3Q4btLBkmC9M4ncxXTEizxY92ojlfEG73Y0pAl0AndbJ+9xEuU5tc+zGK6jgk7KgFZS8fBRtwnrGVvqVLiqFfup1EMfP+5YGcYScrR9hkY7nn5qadSEa2IXbAoNz6hraoJ3di8EYEowhqDcr4+MQ5tGjZNlyWzhZOiShvno1YAzJY0e3gLAb9V+L/46xePLO7cdR643vbJulIkhQ3976qRY2HrgIUTH/MMkptoCEAj2eUJGx8IwK8E2oFpVAqb98nFMCxGXpYYnAWtOGZizWxT+0MKN5vkCXTGW8wEi5mwz7nhKjLcwajZiTHTRT1flDiPrQIL1uomla3K1kHBwr0ncf1BebCgIlSYs8YVjFQyYWuvrMFpTXtk2Opl/lZQiqiXEpeOrxVoCO6GYZwkTcAJeyPD1b0DonaCdWGhQMdYcejAMisx3Lb2N8RBsosI5r5LKHHMD1fZaXvmvXvQpTdMITh7Hj04SZQPASHW0SNIssN8Xdth/aMPAJC5kr0sWULvbFkay6SScyXx4G87KTi/eTBuTYNOu4RhQAG9tS36qe/JJghL2S4DSTHWoooMBFKT4mxBZBxRqFNFhK5JuGfvDBe3ukAtJbYIyinW2kPSovCLCAiIGnNYlBv+hKLIGZg8PHPCUltg0FTerxq8x0npFuulwyrIvo+oBRSK9sAvaGljK2ClRkOijR3mtnuAsNCKcSJEaQ4TffAeOaYZ9TQeNZJTp1h53Wv51a9XrDS/j3e98mU885bFegOTw5//ELzg9XDT0x+/A/1MjSiGb/svXvZ01y9WLy9+5Vfyee/9S57ffyKf9/M/zpvf8/bH7xinMY3/SUIp9SKl1EWl1BWl1I89wvt3KKV2lVInw3/f+8nvOzBnQJRbWtk+i70rIaHxScje6j6jAHruvrzLqdWDh+0DmAA3quwdJVR9v7DG1/QUtmLqrC1d5YQiT2n1hxTpiPu6nq3ThYXg1qbsmHQQcGJRHqKF/RceoLk6qXH5EHYv+mR2rLRKVJAwiUINh8yduI/Z5V10YgDNyPi6EuUcqwcJw9wwuz+glxaM8pBAGQmJm6pqncpk0Iiwmq+xmhzU5mhSJvcl4BKK3OACIFoYBEfI9nLF3iG+EbFCsd4e8YmVNmsb76Y5Wqvrmsp6JAfdpGA22Yb0gGbewaWBDQxjcn6zS2eUc6x9injom2Lb0nEyOExKYGiKUFNYOFvtv7yeBj9vqiteeLMMcUHuqBWx8/U7jSAttWh0P2H+gVWY9UoWJQKRxmS+BtJpz6Cl4lmzSAKYD33mosBYApxc2WIyShhHNXtxwvFLG7jNAw+8xLOxg9sPk842AhvkP9fqp3i7dF0BWyVCwwyY2bofROglhr3QrqF0LXVAkheIeGdIbR1OB9t48S0n4tJYJdwsZucS2drSxLErFVV4UYuXFbtgboIIa+0Rw9EOLqyIaFdw08op3GwLAZqFsNDS2MAYz2a74T4rr5JCnAGl2Ex2sTYP19RfX20yJNbVbezEQSPGKhXG1M9ErTwgdA4aaYEo5eWRgckzxrKzM2CnnzFICi5l14h6I+K9PgphvgFkOVLYANI9yNNlU+owrjMXznqDoqi0wBeifOTHZ6ztR9kg3ra8M6sdZSwlu+WwIcGF0kYzKFfUbKRAKgVOIHnu52BnWliXocTXQcY2Q6cdtvIOV4qt8BlX9VxDhF5YaMmN4AIq6eZdrEtoiEajMGKY3x2wlXfZGh1QBNOlkoUU6yjtOKTwi1t+oUMHsDWAnfMMMkNh/BLBUxefzs6eZq0/oJsUE/WCc8MtsDnKOWYPuhy90oEsZ7v7EG03QImllWzTJKKpYjaSTTIRImMqN+3G2Yt+7ojwtGPzNGJN1BnyxNOXw7312DEFaNN4xMiuXePqq7+XN70Azh19Fe+847t56vH5yY3+9tegOQ9f/v2Pz0FOA2aPwHe+Ax54C5z64+rlxm238eXv+gvUl/xTnvPTP8u7f//3HseDnMY0Ht9QSkXA7wAvBp4NvFwp9exH2PQdIvJF4b83fzL7zp1hYWef1l6QBJZMWvcCxlk+sdxmb5ChVvfpZz4BOBgmKGdqCRlUwqwySVDiUM63L1bNecRZBmlBdLDLzRfWmTl9Eb8K7/cQ1uQBX0Nms85kE2MbGv9WxVOm+j5drh07B50VdByhxNEe5SyvrWCuf8SzTmLHlU5VUqetIjlYpxh1iNuD6v3zxTWfNFYW8+WRllEbAsxmbbTJAZ9QlcmJE4MpPKopjTOcUhwkg6ppti0sRkyV8cwN17FFhiDowoZ1diEmqlwL46JHZNPKEdPLxIRiDCAbm9PMO7S3Nyeu+aWNNrn1K+mN3lUWBtfp71/EN7wmMGgBoJmcoUs5l4S6qeCuKYBTgnL1kIgGBnuQp/5ctUIL3BQfrYDM/Oo2UWBHmDlUg6M4Yr/ve2K5uBTd+nMxzgNYh/gyNCAKElm965Pm9qjsmwdJYegNExKT+tqezI6Zk4Q5pBS2EYfmx7XSMMoNvWKf1WKnAsbzaob5YaghVOOGKc5LeAGL4qG1NoPMMCoSb6oTRcwP1tDrnwAz8hWBYwzazkGX9d1JUxNdWrKL86BJ6wrcKwfdIuVguIwN7QCaeZtG0sHNthg95zaaKmamqUiKItwVqgKpCMycXWH2wSuoUKEYWW9jr51nsxtpB7Gjio0plZ9OqcBSQ9mbS4VFiCj397krCi9v1RoZczw1TujLEJ3lKBTDz7qJY4dmeML6FnNLu+Ge9v37lHM43cQZfz0P9S76OR6PPQsEkiShPazvmai5QHJ0IYytUAQQrHKDdDzjVq3NVHV/NcxQ4rA6AgXHzn2QWy8/RJGmzAz2AUXbJmPzp66z9cAvXDulcdqPt3OO3IwqgObEMNNJcAjGWLBDtGQ4HMmFZeTEVRoqsO/W9yUbX2qIhxtgc3b6GcOsQIlDCyzqQ4xsziC1JHntitvMu2SFZbTW5fjSmp8HzrGXbbFne2SDyyz0Lnnwh0Ibi1OeQeuNivC8dBz0M6z111qLY3bnAK0028k6A/vYbZGmAG0aDwuzu8uV73kF73xuxn1PvoN3f88redJNc5MbbZ2Be34LvvG3vY3pNB6/uOnp8LL/Bn/xOli5r3pZxTFf92tv5sK/filP/vXf5AP/8T9Uq5zTmMZnWHwZcEVEropIDrwd+Ma/jx0r4Pb1EbMbPtEtV/dX7C4ru/cwk+wwyg2iIYo8U6XCj3epNlTGVivSBlvVpSg31kTVeOv6qN9FF86zTdZVzE2plhRgNtnE9JYZV/aIK3xy7oRd2+XKYA0Ah0WhQgLtPxDHDbCOQWrIUm/asNVNgdr63H+Tprf4dLQITjt2spXJsREPqHyyqskWZybGDQSVFqCUB3FlDZrUZhKxAoIk0iFVkvfgxnXvCglgLU5M1ZsJ0ex2EhyKbOQbYDsRD0qAQ70rdUuA4Iw4cCl9SbFmDKCVJizln2H/2uaQpGilsEHav1nWuBGYtlLiaA2Zjklmb6FtB4xsaMhdiRXHQKs4pLsGB9co3QpjGyRewcVyYX2bxnY7DGKo6XGCRJHvfVcm+AgWR6QiLDb0+fLH2k8KnBNuvrxNc2mpOooy+qkh7e5yor3CUFKaeQAKzWZ1TVHKS02D1KtWjwm9Yt+n7s4D4yN6jhkaftux+kzrxPesAqwCvbbF/KUd9pI9tLWI1kQ2w1lDa7RFhF/UkDDmzhrUmCsoUi8GOCdoMehDc7TKRYOxerXE+nqj2CSe6du/gGs1UCgaOsJKWffmjVuwCeAbZgOk4mW3WhzOOgguoapq3O2v6VYSGl0HgxQ/j4PTpQvHaT24c1nqE/vQ9kGPMeOiNS5SoQxRI1F4Ly9obPXB+Vo+FbSjJUADkDiuANrVYhudjSjWHuRgEGSe4f/9Q08PmMuRKD+uresHxBe3/Nx04WikbqlxpHPGSz6dDc0GFLgRs+0R5vQyt55d9s8vVy8iFS5Ir4drxMWgmjsajVOKzW6OcUI+2Gc28ffJxsg/W5wIhbUcVp51u2K2OLN+jWGWEEkwYbJ+oJ0TcicYqR0v6/MVGpsnOFoEZYEQmLWwnQiZcfSTchz9/aScDbWJ/ng8A658bbCKUM6hEa7tD1E4enknrF4IrYMerR2vnLjlgfOY0cPbd4zHFKBNYyLsYMDFV/5rPvDELn/9+d/Fu+94Nbcfmb1hIwN//gPwz18HNz/r8TnQaUzGU74CvvZX4G0v8+B5LF7x2jfw4X//Crjz3dz3PXdgB8PH6SCnMY3HLW4HVsf+vRZeuzG+RSl1Sin1TqXUJ21JGxPVXgVasWb2SSQnStvMJutVou/LR3ya0tRSsWXze8MqGes0DWvFHqO8A0WfzIFT86SnrwHgZmcBwc7MIE64tNUPUAlAkIBSMikq3aSAd8jD1wUNJGUQrMlt6NsTF7430nPmb59o7nt4db+qxVIiiPVOgJ5BC6ccTC3Go6zycuFv9fshHVSgTMrMJ06jgFjEW8yXAFYJomIi36Xag0xVQpsxEKUsB0UXJ5ajUZD8ORgMM7qZcG2r63tq4YiDK6OMpT7qRlZvsM1s0WYm1hhX2uqX0qdQZ7Z2lejCdZQCK+L7jomwN8hoj/JQV1Rw5PoBtvCsh41m2Xd9nBVmk63yVMdMOSA1lrwIzGpgipQTYqXD9fMMncS+dijaHxBve6t2It9CQaEqAGPxdUYiUEgeGDTF2sGIUWZojQzWle0JxllOf32c80xUK3PYRhxkf7WcUQCc9c6c47gdQGl01g+GEIrGKEMeuIw9ed43ZC9H1Rj/XQjRzh6Nfka/e53IFrgYEGGzl3rA4rIJBg2xkwCNGqBV7M7sjJf4Aoir2iJ0rQe5zbzrZb9aIVGEqJimjnFSoCWAiqq2EmwjQkTIpGA/6xAZ8UC0nCtOsDostjQbbD7rSSwnG0TGjRnlKJzyjoXW+dYYojT58poHi5H2YLaSEQpaRdX8N8aiVJjFVZ8KGetR5m4AaA1uXpxlvn/Vc8nOeVCloqr2SongdOTNaFzOebtZDWNhfI1duTwSutJN1HYp8WwggBWHnWlgAmQreTatYNf2GNrajKOUr4JndZ0O4KiwzKwsEzkwpmbZDA6xhpGMOH6ohSjN9V6H1BW1xNFRKQCW9kZcPVjCrW/WC9QSxlQUc+TVa+ViV1mD5se+nlsCzO37561y/rqrwoDSoY1KqVCor5vp7PrvQ2ghGCth0UpQf0fP2ilAm0YVkuece/UreECv8Y7nfQd/escPcdvh2YdveM9v+DvgK374U3+Q03j0+KKXw7/8MfiDb4K9yxNv/dvv/DE++Prv5vLuSU5/0zeRX7/++BzjNKbx+MTDa+EnSQOA9wJPFZEvBN4PvPVRd6bUq5VSDyilHkizFK11BbaM8kXzrY26xsw6i6iQSymFEkdDS510NGOOtmKGx454a/XLqwzvfRvd/b9lPW+wM6p/qu3cvE8a45gkL0KSVIIMwUUapTSF+MS5DFcyaGO1H/51F+RaHmRESvveUc43iJ7dH1IUBc3SmKHCM3WSrg6SyjihGlYpQanf/3hvMYDBLYs08zY62Jdr4+2pfZJDAGiaWIlHtk7ouOGYRKy6FuwXPcBVMtFbtzsUeYKbm0FbhwrGB00VMdu+4HsTlav21njmTkpJmqORd0GpauW9OtdwTvHIMzI69KcrwWduhUFmECuIKWh1E6LRFpEbkzKNsTgSEmNR8JT4ONJvsddLK8ZFtEYZR6QUzXSfsrmxRBHGCc2VPZpXg8RNazCWRlJQzM5UQFahidDkkgKKXmJ84mxqBx8AACAASURBVFcYmjrGhGMzYhm6lMwOEKWIiFAKTDxLnDtss4EkCbfff5pS4ihKhQbTUs+GMaQ+t7lSmTg0BzlWLNeK3Wo8jbU0N3dQKEwAeZ3CsrV0gkOdy5NujeW0Y6wPmlIoMzbJEbTz94ALDZxN6EEFDmcF8caCOBkDMM5Ttt4pVNPUEUYKlChQMWFCTtR6lREPcpy1bGTLJJIHJsv6+aT8fLYYlHVoDQUejDm8HLG5scvhtQNAeaY2zX3dVDVPArusy0WTcD4lerRCN8k5GAT2zTl/rsXY+UURs9JnI7kUnhKKQVJ4R8TAcCsBiTQNUcwN16qeg8NhTu6EWFPdezIGHAEamweeBRwdoJQ3dVFaUTQDYJLyuaG8mceYkseNNezWKgrySSHWDX8NwnUvXSKdODAZ2uZotK+DFT/X51WLBT0PiQlNzf3DqrW6iyxdq9cOwBudOCHaH5ZTp3JYdIEVqw6+/JzAoc3ahr/RGRInOS5cL1ERzWKALh1cA1OoBDCWxZUtZgbXvaMqYLOpzf40PokQ53jotd/PlZ2L/JcXfDd/9j2v45ZDMw/fcOs0fPQ/wUvfCFHjU3+g03jseN5r4Hn/Bt76DdC+PvHWz3/9v+f+H/xm3vOkDhe/9dsZ3X//I+9jGtP49Is1YJwR+yxgY3wDEdkXkTKTfhPwJY+2MxH5zyLypSLypa1mi5jaVr1kreZ3htVi6kG2A6FXmjhoKEdChmlFVWobm4SnN5uIczRGOc0k84mC1iTGkhkTVpt94uHQLG0PKiAUjMGRSKMU5OH1orGI6KhauS8TPwHYOYfF9yib1U2eNvsMfwpR6PVUmjE4S6wVkac6UEqzZ/v1OW92me0sBee0apSI0wIblqDlBoA2umkOAfaDWYQtzQ6Am5hDKUgwRJEKzbWFtgyrVfwylAqJokDxxNtwsUaL4+zRhP2ZlgdoIck6rg9XwEwFdHxkcw0ltem+XfGmHwpFHvoU1XJI/+dw46wfcxUSTFVxCTjnWZESmJgKuFZzp8IxTkl1zrdFNzEXktXUZBTKkc83aXUSWpIzM9olCuxavtelPSwqAwTnBKcVNs9wsaaYm8HhZZ0qABQ3GpIklqW9Ido4ovNXiYkxtnZx7DvfQ8/FumIpk+ZhhvNPxcUx1lkWB8tcy7YYiTcD8XVPgoiqwUMYq2Zoep3OPAF0ACWl+whghwmLly6iVWmi4pksJ36fEfkEg6EJ41YuFoRFhfHQznmwOtaMvQRWOMFEhEUNi1YhCbYOG45LKTiqNMVomcPn19DiMGrGs0AiqFG3AogKYWa7R7M9BHw7CpwLhjV+Gy1ZVY+m0exIj5JBQxyz17dQ4ujfchSjwKUFjAFQUcobEKnIM8xxg+ymRdB+XjlxjJSiM8q8y6OziFaVcRDAjD1gYf80Rpx/NliHya03ipFyvUX8dxFV55obS9oe0dSaVo3QKonjxe45jDji3S7aGqRzHV0MvRBVQdEIUlSCxFGXOeM4LVX/PULT0IFxu/lmjPPscxCGA3B1b0jUz9AmIY50xYbv9TOaKqapGtjcokYeeB3a2MPlOZvp9liHyQiFQStNc2cvzJExma4TovF5JWUNrCY9OkN2aAac0Mt6vtZNR2jryBtHwHmGHGDV7CB48Bb3EmLnn0Xa+abrslvLoh8ppgBtGgCc+On/nZ2HPs4bX/QK/uR7f4SbF1sP38hk3tL9X/wo3PJI9fXT+J8i/vm/hS/6TnjrS3wtQwilFL/xop9i82X/it/5Fy2Wvu/76b73sftwTGManyZxP/AMpdTTlFJN4DuAiR4USqnbxv75DcD5T3bnWtUMWvkrr/ErsTk+EYvNiMwVXJFtoo++h7P9JXbyARIAhrKW2WyXRmdAHlbZIyfeOAJfr7NKm5XRGcCFlWNHntQNnJU4XOS7OLnIM1FZ6xh586g3TaAGJkluIO3hnCWuwFNwoosilC1bIgsmGJp42ZuqkqXK0MQJ2qTEpm5Yi8Ch1Q4HUpuGjCdmNsi1VNhvHJJyUMxKg6FNaLshKgKc0CQiVh7Qis2Ig/11pXbDETU02aEZIhyd409l2/R9ohWOs6HqHlkzuzVsKiHUtu2w0/dshlIwspMW9E4cqXF0i12fpFamD4r2MAcULoroDLOKbfPjroi1ohF58wfPQFFJnXwirim7ld/f9i5vZraBEiESgzhXmUbUuWNwBrSObdWj50bkc02c1hixnNjfxlgv5Vo4d525y7uAYuHCGoiXTtoxJknFEYJnPxqxwgnefEU3cLp2NlRO6ElagW6F833eFCGdhiir5VsumvESwlL6FVLltDvwEj+lwFkvnSvnhQiKwkvugLmWr6FyKq4u+iT09aELExwLA4NbckZWECvYYIKhpOzLJTTwJh7+mmmeYAIDE+rlkpnjGBE2uykYw3ysKxJZoYjSItQgxWAdLt0lSjtoV7CYLMGsByZa+7Hb/dwnU8w1/bwUhxKLmZlj1zj29gagNc28WxnFiPHHOrvRxi4uorRGaaklzJGma/rsmQEL+3sQpLeIZ8eONQ5oxOVY+euqnFSMc+kv6dszKFRhWNjsVWMaRzXwlrAoofAywiLMH+0KnBKaoy0sllhpjKrrvXCCPfwklG4EGaYPG1qFAMQqRtJdVKvNwtyxqj42mHdW0Vptg/KNtkXp6rGy2U5xszOYuRbamPHHDbtpl7UD/3ySqOnlh9Z5p0+liHJDlPtzEXEVQFMi3Np6EhG+/k85i21EjFwaWH9vZqOsNy9yusXiTFzLKb1G1isXwjNhITrkvyfUMz5aTAHaNLj3P72e5M7381sv/i7+22t+mOMLjwDOAD78S9BcgK/4oU/tAU7jfzy+8ifhuS+H/+dFsH22elkpxW9/9RswL/xifu4lx7j+Mz/L7u/+3tQ8ZBqf1iG+KdUPAn+NB15/LCJnlVI/q5T6hrDZDyulziqlHgJ+GLjjk91/NAbQlICLwsqxQCY5Yn0C11tcwCnIXM87JzpVKhPRTtChkD8V3wB1brNDaTYhwOrnHQPxQGA/yxiZlG3rV2G1EyIz9HVLSpEL9BLPAOXxvGdExFUAbZAZ+iZDmZw41CypcA7tzJDmlkZivPV3L/H1TUqBE1RIkLeKdYxkHryJ+CRtgt+KSJUJpgmTzxgP/bxbGsDNizNEAYDMSoNmpKFpfCJqhaY0yJUHigw2mcm8VK4Eiwg0xbsNKiU4pcnQRFaIKpZReyZRXOVA6T+qaT/HE6yDIEvSIbEaj36asdFJqh5LpUSsVPWJ0gwWn0yiZqtxEBzzzYjbDgdFivNSTpTy7KKDW572bHae8HxvTe88K4VWwRVf+6a6ztEY5pTOgn5f4XsLW9edtRqIUrRUg6FkJBS+Vg5H42DE0eVNomDMocR6d0ulOR4tYiJopVteJojFBYMFhUIiFZJpfw6bruMZsZDs58Yx34yRUPt08/m1GquHflsOF1oq+DdWdgckhYdQxhhWzR6i4OaH1mh1U24/pJlVMc3MsdCMQSnymZuq++tKvsnQTTrhKSf4EjBHM9LYcpaIr52zGmbSIKvVCiWKWILFR+jvBnBotkEzADERMOKP2zqHWyyN0TwjqK13Ck3jQ/QXnu7dN4sR80NvxCOHWmFO+X0XiwuYZlwSM/46tOZxsSIf5R4omaEHq5EO8uGI4dztoWYMLqTrJM7fey7SFKHPXbOXYmYbZNZyrZtiv+Cz+eznPLkCtUpHFeupHSxu9rj59HqoQdPEotB9b15RBBWVCnOlhHOlc6eyWe1C63LEKXAFhVgaKqYI991O0WdYpP65UVGoYZGnak8BMTGSHqDFMBMvjn13YE/DHE/nm2igoSNuPdSqsJvLg2mO1syqsPhRQsThHtY64hFkTd/L92BUoMXWffXK/ThHJJA3j/pnc7jr3JgUVIugg7mTi0qZJVg9g0JRVPJoRauXMnfuKqT+WTerZ2gsHqZrpyYh03iM+Js3/RTRf/0zfv3F/ytv/qHXPjo4W70fPv4m+Kbfnbo2/mMIpXx/uue/Fn7/xXDpb6q3Ih3xu1/9q7Se9xR+7NueyLU/fAcbP/56JH9sPfQ0pvGPOUTkr0TkmSLy2SLyhvDaT4nIe8Lff1xEPl9EnisiLxCRC5/svrUYomQ3gC7BxboCIEPJQsF/hGnOoY3QaKaoKMKUgEL8CrMKBgTgk0ETzzNc+Cx//M150Mq72ukmfdOuAIRf9xeaWRsXRx4wxpokK93L4FR3w/+lLHzDW44vEFWJYwnQdkaWYVZweGkPhaCXtpndGQT2BJpxxFwjxrgCHXRnyglWDAOpWSetvUSrFYXCn9JEIJrBROVvjedBdCMiDgBNi3B74wiHZme83DIU9HszNJnIXFTF4kALb4yiUKAjMhEaSqrzolwFh6qHW9jJRJJWJnxxMagSdoCh6dPMOihxJDPHKfotispIBJrNBqYxQ9vNV4mnCLTiiEbkk32VGuLUJ9MOh5o5AlEDF88EB0th92DkQYhSgCYSb+oQp2NsF1Q9lvbSLjr8Lrs4BgWNYIjilE9wDRZEM7fXrc/P5pSumE0a5Np5sxgXwGyYxUppJI7R4oW02sqYrNM3TQbFbCu46OFbPZTgWVTs5weCNGK/UIBvuw0+OU+M7++lnFSAf7bhU+xnrTo+d+7W6rqVRjhOHCkZaWHpJOXvlyIGlDUcuAEfGJwP18H3yjKRIrIJ4zi3qRTttPC9ucL1nglGLArPapbmnsY47BEdQKJftJjd9xLH7XwbdAMbKZxuVsA9DoCSMOeUVhjlfTbF+fYV+cxiqZnFaYXTsR99rYhGGbExGO0Z1URyJFLkznjZdKQwrqjm8vD4Ak4FqWAj9tLkauL4Pn2HV9toHFFuggQ0fJeoiulx1ccUNh9VAEaKwj8X8iF2rDGi0+XzyNEIBiQlHM9NgS4hU7j+5TUEoRFpYhWTlzWdoUWIUqqy9ilZLWscrhGhRTHfHYIIx6NF4ivLxGmOjRRxOgl+SvCqo4i5ZpNDapZIaQ/QIg3UTJyIJVKavHm4nAEcnWvQiHVg4Go7f4LEsfyOvHkTcvjJQTqumG3G9Vwb+Qbe88NVIgrS6FHy7RBTgPYZGiLCO/7wJzn22+/kt7/q5fzm6x6l5gwg6cC7XgVf9TNw7LM/lYc5jf+/8bzXwEv/M7zre+HDv1z1L2lGTd741b/J4c+f58e/7XYuPHCW69/3amyv93fscBrTmMaNMd+7SpR1ICRzEmufRAmMXEJzuOl7+USaRqxpmT7NKMaEhLA02PCJQimtMeStWUzs5VHun36tlz3aFKcbvu9OkGB5y/baLa2ptGfSBJIjfiV6kJnAoNWskBGHRlcAraRklFYUC43qNeO8iUZZ7xXpiLh0i7z1mP9mWwOh+hv8ivlMM5rgz5LZWzGNhQANAosYR9zWahH0WWgUaE3caJAXjqyw3vmuOkoftY+E0ChXwxWoOEZ0RJMgQ1JRlSASXPOKxuHwSeWt6rUq/S9C8/GCZlRL65QIzaLjGQ/VoLG1Q5L2q6TeCVWT4vhMbV+vQiGVjWeZW/GMZ6y0dxSM58MXRh4IhZV5F+v6BEvXPUctE1SgjGNUJKyP9igaQqQ9kHKBaShd/xTe8EQTl1cl7KN+PyYiV45ifob86BxoGB2dY3j8EAp45qFnc3N8BKFuym5Cb7DK8W5i75558C9FaAduJsY1Z4kKz8rZsbrN0uwjmnC28wBG41lqpTxL6SLN3GgdxDKaezKjwpIbR2E9QycOVJF59tofoGeOA4MGHjSXh7vYiMlQbPeyh8kmS4BmxbOFaW4Dc+jP/dbGomdkI+VBfzyD91EcWwAIk1Q3gtQRweBlwSVDbuYXsTHkkgX2tOFvhUizuNLm6KVVf5878U6GsaYIkmTR/voe14u0ULhGFNZCyvGvj+H/Ze/N4y27yjrv77PW2uOZhzuPdW/VrblSValMlRmSkATCZBgFiaIgNM6g2EpDtyPNK6KgAooKr83gi6iAgK2vjRM4tfhxwgkIQ0IIIVVJ6k7nnL1X/7HW3uecqkrg020nEO7zR27q3n32Xnvt4Ty/9fs9v8ctsHvWa+w8ncRRr2+j17ccq+n/ZrGo+/z7DWGwveV3kQ+vIYX82YX2lWNF2MyxjRbAWjqV0F1hm9GpRSgRjBiiO+8un4di/pUHcQbHVK1HU+45yzKSO08PrxP+1HKLuvMLY9dxq5lgNnoIlplmSqTCcnOrxLU28P8Otk+ji1vTrwclRpP7hajc136GZ/pAjgnDsg2I5DkSpfRMglJCaPQYW1+MVdse23bAQ8UOQPs6jCzP+Pl3fh+rr3kPv3z5rfzYD34fs2db6RdhLbz3O2D2KJz4lod3oDvx7xN7b4Rv/X34x9+Gtz0JvuQShzRIedMNP09j1zY/8owWf3HvgE8881n077jjER7wTuzE104ofM2X9fVU1rvIeRiR+5RaIWVySH+LinUAyQpU73ILI5UgQXpDC+ozgw02rVsJzvxeVLZJrkJMr4/6onOKLBgHl8B6ALXVp3rnKczWUAJmbeaAmo9BIVUqcrfM9ccSpVifcXUSCQF5VCc3icubipohHzrQWCtYO6z7KbuVKYUViIyilj+AzocMEEox7AXmgE1dHJCTHJQVUMb1ZPMM2tAn0v3UYzVilsAfz/1LYZWics8pVJ4xMIlzfbOW+MwGIHTriasLE8eG3Lt7gi0v/1vfO4lVisSzX3ak6bfOtshF0WebfHNrxPbdgSJz5xcYSJ9IwhJQAfQrM+R+1dyIHmPnlDKIiFt57zujDusZtDy3ZKrKeroLsJw6s01mnT35qfUeG9t9JITFpEEtqDPqcnkmnWUjmsSKEBBS9E4DlygWbRCMKDJlObN7kn6zigJMGPneYIooqmGsrxPLXFOrvq8ZlJEkfRTgDBk0Td80sFrQ/hZwPcFyNrpV7ltaK9tMFNFIApDM14UVe3K1elYroq27/Y70WZAKl7fYAQrFvet9d0+4gjp6URUTdujOLlIghjB3iyrFR4cnMHymM4FIKwdKxxgpGT++UmRpZ/x3IkzUYoI09ueu6IcVzOnPQHaGB6YbECWc1hnW9kfYXIWo3N//ziCoGnjgohV39927wirFPasdIgLCIBjW+/lxKhmp/xNVXq8hsPbHU+7/9PoWCOTKOXnGkUKLLkHXwGZO3oel6H4GkIktvVm0Uo5ZL5iyPEe0YtAOULYPrcSXXOZlDWzjzO10E41SwoY3DbJiXJ2YtUNTEwtoRf++B1AbvbFzUOANa4oL6X4/SAJ0b4BVMWKG4NlaqAYVlDcwKfo86rGnxO09cxfFgXFfOwgQJeFw4ci6xaVBOd/uGS7fc/6nUcKW3bHZ34mR2Bps8ePvejEXvea/845jT+Jl/+UHzm1CPRp/+Utw19/CE18//lLaia+tmFhzIG3uOLzpKvjwT0J/i0bU4Fdv+mUW5ixvfnrG+6rL/NvTns7m3/39l9/nTuzEThApA8iIYx4+uVYjSYNyzYb1MHEIRTHIcvojXviBMjSVex9ngS4d2cB9+efKoPM+VjyrMwKIlLW0KyH9eopBUIOcSAL6tYpzYAMGjMvTByryi+vuGMnWXc5cQMSZJuBW1nMLA1NBELqqjVaK3kQVcOYXGxMXMNA1xObUVVomuaGKQISULVr5KdQoQENGHeeRQBMO3O8ls6QqBFEEJvCgEGdQgMUXGbm5HfleMlqDTzIHXrZots8Q33MXoFzCZ6H1ibsBIQoMWmCzn3N6fZNBEpRjUt6wQ2uDAA3TKY+ThUIumgE5s5WqS2YlYKBj19uKnL7NiCQELEqPcEuFVb9od66OYnNMllL07QDVz7zE0YHc2WbCRDVGDVztW64jtqshmRhyFbpaRzKUOFA6CtCm0yXiz97nk3FNOw2pRYbsyBoC1GJNEmiUKEQ0W7bv7mOBQAL/tS8oY9DZppufzAtBxYPmEQbtzFRz5AoX0j4n4x0YMCYs/xYaV2eX65hWHjPQw4XiwDd1xxa8jbeyUI6Jvn1wt18UGUlj/bWLfO+8olYyI3OJdZ5zTxSQpYtQncIad/+bQY54ExI5D6th7TbWQqB8o2RvloLNy/oksY6V9SrFcwCaAL2FNv968jHkUY2+cf0MVX+TPVOHUWg2Au1q5byRSRgYkjAHm4FxBjlqsuX2qaU8iFKKTDLyXNChcfJTGf5dAJRzdMUETPt+geosgCAF2N129vY9UyU2xvVcE8VKOI2IAuXeL4W9ffE0DoLayKQNrx2A9TWsDwx8Y+gkAgu9fIBBsRh2EYTECFGoSO78M4L+fSABWhTW92ET65hkEaFlUggrlJWodrg8MARofmyRQXo5vXQGq/170GhyLHPhBKFy4LmlU68UcPeZWEH7/ffvvx9joRs0S2lnqxKAHkocxVqUYliLK5SMdjnPgKYwrHnw2AFoX0dx98bdfPfbv5Hrf/qj/PbaDXzrj/8gy93Kg3/gzr+B3//PcOuvQNx4+Aa6E/93Ikjg+v8Cz/89+OSH4RdOwt+9m7pOefMNb2LfTJsPPuUu3rznCj552zfzwIc//EiPeCd24qs+3PqoA0u5tcypNv0kYKNdLwHajG4S6YgtthEvM26ZlNO9LT6fO4nOoJ2i/Bd5bhS9mnMic0DPM3F+lbeonyqShG3rit0fyDf5dGUTvW+Bqg4xzRb37ZpjrpmwWVkgi7vluLejDuuJS7hG84TCVS+Hsh7O5hmZSek3Vwitk09uLLpE0SjBKoPyRTp1ScjqczTU8HzkzBcY80uHoQNgkd8bjfaJ/3wjppUYLugeoRXWUFmO2RrKOUdZjtF1Q6MUuQcnlTgiMoo4UIgd0KnFFGlbmbz5JHZ9AEOjfPf7wp5e+7muBu1y3L1qRC6aHEiDAIuwUZkjU24lvedrcLRoTuXrbBVOiTKUZxox5GEM4mpUMg9CASpEQ4mjKOLAEGqNZBmZUuQq5HRrL6fW+/RSB2rW8z6qVXXugIB1BAaVyuTwtLCEWqF9ku3uq2KV39Un9+3AywkFURE2cKYHxvh7wc9dP+16B9Ihg1YAqNO7XN2kKVNMB7EyI6RJRLcaMVmNmKyFzDRTphsJ6ZkBvXSY4IvgGjb7pFu1d/m/aHJN2aNu/K4CnW3T6n+a6OoZ7ts/5a9Zjt7oYa1mO+06UCfQq845A5ZBTqUo8Ri5uQTYZsADmbPWV9YB916eF2frWwc45J9rB2aykQu9YqbQengtB1GCEsi0dgAm28LoFCWadb9dAdBEuX5u1k9IbjTB7ITbJkqZidx8Wa3IyBxPE2kQ2IhHrru/vr2wiYO64xLH4hFaa16GFeOYzdouemELUY69V54Jcq08BMlh0Kn4nmGWjXSWjebqsKcZRcP5gkEbgBK2fc1mO6hgvGx427qxu20dkFMi6GwLUQojGjXYxMkdffuOSsB0ZYblmRPcuf9oCdTdNetRONIWVzMLDVke+mvv3qn93StubEFCL2oiwKRpkEiI4BxZp3SrFGvKF76IsQqlh8y4BawJh/eCdXb9Gb6xH76+zU9yphMGQQOjZUQ/cP7YAWhfJ/EP9/wDL3jn03nemz/DH8xdxVN+8ofZ7yUs542Ne+H/u825Ac4df9jGuRMPQ0wdgNs+ANe83NWlveEEyd+8k9dd+WquXD7Cnz/uz3j1JY/h09/7Uk6969cf6dHuxE58VUdRQ+UkLjmJcjKy++daY9sopVi364hAwyRE4pKqIivf3tVGjEuAetWoNA9xNVWK+zcH5EUfIRUSq0pZ/5GrkGBwpkxGlFIcqM64RseFe5s2DKwDVNtRh4GpcKaXEZvxNCC3buV+QNHDS2HzHI1BlGO3hr2IBa0VmQXtDSycuUTB1Ck20nk3PyONd8GBzCLJyS2gvVmJ4Oo/RBBlSJOAeL3nek2Jk+Tlfsi5ClFFfQ+gxJZOb0uTNRZrAZHRLLZTJ+3a2Kb1cdejyCJlTa7FlGMZFEyK36/21zf3NSu5ivxPxSBpOQMGPx8WmKqFtCoBSnxiBqVbWx4OG2Rr0QTdwwA0Ys86eXBZVwlHoxVXQ6YMOo4wfQgGlkx7c3jlpI+9qgMWORZZm8cax+jaAhTFSXmP5XECUYgxhbxSyJJumSYqpegxKBPYM60jSBAhIhgpWhW4GiathwyacwYdApusUmGqfQU1lZIFmsIHL1MCKkCJazmAdc6lusDLY2hLEF3aSpSLHbly0s8v7p9mq5lggc3KHNVanUaiuW/fPOsHpnnAbLAZFdclo/rpe5Fen1otZrKe0IgaIILuZbST+gjLOSrXhAECJgYZ9obukXl85mxOHIOW00sTLycE5QFCGhii1rQ/JUGLu8+37cCzPmBVgIhisyx8Kqgv5Zgy/3xYEScdBLLZQywsXM6FnVlQRQNocSYs+Fo94N7sjOsx56/VSrpYsrjKjk04YLHKONMhESbihbJdiFuE8mPz76YsNZyxm5zJt8hVSCbO5KgeB0xXY9czEXe4wSBDeVAK0NIJy2aSBomf92IIlmzkXtCDTUJlCLdc+4DaPeuYrR55NSK98lqUMuSBGYE6jk/bygv5s2dRQ43u9Wh94rOlnDU3ESjFVtLlzkuOgsB2PyfQijg0NOqJa1ZevBusY/tQhvt2uQWbSGkQZ+iSO92ua0eCr5kD7IhgsgDbmpyBPDQE2wFoXwfxoU99iJe891t4+Tssf9E4wsmfeAUnltsP/oFBD971XFi8FC554cM30J14+EIpOPJ0ePGfwQ0/An/9NoKfPsgrv3SaH9j9FD5+8sO84uaLuf3/+Wm+8FOv3bHh34mdeKgoq99dWBFXSO7/rVAMJCulNYJjewYjDoCIq00p6pgQoRc0ffNY4a4HtkoGDaUxytC3DmDloseTLXEr15kMDUC0CIPMuaf1gxpWFD2rMNrZl5eyydw6SaT43l3G1a4ZFSLGNWQd7WmllbB1ZgvpZeV53L93CQu0TJ1Cry8tbAAAIABJREFUhBYYRacSDuvIpDBEEQZKQItjdoBR7aMKDEmaDhN0J3RiK55gM5keSWKUW99XhVOeQXw9me+Si2z30FveYl6JN0wQn3y78d+7e4J79k+BEjqqVjJNtjAVEDdfGa7OUAdJyQYOKlVqaUinFrp6mYIlBGwcMqhVhvMmmjR0Cpa19t5yO3AOjKIKiWOACg1EMSaD3NfPWO1ktfV64ufFGWkURgfFXKbdDgPfvmEw0UFlGXEU+OMJeVwrj2xNhd4ogybagySh31om0wnzusOc7pSsUAFOljsVPz+CVZqBqY48E44d6WtbukArlGMZtHN1NKK5Z3WuvJqx6NK4QUbu7cJwIQ819y216UcBmQQYpahGhjS7E+IAth+g55nLwEtiUww6NEzXEy6YOkZqauSS0I5HmLuzvuoypR1LLI55MkpTU6k3CcnJvHwxtMKXVubo79tPlltU5qR8Rqmh87WAEldriDZotGNiVYAWw8AYJ5FUoArGqeMauhf3Q+jnXQUhxHVqJmKqFXsmWpBQl/ejFcu/DO7k9u0vkSsIVEw1SIYAbfxUkSxnM5lnPZ1GiSLVbuGg6FdYOKAqEdRIA/Z16841y60zdVFCULi1aMXpzR6DbODeNaXE282JLvrmFc++zelntjTp6LYrhO01lvWUq6MsahUFdBgRKENmTPl+CMQwUY1KYx4Q7jx4AKtdzSu4mj0Bt+ClNVb52ldge5ATGAeEu83Uv1edZX+e+758oug1EuTAHrpBFRsnYB1AC0UIjSKzOXWpOJAtkFdjskZMpxoz10oxIgy+TNnQDkB7FEc/7/Pav3otr/njH+U1753g72WeXT/6Kq7ZN/XgH7IWfud7IR/ALT8zrh/ZiUdfKAX7b4EX/A943vvAxDzpw6/n105tke39GK96ZsC/vuc3+exLX7Zjw78TO/EgIVE4LrXy8iwRZ4agxIGPXCmscYxHULj4Af2gTmEPX/aOEi9lVI7pECVsJjOcqS4DgpaAPhnhCBsyOgAllDUx4Czvs9w98kXkQK9z0LECoUvGMgtam5IlG5B5y3iFaL8SPMIYiRLWp+bIvK38em0P27E7x4jU2VGLYK0tk0t3fi4p6k80OLV3ElEOTOZGw5Z/14ggWmOLmh9xq/lbA1fvUzZ3pmDQYDVy/czQmsFEhbxdcd9nIth8WC8jCER1evVlRBSNumOzrFEMjKKuY1q6Wp5rbhK24ikyXQA11/tOJ20GgQMn65OTKJzETfljNJIA2ssFFi2vrxIpjQmGpisFmNZuzkTAm4fYegMlumQLrFLOWTA05bWoeIklInRNg73BHBcfWeS+2clyTgIRqknEnskqxV1TSt2M+7x4x0Qt2slURVBRwv31Pa5WzSq0By1KQPIcPaqTVZrROjgR1254qxaMXC8HctEGyXKWqg16abX8TKLDskn72aCpiIpEfP7wHnpJ5CWpBZ8FbN2HiBCKZhA1wUKMQUKDFscyihK2KouI1qWNw7jhibtfittWWUs6dbIEKtic3AM0bYGkAjNT5LgWDXGgmarGDoyJMzhRKC8LDhh0DmFVhNUGhSYLXLVTRo7ONjlQWSAPDWdm6sTrPXRvgPHzqsNoeO8Y5Z9vC0aXU1BcEVOdKreNQ03uWVd9tuw4qZCbkK204fYrAVNBg9xatJc9IwqtHBNcNGOfqEVMVCNs7t4fJQGYj0xebjEmpB4HHGhOUNPjktKJNGG6HkNuSWNTus02LtuLSlsOwOU56X1bgK+DCyvEJiILAwZtd+/EFupBOqyME7hvchfayzPdibunMzORk9EqTU7GIA7IfcN1RGFDA17CK9a1hVAW0O65vXfxWhpXXEAWR1jgdH6K1LiFJ2MUlTBgc2maz164n83rbmZr7yRRoEkCQ6hk/GV8ntgBaI/SuPPMndz2odv4m8/+BT/zgTn+7YEY80Ov5InHFh76gx99A3zqD+GZ/w3MQ/do2IlHWcwcgRt/Ar7vn9h78+v4jc4V3JKe4hXPuZd/+djv8s9PfwLZ3Z95pEe5Ezvx1RV2mGBXo0Je5FLfXLsEV0Q5eY9SbFdmodJl0Jz3n3f9zhya8zUeZUNXyj5iWgmIRkyEIBgJy2aoYi3bE0NzBnyONvBMCDhAsJnEYzCu+L3bicJOtMhQzkJfxNdSZNy3tuy2CTxwUMMMMA40vblFl9iKkAU1Mo/uJutx6RA8QqYADpR+4fAsammSqYmqY9BQbDZqznK9AJZGkxihXXGmITk524VJRREHZwBLEijnhohjx/LZ3eS7p1zvNxNjP3XncIrUyHnbnI3aEq25NcD3lVNCL2yX9uS5ChiYhH5Q577mXmduYXN0bZqNdBYAIyE2G3jZm7vukdHYKCZrVhl0GmwlrrRAF/cEkD1wxv30sjhTQidK9qV3wT6+dPSIH7NjaAe5hdCQSkRFpdR0wkI4z2LlCFppEhU6yZ1PknfPNlCi0FrTSMISlITefl9LYQPve7aJu2cFcbV9olmv7nLbpk0yE6KUQWVDQ3YngRumlr2oS1HTl9djKoFLpKUxT550Xb+r3NJMQ6pphTMzdVbMNC1TKZ+HUew37Rt+h6JJJcKKdfebULrqXZDMlzebQWODhF7SRdf3IIHBFCwrggwyxKiRteghaMmtd2ktGDQraKWd7M1CRyd0ohb4Gq2BtXxh83Yn0UOoJwGt1MnolqKOK0WS4fOVI2Qqom+qKFFkOnD3nXYy0G7qnlcrQiNyz3fgr2UQxphO2y9cuP1tqgqEmnoSIv5ZsSIEtRlQTvoXGk1nuUurEjJRdYsNG+miO06lyj2XXkovCjCi6KMdo0dOsPFFeoMcJW4Bh3zIoht/Tpn1DJqfK/KMga+JFAtGO9OZxAylvvfX3DNntCINnRNsPQmotWrDq2Ei11cO69lMWDVdVFLBKENmNNuLUwgZZnCG7KILuZf7Sbzccz7di0I4teTrSMta39g9G0pjbc7p1a5b9PDzabXr5SaeRYsMRKJQ3kzF6Co05sgD/44T8X38LBP1gOlazNbClANiY2SHci6p8U4ftK+7+IPP/AFPf//TubB+mP/0npB//lLG7d/5Cm67eu2hP/j374E/fA08611Q6T70tjvx6A0dwOq1BDe/hm/99r/lVx/3Bt7+jBk+Ip/jL558PZ943WPhI6+He/7tkR7pTuzEIx9B4qQx0bDmDIFONWZ97xxGOemiUkJY6bgctjqFNBcJdfGl7Qvriy9xy1iT2zg0pStYUfKklXEujuIBoQp8MufYr0KipgRWmivEgeJMrz/C1rgVcOUZgFwC8m6TrXiSrL0HRNBRStZd5P7WopNP+YRnKhiCwUApOu06eu8JBqFGUM5Bzrq/FUnSqEw6Cw25CuhHDSLjJGQoB2YHkWNZpDAY0QotUAkNaNc8ORPKWhyai1SWZtkV3E9iN0lURNVEWK3YO3GItdqcm1ATjrnrlQ2uBR6YKiT/w/lvBomv47GOZRux2bfA5uysW1H3PecAmtEMkudlDU+RYm1cdojt/ctkU21OH76SXtgklADlZV62P6zfKwZVMpUisHI1OkocQ1OMoTj/wNBJYx47uRsBYhVRT+Y43TiIV29ilRAZRbueOJmdLs7FHSfw51bIzYxPQLUYjDiRaqfbZKIWIZ05TrcvIGl02HXBleggQA9G3DlFkUcheZyWeK08N4GZ6gzTYQOlNJk2EKRDSato+mlIFjrDBqsFrYTKCPNaT1zCr4v71ubk5KxV56G/gQAzYaO8nKJClBammkeQZBoC4+ZdHPursxxZvJgTjX2ArzX0i9PWwkIwgyBsJROQdNDasGS6LAUTLEQtamENIQftQOyXtu8q7zNlvexUm+H9hnJMsn8v9E2dM801NybtLOZzE9OqhG47z6TGJmZXp0KgNYeDRS6euZhozx7Humvhwsqyk5emNSLjIHbmGTKheMM44N+uRgTKdcVzfeXC4fVDWK8YjApd3y+clFbnPQY59MX6+0MRiHD/XNM1XBcYZO4tpXyNGoMBm7NNthoJkucEulgAEMLZDnk1cWNKRup1PQ2nJxp84YI5B9AZ1n4az37ZLEclFeo338SBxsW0dbUcv202USLEobtv2nHL9YwTCxb6HqzmKgSlULbvzV6gjyFWATaI3XNoAT+GpDvP9PwagWeqI5Wili4DHbIdtRwj5++bLM9JD6+WixUyypa51yP3H1jmoWIHoD2Kop/1efVfvJpXfuSV/NiJV/LUX/pnPn73Jn/03Jfy/bcceegP/9MH4H3fBc96uzOR2ImdABBhaeVa3vKC3+XUi3+RP1pe4J633cnL/+BXecd/u4FTbzgOv/tDcMdfw06d2k58XYciL1zVRKglIVNRB+ObmipR6KTJTLrEXHWO5doSF9YWWTKTpD65ECVlcrc7mKEdzjDZdP27Brkl0IrUs3S1OOCe3fMIUEsCGq2as8B2I3BAzhiUCBVTIQp8ojACTCzOGGKzc5CtuAMIA6uQ6jRL3ZRGrUqctIj0BA7kuK5v7bDK7miKUpKnhO3Fw6xXlx1jU5BTdlBuM8pMfP7oPk4stVmvLLA+edyZMCgnC3Q1VlIyVxiNcxsQrHGsQIYMHSJ1CDiGRiH0ozqpt8xeba7SMK5tgUtS3bz0pmqktYSD3YOEKmSz2yHWldIYZEo3OViZdYyghc2JNr3FleGlFmF9cZHeorcdRxyY0Ro70xkBaMPtIx1x+exVzKYHASGWqGTQAK7bP8VFM1OsmGk/3iGL6tJ6jRbDdC2in/ewvjaMwFCPQrqV8dX4zK/qS4n0KOc02VWYVri5jj1Aq+DmraYSD/ANUe7qFuu1hMV2BdtsE6iQxdqykzmaAN0fArSC2+x3J8Eqn+AKZ/auoBBikzId1jGi6Qcp1KcQX1c0m6zQbkxxas9lyMwFWIFOM2EiDSFIHXvrj1PU+FkszWCSqo4h61OiUmDQWOFU5yJvHy8gBmWK+jrPLA9yqDRpeYZqvbIIU17uikX7+rZe2ISogdaa00s3o9IZPxA3d5GJwUtDc+sZ0MzZ8BPEFI3XW2lEMw0AoWYaiLUcW2qzdzrCiqYWVVlK19ibTA9PRSBQIU7NKFRUTGJSxAwXDbrVmKB/P4OpffQm9wNCjnv+cpvzj5ufZzvfLDAaALEp/jF8NgXh1GybUIfjzd0B266zfXA/HeNY4FYUc/mRQwyU+1wvc1Jk0QGc2QJlyaPAOZLmEJiA7f37qB/aja7VyOcmaYRd9sxdAgVItBYVtOg3V1EIDZM4Zk4EtWeCsLuL6ck61ZkW6BBRilAN2zN0a+lYI3vwdYBQ9iBc9/0TrSju7R5jvbZEbnP6/uadXbyW5cWL8Dpxwt5pbC0mVxoV1qmaGtZCpFO0EtrJBP2gQa4VldCZ6Ax8XeXZ8mVwLCpi0cEOg/Z1EZ994LN80we/iX/40j/w69f9vyz/+Dv55y+u844nfgevftZFpdPVeePf/n94zwvgab8Cy1c8fIPeia+ZEBG+7/rLueA//hq/cuSZPOvdA+45dZDrG/Ad936UD777GWz8/CXwJ6+D++/88jvciZ14FEVhYtD0UjfX8HUIIuZbCQfbq8zU95PoKkYFKIRIGRIJqBuXHEppnGFJVYzyX/BbuxbYqNXopBFT1YiZZkr7lpv54vwR8soU1YlZVg/v5cyxBfd55QGLr1+qXHEFg+ku9093qXqJ2f2LM4Bj0KxJAAVGM9AaHUZemikcmTjMXMUlfMp48Z0IyhcIWc9mWWvJvXTMFggt640ZfgAcn1ouHYRzFZDFHcdYaFefkxszVs8nyhmnCIDW5L4ASIlQN17i5U0MlIIz1RU6S9dyzdK15T5G97fdTGkdmiG88hDVvQdZqu1mT/MSWuE0F7WPAY45cq0GNBmW+/cs0J+aGY5JXD1KLt6axH+/dqsx2fIceS1hshIzPT1bsgNJkNCIqyilmQomSVWEqCFAq0SGamRKVm2M6RMns9MSEPlGxZlxvcts4IWKZy+QSVEFN8KYpjGbx/cS1d1Cwn3NA2glzF13nKV2BZMucjhaIBDjwUBAf65LdvxqBIj27GGwsoetXWtUGt7uPQidocVwcgChN7/EqaNXkAUVBJicn3B1bR7QGNH0sm135Xz9YjudpzFxPbmJQfl+XoXLaFRDdddK0Oqq2pwbpLNfz0vsoTxTY8PEG3I4NtOKRhnlGENPRassQ0xQmtesTtWdrFS5RBsR0iBECRjlkv2eDSjSZ93ushbMMOGfYSUOEAGo4t43rlUBecbhuZZnjZ20MwfS0LCntYfVdB+rew5ial0SFSA6dPc7YHPPHBYLEwK62SScbJFXQiLj6xnjFIlqCEIYwWwzIfMNp9PQYMrKV0iMck3YR24b5Rd5CrarBBjWnfLeyi5qQerYJVHUgohBp8LAs9BGcKB15ij9iZRZ6dIyNcegmQAmVmhe+gyoz/rnWDvZ6+wxogOHUVjyMGB6aS83tg9S0zG9gWunniu3b7U8hQrMSBmOUI/dNa0n/lqOrY8Ip5uHeKC+6Nw6fS2tVQG9iQX6jQlq8dDx1oQxJvDsmihUtk2mDT2bOXBvAk4m+zxoFiarc0zGCzQrMUmo6WeOkYvVsOby7OiaGmYHoD364wOf/ADPeN8zuHT2Un7x8p9h+7t/mE/du8nrrv42fuGbLyMO9IN/+JN/6Oz0n/om2H3dwzbmnfjajBsPTfOiH/kufuza7+TyD97Lr//rjVx04Dm8deU419Qzvv9Tv8H/ePMl9N72ZPin3yltrHdiJx7NIdYJcaZin7QqQcTSlAoLs1dRnZ9k+vrr0aKHeMVaIuO+vPdMOUmW9nVfvbDNRnXB5RhKMViYda6Qyq2gVyNDoL05SFiFICYNa9wwdcwf35tmBAFKgWk22dq7yGa7waDd4t7lWTZmuljP7EkhpVOKrWPHMOEwcdCiGODNS7yJgVVOplXgMOceaNlo1cmjmAxLLgaCCuPwCOZrXa5dPEk3cTJ6hThwoQTVmKeSBJjS7E3Quy6id/CQM8tQUtq7V8M6rahggjyLFYVl4piEhRxwKO7qhS121Q+634YRKokxSmO8cUsBXjNTAxG2kik2mrsYy/ZwdUDW938aYwyV4r7t+/j89pcItCaKI0hd4nrR1EWuHk0px5jKUFJYhPJGEqFSrv6tGLnASmOVihn2I7XdGQZ7LkCiEDm4gK4m48OUkSS1yLEV5PVKmXQfnW8TGUWqQ8eChl3qKi1ZQaMCrlt+PKreIbcQ711DwpD+7LxDKziAJvnAz7JrwVDI9e6fP1o6Zc63Yz9HQ4Cm7rqHVtrm8uu/meSSa8hVyEZlntlWWp5DCUCLe6XA/mGT+xoHqPnEXHLnJLh7skYcKJg7ASZBobH1CljYXlji2sUbiLSXr4l4gDbMkdIiaW4ukoaGyXpMYBRrE1W0374/opWtnrwUtTBNPj/JiaW2q9kqHA5z30JZOwMcmw9KAATCRDRNJ5pBaUUrqVML2sRTLXITIQLBniNs1leYkjqVMGZfe79vczC8tNW9S5zs7iMJPAMVR74kTRiQkyQVBpUJ7OQ+pusRKu+XLo9kvbKnYgHkC+a3ampjvxcElQ/ohY0ShLvrqOgtt+ntXwbwbSc8k9hooYM2Uf0QeV4jDhJu3HUjKm1De4WtZNywLpiaYPuiJfpLkzTak6x03fPYy3x9q9/OhpFj3EZkx40kZHWi6pwjC3bU/80ZNBlyHdCITemmm+mI/tQsjz15gKVOypyvl9WiEKXdcy3OvXM9cIBVN+rsufg6mmsjZEYYEqtqaYiyPdUkWJghqKZjknKAzSOL4JnBxy7exEPFDkD7Go6N/gav+NNX8Jq/eg2vvfa1vGTledz5/Bdyh435j8efyy9+2+U00uDBd/D3vwHv/EZ44uth3+MfvoHvxNd0XLDQ5Kdf+Wx+4paX8+k/+Vuu/Kk/4teueCPvftJvsnrkOfzM6lGukc/xqj/6Qf7h54/Dn78ZeuuP9LB3Yif+r4TNc+wgY32iXiZfVomTVYnQuOQKosUpxH/BF73LNv76Y+j+gN2TNYKkCjNHUdo5G27FXR6o7/HmIlICID3yZR8U8jjx9Ug6cKYOQKiMa6GkjHc3dM5xAF/aN8P6RAuthJ7dKvf5wMlrEaXoZzmBKWpSXA3QwMuclPEADeikMXONxDWYVopQxdw/N8n6iZNOAlhfJW+vnOsOAnSSDpEpXOiA2gzSmIHGPDccO0lFx97TW1DdXfT37YeJffTiJoF2hEhQ1FH5JGpwyS6CWgLeubKs+bBD58b16jJEs/64aghiSnt/xwT1o46Xlil6YcXJQUVohVMkukYraWGxZOJq0IqEUBfXROmyH9bwegXOmEQ58IPISHek8ZD5C2kcuK48PxFhtjoztvXexiWEc/uc+2WjkHEOE2frQZb7f/d/iYzWGg1POywSXZt7jeIwLS/UNwPPchVinG1vI99oVGlEeMc7WE73slw5NHoUx1bEIyY2QFjU5ogQTkwSHn9MyWLtnXYM65mZg2RGlSAh8HJWgDSK6Mddb5ohFNbxRX81fB2dEsX2yhzrJ6+mN7tIZELE13Z1k2naQccxKnmf4+EKM9WZcnKM1rTaE05OWZBhSnF6o8f2wAvpgoRscRdUfHLv3RTlrGvrYOagbHyulCJAUTUtRCkqkeHS1Wkio7DKUL/8MNHqCv2ggdJVZlopsYlLU8TRxL9hErRSZMf2sdBaph25ZtwbrV0wsZ9/OfUvEBT98KS876X3ANZLC7OL9tGuhGS+NUEaVMDa8niO2Lc8UN3F6bmryn2UgNFvp8vHzmKn1jg1dQ2yEdMP6l5y7TeodDnlGevyTtHGMaxn9QdrxI6x7BcLDSZAdj+mfO7KvowilN6bIhxIvHmP0rTSkNlOxRmRaM3W/kWyuMZVeyZIQvdeM0ajalWMdu6x+DpSsXkJaiWMmOhO0pgdSp4ldExawaTbMCDat6e8z0Yjr0Tl7xreTfPBYgegfY3GP937Tzzj/c/gns17ePct7+aEWeXT3/Q87mlN8+LVp/KL33qS+WIV6nzx0Z+H938vPOsdcPDJD9/Ad+JREXPNhF9+6Y287Zkv5yP3KT7x1G9g4vbTvPCCF/KbT34vv/qEt1M99lxe0Ix4zsffxIfeeJzsI2+A/taX3/lO7MTXWAiWB6Y7mJlhQ1pau/jC1NWEE5NOjuOTjiw7S4pWSNNMhOD68PhKe/ZN16nGIcrLrZQ49spaS+TrT1wOIYh2xzgS7OJQZRZB0CYu622KlfEsdxb2aVB1LoRSZnwYJfQzS2CGJhIKS1Ym0y4x7PcyAqUZXHqA/vwkWoRa0OZw82oAvnBmm0Hm6+CsJU9czU2xT/df/1MEkiZM7wdgdfaQA6bpePJirjpJr+FYpaVOhUrsxri/vc/tTYbW7+eL4vwnag4YFlK7ghF0k+MA2kZ1F+x1q9t97cdqAtTlT2IyXiAJY3Kb0fNukkt+pT/0veTQZ3Nj/hy8O2fZXPtBynatMqTRiMTMj71iWmPbFXVqZ4MBt3M1MuUe1JiYm1duLjcpwEJh3W49CzM0tHA/J2sxrcKNz/+uHbccuNrOmUqrVI+5OvfE1Ai8oYMgZQ2crXRg4ZLh8Ir77jxW4/n6OgLkYUjmGdP4wAFWmyvDuQgDrt07RaCEfrsNWLJ6NDYTrhZQEwYaqw2D3Ho3VJc4p0GViWAaUYpIWUIxyOiVa69AdcrNh39OjTpLkRTVvUS4OC8hy3MiZajoiEiZoetpnpfnrQYZ3HGHY2H93+tpRGAc21Nc99zL80rGSglH55tDF9KZo9DZjRahb6osN5Y5NnkCQejbvHQBzdo19h57LLR2uRYeyhlgZDri3j3LtObaXLlngr7dKs+jjLgBCHnHMbjKg2IRRTB5AOqz5UKP6BjCCr2s59oLKENvzdX09RmRwgJpFIwzTEq599FZz/DykmsTUX7anK0KKyjs4XVyTK6XaxrF6kSVSd8z0GqFjUNMlNKqDBctRODYDcfduWvX4qSf42ppy14L/lrFAU866vv2hWE5fgC1vknoWfnzvY2M741WT3YA2qMqrLW8/eNv57YP3cata7fyc4/9Oaqfv5/bn/2NnFk7yPOnb+L1z7mIg7ON8+8gG8CH/iN85Gfhmz8Au658eE9gJx41UYsDfvH5J/nH5303b126mttv+xbufetbAVhrrfHSi17G7z/zD3nyyR/k56YXeOq//BIfeuNx8r/61R3p4048isL1U7Jq2H/IesA0CKpI4dRYALSHMNMZy0tEiHyPs0AZvyps6a3MsnVsjXgEROH7KuUIUVB34G3yIJuTl3JB5yJgmAcfm7iUA42TtMKWP8zwoMaP1Xi2rx6kXrLkAUVvgCAMtnsEorGpk4oNc5ehfKv8r7Vs7Zti8/CwCbGfNf/Tg79qhcYThkoOnUQ0bnrcUHpVqbCRLpCFTnq1ec2lw2MlLQdIRcpG1aNhcYn53uk6Mw0nfXRW4fkIqwViMwSIdAKhS+YGIwCiADBaKUIjhKEDvoVxS1TI47w1/dlX2gHIoaV7yHnKD9QQuLZVlUhFJaOlRBGqccc9N4Mj833WfgC2alUeWHIgZjRCHXF00rEYtYv2s71rt1eWunNeaqZMVCMuW+2UpRLF2Ntxh+uXricUw0plnrjqrkteqYwdQ3mp6kJtgdXmKvXH3eB+fxZQH/tMkgwT4jBCkhYSmLFtnUOpopGGhKtrNE4eZGvflNvGg+/+wDlQaiWQO2bLKCn7YClv4IPSJDrnxFL7nLG4qRwm/sU9rgdeFRKmbLT2c1/DmatpJeS5hQOrHDt5OZEyPGb5Oj9x+ZBByzLH9dgRR1IdkAaGQ/OdEiCt1Y5T7eweG48ZcbWkuxvSNqLg7umrnYOiBycFGwZg44jO7IoDbLpg0YRmvc7M3BD4F/eX8myUQujPdFg/vgKVxN0Hvs+dCATVKWgsEHnnG8uzAAAgAElEQVQmLm+vQFTDqKAEpuHkJJ1qSD8bcfsELt/T5cj8SK6qtGPkzgJolUsvZevYUQ4mc+xqrJRtI4pY6lQc0Dr7VvL/LloTlJ8TRTZ/GTMTD+FWrhQoxWSjwmQtLD+bn2cBSMJi8cKHtQS+z9tV81cTjZiY3DR5MXFhiHKed9XYEB7yrzvxVRVbgy1+6E9+iLf949t4y+PewvMOPo+tv/4Yn372s8luvIXn1a7mPz3pMFetTZx/Bw/cBW97InzmI/D834Opgw/vCezEoy6MVvzoUw6z8vzn8v1Xvog73/prfO4l30F2+jQAiUm4de1WfuvW3+XbrvxR3jA5xdP+9rX85VuuhM/+5SM8+p3Yif/zUJUULc6moviCtiJljmFKKZ4HaGeZZshYsiF0dY3peNlL1AAldCqRM+GwORhN1qySBoV83ToGSBm01pypLrvEM66SBQntpO337AbUjOsEKiIuairUsMYn0IpMRwRefrgUd8vzEATT6xMoTUxOwyTsauzyfxwmLWMslk9sbaDJq4mXDY4wZyMfHUvUBXQtHd8O1zbAFnK8AjQqBZUJzMReJy+rNzGXXHb2ZQJcn7pgruhZZrB57gBokSDnGY/vHGJPZ244znB4fa7aM+HPUXNytUMUKC+VdICsAGjWNz6u+gbWi/VFd0zXtZzcJGAt3focBzoHqN9w/XCQugBCQnvmKeybvMABND89amSeZhoJoTm/UHJQb2KjIhEUZKo2Mr/nAUVRwGK3RjUyWG/PvnuiQmjG00QZ+R+jDBc/9UXULj8KkrN+yQq5B2oOKKhSepiYhL3tvU7qG1ZRsZMxij43DY337UM/9io3l9P7kc6qdywUxzzEhqlaBUFIAs1j982XAEoWLoXdj3Vz4BkrZ9whJYMmWg+nIMtKcwgA0+0AUAkqJHvWqN90o1sc8fsvGOftqOuMLoAsqnGmtovpyrSXOOaoJEJ72aNKknLeSgYtc5bvuR3pyefv7Zm2mxtsTiVoYhvtUiJ93sizclzaGxQJapwFY2QxZoRdjcKIqckhSJpJVjnUvAo1PUN/coZ88hCkXdLQHf/qtQk3FnGtMAozkU7S8U243Tgm0gmuX3kc4N4rjTggGWEawbXhMKMMqlIY0aDOkuIGAZfum+Hg1ASNqIE9i0E7PNck1IrkwB6S1TnX0sMDzOI45XkDMsg4sX8Ph+YehMgAp1rIc5qVhGoUlAse+XlQkyjFxsGj5LGbi62DKwTVSdAhC80WTz184XBb/04s7o2Hih2A9jUSd63fxW0fuo27N+/mnY9/Jwc7B7n/gx/ksy94IeH3vIzbegf49mt2c+uF8+ffwaf+GN54JUzuh2/5XWh+mYbVO7ETX2GICN965QovecHj+eZLX8JnNnI+ecsTOfPHf1xuo5XmCatP4Lee9ns8/bKX8z1xj5f+znO46z3PhzN3P4Kj34md+D8LMQG1g7vIohDx4KtwcYSR1XGlOb7YYmWiOvb5eGWW8OTFAOS5s2auBcMGyVaEZhqR2wG9gWOerbWEnkHr7VqmtzqH1YbQGK674iQceooDHlqfk6QV/06CkLsOrqKiocxmffUmNtNZkjDiWHURjS1JlziNyasJRy49wMJ+BziK/lnbfbfRJbs6rE3VhoqjYse1aWYmDjEdDBOi0uYcYaW5wlJjqfxb4+LdhDMdJwccSVOGbQQg9UYmofK9lQpZGIKt1UfO2MIJVy+iqhXC3Qehs9slytaWyfHRhSaTs8ssLyxyyYpL0g/PNdk9M1Um84UcKjCuB5314y8AaSFLtIGrSUlNwtHJYxzqupos7fu92d2HuWP5iQRTU3RufHy5Ag+QV4dtAbbj7pBnKuZLFLXYsGeyxrHFlgPYIrB46RiY6i2tgpeJrk3VqMfBUE576Bvg8K2cHccWW2UTZX/Uc7YpflXMm67V3D3kVRHFOC9d6RCqmKmVy8nbZzEVUwdR4TjTBq6++YJ5V6vWzz3bUtQBGeNLqIRuNWIiGdZ8xnpEKhZVwO+7U42YqaeIwCBzz00hMy1MQiTPHCiOahDVqFzqmNk9rTWOTB11YA4pnVoLQJEu70XN7Rubp+NTx2nFLe+8OLJoUa8Trq6Qp/EIA5f5si47BIu6AGH+F3lWujieF4UXkWdjzbwdg6bHmbZyLh0zPlwZGVmgAO8WqlGHLyCvNVDGGe9M1iucWGo7Bi1ytYA2MGj//A3yAcvdCrVoCJ5UyTgLRyePMlsdOqEW4x4fn3Hyv+Bc6V80uRvV8XVf/pqcHcGJJxNe85zh7kpZqOJw97Dro1hNyZPzuycqZOgMaTR5JXGOjvUK/SnHMhaLF2cNnKzZRhd/0goVVuHAEwHY017m5pWbuaaxF4D6NZcQLUyedwzj49mJr/r42N0f45nvfybHp47zxuveSCNqcM8v/AKff9V/pvHan+H5n2tzywWzvPDq1XM/3NuA//7D8M5nw+N+HB7/UyPWpDuxE/9+8biD0/zyi6/he3Y/lY/c+FzueOnL+PyrXkW+PjQIMcrwjP3P5v1P+32aB76Bpz7wP/mNt1yG/egvlPUfO7ETX1MhMDM7w5H6SXJvpLCdOKZFRIbGEQgL7bSs5Sk/bjS1rktcNnojCYt4rsTLlQ7N15j2ZhC5dcxAJTLU9+4mr1fIC+twwGa5K1rXhtEcbaoWM+udymJj6KfxWAPmMHJ/M0pcs9+sXzZR7l1xMb21RZI9qwQTRVsAd7xe3533dCMum+vmZihJk9Yyx2YvZXV5N4GXFUVFnZJS7Gvvox6Ogqrh5Jar/gLzrQqtNGBfMs1FMxdwfLFFkbkWBhMrE7WxuhIAa4aGFIhA2kH7FfIiN51pJKjaBKw+xh9PCI3i2K5LuWTymrH9aeWs1DNxLpgoYbnrXOAm00l6e+Y96Do/g5EeOsS1B6bpVEJUNP59nC/OYq+8tgRAbsxjM8JENeLAbJ3CFEQQBy4ic84CALh6GXfor6xXZWGrcr4auQLgjxrWINpb5Q+BSc3XCMb7L2TzwAXn7qdIP0cY5W41YrnrwNV0ddq3qfB30UjPuEtrKyynMyWIG2VmRhP3ZhJweK6NFikPUzBojgESJM+doczu685xsh42F5ZynMWCy+pNj6F61VV+ToafCbWzIBXB1bD5SB77jbAwZFJ0npfgq1xEUUNW3P3InVwSzst6llHpoqJKeX79LPeS35FzGfl8Pa5zYX0ZgPVrjo81kS+vb0k6ecZPD98TpaQv8qwmMLADurUImyYlCBV/zdy9LGPHAS9lHQmJa3SCyvlz1LiBzB8HOIdBK8NEEFa8HNwBs9nWBQQ6YKHuSInNE/sYzE2cX1orCvy9JEqRdRrUrruWypFVbCXCRiFLuy8853NFhCO7DPS5jGeqx2s8v1zsALSv8njfJ97Hi37/RXzvie/l+y/6ftTmNnd89/dw33vfx9Tbfo1v/3s4utDi5TfuO/fDt/8pvPFy+Pzfwrf/MRx52sN/AjvxdRWH5xv81n+4nHdW9/G6Z7+Kjc98jk8++Smsf/SjY9s14yY/fNWP8/ob38JbZpZ40cffxF2/dA3c8T8fmYHvxE78b4dgfL+cXt8tMqxXnJJhsZ0SB+OSvPNFYhJuXrmZWuK/1K3FjkgjBSGNIA2K+hDHmuyfrhN4q36bTsC8rzeLQidDKtgVHwvttKwlUkX9Q67LtH267laug6EVW/nZRjwOetyZu+0WOykL7dSPzR3vnuNHy1N2CZMinGyRXuzYwsLFMZDzSLeCc+U/grA22SIxQiuoEOihwUAjajBbcZbdB+eaw/GfdQ7FgA52D3KwcxCb56VkUKvzX5+w2RpLtia7TboLM+TWcWgijkkwygHT5cayY/70g9eXaBEPms5zTAUEAe0x84LhdqGOxn8vMiZ7lGhEyugm4EHH8WBRdoLIz275O8SKY94eyoAd+DG5X0UeFA/ONsUp9uOT+PxB6pEDFTCdumbNys+teLYyVoGTlQ02/L7G5bGjoZUecRj0gL8AEOBYb6UY9j4bPS8PTkTKXm0FQDNKlQBu9PhCjlh3XzC5H+ZPuPMxMSjNoOgZF4bl3JQArXjmi22sLSXRuna+BQwfSRO1//E86egcgVau2b2MS1/H6vf2PwHSznl3NZRiDhluNxVDgKaUdu+WYFgvluUZCkV/eZrBBZ5Z9M9AZBTB9BTh4uL4sYIAMzFSktNeZuHEC7h55UFcxYu6W60I1Pkkn/5dWDjAirB10ZWsbw8XfyP//JwNoCId0Q2qQ/bOg1ATuPfantoSK7c8k3R6vJYWhvd87E1Rbtp1E3PVc7dj/gRMHyplv18udgDaV2lYa3nL372F//qX/5U3XvdGnrj6RHqf+xy3P/sbsVtbzL797Xznn95LpxLyk99weLwR9Zm74bdfAu94Flz+XfBNvw2t5UfsXHbi6ytmmwm/+eKT1OdmefbqM7j/G57DHd/9Pdz5Ay9ncOrU2LYXTl3Iu5/yfhb238rT0m0+/K5b4Xe+DzZPP0Kj34md+N+LQGsSPZ4dHl1oYh4iUXcxAqA6tZIBsSOuYUoU/bw/NBgYkfqVq9JKoKjrCYLSjEE/CDC05Cx3KqWzGUAzDXn84RkHVnZfh0yuAXDDgWnmW+dK0ookbr5V8WzWCA5VapxaKCRrsZM5FglWKzkbUAC7i5osO1KrJoQ6hP7GOWvfl89dzmw62hPt7HNl7G9L9SUaUQPs0EHvbHymooj02NFzGK5Dz7iFuN0oAZrCOcIVJ1+yB/Lg1/0rWUCvRIbLVjoObPoPVCPDWnuF/Z39YzsrpJ3EDQb1s8oXRqWED2FQU+4OqEjErrh7XmxXXI+x+0pp6G9Cf6N0OSy2G+Tn7qR2w/XlPD0YQAOYqcwwW5tj6Kzo2MRAOWBwdk3Tieoya821sd8pUUOQ4cdkpiZR1eqw9uzBzBr89vmhvfSXZ8b2MQroR6diO99iYLyDdpCck3sNPJDdPLxGFvsatbPvhzyD2WPQXGTvdI1d3QrpRSfGaxUfImYbMbsn61itOTxxuPx9CbjDEDmvVG94LsX5DZ+PIUCj0WC7XS+logCZzdjf3g8i6JFn5pq1SY7MN0lPnCCYnj73eGdrMx8iVBjSeMLjuXrxmmE7BHA90UY+b0du3Mt3dzk4UmtWDd37NdLjz/W1i9dybP5K6OymcvlJ1C5fN+oXkvbUFsta0geLi6eOsbe19uAMWWvZP49f2aLJDkD7Kozc5rz6L1/Nu/75Xbz1prdydPIoD3z4w9x+69OoXnMNkz/7er7jvf/KVj/jDc8+PlwtHPTgI6+H15+ArA//4c/hwtu+sm+DndiJf8eIA82rbz3Cy27czzd9forf+4GfJcsyPnnTzZz+jfeMrcymQcoPXfbD/Mg1r+EVM7O8+tRf0/+5i+Hv3v0VJRQ7sRNfDXH9wRkm9y5jJ89ygivfv17edvFFSDiyejv6el67EWZ8byCtMcqU9U2DfOBkeQzt0cF9XwDnyIfAMWHt6rnMF4BRlm41GpFg+t8X3ydJk2B5N7pWJQl1KXUajSLJGXfXK/YfoopWAAikXWcJ7s8hUAFH5pocmD6Pc17Rh02GPbAEcbVGSQdpnidROmueR6NkmM7+Lszz8vvzfElVMDd33o8pUQzyQcmUZA1nWGGzrJTuFc2ezxftyvmvydkx6RnNYgCP3T9F2uq4RsvlmGVovrLn+jH28Y75x48DtLPCdNroxrlGCSKuke753r9nJ/CAY9BO3Q7AJasdbjzkEvGTq92y+e/YPkbuOZs9OECbqkxx4MhjSI4eRXlG47L5y33vLWFvay837rrRbRykTLZXx5gREWEimWCm6tjVYsjx2hq6VnM1aMI5RiXJkcPj45zuMug0mKvOI3F8zvmPXuX/1d6dx8dV1Y0f/3zvnSUzk31r9qRNV0pbWtpCF0ppWQpICwoIPirLg4oboKiP+Pv9fGR5UJFHeX4/FVRkVxbZcUFRENlaaAuyFIHSQje6pWmWJs0kk/P7496ZTJJJMmm2afJ9v17Tzty5M3PnzMnM+d5zzvdUZZUTzDiCHeWJe4GiPWi2z0ubzx8XYHeRVw0eH5V5IWaWZTtz4Xw+GHck5E/uvn8cESHk9dAe8HcMHRZivYBiWdiZwU77R0UD0KDPXVDcssDr6TQU2vL7aJzkLOXRkfwoEhtGaGVnkb7EyRCeFfR2SzTT5WATX+9FyNulTtseZ05lgp7U/HQ/6f6OY48Oi/V1SURiiYWUz4O0LDw5OUTcANa2O76LehL9M/Fiderh7vkByfWgefreRQ2ncCTMd57/DpvqNnHPafdQ4Mlm1w9voO7hhyn+/vX4lyzly79dT31zG7dfNI+Az/0Cefcv8OerwJ8Jn34IyueN7BtRCjj76DKOKs/mygde48nKVdy49GRqfvpjau+7j3FXXUVwTsdClUvLl/K7lQ/xrX98i8+kpfGjp6+m/NV7nHmTeQnmVyqVKqqXQVoW3sIC2ku7TP6WzsGBt7CQ5p6exxsA24ffI+D3keYJcHzF0WyXRloiLbEeg/getGiDL3pmON5pM4q7bQOnR6yu1Wb9rq2dssN1bR4FZnQsOGwlOJ+bldY9uDIGpmUuYHZ5Id431zrbME4jKr8jXXheII/Tqk/uPPojnpvEwtPWHHsOv+2HnEqnd6GbHoIwYHJwHNk547q9QeMGaLH1jHpQkhVg34Fw7LYlFs1tzdjG/Sxsi4OLj8JbWIi01AFgB7LA030YVl+vVZ4TIKugSwM07j2lTT+CtClOA935TKJDHDsC2a6qs6thz2a6nrkPLXCzXb7xYGxbbshHc3QuZIKGZCwM7tQY7qgbIZ8ntk5VQYafmgMt3Z/Dtp0lFW57tdceNADxePCVlcZO6onX57yNtEwnwIoe0eQVncrJEoug11nceW7RXLbv3E73Gh7tQetct30VFTS//kbcEEcLPDYTz72QvY0tsHFvpx7E+KKYkjuFf4XSO+aOxUn3pZMfyGdP0x4sgYhJ0EsycTl0WdS7k5zKnu+L47E8RLIzOnp06RimCZB29ELSKouAdsYFxzlJNJwdAeezW3VUKbWVSwjXrMP2ZUNL9JtL3AT8HQ9ojUvp77E82Jm9DMmMF1/2Xf92fSEIH6C//LafcA/z1MKRsPtSvQeDEePWy7h1InsSnUtoA0nNpE/yxLMGaCmkIdzA5c9cDsAdK+4gbXc9H1z5GUQsxj/8EO3jivjKb9dTeyDMnRfPJ+T3OHN2/no17H4bTvwezDq/25eNUiNpYmE6D31xITf//X0+8Y9NXHL5jZy/42W2Xnop6UuWUPi1K2JnqotCRdx2ym38/LWf88nwfVyTns+Jv1gCCy+DxVdoghuVmoJuoBLMJ+zLAbo3SuMbhyYctx5Qgh/+2eU5pB1RzMGPNuD1+LGtZlraWsj2Ow03O25IVmt7a6fFh5MR8NkEfEWcNuE0mmpeiTUmewyWSBAAzjibIE7muq7zOXx2GtUF6TQFe08l7bP77kmKBqWN4UZy3YAwYeNKeg7QvGKT7g10e5wnJ4fw5g/6PIackI/jJnXMlbHEoq6ljmwrSOxzdXtFY8FzbjW0Cp7CHpa96UFxVgB8PTfNRATcoDroDYJAq4n02oCckjsFgm/0/KLjl8SCsayAj6yAj7pNdGrQd7x+gsdb8ceb/IidtoJsvAU5fe8IHQk7BDjirB7ni0Utq1iWoJ50WeIiOny2hzZT9P78QD67Duzq8nLxz939PSda73BJmZNUpK29jUhrkB28iafbWZHkyqMv6SefRNve9bHedkEwcUGjFEyGgsmw6Y+ICDlpzut2zfrqCaVDrYWVXQnRHjJx/okulA7EEiQBPcwP60l8oNulMKqXJd3bFG9e0Twac56D/U2dtk/NnUpeII9tjdv6fI4sX5YzTzY6WqGXHrToPMGkB6sVzYDMxCfP4mlLPkXsbtrNhU9eSI4/h5uX30z7439h81kfJzRvHpV338XBvEIuuv0V6ppbuePi+YTqN8H9n4G7zoLxx8Flr8Lsf9PgTKUkj23x1eWTeOiLC1m9tZ6P76nkw5/cgRUK8f4ZK9l57XW07dnj7Gt5uGzOZdxw/A1c3fQONy76LK2bnoabF8J7f9Vhjyp1+YLUlxzXfXvJ7E4Nr05DHBPweix80TWs2tvxuPM/okMK4+fexJIOJMFK0MiQtLSE84S6KgoVdQwn67I9XiwruAjBOXNomn9E0sfXk6l5UynPKI8lFkmY/j2ZwKDL76O3pKTT4tjJsrBoN+1kuEHrrMJZzCpwMhV2nScYcpOiJC1BT6idnbhHxRJnflqriRDrQYt2CnVtLQbzIKuH5XXSCyGj+/ygRMNmEyc2iQ+Wkv9+bpk+gfFFydcPOzMDb3Fxz3PG4vhsX7dAoWs177QuWMIXdF6nMrMydhIkM81LdpdMrP4EQ/i6rncYb1LOJPKD+bQZ6Uervn+CAacHyxKLgCdAfiA/4Zpz0Plz7t7HGK1XnYdB5nizKPVnd8z/cs0vnh9bViIpvb19jz9hwqC++GwfgWCw2/YJ2RPI8mcxPa/vNYDTfeksr1geF5j1fKDRzzpt5kxCCxOvwdiJPz2pvBDag5YCNtVt4tKnLuWE8hO4suoidn75MsKbNlN+888Jzp3L7oaDXHjbK1TmBfnJqfmkPXkFvPkwzLsYLn+t4+ytUiluSlEG937uWP7wxkf85x/epmjcSXzzprPIeOI3vH/KCrLPP4+8iy7Ck5/P4tLFPPCxB7jy2Su5pHgcP8pdReFjX3aGOy7/LlQcO9JvR6lu0rwJGkFdhuimH388B998k9bmyUhFgh90YzrO6re3x3rM/LafJWVLYoFKf3ms7j/53tJSWt/YlNTjEwV4vRGfDxNM60efSmITspx05bHhSYmeMYmGrpWg0XYoovPx8iYeSagqjaz0jt/g+IW1+51BcdoZ3c7U9xVAHpc1haDZCp7Oa0d5baGlLe71q0/o37HkVSeeg5ZoX9tHIDqnpx8nDPrb8xtNa3+o2ru8n76ThHSv7z6PxfGTO/eKTivOjC0PEHtoH/XRFicj5VCdUk/zROfK2SwtXxrbHp0b1pNE8y3j/4/uM7X0FApyDHj8HFfW8Zz5gS5r3vUh+j3nK+9hDd9DlFZeiJ3e/+Cum2jdSGKIo+X3d0sqNKCXHrRnUofktd2vccGfLuCTk8/l0u1T+GDlmXhLSpjw6CME587lrR11fOLmF1lcCj/Lf4i0WxY4FeUrr8DJ12lwpg47IsLHZpbwzDeWsmpWCZc/t4evVZ/Frh/+nNbtO9h40sl8dPXVhLdupTi9mDtX3MmU3Kmc88H9rDn3VzD5FLj3PLjnE7Dp79qjplLKUeXZVOalxxpIiVh+P+JPc3rVMsb1+nzi88V6ZdLsNNJ96bGegbKMMgqDfS94GmUnyCzoyc3lwJwkzvomyRxCWvdkeeMWxY2KNbKDeVAwpcfHpi9eRNqR/Tiz34vofLyMQBae3M6/wbEG7aGMZvH44xYrTk5GwVTscdM75ku55RHyD/D8ezC/hyGOCRqqto9Kfy4nZk9zEpTF6daTN4K6dmqFwk0UZPh77NG2Aj3/DcezLemUiAIg4O29l8+ypNsyGIPJEovTJpyG1/K6yVCcS19zw8pygmQFOidaiT5flG0JlscLIScYy/BlkOFLkI01CXaOM7IgMKv7WnkDIV4PvnGD0D6Ovu9e2hm99ZYOhPagjaCnPnyK777wXf6z+GKm3fAsNXt2U/qTHxNauBCAB9Zu5UdPrOeW6tXMee8epHoZfOFZyJ80wkeu1MCleW0uXDSeT86r4N6Xt/C/XtyMp2wVX/ju2SxY+ySbVq4ifenx5H7qU1w1/ypmF87miuf/gwumX8Dnvroee93t8MilEMiFY78IR36814xlSg2H7KCP86af2sMwvDjRuxOl4XcbA5mnrkBsG7vZafR27TmbWTCzX8eWKJEIQGbAw4Fw6i8ULyLMK5rnpMiPbov2ilm2M7ejBz0NEzwU0SC0a6pu6Aje/BMnYTX0P8FBvwVyOg2f9bnD2GaWZePpZU5hUpLtQfOkOcsgiKdbr1N1QXrSWSuHkt9jdyRVi26zhGBxQSxDZLzMFacgnkNrIvs9FuW5vffeWALGzSKZSsbnhxgf1xsYn0U1amJhet/fb0nyVVR0Wx9t0CQYMdD/53DrTC9z4br2zA4WDdBGgDGGO966gzvX/4qff7iY4I23ELzwQvK+8Hksv5+Gg61c//hr5L1zLy8GHsXLTGcts9I5I33oSg26gM/m4sXjuWBhFX99exd3vPABV1uLOPNLJ3D29ldo+trX8eRkc8z553PP8b/iP9Z9jxe2v8D1x11P+YKvwoZHYc0v4Mlvw7SVMOs8qFqc1DwFpYZCooZ7V33Of6EjzXe05yuZ5+3JsoplCYc4AsypyBm0juiynGDnhaIHWUGw8/AyT17/hlQNhmiq7kTlGf1cfRXl/UyWMDj83uhCyjLgXjSToGegMNPPttoueUijJw7K50NW5yyVtiXkp498cqcTpxV2660KLVqIHUp8Uu9QgzOAFUf2nQDCtgTj8aREgNZbr3e05yw+MZG/hwyJKWcwArSY3nrQBvFl4miANsxa21u5fs311D7zN27+m59g6W6KHvwd/gnOGPu/vbmNNY/8lCt5mKyCErwn3wYTlo7oMSs1HGxLOGV6EadML2JLTRMPrt/GZTVgnTqbz9tbmfv4H7Bu/G/+54zT+fP0Ns59/By+Of9bnDXjHGTmubDnXXj9Pnjsy9DaBJNOdi7VyyAweGfPlRpMiYY4RYf9xG67jaPehk32pbfHyiCeybctoaTL2ldp9qEfd28yTjqx0zIBwyVR8owoj+Xh5KqTewyGh5pvEIPjRL2OxVkBTp/ZpXcoWrcye19CYCR5EpSLJ2dwMiYeCkuEcFkVkdK+g7mh1lt9TtSDdljIqRrcKQAGu5wAABLWSURBVEC9lFFbgqHAg0EDtGHUEG7g+/d+kYUPvcvEGi9F3/w6WatWISJ8sLuOfzz0U5btuoO5WblkrvgxMvX0Icvwo1Qqq8gL8vWTJnPF8km8tKmGx14bx48OljFj4j4+s2s9C558nmOygvxp9Y/4znF/5PLTr6OoYLKTPOSE/w07X4f3/gIv/RQe+QKUzYfqpVC52OmJ1nT9Y46IrAD+B2e5mluNMT/oYb+zgd8B84wxa4f8wKy+k0J4xIMlVrfFVQ8HS8uXdjr7PpiSmpBfOgf6WGurvyoyK2JpyRMZqeAMOjIKtkUG1iWaefppyc+P8qbBEat01EI/+GwLbBsrfeSCxGQkmoN2WCibO8hP2PPfU2VeqNO6eINFA7Rh8va/XmD1dVfwyTeaKbjoYsZd8gXs9BBb9zbw/KO3sHDrrZwSCJJ+5g8JzTxT0+UrhTORetHEfBZNzOe6M2fw3Ht7eOKfU3k6fyknHdjM6R+tpeDal1h/84nIkmNY/Kmvkz75CKTkKCg5Co7/FjTugY1PwebnYN1dcGCPs5B75WKoWgSlc50Ghhq1RMQGfgacBGwDXhGRx40xG7rslwFcBqwZ6mOKnbVO4rs+6A2yoGTBkCUU6Jd+ntwIegcnc+Ihy50w6E9pidVpHlwqERGq8kKE/AMLlvpd1/qZ3GSsiyZzGaL8Ev2SzBDHwy5AG2y99KCVZgcozR6EjJFdaIA2xFr37GH1Dd8i/cnVTFw8k2lP3oS3qIjV7+3g3b/exHG77mFZmg/v6deQO/ccDcyU6oHPY7F82jiWTxvHwdaZ/P2d3Tzx1mJenLiF+bUvMfetZ3jv3HOw8nIpOnUlGUuWEph9FFZ6ARz1KecCsH8LfPACfPg8PHYv1O9wzrJXHAsVC5x5FIO0WKhKGfOBjcaYTQAich+wCtjQZb9rgRuAbwz5EcWtF5aMlAkIcioh1L/Fl9XwmlU+gCHd46ZDzcbBOxjVq6HMejoYokMbx3yANgKfkwZoQ6Sttpatt/w/6u97gA+n+Jlx+39TOX4pd67dgHX7dawK/54JoSLSTv8uWXPP1aEBSvVDmtdmxZHFrDiymNazZ7L2g0U8teHT/PK9PzG+5jEWrH+A+Y89QnpDM6Gjjya0cAHBYxeQNm0qkl0BR1XAUec7T1a/A7ashi0vwdPXwu63IX9KR8BWcSxk97DAqzpclAJb425vA46J30FEZgPlxpjfi8gwBGip3TDrlW+Ee8XU0Cmc5lzUsBjpHrTq7GrKMnpeg+ywHeI42Ebg+1oDtEHWVlNDzV13sefuO3m1sp0t31xOsPjf+fULb3DM4xfyWftZ6osWkH3i3XgmHKdzzJQaIK9tsaA6jwXVefwfcwTv7vocv1j3KLfvvp+M2ghz329gyV+eZfyvbsMTaSM480jSZs4k4F48+SVOiv4jP+484cE62PaKE7StvxMe/4qzJlDlgo6grWCa9nYfXhJ90cZ+cUXEAn4CXJjUk4l8Hvg8QMUhp4g+jAM0pdSAzSjNIjs4svNKp+T2vHZglCXW4ZckZLCNwFBt6S17S1dz5841a9cO/Zzpw1H4ww+puf12ah99hDcm+Hh4QYg99iqW12/h02kvUNK2HWacjXfhl3pdTFMpNTiMMby841V+8drdrN/7LJ62MjLfr+LY/TnMb9pP+a4PCG7dhKewkLRp0/BPmoh/0iTnUlWFRNfGaQvDR/90etiiPW0mAuXHdARsJXN0HlsfRGSdMWawZ24n+9oLgO8ZY05xb18FYIz5vns7C3gfaHQfUgTsA1b2lSjkUH8Xm19/nfCWrQmTgiilVKrYUr+FsoyysduL1nrQmXs7BB0qvf0uag/aAJhIhAPPP8/O39xL8+oXeWF6Bo982sd4TxX/FdnDhMbrYfwCrNnfgKkf02EhSg0jEeGY0jkcUzqHxnAjz2x9hifK/8gfdz7Bc3Yx3vAU9u9YRNVOH4toYdqGbRQ9t4bA9g+gsRH/+Cr8kybhq67GV1mFr3IZvo9dhJ2eDnvfgy0vur1sdznDJEu6zGMbzBS/aqBeASaJyHhgO3Ae8KnoncaYOiC2oJaI/B34xpBmcTychzgqpcaMiswhWkj6cDFCJ181QDsEB7Zs452776f9949wMNLE0zMNqz9n8TH281B9DaHSamTqJTD5VAjljfThKjXmpfvSOaP6DM6oPoOm1ibW7lrLSzteYl3+Wt4tf5e93kL+YipoaSpn774jyagPsSDi4ahwA1VvbiL3uZfw7dxOZOdO7Lw8fJWVzqVqDr4pZ+HLD+GT7Vi71sEz/wW7N0D+ZKeXrfwYJ2DLnaBDmkeIMaZNRL4C/Bknzf5txpi3ROQaYK0x5vEROKhhf0mllFKHBx3imITGA428s/olGv74BL51r5Cxp453J8DjcwRPcYSzfUWcVHUSnglLnbUXdI0lpQ4bzW3NbKjZwLu177K5bjOb6z7g/dpN7Dm4CxsfNhm0hUOEw0GyjY8JzYaqxghl9a0U7W8mZ28jmbsbCNS1cCDbT11hiNp8P7XZhn0ZYfZmNFMTaKLFZ9HqD3Fc9hS+OutLUDIbvIOfmjdVjeQQx6F0qL+LTa++Suv2HTrEUSmlxigd4tjV+09D836ItEJ7K+1tYVrDB2mu38fB+r20HaghXF/Dga27aN7WgLUD0mosNldaPDfXIjx9HEvHL+THU88hb9wsPSuu1GEs4Alw9LijOXrc0Z22hyNh9h3cR83BGmqaa9hev4cP99Wxs6GRTQ1NvNx4gH0HWmg4aMj0+yj1epl4MEx5YxPF9QeYsK+B2RtrSdvZgl0vRLKDmFwf3vTN7L7v8/i8tXjLK/FOOhLv5KOR0llOimt/xgiVhBpW2oGmlFKqB2MqQKtrqeOtmrdoXXczrc37eK/2ILWt7dQLtIUNgf3tjNsVoWRnmKqP2mj2wb8mZdCwspqs445n1vgFrMqfgVcXZFRq1PPZPopCRRSFinrdr7Gljfd3N7JxdyNb9jWxfX8zL+9vZsf+ZnYUHcTMMJT42pnQVk9Fy35KmvdR0FhD/v4dZP1jO97HXkQiz2EHwJsWRjJ8kJWJycunraCY7IlTyJw8HW/FJKy80sGfy2oMtEcgEnYu3oCOAhgWGqEppZRKbEwEaKa1lUh9Pds+XMcj/7iJvPp2cvbbZNd6qd59kIKdzfgOttJckkfLtCo8S6aTNX8x06fNY5E2VJRSvUj3e5hVnp1wcdj2dsPexhb2NobZ3xSmtqmV2qYwO5rCvNXUSnNrhJZwBKu+Fn9tDem12ympfZ/cuh1kbd1H+r9eg7+uYV9jG+0tIHY7tt9gB2zsgMf5P2hjB73YaV7EZ2PZ7Vg2iCeCZbUjdgRLImDawLQh7c7IAdrjrktcsHDajTDpJOe6MViBAJ4CXZh40OkcNKWUUj0Y9gCt5tZbaXzueeeGMR0X3BXVo79ZXe7DmMT3x12PrchugEiESGMD7fvraG9qAsvCm5nJpXl5eEvK8BYX451UhG/8BPwTq/FVVHSk1VZKqUFgWUJhZhqFmQPPAtXe3Exk9w4iu7cS2bWdyN6dROrqidQ3EKlvoKWxCdPQSntrGybcRntLK+3hNkxLK+3hVvd70ll+ILY6arvBmHa3F804w7Wf+TnIzbGh2xnLllHywx8M+PhVFxqgKaWU6sGwB2jB+fPxlpURWzdUxLnqNgZEpGNOl4izX3SKl0ji++OeJ7rqOZaFlZGBnZWNnZWJFQohurCsUuowZQUCWJXVeCurR/pQ1CDwT56MnatZfpVSSnU37AFaYOZMAjNnDvfLKqWUUinDzszEzswc6cNQSimVgrRLSSmllFJKKaVShAZoSimllFJKKZUiNEBTSimllFJKqRQhph+ZpERkD/Dh0B3OiMkH9o70QaQ4LaPkaDklR8spOaOpnCqNMaMuX7+INADvjPRxHMZGUx0fCVp+A6PlNzBafgPT4+9ivwK00UpE1hpj5o70caQyLaPkaDklR8spOVpOqU8/o4HR8hsYLb+B0fIbGC2/oaNDHJVSSimllFIqRWiAppRSSimllFIpQgM0xy9H+gAOA1pGydFySo6WU3K0nFKffkYDo+U3MFp+A6PlNzBafkNE56AppZRSSimlVIrQHjSllFJKKaWUShGjOkATkRUi8o6IbBSRb/ey39kiYkRkbty2q9zHvSMipwzPEY+MQy0nEakSkWYRec293DJ8Rz38+ionEblQRPbElcclcfddICLvuZcLhvfIh88AyygSt/3x4T3y4ZXM35yInCsiG0TkLRH5bdz2MVGXUl2y35tjmYiUi8gzIvK2W48vd7fnishTbh1+SkRy3O0iIv/XLdPXRWTOyL6D1CAitoi8KiK/d2+PF5E1bvndLyI+d7vfvb3Rvb9qJI87FYhItog8KCL/cuvhAq1//SMiX3P/ft8UkXtFJE3r4DAwxozKC2AD7wMTAB/wT+CIBPtlAP8AVgNz3W1HuPv7gfHu89gj/Z5SsJyqgDdH+j2kSjkBFwI/TfDYXGCT+3+Oez1npN9TKpWRe1/jSL+HFCqnScCr0XoCFI6lupTql2S/N8f6BSgG5rjXM4B33d/XG4Bvu9u/DfzQvX4a8CdAgGOBNSP9HlLhAnwd+C3we/f2A8B57vVbgC+6178E3OJePw+4f6SPfaQvwJ3AJe51H5Ct9a9f5VcKbAYC7u0H3N9xrYNDfBnNPWjzgY3GmE3GmDBwH7AqwX7X4vyxHozbtgq4zxjTYozZDGx0n280Gkg5jSXJllMipwBPGWP2GWNqgaeAFUN0nCNpIGU0liRTTp8DfubWF4wxu93tY6UupTqt60kwxnxkjFnvXm8A3sZp8K3CaTjj/n+me30VcJdxrAayRaR4mA87pYhIGXA6cKt7W4BlwIPuLl3LL1quDwLL3f3HJBHJBJYAvwYwxoSNMfvR+tdfHiAgIh4gCHyE1sEhN5oDtFJga9ztbe62GBGZDZQbY37f38eOIgMpJ4Dx7tCLZ0XkuCE8zpGWbJ34hDs04kERKe/nYw93AykjgDQRWSsiq0XkzASPGy2SKafJwGQRecEtjxX9eKwaevo59JM71Gk2sAYYZ4z5CJwgDih0d9Ny7e4m4FtAu3s7D9hvjGlzb8eXUaz83Pvr3P3HqgnAHuB2t51yq4iE0PqXNGPMduBGYAtOYFYHrEPr4JAbzQFaoog9lrJSRCzgJ8CV/X3sKDOQcvoIqDDGzMYdguGesRqNkqkTTwBVxpiZwF/pOIs0VurTQMoInLo0F/gUcJOIVA/NYY64ZMrJgzPMcSlwPnCriGQn+Vg19PRz6AcRSQceAq4wxtT3tmuCbWO2XEXkY8BuY8y6+M0JdjVJ3DcWeYA5wM1uO+UAzpDGnmj5deHOz1uFM92nBAgBpybYVevgIBvNAdo2IP7sfBmwI+52BnAk8HcR+QBnvPHj4iTA6Ouxo8khl5M7BLQGwP0BeR/nzP9o1GedMMbUGGNa3Ju/Ao5O9rGjxEDKCGPMDvf/TcDfcc62j0bJ1IdtwGPGmFZ3mPU7OAHbWKlLqU4/hySJiBcnOPuNMeZhd/Ou6NAx9//oEF4t184WASvd3977cIaV3YQz9M7j7hNfRrHyc+/PAvYN5wGnmG3ANmPMGvf2gzgBm9a/5J0IbDbG7DHGtAIPAwvROjjkRnOA9gowyc0048OZrBjLDGeMqTPG5BtjqowxVTjJL1YaY9a6+53nZqMZj9Mwenn438KwOORyEpECEbEBRGQCTjltGv63MCx6LSeIfdFHrcSZbwHwZ+BkEclxz0ad7G4bbQ65jNyy8bvX83EaJhuG5aiHX5/lBDwKnACx8piM87c1VupSqkvmMxzz3LknvwbeNsb8OO6ux4FoBtILgMfitn/WzaZ3LFAXHYo2FhljrjLGlLm/vecBTxtj/g14Bjjb3a1r+UXL9Wx3/zHbe2GM2QlsFZEp7qblOL8rWv+StwU4VkSC7t9ztAy1Dg4xT9+7HJ6MMW0i8hWcxosN3GaMeUtErgHWGmN6/DF193sApxK2AV82xkSG5cCH2UDKCWfy7TUi0gZEgEuNMaPyTEmS5XSZiKzEqTP7cDIdYYzZJyLX4jTqAK4ZjeU0kDICpgG/EJF2nBNHPzDGjMoALclyigZiG3D+tr4Z7a0eC3Up1fX0GY7wYaWiRcBngDdE5DV323eAHwAPiMi/4zQAz3Hv+yNOJr2NQBNw0fAe7mHjP4D7ROQ6nGyvv3a3/xq4W0Q24ny/njdCx5dKvgr8xj2RsgmnTllo/UuKMWaNiDwIrMf53X4V+CXwB7QODinRwFYppZRSSimlUsNoHuKolFJKKaWUUocVDdCUUkoppZRSKkVogKaUUkoppZRSKUIDNKWUUkoppZRKERqgKaWUUkoppVSK0ABNKaWUUkoppVKEBmhKKaWUUkoplSI0QFNKKaWUUkqpFPH/AbcLK9sMBJChAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(fit_T)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inference for Stan model: anon_model_4d1ec45e7f8ca5ad8aff3c29b11001e6.\n", + "4 chains, each with iter=2000; warmup=1000; thin=1; \n", + "post-warmup draws per chain=1000, total post-warmup draws=4000.\n", + "\n", + " mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n", + "alpha 0.68 7.7e-4 0.05 0.58 0.64 0.68 0.71 0.77 3961 1.0\n", + "beta 0.07 1.6e-3 0.1 -0.12 5.9e-3 0.07 0.14 0.26 3816 1.0\n", + "sigma 0.49 5.5e-4 0.04 0.43 0.46 0.49 0.51 0.57 4067 1.0\n", + "lp__ 21.44 0.03 1.21 18.32 20.87 21.72 22.33 22.85 2224 1.0\n", + "\n", + "Samples were drawn using NUTS at Wed Nov 18 22:09:16 2020.\n", + "For each parameter, n_eff is a crude measure of effective sample size,\n", + "and Rhat is the potential scale reduction factor on split chains (at \n", + "convergence, Rhat=1).\n" + ] + } + ], + "source": [ + "print(fit_Eff)" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
meanse_meansd2.5%25%50%75%97.5%n_effRhat
alpha0.6368260.0015150.0707300.4969190.5916610.6371430.6845250.7752562180.8304051.001008
beta0.0786890.0020680.097416-0.1111700.0132130.0795360.1422970.2739242219.1833621.000176
sigma0.4909870.0006780.0349720.4284400.4659600.4894400.5135020.5650162661.4769811.001984
lp__21.4788470.0311851.23563718.33081020.88939621.81398122.38766222.8927031569.9896741.000456
\n", + "
" + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% \\\n", + "alpha 0.636826 0.001515 0.070730 0.496919 0.591661 0.637143 \n", + "beta 0.078689 0.002068 0.097416 -0.111170 0.013213 0.079536 \n", + "sigma 0.490987 0.000678 0.034972 0.428440 0.465960 0.489440 \n", + "lp__ 21.478847 0.031185 1.235637 18.330810 20.889396 21.813981 \n", + "\n", + " 75% 97.5% n_eff Rhat \n", + "alpha 0.684525 0.775256 2180.830405 1.001008 \n", + "beta 0.142297 0.273924 2219.183362 1.000176 \n", + "sigma 0.513502 0.565016 2661.476981 1.001984 \n", + "lp__ 22.387662 22.892703 1569.989674 1.000456 " + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summary_dict = fit_T.summary()\n", + "df_1 = pd.DataFrame(summary_dict['summary'], \n", + " columns=summary_dict['summary_colnames'], \n", + " index=summary_dict['summary_rownames'])\n", + "df_1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ignore for now" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def abline(slope, intercept):\n", + " \"\"\"Plot a line from slope and intercept\"\"\"\n", + " axes = plt.gca()\n", + " x_vals = np.array(axes.get_xlim())\n", + " y_vals = intercept + slope * x_vals\n", + " plt.plot(x_vals, y_vals, '-')\n", + " \n", + "alpha = df_1.loc['alpha','mean']\n", + "beta = df_1.loc['beta','mean']\n", + "\n", + "sns.catplot(x = \"Rotation\", y = \"mean_NEO\",order= ['clock','counter'],data=df_Mu_neo_condt)\n", + "abline(alpha,beta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References \n", + "\n", + "Costa, P. T., and McCrae, R. R. (1992). NEO Personality Inventory Professional Manual. Odessa, FL: Psychological Assessment Resources.\n", + "\n", + "Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573.\n", + "\n", + "Topolinski, S., and Sparenberg, P. (2012). Turning the hands of time: clockwise movements increase preference for novelty. Soc. Psychol. Personal. Sci. 3, 308–314.\n", + "\n", + "Wagenmakers, E. J., Beek, T. F., Rotteveel, M., Gierholz, A., Matzke, D., Steingroever, H., ... & Pinto, Y. (2015). Turning the hands of time again: a purely confirmatory replication study and a Bayesian analysis. Frontiers in Psychology, 6, 494." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "341.333px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian estimation of 2x2 Between subjects ANOVA.ipynb b/wip/Bayesian estimation of 2x2 Between subjects ANOVA.ipynb new file mode 100644 index 0000000..70abc7f --- /dev/null +++ b/wip/Bayesian estimation of 2x2 Between subjects ANOVA.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
    " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Import analysis packages\n", + "%matplotlib inline\n", + "import pystan as ps\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as ss" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Maglio%20and%20Polman%202014%20Experiment%201.csv'\n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    directionorientationstationsubjective_distance
    0EAST115
    1EAST114
    2EAST113
    3EAST113
    4EAST114
    ...............
    97WEST221
    98WEST221
    99WEST222
    100WEST221
    101WEST223
    \n", + "

    102 rows × 4 columns

    \n", + "
    " + ], + "text/plain": [ + " direction orientation station subjective_distance\n", + "0 EAST 1 1 5\n", + "1 EAST 1 1 4\n", + "2 EAST 1 1 3\n", + "3 EAST 1 1 3\n", + "4 EAST 1 1 4\n", + ".. ... ... ... ...\n", + "97 WEST 2 2 1\n", + "98 WEST 2 2 1\n", + "99 WEST 2 2 2\n", + "100 WEST 2 2 1\n", + "101 WEST 2 2 3\n", + "\n", + "[102 rows x 4 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Reduce the dataset to demonstrate 2x2 for simplicities sake\n", + "dfReduced = df[df.station < 3]\n", + "dfReduced" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "TbTBSA = \"\"\"\n", + "data{\n", + "\n", + "int N; // Number of observations\n", + "vector[N] y;\n", + "int K; // Number of predictors\n", + "matrix[N, K]; // The design matrix\n", + "\n", + "}\n", + "\n", + "parameters{\n", + "\n", + "vector[K] beta;\n", + "real sigma\n", + "\n", + "}\n", + "\n", + "model{\n", + "\n", + "// Priors\n", + "beta ~ normal(0, 10)\n", + "sigma ~ normal(0, 10)\n", + "\n", + "// Likelihood\n", + "y ~ normal(x * beta, sigma)\n", + "\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian estimation of Linear mixed model.ipynb b/wip/Bayesian estimation of Linear mixed model.ipynb new file mode 100644 index 0000000..cb84058 --- /dev/null +++ b/wip/Bayesian estimation of Linear mixed model.ipynb @@ -0,0 +1,924 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

    Table of Contents

    \n", + "
    " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the neccesary python libraries for Bayesian analysis\n", + "# and data visualisation.\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "import pystan as ps\n", + "import arviz as az\n", + "import statistics as Stats\n", + "import scipy.stats as stats\n", + "from patsy import dmatrix" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of linear mixed model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The classic linear mixed model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following the quick description of classic linear mixed model above its important to keep in mind that Bayesian analysis inference are all derived from the application of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to a classic linear mixed model, it is fundamentally different, because its uses fully probabilistic modelling and therefore the infernces are not based on sampling distributions\n", + " \n", + " For a fuller description see the practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. In the case of scientific research publication is the goal, as such the priors must be accepted by a skeptical audience. Much of this can be achieved using prior predcitve checks to acsetain os the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then interprete and the posterior\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation\n", + "\n", + "Within this notebook the example linear mixed model anlysis will be conducted on a peadagogically standard and coomoly refered to \"sleep study\" data set from the LME4 package (Bates, Mächler, Bolker, & Walker 2014) taken from Belenky et al (2003).\n", + "\n", + "The \"sleep study\" dataset consists of repeated observations from 18 participants of their average reaction time (ms) across 10 days of testing. Baseline scores on day 0 consisted of the reactions times scores for each participant before sleep deprivation began. for the following 9 days sleep was resticted to 3 hours." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data cleaning and exploratory data analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Sleepstudy_data\"\n", + "\n", + "#Import data .csv file into pandas dataframe.\n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert to long to change the particapnt ids to 1:18\n", + "df_wide = df.pivot_table(index = 'Subject', columns = 'Days', values = 'Reaction').reset_index()\n", + "df_wide['Subject'] = list(range(1, len(df_wide)+1))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df_long = pd.melt(df_wide, id_vars=['Subject'], value_vars = range(0,10),\n", + " var_name='Day', value_name='Reaction')\n", + "df_long['Day'] = df_long['Day'].astype(int)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.lmplot('Day', 'Reaction', data=df_long, hue='Subject', fit_reg=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case such as the one with this dataset with few subject but multiple observation veiwing the data toghether can provide useful insight. What the plot seems to show is that generally reaction times descreased as the number of sleep deprived days increased but that there was variability between the subjects in terms hoe much their reaction times decreased" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "y_{ij} &\\sim Normal(\\beta_0 + u_{1j} + x(\\beta_1 + u_{2j}), \\sigma)\n", + "\\\\ \\beta_0 &\\sim Normal(250, 50)\n", + "\\\\ \\beta_1 &\\sim Normal(0, 50)\n", + "\\\\ u_1 &\\sim Normal(0, \\tau_{u1})\n", + "\\\\ u_2 &\\sim Normal(0, \\tau_{u2})\n", + "\\\\ \\tau_{u1} &\\sim halfNormal(0, 50)\n", + "\\\\ \\tau_{u2} &\\sim halfNormal(0, 20)\n", + "\\\\ \\sigma &\\sim halfNormal(0, 50)\n", + "\\end{align*} " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3 - Specifying priors for the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stan model of varying intercept and slope linear mixed model" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "Linear_mixed_model = \"\"\"\n", + "\n", + "data {\n", + " int N; // number of data observed\n", + " int J; // number of subjects\n", + " int K; // number of fixed effects\n", + " vector[N] y; // obsevered data\n", + " vector[N] x; // predictor\n", + " int subj_id[N];\n", + " int onlyprior;\n", + "}\n", + "\n", + "parameters {\n", + "// Fixed effects parameter \n", + "vector[K] beta;\n", + "// random intercept parameters\n", + "vector[J] u_1;\n", + "// random slope parameter\n", + "vector[J] u_2;\n", + "\n", + "// residual error\n", + "real sigma;\n", + "\n", + "// Population distribution for the subject variability \n", + "// for intercept and slope terms\n", + "vector[2] sigma_u;\n", + "\n", + "\n", + "}\n", + "\n", + "transformed parameters{\n", + "vector[N] mu;\n", + "\n", + "for (i in 1:N){\n", + "mu[i] = beta[1] + u_1[subj_id[i]] + (beta[2] + u_2[subj_id[i]]) * x[i];\n", + " }\n", + "}\n", + "\n", + "model {\n", + "// Priors\n", + "beta[1] ~ normal(250,50);\n", + "beta[2] ~ normal(0,50);\n", + "sigma ~ normal(0, 50);\n", + "sigma_u[1] ~ normal(0, 50);\n", + "sigma_u[2] ~ normal(0, 20);\n", + "u_1 ~ normal(0, sigma_u[1]);\n", + "u_2 ~ normal(0, sigma_u[2]);\n", + "\n", + "if (!onlyprior)\n", + "//likelihood\n", + "y ~ normal(mu, sigma);\n", + "\n", + "}\n", + "\n", + "generated quantities{\n", + "vector[N] yrep;\n", + "\n", + "for (i in 1:N){\n", + "yrep[i] = normal_rng(mu[i], sigma);\n", + " }\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_03e0a14c53633e6cf81f3726ee6dc79a NOW.\n" + ] + } + ], + "source": [ + "sm = ps.StanModel(model_code = Linear_mixed_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "X = df_long['Day']\n", + "data = {'N': len(df),\n", + " 'y': df_long.Reaction.values,\n", + " 'K': 2,\n", + " 'x': X,\n", + " 'J': df_long.Subject.nunique(),\n", + " 'subj_id': df_long.Subject.values,\n", + " # set to one for prior predictive checks\n", + " 'onlyprior': 0}" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "fit = sm.sampling(data = data, iter = 2000, warmup=1000, chains = 4, seed = 1);" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inference for Stan model: anon_model_03e0a14c53633e6cf81f3726ee6dc79a.\n", + "4 chains, each with iter=2000; warmup=1000; thin=1; \n", + "post-warmup draws per chain=1000, total post-warmup draws=4000.\n", + "\n", + " mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n", + "beta[1] 249.2 0.86 50.88 148.58 215.57 249.1 282.73 348.95 3530 1.0\n", + "beta[2] 0.21 0.75 50.09 -97.87 -33.2 -0.57 34.18 97.51 4512 1.0\n", + "u_1[1] -0.25 0.69 48.87 -106.3 -20.45 -0.15 20.71 105.39 4992 1.0\n", + "u_1[2] -0.69 0.74 49.36 -104.5 -21.62 -0.23 20.01 104.1 4397 1.0\n", + "u_1[3] -0.16 0.76 50.11 -106.8 -20.19 -0.18 21.72 106.58 4326 1.0\n", + "u_1[4] 1.28 0.64 48.11 -106.3 -18.01 0.68 21.81 103.77 5697 1.0\n", + "u_1[5] 0.1 0.65 48.99 -109.5 -20.33 -0.19 19.14 110.28 5595 1.0\n", + "u_1[6] -1.06 0.66 48.96 -104.8 -21.6 -0.24 19.81 100.25 5476 1.0\n", + "u_1[7] -0.32 0.66 50.81 -111.2 -22.02 -0.29 21.18 112.02 6009 1.0\n", + "u_1[8] -1.3e-3 0.69 52.64 -114.2 -21.32 -0.09 21.09 113.68 5825 1.0\n", + "u_1[9] -0.77 0.67 50.62 -110.6 -21.43 -0.16 19.48 108.47 5689 1.0\n", + "u_1[10] -0.08 0.66 48.07 -106.3 -20.12 0.22 19.88 103.03 5377 1.0\n", + "u_1[11] 0.21 0.71 50.3 -108.1 -19.61 0.22 21.39 108.86 5002 1.0\n", + "u_1[12] 0.45 0.68 50.62 -107.1 -19.69 0.03 20.73 114.23 5528 1.0\n", + "u_1[13] -0.5 0.72 49.3 -106.2 -21.38 0.05 20.15 105.46 4698 1.0\n", + "u_1[14] -0.76 0.72 49.2 -109.7 -20.88 0.23 19.59 102.42 4616 1.0\n", + "u_1[15] -0.2 0.67 49.56 -109.8 -21.6 0.24 21.58 106.48 5461 1.0\n", + "u_1[16] 4.8e-3 0.73 48.42 -105.7 -18.74 0.08 19.38 107.16 4371 1.0\n", + "u_1[17] -0.28 0.67 49.02 -109.3 -21.85 -0.75 21.25 103.66 5402 1.0\n", + "u_1[18] -0.05 0.72 48.54 -101.0 -20.84 -0.06 19.52 109.31 4514 1.0\n", + "u_2[1] 0.09 0.24 18.66 -39.19 -7.69 0.15 7.88 40.83 6174 1.0\n", + "u_2[2] 0.06 0.26 19.66 -40.33 -7.76 -0.14 7.76 42.93 5816 1.0\n", + "u_2[3] -0.37 0.26 19.05 -41.84 -8.41 0.12 7.4 39.88 5283 1.0\n", + "u_2[4] 0.19 0.27 19.04 -41.56 -7.56 0.23 8.03 40.24 5115 1.0\n", + "u_2[5] 0.19 0.29 19.58 -41.54 -7.96 0.14 7.7 41.53 4617 1.0\n", + "u_2[6] 0.27 0.27 19.26 -43.08 -7.7 0.13 7.63 43.0 4962 1.0\n", + "u_2[7] 0.57 0.26 18.84 -39.55 -7.41 0.19 8.61 40.77 5283 1.0\n", + "u_2[8] 0.47 0.26 19.43 -38.79 -7.55 0.15 8.05 45.22 5433 1.0\n", + "u_2[9] 0.03 0.29 19.71 -42.1 -7.86 0.03 7.89 42.88 4551 1.0\n", + "u_2[10] -0.02 0.32 20.06 -41.83 -7.69 -0.15 7.81 41.59 3973 1.0\n", + "u_2[11] -0.31 0.31 20.61 -44.9 -8.51 -0.09 8.18 41.71 4550 1.0\n", + "u_2[12] 0.15 0.34 20.87 -43.5 -7.97 0.03 7.92 46.05 3768 1.0\n", + "u_2[13] -0.07 0.31 20.13 -44.01 -7.83 0.06 7.88 42.89 4240 1.0\n", + "u_2[14] -0.22 0.31 20.6 -46.79 -8.59 -0.12 8.29 44.49 4331 1.0\n", + "u_2[15] -0.05 0.26 19.19 -40.53 -7.73 0.04 7.87 40.95 5646 1.0\n", + "u_2[16] 0.53 0.29 18.45 -38.91 -7.5 0.16 8.61 39.76 4071 1.0\n", + "u_2[17] 0.26 0.27 19.49 -41.98 -7.7 0.08 7.97 42.43 5136 1.0\n", + "u_2[18] -0.47 0.26 19.88 -42.56 -8.55 -0.19 7.65 40.68 5728 1.0\n", + "sigma 39.9 0.51 29.83 1.95 16.11 33.94 56.85 112.34 3380 1.0\n", + "sigma_u[1] 41.58 3.25 27.96 3.81 19.28 36.79 58.7 106.59 74 1.07\n", + "sigma_u[2] 16.11 0.89 11.11 2.12 7.68 13.43 22.03 43.16 156 1.01\n", + "mu[1] 248.95 1.11 70.75 110.95 204.53 248.97 293.6 386.88 4079 1.0\n", + "mu[2] 248.51 1.13 71.69 107.28 201.5 248.85 293.14 389.02 4012 1.0\n", + "mu[3] 249.04 1.15 72.25 101.13 206.09 248.95 293.95 391.19 3944 1.0\n", + "mu[4] 250.49 1.09 69.43 108.51 206.47 250.09 295.65 382.95 4051 1.0\n", + "mu[5] 249.3 1.07 71.08 107.73 204.62 247.91 292.79 394.54 4386 1.0\n", + "mu[6] 248.14 1.07 70.65 104.1 204.01 248.96 292.91 386.53 4322 1.0\n", + "mu[7] 248.89 1.06 70.98 103.85 205.17 249.76 292.64 391.58 4525 1.0\n", + "mu[8] 249.2 1.08 72.91 108.68 203.47 248.28 293.47 393.92 4561 1.0\n", + "mu[9] 248.43 1.07 71.48 107.43 204.33 247.16 292.35 393.18 4463 1.0\n", + "mu[10] 249.12 1.08 69.58 114.39 204.9 249.32 290.61 385.9 4140 1.0\n", + "mu[11] 249.41 1.12 71.21 104.64 205.33 249.28 292.51 389.61 4067 1.0\n", + "mu[12] 249.65 1.08 71.55 105.36 206.75 247.88 292.44 395.0 4382 1.0\n", + "mu[13] 248.7 1.09 69.59 111.15 205.71 248.68 291.04 382.52 4103 1.0\n", + "mu[14] 248.44 1.16 71.84 99.69 203.06 248.3 294.16 385.64 3823 1.0\n", + "mu[15] 249.01 1.06 71.06 109.52 204.34 249.74 293.32 389.91 4468 1.0\n", + "mu[16] 249.21 1.13 71.26 105.79 205.01 250.34 292.83 395.3 3947 1.0\n", + "mu[17] 248.92 1.06 69.45 111.44 204.97 248.63 292.84 389.51 4319 1.0\n", + "mu[18] 249.15 1.15 69.58 113.5 204.63 247.69 293.01 395.02 3667 1.0\n", + "mu[19] 249.25 1.33 89.66 70.58 189.66 248.88 311.26 421.01 4534 1.0\n", + "mu[20] 248.78 1.34 90.01 75.12 188.61 247.28 309.58 425.71 4535 1.0\n", + "mu[21] 248.88 1.36 90.12 73.51 188.96 248.53 308.89 422.51 4374 1.0\n", + "mu[22] 250.89 1.33 88.7 75.99 192.07 252.16 309.92 427.54 4417 1.0\n", + "mu[23] 249.7 1.35 90.08 73.64 191.3 247.97 309.7 425.81 4483 1.0\n", + "mu[24] 248.62 1.36 90.89 69.01 190.09 248.53 308.59 425.73 4475 1.0\n", + "mu[25] 249.66 1.31 89.88 73.48 191.14 250.08 309.06 424.96 4728 1.0\n", + "mu[26] 249.88 1.32 90.94 73.24 190.66 250.13 309.21 428.92 4716 1.0\n", + "mu[27] 248.67 1.36 91.66 69.79 188.76 247.46 308.33 433.0 4534 1.0\n", + "mu[28] 249.32 1.36 88.75 72.6 191.36 247.19 305.11 430.38 4252 1.0\n", + "mu[29] 249.31 1.36 89.63 70.23 192.41 248.88 308.06 421.61 4361 1.0\n", + "mu[30] 250.0 1.34 91.17 74.35 192.04 248.06 309.75 429.56 4637 1.0\n", + "mu[31] 248.84 1.34 88.57 75.67 191.27 248.03 304.82 425.13 4394 1.0\n", + "mu[32] 248.43 1.35 90.27 71.02 189.97 246.38 308.66 422.2 4477 1.0\n", + "mu[33] 249.17 1.26 89.45 74.17 189.96 247.92 308.15 423.51 5013 1.0\n", + "mu[34] 249.94 1.38 89.75 71.16 192.47 250.42 307.91 425.88 4213 1.0\n", + "mu[35] 249.4 1.29 87.77 75.6 191.58 247.76 306.1 426.27 4640 1.0\n", + "mu[36] 248.89 1.34 88.56 73.56 191.62 249.82 306.31 420.45 4349 1.0\n", + "mu[37] 249.56 1.88 129.72 -12.79 160.92 249.76 337.53 500.91 4770 1.0\n", + "mu[38] 249.05 1.87 129.54 -5.15 160.77 247.63 336.34 506.69 4789 1.0\n", + "mu[39] 248.72 1.92 129.48 -2.39 161.13 245.97 334.41 506.19 4530 1.0\n", + "mu[40] 251.29 1.91 128.52 -5.97 166.23 251.61 336.19 501.15 4540 1.0\n", + "mu[41] 250.09 1.95 130.36 -6.8 162.65 249.03 340.18 509.68 4481 1.0\n", + "mu[42] 249.1 1.95 132.04 -14.3 160.31 248.69 337.06 514.77 4595 1.0\n", + "mu[43] 250.44 1.91 129.86 -7.77 163.98 251.32 335.63 512.91 4635 1.0\n", + "mu[44] 250.56 1.9 130.3 -7.57 165.91 250.18 337.86 511.95 4719 1.0\n", + "mu[45] 248.92 1.98 132.8 -15.14 162.27 247.65 337.3 508.3 4511 1.0\n", + "mu[46] 249.51 1.97 129.69 -6.26 167.17 246.56 336.96 504.64 4341 1.0\n", + "mu[47] 249.21 1.93 129.73 -1.23 161.08 249.9 334.78 505.11 4539 1.0\n", + "mu[48] 250.36 1.94 132.55 -8.71 165.86 247.72 336.04 506.2 4662 1.0\n", + "mu[49] 248.97 1.94 129.33 -11.93 164.15 248.07 332.67 504.51 4461 1.0\n", + "mu[50] 248.41 1.88 130.18 -8.05 162.4 249.06 336.32 506.21 4800 1.0\n", + "mu[51] 249.33 1.82 129.21 -5.49 162.13 247.92 334.64 502.89 5045 1.0\n", + "mu[52] 250.68 1.93 129.36 -10.22 166.95 249.87 335.87 501.88 4475 1.0\n", + "mu[53] 249.87 1.85 127.87 3.03 166.04 247.65 333.46 505.4 4777 1.0\n", + "mu[54] 248.63 1.92 129.33 -12.5 164.15 249.73 333.45 503.24 4540 1.0\n", + "mu[55] 249.86 2.55 177.13 -104.8 128.97 247.99 369.78 595.71 4833 1.0\n", + "mu[56] 249.32 2.54 176.57 -93.9 129.43 247.71 369.1 601.25 4846 1.0\n", + "mu[57] 248.56 2.61 176.48 -94.39 128.29 248.09 365.03 598.72 4566 1.0\n", + "mu[58] 251.69 2.59 175.42 -106.0 134.83 252.32 369.35 589.3 4587 1.0\n", + "mu[59] 250.49 2.66 178.01 -102.3 130.99 247.69 372.25 597.12 4462 1.0\n", + "mu[60] 249.57 2.66 180.31 -104.9 130.52 251.17 367.58 613.85 4598 1.0\n", + "mu[61] 251.21 2.63 177.17 -95.41 130.97 252.23 368.27 608.59 4549 1.0\n", + "mu[62] 251.23 2.59 177.31 -101.5 135.31 250.9 369.9 605.47 4694 1.0\n", + "mu[63] 249.16 2.7 181.15 -108.4 129.73 245.82 372.48 603.67 4486 1.0\n", + "mu[64] 249.7 2.69 177.97 -100.5 132.68 244.99 368.99 603.79 4377 1.0\n", + "mu[65] 249.12 2.61 177.37 -98.75 130.53 248.44 367.22 602.72 4611 1.0\n", + "mu[66] 250.72 2.66 181.37 -105.0 134.08 246.19 368.3 605.49 4635 1.0\n", + "mu[67] 249.11 2.65 177.44 -103.4 131.86 249.85 364.58 598.89 4471 1.0\n", + "mu[68] 248.4 2.55 177.63 -104.6 129.35 246.98 365.75 602.31 4845 1.0\n", + "mu[69] 249.49 2.5 176.44 -99.4 131.81 248.41 366.0 596.3 4990 1.0\n", + "mu[70] 251.41 2.6 176.41 -104.6 135.79 249.9 367.93 597.21 4597 1.0\n", + "mu[71] 250.34 2.53 175.41 -90.24 134.89 248.33 367.08 599.44 4820 1.0\n", + "mu[72] 248.37 2.61 177.45 -106.7 131.39 248.93 364.83 602.0 4606 1.0\n", + "mu[73] 250.16 3.27 227.33 -201.6 97.85 247.34 403.34 689.69 4846 1.0\n", + "mu[74] 249.59 3.25 226.47 -188.8 97.69 247.48 399.59 701.72 4850 1.0\n", + "mu[75] 248.41 3.35 226.43 -187.7 91.99 249.77 394.9 702.61 4575 1.0\n", + "mu[76] 252.09 3.32 225.01 -207.4 102.93 251.68 404.28 687.26 4605 1.0\n", + "mu[77] 250.88 3.43 228.46 -198.4 95.85 248.45 407.93 697.68 4448 1.0\n", + "mu[78] 250.05 3.41 231.28 -206.3 96.32 249.29 403.24 718.51 4591 1.0\n", + "mu[79] 251.98 3.39 227.29 -190.7 96.48 253.45 403.91 706.61 4493 1.0\n", + "mu[80] 251.91 3.32 227.27 -202.9 99.36 251.51 404.1 708.76 4673 1.0\n", + "mu[81] 249.4 3.47 232.25 -208.5 94.84 245.41 409.55 699.69 4469 1.0\n", + "mu[82] 249.89 3.45 228.99 -203.1 97.67 245.27 404.65 699.32 4393 1.0\n", + "mu[83] 249.02 3.34 227.87 -200.7 98.2 248.01 401.28 696.45 4641 1.0\n", + "mu[84] 251.07 3.43 233.0 -205.8 98.82 247.36 401.94 701.28 4609 1.0\n", + "mu[85] 249.24 3.41 228.31 -206.3 98.53 249.81 398.9 700.52 4470 1.0\n", + "mu[86] 248.38 3.27 227.97 -206.2 93.82 248.28 396.63 696.66 4845 1.0\n", + "mu[87] 249.65 3.22 226.52 -201.0 95.6 248.06 399.42 691.24 4941 1.0\n", + "mu[88] 252.15 3.32 226.3 -196.5 101.31 253.59 404.03 697.74 4655 1.0\n", + "mu[89] 250.81 3.25 225.73 -187.7 100.55 249.76 400.97 694.14 4833 1.0\n", + "mu[90] 248.11 3.36 228.33 -209.0 97.6 245.93 401.17 700.88 4629 1.0\n", + "mu[91] 250.47 4.01 278.83 -299.7 65.26 246.19 437.19 789.32 4844 1.0\n", + "mu[92] 249.86 3.99 277.69 -282.7 62.56 245.22 435.66 799.91 4842 1.0\n", + "mu[93] 248.25 4.11 277.73 -284.0 57.62 247.71 427.93 800.33 4577 1.0\n", + "mu[94] 252.49 4.06 275.84 -307.3 68.89 252.07 442.83 789.61 4614 1.0\n", + "mu[95] 251.28 4.21 280.21 -294.8 61.22 246.09 441.91 795.74 4438 1.0\n", + "mu[96] 250.53 4.19 283.51 -305.5 59.31 251.0 436.05 822.02 4583 1.0\n", + "mu[97] 252.76 4.17 278.7 -283.4 62.36 253.95 438.19 804.86 4457 1.0\n", + "mu[98] 252.59 4.08 278.6 -301.0 65.44 253.64 438.86 810.83 4658 1.0\n", + "mu[99] 249.65 4.26 284.62 -310.8 61.26 245.97 443.36 799.56 4458 1.0\n", + "mu[100] 250.08 4.24 281.26 -301.5 62.36 247.49 439.8 803.06 4401 1.0\n", + "mu[101] 248.92 4.1 279.69 -299.9 63.83 249.04 435.75 797.97 4656 1.0\n", + "mu[102] 251.43 4.22 285.92 -304.0 64.95 246.17 436.61 802.56 4590 1.0\n", + "mu[103] 249.38 4.2 280.43 -304.8 63.7 250.03 432.76 796.12 4466 1.0\n", + "mu[104] 248.37 4.02 279.63 -309.4 58.77 249.87 430.77 788.54 4836 1.0\n", + "mu[105] 249.81 3.97 277.91 -303.7 60.23 247.49 435.5 791.27 4905 1.0\n", + "mu[106] 252.89 4.05 277.49 -297.7 67.77 254.43 437.46 800.13 4685 1.0\n", + "mu[107] 251.28 3.99 277.33 -287.7 65.87 250.29 436.91 794.32 4837 1.0\n", + "mu[108] 247.85 4.12 280.46 -311.5 61.7 244.6 434.53 798.14 4637 1.0\n", + "mu[109] 250.77 4.76 331.01 -398.7 27.95 245.92 472.23 892.07 4839 1.0\n", + "mu[110] 250.13 4.74 329.63 -383.3 27.1 246.35 469.34 898.58 4831 1.0\n", + "mu[111] 248.09 4.87 329.75 -383.6 21.73 249.1 462.22 900.3 4576 1.0\n", + "mu[112] 252.89 4.82 327.35 -403.6 37.32 252.06 480.59 883.74 4618 1.0\n", + "mu[113] 251.67 5.0 332.66 -396.8 25.24 244.96 476.97 892.49 4430 1.0\n", + "mu[114] 251.0 4.97 336.4 -407.0 24.43 252.79 471.42 922.57 4577 1.0\n", + "mu[115] 253.53 4.97 330.8 -383.3 27.73 255.17 473.69 906.78 4432 1.0\n", + "mu[116] 253.26 4.85 330.67 -405.6 30.57 254.21 472.58 908.19 4647 1.0\n", + "mu[117] 249.89 5.06 337.68 -415.4 25.68 247.91 477.88 904.78 4450 1.0\n", + "mu[118] 250.28 5.04 334.2 -408.2 24.86 248.73 477.77 900.24 4405 1.0\n", + "mu[119] 248.83 4.86 332.2 -404.4 30.9 250.24 469.36 906.98 4663 1.0\n", + "mu[120] 251.78 5.02 339.53 -408.2 29.35 245.97 470.19 903.59 4575 1.0\n", + "mu[121] 249.51 4.99 333.22 -402.5 27.39 248.58 466.53 899.75 4463 1.0\n", + "mu[122] 248.35 4.78 332.01 -417.1 26.03 249.46 466.87 889.23 4825 1.0\n", + "mu[123] 249.98 4.72 330.0 -406.6 25.28 248.02 472.78 896.0 4878 1.0\n", + "mu[124] 253.62 4.8 329.39 -392.6 34.2 255.57 473.06 901.2 4702 1.0\n", + "mu[125] 251.75 4.74 329.6 -393.8 30.26 249.04 471.31 892.93 4838 1.0\n", + "mu[126] 247.59 4.89 333.26 -416.6 26.77 244.08 471.03 899.39 4640 1.0\n", + "mu[127] 251.07 5.52 383.59 -501.4 -7.54 243.63 507.44 993.1 4834 1.0\n", + "mu[128] 250.4 5.5 381.99 -483.4 -9.13 247.1 506.34 996.0 4821 1.0\n", + "mu[129] 247.93 5.65 382.2 -486.0 -14.74 249.46 497.41 996.82 4574 1.0\n", + "mu[130] 253.29 5.58 379.24 -496.9 3.57 253.82 517.87 989.47 4620 1.0\n", + "mu[131] 252.07 5.8 385.51 -496.4 -10.32 245.07 514.02 995.84 4424 1.0\n", + "mu[132] 251.48 5.76 389.69 -511.6 -13.44 250.85 506.98 1031.8 4571 1.0\n", + "mu[133] 254.31 5.77 383.31 -486.0 -6.65 258.74 509.77 1012.3 4414 1.0\n", + "mu[134] 253.94 5.63 383.17 -512.3 -6.77 254.68 511.4 1005.8 4638 1.0\n", + "mu[135] 250.13 5.87 391.13 -525.1 -8.94 243.36 514.27 1010.5 4444 1.0\n", + "mu[136] 250.47 5.84 387.53 -509.2 -9.28 249.57 512.88 1002.5 4408 1.0\n", + "mu[137] 248.73 5.64 385.13 -506.2 -4.75 252.63 505.92 1014.9 4667 1.0\n", + "mu[138] 252.14 5.83 393.54 -513.9 -7.46 246.85 505.89 1009.9 4564 1.0\n", + "mu[139] 249.65 5.79 386.41 -505.0 -10.18 249.88 502.21 997.46 4459 1.0\n", + "mu[140] 248.34 5.55 384.81 -523.4 -9.43 248.68 502.53 989.69 4816 1.0\n", + "mu[141] 250.14 5.49 382.51 -508.5 -11.33 247.84 509.26 999.41 4858 1.0\n", + "mu[142] 254.36 5.56 381.71 -496.9 -2.67 257.71 508.6 1008.7 4713 1.0\n", + "mu[143] 252.22 5.5 382.27 -493.4 -2.44 249.5 508.54 997.85 4838 1.0\n", + "mu[144] 247.33 5.67 386.45 -521.1 -9.19 244.0 504.63 1001.0 4641 1.0\n", + "mu[145] 251.38 6.28 436.44 -608.7 -42.14 244.71 544.35 1094.3 4828 1.0\n", + "mu[146] 250.67 6.27 434.61 -579.6 -44.11 245.07 542.64 1098.0 4812 1.0\n", + "mu[147] 247.77 6.43 434.93 -590.9 -48.17 251.07 532.49 1100.0 4572 1.0\n", + "mu[148] 253.69 6.35 431.39 -601.5 -30.6 254.25 554.13 1086.3 4622 1.0\n", + "mu[149] 252.46 6.6 438.62 -600.6 -43.97 245.74 548.17 1094.7 4420 1.0\n", + "mu[150] 251.96 6.56 443.23 -610.9 -47.89 249.63 545.13 1139.4 4567 1.0\n", + "mu[151] 255.08 6.57 436.08 -588.1 -42.59 260.33 545.15 1119.8 4400 1.0\n", + "mu[152] 254.62 6.41 435.94 -614.2 -38.31 256.27 548.32 1111.8 4631 1.0\n", + "mu[153] 250.38 6.68 444.84 -628.6 -42.27 242.71 549.79 1106.4 4439 1.0\n", + "mu[154] 250.66 6.64 441.11 -611.1 -44.17 249.91 552.11 1110.0 4409 1.0\n", + "mu[155] 248.63 6.41 438.32 -607.5 -41.68 251.44 541.61 1114.5 4670 1.0\n", + "mu[156] 252.49 6.64 447.82 -617.8 -44.9 248.19 542.0 1112.3 4555 1.0\n", + "mu[157] 249.78 6.59 439.85 -605.5 -45.59 251.13 539.14 1101.3 4457 1.0\n", + "mu[158] 248.32 6.32 437.87 -623.8 -45.79 249.38 537.14 1095.0 4807 1.0\n", + "mu[159] 250.3 6.26 435.27 -617.9 -49.05 245.08 546.74 1100.6 4842 1.0\n", + "mu[160] 255.09 6.32 434.29 -592.6 -38.28 259.93 543.43 1108.6 4721 1.0\n", + "mu[161] 252.69 6.26 435.19 -592.1 -39.04 251.36 545.34 1102.4 4837 1.0\n", + "mu[162] 247.07 6.46 439.9 -623.2 -45.26 244.88 540.73 1102.2 4641 1.0\n", + "mu[163] 251.68 7.05 489.46 -716.7 -76.66 246.87 580.51 1196.8 4823 1.0\n", + "mu[164] 250.94 7.03 487.42 -683.5 -79.98 242.92 579.91 1198.0 4804 1.0\n", + "mu[165] 247.61 7.22 487.84 -690.6 -85.04 251.31 567.14 1196.9 4570 1.0\n", + "mu[166] 254.09 7.11 483.71 -696.4 -66.75 256.0 590.9 1188.8 4622 1.0\n", + "mu[167] 252.86 7.4 491.91 -706.6 -80.03 245.59 584.91 1195.4 4417 1.0\n", + "mu[168] 252.43 7.36 496.94 -712.8 -81.26 250.39 583.03 1248.5 4563 1.0\n", + "mu[169] 255.86 7.38 489.03 -688.6 -77.59 261.7 580.18 1222.0 4389 1.0\n", + "mu[170] 255.29 7.19 488.91 -722.2 -72.86 258.19 581.33 1216.4 4626 1.0\n", + "mu[171] 250.62 7.49 498.73 -730.7 -76.02 240.64 587.53 1210.0 4436 1.0\n", + "mu[172] 250.85 7.45 494.86 -716.9 -80.78 251.98 589.46 1219.0 4411 1.0\n", + "mu[173] 248.53 7.19 491.7 -710.9 -76.83 251.83 575.15 1221.6 4671 1.0\n", + "mu[174] 252.85 7.45 502.27 -725.6 -80.94 246.26 581.96 1214.3 4548 1.0\n", + "mu[175] 249.92 7.39 493.46 -709.2 -81.54 251.41 575.2 1208.5 4454 1.0\n", + "mu[176] 248.31 7.09 491.11 -732.2 -82.68 249.21 573.74 1200.7 4800 1.0\n", + "mu[177] 250.46 7.03 488.22 -717.2 -84.99 245.09 584.6 1196.8 4829 1.0\n", + "mu[178] 255.83 7.09 487.05 -697.5 -72.47 261.15 578.11 1212.3 4726 1.0\n", + "mu[179] 253.16 7.02 488.28 -691.9 -73.11 250.38 582.74 1203.9 4836 1.0\n", + "mu[180] 246.81 7.24 493.52 -727.8 -80.85 242.6 577.87 1211.1 4641 1.0\n", + "yrep[1] 249.42 1.36 87.12 77.29 196.67 250.56 302.75 422.54 4118 1.0\n", + "yrep[2] 249.15 1.39 89.75 64.47 193.69 248.69 305.36 425.47 4165 1.0\n", + "yrep[3] 249.58 1.4 88.05 61.76 197.46 249.58 304.03 428.38 3975 1.0\n", + "yrep[4] 250.14 1.4 84.15 81.91 198.39 249.36 302.53 418.5 3599 1.0\n", + "yrep[5] 249.41 1.36 88.38 68.65 195.31 249.01 302.28 432.76 4249 1.0\n", + "yrep[6] 248.26 1.34 87.81 71.78 194.22 246.29 302.42 429.97 4263 1.0\n", + "yrep[7] 249.67 1.33 85.88 76.73 196.2 248.95 301.62 420.86 4174 1.0\n", + "yrep[8] 249.75 1.36 89.81 66.75 195.37 248.94 303.92 433.69 4362 1.0\n", + "yrep[9] 248.63 1.34 86.38 81.89 196.35 247.14 299.39 426.85 4131 1.0\n", + "yrep[10] 248.57 1.38 86.14 79.83 194.98 248.92 300.36 424.75 3907 1.0\n", + "yrep[11] 249.05 1.35 86.99 69.79 195.94 249.71 301.71 421.86 4137 1.0\n", + "yrep[12] 251.09 1.34 87.67 81.73 197.54 249.35 302.21 436.89 4260 1.0\n", + "yrep[13] 248.84 1.34 85.98 75.73 196.86 248.08 298.99 422.98 4137 1.0\n", + "yrep[14] 248.18 1.37 86.54 69.52 195.1 249.1 300.97 422.49 3997 1.0\n", + "yrep[15] 247.95 1.23 86.25 68.67 197.38 247.41 301.53 423.91 4883 1.0\n", + "yrep[16] 248.2 1.41 88.1 68.54 192.9 249.02 300.63 427.34 3892 1.0\n", + "yrep[17] 249.21 1.33 85.73 81.28 196.59 248.84 302.87 420.24 4175 1.0\n", + "yrep[18] 248.67 1.49 84.7 78.46 195.87 248.17 299.1 421.74 3244 1.0\n", + "yrep[19] 249.91 1.56 101.98 51.22 184.05 248.49 318.87 448.94 4291 1.0\n", + "yrep[20] 249.96 1.61 102.48 48.61 184.46 249.41 317.32 450.37 4037 1.0\n", + "yrep[21] 249.14 1.55 102.38 46.34 183.28 247.55 313.98 456.26 4368 1.0\n", + "yrep[22] 249.55 1.62 102.28 44.61 185.55 250.3 316.79 451.57 4005 1.0\n", + "yrep[23] 248.61 1.6 102.41 45.25 185.14 247.73 314.82 449.08 4074 1.0\n", + "yrep[24] 248.42 1.56 103.24 44.13 181.51 247.96 317.18 454.23 4388 1.0\n", + "yrep[25] 248.25 1.55 103.21 43.78 185.1 249.4 313.24 450.08 4411 1.0\n", + "yrep[26] 248.46 1.61 104.43 39.87 181.0 249.23 315.65 457.08 4220 1.0\n", + "yrep[27] 249.47 1.61 104.68 45.79 180.24 249.62 316.66 461.04 4230 1.0\n", + "yrep[28] 249.77 1.6 102.72 48.96 185.27 249.18 313.98 454.67 4099 1.0\n", + "yrep[29] 248.99 1.58 102.97 47.97 184.77 248.15 315.79 454.4 4263 1.0\n", + "yrep[30] 249.86 1.57 103.59 45.58 184.45 248.79 316.55 455.27 4346 1.0\n", + "yrep[31] 248.67 1.55 101.62 46.9 184.5 248.36 312.33 449.0 4282 1.0\n", + "yrep[32] 249.07 1.54 101.68 48.04 182.02 248.78 316.35 447.44 4365 1.0\n", + "yrep[33] 248.71 1.51 103.14 42.45 182.61 248.92 315.18 447.96 4650 1.0\n", + "yrep[34] 249.38 1.57 102.23 48.21 186.76 250.1 314.58 453.43 4220 1.0\n", + "yrep[35] 246.9 1.53 100.21 44.77 182.88 246.87 311.39 453.31 4276 1.0\n", + "yrep[36] 250.96 1.6 102.63 52.84 184.81 249.73 315.43 455.74 4098 1.0\n", + "yrep[37] 249.41 2.0 138.71 -25.55 157.79 248.47 341.12 525.44 4796 1.0\n", + "yrep[38] 247.06 2.01 138.54 -26.41 156.1 246.39 339.46 523.96 4737 1.0\n", + "yrep[39] 249.39 2.09 140.69 -31.65 159.37 248.86 340.77 522.88 4522 1.0\n", + "yrep[40] 251.12 2.02 137.62 -19.97 160.53 250.41 341.32 519.08 4644 1.0\n", + "yrep[41] 249.76 2.14 139.54 -20.46 155.49 250.78 343.17 525.81 4246 1.0\n", + "yrep[42] 248.57 2.08 141.3 -28.57 154.53 248.52 340.58 536.86 4605 1.0\n", + "yrep[43] 251.32 2.06 137.17 -19.47 160.82 251.48 340.32 526.18 4413 1.0\n", + "yrep[44] 251.42 2.06 138.53 -23.82 163.52 249.48 341.11 531.62 4518 1.0\n", + "yrep[45] 249.13 2.14 141.83 -34.43 156.28 248.74 343.1 538.26 4399 1.0\n", + "yrep[46] 249.28 2.11 137.93 -20.63 159.22 247.71 338.29 524.35 4282 1.0\n", + "yrep[47] 249.8 2.07 139.45 -21.29 157.5 249.79 341.98 526.12 4541 1.0\n", + "yrep[48] 249.53 2.07 141.88 -26.14 160.54 249.09 339.62 522.49 4697 1.0\n", + "yrep[49] 250.47 2.11 138.1 -19.74 159.56 249.46 340.92 525.02 4293 1.0\n", + "yrep[50] 248.69 2.09 139.75 -27.79 153.16 248.69 343.24 524.67 4487 1.0\n", + "yrep[51] 248.48 2.03 138.93 -23.5 157.85 249.55 338.48 515.89 4705 1.0\n", + "yrep[52] 252.08 2.11 138.52 -22.49 162.32 250.63 343.03 527.76 4306 1.0\n", + "yrep[53] 249.98 1.98 135.69 -21.52 161.75 249.07 340.02 517.77 4689 1.0\n", + "yrep[54] 248.94 2.03 137.97 -27.26 157.49 250.59 338.58 520.93 4612 1.0\n", + "yrep[55] 248.34 2.67 183.62 -119.6 124.98 247.67 370.87 610.3 4726 1.0\n", + "yrep[56] 248.82 2.67 183.34 -111.6 121.81 248.85 372.71 611.56 4733 1.0\n", + "yrep[57] 248.14 2.69 182.5 -106.6 125.94 247.57 370.98 610.91 4606 1.0\n", + "yrep[58] 251.91 2.71 182.42 -122.1 134.52 253.71 375.89 610.88 4538 1.0\n", + "yrep[59] 250.08 2.75 185.44 -109.4 124.86 252.39 377.1 612.85 4548 1.0\n", + "yrep[60] 248.57 2.78 186.78 -111.4 123.23 247.71 369.65 627.67 4528 1.0\n", + "yrep[61] 251.79 2.8 184.86 -113.2 126.84 254.33 371.09 625.66 4344 1.0\n", + "yrep[62] 250.57 2.75 185.08 -120.8 128.28 248.55 374.28 620.91 4522 1.0\n", + "yrep[63] 249.15 2.81 188.98 -118.0 127.23 248.96 378.16 618.23 4514 1.0\n", + "yrep[64] 249.43 2.83 185.52 -111.2 126.5 244.98 373.56 619.75 4298 1.0\n", + "yrep[65] 249.44 2.75 182.92 -112.1 129.07 249.78 370.57 607.62 4441 1.0\n", + "yrep[66] 250.52 2.8 187.76 -119.4 128.62 246.67 371.79 622.84 4486 1.0\n", + "yrep[67] 249.17 2.75 183.98 -111.3 127.79 248.47 371.27 612.81 4472 1.0\n", + "yrep[68] 248.15 2.68 186.47 -120.7 125.13 249.38 371.3 619.17 4847 1.0\n", + "yrep[69] 248.85 2.6 183.37 -107.3 124.6 245.26 368.33 607.44 4964 1.0\n", + "yrep[70] 252.07 2.68 183.44 -113.4 130.38 251.27 372.88 615.75 4696 1.0\n", + "yrep[71] 248.44 2.67 183.53 -112.4 126.87 248.13 370.06 607.22 4718 1.0\n", + "yrep[72] 248.68 2.74 185.01 -119.7 127.41 249.86 368.21 616.37 4558 1.0\n", + "yrep[73] 251.6 3.36 231.49 -205.1 95.98 250.22 409.69 704.18 4736 1.0\n", + "yrep[74] 247.97 3.36 231.67 -209.9 91.97 246.47 406.83 703.54 4763 1.0\n", + "yrep[75] 248.87 3.49 232.78 -207.7 93.14 250.62 400.84 706.47 4447 1.0\n", + "yrep[76] 253.49 3.44 230.59 -209.6 98.72 253.38 410.67 708.79 4491 1.0\n", + "yrep[77] 251.78 3.51 234.22 -203.2 91.98 250.15 412.79 711.57 4445 1.0\n", + "yrep[78] 250.36 3.55 237.09 -220.4 93.37 247.94 407.94 721.57 4462 1.0\n", + "yrep[79] 253.14 3.53 232.44 -195.7 97.04 253.43 406.85 724.8 4332 1.0\n", + "yrep[80] 250.61 3.41 232.13 -208.7 96.08 252.59 404.75 706.46 4647 1.0\n", + "yrep[81] 248.31 3.54 236.59 -214.9 93.56 245.88 408.3 699.68 4463 1.0\n", + "yrep[82] 250.8 3.55 235.15 -209.7 93.89 246.99 410.44 711.91 4394 1.0\n", + "yrep[83] 249.11 3.39 233.4 -209.5 95.1 247.07 402.62 714.25 4731 1.0\n", + "yrep[84] 251.1 3.57 238.68 -218.8 93.31 247.87 406.05 713.47 4459 1.0\n", + "yrep[85] 250.18 3.49 234.12 -211.3 97.25 247.94 403.31 716.54 4496 1.0\n", + "yrep[86] 247.54 3.41 233.75 -215.6 89.33 248.53 405.56 711.59 4693 1.0\n", + "yrep[87] 248.45 3.3 232.5 -215.5 93.71 246.9 407.66 706.52 4967 1.0\n", + "yrep[88] 252.15 3.39 230.68 -213.6 98.12 253.08 404.84 709.39 4637 1.0\n", + "yrep[89] 251.44 3.4 231.88 -201.7 94.36 252.19 407.39 708.8 4654 1.0\n", + "yrep[90] 247.78 3.45 234.35 -220.6 96.45 246.78 406.38 707.46 4621 1.0\n", + "yrep[91] 250.25 4.11 283.92 -305.2 60.69 249.09 443.29 809.87 4768 1.0\n", + "yrep[92] 249.94 4.08 281.68 -288.7 64.55 247.78 439.77 798.83 4766 1.0\n", + "yrep[93] 248.17 4.19 281.88 -301.3 60.52 249.26 432.23 805.69 4526 1.0\n", + "yrep[94] 253.55 4.2 279.93 -314.4 68.32 250.26 447.84 798.49 4442 1.0\n", + "yrep[95] 250.59 4.21 283.94 -298.1 54.63 246.09 440.73 801.63 4544 1.0\n", + "yrep[96] 249.96 4.28 288.95 -328.0 58.75 249.58 441.22 820.8 4550 1.0\n", + "yrep[97] 252.99 4.3 283.62 -300.5 61.09 252.15 442.73 819.6 4348 1.0\n", + "yrep[98] 252.77 4.17 284.33 -318.5 58.34 254.99 443.17 821.78 4646 1.0\n", + "yrep[99] 250.13 4.29 288.61 -312.9 61.67 248.64 449.98 802.82 4528 1.0\n", + "yrep[100] 250.62 4.31 286.25 -300.9 57.99 248.99 443.35 817.7 4410 1.0\n", + "yrep[101] 249.63 4.19 283.56 -309.5 60.37 244.5 441.64 803.61 4588 1.0\n", + "yrep[102] 251.62 4.23 289.47 -322.6 64.64 248.51 445.37 815.31 4691 1.0\n", + "yrep[103] 249.53 4.29 285.09 -315.0 63.27 247.26 434.16 808.58 4416 1.0\n", + "yrep[104] 248.59 4.13 284.94 -326.3 58.19 252.55 438.37 798.6 4749 1.0\n", + "yrep[105] 249.1 4.01 281.17 -314.7 59.94 252.21 437.57 796.32 4924 1.0\n", + "yrep[106] 253.06 4.15 282.38 -307.7 65.75 254.84 437.31 815.71 4631 1.0\n", + "yrep[107] 250.56 4.07 281.37 -299.9 63.37 248.94 438.68 805.9 4781 1.0\n", + "yrep[108] 248.25 4.16 284.54 -315.3 56.83 246.13 440.06 811.24 4686 1.0\n", + "yrep[109] 249.51 4.84 334.54 -409.0 22.23 246.04 472.79 900.7 4773 1.0\n", + "yrep[110] 250.87 4.8 332.28 -396.3 23.88 250.66 475.02 899.49 4792 1.0\n", + "yrep[111] 247.4 4.98 333.43 -402.5 17.38 250.38 465.91 910.12 4487 1.0\n", + "yrep[112] 252.91 4.91 331.55 -405.2 34.91 246.36 479.81 913.82 4562 1.0\n", + "yrep[113] 252.4 5.04 336.07 -405.4 27.74 246.09 478.17 916.7 4448 1.0\n", + "yrep[114] 251.35 5.0 338.8 -410.8 22.66 251.74 473.88 919.11 4597 1.0\n", + "yrep[115] 252.25 5.03 335.52 -407.2 21.21 251.91 476.04 911.48 4442 1.0\n", + "yrep[116] 253.74 4.9 333.48 -410.3 29.72 255.32 479.79 928.89 4631 1.0\n", + "yrep[117] 250.4 5.11 340.42 -425.6 25.12 249.74 480.62 910.79 4438 1.0\n", + "yrep[118] 250.13 5.05 336.93 -404.2 21.06 249.57 477.25 921.11 4445 1.0\n", + "yrep[119] 250.0 4.96 336.25 -408.1 25.5 248.44 472.26 917.66 4602 1.0\n", + "yrep[120] 250.86 5.07 342.9 -414.1 22.19 247.66 474.4 919.75 4580 1.0\n", + "yrep[121] 250.24 5.07 336.53 -406.1 28.08 252.77 468.51 903.09 4404 1.0\n", + "yrep[122] 248.99 4.84 336.17 -423.2 26.7 251.59 472.73 904.41 4824 1.0\n", + "yrep[123] 250.14 4.78 333.25 -414.3 25.45 248.55 477.33 893.78 4870 1.0\n", + "yrep[124] 254.19 4.9 333.13 -404.2 28.67 256.2 480.51 904.89 4619 1.0\n", + "yrep[125] 252.0 4.81 332.65 -401.5 31.38 252.68 473.95 901.19 4784 1.0\n", + "yrep[126] 247.07 4.99 337.92 -429.3 24.45 244.95 466.25 905.87 4586 1.0\n", + "yrep[127] 252.58 5.56 386.93 -502.7 -8.85 246.95 508.71 1009.3 4840 1.0\n", + "yrep[128] 250.67 5.55 383.7 -493.3 -6.8 247.21 506.77 997.98 4785 1.0\n", + "yrep[129] 248.63 5.71 386.27 -491.8 -13.49 246.89 505.81 1012.1 4572 1.0\n", + "yrep[130] 255.31 5.64 384.3 -513.3 0.66 255.55 523.84 1010.0 4648 1.0\n", + "yrep[131] 251.68 5.8 387.29 -500.2 -10.52 249.15 508.53 1009.6 4466 1.0\n", + "yrep[132] 252.32 5.81 392.63 -516.7 -9.24 250.42 510.66 1035.5 4567 1.0\n", + "yrep[133] 253.19 5.83 385.84 -501.6 -8.5 255.26 508.6 1026.6 4379 1.0\n", + "yrep[134] 254.0 5.67 386.9 -516.5 -7.42 251.62 510.95 1011.5 4655 1.0\n", + "yrep[135] 249.65 5.92 395.75 -522.0 -10.4 249.51 522.12 1012.4 4476 1.0\n", + "yrep[136] 252.0 5.91 392.37 -511.4 -10.02 250.53 519.77 1028.7 4408 1.0\n", + "yrep[137] 248.82 5.67 388.56 -503.7 -8.36 248.88 503.13 1018.5 4700 1.0\n", + "yrep[138] 252.29 5.86 395.56 -519.2 -5.67 247.35 512.07 1016.9 4558 1.0\n", + "yrep[139] 249.23 5.81 390.0 -510.2 -8.46 244.75 507.74 1011.1 4507 1.0\n", + "yrep[140] 247.82 5.58 386.89 -525.0 -8.24 247.09 504.92 993.72 4801 1.0\n", + "yrep[141] 250.71 5.58 386.8 -523.6 -7.77 249.85 512.35 1013.3 4804 1.0\n", + "yrep[142] 255.52 5.59 384.16 -507.5 -5.69 260.76 509.45 1018.9 4716 1.0\n", + "yrep[143] 251.63 5.54 384.5 -492.7 -11.29 251.63 508.31 997.44 4812 1.0\n", + "yrep[144] 246.87 5.76 389.02 -524.9 -15.3 244.71 502.13 1011.0 4558 1.0\n", + "yrep[145] 250.99 6.31 440.07 -607.0 -43.69 245.99 548.37 1098.1 4870 1.0\n", + "yrep[146] 250.06 6.25 437.5 -590.6 -47.6 243.17 544.92 1111.7 4896 1.0\n", + "yrep[147] 248.86 6.41 437.43 -591.0 -46.31 246.29 539.36 1110.2 4650 1.0\n", + "yrep[148] 251.52 6.38 433.3 -618.9 -37.25 250.15 548.21 1087.6 4614 1.0\n", + "yrep[149] 254.18 6.67 441.04 -598.1 -45.01 247.4 553.3 1094.5 4373 1.0\n", + "yrep[150] 250.28 6.6 445.31 -624.2 -47.19 254.02 544.68 1128.9 4550 1.0\n", + "yrep[151] 254.93 6.69 439.76 -592.0 -45.53 253.22 546.94 1118.7 4325 1.0\n", + "yrep[152] 255.91 6.5 440.22 -614.8 -38.1 260.77 548.37 1118.7 4590 1.0\n", + "yrep[153] 249.86 6.7 448.84 -632.6 -45.52 246.21 551.7 1112.9 4490 1.0\n", + "yrep[154] 250.55 6.68 444.45 -621.5 -51.47 249.59 554.08 1127.2 4426 1.0\n", + "yrep[155] 248.2 6.38 440.21 -605.1 -40.96 247.83 536.23 1118.0 4766 1.0\n", + "yrep[156] 253.22 6.72 450.26 -619.0 -44.9 251.39 547.24 1123.4 4490 1.0\n", + "yrep[157] 248.79 6.66 443.43 -613.2 -40.07 246.82 539.1 1109.1 4438 1.0\n", + "yrep[158] 248.98 6.32 440.32 -614.3 -48.38 249.45 541.8 1105.0 4859 1.0\n", + "yrep[159] 251.58 6.3 438.55 -615.4 -47.96 249.09 551.54 1096.6 4839 1.0\n", + "yrep[160] 254.86 6.32 435.87 -599.0 -42.04 264.64 546.2 1111.2 4749 1.0\n", + "yrep[161] 252.71 6.28 436.51 -607.4 -37.02 249.45 547.54 1107.7 4830 1.0\n", + "yrep[162] 245.63 6.48 443.17 -630.9 -47.4 244.73 538.25 1102.5 4674 1.0\n", + "yrep[163] 251.44 7.14 492.86 -717.1 -77.74 246.69 579.81 1219.2 4761 1.0\n", + "yrep[164] 251.2 7.04 489.96 -699.4 -78.52 251.41 584.34 1194.2 4842 1.0\n", + "yrep[165] 247.58 7.25 490.32 -695.2 -82.88 251.79 562.44 1212.4 4577 1.0\n", + "yrep[166] 255.2 7.17 486.66 -700.6 -64.89 255.87 589.35 1209.5 4608 1.0\n", + "yrep[167] 253.82 7.47 494.56 -707.4 -74.46 247.56 592.04 1198.7 4384 1.0\n", + "yrep[168] 251.45 7.36 500.87 -725.9 -85.85 248.56 582.18 1250.5 4633 1.0\n", + "yrep[169] 256.1 7.44 490.57 -696.1 -80.54 259.58 582.27 1224.8 4344 1.0\n", + "yrep[170] 254.67 7.25 492.68 -724.0 -77.49 254.36 586.79 1219.4 4619 1.0\n", + "yrep[171] 250.06 7.5 501.28 -737.7 -76.94 241.45 592.42 1220.3 4462 1.0\n", + "yrep[172] 249.82 7.41 496.68 -717.0 -86.12 252.32 588.77 1237.9 4488 1.0\n", + "yrep[173] 247.6 7.22 493.83 -714.7 -80.78 245.34 577.21 1215.9 4675 1.0\n", + "yrep[174] 252.48 7.48 504.8 -727.4 -81.97 248.89 574.55 1223.6 4553 1.0\n", + "yrep[175] 249.9 7.4 495.77 -716.4 -86.91 249.36 576.69 1204.3 4486 1.0\n", + "yrep[176] 248.28 7.18 492.7 -739.4 -76.59 248.04 573.7 1194.9 4706 1.0\n", + "yrep[177] 250.26 7.08 491.65 -726.3 -85.15 242.43 582.53 1216.2 4815 1.0\n", + "yrep[178] 257.67 7.1 489.44 -695.2 -70.48 262.27 584.82 1227.3 4749 1.0\n", + "yrep[179] 253.3 7.09 490.72 -693.2 -74.42 245.85 584.62 1199.8 4797 1.0\n", + "yrep[180] 246.64 7.28 496.28 -746.3 -81.63 241.77 576.27 1212.4 4649 1.0\n", + "lp__ -118.6 2.12 19.12 -152.5 -132.6 -119.7 -105.8 -79.63 81 1.05\n", + "\n", + "Samples were drawn using NUTS at Mon Jun 14 13:47:03 2021.\n", + "For each parameter, n_eff is a crude measure of effective sample size,\n", + "and Rhat is the potential scale reduction factor on split chains (at \n", + "convergence, Rhat=1).\n" + ] + } + ], + "source": [ + "print(fit)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Posterior predicitve checks" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "#Convert pystan fit object to IO for Arviz functions.\n", + "data = az.from_pystan(\n", + " posterior=fit,\n", + " posterior_predictive='yrep',\n", + " observed_data=[\"y\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "plot_ppc() got an unexpected keyword argument 'observe'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\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# Plot posterior simulated data sets for posterior predictive check\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0maz\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_ppc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mobserve\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_pairs\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"y\"\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;34m\"yrep\"\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_pp_samples\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m: plot_ppc() got an unexpected keyword argument 'observe'" + ] + } + ], + "source": [ + "# Plot posterior simulated data sets for posterior predictive check\n", + "az.plot_ppc(data, data_pairs= {\"y\" : \"yrep\"}, num_pp_samples= 100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of Bayesian linear mixed model\n", + "\n", + "# Reporting the results of the Bayesian one sample t-test equivalent\n", + "\n", + "As Kruscke correctly points out there is not standard formula or presentation method for results like the APA guide for reporting frequentist analyses using the Bayesian framework. It is likely there never will be, because as McElreath (2020) explains, Bayesian data analysis is more like a engineering approach to the problem and the resulting model that is fit will be analysis specific. In addition, as Gabry et al, (2019) have argued visualisations maybe even more key, so all the visualtions above would have to be included with any write up. Anyway, the write up below generally follows the advice of Kruscke (2015) chapter 25. In any application though it comes down to the problem to be described and the audience that needs to be convinced.


    \n", + "\n", + "

    Write up


    " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References \n", + "\n", + "Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014). Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:1406.5823.\n", + "\n", + "Belenky, G., Wesensten, N. J., Thorne, D. R., Thomas, M. L., Sing, H. C., Redmond, D. P., ... & Balkin, T. J. (2003). Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: A sleep dose‐response study. Journal of sleep research, 12(1), 1-12.\n", + "\n", + "Singmann, H., & Kellen, D. (2019). An Introduction to Mixed Models for Experimental Psychology. In\n", + "D. H. Spieler & E. Schumacher (Eds.), New Methods in Cog." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "341.333px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian estimation of Welch's t-test .ipynb b/wip/Bayesian estimation of Welch's t-test .ipynb new file mode 100644 index 0000000..b492aea --- /dev/null +++ b/wip/Bayesian estimation of Welch's t-test .ipynb @@ -0,0 +1,946 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

    Table of Contents

    \n", + "
    " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Import analysis packages\n", + "%matplotlib inline\n", + "import pystan as ps\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as ss" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of Welch's t-test\n", + "\n", + "## The classic Welch's t-test\n", + "\n", + "In order to understand the Welch's t-test from classical statistics it is important to first outline the student t-test (apologise to any readers who have already read the Bayesian estimation of between subkects t-test notebook but this is essentially all repetition).\n", + "\n", + "The student t-test in formula form can be denoted as $$\\large t = \\frac{m_1-m_2}{\\sqrt{\\frac{S^2}{n_1}+\\frac{S^2}{n_2}}}$$\n", + "\n", + "$m_1 m_2 =$ the mean values for the two respective groups.\n", + "\n", + "$n_1 n_2 =$ the sample size for the two respective groups.\n", + "\n", + "and $S^2$ estimator for the varaince of the two groups being analysed \n", + "\n", + "$$\\large S^2 = \\frac{\\sum(x-m_1)^2 + \\sum(x-m_2)^2}{n_1 + n_2 -2}$$\n", + "\n", + "with the denominator $n_1 + n_2-2$ being gthe calculation of degree of freedom for the test.\n", + "\n", + "One of the assumptions of the student t-test results then from this aplication of a pooled estimator. That assumption being the homogeneity of variance (homoscedasticity).\n", + "\n", + "Like all assumptions then the big question is for any analysis is are they reasonable within the application at hand. in recent years there has been arguements that the student t-test is inappropiate for research where variance between the two groups is unlikely (such as psychology/behavioural analysis), with even arguements that researchers should default to welch's t-test (Delacre, Lakens, & Leys, 2017), the impotance of a need to for default comes clearer with identifcation the prolific use of t-tests within psychology (Wetzels, et al. 2011) considering the variety of statistical tools avaible to researchers.\n", + "\n", + "Nevertheless, the welch's t-test is different from the student t-test because it can analyse two groups of data within classical statitics framework even in cases of heteroscedasticity.\n", + "\n", + "The Welch's t-test in formula form can be denoted as $$\\large t = \\frac{m_1-m_2}{\\sqrt{\\frac{S_{1}^{2}}{n_1}+\\frac{S_{2}^{2}}{n_2}}}$$\n", + "\n", + "$S_{1}^{2} S_{2}^{2}=$ Standard deviation of the the respective two groups\n", + "\n", + "The df are calculated as $\\large df = $" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following the quick description of the classic welch's t-test above its important to keep in mind that inference within Bayesian data analysis is all derived from the application of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to the Welch's t-test, it is fundamentally different, because it uses fully probabilistic modelling and, therefore, any resulting infernces are not based on sampling distributions\n", + " \n", + " For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. If scientific research publication is the goal, the priors must be acceptable to a skeptical audience of peer reviewers. Much of this can be achieved using prior predictive checks to acsertain if the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then interpret the posterior for any statisitcal inferences.\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks, to identify if the model is reasonable and therfore also check if the infernces are reasonable in addition. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data overview and study description\n", + "\n", + "The data analysed below have been downloaded from https://drive.google.com/file/d/0Bz-rhZ21ShvOdW1wV0pmUTJSSk0/view. The orignal dataset is from a study conducted by Schroeder and Epley (2015). These researchers investigated whether the chance of employment would differ based on if indivduals verbally described there skills in the form of a little speech, or if that speech was in written form and the potenitial employer read the speech instead. The researchers predicted that tone and pitch avaible to spoken word would convey more information of intellect than the written words.\n", + "\n", + "To test this 39 recruiters from fortune 500 compamnies were randomly assigned to one of two conditions. These teo condtions were audio and transcript conditons. More specifically the recruiters assigned to the audio condition the recruiters listend to an aufio recording of the pitch, whereas in the transcript condition the recruiters read the same speech that the audio condition heard. Post hearing or reading of the pitch the recruiters produced rating on the candidate's: intelligence, competence, and thoughtfulness. These scores where then averaged to give an overall intellect score. With high scores relating to a higher intellect rating by the recruiters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    CONDITIONcomptthoughtintelllikeposneghireagegender...pnummeanhiremeanintellectmeanimpressioncenthirecentintellectcentimpressionIntellect_RatingImpression_RatingHire_Rating
    017777717292...14.5833335.7222226.6388892.4166671.2777781.3611116.0000007.0000006
    116866665272...24.6666675.5777785.7777780.3333331.088889-0.1111115.6666674.6666674
    217869916272...34.6666675.5777785.7777781.3333331.4222223.5555566.0000008.3333335
    304366665402...44.5833335.7222226.6388890.416667-1.388889-0.9722223.3333334.6666674
    402312282322...54.5833335.7222226.638889-2.583333-3.722222-4.3055561.0000001.3333331
    503332262242...64.6666675.5777785.777778-2.666667-2.577778-2.7777782.0000002.0000001
    \n", + "

    6 rows × 26 columns

    \n", + "
    " + ], + "text/plain": [ + " CONDITION compt thought intell like pos neg hire age gender ... \\\n", + "0 1 7 7 7 7 7 1 7 29 2 ... \n", + "1 1 6 8 6 6 6 6 5 27 2 ... \n", + "2 1 7 8 6 9 9 1 6 27 2 ... \n", + "3 0 4 3 6 6 6 6 5 40 2 ... \n", + "4 0 2 3 1 2 2 8 2 32 2 ... \n", + "5 0 3 3 3 2 2 6 2 24 2 ... \n", + "\n", + " pnum meanhire meanintellect meanimpression centhire centintellect \\\n", + "0 1 4.583333 5.722222 6.638889 2.416667 1.277778 \n", + "1 2 4.666667 5.577778 5.777778 0.333333 1.088889 \n", + "2 3 4.666667 5.577778 5.777778 1.333333 1.422222 \n", + "3 4 4.583333 5.722222 6.638889 0.416667 -1.388889 \n", + "4 5 4.583333 5.722222 6.638889 -2.583333 -3.722222 \n", + "5 6 4.666667 5.577778 5.777778 -2.666667 -2.577778 \n", + "\n", + " centimpression Intellect_Rating Impression_Rating Hire_Rating \n", + "0 1.361111 6.000000 7.000000 6 \n", + "1 -0.111111 5.666667 4.666667 4 \n", + "2 3.555556 6.000000 8.333333 5 \n", + "3 -0.972222 3.333333 4.666667 4 \n", + "4 -4.305556 1.000000 1.333333 1 \n", + "5 -2.777778 2.000000 2.000000 1 \n", + "\n", + "[6 rows x 26 columns]" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Schroeder%20and%20Epley%202015.csv\"\n", + "\n", + "#Generare apndas data frame with the study data\n", + "df = pd.read_csv(url)\n", + "df.head(6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The two elements crucial to identify here from the dataframe is the condition column where the condition 0 is the transcript condition and condition 1 is the audio condition and the avearged intellect rating score." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploraory data analysis and visualisation" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAChCAYAAADX50R7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dd3hcxbn48e+76tWqlmVJtlyxhXEVtnEDjAnd9F4TSkJCILT8uKHcNH43kBBuSLgEJwQIsSk2zQQDNh0bF7lI7nKVbFmyZMuqVt+d+8cecRehspK3SKv38zx6vNqdc867qzmvZ+fMzBFjDEoppfo+m78DUEop5Rma0JVSKkBoQldKqQChCV0ppQKEJnSllAoQmtCVUipAaELvJ0TkURH5q7/jUKozIvK5iNxmPb5eRJb7O6a+RHQceveISK3Lr5FAI2C3fv+hMWah76PyLBH5LZBujLnF37GovkNEPgcmAIOMMY0nsI9/GWP+7sHQ+g1toXeTMSa69Qc4AFzk8tx3krmIBPs+yt4XgwpsIpIJzAYMMN+vwfRjmtA9TER+KyKvi8irIlID3CAip4nIGhGpFJESEXlGREKs8sEiYkTkhyKyR0QqROQZl/2NFpEvRaRKRI6KyCKX104RkY9F5JiIHBaRn3cSw29F5CXr9ZHWMW8XkWLr517rtQuBnwPXi0itiGzw2Yen+rKbgDXAS8DNrU+6dqFYv98iIitdfj9bRHZa9fsvgHRSdoaI5Fhlc0RkhpffU5+jCd07LgUWAQOA14EW4B4gCZgJnAv8sM025wNTgEk4E/A86/nHgfeBeCAdeBZARAYAHwPvAanAaODzTmJozxxgJHAe8IiInGGM+TfwJLDQ+tYxpftvX/VDNwELrZ9zRCSlqw1EJAl4E3gE57mxF+f50V7ZBJznwTNAIvBH4H0RSfRI9AFCE7p3rDTGvGeMcRhj6o0xOcaYtcaYFmPMPmABcHqbbf7LGFNljCnAmZgnWs83A5lAqjGmwRizynp+PnDQGPMnY0yjMabaGLOuoxg6iPNXxpg6Y0we8DJw7Qm/c9XviMgsYCjwhjFmA87EfJ0bm54PbDfGLDHGNAP/DRzuoOwFwG5jzCvWefQqsBO46MTfQeDQhO4dB11/EZExIvK+1S1SDfwaZ4vElWtFrgOircf3AyHAehHZIiKtX2czgD3uxuBGmUJgsBvbKNXWzcByY8xR6/dFuHS7dGIwLnXQOEdodFRvB+Oso64KgbTuhRrY9GKZd7QdOvQ8zv7Fq40xtSLyAHChWzsypgRoHcY1B1ghIl/irPiXdiOG9rj+pzAEKO7GtkohIhHAVUCQiLQ2SsKAOBGZABzHORqs1SCXxyU462DrvsT19zaKcX4LcDUE+LDn0QcebaH7RgxQBRwXkbF8t/+8QyJylYi0tkIqcSZbO7AUGCIid4lIqIjEisjUbsb1qIhEiMgpOFtUrX3tpUCmdYIp1ZlLcNbHLJzdhBOBscBXOPvVc4HLRCRSREYCt7ps+z5wsohcZo3EuptvJ3xXy4DRInKdNZDgauuY//bGm+qrNKH7xv04E2YNztZ6Rxcp2zMNyBGR48BbwE+MMQeMMVXA2cDlQBmwi+/2y3dlJbAPWI6zD/9T6/nXgVDgmIis62hjpXDW6xetOnm49Qf4C3A98DTQhLOR8DLOi6YAWF00VwK/A8qBUcAq2mGMKcf5rfZ+q+zPgQtdunkUOrGoX7JaSruNMdoCVyqAaAtdKaUChCZ01W+JyLkikm9N6HqogzJXich2EdnmOqlLqd5Iu1xUvyQiQTivO5wNFAE5wLXGmO0uZUYBbwBzjTEVIjLQGFPml4CVcoO20FV/NRXYY4zZZ4xpAl4DLm5T5nbgWWNMBYAmc9XbaUJX/VUa357EUsR3J6mMxjlUbpW1Fs+5PotOqR7w28SipKQkk5mZ6a/DqwC3YcOGo8aY5E6KtDfCp23/YzDOoXRn4FxH5ysRGWeMqfzOzkTuAO4AiIqKmjJmzJgexa1UVzqr235L6JmZmaxfv95fh1cBTkTaThNvq4hvz0pM5/9myrqWWWOtM7JfRPJxJvictjszxizAuUYP2dnZRuu28pbO6rZ2uaj+KgcYJSLDRCQUuAbn7FtX7wBnwjcrA47GORFLqV5JE7rql4wxLcBdwEfADpwrBW4TkV+LSOsNGj4CykVkO/AZ8KA1Y1GpXkkX51L9ljFmGc41Qlyfe8zlsQHus36U6vU0ofvIorUHTmj766YN8VAkSvUu7p4beg50TbtclFIqQGhCV0qpAKEJXSmlAoQmdKWUChCa0JVSKkBoQldKqQChCV0ppQKEJnSllAoQmtCVUipAaEJXSqkAoQldKaUChCZ0pZQKEG4l9K7uji4i91l3Rt8sIp+IyFDPh6qUUqozXSZ06+7ozwLnAVnAtSKS1abYJiDbGDMeWAI86elAlVJKdc6dFnqXd0c3xnxmjKmzfl2D83ZeSimlfMidhO7O3dFd3Qp8cCJBKaWU6j53bnDhzt3RnQVFbgCygdM7eP2bO6MPGaKL1SullCe500J35+7oiMg84GFgvjGmsb0dGWMWGGOyjTHZycnJPYlXKaVUB9xJ6F3eHV1EJgHP40zmZZ4PUymlVFe6TOhu3h3990A0sFhEckVkaQe7U0op5SVu3STajbujz/NwXEoppbpJZ4oqpVSA0ISulFIBQhO66te6WtbCpdwVImJEJNuX8SnVHW71oSvvcxhDaXUDpdUNNLcYYsKDGZIYSWSo/om8xWVZi7NxDs/NEZGlxpjtbcrFAHcDa30fpVLu02zhZ40tdlbvLWft/mNU1Td/6zWbwNjUWM4em+Kn6ALeN8taAIhI67IW29uU+w3O9Yke8G14SnWPJnQ3LFp7wCv73V1aw5sbi6huaGFkcjRnj00hPT6C0GAbFXXN7CypJqfwGDtKqgkLCeKHc4Zjs7U3cVf1UHvLWkxzLWDNscgwxvxbRDShq15NE7of2B2GZVtKWL2vnOSYMH40dQhDEqO+VSYuMpRhSVHMHp3M0txDPPHhTnYeruYPV04gJEgvfXhIp8taiIgNeBq4pcsd6bIWqhfQhO5jDc12Fq07wJ6yWmaMSOSckwd1mqCjw4K5duoQKuub+f1H+bTYDX++dpK21D2jq2UtYoBxwOciAjAIWCoi840x6113ZIxZACwAyM7ObnetI6W8TRO6D9U0NPPiqgLKahq4fHIaU4YmuLWdiPCTM0cSGmTj8WU7GBgbxmMXZmElGdVz3yxrARzCuazFda0vGmOqgKTW30Xkc+CBtslc9Vx5bSOf7izlaG0TE9LjGJ0SrfX6BGhC95HjjS38Y9V+jh1v4qbTMhmdEtPtfdw2exjFVfW8uKqAtLgIbps93AuR9h/GmBYRaV3WIgj4R+uyFsB6Y4wuYeFFe8pquPkfORRX1hMWYiP3YCUXnJLKzJFJXW+s2qUJ3Qfqm+z8Y9V+ymudyXzkwOge7UdEePSCLIor6/mvD3YyMSOO7Ez3WvmqfV0ta9Hm+TN8EVN/UFnXxPdfyqGxxcGdZ4wgdUAEr647wAdbS8hIiGRIQqS/Q+yT9Oqal7XYHbyyppCymkZumD60x8m8lc0m/P7KCaTFRXD3q5uoON7koUiV8g1jDD9fspnSqkYW3DSF9PhIgmzClVPSiQwN5uMdpf4Osc/ShO5FDmNYsrGIgvLjXDElvUfdLO2JDQ/hL9dN4khtIw8uycMYvQan+o6lecUs317K/d8bzeQh8d88HxYSxKyRSewpq+XgsbpO9qA6ogndiz7ZUcrmoirOyUphQnqcR/c9Pj2Oh84by8c7yngt52DXGyjVCxw73sQvl25jYkZcu9eApg1PICzYxrqCY36Iru/ThO4l24qr+Cz/CFOGxjNntHfuzvT9GZnMHJnIb/69nYKjx71yDKU86fH3d1DT0MLvLj+FoHaG3oYFB5GVGsu24iqa7Q4/RNi3aUL3grLqBhZvKCI9PoL5EwZ7bRiWzSb84coJBNuE+97IpUVPANWLrdpzlDc3FvHD04czZlBsh+UmZsTR0Owg/3CND6MLDJrQPaypxcGidQcICbJx3dQhXp/VmToggt9cMo6NByr56xd7vXospXqqodnOL97eQmZiJD+dO6rTssOTo4kKDWJ7SbWPogscmtA9bNmWEspqGrkqO524yFCfHPPiiWlcNGEw//3xbrYUVfnkmEp1x58/3U1heR2PX3oK4SFBnZYNsgmjU2LYVVqDQy/4d4uOQ/egrYeqWFdwjDmjkhg10DMjWtz1m4tPJmf/MX72+ibev3t2uyfNiS4ydt00XaNEdd/Ow9U8/8U+Lp+c7vakodEpMWw6WMmhinoydEy627SF7iE1Dc28vekQaXERzMvy/XK3cZGh/P7K8ew9cpzffbDT58dXqj0Oh+E/3tpCbEQID18w1u3tRg2MRoD8Uu1H7w5N6B5gjOHd3GKa7Q6uys4g2Oafj3X2qGRumZHJS18XsHL3Ub/EoJSrhWsL2XSgkkcuGEtClPtdkJFhwaTFR7DvSK0Xows8mtA9YPOhKraXVHN2VgrJMWF+jeX/nTuGEclRPLA4j6q65q43UMpLDlc18MSH+cwamcSlk9K6vX1mYhRFFfU6fLEbNKGfoOONLbyXV0xGfESvWFQoIjSIp6+eyNHaRh59d6u/w1H92H8u3Uqz3cHjl47r0dDdYUlRtDgMRRX1XoguMGlCP0HLtx+modnOZZPTsfWSZT/Hp8dxz1mjWJpXzLu5h/wdjuqHPs8v46NtpdwzbxRD29y8xV1DE50XQwvKddKcuzShn4CiijrWF1QwY0QSKbHh/g7nW+48YwSThsTx6DtbKanSFo7yHYfD8MSH+QxJiOS2WT1f4jkyNJhBseE6C7obdNhiDzmMYWleMdFhwcwdM9Df4XxHcJCNp6+ayHl/+oqfLtrEwtundb2RUh6wNK+YHSXVXJ2dwZINRSe0r8ykSDYWVmJ36Hh0d2gLvYc2FlZQVFHPueMGdTlRwl8yk6J48orxrC+s4JG3t+qqjMrrGlvs/GF5PicPjuWU9AEnvL/MxCia7A79lukmTeg9UN9k58NthxmaGMnEDM+uouhpF00YzN1zR7J4QxEr9+hQRuVdb6wvoqiinp+fO8Yj15Qyrf73/drt4hZN6D3w+a4y6pvsXl14y5N+Nm80F5ySygdbD7PxQIW/w1EByuEwvPDVPiZkxDFnlGdGfMVGhJAYFaoJ3U2a0LupuqGZNfvKmZARR+qACH+H4xabTXjqqgmMSI7izQ1FbCvW9V6U532ys4yC8jpumzXMow2dzMQoDhyrw6H96F1yK6GLyLkiki8ie0TkoXZenyMiG0WkRUSu8HyYvccX+UewOwxn9cILoZ0JDwnihulDSY+P4LV1B9l6SJO68qxX1x0gJTaM88YN8uh+hyZGUtdkZ99RnTXalS4TuogEAc8C5wFZwLUiktWm2AHgFmCRpwPsTSrrmlhXcIzJQ+JJjPbvjNCeCAsO4pYZw0iLd96QV7tflKeUVTfwxa4jXD45nWAPLxnd2o+eU6D1tSvufPJTgT3GmH3GmCbgNeBi1wLGmAJjzGYgoOfofpZfBtArhym6KyI0iO/PzGR4chRLNhSxel+5v0NSAeDtTYewOwxXTEn3+L4To0OJCg0iR29L1yV3Enoa4HrTyiLruX6lvLaRDYUVTM1M8Nk6594SFhzETadlMnZQDO/lFfNZfpkOaVQ9ZoxhyYYipgyNZ3hytMf3LyIMTYxiQ6G20LviTkJv7+pGj85+EblDRNaLyPojR470ZBd+8+nOMoJswukneef+oL4WEmTjumlDmZgRx4rtpSzbUqI3E1A9kldUxe6yWq+0zlsNTYyksLyOsuoGrx0jELiT0IuADJff04HinhzMGLPAGJNtjMlOTu47ibG0uoHcg5VMH55IbHiIv8PxmCCbcMWUdGaMSGTV3nLe3FDUr2bkuXGx/z4R2S4im0XkExEZ6o84e7slGw4SHmLjgvGpXjtGaz/6em2ld8qdhJ4DjBKRYSISClwDLPVuWL3LJzvLCAm2MWdU3/lPyF02ES44JZV5Y1PYdLCShWsL+8VypW5e7N8EZBtjxgNLgCd9G2Xv12J38MGWw5w1NsWrjZ3UuHDCQ2zaj96FLhO6MaYFuAv4CNgBvGGM2SYivxaR+QAicqqIFAFXAs+LyDZvBu1L24qr2HqoipkjkogKC8ylb0SEuWMGMn/CYPIP1/Diqv00NNv9HZa3uXOx/zNjTJ316xqc306Vi3X7j1F+vIkLT/Fe6xwg2GZjQnqc9qN3wa3xRcaYZcaY0caYEcaYx63nHjPGLLUe5xhj0o0xUcaYRGPMyd4M2peeXrGL8BAbs3rBWufeNn14IlefmsHBY/W8sDLgk3p3L/bfCnzg1Yj6oH9vKSEiJIgzTvL+yK9TMxPYVlzN8cYWrx+rr9KZop3YdKCCj3eUMWdUMhGhvXMBLk8bnx7H9dOGUFJVz8urC2hqCdjuF7cv9ovIDUA28PsOd9aHL/j3VIvdwUdbDzN37ECfnB/ZmfHYHYbcg5VeP1ZfpQm9E08t30ViVCinjUj0dyg+NSY1lquyMzhQXsfCtYWBeqHUrYv9IjIPeBiYb4xp7GhnffWC/4lo7W65wMvdLa0mD41HBO1H74Qm9A6s3lvOyj1HufOMEYQF94/Wuavx6XFcOimN3WW1vJt7KBDHqXd5sV9EJgHP40zmZX6IsVd73+puOdMH3S0AseEhnJQSo/3ondCE3g5jDH9ckU9KbBg3TO+/I9WyMxM446Rk1hdW8OXuwFp6152L/Ti7WKKBxSKSKyL9anRXZ1rsDj70YXdLq1MzE9hYWEFLPxiJ1ROBOWzjBH25+yg5BRX85pJxvfbmFb4yb2wKx4438dG2w3yWX+az1pgvGGOWAcvaPPeYy+N5Pg+qj1i9r5zy401c5MWx5+2ZOiyBV9YUsuVQFZOGxPv02H2BttDbMMbw1PJ80uIiuDo7o+sNApxNhMsnp5M6IJx7X8/l4LG6rjdSAe/feSVEhwX7ZHSLqxnW9axVerOWdmkLvY0V20vZXFTFk1eMJzS49/x/t2jtAb8dOyTIxnVTh7Dgy338ZNFGFv/otH55XUE5NbU4+GBrCWdnpfj8G2xidBhZqbGs3HOUu+aO8umx+4Lek7F6AYfD8McVuxieFMVlk/rd+mOdSowO4w9XTWBzURW/fm+7v8NRfrRyzxGqG1q40MfdLa1mjUpiY2El9U0BPU+iRzShu1iaV8zOwzXcM2+Ux9d0DgTnnDyIH84ZzsK1B3g395C/w1F+8l5eCbHhwcz201IYs0cl0WR3sHqfdru0pV0uFte7lV80frC/w+m1HjznJNYXVvDw21uZmBHHUGvRJNU/1DfZWbalhHFpA1iyocgvMUwdlkBUaBCf7Chj7pgUrx/P3e7O66YN8XIkXdNmqOVfaw5QVFHPQ+eNwWbr/Td+9pfgIBt/umYiNoG7X90UyDNJVTs+2FpCY4uDSRlxfoshLDiI2aOS+XSnruPfliZ0nDd+/sunu5k1MslvXyP7kvT4SH53+Xjyiqp4akW+v8NRPvRazkESo0IZluTfb2Zzxw6kpKqBbcXVfo2jt9GEDjz/xV4q6pp56Lwx/g6lzzj/lFSunTqE57/Yx5e7+sfaJf3d7tIa1u0/RnZmAiL+/RY7b2wKQTbh/S0lfo2jt+n3Cb20uoEXVu5n/oTBjEsb4O9w+pTHLsxidEo0972Rx5GaDpc5UQFiwZf7iAgJ4tSh/p/QkxAVyqyRSbyXV6zdLi76fUJ/4sOd2B2G+7832t+h9DkRoUH8+drJ1DQ0c//iPByBuYiXAoor63kn9xBXZacT2UvuC3DRhMEUVdSzSVdf/Ea/Tujr9h/jrY2HuH32cB2t0UMnDYrhkQuz+HLXEf6+cp+/w1Fe8uSHOxERbp8z3N+hfOOck1OIDA3ijZyDXRfuJ/ptQm+2O3j0na2kxUVw19yR/g6nT7th2hDOOTmFJz/MJ09bSwHn671HeSe3mDtmDyc9PtLf4XwjJjyEiycO5t3cYqobmv0dTq/QbxP6y18XkF9aw6MXZhEZ2ju+QvZVIsITl49nYEwYd7+2iRo9uQLGwWN13P3qJoYnR3HnGSP8Hc53XD9tKPXNdr+10h3GcOBYHWv3l/Ph1sN+v8tXv8xke4/U8vuP8pk7ZiDnnOz9iQn9QVxkKP99zSSuWbCax97dxtNXT/R3SKoDZTUNvLK6kBXbSykoP44gjBgYxeQh8UwZGs/49DgiQoL4eu9RHn9/B012BwtunNIr76k7Lm0Apw1P5Pkv93HD9KE+XVumtLqBtzYWcbCiHoB3c4tJiArl8UvGcZ6PbvrRVu/7C3lZi93BA4vzCA8J4r8uO8Xvw68CydRhCdxz1mie/ngXs0YmcfkUvadyb+JwGP6xaj9/WJ5PU4uDacMSmTUyCYeBnYereXNDEf9cXfitbcYMiuEv101m5MBoP0XdtXvmjeKaBWt4+esCfni6b75F7CmrZeHaQoKDbFw6MY1RKdFMyIjjyY/yuXPhRn5x/hjumOP7bzT9LqE//+U+Nh2o5JlrJ5ESG+7vcALOXXNHsmrvUR59dytjU2PJGhzr75AUzlb5A4s38+WuI5w1ZiCPXphFZpvJQS12BztKasgvraGh2c6ogdFMHeb/MeddmT48kbljBvKnT3Zz4YTBpMVFePV4heXHeWVNAQlRodwyYxgDIkIAmDkyicWZCdz3Ri7/f9lO4iJCuepU3y7B3a8S+td7j/LHFbu4YHyqzxfm7y+CbMKfr53EJc+u4taXc3j3JzMZqP9xeoW7a4zsLKnmzU2HaGy2M3/CYKYNS+DrveV8vbe8w21sIuw9cpy9R457KtwT1tn7nTIknq92H+H6v61l+b1z3Fr6uidLUh+uauDl1QXEhodw66zhRLfphgoNtvH01ROpqm/mkXe3MiY1hvHpvlsmod9cFD1UWc9PF21iWFIUT1w+vte3OvqylNhw/n5zNlX1zdz2z/Ucb2zxd0j9Un2TncXrD/LPNYXEhAXzkzNHMn14YkDW/fioUC6dlEZB+XEeXJLnlTWGymsbeXHVfkKDbPxg5rDvJPNWIUE2nrlmEsnRYdz5r40cO97k8Vg60i8Sem1jCz96ZQNNLQ6ev3FKh38I5TknDx7AM9dMYuuhKm57eb2uXe0jdoehuLKe5dsP84fl+eQVVXLmSQP58ZkjAr6LcWJGPOdkpfBubjFXL1jN+oJjHptFWlnXxAsr92M3hu/PHEZ8VGin5eOjQnnuhskcqWnk3tdzfTbpLuAzW32TnR+8lMP2kmr+dtMURiT33os7gWZeVgp/vGoi976Ryx2vrOdvN2X3+3u0nqiGZjuf7Cjjq91H+HpvOfVNdhpa7BjjHELX0GzHYUBwXtCcl5VC6gDv9in3JqefNJALJgzmV0u3ccVfV5MYFUpafATGQIvD0Gx30GJ3kBQdht1hyEiIZMygGOIiO07QR2saeWl1AQ0tdm6bNdzt/xjHp8fx2EVZPPLOVv765V5+fIb357sEdEKvb7JzxyvrySk4xp+umeSTtZPVt10yKY0mu4OfL9nMTS+s4/kbp3TZulHf1WJ38OKqAp77Yi/HjjcxICKEhKhQUgaEEx5swyaCiHM5hoExYYxIjiYmPMTfYfvF/AmDOfOkZD7ceph1+49RVtNIkE2wiRAWbMNmE8qqG9hVWsOmg5UszYP0+AjGpw1gXNqAb5J7Y4udDYUVrNheSpBNuGXGMAZ384Lr9dOGsHpfOU8t38WpmQmcmpngjbf8jYBN6IerGrjtnzlsK67micvHM3+C3rTCX67KziAiJIj7F+dx2XNf88LN2QzXb0pu23m4mgcXb2bLoSrmjE7mjtnDOW1EIq/rlPcOxYSHcGV2Bld2cqP3RWsPUFbTwI6SGrYeqmLZ1sMs23qYmPBgwoJtVNY10+IwjEyO5tJJaT1qiIgIv7vsFLYequLHCzfy1p0zyEjw3mzbgEzoX+89yr2v51Lb0MLfb8rmrLHaMve3iyYMJnVAOLf/cz0XPLOSRy/M4tqpGQF5gc5TmlocPPvZHv7n8z0MiAjhf66fzPl+mrASqAbGhDMwJpzTRydztKaR/NIaSqoaaLI7GDsolrGpsQxNjDyhehoTHsLfb8rm8ue+5pYX1/HmnTM67eI5EQGV0Kvqmvndhzt5dd0BhiZG8vIPpjJmkI6D7i2yMxNYds9sHlicxy/e3sIHW0t4+IKx+jdqx+aiSn6+ZDM7D9dw6aQ0HrswS7uqvCwpJoykmDCv7HtUSgx/uymbG19Yx60vr+fF759KrBe6xNwa5SIi54pIvojsEZGH2nk9TERet15fKyKZng60M+W1jTy1PJ9ZT3zK6zkHuH32MD68Z44mil4odUAEr/xgGr+8KIu8g5Wc96evuPf1XHIPVvplXeveVrfLaxt55J0tXPLsKirrmnnh5myevnqiJvMAMG14In+6ZiJ5Byu58rnV7DtS6/FjdNlCF5Eg4FngbKAIyBGRpcaY7S7FbgUqjDEjReQa4Angao9H66Kkqp6v95SzbEsJX+w6QovDcN64Qfx07iidndjL2WzCLTOHccmkNP7n8738a00hb286RFZqLOeOG8TcMQMZmxpLkJfv7dqb6vaB8joWrTvAwjWF1DXbuXH6UO4/5ySvtOKU/5x3SiovhYdw16sbueCZlfz4jBHcPDPTY39nd7pcpgJ7jDH7AETkNeBiwLXSXwz80nq8BPiLiIjpQZOrqq6Z2qYWGpvtNDQ7ON7UwpGaRo7UNHK4uoHdpbXkl1Zz8JhzQZxBseHcOmsYV0xJZ1RKTHcPp/woLjKUX5w/lp/OHck7mw7x5sZDPP3xLv64YheRoUGMGzyA4clRpMdHkB4fSVJ0GNHhwUSHBRMZGkRwkJAYFXYiid9nddvhMNQ322lscdDQbKesppGiijq2FFWxZl85eUVVBNmEeWMH8uA5JzFyoNblQDVrVBIf3DObXy3dzlMrdvHcF3uZOTKJmSMSOWlQLKkDwhk0ILxHQ3zdSehpgOvl9CJgWkdljDEtIlIFJAJHuxvQrTvLdn0AAAhzSURBVC/nsL6wov1gbcKwpCjGp8Vxy4xhTB+ewNhBsdi83JJT3hUTHsKNp2Vy42mZHKlpZOWeI+QdrGJzUSUf7yjlaG3HM+1yHp5Hcs/7PX1WtwvKjzP3qS++83xokI0JGQO4/+zRXJmdwaABgT35RzmlDojgrzdOYUtRFa/mHGDl7qOs2F76zeszRyay8Lbp3d6vOwm9vWzZtnXiThlE5A7gDuvXWhHp9i3j9wIfd3cj9yTRg/+AejGPv5/rPbmznvnOexr4RIdlh7qxv15Rt3fjbPrf7e4G39Vb665f4uqinnotphM8P74VVyGw6PYOy3ZYt91J6EWA62DOdKC4gzJFIhIMDACOtd2RMWYBsMCNY/qciKw3xmT7Ow5PCbT3A155TwFRt3vr37o3xtUbYwLPxeXOKJccYJSIDBORUOAaYGmbMkuBm63HVwCf9qT/XCkf07qtAkqXLXSr3/Au4CMgCPiHMWabiPwaWG+MWQq8ALwiIntwtl6u8WbQSnmC1m0VaNyaWGSMWQYsa/PcYy6PG4ArPRuaz/XKrqATEGjvB7zwngKkbvfWv3VvjKs3xgQeikv026NSSgWGfrEeulJK9Qf9PqF3NfW7LxKRAhHZIiK5IrLe3/H0hIj8Q0TKRGSry3MJIrJCRHZb/8b7M0ZfEpEMEflMRHaIyDYRuaedMmeISJX1d88Vkcfa25cXYuu0vonTM9Y5tllEJns5npNcPoNcEakWkZ+1KeOTz+pE6rGI3GyV2S0iN7dX5juMMf32B+eFsL3AcCAUyAOy/B2XB95XAZDk7zhO8D3MASYDW12eexJ4yHr8EPCEv+P04eeRCky2HscAu9rWVeAM4N9+iK3T+gacD3yAc0z/dGCtD2MLAg4DQ/3xWfW0HgMJwD7r33jrcXxXx+vvLfRvpn4bY5qA1qnfys+MMV/y3fHeFwMvW49fBi7xaVB+ZIwpMcZstB7XADtwzmLtCy4G/mmc1gBxIuKrdYDPAvYaYwp9dLxvOYF6fA6wwhhzzBhTAawAzu3qeP09obc39buvnCSdMcByEdlgzWAMFCnGmBJwJjhgoJ/j8QtrxcdJwNp2Xj5NRPJE5AMROdlHIXVV3/x5nl0DvNrBa/74rMC9etyjzyyg1kPvAbemdfdBM40xxSIyEFghIjutloLq40QkGngT+JkxprrNyxtxdi3Uisj5wDvAKB+E1VV988t5Zk0Wmw/8Rzsv++uzclePPrP+3kJ3Z+p3n2OMKbb+LQPextm1FAhKW7+qW/+W+TkenxKREJzJfKEx5q22rxtjqo0xtdbjZUCIiCR5Oy436pu/zrPzgI3GmNK2L/jrs7K4U4979Jn194TuztTvPkVEokQkpvUx8D1ga+db9Rmu0/BvBt71Yyw+JSKCc9bqDmPMHzsoM8gqh4hMxXl+l3s5Lnfq21LgJmu0y3SgqrXLwcuupYPuFn98Vi7cqccfAd8TkXhrFMz3rOc656urzb31B+cV+F04R7s87O94PPB+huMcrZMHbOur7wnniVgCNONsrdyKc9naT3AuUPgJkODvOH34eczC+ZV7M5Br/ZwP/Aj4kVXmLutvngesAWb4q761iUtw3khkL7AFyPZBXJE4E/QAl+d8/ll1px4D2cDfXbb9AbDH+vm+O8fTmaJKKRUg+nuXi1JKBQxN6EopFSA0oSulVIDQhK6UUgFCE7pSSgUITehKKRUgNKG3ISK1bpT5mYhEulHucxHJth4X9GQmmrXM54wuyvxSRA5Zy4BuF5Fr3djvJSKS5fL7r0VkXnfjU32H1u3Apwm9Z36Gc+KCL5wBdFrpLU8bYybiXMnteWuaeGcuAb6p9MaYx4wxH/c4ShUotG73YZrQO2C1Hj4XkSUislNEFlpTl+8GBgOfichnVtnvichqEdkoIoutBZQ62/cNIrLOanU8LyJB1vPnWvvIE5FPrFX1fgTca5Wd3VXcxpjdQB3ONZQRkdtFJMfa55siEmm1iuYDv7f2O0JEXhKRK6xtCkTkV1YsW0RkjPV8sjgX5N9oxV3ow/UvlIdo3Q7cuq0JvXOTcLZYsnBOcZ5pjHkG5yI5ZxpjzrT+6I8A84wxk4H1wH0d7VBExgJXW/uaCNiB60UkGfgbcLkxZgJwpTGmAPgrVgvFGPNVVwGL824wu41zoSSAt4wxp1r73AHcaoz5Gud6Eg9a+93bzq6OWu/nOeAB67n/BD61nn8bGNJVPKrX0rodgHW7vy+f25V1xpgiABHJBTKBlW3KTMd5UqwS51o/ocDqTvZ5FjAFyLHKR+BcbW068KUxZj+AMabtovhduVdEbsd5crouhD9ORH4LxAHRuLPAj1Pran4bgMusx7OAS634PhSRim7GqHoPrdsBWLc1oXeu0eWxnfY/L8F5Z5EuL9a4lH/ZGPOtNZpFZD4ntkb008aYP4jIZcA/RWSEMaYBeAm4xBiTJyK34Oy3dEfre3d93+2t0az6Jq3bAVi3tculZ2pw3tcRnCu1zRSRkQBWP97oTrb9BLhCnDcDaL1h7FCcLZ/TRWRY6/PtHKtLxrlO9nr+b3nOGKBEnBeSru/gPbhrJXCVFd/3sPoyVUDRut2H67Ym9J5ZAHwgIp8ZY44AtwCvishmnCfBmI42NMZsx9kvudwqvwJItfZzB/CWiOQBr1ubvAdc6u6FI8uvgftExAY8ivNWZSuAnS5lXgMeFJFNIjLCzf3+CucazRtx3jygBOfJowKH1u0+XLd1+VzlNhEJA+zGmBYROQ14zrr4pVSfFih1W/vQVXcMAd6wWkdNwO1+jkcpTwmIuq0t9D5ERB4Grmzz9GJjzOP+iEcpT9G67Rma0JVSKkDoRVGllAoQmtCVUipAaEJXSqkAoQldKaUChCZ0pZQKEP8LoaHT6DIXopsAAAAASUVORK5CYII=\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplot(2,2,1)\n", + "sns.distplot( df[df['CONDITION'] == 0]['Intellect_Rating'] ).set_title('Transcript');\n", + "plt.subplot(2,2,2)\n", + "sns.distplot( df[df['CONDITION'] == 1]['Intellect_Rating'] ).set_title('Audio');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What a visual inspection of histograms at least show for the sample at least is that there might be a differnce in the variability of the indepedent conditions and thus the data may be a candidate for welch's t-test under the classical statiscal framework. In the case of Bayesian model applied below our assumption of of heteroscedasticity will be baked into out model in our attempy to model the data generating process, and in the case of recruiters ratings by audio or transcript a differnce in the variability of ratings data of two indepednt conditions is reasonable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "y_{i} &\\sim Normal(\\mu_{i}, \\sigma_{k} ) \n", + "\\\\ \\mu_{i} &=\\alpha_k\n", + "\\\\ \\alpha_k &\\sim HalfNormal(0, 2.5) \\hspace{0.2cm} for \\hspace{0.2cm} 1...k\n", + "\\\\ \\sigma_{k} &\\sim Exponential(1) \\hspace{0.2cm}for \\hspace{0.5cm}k\\hspace{0.2cm} 1...k\n", + "\\end{align*} \n", + "\n", + "The formulation for presenting statistical models here follows that used by McElreath (2020) for its intuitive nature. In plain english the model specifies that the dependent variable $y_{ik}$ of the raters Averaged intellect rating for the two independent groups are distributed normally in terms of the Likelihood an unkown $\\mu_k$ that is to be estimated with a halfnormal prior probability distribution on $\\mu$ that has a $\\mu = 0$ and $ \\sigma = 2.5$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3 - Specifying priors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4 - Use Bayes rule" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stan model of Bayesian Welch's t-test" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "Welch_t_model = \"\"\"\n", + "\n", + "data{\n", + "\n", + "int N; // number of data points\n", + "vector[N] y; // vector of data points in long format for both groups\n", + "int K; // number of groups\n", + "int x[N]; // array of interger values of the indicator variable for groups\n", + "int onlyprior;\n", + "}\n", + "\n", + "parameters{\n", + "\n", + "// Model parameters to be estimated\n", + "\n", + " vector[K] mu; //\n", + " vector[K] sigma; //Standard deviation bounded at 0\n", + " \n", + "}\n", + "\n", + "model{\n", + "\n", + "//priors\n", + "mu ~ normal(5,2.5);\n", + "sigma ~ exponential(1);\n", + "\n", + "// Conditional statement whether to run prior predictive check or \n", + "// full analysis\n", + "if (!onlyprior)\n", + "// Likliehood\n", + "y ~ normal(mu[x], sigma[x]);\n", + "\n", + "}\n", + "\n", + "generated quantities{\n", + "real diff = mu[1] - mu[2];\n", + "real Cohen_D = diff / (sigma[1] + sigma[2])/2;\n", + "\n", + "\n", + "real yrep[N];\n", + " \n", + "// Generate data for posterior samples\n", + "\n", + " yrep = normal_rng(mu[x], sigma[x]);\n", + " \n", + "}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_bbb570752a79c1164a9a269da373b93d NOW.\n" + ] + } + ], + "source": [ + "# StanModel function can be called and be passed the model string specified above to compile into C++ code.\n", + "sm = ps.StanModel(model_code = Welch_t_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "df[\"CONDITION\"].replace({0: 2}, inplace=True)\n", + "data = {'N': len(df),\n", + " 'y': df[\"Intellect_Rating\"].values,\n", + " 'K': 2,\n", + " 'x': df[\"CONDITION\"],\n", + " # 0 means run with the likelihood\n", + " 'onlyprior': 0}" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "fit = sm.sampling(data = data, iter = 2000 , warmup = 1000, seed = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    meanse_meansd2.5%25%50%75%97.5%n_effRhat
    mu[1]5.6187000.0066830.3621994.9176765.3845875.6209765.8559566.3226482937.1295381.000619
    mu[2]3.6904250.0069690.4484712.8252893.3994403.6848233.9818454.5748244141.1602041.000209
    sigma[1]1.6458020.0047670.2674931.2242441.4560361.6099031.7944662.2578123148.1832870.999418
    sigma[2]1.9363350.0056930.3318201.4010171.7007351.8889352.1281002.7198203397.4046521.000622
    diff1.9282750.0098990.5711310.7934141.5511051.9351042.3118613.0664203328.8847311.000240
    \n", + "
    " + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% \\\n", + "mu[1] 5.618700 0.006683 0.362199 4.917676 5.384587 5.620976 \n", + "mu[2] 3.690425 0.006969 0.448471 2.825289 3.399440 3.684823 \n", + "sigma[1] 1.645802 0.004767 0.267493 1.224244 1.456036 1.609903 \n", + "sigma[2] 1.936335 0.005693 0.331820 1.401017 1.700735 1.888935 \n", + "diff 1.928275 0.009899 0.571131 0.793414 1.551105 1.935104 \n", + "\n", + " 75% 97.5% n_eff Rhat \n", + "mu[1] 5.855956 6.322648 2937.129538 1.000619 \n", + "mu[2] 3.981845 4.574824 4141.160204 1.000209 \n", + "sigma[1] 1.794466 2.257812 3148.183287 0.999418 \n", + "sigma[2] 2.128100 2.719820 3397.404652 1.000622 \n", + "diff 2.311861 3.066420 3328.884731 1.000240 " + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Because of python print statement a large number of model outputs do not scale and pRICULAR outputs cannot be selected,\n", + "# so it is easier to put outputs into a pandas dataframe.\n", + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns=summary['summary_colnames'], \n", + " index=summary['summary_rownames'])\n", + "\n", + "#Output model results.\n", + "fit_df.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit-visualisations - Bayesian one sample Z-test\n", + "The arviz package offers many useful functions for plotting MCMC samples of the posteriors produced by Bayesian data analysis with Stan." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior plots" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Using the arviz package the posteriors can be plotted from the MCMC samples\n", + "az.plot_posterior(fit, var_names=(\"mu\"));\n", + "az.plot_posterior(fit, var_names=(\"sigma\"));\n", + "az.plot_posterior(fit, var_names=(\"diff\",\n", + " \"Cohen_D\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Differnces in sigma\n", + "Of crucial evaluation here is the individually estimated sigma terms for the independent groups. Which" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autocorrelation plots" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Using the arviz package the autocorrelation of the 4 MCMC chains can be plotted.\n", + "az.plot_autocorr(fit, var_names=(\"mu\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MCMC traceplots" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Using the arviz package the traceplots of the 4 MCMC chains can be plotted.\n", + "az.plot_trace(fit, var_names=(\"mu\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior predictive checks" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Convert the pystan object into Arviz inference object for use in plotting functions\n", + "data = az.from_pystan(\n", + " posterior=fit,\n", + " posterior_predictive='yrep',\n", + " observed_data=[\"y\"])\n", + " \n", + "az.plot_ppc(data, data_pairs = {\"y\" : \"yrep\"}, num_pp_samples= 100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The posterir predcitive checks shows variation in generted data from model when compared observed data, this may suggest need for more modelling but at the same time may simple reflect an example of small sammple size from a niche population of fortune 500 recruiters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "Delacre, M., Lakens, D., & Leys, C. (2017). Why psychologists should by default use Welch’s t-test instead of Student’s t-test. International Review of Social Psychology, 30(1).\n", + "\n", + "Schroeder, J., & Epley, N. (2015). The sound of intellect: Speech reveals a thoughtful mind, increasing a\n", + "job candidate’s appeal. Psychological Science, 26, 877-891.\n", + "\n", + "Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E. J. (2011). Statistical evidence in experimental psychology: An empirical comparison using 855 t tests. Perspectives on Psychological Science, 6(3), 291-298." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian estimation of repeated measures t-test.ipynb b/wip/Bayesian estimation of repeated measures t-test.ipynb new file mode 100644 index 0000000..b6248a5 --- /dev/null +++ b/wip/Bayesian estimation of repeated measures t-test.ipynb @@ -0,0 +1,990 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

    Table of Contents

    \n", + "
    " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "# Import relveant libraries/packages.\n", + "import pystan as ps\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import arviz as az\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats\n", + "plt.style.use('ggplot')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of the repeated samples t-test\n", + "This notebook presents an example for a Bayesian estiation equivalent for the repeated samples t-test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The classic repeated sample t-test\n", + "Before proceeding a quick review of the repeated samples t-test within the classical statistics framework is presented. The classic repeated samples t-test calculates a t statistic from mean differcne between the two repeated measures follwoed by calulating the quotient of the standard deva\n", + "\n", + "$$t = \\frac{m}{s/\\sqrt{n}}$$ \n", + "\n", + " $m = \\overline{X_1}-\\overline{X_2}$ = the difference between the sample means\n", + " \n", + "$s$ = is the standard error of the difference scores\n", + "\n", + "$n$ = sample size.\n", + "\n", + "This t statistic is then used to calculate the p-value for the degrees of freedom $df = n - 1$ If this p-value is $\\leq$ to the 𝛼 level pre-determined before the analysis under the Null hypothesis significance test the results are determined to be statistically signicant and thus null hypothesis is to be rejected.\n", + "\n", + "$$ \\large H_0:m = \\mu_0$$\n", + "$$ \\large H_1:m\\neq \\mu_0$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following on ffrom thw the quick description of the classic repeated samples t-test above it is important to keep in mind that all inferences in Bayesian data analysis are derived from the application of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to the repeated samples t-test, it is fundamentally different, because it uses fully probabilistic modelling and the inference is not based on sampling distributions\n", + " \n", + " For a fuller description see the practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for the question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. If scientific research publication is the goal, as such the priors must be accepted by a skeptical audience. Acceptance should be achievable using prior predictive checks to acsertain if the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors for the chosen model. Then interpret the posteriors for the model parameters of interest to conduct your inference.\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for the question under investigation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Study description\n", + "\n", + "The data analysed here were produced and analysed originally by Mehr, Song and Spelke (2016). The data were downloaded from https://sites.google.com/view/openstatslab/home and stored in the same Github repository where these notebooks are stored.\n", + "\n", + "Mehr et al. (2016) research focused on the seemingly universal act of parents singing to their children and that this singing from paraents appears to focus their childs attention towards the parent that is singing. Mehr et al., aim was to study the function of this singing specifically between mother and child. In particular Mehr et al. hypothesised that these specific melodies convey social information to infants.\n", + "\n", + "The resarchers argued that social groups share melodies and that different melodies exist between social groups. Therefroe, it was argued that hese shared melodies might signal to infants that novel individuals who sing these songs are from the same social group. Therefore, if a novel person sings a familiar melody this may signal to the infant group membership of this novel individual.\n", + "\n", + "All together, 32 parent and infant pairs were recuited with the aim of testing the hypothesis that melodies signal group membership to infants. The methodology they used to test this hypothesis involved taking each infant and mother pair in to the lab and teaching them a new lullaby during their first visit. The parents were then asked to sing the new lullaby to the infant every day for a 1-2 weeks before the return to the lab for the experimental session. \n", + "\n", + "The Experimental session consisted of showing the infants side by side videos of two unfamilliar individuals, on seperate screens. As with many infant studies the infant gaze is the standard behavioural measure for assessing infants attention. During the initial period of the experiment the two unfamiliar faces were presented with them both smiling at the infant silently. This allowed expert raters to produce baseline gaze proportion scores for each infant studied. This was followed by the each of the unique indivduals singing their lullaby (either the one the mother had been taught or one that had the same rhythm, and lyrics but the melody was different). Following this singing of the lullabies the infants were then left to view the two individuals when silent and smiling, to record another gaze proportion score.\n", + "\n", + "The data here comes from the same study that was analysed in the Bayesian (one-sample t-test) estimation notebook. The primary difference here is that the difference between baseline gaze proprortion and post lullaby singing gaze proportion scores is being analysed in this notebook. In addition, because the gaze proprotion scores come from each of the studied infants at two seperate time points the scores are not independent and must be analysed in a repeated measures framework." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    idstudy_codeexp1exp2exp3exp4exp5dobdot1dot2...dtword13dtnoword13totsing14babylike14singcomf14totrecord14othersong14dtword14dtnoword14filter_$
    0101\"LUL\"10009-Oct-1229-Mar-1305-Apr-13...00001
    \n", + "

    1 rows × 153 columns

    \n", + "
    " + ], + "text/plain": [ + " id study_code exp1 exp2 exp3 exp4 exp5 dob dot1 \\\n", + "0 101 \"LUL\" 1 0 0 09-Oct-12 29-Mar-13 \n", + "\n", + " dot2 ... dtword13 dtnoword13 totsing14 babylike14 singcomf14 \\\n", + "0 05-Apr-13 ... 0 0 \n", + "\n", + " totrecord14 othersong14 dtword14 dtnoword14 filter_$ \n", + "0 0 0 1 \n", + "\n", + "[1 rows x 153 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Importing the data from github repository using the relevant URL\n", + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Mehr%20Song%20and%20Spelke%202016%20Experiment%201.csv\"\n", + "\n", + "#Import data .csv file into pandas dataframe.\n", + "df = pd.read_csv(url)\n", + "\n", + "# Output data frame for evaluation\n", + "df.head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract the first 32 rows as the Mehr, Song and Spelke \n", + "# dataset as these data points are for the first experiment\n", + "# to create a reduced dataset.\n", + "red_df = df.iloc[0:32,]\n", + "\n", + "# unmark code below to output dataset for any checks that see fit (i.e. that extracting only experiment one)\n", + "red_df.tail(5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data visualisation and exploratory data analysis " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate differecne scores \n", + "diff = red_df[\"Baseline_Proportion_Gaze_to_Singer\"].values - red_df.loc[:,\"Test_Proportion_Gaze_to_Singer\"].values\n", + "\n", + "# Plot differnce scores in as a histogram\n", + "sns.distplot(diff);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "y_i &\\sim Normal(\\mu, \\sigma) \n", + "\\\\ \\mu &\\sim Normal(0, 0.2) \n", + "\\\\ \\sigma &\\sim Exponential(0.1) \n", + "\\end{align*} \n", + "\n", + "The formulation for presenting statistical models here follows that used by McElreath (2020) for its intuitive nature. In plain english the model specifies that the dependent variable $y_i$ (the difference of gaze proportion scores between the repeated conditions) is distributed normally in terms of the likelihood with the prior distribution on $\\mu$ parameter being normally distributed also, with a $\\mu$ of 0 and $\\sigma$ of 0.2. In addition the prior on the $\\sigma$ parameter is exponentially distributed with a $\\lambda$ of 0.1. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3 - Specifying priors\n", + "\n", + "## Prior predictive checks\n", + "\n", + "Following the description of the statistical model above the readers should rightly review and criticise the model freely, especialliy in asking why I selected these priors. The priors were selected using the prior predictive checks i ran in the code below to determine if my priors can generate data that falls reasonable on the dependent variable outcome space for the data generating process i am trying to model; which in this case is the difference between baseline and post experimental manipulation of expert ratings of the gaze proportion scores of infants, before seeing any data.\n", + "\n", + "### Visualising priors" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualising priors is a key step of prior predictive checks\n", + "\n", + "# Visualise prior on mean parameter.\n", + "plt.subplot(1, 2, 1)\n", + "x = np.arange(-1, 1, 0.001)\n", + "plt.plot(x, stats.norm.pdf(x,loc=0, scale=0.2));\n", + "plt.xlabel(\"prior on mu\");\n", + "\n", + "# Visualise prior on Standard deviation parameter.\n", + "x = np.arange(-.01, .1, 0.001)\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(x, stats.expon.pdf(x, scale = 0.01));\n", + "plt.xlabel(\"prior on sigma\");" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set seed to allow for the reproduciblity of notebook.\n", + "np.random.seed(1)\n", + "\n", + "# Simulate data from the prior for the mean of the model specified above.\n", + "sample_mu = np.random.normal(loc = 0, scale = .2, size = 1000)\n", + "sample_sigma = np.random.exponential(scale = .1, size = 1000)\n", + "\n", + "prior_PC = np.random.normal(loc = sample_mu, scale = sample_sigma, size = 1000)\n", + "\n", + "# Plot the simulated data\n", + "sns.distplot(prior_PC, hist = False);\n", + "plt.xlim(-1,1)\n", + "\n", + "# Plot vertical line of the 2 standard deviatons either side of the simulated data.\n", + "plt.vlines((np.mean(prior_PC) + 2 * np.std(prior_PC)), ymin = 0, ymax = 1.3);\n", + "plt.vlines((np.mean(prior_PC) - 2 * np.std(prior_PC)), ymin = 0, ymax = 1.3);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the statistical model and the specified priors assumed above the difference scores have a $\\mu$ around zero with a difference of -.5 and .5 within two $\\sigma$ before seeing the data. This seems reasonable for the scores being proportions between 0 and 1. In addition, the prior predcitive checks show that model is skeptical of any differnce between the gaze proportion scores (greatet probalilty density around 0), but with 95% probability of observing difference scores up to $\\pm$ 0.5 s before seeing the data. Under this model which means that data will have to convince the model that there is difference between the gaze proportion scores for baseline and the experimental condition." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4 - Use Bayes rule.\n", + "\n", + "The software of choice to conduct Bayesian inference on the data here is Stan (Carpenter et al., 2017) and the model is specified below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stan model of a Bayesian repeated samples t-test\n", + "\n", + "nNte this model is very similar to the one in the Bayesian (one-sample t-test) estimation notebook. The primary differnce is that like the classic repeated samples t-test the difference scores are calculated between the repeated conditions and this is what is being modelled and thus this changes the thought process of specify prior of the model to reflect this (as described above)." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# Stan code to produce a Bayesian estimation equivalent to the repeated measures t-test.\n", + "\n", + "Within_t = \"\"\"\n", + "\n", + "data {\n", + "\n", + "int N; // declaring the number of data points \n", + "vector[N] diff; // vector to contain the differnce between the gaze proportion scores\n", + "\n", + "}\n", + "\n", + "parameters{\n", + "\n", + "// Declaring the parameters to be estimated\n", + "\n", + "real mu; // mean of the differnce score\n", + "real sigma; // Standard deviation (bounded at 0)\n", + "\n", + "}\n", + "\n", + "model{\n", + "\n", + "// Priors\n", + "mu ~ normal(0, 0.2);\n", + "sigma ~ exponential(.1);\n", + "\n", + "// Likliehood\n", + "diff ~ normal(mu, sigma);\n", + "\n", + "}\n", + "\n", + "generated quantities{\n", + "\n", + "real Cohen_D = mu/sigma; // Calculating cohen D standard effect\n", + "\n", + "// Generate posterior p-value variables\n", + "int mean_pv;\n", + "int sd_pv;\n", + "\n", + "// Generating a vector of real values for\n", + "// Conducting Posterior predictive simulations\n", + "real yrep[N]; \n", + "\n", + "{\n", + "for (i in 1:N) {\n", + "yrep[i] = normal_rng(mu, sigma);\n", + " }\n", + "}\n", + "\n", + "// Callcuate Psoterior p-vlues for mean and standard devaiton of similuated data sets\n", + "mean_pv = mean(yrep) > mean(diff);\n", + "sd_pv = sd(yrep) > sd(diff);\n", + "\n", + "}\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compile the Stan model." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_7ab02f458d5325036b49d9fa6080dcbc NOW.\n" + ] + } + ], + "source": [ + "sm = ps.StanModel(model_code = Within_t)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate python dictionary to pass to Stan model to sample and run Bayesian one sample.\n", + "data = {'N': len(red_df),\n", + " 'diff': diff}" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "# fit Stan mdoel to the data.\n", + "fit = sm.sampling(data= data, iter = 2000, warmup = 1000,chains=4, seed= 302675)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    meanse_meansd2.5%25%50%75%97.5%n_effRhat
    mu-0.0701360.0005510.030902-0.132164-0.090776-0.070077-0.049752-0.0089773141.0970280.999819
    sigma0.1764770.0004060.0234920.1379610.1599480.1742190.1902000.2297783342.9237470.999365
    Cohen_D-0.4050780.0032060.183658-0.773425-0.527339-0.402945-0.279668-0.0472343282.1911090.999666
    mean_pv0.5235000.0080890.4995100.0000000.0000001.0000001.0000001.0000003812.8986491.000710
    sd_pv0.5370000.0080670.4986910.0000000.0000001.0000001.0000001.0000003821.3872531.000854
    \n", + "
    " + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% 75% \\\n", + "mu -0.070136 0.000551 0.030902 -0.132164 -0.090776 -0.070077 -0.049752 \n", + "sigma 0.176477 0.000406 0.023492 0.137961 0.159948 0.174219 0.190200 \n", + "Cohen_D -0.405078 0.003206 0.183658 -0.773425 -0.527339 -0.402945 -0.279668 \n", + "mean_pv 0.523500 0.008089 0.499510 0.000000 0.000000 1.000000 1.000000 \n", + "sd_pv 0.537000 0.008067 0.498691 0.000000 0.000000 1.000000 1.000000 \n", + "\n", + " 97.5% n_eff Rhat \n", + "mu -0.008977 3141.097028 0.999819 \n", + "sigma 0.229778 3342.923747 0.999365 \n", + "Cohen_D -0.047234 3282.191109 0.999666 \n", + "mean_pv 1.000000 3812.898649 1.000710 \n", + "sd_pv 1.000000 3821.387253 1.000854 " + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Because of python print statement arge number of model outputs do not scale and cannot outputs cannot be selcted,\n", + "# so it is easier to put outputs into a pandas dataframe.\n", + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns=summary['summary_colnames'], \n", + " index=summary['summary_rownames'])\n", + "\n", + "#Output model results.\n", + "fit_df.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit-visualisations - Bayesian within subject t-test\n", + "\n", + "## Posterior distributions plots" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(fit, var_names=(\"mu\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autocorrelation plots\n", + "After running any MCMC Bayesian model check the traceplots to review chain mixing" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Using the arviz package the autocorrelation of the 4 MCMC chains can be plotted.\n", + "az.plot_autocorr(fit, var_names=(\"mu\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The autocorrelation plots do not show any serious autocorrelation problems, as the values quickly decrease to 0." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MCMC traceplots" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(fit, var_names=(\"mu\", \"sigma\", \"Cohen_D\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The traceplot show good mixing of chains and show a hairy catepillar." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 5 - Posterior predictive checks" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a pandas dataframe of simulated datasets genreted from the posterior check. #\n", + "# the dataframe is a subet of the first 202 simualted data sets. (Stan generates as many datasets as Iterations)\n", + "yrep_df = pd.DataFrame(fit['yrep']).T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## posterior p-values\n", + "Posterior p-values are type of posterior precitve check. Tehy funtionon the basis that if the model captures the observed data well summary statistics (i,e the mean) should be similar between the observed data and the replicated data. This can be examined through a p-value type statistic (warning not a frequrntist p-value) which caluclte the proability that the choosensummary statistic exceeds that of the orignal data. For the end user posterior p-values close to 0 or 1 are a cause for concern and values closer .5 the closer the summary statisitics calculted from the simulated data are to the observed data, suggesting the model is captuing the observed data well." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "yrepMeans = []\n", + "for i in range(len(yrep_df.columns)):\n", + " yrepMeans.append(np.mean(yrep_df[i]))\n", + " \n", + "yrepSD = []\n", + "for i in range(len(yrep_df.columns)):\n", + " yrepSD.append(np.std(yrep_df[i]))" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(yrepMeans, kde = False).set_title('Posterior p-value for mean(difference scores) = 0.52');\n", + "#Inset line for mean differnce score values \n", + "plt.vlines(np.mean(diff), ymin = 0, ymax = 300);" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Plot The \n", + "sns.distplot(yrepSD, kde = False).set_title('Posterior p-value for sd(difference scores) = 0.54');\n", + "\n", + "#Insert line for standard deviation of differnce between the gaze proportion scores from the observed data\n", + "plt.vlines(np.std(diff), ymin = 0, ymax = 300);" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Convert pystan fit object to Inference object for use in Arviz functions.\n", + "IO = az.from_pystan(\n", + " posterior= fit,\n", + " posterior_predictive='yrep',\n", + " observed_data = 'diff')\n", + "\n", + "# Plot posterior simulated data sets for posterior predictive check\n", + "az.plot_ppc(IO , data_pairs = {\"diff\": 'yrep'}, num_pp_samples= 100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The posterior predictive check shows there is good model fit to the differnce scores from the orignally observed data. There are some discrepanices and the fit is by no means perfect, this may suggest some more modelling is needed, or could simple a result of the small smaple size of the study and the uncertainty that naturally arises from said small samples." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the Bayesian one sample Z-test equivalent\n", + "\n", + "As Krushcke correctly points out there is no standard formula for presenting the results of a Bayesian analysis in a journal article like the APA guide for reporting frequentist analysis. It is likely there never will be, because as McElreath (2020) explains Bayesian data analysis is more like a engineering approach to the problem and the resulting model that is fit will be analysis and problem specific. In addition, as Gabry et al, (2017) argue visualisations maybe even more relvant than written descriptions. Therefore, all the visualisations above would have to be included with any write up. Anyway below a write up for results is below, which generally follows the advice of Krushcke (2015) chapter 25. In any application though it comes down to the problem to be described (e.g. the wrtier of this notebbok is not an expert on babies gaze to lullabies) an the audience that needs to be convinced (any readers who intend to apply Bayesian methods to their own research should be able to be more specific than the general write up below).


    \n", + "\n", + "

    Write up


    \n", + "\n", + "A Bayesian normal likelihood model was fit to the differnce in infant gaze proportion scores calculated between the baseline and experimental conditions using the Stan probabilsitc programming language. the use of a normal lieklihood meant that there were two model parameters to estimate: $\\mu$ and $sigma$. The choice of priors was determined by usign priro predcitive checks which were informed by the data type (proportion scores) and the outcome space (0,1) of this type of data. Posteriro preditive checks are provided above and final values for the priors for the model parameters were that $\\mu$ distributed as normal(0,0.2) and the $\\sigma$ distributed as an exponential(.1). Four MCMC chains were ran each sampling 2000 times with 1000 of these samples within each chain used for warmup, resulting in 4000 samples per model parameter. The credibile interval (CI) and the mean for the posterior samples of $\\mu$ for the difference in gaze proprotion scores was calculated: $(\\mathbf{E}(\\mu_{diff}) = -.07, CI = [-.13, -.01])$ \n", + "\n", + "Posterior predicitive checks showed that the model could generate data similar to the difference scores from the sampled data with the posterior p-values close to .5 with $\\mu = .52$ and $\\sigma = .54$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
    References
    \n", + "Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389-402.\n", + " \n", + "Kruschke, J. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS and Stan. Oxford, England: Academic Press.\n", + "\n", + "Lambert, Ben. (2018). Bayesian statistics: A students guide. London, England: SAGE.\n", + "\n", + "McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. Boca Raton: CRC Press.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian estimation of simple linear regression.ipynb b/wip/Bayesian estimation of simple linear regression.ipynb new file mode 100644 index 0000000..e9787f6 --- /dev/null +++ b/wip/Bayesian estimation of simple linear regression.ipynb @@ -0,0 +1,1228 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

    Table of Contents

    \n", + "
    " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import relevant packages for analysis below.\n", + "%matplotlib inline\n", + "import pystan as ps\n", + "import patsy as pt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of the simple regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The classic simple linear regression\n", + "\n", + "Simple linear regression is the statistical method that is used for the estimation of linear assocation between two quantatative variables. The statistical model is specified as $$y = \\alpha + \\beta x +\\sigma$$\n", + "\n", + "Under the classical Null hypothesis significance testing framework the null hypothesis is specified as $$H_0:\\beta = 0 $$\n", + "\n", + "and the alternative hypothesis is specified as $$H_A: \\beta \\neq 0$$\n", + "\n", + "As with any NHST the alpha level should be choosen before condcution of the analysis or even better idenfied suing a power analysis before data collection. Nevertheless if $p < \\alpha$ specified the NHST is declared as statistically signifinant and the null hypothesis can be rejected." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + "\n", + " Following the quick description of the classic ordinary least squares regression above it is important to keep in mind that Bayesian inference is all derived from the application of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to the classic simple regression, it is fundamentally different, because its uses fully probabilistic modelling and the infernce is not based on sampling distributions.\n", + " \n", + " For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for the question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. If scientific research publication is the goal the priors must be accepted by a skeptical audience. Much of this can be achieved using prior predictive checks to ascertain if the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then use the posterior for conducting your inferences.\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## STUDY DESCRIPTION\n", + "\n", + "Reasoning about cause and effect is fundamental to being human. However this reasoning can often take the form of thinking using heurtistics, as analysing all potetential casues is cumbersome cognitively or outright impossible. These heuristics may generate biases, which they can then potentially use to explain the \"staus quo\" for the way things are. People who reason from these heuristics then are at risk of conflating the way something \"is\" to the way it \"ought\" to be.\n", + "\n", + "Investigating this was the focus of the study by Tworek and Cimpian (2016). They approach this by quantifiying and recoring the the inherent bias of a sample of individuals and whether these biases were related to how they explained if the outcomes of events were desriable for themselves.\n", + "\n", + "122 individuals were sampled. it participant read 6 ssatus quo articles for coietial practices. Theparitpants then answered questions that assesed the level that the soietal practices ought to happen: this measured the level of ought inferences. PArtiipatns completed a 15 item quuestionaire designed to measure inherent bias. Finally participants also completed intelligence, political beliefs, belief in a just world and genral demographics (level of education). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Assinging the URL to github reostipry where the data is stored.\n", + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Tworek%20and%20Cimpian%202016%20Study%201.csv\"\n", + "\n", + "# Import Data from github into pandas dataframe. \n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    excludedRavensProgressiveMatrix_sumInherence_BiasShould_ScoreGood_ScoreOught_ScoreBelief_in_Just_Worldinstructionsâ onthefollowingscreensyouwillbeaskedtofilloutsevestartnextsurveypleasereadthestatementsonthefollowingpages.thinkcarefullyaboutho...ageconservenglishthankyouforparticipating.pleaseanswerthefollowingfewquestionstoh@1.didyoufindanyaspectoftheprocedureoddorconfusing@2.whatdidyouthinkwewerestudying@3.doyouthinkthattheremayhavebeenmoretothisstudythanmeetstheeyei@4.doyouhaveanyadditionalthoughtsorcommentsaboutthestudyattentionfilter_$
    0057.6666676.3333336.6666676.5000005.65111...26711nounsureyes but unsure whatno11
    1157.3333336.6666678.0000007.3333334.60111...28311not at all; it was very enlighteningopinions across different demographicsi dont believe sonone at all10
    2045.3333335.6666672.6666674.1666674.20111...35611NoPatience for answering narrative questions aft...No.No11
    3025.8000008.0000007.6666677.8333334.85111...48711Not really. The surveys were nothing alike.I dont know. Americas habits and buying trends...No it was kinda odd that the parts were so dif...No11
    \n", + "

    4 rows × 191 columns

    \n", + "
    " + ], + "text/plain": [ + " excluded RavensProgressiveMatrix_sum Inherence_Bias Should_Score \\\n", + "0 0 5 7.666667 6.333333 \n", + "1 1 5 7.333333 6.666667 \n", + "2 0 4 5.333333 5.666667 \n", + "3 0 2 5.800000 8.000000 \n", + "\n", + " Good_Score Ought_Score Belief_in_Just_World \\\n", + "0 6.666667 6.500000 5.65 \n", + "1 8.000000 7.333333 4.60 \n", + "2 2.666667 4.166667 4.20 \n", + "3 7.666667 7.833333 4.85 \n", + "\n", + " instructionsâ onthefollowingscreensyouwillbeaskedtofilloutseve \\\n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "\n", + " startnextsurvey \\\n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "\n", + " pleasereadthestatementsonthefollowingpages.thinkcarefullyaboutho ... age \\\n", + "0 1 ... 26 \n", + "1 1 ... 28 \n", + "2 1 ... 35 \n", + "3 1 ... 48 \n", + "\n", + " conserv english \\\n", + "0 7 1 \n", + "1 3 1 \n", + "2 6 1 \n", + "3 7 1 \n", + "\n", + " thankyouforparticipating.pleaseanswerthefollowingfewquestionstoh \\\n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "\n", + " @1.didyoufindanyaspectoftheprocedureoddorconfusing \\\n", + "0 no \n", + "1 not at all; it was very enlightening \n", + "2 No \n", + "3 Not really. The surveys were nothing alike. \n", + "\n", + " @2.whatdidyouthinkwewerestudying \\\n", + "0 unsure \n", + "1 opinions across different demographics \n", + "2 Patience for answering narrative questions aft... \n", + "3 I dont know. Americas habits and buying trends... \n", + "\n", + " @3.doyouthinkthattheremayhavebeenmoretothisstudythanmeetstheeyei \\\n", + "0 yes but unsure what \n", + "1 i dont believe so \n", + "2 No. \n", + "3 No it was kinda odd that the parts were so dif... \n", + "\n", + " @4.doyouhaveanyadditionalthoughtsorcommentsaboutthestudy attention \\\n", + "0 no 1 \n", + "1 none at all 1 \n", + "2 No 1 \n", + "3 No 1 \n", + "\n", + " filter_$ \n", + "0 1 \n", + "1 0 \n", + "2 1 \n", + "3 1 \n", + "\n", + "[4 rows x 191 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Output head dataframe for viewing. \n", + "df.head(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data manipulation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "scrolled": true + }, + "source": [ + " The paper focused on american participants but sampled non-americans, their scores though were coded as 0 however,\n", + " in filter_$ variable of the dataframe and are removed below by extracting all values in that column equal to 1\n", + "# into new dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "filtered = df[df['filter_$'] == 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data visualisation and exploratory data analysis " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(filtered['Ought_Score']);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visual inspection of the data suggests that the data is roughly normal distributed. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot dependent and independent variable onto a scatteplot\n", + "sns.scatterplot(filtered['Inherence_Bias'], filtered['Ought_Score'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "y_i &\\sim Normal(\\mu_i, \\sigma) \n", + "\\\\ \\mu_i &= \\beta_0 + \\beta_1(x-\\bar{x})\n", + "\\\\ \\beta_0 &\\sim normal(5,1)\n", + "\\\\ \\beta_1 &\\sim normal(0,.5)\n", + "\\\\ \\sigma &\\sim halfcauchy(0, .1) \n", + "\\end{align*} \n", + "\n", + "The formulation for presenting statistical models here follows that used by McElreath (2020) for its intuitive nature. In plain english the model specifies that the dependent variable $y_i$ is distributed normally in terms of the Likelihood with the $\\mu_i = \\beta_0 + \\beta_1(x-\\bar{x})$ and $\\sigma$ to be estimated. With the prior for the $\\beta_0$ parameter being $Normal(0.5, 0.2)$ followed by the $\\beta_1$ parameter being $Normal(0.5, 0.2)$ and the $\\sigma$ being $halfcauchy(0, .1)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3 - Specifying priors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prior predicitve checks\n", + "\n", + "The model above may seem arbitary, but it is informed by the prior predictive checks below which determined that the priors specifed are reasonable for model parameter values for the outcome space of data that Tworek and Cimbian (2016) collected, when analysed by simple regression model.\n", + "\n", + "The neccessity for these prior predictive checks becomes essential with increasing model complexity that can create surprising interactions between parameters and thus surprising data the model is able to generate.\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualising priors" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "#Simulate 100 intercept and slope parameters based on the model priors\n", + "np.random.seed(1)\n", + "N = 100\n", + "beta_0 = np.random.normal(loc = 5, scale = 1, size = N)\n", + "beta_1= np.random.normal(loc = 0, scale = .5,size = N)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the lines based on the priors\n", + "x = np.linspace(0,10,100)\n", + "xbar = np.mean(filtered['Inherence_Bias'])\n", + "for i in range(N):\n", + " y = beta_0[i] + beta_1[i] * (x - xbar)\n", + " plt.plot(x, y, '-y', alpha =.1)\n", + "plt.xlim(0,10)\n", + "plt.ylim(-5,15)\n", + "\n", + "# Plot horizontal lines for maximum and minimum scores for the dependent variable\n", + "plt.hlines(0, xmin = 0 , xmax = 10, linestyles= 'dashed', colors= 'r');\n", + "plt.hlines(10, xmin = 0 , xmax = 10, linestyles= 'dashed', colors= 'r');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now simulate data from the simulated parameter from the statistical model defined above." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Set seed for notebook reproducibilty\n", + "np.random.seed(1)\n", + "sigma = stats.halfcauchy.rvs(size = N, loc=0, scale= .1)\n", + "sim_data = np.random.normal(loc = beta_0 + beta_1 * (x - xbar), scale = sigma)\n", + "\n", + "sns.distplot(sim_data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All together the prior predictive checks show that these Weakly inoformative priors that result in skeptical parameter for $\\beta_0$ estimate and that a postive and negative relationship with the $\\beta_1$ is credilble before seeing the data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4 - Use Bayes rule\n", + "\n", + "## Stan model of Bayesian simple regression" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "Simple_Regression_model = \"\"\" \n", + "\n", + "data {\n", + "\n", + "int N; // Number of data points\n", + "int K; // Number of predictors\n", + "matrix[N, K] x; // Specfiying design matrix for the regression intercept and predictor values\n", + "vector[N] y; // vector of the dependent variable data points\n", + " \n", + "}\n", + "\n", + "transformed data{\n", + "// Creating a new design matrix with the Independent variable of Inherence bias\n", + "// centered. This may potentially be simpler to do outside of Stan but it demonstrates\n", + "// some of the options of Stan\n", + "matrix[N, K] x_transformed = x;\n", + "x_transformed[,2] = x[,2] - mean(x[,2]);\n", + "\n", + "}\n", + "\n", + "parameters {\n", + "\n", + "// Specify simple regression model parameters specified.\n", + "vector[K] beta; // vector of beta coefficent that includes the intercept term\n", + "real sigma; // Standard deviation\n", + " \n", + "}\n", + "\n", + "\n", + "model {\n", + "\n", + "// Specify priors for the model parameters.\n", + "// priors\n", + "beta[1] ~ normal(5, 2);\n", + "beta[2] ~ normal(0,.5);\n", + "sigma ~ cauchy(0, .1); \n", + "\n", + "//likelihood\n", + "y ~ normal(x_transformed * beta, sigma);\n", + " \n", + "}\n", + "\n", + "generated quantities {\n", + "\n", + "// Simulate data sets from the mdoel for posterior predicitve checks\n", + "real yrep[N];\n", + "yrep = normal_rng(x_transformed * beta, sigma);\n", + "\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_005dc177e9d0185632c55fedd05da3c1 NOW.\n" + ] + } + ], + "source": [ + "#Compile stan model to C++ code to sample posterior for the model below\n", + "sm = ps.StanModel(model_code = Simple_Regression_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" + ] + } + ], + "source": [ + "# Generate the design matrix for simple regression which is then convert to numpy array \n", + "# to pass to the data block of Stan.\n", + "x = pt.dmatrix(\" ~ Inherence_Bias\", data = filtered)\n", + "x = np.asarray(x)\n", + "\n", + "# Create python dictionary to pass to Stan data block\n", + "data = {'N': len(filtered),\n", + " 'y': filtered[\"Ought_Score\"].values,\n", + " 'x': x,\n", + " 'K': x.shape[1]\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Fit and sample the model parameters.\n", + "fit = sm.sampling(data = data, iter = 2000, chains = 4, seed = 302675)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python print stament it is easier to extract the results and place them in as\n", + "# panda data frame for easier expression of results.\n", + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    meanse_meansd2.5%25%50%75%97.5%n_effRhat
    beta[1]5.6627490.0016040.0955475.4787265.5969895.6612285.7284375.8501793547.8942611.000774
    beta[2]0.2873800.0014220.0840880.1238360.2315620.2871270.3420370.4591303497.7254071.000510
    sigma1.0730850.0011710.0695260.9491161.0250311.0688061.1172101.2228753522.2102500.999958
    yrep[1]6.1928320.0168161.0816874.0733035.4590416.1915846.9087248.3209364137.4731120.999941
    yrep[2]5.5215690.0174401.0874283.3648124.8123905.5226636.2482007.6000503887.9254021.000155
    \n", + "
    " + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% 75% \\\n", + "beta[1] 5.662749 0.001604 0.095547 5.478726 5.596989 5.661228 5.728437 \n", + "beta[2] 0.287380 0.001422 0.084088 0.123836 0.231562 0.287127 0.342037 \n", + "sigma 1.073085 0.001171 0.069526 0.949116 1.025031 1.068806 1.117210 \n", + "yrep[1] 6.192832 0.016816 1.081687 4.073303 5.459041 6.191584 6.908724 \n", + "yrep[2] 5.521569 0.017440 1.087428 3.364812 4.812390 5.522663 6.248200 \n", + "\n", + " 97.5% n_eff Rhat \n", + "beta[1] 5.850179 3547.894261 1.000774 \n", + "beta[2] 0.459130 3497.725407 1.000510 \n", + "sigma 1.222875 3522.210250 0.999958 \n", + "yrep[1] 8.320936 4137.473112 0.999941 \n", + "yrep[2] 7.600050 3887.925402 1.000155 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print out results of Bayesain estimation of simple linear regression.\n", + "fit_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit-visualisations - Bayesian simple regression\n", + "\n", + "## Posterior distribution plots" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Posterior plots showing the default 94% Credible interval of the arviz package\n", + "az.plot_posterior(fit, var_names=(\"beta\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Posterior autocorrelation plots" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Using the arviz package the autocorrelation of the 4 MCMC chains can be plotted.\n", + "az.plot_autocorr(fit, var_names=(\"beta\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The autocorrelation plots do not show any serious autocorrelation problems, as the values quickly decrease to 0." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Posterior trace plots " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Trace plots\n", + "az.plot_trace(fit, var_names=(\"beta\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The traceplot show good mixing of chains and show a \"hairy catepillar\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 5 - Posterior predictive checks" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "#First take a random sample of the posterior parameter estimates for plotting\n", + "posterior_samples = pd.DataFrame(fit.extract(pars = ['beta[1]','beta[2]']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Maximum aposterior estimate (MAP) for the fitted regression model " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x_vals = np.linspace(0,10,100)\n", + "y_vals = np.mean(fit[\"beta[1]\"]) + np.mean(fit[\"beta[2]\"]) * (x_vals - xbar)\n", + "sns.scatterplot(filtered['Inherence_Bias'], filtered['Ought_Score'], alpha = .9);\n", + "plt.plot(x_vals, y_vals, '-');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MAP is the most probable result, but it ignores the uncertainty in this estimate, so below the other credible regression lines within the posterior are plotted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Uncertainty in the regression estimates" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x_vals = np.linspace(0,10,100)\n", + "for i in range(100, 2000):\n", + " # Mean centred\n", + " y_vals = posterior_samples[\"beta[1]\"][i] + posterior_samples[\"beta[2]\"][i] * (x_vals - xbar)\n", + " plt.plot(x_vals, y_vals, '-y', alpha=0.01)\n", + "\n", + "sns.scatterplot( filtered['Inherence_Bias'], filtered['Ought_Score'], alpha = .9);" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "#Convert pystan fit object to IO for Arviz functions.\n", + "data = az.from_pystan(\n", + " posterior=fit,\n", + " posterior_predictive='yrep',\n", + " observed_data=[\"y\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Posterior predictive checks" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot posterior simulated data sets for posterior predictive check\n", + "az.plot_ppc(data, data_pairs = {\"y\" : \"yrep\"}, num_pp_samples= 100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Posterior predictive checks above suggest that the model was able to capture the observed data fairly well and suggests no serious issues with the model used to analyse the data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the Bayesian simple regression\n", + "\n", + "As Kruschke correctly points out there is not standard formula or presentation method for results in journal article like the APA guide for reporting frequentist analysis. It is likely there never will be, because as McElreath (2020) explains, Bayesian data analysis is more of an engineering approach to the problem and the resulting model that is fit will likely be analysis specific. In addition, as Gabr}y et al, (2019) argue visualisations maybe even more key so the all the visualtions above would have to be included with any write up. Anyway the write up below generally follows the advice of Krushcke (2015) chapter 25. In any application though it comes down to the problem to be described an the audience that needs to be convinced.


    \n", + "\n", + "

    Write up


    \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389-402.\n", + " \n", + "Kruschke, J. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS and Stan. Oxford, England: Academic Press. \n", + " \n", + "McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan.Boca Raton: CRC Press.\n", + "\n", + "Tworek, C. M., & Cimpian, A. (2016). Why do people tend to infer “ought” from “is”? The role of biases in explanation. Psychological Science, 27(8), 1109-1122." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "341.333px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian estimation ordinal regression.ipynb b/wip/Bayesian estimation ordinal regression.ipynb new file mode 100644 index 0000000..69c9e02 --- /dev/null +++ b/wip/Bayesian estimation ordinal regression.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

    Table of Contents

    \n", + "
      " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pystan as ps" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "ordinalRegression = \"\"\"\n", + "data{\n", + "\n", + "int N;\n", + "int K;\n", + "int y[N];\n", + "int cuts; // the number of cutpoints\n", + "matrix[N, K] x;\n", + "}\n", + "\n", + "parameters{\n", + "\n", + "vector[K] beta; // beta coeffiecnt including the intercept term\n", + "ordered[K-1] cutpoints;\n", + "\n", + "}\n", + "\n", + "model{\n", + "// Priors\n", + "beta[1] ~ normal(0,1);\n", + "beta[2:K] ~ normal(0,4);\n", + "cutpoints ~ normal(0,1);\n", + "\n", + "for (i in 1:N){\n", + " y[i] ~ ordered_logistic( x[i] * beta , cutpoints);\n", + "}\n", + "}\n", + "\n", + "generated quantities{\n", + "int yrep[N];\n", + "\n", + "for (i in 1:N){\n", + " yrep[i] = ordered_logistic_rng( x[i] * beta , cutpoints);\n", + " }\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_80a8cdbab18e0404c3c21e7f0a859ef1 NOW.\n" + ] + } + ], + "source": [ + "sm = ps.StanModel(model_code = ordinalRegression)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian fisher test.ipynb b/wip/Bayesian fisher test.ipynb new file mode 100644 index 0000000..6eb4a03 --- /dev/null +++ b/wip/Bayesian fisher test.ipynb @@ -0,0 +1,331 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian Fisher test, based on Bob carpenter lecture, and\n", + "# https://www.tjmahr.com/bayesian-fisher-exact-test/\n", + "# Count data analysis by Kruschke (2014), chapter 14, only implemeted in JAGS, only resource i could find for similar analysis to chi-square. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Import all relevant librairies for analysising data with pystan\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import os\n", + "import numpy as np \n", + "import pandas as pd\n", + "import pystan as ps\n", + "import arviz as az\n", + "\n", + "#Set working dircetory to wherever data is saved on local machine.\n", + "\n", + "os.chdir(r\"\\Users\\harri\\OneDrive\\Documents\\Stats 2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Data is a simple 2x2 contingency table of counts for males and females with and their handedness " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      leftright
      0943
      1444
      \n", + "
      " + ], + "text/plain": [ + " left right\n", + "0 9 43\n", + "1 4 44" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Example data set generated in R and saved for upload into jupyter.\n", + "# Data \n", + "df = pd.read_csv(\"Handedness\")\n", + "df.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$left handed \\sim Binomial(Total group, \\theta)[likelihood]$$ \n", + "$$Beta \\sim (a,b)[prior]$$" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Compile stan code\n", + "\n", + "Baye_FisherT = \"\"\"\n", + "data {\n", + " int beta_a; // Specifying the a parameter value for beta prior.\n", + " int beta_b; // Specifying the b parameter value for beta prior.\n", + " int n_total_1; // Total male numbers for both right/left handedness.\n", + " int n_total_2; //Total female numbers for both right/left handedness.\n", + " int n_hits_1; // Number of left handed males.\n", + " int n_hits_2; // Number of right handed females.\n", + "}\n", + "parameters { \n", + "//probability of left handedness. The probabiltiy are bounded between by 0 and 1 as required of beta distribution.\n", + "\n", + " real theta_1; //parameter estimate for males.\n", + " real theta_2; //parameter estimate for females.\n", + "}\n", + "model {\n", + "// prior specified as a beta distribution with alpha set as 5 and beta 40\n", + "\n", + " theta_1 ~ beta(beta_a, beta_b);\n", + " theta_2 ~ beta(beta_a, beta_b);\n", + " \n", + " //likelihood (link to data) for this model specifies the data\n", + " \n", + " n_hits_1 ~ binomial(n_total_1, theta_1);\n", + " n_hits_2 ~ binomial(n_total_2, theta_2);\n", + " \n", + "}\n", + "generated quantities {\n", + " // generating diff values between the thet \n", + " \n", + " real diff; // genrate diff value as a real number\n", + " diff = theta_1 - theta_2; // assign diff value as the parameter\n", + "}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:pystan:DeprecationWarning: pystan.stan was deprecated in version 2.17 and will be removed in version 3.0. Compile and use a Stan program in separate steps.\n", + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_10bdfc6362f3af35b390918305ad4fe7 NOW.\n" + ] + } + ], + "source": [ + "#Values hardcodied below\n", + "\n", + "FisherT_data = {\n", + " \"beta_a\": 5,\n", + " \"beta_b\": 40,\n", + " \"n_total_1\": 9 + 43,\n", + " \"n_total_2\": 4 + 44,\n", + " \"n_hits_1\": 9,\n", + " \"n_hits_2\": 4}\n", + "\n", + "# Fit Bayesian Fisher test using Stan\n", + "\n", + "Fisher_Fit = ps.stan(model_code = Baye_FisherT, data = FisherT_data, iter=2000, chains=4,seed=340)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inference for Stan model: anon_model_10bdfc6362f3af35b390918305ad4fe7.\n", + "4 chains, each with iter=2000; warmup=1000; thin=1; \n", + "post-warmup draws per chain=1000, total post-warmup draws=4000.\n", + "\n", + " mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat\n", + "theta_1 0.14 5.7e-4 0.04 0.08 0.12 0.14 0.17 0.22 3888 1.0\n", + "theta_2 0.1 4.8e-4 0.03 0.05 0.08 0.09 0.12 0.16 3965 1.0\n", + "diff 0.05 7.3e-4 0.05 -0.04 0.02 0.05 0.08 0.14 3998 1.0\n", + "lp__ -70.61 0.02 0.97 -73.2 -71.0 -70.32 -69.9 -69.64 1767 1.0\n", + "\n", + "Samples were drawn using NUTS at Thu Nov 5 07:50:05 2020.\n", + "For each parameter, n_eff is a crude measure of effective sample size,\n", + "and Rhat is the potential scale reduction factor on split chains (at \n", + "convergence, Rhat=1).\n" + ] + } + ], + "source": [ + "print(Fisher_Fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The model uses an informed prior (Beta(5,40), skepticla of a difference) and shows that the diff between left handedness males and females has a posterior mean of .05% but with a 95% credibilty interval [-.04, .14]. If you have to make descision in the vain of confidence intervals in a frequentist analysis the values of handedness cross zero, therfore there is little evidence suggest a difference in left handedness between males and females." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting Posteriors" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([,\n", + " ,\n", + " ],\n", + " dtype=object)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(Fisher_Fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Trace plots for MCMC sampler" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ],\n", + " [,\n", + " ],\n", + " [,\n", + " ]],\n", + " dtype=object)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2gAAAG4CAYAAAAwt8Q7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydd5xeRb2Hn3m3pfeETgJREFCKgNI7SEdAFBUEqYq0i1goInqxgXixIGKDq+gFDQihhBYS0hPSk02yJZvtvb29nHNm7h+nvOdtW9J2IfPwCbv7nnNm5swp73znV0YopdBoNBqNRqPRaDQazdATGOoGaDQajUaj0Wg0Go3GRgs0jUaj0Wg0Go1GoxkmaIGm0Wg0Go1Go9FoNMMELdA0Go1Go9FoNBqNZpigBZpGo9FoNBqNRqPRDBO0QNNoNBqNRqPRaDSaYYIWaBqNRqPRaDQajUYzTNACTaNxEEI8LIT43VC3Q6PRaDSaoUJ/F2o0Q48WaJo9GiGEEkJM2cEyZgghIjuhLbcJIdYKIUwhxL07Wp5Go9FoNANBfxdqNMMLLdA0muFDM/AgMHuoG6LRaDQazRChvws1ezxaoGn2WIQQc5xfa51Zv2OAMUKIl4UQYSHEfCHENN/+ZwkhVgsheoQQc4UQM5xNc4DRQoiI82+yEOIyIcRGp5wKIcTn+2uPUuplpdRrQGjnnqlGo9FoNPnR34UazfBDCzTNHotS6gLn1xlKqTHAGuAK4BfAZCAM/BeAEGI68H/A7cAU7C+ivzvHXwBElVJjnH9dQBC4EhgPfBf4mxBi0m45MY1Go9FoBoj+LtRohh9aoGk0mbyplFqqlEoB/waOdD7/CvCCUmqJUsoCHgeOEkJMzVeIUmq+UqpCKSWVUq8AW4GjdscJaDQajUazg+jvQo1mCNECTaPJpN33ewwY7fx+AHCzEKJXCNEL9AAlwL75ChFCnCKEWCKE6Hb2/xSgZw01Go1G82FAfxdqNEOIFmgazcBoAn6rlJrg+zdSKbUOUHn2/zvwNLCXUmoCsAEQu7G9Go1Go9HsbPR3oUazG9ACTbOn0w7MGMB+/wS+LIQ4UdiME0Jc7mzrBEb6g6iBsUAXYAkhvgx8sr8KhBDFQogRQBFQLIQYIYTQz6hGo9FodjX6u1CjGUboG16zp/MT4HXH9eLoQjsppbYB1wBPAL3AJuyAaJRSUeCXwCbH7WMScAfwR+wvplOBpQNoy4NA3KnnZ87vp23faWk0Go1GM2D0d6FGM4wQSuWzSGs0Go1Go9FoNBqNZnejLWgajUaj0Wg0Go1GM0zQAk2j2Y0IIV71LeLp/3fMULdNo9FoNJrdgf4u1Gj6Rrs4ajQajUaj0Wg0Gs0wQVvQNBqNRqPRaDQajWaYUDyYnadMmaJmzJixi5qi0Wg0mj2FVatWdSqlpg51OwaL/h7UaDQazc6gr+/BQQm0GTNmsHLlyp3TKo1Go9HssQgh6oa6DduD/h7UaDQazc6gr+9B7eKo0Wg0Go1Go9FoNMOEQVnQNJqdhUqlUIaBKClBlJYOdXM0Go1Go9FoNJphgRZoml2OjMWILl1KZOFCEhvLSdXWIiMRb3tg3DhK9t+PkUccwYgjjmDEpz7FiMMOQwS0gVej0Wg0w4s3at7gE5M/wcHjDx7qpmg0mo8oWqBpdhmpxka6n3mW4CuvULL//ow5/XSm3HYbZTMPpmjyZERJCcowsDo7SdXVES8vJ7JgIR2//R0iEGDM2Wcx9uxzGH3CZxElJUN9OhqNRqPRANAd79YCTaPR7DK0QNPsdKxQiI5f/4be//yH8RdfzPR//IMRhx6Sf+eyMorGjKF0xgzGnH46AEpKEuvXE547l7ZHHsEKhxl/ycWMv+IKRhx66G48E41Go9FoclHoNWQ1Gs2uQws0zU4l/N48Wn/4Q0YefTQHv/IypQccMOgyRCDAyKOPZuTRRzP1nntIbNhA70svUXfNtZROn86ka69h3AUX6Ng1jUaj0Wg0Gs1HDi3QNDsFlUrR/vivCL7yCnv/6EeM+9x5O6VcIQQjjzySkUceyV7f/z6hOW/S9ez/0vbLXzLx6quZePXVFE+evFPq0mg0Go1mICilLWgajWbXoQWaZoexgkEa77wLlUhw0EsvUrLvvnn3U0qRsBLEjBgJK0FJoISxpWMZUTQCIUS/9QRGjGDC5Z9n/OcvI75yJd1/+xvVZ5/DuAsvZPKNN1A2c+bOPjWNRqPRaHLQLo4ajWZQGAkw4zBy4oB21wJNs0MYbe3U33ADIw47jH1+8giBsjIsaVEXqqO8q5zyrnK2dG+hIdxAR6wDhUIgGFE8AlOaGNKgJFDChLIJzJwwk09M+gSHTjqUo6YcxQHj8rtHCiEYdfzxjDr+eDsRyf/+jdqrvsioE09k8k03MuqYY3ZzL2g0Go1Go9FoNAUIN0OoBWacPKDdtUDTDJq2UIJtnVHaaxvZ90ffpvXjR7H01M/T8upfaDPX0masJyDgE+MP5dNTDuHq6RcwfcoRTBuzD6NLRlNWVIYQwrOohVNhuhPdVPVUUdFdwctVL/PjpT9mr1F7ccYBZ3D6/qdzzLRjKAoU5bSldP/92fuB+5ly2zfp+cc/afzGNyn7+MeZfPNNjD7ttAFZ5jQajUajGQzagvYhJRkGMwmjpwx1SzR7JAN/b2iBpukT05KsbwqydGsXa+p7Wd/YS2/M4IiRJve88TibZk7hb6dX0dl7G4eaI/lcPMFZ4U6OSIYRbMYkACJAERJGT0OM2xf2/iQceCLiwBMYOfEgRo4aybRR0/jEpE9wycxLAEhZKT5o/YD5DfO5b9F9WNLi/IPO5+KDL+awSYflCK/iiROZevu3mHzD1+md9SItP/oRRWPGMvmmG+2EIjpNv0aj0Wh2EkMegxbt1CJje6hdBKkofOoLQ90SzZ6GUva/AaIFmiaHjnCSdze3MXdzO8truhhdVsxJH5vMGYdO5a6zP44V3UjsG3exenqClWeF+FZHJ+dMOYaxh5wG+xwFkw6GUVMwAmWsb4nyTnkb89dWMCXRxWX7Ss4vbWXs+hfg9Xth5AQ45Hw49EI46FQoLgOgtKiUk/c7mZP3O5n71f2s61jHazWvces7tzJpxCSu+PgVXDbzMiaMmJDR9sCoUUz62rVM/PLVhObMoetPf6bjiV8z+ZZbGH/F5QR05keNRqPR7CBDbkGrmQ8Hnwmjd16SLGUYRFesYMzJA3PB0mg0g0ULNM0gCSUMXlnTxMtrm9nQGOQzB03inMOmcd+Fn+DgKaMBWNW2ij8su58Lf7OGsvEml110BLd/6mo45HO20MqiBDh2ehnHTp/Ed8//BCvrenh+RT0PLW7l4iMv5dYbD+RjyS1QOQfeug9CzXZZh38ePn4ulIwE7Jizo6cdzdHTjuZ7x3+PhU0LmVU5i9+t+R1nTz+bqw65ik9P+3SGVU2UlDD+0ksZd8klRObNp/P3v6fz6aeZfPNNTLjySgJlZbulXzUajUbzEWQ79NmipkWUFZVx/N7H76Q2yJ1TjoOMRrF6endqmRrNUGNJhWFJRpTkhskMZ7RA28PZ2BTk2SW1vL6+haMPmMBXP3sgZ1+3F+NHpV0Cq3qq+NmSH1HTsZFHXoix39ipzHj6r4i9Br5odCAg+MxBk/jMQZO457wYf164jUt/v5zzDt+Le879Pgee+2PorIZNL8OCR+E/33DE2mXw8fOgdBQAJUUlnHXgWZx14Fk0RZp4sfJF7n3/XsaXjucLh3yBS2Zewviy8V69QgjGnnUmY848g+iCBXQ8+Xu6nv4jk2+6iYlf+qJeS02j0Wg0g2Z7LGihZGgXtGTn4bptKqV0/LbmI8Pahh4ae+JcetS+WFJRXBQYusYMwsVxCFupGUqq2sJ887lVfPmPy5g8ppQ5d53K/91yAld8en9PnMWMGI+veIxrXv0Sx29dznMLprBvyaEc+I+3ByXOstl/4igevvQI5n/nDMaNLOH8Xy/gBy9vpL1sfzjtXvjGIvjGQjtWbeHj8NhM+Nd1UP4f23fcYb8x+3Hnp+/k7S+8ze3H3M7CpoWcO+tcHlj0AGvb12bECAghGHP66cx44Xn2eeQRgrNns/XiSwi9+dbQxxJoNJo9l2RkqFug2Q6G3MURYGeLKPe7UH8naj5CxFO2pXlTS4jXN7QMcWu0QNMUoDeW4nuz1vP5Jxdz4KRRLPjumdx3wWHMcNwYXRY0LuDSly6gauM/mRUr40vqa8S3SQ74018oGjO6QOmDY9rYEfz4sk/y5l2nEUmanPnYfB57awvBuAGTZ8Kp37aF2jcW2bFti/4HHvsY/OtrsPElT6yVBEo4Z/o5PH3u07x46YtMHTmVu+bdxZWvXsk/N/+TcCrs1SmEYMyppzDjheeZeuedtD/6KHVf+SrxjeU75Zw0Go1mUFS+CanYULfiI4GyLMyOjt1T1y4SMc298V1S7oDQAk3zESYUN4e2AYNMEqIF2h7E+5UdnPs/C4imTN679wzuu/AwJo7OdPFLWkl+tuynPDDv23ynqZ6nPv41Jh7xCzqee5UD/vAHSvaattPbdeDkUfzPl47mxdtOoqI1zOmPzePp97eSMCx7h8kz4dR74NYF8M3FsO8xsPjX8OhMeO5KWPYUdFaBUhww9gDuPvZu3v3Cu9xy5C28V/8eZ//7bB5a/BAbOjZ4X6oiEGD8xRdx8Jw3GHPmmdRfdx2tP/kpVkTPZms0mt2ENyDeubFEeyqpujqiy1cMdTO2m2jS5IPa7qHz6pAy8+d2sKBxAXEzLTLfrXuXzV2bd7Rl/ZLcto1UXd0ur0ej2TF0khCNDykVT7xbybNLavnZFUdy0ZH75N2vI9bB3XO/RWl3LbPCkr2u/g8pazLNX7iKfX7yCCMOPWSXtvMTe4/jz9cdz8rabh59s4K/Lt7G3eccwlXH7p/2GZ50MJzyX/a/3nqongvV78J7P4FRk+Bj58DHzqHkoFM5f8b5nD/jfOpCdcyqnMVtc29j79F7c9UhV3HRwRcxumQ0gbIyptxyM+MuvJC2Rx6h5oIL2euhHzDu3HN36blqNBrNR8JSYSa97LtDjTJ23wz5sHBx3MmonWBBS1kpTGlm/N0R7+AwDtvR5vVJonwTAKXTp+/SejQfPtxndVg8s9qCpnFJGBZ3/N8a3ixvZfbtpxQUZ+Wd5Vw9+woOa9rIHyd+lr1uWYgc/3Eab/sWE77yZcadd95ua/NxMybxwq0n8PMrj+RvS+s46/H3+cuibYQTRuaOEw6E474OV/8DvlsDn/89jBgH838Kjx4Mz14Mi55gejzCt4+9h7lXzeXrR3ydN7a9wbmzzuWJVU/QEbPdYUr334/9n/o9ez30A1of/hHNDz6IjEbztE6j0Wj6Jvja68jYHuC22F0Dm18d6lak+YhYIpfWdBFNDoE7lmM525FhrFRyeAyE82D16gyVwxWzo4NUfX3B7amGho/AO1WhY9A0AMRSJtf9dQXBuMGsb56UE2fmMqfmDW6c8zVubW3iwc8+QMmlv0WVjKT5vvsp2W8/pt5xx25uuR0rduah03j9jlN46OLDeW9LGyf+7D1++MpGVtf35LqAFJfCjFPgnIftmLW7N8DRX4GWdfDMhfDUyZSufJYL9zmJZ89/lj+f92eaI81c9J+LeHjJwzSGGxFCMO7cczn4lZcx29vZdsWVxDds3O3nrtFoPvwMzF16eA5kB0x2gpOOyiG1CirL2n117cB5die6+9zeEU7SEU72Vfl2190nO8HFUaGGPPFWV7yLpJXZfzKRILJoMcr6aIj4jxqxNWuIr99QcHt83XqSlZW7sUW7iEE8GlqgfUSJpyxuePYDRpcV85frj2PciJK8+72w6R/8ZNED/LYnwRevmgXHfBWArqf/SLKqin0fexQRGLrbJBAQnHP4XvzjphN48ZsnAXDr31dxyi/m8chrm1hY1UEslWemcezetkC76hm4twpO/w5UvA6/Ogxm38nhRWN59PRHeenSlygSRVwx+wp+vuLndMW7KJ4yhQOefpqJ11xD/fXX0/Pvf+/ms9ZoNB82lFKY/sHfQL6Ih5GLYzBuMHtd8+AOUj5BJCW0rgdjCGe5d0BYDJYMK1GsG+qWDPjYxnBjv/sUBXZ/mnvl9t8O3JfDwXq2vGU55Z1Zib+G0bOmyYPs//qoAewzrFHagrbHY1qSO/5vDSVFAZ665tOUFedfnO+5tU/zuxWP8idzIsffMB/2+zQA4fnz6frrX9n/yScpGjt2N7a8bw7deyw/uuyTLLvvbB7/4lEYluTHr27iqB+9zRW/X8x/v7aJf69sYGNTMJ1gBGzr2hGXw9desa1rAE9+Bt74DvtTzA9O/AGzLplFd7ybi/5zEU+te4qUTDHp2ms48K9/ofM3v6X1kZ+gzCHOAKTRaIYt25fCefgMOHqiqcFbPjIE0TDIACglu6tPM/qqtx5CAxe3gv7FV58Czat754q4+Np19i87InQLdH9nvBNL7iYLp7RQiWCBjXp9t/5QlpUW67uqjuz3xIDck3f9s73Lrb86Bm3PRSnFD2eX0xZK8Idrji0ozp5d/hh/WvM7/jLmSA772hwYMxWAZM02mr/7Pfb9xc8pO/ig3dn0AVMUEJxw8GR+dNkneeee01ny/bO58ZSDKSkK8Nr6Fm783w844odvce6v3uf2f67md+9V8XZ5K/VdMeTEg+HS39hCLRGC3x4LS5/kwDH78ejpj/LM555hectyLn/lchY3LWbkUUcxY9Ys4uvWUX/TzdqHXaPR5CWScCZwehugtbCrTgbul/UwWBR4u4YlfgvaMIj/6grFWVXXs1vqyrAUDfL6+ReBVoaB2Z3r8ljcpwVtxwaRMSPG3Pq5hUvfgeJlgftgU9cmepO76fszWI9oXrPdh5uWZENjIYH30Sf87lxiy5btsvLrQnXM2TYn47MBCcLdMPkzZ9scLzfBrkFncdxjeW55PfO2tPPK7acwuiz/5X1mwQ/4W/VL/PXAy5l59n97Xy5WJELj7bcz+evXM/bMM3dns3eIqWPLuOjIfTISoHRHU2xpCbGlNUxFa5h3N7dT2RZGAMccOJHPHDSJ44/6KccedzOlr90Bm16By57ksCmH8cznnuHVmle5f9H9HLvXsXzv+O8x/bm/0/LAg9Rdey0H/PnPlOy119CdsEajGXZ4g+5Q005x85PRKOF58xl/8UU7XNZA2K6ZY5lPoA2dBS0WT23XcaG332b0iSfuNo8RvwUtsaWCVF1dznUWu1C09yZ7SZp9xbhtn9h276F895JSqqB42+lI2YedrP/7szuWoqYzwqf2H78zW/WhQRkGVnDXCdRgMk/ZWZfllbVNfOagSewzfqRvH2enRND+feSEXdK+qBFlKlN3QcmDezdqC9pHiJW13fxizhaevvY4po7Nn/b4pXfv5Znql/jLkXcx85xHPHGmLIvmb99L2cyZTP7GN3Zns3cJk0aXctLHpnDDKQfxiy8cycvfOpmND3+O1+48lcuO3pfGnhjff2k9x/y5kzvH/orNI49B/fEMWPEnBHDpzEuZ/fnZTCibwBWzr+CNpnfY5xc/Z/RJJ1H75S+TrNk21Keo0WiGkM54Z0Y6cdfgkahpGEQphb+wZaoPsVG/DLq2DqKeHWmJTWVPJYbMyqTrO//hsMjxgN2yol0Z7VQpAyuPFavPunZEiPrVw/a4/e1gH/crlLaz/Jx05vFe8N0zAxVo725qyx9bnrdSBZFci4dAgJmCUAvv1r1LZU+V28gBlJn/Y8MyMtZ4+9CRDINl9L8fDIuYvVTNYmhZn/7AbVPVO/bySh829ELVeyahhMFdz6/lwYsOyz/rIyVzX72ZXza8ye9PeJiDj7slY3PHE7/GaG1l35//bJfO3A0lgYDgoCmjueq4A3j0C0fx/nfOZPYdp/DJ6dN4KHQZV8Xvp/udx+j8x80oI874svE8dOJD/PL0X/LEqie4d+F3KL77FiZ+6WrqrrlGZ3jUaPZgVrSsoLq32vvbfW0m6waRaKMPUeMmZ8orOoKN0FWd+zmQMiXBWP5B2IaODSxrye+61N+4obqnmq54V9ZBeSxoO2AlSVZXIxOJAe//xoYWNreEctvQHzXzINKe+dlgB6QZuw/SxbHA/kU171FkRp3i87dHGQbxTU4CjHhhd04ZixW0ghSyloqAsE9lewVa9nHV70KnnXlPMXALWjRl0h0doDU02Ajb3s+/rX0T1C0mZaXozr53+6BQLoplLcuYVz+v74M3vmhbeAZcmYQNs2wx2Q8bGoMkzR2I46t8C5pW534+iPjJXUF9qJ6okZv1tiTS4t0/sBOShGSHvSlFytzdrtlaoO1xPPxKOZ/cbxxfOv6A3I1GgpX/+iIPdi3jlyc9wicPvypjc/C11+mdNYv9n3ySwOj8qfg/qsycOoZbTpvJv79xEo/ffT3PHfm/bNu6maqfn8LcZauRUnHivify0mUvMbJ4JFfMvoItFx/O1Lvvov6mm4hvGGCsiUaj+ciRMNNiYscmtvJ8abvZcweZnGhdYy/zK9vzbmuKNNEdL2Qp6n/gkCMs8rk47oBAM5qakO7yBMEmwG5zIUFhWJLOiM9VbwAp1Jt642xo2nH3rT5j0OqXQWdVzjGmVNR1xTL70X9u8R7Kkn1b8szublLbau0/WtZ6n2/o2EDKSg/yo0uXElm4KG8Zkvz9pJRCFBXnTApIqXhlbRNWPwNkhULEElghX//6hEehevPRX10uXbF2goWsWgFfDL7T5b3xJLIfAapQTO5YAd2ZnjIJawCTB0plTp5I2XfSFXeSQ/b/nNd0RmgPZbqmGq2t/bfJT9byA5hJ1pQ/T01XxeDK2Yls7NxIYyQ3s2nOK7Wf6yaVZEXLigG7a1e2RZizcbCJnXYQbUHbs3h1XTOLqjv52RVH5g4SYt1U/e1C7kpU8cMTH+akQz+fsTm+sZzWhx9mv18/Qen+++3GVg8/pk8ezZ2XnMhR982DA0/imLcu57bHn+HVdc2MKR7LT075CQ989gG+u+C7/O/MZqbcew/1N92sLWkazR6K3+Vv++RZH1/Wrvv5ILOpGdu5ztNAxg0BkTVkUDs/i6OS0i6jfikkQqxrX5c/ZsUhQ+wMoK9ag3HbCpF1LoONwevTxTHYmFeghRMGHZFEbj/mryAvoigr8VciBIkgDeGGDAtnX/dNQUuWIu2r68Ny+sawJKRiUOsTfnVLvCUGFIpRKzaRXLzcV6YtQJRSyEHcy1LBGzVv0JvoO7HI8o61LA3luvsGhADh6yupQFpUtEb6XmPO2XVEsgOaHWuTmWL2umbquwYYW+q/l2rmQdVb6U1SZq7X516L7ZjgSWzaRGzlKlRf7tD9oRQtqSBVvfkt8juLV9Y2EU7Y78t8z04+q7I7nk0/m30/o4Y0bNdzlSl2Cz3abnt2HzrN/h5Fc2+cB1/eyGNXHcWk0aWZG7u20vmXs7m9JMStx97N+YdembHZ7Oig8VvfYtq932b0Zz6zG1s9vCktLeWQ637L+PMf5DfJh5g7599c+YclrG3o5bwZ5/HCxS+wvGU5d49+lbI7bqbhppuIbyzvv2CNRvORwi/QAttjQesrbsv9rODiyzvXFb2vYYM7QMqZAPS3eydY0JRSdkNcy5zPAiJTqbxxef4mDUTMeoNjv0gKt0Dr4CbaMgVd39eiM5Is6HY66OQsjkDzzqPqbTsmZxBYGzblWBu9a1xUlCl0E6GM/Yi0QthntQk1ey5yKt/97FxLhdouC1okj+tbBlJSVEjwOvdPypTI9k3QvBJQ+TRoBjnnsXk2pfEOwslBxMW5xHsgFbXdGBNBYh+sJPyO73q59/p2TGy4sfA7I1rM2g0JXCJ99l8egeb+MsD1+bx7eIDvxiFZVs09h2Q/9zVaoH2oUUrxvRfXc/kx+3H6IVkZZxpWkPzLudw1ZQInf/xSrv3k9RmbZSpF4x13MubMM5h49dW7r9EfIoo+ezOll/+O/5GPctvU9Vz75+Xc++91jBTT+NsFf+NTUz/FtcXPErrp8zTceCOJiqFzEdBoNLsP1wLht0SkhcJOEk5uRrwCAq2QFWSgg5MC1eXFcqwgfZbtDWp3ZA0tZ4bZs7rYHwshiMybT2Te/JxDMlo0oFTdrkDzHRlqsq1eBduU5+N+h8Xp7Y09cVpDA3WHFVlHZ20NBOza+4udUQpSmYNAGY3alqymVkQiy4rk9J2MJ4gudeIUzaQtAI04RWYB61EgnS26vz4ZTBZHWWhSwCFhWGxsCgIyxyKpcASe07aG3hjRUNDr1P4mU/Jd8uJID+NqdzBWKxHE6u1F+ePIvDjOwn1X2xmluj1cuNwdslrvPpViWoOry8LpGyvPM9sHA03g06cb7YZZ3sRD+N13B7wO7qKqTrZ22M/dts5oZsygf6Hqyjf7jCEFLdA+1Mxe10xNR5Tvnn9o5obyl1HPXckPDj2ekZNnct9n78tcd0UpWu5/AFFSwt7337+bW/0h44jPI778POdu/RlLzqpCKsW5v3qfV9e18Z3jvsNDJz7EnaNnU3XFsdTfeBOpurqhbrFGo9nFuAvu+geclsq2kAwmXVxhC1ohgfZm1zpao4OLP+lr4NLXtsID651rQbMrk148jr9eZRgoo2+XpOy+MizJltZMC1C2xapPC1bzWjvpQxZrG3ppD/Ufj5SoqEQp5bNA2p8PRET3OeZWqn9roWVCW7n90yE8bz4yFLKvdVHW8C9feW7/1y1m79YCyTGK0xmjvTT7+Zo8iCQh0H8MWmswYQ+EpZVTY0ckSVV7hLAhMaVEmtKxztoW2u0SaO3tjG4d6PpYBeV15oxCMuKzoBXum3WNvZQ3hwpuz8cra5tIGANLKFIqigFFTbBmUHUMlr7cr4X3P2iJtBA1gyyIVJCQxi5bPLq/WETqlqBME5lIImOZExRWOEyyJre/uqJJGrrtfdc39lLbmTWx4a+zr6Uu0ALtQ0swbvDfr23mx5cdwahSZwZLKVj8G3j1Lv5wwlfZLGM8fvrjlARKMo7teOLXJDZtYv/f/gZRWpqndE0GB50K17/O2A9+y68mz+bXXzqaX71TyXXPfMDh40/h7xf+nT8cUs+KEyZQe8MNGG1tQ91ijUazC2mR8MgAACAASURBVDGVSdK06InZyQm64l2saHcyyQ3G1XEgA48+BuJRIwpKYUiDbcFthFKDG8Tla0qqrg6jOdNSYCbiIGXfM9M7YEFTlkXwtdftmXKlvEHrgFzi+khZ3xpMUNHqWB6inWCmENnJGPpyn4p15q3SlJJg3CcW81xzZUmSVVWoZBKpMgVwhkBLRqE3PbGn+hFvhmnZMWH9ZfPLim2KG/a9KuNxZ8Cbrif42utY614nFWommS8RRl8DSb9A24kWtIEmCUFJUu51dzOpmvY6aEu39tAVSYEyQeLcTcLLv1O4nXnqFgN4tAcjJCwDKt/kjW1zaE+F+zy2uL8GFzg2YVhYvf0vDj6heBQlgZI+Yz3zYVgGb9S80a+AktKNYXQEfL6stUJ4mWtXta2mNrKBAMJ2vZTSvp8H2r8D3K1fgQaQsp+bbAuaDIUwC4z1/KXmitJc999CaIH2IeXRN7dw3PSJnH2Ys2CyZcLr34YVf2LeRY/wj7ZFPHn2k4wvy0y53/PCv+h98UUO+OPTFE3YNYv8fSTZ+1Nw49tQ/jKnbfkxb915EgdPGc0FTyxgY+0I/nnRP1l+wXQWTI+x9fqvYfb0bbrWaDQfXixpsbUjysZmO+NewkoQ2C7Xwv5j0FTBgbiwRcfGF+mIdbC5azOLGhcRNftwheqrJU4T4hs2Elu9JmNb9N25lFY39VeC82M7LGiO5UuZrkBzBkNSUtQZ7HMA6Bc7lmUSNUP2+lvZ1MyH1vU5MT/xdevZHrfUwbg4KqWwpPJcvDLc9mJddkyXtDJFXKTFdrPKYt6WdrY1NPVvQfPFUbVGW5lXN9f+M5HwVivzI3s7qOhcz+auzXnKStdVE26kOZnuXykCNCR7vPMs3J7tE2jZ1saEYWUOepWVcy2kVN5xClDSQghhxxwpRdLnHpqzth8+u7b/fAYy8aIUC4KVGIXWGssImHSvj0WvFSPnekjlWcCKi/oR7VaKeCxEfP36nGUqIosWY/azxp9CMbpk1KAtVSk5sOQkbql9JzASXlyo1wzntJUlofED6C3ghuzVozJ+FmqHiyUVgVjfsWDuc+Za75sjzSxvWW4bYwfgUm1K5RN3KvNd30/mTi3QPoSsru9h9tpmfnjp4fYHyQg8/xVoXk3d1c/ywMan+OkpP+XAcQdmHBd5/33aH3uMA556itL99x+Cln/ImTgdbngLWjcw+pUbefiCmTz51U/zk9c384OXqvnvkx7HuuM6Fo1rZdPXv4IViQ51izWaPRohxPlCiAohRLUQ4vt5tt8jhNgkhFgvhJgrhJg+kHItZVEsirGUhVS2+9RAEvOBnSZdRvt/N7iDpT73TdnuM34viaRVeCHdQbsKbXoFurchlSSQSPYzAN9+F0d/qUrhs6BZjNi4FRUsbBn0J3xoDFXTmWiA6neRwW5Udycj4m3IVIpkQxtIE+W1z5nVd62F2ecW7cwv9PLS9wBaKqhuj1DbZV/LDIHmWqCSoYyiRDL/ORfHWikO1vYfg+Zzn01ZKe9PmUyikLmjVaWwciyWmVn0lIItoRrKY2kLa0cqxMaeWjs7oVtoge5wBZrf7bMQbl9l81Z5K8tquni/6W1MaTCivCkn4Yn0xJ1CSjt7pPIJxPrumLf+1Tu171AXygxNyNc2YwBiJG7GiFhJjIID7zwd47Qt+7kpbw7xVrntwlzcT1aT9e3reX/LHFL1DVihPPdNwURDePd9QAS2e/H1/i2n/Zcr7EZQG6xFKmWX6T4nbt8k+558yr5uH9R2Z1q687Rr9JoVfXs9OfeSmykznS1V5WQZmVs3l7iVFnxjli3A7O4m9OZbpBoa3Fb6GqwtaB8pLKl48D8b+a9zD2Gf8SMh2gX/ewkIQeyaWdy94hGuPexaTtv/tIzjYqtW0fTte9n3l48x8lOfHKLWfwQYMxWuf81+UTx3JaceUMpbd59GwpBc+tslnDztyxzy6BNsVE18cP0VWINYdFWj0ew8hBBFwJPABcDhwJeFEIdn7bYGOE4pdSQwC3h0IGXHzTjFjigypWkLNGdbobFIzIhR2VNJdMlSostXALbFxzmqYF1GU4FZY5Ee6Bf7EjWkBrJWUx78g6hwKmxbASwDwi1IVH4LSEYWx+23oG1qCpJKxOz1o5QkO0mIV2+sy86IlwdDGsSNtCUitmoVatFbTO5aSbKikkRdGyTDjG/Ks6ixIHcR3H4WG1ZK2Zkfm9fk38FXXvbAUSDskzNTvpO0yN4rf8WOKFWFy/d2tDfa94llgplAJZP579G+g96c/7uD+XTb2pI9jFrbTLKiIt2OLJc8gciIQXt3czsf1PaQqKjo0wpRaOAfSdqDblOlCARjBJzsgO7zYCnXgmZbLlHKboGvKr8LZczIjBOyD1EkHaEVs1JUJ/pPAtavi7Hw+0m69UtyLCtA3Bc/VtSPQEuaCYSZaRn2416XlBuLl7kVhSKA6FM0J00rZ3mCgU74DEigOX1TG6p1TsHnhutOEPRjsYovX4GIJbz7prk3TkswXvDt6nk3Jwu78Co3C6kj0Lx3kW1Cy9g3aSUJptq9SyssE6vHnuSRsZhzjO9a93M+WqB9yHj+g3osqfjaidOhtwH++jmYdjjqi8/x8MpH2XfMvtx61K0Zx8TLy2n45m3s/fDDjD3jjKFp+EeJsrHw1X/D6MnwzEVMlD08dc2nufGUg7j6j8toaD2Y459+nvZYO+/ccBGJ1ADXTtFoNDuTzwDVSqkapVQKeB64zL+DUmqeUsp9QJcBA3ItWN22GktZ3sDTHRAIaaKi+ReJbgg3UN1jrzWkTIO2aBtvNcwtXEmBwW4GrkuQbwiSkn0Hng+ElmgLXQlnTS0lPauTQtmZx5TKdVcqYEGTqVS/A7mazgjh7laIdngujt1GFOkJNafMrmpoXGkLR4fsLH/e8NeyvN9l3LnEqQh+4WJvdN0pt8N60LEFunxrcLWsS/+eTkGZU7RA2OukbZ6d2w738D6scgEhMgVavp18g7+ACED3VmhZ57jA5QoCJSUqILAyrFvuwDhtQQMIEIC2TQA0OveJMoy0i1mOoHCswc51jKVM1rdvZsWSF5GRCDKRIDx/fs4pKJV2caxuj9AeticfhE8wxlMmMcPyWUYhEE0gbH9GLGVb0VBk2Aff3pROsJObKVLRaHUxt3cLQNqy2M8tknIW5VahRqh8u++dXXwxkEopltQ05CzJ0F8MWgCBMAoLNJTtGrq+qZfOaJYlUNlXrT8L2rqGIEu2ZsZkete7n+e7r80tkfRC0ULYItG1xSr3Ssv875ZszM4uirrDGe0p6sM11V3fT5kWb9S8kT8GzxG+bgIi/xIM2YJRJA2MaGZ4S27iSvvZk/EkMtq3e6UWaB8igjGDx9+u5KFLDqe4qxL+ch584iK47Hf8o/IFNnRs4Ken/DQj5WyypoaGm29h2j3/xfiLLxrC1n/EKC6DLzwDBxwPfz0P0VXNdSfN4LmbPsvv523lyflJjv3zy4xoD/LiLZ+jIzbQ7E8ajWYnsR/Q4Pu70fmsEDcCc/or1B/nIJxZZ6kkAQFlyW5Q0GNEc6xNGanApcxc30kp26XOP9hPV1iYnIVcC7hitayHxlV9n1dGPb4/pJXOgAdQPRdCzbyxoYXeuK8u38zyK2ubPBey8NvvkKys9JWXf5Cl3CQATpKQZeEawqlwzvkR7YCKN7w/cwwSni+fRcLItKzY1fvERyoKTaty63D3sCTxRKJwTFF2I3yLU6ebk+vOp/DNvrtuTp4bouvjWNiCJoQgUddqx+aQeU/6iaVMQNmCyjIJxg1kImFbRAuUXdcVo6ojf8ySe1SRENC+KXNjUZGv/qy2N6xAobzlGgDCqS6UkkQWLCSxcSPSCQnw95V3qaRF74p/sr6+12uHO3APxlK0h5OemJfxOJO3NFMciiGUcqw3dtIQJXOFqd3azPZKBcmsrKz+SwMUyKAq0/vmc1H1vwNyLKeKtlgb79a9T21XNOPy92dBKyKAcN0YZdpKpnzPhGvFyk2+Yt+fGTG0qVzhYN9LOUdm/CxEXwKtvMtdQ9a2oCmU4/KpQAgakz20RR0XxH6t87nt8fddT7Ij4/3ovQscTwb3fZNRojRZ07YGw7TvBy9xke0zm7HviLWVTF67zj4H5z1n5jn5zngnm5csIzLnP32ejRZoHyJ+PbeK42dM5OSyGnjmAjjxNjj3R6xqX82Ta5/kiTOfyEgKkmpooP6GG5n09ev1Wme7gkARXPQrOOZa+PM5sHUeRx8wgdfvPIVQwuS652vZ69cv8PGaBM/910X5g681Gs2uIt+oJu9QQQhxDXAc8FiB7bcIIVYKIVZ2dnSmCxeZFjTs6B7qkt10JzIHuUUivehybffW3MFAW3mmu5xvprYgrkDznZbps6CZrpWrsxJ6thUsxpSmL3ufynBJUl7yilzLSsr0t00hkwbvLfwXIaOThC+5iYw6FiwzCRWvY0g7+1sGyo6LUjLt4uhZ0LIvm9+C5h6eLYIsi47sVPhKsTheQaNpi4N0OSKvcIxXNbBi/lzmVszBsKRP3A2AfELDjxt75mRXJEsE9mWVCAAykcKK2P2ab0/DtGgNJbCkdCwT0BlJkIqnUEoyakWWwJIKAoLw3pPZ1rmarZvX0hZyMtg5A2OrvZ3SbW224MtCFJek25z95Fl2qvTMTJa+e7azy3feefrAcUHMjB/MFL7uNukMtgMyLUikYz3rJw9pThtUlqXRrc+wDFa3rSZlpUiYiZztA6zF+WF59XjrDRoxJm57zduzP4EWQEBfFjT/xzn6zG6tqF+KijnvrLZyMDKfnUTKoDgVgoYVYJn0RFNeEpOBxqDla5o7ceV/n8ZSpud4WZPoYGPHBqegvgWaHbfmnpZdmb/vVrevojWeTo0vvUvg9Hu+bKymSX1PjDX19iS7d73zuDgKIy1i3aQi2VMzKEVN71Y6jUi/lkct0D4kVLeHef6Dev77k+3w3JXwuZ/CSXfQHmvn3vfv5f7P3s+hk9LroaUam6i77jomXHE5U26+eQhb/hFHCDjtXrj0t/Cvr8GKPzFhVCl//tpxXPHp/fjiv6rpeOApzl6v+OePv8pbtW8NdYs1mj2FRuAA39/7AzkrzQohzgEeAC5VSuX1D1RK/VEpdZxS6rgpU6fYxzn/uQPP9ABIedsBb6FS/5d/T6KH5kgzmV/fWV/WBUdU9uDDkAq5roJEXWt6XyPOyJaFFBtRDEvy+oaWvDPf2axpX8PKjoUZbUkPjlO2NVClBROO2AwI21VHRbtASVJt3QSau4gYvSiFk3zB137LADNJVzROJGmkLVMKUM7w2RVpgMx2K8qD263xVCSzp6zc9bGkgrgpialEjtiypKQz7nPhUgqZsGfbt25ewBsbWliytSvjGMNLk203IlZRn04G4LbZspBZ67clDIukO5hz+1SamB29aXdCp0xLKlqCrlCyt2V7vOXrH9PXd0WiCEvZg19MI+9x7u/RSeMpsnro2boiZw25VHU1JU1dedcREyXFPoGSSzBh0Njjix/01W1ZBi3RlpxjpLLFlXLdP5WbKF85xlbHQoqw71Hhi6OyFGWbyjHDdlyiUKLvMLuser3z6MOVVKF4r/49klbSOSU3CUqhku32rW1fk44/VY49U0nvnVGUChJQkpKUbTHsL4FkQAQQyRi0b84Iccpwx8s6t8yzcLwBkqGC750RXeXs1b4Qeush3MyCqg7WNviyeUajyFT+RCo53RGPe5+68bP2uQtQEEqYZCQJ8cRsrkDzP8e2ldPuYzPWS8BK9LnmnWcBdN/RTv9nrBtsmVhSUVL3Pl1t6+2EO9h9mxMT53k0QDyWzCg7u+aBoAXahwClFD9+bTMPH97KtDdvhcv/AEd/GcMy+Pb8b3Pu9HO5ZOYl3v5GczP1113H+EsuZcoddwxhy/cgDr8UrnsVFv4KZt9JwEpw2xkf40/XHcfDK3uZc+3DXL1E8PYfH+SptU8NKt2wRqPZLj4APi6EOEgIUQpcDcz27yCEOAZ4Gluc5Q8eK4QQ6Rg036DG/9UbTZq8udF2hQrkS/Pot5JlD5wKWNDawwkq2sJs64yhukMYbd1eIDtmHENZFJthOiP2AGEg60nFjFhWUxTSNFGWxGj3WTfcegJFThcIwqsqMBbPsq0kyZTjLmT3SXlnOZFUhKjpDMwdMbK4qpMtrWFfmm7n/O2RanrgVMiCloE9KFrcsChdFmSsW+QenfINljL7RdGc6OGDbYvIh7AkIh6jK5qp3+eFMhNHGB29pLa5lkrHytjURFFlpvfE4upOlrnxPE7Hy1iEZGUDAS8Ln31eyxsqeKfCrqc5mAClKMpSaPn6xxs8brEtMaaTuHFbd5WXFMN/nJQynRLTnWTIvgeL7cG06w6nlPKCbEQgQDiZojeWOUgv6o5S1NxLS2MdrTXreGODI8R8/R9JhmgL1UNnldeiogBIw2LemhqqNq932pt2IZS+AXKAAFJmWl2LDIuSSAQr4ohxR+zldXHMGsRn7JKxnlyWPcTZ0XQEZL8uf0J4LnJhw7Wgu/e7otcfeyZgWvtiCLf1nSO0p4aiUCsiFrTdKpW7KHd63bCOaDtLm517W1l5YyWLRCDDlTm7mwKW77o6fW24bpXKXgQ95iQ/yiYteBVWOEzgpeegp94u130vCuHoM5XdzQjl9ECWIEoYFq+ub84RS8n1G0iuns3UjmU54nZMZBtIi9jqNYzYuNb+0EoLfbCXUekOdnvvQbs/JFbUN0Fj34RZDRUYKkV1aB1/WPIyQaMjc/20nOepjL7QAu1DwLyKdsY3LeCqbQ/C5U/bcWfAL1f+EoDvHPcdb1+jpYW6665n3AXnM/Xuu/KabDW7iH2PhlvmQ08t/Ols6KjghIMn88adp7KuZDK/P/tb3PimZOtbs7j3/XtzMkdpNJqdh7Kn128H3gI2A/9SSpULIX4shLjU2e0xYAzwbyHEWiHE7ALF9VWPnWqfLEtGw3IvCF1K5RNoA5zGT1eQ8efqul7CCZ9rHj7hZCYxlMXoSD2Wa0EZQBX+CSOF7QpWu24BW2sqsWJxpJM1UDlrRrnixh3Py5QBVsq2ODn7uIPW5mgL69udAaHrhuYMolNWnhg2mU5KIi13//RZrKzrtl2gVGYbRFY/qZb1pOINBGU0bcySFkVCIHwDeq8POkKMWraRWMr04udcxjX0Mmb1spx+M7MWg84s0BmUpgzv+hQLO/OnQnkWLve8I0tX4j8h14JW1buZ2uhGuz5LOpYWG6MrhEymvGuc4erqurcqZ2DsWNCCiaBn8QHojCSJJAy2NAc9C0Qad7DuCKPiYoh0UORLTiN8yWJqOqJ0x4yMe7akphvquiiNtVCWaE0nl3H3aV6FiAch1o1qXptxXUevq2bvJW9idlT7uwrlPm+O2AkERFocuSn2paS0OEDKl0CkkIuqOzBPVFSgLCt9vzkTBoWyELp1vt/wPqGEwXtbWjM+7+uYkJuQwqtLsqK2K/cAI8aI3irGhuz4RksqenyJPlK9rVjdDQjTbnfSMKFtA/s1zfHKbou1EXJcqhtbWvnn6n9R1ePGS9oCsbk3TtzMtTqnOyn3vpCetdf+2dS9jbiZu8xHRve5QspZwsJ1/fa7OAaMCMVm2toq/HGjecqNG1Z6kXsBRmMjFVua7KRNWaczKtYM8R6M5mYCvZlu6O54eWPVWlYvXowVjqXfrQpvYsSwJCnTZFNTr+fm6faRpUyCRhemGceQKZRp0hyMsa4hnTzEnXjCtzxKPrRAG+akTMmbL/+TX4n/QVz+R/jEhQC8uvVV3qp9i8fPeJySIvsiJ2u2UfuVrzLuc+cx9dvf1uJsKBi7F1z7H/jk5XZc2prnmDqmlL/f+FlmnnUyjx77Va5/vpdxlS1c/+b1BQKNNRrNzkAp9YZS6hCl1Eyl1E+czx5SSs12fj9HKbWXUupo59+lfZeYRiDs2B6fxacyuoF6N/YsGaZICEbE25GbX/UEWn7ruaLQ7Lzatihn/R9vT4Hj0pMWaBJFWaId4cST5BtbZmcsk/7onM4qiLQRj0eojbSBZSHGjbGTLFiuQHNczfwLybaVYyaSKEd0retc7Zyv3TYrFPIGZ5bjmuYJNH88h1Ksru8mGU1hzl+Sp68gaUq6nEGq9z3nG8S5fdeRaqPCaE73l1RuLoLM3lbpXn1nUxtLazIHyoGCa44J22rWmOue5/WntLyLUBwopaxoFKAIeNvtsotGj0BhC4uMuv0JThQI0gPCVHMn8coGYusdAaMUvbEU7eFEWlQ5n1uS9PpSbhuVYnFVB1s7o97gVoncNnhZSgOua6ugKt5ux0y5g3QpESL3Hu6JJOmIJMm4LokGvJyKlgmJkH0vy3TrigQQS+APaBNIyrZWUNJY66xvZguKgBL2+QqRdsOVirKitHATioLJUTx35KpqrFCI6mA53qIHfquHe797wjVdXjBmZPS5H6kUcWkLV/cQU/ncNoNxgm++m7dtmElG9WxmXMhOtLOusZcFVemkY429ceo7o2BKWsNxFla0g/vsx5xJYH9GT2XSnUjSEUs7DCgF4YSdRCbtRuhYkxrXeOX5DwimOhApe7Io6ixz0BJpoT5U7+32ytom6rti6XeZAoqcWFzDniTwexYIIVCGyYhgDeNCVd4khbtPtkuhex2iSSu9Dp9jBDSU4bwb83drukG5bp9bW4KEYoZ9P/qWQnHF5IamIMu3dRNNpDLWWfOylzrvk5JAGco0aelN0B01IOue2hrvY/01tEAb9syZ8zI/TD5K0ZVpcVbRXcFPl/+UX57+S6aNmgbYqfTrrrmGiV/5MtPuvVeLs6EkUASnfQe+8gK8/wt47gqKemu557xDuf7ea/jj0Vfw+T9Uc1pqJle/djXrOtb1X6ZGoxlWBJJ21r2OUIJ5W9owLEVMRonJFIZMLwRcluxAGgmEEiRDHUgna2BpRT3FjZ2FK/CPGRJBlFJs7d3qiAuVsY9SFpZUnsuRhcwJJekwMkVeRhKTtGcTxLtR8aD9oVR2DJWT1t2zoHnWH880BcCG7lpvMNiT7PF2EYkU0SVLScdb2T+jRtQrJ2UlMZWdMbInmiIeSaXHxtlDXmW7CWZ8pOxzFgrCSzd6RwhfOgo7Wx22EOkj4UDGrLhbYYFPrFgCK5wnXbbbdtPyLIG2XcwWDUWBLAHgWByFu+4SgoZkD/7k8EopnzUByqNNtPd2YoadGDUUi6u7WLq1yxPR7t4ZZ+ufD/C712YrV6UIWwlvPTDhujgqqIq3ETfjJFKmc7+nLXsZItDXDumItOZ4NUmfhSSt9G0Bk7CiJFUE5R/vYrthlrY2UVq3LX8MWrAJFetx+lEiBBQpXx19DdZ959wWbyYsXUuQyhF27vllT7b47zM/1Yl25vVugc4KL7uj/xlS4Zij/3KzsuIkpZCimEJYlkRYkqiEvVrno4w4hGMkFy8CaaIsI+fU05ahdAxaQPhfBBCqXsaatR842WXTY8pQvJv68BomtC5lW0MD6xt7vbI84ekQjBvpZBy+z4UTCxkQAVAKIQJY4QgjF65BuHFeCHpjKZKxCE4azoyy17SvoTq8mnjKtqApAKmQSGqsZgxl+t6VirJgGIRgfuMqehK++DllQSKICDbZbZPpY7wEIsp3HyqFYVgI5341pckHrR9kWhm9+Rn7+JIi/5hcgoBwHmujHy3QhjHd29Zx+qo76Dj1JwQOs90ag8kgd8+7m9uOvo3j9j4OgOiKFdR//Qam3nWXTggynJh+Ety2DPY6Av5wKiz+NWd8bCLf/597mH/chRz70/lcO/6L3PrOrbkZzTQazfDE+eKdumYTY9q7Wd3Qg0KRNO0FZ1NJPBck/4BtW2eYbTVNKGURTxnEt7VQ2tDu7pDH1OXM2DufR40oFd32YsA5gkVa1HVHqW4PUywCmFgZ+wTNOB+EazMOWVPXQ3W7Ldok0nNbTKTsgY6wLHugIi07K4WyB3UNyW5vvScvXsWyhZU1Zm/IWjMqYZi2G5yy/1nRBCPq7OUEmiJNXnuqE01sjXfY9TgDmHTMUf6R9dhQFcXxDixpUdcZIejM5EeNtAufP3LI7eIAYLqucCVFGTVUhVelL0WBic6ATFKS6MDL/ui/dj6xYXeuhZKKQMh2IfQSQXjWJjfNvjOI9gnHDdHGjGyHdpPTcsFQknAy5rPy+MSDz/XQ2FDOiOag3Z+JIPTWQbjRExhKKc+6OLF3Q7pGBQuDVayK1LIsWYmM2R4fwmmyYRk0dsfoiaZQVjLnOilfN5B93/qEpoh1QTK9NEVFaAVV4TVkXj27jDKrm5JkN1JBUaqXYivhLLIsIdiAVbuQgEyBJe2+866Ds+7VAGLQMkWqtC0s/sQRruUlU/YiPBdehSkl1C4GIGQ4i4NH2lHd22xLty9rqrLSbr12+Wmx62b3lEUjGF2zlrGL5jJyU3pStzggCFhJUrEUqrjI7mcz5aXdjzV+gGxemyNOS+d9gNnZiR2bZ6fZDwQy+9vqqiEfqmMLE3rLHRduQVlJWkpY0mJrR8QWTVn9lYETu5g0LWJJMyMbqwKUYzXrjhnE69bb10hadMY7WR2pJxhPURdsJmoGMaUkkYohlWVfA2VPBBQFAigFLbFattW9zV4VtQAsqa9jc3v63aOkgmQEEbcnrVx36erVa5G+d53/LEqTXYyO1GJZFlEjai+l5LtH7DJU2uUWCMdTrO1qoSveTW6q01y0QBuu9DYQ+MeVvD/ta8w46wbAnq25b+F9fGrKp7jmsGsACL/3Ho3fvI19fvQwE7/0xaFssSYfpaPhvEfg+teg/D/w+xPYt+Vd7njqh4ROOYfpP3ieS8vu4GcrfsaTa5/UyUM0mmGO302syLLdapRKrz3kX1PJy0aoFObmjexdXotUks5wks5Idsaz9Nd/bOVKzPYORCCQV7hlz9QrZWFaCqGgWBRh+tpTSNx0xRLU8SNaWAAAIABJREFUdcVIVFYi2rqQSlHc2UZrOEFKKeiocFx8DJAJx4JmsiHaREv2Ytzu+lKBIru5ynY9CyUMGnpi6e3SQiaSFPd0se+6ynQslNNWU0nmbW7BW0/Kix2yIJh2nXLPaFyokhGd5ZjJONQ7AySlqIy3kXKsPgGfm5OUEiElJUnDs2AESkspnTYp3fdmiOzRrN9zL56yGBVrYUTYbk+htbVQisaeGK09UQK93Yze4K5DZ1tyisNhpGE6JkbhWRWx0ha0PIUiVKY9JyDSbnx+wSB9Qk82tVDWEUICItZpW+uMpNO/irgZ8lwEEcKLLaoPbkGkTCLOdZIdG51m2GUb0kgL8cp51HVFvHPPbbm7ALH7nOTuY0lJTaddRpEQduyjQ6+MEjejjEq0UBZrRqEYE7JdO4tU2rWx14hSZKVAKmd5AekNlk1loWIxRDDT4pmxDprKaqFSyGSK4mQqbeHCfe58z6wZ9SYUWkNxO8NhxHZhq2wP0RlxBJkZh2AjVqOdUEPFemjqjCCVoizRydjmDkreftn/FrH7PlDCpLZVlKZ6Ke5Ju+CWFdvPnIh0ooqLnHs1LSgro02Eks49HY4SqKxz2i6RUduKaSrXgmY/P83JXlpi/YdfpCwLGShmZGl6CRFLWWxsClLdnu7jRY6125+ERFgSUlE2NyXZ0tTtm2TwZke844XAmSSSNIYbqYv1UNUesSeYlO1y+NbWudQF6xwLmkJJKC4K0BltoaNzCSllL9DulmopRcKKYirDyRKqPKuiK9DajBDJWMR5rWS+SUdH6ilN9dhWeX+iE++K2SY35XMZre6IsqjLl9k2X+IoH1qgDUdi3SSeuYyXjBM54Zofeh8/vf5pWqItPHzSwwgh6Pn3v2n+7vfY74knGHfBBUPYYE2/7Hs03PQenPZdePM+ip69gPPuuYh9Tj6BTz/xd/brvIPXt77Jdxd8N2+QrUajGR6EEgadYcdCFijyvrgzUnM7v6UFBpS1rqXECCPxxR+hWBHelnEUgNHaRqq+LnNGNiPltD1zbUkntbgv4YVrQUvHV+Q/j95UO6u63iNZWUVJVQNl3ZsZt35x+hyka0FTyDZ7HSI33bmVFWujLMtpkj3AL012o5JRW5ehEFKi4r1QvxSkIpCIU2ZAykxmJSixBQhuvcon1ELpOK+M9PBAvGYrY9q6UIH0UNu9HkKSHuspGNMcYlxFSzpZBSqdaQTYa9PW3GQSvvre3tSKN3Ry4qbypf/uMWJUdzVT3xHJcLNyr2Pppk0Eq+tZ1r3JFraWgQgECHgugU7Kb3eA7qznlR0flu3CmW6zL0mIa5XIkutWl72wemu8jqhMeC6smBKicYzWbZQ2Bb1jPLdJV1BLE4FCCDtxgvLcM3MnByyZzlaY3afe5ZGSitaw96F7ppZlscVoojvZ4iWSUKQXARaO+2JUptgUtVfSEFJ66fjdOquSrTT1bEN0BzP7S2TdUxndaJFaV8OornRSj/LOcgi3In335Iq2RV49cSdbaHRDNUZHLxKFaWX2iVpaiRWJYymFkTKxpGRi9zrGBpsIpIKe5cWzUudNRGMLC5QE00QVBxyroZvRMh3XqRQEesKIqG98ISVmVwOmJT3XW1C0G2HaYm2OAT/TeuiegyBtyy0O4GVkdZMDmVlxqt5VcCe4pIKKOUipmNy9ERVq9u/lXBdnjTT33aJs91r/snCTetYyrncziYThxUQqxypYJASbmt9jRLTJs8iVdobtMxDQlqijO9ns9FOmQBPCdnUOOe7D2RY0N6ul6wJp96ft9i6ldFwi00eUdPrizQY4Ea8F2nDDTKFeuIZliQNJnPEQe40bAcDCxoX8fdPfeeLMJxhZNIK2xx6j49e/4cC//oUxp54yxI3WDIhAAI76Ety+Eg69APF/X+LwT2/lsI9N5htvzqJl3XVsbmvj629+3TaXazSa4YeUpNwYpYBwMjhKJ4U3vpngLAuaKzYE+COCOo2It38Gzuxr0jLIcIcRAolCKAtpJpCJFDKRcHZRFBGwrXwqczCdzYjOZZQ5a08pJRGpKCVW1KlaOBYv1zImEJaFVW5bLCwvrXi6rU7jAEGq5wPGLniP4h57MISUEOlIt0dJikQxKHhpTa03Y62U6yaWNcDPXrPMyExd7Qk5JxW6wp4hFyimbmjGaHbOU0oC0rYW2FkO3QF6WuSURhP51y5SikAk7NSTPl8lZVY8m72xItZMd6oVIa2MWLqANClO9RIQkHQSRyCK7fXJigK+wVumpck0TcdtMW2FgHTK+2wXQrfLEnWtmQNjH/HlK7xslnVGO92RuHduRZV1lKoAliFp6rUHqe5Cyt6CzamIbayxOxdLZq735mdsYw8Ty9MJW1yrpFKKlCmdezbzvFyRaq4ptz8LFDsjfgGWRYvRjmv/UkrS5Isrsi1oZAlVx+rndI5sWA6hJruEbAuO0w/ht95BGmbaiqoU7bF26K1Dta7PPMmsZ83sjRBt+n/23jzekqyq8/3uHRFnunPenKsqa4KqokAGfdK8BzYqoKiAQyPiWND4kYaHKI8nou0EOLQgIsrQ2ghIoUyOiKCigAwCBVVQc1VWZeWcd57OGONe74+9Y0ecmyllvwaS1rP+yJv33HMiduwYzvrt32/91rpnNgdFwilXm1mIUAxiK7UVCwQxQlj07HnKK3MKP6D65jePw9mb7Uuu558J1NgzqLxPtQD1uSnHuvkAx971xywfXaJzchVJ7PXY0pEH9p9N72N55VaCogJ2+ShBx5k7LiE3BXdv3A1SNdu+UHsPqY2tdP+UIrVTncesdGMGaVUvV/7sjTLWBwkUKfr4P5233Wa65WsNlQiFyREjBKGu9umeDe0zG4B4qx0RrMmRCKasaXRj68YZx5e3UVpzf77Mai9ha5gSqIDASVTFqSh0bwgbDxAWA9QoqR4t5c94CKVpy4UfyefFBKB9LYUI/M1L2OwN+dXghTzvm64C4HTvND/3yZ/j1x7/a1wW7uPsT/0Ug098kivf827aj3zkRR70JP6nI2rBE34afvo21JFv5LKrPsGV2XH++ORfUZx5DieXFnnm+59tH3iTmMQkvqZCxyOCrgMyOuD+3hfYSM7VLKdr4Wo0jEhZcjGew9WSvmGR8PGdo7WPCueGS9wxqGol1GhE4947kDy3CVPPypCK02fLNLXqzQbMfOojmHg0LuFyO1aSE5R9yMo6m1Ia6VarVWE4NlxBlELFKbLkVoFrq/uJce515eeUpsAQDFIaG9s04zVIRmwNMz5/YsOtcNsV+0BC8iIhyCuTC0zhrfqlzqCVkeXEn7+HcNUe+yd6t7GdbLvzUXM8dElxKTraGWUsf/J2wjjDKMMgs7LNYZKTFdkucLMrg3IszNStn3Mv2MQvNzUQuytCI4T5gGDpIwTJDohBmZRG2mVqcMIlXy6h1oF1Mwy078m0k+6gu7GXPO588P00P/w2lBRsFH2+2LcNsZVSJFnBqY2BP4al0QNsJBYQZ6tb5cxeMC804tzlBGKj/YwBhAQUNcOUwhj6cc6m67GXHftHz/MNTcaZ4T0ArHZt8rqWVTK3Ri9GZQWNsycJ0oyrpr7OnSfh3M6IflbQv/leVGyvhWa8grjrIi2ZEZN7tmv/Z+90V7sdfj/NWO7F1bkwxhGcjh2pJfDlL5tn72O0cWYMoI3VeLpFEgGCfECr+8DYJPpKseU7UEkPsgw9SMZm+uR6zwPPvz95gnNDB/IRTJxQuPpMMQViCoISoNQZtNo+vUgvG2KMO67y2aO0M66wqFmtWmArRlA1WfKBW8/6K+JksoHux0TbA/KzS+zEWzSUrctcGdnnXHZylTCtzmX8xfvZe++q3YcISZ6T5jn9OCMvLMjrxhl60Iezp/29KiJkzhxElf3zihQtkAuc2RqyMWYOBOEoQxthJyncWpG+4HUclk2yjeH4zgkAlnILxnRWly4qO2ZTPnOMXaSQikHTtfs5LBLQghLh6EqXY2t9Grrt6w119ywiQvvme9DDDCVCMIztefBHAWeH93N/bMelt3vYybvAgdRiAtC+luKffxdz/BP8UO8n+dmnPYpmGDDKR7zkoy/hWdc8iyc0H87JH/0xTJJw+Z/8MdEll1zsEU/ifyVac/DNL0e/9Itc+pPfTuvoZ3nrLa/kpZd8NxtnH88PfeAG/vTuD13sUU5iEpPYFZI6pqDWMLi1skGYpJWEyRhECsKs78kosPKnwuyWuAjr951gcGbdJ4q3nNqin1rJDKc+DWtH0fd/lpkHboLEJiPGJZtHt4/5LXmJX+ZATrK71g1f71ToFree2XaSIFjNVmsMh932WtLzwMf4Anw/bO4eLrOW9fnI9r2OidLuPa4mLutBvMMgLarVfZOjlEKjaQ1Ps3/l4+XmaCYbhP2jbkW7Spp9xKmt+3DJowj0HBCwbJNjLWtp3O1ntvnc9ilWkz5BVhBLymfWbd+xs1tDzm7H1GMjPgO9c35M7P6fsi1wTm6UBh3np4wRmqBIWUo3CPMBU4PTLGzdikacU6NBaSsUwyXEKtBewnjL1q2071mldc7WG91+9jOwvY4CBiYhyQtWezGDJGcnzljrxg7UP8BqfJK12KkwHPC2c3K+66CR3CWu2rp11pLGkMA7dE4td8nXdhhmOatO9pVJgTYFCrh9eMaCIik4tzOCLOaWvqt38rU5QuvEMaZXN5kKphmmhmFqAalxwEsP7XltDc6iRjbBziUnTHILlnxDY5xU2IK0U/0zFGKsFw2lxLG8dmogRwSVpDSP38ex9T7ndoZjgLx0AxXEymDd57VYI4tgp0/nM3f44zq+MWB7Zwud7NDY2qJxZmfsajC5IXfzfjbfIssrVn19o++NS8RYV0Cd20USZYT05ttY2y6lcePMHiKc2Bz4HmBGn5/OG+eUecHadnePplL1VAx7I2659cO+tcMJ1zS9OLOOGlj2Z2uQcmptQMVVw7G1Pqe3Rqx0E+T0Z9mzeQu9OKf1wL2oe++mfd9dAJwZHONjpz9KXoivkdQmRQmc3Y4J8qG7h50MXGDx6Cqd5a7XSlpDGEF1x+sIg9jVs7pWDUqE1WIbRNhz5zKdzR1238Pl/iXeITq3wd2327pAJcabCLFxG+3RGXcJnY+oRqc+xq2fr/K0IgqJhrHbl3jTn1wydjIrk22cWqEQYX3wLzPO9lgn8bUR9/wNfOK1vO3If2P/wUt58sP2IyK86tOvYrG9yI+3nsSJH/gB2o96FJe96U0E09MXe8ST+HJFZw/B036VI+/+a4bHB3zTW/4Ln7vkNN8SPptf+fQvc8Of/bdaY9pJTGISFzOUMZBlbqW9+gqdP36OmaUNn7QYEYi7tJI1B87s68vduCaJqxKnU7c9wOCupTG5XG4MygijY2eRnbNWtmVyj/YKn3eWqUONQXNymgslFZLaxK0wBXlhWNm27x0WfSi1YOJWuUWQeqYgIOvrlp3zUipDYlJA2QRaqjcLgAodwyi141NoAWUc42DsdAzjZe7Oztj3lsBiUNnx67VN6zo3GvohFVKgUEwNTtKKV8mN8YymQjHIuxgMI5OgxLJOwzTzbQnYldyuDh+Afs0IpQbC9KDP4VuOU7gVcjGmMvgAn0iGrkpHOfBLMcIRGyhnolIacuRKIUaQGkDzk1huO96BQKPdNVP2X+onZR8qm3DvpGtjH5ey8bdf0a+HULheYsrIebVGWhTzuoUCpld6FI6lMKZAD1MyUxCYzKnHhLmde5ganCbMBnDsI+fNiWfylEIJrPYSlrpOOueaTZfv1Cj6SeEhtzLC3Pad1aFJ1ThYiWXAYsk8mFfOlTL358Y44CaQZjTOnfb36omNgU+kxd+riqVu7BcKyntdzqySDSygz86tE372LrZHGdHWgKi74+RxdrtxVlCYnJGkHliJez0LFKsbPc/CijGudqkElIaBxKx1Vz3gcJPpxlmdzWBgwYkofwAgVc1gubCy+9zbafdcLkF3SHTvSf+OoSTVzLnPx2Uza1Utgkgd+BcZzWSzvgsAVgbHODs4DtmQ1V7M9tD1MTSl+YphtnsvCsPMAxtMrVZtQXRhvERRoQj6KfrYmbGjUXFG0YhsvWu5bxHC7VLtoCq5oVSFqZ3BKeTcF2mcWENOb5LmZqyVxVayaSXeVIBU/DNSuGN4Brn3fv/+tNMkihOPz/wx7JqQ3NhFni8VE4D2tRDr98FfvICVp7yR135R88tPvx6lFO++993cvHIzr5BncPqG57L4vOdx8Jd+0fcimcS/rQgvv5bL3/d+ejtX0v3YGq89/lvcOPV13NX7W57w1ufz9n++v1bYPolJTOJiRGoSer2EJDfoSgyFQYgcWwYu3ystsuvJsap9c5eLtPGmL5XJa3U8SiswQrq0QXz7HSgjiA59cpYUhU+elKqK2wVBRq5Gwoxb7gOsPmBrZ4J4QJj1iUcp7fWBSwprzorGsmCVdNAlej0H/ur1UgI2paiS/MAl/4PMJbnGWCCCEJoEZWwNV5lRCSB5VadRAkBz971+utSOnWO1vUFWpJTABGyCqsT2UUtMAUqhUGwkLpkrgYKCYSLceXaHen2KjxpI9gm/+3O0uUqUdau3FgaMYWQy4tq5i7ZjlNhkLyPneHaOjMIBtJwgTxClWO+NuH/DnisVBB6QLe3E9OKMxvqOHVqeQBgQivGGE+WciJt/wTbHrY9XpDY/F4BohWMwrOSqOnc9M0IZQ8NoOspuszJtgfYdy+SDmMDkbp4hLMqkO/P9u8rzMjZW95ty9ZIgiKsD9EYNur584VgVk1iDmiIBkzK1UTKnznlSWTOVkjlTUjlNSu1cArRGZW0eZHkNgGRWYphTNuEen7WVcxuc2bKg8tjaCRpDC+Kmji7TXN/y9vEAG/2EwhSoWiKeFVb+dyYrUEmKGLvoI86URxkhN0K21UeAtLyuPMg6f8El6o5QhbHXf3Wwvtm2SnLPhFbh7scStLj6UXue7SbCY2dci4xqr80wcP3s7Myo2vVVvxftr5bFbGhhpti2+1qxbFpp5hHmPQLBy5wRaO7ETDkzD78pbRd+grIn4y7A2R+m3L81cveuHZfSimjbXiNt1WH3FeVGTEEAApu9mAdWt718EZxTr3YS2Bp7qbZ7jCQjMcXY9ZG1m0ztrNj7ogZs64AWhO3hgy+6TwDaxY6kD+/5EeT/ehEvv+0AP/jYIzxk/wyfX/48v3vz63nd+pPZ+a+v4PBrXs2eH/2Riz3aSXyFIzp8mMvf+U56D2Ss5s/h0aT8/doprps/wxvueilPet0H+Ksvnj3fZWwSk5jEVy20K+CfP3ZybOU9LAY+KbpvreuTzdyMp6Vq1+372dUvUvb+yndOU08fyshWt1ndHmF0AE5iOcoz56hYJQ+lq59JS8nP+Ys6ozSjtTnk8BfuZqZ3H4O98zR7VpazbQZYIAZtQjBCroSdUcq6q/ExZebgko/T8abdvcIm6yIURrwv3NYg4a7+A9yU3M+ntx5A50Om+qddnUYFHgRoOBOF0vjB7mZ8wtT1V7GcHOfW3n1jNUP1eY0La2GvawmtGx4aiIgobr3VzqUeN32v16DU/zfIu0RLd/ktKpydvRjuuvUW7hkuw7LtUaXObXmAVpeYabGyqrAY2POdGzLjEkCt/fmytT2GcOMsOs0wwPHCSf5qIMC4oz90x31khSHS1lgsdMxIkhb0Yiu9lbo8FZDCOd9h7ClXyoOplcJKXymEh0WXWhDkJafldV3O8fhkKWO4u1Y7aV+szEwUiizL0aiqVlA5M53eUWa79zuTEPz5VWKP9b7sHGfzTZrxdgXQRNBdW5NnaiBGKYHNbTCGbpyTG/FMVDPZgOGIaJRStqLuJzn/dLSU+SrL3JT7d68W8YipgW2xkIROducAWC/f8kAJEZLCQJpz8M5lmlsV4wuQtkPIcsfAOgbNgbBBmtM/YY1tlpINbhrdjyoXR9w4PnbPuA2+IG6BooKUpdtqtDageWrdzU/l7Hh6u2dZeuxzoy7pZPs0qjskSAtfnwogeQVIDIb28OwYg7YxrCTV5WJMZ3CGfas329pS9+HW1oizx5Zoba3Q6FZgXtVhTD3PUYqVnZG9x52VfpBWICfPDSYMPLosyUQ9sospwRjcMWNWkKIjO4MBzGzcRpjW2DtR4MxXxM1Tc7vrn8Hbg9SzgXZbimaySZD1ySUjLarzXl57pcPlBZ05azEBaBczROD9PwkLV/DB+R/mnuUeL3nKNSwPlvmZj/w/vO4LDyd69we5/MZ3MPPN33yxRzuJr1JEhw5x+Y3voP/pW1i59xrmv/ctvHNrm+9QZ9AHX8tvffTjfNfvfZKP3LNyXuIyiUlM4isfOi/txE1V++DC35NSAYy1XkI2xmJcYDUbm1TkZz5rF+6wTEsZmSlIswIxhujUMhhhOx9xdLhS4TOpSRxLOVphAcIgzcf2GNR+j+em0IWtjTJYcJVlBQ9p7iVAsWViNgYZW0MrERSlWNj8AvFga/wwSoc9scnubnOS/XcucWZlE20S+zcjY8ldJS+zv5hkBwZrlg0Qw/2jVVCKjbQgI2d6pUdje+idCOuRFKXTZJXUUgPHuijQW1aKVU+UtMlYPHczsUuQEZv0KhHWkzPsrN1T20vhGLSCZjplX4l3fA82SqahdLIQIcr7HgBkxuo6RWlOrg9YHsbeZr8cu8qG3gBhS8UEUiWl2sMYCLKCtChQSnPp2ZFPxNd6MUdXV+x8llmmi/js5ygSW+MUDVNEjzOgpnDOgiqgoUKK8ly5MWYmR4tynQaqeS7MiOPxenUctctAMGgROnfdavuU+TPkJGe9BwjzPk0dUtqse78MKilmI499jl2/iowpLeNtZFIwf2aVrdzW/+jc3q/KFMipJRaPrrLaP0W2dI6iMDVGF1RhGGZDBxncWTO2FUReiNMYuzGVLR7caybPHbg0aKU8i1bORR46oqtwAE0KlClQAqeLdVLJ7dzUAHspqzs/qtfywnBstW8XVqS6L4LYXkNnig13jgpu3riDtHQuBAti08pspjzfApAkMEo4e+woy8WWOy9CYBIP2JXA5qACK73h/Rxb+aR9jg1idFE9K6NhxvDoaWaPnmJ6aad2KNW9WjpZppKzZLbpJznm7Cad+9YQ4PCt1lSpEWoCBBMEFGk+PkVxl9wI2pQSSXtAVV0kHN8YuPFbhjvY1erItu8ogbqyMnP3aWWsCykCm4ME5f0hAwamz5nBvX47e+6yTP7ZrSEXOou7YwLQLmZ85s1w7gvsfMcb+ZUP3M0rnvFwwiDnZR96Eb/ylw0OnOhyxXvfQ+u66y72SCfxVY7owAGO3PgOhp//POfe/EHC//wxXnn987hh6yzF4qt54sNO8fN/fgfP+v1Pc9PxzYs93ElM4t9VlDbMxjEkQZGgJKO9OayxDLAVb/GF9AGSsm6DanV7f+sIIKg4o/2F+z1Ey0yBEVjY/AKBcyNzH6S1MwKTuJocQ1YYVodlA9aqfkfAG5mIKRilBSvdSrqoZZxXksCyBQpDIYbuIKa7ZkGiyXKGkhIUCc14lUoSCHd95lO18Skqu3r7r/ZZSAU6wlGGKmK7am+Ml2iWkql65tLtL0PcxSAMTUqvSNgp+hYDak1zZ8TMsXXvdFjPzGI5P72pvxKePMF9vXtYL7oItjZwrZegJKfRi0nOrNHtx+z4+l+77WHbQWnHxpjCkK1v+rqV2wdn+FT3mH23A0kro5MEha1LaSUbfnvHNwc2mVeaLC+4OT7NMKtW70sy4+Ddx9wUK0JTAWutxpm/3DlkdrYqQwQjhmHRc78K26OUkWtTcGy0xmp80r63BNcl6FEKkbxi9ByDpgAxhs1BAnecYebcNoURdoapB79RkI8DCVXW3dkFjJmldXc+FCkWiKBgLeuxnjvQrwQJKnMRLU7e5jepPChShWEXdvTzk0vO/MaQyEnKShZDUXDS2Hq9xt0fZu3v3g4KWsMlOmu32z0Y4ejO/d6MR6T6/KmtIcrdM/bcazuPhWXC70xOcLpYx+CA9C7VS2aLIa3Dohi2ZMjZeM3PW2XIU1TXklRM3lg4ArYEf6OVTfTJc2Q1gIYRhib2gKyQwssxVV3+OFh3cFSqfociDE4co7j7fkY16arnuqQCLGMxPEWQbEHWp3F2k9bapmfFtCpVl8UYoM4Le7xBVtBYrqTEW2LvlcFWn8JUBkJ2/JuobIgJNHmWe8YVZRlkgCgbEuaxfWYjZEHVXLt8PuvC0Fnu0oirNkeCfdY0egnTdxxlz8lz1bXs/h0mKUV/1TK02Gvfgr3C3fdCw/WVC0cZ0ZZ1cZQHgWATgHax4uQ/w8d+A37gRl79sSW+/sg8T7n+AL/9Nz/Hj7/xOFceeBiX3/gOov37L/ZIJ3GRItq/n8tvfAfZuXOcfvFPI496Ds99zqf4teAS3n/2N/gvj/sIT3nYAZ5/4+d5wTtv9r1qJjGJSXxlQxcVg6ZEmNu5m87QMi4jsSvIXTPg85u3ksiunmEuIWvqDogQ9BN0P/bA7fajt3Omf5ZCDFkN2OwUMdEghSJjxwzQpVGIy0inVm8hzHv4cvYspTM4zYkTS/SW7CJO467jtD9/F4GqffUrEC2OuTFjjJZCWcmWVkT50LKDpd253QsCLO2k9JLSYU/7RH2YWnlPXXpoS/AMSimKIuN4fNyxjq4exiVeaWZY7blkMOmysjNie5iyVlhHt7IuTpuU9a0dMtcctoxha2F83v3B2jq9IM3JptskkpGjKApDL8nHbNi3B6ntlya2/klJTkE+tlVjCosKVABBRD9N2R4lrledbV9QZ0IVrncakEvVHjiXHAl0zXCk2oc2Kb3Y9nbKij4DsfOiPaBySe8/fJx9N32eMLOyy+V8iy3TpzQIKc9svc3bvntPWamp0j7BT6N5jIowjtHxpjQOSElhXe6SLCdIbXJbuHMnWvueXvV5L7e9lZ5Fu1o9LYq1ostVs6isAAAgAElEQVSmsaziVj5kYGotF9wmjAhalDcjASg6TXRRoBR0zm37BQ6RSr2mwoBcDCEh08tlo2kLfrpm6FkUZYQT3dPcs3Un08fvYPam+/3r3jCn3EMpETRim6Ajrk7L0EidPNcYpu4+61kwhXLzVEWhnHOlq8tcC4YEtZYGJdPVufMsIhD0eqidfnVd1AxAYseqniwswFOuqXZGbZ+54YG8Mr4RMb4urdFPnBS4trwiVsKojKEAVs022x7ol1M5PhZlhKifEBw9QW4yRq5X2JlkGUFobO/48ox+krPmequpMdgpZO497aWqd1t5rpa7I/pxVh8GR+NTNLZPMdO/Hxl0Iek52aZ4QDjXPUaU7bCjrSnRKMstk4ZQGOOVD1PLvV0sZbVQoEdDOls9f48KQmdtQJDHjLrVwoL9YWjFK0wNTqNNYh0qJSeqySEvUE44FhOAdjGitwzvew5852u4ObmU9996jlc84xH85ftfw5Ne9fdc8e3fx2Wvfz263b7YI53ERY5gbo4jb/1D0IqTz30ueRLwLT/017zt4S/kXSf+kuNnfpy/ff417Jtp8u2v+zhv/Oj9bjVoEpOYxFcqdFGtLJcr4/XCcoCuGXkraWsS4r6N3WuNdAd6yx5oiCuoz5KEtdEqx/MVtgvreJZmBSulMYXJMAgzNOzvAiod1JgBiCXjtvVbaCbrmDtuYuuOEwRxRrCyhe4O0OjxREsrdG5qvXtw27JyRQJVtkajO8qQ2CaAvTinH1vwkTmTBlH4OdE7J8s9VDI3EQwFGo2+6Vbamz0YjLwxQflvaYgUZwWbS8cY5UVlfa00U/3jAKSFYW1l6bxzlJVzT+WqV25doWjG69ZNEFVhCZ+ZCkvbI0Z5BaxmesfoDM/641Ai7L/9HNko5czmwH40aLA1yljtJWRFYeuG/FmxoHDurrNuDgLiRsfvNpXMnYcRyjjzE6UxUYegSMgLgykMcV6xChqNYLzbngXQLnksMvoSs2UGPv2VXdcoQJjUAad2ILvsL+bYm1pPMStNtdsp8J3I/DkXDWJypJtUbFANOavBKaaGtj6ttHM3CFkhDszaLUYbfRp9yw4LxtbUKcuMxItTpIfn0RiSxiKiavy0WPORgYw4uXMnIxJCVRmrKRG6MmS12KkS5NxQIBZsjyoZni4ECXXlVlgD20qE9e2RNaMwgipS17xYkML4+7EEaNFqn0a/2rZSUFgXE6QokEaAzqrPldeFiKDTDLVaSUaNGN8QOpYMBawGB0i1rsCFCMOiAgOqMLQ3B2PbVnW6cQygCaoErwLnugMH2GRMnZ1j5ZlBv5Iozh1bhSTlzp1P+msjxfYZVEUFhACWamY7Q4nJpfAOi9YLZJyFjSVmaGLHFlZ/G5rEyklDjequQNnzTVWMpxYoogBVGGaWNlEijPbM0FtospKv041rwKkoWWM7I6bWJqIIQ6r7xaoaApMydKy06EoSXRbChfnQLT4Z/93xr4kJQPtqR5HBe2+Ahz2d+Prv52f/7Db+32+7lnMf+kOO/NLbmPvJF3Dk5f8VdYGeFpP49xm63eayN7yB1jXXcPxZzyI+eh/XPvaFvOd7/potyXjBB76L5112E+95/uP4yD2rfPvrPs7H7l198A1PYhKT+P8VqqhWUHcDM4A8nEZ0k2DbSdhqIKGsuInygcuSFPSWUVJLEHYVkWeoyhgot+xMJJVxc3TsBHP32HteBNIsJugOEa0Jctv4tb1Z1j0Yu1peJpBKIcF4YuiP0/2s97HqDlKK3kYl5UTAKLc5Zzfg2T23nbocSQSd9dEolrdGTK/tQK8/lljWfjDKCvpJ5pP5cmCqnvQ7+Ve9ri4Xa81tdjGCRdBBwhbaGLQMsWI3oSsjFDZBVGIY5YU/RiVlci7W3MMNUBkhLQzbyZDT212GqXFAU1jpJeddG41Bik4tS7eRdzmbr/lzmJkC0YrGcAV9+n10hucogg4maNHROUq7OTapB/vagcstY8eUZHbMzayLqjWJbo+W0GnXA43xhftyHisWowShnkFztY1GBO1YH7BgvR5lfaKonOQLZzCrtV5VPuEvcDScl2sqIBdx7WTs9dnYKZk0S8Zp0YS9EUGaM1rskDcb1sxFKWsOgXOCdGAyF0NP2XtK1xjjzOT0jQVKHbfIofLCH385B9EwYf7EBirLSUvQK9CMS5ZMSOPMgsZa/Z0ApqiOy2CNagqRMTdHoxTL6Trz965SKJBmRJAVYyxw4RYLwmGGLsq5Nqwnp7l9cJt/XzLXxgShW1+wrF8qBYUI04F14NTJbtdAM8Zy7r4mShG0KqzUUSEWkNWu6R3TZ2qtz9ztt9KK1/3kFaUMV+qyawExNGuyxco630bJDNeePP6tWiuWzRZDST1wLaWGe24/RzBIMVphMuU3URrmlhb75WLYwulzHE4Mg8VZhotTIJDn9RUr8dsAqRbRgChOWTx2amx0Ywy5m39ljDdtKcepTeEadIu/H75UTFDAVzv+/hdBCvj23+A3//Ye9k83ePzn3kvxm2+k+ysv4LobXnSxRziJr8FQUcTBV76SxRtu4OSP/Ai9j36U+T1X8aYf/jhPvvzb+MFbf5ulTzyX9/3YdbzoWx/KS997Ky997610J/3TJjGJL3vooiAwCVHW873CznuP0kzdeZLmTjzGDAkOpIhlwk65ujVx0jub1Y0bCmxEATujzKUyQhpO0TLjX99BXCZFijDOCNMNVjoZSpxZQaDZHqZs9hOmzmx557my/qdMiuqGGf5/rkjeYNlAY4QH8mX/HtERWTTr5JKBX5lPs/PZfGUgpfBJc5hkkBde3qiSnH13LhG5Z5ddhJYKoJUMTz3ByTKivE/eivxLqRQ0opZtBFw7HmOMBXci2IJ+oadswq5LeWHJBjnGyDp815gNVZmN5IVhKVtlMxkwrPc12sWElK8ppQi6Q2LjmpA75jA3OaItwDhXbFSfDZvoIvVW36fSVZ/cRVo7AGDn8sTOGpmJq+MYC8Fw/nVKbZFB+UbVlolRsWUrKCxoKrDnQRnjXfE871I7XiPWW1LScXlv/ZRN904wNTzntmH/kOTGsXWKIHMMaLuJESHUiuzkmWqcYVg1qjaGohkhUTCmrBStWOg02NNu+NcUQlwuhhTGMrm58WChHOSe+yx4zgrDugO7Qtk6AjprfXQmmMgBNO+OCFLWdrlGHLvNcgAORgtWAikQdwLW09wCuNo8Fu761KlrRi3WNCQ1sTfmEARz1WHOfP21iFbclZyyslYqhnL3WffnwYwvXpQOkEbwAE1LxWQVGCfrtHFovs2sgvbgNGHepzTI6LSqrfqfyjJ2qlexiKK0A9SufyKq6lFmbT39e9vNyN8vbvBMDaxzpy6EgAAJNP3EXnMad/+KBVjaVAce5n2mukuuK4iqes9hGfmq7tLtyREmfqzl+MsFjxpolbLwtmyqLrVFPDHuuVstjn2pmAC0r2bc/qdw+/vg+/+ITxzf4a8+d5Jfuud9LL/nndz2qmfzLd/74os9wkl8DYdSij033MAlv/Uazv3sy1l7wxtRRnjhk1/Hq5/4Gl4VH+dXb3wC39X5An//kv/IIMl56us+zqfuX3/wjU9iEpP4V4cuDM14g2aywd5jp8ekS/49aBDDzFoftrs+sSjFjtpZTitX8O6TZ6WqGi/3Bd4rTTCcKUSuNY1/QSkT5wWS5RSNkHXpoZxMTbRiZ5TRixPaGwMazm3NyvzqK7p1psr93zdqNUyf66JP1xz6EISAeOqwfcEY9rhnTu4By/jqdCypS/CNdcQsCi+/K6SFzg1BUq1K29yqTGoYmxsAlWVsXn2A9Wurmm0RRafRGQNWUNqwu/5uSkjJ6bmVe1VazovQM0O6ma3pC7WqK7tQIuy7ywLUzAiF5ID6lxNi/zmb+HZOrZPlZmyqcykwgXbuiyVYhiQXK5XUzlCitkWNxhhDoDRTeoq0yMglQytNfAFwnHMBwFxel+p8+4lgMLBgaEwuChghcrLBXHaBUmcuIuAp1Iqjqw44ynuELgXVZUKOBVBpa79nQ0eXHSSdbhKpoDKdUWCCpgVoDjAarSiiadtXrNxPoAm1pqPSCoTWL0VjHRbLcdoqzHH2ZnlnVDuCiu2ZWrOsr4lA75rrNNpDI6iquhRwIFgYMwo5HC4iKkQQkmbAjh6gc8ugbV++B7CAyGC3rwrDSrHNyeR4fXgWoCsNSpO1G4SjjOVixwPDjZ5dlAELcttRSKAUJzcGZMl4bWR9GaKcw6DyX6EQg+5Xte7tpS6RgHaMZMkUqRLFSakXcNWqppJE+3MgdoaKppMOSuVoWwe3KrAMcnmV7l5sCNG0W3Y+y8Ue4+hXUcoCzZq1vir7qdVkkD6MYVq18PdhrQ+kony/ogJo1flXyjCvO+wL9jjwB+3ROZyYmnCUeim5mgC0r5FYuRM+8BL4/rexHe3jl9/5z/zBHX/EmXs+xQd//ok897t+4WKPcBL/m8T0E5/IFe95N71//EdOPfc/k62s8oSrvoM/e9aHOXvgWp79zy9n/e9+gjc/82p+5qnX8sI/voVf/+DdkybXk5jElymUqWrKgmJEa+t8gx6dW0e9aJQRHj/nAYjUktTS9lzK//smrNVq/Kh9iO3ZKwEoopDNICYXQ0t2f7lXq/adk+sYl5j49EiXEjM7Ai8HVDbBkAvI6ssEwn7W9SnrJ94JDvDOdqaAigLE76s+Nr9/Va7Ql72VLJOQTl/CVmNu7CPijBjKBN7LLes4MksxoSZtLtbm1ioP6hvzTb1dAifaAqNzYs0IomzHNwteLbps5T20UoRKs1l0x1L3g4E1IUkNnvUwZWaJsDDdqLkOunNjxLEq7jV3jOfSs7amKFA1kxAAl5Q6Jk+MOGt6xwRojcEmzvP945580areQt29hh47b3a8dUrCnbvaOSyXDfR2z0kc7fkKatK1ErRZZhhLvpUSt6R2LIra/FkWQWY67A47R6GTgrmrV0EjKQjK9hZKQRBWCW5hjRxMYxYRQz7dYuOa/eRTVt4X1OZUiZCHrvbP2FYQze0RZZGlh8Be3lp9tm5AAiBaUzQVnbUBdenfqHMZKAuwdswA7dLzes2XKIXWoVs80UigCByDlk433WFZZrmz2oc0q64bLOuzbawlvhaNoMhbViapC+MXggojlkUWy6i1GhojbrFgZ7w3mz9IB4yS5iKhg0oajcHUD5PG2R1bV+ZG1nGMaGzS8kT7n4nJUaYgK8ah1VzH3qNFM3RjdjVoWPCWtA9idMNKfGvbrG9DOQVA2AxRYlwdomPLBSTQaGOfgUbZ/ZWAFKUIinjMWl/lhkiF5a08Zsmv3L0l/j7edY0ghBLQIPQMGpSAUhGNsnJDqImL49dAjLbh3T8MT3wZcsU38eq3/gO/+g+vZ2Ouyx89/0p+6amvGdNIT2ISDxbNK6/kine/i+ZDH8rx7/1eeh/5KHvbe3nz9/wZ3/eY/5sb+l/kbW97PM+Yvpu/efETuOn4Jj/w+5+eOD1OYhJfphBtbZrbo5ULvyEvSLIcraxbW5XQgpe4SJUIiphaw1/3pa6hCJoMm7MA9A7M0yNBlKJzpupBttqLMaZMHm3dVTzbpKUjBkWfjKLGOAnULKaXi22b1OjqNfsuVWWjWjkTcWtcke1KWhG8IcqWszPPW9Eua/Eqs9UoDsw2aQ/PAY5BQ8hMQZxXzYft9pXzliwL2hS3pSfIa1+ZOk0pgsDVP1lmpxCFDiO34m3fd2C2yd4pl6CZmnGJUrQaB9AmtSv0Rjy4UigipdnGsULu9bZqoIC0EKaaAfOdhk/Y5u9bRQFTTTuWwLnZlTU5JgqI981QB0eZDomCBsbUIby7XhROdVjarNcYNFyvrbF0bvxcAkSE5722OUhoxatuLzVg5oC9cYm9WtlAYevEBGioimGp2MEysQcpMhpENNdrAKDE2FJBxw7N8k8+lEA4EqRlx6uMPcKp5ZqphwIhcIylTb7T5iyNVFBpgdKKvB2RHpipjc1/1P9bFIU7D/Y1EVhKqwbbojVbV+5FUCzoafYGszWmGUyoyWZCkMA3BhdA0szfOqnk7jOamaWqhYK9pzQicDjahwl1ZXdfssTYcy4AxhCg0CajkW6jROiZkQXQ7r5HW6mgMmLliFRMUznrIdoD87oJh9Kqds9ZKasoTWDs/VceX2x21R3W6u/Aym5TM6IR71QABzsFuauDG188sJ8votA99uoUp4By13LgFh3cn/umuqdaqkEjnEFFmrrU1Lcq0JZBy8MORdjyUxzlPXcM44vXpfy7NEZCCbPtiAMzmqabe1HWoKcdBWPgXCnYP9Nk33TkFr+qhuFN92yq7uEJQLu4YQz8+U/A4UfD//ki/vItf8n3/o9fovutV/Pa78x53be9gVbYevDtTGISu0I3mxz8xV/g4Ct+haVf+AXOvuxlmO0dbvj6F/JHT3sPH9p7mB/72E+R/tNP8d7nPIJvuHyBp/3uJ/joxEBkEpP4X4790cEv+XeV54TRDL1HXmapFSdTNChrboBlMEoQk2MbAruszL8XIK8Xfri/hbsAVWy0k+3YlCxenMEoWM3WWC+6NUbAUNRqtfoSWwZt9yKhwgO0ikETjG4S6yrZN9gkxBQW8OQC6VST/sFZn0zVazzAJjEzgUuUdMzpfANjhMz1QKo+g0vGxbcTEAVDSX0S2wytVFJCCzCkliiHQQgKGrpJU0VorQi0hbDKCPPRlD8+HZZsjk3qSuleLjmB4jyGUVpN4sUpMhEiDWGgMAQWYAxTjDKYzuJ4E2yX+BXtxjhTpaDXOYy0DyKmGJMC+gnR1nlzMZjxIMH2krLnO1SB7bkEjmWzWx8tnM9SlZs0tfMjPhuFRhigsMYlAydJU0qTS0FThUyFQXlq6ri7ysSNoalbTOmme905HTrgV+gGxXVX0NQRnWiBsg5SAdPLXRqjHJlp2eGUfbFq86WUQgXNqgquMIjWliE6u0PZ4HpPYxoJFHWB6ljLByPknsWzc1Y3lUmnGqTTDWu2oxRzegoNrF93oNoGOaICZlTbzamw2R2OTYxGQSMaBwJK+Tq+uWAaCXR17SpFRzXLqfP1h0pBlPWIkk3/HBD7RHHjV56FHzgAU4Ghao7L3+vXmUIRZTsIFohv9Gzj8kAUdamhB0buGFpGe+Z2vtNgz1REIjkzvWME+XAMb+01M7vGZM+lwbZnMJiqXkuV9XvuLAd2P+Wzsy8jGipEGeFIYy9Fax+mNYdemKOIpjHRNCZ3CzHOJMQEgT8vCsVljW0PaOuQUbuaS9HVczLSinbeo5Vuueu1Ou6xJgHa0NCKQMQ/N3PJCalAfDkp+kHMACcA7SsdH381bJ9Cnv57fPH1v8+R330lp1/wDF51/V284clvZG9778Ue4ST+N4/ZpzyFqz7w11AYHnja0+n+7d9xzZ5r+ZNnfpAnPPI5/NDOTbzzHU/gZVef4Df/0yN58bu+wB9+8vjY6tkkJjGJf30oFK2gWlhT/ou6FkVBJEN0FGJChUoreVY875LC3DBzzvYGyslpqpBotc+023Z5h/raIQeYrmheQbibJVGaQIEubMKjlKZQjK3u2jBIo/ps6TIm6nzWRWkr49mJc1QtrUql8MllmaW01jZJCwcLFNZuukxIpSCquQrOBC20tls0UWATM4RhWlSz6MkEm/54s49dtXJKuQRW2wR+1N7PqH0IQVX26mJoqcjOaAlUjTBF25+Thak2SgxhPvJAKnBAp6yUAqwZittvpDVDMqaDBlppkmiGRhyg0eQYTGuOIqgAUmfDui0WyrBj6smrIpeMKGgiYugs76DTgoVwwcOFrDCehYpn26RTTQKlySncWryiPVpyIy2hiyaZc9fSmGnJ+LO/BHZ+bktJlxjPwJQMakNFaBEn2qtSyPbGgJnlLqCcHb/2snrBATRcMqsCUJqthUdidMOPp9x9kOIZNBNpx0BXcMICtqaTyTpgFYZ0GiF5UVWRlcYX5cU032mMs7oiDOYeQioh841Z79RZj0a6DSoYO9Y8steNfW9hWVv3EMhyg3KW6ztHrAw2VJr08kNj27UStxIs2G2boEGA5rHNh/j6PEFI5ttMNUMWpxse8Dd7sb03xLGZtdtCCc6VtAYdylYJtSHU29WX/xesLFKhMKG2tVvuPXv0DEXt2RHpEJ1XksVAK+f06JjXGluqUMybwO/DD8st9hitcdWy1fVnard7UM5VnQ21YNQu0ShU1CAI7N2QR9Ncev8yHQeUItEOMFUArdUIx9hKHyIkzb0QROWIHGA0KAWt0TK4e6P83BWzVpqtnBGQMoU3E0rJiVRgF+Bqe7uQeUw9JgDtKxlH/w4+8ybM972dB37x1+jeeCOf+9kX8JrZD/E73/I7XLNwzcUe4ST+jUS4Zw+XvPa3OPTKV7Dy67/O6Z94PubkGV7wH17G25/2bv5u8RDP+tTPsHDbC/iLH7mCt37yOD//F7dP6tImMYn/yVCO0UGELLJfyqFYuZPtkeOiEAgCAqUxgXZW3hYMSDutGj9jv667zUWmtU2m5wPL7HRdEX/mGbRK2qYVmECz+vBDbruOQTOCaNtPSuor5p5BE1RUJVmitF21vkA2oMKQNC/YLA1FXO1QJoamq+U4HCzSOzhPY7tPL7FmGaIVe2eaPhkOTDq23VYYUtaAFG4slgmrJVDleN2eS5nQ7tI704zoXrrgZtFKHE3YBKV9uxpxEj4vLxXGEnHR0G46y3XJfa+iPa4WKFBcgGG0k7tedJkNI9vXSsAELSIVWGlbHQu45EwpiMkpwo492rpTXdgiF0NnpcfUWp+ObtcwnHK1foIEAXkrtBI/sQl+PdkLaydTAk3/4Kyrj6tDnCqCYoTUAP9UME1bNfyc2vOg/baVA0Fa1fYqEA1S2wdNBBO0iLPKLAJput1beRiBPd8liOjolmUHgfbmAGmGbD/6oRTTHVf7pBgdnvNzoYM2WlvGaNQ+SNLZT6DH3T3L/zUDmGtHRIHe9Rcw4TTDcJ7FcAaV9gnzWmsAsMC/VnOnVNUXyyMIpW2T8XLGshyjFbFjLwM0xXQJ6qrt+s3sdUAunOJgsIDWAcpdb0YURRQw1QyIwsiPfe7MugNcUgNariG4VADAM4JSsCeY4ZJmCSQMLdeH7Eh7gdDdK6O0bK4NJgwIjfKulwr8cbqD8PVu5d8LERo6tM8ZqS0GKGikma/hAlAmJ545SG/6cutgKkJY1oK5a2XroVfY30uAVrt0IwIetRQ5xliQIHSdAe3jrCPCXLuBaEVHIqaDGf9Z5c6VKGXZdRm/v5POAYLCOvAaZa8zu13F4OAMETm5k3pPqRbN1jSNwPYRVLpJmA88m5lKQaB0tajlxzABaBcnNo7Bn/8E+Te/hhM//QruveVu/uanXsQ7grfzyse/km88+I0Xe4ST+DcYM096Eld/6IO0rruWE//pmay+9rd5aPMy/uSZH+IHH/sz/HR2gv/x4Wfwzm+8ifuWtvmxP7yJ7WH64BuexCQmAeCTPCWGULcsU1MYTKCRZgnQrB12KUsTrcAUjBY6NinQ1hmwxAgiQh62mXIAbXe1UE5BOt1E7em4BMWtMivFoeYia9cfRJTlTbQRCDUhAYO9UzWHNdyKrqCaAfsDm6iJCnZJHIWkuUAWzaECTZpb+dtiMEsSzhC39gKKyAGOBiFmdh49StgaZbY2QymiKKiaF+9KRMJAodsNK1mKAlqRphG4tfGSxKkBl4SMjcImzqEDrSU7t/Pwg4z2TIFPK/HJ5vq+xyGEaJfCiohjFB0r5IelCFAkM3b+SyOBuaDNgfYUkQ4wurJqt/PmUjylrcOgUs5EImB2KhhjJ3ZH3NhDf/oISCl7tO+dCdoMJCaTHJ0VBFr71He4dwqdG2ZLUK9VxfA5IN4/OEv7skd4h0V7LNo3W15/hHXavJB4wgRVDV0nnPY1eMZZ1HsIoAPCXop2sHB3lIArj2Z8Mt9RCmUgDANb76g0KMXV+2ccMysEKML2AYbtw4huYA5cTSOI6BU95mcj9s+2MJEud4LWLdcWICCPpkm1Bcla4VmmqtmyoelkmwCL4Z4qWdaa7iGrZFLZkFArK88dm5waK6cYA4FS7s9tLs4NFClaay5ZcKAMjSrZkxoTrMSyVNKy72s35mg7WahGOTAYOJZZMNqyQ2X/RQvAKvnhXDsaawGh1Lhks60bRNhazTDrIcoCjGYQELpzFWfGy3tFa99vMZHMbs+9T0/tcWsUJdSpMaBKe2Yr6xwoTxlacsCagaxf+jDi9gH7jFSBBWiIY2rL60jIO1N268H5cCVSIVM9+zwwKHSgESXsjHLitFYn64C8VkF1baCQvUfozV6NQvkFoOozmtLdNpltIY2GqwFVJPMdRIXElDV1QHsP5YGq1iw6HYBrrJ5LYfv4lYs0Zf8/+dIQbALQvhIx2oZ3/SCjg8/k+Mv/O58PF3nrM3+Mzzb/gBc/5sU85fKnXOwRTuLfcOipKfa/9KVc8ad/SnzvPRx7yrexc+M7eea1P8RfP+sfaV79rTz79B/zH5sv5RuiO/ieN36KY2v9B9/wJCYxiVoYLmkdYWb/YYKyx04tccMBtLKGROWGohmy+ZiHoJQm0MrXAGWFoaFbhAQ8cupSGmUy57ZXkNN8zCUszLf95pWxyeH+YM4lHRqtFVoUKtBW/hbZvkKFbiAETuJjaCxeTTBzHVACNEM9V8iiOYy2Re4jJ9dq64g8bFuGShTa1cBlj7oGwsD2aHLbQ2HHMO55AEDS3INWiqBja4x0I2C21aCmPrLhEtk5PXVBQBE4a/PCiFutF0qllwde2oKO0DmylUlYKZNq6CYt1eDQfJtAlRKoKvY3ZnjU/GHUzEHSqGJvyp/TrYiFqSZaKQ609xKoEK00V42VLlQir/KnSO4TxfkoQFAsNA7QUVXNiy6MdWMUBaGmaIYEaU603rWgINBEuqwF0xThDIM9C3SmZ2+U9uQAACAASURBVFBi6F4yR+k2VzRmLXj8EilfHnT8GJWqoFd2YMHuoQTwc9N2nr1Er5LGAYgOSDsN+kcuZ3vf1wEQKigaIY3L5sujg0Cjg4CyATYoCNuYwJqvmNlpHnZgjtSkaKWYCZtkgbaywShA6RYqCl1KD7ERLpu5gnptn5f+7b6Aaveq6IDuJXs5vTHw/dYG+6fH3m469fOp/L3SaVigaSWXbr6UgiwlDBUNbZPytmpAsGvZxc2f1MZigsgfD1Nt24hcBZjQ7iN99FVupm2T5hIcBe7c7HWulVlomSLjelGvPOoS9h65gmndpGSppyPNHvd+xTjzuDVMrXzPMUAtx3KbVpNkoUM7Ctg7P+/75hlguhkSX2/rciOlKYIpmqH2iyaHo3m7J0v6snnZpRhtTXxmokWioEHhmG4pTYykqtNS7jVdO5WLetqNH0LddCsVziiF6rxXzq+azavsGNOHHEaFIUaH/hJZu/4gmw/ZZzceBJV8WinMVBODEGirELCMec0dtLNA7hxoAxWiV7d90+qMAuVkstPNkMMdISjiB+HPJgDtyx9FBu+7gZ2l/Zz6vX/ik4/9Lv7wcU9me+9/51nXfj/Pvu7ZF3uEk/h3Es2rruTIH/wBl/zO79D94Ic49tSnoj/0cV7xpDfwjqe9m7vn9vN+eR3fM/ervOhNf8qnj208+EYnMYlJAFROXs2IoLB1Dk1dM3wyjuUS5VaHbfItUOanXBXZvl1xZvwquFYKVXZ69WYh0GmEKAX9A9PkC3vtn8okWdXYMSPoUNvGrW4oeTTlmbwrG5dy9b7riK+3yfP+2SmU5JTGCnX1m19TdgRB3SBbtztkj7nWJvO11W1xq+EEiiv1PjdXTqoZzZCF07YAf991zOgWqrT/r8npoGLQOmqcuSpj+8gC3esOM4gWayYkVm6mcPOhAxbDORZCu7ptZYe+qoSpZoODwTytSBN1Zkmbe0gb88y3L8edJjsWXdWqKKxV95V7bQLaiqzU8JonPIlAWbDVCRr+9O1uRps09zFsLrh5AW2MAzwBAdYtsDx+rapzWNQMC0DR1A0WmyVDA82FGdKpaXTLJpy9PZd4Kdlozz7yRmAlgZyPV/KwMwaOldYoUaSdJsV8lQSLgMyVv1+YQSPQbD5kH/3D+8lb1YJCOtMhvOIAoz0djNbsnW46GV9F95QsTj43D9qytDkZ+xrTRFP7yI0QL3Rs3ZVuopqRcx+19998c95XbkHZqPn8/m5KB6SzrXIqEaUJi5hksMPG1XvHFloirZCgmvu6LG26YRmZw9GlzDLNkXAfi415rlls0G5aRu+QXiBQToLqDCnK41XA7FyLTiPk+uhSZptVH7/0msus82HQ9jLgoiG+r2FQxAwkZiQpV+yZ4vpDsyil0EZ8nWQvzm0dKnDZ1Q+3CyNaOb6yklyH2j5HHhId8sxi74pFe98boVGya1ceJFmc8vNmDsxVIFErJFAkVy8SNCKuuPQKuwilIuL2fubDNkaFBDLOVtkFFU2jc6mVENYWQARoNx2IDNzf6hdv7b2dcI4nH3i8dTtVisA5RgZKIaG7KnTVdNq0migdWAbPhYkCsk5E79AsuNo4cfb+SmlEhIVOwzGZpXyzJsVW8OjGlexpLdjnkGP5c3JKS5jy+NJononN/lczRJD3v5TlD55h5WPbfPy5P8fbD16NOfhmnn7103jBo15wsUc4iX+HMfUfHsvl734XB3/+59l4y1t44Lu/m0OfO8Wbvu/9/Na3vp6bphLyS1/Nu/7iR/mzT99+sYc7iUl8TYcSa9ZgrdM1SivCXLguupQ9QU0aVTJowKE9HcRomww6WZzWGo1mcGCGeL7telfZiB5ymd2XS+aM0yxpIN47zfYjvonenq9DlJVCaYVlyAQoBBXqyhZ7vs3idJvI1f4EYogabWTGJtphFKEkJ57v+ESwfgh2IIrQpJRZ/M4l8+SHF7kmOmyTwhKgKbvib3s8adL2YfpTl9fmTuycKYUOQvYFcz7JKsHDodZV7r1uLlTI4Slnl14fWyPkiulLefThh9EMlZdwlj2n7HA0C8EMkW6g0CRuxTvUmrl2ZM8DCqI2rdYUScMCtLxz0O1P++34Y1CK4dQlNGZKSZPdp+606V16GE1Va5JPt30dHAiz7SNsXvMYti85yExjkauDy3hE+1J7ZNpKTUsZ1BEWbHLpdJ+dZkg7KlkYRSds+2TPNOZRi1fZETcapN/wKN824ZLGfsxUm43rDoyBCz+PukHi+sf5uj8HHoooPO8TgQ69tC265mrau6SfJijbHBiShQX/ejI7xTcfehzKCFEQcMXeaVCBr1MSNJFjSUIV8Z1HnoKOt9HJBlEQohrTzB++kqmGBWQKKK45SNFwbofaXldK4RPjAJvQ7wZoWXMO45J240Ccwi6kzNbkjVpVTpcAsjBL99r9XL3PgdROgyP7plnoTFMINFSIDgJMmlpgvufK2pzb+rBGCSq0YvFbHg6POIQRmNUdiNp+oSTAulJmjXl2DjwC9s1h8uS885dKznzUptOw7G3DaJraAWMVkof2eJK911fHoQOuau7lytZe9s62aDdCL0ct2bi000CwdWH4Idtn1D63zWJxmvSKxdr8KvLFKYIgpOF6zxkVuHpL7Rgz2zKjaDXY9x8f4T83mL+K/vQRFIp4ru2xV6dV2uIrAhXahSalaNRkvMpdETooa/d0tcDzf1znZZnVUxTHGGovp6wvFAz3z/hrou/YVKWsTDiYP8KhPW2i2Q5ZKyKbaVmw7WS7DR2igsh+2jn35mLGniEAWWN6rJ7uQjEBaF/GyD70ak6+7sPEchUffslreMsAmpf9AU+98tt48WNezIN1DZ/EJL5SoZRi5slP5qr3/xWLz/tx1n7n9Tzw9Gdw7a0D3vmsj/KLj/sFzi2u8ua7ns0r3vHjpOmkX9okJnHBKDJayTra2H5k/Zmr7BfpLoOCPJwmb86hRUGoMNrWZlknMAuqymbSpUSxNA0ZlHVW3kTEfZOH1uVN6YA9s9McmGsDiv3RvG3CWhgrK4wsSFBYJ7kwCmk47V9QJOj2LEoKplWbVtBCidA/tIfNh+xFIexcsp+V6y/31vZQ2lvb4xvsn0emK3dCVeurprRm38Megbr0McStAwRj7Jpl+wIUugQbqvohChrOwXIqrD7XqL13/dqKZQgJePSBK9k73QBvDaBKFZdPiq49MM2BPR0HjhVqapFmZ9YCOpdYGREOzbj6H1UmcYz9XhuuTaRdcq9RoCPmFmdBhQTlKn0YeoCmUUSzj2B0yWXk7SZahbR6MbrT+v/Ye+8wSa7y3v9zzqnYOU3OeWZzVFqtVqucsGTSTzZg+4eNIw5gg33B9zrei6/z9XUO2MYm2tiAMdgIYzAGYYIAZSEhrdJKG6QNsxN6urvq/nGqqqt7wkZpF1Sf55lnZrornDp1uvq8533f70vaKJAyc2jfRiDG0QApVeTF7Cq4ZB0DEAyZffRY5ciD1bAy+GZKX08gxx7WjyqrXNQPUeHx4Jg1M8N8qjcSMGkKmQSCM6Jp7NbK2ZZj2EMdZDo3MGA0i4MD+IGBVqfBYqUUSdJ7UoFbRAYGWtjBocCDEDIm4gEoE8dQlNIWRhAe5tgGPamsTv3zBMo2qaWDotNSBu0GIvGruKdDU7dyzI6sZ7GSoW5kaBi2FsoJOsasjNIIjU4RZsjp+7yU7WT7+lchBTi7RqnNdGFIiSEl9TC3yTDwlhb12DBjnxGhQAiKwRgzlSKddkBJwnSp+tRGTqzbFo0XKaBkdWOaKRjtwisOR1cVyvAD5ANVSVMqhlSFTmeQuY4Mx7vG6UpPstkajiTjdWaVIC1NMspGGvp5wsSgvk+22bzPQkT5d6GHqDl6tAHpFdOxvDr9y5CKLd27qGe6MIT2ckrAlwb1WLF02VmMxpcnJUvekja8LYO6q6/PMgzyrknOcuhUeYTv02t0MqCaYafCtWnky0jTpJZ38WIeTylFJCwUF/qRUgaPC/1aT9s4DkN450ppff3BYps0bRzLwJ8Z4rmpTub6C3jCoCfVTa/VozvBcEBIVL1Zn1AKI4oW0L0oo3OvRmKgnSPm3vcbPPa2v8TefRN//qq38d59+ymOvZOrh67kzdvfnBhnCRcEQikK33kbox/7Zyo/+qPao3bTzWy8z+AD3/053jj2fdy1+AVu/JuLeN9nf5WlRiIgkpDQgpSR0pvwGyxm+/UEXsqWCUBDucwWptEWifYsAfiB8psSQhtoUQK7gLTLYq3B4RMLHBssciIotFulCn07EE4eQyh2jpToyaUoWxkMJJN2L7ZMYSqBqNbANnUImEBPsqSJaGhjUHpLWKkswm+Qk66enAHIZo2ghUKWumvTiK3wtoezxb0xRUt7SnwhcK6/iC2XvZq9ozcwXMnQX3ADhUaoBttJKZCWCYZBLWPHjtc8ZtZSKCGYzY4hs9ooM1DkjHSzTUKSsjJ0WflIGKMlnya4JkdJrIEKFIagMMjiRTsg2x2TnteGcyrwIKrgWOE1D2UHMERs4gqYxTxLfXmWhip6MufmGenIMlrJRklwprE8r023WwsW4PnIQpqy3Ytp2gi/QUpYdBoFbE9798Imeq7JUl9eX6E7iKHMpsHlEwZQBXNoLwrdEohocrnOHKRD5Vs8Hp3OYLNdQYiulBLpe/RkdT/Utq6j2h16w3xmt/YzsOtyTmRGAFhwYzUBrUA23dd9FaluSgHSoOiYFFJhuK82fbQ3M5A9D9qF0q8VXBNbau8QVjoyYBdTvcjKdGBE6n7WH0MZub1ynTPg5FvCdgG8dIaGZdBQNvg+DeUwl9GeXmE4hONQBtdRy6XJCpf5VC+ysh7wUYGxDNpb3AgNNCF1geR8DxhNT5AeY1qaXowO6nbb2jsTlpBQ5U6OjF4edJc2kmt1g1pDUfcbzFvN8htGYOQOqAqONLmi/wouLQT9IRRzvXmOjwzhTW3CFRae0CIpSgq6SnnSTqCkarn4hgOBYRZ+HsfNHvqdkRaxHiGULmIdeqhlaGC3ogZ2UjSyNNw8CzPrqGVTePl+Fp0ulsyUDqkNqHfpcVUTDXzCKAI/6jfDMElZBjPpXlxhMW32YprFljm1MBRzm3boBY2UxYHpkWZbpDaM6yrFgtNc3MmoFJd0aGPYMiTV4jayIlY6BaHDJYMFj5Ry8fFAGPgERbCB9akBKmYHSho4SudnYjh62cbT5T1EMC6QqvmIE5JGqhkCvBKJgXaW+L7Pc//7Z3nqf/4F5Z/4QX5p+rv54rOPYA3+EdcNX81bd741Mc4SLjiEUuRvuZmRD3+Yzre+hSPvex/fvOFGrniyh7+87TNcsrCTDz7wbq7/24v4qzvfwVxt7nw3OSHhwiCUv5YmS3YJ3zCopXvxY3lKEcpA+pLH68+TyXr4Ajy/oSPahBYJMZXOC9F1hwz2H1tACoHRnY0VlA5C4AT0qVKwEisYczqCsC6BjxYemco5+I5Jwyw0myMVzrF5COT6nVSayd4ykUgB4CtrWfsbPlRUjiGjEkoTRO2Jf60Np4ewgsl9LmPTm+nFtlKUMzYqZqBkHe2Z0B40RX3jJHU3NHza+i5I4aubOSZf/iN0OEOMml10maVoEwOJlCbbs0Pg+/QWU1F4XqvB5yOLGZ1XIg0apRwXv/bNLTl2OgwynrvUbJStbNJGnoZlIMa7ObZ7C9ZAN5iKRs7Rhp6TQ0qp86ikhEwHA4UuLusbJROqewar8koKTKUnwilbS6frvC8tDJIzA2+VFgGl4RggJLW+PEJoERc/m8Ia7gnvBkLqe6lo5inpa5GR3L0plJbg9/3AKBDImGboQHYbg6pC2tAhXmrhEAJBVz5FLp3C88CveaB04WYvKpYeM9az2iAfSE0AcCKrQ1ZrEpAGKUNFghqtSNKWgWsGk1hpUgvC5Dqy/Ugnh58q0CiO6rEoTVS+H5CU7B6GspsRwJBVIoXJUCnFxp4ZNheniVtofuDtFEox213As/T4qxvZwFOiovserreImfUYUlAzszoU1wgn8jp/sFk2QBuIS94SODmQEq+7jN9R1B5NIeg1c4yYXRzovw7ZvYnwRG4gFtPsDX1PtePb4JnaUUzlRAsRdrAgZAUCOBkrgyubzwuFxHdsGuXAMySbxq8yzUjZNMrJlBJvqDcS6DANC9sqIKs1TCVRKlgwABrT/dGx9BgNlCTDcOVslsUHHmCm/xIyvevxlMLP9lE3M9iZaczMdHSdi5ODeI5LQ+pQyryrwyAX+oIFKl/QKLhI1wbDoOErFlK9rUNHCHxpINL62dBQzXIEjmHjC6HDLL3m8w4hKDvNEFxfSKTR9EqKwPcVLjQI38fzwb94azCOPGZzU8zseDn9o5sxKmWqbieHOy7RfS0kJ7pyunSKAFPYWngESCmLTEGHfa5FYqCdBY3ZWZ5+w+s4+sEPUfxfP8MPVi/h2erDLFR+j9eu+27evCPxnCVc2AgpyV17LSMf/CA9v/gLHP/4xzn8spv58domtsjf4sqDk/zn3X/Nde/Zxe9/7pd5fvH5893khITzjH6m140sjmkEhpNEV3xu+0pVujipL3XOlZ7NNRgopZFCG0BCKh0th6QWhBQK4ZO1m2E6S+nAswHaAxLNHJun8qXUOQ21BtiK44V1mB27mc2OafGQhpaX94SBaRr0j0zTPbkjyjNTpkNHxibvmox0ZJBCt2fAzdOryvhWHk+F+UatAhHh6/HaYmFo30x5Bl8KMsKNGSogU83Jkb5mQSpWpyhOOp0nJV09gY6FU3ZnUyANnGBObypJyerBUnbo3Agmjbp47ML2adK7dlF2yzhWivHOLN05hxs6tpG1sgjfp5pJkTKbhqS+Fp3/Us05SMtksisbeRD9xmJ0rwXaMyaUZEvPxUyWJqm42cDg81nq0RNbJQWWStGbdzHNIGdPKY4UN2o5/8CoVUJxbOdlnBivMOo2V//D/EazV4d51f1G4A2UOsTR98in7Og+tHpC9X157kSVuUqWRrbZ57ViJ/7w67CkFRkIABnXZCJbZtjqwDfNwCmsmqGR4WTYlJi2pRcYglDVemCIesoAO9sUz9E7xiLmdMicIYN8nlDVrzCEGNyJtNI6jFgZNDwfQygspfPRlDB0iKgQVOwMwiNYGNAhrPmYxwYhtOgLMNtbappuMr5QENz34P+aMFnMDuEZri4/UJ6AwiCO0BPxJasYM+r0qJGz84BgYssYXr/O//OkQnUEeVdSIYN2bB/MM9WdbSkWLxEYwTEVijlviXKqQi0IEUxbBqW0HS1I6HM3F1DCsNCqXeZIcRN1u4TvppHZFPXOUnR9SCu6Xr+Uo6aKUT/VrDyeZyKFoJSyojxRDEl/ppfpTG/TyIithxhdXfgND6O7F4XJ0ZFezEt34StFweqkZHfTFyjSSkNxbOt2Gl5dR4UqvYBQzbvMdedYqPksTvcglBZkWslsicKIM52cKM7owuEBQ26ZclkbdHKp2mxkeLN9L7rbmYnddOTT0eVM2N0YGCihQ7x930MWCzqv0ffwzCxOsYdcJo09NsbizsuoOsFnUkI175ATLhNmL6a0ou+HXqtAJesw1V7OoY3EQDtDFu6+m8e+42Z48r9Y+vk3cePXhujuepIDqd/jTdvfxOs3vP58NzEh4ZQRQpDZs4fh97ybgT/5Y+r79vGKP/h5Ln+whxMP/gS/bG7jkXv+lhvev5df++zbeebEM+e7yQkJ5wlBOWNH8syuVSRn5pvhhEBHxqGv4GpRDoL6aKZL3TXw/AaOaercCHy2uCN0qQJZs9RUFJMC19KTtacv2kTdCXKjgre9toU/IaCvnKXgmDokSUqEtMjlhqiZWRAg8EjPPYkvFKahEFLixFT2DDtF2jGwDUXKMgCfRalieUFSTzIgCFvTbTjYuYv57BBa+nt51rutdBK9bO6Ct+M6zJGp1rkS0GEPtOzrGZJauQOE0KqJpozyqVxDMlLOgAw8Mr5Pwxc4Kk3BLkc5ND76PYTAy6bo6h3n4p6LAUjbZiSuoNsgODgzwkhHhqu6xiPBBKRCCoOalaLmVFraiJ3DdovBxF/Xu9ITxsCjEFzjsaES1RHtVXIthSldbdQH20olWXS7qdpNFUEpBSPF7W01oMJKY00aaBVOXwg29+UQeJQzTW+AHysOHA+bPDbYGcm/P7ljHbVB7e1iz54WT6RjKCxDYWzbADk38OrIpkERH46mEXjIwol7qKAnoDDA0u5rWdw8A66W3K+6XfipjuAgioyjc46QUsvvh32JCMLCOjiUmybr2BhKxcacDhN2TRXlEob9mFIWnWbTEJXKxBC6BAXAYCkVuCv9yFCL+s6HJ44sUE9VIs83UoE0ooC8gXIz7FYYCj9WsD5ayAhyFs2YJytcwLekwJDN/nRNpfvXkAx1FVBCMudVsQNhmKW0rcUomvZYeMTovIpQOVIynx7g6eNLzN/2kziv/gl8Q4fdOTv3srR5K2KgI8ojWzKaHmqEIJuy8a0wNzGo9Yegku6mYKYxgvDtZlsEKhT2sGxsmWVL515UPsfc5mb93568fvYoKbh6upMGumyAfo76urwEYIWLP0KAbekww/bnTGA4SiFo2Fp4qD9vkVIWljQYsPTihlpaou7ERG2EpKEcvODza0iJbYQlBVK4hsmkNUhP1zVIZUY15/S6QuA1jC0Y+U4qGnNZI8OU2Rs9+3whqY/0MD9eiQyvtJGEOJ5TfM/jub94J0983/dSGnqS+1/7Bl591wg37XqUuxZ/l1/d9Su8fOLl57uZCQlnjLtlC/3/53cZ/ciHWT/SyX//9z/nmT8/zhW1n+U9uZ2cuOcD3PrBG3n7v7+ZR48+er6bm5CwKkKIG4QQDwkhHhFC/NwK718hhLhLCFEXQrzyVI7pp2ytrhckottWlqKdBynwTP2aaQidd2Xooqz17i4a5TK1lEXVziOF9hY0lItSim6Vx5R207MArA/CeOKTrmbA3vLIDNuytD3geQilDZm8Y7fsJ/CxLCsS7hBC4ikba2Abuyd2gxlsL7Sj41i5SOGyDVGz4op0dnCMmlVoTtTbJk5GKViNV8FkNHx7rB9hWfjIWLFobfjJWB9UCy6L0xtBCCzqiP5ubNEqLoJU4DcYqbjkg9ympq0cTOx9L8q1M2Kr6yrtkJoZjv7f0bmFzf0FLKUwYg0TUpJSWfK9N9GwC8TJdq4j1bM1OKcM8hFj+yotXNEIhE58PHrzLkqa2CoMCxMtnsHIQAuFGlput4hyrHzfp3d0I93WIMNDPYx35nCNwPAIwql0bpdJNvBexvs31E8Mzxm9Y6WYTw9FNacsQ2EZktlqnbqO6sOQhjb8zDAXKzjMUgPLlC3eINAeXt1hWRrdXTB+jc7lUQ6encNHIqQ2TsY6tDHV8D1QVtQfvhRBwWabjKVz8CopK1rMCBcTjKg5zbC78KVCykIFHtbhSpotg3k6sw5dOYcTmWEw7GiY1u0yVbuCh1Y39AmKKYvAaAy8lmFhdABP5akNdDZzAGOfHU8IDMPlcMfFCKmadenaiiSPVNJIBPMbe8hefUVkkDtmCvfKMRYHSxAIq8SvDalIWWEeZev0frHW0G2RApSBMBT2xAT1rjJqpKd5/2LCQDoH0MIPcjPDhYyF/suD+wKOZeKmfUzZFM1RduDxd2yqdQ/H1LXtfDfmyYwhJXQ5w/q5k3dZKKcj9cwbJzdAUNjZN5QOfcbHz6YpuhZ5x6Rh6/GipDbSfQGbOrcx6XY174s0KVCg7tgc2NwXnFjnOfrprqDPFGYQfzs3vY7sNddAfj2FyjaE59EIPPFAMA5U5L3zfV8vKIReT+GTV2l6KyV6Ci515VJVBvWCiznQSa2/Ezcx0M4d9eef58kf/mGOvvddDFz1HH+6/vv5v8cu5oY9n+PO5/6Ov7j+L7h66Orz3cyEhHOCNTBA98+/nelPf4qdN11Bz1/+Gc/89uO80fxJPmRtInvfR7j9w9/JT/3L93PPoUSeP+HCQgihgD8AbgTWAd8lhFjXttkTwPcB7znlAwcT6TDR3VdK5xEJ0bJyDlCVwWTHkCxumeD58lbmMv2ogYuQoMMPg/wGgMFi8ws7rVoVBfVFBau7XpshJEUwIZRIaSCVFk5wjJhQBIIR1clQR554KKaHz0BHkXUdY5T6LsItDEUzPsc0yGecyGCLG2jxSbkXyrP7PlcOXBm9LjNaBMEXum6XDCY/XuyStrpDMaNBYJsxoRWzWecLZSKyLrYwuaSjj45sWMdKge/hGE3vWsrIaDXIeLhaOHGSrYaDWc7FLd9WJcFoIwshBAMd/XTnA+9Ao9a6TaAiKKUOcSQ0rqTAMhRdk9uCvmpovQypdJuDia0RrNzXRkdoDPW0tBnaJtzBhN4H7EqF2qV7qWzZxm1DexG+h/Abuj1IDKWY7inSkXMYrWToSLst/rflIvQgpKJhZJECpruzmEoX2E2lMlFXpZSNLwyy26c5MtSNQFBPZ6h15sg7Fteu79HehjBnM1oUaF6TDHLy6K/o8MVI8S7YZ+AicApNw19qMRcA09AGTk/O0UZJaOQMXNysDyFE9JkJb7JjKpSyOFGYJl3q1zlvQH/R5dbpyxjMDbKtU4ei1twyc+n+wPCO9U9kNDcl6Itpm0a6wPHx63AmJpi7Yovu3471+loCD5ohTWpWDoHOGwvvQhxDSVxlYLsWpu3gmwW8XD9ltwxSopSiYmYZS3XQk3dR48G8c/BSKmPbtRcL7c0FXUNxqeE3P79S6XDI0LsZvF4zs6TXX0than10TT7gm2F+nc6bFWazhpyAKM809LmGBlomk4qeDWvh45O3OhBAo5gC6eEH1cAdw4RGLQjH1Eq3PgJvoJti2qKcsZnv3qHbJ8J7I1BWShuRgDlzE8/tvBFv8+VRFMDx3CRkOuiwB+gMymqoYCGpK2cjlYk1tYfZ3DgNz0f29lB3jaDWoY/XLp0f5KiFxzBufQAAIABJREFU5I0G/cU0Tv9mzN6NlPMZBAJTSeyhLryMC05rmHc7iYF2isx94b947NbbkGqe3l3f4C3pH8LafiPlsb/imYVHed8t72Nduf27PyHhWx9VKDD1Mz/B+s98ii/tupUv/83HOPrbD/H6Y6/hY+pSxh/+DD/8sdfxAx95FZ/f//mYMlpCwnnlIuAR3/cf9X1/CXgfcGt8A9/39/m+fzexmswnI/xSnh9ZR6O7F6T2LuD7eMFkL0zkv2HjRq7qG+aSwmS0/0J9EWXahDF/otGI5mdpq2ngRbW84l/T2R4OdF3RNukJCvwKAT1bURgYpuTGDb2R0pvnuqRtg9FCFmLhSNIw8f1mkeyLh69mtGszuaDQrYwmXrqJpbRDT85hoJRC+M0ZazQx8X1ScWnxoMjslNNLuncQQ0pGK+mortui00nB7cSMTaI7szY7K33g5FjMDoedDh1TLBS1uIBrxTxcMpycN8sA2MpmID1JI5uP2hUZaG2enRbMUAmyFZXPM1xOs328k0yYGxgo3Da9F5L+yig5M4ewmmINodCDk9ZhcHVvCdtQ7A3k58OZvx3c+0alDEGh8vA+bXeHGXV10W8fIoM+NGz9QPFPSKn7wveQUlFO20x1Z4OJqk8pbZGxjNj48bFUmoLVSbczEp3PMprhd65pRA3JuyYN9HhxlMVNm/oZ7sjQXUphCsXsyCR+SquHOpZJX0GPBS2c0lTZDCm6BmMdGfx1I4wPjyLMZv4PQCM0pkPjS0qtghp0mxRasbG/mOKy8WaOnh8NyHDhImYYCp3H5lslPXYiBEpKNlQ2cEXXBIOqg3Bpo5QaDLzTDR3iF3quPA+ExBoaZHCil8a6MRrFcssY89N68m9Lmz6zq6WEQ3e6m+uL62nHUpLN/cUgZNanxx3nmvWvJ2fpz2XaNnEMg4KZ0QamE4xz09G5egQhjmEIpSEjNUIppFbIRLRc/0JmgINdV2CnyxTznRhSsKFbH8uzDGxpYCiF5/somiGPLQSGqwxq05VL2rO1VPfW1GPwAmVbIbS6a8ZWWGZYfyzwgCN0qK/vcfzSa3h26Npo/8UgyVKH5WqxFSFEZKBtGOnDyxe5ZPNI9Ky6eGYEgN7UOOlg3EWLIL5+zoeGpw+w6RIWbrg1WuDxRetYJvSgBSgl6cppuX2sDCnLQAhtoK1Uj3AlEgPtJPi1Ggd/93d56sd/nPqeCfJdn+I3Sj/P1bfv4qOH38JgboB3Xv9OKm7l5AdLSPgWJpdN8aZ3/BhHf/2P+G8X/wD3PHiQw7/5X9z24LV8pLqb3fu+xs9/4ke4/QPX8slHPhrJ0CYknCf6gCdj/z8VvHZ2CEF9ahvVvlFqU+siz4lvGpFscmg79FS6yDsWJTPLYG6QnK3f9wPPweL4NJRyUQhTfHEj8lSYJpv7C1TcCr3ZPupmq5BGQ9naWFEGxsEjOixNaHGEsFj1wo4ZpBDaAJQqmrSa/X14aTcWAykwpYUpDZbMLNXSDMx8h/aE+Hqy7lqKjG0Sz4PyfB/HcJYVXhVWsIKtTOguM7z7Rt3m4NlwpLyV6tj1FMw0KbfMpoESlYxN0dK5HDKsZxSEToXekFG3gw2p4FYKBV4DW8TU9wTURyfwUmnqfd3Ew8EM2erlDMlceSWZ7TpHRpqtxZet4RH6L78YoxKrvdSoBvepOfHv7Bym85ZbtOJceBPLY3jFCr6rj1n3tedNKQlevTmJDg2/+CTP00ZgwUjFPGg6tG6qKxuJrkTKdFIhfU+HOApJ1rRIBQaWQuqJfL5fFwgHsjKLlIq8WaHLHY6uRefhCK17E/OWOmYQlhdM9JUyolw1A8WO8s6YAqYO7fQNRX17P+uGl8+RhBCkAmO24eaRVmvIV/gd4tcbUR97kYEmIu+zISWlgW7Suy7T24cHcHLRPYsFYYKUjGY3satv14oeRNAFhwFeNn0peUcb06KuQ+1kWG8v2NfduBGVdqJQPBkLeYxC4IQgHYmVxDyjo1dC90bdB6H1kOvV99uwEUIbN/F2TlUqwQJFM0cy3qegjY1wcccKvJedWZuslWVdZ6geGYq8NMdQ3JDya3UdBGsqptxuSJU5kp3GUM0w47ixIYPPqalM8rfcHH3+G97Ki7Y9mZ7g/EGZAQF0rqNzbDuT2Rk2W8P6w1wchp7NzQerEDTCchtOnvmYCo4MjEQhBHJwM0xc13LORrAWl3bd2D7B9aKNOr3UowVrhsppNvcXtNEtRPT8SNkyMsSlY6NyudbFaQEYzXzGnGOyqbc1PLrdc9rOyk+qBACqjz7K/re8lYWGz+FruxkzP8sXr/pbSpn7+c2vvo23XfI2bhm95Xw3MyHhRUMIwWsvGeKS0e/ip94/TWHjy3hb/QHm3/UhdlfWc/OecT6zdCe/9Zm38gd3/gpvGHsF1+/4cZS1dqx1QsILwErLlGfk3hVC/CDwgwDdQ91UN4zB8eYkzL3mavwv/hO+FCzmXSbS/fjl0WiCgltkQ2UDC7Uq+088o4v5ViaoVbpRxx8M5OhT4PtNue/Qg2EZmEowVZoib+fpdReppG1m0ZM/gcDZ9mr48B9h7XsG6ekcKCUUMpy4hYWefXSeWTBpdNavZyF3KFIrDA2cicIE9x9JUStPgmGFNgFSCBYmBkAsMFzOYGPxNFBreIzmhzm02Br2J1PBJCo4X1/XBE/y8ZbFG0tJSk4BvzQZiWEIKZnODuHnNnAs1i5TSaqAK00GnUDMIDBcLsqNU++c4Qt8FjEyht/RB4s1rTBZryKEvhctIaOdM3DwAQBURrf1ptGbYBSO/f0ROPQg9vqtmL39LCNVBlMXCid2v0LDJDIW890sXLU3mrQ7KgMc0e+XRmFyG+43P0c5q5+RS3YRZnUdvUaohBif4Ap9M7JOq5iFfk97E4XfQEiTipkBKdjdvxtj9pie6KaOkpcpDjWOIYFyxuJYmzivbcjWfkqV4Ng8liHJSpfLCoHXJ7imdcVpquobut6a3wwvFEJ7GnzLgOBa2r0H1UAFc64+jymDun+BYdrwA8OsoQ2f0cIoR+aWgIM63wip1UmDYW4UixiVMv5BB3J94ORxt2/DLc9z4NOfB6DX7SBdTFNI2+TtDL3p3mA8HmvtYyGoFjJU0g6PB48Nw/copixy9RyHIqGsuOkXGhpNgylSLo2FeyKa3lEygedv3a14Dzyn/x66DO75ezDcyIsTGjnXFtehkMxylOhxFjOqwj9V57VkbP0ZWQoKd3fm9D0opcrUlAlxo7/tvviAs24GT87g7/s6UggsJ8PighdFCNC22KGNd0G7t6zuNbNmF6bWg+cjOBDlgxpCLyKkLIVnOWA7rBvsxbSO6nZluqA4FIx9Dz/u+XSLTHakqQ8WgyuRUSekdl4MTi6q5abTYMNw2ZiQSxiO7AMdM1SdBRxPeyC3DGij6pnFML9R71dMm1zWo73a2WuuAcDzY2PIcHXdxcefjW6MY8Q89MHneC0SA20FfN/nyLvfw4Hf+R3uv+gqBiufYbxoM/fq9/Oue36XhaMLvPeW9zKUGzrfTU1IOC+Md2b4hx/ZxZ999lFe9e+S7/rpa/gB71Fm3/seNj7t85e3vJy7Bh/jjx/8G/7wgb/iBzJT3DxzO+bIlZDtOunxExLOAU8BcVnAfmD/mRzI9/0/Bf4UYPuO7f7ewb184t7mrFYZoQCExpAKa2xST1TWvzyaMHjUMZXEw0OkSsEOAteQdGUcUD3MpzKk0UppvQWXQkeWxcaJ6Fyd2VgxVVNx0XARcg5PhG1RDr6YRwqJHXigNlY2Uh/Yj+/Pg9ss8hr9bjPQ8k6eDkdiqHiIo67RVe/rAK9BLj9BpjgC9x6m4fmMVDIMxzwHAGZXJ87MNGL/Xfr/bJaJV3wf+UBif9d4hVLK4sTQZVCLGXdC4CgbS7kt7RqpZPDsNmnq4Botr45lpuOH0H2uXDj+JFhaQdGMq/R1rQ8MtBVs+TBsrC1nLeqr4d3gz4JhobKxY7ZMlsWy/TudQW4avZSlp57WIViWy7qePCrwsFWdDo6N3UrPkf+gkNGvLSzVY8cPQzpXCIASEt+vI/C5duAqqsaDIARZKwuTN4CdQ8i7Y9cCfcUU46LEp9Ey+SeqHpYhg+C+gP6L4PjTWJkScLB1Al6ZpDCb44n0II5bZrrvMmrHn9bdm3MpHLFgieg+xWkcO0Y2CNtbaCw0Xw/qn4UeCb+h/58uTfNA/amom6WQELwXkr7kEhhLw+wTQXeZgedD91fZrTAxUo62H84PM5wf5vg9/4pfb/ZzY8sUncMX05PpAp7F8+Gy4QKplMk2Zxue3cHnj7yn5X6HoZh9mT6OVY+x1FjCVJLOrMO+umC0U19rxjboyrctWiqzNXR5dC8XeUuUnBKwPypmbcZDdFeMUgk8aKmB0G5lvDPTMsKFEJDv12UPCKRi2gVHfFDZLAxsxK8LOPxIpHAYGaDjV8GBLKQOYfk+9bC0QWxsru/NteR11iv6+9/kAIY02Du4F9dwuXJylrvutVDSYKo0RdY3EMHC0obKhiC8M/BathmABVPgllJB+1WsdELrZ0RA00hu+1w23LTeQio8abaEcAPRNSmpwEpTd/PLwqXLaYujC8FzrG8rYmA7zuLDLD70DR0RQOx5u0bIZ0hioLVRO3CQJ37uv/H8I4/xT5fs4S3dH4Dpm/nwzE5+/z9+hFdPvZo3bnljJP2ZkPBSxTIkP7Z3nO/Y3MsvfOQ+XvZ0lje+6de51Xqeufe+h9G/u58/3HstD19W5PeX/pU/uusdfP+/vYXbrB6soV3Qu0WHLXTM6FX6hIRzy5eACSHECPA0cDvw3Wd7UIHANVz6iynyrsF9C7VmnktsYuKub/UyACwFeUuekqS3bMF/vpnzAEDHJI1HHwD0ZNUxJI5psNhg2ao0gOiahr5tLa+p8hj0DgKQs/LM5C9lIDfAiUwvZNJwYq7p3Wnz/oS/Q/lwMwzPUjbHt+5CdStgUYfSFQbBsDGVpNbwWsRO4thjY4jH+2G+hhDQm296oyqhFLxsmwzZFg0r05I3BFqMwDZjq/deXb8XeMkQknqhhOzqZqyS4a4njkRiHvONsE8dTov2sM2WNgkwDDJ7LottEJeFb9YSayc6TJAnKJSKMiGlEPSVUuDDFRMduF46tqNcwQ8cHMxKIeafQ3pLKKPNIHIDD0PMcyAQyJiwjWMqrp7pwvN8urJpjqk8wtYFl1l3GxZwfL+PEgeax+3ZhFl9mqrTgTINRrq3Qrcu5puxDca6CjA3AkYs7DMgtXMnKcBO+YjsBpwTDSbr86SyOnw19KD5MSOs4nawsbAnCnGksYKREt0Doom4JcJ+Xvm7xt26hfqhwy392eX2RzlHnufj17QBp6QWGok8IQFeIEDRm+mlNxMUU144GrXJtQzqwOWTnRil5TWwWkIB02XiQaGy/fNfHAYzBfXFFa+n5dpMRSkdu26loNAXfe9aslnYOSTKUpSCRkcJd2IgUm41gv2Ek6VhpcDOMlnZyKz9NPd7R1u8r+Od2gjUns9WDGlESoYZV/enJ2GsMAazTc/TYG4w7AQAtgyV8HxITQ8xf0C0GNaRNwyWK2kKQUblyUm3pZyCsfNiSnUJ6YMcmA8l9FuNu/CalFDQtZ6l4jRWbbZlm4tGSvg+zD7WfE7YExM0ZmepPfJcdA89fEYKo6QOrV1XNjHQYhz86Md45hd+kc/0bGDDywb4hcbf8cR1/51fOPhZZr/5Yf78uj9nQ2XD+W5mQsIFxUApxV987w7+4+HD/PYd3+CPji3yY6/6cb7zzT/N/N9/gL7f/gC/0dPDwetv4A8Hv86fLD3D/984zCvu/wjup34VFo5A5zptrHVvhK4NemXbWbuIY0LCWvi+XxdCvBH4V3QQ1Dt9379PCPHLwJd93/+IEGIn8I9AEXiZEOKXfN9fnrW/AtuHihw8ridHKpCnzxlZskbnqvuEC3ue72EP9MPz+/GtNDj1sNHRtgIBTj6aGMgVPCaicwxyvS2vSWmAFcpiCyzpIIDM5bvwPY+5z98ZTVzi9b9ajhsTFwA4PHAdJ6p1irlnwVts2Wb3RIVq3dP+ylVoNwDbkbZNo9qcwMmNN9B4en8UMhbtFzcApYpyfjBsLdphuiys34KZzzFQSnHXE0dYsLuAx0ittmDdtz2qyXUmLEv4FwKVceEoGD09SNuG5x5evmNU8y64BzEDTR9G4Ps+xbQFpoopGcqgftcKHjS3iOeWgGNIpaAwCL1bW5sXz4/Cx1q3jow3DvvmoiuRUjBayfPVWjf+6BXBizoQ9qor9sCB+/SxA6zAaI6M53bSzc9EvL/C/LAOgJT+I3Xf/VGez9bOrVQbVXiqWc4l9JxpSfUgxLGNlryyMPcwCE0TauU2ml1dmF1dzZO00fD9NmNbtP6euolDS8+ueGzwdY5Xm9emndUUD6+Z6SJtx9rtFhGlHP5SLfKCtXBS70wzpPHKgStJ7f8a+48sRbtW0ham0ZSPR0qsriKGF4QaZ/NkLt8VtFn3v12uUEwvcf+J463hsQHhle2e0GqNdx5Yng9adIpk80FkWmRIN69lojtL9ahDR1h3rpKHgydaDDRdQH315814ZiOd88dbQhxFscj2Uor7Dj8fXbNou4bQW6bbLKh6S8scNSII623H3boVd7gMT30x6Auf8cIYJxbXriebGGjA/PNH+dJP/zzWV7/I3dddxxt6/xU/V+IPpn+Qdz/457x23Wv5oU0/lHjNEhJWQQjBnskOrpio8OmHDvF//u1hfvMTc7xq+3V8999/D+Uvfxb53vfx9m8+zvw1F/E3Uwf5M/cQ33P9z/Kq3j3kDj8Cz94Nj/0HfOEP4cg+yA+0GmzdG/Wq4SmEBiQkAPi+/zHgY22v/Y/Y319Chz6eGeFCbeAdkF6NjLl6vuW2rm3cse+OQKJZ73x88BpG1vsYzz9P/Tk9QTjYeTlT093wzBeiicFKkw1VLi97DeCivkvI23lm5+eDfUEWCjROzAUNXtkwA10HUWazwGxkoIUrv6OFCWYbKR479li0b9YxydKSwXPapC6+mNk7Ptm8rnweISU9qRJLfrywbNDe8avhyS9qrxk0jRfTZfdEUOiYwOPmZiE/wODIVQyWx5afvDSyZtuaiozB/6sYs80XJEYxS37XTc2wvueWH1cGqo6hwSQNA+IpfCII52rDQ8EqogsATipLd87RnrJsD+S6W88rBLPZCcSRZ/Clg7RMlJMC5tq2U4DP4koemq7WNYzQQDONVcJBz5COlM7xma03DdzIiBRgSWtZvS9oFdsh8NA40qRguAi5Qu7eMsIJfvOVI5t24m6OpbUIgSstouBjK4WnmsXBY61pHrPNI3yqtBhnAxeBk0cdfpD64cPLtrUnxllUR1tb0Gb4aYl9/bdWXRUttRWHK5nmvrExGFbAUEKhCnqb0EAze3qQc89wSef2Fb3UYRsKrhmpw8ZrEiIEQ7lhMoXh4CTL71M65eCr1jHmblyHGpiJ/reUitnO7aHcRMXJWwy0Nls7bZuYS63nCXNIw+dxtVHVocOr0R7qrJpqqL7PKY2Bl7SBVmt4fOIv/5H8H/0Wz3cNcPEbt3DJoXfxbxtu59ePfo2hE4/z3lvey2h+9Hw3NSHhWwIhBHunO9k73ck9Tx3jb7/wOLf8yRdZ39vBjW/8X1xnHyf30X/kh37vTmqTQ/zLvn/m+p4/5oaJW7h94+1M7XmrPlD1BBy8H569Bw7cC5//lF61tdIwvAuGdsHoXqiMn98LTkig+eXd4UJXOUVjYeUJdJj/FJ/0SAFGqYRRKqFKz9NZlcj+fmQw2W3K7bd+oedvuXnV9oSqwrJ9ghIYG+0erTD0EsDqbwpd2kZT1AC0fH3W7eWxY48tWyUXhtGykh1nWXhW+/u2jXRsvMUqmct3IfN5hBAMAa2Z3rGZ1MieZg5OTO0xHsp13bpulhoHafi9sJJxdgpYAz1rvr/cgxYXXtAM5Ydwlcs9sXlzVCMuqNklTCOMbI0dZ7l3SAsutU24Y7lIwkzRX0zhyXD8tDVPQN3MsJAZZCGzdh79YHaQrtTJc4bDEgFhzuJanJLR1raJymXxq62GohCC7nQ3bm6Qdlo8aIYFG1+JvPNehp1KzDNzetSdVFP0J2jk1swg9VSp2U4pVlQstA0FS4L6kePQ0bvs/dMi8Fw601Oc+M/lBpozNUVt4em1j9GWm6VFUVZftAkp2inGs9tatggNNJXPwxyUOlcOQIjM1Fi4YXGlOmBh21a4TyqbIntxrJxVcRire2MzfJZwkWBlQ1iIuIG23LAPoxQytsFNM62Cv60eNG2gleXKC2QrnTv8f1O6n6yy8RIDbWWq9Qb/+On7mP/t32Tj/gdIveJyLnM/zj3uFG/buIsnj3yFt+58K9cNXbdqSEZCQsLabOzP879fuYm33zLDHfcd4OP3PsOvPXyY6e5r2P3fX8aefV/mtk9+lJuPmnzjsvv4uYHXkZuY4fbp27lm8BrMgYv0amGI19AG2+Ofg29+Cj75S1pwZOpGmLpZFyhd4aGbkPBCIdpW2pVfR9oZGiK16j4X9VyEo5orzHHjxSiV2HCFnvDN17T3K8yBOZPvonbDSFpWi2G3Zk0wiIr4Ng295nvLPEmWtaqB5g1dxnPPzq74XjuqcIrhhmZslX7wUvBqyzaxDEnd92gse+fUEIZa1u/t190sNtzcop31ZT1pvYfmxFlISWrH9uh646GHeql/hfvdtwN3/9OMZvKE1lwlbWHmY5PZfD8c/kbMUFx2VQD0psdJueOtRc/bTtmd6tIlEk5CuJhgtXvQVsqbPMk4drdsXjYG3G3bIi9pOBZVkIMmhEQ6rR6bgl3gqdkVYm6Lw2v0Swzfo5y2SHdoL+dkV5al+nJj2RASI+Y1M6SMBE7idOUcKqVO6sYwi8dYJmxyJqhCAWtokPqhQyu+bxuKalCeYHnKolhhfK0Sfhz219BlGCpLev74sns4d+U2jHIZyq88abvDfW8cuXHls4s2Ay1+LreITMfGWP+OZccvOUUifY+VQlXVcu9e2KbVSnDE3wufmQv1hZPks67cv/322sWpW855ylt+G7BYa/D+Lz7BV//qA7zmyx/E3jTBzHUpnjQ+zZsGt/CluSd4/dDrec3Ma6LExYSEhLMj55i8Yns/r9jez/HFGp9/5DB3fvM53u5Ps29zPy+Th7j+8S/yK598hIXuJ/jM+l/jD6Z+lUs33sQtY7ewqbKpqYbWu0X/XPpjOrxp33/CQx+Hv3+9frBvfCVs+v+gKykan/DCE67USxFIaPu+XlR4ZrVcFJbVzFzNuxQZZqt40Fai3tn65b+KPkWEEILeTC/7TywXt7xqujOSco9HAK2WT5bathVvrjVMLsQu9rPYruV+NrT3mbW6QXzWk+F2IzZ26muHr20N04KYAXDyig5mdyz80Gg/zgo7SO0diDuqhisZ6Ms3X0iVYN1tEKsdthLG6AbK6RQpa41p4CkWpRBCsHmgQD51cu/Uycax1b884jhuvJptYbfZa65Zdo2DucGmsEScU/WeLc0xUslAoJg607NCPnTkCoq1TQmqK69RoKREDfayeM/zbZ64M8fduHGNd5s3b1lqm5Btn18Ry9tq3XR9eT0juRFwClieDxxv8RJmrSwnlk5wMk6iKN9sSaj4KIQex/GcwfGrT7p/T6aLzeWrsVNzCHt5yGndzPDsYLM81kxPjs6s3m40P8q+4/uoNZYv9vSke7C6rcg2mK/Nr7A4s9aFrexRW4uXhIF2aLbKu//rcT5xx5d5w9c/zBuOP8ngd3bwROrz/OzAJv5zcYHb+y7ilzf8CXk7f/IDJiQknBE5x+SGDT3csEGHDR2arfKFR5/jzsd38sCOV5K+6wtc/bWv8o6PP8gTw//C3018iHdsKHDJ1pvZO7iXjZWNzRU9w9YP7PGr4cZfhyc+D3e/H955g66ZsvV1sOlVkXpZQsI5J5h0KCFoOGWy7vxpH2K172k7WJmvhgWRT/KFXrtyB9XaLPnRm6LXDHVyb8FqudXxOlsrGZHtIY6qUFjV+9WTd9gxXFrxvdNBmGGbTt2bKLNZOHDwtM9lj48hx/t1rbR4G2LnNlfKZzIdLcyxgudpy0AhqmvVcq7REczOTjjc9IYIKVvtI2kAHkyN4nbOwJEvxBrVHhNoNA20FVjqHcAYHOWaqdUFbYBTn1VLqWXs2yIYVhKLONuoJFO1GhJyhUn4ivRftGbuXgv+qRj1y8PoVrq3UR8KXZ9rrdDk3oKLY6zt1T5VfB9u3dLHvsNzlNNtYhZyuQdttZ6xlBU9I8Lrq8WEWSaLk6SMNRZIouOfpO/blBf1CU/fRBmtpKlkLJxU37L3ws9uPAx6squZR6akIm2mOdo4umxfKWSUExmypiNn2VBo88Srk9/nb1sDzfd97nriCO/94pP82137ePOBz/GbX/kEpa05Hr3yGd48nOI+r5vbR67m7TOvoeyuEUuakJDwgtCRtXnZ5l5etrkXWE+tcSUPPTvLvQ88zok7/o0tX/08r7zjAZ4pvZuPTL+L/zll07tpLzdMXMmlvRc3F1SkhOHL9c+NvwEPfhS++jdwx/+A6Zth2+tg+IokBDLhnBLO96QUXHLlLbA0x/z93zytY5wsP2uhrkPZTupBU6pVZAI9Edkz2bHy9uE2Tol9x/atuU08b+R0PHrN/QV9hbOLSslddy1i9kmtRmKc4qQcsKemsCcnT/t8zvT0ae8TsUq+21A5veLrzrpWj79jqJW9hP4JGOzFqvTDkeD1sau016wNEXrkVpkIrqYYGCJTKYzOkxhw0cYxr0eMsfwYeSvPVw58hasGrzq1Y50EK1h0OFn7l6EM8Gqo3BrCDiGje0++zQoetGLK4tjCcu9LsOFJwyt3noNFjOhsQfOGKyuMuTVz0NoOsALxcM+0mWaqNHUWLdWEIiJna8BLKSis4smeLIDUAAAgAElEQVS1zWaO2WrUvVVcoG2czJnjzbUt1sXvvbKQ7smfh992BtqTz8/zz/c8wwe+/CRzc1V+qnY/b/j0+zALS3zp5Sd417hLzRrju2Zew+9Mvur0XJQJCQkvKKaSbOjLs6FvE1yzCXgTJ44ep/FPn+TyT36SG9/9X3h/+1HuHfhXfmm0ypNjA/RMXsYtk3vYPbhTr2iZjg513PhKOPI4fO3d8KEf019KW14LW74bCgMnbUtCwsloWRVWJrgFzL4+vMWT1yYCuGS03FqfqI0tnVsA+NrBr5104jKaH+WQuTwfZbXJSkh3upvrh69fc5vwzFI0vUYvdn62sCywMvrnNAw0sWK+zVm04wW+7rRlsGO4iCdHWyd5VkaLJ7WffgXjDPQK/areGn+5P6Mz6zBYbnpCsledgpESnmuVhS8lVVBo+dz1W3ic+ql6w8L9lMSvQeqSS06+sX0K88Jwwp1pGrEb+/Irh0NCixH0YmCba3ho2j8THdOURJUDhwOxoOIwrKFGW1up9txJ6MjYaxqgwjCQjr081PccceuWpkfNXCOy4FTV2nPW6mWAnOkp/KW2um9hf3dv0nmigD22tgDht7yBVm943Lv/OJ956BD/et+zPHr4BFeOl/k14z6Kd/w1C/4JPn6l5D3TBtt69vKWmdewu293FN+fkJBwYZMp5Nj+upfD616O73ksPvQQlU//J+v/4z9w/v1u5qwP8lD3P/Cr/TX293djz2zl0omLuXJ4J4OFQcTet8Gen4VHP629ar+/Q6tAbnsdTN10WpO9hIQ4K80Rze7u1tyiNejKrV00uTfTy9FFHW5zMo9Vd7qb7vSpnbedk30fxkO3HMM5qUH3gpHtgqkbzs+5XyRcS+kJ5MhI88WhyyBVYeN8B5VUZfWdT4N2B9SlY2cRRRQaaGt4tc5Wcr+dldQS1yJ96aX4tRryHOV/kSrp8HqnGdYrpcBaNfFTcDqhuWfLWqGSKpPB6Iqpczo5JvvhgcNPaztiBfGNkFLaOulzayWEEPSu4UUXUup8whcBY43k3O1d2yNlyrVYy5Czx1dSlw7OaaWjnFlnZmaF7WLtPGkrLiB83+epIws8+OwsDzxznK88foQv73uejGOwa7zCz2xzGf/0ezj6x59lnjr/d4/BU1t6uXHmVfzD6M30Z8+83E1CQsL5R0iJOzPD6MwMoz/yBvxajcUHH2TkK1/lqTvvpP4vd5N99z/zVOkO/qW7zpNdJguDI3Ru3snOdRczc+M76L7ptxD3fAA+8xvw0TdrUZFtr1tW2ych4WS01xd6ITnXE9zTIVyND9vwUl7gfKHvw4pDKihGPpA7d57/czp21zDQQl9d2G+TxUmeXzw7wZhKxqYre3pGQlh37pxyOvnN0jijnKozpZJZ3YCQ6TTOCmG/O4ZLzdIaq7B7Yu2Q6W8F1vKgrZhXehbbRZyBB/mCMNAeOzzHvsNzLDU86g2fxVqDI/NLHFuo8dzcEs8cXeDpows8fWSBhu8z1Z1jXU+Wm9aV+bltRzjyhfdy4kNfoXjvIl/sltx3Q5GOG27jxydvY6I4cb4vLyEh4QVCmCbuxo24GzfS833fA0D9yBH6776Xni99lZGvfxnzo4/Q8dfvYtZ9N3eWfA6WFEu9neTHNtE5djXDx59i/J03YJbHYetrdWikk4gFJZycF9E+O68lX5ywQu0L2AT/DMKmzgfn01A+l5zLoRuNzRU+EOF7oWDI2Xh6Q3aNnxsv4otCGJZbfvFqdt60sWdNI2Q1zjZP9FuFU6nXdzJO20ALOY3n+AVhoH324UN88K6nMaXAVBLHlBRSFoWUSXfOYetAgaJ9BGfxGY6duIcnDjzIiXv38fw/znPoIXAagsWtHRz69ddz9ZWv45UrFb9LSEh4SWAUixT27KawZ3f0Wn1hkUfvuo/Dd92PevBrWPseIf31r5M/fgLm6tzlZjiRP0z9w7+DtH8Np5wnPzhJ58arKMxcgdnbg8xkkrqICS3kU+YLPiYiWfvz6UELi2a/kAZabTVxhQsLb4Xi0eeSk6rdnavzvBCnWeGgpjS5fvj6l67X1bBe9LDcMzHOXkoY50As7FTqA7ZwBgXSz7uB9g8P/wMLqUNct6tO7dl7qB7Zx3FvkePVJR5dWOLYs1U44tF92Gf6IMzsl+ze71Erusgdl9D/Q99L6dLdpyRZmZCQ8NLEcB0md21nctd24HUA7D+6wJf2Pc8nHtrP49+4l4UD91NeOsBQ/SCds8+R/sLXefqOr1I68Vvk5qFuCGpZG6+UR1U6sDu7SXf1ku7sw+roQJXLGJUKRqWCzGYTY+4lQM4x+Y7NvS/Kuc7neApVz8xEBZWlxtLJN/oW4LRVEE+B1ep7vWSNs4QLkqxz9qaPrU4zd90MBHhWqLG2Gi+6gVZ9+GEW7r0Pv16Deh312KcoLM5h1n3sI89jz87hLoI762Ed9ZDHG8hsGmdyCmd6Bue29aQvvhizp+fFbnpCQsK3Eb0Fl1u39AXqTjvxPJ3j+o0Dszx0YJb7n5vj6SPH8Y5/hezCl+lt7CNXO4qsPsVS9QDe0fsxn/HJz0FpTpKfF+TmfDILDepKMpdxmE9nWEhnqbpZqm6BJSfPgpNjwU4zb6dZNC2q0sRIpfjFV21D2DbSdRG2rRedZFtBUd/XBbobVf27XoXGEtQX2/5e0sICp6JGlnBBo9oLJZ8HOrJ2iwraS5mwLt0LxYq1tF4AzrV5lrvxhmShPOGC51w8x3b37yZrnUK5hjjh4lZt4ZR3efENtG8+yvF/+TjCNBGGyQ6ziDA6EI6NmsghsxlULo/R1YnZ3Y3R1YUqFJLV6ISEhBcUKQWD5RSD5RTXrIspXLEb3/d5bm6JI3NLzM7NUTv0TeThB+DoEzQWnmSpup9q7SD7vaPMejVmq4q5pSWWqvN4i8/hL/nIRR/3BOQOQf8CZBfBXQK7DmYNHvwrH1UHGYugqg+BvL6GW1/CrVdx6ku4vo/t+wghwXB06ITh6FAaZTf/fsVfnLWB5vs+Db+B53vUvTqe7+EabrIi/iKSsTLsGdhzvpvxotAsRH3hUjuNFfAzoZI5xZX5iWvP6jzn2g5MjLOElwqnbZzFcVaX52/nRTfQcjdcT+6G8yTRm5CQkHAGCCGoZOxg8pSF0W5g18obew2oHofFY82f+hI0lliszXF8aZZjS8c53qhy3K9xwFviWKPKca/K8UaV2aU55hcXmK0tcsKvctxssNCostBYZCFYvRcIHMPBNVxsZSOFxJAGUkiUUCihkJ/7WZRQNPxGZGQ1/AYNL/Z/8HfLNl7r/+186NYPMVZYuRBvwgtD2nwBFOguMJzpqRdGae8cM12afsHyxC4dLZ+6gXYWQkY7hks4U6dYhDohIeHcsPGVp7X5ec9BS0hISPi2Qiotv7yCBLMT/Jzp1Mj3fRYbiyzWF1moL7BQX6DaqK5qfHl4kdG2ohEX/i1Vy+/4NvH3pJAYIvnaSDj3rFw76MJjtLB2cdmzofMM6kudLtbgAJmhIZSdfI4TEi5kkk9oQkJCwrcIQghcw8U1XIokarUJCQmnh7tp0/luQkJCwimQSDIlJCQkJCQkJCQkJCRcICQGWkJCQkJCQkJCQkJCwgVCYqAlJCQkJCQkJCQkJCRcIAj/NIoVCiEOAY+f4bkqwOEz3PelTNJvZ07Sd2dG0m9nTtJ3p86Q7/sd57sRp4sQYhZ46Hy341uc5HNy9iR9ePYkfXj2JH14dqz6PXhaBtrZIIT4su/7O16Uk30bkfTbmZP03ZmR9NuZk/Tdtz/JPT57kj48e5I+PHuSPjx7kj584UhCHBMSEhISEhISEhISEi4QEgMtISEhISEhISEhISHhAuHFNND+9EU817cTSb+dOUnfnRlJv505Sd99+5Pc47Mn6cOzJ+nDsyfpw7Mn6cMXiBctBy0hISEhISEhISEhISFhbZIQx4SEhISEhISEhISEhAuEszbQhBA3CCEeEkI8IoT4uRXet4UQ7w/e/y8hxHDw+rAQYkEI8bXg54/Pti3fapxC310hhLhLCFEXQryy7b3vFUI8HPx874vX6vPPWfZbIzbmPvLitfrC4BT67s1CiPuFEHcLIf5NCDEUey8Zc2fWby/pMfftwsnGQIJGCDEghPh3IcQDQoj7hBA/GbxeEkLcETw/7hBCFIPXhRDi94J+vVsIse38XsGFgxBCCSG+KoT4aPD/SDCPejiYV1nB6yvOs17qCCEKQoi/F0I8GIzHS5NxeHoIId4UfI7vFUK8VwjhJOPwRcL3/TP+ARTwTWAUsICvA+vatvlR4I+Dv28H3h/8PQzcezbn/1b+OcW+GwY2Ae8CXhl7vQQ8GvwuBn8Xz/c1Xej9Frx34nxfwwXed3uBVPD3j8Q+r8mYO4N+C/5/yY65b5efUxkDyU/UVz3AtuDv7P9j77zj5CjLB/59d68ll0YqkAABpKOAIKCAgiICKgGRzk8QDEqRYgcbitiwIigooDRBeg0QICSEJKSXS8+1XO+3vU15f3/MzO5su9srubvk3m8+l92deeedd955d+d53qe8wHbgSOD3wI/s7T8Cfme/Pxd4AxDAycDy4b6GkfIHfAf4L/Ca/fkZ4FL7/QPA9fb7nHLWaP8DHgW+Yb8vASapcdin/psJ1ABj7M/PAFercTg0fwO1oJ0IVEopq6WUCeBpYE5GmTn2lwTgOeBzQggxwPPuCfTad1LKWinlBsDMOPYLwNtSyi4pZTfwNnD2UDR6BDCQfhvtFNJ370kpI/bHD4FZ9ns15vrXb4o9g0KedQpAStkspVxjvw8CW7AEPbcs8Chwvv1+DvCYtPgQmCSE2GeImz3iEELMAr4IPGR/FsBnseQoyO5DJWe5EEJMAD4NPAwgpUxIKX2ocdhXioAxQogiYCzQjBqHQ8JAFbSZQL3rc4O9LWcZKaUO+IEp9r4DbfP9IiHEaQNsy+5GIX23K47d3RnotZcJIVYJIT4UQpzfe/E9ir723bVYM4r9OXZPYiD9BqN7zO0pjObx329sF6fjgOXADCllM1hKHDDdLqb6Njd/AX5AaqJxCuCz5ShI76ee5KzRykFAO/BvW858SAhRjhqHBSOlbAT+ANRhKWZ+YDVqHA4JRQM8PpdmnJkWMl+ZZmB/KWWnEOJ44CUhxFFSysAA27S7UEjf7Ypjd3cGeu37SymbhBAHAQuEEBVSyqpBattIp+C+E0JcCZwAfKavx+6BDKTfYHSPuT2F0Tz++4UQYhzwPHCrlDLQw0S66tsMhBBfAtqklKuFEKc7m3MUlQXsG60UAR8Hvi2lXC6E+CuWS2M+VB9mYMfnzQEOBHzAs8A5OYqqcbgLGKgFrQHYz/V5FtCUr4xtIp0IdEkp41LKTgAp5Wos//5DB9ie3YlC+m5XHLu7M6Brl1I22a/VwEKs2d3RQkF9J4Q4E/gxcJ6UMt6XY/dQBtJvo33M7SmM5vHfZ4QQxVjK2ZNSyhfsza2Oy5j92mZvV32bzSnAeUKIWix32s9iWdQm2XIUpPdTTjlrKBs8AmkAGqSUy+3Pz2EpbGocFs6ZQI2Usl1KqQEvAJ9CjcMhYaAK2krgEDujSwlWUGBmlrJXACfj21eBBVJKKYSYJoTwAtgzy4dgJR4YLRTSd/l4CzhLCLGXPcNxlr1tNNDvfrP7q9R+PxXrIbh5l7V05NFr3wkhjgMexFIy2ly71JjrR7+pMbfHMJDf61GFHXPyMLBFSvkn1y63LHAV8LJr+9fsLHonA37HBW20IqW8XUo5S0o5G2usLZBSXgG8hyVHQXYfZslZQ9jkEYeUsgWoF0IcZm/6HNZvrxqHhVMHnCyEGGt/r50+VONwKBholhGszDfbsSxgP7a3/RJLUAEowzKLVgIrgIPs7RcCm7CyYa0BvjyY2U92h78C+u4TWDMSYaAT2OQ69hq7TyuBrw/3tewO/YY181Nhj7kK4NrhvpYR2HfvAK3AOvvvFTXm+t9vasztOX+5xoD6y9lPp2K5NW1wfR/OxYpFeRfYYb9OtssL4H67XyuAE4b7GkbSH3A6qSyOB9lyVKUtV5Xa23PKWaP9DzgWWGWPxZewMhCrcdi3PvwFsBXYCDwOlKpxODR/wu5UhUKhUCgUCoVCoVAMMwNeqFqhUCgUCoVCoVAoFIODUtAUCoVCoVAoFAqFYoSgFDSFQqFQKBQKhUKhGCEoBU2hUCgUCoVCoVAoRghKQVMoFAqFQqFQKBSKEYJS0BQKhUKhUCgUCoVihKAUNIVCoVAoFAqFQqEYISgFTaFQKBQKhUKhUChGCEpBUygUCoVCoVAoFIoRglLQFAqFQqFQKBQKhWKEoBQ0hUKhUCgUCoVCoRghKAVNobARQtwphLhvuNuhUCgUCsVwoZ6FCsXwoxQ0xahGCCGFEFMHWMdsIURoENryJyFEjRAiIIRYLoT45EDrVCgUCoWiN9SzUKEYWSgFTaEYOfiBM4FJwN+Al4UQpcPbJIVCoVAohhT1LFSMepSCphi1CCHesN/W2rN+xwHjhBAvCSGCQoiFQojprvKfFUKsEUJ0CyHeFULMtne9AZQLIUL23xQhxBwhxEa7nm1CiPN7a4+U8hdSyioppSmlfALwArN7OUyhUCgUin6jnoUKxchDKWiKUYuU8hz77Wwp5ThgLfAV4HfAFCAI3AYghDgAeAq4CZiK9SB63D7+HCAspRxn/3VizQBeCEwEfgA8JoSYXGjbhBCHAmVA3YAuUqFQKBSKHlDPQoVi5KEUNIUinTellMuklAngWeBj9vbLgf9JKZdKKQ3gj8AxQohpuSqRUi6UUm6zZwBfBqqAYwppgO3K8W/gbilldKAXpFAoFApFH1HPQoViGFEKmkKRTpvrfQQot9/vB8wVQviEED6gGygG9s1ViRDiVCHEUiFEl13+o0Cvs4ZCCA/WbGQN8Jv+X4ZCoVAoFP1GPQsVimFEKWgKRWE0An+TUk5y/Y2RUq4HZI7yjwMPAjOklJOACkAUcJ77sVxBvi6lzFWvQqFQKBTDhXoWKhRDgFLQFKOdNgoLPv4vcJkQ4pPCYoIQ4gJ7Xwcwxh1EDYwHOgFDCHEZcHRvJxBC/AL4BHChlFLry0UoFAqFQjEA1LNQoRhBKAVNMdq5G3jddr04Nl8hKWUNcCXwF8AHbMYKiEZKGQb+AGy23T4mA98G/on1YDoNWFZAW36G9fBqcWXBOq3fV6ZQKBQKRWGoZ6FCMYIQynKsUCgUCoVCoVAoFCMDZUFTKBQKhUKhUCgUihGCUtAUiiFECPGqy2XD/XfccLdNoVAoFIqhQD0LFYqeUS6OCoVCoVAoFAqFQjFCUBY0hUKhUCgUCoVCoRghFPWl8NSpU+Xs2bN3UVMUCoVCMVpYvXp1h5Ry2nC3o6+o56BCoVAoBoOenoN9UtBmz57NqlWrBqdVCoVCoRi1CCF2Dncb+oN6DioUCoViMOjpOahcHBUKhUKhUCgUCoVihNAnC5piz8cwJb5IAiEEE8cU4/WI4W6SQqFQKEYpUkqEUM8hhUIxulAK2ihHSsmaOh+vrm9iWVUnVe0hDCmREkqKPBw6YxynHTKN847ZlyP2mTDczVUoFArFKOKNmjc4fb/TGVs8dribolAoFEOGUtBGMQu3tfHH+dtp9keZc+xMbj/3cI7cdwJTy0sRAloDcSoa/SzY2srFDyzjsL3Hc8uZh3DqR6aqGU2FQqFQDAmaqQ13ExQKhWJIUQraKKTFH+OnL29kXb2PWz53CBefsB8lRdnhiHtPLGPviWV8/sgZ/PRLR/K/lfV855n1HLHPBO788pEcNG3cMLReoVAoFKMBtU6rQqEYragkIaOM+ZtaOOev7zN9fCnvfvczXHnyATmVs0zGlhTx9VMOZOH3TueIfcZz3n1LeGpFnXqAKhQKhWKXYEoTAI9QoopCoRhdKAvaKEFKyd8WVPLvJTX84aJj+NwRM/pVT3lpEbefcwRnHbk3Nz+1lve3t/Obr3yUSWNLBrnFCoVCoRjNOAqaQLnUKxSK0YWalhoFmKbkjhc38sKaBl644ZR+K2dujj9gL+bdchoej+C8+5awozU4CC1VKBQKhcLCxBzuJigUCsWwoBS0PRzdMPnus+tZW9fNc9d/igOnlg9a3RPHFHPfZcdx2Yn7c+E/lrJoe/ug1a1QKBSK0Y1jQZMoV3qFQjG6UAraHkxCN7n56bVUtYd4+rqTmTqudNDPIYTg+tMP5p6LjuGmJ9fw6NLaQT+HQqFQKEYfToyzUtAUCsVoQ8Wg7aGYpuR7z66nxR/jiW+cxISy4l16vi8ctTczrxvD3MdWUdkW4udfPpIir9L/FQqFQtE/khY0lYxKoVCMMpQEvQcipeRXr29hW0uQf1994i5XzhyOnjmRl288hfUNPuY+topwXB+S8yoUCoViz0PFoCkUitGKUtD2QB58v5q3NrXw6DUnMnHs0ChnDtMnlPH0dSfjEYJL/rmMtkBsSM+vUCgUij2DpIujsqApFIrhJh6ERGTITqcUtD2Ml9c18uCiKh695hPsPbFsWNowtqSIB//veI7dbxIX/H2pyvCoUCgUij5jSANQMWgKhWIEsP0t2P7GkJ1OxaDtQayt6+bHL27k4atO4CPTx/dY1pQmdYE6tnVvoyXcQlesC83U8OBhfMl4po+dzv4T9ufQvQ5lfEnPdeWiyOvhrjlH88+9qvnqA8t44Mrj+eTBU/p7aQqFQqEYZagsjgqFYkQxhNZ8paDtITT5olz3+Gp++qUjOOmg3IqQZmosql/EO9Xz+KBpCbqpcZinnJm6wWRdp8yIYxgJWj0eNniL2OmFBo/JId5xHFc+k+OnHsMph13IuGlHgOh94VAhBN/8zMHsO8lKHvKrOUfxpQPGoDU1oTU2Wa/NzRjd3ZihEEYoiNQ0RHEx3omTKJ4xg5KDDmLMMcdQduQRCK93sLtNoVAo9lwMHZrXwawThrsl/UK5OCoUKcJaGEMaTCiZMNxNGZ14S8BIABB85x1KDz+cklmzdtnplIK2BxBNGMx9bBVzjtmXSz6xf9b+7lAr/13+O55pXMRkQ+dcv4+vl+3LIVOOwTP1UJh8EIzZC8omQul4MHXQoqCF8fvrWd+2ljX+HTxW9SJ3VP2PkxMGZ5bN4vQDzmbiwV9ATtgfqesYXV3oHZ3onR0YnZ3J98e0tPL0znr055rZJg1K9t6bkn33pXjmvhTtuy8l++2HZ9w4POPHIYqKrLp8PvTmFiIrVtDx4AOQ0Bh/9tlMuugixhx91DD0skKhGIkIIc4G/gp4gYeklL/N2P8d4BuADrQD10gpd9r7rgJ+Yhf9lZTy0SFr+FCgx8BXt9sqaMqCplCkWNK4BN3UOfegc4e7KaOT4jFJBc2MxdHb2pSCpsiPlJIfv1jBlHGl3H7uEe4dRKoX8PCqP/NEpJZPGl7+svcpHHvo+Yj9T0Z6yojX1pKorCSxrB69bTt6ext6VzcyHkfG45iJODKhsXciwTm6zjm6B1P3IjUTYTbQwkO08JB1PgHeCeMpmjYD79SpFE2ZQtHUKZQcMJvyk05i6r770lQ2iWtfq+FTh87grjlHF5yGX0pJfMsW/K++Rt0111B21JFM/973GHOUUtQUitGMEMIL3A98HmgAVgohXpFSbnYVWwucIKWMCCGuB34PXCKEmAz8HDgBkMBq+9juob2KXc3uq9woxUyhSKGymg4znqFVmZSCtpvz9Mp6Pqzu5PWbT8PrEaAnYNMLvLf8z/yqKMzBY2fw+Cm/5eDppxJZtYrW51YQWfkA8e3b8U6cSOnBB1My+wCKps+g9LDD8E7eC0/ZGERJCZ7SEkRpKaK4GFFUBEXFiCIvoqgIUVREtx7k/cZFvFH1AmsDlZwWD/ElPcpph3+CkuO/DpP2S2vrR4DnDtiPbzy6kmsfXcV9lx/H+AKWABBCUHbkkZQdeSRTb7yR7ieeoO5rVzHxgguY/r3v4ikbnmQoCoVi2DkRqJRSVgMIIZ4G5gBJBU1K+Z6r/IfAlfb7LwBvSym77GPfBs4GnhqCdg8RsrCYCdMEaYB3aLP+9kYyScgwuzgGYxpjS4qsZ6xCMUx48CglbRShFLTdmI2Nfu5+fQuPX3sie5UJWPkQ/sV/4HcTx7KkvISfHnMXJ+wQBP7wCjs+/DGlhx7K2BNPZNotNzPmox+laMrAknZMZRJfmXolXznmSlrCLcyreo37tj3Dzxpe4AtbH+NLEw/n2BOux3Po2eCx4semjS/lqetO5rb/reOiB5bx8NWfYOakMQWf0zuunKnf+iYT55xH0+13UHvxJcy696+UzJ49oGtRKBS7JTOBetfnBuCkHspfCzhpuHIdOzPXQUKI64DrAPbfP9uNfLenYSX46+GjXx3ulqSRjEEbZkvagq1tHL73BA7bu+8JsxSKwUIUEPuv6BthLUx5cflwNyMnKs3+boo/qnHDk2v43ucP4bjwB/D3k1m89l98ZZ+pjB3zCR7dfDqz/u8ufM88w7jTPs1H3n2HA597lhk/+D7jTz99wMpZJnuX7801H/sGL140n0e+/D/KP34136edc5f9iHsf/BjV7/wUopbn0NiSIv5xxfF8+tBpnH//EtbX+/p8vuJ99mH/hx9i/FlnUXvpZYSXrxjU61EoFLsFuSSWnNK8EOJKLHfGe/p6rJTyn1LKE6SUJ0ybNq1fDR1WerNAxfxD044+4ihmw21BUyh6QkqJbux6y5ZHieyDzqL6RbRH2oe7GTlRFrTdECkl33t2PZ+bHuCqypuRK2t56KgzeH/dcu5ZOJXyrasou+AC9nnheUoPPHDI23fY5MM47JM/4daTbmdl8wpe2/Awlze+wuwnnuNLU47h7E/dwdS9P8Yd5x7B7CnlXPnQcu656GOcffQ+fTqP8HqZdtONlB50IA033si+9/ye8WecsYuuSqFQjEAaALcv9SygKbOQEOJM4MfAZ6SUcdexp2ccu/4/3F8AACAASURBVHCXtHK4cBQbKXvJvFuYAlTtr2Zy6WQmlU0aeNv6wHBb0ACKvQJfzEd5STnFnkF0BZXSSjxQVDp4dSqGlA0Nfmo7w8w51jLASylB0xAlJYN7ImVA2yVopjbcTciJUtB2Qx5etJVPNTzE1bxC/MS5/KFsJoc+uIw76gTT5n6RvR64BO+E4U/D6vV4OXnmJzl55if5sR5l4cYneG3T4/z5zcv5RNFefPnor3H+x69g/8nHc+N/11DbGeGbnz6oz2b8Ceeei2f8BBpvu00paQrF6GIlcIgQ4kCgEbgUuNxdQAhxHPAgcLaUss216y3g10KIvezPZwG37/omDweDo+B0Rjsp9hQPnYI2/HoZmm0ZKSnysLRpKbPGz+Jj0z42eCdo3wqtm0ace6micAKxdAE/vmUL8eoaJn7pi4N6HqE0tIFjWnGtTtgNDGQCKPf9kFIikXjEwCyeyl66m1GxZimfee8iLt1rB60XPcnjz73Hl3/9ASefcD6Hvv02U6+bOyKUs0zGFI3hnGPncv8V7zP/3Kc5bewsnlz5J8747yeZV/tzfvJVD49/WMPtL1QkH4h9YdxppzLzz3+m6fs/ILphwy64AoVCMdKQUurATVjK1hbgGSnlJiHEL4UQ59nF7gHGAc8KIdYJIV6xj+0C7sJS8lYCv3QShhSEFh28C9llOBa0Xn5Te3AhNEyJYVr7TWmOCKWpYCqesxJnDYCEbvWdM3E46LPticjg1rc7IiWERqabWSFkKk5GMGS/Bonv2DFo5xmowK8AdsyHynfTNmW6UG/q2ERjqLHfp1jTtoZ3dr7T7+MdlAVtd0FKgov/wYELfknNkddTOeMo2q/5NodPnsohr7zG2NlD78rYX6ZMP5orvvIUV4TaqH3/17y+9TUeaf6Q0v3LWdZ1Klf8+zP864rPMnFM39xIxp12KjNu/xH119/A7KefomS//Xo/SKFQ7NZIKecB8zK2/cz1/swejn0EeKTPJ/XVQ/3yYbV6vLmxmZMOnMJe5QW4UfUjhivR0EjxzH1ZtN0yOn728BmY0hxSd8NBiUHTwlDUf1cz3Uxvg+HMwA8WyigCoTaoXbznWBHtCZH4jkq0piZKDzlkUKpVFrSBIQ0DkQhnb8/4TdsZ2ElHtIOZ43LmjOoVX8yHbur9OtaNUsd3B6LdyP9eQmjRffzn4L/ir+xGu/mniAvO4dQXF+xWylka46Yz+9y/cOPXlzFvxjn8tnEnx5UtZnvRj/jc49/g7cqVfa5y0oUXMunii6i/7pvo3XvYckYKhWJkoMd7L7OLiesmHaFe2pFUbHpTcLL3R9etw/D5CMZ0gjHdLiWHJR5sQOfso0JlxuPEtm93nzwNJ/X/4KGE7l4tvCOcrKgM07mewf2uqCyOAyPwxpvEGwuz1I6EvlYK2kinbSv867Ns7ZLcNuY3HPnWf0i88Q7i4Xs44zv3IDx7wC0cNw3xhV9x4rdW8ZcZp/NGSzvniAa++/4NXPzyVSxrWtanGdRpN99M2UePpvGWW5H6wGcxFAqFok9UPAfx0HC3IkUfLVDO723m88WU5pBmVExa0Poj6Drt7KuCFgqhNWa7NznVDbqCNgIEQUUBRLuhdknOXZl3UDphGoP8XRFSWjGLin5jRmLpG6QcsW7be4B0vwez9XV4+Cwq97uQb9edww2v34U/3Mlhz73IcZ/40nC3bvAZNw3Ouot9bljJXdOP4P2mRiZXtfHd937I5a9fztLGpQUJB0II9rnrLsxQiPZ7/zYEDVcoFIoMEoUraNIwMON9t8r1LtsXaEHL/F11JrZsBc2ZTXaC34eaASmFfXU1yhDYMq/X7Ke1xzTzXYNS0HYL/A0QbM69L0tD20UKmh7bJUtiRBMGGxsHoV5Dg5aNg37dg0v6zSpftBbTl33tOd1Jh3gyRSloI5Wlf4OXbqDj7Pu5bWk5dy/4E/XHzOCspxcwc+/B8WcesUzYB+bcx6S5b/PbccU8X1nDEV2l/PiDO7h2/rWsb1/faxWe0lJm3vtXuv/3P4LvvTcEjVYoFKOFznjf127siVhFBcG3Bx5UnpdelYp0gUo6Llp2UgKvo6ANsYvjgBaqTi4x0IvFyzTThF7r2lPnc69UAGBo/Us68uqGJmo6suNflAVtd6Hw+5T8/gyyorKrkoSEEzqtgVjvBXtj88uWhW8EuIDnJcdtNGPZCZ+Ui6MiG9OEN++AD/9B4qrX+dHL6/npO/+i5cJPcdmfX6W8dNxwt3DomHEUk657BfnlB7msdh3/qe7k5OJp3PDODdz23m00hbKWO0qjZNYs9v3Nb2j+0e0kGhqGqNEKhWJPRiJZ3lFBfBCCwAHmVc8jEQ727aBQW+9lIFu7KBTDUWqs47y2pDBsWRwHcs7e7lO4HRpc8c5S5uwvicTb4adk0ap+WTsBgrEcGSBtoVszCogpVAwPehyM/Pcmy9qS11o6UOzz9PH7nDB6nlQYdIOX8EBXDQR6ltFGCkmF2sVISMiiFLSRhB6HF+ZC1QLkNfP52ROP8c0XnyR23cXMueOfozbF6r4fP5cp3/mQV8u+ysUfPM3L5iymF43lK698hQfXP0i8hx/O8Z89g0kXX0zjrbchEwNLt6xQKBQAiMFZPDmZGZA+uM1pUah5v3fLUPqZemtI+scMC4DHnk0etiyO9uvr76xl+0vzejok7WjAlbAhXzEjvXsyFDT3LmEYlpK6C2Kbt7UEWVLZ0WOZAWeHkxKig2v9HRVUvWcpHDbSNPG/9nrye5JlbLEt1nKQFTXne9gXjcof9/ea8l1K2WuVgZiG7loCqSMUJ5rI9xskoXG1lenWrr8/yyftEnJZxnJcu1LQFCm0GDx9BQQa0a56hZ88+Uu++sRryNtu4tPX3zncrRt2Jo8rY+63f8LvDn6cpTXww8WP8ui+X2Jp0xIuePkCljYtzXvstFtuRpSW0vanPw9hixUKhaJnkspOXybf7KQXoi/T3v21oEnHguaKQRvk6fbYtm1ora09lnH6qaiznWBXoG8n6E2RzbweM3ciFCmt/yS9C7OFnspC9LAvnaVNS6n0Vfbv5ACBRqjsxZW2YRX4+78G1C7DlexFGgbmUE64ahmuqbaC7iQhy9bPClRGgi19UphTSkPhA7CQdfvMAvJkvLe1jQpXnFpVW4i2YB63SJn+u7apKcC8ijzxeyMAmWuCbPj1M6WgjQi0KDx1KRgJui96hO88dQPn/WsJ0bnf5sSrbxzu1o0YSou8/Pry06g++S6u1m9n/3Uv8Z/WTr558Ff5wfs/4OdLf04gkf3wFkVFzLzn9/hffJHgwoVD33CFQrHHMRhqSlIRKDAb7/za+axLxuDmaIGUEO603uvxfqfZzxdDYzL4FrT4jkri7rT2aa0awDpoySyOvVicZHrMWZYFLRkH5y42iH3giu8rhAGtw2YUsMh2dy105L4fw0YiApteTN6X6Lp1BOe/XfDhWnMz/tde31Wty45XcixnvY3b2g9gZ+7MkLnw7KK4KGvSoffxpxkyOf7MHJ7AnaE4OzvDkDHx5I/mH3eRtWsJLVpUwLm1wZkcymlB66N1L6mADrw5PaEUtOEmEYH/XgLAjnN/zY1P/R+XPbCdpi9exWk3XT/MjRt5CCG49cxDueDLczi1++dUFX2EOfPu5KUDryAYD3DBSxewqD77y168777sc/evaL79jl5naxUKhWIoSFnQCiuvmzpN4WZaEwHbgpZxYLAZqu2kSFteTcVW9VUAybCgOfLmLnNx7EVBTfVTdkdphsa86nk5hLdC4+9yKGRpWRxd261G5K+qY0fh8YEO0eFdr7OhO8I7m0f4MzFiTzrY49gI5o7ZjK5fj97ZmbVd7+raZU3Lidw166D1NwatNwr1xIzqEd6qfctuQvYvQbM/RnvINTFkK2h6DyfQW1sxgr1nvH1759tU+aoKa2gfyeWKOmgujo1roLN/7VYK2nCixSzLmaeIt0+Zy00vfYMbHgmw6ZhzmXPX94e7dSOaC46bxT+uPoWLqr/Is4few5Rlf+dPzU388GPf4mdLf8adS+8kokXSjhl/5plMOPdcmr73faQx2IuNKhSK0UTe2dw+CE9JEacv61lKEyPT6uOQaV1xUv331qbMGDT79zFpPXK97op10IS3KE+7nJf853RikPOlv98SqCGY6CEJSw4Xx5z9JSVg9XtnJE+sWPN6aFqX/1yZRLowmqoBKA43M7OhFyuPoQ16mvVmf4xwYoSsF5pvbMUz7l8egT9R30BiZ132jkJdDgshRxvzLlS9y7I49l7vxkY/21qCSUWjp+9tLmUrF5qZive3LGj5jkpX0PIvMdE3eso34LCwfiGbOjfl3V+oEbLHLI499GVCzxhrXdXQtoUPqzvTljLoiPYcbwpKQRs+DB2evxZdGvzxsJP4zdLf8JPnJ7Bp+jF89W+/wOMZAQ6wI5wTD5zMCzecwoM79+bmSfeTGDOVs169g+eP/R4tkRYuee0SNnWkf1Gn/+D7GKEQHQ88MEytVigUuzV9VHbAdhmM+WmtfIudgZ12MYk/Yrn+iL7EoEmTEk8RuzSdYsYEVspRctecU3hzX79MaWj2a3b8l7NwtJkZR2IXrAk3Jfs890lyuDjmSLM/ZucC6KhCSsmaltW91Ackwr2OFcPnI7TeiinzRrMtP6mCuuVt07nDihEbrUgTXyRBLFGAq6abHhS08IoVRDds6FMbMsmKQetH9lR/VOtVkfHYIntvMW4N3RGq2kNsbUmFfPT03c2TuDS7nKsOsyelLsOCZpi9uSpL4nrvk+ZCCKhaYK1Jl4eIFqEt3JMVO1u27nsWx9zXEdMM3tiYO9auNRCjritlNFjRvILuWM/Wc6WgDQdSwmu30uHfydzpU1jZsobfLzyMxuhYTr3vd+xVXjrcLdxtOHBqOS/deAp6UTlnV11Ey8k/ZeoLN/CPko9w2WGXcu38a3lk4yPJHwZPaSkz//hHuv79HyIrV/ZSu0KhUKRj+APgq8sp8EgpafanW+4TdXUE5r0Bvjo2NC1PThrVdUVYtN12LevLhJw0EQhEhqBY46/h/ZYVeY7pawxauoAp5a6zngHg9fa4O1dfRzdswEwkklkN+902aTKvYy3+uN/+mNuCJhLBAhcft4/d9oY1e94Dpq7Tqdl19qSk1y+HbfP6vui2i45AC9XB/i03U0gGvphmENMG6JnSyz3siLTzfMVatjb1LVGM7KH9els7ibr6wiszjdRoNJwJlswT2i99GJPr6n10hHu2EHltS7u++SUI57fAVLalxqlwZWDNR+HNzFDQ8mtoztkBy8VRmAk+ePcVdrQG7XNKtEYrGc0Hdat4eMXiXs/uFV6IdIGv5/uVNVnjxnWvelpnsScFbXPnlpzLPDnfk0InsnorpxS04eCdO1nXuIRLJhUxe6+DuXvzx+neWE3Zr37LUftPGe7W7XaMKy3i71d8nItO2I/PvzuD9z/9FGLDM1y+9mUeP+M+Xqp8iZvfuzmZQKT0oAOZ8ZMf0/i976N3D6//v0Kh2M0wdZBmzkdrezDOypp0S4gZtTOdSYn7KM0wkaYBmo7si8NEMg4svQXN4WZCRvaCq/ZBqXeGgdbYiK73FHSfHc/lCHimNDFNmXtNr34ielPQkjPyqY5K1NVjtLcnFbRsAbRw4bj8w2qWbX07FX+WlmY/ZT90W/LyN9bVjkSOhaldBLUw9fFuTMNA9jRjr9n3tRcFrS0Ypt6fewa/6qUnqdzWx0nJaDcEmplX0UxDd6THogu2tvHOluxYtu5wog/ru/V8zyo6N9EY2dGrYLt6Zxdt7oWX+xqDaROK5+hvd12bX7GSquSjD3MGptl7dlBHadAxIRFGSsmCugVZiWM0I/fkUd5zy+zfk/zlnMkayPxdIZgx3h0XRykpSfgZE2vFZycMMYNBImstd+CEaRLRe3fdLXipKQkEW6Hiuex9OVwXcypoQmAEAgTffTdrX62/hrZI/rjNrK52EgH1cQ5JKWhDjPzgLzy54zm+NbGYmz5+Czc2Hk3X/15gzbd+wvmnHT7czdttEUJw/ekH8/crPs4t70b44+wHMEvHc8jTX+ep435IqbeUS169hK1dWwGYdP75lJ98Es13/HjXzQorFIpdjhDibCHENiFEpRDiRzn2f1oIsUYIoQshvpqxzxBCrLP/XinohHqc6o4weo5setL1f/IcxcWp/a7fGlPC2O1bKV+yoWAXR4/Hk3TX6i3N/tJAFS1OVluXUGl0dRFZu453Nr7E+vb1EA9lSw4ZLlqmTM1KSySd4QSrd/Z/cktqWnqadE9uBa2nGW7rOA+6TFfQtB3riS5+zV1JL42xvStCEctdUvaQZr8AYtUNhJbYmflkbldRAJlIIPyWNUE3DArKFGP2nKTluYoVPL/5/bz7PdE+pqZ3ZRmM5F3zykIzzKQrm5ulm2tZW9dDKvmYP6uf8mPVL3p18YtS3ZFSFqSW6FMyFscS+O6WVtqDGcqldFlYJbYSnufeZcRw9kTPFimnOuu6dWmAx4tu6sT0GN3x9GvLZfHs0cWR/OPbXdd2/7pkXZntTezcidhR5zTUerUVE/e4cPeU4bJWF/Ib6BU9T+Q4SGR23GK+cuR3GdU7OlMTbAWQslZmWhpzd25v40IpaENIZPUj/HDjgzw+dW8eOfs/nNU1k6a77uaJL93Ibf93xnA3b4/gtEOm8cpNp7KgOsyVXd8geMKNlD95CfdM+DhXHnklV795NS/seAGAGT/9GYnqaroff2KYW61QKPqDEMIL3A+cAxwJXCaEODKjWB1wNfDfHFVEpZTH2n/nFXJO55GaY5I6Y1bZeliLEktBk2a61c2UEk8sklZnb0wunQzktqBlfvTpEZoTPnfD0AyThJMQQkr80S7Y/ma2ZcYRLAMt7NW5NuniaJ3GEjjMAoTO1Tu72bnwsSxXv/DSpQTnv50UjBI7dxacaS/trB4Pmu1mJpEEE0HWLnycxMYVLonTam9HKE5nLkuOKwZNSmknCUnt1g09GedWyNpzWlsXRrfd73lS4kspiW3ejFZZA0DC0OhRQUsuf9WzImNIvef7MgjJGgKJAJs7N7OwfmFyW5YSA5jhMEYgwIzWhZRHe1hXbcfb0GrHivdm0e1LAh530b4sHRDt5q1NLfgiljKrZwrvrnuQME1AIITV91mLiDsTIz1aXFOTII7CYEajBN62lhGobg9l9a8uTfAUJSdNMpPguJUqmazfzoAZyHYPzTdmusKJnOuXORa0tKNME2krUAkjTtCIpVnQMr87Uc1gXb0vpaDhYWnTUrZ3W/ep2R8lHI+jmSlLf9KCZiSgagFxPbdbbS53zh6VoRy7BCLHV7JQV4dUufm+zawP5UhcUwBKQRsiajc+wxVr/0Bo1sf535wXONg/huobbuKfJ13KHd+/mJIidSsGi/0mj+X56z/FgdPGccaig1n76X8h3vk5V+zcyAOfvZ/7193PT5f8FH1MEfv+6Y+0//WvRDflz/qjUChGLCcClVLKaillAngamOMuIKWslVJugJ4CE/pOvge+SG62BQ/bfU/qenqQvemawfWmMq0ZoXD6mk2mQUeohfZXX4JwFExbOcrMvJhHzdMDYaIbrd+3pdtb2Pj+i2lH9UhXDWOjTfaMear7TNlzYjxHaKrvClMZ6IJQe9p+I2THyNjJSBK6ia8pvYz7mvLO/guRShIiTdqj7YRlPHm0+2VJZQfLqnMk4nBbF6WRdV8fWfcq1aGMzIymkTVDn2hoJF7fmt6leRS0N2reIBoPJ69LM3Wk43o1gIyDuQTTl9c10hqIAdlCcn/4oOEDav21ySzJcSNObWe2K2fogyWE3rfjioxeYueS67MV2L48fRTVdBp9zqSHq65CszaH2qDScmnL5SYIQPUi6KgkktCpsuOpBLDZv5SljctdBWXKMlPAPXVbpIxAABm3FMSKRj9Lqzqs75BujW1dGiA8SdfGnq1j6d+h0PuLs5YaylK2bPIl7kg01GPoevpPkOvD2vYNLPbvyBlX6Qxzp3+l/VtWogXxxXzJ2K4VNV38d9NrLG9envyOJxW0eAAiXSza1s5bm1ryXnPWRbobAJh5sm1KKWkPxukO53Pjzu1CGtXSx96aum7iuk7IiOVsl4pBGwG8u/FxLl/5S8464Ezu++LjlEdMdlw7l2cP+jTX/ew6ZkwoG+4m7nGUFXu5+4KP8ovzjuaqdzzcf8hDmHUfcuxbd/Ls5/5Jc6iZq9+4msDsqUy75RYab71NxaMpFLsfMwF3xHiDva1QyoQQq4QQHwohzs9XSAhxnV1uVTRmCUnuR2tUj/JB4wfpli1n5jpiP7TdmecSEUS02xVbZW83DMxwRiKKju2s2P4SO301EAyCHckmMHtNGS0BraWLRIMl9EQjQbyaayHZfAK7Yy0znbgzlzAhUy5O+XhrUwuRhE5Q72KL1kNiCruOHW1BVuy0LGhmNEpg/vy0ta4ylSanXUKILAuB7KFTcrng4YotM13ua6l6DWJGOL2r/A2w/a20WsxIGDOWSLPI5YoZy7UsQMLQUuOpYHe/FIZpEEqEMMl9bHKh4JzZ6goj3+1+d+e7RLQc7mQu5TTnPYmHXO0pbDymrC32586qtDijZl+M6vZwWpWQ34XNrixnm/Oix8BXj25asZhGMISIRjClwZr6VvxxP1E9wvaKFUSi0Z6vy4WT6j66cRNmMJVII0nNImTrRgBips68uneTikshLpRpZXQ9a1+uKvIlyoiuX4+nsz2vgqGZ9nhLU9AyyjpjQkqK4n5mL3snq51CQEgLpSzYrnXggjGNWDiKiGe7IObsD3tbdzhOeyiWti3zOtpCcba3hlhT17NM6E7FX+mr5P3GRe5qqe+KEIjqlHryLCPSC0pB24Xops6fP/wNd678Pb/f50yu//y9kNCovO56Fo+ZyUk/+Q4fmzVpuJu5R/PFj+3Da98+jfkNXi7Tfka4bG8mP3o+Dxx9I8dOP5ZLX7+Uui8czZjjjqXxtu8gtcELfFcoFLucXBJEX8wE+0spTwAuB/4ihDg4VyEp5T+llCdIKU8YM2aMfZKUFasr1kUg7rgOpVzrjECA2Jat9kdnNt2A2sWUNyxKNd4RZHJlEDS0pNBueVDmcG/MQ9aMrSmdBYzyCo1SStAT+P0+pJHKkJju4pjfLcopZ5gyGTOTo2HuF0xpCd2VbUGWbmpAJjTMaDT/DLNjEfF4UhYCKS0BzllTzuXi6JC5tpFmmMQ1LVnGikFLKQNuQU+6FLmcFhm3GULLl6yFpOXJ6UcATddTY6EQJSGcbgl8r3odb9a8hymtDIN5n2ODakNOkTBjmNKgNVqbc7/MJWpufxM6K3us1wiF7InTjHuZHDi2oqH3koSkBwUtaeHOKONYBd1KStTULNe9otLk1zm0Yh2e1Vb2VCm9LGlcwraubRTpIbSQn65wHMPlclgfqGdr57bsJkprDCdqa0nU2S5xGeNsS1M3cd0kaiZAypQFrQcFLXMCI2cZrPGoGyYdy5+BoGWV8iT1IZl9QI9JTfK3Z0v3OlY0p7LNOm7h3kS2NTA5b2UaRDUDw3HVNnVeatjC2HUrGLdqaY6zu7VzZ7LEuv6mQIydnfZ3cOPzVmxixoWkLXfQvtUVv5gxDtu2Qb11LWEtnPOqDWlSLCwFLWaEMfPEuOZCKWi7iM5oJ9+cP5dlW5/l6QnHc+oX/oyUktof3s7Wjgj6LT/ki8fsO9zNHBXsP2Usz37rU3xs9gw+teWrbD7waxQ9dh4/HHMwNx93M99693pWXnUCZiRC6+9+P9zNVSgUhdMA7Of6PAvIzn+cByllk/1aDSwEjiv82NT7zCxqDmkB5k75xlW2S1dK0UpbEDpLGDJTh3sEjviRmWY/p1AClpRjlxWOUimdOKtsQSG0aBHRxa/RWb2aUMy9MK2ZbKMtn1nbw+E07wNnu1sZMkIRDH+OLG0Z19rQHcXnpBrPVI70hOuw5ElS2SUd7SNLZU/Vk1zNoHkDNK5maVUnS7en1kwypZm2yHCmgbNHS4hpC17SgJaKZPvAUjSK2loQ1ZXE9FjyGpIxR9J0KfAFpNKvfi/t49ZWHw3dEUxp4tF0Am/NTxMCtzQHMEzZa3KN/mJInagRpDVem9wWSASRSY0wn50usz/TP4cXLya8JFsA9xgJzGAraxsC7Ai10xxqYvXOLrqduLFQF8G1C5KKapryFWyBjh2uymwxWNfBZZFt8mUr2R8Gqi3XPU+JtWS5lNa12ccUe4rTDzBNdrZ20d6ecil8c8dqnqvIXssuLemG83uQYyJA003Mug5KN+9MWdD64OIIQN0y6KxCa2vD/9rrycmWuq4Ite0+y9UTkrctc1FqSWqeJ7nNNXGU5uad4d7q1zrpiHYkraBOUU+OMeJ8Xw1psKnJT2WbZVlMYBIwI3gS6Yq5aVssTWmypq7bdgVP/W7l7BstmpxIMkwDf7gdCfi1dpqjVVYSm8x115yqgk0EWqoAy2o4flkFIhpJ6YTSxDCkvW4lbPWvoNZfm6qndUtWm9Kuv8e9in5R0V7Bxa9dzKyueh43pzNzzoMgBM1/uZf6D9ey+pofcdNZRwx3M0cVJUUefvzFI/nzpcdyZcWx/Hu/u5Fv/IALatfy4Of+wb2bHuClaw4l8NZbdD/77HA3V6FQFMZK4BAhxIFCiBLgUqCgbIxCiL2EEKX2+6nAKcDmQk8sSVm7Uq5GViKJjlCchu6wlT3OKe9YeACkme426Ly6BDK9q8sSOKSRbjiQjrKUYR1Ll5asF80g0dptCf1a1FLqbEFK5LHEmaFw0opj2jP/44JVmJFOuw2OUmG9hlesSBOine1OPAZAaOUGQos/yNGJbqFP4PWkhF23BUtKCVteoUhLj11zH2/a68NJR+DOcW0eR2ns2A5dNUQTBlkxaK414LLn8t1Nz+j/rvyLYYcWLmLMjs3g9yUVYf3B6gAAIABJREFUSVMayTZKw6Wg9cPFsTxcj9d2cSyKBgBJ8K35aUpxrrjFXslUkjNIJp6QVhZKdy9VdVfSEbHiCs3MWCRHYYqmJ7FxXuM7dhDbvDlru9NfxQk/WnsVvkCQ9aEG1revp6E7pVAlgm1EmisxYzHMunUQcCW6aFwNzetd12i/mKb7Y04M+3pD6IQSweQtc5SNUm9JxgEmE/2b8VS/Q7vdF4aZ2/rsNtpIO/ZLahmuiM5ri4+izkDyd6cnclrQoj7w7cTotL/T9tBItSG9fYYpM+6/5eKcdR1S2tZSe3uoDTa/lFankNLKRpt1b51S7sklkbZNM5x4U6dM+oRD8N0FFLV2YZqWK6NpypRFuge3R6ePavw1LFl1H96YNS79ekfO8lbTBBHNYLsdh6ibOiKh4w0FXb/qEkPKdOVTkPS+kL1YkJWCNsi8uONF5r49l2+VH8Iv2jsoveRJKCql68WXaH7sSV66+Dv8/PKTs1wtFEPDZw+fwbybT+ON8CF8vege4tve5Zj5v+Tpzz3AKqOaR/9vb1p/9ztCi3tfNFGhUAwvUkoduAl4C9gCPCOl3CSE+KUQ4jwAIcQnhBANwEXAg0IIJyPQEcAqIcR64D3gt1LKghU002WBSregmdR2hmmpeA+ZSOCP+1jXthZLBbFLmHr6jHQy5islRIeXLiO2dWvK4uWeC5dWqvHowoWsqXW7uyXT/VkvtkWB7lrY+rol/DvnMPU0OewDfyUV1fPtxZhtYSpmuW1O9G/FaNrgnB0pZSqey36ZVz2P9kg7ppQEtE4+aFqUU9jVDDO1hlqG0OQRImXlyaEcCGlbRAyXlSvDCplz0e8NW8AwsnaVFAmEM8PvKL45BMcxnT4Syze5+ktkX1tndbYQmBSKUwp4aj05mdxsmmbKctaLi2OuPi3WAngMy82wPFAJ4Q6krltKsSOgm2RncYz5mVz9CsJMpLsJFiifOLFGhtST6pmU0ooN02PEHWthpoKr28qU3wkfTYm0/ohGaMtW4tU1KWWZzHtix2HaLp1Z6delmeznzleeJRJwreGWQ6mwqrQyMuYsk8Hq7u3UBKoAE0TKYuTNWC7CscxURVpZ2WKtQVdqJ4TLjIdMiwNzxkC+5CrCmlwxuqrBTE8+lEnefXoCGbf6xbQnI5zv0OL2dVZGyqQlKF1Bc5IGeRLB7L4Sotf+c1sanZKZafZFIoGQEtOURBLp/ZBabCL7PCKhJfdZP9EZCppzSOOalH6XMdGG0du6fRm/OVjfBcOU1vVHHCu547lgfUcAyrxWzokxq7aibcw/qQNKQRs0NFPj18t/zb1r7+WBw7/BRevnwWVPQ/kUQitWUn/nL3n03Bu4+6ZzKPKqbh9O9p5YxlNzT+aYj36UU9t/SG20jGlPXMwjx30fecRH+NcF5dTfeiuRtWuHu6kKhaIXpJTzpJSHSikPllLebW/7mZTyFfv9SinlLClluZRyipTyKHv7UinlR6WUx9ivDxd0PqAkFE9zR8w3ky0TiWTMkVs4lvZC11nKhWmkCzcyY5utYZi265CvK0BDW0+ug+kCTNKCpsehZUOa9Shgxmhp+BDatqQEM9fxhhM75RIkpZRJmTYU1+mIdiFlShjJxdbmINtbgyRT2pNSKz1CpLkYprrBNXNtaoTDbTTb2d7SLATxOEVORkG3QNbagcdO5JBMYAAUe10z+aZMdzV0WQi8hq0QJGyhVZAt+Er7z63cJN2rQJh6egr/SBfS1AjENBq7Qtb+HpCmSVF7CGzryrzqeUl3ydT1ypQ5xGb80vfwdnUgyeHimLDu6d4t78OO+T2e303I34HRVUvCsCYArPst8Ahh9UvTWoh0kLCz1xVvrMC/dXsydT1GxnpsrvYu3N5GXWck735IjZcdwc14womUZdQpjmkJx1LS2B2jye9yV8yzoLmjDBa0aLM08Yaa0ywlAF6RruD5En52mK10R+NEM9LBu9P3OzFPyd8DewIi28XRKWe5OustFRBuLyimKSsGLRGCmsXUBeuI2gqzo6QFtQhxI46nfSvlodqseFLnt29c3QIItSabJgS2BS1Vtq4rPcOnEFDiKUlmFHW+YwKsBCz2pYxb+QFlLV3UdoR5b2ub6+pTik++ZSOklEnl3VF2Y4kouv3d16RB7c7VqUZnV5Cz3vRxkTpuY6OfWDxOSyBGcWsTY1dstEun7pdmxvHGEniEByklnnAUM9jz4u9KUxgEOqOdzJ0/l02dm/jfqfdw7Nt3w5z7YPrhxKqq2PGtG3nq1Mu5847LGVvSv2wuisHF6xHc9vlD+fvXT+WKzm/wvOcLlDz6Ze6efBIfnfN1HjoTar45l/iOHb1XplAoRhV7VXVgkhLkDFcSD+F6hkvDSJtVT06QSwO3HJgU9k0zPXzM67EESsOaMy5at42SnZarVGbK9DSFIZGRCdKpTxrWcVrEVv7cSkHKNTDVnlSdZiIlTGhvz6dkZ3VqxhjY2hKw46DyzW07VVp71tR123E+qZKZLo6ui0tS3rWZuopnaI20plvQkMgmV/rwqL2ummn3tUewLbCCpY0pd8xirwePLQQL00xa0GKaTlvAUi5KQhE8um5lQjQhqlkJGjq1MNu6UskeUjE4LrHK6YuYn0m+ijTrmNG2hVhXJTHNwB/V0I04nUYwpUBoUWjZmOq3SJzSmi78bQE67Ti9sOYIv6nBVJRjrTdvOGTdqyzB0/rsMeO9JtpwH9pUV4WvrT6poJV1VFAa70SIdEXANE1aDB/4u6lYu4M3Nlc5O3K2gwyhPV8jbDWQku2VjNvSitclLJfFWhGmLZy76unUQmzp3ELWyExOCJiOhoHH1HMqzElZXtcRWtSu3hWDZk++SyRNejdxaS2fEIjHqe9KF8bjmkaH7bKb/M64mlYfrKM7nOFi53SD/Z9hj1cnJX9aW02N4ramtLis7IokXdFOOqOWkmdKiYkklNAQCLztm5nk22Qn50hNzJhSUqRHrEbY9Xa11xE3tSwLWlswI6ZUQpGnKHnNpjtDY/N63AvFexOanY4/XYFNXUvumEopUzG6/miCl9c1sqjuveQaa7V6K681b3O5UGeudZf+MbJxW3ocY8b4rWoPEQhFKY134tFiruOd9eIkoqOFfSq244497W0iQCloA2RL5xYuff1SZk+Yzb/PuJ/pL98KJ3wdjjyPRGMjmy6/ijeOOpPv/vbbTC4v6b1CxZDyidmTmXfrp1k09VJuMW5Ff/lmrm5rYM5Nf+LZk022XHU58Zqa4W6mQqEYYbgVonQXx3TFwmsHiOuG5YoUN0wSuomTbMMq5wgKGbP3Hg91nUG6QzG7iKSo21k/y6Tct41x/vQ4Bv8HG0h88EzONqcsaDGQEl8khuFKdZ50U7O32MvwIhDobiFJmngjYSv7oi21zlq92Zpp76xmir24dRYNq9KsJ2t3drG+IWUB9AgQycx07hloWzCX0hKcXS6hbguaMGVS0ZB19ppUholpaExt/xDNjCeVmpaEH11GLQHTtggY0iAa19jUFGB5dSdSwowtNYxvtpRi3TBoC8TQDKhPdFHlq3Ldq2IYMzl3anFH+dF0DGcNKmlSH+uw35rUxxrYoTeDlLy8rhFfU6WVQS5ZlVVXXEstzptmWZRWX4yv9+W0CljWBEsIX9qwnIr2ivQCXpfbmSSZkAFggm8LppQ0+aI0O9YomXJx9MY6KI802MdKVz0mtXobfs1HlxFgR3C1fYxL8ZeSeHJMFGK5sD9LKAlHLYXK1efjg1UUx304MaJOHzUlfNT4a9C6fCkXWbu9wiOQsRi6z4cJTG9bzIzW961+zexKR1lJNkuiOYpqsWWRNU2TiIyRwEAKiEoNU0prTNnVvL74WRb7qtjQ6CPDWxiwJv4bfe4VRFJlpOPiKE28vjBy0bIsK9qMlkXMWPki+rZK+7hs5TfdaugYeCV1XRHCcT25oIc008eaKaW9XAdgJDATCbZu+5BGvcuy7vVo0TOt5THscxsZyo6URtL909aXU78DGUpdzvFi/254omHMuEYgHKMqaK1jqNm/HWbGcdnLMKR/1lrbkbFY7smj5CEmxVqQsq56l+JnvfpiCeqCFXbznH+9dBNKQRsQixsWc+1b13Lt0ddy5yd/Tsm878G46XDGj9G6ulh36ddYvP9xXHPfz5g2vnS4m6vIw8Qxxdx76bF85pxLOC9xF22rXuTU9/7C1374DxYcX8zGSy4gsLmi94oUCsWowZ10IX09IvdTNxUgnjAtIa6xO0plWwCjrRtvJJw6ztSpaa0gkmH9qm/tpnh5Fc6j3SOtwPMpHasp0kOUxdrIxAjlWKwXKOtcQcyIJ5Wk6tYATb6Um1xSGM0QMPAWpQkdEon0CEwpieq2m6KTSMBXS0nCl3be7kSQdW1raW5djzfmXlvISu0NUFZbiced5h6ZJegksQW4tzY1U9lu9ZchDQg0ZZURpkRqcYq1IJg6QgjMuEZ9vJug5k9ZSkxLsO4IOv0hk+6p4/3V9iZhC/2pfkq6sOoactIBmNJka6TZdgPLSDyhxTHWWVYxw1YyrAJu10o7EUXcbcFxr/cmXV6gkrgRt62pPWdoNKVEaAY7O8Ms2LaR+mA9Gxp8dDluh96UjBLVDDY1BZLjZ3yomq5wgpW1XTS6shs6FjS3u2/ciPNmtxXmGdOtvunWOqnX6pjctY7qbev5YEdqzG6JNPOub0uyP90kXWWltJQq02B8cwfOd8HpK29G/FJxvDsrG2bCMAnENCIbq0i0uOI2JeDxElm3jpbFi+jSwniNGF4jO4tjcWU7Y9c34VhGomaChnArNcFtyeZH4hr1HfY6ZgKkR1ihn51BOmobLIVQNyiv3kQ0kiAUT7gsaJKYHktZiLQwMxtSi9YnJ07s76kuU9FYhi/9O+c143jMGGZzC2ipmNe1dd2E4rnXxXMsaEJayUysISowpEQYMTzCQ7G3mCpfFVWRjdbtMjQwrHFlJi3q+TUPIWxrmd0eLYel0kmnn9KNMxSqzLgym85QHMO0EjCVVmwksrEapE5It1Lpe4UHd+tMieXma5poO7cjq7alVZtWe5rrckbaINPA44qTS/2CWf1b2xlGelIJT4JvvElTd9ReQD4/SkHrJ89vf54fvP8DfnPab7j08Evhw79Dw0q48CESoQjLLvoaG/c6gIv++Tv2nqgWoh7pCCG48PhZPPDtC7lp7O95q6mMAx6/lGuvu5Xln5/Jjisvo/HD94a7mQqFYoTgWLQg5XKjmzqGW+AwjeRsbdzQrBgmQJcgdzbjfpTTXUP19tdoCDZYcWDRbjBNPPG45fpnI5w4HxvDMya7cbYw0amF6dRCICXd4QQbEzupTDQnY6GENNgcbqIp7rPb4iR6sFtle31popS67hCGriG7fEgJxR1tVG6qZl1dumDo1hOKE5ab4c6oJZA3xn1ItzAt05qL11mnzdUITzyRpWg6bqQCCEQTqbrciyMnNIxIzErMYdc3oWsDXi1GcOUWzIDtomVaQq4wTSvDouv8i+oWZHYsjuIoJdBawcL6hVZxPQ6iCAOTmKmj2xYcM2O9qNQkfEoIldJSPiZXticXNndueaftsphotpSKNi2AbmeA3N69nXd3vpvWJ/kwbcUgrptM8m0BQ6PZ53K786bCL5yq/O+vRfdbCnAkYbgrQ+gmMT1OVUcIXzRBaayN8kA1YS2cyu5oGghb0jfs7Jod/m5CsZQVNWJmu+c5VLRXEEj4CetRAss2UrytjkkNrUnh2Fl0InPdtzHRZkqDtWmWke5I3Mq45zJdOPegMdpMa7CZqmAty4PVedvjDcURCQOifpCwXWuiI9ZtfZeEwMREjwYZG7UmCkxhKSwmkqnbahmzZQOGlJSGI5S0dFIU1dLGtgS2dm2hNWytQ0YiW0kUrjg3AzM1MaLnjmE0seOdpElHtANdamhOjFtG7Js7GYjp/HIJgRnuYkbLAsuWridI6HE0I2Lt1+NJi6RAULRmC9j3w22tc9LqCAmemkbMbut3Q89Q0GS4A9osy3FZQxt7ba9gRtO7rh6yk+q4PjvUdobxRa0kIQHdT1cskLzX1q3wpFlVneNluJ3oW0/Bkndy9qF9Utf5XBl5gZLGespqGpL7wJpgcfelaY9/U9fBMIkbJjG954RASkHrI1JK7lt7H/euvZd/nfUvPrPfZ6DmfVj4W7jkSSKJIhZ95UpaSycw5/H7mLnX2OFusqIPHDClnCevP4PNH7+Tm6NzKX31h8w9YX+2XXo8Ld+6kYrXHhvuJioUihGAWzRwBNLlLUvYEVqbjOdASuKajikhVt/I2HW5l2hzFofGlHjwQDwAkQ5LuHAlrZCBFoRpom/YmZz9Njxu74wMjQeoj3ejm5KqjhBGcmkAW6CSBrXxTqpj1rkEkNBNqjqc2BgTw2spgIGYTkKL41mzKVl3IhKxE2ZkJ9aQwJROV6IlaVrCesVG17Z0AcsjBGXRZmvtISmRPj9jP9yMjDqCqlU+bGdZ82ohpP3ecC1HgJREttYRWrMdYVvGHKuHx1lCwDRwJypBSkzDYEzDUorjPrvv0+9TezCRVG6dNZQAzFjMEko9RaTNrUvJqxua2Nrsd12ubfFwV25KhDQpDieItlgukAJLwP2gsoO4pqO1++x2S8JmHCTJ5A52Jam3OVwc0xbfBTAT6bkohYeX1zUSSehpFgQjZJ1D+FPudhMbuyhdXceK2ja6wwnGbWnA4wtRlPCxeqdjZbPUJ2HHEHk1jaKohgcjGT9Z3RGyYv2cE/qys9qFtQiLOtbRrYXxOG1J3ht7rGUk4MB296TTrWyJ1P3MSObRGm2jKdSY3N9i2G63TqyoU62zhINhTRpYSya4Et1I8Ka5eFpWNFOmDU08uomJVbcw48jGNe7muLIJOpYiE60zwIQtjda4sJufsF32JCllK5P6QJ21vhuSZY3L8SVaM0xIIKVIfnRia03bfVgikEYCKaHcvwPRtAbCbXgcV2MtYicychZaEGj28gB6WpvsCRcjgae6Hm3HNruMnrafrmqkK/ZyXGNLso9T6lFqBsmQ/8/em8fbllX1vd8x11p773P2ae45t62qe+tWAUUBojTSRYHkQ0ITnqLBDvOMiglRUIldNIlGoygiBlQIKo3IMwqEGAIqIIICpSVQVUD1za3b33PuPX2327XXmnO8P+ZczT73FuB7mtLijM+n6py792rmmmuudcZv/Mb4Dctyr1Z7Gmot17N1zqbrpEVGgoZ3a23/u3uL/rtdAF8VzOjyz2onGftO8ir4Bp4BvH9ph4kH7iVKc88IFi0DHgJIX8n2ANrfwJw6XvvZ1/KRMx/h91/8+zzxwBNh6wL8z5fDN/waq+YaPvnS76LfbPOi9/0O+2f3wNk/REsiw4+/4Ea+53t/gG+VN3DnyUVexs2sf+8zGf30L/OpX/8PD/cQ92zP9uxhssq30SoSX7JkQ1I34P58kVtGD7LYXeTEcofVbko29M6lCRIHqWbBFaICTapj9TSqyj3pSe+E2Yyst8nicoeLi55dO5+tcWf3Tj5/+k/RQVXL9dBtXAyiyuJgFVBS56PgkxtDoq0BqbUU5Tmpy7zTJyZcoQYnUENTXfXbipQRbe8buZDCVHOkADbOMOr3yTdWrzCbYXQGZjfvxa35a6bX925+TQRhqBmbof7l4OpnaG/eX92DGjh0o5FnCpxFncWsbHLojkVG2ZCl0Y5PPQvMmd9BcS5DbUbkQs3YLpVJ78gqquMsZuejHyHv9KAus14DEHXbHngGY0z5U5Uo99f0mRPe2YxMldaotUi7hBTXMTXCenuAh7ACXJT1V7sl3MP+w6zOFFS/zW98AVMA46Hfd3PQDyxvBuEede9/kFGRnqmuVI5sr2+x/8SKV9dTy07aY2O9y+ag5giv1Br3huvLXAZKLbjgx1pXSNddqqGKd5J/585P7Z4FsjDvw8yWKokaReX9UqDjPLM4s3OS0Z9/jOG5wGiZCtgpysjl5fwYkTCm6vm1YR911WpxDkxot1BUI8lGASR33UOtAin55g5RmtdS9Py1mOol4vdJi9rB8XfAMMv4wvkNDBEDl3FisFzOZ3zRg2+7q6bTKSCm7NmogTnUbICoZTh1FHYWA+iomimcOH2BnWFWpi/XL625cV+YQv9Bvus5WOum3H1xp/rIRLUrLsZWzibbo1U+t/y5+qThVGlHkyjQz2rvxfHZZS0Lja/TARv9oWe9gUZvyDV3nKDZq+otUcenT635d4oyHgRxdkwhtXg0k7VVGtuDAOTDmHO7+y4/pO0BtC/TrLP8/Kd/nluXbuVdL3oXx6aPQTaE9/0reNLLuL/9HG7+tu8mnpnhn//hO5meukLayZ79g7JnXD/Pu3/0G3n38dfw48N/zXNGnyb6rqtJ3v3H/MmrvonR8ItLpO7Znu3ZI9e09v8xhTSndIKTNMqHPtKu6huZAlHmyJ3l7GiVXr4V/vj7ELs4xpuaKqRaAyeZ9UIYKFjHUDPUWc6euZn0Uq1OdpcnUrozIqCG1cGOTzXKc9Qq8alVmmc3WNgYsDXwKoUX7BqdbIigdGyPk/lSeaRLvQX6diewMlI2XyYdkp4PTYGdvSxavG0HnO7WlHEvY9D8KdREDO66Gz236NOSRpXCoAdi9YvLvZO4dHfFJgD9YcaFzT555iXXo26/nLeOHZbpXJKPvL/lXK15tBJvbTB183jtcb1t7tjI7ciLhNTq0xSln46LX6jCWjelO8zKpseUyof+6KZQtZOK33I1gOYUdJRhtqpaRbms7scfq5tV26gR37OrW7R9yMh1nCUoxl3DQaXtuAF3bN00tq1VS5yGvlNxRCRC8/xZmkVtTZ6G9VqzS4us9y5w2xc+S3TfaeZvP0+02Sd1ealqCPiSkc4l8rTDlWylMyyV+i7rHacOp0p/V/qcoJzoLWNV+eg9S5wr5PwL9dByaYX/i0HznGzNO/lO/Hm1vgCKSXKOZHkVVxuLE0UL5iQ8k62FJebOXcLiWac1u0MaxnkZxi4CBLVaRik0LFVJXU4SGkN/euFmbl+5HU58lDjr4UJvuGFuQYQLnYIhFFbyHU6GtGPUse+Wj1CoJ57KPRhdG6wHMCtYp9yZnaOXZwxHFnW5B42NWT/3eRbmVzBuSGu4Rm6V3FYXVKY47pq2QmBJgX6a0x9ZejgW+w+GbcYZaaixz3rZk4hqIAbD/bS9HeZPLyJZXipMljWt6hnDC2eWuGvzEush8CO5I7IDWv0qmFSwgjakYddfsWJduc4lgNnmTo9CZsnV3lmaZ+guBu6hbA+gfRmWu5yfvvmnuWftHt75ondycPKgv/kf+jFoTvOJue/mvu9+OQcPz/NP/8fv0JjYqzl7pNhMK+EN3/4kXvjSl/P84evZnngM179wm9b9p/nES5/L0rkvu6/tnu3Znj2CrJBm3vnwh9GVoManMHtxhYP3eienJQmKFwrJrXeU5u+9yO3DMwBkrmhoqiULYrSKLoPv51OABgWMupAaVTlvF7cGXNwcYJ3zvaZq0d16zVoR49/q+xS5yfVtphe3/PfO11XYEqjAcr7Nar7Fmg3CHtanBWaa49RW6YxhLNHyMvk5D9BapxaZvPss8yfrjNnuSdz1T6u+OS0Rucvpb3bY7o/Ih0MvrlCDP+WEqzLYWGC4fg5xympnSDfNyEPam81ynLPBjQXJLVZtACKKDD34kyz3QCk4WmbQh6zrWxOUXqWE9LqagAnqndQA0IqxWVXuXBivz/NEnUOd1gCagHWYXgFqLM7m3Peh92JD2lQ6SunXBDnsmWXMbfd7YNhZx4wyxI6YW6+YhNxl3Lv2QBk8UFXsbLtsoou1XEwvVwmE8dqhrNPHnL1Iz/mWCLNb9xHbQro9DzOgtCKhERs2RpcqUGZTjHVlimlshNUHP8/mpbvGUsKiTsqJwTK3dM6EKfECNIz67KzeB2oZupxuOgxiC1VaKoCzlkPLf1kKvlSPj46hnrmNO1juDkvJ+1EAvmXq4m7wjzDILBqc86J+spDmEPUpwQDt9XXmP/sRSHvl8+dUPShWD60AJhZrPb0ULth1zg3XPUDadR8Khk+0zqAGh7+/Qao5DWIUZTjqs9r3xzZuRDG7F7d8Cmln1AEUhyWWqJqX7QXUOSSoJ/Y0BZSTizdxKl3x97deC6cFEFFcaDi90/HBD0HINCfXK6Q42hxZXLnsOJVSKyztpCjKQFOc7SNZD60FKqrAQTGGjMkr1QyWz6HwwNKdtNe3aN15EiMG2ezUAJrPCoj7gyrIBBiUxmib1rASkxllxXs4WFYT+LAW6VfPuioceuCs/0cIwBRpvWpzLm0VDdy/uO0BtC9hucv5qZt+inPb5/idF/4O8615/8Wt70DPfIq3N1/N4NU/yLWPvY6v+/23EzX31BofifaiJx7hf//oC/nv06/gRyZ/hSd+xw1kU13OvfRb+cIfvePhHt6e7dme/R82BwwfOIE6RTZ3glKaI6o5nlI4CgK55uUf3LKHme2j6U5g2Sw45czmaR4slBnVA5aCHxEgCalww9w7UkV0XYzQ6Y+8Kl8NoI39kVdAPVhUhTgb0drojTnkTuFs5mvlumfOcTpfJCq+Duk5LgAcq8pU7zT5RV9DY4MghwDST1Eg6T10U2LZ5aJodwfZ6qBiuG3pdk6tbrHey9jorvDgcIX+cJFN20MUtvojrDq2XY+LW0POb/Rh6zwAqbWl1ofNHOpcmZ0mzpFrARocjFLsZOyBkQuNqINjL50lGqNaI3AtKD4/8p1hxufObXpwHRrQVmlY42mHXTfkUu98iMC7MrVLjbBlu+h9Z8PasFg7YmAHDC554H/X1hmfkhbmb3XY5eL2AN04y+QnPsnsPWeZvlCrw8GDiIXNAcloC9Gc7jBjfZCzlQeWarD+kB5ivY4wX9lCNncw/ZDeGNI/fY2cLUUqjFQOeVTWNimm7qSLB1cujsqaKQEyar/hAAAgAElEQVQ0EkbWeYGHXY47AP11FgbbLGz1GM5MYa8+EvYNDSec5a7VT6Ph2spnwo0dBXFKrpbNUDtozp4ux7VbZEUxNNNVVlaXK2AQFSmO1a1d6w7JnMOoFwSSbFiyZr3hKipSst4z2/fX6vu0DAZ45cS8tlwE1k9BZymMOy/vx1pvVKaFZi6nUQMwFXjKuW30oK9RA/Lcp9Q2O30yN+RMukb71guk2x2vwoh/J22kHpA0t/sk5+8L929EsnRbOS/JSlXTVQDWO+45G6rPYNN2yWwWgEk1qdHWJrLsj98ddbh7445wmNr7QKtQ0mR/kai35FlMLRpPh+DHwAOcKOshbuRnZtSnNVhG0i7xxVv9XBiDKdj3PPUjzHOiXsrEeo/+uc2wZpWEWnqyw4PP2poYBZCehYCZbF8gV/8cJ6Ntou5StXttDStVXR/AybUTXOotP+SzV7c9gPZFzKnjZ2/+WRa7i7ztBW9jtjnrvzj3adzHf57XZj/IDb/+SzzmOU/nqW9/M9LY63P2SLaD003e/t1fy7c8/7k8/9L3s/kvfobu86Zw//kN/MWrX4QbXDkVY8/2bM8eOVb8Xb2nc4Glu27nc+c36VvLxF/dSWthhfpfXlXPOBkgq9X8FE5JPFhGuyusbA+8sxuAgWdLirSrytn1IM07f2fSS2MDSjPLTmhgLGMAzXg0mdsgZODrw/ppjrHeKcxr0eOxNKvwexSOVziaGupnnIMk6zAIDq+6jK2s6/t57TYTcXnu5biXsnHLx8iHQyyG3Cqq1ivHZRk7+ZC17n3sON/7anOQcS5fIdOiLqdyguqHtdZCNvDOjgJ5jlWHCz3BJE2xkw0kt7iP/yWu0ytHuUsk38+BhooSa4lPhUbCtSbHnVCn5bRiLBujLXZcn26+U9Yulk6cEaxpVdLiLi+Bx+iiP36eV6mITmHN7YRxOSTzQDDppdVY006pADjRPcXEAzeTjXK2hhnL2ZYXlsn6Y0p0hW0PMm66cJ57t/+aesqmOFelFAKCwVFr0A6kuWdiTI2hiGxI3Qz/lhBUGBO1iAynumv005zU9elmO+yu1KmPtPaEoUBmU+ZOrJC7gmUs7lVgncOVTl/0YHuEn0+9EERJitza2rHVJDSH60x3TpEj2N6grN/Tso4orDkFE3g1Z22ZzijppgdrCkJOZAc452jGoa6zrPfSMjUOqHoFjnwwxmjuAxDhWjqjDOscE7ecI1m65MVqnJZ1heJyFEPQLyW3Dussh06cI9dKOXJ7TGRGeWDrCxy+Y5H9p5Zpn1i9YgPt5sKmZ/DxtZiIIT5/prxLCiWDOga+ivffF3kFJD1fN1rVpZnqmGHtfXTzHlZuvZvGztA3WC/Q8toJJgYXMaMB7Xt8OvYwh82N4JdtL2DyAugqMwtbuIUtemlRPyc+fdUqxjlUoiqQhjIaWbq2x8e37iNZWAKEu3sL3DE6i9iaQFEttKAUGQHO31+FxY0uO6N1Kvj20LYH0B7CVJVf/Mwv8sDmA7z1+W9lujHtv9i5RPbef8WbN1/KC/7wD7jhJS/ihte/FomiL37APXtEmIjw7U87xodf/Rz+ZOfRvO7I6+n8zPdibz/PJ17yDDbf/x+ht/ZwD3PP9mzP/o5tZb3HQserH96/3OHS1gAzHNX+qFPJT4uQu7ysLyui9U48sDq12vUpbzWZ+fuWthmOcu/Mlu5g5RaKcyiG1rB63wyG4zVFol5IwKJEdz3oUxFVUAnS8rYQ9/DHTbMhW7ZX7p8H/9MoqAjGKnlvg+113yKgaChb1g7ljtVsG4cyGNT6OQGIwQx8k2JjQ3pQOVUO1HJy/Rw7ro8lYNMiYj4clc4mwNbAq8pxdpH2xU2afa+qp1dwKJ21SLpTzp6fN1DnhcRllJI1Y1+jospQcwTHweWbGYxscAzrwu7hBP2U5nqP1mYAS6G+5Vy4H+ml9RJgd9Kl8v5Zhe6Fe9kOUvNqBJwXmSjnpHDcC2GMYa02a4ztfAihkO4y6ZoXT7loN1lIF8hdhorQd6kXXlBXOoBOFZv58XSGOX0dkLmU7rASwRCrY7oIRmDqwkWavQFB6w9Umegv1erOlDj8GmvB1DiM9Wu9TL8s6rRU2c5WWeqfZ2DHmdeCWRHVsqdUAY5cKUbhU0cLARZ/77RsUzERQE5cCPGUF1cIslRrKJeYNDQYz4HuFx4kCj3gmvct+121epbLFWJtreeV1FIc/Tkly5hpJWFpFAANDi3fhOl7hilbvs83cB5ZyHOMy+iNPHNz8I4FD97DNTdWd9DtBcxOj4mbbiff6aH9s2OzhnNlSm0dNJ1PN8rfk9UlnK1SKatwh08Tng7puoVaJShcWsIOszLdu0wjDrcju7BM9IX7SUbbfl7wAYDUunEmMfw2d2YdRUtlR09Y1+GOEp1bZ+gyGt2U2A5xQPOuU9x/n5fIb587gxlkiEvpjhRTC26UfQlr61hEwuf+PJO3na/uaS3QMMp84+4sdzTu8uxfqhkZNlxbNa+9soehehVZdeVkruwM/Jr4Mii0PYB2BVNVXn/r67l16Vbe9vyKOdNsyOo7v50Pn72R5/3Fp3nUK/8N1/yn//BFFLP27JFqx+Ynec8rnsV3PONafujuJ3P3T/0um8cOcfI1H+DOf/80+KNXw+oDD/cw92zP9uzvyKLc0U0DW2KEbDTEpJtj22iIqscmDlHhANDK4vnqb4d1tnJ8gPV8g94gq7EWVa8hD66ULJocI3hMqAXbXt4kWdqh9YCv/1oMjaMP33URUXASHNcQUR4G5/nQPUv+9+CYZIExixCi1BFtb+F2LhGn6x7gqHfNe27EvsYhcFXj6f7q7vompXnap9XNbt9HMip6r0F75zRzm3d6dgUvCb/ardhAl+WMOj2SkNq1M8iZ6p4FYHJph/kTVUqoP1PNvXSOVDNOjDyrVygiqnNI2iHpb7GYWdKFe8ltxqnRCs5lnv1ShZDG1HGD4Nl5MYWiYujQg2e5aeFOlndqDBaQXVjxdyrtse469DUNQp0O7W2yHlIG1ZgyPQwI7FIAzpllon9xrMfV9IUtxPnIfOEYS2Bf6yVXp7q+L5NVVzmkIqR5AJyqzHS8YMv9nVPc+tEPEW+seaAYjrHaq9XZBCa4MMEwdXGJfReWA1PgApjPaNbAZpRDnHeYGK6UoEScA+c4nfu0zDy3ZV1X67xntfIa+MydsjPIMC6jOVylORi/34N7Fsr7fjJfYtV6FcASjO92hgOAXNjskzs3DviCrS6e5N6NIAU/GhcbcbVzi1qi0VZglGDq7nNVvREegJvMekEeVajVNFZ9yByRSxluLHJ6rcdKJ2V9kCHL20T3nkFcXop+FNdZWCwGTExy92dhuI3mlpPbn/Ept8V462qKVwAGCrRO3EvSHYx96IVWUkadLpPrvbHna2ZhE06fpn/v2SCMEoIUAagL4LZ9sOfQyl8hQekRYKvnn+PtzLN0dbBy+I7FikEL70N1fn07tSRLHUSE9moXsf6Zi1fWWC7af4R2G+IsQkQUGGYf96iAfGEi0NchXa3qgaV4fYT7k7uc8+c+T4xhJbyXCkZeEXCWTgiOqSqjvMYgOsWWB/TPt5RJ63sM2t/Y3nrnW/nEhU/wjhe8g/0T+wEYpDmffcv3cfHOATd8dpnjr/l59r/85Q/zSPfs4TRjhH/znEfxv1/1dfz56RHvfvprOf/q76T/2Uk+9Z5byd7yPPi9b4Z7P1jmee/Znu3ZI8NMZumkKY04Isq2mRxcJOotU7jah+9YxF5cpjFcI7GjwKAFq7NsAOr7IkleyTWb0TbJ0ud9g1WUzOXspGnpyPs0HBlzBg/ddRaAxYV1GrUG0rnmY3LWZdpViJj3Rw7JC+EFhynV/aSMqs+fWKV1972lw5v3TnKqczcpI/rXz6OT0/QvrpTMkODQemGP1hw4ArsRxh4F9bTWcJWODioBDUCjCB2OsPddIB5W9TKluQyRKCilBcZNHRuhVk+dY+ByiL14Vy80B05OrrDv1j8Fm5O3Ypx1vgYNfGrl2pZnA8JpekHQpejBVRshO8OUjX42dl/Pr/kG4Ts3vasaqvpaI1SJeik7w5EHaOGYAM3eJQr4N7Apbrhdc+T9f/EwZ7K/WEqqS03cxI9fyaxlqnt2zJlXIaTSBTYnfD7IBwzdiIn77qTWE51RvbecU0xxPzXs61KStFPOU7kWnZZKenGmuKYZa7AsztHpV38T7ak1zDBDFSYXFgA3xpgWYh5x3qU52iqvSQKDFm1WdVF5mN/iXl01N3lZDzhb9hfL0c6yd+rDdRUMU7K0ydSyn18X6o/q91zDHER2QDzYQFD6WU6WWUxeMTUqwtRKB1HlnF1BnaVE5LX+YwDbwwoIumK+rMVohkocrlnK6+umOcs7KVqAN5ex1qkEKCrF0zpPRQlYxk2rvmsUDQDgzuwcX9jywQ3PuJfoElXBDTNGk81qbIynOJZptJcpbcLWaLVkO+vjmTi5WNvMsfOFzxOlIwZ5dZ+r77U89prdCaBJAYcRQ5TlJQBUPDgq33V4EGRqZ3dAY6vrg0XhOtKsT+fk3ehwSDzIcKrsDGvCLTXmcaADcs1IpyZwSULSGzF7epW5xYu0e+cw1gWA/qUSHPcA2mX2wZMf5N33vZu3Pf9tHG4fBuDMWo93/NpPc80tt9G8y3D8t3+TmRe/+GEe6Z79fbEbDk/z/ld+PU8+OscvXngWZ9/w66ymOZ/7s4Ncym+AT7wW3vgE+PjPw8aZh3u4e7ZnjygTkReJyAMiclJELmtSKCLPFZHPi0guIt+667vvEZEHw3/f8zc6r3XkNqcVGyb6Puq/+w+uGw1opqu0hhveWSlqucKGkR2igOmf54LbRjILtvaHO+1AiMh2h1lwhD3IkSBx317usP+BlTEHfbetdTPO5gXrIJ4xUIhz78x10hxtzlx2DQWb8ugDkyQizDXmcIE9ci6ll3c4n62gCCIGN/RtBQ7cuzSWmlmKBYhhvH9XxUL4+Rgw0pzTvcB2obgkIVo5gWslV5xjay1qGnT3Pb6UTd+yPS5lISUrOG+Fo7h26Txp5gGQ2eqikUGNwTnoj9LgpFtQDWxTiKLjGSLPVGmVvqou+No65jjiHEKliAnQ3DnD1M6DNLoj1FqGmWdv6mIWcd5jeut+zHCTM+k6nVs+QrRa1DePX72rNfkVa5m54Bnc7jBns1eX+K+laKqXfpfMEneH5eeqynq6yH3dM6j1IGfBrpbnFFWi2vkNhsZwjVa6Sil4Eb5Oc8fStl8LSQZ5MyIsOcTmNIdbZAWICYgwzv06SQYXQRXrHFnx36hq2IwW7SIq0Fpc3cpoMYyVcsw5jpP5pZK1akhMPx+BOuY270K2zkG/UusrBWIkQgW2Xa9kNIvrK1rn1YUyRDNSzRiO8iplE6jnhebq6/iKdslTi9u0NvsMbMbpbIm8lnundaR86Rx3j85W3zlHZ5iTW0emUTkBdrrFxc3QOkC82qPg0GGPxsJqYFq9JPxmLyV3RdojtHvnmVqrNZEHnPNgYiv0C2t0R+PJvgKIoTlYLFOtFQ/QfHZiFTAxeV7OGa5KXz2ztkOajQvqmP44cwsQZRlbg5oCJgWMhOEoZZhZtlyv/FIBkQiT58QhWOPCuKbPVmnhLjJILZC02Rth1oLYTAE0h1uoHdG69zT7T6yQWcewaI4utfo6oGs75b755ARJb0Rze0Cz60F6o9uvBUG+ePbdHkCr2WcufYbX3fI63vS8N3HtzLUAfPSeJX7hTW/lpZ/+nwwvHOL6d/8B7Wc842Ee6Z79fbNGbPjZb3wCv/ItX8PrboJ7v+9t3P+Sr+bSb3yIW1aeib70d6FzCX7r6zyrds8H9li1Pduz/58mIhHwFuCfA08AvlNEnrBrs/PA9wLv3rXvPPBzwDOBZwA/JyJzX+65Zxa3ceREpoqFev+qSEUEt+3rReKSLapFn4sDKbjuKfqxEnXS8Me+lqQXNtyspZs5JKRJCnGaEw8zclv1CrvcdOxX7/wJ9R5VI6dk7QY7rtbfUQRRx7l8hX3JDPuiybHUMxfS7KTeXBuIMguuqjNZ2h4GYCY0BqvlloV7YoOYSmFLg0KNL0ckRzM37svsQmlqGqSt/WQhkp3XGQNrsUjpFEU2rcQPcgtRBEZwolzY6DDM8hrwqQBejkXKRtVK8sAKauISoLnAvixte1W/6uaNR/udOowqZS+kyIOjwuFTVSa750onP3M5ZjDCqtaYoNoEhO3i4Yjmlr93cplnJ2M/ECHaSWkueRCr4s/by7cx951l5t6zHqSo49J2xRxG2JJ9GLsdWoh/6K6SD/V9/5pRGKb/LsqHpePtRDkYzfCs9mOYoEU82sKoZSPvsdUfsdkbkZz0DnUUHPt6xY/WwJqf4dr51Y2tBT8CYSndwK2cZeBG3NG9gFbUNo2VLvvuOI+YGERYtTu+5NCmpGMNsWvnVIjyXWqlxbzK7u1LnVNM5pha6rCWd1hxO7g6/KnNY3T6QRrdLFydlLWjxsCoMVeuV6eWXqjdi/M+Di8XH62doHXiDJODZSb7i6EuqmyzFoI1PZobVf2pKuQOYhFsACD7zm1QVk+p+rRsMZh0ncgO2HY9ejqsGG6npDri1tHJUsCkvdql2VkoZ8NqVoL1Ypb6Lq3NRAgQOKW9fhcb/RFpYFSX3BZDHbHRGZTs4yDP6adFL0GDyR3NdD0cuXiPVCvCGsHs8sVsaAshtRRUZ8dTmOsqnMW7zV9yIdTisFFcbR8eyqm1zZJt3hMJ+TLtxOYJfuyTP8Zrvv41PPnQk7FO+ZU/vZ83/Y+P8fOffSvp4Fqu+8P307zhhod7qHv299he8FVH+JMffjZfON/ngzM/wIVf+0+s/PUn+OtX/gTrj/sB+NF74LEvgk/+smfVPvZzXk53z/Zsz/6/2DOAk6p6WlVHwHuBb6pvoKpnVfVO6hlK3l4IfExVN1R1E/gY8KIvdcLC95g1bVQcuDq4qJwqwdAPwhPGOhY2e5XMfg0YZdbXNGkjQjJHvLROkfGmztdDRHZIaxAkt4PzJnbcGTV5DrGgh31afm/kI9b9Wv2Mi41PcTMCJiodPQisQD1qX1xPAchCKl6u405FwS6UVTzhe2PdmMy238i7W049s9TrnWO1ew+nsmVc3VkJzmlzsMTkcAFnXdXAW8dBjz9ZNOYw9YrmyQBqx1gWanuLOohCbysRxKXh/uaVEl8BvmsgAOfQRhslogCaVpRzW2cBSEeh9vCuB8uzzU54YQhH6HVXHE2EWGsAV90Yy6ihQ9N6N2WzPxq/AFUwlRMIvv+arac71q/bGCZJAGGnn2PSzIs4FMct0r+ynInAVqSFMmBmmT9/D4fuuUTSH2EwtQbKhUiIP0bJbimI8yyFwaAhmWxyrdZAG4g04uLmAFH1qY1qydTSZsLfm1rqHwrO1K5LzBj70toelMc3u5ivwibXe0T3fJZ153uDMdgs51wyS1eHbOS1FGGcVzwMx9keZhT94iulyCo9sFhrfauUkQH1jLQJ9Vme+JKycwPAud49ZW2lGmE7HzByQSjImMCIUSKrYebVQ621rHR8YODB0SLTi9vEIZXXtvxzYXbOE4UG01ilJQ32xZNj89IMIiiJxAxGFvfZ+zl475IXdQGORvvH5vNsb4F+Zsv03+LKxTo0aqBKlaqa+x5nU5d2yvMlWYeJTq1xfTCLq4B+rd+jqiO3rgRomeaoGM8Shp5sqpSKqEZiTEhPvTrZ54NJjC0Xf+zay8HU/lEATadgBmuXvc/8q0jHUhxHGmpnnRJp9/J3YHie5bLPL7c9gAYs95Z51cdfxaue9Cr+2fF/RjfN+f7/fhu33X6C/3bzr6KNQxx//5+QHD70cA91z/4B2NG5Sd73/f+Ip183z8/91SGyX3kfp598iHMv+5d87q2/jj79FfCqz8B3/D50V+C3nw3/z0vg7vfDFaJwe7Zne/aQdg1QV6NYCJ/9ne2bOe/cHDTTXj7fpZVDWttOgO3BCCeCwWDylKimHldsm1mHw+HaDbJDU7XjeLalBFFaHVfxEdo66phc7xOn21UPpjTHOUc3rZzbohzfbzPOoCngzG6XQKo+VibGqMNeSRRLDNOdUzSGq2VUOB7lteavGrx1f/wLdp2F9AK99b/E9Hx6qES1sRjxooguR4xBnXI4CSmYWqWwVRYxcX4BCY5bV4ehzxlBYc1UJFJgogoOS4MCsxO8+Akw2jfJJVso3FVi+1o45EF8AhGM8zL/A01ZDexgP8u9s5plNZW/GpiuhQqsQIMIFxts4hsIK1oCRK0tFldbAwDJxR0Qw+47sj248t8RFZgUXy+01c8gOK9JP6NoFaAKjYmE9pYHaEvWS9MnK1vMnqtU/4paqIbEOLx64oy0uC45xOwoQtSxttPDONDYEEXF+hq/eTmW3HoH3DifPisuI3OOtmmW9wYgHmY0eikOyB5/PMxPdcx6GwD/peJqfcJcHGFnfC2idUOGOmI4N+mfh3QH1KFBwEelWjPW5XioPp66VztR6XAnRnCmhTUN+gfa5fMoFFgtIDvj57pgswF2gtrntuuVwZICsLoiCAFlnao3UzJcVi3xIGNyrcv0xe2yUXYZYylBj5bD2H0dfqyCVSXVjNg6TGYZ7psgv2p/7T2kjFzOwtaQDdctPvTnCcEjlagMrGwOV9iwVSsiGW7RTNexaq8IRKJd9WyolgI/ddu5Zg5xtqrDK69EMU4wzjGYn/Tj6K6W78/xKw5jEoiNlI3VxXm23M1cBblXhqzvE2/1MbmjUTJ0MG23SLJtkmzAVP/y4LtxvrVJEjEelLqCfcUDtO6oyw/++Q/y/OPP57ue8F1c2OjzLb/510x3NnndR36BxkyTY+/7KNHU1MM91D37B2RJZPhPL348b/z2J/ELf3KBC//4daz+yqtZ/8D7+etv/Mds3n8XXPtM+Be/BT92LzzuG+Cm/wpvfLyvVessfemT7Nme7dmVkvi/dGjyb7iviPxbEblNRG4bjUL6jQjNqMkg65RORHOpw+TGTrGPBz1R0wM0zcpGvo1eOnamInXKzrbCKJS+pgxdzmyoK6o298M2ztcSFeHfybUuUTYau6rdlKFoSNsxoCpjTYQ3e6PLGDQRmFj3aU8SGe8IVTxgeUxVIc56JKPKAcsnYhaOj0foEcGpj3znsRCZqnfoelDem0xiEMWIcGQmYWYiAQeR1up6dl+XiWgtLGIG42meEuZSkRoDB4PMstpJSXNXNh92IiWgTA8mpbJl5fRrSHF0YL1Ev4ohGe0AjvV8c0yMYsltlfNTmy7PoNlqOw+2wUbCcN+EB4461tXtIVOhZFdT8sKGdvedJ6RiZmXT8YY0ScM1mtyVLKOixM5x4MHxXnYanP00AJyijupotJ8IQ+YyBGiahPZAiewwnNOQTSZjzETdcnU0STCnFpg5u4FTRzJYIx05Yq7cvuicXeU2E5QbdzFoYyy2VRZHFahsx4b2ZIIiuGwbm0Sc3t+mO8qhtw6jHq5Q4wvBhEgiz8gEYjAy1TqpaaZQ9LybaUU0YsOlpz6afLIxphBpRIizPtHWGSQ23lEfZPTOrtPcHjKzUI21Xsemmocz+BTHsp1EeP4LgOas9anPIsTDDIdDDVyTzHqytUKc5TorzlH/WTcjQtLPyJsxNBt+HsLq9PsbEomYlonavPusgMHc48vlGfcujfVbNIP18pxJ3r3s3KZUoQwMdGLK56Ye7MhaTQRb3q9y/lDP8pIzmJskd+rTFMvxV9eskZSt8IrnoP79pptglNvyvqsqg/lJX7u32qWRbdeWoJBkPeKsyyrdcqwlgM86bA8yjFbM40PZVzRAy1zGj3/qxzk2fYyfeNpPcMuZDb75LTfzTftHvOo9P0P7YJ+rf+/DmInJL32wPduzK9jzHneYP/7hZ3PL2U3eduZruOr3P8CJJ8xw9jtexi2v+0k0y2BiDp75b+GVN8N3vhc2z8KbngIf/KE9qf4927MvbgvAsdq/jwIX/7b3VdW3qerTVPVpjUaz/LxpmgzzHnEpAlf9STUFW6UONY1SBh1gcnm8qb0EEKGxwbmKdxpegVEvazOcd44Km5JWGEPlXhyMpms7ileEVEEDU1alOHqB+zpA6+9v++OGsar4FMe6I1QQAlaUJDKAxYQ6ocFck3Q2OG0B5OTOM4YAiUkwNaduwKiYOMAS274X2oi8A77eKZwZvSxHSZFQN1L7TGHd7WBzRz4a1ICMjm8UVwxaYZnachZnTXss6i6qSO6roPJk1s9rqK/Ka8AooxAncRycbnk57/1tXCREdUEXkZKNwgga2D0EXDMhrqXWjY0bgrrj5cDHXQbNldZwlcZoM4BMmKDBQEekLsNYx3o39c4/BUMU0sRqRzHgWT4KR1a9CyywPNpGNKTx7Ur1dY2YyPiti0tpik9JSyQu09ka3ZSsYYodacmVhWF2XCUHX6xLrrClUVtCc6dwMJkkTnxKqBlewoVrGRb3IxuSd6t0z1nTxpjIi3sgDDNLM/FSKa2kUsL0qZzF/fYzUypaGkHUEuc9jIEojN3EFfhsL25hMktcDzAUZHsUQQHQ1AM0WwY2BMTgSgbNIbl6QJz7IEIiEYfNTNg6gD5XyM6Hc6gy0rycvrF2CiI0eikaGSS8NzzY0xpgqa1C8YEfDecpzumcJQ/PxMbBKbYHVd2XIGT7xv3saFevOhMCI8UeeezfTzaO/fVcIQAgGFzsmdDVzohhngcQVq2TzCkaR8y1m8y3G/7u1ZjY1OWcWt/wNaD47ImrZL8PNgHtlU59mAHoKZHNGER+3Rit2qWYWo3yXg3aQ5iq8ppPv4Zu1uWXn/PLfPiuZb7vXbfySzfCP/tv/5G542scftO7kemDD/dQ9+wfuBUpj086uo/vedcZrn/Fu9h4079n/WMf4a9e/PVc/MLNfkMROPZ0+LbfhVd9GpJJePvz4N0vg19l/UgAACAASURBVAu3PqzXsGd79vfUbgVuEJHrRaQBvAz4oy9z348CLxCRuSAO8oLw2ZdtkUTkLifWkJKHK/8Q+0i3QzVHTZProkNEg1qUGcq0nCj19WISNzi30btMFhxgzniHRJPI1zCUqY9+23kzTffq2bEmrFHJc2ip3Kih7kXtmJyC3y4AtCyZxTUi6s6/Rr7myElxTJ8+GI1y8iimGckYMFBTB3JFmmDdcQcTHE2rShQcrM5XHYbI0eyv4NQiRhiMcgZpJce9O8VRxZDaIX1XObiqsOX6ZJnD1RzI3fu6ph+DqwHPTKrrmJQGIt7x8uBUIQi0FM2pCwEY7adlj7u8mVTiGeFY+URCNpHQ2qwDDD9YVd82QQMA3RnmXLhmhig0463PW8HYduUIW4PxHl3+2pVRY1/9E197VAdPJSOpvn1BKdohUOvb5Oo/xdcxQnEf/DFn4zkqkRDGhEJEIo7OTdB/xvExRqQYx6h9fOzqslbsjx2O42s9d11g7d/jKY7jN3dqedN7uQKjZIZrJw4xOzGNoORqaTf8MzUI9ZJb/RFu4IhEQoqjEJmIvOh9p0ocnhET0jUlPGNlvRtFvWSlUilqSbId4iIwIhBLDTDh61TjmtBP8fyoiYKqqPj3y9gVepH4IjiROi/x7+KIvBn79WcCkNca81QoaIajLNttzuerFVyozWNxvS6onRYbReIFYUzmU0O9UJDgTBAxUZ/qWx7SVinPRfCnfjtHMxNjn0WYAPz8CSOnxDvDcv76+31Wm0viILO/m0EDkRiXGK9aigdbm9pjt6VAJEJkCja2Slk9NVyhG9h9F+oHByPLyOW7+suF+QrvdHG2YuYg9H2EyRAUMGXnv4e2r1iA9tY738rnlj/Hm5/3Zt7z2Uv8zAfu5l1PyLj+V36KQ1+9zoGf/W/I1U9+uIe5Z48QK1QeX/svnsir3/MFbh99Hc/8wCdZeNajWPqef8Of/8eXk3a2qx3mroMXvx5+5C645qnwB98K7/4OuHTnw3YNe7Znf99Mfe7PD+GB1X3A+1T1HhH5BRF5CYCIPF1EFoBvA94qIveEfTeA1+BB3q3AL4TPvvg5a+5yZCB3I+LQ2khdJcsuCDs6IHUDGqZJQ+IqKuzHhQuF7ckg48jMo5g1+8J14VOXar2DNPaR/96jjzA4sq/W7BoORNPEEjGcbVUhfQgy8YR0vOLEwWms1XMMWwf850Ewo4zQV2Fxf70q2ELQQoRmd4BtxOw4SKJoTOGxzsZFV1KYHFykb7vl9UbF9qbekBdMqBG7vO6sZhIxtEOW5qs68VIrzznyuF0SaAaYCZL9thFh5z3LWGfQ6qlY9dOqRIizNFeDst/IeQcyzHfutKwP6lw1y1ojRXDMxhMUc+hMaJQQGT8mI5hAZ6gRcIGZEGEYnPTusMY21Ma5tTmkn13uJDoUG7WqfYqUSZEyVbSe3uZl0RXUeUe8dg4bwAkokTG4OKKVGFomoRF6c7Wiae/QOs+g1VPQRIRGZNiFLDBUKYTVZwEA6vjm9Xsg7f1jkzDILXlua1vu3tPXemXRBLQPc/30EQ5EgVGKw/MXG3KnjKxPIoxqdWHGeADk8Ay4KQAaMC8V0DB2vNaz/D1s35IGev18yTDK7nRi68aeaZVw3UkB0HatRROQJ0IW1rNz1ovzGCGbbKAoIr4lhNbeS9Tk9X3d6/jDVQRU2s2Y4aO9MIhfr1IC+zgyXDs/wb2RlwEp3mwaGcQ6tgcj3+suXOZIGlV/w11rIY8mUeNBWSIxvodZBV4BIueYXPA+Ul346HHtx5asZd38tRtceKeZcFX1fpB+LEJ39trxj1SxTb+2MxE6B+f9/OJqa1vZji4X/S0CD16AKKoAfEhjN6a4D1+qAu0rFKAVvc5+85/+Jm//5Aq/9clTvPfaNaZf/1+45jkD9r3iJ+Fxe33O9uxv3170xKv44x9+Nn9x/wo/+J4H+ac/+S7i3/01hnffzeee/2xu+R9vHi9AnpyHf/yT8O/ugCNfA7/7YvjD74PthYfvIvZsz/4emap+WFUfq6qPVtVfCp/9rKr+Ufj9VlU9qqptVd2vql9V2/edqvqY8N/vfjnns+oYqE+DisTgXE7kwDuCWjJoo8YcKhEtiYlMcywq7X3hyqlQMdyeXk03986BUw+o6ilzbsanAO2L2myLl62vGg4Hp1+gUEqECqB50QnhwFTTszQiaF6JjNjIR69LUFU4hwiPio+QLHUwQ1/fVrSkEnUcGg4QY+lrykQSoeJKx7s4VgGGwnyXv4tCbismqfS5d+lIFGlVE5pgo1Yp1FG3Yh668753aUNiKNgcq2TJDEdbB5k3014iPzhtO4+9CkLKW138JBdbDqFsoUAAaFjEWTJbpFW5cn7raVuIsKZ9RB0Hk2k/pyL0Jq6hUO/zPz0LlxifYjp0KWky7vhlWtXYyG7vFrmCo6fEktT/GSipyo216lAxY6zndO98CdKLm5BqxrL1TbtjI6FmT0iIuDb2GUYSNXx6Y6GeVxtjvn9+7LOqJK9w9CsgiQh50iJtX4MNfWgFaHQ8wI+NMN1qsLnviTVRCClnYPe6EK077sJkI8I0k8qJbsQYhCiOsK4CBWKq+yImopdl5M4yIU1mo4rpOcRMuY9xWqbDeiAz3uftWQeP0mpPsq/t0z2jGsOsRtinEyRjfdD89+nRQ9gYxFXXAZA3muTJFIgpVTut+vrG3vTVaGQC6ylImYKtIFHJoDlV7Nw4cwU+NbQ4k5sMDHMskPh01NwVTJ7h4NwsU62KbXexCSIhlGxUY2eIWv9M2WjisnRErX0m+L552dEDgCvvqe+jNgbVEYTJxiz1atu5th+v7zsZ+fkPokjq9Iq1kOuPOlqNRT14yloN8ngKZq7x71SpIJVUL0gARs63RvEzIhBSf200/rSqeLg22DfNxo1HyPRy9rtuX3EAreh19sZ/8hu8+c+2+Ohdl3hP+wGit72Za78hZuqFL4Fn/cDDPcw9ewTb8f1t/tcrv45HH2rzwl+7iVPmibzwA5+m//3fRv6G3+ZPv+XZ3HfHX4zvNLEPnvfT8O9uh9Y+eMuz4FO/CtkXLzLdsz3bs79ds0nkxRwOzROJYDUnypxPgVMPXLJkhqx1AGcSDkbTOB3/U6tcua90J7OAeuVFEbQm31wgmAnTJDaxrxmR8e+ckTEnpopCU6rFlY5+DfzFRogkrrFePr1LwjEaC9slq5IXKU52yNVpHzXCdCthMji7xSGGk57Niprtcgx1kGpyy9TyBlPNmNmJSiyk6uIaHJ4AGGKFdqipM7sc8eL6VYQZmeDaeL8XqksiTGBFmiZhQhLftsAI+9uN8dS52u9ZzeFrMN7LSNR5cB0AF4FBm2jEvv9Zua3gJELUeqctErLGNL39cwiCsUpB1AjKRBKRxxOcbE0zaB4cG5sHR+G+FWmohSNapFnWrHDMe4emw6UFJslIYCILpbrL4/harKHax8MoNPY2EiTz/RFHDQ8kNWr69Eb1qVuCEBds7PxseYuMCL1JL5Rq8Mzv1MyxsXvoIqHRmOPQWOpj/TYVbHA11/UatCSOmJ1Iyn8XyqEmTHaUxCU4dI3JANAMViFpeIGPQVBTxSnGxGTO0hnltE3Ls4HFeZ0rQbZYSyKRTz+VCjQWz1RiDHNJVWdVBwoqQuR8MKH4NAvXkEeGfDIpb0cRv+hOHSNrztGIY7LAIDoR9kUz5EkbjTxjJGJqDc8Ak+DKnnvF/R43E/qqiQCthH2mDQjN9gSjqQlGueOGac+6TzRjYhNYQXV0Zq8PKY4KzhFHhrkz69j6WYp3STwVnoHIM7cE8CPQDOymBPY9Gaa7l7lfh+IVQ4uZK5j4IsWxDvyKFMU6uwxg4+r9o4DGhs3r9mOjppfvD0y30wrg1cH/eVulh8ZS6E8qLjJ0ju5j52iRbuzh29oN1xKNOpeNY7d9RQG0BzYe4Mc++WP8zDP/C2/5SMbJ5R3e2fsr3Af/F8e/8xATN14HL3ztwz3MPfsKsFYS8Yvf/NX8xsuewn/96Ale8Xuf53Hf/BM88c8+gXn0dQz/1Q/y3n/3DZxZvGd8x/YB+IY3wss/DKf+HN7yDLj/Q5f9gd6zPduzvxuzScz28XncNYdo5B2M6xOPivqogkErwFBEw8S+cuQKz+jYJ+LrWdae6ftsGzF0TCVIUjaDFmEmnmbMjHcKHAIjz+JEUkV5i7TMyBiSZuI9L+uj4E+fO8rh2RZGKpGQy2t+gvPUbOCkAiyxFZqNmLlJ78R4ksaPM4tDZD44P25i3vf4qlmjP8CIMJlEIR4uaNxilMzWTh2Aiauc7d0sUql4ZySk1AVJ8pBuhQiRmBrX4lmOdbqsBBl5GzVK5yvDkUxOcizaT9Mk3JBczayZrFibMk0qiEGoEhtzmZigGq982TQxB5IpIonpHT7gR69aRuYPtps0Y+FA6wgqES7UdoFPBTww3WR+aoKD060SfBRiHQX82nzsweK0Qe3PlPVixUJQEeLOgCfdO2AumgOE0cwEGoB7Ma+eIQlsgcC+6Qm2v+oo2VUzHDu2DzFgsTgT2KjIA99RYx4jHtTOxH7t2loKYxxBWlsC+8wUo+OP5vCzvxr7+Ot92qWBBvuYih+iZ3yoDSvXtsQYVzCXSjOOaMZFWmyNsS1Ys2bi14Ex0PTr3kSeXXFGaCWGbKZF99Cc72VlDBZlbTSiIRGmnqZcMpOCcbbgseldf6B83sdqMRU0KQBjzf0Wv46Mq94JeStGjQ8yqBRN0sfn4Ph8m0NTMYOJBmlrHgfEEmMbCfPxDHl4BrTWCNqZBnnxvnJaC4h4u6bodRbmzESCmdtH3oqZnUhwKkRxQjtu4FdbxPDYHIMbPQuVJ03PJp5bZzVdIIkNh6ZbV+xz6CQuAVl/8lh1n0RoxI2xe9zaGBdWgqD0KJEPRJTkfxA8QjHhXVWki1YZpOJVKcN3rtZLMDbi56Roc+AcRd3u9ETMoekWliIV2G+TNvdTDDaRiFgiWuIVLwf72z71vBxd0W8vLVPcH8q+YgDape4lXvXxV/F9T3glb//TKUye8Rsn34/7/G1c94on0Gxuwre8Yyz/fc/27O/anvvYg/zZjz6X4/vbvODXbuKNN63y1J99J9e89w84vDJi5Ru/jd9/zf/Npa1dKY1XfQ28/CPwvP8MH/pxX6O2efZhuYY927OvVJvItpiTPofbzVJWX8p0NEElJkJw+P5Wk43q70tRZ1SY4sFF4VMbMWzYnMHEVUCdLDM1gCbFxkTGoEawh+aZuO4ISeQl7SPxP4vIfqOovzJTZMkMc9c/i2ZgBa6khOaP78cdP/FRYJpkTQ8GYgtTE00iI4hKqD/yNt3Yz+FoFhqTjJr76T/jaZTCCWVU2zNRZcoZAkkTN1G1ozPBAcZVjbkLLcBHx0cAiGS8WbMRX8ekRbqVCIkYWqbBjdHVaBSUF0VCs2LvuFqFg9MtcuMYPv4ozajJbvOy7gUfEKL2ebesTRrbNooRzYniBsdaB2iYBhhD58arWLvxEJF4B7ERer2J+HVia+wQ8SRm9hgycySkz/mP+9OHwwbeGbUhFW1molECNA31YI+Tfcyb2XK5TNFiTqeYjtrYctjh3glsHm6XDI6qd1ptLIyO7kOTiOON/cyIB6xJbJCoge85lniAbGKuDiIlUmulEBmD2+1jmZirD0xDZDgUzfLVreNMN2ZIc8fEvqq90WC+7ee8VSgSarhvCXOht9v20fndd6CcsIJ5lGaMPXqQ7Wv3YafbXoo9NjjVsj6wZGTVYUyEU0dqLA3T8AxWATqdDyCogLGeKc3nJrAzTUp0IdWPSAyDJxwp58IgLD/pGr+/0zFAaYzQ/dpjuCTGorUgRMLq8Rv9mo4Mren9LH7VP+Lc05+CA6ZNm2vnnkY7nmSEwyQRGsQ6FAWTkKjSlqbvA2giUGhEVfuEjSdf61MkJxKaJia77gh5YxLbvoqIiLw5XwIpxLcH0cnQsy6KPCO8NaBve7V5MJd1hIhM4tcPQr99BJXYQ9wCoAWrM1yrjz/M6hOOUKxXZ2L6B9rlRFf3TxETUz00ymC6xWg69AG8PgBRkTLwAzA72eDqfa0SYGfWlWy5MV4kpmkadK+eLc+ZxxUT1jAx10fzTKjfZ3p2vvaerxhSz4l+cQj2FQHQttNtXvnxV/Lcq1/Aez5+jBunhf988ztgbY3j//ZJJKt/Cf/yfdDc63W2Z//nrd2M+S8v+So+9OrnsNkf8U9+9RP85skmh9/wh1z9xjdw/V+d4f4Xv4C3vvn7OLl5stpRBL7m2+CHboODj4Pfejb85RvBZg99sj3bsz37WzFBmG76/k6NOPIS+SGCriLMtROcMSECL7TiiHajli4XjkLtE8FgghqGE6U/suU2UnMiCuGM0vkTw1SICBMbzOwk6cxxcJRsggkpjUVjZtQwmjwAwbmQAgiF31VqfoVEZPPTEEUcm7iB2Tg43065pjXHVY3ZIgvI9zEDbCPh+vgwjaiJbczghCCi4pmuQiYbYCry4iaHolmub12FwdAKoigahONKhxDPREyKB8XHogM849rjTBfXj2BCuhSRhOsW4igIWkiMnZ/EhhYABaTc124QiTD4mqtQYLbVGAPQleqhAXU+PazmQkW76klUhNFkyzOqjWkk1G4hoBNNNg89wdfCiRCpb4hrxGDUpx8WhImYJsRNiBowfx2CkCWzdGeuKs9TmACNyLBqtwPLEvxTzZgxzXC1YXurtBsNnAQH1K8+FGF41Sy2GZerMxJhKMKTZ64Pc5gEpUZDEglPOX4QsY5MGmP7JJEZC3obKkZJxXBsbpIXPPGq8KXxfcLCz1yF6Lqry327M1eTzVxHM2p4Bz6AJGcS2qbF9Nd+La4Rjd2yyA5xE94hF/HCHJGJcAfnSGcnMJFn00xgP1WCGmuoW+rM3AjRBCMsO9NHSaSBHJmjePBuPDxTPTfOEWMQI4gYCvXOsqZPBKOCNiJGR2dLgAbhfM6RhLS+SASLxwhWFWcqZ77zqOvYPH4UDamazSQmS6ZRE4fxC432flpRi/mpJvvazRKg+XluMCtN5iLv747Etw2YT+ZC+mZghZ58NaOnPobnzN6AIBycn2M09xiMhmusrXWRal2pqbdZqHqtHZhp06q1FpiRCQ40DjPdSgCh1W7RuWbWBxZiQytpgvp0xKyZ4KYCAGzEZXsEg0FDavZ4SrB//o1E5TM8IQ2akpAHkY5CPKRQRL226cG95wRrqaaBgUeqPnQminhM4+pyropwwVPnj7DPtHEm9G8USMSM9cKLsUz2F9GZCQ4+9Tq+mD3iAVpqU37kEz/CkYlH8Sefegr/19EG3/+//yvx9DTH/vWTiB74n/DdH4TpIw/3UPfsK9yuP9DmN172FN7/qq/HOuVlb/s0r7xritt/8l0k3/PDPO29d3Lipd/Er/76d3DrpVuqgvvmFLzwl+DlH4L7/hje+lw4/9mH92L2bM8ewTbfTnjUgXbpiBiEdN8ECUnohyQBaBkiMTWBB8qfx6IDY8dUvGLeeHvW6mfJMgUnDGDnyIHKoQTa0uT20RluT89RpvNJSL0MjsjkNfv9AHKtwBohNSh4BPOTDfZNNth5VBijGCRp+to5hFY0xaS0sKbJVRPzPGXqWl9jdODRTDQi+vvbZEnEYw5OEUURCCV4TSJDf+poORtHk3mON+f56omjTJkW108e5cbD00zNHYT2Aa92GcBVVWpUMQ1Nk4D4NMBDMy2caWASn/JpC8lv71aV+2psSG88NHZPZv5f9t473rasqvP9jjlX3jmdnM89N+dU4dYtKkNVIRTBVrEVFOXZhmdqEZ92a2srbb9Py2sbtBuFFmwVtMAIooCCgSAgRZGpCFTkVt1bN5y895rvj7nW2nufe26sXLV+n88+e58V55prrr3Hb44xfiP0aJZ8vMgSt7IOcROD0vR66HoLIwu21psB5dn8Ip0UV54vjHFiaIgRCW3/KQVihRvaTomxVoMBr8ymYKIbyqWtN6E3xFH6PE49GnKJgek6mlNjVZaSYtO9an1p3mE6klJhEgDaMY7WxCrtBeuJG6mEDBRdyjqikeT8xZtHOOl4RBtexI217QRTo0m/JONaayhMUrz3YRsuKG73vD1hXEq6HjQDuFohaV1B1UNkJCm90COyMD43Qm3zODtb2wEYUFWrjCgCvkfJc4iTrlra1KKTTIYcPvRiKqFHoxjgjVvC56ApeBoRF19cpFBn2S0lHjTbT7FoiA2u8liRNrHnW8Ibdb2q2gjK0bZfY2PvfUaKDXu8GWJXc2TLUHL99vpWRyoogfFaIbtmTIfmxBYWw0FLgLSmE9u2dFTXy9PBsG2kwuxINXUXWX6YjFExMZ0gQqLAtsPRxO3UgwYdJ0Q7DiDMh8PcM58Q3SQfLBXAaFQjJra8iEjb++c5CkeUfY5EJSNmHQoh0lVkTcejG+KtKVkVN6tocZPj2LE33ypaJU8t+K7tZ+M1QQlLY3UWGgUqKsqOXBAfIzqZpFCcLM+wcHBz1ldaOQxVg6R0hFBQASc7iwQqwDj9ubjR0K5u45yk5iOKdhJ2uhQ0u4I6SuGLy2Pj9jvEJGO66kbJvXAzT+CgW2a3P50c1k7YBUtHQAmro/2/AWvxnCZoq51VfvojP82ppZh//vi1/NSWiJf8zi8S7t7F6HdvQ33qty05q0093U3NkSPDpqESv3LLdv7l56/jBw/PcOcjC/zUI3O89ur/xO1bv50r/vBOvvWq1/Izv3Yj/+fz72W5nUhYD++CH/gQ7Ps++INvh7/8cVg89vReTI4cz0FUQy9RC0vDalJTxeYYGOnOIaskzyixpWwekDiWWCBZ/S/EGl8KQyie9TiJFVuweUddeifFAse2D9P2XeqRx8jsEHrTJGUV0RaYLwxmhqL9Y432+YMzmJkhCr4D7Q7NYleIYblSyIxa33Mo+Q7tJBxy+/bLGNt8AJMUqvaVy4hTYyls9RXcLfou/tBWUiXJ6oHvYP7wXpvMH8cQW2XFtuNnnh+deMayblBCOShaBdugQnv0Ehv61BOKZAw0ix6T9QKDpQCVhEYJMF+cwalXmG8W0SnBSvpOi+DQ73XKRFWSZVUnUcts7c1IQ29xbiMK4k4m8mIElvwGsiYCp+0WWSw1rQdH6YxYxybmRH03zXJEtGczQbnao2CpUAbinly9x3Zvyj6nXtSHdk5muXmM1Fls2nOn8vG+Y8Pu+rxrxmQKdwB0YrTWlJNxbGuw2fBEJTDkNikmhcRV5LECRF7BhuEOW8OyXU5yBRMiGIslax3tp+XHbJhZenmJV+/4eJNYOZQv346Kuv1mxOF4ZZMdI5lkfyIS47vEQ3WbuyXWoC/6mvlGjc6mSSYahczTYZRQ8h0i30GUQ+AmeWmJd84Th1rB43htOwx/GzK4gSW33vV0DB2i7YZgYnxlx2pHq8RL3OPJNjBcK5KG4DqSStHbchueWAGNOAlt7vU7xQh6aKe9X66mGia5dYkOqlKKduJJ7WiyfKqqLhO6OsulE2DvRJVG0afQsYIZuD6EAScumUE5Dqx2EgGhGMGh3BxjsFUiNsLJ8hTzhQlOVWZ6lDEtdrd2d71jxhB6yiqkJiHcWS7ZwA6Wgm6JCyfLr7PCGqFXoOfSAVgab7IyOZn1i1ZCJeG+sVYoR7MlGGE4mLJqo5WAk2NVNjl2cmC4vI9iMGLzx3o8VCbws+sYKEfdEGksoZTYZEXSAXQimJI+W8bXdGYHgBidCIMgQseN7ESXCEZbgnZqsJa1F8CbGISxgyDK3kVly1oEYp+7atSjaEs3p/ZMeM4StHbc5g3/+AbuOXaEL9/2Ct60M2Lff3sDlZe/jKEbR5CP/Bp8z3uhtfHpbmqOHOsicDUv3T3Km75jN5/8f67lPT/+Ara97nV88pfeyT27X8Utf/oI1R/5T/zYz13GFb/9E7z2Dz7Ab3z4Lv7Mu5m7/82HMIvH4M0H4PO3kouI5MjxBKHXQEvfPcfWkhKFrxN1L0mImiRbJmzJKKGTiH8YERp+V9lNNCgxjDoNVoOu18gtB137RmCwXEB8h6rXRCtFoRgilWIi7RyxUhpKvGySedJOjFZs4TYSD5wxlMLurPbxjROZgZom7qd5O6VaC9f1E/l/RcWz4ZoGxxrMKlXG63r3YgwoTay6HhGTqKi1Az/rPVXuXr/tE8VUaZJmZNsW98aspZ8TdTitxJJN1W/KiOcwP1rF8TQDpYCRWkjgKMZqEdONyIYLAkVfZ4Zj5IbsLI4z4lVZ9ps4OgIltHVER3Wl2a07sMOjCyvdE6qAeGIQV3kEifFnyxlou5/SOI0yceAn2iCJ9yvwu0QaktAxoUNXodPRPvNX7e27vo7jZgTNFIIsmiItTF4JPaqh12cUizH9tbdig3Y9lt0lTg5WmB+o0FEaWiUwMaKscMvSvjEEW1dN9+STASyPzdhTJCQsBrSrAYNSwlJlps8DqJT1ED4wc4DHpgYzryLAQjTGgwf/DSdHdiZEFrRA0R+hIIG9x+n9V8JkPSJ0HTquC1qjkzIFJiEO4+UG+6tjPZ45SMM+HdGY1hZWlcZxini+az2bYp+WLYOXJx3dQSllc/C0mylRLvs16yWKbY6aEaATJwRUErGZOLlmYaZpPWW95Qc6cQclisVgiM2FETb6LZbaS92JC5V40IBVT6EST2RcsM/L6sQgadivVhC4imnTyCTyl/0WxokQx/rkjVYsVELbPqWobxrn+PQ4Rmv0iw5aoZfUu6kcSMg4wHA1IHQ1pVINFwcRZfMk0/vhhn2F3jsDBR7d2OL+nXMYIFAe4+WJPoJq256Qd1EYDK3xTUStMVbKAY5yaKelSESyOmIFz8HVitWwzqnSbB+plNhAdYJVt0jRdxgsF5gMmtkYExFiR9Eu1PC0UO+ZsMi+WjwHCTzEdHCUphPDycEGopys4L0RB7fnvKnIiFMIaRQDyzyv3AAAIABJREFU5kZqxMO15DrXE4eS7LrOhuckQYtNzH/85//IbQ/exTe/9Cp+b6Nh/NdeT+snfpzW7hj5yBvh1X9hPQ45cjwLICLMtop8264RfuKmbfzom36Oyz/2MS77sf/Aj36uxn9750cZ+tjP8nff/CV+91//kpf+/p0cvON7eWvtJ1l4/y/QecctcPTup/sycuR41qPPIBCBsIzaP2tV2ETjJupekoUfJYZ6Yog3KzsoV/Zlx3DFoa5KVL1BJlsR2rTp7JiDqTogLAc+i9uGst/ygucy2yyCMehExELqs6yGjcRAT2dmE7KlNOFQmcJUPfMwxBgCqWUKiwCe9Is5IFbBDUC0JWBxDAvb9zA1VqMUOAgGFfpQSzxxQg9BI3m3OU6ThRmMiRGBo7OTGVnQhRCVeANsnypUGGZkw3Z6+ra+QdNr9qV2jyBZGOClI/uT6xIbgJi0caAUIAKeaHZX51CiaJuY+cIYWlu1y5OVTX3nUvFyVncK4HhlI6VwAtOsMBJuYFB3hSqG6yUaReuhCsYHiYPkfiXXZr0R/QRUDDYHLbmq6Ub5tH5AJCHbIMqGTYauwqFLOA2psIEiVj5p6F7maxCFGqzRrkecGq5ybHKUB/Zvg4EyGIgbdUy1ZAukV0bYML2BwOuOkROHrmd5IFHec23umUFwHIfpekjRc07zoJF6mJRisRZly2x7NO2B4czL1Y7Bj3wqoc0dQncJ2u7xGsXAxU32nW4WrKdad+citQ4JlAud1a6fu4egivbQ4uAoHwIfRxSm3WHGSwx66Sp01ks+cZrLKcLE0AC+o21dLa0zDxoIl1c2EKBZxy5HBT2iMyI0wxb15mEKjo+HYkN1g/UGC4hWSf6WYcXXKHE4MVolDpOahZ6LCTwrTGLaCLAUDLMU2XDKY61LQNl8KZOIpcRiSHzWeJUCxQmb/9cohSxNb7CeXiUcre+E1hbbzsYsA8UAESGKijwyeAXj5U2U/HrfpFH6vWiApekGI6NVOr6f1CMTGkHD3qPuLj3kXYgRTHEA9ZIXslL0Ua5DJzYopfFOLaGXbDSQqxXNyGdpYpr5XQeIxeElw1vJRrYbZG0REdyhbbSdAEFQonhkbpaT2zdbkrdtiGOzaZihYrVVYHnaPr+6s0SoA5aigNh1WBwctB40x2c5mTzaWNjOqaEyj41bwR4duJRfeAOVF1xKacMQgWND3HvRrkcIBsc5N/16zhG0Ttzhlz72y/z9PZ9l4Ruv5Y+bj1B6068y+qY3URu4Fz72m/Ca9+fkLMezHsrzaHz7K9nxNx9i23/9Tb7/1E7e+OYv8eJPvIMNI2/kmss/ycMTm3lN4S383t0llv7H5Xz9PT+PWT5drjZHjhznB1f5jGcJ5QKFAbRXInYL6KQGTqTLFN1CVpg0VXUUwNfFnuK8gqOEhirii29FElZOYByXjSVrOMSCJUukeRVkdagclSbOh5wcPoSSJHfC0V1D3PGJXG0LRisXnJA4NmgU7cSQ2dHcQcNtWo9e0c9ClBoFl7FKaGfXxSb8x9Um7tg0aI8Vr4TsvAac0BK15CqPDczRTq9RrGiAp0PaxWFWoyGMVnxr94xdX25CZdza3p6LiCLctQu13/5GFwKHMJEmr7pWeW2NWj9e4hFbrVg1t0wwvqdmFYkAA14RIju7vTEcTOS/JSO1nbTAsGPzeNJrSImnmJhOKnxQ2sBUaSclt8Z1G2+iXall220erjBcCSn5HpAUzV5TJiAlaOm9lUQkpGM6mb25odYNHcvIl3QlviUxwGf9QVQ4Yq8v3Tb12tL16AhCsRzZ2lu1CnHRx4hiIRplITFkIUaiCJkZZ8pvYNyIYuBlZGqlE1svS9Jud9gaqDEG5Shc1SXqpqfWU0y3kHkpTOVK7fu+iSpXbRrgui2D2fau5+LMTOCk0vprPA7jQYOpwvZMfELHC8mOESZIwi97SiIg3aypa8evpuYN2YLeYuX11UoHTxQkyoxaKUQMnVJA27cktFesR2KDaIeqU2JampwY2k44NWTv9xoZoMoVO9ElS65qTsGGRCqHpj9mRYY6MUWvhJ/kWNmC9WBMTFQOEHGQOEb3TKoUHOvlWo1XERGO7nohJy69KbvPGDCOZKTVJO0XFMp12DhUYsuQnQDoVBuJl1LZe5uSp5E9UJ+G8ghe0s+BW6Tyg29gpjqbXWM2eZLkEKbF4FenBrJeUEaIk/FgxVk0VEZYdcu4jmCMyZ4/rYR4agSlFLqzkOT22iEwt+m78d0ScdGKo5Q9m4sY6rTmYuo1VVBoshCNsViYQImiA6gkry4uBawk4iPRFZexMlXHBN3JokZQp+HakMrlgUGWNrVg60FW/Fp2DfODJVaSvFXl+4jrIo5L5LqMVEPi0ghHWpfadnmaxtYNTDYiKqH//PKgrXRW+Im/+xne97VPMrLwY/zRyufR73onk//77RRP/gV85vesNPnA5qe7qTlyPGEQEYqHDzP7jt9n7g/+iG+rXcmvvuUE1/7uR7j7Uz+LmnwrSy+5nv+z6808/IV/4Oh/2cEnbv0NlpZXzn3wHDly9KEShOwoJMV2U/I0dQULg3MgdvZfK59WOEDdaeFpW6snsZURoJEYBa2ST9F3Eul2w9biOHuCcdpukW3VCQJdSOqqwdxAkVrBY6pV6v6uJ4bGiunY2WbEKgwm3oY0yJHKGJHy2FrdyGUTV7Hot3DcGriJapxyWO3Y+B0tXRU27Qieq0E0RnnExljRj6hOPLQbxEGSHG4/qYOE53JicCRLqC/6JRu6ZxSxDjBuAa1cxHM5MVZFdl+HM7HZzphvm7XeR8dB1axSpBvH1CKfVtHHTcIHxRjKaZ01wNUu84d20i5GCDasrE3cX+OpOmUnZof3QGgNrNLQLhbK49m9QaDuFAh0RNgc51uHvpeTlxwGYGhuCLcnlHKlGBIfvB43IclKaQ7s2ZCdMvC6Hhcgy+WZaETsmbDkwVVdlTmj01pt0OlRhGyFVW6cvpGVqWEwMBU0qDg1fDcJm3M0C2YFXRzhxPQ+Vse7YauxVokH1+a26aQQlFsssGmkSbWa1BpLSMfcUMmSIWPYMlLloaFrCJVLG4NOFfG2vITVTsyyV7MkZvdGnKadTDDENicxIcwigO7ep5JTRBA2DJRolvpLIxTrg1RCl4LvsFKpgYjN2RFFQQL7jEiXBBhjiz/rymDmrbbLY8zIduKgYsnFwNbk/griR11Hi+6qO940cxNqbozFeoQ4bhZOOdkMmRsssbyhSdu3wg+9OWgmjonckNAJCDqK1eYQ4jp9cvm9SJ+rzdEQe4pTANy8Y5hKwc/aVfdHKOswydsEiKmVPbS46JUO+6a793eyMmWFV+LYEmI/gEo1GXfWmy7aevNSAuUolZ1Li7Ihwsn2C6NN4rGeCYEUA1vBjbJ908srhVXKvh3L8Zr8tYQO0mmUsj5bqc2x4lUY2bjf3jNR4ITMDhSYbdmJhTZtjtZ3c3z6ZtqORjkup4Z665xBvTzRnQAQnX0OEmXKWjHAT2orKq1puyGrYcMWJVeCojcv0r475bIdXyPdcGLX0WhjCbNSLp1qiIwN0anYa171apwqThK7GvEdtNtVku1yL8WK32AxHEKaM+wujqN7JnzOhucMQZtfnedVf/mDfPTuu7khfANv+uIHiD/1Sabe8VaCz/4HuO9TVkChMft0NzVHjicNwZYtjP7XX2fugx9kz6GX8XN/W+DnfvMh5E//mHc+8kbedeUB/mzP6xj88v/mgTfu4R/e89ssr+RELUeO84Xb45URx6FTLqCUZnF8iKN7NthoJwye9vG2XY7rWBXH1U6MIMjyMrXIw1SKVmq7uZET5Y0QGwLt0dQBbSdCOYpHtm/i6FRaB01wlcpmsQFW3AL11hYatWmMMd2wwGQWO4N2iLTHVHWaWlDjii2vwHWKEFnjw984RyeRL7fbu4lBlMaLKVbqGzk+eIkN1QzDrkcpOYVS/UIIaVHqg0MHGY0mKThFK/ygwBGPTaU5FhsFvLBMuGsnhYOb+/bXiccr0D4YYwUjEm9IRwUMuN1i3bpYslWQ0/uSepocBaVhvLHRxBgMbHHepJSBbmygEzTtORMPWtkJ+JnDL2OkGoIbZp63wHGYv3oPi6ENITNKgdZdOXyBKHC7BYgzA0yszH5ybY4S6kkeUSoQo0QwvgNao4zgmU52L1RizK1ODdOulag6ETPRZjZWDxDpEpXIGuQrrR0szm1nZbxuFeJiaEcex7aPQhAAhk7oEhcC0BqvWEeP7Ejvnh0HjiJUtqZZObSeNS2KVXqIieMxcsX3sG/nDvZO1BkdKCeEBpsLV25ZO2tkj/WsuNZjMhsMMFkYQxCGo2F2JZL9AEwdhmqPYM3MxqQLJZk0UFw/fhW7B/dkXeoqTeXwHtqtxOPmWcJ6bMcAJlEwRHvg+IRemZXtG3C3X4bZdFMyaPqLBOu5UY4ObKPd3Awi1P1RRotDeE7P/ZSuZ3DQLeEYYSRqUXJL1vuTXOuljTEOVmz456w3yMZwMLuXtrfFEl7A6QndBEsEIu0T+w4r7Q6uiumYGEkIsna87F4okeyzTp4/3Wv4G2O9gQAqIHQVG4cqXS9grzcQQTaMYhqV08Izs2c9JUXJBuGO7ai5aURgxW8QpxMoGZFOTqFTr589XxCV7BnTXEytMofdZGmSmjeIaJeHW1cijsfR8ekeQtNPbGJxshDOtE8roYtE1hvfCgeYLu1GK4WDFVxSPXmRY9WI7aMV27+28dntcHrKCThiw0lVuUR7dCzpB2HFq2G0Jjw0023j6kL3eyxZ9s29O1nYa5830WtKeJwBzwmC9sDJI9z47lfx1QeXeePE6/nBP/wNWF5i8n/8Cu5ffTcEVfi+90NSNyRHjuc63IEBWj/8w8x9+MNMv/4XePGDg/yvt8S8+E++ySe+8WF+YFOLt28/gPu1N/PgG3fz8T99C6srS093s3PkeFYh2L+RpZ2ztthtkoexuGkQx0/qI5XKpDRmuafgqTM6SjwywGQ0zEB5glj7Nm5PBGVWaSeeBl0q4tfsrHA6K696f9iVMNHcRuSXenI7uvF/qcy+fRNbTwtrlABEDRuq6Q4PMzY+Q3lmPzRmM0GC7nmsJ2MpGEBE8KamUEkuTKqSttZ8Mkk7Aieg6JXAKGrBFGESfqnErvdcq4LnpAQv03QQFi7dTmlmI7srs3af2NZ4bOt+YZHSVVexf/hAlyymhqQS8CKcVo/Ed9zJjHNRTrfAtBJbT3LqcJ+BmUKAsWpIvZnkmJkY3+kRXUleTmOGTtjo7jdxCYS1zIOmBIpukRunb+zhcELsO2jtUBusUV1e7DqLemX2U+KZdFIrGGewNWX7MakblzlXE2+ccTWxaw1k4zsMHtzEQDUpSpyWL+gxfpO9shuhRKwHracdgatpFn3G6kU8rayXBmu4m7F9oD1LYKUb4mhTzxRlt8lcdRPN0njPDRzsM1jbfsipA4eSi7VvjuPQKliic/noFUy1diDNDQCsbn0FMnMVsXIxImxvbmesmBQ71w6zM9fygn0vR7TGDYpUDnwXvpd6+ex5Xa2oFYu4iex/K5ig4BazUD+DreuWBg83vBKmE4Pj0PEKGKzSH8BQFHFw0J5/1KmxYdt32L5MFSnXqvf1lENolULKh7cirqJhaniOwWB4bN9+Tg6XEeUyVBiimISyZqHPyXXovpkZgxI40djB9PBVjE++PAn/PX18v2T3GMVCYb3UuTWwuagA7ugoemYKAVbdIguF0b4td4xWqBYcJPk+yUh++n2hUsLXxVRlgr2De/AcxYo4aBEWCmMIsM+bSXbvNtwoTUG51JxmpnSJKKq1BkoplFKU3DqOFjYNlqlHHuldFARH2xqVvQSwkHrF47j7vas8+x1v1ny/pnv1dXsn85YOVSL2TdboeDZqgLH0e0pxLgr2rCZoxhh+55Mf5UV/8gpYGeUvR7+XDb/4U5Suv47x/+tK9B++GHZ8O7zy7XYmLEeO5xnEcShffz0Tb387M+99L9vG9/Hv33mK//6uAjP3xPzMSIGfnBvltnv/F/f/+ma+8q6fJz7+4NPd7Bw5nhXQOpHuVioL2bl6bC+Xbd5qC0f7JURBNfIIHY0/PUJ7aIhg124IPBqDo2iV1i9KyjGbTlbEec9YlQ0jdWskLllyIgjUpjGmR+FQKeJM991kJlinXiZVelMiXbW9hXl2jlXZO5d4H0SYbkY0azZ0x6R5WakJomwB4E5sMo9OdOhyTu29tEskeiiapwIip5L1kxKh4wcs7b6Kpcu22fboJKdEacjULiG1GpUoK4KQEgOBpWCAZb/BilfNPC6FK68GIHS6haez/J+eIsHBVit6YOKYVmmE5sabUaLo5uorcCNLFvpaAkv+ICpRxlw8aI9T8DXXbB7seisTgjY2MsK1W6/JLLY+aXXptlBE6EQ2P0cqYxjfsQZsrYo+NZ8Z3n1WdCbtrxkealDbvoVGvcJwJcBf4xFqlnymGgVGqyHD9UJ2nFYpwPcSgznp+1XX5iGVvVL3RIlHMdCajvSH9nWRscguMUw8mZ72KLkV4iTEUYIqFBqU3Dqe49vwwy0vWeeYdpyZrD5a0o+JgAVAyS+hpg4hwzsZqYYUfYdm2KRWP0THr1JwC+jEWFdhiA6jLE8RYG6wdJoHGGBkvI5yHVbbcXdFYPtm21iV1uxezKgNgXNQmKVlVBCwHLV4rL4bk57D8WHjC7N+JcmHi0uDMLybQLnoQtcm7RUvmWgUCROBFYXgeclY9q2qKF7I7oHdXDl2pV2+Jq9xPceMEQcXjecWSdVdAVpBtw6XFg2FFkdaV6x1UvWNYegnJq5y+9ab9LvGgO9oOsSIk5L0fo9dd8bBJAImdpwd2tC03xmVGnLVNcwUd3GgMIWbCqisuTalhKrX6CGeChWWsbK4KpkAkkRERxBli3NLDy8QY/CVy0x1llbqnTcxMw37ueZZz/mjS4/2nNsw1SgQeQ7lnlBexi9FzVwFgCPCaDVkY2k/lw5dDkqjTFr8mrPiWUnQjDF8/K5HuP5t/4Xf/NJPc8vwd/DHD42x/Ku/zMiv/AKtoc8gH30jfNcfwVU/e16uxBw5nuvwp6cZ/Lk3MPfRjzD23a/h6k8s8LbfUvzM5ya5u7qFl0/W+S9HP8g//vYBHv6dWzCfvxVWFp7uZufIcVaIyItE5KsicqeIvGGd9b6IvDtZ/0kRmUqWT4nIoojclrz+5wWd2A1xpq8C7Cz2ZGmWYd0EgShRa7t04zDe1OVMNwsMV0O84Qark5NW+Q2Its91j2esUShArFxEO3h+ZGefR/cDifG34xUwtJ2FwX0QJIVuE0EBAMGgFbSrJTrVIsvDFVbGBuyMbhLGE5+ax9XKhlcle/Ui87ylySZOQBzDI6eWM90NHQSYMOr36AGiNZvKlzBV2NFddu0L6QQRq56PKoQIQtMr2bIAifpdOvMfZ4Zm73EtqYm1T6kwR8MdysKmnK3JPei63rqhWE6XwPozM6jAx6yusrW5jYPDB1GirLCKPSFrkQkdeGVoWG8NiefFUWLD36TrQUvP6+muAEDvdRzvHMfVVtQBoD1xBfeP3WQlvAMHjUIVi7C82t0vMe57PVhXbhpi23iNPS/Yj6tcQlcTeSlBTQz6JHLOcxyKSW5eyiNTYQ7tODB2gKXEUD/Q2m1PkNR+euH2YaqRbw3e9QhaGs6pFK5y0OIgy7Yup46KjEQTGMeF8Utg/CB+WKQSOIlgjepTEe1FpyeHa6haZKAUpC44WHOvDkzVrTqn0pQre1iszNgJhuQY4jiUrr123fP0YltzG3PD2xl68cs4tdzunmNwKzTncByN8iI6gTXYVZIHpwoRaE3Bd6zXcPPNsOF68EusOsWk05NxFLfB8dGiCLfOUH7hDUkbNb3PoPV2e4gIjmPQSf8/Vt2GlPojwbrjPgnb7fWgmThrJ8ZgghBF917Wgxolr5Qd5+qJayBosK4EZYJOoZSVdQCYqc6wd3BvlouZ9s3+ks2Pe2xoR0aEgh7PtyB9dfri9H5lz41d7ngeJbdOzSlgRNHpxH2PaqxsjURjejyDgBrY1n0IRAhcjRM1oNAk0XKlL8RTNNdWN7O5saXrgfeKmUCRUg6bo2E2VDZk+/gqYrRSZutwmcmgbid4ANwAkrqIaf3DG7bM0CiU7GRG8nydC88qgnbfsQV+5x/u5qr/70/5oQ/+MIvh3/OOmZ/n1W/5a5a/+jVm3vgDlD7/7+3G/+6fYOrQ09vgHDmegVBhSPUVr2D6Pbcy8ba3MR2O8QNvuZs/+sspvm3xAL88MMNL1H38ykf+M3e/aSO893VwxwchLYidI8czBGITB94C3AhsBb5LRLau2ey1wDFjzAbgTcCv96y7yxizO3n90HmfePPNMHcDhSTPZLG9yGRplkh5SeiO/Wkthj0z6tUJOmOXsBCNZWF1pr2SXoiV6RaFEmt0mK230Cx1Q4b8m/4tC9e/KDNCvn/vi/k3Bw4AEC8sYoyhrEJ2tpqMVENmrv5O4kqBlckWcaVg90uMfG9qEm9qKmtnr62w9YXfSSu0BnvdKRBc99044xtwEsn9eI2CYuo5FBEWDm7FHNiJEtVHKDxH8Y2jdrInJXgtr8RBf452csDUKOokx0/zSazHcIpweBOzrSIlt07VbXVziNbxVKU2qvGTmmRJnq24LmZ5JdtHiaJWcPG07hZ+7sG+yVr22emR7rbv3QLXKm1HT0ceHr/ytHZdPzOL7+iuNyed8N+5hXazSNF1GRgapBNDQYWcKk5CZTTpl052nNDzsjHkJLXJCglBI5GGHwgHslM4bj8REqffQC0k3k6nTxJf8LSyoXOis/uxHkQp5qqbGRjYiz87g65UKFxyEBFBpV5bE+NqxeahMoF75mPZdnT7zHUD641O2tT3vg663OH8an+mh5osT7KhtgER4dLZBpdusGGxEkQQ1rNnpWM6ffurMOLghkFmWgUu2TBgo7XcoPcM2UncNHR4YCsysgtJRHpUGPQdUzbdSCy2tMWJ9oLNQUMRrw09Xge9OWimPEbcsLXIaDRhKsnl0l15e9PTT6kXermzuKaPejx8uzYz8cqXZv+7ymUgGqTqDTAabUzaoBDTITYxp+JlqlddQ3RgPzWvxUhodSBCV+MGHlW/Sskrs9BZwNPd/LrMI5tO3DQ2UhrbQbXg9Xv0RGfy/dJDaVQS8osxXLVpgJFqiB7ZC36ZucES9YLXN7mk/CJsuA5EKOuAWBzaysVJvyvQzOz5PobqXR0LR3lcO2nJvwqq0OxOuGV5gkluZFZzLWkvqGzy5Uxwzrr2acZb/v5O7jpyikdOrXDnwyd5dGGe2dnbWGi9j+8aeRHf868FTv3Gr1J99SuoFz6C3P6bcPN/g0035V6zHDnOA+H2bYTbtzH4+tdz4q8/gH/rrfzW1xaYv2o/vze2wC2t25k49kW+94P/wk2PHaE4/QLYeIOdIayMnvsEOXI8uTgI3GmMuRtARN4FvBT4Us82LwV+Kfl8K/BmOR8JrbPBTb0VduZ9MBrk3qUVJG7bGdOemf5o/z4Wb7sNUxrGVMYwD30LVysmLnslXjFk6at/x+bBEisDBZATpHXTRGCsNEbkRnzigU+gSxVMuRuaVQnsDO3y1q24oyPE3zhJIA6XVmf4a5Ge+jvWAIsxmQfNGx+HcYiXlrJ2poZavdjCiwaZHBnDHR5Ghm0dsC3DZb728EmW2l25a0iIVX0G0Q7m2JeR0Ba1zYxqYKZV4CsPnUj6TCh4DtXmMPffeXuiDNc1rLqewJ5b5Pjg+FR7NvLmNuMNdwWOuh6ergoljmZp+wzO0FDyv4NZPdVH0MqBS7FRYL356pRIxJ5Ha2ITh6oBH7r3HzA9W9taY11PXYp0DPRex7byNNtGuzXwsm2HWvCIsHekxoOlOnFsKLoBe8qX923X9Zb0tDW5loIXAAtADAZqQZ39xSkCr4Cz5KbdZn2RSe5ZypZHwzm+dvLTWbhtdo4kb84alWebz7cerM74FG6thnv4CkynA8ViRtDaPcqU58KVG1t2HHwNTKJWSWcV3NPDEnuxd6JG/EhCds6gpHh6y08/WjlwWQlcFrHlbIjJirhX/SpHtA/YsacrJeLHloFVaqXgtGNJ5raE8cIwwyO7slDjFGmJip5GserXiBfbOKEDS8d7iMv6V59OGPR9tbkRFEZY2NeA0TosxzSLA8QrKTFYv4+0crIcN4DI6Xq+to+emVQ44jBd2Ys3tow0ZuCxO2iGTYaGLKkZHqqhT3jcB8wNlBgYHWFxcZ6TKyeI45hiWDztmKlISzy4g82jq/C1+3l4bRcoh5lWkbhY4As91wB2zFVCl+Mi0LH/TzeqfP0h1deXtqal/YbRotjojVNwi2iBudJ+ykEhy+FdD2rqMBS73k0lCkb3IZWx/g0lOW9nyeb2ngXPaILWKHj4TplCEHPfli/wN/f9IY1wiP/32Pfi/eK76UyNMPX9M/jH3wy7fgIu/7E1Mxc5cuQ4H6goovqKl1N9xctZvuMOHrv1Pfzou/+cH2mN88FNI7xp9D5+bURxmZzk5V/8Aw7/9esJGnMwfSVMXQETl0FUP/eJcuR4YjEKfLPn//uAS860jTGmLSLHgVTFYVpEPgucAH7BGPOPF9qAybLNhVKyits+hVDo8eqAOzTEUhBgTs1nnh0RGGjUMYn3qOA7NIZqoJZsZKE4XXn2xIg4kwfDn5kGYO+kZuVEE4lqQCoykYprC6c6S5kH7TT0GnXpeYd3IMOnC2ul7fJ0KiIBiEbKI3DsyyhUXy0rsKGCNgzJGoS7J2pEzXF2vPD7ut6UrbfA19/eJWg9IYKqWCA+Nd9tIgZdraN3fm9PsyUL2XT9AGdJMeCWOdmsWiMbusfoJCSTHoP/LJx94eAVuIOD9JqmfQFpqRHeQ2JSD6I/O4tz9ARwF2upRaZ4mRjKWjRfqBJRAAAgAElEQVRhoULbGCQ2fUXRD48dZuX+CE5+PjMkAUpemRGvmtRbo8/FOeCVwAlYTGp4ZaGDaQhXsl3olNhUPggIY36NZdO216Jd3E03w8OfOjtB8yLruFMq8+CI1niHroAHLTFf7diwTnMexCnyEtN0y7fB3V+3nzvLWe+lGZJrMV6P+PxjZ/fOrcWZb3tyHZ6HLHe9YOWgypbGVlj4JOHsGOIFSCDAYiaW0ou+kEOl+r2U6TblCH98kOU05VMUK36dowMzDNQfgPYi6uQ5zPVMcLX3guwIm9ceSx17CZVLD2FWV+HIP0F7fWGwTeUDHBrpTsBGbsRNMzed/fzA/K79EBUZrS5SKI/hnLiHsVKXoAxeexWDSjETLxEufghVG2aRO0i9jCWvq8yaFoMfqYZ8+cETdu7Di2D7y5ETtt3Nom+93He5lEKfdqEA2Gc7cEPgZB9RjxcWuGn/TXD8fgBaXoUHlk+sey01XUbVmnhjQ0SdEi/Y2Fp3O4CdrZ0MROuUJ1DO6YRaaTuBFHe4bvK6Mx4TnsEErR23mZ14mL+99295353vY1Npll+dv4HWO/8Js/BuBq4JKPofQza9Fi59MxTX6ZwcOXJcMPy5OQZ/7g20fvqnOPWhD/GSW2/l2r+9jwdmNvLeEZdf3rnC0uQU19fnuGnlES756K/jPvxFaG2xZG3qEEweyglbjqcC65lXay3AM23zIDBhjHlURPYBfyYi24wxp/1ii8jrgNcBTExMrNsQJXCkdSmdmRb6a/ekO9r3xGiWNaQr834cuhxdrcL9j0IS4pg2Ot02ciMuG7ls3XODNWqjPS8FhJFHbqfqlvlWek6BhXj1dIK2TshYGna1ngdi32SNcmjXO1rx0t1dI64rUtDjUepBwdM2t4duqFdfqFvi1Ulz0HoJaeHgQTonTrDw6c8AoBbmT7OsFYpG0WNyoIi34QW8enUU1T7JneWufHs6g945ecqeI+mPjkn0/1kTv7kOXK3oiMpyZhCywth9PDe5Dnd0hHByFr547xntlDR8UYsQ+UXqhQB/cYHFHlJU8kqszm5kNSz23UdHOewujoOneenuUf76E2kOWk9j/ELiDUsehmSd2zOhHWi7TcUJ2VucyMhmpdDCd/w+47kPO14JgHfwEjqPdPru/YZWkcFSwD8/hC2m7Lo4jcb6x1kPjm+fVL8MUTMLzTubA3zPwB4aQYOT5+lB6y2b0Yvs0XBdWKYn/627jTfSgtZGVLgId3/qtOdr70SNYL4nj3sdcgYgE5cQzISs/PNnUZHN0bRiP8oe0yvipmJCZ7r2ZHHqcbp8tsmnjujsOu46coqJetTt//kGhDXMidP7SYvTF6J8LmRfc8Uy9YLHtpYN/b5h6ob+7ZKJkiJF2HbLaQdpBN2xcWLJPqtF3+H6rYNd0t6DQxsSkZPpKyk0liAMGJz/GhPNbZSLwyxxcv0GJ8Ivo2GDbU6Jf/FP9woaBLX/IMVWEW5/oO/rsFqtQVcrpI+Epih7ZWars5T9cv+KJMoC6BOuWQ/PCIL2sfs/xqNLjzK/Os83T36TO47dweeOfI6yX+Ylxct527duxvmt96Odr9KYO0Z5awnZ/zLY/yfnjOHMkSPHxUF5HuWbbqJ80020jxxh8AN/w9Rf/RWLv/Uwnx2Z5l/nlvjV7d/kZNlww7Z/x3XeAAeOPYT7j78Bf/IaaG6CiUtg7ACMHbS1cfLQ4xxPLO4DevS6GQMeOMM294mIA1SAo8ZO5S8DGGM+IyJ3ARuBT689iTHmrcBbAfbv37+u5ScirHpViGog96YL7f6Zx2Z9iGsFAVIrIFZ+5qlye9T5akFt3f0zJEbV7oHdPLawgicOIitcOXIIs7CYhTj2NLr7ns7eJ/L5WfhjD8Zq0WnLTruWM1xlmBC0w2OHCfT6kS6+o6lG/fLnYD38Kuo/t1NfMwEk9tzBJXvwpmaRu+4F47Ktua1nJwdWVvFnrVy3nxQrXohX8GfH0DNnniXPDqGE8aBBs56IBShtScmaHDRZ63Ha9rLTjpVurpXGFQ1+mcAJcKYGKS2c5LE1YZfuyAjuyEj/QdYoVGciIZ6HDXkE8QuM1yLq04eIlk+xWmjCI49SC2tcPXE1f/uFo9nevUdKce3EuUU23GYDHvlW3zKlhEqUFFPvrFC6/rqL+A0wMLAF/CIClG+4vjuJsA6GkzCz8/HUXbtlsC/frQ/pZIrn4WufJbXaXaUE09hgI0f8Ekr83l0yFH0Hlnru4RkIGlU76VO65moQ4eTSEcZqEXPlBvfOP8iegT18+tS5yuDY6/UTgtYq+XCkm1em1ZriyLNXn/lI58dtM3haMVGP+MbRBYYrFxbFVnnxzUTxKttPPchg1PW8bx0uM9VIyoGsJWdrb5lfwmnZCYR9xX0cVw911yUXU7ru2i7J9oqUdEDZq+C2j3JodI1eRaHJQsGGWWglOErh6u5JL9kyjdn8g+yKz9xRnvbYVN90+oqoedYJhl48Iwja++55H48uPkrkRoxHI7xsdSs/+0AN/Q+fZumeP6Ewskptn6Zw3U3I7lfB6L7c0MuR4ymE02pR/55/S/17/i0r993P4Ac+wI4PfAj9d9/inmqDr226nbduOsHPDC1yaPMLuOaqH+OKtkPxoc/D7e+G97/ezpCP7ofxgzC6FwZ39Ela58hxEfgUMCci08D9wHcCr1qzzV8ArwY+DrwS+DtjjBGRFpaodURkBpgD7r7YhnRDEnuQLkvEKdY6rETEFntNChdjOqSy+Ok2BbfA9ZPXX3B7Qk8TiMcNrV0U3SLo4LQZ/qyt6/yeinNh5sF6tZV6sWusytH5FUremUnejtEKJMIqZyJ6KVSh0Pe/q1xmq7O0CkM21GydkLzi4cN237BLaoaLwzwIiOPiDgyd9ZwpBscatKasUW2U7srs9+SgnT1nyyK9wq2NrWy57GdBuzhKsbJhlNLxb7F/snnW/QGrFpd4sQB2FyZYaD5mRWAWb7Pt8iJcrZhsbYb6NHzzm/B16wIInRDfUax21ijLnUf7e1EOXF64bf3+qwU1BqKBdYVYzok1bCH1wjwR6M2TXAtVruBNjKPKZS4vXE5ndYX2Hf8IBnStRjs2UGhkbXIadSRYQ06Gtvd7Tc8itAJd77WIrbnWKoXcO2+9p0X/HKwpWd3rlb564mp87eN2TnLXkVNnCCU4/bjxBTI0EWHPRI3Ic85rEmctXOUyUe6PTAg9Teit31/n+m7oRUrUVe+9EeHwoTfYmojrhTjOXMXJE/cTJePj5p2nh3rbe3QRPEQp1MQhWDp6zk2fcoLWeewxVm/7IOaRbxAvzNM5cYKfeuBhVh58iJUHj7J0/wKiOvjDmuKucUo//lr0jhvtDEpOynLkeNrhjY3S+oHX0vqB19I5eZL6hz9K431/y+FbP0MwP8/XB/+Zr078E+8dnSfYuo3du1/C5Tf9Ghs7oO77NNz3KfjSn8ORr9pcisHt9odscId9r8/muaQ5zgtJTtmPAn8DaODtxpgvisgvA582xvwF8Dbg90XkTuAolsQBXAn8soi0sYkLP2SMOfev5hnQrXcs3fCyNITx8suh0yHNoun9JfMme0Lw4nY3tLHn987V51ZvWwvfWce4OZMHbQ3KN9144cb0OX6eC75D4SwGcQY3LYC9vijCSDUkPoM8e9+M9dg+WDret76XmKXY3tzOeOlzF0RIvG2TuI0kD0wpQlf3edD8uQ3opI7XejlHKXrzhcTtChBsb25jeGkFt3zhxu6QV4H9e2DDBvj8bcmxkzYkeWBrr/WqTQmJWO3prwskaMAZ1RnPFpp7Luhy+dwbrYcL9AKddt5igXDnTsAWWzfK4wSAiYkOHMjyR1MULlvnGr2CJcQpzjNs0FkTzqhEUUzGU1qiAWzfxItLuNplNV4mpF/FMfWeRQnRaa+VYOV0T+NYLeKh4+fy1q2PTUNnCIN9gvGEUAERO2l8hlSMF+8cWZPP98RBlM7KhJwNTzlBO/6Xf8Wjb/4NRLVRrkZFHl6jhDsySLTvAOHe/bi7XoAULiBOOUeOHE8LdKnE6C0vZvSWFwNw4hv3sfR3H0d/4tNs+vjnGPyL20E+x51N4R9aDp2JKca27mX7gf/M+OxOnOUH4KEvwMNfgNv+AB7+Iiw8CpUxqM/YV2PWvtemrXKkX84na3JkMMa8H3j/mmX/sefzEvDt6+z3HuA9T1Q70qT2VPIZyMZpKlLBSqqAeIbxO7CV1U6RM6VOXCh2jlXxPMfWWIOzGoi9ROJiPB3qiajas/lmSIvaJsQscPona8Znx4j27j33scKafZ0DrnJpusUzfqdcMt04baZcBrZCw0qKK9ehVvD6CJrTaiEi5xRWqEYeh+dOD6ucKE2A3H5RJIk1MvAguENDdHZdbb9HAV3tTw3pEqseQngx534S4I6MUFkb1vk0IHsmjEEc5wJ8OD04z5ScZtjk0OihTNJfK82OsSpfnS/SjrtqmOG+fYRxzLalb9Hwj3H8+PoTOWle2rH51XXX92LvRJWzRO49I+A75+GdDtOohIs7x5NFzuDsOZS9eMoJWhomlSNHjuceyhNjXPKab4fXWHt4YWmVL332K6x+5vPIV/4F/84v0/70n/HgyXexNG84FWpO1Svo1jD10Y00J6/HHyni+G0cZx6ncwzn/s8jX/pzOHYvzB8BrwjlUSiPWMKWfi6PJe/DEFRzEpfjKUUt8hgoBYSeZqFHgbAX5/zRj+p0miGcvGhHXh88rexkx5kIWiq6oDVDhSEOjx1+3Oe8kPCj09CTTxW6IVW/SmVNAr84zhMa5tZz5HWXDq3Jqdk3uI96NJB9v1y7bZiFT9u0x+x+X8B3T1Yfqa8pqTv2whQJARu2tQYqiogu7+Yc6WKRyotvPn1fN7B5Vd/4+DOGoF0snEZ93TzKx411vFDnhe2vuKBxUfErHE3C4JQoXFeztTXRl4cqSoFSjBRHGCmOwJCtF7wWY7WIz3z9GPMrp5c6WBviKCJcTOTeU4lS4HLzjtPDDlOUb7ge8TwWb7vtKWzV+eN8wp/hGZKDliNHjucmosBl/2U72H/ZDtLUoOV2h3seOckn7vocd375Hzl13+eR49+gfOLLVD/mUj/l0ZxX1Bc6lE4touKYuFRG1zfi1C8hqBZwCw46BMdbQesv4qhPoM2jOO2HUatHEMe3+W3FoeS993PPe6F5cUZQjhxrUCt4XDabRH6cwRDzHMWN24fPStQeF8FZi6232DCezqqtibT2XEr1GepnVOo7Dzze0nJr4Wufy0cvP225+GeuRfS4cJ7tHyysyZvtqXmnSiW8sdHT8uMuojFr3i8AXgSL3fpwF0y0MpL57CZo0aWXXrjaxXngog95Ec9HGn7oiDXV9w6e23M8Wg3XDW/eO7G+N3lnayeLq4vrrnsmwzmD+iaszVN85rkDz/c7PidoOXLkeErhO5rNQ1U2D70ADr0gW764usjnHr6Dzzz4FT796B184+Q9HFn8Jmr+KMWFU5TnVyidOE75pEvtYUVtCWqLHcqLHcqLq5QWlokWXTp6jMXIZ6Xgs1pYoh1+kzi8D4IOBG20t4z2FnCck+B36ERlYq9Cx6vS8WvEXg3tlnDdMtqr4ng1tF9nbGCY0VbDxo5rD7Tb/XymkDBjen7Rz/IZrMJXThaf8/DOEZ5TDJ7An+WsILELm89dx+jxIJ0VXumsnGPLi0fpmqufPIJ2kZAe6XcJAsLdu5/Ag18ESZp+QdeLNnvNWYvrnhX62W0eyhpVzScM5iI9aBdzqlSR8wKuQ0SsguMajNfXz2dshk04PTUzx5OIHc0dnFhZv/5aL57dT2COHDmeMwjdkEvHdnLp2M7T1i2sLnBk8QgPzz/M148/xMOnjvHowgnuWTrFqdUFFtrzLLUXiVdX8U+tEJxaIjq5TGF+heL8CsX5VYonOhQfalOeh9KCR7RQIVwxLLvCyWiBxWiRk9FDnArheMFwPIIFz7DowKoDHQ1oUDrGE4MfGwITExjwMYRxjG8MgekuD0yM58X4xTYFY4hiQyGOiYwhimP6Mga+692w6UVPVXdfFDpxByXqCfeWPOfwOPqn6Dt99cWebZhfnT/3RheJtVL7TxjKo1A6c8jU2bBeceLHDceDmavt+4VCu/YFF1mLMi2oVbyIfZ8HuNgQx4tAKvKR4+LxpDyfjxORGxGtE9GwFjlBy5EjxzMekRsx6U4yWZ7k4MXZUesiXlykc/Qo7aPH6Bx9lPajR3vejxIvLRHPL9FZWqK9vGj/X14ibrcxpkNsYkwc23cTExuDMTEGQxzb969sL/P+Aw3m2wsstBdZWF1gsWNzI1zlUnAiIjck+sr/Irrr9yk4BVsLSTk4ScFQJQpHOWjRaNE4ykGJIjZx/4v4tGWdpJ3tuN33Wo1XaZv2acuzl0m26VlmMPz5S/+cmerME3cTnpN4fhLYy0YuO2fx1WckJi9eZVAViziNiyFC58DTJZSWFPFdLyT2+Y5oz26cwaeuNEzBLZxTaCbHmVG88jCq+OydaMgJWo4cOZ63UGGIGh3FHX3yPBaXAd+3Zlkn7rDYXmShvcD86jwL7QUWVu1rfnWexfYiHdOxr9i+t+N29n/btIlNjCAoUWixJO5Mr5TU9b5ccU9blr3EwVXrry84jze/5rkPd3T0rMV0n6s4ZyHt5yDEcdaXWH+2wivA+CX99btyADypvxM5nnhcdImGZwhygpYjR44cTzG00hS9IsU8jOg5CXdwAHcwN3BzPEtRHX+6W5Ajx/Mez26Znhw5cuTIkSNHjhw5cuR4DiEnaDly5MiRI0eOHDly5MjxDEFO0HLkyJEjR44cOXLkyJHjGQIxF1B1T0SOAF9/8przjEETeOTpbsRzFHnfPrnI+/fJQ963TywmjTGtp7sRFwoROQl89elux7Mc+bP0+JH34eNH3oePH3kfPj6c8Xfwggja8wUi8mljzP6nux3PReR9++Qi798nD3nf5oB8HDwRyPvw8SPvw8ePvA8fP/I+fPKQhzjmyJEjR44cOXLkyJEjxzMEOUHLkSNHjhw5cuTIkSNHjmcIcoK2Pt76dDfgOYy8b59c5P375CHv2xyQj4MnAnkfPn7kffj4kffh40feh08S8hy0HDly5MiRI0eOHDly5HiGIPeg5ciRI0eOHDly5MiRI8czBM8rgiYiLxKRr4rInSLyhnXW+yLy7mT9J0VkKlk+JSKLInJb8vqfT3Xbnw04j/69UkT+VUTaIvLKNeteLSJ3JK9XP3WtfnbgcfZtp2fs/sVT1+pnD86jf39KRL4kIreLyIdFZLJnXT52nwc41xjJYSEi4yLy9yLyZRH5ooj8eLK8LiIfTJ6TD4pILVkuIvKbSb/eLiJ7n94reOZARLSIfFZE/ir5fzqxTe5IbBUvWb6u7fJ8h4hUReRWEflKMh4vy8fhhUFEfjJ5jr8gIn8kIkE+Dp8aPG8Imoho4C3AjcBW4LtEZOuazV4LHDPGbADeBPx6z7q7jDG7k9cPPSWNfhbhPPv3G8BrgD9cs28d+EXgEuAg8Ivpl2aOx9e3CRZ7xu5LntTGPgtxnv37WWC/MWYncCvwX5N987H7PMB5jpEcFm3gp40xW4BLgR9J+uoNwIeNMXPAh5P/wfbpXPJ6HfDbT32Tn7H4ceDLPf//OvCmpA+PYW0WOLvt8nzGfwc+YIzZDOzC9mU+Ds8TIjIK/N/Y377tgAa+k3wcPiV43hA0rPF0pzHmbmPMCvAu4KVrtnkp8I7k863AtSIiT2Ebn804Z/8aY+41xtwOxGv2fSHwQWPMUWPMMeCDwIueikY/S/B4+jbHuXE+/fv3xpiF5N9PAGPJ53zsPj9wPr8fOQBjzIPGmH9NPp/EGsWj9P++vgO4Jfn8UuCdxuITQFVEhp/iZj/jICJjwM3A7yb/C3AN1jaB0/swt116ICJl4ErgbQDGmBVjzGPk4/BC4QChyP/P3nnHyVXV/f99ZrakQRqhd6SIoIA0UUEUfBAVfKj2QpAHFLAhys/yiPg8IiiC+liwgAJKVUASSAKkQ+pmUzbZbLbX2d3Z6fWWc35/3Hun7M5uZpPdbELO+/XaVzIzt5x77pk738/5liMqgClAD3oc7hH2J4F2BNBR8LrTfa/kNkopC4gCs93PjnNDDZYKId4/3o3dBymnf8dj3/2B3e2fSUKIdUKIVUKIT+x88/2O0fbvXODlXdxXs2+i7/Mu4IY4nQmsBg5RSvWAI+KAg93NdN+W5kHgTvKTbrOBiGubQHE/jWS77K8cD/QDj7i225+EEFPR47BslFJdwM9xInR6cMbVevQ43CPsTwKtlIofXMJyuG16gKOVUmcC3wT+7s7OaPKU07/jse/+wO72z9FKqbOBTwMPCiFOGJtmvWUou3+FEJ8FzgbuH+2+mn0afZ9HiRBiGvAc8HWlVGykTUu8t1/3rRDiY0CfUmp94dslNlVlfLa/UgGcBfzOtd2S5MMZS6H7cBBuuP6VwHHA4cBUnFDQwehxOA7sTwKtEziq4PWRQPdw27ju3OlASCmVVUoNALgPzCbgpHFv8b5FOf07HvvuD+xW/yilut1/m4ElODPamjxl9a8Q4hLge8AVSqnsaPbV7PPo+zwKhBCVOOLsCaXUP923e72QMfffPvd93bdDeS9whRCiFSec9oM4HrUZrm0Cxf1U0nbZkw3eC+kEOpVSq93Xz+IINj0Oy+cSoEUp1a+UMoF/Ahegx+EeYX8SaGuBE93qM1U4iY6DK9q9CHhV2K4BXldKKSHEHDdJHCHE8ThJpM17qN37CuX073AsAD4shJjpzth82H1P47DLfev2abX7/4Nwfvi3jltL90122r9CiDOBP+CIs76Cj/TY3T/YnefbfoWbc/JnYJtS6oGCjwp/X78AvFDw/ufdKnrnA1EvBG1/RSl1l1LqSKXUsThj7XWl1GeAxTi2CQztwyG2yx5s8l6HUioAdAghTnbf+hDOb58eh+XTDpwvhJjifq+9PtTjcE+glNpv/oDLgQYcD9j33Pd+jGN0AUwCngEagTXA8e77VwN1wEagBvj4RF/L3vhXRv+egzPDkgQGgLqCfW9w+70R+NJEX8ve9rerfYsz27XZHbubgbkTfS17418Z/fsq0AvUun8vFuyrx+5+8FdqjOi/kv30Ppywpk0F35fLcXJRXgN2uP/OcrcXOBUym9xn1NkTfQ170x/wAeAl9//Hu7ZJo2urVLvvl7Rd9vc/4AxgnTsWnwdm6nE46j68G6gHtgCPAdV6HO6ZP+F2qkaj0Wg0Go1Go9FoJpj9KcRRo9FoNBqNRqPRaPZqtEDTaDQajUaj0Wg0mr0ELdA0Go1Go9FoNBqNZi9BCzSNRqPRaDQajUaj2UvQAk2j0Wg0Go1Go9Fo9hK0QNNoNBqNRqPRaDSavQQt0DQajUaj0Wg0Go1mL0ELNI1Go9FoNBqNRqPZS9ACTaPRaDQajUaj0Wj2ErRA02g0Go1Go9FoNJq9BC3QNJrdRAixRQjxASHE74UQtxe8/3MhREgIscx9/XUhRL8QonXCGqvRaDQazRijfwc1mrFFKKUmug0azT6NEGILcKtSaknBe8cAtcAxSqmYEKIKiAEnK6XaJqalGo1Go9GMPfp3UKMZW7QHTaMZH44CupRSMff1wYClf5Q0Go1Gs5+gfwc1ml1ECzSNZpQIIc4XQtQJIaJCiPsK3n9UCHGHEOI9wCLg7UKIhBDiv4HtwFT39Z8mqu0ajUaj0ewu+ndQoxlftEDTaEaBEKIaeA64F5gDJIBTCrdRSr0JfATYppSappS6G3gHkHRf37iHm63RaDQazZigfwc1mvFHCzSNZnS8B4gppR5TShnAT4H0BLdJo9FoNJo9hf4d1GjGGS3QNJrRcSjQ4b1QSplAYOKao9FoNBrNHkX/Dmo044wWaBrN6AgAR3ovhBCVOD9WGo1Go9HsD+jfQY1mnNECTaMZHW8CM4QQn3F/lL4DTJ7gNmk0Go1Gs6fQv4MazTijBZpGMwqUUlngGuB7QBA4EKif0EZpNBqNRrOH0L+DGs34oxeq1mg0Go1Go9FoNJq9BO1B02g0Go1Go9FoNJq9BC3QNBqNRqPRaDQajWYvQQs0jUaj0Wg0Go1Go9lL0AJNo9FoNBqNRqPRaPYStEDTaDQajUaj0Wg0mr2EitFsfNBBB6ljjz12nJqi0Wg0mv2F9evXB5VScya6HaNF/w5qNBqNZiwY6XdwVALt2GOPZd26dWPTKo1Go9Hstwgh2ia6DbuC/h3UaDQazVgw0u+gDnHUaDQajUaj0Wg0mr2EUXnQNJp9EVsqBpJZsqbE5xPMmlLF5Cr/RDdLo9FoNPsgtrTxCR9CiIluikajeYuiBZrmLUc8Y/J6fR/LGoJs6ozQHEwigEmVfiwpyZiSg6ZVc8ZR03nXkTN49zEzOee4WVT6ix3KSkqMlhYydXWY3d2YgQB2JIqoqsRXXY3vwAOpOvoYJr/zdKpPOgnh16JPo9Fo3uosaF3A22a+jZNmnjTRTdFoNG9RtEDTvGVo7Ivzh6XNzNvcw8mHHsDFJx/MVWcdwcmHHsCsKVX4fM5sZ8qwaO5PsqkzSm1HmGfWdxJJGXz8XYfzuROncsiWNSQWLyFVWwtSMum0d1B15JFUHHoo1SedBKaJzBrY4TCJJUvo/9WvQEoOuORDzLjueiafftoE94RGo9FoxpNoNjrRTdBoNG9htEDT7PP0RNPc98p2FtYFuP6co5l3+/s57qCpw24/paqC046YzmlHTOfT5x2NnUrR8OS/CP7+t2Q7mll+9Ckc9OFLOeuOb1F9wgkI38ipmkopMnVbic2bR/uNNzLp5JOZ8/WvM+WsM8f6UjUajUazF6CUmugmaDSatzBaoGn2WZRSPLG6nXtfrueKMw5nybcvZs4B1WXvn92xg/BTTxN94QUmHX8cp9/0BXwXXczGHVHuXdLEoYv6+IfOEU4AACAASURBVI5/FucdP3vE4wghmHzaO5h82juYc9uthP/xDzpuuYUDLr6Yg79zJxUzZ+7upWo0Go1mL0KhBZpGoxk/tEDT7JOEkgZ3PruJht44j809lzOPLl8EpWpqCP7+96TX13DgFR/nmMf+xqRTTsl9/vmDZnLd2UfxxOp2bnmihjOOmsHdV7yDo2ZN2emxfVOmMHvuXKZfeSW9P72Xlis/weH33cfU88/bpevUaDQazd6HVHKim6DRaN7CaIGm2edY0xLi1r/X8P4T5zD/a+9nWvXOh7FSitSqVQR/93uyDQ3M+uIXOOLnP8d/4IFkrAzre9dTF6yjLdZGX6qPlJUC4JzzqgiEqvjIo5P4xKnn8M0LL2HWlBk7PV/FQQdxxC9+TvTf/6bz1ls5+Dt3MvPaa3f72jUajUYz8WgPmkajGU+0QNPsUzyzroO7/72VH1/5Dq4668iy9klv2kTfffeTbWtl9g1zOep3vyVdBS+0LmDhmoWs6VnDoVMP5bSDTuO4A4/jtINOY3LlZASCtJWm/9B+amc1Mr/zj7zw9D2cOut0Pv62y7jkmEs4dOqhI557+sc/TtWxx9Jxy1cw2zuY842v7zSnTaPRjA1CiMuAhwA/8Cel1L2DPv8mcCNgAf3ADUqpfXIBbc2eReegaTSa8UQLNM0+gZSK+xdu55l1Hfz1hnN49zGzQEro3wadayHSDqkQSAsmTYcDD8ewD6LvySUkV69n9pe/zFGf/yMBK8S9Wx7ixaYXOXnmyXzkuI9w93vu5pCph4zcgHeCZUsefL2WR2vnYxiLebDmQU6dfSpfOPULXHz0xfhEaeE1+fTTOfbJJ+m4+b+w+vs57H//R4s0jWacEUL4gf8DLgU6gbVCiBeVUlsLNtsAnK2USgkhbgHuA67f863V7GvsSohjMB3EL/zMnKTzkjUazchogabZ67Fsybee2ci2nhj/+sp7OcofhoU/gM3PgpWBo86FWcfDgUeAz48dDRH80z8Jr+lmxvEpTriikujUV7hn3svMS7Vx2TGX8NhHHuPEmSeOqh0Vfh93XHoWHzr5OL72ZC2nH/JF3n9EO79Y/wserHmQL77ji3z8hI9T5a8asm/VkUdw7OOP0/a5zxO45x4O/eEP9SKnGs34ci7QqJRqBhBCPAlcCeQEmlJqccH2q4DP7urJbKnImDZTywi51uz77EqI45qeNQBcfvzlY90cjUbzFkNP42v2aixb8vWnamkJJnnmi6dz1Jp74DdnQ6IPrv4TfLsJPv0UXPZT1IV3EMucTvPPV5KteBvHv/QKs5/ZxhOX3sjHZRuZbJQXEpX8ePEfOfGx6+GfN8HqP0DTYgg1g22W1aYzj57JvNvfx8zJB/D7fx/Cd097hFvPvJWnG57myuevZFHbopLhL/4ZMzj6L38m9cab9N3/cx0io9GML0cAHQWvO933hmMu8HKpD4QQNwkh1gkh1vX395fceXNXlFe39e5qWzX7GPr5rdFoxhM91afZazFtydefrKUzkubvH8oy7S8XwGFnwC0rHY9Z4bZdXQR+8j9k6uo45Hvf44APX8q63nX8eN6tTK2cym//40+ccfAZzsbZOPRshM510LoC1v8VIm1gphwv3PQjYfJMJ1Ry0gz334K/aYdwwMxjeODad/HCxm6++vdaPnf+MTx22d95rWMh96+9nye2PcGd59zJqbNPLWpnxZw5HP3oI7R95rNUzJrJ7Btv3FPdqdHsb5RyUZe0qoUQnwXOBi4q9blS6mHgYYCzzz675DEMS1f125/QVRw1Gs14ogWaZq/EtCW3/2MDgUiKp05ZxqTn/wAf/QW8s7gSojJNQn97jOBvf8v0q67i8Pvvw5jk57619/FC4wt88+xvctWJVxXnh1UfAMe+z/nLHUhBOgzhVoh1QToCmShkIvn3vdfxHoh2gr+KK+eczMXvOovf1x3EzY3v4sefuZgXP3Exf9v6N25YcANXnXgVt515G5MrJudOVXnYYRz1x4dp/dSnqTr+BA744MXj25kazf5JJ3BUwesjge7BGwkhLgG+B1yklMru6sl8OmJ5v0KiBZpGsy9QH4jR2JfgY+88fKKbMiq0QNPsdRiW5LZ/1BCJxXn6oEeorN8GNy2G2ScUbZfZvp3uu+5CIDj60UeZfPppbOzfyPcXfZ9Dph7Cc1c8x2HTDivvpELAlFnO3xFn7Xx723KEXO8WDuxYzR0zliI7f8mGh04keubV3HTJF/jY8R/jh2/8kOv+fR33vPeevAcPqD7hBI64/z667vg2xzzxOJNOOmk0XaTRaHbOWuBEIcRxQBfwSeDThRsIIc4E/gBcppTq252T6ZzS/YzdiHA0bZNKf+XYtUWj0QxLMG5gS8VAIktvLMuphx840U0qCy3QNHsVhiX56t9rSMcjPDHlQSqyAm581QktdFGmSfDhhwn9+S/MvvlmZt/wJUwheajmIf5R/w++ftbXue7k64atqjgm+Ctg5jHO3ykfxQf4UiGmLfkH4TVPY2y8j4NPv5o/nv81nolt45ZXb+Hak6/ltjNuy/0wT7voIg66+WY6v/JVjn3maSpm6speGs1YoZSyhBC3Agtwyuz/RSlVJ4T4MbBOKfUicD8wDXjGFVjtSqkrduV8fu1Cm3D6YhnebB7gyjNGSjUcG3ZnHbT6UD2nzzl9DFuj0WiGw/uuNvQm6ItntEDTaEZL1rL56hM1JFNJHp/8AP5J0+GaR6ByUm4bo72drju+DUpx7NNPUf22t9EQbuCu5XcxtXIqT3/saY4+8OiJuYAps3j75V8l8N653PHEfC6uf54rt17GdceczwXvuZvvbP8rX3zli9x/0f0cPs1xtc+64Utk6uvp+e5dHPn73+lZeI1mDFFKzQfmD3rvhwX/v2SszqX12cQTTZdX6Gks0DlomlHRsgyOPAcqJ+98W824sK8tLq+rOGr2CrKWzS2P1xBPGzw261H8Ph9c+2iROIu++CItV1/D1Pe8h2P//gSVJxzPX+v+yudf/jyXH3c5j/zHIxMnzgo4dPokfnnzf9Jx7g+4wPgNW3wnc8TTc3k0UcFZ04/nupeuY2nHUsAJizrsR/+N0dZG6NG/TnDLNRrNruLbHydXpA2J0lUt3+rsTg7auEZ3aFjYupDWaOtEN6OYRJ/zpxlXMlZmopswZuinhGbCyZg2Nz+2npRh8fgx86kI1sP1j0NFNQB2IkHXnXfS94sHOPLXv+bgb3ydQDbIjQtv5PnG53n0skeZe/pc/D7/BF9JHr9PcPuHTuShL1zIf7VfwjcO+QvmpIP51rK/8JMDTud7K/4fD65/EFva+KZO5YhfPkDwN78hvWnTRDddo9HsAvulQAs2QMvSiW7FhLA7ZfZ1pMT4YkmLgczALu+fzFq8UNs1hi1y0fd9XDFtk9fbX8eWdtH7ua9qGV/Z1T2rSZrJsW8cOJNZvXVlb64FmmZCyZg2//XYerKW5G+nbaRy63PwmWdg8gwAso2NtF5zLTKZ4rjn/8WU887lpeaXuPrfV3PKrFN48mNPcsqsUyb4KobnvONn88rX38/kGYdyQe2lvPb+f3BRfwdP9cdY3jyf2xffTsJIMOntb+fgO75F1ze+iR2PT3SzNRrNKPEV/Joque+Fv6UNm3WtodHtJK3xacxejmFJ7N24x9qDNv6IkqtslEcyu2+Na1OabynP0a5iK0eY7U4o40B6gP7UOEUF9G6Bvm1lb66fEpoJI2PafPlv67Cl4pHze6la9r+OOJvhVMaOLVhI66c+zYxrrubI3/ya5BQfdy67kwfXP8gDH3iAO8+5k2p/9QRfxc45YFIlP73qdB765Jl8b7nJV/w/4sD33MVjrU1UB+r4zLxP0hZrY8YnP0n120+h939/OtFN1mg0o8TzoKW31BGb/zJ2LLZbx1NKYXaNwyz+MKRNm1DSGN1Og2aqJ5o9lWGyqStCZzi9y/vvjngYT2R20CoTSkFf/ZgcO5AMYMo9lyNY6KVsi7URzoTL3tfenUXIg40Qatn1/XeB2r5aXm9/vei9/vgurxiiGS/ciZmUmaI32bvTzbVA00wIacPmxr+uA+DPH5JUv3Srk3N26Oko26bvgV8S+O//5ogHf8nsG29keddyrnrxKoQQPHfFc5x/2PkTewG7wEUnzWHBNy5k6qRKLnz5IBa//5/c7z+Sy3qa+cy/r2NtYC2H3X03iaVLib/++s4PqNFo9hpyAq25GQCZ3nUDHsCOREhtqN3tdpV9PrkLRungQhmZ3ROlu0vSTOyxc5n2W8+DFl/0KrGXX4b+BujeAFbGmfW3d9+jVNNbs0fzwgpFcF2wjq39W0itX19WaGqp70Iia5W3GH1PLXStH1VbS6GUKu98QNoa+qx5oylINLXnBPF4o2x7p/duOM/ZKCIcxxf3N2JLcAvre3c+RvbOp4TmLU3asJn717X4fII/fXQG1c98Bj7yMzjhg9jxOB0330Ji2TKOffYZ0meexLeWfIsfrvwh3z7n29x34X1Mr56+85PspUyfXMnPr30XD37yTP53yQBzU7dx3bt/wA/6B7h94U3M73+NQ+/+ET0/+CFWaJThRhqNZsLwJuzHyggQfienVtmlvVSBZABrDEMMldqVwKCCPZSCHQvB3D1huqtY0qKm/w0sOUov4AQw0R40pRTWQOkcLWVLCGyCgSbItXNsRvWEVr40TMyeAJQRmlpKB7y2rZf1bWV64XzDFUgv/75v7Ynx8paesrcvxb5WtXAkYi+/guFOfg2HJ+DG5LrrnofkrucxlsSdmCm3fVqgafYo0bTJ5/68muoKHw9fdTTVT10H590MZ3wao7OL1k99Ct+UKRz998d5PvkGV75wJQdUHcALn3iBy469bKKbP2ZcdNIcFn7jQo49aBofmD+LyLue5Hf+o/j5m/fwxOSVTH3f+wj86O7dSkTXaDR7Du+rmrP/dve76xY9UlZpEVbTW0N7rH3EQ8Qy5c+g20qNvsmFOyjJlmQXLZGRjajxwjN6LLX3eg28sLsx8aANMh6zdvkhbVZfH8k3Vw37+Xj97lhqz+V27U4hlpLeZGljRiLlHWBYgVY+yezeFT484RgJ7NAYC6aRkBYkxzoXbXRjUgs0zR6jP57lkw+v4rAZk/nD9W9n0jOfhuMuhAu/TXrjRlqvv54DLv4g6R9+hS8tuZkntj3Brz/4a350wY/2aa/ZcEytruBHV7yDR284lz9tSPKT2Hd56NCreWn70/zpjB2kN24kNm/+zg+k0Wj2IlzjrlwjNxMd+Wjmrhm1SikW1/eVHebkGKWjNMwLc9CUpD0bojm2Z/NvBiNVGYZt/fwJ8/TBGFVxbF5cJNJea3uNYDpY3r478yJ5AsVtp1KSeZt6di0Mtui05XnQDEuSMXdPoOyOl7JUDlpVVwfVNWvKO4C/cpfP7aELPg6itw6CO0bcxJukib+8oCgCKedZm+j5bu1B0+yNdIRSXPv7Nzjr6Bk8eO1pVD3/ZZg8Cz76ALEFC2m/YS4H3HYzj7zf5LOvfI4LDr+AZz7+DO8+5N0T3fRx593HzOSl29/H+06cw2ffPJ+rZ36PxlQDT34wReCee7D69891hjSafQuFzzagfxRFFZSCHYvASJU8HgDWCAJrBCMu7Rq4vjJ/5XepKGFhyJr7/wnLr3K7yy7Hg2amdiqMd68tqqQ16BmKu9NHMpMhsXy5e8Dim2bYuxneKZwwxNgg8WpJhSXlbuXdQb7K3s54vb6XFTvKFJu2WbIy3rACrQwrXZYSoqMJzxzOgzYK1TUafTbRIbN7CmWWOb5tG3uYEN5RMdYqeZTH0wJNM+7s6I1z7e/f5KPvPIyfXPkO/K98B+LdqGseIfjnR+n54Q9p/O41XM/D9KZ6ee6K57jljFuo8ldNdNP3GNUVfr5x6Un88yvv5YWeY7GSP2HgaMHqY+K03fUtHeqo0ezlKAVVRhiRjeXfKHvnEoarN+s7TIgjALYNkY6SHyXcUuHltkLuUohjoUBzdvZPkLGo3Hnp8sPoituplEImx2j9o63PQ+faEc68G96daBQ76o6xXTUgR9ivNxlgZdT1VKhReoN3Qrk5aFlLki2zQAaxrpJrSw0WwV6fS1vyQm3XiN7Akh8JX/mqabAHTf9+l41SKm/v2FbxzNEw+biF+0L5z7y+WIblO0aaAHdu+Pzm+WNTgdTzoJU5HrRA04wrGzsiXP/wKm5437F8+z9OQbzxEOxYhLzmCXp+fC+9/3iMB26aw5+r13D/RffzwAce4MgDjpzoZk8YJx96AM/d/B7OPuUUlrR9nx2XHEbvxrW0/FmX3tdo9n5UPnylnB/hcirKhUcoTBDrgo7VJT/yKsCVaxvau1IkpJQHbQIFGoBdKNBsC4I7io0+j0ID3jIwGrcTX7ykeJuBJmhdOfrGSBuSjgfIaG8ntbZYrO2OQMsVjynlodud4gj92yGbwEaWaN3YCIxyPWgAVrku3eGWeii4CNPOr1vnhS+OdHxZ6kvjG8U9E/7i16MQaJbrpRwL5025p13cvpjN/Zt3/4QjsL4tVJYHdktwCwvaFjgvts+HlqW5z5RbTTRj2rmFxAvzbAvHfznfj45weqdLi3jHKfROZ0ybrLULIbij9JxrgaYZN95oCvLZP6/mO5edzE0XngAbn4KVD2Fe9jCNN3+N+s2LueOzNh983+d4+mNPc95h5010k/cKKvw+vvXhk/nt597DguB3WH7ZcQz85jG2L/vjRDdNo9EMg4K8RdRdM5q9hvnI+cwMBIbdRAyOX4x1Q6KvcPeybeuSYV07Y28SaLk8k3ybYn1trFu1mNTqNSRXuELLM+gLjaWWpajtrxYfMB5wSs3HR66kl6qpIbFieBFntLVh9vYVt3V3BI8r0LAlIJzwvsCWIZuZ3d1kd5TO2TF7hl6T6tkEUdcbq5TbP26fjmJsvNn95rCLJnsCLZm1djvHLMcwAq1QBG/sjNDc73pHd6XM/pZ/4rfLWwg6aqVLqCvveIPeL9GWeZt7aOpPDN12BAbnNI4m4iZmxEhbaQKp4Z8zY0FnOM1AYuchiqFMKJeruCpcj53sH3I93kLiiazF4vr8dyvvoS3efvje2Ek/CVHyu/rqtt6i85aPm9Opc9A0E8nCugD/9bf1/Ozqd3L9OUfDjldh/h1E3nU3W+bexsLqRlZ/93Ke/Ox8rj/leirGoOrRW40LTjiIl2+/kA1H/YDGt81hw88eoHbtbye6WRqNpgRKgcglQpnlzV6PtJH3mSvCbGlT0ztI+A2aqQ82LmT++v8rPkyZxoAclDa1I7yDZrcio9nTg0yVyJMrCllT+FIG/szELpCryLcpF+aZzWBH3Zwzr82eUZsKQTY+9EBGeeGOZk8PdsnqfkM9qekxECWOMa6ccvjghPeVyHtM19XRs2GocFNKEWtrpLZvQ7HhK5XT5Gzc+b8vP7ZUeoBJqWGEamAzBLaQtWwWbe0lYSYwhlnqIJU1WdMS4tVtvSwfIcfMExy2VCjDwOzuHnZb3KUmlGGgzLw3ZbCX0thJeFzRIXNi37uHkgo7UXTMQLJY0Cil6I2mWRlrJDF4XbJS33Pbgi3PgW1hhcNEX5qX+yie2bXCQFlr52uFDWZdwFmPdiyX7BiOcrxO3vNKKUXISpKRJrhh3kK412am8Fvp3LXu0uQSwz9+TWnSkQ3l2jEYW6ryQ3ALKRTSw0xiFKIFmmbMeW59J996eiP/95mzuPz0w6BzPerZG1he+RFavvMzll16CB/+9bPc9d7vvyWrM44ls6dV89jc82i87hcc3VvNY0/+hpUr753oZmk0mpI4Rq5VdihXGYaFa3ykrfQQo1AUeIEa+xI815bPxSk3fciOOflMtlRFYm5HeAf1IcfwT62vIVM3NM+neB00yeQtASprGnZ2RVjSYlnnspE32vwsWOWLvUg24tTmKGiTZw+JSZNz7/VFkzT0ujlcmRg0vc5w90FmDazoMItf74KjsCGQIW3aRW20BgZIri6zOiCuwagUqkSIXqExmTUlzf2JId4gZZpkvH4tGhxuFU/XcZYxLAzXoBbtq5gdGsYr3L8d+utJZCxShoVUstiotYxcEZyBZIaeqCNeRlqE2YsmtKQku3UjqYVPDbutl78ZX7yExPIVubcHe5U8cTX4+xBIBoYUVyn1nVEF+UOmbVLTuQIjnRfm3dEMyxt73Pb7B+89bLtRNnZ4qMDflRDHV7YEaB1I7VLK257Icx9J1MhMBjuenygpClfMiWvnvaqmRRwaeJ2IW6F2sKBW7nN4MIMvcbgr7oh3sDnZNaQdhfhjEdKbNg17PSUpXCyzZyOkR15XTws0zZjyyMoW7pm3lUdvOJcLT5oD/Q20PHYdz246hMonVxL9n9u4/UcvcdLMkya6qfsMlX4fP7juHEK3/phPL6rgnvWPsWDF/0x0szQaTQGFRsGWZDexTBlrJpXjQRscrlO4j2cISkkgmsEsMMZzM9Ejnd6ySCxbjjSMoUVCUgOQHlqqetj2eyGOg4SDJS2SZpLuSDoXFpS1sySMBCmzVPVK8mFrdunEfCsUGlI8xfMuFrZTDPkPdIaTxDwPhedNG+Y+pOrbSW52vIgtwSQdoaHt3WnJ/ALjsco3aYhnx+zsLKrU6wmYnR7TKhGqieNptaRVlO9jSpOOmBO+qAyTUt49z4Uq/BUIpVjX76OmvcyFmcn3w5Bx0rrMySVikNGNItvYOIzQ9K5FgTTAHMHb4HnQTLPIyzu4n31WGrLxXBicd46a3hoaI40lD114KaZQjn3tiYXAZmhbWbCtwpKZoTuWei3t4vfc3CqlFGkr7ra/fAqv1Qv/G+2+hUK+NdpKZ7xzVMcph5woH3z9QPKNN0gszU/aeONIIFDeWM99X53X3vi0C4UZwz/zvPdXNQ/QG8sM+/j15wR26RBHgMqeToz24gJNwXRw5EI4g8vsWyOHfGqBphkTlFI8+GoDv13SxD++fD7vPmYmZt9W/v7rK2l7sZqDxWxOm7+ID3zslrFZA2Y/5GNfvAL1oY9yywuz+VHDEzy77EcT3SSNRlOAKBBp5YUMDe/myodXjSCxcgLNKppxd7wYg44zElIOLYyQjReF+aUHh205Ry9sMDDUqNi2cTHLti+gM5zOJfQr6cx6L+lYUrStHYk4BrsnzEok1ZtdXSTfeBMz0DvMxQwVaIW/OaLccumDSuUnMtaoQ88K1yXzDGC/zzdIZBcb12taQiPfM+V4upSURP/2ENnufDlxhWJt71pea38N0zVqpVJ0J7rZHNwMySDKNPIGoiuOAskAASPmHEGIXM5Z1rKRStGS6cdStnPqZNDxbubaX3yPhhioI6w3l6nfjkwM9VB6V29J5faPIpoyi/LWepPu/ZfFxnvOsB9kZxwQroe+rWAb+C1HyG3qjNAb23moWco22J5yPMipbIK6AdebPOg7bqpsUfuHXpFL3b+Kqnx6/R1I9NAQX4dSqrSdVCoUtwTDCpTOWqz2Yg+3d54NHeHcBMTWga1s6i/hHbLNIUVzRhqrr9f30lfQv7lJg7p/QaD4+DKVLjqeQuW/G+49VrZ0vN6Dr8sbzu4YiGfjWIVVFwc1sTeWoT2UGrbtRX0/3HO0YNyb0mRVzyo2vPE8bSsWlDymu5N7sGKhORxaoGl2GykVd/97K8+u7+SZ/3oPbz/sQDZveJYnbruGU58XHHrDl7nwiZeZdvDhE93UfZ6zfvJ93mZXctWKd/FQ4zM8suR7E90kjWa/xJYKw5JEs1Fs6RqvyLwtMJxXZt06sk1Ng7YZfs53sNBTqAIjFFJbW7GjkaIZd1vajHTkV1pe4aUtjWzrzi8J4IQ4Fp6oeM9N/ZuGXwg50V/SEwJgba2nqrUnV50OYFVLiLruoYZWYsVKjOZmkCaWtGndvBKZKTagjbY25z8likP4E2lE1g3fC2yhqmc9/g31xUU6duI1K6JQf6JKV/cTwgnFDBZ7YSxls6ZnDaYbPufN8rcFd+4hK6tplqQ1E2QgXHxPYtkYtrSLvKlydQ1VDe3QvASVjg8Zo9uf+hMb+50xub0vQSRhIFAINw9oe6qXqEw5RnPzkuKGuPnjAseILRyfADErQ9D0RFh5YXRKKSp8PqRUzrhWkiUNfaxqHsh9vr53/VAPbKQN2b2Jyau3Qk0d6c1Dc/BE25scGlgMOF7RnmhmiLet6LsjJYayHOGqFF2xTroTpXPibC/3brC4ylUCzNKT6KEu1U042p7/2F3r0DKcsT6z6QXH4zeYhgVOIaDB18Qw3stBZNYtJfnaiyX3Bdjc7wjPKlvkPbSFZONFRXMM2+DllpeHPW88YxEoEGhFwmeY8D6FAiuD6q7NiyDv+EYSdiwcso8tFcq2cwKtI97Om92rMIfxwHsMl7qW86C5RUKyls2q5kHPvoJLSRpJQukQlZ39ZLuGeh5T69c74ZueB817pg9XgdRFCzTNbmHZkjue3cjKxiDP3nwBh01VPP+Tz5O+4QecqGZxyssLOeXL39BeszHCN3Uqx973Uz66qYlzmz7IY03P8+vl39frpGk0e5gN7WFe3tLDyq6VNEWbnCIhZXwPzUAvqYbt7quRvWS9qT429G5wt8yHyqlIOxgJQGGGYhjdXaRtxwg2bUl7OIFSiiM650F2qIdCKklfMkxPJJU7vxPi6J7DstwwpKHhikV4zW5ZivIMSiUxbGPIukGFgmEgmR5aMEApMBJOvom06I0GaatZjh2JYHR2kVy1CoBVW9rJVE8u6jOv3TM3NjDTM8r760sUzVMFHrTRPTOVcrxRJXOn4j3QU1v0Vl7MCUzbWddLoUhkrZ0WbjELxO7Wga3F+XquBw2liFhp+oxir4oQAkyLSDqU29wXTVLR04eSEqsnUOQVsF0j0ZcxnX0Fbu6Zs5GlnHanVLZ0NUd3zS8hcAq0qOJQxhXR7ayJt4CZRKQLFg/2+kcIZ6x5XhL3/Qq/cD1ofSrDPAAAIABJREFUgFv4xfNCeoa4HFxFNB7A7tqEL52BYCgv5gtxw8rKEsFKgbKxlcKpT6EQBRMNQw/hfJaxLIKJwvxJZ8vWRCcb+jbQlhkgaBYslO6G6yrLyh10at96AKLZKNFswbYjiA6FdB2/w1ycVIAg0zA0T3RqxXSybojm9PnzmVzjFp5Jh8E2SWYtOiPFIZxe7t5I41kghrQnYER5Pbhx2H0INTtLXOQEjTep4h5zkD1pK0Vq9WqMZW/k3rMsm1e3BQgXlNH3nomeB3W4dvvI5xs6Ak1i9zcUP0sLPGiFxylVIMfsCTiFbnIeQav432HQAk2zy2RMm1ueqKGpP8mTnzyV+F9/xab3n8/U19Yya+77eO/TK5h0xP67ptl4MfXcc5n1iSu5sT3Be3rex0sN/+JnK75f9iKgGo1m91nXu4q46RjB+Vnogp/qESzATT01dCW6SnvQQi258LqsnSl5nHBvM/Wt+Zn0UCbElrBTaGIgYbC+tTd3RJEsXQ66yGugVNFscuyVBVQGwu5HqvQ+g1AF64+91v4aq7pXFV2bPYyHzRMIRNrzCw7bVtG5sg0NWMEBSPQjbJO0JYs8doWLU4uiSn651jnXNW8+IpPEUvaoF55VOB6Xl7eMXHa/8LoqeoIopdjUGWHp9n4YLnRtEK9sCeT6PZgOkjAKDEMv9DKXo1MYDuvco6krNzHgenkUCiF8CNsi2x/B6OigcGJge18Yw84LK386w7T2MJ4ItN3flbTKljZocx40x9tQuaOT7LrCgiKCqf5q6G+gKlQgDArHdcMCaF5c9HalXziCzCfc8Zm/39IVQhKJlN7EgiPmciJySJEQ97RerlxhKGyJe1Kd6Yf2N0Da2ErmPGiMtD6b+1lrMMnKxgKPi3tRVb78Ata2ZaNMC5RCGWnHWLdsBDapaJiVwRXEzRAru1aysqswrLD4Hnj5i67vssQWFOR8KZA22SVPD7l2v6jEu+fYBj5v0fbG1yCwmYa2TprrN7hN8NaUy4tqpRRpY6hHSIj8PY1kg6wNrKXXjJOxhxYAEhUVzsbZuBviOMiDNoygUhLseAJVkIPY0BcnZVj0xfPjNpp2vvN+79zDetDcvDy34I1tS6ZF6mGgEcM2SFlxVFEUpDfmhg9tT5kp0p6g1QJNM54ksha3/2ohb1+1gF+u+SOBD1/MhgV/o+HSLBfd+zXedqtes2s8Ofib36Cyt5tPH3wBH+0+nZUNL/Lfy76bN3Y0Gg1CiMuEENuFEI1CiO+W+PxCIUSNEMISQlwzmmMnrBg9aaeARIWvooTx6r6Ol86VSrgeMGfTgn271jul3wuMbsAx/lxvRjRlYksbkdstv7iwHx/TotvzDophCnF45dqd06ucd+KF2i5MWyJShvOxmS8qMdSQLfBi2V7hDcdYS1nF5zVt5VhSA00oJalyPRGmNFnXGiJlegUEJFkzi8/z5kmZK/4g6xcxNdGGFD46QslcTkvxc0/lii7kJqwLulekkmw221ja/QYjM7JXIP+itOCybAvf1laMcD8oJ8fPk1TlLH3gGZMeK7tWOiXR3X4x+70iNAq7YGmDXLgbuPcCfMIHtoltWXimPO7nm7tCRFKm4/0VTkih37Bz3mBL2UzyVZJSxjBhrCL3j1KSiv4Idp9b9CQbLwjjyl/1xvBigpl8KJjKxMm6XmVvmwqfz/Gg4Ri9m0NL8iK/wAO4ri3Ejt64I35LeGtyrczlNpWu5jiYaYlWJ5xQSWfxbve7Jwr288RexsoQzUZyVzhkMsIVNNUFAk02dBFbvdW5mNaVEG5DWSZC2mS2NeNPW6TteO6YXvGP3liGTZ3OvVeGQXL1GpRpojJxDu+cXzS2MpEBlFKYtQuQ/a3Om1Yaw0wNmaDwK4lUQ4t3OI218RtxJqX7iq4nV3AFRWcgzMJNpQuLeEfsTXfTn3LXNStxHlHhx1vHUPkryI2tQfd9MLZSWFIRTg0Vff4SKicv0kvj3WNbORVXD1i7DV+LU0F3U/8mdsTX4bfSgMpfj0sglqYzPPSZu753PW94XkP3ejI7WcpDCzTNqMi2tNDxm9+x8sNXcNtfvsMnsi2seLvkrq9N420fTPCZz9xL1Xk3T3Qz3/L4pkzh8HvvpeLh33DRxXfy2a4jqGt6lTuXfnuncdcazf6AEMIP/B/wEeBU4FNCiFMHbdYOfBH4+66cI2074WUVoiIX4mjakrTpipV0BFqXD2qY84+ihJHivo4ufK0gPMvdpmMVdG/AyYWSrnHtGGoVPn9OJ/jwIZWR32+QQPOMKoEvv26blMSNGBvDizmicx6mkXGtA4UZCCCVoiecZG1LPm+kcM0pAJV77jjH9A8qNS6lojo74Cz+nM1wZK1jkBvSoCuSpjfh7m+mWbqtm7D7unBGOr6uHoEjR1v7k7miHfbgZPutzw/q1oJ+lpKssjCG8aBlTLuoAmKpY+QEWscaZ6HoEthK0hFOE2hYx6SM58VUQ72Qwwm8woqcSpGqr6d5Uy1rWgYYSEVo7G5xLqc/zqZX1g9tr7SZGa51xiCAbbGpK4SVTuSMYKTEVm4/SIWpbGxVOKuvsJTkQP9kMsrElibmoEnAtG3wSmiLuzXYk6vyhm/DAnKFEKQE8mG0WW9cCoEVTpDpCOauFaDCJ7Btp3JixjYcgaTynrP2UIrGvhgZ0yKedYSFjcIWgJHECmwjYxZci/e9U0ON81KeYZ80sCJxlLSca/ZCHAvGgRWOYbS2sqFvAxv6V0NuDb7B4yfv7fSwU264oHS8aPgrUbaFz6voKMCQGVDOhPir23rJmDabOsO0BB3D3lueIZLOYBsZR3q7jxV/eIDN8/5IQ7iBVG1driR8Y7SfNZGOIUVADgmuQyR7KTlGI20gnYkapRQy44QzFwo0Y+lipmwrDlu0pEnSjOdDpwu2HyyPjEEetbbMAAiBKe3cmn/DiW9bKrqjadoGigVPVWd7PicViGb7MN3xXRtcSdZdbsKUJss7lxfkNLoTP954TWcgnsptW2ElmB3eQMJMsrZndZEojqQMajo7SkY05T2Ozr+v9wy/wD1ogaYpg2xjI30PPUTTxz5G89XXsGz+Shov/DgHLnyauz4RY8kxHfwl2sdZ//konHb1RDd3v2HKWWcye+4NHHD/jzn26t9zZzt0dazm9sW3kyljEUSN5i3OuUCjUqpZKWUATwJXFm6glGpVSm1iqEU1IoMnQXzuYtLrE2sIZzLEM5aTq1TwI21bJgOpIKLCmUV3fqwHedByFekUVr9jsAov7yYZBMt0opSURCAJJ7xCAnkDwed6aNSgQ+faIW0sqVBI4oYTopnZVk/cyBft8NmGa00q7PY6VKqPeMrMzVAb7e3EFiwsPrgneNzZYcM2yBYYXb5oBL/t5bBYzlpXUiLd7XO5YVYGoWy3qIjCLgwDUriGuMC0bJbv6MeWTiGVroEkkbSJ9A01touMcc8Alo6RuLU76pTcBlJmkgV1Ada2OP0ilSSRMQkljXzIqGHgswrCMjP5/KDk6jVkW1py/QyOUBODxd3OHWhE0yYrdgSJpk3Wt4eZ1jmAXd9OKJFlXWcbHcmY290FQs4L8/P7QUosZWM0vIJpOwsYKylRA40Qd7wBSoGtLPdWK7YkO0jaXhERRcWO7ZjrG6l2wxjtRB+LIludxYNdYlYqVxpHIVGhhty6Z6XuQt6YLfS+2rmKpDkPmt+HVIqtPXHCmQzCdsas01RFXzxDQ1+0KPytdSBFbywLRpK+cJS6rjB2T5Pb7w61XdFc/3p9UApfKkF6SysyGnHWNVR5z2NVQweTN3eTqW+jY+0SbGUj3HBF56DS+Q7lLtD9DhecLFfGR9rOWBQ+lC3xmybKba9AIBVkIgFQ9pD8RzsaxZaSrV0RwhnniF4xHuEuB5CyUk7b3QmVlsQAqaxF1rURhJTQsRofAl8khEwknLDZQRMHZrIboSTZjj7ir77mXk/+flT4fIhstqhcf0eqntqBVcQXLaIi2JvrP8uWQ7yxTZEmkq5XCqAx7UQebGoP0xEqFl6DhZpyQ7QH38rq9iZ8XR2529KW3Mx6oxlLWmRkmqQdw7AzLGpdRNyIE8lGio5vm6YbXQDSh1M0RLkiXQinQmlBNU5vlNVHakoXkslFQ4y8jIiHFmiaktiJJKG//pXmq66i9ZOfwgr0wk23ccvV/0vjLXdx7lfO5rPL5nJeqJuHoyYHzV0Mx39gopu93zH7ppvwT5/OMf98nNTlf+FnLV2kgm3c8uotxXkLGs3+xxFA4UI1ne57u03WzlJREK5UaDBY7q+wWVAEY0NbiFcX/JJlm/+B8OcT0AuNS68ggYOAtjec6oBeQn59B1M2OT/6jmGkmFFb5+5dsCiz8LmhObkj59ph2AZJM0lH42ZCqWYao84sutXfP6hCocztJzu34GcAn/tObV8t6VgYqRRvNgdJbG7CTmVQrkGaLnjubBtwvEv+aJLJm9fjtzPYUuYDMqXK5RMJ79rNNEK5FRMVyBJh21KInPiNZ0wsaRGKOwanKohBS0sv50M5nsRoJ1P7nDwar6pi3DDpj2foyoZpCDewLfomScMxyHtjGfoSWd5oCuZuVf/yP3PoxnqSGYtIRhJOGfS757b6+3OeT094KqWK74UYPsSx8N3uSJpQPEnWDf30CR8+n2BGZCsou2CG3jla2i1Bnw9xVLRafbzUtZ2tPTGkko6QQOQ9aO57CcOia6BQVLmJQ6EgvYkQQjlHVe0r3b4rNK5V7h/lFdOws0UGuDO2ZdF1K5WfjFC2BOEn29yMtBwx6RMKWynCiTSRdBYh7ZwQ9K7dtK28F9j913AXXJfA5HQv9uZXmdERQCi3+Ilyvn/rWkNUGDF8cmhYnFIKf8IRwMowsCjw/ChFRW8IX9rCNkw6450kjDgzVtfhjzvirzLTz2E9ixzx3rs11zaFxBdN4A+l8n1YsCaYkhKfXVhExrk6X7iFyekAzcEEwrbxJfPfsca+JNI2HIGHQlreMgqe18rzpBvFA8ybSLFMspZkSmeIKVtaSL45NPQ3YqWoSWxBIFFZA5QkkAzkQgClkrnnT2HxH+mK/9bgDvzRcO66GnpjDCSL+92UpjMZUzS2fFRtbKRpy1K6zbwYH/ztsZUadoatWGY6W1lGkKpkL9FsiKbEhvzZcouRO9uFXv4325Y979ZWEUip8t56IYgk0kTSJtG0weTVdQi32Isvnckdy8MfLQxpd4+h10HTjAYrHKb/V7+m8UMfIrF0KQfddBMnrlhO9Pbv8snNfv7zvOM58+1N3PbaV7grFOObB55GxdxFMPOYiW76fonw+zn8/vuIzZ/P2dF+tp73a35ZX8OkjMGXF36ZSDmL5Wo0b01KxY6V4bsocSAhbhJCrBNCrOvv78eUJgd2BfEb7iw8Cls6eSpeSJ4TEeWcrjOcImmatAWC4PNj2JKeaMGssFIs2d7PmmZvweL8TLyHTKQRhu0aiY5XRrg/8EKo3MUK5bQnkOz2Dg04SeqrelbxRvcb+FID+I1wzrjN2hnH45OzdaUzs27ZKMNCVfkdz5ySDIQaMS1nplwAZiSBFYqhXK9M2sxXFexOdGPYZs4Y9EmTLd2xnHEupMwZkD4vtM50ZtG9dlv2oER6aTtmllL4YxHCS1egCkPP3MT+NfEWVsR3kJZZtgViyIEWiHVhtweoTBq5jlmb3UGjGcgZ/1ZBpTbTyi9q7HkGfJaNz7YwbEl9X5JQyiSYHGpoWTkBQkEYY4nwMY9YD6pgnStbKg7vXkBFJh/6J4Rgcqo774mEnByr63bEgWkrpwqkO3aStkGlEQEjjVKSUDKLYRXkC7pLRAhF0QSBQGFju8fI4sOHkUhSEUy6jsxiY3l7eJsTHqkcL15uQecheJ6jApPaluDzkdm6DSs0QHNiI5tDqx1PtXIkvE/aQ8LG0nYCQ2bZYrTnBKHP5yOZtZxQP+HDTKQ5IDDgjFVbFfXdIX3LOTC6neFQOAJNKeXmRDoCVBRem5VFphwxVxmNet0KQEvdamLtG3Nrd0kpmbRsEZMag/kFObwQRxwPp9/1dAnvPVfg+6SFJRUVPd1MrV2TO32lXyBtA5HJULmlGWIFYch48l1im9liYe3+v74nTlckTVU862xdcG3ePbaULCoJn0n2UdP6OrayqWmPsLU75uQ6ZjOkuotzbgXC8Uz5nPwykcowY1MHtl0o5KSzdmPh41mRG0OJSAe9dqLAQ1l8n6TyhtOgD1RxBLE3dzMt0UJVopNAugNDZoikne+vF5ZtDzhVebd3NdPQ3urkj/oE0otOEM6TsyucIpQ0iKSy+NLeGniKGTs6qO2rLbK//JEEeCG33n0oUSilEC3QNIATYhB6/Ama/uMyMtu3c/QfH+bov/yFAy+7jA29aT71x1XcctHx+Ge9zC/evIffBfq4/Ozb4BO/g8rJE938/ZrKQw7h8Pt+Rs/3vs8HT3oHq972HX68cTlHVs/hSwu+NPzaRRrNW5tO4KiC10cCpRcw2glKqYeVUmcrpc6eM2cOUkkO6Oxlan++0qGTFwadbmVHz+iyUxmkmXUNcx/C7yORsdjc1sSbnSsAsIJBYokUwZjrxXCNNuXzO8eJdkGFm9OViTmekAJjy5k/diwRHz4idpTQ/CcQbvsSRoIlHUuKFptWiJy9s21gGz3RrcyIuCXqpaSyO8KU2nakYaKqK/HbBnMCS1HBHcicCCsIpTQSTsl1Mw1mXnzK3Iyz849pS8dbJqBS+PNGmWewW1mE8nwgCrtowVnXg+KGCVYM9NMbaMCwDUdQQs6Q9NbeCso4pmVjuGtMKcNiSjDhChLFIZu6ybTlixscUbs9ZxjmIpKUoifq9F2VGWZqst3xGLnWn0CQliYZK0NnKEVDbyxXKKLbCpGXUc62sXRx6W8A2lbi687nknmCULh5MtK5YwBUZ4KgnHL8AkFGmXgheFu7Y0WL8CoUU5IdYKbo7WokmjWIZgoLuuTXhbOVwphaBUIgunqQ7mTDSZMOxYcg29xNdfMACkVs3jxkOh+S1pXsJCud80oEsQULCYcGivpRFfw/YyeJZMKuSJTgc0McbUnSjpCVKVb2vE7SiiOVxCdlbix5Qq0300rMjpNQacc75i4TYNrutQs/fZEUKElFLI2UigprUGSJsnMFbYyODpRStCeaSMssQTOOMrOOmHKFUqG8lijCA21EGjeAyIcjewz0tNIdSUPH6tweuQqIbk+01j+PYTvFW5C2G+Lo5pLhCGgUCHcCQwwSvhKbmaFNVPV1g2kiEhHv0ZPvKwXbQ01sTHTkc/FsSUcoRcbz0HoFVlSujEqRIM49bxSoTBTSYSxpoZQiljFz93XtpmZMW/LvxnmOt0nZjvfV70cq5YSCovAXdKRXrt/LrfXwGTYGFhVZw6nmWQKfnSGbNehMtpKyCtZWdL8PhUtDTOsdYE5dgMGLqzf2JZiyshZq/gXxXhpbmhyvuBKkfD5nnTWf87x0+kQ435Fc+/PPKFkQvTCQGSg6D96Ej7Sd8b6TyttaoGkwAwHav/glIk89xVG/+y1H/d9vmPzOdwKwfEc/X3xkDf/v8pPYkbqPBRv/zOPpSZz5uflw3k3DJjhr9izT3v9+Zs+dS+dXb+XSK25k4/TLuGXNCs466J184eUvDLuwpkbzFmYtcKIQ4jghRBXwSeDFneyzU7ywngor7RS9AFSiF5EKUGWEchUDDTNDS6yD7a/VcuBWZ8ZbCkFNZ8wx9GJNBAd2sDHZSXLdBqq6O/B5YsY2854XKwvtbyJci0Y1voow0+AKGHBzW/LF9HIVA0U661Rxc0WO8ELXBEjhI28ugzIG8CmLHUa34+FSEpF1vF+qwkeVEUUoE6WsvKjIurlZIh9oRkW1kyvnHXdIEpxNRSREtW8Sk838sQyZcWb4bacYgXRzxLxcLktKOqIpMjKLhcgZjL3JbroT3TkPmhi04Lbl5vr5pCNisqaN8gl8wR25bUSieFFgTxR5zHn171S7RTmUq5P9MostfLnqhV3ZMH3pPvpiGWIZKyduTGxAUGHGXQ+VYG1biP54lkQizhtbW8gYcYID/aTXOwK5or8X2y1u4OU3qmySSbFWt0WSjCVzazx126HcrcwXF/FC3HB9KA5xlcLywi9tmfdmKkVfIkv8iOnO9fX0YSlJJX56Bpw10mSFM8ik14/ZfAiulBJbmrn7YJopapo2uS0pGKfu/9NGiNZwg2NIWzaF5qjALc9uWlS2tToCxfOgNS1GecsxFBxbRbtAuIsWe+JGCEzTKV0/qSfs9M0QH7qiJ9FD0kyS3riJ2Lz5dCZaaLJ66DQiKMNww2xdgTxo/4ytSMRc74myc+f2sOyC79igHLTmYIItyS62DvSBz0d7MIHfMl3vmus9k87zJl8aXhE1+tnghupaUuJTUBkNO2fNpkBarqcwf76UlSJTWABG2k4eXu7+u08UlfPt5cKPVcor0uKGYkubSVtaiWedSY9JVb78tSl4rGYpGzrCpO04k3tr8UW7wOd3lkVwQ3wrhMLs6SE6b17u+eStO+YdSNqKgB12xoJbrz9qpVkUrcOKxZm8uYbDel4juH0FMTPqFG0qym1TVLctZnrfWvxWkknRmJs/6j4sbSsn2EUmg4wnIdIKQNZwtlPVVc7zVED95mYCDZ3O/gKmJtpAqaKcZKfmp3t4ZSOlojuaIpIyCnKMJUpUEM/oEEfNCMRffZWWKz9B9SmncOyzzzDl3e/OffbKlgBfebyGn33iOJY33UR721IeP+HTHDV3MRzyjglstaYUs2/6MtUnnkjgrru44KZfEWYGV67dyKXHXMoXXvkCrdHWiW6iRrPHUM7CXLcCC4BtwNNKqTohxI+FEFcACCHOEUJ0AtcCfxBC1A1/RIfUQB9Wfz9VmRDVhht+1lXDlJ438EsjZ2B0xNqpbdtA1pZMH3A9U8JP1nbEhxPeZxNZvhFQqIoqBJKUaVHXMUDQSBAyI+S8L55AQ+Ezk3gGHDgzsrkQR3yOiaAAv68gzMlFWkztjTO9uav4wlzDJmTF2BzyKhMqkArlAx/KyUxzvS5KQeWW+mIBVjEJx6zIv+fN4Hv4uvqZ2tRIRTZCtq02N0v/5sA6mrMhyESYHt2G4RqT0jUMk1mLrkyYbrMXU7pCU+XXbxO2RPkEKIusaWNmLQ70TylsCYaUWEq5ni8BbqEEn8zPhldYSQ7pWlR0DZPTPRzc8pp7Ae66UXYG5c7ECyWRrschlA3QENqBZdtF131I7zImp7qdpQoUxF94juyiR5gV3kh79xpq27aiQv1UZ/qZ3FBHRb03Ztw1mSy3cMsgwZvPHVQYlp33iLkd408Xi82gHSdiO6F4VjLuLOrsqjjl8zt5Rq6vTmJhWo6nzofAdsegYVts7egns2NpgZdRImUWZStIBqGnloqBVFF7Hael83paoh0iHaAUoWCMTM674HzuQ0DWdO8cjhcsEyQe7yIVaswdsy3reIVkagAQ2KLA6wPYto0vt8C1QhgmuBUUvbaIjEFyxaCKesrJWVPZNHZ9NzM6+pDKLipao3wCIRU+r3hHrspkHtOWmP0RlGk5Y13kRS44gjqRMbGFIN2whArL8T4LqTh41VpkwvHuHTItn/MaswboSfTQPpAkYxpUKIEv6yxybaXStG9ezpw+53pk4RpohUhJX6oHK+6sz+d3hWVHMEEgml8nzJQ2Rm3T/2fvzaNtyaoy399cKyJ2d/pzm3Nulzd7ICETEcESQQQTfJRUWZTv+WzqpVUIFE+LLKUUQQo7HlpQDGGotFI00mhaYoeALdIoJH2SfX/75vTNbiNirfX+WCuafW4CwxqjvGTmmWPccc/ZO3bEirVW7DO/+c35TaLtPg7IjaE7HJGvdPnMfT6dsRH5RvRLw+OM8i7ba3f7gAigsgxVpH27KoAQbw0Y/c17wFrSc7dCVonNFBdv3na6XEeU/3zP+L5mdm2daMunEKbDLUbG+V5u4fydJPRUc5bG4DwL5/6eid4JAPrOA6NG/zRTW9VeqtQmHQrHpu2RDEdYC6o7oHvbPaRfvgs9SgkSLoix3Lt2RzWtrgqcZSbnz792hrMbQ9b6WcmwPri8yWo/Z3n7G6c4Rt/w3V17xJpzjpW3vIX193+Axd/4dSa/93vH3v/Tr57m1X98G++8fsTbv/RDRDrhnc+/ifbCEy7SiHftm5mIsPi613H8R/5vum99C0de9CH6b30mz7jzGiau+2Fu+PgNvOP6d3D13NUXe6i7tmv/LOac+yjw0R2vvab28xfwqY//JDMbG2U6FcAgy8A1xtIGtwYZM/fcN/Y5Jwrf+DaoCRbpSs7hkhjlLFluEZdzcrQGTFUObq2hj3Gh5qoIWluDqCrdzhrPTLm6IAlBOdLltFZ7aLVJdHjoe1VBJergfD3dKMtpaF8XYvERdr26hZuNsM7Q7fXBwa390zw+n6eDg4l90D3L6sm7kOY0c0ceh8VydjNlvwblLBjPCJjccna9z8nbzzIT76HzwDkyHC5PQTd5cHSeg67D4JbbObYRcXC2VbpveRhn8Z/Lc/bdfj+5dXR6x/j4aeBLxzl4zWO5yx8FeAyaaEUSa+98jnxKlK75hVJzsMYXL0iMq+I4U+s+59UorXP0Bms0Tp8iP3wIoXLYz5r1AH0EcRn6/Ncw+2cBaKmYTZdinEOH9djKVjmfnWGCywDBYr2DN0zHRjcqer5ZL9AQqQYN1QI8Kzh7zzJOTz1kNlX/q19h5r772cB55lE8DCf0yDNYohDHHwNoWY42I6S3iksavlbRWYxLvYNcY2FkmHm411L+53QUxuv3fmpSzp/fZFv6XDabMl3MUcD5BSOsnGN6+fN84ezdDK9eBC715wnXMc7SdyPO9I6Xjm2nGbM9CmmYzrNr6kHfIF6uqMQ49MoSdqUJrfmw9n6tt4c5bjTErXeBiDP94+Q3r1Zpjknsa0ELlqzW860EzmmAHKyUAAAgAElEQVRG/4QHMvbqqsl5MUPGhICH+JrMONsOQBZwBjcY4Jyjde40ve0+4jrl3C53R4xMhm4JZBaXRAy6fZbXhyQ+Hw+OPRhqnSQ09S4G4OiabRbuOEYsK4ibxFjL1rDoe+hYGixxy8YdXAeIzbFY7tpa4cGVs8xMNMuWDJlxnFrrMzA9Wst/T0vARh22Jy9DjEVEe7XKkP5nRTNx7zJmbgI6fbLlJRisY7F8+dgal4e0zsTWgG/Yk/W9PzQ9ctNnSiZCDZsdS3UW9ZBPMseHp5iKYfr+87h8i7U5P+G2zr6FiWqvbzOotWYwzrB46/2wfz8O2Hf7OTJ9xgvCRE0AJrsPIFuXkt3fRXWuq84Z9sf2IAVT1Q1/Pdtl0B6F5vKcc695DZt/+mcc/f0PXQDObvriSV73J1/kjx77Z7zpzlcwM3sZv/Njn9kFZw8D0xMdDr/tbWz+yZ+g/+ZvSf/PD3D5fe/mqUttXnLtS3jhX72QW5Zv+eYn2rVd27WHtMxY7l3a9jIKIRp+71KXXlZXlIPV7Wzsd/AAbW71y+j+MsrYsqEyAKLQQZShiD4jUolfFJH3UMMhtXSgpVqdqcOF5tLh93rKVbeP2tjGaS/Gf+iLn4WlEP21laO/1hv5lJy0531bLeg0p3FyiVNrfZy1bD9wM8plGGfZzPs4VcV7N3sDNlfPeUd9sEE/qCKKtWAK2XkphRAwhubShnfkqLFCzrM3mbFjcvJ54cE6z+rJ5nY5bxbHxmCT3BnydZ++lJsR2CBJH7cQpbEWtvuhlYHzTqa/rPj0PVdzZsNESr9b1cI4G9ZXyMyQ+4bLJdsHcHy1Um1LXcrxfBlrU3Q+ZGb9Np+KF9ZUOYVYx9BmtL/4RTbSJc4Pj7Nqu2wHwGZMjhiHOjdeU5yXTqUlt95JbevJikmr74vS/HXv37ifjWzZpxQ6D9CUy/1c4BjkOUVLOIUiU/4cg2GKE8FaS7+3TefLp2kcP4e1mZfD11Hl4N52jsZtZ31m7ZdO0r7za2HO/ftbn/okw3zImXyJle0UZx3KGPZ84Y6SHXU435IhN+iNAZIbr2opmoWvHkNlhhzLhuuHy1q6bkhPtrl/dK7YSn4GQsooAQjhgOV7eODEp+im2yFF0iFB7fG+s6tjjJiktZrIJPKgeUdaKcBt2XFuy46PMYi97QHGjR+ZW1+DZ/F7R6zFBYZ9Yvs+1Ordfuwnz3DXqVsY2sHYOZPRMsoJeZZyX7SO5KlXQcUigzXU5/4er9ZpOb9RS+W1tmz1EfdStHVsjTLSWu+4zx57oLpUERARv+9trSYwt5Y43WKiewybDzDOokNdnzIOURqxNkjhu7G9eMuZO/mrM/eA6FC/aOmv9Vg+v80grdVKKgHrMFmG3h5icZwfHuee/AxOTCD7DckDZ8A52hv3oK0Xi1kym9yVVXWme+45j+6nTCydI+lWLNb48x7WW2yp+Dq046nQhanB0LPG3eWqVnBrC3PuPMlpz9qJyyDsnSjfxkTthzzX2Hm/6RG79ogyOxpx6qf/E8O77ubohz5IcsklY+//3meP8Td/cRMfmfpFXj26mcsvfy5v+MH/SRI3L86Ad+2fbPHBgxx+x9tZftOb2X96lfuf/iYu+fTL+W6u5Oee/HO89K9fyufPfv6bn2jXdm3XLjDrYLs/QlBMnd5Af+1+HI5sh0Os3LgjIggqs6jhALIByhhfLA4l2FBUEXjAO/CjHt1b7ivP1Lv5Tmzf16C5SLOe9VgdrKHTNFxXQt2KH2w9raz9xbtoffVenJZS+KC4WjSo+iKJzak7m0Z8w2AH5N0hZrPq/VU6NZMHwn1WE+VOfYHhRk3VzRmwlihbp5mu+7E6i876/nrBeTaYMGbfg8gx3kNqZbRKPthEDQfe8T/mU6Ha/dM4nE/3Aja2vDLdmeEDrOY9cusY4p1R6+CujXuJRGHTjGMPnmN7lONE2LYDTq9XQiepzZGtHp2v3IyJ/Rgb2wPY3II8Z9tsVQICYR8MRqOxOdx/y2nEORrZFlG2VVvlKg10aHOcs2xmKxCEE1zRt62/gsOxbQcMXVW7UjiPOOcBmnOIKOpNgc+bjYdkEqp+mZ5BE3K0HQbg4lCDQQmMhQoMZmnmwbaDB+867cVKshxjU9a6Q9bSLnf0zxanZpgbBlnuWaI8HxtDbot+gMLIDumm20SjFJXlOBMEJvDMZlGPSJaSmAEERcaO0XTNiHXlaTfnIHM5y+npkFYWGFTnINL07JA7Nj4x9nxmznC2u8qXT6xC7dnd2qwaLe8014iqwAOgRiPU1gCHT6Pru5SBGXFi06/3ifU+G8NK+RV8AMKLDBmizR5J16fwYTx7XX4jWC+sk7vi2XQMR0u0+ufQPpKBaceoUSUqg83L58YOUxqbNYDhbFCeLV4I4aRCTdFazqwuk2bG192G11U4uzGGzv2+x1xuDNp4QJY7g6GmKmttyByw9NOM7fs/jwQm7fR6n3Nrp+j2fJsAOxhw8Au307ztHHP3rdTAoQUlLKXbnL77Hjp3L6M2tkswpLMtxFo63WNEp075451jmK9xfLTCUr7Jhu2XQS49ymlsDMgbUU1HwQc3KHZL1cWC/uxUiMtUaqz1/4uaX1yOshkOYWV9k/V+WqaOd3qnkb6vWY6G25AnD7mn6rYL0B5F5tKU0y+7ETsccsl730M0Pz/2/ns/cQsTf/WzvL7xZm48dICjR5/Frz7zjURqNxP24WbNxz6WQ2/6Tc6+8lVcPXGUWy9/MdFNP8az5r+TX3nar3DjJ27kkyc/ebGHuWu79rC0hhhfSwQs3/uAT33JM+bvXiqPaW5sV3/kw7H77jzlmQDnHZci5aU7ytBZl1Yv1IUVKWLioLeC2e7jxDsC1jnWRlvewWo1eNBsEJ1bZeEznyqv7aXTLercCub0eQ8AhqGPkHU4VTjwVTh/3x0nUJlBZyYANG9L6Ra5+Kok5xzz9yxhVyp1MuccrtHBLlxbngtg/61n4PQWW8MMZSohitxkjPrHPGtjHa3eCRbOfSI4YZqNrMeX0gfK85vgwObWsO28g3mq/wDb524n2giiLOsVYLTixQUAVJ6XggunsnXOZZucypbR1tE3KRuDDebUJBZLr7fhWUOEe9PTmMyDoK4dcsIsl45WwTTtufckyX3H/DXDellrSzZlmOW1BtUFyPHOaobhWLpMTS+P4Sjj+HCVY6MHAdD5gEP3bqC2KnlxB5w3461TCoCmnIVbbqe5tIKIKtsTlCIaDwEymlEReHUhnbCgDao6wmILR6nBBlGD3BifdOksycllrHXYWGNtilhHz4wzDav9oW8gjSIL4NK5APpMRuH4nti+jy+d+Qdf2yNC6+xKybIKrnxexFhmt+5hOtRKRmhGNvcAvxYn0QzL9SjYMhdpn7qZDUpWLbVemS8PzGFdaMKkfTbMoJiiMTszCH2vQu+v0fKDNO9eGgN+d6QnuKtfBSmqdGMX5tKybfucGi2hBiMaW0O27MDXVOLKtETjcvY3IxrrgfnbPk+2+VXAoRyQG2w7QY383Pt6N4V1lq18gDHjASRMwQyF/UPFDAPQO8/06pfBOdJCPROHhGIw5yzNJf99p9aPl20ydu4zn/7p+xb2RyOGw4yRy8iNZWQsOvf3iinEWMLnrC2VTFd7Q3IRTo/W2UqHbA8zMJZO71Q5rqRQls1TCABwO13mfL5FvDVAdjT5FuswkaoiTLZi6R2ubPiNydBu6Oscd+yA4rfNQeiF6ExoYg1bW9usdAe+RrFoWh7OOXl6nT13n7ugGfhO2wVojxJzec7pl/8XTLfL4bf8Dqo9Tq9++MO/z3M++QKedlTxssd/Fwv7Hs/rnv46dJC+3bWHn3W+67tYfO1rOfVTP8UTH/MDnJi4jtNv+yGevfh03vA9b+AXPv0LfOzBj13sYe7arj3szBmDBIGIeOAdsjPLJ4iGVfpTPBhybmvAfdlZpNunfc/p0pGzSsYYtC+eeZCp7fvZ6t3LZ7v34YoouascltxaVrojTq73OdftIjhym3oAtLmOCg2ifQmQQfKRZ7weOIHZPg/LdxWjx0UFL0H4jEObATMPrjL7wKov6g+Oy5l0kxXTC5/0/7JeAGjhmI1+yqBUJBNU6scdD3f0wnKO1XSDkfPzNPXgCslmAD/OIiqiH5pLS/CoCwagm1aMlgl+jTIjGlvHSrYJwAqh8bGg01orAueZMBox2jk21ro0P/0AjVopfh7YlhyLhDRTM5bg5tPvnOhSpt+f25QsSOGgbuVb5RwXkXlxFoVjYEf07Mi/KoJNB2xtBMcThzhDc7hCPMhJzp9honeCwSikto7PaNnbqrm5TX5umcmTZ5CQplm3Qk49zBLYHFVrlCvWYkXVnMbABoZtMnvnOdxqEBfJcy+MklbX0Ot3YwJAc3rHKKUQHfFsZjzapLXeIxFNZrISpiozRC3dSbt7AgGay14kxwFiDFtBQAPjUKLYf9spz7BFE0EhUCH5CF20eVBS1SQVq6EUgmLm7LGyz1s3tB0YZluc79+HGIdxFqcUG6PzniVjnBEHWMu8tL+YnLS7UbtIZdpVc2HxaoD+FvyBeqWHOFgrUi7x7F9R12YM5MkUuU1JBjkq1PCVDCsQj/zxphPDaBTqvfxoDZYNM8BYg64DgiwL/fBC8CAI55QNxI0pxYbywN65kmkt0jL9sY2129DDANbSnOnjRasRD4QSB6MTX6CzcnP5fIxqgGnP3UteObW/SrhKqD0Me3u4RB6yDNLcBsxdTfStg2NlTWPB/gJEzoB1dB5YprO0Pb401mG1QmcWlfrnrlf0cLOO1ppnBKP+edr902H9/HgSScK1/Bnjgq0OQQeHoM2QgelhbIoOAYs0Nb5dh3V4+PWNIdguQHsUmLOWM698Fdn58xx++9vGwJnLR9z8zpdx/dd+BvO9r+CVC1NMtfbw357+33aZs0eATX3/c1n4pV/i1P/7U1z1tP9CmhvufscNfPeBp/Hbz/5tXvu51/J7d/zexR7mru3aw8aUgM190b03YehSBsPe2HGd3qlaJowj6hfMgcNojc6NFwoBtswmOMu66dI1I1bzde9jDCtm6PiqP79xDkYj5s73yG3KyEG9r48gROkWca8QJHCc+dKny3GgKMVDwEd/vQMK8aAAmEV9VfhNeXKicEhMf4NRqPdyDs5sDjm1Vt3/3jvPBQawHEJ1/do4AZLb70CtbgYGLRrrWwQOY3KavZNexRDQZkC7fxoRX8vhrAnS/N4MzsvHx+PBReccBmF0dA/KOeJBRpbltUGCMilRPkAcKJeVaZbVaDxQyJOYcWU8izVFSwD/2tTJ9bIVQJnKag3KgQrrlbnCqbToUQ7iGxK3Vcs7t6JxxhEryyg4pmONd5ESoB257WbWV84gWYYy9oIaNGMtqkzx2ob148j6MSa6x0jSTZTB1/kUREpBrrqC/avAqjOWdv8keWizUDjFLrQncEoY5VU/KFsqkHr3dd+J25g8v0asIvLQJ0vbIUnP1w81u2uIULZaKPbnSuafh9ZdSyHVj8DEtrEOTBzRkpgpFXqzaglAx5+jAPtOJUyfXGfT9sfmaP3MbSS9Y7jUMx9WC7pkkz2FttJNOb0ePheApzKWLK16wtXTcQuRldyZ0HLB22rfP3edB9fAOQajPhu2CoRIbhGBA3oGh5SNz+t9A134vbk5ZDjd9MGHbI0zg7M4axGlyZ1jMMoxxhDVgHP6hTtIz1f1jAohsYb2pq95wxjilS3E+oAFRU1m2EPOegY1Go5ITc6xzNf6RdsDmmWtm+USPctWukm6fgvRIBuv86qZEuH8YNnfnwgiUrJ+4hyZdfRTUwp51E+z59az6FrqbH+U4oxXYgS/R3Q6HrAYjXKM9gD+4C2+3cOpzVXyYUZzZZvOUhBPcg6lAnPtnG9m7UCdXirH0M57KHyrAcFhdaNs69FNV1Eh6LTRHfGXS3fj5VYKkPb1bRegPQps+TffxPCOOzjyznegJybK193yPZz/zWfQOfNZVn/847w2+zJaIt74zDcS6/gbnHHXHk42/fwfYP8rX8nyjTcy9ZTX0Fz5Gvf9wSv59v3fznu+/z289/b38vovvH6sKeWu7dquPbRpJZg8KwGGE3yj4Gz8+VF2VKr41V18h0+zEmMqZT0HWXeTrb5vLL1te8FJ8o7OMDOwtl0eG69sMXNmDZVuX9DAVRAwWSUOYg1bZ4JkOxajhMyCJH2whtVeaFxcd/x3fBeMnAni+f5OejblpFmh71KfdolPe0OEWtsn1nqF02SJ082yRsyP0zv822YTzq14gCaKE0XtV+hZNRiscrp7K8lq1QEhCoyeyof+PrVgC8VKcV5WPdE1B6dIK4MojryDlVuyHSxTa3je90VyjpO9U+QhzYzaeXSeYxoJiYpIXSG37Th/fpvlpVPEWQWqoyCUUN6ztcTdHjO3n/aMHrXIewGSsy0m+uc8QFYJNmrSaLTKKakvlBapatAAAosqQQK8btaBRA8ddI3ynhf3ULIj7cqWDrUglTR8SFm1I6+EWPTHcvnIgzwcm4OsZPFcIcwR5srXhVli0RiT+2eiRtxNH18HgUE+nvJWbwSd9EblgxUNDcb5eqoJ1fROND4QIc6hCWA9sJ4mvH+8f4aZYx4gOTyDKs6y79bPMn1mBacEbSoONUo3kLX7iTcfLE+HeKn9oCmyk0BDhzfu7J9l1XYrgOMTXr1yp2NcMAjPPKWSk7gYUFhXCWYAlQIsHiBarRCBbrbM+ZFvuB1JRJob1vspg1FGUgvMnNsYYHu92oiFxIzIA4h0Dtqn1lEjX8cm4fukqFmzzuFczuKt9zGqpSa62r5T1qBHeXmFx621mAjA50Im2DHcWiZKa82ma7PZy4xvR1AEDmrvBfmk8hMr2wOstWgRtoY5Q5vT3BzQLGvw/H3Y2nzgLBv9lLWlbfTQYnQz3GcIaIkQj5aI8wEw/r2hrWeBnStCEF4+UosiM4My1dlZS9cMiPopxjqmT6zzjWwXoD3Cbf2mm9j44z/m8Nvfjp6ert742h+Svu2ZfGT0RDov/UveeOJ3GeUj3vysN9PQjYs34F3732IzL/g3LLz6Fxn94n9l7cjPMXvXBzn/ibdx5eyVvP957+dzZz/Hz3/q5xmZb9yXY9d2bdfA5VlFZYiQYZg0EbNq4iGPr2oXfJqUjRTN3NFwhTS+w50/z9xdPiorzrLaHZHawiH2/Xaq6+doFCrb9I5tqIEK3b2YPrVWXjEfrteIHsdm6qPhNvLjsUnMRn/0DSWfNwNQLJyiNLAKhetqnGByD9DObw3LzxVMkQBJulE6wgC24YOAEsQHjDEc3+qzFGpJHPjeSMEZrjf8LQarzSiweLYEFjY4zGiFnWyHa/jjjXVo7YGbMhblcgRhON0Kx4XUTDT3LN/DA/n50h8e2Yws76KzDNeIaUjCiMw7Yxjm71nC9NdLARDAR86VLtPGlE1pn19FsjwAgiDD72q92QipWc7DmXg5o3NmNaiMewfQKJ9iJVIxaABxtk2crvv0252iNSIV0RrmryyRdAH81Bi0zGVhfaX8iHGW1FiWt/qMbMbn1+8Njjo0T20y/cBJD/LCKpf/j7F+3mkX5xiMLKvDVc/QAGnu2zsIEA3XUSbFuJQtN2DWNtnzQFV/N3Pfigd7OLRx5CrMYV41P3AheNGJJnHOi7D0zZBt/N+51vqgFM6o41zbDWy3Vmjjm2grUejRZhkP2RykPp1WKOeu2GfOUab5Dpy/llGRn5EC8LoKysypibJuspyn3KIAffdJpo6tlul1xaqtbYdeboAO4ForYd31aK/2iU4voxFy69h83EEERxRVLr82fWIzoDFcLdc3ca4EaOu9FIelP8w4vdFn4uQG6sQ5XLear2JfmxpwLpU3rWP+2FnU0jam4/drY3NAsrP3V9iULRWzNciIsy2KRME6QJMAprL8QgatmM/lxy2wNcjK/aycEA8zMpez08TasUyC4oydc9vE28Paqw4R8QEq52gPl8o5O2c8wNJ51a9P8Kmx4hyzehayPsWaKeu8sFQIXLQ3qus8lO0CtEewdT/zDyy9/g0cfutbSA4d9C+aDD72C/T//Of5GfULPPslb+C37/j/2Bhu8DvP/h1aUeviDnrX/rfZ9POfz4H//gYm3vJWviQ/SeeTv0r/yzex0Fngvd//XtaH67zkr1/C5mjzm59s13btUWzpYECeB1bEWOxgQJy7qtAeQgKLKl0N8IDCAzTNE7YmuPJUwazkkG0gAmprWHqLa33fG2upO2IN/1xm5Ng8RyO+5inIT4NXWhQHOs3LGpTlbo9TPWFr5FkLV5AJyqdn3Zufqbng4ERqjEc4VqBJoxRGyYPzn7oc62DoUvI885+rMTCqRAT+fiYbVdqhqJCAFJo8G2MY5o5R3ZkadcuUx7oT6EQYmB69fA2LeHGBGmAW4/ztTHTG7sM40JH2QDa3EPnx5M1xZqkjDYbZkNTlpTDJSbPCau9eX4PT6qDDyvpm1QFoidQc6SD7r+ISqChjaZj1cN+wbgd0bVDt09Xu0SECr7xcBhOnVwDxLJiS0qnVjDNoDtB5L9THWdJ4pnwvbe8nj8cDCG6MZZOQ4uhHcdas0s3D34Jtr4A3sF7cwVnLkAyX5azZba+p4RzN7T5OqTHQCBVQAmiM1komYzSyDPMR67Ybapxgs++dXT3aoD04Q2/kU3Xnlh9gzC0v1tuBGOhLTtxPiY4tVWBLVfNkcRzLlziRL9O3o3CNIGxhLd1Rte90kHd3WtgY+LmPAgvngHSywSgvhHw8QNPWK6sWtvfOc8S9tEIsKjTlKtGjY8V4MK+SpFQHBWiSYHPrP+JAjwy2aIMR5mB5qxJiUXgwmUQKJ9Be7eIb2g8QiZmdiNk3mYw5/I3RClOb95TiHgCJaEYu8wGTIpgS7tMNMvTqJi6rp1qG+11aK8/hsOyZbHi2OCjDZvMTDGdataTwMegFwCXNumhdtV+aAVSOCuYuBKq2aumpgg+uOIHtoa+PFAcK30LgQnO+DFNXjebJ/FijfhYERUJwwX+R+5RbbPhO38GgZSkiHtT6oQtYmOo+QGfrwQr9W0c0rOZ7Z6bCTtsFaI9QS48f5/TLX87ir7+O1hNC/7LeKrzvBzl/x6f4YX6Dl7/4J3j7Xa/jdPc0b/m+t9COv3lfhl17eNvkM5/J4be9lUs++hf87enn4/7sRsydH2UymeSt3/dW9rX2ccPHbuBM98zFHuqu7dq3rEXLdxLnXYxuooxl/u4lkpEtAQx4B6ajWgiqctqV8WAkijxr1vNOQZJt+JoaEZLVIDmPd3+s84IFiKMdR6xazzBpUT7lTIQidiv4aDpAHinuWTCs2G2cg+VND/x82pcHXc5RMk/VyKWsGbqitS+8JHR0i05DEWmFCczPpu3hnKNvh6R5CjtS5FT5syUZrTIzGJbMTsWuucCCORw1AFf4NGEu8prYQ8gnYjVbxqkG1lo2Dy/Q2zuBE++YORGINFqEVqSCO+kl6BUOlVuIPTAbj6RDa63HnlvP+jSzmgMrWKw0IEm4YsIgyo7V/zUTPcZEijM4FVOIvRTMXiE3cdZssmV7wQlU5J24XFtxjkgEVyxU2Av186ua2iLhJ2VGTG/d7e+2BoycbowXsAH13nIOmFAXBmgFQZ86X6X04gFNzw5Rg3HJ+Djb9MGB8Pv2MKef5qCE1orvC9fc3kTllinVBhRu/XgIY4T6OOtwOq4ELYr94iCvMbDFnAhA7oKzvcOl1cr3dkNTsNdS6z8XBSCWZ5bBjnRXqBx4p8TPtfPMkOs0iLWi63qIKM9uK/EMrKvmQ2Wm3MeZ8SxP2tzjrz0ydAP4l2aD7qEZVq/aF4515JlB4Z/xuJ+VAK44tw7ptw5fI+WUkOhC5MVxxqwhZgQSkVL1HizvDcegUD4M77SlycCNAxplCBVTBSQr9iJokxNLTLRUpep5PCPsbcVBUATMRMLexny5h6RYp3qtXu05csBUK2G2HZO15ss1cEi5Z/uBmazO4YWBorznRTlq7RUKMyrBqsSnE7tKzKbdP+N7PpbXd2NjE/Hpo8Z5gFZYkTKqa+BeBfZu6vSGB+/OUmJv68YyIUTGx7fTdgHaI9Bsv8+pn/5PzP3YjzJ1/fX+xdX74V3fx13DWV4w+K+86cXP4333vpH71u/j7de/nclk8uIOetf+2az95Cdz9EMf4nHHHuQTX3oK2R+8EO7/OxKd8BvP+A2+5/D38KN/8aN8bflrF3uou7Zr33pmDWrUQxCyuPrejAzQqHrbOBzNoW8C7J02fCG/AxtpHwl3sBTSabIgluBEyii5E2G9l5K6rIzWJ0QkEvlIrgOUYKxhkPuUNxXEBbCOgcsZupRENZAcwEeWG+jKeXcV+1aOPbynnGKUewU3FRw0n9BXORZ7oilwQpb5tM+CfZtoRKiQ2xblvreWObWEC41wNYrtI7MexBQ1TGPMXQVK/MjHGTQpui2piONrXfp7ZugemAYRX4MmwEQIOkpQdxSH0j4VTecGGrFnbnbW8Tlff1Kveapf2yWeT3E2Z936dD/wrGB1jsCqoVjNV8vziaV0dguxAIN38Dcu9857waXFWmFCPVmdwSlMF+RpMJN4IKKtIcq2KVw8Jxp2KDIPsnxH7z5hIfLO8JTqUKSYCYTG5uH+HThrvIOcV6mJ9RTBs90qdasbesu1V/u0k6jcZ/v0DInWZMahy3EqL68ed0pw30k8iFbWkXUS7jw0w9ZwHETEzpZBhZZUz2A+cxCrEhrSoJdntckL+3KYe6f96xAZVivOX3eQ/tF9tFSbjmrggHZgXDdsFxsYNBvFfs1FcEVfNGNLR3+lmzJ9Yp0iAVPVFi5KmuRKkTWq2n+xAXhZENHkuhLM8IOrAGU7iViYaaEDg134/SpAoVwcsSii2v60ztHcGk+x0yGYVK/vUibsBAenzeqYjOnypLQAACAASURBVP5ktsyUzr1q6g7TxqLTEfbqg5h2wsRDNGbOseVeT9PxvRhpIVGC1S2sxGMCNnUrnlGxBieCciO0GXoAhmVSakEH8YAd54j7aY1B25kCeaFaKuEbRxH0PJ0jCeyeMrVUYGcQL8/qKy5txcaJtahaKqvIQ9xQzXYB2iPMnHOcffWriQ4ssuenf9q/eOJmeNf1fHH6Ofy71Rt474u/mw/d/2ZuWbqFdzznHUw3pr/xSXftEWeNyy7j0pv+gCvbbf7h765i+J7/B+77W5Qofubbf4aXPellvPivX8zHj338Yg9113btW8o8X2VDNLj6AxvHTZDKCfaAykfeHZ6xGk01ffpQpBjnPkDHlMcVtVCFN1uoJRZpYC1plI2mnQhplnvXwTnEeJHqklwDEokR45UgsyxHIUy0dghB1dm/AFjWuimbgwzEO6RW+//rKWwtEgRNmmec3hqWUelmokuAlqTr3J+fIy3rmkCai+RBKdjiSgGMkjkLl9gJ0IpRxlmX7UOz3pVyhiw4iU4L7Y0BOIe0Wgz2dPyaBcU9URo1yol7KS5JPADZAdCcagFeSKAe2Q8ThcSaKDizK3arXEfZKS/vz1I61b4uxvmUysAGWed8lwEloBUrV+/DHF1ErCMS7blRVwiPQzOqsYy1NUu0YjjVKEYYrh0UBKMitbE6fnuYsz3MWdCzxcnKdxOJmZZ2BQZUBdAmpUm8soUTYfPSOcALPHRHGduLU6xfs+jTMMcnAasVrViIo8QLMChhMo4ZBMDuBM+g5hat/H3MTzSYDGBIrEMiz+5mefXkJFpIhxlWK5RERFmjXA+n/VzFKqIMe9SYJDEWF0+HtLmv7yzbOGJaz7Bfz3gWLlIBqFRgy2ldAjR9arkas3NMqw5N5UF/kVq8fWi2PL+KY38+Xayt1IIDAihMeDBKwZmawpAgRBMz/vtBinsM50HIxNJSyZgQmMOiUsO+yaavTwz3Ulw2Eo1TCmUZa4xeB2hxpJhIojHBkvKeRrkHLiHN2LclCfMVpvpYvkRGzkE9z8qOVMQ6Y+sDP/77VmrX36un/HMu+FTNerDFgXOWaOw7ecdzri+EQO4hgjIifjyGSgnVAfl0k+0D09VYRaFcAIplXqQp9wXGlq1WAHoTRy+4Vt12AdojzNbe/R4Gt93Owde/3kfzbv8TeP+/5e+P3sh/PHk9H3zRU/nw8bdy89mb+d3n/i5zzbmLPeRdu0gWzc5y5e+9l/lrnshXPnaAwTt/Au75SwBecOUL+K1n/Rav/dxredstb3vIJqe7tmuPVsudLdmuwpIoucAxLSBcq6EYLs7glK+FIY4pabXiWG0ZtRdCTZM3V/tfqaJ2I6T8ORNSFSWwTz69TuXGO0DWjQGPJO3TGpwnM0GAoowew9binjHXxSnFhDSrFMXAoOTNeVCaNIChvBGhVAMbTZBmGb3MYMPwzb5J30CXypm8d7hE6nIO6z3EyXwQToBOU9ecJ/+hLDdk1pYs5ZjKrIgXHcGCUtQj/uL8+2KrchAH9LPgJNWYJBcYC6cEk1SBSmWD6qFzYylNRSqpxqFF1UQ0gtO8A6A5KYS+Ayi1Dm3ARMqr1qWGLdtjywwgrMcVk5cQJ54FipVgnA8JFBBJipsCNq5aBCrWKFN+JiTcc71VzkPBD4MZT5ksmnGHf83hkgeBAYyUEDk04V5r1EG+oElZWJihkYy7lp4tVthOEuZKQCk6OqEjzfI4pzSttT552ERahOzQJMOZFtqAjnwKnwgciGq+izH0isbOuEpQRmsKkNIKCpbKuXFHXSVj6WwPZfPNOQ9inA98aC1hSnxfM20lgMEQRgigU4XnpCER03rK32N4vtOZitnRcRKEYgIjww72VjTGjqs4Fqly2eykZ3X2XYlqTFaLT9juzpK5HC1qbBO4ML8A+/SUZ9+l2ssT0sRqQfLxuXFAM7SwUIDO+xc0cAaY2BpiGpEHV84hukr/lR3QoynxeLRKxoMPEkBP+TAHi4lIrMYWwnZSpVjHLg6qitV5WiHDocpQqM5lEUysWTl6oU88Mj4rIceUtadWwLVishDommzECJBO4RVRC9BrbeiDJjQ2xxlLp5ILrlW3XYD2CLLe5z7HylvewqHf+i301BT842/Bn9/Ix5/wRl5+9+P4wE8+hY+efjd/d+LveOdz3sme1p6LPeRdu8imkoTr3vImtr/vX3PXx+bp/vaL4M4/B+A7Fr6D9/8f7+cjD3yEV33mVaTmoYptd23XHl2mRXsGLTgCg9Z+BIh0goxFa4u6MGGyqb0zIMKwuYe9E97drvdLCtUkNM55YQY15kw5JptJCdD2dBpkmMqJc843TxbBmAwRH2kuy3hCWo/g02zEudJZixTY6R55OymvhsBCNFs5NyFtqnBtC9ZjONdm67rn43SLLChbdvfPMJhr4xoRemfmUzhdCfyCB3IqXQuJVZUDZp0vuh/Ek+S6NSa+UUqai5QMWmHKhVG7Ih1JGGahJXGIsJeOYhSYFa3IasHKgvljB0AravdEDK1rLsXhyNpxBaRFdriqUkrog5f217llNNPC6Er63TkYtfbgHMyqCaJIs3eigWpMYR30js6WTq0gmOCQqokkTGNw+qPiqiFlsOjDhoT9Gl63lOeqov9Spl/VnVoBpNsvAaIWNeaQp5ON8rgk1kRRXK6Plbi8R5VDPt1C8EAkjjQqMHOWgu4NILTmuOb7Jr2z6xxKK1/rJeKdeqARhZq+SF3AshYiMIpqXdTYAYDytVJR2F/9PTUhlfChU5spusCpgNK6TDlGhNb803FKiLMu+dQkLtQ2inHF41Q2tndOGFw6h9SY0M7efRgHB6L91bzb6vm1zRZZEr5vrK9bU6FPWz7RCCSbAt0IDHzYb05B1PEKj7X5BWjFmrl2AVgq6JQGxrIpCU57Bq1uDocuWSpBjzaqiSqOiRXNrSGPXbwKl0ySLz5pHHDVjp2ZPTL+3g5w5vAM8LC1B4WiVWO9BGFST2EasxyN9nFltIgOLH9kVMh0gMuSBQAaUURbNQHHxtF58loWwRqTrDxmf6VCGuZwb6fFbDshURrjLA1dsP7hO1qETjQDkwsMG3uwSlDWoMI+HdkhD/TvK9lsU+vP+PU5W2+7AO0RYtmZM5z+2Zez8Cu/TPPKy+GjPwc3v50//fZ38aqvzvGBFz2VT5z/IB954CO867nvYqGzcLGHvGvfIiYiXP/rr+JzP/gSHvjEDGu/fiPu878LwNHpo3zgeR/gXO8cP/lXP8nacO2bnG3Xdu2RbYXvEKM50DhKW0+iRFBRVDphUKUK+QoiH2l3SogizWSr6JdTO29wImcjnxKkRNgbnF+zd8ofrz0YkHaDadVmmiaNoQt1PgrlHJnNq2zFWh3PsHMYq5s+d9BRIkCFoPP+mLNQ9gcqkUc9zdD/HEvEfj2LabZRuaWf9nBKGM622To864UV3LgL0gyMjkbhnJQOdeVWqyqFKfyXOQWTi0xP1Xp4Fg6iwGQzIRZLlPsifymwp8Te8RR/q1YEG96MtSBH5wLjKcSxZu9kg2ZgopStxq3GZiYwSTohjmKss2xcNl0JWpRrCXsnmv5YjxZxInTOpkTbQ5yGbLod7tGnd7ooqkCECHpyATt9CQ5FNt0cc9amJh8HwHQ7Lq8HMBIz9rtVBUOox8gHWwI3N+ac2+Z+HhsfpKNa5fVMxzM9+VTRsqBatwhF44kH2V6c8sSNAEqTdWKyeJpB2/sZYiwqy3GNII4jwoG5VhmE6DQj/3wUwjEBfJmJxBfaiQckOqraNoAw3YqZjOGSqcOMELTs6PNWY1QakSbWyjPLY4AgIo3nfLqpVmwfrJV8hPEYHJ3BGawzXNHYj+iqlYBKDZFKyAIhZqf2kYWaRJWbmjiHP5e1vt9acRc2iWjOzXFNdJiFuAbQnAuph7Bx7dVkkRtbO2VCvZsK6dJJjJu7bCyyYycXscl0uHq1kycaERPNuGyiXQAiRZWCGaGYijpI1QO+FC0RYNjc64NCjHfcE8AG4Dc7s+i/E0WVYLwwE2rLVHuWQWvRh7ME1IHpHcBEGDVm6XcW6DRjZmqgSvBsN0oTiWZeTyJKYXQL5+IyT1rpmLY0mEmmcTg2LpllNN1kfqJqKWVEe5EjNR5kaWpFpxmhxa/5XLsRekx6cwKdaArXniGP2hgRVJ7RijUHZ9tlyrYoH6xJJxpl2uVuDdqjwOxoxKmX3cj0v/pXTF//TPj9H4NTn+fDT3ovv/xZy/tf+FQ+s3wTN919E+96zrs4OHHwYg95177FTET4D695MW//t7/A8bv3cfbX3oD92GvAOaYb07zj+ndw6fSl/Ohf/Cj3rt97sYe7a7t20axwaGMiEuXrzlSoHxLZ6VrUo6TeWbciZTpb8Vc+neng2rOMrKY/eZT9egYlQhSOszWlxbwZIddewqRqs09Pkzej4FRocNAhQTcSVq/cWwK0mTSjfXbDp72P1baEdB+p0jWXrlkMYhM1q6kHusg7NW1psD+aIU9aJKOctcHpkqkDQAm6lqYVS8TBxMu+qwCcqhTOktLhsIynGGXOsWeyQSOpnO9CTMSJMNlK2N+KiDPfpHmw6B3stLkfCYyWxqdaGbHlnEx3EtqJT0uan2nSjHTpsOoALL0uQW0Fg7NtJ/YTRZ65yKMO6nuv4fFXPZNGHNOMFXOdhKta+2ioBpTVhsKwuRfBpzgWLKI4GGY5NtK1VHJBRBMpXVAKDI/uK8ehdgKRYKPJqgYtjzrkOqHXOUQWTY7dR4WBq/Xp7Zti/bHPYlp1YG6eYn+oYq6bSckGFcxEFBx0JDB0SkA06Xyn3E/rl+8hSg1pazKc0gEqKOP5JyRSBDAVGK/w/1Ro+1Owe0nYl6mxGOP8c4eQ6ITHNQ5zoHFkfEIERPv+Y9ONiNl2gjhotebQC/sCkPdgARx7Jhrsm2xw/gkHqrlCwNky6DERJ6goDnPoygbjLoDHM0PHib4XhVF56Dk41SlZzgghNT5lNdKKI3oPOmnRliai43LgYh2RKr5BovL8ZTjDFVI9YU82YpSqpwqDUzEK7YFPrSehDiyyjInyVN9WLqzvYrIHsY52ePZK5VjASUzBwTpnmWxGREoQVAnQWulJJO2yNTAhtbga26C1n37nYIUnHQzahzzrKTvHBYS6Lqk3L0cQZ7FRxNZTvifcs2LUmAMV+xRNAZRwIJrjQPuAF0HRlZBRERQYmGLOdoTOCvAa/k+0ohlrrwjrPKCzKgrf/55Zi5ylESuSSJcPmysCcFMNDjRC+4sL6lvHbRegPczNOce5X/1VVKfDvpf8OLznX4IzfPjad/Krn1zl9174VP5x9SY+cOcHeNdz38WRqSPf/KS79qi0RqT5pZ99AT/33FdwVh7H8V//I7J33wB5Sqxjfvlf/DI/8pgf4YaP38CnT336Yg9313btopgWRUeaxCrCiaClEbKzZAygFemAXmbfBfbG97VCa/i2q7DBQZJ4ClpzIMLawDGlmkw2K8U7U8szsqH+qfBb+gvTLD3hgO9x5RzzapJr9l1J3k7Kzx9s7SMejkrFxyL9cdSoeg9VAEyVjrIDsnja/+S8Y553fN2TTxUTskabxnBE0huEuotg85eNKZbFaDqqyT7tlRbLrLbiQsE2Zp9Isj2kvTQgi6ewuAs6D1UMmgcHzhkmQo+11ky4p1r6aCIN5qIpjKPsjeVEvOod+PoYARPYSwkAbbadPEQakiBKoef20D0yR2/iEvZf9WwOTO4jUo7ZVoJWHnwcjPdzOF5Am9FYzyPXjGho77y1YoV1XoiihGfKCypEqqq5mlCtElDUZqL8KW/F2JA+JeH+cBYnEQEPl7LgLsxh2mqUrQa2D8zhpr3jmB9arBzTgrVSiu3H7id9/KWkLieNp3DxnE8vDOvutARH3FYgGr/Phu0ZxJrAFKtQA1kwyf4+jG4yaB1A6RZP7BwiEuXTPtsJM82YONY06ixawRCKEOnkAgbNIWjRtAZnx6bMLR5GZmfwqaASZP87aB2CIqoAKp79cVKJQ2gRJK6AFAitWJfAZUAl8DFdNK/WGttolJ9f62ckotHigxV27vJwH378eyZ8XVwBjpGYdHGK/nUHWH/8Jdg4Qoyvy3I2CBaFdcx1q6zblKg5xo6hY3yTCW86qs2XVICgEOwZtBc9+ynF1LmK9Q44poAz5XUEbDMin2ujJxNGWc7ZrUHJBhYXW7nicPipmGtCmqYaq7Msl008+JOsJlOPoHOLTWJco83leyZQZasBMFFI4VXVHja1LAARWL90T7h++M4LDFrxHVOMoqU96IxEMdWKODIXKFMRnIpxSvtMCqWZm2gEsFoDy9ZhnUPHupznvP2N+w7vArSHuW38wR/Q+8fPcvAX/yPynu+HA9/GH131Bn7lr47z3n//FD639od84M4P8D+e+z+4dPrSiz3cXfsWt31TTd74ou/lP1z3IrpPfT4P/s6X6P/G82CwgYhwwzU38Lrvfh2v+NQreP8d798VD9m1R50JwmI0Wzok0Xf9ax7bOBAAWp158pFtBWXT6PIcAjrUsTjR6JknctXsVSw0jzKT7KcVRyV4AOi6IOOOd4yiWkPo0hlRMVt7nkgaz9Io6hyU0JSEhvbRbnRwHh3EsSKPmhTFNHV3qC53n0dB0VAqR2zQOoBtNXHz0+Rxgmu1aG1sE0WKVri2ndmLDr2lYq1YmGqhFEQTV4eLOK5qHOaI3ouuzVtEzMTZLRxNRo05XGAvtkJzYT+mJoPWAtuTR8kO7iWfanBoxjvAl0b72RdNldil6AulVOhHp4JAi0AjOF3FzY+aPsVM19Bg2qgYPVfmKwmihHyP77v2nYvfCaJZjKc52PDqfBrvrElyoeiAa0QszrQ9aAqAI5vq4ICNmWvKvmCRijBPfQ7PnHgMM2qiIhNKp7baVfVsUilrBmsmMNkKQgadhNWr9rF69WLZGqKe9rd3sgkE1iQqHFeFaScQJ5hYM0rmWGwsctXE3ooNFc+gFTbfaQbH36filXhcCfHiPI1IexCKF7Q5ONvmyP5Zrlqchcl9gJC6nHZDM930UuuFqiMiuPa8fyac0Ju8lHyHlLuLYiaiJspm5bXLWjat0KFH2pHZNk+aXwAZfw48v1wwhOVlUVGxb0Kao9SeR61KgKZHOUnXB0YKwRybexY3kTgEdmK0LoCmn7tGpJloVODJon2KXCPCxBobxyhjAIdNEmRibxgH5DLPqDHP3U97DqITz1QiRNd+JzSnfXAnmNYRaB+EsAc84y7WlfsqTzqoUepZIa3oz7XLVMT9U21mWgkWy7YbhOCBB1FOC6Mr9qJiXaZk9ifq5ECVZqpUtZcFhUSa7tH5aj5ra5EtzIafqlVqOAVK0WsfYqIZBRVZFc4F3WsvZXE+pEerKPRwdFwdHUAQhlMdWpIQFXWP4fuiElgSzESDucSDqbjY364CqlYiX6+HsHDo/0JPHhi7t+KeAeJYgxNMEpWBoK9nuwDtYWz9r3yFpTf8dw694gaiD/8wPOVF/MmBl/Mrf3E37/7338EXNv7nLjjbtX+yXXd4hl/6N9fyE1PPRr/sVZz8w3Os/+dn4JbuAuCZh5/Ju7//3bzvjvfxa5/7tTLfftd27VFlQaDCNVs0jixi98wQq6o+ou5YOMAq5f+YYz1sq0WJRcXMtiZ5ytEDKNGhRqWyDQbhVN6ZjZVUOvTF33glOAujZJZmxyvGNeOaM4tn+MQ6OommEUcUAJLghFZjr9gPCGmLLjR6BoxOyGZnQClya8nnZ1B5jqhKjsE1YlRa1UQtNPyYjK7ESBIVk6goiFb4RrsNaSDWsb8zxVQ8H4YvY+lA3ckjGN0kjzuklx9g+NgFNHB4tu3BCXJBLZtSvrWAK9gPESYDK1Uqv8URrViPOU6ClH26ypqmcI16g2WU4lBzlpkAEpRSLM50yONJ7NwVdDveQe0uHiVv7yUSTa47RCKsHj1M0poKjcOVr41zoQ6xfRQR4crF2VLUobhmJdZRjAm6C1PhFf/evuaR8hNaFJ1GRN6MMI2I1FbMR920Egj1gqpgWYJ4QpHWhcCMmuTaiUO0Gz7YoFQEZacvmGqFNDjBq4yWvR+Exr5ZWvNTLEw3GRyZ8/GDAOIaOoKoXTUJL3BxZ/+Ycy6qUgbcnrmyVsMWaqQak+xJ5mszxgWffcK+y/iOhScTHXrSBfMAIe3S2bLOUAAC41ooCyoRelOXM2ofIukcZJh4wOSaEdEwD/flPz+R+Hq7RCLyTgPmZtCB2Rw293Lu2iPIkT2+ZxwFRxdV4BbrW0UEBi2bbGOe95xyvPPxfpQobBJTAEwBoic/zS9jze1vRHE5uW5+pry/sjZRtH+GrWcxe3snOHf1ogd8l+xnLunQr/39L58GEQiqk8V3mVON8SBQfT7LEwiiNZcffBxmumKo/PHgkvE2EiKwj0munLuWPJlCZo94Bkw8izbTTnjM4iztImVbdBlkmdUTPHnyEpyO/etFjaD233cmSmhEikgUth2zsHc/z9h/lIaOyOcnStGihkrYMzVBp9XwALk1gygdYkD175FgSmgn2qftPoSkf912AdrD1PLlZU7f+J/Z/+PPovWVV8Pz38yfT/wQ//XPbuddNzyZT6+8jw/d+aFdcLZr/0v2gicd4seeeoQXLR9k9p3vY+VrCede+APY27zC49VzV/PBf/lB7l6/m5f+zUvZHG1e5BHv2q79M5uS4LBHRIf2QLuJUrqUDc/iSfJ4hlBegxVfg2FsGlIWJaRFVYqQcRxxzcEZ9k8F6fHwetaKYeYSbNTyMt9KMQxsjyBESoh0A3GOQXOB9tXfw+HZVimqoArGTSvmbZv9o6QEiIXjcO3hJzIM0t+ukKKXipkR5zxQK3+vpiIXhc4MohTNgg0I9V0AT5o8zIRuFGrz3mqfL16+dPKaoDIJ851GySL4odbSR1Utiq10AKuWOCqK76lSHAUOzbQ4ONOmlSgPPBz8i+krqhRHgdFVR5h/whW05tpMJLVaIIQZ1SGWKNy0hIypHQANweW2ch6pnNCpZlSOf+Wq72Q0eQn68JM5tLCPRqQYtvayJ57nKckVYTwKcd7xd6GHVztW1XjbU/T3TDCj29X84J3vNCg7FlKNsWqweXDf2EiLqTdO1ZehuhPt65cAVKxZn72uZBQya6p9GXtmwqdTOvr7ngT5aBzsB2ztlE8NLRN/yxQyId3TCUxVMT4Hk/trcxlj916F6o/KU/fbhzDT00GEQnC6YpwWp1o4iTk6fWX5bNWBgIhCtEah2DPZoWE1hJrCcQvPeD3cIgJxEKAJLLmIkMcdkmQOSZq48B1gWgk6zb0cfaEK2PYgo0OT/mMWcAcXSlXENJllff4JNFrt2ggELTFeBl9hncXpiNiasMlsmVqtRZjSMyy2Lg+fVWWwoaEbYC1z7aRk2Oebk9CeH1v8Qthn9G1X4pQH3iYp0oILxU2wM5Ncds0TiCNBdFSm9ClUWeMlImzsfVIxceW+aEYRV+6fqC4b5iYzDrSglTB8zP5wTSk/X9TMFSygIChriYKqqdi8ujaCxtKYnK2BI8WcnuVwaNEwqZs40XQnLsMBW3PXghLOX7fI0qXXMLF4BUeb8zx9+mraV13K1AE/ptFlc1hnmZg+yqVzz2D/wgGi2Kek26RBAXOLtiuJ6FJ5FeuYbsWhV9oug/aIM5emnHrZy5i4cpKZ7I/gx/+Ij9mn8qoP38o7/t238dFzv81fH/9r3ve89+2Cs137X7afuf4qnnhkhpd+6f9n773j5LrK+//3Oee26TO7O9v7rnalVS+WLFm2JcuyDQZMMU7ABEIIyRf4Qii/AAlJgG9CaAkkTmgOJJRAsA0hmNCbSRzAYGJsbOMCxk2WZfW2bcr9/XFum93ZZpWV7Pt+vVY7mrlz77nn3pl9nvM8z+cZp/3fv8qEOczDr3oTpS+/E6pVmhJN/POl/0yj08hLvvYSHjry0GIPOSamBiHEZUKIe4UQvxJCvLXO67YQ4jrv9VuEEL1z7zVMc9O/Q8GHqhP2dKpIhzGvXsvFJduklfSOVg5RcXVdR8lpoirt0PkQAsc0MJXEkSYI2LO6g7JjgjK02p9XgzbuFDGEFh7pyCcQQul0wKruDWYqGfFpdE1UUjk0GDmSbihj7hsJRnuRwz0NpC2DsqlrsdzuVkorN9JlN+B3GzM8o0OpcB9+dx9XSQYacnQVErruw0sNyyam9/vRdXBTXQY8lUnXq3vx5xianLBezq8pKRjNnvVf0UZeWgtplIY72L+0J9g8YSpsL3KBpY1fR5mBSAVSUG5vomnVCELpaMeg2RYcvqDS9BhFXOU5HJ6xmTJDiX2rqxMjnwnPD4E7dgiE79D5c2R6og1Kz6UuvsF1XaSQCLeKKSV4DhpCRm1bHaHLNXG0I0eXXdDjBBByipR6JLrm//Ielj2P2BWSiGAloOvFyvk+yqaOxCnLpLMxjduymrIrSJMM9nwkOwzLnwemomIkkYYViFH4PeOaszYpU+EKxbreFcEgpJ8za+oWCinlsCSrHYtKtYKOXnnGdlWAkaDckAnmYcIpgpT6PnEruIahUyrR6XiuELQk28IauogxXDFSSCnZtXW7dqjKJYSSuNkOb1EidHldoWg0suFsCnCVha/f4bloYY2kMoLotm+gVxpzlOwi7blzEEhcKWlSWS5ML9WKnf52ymIkt4X+vHdNvYUIJfVih+V/xqUi51ZJpSwE1VAAJHBGvc+oVJSKjSDQ0X23oh0qb6gJKwFWyjuWpDLYTckJ1VInbV1/KryFkqrQqYl9iQa2dGzFamuk5dzVFNO2jhL6LS6886maKcbNQnCvAYzbRUrp9iBiVYOApGPSnnWCBQb/fa4QQWpp0BMQAdVKWGNWKUVULAVUvTYZweVU5I0szalQLTO4qIBlhd9Tx1oamRzqwRCKtGGjkg5WSwN+FL3iVpHZLo42rAJpJT19lAAAIABJREFUBKI3bmQfQb835ejm7EDQKF0IHFGbkjuV2EE7C3n8HX8B++6ldd1BeOV3+daRbt78hTv4x6tXcsMj7+GufXfxycs+Gas1xpwQQgje8/xVJEzFH3/vUTr/7Us4FzyHB995HaMfeD5MHMNWNu85/z08q/9ZXP21q7ll9y2LPeyYGACELgj7EPAMYAR4kRBiZMpmrwAOuq47CHwQeO9899+eT+D/5Z9s1avE1fbmmpqlYsYh45iMUYZknq7WDM6WEc+gEOwx2plMtdGa9YvFQyOy12kM3RbfKG1Mc7grr1O4BpciVw5gGZY28AS6ut91A4cv6F0moCc5TIuRB9fV9qOvgBf4hqE5MG43cTQzCIZBS+sGVqY6wHOoMrZJU9qme2g9iRXP1ufti1IqbTCbvhiFWSvaYBkSI/AfQpWQhNTpWKaSiIru7xYtsW9PtdGZ9pT1zDQVL0qXUhnPQavqNDQzSdlI4WYSlBNO5Mi67shtWgJ2qmZMgtD5sc2EnpdIM2tb2JG9CLTRrsdWTCXZ0Ksdx8Tq1SSGukK/HUF163MYHVldc10JHFwDc9cBRKkSRiaAjK3oKaY9v1GGDlp0zFWX3sZkkMIqgKoyOZ4ZQnrNsnPJsD+ZPx6sjFacCweD6f/fdUlYin3Fc6k29FHyaudaso7Xky3NwQldt+WoSL2OVJSzDsdG+iCZ8mQ30KIoApKWgWM4uFJQdDIcW9nD3uHO8FzSrUw2DqGQpKwUSxuW0ZzSjna14BmwniJFubtdxyYCkRippd+FgWsYtb0DvYhLf3M6SH0FKFk5Sk6Roy07UGkdYXNLJS384akbWsKmWxVxBXRbHTSqXLAwoY1rSSnRFKQ4Cs9or0qpxSI8B831roWwLcpGSjuVQnIov1zfA95n0PDSBF1pYUob6TcAVxaHCiMYQgv+WMLAKB9BiArmkXHcvMMRd8xLLY1ea/35K2/ZzN7BFVSTRX0vWUlQVnDD+yIhwUc/k2Q8GYp3VI0EZSNFIq3TE6sCMo6BrQwswwHXZdhbhCJy7OC8pcIQ+rutkkqDYVAxHKpmMli0cCyT5kz4GVOmImWG6of+STXnk7TlvTRpv/E7QLVK1fu8ymopEMrRE1qNLBgAwqDcXAT/vIO6QQW4tGRtuhuSgeBQ8DbvOqtMkvTm5ToF0q3qRvCBU6zbIgQOmjACkRfdXsTbzk/5FAKD+mqsPrGDdpZx8NoPcOybX6bzJSsRr/w239hl86brb+d9vzXAp3/z5xwYP8AnLv1E3IQ65qRgGZIPv2QdD+4b5U+/8kua//L9FN/8pzzy6fs5+IYLYP+vEULwh6v/kD8/9895/fdfzw333bDYw46JAdgI/Mp13Qdc150EPg9cMWWbK4BPeY+/AOwQczWn8RGC7gZP9c/yi9AVh9sGvNehJeOQT9iMCS0FnlE2sqNHF+Z7nkGl6tIbbY5bh6BeozlHKaFT3srNbaikw6DRxrbcMEGTZ7carNwLvLoxIXVdhNR9xvJe/6zephS2oVMvcwnfaCEwOnyHBEB6DpUQOmUvadnkclrS3vRT3ZQkp0JlsoSdolGGUSWE7l0FsLI9jIasLbRxYcsAA8UUTUlDCzlEQj6GNIKaqPFEa6B2l8l00ZPthWqVpakOdrafV2fugGoZvx8TdS6v75xahgNNQ5ScIsdSfaxJdpIxwmujXF0L6Kss+o5lFCUUK1LtSCHIDw2TKjYFZ9Lm9GsBFkEgjCInyt779BhSpiRlWnRlurSioFfvFhqCYBw7Gjb7jsytq2yv/qoV24/CRMfngm2HPVBTKoNqbwnq1ixDcsWaDq24J6AxbWNJHSEWVZeJVDtJy6AtV6s+V1ApWnM9KKmCFgUiiIBASiWC3npVy2Ii7TvJrlbEVKECqmM4gRpjudVTI0y26Pitk8fN91AKGiyDbBkBqR00N7hv9atCaKlzU0WSyYQWeEk5bTQnerXD7IIwTWzlIABHJrA8o1p4Yh6++I1OWXaRUhv1haSlhV48J22CSZoS2gmq5HQUVwrJ6OoN4WXwLmdjyqYl66Cs2kiKMA2GEn6Kn0QI/Zm3pKLqVkiPPqLHlrKYcEvhveCfpOeA3HewwoRrUC30IoRgacs6OnrOB2BJohnpOfjRe2mikNPNlIUI6zWDGi7BqBiriUZGvy6T0grUXfWLElsmWV3YDpZNZeVg5CQlDVYbPZkeT5TGf9p7r1tloqfA4f4udq0eoq29QMK2yKoEaelvr+/LciBLaTLemGL3ikE9Rld/9wUrXXKK40X4vOs5WIYUkVTpiIvUvblmfBW3qvuZ+dG7qrf2Ir3WE5lW6FhHQ2OKprRFb2KQ0cYUpaZU8HmOKrvWI3bQziJGr/9bnviHa+l801UYL/0kX/nlIf74hjt4+wsa+Yd7XkdzspmP7fwYGSsz985iYuZJ1jH5zCs2ctvDh3j7jXeR+62r6f7UZ9n3M9j9e5fi3v7vAFzaeykfv+TjfOTnH+G9P3mvl6YSE7NodACPRP7/qPdc3W1c1y0Dh4FGZiFYpVYyME78FCW3Zgu9Ci2Bklum2WnQaYuAL+gQpCQFctrhe2XEKPd3mbIUfU0prYonIJcwWd/VSFJZqFKJ/AOP6hRHFdaQVd0qyY1rtXiGAkGVpGV48u0SnCxLWnLkk+EqdsLQf0PGPSECvavagnZVcSl7MvqXLt2s96UUI+lOVqZ0hESaJgU59e+RrtuTVorDDcsZS7RjKIVjJ1FSUrAkVWGDkw9O3BoeAmngCIu0qZ3CR85ZTjWTQ0gDXN06QHrzq6JzBzBxXM9x9LmglijEUpZOUUNQsvOhsegRSG8LfR7Lkm2sb1nPVPxGtJlEgm3LtKEthcJSjnaSAekdR5SrrOhoZzDvCUtUtcpfY6IpMOSCCCngph0m+gZrjpeWCWzpINF9sCrK1A65N4NjuTQVL/0vKVMkhYUSOlXPSCU43pIh2m5Yn6ZvHgoSK1dQam1h0szpusApzuHqRA8FlccQFkoIbEPRbXXTkPD6qbkEyqAVYdb4jAKBYxl0NyYjoRzvXFNFnYLnQtbW110kCjwx1MexDVt0XRZaih9lhJ0VXBcXqaNiaqpDrquTwpYD3jU1TZY3LvU28Q15iRCKtqxN0jZozTqkHQPhVgOn2za1kT5p2xzobWOCMXK2QaSjA+PpdqrJNNWk55h6DrBlSHoak5jZIrs6Lw9HKARJL9LlumBI7SQaniOhqIIyUJZBIWVgKN/Rkhxfu5HRNZsAUJ5j59eGOUYCJbRYUUJaKK8JebTR+r6WDI+vGiDX0s/awTaaczaur+Spc3OnTGf4/1aV199bkQhnJYgY6ckYLWQp5XOc37GNjFnQvf6i3y2R79VyS4bxxpwneALCMOhPNDFQKHg1byAOHaXkX6+e8zjStIZywqa7KQVV7WSJdDPH0gNen7kp43fd4N5UXkQYIGs2sbq42juxlZAugpNHeN+EVapazMafhpqUbUAZSMNGOEaw+HK0M09TthHHa9EiJyeZjdhBOxuYPE7p06/k0Xf/Ey2vezmJF7+Tf79tF3/6pV/w2mdN8oG7XseVQ1fy11v/WheCxsScZBrTNp995SZu/tU+3vXVX+KsWU3ff36TCTnEQ6/9E8rXvwHKkyxvWs7nLv8ct+65ldd///WMlkYXe+gxT1/qRcKmymbNZxuEEH8ghLhVCHHr6Ogo1bYmhGUGf0GlCleYffaMdCNHhv2QFKY0av+AC23wNGfsuoNQNak5vnXu9+8Jo0t+OiFAU7XM0pZMGG0R2pBQuayW5q/oVECcnDY4AAy/3kO/p8Ms0Jlahl9dQ7Da6xKdLlmFspfK1dmc1almhkS0rEA16ijiRLLIpFWoOS9bwaHCSlxlM5ZsZzzRAm2roFE7HbmkxerhLTTneoKj2YODIBUdTgtpI1d7bXxHxK0EBn6DkSZn6hS9srIR1cmg6XPaSlM0ddNkYRpU1i0L9uWrcE69AY6ndCREO2hes2shsKVBy9R6liheKmbUpg37RXm1MS4UM1kaUjoqtPfYROi4R66jP/eV5izlhjBDxpSSpLQZtDsRwquzi8Q4xlafQymV4NDyfkbPWUZzssCAqaNomWSSiYFn6qFWwu/qaERFNWawenrAMihXde2kMGpNRwHgugzmRrCEJOMYrFqxrqb+x+/5V1ZWcC646JQ7QOZTUxong9s0CC0r6GkfwTY9BzOZpr/393Ft/X8ZWdwI/DPgkY0rUE6S6QFxbYRLKSgkrSBagmGgPEEW/z5SQpFS2UD5L2l5n2EvfTOYa6WoCjheSHsCNoKuTIakt+8xPxpWs3gTPrKMmUxxwWhPL45KcXF+GUJaOhVWSVxlUUzlSTi66XfKTFFMFHn2lmFcJ4ESBo5K4UtRSDH9mlmtDbiGQtopxhKt7G88h5SR40h2CeMta2gZ6sUY6gQlgrRWgaj10aQIFp4EgoKRwfLVFiONrf0z3j/YxbHlS1CyNs3Qx5UiTG+cOhveZ92UEil1aikTk0z6a8GGhevVqZmeoqwUCorDlKys/sxH9u1/SizHpGh3YSoDmkeYbBjGlgkKfkPpSLql/si6HCiPknZsnr3Kk9SvRtbTwnBt4Nz6559Vjnby5pGoETtoZzq776D6jxfyyD/fSu65V5F/5Vu4/qeP8PYb7+CKbXfyyfvfzbu3vpvfW/F7db6IYmJOHs0Zh8+9chPfvPtx3vmVu5GNTfRc9x/YW5/Db973bcbefTEcfpTWVCufuuxTVKnyu9/4XZ4YfWKxhx7z9ORRoCvy/07gsZm2EUIYQA44MHVHrute67ruBtd1NySTSdzWJpyhbszztgLaSFzVmachFabqlBMOsiEfFLRbhlWj2qVdH4lpyDCVJroA6684JzsZS4RpaXqz0EHDcKB3K/3FFNmESTGhAkPXs/91vZOX9jSwshsybZD3+hIJEaQvAtjCCMZjKk85TYTRlGAMrq6xK6ZtZCIRGraZFijqSMThhhUcy/SCkJiNWeyV51JsbmV1V16nlQXGcUKr6Hm0p9uxlB0YUH6Ko2snEEKQdUy6Cgl2jrQEUTNZKQer3wlpMZTVq9+uMqFawUg4VNMJCnaBDt/w8s/FOy1TmmFfuWwOp1fPu19XqKWxK7P8rQ2Nv+SyHj0eIWqcaH8WtcHsRV6lDN57NNMHA9v1OXmHkZEiNNsTkdH7EXQUEv5GkXSucHsVqQd0U4madLLBrg6UneB4qpvjqbBPlZJaoKSacVCWb/CawWvlwWaOrVkSpjoKLa5vSpMhp5lVqc7p0TC/TshOh+fdusqLasBoc5LsZZfq3Vkm6QvOD/ps9p3/DDKXaCl5KQXrezzhCRckCidSd+Rm04i1vXQ3JGnJJupEfEBJA0tJLhgqBmlm0hf4EQQCFgPOIJZKTGt7IfCUE4VgbGU77rqNAFTcMhWhnTfD0E68Iyz6GxuCa4QQVJWKuNBhBD6KWcyTW78C2b0UEFjSQCgL10vbc4WBY+kxC6G4sOtC1jSvCd6/In8+hrT0vSq0I5S5ZKd3ApGjK0WmcxkHGtcznmim1enXQ0UrXea7OnGFYHjoWUw6jTWLBXpXgiWJFkaSXkrt+nVkvXRA/4OVsgy6Gz3hIbyectFG0NIAr5E3QjA5Q+ueQNVTSo71hrW+CVOFcxg4YPqzIITAHhig1Kw/y7I6PWpl2xaOStGebgcziWulg3nzduoNIGyXUPVUbQM1UqWCW210yyomhrpJrFqJa2hH0UWw1Oygv2UtFJdSNWrTLesRO2hnKq4LP/4I7ieeya5biphLN9H8Z2/nMz96kL/61s0sWfMZ7jt6C5+9/LNc2HXhYo825mlCWy7BF/7PFn78wH5e9/nbmJSKtr9+P01vehsPf+Egh960De7/NkkzyTXbr2Ft81pe/NUXc++Bexd76DFPP34KLBFC9AkhLOC3gRunbHMj8DLv8ZXA99w5u6973cEMhUzq+iQpdQ+vqaEwmU6j+ltAWZiNS6BNG1Abs4NhdATqpjgCnJcZYNxpYizRSs7OkfNqvWR0BdZKQkZLdQugcuxYkFtVtQ1yMoUQkkLKgnKFbHMOhNQCHl4tlfCiZRfkB1lbaGdjn87yNJSk6goe2nvUGydMDugsUVnRctFbBpsQnnElqa2pcP0eWm2rST7vNRgj54KQOpoHOj2xHlPm0ZQmuee+gIOt61AChloytGQTJC0jiGZJt4yfmhadRhcJ1TJWLsPkYFcQSVBJZ1qkTBf968fjA8NYrWG262iyE1cKyoZ2EgV16kcit47Z6KXkeYNJ2wbL2rKErnlYd+VKGaraCQWZYs15aC0JL2JqSJ65UhvDplA4LZ6hmnQQQmEoQcprcZA3i14KWXRu9X6O5oYRnsR6X/EZ9DddFmwi/QhtJG2rPdXOcHajrkczDQYGW9nYp48tIIiUNBlJOr1m3UQMcVdJWP58unsGWdbuRUFlGF0Yq5bCyFU6jcpmA3l7IbWzAFCtVuksJNnU10h/oYfmZJHOQoJnrGjT028Z0L+R5oyjPxM16ccaR6XZPKDP3S15zoAnFCEiczRpFRhLtJL0mmNbPV1kN414TpJ+WzWTQKRTSAxsmaQs9H1RQUdrOo1GGlMJr7m897k0wtqlmUgOd7OmazVKhLV1KJPOhlAh1fQFKWbZl8AXmRHIiMIgQkCHFjeSQpcyAEFky5fiH0i1s61hGQKdGlkrDa9nVSUKOj0YwK2ypmkV23LDwTxmHN2Y3R+R60UgAVLnbiJ18RXsufClPLZyCRiStLSxvXEEPcwAkcuSWNJFQloYTrigs7w1w2XLW4P9A1RX9DO+pIlkaRyztZXxJSM62dPrz/ZY+87gTKTnLKVqxEGiXyL1/yQUk1rMJrPjIuhqD99hm5TbmzAbmnBNGayZ5GUKM98FTkY3NK+bOxESO2hnIkd2w2evhJ9+nD2jL6RUStD+N3/D+799Hx/40XUke69ha9c5fPaZn41l9GNOOy1Zh+v+cDNPHJngFZ+8lWMTZQovehFd//Jpnrirgcf/+FW4X38bqlrmTzb9CS9f8XJe/o2Xc/Oumxd76DFPI7yasv8LfBP4JXC967p3CSH+nxDiOd5mnwAahRC/At4ITJPinwkhCIy/IB3R1bVG3hb63yXbIVXEdJKIRD7YXnqORfRPtNUVDfgBrqsL7PHUGD35e21Ay2AbfTTPMDk+ipCSdivP+sIgDRs2IqWg+4LNrD53CCNtkX3GJbhJJxC78BUVVzU3kXHMwDhT3or/vmMTejuhKHW1MLGsF3vl8shc6HQj4TlkvmHtKkPvW1lg1gpL+HMYRaZTdTcwvShLRTnT3uQ3sVbVsmcAi5oUItcz9AKnUAhyW1chEzb728LFzeVNy7GUxcqmlUihPHGGcBwTTpHDhWEqKqHnpV6Nbb4bkaov0NWcsbVDiXbWlFCsLizzxigZzQ+xr2ljeE6R3zJ6n3gPduSXkTUcEkNdVFYN4XZ1gFAIIehvTuOaBpZySNoGjkqisFlSWBKMJ2py2iqJrZLBPSSlwEg2kGtsJCm14e2YCkeltG/A1Guga8uEAFw9LzJZK3xRlRKk7uVmBinByouSadnyaLsJ0PWTUc5tP5e1zWsBaM059G7YTnbFavLPfhaWIUk7xrT81MBB8z4nF3WvYfNgYxh184MjSgVzKxC4LY0c613BqqVDtOc8Z6Brs05ttvRcmRF1vmW5zfSlVwW1VlXcmrGc01tgsEnPSVWpQPSmLsnaMlg3WHjQgiqNS1pxt6/ElEqLUsySPeXfO1NTHLGS0NDPZH87sq8n+MwawmRdd4HBYvhZFEEEWFJJFRHNy2v31TgAHX4tpoupTF0X633mpPc9ArqHY5WKnx+KTCQxikXcZJ6Ko9NfM4bNjrz+bHQWkrx6k148kEphtRRo2LqWLa2rwvHhakEd/1iA2VykmrJJWNmaofqJsK60EAhS6SxtjWnW9xSmz1FAJE3Tj7YLGdSaykQCOlo4uHVNzbuUVFTySW9uRbgPgtOfldhBO9O484vwkc2Q72F/8g85+sOf0/ahD/Oa//wh1z3yDnId3+SD2z/AG9e/MfijFRNzusklTD79io0kLcVzP/Q//OqJoyTXraXvP/6TMbWCh//2q5T/7gLYfQdXL7uad5//bt78gzdz3T3XLfbQY55GuK77Ndd1h1zXHXBd913ec3/huu6N3uNx13Vf6LruoOu6G13XfWDeOxc6tQhAJrQRYE4epskOdUiOl44jk0lcQ2FlQwMgMLiF8BTzvJTCzk6S69dFx1/7ONkAXZtg2bNCR6Xe6q6UrEl3UTB8UQKJ2dGBSjpQrQSRK9fvVRZNmYzg1+e4uJSXLuHYyiVIKSm3NGDmatMEj27cSrllinMyQ/SmHs7SYVJbttQ8N1Es8MRwL5YMV/6NKYat8BxH6QrdK07UHrPipeaZ3vMq8vqEr9AYGVfayeIrPAQRLkPvYzKbxhnuJJFNIOo5aF0bIdte9/x8A3jnslaKGdurQfMcESUoYzDhFLlkJExn9aMVSgjw0xq933Z0HpSkaiaQwgjqx7Y+7zVs7O5kfW8jw9lNjOQ2T3HQ5IyGvRKC8Q1bWbt0TXAcy3OqpBDaMYgw2r2DQzmdikeqCHaG1ObNcME5+CmeFWu6Q+KWq8G8lN3pRTzuFG+rwWmgLR3KuhsNDdgDA8H/mzMOy1ozNbVeZlsjQoigjYFt2hSaausi9TGjacaCanuRata7xz2VVrdUguXPRzT0e0Es/bnRdVkKKST92dU0pYp0WGGtpGsmac46NCW8eZOCw8UNNed6xZoOLCW1E++luIb3hw2FXkSyiapbJZ+0kcrQ97SydK3fjHhj9FOYlUODkQJPTK7c0oAc6mepp+SphKnl4qPNuQOREO3wCtOZ8lHWDlguYdKYDJuym8E9E1lskEILFzkGjqmC+7lmf241qN8UQgS2rt8+QCiDjnyCytJevXkl/Cz64i9D+QHWprsRHeH3qYDahZVlz6GzbwjDDB3tmm3rzqbQi01mctrzapogDVRTFpMrOxkfXhamaCLoLqToK6ambR8ldtDOFEYPwBd+D775NnjBxzk0sZX9//Qv5K75e57z9X/kh+N/yvOWr+Orz/8Km9s3L/ZoY2JwTMVHX7Ke563t4Hkf+iFfuf0xzJZmev7tesxNl/Obf68y9jfPgv/6Gy5sP49PXPoJrr3jWt7/0/fHCo8xZz0668wzOpSuNWrNOzSnk4HBZBs2Kl9gdOtqbMMOt0c3L65kdOF6uHIbrjRjpRE95wbHMywHstqokkLSWUhQSFr4q7ulNUuZ7NHGvTDDon39QNb+9gzsUn87pbYGT8ckdPj8qIMRqZ0qp9OUG7LBuakpjlB7doTNrTsASBraePFTHJNra1eWo3M4uly/JjOZ2hQsoGQqJrKpGkfCVjY9uZ7IPrwImpCgLPY3ncOx7m3B665UCMIoZ8III3kVT+QkWlsilPJSknTKqiEicuOOhVjSqVPKvFSpqVg9XRiFDAw/M3iu3No4NbBTo9LpGHagdpeIpnRFLp9KOoyuaYfc9Oa2qSUX0rbsPM7pK9LQkEA6DtKTmLdMxUVLm1nn1W2Zra1UC9lw7iL4dTyBCF/0vIxwcWFNqovzO88PXnOdLK6vUNp7Piy5BGFZCFunkWYcg5V9ftqmCKJs7mQpSI2U3nWMHnnObON6uLWOnTQUwjKoGl6NmVRhvRlgtrVi9fRoxyNRQOS6yKhQbRGA/m3QsU47aEHk24vFCFHjXGTMRrpyPVjKxFWSgw1rcR3P0fMcCSkEmcba/mEAFy1rZttwMXwiEdZZ0bmBclSOXwhP5t2c0dEGgixT/3MrhWRo56ux1m4Lp8x1ac/r1F0pJBvbNpL0HRAnh5FPk9qymfPbh8hY5gypeYJMwmSgKVTjDG/lsKth2jFwLDCUYnl7LkjP7CwkgwgzlZKn4uqfqh9RDiOsUghIOIwlOoLG4AC2d5+a0qTNytUsElmGrBEJ0e0IBCQTwSj1fEw5tYj4ngBoXg752mwH6WdFUPu5QQiqhUYqDY3B/0GQsg0y9uxBlthBOxP41XfgI97K4at+yJEHqux5z3u4561Xc+mtf8RxdTefu/xf+bMtbyFlzu5xx8ScTqQUvGb7IB/9nfW848a7eNuXfsGoK2n7q7+i6bVv5OHvN3H4i9fBP1/KslKFz17+WW7ZfQtvuOkNscJjzFmNiKxKSyFAWSRNg2XtWZTQ0Zf+XH9gLFpeSg2AbF5GKT/A+MAwx7euCQwPIUVQz5U6dwP2Kp02tCy3mZErXwGp0JBY39Og07Q8a8JtyOEmteGpsmFfK2/H+refMuY5V8mefiaHupi2eiwEwg2CNt5bQwMPpjto+jm9n4JT4Bl9z6Dc0MTY8ArMjqndDTRruwuMLO9DJhOBMSyUROW1cVauTBcLmGoaCr8GDX0NqsrGtUOJfhfAzgR1fjk7By3LtbJbJSz+D3coGWxOs3moCELQUUhS9SJo/U2poFanboojYA8MkFreVzsvK5eRbunwxu/dA96hl6faWdq0nMHmNOf216a2BQ6afy51olAAy7qbKeZSNCQzKEORuWBzJCQhyDhmkNKXXL8OUgmddBUx7C8cKrKmS897IHwQmRe/D5ghdWpftJ1PJBM0cGCmvWhFjFG/R1rVpUoVcDkvNxhcBxGJ3C4U161NLfSUInBzXmRzSvQvuX49iZUrtKOgFM9c/fu0GFMiwcoEaQZpm0JIUrZBMa1TbqcZ9FKSUjYbNj2T4yvDNgzViQkA1nbl9VwXh7WEu4ft9WwDYMULoDhUs9tJb1HAnxdT6FTJuWqZQNSk76lMhsSK5dO28h3imj66Lcth+fMwGhpoz+dr21XM4UBbMvK69562XIJCMiJP7z2/oiPHSFs2/Cx2b4Y2LfTjf9eIwCkKz7dspILvO4DGjEU2YUY+PPq9V6yZPwBuAAAgAElEQVTp0HMbSZsN5q23k8xF26eM3nutbbVO4Yy+p45DLIUMvv9qXu3YQKVpKPKsILnxHCb721HdncxG7KAtJpPH4T/fCF/8fbjkr+DKf+bYz+7i0be+hY+8sMCf7fsi67NX84OX3MAKTxUrJuZM5LzBJr76uvPZfXicSz7wA266b6+uS7v2Wvb81GLPzwu4H7+U1pv/gU9d9I+Uq2Ve/s2Xs3d072IPPSbmSRGNOhlS1KyydqlGVqS0YVb2jCoVMQxloZeJ4ioduTEiqWZCYBQKZM5ZhpFJIT1nzZIOhjQ4NHEIgLQVbWwdGkGVbAqZcBBWWDe0oadhukEhFTt7drK2ZS0YiempP340KSI+UEFHCnzjsF69RlSNTggBUlJuag6f8yN7nhHamnPoL2pVP18EInPxxaQ2bvTeUVuD1NeU0qqF0bZJQjupSsigrkdKSHmOVEsuqRuDA5f0XqIdtOZl0LoyiFoRdTal7uPVlNHOsIsbRtCiMzRTFsCUND2ACzsvZCi/ZMpmul7OFApl6mbHLVknskFooMk5BCX8azeQX8L2vN/LS0R/hXhiDm4kqgGQ9xsuUyuz75NLmly6vJVM/0bo3FjzWuhI1kFKkKrWufSiWVZPD9WedsDVTb+FJHXeFpxVur5ooRE0mUxidg3i2pHee/4Chr+vGYVp5LRoGIDhpa1lL7sUJ+LUKCm9putimiMplEIIyKcaqWbDVEd7cAmJoWFM5UW9WleGaqrTxjN9Nv3vCVMo+tKdnoOmZpj4KFN6dC0EIQKnVhT6INs24+F81dPgexF9X06Wq9imImsbJAPFTW/OVK3DLJqGoHUVGBYYWpLeT2sWdrZm/7vbLubgBZdjLwtbZSQtxVBzhmBSpl5vJ+IcBttI7XzXrCsIPUSjtu5VRN8ZuT+zVjbQaqltpyIpU62R1Tebm8E0MJfPbtfPUqUYc0p55CfwpT+EQh+86oe4mTZu+cI/4vzlR/jIc9P8jzyXt636XV60sW/ufcXEnAG05hw+8bIN3Hj7Y7zp+ts5f0kTf3zpCH1f/AKPvvZ1PHzofDqa7yJ153auuexdvOfo3bz4ay/mQzs+xFBhaO4DxMScAQgvnUZH0CL1E5Ga4KaOYYpD2mgoRSSjwxo0X1mNGrER3xCQkdSXgWKayYqnDOcZ4r7kOVCzIuwmbDI7dtQZ9BQDXyhMKbXUnPQkvyePQboZjj3hjcetMdSDOjlpUqqUahxO0CvgHfnpQiBRVDpN5uIduOUKVMIUQaOhEKSdCdMMjOnljQOofBhV6iwkKY0dZn/EIDaVqeWuvchN0jJozjjkmxJUD+c5sG8clI5cTK1fa8k6JCyjxkELjCupHedqNXTQ0naS4GqmmmZIc6xvWCdWraJ65AhHhL6GKrI+LqbW6qGdY+m69DamMA+rmus8Dd+AlVI7OkyPXATMo3Y9vB1Fzb4cU9VEE8JD1DfZhc7fRZy/FojIm1tpWPpMEkB5zwQcuD8Yq1EI68MyVobDk4fnHG+wvRcFcff/MvLsVAetvry5vudFzbk4pqLZa0vgC+B4J4YOpOgH/q6zjsn5S5oY+6/79CJAqkh/0xGa0nqhxRkeYq07+ORSNwmXK6q4rMj1M3pkL0hVU6NZ931P7nDTkIatUyrxUj37LoDKJDz0MwCsNj/y5s2h9/mYrFQxrQRDrVmOpVqpuJXQuZm6+JBsmBY59L/vKl5qbOB0CxV8Tqcz/XpnL7sUvjG108qTQEScO4/ubDcZ1cJ/3b9v2ubVSJ/J2epwpxI7aKeb0hj84L3wk4/DzndQWvdSvvnQt/jRP/49V163ixuu3MltxiV85qotrO2uU8gaE3MGI4TgijUdbB1s4gPfvo9LPvhfvGhjN6++9p+ZeN9f8+Dnf0L7a19N8pt/xtsal9AzvJPf/cbv8v4L3s95Hect9vBjYuYmujjqPVZCaIGI8cNw/27yLd3kinoVv8aZ8pCB/MMUgY4aY0UbGCs6ahszT2M+xt5UBy1Y6vWP5O2j5zwoTyB2ayMjavi4Qo/Zkhbb+rdNO8RAMT3tuVWd+SDtJzh0pP7HJ7GmtkZNCIHd38eS1laWNTREXwEIJb0BQxiAGzg8O0e8xtHVClJ5URErVdcwWt9TgPwlPPj4jyMDDNUeAezhAUpGF5vtcVLdW/hF6UF2H9uN6LuwvrEVSS2MotJpVDoNRx/Rh4m+16hjikkFlaruGTebj7L8+dON3Ehfu+kOWiSCNpNjtcBoS5jiOIujNgOWtGa8hze2bvRSIBdGTUQr1wX2w1QnxvRYTLv+m6Kpex4ZZwYT2U9DVHqVxT9a1dVqgkIp3FQjCMHKztrPb9Cg/Engq1pWXRcSBQwhQZo16abTqXWy6zHfVFL/OjobN2C0tQXzlbl4B0e/812mOSFeinKpXIVkEZZezjpPzbU6Pu5tOsdkiHBhK3DQfKd7RtVFpqVzg+dke4e7dHlrWNtW74IIMFuaqRzYX/u08L+vp7/HF1WKnlJLqoW8nec+xiK7nuW4EWIH7XTy4M1w4+sg08a+l/0HN+z9KTd88TK23SX47Rv389Edr+FI+wa+etUaipkZvkRiYs4CGtM273reSl6xtY+//dZ9bPv7H/LiC36Hq5et4JG/+iCNr3gVjZ1H+Z3vXUPHkvP4/256I2/Y8CauGr5qsYceEzM7vnMgwj/RSgpQKbBSwO6azXtzvbSmdOqPb2BJUesEBFL5UUPbnC4GUX8881Hz9Y6z8sqaZ33hAF8uGqm0hLiUlI0kOJkp+xALMi77mp58zbQzMjLja4P5QTrTun7DVCbku5FNK+tum3Qy5FI2rHhu/Z3lOmF/rfhClObhfi7K9yN+cQSVyYRtzKfWWoU7qLuf8FUvSkroJNeLoNU0WBaR2hnflm5ept88V/rjDA5ab2KI4cIwdx6fXgvsCy1oQY+5jXf/Pi5V6jhT2XbdvPzow3XfO9I4wqCRgUdvnfaakgrF3A19p1LjcHRugPZjtO59iPFEIUj/nXYOpokwjWAPsyGk4vj5K0nvfwCQQUQsqGmUCjh5QlhpW4/LV6Isd2+EZANG3wWw939Jm9MXR3ws6QRy9z4yN/OiT730Vp8gxbLYPIdjJXQ03s7AEwQZANFWG9JxyD3r8ln2MZ3ytIj1LGPwPy8zfD4cUwXXLfg+juxPIJCpFMkNG+q+v97pG3K647W+Rae63yvu8t4TR9DOLMYOwXfejnvXl7j9vFfxuepBvv/9V3F+x1b+9jebUV/9Dm/f8n94xnMv4RVb+4IC3ZiYs53+YpoPXb2Oux87wkd/8Gsu/VUjL3vFO7nixg9zvK2Z9j//Bhfd+TE+/uCPeN1P/4ZfPP4z/vS8d9QorcXEnFFEVm2F8GXyZ/7OlkIGimi+4aikqvljLXy71jcmlj4rdASn7i9qcAxdNns/pTpjjhKmKdYapNJxcKXJZO922KfTA2ddrT7NROfUkAYkG1HT6rT13G5ZuoOjB+9YQGpRZLvBi8HOkpISPENtTkGGOY7jRwFMDKqAigikRJHJJJWJyWn7DB61TBd5CJklgiYE+5o2knaKOIYDTHfQHFPx7FXt8Esxl69SM6bDo7WiLkIIyHUhjJkXnJVUJ//7fmovNKVoTDSRXL9O96yqg9HaitGs6yXFDAqdU/aKI02OSRmkEJb99Gcl59Xnaj4Mt2YoeimS/veHrz4qs+08c4a2Dj5Xrh1EyrD+cS6naMaoITPf+379qP7+csP0R6CzdADbWLiTDdCQaKhxPsuud138RYN6n7UgnDndQZ7xe3re12rKjTVl8cI2JGu68jw2kWSiMjHDsWZubzGV2EE7lbgu/OIGjn37z/laax9fWLqex3d9nSuHruTGZ/07h9/1Yfb91818+PI38Y5XXsKqzvzc+4yJOQsZac9yzYvW8vD+UT5x8wNcvf7/8Pr7v8a6334l7f/vnSx/6au44dt/xlvv/TIv2fVDPnDxh+lprr8iHhOz2Ay1ZLCUTvlTUxfUZvnjW9t4N2LuTI2gmdPTAH1MEYmY2aHxMqvjMMuY1javxTlcWzehEk6dt82tFbcY+Apv00RLgno+h8z2bXVTK30MaZD3ZL1F9HomZv6bPLORVT/F0ed46bh3TMUkkF67pO71SW7YgFvyHYU6ebWzMVuKIzDhFKc9NxUp66dx1aPseShzBfNmJN0MfRfOvd2TxHcgzLbp0vbBNkIEghXSqxud6Y5f37KesSO7aCxBudDD3d75b/ZVOKVaUK3RbPj9yQDdzBtQddKmZ2KuBX9b2TQ4Oo340uWt07/PovuK1NBGEaZJ9pKdiPu/5tWdRSJIPQ0shOicn9t2bs1rpjQh0wZODvbew7bh5lDox6PkeosEs9Vs+gtjU1Ica6/3wq+fEIKexhQd1XOmNVkvG2mO5Ib1vT5PYgftFOHu+l9u/8Yb+WJlP99sTrOiqZ2XLXkBF/dcTGnPQe64+jXsOTTKY295P//ynPVPeoUhJuZsorsxyTuvWMEfX7aUL922in/5ty9z5Z/8OWNLV7L0Xf/AR6zH+Nj33syLvvoi/qK4lct2vGdWIykm5rQjRCC1LsT0lCBn+XKs7vrKbFP/aIdGwPyr+KeKc8y8oaUL+GHGCBqgG/8atemUvpy49MZbMjOMJtsR4ol5j/OUUMdmsj31zHqqkvoFhUzOnmp5Se8lkWOcoGE9x/sH84O0p9rh3oeYzQiUtg2eimfNPudKOTRsSIbCKvWOcP6S4rTawBPBjxzVy3CcSv16HwHpuZ3G+TKtpmq+nxmP/gabh/fO3AYmZ+fIpSog7sY0ErhesKTRi3QJNf8oyULwvz9WN9fvK/hk2NETigo55uzzFPYjm35uwrJmXRQ4UbZ1bdMR38IQlCfg0MOeimYtVb8n2gxR28dbtzP1UzH1u8MFKu5cUVQxo9jLVDEiAFcaHM/WX4yZidhBO8kc3v1zvnLTn/HF4w9wwE5xxdKXcsPwVfRke3Bdl29d/22y7/kL7luyjnM/+lc8r2thqwsxMU8F0rbB75zbw0s2vZaf/uI5HHrXu3n0uc/h85e9lLUv/zzvnfgGb7/97/jeJzfxtiW/TW7LH2l1p5iYRUY6YYqUYyoaUrWpiNK2A3n8qYRF7mCqBRjdEebdC3PJTjj4IOy5a8EGk5CSseEVyHQG9h3giZYLvFf2LmoczRkZmdZTzZAGz+x/5vSNn6SxaHZ24s7H05iLGY5rKQtLWYy5Dy5kZ/PfdNmz9e/JyRk3mXrPznzY+R23VK5NvfPx1QWj90xvtpcnxk6toz/VcBZqYaG95qSgfsVcBN/pE2r68spJjKBFqfoqrrOkjJ5K5itu8WSiT3ORjNbkGjYsrfOZ948uhK4tHZ6+TcWYXtsrmO54lqrjJzDa+TGXEx87aCeBcrXMD+/5Ijf+/GP8YOIJ1jlFXrX1nWxf8lxMpSWDb7r7MX7+vn/ggp99g+OvfC0ve+3LT8kKS0zM2YQQgo2retl43cd49FvfY/s73sEDt97E5za8gJ0X/RO7Sx/mebtu5B0f+SQXLLsKzn01NPQt9rBjnsYIJzSOHFOxsW/+CwfRCFp3Q5KcY/LDPb+Yt4O2vXt73dXZupgJSLd4DtpcQhJyWkpQuakZpab8jVrkP1nStpHN808RAmas5ZvxGI6DM3zq234sSGr9FNoKc8hhzGsfvghEIJLh0ZvrpTnZjG3YgYHdle2iK9s1bR8nk2kRNLXADCUrTVUem33afWEdIad9fBfqEM6X6imMUM2HWVUPazc8DaOZmUbHiyBb9YWWptre9WzxUnXmBQ7NwnsXJK2F3Yexg3YC3Lv/Hr5820f52q4fkC5N8JzsEG/c/n7a27wGpZUqX71jN5+/8ce88FsfZ6sNQ1+4jvRp+PKPiTnb6LzkItrP20THP32cZZ/6AHfv3sp/tF5G87L1vLn5X9ly6Ge84dqtdPXvgC2v0+pcMTGnHaEV9J745dybTiHqoAkhyKcsWlOt2IcF85ElWLCYQmDQLdxBg9kV3c4Klj17xlSnJ8vcC6vznDOXJydcMl9mE1I4yQy1pHn04KiWf48ghQyaqrenZxezOJkoUWsIi4U6aIM7ePz4LmZdCpCh8uk0Z/sURdDSiUaOdqw76ftdKDN+BjxZ/XkJFy103/NkZ8/OmdOdPawpDrSvqBp1PC05c80qLLyJ+qXLWxf8fRo7aAtk7+hevnbP9Xzl3ut5bOIAl42X+bu+Z7P6vDcj0rpJ3yMHRrnh1kf44k8e4vL7buItd3yTxhf9Fi1veD1yBonXmJgYkKkUza//IwovvJLcBz7Ip777Xh4bv4QPtb2O72T+m++3HeFK9xiv/dyVZJuWwsY/0Ip3Rvy5ijmNqCdn9E+vQYN1LesY3XXbiY5oBhbgoNVhak3KWeeunYJUMMnJiY4I05j7ugQbRyInM0QFpr3FV+x40sod6GONzRVJgIwzf9GK08FQwxCdmc7wiQXWoKFMWvIZuhtnmevACRHTGkHrCNrJ/7Rsats0755lp5LZBYmm95ObL23pNvL2idWcm/NoO+K3kfDxncKB/AC/PvRrmtI2y9rOmXUfVdwFBdHmqu+rR+ygzYO9o3v59n1f5Jv3f4k7R3dz7tg4r8wMsm3jG7CHLwdl8tD+43z7tgf41t17uOPRQ7wsc5iP/c+/kkzYtH3mUyRWrljs04iJOWswOzro+Nu/YeLXvyZ77bW864b3U95yIV/uewX/lriJG5pbucIq8sYfvI/c198Ma66G9S+Dhv7FHnrM0wFnjubRM1DPQdOcBKNrVptorpSk6Ub8c1a310kFWviwnmrIuRwe5YkliNkNMmfZMqzeXrDnY7h5E9+6GpHrnc8wtbLeMy5bePQoSu8F4M6vn9eOZS0LjiqcKkxpkrPDz+hMvc9mY1N/4+wbiLBFRd2UylPwYZl3evMpZtYIlTVzT7a5WNu89km/dyFYMzhohjRImklGGWUoop4ZUI4sVpyGe/3MuNpnII8efpgf3HM933rwG9w5toeN4xM8L9nNNcteTXbk+ewqpfn6gwe55cv3cMtv9rP70DgXDhV5aWuF5f97I5Pfuo3ia15N4cUv1t3LY2JiFow9MED7e99L02tfy6EbvsAL//1zXJXO8MuRjXyu8CBb20sMpzbx6l13se0n5yE71sOKF+jUplTTYg8/5qlKujit6fN8mNFBq54EUYp6zDvFsY4q25TnLlhS5Ed71NO+dnpq+tw0pNTfQXMgDEM3vp4P/pwb9oJq6ubjnA01ZwJV0mksIDPBb6Z8JmIPDmA0nzyVSCCMTLrTIylCyrMw3Dx/Zv0OUGfufeAz1UGLRsV7s70cmTxS/41eiwwIm4Y/WWzDnrXBOMQOWsBkZZL//c13+O/7vsh/77uD3dUxNk5UeEail9e0/AH3ORfw4wOCT996lPu/ehuTlSpruvJs6mvgL69YwYqxPRz71Cc5+r3vkbr6arrf9x7ULN3aY2Ji5o/V2UnzG15P8f++hmP/fTOp732XpV8/wGTZ4Nctu/hh4yGua+ylpyy5eM8nWXvjW1BdG2Hk2bi926m4KapHj1A5cpTKkcNUDh6kcuAglYMHKR88QOXgIapHj1KdmMAdH6fh915O4YUvXOzTjnmKsaltE5OVOiljp2w1dp6iArM4cFsH9UJHYb7Kf09x5qpvOTWcOms/lzTJJc+sFMWTjVAKo1A4ZfufmuKIMp6+CxlnSJRvJjoLCQaKtY5R9Fr1zhahjnxPu7P2WZubHd075tzmzJ7JU0C16nJkvMSuw0e4/cGbuP/Rb/HrY7/kHg7RVCmzYiLFmokh7CPn8vXxPn6ccmg77NDbWGG4JcO24WaGWzN0NySRlTLH/vtmDr77/Tx+++3kf+sqBr7+dcyWBapMxcTEzAthmmQu2k7mou241SoT99xD5913s+HOO3n09p8wuedRzKNj/KrUCPwa+Dvg7xAmqKSDyuWRjS2oxiaMQgOqUMAZGcEoFJCZLNKxEY6D2dk5x0hiYhZOykzVlcl3p1l4J5knWYMGYW+nGM2cEbRTwdPV2D8rmJ7iKKQ4I67ZVDGME2VeLTbOcAetXuPsedeVDu7Qqa2P3exd81P7vX1mz2QdRifLPHZojHLVpVxxKVWqVKouY6UKR8bKHB0vcXS8zBH/91iJPceOcXD0fszSbRjyV4wm9vC4PUFrucJAyWGAPjakr8DuupSmQoG2vMPv5RK05pxphX1utcr4XXex7xNf5/BXvoJKpchf9UI6/u6D809XiImJOWGElDgjIzgjI+SvvBK/O1K5Uub799/CjffcxN3772R/9QFcY4LOqsNQ+SCD4w/QYeboKQzS1bKWhs7liOZlkO04sYL6mJgny6mKoM03xbF5BEb3zbm7wcJgKGH9NGVRImhmAsYPn/7jxsyN66LkFMflFNWgLZTtS09usCBlpuYW4TjDHbS6zPdSJcIo7OmotzzrZvJnDx3k9Z//OYYSGFJ6vwW2WcV2jpCyHictHkK6j3Dc3c0eDrFbTdKUrNBRlQw4zawrbmVd/yU09e0Ac3YpTYDywYOM/vSnHP/Rjzj2ve/jlstkduyg85prSKxd8/QNZcfEnIEYymDn0vPYufQ8QLe7uPnB+/n+Az/nrr0P8BN+Q6XyMOr4Lo49fD/y4etpqlT0j7QpWjmKiSaaEk00JVsoZjoo5npoKvSjUs1gpUIhgJiYk8EJpsvMyVz3ar5L/8zBUCFuEbMoDlrXJqhMwqM/OP3HjpkdIVjfU2C8FH6GZTKJTM2zofwpQApB1XWflHLgbFjKYmfPzjkOvggR5hPkyXymq9kUZvupbR2x6A5a1a1ScStUqhUqboVytczYrls5fnwvo+XjjJbGGC2PMloe5Xh5jCMTR7hq5SEOlo5ysHScg5Ux9lfGeZQKabdK+2SZAgYddo7udAdLGrawpO0c8t3nQWr2lT/Xdans28f4vfcxce+9jN97DxO/vIeJ3/wGZ2SE1KaNdHzwAyRWrz4xZaSYmJjThqEk2waG2TYwHDx3bKLMvY8f5f49h7lzzy7u3/so+4//igOTD7Hb3EfSPkLFeIIxdRtHZIkDskoVl6ZKhZZyhZZKhZYqtLqSFkxahEkLimLbOswXfHwRzzYGQAjRAFwH9AIPAle5rnuwznbfAM4FbnZd91mnc4w1nITV2OWNyzkyMbW4/cxItXoqsSgOmjL1T8yZRe/5kGzEVgrbCG1Co7ERo3HxIs07ljVzbGI+nRVPAWdhBG1eqZsRNrZtRFy+CTNxaq/xaZ/Jz9z9Ga7532sou2Uq1Urdng4JJAnXJekKljwOIw+4GEqgpCRhGCwxbBKmQ9LOkEq0k840U8h3k2roRGRbEE4SYZpaTaZSxh0tc/zOX+OW76F67LgWCzh8hMrRI1T276f02G5Ku/WPWyph9/djLx3GGR4m95wrSKxZjUo/eenQmJiYM4u0bbC+p8D6ngLahteUK1X2HJ3gsUNj7Do4xq5D+ufxw2PsPX6Ag+OP88vyHu5092LZh7Hto7jGEcrGUSbkMSrHbsH81Hk4okBCNtT8pJT/uxFbJhFCBAJgrkvQ5NV1XaquV9ngvb6pr4Er1nTUO5WY+rwV+K7ruu8RQrzV+/9b6mz3fiAJ/OHpHNxUnFWrqR47ekL7yNm5GmlxwOtJFC8mnkwWpQYt5swk07LYI6hL0jJIWovkKC1AZfRMIO/kaXCm16XNRlPi9ChEn/Yr+NzB57KzZydKKJRUKKEwpFHz/+gK1ehPf8rhr30NyhXcchm3UoaydrrcoxXcUgm3dIix0l5GSz/2/j+JWypBqQyGgTAMHfEyDVQqjcxmUZkMMpvB7Ogkec45mG1tGG3tmC3N2rmLiYl52mEoSUc+QUc+wTm9M283Ua5w8HiJA8cnGZ0sMzpZ4fhEmYMTh9k7upd9Y3s4XNrPsbL+2Vf5DQ9P7Od45QDj1cMoLGyZwZZp7yeDLdLYKo0jM5gigSltTGFjKoe9pUbu3HeQhJHAUhamNGf8DjVEqCDmurqAvepWPYev/uOqW9X/pxo+dvXjqc+1p9uxzvw/wlcA27zHnwJuoo6D5rrud4UQ26Y+f7pR6RQqfQpSoqwkDFx08vf7NGZxVBxjYs4C+i6E5MKcncVmS/uWxR7CjJx2By1jZchY8xfTSJ5zDslzZu/oHRMTE3M6sQ1Fa07Rmptaw9oGLJ31vaVKiX1j+zg8eZjDE4c5NHGIwxOHOTJ5hMMT+rnjpT2MV8Y5XB5jvDzO/Y+N8YWH9eOJykSQDl5xK3V7a0khA+dsIUghkUiEEPqx/xN57tPP+DR9ub4F7XcRaHFddzeA67q7hRBPX2ldp07D1ZgnzZnSLDgm5owjfZJ7zT3Nib9pYmJiYk4jpjJpS7fRRttJ2V+9Ot5ytRw4V0IIBNq5EojQ+UI3U5WE251NCCG+A7TWeeltp+BYfwD8AUB3d/fJ3n3MWURToonN7ZsX5dhDhSHa0ifneyMmJubMJnbQYmJiYs5ifEfMlE+v1GzXdS+e6TUhxB4hRJsXPWsDnjjBY10LXAuwYcOGU6+vHHNGU3BOXdPj2RgsDC7KcWNiYk4/cTJ1TExMTMxTjRuBl3mPXwZ8eRHHEhMTExMTsyBiBy0mJiYm5qnGe4CdQoj7gZ3e/xFCbBBCBH0QhBD/DdwA7BBCPCqEuHRRRhsTExMTExMhTnGMiYmJiXlK4brufmBHnedvBX4/8v/zT+e4YmJiYmJi5oNwF9AgUwixF3joJB27Cdh3kvZ1phGf29nLU/n84nM7O3mqnluP67pnneyXEOIocO9ij+Ms56l6T59O4jk8ceI5PHHiOTwxZvw7uCAH7WQihLjVdd0Ni3LwU0x8bmcvT+Xzi8/t7OSpfG5nI/H1OHHiOTxx4jk8ceI5PHHiOTx1xDVoMTExMTExMTExMTExZwixgxYTExMTExMTE4UFq1MAAAfmSURBVBMTE3OGsJgO2rWLeOxTTXxuZy9P5fOLz+3s5Kl8bmcj8fU4ceI5PHHiOTxx4jk8ceI5PEUsWg1aTExMTExMTExMTExMTC1ximNMTExMTExMTExMTMwZwil10IQQDUKIbwsh7vd+F2bY7htCiENCiP+c8nyfEOIW7/3XCSGsUznehbCAc3uZt839QoiXRZ6/SQhxrxDi595P8+kbfX2EEJd5Y/qVEOKtdV63vevwK++69EZe+xPv+XvPxGavT/bchBC9QoixyHX66Oke+1zM49wuEEL8rxCiLIS4csprde/PM4kTPL9K5NrdePpGPT/mcW5vFELcLYS4QwjxXSFET+S1M/7aPZWY61rFaIQQXUKI7wshfimEuEsI8Ufe83X/ZgrNNd683iGEWLe4Z3DmIIRQQojbfNtoJptotr/NT2eEEHkhxBeEEPd49+Pm+D5cGEKIN3if4zuFEP8mhHDi+/A04bruKfsB3ge81Xv8VuC9M2y3A3g28J9Tnr8e+G3v8UeBV53K8Z7scwMagAe83wXvccF77SZgw2KfR2SsCvg10A9YwO3AyJRtXg181Hv828B13uMRb3sb6PP2oxb7nE7SufUCdy72OZzgufUCq4BPA1fO5/48U35O5Py8144t9jmc4LltB5Le41dF7ssz/to9lX7mc63in2Cu2oB13uMMcJ/3N6Lu30zgmcDXAQGcC9yy2OdwpvwAbwQ+h2cbMYNNNNPfr/+/nXuJkaKI4zj+/cdVAd+vqHElQoIevIiiMT6JEhVEMNGDCb7xauLJxBAPejZGDh5MMEaNhggS5GZ4Gi+ggArik4eBhRUQ5BE1EOPPQ9XquM7s9kzvzPSyv0/Smeme7kn9q2q6urpraqwvwNvAM/n9GcD5rodN5d8VwC5gfF7/AHjS9bAzS7uHOM7NPxDy64P1dpK0Gjheuy0iArgLWDrc8V1SJLZ7gZWSDkv6FVgJ3Neh9DXrJmC7pJ2STgKLSTHWqo15KXB3Lqe5wGJJJyTtArbn76uKMrFV3bCxSfpJ0hbgr0HHjob6WSa+qisS21pJv+fV9UBvfj8ayu5UUuQcYoCkfkmb8/vjwLekC71GbeZc4B0l64HzI+LyDie7ciKiF7gfWJTXh7omGq3tV9tExLnAHcCbAJJOSjqC62GzeoDxEdEDTAD6cT3siHZ30C6V1A/ppA00M4zvIuCIpD/zeh/pJF8VRWK7AthTsz44hrfy0KsXK1CJh0vrf/bJ5XKUVE5Fju2mMrEBTMrDTD6JiNvbndgmlcn7qpcblE/juIjYGBHrI6JKN3ig+djmk+7wtnKsleP8bkEe4jQV2EDjNtN5W99rwPP8e+NpqGuiodqvsWoycJB0nfVFRCyKiLNwPSxM0l7gFWA3qWN2FNiE62FH9JT9gohYBVxW56MFZb+6zraOTjk5ArENFcM8SXsj4hzgQ+Ax0hCtbimS34326XpZDaNMbP3AREmHIuIGYHlEXCvp2EgnskVl8r7q5Qbl0zhR0r6ImAysiYitknaMUNrKKhxbRDwKTAPubPZYGxHO7yZFxNmktu05SceGuAfpvB0kImYDByRtiojpA5vr7KoCn41VPcD1wLOSNkTEQtKQxkach4Pk/+fNJf115QiwBJhZZ1fXwzYo3UGTNKPRZxGxPyIul9SfHxUfaOKrfyE9Yu7JPfFeYF/J5DZlBGLrA6bXrPeS/ns2cGcCSccj4n3SEJpudtD6gCtr1uvl98A+fflx93nA4YLHdlPLsUkScAIgN5Y7gKuBjW1PdTFl8r5h/ayQUnVL0r78ujMi1pHu5lelg1YotoiYQbopdKekEzXHTh907Lq2pNKg+ue4SomI00mds/ckLcubG7WZztv/uxWYExGzgHHAuaQnao2uiRq1zWNZH9AnaUNeX0rqoLkeFjcD2CXpIEBELANuwfWwI9o9xHEFMDC72BPAR0UPzBfGa4GBWdmaOr4DisT2MXBPRFyQ70TcA3wcET0RcTH805DNBr7uQJqH8jkwJc/OcwbpD56DZ72rjflhYE0upxXAI3kGn0nAFOCzDqW7iJZji4hLIuI0gPwUZgppQoaqKBJbI3XrZ5vS2aqW48txnZnfX0y66PmmbSlt3rCxRcRU4A1gjqTam0CjoexOJWV+Z2NKHq7/JvCtpFdrPmrUZq4AHo/kZuDowBC0sUrSC5J6JV1FqmtrJM2j8TVRo7Z5zJL0M7AnIq7Jm+4mnf9dD4vbDdwcERPy73ogD10PO2GkZx2pXUhjT1cDP+bXC/P2acCimv0+JY0V/oPUA783b59MutDfTnq0emY709um2J7O6d8OPJW3nUUax7sF2AYspAKzHpJmMfqB9IRhQd72MuniENKdvCU5ls+AyTXHLsjHfQ/M7HYsIxUb8FAuo6+AzcAD3Y6lhdhuzL+r34BDwLah6mfVllbjI93p25rLbiswv9uxtBDbKmA/8GVeVoymsjuVlnpl5aVuPt1GGta0pabezqJxmxnA6zlft1Kh2Y2rsJCelA/M4lj3mmiotnksL8B1pNEuW4DlpBlvXQ+by8OXgO9IDxHeJc3W7XrYgSVyppqZmZmZmVmXtXuIo5mZmZmZmRXkDpqZmZmZmVlFuINmZmZmZmZWEe6gmZmZmZmZVYQ7aGZmZmZmZhXhDpqZmZmZmVlFuINmZmZmZmZWEe6gmZmZmZmZVcTfxtGoEoGjfwgAAAAASUVORK5CYII=\n", + "text/plain": [ + "
      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(Fisher_Fit)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian multiple correlation estimation.ipynb b/wip/Bayesian multiple correlation estimation.ipynb new file mode 100644 index 0000000..4b664b4 --- /dev/null +++ b/wip/Bayesian multiple correlation estimation.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

      Table of Contents

      \n", + "
      " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Import analysis packages\n", + "%matplotlib inline\n", + "import pystan as ps\n", + "import numpy as np\n", + "import pandas as pd\n", + "import patsy as pt\n", + "import seaborn as sns\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as ss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The classic pearson correlation " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following the quick description of the classic one sample t-test above its important to keep in mind that Bayesian analysis inference are all derived from the applciation of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to pearspn correlation, it is fundamentally different, because it uses fully probabilistic modelling and the infernce is not based on sampling distributions\n", + " \n", + " For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. In the case of scientific research publication is the goal, as such the priors must be accepted by a skeptical audience. Much of this can be achieved using prior predcitve checks to acsetain os the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then interprete and the posterior\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      PSPD_15PD_30PD_45PD_60PD_75PD_90PD_105PD_120PD_135...redist3redist4Household_IncomePolitical_PreferenceagegenderPopulation_Inequality_Gini_IndexPopulation_Mean_IncomeSocial_Circle_Inequality_Gini_IndexSocial_Circle_Mean_Income
      0233274821000000...61540238.7829382971528.05673821150
      11573900000000...3420559237.21451112363024.32338865355
      227500500050000...55100541220.7500006000014.442577107100
      31119141717178752...34150859235.3795805935526.92590086640
      45268320000000...45500535116.8750001536021.40105556850
      ..................................................................
      300195561351412101410...45275000425129.7117128325012.583830113175
      30110171319231411433...44350000443132.7532675739022.719525139050
      302622622171296222...62566142.527390418958.55737791500
      303227662014000000...33526225.5551781767020.92758024855
      3042536321616109432...43333136.6854814969534.83105065700
      \n", + "

      305 rows × 37 columns

      \n", + "
      " + ], + "text/plain": [ + " PS PD_15 PD_30 PD_45 PD_60 PD_75 PD_90 PD_105 PD_120 PD_135 \\\n", + "0 233 27 48 21 0 0 0 0 0 0 \n", + "1 157 39 0 0 0 0 0 0 0 0 \n", + "2 275 0 0 50 0 0 50 0 0 0 \n", + "3 111 9 14 17 17 17 8 7 5 2 \n", + "4 52 68 32 0 0 0 0 0 0 0 \n", + ".. ... ... ... ... ... ... ... ... ... ... \n", + "300 195 5 6 13 5 14 12 10 14 10 \n", + "301 101 7 13 19 23 14 11 4 3 3 \n", + "302 62 26 22 17 12 9 6 2 2 2 \n", + "303 227 66 20 14 0 0 0 0 0 0 \n", + "304 253 6 32 16 16 10 9 4 3 2 \n", + "\n", + " ... redist3 redist4 Household_Income Political_Preference age \\\n", + "0 ... 6 1 5 40 \n", + "1 ... 3 4 20 5 59 \n", + "2 ... 5 5 100 5 41 \n", + "3 ... 3 4 150 8 59 \n", + "4 ... 4 5 500 5 35 \n", + ".. ... ... ... ... ... ... \n", + "300 ... 4 5 275000 4 25 \n", + "301 ... 4 4 350000 4 43 \n", + "302 ... 6 2 5 66 \n", + "303 ... 3 3 5 26 \n", + "304 ... 4 3 3 33 \n", + "\n", + " gender Population_Inequality_Gini_Index Population_Mean_Income \\\n", + "0 2 38.782938 29715 \n", + "1 2 37.214511 123630 \n", + "2 2 20.750000 60000 \n", + "3 2 35.379580 59355 \n", + "4 1 16.875000 15360 \n", + ".. ... ... ... \n", + "300 1 29.711712 83250 \n", + "301 1 32.753267 57390 \n", + "302 1 42.527390 41895 \n", + "303 2 25.555178 17670 \n", + "304 1 36.685481 49695 \n", + "\n", + " Social_Circle_Inequality_Gini_Index Social_Circle_Mean_Income \n", + "0 28.056738 21150 \n", + "1 24.323388 65355 \n", + "2 14.442577 107100 \n", + "3 26.925900 86640 \n", + "4 21.401055 56850 \n", + ".. ... ... \n", + "300 12.583830 113175 \n", + "301 22.719525 139050 \n", + "302 8.557377 91500 \n", + "303 20.927580 24855 \n", + "304 34.831050 65700 \n", + "\n", + "[305 rows x 37 columns]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = 'https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Dawtry%20Sutton%20and%20Sibley%202015.csv'\n", + "\n", + "df = pd.read_csv(url)\n", + "df.dropna(axis =0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define the descriptive statistical model \\begin{align*}\n", + "y_i &\\sim MultivariateNormal(\\mu_i, \\Sigma) \n", + "\\\\ \\mu_i &\\sim Normal(0,1)\n", + "\\\\ \\Sigma &= \\sigma \\cdot \\rho\n", + "\\\\ \\sigma &\\sim Lognormal(0,1)\n", + "\\\\ \\rho &\\sim LKJ(1)\n", + "\\end{align*} \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4 -use Bayes rule" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stan model for multipke correlation estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "Correlation_model = \"\"\"\n", + "data{\n", + "\n", + "int N;\n", + "int K;\n", + "matrix[N,K] y;\n", + "\n", + "}\n", + "transformed data {\n", + "matrix[N, K] y_std = y;\n", + "\n", + "for (i in 1:K){\n", + "y_std[,i] = (y[,i] - mean(y[,i])) / sd(y[,i]);\n", + "}\n", + "\n", + "}\n", + "\n", + "parameters{\n", + "\n", + "vector[K] mu;\n", + "vector[K] sigma;\n", + "\n", + "// Correlation matrix\n", + "corr_matrix[K] rho;\n", + "\n", + "}\n", + "\n", + "model{\n", + "\n", + "// Covariance matrix\n", + "matrix[K,K] Z;\n", + "Z = quad_form_diag(rho,sigma);\n", + "\n", + "//Priors\n", + "// Stan std_normal() is a more efficent implementation\n", + "mu ~ std_normal();\n", + "sigma ~ lognormal(0,1);\n", + "\n", + "// Uniform prior for correlation parameters\n", + "rho ~ lkj_corr(1);\n", + "\n", + "//Likelihood\n", + "for(i in 1:N){\n", + "y_std[i,] ~ multi_normal(mu, Z);\n", + "}\n", + "\n", + "}\n", + " \n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_9b16b85a03d777d0f7a4bd0fbd10ba3d NOW.\n" + ] + } + ], + "source": [ + "sm = ps.StanModel(model_code=Correlation_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'N': 305,\n", + " 'K': 113,\n", + " 'y': matrix([[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", + " 2.9715e+04],\n", + " [1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", + " 1.2363e+05],\n", + " [1.0000e+00, 1.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", + " 6.0000e+04],\n", + " ...,\n", + " [1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", + " 4.1895e+04],\n", + " [1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", + " 1.7670e+04],\n", + " [1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00, 0.0000e+00,\n", + " 4.9695e+04]])}" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#df[[\"Household_Income\", \"Population_Mean_Income\"]].as_matrix\n", + "x = np.asmatrix(pt.dmatrix(\"~ 1 + Household_Income + Population_Mean_Income\" , data = df))\n", + "\n", + "data = {'N': len(x),\n", + " \"K\": x.shape[1],\n", + " 'y': x}\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Bayesian one-way ANOVA (between subjects).ipynb b/wip/Bayesian one-way ANOVA (between subjects).ipynb new file mode 100644 index 0000000..5ada1f3 --- /dev/null +++ b/wip/Bayesian one-way ANOVA (between subjects).ipynb @@ -0,0 +1,1730 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

      Table of Contents

      \n", + "
      " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Import relevant packages for analysis\n", + "import pystan as ps\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "import arviz as az\n", + "import scipy.stats as stats" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of the one-way ANOVA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following the quick description of the classic one-way ANOVA above its important to keep in mind that Bayesian inference is derived from the application of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent to the one-way ANOVA, it is fundamentally different, because it uses fully probabilistic modelling and the inferences are not based on sampling distributions.\n", + " \n", + " For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for the question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. If a scientific research publication is the goal, the priors must be accepted by a skeptical audience. This can be achieved by using prior predcitve checks to ascertain if the priors are reasonable.\n", + "\n", + "4. Using Bayes rule, estimate the posterior for the model parameters using the likelihood and priors. Then use the posterior to conduct your inference.\n", + "\n", + "5. Conduct model checks. i.e. Posterior predcitive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data overview and study description\n", + "\n", + "The data analysed here have been taken from https://drive.google.com/file/d/0Bz-rhZ21ShvOM1cxWUpUNlQ0UlE/view, and stored in the Github repository for these notebooks for ease of import. The dataset is orignally from James et al. (2015). See the original paper here https://journals.sagepub.com/doi/pdf/10.1177/0956797615583071?referrer=&priority=true&module=meter-Links&pgtype=Blogs&contentId=&action=click&contentCollection=meter-links-click&version=meter+at+null&mediaId=\n", + "\n", + "A reality of trauma is that individuals can experience flasbacks which have been termed \"Intrusive memories\". A form of treatment that has been argued to be effective for suffers of intrusive memories is to use reconsolidation methods. As such, James et al. (2015) wanted to investigate if a video game treament (tetris) could reduce the number of intrusive memories a traumatised indivdual experienced.\n", + "\n", + "The participants with the study were split into four conditions (n=72, with 18 particpants per condition).\n", + "\n", + "1. No-task control: These participants completed a 10-minute music filler task.\n", + "2. Reactivation + Tetris: These partipants underwent a reactivation task to (trauma film) to reactivate their traumatic memories, which was then followed by 10 minute filler music task. This was followed by playing tetris for 12 minutes\n", + "3. Tetris: this group played tetris for 12 minutes\n", + "4. Reactivation only: Participants only watch the trauma film" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      ConditionTime_of_DayBDI_IISTAI_Tpre_film_VAS_Sadpre_film_VAS_Hopelesspre_film_VAS_Depressedpre_film_VAS_Fearpre_film_VAS_Horrorpre_film_VAS_Anxious...Day_Zero_Number_of_IntrusionsDays_One_to_Seven_Number_of_IntrusionsVisual_Recognition_Memory_TestVerbal_Recognition_Memory_TestNumber_of_Provocation_Task_IntrusionsDiary_ComplianceIES_R_Intrusion_subscaleTetris_Total_ScoreSelf_Rated_Tetris_PerformanceTetris_Demand_Rating
      0121330.00.00.00.40.30.8...241518590.6299999999.00
      1123271.90.70.50.80.20.2...231719490.6299999999.00
      21110422.21.20.90.20.10.4...5612210100.5099999999.00
      3111411.21.00.65.10.40.5...021619080.5099999999.03
      4121270.20.10.02.90.00.7...5314221081.0099999999.0-7
      ..................................................................
      67422340.50.01.02.11.53.4...211520470.5099999999.00
      68422280.80.91.00.00.00.0...241421681.5099999999.0-5
      69420231.60.30.50.00.01.3...341824790.5099999999.00
      70424422.25.02.30.00.00.0...1271317370.5099999999.0-1
      71414540.90.00.00.70.53.3...341721290.6399999999.00
      \n", + "

      72 rows × 28 columns

      \n", + "
      " + ], + "text/plain": [ + " Condition Time_of_Day BDI_II STAI_T pre_film_VAS_Sad \\\n", + "0 1 2 1 33 0.0 \n", + "1 1 2 3 27 1.9 \n", + "2 1 1 10 42 2.2 \n", + "3 1 1 1 41 1.2 \n", + "4 1 2 1 27 0.2 \n", + ".. ... ... ... ... ... \n", + "67 4 2 2 34 0.5 \n", + "68 4 2 2 28 0.8 \n", + "69 4 2 0 23 1.6 \n", + "70 4 2 4 42 2.2 \n", + "71 4 1 4 54 0.9 \n", + "\n", + " pre_film_VAS_Hopeless pre_film_VAS_Depressed pre_film_VAS_Fear \\\n", + "0 0.0 0.0 0.4 \n", + "1 0.7 0.5 0.8 \n", + "2 1.2 0.9 0.2 \n", + "3 1.0 0.6 5.1 \n", + "4 0.1 0.0 2.9 \n", + ".. ... ... ... \n", + "67 0.0 1.0 2.1 \n", + "68 0.9 1.0 0.0 \n", + "69 0.3 0.5 0.0 \n", + "70 5.0 2.3 0.0 \n", + "71 0.0 0.0 0.7 \n", + "\n", + " pre_film_VAS_Horror pre_film_VAS_Anxious ... \\\n", + "0 0.3 0.8 ... \n", + "1 0.2 0.2 ... \n", + "2 0.1 0.4 ... \n", + "3 0.4 0.5 ... \n", + "4 0.0 0.7 ... \n", + ".. ... ... ... \n", + "67 1.5 3.4 ... \n", + "68 0.0 0.0 ... \n", + "69 0.0 1.3 ... \n", + "70 0.0 0.0 ... \n", + "71 0.5 3.3 ... \n", + "\n", + " Day_Zero_Number_of_Intrusions Days_One_to_Seven_Number_of_Intrusions \\\n", + "0 2 4 \n", + "1 2 3 \n", + "2 5 6 \n", + "3 0 2 \n", + "4 5 3 \n", + ".. ... ... \n", + "67 2 1 \n", + "68 2 4 \n", + "69 3 4 \n", + "70 12 7 \n", + "71 3 4 \n", + "\n", + " Visual_Recognition_Memory_Test Verbal_Recognition_Memory_Test \\\n", + "0 15 18 \n", + "1 17 19 \n", + "2 12 21 \n", + "3 16 19 \n", + "4 14 22 \n", + ".. ... ... \n", + "67 15 20 \n", + "68 14 21 \n", + "69 18 24 \n", + "70 13 17 \n", + "71 17 21 \n", + "\n", + " Number_of_Provocation_Task_Intrusions Diary_Compliance \\\n", + "0 5 9 \n", + "1 4 9 \n", + "2 0 10 \n", + "3 0 8 \n", + "4 10 8 \n", + ".. ... ... \n", + "67 4 7 \n", + "68 6 8 \n", + "69 7 9 \n", + "70 3 7 \n", + "71 2 9 \n", + "\n", + " IES_R_Intrusion_subscale Tetris_Total_Score \\\n", + "0 0.62 9999 \n", + "1 0.62 9999 \n", + "2 0.50 9999 \n", + "3 0.50 9999 \n", + "4 1.00 9999 \n", + ".. ... ... \n", + "67 0.50 9999 \n", + "68 1.50 9999 \n", + "69 0.50 9999 \n", + "70 0.50 9999 \n", + "71 0.63 9999 \n", + "\n", + " Self_Rated_Tetris_Performance Tetris_Demand_Rating \n", + "0 9999.0 0 \n", + "1 9999.0 0 \n", + "2 9999.0 0 \n", + "3 9999.0 3 \n", + "4 9999.0 -7 \n", + ".. ... ... \n", + "67 9999.0 0 \n", + "68 9999.0 -5 \n", + "69 9999.0 0 \n", + "70 9999.0 -1 \n", + "71 9999.0 0 \n", + "\n", + "[72 rows x 28 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/James%20et%20al%202015%20Experiment%202%20Data%20Set.csv\"\n", + "\n", + "#Generare apndas data frame with the study data\n", + "df = pd.read_csv(url)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Set them of seaborn plots for nicer display (not APA, work that one out soon)\n", + "sns.set()\n", + "\n", + "#Genrate grd to dspaly th four separate condtions\n", + "g= sns.FacetGrid(df, row=\"Condition\");\n", + "\n", + "#Genrate the four hisogram plots for the 4 condtions\n", + "g.map(sns.distplot, \"Day_Zero_Number_of_Intrusions\");" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g= sns.FacetGrid(df, row=\"Condition\");\n", + "g.map(sns.distplot, \"Days_One_to_Seven_Number_of_Intrusions\");" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(df[\"Days_One_to_Seven_Number_of_Intrusions\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - Define the descriptive statistical model \\begin{align*}\n", + "y_{ik} &\\sim Normal(\\mu_k, \\sigma) \n", + "\\\\ \\mu_k &\\sim halfnormal(0, 100) \n", + "\\\\ \\sigma &\\sim halfnormal(0,1) \n", + "\\end{align*} " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 3 - Specifying priors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prior predictive checks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualising priors" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualise prior on mean parameter.\n", + "x = np.arange(0, 300, 0.001)\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(x, stats.halfnorm.pdf(x,loc=0, scale= 100));\n", + "plt.xlabel(\"prior on mu\");\n", + "\n", + "x = np.arange(0, 4, 0.001)\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(x, stats.halfnorm.pdf(x, scale = 1));\n", + "plt.xlabel(\"prior on sigma\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simulating data based on priors\n", + "\n", + "Following the visualisation of the priors for the parameters of the model to \n", + "check how they interact it is important to run prior predcitive check by \n", + "simulating data based on the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4 - Use Bayes rule" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stan model of Bayesian One-way ANOva" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "One_ANOVA = \"\"\"\n", + "data{\n", + "\n", + "int N; // Number od data points\n", + "vector[N] y; // Dependent varaible data\n", + "int K; // number of groups\n", + " // array of interger group indicator variables\n", + "int x[N];\n", + "\n", + "}\n", + "\n", + "parameters{\n", + "\n", + "// By specifying the lower bound the normal for the priors\n", + "// become half normal prior distributions\n", + "real sigma; // homoscedastic standard deviation\n", + "vector[K] mu; // mean parameter for each group\n", + "}\n", + "\n", + "model{\n", + "\n", + "//priors \n", + "mu ~ normal(0, 100); // prior on each group\n", + "sigma ~ normal(0,1);\n", + "\n", + "//Likelihood\n", + "y ~ normal(mu[x], sigma);\n", + "\n", + "}\n", + "\n", + "generated quantities{\n", + "\n", + "// Calculate the pairwise comparisons\n", + "real group1vsgroup2 = mu[1] - mu[2];\n", + "real group1vsgroup3 = mu[1] - mu[3];\n", + "real group1vsgroup4 = mu[1] - mu[4]; \n", + "real group2vsgroup3 = mu[2] - mu[3]; \n", + "real group2vsgroup4 = mu[2] - mu[4];\n", + "real group3vsgroup4 = mu[3] - mu[4];\n", + "\n", + "// PPC\n", + "vector[N] yrep; \n", + "\n", + "for (i in 1:N) {\n", + "yrep[i] = normal_rng(mu[x[i]], sigma);\n", + " }\n", + "}\n", + " \"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_13583df338ad8ac48f591a9a1732ef31 NOW.\n" + ] + } + ], + "source": [ + "sm = ps.StanModel(model_code=One_ANOVA)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "data = {\"N\": len(df),\n", + " \"y\": df[\"Day_Zero_Number_of_Intrusions\"].values,\n", + " \"K\": max(df[\"Condition\"].values,\n", + " \"x\": df[\"Condition\"].values\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "fit = sm.sampling(data = data, iter = 2000, chains=4, seed= 302675)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      meanse_meansd2.5%25%50%75%97.5%n_effRhat
      sigma2.2098920.0026400.1770521.9038112.0853172.1963232.3205282.5984724497.5311011.000250
      mu[1]3.5463490.0079590.5129452.5295493.1960063.5487203.8891034.5466144153.1813931.000008
      mu[2]3.1107180.0078690.5023322.1309422.7721753.1138793.4399884.1047194074.6309400.999582
      mu[3]3.1742390.0075860.5182212.1461482.8274083.1656663.5229974.1848804666.5200740.999520
      mu[4]3.4494290.0073770.5280562.3894423.0926443.4487763.8085704.4787165123.7119010.999797
      .................................
      yrep[69]3.4979800.0365292.259036-0.8200901.9773373.4532684.9971608.0642313824.4775900.999764
      yrep[70]3.3895940.0360212.261581-1.0662201.8789903.3823864.8976877.9275993941.9510541.000645
      yrep[71]3.4938970.0351612.304058-1.0337941.9233993.5239385.0515268.0140154293.9303100.999829
      yrep[72]3.4404620.0361972.234919-0.8933351.9238713.4501574.9077897.7954803812.2629280.999764
      lp__-91.7432880.0390101.612658-95.733545-92.593771-91.386903-90.568616-89.5792481708.9475091.001719
      \n", + "

      84 rows × 10 columns

      \n", + "
      " + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% 50% \\\n", + "sigma 2.209892 0.002640 0.177052 1.903811 2.085317 2.196323 \n", + "mu[1] 3.546349 0.007959 0.512945 2.529549 3.196006 3.548720 \n", + "mu[2] 3.110718 0.007869 0.502332 2.130942 2.772175 3.113879 \n", + "mu[3] 3.174239 0.007586 0.518221 2.146148 2.827408 3.165666 \n", + "mu[4] 3.449429 0.007377 0.528056 2.389442 3.092644 3.448776 \n", + "... ... ... ... ... ... ... \n", + "yrep[69] 3.497980 0.036529 2.259036 -0.820090 1.977337 3.453268 \n", + "yrep[70] 3.389594 0.036021 2.261581 -1.066220 1.878990 3.382386 \n", + "yrep[71] 3.493897 0.035161 2.304058 -1.033794 1.923399 3.523938 \n", + "yrep[72] 3.440462 0.036197 2.234919 -0.893335 1.923871 3.450157 \n", + "lp__ -91.743288 0.039010 1.612658 -95.733545 -92.593771 -91.386903 \n", + "\n", + " 75% 97.5% n_eff Rhat \n", + "sigma 2.320528 2.598472 4497.531101 1.000250 \n", + "mu[1] 3.889103 4.546614 4153.181393 1.000008 \n", + "mu[2] 3.439988 4.104719 4074.630940 0.999582 \n", + "mu[3] 3.522997 4.184880 4666.520074 0.999520 \n", + "mu[4] 3.808570 4.478716 5123.711901 0.999797 \n", + "... ... ... ... ... \n", + "yrep[69] 4.997160 8.064231 3824.477590 0.999764 \n", + "yrep[70] 4.897687 7.927599 3941.951054 1.000645 \n", + "yrep[71] 5.051526 8.014015 4293.930310 0.999829 \n", + "yrep[72] 4.907789 7.795480 3812.262928 0.999764 \n", + "lp__ -90.568616 -89.579248 1708.947509 1.001719 \n", + "\n", + "[84 rows x 10 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit_df" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      0123456789...3990399139923993399439953996399739983999
      04.1118425.0124841.1135941.7794709.4937080.2387794.4338384.1028514.7298022.643693...6.2475874.5035123.2994811.8345836.6873044.5623515.0902762.0505070.4607023.818417
      11.8416874.6163773.7613165.2168682.6415822.1770237.4326026.0847534.2397415.109619...3.8966097.055100-1.9191411.8869161.9587214.7697203.1528193.4346245.5234025.290638
      23.4116161.4969522.6144422.1922998.3249141.9307481.6630805.8571244.5181461.558030...3.9175526.0627201.1766992.4086873.447087-1.3478285.777794-4.9316373.4511692.157250
      34.8285270.7874162.2242585.590150-0.6339705.4882162.7602532.6313532.8799274.583480...4.4147625.3724504.3484174.6044497.7741576.0554761.7101351.2866020.3293308.133375
      41.9158746.0044771.0054266.4001743.9941923.9386953.2919920.8810424.5391864.388841...7.6555382.8750933.7067070.525722-0.3818474.1971750.0231632.1952646.3491832.791978
      ..................................................................
      673.3736402.0622113.8392202.2120302.6665281.0223342.0733026.7852500.8518315.977468...2.8066504.9615905.4901802.0550035.636176-1.0013835.5004707.9869044.7831156.678640
      684.4819342.3856013.0071443.7040170.5228512.7607661.9784901.547877-0.0164805.343790...2.7272450.6379677.2243511.9332393.1414384.5241104.3409783.1091294.6996848.262018
      692.8636393.8823490.4560382.9887722.5057336.7018333.4705462.3287265.8868133.670698...2.2248593.3504021.702927-0.7559673.7752302.4454355.2379283.9827834.6772740.054098
      702.1184204.4822854.1992140.5373752.1370161.2712971.608497-0.0293933.3082981.831304...2.3877797.2603102.8266111.3099634.8914075.4760963.6161683.9211760.8683003.411840
      713.7435451.8958344.6036377.3760354.6472265.5555122.1991592.1877356.5739445.242526...1.3512177.8503315.2883023.027824-0.5311953.7898593.5501241.6707184.3807042.915631
      \n", + "

      72 rows × 4000 columns

      \n", + "
      " + ], + "text/plain": [ + " 0 1 2 3 4 5 6 \\\n", + "0 4.111842 5.012484 1.113594 1.779470 9.493708 0.238779 4.433838 \n", + "1 1.841687 4.616377 3.761316 5.216868 2.641582 2.177023 7.432602 \n", + "2 3.411616 1.496952 2.614442 2.192299 8.324914 1.930748 1.663080 \n", + "3 4.828527 0.787416 2.224258 5.590150 -0.633970 5.488216 2.760253 \n", + "4 1.915874 6.004477 1.005426 6.400174 3.994192 3.938695 3.291992 \n", + ".. ... ... ... ... ... ... ... \n", + "67 3.373640 2.062211 3.839220 2.212030 2.666528 1.022334 2.073302 \n", + "68 4.481934 2.385601 3.007144 3.704017 0.522851 2.760766 1.978490 \n", + "69 2.863639 3.882349 0.456038 2.988772 2.505733 6.701833 3.470546 \n", + "70 2.118420 4.482285 4.199214 0.537375 2.137016 1.271297 1.608497 \n", + "71 3.743545 1.895834 4.603637 7.376035 4.647226 5.555512 2.199159 \n", + "\n", + " 7 8 9 ... 3990 3991 3992 3993 \\\n", + "0 4.102851 4.729802 2.643693 ... 6.247587 4.503512 3.299481 1.834583 \n", + "1 6.084753 4.239741 5.109619 ... 3.896609 7.055100 -1.919141 1.886916 \n", + "2 5.857124 4.518146 1.558030 ... 3.917552 6.062720 1.176699 2.408687 \n", + "3 2.631353 2.879927 4.583480 ... 4.414762 5.372450 4.348417 4.604449 \n", + "4 0.881042 4.539186 4.388841 ... 7.655538 2.875093 3.706707 0.525722 \n", + ".. ... ... ... ... ... ... ... ... \n", + "67 6.785250 0.851831 5.977468 ... 2.806650 4.961590 5.490180 2.055003 \n", + "68 1.547877 -0.016480 5.343790 ... 2.727245 0.637967 7.224351 1.933239 \n", + "69 2.328726 5.886813 3.670698 ... 2.224859 3.350402 1.702927 -0.755967 \n", + "70 -0.029393 3.308298 1.831304 ... 2.387779 7.260310 2.826611 1.309963 \n", + "71 2.187735 6.573944 5.242526 ... 1.351217 7.850331 5.288302 3.027824 \n", + "\n", + " 3994 3995 3996 3997 3998 3999 \n", + "0 6.687304 4.562351 5.090276 2.050507 0.460702 3.818417 \n", + "1 1.958721 4.769720 3.152819 3.434624 5.523402 5.290638 \n", + "2 3.447087 -1.347828 5.777794 -4.931637 3.451169 2.157250 \n", + "3 7.774157 6.055476 1.710135 1.286602 0.329330 8.133375 \n", + "4 -0.381847 4.197175 0.023163 2.195264 6.349183 2.791978 \n", + ".. ... ... ... ... ... ... \n", + "67 5.636176 -1.001383 5.500470 7.986904 4.783115 6.678640 \n", + "68 3.141438 4.524110 4.340978 3.109129 4.699684 8.262018 \n", + "69 3.775230 2.445435 5.237928 3.982783 4.677274 0.054098 \n", + "70 4.891407 5.476096 3.616168 3.921176 0.868300 3.411840 \n", + "71 -0.531195 3.789859 3.550124 1.670718 4.380704 2.915631 \n", + "\n", + "[72 rows x 4000 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yrep_df = pd.DataFrame(fit['yrep']).T.iloc[:,:]\n", + "yrep_df" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(yrep_df[50]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit-visualisations - Bayesian one sample Z-test\n", + "\n", + "## Posterior distributions plots" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Using arviz built in Bayesian anlysis plot_posterior funcion to display the posterior probabilty distributions\n", + "# for the fitted model parameter estimates.\n", + "az.plot_posterior(fit, var_names=(\"mu\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autocorrelation plots" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Using the arviz package the autocorrelation of the 4 MCMC chains can be plotted.\n", + "az.plot_autocorr(fit, var_names=(\"mu\", 'sigma'));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MCMC traceplots" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(fit,var_names=(\"mu\", \"sigma\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MCMC traceplots show good mixing of the chains, showing a \"fuzzy caterpillar\"." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 5 - Posterior predictive checks" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "#Convert pystan fit object to IO for Arviz ppc functions.\n", + "postFit = az.from_pystan(\n", + " posterior= fit,\n", + " posterior_predictive='yrep',\n", + " observed_data=[\"y\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot posterior simulated data sets for posterior predictive check\n", + "az.plot_ppc(postFit, data_pairs = {\"y\" : \"yrep\"}, num_pp_samples= 100);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PPC shows a poor model fit with the data and that the data is likely not normally distributed. see caveat section for fuller explanation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the Bayesian one sample t-test equivalent\n", + "\n", + "As Kruschke (2015) correctly points out there is not standard formula or presentation method for reuslts in journal article like the APA guide for reporting of standard frequentist analyses. It is likely there never will be, because as McElreath (2020) explains, Bayesian data analysis is more of an engineering approach to the problem and the resulting model that is fit will be analysis specific. In addition, as Gabry et al, (2019) argue visualisations maybe even more key, therfore is arguable that all the visualistions above would have to be included with any write up. Anyway, the write up below generally follows the advice of Kruscke (2015) chapter 25. In any application though it comes down to the problem to be described an the audience that needs to be convinced.


      \n", + "\n", + "

      Write up


      " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Final Caveat\n", + "The poor model fit above and is a result of OSL pedagogocially (regular ANOVA is the domiant analysis method) suggesting to model this data with a one-way ANOVA when the data is a count variable as such an alternative modelling option using a poisson likelihood would be more appropriate. See, poisson notebook for this alternative analysis. A benefit of this notebook though is that it shows that modelling evetyhing as normal when it is inappropriate and despite being common should be avodided and the Bayesian worflow with posterior predictive checks exposed flaws in the model and thus helps us avoid of mindless (Gigerenzer, 2004) statistical testing. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389-402.\n", + "\n", + "Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587-606.\n", + "\n", + "James, E. L., Bonsall, M. B., Hoppitt, L., Tunbridge, E. M., Geddes, J. R., Milton, A. L., & Holmes, E. A. (2015). Computer game play reduces intrusive memories of experimental trauma via reconsolidation-update mechanisms. Psychological Science, 26, 1201-1215.\n", + " \n", + "Kruschke, J. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS and Stan. Oxford, England: Academic Press. \n", + " \n", + "McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan.Boca Raton: CRC Press." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "341.306px" + }, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Data/.Rhistory b/wip/Data/.Rhistory new file mode 100644 index 0000000..c5d6a9a --- /dev/null +++ b/wip/Data/.Rhistory @@ -0,0 +1,512 @@ +as.integer(factor(x)) +15/7 +-(4/3) +-(4/5) +-(4/5)*5 +-4/5*5 +-2*-10.5 +-11/8 +-1.375*8 +-1*-1 +-1*-.3 +contr.poly() +contr.poly(3) +M <- matrix(1,3,4,5) +< +M +t(m) +t(M) +## Simulate Bayesian Binomial updating +sim_bayes<-function(p=0.5,N=10,y_lim=15) +{ +success<-0 +curve(dbeta(x,1,1),xlim=c(0,1),ylim=c(0,y_lim),xlab='p',ylab='Posterior Density',lty=2) +legend('topright',legend=c('Prior','Updated Posteriors','Final Posterior'),lty=c(2,1,1),col=c('black','black','red')) +for(i in 1:N) +{ +if(runif(1,0,1)<=p) +success<-success+1 +curve(dbeta(x,success+1,(i-success)+1),add=TRUE) +print(paste(success,"successes and ",i-success," failures")) +} +curve(dbeta(x,success+1,(i-success)+1),add=TRUE,col='red',lwd=1.5) +} +sim_bayes(p=0.6,N=90) +library(dplyr) +num_trials <- 10e6 +simulations <- data_frame( +true_average = rbeta(num_trials, 81, 219), +hits = rbinom(num_trials, 300, true_average) +) +simulations <- tibble( +true_average = rbeta(num_trials, 81, 219), +hits = rbinom(num_trials, 300, true_average) +) +simulations +hit_100 <- simulations %>% +filter(hits == 100) +hist(hit_100$true_average) +#Visualise the results with a histogram +hist(hit_100$true_average, density = T) +#Visualise the results with a histogram +hist(hit_100$true_average, density = TRUE) +#Visualise the results with a histogram +hist(hit_100$true_average, density = TRUE) +#Visualise the results with a histogram +hist(hit_100$true_average) +simulations %>% +filter(hits %in% c(60, 80, 100)) %>% +ggplot(aes(true_average, color = factor(hits))) + +geom_density() + +labs(x = "True average of players with H hits / 300 at-bats", +color = "H") +library(ggplot2) +simulations %>% +filter(hits %in% c(60, 80, 100)) %>% +ggplot(aes(true_average, color = factor(hits))) + +geom_density() + +labs(x = "True average of players with H hits / 300 at-bats", +color = "H") +library(tidyverse) +library(Lahman) +install.packages("Lahman") +library(Lahman) +career <- Batting %>% +filter(AB > 0) %>% +anti_join(Pitching, by = "playerID") %>% +group_by(playerID) %>% +summarize(H = sum(H), AB = sum(AB)) %>% +mutate(average = H / AB) +career <- Master %>% +tbl_df() %>% +dplyr::select(playerID, nameFirst, nameLast) %>% +unite(name, nameFirst, nameLast, sep = " ") %>% +inner_join(career, by = "playerID") %>% +dplyr::select(-playerID) +career <- Master %>% +tibble::as_tibble() %>% +dplyr::select(playerID, nameFirst, nameLast) %>% +unite(name, nameFirst, nameLast, sep = " ") %>% +inner_join(career, by = "playerID") %>% +dplyr::select(-playerID) +View(career) +View(career) +nameFirst +career <- Master %>% +tibble::as_tibble() %>% +dplyr::select(playerID, nameFirst, nameLast) %>% +unite(name, nameFirst, nameLast, sep = " ") %>% +inner_join(career, by = "playerID") %>% +dplyr::select(-playerID) +Batting +career <- Batting %>% +filter(AB > 0) %>% +anti_join(Pitching, by = "playerID") %>% +group_by(playerID) %>% +summarize(H = sum(H), AB = sum(AB)) %>% +mutate(average = H / AB) +Master +career <- Master %>% +tibble::as_tibble() %>% +dplyr::select(playerID, nameFirst, nameLast) %>% +unite(name, nameFirst, nameLast, sep = " ") %>% +inner_join(career, by = "playerID") %>% +dplyr::select(-playerID) +View(career) +num_trials <- 10e6 +simulations <- tibble( +true_average = rbeta(num_trials, 81, 219), +hits = rbinom(num_trials, 300, true_average) +) +simulations +hit_100 <- simulations %>% +filter(hits == 100) +#Visualise the results with a histogram +hist(hit_100$true_average) +simulations %>% +filter(hits %in% c(60, 80, 100)) %>% +ggplot(aes(true_average, color = factor(hits))) + +geom_density() + +labs(x = "True average of players with H hits / 300 at-bats", +color = "H") +career <- Batting %>% +filter(AB > 0) %>% +anti_join(Pitching, by = "playerID") %>% +group_by(playerID) %>% +summarize(H = sum(H), AB = sum(AB)) %>% +mutate(average = H / AB) +career <- Master %>% +tibble::as_tibble() %>% +dplyr::select(playerID, nameFirst, nameLast) %>% +unite(name, nameFirst, nameLast, sep = " ") %>% +inner_join(career, by = "playerID") %>% +dplyr::select(-playerID) +View(career) +career[max(),] +career$average +career$average %>% max() +career$average %>% min() +library(stats4) +career_filtered <- career %>% +filter(AB > 500) +# log-likelihood function +ll <- function(alpha, beta) { +x <- career_filtered$H +total <- career_filtered$AB +-sum(VGAM::dbetabinom.ab(x, total, alpha, beta, log = TRUE)) +} +m <- mle(ll, start = list(alpha = 1, beta = 10), method = "L-BFGS-B", +lower = c(0.0001, .1)) +ab <- coef(m) +alpha0 <- ab[1] +beta0 <- ab[2] +# log-likelihood function +ll <- function(alpha, beta) { +x <- career_filtered$H +total <- career_filtered$AB +-sum(VGAM::dbetabinom.ab(x, total, alpha, beta, log = TRUE)) +} +m <- mle(ll, start = list(alpha = 1, beta = 10), method = "L-BFGS-B", +lower = c(0.0001, .1)) +m <- mle(ll, start = list(alpha = 1, beta = 10), method = "L-BFGS-B", +lower = c(0.0001, .1)) +library(tidyverse) +library(ggplot2) +install.packages("Lahman") +library(Lahman) +num_trials <- 10e6 +simulations <- tibble( +true_average = rbeta(num_trials, 81, 219), +hits = rbinom(num_trials, 300, true_average) +) +simulations +hit_100 <- simulations %>% +filter(hits == 100) +#Visualise the results with a histogram +hist(hit_100$true_average) +simulations %>% +filter(hits %in% c(60, 80, 100)) %>% +ggplot(aes(true_average, color = factor(hits))) + +geom_density() + +labs(x = "True average of players with H hits / 300 at-bats", +color = "H") +#Chapter 3 Bayesian estimation, binomal mdoel. +career <- Batting %>% +filter(AB > 0) %>% +anti_join(Pitching, by = "playerID") %>% +group_by(playerID) %>% +summarize(H = sum(H), AB = sum(AB)) %>% +mutate(average = H / AB) +career <- Master %>% +tibble::as_tibble() %>% +dplyr::select(playerID, nameFirst, nameLast) %>% +unite(name, nameFirst, nameLast, sep = " ") %>% +inner_join(career, by = "playerID") %>% +dplyr::select(-playerID) +career$average %>% min() +#Estimtate prior from data. EB is an approximation to Full +# bayesian methods which becomes more accuracte as n increases. +library(stats4) +career_filtered <- career %>% +filter(AB > 500) +# log-likelihood function +ll <- function(alpha, beta) { +x <- career_filtered$H +total <- career_filtered$AB +-sum(VGAM::dbetabinom.ab(x, total, alpha, beta, log = TRUE)) +} +m <- mle(ll, start = list(alpha = 1, beta = 10), method = "L-BFGS-B", +lower = c(0.0001, .1)) +ab <- coef(m) +alpha0 <- ab[1] +beta0 <- ab[2] +install.packages("Lahman") +num_trials <- 10e6 +simulations <- tibble( +true_average = rbeta(num_trials, 81, 219), +hits = rbinom(num_trials, 300, true_average) +) +simulations +hit_100 <- simulations %>% +filter(hits == 100) +#Visualise the results with a histogram +hist(hit_100$true_average) +simulations %>% +filter(hits %in% c(60, 80, 100)) %>% +ggplot(aes(true_average, color = factor(hits))) + +geom_density() + +labs(x = "True average of players with H hits / 300 at-bats", +color = "H") +career <- Batting %>% +filter(AB > 0) %>% +anti_join(Pitching, by = "playerID") %>% +group_by(playerID) %>% +summarize(H = sum(H), AB = sum(AB)) %>% +mutate(average = H / AB) +career <- Master %>% +tibble::as_tibble() %>% +dplyr::select(playerID, nameFirst, nameLast) %>% +unite(name, nameFirst, nameLast, sep = " ") %>% +inner_join(career, by = "playerID") %>% +dplyr::select(-playerID) +career$average %>% min() +library(stats4) +career_filtered <- career %>% +filter(AB > 500) +# log-likelihood function +ll <- function(alpha, beta) { +x <- career_filtered$H +total <- career_filtered$AB +-sum(VGAM::dbetabinom.ab(x, total, alpha, beta, log = TRUE)) +} +m <- mle(ll, start = list(alpha = 1, beta = 10), method = "L-BFGS-B", +lower = c(0.0001, .1)) +ab <- coef(m) +alpha0 <- ab[1] +beta0 <- ab[2] +dbetabinom.ab +undebug(ls) +(101.2-100)/21.2 +x <- matrix(c( .8, 102, +1.0, 98, +.5, 100, +.9, 105, +.7, 103, +.4, 110, +1.2, 99, +1.4, 87, +.6, 113, +1.1, 89, +1.3, 93, +.8, 102, +1.0, 98, +.5, 100, +.9, 105, +.7, 103, +.4, 110, +1.2, 99, +1.4, 87, +.6, 113, +1.1, 89, +1.3, 93), nrow=22,ncol=2,byrow=T) +View(x) +plot(x[,1],x[,2]) +# clears workspace: +rm(list=ls()) +library(rstan) +#### Notes to Stan model ####################################################### +## 1) Multivariate normal distribution in Stan uses covariance matrix instead of +## precision matrix. +## 2) Multivariate normal distribution can be (and is) also vectorized. +## 3) Warnings may occur during sampling, ignore them. +################################################################################ +model <- " +// Pearson Correlation +data { +int n; +vector[2] x[n]; +} +parameters { +vector[2] mu; +vector[2] lambda; +real r; +} +transformed parameters { +vector[2] sigma; +cov_matrix[2] T; +// Reparameterization +sigma[1] <- inv_sqrt(lambda[1]); +sigma[2] <- inv_sqrt(lambda[2]); +T[1,1] <- square(sigma[1]); +T[1,2] <- r * sigma[1] * sigma[2]; +T[2,1] <- r * sigma[1] * sigma[2]; +T[2,2] <- square(sigma[2]); +} +model { +// Priors +mu ~ normal(0, inv_sqrt(.001)); +lambda ~ gamma(.001, .001); +// Data +x ~ multi_normal(mu, T); +}" +# Choose a dataset: +dataset <- 1 +# The datasets: +if (dataset == 1) { +x <- matrix(c( .8, 102, +1.0, 98, +.5, 100, +.9, 105, +.7, 103, +.4, 110, +1.2, 99, +1.4, 87, +.6, 113, +1.1, 89, +1.3, 93), nrow=11, ncol=2, byrow=T) +} +if (dataset == 2) { +x <- matrix(c( .8, 102, +1.0, 98, +.5, 100, +.9, 105, +.7, 103, +.4, 110, +1.2, 99, +1.4, 87, +.6, 113, +1.1, 89, +1.3, 93, +.8, 102, +1.0, 98, +.5, 100, +.9, 105, +.7, 103, +.4, 110, +1.2, 99, +1.4, 87, +.6, 113, +1.1, 89, +1.3, 93), nrow=22,ncol=2,byrow=T) +} +n <- nrow(x) # number of people/units measured +data <- list(x=x, n=n) # to be passed on to Stan +myinits <- list( +list(r=0, mu=c(0, 0), lambda=c(1, 1))) +# parameters to be monitored: +parameters <- c("r", "mu", "sigma") +# The following command calls Stan with specific options. +# For a detailed description type "?rstan". +samples <- stan(model_code=model, +data=data, +init=myinits, # If not specified, gives random inits +pars=parameters, +iter=10000, +chains=1, +thin=1, +# warmup = 100, # Stands for burn-in; Default = iter/2 +# seed = 123 # Setting seed; Default is random seed +) +r <- extract(samples)$r +#Frequentist point-estimate of r: +freq.r <- cor(x[,1],x[,2]) +#make the two panel plot: +windows(width=9,height=6) #this command works only under Windows! +layout(matrix(c(1,2),1,2)) +layout.show(2) +#some plotting options to make things look better: +par(cex.main=1.5, mar=c(5, 6, 4, 5) + 0.1, mgp=c(3.5, 1, 0), cex.lab=1.5, +font.lab=2, cex.axis=1.3, bty = "n", las=1) +# data panel: +plot(x[,1],x[,2], type="p", pch=19, cex=1) +# correlation panel: +plot(density(r, from=-1,to=1), main="", ylab="Posterior Density", +xlab="Correlation", lwd=2) +lines(c(freq.r, freq.r), c(0,100), lwd=2, lty=2) +# clears workspace: +rm(list=ls()) +library(rstan) +#### Notes to Stan model ####################################################### +## 1) Multivariate normal distribution in Stan uses covariance matrix instead of +## precision matrix. +## 2) Multivariate normal distribution can be (and is) also vectorized. +## 3) Warnings may occur during sampling, ignore them. +################################################################################ +model <- " +// Pearson Correlation +data { +int n; +vector[2] x[n]; +} +parameters { +vector[2] mu; +vector[2] lambda; +real r; +} +transformed parameters { +vector[2] sigma; +cov_matrix[2] T; +// Reparameterization +sigma[1] <- inv_sqrt(lambda[1]); +sigma[2] <- inv_sqrt(lambda[2]); +T[1,1] <- square(sigma[1]); +T[1,2] <- r * sigma[1] * sigma[2]; +T[2,1] <- r * sigma[1] * sigma[2]; +T[2,2] <- square(sigma[2]); +} +model { +// Priors +mu ~ normal(0, inv_sqrt(.001)); +lambda ~ gamma(.001, .001); +// Data +x ~ multi_normal(mu, T); +}" +# Choose a dataset: +dataset <- 1 +# The datasets: +if (dataset == 1) { +x <- matrix(c( .8, 102, +1.0, 98, +.5, 100, +.9, 105, +.7, 103, +.4, 110, +1.2, 99, +1.4, 87, +.6, 113, +1.1, 89, +1.3, 93), nrow=11, ncol=2, byrow=T) +} +if (dataset == 2) { +x <- matrix(c( .8, 102, +1.0, 98, +.5, 100, +.9, 105, +.7, 103, +.4, 110, +1.2, 99, +1.4, 87, +.6, 113, +1.1, 89, +1.3, 93, +.8, 102, +1.0, 98, +.5, 100, +.9, 105, +.7, 103, +.4, 110, +1.2, 99, +1.4, 87, +.6, 113, +1.1, 89, +1.3, 93), nrow=22,ncol=2,byrow=T) +} +n <- nrow(x) # number of people/units measured +data <- list(x=x, n=n) # to be passed on to Stan +myinits <- list( +list(r=0, mu=c(0, 0), lambda=c(1, 1))) +# parameters to be monitored: +parameters <- c("r", "mu", "sigma") +# The following command calls Stan with specific options. +# For a detailed description type "?rstan". +samples <- stan(model_code=model, +data=data, +init=myinits, # If not specified, gives random inits +pars=parameters, +iter=10000, +chains=1, +thin=1, +# warmup = 100, # Stands for burn-in; Default = iter/2 +# seed = 123 # Setting seed; Default is random seed +) +print(samples) +setwd("~/Repositories/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/ +/Data") +setwd("~/Repositories/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/ +Data") +setwd("~/Repositories/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/Data") +df <- read.csv(file= "Crime.csv") +df <- read.csv(file= "Crime.csv") +df <- read.csv(file= "Crime.csv",header = T) +View(df) +cor(df$ï..CrimeRate, df$BelowWage10) diff --git a/wip/Data/Atir Rosenzweig Dunning 2015.csv b/wip/Data/Atir Rosenzweig Dunning 2015.csv new file mode 100644 index 0000000..529ed5f --- /dev/null +++ b/wip/Data/Atir Rosenzweig Dunning 2015.csv @@ -0,0 +1,203 @@ +id,order_of_tasks,self_perceived_knowledge,overclaiming_proportion,accuracy,FINRA_score +1,1,5.5,.444444444,.25,4 +7,1,4.5,.555555556,.194444444,4 +10,1,3.5,.166666667,.347222222,5 +12,1,6,.722222222,-.055555556,4 +14,1,2.5,.388888889,.166666667,3 +17,1,7,.944444444,-.041666667,4 +18,1,6.5,0,.847222222,5 +20,1,4,.111111111,.5,4 +21,1,4.5,.666666667,-.083333333,2 +22,1,7,.777777778,.152777778,5 +25,1,4,0,.611111111,5 +27,1,3.5,.166666667,.152777778,0 +29,1,4,.111111111,.166666667,3 +30,1,5,.222222222,.611111111,5 +31,1,5,.444444444,.222222222,5 +33,1,5,.111111111,.486111111,4 +34,1,3.5,.111111111,.166666667,2 +35,1,3.5,0,.25,4 +37,1,6,.5,.291666667,5 +38,1,4.5,.333333333,.416666667,4 +39,1,5,.833333333,.027777778,1 +40,1,6,.5,.180555556,2 +41,1,5,.444444444,.222222222,4 +46,1,4.5,.555555556,.291666667,3 +48,1,6,.444444444,.347222222,5 +49,1,3.5,.222222222,.402777778,3 +50,1,4.5,.111111111,.486111111,4 +51,1,5,.166666667,.486111111,5 +54,1,4.5,.222222222,.222222222,3 +55,1,3.5,.333333333,.027777778,5 +58,1,4.5,.5,.083333333,4 +60,1,4,.388888889,.319444444,4 +62,1,3,.055555556,.277777778,3 +63,1,4,.277777778,.222222222,4 +64,1,4,.333333333,.263888889,4 +66,1,3,.111111111,.180555556,2 +72,1,4.5,.111111111,.513888889,4 +75,1,5,.388888889,.208333333,4 +76,1,3,.166666667,.527777778,4 +77,1,4.5,.666666667,.111111111,4 +78,1,5.5,.611111111,-.125,1 +80,1,6,.555555556,.236111111,5 +83,1,5,.166666667,.472222222,2 +87,1,4.5,.166666667,.444444444,3 +89,1,4.5,.722222222,.055555556,3 +90,1,5,.055555556,.847222222,5 +91,1,5.5,.722222222,.083333333,4 +94,1,3,.333333333,.138888889,4 +95,1,1,0,.319444444,4 +96,1,4,.388888889,.166666667,3 +99,1,5.5,.444444444,.041666667,4 +100,1,4.5,.444444444,.263888889,4 +101,1,6,.277777778,.305555556,4 +102,1,6.5,.555555556,.152777778,5 +104,1,4.5,.333333333,.138888889,3 +107,1,4.5,.111111111,.652777778,5 +109,1,3.5,.222222222,.097222222,2 +111,1,7,.944444444,.041666667,5 +114,1,6,.611111111,.222222222,5 +117,1,4.5,.555555556,.027777778,5 +121,1,3,.444444444,.027777778,2 +122,1,4.5,0,.527777778,2 +125,1,4,.5,.111111111,4 +131,1,6,.555555556,.333333333,4 +133,1,4.5,.222222222,.305555556,4 +134,1,6,.444444444,.388888889,3 +135,1,5,.111111111,.138888889,4 +137,1,5.5,.5,.333333333,5 +139,1,4.5,.5,.25,3 +142,1,6.5,.444444444,.513888889,5 +143,1,4.5,.277777778,.208333333,2 +144,1,4,.111111111,.416666667,4 +146,1,6,.055555556,.638888889,4 +147,1,4,.166666667,.180555556,4 +150,1,6,.5,.069444444,4 +151,1,5,.166666667,.277777778,3 +152,1,5,.055555556,.513888889,5 +153,1,5,.833333333,-.027777778,4 +154,1,4.5,.666666667,-.027777778,1 +158,1,3.5,.444444444,.277777778,5 +161,1,4.5,.055555556,.5,4 +163,1,5,.222222222,.5,5 +166,1,7,.055555556,.930555556,5 +169,1,3.5,.222222222,.25,5 +170,1,3.5,0,.25,1 +172,1,6,.555555556,.25,5 +173,1,4,0,.569444444,3 +174,1,6,.277777778,.527777778,4 +175,1,6,.5,.416666667,5 +176,1,5,.388888889,.305555556,3 +177,1,3,.166666667,.236111111,3 +182,1,2.5,0,.277777778,1 +183,1,4.5,.5,.347222222,5 +186,1,4,.055555556,.444444444,4 +190,1,4,.388888889,.083333333,4 +191,1,5.5,.777777778,.083333333,5 +192,1,5,.555555556,-.055555556,5 +195,1,3.5,0,.291666667,4 +196,1,4.5,.777777778,-.041666667,1 +198,1,5,.055555556,.736111111,4 +201,1,4.5,.333333333,.319444444,3 +2,2,4,.388888889,.319444444,4 +3,2,3,.055555556,.222222222,4 +4,2,2,.111111111,.152777778,2 +5,2,3.5,.333333333,.055555556,4 +6,2,4,.111111111,.375,3 +8,2,3,0,.486111111,4 +9,2,4,.111111111,.263888889,4 +11,2,3.5,.055555556,.527777778,4 +13,2,3.5,.277777778,.555555556,5 +15,2,4.5,0,.541666667,5 +16,2,3.5,.277777778,.263888889,3 +19,2,5,.277777778,.402777778,3 +23,2,6.5,.222222222,.569444444,5 +24,2,5,.833333333,.013888889,1 +26,2,4,.555555556,-.041666667,0 +28,2,3.5,.055555556,.430555556,4 +32,2,4,0,.5,4 +36,2,5,.388888889,.347222222,4 +42,2,4,.166666667,.361111111,4 +43,2,4,.388888889,.458333333,3 +44,2,1,.055555556,.472222222,4 +45,2,4.5,.444444444,.069444444,4 +47,2,4,.277777778,.236111111,3 +52,2,4,0,.833333333,5 +53,2,4.5,.388888889,.305555556,4 +56,2,5,.722222222,.166666667,4 +57,2,5.5,.5,-.041666667,3 +59,2,4,.166666667,.333333333,4 +61,2,2,.166666667,.166666667,3 +65,2,5,.055555556,.625,4 +67,2,5,.222222222,.333333333,3 +68,2,4.5,.333333333,.152777778,3 +69,2,5,.222222222,.486111111,5 +70,2,3.5,.055555556,.722222222,4 +71,2,5.5,.666666667,.055555556,4 +73,2,5,.055555556,.555555556,4 +74,2,4,.222222222,.375,5 +79,2,4,.111111111,.458333333,4 +81,2,1,.055555556,.361111111,1 +82,2,4,.388888889,.166666667,3 +84,2,4,.111111111,.472222222,4 +85,2,5.5,.166666667,.5,4 +86,2,4.5,.166666667,.388888889,4 +88,2,3,0,.361111111,2 +92,2,6,.388888889,.277777778,3 +93,2,5.5,.222222222,.208333333,4 +97,2,4.5,.333333333,.486111111,3 +98,2,6,.722222222,-.069444444,4 +103,2,5,.444444444,.194444444,5 +105,2,4.5,.444444444,.263888889,5 +106,2,3,.055555556,.291666667,4 +108,2,4,.333333333,.222222222,4 +110,2,5,.333333333,.5,4 +112,2,5,.166666667,.333333333,5 +113,2,3.5,.222222222,.333333333,4 +115,2,4.5,.388888889,.25,4 +116,2,4,.388888889,-.013888889,3 +118,2,4.5,.166666667,.583333333,3 +119,2,5.5,.388888889,.263888889,4 +120,2,5.5,.222222222,.319444444,5 +123,2,6,.666666667,.319444444,5 +124,2,2.5,.444444444,-.027777778,3 +126,2,5.5,.555555556,.25,5 +127,2,3,.111111111,.222222222,3 +128,2,3.5,.333333333,.319444444,3 +129,2,5.5,.333333333,.513888889,5 +130,2,2.5,.222222222,.097222222,2 +132,2,7,.111111111,.791666667,5 +136,2,3,.111111111,.319444444,1 +138,2,3.5,0,.583333333,5 +140,2,6,.555555556,.013888889,1 +141,2,4.5,.611111111,.208333333,4 +145,2,4,.166666667,.347222222,4 +148,2,5,.666666667,-.111111111,2 +149,2,5.5,.166666667,.555555556,5 +155,2,2.5,.055555556,.347222222,4 +156,2,6,.111111111,.666666667,4 +157,2,3,.277777778,.236111111,5 +159,2,5.5,.555555556,.097222222,4 +160,2,4.5,.166666667,.5,5 +162,2,6,1,0,4 +164,2,3.5,.055555556,.638888889,5 +165,2,2.5,.277777778,.180555556,2 +167,2,4,.055555556,.333333333,4 +168,2,4,0,.486111111,3 +171,2,2,.222222222,.375,4 +178,2,4.5,.333333333,.083333333,1 +179,2,6.5,.333333333,.347222222,4 +180,2,4,.277777778,.194444444,5 +181,2,2,.055555556,.125,2 +184,2,5,.5,.291666667,5 +185,2,4.5,.611111111,-.125,5 +187,2,2.5,0,.666666667,4 +188,2,4,.166666667,.555555556,4 +189,2,4.5,.611111111,-.083333333,2 +193,2,4.5,.166666667,.222222222,4 +194,2,4,.611111111,-.194444444,2 +197,2,3,0,.305555556,5 +199,2,3,.166666667,.319444444,5 +200,2,2.5,.166666667,.125,0 +202,2,4,.277777778,.430555556,4 diff --git a/wip/Data/Awards.csv b/wip/Data/Awards.csv new file mode 100644 index 0000000..5404420 --- /dev/null +++ b/wip/Data/Awards.csv @@ -0,0 +1,201 @@ +id,num_awards,prog,math +45,1,3,41 +108,1,1,41 +15,1,3,44 +67,1,3,42 +153,1,3,40 +51,1,1,42 +164,1,3,46 +133,1,3,40 +2,1,3,33 +53,1,3,46 +1,1,3,40 +128,0,2,38 +16,1,3,44 +106,1,3,37 +89,1,3,40 +134,1,1,39 +19,1,1,43 +145,0,3,38 +11,1,2,45 +117,0,3,39 +109,1,1,42 +12,1,3,45 +37,1,3,40 +69,0,3,40 +43,1,2,43 +196,1,2,49 +36,1,1,44 +155,1,1,46 +6,0,2,46 +4,1,2,41 +25,0,1,42 +107,0,3,47 +5,1,2,43 +47,1,2,49 +140,1,3,40 +22,1,3,39 +18,1,3,49 +30,0,2,42 +40,0,1,43 +176,0,2,41 +126,0,1,57 +197,0,2,50 +46,0,2,44 +49,0,3,39 +8,0,2,52 +124,1,3,41 +13,0,3,39 +111,0,1,39 +142,0,3,52 +193,1,2,48 +105,3,2,45 +58,2,3,40 +129,3,1,46 +38,3,2,50 +182,0,2,43 +115,0,1,43 +14,1,2,54 +175,1,1,42 +44,2,3,45 +86,2,1,54 +72,3,3,47 +41,1,2,45 +191,0,2,43 +138,1,3,40 +9,0,3,52 +151,1,3,52 +119,0,1,45 +55,1,2,49 +73,1,2,53 +28,0,1,54 +90,2,2,50 +17,0,2,48 +102,0,2,51 +70,0,1,41 +148,1,3,51 +54,0,1,46 +42,0,3,55 +87,0,1,46 +21,2,1,61 +181,1,2,45 +165,1,3,54 +78,1,2,54 +76,1,2,51 +29,0,1,49 +91,1,3,56 +52,2,2,53 +10,1,1,49 +85,3,1,57 +50,0,1,42 +56,1,3,46 +64,1,3,45 +130,1,1,55 +141,1,3,47 +74,0,2,50 +83,1,3,41 +31,0,1,52 +172,1,2,57 +184,1,3,53 +75,1,3,51 +187,1,1,57 +113,1,2,51 +162,0,3,40 +110,2,3,50 +150,2,3,57 +167,0,1,35 +77,1,2,49 +35,0,1,50 +158,1,1,55 +112,0,2,48 +48,0,2,52 +147,1,2,53 +7,1,2,59 +65,2,2,66 +168,0,2,57 +190,1,2,54 +178,0,3,57 +159,1,2,54 +120,0,2,54 +116,0,2,54 +79,2,2,49 +98,1,3,51 +122,3,2,58 +179,1,2,60 +198,1,2,51 +189,1,2,63 +199,1,2,50 +156,1,2,53 +166,0,2,53 +160,0,2,55 +152,1,2,56 +183,0,2,49 +94,1,2,61 +149,0,1,49 +131,0,2,57 +24,0,2,66 +99,0,1,56 +171,3,2,60 +104,1,2,57 +81,1,2,59 +97,1,2,58 +20,0,2,57 +163,3,2,64 +195,0,1,60 +84,0,1,54 +27,1,2,61 +118,1,1,58 +71,0,1,56 +63,0,1,60 +185,0,2,55 +127,3,2,57 +177,0,2,62 +188,0,2,56 +60,0,2,51 +66,2,3,56 +173,0,1,61 +186,1,2,63 +96,5,2,61 +101,0,2,67 +3,0,2,48 +170,1,2,61 +92,0,1,57 +62,0,1,48 +135,2,2,65 +26,4,2,62 +139,1,2,61 +121,0,3,53 +144,1,1,58 +146,1,2,64 +137,3,2,65 +123,1,1,56 +169,1,1,63 +34,3,2,57 +33,2,2,72 +32,0,3,66 +114,0,2,62 +125,1,2,58 +59,1,2,63 +23,3,2,64 +161,2,2,72 +103,0,2,64 +194,6,2,69 +136,4,2,70 +154,1,2,66 +157,0,1,58 +93,2,2,62 +39,2,2,67 +88,1,2,64 +192,2,2,63 +80,1,2,68 +200,1,2,75 +180,0,2,69 +82,1,2,65 +174,2,2,71 +95,5,2,71 +61,1,2,60 +100,2,2,71 +143,2,3,75 +68,1,2,71 +57,0,2,72 +132,3,2,73 diff --git a/wip/Data/Birthweight_reduced_kg.csv b/wip/Data/Birthweight_reduced_kg.csv new file mode 100644 index 0000000..9ce4dd1 --- /dev/null +++ b/wip/Data/Birthweight_reduced_kg.csv @@ -0,0 +1,43 @@ +ID,Length,Birthweight,Headcirc,Gestation,smoker,mage,mnocig,mheight,mppwt,fage,fedyrs,fnocig,fheight,lowbwt,mage35 +1360,56,4.55,34,44,0,20,0,162,57,23,10,35,179,0,0 +1016,53,4.32,36,40,0,19,0,171,62,19,12,0,183,0,0 +462,58,4.1,39,41,0,35,0,172,58,31,16,25,185,0,1 +1187,53,4.07,38,44,0,20,0,174,68,26,14,25,189,0,0 +553,54,3.94,37,42,0,24,0,175,66,30,12,0,184,0,0 +1636,51,3.93,38,38,0,29,0,165,61,31,16,0,180,0,0 +820,52,3.77,34,40,0,24,0,157,50,31,16,0,173,0,0 +1191,53,3.65,33,42,0,21,0,165,61,21,10,25,185,0,0 +1081,54,3.63,38,38,0,18,0,172,50,20,12,7,172,0,0 +822,50,3.42,35,38,0,20,0,157,48,22,14,0,179,0,0 +1683,53,3.35,33,41,0,27,0,164,62,37,14,0,170,0,0 +1088,51,3.27,36,40,0,24,0,168,53,29,16,0,181,0,0 +1107,52,3.23,36,38,0,31,0,164,57,35,16,0,183,0,0 +755,53,3.2,33,41,0,21,0,155,55,25,14,25,183,0,0 +1058,53,3.15,34,40,0,29,0,167,60,30,16,25,182,0,0 +321,48,3.11,33,37,0,28,0,158,54,39,10,0,171,0,0 +697,48,3.03,35,39,0,27,0,162,62,27,14,0,178,0,0 +808,48,2.92,33,34,0,26,0,167,64,25,12,25,175,0,0 +1600,53,2.9,34,39,0,19,0,165,57,23,14,2,193,0,0 +1313,43,2.65,32,33,0,24,0,149,45,26,16,0,169,1,0 +792,53,3.64,38,40,1,20,2,170,59,24,12,12,185,0,0 +1388,51,3.14,33,41,1,22,7,160,53,24,16,12,176,0,0 +575,50,2.78,30,37,1,19,7,165,60,20,14,0,183,0,0 +569,50,2.51,35,39,1,22,7,159,52,23,14,25,200,1,0 +1363,48,2.37,30,37,1,20,7,163,47,20,10,35,185,1,0 +300,46,2.05,32,35,1,41,7,166,57,37,14,25,173,1,1 +431,48,1.92,30,33,1,20,7,161,50,20,10,35,180,1,0 +1764,58,4.57,39,41,1,32,12,173,70,38,14,25,180,0,0 +532,53,3.59,34,40,1,31,12,163,49,41,12,50,191,0,0 +752,49,3.32,36,40,1,27,12,152,48,37,12,25,170,0,0 +1023,52,3,35,38,1,30,12,165,64,38,14,50,180,0,0 +57,51,3.32,38,39,1,23,17,157,48,32,12,25,169,0,0 +1522,50,2.74,33,39,1,21,17,156,53,24,12,7,179,0,0 +223,50,3.87,33,45,1,28,25,163,54,30,16,0,183,0,0 +272,52,3.86,36,39,1,30,25,170,78,40,16,50,178,0,0 +27,53,3.55,37,41,1,37,25,161,66,46,16,0,175,0,1 +365,52,3.53,37,40,1,26,25,170,62,30,10,25,181,0,0 +619,52,3.41,33,39,1,23,25,181,69,23,16,2,181,0,0 +1369,49,3.18,34,38,1,31,25,162,57,32,16,50,194,0,0 +1262,53,3.19,34,41,1,27,35,163,51,31,16,25,185,0,0 +516,47,2.66,33,35,1,20,35,170,57,23,12,50,186,1,0 +1272,53,2.75,32,40,1,37,50,168,61,31,16,0,173,0,1 diff --git a/wip/Data/Cholesterol_R.csv b/wip/Data/Cholesterol_R.csv new file mode 100644 index 0000000..a403b43 --- /dev/null +++ b/wip/Data/Cholesterol_R.csv @@ -0,0 +1,19 @@ +ID,Before,After4weeks,After8weeks,Margarine +1,6.42,5.83,5.75,B +2,6.76,6.2,6.13,A +3,6.56,5.83,5.71,B +4,4.8,4.27,4.15,A +5,8.43,7.71,7.67,B +6,7.49,7.12,7.05,A +7,8.05,7.25,7.1,B +8,5.05,4.63,4.67,A +9,5.77,5.31,5.33,B +10,3.91,3.7,3.66,A +11,6.77,6.15,5.96,B +12,6.44,5.59,5.64,B +13,6.17,5.56,5.51,A +14,7.67,7.11,6.96,A +15,7.34,6.84,6.82,A +16,6.85,6.4,6.29,B +17,5.13,4.52,4.45,A +18,5.73,5.13,5.17,B diff --git a/wip/Data/Crime.csv b/wip/Data/Crime.csv new file mode 100644 index 0000000..ec78801 --- /dev/null +++ b/wip/Data/Crime.csv @@ -0,0 +1,48 @@ +CrimeRate,Youth,Southern,Education,ExpenditureYear0,LabourForce,Males,MoreMales,StateSize,YouthUnemployment,MatureUnemployment,HighYouthUnemploy,Wage,BelowWage,CrimeRate10,Youth10,Education10,ExpenditureYear10,LabourForce10,Males10,MoreMales10,StateSize10,YouthUnemploy10,MatureUnemploy10,HighYouthUnemploy10,Wage10,BelowWage10 +45.5,135,0,12.4,69,540,965,0,6,80,22,1,564,139,26.5,135,12.5,71,564,974,0,6,82,20,1,632,142 +52.3,140,0,10.9,55,535,1045,1,6,135,40,1,453,200,35.9,135,10.9,54,540,1039,1,7,138,39,1,521,210 +56.6,157,1,11.2,47,512,962,0,22,97,34,0,288,276,37.1,153,11,44,529,959,0,24,98,33,0,359,256 +60.3,139,1,11.9,46,480,968,0,19,135,53,0,457,249,42.7,139,11.8,41,497,983,0,20,131,50,0,510,235 +64.2,126,0,12.2,106,599,989,0,40,78,25,1,593,171,46.7,125,12.2,97,602,989,0,42,79,24,1,660,162 +67.6,128,0,13.5,67,624,972,0,28,77,25,1,507,206,47.9,128,13.8,60,621,983,0,28,81,24,1,571,199 +70.5,130,0,14.1,63,641,984,0,14,70,21,1,486,196,50.6,153,14.1,57,641,993,0,14,71,23,1,556,176 +73.2,143,0,12.9,66,537,977,0,10,114,35,1,487,166,55.9,143,13,63,549,973,0,11,119,36,1,561,168 +75,141,0,12.9,56,523,968,0,4,107,37,0,489,170,61.8,153,12.9,54,538,968,0,5,110,36,1,550,126 +78.1,133,0,11.4,51,599,1024,1,7,99,27,1,425,225,65.4,134,11.2,47,600,1024,1,7,97,28,1,499,215 +79.8,142,1,12.9,45,533,969,0,18,94,33,0,318,250,71.4,142,13.1,44,552,969,0,19,93,36,0,378,247 +82.3,123,0,12.5,97,526,948,0,113,124,50,0,572,158,75.4,134,12.4,87,529,949,0,117,125,49,0,639,146 +83.1,135,0,13.6,62,595,986,0,22,77,27,0,529,190,77.3,137,13.7,61,599,993,0,23,80,28,0,591,189 +84.9,121,0,13.2,118,547,964,0,25,84,29,0,689,126,78.6,132,13.3,115,538,968,0,25,82,30,0,742,127 +85.6,166,1,11.4,58,521,973,0,46,72,26,0,396,237,80.6,153,11.2,54,543,983,0,47,76,25,1,568,246 +88,140,0,12.9,71,632,1029,1,7,100,24,1,526,174,82.2,130,12.9,68,620,1024,1,8,104,25,1,570,182 +92.3,126,0,12.7,74,602,984,0,34,102,33,1,557,195,87.5,134,12.9,67,599,982,0,33,107,34,1,621,199 +94.3,130,0,13.3,128,536,934,0,51,78,34,0,627,135,92.9,127,13.3,128,530,949,0,52,79,33,0,692,140 +95.3,125,0,12,90,586,964,0,97,105,43,0,617,163,94.1,134,11.9,81,571,971,0,99,106,41,0,679,162 +96.8,151,1,10,58,510,950,0,33,108,41,0,394,261,96.2,161,10.1,56,515,1001,1,32,110,40,0,465,254 +97.4,152,1,10.8,57,530,986,0,30,92,43,0,405,264,97.8,152,11,53,541,989,0,30,92,41,0,470,243 +98.7,162,1,12.1,75,522,996,0,40,73,27,0,496,224,99.9,162,12,70,533,992,0,41,80,28,0,562,229 +99.9,149,1,10.7,61,515,953,0,36,86,35,0,395,251,101.4,150,10.7,54,520,952,0,35,84,32,0,476,249 +103,177,1,11,58,638,974,0,24,76,28,0,382,254,103.5,164,10.9,56,638,978,0,25,79,28,0,456,257 +104.3,134,0,12.5,75,595,972,0,47,83,31,0,580,172,104.5,133,12.7,71,599,982,0,50,87,32,0,649,182 +105.9,130,0,13.4,90,623,1049,1,3,113,40,0,588,160,106.4,153,13.4,91,622,1050,1,3,119,41,0,649,159 +106.6,157,1,11.1,65,553,955,0,39,81,28,0,421,239,107.8,156,11.2,62,562,956,0,39,85,29,0,499,243 +107.2,148,0,13.7,72,601,998,0,9,84,20,1,590,144,110.1,134,13.9,66,602,999,0,9,87,15,0,656,151 +108.3,126,0,13.8,97,542,990,0,18,102,35,0,589,166,110.5,126,13.8,97,549,993,0,19,103,34,1,659,160 +109.4,135,1,11.4,123,537,978,0,31,89,34,0,631,165,113.5,134,11.3,115,529,978,0,32,93,35,0,703,175 +112.1,142,1,10.9,81,497,956,0,33,116,47,0,427,247,116.3,147,10.7,77,501,962,0,33,117,44,0,500,256 +114.3,127,1,12.8,82,519,982,0,4,97,38,0,620,168,119.7,125,12.9,79,510,945,0,4,99,39,0,696,170 +115.1,131,0,13.7,78,574,1038,1,7,142,42,1,540,176,124.5,134,13.6,73,581,1029,1,7,143,41,1,615,177 +117.2,136,0,12.9,95,574,1012,1,29,111,37,1,622,162,127.8,140,13,96,581,1011,1,29,115,36,1,691,169 +119.7,119,0,11.9,166,521,938,0,168,92,36,0,637,154,129.8,120,11.9,157,524,935,0,180,93,27,1,698,169 +121.6,147,1,13.9,63,560,972,0,23,76,24,1,462,233,130.7,139,14,64,571,970,0,24,78,24,1,511,220 +123.4,145,1,11.7,82,560,981,0,96,88,31,0,488,228,132.5,154,11.8,74,563,980,0,99,89,29,1,550,230 +127.2,132,0,10.4,87,564,953,0,43,83,32,0,513,227,134.6,135,10.2,83,560,948,0,44,83,32,0,589,234 +132.4,152,0,12,82,571,1018,1,10,103,28,1,537,215,137.5,151,12.1,76,567,1079,1,11,105,27,1,617,204 +135.5,125,0,12.5,113,567,985,0,78,130,58,0,626,166,140.5,140,12.5,105,571,993,0,77,131,59,0,684,174 +137.8,141,0,14.2,109,591,985,0,18,91,20,1,578,174,145.7,142,14.2,101,590,987,0,19,94,19,1,649,180 +140.8,150,0,12,109,531,964,0,9,87,38,0,559,153,150.6,153,12,98,539,982,0,10,88,36,0,635,151 +145.4,131,1,12.2,115,542,969,0,50,79,35,0,472,206,157.3,131,12.1,109,548,976,0,52,82,34,0,539,219 +149.3,143,0,12.3,103,583,1012,1,13,96,36,0,557,194,162.7,142,12.2,95,612,1003,1,13,97,36,0,625,196 +154.3,124,0,12.3,121,580,966,0,101,77,35,0,657,170,169.6,134,12.2,116,580,987,0,104,79,36,0,719,172 +157.7,136,0,15.1,149,577,994,0,157,102,39,0,673,167,177.2,140,15.2,141,578,995,0,160,110,40,0,739,169 +161.8,131,0,13.2,160,631,1071,1,3,102,41,0,674,152,178.2,132,13.2,143,632,1058,1,4,100,40,0,748,150 diff --git a/wip/Data/Dawtry Sutton and Sibley 2015.csv b/wip/Data/Dawtry Sutton and Sibley 2015.csv new file mode 100644 index 0000000..697866c --- /dev/null +++ b/wip/Data/Dawtry Sutton and Sibley 2015.csv @@ -0,0 +1,306 @@ +PS,PD_15,PD_30,PD_45,PD_60,PD_75,PD_90,PD_105,PD_120,PD_135,PD_150,PD_150plus,fairness,satisfaction,SC_15,SC_30,SC_45,SC_60,SC_75,SC_90,SC_105,SC_120,SC_135,SC_150,SC_150plus,redist1,redist2,redist3,redist4,Household_Income,Political_Preference,age,gender,Population_Inequality_Gini_Index,Population_Mean_Income,Social_Circle_Inequality_Gini_Index,Social_Circle_Mean_Income +233,27,48,21,0,0,0,0,0,0,0,4,1,1,50,24,26,0,0,0,0,0,0,0,0,6,3,6,1, ,5,40,2,38.7829379101464,29715,28.0567375886525,21150 +157,39,0,0,0,0,0,0,0,0,0,61,5,2,19,0,0,0,53,0,28,0,0,0,0,2,2,3,4,20,5,59,2,37.2145110410095,123630,24.3233876520542,65355 +275,0,0,50,0,0,50,0,0,0,0,0,5,5,0,0,11,0,0,0,35,0,54,0,0,5,4,5,5,100,5,41,2,20.75,60000,14.4425770308123,107100 +111,9,14,17,17,17,8,7,5,2,2,2,7,7,2,7,7,11,11,13,14,14,14,5,2,1,3,3,4,150,8,59,2,35.3795804902704,59355,26.9259002770083,86640 +52,68,32,0,0,0,0,0,0,0,0,0,4,5,0,0,57,0,0,43,0,0,0,0,0,4,5,4,5,500,5,35,1,16.875,15360,21.401055408971,56850 +11,10,31,6,14,8,8,6,5,5,7,0,1,4,8,25,8,16,7,11,11,9,3,1,1,6,5,6,6,600,3,34,2,40.9244791666667,57600,37.0882426673352,59835 +76,1,4,30,8,19,7,5,4,2,10,10,3,3,1,29,0,0,20,50,0,0,0,0,0,5,2,5,5,600,4,36,2,35.7271038454393,80745,23.6694356217933,61395 +90,2,20,10,7,18,4,4,10,10,10,5,5,4,0,0,10,80,10,0,0,0,0,0,0,3,3,4,4,600,3,39,2,36.5702697604226,79515,7.14285714285707,52500 +93,33,23,10,10,9,4,4,4,2,1,0,5,3,50,14,9,7,6,5,1,1,4,3,0,5,4,5,5,600,2,40,2,44.7781120722076,39885,49.3891213389121,35850 +104,10,13,11,16,23,12,8,3,2,2,0,4,5,6,10,15,21,23,11,5,3,2,2,2,4,4,5,5,680,3,31,1,31.7237851662404,58650,30.9116075764935,61770 +26,33,26,11,4,2,6,3,3,3,6,3,5,4,5,8,8,10,6,9,8,8,6,10,22,4,1,4,2,1000, ,53, ,51.4852752880922,46860,35.4034707158351,103725 +259,79,21,0,0,0,0,0,0,0,0,0,4,5,45,51,0,4,0,0,0,0,0,0,0,3,4,3,4,1000,4,19,1,14.2629355860612,14205,21.7667984189723,18975 +255,27,32,27,7,5,1,1,0,0,0,0,1,1,68,20,9,3,0,0,0,0,0,0,0,5,3,6,1,1100,5,27,1,32.3350331463539,29415,26.4480408858603,17610 +176,20,25,10,10,5,5,5,5,5,5,5,3,3,40,40,10,10,0,0,0,0,0,0,0,6,2,5,2,1200,3,49,2,47.7078507078507,58275,29.5,22800 +51,2,9,19,22,21,15,6,3,1,1,1,2,3,3,4,8,12,14,18,15,12,9,4,1,4,1,3,4,1400,5,64,2,26.7780493766805,61365,25.5443916697114,82110 +92,22,20,20,20,4,3,3,2,2,2,2,3,1,30,10,11,11,5,5,4,4,0,20,0,4,3,4,5,1600,3,47,1,42.2618888144675,44790,46.8279301745636,60150 +18,16,28,35,21,0,0,0,0,0,0,0,5,5,10,19,19,12,12,12,16,0,0,0,0,4,4,5,5,2000,9,34,2,25.4652455977757,32370,33.2714285714286,52500 +122,0,13,29,40,18,0,0,0,0,0,0,5,5,5,21,36,22,0,0,16,0,0,0,0,3,4,3,4,3800,5,28,2,18.111821086262,46950,30.2039151712887,45975 +206,6,12,19,16,10,10,10,8,4,3,2,2,2,6,37,30,12,5,2,3,3,2,0,0,5,1,6,4,4000,2,26,2,34.7098681218736,65970,33.1297257227576,40470 +294,0,32,18,50,0,0,0,0,0,0,0,3,3,0,24,24,40,12,0,0,0,0,0,0,4,4,6,5,4000,7,34,2,19.4477611940298,40200,20.5379310344828,43500 +123,1,2,8,18,22,22,15,9,2,1,0,9,9,15,24,23,19,6,3,3,2,2,2,1,1,3,2,5,4200,8,24,2,20.6921533212924,74745,38.6588628762542,44850 +83,18,19,21,19,16,2,1,1,1,1,1,5,4,12,20,21,17,17,13,0,0,0,0,0,4,3,4,3,5000,5,30,1,36.20041393584,43485,30.8291054739653,44940 +156,10,20,20,30,8,5,3,1,1,1,1,5,7,10,11,13,20,16,13,8,4,2,2,1,3,4,2,5,5000,5,36,2,33.0158982511924,47175,33.0323785803238,60225 +220,2,10,22,41,20,5,0,0,0,0,0,3,3,0,0,40,50,10,0,0,0,0,0,0,4,1,5,1,5299.99,3,56,2,19.3998797354179,49890,12.3125,48000 +185,3,12,14,13,8,16,8,8,9,4,5,3,3,0,0,5,11,19,20,29,11,3,1,1,3,2,6,3,5583,5,33,1,34.1440743609605,77460,17.7922192749779,84825 +48,22,27,25,12,0,0,0,0,0,0,14,1,1,8,21,31,30,0,0,0,0,0,0,10,6,1,5,4,6000,5,57,1,51.5130586186883,51690,40.5964611872146,52560 +75,9,3,12,18,16,10,8,8,7,5,4,1,1,6,7,10,12,8,4,9,4,7,11,22,1,2,1,6,6000,6,31,2,33.4945295404814,75405,36.7595757218621,101820 +81,4,5,6,25,26,14,5,5,5,3,2,5,5,1,1,10,19,23,23,10,4,4,3,2,1,2,2,4,6000,7,44,2,27.1851539434838,71130,23.341021092056,76095 +114,8,15,16,15,13,10,10,6,3,3,1,2,1,31,15,17,37,0,0,0,0,0,0,0,2,2,5,2,6000,3,59,2,35.1238164603059,61785,30.7838577291382,32895 +243,10,15,25,20,10,5,5,5,2,2,1,1,1,40,25,20,15,0,0,0,0,0,0,0,5,1,6,6,7500,3,42,1,35.8293370944993,53175,32.406976744186,25800 +15,25,19,10,8,7,6,5,5,5,5,5,5,7,1,60,30,9,0,0,0,0,0,0,0,2,3,2,4,9000,7,53,2,48.2097186700767,58650,18.5600608210846,29595 +229,57,20,14,3,1,1,0,1,1,1,1,1,3,76,0,24,0,0,0,0,0,0,0,0,3,3,4,3,9000,3,27,2,42.9563679245283,25440,27.6688741721854,18120 +258,14,19,21,23,9,4,4,3,1,1,1,3,2,34,39,27,0,0,0,0,0,0,0,0,5,2,5,3,9200,5,42,1,36.3511280584684,47205,25.1187989556136,22980 +237,11,17,17,16,10,10,8,5,3,2,1,1,1,50,15,10,10,5,5,5,0,0,0,0,4,2,4,2,10000,2,30,1,37.1074249605055,56970,43.7073170731707,30750 +268,25,18,14,11,11,8,6,3,2,1,1,3,3,71,16,8,5,0,0,0,0,0,0,0,3,3,4,4,10100,2,24,1,42.5015873015873,47250,27.7590870667794,17745 +67,17,20,14,10,9,8,8,5,4,3,2,1,1,20,73,4,2,1,0,0,0,0,0,0,6,2,6,1,10500,2,55,1,42.4060090401489,56415,16.7006802721088,22050 +124,32,12,11,10,8,7,6,5,4,3,2,3,3,51,19,12,10,8,0,0,0,0,0,0,5,2,5,2,12000,1,25,1,46.8269675925926,51840,37.5918966529654,25545 +230,32,19,15,10,8,6,4,3,2,1,0,5,5,30,28,17,9,5,4,3,1,1,1,1,1,3,4,5,12000,7,64,2,43.7154471544715,40590,43.1237113402062,36375 +238,28,23,20,6,5,5,4,4,2,2,1,1,1,29,26,12,0,22,11,0,0,0,0,0,2,2,2,3,12000,5,41,1,45.4029692470838,42435,39.0174811283274,37755 +266,10,20,20,25,7,5,4,2,2,3,2,1,1,3,5,26,34,17,6,2,2,2,2,1,5,1,6,3,12000,2,43,1,37.5706051873199,52050,26.2302839116719,57060 +4,8,52,11,8,7,5,4,2,1,1,1,5,5,30,58,12,0,0,0,0,0,0,0,0,1,3,1,6,15000,8,28,2,38.7836840162421,40635,19.9148936170213,21150 +50,42,17,14,10,5,2,3,3,2,1,1,3,3,5,19,38,30,7,1,0,0,0,0,0,6,3,4,3,15000,3,50,2,47.2261368291684,36615,21.8423005565863,40425 +99,17,31,22,8,4,5,5,3,3,1,1,1,1,40,43,15,1,1,0,0,0,0,0,0,4,2,6,5,15000,5,27,2,41.9869684499314,43740,25.8450704225352,21300 +151,20,18,13,13,10,8,6,5,3,2,2,1,1,35,47,15,1,1,1,0,0,0,0,0,5,1,6,1,15000,1,30,2,42.4929971988796,53550,26.4448160535117,22425 +68,2,10,30,13,12,10,9,8,3,2,1,3,5,16,24,22,10,3,5,10,0,0,10,0,2,5,3,4,16000,9,62,1,31.5941719971571,63315,42.8705120659211,50970 +108,15,20,21,15,13,11,1,1,1,1,1,1,1,80,20,0,0,0,0,0,0,0,0,0,1,1,4,2,16000,5,29,1,36.491961414791,46650,13.9148936170212,14100 +174,6,20,17,12,9,8,7,7,6,5,3,1,1,40,20,0,20,0,0,0,0,0,10,10,6,1,6,1,16000,2,47,1,38.8365581607102,65895,55.8655462184874,53550 +295,61,20,0,0,0,0,0,0,10,5,4,1,1,75,0,0,0,0,0,0,0,0,10,15,6,1,6,1,17000,2,33,1,57.4549943030763,39495,61.3571428571429,52500 +145,5,10,20,10,10,10,10,10,10,5,0,1,1,20,50,10,10,10,0,0,0,0,0,0,3,3,4,3,18000,6,41,1,32.9181532004197,71475,31.1836734693878,29400 +165,7,22,30,12,10,5,3,3,3,3,2,3,3,5,69,0,5,10,10,0,0,0,0,1,3,3,4,4,18000,3,25,2,38.0231362467866,52515,33.7689075630252,35700 +209,10,10,13,30,12,6,5,6,3,3,2,1,1,69,27,3,0,0,1,0,0,0,0,0,6,1,5,1,18000,2,64,2,34.8074074074074,60750,22.0395584176633,16305 +221,13,14,20,19,11,9,4,4,3,2,1,5,5,5,20,20,13,13,14,0,0,0,5,10,1,4,2,5,18000,8,46,1,36.8895671476138,54060,41.3841807909605,66375 +77,11,13,15,17,19,11,6,3,2,2,1,3,2,60,21,9,4,1,1,1,1,1,1,0,1,1,6,6,19000,5,52,1,34.1629125196438,57270,40.7848101265823,23700 +131,30,50,20,0,0,0,0,0,0,0,0,1,1,50,20,10,10,10,0,0,0,0,0,0,6,1,6,1,19000,5,41,2,22.6040268456376,22350,38.2857142857143,26250 +213,15,22,18,15,10,5,4,3,2,1,5,1,1,31,41,5,5,5,5,4,1,1,1,1,6,1,6,1,19000,2,41,2,43.169014084507,53250,44.6551126516464,34620 +37,20,17,22,13,10,7,5,2,2,1,1,5,4,31,25,21,11,11,1,0,0,0,0,0,5,1,5,2,20000,3,30,2,39.8608,46875,35.0643302928468,31245 +128,5,17,18,20,14,12,6,4,3,1,0,3,3,4,10,25,21,17,11,6,2,1,2,1,4,2,4,4,20000,6,25,1,31.6569536423841,56625,29.6492092299715,57855 +134,5,10,20,22,23,8,5,3,2,1,1,3,3,17,37,45,0,0,0,0,0,0,0,1,2,3,3,4,20000,5,30,2,29.2345360824742,58200,25.1819116135663,29190 +161,13,26,14,12,8,7,6,5,4,3,2,4,3,48,52,0,0,0,0,0,0,0,0,0,4,2,4,4,20000,3,28,2,42.1522273845313,54885,17.0103092783505,17460 +225,8,15,24,25,8,6,5,3,3,1,2,1,1,0,31,45,19,0,0,0,0,5,0,0,5,2,5,1,20000,2,29,1,35.0857775318207,54210,25.6492537313433,40200 +247,2,4,4,4,8,12,13,15,15,12,11,3,3,9,16,16,17,0,3,0,13,12,6,8,2,2,4,4,20000,4,28,1,25.5067822682142,107265,41.7898365176285,76155 +300,10,15,5,16,3,9,10,5,4,3,20,3,2,0,0,70,26,0,0,0,0,0,0,4,5,2,5,2,20000,2,55,2,42.1056218057922,88050,20.0754716981132,47700 +1,10,19,26,20,9,6,3,3,2,1,1,2,2,4,31,36,17,7,3,1,0,0,0,1,4,3,4,4,21000,2,37,1,35.2633996937213,48975,28.1466066741657,40005 +19,10,13,18,13,12,8,7,5,5,5,4,2,3,8,18,40,19,15,0,0,0,0,0,0,5,2,5,4,21000,3,28,1,39.1383219954648,66150,24.2819745699327,40110 +23,12,12,13,18,13,7,7,6,5,4,3,7,7,24,61,7,5,1,1,0,1,0,0,0,1,4,4,5,21000,8,32,1,38.3210881190421,64515,26.3431372549019,24480 +41,5,37,11,11,7,9,5,4,4,4,3,1,3,1,28,16,29,0,17,0,0,0,0,9,4,3,4,3,21000, ,53,2,41.8578947368421,57000,38.9650455927052,59220 +72,2,9,14,19,21,13,8,5,4,3,2,3,3,0,40,20,0,40,0,0,0,0,0,0,6,1,5,2,21000,2,28,1,29.4211897524967,69090,26.8275862068966,43500 +115,19,17,7,8,7,7,7,10,10,5,3,4,3,17,49,24,0,7,0,0,0,3,0,0,5,3,6,3,21000,5,27,1,42.8049977688532,67230,32.3561979421852,30615 +87,18,20,30,11,4,5,3,3,3,2,1,4,6,39,30,23,5,3,0,0,0,0,0,0,6,2,4,3,22000,3,29,2,40.6652360515022,45435,31.3339404978749,24705 +222,16,24,28,13,6,3,2,2,2,2,2,3,3,0,0,16,22,26,5,19,8,4,0,0,3,2,5,5,22000,3,61,2,40.4714573539288,44670,22.0062630480167,71850 +154,30,19,10,9,8,7,6,4,3,2,2,1,1,10,30,40,0,0,0,0,0,0,10,10,3,1,4,3,23000,4,56,2,46.8885448916409,48450,47.978835978836,56700 +24,51,30,9,2,2,1,1,1,1,1,1,1,1,20,60,20,0,0,0,0,0,0,0,0,6,1,6,1,24000,5,25,2,43.0800915331808,26220,19.4358974358974,23400 +57,0,50,10,10,10,5,5,3,3,2,2,4,4,50,30,0,17,0,0,3,0,0,0,0,2,5,2,4,24000,5,25,1,39.5255255255255,49950,37.5060975609756,24600 +97,20,22,30,5,5,5,2,3,3,3,2,1,1,20,30,30,20,0,0,0,0,0,0,0,6,1,6,1,24000,4,42,2,43.7785016286645,46050,27.3398058252427,30900 +169,22,17,11,19,16,6,3,2,2,1,1,2,2,40,57,0,3,0,0,0,0,0,0,0,4,1,5,2,24000,5,25,1,39.3416640303701,47415,19.671875,19200 +245,20,14,11,10,10,7,6,6,5,6,5,3,4,18,19,17,14,9,7,5,4,3,2,2,4,3,3,3,24000,5,25,1,44.4252607184241,64725,41.9515011547344,51960 +53,5,8,12,15,12,10,9,9,8,7,5,1,1,2,3,8,11,15,11,10,10,10,10,10,6,1,6,1,25000,3,44,2,33.8348968105066,79950,29.5384375972611,96390 +61,10,27,16,20,11,4,1,2,2,6,1,1,1,12,33,7,11,11,1,5,5,7,4,4,6,5,6,2,25000,1,44,2,39.2776957163959,50775,45.1088295687885,58440 +71,20,40,25,15,0,0,0,0,0,0,0,3,3,20,0,80,0,0,0,0,0,0,0,0,2,1,2,5,25000,9,52,2,27.1047120418848,28650,14.5925925925926,32400 +110,17,23,17,10,7,6,5,4,4,2,5,2,3,5,50,30,15,0,0,0,0,0,0,0,4,2,5,4,25000,4,25,1,45.5542363734484,55590,21.7699757869249,30975 +117,15,24,17,14,13,5,3,2,2,2,3,5,5,5,23,34,28,4,6,0,0,0,0,0,3,3,3,4,25000,5,24,2,41.6,50250,24.6366972477064,40875 +132,10,26,14,11,13,13,3,2,2,3,3,1,1,30,30,22,0,14,0,0,0,0,4,0,6,2,5,2,25000,3,54,2,40.1231393775372,55425,40.8533333333333,33750 +190,31,17,11,8,7,6,6,5,4,3,2,3,3,9,17,74,0,0,0,0,0,0,0,0,4,1,6,4,25000,2,31,1,47.7241276468834,50295,13.4712907671107,32655 +207,10,30,30,10,7,6,3,1,1,1,1,1,1,50,50,0,0,0,0,0,0,0,0,0,3,1,5,3,25000,3,23,1,36.0788091068301,42825,17.2173913043478,17250 +251,10,30,40,10,6,2,1,0,0,0,1,4,7,39,0,22,0,0,39,0,0,0,0,0,5,1,4,2,25000,3,32,2,29.5091649694501,36825,39.1842367808447,45105 +269,30,9,12,23,4,2,3,2,2,3,10,1,1,40,40,20,0,0,0,0,0,0,0,0,6,1,4,2,25000,3,25,1,49.9457364341085,58050,25.0985915492958,21300 +276,10,26,64,0,0,0,0,0,0,0,0,7,7,30,10,60,0,0,0,0,0,0,0,0,5,2,5,2,25000,5,34,2,16.1739130434783,31050,22.4761904761905,28350 +282,20,7,8,9,10,8,6,5,4,3,20,4,5,10,50,20,0,10,0,0,0,0,0,10,2,2,2,5,25000,5,35,1,44.7349823321555,84900,47.0649350649351,46200 +304,21,19,17,15,12,6,3,2,2,2,1,1,2,34,62,4,0,0,0,0,0,0,0,0,4,3,5,2,25000,7,23,1,40.7252252252252,46620,17.0138248847926,19530 +74,12,35,24,11,6,3,3,3,1,1,1,1,1,24,49,21,2,2,1,1,0,0,0,0,3,1,6,1,26000,5,26,2,38.2241068206424,41565,27.9284064665127,25980 +103,15,21,30,15,7,4,3,2,1,1,1,1,1,30,39,27,2,1,1,0,0,0,0,0,4,3,5,3,26000,4,36,1,36.7586206896552,43500,27.6766467065868,25050 +129,12,22,20,17,12,6,4,2,2,2,1,2,3,0,0,0,0,35,25,17,12,5,3,3,4,3,4,4,26000,4,26,1,37.4898207231844,49365,16.8761354252683,90825 +21,20,19,10,8,8,7,6,6,6,5,5,3,3,0,40,50,0,0,10,0,0,0,0,0,5,1,5,3,28000,3,50,1,45.7723480333731,62925,23.25,36000 +179,7,16,28,41,2,2,0,1,1,1,1,3,3,13,24,45,11,1,1,1,1,1,1,1,4,1,5,1,28000,2,49,1,27.289124668435,45240,31.7393026941363,37860 +186,4,5,11,20,16,12,10,8,5,4,5,4,4,8,16,20,29,24,3,0,0,0,0,0,4,3,4,4,28000,2,27,1,31.280627056319,77505,25.0587467362924,45960 +298,23,19,22,9,10,9,2,2,1,2,1,1,1,22,59,6,5,2,0,0,0,0,0,6,4,2,6,4,28000,4,30,1,41.417004048583,44460,43.6719858156028,33840 +305,12,17,22,18,14,6,4,3,2,1,1,4,4,9,14,21,29,15,9,3,0,0,0,0,1,3,2,5,29000,5,49,1,35.8288341738356,50565,27.4982742390963,47805 +31,6,4,4,3,3,2,2,1,1,35,39,1,1,2,1,84,2,2,2,2,1,1,1,2,4,2,5,2,30000,1,51,2,24.9095531753759,138645,21.1864740643467,45690 +32,6,10,10,18,19,11,10,5,3,4,4,7,4,0,21,13,22,10,15,9,3,2,1,4,1,3,1,6,30000,7,26,1,33.3370072064434,70770,33.588785046729,64200 +44,1,3,9,13,23,16,7,13,9,6,0,5,5,0,0,0,40,33,27,0,0,0,0,0,2,2,2,5,30000,1,30,2,24.4225013855533,81195,12.0022883295194,65550 +107,4,7,10,12,18,13,11,9,8,6,2,4,4,0,0,0,40,35,10,5,5,5,0,0,2,3,4,3,30000,3,27,1,29.8321865443425,78480,16.8924731182796,69750 +125,3,17,20,17,13,8,6,5,4,4,3,5,5,4,30,36,14,5,3,2,2,0,1,3,3,2,3,4,30000,5,42, ,35.8399625643425,64110,35.1096795507103,45405 +138,4,8,57,20,5,1,1,1,1,1,1,4,4,4,10,86,0,0,0,0,0,0,0,0,4,2,4,4,30000,5,27,1,24.2790156301962,45105,8.3156089193825,34980 +146,2,3,11,16,20,23,25,0,0,0,0,2,2,8,41,51,0,0,0,0,0,0,0,0,6,4,6,4,30000,5,38,2,20.145966709347,70290,17.4257932446264,29310 +187,20,30,0,50,0,0,0,0,0,0,0,5,5,21,12,14,12,15,11,4,6,1,1,3,4,1,5,1,30000,1,30,2,27.9322033898305,35400,40.9825992387167,55170 +246,17,16,14,12,19,0,9,4,0,2,7,5,3,11,11,14,16,3,5,7,9,6,9,9,2,2,3,4,30000,5,22,1,43.6161565479177,59790,41.0488736158839,78570 +277,11,17,21,22,5,5,5,3,3,2,6,3,3,0,30,30,40,0,0,0,0,0,0,0,4,1,4,2,30000,1,29,2,41.6506516739075,58695,19.3076923076923,39000 +302,4,18,34,22,9,5,3,2,2,1,0,3,2,5,12,21,18,17,10,8,4,3,1,1,5,1,4,3,30000,5,40,1,29.6986301369863,48180,31.5288220551378,59850 +60,6,13,19,16,13,19,4,2,5,2,1,4,4,10,20,23,0,30,0,15,0,0,0,2,5,1,5,3,31000,3,33,1,32.6726556731479,60945,35.3559322033898,53100 +65,0,3,10,17,19,17,15,13,3,2,1,1,1,1,5,17,23,23,14,9,2,2,2,2,5,3,5,5,32000,7,26,1,23.1440922190202,78075,26.1634249944109,67095 +182,17,13,5,31,34,0,0,0,0,0,0,3,4,35,40,25,0,0,0,0,0,0,0,0,4,1,5,2,32000,5,32,2,26.7694562031911,46065,25.0398671096346,22575 +39,13,17,13,10,8,8,7,7,8,7,2,3,3,5,60,30,0,0,0,0,0,2,1,2,4,2,5,2,35000,1,28,1,40.6777551482236,66285,31.0496613995485,33225 +88,12,29,20,14,12,10,2,1,0,0,0,6,4,20,31,49,0,0,0,0,0,0,0,0,3,1,5,4,35000,6,29,1,33.7230113636364,42240,21.5621621621622,27750 +118,4,4,3,10,15,12,11,10,11,10,10,3,4,0,0,39,39,22,0,0,0,0,0,0,4,2,4,4,35000,3,29,2,29.3005229160258,97530,14.2972972972973,49950 +119,15,20,40,10,3,0,5,2,1,2,2,1,1,10,20,40,20,10,0,0,0,0,0,0,6,1,6,1,35000,9,32,2,38.451114922813,43725,24.6482213438735,37950 +136,10,20,20,19,5,3,2,2,2,2,15,1,1,0,0,97,0,0,0,0,0,0,0,3,5,1,6,1,35000,1,35,2,46.4983425414365,67875,12.854351687389,42225 +200,21,20,18,12,8,6,4,4,3,2,2,1,1,22,41,37,0,0,0,0,0,0,0,0,6,1,6,2,35000,4,38,1,43.5800182204677,49395,22.5839160839161,25740 +287,3,7,13,10,21,16,12,8,3,1,6,3,2,10,21,36,8,11,8,4,2,0,0,0,4,2,5,2,35000,1,27,2,30.5477719400662,77085,32.2020547945205,43800 +257,8,18,15,19,11,7,4,4,4,5,5,3,4,20,25,20,15,10,10,0,0,0,0,0,4,3,4,3,36000,6,48,1,40.0362234166861,64185,34.6953125,38400 +33,3,7,22,27,26,4,3,3,3,1,1,8,8,5,6,8,9,10,11,11,13,13,0,14,1,6,1,6,37000,6,69,1,26.7986714358712,58710,32.895486935867,94725 +130,4,10,13,14,13,13,12,9,6,4,2,1,1,0,10,33,5,6,17,9,0,3,10,7,5,1,6,5,37000,1,39,1,31.6262749898001,73530,36.1829871414441,75825 +293,19,20,30,10,9,6,5,1,0,0,0,2,3,25,15,13,22,19,6,0,0,0,0,0,1,4,1,6,37000,9,25,2,35.3952363230368,40305,33.9500924214418,40575 +43,19,18,10,10,9,11,6,7,6,1,3,4,3,0,1,16,32,43,5,0,0,0,3,0,4,2,5,2,38000,3,53,2,42.7884322678843,59130,16.6625310173697,60450 +113,9,18,32,21,11,2,1,2,2,1,1,3,3,1,30,51,13,3,1,1,0,0,0,0,4,2,6,3,38000,3,32,2,32.8014230271669,46380,20.2329103561195,36645 +135,43,13,4,3,3,2,2,1,2,6,21,4,4,0,82,18,0,0,0,0,0,0,0,0,4,3,4,4,38000,3,28,1,56.1749571183533,69960,10.7857142857143,25200 +178,7,16,15,13,12,10,8,8,5,4,2,2,2,11,38,10,17,5,6,11,0,0,0,2,4,1,4,4,38000,1,26,2,36.4278573051579,66015,39.4919221892516,45495 +29,3,9,18,21,17,12,9,5,3,2,1,7,3,0,15,37,37,0,0,11,0,0,0,0,4,1,5,4,39000,3,25,2,29.4516728624535,64560,23.2278481012658,47400 +5,2,10,15,15,15,15,10,7,5,5,1,5,5,10,20,30,15,0,0,20,0,0,0,5,1,3,3,4,40000,8,58,1,30.1548941521693,71565,40.3980582524272,54075 +20,16,12,16,16,9,4,5,5,5,6,6,1,1,13,3,4,4,4,3,2,1,2,3,61,6,1,6,1,40000,2,52,2,43.4097985347985,65520,28.5982410982411,139860 +56,11,15,17,20,15,10,4,2,2,2,2,1,1,14,22,20,21,20,1,1,1,0,0,0,5,1,6,5,40000,4,60,1,35.9379907933929,55395,30.7438672438672,41580 +63,5,19,45,14,5,4,3,2,1,1,1,1,1,20,50,15,10,2,2,0,0,0,0,1,6,2,6,4,40000,5,25,1,30.9703947368421,45600,32.5521628498728,29475 +78,11,12,13,14,14,11,8,5,4,4,4,5,7,10,20,24,24,15,7,0,0,0,0,0,1,1,4,5,40000,8,63,1,37.9000673703122,66795,28.3229166666667,43200 +82,4,7,11,5,1,8,5,8,15,13,23,2,3,2,14,50,30,4,0,0,0,0,0,0,5,2,5,5,40000,1,28,1,32.2458316659997,112455,17.1855136733186,40590 +86,10,10,20,25,10,10,5,5,2,2,1,2,2,2,70,20,8,0,0,0,0,0,0,0,6,2,5,4,40000,3,38,1,33.798418972332,56925,17.6511375947996,27690 +96,15,14,11,9,7,7,7,6,9,8,7,5,6,10,60,20,10,0,0,0,0,0,0,0,2,3,3,6,40000,6,25,1,42.570564516129,74400,21.8360655737705,27450 +98,5,15,15,10,15,12,5,8,5,7,3,2,3,1,0,7,90,2,0,0,0,0,0,0,5,3,5,5,40000,7,29,2,35.9598308668076,70950,5.21443178498393,51345 +100,6,10,16,14,13,12,11,10,4,3,1,5,5,10,17,25,19,15,14,0,0,0,0,0,4,2,4,2,40000,3,27,1,32.3520755545794,68295,29.2312703583062,46050 +144,15,34,30,12,5,0,0,0,0,0,4,5,5,0,35,65,0,0,0,0,0,0,0,0,5,3,4,3,40000,9,35,2,37.8880157170923,38175,12.5813953488372,32250 +170,24,49,22,1,0,1,1,0,1,1,0,1,1,20,20,36,17,4,0,0,1,1,1,0,6,3,6,4,40000,1,25,1,31.0993377483444,27180,31.9707112970711,35850 +175,3,19,27,24,16,6,2,1,1,1,0,1,1,9,53,28,6,4,0,0,0,0,0,0,5,2,5,2,40000,5,34,2,27.8659097882786,48885,23.5293817066939,29355 +177,9,13,27,18,11,7,5,3,3,2,2,3,3,2,5,29,36,17,8,3,0,0,0,0,3,1,4,3,40000,6,34,2,36.0202319935258,55605,20.349252013809,52140 +189,10,20,25,25,10,3,2,2,1,1,1,3,3,0,10,10,10,20,10,10,5,3,2,20,4,2,4,3,40000,5,51,1,33.3273905996759,46275,35.1594896331738,94050 +204,10,10,12,13,14,10,8,7,6,6,4,9,9,0,0,5,15,20,10,10,10,10,10,10,1,6,1,6,40000,9,31,1,36.9230769230769,72150,26.5833333333333,99000 +223,10,17,21,27,11,3,1,1,2,3,4,2,3,2,3,26,30,7,14,12,3,1,1,1,3,2,5,3,40000,4,45,2,37.9469273743017,53700,26.4645517904349,62415 +224,10,0,40,28,22,0,0,0,0,0,0,5,4,7,5,4,6,7,8,10,9,36,8,0,3,3,4,4,40000,4,25,2,20.8393442622951,45750,25.1078694212269,95115 +232,7,20,22,17,11,8,7,3,2,2,1,1,1,5,12,13,28,27,15,0,0,0,0,0,5,1,5,4,40000,3,26,1,35.4559068219634,54090,23.1907433380084,53475 +240,9,23,42,12,3,2,2,3,2,1,1,3,3,0,7,18,72,3,0,0,0,0,0,0,4,2,4,3,40000,5,28,1,34.1054823039556,43230,10.7757009345794,48150 +244,16,23,17,12,11,5,5,3,3,3,2,3,3,10,43,47,0,0,0,0,0,0,0,0,4,1,5,5,40000,2,32,2,42.2524723676556,51570,18.4263157894737,28500 +248,15,9,15,14,10,5,9,11,3,2,7,8,8,15,9,22,22,9,8,13,2,0,0,0,6,1,6,3,40000,1,52,2,41.0845986984816,69150,33.0937042459737,51225 +280,14,20,20,15,8,6,5,3,3,2,4,2,2,7,25,30,22,10,0,0,2,1,1,2,4,1,4,3,40000,2,28,1,42.2932454695223,54630,33.3764627214978,44865 +296,2,8,22,17,5,12,12,8,8,4,2,1,1,1,23,53,10,13,0,0,0,0,0,0,4,1,6,6,40000,4,31,2,32.0250719276613,72990,20.3517030233448,39195 +89,8,16,40,21,6,3,2,1,1,1,1,4,4,8,4,22,35,20,11,0,0,0,0,0,4,2,3,6,42000,3,28,1,30.5001672800268,44835,22.3678025851939,51060 +234,23,27,15,9,6,5,4,2,2,2,5,7,5,20,30,50,0,0,0,0,0,0,0,0,1,5,1,6,42000,9,34,1,48.0086313193588,48660,21.4623655913978,27900 +303,19,18,17,15,12,10,4,2,1,1,1,5,5,7,29,54,7,3,0,0,0,0,0,0,5,5,5,4,43000, ,47,2,38.7624135763671,47730,19.7879333633498,33315 +55,20,20,30,10,10,3,3,1,1,1,1,3,3,3,2,2,2,2,3,5,9,14,24,34,6,1,6,3,44000,5,28,2,38.2558983666062,41325,21.5049898057732,139785 +163,2,3,18,15,17,13,9,8,6,4,5,3,3,1,1,40,4,16,4,2,20,10,2,0,5,2,5,3,44000,5,31,2,30.4634611715321,78615,29.6834276136602,71595 +80,5,18,26,18,6,5,2,2,5,6,7,7,6,1,2,51,39,7,0,0,0,0,0,0,6,1,4,5,45000,3,44,2,41.3194444444444,64800,13.6782492482459,44895 +84,2,4,4,6,19,10,10,5,9,10,21,5,5,0,9,0,50,25,0,0,0,16,0,0,3,3,2,5,45000,6,28,2,30.2546481200936,108915,23.5858123569794,65550 +102,0,1,13,26,24,10,10,10,2,2,2,1,1,11,34,40,3,4,1,4,0,0,1,2,4,1,5,1,45000,1,49,2,24.2632653061224,73500,35.0485703094399,38295 +184,10,12,14,15,13,10,8,7,5,5,1,2,2,0,3,36,39,20,1,0,1,0,0,0,4,3,5,2,45000,3,54,2,35.9298043728424,65175,16.311377245509,50100 +192,17,33,39,11,0,0,0,0,0,0,0,1,1,10,20,29,41,0,0,0,0,0,0,0,5,1,2,2,45000,5,30,2,24.4344550477147,29865,22.2716535433071,38100 +217,20,20,20,12,10,7,3,3,2,2,1,1,1,0,47,40,12,0,0,0,0,0,0,1,6,1,6,1,45000,1,36,1,41.1664,46875,22.2328159645233,33825 +284,20,0,38,20,0,0,0,11,0,0,11,3,2,3,19,27,51,0,0,0,0,0,0,0,4,1,4,5,45000,4,38,1,44.8154981549816,60975,17.9577464788732,41535 +285,15,5,13,16,11,12,10,6,4,5,3,1,1,24,27,42,4,2,0,0,0,0,0,1,6,1,6,2,45000,3,29,2,37.0330396475771,68100,29.6995515695067,30105 +196,11,13,15,14,13,9,7,6,6,4,2,1,1,21,39,25,7,6,2,0,0,0,0,0,5,1,6,3,45900,2,49,1,37.6766285314032,64245,30.7983025461807,30045 +120,45,25,10,2,2,2,2,2,0,0,10,1,1,100,0,0,0,0,0,0,0,0,0,0,6,1,6,4,46000,2,29,1,56.5414462081129,42525,2,12000 +8,5,14,12,22,17,10,6,5,3,4,2,2,3,0,29,54,3,10,0,4,0,0,0,0,5,2,4,3,47000,2,32,2,33.372526193248,64425,22.9307692307692,39000 +148,18,35,15,10,7,5,4,3,1,1,1,3,3,7,15,16,7,2,4,8,17,1,6,17,6,1,6,4,48000,7,31,2,41.541921554516,41685,40.9165535956581,88440 +150,8,11,14,17,12,9,8,7,6,6,2,1,1,3,16,20,26,30,3,2,0,0,0,0,2,3,5,4,48000,7,53,1,35.6639094471049,68910,23.2946670683941,49785 +256,25,42,12,15,1,1,1,1,1,1,0,3,1,0,29,70,1,0,0,0,0,0,0,0,6,3,5,2,48000,2,27,1,36.6578313253012,31125,11.7207207207207,33300 +265,4,6,16,1,9,10,17,15,12,8,2,5,3,0,0,0,96,0,0,4,0,0,0,0,1,2,1,2,49000,7,56,1,28.7810446212383,86730,5.18232044198896,54300 +13,42,8,5,9,3,4,7,5,5,8,4,6,7,12,6,13,20,10,5,25,1,1,3,4,3,4,3,4,50000,3,29,2,51.6069730586371,56790,35.1997348652231,67890 +25,5,5,33,31,7,19,0,0,0,0,0,6,5,9,14,23,16,21,1,16,0,0,0,0,5,6,6,6,50000,6,26,2,23.1122599704579,50775,30.7512293896442,51855 +30,2,7,7,12,19,16,7,8,3,6,13,3,3,0,0,6,18,30,23,11,7,3,2,0,3,4,3,4,50000,5,55,2,32.645573824946,90315,18.4763779527559,76200 +35,30,40,5,4,4,4,4,3,2,2,2,1,1,39,39,11,11,0,0,0,0,0,0,0,4,1,5,3,50000,5,29,2,48.3563218390804,39150,30.0044958253051,23355 +64,12,18,9,10,7,7,6,7,6,10,8,8,5,2,5,9,4,0,78,0,0,0,2,0,2,4,3,5,50000,7,54,2,42.5996835443038,75840,14.3910048622366,74040 +133,14,15,13,10,10,12,8,8,4,2,4,3,2,11,9,13,15,15,9,2,8,2,8,8,5,2,6,2,50000,5,49,2,40.2054986020503,64380,39.7489378919684,74145 +171,68,32,0,0,0,0,0,0,0,0,0,3,3,64,0,0,0,36,0,0,0,0,0,0,5,5,5,5,50000,7,24, ,16.875,15360,41.984990619137,31980 +188,5,10,10,10,10,10,10,10,10,10,5,8,8,0,0,0,0,30,0,30,0,0,30,10,1,5,1,5,50000,8,49,2,33.5514184397163,84600,21.5302013422819,111750 +197,2,6,23,31,8,8,8,4,4,2,4,1,1,0,12,20,55,13,0,0,0,0,0,0,1,1,6,1,50000,5,29,2,31.7479711451758,66540,15.6771159874608,47850 +202,5,10,20,20,12,10,8,6,4,3,2,5,4,2,34,24,5,2,2,6,10,10,4,1,4,3,4,4,50000,3,36,1,33.3598615916955,65025,40.5846038490377,60015 +281,76,24,0,0,0,0,0,0,0,0,0,5,3,60,40,0,0,0,0,0,0,0,0,0,1,2,2,2,50000,3,25,1,15.1900826446281,14520,17.5555555555556,16200 +292,10,16,17,15,12,9,7,5,4,3,2,3,3,5,14,16,15,13,8,4,4,5,7,9,4,2,5,3,50000,3,25,2,37.7810945273632,60300,39.4123505976096,75300 +10,7,10,16,17,18,12,8,5,3,2,2,3,3,0,22,24,29,9,8,3,2,1,1,1,3,3,5,4,51000,6,63,1,32.7438538983844,64065,29.4702467343977,51675 +249,5,25,20,10,15,10,5,3,2,1,4,3,3,0,15,60,5,5,5,5,5,0,0,0,5,2,5,2,51000,1,24,1,38.2156862745098,57375,25.8709677419355,46500 +143,10,25,17,15,10,8,5,4,3,2,1,3,3,0,33,14,11,12,10,10,10,0,0,0,4,3,4,4,52000,3,26,1,38.5078683834048,52425,33.5537634408602,55800 +9,13,21,32,20,5,2,2,2,1,1,1,2,1,1,11,19,22,28,13,3,2,1,0,0,6,4,6,3,55000,3,51,2,34.5675105485232,42660,23.5754642950562,57345 +28,13,6,5,12,12,12,12,10,8,6,4,5,5,1,0,0,85,0,0,13,0,0,0,1,4,1,3,5,55000,6,34,1,34.6220652796335,78585,13.4269833249116,59370 +193,19,15,14,12,10,8,6,6,5,3,2,2,2,0,0,0,30,30,20,0,0,0,0,20,5,1,4,1,55000,2,25,1,42.1307512347284,57705,29.0491803278688,91500 +215,40,43,17,0,0,0,0,0,0,0,0,5,5,0,0,98,0,0,0,0,0,0,0,2,3,2,5,4,56000,2,27,2,24.2374100719424,20850,9.59409594095942,40650 +219,8,10,15,15,15,10,7,6,5,5,4,9,1,0,0,20,45,15,15,5,0,0,0,0,5,1,6,5,56000,5,44,1,36.2349914236706,69960,17.2564102564102,58500 +167,9,12,15,18,15,13,8,4,3,2,1,3,3,1,13,15,17,27,21,2,1,1,1,1,3,2,5,3,58000,2,50,1,33.5111331024246,60630,25.580088713652,60870 +198,6,10,15,18,11,5,9,6,3,10,7,9,9,0,19,26,21,22,12,0,0,0,0,0,2,4,3,6,58000,5,28,1,37.6535233261761,76845,24.0301204819277,49800 +49,12,18,20,16,12,10,5,3,2,1,1,3,3,10,20,30,23,10,5,1,1,0,0,0,2,3,4,5,59000,6,40,1,36.4776076278532,51915,28.7392857142857,42000 +288,7,9,14,25,20,12,5,2,3,2,1,3,3,0,0,30,36,24,9,0,0,0,0,1,5,1,6,4,59000,5,25,1,30.1127670144063,60390,18.114401076716,55725 +2,14,6,12,7,30,7,7,5,5,5,2,5,5,0,0,23,34,14,29,0,0,0,0,0,1,4,4,5,60000,7,34,2,35.0161001788909,67080,17.7418546365915,59850 +66,4,29,43,9,6,4,1,1,1,1,1,2,2,0,0,0,0,53,25,13,4,5,0,0,5,2,5,2,60000,1,31,2,30.9249195566679,41955,12.3208255159474,79950 +94,9,17,26,10,10,9,5,5,3,3,3,9,8,1,18,15,48,5,10,0,1,0,0,2,4,6,2,5,60000,1,34,2,39.1855458739108,58530,25.5820505373221,51645 +109,9,10,16,18,14,11,9,5,3,3,2,9,9,13,14,15,14,14,11,7,5,4,2,1,1,6,1,6,60000,6,29,1,34.5719331292677,63705,36.9544057377049,58560 +140,8,7,8,12,14,12,9,10,6,7,7,5,5,18,18,19,45,0,0,0,0,0,0,0,4,3,4,3,60000,3,56,2,34.5453718857974,82485,25.588474025974,36960 +231,9,12,14,15,14,12,11,7,4,1,1,1,1,3,4,5,5,4,4,39,19,4,6,7,5,1,4,2,60000,2,62,2,33.6740881099006,63330,23.2982937233394,98460 +263,0,2,6,11,18,23,20,5,5,5,5,5,4,0,10,10,9,60,9,0,0,0,0,2,2,3,4,5,60000,7,27,2,23.5507246376812,87975,18.8057553956835,62550 +272,11,27,19,14,12,6,4,2,2,2,1,7,7,1,2,9,34,34,15,4,1,0,0,0,2,3,4,6,60000,5,25,1,38.2602230483271,48420,16.6615978759353,62145 +40,6,18,19,13,13,11,9,4,3,3,1,3,3,9,13,20,21,15,9,4,3,3,2,1,4,2,4,3,61000,3,38,1,35.1412471825695,59895,33.9501607717042,55980 +216,6,23,23,15,10,8,5,4,3,2,1,2,1,5,15,30,20,11,8,5,3,2,1,0,4,1,6,3,62000,1,57,1,36.3519397153224,53745,30.8730158730159,51975 +250,5,11,29,16,12,11,6,4,3,2,1,5,5,0,5,42,42,11,0,0,0,0,0,0,5,5,4,5,62000,4,24,1,32.6333333333333,58500,14.7669902912621,46350 +17,14,16,15,20,7,6,7,4,4,4,3,4,3,5,15,20,30,4,6,5,15,0,0,0,3,3,3,4,63000,6,67,1,40.9847599695199,59055,31.7523178807947,56625 +155,8,14,24,21,14,9,4,3,1,1,1,5,4,0,0,11,30,42,17,0,0,0,0,0,2,2,3,3,63000,3,29,2,32.4978747520544,52935,13.5879518072289,62250 +226,47,53,0,0,0,0,0,0,0,0,0,5,5,0,61,0,0,0,0,39,0,0,0,0,4,4,4,4,63000,9,29,1,16.8906917164817,17565,36.4782608695652,51750 +235,20,15,13,13,9,8,7,6,5,3,1,5,5,1,4,23,69,1,1,1,0,0,0,0,3,2,3,4,65000,7,33,1,41.7083888149134,56325,11.7620229599752,48345 +252,10,16,29,9,8,8,4,4,4,4,4,8,8,7,19,7,12,25,1,0,0,29,0,0,1,2,1,5,65000,1,64,2,40.9621212121212,59400,35.4920323073565,68715 +267,0,0,0,37,43,11,3,2,2,1,1,4,4,0,0,0,11,52,31,6,0,0,0,0,3,3,4,4,65000,6,35,2,15.64043715847,68625,10.0373443983403,72300 +273,10,20,6,20,10,7,6,5,6,5,5,2,5,0,30,40,0,30,0,0,0,0,0,0,3,2,4,4,65000,7,68,1,40.6976483762598,66975,24.5,42000 +228,5,10,18,24,20,11,4,3,2,2,1,5,5,4,8,20,24,21,16,3,1,1,1,1,3,2,4,5,68000,5,43,1,29.6388888888889,59400,26.6453682319733,58455 +34,10,10,10,5,10,15,15,10,5,5,5,4,4,10,10,15,20,10,10,5,5,5,5,5,4,1,4,2,70000,7,40,2,35.030739673391,78075,38.3721185510428,68325 +79,2,3,6,7,8,10,10,12,12,13,17,5,5,0,0,0,50,0,0,47,0,0,0,3,4,2,4,3,70000,7,59,1,28.0854897710337,110715,20.0779595765159,77925 +166,5,10,20,20,14,9,5,5,4,4,4,4,4,0,6,8,6,19,42,10,6,3,0,0,4,2,5,5,70000,3,25,1,35.1301907968574,66825,18.7250996015936,75300 +180,5,10,14,19,19,12,5,4,4,4,4,7,7,0,0,5,21,21,50,3,0,0,0,0,2,3,1,6,70000,7,26,2,33.6521264994547,68775,12.9073684210527,71250 +27,11,13,16,27,5,5,5,5,5,5,3,1,1,3,2,3,5,11,8,9,8,6,25,20,6,1,6,1,71000,2,54,2,39.0781932977173,61770,26.7043874067518,118635 +218,5,5,10,50,5,5,8,4,4,3,1,5,5,0,0,0,38,29,21,12,0,0,0,0,4,4,4,4,71000,3,28,1,28.0859188544153,62850,14.3041575492341,68550 +271,5,12,17,29,15,9,4,3,3,2,1,1,1,0,14,23,31,24,8,0,0,0,0,0,5,1,6,5,72000,1,27,2,30.7557840616967,58350,21.0176991150443,50850 +3,9,23,29,19,10,3,2,2,1,1,1,3,3,0,1,14,46,27,4,2,2,2,1,1,1,2,2,5,73000,7,31,2,33.832005312085,45180,19.2227662178702,61275 +297,10,10,13,13,14,13,10,6,5,4,2,7,7,3,10,15,15,15,15,9,6,5,4,3,2,2,3,6,73000, ,32,1,35.0442477876106,67800,31.996253122398,72060 +42,11,32,39,5,0,7,0,3,1,0,2,5,5,0,21,24,5,50,0,0,0,0,0,0,1,6,1,6,74000,5,44,1,36.3258511036289,40095,21.8622754491018,50100 +45,15,15,16,14,10,7,6,4,4,4,5,5,7,0,2,60,10,10,14,1,0,1,1,1,1,3,1,6,74000,9,45,1,42.6077481840194,61950,23.7338129496403,52125 +262,14,12,13,15,12,10,6,5,4,4,5,3,4,4,10,33,35,16,2,0,0,0,0,0,5,3,4,3,74000,4,26,1,40.4389741241127,65505,20.4056172436316,45930 +16,10,25,26,18,9,4,3,2,2,1,0,1,1,0,0,0,50,50,0,0,0,0,0,0,5,2,6,5,75000,1,25,1,34.3232323232323,44550,8.25,60000 +116,5,11,23,18,13,10,7,5,4,3,1,1,1,3,10,21,39,20,5,2,0,0,0,0,4,2,6,3,75000,2,66,1,33.002421307506,61950,20.9869397447314,50535 +139,7,10,16,20,16,10,7,5,4,2,3,8,8,2,4,15,18,23,25,7,3,1,1,1,1,3,2,6,75000,8,51,1,34.0924641701341,64890,23.4766793126534,67215 +142,6,10,36,20,8,6,6,4,2,1,1,3,3,15,17,22,0,21,22,0,0,0,0,3,5,2,5,2,75000,2,31,2,31.9952420934789,53595,36.8530259365994,52050 +162,6,9,15,19,17,9,9,6,5,3,2,2,2,10,14,21,23,18,13,1,0,0,0,0,3,3,4,3,75000,2,32,1,32.7326203208556,67320,28.1931464174455,48150 +199,21,16,16,15,11,8,5,3,2,2,1,5,4,17,23,40,12,6,1,1,0,0,0,0,5,2,6,3,75000,5,28,1,40.6874244256348,49620,28.2710606721956,34365 +241,39,31,12,1,0,17,0,0,0,0,0,3,6,28,38,32,0,0,2,0,0,0,0,0,4,5,4,5,75000,4,20,1,41.390815828041,30705,26.9014084507042,25560 +6,11,13,16,17,14,8,6,5,4,3,3,3,2,4,9,18,21,19,12,7,8,0,2,0,4,2,6,2,76000,2,29,1,37.9394379844961,61920,28.2568093385214,61680 +106,10,15,18,14,11,10,7,6,5,3,1,4,3,0,2,88,8,2,0,0,0,0,0,0,4,2,5,2,76000,3,31,1,37.002478314746,60525,6.96923076923076,39000 +278,1,3,10,14,19,22,11,9,6,4,1,5,5,1,1,2,7,61,20,5,0,0,0,3,2,2,2,6,77000,8,36,1,24.2167805618831,79020,14.8573185731858,73170 +59,19,11,6,13,7,7,6,9,8,8,6,8,7,11,15,23,24,18,7,0,0,1,0,1,1,2,1,6,78000,3,52,2,42.3674342775627,73605,30.4633524537922,47070 +274,6,14,17,21,16,7,3,4,5,5,2,3,3,0,0,0,0,0,21,29,24,19,6,1,3,1,5,2,78000,4,35,2,35.4174246050742,62670,11.8958770090845,107325 +291,10,14,31,20,13,3,2,2,2,1,2,3,3,0,15,21,41,15,8,0,0,0,0,0,5,4,4,5,78000,7, ,1,34.4759036144578,49800,20.4424242424243,49500 +69,3,19,39,10,23,4,0,0,0,0,2,3,3,0,12,30,37,8,3,10,0,0,0,0,5,4,2,4,80000,7,37,2,28.6487773896475,47235,23.1235294117647,51000 +160,6,11,18,20,13,7,5,5,5,6,4,9,8,0,4,10,15,28,30,5,7,0,0,1,2,5,3,5,80000,5,30,1,36.5773966578716,68220,20.0095238095238,70875 +181,5,11,18,23,17,11,6,4,2,2,1,7,6,30,50,20,0,0,0,0,0,0,0,0,3,2,4,5,80000,9,32,1,30.6832917705736,60150,22.6040268456376,22350 +205,1,13,27,32,21,1,1,1,1,0,2,2,2,0,0,50,30,20,0,0,0,0,0,0,2,1,5,3,80000,4,29,2,25.7131782945736,52245,14.8125,48000 +211,20,30,15,6,4,3,4,2,2,4,10,1,1,41,36,0,0,0,23,0,0,0,0,0,4,1,5,3,80000,2,26,1,51.2605263157895,57000,43.1500234411627,31995 +70,27,20,13,9,8,5,4,3,2,3,6,6,6,30,20,0,0,19,11,0,20,0,0,0,2,3,3,5,81000,8,37,1,49.3731130731985,52665,42.8828571428571,52500 +210,2,14,23,19,15,8,7,5,4,2,1,7,7,1,2,12,12,12,13,13,12,10,8,5,4,3,3,4,82000,7,42,1,31.8469663473348,61065,27.4582917912928,90270 +36,25,18,6,6,6,5,6,6,6,6,10,3,5,30,13,0,17,0,0,0,0,20,0,20,1,2,1,6,85000,6,53,2,48.9782372143635,68925,49.9774859287054,79950 +73,3,9,11,36,10,7,5,8,2,7,2,8,8,5,10,10,10,30,10,5,5,10,5,0,3,3,2,4,85000,7,51,2,31.5355023081996,68235,30.117709437964,70725 +147,0,4,7,9,18,20,16,10,7,5,4,9,9,0,0,0,0,16,22,31,21,10,0,0,1,5,1,6,85000,8,36,2,24.5787671232877,87600,12.5792778649921,95550 +152,3,9,16,19,15,13,8,6,5,4,2,7,6,0,0,7,28,51,3,2,2,2,1,4,1,4,3,5,85000,8,28,2,31.3346956334696,69735,19.9444444444445,70200 +153,9,16,16,6,2,10,14,5,2,8,12,3,2,4,16,46,22,2,2,1,1,1,0,5,4,2,5,5,85000,3,26,1,41.8338017612891,80055,33.0906029996939,49005 +260,10,10,10,37,3,3,3,3,5,5,11,5,5,11,5,11,17,11,45,0,0,0,0,0,1,3,1,6,85000,7,40,1,41.099481865285,72375,24.3315013739695,60045 +121,3,4,8,11,17,19,9,8,7,8,6,3,3,1,1,26,31,22,6,5,7,0,0,1,5,2,6,5,86000,2,37,1,29.1714088635972,86985,23.3178905864958,60870 +168,6,9,26,20,15,12,5,3,2,1,1,4,4,2,8,22,32,16,10,5,3,2,0,0,3,3,4,5,88100,6,28,2,30.6527668502492,57195,24.8877766069547,56940 +112,10,10,25,28,17,4,2,1,1,1,1,7,7,4,8,11,28,24,16,2,2,2,2,1,1,2,2,6,89000,9,33,1,30.0584707646177,50025,26.2985255015712,62055 +12,20,30,15,10,7,6,5,3,2,1,1,6,7,26,31,16,9,7,4,3,2,1,1,0,2,5,3,4,90000,7,35,1,42.6136752136752,43875,41.0269607843137,36720 +38,30,22,14,7,6,2,5,3,2,2,7,3,3,16,42,6,30,6,0,0,0,0,0,0,4,3,5,2,90000,5,36,1,51.5081723625557,50475,30.0287253141831,33420 +47,20,0,0,40,0,0,35,0,0,0,5,6,5,0,10,0,0,0,0,90,0,0,0,0,4,4,4,5,90000,4,30,2,34.5696767001115,67275,9.5,90000 +85,9,14,24,14,10,6,5,5,5,5,3,3,3,1,1,7,11,43,23,5,2,3,2,2,3,3,4,4,90000,3,27,1,39.1941091260261,62130,19.6492041104171,74445 +159,2,31,10,40,0,0,0,0,3,7,7,4,5,3,0,0,42,55,0,0,0,0,0,0,1,3,2,5,90000,6,56,2,40.7192628124211,59415,10.2154195011338,59535 +183,17,14,13,10,8,8,8,6,6,7,3,2,2,0,0,2,1,26,26,23,20,2,0,0,5,2,5,2,90000,7,40,2,42.1955922865014,65340,13.851282051282,87750 +289,12,20,13,33,15,7,0,0,0,0,0,3,3,1,4,19,16,19,19,15,7,0,0,0,3,2,5,2,90000,4,43,2,28.4209809264305,44040,23.2176326893182,67545 +301,14,13,13,10,9,10,9,7,7,5,3,5,5,7,16,15,13,12,12,11,5,4,3,2,3,2,4,4,90000,5,27,2,39.7574883514533,67605,35.6472913275982,64515 +46,11,12,13,13,13,8,7,7,6,6,4,3,3,2,3,10,30,31,17,2,2,1,1,1,4,4,4,5,98000,7,69,1,38.6952503761014,69795,21.027563653352,64215 +201,12,11,15,13,20,13,5,4,2,3,2,1,1,2,2,3,5,12,13,17,27,10,6,3,1,2,1,6,100000,8,45,2,35.5126050420168,60690,20.8625789554768,97365 +203,11,36,15,12,7,6,4,4,2,1,2,1,2,0,11,68,0,0,0,0,0,0,21,0,4,1,5,3,100000,2,29,2,41.3190612115446,47295,34.6217616580311,57900 +208,17,18,21,21,5,4,2,2,4,2,4,3,3,0,18,27,24,17,5,4,2,1,1,1,5,2,4,4,100000,3,24,2,42.8070328392909,51615,28.5768688293371,53175 +242,4,8,13,16,15,12,12,10,5,3,2,4,3,10,10,21,21,19,11,8,0,0,0,0,4,2,5,4,100000,4,20,1,30.5710180623974,73080,28.7435158501441,52050 +299,5,15,24,11,10,5,5,5,5,5,10,5,5,0,10,10,14,10,13,11,8,5,5,14,2,3,3,4,100000,8,30,1,41.2748717948718,73125,33.8752052545156,91350 +127,3,9,12,14,16,15,12,8,4,4,3,3,7,0,0,0,0,0,28,18,17,22,13,2,4,2,4,5,104000,2,37,2,30.5918940609952,74760,13.9619047619048,110250 +191,12,24,39,12,5,3,1,1,1,1,1,3,3,0,0,0,0,0,50,50,0,0,0,0,3,2,2,4,109000,7,42,2,33.5039018952063,40365,6.16666666666666,90000 +126,1,3,38,23,6,2,10,6,6,4,1,5,5,11,25,41,15,6,0,0,0,1,1,0,4,3,5,4,110000,3,36,2,30.797121634169,64620,27.9005278116119,36945 +164,16,21,31,11,5,3,3,2,2,1,5,5,5,47,0,19,0,3,29,0,0,0,1,1,1,3,3,6,110000,4,52,2,43.3289271441746,48795,44.7377049180328,42090 +172,10,10,15,17,25,13,6,1,1,1,1,2,2,0,1,2,5,21,21,21,23,2,2,2,5,2,5,4,110000,2,27,1,30.5439056356488,57225,17.500826446281,90750 +270,10,0,51,14,0,0,0,10,10,0,5,9,8,0,5,30,35,10,10,5,5,0,0,0,2,3,2,4,110000,7,47,1,38.1941391941392,61425,22.8666666666667,56250 +236,10,10,25,22,10,9,6,3,2,2,1,3,2,10,20,20,20,10,9,6,0,0,0,5,4,2,4,2,115000,4,32,2,33.9442176870748,55125,38.4558204768583,53475 +264,10,13,13,33,8,7,7,7,1,1,0,7,5,7,27,25,18,9,4,3,3,3,1,0,2,2,3,4,120000,3,34,2,32.2540983606557,54900,34.5544840887174,46665 +286,19,0,25,0,34,22,0,0,0,0,0,3,3,0,0,0,10,39,22,29,0,0,0,0,4,3,4,3,120000,6,56,2,28.330110889963,52755,12.4961538461538,78000 +95,2,4,16,14,16,17,11,9,6,2,3,6,7,0,20,39,0,22,0,0,0,19,0,0,1,2,1,6,125000,8,59,1,28.3256224269751,76515,34.4587628865979,58200 +14,11,11,13,21,11,12,5,4,5,4,3,8,7,0,9,11,13,21,20,9,5,6,2,4,1,6,1,6,129000,9,31,1,37.1674440298508,64320,28.3516699410609,76350 +261,5,10,18,20,22,6,6,3,3,3,4,4,5,0,0,20,50,0,0,0,0,30,0,0,3,2,4,5,129000,5,28,1,33.717416378316,65025,27.2083333333333,72000 +22,21,26,19,13,8,4,3,2,2,1,1,2,3,36,14,0,23,19,8,0,0,0,0,0,4,3,5,3,130000,5,45,1,41.1588319088319,42120,37.8729792147806,38970 +91,2,5,12,21,16,13,10,7,5,4,5,3,3,1,3,10,20,30,18,8,5,2,2,1,6,1,6,5,130000,2,53,1,30.2695049123483,77865,22.5029610829103,70920 +158,12,15,17,15,10,7,6,5,6,5,2,2,2,24,0,11,24,19,0,3,9,4,0,6,5,2,6,1,130000,3,59,2,39.6584062196307,61740,41.0264932562621,62280 +254,0,50,37,0,0,9,0,0,2,1,1,4,4,25,0,50,0,20,0,0,0,0,0,5,3,4,3,3,130000,7,28,2,31.5808966861598,38475,38.5833333333333,45000 +283,2,6,14,24,23,14,7,5,2,2,1,4,3,17,13,10,9,9,12,10,10,4,3,3,4,2,4,5,140000,3,25,1,26.0572268717485,66315,39.9460694698355,65640 +105,5,10,50,10,5,5,5,4,3,2,1,8,8,0,0,0,0,0,40,20,20,10,10,0,1,4,2,5,150000,9,36,2,32.756446991404,52350,12.7352941176471,102000 +279,7,12,14,16,13,10,8,6,5,5,4,4,6,19,20,0,26,30,0,0,0,0,3,2,3,4,4,4,150000,2,21,2,36.3474630700064,70065,36.2993552348787,48855 +58,3,5,8,9,9,10,12,11,11,11,11,4,5,0,10,11,13,16,17,16,9,4,2,2,2,4,3,5,159850,5,43,1,30.3781895504253,98760,27.2599206349206,75600 +7,20,15,15,11,9,7,7,4,4,4,4,3,3,0,0,0,0,49,0,30,0,0,21,0,5,2,5,5,160000,2,53,2,44.3027989821883,58950,18.219512195122,92250 +194,5,6,8,10,11,13,14,14,8,8,3,5,4,13,18,22,17,27,3,0,0,0,0,0,1,3,3,5,164000,9,39,2,29.3732394366197,85200,28.94963780614,43485 +173,1,4,7,7,10,15,24,20,6,6,0,5,3,3,6,3,7,12,25,13,15,9,7,0,5,2,4,3,175000,3,26,2,21.4109774690835,88545,23.4901722038615,86235 +141,1,8,20,20,15,10,7,6,5,5,3,3,3,0,0,20,20,10,10,10,0,0,0,30,4,2,5,4,180000,3,35,2,31.6941870261163,71220,35.4074074074074,101250 +149,37,30,14,4,4,3,2,2,2,1,1,1,1,80,8,2,2,2,1,1,1,1,1,1,6,1,6,1,180000,9,55,1,45.7154255319149,33840,43.1423728813559,22125 +137,9,10,11,15,12,10,6,6,6,5,10,5,5,0,0,2,5,7,9,10,15,19,17,16,1,3,2,6,185000,7,55,1,39.0523200305518,78555,21.0197044334975,121800 +290,7,7,9,10,17,13,12,9,7,5,4,8,8,0,1,2,4,6,9,11,14,14,16,23,1,4,1,6,190000,7,46,1,32.1362858772097,78915,22.3526970954357,126525 +54,3,17,14,12,13,13,11,5,6,3,3,1,1,8,20,22,24,0,0,26,0,0,0,0,6,3,6,3,200000,5,27,2,34.4744809688581,69360,33.3681765389082,51660 +214,7,10,12,18,20,10,9,7,4,2,1,3,3,1,3,5,9,19,20,19,11,6,5,2,3,4,4,4,200000,5,32,1,31.5941230486685,65340,22.5498001042934,86295 +239,7,9,16,25,15,12,6,4,3,2,1,3,3,0,0,0,0,0,0,0,17,39,44,0,3,3,5,6,200000,5,60,1,31.2380718150516,60990,6.41847206385405,131550 +212,5,10,15,20,15,10,8,4,4,2,7,5,5,0,0,0,0,0,10,10,10,10,20,40,1,5,2,1,240000,8,23,1,35.6778947368421,71250,17.5555555555555,148500 +195,5,6,13,5,14,12,10,14,10,10,1,4,5,0,0,0,0,0,18,19,25,20,17,1,2,2,4,5,275000,4,25,1,29.7117117117117,83250,12.583830351226,113175 +101,7,13,19,23,14,11,4,3,3,2,1,3,3,0,2,2,4,4,6,11,11,10,10,40,2,3,4,4,350000,4,43,1,32.7532671197073,57390,22.7195253505933,139050 +62,26,22,17,12,9,6,2,2,2,1,1,2,1,0,0,0,0,0,60,20,20,0,0,0,4,3,6,2, ,5,66,1,42.5273899033297,41895,8.55737704918035,91500 +227,66,20,14,0,0,0,0,0,0,0,0,5,5,19,52,29,0,0,0,0,0,0,0,0,3,3,3,3, ,5,26,2,25.5551782682513,17670,20.92757996379,24855 +253,6,32,16,16,10,9,4,3,2,1,1,3,3,10,10,10,10,50,0,0,0,0,0,10,4,3,4,3, ,3,33,1,36.6854814367643,49695,34.8310502283105,65700 diff --git a/wip/Data/Diet.csv b/wip/Data/Diet.csv new file mode 100644 index 0000000..d64ea76 --- /dev/null +++ b/wip/Data/Diet.csv @@ -0,0 +1,79 @@ +Person,gender,Age,Height,pre.weight,Diet,weight6weeks +25, ,41,171,60,2,60 +26, ,32,174,103,2,103 +1,0,22,159,58,1,54.2 +2,0,46,192,60,1,54 +3,0,55,170,64,1,63.3 +4,0,33,171,64,1,61.1 +5,0,50,170,65,1,62.2 +6,0,50,201,66,1,64 +7,0,37,174,67,1,65 +8,0,28,176,69,1,60.5 +9,0,28,165,70,1,68.1 +10,0,45,165,70,1,66.9 +11,0,60,173,72,1,70.5 +12,0,48,156,72,1,69 +13,0,41,163,72,1,68.4 +14,0,37,167,82,1,81.1 +27,0,44,174,58,2,60.1 +28,0,37,172,58,2,56 +29,0,41,165,59,2,57.3 +30,0,43,171,61,2,56.7 +31,0,20,169,62,2,55 +32,0,51,174,63,2,62.4 +33,0,31,163,63,2,60.3 +34,0,54,173,63,2,59.4 +35,0,50,166,65,2,62 +36,0,48,163,66,2,64 +37,0,16,165,68,2,63.8 +38,0,37,167,68,2,63.3 +39,0,30,161,76,2,72.7 +40,0,29,169,77,2,77.5 +52,0,51,165,60,3,53 +53,0,35,169,62,3,56.4 +54,0,21,159,64,3,60.6 +55,0,22,169,65,3,58.2 +56,0,36,160,66,3,58.2 +57,0,20,169,67,3,61.6 +58,0,35,163,67,3,60.2 +59,0,45,155,69,3,61.8 +60,0,58,141,70,3,63 +61,0,37,170,70,3,62.7 +62,0,31,170,72,3,71.1 +63,0,35,171,72,3,64.4 +64,0,56,171,73,3,68.9 +65,0,48,153,75,3,68.7 +66,0,41,157,76,3,71 +15,1,39,168,71,1,71.6 +16,1,31,158,72,1,70.9 +17,1,40,173,74,1,69.5 +18,1,50,160,78,1,73.9 +19,1,43,162,80,1,71 +20,1,25,165,80,1,77.6 +21,1,52,177,83,1,79.1 +22,1,42,166,85,1,81.5 +23,1,39,166,87,1,81.9 +24,1,40,190,88,1,84.5 +41,1,51,191,71,2,66.8 +42,1,38,199,75,2,72.6 +43,1,54,196,75,2,69.2 +44,1,33,190,76,2,72.5 +45,1,45,160,78,2,72.7 +46,1,37,194,78,2,76.3 +47,1,44,163,79,2,73.6 +48,1,40,171,79,2,72.9 +49,1,37,198,79,2,71.1 +50,1,39,180,80,2,81.4 +51,1,31,182,80,2,75.7 +67,1,36,155,71,3,68.5 +68,1,47,179,73,3,72.1 +69,1,29,166,76,3,72.5 +70,1,37,173,78,3,77.5 +71,1,31,177,78,3,75.2 +72,1,26,179,78,3,69.4 +73,1,40,179,79,3,74.5 +74,1,35,183,83,3,80.2 +75,1,49,177,84,3,79.9 +76,1,28,164,85,3,79.7 +77,1,40,167,87,3,77.8 +78,1,51,175,88,3,81.9 diff --git a/wip/Data/Harvie et al. 2015.csv b/wip/Data/Harvie et al. 2015.csv new file mode 100644 index 0000000..f3d9538 --- /dev/null +++ b/wip/Data/Harvie et al. 2015.csv @@ -0,0 +1,49 @@ +Participant,DirectionofRotation,Understated_Visual_Feedback,Accurate_Visual_Feedback,Overstated_Visual_Feedback +1,1,.88,1,1.11 +2,1,.94,1,.92 +3,1,1.01,1,.97 +4,1,1.21,1,1.04 +5,1,1.3,1,.78 +6,1,1.32,1,.85 +7,1,1.4,1,.89 +8,1,1.16,1,.94 +9,1,1.28,1,.77 +10,1,1.15,1,1.1 +11,1,.97,1,1.07 +12,1,1.09,1,.81 +13,1,.84,1,.97 +14,1,.98,1,.99 +15,1,1.06,1,1.09 +16,1,.94,1,.97 +17,1,.89,1,.79 +18,1,1.05,1,.88 +19,1,1.09,1,1.04 +20,1,1.21,1,.85 +21,1,.93,1,.9 +22,1,1.05,1,1.16 +23,1,1.19,1,.85 +24,1,1.03,1,.92 +1,2,.83,1,.85 +2,2,.95,1,1.08 +3,2,1.04,1,1 +4,2,1.06,1,.91 +5,2,.98,1,.69 +6,2,.91,1,.86 +7,2,1.33,1,.92 +8,2,.94,1,.82 +9,2,.89,1,.63 +10,2,1.03,1,1.02 +11,2,.97,1,.93 +12,2,1.17,1,1.07 +13,2,.97,1,.94 +14,2,1.13,1,1.02 +15,2,1.07,1,1 +16,2,1.07,1,.88 +17,2,1.26,1,1.13 +18,2,1.18,1,.86 +19,2,1,1,.97 +20,2,1.26,1,.88 +21,2,1.12,1,.71 +22,2,.96,1,.91 +23,2,.98,1,.81 +24,2,1.07,1,1.15 diff --git a/wip/Data/James et al 2015 Experiment 2 Data Set.csv b/wip/Data/James et al 2015 Experiment 2 Data Set.csv new file mode 100644 index 0000000..0ab7391 --- /dev/null +++ b/wip/Data/James et al 2015 Experiment 2 Data Set.csv @@ -0,0 +1,73 @@ +Condition,Time_of_Day,BDI_II,STAI_T,pre_film_VAS_Sad,pre_film_VAS_Hopeless,pre_film_VAS_Depressed,pre_film_VAS_Fear,pre_film_VAS_Horror,pre_film_VAS_Anxious,post_film_VAS_Sad,post_film_VAS_Hopeless,post_film_VAS_Depressed,post_film_VAS_Fear,post_film_VAS_Horror,post_film_VAS_Anxious,Attention_Paid_to_Film,Post_film_Distress,Day_Zero_Number_of_Intrusions,Days_One_to_Seven_Number_of_Intrusions,Visual_Recognition_Memory_Test,Verbal_Recognition_Memory_Test,Number_of_Provocation_Task_Intrusions,Diary_Compliance,IES_R_Intrusion_subscale,Tetris_Total_Score,Self_Rated_Tetris_Performance,Tetris_Demand_Rating +1,2,1,33,0,0,0,0.4,0.3,0.8,1,0.3,0,0.3,0.6,1.2,9,8,2,4,15,18,5,9,0.62,9999,9999,0 +1,2,3,27,1.9,0.7,0.5,0.8,0.2,0.2,1.1,0.4,0.4,2,5.8,5.5,10,2,2,3,17,19,4,9,0.62,9999,9999,0 +1,1,10,42,2.2,1.2,0.9,0.2,0.1,0.4,6.7,2,1,0.7,3.1,0.4,10,6,5,6,12,21,0,10,0.5,9999,9999,0 +1,1,1,41,1.2,1,0.6,5.1,0.4,0.5,5.1,0.6,1.8,5.3,3.2,3.6,9,8,0,2,16,19,0,8,0.5,9999,9999,3 +1,2,1,27,0.2,0.1,0,2.9,0,0.7,4,0,0,8.4,7,8.4,10,7,5,3,14,22,10,8,1,9999,9999,-7 +1,1,1,25,1.6,0.7,0,0.6,0.6,0.6,4,0.4,0.9,4,4.9,6.6,9,8,4,4,13,15,0,9,0.88,9999,9999,-2 +1,2,0,25,0,0,0,0,0,0.1,0,0,4.4,2,1.7,1.8,10,8,0,0,15,19,1,10,0,9999,9999,0 +1,1,4,46,0.5,2.1,0.5,0.5,0.3,0.1,7.6,3.9,6.9,1.4,3.9,0.6,10,9,4,4,16,16,7,8,0.38,9999,9999,2 +1,2,0,21,0.3,0.1,0.1,0,0,0,4.8,0.4,3.9,0,2.5,0,10,7,3,2,13,22,5,9,0.38,9999,9999,0 +1,1,11,58,0.7,1,0.7,0,0,0.6,2.5,1.1,1.2,1.8,3.1,7,9,7,5,11,20,23,3,8,1.75,9999,9999,5 +1,2,0,25,0.5,0.9,0.6,0,0,1.6,1.9,3,0.7,7.7,6.5,8.1,10,7,5,16,13,18,3,9,0.75,9999,9999,-1 +1,2,8,41,2.6,0.6,0.9,0,0,0,6.9,1.2,6.6,1.1,5,0.3,9,8,5,12,15,21,2,8,1.13,9999,9999,-3 +1,2,1,30,0.3,1.2,2,0,0,1,2.5,0.4,0.8,0.4,6.8,1,10,3,1,2,15,20,7,9,0.38,9999,9999,-4 +1,1,0,34,0,0.8,0,1.1,0.7,1,0,0,0,0.6,0.5,0.5,10,5,5,7,18,20,4,9,1.5,9999,9999,-3 +1,2,4,27,0,0,0,0,0,0,1,0,0,0,1.3,2.9,10,4,4,7,17,22,5,7,1.25,9999,9999,-3 +1,2,15,48,1.1,0,1.1,1.2,0.4,0.8,2.2,1.9,3.5,3,4,1.5,10,6,3,6,13,18,2,8,2.25,9999,9999,-5 +1,1,0,29,1.2,0,0,0,0,1.5,1,0.4,0.7,0,2.6,1,10,6,1,2,15,20,0,9,0.25,9999,9999,-3 +1,2,0,22,0,0,0,0.4,0.4,0.3,1,0,0,3.8,5.7,6.8,9,7,10,1,13,23,3,7,0.5,9999,9999,-3 +2,2,8,46,0,0,0,0,0,0.4,7.4,0,3.4,0.5,2.2,0.3,10,6,3,1,16,21,0,9,1.5,16413,6,-2 +2,1,2,29,0.5,0,0,2.3,0,0.9,1,0,0,0.5,1.7,1.3,8,6,2,2,16,19,0,8,0.75,3231,5.4,-1 +2,1,0,36,0.4,0.9,0.5,2,0.7,3,4.7,0.6,1.3,3.1,5,4.8,9,6,9,3,19,15,0,8,1.25,12505,4.7,-5 +2,2,6,42,1.8,1.3,0.4,1.2,0.3,2.7,5.7,0.5,2,3.9,4.2,4.1,8,6,2,0,13,18,0,9,0.13,20567,3.1,-6 +2,2,0,25,0.3,2.2,0.3,1,0.5,5.6,4.7,2.2,3.2,3.3,4.9,4.3,10,7,2,2,18,20,3,10,0.75,4816,4.8,-1 +2,2,2,38,0,0,0,0,0,0.9,0,0,0,0,5,1.4,10,6,2,3,16,16,2,10,0.25,24233,1.8,8 +2,2,5,31,0.2,0.2,0,0.2,0,1.4,5.7,0.5,1.3,5.2,1.6,6,9,8,3,2,16,20,4,8,0.63,22672,1.8,-6 +2,1,4,39,1.5,0,1.1,0,0,0,7.3,5.1,5.2,0,0,0,10,8,5,1,13,17,6,9,0.88,44650,1.8,-2 +2,1,1,34,0.5,0.2,0,0.3,0.2,3.8,1.9,0.7,0.2,2.9,6.1,6.4,10,8,2,7,18,22,1,9,0.38,90077,3.6,-4 +2,2,4,26,1.2,0.7,0.7,0.2,0.3,0.5,5.5,1.2,1,2.3,5,6.2,8,5,1,0,17,19,3,8,0.38,21648,2.7,0 +2,2,0,30,0.7,0.5,0.3,0.2,0.1,0.1,1.9,0.6,0.3,0.2,0.2,0.2,9,4,1,3,13,19,1,9,0.38,26590,2,0 +2,2,3,36,0.5,1,0.4,0,0,0,4.6,3.3,3,2.6,2.9,1.6,8,5,8,2,18,17,0,5,0.5,29027,3,-4 +2,1,2,27,0.5,0.4,0.5,0.1,0.2,1.6,1.6,0.7,1.2,4.1,7.6,4.5,10,10,2,2,12,18,0,6,0.5,53526,0.6,5 +2,2,1,42,0.9,0.2,0,0.4,0.3,0.7,2.6,0.6,0.7,3.3,8.5,4.7,9,3,4,1,16,16,0,9,0.38,107782,1.2,0 +2,1,0,37,0,0,0,0.6,0,3.2,2,2.1,0,0,9.5,9,10,6,1,0,16,18,0,9,0.5,34285,1.6,-3 +2,2,5,51,1.3,6.8,2.3,0,0,0,6.4,2.1,3.7,0,1.8,0,10,8,3,1,14,20,0,3,1,1425,2.2,0 +2,1,0,24,0,0,0,0.7,0.5,0.4,2.8,0,1.4,0.9,3,2.1,10,8,2,0,13,18,0,10,0,32853,1.2,-7 +2,1,5,31,0.1,0.7,0.5,1.6,1.4,1.1,3,0,0.3,2.4,2.3,3.2,9,6,4,4,18,17,0,8,0.88,26885,5.7,-4 +3,1,5,27,1.6,0.6,0,2.5,0.1,1.3,4.6,7.7,1.5,1.9,4.3,2,10,9,4,2,15,25,5,9,0.5,27116,2.4,-6 +3,1,1,28,1,0.6,0.4,0,0,0,2.3,0.8,1.1,1.2,2.3,1.8,10,7,0,2,10,15,0,10,0.25,14360,2.1,2 +3,2,0,21,0,0.1,0,0.3,0.7,1.1,1.5,0,1,1.1,2.5,1.2,10,8,6,2,15,17,0,10,0.87,37782,1.6,0 +3,2,6,27,0,3.1,0,1.3,0.4,4.5,3.9,6.2,2.6,1.3,0.9,2.7,10,4,3,3,13,24,0,10,0.5,1035,5.5,0 +3,2,18,53,0,0,0,0,0,0,0,0,0,2.9,5.8,2.3,10,3,4,2,14,19,8,9,0.88,39099,0,-4 +3,1,5,54,0,0,0,0.9,0,4.1,2,7.9,6.8,3.2,3.1,3.8,8,7,3,8,15,15,4,7,2.25,90276,2.3,-2 +3,2,1,31,2.7,0.5,0,1.7,0,0.3,9.1,0.6,1.4,6.5,8.3,8.8,9,8,4,3,12,18,6,9,0.75,22095,5.5,3 +3,2,2,36,0.5,0.5,0.5,0,0.1,0.1,2,1.1,1.7,2.3,3.8,2.9,9,7,5,12,14,21,5,7,0.87,53748,3.7,0 +3,1,11,33,0,0,0,0.3,0.5,1.1,3,1.6,2,1,2.1,2.3,9,5,4,5,16,20,5,7,0.75,8865,2,-6 +3,2,2,37,3.8,0.3,2,0.7,0.5,1.9,5.1,3.8,3.8,3.8,7,5.8,9,8,6,5,13,17,5,7,1.25,10158,5.4,0 +3,2,0,41,0.3,0.3,0.1,1.5,0.3,0.3,0.3,0.2,0.3,0.3,0.7,0.2,10,0,2,1,17,16,0,8,0.25,25842,5.1,0 +3,2,0,35,1.5,1.7,0.3,1.1,0.7,1.8,4.2,1.3,3.3,5.7,3.9,6.1,10,7,2,5,14,13,4,8,0.63,18272,6.1,-2 +3,2,2,24,0.4,0.4,0,0,0,0,2.2,0.4,2.1,0,7.5,3.3,9,3,1,1,16,22,1,9,0.25,31359,2.2,-1 +3,2,1,30,0.6,0.3,0.4,0.5,0.6,0.9,0.3,0.2,0.5,3.3,4.8,5.6,10,4,2,1,13,20,6,8,0.13,45822,0.2,5 +3,2,0,35,0.7,2,0,0.7,0.4,0.5,0.3,0.2,0,1,4.9,1,9,9,3,4,16,19,2,9,0.88,3686,8.3,-7 +3,1,5,34,0,0,0,0.2,0,1.5,8.1,0.7,0.4,1.5,3.9,5.1,10,4,3,2,15,21,2,8,0.25,65910,1.8,-2 +3,2,4,33,0,0,0,0.5,0.2,6,6.6,6.4,7.7,3,6.7,6.9,10,9,2,7,12,19,1,9,1,13096,4.8,-4 +3,2,2,32,0.3,0.3,0.3,0.5,0,0,4.1,2.1,2.4,1.2,2.4,1.7,10,8,3,5,17,18,2,8,1,16768,2.8,-5 +4,1,0,35,0.2,0.3,0.2,1.3,0,1,0.2,0.2,0.3,0.5,1.5,2,8,6,5,4,16,12,6,8,0.63,9999,9999,0 +4,1,0,47,0.2,0.2,0.1,1.1,0,1.2,2.8,0.2,0.3,2.5,2.1,0.8,10,8,4,4,14,16,3,9,1.63,9999,9999,-3 +4,2,1,29,0.1,0.1,0.1,1.1,0.4,2.7,1.8,0.3,1.4,0.2,4.2,2.3,10,9,3,2,14,16,5,10,0.75,9999,9999,-2 +4,2,7,28,2.4,4.9,0.2,0.6,0.5,0.3,5.3,3.3,0.4,2.2,1.4,0.1,10,4,1,3,12,16,0,9,0.87,9999,9999,3 +4,1,5,32,0,0,0,0,0,2.5,3.4,1.9,1.4,3.4,9.2,6.6,10,8,1,2,16,21,4,9,0.62,9999,9999,-5 +4,1,0,35,0.4,0.5,0.3,0,0,0,6.5,1.6,1,0,3.2,0.5,10,7,6,15,13,21,0,9,1.25,9999,9999,-8 +4,2,0,29,0.9,1.2,1,2.9,2.5,3,2.3,0.5,0.5,4.9,5,5.5,9,5,2,6,17,25,4,8,0.62,9999,9999,7 +4,1,0,26,0.5,0,0,0,0,0.7,0.4,0,0,0,1.3,1,9,1,2,3,10,15,6,10,0.75,9999,9999,0 +4,1,0,32,0.3,1.1,0.4,0.1,0,0,6.8,5.5,5.9,0.9,0.5,1,10,9,6,7,14,15,5,8,1.5,9999,9999,4 +4,1,0,24,0,0,0,0,0,0,2.5,0,0,3,4.2,3.2,10,4,2,5,12,17,6,9,0.63,9999,9999,-3 +4,1,9,38,2.8,0,1.8,0,0,0,1.6,0,1.6,3,4.3,1.6,9,7,1,1,17,20,2,7,0.38,9999,9999,3 +4,2,0,32,0,1.6,0.9,0,0,0,3.3,2.5,2.7,0.7,7.1,2.6,9,3,3,6,14,22,6,8,0.63,9999,9999,0 +4,1,4,40,2,0.5,3.1,0.4,0.3,0.3,1.5,0.5,3.1,6.7,6.9,6.3,9,8,4,9,14,21,6,8,0.75,9999,9999,5 +4,2,2,34,0.5,0,1,2.1,1.5,3.4,4.3,0,8.9,3.4,6.8,2.8,10,9,2,1,15,20,4,7,0.5,9999,9999,0 +4,2,2,28,0.8,0.9,1,0,0,0,3.6,1.5,3,0,4.9,4.8,10,5,2,4,14,21,6,8,1.5,9999,9999,-5 +4,2,0,23,1.6,0.3,0.5,0,0,1.3,3.2,1.8,0.3,7.1,6.3,6.3,9,2,3,4,18,24,7,9,0.5,9999,9999,0 +4,2,4,42,2.2,5,2.3,0,0,0,5.4,3.4,4.2,1.7,4.8,0.9,9,6,12,7,13,17,3,7,0.5,9999,9999,-1 +4,1,4,54,0.9,0,0,0.7,0.5,3.3,2.6,0,0.7,0.4,1.4,4.9,9,8,3,4,17,21,2,9,0.63,9999,9999,0 diff --git a/wip/Data/Maglio and Polman 2014.csv b/wip/Data/Maglio and Polman 2014.csv new file mode 100644 index 0000000..5f46fac --- /dev/null +++ b/wip/Data/Maglio and Polman 2014.csv @@ -0,0 +1,203 @@ +direction,orientation,station,subjective_distance +EAST,1,1,5 +EAST,1,1,4 +EAST,1,1,3 +EAST,1,1,3 +EAST,1,1,4 +EAST,1,1,1 +EAST,1,1,4 +EAST,1,1,4 +EAST,1,1,5 +EAST,1,1,3 +EAST,1,1,2 +EAST,1,1,4 +EAST,1,1,5 +EAST,1,1,3 +EAST,1,1,3 +EAST,1,1,4 +EAST,1,1,2 +EAST,1,1,4 +EAST,1,1,4 +EAST,1,1,5 +EAST,1,1,2 +EAST,1,1,3 +EAST,1,1,4 +EAST,1,1,5 +EAST,1,1,5 +EAST,1,1,4 +WEST,2,1,2 +WEST,2,1,4 +WEST,2,1,3 +WEST,2,1,3 +WEST,2,1,2 +WEST,2,1,4 +WEST,2,1,3 +WEST,2,1,3 +WEST,2,1,2 +WEST,2,1,1 +WEST,2,1,2 +WEST,2,1,4 +WEST,2,1,3 +WEST,2,1,2 +WEST,2,1,2 +WEST,2,1,5 +WEST,2,1,2 +WEST,2,1,2 +WEST,2,1,4 +WEST,2,1,3 +WEST,2,1,2 +WEST,2,1,1 +WEST,2,1,2 +WEST,2,1,2 +WEST,2,1,3 +EAST,1,2,3 +EAST,1,2,1 +EAST,1,2,4 +EAST,1,2,1 +EAST,1,2,3 +EAST,1,2,3 +EAST,1,2,2 +EAST,1,2,4 +EAST,1,2,3 +EAST,1,2,3 +EAST,1,2,3 +EAST,1,2,2 +EAST,1,2,4 +EAST,1,2,1 +EAST,1,2,2 +EAST,1,2,3 +EAST,1,2,3 +EAST,1,2,3 +EAST,1,2,2 +EAST,1,2,4 +EAST,1,2,5 +EAST,1,2,3 +EAST,1,2,3 +EAST,1,2,1 +EAST,1,2,3 +EAST,1,2,3 +WEST,2,2,2 +WEST,2,2,3 +WEST,2,2,1 +WEST,2,2,1 +WEST,2,2,2 +WEST,2,2,1 +WEST,2,2,3 +WEST,2,2,1 +WEST,2,2,2 +WEST,2,2,1 +WEST,2,2,3 +WEST,2,2,1 +WEST,2,2,1 +WEST,2,2,1 +WEST,2,2,2 +WEST,2,2,2 +WEST,2,2,1 +WEST,2,2,1 +WEST,2,2,1 +WEST,2,2,3 +WEST,2,2,1 +WEST,2,2,1 +WEST,2,2,2 +WEST,2,2,1 +WEST,2,2,3 +EAST,1,3,1 +EAST,1,3,2 +EAST,1,3,2 +EAST,1,3,3 +EAST,1,3,1 +EAST,1,3,1 +EAST,1,3,2 +EAST,1,3,1 +EAST,1,3,3 +EAST,1,3,2 +EAST,1,3,1 +EAST,1,3,1 +EAST,1,3,2 +EAST,1,3,1 +EAST,1,3,3 +EAST,1,3,1 +EAST,1,3,2 +EAST,1,3,1 +EAST,1,3,1 +EAST,1,3,1 +EAST,1,3,2 +EAST,1,3,2 +EAST,1,3,1 +WEST,2,3,1 +WEST,2,3,1 +WEST,2,3,5 +WEST,2,3,1 +WEST,2,3,5 +WEST,2,3,2 +WEST,2,3,3 +WEST,2,3,1 +WEST,2,3,1 +WEST,2,3,2 +WEST,2,3,4 +WEST,2,3,2 +WEST,2,3,3 +WEST,2,3,3 +WEST,2,3,2 +WEST,2,3,1 +WEST,2,3,2 +WEST,2,3,2 +WEST,2,3,1 +WEST,2,3,4 +WEST,2,3,1 +WEST,2,3,1 +WEST,2,3,2 +WEST,2,3,3 +WEST,2,3,2 +WEST,2,3,2 +EAST,1,4,4 +EAST,1,4,1 +EAST,1,4,1 +EAST,1,4,4 +EAST,1,4,3 +EAST,1,4,3 +EAST,1,4,4 +EAST,1,4,5 +EAST,1,4,2 +EAST,1,4,3 +EAST,1,4,4 +EAST,1,4,2 +EAST,1,4,1 +EAST,1,4,3 +EAST,1,4,3 +EAST,1,4,2 +EAST,1,4,4 +EAST,1,4,3 +EAST,1,4,4 +EAST,1,4,2 +EAST,1,4,1 +EAST,1,4,2 +EAST,1,4,2 +EAST,1,4,3 +EAST,1,4,2 +EAST,1,4,4 +WEST,2,4,2 +WEST,2,4,5 +WEST,2,4,4 +WEST,2,4,5 +WEST,2,4,3 +WEST,2,4,4 +WEST,2,4,4 +WEST,2,4,3 +WEST,2,4,5 +WEST,2,4,6 +WEST,2,4,2 +WEST,2,4,5 +WEST,2,4,5 +WEST,2,4,4 +WEST,2,4,3 +WEST,2,4,4 +WEST,2,4,5 +WEST,2,4,4 +WEST,2,4,2 +WEST,2,4,5 +WEST,2,4,3 +WEST,2,4,4 +WEST,2,4,5 +WEST,2,4,5 +WEST,2,4,3 diff --git a/wip/Data/Mehr Song and Spelke 2016 Experiment 1.csv b/wip/Data/Mehr Song and Spelke 2016 Experiment 1.csv new file mode 100644 index 0000000..dc06cc7 --- /dev/null +++ b/wip/Data/Mehr Song and Spelke 2016 Experiment 1.csv @@ -0,0 +1,97 @@ +id,study_code,exp1,exp2,exp3,exp4,exp5,dob,dot1,dot2,dot3,female,dad,train,Baseline_Proportion_Gaze_to_Singer,Familiarization_Gaze_to_Familiar,Familiarization_Gaze_to_Unfamiliar,Test_Proportion_Gaze_to_Singer,Difference_in_Proportion_Looking,Estimated_Total_Number_of_Song,totskypesing,stim,othersing,comply_no,module,skype_before,ammat,ammar,ammatot,ammapr,ipad_num,famtot_6,unfamtot_6,totprac,totw,totnw,age,length,delay,mtotsing,mbabylike,msingcomf,mtotrecord,m_othersong,pright,diarymissing,comply_fup,survey_completion,smsingrate,smtalkrate,gzsingrate,gztalkrate,famtot,unfamtot,totsing1,babylike1,singcomf1,totrecord1,othersong1,dtword1,dtnoword1,totsing2,babylike2,singcomf2,totrecord2,othersong2,dtword2,dtnoword2,totsing3,babylike3,singcomf3,totrecord3,othersong3,dtword3,dtnoword3,totsing4,babylike4,singcomf4,totrecord4,othersong4,dtword4,dtnoword4,totsing5,babylike5,singcomf5,totrecord5,othersong5,dtword5,dtnoword5,totsing6,babylike6,singcomf6,totrecord6,othersong6,dtword6,dtnoword6,totsing7,babylike7,singcomf7,totrecord7,othersong7,dtword7,dtnoword7,totsing8,babylike8,singcomf8,totrecord8,othersong8,dtword8,dtnoword8,totsing9,babylike9,singcomf9,totrecord9,othersong9,dtword9,dtnoword9,totsing10,babylike10,singcomf10,totrecord10,othersong10,dtword10,dtnoword10,totsing11,babylike11,singcomf11,totrecord11,othersong11,dtword11,dtnoword11,totsing12,babylike12,singcomf12,totrecord12,othersong12,dtword12,dtnoword12,totsing13,babylike13,singcomf13,totrecord13,othersong13,dtword13,dtnoword13,totsing14,babylike14,singcomf14,totrecord14,othersong14,dtword14,dtnoword14,filter_$ +101,"""LUL""",1,0,0, , ,09-Oct-12,29-Mar-13,05-Apr-13, ,0,0,2,0.4371257,248,419,0.6027398,0.165614,35, ,"""C1""",0, ,"""""", ,17,26,43,26, , , ,15.49416,793.392,0,5.848049,7, ,5,3,3.857143,0.5714286,3.285714,0.8,0,1,1, , , , , , ,7,3,3,0,6,293.0226,91.92855,5,3,4,0,8,53.27683,25.58346,5,3,4,0,0,79.91525,0,5,3,4,0,9,159.6061,18.74565,4,3,4,2,0,78.93342,0,5,3,4,2,0,76.71582,0,4,3,4,0,0,0,0, , , , , ,51.92201,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +102,"""LUL""",1,0,0, , ,16-Nov-12,10-May-13,17-May-13, ,0,1,1,0.4125326,406,307,0.6830266,0.270494,239, ,"""C1""",1, ,"""""", ,27,28,55,62, , , ,5.421422,153.7889,0,5.979466,7, ,34.14286,2.142857,4,22.28572,1.714286,0.8,0,1,1, , , , , , ,6,2,4,20,0,55.80707,25.28744,25,2,4,50,12,24.16411,0,50,3,4,30,0,0,0,30,2,4,16,0,0,0,40,2,4,30,0,0,0,50,2,4,5,0,24.7315,0,38,2,4,5,0,49.08626,146.209, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +103,"""LUL""",1,0,0, , ,26-Nov-12,11-May-13,20-May-13, ,0,0,1,0.754491,154,218,0.7241379,-0.0303531,102, ,"""C2""",0, ,"""""", ,31,34,65,84, , , ,5.451066,0,223.847,5.749486,9, ,11.33333,1.333333,3,0,0,0.96,3,1,0.6666667, , , , , , ,8,2,3,0,0,24.555,78.63,25,1,4,0,0,24.555,145.217,8,2,3,0,0,0,0,15,1,2,0,0,23.962,0,6,1,3,0,0,0,0,6,1,3,0,0,0,0, , , , , ,30.145,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +104,"""LUL""",1,0,0, , ,19-Nov-12,11-May-13,18-May-13, ,1,0,2,0.4388778,502,666,0.2816538,-0.157224,27, ,"""C3""",1, ,"""""", ,25,24,49,44, , , ,22.339,1163.583,0,5.913758,7, ,3.857143,3.428571,2.428571,0.4285714,0,1,0,1,1, , , , , , ,3,4,2,0,0,13.81173,76.20277,6,3,3,2,0,384.781,0,6,4,3,0,0,185.3951,100.5546,0,3,2,0,0,321.9076,0,4,3,3,0,0,26.45007,0,4,3,2,1,0,79.3502,0,4,4,2,0,0,106.1017,0, , , , , ,45.78514,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +105,"""LUL""",1,0,0, , ,29-Nov-12,15-May-13,29-May-13, ,1,0,2,0.474645,311,245,0.4985423,0.0238973,60, ,"""C4""",0, ,"""""", ,29,28,57,68, , , ,2.793547,157.9989,0,5.946612,14, ,4.285714,3.142857,4.071429,4.428571,0,1,0,1,1, , , , , , ,8,2,3,2,0,131.3605,9.613913,5,3,4,0,0,26.63842,0,6,3,4,0,0,0,0,6,3,4,12,0,0,0,5,4,5,10,0,0,0,6,4,5,0,0,0,0,4,4,4,3,0,0,0,3,3,4,4,0,0,0,4,3,4,0,0,0,0,3,3,4,6,0,0,0,4,3,4,6,0,0,0,2,3,4,4,0,0,0,0,3,4,15,0,0,0,4,3,4,0,0,0,0,1 +106,"""LUL""",1,0,0, , ,05-Dec-12,16-May-13,29-May-13, ,0,0,2,0.8709016,639,533,0.9509202,0.0800186,126, ,"""C5""",0, ,"""""", ,33,33,66,86, , , ,10.28441,317.7464,0,5.749486,13, ,9.692307,3.923077,4.615385,3.769231,2.153846,0.64,0,1,1, , , , , , ,9,4,4,20,0,26.45007,0,12,4,5,6,0,79.33268,127.4757,10,3,4,0,0,52.89915,50.00899,9,4,5,8,0,26.45007,25.56155,8,4,5,0,0,0,0,7,4,4,0,0,26.55729,45.15263,10,4,4,1,0,53.07892,25.54979,7,4,5,0,0,0,0,16,4,5,12,0,0,0,8,4,4,0,0,0,0,12,4,5,0,28,0,0,8,4,5,2,0,0,0,10,4,5,0,0,52.97826,25.5694, , , , , ,0,0,1 +107,"""LUL""",1,0,0, , ,13-Dec-12,17-May-13,29-May-13, ,1,0,1,0.236715,28,460,0.4177546,0.1810396,134.6667, ,"""C6""",2, ,"""""", ,28,33,61,76, , , ,15.65549,0,259.2986,5.486653,12, ,11.22222,2.888889,4.111111,3,9.444445,0.96,3,1,0.75, , , , , , ,12,3,3,2,8,171.8019,81.36234,8,3,4,4,7,196.3433,177.9362,16,4,4,5,0,90.86707,0,10,2,3,0,21,73.66531,0,12,2,4,0,13,73.68774,0,10,3,5,5,9,0,0,6,3,4,2,10,0,0,15,3,5,5,10,0,0,12,3,5,4,7,24.5551,0, , , , , ,49.1102,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +108,"""LUL""",1,0,0, , ,07-Dec-12,17-May-13,31-May-13, ,0,0,1,0.7592593,537,440,0.9382023,0.178943,63.77777, ,"""C7""",2, ,"""""", ,30,28,58,70, , , ,9.270391,556.2234,0,5.749486,14, ,4.555555,3.888889,3.666667,8,0,0.84,5,1,0.6428571, , , , , , ,5,3,3,10,0,293.8942,0,7,3,4,15,0,24.57818,0,4,4,4,20,0,0,0,4,4,3,2,0,0,0,3,4,3,3,0,24.55023,0,4,4,4,3,0,0,0,4,5,4,6,0,0,0,5,4,4,3,0,0,0,5,4,4,10,0,139.5715,0, , , , , ,0,0, , , , , ,73.62939,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +109,"""LUL""",1,0,0, , ,24-Nov-12,20-May-13,29-May-13, ,0,0,2,0.4163347,269,520,0.5,0.0836653,44, ,"""C8""",0, ,"""""", ,19,27,46,35, , , ,4.592237,0,249.0842,6.110883,9, ,4.888889,3.111111,3.222222,2.222222,7.666667,0.52,0,1,1, , , , , , ,5,2,2,4,16,26.45007,198.0267,10,3,2,0,15,0,51.05744,5,3,3,3,14,0,0,4,3,3,2,12,0,0,4,4,4,0,0,0,0,4,4,4,0,12,0,0,3,3,4,0,0,0,0,4,3,4,5,0,0,0,5,3,3,6,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +110,"""LUL""",1,0,0, , ,21-Dec-12,28-May-13,04-Jun-13, ,1,0,1,0.7995338,629,649,0.5862944,-0.2132394,55, ,"""C1""",0, ,"""""", ,27,29,56,65, , , ,1.005501,60.33006,0,5.420945,7, ,7.857143,4.142857,4.857143,2.857143,2.428571,0.92,0,1,1, , , , , , ,15,5,4,4,0,49.463,0,10,4,5,2,17,10.86706,0,10,4,5,3,0,0,0,5,4,5,0,0,0,0,5,4,5,3,0,0,0,5,4,5,3,0,0,0,5,4,5,5,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +111,"""LUL""",1,0,0, , ,06-Dec-12,30-May-13,10-Jun-13, ,1,0,2,0.3786765,437,524,0.4726225,0.093946,88, ,"""C2""",1, ,"""""", ,28,25,53,56, , , ,0.0292358,1.754148,0,6.110883,11, ,8,4.222222,4.666667,3.222222,0,0.8,2,1,0.8181818, , , , , , ,15,3,4,0,0,1.754148,0,8,4,4,6,0,0,0,12,4,4,6,0,0,0,5,4,5,3,0,0,0,6,4,5,2,0,0,0,8,5,5,2,0,0,0,5,5,5,0,0,0,0,9,5,5,10,0,0,0,4,4,5,0,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +112,"""LUL""",1,0,0, , ,23-Oct-12,03-Apr-13,10-Apr-13, ,1,1,1,0.6978922,603,0,0.5083799,-0.1895124,53.66666, ,"""C2""",0, ,"""""", ,22,22,44,29, , , ,0.4121917,24.7315,0,5.552361,7, ,7.666667,2.666667,3.833333,13.33333,0,0.96,1,1,0.8571429, , , , , , ,10,2,3,15,0,24.7315,0,7,2,4,15,0,0,0,4,2,4,30,0,0,0,0,3,4,15,0,0,0,15,4,4,0,0,0,0,10,3,4,5,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +113,"""LUL""",1,0,0, , ,13-Dec-12,30-May-13,06-Jun-13, ,1,0,1,0.5934066,546,613,0.8111888,0.2177822,59.5, ,"""C3""",0, ,"""""", ,20,25,45,32, , , ,2.795225,167.7135,0,5.749486,7, ,8.5,3.5,4.333333,1.666667,19.83333,1,1,1,0.8571429, , , , , , ,10,3,4,3,0,122.7755,0,5,3,3,2,21,0,0,7,3,4,1,18,20.38289,0,10,4,5,0,29,0,0,12,4,5,2,26,24.5551,0,7,4,5,2,25,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +114,"""LUL""",1,0,0, , ,12-Dec-12,30-May-13,06-Jun-13, ,1,0,2,0.6149068,625,532,0.5718015,-0.0431053,94, ,"""C4""",0, ,"""""", ,20,22,42,23, , , ,2.809817,92.104,0,5.782341,7, ,13.42857,5,4.285714,7.428571,0,0.68,0,1,1, , , , , , ,15,5,4,15,0,18.276,25.495,8,5,4,2,0,31.861,50.99,8,5,4,10,0,23.053,0,15,5,4,12,0,9.828,0,20,5,4,5,0,9.086,0,20,5,5,3,0,0,0,8,5,5,5,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +115,"""LUL""",1,0,0, , ,19-Dec-12,07-Jun-13,14-Jun-13, ,0,0,1,0.6149068,401,321,0.7774481,0.1625412,54, ,"""C6""",1, ,"""""", ,31,29,60,74, , , ,0.8992158,0,29.22145,5.815195,7, ,7.714286,2.857143,5,4.714286,0.8571429,1,0,1,1, , , , , , ,0,3,5,1,0,0,4.953094,12,2,5,20,0,0,24.26836,7,2,5,0,0,0,0,9,3,5,0,0,24.7315,0,5,3,5,3,0,0,0,18,4,5,5,6,0,0,3,3,5,4,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +116,"""LUL""",1,0,0, , ,11-Jan-13,10-Jun-13,17-Jun-13, ,0,0,1,0.3168317,611,548,0.2628458,-0.0539858,26, ,"""C7""",0, ,"""""", ,32,33,65,84, , , ,0.4212921,0,25.27752,5.158111,7, ,3.714286,3.571429,4.142857,2.428571,3.142857,1,0,1,1, , , , , , ,4,3,4,3,14,0,25.27752,3,3,4,2,0,0,0,7,4,4,3,0,0,0,4,3,4,2,0,0,0,2,4,4,0,0,0,0,3,4,4,3,8,0,0,3,4,5,4,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +117,"""LUL""",1,0,0, , ,13-Jan-13,10-Jun-13,22-Jun-13, ,1,0,1,0.3104167,525,662,0.5079365,0.1975199,44, ,"""C8""",0, ,"""""", ,27,27,54,59, , , ,8.838828,354.8344,0,5.256673,12, ,3.666667,3.416667,3.25,0,0,0.72,0,1,1, , , , , , ,4,3,4,0,0,74.1945,50.74892,4,4,4,0,0,74.1945,124.7464,5,4,4,0,0,49.463,0,5,4,3,0,0,49.463,0,4,3,3,0,0,58.05636,0,0,1,3,0,0,0,0,5,4,3,0,0,49.463,0,4,3,3,0,0,0,0,3,4,3,0,0,0,0,3,4,3,0,0,0,0,4,3,3,0,0,0,0,3,4,3,0,0,0,0, , , , , ,0,0, , , , , ,0,0,1 +118,"""LUL""",1,0,0, , ,15-Jan-13,11-Jun-13,18-Jun-13, ,0,0,2,0.5043668,506,472,0.4369748,-0.067392,23, ,"""C1""",0, ,"""""", ,27,31,58,70, , , ,3.599689,155.884,0,5.059548,7, ,3.285714,2.714286,2.857143,10.71429,0,0.84,0,1,1, , , , , , ,4,1,3,3,0,26.53506,9.130268,0,1,2,15,0,0,50.96708,7,4,3,15,0,0,0,5,3,3,12,0,0,0,2,3,3,0,0,79.3502,0,3,3,3,15,0,0,0,2,4,3,15,0,0,0, , , , , ,49.9987,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +119,"""LUL""",1,0,0, , ,17-Jan-13,17-Jun-13,24-Jun-13, ,0,1,1,0.4693396,603,573,0.5421053,0.0727656,38, ,"""C2""",0, ,"""""", ,20,26,46,35, , , ,19.23616,764.5626,0,5.190965,7, ,5.428571,3.857143,4,1.285714,0,0.56,0,1,1, , , , , , ,8,4,4,0,0,208.2161,177.0181,6,3,3,5,0,225.1051,48.65501,7,4,4,0,0,243.5671,25.37446,5,4,4,0,0,49.463,0,5,4,5,0,0,38.2113,138.5596,3,4,4,0,0,0,0,4,4,4,4,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +120,"""LUL""",1,0,0, , ,15-Oct-12,05-Apr-13,12-Apr-13, ,1,1,1,0.5040816,436,432,0.6008968,0.0968152,31, ,"""C3""",1, ,"""""", ,25,29,54,59, , , ,3.335714,0,116.1673,5.880904,7, ,4.428571,2.428571,3.285714,3.285714,0,0.8,0,1,1, , , , , , ,5,2,3,1,0,0,71.62625,8,2,3,15,0,24.57735,43.708,7,2,4,0,0,0,0,3,4,3,3,0,34.85505,0.833062,1,4,3,2,0,0,0,0,1,3,2,0,0,0,7,2,4,0,0,0,0, , , , , ,24.54313,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +121,"""LUL""",1,0,0, , ,19-Jan-13,21-Jun-13,28-Jun-13, ,0,0,2,0.5640327,367,190,0.4186747,-0.145358,78.4, ,"""C5""",0, ,"""""", ,18,19,37,12, , , ,1.314338,53.27683,0,5.256673,7, ,11.2,3.6,4.6,1,0,1,2,1,0.7142857, , , , , , ,12,3,4,0,0,53.27683,25.58346,18,4,4,0,0,0,0,10,4,5,0,0,0,0,6,3,5,0,0,0,0,10,4,5,5,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +122,"""LUL""",1,0,0, , ,21-Jan-13,24-Jun-13,01-Jul-13, ,1,0,1,0.2566372,282,465,0.7894737,0.5328366,135, ,"""C5""",0, ,"""""", ,28,29,57,68, , , ,8.793594,0,343.7641,5.289528,7, ,19.28572,3,4.428571,10,0,0.96,0,1,1, , , , , , ,30,3,4,15,0,112.7346,212.4499,25,4,5,0,0,0,0,20,3,5,5,0,23.19954,47.27182,15,3,4,25,0,0,0,15,3,5,10,0,23.18593,34.69049,20,3,4,0,0,0,0,10,2,4,15,0,0,0, , , , , ,24.7315,49.35186, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +123,"""LUL""",1,0,0, , ,17-Jan-13,28-Jun-13,05-Jul-13, ,1,0,2,0.7,517,432,0.7601078,0.0601078,26, ,"""C6""",0, ,"""""", ,26,29,55,62, , , ,3.623107,0,137.9676,5.552361,7, ,3.714286,3.142857,4.142857,8,3.285714,0.88,0,1,1, , , , , , ,5,3,2,20,0,26.45339,25.48354,6,3,5,20,10,0,37.9253,5,3,5,0,0,0,26.16751,3,3,5,10,13,0,0,1,3,4,2,0,0,48.39124,4,4,4,4,0,0,0,2,3,4,0,0,0,0, , , , , ,52.96546,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +124,"""LUL""",1,0,0, , ,25-Jan-13,29-Jun-13,13-Jul-13, ,1,1,2,0.3823529,525,421,0.6238938,0.2415408,120.9091, ,"""C7""",1, ,"""""", ,33,33,66,86, , , ,0.4439736,26.63842,0,5.552361,14, ,8.636364,3.272727,4.727273,7.181818,0,0.8,3,1,0.7857143, , , , , , ,3,3,2,1,0,26.63842,0,12,4,5,20,0,0,0,12,4,5,0,0,0,0,5,3,5,3,0,0,0,20,3,5,5,0,0,0,5,3,5,10,0,0,0,12,4,5,20,0,0,0,3,3,5,5,0,0,0,8,3,5,15,0,0,0,5,3,5,0,0,0,0,10,3,5,0,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +125,"""LUL""",1,0,0, , ,25-Jan-13,09-Jul-13,16-Jul-13, ,0,0,1,0.3718593,519,578,0.3664122,-0.0054471,13, ,"""C8""",0, ,"""""", ,26,29,55,62, , , ,1.650107,73.73187,0,5.650924,7, ,1.857143,4.428571,4.857143,1.428571,0,0.96,0,1,1, , , , , , ,1,2,4,0,0,49.13915,25.27456,4,5,5,1,0,24.59273,0,0,5,5,0,0,0,0,1,4,5,5,0,0,0,1,5,5,2,0,0,0,4,5,5,1,0,0,0,2,5,5,1,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +126,"""LUL""",1,0,0, , ,12-Feb-13,10-Jul-13,17-Jul-13, ,0,0,2,0.2844639,592,554,0.4615385,0.1770746,245, ,"""C7""",1, ,"""""", ,34,31,65,84, , , ,6.138217,0,77.21062,5.092402,7, ,35,3.285714,4.857143,24.28572,0,1,0,1,1, , , , , , ,40,4,4,30,0,291.0824,77.21062,50,4,5,30,0,0,0,30,3,5,30,0,0,0,40,3,5,20,0,0,0,25,3,5,30,0,0,0,30,3,5,10,0,0,0,30,3,5,20,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +127,"""LUL""",1,0,0, , ,23-Jan-13,12-Jul-13,19-Jul-13, ,0,0,1,0.7678161,425,336,0.8995215,0.1317055,66.5, ,"""C3""",0, ,"""""", ,16,19,35,8, , , ,0.8302947,0,25.27456,5.815195,7, ,9.5,4,4.666667,8.5,0,0.68,1,1,0.8571429, , , , , , ,20,4,4,20,0,24.54313,25.27456,10,4,4,15,0,0,0,10,4,5,5,0,0,0,7,4,5,5,0,0,0,5,4,5,3,0,0,0,5,4,5,3,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +128,"""LUL""",1,0,0, , ,23-Feb-13,29-Jul-13,05-Aug-13, ,1,0,1,0.4737864,408,352,0.5311005,0.057314,63, ,"""C4""",0, ,"""""", ,30,31,61,76, , , ,7.668932,375.9897,0,5.355236,7, ,9,3.571429,4.285714,9.285714,3.714286,0.8,0,1,1, , , , , , ,8,4,4,20,0,264.5874,69.73813,10,3,5,20,0,0,14.40809,8,3,5,10,0,24.57798,0,6,4,4,0,11,0,0,15,4,4,0,15,86.82429,0,6,3,3,15,0,0,0,10,4,5,0,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +129,"""LUL""",1,0,0, , ,27-Feb-13,29-Jul-13,05-Aug-13, ,0,0,2,0.8212181,621,493,0.5418994,-0.2793186,57.16667, ,"""C8""",0, ,"""""", , , , , , , , ,2.433233,145.994,0,5.223819,7, ,8.166667,4,5,13.66667,4.5,0.72,1,1,0.8571429, , , , , , ,8,4,5,15,8,79.42399,0,12,4,5,15,6,0,0,6,4,5,10,7,26.48727,0,8,4,5,20,6,0,0,9,4,5,10,0,0,0,6,4,5,12,0,0,0, , , , , ,40.08272,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +130,"""LUL""",1,0,0, , ,11-Oct-12,05-Apr-13,13-Apr-13, ,1,0,2,0.5901639,662,608,0.7003891,0.1102251,29.71428, ,"""C5""",1, ,"""""", ,24,29,53,56, , , ,0.441034,26.46204,0,6.045175,8, ,3.714286,3.857143,4.714286,4.857143,0,1,1,1,0.875, , , , , , ,2,3,5,3,0,26.46204,0,4,4,4,5,0,0,0,4,4,4,6,0,0,0,4,4,5,3,0,0,0,5,4,5,3,0,0,0,4,4,5,7,0,0,0,3,4,5,7,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +131,"""LUL""",1,0,0, , ,17-Oct-12,06-Apr-13,13-Apr-13, ,1,0,2,0.4220374,580,645,0.7629629,0.3409255,70, ,"""C4""",0, ,"""""", ,24,21,45,32, , , ,4.416657,264.9995,0,5.848049,7, ,10,2.285714,4,2.857143,0,0.76,0,1,1, , , , , , ,10,2,3,6,0,78.54792,0,20,2,3,0,0,79.91525,0,8,2,4,4,0,53.6758,0,8,2,4,6,0,0,0,6,2,4,4,0,52.86048,0,12,3,5,0,0,0,0,6,3,5,0,0,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +132,"""LUL""",1,0,0, , ,01-Dec-12,08-May-13,15-May-13, ,1,1,2,0.4354839,452,489,0.460274,0.0247901,140, ,"""C6""",1, ,"""""", ,20,23,43,26, , , ,2.992643,26.53624,0,5.420945,7, ,20,3.5,4.166667,3.666667,0,0.88,1,1,0.8571429, , , , , , ,10,3,3,4,0,26.53624,25.53787,15,4,4,0,0,0,76.5043,45,4,5,4,0,0,0,20,3,5,5,0,0,0,10,4,4,4,0,0,0,20,3,4,5,0,0,0, , , , , ,0,50.98018, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0, , , , , ,0,0,1 +201,"""ALF""",0,1,0, , ,17-Feb-13,30-Jul-13,06-Aug-13, ,1,0,1,0.7181818,548,422,0.5825243,-0.1356575,43.16666, ,"""C1""", ,0,"""A""", , , , , , , , , , , ,5.585216,7, ,6.166667,3.714286, ,4.571429,0, ,1,1,0.8571429, , , , , , ,8,3, ,5,0, , ,5,4, ,3,0, , , ,4, ,5, , , ,7,4, ,4,0, , ,9,4, ,3,0, , ,4,4, ,6,0, , ,4,3, ,6,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +202,"""ALF""",0,1,0,1, ,25-Mar-13,03-Sep-13,10-Sep-13,29-May-14,0,0,1,0.4505263,531,654,0.6104783,0.159952,63, ,"""C2""", ,0,"""E""", , , , , , ,125,177, , , ,5.552361,7,8.804928,9,3.4, ,5.2,4.2, ,2,1,0.7142857, , , , , , ,5,4, ,5,5, , ,7,3, ,8,10, , ,15,3, ,5,0, , ,10,4, ,4,0, , ,8,3, ,4,6, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +203,"""ALF""",0,1,0, , ,17-Mar-13,06-Sep-13,13-Sep-13, ,1,1,2,0.5460526,670,589,0.4673684,-0.0786842,35, ,"""C3""", ,0,"""H""", , , , , , , , , , , ,5.913758,7, ,5,2, ,3.166667,0, ,1,1,0.8571429, , , , , , ,5,2, ,0,0, , ,3,2, ,2,0, , ,6,2, ,2,0, , ,6,2, ,0,0, , ,5,2, ,5,0, , ,5,2, ,10,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +204,"""ALF""",0,1,0,1, ,27-Mar-13,07-Sep-13,14-Sep-13,16-Aug-14,0,0,1,0.4533333,437,503,0.4271357,-0.0261976,100.8, ,"""C4""", ,0,"""I""", , , , , , ,287,264, , , ,5.61807,7,11.26899,14.4,3.4, ,0.8,0, ,2,1,0.7142857, , , , , , ,20,3, ,0,0, , ,20,3, ,0,0, , ,12,3, ,0,0, , ,10,4, ,4,0, , ,10,4, ,0,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +205,"""ALF""",0,1,0, , ,25-Mar-13,07-Sep-13,14-Sep-13, ,1,0,1,0.4210526,417,319,0.5303867,0.1093341,69, ,"""C5""", ,0,"""D""", , , , , , , , , , , ,5.683778,7, ,9.857142,4.142857, ,1.714286,0, ,0,1,1, , , , , , ,10,5, ,1,0, , ,10,4, ,0,0, , ,20,4, ,5,0, , ,10,5, ,0,0, , ,10,4, ,0,0, , ,7,4, ,0,0, , ,2,3, ,6,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +206,"""ALF""",0,1,0, , ,01-Apr-13,18-Sep-13,25-Sep-13, ,1,0,2,0.6791809,404,206,0.7116279,0.032447,66, ,"""C7""", ,0,"""C""", , , , , , , , , , , ,5.815195,7, ,9.428572,3.285714, ,15.71429,0.7142857, ,0,1,1, , , , , , ,5,2, ,15,5, , ,8,3, ,15,0, , ,20,3, ,25,0, , ,9,3, ,12,0, , ,15,4, ,13,0, , ,3,4, ,10,0, , ,6,4, ,20,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +207,"""ALF""",0,1,0,1, ,29-Apr-13,28-Sep-13,05-Oct-13,26-Jul-14,1,0,1,0.2857143,439,673,0.3844086,0.0986943,105, ,"""C8""", ,0,"""E""", , , , , , ,217,265, , , ,5.223819,7,9.889117,15,3, ,22.57143,0, ,0,1,1, , , , , , ,15,3, ,8,0, , ,30,3, ,20,0, , ,10,3, ,0,0, , ,10,3, ,30,0, , ,10,3, ,40,0, , ,10,3, ,20,0, , ,20,3, ,40,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +208,"""ALF""",0,1,0,1, ,11-Apr-13,30-Sep-13,07-Oct-13,15-May-14,0,0,2,0.3316062,371,662,0.1522634,-0.1793428,270.6667, ,"""C1""", ,0,"""C""", , , , , , ,226,182, , , ,5.880904,7,7.457906,38.66667,3.2, ,16.16667,2.333333, ,1,1,0.8571429, , , , , , ,20,4, ,15,0, , ,40,3, ,20,14, , ,45,3, ,40,0, , ,42,3, ,8,0, , ,33, , ,12,0, , ,52,3, ,2,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +209,"""ALF""",0,1,0, , ,03-May-13,30-Sep-13,07-Oct-13, ,0,0,2,0.6731517,666,647,0.6527546,-0.0203971,62, ,"""C2""", ,1,"""F""", , , , , , , , , , , ,5.158111,7, ,8.857142,2.142857, ,5.428571,2.428571, ,0,1,1, , , , , , ,4,2, ,10,8, , ,11,2, ,0,0, , ,5,2, ,0,0, , ,6,2, ,0,0, , ,12,2, ,10,0, , ,12,2, ,12,0, , ,12,3, ,6,9, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +210,"""ALF""",0,1,0,1, ,27-Apr-13,08-Oct-13,15-Oct-13,08-May-14,0,0,1,0.5398773,601,375,0.4360656,-0.1038117,80, ,"""C3""", ,1,"""A""", , , , , , ,180,180, , , ,5.61807,7,6.965092,11.42857,2.714286, ,3.285714,1.285714, ,0,1,1, , , , , , ,6,2, ,0,0, , ,20,3, ,0,9, , ,10,3, ,0,0, , ,10,3, ,0,0, , ,10,2, ,0,0, , ,12,3, ,20,0, , ,12,3, ,3,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +211,"""ALF""",0,1,0,1, ,03-Mar-13,31-Jul-13,07-Aug-13,29-May-14,1,0,2,0.5721519,285,349,0.3806306,-0.1915213,128, ,"""C2""", ,1,"""B""", , , , , , ,237,168, , , ,5.158111,7,9.921971,18.28572,3.714286, ,0.1428571,8.571428, ,0,1,1, , , , , , ,20,4, ,0,15, , ,35,4, ,0,11, , ,25,3, ,0,8, , ,20,3, ,0,7, , ,10,4, ,1,7, , ,10,4, ,0,6, , ,8,4, ,0,6, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +212,"""ALF""",0,1,0,1, ,01-May-13,16-Oct-13,23-Oct-13,22-Apr-14,1,0,1,0.605598,616,591,0.7182741,0.1126761,104, ,"""C4""", ,1,"""G""", , , , , , ,57,80, , , ,5.749486,7,6.176591,14.85714,3.428571, ,16.28572,22.85714, ,0,0,1, , , , , , ,25,3, ,10,41, , ,24,3, ,14,24, , ,25,3, ,8,23, , ,5,3, ,20,0, , ,8,4, ,12,29, , ,5,4, ,22,22, , ,12,4, ,28,21, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +213,"""ALF""",0,1,0,1, ,01-Jun-13,25-Oct-13,01-Nov-13,08-Aug-14,1,0,2,0.548,424,35,0.5931035,0.0451035,49, ,"""C5""", ,0,"""E""", , , , , , ,109,48, , , ,5.026694,7,9.429158,7,3.5, ,1,0, ,1,1,0.8571429, , , , , , ,10,4, ,0,0, , ,6,3, ,0,0, , ,3,4, ,4,0, , ,3,3, ,0,0, , ,10,3, ,0,0, , ,10,4, ,2,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +214,"""ALF""",0,1,0, , ,05-Jun-13,30-Oct-13,06-Nov-13, ,0,0,1,0.3342037,303,325,0.438961,0.1047574,80, ,"""C6""", ,1,"""F""", , , , , , , , , , , ,5.059548,7, ,11.42857,2, ,1,0, ,0,1,1, , , , , , ,10,2, ,0,0, , ,15,2, ,0,0, , ,10,2, ,0,0, , ,20,2, ,2,0, , ,10,2, ,0,0, , ,5,2, ,0,0, , ,10,2, ,5,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +215,"""ALF""",0,1,0,1, ,15-May-13,31-Oct-13,07-Nov-13,19-Jul-14,1,1,2,0.4229249,506,471,0.7230216,0.3000967,51, ,"""C7""", ,0,"""G""", , , , , , ,221,120, , , ,5.782341,7,8.574948,7.285714,3, ,0.8571429,0, ,0,0,1, , , , , , ,7,3, ,4,0, , ,5,3, ,0,0, , ,5,3, ,2,0, , ,8,3, ,0,0, , ,10,3, ,0,0, , ,7,3, ,0,0, , ,9,3, ,0,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +216,"""ALF""",0,1,0, , ,03-Jun-13,31-Oct-13,07-Nov-13, ,1,1,1,0.3516484,41,43,0.6082474,0.256599,114.3333, ,"""C8""", ,0,"""B""", , , , , , , , , , , ,5.158111,7, ,16.33333,4.333333, ,96.66666,2.5, ,1,1,0.8571429, , , , , , ,20,4, ,100,15, , ,30,5, ,100,0, , ,15,5, ,100,0, , ,10,4, ,80,0, , ,3,4, ,100,0, , ,20,4, ,100,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +217,"""ALF""",0,1,0, , ,26-May-13,02-Nov-13,09-Nov-13, ,1,0,1,0.7666667,537,47,0.6912752,-0.0753915,168, ,"""C1""", ,0,"""A""", , , , , , , , , , , ,5.486653,7, ,24,3.4, ,11,0, ,2,1,0.7142857, , , , , , ,40,4, ,0,0, , ,10,3, ,20,0, , ,20,3, ,20,0, , ,20,3, ,10,0, , ,30,4, ,5,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +218,"""ALF""",0,1,0,1, ,03-Jun-13,04-Nov-13,11-Nov-13,16-Aug-14,1,1,1,0.8,281,87,0.8617886,0.0617886,105, ,"""C2""", ,0,"""F""", , , , , , ,163,143, , , ,5.289528,7,9.36345,15,2.857143, ,2.571429,0, ,0,1,1, , , , , , ,10,2, ,3,0, , ,10,3, ,5,0, , ,20,3, ,3,0, , ,10,3, ,0,0, , ,10,3, ,2,0, , ,25,3, ,3,0, , ,20,3, ,2,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +219,"""ALF""",0,1,0, , ,23-May-13,06-Nov-13,13-Nov-13, ,0,0,2,0.1749049,357,540,0.4307692,0.2558643,37, ,"""C4""", ,0,"""N""", , , , , , , , , , , ,5.716632,7, ,5.285714,2.857143, ,12.85714,0, ,0,1,1, , , , , , ,5,3, ,10,0, , ,4,3, ,10,0, , ,6,3, ,20,0, , ,5,3, ,30,0, , ,4,2, ,10,0, , ,6,3, ,10,0, , ,7,3, ,0,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +220,"""ALF""",0,1,0,1, ,19-Feb-13,05-Aug-13,12-Aug-13,23-Jul-14,0,0,2,0.5904762,639,570,0.438114,-0.1523623,38, ,"""C3""", ,0,"""C""", , , , , , ,203,271, , , ,5.716632,7,11.56468,5.428571,2.714286, ,19.14286,0, ,0,1,1, , , , , , ,5,2, ,15,0, , ,4,3, ,5,0, , ,10,2, ,6,0, , ,9,3, ,3,0, , ,3,3, ,15,0, , ,3,3, ,50,0, , ,4,3, ,40,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +221,"""ALF""",0,1,0,1, ,11-Jun-13,11-Nov-13,18-Nov-13,19-Jul-14,0,0,2,0.9003322,438,474,0.6872964,-0.2130358,45, ,"""C5""", ,0,"""J""", , , , , , ,180,151, , , ,5.256673,7,8.213552,6.428571,2.428571, ,4.571429,0.7142857, ,0,1,1, , , , , , ,12,4, ,0,0, , ,6,3, ,5,0, , ,6,2, ,4,0, , ,5,2, ,6,0, , ,7,2, ,6,0, , ,4,2, ,3,0, , ,5,2, ,8,5, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +222,"""ALF""",0,1,0,1, ,27-May-13,11-Nov-13,18-Nov-13,21-Aug-14,0,0,2,0.5758621,600,512,0.2964602,-0.2794019,48, ,"""C6""", ,0,"""K""", , , , , , ,329,224, , , ,5.749486,7,9.297741,6.857143,4, ,5.142857,16.42857, ,0,0,1, , , , , , ,7,4, ,5,17, , ,8,4, ,5,7, , ,8,4, ,3,19, , ,5,4, ,7,19, , ,5,4, ,8,21, , ,5,4, ,3,16, , ,10,4, ,5,16, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +223,"""ALF""",0,1,0,1, ,10-Jun-13,11-Nov-13,18-Nov-13,21-May-14,1,0,2,0.62954,656,587,0.4403409,-0.1891991,84, ,"""C7""", ,0,"""L""", , , , , , ,224,183, , , ,5.289528,7,6.275154,12,3.714286, ,7.428571,0, ,0,1,1, , , , , , ,5,3, ,0,0, , ,4,3, ,5,0, , ,10,4, ,5,0, , ,8,3, ,15,0, , ,12,4, ,7,0, , ,25,4, ,10,0, , ,20,5, ,10,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +224,"""ALF""",0,1,0,1, ,06-Jun-13,11-Nov-13,18-Nov-13,17-Jul-14,1,0,2,0.464,512,633,0.5645161,0.1005161,99, ,"""C8""", ,0,"""M""", , , , , , ,244,245, , , ,5.420945,7,8.147844,14.14286,2.142857, ,1.714286,0, ,0,1,1, , , , , , ,15,2, ,0,0, , ,25,3, ,4,0, , ,10,2, ,0,0, , ,12,2, ,0,0, , ,15,2, ,4,0, , ,12,2, ,2,0, , ,10,2, ,2,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +225,"""ALF""",0,1,0,1, ,26-May-13,12-Nov-13,19-Nov-13,25-Jul-14,0,0,2,0.566586,507,240,0.9110577,0.3444718,38.5, ,"""C6""", ,0,"""H""", , , , , , ,161,145, , , ,5.815195,7,8.377824,5.5,2.5, ,9.166667,0, ,1,1,0.8571429, , , , , , ,10,2, ,20,0, , ,3,2, ,5,0, , ,10,2, ,10,0, , ,5,3, ,15,0, , ,2,3, ,0,0, , ,3,3, ,5,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +226,"""ALF""",0,1,0,1, ,27-Jun-13,26-Nov-13,05-Dec-13,22-May-14,0,0,1,0.5647668,707,609,0.6533333,0.0885665,74.57143, ,"""C3""", ,0,"""E""", , , , , , ,178,160, , , ,5.289528,9,5.815195,8.285714,4, ,0,0, ,2,1,0.7777778, , , , , , ,10,4, ,0,0, , ,10,4, ,0,0, , ,10,4, ,0,0, , ,6,4, ,0,0, , ,10,4, ,0,0, , ,6,4, ,0,0, , ,6,4, ,0,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +227,"""ALF""",0,1,0,1, ,23-Mar-13,16-Aug-13,23-Aug-13,03-May-14,0,0,2,0.3415179,643,657,0.4628572,0.1213393,49, ,"""C4""", ,0,"""D""", , , , , , ,311,256, , , ,5.026694,7,8.542094,7,2.166667, ,0.3333333,0, ,1,1,0.8571429, , , , , , ,8,3, ,2,0, , ,20,2, ,0,0, , ,10,3, ,0,0, , ,2,2, ,0,0, , ,0,1, ,0,0, , ,2,2, ,0,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +228,"""ALF""",0,1,0,1, ,09-Mar-13,17-Aug-13,24-Aug-13,31-May-14,0,0,1,0.4360087,507,557,0.4236902,-0.0123185,28, ,"""C5""", ,1,"""E""", , , , , , ,360,261, , , ,5.519507,7,9.429158,4,3, ,5.285714,0, ,0,0,1, , , , , , ,6,3, ,6,0, , ,6,3, ,8,0, , ,4,3, ,2,0, , ,3,3, ,6,0, , ,2,3, ,4,0, , ,3,3, ,6,0, , ,4,3, ,5,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +229,"""ALF""",0,1,0,1, ,25-Mar-13,17-Aug-13,24-Aug-13,29-May-14,1,0,1,0.28,198,419,0.1081081,-0.1718919,64, ,"""C6""", ,0,"""F""", , , , , , ,89,105, , , ,4.99384,7,9.36345,9.142858,1.714286, ,1.428571,1.142857, ,0,1,1, , , , , , ,12,2, ,5,0, , ,8,2, ,0,0, , ,15,1, ,3,0, , ,6,1, ,2,0, , ,6,1, ,0,0, , ,8,2, ,0,8, , ,9,3, ,0,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +230,"""ALF""",0,1,0,1, ,21-Feb-13,20-Aug-13,27-Aug-13,07-Apr-14,1,0,1,0.5277778,629,638,0.5987124,0.0709347,86.8, ,"""C7""", ,0,"""G""", , , , , , ,234,225, , , ,6.143737,7,7.556468,12.4,2.4, ,6.4,1, ,2,0,0.7142857, , , , , , ,10,3, ,12,0, , ,15,2, ,0,0, , ,12,2, ,7,0, , ,15,2, ,8,0, , ,10,3, ,5,5, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +231,"""ALF""",0,1,0,1, ,17-Mar-13,24-Aug-13,07-Sep-13,10-Jun-14,1,0,1,0.6619318,663,493,0.754717,0.0927852,49, ,"""C8""", ,0,"""A""", , , , , , ,123,67, , , ,5.716632,14,9.52772,3.5,3.3, ,1.4,0, ,4,1,0.7142857, , , , , , ,3,3, ,5,0, , ,5,3, ,0,0, , ,4,2, ,6,0, , ,4,3, ,2,0, , ,3,3, ,0,0, , ,0,3, ,0,0, , ,3,4, ,1,0, , ,6,4, ,0,0, , ,4,4, ,0,0, , ,3,4, ,0,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +232,"""ALF""",0,1,0, , ,29-Mar-13,24-Aug-13,07-Sep-13, ,1,1,2,0.1762014,430,678,0.452381,0.2761796,182, ,"""C1""", ,0,"""B""", , , , , , , , , , , ,5.322382,14, ,13,3.666667, ,6.125,0, ,5,1,0.6428571, , , , , , ,15,3, ,1,0, , ,25,4, ,10,0, , ,20,4, ,10,0, , ,7,4, , ,0, , ,10,4, ,4,0, , ,5,3, ,4,0, , ,10,4, ,0,0, , ,15,3, ,10,0, , ,10,4, ,10,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +301,"""LAS""",0,0,1, ,1,07-Oct-13,12-Apr-14,19-Apr-14,14-Mar-15,0,1,1,0.3877005,454,505,0.7536232,0.3659227,43.16666,45,"""C1""", ,0,"""""",1, , , , ,1, , , , , ,6.373717,7,11.03901,6.166667,2.666667, ,10,6.5, ,1,1,0.8571429,0.0199494,0.0160402,0.6726609,0.5517117,939,941,7,4, ,10,6, , ,4,3, ,10,7, , ,4,3, ,10,4, , ,6,2, ,10,7, , ,8,2, ,10,8, , ,8,2, ,10,7, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +302,"""LAS""",0,0,1, ,1,23-Nov-13,29-May-14,05-Jun-14,22-Jan-15,1,0,2,0.4862637,482,460,0.6496746,0.1634109,61,59,"""C2""", ,0,"""""",1, , , , ,6, , , , , ,6.373717,7,7.819302,8.714286,4.142857, ,16.57143,20.28572, ,0,0,1,0.0042404,0.0009268,0.4511431,0.3566265,851,777,5,4, ,6,12, , ,10,4, ,15,20, , ,8,4, ,25,15, , ,8,4, ,15,15, , ,9,4, ,15,25, , ,9,4, ,20,25, , ,12,5, ,20,30, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +303,"""LAS""",0,0,1, ,1,24-Dec-13,02-Jun-14,11-Jun-14,12-May-15,0,0,2,0.5746269,895,479,0.3869565,-0.1876704,48,42,"""C3""", ,0,"""""",1, , , , ,4, , , , , ,5.552361,9,11.30185,5.333333,3, ,5.833333,3.166667, ,3,1,0.6666667,0.3278756,0.3274321,0.7798964,0.7547765,1167,833,5,3, ,10,4, , ,7,4, ,3,2, , ,5,4, ,5,3, , ,6,3, ,4,2, , ,4,2, ,12,5, , ,5,2, ,1,3, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +304,"""LAS""",0,0,1, ,1,11-Jan-14,23-Jun-14,30-Jun-14,28-May-15,1,0,1,0.44375,371,475,0.4532374,0.0094874,61,58,"""C6""", ,0,"""""",1, , , , ,8, , , , , ,5.585216,7,11.13758,8.714286,3.571429, ,9.428572,6, ,0,1,1,0.0636816,0.0230114,0.2462687,0.246875,851,1105,8,4, ,4,3, , ,10,4, ,18,4, , ,10,3, ,3,8, , ,8,3, ,15,3, , ,8,4, ,20,5, , ,8,4, ,6,9, , ,9,3, ,0,10, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +305,"""LAS""",0,0,1, , ,25-Jan-14,26-Jun-14,08-Jul-14, ,1, ,1,0.6639344,615,477,0.6959799,0.0320455,87,83,"""C7""", ,0,"""""",0, , , , ,5, , , , , ,5.38809,12, ,7.25,4.272727, ,12.83333,7.166667, ,0,1,1,0.1352646,0.0406805,0.6993613,0.6193294, , ,6,5, ,20,3, , ,10,5, ,10,5, , ,8,5, ,15,5, , ,7,3, ,10,10, , ,8,4, ,15,5, , ,8,4, ,15,5, , ,8,5, ,15,10, , ,0, , ,5,8, , ,8,4, ,8,10, , ,8,3, ,1,10, , ,8,4, ,20,10, , ,8,5, ,20,5, , , , , , , , , , , , , , , , ,0 +306,"""LAS""",0,0,1, ,1,28-Jan-14,26-Jun-14,03-Jul-14,23-May-15,0,0,2,0.7915832,439,458,0.7363636,-0.0552195,53,45,"""C8""", ,0,"""""",1, , , , ,6, , , , , ,5.125257,7,10.87474,7.571429,3.714286, ,12.16667,3, ,0,1,1,0.0721519,0.0287632,0.821308,0.6573346,1008,1028,4,4, , ,0, , ,8,3, ,12,0, , ,7,3, ,30,6, , ,7,4, ,10,12, , ,8,3, ,4,0, , ,10,5, ,7,1, , ,9,4, ,10,2, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +307,"""LAS""",0,0,1, ,1,14-Jan-14,28-Jun-14,08-Jul-14,10-Dec-14,0,1,2,0.2876405,609,575,0.4588745,0.171234,55,77,"""C1""", ,0,"""""",1, , , , ,4, , , , , ,5.749486,10,5.420945,5.5,3, ,15.7,19.8, ,0,1,1,0.030836,0.0660338,0.7520087,0.7111019,770,738,5,3, ,15,15, , ,5,3, ,0,13, , ,6,3, ,30,30, , ,5,3, ,10,15, , ,5,3, ,30,20, , ,6,4, ,17,25, , ,5,3, ,2,15, , ,6,3, ,13,15, , ,5,2, ,10,20, , ,7,3, ,30,30, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +308,"""LAS""",0,0,1, ,1,29-Jan-14,04-Jul-14,12-Jul-14,26-Jun-15,1,1,2,0.2613065,636,343,0.5277778,0.2664713,16,44,"""C2""", ,0,"""""",1, , , , ,8, , , , , ,5.38809,8,11.72895,2,3, ,5,10, ,7,0,0.125,0.0582011,0.0338157,0.8704963,0.5663731,1131,1044,2,3, ,5,10, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +309,"""LAS""",0,0,1, ,1,06-Jan-14,08-Jul-14,15-Jul-14,18-May-15,0,0,1,0.4502924,151,174,0.8352273,0.3849348,47,51,"""C3""", ,0,"""""",1, , , , ,1, , , , , ,6.2423,7,10.31622,6.714286,3.571429, ,4.142857,12.57143, ,0,1,1,0.0991907,0.1048288,0.3835838,0.3717296,1001,1047,5,3, ,0,10, , ,7,4, ,2,12, , ,7,4, ,2,12, , ,7,5, ,0,13, , ,7,3, ,0,11, , ,7,3, ,0,10, , ,7,3, ,25,20, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +310,"""LAS""",0,0,1, ,1,10-Jan-14,11-Jul-14,21-Jul-14,01-Jun-15,0,0,1,0.8538813,615,494,0.5735294,-0.2803519,60,60,"""C4""", ,0,"""""",1, , , , ,4, , , , , ,6.308008,10,10.67762,6,4.5, ,5,3.6, ,0,1,1,0.1975178,0.0611714,0.6847109,0.5065799,801,805,4,4, ,10,1, , ,7,5, ,0,12, , ,6,5, ,8,2, , ,8,5, ,2,2, , ,7,4, ,0,2, , ,6,4, ,20,1, , ,6,4, ,1,4, , ,6,5, ,4,4, , ,5,5, ,0,6, , ,5,4, , ,2, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +311,"""LAS""",0,0,1, ,1,10-Jan-14,11-Jul-14,21-Jul-14,06-Jan-15,0,0,1,0.1076605,618,658,0.0175824,-0.090078,60,66,"""C5""", ,0,"""""",1, , , , ,4, , , , , ,6.308008,10,5.880904,6,4.5, ,5,3.6, ,0,1,1,0.1021917,0.0277657,0.6746765,0.4120029,877,817,4,4, ,10,1, , ,7,5, ,0,12, , ,6,5, ,8,2, , ,8,5, ,2,2, , ,7,4, ,0,2, , ,6,4, ,20,1, , ,6,4, ,1,4, , ,6,5, ,4,4, , ,5,5, ,0,6, , ,5,4, , ,2, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +312,"""LAS""",0,0,1, ,1,13-Jan-14,11-Jul-14,18-Jul-14,15-Mar-15,0,1,2,0.52349,563,636,0.7555556,0.2320656,50,43,"""C6""", ,0,"""""",1, , , , ,5, , , , , ,6.110883,7,8.114989,7.142857,4.142857, ,1.4,1, ,0,1,1,0,0,0.5171806,0.5948387,986,932,4,4, ,0,1, , ,8,4, ,4,1, , ,8,4, ,3,2, , ,7,4, , ,0, , ,9,3, ,0,1, , ,7,5, ,0,2, , ,7,5, , ,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +313,"""LAS""",0,0,1, ,1,07-Jan-14,15-Jul-14,22-Jul-14,21-May-15,1,0,2,0.6467066,544,296,0.6341463,-0.0125602,32,39,"""C7""", ,1,"""""",1, , , , ,2, , , , , ,6.439425,7,10.1848,4.571429,3.571429, ,6,5.285714, ,0,0,1,0.0283623,0.0686185,0.5443733,0.516011,1080,736,3,3, ,0,5, , ,5,4, ,5,3, , ,5,4, ,4,4, , ,4,4, ,7,3, , ,5,3, ,2,4, , ,5,3, ,4,3, , ,5,4, ,20,15, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +314,"""LAS""",0,0,1, ,1,07-Jan-14,16-Jul-14,23-Jul-14,28-Mar-15,1,1,1,0.5802998,465,454,0.747449,0.1671492,48,43,"""C8""", ,0,"""""",1, , , , ,3, , , , , ,6.472279,7,8.377824,6.857143,4.857143, ,9.714286,23.42857, ,0,1,1,0.3213578,0.0939784,0.7541726,0.5802297,1083,924,6,4, ,15,4, , ,5,5, ,10,16, , ,7,5, ,10,10, , ,7,5, ,2,25, , ,8,5, ,6,20, , ,7,5, ,7,10, , ,8,5, ,18,79, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +315,"""LAS""",0,0,1, ,1,14-Jan-14,17-Jul-14,29-Jul-14,03-Jun-15,1,0,2,0.0930233,675,671,0.0581162,-0.034907,53.33334,59,"""C2""", ,0,"""""",0, , , , ,8, , , , , ,6.439425,12,10.5462,4.444445,3.666667, ,18.57143,4.111111, ,3,1,0.75,0.0943555,0.0364855,0.4057849,0.3361132,861,712,3,4, ,15,5, , ,1,4, ,15,5, , ,6,4, ,25,10, , ,6,4, ,30,5, , ,4,2, ,15,3, , ,4,3, ,15,4, , ,5,4, , ,0, , ,5,4, , ,5, , ,6,4, ,15,0, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +316,"""LAS""",0,0,1, , ,14-Feb-14,29-Jul-14,05-Aug-14, ,0,0,1,0.67713,490,551,0.6810126,0.0038826,26,26,"""C4""", ,0,"""""",1, , , , ,3, , , , , ,5.650924,7, ,3.714286,3.285714, ,9,3.714286, ,0,1,1,0.0215012,0.0207856,0.3846755,0.4121113, , ,5,4, ,10,4, , ,6,3, ,5,2, , ,6,4, ,10,4, , ,2,4, ,10,6, , ,3,4, ,10,5, , ,0,1, ,12,3, , ,4,3, ,6,2, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +317,"""LAS""",0,0,1, , ,04-Mar-14,30-Jul-14,07-Aug-14, ,0,0,2,0.7567011,656,674,0.6759657,-0.0807354,44,41,"""C5""", ,0,"""""",1, , , , ,8, , , , , ,5.125257,8, ,5.5,3.5, ,2.625,8.375, ,0,1,1,0.1804575,0.0363663,0.4456368,0.4846371, , ,6,4, ,10,6, , ,6,3, ,0,9, , ,5,3, ,5,10, , ,3,4, ,2,6, , ,6,4, ,2,12, , ,6,3, ,2,8, , ,6,3, ,0,8, , ,6,4, ,0,8, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +318,"""LAS""",0,0,1, , ,03-Nov-13,30-Apr-14,09-May-14, ,0,0,2,0.3895487,264,350,0.375817,-0.0137317,53,78,"""C3""", ,0,"""""",1, , , , ,1, , , , , ,6.143737,9, ,5.888889,2, ,2.625,1.666667, ,0,1,1,0.1778048,0.1208639,0.7156743,0.6233406, , ,5,2, ,3,1, , ,3,2, ,6,5, , ,3,2, ,1,0, , ,5,2, , ,0, , ,6,2, ,1,0, , ,8,2, ,3,1, , ,8,2, ,2,2, , ,8,2, ,3,5, , ,7,2, ,2,1, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +319,"""LAS""",0,0,1, ,1,08-Feb-14,31-Jul-14,09-Aug-14,17-Apr-15,0,0,2,0.5762195,694,553,0.0880503,-0.4881692,25.2,29,"""C6""", ,0,"""""", , , , , ,7, , , , , ,5.979466,9,8.542094,2.8,4.8, ,2.5,2.6, ,4,1,0.5555556,0.0087294,0,0.2870999,0.3284069,917,1000,2,5, ,1,4, , ,3,5, ,5,5, , ,2,4, , ,0, , ,4,5, ,2,2, , ,3,5, ,2,2, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +320,"""LAS""",0,0,1, , ,06-Feb-14,31-Jul-14,07-Aug-14, ,1,0,1,0.528436,416,619,0.2123142,-0.3161218,29.75,25,"""C7""", ,0,"""""",0, , , , ,6, , , , , ,5.979466,7, ,4.25,2.5, ,9,5.75, ,3,1,0.5714286,0.0129416,0.0093163,0.679958,0.618174, , ,4,4, ,5,5, , ,5,2, , ,0, , ,4,2, ,10,10, , ,4,2, ,12,8, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +321,"""LAS""",0,0,1, ,1,28-Feb-14,02-Aug-14,09-Aug-14,02-Dec-14,0,1,2,0.4987593,558,691,0.3255269,-0.1732324,35,25,"""C8""", ,0,"""""",0, , , , ,2, , , , , ,5.322382,7,4.008214,5,4, ,2.571429,8.714286, ,0,0,1,0.0183838,0.0143488,0.8391421,0.6556292,1001,985,10,3, ,0,6, , ,5,4, ,7,7, , ,5,4, ,1,12, , ,5,4, ,0,10, , ,0, , ,3,6, , ,5,4, ,2,10, , ,5,5, ,5,10, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +322,"""LAS""",0,0,1, ,1,02-Mar-14,02-Aug-14,09-Aug-14,25-Apr-15,0,0,1,0.1736527,565,365,0.0927835,-0.0808692,43.16666,37,"""C1""", ,0,"""""",1, , , , ,5, , , , , ,5.256673,7,8.73922,6.166667,5, ,13,8.5, ,1,1,0.8571429,0.4555556,0.0634781,0.8682539,0.6504315,943,865,5,5, ,30,15, , ,8,5, ,10,5, , ,6,5, ,10,8, , ,6,5, ,8,5, , ,5,5, ,15,10, , ,7,5, ,5,8, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +323,"""LAS""",0,0,1, ,1,23-Mar-14,04-Sep-14,11-Sep-14,04-Dec-14,0,0,1,0.3853503,465,212,0.5707317,0.1853814,28,31,"""C3""", ,0,"""""",1, , , , ,8, , , , , ,5.650924,7,2.989733,4,3.714286, ,2.285714,2.428571, ,0,1,1,0.0174658,0.0012263,0.6825342,0.6264892,880,928,4,4, ,0,2, , ,4,3, ,5,2, , ,4,3, ,1,2, , ,4,4, ,5,4, , ,4,4, ,2,2, , ,4,4, ,0,3, , ,4,4, ,3,2, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +324,"""LAS""",0,0,1, , ,01-May-14,27-Sep-14,04-Oct-14, ,1,1,2,0.4419889,623,622,0.3538083,-0.0881806,33.83334,26,"""C4""", ,1,"""""", , , , , ,3, , , , , ,5.125257,7, ,4.833333,4, ,15,3.166667, ,1,1,0.8571429,0.0634807,0.0652316,0.8412982,0.6829746, , ,6,4, ,32,2, , ,4,4, ,15,4, , ,4,4, ,10,4, , ,4,4, ,15,3, , ,5,4, ,16,2, , ,6,4, ,2,4, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +325,"""LAS""",0,0,1, ,1,22-May-14,15-Oct-14,22-Oct-14,14-Jan-15,0,0,1,0.41,691,270,0.5,0.09,33,33,"""C2""", ,0,"""""", , , , , ,3, , , , , ,5.026694,7,2.989733,4.714286,3.857143, ,10.71429,23.71428, ,0,0,1,0.0730885,0.0017144,0.7181409,0.5636894,977,695,3,4, ,10,6, , ,5,4, ,10,30, , ,5,4, ,10,30, , ,5,4, ,5,20, , ,5,3, ,5,20, , ,5,4, ,15,30, , ,5,4, ,20,30, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +326,"""LAS""",0,0,1, ,1,07-Jun-14,31-Oct-14,07-Nov-14,02-Apr-15,0,0,2,0.5978495,674,626,0.817372,0.2195225,25,28,"""C5""", , ,"""""", , , , , ,3, , , , , ,5.026694,7,5.026694,3.571429,3.571429, ,8,6.714286, ,0,1,1,0.1831818,0.1322,0.7236364,0.5152665,933,829,3,3, ,6,8, , ,3,3, ,10,5, , ,5,3, ,20,5, , ,3,4, ,3,10, , ,3,4, ,2,5, , ,3,4, ,10,8, , ,5,4, ,5,6, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +327,"""LAS""",0,0,1, , ,13-Nov-13,13-May-14,23-May-14, ,0,0,1,0.4834711,400,702,0.4623116,-0.0211595,72.22222,65,"""C4""", ,0,"""""",1, , , , ,3, , , , , ,6.275154,10, ,7.222222,3.888889, ,13.33333,8.666667, ,1,1,0.9,0.1024769,0.0820385,0.7467217,0.7143153, , ,5,4, ,5,4, , ,8,3, ,3,2, , ,8,3, ,7,4, , ,8,4, ,5,3, , ,7,4, ,5,10, , ,7,4, ,30,15, , ,7,4, ,15,5, , ,7,4, ,25,7, , ,8,5, ,25,28, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +328,"""LAS""",0,0,1, , ,13-Nov-13,15-May-14,23-May-14, ,1,1,2,0.3992674,593,589,0.4628572,0.0635898,66.28571,65,"""C5""", ,0,"""""",1, , , , ,4, , , , , ,6.275154,8, ,8.285714,4.571429, ,8.285714,5, ,1,1,0.875,0.0647795,0.0651485,0.8189076,0.7071287, , ,3,4, ,4,3, , ,6,4, ,5,4, , ,9,4, ,4,5, , ,10,5, ,6,2, , ,10,5, ,12,5, , ,10,5, ,15,8, , ,10,5, ,12,8, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +329,"""LAS""",0,0,1, ,1,11-Dec-13,23-May-14,30-May-14,02-Dec-14,0,0,1,0.3125,677,676,0.2558824,-0.0566176,45.5,52,"""C6""", ,0,"""""",1, , , , ,4, , , , , ,5.585216,7,6.340862,6.5,3.166667, ,3.6,4.166667, ,1,1,0.8571429,0.0373936,0,0.4844502,0.4635123,940,930,5,3, ,3,5, , ,7,3, ,0,5, , ,8,2, ,0,5, , ,6,4, , ,0, , ,6,3, ,0,6, , ,7,4, ,15,4, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +330,"""LAS""",0,0,1, ,1,28-Nov-13,27-May-14,07-Jun-14,30-May-15,1,0,2,0.6793349,501,484,0.4908676,-0.1884674,86.9,75,"""C7""", ,0,"""""",1, , , , ,3, , , , , ,6.275154,11,12.09035,7.9,2.777778, ,10.44444,7.1, ,1,1,0.9090909,0.0115552,0.0762679,0.4303845,0.6142083,1090,833,4,3, ,6,5, , ,9,3, ,10,5, , ,8,3, , ,0, , ,10,3, ,20,10, , ,8,2, ,20,10, , ,10,3, ,3,3, , ,10,2, ,0,10, , ,10,3, ,20,5, , ,10,3, ,10,3, , ,0, , ,5,20, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +331,"""LAS""",0,0,1, ,1,29-Nov-13,27-May-14,03-Jun-14,25-Apr-15,1,0,1,0.4516129,545,651,0.2956522,-0.1559607,57,53,"""C8""", ,0,"""""",1, , , , ,2, , , , , ,6.110883,7,10.94045,8.142858,3.571429, ,30.83333,10.28571, ,0,0,1,0.0830479,0.0869418,0.349101,0.4350484,1015,1062,5,5, , ,0, , ,8,3, ,0,2, , ,8,4, ,20,5, , ,7,4, ,30,10, , ,9,3, ,80,25, , ,10,4, ,30,20, , ,10,2, ,25,10, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 +332,"""LAS""",0,0,1, , ,13-Dec-13,28-May-14,04-Jun-14, ,0,0,1,0.4227273,691,677,0.6629527,0.2402254,49,56,"""C4""", ,0,"""""",1, , , , ,1, , , , , ,5.683778,7, ,7,2.714286, ,2.857143,4.285714, ,0,1,1,0.0530738,0.0333863,0.6706967,0.6228582, , ,6,2, ,0,4, , ,9,3, ,2,2, , ,6,3, ,5,6, , ,6,3, ,4,4, , ,7,2, ,5,5, , ,8,3, ,0,5, , ,7,3, ,4,4, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,0 diff --git a/wip/Data/Schroeder and Epley 2015.csv b/wip/Data/Schroeder and Epley 2015.csv new file mode 100644 index 0000000..36e1696 --- /dev/null +++ b/wip/Data/Schroeder and Epley 2015.csv @@ -0,0 +1,40 @@ +CONDITION,compt,thought,intell,like,pos,neg,hire,age,gender,time,wordcount,negR,intellect,impression,speaker,pnum,meanhire,meanintellect,meanimpression,centhire,centintellect,centimpression,Intellect_Rating,Impression_Rating,Hire_Rating +1,7,7,7,7,7,1,7,29,2,135.045,36,10,7,8,9,1,4.58333333333333,5.72222222222222,6.63888888888889,2.41666666666667,1.27777777777778,1.36111111111111,6,7,6 +1,6,8,6,6,6,6,5,27,2,127.16,71,5,6.66666666666667,5.66666666666667,18,2,4.66666666666667,5.57777777777778,5.77777777777778,.333333333333333,1.08888888888889,-.111111111111111,5.66666666666667,4.66666666666667,4 +1,7,8,6,9,9,1,6,27,2,140.512,54,10,7,9.33333333333333,18,3,4.66666666666667,5.57777777777778,5.77777777777778,1.33333333333333,1.42222222222222,3.55555555555556,6,8.33333333333333,5 +0,4,3,6,6,6,6,5,40,2,59.906,85,5,4.33333333333333,5.66666666666667,9,4,4.58333333333333,5.72222222222222,6.63888888888889,.416666666666667,-1.38888888888889,-.972222222222221,3.33333333333333,4.66666666666667,4 +0,2,3,1,2,2,8,2,32,2,108.722,51,3,2,2.33333333333333,9,5,4.58333333333333,5.72222222222222,6.63888888888889,-2.58333333333333,-3.72222222222222,-4.30555555555556,1,1.33333333333333,1 +0,3,3,3,2,2,6,2,24,2,75.19,49,5,3,3,18,6,4.66666666666667,5.57777777777778,5.77777777777778,-2.66666666666667,-2.57777777777778,-2.77777777777778,2,2,1 +1,5,4,5,4,2,6,2,29,2,151.021,54,5,4.66666666666667,3.66666666666667,14,7,5.41666666666667,5.88888888888889,5.94444444444444,-3.41666666666667,-1.22222222222222,-2.27777777777778,3.66666666666667,2.66666666666667,1 +0,2,3,3,3,2,8,3,23,2,244.342,76,3,2.66666666666667,2.66666666666667,14,8,5.41666666666667,5.88888888888889,5.94444444444444,-2.41666666666667,-3.22222222222222,-3.27777777777778,1.66666666666667,1.66666666666667,2 +1,6,6,6,4,4,3,4,28,2,126.888,75,8,6,5.33333333333333,18,9,4.66666666666667,5.57777777777778,5.77777777777778,-.666666666666667,.422222222222223,-.444444444444445,5,4.33333333333333,3 +0,4,6,4,4,4,8,3,34,2,38.998, ,3,4.66666666666667,3.66666666666667,9,10,4.58333333333333,5.72222222222222,6.63888888888889,-1.58333333333333,-1.05555555555555,-2.97222222222222,3.66666666666667,2.66666666666667,2 +0,6,7,7,7,8,1,5,33,2,101.133,54,10,6.66666666666667,8.33333333333333,18,11,4.66666666666667,5.57777777777778,5.77777777777778,.333333333333333,1.08888888888889,2.55555555555556,5.66666666666667,7.33333333333333,4 +1,10,10,10,10,10,2,9,28,2,142.103,48,9,10,9.66666666666667,14,12,5.41666666666667,5.88888888888889,5.94444444444444,3.58333333333333,4.11111111111111,3.72222222222222,9,8.66666666666667,8 +1,6,8,7,6,7,4,5,28,2,805.438,42,7,7,6.66666666666667,18,13,4.66666666666667,5.57777777777778,5.77777777777778,.333333333333333,1.42222222222222,.888888888888889,6,5.66666666666667,4 +0,5,5,4,6,5,6,3,25,2,126.428,56,5,4.66666666666667,5.33333333333333,14,14,5.41666666666667,5.88888888888889,5.94444444444444,-2.41666666666667,-1.22222222222222,-.611111111111111,3.66666666666667,4.33333333333333,2 +0,3,1,1,1,1,9,1,29,2,61.496,49,2,1.66666666666667,1.33333333333333,18,15,4.66666666666667,5.57777777777778,5.77777777777778,-3.66666666666667,-3.91111111111111,-4.44444444444444,.666666666666667,.333333333333333,0 +1,10,10,10,7,8,3,8,28,2,138.58,35,8,10,7.66666666666667,9,16,4.58333333333333,5.72222222222222,6.63888888888889,3.41666666666667,4.27777777777778,1.02777777777778,9,6.66666666666667,7 +1,3,3,4,3,2,6,2,48,2,130.437,75,5,3.33333333333333,3.33333333333333,14,17,5.41666666666667,5.88888888888889,5.94444444444444,-3.41666666666667,-2.55555555555556,-2.61111111111111,2.33333333333333,2.33333333333333,1 +1,7,4,6,3,4,6,4,31,1,123.762,21,5,5.66666666666667,4,18,18,4.66666666666667,5.57777777777778,5.77777777777778,-.666666666666667,.0888888888888895,-1.77777777777778,4.66666666666667,3,3 +0,7,5,5,6,4,6,4,27,2,108.255,73,5,5.66666666666667,5,18,19,4.66666666666667,5.57777777777778,5.77777777777778,-.666666666666667,.0888888888888895,-.777777777777778,4.66666666666667,4,3 +0,4,6,4,6,6,6,4,24,2,106.662,51,5,4.66666666666667,5.66666666666667,9,20,4.58333333333333,5.72222222222222,6.63888888888889,-.583333333333333,-1.05555555555555,-.972222222222221,3.66666666666667,4.66666666666667,3 +0,9,8,7,8,8,2,8,32,2,46.101,43,9,8,8.33333333333333,14,21,5.41666666666667,5.88888888888889,5.94444444444444,2.58333333333333,2.11111111111111,2.38888888888889,7,7.33333333333333,7 +0,5,2,1,2,1,9,3,31,1,907.291,76,2,2.66666666666667,1.66666666666667,14,22,5.41666666666667,5.88888888888889,5.94444444444444,-2.41666666666667,-3.22222222222222,-4.27777777777778,1.66666666666667,.666666666666667,2 +0,7,8,7,7,7,1,6, , ,89.009, ,10,7.33333333333333,8,14,23,5.41666666666667,5.88888888888889,5.94444444444444,.583333333333333,1.44444444444444,2.05555555555556,6.33333333333333,7,5 +1,6,8,6,8,6,2,1, , ,195.625, ,9,6.66666666666667,7.66666666666667,9,24,4.58333333333333,5.72222222222222,6.63888888888889,-3.58333333333333,.944444444444446,1.02777777777778,5.66666666666667,6.66666666666667,0 +1,9,9,8,8,8,4,8,24,2,124.705,18,7,8.66666666666667,7.66666666666667,14,25,5.41666666666667,5.88888888888889,5.94444444444444,2.58333333333333,2.77777777777778,1.72222222222222,7.66666666666667,6.66666666666667,7 +1,9,8,6,8,8,1,6, , ,139.743, ,10,7.66666666666667,8.66666666666667,9,26,4.58333333333333,5.72222222222222,6.63888888888889,1.41666666666667,1.94444444444445,2.02777777777778,6.66666666666667,7.66666666666667,5 +0,3,6,5,6,5,3,2,32,2,57.653,50,8,4.66666666666667,6.33333333333333,9,27,4.58333333333333,5.72222222222222,6.63888888888889,-2.58333333333333,-1.05555555555555,-.305555555555555,3.66666666666667,5.33333333333333,1 +1,5,7,5,6,6,2,5, , ,132.961, ,9,5.66666666666667,7,18,28,4.66666666666667,5.57777777777778,5.77777777777778,.333333333333333,.0888888888888895,1.22222222222222,4.66666666666667,6,4 +1,8,6,6,8,9,3,8,38,2,189.319,41,8,6.66666666666667,8.33333333333333,9,29,4.58333333333333,5.72222222222222,6.63888888888889,3.41666666666667,.944444444444446,1.69444444444445,5.66666666666667,7.33333333333333,7 +1,3,4,6,5,5,6,5,27,2,127.853,80,5,4.33333333333333,5,18,30,4.66666666666667,5.57777777777778,5.77777777777778,.333333333333333,-1.24444444444444,-.777777777777778,3.33333333333333,4,4 +0,6,3,4,6,5,3,3,27,2,105.149,66,8,4.33333333333333,6.33333333333333,9,31,4.58333333333333,5.72222222222222,6.63888888888889,-1.58333333333333,-1.38888888888889,-.305555555555555,3.33333333333333,5.33333333333333,2 +0,4,3,3,5,3,6,2,28,1,53.18,30,5,3.33333333333333,4.33333333333333,18,32,4.66666666666667,5.57777777777778,5.77777777777778,-2.66666666666667,-2.24444444444444,-1.44444444444444,2.33333333333333,3.33333333333333,1 +1,7,7,7,6,6,6,6,38,2,132.024,46,5,7,5.66666666666667,14,33,5.41666666666667,5.88888888888889,5.94444444444444,.583333333333333,1.11111111111111,-.277777777777777,6,4.66666666666667,5 +1,6,6,6,8,8,2,9,25,2,161.854,25,9,6,8.33333333333333,14,34,5.41666666666667,5.88888888888889,5.94444444444444,3.58333333333333,.111111111111111,2.38888888888889,5,7.33333333333333,8 +0,6,5,6,8,7,5,6, , ,75.072, ,6,5.66666666666667,7,14,35,5.41666666666667,5.88888888888889,5.94444444444444,.583333333333333,-.222222222222222,1.05555555555556,4.66666666666667,6,5 +1,6,9,6,8,8,1,8,47,1,138.268,64,10,7,8.66666666666667,18,36,4.66666666666667,5.57777777777778,5.77777777777778,3.33333333333333,1.42222222222222,2.88888888888889,6,7.66666666666667,7 +0,7,8,8,8,7,7,8,42,2,116.805,71,4,7.66666666666667,6.33333333333333,18,37,4.66666666666667,5.57777777777778,5.77777777777778,3.33333333333333,2.08888888888889,.555555555555555,6.66666666666667,5.33333333333333,7 +1,6,6,6,9,9,1,6,31,2,143.935,148,10,6,9.33333333333333,9,38,4.58333333333333,5.72222222222222,6.63888888888889,1.41666666666667,.277777777777779,2.69444444444445,5,8.33333333333333,5 +1,6,7,6,7,7,5,6,33,2,143.669,38,6,6.33333333333333,6.66666666666667,18,39,4.66666666666667,5.57777777777778,5.77777777777778,1.33333333333333,.755555555555556,.888888888888889,5.33333333333333,5.66666666666667,5 diff --git a/wip/Data/Sleepstudy_data b/wip/Data/Sleepstudy_data new file mode 100644 index 0000000..2ad7959 --- /dev/null +++ b/wip/Data/Sleepstudy_data @@ -0,0 +1,181 @@ +"","Reaction","Days","Subject" +"1",249.56,0,"308" +"2",258.7047,1,"308" +"3",250.8006,2,"308" +"4",321.4398,3,"308" +"5",356.8519,4,"308" +"6",414.6901,5,"308" +"7",382.2038,6,"308" +"8",290.1486,7,"308" +"9",430.5853,8,"308" +"10",466.3535,9,"308" +"11",222.7339,0,"309" +"12",205.2658,1,"309" +"13",202.9778,2,"309" +"14",204.707,3,"309" +"15",207.7161,4,"309" +"16",215.9618,5,"309" +"17",213.6303,6,"309" +"18",217.7272,7,"309" +"19",224.2957,8,"309" +"20",237.3142,9,"309" +"21",199.0539,0,"310" +"22",194.3322,1,"310" +"23",234.32,2,"310" +"24",232.8416,3,"310" +"25",229.3074,4,"310" +"26",220.4579,5,"310" +"27",235.4208,6,"310" +"28",255.7511,7,"310" +"29",261.0125,8,"310" +"30",247.5153,9,"310" +"31",321.5426,0,"330" +"32",300.4002,1,"330" +"33",283.8565,2,"330" +"34",285.133,3,"330" +"35",285.7973,4,"330" +"36",297.5855,5,"330" +"37",280.2396,6,"330" +"38",318.2613,7,"330" +"39",305.3495,8,"330" +"40",354.0487,9,"330" +"41",287.6079,0,"331" +"42",285,1,"331" +"43",301.8206,2,"331" +"44",320.1153,3,"331" +"45",316.2773,4,"331" +"46",293.3187,5,"331" +"47",290.075,6,"331" +"48",334.8177,7,"331" +"49",293.7469,8,"331" +"50",371.5811,9,"331" +"51",234.8606,0,"332" +"52",242.8118,1,"332" +"53",272.9613,2,"332" +"54",309.7688,3,"332" +"55",317.4629,4,"332" +"56",309.9976,5,"332" +"57",454.1619,6,"332" +"58",346.8311,7,"332" +"59",330.3003,8,"332" +"60",253.8644,9,"332" +"61",283.8424,0,"333" +"62",289.555,1,"333" +"63",276.7693,2,"333" +"64",299.8097,3,"333" +"65",297.171,4,"333" +"66",338.1665,5,"333" +"67",332.0265,6,"333" +"68",348.8399,7,"333" +"69",333.36,8,"333" +"70",362.0428,9,"333" +"71",265.4731,0,"334" +"72",276.2012,1,"334" +"73",243.3647,2,"334" +"74",254.6723,3,"334" +"75",279.0244,4,"334" +"76",284.1912,5,"334" +"77",305.5248,6,"334" +"78",331.5229,7,"334" +"79",335.7469,8,"334" +"80",377.299,9,"334" +"81",241.6083,0,"335" +"82",273.9472,1,"335" +"83",254.4907,2,"335" +"84",270.8021,3,"335" +"85",251.4519,4,"335" +"86",254.6362,5,"335" +"87",245.4523,6,"335" +"88",235.311,7,"335" +"89",235.7541,8,"335" +"90",237.2466,9,"335" +"91",312.3666,0,"337" +"92",313.8058,1,"337" +"93",291.6112,2,"337" +"94",346.1222,3,"337" +"95",365.7324,4,"337" +"96",391.8385,5,"337" +"97",404.2601,6,"337" +"98",416.6923,7,"337" +"99",455.8643,8,"337" +"100",458.9167,9,"337" +"101",236.1032,0,"349" +"102",230.3167,1,"349" +"103",238.9256,2,"349" +"104",254.922,3,"349" +"105",250.7103,4,"349" +"106",269.7744,5,"349" +"107",281.5648,6,"349" +"108",308.102,7,"349" +"109",336.2806,8,"349" +"110",351.6451,9,"349" +"111",256.2968,0,"350" +"112",243.4543,1,"350" +"113",256.2046,2,"350" +"114",255.5271,3,"350" +"115",268.9165,4,"350" +"116",329.7247,5,"350" +"117",379.4445,6,"350" +"118",362.9184,7,"350" +"119",394.4872,8,"350" +"120",389.0527,9,"350" +"121",250.5265,0,"351" +"122",300.0576,1,"351" +"123",269.8939,2,"351" +"124",280.5891,3,"351" +"125",271.8274,4,"351" +"126",304.6336,5,"351" +"127",287.7466,6,"351" +"128",266.5955,7,"351" +"129",321.5418,8,"351" +"130",347.5655,9,"351" +"131",221.6771,0,"352" +"132",298.1939,1,"352" +"133",326.8785,2,"352" +"134",346.8555,3,"352" +"135",348.7402,4,"352" +"136",352.8287,5,"352" +"137",354.4266,6,"352" +"138",360.4326,7,"352" +"139",375.6406,8,"352" +"140",388.5417,9,"352" +"141",271.9235,0,"369" +"142",268.4369,1,"369" +"143",257.2424,2,"369" +"144",277.6566,3,"369" +"145",314.8222,4,"369" +"146",317.2135,5,"369" +"147",298.1353,6,"369" +"148",348.1229,7,"369" +"149",340.28,8,"369" +"150",366.5131,9,"369" +"151",225.264,0,"370" +"152",234.5235,1,"370" +"153",238.9008,2,"370" +"154",240.473,3,"370" +"155",267.5373,4,"370" +"156",344.1937,5,"370" +"157",281.1481,6,"370" +"158",347.5855,7,"370" +"159",365.163,8,"370" +"160",372.2288,9,"370" +"161",269.8804,0,"371" +"162",272.4428,1,"371" +"163",277.8989,2,"371" +"164",281.7895,3,"371" +"165",279.1705,4,"371" +"166",284.512,5,"371" +"167",259.2658,6,"371" +"168",304.6306,7,"371" +"169",350.7807,8,"371" +"170",369.4692,9,"371" +"171",269.4117,0,"372" +"172",273.474,1,"372" +"173",297.5968,2,"372" +"174",310.6316,3,"372" +"175",287.1726,4,"372" +"176",329.6076,5,"372" +"177",334.4818,6,"372" +"178",343.2199,7,"372" +"179",369.1417,8,"372" +"180",364.1236,9,"372" diff --git a/wip/Data/Turning_Hands_Data_Final.csv b/wip/Data/Turning_Hands_Data_Final.csv new file mode 100644 index 0000000..0604294 --- /dev/null +++ b/wip/Data/Turning_Hands_Data_Final.csv @@ -0,0 +1,103 @@ +ParticipantNumber,Condition,q1_check,q2_check,q1_NEO,q2_NEO,q3_NEO,q4_NEO,q5_NEO,q6_NEO,q7_NEO,q8_NEO,q9_NEO,q10_NEO,q11_NEO,q12_NEO,mean_NEO,q3_check,q4_check,Include,Rotation,Age,Sex,Student,Major.Occupation +1,1,2,6,4,2,1,3,4,4,3,4,5,5,4,5,0.666666667,5,5,TRUE,counter,25,M,Y,Rechten +2,2,3,5,5,3,4,4,3,5,4,4,4,5,5,4,1.166666667,8,1,TRUE,clock,20,F,Y,Taal- en Cultuurstudies +3,3,7,3,4,2,4,4,4,5,5,4,2,4,4,4,0.833333333,7,2,TRUE,counter,25,F,Y,Politicologie +4,4,4,5,3,3,2,2,2,3,5,4,3,2,4,3,0,7,4,TRUE,clock,19,F,Y,Psychologie +5,1,3,3,1,1,1,3,3,4,4,3,4,2,5,2,-0.25,5,2,TRUE,counter,20,F,Y,Geneeskunde +6,2,3,1,3,5,4,5,4,5,4,4,4,2,4,2,0.833333333,6,2,TRUE,clock,26,F,Y,Communicatiewetenschap +7,3,4,7,5,4,2,5,4,4,3,4,3,2,4,4,0.666666667,5,7,TRUE,counter,23,M,Y,Psychobiologie +8,4,7,2,4,5,4,3,3,3,3,5,4,4,4,4,0.833333333,7,4,TRUE,clock,22,F,Y,Psychologie +9,1,5,3,5,4,4,4,4,5,4,5,2,3,3,4,0.916666667,7,6,TRUE,counter,18,F,Y,x +10,2,4,6,4,4,2,2,4,4,4,5,5,4,4,4,0.833333333,8,2,TRUE,clock,19,F,Y,Sociologie +11,3,4,2,2,2,2,4,4,4,4,4,2,4,4,3,0.25,7,2,TRUE,counter,24,F,Y,Psychologie +12,4,7,2,3,1,4,5,4,4,5,5,3,4,5,2,0.75,8,2,TRUE,clock,20,F,Y,Psychobiologie +13,1,6,3,4,3,2,5,3,4,2,5,4,4,4,4,0.666666667,6,4,TRUE,counter,21,F,Y,Psychobiologie +14,2,3,5,4,4,2,3,3,3,4,4,3,4,4,2,0.333333333,5,5,TRUE,clock,19,F,Y,Psychologie +15,3,4,5,3,4,5,5,4,4,3,4,4,3,3,4,0.833333333,3,7,TRUE,counter,21,F,N,Tekenaar +16,4,7,4,5,2,3,4,4,3,4,3,4,4,4,5,0.75,7,3,TRUE,clock,19,F,Y,Pedagogische Wetenschappen +17,1,4,5,1,2,2,4,3,4,2,2,4,3,4,3,-0.166666667,7,1,TRUE,counter,21,F,Y,Oudheidkunde +18,2,0,3,4,1,1,5,2,5,5,2,2,4,4,4,0.25,6,4,TRUE,clock,26,F,Y,Bestuurskunde +19,3,2,2,5,3,4,4,4,4,2,4,5,5,3,4,0.916666667,5,4,TRUE,counter,21,M,Y,Psychologie & Wijsbegeerte +20,4,0,6,5,5,4,5,5,5,4,5,5,5,5,5,1.833333333,8,5,TRUE,clock,23,M,Y,Psychologie +21,1,0,5,3,3,4,4,3,3,3,4,5,5,5,5,0.916666667,6,6,TRUE,counter,41,F,N,Boekhouder +23,3,3,4,5,5,5,5,5,3,5,5,3,5,5,5,1.666666667,5,7,TRUE,counter,25,M,Y,History & Philosophy of Science +24,4,6,2,4,4,4,3,5,4,5,4,3,4,5,4,1.083333333,7,4,TRUE,clock,31,F,Y,Psychologie +25,1,3,5,1,2,3,4,2,1,5,5,5,4,5,1,0.166666667,7,2,TRUE,counter,22,F,Y,Algemene Sociale Wetenschappen +26,2,1,7,1,1,1,3,1,3,5,3,5,1,4,1,-0.583333333,7,0,TRUE,clock,19,F,Y,Psychologie +27,3,4,0,5,1,2,2,3,5,4,4,3,5,5,5,0.666666667,8,1,TRUE,counter,23,M,Y,Psychobiologie +28,4,5,7,3,3,3,2,2,4,3,3,2,2,4,2,-0.25,8,3,TRUE,clock,19,F,Y,Psychologie +29,1,4,4,5,5,5,4,5,5,4,5,2,1,4,4,1.083333333,6,5,TRUE,counter,19,M,Y,Psychobiologie +30,2,0,1,4,4,3,4,4,2,5,4,2,4,4,3,0.583333333,3,6,TRUE,clock,21,F,Y,Taal & Communicatie +31,3,1,7,5,3,4,4,4,5,4,4,3,5,4,4,1.083333333,3,8,TRUE,counter,21,F,Y,Psychobiologie & Wijsbegeerte +32,4,7,2,2,3,3,4,4,4,4,5,2,5,5,2,0.583333333,6,1,TRUE,clock,21,F,Y,Psychologie +33,1,10,10,5,4,3,2,4,5,5,5,5,2,5,3,1,0,0,TRUE,counter,21,F,Y,Psychologie +34,2,5,2,2,2,2,4,1,2,4,4,2,2,4,4,-0.25,6,4,TRUE,clock,20,F,Y,Psychobiologie +35,3,4,1,3,1,1,3,4,5,4,4,3,1,5,2,0,3,1,TRUE,counter,21,F,Y,Algemene Sociale Wetenschappen - A&O + COM +36,4,1,4,5,4,2,4,2,3,1,5,1,5,4,3,0.25,6,1,TRUE,clock,19,F,Y,Psychologie +37,1,7,2,5,5,5,4,4,3,4,4,2,1,5,4,0.833333333,4,4,TRUE,counter,22,F,Y,Psychologie +38,2,0,3,5,1,2,5,4,5,3,1,5,5,4,3,0.583333333,6,2,TRUE,clock,19,F,Y,Psychologie +39,3,3,4,4,1,5,5,5,4,5,4,3,5,4,4,1.083333333,8,1,TRUE,counter,22,M,Y,Bedrijfskunde +40,4,9,2,5,4,3,4,4,4,4,2,3,5,4,5,0.916666667,7,3,TRUE,clock,22,M,Y,Media & Cultuur +41,1,3,7,3,4,4,2,4,4,4,4,5,4,5,2,0.75,8,2,TRUE,counter,26,F,Y,Rechten +42,2,0,1,3,4,3,4,2,4,3,5,4,5,3,3,0.583333333,4,6,TRUE,clock,21,F,Y,Psychologie +43,3,3,4,4,4,2,3,2,5,2,5,5,4,4,4,0.666666667,6,7,TRUE,counter,22,M,Y,Psychobiologie +44,4,2,1,4,3,2,5,4,3,3,4,5,4,3,3,0.583333333,6,2,TRUE,clock,20,F,Y,Psychologie +46,2,1,3,4,4,4,4,2,5,4,5,4,2,5,4,0.916666667,7,1,TRUE,clock,22,F,Y,Psychologie +47,3,6,6,4,2,3,4,3,4,5,4,3,3,4,2,0.416666667,8,2,TRUE,counter,25,F,N,Orthopedagoog (ZZP) +48,4,8,8,4,2,4,2,3,4,2,5,3,3,3,2,0.083333333,7,2,TRUE,clock,19,F,Y,Psychobiologie +50,2,5,4,2,4,4,5,4,3,2,5,5,3,4,3,0.666666667,3,3,TRUE,clock,24,F,Y,Psychologie +51,3,5,6,5,2,2,4,2,5,4,4,4,5,5,4,0.833333333,6,3,TRUE,counter,19,F,Y,Psychologie +52,4,3,3,5,2,3,2,3,5,5,5,2,2,4,3,0.416666667,7,3,TRUE,clock,20,F,Y,Psychobiologie +53,1,5,4,5,4,4,3,4,5,4,4,2,4,4,4,0.916666667,7,3,TRUE,counter,22,M,Y,Toegepaste psychologie +54,2,7,2,5,4,4,4,4,4,3,5,2,5,3,3,0.833333333,4,5,TRUE,clock,28,M,Y,Psychologie +55,3,2,4,5,4,1,5,3,4,5,5,2,5,5,3,0.916666667,8,1,TRUE,counter,20,F,Y,Psychobiologie +56,4,4,4,2,2,3,3,3,4,4,4,4,4,4,3,0.333333333,6,2,TRUE,clock,21,F,Y,Psychologie +57,1,2,5,5,4,4,4,4,5,4,2,4,5,5,4,1.166666667,5,7,TRUE,counter,25,F,Y,Geschiedenis/Hebreeuws +58,2,6,2,4,3,2,4,4,5,2,4,3,4,4,3,0.5,4,2,TRUE,clock,20,F,Y,Algemene Sociale Wetenschappen +59,3,2,5,4,5,5,5,1,5,3,5,4,3,5,5,1.166666667,6,3,TRUE,counter,18,F,N,Geen +60,4,6,3,2,2,3,2,2,4,3,4,4,4,4,2,0,7,2,TRUE,clock,20,F,Y,Psychologie +61,1,2,8,5,4,2,4,2,5,2,5,5,5,5,4,1,7,3,TRUE,counter,23,M,Y,Psychobiologie +62,2,4,2,4,4,4,4,4,2,5,5,5,5,4,4,1.166666667,7,3,TRUE,clock,21,F,Y,Psychologie +64,4,8,1,5,4,4,4,4,3,5,4,5,5,4,4,1.25,7,2,TRUE,clock,21,M,Y,Geschiedenis +65,1,7,7,4,4,4,2,2,3,3,4,3,4,4,4,0.416666667,6,4,TRUE,counter,17,F,Y,6 VWO +66,2,3,4,4,2,2,2,4,4,5,3,5,5,5,5,0.833333333,7,4,TRUE,clock,19,F,Y,Biomedische Wetenschappen +67,3,3,7,5,4,2,3,5,5,4,3,4,5,5,5,1.166666667,8,0,TRUE,counter,23,M,Y,Psychobiologie +68,4,4,2,5,5,3,5,4,4,3,5,5,5,5,5,1.5,7,2,TRUE,clock,25,F,Y,HBO Sociaal Juridische Dienstverlening +69,1,1,2,3,4,2,3,4,3,4,3,3,3,3,3,0.166666667,1,9,TRUE,counter,38,M,N,Scheikunde +70,2,5,0,4,3,4,3,5,3,5,4,3,4,4,4,0.833333333,10,0,TRUE,clock,51,F,N,n.v.t. +71,3,7,0,4,5,4,5,4,3,5,5,2,2,4,3,0.833333333,7,2,TRUE,counter,21,F,Y,Communicatiewetenschap +72,4,8,1,3,5,4,4,4,2,5,4,4,4,5,2,0.833333333,9,1,TRUE,clock,21,F,Y,Psychologie +73,1,2,1,2,2,2,3,2,2,4,4,3,2,4,2,-0.333333333,7,5,TRUE,counter,21,F,Y,Communicatiewetenschap +74,2,2,8,4,4,4,4,2,4,4,4,3,3,3,2,0.416666667,3,5,TRUE,clock,21,F,Y,Psychologie +75,3,4,5,2,4,2,4,3,4,4,4,3,2,5,4,0.416666667,7,2,TRUE,counter,22,F,Y,Communicatiewetenschap +78,2,4,6,2,2,3,4,4,4,3,4,4,5,4,2,0.416666667,6,6,TRUE,clock,21,F,Y,Psychobiologie +79,3,3,3,4,3,1,3,4,4,3,5,4,4,5,4,0.666666667,7,1,TRUE,counter,18,F,Y,Psychologie +80,4,2,2,5,4,4,4,4,4,5,4,3,5,4,4,1.166666667,7,2,TRUE,clock,24,M,Y,Spaanse Taal & Cultuur +81,1,1,8,4,2,4,2,2,4,4,4,5,2,5,3,0.416666667,4,3,TRUE,counter,20,F,Y,Communicatiewetenschap +82,2,5,1,5,5,5,4,3,4,5,5,5,5,5,4,1.583333333,7,4,TRUE,clock,19,F,Y,Geschiedenis +83,3,4,3,4,4,2,2,2,4,5,2,5,1,5,3,0.25,8,3,TRUE,counter,27,F,N,x +85,1,3,4,2,2,1,4,1,3,5,4,4,1,4,2,-0.25,7,5,TRUE,counter,21,F,Y,Biomedical Sciences +86,2,2,2,4,3,2,2,3,3,4,4,5,4,4,4,0.5,8,2,TRUE,clock,22,F,Y,Psychobiologie +87,3,4,4,3,3,2,4,3,2,4,4,2,4,4,2,0.083333333,7,3,TRUE,counter,20,F,Y,Biomedisch +88,4,6,7,2,5,4,2,4,3,5,4,4,4,5,3,0.75,7,7,TRUE,clock,28,F,Y,Psychologie +89,1,8,0,5,4,4,4,2,5,4,4,5,3,5,2,0.916666667,9,1,TRUE,counter,18,F,Y,Psychologie +90,2,7,8,4,2,1,3,2,5,4,4,3,5,4,2,0.25,5,6,TRUE,clock,21,F,Y,Psychobiologie +91,3,6,6,5,5,5,5,5,5,5,5,5,5,5,5,2,4,7,TRUE,counter,25,F,Y,ACW/Kunstgeschiedenis +92,4,3,5,4,5,4,3,5,5,5,5,3,4,5,3,1.25,6,5,TRUE,clock,19,F,Y,Nederlands +93,1,6,5,5,4,4,5,3,3,4,4,5,5,3,4,1.083333333,7,3,TRUE,counter,22,M,Y,Psychobiologie +94,2,3,2,4,3,2,4,2,3,2,5,4,3,3,2,0.083333333,5,4,TRUE,clock,19,F,Y,Psychologie +95,3,3,6,5,5,4,4,3,3,4,3,2,4,4,2,0.583333333,7,3,TRUE,counter,21,F,Y,Psychologie +97,1,3,5,4,2,4,2,4,4,5,5,5,2,4,5,0.833333333,7,2,TRUE,counter,20,M,Y,Media & Cultuur +98,2,0,4,5,5,4,2,5,5,4,4,2,5,4,5,1.166666667,6,4,TRUE,clock,25,M,Y,Bestuurs- en Organisatiewetenschappen +99,3,1,7,5,2,2,4,4,4,5,5,3,5,4,5,1,5,3,TRUE,counter,21,F,Y,Psychologie +101,1,3,7,5,4,2,5,4,5,4,4,3,4,4,4,1,5,4,TRUE,counter,19,M,Y,Psychobiologie +102,2,2,6,1,1,1,5,2,5,3,4,3,3,4,4,0,5,3,TRUE,clock,20,M,Y,Psychobiologie +103,3,5,0,4,4,3,4,3,5,5,4,3,3,4,2,0.666666667,6,4,TRUE,counter,19,F,Y,Psychologie +105,1,4,4,4,4,4,4,4,3,3,4,4,4,5,2,0.75,4,7,TRUE,counter,22,F,Y,Psychobiologie +106,2,4,2,3,1,1,4,3,5,5,4,3,3,5,3,0.333333333,7,3,TRUE,clock,19,F,Y,Psychobiologie +107,3,5,7,5,3,4,4,4,5,4,5,3,5,4,5,1.25,8,0,TRUE,counter,20,M,Y,Psychologie +109,1,6,4,4,1,1,4,2,4,4,3,3,4,5,2,0.083333333,3,7,TRUE,counter,19,F,Y,ASW +110,2,0,10,5,4,2,5,4,2,5,4,5,5,5,2,1,4,0,TRUE,clock,22,F,Y,Psychologie +111,3,5,5,5,4,4,4,4,5,4,4,4,5,4,5,1.333333333,7,1,TRUE,counter,22,M,Y,Psychobiologie en filosofie +113,1,0,10,5,5,1,2,2,5,1,2,3,5,5,4,0.333333333,5,10,TRUE,counter,27,M,Y,Economie +115,3,2,6,5,4,2,4,4,5,5,4,5,4,5,3,1.166666667,7,4,TRUE,counter,18,F,Y,Psychologie diff --git a/wip/Data/Tworek and Cimpian 2016 Study 1.csv b/wip/Data/Tworek and Cimpian 2016 Study 1.csv new file mode 100644 index 0000000..3e06082 --- /dev/null +++ b/wip/Data/Tworek and Cimpian 2016 Study 1.csv @@ -0,0 +1,132 @@ +excluded,RavensProgressiveMatrix_sum,Inherence_Bias,Should_Score,Good_Score,Ought_Score,Belief_in_Just_World,instructionsâ onthefollowingscreensyouwillbeaskedtofilloutseve,startnextsurvey,pleasereadthestatementsonthefollowingpages.thinkcarefullyaboutho,ih01stop,ih02beds,ih03diamon,ih04dollar,ih05chipmu,ih06oj,ih07pink,ih08white,ih09black,ih10mint,ih117days,ih12weeken,ih13limbs,ih14eyes,ih15sounds,ihc1,ihc2,beginnexttaskâ thefollowingisapatternmatchingtask.inthissectio,rmpprac1,practicequestion1answerâ â hereisthecorrectanswertotheprevio,rpmprac2,practicequestion2answerâ â hereisthecorrectanswertotheprevio,begintestquestionsyouwillnowbeaskedtocomplete12questionswithoutr,rpm1,timingfirstclick,timinglastclick,timingpagesubmit,timingclickcount,rpm2,timingfirstclick_A,timinglastclick_A,timingpagesubmit_A,timingclickcount_A,rpm3,timingfirstclick_B,timinglastclick_B,timingpagesubmit_B,timingclickcount_B,rpm4,timingfirstclick_C,timinglastclick_C,timingpagesubmit_C,timingclickcount_C,rpm5,timingfirstclick_D,timinglastclick_D,timingpagesubmit_D,timingclickcount_D,rpm6,timingfirstclick_E,timinglastclick_E,timingpagesubmit_E,timingclickcount_E,rpm7,timingfirstclick_F,timinglastclick_F,timingpagesubmit_F,timingclickcount_F,rpm8,timingfirstclick_G,timinglastclick_G,timingpagesubmit_G,timingclickcount_G,rpm9,timingfirstclick_H,timinglastclick_H,timingpagesubmit_H,timingclickcount_H,rpm10,timingfirstclick_I,timinglastclick_I,timingpagesubmit_I,timingclickcount_I,rpm11,timingfirstclick_J,timinglastclick_J,timingpagesubmit_J,timingclickcount_J,rpm12,timingfirstclick_K,timinglastclick_K,timingpagesubmit_K,timingclickcount_K,beginnextsurvey,instructionspleaserateeachofthestatementsonthefollowingpagesusin,bjw1,bjw2,bjw3,bjw4,bjw5,bjw6,bjw7,bjw8,bjw9,bjw10,bjw11,bjw12,bjw13,bjw14,bjw15,bjw16,bjw17,bjw18,bjw19,bjw20,belowisapassagefromanewspaperarticleorpressrelease.afterreadingt,timingfirstclick_L,timinglastclick_L,timingpagesubmit_L,timingclickcount_L,shouldpizza,doyouthinktheamountofpizzasoldwillgrowinthenext5years,whatdoyouthinkaccountsforthecurrentpricesofpizza,howfarbackdoyouthinkdatahasbeencollectedonpizzaconsumptionintheu,whatareyourpizzaconsumptionhabits,â belowisapassagefromanewspaperarticleorpressrelease.afterread,timingfirstclick_M,timinglastclick_M,timingpagesubmit_M,timingclickcount_M,goodfootball,doyouthinkthenumberofviewerswhowatchthegameslivewillstayataround,whatdoyouthinkaccountsforwhyonly6offootballviewerswatchthegamesl,howfarbackdoyouthinkdatahasbeencollectedonfootballviewershipinth,whatareyourfootballviewinghabits,â belowisapassagefromanewspaperarticleorpressrelease.afterre_A,timingfirstclick_N,timinglastclick_N,timingpagesubmit_N,timingclickcount_N,goodwork,doyouthinkthepercentageofpeoplewhoridetheirbikestoworkwillcontin,whatdoyouthinkaccountsforwhysofewamericansridetheirbikestowork,howfarbackdoyouthinkdatahasbeencollectedondrivingratesintheunite,whatareyourdrivinghabits,â belowisapassagefromanewspaperarticleorpressrelease.afterre_B,timingfirstclick_O,timinglastclick_O,timingpagesubmit_O,timingclickcount_O,shouldtv,doyouthinktheaveragenumberofshowsandmoviesthatamericanswatchperw,whatdoyouthinkaccountsforthegrowingnumberofavailabledevicestowat,howfarbackdoyouthinkdatahasbeencollectedontvviewingintheunitedst,whatareyourtvviewinghabits,â belowisapassagefromanewspaperarticleorpressrelease.afterre_C,timingfirstclick_P,timinglastclick_P,timingpagesubmit_P,timingclickcount_P,shouldemail,doyouthinktheoverallpopulationofinternetuserswillcontinuetogrowi,whatdoyouthinkaccountsfortherecentriseinthepopulationofinternetu,howfarbackdoyouthinkdatahasbeencollectedonemailandinternetsearch,whatareyouremailandinternetsearchhabits,belowisapassagefromanewspaperarticleorpressrelease.afterreadin_A,timingfirstclick_Q,timinglastclick_Q,timingpagesubmit_Q,timingclickcount_Q,goodcoffee,doyouthinkthesinglecupbrewingformatisgoingtogrowinthefuture,whatdoyouthinkaccountsforthesuccessofthesinglecupbrew,howfarbackdoyouthinkdatahasbeencollectedoncoffeeconsumptioninthe,whatareyourcoffeedrinkinghabits,pleaseanswerthefollowingdemographicquestions.aswithallanswersint,gender,educ,income,relig,ethnic,age,conserv,english,thankyouforparticipating.pleaseanswerthefollowingfewquestionstoh,@1.didyoufindanyaspectoftheprocedureoddorconfusing,@2.whatdidyouthinkwewerestudying,@3.doyouthinkthattheremayhavebeenmoretothisstudythanmeetstheeyei,@4.doyouhaveanyadditionalthoughtsorcommentsaboutthestudy,attention,filter_$ +0,5,7.666666667,6.333333333,6.666666667,6.5,5.65,1,1,1,9,9,9,8,9,8,9,9,9,9,3,3,6,9,6,3,7,1,8,1,3,1,1,7,7.69,7.69,9.035,1,1,14.654,15.592,16.464,2,4,9.773,9.773,10.721,1,6,4.248,4.248,4.963,1,2,6.598,6.598,7.934,1,8,6.27,6.27,7.45,1,3,19.515,19.515,20.529,1,5,2.611,4.148,4.884,3,8,5.947,5.947,7.291,1,8,13.876,13.876,15.275,1,8,6.025,6.025,6.994,1,2,4.714,4.714,5.556,1,1,1,2,3,5,4,4,6,6,2,4,8,7,6,6,8,7,6,1,4,9,9,1,31.442,54.098,54.958,5,3,1,"Supply and demand, and convenience",2,i eat pizza twice a week usually,1,11.41,35.969,36.845,5,9,1,ticket prices are sky high,3,I wathc the games of the teams I am most interested in.,1,106.211,133.833,135.06,4,6,1,The roads are not generally safe and a car is much quicker.,4,I drive everywhere I need to go.,1,9.195,36.865,40.704,6,9,1,convenience,3,"I watch TV daily, and even if I am not watching it it is on in the background.",1,32.415,82.595,83.358,7,7,1,our society is obsessed with instant gratification and technology.,1,I use email and search websites multiple times per day.,1,33.783,59.25,60.26,6,5,1,It is more convenient and less is wasted.,1,I do not drink coffee.,1,2,3,22000,pentecostal,white,26,7,1,1,no,unsure,yes but unsure what,no,1,1 +1,5,7.333333333,6.666666667,8,7.333333333,4.6,1,1,1,7,4,4,8,8,8,8,7,8,9,8,7,8,8,8,7,8,1,2,1,3,1,1,7,8.697,8.697,10.044,1,1,15.966,15.966,17.423,1,3,15.699,15.699,18.939,1,6,15.646,15.646,16.437,1,8,13.962,13.962,18.51,1,7,10.647,10.647,11.588,1,1,11.757,11.757,13.35,1,2,21.914,25.612,26.492,2,5,9.379,9.379,10.245,1,6,10.74,10.74,12.575,1,5,11.594,13.362,14.074,3,6,8.885,9.816,10.48,2,1,1,7,2,4,8,6,7,6,6,3,7,3,4,7,7,8,8,4,8,7,4,1,8.922,47.868,48.557,6,8,1,abundance of supplies and resources to make and create pizza,4,i rarely eat pizza only 3-4 times per year,1,8.366,44.312,50.961,7,8,1,the cost of tickets to see football live is too expensive,3,i rarely watch football at all,1,5.206,49.915,50.89,6,8,2,it is an inconvenience and much harder and longer to travel to and from work,4,try to carpool with people; use of automobiles religiously,1,24.385,62.367,80.24,5,7,1,"online services such as youtube, hulu and netflix",4,i try to minimize tv usage throughout the day but in the end i would probably watch tv for about 3-4 hours per day,1,6.567,81.148,81.953,10,5,1,use of smart phones and tablets,2,i use email and internet search on an everyday basis to get information that i need,1,6.323,24.871,34.905,6,8,1,keurig home devices,3,i rarely drink coffee; i usually consume tea based drinks,1,1,4,10000,roman catholic,vietnamese,28,3,1,1,not at all; it was very enlightening,opinions across different demographics,i dont believe so,none at all,1,0 +0,4,5.333333333,5.666666667,2.666666667,4.166666667,4.2,1,1,1,3,6,7,1,6,1,5,5,7,8,1,9,7,8,6,3,7,1,8,1,3,1,1,1,10.006,10.006,11.699,1,1,37.25,37.25,38.578,1,1,29.487,29.487,32.458,1,6,15.813,15.813,17.075,1,2,35.62,35.62,36.915,1,3,34.663,34.663,38.692,1,5,23.577,23.577,25.041,1,3,37.88,37.88,41.594,1,7,20.162,20.162,21.603,1,2,41.18,41.18,43.51,1,8,23.528,23.528,25.149,1,1,19.76,20.389,21.594,2,1,1,4,2,4,5,2,7,5,6,2,9,4,5,8,5,6,5,8,3,6,8,1,25.057,63.207,100.514,5,5,1,Price of ingredients and pay levels of workers plus rent for business.,1,I eat pizza about three times a month.,1,7.276,41.567,42.941,5,5,1,Expense of tickets and distance from venues.,3,I don't watch football.,1,70.132,113.368,123.201,5,1,1,"Lack of dedicated bike lanes, bad weather, and lack of changing and shower facilities at most workplaces.",3,I do not drive.,1,13.767,80.324,82.485,5,5,1,Interest among consumers for customized viewing experiences rather than mass broadcasts.,4,I don't own a TV and don't watch TV.,1,17.035,59.729,73.254,7,7,1,More people using computers.,1,I search regularly throughout the day and exchange emails daily.,1,10.411,83.511,84.368,6,2,1,Outlets such as Starbucks have increased desire of many for individually prepared coffee servings.,3,I don't drink coffee.,1,1,5,68000,Roman Catholic,Caucasian,35,6,1,1,No,Patience for answering narrative questions after unrelated tasks?,No.,No,1,1 +0,2,5.8,8,7.666666667,7.833333333,4.85,1,1,1,7,8,6,6,4,5,7,8,6,3,5,7,2,6,7,3,7,1,8,1,3,1,1,7,16.059,16.059,18.486,1,4,36.732,46.946,48.261,3,8,23.057,23.057,24.931,1,6,27.254,27.254,28.169,1,7,34.541,34.541,35.719,1,7,14.869,14.869,16.583,1,5,16.353,16.353,17.316,1,7,13.193,13.193,14.244,1,3,33.46,33.46,34.711,1,6,23.169,23.169,24.237,1,2,26.858,26.858,27.989,1,4,10.129,10.129,11.484,1,1,1,4,4,7,3,6,6,6,6,4,7,6,6,8,4,6,7,7,6,8,8,1,70.252,355.2,386.187,5,8,1,Competition. There are so many people eating it. The vendors have to price the pizza competively to sell it.,1,I order a pizza once or twicw a week almost every week.,1,50.581,115.409,116.529,5,6,1,Cost of tickets and driving and parking expenses.,1,I never watch football.,1,38.807,117.145,121.088,5,8,1,Weather conditions and distance to work.,1,I walk to work because it is only a couple of blocks away.,1,38.987,99.43,100.495,5,9,1,Consumer demand and competition.,1,I watch about 20 hours of live tv a week.,1,41.215,130.24,131.167,5,7,1,More internet options. A lot more devices you can get on the internet with.,1,I dont email a lot. I do surf the web and search daily.,1,35.137,72.666,110.98,5,9,1,It saves a lot of time and prevents waste.,1,I make 2 or 3 pots everyday. I drink a large amount of coffee.,1,2,2,24000,christian,white,48,7,1,1,Not really. The surveys were nothing alike.,I dont know. Americas habits and buying trends maybe.,No it was kinda odd that the parts were so different.,No,1,1 +0,2,4.266666667,7,3.666666667,5.333333333,4.65,1,1,1,5,3,3,5,5,3,5,5,5,2,5,5,3,5,5,3,7,1,8,1,3,1,1,7,12.871,12.871,15.761,1,2,23.05,24.964,25.429,2,5,13.5,16.13,16.971,2,1,29.145,29.145,30.235,1,2,21.36,21.36,22.917,1,8,21.017,21.017,23.803,1,5,13.96,13.96,16.242,1,3,26.513,26.513,28.587,1,5,40.802,41.816,43.02,2,6,17.373,17.373,19.248,1,5,17.434,17.434,21.97,1,3,19.003,19.003,20.401,1,1,1,5,5,5,5,5,6,4,5,4,5,5,5,6,5,4,5,5,3,4,6,1,27.02,76.749,78.09,4,5,2,Don't know.,2,Two or three times a week.,1,40.478,51.833,66.177,4,5,1,Easier and cheaper to watch by other means.,1,None,1,43.287,71.383,78.243,5,1,1,Less convienent.,1,None,1,60.332,91.113,92.029,6,7,2,Companies catering to consumer demand.,3,None,1,85.729,132.556,133.732,7,9,1,More people getting online.,1,Daily use of email. / / Mayby 2-3 times a week for searching.,1,24.858,40.598,41.953,6,5,1,Don't know.,1,None,1,1,3,8000,None,White,45,5,1,1,Not really.,Not sure.,"Yes, but not sure what.",Nope,1,1 +0,1,5.266666667,7,5,6,5.15,1,1,1,8,6,6,2,6,7,3,3,6,4,3,8,3,7,7,3,7,1,8,1,3,1,1,7,5.279,5.279,6.375,1,2,8.255,8.255,9.185,1,7,7.527,9.758,10.676,2,2,9.714,12.152,13.243,2,7,19.276,19.276,20.357,1,3,24.401,27.474,29.384,2,5,9.318,11.176,11.951,2,8,16.361,23.027,32.418,2,3,16.251,16.251,17.445,1,6,23.15,23.15,24.345,1,7,10.193,10.193,11.513,1,7,31.495,32.453,33.407,2,1,1,4,4,6,8,7,7,7,4,4,5,5,6,6,4,6,6,4,4,8,4,1,34.64,49.879,75.694,5,8,1,"the prices? I'm not sure, maybe the competition",1,at least once a month,1,58.15,91.393,102.375,5,5,1,its expensive and the view isn't always great,1,i view them on my laptop or big computer monitor,1,44.777,75.377,105.89,6,3,2,not enough good public transportation from suburban areas into the city,1,"i live too far from my downtown where i work to take transportation, the time quadruples if i were to take a train and two buses.",1,48.205,108.09,108.984,5,6,2,the usability and choice to watch when you want to,1,"sometimes, a few times a week.",1,471.65,568.128,569.3,5,7,1,all of the different types of media that's available and the freedom to use it without going anywhere.,2,email and search everyday and at least 3 times a day.,1,36.641,97.459,98.412,5,7,1,it seems that it wastes less and can accomodate different flavors for different people in a household or company,1,i make coffee in a manual drip funnel and at least 5 times a week.,1,2,4,11000,christian,asian,37,3,1,1,the puzzles,thought perception?,"yes, maybe how what we are prompted in our minds effects are problem solving",no,1,1 +0,3,6.6,5.666666667,4,4.833333333,6.15,1,1,1,8,7,8,6,3,5,8,9,7,5,4,8,7,7,7,3,7,1,8,1,3,1,1,7,15.28,15.28,20.293,1,2,35.052,35.052,38.639,1,3,15.041,32.766,35.697,2,6,16.02,16.02,19.551,1,5,22.135,28.198,31.239,2,7,9.92,9.92,11.241,1,5,28.751,28.751,29.562,1,7,14.024,14.024,15.272,1,2,26.208,26.941,27.877,2,3,20.795,20.795,23.384,1,7,6.162,38.548,39.765,2,7,15.943,15.943,17.066,1,1,1,2,6,4,3,4,7,4,3,8,3,7,6,4,6,7,7,2,2,8,4,1,24.793,93.613,94.342,9,4,2,I think the current prices of pizza are determined by numerous factors including the price of key ingredients and increased competition in the pizza industry.,2,I tend to eat pizza twice a month mainly due to its convenience.,1,24.515,63.641,121.285,5,4,1,"Most people have commitments (e.g. work) that prevents them from watching the games live. In addition, watching the games live tends to be expensive and time consuming.",2,I occasionally watch football games approximately once a week during the NFL season.,1,28.291,75.424,76.384,5,2,1,I think that few Americans ride their bikes to work because it is inconvenient for them.,4,I tend to drive to work on a daily basis.,1,22.504,93.14,94.103,5,6,1,I think the advances made in technology have made it more accessible to a growing segment of the American population at a price that most Americans can find affordable.,4,I tend to watch television for about 3 hours a day or 21 hours a week on average.,1,32.944,73.123,142.084,5,7,1,"Since the advent of modern technology including the Internet, it has become an essential part of society. People use the Internet to communicate with others, search for their favorite restaurants, and to learn valuable skills for the workplace.",1,I check my email about once a day while I search the Internet multiple times a day.,1,39.679,140.063,141.11,5,6,1,I think what accounts for the success of the single cup brew is its convenience and superiority in taste and freshness when compared to other methods.,3,"I tend to drink coffee twice a day, mainly purchased from local gas stations.",1,1,3,3000,Muslim,White,22,4,1,1,Not at all,I think you were studying how participants make judgements based on the specific situation., ,Great study!,1,1 +0,1,5.266666667,5.666666667,3,4.333333333,4.85,1,1,1,4,4,5,4,7,4,8,5,7,7,3,7,7,3,4,3,7,1,8,1,3,1,1,7,4.4,4.4,6.708,1,5,4.406,4.406,5.401,1,4,7.549,10.677,12.212,2,2,6.276,6.276,7.957,1,7,4.172,4.172,5.514,1,7,3.074,3.074,4.312,1,8,3.974,3.974,5.047,1,2,4.073,4.073,5.624,1,3,19.965,19.965,36.598,1, ,0,0,60.011,0, ,0,0,60.142,0, ,0,0,60.008,0,1,1,7,3,5,7,4,4,4,5,7,3,4,5,7,8,7,7,3,7,3,7,1,33.525,180.321,191.903,8,3,1, The demand for pizza determines the prices.,2,I eat pizza 3 times a month.,1,3.256,64.322,65.465,8,5,1,TV is more convenient than watching live.,4,I watch football every Sunday.,1,189.978,226.722,227.95,6,1,1,Cars are a sign of status in America.,2,I drive only on the weekends.,1,115.507,196.979,197.788,6,5,2,I feel the size of the family affects this.,3,I watch tv at least 2 hours a day.,1,45.889,73.274,86.321,5,9,1,The popularity of the internet has rose.,1,I use both services many times each day.,1,107.507,142.538,143.691,5,3,1,Americans feel the need to multitask and coffee helps this.,1,I drink coffee once a month.,1,1,3,24000,Christian,African,23,4,1,1,No,The purpose of this study was to determine my personality traits.,No,No,1,1 +0,2,4.8,7.666666667,2,4.833333333,3.85,1,1,1,6,4,5,1,8,5,1,5,5,6,6,4,4,5,7,3,7,1,8,1,3,1,1,7,8.217,9.464,10.461,2,1,20.367,20.367,21.233,1,4,27.973,27.973,28.977,1,2,6.468,6.468,8.208,1,6,16.933,16.933,17.696,1,4,32.893,32.893,33.952,1,3,9.332,9.332,11.046,1,3,7.593,7.593,8.555,1,7,7.452,7.452,8.72,1,4,13.165,13.165,15.105,1,8,9.655,9.655,10.371,1,2,8.765,9.757,11.2,2,1,1,4,1,4,4,4,5,4,7,3,5,4,4,9,6,4,5,9,2,6,9,1,9.926,29.133,47.226,5,5,1,"Raw ingredient costs seem constant, and so does the price of my pizza.",4,I eat pizza every week.,1,35.826,140.837,142.812,5,1,2,"Only a limited number of people fit in a stadium, and it's necessary to travel and pay for parking or wait for mass transit.",4,"When I visit a friend's house, I sometimes observe that football may be on their TV. It never enters my own house.",1,17.061,50.706,68.48,5,1,1,It is dangerous to share the road with crazed automobilists.,4,"I hold a license, but do not own a car or drive. I use public transit.",1,10.476,54.962,55.808,5,9,2,It's convenient to watch in a portable format.,4,I watch TV every day because it is more important than brushing my teeth.,1,9.759,60.044,61.395,5,9,1,Advanced technology and technological literacy. Ease of use and access.,2,I use the Internet where I would have used the telephone a decade or two ago.,1,14.438,49.916,51.979,5,4,2,Convenience (over price and ecological soundness),4,I drink several cups a day.,1,1,3,46000,atheist,white,59,1,1,1,It was very long. Very very long.,I cannot imagine anything was left out. The entire syllabus must be here.,This is opaque to me.,"Thank you for allowing me to provide your data. I am sorry that so many written answers were required, but ""sprung"" upon the subject without preparation. I shall not make a point of recommending this to some of the other prospective research subjects I know, who are more sensitive to the rate of pay for writing assignments.",1,1 +0,4,6.133333333,8,4.666666667,6.333333333,5.45,1,1,1,8,8,4,4,4,4,6,6,7,4,7,8,8,8,6,3,7,1,8,1,3,1,1,7,10.802,10.802,16.314,1,1,35.271,35.936,45.934,2,3,12.19,12.19,15.42,1,6,42.762,42.762,54.476,1, ,0,0,60.003,0,7,35.757,35.757,60.001,1,5,17.427,17.427,50.29,1,6,46.544,50.035,60.003,2,3,37.393,37.393,44.338,1,5,27.747,59.656,60.002,2,8,40.295,45.981,60.002,2,2,37.829,37.829,42.506,1,1,1,3,5,4,6,3,7,6,4,2,6,5,7,4,7,7,4,4,3,9,9,1,42.296,159.551,161.182,5,6,1,The prices have increased because the items needed to make them have increased but it seems that many places offer specials that reduce the price quite often.,1,I eat pizza a couple at least once a month.,1,76.581,162.746,187.546,11,8,1,I believe that the expense of going to live games account for why so few people go.,4,I view games on tv regularly.,1,52.851,89.066,128.763,6,4,1,I think people like to be able to run errands after work and have the convenience of controlling their own time.,4,I drive to work daily.,1,62.139,348.935,350.296,24,9,1,Because being able to have access is important to people so therefore in order for companies to maintain being competitive they have to make devices that are in line with what people want.,4,"I watch TV many hours a week on TV, Computer/laptop, and Phone daily and I use the DVR, on demand, and NEtFlix regularly to catch anything I have missed.",1,2.752,247.296,261.5,8,9,1,"More companies use the internet for business, Therefore people use for bill paying, banking, job search, entertainment, and shopping so as it become the major source for interaction with business and companies more people must use.",1,I use both several times daily,1,36.943,91.911,101.69,6,2,1,It is convenient and less expensive to brew your own at home.,2,I don't drink any coffee.,1,2,4,30000,Christian,Black,46,5,1,1,No,Really not sure from questioning,I do think it was more. Again I'm Stumped on his one,no,1,1 +0,4,5.4,2.666666667,4,3.333333333,5.25,1,1,1,5,5,5,5,5,9,5,2,8,8,1,5,5,9,4,3,7,1,8,1,3,1,1,7,7.269,14.263,16.766,4,1,0.775,14.201,15.168,2,1,17.149,17.898,18.95,2,4,0.812,43.679,52.33,2,2,6.264,6.264,13.931,1,4,0.772,27.882,37.177,2,5,0.867,28.366,39.722,2,5,0.88,28.355,40.716,2,1,0.863,10.775,15.365,2, ,3.999,3.999,60.009,1,7,1.069,18.647,20.258,2,2,0.6,15.452,16.699,3,1,1,3,6,5,7,6,7,3,3,4,9,5,7,7,5,7,5,3,4,9,4,1,4.916,102.519,108.143,13,3,1,Everyone enjoys pizza.,2,I eat pizza at least once a month.,1,20.107,67.818,87.775,9,5,1,"Because the price of tickets, food, and drink is super over priced.",2,I watch all the games for one team.,1,40.238,125.316,170.125,8,3,1,Because the obesity rate has gone up so much.,2,I drive myself where even I need to go. I don't like buses or other forms of public transportation.,1,1.468,152.792,157.137,16,2,1,Consumers wanting other devises to use.,2,I watch about 20 hours of t.v. a week.,1,1.205,82.71,86.672,11,3,1,So many things can be accessed on the internet and now the inter is usually the only way you can access certain things.,1,I check my e-mails three times a day and search things at least once a day.,1,4.373,44.603,86.334,13,4,1,I think because of how easy it is instead of a whole pot.,2,I do not drink coffee.,1,2,3,0,Christian,white,23,7,1,1,No,My views of certain things.,no,no,1,1 +0,3,6.666666667,6,5.666666667,5.833333333,5.75,1,1,1,8,8,8,4,7,4,4,6,8,8,8,7,7,6,7,3,7,1,8,1,3,1,1,7,6.537,6.537,8.116,1,5,12.993,12.993,20.919,1,1,17.375,17.375,18.956,1,6,18.078,28.069,29.214,3,2,13.937,13.937,15.385,1,4,11.654,11.654,14.729,1,5,21.07,21.07,22.3,1,8,22.796,23.593,38.651,2,1,30.352,30.352,31.642,1,3,12.057,12.057,13.505,1,7,29.049,29.049,30.145,1,2,20.026,20.026,21.472,1,1,1,3,7,6,6,5,8,3,5,8,8,7,6,8,7,7,6,3,5,8,3,1,23.256,46.322,47.29,6,6,2,The economy,4,I have it about 2 times a month,1,14.818,37.699,53.743,6,7,1,Price of tickets,4,I watch NFL and NCAA football regularly,1,6.743,47.115,48.074,5,4,1,It is much more convenient to drive,3,I drive everyday sometimes I ride my bike to close shops,1,8.943,33.009,33.955,6,4,1,Growth in technology,3,I watch tv everyday,1,37.478,74.117,83.1,5,8,1,Growth in technology,1,I use them everyday,1,22.369,38.843,52.632,5,6,1,It is very convenient,4,I drink coffee almost daily,1,2,4,0,Roman Catholic,White,25,3,1,1,no,I am not sure,"Probably, since you asked the question and I am not sure what it is.", ,1,1 +1,3,5.733333333,6.333333333,5,5.666666667,5.45,1,1,1,4,7,5,5,7,6,7,3,3,8,6,7,5,6,7,8,7,1,8,1,3,1,1,1,6.237,6.237,7.589,1,3,2.108,2.108,3.458,1,4,2.301,2.301,3.953,1,6,3.807,3.807,4.761,1,2,3.037,3.037,4.677,1,2,12.159,12.159,13.142,1,3,4.013,4.013,5.237,1,3,1.587,1.587,3.136,1,2,3.673,3.673,5.138,1,8,3.113,3.113,4.531,1,3,1.693,1.693,3.249,1,3,1.757,1.757,2.918,1,1,1,6,6,9,5,6,5,5,3,7,6,8,7,6,5,7,8,6,6,7,7,1,6.791,9.292,20.634,3,6,1,food prices and shipping costs,1,I usually eat it once a week,1,6.162,25.004,25.878,4,5,1,I'm not sure,1,I only watch a couple games a year,1,11.953,58.403,59.238,9,4,2,the distance to work,1,I usually drive if the distance is over five miles,1,8.095,10.206,28.73,3,6,1,the growing number of computer devices,1,I don't watch a lot of television,1,9.012,27.202,32.103,6,7,1,I'm not sure,1,I use multiple devices,1,6.831,12.753,28.319,4,6,1,People want their coffee personalized,1,I drink one cup at a time,1,1,2,40000,christianity,white,22,5,1,1,no,I don't know,no,no,1,0 +0,4,4.266666667,5.666666667,5,5.333333333,4.15,1,1,1,6,5,1,1,5,5,2,5,5,5,5,5,4,5,5,3,7,1,8,1,3,1,1,7,5.688,5.688,9.225,1,1,19.782,19.782,22.375,1,1,10.821,31.779,34.094,2,6,21.381,21.381,30.262,1,2,20.691,20.691,21.652,1,6,34.944,59.831,60.002,2,5,23.15,24.421,33.103,2,8,30.991,34.222,35.552,2,7,23.357,24.324,40.869,2,1,11.098,28.841,30.506,4,8,17.206,17.206,27.375,1,2,5.716,34.955,37.06,4,1,1,5,5,5,7,5,6,7,9,5,5,5,3,9,3,5,6,4,3,4,8,1,5.176,43.247,52.201,6,3,1,Quality and quantity of the product,2,a pizza every 2 or 3 weeks,1,3.063,33.278,33.921,6,5,1,"Monetary limitations, physical discomfort of being at the stadium",4,i dont watch football,1,8.029,48.147,60.054,10,5,2,They are lazy and in poor shape,4,I take the bus or walk,1,4.289,41.551,63.233,7,5,1,By expanding the TV market to multiple devices you increase viewership and ad revenue,3,"A tv show or 2 a week, mostly stick to netflix and youtube",1,9.946,54.424,66.356,10,9,1,accessibility of computers and internet,1,"entertainment, personal, and proffesional",1,4.873,58.494,63.498,7,5,1,People need a quick easy drink that gives them energy in a good portion when they are in a rush.,4,a cup or 2 a week,1,1,3,0,No Affiliation/Atheist,Puerto Rican,18,5,1,1,No,"If there is a link between comprehension, an innate sense of justice, emotion, and intellegence","The questions on how innate things are ,may have been to test morality, empathy, and bias, the images where a type of testing used in IQ testing",None,1,1 +0,4,5.866666667,5.666666667,3.666666667,4.666666667,5.8,1,1,1,8,8,7,7,6,3,7,4,6,8,1,8,3,6,6,3,7,1,8,1,3,1,1,7,8.057,8.929,9.84,2,1,23.042,23.042,24.283,1,5,28.528,28.528,29.663,1,6,33.643,34.351,34.881,2,2,30.571,32.876,33.455,3,1,42.86,42.86,43.385,1,5,25.444,25.444,25.879,1,6,56.642,57.282,57.804,2,7,26.625,31.37,34.023,5,3,26.175,28.281,30.415,3,7,59.491,59.491,60.008,1,6,24.605,24.605,25.561,1,1,1,4,7,6,7,6,7,8,6,7,7,7,6,7,6,8,6,2,6,9,6,1,121.649,186.265,188.028,6,1,1,the amount of people that eat the pizza will cause the price to increase,1,i eat pizza a lot,1,160.502,266.618,267.403,5,7,2,the amount of money it cost to watch a live game,1,i dont watch football,1,77.658,137.183,162.212,5,3,1,takes to long to ride a bike,1,I drive everywhere,1,359.674,399.085,412.69,6,7,1,dvr and netflicks,1,I don't watch much tv,1,254.574,344.27,361.794,5,9,1,easier and faster than snail mail,1,I use both everyday,1,250.612,277.101,278.402,5,1,1,i dont know,1,dont drink coffee,1,2,3,40000,christian,hispanic,24,2,1,1,no,no idea,i dont know,no,1,1 +1,2,6,6.333333333,4.333333333,5.333333333,5.4,1,1,1,9,6,9,2,2,3,2,7,9,9,1,7,6,9,9,3,7,1,8,1,3,1,1,7,6.362,6.362,7.374,1,5,8.677,8.677,9.478,1,3,10.839,10.839,12.693,1,8,5.266,5.266,6.468,1,5,3.855,3.855,4.673,1,7,7.126,7.744,9.067,2,2,9.983,9.983,11.145,1,6,5.564,5.564,7.049,1,5,5.319,5.319,6.304,1,6,5.121,9.238,10.126,2,5,7.423,7.423,8.352,1,7,7.921,11.133,12.207,2,1,1,2,3,6,4,4,7,6,5,7,6,7,5,7,1,6,6,3,7,4,4,1,4.912,5.522,79.224,2,1,1,"I'm not sure if this means they're cheap or expensive, so I don't know how to respond. They seem to be rising, but so is every other food we eat.",1,"I don't each much pizza at all, because I try to stay away from carbs and gluten.",1,10.009,78.275,79.106,5,9,1,"Because it's expensive, and it's also a social sport. People like to have big football viewing parties at home, and/or go to bars to watch the games.",1,I only watch during college football season. I don't follow the NFL.,1,65.571,115.884,124.431,3,3,1,Because it's not safe.,1,I always drive. I don't like public transit because I supposed I'm somewhat antisocial. People scare me.,1,5.722,30.619,70.048,6,9,1,Affordability of the devices has increased.,4,I watch a couple of hours of TV every day.,1,10.168,47.1,57.8,5,9,1,More accessibility and the younger generation is growing.,1,I use it several times a day. It's how I make my living.,1,17.804,40.472,56.183,5,1,1,The variation in the flavor pods. It's more fun.,1,I recently stopped drinking coffee because it was destroying my thyroid.,1,2,4,62000,Spiritual,White,31,5,1,1,No,"Couldn't tell, haven't seen a study like this one.","I'm sure that there was, but I can't figure it out.",No,1,0 +0,4,5.2,6.666666667,5.333333333,6,5.3,1,1,1,7,4,6,3,5,3,5,3,6,7,6,6,6,6,5,3,7,1,8,1,3,1,1,7,13.677,13.677,15.257,1,5,18.194,33.621,34.795,2, ,0,0,61.288,0,6,10.588,10.588,11.947,1,2,19.601,19.601,20.728,1,7,36.969,36.969,38.26,1,5,40.168,40.168,42.525,1,8,27.453,27.453,28.721,1,3,39.48,39.48,40.852,1,2,34.222,34.222,41.246,1,8,30.487,30.487,32.154,1, ,44.935,44.935,61.444,1,1,1,4,5,6,4,7,4,6,5,5,6,6,6,6,5,5,3,5,6,9,7,1,63.218,129.643,142.73,6,7,2,The cost to make the pizza and the cost the public is willing to pay. Quality also comes into play.,3,I don't eat pizza.,1,54.265,124.129,125.428,5,6,1,To expensive to go to. I have also heard people say they can watch better from comfort of their own home.,4,I don't watch at all.,1,55.348,132.697,134.156,5,4,1,Too inconvenient. Americans like their life easy.,4,I drive to work. Too far to pedal.,1,69.927,118.877,119.976,5,5,2,Americans want what they want when they want it.,2,I don't watch tv at all.,1,131.792,208.952,210.734,6,8,1,As more use it and talk positively about it more get the courage to give it a try.,1,I use both more than once a day.,1,63.044,153.095,154.409,5,6,1,The convenience of a fresh cup when ever you want it. The coffe doesn't sit and get old.,4,A cup a day.,1,2,3,50000,Jesus,White,47,8,1,1,No. Varied but not odd.,I don't have a clue.,"Def, but, once again, no idea.",Thanks,1,1 +0,6,5.533333333,6.666666667,3.666666667,5.166666667,6.2,1,1,1,6,6,6,5,5,3,5,7,5,5,6,4,6,8,6,3,7,1,8,1,3,1,1,7,7.35,7.35,8.548,1,1,11.161,11.161,14.271,1,7,29.638,29.638,32.654,1,6,18.061,18.061,20.787,1,2,28.614,28.614,32.603,1,5,21.6,31.042,34.611,2,1,19.608,19.608,23.693,1,5,20.23,20.23,24.74,1,6,13.021,13.021,16.421,1,4,9.903,9.903,11.543,1,7,14.14,14.14,17.781,1,1,20.743,20.743,24.773,1,1,1,2,6,6,9,5,9,4,2,6,6,6,6,4,6,8,4,3,8,8,4,1,79.04,126.786,127.951,4,5,2,Pizza is extremely popular,1,"I eat pizza at least once per week, sometimes even quite a bit more than that",1,69.591,138.878,139.93,5,5,1,"There are only so many seats in a stadium, and only certain people in the area can afford tickets.",1,"I do not normally watch NFL, but do watch college football.",1,65.484,150.242,151.31,7,4,1,"Many people do not have the time to take to ride a bike, which takes longer than driving in most cases. Also, if you have to dress professionally, it may be difficult to ride a bike and still look acceptable when arriving at work.",2,I drive everywhere that I need/want to go.,1,191.265,248.759,250.211,5,9,2,People are always looking for more convenience.,4,"I only occasionally watch TV, usually only because someone else in the family is watching it. I normally only care to watch the news.",1,59.311,101.295,129.982,5,6,1,"More availabilty of computers and internet, more people learning how to use computers.",1,"I check my email many times each day, and search at least once per day normally.",1,69.146,96.514,118.46,7,2,1,"It is easy and convenient, and in the long run is more economical.",2,I do not drink coffee.,1,2,3,60000,none,white,29,3,1,1, ,patterns,"absolutely, but no idea what", ,1,1 +0,3,2.933333333,6.333333333,3.333333333,4.833333333,3.7,1,1,1,1,1,1,1,7,1,4,3,5,6,5,6,1,1,1,3,7,1,8,1,3,1,1,7,0.824,10.32,12.513,2,1,0.852,34.732,36.743,2, ,0.5,0.5,60.005,1,5,1.18,34.964,44.62,2,2,0.597,52.189,53.19,3,4,0.474,23.696,25.308,2,5,0.899,26.044,26.838,2,7,0.539,28.429,28.918,4,3,0.837,14.824,15.967,2,8,0.986,38.175,40.183,2,1,1.511,29.387,30.51,2,7,1.536,19.373,20.689,2,1,1,4,7,2,1,3,4,1,8,3,3,1,1,5,1,3,5,7,1,5,9,1,1.482,62.249,68.793,6,5,1,Cost of the raw materials.,4,I eat pizza rarely.,1,1.53,60.074,70.292,8,5,1,Limited number of seats. Cost.,4,I watch my favorite team whenever it plays.,1,1.992,87.766,88.367,6,1,1,Work distances may be too far. Danger of riding a bike on streets with cars. People don't want to get sweaty going into work.,4,I take the NYC subway to work. I don't own a car.,1,1.361,54.313,80.405,6,5,1,Advances in technology.,4,I watch a couple hours of TV a day.,1,1.55,101.387,138.752,8,9,1,Greater accessibility to the internet through public locations and free wifi.,1,I use search and email daily. Several times a day.,1,1.279,44.654,49.972,8,4,1,Ease of use.,4,I drink coffee very rarely.,1,1,6,152000,None,Hispanic,34,1,1,1,No,Not sure,Not sure.,It was way too long for the amount paid.,1,1 +0,6,5.066666667,2.666666667,3.666666667,3.166666667,4.35,1,1,1,8,6,1,2,2,3,1,7,6,7,6,7,7,7,6,3,7,1,8,1,3,1,1,7,5.433,6.318,7.729,2,1,9.771,11.287,13.038,3,3,9.715,10.339,12.169,2,6,36.957,37.436,38.466,2,2,3.274,22.073,24.208,6,7,36.086,37.204,38.601,3,3,22.487,22.487,24.44,1,2,28.715,37.74,39.17,3, ,6.809,6.809,60.007,1,2,15.505,16.853,17.385,2, ,0,0,60.004,0,1,56.091,59.331,60.004,2,1,1,4,2,5,2,6,5,6,2,5,6,2,3,6,1,6,5,6,4,3,8,1,2.505,41.249,67.122,12,1,1,"Popularity, price of ingredients, quality",2,I eat lot's of pizza. I make it with a cauliflower crust by hand though.,1,78.784,117.581,118.222,12,5,1,Because games are too expensive or far to get to.,3,I don't watch football.,1,13.266,81.423,81.874,8,1,2,"Bad weather, laziness, out of shape, too tired, not as safe as a car, distance",3,I don't drive. I walk everywhere and occasionally bus.,1,20.185,64.704,65.715,7,2,1,"The popularity of movies, demand",4,I watch 20 or so hours of tv per week.,1,4.019,73.998,87.057,13,5,1,You cannot survive in society without being active on the internet. It is even required for work and school.,2,I check my email daily and I search for things on the internet daily.,1,12.009,55.699,56.506,9,5,1,"People are very tired, people need energy, its convenient",3,I drink coffee almost every day.,1,2,4,5100,Jewish,Caucasian,25,1,1,1,No,Perception?,Yes but I have no idea what.,no,1,1 +0,6,5.466666667,6.666666667,4.666666667,5.666666667,5.9,1,1,1,5,7,5,3,5,7,2,4,7,3,6,7,7,7,7,3,7,1,2,1,3,1,1,7,6.985,6.985,9.302,1,1,25.963,25.963,26.703,1,8,45.593,54.338,55.312,3,6,14.626,14.626,15.269,1,2,16.923,16.923,22.473,1,8,31.258,31.857,32.891,2,1,10.037,10.037,10.827,1,2,21.134,21.134,22.949,1,3,36.558,37.083,38.423,2,2,8.082,8.889,9.471,2,5,14.331,14.72,15.621,2,3,13.795,13.795,15.49,1,1,1,4,5,6,4,4,6,6,4,5,4,7,5,4,7,7,4,4,5,6,5,1,24.973,75.174,91.314,10,7,1,Maybe,3,Whenever I can,1,21.779,75.007,75.79,6,6,2,Methods of recording the show to watch it at a later time are probably the reason why.,3,Hardly ever watch unless someone else puts it on.,1,34.367,166.883,168.143,9,3,1,"The weather, the inconvenient roadyways, dangers of biking such as getting hit by a car, not in shape enough to ride a bike, etc.",4,I have to dribe most places because I live so far away form anything. Biking would be the next option but I am still a little too far away for biking to be convenient.,1,43.876,117.985,118.939,5,5,1,"A larger population and developments in technology, like moblie phones with video capabilities.",3,I watch TV daily during the summer but during the school year I rarely watch TV mostly because of how busy I am.,1,129.592,145.351,189.556,5,8,1,Increased availability of computers (libraries for example) also the price of to access the internet has drastically decreased over the years.,2,Almost daily,1,4.977,182.626,183.342,7,5,1,"It is convenient and quick for most people on the go or for those who do not want a large mess (ie grinding coffee beans, using filters)",4,"Occasional, about 1-3 cups per week",1,2,3,1000,none,white,20,4,1,1,Nope,Reactions to certain scenarios to determine who we are as a person,Not really sure, ,1,1 +0,6,8.066666667,8,6,7,5,1,1,1,7,9,8,6,8,8,9,9,9,8,6,8,8,9,9,3,7,1,8,1,3,1,1,7,6.236,7.212,10.938,2,1,9.82,14.659,20.398,2,8,12.184,33.308,41.137,3,6,14.517,14.517,21.205,1,2,17.787,17.787,21.836,1,1,19.173,25.466,30.117,3,5,11.666,11.666,30.438,1,5,24.267,24.267,31.323,1,3,9.965,9.965,11.169,1,2,18.427,22.194,25.158,3,8,8.592,23.815,27.009,2,1,14.326,14.326,24.436,1,1,1,4,3,6,9,7,8,2,6,3,8,7,7,7,8,9,5,6,7,6,4,1,17.789,202.813,203.51,5,6,2,"I think that basic supply and demand is allowing pizza prices to stay the same, or in some cases drop. There are a number of options, from hand-made to frozen to ordered, and since pizza restaurants know this, they must keep their prices low in order to compete with other restaurants and the grocery store pizza market. I think that since so many Americans eat pizza, the cost of ingredients is fairly low since many companies can buy in bulk.",2,"I cannot have red sauce, so pizza is more expensive, generally, since I have to eat a specialty pizza. I still eat it about twice a month on average, and always have pizza with my fiance and kids. My kids have pizza slightly more often than I do, closer to 5 times a month.",1,41.559,176.949,177.828,7,8,1,"I think that with the advances in technology, many people can get a more clear view of the game from their own television. I also think that it is incredibly expensive to go to a game in person by the time you pay for tickets, parking, and food and beverage; and I think that since most Americans are still struggling to afford normal expenses, many enjoy watching the game for cheaper or free at home.",4,"I watch football as often as I can during the regular season, and every game of the playoffs and championship. I always watch at home, and occasionally my fiance or sons watch with me.",1,37.389,195.554,197.307,5,3,1,"I think that riding your bike to work would primarily depend on the distance between work and home. I think that more often than not, Americans don't want to ride a long distance to work because they will sweat, ruining their appearance.",3,"I drive my fiance to work and my son to school which combines for approximately 15 miles. Then, I drive myself to and from work, driving approximately 50 miles in the round trip. Generally after I get home, there are 2-5 people in the car if we travel anywhere else that evening.",1,38.595,1944.661,1946.689,5,9,1,"I think that the advances made in technology recently allow most TV to be streamed to any internet-ready device, since most television is being transmitted through a fiber optic or network signal, rather than a coaxial connection.",1,"I watch TV almost exclusively from the DVR. On average, during Fall/Spring TV season, I watch about 25-30 hours of TV on the same night that it originally airs. During the summer season, I typically watch less shows but the same number of hours, since I subscribe to Big Brother Live Feeds.",1,50.71,61.761,236.49,4,9,1,"I think that technological advances and the growth of the internet is a major reason that the population of internet users has risen so dramatically. More recently, I believe that services being offered online that previously had not been (such as watching television) and an increased acceptance of certain activities (such as online dating) has also caused such a growth.",1,"I use E-Mail and search engines every day. I search for something using the internet at least five times a day, and I'm constantly connected to my E-Mail via my smartphone.",1,40.232,165.868,166.927,7,7,1,"I think that when coffee is made from a drip-brew system, many times a lot of the coffee ends up getting thrown away, so people percieve single-brew to be more economical. I also think that the variety of choices of coffee contribute to the sucess of the single brew cup, since everyone has different tastes.",4,"I do not drink coffee, except for on rare occassions when I feel like enjoying the taste of it. I do not like to use coffee as an energy supplement due to the jittery after-effect of it.",1,1,3,51000,"spiritual, not religous",white (caucasian),28,6,1,1,"No, this survey was rather straightfoward.",I think that you were studying whether or not the patterns of American habits are consistant with the results of major research taken recently.,I do not think that there was any more to this study than meets the eye.,"I do not have any additional thoughts or comments, thank you for the oppurtunity to participate in this study.",1,1 +0,2,5.933333333,5.666666667,5.666666667,5.666666667,5.35,1,1,1,6,7,5,7,8,4,6,6,4,7,6,5,5,7,6,3,7,1,8,1,3,1,1,7,11.247,12.064,12.486,2,4,21.395,21.395,22.222,1,7,23.836,23.836,24.578,1,2,20.212,20.212,21.196,1,2,21.494,22.212,23.156,2,7,26.945,26.945,28.698,1,8,22.858,22.858,23.914,1,3,12.061,20.609,22.733,3,5,18.222,18.222,18.982,1,3,22.809,22.809,24.82,1,7,19.868,20.779,21.803,2,3,18.063,18.509,19.589,2,1,1,6,6,6,8,7,8,8,6,4,4,7,6,4,7,7,5,6,5,5,6,1,22.23,139.864,166.845,5,6,1,Supply and demand. 94% of Americans have eaten pizza which makes it a popular choice for a meal!,1,I have pizza maybe once a month. I try not to eat so much; most of the time I'm on a diet.,1,7.041,94.229,95.067,8,4,1,The cost of tickets and the crowd; plus people don't want to leave the comforts of their homes.,1,I don't watch football.,1,62.229,168.851,169.669,6,7,2,"Convenience, many don't want to wait or go by a public transportation schedule or rely on others for carpooling.",1,I mostly drive to work which is several miles away from where I live. I don't drive as much but when I do it's to go the gym and grocery shop.,1,46.987,166.722,167.673,6,4,1,"Accessibility thanks to Youtube and Netflix. In addition, many people are cutting their cable due to high costs.",1,I don't have cable but I watch Netflix every evening on my iPad. I estimate 2 hours of time on Netflix.,1,36.673,123.019,167.422,5,7,1,Work and school. We use email at work to effectively communicate and send reports instead of traditional mail. Many students turn to the internet for research rather than the traditional encylopedia set.,1,"I use email mainly for work and personal use. I do indulge by going on Facebook and watching occassional Youtube clips. With the accessbility of a smartphone, I'm always on the internet.",1,8.263,72.591,84.084,7,6,1,Awareness and brilliant marketing. People want convenience and a single cup brew is quick and efficient.,1,I have 2 cups of coffee every morning.,1,2,4,30000,Catholic,Pacific Islander,35,5,1,1,no,Our opinions about trends.,No,No,1,1 +0,7,5.733333333,8.333333333,6.666666667,7.5,4,1,1,1,7,6,3,5,7,5,5,3,7,6,7,6,5,7,7,3,7,1,8,1,3,1,1,7,26.493,26.493,27.294,1,1,44.464,44.464,45.721,1, ,0,0,60.004,0,6,38.91,38.91,39.543,1,2,57.363,57.363,58.116,1, ,0,0,60.001,0,1,37.278,37.278,38.336,1,6,33.464,33.464,34.762,1,1,51.607,51.607,52.416,1,2,37.005,37.005,38.175,1,6,21.574,21.574,23.47,1,2,23.597,23.597,24.502,1,1,1,4,1,3,8,6,6,4,5,7,7,4,7,7,3,3,7,4,3,5,8,1,27.915,64.009,65.373,7,7,1,Dairy and meat are expensive,4,Occasional,1,32.803,75.826,76.414,5,5,1,"Pro tickets are insanely expensive, plus there's transportation costs to consider.",4,I don't.,1,25.53,45.89,74.294,5,6,2,the rise of urban sprawl- it's too far to realistically bike,4,I commute alone.,1,53.209,153.143,154.028,5,9,1,Free market innovation,3,"I have certain shows that I follow, and some DVDs that I like to replay. Otherwise the tv stays off.",1,41.583,73.759,118.216,5,9,1,Access and demand due to business & consumer needs,2,casual- on a need basis,1,45.757,96.397,97.008,6,9,1,Convenience & selection,4,"Every day, religiously!",1,2,4,25000,protestant,caucasian,37,5,1,1,no,cognition,no idea,none,1,1 +0,6,7.133333333,7.666666667,5,6.333333333,5.65,1,1,1,9,9,7,5,9,8,6,8,6,7,5,8,6,7,7,3,7,1,8,1,3,1,1,7,13.25,13.25,14.313,1,1,20.97,24.37,30.576,2,1,45.815,49.838,54.591,2,6,26.638,26.638,28.125,1,2,42.264,42.264,46.127,1, ,0,0,60.005,0,1,26.826,26.826,32.042,1,8,50.864,54.288,55.984,2, ,0,0,60.003,0,2,26.822,26.822,40.265,1,7,10.367,10.367,23.686,1,3,36.172,36.172,38.634,1,1,1,2,5,5,8,1,7,1,2,5,5,9,7,5,5,7,7,7,5,9,5,1,28.201,99.623,278.014,6,5,1,"Most likely due to increased costs of obtaining the raw products, processing them into a usable pizza component, transportation of the products, pizza chef's salary. :)",3,1 Large pie every other week.,1,67.15,137.243,138.214,5,5,1,Ticket costs are outrageous. I couldn't afford to go to a college game let alone an NFL game...,3,I don't watch football!,1,4.302,225.142,238.338,6,5,2,Lack of suffient bike lanes in metro enviornments. And possibly that people are living outside of a metro area due to cheaper cost of living and it simply isn't feasable for a bicycle as the primairy means of transportation to and from work.,4,"I work from home, so I only drive to the grocery store or to visit friend's around town.",1,71.086,514.702,519.945,8,9,2,"People can watch their favorite shows/videos on demand and on the go, as our society seems to be so wirelessly connected these days and almost everyone has a person device that is wifi capable now a days.",4,"I usualy download the TV shows I watch each week which already have the commercials edited out. Makes it much easier to binge watch shows without the annoying breaks in the story line for commercials. I then cast them to my TV via my Google Chromecast in full 1080P resolution. I would say I probably watch about 12 hours a week of shows (without commercials), and only really watch live TV for the local news. I have cut the cord with the cable company and don't miss it one bit!",1,67.869,150.994,151.932,5,9,1,"Cheaper accessable devices. Smartphones, tablets, laptops, netbooks, wifi....",3,"I use both multiple times a day, everyday.",1,104.976,269.594,270.79,5,5,1,It mean there seems to be popular for people to have 1 cup of coffee to get them going for the day... Not is the case with me...Takes me at least 1/2 a pot to move...so unfortunately single cup brewers aren't as cost effective for me.,4,Heavy. I have no problems drinking a full pot myself.,1,1,3,30000,Christian,White,43,1,1,1,"yeah, the shape tests were a bit confusing. Maybe I am just geometrically challenged. :-)",No clue,nope,Nope,1,1 +0,5,7.066666667,6.333333333,5,5.666666667,5.8,1,1,1,6,7,9,7,4,6,7,8,8,8,7,8,7,7,7,3,7,1,8,1,3,1,1,7,6.588,6.588,16.572,1,1,18.344,26.592,35.695,2,8,21.405,42.834,59.998,2,6,10.984,10.984,57.168,1,2,31.815,31.815,46.254,1,6,50.908,50.908,60.01,1,5,24.093,24.093,39.008,1,5,40.237,40.237,60.011,1,5,50.016,50.016,60,1,3,25.154,25.154,42.967,1,7,30.439,30.439,60.002,1,2,44.613,55.983,60.013,2,1,1,3,6,7,4,4,7,4,6,7,6,6,6,7,6,8,6,4,8,7,6,1,3.198,117.088,149.703,9,4,2,"Demand. More variety and types of pizza, specialty pizzas and toppings.",1,I eat it maybe weekly at the most.,1,10.83,142.56,146.767,9,7,1,"Location of the games, cost of tickets, transportation and associated costs, inconvenience, children, other responsibilities.",3,I usually only watch major games or if I am invited to a football party.,1,10.007,101.599,175.301,7,3,1,"Longer commutes. A more rushed society, workplace. Many roads are not bike friendly. Many Americans are overweight, lazy and do not like exercise.",1,I alternate between driving and biking.,1,16.664,226.692,227.99,12,8,1,"Advances in technology and their affordability. People spend less time at home with their families. People demand convenience, and gadgets which tailor to their lifestyle.",4,"I mainly watch streaming shows and movies. I watch the news daily on regular TV. I probably watch about 4 hours per day between the two, although much of that time I am involved in other activities.",1,6.1,142.895,181.642,10,7,1,"The internet is a fast, easy and accurate way to ge the answers, services, communication, information, entertainment. / / Services, trends, convenience, knowledge, advances.",2,Multiple hours of internet and emails daily for work and personal purposes. I use search engines every day to conveniently find all sorts of informaiton.,1,7.928,105.669,146.642,11,5,1,"Convenience, flavor, trendy flavors in a variety pack. Coffee doesn't sit and get old/burn. It comes out fresh. More choice.",2,"1 cup in the morning, 1 cup in mid-afternoon, and a cup of de-caf after dinner. So three cups per day, all from a single cup brewer.",1,2,4,36000,Christian,Caucasian,33,7,1,1,No just very long.,"How people perceive the world around them, their habits and personalities, and judgments.",Not sure.,no,1,1 +0,6,5.866666667,6.666666667,4.333333333,5.5,4.55,1,1,1,4,6,7,2,8,3,7,5,8,4,8,8,4,8,6,3,7,1,8,1,3,1,1,7,8.812,12.17,13.305,2,1,12.707,12.707,15.407,1,1,56.132,56.132,57.688,1,6,23.2,23.2,25.143,1,2,24.962,24.962,30.72,1,7,28.866,48.867,60.001,2,1,23.624,23.624,25.149,1,2,44.031,44.031,47.822,1, ,0,0,62.877,0,2,22.095,23.427,25.3,2, ,0,0,62.14,0,6,48.906,48.906,50.743,1,1,1,4,5,4,6,5,8,6,5,4,7,3,3,7,3,7,7,2,3,6,8,1,11.073,32.133,50.968,5,2,1,Demand,1,Couple times a month,1,93.986,195.452,195.84,10,5,1,number of available tickets and prices of tickets and distance to the games,4,Never,1,66.11,110.438,110.808,6,3,1,Distance,2,Drive myself or carpool with a roommate,1,23.979,63.775,77.122,7,9,1,lower prices of such devices,4,Usually have it on while doing other things,1,14.367,132.069,132.958,5,9,1,growing awareness of the effectiveness and amount of information that can be obtained from the internet,1,Email every day and always use the internet with search engine,1,10.282,122.122,122.862,5,5,1,college,1,"Every day, college life",1,2,3,40000,Christian,Caucasian,20,5,1,1,No,How people perceive social ideals and behavior,Unsure,no,1,1 +0,5,6.266666667,4,1.666666667,2.833333333,5.35,1,1,1,7,7,6,3,6,6,7,7,7,5,7,7,5,7,7,3,7,1,8,1,3,1,1,7,16.837,16.837,19.243,1,1,29.065,29.065,32.574,1,4,27.872,27.872,29.349,1,6,41.034,41.034,45.014,1,2,19.241,19.241,20.222,1,4,13.032,13.032,14.238,1,5,22.702,22.702,24.299,1,5,16.346,16.346,17.128,1,7,13.734,13.734,14.603,1,3,19.124,19.124,20.234,1,8,20.433,20.433,21.47,1,3,12.393,12.393,14.742,1,1,1,5,7,5,2,6,6,7,4,6,7,6,3,6,9,5,4,6,5,5,7,1,41.139,90.875,91.857,4,2,2,I think that supply and demand accounts for the current prices of pizza.,1,I rarely eat pizza. I would say I eat pizza once every month.,1,63.464,84.391,125.07,5,1,1,I believe this is the case because of limited seating in arenas as well as because of the increase cost of seeing a sporting even live as opposed to on television or on a computer.,1,I do not watch football ever.,1,30.114,131.787,133.594,5,1,2,I think primarily Americans are extremely lazy and out of shape. They also have been tricked into believing that they HAVE to drive to work when in reality there are better options.,1,I do not drive. I own a bicycle that i use to commute everywhere. In situations where i cannot ride my bicycle I use a friend or a company like Uber.,1,151.065,271.239,272.295,6,1,1,The advancement of technology and internet accounts for the growing the number of available devices to watch videos,1,I watch about 2 hours of tv daily. This adds up to about 14 hours a week. This includes movies as well.,1,28.401,96.313,97.079,5,9,1,I believe that the increase in size and depth of the internet as well as the increase in internet speed accounts for the recent rise in the population of internet users.,1,I use email and internet searches every single day.,1,38.265,103.687,165.351,8,3,1,"I think that overall the american culture is starting to slow down. More and more people are relaxing their lives and appreciating the small things. In addition, single brew coffee is significantly better in quality than a traditional drip maker. I know this because my boyfriend use to work at a coffee shop. It is safe to say he is a coffee master!",1,I do not drink coffee ever.,1,2,3,30000,agnostic,caucasian,24,2,1,1,no. directions and procedures were clear.,I really am unsure. Maybe studying the relationship between problem solving and statistical understanding.,potentially but i am unsure,clear directions,1,1 +1,2,5.533333333,6.333333333,4,5.166666667,4.9,1,1,1,4,6,6,4,4,6,6,7,7,7,6,4,6,7,3,5,7,1,8,1,3,1,1,7,11.857,11.857,14.444,1,3,19.764,19.764,22.332,1,3,27.605,27.605,29.238,1,1,42.051,45.153,46.503,2,6,17.426,20.599,21.717,2,5,15.411,15.411,16.711,1, ,0,0,60.004,0,6,2.724,2.724,3.841,1,2,5.042,5.042,6.617,1,4,3.794,8.518,10.192,2,2,16.909,41.104,42.659,4,3,15.609,25.088,27.442,3,1,1,3,6,6,6,6,7,6,7,7,7,4,6,7,3,4,7,4,4,6,4,1,8.22,54.795,55.963,5,5,2,It is in such high demand.,2,I eat pizza probably once a month.,1,17.802,61.761,62.861,5,6,2,Tickets are expensive.,2,I watch when the season is going on.,1,62.474,113.892,114.748,4,3,1,It is more physically demanding and inconvenient.,1,I drive everywhere,1,84.133,126.983,128.163,5,6,1,The demand of watching videos.,3,I watch Tv daily,1,51.616,132.734,134.01,5,8,1,More and more technology friendly world. Everything is dependent on technology.,1,I use it daily.,1,7.143,25.337,34.048,6,3,2,The demand of coffee,4,I do not drink coffee,1,1,3,45000,none,latino,27,5,1,1,no,it was long and tedious,no,no,1,0 +0,4,4.333333333,3.666666667,3.666666667,3.666666667,5.15,1,1,1,4,8,1,6,1,4,2,7,1,4,6,6,2,4,9,3,7,1,8,1,3,1,1,7,8.853,8.853,12.547,1,3,28.489,28.489,37.593,1,5,33.392,42.333,60.003,3,4,10.002,10.002,12.901,1,2,23.769,40.51,41.755,3,1,26.615,30.359,31.756,2,1,6.304,20.956,22.713,2,5,53.109,53.109,55.657,1,2,45.843,45.843,47.945,1,8,39.573,39.573,41.828,1,1,59.223,59.223,60.002,1,3,18.973,18.973,22.785,1,1,1,4,9,6,8,3,3,4,7,3,7,6,6,7,5,7,7,4,7,8,4,1,49.14,145.814,147.135,9,2,1,pizza is a pretty competitive market so the price cant be changed to much so most likely ingredients,2,i try not to eat it but it happens on occasion,1,35.826,109.841,112.824,5,6,1,there is a limit to the amount of people who can sit in a stadium therefore the demand is very high causing prices to rise,2,i watch on tv,1,34.581,91.206,92.393,6,2,1,most have cars and are paid well enough they can afford to use it,4,drive just about every where i go,1,39.236,134.316,135.141,8,4,1,the cell phone industry,1,i dont watch television,1,48.03,160.569,163.134,8,5,1,the amount of people who are able to use computers keeps rising and we have smart phones that are basically a computer,1,I use my email about once a week and i use google multiple times a day,1,41.081,105.387,106.557,7,3,1,It is less wast full and it is fairly easy,3,i do not drink coffee,1,1,3,60000,none,white,20,5,1,1,not really,my opinons on certain issues,most likely some of the questions seemed a little funny,no /,1,1 +0,4,5.533333333,5.666666667,5.333333333,5.5,5.15,1,1,1,9,5,5,5,8,5,7,4,5,5,5,5,5,5,5,3,7,1,8,1,3,1,1,7,7.393,7.393,17.553,1,4,13.663,13.663,15.551,1,7,17.966,26.032,27.139,3,2,23.897,23.897,29.708,1,2,31.591,31.591,35.972,1,5,15.447,15.447,17.69,1,5,16.163,17.985,19.796,2,5,6.881,6.881,8.771,1,7,15.736,16.71,17.632,2,2,24.782,24.782,27.298,1,7,23.193,23.193,23.921,1,8,22.702,22.702,23.332,1,1,1,7,5,5,8,7,8,7,6,5,5,6,7,5,8,8,5,8,4,8,7,1,6.342,36.356,37.933,7,5,2,Toppings,3,Barely,1,19.461,66.5,67.466,5,6,1,Cant afford it,1,Rarely view,1,92.443,163.488,164.587,7,6,2,Want to get home from long day at work faster... bad weather... dont wanna mess up clothes,2,Dont drive much,1,14.33,57.272,58.526,7,4,1,Reality tv advertisment,2,All t h e time,1,11.138,50.989,52.043,5,8,1,Employment,1,Not often,1,7.489,32.421,39.782,5,4,1,Quicker,1,One cup a week,1,2,3,25000,baptist,black,24,5,1,1,No,Patience,No,No,1,1 +0,2,4.8,6.666666667,7,6.833333333,4.25,1,1,1,7,6,7,4,3,3,2,8,7,7,3,2,3,7,3,3,7,1,7,1,3,1,1,7,4.274,4.274,5.597,1,7,5.149,5.149,6.219,1,8,6.271,6.271,7.559,1,8,9.051,9.051,10.542,1,2,5.851,5.851,7.232,1,8,4.669,4.669,5.963,1,8,4.462,4.462,5.953,1,3,3.659,3.659,4.844,1,7,3.962,3.962,5.197,1,7,7.103,7.103,8.387,1,2,4.067,4.067,5.459,1,4,3.22,3.22,4.551,1,1,1,7,5,2,3,7,7,3,5,4,7,4,3,7,7,7,7,7,3,7,7,1,24.923,79.793,80.786,6,7,1,I'm not sure.,1,I eat pizza about once a month.,1,7.311,553.273,554.428,5,7,1,Because so many more people can watch on TV than in the stadium.,1,I do not watch football.,1,253.49,454.073,455.377,5,7,1,Hard to drive a bike on most roads.,1,I drive to work daily.,1,52.27,70.819,85.465,5,5,1,The growing availability of smart phones and tablets.,2,I do not watch live TV/,1,45.32,61.364,103.424,5,8,1,Easier access to the internet.,1,I email and use internet search a lot.,1,35.337,60.011,86.336,5,7,2,It's easy and convenient.,1,I drink coffee daily but do not drink single-cup brews.,1,2,4,28500,pagan,white,33,1,1,1,no,not sure,not sure,no,1,1 +0,2,6.933333333,7.333333333,4,5.666666667,4,1,1,1,8,9,7,7,2,6,6,7,6,5,8,8,9,9,7,3,7,1,8,1,3,1,1,7,35.033,35.033,38.366,1,1,42.081,42.081,44.352,1,1,54.245,57.152,60.005,2, ,0,0,60.018,0,8,20.631,20.631,22.043,1,3,40.578,40.578,42.75,1,5,33.517,38.964,40.241,2,7,39.933,39.933,41.883,1,7,36.166,36.166,37.514,1,4,34.214,34.214,36.516,1,5,24.408,24.408,25.764,1,1,53.406,58.015,60.017,2,1,1,4,3,5,6,6,4,6,6,3,4,3,4,7,2,6,6,6,2,6,9,1,16.106,87.437,97.4,7,5,2,Inflation. People are not eating as much pizza and this is reflected in the cost. Prices must be inbcreased in order for the company to profit.,3,maybe one to two times per year,1,30.962,94.146,95.686,6,5,2,"Price of tickets, availability in getting to games, weather, work schedules, large families could be costly to take an entire family",3,I avoid it at all cost.,1,34.142,101.079,191.259,7,3,2,"Probably for multiple reasons. First, most people are not use to riding a bike anywhere. It is more convenient in most situations to drive. Weather could also be a contributing factor. Additionally, if someone is shopping it isn;t always practical to ride a bike.",4,"I walk to places within a few miles from my house,otherwise I will drive. I try to combine activities if I am driving to save driving time and gas.",1,10.058,154.06,155.603,9,8,2,"public demand for new and novel technological gadgets, and the desire to develop a newer, more advanced device to sell for profit",4,"I usually watch 30 minutes to two hours per day, depending on the day, my schedule and mood. I predominetely watch tv series but occassionally watch movies or documentaries. I do not have cableand usually watch netflix",1,65.325,239.126,243.683,10,9,1,"devices are flooding the market which makes them more affordable to people, therefore more people have access. Most companies advertise online and social media draws people to interact ona daily basis.",1,"check e-mail 2-3 times per day. Morning, afternoon and evening. I usually onluy search for something onlinewhen I need to fine an answer to wome question, or to look up local businesses information or for online purchases.",1,16.612,107.506,108.653,6,4,1,busy lives require fast and convenient coffee. It is difficult to measure one to two cups per pot of coffee. It is also novel right nowto have a special 1-2 cup coffee brewer,4,I drink coffee about 1-2 times per month.,1,2,5,50000,none,white,34,2,1,1,no,to see if people actually took the time to read the article when they knew they didin't have to in order to answer the questions,looking at the amount of time people spent reading the article,no,1,1 +0,4,5,7.333333333,6.666666667,7,5.45,1,1,1,7,7,7,4,3,4,6,6,6,6,5,5,2,1,6,3,7,1,8,1,3,1,1,7,9.5,9.5,30.563,1,1,26.933,26.933,41.804,1,7,26.939,26.939,44.018,1,8,41.69,41.69,60.014,1,2,43.699,43.699,60.006,1,6,42.659,42.659,59.989,1,5,40.641,40.641,60.015,1,5,35.537,35.537,60.013,1,7,43.317,49.445,60.009,2,3,33.596,33.596,60.006,1,7,21.961,21.961,60.009,1,2,34.799,48.087,60.013,2,1,1,4,1,6,6,4,9,7,4,5,4,5,7,8,7,6,4,4,3,9,8,1,104.329,152.585,182.466,5,6,1,Demand,2,I eat pizza about once a month.,1,44.201,138.721,167.107,5,7,1,Maybe they live far from the stadium and the prices of the tickets are too expensive.,1,I don't watch football.,1,64.829,164.541,172.591,6,6,1,"Their workplace is too far, weather conditions ( snow, rain, heat).",2,I drive almost everyday.,1,50.198,122.549,142.176,6,8,1,New technology.,2,I watch about 10-15 hours of tv each week.,1,47.233,100.633,140.373,6,8,1,"More access to the internet, smart phones.",1,I use email and the internet everyday.,1,33.62,198.908,206.142,20,7,1,"Convenience, variety.",3,I don't drink coffee.,1,2,4,47000,Christian,Black,31,1,1,1,no,I'm not sure.,I'm not sure.,no,1,1 +0,9,6.933333333,8,8,8,5.85,1,1,1,6,7,7,3,6,5,7,8,8,8,7,8,8,9,7,3,7,1,8,1,3,1,1,7,5.974,11.686,16.035,2,1,15.541,15.541,19.202,1,1,27.341,27.341,36.835,1,6,12.982,12.982,13.995,1,2,14.821,14.821,20.249,1,8,44.528,44.528,50.231,1,1,16.304,16.304,19.421,1,5,28.867,28.867,33.486,1,6,30.925,39.404,58.107,2,2,19.166,34.996,38.212,2,6,27.886,48.63,50.988,2,5,33.553,33.553,46.968,1,1,1,2,7,7,3,7,8,3,6,7,7,7,8,7,6,7,4,2,4,8,7,1,13.327,184.995,220.002,11,8,1,"The price of food, including the igredients needed to make pizza is going up, but there's a lot of competition in the market, so prices are held down somewhat.",3,I order pizza delivery from a national chain probably 2-3 times per month.,1,40.978,129.992,181.929,5,8,1,Because there are only a limited number of seats in the stadiums compared to the total number of people interested in watching the games.,4,"I watch multiple games (college and pro) every week of the season -- usually at home, on the tv.",1,672.631,792.893,802.25,11,8,1,They live too far away from where they work and like the convenience and speed of driving.,4,"I don't work outside the home, so I don't drive to work. I drive while running errsnds.",1,33.744,174.447,175.665,5,8,1,The availability of high speed wireless and broadband networks in and outside the home.,4,I watch a lot of sports and follow 4-5 series that I watch each week. I probably watch 40 hours each week.,1,41.037,214.835,252.78,13,8,1,"As it has become easier and cheaper to use, more and more people have accepted it, including people who may have been reluctant to use it before. Also, capabilities of the internet ghave increased, making it more useful (e.g. video streaming).",2,I use both email and internet search on a daily basis.,1,42.237,114.715,168.934,7,8,1,The convenience of not having to make a full pot for people on the go. It's cheaper than going to Starbucks.,3,I drink 4-5 cups of coffee each day. I use a drip brewer.,1,1,4,115000,Christian,White,52,9,1,1,no,cognitive ability and perceptions of naturaness,"There probably was, but I don't know what",no,1,1 +0,3,5.733333333,7,6.666666667,6.833333333,4.7,1,1,1,6,7,6,7,4,6,4,4,4,6,6,8,6,6,6,3,7,1,8,1,3,1,1,8,11.671,11.671,12.959,1,1,6.782,6.782,7.871,1,8,21.514,21.514,22.579,1,8,9.052,12.986,14.38,2,2,11.48,11.48,12.806,1,4,18.796,18.796,21.108,1,5,9.053,9.053,9.645,1,5,11.432,11.432,12.41,1,6,22.173,22.173,23.054,1,3,11.014,11.014,12.467,1,1,15.013,15.013,15.787,1,3,4.435,4.435,5.534,1,1,1,3,7,6,7,7,7,6,8,3,8,6,6,7,6,6,7,7,4,8,7,1,9.564,68.907,69.733,5,6,1,The demand and competition,1,I might eat it once a month. I do love a good pizza but I live in a rural area and there isn't much option for delivery or time to get it home warm,1,34.205,50.221,66.29,5,7,1,Finances or time to go,3,I watch every Sunday at home on my TV and rarely attend them live,1,12.835,36.579,37.35,5,7,1,The distance to work,1,I drive to work myself without others,1,32.358,65.852,66.506,5,7,1,Technology,1,"I rarely watch TV, my kids watch it most of the time and I find it unstimulating",1,14.943,39.51,53.131,5,8,1,The ease of it and how much information is out there,1,I use it daily and often. It is my main source of communication with family and with my job,1,6.463,50.101,60.404,6,6,1,It isn't near as wasteful as people would make a whole pot and not drink it all and be wasteful,1,I drink it alot in the winter but not much in the summer time,1,2,4,94000,christian,white,39,2,1,1,no,no idea,unsure,no,1,1 +0,6,6.666666667,7,4.666666667,5.833333333,4.7,1,1,1,6,8,7,6,5,6,4,6,7,7,8,8,8,7,7,3,7,1,8,1,3,1,1,7,10.947,10.947,16.782,1,1,30.439,30.439,44.083,1,3,30.558,30.558,33.288,1,6,19.942,19.942,21.406,1,2,20.245,20.245,21.802,1,3,38.981,38.981,41.912,1,5,26.858,42.34,43.779,2,5,30.563,34.258,37.165,2, ,0,0,59.987,0,3,10.088,10.088,11.28,1,8,11.763,15.223,20.025,2,2,19.08,19.08,20.884,1,1,1,4,4,6,8,9,5,5,7,4,5,5,6,5,4,6,5,5,2,8,3,1,491.601,619.154,669.148,5,7,1,Gourmet pizza has been on the rise. Gourmet costs more due to the higher cost of the special ingredients.,1,"I would eat a slice of pizza everyday. On average, I eat pizza at least once a week.",1,76.644,168.595,235.956,5,7,1,"Live games are extremely expensive, annoying to get through the security checkpoints, finding parking is a hassle, the beer and other snacks are ludicrously expensive and the view can only be decent in one shells out a lot of cash. The audience is also subject to the weather if it's an open stadium.",3,I used to watch the Dolphins games at home with my mom when I was young. I stopped watching for a long time because I'm generally just not into sports and the game seems really complicated. I recently got into watching the Bears games because my boyfriend is a big fan and it's fun to watch with him.,1,128.046,277.359,283.306,6,2,1,"Riding your bike along side car drivers can be very dangerous. During certain times of the year, one may get very hot and sweaty trying to ride their bike to work. Many people look down on those who ride their bikes to work. The comfort and convenience of a car can not easily be given up for the fresh air, exercise and environmental benefit of riding a bike.",4,I do not own a car. I do not have the option of driving a car.,1,101.636,222.942,224.814,6,6,1,"Logically, as soon as we were able to take pictures and view them on our phones, audio (music) ought to be at our fingertips for listening, followed closely by audio and video combos.",3,I mostly watch shows and movies on my laptop but I will tune in for my favorite network or cable shows if I happen to be at home to watch them.,1,71.57,306.862,308.7,5,8,1,"Younger generations are more likely to adapt to new technology and as they grow up they quickly become the main users of the internet. Older generations sometimes prefer to use the internet because no matter what someone's schedule, they can always contact someone and expect a reply at that persons most convenient time.",1,I check my email everyday. I typically leave my browser open on a social media website in case someone wishes to chat with me. I enjoy the easy access of search engines even if I'm just looking up the definition of a 'big word' to make sure I'm using it correctly in a sentence.,1,69.457,211.627,218.431,7,5,1,The growth and spread of Starbucks and other such cafe's and coffee shops that specialize in made-to-order/customized brews where one can add or take away a wide variety of ingredients. Coffee manufacturers as well as coffee maker manufacturers have noticed the trend and now offer products that provide the customization that place like Starbucks offers.,4,I don't like coffee at all.,1,2,4,75000,Atheist,Caucasian,26,5,1,1,no,I don't know,It's likely. I don't know.,No,1,1 +0,5,3.666666667,9,2.333333333,5.666666667,4.1,1,1,1,5,5,5,2,2,2,2,2,5,5,5,5,2,5,3,3,7,1,8,1,3,1,1,7,2.083,2.083,3.224,1,1,10.737,10.737,11.598,1,5,0.205,0.205,0.883,1,6,1.318,1.318,1.842,1,1,7.04,7.04,7.821,1,3,0.576,1.583,1.892,2,1,1.947,1.947,2.696,1,2,0,0,0.702,0,5,0.798,0.798,1.434,1,2,0,0,0.614,0,2,1.134,1.134,2.09,1,2,0.677,0.677,1.425,1,1,1,5,2,2,2,6,5,5,8,4,5,2,5,7,5,2,5,5,2,8,7,1,29.631,47.192,48.423,5,9,2,"price gouging, corporate greed",1,none.,1,16.008,43.528,46.549,7,1,2,expensive. time consuming.,4,none.,1,13.415,58.13,58.616,6,1,1,"weather, too far to bike, insufficient public transportation options",2,drive once a week for errands. i walk to work.,1,37.304,80.764,81.282,5,9,2,price drops in tech,4,"usually stream online, netflix, hulu etc.",1,79.358,128.127,137.219,7,9,1,"accessibility, necessity",1,daily usage for both.,1,8.463,25.778,26.288,7,5,1,"advertising, novelty.",4,none.,1,2,4,70000,none,white,29,1,1,1,no,not sure,"probably, no idea",n/a,1,1 +0,5,6.066666667,5,4,4.5,4.8,1,1,1,5,5,7,5,8,3,9,9,5,6,7,7,5,5,5,3,7,1,8,1,3,1,1,7,19.101,19.101,20.924,1,1,29.488,29.488,32.122,1,5,28.259,28.259,29.757,1,8,26.442,26.442,33.823,1,2,18.028,18.028,21.165,1,4,55.622,55.622,57.347,1,1,38.64,38.64,40.029,1,5,37.479,37.479,39.813,1, ,0,0,60.007,0,3,19.528,19.528,21.279,1,7,13.886,13.886,19.548,1,1,57.06,57.06,58.721,1,1,1,6,4,6,4,7,4,4,5,4,6,7,5,6,5,4,6,6,6,9,6,1,28.919,55.46,57.374,5,3,1,demand,1,once every couple of months,1,25.118,54.092,55.011,5,3,2,the price and availability of tickets,3,never watch it,1,20.148,48.965,60.795,5,3,2,"takes energy, weather permitting /",3,drive to work walk the rest of the time,1,162.596,208.689,210.108,6,4,2,growing technology,4,rarely watch and scheduled programming but watch movies on dvd frequently,1,83.54,146.366,173.854,5,8,1,you almost have to because everything you do requires you to go online. it's faster and easier for everyone,2,I search the internet and read my email on a daily basis for a majority of the day.,1,25.542,57.244,58.3,5,6,2,to reduce,4,ocassional,1,1,3,20000,christian,white,34,5,1,1,the first few sets of questions the ones that were asking things like why do i think the sky is blue lol,"not really sure, how people think?",yes but not sure what,I enjoyed it... thanks!,1,1 +0,3,6.133333333,6.666666667,5.333333333,6,6.05,1,1,1,8,7,5,5,4,6,7,7,8,6,2,8,6,7,6,3,7,1,8,1,1,1,1,7,11.703,11.703,13.034,1,8,9.618,9.618,11.239,1,4,18.184,18.184,19.915,1,8,11.649,14.279,15.38,3,7,21.613,21.613,22.587,1,7,7.151,7.151,8.163,1,5,16.359,16.359,17.134,1,5,23.654,23.654,24.417,1,7,12.384,15.674,16.859,2,2,15.301,15.301,17.695,1,7,8.722,8.722,10.382,1,1,16.196,19.784,21.131,2,1,1,3,6,4,6,2,7,8,4,6,4,6,4,2,7,7,4,4,4,7,6,1,14.933,73.14,90.94,7,5,1,The supply and demand of the people wanting the pizza.,3,We usually have a pizza once every 2 weeks. Normally 8 slices.,1,31.83,86,86.702,4,8,2,Because most people do not live in a town with a team there. And there are only so many tickets available.,3,"I watch football on TV, not live, every week.",1,24.552,71.574,76.887,5,5,1,Because more and more people are living outside the city and further away from their jobs.,3,I drive 25 miles to work each day. 5 days a week.,1,6.605,53.377,54.004,6,7,1,Better technology in the household.,3,"I watch about 30-35 hours a week, with most of that being saved to DVR to watch later.",1,30.723,50.883,103.674,4,8,1,The recent spike in fast internet being available to more and more people.,1,I use the internet and e-mail many times a day. Aprrox 40% of my day is on the internet w/e-mail.,1,6.881,44.987,45.763,5,3,1,The opening up of so many coffee shops in the US.,2,I do not drink coffee.,1,1,4,94000,athiest,white,39,2,1,1,No,Shapes and how they combine to make something,No,No,1,1 +0,3,6.533333333,5.666666667,4.333333333,5,5.45,1,1,1,9,6,8,4,4,8,8,9,8,8,1,4,6,7,8,3,7,1,8,1,3,1,1,7,5.611,5.611,10.234,1,8,14.173,15.712,18.903,2,7,18.285,18.285,19.268,1,4,32.213,32.969,36.583,2,2,32.334,33.199,33.997,2,7,12.254,12.254,13.351,1,5,28.226,28.226,28.975,1,5,33.978,33.978,34.732,1,6,31.299,32,32.862,2,7,27.744,27.744,28.796,1,8,30.194,30.194,31.058,1,7,30.276,30.276,31.144,1,1,1,4,4,8,8,3,8,8,8,4,7,7,7,8,7,7,7,4,7,4,3,1,20.281,63.87,64.476,6,1,1,"The toppings are cheap and easy to make, which is why they can sell pizza for such a low price. Also, it's able to feed many people.",3,Maybe once or twice a month.,1,11.774,78.13,78.709,6,9,1,"The cost of attending a game is expensive and it takes more time than necessary. It is usually an entire day, which isn't a good thing.",3,"Every Sunday, I make sure to watch NFL games. Will not miss my team's games at all.",1,9.384,82.535,84.19,5,2,1,"This could be due to the weather and people are working more and sleeping less, which makes it unbearable to ride bike to work.",2,"I rarely drive during the week. When I do, there are other drivers with me.",1,57.717,138.511,139.682,6,7,1,"DVR allows ability to record more shows so you don't miss any. Also, streaming services like Netflix makes it easier to binge watch missed shows.",3,"I mostly use DVR to watch the 4-5 shows each week. During sports season (NBA/NCAAB and Football), the viewing is increased.",1,9.11,73.433,74.438,6,9,1,"More availability of internet, especially with the smartphones and tablets you can get internet access anywhere.",2,"I usually just go onto the usual bookmarks that I have, they consist of forums and such.",1,19.948,47.68,48.825,6,2,1,Its readily available and it's the hip thing to do now.,3,I don't drink coffee.,1,1,4,50000,islam,asian,32,3,2,1,The amount of questions are too many.,Learning about our thoughts on basic things like pizza and cars.,"Yes, I'm not sure what, but there usually is.",Nope,1,1 +0,3,7,8.666666667,6.333333333,7.5,5.2,1,1,1,9,1,5,7,9,1,9,1,9,9,9,9,9,9,9,3,7,1,8,1,3,1,1,7,10.22,10.22,13.546,1,8,8.671,26.347,27.639,2,3,19.734,19.734,23.842,1,6,19.689,25.57,26.385,2,5,17.94,17.94,19.155,1,4,18.74,20.367,21.762,2,5,21.768,21.768,26.262,1,6,15.438,15.438,16.438,1,3,23.091,23.091,26.875,1,3,19.15,19.781,23.737,2,1,15.945,15.945,19.891,1,1,10.228,10.228,14.58,1,1,1,1,1,9,1,9,9,1,9,1,5,1,9,9,9,9,9,1,1,9,1,1,13.283,78.979,95.909,5,9,1,the cusmores willing nice to buy and the quality,1,regulary i love pizza at least once a week,1,13.793,85.117,100.015,11,5,1,"Weahter, ticket price, travel ease of watching from a pub or sport bar",3,i dont watch only suberbowl on tv,1,12.167,87.246,88.068,10,5,2,to far to much traffic more convenit for car or bus,1,I walk or my husband drives in car with kids. We also use bus togo into teh city,1,15.802,72.814,73.921,5,9,1,people schedules kids and time,1,i watch my nightly shoows not much in day though,1,41.254,152.131,152.949,5,8,1,"more kids, social media sites and shopping, even work",1,i email more than i write and always use the internet,1,39.342,388.276,389.846,5,9,1,less waste and clean up,1,I cup in the morning and a few at night,1,2,4,48000,Catholic,white,31,6,1,1,no,different things like how miuch time in front of tv,yes probably how we precieve thingss through our daily habits,no,1,1 +0,5,5.266666667,6.333333333,3.666666667,5,2.6,1,1,1,6,5,1,7,8,1,7,1,9,5,6,1,7,6,9,3,7,1,2,1,3,1,1,7,8.805,9.269,11.974,2,1,10.896,10.896,11.929,1,5,18.316,18.316,23.652,1,6,6.653,6.653,13.206,1,8,36.482,36.482,38.916,1,7,13.538,13.538,14.5,1,1,38.663,38.663,40.073,1,7,4.777,28.522,29.339,2,1,17.349,17.349,18.063,1,3,19.774,19.774,24.056,1,8,42.429,42.429,44.398,1,5,12.778,12.778,14.019,1,1,1,9,1,1,9,9,6,4,9,1,9,9,9,9,1,1,9,9,9,1,9,1,19.321,107.24,108.002,8,9,1,"Available raw materials, dairy prices, fuel prices, how much profit an establishment deems reasonable.",4,I probably have 4 or 5 pizzas per month.,1,31.179,80.195,81.709,8,1,1,That is the max capacity of the stadium.,4,I watch mostly on mobile devices.,1,14.503,79.663,80.017,10,1,1,"Honestly, I think Americans are very lazy and have become overweight bums.",4,"I try to drive as little as possible, along with the health and environmental benefits, it saves a ton of money!",1,19.663,69.983,108.752,8,1,1,Lowered technology costs. Anyone basically can afford the $150 for a tv. Also people seem to prioritize it so they will find t he money somehow.,4,I watch only the specific programs I want to. I try to watch them either online or through my computer to have the least amount of ads possible.,1,6.018,106.329,119.003,11,9,1,"Lowered cost of entry and ownership. Also it is a necessity for a lot of jobs, programs, and daily life.",2,I use them both many times throughout the day.,1,22.565,48.462,49.103,6,9,2,Laziness.,4,I brew and drink roughly 10 cups per day.,1,1,4,125000,None,Caucasian,29,5,1,1,No /,Peoples opinions based upon their level of problem solving and intuition.,It seems like you wanted to distract people then get to the info you really wanted.,nope,1,1 +0,5,6.733333333,7,4.333333333,5.666666667,6.45,1,1,1,7,8,6,2,7,2,6,7,7,8,8,8,8,9,8,3,7,1,8,1,3,1,1,7,5.512,5.512,7.102,1,1,14.137,14.137,14.968,1,3,7.951,7.951,8.795,1,6,7.131,7.131,7.86,1,2,13.892,13.892,14.782,1,6,20.708,20.708,21.516,1,5,30.61,30.61,31.681,1,3,10.311,10.311,12.975,1,3,31.489,31.489,32.275,1,3,17.232,17.232,18.618,1,5,32.129,32.129,32.879,1,2,27.928,27.928,29.281,1,1,1,2,7,6,7,3,9,5,3,7,6,7,8,6,7,8,3,2,7,8,8,1,33.258,79.416,116.474,5,5,2,Restaurant pizza is very expensive because there is always high demand for it.,4,"I probably eat pizza about once a week, usually frozen, grocery store pizza.",1,58.851,130.387,131.571,5,5,1,Tickets can be expensive. Also many people live to far away to go to games all the time.,4,I watch college football.,1,84.852,164.393,196.836,5,3,1,"Is just part of our society. Also, in parts of the country that have cold winters, riding a bike isn't much of an option for about half of the year.",4,I drive when I need to but walk when I can.,1,32.189,142.184,144.231,6,8,1,It's just the course of technology.,4,I usually watch TV at night when going to bed,1,82.891,139.189,140.073,8,8,1,Cheaper and accessible technology.,2,Pretty much every day.,1,4.324,32.247,55.022,5,5,1,People like fresh coffee that's brewed right before drinking it.,4,I drink coffee most mornings.,1,1,3,22000,Catholic,Caucasian,33,3,1,1,No problems,Not sure,"Probably, but I don't know what.",No comments,1,1 +0,5,7,8.666666667,7.666666667,8.166666667,6,1,1,1,9,7,9,5,6,5,5,9,7,7,9,5,8,9,5,3,7,1,1,1,3,1,1,7,5.846,8.59,9.02,2,1,6.65,6.65,9.577,1,5,38.267,38.267,39.177,1,4,24.052,24.052,26.62,1,2,28.157,28.157,30.293,1,2,20.237,20.237,24.258,1,6,10.962,10.962,13.795,1,8,17.119,19.073,19.96,2,8,14.234,14.234,16.81,1,3,10.609,10.609,13.953,1,4,8.457,8.457,10.739,1,5,6.496,19.356,20.291,4,1,1,3,5,8,5,5,9,6,5,5,5,7,7,5,7,6,6,5,5,9,5,1,8.864,86.006,86.779,5,8,1,"Consumers market drives up price. More demand, more money.",1,I have pizza about once a month or once every two months.,1,14.061,33.565,40.437,6,7,1,It's expensive to watch games live.,1,I don't watch football at all.,1,28.032,168.48,173.999,14,9,2,A lot of clothing for work is not suitable for riding a bike. Also a good portion of people live too far away to ride bike to work.,1,I live in the country...at least 5 miles away from town. I usually drive.,1,2.787,488.68,490.788,7,9,1,The demand by customers to have such devices.,1,Probably 20 hours a week.,1,5.337,30.439,45.102,5,9,1,Cheaper and easier access.,1,I check my email and use the internet 5 or more times a day.,1,13.518,47.685,48.266,5,7,1,Because there are a lot of people like me who only drink one cup a day.,1,One cup a day.,1,2,1,50000,Christian,White,54,3,1,1,No,Not really sure.,"I would imagine so, but have no idea what?",no,1,1 +0,5,8.666666667,4.333333333,4,4.166666667,5.85,1,1,1,9,9,9,7,9,9,8,9,9,7,9,9,9,9,9,3,7,1,8,1,3,1,1,7,12.022,12.022,12.923,1,1,27.632,27.632,31.711,1,5,31.582,31.582,37.479,1,8,27.177,27.177,28.304,1,2,51.127,51.127,52.053,1,7,20.672,20.672,21.711,1,1,32.323,32.323,34.337,1,5,15.382,20.982,24.724,2,2,41.206,41.206,41.965,1,3,10.767,10.767,22.663,1,7,31.109,31.109,34.557,1,1,30.993,30.993,31.745,1,1,1,4,4,6,2,4,7,6,7,7,6,6,7,7,6,7,4,2,6,7,6,1,54.894,144.724,145.426,7,3,2,"I think pizza is relatively inexpensive considering you can get a $5 pizza from most pizza restaurants these days. Because of the amount of pizza we consume here in the USA, restaurants have to offer very competitive prices since the availability is so high.",2,"I tend to consume pizza about once a month, maybe 4 slices in a sitting.",1,85.34,177.571,178.726,5,6,1,I would say the price of the tickets and convenience of getting to and from a stadium accounts for that rather low percentage. Many people are struggling with the economy right now and cannot afford ticket prices.,2,"I don't watch football unless I happen to be at a friend's house where they are watching it. Usually in that case, they are explaining ""plays"" to me and telling me about the game mechanics so I can follow along.",1,45.538,85.673,110.678,5,1,1,"Convenience. Many Americans are pushed for time, and would rather take the fastest and easiest route to and from work.",2,"I drive to work most days of the week, but I have been trying to make it a habit to carpool to work with a coworker at least once or twice a week as of the last few weeks.",1,57.714,66.681,161.318,5,1,1,"There is such a demand for more convenient and inexpensive ways to watch TV shows here in the US, so the wide growth in available devices to watch shows on was just a question of when it would happen, not if it would.",4,"I tend to watch no more than 4-5 hours of TV a week. I used to watch a lot more TV, but I no longer have that amount of time available.",1,39.318,138.173,139.609,5,9,1,I think people use the internet for the same reasons I do. A wealth of information is available with the click of a button. I can only seeing the population of internet users growing in the coming years and I see it as a good thing.,2,I use email and internet search engines frequently. I have gained a lot of knowledge using search engines and find that I can be really resourceful just finding facts by myself instead of having to depend on (and pay) experts to find me the information I need.,1,55.295,131.918,133.579,6,5,1,Many businesses have purchased these machines for their workers. It is much easier now for a occasional coffee drinker to get a beverage that they like because they can customize it with different flavors of coffee.,4,"I do not own a single cup brewing machine, but I drink 3-4 cups of coffee a day.",1,2,3,45000,Non religious,Caucasian,30,5,1,1,"Just the last few questions of the IQ test being a bit confusing - other than that, the survey itself was pretty straight forward.",There were so many mini surveys in this one big survey that I'm not entirely sure what you guys were studying here!,Yes. Perhaps studying attention levels?,No. Thank you for allowing me to participate!,1,1 +0,9,6.133333333,5,3.666666667,4.333333333,4.25,1,1,1,8,5,7,3,4,3,2,7,8,6,8,7,7,9,8,3,7,1,8,1,3,1,1,7,9.26,9.26,11.282,1,1,21.286,21.286,27.983,1,3,18.208,18.208,21.93,1,6,15.085,15.085,16.119,1,2,16.519,16.519,28.249,1,5,44.55,44.55,45.24,1,1,25.646,25.646,26.527,1,5,30.938,30.938,31.596,1,7,51.528,51.528,52.689,1,2,23.19,23.19,24.283,1,6,23.811,23.811,25.132,1,2,31.099,31.099,34.117,1,1,1,4,4,4,7,6,6,3,6,4,5,4,6,6,3,3,7,6,6,7,8,1,18.694,49.494,51.276,7,5,2,"Demand, and fuel costs.",3,We usually order once a week.,1,31.996,74.915,78.13,7,1,1,Insane prices to watch mongoloids running into each other.,3,NEVER. Not. Ever.,1,12.989,40.917,56.643,6,7,1,"Weather, laziness and the need to transport more than just themselves.",4,I have no public transportation where I am. I must drive.,1,15.424,70.255,70.974,5,1,2,"People are sheep, and TV is a very easy way to control them.",4,"Very rarely. There's little onthat interests me, and when I do find something it gets canceled.",1,16.544,43.254,58.94,6,9,1,The internet is the most valuable tool man has ever created.,1,"Yahoo for email, google for searching.",1,46.699,80.409,81.616,7,3,1,It's less wasteful,3,Only on long drives,1,1,3,0,Atheist,White,41,3,1,1,The information contained in the articles was extraneous for the questions asked. You could have done it with a simple line.,Laziness?,No comment.,No.,1,1 +0,2,4.866666667,5.333333333,5.333333333,5.333333333,5.15,1,1,1,6,6,7,3,2,4,6,7,5,6,4,2,3,6,6,3,7,1,8,1,3,1,1,1,15.227,15.227,17.963,1,5,16.766,16.766,17.789,1,5,53.415,53.415,54.471,1,6,32.519,45.437,47.195,2,7,27.652,27.652,28.624,1,7,23.923,23.923,38.322,1,5,26.665,28.243,31.776,2,5,29.819,29.819,31.209,1,3,30.933,30.933,32.119,1,3,41.643,53.965,55.043,3,7,15.722,15.722,16.559,1,6,12.876,12.876,13.916,1,1,1,3,7,7,6,4,7,3,4,4,4,6,6,7,5,4,4,4,4,3,7,1,58.524,171.334,173.417,6,8,1,"Supply and demand - the article is right about the popularity of pizza. Guess what I had for dinner last night - yep, pizza!",1,Probably 3 x a month but I rarely buy it as I'd rather make it at home.,1,48.082,98.078,107.593,6,5,1,Two reasons - 1) cost of the tickets and 2) it's so much more fun being able to relax at home with your feet up snacking on chips and a beer.,1,Don't watch it.,1,21.45,189.682,190.951,6,5,1,"Sounds nice, romantic even the the reality is riding a bike in traffic can be darn dangerous. Plus you end up sweaty and tired by the time you reach work, and if you have errands to run or cart work back and forth home it's just not realistic. Again, I don't fit in with the norm but I live on an island surrounded by hills so you won't see many people here riding bikes except in the central valley. I do however use the bus on a regular basis.",1,I don't own a car - I take the bus.,1,29.721,160.738,161.623,10,2,1,"I suppose to some degree it's a matter of status, plus people seem to be consumed by the need to watch other people's lives.",3,I have a TV but haven't turned it on in over 2 years - I don't have an iPone or other similar device and honestly don't miss it.,1,33.51,238.91,239.961,7,6,1,"Well for one thing more people than ever are trying to find the magic bullet that will enable them to earn a few extra dollars online - no where near as easy as some would lead you to believe but that doesn't stop people from trying. The other thing is the social aspect of it - if the amount of time some people spend on Facebook is any indication there are a lot of people who really need to get a ""life"" offline.",1,"I work online so I'm probably not the average user. I am a professional blogger, author and I sell online courses so I spend a lot of time online and I do the bulk of my research there so I use search engines and databases a lot. Email is something I tightly manage to avoide wasting time, so compared to other entrepreneurs I probably spend less time on email.",1,23.637,118.508,119.512,6,6,1,"It's quick, easy and coffee just doesn't taste as good when it's been sitting for awhile.",1,"Nobody's ever going to get me to give up my coffee - and yes, I use a single cup brewer because I like the ""fresh"" taste of just brewed coffee.",1,2,3,50000,none,white,67,3,1,1,no,attention and perception,no idea,no,1,1 +0,6,6.466666667,7.666666667,5.333333333,6.5,5.2,1,1,1,8,9,6,3,5,3,6,7,9,5,7,5,8,9,7,3,7,1,8,1,3,1,1,7,7.492,7.492,10.778,1,1,11.417,13.345,14.242,3,5,19.891,19.891,21.115,1,2,17.59,17.59,19.972,1,2,13.128,13.128,18.067,1,4,19.191,20.791,22.383,2,1,21.995,21.995,30.06,1,5,29.507,29.507,31.075,1,7,19.323,19.323,20.15,1,2,25.848,25.848,26.994,1,7,15.15,15.15,17.508,1,1,11.083,11.083,12.337,1,1,1,5,4,4,3,3,7,4,6,3,6,7,5,3,4,5,4,4,5,4,4,1,41.897,119.346,119.734,9,6,1,"Pizza prices have been dropping, or at least it seems so to me. I think this is due to the shotty economic conditions right now and everyone is having to lower prices to get consumers.",3,"I love pizza and will eat it any chance I get. I probably eat it 2-3 times a month, indulging in 3-4 pieces at a time.",1,64.163,103.888,133.618,7,5,1,"It costs a lot of money to go to football games, and unless you live near a stadium, you have to travel a good distance.",2,"I enjoy watching certain college footabll teams on TV, but not much NFL",1,33.091,89.471,90.155,8,7,1,"Long distances from home to work and if you work in a proffesinal job, showing up sweating in your suit probably won't be ok.",3,I drive a motorcycle to school/work and a truck at times when it rains or is cold.,1,36.743,122.572,125.564,9,8,1,The growing number is due to the expansion of electronic devices that can stream shows now and due to the excess number of shows that continually come out.,2,"I watch an excess amount of TV on a weekly basis, probably more than 23 hours. I have a DVR that comes in handy.",1,25.389,54.724,71.475,5,9,1,"You can find anything and everything on the internet, and so people default to it for information and communication.",1,I check my email multiple times and day and am on the internet almost all of the time.,1,35.733,119.022,119.801,8,4,1,You can easily make a single cup without wasting coffee for a whole pot when you only want a cup. Also individuals can each have their own kind of coffee.,2,I drink coffee 2-3 times a week and generally buy it when I am on school campus from Einstein's Bagels,1,2,4,68000,mormon,caucasian,25,3,1,1,no,understanding of individual's perceptions about why things are the way they are and if people agree with things given a reasonable explanation.,"yes, but I am unsure what it is.",no,1,1 +1,3,6,7.666666667,6,6.833333333,5.65,1,1,1,6,6,6,5,7,5,3,4,4,7,7,8,6,7,9,3,4,1,8,1,3,1,1,1,3.904,5.062,5.949,3,5,14.135,19.593,20.256,2,1,16.253,16.253,17.514,1,6,9.719,9.719,10.758,1,3,18.552,18.552,19.375,1,6,12.199,12.199,12.974,1,4,9.044,9.044,11.087,1,5,3.898,3.898,4.638,1,1,16.352,17.564,17.997,2,7,17.498,17.498,19.757,1,6,3.491,3.491,5.84,1,3,3.05,3.05,4.201,1,1,1,4,5,6,7,4,6,5,4,4,4,5,7,4,5,7,3,4,7,8,8,1,2.76,22.494,23.426,5,5,1,its a lot of money,1,maybe once a month,1,14.457,52.47,53.247,5,9,1,it is very expensive,1,i don't watch football that much,1,2.44,44.282,45.048,7,4,1,they are lazy people and they are always going to be,2,i don't have a license,1,4.91,16.03,21.222,5,9,1,the children,2,watch tv all the time,1,3.256,29.076,29.922,4,9,1,More people are learning technology,1,i am on every day,1,18.649,31.91,37.444,5,5,1,its quick and easy,1,once a day,1,2,3,20000,christian,white,19,6,1,1,no,not sure,yes but not sure,no,1,0 +0,8,6.666666667,6.333333333,5.666666667,6,5.6,1,1,1,8,7,8,4,7,7,3,8,8,8,6,6,7,7,6,3,7,1,8,1,3,1,1,7,8.132,8.132,15.539,1,1,20.421,20.421,21.74,1,3,31.545,31.545,32.766,1,8,9.762,33.346,35.25,7,2,6.068,26.868,28.302,2,3,3.021,35.896,43.542,5,1,2.564,19.895,24.941,2,5,13.127,28.682,29.803,2,3,20.358,20.358,23.429,1,2,21.095,21.095,24.951,1,7,9.548,27.341,28.911,4,5,15.58,28.686,30.179,3,1,1,3,2,8,3,2,8,5,6,8,8,5,6,8,8,7,8,3,7,7,8,1,31.639,142.851,143.64,9,7,1,"Pizza restaurants are competitive, and each one tries to find something that will differentiate their brand from another. Pricing is one aspect that allows for a more thriving business.",4,I eat pizza on average about once every two weeks. I typically eat either a carry-out pizza or a baked frozen pizza at home.,1,64.343,233.124,235.454,10,5,1,"1. Tickets to televised sporting events are often very expensive. / 2. Football is much more easily viewed on television rather than in person. / 3. Going to a live sporting event, finding parking, navigating through crowds and then exiting are all time-consuming, frustrating processes.",4,"I will rarely watch football on network TV if there is a game that I am interested in watching. I have been to a few live college games over the years, including one in 2013.",1,116.129,614.382,617.436,13,5,1,1. Discomfort of biking in all types of weather / 2. Necessity of changing clothes / 3. Desire to get to and from work faster /,4,"I drive about 12 miles to and from work daily, Monday through Friday. I will also drive to various stores and run errands on weekends and some weeknights.",1,204.448,480.856,481.769,10,5,1,1. The desire for instant availability to entertainment / 2. The lower cost of sophisticated electronic devices / 3. The rise in technology that allows for mobile data transfer.,4,"I rarely watch live television. I will watch the local network affiliate during bad weather to check for storm related news, and I will also watch some sporting events. However, I do not have a cable TV subscription. I do watch Netflix, though much of my watching is passive meaning that it usually someone else in our family that is watching and I am merely in the room. I would estimate that I actively watch about 5 hours per week of programming on Netflix.",1,28.066,290.39,291.675,7,7,1,"The younger generation of persons grows up in a nation of internet users, and as the older generations pass on, this accounts for the increase of users.",2,"I use email, both personal and work accounts, quite extensively. I also utilize internet searches for work and personal business on a daily basis. I cannot think of the last day that I did NOT use either email and/or search online.",1,169.959,256.857,258.08,5,7,1,1. Convenience / 2. Less waste / 3. More choices of flavors,4,I do not drink coffee or any type of coffee-flavored drinks.,1,1,5,41000,Christian-Protestant,Caucasian,42,8,1,1,Some of the questions were strangely worded or nonsensical.,I am not certain.,"Perhaps, but I do not know.",No,1,1 +1,1,8.866666667,9,6.333333333,7.666666667,5.4,1,1,1,9,9,9,9,9,9,9,9,8,9,8,9,9,9,9,9,9,1,6,1,3,1,1,8,8.539,8.539,9.74,1,2,30.907,30.907,33.755,1,1,9.825,9.825,12.821,1, ,0,0,60.016,0, ,0,0,60.034,0,5,11.182,11.182,12.248,1,8,18.589,18.589,21.584,1,3,17.876,17.876,19.424,1,5,26.155,26.155,27.296,1,2,5.838,5.838,7.3,1,1,23.764,23.764,24.459,1,6,18.807,18.807,20.381,1,1,1,9,9,8,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,1,1.792,281.695,282.538,7,9,1,The demand for it.,1,I LOVVEEE PIIZZAAA!! I eat it about 2 times a week. Never miss a week!,1,36.465,411.629,412.722,6,9,1,Football tickets are price because there is not very many games per season.,1,"I only watch my local team play, and I don't watch each game they play.",1,13,126.858,157.289,6,9,1,"People think it is ""un-cool"" to ride their bike to work.",1,I do not drive.,1,21.345,114.315,115.111,9,9,1,A lot of people want to be able to look up a certain video before they wont remember to look it up.,1,I only watch TV when the Astros game is on.,1,7.733,101.953,103.055,6,9,1,There are more options and things for you to do everyday on the internet.,1,I check my e-mail about once a day and search on the internet all day everyday.,1,17.899,86.4,117.338,6,1,1,The simple fact that people do not have to make 4 cups at a time.,1,I do not drink coffee at all.,1,1,2,20000,None,Caucasion,24,9,1,1,No.,I'm not sure.,Maybe but not sure.,"No, Thank You!!",1,0 +0,7,5.866666667,6.666666667,4,5.333333333,4,1,1,1,6,8,4,2,7,3,3,7,6,8,6,6,7,7,8,3,7,1,8,1,3,1,1,7,13.049,13.049,14.511,1,1,24.24,24.24,27.562,1,3,30.644,30.644,32.394,1,6,30.347,30.347,31.437,1,2,28.842,28.842,30.904,1,2,48.548,48.548,50.111,1,5,35.666,35.666,36.553,1,2,41.13,41.13,42.612,1,1,34.004,34.004,35.166,1,2,24.596,24.596,26.158,1,8,33.64,33.64,36.219,1,1,37.44,37.44,39.182,1,1,1,4,3,4,4,6,7,4,4,4,8,4,2,6,2,6,7,5,3,2,7,1,27.885,78.132,99.312,5,6,1,"The price of ingredients, and the price of labor couple to corporate greed.",4,I haven't had any in a few years now.,1,37.998,107.673,130.023,5,5,1,The cost of the tickets and beer and food.,2,I don't have a TV and don't watch on a computer.,1,41.172,138.675,208.58,6,1,2,"It is hard work, and you get sweaty when you do that. Also there is rain and ice and sleet etc. Bad weather in other words. Long distances are hard to do in work clothes.",1,"I try to limit the amount that I drive, and participate in car pooling when I can. / I try to combine trips to the gas station and grocery store to limit car use.",1,29.647,142.234,143.055,6,5,1,There are fewer and fewr people working who need to be pacified some how. Videos are a good way to hypnotise people so that they don't create disorder while they are out of work.,4,I don't have a TV.,1,119.391,217.967,220.386,5,9,1,The usefulness of the internet and how many activities are tied to it like online banking and shopping.,1,I check my email several times a day. / I do several searches a day using different search engines.,1,53.239,178.866,179.759,5,6,1,"You have less waste. People are more cost conscious now. Some people only drink one cup of coffee at a time, and don't want to be wasteful.",1,I never developed the habit.,1,1,4,12000,agnostic,white,62,3,1,1,Just this question :-),I am not sure,I believe that each phase was designed to have an effect on other phases and that data was collected to measure what those effects were in order to test some hypotheisis. / Some deception may have been involved.,thank you,1,1 +0,5,5.2,6,3.333333333,4.666666667,6.3,1,1,1,5,8,4,3,6,5,7,5,3,5,6,4,5,6,6,3,7,1,2,1,3,1,1,7,11.543,11.543,12.73,1,1,30.391,30.391,32.741,1,5,30.977,30.977,33.108,1,8,41.934,41.934,45.689,1,2,29.053,29.053,30.479,1,7,41.986,41.986,44.468,1,1,55.007,55.007,58.285,1,5,54.926,54.926,56.605,1,5,47.354,47.354,48.24,1,3,24.081,24.081,27.761,1,8,56.61,59.453,60.011,2,1,29.218,29.218,31.512,1,1,1,3,6,7,6,4,7,8,3,4,5,7,7,5,8,8,3,5,5,8,5,1,92.815,250.551,255.162,8,4,1,The demand for pizzas.,1,2 pizzas a week.,1,52.571,280.745,281.341,6,5,2,"It might cost too much to go to a football game so the 6% of football viewers that watch it live might have lots of money to spend on this type of outing. Also, not every city has football games so it might be too far for someone to travel to these games and watch them live. So maybe the 6% of football viewers watching the games might live close to the football stadium.",1,I do not watch football.,1,59.426,255.515,256.91,11,1,1,"They are too far away from work. They might be close but are lazy. It might be weird or uncool to coworkers and boss to ride your bike to work. OR, It is quicker to drive rather than ride a bike to work.",1,I drive my car a couple times per week and the other times I ride my bike.,1,66.401,168.088,298.849,7,5,1,"Companies can make more profits by producing more devices such as iPad, etc. so they can reach the population interested in watching shows not only from home, but while traveling, during their lunch breaks, appointments, and more.",1,Watch a couple of TV shows a couple times a week for a couple of hours.,1,93.776,300.351,301.128,8,9,1,"It is one of the better ways to communicate with others (quick). You can also look up things quickly, pay bills, and enjoy leisure time. Also, computers are not as expensive as they were many years ago so everyone can possible afford a computer so that they can use the internet and email.",1,Email and Internet search daily over the course of the day.,1,159.597,299.148,324.033,10,4,1,"You do not waste the coffee. Also, It is a quick and convenient way to drink coffee in the morning before work.",1,I don't drink coffee.,1,2,4,80000,Christian,Caucasian,40,7,1,1,Maybe the find the correct graphic.,To see what some our views were on healthy lifestyles.,Possibly but not sure what.,Maybe to find out what the study was about?,1,1 +0,4,8.933333333,7,5.666666667,6.333333333,5.25,1,1,1,9,9,9,9,9,8,9,9,9,9,9,9,9,9,9,3,7,1,8,1,3,1,1,7,6.521,7.511,8.204,2,2,2.306,2.306,3.967,1,8,15.177,15.177,17.676,1,6,5.294,5.294,6.488,1,4,8.196,8.196,10.864,1,4,17.124,17.124,18.494,1,1,0,0,0.885,0,5,6.457,6.457,7.878,1,6,13.713,13.713,14.758,1,6,28.16,28.16,29.002,1,2,15.938,15.938,16.957,1,3, , , , ,1,1,3,6,8,6,6,4,6,5,4,6,6,6,9,4,6,5,3,6,9,7,1,5.704,20.533,26.908,5,6,1,competition,4,2 pizzas permonth,1,32.767,56.29,67.736,5,7,1,convenience and cost,4,I do not watch football,1,20.572,47.37,48.025,5,5,1,inclement weather and it is a hassle,4,I drive alone to work,1,67.462,89.872,91.055,6,6,1,Profit,4,7 hr. per week,1,26.549,48.144,49.396,5,9,1,more people own comuters,4,use them daily,1,18.625,42.8,43.634,5,5,1,use less coffee,4,2-3 cups per day,1,2,4,20000,protestant,white,59,1,1,1,the repetition was a little confusing,no clue,of course but I don't now what it could have been,no,1,1 +1,5,8.333333333,4.333333333,3.666666667,4,5.9,1,1,1,8,7,9,8,8,8,9,9,8,9,9,7,9,8,9,8,8,1,8,1,3,1,1,7,14.734,14.734,16.576,1,1,14.359,14.359,15.371,1,3,21.115,21.115,22.331,1,6,10.6,10.6,11.36,1,2,10.591,10.591,12.035,1,3,11.213,11.213,12.032,1,7,14.666,14.666,15.843,1,4,1.485,1.485,5.506,1,2,0.051,3.768,4.576,2,3,0,0,2.056,0,1,0.216,0.216,4.599,1,3,5.796,5.796,8.501,1,1,1,7,8,9,9,6,6,8,4,7,8,4,9,3,7,7,7,5,6,9,3,1,4.883,4.883,21.046,1,1,1,way to high,1,once a week,1,7.476,33.272,42.482,6,1,1,Because the game is better,2,One game a week maybe,1,12.794,16.175,48.384,3,9,1,because of the distance and large hills,1,I drive the speed limit and don't ride butts,1,6.641,21.57,31.873,7,5,1,new technology,2,too much tv,1,6.922,13.789,30.152,7,7,1,so no physical contact is made,2,Everyday,1,4.851,34.185,36.939,5,1,1,for a person who lives alone,1,I don't drink coffee,1,2,3,20000,Baptist,White,31,6,1,1,Some of the patterns,Peoples intelligence,no,no,1,0 +0,5,4.333333333,5.666666667,4.333333333,5,4.45,1,1,1,7,4,6,5,4,6,5,3,3,5,5,5,2,1,4,3,7,1,8,1,3,1,1,7,4.466,4.466,7.941,1,1,13.133,13.133,13.843,1,7,14.592,47.52,47.978,4,6,17.052,17.71,18.228,2,2,14.575,14.575,21.905,1,7,15.125,17.429,25.543,2,1,13.252,13.252,13.926,1,6,9.458,9.458,11.459,1,3,20.274,25.29,26.058,3,3,9.119,9.119,10.134,1,8,12.103,15.095,16.501,4,6,14.808,14.808,19.119,1,1,1,5,3,3,7,4,7,2,6,2,6,6,5,8,4,8,6,3,5,5,6,1,28.892,49.756,90.65,5,5,1,"Pizza is in high demand, which could drive prices up. However, if prices go too high, then not everyone can afford it. Pizza is considered an affordable food in the US.",1,I probably eat pizza once a week. I definitely get a craving for it.,1,33.316,83.945,84.741,9,5,1,Live games are expensive and not everyone is close enough (distance-wise) to a professional team or arena.,2,"I watch big games or interesting games. I don't really follow any particular team, so I'm not a consistent watcher.",1,17.071,37.878,65.629,7,1,2,"It's somewhat dangerous in places where there are no specific bike lanes. Also, distance could be a factor since lots of Americans commute pretty far to work.",1,I live in a major city and cannot afford to park a car. I have no driving habits.,1,28.966,45.149,65.504,5,4,1,Advances in technology.,1,"I don't watch a lot of TV (no cable), but I watch shows and movies on my tablet or Netflix.",1,19.435,52.189,69.866,6,8,1,Access to interet has increased dramatically in the past 5-10 years. Now virtually everyone has internet access.,1,"I use email and internet search every single day, probably about 2-3 hours a day.",1,13.009,63.993,64.438,7,7,1,"It's perfect for people who don't live in big households. Sometimes you just want a cup, not a whole pot. It's very convenient.",1,I drink coffee every single morning. Just one cup that I brew at home in my Keurig.,1,2,4,26000,None,White,24,5,1,1,No. It was long though.,I have no idea. Consumer habits? I don't understand the weird pattern test.,"I'm sure there is, but I don't know what it is. How spatial reasoning relates to consumer habits?",Nope.,1,1 +0,8,4.866666667,7,4.666666667,5.833333333,5.4,1,1,1,4,6,2,4,3,3,8,7,7,8,6,6,3,3,3,3,7,1,8,1,3,1,1,7,3.924,3.924,6.479,1,1,16.015,38.239,44.523,2,3,16.759,16.759,18.034,1,6,15.201,39.552,40.547,2,2,34.41,34.41,48.309,1,6,15.608,15.608,31.629,1,1,32.164,32.164,33.479,1,2,17.127,18.343,60.003,2,3,35.419,35.419,60.004,1,2,18.793,18.793,24.219,1,6,28.037,55.325,56.424,5,7,43.172,54.722,60.006,2,1,1,4,7,6,7,6,7,4,3,6,7,6,7,6,6,6,4,7,4,9,6,1,56.375,156.759,163.823,6,7,2,"The prices of pizza have stayed relatively the same, when using coupons. Any increase would have to account for general food increases because of the economy.",4,We usually order or make a pizza every Friday night. I usually consume 2 pieces and maybe 1 piece of leftovers on Saturday.,1,36.851,118.891,161.009,5,5,1,"The price of tickets is very high. And with all the big screen TV's, it's cheaper and more fun to have their buddies over and watch on the big screen.",4,"During football season we watch our local college team, and on Sundays, my husband has the TV on football. I watch intermitingly - mainly to check how my players on my fantasy football team are doing but I don't have a favorite team.",1,23.969,149.086,149.99,5,5,1,People have more things to do after work - their kids are in more activities. So Americans want to get home faster and don't want to wait for someone else to carpool. Biking takes longer.,4,"I drive back and forth to work by myself most days. My husband and daughter do not work in the same area as me, and a couple of my co-workers, who live in the same are, have different work hours.",1,48.811,136.706,277.06,5,6,1,"Technology is continuing to advance and people want the newest, ""best"" thing - it's becoming a status symbol to own the newest smart phone or iPad or tablet. Kids growing up on technology use it more and more, so an increasing # find it being part of their normal everyday activities.",4,"I watch about 8 hours a week. I have my favorite shows that I've watched for several years or more and don't tune in to many new shows. I think less people spend time outdoors. And it used to be people visited their family. But with Skype and other forms of media, they can just sit at home and ""talk"" to their family members over the internet instead of in person - that part is good and bad. If it's long distance it's a good way to stay connected to kids, parents, grandkids, etc. But if they live close, it's still better to stay in touch in person.",1,53.412,99.508,203.204,5,8,1,"The older generation that did not have internet growing up or as young adults are dying, and the population has grown up with or been using the internet at an early age. More % of people have lived with the internet most of their lives now.",2,"I use e-mail at work and at home. I use the internet to search for product reviews, best prices, etc, especially when buying appliances or higher dollar items.",1,32.478,102.941,122.043,6,4,1,"The great amoutn of versatility in flavors, Plus, it doesn't waste coffee, and multiple drinkers in one household can each have their favorite flavor without brewing a large pot.",4,"I don['t drink coffee, but I have used the single cup system for hot and cold tea.",1,2,4,61000,Nazarene,Caucasian,55,8,1,1,no,Maybe to find out how logical a person thinks.,"I'm sure there was. The questions at the beginning I'm sure were related to the puzzles, just not sure what.",no,1,1 +0,2,5.8,7,5.333333333,6.166666667,5.1,1,1,1,6,5,6,4,7,5,6,6,5,7,4,6,6,8,6,3,7,1,8,1,3,1,1,1,9.074,9.074,10.343,1,6,13.347,13.347,14.13,1,7,22.64,22.64,23.823,1,8,14.27,14.27,16.844,1,2,7.927,7.927,9.084,1,7,11.035,11.035,16.177,1,5,21.551,21.551,22.374,1,5,13.329,13.329,14.953,1,8,13.319,13.319,15.596,1,4,17.922,17.922,19.543,1,5,16.318,16.318,18.331,1,1,27.357,27.357,28.612,1,1,1,2,2,5,3,6,8,4,5,7,5,4,7,7,5,4,6,2,3,8,9,1,17.887,61.516,97.877,5,7,2,"I think they have gone down especially since some are selling 5 dollar pizzas, the other companies have had to lower their prices some.",1,At least once a week.,1,119.28,179.854,212.577,5,6,1,too expensive to go to games. Annoying to fight the crowds and traffic.,3,only big games so only a couple times a year.,1,31.71,128.495,129.989,6,5,1,"more convenient to drive everyday, than to anticipate the weather, waiting for bus, riding bike etc.",2,I live in the country. It is 15 minutes to work by car. No buses here.,1,46.498,128.692,129.833,5,9,1,technology and lower prices so most people can afford them.,2,watch in the evenings before going to bed. Usually about 2-3 hours a night.,1,46.949,134.687,136.075,5,5,1,Computers in almost every home now. People have a lot of free time. Wish I did.,1,use at work and some for personal use. Not as much as I used to. No time.,1,62.083,125.641,176.56,5,5,1,convenience of those single cup things. But the coffee tastes like instant coffee to me. I would not own one.,1,"I drink a lot of coffee. The one cup thing would not work for me, but would work for someone just wanting a cup before they left for work.",1,2,2,38000,christian,white,55,6,1,1,the puzzles,trends,I don't know,no,1,1 +1,2,6.666666667,5,3.666666667,4.333333333,4.35,1,1,1,9,5,7,1,9,7,6,8,6,8,6,7,6,8,7,3,7,1,8,1,3,1,1,7,10.817,10.817,12.795,1,5,40.456,40.456,41.841,1,1,35.162,35.162,36.197,1,1,15.758,15.758,17.12,1,5,18.565,18.565,21.855,1,7,38.513,38.513,44.259,1,1,47.962,47.962,55.106,1,7,12.698,12.698,13.916,1,1,11.71,11.71,13.744,1,1,32.851,43.594,44.332,3,2,16.962,20.881,23.277,2,1,24.412,24.412,38.494,1,1,1,5,7,5,5,7,7,5,5,1,6,7,5,8,5,4,7,9,3,7,7,1,8.109,50.829,85.416,6,5,1,"The price of the products to make the pizza, and the price of the toppings, plus the price to bake it.",1,"I eat pizza once a week, for dinner.",1,34.539,79.587,80.51,5,4,1,too expensive to go in person with ticket prices.,3,"none, don't like it",1,49.277,104.701,105.4,5,1,1,the distance and being lazy,2,"as little as possible, I take public transportation",1,13.634,69.178,69.756,6,3,1,demand for people on the go.,2,I watch around 6 to 8 hours a week.,1,7.021,43.757,61.4,6,7,1,Instant communication and convenience.,1,"I use both each day, for work and personal.",1,9.374,130.256,130.833,18,6,1,"better taste and flavor, also less waiste, versus a larger brewer.",1,"2 cups in the morning, each day. black",1,1,4,59000,none,white,44,1,1,1,the logic shapes did not really make any sense.,"views on people, by the people",no,"not really, except the logic shapes did not have a specific patters or rhythm.",1,0 +0,6,4.933333333,7,6,6.5,5.9,1,1,1,5,6,4,5,5,4,6,5,6,3,5,6,4,5,5,3,7,1,8,1,3,1,1,7,22.298,22.298,25.223,1,1,29.182,29.182,31.123,1,3,44.022,44.022,45.764,1,6,35.134,35.134,36.812,1,2,57.553,57.553,58.958,1,7,18.107,18.107,19.04,1,1,34.631,34.631,36.196,1,6,58.09,58.09,58.992,1,7,30.171,30.171,32.608,1,4,22.061,22.061,23.426,1,7,34.235,34.235,36.096,1,3,23.038,23.038,25.476,1,1,1,4,5,5,7,3,7,4,2,6,5,7,7,2,5,8,6,2,7,4,6,1,36.011,101.831,102.585,5,7,1,cost of overhead for the pizza business.,2,While we love pizza we do try to limit it in our diet somewhat. We do order delivery from time to time as a treat.,1,10.13,50.445,52.039,5,6,2,"price of tickets, travel, dealing with large groups of people.",2,don't watch it at all,1,24.085,101.369,103.092,5,4,1,I live in Portland where it's popular to bike to work. I think biking to work is not always an easy option depending on distance and dress code for your work place environment.,3,"I work from home, so I don't drive to work.",1,6.519,122.622,124.224,5,5,2,Mobile devices in general have exploded in growth and I expect this to increase.,2,We watch a couple hours in the evenings. We are very specific about what programs we like to watch so usually watch shows on the DVR.,1,38.314,72.195,133.386,5,9,1,"Once the internet is in the home/work environment, the ease of finding information, entertainment and communication most can't resist!",1,I use it for work and fun on a daily basis.,1,5.995,54.071,54.817,5,8,2,"not really sure, I personally think it's a fad.",2,everyday coffee drinker,1,2,3,30000,none,white,51,3,1,1,"no, it was fun",I have no speculation on what you were studying,maybe but no idea what,no thanks,1,1 +0,5,5.266666667,6.333333333,4,5.166666667,3.55,1,1,1,7,7,6,5,3,4,7,7,7,6,6,5,3,3,3,3,7,1,8,1,3,1,1,7,7.885,7.885,11.156,1,1,30.881,32.144,37.384,2,8,30.588,30.588,31.946,1,8,26.005,26.005,26.945,1,2,31.276,31.276,32.464,1,6,26.522,26.522,27.54,1,1,30.028,30.028,31.254,1,7,26.774,26.774,28.154,1,7,16.812,16.812,18.131,1,2,42.305,42.305,43.647,1,7,20.971,20.971,22.182,1,7,22.702,22.702,24.262,1,1,1,4,1,5,9,6,2,3,4,1,8,4,6,7,3,3,6,6,2,3,2,1,35.885,92.965,94.064,6,5,2, High demand of delivery of pizza.,2, Eat pizza maybe 1 or 2 days a week.,1,78.884,143.719,208.09,5,3,1,"Because so many people are out of work these days that not many can afford to even go to a game and also, some think that you get a better view from the tv than a cheap seat in the stadium per say.",4,I only really watch it when teams I like are playing and are shown. I also watch the playoffs.,1,54.62,202.872,204.707,6,4,1,"Its unsafe to ride to work when the bikers have the ""right"" to be on the road with the traffic. If they made bile trails along side all the states highways, then bike riding would be a great idea to get to work. If the job was not that far away that is.",3," Where I live, we don't have transportation other than taxi service, so we use the car to commute.",1,71.888,156.354,203.356,5,8,1,The study of more modern technology that lets people watch videos from the devices rather than spending money on a movie.,4," I watch quite a bit of tv, especially series shows that are on. I also watch a few movies, but not as much as tv programs themselves.",1,57.582,106.414,107.575,5,6,1, The ease of finding any kind of information in a short amount of time.,1,I use the internet and email daily.,1,54.036,123.486,126.605,6,5,2,It takes less time to make a single cup that it does a pot of coffee.,1,I drink 2 - 3 cups of coffee a day.,1,2,2,0,rather not say,white,59,5,1,1, The shapes part of the survey., I really don't know., Have no idea.,no,1,1 +0,7,5.533333333,8.333333333,5.333333333,6.833333333,4.65,1,1,1,6,9,4,2,5,5,6,3,8,7,4,8,5,7,4,3,7,1,8,1,3,1,1,7,10.386,10.386,11.527,1,1,18.733,19.541,21.438,2,3,20.134,20.134,22.766,1,6,35.182,35.182,37.812,1,2,17.546,17.546,19.273,1,6,46.375,46.375,47.185,1,1,19.682,21.368,22.534,2,5,19.867,19.867,20.772,1,3,7.532,7.532,8.588,1,3,8.531,8.531,9.674,1,2,31.775,31.775,32.806,1,1,17.44,17.44,18.611,1,1,1,4,3,5,4,7,7,7,8,1,7,5,5,9,4,8,5,5,5,9,7,1,11.921,27.177,27.828,5,9,2,Price of ingredients.,4,I love pizza!,1,28.724,44.601,61.008,5,7,1,Too expensive to go to a game. Don't live near a football stadium.,1,I do not watch football.,1,50.534,134.66,135.573,7,4,1,"People don't want to be sweaty when they get to work from bike riding. It can be dangerous, especially during the morning and evenin commute. It may take longer to bike to work than to drive. Many Americans live in rural or suburban areas where it is not convenient to bike to work. Some people may work a substantial distance from where they live.",1,I drive to work by myself.,1,28.762,58.707,66.936,5,7,1,technology advancements.,4,We rarely watch live TV but watch shows recorded on the dvr or on the internet.,1,24.872,38.837,43.175,5,9,1,More access to computers.,1,Use it daily for both work and personal use.,1,18.222,27.146,48.864,5,5,1,Convenience and affordable nature of devices like the Keurig.,4,I do not drink coffee.,1,2,5,80000,Catholic,White,31,5,1,1,No,How well you do on non-verbal tasks compared to how well you use/rate language,Probably but I'm not sure what,No,1,1 +0,3,7.4,9,5,7,4.6,1,1,1,9,9,8,5,9,9,8,9,9,3,9,1,5,9,9,3,7,1,8,1,3,1,1,7,10.405,14.486,19.574,2,1,8.191,13.704,17.346,2,1,13.141,31.102,59.705,3,2,27.473,27.473,34.184,1,5,22.793,22.793,23.741,1,7,20.846,20.846,22.064,1,5,18.802,18.802,24.635,1,5,11.597,11.597,12.459,1,6,17.906,17.906,19.426,1,4,11.835,11.835,13.444,1,2,12.679,12.679,14.598,1,7,22.404,22.404,24.148,1,1,1,3,6,9,9,9,7,1,5,1,9,5,1,8,9,9,5,3,9,5,9,1,43.833,67.886,89.359,6,9,1,because the demand for them,4,1 a month,1,39.957,115.086,116.907,8,9,1,Because of the game ticket prices and because of the high definition tv's available today.,4,watch as much as I can,1,44.33,87.262,87.942,5,1,1,to far to ride a bike,4,I am disabled but do drive,1,56.498,135.562,136.638,7,9,1,phones that cam stream live tv also computers and all hand held devices,4,3 to 4 hours a day,1,58.4,92.474,151.389,5,9,1,new smart phones plus a person almost has to use the internet today.,2,i use them both daily,1,64.194,105.806,123.342,5,5,1,able to control the flvor and richness better.,4,3 cups in the morning daily,1,1,2,23000,christain,white,51,4,1,1,no,perception?,I believe so but not sure what that is,no,1,1 +1,1,4.666666667,7,6.666666667,6.833333333,4.95,1,1,1,5,4,3,3,1,4,5,3,5,7,7,6,5,6,6,6,3,1,8,1,5,1,1,4,19.331,19.331,20.499,1,7,11.351,11.351,19.254,1,4,5.263,13.505,17.075,4,2,0.92,8.905,13.18,2,5,6.673,9.01,14.204,2,6,3.617,6.927,11.181,2,1,4.785,4.785,7.72,1,2,3.719,6.13,8.431,2,1,2.886,2.886,3.78,1,3,4.616,4.616,5.856,1,4,4.668,4.668,8.751,1,1,2.823,3.778,9.399,3,1,1,4,6,4,6,4,6,4,4,4,5,4,6,6,5,3,5,6,5,8,6,1,24.177,74.207,75.306,7,5,1,The demand for the is low.,3,I probably eat pizza once a month.,1,4.146,27.863,50.812,5,9,1,Expensive.,4,I am a avid NFL football watcher. Big Miami Dolphins fan.,1,9.269,30.732,31.621,6,6,1,It's a safety issue.,3,I drive to work each day.,1,5.7,41.283,41.629,7,7,1,More and more people are making videos.,3,I watch a decent amount of TV.,1,7.582,43.186,43.872,5,9,1,As technology gets better the amount of people who use it grows.,2,I use the internet and e-mail everyday,1,268.934,292.786,293.501,4,5,1,It's popular because of the amount of caffeine.,4,I don't drink coffee,1,1,4,50000,Catholic,White,25,7,1,1,No.,Common core issues with the American population.,I don't think so.,Great study!,1,0 +0,4,6.333333333,5.333333333,6.333333333,5.833333333,4.4,1,1,1,8,5,7,5,5,5,5,7,7,5,8,7,7,7,7,3,7,1,8,1,3,1,1,7,1.799,16.753,18.289,3,2,2.09,48.343,50.664,3,4,1.618,55.686,58.479,5,4,2.228,26.475,30.18,3,2,1.243,35.47,37.509,2,7,1.366,33.378,35.703,2,1,1.491,53.402,55.353,3,8,1.396,59.842,60.002,2,3,25.512,30.74,33.243,3,3,18.406,28.181,30.716,3,6,2.079,35.198,37.473,3,2,22.993,24.144,26.866,2,1,1,5,5,5,8,5,7,2,5,4,7,5,5,8,5,6,5,5,5,4,7,1,38.938,109.832,111.512,9,7,1,cost of ingredients and labor,2,minimal at best.,1,10.136,102.202,103.743,9,9,2,cost of tickets,2,Computer screen,1,39.352,80.954,82.639,9,3,1,distance to work,2,I rent cars,1,52.994,155.822,157.496,11,4,1,profit from both the programming and the devices,2,Dvr. not live,1,52.49,192.443,194.222,11,5,1,population growth,1,I use search engines every day. I e-mail occasionally. My primary form of communication is texting.,1,26.746,73.559,74.549,10,7,1,better coffee. little waste.,1,one cup per day,1,1,4,16800,non,white,62,6,1,1,no,not really sure,I am sure there is. I have no idea,no,1,1 +0,3,6.133333333,5.666666667,6,5.833333333,5.1,1,1,1,7,7,6,4,5,6,6,9,8,7,7,5,5,5,5,3,7,1,8,1,3,1,1,7,15.123,27.434,28.366,2,1,3.564,18.603,19.31,3,7,25.224,25.224,26.082,1,4,9.237,13.413,14.967,2,7,2.457,2.457,4.668,1,7,3.164,3.164,4.279,1,5,10.66,10.66,11.847,1,5,4.546,6.21,6.717,2,3,3.127,3.127,4.201,1, ,0,0,60.019,0, ,27.116,27.412,60.049,2, ,0,0,60.065,0,1,1,4,4,6,6,6,7,5,5,4,6,7,6,5,5,5,6,5,5,6,5,1,1.282,107.105,107.949,19,5,1,I think the consumption level of pizza accounts for the prices.,2,"I eat pizza, on average, about 2-3 times a month.",1,134.875,186.176,213.468,7,7,1,I believe the price of tickets to attend games is a major factor of why more people are not viewing the games live.,1,"I occasionally watch football on television with friends, usually in a bar or other social event.",1,24.719,170.589,202.448,18,6,1,"I think the accessibility of people using their own vehicles to drive to work and get their quickly accounts for less bike usage. Many people want to get to work feeling refreshed, not sweaty and tired.",1,"I drive everywhere I have to go, including to work. I like the ease and quickness of being able to get to one place and another, without having to rely on someone else's schedule or time.",1,15.062,112.107,112.943,10,5,1,"I think that the more and more access we have to sites such as Netflix, or movie rental boxes such as Red Box, make it easier for people to be able to catch up on, or view things at their own pace, on their own time. The more access we have, the more people will use it.",2,"I watch TV on a daily basis while at home on my down time, usually before going to bed after all of my tasks for the day are finished.",1,62.179,130.119,174.387,20,7,1,I think the ease and at-your-foot access to so much knowledge is a popular idea and strikes the interest of people who never used to use these things before.,1,"I use both my email and internet searches on a daily basis, usually multiple times a day.",1,88.222,175.683,176.183,5,5,1,I think the ease at which coffee is made makes the single cup brew more appealing to coffee drinkers. It also allows for multiple people to drink a variety of different types and flavored coffees.,1,"I personally only like drinking iced coffee from one particular place, aside from that I do not drink coffee regularly at home.",1,2,4,42000,Catholic,Caucasian,27,4,1,1,Not at all.,How people follow rules.,No.,Not at this time.,1,1 +0,2,5.4,5,5,5,4.6,1,1,1,7,7,5,5,5,5,5,7,5,5,5,5,5,5,5,3,7,1,8,1,3,1,1,7,6.32,6.32,8.93,1,4,25.305,25.305,29.347,1,1,34.569,34.569,42.028,1,8,33.012,38.083,42.653,2,2,18.926,18.926,31.624,1,7,34.145,34.145,35.923,1,5,27.064,27.064,28.194,1,7,42.541,44.365,45.599,2,7,33.836,33.836,35.582,1,7,34.248,34.248,38.626,1,8,15.363,15.363,17.269,1,6,44.949,44.949,46.439,1,1,1,6,1,5,4,7,6,7,6,3,5,5,6,8,4,8,5,6,4,6,6,1,10.872,84.567,92.155,6,5,1,The high demand for it,4,I eat pizza atleast once a week. /,1,263.48,278.639,286.845,5,5,2,Financial situations,4,I don't watch football at all.,1,5.534,88.228,88.811,6,5,2,"Most people are becoming more environmentally conscious and choosing to ride bikes or carpool. I also feel that if you live in a large city it would be easier to commute on a bike, instead of fighting traffic.",4,"I work from home, so only go to town for things I need or to run errands. I do not drive everyday.",1,2.838,70.334,80.746,6,5,1,"The growing available devices are readily available for all different prices, and it is more convenient to have a mobile device than just your tv. People don't spend all their time at home in front of the tv. Now we can take entertainment on the go.",4,I watch TV a little each day.,1,235.58,313.748,314.522,5,5,1,"The internet and computers have become more readily available to more people from different financial brackets, making it more easily attainable.",3,"I use email and internet everyday, several times a day, as I do technical support over the phone.",1,3.233,18.072,49.206,5,5,1,"It is fresher than making a whole pot and letting it sit for so long. With people on the go, the single cup brew is effective and not wasteful.",4,I drink coffee daily.,1,2,3,32000,none,white,28,5,1,1,no not at all,Entertainment and the way people use it,not sure,Interesting study! Thanks for allowing me to participate!,1,1 +0,4,6.666666667,6.333333333,3.333333333,4.833333333,5.3,1,1,1,9,9,9,5,1,5,7,5,9,9,8,9,5,5,5,3,7,1,8,1,3,1,1,8,13.347,13.347,15.372,1,1,41.178,41.178,45.947,1,7,33.201,33.201,57.776,1,6,26.293,26.293,43.634,1,2,52.762,52.762,58.64,1,6,23.056,23.056,24.858,1,5,34.819,34.819,36.173,1,5,39.978,39.978,40.877,1,7,30.967,30.967,32.541,1,1,38.501,38.501,40.687,1,8,30.016,48.574,49.876,3,1,27.494,27.494,32.26,1,1,1,5,7,9,9,5,8,5,6,3,8,7,7,9,9,7,9,2,5,9,7,1,51.789,112.385,113.299,7,1,1,Demand,4,Maybe once a month.,1,73.069,183.51,184.422,7,2,1,"High cost of tickets, convenience of watching at home.",4,"Not a fan, don't watch",1,127.733,284.943,286.527,5,1,1,"Laziness, health reasons, and/or weather conditions.",3,"Commute on rapid rail, unless I will be working late. Drive on weekends.",1,99.526,252.694,254.399,5,9,1,"Demand, technology.",4,"Varies, but averages about 6 hours a week.",1,167.491,279.733,323.294,5,9,1,"Technology. It's also more affordable, and there are many more providers of service.",2,"Use both daily, extensively",1,32.561,67.147,110.875,5,7,2,"Convenience, selection.",4,One cup per day,1,2,3,143000,Catholic,Black,69,5,1,1,"Yes, the patterns threw me!",Comprehension of shapes,N/A,No,1,1 +0,4,8.133333333,9,6.333333333,7.666666667,4.65,1,1,1,9,8,9,6,9,8,8,8,9,9,8,7,8,9,7,3,7,1,8,1,3,1,1,7,4.508,5.068,7.277,2,1,11.524,11.524,13.813,1,4,9.044,50.603,54.332,6,6,42.1,42.1,42.886,1,8,54.09,54.09,55.147,1,7,30.609,50.032,53.434,5,1,12.683,12.683,36.085,1,2,12.258,35.867,39.027,3,8,19.628,35.988,37.173,2,3,27.231,27.231,28.362,1,4,17.281,18.497,18.787,2,2,11.766,11.766,18.511,1,1,1,4,3,5,3,6,6,4,8,3,5,4,5,8,4,6,4,4,5,8,8,1,63.236,450.153,468.485,13,9,1,"I would say that's kind of subjective, pizza ranges in price form 3 dollars to 20 dollars a pie so its mainly the quality of the ingredients and the cost of those ingredients that determines the price.",2,I love it. I would eat it for every meal if I could.,1,23.108,90.771,91.302,7,5,2,Because it is really really expensive to go to games in person.,4,I do not watch football at all I think it is stupid.,1,7.874,71.992,72.563,6,5,1,Because most people work very far from home.,1,I only drive to the grocery store. I do not drive to work since I work at home.,1,61.104,260.742,261.29,12,9,1,Over time the cost of these devices is less and therefore more people can afford them.,2,I only watch movies. sometimes I might watch a few tv series type shows off the internet. generally only for about an hour or two. a day.,1,22.165,94.043,95.71,9,9,1,There are more people in general and there are more areas that are offering broad band than ever before.,1,I use it every day almost every day.,1,11.241,76.632,79.522,7,9,1,convenience and it is less wasteful.,1,I drink it when I have to be awake early in the morning and it is noce to drink it when I am reading because I have a tendancy to nod off when reading a book.,1,1,3,20000,Spiritual,White,30,5,1,1,No,Not sure. It was three different studies but they may be connected somehow although I don'y know why.,Well maybe it seems that this might not have been three different studies but one study with three parts.,No.,1,1 +0,3,6.066666667,6,3.666666667,4.833333333,5,1,1,1,9,7,7,5,2,1,6,7,7,8,9,4,3,8,8,3,7,1,8,1,3,1,1,7,7.643,7.643,8.665,1,4,16.451,23.154,25.091,3,3,17.823,17.823,19.628,1,8,11.164,11.929,13.291,2,4,19.186,19.186,20.518,1,1,16.359,17.407,17.993,2,5,15.959,19.235,23.168,2,6,21.115,22.243,23.687,2,3,16.232,16.232,20.906,1,6,17.222,17.222,18.293,1,2,14.384,14.384,15.879,1,5,14.962,15.728,16.657,2,1,1,4,1,4,3,6,6,8,8,4,6,4,6,4,3,6,7,3,6,9,6,1,17.947,59.242,60.344,5,6,2,The cost of food and gas prices are higher.,1,I share one pizza per week with my family.,1,12.971,47.18,47.899,5,4,2,No one likes how there are so many stops in game play and commercials.,1,I watch football only when my team is playing.,1,61.286,110.314,111.357,6,4,1,They work far away from home.,1,"I don't work, but use my vehicle for errands and outings about 4 times a week.",1,47.826,85.013,101.389,5,5,1,They are more portable and have more shows available. Plus you can stop/watch it at any time.,1,I watch about 15 hours of TV per week.,1,36.045,81.744,83.199,5,7,1,Smart phones and tablets make searching and emailing more convenient and faster.,1,I use the internet every day from my smart phone. I don't search as much as e-mail.,1,25.407,56.17,57.221,5,3,1,It is portable and quick. Plus there are many different varieties and flavors.,1,I dislike coffee.,1,2,3,90000,Agnostic,Caucasian,33,5,1,1,No,I honestly have no idea.,I have no idea.,No,1,1 +0,4,6.4,7.333333333,6.333333333,6.833333333,5.15,1,1,1,9,8,4,7,2,8,3,7,8,7,8,6,7,7,5,3,7,1,8,1,3,1,1,7,5.043,17.278,19.122,2,1,19.74,19.74,21.498,1,5,33.962,33.962,36.016,1,6,14.442,14.442,16.432,1,8,18.844,18.844,20.097,1,6,45.404,45.404,51.803,1,1,39.615,39.615,42.467,1,7,17.312,17.312,18.885,1,5,14.139,14.139,15.196,1,4,9.985,9.985,11.792,1,5,32.94,35.066,36.148,2, ,0,0,60.002,0,1,1,4,6,6,6,1,7,6,4,4,5,2,5,7,5,7,6,5,3,5,5,1,19.133,113.799,135.885,8,6,1,Pizza is cheap and quick which is why so many Americans go first for that.,3,I don't eat it often.,1,56.503,105.231,106.438,7,7,2,People can't afford to pay for tickets to see a game live.,1,I watch it occasionally at home on the TV.,1,25.595,73.997,75.019,6,5,1,Riding a bike will take you longer to get to work and once there you'll be tired.,2,I drive 3 times a week or less.,1,32.415,92.193,93.264,5,7,1,It makes it easier for people to watch shows they are unable to watch when it airs.,1,I watch about 8-10 hours a week from a DVR.,1,30.559,84.591,86.521,7,9,1,Using the internet is easier and a lot faster. Everything you need is at your fingertips. Sending a email to say hellp takes a couple minutes.,1,I use both daily.,1,37.257,102.701,103.87,5,7,1,The single cup brewer is better in the long run so you make enough coffee for one cup which is what you will drink. Most people make a whole pot and then it gets wasted.,2,Daily!,1,2,3,35000,Hindu,Asian,32,5,1,1,No,Not sure,"Yes, if we can figure out patterns",no,1,1 +0,3,4.4,4.333333333,2.333333333,3.333333333,4.55,1,1,1,6,2,2,5,5,2,6,2,2,7,2,7,7,7,4,3,7,1,8,1,3,1,1,7,14.435,14.435,15.809,1,1,25.507,25.507,29.576,1,5,37.409,37.409,38.408,1, ,0,0,60.011,0,5,30.899,30.899,31.824,1,4,29.363,42.025,45.432,2,5,32.888,32.888,34.075,1,5,39.735,39.735,42.82,1,7,37.81,37.81,39.605,1,3,27.758,27.758,32.208,1,4,25.414,25.414,27.309,1,6,25.553,25.553,30.265,1,1,1,2,5,4,9,4,6,4,4,2,4,1,5,6,4,6,6,1,2,5,7,1,55.14,177.43,178.585,6,1,1,"In my community, there are many pizza shops, so the prices are becoming lower as the owners compete with each other. I think many pizza shop owners charge for the convenience of the ready made meal.",4,I don't like pizza.,1,32.876,119.505,120.823,7,3,2,I don't know. I don't enjoy football enough to watch a game that had been played earlier.,3,"I watch when my home team is on, but not every game. I used to watch all of the games they played. I watch on television.",1,74.189,232.266,233.673,5,2,2,They don't want to ride their bikes in bad weather. / It's difficult to ride a bike in snowy and icy weather.,3,"I haven't worked for 2 years, but when I did, I was a visiting registered nurse and had to drive from home to home and averaged about 6 to 7 different homes per day. With the equipment I had to carry, I had to have a car with a trunk.",1,71.053,353.83,354.988,12,5,2,People are busy and working more hours with less hours to be available when a show airs during it's regularly scheduled time. / People are looking for alternatives to paying for cable and satellite tv services which are becoming too expensive for most families to fit into their budget.,4,"I have two opposite habits. 1. I find a show that I want to watch, and sit down to watch that show and then go back to my household activities after it is over. / 2. I often leave the tv on during the evening to ""keep me company"" and it is usually on for sitcoms that don't require much thought. It's just banter in the background.",1,66.824,277.093,278.397,8,7,1,Programming has made it simple for those who were computer illiterate in the past. There are many people who didn't use to have computers who rely on them for personal business and to keep in touch with out of town relatives.,1,"I try to keep my email account to below 50 if possible. If I get an email that is not important, but that I want to remember or read later, I store it somewhere. / I don't search as much as I used to, but enjoy having facts and trivia information available within seconds.",1,38.168,148.056,149.359,5,2,1,I don't know. I'm not sure what a single cup brew is. My guess is that people want fresh hot coffee with each cup that they drink.,4,"I have a cup every morning, and sometimes almost 2 cups. I don't drink coffee for the rest of the day.",1,2,3,11800,agnostic,caucasion,58,2,1,1,No.,American consumption habits.,Judgement of other people's habits,No. It was much longer than I thought. I don't do well with pattern puzzles and I don't know why.,1,1 +0,5,5.8,7.666666667,4.333333333,6,4.55,1,1,1,9,8,4,1,3,2,2,7,9,8,3,7,8,9,7,3,7,1,8,1,3,1,1,7,8.2,8.2,9.676,1,2,14.944,14.944,19.781,1,3,33.616,40.496,42.155,2,6,16.892,16.892,23.347,1,2,31.694,31.694,33.083,1,5,24.928,24.928,26.411,1,1,27.791,27.791,28.872,1,8,26.673,26.673,29.561,1,6,23.543,25.307,26.291,2,3,6.938,6.938,14.041,1,8,13.769,13.769,15.183,1,2,24.645,24.645,25.973,1,1,1,2,6,6,4,9,6,4,4,2,8,6,4,8,7,4,7,6,4,4,4,1,27.366,78.264,97.919,6,5,1,"The fact their relatively cheap, compared to other dinner options, it's a quintessential ""party food"", and because it's so customize-able to everyone's unique tastes.",3,I eat pizza twice a month.,1,16.129,220.971,231.926,11,7,2,"It's too expensive to buy tickets, the convenience of staying at home, spectators don't have to deal with traffic or bad weather at the stadium, they can eat their own food that may not be sold at the stadium, and there's better technology to watch the game when you're at home (better cameras and angles).",4,I watch almost every game that comes on network television.,1,48.905,209.87,210.554,7,1,2,"Not everyone is healthy enough, green-conscious enough, or live close enough to their workplaces to ride their bikes to work. Furthermore, not all cities are pedestrian-friendly, so this fact may deter potential bike-riders from hitting the streets (i.e., for safety reasons).",2,I don't drive at all.,1,49.879,186.442,199.231,7,9,1,"More and more, technology is allowing us to do things in a mobile fashion. Laptops were the mobile versions of personal computers, just as cell phone were the mobile versions of telephones. The increased accessibility of the internet aso makes it easier for us to watch videos on the go.",4,I watch about 15 hours of TV every week.,1,56.372,120.602,187.417,6,9,1,"Technology and internet service subscriptions are becoming ore accessible everyday. Soon everyone, no matter their socioeconomic status or status, will be able to use the internet.",1,I check email 5 to 7 times a day; and search for things on the internet about 30 times a day.,1,13.444,112.958,114.029,9,5,1,"Because people don't want to feel like they're drinking too much coffee in a day, and because more people live alone, even when they're in a long-term relationship.",4,I drink coffee four times a week.,1,2,4,5126,None,Black,28,1,1,1,No,I don't know,"Yes, but I'm not sure what that is at the moment.",No,1,1 +0,3,4.866666667,6.333333333,5.666666667,6,5.45,1,1,1,6,6,6,5,2,6,6,1,1,6,7,6,2,6,7,3,7,1,8,1,3,1,1,1,19.675,19.675,23.184,1,3,12.322,14.073,14.76,2,3,30.615,30.615,32.695,1,8,18.35,18.35,22.175,1,2,24.382,24.382,26.692,1,6,30.465,30.465,36.12,1,1,19.697,19.697,21.472,1,8,29.207,29.207,33.054,1,7,25.986,34.993,38.703,2,3,17.672,17.672,22.414,1,8,31.363,31.363,36.922,1,1,28.701,28.701,30.381,1,1,1,3,6,4,4,4,7,6,6,4,4,7,6,4,6,6,6,6,4,7,7,1,12.563,37.987,39.235,4,6,1,okay but I usually try to find coupons.,4,twice a month,1,5.48,16.703,44.631,7,5,1,lots of people need to record it and watch it on their own schedule,4,Never,1,4.322,45.728,46.817,5,6,2,lack of bike lanes and other bike friendly ammenities.,4,work commute and errands.,1,7.016,40.804,41.423,5,6,1,people need entertainment on the go,4,Don't own a tv,1,41.793,68.656,82.629,5,7,1,ease of transactions and information gathering,1,daily and used for most things,1,8.781,24.18,29.887,5,6,1,ease and convience.,4,twice a week,1,2,3,45000,none,white,36,3,1,1,no,how people feel about their data being tracked,no,no,1,1 +0,1,6.866666667,9,4.333333333,6.666666667,4.45,1,1,1,9,9,9,1,1,5,9,9,9,9,9,9,1,5,9,3,7,1,8,1,3,1,1,7,9.938,9.938,10.747,1,3,31.313,31.313,32.5,1,5,30.353,35.195,36.118,2,8,29.542,29.542,30.266,1,7,16.698,16.698,17.563,1,8,13.802,13.802,14.899,1,5,28.508,28.508,29.979,1,3,12.779,12.779,13.686,1,7,9.907,9.907,10.842,1,3,15.748,15.748,16.994,1,4,24.091,26.837,34.049,3,2,9.33,9.33,10.217,1,1,1,5,3,3,9,2,5,4,5,4,3,3,7,9,7,8,7,9,2,9,7,1,14.25,51.341,53.897,8,9,1,Supply and demand,3,2 a month,1,32.94,97.832,98.438,5,9,1,"Cost, availability on TV and location of teams",2,"Watch weekly, do not attend live games",1,35.957,52.923,66.744,7,3,1,Less hassle and hurried lives,4,Always drive,1,109.615,151.333,159.839,6,9,1,Growth in technology and adapting to busy lives,4,20 hours per week,1,45.927,133.144,134.843,9,9,1,Availability of the internet to more people and it becoming almost a necessity.,1,Do both multiple times daily.,1,30.273,54.729,55.41,5,1,1,Less waste and hurried lives,4,Do not drink,1,1,3,59000,Luheran,White,47,4,1,1,No,No idea,No,No,1,1 +0,7,5.266666667,5,4.666666667,4.833333333,4.2,1,1,1,7,7,5,3,5,3,5,7,7,3,5,5,5,5,7,3,7,1,8,1,3,1,1,7,14.604,14.604,16.867,1,1,19.11,19.11,22.042,1,5,32.336,32.336,50.147,1,6,23.573,23.573,25.98,1,2,20.645,20.645,22.249,1, ,0,0,60.015,0,1,24.165,24.165,34.127,1,8,32.143,32.143,44.56,1, ,0,0,60.065,0,2,13.912,34.846,47.867,2,8,28.874,42.034,44.408,2,5,19.925,19.925,22.795,1,1,1,5,5,3,1,6,5,6,6,2,6,3,5,7,5,6,5,9,3,5,9,1,33.914,131.725,137.824,5,5,1,I think the combination of high demand and high supply accounts for the current prices of pizza.,4,"I eat pizza several times per month, eating roughly four slices each time.",1,22.266,194.961,206.344,6,5,2,"I think improvements in the at-home viewing experience, increases in ticket prices, and the geographic dispersal of fans all contribute to this.",4,"I watch football (both professional and college) regularly during the season, usually watching several games per week.",1,22.971,62.997,109.068,5,4,1,"I think commute lengths, as well as poor accomodations for bikes in many American cities and towns, account for the low rate.",4,"I drive almost daily, but rarely for long distances.",1,23.775,103.205,104.142,5,5,2,"I think technological innovation, as well as growing demand for ""on-the-go"" viewing options, accounts for this change.",4,"I watch TV daily, usually for several hours.",1,28.686,111.813,113.182,4,5,1,"I think the increasing availability of internet accounts for this rise, as does the increasing use of devices such as smartphones and tablets.",2,I use email and search engines daily.,1,33.227,139.347,141.944,4,5,1,"I think the desire of many for a single cup of coffee each day, but not for a greater amount, accounts for the success of the single cup brew. I think that increasing awareness of the single cup brew has also caused the higher level of ownership.",4,I do not drink coffee.,1,1,4,110000,Catholic,White,23,2,1,1,No.,The effect of reading about a trend on personal beliefs regarding that trend,"Yes, but I am unsure of what it might be.",No.,1,1 +0,8,8.466666667,9,5.333333333,7.166666667,6.85,1,1,1,9,9,9,5,9,9,9,9,9,9,5,9,9,9,9,3,7,1,8,1,3,1,1,7,15.065,15.646,31.242,2,1,21.705,21.705,25.467,1,4,44.012,44.012,60.007,1,6,21.881,21.881,23.75,1,2,22.101,22.101,25.301,1,7,34.707,48.622,60.016,2,1,22.67,22.67,51.201,1,5,11.951,44.71,60.008,2,3,59.601,59.601,60.016,1,2,49.339,49.339,60.007,1,6,33.485,54.061,60.005,3, ,0,0,60.015,0,1,1,3,7,8,6,2,7,5,3,5,5,7,6,2,9,9,3,3,7,7,3,1,62.1,196.942,202.413,6,9,1,"Pizza is not your typical dough and cheese. Many pizza pies are very fancy. In addition, people love pizza and are willing to pay for a good slice.",2,I have pizza at least once a month,1,45.015,131.697,133.408,6,9,1,"Football is traditionally a winter sport. It gets very cold to go to the stadium. Besides, you can enjoy the game with friends and family in the comfort of your own home",2,I watch on a television.,1,71.46,209.098,211.046,9,2,2,"Because many people live far from where they work, by the time they reached work they would be very sweaty and uncomfortable. Many Americans do not have facilities where they can shower and change at work.",1,I tend to drive only when I have to visit destination that a long way. This is for personal reasons. I commute to work using the train.,1,80.829,192.492,194.026,11,9,1,People love gadgets. Everything we purchase today has video capability,2,I watch lots of tv. I watch dvr and dvd mostly because I prefer watching exactly what I want to watch when I have time to watch it,1,47.519,148.815,172.816,7,9,1,More and more people have access to devices that allow them to connect to the internet,1,I search and email daily. I am on the internet at least 6 hours out of the day,1,81.99,162.95,167.667,5,5,1,There is no waste. You brew just the amount you are going to consume,1,I do not drink coffee. I only drink tea,1,2,4,225000,none,african american,54,8,1,1,no,Opinions about popular trends in America,no,no,1,1 +0,3,7.066666667,6.666666667,4,5.333333333,6.45,1,1,1,9,8,7,6,6,4,7,7,7,8,8,7,9,6,7,3,7,1,8,1,3,1,1,1,7.766,7.766,10.491,1,1,9.493,14.237,17.14,2,8,16.503,16.503,18.513,1,6,19.866,19.866,21.65,1,7,21.453,21.453,24,1,1,20.721,20.721,22.591,1,8,22.459,22.459,24.445,1,2,22.22,22.22,26.204,1,3,19.849,19.849,22.51,1,4,6.503,6.503,19.103,1,8,25.578,25.578,27.77,1,5,25.842,25.842,29.16,1,1,1,5,6,6,4,2,9,6,5,7,6,7,6,2,6,8,3,1,4,9,7,1,35.149,69.791,71.533,5,4,1,Supply and demand...competition,1,Maybe once a month,1,42.327,84.137,86.336,5,7,1,It's easier to watch at home on a big screen TV,3,Never watch it,1,77.906,102.054,107.341,5,2,1,Lazy,4,"I walk, ride the bus.",1,56.632,121.746,123.09,6,7,1,People do not do well with some sort of entertainment.,4,2-3 hours/day. That includes local and national news.,1,44.887,68.962,70.585,5,9,1,Convenience,1,daily,1,39.46,87.823,89.61,4,3,1,"less ""family""; more singles. Or everyone wants a different flavor.",2,never,1,2,5,60000,Protestant,White,49,4,1,1,some of the graphics,logic/perceptions,how humans associate different things,no,1,1 +0,2,6,9,5.333333333,7.166666667,3.25,1,1,1,7,8,5,9,3,1,9,5,7,9,9,9,2,2,5,3,7,1,8,1,3,1,1,7,7.768,7.768,20.846,1,1,29.044,33.752,37.903,2,2,35.358,36.484,38.535,2,8,38.971,42.504,46.765,2, ,2.229,2.229,60.016,1,4,1.21,41.424,44.033,3,5,1.208,40.1,58.625,3,3,0.972,46.392,48.648,2,7,0.813,36.336,37.37,3,7,0.825,47.283,53.115,3,7,1.248,39.831,42.159,2,6,1.485,23.419,24.809,2,1,1,7,9,1,2,6,7,3,5,1,3,5,1,9,1,1,9,9,2,3,9,1,76.405,358.665,359.902,9,9,1,all the materials it takes to make the pie,4,I eat pizza at least 6 times a week,1,2.308,449.552,450.794,7,6,2,the price of tickets and technology,4,I don't watch sports,1,12.316,50.484,51.518,5,5,2,the price of fuel,4,I drive evryday,1,814.605,878.861,880.376,5,9,1,I think technology is out of control,4,I watch from the time I open my eyes till I go to sleep,1,279.486,838.495,839.459,10,9,1,internet is the trend,1,"mostly yahoo some aol and google ,google is my fav search engine I use e-mail every day",1,16.219,90.068,91.272,5,5,1,starbucks is to bleme,4,every now and then,1,2,2,1450,none,white,40,5,1,1,no,don't know,I say yes but not sure what,no,1,1 +0,3,4.933333333,8.666666667,7.333333333,8,2.7,1,1,1,8,9,8,1,9,1,2,9,8,3,1,1,1,8,5,3,7,1,8,1,3,1,1,7,7.032,7.032,8.192,1,3,7.842,7.842,12.082,1,3,16.109,16.109,17.062,1,4,9.25,9.25,11.547,1,3,12.925,12.925,14.398,1,5,7.237,7.237,8.134,1,1,14.144,14.144,16.777,1,2,12.143,12.143,13.327,1,7,10.43,10.43,11.447,1,3,10.63,10.63,13.958,1,7,20.882,20.882,21.843,1,1,15.382,19.423,20.823,2,1,1,7,1,2,9,7,1,6,9,1,8,8,1,9,4,1,5,8,7,2,8,1,30.177,61.41,76.035,6,8,1,American obsession with fast food,1,I eat about 1-2 pizza every two months,1,9.015,36.254,50.25,5,9,2,The love of the game as well as sports betting,2,I watch home team games and the Superbowl,1,4.699,46.012,71.693,5,8,2,Its dangerous and the weather does not permit it in most places as the only way to get to work,2,"I drive to work 5 days a week but we do have a ""work from home"" option available sometimes",1,12.576,41.456,69.803,6,9,1,Because the devices and the subscriptions to show are getting cheaper and more affordable,3,I watch approx. 3 hours of TV per day,1,38.962,104.834,105.924,6,9,1,There are no other reference material readily available and its fun!,1,i do search and email several times per day for work and personal,1,10.463,56.633,57.475,7,5,1,The stimulant effect as well as the increase in coffee shops available around the country,1,I drink about one cup a day,1,1,4,70000,Christian,White,52,9,1,1,the pattern matching,Attitudes toward customs,Yes it could have been age vs cognitive ability,no,1,1 +0,6,4.2,8,7,7.5,4.2,1,1,1,7,8,2,2,3,3,3,2,6,6,3,2,7,3,6,3,7,1,8,1,3,1,1,7,11.938,11.938,15.396,1,1,31.15,31.15,33.464,1,8,35.189,35.189,42.375,1,8,42.098,42.098,43.077,1,2,17.422,17.422,26.669,1,7,26.532,26.532,27.287,1,1,36.608,36.608,37.914,1,5,36.764,36.764,38.414,1, ,0,0,28.733,0,3,28.523,28.523,30.557,1,5,16.328,16.328,17.45,1,5,38.431,38.431,39.041,1,1,1,4,4,5,8,4,4,4,4,9,8,4,3,8,5,3,8,4,3,6,8,1,3.706,38.884,39.775,7,8,1,Whatever is going on with the economy,1,"I have pizza a couple of times a month, usually on weekends.",1,6.507,51.594,52.076,5,9,1,"Ticket prices are high, a lot of people might not be close to a stadium",2,I watch SEC football in the fall and occasionally I watch the NFL but usually only the super bowl,1,12.168,68.725,69.335,5,5,1,It's not safe for convenient for a lot of people to use bikes. There aren't bike lanes in a lot of places and unless you're in a city most people probably can't get to work on a bike in an acceptable amount of time,3,I commute 30-40 minutes to work every day,1,109.714,177.137,198.258,5,7,1,"More interest in technology, more need for people to have devices to use on the go",1,I watch a few shows regularly but usually not at the airing time; I end up seeing them later in a stream,1,152.873,200.053,200.769,4,9,1,More access to the internet available to more people; more necessity to use the internet as more and more goes digital,1,I use e-mail and internet search engines multiple times a day,1,22.725,45.791,80.299,5,7,1,"For a lot of people like myself who live alone and work, it's easier to have a machine that quickly brews one cup. It's less wasteful as I can't drink a whole pot of coffee on my own.",1,"I drink coffee several times a week at work, usually one cup a day.",1,2,5,35000,none,white,30,2,1,1,no,i don't know,i don't know,none,1,1 +0,3,5,3.666666667,4.666666667,4.166666667,4.6,1,1,1,3,6,1,4,7,4,6,4,6,6,4,4,6,7,7,3,7,1,8,1,3,1,1,7,8.334,8.334,9.226,1,1,12.04,12.04,12.876,1,3,8.044,8.044,8.705,1,8,11.258,11.258,12.806,1,4,7.678,7.678,8.681,1,4,9.405,9.405,10.232,1,8,6.809,6.809,7.54,1,7,9.545,9.545,10.211,1,6,15.052,15.052,15.846,1,8,4.433,4.433,5.179,1, ,0,0,60.005,0,3,7.301,7.301,8.263,1,1,1,6,6,4,6,4,6,5,7,4,6,6,4,6,6,4,6,6,6,4,6,1,9.74,87.85,88.431,6,3,1,food prices,2,once a month at most,1,16.786,34.217,34.556,6,5,1,they like live games a lot,3,don't watch football,1,13.725,30.78,31.199,6,3,1,laziness,2,drive to work,1,14.525,24.292,31.967,5,1,1,laziness,2,don't watch tv,1,25.86,31.323,44.134,5,7,1,availability,1,daily,1,7.507,44.937,45.549,8,6,1,convenience,2,1-2 cups a day,1,2,4,60000,none,white,34,3,1,1, , , , ,1,1 +0,1,6,6.666666667,7,6.833333333,6.1,1,1,1,9,7,6,3,7,3,7,4,8,9,6,2,6,7,6,3,7,1,8,1,3,1,1,8,5.223,5.223,5.924,1,3,6.091,6.091,7.175,1,5,3.834,3.834,4.435,1,8,7.597,8.26,8.49,2,3,2.804,2.804,3.573,1,4,0.062,2.481,3.408,2,1,15.52,16.06,16.243,2,3,3.801,3.801,4.547,1,3,2.99,2.99,3.645,1,3,12.101,12.101,13.06,1,4,5.593,13.198,14.079,3,6,2.987,7.689,8.288,2,1,1,7,7,8,3,6,9,6,3,7,7,7,7,2,7,8,7,7,8,3,3,1,8.541,68.841,70.016,8,3,1,I think that the ad's and commercials are a big contributer to this.,2,Rarely eat pizza.,1,2.139,92.893,93.19,6,9,1,Because of the weather and most people like to be in the comfort of their own homes. Instant replay is a plus.,2,Big fan. Watch it every Sunday and Monday.,1,21.917,84.558,85.113,7,4,1,I think it's because Americans are spoiled and a consumer nation focused on easy means.,2,Drive to and from work. Drive to run errands. Drive to meet up with friends.,1,12.943,62.77,62.999,9,8,1,Demand for it is growing so of course the number of available devices will grow as well.,2,Only sports.,1,3.771,62.776,63.006,6,9,1,"I think that the Internet has become easier and much more accesible to use. Wifi networks are everywhere, computers are cheaper and much more user friendly.",1,Browse around 5 hours a day. Mostly work related.,1,16.263,103.45,103.871,8,8,1,"I think that it grew because of cool trends. Americans like to do whatever everyone else is doing and when a trend catches on like wildfire, it's inevitable that the success of the single cup brew will rise.",1,Drink it every morning and afternoon.,1,1,4,22000,Christian,White,27,2,1,1,No,Americans view on certain things and their preferences.,No,"I think that this study is far too long for $.75 By placing that type of wage and saying that it takes around 30 minutes to complete, you are saying that a workers time is equivalent to $1.50 hr. Just a suggestion: make it worthwhile for people. We take your surveys very seriously and we want to help you with your research but you have to respect the workers as well. I hope that my input could benefit your research.",1,1 +0,0,6.333333333,6.333333333,3,4.666666667,4.2,1,1,1,5,5,5,5,5,5,7,8,7,8,5,5,8,9,8,3,7,1,8,1,3,1,1,8,5.905,5.905,7.283,1,6,11.92,11.92,12.893,1,2,16.731,16.731,19.121,1,8,7.185,7.185,8.896,1,5,10.65,10.65,11.671,1,7,8.562,8.562,10.197,1,4,14.808,15.957,17.652,2,4,13.625,14.774,15.392,2,8,9.437,9.437,10.738,1,1,9.883,18.416,22.469,2,1,7.083,7.083,8.068,1,3,9.181,9.181,11.275,1,1,1,5,2,5,9,8,5,5,9,5,5,5,5,9,5,5,5,5,5,9,7,1,14.738,34.797,68.953,5,5,1,Consumption level and the attractiveness of it to people plus competition strategies on the part of those who sell it.,2,I rarely eat it.,1,14.974,41.984,43.024,5,5,1,Unsure,1,I don't watch it at all,1,17.844,44.56,52.858,5,3,1,Because we have been conditioned to rely on cars for so long.,2,I don't drive at all.,1,15.329,36.479,40.148,5,5,1,Boredom,3,I very rarely watch TV.,1,21.83,56.861,73.359,5,9,1,It is more convenient than regular mail.,1,I am an avid and regular user of both platforms for multiple hours a day 7 days a week.,1,18.729,69.604,70.726,5,1,1,"People ""trying"" to tell themselves they can drink it without repercussions.",1,Very rarely-maybe once or twice a week-one cup,1,2,5,10000,Christianity,White,48,9,1,1,no,Unsure,no,no,1,1 +0,1,6.466666667,9,2.666666667,5.833333333,4.75,1,1,1,8,7,7,6,7,7,6,5,6,6,6,6,6,7,7,3,7,1,8,1,3,1,1,7,8.475,8.475,10.245,1,2,14.697,14.697,15.898,1,1,18.708,18.708,19.854,1,8,17.622,17.622,18.719,1,1,14.523,14.523,15.394,1,3,8.113,8.113,9.675,1,2,16.62,20.371,21.604,2,8,21.01,21.01,22.883,1,5,13.509,13.509,14.038,1,3,2.505,2.505,3.599,1,3,2.876,2.876,3.858,1,3,2.104,2.104,3.374,1,1,1,3,2,5,7,6,7,3,7,1,3,6,5,6,5,6,3,3,1,9,7,1,56.032,115.48,116.424,5,9,1,the economy,3,1 pizza a month,1,32.392,53.536,54.395,4,2,1,The ticket prices,3,I don't watch football,1,36.242,97.571,98.17,6,1,1,because so few work places are close enough.,4,I don't drive,1,34.686,73.245,81.27,6,9,1,the technology keeps becoming more assessable to a larger audience and prices drop,4,I watch a few hours a week,1,75.728,88.879,166.831,4,9,1,"The information, educational and content people find interesting all account for the rise. Also, the availability of the internet now as opposed to 20 years ago.",1,I do both daily,1,15.146,46.547,47.114,6,5,2,How good it tastes,4,I don't drink coffee,1,1,2,30000,atheist,white,48,1,1,1,no,I have no idea,Perhaps there was but I could not figure it out.,No,1,1 +0,1,7.733333333,8.666666667,5,6.833333333,4.8,1,1,1,9,8,9,7,5,6,8,9,9,9,5,8,8,9,7,3,7,1,8,1,3,1,1,1,17.186,17.186,20.337,1,5,19.699,19.699,21.151,1,7,23.111,23.111,24.687,1,6,17.943,17.943,20.093,1,3,14.721,14.721,16.533,1,3,12.533,12.533,13.963,1,3,12.927,12.927,14.166,1,6,13.343,14.131,15.909,2,3,8.852,8.852,11.103,1,6,12.83,12.83,14.125,1,7,13.654,13.654,14.994,1,7,7.634,7.634,8.917,1,1,1,5,8,5,9,8,6,3,8,2,6,6,6,8,6,6,4,2,6,9,7,1,22.522,198.648,199.943,5,8,1,People are increasingly demanding pizza that has more quality ingredients and are willing to pay a little more for a higher quality pizza. There is also a growing market of inexpensive pizza which I believe is aimed at families who have children to feed and children tend not to be as picky about their pizza as adults.,1,"I eat pizza about 3 times a month. I find it to be an easy way to feed the children when we are busy or want to spend Friday or Saturday night watching a movie, or ifI just don't feel like cooking.",1,65.31,133.521,134.636,5,8,1,"With the high prices of tickets, it is easier and cheaper to watch the games on television and many people like to go to sports bars and watch games.",2,"I am not really a football fan, but do watch a game every now and then with friends and will usually watch the Super Bowl.",1,69.229,146.249,201.907,6,1,1,For many people the distance to their jobs is too far to commute by bicycle. Other people are just too lazy to commute by bike and for others the weather or geographic conditions may be a hindrance to taking their bikes to work.,1,"I live in a rural town and need to drive a long distance to get to my job. I try not to drive more than I need to, mainly to conserve fuel. I try to run multiple errands at one time so I am not making a lot of trips and wasting gasoline.",1,24.454,76.161,110.139,5,9,1,People want to watch movies when they want to and not have to watch it when it is scheduled.,2,"I watch 3 or 4 hours of TV a day, some on cable and also Netflix.",1,17.353,91.269,92.485,5,9,1,I think the rise in internet users can be attributed to more people gaining access to the internet.,1,I use email to communicate with friends and family and I like to use the internet to look up various things that interest me.,1,20.694,67.642,68.589,5,6,1,"It makes very good coffee and is quite convenient, it is also seen as being chic or trendy.",2,I drink at least one or two cups of coffee per day.,1,1,3,30000,Catholic,White,41,1,1,1,I found the very beginning to be odd.,I think you are studying the cognitive reasoning ability of people.,I think it is to see how people see patterns in every day life.,I found it very interesting and like to share my opinions.,1,1 +0,6,5.8,7.333333333,3.333333333,5.333333333,5.15,1,1,1,8,8,3,5,6,5,3,8,7,6,8,2,4,7,7,3,7,1,8,1,3,1,1,1,3.967,3.967,9.043,1,1,16.537,16.537,17.922,1,3,17.198,32.335,34.232,2,6,27.722,32.57,34.74,2,4,20.925,21.509,23.054,2,4,18.78,18.78,21.406,1,1,9.653,9.653,10.455,1,5,25.447,25.447,26.08,1,3,19.559,19.559,21.569,1,3,13.523,13.523,14.284,1,5,19.423,19.423,20.36,1,5,44.446,46.086,46.592,2,1,1,4,7,6,3,7,6,2,7,3,5,7,5,4,1,6,5,6,8,7,4,1,28.266,59.827,60.14,9,5,1,Demand,1,a few slices a month,1,2.994,32.171,32.677,6,3,1,Tickets are expensive and travel is as well.,1,I don't watch football.,1,1.684,73.493,74.062,10,3,1,Distance,3,Drive 40 miles round trip 5 times weekly.,1,1.299,37.073,37.651,10,9,1,"Ease of use, or need for multiple viewers.",2,A few hours a week.,1,7.613,41.918,43.928,7,8,1,"Younger generations growing up with the technology, as older generations pass away.",1,I use both daily.,1,6.802,41.714,42.509,10,4,1,"Quick, easy measurements, with uniform taste.",1,I don't drink coffee.,1,1,3,32000,n/a,white,26,6,1,1,No.,Commonly held conceptions of activities of the average United States citizen.,Something with psychology maybe? Hard to tell.,None.,1,1 +0,5,5.4,6.333333333,4.333333333,5.333333333,4.25,1,1,1,6,7,1,4,9,5,3,3,6,6,2,5,7,9,8,3,7,1,8,1,3,1,1,7,6.512,6.512,9.664,1,1,15.577,15.577,16.859,1,1,13.554,13.554,14.222,1,6,12.886,12.886,13.849,1,5,14.314,14.314,15.228,1,8,22.467,25.424,26.898,2,1,20.341,20.341,24.006,1, ,0,0,60.044,0,1,10.846,10.846,25.749,1,2,21.018,21.018,22.025,1,8,12.321,19.473,27.568,2,8,30.327,30.327,32.079,1,1,1,5,6,5,4,6,7,6,7,1,6,3,4,7,5,7,6,6,4,3,9,1,40.934,66.612,67.249,5,5,1,Ingredient prices and consumer demand.,1,We order pizza about one every other week.,1,51.358,91.989,97.608,7,5,1,The expense of tickets.,3,"I watch occasionally, usually a ""big game"" such as the Superbowl or our local college team.",1,16.738,49.33,50.271,5,3,1,Lack of safe bicycle lanes/paths.,2,I carpool with my spouse.,1,24.922,78.919,79.515,5,7,1,The unwillingness of people to (1) pay for cable; (2) schedule their lives around the TV schedule.,3,"I watch at least 14 hours of TV per week, sometimes more. I watch shows via DVR almost exclusively.",1,19.27,63.226,64.017,5,7,1,"Increase of population in general, and younger generation more used to/dependent on computer usage.",1,I use both multiple times every day.,1,17.983,71.275,72.018,8,5,1,"Convenience, and ability to select a different blend/flavor with each cup.",3,"I use a single cup brewer at home, and a drip machine at work. I drink at least 2 cups of coffee per day.",1,2,6,140000,None,White,39,1,1,1,The pattern recognition was much more difficult than I would have expected.,I have no idea.,"I'm sure there is - otherwise it makes no sense. Perhaps the study was about people's comprehension of different types of information? Visual vs. written, for example.",No.,1,1 +0,3,6.066666667,6,3.333333333,4.666666667,5.1,1,1,1,6,7,8,7,6,7,3,2,8,8,6,7,3,7,6,3,7,1,8,1,3,1,1,7,8.529,8.529,10.037,1,5,6.262,6.262,7.602,1,7,11.371,11.371,12.695,1,6,11.692,11.692,13.816,1,7,7.346,7.346,11.623,1,6,2.109,2.109,2.953,1,5,13.865,14.385,14.694,2,5,4.638,4.638,5.227,1,5,8.413,10.829,11.394,4,7,12.201,12.201,13.086,1,2,4.81,5.538,5.893,2,3,2.825,2.825,3.597,1,1,1,5,5,5,4,4,5,5,5,4,5,5,4,4,5,4,5,3,5,5,5,1,2.791,19.175,20.573,8,5,1,mass production,3,quick,1,2.211,8.731,12.033,6,4,2,no idea,2,never,1,4.429,16.909,23.387,5,3,1,"lazy, safety concerns",3,drive daily,1,2.7,23.459,23.955,9,5,1,"convenience, lazy culture",4,dont watch much,1,6.992,30.936,42.43,8,8,1,"convenience, connectivity",4,I use email mostly for work. Internet at home for surfing and facebook,1,3.692,19.764,20.194,8,3,1,busy scheduels,2,every two days,1,2,5,53000,christian,white,27,5,1,1,just long,i dont know,not sure, ,1,1 +0,3,6.8,6.333333333,7,6.666666667,4.55,1,1,1,8,7,7,4,7,8,7,7,6,7,7,6,7,7,7,3,7,1,2,1,3,1,1,7,4.39,4.39,5.207,1,8,2.007,2.007,3.201,1,8,11.641,11.641,12.887,1,8,5.733,5.733,11.607,1,3,3.272,3.272,4.11,1,6,3.528,3.528,4.378,1,1,20.289,20.289,21.355,1,3,3.828,3.828,4.507,1,3,4.183,4.183,7.143,1,2,2.786,2.786,5.674,1,4,5.474,5.474,6.427,1,1,5.475,6.386,9,2,1,1,6,4,3,7,6,5,6,8,4,4,6,5,7,5,6,6,5,7,6,7,1,19.405,38.268,38.672,6,6,2,gas prices,1,every now and then,1,6.181,20.916,32.043,5,8,1,easier to watch on tv,1,a lot,1,7.458,20.329,20.841,5,7,1,cars are easier,1,every day,1,10.789,25.499,26.319,7,6,2,people want it,1,few hours a day,1,11.439,23.198,27.326,5,7,1,people want information,1,once a day,1,12.136,24.287,29.883,6,6,2,easier,1,dont drink coffee,1,1,3,45000,christian,black,30,1,1,1,no,american's habits,maybe,no,1,1 +0,2,8,7,5.666666667,6.333333333,6.45,1,1,1,8,9,9,9,8,9,7,8,9,8,7,7,8,8,6,3,7,1,8,1,3,1,1,7,6.297,6.297,6.789,1,1,10.74,11.188,11.689,2,7,13.202,13.202,13.83,1,1,5.233,5.233,6.174,1,5,2.372,4.99,5.521,3,4,8.181,8.181,8.779,1,6,5.449,5.879,6.147,2,6,4.411,4.411,5.01,1,8,3.361,3.361,3.999,1,4,2.999,3.214,3.73,2,7,6.922,6.922,7.463,1,3,4.212,4.212,4.864,1,1,1,3,9,7,3,5,9,9,3,7,6,9,9,6,6,9,5,9,4,9,8,1,4.362,31.129,31.462,5,5,1,The cost of the ingredients.,3,I eat pizza a couple of times a month. Sometimes we buy frozen pizza and sometimes we go out.,1,17.912,48.099,62.167,5,5,1,Because people can't afford to see the games in person.,2,I watch games every now and then on television if other people are. I'm not generally a big football fan.,1,21.681,60.298,60.607,6,4,1,"Because there aren't enough safe roads that they can ride their bikes on (ie to get to work they have to take the interstate), and many people commute many miles.",4,I commute to and from work every day (30 minutes each way).,1,40.811,85.083,85.331,7,7,1,"Devices such as televisions and tablets are becoming more affordable, allowing more people to use them.",1,"I generally watch television at night when I am eating dinner. Maybe a couple of hours. I don't watch a lot of shows, there are only shows that interest me. I watch a lot of netflix.",1,4.568,38.513,38.985,6,9,1,The internet is becoming more available to populations that aren't within large cities.,2,I use the internet and email every single day at work. I also use it at home. I search the internet for entertainment purposes.,1,29.603,69.948,70.523,6,8,1,"More people are on the go and don't have time to sit at home and wait for a whole pot of coffee to brew. More people also live alone, negating the need for a whole pot of coffee.",2,"I drink at least one cup of coffee every morning, and more on the weekends.",1,2,4,65000,christian,caucasian,31,7,1,1,no,i have no idea,i dont know, ,1,1 +0,6,6.333333333,7.333333333,6,6.666666667,5.4,1,1,1,6,7,6,7,6,7,5,6,6,2,8,8,7,7,7,3,7,1,8,1,3,1,1,1,1.37,5.832,6.95,2,1,2.048,18.06,20.856,2,3,2.036,30.329,35.286,2,6,17.509,20.562,24.005,2,2,17.167,17.167,18.874,1,3,47.447,57.682,60.018,2,5,1.382,32.466,36.685,2,2,35.568,35.568,40.14,1,3,19.062,19.062,37.269,1,2,18.935,18.935,22.847,1,6,40.654,40.654,42.437,1,2,15.075,15.075,16.817,1,1,1,3,5,7,4,7,6,5,6,4,7,5,7,6,6,7,4,2,3,7,5,1,53.171,75.756,98.798,7,6,2,"Where you're getting it from, prices of ingredients, and demand.",4,I rarely consume pizza.,1,44.849,180.827,182.287,7,8,1,"The expense of tickets, travel, and accommodations. It is also easier to follow the game on TV. It is more convenient to watch on TV.",4,I watch it on TV.,1,67.935,126.391,183.42,7,4,1,"Many cities are not pedestrian or bike friendly. It takes longer than driving, is less comfortable, and is not convenient in certain weather.",4,"I drive as a method of traveling between cities. I normally take the bus or walk, but my community is very centralized around a college campus.",1,3.084,264.359,265.483,9,8,1,Advancements in technology that is affordable and available to the general population.,3,"I watch a couple of hours of programming a week depending on available free time and the kind of programming that is airing. During sporting championship series I watch more TV, or if there's a movie on that I like.",1,75.458,109.293,145.079,6,8,1,"Internet is more readily available across different platforms - smart phones, tablets, iPods, etc.",2,"I check my email everyday, and search for something at least once almost everyday.",1,156.639,186.115,189.56,5,6,1,It's convenience.,4,"I drink coffee, or coffee with milk, almost every day.",1,2,4,11000,Roman Catholic,White Hispanic,23,6,1,1,No.,Comprehension and attitudes,Yes. Some connection between English nativeness and perceptions,No.,1,1 +0,2,6.333333333,5.666666667,5,5.333333333,5.35,1,1,1,6,7,6,6,7,6,7,7,5,6,7,6,6,7,6,3,7,1,8,1,3,1,1,7,7.217,7.217,12.81,1,5,7.223,7.223,8.4,1,7,24.39,24.39,25.36,1,6,27.931,27.931,40.26,1,5,47.492,47.492,50.677,1,4,23.598,23.598,26.888,1,5,11.795,11.795,13.637,1,6,30.388,30.388,31.309,1,3,44.518,44.518,45.488,1,7,27.464,27.464,28.418,1,8,12.758,12.758,15,1,1,16.017,16.017,17.914,1,1,1,6,7,6,3,2,6,4,3,4,5,6,6,7,6,5,6,5,4,6,6,1,21.26,119.94,120.566,5,7,2,"The current price of pizza is based upon the ingredients to make such, along with the demand for pizza.",4,"Pizza is a wonderful food. It is diverse in that one can change the toppings and crust type. I eat pizza typically two, maybe three times a month.",1,18.111,113.966,114.809,6,5,1,I think that the cost of tickets contributes to the lower percentage of persons actually watching a live game. There is also a time and location hinderance. Not everyone can attend a live game.,3,I do not like football. I do not watch football.,1,35.945,141.177,143.324,6,5,1,"Work attire is important. I think few Americans ride their bikes to work due to several factors: weather, the physical exertion, and traffic related factors.",2,I drive a car. I do not own a bicycle.,1,47.7,185.548,187.167,6,4,1,"On demand services accounts for the the growing number of available devises to watch video. Shows can be recorded and played back to accomodate the consumer's schedule, not the broadcaster's.",4,"I have a television, but do not watch it. We have an antenna but no converter box. Most of our viewing of movies, shows, etc. comes through the internet.",1,46.553,206.105,206.939,6,6,1,"We have not had the internet for a long time. Overall, it is a fairly new invention. These days, children are almost raised with an electronic unmbilical cord via the internet, computers, cell phones, etc. It is like second nature. This being the case, the internet's recent rise in popularity will continue to grow.",2,I typically utilize both email and internet searches on a daily basis.,1,122.536,249.624,251.363,5,5,1,"Typically, when making a pot of coffee, some coffee gets wasted. The single cup brew coffee makers make only one cup at a time and there is not much waste. The coffee is always fresh and hot too.",4,My coffee drinking habit is typically two 6 ounce cups in the morning made from a drip coffee pot.,1,2,3,9000,None,Caucasian,47,5,1,1,The sequence of articles asserting percentages followed by the shape games seemed to focus on predicting outcomes based upon patterns. This was a bit similar. I did not find it to be odd or confusing.,I think you were studying prediction perspecives based upon patterns.,I think there was a strong similarity between the percentage articles and then the pattern games. I think there was twelve of each.,The survey format was well laid out and easy to complete. This was entertaining. Thank you. Best wishes on your study.,1,1 +0,5,6.2,5,3.666666667,4.333333333,5.8,1,1,1,9,8,7,5,3,5,8,8,8,4,3,7,4,7,7,3,7,1,8,1,3,1,1,7,23.802,23.802,25.793,1,1,59.782,59.782,59.997,1,3,18.729,18.729,21.241,1,6,18.47,18.47,20.464,1,1,21.185,21.185,22.062,1,3,19.043,19.043,19.981,1,4,26.067,26.067,27.191,1,7,14.23,14.23,20.958,1,6,18.558,18.558,19.457,1,3,12.329,23.589,24.566,2,2,17.608,17.608,21.403,1,5,20.539,20.539,22.797,1,1,1,3,2,3,7,1,7,5,4,7,3,3,8,2,6,5,5,6,5,9,3,1,43.599,159.609,160.123,9,4,1,I think cause its pretty cheap and you can fill an average family with a large pizza.,1,I eat pizza atleast twice a month,1,73.018,185.74,191.735,6,5,2,Because its very expensive to go see a football game live.,1,Thru tv at home or a bar.,1,13.982,73.908,87.583,5,3,2,Lazy,1,I drive almost everywhere in my car but atleast 2% of that is on my bike.,1,15.164,102.979,103.406,7,3,1,Technology is growing so fast everyday something new is right around the corner.,1,atleat 4 hours a day.,1,6.558,143.7,144.289,8,8,1,thers a whole new generation thats growing and they are born into this technology.,1,I am logged on atleast 10 hrs a day either thru my pc or my smart phone,1,10.253,154.222,156.418,10,3,1,Its quick and easy,1,I stop drinking coffee over 2 yrs ago but when I did I WOULD DRINK ATLEAST CUPS A DAY.,1,1,3,42000,n/a,n/a,38,5,1,1,connecting the shapes was a little hard for only minute of time,not sure I thought it was to understand psychology but than the questions threw me off.,yes but i don't know what it is,none at this time,1,1 +0,1,6.466666667,6.666666667,5.333333333,6,3.5,1,1,1,5,7,7,4,8,8,6,7,8,6,5,5,5,8,8,3,7,1,8,1,3,1,1,7,5.482,5.482,8.163,1,2,10.309,10.309,11.75,1,5,11.088,11.088,12.193,1,8,18.007,22.369,23.338,2,8,13.298,13.298,14.346,1,3,15.957,15.957,17.298,1,6,14.573,14.573,21.905,1,3,6.194,6.194,10.22,1,5,16.849,16.849,17.892,1,3,9.34,9.34,14.68,1,4,10.163,10.163,11.861,1,7,17.282,17.282,18.773,1,1,1,5,1,4,8,9,7,4,7,1,8,7,4,7,2,5,9,9,6,9,8,1,39.261,97.961,123.304,6,7,1,It's the perfect food.,2,Every Saturday night is video gaming night with friends and family so Saturdays are Pizza night.,1,64.685,98.586,99.565,4,5,1,They can't afford the tickets,1,I hate football or any other sport for that matter.,1,70.348,119.36,120.11,6,3,1,Their lazy,1,I carpool to work most days,1,7.021,113.162,113.855,5,8,1,The drive to own the newest and best technology,4,I hate the direction American TV has gone in. I DON'T care about reality TV. I usually end up watching Anime or Asian dramas because they still actually do TV in a good format. Not reality tv.,1,93.072,121.586,122.375,5,5,1,Not sure,1,I only use them for work.,1,9.542,29.593,36.117,5,8,1,No left overs to throw away,3,Twice a day,1,2,4,45000,Agnostic,Hispanic,39,4,1,1,nope,not sure,nope,Thank you and good luck,1,1 +0,2,4.933333333,7,4.666666667,5.833333333,3.6,1,1,1,5,6,8,2,2,2,2,6,6,7,8,4,6,6,4,3,7,1,8,1,3,1,1,7,11.226,19.214,22.592,4,5,19.918,30.182,32.146,2,8,25.823,25.823,29.256,1,8,34.292,34.292,35.939,1,8,38.368,38.368,48.297,1,1,30.576,33.086,34.819,3,1,24.792,24.792,35.483,1,3,22.556,22.556,30.029,1,7,43.549,43.549,45.772,1,3,17.264,17.264,18.717,1,7,14.868,14.868,16.102,1,6,26.606,26.606,30.648,1,1,1,3,1,4,3,7,2,1,5,1,9,4,2,4,1,7,4,6,4,4,8,1,22.523,116.729,119.309,8,4,2,"The over saturation of the market with pizza places. Also, the cost of ingredients along with transporting these ingredients.",2,Once a week on average I get it.,1,18.076,85.021,86.019,6,5,1,Price of tickets and drive time. It's much nicer to sit in your house than go to a stadium where everything is overpriced.,3,Big games and playoffs usually.,1,40.885,119.464,172.385,9,3,1,"Laziness. And it is much easier to get in a car and drive. Commute time as well. It would take too long if someone has a substantial commute, which most people do.",3,"I drive everywhere. I plan on buying a bike this summer for work though. I luckily live nearby. But as far as groceries, etc, I drive constatntly.",1,35.012,123.733,125.565,6,8,1,"There is money in making the devices, so more companies put out products and they have to stand out, so the devices become more frequent.",4,I have a few shows at a time I watch online. 5-6 hrs per week.,1,234.274,300.323,346.721,6,9,1,"You can't do anything without the internet for a lot of things. The younger generation that grew up on it uses it constantly, while the older generations are dying out.",1,I don't use search engines primarily; I usually know the sites I will go to. I have my personal and work email up in a browser all day.,1,17.197,58.101,58.804,7,6,1,Simplicity. You only have to make one serving at it takes a few seconds.,3,Cup or two in the morning.,1,1,4,75000,none,white,35,3,1,1,Not particularly.,How different people account for consumer habits.,"I assume so, but I can't think of the ulterior motive.",I do not.,1,1 +0,1,8,5,5.666666667,5.333333333,4.8,1,1,1,6,5,9,9,9,8,9,7,7,9,7,8,9,9,9,3,7,1,8,1,3,1,1,7,17.52,17.52,18.852,1,8,13.166,13.166,14.205,1,5,13.702,13.702,15.064,1,8,16.954,16.954,17.474,1,7,13.201,13.201,14.346,1,5,14.321,14.321,16.126,1,8,5.87,5.87,6.407,1,2,9.75,9.75,11.478,1,8,6.591,6.591,9.583,1,3,6.998,6.998,8.679,1,2,40.196,40.196,42.681,1,8,10.029,10.029,10.736,1,1,1,7,9,5,8,7,6,5,5,3,6,2,6,9,8,8,6,7,4,9,4,1,46.737,124.387,125.482,5,2,1,The laziness of Americans who don't cook and that have a lot of money.,4,I love pizza but I refuse to eat it more than once a month.,1,14.888,137.972,152.8,7,9,2,I think that the price for live football tickets are still too high for most Americans to purchase.,2,I view every football game and Superbowl every year?,1,36.682,97.389,99.012,5,5,1,Since Americas are getting better jobs they are able to afford cars to drive to work.,4,I drive to work and school only.,1,34.585,99.648,163.674,5,4,1,I believe that researchers and developers are the main cause of the growth of devices being built.,4,"I watch TV about 5 hours a week, including movies.",1,64.574,197.697,198.781,5,9,1,The reasons why internet use in America is increasing because of the constant web updates and technology that keeps being produced that uses internet. Plus its very easy to use the internet whenever available.,1,I use the internet and email on a daily basis.,1,6.446,69.788,70.364,5,3,1,The success of one cup is giving an adult the energy to focus on any task necessary.,4,I drink coffee daily.,1,2,3,30000,Catholic,Hispanic,24,2,1,1,No I understood the question.,How the participants read questions carefully and viewing the opinion of the American people.,No I believe this survey was on point :),No I liked this survey very much!,1,1 +0,6,5.333333333,5.333333333,4,4.666666667,5.2,1,1,1,6,4,6,5,5,7,4,4,6,6,6,6,5,5,5,3,7,1,8,1,3,1,1,7,6.401,12.242,17.504,2,1,27.588,27.588,29.579,1,3,43.472,43.472,44.783,1, ,0,0,60.007,0,5,22.24,25.513,30.023,2,7,12.093,12.093,25.991,1,5,23.658,23.658,25.139,1,5,21.348,29.845,31.142,2,1,31.52,40.301,43.16,2,2,24.411,28.72,50.718,2,6,28.25,32.664,33.908,2,7,37.892,54.457,59.241,2,1,1,5,6,6,4,6,5,6,5,4,4,5,5,4,4,6,5,4,3,5,4,1,12.48,138.09,139.088,5,5,1,fair,3,1 a month,1,6.18,41.252,42.546,6,5,1,ticket price,3,none,1,6.7,33.843,35.615,6,2,2,lack of infrastructure,4,rarely driving,1,7.516,47.412,53.365,5,5,1,peer pressure,3,no tv,1,14.967,34.672,41.439,5,6,1,availability,2,daily,1,10.932,45.734,46.775,6,5,2,laziness,3,quit coffe a few months ago,1,2,3,60000,agnostic,caucasian,34,3,1,1,no,manipulation,manipulation,no,1,1 +0,5,5,4.333333333,2.333333333,3.333333333,4.85,1,1,1,7,5,1,5,5,5,5,1,7,8,3,6,6,5,6,3,7,1,8,1,3,1,1,7,3.951,3.951,4.761,1,1,11.036,11.036,12.645,1,1,12.345,12.345,13.541,1,4,24.894,32.362,33.032,2,2,14.285,14.285,22.733,1,3,17.89,17.89,34.459,1,1,16.762,16.762,19.777,1,7,21.26,21.26,22.63,1,6,22.921,22.921,24.175,1,4,15.177,16.426,17.562,3,6,24.372,27.308,28.38,2,7,22.846,23.569,24.323,2,1,1,3,7,5,7,6,6,7,6,2,5,4,4,6,2,3,3,6,7,4,2,1,23.534,46.43,50.661,6,1,1,Capitalism.,2,"I like pizza, but I prefer making pizza at home from scratch rather than ordering it.",1,37.504,56.71,78.15,6,4,1,"It's easier to sit on your couch at home, and game tickets are expensive. I suppose being able to have parties with friends is also a factor, but I dunno.",2,I don't.,1,26.268,166.178,166.691,5,1,1,"I think it's a combination of a lot of things. It would depend on the area; the area I grew up was not a safe place to ride bikes, due to high traffic, no space on the side of the rode to be passed in, and very hilly. The weather would also be a factor - if it was freezing most of the year, or often had precipitation, I wouldn't want to dress nicely to work. Also, for women who wear dresses and skirts, it might not be practical. It's also difficult to carry a briefcase on a bike. But the major reason is laziness.",3,"I both drive to work, and take the bus, depending on the day and which site I'm going to. If I got to the usual office, I take the bus. On days where I need to attend meetings elsewhere, I drive there.",1,45.803,84.35,85.092,5,3,1,"People keep buying them, so why not keep making them? Capitalism, folks.",2,"I will have the TV on while doing other tasks, mostly for background noise. I listen to books on tape just as often.",1,140.201,185.183,185.981,6,9,1,"There are more ways to use the internet, and it's cheaper. Also the internet is pretty awesome.",2,"All the interwebs forever! / / I communicate with email for work and school, and I'm great at searching the internet.",1,19.92,61.591,61.977,9,2,1,It's easier. Current society is all about convenience and instant gratification.,4,"I drink too much coffee, but I brew a whole pot and use it for a few days.",1,2,3,10000,none,caucasian,22,2,1,1, ,i have no idea,"yes, but i really don't know",no,1,1 +0,7,7.866666667,7,6,6.5,5,1,1,1,9,9,9,5,9,5,9,1,8,9,9,9,9,9,9,3,7,1,8,1,3,1,1,7,4.162,7.215,27.844,2,1,7.948,7.948,24.499,1,3,1.893,36.893,42.424,2,6,41.479,51.781,60.009,2,2,23.1,23.1,56.385,1,7,54.407,54.407,59.993,1, ,0,0,60.011,0,5,35.946,55.188,59.986,3,6,43.022,43.022,60.003,1,4,26.635,51.023,57.57,2,6,17.684,33.332,42.973,2,3,55.541,55.541,60.008,1,1,1,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,1,61.201,163.139,234.123,6,3,1,"I don't even know what the current prices are, because I don't eat it. Last time I had it, someone brought it over, about 4 years ago. If they're high, it's probably because of consumer demand.",4,"Don't eat it for a number of reasons: GMO cheese, GMO flour, GMO tomatoes, high salt content, fattening.",1,3.869,240.2,245.16,6,7,1,"Too expensive, too far, too cold.",4,"I don't watch it. BTW, somebody should check out those statistics on females. I have a sneaking suspicion that the reason 55% of women watch it is related to their husbands/boyfriends, such as they watch together. If they were single like me, would they still watch?",1,2.171,135.049,220.047,6,9,1,"It's too far. But that's no reason to create sustainable communities where people live like rats. Sorry, but I still believe in commuting to work.",4,I don't commute and mostly use the car to go shopping.,1,81.881,285.154,290.103,9,9,1,Consumer demand,4,"During daylight savings time, I probably watch a couple hours every night. When it gets dark early, I'll watch more. It varies. I'll watch a DVD once or twice a week usually.",1,72.568,187.681,190.532,5,9,1,"Older adults have picked up usage, such as Baby Boomers and X'ers.",1,"I check email daily multiple times and google whenever I want to find information. Sometimes, it's for buying something and other times it's just for information.",1,69.82,90.567,126.442,5,2,1,"It's convenient and fast, easier to clean, no leftovers.",4,Don't drink it.,1,2,6,2500,Christian,white,57,8,1,1,no,If rational exercises clarified thinking,"Yes. Perhaps, to expose our own lack of logical thinking when it comes to certain activities.","I'm on dial up, so my time was a bit shortened on the beginning exercises. Thanks, & Jesus Bless.",1,1 +0,2,6.066666667,5.666666667,3.333333333,4.5,5.8,1,1,1,8,7,3,4,8,7,7,5,5,7,7,7,5,7,4,3,7,1,8,1,3,1,1,7,23.714,23.714,26.593,1,4,32.431,37.542,40.054,2,8,36.55,36.55,40.717,1,4,22.304,22.304,24.239,1,5,18.264,18.264,19.766,1,8,37.516,37.516,39.659,1,4,18.764,18.764,22.412,1,5,29.37,32.523,33.464,2,5,17.702,17.702,21.917,1,7,17.823,17.823,25.373,1,7,14.231,26.727,28.106,2,3,8.114,8.114,10.268,1,1,1,5,2,5,2,7,7,7,2,4,3,5,5,2,9,5,5,3,3,8,5,1,8.931,50.938,72.852,6,7,1,People love the taste of pizza.,3,Very often. Twice a week.,1,111.862,205.682,207.996,5,3,2,It cost too much money.,2,Not very often. It is a violent sport.,1,35.498,66.225,177.139,6,3,1,It is more easy to drive a car.,2,I take the bus.,1,22.536,102.02,103.066,5,5,1,"Commercials make a lot of money, so they make it easy for people to watch TV.",3,"Everyday, almost.",1,10.102,141.62,142.685,5,5,1,Computers are more and more and it is hard to complete certain things now a days without being online.,2,Much. A lot. Every day almost.,1,47.501,96.995,98.453,5,4,1,People are hooked on caffeine.,2,Not much. Maybe once per week.,1,1,3,25000,Pentecostal / Christian,black,44,4,1,1,"Yes. It seems too long. They say 10 minutes or so, but it seems to take much longer, more like 35 minutes.",Shopping habits.,Yes. I think that it was mainly about shopping habits but some of the questions seemed tricky.,none,1,1 +0,5,4.6,6.666666667,4,5.333333333,4.5,1,1,1,5,5,7,5,2,4,5,5,6,5,6,7,2,3,2,3,7,1,8,1,3,1,1,7,10.728,10.728,16.934,1, ,0,0,60.004,0, ,0,0,60.014,0, ,0,0,60.005,0,2,23.337,23.337,24.255,1,7,37.624,37.624,40.948,1,1,29.931,29.931,31.431,1,5,32.594,32.594,33.55,1, ,0,0,60.004,0,2,1.855,23.31,30.224,3, ,0,0,60.006,0,2,52.884,52.884,53.952,1,1,1,5,3,5,4,5,7,3,5,6,6,5,5,7,5,3,4,6,4,3,7,1,51.978,136.779,144.606,8,7,1,increase in the price of ingredients,1,"I don't eat pizza parlor pizza very often. Maybe, 4 or 5 times a year. I make homemade pizza on Italian bread and eat that more often. But, I love pizza and could easily eat it more often.",1,74.511,99.402,106.546,5,5,2,It's more convenient to watch it when you want to.,3,I don't watch football.,1,46.985,255.314,256.464,7,2,1,"We are not set up for that mode of transportation. If people want to bike to work, they have to have somewhere to put the bike when they get there. Or, they have to have the kind of bike that folds up and can be brought in to work. Also, many people work a long distance from work and cannot easily ride a bike there. People are used to having their cars to do errands before and after work which might make it difficult to use a bike instead. Also, many people have to transport their children before and after work from school or babysitters.",2,"When I was working I used to the subway about a mile, but I took the subway into work. Now, I only use my car for longer drives and to drive at night to visit people. During the day I walk and do my errands during my walk.",1,52.117,1843.188,1847.275,21,8,1,People don't want to watch tv when it's on. They like having the ability to watch something when it is convenient for them. Now that the technology is available they want the convenience of being able to watch things any time and any where they want. People don't seem to be able to entertain themselves any more so they are constantly connected to various technical devices.,4,I watch too much tv. I watch about 7 hours of tv a day.,1,40.337,92.338,93.546,8,5,1,A lot more people are around who grew up with the internet. More businesses are using it so people have to use it for work.,1,I use email and the internet almost every day.,1,30.612,95.559,99.073,6,5,1,"A lot more people are living alone and don't want to make a whole pot. It's faster, you don't have to wait for a pot to brew. And everyone can have a different type of coffee if they want.",3,I don't drink coffee.,1,2,4,22000,Catholic,Wihte,57,3,1,1,no,I have no idea,"There probably is, but I don't know what it was.",no,1,1 +0,6,6.466666667,6,5.333333333,5.666666667,4.75,1,1,1,8,7,4,5,5,6,8,7,7,9,7,5,5,6,8,3,7,1,8,1,3,1,1,7,10.88,10.88,12.037,1,1,20.631,20.631,22.485,1,5,19.799,19.799,21.455,1,6,30.306,30.306,31.804,1,2,10.614,10.614,17.211,1,4,15.039,15.039,16.917,1,5,14.649,14.649,20.501,1,8,12.863,12.863,14.031,1,3,13.319,13.319,14.437,1,3,8.21,8.21,9.511,1,6,23.88,23.88,25.035,1,5,6.4,6.4,7.714,1,1,1,4,4,4,8,7,6,3,7,3,6,7,5,7,7,7,4,3,4,8,7,1,27.543,51.204,51.983,5,6,1,the cost of transporting the goods,2,one pizza a month,1,18.258,77.101,86.324,6,7,2,the cost of going to a game and the games location,1,i watch occasionally when i have time,1,22.929,44.145,51.566,5,3,1,the cost of gas,1,do not drive to work,1,20.31,109.108,113.772,7,4,1,the number of companies who want to compete for your money,1,rarely watch TV,1,17.405,40.974,65.873,5,8,1,cost of stamps and the quickness of emails vs regular mail,2,check my email multiple times a day and am on the internet often,1,24.613,50.381,51.343,5,6,1,quick option for people on the go,1,have one cup a day,1,2,4,24000,cathloic,white,32,5,1,1,no,understanding patterns of thinking,no,nothing additional,1,1 +0,2,6.466666667,8,4.333333333,6.166666667,4.7,1,1,1,8,8,8,7,5,5,8,9,9,6,2,3,2,9,8,3,7,1,8,1,3,1,1,7,7.689,7.689,8.994,1,3,11.564,11.564,12.992,1,1,29.422,34.292,37.572,2,6,10.684,10.684,31.224,1,8,22.472,22.472,43.368,1,5,14.021,18.229,22.614,2,5,16.479,16.479,21.132,1,6,14.2,14.2,43.668,1,6,25.227,25.227,58.745,1,3,14.921,14.921,16.252,1,5,23.869,51.447,52.382,2,7,23.223,42.855,45.654,2,1,1,3,2,4,8,5,5,2,7,3,7,6,7,5,4,4,2,6,6,7,3,1,62.388,111.89,115.761,6,7,1,need and demand,1,1 slice a month average,1,67.445,121.215,122.608,5,7,1,Expense and time involved in attending a live game.,2,I don't watch,1,83.575,153.107,154.157,6,3,1,Convenience,1,"I try to drive as little as possible, and attempt to arrange safest, fastest and best route to get everything done in one trip.",1,90.167,164.144,165.121,6,9,1,The quality and availability to show and movies are more readily available and streaming availability has improved.,1,about 4 hours a day,1,91.691,205.825,206.837,8,8,1,Easy access and portability to these items as well as more people have learned how to use them.,2,"I use search engines regularly and check my email at least once per day on my computer. In addition I have email, text and internet access on my phone and usually use one of all at least once per day",1,82.62,130.872,146.853,5,3,1,"People are trying to consciously cut back on their caffeine consumption for improved health, and single help encourages less consumption.",1,I drink an average of 2 cups instant coffee per day.,1,2,3,0,christian,white,46,4,1,1,no,not a clue,yes. Not sure,no,1,1 +0,2,6,6.666666667,6,6.333333333,5.2,1,1,1,7,6,6,7,2,6,7,6,7,7,6,6,5,7,5,3,7,1,2,1,2,1,1,7,14.539,14.539,16.078,1,6,10.869,10.869,11.812,1,5,22.633,22.633,23.608,1,6,14.533,14.533,15.886,1,8,25.485,25.485,26.894,1,1,11.242,11.242,12.449,1,5,20.441,20.441,22.128,1,8,20.185,20.185,21.47,1,5,16.138,16.138,17.329,1,5,12.041,12.041,13.13,1,7,21.249,21.249,22.734,1,2,11.103,11.103,12.362,1,1,1,4,5,6,4,3,6,5,5,4,3,2,5,4,5,6,5,6,2,8,6,1,24.744,98.339,109.11,10,7,1,Because of how many people eat pizza.,2,I eat a pizza once a week.,1,17.002,107.708,108.815,7,7,1,"With the great televisions now available and computers and tablets, its more convenient to watch the games on these than in person.",2,I never watch.,1,13.462,88.592,89.597,6,4,1,Perhaps laziness. Also many people live so far away from where they work at.,1,I drive everywhere and usually by myself.,1,46.36,111.068,112.206,7,7,1,It is the demand. Consumers want these devices.,1,I watch a lot of T.V. about 20 hours per week.,1,61.15,218.715,233.482,9,6,1,"Almost everyone has a computer or iPhone today. Back in the day things such as phone books, etc. were available to people and these type of research and information materials are going away.",2,I tend to use e-mail over any other kind of communication for the last 5 years. I also tend to Google search just about anything I do not know or have a question about.,1,27.214,94.206,95.112,6,7,1,Companies have come a long ways with the single cup coffee makers. There are also more single people or single households.,1,I drink about 5 cups a day - throughout the day.,1,2,5,88000,christian,white,49,5,1,1,the shapes.,peoples perceptions,yes I do however i am really unsure what it may be,no,1,1 +0,3,4.933333333,6,4.333333333,5.166666667,4.85,1,1,1,7,4,4,4,5,2,7,4,7,6,5,5,5,5,4,3,7,1,8,1,3,1,1,7,6.807,20.072,24.324,3,2,26.14,26.14,31.145,1,8,27.994,27.994,29.464,1,8,9.248,9.248,10.769,1,5,12.478,12.478,14.564,1,6,9.237,10.698,13.31,2,1,19.992,19.992,21.27,1,5,7.23,7.23,8.723,1,3,14.591,14.591,19.793,1,6,13.993,13.993,15.234,1,4,12.451,12.451,14.677,1,6,5.976,5.976,12.104,1,1,1,5,7,3,7,5,5,5,5,5,5,5,5,5,5,6,4,5,2,5,5,1,29.632,69.68,70.74,7,6,1,THE POPULARITY OF IT,3,"ABOUT 3 TIMES A WEEK, EATING 5 PIECES EACH TIME",1,10.54,31.792,42.285,6,5,2,THE TICKET PRICES,2,I ONLY WATCH THE SUPERBOWL,1,14.769,34.268,37.862,5,5,1,SAVING MONEY,4,I DRIVE MAYBE ONCE A WEEK,1,7.903,42.516,51.941,5,5,1,THE ENTERTAINMENT FACTOR,3,2-4 HRS A DAY,1,11.43,32.552,44.445,5,7,1,FASTEST WAY OF COMMUNICATION,2,I'M ON THE INTERNET MAJORITY OF THE DAY,1,15.853,34.989,41.334,5,3,1,PEOPLE ARE BECOMING SELFISH,4,I DONT DRINK COFFEE,1,1,3,40000,BELIEVER,BLACK,32,5,1,1,NO,HOW THE BRAIN WORKS,NO,NO,1,1 +0,4,5.133333333,5.333333333,5,5.166666667,5,1,1,1,6,6,5,6,4,4,6,6,4,4,4,6,6,6,4,3,7,1,8,1,3,1,1,7,5.754,5.754,7.088,1,8,6.636,6.636,8.368,1,4,15.56,17.043,18.857,2,6,15.97,15.97,17.049,1,3,12.789,12.789,13.882,1,3,6.586,6.586,7.97,1,1,12.461,12.461,14.949,1,5,20.863,20.863,21.421,1,6,8.494,8.494,9.114,1,7,1.144,1.144,2.786,1,5,7.547,10.694,11.432,2,6,1.478,1.478,2.016,1,1,1,6,4,6,4,7,6,6,5,4,6,5,4,4,6,5,4,4,4,4,4,1,2.057,14.908,19.02,5,5,2,travel and food prices have risen,1,i never eat pizza,1,2.359,24.909,25.379,5,5,2,not all are avalible on regular cable,1,i want the superbowl,1,1.963,16.899,21.818,6,5,2,not everywhere is bike friendly,1,i almost never drive,1,5.715,41.597,42.235,5,5,1,urban spread. not all places have cable hookups,2,i watch the local news,1,4.489,32.384,32.941,5,6,1,more countries are allowing it,2,im online all day,1,5.736,23.186,23.678,5,5,2,kuerig,1,i drink coffee daily,1,2,3,20000,christian,white,27,5,1,1,no,unsure,nope,no,1,1 +0,3,6,5,5.666666667,5.333333333,2.95,1,1,1,8,7,7,2,7,7,7,8,7,8,3,7,3,3,6,3,7,1,2,1,3,1,1,7,20.963,20.963,24.038,1,1,46.334,46.334,50.882,1,5,36.752,36.752,37.743,1,6,27.432,27.432,31.771,1,5,32.653,32.653,37.141,1,6,40.994,40.994,42.754,1,5,34.551,34.551,36.904,1, ,0,0,60.013,0,5,50.151,50.151,51.428,1,1,47.748,47.748,51.831,1,8,42.584,42.584,44.493,1, ,0,0,60.012,0,1,1,5,2,2,6,8,7,2,6,2,6,4,2,8,2,3,8,8,3,3,8,1,19.82,91.689,178.597,5,4,2,"There is a wide range of prices. The chains are trying to woo customers and develop loyalty by providing bargains, on the other hand the upscale places catering to a more gourmet set are becoming more sophisticated and expensive... so there are a few pricing levels",1,Very litle... I ate more in the past when the children were small and we would order pizza routinely for dinner. Since I live alone i usually only pick up a piece when i am near my favorite pizza place.,1,40.798,105.388,106.519,5,7,2,"Really expensive, hard to get to, better coverage on tv",4,Only watch the last 10 minutes of really important games with local teams,1,26.402,125.071,126.596,6,2,1,"Jobs are toofar, It is hard and you get to work dirty and exhausted. Few places have a way to freshen up and make yourself presentable for work.",3,Drive when I have to. I drive to my job because it is the only way to get there easily. I did casual carpool when I worked in an area that I could.,1,51.013,169.391,170.908,5,5,1,People are really bored with themselves and have become completely dependent on created extenal stimulation.,4,"When I teach during the school year I inly watch on weekends. During the summer I watch almost every day. I watch mostly news and crime dramas, never reality tv.",1,29.481,135.2,136.381,5,6,1,Easier access and more familiarity. Devices are becoming cheaper and more mobile.. they are always with you,1,Check the news on the internet a couple of times a day. Keep in contact with my daughtes through email several times a week. Internet is my first choice for finding ANYTHING I NEED KNOW.,1,20.968,103.293,104.761,7,8,1,smaller households..convenience. My mother stayed home and always had a pot on the stove.,2,"usually one sometimes to cups a day, French Press, Peet's coffee",1,2,5,55000,unaffilaited,white,72,2,1,1,the matching of forms or maybe its just that ai dont like it,beliefs,"I'm certain there is because there alwasys is, but i can't figure this one out.",no,1,1 +0,1,7,7.333333333,6,6.666666667,3.1,1,1,1,4,9,8,4,5,6,6,9,9,7,9,8,5,9,7,3,7,1,8,1,3,1,1,7,4.088,4.088,6.5,1,4,12.596,12.596,14.158,1,5,22.261,51.593,53.801,4,8,20.133,23.309,24.748,2,4,40.074,56.456,57.403,4,3,41.371,41.371,44.067,1,5,43.985,43.985,45.893,1,6,8.636,8.636,12.583,1,7,30.564,30.564,31.271,1,3,27.77,27.77,29.114,1,1,16.211,16.211,16.816,1,3,28.886,28.886,29.455,1,1,1,5,5,5,9,9,8,1,5,1,9,1,5,9,1,1,5,5,1,5,6,1,3.514,65.781,87.274,9,8,2,the demand determines the pricing as well as the pricing of ingredients.,2,I eat pizza more than 2 times per month usually.,1,42.657,84.997,115.458,8,8,1,"I would assume the majority of people cannot afford to see a game, or possible they do not have a team in their state.",2,I watch footbal from a television. Very rarely I watch it on my laptop.,1,100.449,154.784,193.769,5,3,1,"Laziness, convience of driving, distance of working (a lot of people live on the outskirts of town).",1,"I have two jobs one of which i drive to on a regular basis. The other I split between driving, biking, or using the bus.",1,34.293,122.658,134.462,9,5,1,Advancements in technology and availability & simplicity for the viewer.,1,I rarely watch live tv. I usually view the shows online after so I can skip commercials and I stream from my laptop to my television. I also sometimes use dvr.,1,78.136,143.438,166.952,10,9,1,"the need for internet savvy people in different employement opportunities...it has pretty much become a requirement for most if not all jobs, starting from application to employement.",2,"i use both multiple times daily, its a necessity.",1,47.445,63.349,95.083,7,7,2,people wanting to spend less money at companys like star bucks for something they can do at home.,1,i do not drink coffee.,1,2,3,14000,christian,African American,23,3,1,1,no,I have no clue...nothing really matched up to create a single research topic,i do not know,no,1,1 +0,4,5.733333333,6,4.333333333,5.166666667,6.2,1,1,1,8,7,7,3,5,6,4,7,7,7,5,2,6,7,5,3,7,1,8,1,3,1,1,7,7.895,7.895,9.592,1,1,47.484,47.484,51.381,1, ,0,0,60.01,0,8,0.543,0.543,19.704,1,2,31.344,31.344,55.985,1,1,47.754,47.754,60.009,1,5,44.701,46.757,50.79,2,6,34.028,35.412,46.236,2,3,42.075,42.075,60.023,1,4,19.239,19.239,20.528,1,6,22.079,52.215,58.256,2,1,44.775,44.775,60.019,1,1,1,4,5,5,1,2,8,5,1,6,2,5,5,7,7,6,7,3,6,9,6,1,26.316,71.788,114.232,7,5,1,It can be relatively cheap when thinking about what to eat for dinner.,1,i eat pizza 1 or 2 times a month,1,59.085,102.805,116.033,5,5,1,Too expensive or too far away.,2,I only watch the superbowl.,1,47.286,86.006,86.865,5,3,1,It's not convenient.,3,I drive everywhere.,1,61.757,122.524,123.351,7,5,1,Americans like convenience and fast results.,4,I watch about 4 hours a week,1,52.042,93.058,127.782,6,8,1,"COnvenience, speed and new technology that makes it easier",1,I email and search daily,1,52.504,118.92,123.331,8,5,1,quick and convenient,3,I brew it in a drip coffee maker and drink 3 or 4 cups a day,1,2,3,12000,none,white,53,1,1,1,no,consumer behavior,not sure,no,1,1 +0,5,6.4,5.666666667,4,4.833333333,5.15,1,1,1,8,7,6,5,6,6,7,6,6,6,8,7,6,6,6,3,7,1,8,1,3,1,1,7,8.72,8.72,9.341,1,1,27.279,28.697,29.086,2,7,25.819,33.213,34.629,3,6,23.108,23.108,23.941,1,5,29.804,32.276,33.304,2,6,43.408,43.408,44.051,1,1,26.037,26.037,26.697,1,5,11.62,11.62,14.612,1,5,52.478,52.478,55.123,1,4,17.154,17.154,18.548,1,7,25.28,25.948,26.304,2,7,19.074,19.074,19.763,1,1,1,7,6,6,3,5,7,7,5,5,5,5,4,6,5,6,7,5,6,5,6,1,53.419,102.455,146.643,6,3,1,"The price it takes to make pizza, how much it will profit for, and how much competitors sell for.",1,I eat pizza on occasion.,1,31.435,59.611,59.933,7,5,1,The price of tickets and being able to take time to go.,1,I don't watch football.,1,5.294,55.129,55.468,10,3,1,The commute is often too far and there are not enough bike lanes.,1,I do not own a car.,1,9.993,30.773,83.152,7,7,1,Companies realize how much people watch television and that it would bring in more money to make it available.,1,I watch television occasionally.,1,20.176,60.094,68.54,9,7,1,Growing population and more people becoming interested in internet use.,1,I use internet and email on a daily basis.,1,16.565,86.285,88.993,14,4,1,Convenience and saving money.,1,I drink coffee on occasion.,1,2,3,9000,Agnostic.,White.,18,5,1,1,No.,Unsure.,"Possibly, unsure what it could be.", ,1,1 +0,5,5.533333333,6.333333333,5,5.666666667,4.75,1,1,1,6,6,5,5,5,5,6,6,6,6,6,6,5,5,5,3,7,1,8,1,3,1,1,7,5.424,5.976,9.112,2,1,33.078,33.078,41.854,1,1,46.1,46.1,60.011,1,6,21.587,21.587,34.563,1,7,34.474,53.08,60.005,4,7,22.503,47.415,55.768,4,1,13.113,14.585,27.338,2,6,54.643,55.283,60.012,2,3,45.114,46.978,60.011,2,2,12.52,40.863,60.011,3,8,15.848,54.192,60.011,5, ,41.158,41.158,60.011,1,1,1,5,5,5,4,5,4,4,5,5,6,5,4,6,6,4,5,5,4,6,6,1,48.17,78.537,96.37,5,6,1,The ingredients needed to make it.,1,I eat once to a few times per month.,1,33.622,84.734,85.441,5,5,1,Ticket expense and they prefer the comfort of their own home.,1,I don't watch football.,1,39.14,66.851,104.301,5,5,1,"Location is too far or the person, no energy in the morning, or inappropriate feeling for work attire.",1,I don't drive.,1,39.002,156.497,157.339,15,6,1,Companies or people are always trying to make more innovation ways to do things. The availability provides for convenience and give consumers a better chance of watching what they want when they want.,1,Daily,1,38.562,98.138,100.405,6,7,1,"More people are becoming tech-saavy and the younger generation are already getting smartphones, computers, or tablets as kids.",1,Daily,1,45.602,104.417,105.755,5,5,1,Quick and convenient amount. No need to worry about excess.,1,I rarely drink.,1,2,3,12000,none,aisan,23,5,1,1,no,Why Americans have the habits they do?,Not sure.,Nope,1,1 +0,5,7.6,7.666666667,5.333333333,6.5,4.75,1,1,1,9,9,8,1,9,8,6,8,9,9,9,6,7,9,7,3,7,1,8,1,3,1,1,7,14.128,14.128,15.626,1,1,32.635,32.635,33.522,1,5,48.215,48.215,49.182,1,6,20.122,20.122,20.908,1,2,23.617,24.175,25.978,2, ,0,0,60.004,0,8,12.925,12.925,14.948,1,5,33.908,33.908,34.852,1,7,36.469,36.469,37.861,1,3,19.608,19.608,21.529,1,7,44.652,44.652,46.78,1,8,27.774,27.774,28.612,1,1,1,2,4,4,6,6,6,7,4,2,9,5,6,8,4,6,5,6,5,9,7,1,9.742,59.17,60.126,5,9,1,rising energy and transportation costs,1,I eat pizza 2 or 3 times a month,1,6.091,28.686,30.03,5,5,1,I have no idea,3,none at all,1,7.352,43.681,70.508,5,5,1,It is too dangerous and the distance to work is often too far,3,"I rarely drive anywhere, maybe 5000 miles in a year",1,40.646,86.27,87.34,5,8,1,people are more technically savvy,2,I watch very little TV or movies,1,49.387,100.723,141.373,5,6,1,the population in general has grown and accessibility to computers has also grown,1,"I use email occasionally, but I rely on google searches to find information",1,7.476,44.142,54.164,5,6,1,a lot of people live alone and do not want leftover coffee,3,I do not drink coffee,1,2,3,23500,none,white,58,3,1,1,no,do not know,do not know,no,1,1 +0,5,5.333333333,6.666666667,6.333333333,6.5,5.6,1,1,1,5,6,4,7,5,7,7,1,1,7,7,6,4,8,5,3,7,1,8,1,3,1,1,7,5.245,5.245,7.136,1,6,7.529,7.529,8.393,1,7,10.61,10.61,12.253,1,8,20.986,25.414,28.441,3,2,23.608,23.608,25.68,1,2,34.659,34.659,52.406,1,1,10.101,10.101,11.624,1,2,14.534,14.534,16.499,1,7,30.077,30.543,32.02,2,4,11.336,11.336,13.195,1,6,11.597,11.597,12.578,1,7,12.21,23.169,25.119,2,1,1,2,7,5,9,6,8,5,1,1,6,9,8,8,5,9,5,1,2,9,8,1,21.196,103.285,104.232,6,5,2,"I believe that the rate of comsumption affects the costs of pizza, in adidttion to risisng food costs.",2,"I eat pizza maybe once a month. Its sort of expensive here in the midwest, its not like brooklyn where you can get a slice for $1.",1,27.218,63.021,63.744,6,8,1,The cost of tickets are too high.,2,I dont watch any football.,1,15.223,151.27,151.88,12,4,1,"Ridging a bike is more energy, and unfortunately America is a lazy nation.",4,I drive when necesary only to cut donw on the rising gas costs.,1,19.507,94.446,94.898,7,6,1,There should be alternatives to cable beacuse it is so expensive.,3,"I dont really watch much TV, but i do use online streaming services like Netflix, Hulu, etc. I own a tv, but i use a chrome cast to view programs because it is much cheaper than cable.",1,21.895,64.964,65.446,6,9,1,More people are aware of exactly how uch the internet can help them do.,3,I check my email severaly times a day and i search something at least daily.,1,50.853,186.771,187.781,12,7,1,I think the success of the single cup brewers can be attributed to the increase in the percentage of people aware of them in the past year. Awareness grew by 11% in the past year. Good marketing and branding definitely can influence the awareness of a product.,4,"I occasionally drink coffee, perhaps 4 cups a month.",1,2,4,45000,christian,african american,22,2,1,1,No,"Im actully very unsure, the sections were varied and i can't form a link between them.","yes, but I can't lay a finger on it.",No.,1,1 +0,7,6.133333333,5.666666667,5.666666667,5.666666667,5.35,1,1,1,7,6,6,5,6,7,7,6,6,6,6,7,5,6,6,3,7,1,8,1,3,1,1,7,13.589,13.589,14.722,1,1,25.546,25.546,26.82,1,5,22.186,22.186,60,1,6,14.304,14.304,15.335,1,2,30.709,55.122,59.136,3,3,39.937,39.937,60,1,1,22.014,22.014,22.986,1,5,27.995,44.781,49.77,2,7,45.699,45.699,60.002,1,3,18.619,18.619,20.111,1,6,27.662,44.423,46.88,3,2,56.215,56.215,59.999,1,1,1,5,1,7,4,4,7,7,7,5,7,7,8,6,6,7,5,6,2,9,5,1,15.319,60.556,61.483,5,5,1,"Marketing, Salary, Food costs",1,I eat pizza at least once a month.,1,6.474,35.297,57.72,5,6,1,Most viewers cannot afford to go to the view ball games live,1,I watch it occasionally with family and friends.,1,22.92,99.406,101.648,6,6,1,It is too far for most Americans to drive to their work,1,I drive once or twice a week to work. I take public transportation. I drive to social activities in the weekends.,1,8.102,25.046,52.14,5,5,2,It will lead to more demand for videos,1,I seldom watch TV,1,6.773,60.39,61.849,6,7,1,"The availability of technology like PCs, Laptop and Mobile devices",1,I use google and email everyday at work.,1,12.352,51.312,52.178,5,5,1,Convenience of preparing a brewed cup of coffee.,1,I drink about a cup a day.,1,1,5,65000,N/A,Asian,39,5,1,1,No,I don't know.,I don't know,N/A,1,1 +0,6,6.533333333,6.333333333,4,5.166666667,5.3,1,1,1,9,8,5,2,9,5,1,5,5,8,8,6,9,9,9,3,7,1,8,1,3,1,1,7,7.126,7.126,8.408,1,1,10.106,10.106,12.429,1,3,26.81,28.993,30.187,2,8,15.232,15.232,21.761,1,2,11.125,11.125,14.062,1,1,25.278,25.278,26.632,1,1,23.955,23.955,24.645,1,2,20.084,24.963,26.669,2,6,14.613,14.613,15.27,1,2,14.908,16.028,18.614,2,8,20.517,25.812,27.31,2,1,17.973,17.973,19.526,1,1,1,5,5,5,2,8,7,8,9,3,7,6,8,5,7,8,5,5,5,9,9,1,33.016,49.807,65.922,5,5,1,Customers' willingness to pay these prices.,2,"In my house, we usually order in pizza once a week.",1,30.716,85.194,86.119,6,5,2,"Because tickets are expensive, and fans have so many other in-home options to watch the games.",3,I don't watch it unless someone else is watching it on TV and I happen to be in the room.,1,118.258,183.536,194.49,5,2,2,"As a society, we've become so dependent on our personal vehicles that few of us are reluctant to give up the independence that those vehicles afford us. Also, America's transportation infrastructure isn't mass-transit friendly, and offers few options for workers who live far from their places of employment.",4,I drive by myself to work and back every day.,1,43.333,89.244,105.333,5,5,1,"Americans want convenience, so companies are giving it to them. I think that fewer people are watching movies in theaters because of the costs, so at-home devices are filling in that market.",4,"I typically leave the TV on during the evenings, but usually only watch it here and there.",1,50.105,78.744,94.906,5,9,1,"The prevalence of Internet tools, and the continual upgrades in computing technology.",1,"I use email and conduct Internet searches many times a day, for work and personal use.",1,31.75,72.805,73.775,6,5,1,Convenience; it can be prepared at home. It also doesn't require that use of pots that will require washing.,4,I purchase a coffee every workday morning from a coffee shop.,1,2,4,60000,Catholic,Caucasian,35,6,1,1,No,I don't know.,"Yes, but I don't know what it is.",No.,1,1 +0,8,4.733333333,9,4,6,5.2,1,1,1,7,5,5,5,5,1,1,1,9,7,7,5,1,5,7,3,7,1,8,1,3,1,1,7,3.278,3.278,6.448,1,1,12.917,12.917,20.788,1,3,17.161,17.161,26.142,1,6,17.582,17.582,26.92,1,2,9.936,9.936,23.286,1,6,25.921,52.574,60.034,2,1,49.706,49.706,60.034,1,5,19.734,39.526,40.288,3,7,46.555,48.85,49.387,2,3,22.202,22.202,37.243,1,6,36.43,50.654,51.277,3,1,16.686,47.739,60.033,4,1,1,5,8,5,9,6,7,5,5,4,5,6,5,4,2,4,6,5,6,9,2,1,38.287,63.747,86.661,6, ,1,"cost of materials, wages, transportation",1,i probably eat pizza once a week,1,9.728,44.585,52.149,5,6,1,because it is expensive,3,i dont watch football,1,78.848,116.217,140.104,5,1,2,"long commutes, unsafe, dont want to get sweaty before work",4,i drive whenever i have to get somewhere,1,72.547,143.549,144.361,5,9,1,"technology, accessibility",1,"i watch netflix,hulu,hbogo, xfinity on my computer because i dont own a tv",1,88.515,185.984,225.182,8,9,1,"internet is more accessible, majority of people have smart phones with data plans",1,i use email less than search engines because i use text and imessage and social media instead of email,1,7.868,81.213,82.979,9,5,1,people are becoming more particular in how they want their coffee to taste,2,i drink coffee 1 or 2 times a week,1,2,3,40000,none,white,25,1,1,1,no,haven't a clue,probably since i dont know how it relates,no,1,1 +0,5,5.533333333,4.666666667,4.333333333,4.5,5.15,1,1,1,6,7,4,6,5,5,4,4,5,7,7,5,6,6,6,3,7,1,8,1,4,1,1,7,5.522,5.522,12.976,1,1,18.235,19.368,25.688,2,3,37.939,37.939,40.982,1,6,29.133,29.133,32.028,1,5,11.844,11.844,34.397,1,4,44.939,44.939,50.123,1,5,33.408,33.408,37.545,1,7,59.543,59.543,59.999,1,7,16.436,16.436,22.858,1,2,19.004,37.762,39.653,2,8,11.791,11.791,12.797,1,2,21.495,24.888,25.96,3,1,1,4,5,5,6,5,6,4,5,6,4,5,5,5,5,6,5,4,4,5,5,1,120.758,140.68,142.858,5,6,1,"demand, cost of inputs",2,often,1,45.433,74.491,76.954,7,5,1,There aren't enough tickets for all those fans.,2,don't watch.,1,5.324,25.749,26.447,5,5,2,Many live too far from work.,3,Don't own a car.,1,6.33,36.373,37.273,6,4,1,need to be constantly amused.,4,not too often,1,20.546,51.145,52.043,5,4,1,Accessibility.,1,I check email multiple times a day and search a few times a week.,1,23.869,47.968,49.066,5,3,1,"can be done at home, and if you're single.",2,occasionally,1,2,4,45000,none,black,22,5,1,1,the phrasing of the last survey,No idea. Reasoning?,"Yes, but can't figure out what.",Nope.,1,1 +0,5,5.466666667,5,3.666666667,4.333333333,5.05,1,1,1,4,6,6,4,6,6,5,6,4,6,6,6,5,6,6,3,7,1,8,1,3,1,1,7,11.815,11.815,13.762,1,5,15.625,15.625,20.742,1,3,22.557,22.557,24.194,1,4,19.319,21.441,24.222,2,2,19.025,21.139,22.552,2,7,20.278,23.668,24.397,3,1,21.498,21.498,23.362,1,5,18.81,20.906,21.539,2,3,14.515,14.515,16.406,1,4,16.711,16.711,19.639,1,8,21.256,21.256,22.444,1,3,15.584,15.584,19.001,1,1,1,4,6,6,6,4,6,4,5,4,5,5,4,5,5,6,6,4,4,5,5,1,55.967,143.496,144.571,5,5,1,Cost of ingredients. Demand.,1,I love pizza but I probably have it less than once a week.,1,60.478,110.526,132.617,5,5,1,"It's expensive, time consuming, possibly difficult to get to a game depending on where you live.",2,Sometimes I watch a game or two during the season (go Packers) and the Super Bowl but I'm not an avid fan.,1,57.968,85.816,109.888,5,2,1,The layout of the land. In cities it's a bit easier but for most the commute it too long.,1,Unfortunately I drive to work but I have biked before.,1,53.747,126.448,127.24,5,2,1,Better technology. Cheaper. More people can afford devices so more are made. Demand.,1,I own a TV but I never use it. A few times a month I'll watch a program on my laptop.,1,39.506,87.652,88.818,5,8,1,Internt is more affordable and easily accessible.,1,I use email on a daily basis for work and search the internet daily for work and pleasure.,1,34.769,86.677,87.709,5,4,1,It's convient and quick.,1,I use a drip. I make a four-cup pot pretty much everyday.,1,2,4,34000,None,Caucasian,26,5,1,1,Yes. The find the shape thing seemed like part IQ test.,I really have no idea. This survey was all over the place.,Yes. It's like your testing intelligence and assumptions and god knows what.,No,1,1 +0,2,4.2,7,7.333333333,7.166666667,5.55,1,1,1,7,2,7,3,7,3,3,3,7,6,5,2,3,2,3,3,7,1,8,1,3,1,1,7,9.804,9.804,15.736,1,1,25.91,25.91,27.909,1,1,16.432,16.432,17.856,1,5,8.683,35.179,38.232,3,5,37.771,37.771,38.966,1,6,13.683,13.683,14.709,1,5,17.895,17.895,18.923,1,7,20.989,28.399,29.524,2,3,19.589,19.589,20.751,1,7,13.533,20.196,21.836,2,7,14.878,14.878,16.068,1,1,8.295,13.842,14.958,2,1,1,6,3,6,4,4,7,7,7,6,4,6,7,5,7,8,3,3,4,3,7,1,32.939,90.822,91.643,6,7,1,The economy has gotten worse so pizza prices have gone up.,1,I eat pizza maybe once a month.,1,19.404,69.777,72.692,5,8,2,This probably occurs due to ticket prices and that the locations of the games are often not nearby.,3,I watch football weekly during the football seaons (on tv).,1,37.544,76.797,77.833,6,7,2,"Often, our workplaces are located a distance that is too far to bike.",2,I don't drive often due to being a student who lives on campus.,1,65.953,129.039,130.277,6,7,1,Humans are becoming more intelligent when it comes to devices.,2,"I usually watch a a few hours a day, though it does revolve around how busy I am.",1,34.791,101.146,102.166,5,7,1,Perhaps the fact that access to the internet is more available and fast.,1,I tend to check my email daily as I am a student and need it for such purposes. I also search often on the internet because of homework.,1,22.089,65.867,66.986,5,7,1,Perhaps the fact that many companies (even starbucks) offere devices that allow for one to brew a single cup.,2,"I drink coffee every once in a while, but I am more of a tea drinker.",1,2,2,100000,Christian,Caucasian,21,7,2,1,"Yes, trying to figure out what the last shape was. Very confusing.","Honestly, I am not sure.","Yes, but I am not sure. There were different parts of the survey that looked at different things, rather than one.",No.,1,1 +0,9,5.533333333,5,3.666666667,4.333333333,5.2,1,1,1,3,3,6,3,7,5,3,6,6,8,7,7,6,5,8,3,7,1,8,1,3,1,1,7,2.213,3.842,5.2,2,1,7.232,7.232,8.683,1,1,38.189,38.189,60,1,6,11.19,11.19,48.533,1,2,25.35,25.35,29.547,1,2,43.514,43.514,47.08,1,1,25.592,25.592,30.349,1,5,24.648,24.648,60.001,1,7,49.402,56.33,59.999,2,2,11.949,13.762,16.019,2,6,23.127,23.127,32.432,1,1,33.729,33.729,35.401,1,1,1,3,3,3,3,4,6,7,4,7,4,3,7,7,5,3,3,6,7,5,8,1,8.402,157.35,164.496,5,5,2,demand,2,eat a couple of times a month,1,14.832,33.993,34.484,9,3,1,cost,2,don't watch,1,155.675,221.205,221.738,13,3,1,"takes time, is dangerous in cities and takes too long to do outside of cities",3,don't drive,1,184.47,205.391,205.954,5,5,1,"advances in technology and availability of programming, also demand",3,don't watch,1,123.032,192.009,192.823,25,5,1,"availability, necessity for everyday transactions",1,do them every day,1,14.862,30.989,32.078,6,5,2,convenience,3,"drink occasionally, a few cups a week",1,2,6,90000,atheist,asian,33,3,1,1,no,no idea!,no,no,1,1 +0,6,6.733333333,8,5.666666667,6.833333333,5.4,1,1,1,7,7,7,7,4,5,7,7,6,7,7,8,7,8,7,3,7,1,8,1,3,1,1,7,4.826,4.826,5.617,1,1,11.609,11.609,12.266,1,3,22.076,22.076,26.521,1,6,32.403,32.403,33.148,1,2,15.521,15.521,16.86,1,1,25.483,26.6,45.264,2,8,13.963,13.963,14.864,1,5,30.06,30.06,30.805,1,7,29.853,31.017,31.362,2,8,55.67,55.67,56.951,1, ,0,0,60.004,0,3,51.531,51.531,53.136,1,1,1,3,7,5,3,7,7,3,6,3,7,7,6,4,6,6,6,3,6,8,7,1,7.317,25.91,26.556,5,8,1,Cost and quality of ingredients.,1,I eat pizza once a week.,1,13.688,40.869,50.035,5,7,2,It's expensive and time consuming.,1,I watch weekly if I am home to watch my favorite team.,1,11.959,33.934,47.996,6,4,1,"Danger, takes too long, requires exercise",3,I drive by myself or with my kids in the car.,1,8.951,49.618,52.132,5,8,1,Interest and convenience plus relatively inexpensive.,2,I watch 10+ hours a week.,1,11.031,37.941,38.688,6,8,1,"Convenience, easy and fast answers",1,I used the computer and email multiple times daily.,1,17.879,37.769,38.197,5,6,1,Convenience,4,I don't drink coffee.,1,2,4,100000,christian,white,42,7,1,1,no,"Understanding things in society, why things are they way they are.",no.,no,1,1 +0,2,5.066666667,4.666666667,4,4.333333333,5.4,1,1,1,8,6,6,2,3,5,7,6,7,7,6,3,4,4,2,3,7,1,2,1,3,1,1,7,14.289,14.289,20.696,1,5,23.173,23.173,23.771,1,8,30.758,30.758,31.868,1,6,15.272,15.272,16.247,1,5,26.482,26.482,27.56,1,7,22.393,22.393,23.056,1,5,18.582,18.582,19.796,1,6,8.442,8.442,9.12,1,3,9.183,9.183,10.303,1,3,17.017,17.017,18.551,1,4,24.933,24.933,26.107,1,7,6.169,6.169,6.943,1,1,1,6,8,8,8,4,6,2,7,2,9,8,8,9,6,8,2,3,8,9,7,1,73.872,112.352,140.76,6,2,1,I think pizza is pretty inexpensive so more people can afford to eat it more often than they can eat other foods.,3,I do love pizza; but I hardly eat it. I wish it were more healthier.,1,43.397,122.716,135.501,5,6,1,"Because no one can afford to go to the live games because the price of the ticket and everything else (i.e, food, parking, beer, etc.) is too high.",4,I hardly ever watch football; but I do like to watch the Super Bowl because of the commercials.,1,46.54,82.158,99.133,5,4,2,Their work places are too far away to ride their bicycles.,3,I drive to work everyday. It would be impossible for me to ride a bike or take a bus to work.,1,76.139,143.753,197.065,5,6,1,"Consumers want more ways to watch videos besides sitting at their computer or on TV,",4,"I watch a lot of TV. Usually, I do something else when I watch like read my mail, play games on my phone or lay on the couch.",1,89.341,137.644,138.237,4,6,1,More people are going online doing searches.,1,I use both every single day. I look up everything on google and I get a ton of email messages everyday.,1,346.595,416.706,417.442,5,2,1,You can make a variety of different flavors of coffee for everyone in your family. / / It's very fast to do. / / It's simple to operate.,2,I don't drink coffee. I like tea.,1,2,5,63500,Catholic,White,50,5,1,1,The puzzle matching section. It was very hard for me.,I am not sure.,I'm not really sure.,None,1,1 +0,4,1.8,5,3.666666667,4.333333333,4.25,1,1,1,3,5,3,1,1,1,1,1,5,1,1,1,1,1,1,3,7,1,8,1,3,1,1,7,6.717,6.717,7.678,1,1,17.328,17.328,18.823,1, ,0,0,60.191,0,6,9.697,9.697,10.656,1,8,14.06,14.06,16.386,1,3,30.573,31.417,31.832,2,1,13.701,13.701,14.857,1,7,2.626,14.669,16.018,5,2,3.232,3.232,4.539,1,1,14.238,14.238,15.823,1,8,19.154,20.707,23.75,2,7,13.008,13.008,13.762,1,1,1,4,2,1,7,2,7,3,5,7,3,3,5,8,1,2,5,6,3,6,5,1,22.231,47.241,58.339,5,9,2,supply and demand,3,about 6 times a year,1,7.71,32.942,33.64,2,1,2,it's not as easy to see a game live,3,I never watch it,1,7.207,12.365,33.563,4,5,1,"to inconvenient, work is too far or no good paths",4,I drive all the time,1,42.903,45.865,79.135,3,1,1,"the more TVs there are, the less people are focused on what's really happening in the world.",3,about 4 hours a week,1,23.036,31.902,69.734,4,5,1,kids are using it at younger and younger ages,1,I search almost every day. I get email everyday but send about 2-3 times a week,1,5.975,6.798,30.053,2,5,1,convenience,3,I can't stand coffee,1,2,4,50000,not religious,white,52,5,1,1,no,I don't know,I don't know.,no,1,1 +0,3,7.4,6.666666667,5.333333333,6,5.4,1,1,1,8,8,9,5,8,5,7,9,8,7,8,8,8,7,6,3,7,1,8,1,3,1,1,7,15.437,15.437,18.203,1,1,25.104,25.104,32.923,1,8,28.949,28.949,31.37,1,5,26.695,26.695,28.714,1,2,19.542,19.542,21.867,1,7,26.781,26.781,28.536,1,5,31.503,31.503,34.765,1,3,16.143,16.143,18.589,1,3,42.113,42.113,43.704,1,5,55.933,55.933,57.793,1,8,38.13,38.13,39.736,1,1,35.148,35.148,36.915,1,1,1,6,1,4,4,6,8,6,6,4,5,6,7,9,7,8,4,4,7,9,5,1,36.426,81.963,83.185,5,6,1,actually more reasonable. more pizza places so they have to compete,1,once a week,1,64.722,91.202,92.543,5,5,1,too expensive,1,i don't watch,1,55.927,82.438,83.846,6,5,2,work is too far away,1,i drive to work every day,1,88.947,123.953,136.31,5,8,1,people want more from technology,2,i watch about 10 hours a week,1,45.89,71.475,80.651,5,6,1,easy mobility. smartphones,1,check email several times daily. use search engine a few times a day,1,27.939,55.522,62.544,7,6,1,easy and fresh,2,i don't drink coffee,1,2,4,40000,christian,white,41,8,1,1,no,brains ability to see what belongs,"yes, no idea",no,1,1 +0,7,5.466666667,5.666666667,4.333333333,5,5.8,1,1,1,7,7,5,4,7,4,2,5,6,7,6,3,5,7,7,3,7,1,8,1,3,1,1,7,9.711,9.711,10.991,1,1,34.17,34.17,38.675,1,1,21.228,21.228,22.205,1,6,23.407,23.407,26.856,1,5,25.417,25.417,26.482,1,2,24.469,24.469,26.429,1,1,32.116,32.116,32.886,1,5,20.093,20.093,28.494,1,7,32.269,32.269,33.15,1,3,40.757,40.757,45.054,1,6,28.771,38.546,39.835,2,6,24.166,24.166,24.943,1,1,1,5,4,5,4,3,7,6,3,7,5,4,6,5,6,7,2,3,6,4,6,1,62.253,89.269,90.19,6,5,2,The demand for pizza.,3,I eat pizza once a month.,1,171.693,277.924,316.334,6,5,1,I think the price of going to games is prohibitive. I live in the Pittsburgh area and season ticket holders sell tickets for way more than face value. A viewer would have to pay for parking and food as well.,4,I don't watch football. Not a fan.,1,25.305,86.528,137.498,7,3,1,"There are no bike roads. It's too dangerous to ride on the roadways with all the fast traffic. Also, many people don't live so close to where they work.",4,"I commute approx. 20 miles each way to work, 5 days a week. I live in rural community and also commute to the city on the weekends.",1,100.161,211.896,235.065,6,5,1,"People aren't home as often, so they like to watch things wherever they are.",4,"I probably watch about 6 hours of tv a week. I watch more while hockey season is in, probably 14 hours a week.",1,107.982,471.735,474.138,6,7,1,Many people work long hours and use the internet while at work. I think many people shop online because it's easier and they can compare prices. The internet also makes it easy to communicate with people.,2,"I use email daily for work and personal use. I use internet search engines daily, mainly for personal use but also for work related searches occasionally.",1,22.733,53.925,54.605,5,5,1,People aren't home long enough to drink a pot of coffee.,3,I don't drink coffee.,1,2,4,120000,none,white,45,3,1,1,no,I have no idea.,"I would guess so, but don't know what.",no,1,1 +0,1,6.866666667,8.666666667,7.333333333,8,5.95,1,1,1,7,8,5,7,8,4,8,8,7,4,5,8,8,8,8,3,7,1,8,1,3,1,1,8,7.179,7.179,9.777,1,1,16.3,16.3,18.49,1,5,20.249,20.249,23.587,1,5,15.314,15.314,16.32,1,8,25.595,25.595,29.388,1,7,29.2,29.2,46.071,1,4,14.186,14.186,16.401,1,7,26.767,26.767,28.931,1,1,33.82,33.82,43.837,1,8,17.875,17.875,22.144,1,1,44.244,44.244,45.572,1, ,0,0,60.014,0,1,1,5,8,7,8,2,8,7,5,8,8,7,8,8,9,7,5,2,2,9,8,1,75.171,202.141,204.414,5,9,1,So many people are eating pizza that the prices of pizza have come down because of the competiton between the companies.,2,"I used to eat a lot of pizza, I am diabetic now and do not eat pizza at all.",1,155.827,297.319,299.241,5,8,1,The prices of tickets are just to high to attend the live game.,1,I have no need at all for football. I don't understand the game at all. Grandson's have tried to help me but I just don't like football.,1,58.274,200.186,201.198,5,9,2,Many people don't have cars or can't afford cars and use their bikes to go to work. I find that others use their bikes to go to work for excerise and to stay healthy.,3,I am now retired; but when I was working I drove to work on a daily basis.,1,85.482,279.54,281.329,6,9,1,Many people cannot afford to go out anymore and with these devices you can stay at home and enjoy the movies for a little amount of money.,3,I watch TV on a daily basis; mostly at night. I cannot afford to go to the movies so I watch TV or movies on my DVD. /,1,270.563,394.499,494.626,5,8,1,More people use the internet to find things that they want to know about and get a quick answer.,2,"I use both on a daily basis, many times a day.",1,21.035,149.471,151.221,5,5,1,No fuss and no mess. Make one cup at a time and the coffee is hot and fresh when needed and no clean up.,3,I am a coffee drinker; I consume at lease one pot of coffee a day.,1,2,4,9090,Christian,Black,68,5,1,1,No not at all.,behaviors and thinking.,I thought so; but I am now not sure.,Only that I enjoyed it very much.,1,1 +0,4,5.2,7.333333333,2.333333333,4.833333333,4.4,1,1,1,3,8,8,1,8,5,6,8,6,4,7,6,3,3,2,3,7,1,8,1,3,1,1,7,3.287,3.287,3.988,1,4,11.117,11.117,14.049,1,3,23.126,23.126,27.659,1,8,25.359,25.359,26.597,1,2,12.733,12.733,15.451,1,7,17.917,49.57,60.004,2,1,21.743,21.743,23.244,1,2,25.42,25.42,27.053,1,3,18.384,18.384,20.767,1,3,21.361,21.361,22.579,1,7,29.398,29.398,30.514,1,3,4.841,4.841,11.653,1,1,1,5,8,5,3,7,4,4,7,2,5,4,6,7,2,4,2,6,5,3,7,1,66.152,202.62,203.597,8,5,2,There's a lot of consistent demand. Its easy and cheap to make so there are a lot of pizza places and it is sold fairly cheaply.,1,I have pizza as a meal about once a week.,1,40.406,168.541,169.195,8,3,1,Because they would need to be in the correct city and buy tickets and drive to the stadium. Also many people watch more than one team and or game a day.,2,I do not watch football if I can at all avoid it.,1,28.592,561.544,562.34,9,1,1,Most people live too far away to bike to work in a reasonable amount of time. Also American roads have very few bike lanes. i know I would not feel safe biking to work. Also arriving to work sweaty is gross and unprofessional.,2,"I drive alone to work 5 days a week. It takes me about a half hour. I like the idea of biking, but I would not feel safe doing it and would have no way to shower afterwards.",1,298.966,331.487,361.046,6,8,2,"I like it, I hate watching live tv.",2,I rarely watch live television. i predominately watch Netflix and dvds.,1,36.057,144.82,145.392,6,9,1,People who have been reluctant to do so are finding they cannot accomplish many needed tasks anymore without the internet. Also those who were reluctant may have come to find the internet more appealing and useful than they thought.,1,"I use both often, everyday.",1,28.547,99.503,103.846,9,3,1,"It is fast, convenient, cheaper than a chin fancy cup of coffee, and tastes better to most people.",2,"I hate coffee, so I don't drink it.",1,1,5,40000,atheist,white,31,3,1,1,the patterns were hard to figure out.,no idea,"I'm sure there is, but when taking a study like this I try to keep my mind from being too inquisitive so as not to bias my results.",nope,1,1 +0,6,3.4,9,5,7,5.05,1,1,1,3,2,2,2,2,3,1,7,7,5,1,5,3,7,1,3,7,1,8,1,3,1,1,7,6.157,6.157,9.559,1,1,19.136,19.136,29.905,1,3,39.019,39.961,41.373,2,6,23.675,23.675,25.342,1,5,26.899,26.899,29.429,1,2,30.179,30.179,32.209,1,1,27.184,27.184,28.424,1,6,22.411,22.411,35.542,1,3,21.807,21.807,23.63,1,3,7.492,7.492,10.444,1,2,14.81,14.81,20.911,1,2,39.988,39.988,43.076,1,1,1,4,9,4,4,3,8,3,6,4,6,4,7,5,1,4,6,3,6,3,5,1,5.149,62.781,72.357,7,9,1,Demand.,2,"I eat pizza a few times a month, maybe once a week.",1,25.316,68.655,69.444,8,4,1,Expensive tickets.,3,I don't really watch it.,1,9.313,52.496,94.214,9,2,2,"In some cases, laziness. In others, people just live too far from their workplace to use a bike.",3,I don't.,1,9.764,67.468,73.036,9,9,1,People want to watch videos.,3,I have the TV on in the background while doing other things most of the time.,1,9.068,64.026,73.624,9,9,1,More and better access.,1,I use them daily.,1,3.106,56.25,74.341,9,9,1,Most people only want a cup at a time and won't finish an entire pot of coffee.,2,I drink a cup or two of coffee most days.,1,1,3,50000,None,Caucasian,25,1,1,1,No.,I couldn't really guess.,"Yes, only because the tests didn't seem to match the initial description of the study.", ,1,1 +0,6,5.933333333,4.333333333,4.666666667,4.5,5.5,1,1,1,6,7,7,3,6,9,8,1,7,9,5,7,5,3,6,3,7,1,8,1,3,1,1,7,7.618,7.618,11.332,1,1,16.181,21.276,59.697,3,3,32.825,42.256,47.813,3,6,34.23,48.181,50.977,2,8,12.232,12.232,24.235,1,7,13.88,20.552,22.609,2, ,0,0,60.005,0,5,12.321,12.321,29.294,1,1,10.015,10.015,15.36,1,2,7.647,57.554,59.955,4,7,15.298,15.298,21.971,1,3,20.595,20.595,60.005,1,1,1,3,9,9,9,5,9,1,5,6,5,5,5,5,7,3,5,5,7,5,4,1,332.616,392.69,393.646,5,1,1,The demand and how quickly they can be made.,1,I eat pizza every three months or so.,1,170.093,190.948,205.361,5,7,1,The prices,2,"I don't watch it much, except for the super bowl.",1,69.664,90.008,139.015,5,4,1,"The workout, which would make you arrive to work sweaty and also the constraint of time.",1,I drive to work solo,1,76.983,142.722,143.55,7,4,1,It will be easier to access information from viewing devices and either bring families closer together or more apart.,2,I watch it everyday while multitasking on other jobs.,1,115.019,166.154,173.445,6,8,1,Children being raised on technology early.,1,I search for the website once or so and then make it a favorite. Email I check everyday.,1,108.372,147.805,160.928,7,3,2,Less coffee wasted for just one person.,4,I don't drink it.,1,2,4,49000,Catholic,Hispanic,21,3,1,1,The disagree or agree statements had grammatical errors.,How we solved logical problems,Whether we paid attention.,Nope.,1,1 diff --git a/wip/Paths to Bayes.drawio b/wip/Paths to Bayes.drawio new file mode 100644 index 0000000..c5cb696 --- /dev/null +++ b/wip/Paths to Bayes.drawio @@ -0,0 +1,13 @@ + + + + + + + + + + + + + diff --git a/wip/Patsy contrast analysis tutorial.ipynb b/wip/Patsy contrast analysis tutorial.ipynb new file mode 100644 index 0000000..a16369c --- /dev/null +++ b/wip/Patsy contrast analysis tutorial.ipynb @@ -0,0 +1,412 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Patsy contrast tutorial\n", + "\n", + "The following tutorial for the patsy package is based on combination of Schad, Vasishth, Hohenstein, and Kliegl (2020), Tutorial of contrast coding for analysis specification using R packages and https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import packages for conducting the tutorial\n", + "import pandas as pd \n", + "import numpy as np \n", + "import seaborn as sns\n", + "from statsmodels.formula.api import ols" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The General issue\n", + "\n", + "Traditionally the data produced from experimental designs are often analysed using some variant of the analysis of variance (ANOVA) depnding on the experimental design. Standard practive is analsye the data with the desired ANOVA check the F-test for significance followed by Post hoc analysis of all the differences with some form pairwise comparison (Bonferroni, typically). This apporaoch is limited though if researchers have a prior theory driven comparison hypotheses before seeing the data.\n", + "\n", + "The tutorial below focuses on Frequentist statistics but its application in the following notebooks is of course Bayesian." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      SubjectFDVF_Recoded
      01F11.1248691
      12F10.6776491
      23F10.6943661
      34F10.5854061
      45F10.9730821
      51F20.1396922
      62F20.9489622
      73F20.4477592
      84F20.6638082
      95F20.5501262
      \n", + "
      " + ], + "text/plain": [ + " Subject F DV F_Recoded\n", + "0 1 F1 1.124869 1\n", + "1 2 F1 0.677649 1\n", + "2 3 F1 0.694366 1\n", + "3 4 F1 0.585406 1\n", + "4 5 F1 0.973082 1\n", + "5 1 F2 0.139692 2\n", + "6 2 F2 0.948962 2\n", + "7 3 F2 0.447759 2\n", + "8 4 F2 0.663808 2\n", + "9 5 F2 0.550126 2" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Simulate data for tutorial\n", + "np.random.seed(1)\n", + "F1 = np.random.normal(0.8,0.2,5)\n", + "F2 = np.random.normal(0.6,0.2,5)\n", + "F3 = np.random.normal(0.4,0.2,5) \n", + "F4 = np.random.normal(0.2,0.2,5)\n", + "n = 5\n", + "\n", + "#Specifying data for two group dataset\n", + "#Put data i to python dictionary\n", + "data = {'Subject': range(n),\n", + " 'F1': F1,\n", + " 'F2': F2\n", + " }\n", + "\n", + "data_2 = {'Subject': range(n),\n", + " 'F1': F1,\n", + " 'F2': F2,\n", + " 'F3': F3,\n", + " 'F4': F4\n", + " }\n", + "\n", + "# Using the specified python dictionary above \n", + "# to generate a Pandas dataframe. \n", + "df = pd.DataFrame(data, columns = ['Subject','F1','F2'])\n", + "df = pd.melt(df,id_vars=['Subject'],var_name='F', value_name='DV')\n", + "df[\"Subject\"] = df[\"Subject\"] + 1\n", + "\n", + "\n", + "df[\"F_Recoded\"] = df[\"F\"]\n", + "df[\"F_Recoded\"] = df['F_Recoded'].replace(['F1'], '1').replace('F2', 2)\n", + "df[\"F_Recoded\"] = df['F_Recoded'].astype(int)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "df_2 = pd.DataFrame(data_2, columns = ['Subject','F1','F2','F3','F4'])\n", + "df_2 = pd.melt(df_2,id_vars=['Subject'],var_name='F', value_name='DV') \n", + "df_2; " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Treatment Contrasts\n", + "The default contrast in many ststisticsl softwares is the treatment contrast. This type of statisical model contrast name is derived from its general use in medical settings, where treatments are compared to a baseline (control group)." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: DV R-squared: 0.234\n", + "Model: OLS Adj. R-squared: 0.138\n", + "Method: Least Squares F-statistic: 2.442\n", + "Date: Sun, 22 Nov 2020 Prob (F-statistic): 0.157\n", + "Time: 13:43:11 Log-Likelihood: 0.24060\n", + "No. Observations: 10 AIC: 3.519\n", + "Df Residuals: 8 BIC: 4.124\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "Intercept 0.8111 0.118 6.867 0.000 0.539 1.083\n", + "F[T.F2] -0.2610 0.167 -1.563 0.157 -0.646 0.124\n", + "==============================================================================\n", + "Omnibus: 0.097 Durbin-Watson: 2.967\n", + "Prob(Omnibus): 0.952 Jarque-Bera (JB): 0.310\n", + "Skew: 0.110 Prob(JB): 0.856\n", + "Kurtosis: 2.166 Cond. No. 2.62\n", + "==============================================================================\n", + "\n", + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" + ] + } + ], + "source": [ + "#This is demsontrated here by fitting a OLS regression to the simuate data set above\n", + "\n", + "mod = ols(\"DV ~ F\", data=df)\n", + "res = mod.fit()\n", + "\n", + "print(res.summary())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## $$ Intercept (F1) = \\hat{\\mu}_1 = 0.81$$\n", + "## $$ Slope (F2) = \\hat{\\mu}_1 - \\hat{\\mu}_2 = -0.46$$\n", + "\n", + "When using Treatment coding the mean of baseline/control group is the intercept of the ouptut and the Slope (Beta) is the diffence between the two groups, please note that tis was all generated by defaukt in the OLS function, so if not specifying the exact cotrasts be careful with interpretation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# General linear model formulation\n", + "\n", + "Why do we get these regression coeffiecients?\n", + "\n", + "We can udertand why we get the observed Bata coesfficent if we specify the model above in general linear form. $y = \\beta_0 + \\beta_1 x$ for the F1 condition, $x = 0$, and for the F2, condition $x = 1$. Therefore when $y = \\beta_0 + \\beta_1 \\cdot 0 = \\beta_0$, but when $y = \\beta_0 + \\beta_1 \\cdot 1 = \\beta_0 + \\beta_1$. This si a very simple case and using the defaults give the results described but the rorder of coding could be changed (see later complex example) by reversing the setting of the coding would simple flip the sign of Slope coefficient." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Formulating the contrasts above as NHST's.\n", + "\n", + "Because treatment coding in a categorical regression test level of IV's against a baseline IV as such in the two level example above (Default) the NHST is specified as $H_0: \\beta_1 = 0$, which is a result of the $\\beta_1$ representing the diffence between the two scores. Thes test above also geneartes a NSHT for the intercept term $H_0: \\beta_0 = 0$, of course this is of less as essntially a ones sample t-test for comparising the Baseline IV has a statistically significant diffenrence from 0." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reordering treatment coding from default" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.]\n", + " [1.]]\n" + ] + } + ], + "source": [ + "from patsy.contrasts import Treatment\n", + "Two_levels_examp = [2,1]\n", + "contrast = Treatment(reference=0).code_without_intercept(Two_levels_examp)\n", + "print(contrast.matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.]\n", + " [0.]\n", + " [0.]\n", + " [0.]\n", + " [0.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]\n", + " [1.]]\n" + ] + } + ], + "source": [ + "print(contrast.matrix[df.F_Recoded-1, :])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sum contrasts\n", + "\n", + "This type of test contrast is different from the treatment contrasts, as the diffence is generated by comparing eaxch facors mean against the grand mean of all the levels of the factor. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Repeated Contrasts\n", + "\n", + "This contrast compares groups in succesive order. i.e if you had groups separated by some level of manipulation with settings of low, medium and high; low would be compared to medium and then medium compared to high." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Citations\n", + "\n", + "Schad, D. J., Vasishth, S., Hohenstein, S., & Kliegl, R. (2020). How to capitalize on a priori contrasts in linear (mixed) models: A tutorial. Journal of Memory and Language, 110, 104038." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "#df['F_Recoded'] = df['F_Recoded'].astype('category')\n", + "#df['F_Recoded'].cat.reorder_categories(['1', '2'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/Practicing Bayesian Statistics.md b/wip/Practicing Bayesian Statistics.md new file mode 100644 index 0000000..9c2bc3f --- /dev/null +++ b/wip/Practicing Bayesian Statistics.md @@ -0,0 +1,160 @@ +##
      Practicing Bayesian statistics
      + +## Contents +- Introduction +- The two statistical philosophies + - Frequentist Statistics + - Bayesian Statistics +- Bayes Theorem + - prior + - Likelihood + - Posterior +- Steps of a Bayesian analysis +- Effect indices/ hypothesis testing + + - Maximum a posteriori estimates (posterior point estimates ) + - Credible intervals +- Markov Chain Monte Carlo (MCMC) + +## Introduction +When Efron and Hastie (2016) identify that “Statistical inference is an unusually wide-ranging discipline, located as it is at the triple-point of mathematics, empirical science, and philosophy” (pp. xv) they make its multidisciplinary nature and complexity clear. This complexity has led to misinterpretation of the standardly taught analysis (frequentist) methods by applied by researchers (Hoekstra, Morey, Rouder, & Wagenmakers, 2014; Lyu, Xu, Zhao, Zuo, & Hu, 2020), seemingly result primarily due to the “philosophy” section of Efron and Hastie’s quote above. As demonstrated dominant use of frequentist methods, whilst seemingly wanting to interpret the results of their analyses under the Bayesian framework (Etz, Bartlema, Vanpaemel, Wagenmakers, & Morey, 2019). The question then is what is Bayesian statistics? + +## The two statistical philosophies +“The numbers have no way of speaking for themselves. We speak for them. We Imbue them with meaning.” + +Silver (2012). + +“Technical mathematical arguments and formula, though valid and of interest, must always assume, tacitly or explicitly, a philosophy.” + +Briggs (2019). + +“An important insight that would seem desirable for any statistical philosophy: Conclusions are only as plausible as the subjective foundations on which they are based.” + +Western (1999). + +As the quotes above point out there is no free lunch when analysing data and conducting statistical inference, but this is often forgotten or ritualised to make it seem as such (Gigerenzeer, 2004). + + Crucially, statistical inference is only possible at all because of the application of probability theory and its interpretation when the data are analysed. However, heated debates about what interpretation of probability to apply have occurred since the inception of statistics and have resulted in what has been termed “The statistics Wars” within the statistical literature (Gigerenzer et al., 1983; Mayo, 2018; Salsburg, 2002). + +Despite these worthwhile discussions, it is more important to remember it is the act of applying interpreting probability, that makes statistical inference possible at all; and crucially understand what separates the Frequentist and Bayesian frameworks is their differing interpretations of probability. + + The two interpretations of probability are objective and subjective probability. Lambert (2018), describes it as a difference in world view by the analyst. As a result, by applying either framework, the analyst is making assumptions about how to model the world through these different views of probability. + +Frequentist Statistics (objective probability). + +The philosophical grounding of the Frequentist statistics framework is objective probability. The meaning of objective probability in terms of data is expressed by the view that any set of observed data is a sample that has been generated from an infinite replication of the experiment from the data generating process. Therefore, any inference that is justifiable within this framework by analyst is that the observed sample is one with a long-run view of repeated sampling; meaning the data is treated as random but the statistical models that are fit to data are an attempt to estimate fixed population parameters. +through the attempts to estimate sampling error and this variation is expressed in varying estimates between experiments or observations and stipulates the necessity for replication and long run error control to understand the phenomena under from a statistical analysis view. + +Frequentist statistics dominates research with the use of by Null hypothesis significance testing (NHST). NHST tools for inference are based on repeated on tis sampling paradigm such as the p-value which is the $P(D|H_0)$ and confidence intervals (CI) which are not probability statements but an interval estimate of the parameter estimates generated from the data. As such a CI is again based on repeated sampling. As CI is a such that 95% of the confidence calculated will contain the true population parameter within their estimated intervals. + +Bayesian statistics (subjective probability). + +Bayesian statistics applies probability as an expression of the analyst’s belief and as an expression of their uncertainty around what they are analysing. This is expressed in the reversal of what the Frequentists do by treating the parameters of the statistical models that they fit as random and the data as fixed. In terms of parameters then a Bayesian does not have to deny the existence of a population parameter but can accept the uncertainty generated by each experiment/acquisition of a sample, that the estimates will vary due sample variation. However, a Bayesian analysis can also specify that differences in parameters is due to our uncertainty based on our knowledge. + + Under this framework, the use of Bayes theorem and data can be used to update beliefs about phenomena being studied through data analysis. Bayesian statistics allows for inference statements that $P(H_0|D)$, which is of course the opposite of that above from frequentist methods. This type of inference is achieved by the application of Bayes rule. + +## Bayes Rule +$$P(A,B) = P(B,A)\: \:\: \: \: \:(1)$$ + $$P(B|A)P(A) = P(A|B)P(B)\: \:\: \: \: \:(2) $$ + $$P(A|B) = \frac{P(B|A)P(A)}{P(B)}\: \:\: \: (3)$$ + +Hopefully by the outlining Bayes rule above it becomes explicit why we would want to use the statistical practices on which it is based. In case it is not explicit, it is because the posterior allow sus to answer a question in which we are interested in that what is the probability of our hypothesis we are testing base on assumptions of the statistical models of which we use to test the data. + +## Steps of a Bayesian analysis +Kruschke (2015) outlines general Bayesian analysis steps which include: + +1. Identify data relevant to the research question including the variables of interest. +2. Identify a descriptive model for the data that you are going to analyse. Meaning the mathematical model and the parameter's should be appropriate to the data. +3. Specify priors for the parameter of the model, priors should be reasonable and will have to pass an audience of reviewers. +4. Use Bayes theorem and calculate the posterior. +5. Conduct posterior predictive checks. + +## Testing indices/hypothesis testing in the Bayesian framework + + Due to the standard training of researchers to use NHST methods in analysing their data and the ingrained practice of using p-values, which has created an illusionary sense of the ease to understand data analysis and statistical inference with decision-making rules such as standard use of thresholds (i.e. ≤ .05) and concluding a result or body of work is meaningful or significant in the standard sense of the word when it is only significant in the statistical sense of the word. With this culminating and resulting in publication. + + A result of the standard training in NHST is likely to lead to the first question any researcher curious about Bayesian methods to probably ask, what is its p-value equivalent? A more general and helpful question though would be what are the outputs that are used for making inferences from the data within this framework? Answering that question is likely to result in trepidation by interested parties in applying Bayesian tools in analyzing data, due to the flexibility of the framework and the variety of the analysts’ options. This can either be seen as complicating or liberating. + + To simplify Bayesian methods of inference is to separate out Bayes factor analysis and Bayesian estimation. There is plenty of discussion in the literature of the advantages and disadvantages of each method (Dienes, 2020; Makowski, Ben-Shachar, Chen, & Lüdecke, 2019), with no general consensus. + The notebooks here focus on Bayesian estimation. For clarity, this is not to suggest a preference because when it comes to statistical tools more options are an advantage but to go over those methods is a project on its own. + Bayesian estimation test indices + Point estimates (Mean, median)/Maximum a posteriori estimation (MAP - the mode) + Posterior mean - minimises the expected square error + Posterior median - minimises expected absolute error + MAP - most probable value on the posterior distribution. + + Expression of the uncertainty in parameter estimates. + Credible intervals Krushcke (2015) (plausibility intervals, McElreath, 2020; uncertainty intervals, Gelman et al., 2013). + + 95% credible interval - Krushcke (2015) 10000 effective samples for stable estimates + + 50% credible interval - (Gelman, 2016) suggest using the 50% credibility interval, which gives the quantile interval between 25% and 75% of the posterior distribution. + Gelman argues usin this interval: Increases the computational stability, gives the credibility interpretation that true value contained in this 50% interval and finally this interval helps avoid certainty. + + ROPE (95%) + + ROPE (Full) + + Hopefully, the different recommendations of the interval size to use when summarising the posterior makes the reader think this is arbitrary like a p-value threshold of .05 because it is and it is down to the analysis to decide and make the argument for their choice. If that choice is due to expert suggestions or for general research practices. + +## Markov chain Monte Carlo (MCMC) +In laymen terms MCMC are mathematical tools/algorithms for sampling form posterior distributions. MCMC underlies all modern applications of Bayesian statistics. this is because many of the statically models to answer complex question in which statisticians and researchers are concerned do not have analytical solutions for calculating the posterior. To overcome this numerical methods such as MCMC must be applied. + +## Why Stan? + + +###
      References
      + +Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint arXiv:1701.02434. + +Briggs, W. M., 2019. Everything Wrong with P-Values Under One Roof. In Beyond Traditional Probabilistic Methods in Economics, Kreinovich, V., Thach, N. N., Trung, N. D., Thanh, D. V. (eds.), pp 22–44. DOI 978-3-030-04200-4_2 + +Briggs, W. M. (2012). It is time to stop teaching frequentism to non-statisticians. arXiv preprint arXiv:1201.2590. + +Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of statistical software, 80(1), 1-28. + +Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of statistical software, 76(1). + +Dienes, Z. (2020). How to use and report Bayesian hypothesis tests. + +Efron, B., & Hastie. (2016). Computer age Statistical Inference: Algorithms, evidence, and data science. New York, NY: Cambridge University Press. + +Etz, A., Bartlema, A., Vanpaemel, W., Wagenmakers, E. J., & Morey, R. D. (2019). An exploratory survey of student and researcher intuitions about statistical evidence. In Poster presented at the annual meeting of the Association for Psychological Science, Washington, DC. + +Gabry, J., & Goodrich, B. (2016). rstanarm: Bayesian applied regression modeling via stan [computer software manual]. Retrieved from http://CRAN.R‐project.org/ package=rstanarm (R package version 2.9.0‐1) + +Gelman, A. (2016, November 5) Why I prefer 50% rather than 95% intervals [blog post]. Retrieved from https://statmodeling.stat.columbia.edu/2016/11/05/why-i-prefer-50-to-95-intervals/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+MyDataScienceBlogs+%28My+Data+Science+Blogs%29 + +Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., Kruger, L. (1989). The empire of chance: How probability changed science and everyday life. New York, NY: Cambridge University Press. + +Hoekstra, R., Morey, R. D., Rouder, J. N., & Wagenmakers, E. J. (2014). Robust misinterpretation of confidence intervals. Psychonomic bulletin & review, 21(5), 1157-1164. + +Krushcke, J. K. (2015). Doing Bayesian analysis: A tutorial with R, JAGS and Stan. London, England: Academic Press. + +Lambert, B. (2018). A student guide to Bayesian statistics. London, England: SAGE. + +Lynch, S. M., & Bartlett, B. (2019). Bayesian Statistics in Sociology: Past, Present, and Future.Annual Review of Sociology, 45, 47-68. + +Lyu, X. K., Xu, Y., Zhao, X. F., Zuo, X. N., & Hu, C. P. (2020). Beyond psychology: prevalence of p values and confidence interval misinterpretation across different fields. Journal of Pacific Rim Psychology, 14. + +Makowski, D., Ben-Shachar, M. S., Chen, S. H., & Lüdecke, D. (2019). Indices of effect existence and significance in the bayesian framework. Frontiers in psychology, 10, 2767. + +Mayo, D.G. (2018). Statistical inference as severe testing: How to get beyond the statistics wars. New York: NY. Cambridge University Press. + +McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. London, England: CRC Press. + +Morey, R. D., Hoekstra, R., Rouder, J. N., Lee, M. D., & Wagenmakers, E. J. (2016). The fallacy of placing confidence in confidence intervals. Psychonomic bulletin & review, 23(1), 103-123. + +Morey, R. D., Rouder, J. N., Jamil, T., & Morey, M. R. D. (2015). Package ‘bayesfactor’. URLh http://cran/r-projectorg/web/packages/BayesFactor/BayesFactor pdf i (accessed 1006 15). + +Muth, C., Oravecz, Z., & Gabry, J. (2018). User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. Quantitative Methods for Psychology, 14(2), 99-119. + +Salzburg, D. (2002). The lady Tasting tea: How statistics revolutionised science in the twentieth century. New York, NY. First Holts. + +Stan Development Team. (2017). Stan modeling language users guide and reference manual, version 2.17.0. Retrieved from http://mc-stan.org/ + +Vasishth, S., & Nicenboim, B. (2016). Statistical methods for linguistic research: Foundational ideas–Part I. Language and Linguistics Compass, 10(8), 349-369. + +Vasishth, S., & Nicenboim, B. (2016). Statistical methods for linguistic research: Foundational ideas–Part I. Language and Linguistics Compass, 10(8), 349-369. + +Western, B. (1999). Bayesian analysis for sociologists: An introduction. Sociological Methods & Research, 28(1), 7-34. diff --git a/wip/README.md b/wip/README.md new file mode 100644 index 0000000..c2b146f --- /dev/null +++ b/wip/README.md @@ -0,0 +1,2 @@ + +# Notebooks diff --git a/wip/Within subject ANOVA.ipynb b/wip/Within subject ANOVA.ipynb new file mode 100644 index 0000000..f8e1723 --- /dev/null +++ b/wip/Within subject ANOVA.ipynb @@ -0,0 +1,1427 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "toc": true + }, + "source": [ + "

      Table of Contents

      \n", + "
      " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Import analysis packages\n", + "%matplotlib inline\n", + "import pystan as ps\n", + "from patsy import dmatrix\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import scipy.stats as stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notebook formatting (code below centralise the plot outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML as Center\n", + "\n", + "Center(\"\"\" \"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian estimation equivalent of within subject/repeated measures ANOVA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The classic within subject ANOVA\n", + "\n", + "Traditoanl ANOVA are based on the use of Null hyptohesis of F-statistic and like all ANOVA require its calculation \n", + "\n", + "For repewithin subject/repeated measures ANOVA the F statisitc is calulated as such\n", + "\n", + "1. First the sum sqaure total ($SS_T$) must be calculated \n", + "\n", + "$$SS_T = \\sigma_{grand}^{2}(N-1)$$\n", + "\n", + "2. After this the within subject varaince ($SS_W)$ needs to be calculated \n", + "$$ $$\n", + "\n", + "# Add to this" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian inference\n", + " Following the quick description of the classic within subject ANOVA above its important to keep in mind that Bayesian analysis inference are all derived from the applciation of Bayes rule $P(\\theta \\mid y) = \\large \\frac{P(y \\mid \\theta) \\, P(\\theta)}{P(y)}$ and as such while the following description of the Bayesian model is an equivalent alternative to the eithin subject ANOVA, it is fundamentally different, becuase it uses fully probabilistic modelling and the infernce is not based on sampling distributions\n", + " \n", + " For a fuller description see the Practicing Bayesian statistics markdown file within the Github repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Steps of Bayesian data analysis\n", + "\n", + " Kruscke (2015) offers a step by step formulation for how to conduct a Bayesian analysis:\n", + "\n", + "1. Identify the relevant data for question under investigation.\n", + "\n", + "2. Define the descriptive (mathematical) model for the data.\n", + "\n", + "3. Specify the Priors for the model. In the case of scientific research publication is the goal, as such the priors must be accepted by a skeptical audience. Much of this can be achieved using prior predcitve checks to acsetain os the priors are reasonable.\n", + "\n", + "4. Using Bayes rule estimate the posterior for the parameters of the model using the likelihood and priors. Then interprete and the posterior\n", + "\n", + "5. Conduct model checks. i.e. Posterior predictive checks. \n", + "\n", + "This notebook will follow this approach generally. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1 - Identify the relevant data for question under investigation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Study/data description\n", + "\n", + "The following example data analysis https://www.sheffield.ac.uk/mash/statistics/datasets.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Call github repository\n", + "url = \"https://raw.githubusercontent.com/ebrlab/Statistical-methods-for-research-workers-bayes-for-psychologists-and-neuroscientists/master/Data/Cholesterol_R.csv\"\n", + "#Import data .csv file into pandas dataframe.\n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      IDBeforeAfter4weeksAfter8weeksMargarine
      016.425.835.75B
      \n", + "
      " + ], + "text/plain": [ + " ID Before After4weeks After8weeks Margarine\n", + "0 1 6.42 5.83 5.75 B" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Output data frame for evaluation of proper import\n", + "df.head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clean the data\n", + "\n", + "Because this notebook is solely focused on modeling the within subject effects for individuals who consumed Margarine here.\n", + "Therefore, some data manipulation is required." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# First, drop all of the type A Marrgarines from the dataframe.\n", + "dfReduced = df[~df.Margarine.str.contains(\"A\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ID Before After4weeks After8weeks Margarine\n", + "0 2 6.42 5.83 5.75 B\n", + "2 4 6.56 5.83 5.71 B\n", + "4 6 8.43 7.71 7.67 B\n", + "6 8 8.05 7.25 7.10 B\n", + "8 10 5.77 5.31 5.33 B\n", + "10 12 6.77 6.15 5.96 B\n", + "11 13 6.44 5.59 5.64 B\n", + "15 17 6.85 6.40 6.29 B\n", + "17 19 5.73 5.13 5.17 B\n" + ] + } + ], + "source": [ + "# Print output to check the code has done what is described above\n", + "print(dfReduced)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data visualisation and exploratory data analysis " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(2, 2, figsize=(7, 7))\n", + "\n", + "sns.distplot(dfReduced['Before'], kde=True, color=\"skyblue\", ax=axs[0, 0]);\n", + "sns.distplot(dfReduced['After4weeks'], kde=True, color=\"skyblue\", ax=axs[0, 1]);\n", + "sns.distplot(dfReduced['After8weeks'], kde=True, color=\"skyblue\", ax=axs[1, 0]);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2 - define the descriptive model\n", + "\n", + "As the The Bugs book (Lunn, Jackson, Best, Thomas, & Spiegelhalter, 2012) shows repeated measures ANOVA type mdoels can be estiamted within the Bayesian framework through the use of multivariate normal likelihood. The multivaraite normal model allows for the etiamtio of group means whule estiamteing and thus controlling for the correltaion between the repated observed data values. BUGS mdoels use inverse wishsart priors for the covariance matrices when estiamting correltion between data points. Stan, however, is not limited like Bugs in this way and the standard practice for stan users is to use LKJ priors (see, Lewandowski, Kurowicka, & Joe 2009, for further details).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stan model" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "repeatedMeasuresANOVA = \"\"\" \n", + "data{\n", + "// The number of data points per group\n", + "int n; \n", + "// The number of groups specified \n", + "int k; \n", + "// Data inputed in matrix form as the data is in wide format\n", + "matrix[n,k] y; \n", + "}\n", + "\n", + "parameters{\n", + "// vector for the mu parameter for each repeated grouped observations\n", + "// lower bound set to specify postive half normal for the prior \n", + "// specified in the modle block as cholesterol levels cannot be negative.\n", + "vector[k] mu;\n", + "\n", + "//\n", + "vector[k] sigma;\n", + "\n", + "// Correlation matrix \n", + "corr_matrix[k] omega;\n", + "}\n", + "\n", + "transformed parameters{\n", + "matrix[k,k] Sigma; \n", + "Sigma = quad_form_diag(omega, sigma);\n", + "}\n", + "model{\n", + "\n", + "// priors\n", + "// Priors based of NHS estiamtes (https://www.nhs.uk/conditions/high-cholesterol/cholesterol-levels/)\n", + "// of average cholesterol within population\n", + "mu ~ normal(5, 1.5);\n", + "\n", + "// Weakly informative prior https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations \n", + "sigma ~ normal(0, 10);\n", + "\n", + "// LKJ prior of one sets unifrom priros from -1,1 correlations.\n", + "// So the prior has been set to 2 as to give less probability to such exterme correltions \n", + "// which are uncommom in biological research.\n", + "omega ~ lkj_corr(2);\n", + "\n", + "// likelihood\n", + "// for loop required in the model as index the row of teh data in wide form\n", + "// to calcuate the means and correlation parameter estimates.\n", + "for(i in 1:n){\n", + " y[i] ~ multi_normal(mu, Sigma);\n", + " }\n", + "}\n", + "\n", + "generated quantities{\n", + "vector[k] yrep[n];\n", + "\n", + "for(i in 1:n){\n", + " yrep[i] = multi_normal_rng(mu, Sigma);\n", + " }\n", + "}\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_5f6dc551bc7346776f5f439d88ce4073 NOW.\n" + ] + } + ], + "source": [ + "sm = ps.StanModel(model_code = repeatedMeasuresANOVA)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "y = dfReduced.iloc[:, 1:4].values \n", + "data = {'n': len(dfReduced),\n", + " 'k': y.shape[1],\n", + " 'y': y \n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:pystan:n_eff / iter below 0.001 indicates that the effective sample size has likely been overestimated\n", + "WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed\n" + ] + } + ], + "source": [ + "fit = sm.sampling(data = data, iter = 2000, chains = 2, seed = 1, control = dict(adapt_delta = .95) ) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "note for the warnings. In the case of the first warning, as dicsussed at this Stan discource thread https://discourse.mc-stan.org/t/what-does-this-warning-means-should-i-change-the-code-and-how/6806, these warnings for this model are fine it is the use of the correlatation matrix. In terms of the second watning this is due to the NAN result of the perfect correlations on the diagnoal of the correlations matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "# Because of python print statement it is easier to extract the results into a panda data frame for viewing.\n", + "summary = fit.summary()\n", + "fit_df = pd.DataFrame(summary['summary'], \n", + " columns = summary['summary_colnames'], \n", + " index = summary['summary_rownames'])" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
      \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
      meanse_meansd2.5%25%50%75%97.5%n_effRhat
      mu[1]6.6754241.315394e-022.791486e-016.1065986.5048536.6788016.8379107.239293450.3593841.005800
      mu[2]6.0383821.107128e-022.384983e-015.5579715.8891826.0382736.1807096.509540464.0605851.007661
      mu[3]5.9802131.166454e-022.344713e-015.5114995.8315835.9812496.1210406.436321404.0584881.009200
      sigma[1]0.7975227.956029e-031.848540e-010.5398910.6667660.7610240.8888371.271714539.8396761.002868
      sigma[2]0.7248868.134675e-031.582598e-010.4979540.6192150.6915800.8013041.139166378.4953731.004925
      sigma[3]0.6997847.723287e-031.569222e-010.4815110.5919890.6663770.7721231.095772412.8234881.001945
      omega[1,1]1.000000NaN0.000000e+001.0000001.0000001.0000001.0000001.000000NaNNaN
      omega[2,1]0.8670375.645459e-031.383990e-010.4605390.8308370.9116940.9519700.984115600.9902901.002507
      omega[3,1]0.8483105.901870e-031.440812e-010.4408360.8056690.8970280.9447700.980477595.9852371.001789
      omega[1,2]0.8670375.645459e-031.383990e-010.4605390.8308370.9116940.9519700.984115600.9902901.002507
      omega[2,2]1.0000005.083857e-192.275849e-171.0000001.0000001.0000001.0000001.0000002004.0120360.998999
      omega[3,2]0.9194273.883656e-031.025580e-010.6239000.9023140.9546430.9771840.993052697.3605950.999851
      omega[1,3]0.8483105.901870e-031.440812e-010.4408360.8056690.8970280.9447700.980477595.9852371.001789
      omega[2,3]0.9194273.883656e-031.025580e-010.6239000.9023140.9546430.9771840.993052697.3605950.999851
      omega[3,3]1.0000001.074109e-184.884941e-171.0000001.0000001.0000001.0000001.0000002068.3395790.998999
      Sigma[1,1]0.6701951.490950e-023.477407e-010.2914830.4445760.5791580.7900301.617258543.9825671.002555
      Sigma[2,1]0.5170121.300923e-022.578613e-010.2195710.3604600.4565160.6041271.207637392.8884931.004845
      Sigma[3,1]0.4898331.285627e-022.497249e-010.2041580.3328090.4303020.5741451.175334377.3057641.004041
      Sigma[1,2]0.5170121.300923e-022.578613e-010.2195710.3604600.4565160.6041271.207637392.8884931.004845
      Sigma[2,2]0.5504931.429708e-022.662571e-010.2479590.3834270.4782830.6420871.297699346.8227811.005237
      Sigma[3,2]0.4849791.284147e-022.345179e-010.2128010.3338180.4288340.5675631.140974333.5202061.002803
      Sigma[1,3]0.4898331.285627e-022.497249e-010.2041580.3328090.4303020.5741451.175334377.3057641.004041
      Sigma[2,3]0.4849791.284147e-022.345179e-010.2128010.3338180.4288340.5675631.140974333.5202061.002803
      Sigma[3,3]0.5143101.321111e-022.576465e-010.2318530.3504500.4440580.5961741.200717380.3380461.001706
      yrep[1,1]6.6821702.216907e-028.756611e-014.9363326.1290576.6922217.2607458.2979671560.1894321.000165
      yrep[2,1]6.6667022.025399e-028.312174e-014.8820466.1525736.6994607.2164748.2625501684.2558191.000058
      yrep[3,1]6.6850792.011059e-028.564000e-015.0344586.1341036.6901307.1951848.4016381813.4414810.999593
      yrep[4,1]6.6705242.236963e-028.609857e-014.8758206.1492486.6824157.2274508.3454161481.4064521.003944
      yrep[5,1]6.6895592.184787e-028.640487e-014.9670406.1505986.7117107.2433668.3671421564.0768081.005925
      yrep[6,1]6.7000172.402646e-028.779270e-014.9318296.1365556.7244667.2659228.3854531335.1721471.002538
      yrep[7,1]6.6649272.065793e-028.947897e-014.8373596.1234906.6969977.2482068.3715171876.1534611.000683
      yrep[8,1]6.6633472.418800e-028.843715e-014.9502696.0794056.6428207.2102198.4516221336.8097471.000106
      yrep[9,1]6.7207301.944811e-028.365748e-014.9997706.1810656.7414927.2620808.3794511850.3533001.000756
      yrep[1,2]6.0441041.990256e-027.756738e-014.4857015.5601206.0524726.5573657.5806311518.9389001.001876
      yrep[2,2]6.0361271.867840e-027.580061e-014.5522625.5843306.0326526.4970007.5449911646.8947301.000362
      yrep[3,2]6.0340351.819081e-027.609688e-014.5303605.5350966.0455976.5143507.6385201749.9666711.001300
      yrep[4,2]6.0506081.969082e-027.800304e-014.4589255.5509216.0611476.5324387.5843001569.2619871.003746
      yrep[5,2]6.0314611.992481e-027.805301e-014.4111205.5546536.0502666.5294297.5559861534.5852771.004684
      yrep[6,2]6.0492342.148586e-028.187541e-014.3631725.5525886.0406056.5693877.6497091452.1161181.003047
      yrep[7,2]6.0294041.765689e-027.946464e-014.3694195.5293796.0566816.5333527.5730112025.4406471.000119
      yrep[8,2]6.0239472.176549e-027.934618e-014.5012865.5027386.0177936.5195747.6165981328.9700771.000008
      yrep[9,2]6.0625241.802885e-027.609460e-014.5353905.5804546.0694546.5501847.6013651781.4413781.001577
      yrep[1,3]5.9832011.899825e-027.533738e-014.4839865.5023616.0034996.4841947.4229951572.5122791.000821
      yrep[2,3]5.9802581.739955e-027.213259e-014.5713465.5193705.9726706.4367837.3674491718.6480940.999897
      yrep[3,3]5.9867511.739415e-027.413773e-014.5686885.4764575.9904696.4784777.4305491816.6544591.000894
      yrep[4,3]5.9982821.841715e-027.454097e-014.5529735.5288775.9983186.4759937.4721431638.1178541.003367
      yrep[5,3]5.9777291.940593e-027.564648e-014.4219065.5010235.9836556.4656457.4618201519.5275131.004664
      yrep[6,3]5.9907571.973952e-027.801515e-014.4092435.4968835.9945866.4933327.4612341562.0138441.003238
      yrep[7,3]5.9655711.692668e-027.625070e-014.3606925.4872825.9775446.4503927.4101332029.2888140.999699
      yrep[8,3]5.9687061.949501e-027.580357e-014.5464375.4678805.9567236.4237447.5297381511.9328801.000197
      yrep[9,3]6.0084531.730945e-027.271879e-014.5302195.5662436.0012666.4690957.4494801764.9277841.001020
      lp__10.2175171.439820e-012.888574e+003.4414488.48896510.69212712.36032914.502308402.4860061.000246
      \n", + "
      " + ], + "text/plain": [ + " mean se_mean sd 2.5% 25% \\\n", + "mu[1] 6.675424 1.315394e-02 2.791486e-01 6.106598 6.504853 \n", + "mu[2] 6.038382 1.107128e-02 2.384983e-01 5.557971 5.889182 \n", + "mu[3] 5.980213 1.166454e-02 2.344713e-01 5.511499 5.831583 \n", + "sigma[1] 0.797522 7.956029e-03 1.848540e-01 0.539891 0.666766 \n", + "sigma[2] 0.724886 8.134675e-03 1.582598e-01 0.497954 0.619215 \n", + "sigma[3] 0.699784 7.723287e-03 1.569222e-01 0.481511 0.591989 \n", + "omega[1,1] 1.000000 NaN 0.000000e+00 1.000000 1.000000 \n", + "omega[2,1] 0.867037 5.645459e-03 1.383990e-01 0.460539 0.830837 \n", + "omega[3,1] 0.848310 5.901870e-03 1.440812e-01 0.440836 0.805669 \n", + "omega[1,2] 0.867037 5.645459e-03 1.383990e-01 0.460539 0.830837 \n", + "omega[2,2] 1.000000 5.083857e-19 2.275849e-17 1.000000 1.000000 \n", + "omega[3,2] 0.919427 3.883656e-03 1.025580e-01 0.623900 0.902314 \n", + "omega[1,3] 0.848310 5.901870e-03 1.440812e-01 0.440836 0.805669 \n", + "omega[2,3] 0.919427 3.883656e-03 1.025580e-01 0.623900 0.902314 \n", + "omega[3,3] 1.000000 1.074109e-18 4.884941e-17 1.000000 1.000000 \n", + "Sigma[1,1] 0.670195 1.490950e-02 3.477407e-01 0.291483 0.444576 \n", + "Sigma[2,1] 0.517012 1.300923e-02 2.578613e-01 0.219571 0.360460 \n", + "Sigma[3,1] 0.489833 1.285627e-02 2.497249e-01 0.204158 0.332809 \n", + "Sigma[1,2] 0.517012 1.300923e-02 2.578613e-01 0.219571 0.360460 \n", + "Sigma[2,2] 0.550493 1.429708e-02 2.662571e-01 0.247959 0.383427 \n", + "Sigma[3,2] 0.484979 1.284147e-02 2.345179e-01 0.212801 0.333818 \n", + "Sigma[1,3] 0.489833 1.285627e-02 2.497249e-01 0.204158 0.332809 \n", + "Sigma[2,3] 0.484979 1.284147e-02 2.345179e-01 0.212801 0.333818 \n", + "Sigma[3,3] 0.514310 1.321111e-02 2.576465e-01 0.231853 0.350450 \n", + "yrep[1,1] 6.682170 2.216907e-02 8.756611e-01 4.936332 6.129057 \n", + "yrep[2,1] 6.666702 2.025399e-02 8.312174e-01 4.882046 6.152573 \n", + "yrep[3,1] 6.685079 2.011059e-02 8.564000e-01 5.034458 6.134103 \n", + "yrep[4,1] 6.670524 2.236963e-02 8.609857e-01 4.875820 6.149248 \n", + "yrep[5,1] 6.689559 2.184787e-02 8.640487e-01 4.967040 6.150598 \n", + "yrep[6,1] 6.700017 2.402646e-02 8.779270e-01 4.931829 6.136555 \n", + "yrep[7,1] 6.664927 2.065793e-02 8.947897e-01 4.837359 6.123490 \n", + "yrep[8,1] 6.663347 2.418800e-02 8.843715e-01 4.950269 6.079405 \n", + "yrep[9,1] 6.720730 1.944811e-02 8.365748e-01 4.999770 6.181065 \n", + "yrep[1,2] 6.044104 1.990256e-02 7.756738e-01 4.485701 5.560120 \n", + "yrep[2,2] 6.036127 1.867840e-02 7.580061e-01 4.552262 5.584330 \n", + "yrep[3,2] 6.034035 1.819081e-02 7.609688e-01 4.530360 5.535096 \n", + "yrep[4,2] 6.050608 1.969082e-02 7.800304e-01 4.458925 5.550921 \n", + "yrep[5,2] 6.031461 1.992481e-02 7.805301e-01 4.411120 5.554653 \n", + "yrep[6,2] 6.049234 2.148586e-02 8.187541e-01 4.363172 5.552588 \n", + "yrep[7,2] 6.029404 1.765689e-02 7.946464e-01 4.369419 5.529379 \n", + "yrep[8,2] 6.023947 2.176549e-02 7.934618e-01 4.501286 5.502738 \n", + "yrep[9,2] 6.062524 1.802885e-02 7.609460e-01 4.535390 5.580454 \n", + "yrep[1,3] 5.983201 1.899825e-02 7.533738e-01 4.483986 5.502361 \n", + "yrep[2,3] 5.980258 1.739955e-02 7.213259e-01 4.571346 5.519370 \n", + "yrep[3,3] 5.986751 1.739415e-02 7.413773e-01 4.568688 5.476457 \n", + "yrep[4,3] 5.998282 1.841715e-02 7.454097e-01 4.552973 5.528877 \n", + "yrep[5,3] 5.977729 1.940593e-02 7.564648e-01 4.421906 5.501023 \n", + "yrep[6,3] 5.990757 1.973952e-02 7.801515e-01 4.409243 5.496883 \n", + "yrep[7,3] 5.965571 1.692668e-02 7.625070e-01 4.360692 5.487282 \n", + "yrep[8,3] 5.968706 1.949501e-02 7.580357e-01 4.546437 5.467880 \n", + "yrep[9,3] 6.008453 1.730945e-02 7.271879e-01 4.530219 5.566243 \n", + "lp__ 10.217517 1.439820e-01 2.888574e+00 3.441448 8.488965 \n", + "\n", + " 50% 75% 97.5% n_eff Rhat \n", + "mu[1] 6.678801 6.837910 7.239293 450.359384 1.005800 \n", + "mu[2] 6.038273 6.180709 6.509540 464.060585 1.007661 \n", + "mu[3] 5.981249 6.121040 6.436321 404.058488 1.009200 \n", + "sigma[1] 0.761024 0.888837 1.271714 539.839676 1.002868 \n", + "sigma[2] 0.691580 0.801304 1.139166 378.495373 1.004925 \n", + "sigma[3] 0.666377 0.772123 1.095772 412.823488 1.001945 \n", + "omega[1,1] 1.000000 1.000000 1.000000 NaN NaN \n", + "omega[2,1] 0.911694 0.951970 0.984115 600.990290 1.002507 \n", + "omega[3,1] 0.897028 0.944770 0.980477 595.985237 1.001789 \n", + "omega[1,2] 0.911694 0.951970 0.984115 600.990290 1.002507 \n", + "omega[2,2] 1.000000 1.000000 1.000000 2004.012036 0.998999 \n", + "omega[3,2] 0.954643 0.977184 0.993052 697.360595 0.999851 \n", + "omega[1,3] 0.897028 0.944770 0.980477 595.985237 1.001789 \n", + "omega[2,3] 0.954643 0.977184 0.993052 697.360595 0.999851 \n", + "omega[3,3] 1.000000 1.000000 1.000000 2068.339579 0.998999 \n", + "Sigma[1,1] 0.579158 0.790030 1.617258 543.982567 1.002555 \n", + "Sigma[2,1] 0.456516 0.604127 1.207637 392.888493 1.004845 \n", + "Sigma[3,1] 0.430302 0.574145 1.175334 377.305764 1.004041 \n", + "Sigma[1,2] 0.456516 0.604127 1.207637 392.888493 1.004845 \n", + "Sigma[2,2] 0.478283 0.642087 1.297699 346.822781 1.005237 \n", + "Sigma[3,2] 0.428834 0.567563 1.140974 333.520206 1.002803 \n", + "Sigma[1,3] 0.430302 0.574145 1.175334 377.305764 1.004041 \n", + "Sigma[2,3] 0.428834 0.567563 1.140974 333.520206 1.002803 \n", + "Sigma[3,3] 0.444058 0.596174 1.200717 380.338046 1.001706 \n", + "yrep[1,1] 6.692221 7.260745 8.297967 1560.189432 1.000165 \n", + "yrep[2,1] 6.699460 7.216474 8.262550 1684.255819 1.000058 \n", + "yrep[3,1] 6.690130 7.195184 8.401638 1813.441481 0.999593 \n", + "yrep[4,1] 6.682415 7.227450 8.345416 1481.406452 1.003944 \n", + "yrep[5,1] 6.711710 7.243366 8.367142 1564.076808 1.005925 \n", + "yrep[6,1] 6.724466 7.265922 8.385453 1335.172147 1.002538 \n", + "yrep[7,1] 6.696997 7.248206 8.371517 1876.153461 1.000683 \n", + "yrep[8,1] 6.642820 7.210219 8.451622 1336.809747 1.000106 \n", + "yrep[9,1] 6.741492 7.262080 8.379451 1850.353300 1.000756 \n", + "yrep[1,2] 6.052472 6.557365 7.580631 1518.938900 1.001876 \n", + "yrep[2,2] 6.032652 6.497000 7.544991 1646.894730 1.000362 \n", + "yrep[3,2] 6.045597 6.514350 7.638520 1749.966671 1.001300 \n", + "yrep[4,2] 6.061147 6.532438 7.584300 1569.261987 1.003746 \n", + "yrep[5,2] 6.050266 6.529429 7.555986 1534.585277 1.004684 \n", + "yrep[6,2] 6.040605 6.569387 7.649709 1452.116118 1.003047 \n", + "yrep[7,2] 6.056681 6.533352 7.573011 2025.440647 1.000119 \n", + "yrep[8,2] 6.017793 6.519574 7.616598 1328.970077 1.000008 \n", + "yrep[9,2] 6.069454 6.550184 7.601365 1781.441378 1.001577 \n", + "yrep[1,3] 6.003499 6.484194 7.422995 1572.512279 1.000821 \n", + "yrep[2,3] 5.972670 6.436783 7.367449 1718.648094 0.999897 \n", + "yrep[3,3] 5.990469 6.478477 7.430549 1816.654459 1.000894 \n", + "yrep[4,3] 5.998318 6.475993 7.472143 1638.117854 1.003367 \n", + "yrep[5,3] 5.983655 6.465645 7.461820 1519.527513 1.004664 \n", + "yrep[6,3] 5.994586 6.493332 7.461234 1562.013844 1.003238 \n", + "yrep[7,3] 5.977544 6.450392 7.410133 2029.288814 0.999699 \n", + "yrep[8,3] 5.956723 6.423744 7.529738 1511.932880 1.000197 \n", + "yrep[9,3] 6.001266 6.469095 7.449480 1764.927784 1.001020 \n", + "lp__ 10.692127 12.360329 14.502308 402.486006 1.000246 " + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Post model fit-visualisations - Bayesian estimation of a repeated measures ANOVA\n", + "\n", + "\n", + "## Posterior distributions plots" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "#az.plot_posterior(fit, var_names=(\"mu\", \"sigma\"));" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [], + "source": [ + "#az.plot_autocorr(fit, var_names=(\"mu\", \"sigma\", \"omega\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The autocorrelation plots show little issue quickly decreasing to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [], + "source": [ + " #az.plot_trace(data = fit, var_names= (\"mu\", \"sigma\", \"omega\"));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reporting the results of the Bayesian one sample t-test equivalent\n", + "\n", + "As Kruskcke correctly points out there is not standard formula or presentation method for results like the APA guide for reporting frequentist analyses using the Bayesian framework. It is likely there never will be, because as McElreath (2020) explains, Bayesian data analysis is more like a engineering approach to the problem and the resulting model that is fit will be analysis specific. In addition, as Gabry et al, (2019) have argued visualisations maybe even more key, so all the visualtions above would have to be included with any write up. Anyway, the write up below generally follows the advice of Krushcke (2015) chapter 25. In any application though it comes down to the problem to be described and the audience that needs to be convinced.


      \n", + "\n", + "

      Write up


      " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "note sort gerneated quantaties." + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [], + "source": [ + "np.shape(fit[\"yrep\"])\n", + "x = fit[\"yrep\"]\n", + "x = x[:, :, 0].T\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., ... & Riddell, A. (2017). Stan: a probabilistic programming language. Grantee Submission, 76(1), 1-32.\n", + "\n", + "Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389-402.\n", + " \n", + "Kruschke, J. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS and Stan. Oxford, England: Academic Press. \n", + " \n", + "McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. Boca Raton, FL: CRC Press.\n", + "\n", + "Lewandowski, D., Kurowicka, D., & Joe, H. (2009). Generating random correlation matrices based on vines and extended onion method. Journal of multivariate analysis, 100(9), 1989-2001.\n", + "\n", + "Lunn, D., Jackson, C., Best, N., Thomas, A., & Spiegelhalter, D. (2012). Bugs bool: A practical introduction Bayesain analysis. Boca raton, FL: CRC Press." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/wip/build/temp.macosx-10.9-x86_64-3.9/Users/eroesch/Library/Caches/httpstan/4.5.0/models/ceykbmxk/model_ceykbmxk.o b/wip/build/temp.macosx-10.9-x86_64-3.9/Users/eroesch/Library/Caches/httpstan/4.5.0/models/ceykbmxk/model_ceykbmxk.o new file mode 100644 index 0000000000000000000000000000000000000000..7a105f59c1b6d1fd6d268566fa3f1fa7e6c557aa GIT binary patch literal 283176 zcmeFa4SZD9nLj=uiHu6TgMvn-8Y-=cR+?1Nq`oDRk#psa;0hWl7!Y}@f|Se%mKTGQ zRBrEHo9(W<>~7u7?!Ru+w{3R&(+yf=0+qy=RD^*&)a$C@NbU}_4#sgefYtDKK_eNq96R@?`-_9rN}}4ioZ96 zzf@8l{B`}4d`Fo+`X5e__^YiA&si3hVfj8KSTn_acS6_wm|EfUQF(cI2L5e`uTDcC z&sUG%)L(7wyxFsB=Pa8&XK}bTJnN3y+Vt|ORXIPQ4&blhzjA!kvX)V~z7ZUg{!?3f zb9h#G!EAle0&Znyc|DtDyB?=b;jeUC4!2KyF4uP-MGR=)-3!8Vmdv{QGWtmUPj6q# zFJ*b7VbFEyEi|d3E6>3l_~& zk(u)wE0g6-r?o|Yd9~}G9ex?fZ%^3;GGgmtw zBSi1QTIyGEP4r%MYt=1PQ*PGH=c~*^y7|F|8YJ(gJi^>0%ud66tJ*wlnEO@S?%S%S zSKVG!J7be>b_jD1M;qoLerl)A9jl$+K)HR|`LaHnH>eq#)Vr!rm`6nC1V1IjlLre= zMi|2493mPE2y>`m&nv>0B9*dOJsI>6Urz6x6Doba`fCmQet{_QgCGCsD|qrq;z=E% zN@l9(eDz$cp3CI(!Fqb)?@>0>* zAI|4D-MVUw-kC=r;d5it`j{*038Y6ROHfl;uPV|V5^9q$bHk^&pWWfdgvna7%#EB6 z9{;JBaS`2DjQKX~dxT-W%eiD-X0TPar;jNTcBIvrV9^-U8V>?gg*hW;mq!-7m`8#S;rIDNp);v*!_^{cNi+w~O|7zD)6D`zb;?ciB$; z&>>+3W5PoJrgjM{uL~UxFBev!XnZML7kz(-7JUg|q1zMsg&AxMHTFhMNeW?|OW$u6 z)_A{B(Typ&AlN3Z40c5-g|$3D)BUnx$|*#7T?qpV8Iv9GX$^#;RQ@xZ{BJn{M~#kq5PDJG8MImk!g- zUt_*wiEmM*(f(ZRyD{CK)UP)_qdna2Z`U4gztO&I*+K30c5d-OEl{u^p5xPsQQq_& zDELbo#X4>>t)a4Zis{kDwhL|ie!Zes*T(l!`3@K}P|#58jh&Hvee~1G08Q1Y&Qzf} zx^>zOR$hW;=~Q9)g?Va%s@Co5F{QdSWlX6s&(O{3V@hu@k;=*?_-)vN6vC?XJ8f_L zH;Nb?PY`a!lxmG(wqCNtX$gh&4((7yAo2%!ZB+Np+ACtc!g{pM5@T=AgHLSWq z!(3j79vRlcc=+0?`fGgQGGSefhWPr=GOTi1aRCQkInPuA1zyf{IM_i%Hy0DWo8h8= zYk8sQ9M5p+!ENTAWTAtIoM#f#(c#pTJUN+1NQ8C}Yh|2)#;Hv^)yXo~-Jb|wP1en$ z!m7opdMR?FZe62yo<#jYtNf^2YanvZZ65D>1AC+}bZXm5h8lkx{%^y&*zk0PM(5?K z`fGjR?WqzBt0>g?M);?O`L=4Vd?x?dt#cFar?=cpdt2HbCZhNIec`b#!uEd7g3oWO zx_!nbZyGaKMjZ>gtNq>CU!(hq&u`N^(%M#!jm(H39yA>^1>Dm6JRTSm{Q}Ig00M%kZr_l7Nz!75Fk$5 zou<0bp`BmJ^u^7S(M{F0Jp>H5N$SS=K4X(hlF(QQ zcs-?o)ldnULlv0;pi{YBdXdLBTP6?vf%-iopDN=c*EgBxk$+P3YKmR~?A_zXa@89C zfp%iES6IMkUghAH2Q<&86oq7P{q5nh4sFtYVWL0!B71b}a!GjfhR+~`d>W4>Hpv}{J1O1C zN43UHv?tv{Wj5hGJV_BDLAk;8MK1Cb@+uYbf+|F7*g;v(MON+ndi?r8^T@66Jz;jC zW#ONfy(G-t3=8f?;lP9QOka;Qzl__T1VIFBel#aMOxV}=i^h(~aA6({S*y^uBlmtT zaWSS{C|m~Apgw31o`HBRx*xf9>v|Au$DxNn-rRnD5%-A*mCe{gk4dQXNkZj)CVU=v zn7Vypq-y(bm`(-7ngdAe0^JHywM(pXNmbCT9#R#c++7~+%UODvATm>1ACK<8w)cfL z+T+EudquQ=h|sR<6cwBB*o`q+GD?m#pT@XD?zg%(fI11E6Gr3>Tb=_w$1{mPG5Q4;NQ!jSJ}1VPEwMHG^BmCn_^JY6Jm9UbUbSR4Pb1e_c+ugp7OvL4s7q>ICL~; z>B+(#-_NwQUh#^et;L~(Ok3xTxxpOPZCKZW64Dx8z{p0A;so>}H+x%xsAR^^pyy;F z!#t=F6Bp}qw{F$?2~0+BGAfecmvsv>6}@UVjyx9f5!olqm0h}}h0G5{^E00VRJw4#nT#gVgH;7_Tw_0H2N2tq}GAl~QZm3CuJX;~NCPuEfD` zC|7Ic?Z%N#H-nfXv;wb&Sxz)@B8c?DybACAy7@}ta(3Ed^sD602!*}O@_T# zJVg;s=}(bt!=dw%SEP#jXc~1IM zbQ`Cy)*S97A7Gf<6Tvv(Fx1!|DHYb0^t>^_>%|(6^#hiLWco1J z4daV4lN6~WRFc;4H`FHZ7}8QDBc9s9LXfpzqH3RztVNFFRQ>Gnst%tspjx!wujVq7 z_m_TNll&$0_lUD;Q+qps&1rzg`_+?C=mvUIrREA-0R2tG83L#HiC}i917_#=B9*6@ zp58p;ao%hf#D6j;78BH)6N`|ql+uNA4&gLhZkTnIh!PZq=VbM)QO}v`IbS^&tLHNL zjMmc=e*ld&GDIAMu!i;HJro-@&ffO&BEz07+y?6A(+;$Yv~ATnS||RRiU#Y6Qd-NG z5dtU@7T20K>B4-n9!a~Y3BnF;7ACeY1b|b4;LJVX)*9vkuzNx#@OP+-6N0xAPv$bT z5Ih}gQk`|Eo$8#Q-2Hj=K7j=!s|G3p!piU6G&C}j_NCRE>6-_U2zz}Y(2F6v8?qNw z8s<-1i0@%P*2?S;$c0L8kfUp0i@+z}4 zm9k2E3d=>VD(9I_o1qLfeq40b|K#8t-{zzZmI2FWquS`y9PCP7$iMf~Z^EW7AXI$y3-dc1WO$koFzovag_j1MP}n=6 zhC97{K39irUR5gxk6M=E5FHwnf_C{+X=mi$cY9=0)rs4s?7i#t=E>-EkJD*x(4-#2 zUbVbR6@>8fSkXR;b=QhkufwN z?CC=RJTgPHJwTC#z12erd_LY7xx}SM9)|If?GyH>kJLWHUNLC} zYzM~ZkG@{0HEhD-hmZIxac5ZTy}mLhGD_IDSBm~k$+LoGFEwt7oKv<% zozVKyu354@$!!6->O<1=e14LrCfIojr5r02b z%-$h#Zz)D(scvq8lt)nRGIwMe0eL z8I>1MPrrQ9FUasU^?2Zb?wS88@Ggj>{csaLf$3JZrGIh}ZTjjcpPF#R@0(C6tVuPZ zqMhbyLJcUhHkzj12{ZlSQNp^l2noZR&tbygBkU}m~ zRNNYs#{&60kz2b|4*4E(JeRTlH~&!ul5 z52cdERDrb!&bf7eAr9iD#>I37olve^;Fs_$? z0*u5&2}C2IE{sY`PQ*{lf*svSAj|PWSQ96UoH$Xs5J#8mIlnT@yGljQc45}l=yoVa zm?2OJH)96&Gj*U{cR*NY3iIZg-tm}%&-aeUn!xcN^l*O%Hg&6RvYrFNqYeZT4iVYa z2idCzQ10M}Qty7EwTh4)wAPy7E=tT_w5Ni*s?3Hl5~f$iLODmPR=?SbgjLZGiwyIy zu2nx@tv!XJk3#t&WbTy(HpU`n$$oc(DV1x5bBm(erKF$wAQSQ0@C?YN3`C|X%nWAF1*0>QvLo(8b zXv7S2POBb0dRE==@pj(jYY5Jv7GTe5PhP0ow+BS@^&W?E&`lgtKt)Ek0We8-0CFcJ z)*g@i0zx7jR=8*5JMu2`^#)@(;gi6z=t|}ST^klEaoX`JQc_dQO`M7-R&}_kI2b9x zifPeH4uM^@>U5O_Q;77$6+e}De2n#yxxupE64yVvx!ikJ8BnOx_(AAThF_9LTG!8P z2}Fuo{E<8K&g%mh86Td0`DF2A;w5nPgmpdkuesR2HWT8z9{bm3>^sFF^G&_v6+QZ4 zj~0Cz=@2?|=`G{!>Pvi8Yfd8divE~U@p|O0#CN%r+rb)~Tjb649hFOakXTIva%zo) ztP)d@DrBz==+U=(9P&*!ugCi{IU)0!khusf_SdzBFUiu1vki*}$MRQGu#716gp5Qm^D)1(FRE`>hBr2%btUa2Pw3#3a{0rMHE*` zag`J|nc^xbZZgHqq`1WtH7SXs5^oo}wJ&7l|Ox66kO@CVL z*DHRdHJp8{_G1zVNQn+`#XSxxQ}nH(|27a7a@ZQgp37uwP@F_^){{L{DKWl^C}2hV z=#kDudy?Xu^tnDZ2HodTJ2{ z#Vy`#zivK7pSelMH#0RjkdV#iOiW*;+M}DR}1674K$uV-zxKLaG+kKDLK>QoY=nN;hlgT$E z6tXBRH9ro$fiuJo-R#2=!f#kN_ze)W8L}0MN|&v4iqX4LvX#C;WGngxN45faavwy? z<&Y9WwxWXo)kn9J-qH?|t;9*_dq6jL7}n(zt-S3@ehS)z81@zM$a@C1O`~E@_zR#l z{Z*=vzv>sGyOIS)MVAz_9$+zRJUmWVImCZM;>zPD_D@t2EMc8Z+G=9~#xj#%@84=< ztpKDc2*#^6QWo=k(FyPau=U>v0i8c&*7&PBDg!x&NvwX-1a-JKTVK)*<0Z1ys-dV&o;mo9HDwlDinnhp$Io=&uNXw9_ zRHZIg&q{up>*r%E@LAAn*-Ptw+~0ryQ=#vHOlG;X+ZN(K5u6|D#kuaZ)$dbA?Llhy+_up7%685@v|sz5_*0ZntuNUEwC6PQdD%8| z2U^gLehj6490Vk(elR{f(e_j3RQstA@x;^dT;BoO)&^@oIgRY=5_{SLvHK{ zZuE6DPR6H`Iva1oW5J>@luO=?&DHJ?Hbdd76~l?O2-e_ZREMyqg0{CQgJB1oK|J

      l|NaiA1pmSSOXyfw6nL?Bkcx}JC5S-$9Qe;TaVLga@^I#+PcvHjNgR2Pf5rPb{4)v{h2e9y@8Nn9Y2cO0Y`=!z&LJcZbKB zRuEKz`zM2s%y8m>sM$#U_O=64Uyts|bFd4VRnRAZ_P|4bhfj2VwZ|>Y0eD)BniIDn z*4cjncrgIp;1J;LB^9gBh6crmZK^DohM*fGwpZaWT2=9M(ERZJ3xf8_9BYyQ9LG#X z0}A|B{cV4Hb;bMluTOrP)zFwe2H)di?iCSoa{c)uhaG^Y3WZ(pA{G-^R9I#OF%ELE zfQ`o3eU~1$2USmzO_aAE7*EC=oorSENn{)eJm(Q~ZYBmgwdl2mRY$N!9sYp=4XyDT zh)qQJQ)Y(sq>nuyLn~qv{|`U3iW0^LM6&L}$v&|H>sJOD0viaYQuf5ZBOpI0q{WS@ zdg?U9C$3NBLR!MFk#iLGj0CBi$W0Cu&nEt}#JV)lO06J$1%!W@)KnW77374U%p8dD z&ocYO_)y|w)u{hUwK#+AQuWj@YBXskI6dMa9BK6Eb`ZvoQ7&OVQc9VL%XJi-G}*n5yiovOPT)xYd$|%RGNm#&VTRy$T+0Wv zY#h9kUCPVN*u&YApTmn&F!H*6rMydx;F39KW@u0Rk#ug6h>Uq8+J%u)k9N)n3BY;H8Q6hkRgNyH zmlO^*BM-Nrv$0E6g8Fv5!M$#~V82Ur(HhC^@ts?1BC;ME#Qk z#0z$2l2kSzZjeg%V5(2`^=^b{K)0s)L)OAT@+7_gX|dxsNNY*G;PTWxBX-G#fcDiW01LmT$?!NmT3{yMytu3?Bc5u3g-g8tC2Gu3V z-R8^BAQ@9O@Ymg+f)^lX7WUfxNiZ-@e2ic6bj*j5=6K%t5n32RDn*C$9;Rz(k zY{LlIhVXrhxeYWS3-0U)ZadMs=ThCXk6-sSRCmAu2ErFK=?*Zu3qfaEIs>re5FVjz z?<*i%oot!2tA_enr2B+bHBnWapx=&D)%;Ih)fcF$>Bq0?Lu~V(uvbr@h{S%N!sGPn zyT`67>*U-{WfTAWIAvdc?6On)z%k(9Akj+~9lxq=NcRaxYz{NW(8czS;PWSnD6U4eiLc7GB&zXDo`O~-Z=#>sMEO{$UDMeytsr*HM&8z} zV6)mDdwF-1Gz;yB+y(4@1GXhnrn1-5F6<RRXT=_66@gcV9wpOw53eS5$k5p zx$mw!7B0IBZ$mpKXg=bZdgQfJ$Qrv+H$h~{r3bFC8Qh%)5{PKkiu3*7fxSuz=Ao_V z-mi@P$`xZvp#^ZJXsZj)S~UC8qI>a%ZFET;_OGI=0XfhbmfvdcMO$(^#-HcgP>Hct z%@4*==H`b`=5UxUuY*&H@FlYsFBYb_T&!C#yXcZj7B2yNNtB9pi!NC_=gwJ+ur-Tf zv2NL2__lP`61)|Ob<|VpA>InbIyh1&qWfHLgh)c!KRUHL5ncu>uMbyW zW%a%_G+<;1=K-3oj>Z}gIeFmCOv7_wq@(dRJi+xNw!kT!%qXl|FmE9eL=ZE}*AZOf z?+luEE|@#FBWUBb`Lxd9zm30V{+!O>_vb9Ct?mf6Ahr#O*4?*c&f<>Xdf$d+&iufn z4%8@BN!Y`oBqS>bbUny$IZ~LWHFyWKrrw+JESTfO_3fU4w zVciggp<*1I5=D5DwMoS*;gQ0w>F%xU&I!LGtYv*XG0E2QxMlU{3gZIH}QLizCzP3pa5jAdCU^yyRR2Asvid?uhic4^`ZU8@LZQJAIh$ z^kI$zS9D0j(c>tW=`A3<0gdu3h{w}A@zVjF`2K)S{IjBf>Zvgyi zx5fy+;lBz0^;(H!yp=Q(7YA?5LYQ4!jE}82_QgbJnY)$LIKgu9n^XXL^Dda%_<;en; z=(1wp{bLjG!j>@_ds3s}NvS!fJ-Su$kmqiO(U#^o3V+-D!2xUD?TUmmjSL#0zBg=kl7WG(p z90IcX2ulFV+hO=INBqmZyeZ^F%6U4fdxP~kkx(y>58ARG2aUu{s68bgNr9@&7mfp2 z5n06j=2i5I)QnMK;h*DG?z^c{=b==E<2YO57K#{vw34SJM*7p=dlA=+R<@ymRB1wrzd2>(0;gwR<(}QdJ^Pw!Lu5rFDl^H#RNC-!$zfEg>Ko8y zM&F)Do~&wR*1tp++|B>U>}KTboNT-9Wp}TL5olVnIH5u5iAJnYw)A?Quq>*thaZ=@yZ60hIk#X2z%IW z5P~uM(?O+6meA}TZ>Ba2I}`(92*yIl?2#mbH6tcqfG$d2741iJ^@@l;Je-}iI5ePc zMf_y6p_>Qv_TOBq=fHk_2kZ=v!l+<~_Sc1IT-i?6&x3yavsww9Ge$QKff3n?cXe~W zUb01d=tm%p_2~O~+5@awZjXqg;RglG1tH!a(Sw(5qGFf!zyXvMg04Yy?+3a)`2#pT z)!W}0qW5Z>OICd|^c2VqJj_twkuw4{MHUzuSJ17@) zxcCF!W zsTVoz=Ajap{>1e|T{^tPMBg5&H7I{8r2AuHu*et9Gm#!W+BZ~tU%)Qbf8b^cjG0c{UTXbKzm-x9)>{e)Z}S$byHE{vy#`z zH)=x!jx)H12;hE?N{)I+r7;ixQhhM9Ibk@?kWIbe*-=(Z#8D5BA@TdMRGDI&E z;Ee{ZYcAyRpH#>UAP4@4|3ljA;4hKBMW^grx;}Vzc?-F?(HhRbd{mRnan>Z6*o_IRgir+hquD*+v?<38(K#)? zXsFfkEYSra7dfC%o+Y?&^QWy;nwnk1`U2VxoU<9?h)3v)Vb1oWPyryGJ(+%L=x3&{ zbNqb5VBr0n0Y^$-zwm2L5m0HhutX%#fhPo;P1#`8C~&y~I$6hB<-#h{VK>W?gOdz^ z9^7Zz!FAA}dwaJca5JO|fKR6rO0yP+KYOq2m~FSA_RNf*gh5PZ1kZ*;;1+s#SBp^S;b7b z?zG1}_dJx3uzdr*yyD2iPie%4Ci#Hg{)9 zkRfl%f2b?*CuGaoevhRlO!_Tpc5*snF7?ii;l%_sCP&lHJo>p0Kg_dnIiKXdq`)cG zVFEi7;!K7Mg}prRauv{E%PxCCJ`Lb#oSh`8MUzdwk%sdalHi{N-ZvPMCM-6N;r612 zD@aYQr+&{cv%wt+FEf1lIr#DST)zVu*lc0==eEORzoz_IWc9ZZ9*U7@uOwd@Ag`tm z1yPaaV8agw8_EfZ91UkeJ$T6(AdG%yD^etyD{sPh9K*kcw>{)gW3cu>A@W3KXOPix z=EjkI5iXL7kcEswSZM^t(-@c9Cc|cbInKfW2tgvmdl%$I^Y!Sw9;|;UyA#nl&DgtI zyn7b`s>dr5?5?o}9%*4TAtBgNyo`2Y|6<2Rh6!IR(Rs9g&7=Km9_?TAuzyW(_OAxq zg7WooUsEe!+>hz?RBUGw!$9yx8jj?}GzF=!A5S3!Aeu(XF)BSrHQZMnBg;LMshEhSbq<_3G+4X|z+JUXY|8 zCx%O-oh`=Y%bjg=GDPWWDV@YA5)rc}Dkc!kMmgp@nV*JZBpj`{0kpe0{8sWG47ZI( zMa5Eo_%ew1KW{^pGQ7t!C*nWOmXBUAQfkJR;&90 z@Od+RW^)I%#-M*b1@XT?Jcfy?dZHM)xe9ECf_!$T7N2)FmLDC{k0NH#&W{2tyTik0ofgW$rD;X zviE80$jJ}=Oj>VnfCNqw9C6S^3zdZ#`k2Z;5%{HlF7S2b);|n5~p}XQA34hUjl!w>{7VIX#Sbzt*y04NzgsfX2IDuRQ z6zoL&=PH8{*M3A@CK=rXp4sjGM1-jMwjaqq*wvmXN}d(%M{>o6AFD`Q473N{Fmpsj zhxXtkN?x)^2fWPqc^KY1nyjzR<#4(Str*OZQ|5b9xVcuId@A7k0R(5WXbf z90@w$om#`qsKT8ST!+>t%&+y~*^Oc95#|l}|5`Wv^+*c1b3%_iCrs#*Z6hY)#%^Ig zLt?vF2)C#~yPO#1;Mbg~oA<&N;(GX@>Wh4dul^`UF0fi1TbT@Ya}(QN>H@AQ!vw%P zVEsOU-_2m@Ig>O(~sW+yTBs2p|P!$myZoN@W#{?x3+|Umwjfh~eU4YS$j1 zrA8gU{F|u>naznX-gf>98ASTA?b^5Aks}$?zWoM=iVG&!psg`338&F|aCFyT4N{!e za6Z>{D*)qt7owXm$~jWYAs&Gr)&6iXO&2wR4x6~&#-p#WCN-Y?eSrQjJgyK366gU0 zMLX04POyqDcyqOq|2f{6;$H_#Kk*Eh>3rsS;e=9kbLcD;+Ae0!7AjJ^ZB6&K6 zPNfiC@bl!!2t^O4JZ(6hFyQBwScE>q;_G$f_&J_7CZmxYlf}~peS< zlKgUcKNejMWmrq$S@6BO5m@EJp*R(N8hHEeG5*8{7vhmvZltd1HOc9@!)~$;E(mljrNKo&@kJ%3Z4aF zb_AyvL8-ouG3kOtS~o|H37IR#0Jf^f1a!JhiL{h(513KX4V53rJ80zKPYBIH86^1g zTwTO_Q+gD39w{#2)~Wr5^c`!^#G%xg7GXg(DAXmaA!yESVR`V^USW@%1vj{L%9%f! zlLwJAf1(Wb&;agE;mL3P9ElNC@I_?B=73oUOl<)=n~IU1wgdk=@jo)aUu#ApF;Wem z+RU9s+E6pn0!4e&-m^U)i%6J;%Kyng02 z0;VRqxNf>uMYYN;>mfM>jNp^`1aLq*G(!cuAed2|D^Q)2kon_032>8dk^twcBY=At zu`*|VE9V3)W9PppgE4}VHrm)wL|psU_mEh*+Wabl_?o|Q3Bz6xsiZw7dQ%b3gij`}~PB$~!TDb9}1e%@E4rqdIU-4!L9@|T{0?Hp=jD~0rwP54o z67=X{sD6@+;F2K%xjqT)c^@EhzAeW1>v7W%yQ!0P~kkO?;a-Vad1R#b?kb@~>m zBDzCwPe4T!A^II52-=)?A~F*O(s|BU`Y5474mTC$z)TsXAT$U)I>zbA_k#~b1TX>oW%Twz)qsS@VtuulD< zZrzBDVM`m49YTW#9&5%LQbqnEHW7f*a@^kilF)Qq5-<_u74CC96UCiQh%a#tO|y3E z&ctwkk0Ou+tTabDPkOyaVV5Kpe^YJ4FAMND}va=4uaA zA%XKfU>v}paw?#mX^NN+R3E^wax6{-fhh+tytEadBq$G~@(7l5$-||zE`d1ka!3Be z5Fv%>`8V^k7`wtq%ykW(CA`E?PEbm1&=JmxGDrSu5#^GO5b=)Mrt#4RIRPcKx5(T? zCG)O>#PX1`dNZMLkCtFrKf$rqcp16dslgAn6*_HUn)!6K#wB%|5Ea8GP{FELAj8BJ z@Xs=dM~tGd;wY+<@c~{O*L+y?jZ}hk%|`-nt{W0`FkK$uN>eUT z_&ABG0>B2KIoYKj%5GEDAsBOmaW~kdg3^6oA+-!=endKlNz4Mg>N(o2&S`;TJvy)C zFi;-#=&a=G&0w3t;AQ)Qbc9JI365kSud^k?m)1eQptITXWE@u6%>XzVG(9}#tTgi& zvw2j0XV#>~+i~<}E(DAwSKudMVYv8UOJzyz8OgH9sy*XOCx|)VY*wp95L4MmHD}TuB zwbGJheLjVQ3@b;+X#`k^u#rJ`cSZO44f}KdKqc6pTg{Jgb7bi+aOnll$Kj!&+CD9HWMhEXh&G%efhLKY94W(VEvT1`K7+x;V<0<<( z=R>Ex4nK04(mb}A=jkxZi9ue1wcIJa-Wc%OW8LNlaX}|@;F0{sJ5pBp@|XZ3KPAeE z0Vnu~{|l-|84^L@hn=6;Gx(2yaWOTf+Kt136#ln1$gI7LSkC?iz()n{DogZ+>k@=iq^$NRl>Bc;C$&rbSFG}KA z+bNzZMj@(=xGDLi$YW(Mk#b;~=q2XJPe}B_loEP~(+>0qZT6g^3LsVudCQI{cBx0| zLhLsll+=Kvr-czfB{HJWsZdN*F6M}cN-I!W=cL%y^fA?TOPOk3K;xu5d#B(RG+E02 zvPEUt4KCv{&xy|=i6LT?5{55Qh$cBXib5<;fMlUl${4`??Q|szr!c`PcYUIs2aznC zh9=i@{fTC3C*YYJcuW1wdhqd9(;O5hFVoHJ0)=3clqM{!$1H(um&Iduq}~}o_LxF8 zM@?L%i`%$`qaANGkP!b}Fc}0oyP?C-tnGI{GVZLURY!v46WEU>% z8(ksAFsjZ$f9HovIxtu#AfzBKWZ_G}e2tN?f(#2-PC+dAG&u!KRbWI3ra$~`;i^r( z6>y?m{T}Gu>G-jY_gGFb8XIs3F7`;&L-cDXE92-x>;)& zj;e4il2NkPBd-WG#taKB`=X(yGC7KI1RY3ZiGWwS2I-$b?3BI4F6)S3a<5dApdfEV za=oiNkl_j-Gx0Mf6!)0UgS_Z|Fa$rqot{u7fM;i#4Qa3Hm7w6lMc^}<6Sj+ot-g0o z-b8D?a{>VFmvhn&!4c#{*S()9IO>BA0nG=78FM1%qf>kE|C96aAs({-srkUfk=TfC zX`E;q9|ELKnrLEPCfQ**C1>-LplVEsy4)T%iI|E$!@Srq>-?5}Dw1Stv#;OX62{=3?aW4(R8YBxgp}zb8 z8i};LGosiaiX{)T4w6XQedlK)9tJi-JiQoa>Owu*1Ig(idvIMLSBo0P7Xcj=Ba;u$ zQuQoX&r0N#0GYt(ZlJrj-i!4U1n5VhgCmxc<@W;{WDu*i(|Pst|5^pc3IB~gjB znqbQS~uPa&IHByd86Xdh>iusiCSxQDY){)QFOZn_w5FAr_ zTnAQ(ham;Zh7>Ow=EF4{`N&LusFS%<5!I=x57twQm|nLKR&8eCEmfpFun+~1V;f)y zMuSp<3yOy(tzhK8rTRZn2Ak!6umBg}5Sl-FsYLUuK!$12n-ME>=D))fCIL@=A?FM> z69@zMI%s|esA?s_J(=>^QVFS(A3Va25FN)w=~35~K2SUPp_0%D@ZpP3C#WO;@D#%8 zw^LoTR4qCD7T=%)A@DL}Kr3SOmF6p!DFlwQ5I7?m4kJKNfk5yIftB#KA0B`Rk3$}d zo$Ba_il;VtMw52_4EZj52Ks%=e;tSc@@6HBR)!k6 zBRbxrDk5#+(`mhVzrnhV)F#aRq&y&U_Pb7_=-T2kfMRhTMCH>69`pLJ*Fsm|nH33g zG*z-yd+5#-Y&@@EBfHnAHWw_is057nD{z4C08$CqDx$AJCE&%f{w9KlYN-!kkUju4 z1ae|1ZB_S^mcVeMpEUuhkFX}dFx6Hk2}KScMl=F+05%SZw6YGsXsH809Sazw1E5NU zjupxN4VL|D4G+q>C*My}EH2IO|pJYUd?j;YLfG-cZBergwRx6HT*)c$b5TbT^9L%8|a+y=RxfQi4BzRL$)6{ zCGi%w6AFs2ATUt2FXj5<>6;_hmooix5XCZmPy{u3kmuJJTLX^#eji|>3;!wkeLf|E z{N5FYLv+4sVW!M}Xjmh8ea)}~N=|GHqC^bJ`V7-iPN2u)1NVxQ2+G&bNT5ujS}`CA;u>tr7;+4HbX~DKAM6lo5p@) zDBkD6#yFku+G_T$ALW^?BGSTZEA1Pu1+s$81J~Ay(LjsACQ^7o6N?#UsRP3@*B3s9 znzV=RNi8y(PeQ=i7g-!%UEJHAWuYaZr@?sFu*2yK&7oIly%}=7P2u&1hE0nj$%2xnuguf@=xi9vs+N7$30KXW*@ z?i^&z0i+<~^me{@kWg^qYD^Vf&|d-Bi0J|e3#CfT0dO4gZYQfb=n}SYb(n@GUPBjX>?u{^*EFuU3xGE>Xlwp2 z=`d+D<}0qnY|wba0tVx};+&^wjGHhj!w%u6`VgW?=jZtqgx{bVM<}CaD-2au5q=xC z3o)Ltivz!PII3Vg)o(fYE$YeS2%vY2KQSo< zGjAXYHNeOZ&7cXi{U{gT6zYT>Kvq5iI0l6nRpJ~J(bF*RDh5vpTl(^HaLXzYFDM?* z?3@GgJ&C4FZQ>m?y1f@FM@5H-BIdZGxmxtwr1$h|{5XnG7gM51_;Di6>qEp>@#93S zB+aFNlxj~gQ0wJ1NPI??M7>RX5vdZ(Tna_byB9tx4X8s0Dc|}+WG9H@iAdgulCl=c z2bSZMi=$0q0hOASi9vqSiHY$D)d={O=UD_~)qn~9uzE9{Dm~0jhixGhwVGu+aZDkn zxUvj(p6(W;Cezi+aTEfsoZ#0QzJW<$9pY?!f#OWY(=85I&7*8&cy*(n*JnjMd^*lM z1o78_8jAt2+avIAw1?X#(s<)u5Qf2-Zr$q_CC>{>GS}^4rNaEq8Umy%zf+Hg7G-|7 zIUejbR0FiqI==N z6qxN+!WLy)nsn$$Teu0!3U9|DJp2r_zETkZihvzjDgw2KpMkMiDx!!YAn+%}$E}f{ z6J8+B=_G2hyswL_>8v`GKVqxEy}^t*;*mu1%OKRdQ$Pl>aCZ{r3>1rLjsK{CWJWh} zyP$v}84*a!#l@sfRVv#1at(W4A(}?|+vdbl z%v&9%WCS#+zL8^8T!xnKU-DLB3N{Vg^HoF_2#K72qvDXRU58s+Q_#8`T=eAuqoTj= z47!qxXasXPq*eV1Vq_Z@1T}|^5|qZ`48y)1#wjlVmBZ$u24(AQSOxTVeyHRbQdVw1 z44aGoA)$&rA?>;&F6&&c*c#Ht6YCsG5n%YNYztoj_Bl>DQ(JKUkB49e_#p0oCXoi! zEHNsx(2<}pcBCXu{BMVUz9l@&FrVQW!l3x|=>2^@t)U9QiPs{-x(m{fcf%vlni%fwf{GWU z4a1x1eIAvI>5{%1aY6JWXw7>y!^6WcU??3<-Cnq;*O+nf;N_Cyo(nlR}24 zwFmd0E{xD`@o+|Hvl9B?&=_GM!y7Qm+$S5E_dDIL{+%QCc{>CN5z82W8$DJk7?xPb z?8at*#R6C{C!(XB;nP^q(8u;&QoayIhn0Gb1(1V1oOLsn2@F7UAWy2R6xOh zL~N329&0~>R9<2pg@GL9HTO8L>|HYT0bv_TlZqSY2HF?Zkl=@O+>TURW2?=Du$%mR zwK)g&o%ts}MBFOSGh;<~U}Qp%}J3dU=|P9}IPqY-D_4wOLnGZ7v^EZO#Ts1A8X4WPEAzudrP{ zhrX`ClU8FBo>-Y|EQ$pc1f{o9p37hy`dqcOuuEPDOSrEb4N_Akx%{^S8%Qoc+2y#> zJ_pMUoF51qv6J#RVkriIhtMqGIyI%KAu_DukTnNRg(47!K>`~xuTm1&IsOD~w;^oZ zgNt9^q%rj1s}mHzrdA<1c}n#9C`w8by=2Zv5J~_w>@N7(tD|eMc zCgrUp8V#8%fpvpY$~Tz2HGC%4UpCoZn#maK!=zDRGE=nbxsMXFKY{4~3D_DY5UE>< zx8aqfXUqC*FwC6*5Sd1a0+UJ+B9e1t;#z{@>2&%4R_2V8DdS{h%&tGY}e}RXXLCyYU0?3n>YbsmzZE zipYPly`;a&?5a}4NnKu9Z}N5~P@|7&-QrYK^?rYrxQyl?YxRE5l*lvCfk+EW zcrT**?P2@jwcVi?RNK&y0tZz)x#I8kdSV3iK_mHZ##Km!B7u-xwl-RVtih` znHHO87tFAKucSF<`oj^NI*ty|Zu*#b0SAQiOuT47v1H@`xk8<#Of+eU8e{}&b?x61 zu#UpIS61)PQd1W3pMw2+F)HP0$YTFa-yq2>!2=Xbq%3*$VCrhj15ozwlnaOKQp%i7 zZ|yNIRwm1(W@Is$j4EJA)}HuVEWy z$RU8p3J;EAoU7=R!rz(az4?S_#FGFV{Pk=gU}Ri15ApfupG6BvTBhMkyMlhKZ(4BQ ztiW0=iOmT(5kuF~^&+#o;N-=;5iQQ;bEXLp^Z$dSh#a@lFUWlfO zeNF}k6ng;b584B)3E(XnB+Goy8lc-$7<_G#lR(;KLxmI+CQuLlp|oiJ8i$^>eCnk; zk@ukWEpRH$T^0SoF0Ju9Xf>@c>NMdWSs?raz(~h{c9{9L7CnzT^JC{5bVWa2Ws%b3 zRTQ9#e#WU1|Akd2LqiaoKC`R;%DoW*<{OoQAiKv|I zEbYE{SHE@jK7logvj>a_qyGwyc)3)GPVtpa{Q&=@Q{wBqWhLP_75wYu=Vg-#S^4j z^Wer0zOSfNjeU@I5u)omgE34G{Fn2Ifi{0Ke(2%?rSU@-y|bzjH2Hz4n-GeN4D z81hpw8FmnGo90I)P6^08HI+M-MmDDi-%Nu98?<2^SC*S2P5p9GSV#*&LPVLRVo=LNY2)$W1LWr!5MJgUc)1{jlC{^ z2$pV(b&?qb8|^FAW&{qp3mWdD4zQ;J_l?G&x}rz3!Ey0bm2{BC9grBYB8v+yg61g> zz@UCgLajvYGktgo9j7FC@qrJ?nW;uJQq$5hdYuK$3p_({$)S7=$1|Q)#1#bj8y%A* zhm!rXfv?!XCmoe+IH^Gw2V2!EAGSiR@}yNm%Bqr9jhymL2o^42-Z+typx~N>buOxz zhz^tlk%Q04Aox6*t%mgj8Y}Ru#}sFBKp(&dxF%G$znO2ND5WbRT-G650hrUr6mlEM zja2oRqU1afo4E7BQ3#aiNu2;4f#6I6Nalh{GT!Ff-np06R%Ejkd@ivRq+PnW2VT3V zNsXit87zAPOSj1|i!cOW1q7d?VQURp3w_My4YpW*Z(l`<*;|?3)w&uiqVPS`zj_>q zgE+$05H}V(+ML#)`MWA}J2%VIZTO3XR2SUc<>2ntF?SbjL7wrFc126ZmnYAo3$KY~ zTZy5RMVOl`#9__;jiZ-MIUrC7 zSzk>26G{fxNw>~pD4@h$4irGTZ_PhY#7**`nmoJF3* ze1*&}hMd;&cyOD%O>rlwG-Hw~Xp(NAiZIzVbQD)qvD+M;mD!L>-BlzTgjpz7hIFN# zDWLIx+~JseTz_qH=oRPb@cU@|QKIDE7N_{32%sW|u?Ys|fb^-P=qRyz)A+$&elHlI zlHXf#5h0r57qP%!K-_LBfdr7K2Yf2#1K$+D%d_}TF@n?KWA(nZa^@v&_`UhR1UU$L zfH;lU$>1N*|K&L)4zsX%sKTfiHDW&H_ZPd&!aqa*my-!EX7UL~*U?xrpRid5!S{3c zgcoAJqK==2j;jfo&54*#xIzYzp7?~@0T~FvfMPR;rsOX^i2!bF{%#bbMgJ4ApuFk{ zba;h+Vh@*}<9fi|17puwAAsF7{@$nP-;#$f8-FkH1q|#*;p}(dHYqq>-3Y!fgFUn=zIm5pKZisGzu+(Ii}8RA%0w&3s6vMU5-pn_j>+-W zeI3gVIr1OjWH9Zr^E+j*2Q4CJcXF-XyM)#i4iX;si7^mC-N=sv4~v3vzGC-@yXgSL z;}1SF*@m?y?xd-7EyKe0{P=Ufl_9Lgljl{ojncF0@_a&gXD*!MO&-~>Y;>|lH* z22F&pY8RW|a5>*qK{{9(iYX{o~9o&-6 z1^IpZXUJ-g+k6~j^7wq-2(fvM?2*B4!eZLDZwFE7Y=+p13Vz04*_ixI8B_^7C?QZ@ z4SYt^hS>T!q9a=o3uJ?w`D-{i?~ng2gWYLpPy1FeB_3!@z6d2r)(Bjn;p{`c=B7af z;nso=j!0?nT13Q&B~y=0KLp(Eo@w2KpCcsG~+wfd$ zjLsQYne7fNx+QDTWEg%v6|9IrM-K@U*vu2P2cN`5+nm!!>Vu^1`Aps-92XO`g0to^QfP`!N z9qLZ>N$3IN1Q6p+HBIrrEHoF=_8U3x9TbR$dVpwVy6tz0n?8V^SiO%1?$fjXhWRDV z#^_VQB{E1-lX_G^L}oG3e}CEb-?LNwpD2TXXhPwYj{P?*mT5U$UP#N~B9Hy|9h8%x zA-|Aw5}Hl3|9u#XSy(l|1^fhrk*pPYCT$+4krNxV#Zg6TC(1=ARu zNHRvfj=o7_R2@c9WQ2M)qSy!(rjL2xUe*}zNOK9)N)hMB$&*d|Uf#y4_ouaHH3MK>O|fywpPZ%znE%4&eCAemk4}_P>E)56Jw? ze)~_>ejD_8CFpaQag(q`UH$@VZsO;j;9>q>iFZJU`8GN;;;I*1taeBKxj5sK--`?Q z>7rzSgYs%IE&qxFB0&QW`%xJV|G>A?VM~Y`rKCZkNRun$#9s(}=fkszo-n>EhAkp& zx@VU|-du@zLGgG_R?iysoJr3Fj-nD6rA_x?GS@cJZMyFzo9=dxP4}&oXt~Fx`-VP5 z%*Bsm)4hOfx*I>aP4@-ABhEs)O?P57+Kq!^41DBrnh>w8xTD1(uX1&GglmJR$S$OnbQ1`*tJm zN3+kN&VDTWoRh)Zz?lm&hx}nO_-WYZOiU1TWwFmGLOV0;a|#i~qXkX`tML!ELWako)D~=_U|GSf2kvo|jJhpKT6+>K~l#b#$h-Vs0sqjpp`%iA>gEg2V1s6}@phxKb zS6I)~^&E6v&F4Fuo|q~5Lpgm=3Ed@oh>qlvOEo3`$8Xbojm_WYrz80b06(lg5@`{g z#$8u(ciAQ7Nls3iO3A6v0>{`UhRH}X!XeHF=tDNbxeN6m9IW?d7FjKD0uaxdnkugm^|9iB%DMzI|@>%5b^bPX)3Os<>jwzofw5jCt zlndC8_ra`oH(YE=K2L8_K2K$EQ!?fA9oG{`epLCqESTi;se--o`A=eZBl*L(R^2kd z-}^_+fx{R0Ar=#y289&&9a;73(()S0~ zcWz<&rB_&I3#*E~%6P^3mEdcTz2v3{JTgp!mhk<4kYrs*8W1JB*|SMSAACu)VaVw3 ze4Si_EW{~0%{9mkHe9n=Qz1WqU+U{n#0>$UxiA8I7!5i0b7XuwV{J#UEpKjTaNWX4 zNAOAVfPD;Dhk7_FQqT$wePDz3%R}wYo<;n zKv4k9ALuNA9rfyF0~2SD`*(g!T`O$9t&>zfxCHE;!L^pM*S!do%=C8nD=BH0qzlLw zvNjGBTmxkee8czKT`N6Myfa$S$y3nBx&`wVqOa>`9lxKg+|SYJ{q))!u-;3y{(-7U zP9!*F+OeC1OqX{CTd3ENc{I_JI@KykIw<@_7a$78$}PBgvxO_9%WMT*L60Ws6Qq>b7@L!Zp&k7|&I zt9w>)DJnn@_YTA7!n@*06i9#Pvllr?9-up}T)f;57yln5_EO8OD-3dngDZ%i)7w9Q zC;93zIig~>_TaOqCsgvDVJ1ZT`|yB6eqEl1UzhF_7~|IAcHBCgB*Xbjg!wS-uzJNS z+Jiqq)-;zcq}FCZ1<2wwbldQa_Lq{>g=W5T`1Kp@J6~s)5*mgebt@TVG0!(l_{BG= zPlv(<23<1TNAt=4TYBAIhDSKKf~u0v^eNN6I?~aXZp|AtHJnDxH@k3!f#c5x-W&*9?Ba|t)F!a?9OyWj ze-hI$Lozfe6$++8FevvWE7`cCgOR{jQNPqpPkvcOOUoQwLbeMH%|I0L8?MfvZ>-c= z1=mgs#(gIWFMs8I?FVt)UqE(Iz~EV%gcEP?+{Iijnt9QZ6`LsmFuYwE33o zh*A^i46ec8<3`CK@_{oTZ-&&PPtV76ab7a!S5-L%uU_^b&oZ~5v$0E6g8Fv5LGsE= zRU@`r1v{A&NnAxVpLzjGrMMap^G4_}bRCWFDxaAA;lYJOB$YMDn7wp!(#Q zg3^ZR#v(~S0g{}-N(~gxhXOIGqn8Inf;bI=YWDh6{UQyTXwsY{H1aaMdNV$+L_f0$7Y=M3+{j^Wd8p;W{L~t#NMP;Mbp7c+*ZbINg68(1xf!f* z-0>vUd}E;Px&UPWifJH)UJ75GT9t6P*xn8Igu7_D+E=|osni?}+l>W1IVPRH}@mFb&ci>i`p!WDLg&k@JB;u?qJ!`S~IEgMxa5Xn>6slP24-Z$X zLIR0yErnakq2!Yteuwp(1BSW7_2Ygn+EljFu|I*{__Ofj-bLQf;gfR3ySlkwFWI6! z^do?o9(^BP{sRaN?qr%=>4eNUUGy#;Oh2!;zk|!&JA&rHswnQxiyj_UH?(YL;sGRt zJb&9_0jKVZb;y@>T=KrtdprYvB=_#Sn#k$&u4Y(J?rMVj^|ToPqS(D0+k@X>yC|{;PNjFDcLCT*=S7-BBp~?;nxgN3!^qe2bbHFqfPUyDcRZ@k z=t15ykT*bi1ISyX_iu{48LDVk7NF7{wT@mN>e6$1;3Ijc)^Ius1$}3Y*apX)j_3Ct zJ=zD6_6+(a@2_&cTT`}y0-O@}3xom`3Q!1WcMANxBUD78A_^5#sF*_7o~Oi1DO64& zh`Y%%(Duj=89&jvkuWA83dWYPJmp{V19%gn^R8pR4R^78^twPMjT4EU?R=?rO}5c+ zUFk76Yo{i0w+$T?sHpH+$?N1>Gu|R+sOSie8hcc7r%9+Qdvn6iGL}M9kR){jo-(sc z6*Ev?5vNX=kMW7d!l6@;7g;$OG&neaw4=0mjSd4!`~s^5d`X ze(+nHltG4a8bJ1*r{LZ!NhO~l(#&!fwplac*q{SLg`IybQ<}kg1h139HB}X_u#&-w zYMddlNkpqA!SW09*+PMo)f>XKBAX{FAFaH32(FFTh_}Dl~rp zlE_k1F47{oMZ_-^A$B@c#H8m8S+FHzI&2!-A{;&~!k@6`2LdAAuZW4PfBuPVAa0wF zj@F|ty>9!Hk=F-W|M*9cND2PN#-@A|*#KjcbdtJWG?NRgd)qiV4DkZO@8%P@(BIlr`@b+15KxFB3qNsVe?$RfbJuk5-ER2kzS=?dFg2@glbc^u!E4B#liGtuJHwto3 zwb$y;CbKqstuQ5QV+dL5QfCG&A}FTHg0y~Rs71rBBE;x!6dzuA+=ZqWS!dDW$X;%X z@EZYMr!d2}PUsmQ7C93ij{QkKRIu`46veY2b!hHpkH88YLN=iga6`I%6s2DCqjw8N&u928-6p zn*H>xrksVvU1KOcq@zYx8nHMa7PqHVCq-|OjKFT&5O_dOxKajn1EDzl-CF6+6PgCU zey!+|0LVvn#C?C1mRT=mB9s*p$##Z02FeVUYRCA(Ub5dv>@T6z#$WeC6ZNb6)HwbT z99cW?&vQ9QQm>1RnIzwY4#UXJ>Z^K&Al$S3k9vM=8$YwIfB=|!K_ip0hXjN^m19xW zQh@Q3JyxT}W&LFgaT!mc!t8b@CPvn}cZDj2Tf3ts6L|n<4g8P5%(dS%tO@0O99f)B z%1Ln5(cjwt)BBOF0kx@PHOmR=2ZR9pv3`|x z8xcql;=#)RFT8n~WL?qP0-n%cS)5`sj^3dK4KI|4{>CP!ArYa4M)$A5pWgDXd(1d` z21U;=5b#XrcZZ>QMhB`72r!e@0g)Zj&@nl+O`!Tb!2^-MJb{de8(NGt4@3yx#TfuC zD*PSLPV!zAlezufn25~5cuVVjkdO9K(SBffhw`EMKgJPtm6IjJ`>Rc=X3^tPDvzE9 zc;&VTzhf1W{`r}-h`RfqWm12L%iw6nolz9efjh(*&JtghC5%*J$wdhdXkG+Ma)*{7 zmW1C{7yn=xi}58e{4ACLVa-V$N8YIz5FSVj@@3;le^+%3vGV7c0+ZxahY?k1k>pV2 zRaRxNg0D|g3x#>t`~Klh^wv!PwJ58wd55BqAo}Kh05wG#kK6<6Uhj*?>xzS(I{{wD znecps$6=emR67RUVudw?csmkm9egGfQj#_BYI+31oSP2$m7Ry^r#9Et0wAWdy<2usTWk!}b@ zi@fn-$qGk*bNZ42rpl;0wQzK5g;af;0VeVb{4X%EX?cr=EWYl;7g8cfVF6amLa0%4 zAiE3zVY~A=CN>3w?9665toVjrt>YWf0KFnEqLVIQ+r>VTv=li8Y~P)%m2QE@w|tH6 zWvb(uCUR7ImmEg-`t;?8`UtPQ%>%f-1xKK&Ep+pvJ77`1c!#&g{g$(+y6y+uyMTZF zy$<^y$@G^GW3Ay${~mG6@Sc5uR}$79dYJCaq4Jsn?p>tE9~=VWiqbHDuLLCNWm1*` z@C|?9Wi~cV&Py5o0M*#=rwLIldgT3?%=~*;UN))E#Pq)83 zzlUC@hwoC$hEf`v?#fH9&x$rBd~f^}{qS?h8+A}+7lxnq3BxauAHaib8Uxh+&;M@h^hA0+_n_8msLaFqYC?eHY8xGH=(^;N2P^JZt&hEaJVuh~OCN7$rHrlENKj z?wn&vu-|IqsGE;TV*AB~x6}EC*U4^+0U@pt1_PZRZ1H1LH`zb3Bc?1+GSxM0%rKOT zhz??C7x#<}50X%FQslvn_WMzn@dLnvc9k|Fc%#rua+65k+Ig6hpe5kfJ`rmmb?gQQ z+nUug@}ROR50W7dsMK-K6AC3hXOy{n^KUKs2C8z4I1#9Nm!U7sj;IiaaewfYJYd4^ zPs^W|k_WpSw8d7NP2)0M+`={;_`q! zy7F(62Ny^loI^XkjFy}_U>w>t9pOgLZ+{#_cEB1!l0SCLISuK z&!Ip#-$Bo>wWBB45W79nYj;-dAsv_~h)0)i#AcLjmA9~Lh6G^%kh*&u>*;_i6YO4Q z8eWQ_HatfC)*7gOy}Q#F-M>UzI9Trhy?$}VQE!8&tl|(nWbd5Jr(vz&D}>2%V$}L2 zt6g68YeX-NgeF%0GyU;jw*TdIx4)r=rO`IjtCa1IQ)SawStB~)9hdKzz{P1ErD0_@ zU8a-FjaBDgWB&_hIkdFK1~w%RC@178)6;-r7Y5YTzuM^_kFEaA+Q!vMR2@~WQVL$+ zC)}Ol{|9Y=(W=2S$C1mxKo{0eNN1g;I&r*M*Z{+EAZmR|73{u1U#3dw%mR^)s|-|~ z2v0V^FsY{{7A(4CFjr-_o-lomH50a$l7mZf#?U>?$BP-aR~tG`7t?G8okYACFeHTe z{L2=?Xm5t3Jq{l8`5vk+-}&Re&^{PrZ9(Uc!M%~>nWf+#4C^>8#&)iv@x`JJHW#ed z?RkHIp&b#4Dk3WkIv(`ve~XaH%O!ANmDhuq+ee`az**~tBAa$-%`{v7f@V<+qxePy7qxBPPU zjM8vWpOw`AdG?F~y&f@>%jy=6YH}GG8fj^GC@r#$I%r^#9~V{ZO)g`t?vebH%~*2_ z9F(JxmTx&=yiqUQ6lmb2a()9R{XV3Pg67nt(2w{<9d$Cu$3QH{VDAkUOb2^V1E$q^ zJy;e6f({)+%TQvy#JbxS!-G58cNWuXoS0F2 zG_&)}hD5yNk+c=&5TtCpbaQXTK1f(;QCxublnQUCJ)$~-If5rW*QtN4mE8u<>Z3>aX<8Vid zq%doj)bl+7s3U#l7v0fn0%JF-O0;C1gGy8xUP2|pAkOw<*-qyF`Ws5hn^wIjOm*7n zMWI|TdhfVZFKWgDF}g0M6Z1r)y$b-2myY*jd(~It7FUlDD6fKdqxIRRx8LuYhB~zAKYk(Nu1`TPNA`iV zdTGsv)^mC@{f{jj^i`ez=JlMFScO-W7uRJD%F85pVG#RYUC&{2Ysc&5ElODRmi?;J zPH(Be^_Kbjta{6M7KrIBF{+d6Eql>A)b*S==4!2^E@2p@Ct@2zX>*Py8Z;whW8A;I zVnc?JC^fcXgY&D7`o&+kUc*>dkbaRx?;F01UO_1_y<)keUZI|K#8H7gSvYHtp2lBn z{0rV`v?}ri=oRD#z2ZM^&wYyidE)Pn^a`S0U8kW{GuE#D)pZ)HzMB6%i+|ewba1j* z$auA0^J={YGVA};dd(T#^SW)pTFZa+dJPHZm)2Tny=F7h-I&(0O?Cbi>ov>f(*Re% zdW|ROEMUFHLx@0Huc7%O4K$e1zi7S2k|0!`y2z0UQL%*;2QQ)W(0UDJ;kaJI21mXu z%ig>uq5NNMQ>UG)Rp~jUI_;F6PF(3Zu*<6S%wU0-(z8x=a-|2>K)BNLh$}tL^Him0 zDq`Uwf=&+nR?PzSQnss!($k?9Cw~dJbqq>5^3b$dHT1nza&$Y{!RDoydM9_`*w$z zzDL82a#VHx#rN&#s)4Gv6leyJ-??jrvm)v$&7$cJDoZVR3647p?R4xk|W%_*h?v(gBW;0<^BLrc(|^j+o@ z%aBZqcP%wcg2}A{LYNZGN?Cl(aY!IN9)TZNf-;eucNq$Iz`WPApTgvg;i)C2KQgBv zF~q+CoGH+i7}>q=?NC^uPM|%lX4Dt^ZxXG zsN;C}#^Z5$>+YCp55|A^)o65iOBHjbCCeDP1FLgrFt<>b=9JWA)aART6iaVU)MsyZ z(_TyO>gn7q>g{3E`=(O`Ch7Z-0w z9yLyjJHt$X!aD>hgh%#4VsVadrUeSD_SR7 z6xhDs(%T1R?cFMU7b1C*3TTZH6hgtx`_cST(i7p0!_9tUY~EvdYCI*8s-<@qG`!w@ zSd{9B5@m}L#iGD$Pt!W^m}c{s#?t~t)^t{dfT!t+mQ_*orAR)A6m5$X&FI_9=p*v| z;T>c1j^SzAm6p=BtcqsoSyrv4FGXyHh^uW8SF?yz;&$+ewRyzy6zxiESrx}pv8=+G zDNu_@T@WeG7AcNVxsOpvh3F3NWSe&~Pt&gCtX@`Su=Fgea_CDD`yk?BTg1aGB9V_H zUVbK55Slvm zhL$KqWKKR`f~X^)IkyDUBFP4wEiOSUu4peR*W!rM1Y6y!-daR?%650I>3CGtQR|9oKxQq#3( z1cW7|k~@pzjySFI#UY5rp%--{zR*KTwxa;Kkdi*eX?xDFS8GhV)x;-^bHs;z>3c2j zBBd_*QX+^b+>che7g?eYpv#!|no&eTK;qwoZH8!6OUHx)-ebr8dl;|9L#qMdxx#t| z`WVb=4~Y@y=mE#!7I!^=SiESInoS2q_`;nqogI(CZ3bGc$%Q0v?){ z)%+xen6Qb!1%xL;Ya%Nl;a4&NOL()*Js>a9n&gn}*+DJpKJMQ=uKgU?$9kSQrx&=~;RTDkq}(g+(lBqOg?WnAvgfYp@#UNC(Q z)-rqNLo%4%IR6OeyjGrJaWEf~`yGuYa>#vF&pYWTI&-mqB8J=#O%nIi-QBzq z6x-dn`ATM`+LfpT7#DGI%CB7+8QqEw^iC;_mrgrq$07vos`Ul6sf&( zpMBf0FWG;pgTOcyr{n-Xpb(3eB$Wp_MC5rOL8NL0?HMBP*b^Cxp!P(r2K+}vt`4;$ zGJ*x}r1WFo_DU%Gwo^XGmx!E%z(izoz|Rob2}lt6n9=?Wk&o?(tjW@|ak47lKO(Y_ zJ&{f`F#{~cV5Y!Z8Uxdyk>ABbOwskI!0>GU z&;%H*Jf9)9>1*Ejup^aiwsUNGq8NiP^+^7kdk^^YvEf_*qu#*J;_a(Bu74OoDHg{d~l z+0On)u3&>O9)zpn3Na(kvYHeyiD_Y=cL?s826GQ?F^%2M!|gzE@0wJ!S1=%;Azp|H}Z?!}7KeR`~|$1;Z6PeMASPJi;O5Ibr!OfQjg| z-oj;o7%u3-7Q3%Swr(K^6{JV!%`wI}I`ptH{Fxqq$V`Ni>vwmk^YEP z2p{Mm&B-JtG_WQfOrkwI|9LjQW-z}v%=1~1$+ANGz>s}LO)QHtx5+T;eKV5h4lJ=R zkTx)Kus$GifZp0f?R}fT(xF}Hn|K674260!m{Ej@7lb}E5m1oifiyfCVLlSxq)q^) zH~|r)88yeR2@Ia9X%x&0m76efW0Ufz6Dv8w%#SNu7{eXYGsx3cQ&#(h?0n_lUjKhw zA7CXfO+l4n6CUC|AnH4^^L@b{*1(!!j2!0i(iN`GpI=Jatj#J z|6KcI^u9!Tf1X_1eM@TpP$?a{abP5Io^C0>uUh9_?DGs z9`Jga$rNw-s3N9zEG#X|Wo6-BgOp}AD-VMu=}Z<)fz25M+|pA_ZdZZH!n%wU>(kgM znXhlwz|Hk7OfFKwTOjc1U{h)WEvaomXk?gTK1-<#D3TZNpOB`8u3*6tSDJ1nFb=6=@Nd<_AvF zlnty}i$a4{1lbjdEhd`W>Jd3ud)#jcN)^QdAC91?G{^VeHwY7Te2?KvD~=X|_gcKq zGZ-_yz4YTSRur=x@C*k2wgaBQz&~=pGZ=U+O2yU&c=`+mzK{c+!N3=Dz%v;5QdYdh zyR2|ysm`*!HSxo{DsWW3n#@m4-?TSXSvEs0C;6*%SiIl0bF1OivVUNQuV?3OXyox81_`zzsQWuOVF#hd)844s*u#ru1LQ764cSh)Myxqq^A54Lj; zvvY^qxkuZ%BkbJc?c9^>+*9q`GliSc+mq2t{zUIw=4bIn&6J4j6pT+wO!Jy@`2n%<%o%@EJ`?j6?o}K%V zojcFYt^I|!E6RUJn8jNI1Z8eu9v1JS1S7*YL_oOTv~!oYbH8Qhu43n|CfqEOD2ul@ z5R^$R=3()!PcRbp69M54uyePtbAM{*Ze!cvrJ+v-Vs1hCX<+l#d{{fNO*w=2=`(;_cA;8D&b~{ zt+#lu1cDOV$UH3GI|xPsdqhCE_X;;f^4?D&_F24-Gc37IGnd8t9QV1x;addnw|GBf zFk6cCeyX~$G>gS1Z)u1cLXR(2nh>@yxv&(Q31OZ30Q*aOQx9V4Y7bT&u^NScQ!GL2 zX|0MJF~TuRIIxtedWCTZTlE)i>YKt%as}=btctB5IhnpIeIJHFb4EJ$wlE~C8&!ZY zOsZp~_XyAqvame%khZ7pU}+hf^O!be&JZUj($a(MCxjf@xwc9K})deqRwh> zKRc`4?N8R7)LAX2k-@8Bm6y!8hu>m3G}+@@i`Iu!^@Zg}Mn=>rbQWHvrMV-UN2^9g z*ev=*MnjoUZD!(RavwA=BoH~dD;$V7 zCym8Z`S6F6c5$+oJ(^%U>1(g^sy1~ZSqPRhpP}0j*`=~evPpWB+`nvgE9S)0IiBog z8ct_$>0G>)7hIFg;Xk71Y=ZqTV-pERY^O4)1w*+0rfe6H;sMO-qqz^J`Pl#A(|qs zF0tMw_P735*Mo?DyZ6Sqepc>;y?*wg3cb?L?DVt#WGO4ML=yYFw0^cA`JdCzp5o-W zRZo4TpJ6}suil^O$TmH&1>h@*_37|OToBkh!6m!v1A zZ|a)Q0nDmQ-p=sArjh?w9dQJDM+bzuGN3wkRIbwojBzAPluO33WaBZ-lr=?%l_6SY zs*#sD91F7zMjO>Y}H!S=`;!DeVlL^LR-)sikc0Cx3U54Ks@C*iig}}4L!Bsq! zO$=-C-bpZ)2$QU#kg|Xg`;dy?hcz`6h0ds9MQ&I6O_Q?uD{MJE#y%4^ixfRheVoZM zj-n%`Am42vJT2az1%Y%Nugq$#1zPzYND72UhFMvE0IZfZ=ymg%Qt~S<#42w5|6Rhk8|I#0r zl$Jz}fyhxRz)5^u#@kFOz$$@jx~l|k#+UdQPcR5fVX%#l3*&TDxvqgsf0@3tAy(DB|_a9Y$KGx zNZ!K%&tTv^t$2%fMd79b&<%zPKu~p6WgggHBN&;~69M_Y9)pos69+tlfp4zj_ok=d zxMd0w$HfP9farE?X&9Xyx+veKg^=+?Q8V!)DZsuFLvTdQwn`n83KpEBWA|np<2Ri( z?@NtiOXt9-Yubr+YuZB&TY~K2pu=b)SmI{4FVH7VXTvNISt)5|E@t@tcIeesX5}qV zNPDaoR2tQ@VQ?2jBTk6f(RcxLVQJs2TyD$mbEq_;|A0m?H%FAsG9=Ctlw(J4P#n>B z0rYu)+XHnA5*Y>Pdst)d3KW4@a%T(ecrmtqtz=T>Ykh|gW0zU^33*Z|$_1=_39@qp z*>wse+hijf%lIaHvgsIlP!5rOpgWOGWgwD+>`5J)#7NzO4=45b7ymM;laT~n&ta{= z%Hcmn>U+;8^~@eds?kPjjenigTW$VnQm0ZMdX}pmXaMi4q<%}E(8N~i)PuQf=b16Q zd*Y6h(jOF%=sSr1m6~jF3j?f5l($H_qO(`y(zQ0h=Hfs2HjH6&n+A2VBX9uUg z*Vh#yKY$~lU`wsJBU`TgpEXE5+n9PkVVevSj4!N4z4@vJOMRX2n^ z2`rQH@Njb})s($g8LWp25Js;ecl_@Z}xw3^5j={)Q`)z(UFc|nk4tNFwU(5l| zVBkwR;28{j1qVEXfv;-Avm!GX_?jwS6dj{K6+1meEqK23jzZp4{#0$wN2o!>nNmz{ zZvmr9oYtgTq9t|Us^+%@gB?8?Y-0z5k$ry$JcEJ%#R1P?;KLm73s_WvwgB2Hax$K6<~Xl}?m>lmn{^X%d0SmIt**LO zS3|4I&+2M!b$x1ewY9oBT3z2*T|KO>-d0zz)iv1a8g6xswz?v%u1U;=^8?<~;mTS= zb*OauM6J;c&z6=n9*cKJbP%f6?^s8%cn1*-e6ZSatV%wE!S*2zcm@MM(gDw4;3FOI z3bby+4a#J+={w zfd|^;Yxr1(j3W|pB4nJ&tzQdD+j zYF5f4!Bi+T;~)$YFv;DUeir@~_H4Rk1(hTHrey^c((+41PyE~|Z#{ie8#gYFG4ufdv$OHBybW zPt~#9bJWxh;$v5ClUp@PEodALohom$g#TQbV?@b_{bw!gmazGH{?ne=qRFwQ@c-9=?_XZxitu22*`Ux!kqjC zX$RS-c^!#CD2-rgvNldBn3XI3stifRfSiVdA6Wk&if9Eg6h_-HTJ1-OGug(_F95~j z2h?_s@*@;RaiCUm)O&=|GSpm-DoZFGLq%~EETc-X3PTO&s0V;zofxVwM_nS6GedpD zQO5|SXQ6w294kcyq#Gi&aBJ< za3-r?*Ud&oL1z_gz4=WBhN+<6W^V@mtb(;Sf5gDIRPgQ1&F$lQY{r@#YNoa7>#`X$ zCMjEkJLod5k`wbKmGktetYrUKo z-!1f^hNZYyO)EGyE9X&Oo?2$>NXfJdR;sK;%$L^EC@t#tY;6suPqUI)3W`PR^4{NBubYU{SWnc8S*cC*;540D?ch$D2`tAyOpj2t2s$-uItMf<9jQ2p z8+14|!O~OCLAjA4Gnrw!2xa#jnXM+G1)#-TX%88pGZNFBo6Cfh*vNz5a;K ziY~x9730Gg^S;w9#+b69KJPQ_1&?60Afa2`Wb=Wa&Sc#NPt#H{BBBrAA5eXw14^+A zt$?;{P`x;?m~R{uq&+J$8>;jl!x|Lon|F=nW~CnJxAFz+(y^=(v6hV%Mm>}K?BokV zq?*oxhe6BP*sorR(hUB9ReP){g__U@NE3EwNJ^m}iY7!+Y2H>2Wnddf@%N}TW^G{~ z%9bkU zL&kpOM_{7GTgDC?ng?5=Ka90J^b|TYFF5FZ6QdY0@eWBWR5IC)x&|Co5737pc90O3 zCslPwZ7SXt9xGElgs(3BYRy+~Z@w^^YeCVRUl{Ff`vP*y0rHOJt$8%InFyP15a?_z zv`azrZ#wUN@(<5=c`_08hZoXuwbvcD*l#y7_IB!K>=W1WwsBY!yi(uEK1X)G{Mqwl ze|$guMdxS4`IdkFzP`tQ-39%8(kct5|OR{l3%$Y=c> z`br;ucnj;u8Bk%5@)aK3h7UFj(&OrVXkwp^33UD4Scj(TO!F{?)`SLaR&)$y4DA5O zR6q9v(s0;PbQWpD3yq->6pS`-9%6r}4FX}JcMKb@w2xJYU}&2QRG5^U${dx!QHL$w z3P-Krs8mWKL-gk2HTi>ptNJVG?nIKiRrhAEGS^DI4Spe zqR85tqjsw(?-o2?%AcrZ{>b12&+U%QAfbALllGD6?q+}bVl{soeG}7nWBs2Q8Os^~ zUZf^lhOG8iUx*i4W@HD?5FY|I2rZnFT>F47+-@Rlus z-YkJiK+u*qdk;oj?e_$d_*j{FTD+?h3|7|Run)l~i$8sF`?M9qgOyhEkmOeVsm#nD zWo}2|z~3%vcvxVBs!FETaF&WOzAYt)j61UcxUWtyGVaac(FB9b0|IEOyP+d|mVpqiSDAHT2o98=Cwr^IjajS1sb{B4pv3D166xKRnM6?xn zNk&BBO?eQmSF7jd!ASXIuw#{?*P za0W?HY_=_h@@7_!GQ2VL6reP-ss1j&6d;f0tspZ z+oN23)j?JWni`*qeX4skNPyJ>D!cBBqn5Fcn#iKQL4-qe(J}@oRhO5+TDX{%5?YK% zVLKI}iQOHxuT*yG+V~>WGP9gTjvFVIz6m$pk|}&Q{~@hDd;fGtn@^#H>k~gxG{rPc zxVjZz;!py?!0%JxA+X+Xn4?TQNCv?uR3?K_cp8zB1cMXMxr5fz(1vwCwuqjdsJ40{ zGhdGhI?gIR{VcSi{4LI29vVXvh|Pc37$Bq*##NGus!3&%iI7o!Ai?bX$!%E`0%{<; zxVdD@N(S$;tc)VODi!ymT&tqha4YGXMJPoxZ%__2 z^4p7Q>-{Q-2Om{DQ0L7whBhR9Rq25(Csjz`NZ=x%OgGhzoY?|yw3{4!sp;J(7)?)7 z+m-c@3WK4Z=pFD32HxEP&tTv^9PkVV-c#V2NGK18a;?I!IDbYkTK9+6P^fnSBX(m4 zJcEJ%*a6RA;6GFG)I+cYiCR>p2_3#}`wluZte4vj8?4|!Sqdlx%k}F zy;-x70gQLKt*y!=4i`5}pTi-}n{2Pn+l%L117E7Xx&$NVMhphi;P$g~2iUn=*ttKo zbGNZ`x3_b5v~zbAZb)d0_cwqjrym#=_L&Gqxdw|+a1XO{huXPE+qom`+~e)slT;XG(2h6*f4i3c`y%5V_DlKvqN{Y_8Kk~wyh=eiIv@@}9iLFkW zq|sIcH67^EyxDZ1{6OLbPU2%mq8Zzdn7y4LB<2BcQy>s&WJIau!D|b`&a?Ma9_|o* zchULYyO=8;$nKMN8oG`5KuQyNgpfDbFos|x7scUe1f$H+9F8Fv^Ptr#jBY?i@ej}* z$_hf!sJxrFPCK**`L+@2tDYb5t5 zFrq?WL_ZJz&~H2>GAR5}qmTwt|7u(@%6ly^qD~9gIfku|JeIKdS_AlpdcvN_J&`L9 zbiT#N3&9J%YSclsc5K@%QO8U5{%lA?`<(nEU@g+p-(76GSihZ*jaucoLrE2?#$S3?>fiLUt(MfHmsw*qSBdrT6{oCI!Pf^x*D{;DCq*B19|4 z5UtcAMFe}m6p4F4#1M%E4T#WH%oj8uobTgA_#JN)4!hFzP>C@2+@VoNl?itCm>IpT zKW`C!s6{wEALJi?EZ0ef&FKg~n9UsM&a%waqDBKEhN)1aTwO&54^?3+Z`uA+K8vti zhlM2}`SrMh$G=zMvA_3yNp#Mw;6!lgW``s*P}T!hpKz!51C-8?Ar8M0mcx zFpSO;vVQoSUIw%3)kTmx`Fa<+Y8}=q5QJHkrQYo67{rqYsV5SlzMikY>(vnXj15Z? zo&a){%1KhWA`PVX@Xec9NiMxee#at9M`avGSfw#s(Z~lihI1O(q^Ts)0~+*~H#0zP zAbCoXr?SXJuI4()x*n3H4>s6EDmV^HrvuVy zZA-^Z*1hgrJ|Fz1p>{qvpMMG2b(cmtsFg=+l|5Q(IO0=rNh+>?Pbv+KD{p2w`88=R z4!o_^DRZ6W`A*7IXL+$xh&_#Y^6L^7Q`@+W*GgZ%BT2u?ZWA=pOgW5>&LNFF`3)&Z zl7bAgwbD<^5p#j2BwozfteM2P0RLK{vT{<36M3$OwbCTbdr>-Rx8}{^I_ZX{INYIH zH%W@py6@FW3$&HaXr*NBRu&cXm5>W{t76v3QL@RNrkRc8+Hbf{)ySW^hil}nvWuQM<;~^-2-#12+mYvTz(YzPqUJrs7g~v3GH@ub6Rm zaVe=-dCcF68Ka6z_luQ;->=2N?JdQXX(g1M#k&%(fyNdeMuUlQ)U^ZNS&)B zP1h;6b@Ch?7OIZvN?zAVSvoN45epEaAiy|9xu?hz6r4*trIfs-NY@ku$WthVFee0v za#C(P$&;NLAO)wA*PWy+CrZJI0)#kI3eHNpvpm5W*agn6W@l-Kb4mCeC&VRZ3YJa5 z?l~(ddigP7f7QDt>ZK)m3bs=Z#1TCOJE=#ob9!aHi+q`|5A?39T%_MzDA-aLAhx+s zuy_{)JK&!6qAkSZttR%MAz?Yfv5;~pbd3qsWmlbk7TS(edh=QdO;yMdyj)9d%Qp3(Ke65G)IA zr?7OF1&b(LMv`I*Q?M0ZSD>_xXD1mc}(&FD=u$&(TX+TGv^6X|z%-%SB373a7hB zPZYyi7io)=exi$%<5cLV9xWy6?@m`=tZ;3q9Lc;qSzpWr^6wq){%i21K32v$Nxe!= zbCRaZZeyIJ;ZB4Y<8 zMmVol`qNrzjI2MQ1xu&OAv)=i2J&%?)*ZrgrnV;J<3VjSDq0QyZYv5oO4h@7r#x9k zbw*GoESjjrZh zopeDri{~-ZTdo`~>&;qug8VkRi(CTEOR_5lmuQXqY^^*|>jKP7ZC6Pe#>j$f`sd=j zJxqTHLl$MTQZ4~!s_dGmm8WRjCu!v|S{Gm@*k$u?%m*_6$BtcpbJp&Ve*<5RYFx%T zYb_ec)QMWpEza7t@Q|b&oy#I;?Gs(u)y~>kirXA#X{%E`U~-*)mZTfbzGq#eb$ST= zb9!Tvi*!>Dfj`N`xWz@9?@|(eu`Xzn7hIGn1|`ddZ5s%I54JLypC=cQQqWaWw3RQo zN;kFPE{x$X(Y{C;(wGle(HV-@KEg&(CWc3daT($z)_I%*9+GenkqOE~)dQSry<9p(1u z&9i~4UnFUX#;{(eWN0c~&}k=W-#o3;ZqSxHtka&92t~n`poRnKm9}<~XuBqc>CAo}5-D{#oN!Q9# zHKUO1Y^~v>R!-Ks0>jHT^L0`P5ap!Kuu_pTbhrVodZC>`50M^$@sG5IxjLav0bf_H zChb6+9lFZX%SdiUeZPs2 zr|7lU<#`A;UhDGESvyVJ5W~5)!cx8Vp_WdmuT@;f>9xC*^3i(jG$+?E2yo|mz+7@h z<2|GIJz|g+xS;VKbun%-NM~KpctZ`wr3PuDp(Olb3~0Q^4a)dJirIjo*j0mQyO4pK znG9S{GO*kZt&}PkgM6Bxah;=;X4}iaR3-yYkqn$JkI+d^NCuA8x?^mbt*v=hD;>7V zz?s4Fw^Ch6s#|=zlk`KSM6GtYT>hX|dsD7-RjXa7dGnlByGK*bq}5*5lthpx8j^$a zbiSjVq>DN)s2YkfN0DYLWuZMN#`B7_Pbmq%vkLsiIw=#Jl<@_&E2;}fWBd&J6gf+~ z5y{R~4965(U9kFE2mCtOkg1Wk%Om05r7`5#)z{1+@(&%mUUjmGmUB*4(K69ldr;^0 z)Jc0rS9XlE_NmS-*GZb`R1=tkBvg`|eK)&E5hPT$=#6t-q!bb=*?J?!)`$9%@QWg$ z0?@687}^L2)MI(fMcd;H)O{i%KLGWdL1YlwJG-@9L$`reh& z=zBMD_hY=VN@{6ov+)4>-l)=sD0K-)s^i)UBlOxy ziu(m;?P8_;eP=D?PO7sM;an4#9n={%>3x?Nq({W`r7p(t25B>O28)Yvgh9IEQWAb4 z)ESl=l*OIn*`w-_gBo)cjfkusJyGm$2XgZfM^sl6hPKpC!T zT*FFg?`xL9y+EgoDyfapg&?IECvvZFq8k{OoL$eC(B9Nf(k?Hl4RhT9@6Cmkvn8}U z3Qvdolsmao-IL&6P)u2fB#Ol$T4wQLXG&;8N>oKct4mZtnA0Vu0e`Ec?*$KO<7;R^ z7hf~(^^oqoRvr@u594+Z>35Hk@Z0Kv7IfJ|$@NgKdN{C6m8T@vg-$|go@6@78PZ9n zFr8#0(@E|yon*K|I!W~3p_3Fa#>cQRehrQBE7%x+fsOG~*%-f>jq#`17=K$!+jM`y z7!O|17`KSVxZjcFB(2L>y>=FjaTaZbEqd)!HpXpKFd5PAQOd7|r<3b%dTFb3Jz#RF zTi?+8o;678XpB4OVoWkfH>q1sG8nfQr1^%D@QbBxeZip2Eu>^QbZcHG&-ClnY%IFV z#-bQD7VTwYQ8pWkhO<%ZFBpq#V+d~w7(+x;z!-9rjUngR7&1cnw~QhG{x+b>^rs}# zc@sDXHJoI+Nn2r}UOQEBzvHZ3rj#Fp=AgJpnN$$JTf=kxnIT zQwO+tKG;4V3SUuci=QXEbkNR~rOp_v@drBIN6s`;uD2R%c5*8WNt%}Tbkb<8@pqlH zN?UWQPCBP`U!ar1bgmP1Qj~5Uf+OB*-{`UCFjdx1)k>o^rRHg|UU(H=>fGV~cG>?| z>h?u#)Lpt?M8eM)UQ`-g6idFSS62zkf@KOflBzzh-L{pawwQZMGh}6#EY1CQwc8fj zC|RPz+xS!WJE5dYQb--CzXp$-Cb7 zz`ikx6*(kF-L10bN$5XQeky%sha@Z0QAK3{Ru*uiQJ6p(l2b!9Zh=dQICWt;lAc8- zPX_Ag0FTvXU^%G0xX4)p9|wXcQQPLeZ9g9Tw-rcXoNBaD_6y4mCL4r>eq!mJ#fPOQ z@V5sre2TzxW{b_oFsdD4AKRAvU&JqL?~B&Ls=$Nz#TpGBjfV9awJvZpPN|K?M~%r5 zU;a7x!q34!#IXNtI<^)@6sYGVs6PKZeTxcA*Q`K<7m%M>8CoFTfez~%1l0bB9#~Vz z$1i`@zG_oibU9h6PA<9!&7X5JpG{LdE3?)|S7WV>YlT|r$Afuez&U**jL(=jV#diU9UpO-#a z$BAUE(c#n(o~|aD!CtrLcf4agynS0gD)+jNe>fXN{9}uLQnmLKrE#1Uv3=fU|HG|r zHMAnOF~m6=;y}G&-R4B++7SP^v3|n+uO8bF%GLpYPi9)-px3}Av4;$pi9 zSi>P;_!-9#)t?)}KS@E4W`A(H3MXf9q=F#>d{$W+S)aU-Hi_;m9#l$h0#_qR^1;uu zG1iCh>jQT*cMrs5JHrQI@Rj&Cj@__=~;)icY9?~BMcO1b7 z_V4$>Ax5M_GoJ_dHCd^1{rZ9;f^90VWaU)-iiF- zo*#$pIUgw`Djstzd{gng4?NLJwD=8}(04QEU@H;jF=gb!e!`i8dn zw#RR}5Bxp?t^wjT=+>=U!*+SYc)CP8OCR4i;^0R9A={+r)o_RSNIt}W3OSD*VhZk<9w5@+=(CqK&Fcnpf`@G4&$dLU}0bk-Tgoh2` z$q)OpU$hsrh3bC!^CBGmRrhmDN5xAa;`{Rcf1IC4_j%tJ<@daJ0`D0AWnqzy_&UZH zc=3JK|K-a6YkIF^YZCxiMjl)m&uB~zI!;eo|cz$QT zYWHA(rDukP`@h%f!apX~?)NBz`*-?m$dU8=qNbeZ{zK9-O0B%oeDwwHzu-a7`(Iri zbm=^Yzc~Nih>{0ZZO`Q4-}2Rec`IsT`W5bfU~#QQ%U?6!&*uK$6&gDEz1SrauX6bF z*L&`mn)vlX!S5B8x^;6u=(F_}_a8E=?#3!*Zp7Z^>2+>A*yUQK^;7S1_}kf4js7P$ z-@4DkFI%eqJ>vcXe!~6BRqeZH(}>Tu3H}TTdSg!KH%?zT!^3}gdh3x-(?6LZ_|vu1 z*ql*EK0hGJU#4H%yQ*DQ6+9}zxgw@dYpY6J#Nk`Tr;JVP<`7lk?f6Al)w-fig&fetydzQaH$X79s zy2|5wX+PO?X|&(oJ0iXMi%tx9fAcC)-Z$U-DLJ)VztQ)&|FzoF*LT0{wNUim>cRK! zJ-J+RqiC;1YxnwkP_r8=F7xn(Ybl17lZxCG^*^ldn3N@wywh49Y53gwKmV}f+D^56 z6XyP;DYD?kW>KHX2PTbAXtg9n)L-(YtEY>XnLJ07=XBZqQ_h_3Fiy1R(Q>ODe&e3p zz0K2Gv$ye+_xjGs5%s-gU5Ul!lm<&hd7q>$nfCL>nz@4hP7kwoE!6&cUbHWduh;tD zzgp^s=zpeb^DkP?nZrf{vR%&EJ3PIBV==t=+9~V|G zsW~F#!;D7*oo6=bJVEgDR)T!6k*3~6!SC#`$KLk-;@g$bw`Jbo@NeZj%Dx@(zLlO|iYik*>Kq&++Q*UJO=l() zd+(B}mn z+#NM`3ICjbL=+EOHfE)0uWKe;YY;rN&?r$qtyT;j)xKV_twKKh^r61O{QCPYiu}qA ze}8n%gGEwBc?VS3Qh%Je_%gwdYO-Imu2Dm*^&9(fe9_bv`__o?)2oi`6+iLNViEqW zmUELWk3FV}^8R29Y<}r}?^(h>DdOV=kvrQ@xXbzX zheUdFgK`FGL;ewVkGH3XX6dtsfya*Agqz2UXdLVudl#>J>Ft(XPexZT%TEgHI95pcyWueE7g_gtDLPEGIWlv8Cx zUEW?Lb6BdnbiD4#HxHk3`f{6n7n!EyMLp)QY1%h~$3Dou_<+-UrPBH!L!;nbg8t<* zR^8p%XicuxM@`>(%gL_`n}%(4;_&VwwQtndY&|UWw<8ryq2-=bSu6baebci;5wFBh z0lRjp|DF5$nj9UEKf$>C`-~CYwu|)3W>!od_n_@m!Joz*I?tGzeCDZ;*B!Gz**?0` z$r+-()T!Ly%Xj-mSVa4+f9bHtxN$$674`Q-E;o5{{d>#q^YSHct1v7usz;`P+s8z7 z?p`}IUW6b1)tFC;_gxqxGHlC{St+hX%`10VYe&yR(1iyQgpO$>Nvg>tG zzAam84t8sO?Wo8#6>(e~Rj56lzvAC($s zl`+R!1bpOC`BJG7B`#j%^xUiUmirIUR}YBtWOynY_8)A2S;(8_yU$iR-u{!>g5IV5 znhu(9_p1?te}_u>)^cvX_Pl_Hz4gP6V%3f>5b)wfXR~_7o=<+n%eQ4ut%B1G(UOOkKKCsA@|p> zF7svclBHHW;qa+38(Wl%Nn0xTU*Y${#$NlM%n|bc*X-J@O;U>((LSqe^KX0jjnP(p z!FlXdUHLofw~F|_Z?tPO`&^f-Ck@s5zVOD8jb6IoG{Fz&#OU_}OLfl{`8&#+B3;VW zyIQl<$fumXhz}<&wKUr@PQc?fuAkF{nt$UXzYb4zE?_fczw-}`;FCXSn@g#AF+G=!Hpdott!F&KaSa+ zuxiAfup-?5?f6!Q2DKjYSR6lOi^b5No3Bo*hb_%64LaIu zuT?&FD!z8!9IwXvMfe_JDlGV)oVxFEy7%OfWjVc02}7GStYA^lJnYvab-TY@}u#?p(js<+W0YF8#Z@>9Y4%@lUA*)uAL~xQ=g0q z2^TK4U+=-`JF)PKiOsz{l3(NSg1Z6z`VQ}zB+7fR^GE4NJJh@>+Jk%W%8LE3R7fbs z!w<^vk8>~CDuY%c=}*~qw{V%1bK6AuTW>7q4H4?!LyQ^S~`nMg70qaNXBVzr1d(??&nUDt@u*`|)Bt zKeT&E(WCKhnMHln^!=aKFaPA{%n8Lf{B>5&krF3|S?N!@r2MkEQL{YJzB3!0zFDP+ zd4edB`SH%7>ie)cn4>SZdFQyGz~>8$8M?X^~-#HvZ&wJzV&H-ZEH%3C{ML{R_W(#@0?#AI&HJkpr zSimQzbXco%9=cP&6Wp6Vs6DsFbfJGa%8w)Ei(g1R+hk(lI5q!~bNsVf-~N1^3x{V< z?Rh-=>pL@rJX|#0t@$CtCszIIlWM7@etd89L=6w$wnp$>%V*cdit@ahG2&t^gC$bX z|JHBK=G|F6#9F>C6BbvE3!HB)&(!)qbnf6|E6=DU+L?vz%CjtYcdmVTF5O5SL6PL@;)$( zE75JNH;@01G^doMx!x12a{vBQv%JEKHN9Gy`RL-^mrY zf8RBk4L)74=(>=1(ki*4KX*F({?<$L z!=@N{`eo*rJLazWV3dKUul;rZmZCdXP8ag_qh@s%Jy5ANa=Ck=D`b>a(LdnCX-uD4DPXe>yTTMvbcZS z)RBFi{HpB|>j9r6b}6(G|KH~Ro%#Bf&=~n{#9DHtWkCI-5BBj;I}1GgL;)#3je4S=XT#0`|ybHFa61; zik5aW76^Yswd0o#w%ztn_>b+nZC=wI_ooW~&qu7NeA>OnL{Wbq4$2$ze z-JI{;#w-&4Thc0Sdv8nFBhel%#NAr@W4vjz@V~g}$CGalU7s%e&#uU7(t7vYeZs%> z>?w;zrFM@K{s$ZH`9xd!TZ{1jVBgXzrr;CN!vA)IvHN`%ZOIh=3>`Jx2;fjd7-9Vw+sE+t6`{bi1VI@!hdjRo8V%Xo7@uZWq8h+=+75PdBVT{ zK-0QyN^Rcr}%(4BKWKI|JjSug-SFii!`Yj^<*2;amng=ahE%cQ(tzPr1_Rd%1 z#e8GU7rRfCKehe5NMDyw@rlur-U44B~e1o|{e^GAEz0t&L z*%_g~O>a=@^J5iW+amP4p_4WYo1m$_L99RMbB5mNQde_c=t~V{?U?P|vNt=$N$fAX za(CyOy8dfLdev79_-x&%ZCk{6+O3M}FXBgbw-g_-rB{^TuaDor!7G-1cvsZd7fQQ( zQLV-<74q!uVG|e3UNtO3)Ys{^*2BTC#ob`kp`d{YNxw+tV2yUS<1t^TXzMYE+tijQ4lH_5rPcDSH?5^J(6# z@99H6Zhg()uWjDuRFjt$URR27JZ^u`crf_>$|(N)D)_-MZ}$mF8~F1lpUc;87mD-G z^X=8Fv}c`rmnDn%{r~0kDjIzq?w{k22d(a>UpyA^@FRc!Y)Gvt2kJWXP3G@Ezq1W7 z^F0z9_YLD)M1PLm66ybrUk{&mNL<`z{fig8zZ*A=SBdxEJizzot>vWgo^5t}_wlprkU^ZcP*O7p3KI4jWkW7CysKx!Sl9Pdb{~|I=8C;`4J-uFYX*vXDfgH zXw$!a{k@}FpMF8ue3ZSLS2L-y$zi^K)+IOB?CAgM8-IVbfBx$0H;fLg=KDwca1*E6 zar-y$=Wk6eS~RJ)zjPIUd`TNQcx}X~VNv}4aBJT+>0Dc@1^j$`IdM|uuHmmwYjZ6A z8IJ}#M>s7?)xq-nz1r71rEa>L)K;E5ZKxZgqIuy3P+a^ZVCs2ia2p zzJ_c0c!v+He4riq>M?(PGx?HdU5A^R2l@T~#i;^_QE{M>{aE( z*Yk*R%KAe^|M7X2`A0jxeP#COm)A#kXTRwoW)N*rLKUdP@-O=y_5AxrEZ|rD^6h4x zncDW@wI&?%ud084<*R&NFl%@>s;obh>&oN*^uuaY^-H7+!-v#9N`Hgp-^h^`(b}i{Q<QllV;H-0kb_fy4CU&zyVk@Oi}JZsWVb+g>vMt})Bz|NSh#2i97v%SGDV81iKv z|5xizpx>5+`7b}C-xYel7e&G{Pphiu`Ts7Ate|5V>I(1zQR4-=hyG_H~Qxq|0`QjIp<-#&+Kw^|EK5Ax3fG{ z)=p}ptp5LvUwOR=SLo%;E8atS^KYP8aEK(p%wH1XZDKC*a0!vfq%MJO!P1~GX^@R* zV5bl_Hxpx@fDmbrr^H=qZrUkC671^|?Cve~2=$W&3AGk-X@JDf)LezoC9D(pbqwj` z;$mWA;U)?8ad!z03GxZ>f}c`}zn%L)M_}B&B|$DBK@y*kU^{mk8>q9C`1u98OF{yJ zXobx$C!H0YxP^ND2Odn#`~y9telG6PiN0?B;l3^b5`Ss13|eX;U?7~lt2)aKOc=O8 zWT}gLP>9TK;D6wX=C0_>m?c4-{mxj*f&$%Kyn-Yizgla_S{wLZ>@zmrev$w$lP>b^ zlzO;G!`-E_5TC#RJI4@k@8Kfv>9Q=%RI6e9JY3u*!66k9n{=Y;vS1Z5F{UOV6J=5# zkBaCmm3WuWZR0RWkdI54#4l88=MnbH6(ud@oMP|G_lL5nY|_acI>b%l?h6ireC#|% z4sPR7mkG(^fv5mc>xr;GO*WQvL{heIkpRtS0P`@FL z7A`jM8$aF^irC37w7ifiV?tn)vcUgYTvL-knKVce;1TQsKU`#zpb#I4U$EVf{z@7> zq`!xwrHhA$V`mqe&TgSTejYX+w7t-FoB!Uh9{J4Vx~U}K}S>m2*ieuG{rr{7GBgG2pY!sL_j zKbk-P^%*;a4(ESu*2?^#i|CF(7x6##ks zugyzMPPdnTq-2pMw`gD0Ta`$?BYmsqZ9{KI)v>Ip4dBYN~UguW$9-3QvF2 zY?}u6ho|<}imJwYOjqNEJJ|T&aa7}0>eUxdXsrf*Rt)G~BH|Jkw0WwZD&j`y=cPQ` zCE|8B?EYhUq=-8emw2by2oYz#E3xq;QxVtcTix6xx+3mH_3MWZ-1rO04@& zJlJqsDVcb9_>k{zrR3_0AL}+XFC}$MT;fVzlo0KUIst1=8`<*O{oN6=Rz<08I=;tw(LEp)0L%Yye^Y3Ke%q2Z1e=jDkKfYhNm{3f< zYY*-#n^R1-AHHof#lD!tUSAdQvSu;al|L?h{ljnM=G`m7TVuZwx9axGK6rg2rRTiX zPdEBTJcf5^H1KN?nLcCV(ZTQ*%bbmKThE z$pg+?Eob*vVra0{`9{!JGOe}RfD2u}k|hHj@-AzBCF)gtd!{9SAuZl!41Kfm3+Xj& zkwLM`7m|=$H&(mt7h(}MU{4izTcdY^hyD+E>%w-|kA+WWekSAk{yZ9M|Cu;@inVR( z0N$`nzx1?_xP+KG)!R`>HuqmN!7`wb1l65U*}%Mze79J-BBk^baU3<(!{YKM(&xk4 z5GVL5Y`%J*@&7n}A`{GJ>|WU56LEPkrP8YBA4#bDi(>QbAIU#0PEB(g_mSlI?fzcF z=p!i}W!I(irw?S(vvzh@kAEOGGi@SIPy9fBo@>`%-TDKm-(gjwrKJU=#4ymtIlh1d z1WiouJGFr5eT)ts*sXx%8UJW{_{)1T|IWBM+hy;`=#x%4w%PflMek3Qea7UI$dHDY zZ{B`KeErg^nUQy7dnfOG!}jJ8yE`@GUewAX+jO#4*pw_f|$GN$ok}kvY8OS!`?(FnsYdqTcm>-V*!A zWW!Ud`gWEN$T`#RQ;z!I!RJc++4V1+VG`VF<1;Ba*`cJy!qZ`-?a7iO$9GRBRckbS z*PqNI1-{b0I(kb;MBmJ;Ta}j+ql@=5{Qqh7Vdx?24TKSy5vnNwBzP`Kln6 zY#Gz?#EchPh|b7IH^`H1~yY)Rs zY@_sZhfRwob?aX_*8B8%QZ#(4Nu7uU(peHTc4p%Xq}Ry!D8Kj%#31b8o)pPN^6q_u z*9WyOkuf&Uc30Vdi8wvpvo6yqkvI>ly|(j@MDlX?_tVne3eWdqrPUOdlGrtVN(9-I@idH>l&8Zu3aPJsx|ypf7*58wKgxak@*dB zeEG(Q2lH=`Uj6rfd$8*!S=M3dl2snbWNTope?B(5MUISlGvaN=Epjh3zth~!x5+R! z*X%5l6!LX*=+(!|Q;1sgV>9=BNg-Mdcg}Sjd57HSbY=9zco^@jsr$Mcq>_3M`}CNz zAeHpJnXG-}Ln>KpWT%%j;x2h3eX=C!;$3pyr|F9m?bFC4wJQtOtV$#6U;1F%w5h`bqW{l%t7V%WkdE^j zO7@R?nwH){4~facw2dV>4@vH-s{xnX9+6p-Gsa(f z@`#L=^}39@cpqc?J+5NnETm%$P;2(`EHe%J5NZH29I@H4tYv^myYN!PJ2qW zYINMRWcV`@pK~&F%9CfL#{Ei93*DZRfXjoIs=a+q`We^Ia<lN`x@3-ue?<-Q;y5HTES6-1LGe?#*Z}FOZGJ1FP^z_%n@P^vB z*)LubE>~kfoXs23HOq5-;NCan*pNm;M76U??Co8WJ=3zum(AxcU&_xW#+wdZI4a2@ zU;5=2m!{_sozpd^_ZXT>QpU7C;qV}r=({bu-P83gS!hQ4Ya?!BhPy4Yu&!bzXioVw}=0G#F+Cjc^=I;{&{@K_(y{`UTVb6o@`s~ zNlpW7j(YAzt^6+TxzZ0%t`rmxG%xCt<(F};qDCXoBZHZ zZO*M$mglN2wK>Vjq^koj)Z)f|z1ce7sTNnD-+RH!f|}g+J$45^FR#gMS~}u*yKXhP zh~jIpXW!~`_b(nftFcL+GwdDe6+TR#+p(;d#x8w*F4lHTxJim0H*oun)>^{^}M^kLvZhm2}mUe~<9n7P%s%NDJD7nxM!rtSWiSL2y3SD2L4voK1R+nI5(N8Rqa z+=Tc2pE-S~%8h*#mb!FDRjx+vQinrhs&ZA1Yb30xTa`<+NuSZ_P8BY9&i;$ui>h#4 z?X!Ee>|cco|6tdBSQV&$Xke9ly)u{HaM6d9sLEV*!xFRagDP{qLqDaCtX-K~VcvDd zi-$Vg-cdfcX2kFRaICbM;e4-fv?!Jwti(xrH?l9CQHdM5vq;NicqQ)eo_*ts z+EwCoR-Doqq*jS*eW>wd;|H3Y{pK+{SMS&4Ok?h@o;gdCYk#fL?02IyxuTxuuBCR; zX>h}KMHT4>X>ihOU5~7^*Wgli z8dja&8pRda=o;mp|)wvyyu1XFDt8=Hja<8vA zsB;dl1`X-dQJvdTSg`zKRdr6*Y26<6Ts3Z_VfYS7k{Txt-?FpXPBm`hfxxGlbJVyC znu)V*q-tC{tH8YE-fCRGI?KkbH&Ekly|3zdP*aUNar?bPrB@;@Fh;a{{bdo?AgR;W z;2k2a#(t~Y^XH1V$F1~lKk*iETMd?N^zA3&8d>VUH)}8A()7Pg>QY_ArB{AhB`f16 zd6oJ8$d&CsNy^y4t84rIB-75u2HY_INxYYiD#?cP%J8s$b$rkLAk$hDt{pV%2N|-h zZbVG~A7q_FtD{|NL4D`ceqA1yk}BI5IWFB=N`7o@@~)R(DcS$gxW|Cbr6h9vyP11` zln}q|D?W`*Dk0xnv`BYZRzkKN%AIj|bP4H@(Q}|<>k=}%w8IAO<9DLhO8as6x$oro zAk)dk^S+awEpHcHa{5k=EeyDSSNxs)+;@FLn|H-zl63m>1o&F1#-|k1b#T5JePvkZ zGTlnsW9VDLlhjE?)4BESS;xaJut1k{P^Mb9C!( zq*k3DpXz=oA~CKvZFMde5vLm4Zg-3+B5!mgBj&po5yRKdPuJ~KL?#5?x_M8%hihF?gl+rx@CeE&>3FV{~SmHe4}TR+gZ+osQ? zb)d=F9^*fgqGRn7y?T8n-$f6nAAs}GT$>w(wX+M!w)(#RtUX;w5@#lrST8ChJN?GT zl(-a<6u&;NADBXY)oDtRGLqIm_&SSR6U;zkM21x z^!Pin$T$7Nxv}ra_tyoBKWe=rk4Ii?r*|-qY)X}_tu;K4tobow`8W8gc>WS{B!0(R zvTl|AM3;eY$u)zIz0*JE5=rVx$Cev%Nxylu<9qhbCFeDMhPHp3Lo90T@A+(L4(Yw6 z^qiwb4l%r(;xO-7Hp$CuXEb3}Hd%A;hhrndY!WnmYDD1eHzatXc8h?JH{?w8#EJS1 z-jLqAi`^X0y(Ssny8ay`ugUF>Pp_`ld`(K@Pj*|p^A#Bo7~WQ^-z&1bYO-(9+bmM^ zO=p+Ui?WEVm+X3*j#(tut=S#NTQ7WRaPd6ecu0V)U-@;@Uz|APYp6jN#we|wns9EbLhBtVf`~mWmE4lsfo|Y zolG>&D z(8&D{NucXprxEe#q(Mr(N6FXVyuIDd0`B&Ga;cyChN9$qq}h+ESL$3&Bi94E{g`*; zF6q?jQMCH%R3iIw-{7(A4zV76#jc=h3R!*5|3rFTGP!wT;`=)mH%R29i{GOil1Q*& zzgMrDUnZv)joRIL`32H$z$m-J_fL}sJ;p5D;2fv&`k;T+O{R@6bt9)r7JaR9co1pX zUDxSYyAi}-YvwNB7zz3LWAmqZ>7GQtfAaJr&VHm9H{7S2_IOg-{N=4x#lb{up4VlQ z<^*DHyDBZG#UyfhOi1zI;Zw-B2QF;q9j^5pTAaRs3~wD!`_%C$a_j2+XQk^FlLGZ7m520>CN3^P zPBz__5>u;Fnb*25gX?aSmZ{duNqoWFTZ4PVkiqJEcXYE`K_+lpf)+ZhBr8kiJ~DA% zMZDJJIkpL1O>7No)$m%dhO8U7?Dgv{Yl+Oc*kN=0I-=XClT-B5^~5`5@T3DjH<0Mn z2d{oH*hKP+tBp9^DwYhsAE7(xY%JM)q`{^X$IT?I<|mh zBCB*uPcrW?xw9xH>`U%pQvc1B295)dkd2$xt(kE62&sOkN6F~bz3w)ga+I`v z82xeW%cI0RWyGmh{f?3DXO_PEe)t&q`t17XXN`^%@p0oPi)J4u$Lr3rty*xLG_h_l zXZWZS+q;^bBwZax&TO*fBsmrGYTiM;Q$*(5sc-GbQ>0B{pOgUw zr^pG5huy|VP7|q<^iZ$6r-`k!vy*zCGh|7t@x#}iIztZXbu85Fc$N$=jr1M0^(=WX zV5!#J2It7?<@@U{jXp=dpO$={q!UkWo?5KgXL>xbzBKc|r{Z{0TeE@Z?9lUMQSJ@+ z{_H%_oE9)?zj*?Q-+m(BAvA#)9e0}Sb2Nd7>*c-w`U%S8uZc@LT_E8H3SLhPxj;Pc zn}`FCT_Doy-3~te0`=KvCSS3*NVqkwL57nr60_Tyv#Z2kBmq<6_jsvYA_sMgpZB!6 zM6TV7X|-YgB~od3(d-GgE|In4EUMnEl}N7K>~Jk^L?T&fygFS!Hjxw`w|4&eI+55# zY5Ti%yi7iackJmDc9|T%=v4Vu{AFS?F0MF5`wH22$9U-2!B6Z$_{cAZom z^|q(ctLvou`zI$2U_IKN6%-M_@CNDJ+tTaD^BW}p;HfdM&2AF)`nLv!FStoIrF^kC zo^g|unj95x>5@!N=D%#%eMvI09Ms%4>P<4)In(I=V(u3C$c+%$uDwOpWn|YlU3iO3 z?Y?sKn?bjU)AFD8se5n3eW&GnyKAP9_7M{!KbxkI)aWq<)!kA^t?oGkqn4(SPL4~8 z0xqNwza0;UocxeNPH#GW)2I0z@_y-yXR{pdkQogc=vJM5hm1VCHna5j9rC#H@ImfJLmm#{wXQxk{PTA$ zQ~x@&j-A-wMnmExR_kT7P>cnLl}MgW$)RB&qxK z)44TYkgX0;%@_B70r%DP_5EkRAO?e)9$%gCf{cA+^Rh+h3sT>`x=o9&FUiBFIy?7I zdP!>E$?W~)%u5ov@KC!OKVFiSnY#|I>6Jx%G8TUyw;+ogEE;^l^KKSdJ3qY6=_ap8 z@Z3i;R{Omoq5JH8x}AGPJP+=?@J{bF(HcF-zu49P*~vZTso4 zTw*xwb@dv}-jddn-%b|2dP_e1obIqAC69E`pZCh9(x3MSTDSRU+-dH`XG@OVJB%sJ ze%kZdgAfLAN-iN+ZBX^i%d4ELQPxFSaW}0Dn z%iw~^E88yHWP)YQGqYChST9-G5z7m!4juH_qWN<}ET0Z4_P-UKFrzh=^KV>!=AGa3 zLvt)|JLkA-t(9ay|7F3RE&kn>b+wCcz}p|O@$#M8c5Q7e7tN?~@pM7fe*SyBQ5V12 z+oy;M`R`|rw6{3)bG2Wr2=yXD8XL*jX zl=;P0MoT`f7Q|(C^e1I`ua!iVMB4D5p8UV6qN<`Qa+X>C$ySxK8qlWSIciU>sb`+m z7qS0-`KCQV`#=Yj)kL)*R&Dx!_=fmZpr+m*eS;N|{P(S8<@4;f41AA@|K2a;J*)VE zyvOA4@(P}>d(oPekq?-2B8OJ+{B8f;?de|?=k5Pl(Q?R31-{*5!|hu^!1LB_pYFf; z_{%6n8=P=pD~jTk`UY#CrcDo;TZ;ePmg`w`X~N_t2zI zp9=1*;Q2w1dd#S|yEKje-gejAFHd4jXU*cj2YvRmhTrb=M^XHGW4C>j`K<|>--@yS zZZCfPF5lkr5dS^zh^x-WyG{za_!apP3le9{8lAU>|6cfAL5tkrF0IyoMBZ~rl{Wel zI@>yl2Mm>c6H*uJxU*eh&vFv`k=ipEKR>>h`d?(Tro|XpL$gWcymj zG)8`Kvi0ehF2k?$>+#Nz!`6#Z>MW^_{JZEu^H2M}xW#|(`op+u<86j*iumtsSMC}( ztJcO!3;E;6!)`OyEw9!#k&m}YGiS-;0WPx}V*Fh#TgP30cH$Yo-hXoGamoLiPOc8} zBDM+VH&(-6yDv@GzEkr@7XSBJM7AU6S9SRmejvMQEk?@g?xZCkaO zKb|zr&im|cWOjltM_;JbZ_Su$WnU(W=C!qa8l8NK&nLF7q)Nn_F&8yC=3g&V%|5&ELVFPh?mX#PwMK|MPm}MQkhUkIlh*!p^?GKFa|4F}4kBHO;DYl|TO0ZhrH8 zVUs>j8X!NQ-=VCX4nFT|a?DYh$+qTRTl^OC{qy5?XpL24eDpz%mH!^!IR5Vk zm^Pj6TEEV*Pi>HIY-PLK>1*=(=2-sp++|$Yft&<>e2;dL9T+^rDVmQrVuz`ruT9@M z{P)Bnww1L9%ViLJc6|e5y}IAb5?Iy6Gec|o8{aw0pZ_)6-Tc^9+34Brk-sq3e8Kq8 zgWFqjOrH|}v?$r>{eypy|2Dw8X~=Eg?y@wa_``hB_1-nIPs zPmzP?;A)BU=ktHx#;dO1IHRE}_p9m;ze*2Gw~QJ)w+`l?aUjut`s<^s>SFm-Xk6QA zk7ixt`QEP8EpGQKKE?n2u2yQbCfC=FywA_S^e6clQD=g$mF+L0-#1@DypqI(RQL8P z%Hmla?X~gqm#;~DJR_0$%4)h%C;52GY&Jye=TE!C$1`v_FmvdDjAMd$W#c_*W4P75 zoW$w;`E+!3Z}p`y_B(2DEU%E^!|T`a?J=D%Yp9(VylPHFG{65?x?DavQ^Woq-+!h? zp*_g78t|sk@9pAC2iklY(0Gy7ettgQ9#(1AFujJ)`0;33+jPXJmVNf~F_dVWj} zxulQ&I;sV2OtDgr;qx^qO#hUy=<@@9eZV+n?Q7j(@$}*MCoFG|_F7|>hA*#Yxv4Fd zzYg=*_A*QVAiq8bmTo>(Gf(XSzyG>wEx7md+n&t&3Cwd!x>Z4q7jEhN`f|$jveCY< zWgE4THx8)%x$o+$3yskKpiymmo|$G{!0)f3z72+zd=cN~*W*tiRt?@P-vv)@ng9Lk ztJmAM()={v-UTD>N47TTl&q?KHx6ec{%}7$oj*RF?ek%;Zj#G>e*YQ1v2)E->jp>o z@n}dH2x}8qk zgC~V7-{s?%yd0sf8O5)UlYSWP6W_=w`}+s_HNrIq8?@Ta|NR4I1^=@S!hUm`aA9HU z(Q0wLzs)v{re!SDjOXKZO*@|R-cRce-~P^dCk{)j4S(|UEp2&|SKd#r##O<5Hfm+K zU!M4C9$&s*X;hsKgX?7S_QuQIimweEc8K?XV@$(#VRqjisiXbFXT|@R9_%uM@6WP# z!mXXUTUlXA+9ud%lGG(c9MD$WI-s?fp7l#Tg2n!la3B9re{pcAOcodvBJ~hcoyaEu zPU3w$#PGu_z+V~=A`S@@TZr8zhDd|ki)A4}-9)P161TGYoe_CRLL_3J03Z5>tj{F* zg@I@wLAi!8+fO>n9D?kLfNZj42QGl0N>L-h+)cS%4%pu7|0c2W-! zBm#kH0Okq00lLUd8ZtpD4FJE?N@T;o-wvHa-zS$(6ZvmtSCjG*d(T`H8sHlcIDz)9 zkCzX8${?ooBJn6z1|MTY;x?Z0E_Rc81_nvR@C`+fyjk$RKD@2(-xdVW%dL%|pN%{N z{XC>W!A2IIMx8y4jl(+oS(*h|n)ru!`uSOg7>An%`y0dN6=39KPX4f?Ztl3H)0dB_G0%yzfk`Gu?Zc2{N1$6zZAZ+^6)1APWVpdW#Nq# zTmLVGH#1fq-s0bBy_u=f@Ft!ArSQKx-%O$NJDLAW;T5J{7aCu_KbUnX>l4#|JrC3W z!8A;Nb;obJ!oSoDroSe`Z{h!?xoB!xHtfHJ|Cd^?yzfo^rA7Ex*UR?1W8ap2bt7E5)k6!4rLj9uo%+8T_zLxhl=2A$6q4y@bUB%^$PTs2DtPV z`6+&>CGv9$Sg^P~VtfQX(z34)87i9G2ks#GKk42Cn+FeyEa zf}7~jHa? z{$PZC-~>3ua}(0PpYOtnREW0}&hMa^aI#5T!*@0BljX7p5_s7IDMJJ?3^zN?hf5kSpD=pH z94ZsTd2Yu)a&s4E@muop(e)26)3k*wV7&dq+l04;nL9qz2d)#u;o|P%4km0Myl=*5e?~7o#R$!wO1eXQ$Oc%Oy5J+1Gi=BS;t0z4Dfhz!`q42SC zu#q$VNF{zwbk_XVMeNCx zl@%&oJbd7j;t+T5HuBF!#k>(cY{BQF<(3@+{e0Xfid*q|j74_{xY&S?m#G(6n5?0u zz1ZBWySPQZxgM%k6||e{xAJmg|$mf*gQR~ zsIGz+Bku^PUsbaQ7Dc`HGFOpUQH|jr{tFUWRU;L=-etza)iW&9>VMcrBCpCmwj4eI zd1LSo&;S1LUr>&{-W6stUWI)$^21f+Ux2)-_~NU~zpD0e$Zu2;KLhzWDtK8E^S?(0 zFTTcja}~U6Iect6dfei+mtNQOsX&9}A_23xgp`qpW=f$aBbd2Ojb-{aCg7Is-pSg+_Gk%DGH$%Rl8RH!Vd^qwBEg0W{%`fJZRmp!wwqgY% zV3f5lt~KNP0gwm7=u!{!42l;y97&rMua@G|7v122~_Ec+RYd<)or6xUDZI2J#p2b0DN_OD3f zqkA*{ihy5@yenNeV5?f`_x2q{US_YH*FD7IM-FB@l|lP48$P4q|hkTq0`;*9voS6Tqg7q^I`AB-=0~;>$ ztkYPP{AYAId~7*q0nKf2b`}NA<#pUqE$kXwK?T`MAMBYJ8 zs{O&IAm2f-e`N9FiwkFffY&|DT*>gVC=C27TYn0WpQ?hFjRgWWRmVU1!UB90{8Q#X ziY`2`4HD#MjrnCr7(W>Rj^BT@f3zFpaT)!Ck47F|!TW9flcE1;DYI8~{7Wf^*Yo_l zy+b*CbUAzq@+HtR#q~QD^H1?+@l_q4^?VqQhv`51#~68`|1|V(?E82BGUQeHk4FBZ zAb*`BER|^gzxz)?UX}j>8+YYWzweVBjjB4)n}K-v1|u$b}t1oltRUbKwyuc7A@_kY)8tSbxX zi9c*Z82?BAI;>#)ap+&g@tKGAdaD`VTEK5Zo?FZKgK(Xt82>CEpEeA(Y>2GrzXk+au2%Vmw}E{1M+3 z`7TWM2frG5;rw?%{{`jv*E{_8_{PZNY5X7YWyqgXk)QDqMywL7AFh~R%1OpQV*T@n z|2?M}ZzR}1v(Y}|EaPPYUhf2R<#3+yc7plMAs>5@@s9=eCy|f5%=mDYUk_FiiJl-Y zy2@(sGV720jgiO8>_2!1VWexqQ1IiP*y zPi8+=_xJY8kQeE)JSwsAWd&v;|EZ_Ps%EQLzhY}KUU+=AILqo2>N6CMZvyhN#*E)7 z=>Oj5SiQJ8Lt+2lL|)dC@xuApJD%0c3>gaJCn7KI$oOf3__pU+eQYO&!uUzZ7nn0% z*uPu?tC#g+SR2L{Hdc`T%6>%Ku&TEZAGSaE$o{NK0-$vNv1h!h?%3#J10H2EcZ#3_p9=KuifPc!? zk7(qnJ@7rKmTs|VpMm@vs8h^8j*suCJbn@%--+=s(~@k;n_<*GE1B`J;mNwLw0181v()0|J_YVAFG9Rfh!Yiv`-JNElyT zz{~jf?u-|XUlj7j9*kEtezC|4$8SB_7a%VjzkPgssq*-Ve0+Li5H?}|r6C`Uym0)o zc>l-?$FC51&g)lv#qra;#OkAw7sjuRd;#*p@iRa^j^6l$O*lSg$jf|x#aG;a{E$yT zUe*1X6y)jk7v#^J$s@8K1<0!!KUcqB@fF)2!RL>>aQvc>kM;j$ubBTf9LP{!X- zvHm;I6FbHYjOk-8<;9psMhZ&4t2%xw=*T(!aW-`8M z<=@v&GvvjK7(WbxE3U7$$VVdIOGW-EOPReC>|y*_fwlxUZW*f*t`E-WKN@*@8mAcF z8+qCCU-pXgD-!tx{0;zRyfGzUqqm8a-9L&$z8~<)>_t17{chluonOTv zPp^-Z`4{bC_RWBYjTPAM@}I`#@UG?X(dF<7t{gt29A0nF z-}B>;H-z3+HomfQ_*mq($OljDkMXOGW2rW2h)6A5Pfbr(TPGS+22f9rsnKgn>F44< z@%Sqe^{B2lN4-}yJl~eVDyPJU|wbpb+M z-I%8qno-^eu8I3!-g8pVH_!}?uXYh38>h-J`2C4Y-D=6ugW(*Plb7y1-c~E@%wF= zUo@lhRaX<*QvsbB=<07_af4tE$u~Z3pPBL;My!9&?TX6+Fx`fpc5Zt{^tHgX8`BE^H8_^FFJeBiQ<^Qb$`)?fG!+$ zWqj2V?=Z`+x*kFumJI-{L1&D* zQ7UwFUVDIU^>G%LzGoxf`1zt_UdK@VDW<3I-TbCk=%2Hoi#^SBeT6!Od6NaYq%%zC zE!5fkny70_9OE*5pRIG$zmKw*46K$nC%d=8;>UPXZ}`WBHsl*WuM~7oK*!}Xf3yCg(}YR43w3{6SKEWm^#k)aS?EvU{KE-!M^V>SsKYXy z_Yt5=C}jTrwhnFsU3vv{H$hiW0bL>JbUv3Kw{cZCK2$*08*~<5nZLiS(|(|HK;0k5 zYlVJ^0iEr)^8K9!oh$18<}V9$&fm-Tr>hIs)2REKKQqvImzM8uH0UBx_cwpDKo|bA ze1H2u7mYeq{@O!7rh#rA>Rt-h`@S=^qRP&qL?Wu!go_bxtsn9e%>hFFfIrW>%s@X+ zo9SbPdWG}D(V#2TXF7U+M85I+DHi4HKj^B|DPOk_bY>OMrGaj21#~5#n^ysy0bJA_ zMx7M%E?egmj(7I-;;#W~$1b4`*CpEDA)ss16x%E8KS6&}{Wj2NcV>F}o`HPh`%$2$ z{h0>(bPJ}3LqFSiJr=311at|ggJ0REL}yR~*6*&&AHGNMYyCzu>dzi@38=&O36$zW zK-b2)e1GdfC$4}l5p?xY_s4aTLVkImGe#Z0Pw+>6w7+ZX!T!*bqF+xgSGxw zo(k(o4CqciWjcKB_e+O%w13WmPW-lfe_5cL_mSz~(9br0oUlm!>DGerMBN|97lr)H zKI=gQo z7S1zFgSNK>bkUWVjy_+JZ+w2Xzpfh%;3lC?EvCcgAWGxfgU+}f)BSOrP&hvf0iAav zrW^2={MLgmx(U-|VS38el|;}vH)T3}4xlu@JkW7sro+cZzjO-YQM(R|M>D3Q&jo(# z7X_U)=n7ge-Dctbgl4p#JwO-UlIid{!LPUqx+u_z44JMc){FY!{Z@tkI|{m@flPehw%h4K3d^$LE?Ko^d>-9nwX9hZp3g||Ij;nxf7U*m%pxXyJ zhYIM@K<8WmT?y!1E1)x|5BqBcboQW=RX`U4x^UDP3ddJrp05X8WCe7Ipqq!fEkb{| zZu9H8+9U^i`I8Rqb($d6jZX!<1*c!Zo)tBxy+8g4%ay-u)}_&`IW%Glc>*w-|42aT%hm& z=XDW%&Jj0H5NEQ$PGMc}plzSe^nvmy5XVKeo<3UUP|wc;on8Vx`l0QnkBoVJciw*s zfgZ=3>I*^dAoQ;x(4!yu_`_Q7iu(JsEwJgl#D$9=e2nT;XcmWl?<%)jBCu14QwSPv8Pnh7+cQm|7sR3c&>nJ1 zU(W34cd_y~UIIJ8c{8=6&p9IT`2~NURmWFgrzGBbusgYd#lzR#L^}P;w@U-N418XJ zufd6QhL>+gpL4X?$>MqP{nANbCm3hizTRNxx{KM-?=k77(^O!mq+cSyE(!0G)9*Cp zc7f&F9R<5Myl+jvuaw&jZ_f^l{CpJbw=~Z@u-kQxziN~ev{eRvv4q91a=D7e zd7!sIeOdnmtJXJ$=K#(^|AUn4>HM+>eK_jrccpaGX(!Mt+0W^74!u;?4*I^h+^&|u zPRYDH3wAnb%#MEND7O=?<4Wub!ESUqv!mZ1%I&_Fzu&coeYDMEW+&p?_qBXG53n+@Ps{VbR z(RIZD*7PF0|6R7<2eidj}6xbz+*mXSpu1s!sxBUJ{1Um;! zW=G%mm)qSC*eMye5*iQhSHI@xNj*V-2<&Kon89;{j4CW1{k}{dPg`KukG*$7^&X&? zRbzVky_sD9Nsu4DmqXVP`kX^ngX!t_XL5Zx{YT^61iS2im|ZsC9^pJxh*LuK&6%El zM<$O`fqtXUIUEd_-9+AQub@3j`ppmQ3R*HddlpWlvr1s6Fpt)Q-nli?)9<+C`Arb$ zmCSScoMUZAW=Fr@lG|M^-%c0q`>!@;cJ#Y0x!sBK?RtYA+|=kYaSk&bYFD2bN`cHvemPx{@AJYEI*g+Awq#{1r7 z`&SLYd>6!P56(w}olYMXkABA`k2gwShx3u@qd@O$%k=cSF1fz1ay{+8v!J)_$Mp0& zFS*`GpjXm=g`kafW_I-ZFS%W(^6lEgbAt3S%&u&lh1Y$8exP}JfL(zrv!maO$>Ryn zYn0?kpL3XdF}t$$x}M;CONm_?*ctmUJNkW@Jf3#>cDnF9V6`8!qu-Uu?Y;{7MM?YU za}JS=+0pNzZu58>#3G9@_8x3~p_#C8ce_JQ8QxY!*>|)ml+V@gmrzGA@ zu&cj;+0pOXGOvzg7!@i*eQu;4R+z%nO)hqpAgsy+DG^E5E}0cv#ZP> zKl&Q|o~Ocd(QTmT&N4lIk0#O?B+#?w%Kx5I8t9GVnVx?CK({~ig7(n7=yQ&F=b7CA zxfR%T5yZjc6V+RTzJ7v0Ur(S{xbNZzdJELQ;r)LV^p`@r*Mq)D=>NGuuaM78n$HE6 z&k)}Kd4XObANrib7WD&p{U+7=_O;>u8tNT+{WR73(V$x{Yhc|M791b z=(ACOl+UNBK#%=t20VSvk$s)@5B=_nZaP29->(edd4UX{Bhv5Xl-eno=cB>S_O@Vt z2=`-!@uSZ<(uMj!#2=I-V=_SAdW&i=yQ%( z)R)Z*Ux8j>UTDH|0@wQj|BeE^LOy2n`2*_9`n8iluaJ)i=;KgdHZN+a*2jQ81@&d~ z;*;P!M8Q9O&LMgr$p5ZteIe+@s4vU^h-$q7yp-X9`m%YkT%cE&7fzs0$YJ@E9e;ua zdL{EB3he6Rb9?$-o_w7Vo-f<8_j2j+=Pc;k6tXzOm|Ubo1aTC`Igk4PCg`W80=+`N zHio~!Xo324-v3X*b&Z05d(dYK{R{8AD9ob>(CZYle9Fc_cs-(!&pw(D>dWTQPC-5j z`X``wMt#{lnyXr`+X#;Ts4v?uy;SS1LBC6we_z#lKhUS6emmcvtp$4Q&+dEGMDNu_ z9gGpHBff?2mS}wv;(Wvrh%Et2fNui$NL|#e9^eze&+xq!tq`MXXYy?Vto*n#{p8kGuHou_4>Eh#e4HBGy5yhFA%)bQ_CTfS8S#fp`oM zZWYwoi#QMK!x3eO-iWSpGaR+h&H#8-$95t9)wAf7@zgt!}V z9pVzinTQh*eGy#{9TEE?c17%j*bcD;Vs${cH`zf8kmmnk3(GqP@gd?J#A}Eb5Kkc< zLfnnG9FY180;K+>Snh{>PefBhL&S!NH4)Vji{Zr^I*&dg<{~~pOhZgWJd3ywaT{U` zViaNoVhExiq6cDsK-vx$_&%7kyX$QvEnUn)hHp+O9r;v|R*{wyPB&)upay@oxgs@_8(u0<;CY z1AyZ|zZ1|Ca24`P0jYi#mZt*t1${7}59s^=rGQ;24|#L|Yy{YVmcgznAl0h@`hl(( zz8CifEC8hSF93a^{20&+@H!xkcN%dEqCI@4PW^WWr2b848FZBpOINeMh)#(1h`kZ55zP?WBi2LI2c+%P!mG3wY9e?i zu3J8!8(;)cq#7IOhL>I*2h=UOOA`(P1 z#P*0^Nfp?*1FODG2bhC$gAkRETH0jXUFK&o$mygt@Hg_mS#{dquYcLI>w z?FOWFQ;;8ryc^bc!20S~uLVf$is0`>P`jIe)b9yEn*Tw-BEW5c#enMp&D2EQRsd!K zE(SD(af}4~2)r90wX+ALetQE_zwH2ffvyH1?S~u7Se!&a+7J5yX+LZQEP(QSK-#Y9 zfYja|kmmIh{;meKF94)=8GzJo7a$$iEr4`gnS{Yz%NmHK@Uc7f zlL<)u-bB2D^?R_q0&xk}2V;3GqBGW8W4SeAGpw(K<$@(FPA(vgdkf2_5RYK}DlE@H zjKF$VEcZvW!TPpXu8mk7>pw1L@tz_+0HkrxV0i~(EY{D$av-7))(^n41)?d|*Tu3L z;&=FaDb)XSK-!--5HDl>UM$A~()~#WNav9^Af10>0O|Z23P|VQKtMYG`U0B3yfO!* z^Y2>}v-yo(V|vn+!fPvsY36T1~fVdY>3iWdVshlH*FbzXkHx%99>Mz6fH6?N0FVPe69KbeJc0nDp1`r0g&ds7m(&1gE$NMa6}oRE21-^ zEh2|#jMxUTKB69?2(c)FyK-`G@GQ&nB$nq##9YLOfHc2rfHc2&!~@9hKwOEq7;!3M7@{ZQSj2&deGttNJ0iA1Y=T$~ zkmgZ3k>&9b@dcoKy+*u+7>{@yaR=f?#Knkn5W^4y5IqscA`Sqg@ht#pKQzJmx`OT{Z`hSFY9+3J!2uS^JLYxIi?Lq;me=k7l-v!G< z0BQa`0I9AkAoXt!Nc|fjHUgynbr6d}S)6RdWI$>k4@l$00n#|Tu)GeC##sPJbfYeSGklJZt`D+NX%Lb%&cK~S~*AP!39zxuVxCU`4;(Ww#L_b6iL?=XhL~BGd z#P)~=h&2FdKA(eGKJO5pBHlwxM!bx80&y?mR>b9q3lV1`PDUILNaINmhaqyv8zZ(s z6eHG0tb(YH_%Vp(os0Ma@e$%xKpJm9mbW9WKwN}48*vI^Afh|sD8&AVy%4)1c0z0m zNaHm?tb+BLSS}pT&Qsn1Qe842od=f@k0I_y+>W>baS7sF#0iLjh%Si35eFgmMeGJh z#Je?`v7M529Vl41f+KR0jb?u<(TAZ^EUKze;uhYO0}T#x&JLxE&(LM#3OWjyX1t4Z=w5oJW!n zCdZtIs6qI>Kll2un|tr)dG_AV|Nr~lul;yGpX*xdTGzVP`mXQydrwp9cNSFKX;Ar$ zg-6ovFsOR`G9`@j6;wSwfvU%=Q1y5Y>iPU`sJbkIiZcajUOl1Ww1Mik<@C^R1JtNg51?onnRa}RSzsCKWO7RFr$ z4<*hWQ1iPP>b>PksP~i8AzLGL9Mrjc1SCXiFR1sLZnp0O)qYE1XulDv{nzGWQ13I> zK-FtJ)cee6sP~zp?cdk_jgy1_6Da>$sCX}#H$%_5-;V{Jdk_Whyy?PmWs6GOjuq1JyDjK{P69NT9= z^_y(}0Q>icn#W#H>)j4&z1zSHmS$V{5&WH(qrTL=0cyS9fa>=oRNRN5#=QY*|DFR? zkAtD+)g7uHU7+f*BUC-Mfm)9*CWQRgL&dods$VHooMBM?20-=OAI9^B@w{O?Zy3)T z#`7NEEYe4LL*=~|s$U&^7OsFx;3IGc;@%Hy(2JnvH5{tl5m4=VLbdA-)ow4ScHfR` z7I~O)-h=m`OJP^qoer0x`@v^m5`3O|{*>P=asd7(q4IecD$ecZHRf67RH$~T@DkV& z9?p2%Le=&6yk?OLv40D79lQ*6-7JUNx3@xFH&;VlH<#Le0aW{wq1unKeVEw+>bm(U zH>}fhP}j{PP}jwsQ2sggkFx(L`!k@%?`Cd3HQX28f*j4MuR{4>FqcBzzwU)>jnp%( z%b=c%J~$=#SDUYxPn!>!_n5buH<SbIobyB=aQmIP*xepLvkEzqyy0WHvW9ofPu@()_?& zWxixCH=i)?Hg7VoGUu4H%&BI+ImSHJ%rXx(4=_8KZOv`XpT~xLzcJUFZ<#NePnnOJ z_nUW^SDACnS>{wT-yCBOHV-w^&3(BRS_7^7o9SjJv#r_M{5dD&`@Ol|e8+szeAax>yxY9qyvjV!oMjf8 z`R0k{2s0h(d2%1fqfBZ$sB>fE2_dfy=6i4&=i#kT?aRz@W;WE%^ZQ$OGrK^YM|(lV zZ4VXa*W*K+pCFH9sb4_(KZNpM33V=C1h0W7!Y>y#{r^ zegW#-uYu}+1ysLT_Mc(s&^THF8r=+N&asJP2vJf7`EwoioWmt%hq`*($! z$4*f9(-u(o(=DT#MF!}81wV%0!h@+pJ=Fd5S*U*ZLdCreYTQfVk+dsemD6Ibc^9&l|?`hVi^dIByuw8}?V; zP9gkAoWj zD5&udx9$Tqeh;Ye+e59(4p95(%VUH7$b7mY$0!yLJv(upR9&0_!Jj(VA>ojve+jqBaW45rp@u(2D!Tby={=3$%m@90r zv931nwEYI_%dF3}o@q|C{S@;A+mAB`n*Gg#pz_$u+|Bm3=5HJ>s>gRw?cT9|!~C1= zORR6Rj+tlLej3y|=0UAv4%`ffL!Aq~q0W{4q0W`P;C#-36u1Vqg;!#44RziAaYQ&@ zeuC;>4>v^kJ^<=D`Btdw{xqoTJ_qW$9|qGRkxqxhpRgYYcfh`WVBia|GjW!g55co& zce{C=?N^u=*goByWcw-BM?l5z4VC9Q4mstw04l$_!-M@IDElBN`z?nBdl8hq8&@Imj-Xx*Fq`%d!7=c7sJa~or*mHRfV#fAK*dS6{^O9)?nm=u zsCMr`wX3!LQS&aS@lJzBGR_Cww7L`jeyD!eLe2X)sCn!LHIGZUNyt73%ANvcKRbhd znjf5w-d~zHd)VFqDvvg{|JJWrWGw6X6I8!7a3K9EpyoTrEQN~qao@22-h&$dQK)@+ zFVucqWc#I1`|)h3{Wu+JKNdml$4OB8F&Ao_F;Mrrp-^%AL+!_Xq2|{NYJM;F3Hxy= z)PB4dYCkT5+KpNfzbP;R~$G|e; z^?+^A$<|-=;(m|*1ggHzSlQfALy`BP9pAk^?IR>geU#2&UjE1#v3_K4izy08E z#A^>VpD)saejjebJfE|^AIg6R)IOR6mEW0WvHfGLhnNH4am;I1sBw0H+Y)!>L1Ew3 zK<%$GY2r<`eLU1SC)z#)YQG*~|9&u;c{Ydp5%2p0L;Lrk_VJxi`Q2#Fg&L;=)cPbr z&3{8`Sho+L)@e1Ay%uVn9)?<{d!g3pcBpl_32L3Lfg0yBsC7CIs(&feI^{#HQzq0p zB|)vzA3ef4ZG>8NcIcltMAYsRsQr8! zYzfbTDdb%Sf5m?Y+>JkH-$Q=#>jbsmf9=M(OZ$(Z^fIXZz7%SG=39@0$}S@>u?9u zI-Cc!4uhfAp&!&bbcb4py`k3OwS7YWo1pgb0;v6Yu{i_&jvi+{0`5Y)zSjG~2>RR3 z;aq+TYG2+5+hDJ-E`*dkbplj<54YY0svfPO>eLKsK5y+E>hm&`eh{iYw?kd;3!&{`HZ$61oPJqn^d`UYyfK832odr<5322|YT zQ0w&|)OuAwt=Fkg`}sJi^*R!2z4}9~SL5y>pAVqoEP>kBcS7y!Yt1=O`}z#)Jg9x0 zW!)P_&>J~~Tf&!N3VE-9I-f6t{O_jO{73bk3bpTtLY>dOtlL2C`|Y6C_oI}cpMa`c zHB>zpL*+XMs*Yz_=R?(TG*lgjLGAl2s5%}7RmZ+ib?gCE$Iejkc7&?q&%1^?z7AE# z>!9j52da*W)9}X~kLGAb5tp8{q_SM%=^R0*4SG7?2 zJ!;-#e}(lNb0*Y&9|<+iV5t4x5~|L>aud})d=M)B9U}HBsBx~f{X(eyKHL7WQ2ROx zl}CHCIaItt7GLqQ%mHR^vxnKm>>x63OQ>=GwlnL`_gf{&A8EeV28K^(6CD^EmTp^H5mII6b7vtAp(Y9Ky1n3N^nm<}i2$_AKi|&EDoO zY+~)t2B`hH0cu}<0JRTRK<&$=Q2TNT)V^E{wJ#Sz?aS+->OBu?U(SK*Ujnr+Plmd_ zdqVBYU$zhXas$-9TnDu;Ux(V4PeZ+To?#vj^&IZ2Ppg6?SlOvsP?~eSjheaRQuny4ezy$Q2XRH>*uX6 zhI-G8!ED+e0ms14T7<2a8FWZFo z+9KGU_=iHp-w$dYA2tv3xEpF7yFuAkb1`auw?o-mL+!U8IV^P@Jq#7+E)n~!P(jr{VM1ABNhemq7k4iPU1K{m=y}kB;zJm<*Rd-3;{e+@F8p z@037)1C_@u=2=kfi=o=*LbcCkg|=T~`=z#@ZTk%CLhBAtKVRMnE+x*Jjgk1zm+yhkb1qy3_4DOEQ1ecOn#bNy z^Joh-kL{u6@%!d5j~a6URQtJ5?aQIsp8?gr5Nh7N&3&Qf9fg|r=1pPVpFqv?ZK(Ob z0_DFED*pvg`HzG8`FIvo{s%$j-x}&1`{SoDkIis1`WvY6*T6>jBGkTk7U~>(0_q%l z80s9m7i!!^Q2XOLsQz=H&apF~u8U)#_E~$V`|CDP=h)^S`MY@7_uoLBV=qGWzr(x) zY9F6#PKP>=PB$moejHT44p8I0y)le87|Pxj%KqLD!JYy2oVNo!5&!(}!#plFV^ICh zFn54z|9e9uejUFF_oeR7K+V6#tb*!ymASXs3~K%td>86{7F4@2Q0p)hs{R9Q?*>)> zPEhr44^{spsQPaQRsTP}4ded=RsXM`;=Th_|L39Ry$EXjLa6$m09F5?Fpc^TgsT6y z--P~;!%F5?4fT9@n>inHfNA=J5;Ykde*J<_4BuLGdw^V?UUJ{w_teo*y!1*$$Ppz8A!RDB+Us?P(k4e@V> zs?QBj{pY~={)DQ}F;Ml{393H7eHrTWJyd-@hpNw|P|tsPa17iVD&DFuBJt<4XQAeE zjrApvTSscK^@;Y6fI9!vq4L|$+{6B6)|*ID{tfUr=Jg!Z^WRd)EittW>V7f-PA8A3 zH0|5jz7y0q+uFW~zwl)&^Z6F4-zunm{Segq&LXJxSHOY9J05EMp=JiuI4eI3>r)GL zA9%>T-K>OK$C=he<}j%DhIE*X|NBqFK3D@sp*Md*-I-?tTuI&sPTXLkbeS#xbK*cLCx#kb;16$dF#7DUuKSkivQ6&VV-ZBFF@I!h8lOV?Khc6 z!x7}Wcx~u+lX)c^ME(as&3{Lz`F{I$*f&o??VAj^C+*Uq=DBfAn9oO0<1K|6|6c3M zpw=N~o@$OVv*2*z?Ey9J4p8-7`BoUe8mhi$!jW+EYBquD3^o1+sPXQBs%r&Q9y4qo z1U3GlW)IuDK($M@2ODd-)P%2p|0;vy_`7vfZ>V}Ng{t#nsO#{0 zsB`ix$RmI1c}7j9^X=Ed{yh?^ zUxwKY4kpj_uZH;5Q1Q=%+BdzR+IKbgfEs5PsBxOvzUh_FZk_pt`5YWd{P|FIo(r`f zVo>!y1!^CRvhEMH4|+iDgDz0}pcB+S=m513l3*LgZvnLret9{>`xJ8YrM>~R5AK56 z2gOkP;1sBRFaoBr4~~J_2VcGvuGd-8L{{Pu@Be|IsvgUr z@_Nv`)Bek?&oigN=X-Og{jqsvICt-dioZz2z7T4h`L@TP&i8Wr$3mUs zQK&rHo6X^L?5F=N#QX7uupVEVE1~x7EpRvTs)X9N`O@s$G4eBhf01^{a4*`mhHAIw z`Ot1A)H)ZNSy1g;!#!x<9IE}Y6`}ncDF5kDdG8DrcL%6;bdrMpw9UdU`ytA zoQNI*w?!Xfdk?6(>}7v*`#)G7>QZN34YeOHgIezbsQgcY+Lt42KNxCXrb6w@eWCW{ zo>2R8SEzlt6I9)|h1!?DJ{#hG0XHz8x1jdrgHZc&7Sz6+1hp?ug4&lOp!VhOwW0ru zP|s(zQ0LOa=B-fQ-(6{aHsq_A)N$4$Ag_?AyF)#nHG?{L);&X>iU*~yhdOr_K-IO_ zdI(f~j)2d?zHl+z54K_6d%+s?E>QJb_jIV!>rnbWsCwNFFQNTSa2329u7`8rh4^Pc zz2A+8s@sWB?>R%E>M{V{jlGBMd)mH>bxWwa|M67l_dQg<&#d2p@~^V4wSOAa`_mM- zl=wqnH*{~P_oxnVCGSyhE(>*d8LAG?z$I`A)cf3Gs5&f!n)mT$PpJ0$LACDy)qW?a z_S-?VU-e|*b5QSpPebK<4OCsuhnoL1sJcwHzaLcoX;At9{zS-oJ=FW(%kTjF6JZYi zpC1prAGW1FH$biT`B3A{Gz(mqV>*?pE;d*)<>Uz2k>Ux?3 zwf?i9uBT$C>uG1G=lS}_!a2VPDqc4Hjd%l~=9z5W0{(&h%SVI14(j}=g=+T*)VZ?& zD$h&IbL^jBeWE!O>U-V&pvK!B^3_r5+mD1gu7EnHW75Pcv%MHv2C+oZFz* z<1DB=`#`OGci5SEb%A(?&}YPevd+pd%wuM7emeWX4~gM)#n2HkB7>8sQKOfA-~Vf525mY7i#{ipuQJ- z8R~nn6;R)cJq|VRbD`!n2M`NgZ*gOnR%_gC(QFHsCiy%o)2~Y zo_Tke-xS!MaSNd4on-qLi^F=Yhk6fs(|q219IB3$*7M9WpvD^uv+;L=8m~Fjd%`|< zalJ5JN2u|Byff5yDb)Rc0n~VXZw>ELyPMxFivPP_oj!!xf6qd#!{g?=H~W3eymg`9 z$IOvX>-f=)VSU~mG|A*^Bz3+p{ zV*ymXPlY4ld)Kl_X}=mOk87anJsT>I)1dOmgD2B82P&Tf%wMkw`81dxLiyi@>i?|m zkC~O`73R6{XvRMbYXA3#x^B~<_ILe)u#azp+TR~t9p?3}SqIhcMW}waLCy1OsQx3M zu7h2m`j^ZP^UH=E7`LPKrmMof{uZj=N~roh4Rzmm#P%wvbLwiS>+~|HbLv942A&Od zPECher_-UX*LDH%MkFY+{x*ybYP&eB<+MW#W!`{O7O_zmuUqZ!SXI*E#-1-UY`>b=Io~K5@rNr9- z>N)VEOT+Wji%`!~7s4*YodMfW|6+5pnQx9Yqi`SEr&zZ&w>ST|B*b|Qs@)6L&zLpl z{pM__c4gL6&57noW;dvIovrsU+nKG+_vVK6e-mo`{|2@GOQF`k8fyJ-hg$zrq1Jzd zbyui;7lm5?-JsUL71a7SgIfRZE)MJe4%GU;4xb~xXQ9^rQR};`Z?wL``aG!hFSC83 z?PINnS!Y`JwC-lz(R$@YVV$3aTIaK%)_Ek-@gC#(del8S47E z7OF0nLDk~|IG;GPq0Xo2Q0LQBsCeU{>M#PT|6r*49|~3fRH*vz2UY*wq3XZ-!oX^% z_P0Z|UjWtq3aIvTpxS4f10jDzA$0)M_`5(|j}dq;?KWNz)?)*de<{@UPz_b5bD-*! z4^^k3P<848m48R5^JL5U;Xd~=)bmCel>H>A``9tCCGq<~-QRYD+oIc=H=Prn6D~4` zK(&uT<<;J7Vf&V|L;ug9{O_3ypq>+2TmL>g_#0prdEW|kKRyF0&qLry`1@HQuOH#b zsF}y)9Fz6 zrzd8G`%g7gofku`>m0Zlo@Jd6wf{y#?Z07A`!5S>{~ZS7?^RIw_kh}eouT6G2se<= z&*fqK*P+J04#wZBVEnxbrcsw-sQcs2F#cXOGdy423>7a6{zkkE^B}1AsrJ^t&Ionh z09D_0Q1x2|mEVKro%UaDeV#cD>V0ZB)HqpC?^Dg8?w^~d`+GE~_=`mB3!%oDZ+i@m zWj^KhkA(xtKMIvcd$T#5j(uKPShq`|)@>%#d`FnGriHrAEe-ZF;fwf7txtySv>tF1 z_P+L~+TY6j z6)N8DFa~$F-cro^;NJk1XFZhvZK(DSnRna2$o`9=@+`JL&+G`5=Pporeoz$heB8Vj z%6>D{{y!Tk&z+(2*a4o4UO6?4{{*}O{gCxS*p_zZK;=8b{;Bp4G7q=EpZ%Sn^4-?{ zO;bESsC@5+%Bu{juE&`JZSM`^&$&?f-gA1$V=`2o#=;eFr1b!}v+53&XVm^(?f>Dl z(C-T<|9YtYe}l?%iTzdP$xwNYf^EoiPpCY9E)4t%%KiaVo=-vLIT9+5q3}6$E2w;b znauZI@CPWp4(>v`l~DQA+P~EP`Q|0|pJ)FhsCuZOE) z9sC%+2=%=51l0ZcUZ`_=5!Cb2)llc~h1N5yCqdoEPq954s=kA5A7FcT+dEsggNnDU z_2%(GZ-5%_ZKyb}SU+Q312x|LwpZDHz4hhRbF8O8jk6Ebb45qEl>R@D<9?6+80z`r zd8qGQ&x8CCwbbcQBv+>a;V|pIh3TAD%njf?BUvq1NkJ zxCA~ZKmG54TCba-)@!tx1+{LypvLVEucLhz_zvsv{IiwLa7S$=0XXf4uc@>rPPh>;P5I`rJ^*=b)a`?uU9#n+r9+ z8Bld7g4(y^p!V%aFurf0+8qoPHw9{a8cq%UK7i`C3aa1BQ2m~R>UR%RzqwHTPP07+ zYTRt7{zpOeKNPBeAE^F&LiLY8_5bLU(60`v-wLRHPeJv26sq6NQ2k<1{U$@Ldmhxd zqhWkp7#|m^e>znEUwGJSgx^5vm!Q^tIlP2;Pr_C39=IOf26de-fLibKp!VNPsP!&{ zTHmoy`(T*uhuPlCdOxUrv6ppQ`&(N7d{XfL02Q|$D(=-#^SvBu-A{v>_h_i=s4vuY zw3&w+)!{p+I(!V5z&D}x|0_^+sD+yMndb3O?T0|MKNzZgFR1q2q1tacG4OM!_lu99 z=JPmIUG9XM|MgJ$Uupj-Q28GZm46qgytjkex8LW4eftX3I8U3G!zleqt@~N;2d^N0 z2RIM5gcrk2C-85%z|Y_Xa1Fc=z5vgKkHHwc6P^XHg|p#BupE{{)vp99k7Lbr+ndAA zJb!+Byz?|~9UcDsWeh5xp)h_Q8x`Vx3H4sI+FS|e(C$wAZ?^p+vm9Q8f293GZ0~J$ zh3DaKY5(sdL;TOoweWoW4?)f69@`g~bK%+eC)$6i?MIs#@ErU*+rPc-KaL3FY=E=y zzW^2gY1{8KE8&^=r`tajs-B0NX=Vp=V|MW0X`W%`n1juO&3(_f@K>TTN zIXnX11Y1HqFExYOr`HV+5a~UEo0S z+7+t*S0t(abx`>&hl>BC?RT0B%sJ)^b0k#zZLK#S5#rUrn~AsBtTgAE)6G1nd45S! zit`MdfnExg*8|p7)>oNx;Ni@p$o`4usrC)XkCHG@gN0OfxaD()1hJWn-yLESHUK%KW8;g0AQ_P=>p@V^4(Uk2sB4Qf4Zgt`yT zgG=DKQ1`)dsQcg)sPU5EFR&%t9O2(*IFx_8j{8_6{E50e4E20)H{1+wmmOXQ_1tkW zRR43~e8q=r;2Cfp_Hj`CN5LQA5Xk>hkKjLwcPLc61EJ#V2d}07UT`f;hM&ULumb-t z146v7pyGW5`Csb0{73QLf{OPXRJ3u(-+*_(dU!Kbym?Uh#h~KNgohBX7%JXr zQ1No0;$_1H^dAi0h6lq>U@xe6U7+H%hl8f{Jq$RGhi6H})7*oU@?f6hg(xhu;!s zEPRzXqu|%H?+D}hz<55em-1l|;`zXMJ}{mS{9gIM*OU+Zj`nv##km10&Q-7{_Di7R zTnH6sCe-yk!OVv7y1^fbI}pB3+=Jl{^xsLO{WkK$pIOW;@H?pY)eUeRTo3hp{07u> z@qOmaP|x=lS^jzs9^2D$Z{3NVqoL^MkUV3>Ci@)bEMyYWufLLjH@P=Gh4<{!2_k@t-lT zhhwq#w?5F^AF6+fdDnqKpJVRXBix5pbr0+L68wnzEQhZ`#)IMDV?|@gsr{FZ}(_k%nIDA_B z!~Wl*yiZ~O5k5{`zJceUAA+~T^P$E&8!G;NdqpBQ!YiPjyK6dzyzhZE=v%EXhdKvJ zY#(R)Fx&fC?+f+Z(g`ksZK0l9wug$>9I8HF>=EMCK%F1+p!%H+)$a_bey2h88wb^I zFjO8XQ2jRU9_;I(`o9C!|5d2|FF^Hw8mj+YQ2oz?>VG;^zZ0SQ9Sha(XsCXNLG{}o zs$VmxejB(LWM2c-|23%o%c1%|3Dy5$sCElsynayqv!VJO2Gy@8RKM;}{q}+C*9NLz zeM;!}6x6EIB$P~I&Z&%^RTaj zx=*|ab>2P$Z-YzWt?*u`xK}~#x4BUJ?L4Ucb~?Nd=0V+Oj)yv*wu72a1TMwDqC>c! z+zfR;nFCdyF;L&9_Jx}7fl%|=26AbpzS}Pif;xBZglfMKmcxtTPKw02SvGsBvC_8s~ZI`=RbH*Foho532u#)+JEwCqVTdW}Ri73e~@{ zRT$?>xHI)y3l;A*sJg!ZpM(#XcS7ZLvv~#7KDfyG9H{%|6x;j3$I$!0N8nCycj~)6 ze1bTiZx_b<5Gw!G*0oUiKLVBiLhEa+&xXoB7b>3t@{V5oU= zyNRrzA7zN|t6GAuYaThak#k9mnGtg~=aTGOI5)%#%`KcC(&c6Y=ZJI-=ZM(FypQW$ zdKuTZm|>Q19ZOg35SYSsCjANfPbAkkA7X!OB=0*RpH88j!f2P?l@a#1E_l37m-%fBj?RGVj0?*zKK8t-51w9#l3TxqN_zZjw zJ`Ep&Pr*fS8LWU$Lha)xU=d_3JDmc@!eOum9s)IwH27=tNT&l}TkdPx&pWY>yThfl z+r<7o7X2C2zWu=ZHS1+?E$?~P!eO+#$o6xri>yzEZ_}4W=W$+#Jd?^2HxCTAa{sHhUbP6oOzYAmwv~OcJa$QZt{x!tZ{v&f8RQtD~ z+Si%OAX9Ju2vontQ2lN;FNdobZyX$of28e0tPi&CY2DeHYdPM23;S<1{ojQ{=+C_- z9)G#@L)Q0L-(Wr8`h4p%txtw;60aY81?~w)5?ANNOX$zJo{P~RLCy0$^L5)_vgX+* z?!O1t(T|_~#QX6xiMV~5IRUa%?HMDIL)^ol>eB+m}GDlCVE_>16RI02Tj4^Dw+z|poJ4NK5`9T_Qx2g3L0&*c$$1M+8$m0!yWbFQiL zO?G}p6=#Xb9*XNclR3mSxyCuj%rrC1v_JivGY2(~BxS-hsB>pMf2D6%I13$xtVL1^ z%z(-EM_@17HIh_s*Z?1Z8{qx09xBdC_!+E$b@X2hIop#eVRLi^{1DECsW1i)gypcF zc9S7{BPkE|fjO`*%(g!h9z?qg$efbW;C-+wycb5H;a69bP@O`)#ZVRidE8!~IRlr6#7iyhi@MmuM<**ZWJx9I` z^Pt-2z%4Kvs{J6S_L*>R>=}?VC8;Y^`zYkuBq;@|eKJ)0R&WpO5%?Bt;C`(38{iMH z9;&^bN7cR#Ho}$g4g57w?W^U7i=o8)xH(n3?oqO8#$QNz5#Z`z5%X*YoXfL!Jptt zsP=m9Rr?yaJN9b08dkw|P|qV9VFgtCx$rl13~HUrq57A=U!a~#G)@k@4raqm*ayMQ zFca2cPlH#(uJB9jQTP>1fv;e11sA{w{04g?H$~3$qz1_M%}Mp}8n_lVV6TJU!@|>Q&7^9$5xbtZe}q+#drwjY8@aiC4I7}^t%Y~OI(QdcX?r#7 zhF%O;62HoZ-yyw5ln`+!dCEZ7=b*qBnrUd=#AqpFnqo522&*NpuQ)1f2{YgRNjS zjKFS;)5vpP4ec7>!`L^#2Vp&2hJ7u33i2!w;aM=L7QTwV2J(!UM67r|i=p;^71aK( zgxdcxsQq6Kwf{?O&w*c{v*DL;5PTV?L1H9zg^OU+_Ezv4bOe42xd+Ab;8`Q`Ihr#& zvH@~7h5X?C=xX=?WKH6B)U@O}>>mtwK-vDc2JyiRZ@Ke^K7JdeK$BKLmtKlbbF?^YJm9QRN z0oTL1Q0-z+?etvy6743NdGJSc4%EKMhKe%?eg!ja&wyW})1a>Ju8`c5qOdJYfjh!v zm;_tFH=)|SfJy#l+XVmfXk=Vh!pG3cmsj_AzP!4}^W~L2o-eQJNhSD+9nYIr^rSpA zLE?Gy=o`StyyKp%seyGjG7U%zO(zARc6f0Gc(Po88Pek)*rWsw5)~7ugZ*> zd1j^=H6vzyH1t~wo72Az{?5AA+FoOOwe3~5SK3}-d(8H7+e>WEvpvW5Y}+$!&#*ns z_NeVCwkO*jvAvO>%P7wVs66XoGxS>9>uj&Jy~>Q4`+pBG_vc1yw3fp70m)l-qd!Fr?X4H(BjUAm2jOPR6`Pg1(d#&yI`I_oeZF`mN zm9|&d9<#mNcKzHfUQgR|Y|pkm)AkJ8(`=90o??5l?Gf7>`8isAf5CYEFrL5db+*^q zuAkS%^S8aq_Ly02e~JBhw&&QMZF{Ee8Mdd{9<@DUHtz2HU_3t<&(HQc+iPvFvAx>% zD%&e(MqZhMLCdA8@+uAeJvoJ`xJX2jIb5#!^VRi=J^sCBJ?>L0Vc-1ZXN^K8$t zJ=^w7+cRuWvps5iitWj^M{IB0)%Ah#`oQ@5+FoaSt?f0oSKD4?d!_9aw#RJGGc(N$ zsClMAjTf~&#r9;|BevJ`^Tv2SQ2pwl`qkQAV|%sjRkl~!USWI8_Hx@xY|pbj$M$U7 zGi}eXJR$rYKhO3|GipXm z{Tw1b9*mC%~As8T+) zW|dh9m3IYH-Z9(r%uF)_s$CjXyQu9cwkO*jvAv#$xA=Hwm01bnR z=2r(bZmsQAW~KcV_Qz~5x4p#nJlk_@&$d0&_NW;#8+jv=+s$2V)B+EqiftFk?2=9xKA?XscTW!fG!BW5EH^=j7u z)vg}K$1|(Un3-p0n)=>c{nMcOM{SRo^}H;^*9)p$9aOto+pEl&nP+C2`hGheAI9U` zo??5l?GfASc`1qKZ&sNxGtbO4qh`dc=Vd0|->foYW}cY?l~*=YUYWM*`{8)LwnuH( z_r$X6dt$YZ*xtxXlk5#p_Ienvm+f`7*Va@$L6&$B(p_H5fT zZO^bh&GxA6DYhru9t$A%F|!=1eF==$%k~`Gvu)3`J!=G#FxSU1yn#82kSK7GaA~cekChI`}gbJpD%s&S?c`qtcN+jD(l)eL;HGb{r$?yH!_Pknmv(3H>+h3RJx9Bq-QExWYWsguJp8lm-;VD^v>vsb7y6u~`Ql$`f8#4b zud|-5{PAzGZmay!$^5WbpX)t-7weTCuebH~%AfW}S?ljTmL6?=Zzh^hdalr?-@u?t ztS76!w4dVk1D(%o>o3;?f0gw%p1;1t*GGSkw))rFe}(g_w_b5b7*F5t=ySgF>v(_Y z|J+NVe{btl=TmMy((x|0p8RM1J$~J6`g5<=4-@ry{q+#9+U=LAo~&o`J;A?=*SG%e zp!=^6{%Y$#G(PQP*7s;XqO+|>X}_YQ*4t}5bi?8h?@_G>y4Jc>*8{r3`UG7s=sfFQ zYlBX+{_@ZITBmsbWIPb!U+VlbJ)bk{=}-N0s)Il8gAlL8{+3?PnElN^4E`$nFZTLY z+JE5Daet(`RMq6qetLZ$vHx(dN1F%3c#B={3D)1MKIBtkeT>(8w)I@E_gw3iUhf6g zqv}KdD(es54f;OoJm*_uy+!q9ycO0_kN1i7bdT5Op^)#9J|72IZ*qN0tQY(GtFper z*Vk(6$=VN$*XrRA?+w>?fOSXbQ(|3sHtl)#Xtl++j zKdjT|Nym#>w{W}`{7_Jzv5ptDzR~eAtatN#%d98XhxPc;`UmgNDg01TpJ{)N&kq&# z*~#_kYQ4buWLv-g=k;R!!^#jpgC8pDb9jBwwbn0tyrcMGqdvnt-YeD{y#J1RI{3T0 zzALO_9)A!&T+`=8kH6Bom+O&P8~g|PdTI4+(5E_nru8E4zY^<@T;B@oYrWsAtiN(S ztF8B4ALh5x`Va5lwbl!~|2J6g;d~mchd7^B{E%OtSv^AbE04dzdZ@==ZGEN3|HOI+*ZW87ot;k${lP#!^PEpR>ld9* zmp}ckPjBlk&ToqKB#$@S`V+6`<<`G@f8TC>mdAU<`caR!!g{pFTW$TD_uD7dXSg0e zT330$wea)iBFAfIo%(0~)@vMZko6|-&m8N+J)g3ap zH;-Ry{eZ`>v+nEp)LZY^o&CV`qJ9{x&(N7@&hxq(!g+qH&+i?Hon%kFKe{Mb3dVt$kSbsl1_jPIDh8i79}YSiSNs|3Y3SCmNY0O;UMCg>U1?o8J?P2SJCp>y z*m~jkpfjxBm>6`q^|iS{53)Y7An3W)*XIYFVm<$ipx0V=o*HzG^_`~&z0&%@Q-a=L zt-qH?^J%o6FeT`&KZW^iI4$T5>z4|HZe=}ea?mN(N1p2bj@N?v@5FZ_5!W{-UeDS{ zJ^N0dN22(f)kcyJkITr+4nbER7W66hSG5jWKMzp*sP!LhgDzpu>eKot$FqOlkwHhS zcVYe2U*DJM)4=;oe0=L2+65i6p8kw=-_U->h@cy&vp$#YV_h8de17hvc-gG0KIdfz zUBNo&v!~lvxc|OxpX~PAyM0<=Xy0@1&_B)n`|lcb9s5F`2Io`f{?9so9qXdcO2@Bo z``;Zun|-Oz32vWV8uY1uws*Xr`QAhGDS0UP`Lm&+UJnG_k?(Efk68B}5_Ij7;LkZG z=!UCGaJ1gj_VZonyL(naU2VKp3k3Jnn1)Y6O(8ImKtKJOyyV2I1v-Gij467s|fDMyl7}&uWN|UNbmokc)#B{ z_)EG4OM~l`Tp9En>*$)G`#JymWkLUOeCQuJ$?Y94wqMZvndx}``nilgGg+6Nl46nG z14F`la$Uvi|6uUz_q*cz;cr3DV4l)>_Urpm>FmD+|B>E5X%7WGuVwHzxV`>-rP?Rk zukSmh)7)Qw-+;7!eyGnMM+Y6bDCk?QD=rOs@<_LD40;{sj{4Wt1f4g;{pSbW&evZo zC+L>G{?e>Z_I|0lBKW^JIP|aQoYv_nHMQC4lOwje?gTBGK zZbZ?e7=bN9sJ@IqgIITI;L-7Ich1#Hr7U<<{2+ zeS&q#4MC?r7yK1jL4UC#=Kv#e`p2YsD&GJgnFpN;OHwkYUZtYe;kW-0Mn$0FIj ze=hfaj1CX&&z~Cn74{!^To^w#J^1zSMQMN4Ul8<}WubkZ+xO%?rTA6W3&#bWX5C?8 z&?VMO9k1&65bs*w-!tvMc|vF(v#!hwI@$U&?lT&%&U%HPFRHBvabJ_a*1FWsBlXrl zoE-cO)-ByX`cuf~($j)J&AJWuO~un6j@RePNkJ!DAHun<`jnIe|AAwJu0J>Ew|pL^ zT^sbh*0CFd{=xU3k|V91Z`AocGdRSn%Mbov9lv^V&<|KA=LUU;??>6^2Yr%tW_Hj^ zd_Rm04LU{j*n#tpKXk6o{hZsf|y9d4GsF0ujaVmYLWCfj^ z9CUSZ&?SBzYqv1ydY@k}Tpx7ymGSP8c2z-FvCs7RXlc;3`h%~0il4N9m!KcH&i#3= z(kJcapwk`>defspM>~i1k3JrBNyng{stmf}_0YcaEkRdt9qaS;V{YFqw4eWk^}a#h zdu`B}Ux)UG-4t})XF*3iepRo~er-+gM-L2o*t2dwFtl%fSI`mGO`kUR1)a>fsLz;N zgO2?X^owra&^N@>@AGOuR=M8Y9tpbQyU_mOBZAI5Cg?WSCBuUL-1$_nuKG;hCbUoU z^PPUUqxf}Gga1XgL45!Fd2)&U$$s8E)VhLmNS_;ge{WbE^y@c-{xR-b`m|gWbky^G z?Y5wkdG64s)7?SWa^KbGCD*6k{RdVDf1UNLJAy88`-S%e9ksrK=PKo2>-MX8PLQs) zzU6jnkH6OA=ULx%fAB|~Z%g-YaQ|U<27ld%knf521|74WvLxs#x4-eBpewAW`TD8$ z{rXUrS@~5>4eNb`{T03+Z*PBz@7KxpH~4tZVlST3=Ht z-xBMW_YXSe{<~tAKhwJ3@%nuzo`?1h{bO#Qw_DH`cM9$6Qi877FX#r&U45=2PJBFT z{e4K%716jml5uX(^>>8&JT?!#J^N>K&=+!!Ne{CA(mJ^@`1S9`$Uphlpl_Ka&Hc(h zkJ)`@&<(!7j9(daz4ad#1f8-a^j~p)(E4RKeXfoLy}|KsxG?BC>v8U%=Jq?D8T=K$ zhyEQjKk~`^E$HRWr`4vQ&zv9p<@UF|D(IE&-{tb4tL^{zil8(82>nmFDCiuwzwDBr zH@Ln29TJTnwO{{!i?n{3NS`^*H_h#b{F%S&w}bW{?H5~*ogH+{`uwY{n{jaQIl%SH zvi1Z14EHb8dZ2%He%-ChJ-^4b-uSy) zAFBO;o^L(W{*n8I@qgEP*Uw@;{9?+{QTg3_UC5td~38{@JHRg zS^WIq{n+}N`1Q|uD^b%j4%oZ6wG3J+xnFzu5h|X}_Q=>|bwNSx2@0_}BV8Xcs>Z=l^d>4D+^9(`g1>O*e&Ru zU4qW``+k4g$*;d8L7&>rK}W9%`fuF#yPb%FhLqeJ{D9zSNCy1&QY zBR<_o?@mGM_aF2b;QB>!gFe#rD>*gjgLaCyZ_$nGLZ7{RqFeJm>;0Q9_aot>$?vO{<+qdxPOWDF4lS0Po;4Nc_QwPyk~#Xrl3>pugeMg^ACx~d6}nQ;N&yU zI+?$uL7xv-%g^<_f6(m@4Z6bT@B9OTuC>17u%N5m{)=~{WAR^_R=iGYg0AP6sP$R9 zHs})TXWj}rk6*&n=Lz?(xBk}tT7EfGpXYqPu1yX4oRdQTssn?*+wrsAzQX-8-F|`l zr@8%c?w`zG_MuP8yCGi8dOPRSVEuJ3=ifQBkM(x@eS%K2PVN%)3)WHJuUe^pYo4dP ze|GkIRX!LWKC-Gce)dz2_e(44tLN`(+UXNThbuSeVMjI9{4Q-1|GWgPzmIC`{yh`) z`m>t)Q`e--zGt?RuV_k*o<68D3x_1uJbx9!x_|I`Uh^*ags z>zaL=`WJE^+B*I%yEN5z9o|%zBwR1E1~m1*kWin_3F94*FkWH8ctaD`>xP8uXEw=h zo$ug-o9Z1B^u-DK+vKM0Z%wGr>V$k+&2HNMvV?k{k)Ycr?5DRAu7^pHrtyX*=*5RL z_4iN6=l6tqADWQ=oP_#yjy8>VMMC>7Jg;nB&n^k$>Gzhl_A{L(df>H9^^OU8?KMsP z(-Zvd6ZY4u-I}(qNzg+Q^o#^OI$?j#?cOxr(${PsxjS9ERaACsW}NXY+| zgz*k3Z`%K}nN4+ag8#OJ@p~ueyaat%!hBaH%%^!m`xy!OFXs7t>-jI)y{UdF;re|e z;rh)<@aHD@KS_xHRYLqD6ZX@my_$}HTtYo_67)R@^T|rkFY&y!_53Ol*6aNQe`!Me z7ZT179w(Z_OG~K##DwubN~r&=51aPi*uSZ6o{(QgLO#6{&WAAx{Wm7er+b3GRYJVM z3H|{I@xMyQKRu!SX$kZHC82%)g!au6#vhpA|2852K?&`*O<0f33HACRVf?g&{@M#$ z?}tkhu9s!6HTCP?P1w5qFA4b_myrMa3HvvaJGEfy_>x(X+}zyp<@vc~lS_+dBF2s? z>t9w-I<>GUzpP+}3_a5)6_n*pE6Xp+Eh#OYSU9C1cS3$ye%v=qX0HL>!ktVO(KZZhp~(-0{Ul(+VdPl;)Ne#Pi4sL5KDk zS6n=;EIn;zX?{sT`S^m8vcaRu(t8)rC@7scrFdp;L1}4mX;!~~saMYl#Z&VOi~g-X z=~Id)6^{Scy9^(d+k0w$+2r)T1vB!eOwTVXEdF!A#4JO>()&;?MwnJsT97|=-1Lcq z#}gwxef;G7(%iDr{KB$ngU4rOW%Ze^`MNCs(6z8=(tp{NaQ*YAOer3pUshZikJW#A z5vATLUT>{!$UALDeraK@axWM>VMbPZUoF&`1*OHg`4dhnE-Wf@3V-j?v**C+WyKQ; zCr%tymOuXVQRFhEpscuP@bFP-x&7nev>~!2)B7+lHr6<{P&}ustgunM+^lV{(&f=bu+(2-Pz^SC@I%xU4uA z@YJakdf2%1tgN1Cx#2X)ol;OVscf<@pTE{v2Zh?qC>YO{j2{>OM*p6@rxs5rn36lb zVAkp5rk0LtG(Ydy||>Hl*4XX{KPBCFD)y~pE7Om z$f3jgWED;-DC%`=ehC*%@7%t}=9iTgmJgmVtgt9OYgnQFFOC1R$coE&!%?)GP%t>S z(>NxwvIytv?(bJ|)~I80CrlWXm7CRP-1NdJ6S5{`jhQlX=;&No;#cH9%=tf`%s-Ak za_9s;qx$^68-7%O&Hw+y1WKl`I@vQ+@_$n9kwZuI&8503GDH8zRZj0UW%{`Iy-|09 zzn`dEt!!aoJdI%tsAP`H*Rt||Y5GrkJcUsi|1UEb8NZ!zgPvYm6hG4j42+;$4in4Zs7TsUkT7Y?T&rHKdY z!<92(SL8p5SU9R*?th%)gnt%s!v7F4y739X}H#mMH z;`S8Z2SvqY+&ei{!tE!$=fKj^{8=aPq|JS5Ft?FjxxOX!`d4pBH2f#GB+jL*8CvNq z9ZI^~CS>J~Ix=@imaobo@w02js6jduLaQ|KsFHAq{lg~x`{wwUXF0(+^xi6m3C;i) z#pKS*|KevS{9ldTJ5;!&cv_*hDi>cU!2gxO`~A7RMSor@|98jl8OEQeH^Be9Q}4-Z zlTNrl>z@1nV-9Iys3`^0rVaK@xXB{N_fg^%@3-VWQ>T{|l=q!p#GA_Le)I~@)xp#I zAMAP$(bG0B!+KBruU---PMJP!vIG8$@6LV3Pbn@c`0x8PeRKX-dTjlk#^L%u4Dsi^ z<)6JZH|_jyJp8AHhkv~tG%4}F`o`UV+T`MyQ}c^vjsLSaVTu0wilZ;zim*`sQSjc= zW)=Ot=U*?UNA>(GTH==+Tf3U9$JUOfFV9d`b`8cm-&+l{u+n0wtBJt%l!A@)6@ALgySRr0{xetod0^O-pZ(#SRDVSy=(n$ zT37b`G3WDMk&`&d$I^PUv*{^?_HxhKU%b-9lt5l5NejJy{r!y|wq!}RoTO=KVF87) z9;1;o8qG_zDWUW7Y{*i1*2ybWZR2vSpuJzN6sT9qtI=A>#hNJgEQIvFo!8KmDyM4Q zYQ(hGM_$OB2n}2iS_2ZHL-%aCS`g)+;#zFi4`(djd(@@i#2S#r(*+kq)6x3&jsz?H zV@V~cf9mGyHlYZ~zX|20M_-ti0=3NjUf_v)ho)fBYm^v)R)s+YaVxcJdJTg?sPf?q zLe;2R0C{)eTs*HL6~S@NKGI3H9ZhGf#Sa&*M9K>bMjewLaF%IM7A^0#>GDg;X^?;5 zICuGRF@S>mCcRycCiz!m|C_@MQ8%h~uD>q&=|z}6TdNawnpSGwhZiqn-6cU-xvO|^ zau=?ml)g&C5OUV=;NvbUmb8DaONU*K)#;w%iWqaR`6wF<_X*YQ%hN*JqO0wJ|s z)DNi#lv$ig89MYVl{@zOzLhR$GYQ_%u=Z}X%6$f%MvBC$%SM$vJx(ic4W1suDwTVb zBGD2(!YokLFJ`=CmntK2h_=i%13|m6S?q>fdB6u~Qcd*%n~L;-x`oqv`WGT(Gy7s` z9@}$n*GN07(R2+12aG?vU(e@FQ=5&6K;?S~F3&lXg zg?uIG=+e)r0#*yIEh$Hb3y-QsK~HAg9?n#u2EwDN5+ppT!W;=p8Z^Fk{K7zfl}{AH z-h&o`ed*Sq*J&X5Dck~uOIf@MuaaO^)bL~FuHriC%NH7b)Lv246wN{qa@O$R<1Qs?12un5loSfrHz|A_muunh`V~LFth1l1 zH|Y%i1Ng!JO5dG-czbdV$5eRS3{%s^?2qvZ<;Y!jj-M%(Mhd^wEiRBFYG!%M2%O%K zpCtW*+-bQHfqWKnjivg6-c2aqG9_UGg*mto6$qlQM8j)PP;nM@pr!yGSxi$al&ot?Oxm%3M9ky;9 zt>k5lgB^AplfvIDg!XTV_NVmM+0)VfC^&=8w##9|o?{|ZkN(1)yU9vDR?o0|tYD^x zXSA{59o^ULP;Gn4L062|)ho8=Bll|YH8_vfjLPIYv(^BTFKPyW76aVlwKJfS54#z=m#rA@ABv=Bak^L+kun9)M-ua>jvc$#fu z{*MQYCqj47HO`kf+#y(uw&+ec0^>_ZeVHup5anQeQu;m7QbKO9{@-F`t+cycE@vA? z+D+agjL)0lb^3*V$)lF@c^axVD99#|u;{E^{FsX)*BB#Y(EnSaS!g%-KYH`HgXjwJ zKL|l{k}t1`V4XB+FYZFyh}v2kBwD?LI|;0?lO5ah;?$jFi{(MFDq2+cvYqtQG5Jp} zCge-X(UXlqZIVKGha=`_JsOh_>IQn;3|9fSdLEwqd|?Y!`BEAyU?G-55iVve;V_(6 z($w%qMNJt`vR9JyQ$)29$t2~aO6fNp1@VLF7<;J57wT$_m@#W<7Y-hmp z8#nyh1vm0gSe#YKa07!!5mx_rxtYmZB>kl8BYyJxBYukiZKezS8*!2J13ClgRo%Uu zA#~%z%{LrSZ$}%xW4hea+8^JOV5l2-?I*L^rfOq_%(f_s6CQ5kuiw)z@Sfq9SJ8)D zm4*JOG8{4fxqTaE-_tW(jKK*ZCocjtd zS;Xy;i+|l@G@ziE-Uv-%%IC~8^k&A=-X_bwFL9Rt1k=fVOF48hT_@i-`eJhOukeY5 zG-Z6#Jmhxh9}d&R^EzMfk0CMc`3)8dw#qg+OfP*Ev71b_0RX3=yg+EN2-6*Sp{t#q7e9R?mbSS zd^lanKNlWphQ%+Ybd!YR8rxUT=RigN~sd1$aHSBPp9)0*NH+YT2Ht(>d=S^c6fU?;vk`n(;S6q zP`ci&!qmTy!kMrky&<24JDh1N92glPiJ%=GTOYhzM5RK&8}J(q@vH=&pfbd%N9~fP zzAzB#J?$p=%0p|;8&^p0)D!~xS2K8|C-XVDyoTAiC$`uuwVQ*9Cb-KUjc{{F0!1&T z=Peu~v`?lI+x(^*nvE~q%QuS$bFUj4Lx$@?Da8S~=V-DsB|X5I6sO7$=H`aaN)t5OPlaT$ z_iRTN)<+Oy5n{o8&l54WVB1?o8nXfUM8Nm<7_@W?_Sqp*eGc*aqFb4P{-M7@n?Sbqq;uh356>+aeqtj;>^)Z|kY z@<_!2ZGdhS(#k`a1h@||k)S)3{cFp7bFA0j9 z7}vEe$l#9-F0n=^#fPy-_$(EAY8e;9R_kn>ej&EFCAruukHI0;K8oSADh=2&XHd~; zv1wQ-SeC&jQfn3*=e z$5pKbUAzp28lID>0`cUOQVJo&Q2Re{WoCkuHXt9qm3H2cvTo!bJvITVrgZlun*e<( zUln=$JB;R~-(r!tuYz5&l!!b^y~&b~oJjXqnaBNF4g)0yT}J+SdkNGa|9o5nY7jjz zm22&hrX5%JVc>{ro4w5MBDkXnL4vdx(1S>oj=GN6R}kzzoA0$PzDKgeseUHwu30v( z$^5+LFbo3W3;y3#Y(i>`$fr$b6LM^VtCJq6E`J*C!K7aiDhca$A9$K5lGb%w@`_51 z+Iiu7b=-apSMTGFIFEFHj1iMGnM?7#nVlv~YF!ZCd*v6aNRn71fMMz(HTZN&E)tPG7e*%z z4TfmDvWls`2&kZ67?Zm4^#hm4X#;PNo6(*_%FU=P)-VE9R&f!?tboIX5-3wfVij00 zUalU1xNck}#&4VWTz|qKPN0)zl(hT>Q)=+K=)oA5Wca)A4gB-Uku_j56|08`!G>VxDb7(j;b#sGyp00lG z*p;Yv{aKHAanPB@aeWn12#E~&QueVhjUxIQj#z$<7+Rw&VXO!UTJur9Gq6<_hz1Sl zC|cE9`}SYofZl(8{twiNuMJn)?UaZ>?x9INMGn%Wj-y)U{n_nkJ|7tbdJ$^9&_%{x z69rr=U@)1!_9gJDNZ?un{a3#PUK0hJE5LCMJhjSoFBbj`k$2MFvGblG>Q4C#kJ8vD z$1_CULHArXePD*@^9-?flKvOMzmobT!TO19JfRGyp#HPRzl!?bt+ik66y5aNwtxFo z*iRoB`~BOm!v4Fpat=)Uz1y$Ce!6$-)m&}=_8r*&dN$?24fhfT=9(|kr_HTh?hGA< z&%v|s0tlV47cn%~oDEd`h_3RgP$*e`T6OU}u(#DaV}wq_bH&z0+1KfP7dn%_sUsBW zu9f3pHWfSa<#=v}uASfXL4GZ6hJn#2IJvB-aMiTJuwex)MI4LrWMUWa&*53I!sf6jK2<|L&S6E8&$SI{G3I@i_huRM}+rX|L=$LxX%0DFN~dk zc$X%ZSHtrU7gjyi{`c_W!>9B!9JPk-?*kGu7OA5u8(bvsQX06Ti?bA~`Wk!Mj;UGMr&_VX2c#6A#D@!Cl42Ii5lo_l&XAlX zr1v}gAMuQr$bp3TH#7awQC~X{SIq(Pc8pdM56TrgS|e)ggkp)v!A(4eaDscON^(TO z)$|H)(!*1++x7B+`+o7nO_ZZlM4cr(>Wp)0b-KVSq&7^zBzAZWMf~s@`593<(o4}= ziNeU?_~`ISR7;^rX&)wrGg=KEuCn=QRT`nItPcl6z^NvMqo@x9N74F4QV!}+*ob}U zpLln@`UT^zSC6MAgNCALRjJ3$s#1@b5g-zZnxj%Za*j&%=oL9d^0i(!wN{mS)T}BB zF(b?bj~yc*jM7|yFRrJ<34a0^`D5_LF&x(+3&49FjFJ+oZicuwh_qeu;p0wd4rLU) zvU4cYHN*wF9bX@ zj$foGe%zI?OV5XyyIp#IL~7%j^paJ!E>!1ONSOYCVY(@7_WC)!Il!<%?@*TeO2BbZF=SL;GOF4rC&2Q-frJ@|4 zbZT}%@Tk+b3xcOAmVA#_aFQ0?aGCWo6i=_hg{p(}!e1YS3BQzR+0hGxQWWedfA_1vXlJDr(!5gc3yBWqs{I}{E^&_aAYesfBX1pN&Y==22juTwOQWEeG!UI0SzpVq4uFhtTD2HPzoai!VP6udY5^ zr2jtoPx|TPTCP5)0)_NvbdPZasO5{+p((1b=2WcIS2L|KLO;*nKu^c(?W&R8Na8Oi zL>h1o(4u1NzcYh1%nXL)-;{$t!7896^gIC=Ph?rd%~vD`;~W%>P~>N^PDC_nrM<~l zK$D1a!K(9)$_GhEKY_+A-qS^cyq^$5m$xZwH#d|Nc*b7?A(gk(?Wly`8Z#)J8Rydl zOU|eC2Tziy|B#Zsol^2;_g8t)obZF@0(lFNNFK^3JbS{;YLM&i)!SI~Vh1ulU>e0V zmCyQ%tD$e2_9Fy_^P3QAMdaD7Ui*r8F@)f1=7StzjQv<6O^GlS!&)v5_kh=q;(hYT z_UrP=0+D2Tn=RCeo%om$w9Gd*75wF|gYXxfNJ^3kpwVN$2z3vmd^n|&YD)ci5K4s| zN#5xUI(ftuM;vPvXV0MS?0KXSJI|gMJz@wDco{5o%f6I3{`E%u3XTow2PTv9!>{^G zS34nN6@PCmfp~j?*II{2eU&5-LirhTW~;oYzULm%NI;FXQ&tG;;V13mo+9D9R(RM+ z%7T2SI%PQ_iCUC}806)YmN$jJaZ@7j*arR`%;F~ zS0j3wxvB67?U)aP!yg@9Bpb@I&g1hp~xpqa1_)V zFwSQ?h7cC{Y?1df!vmw-fQ2++d*+_T&W-gw&E+GQjeA@`g8qYVa_~UBtYB+J0eqUx zY7W99`_OCEnk|E}((?|U8^gDXn}GMM7%Z|M zDZixb(k$|m_>Q3PAw{jyrAR7vtj!$hr==9`$-qjsQ?Zw&qGTXtdhgweD^AFq8o3Tu z0QVo2*Qjg`%WqUBuf>jGd-*uv-OLMfIzfMB)Yc70`!29GveFROZ+#$zXCVVMVdm0}cB z(|Q1S^;2~X8!o*DOK(^`3eE>>xQT;m_cq&7)bVJZE=KbVW+OSaSelV9@aB&FWF=ts z+%WxCF(w5gS2$Aq7=H12YJggwFq=&0E|j8lI48ukTg+Vahg%y(Q>gxgvmAGr?{1gN z*+xBe(#|MJ#t43Oc;pzVSdbQLOt-^fmSsr-)AuE!Gw=s?@b|a`USi69`DXja?5wv( zXAezB#$3Hg3gQXk9~m;(Y}4zmARNe+!YvuUX@mHv#UKCVYeAbE)n;2hfJ2FpX)dTq z(Ek7}QgY(Mwnzzy58EOo@jXb3csUwR+fh@*FpH-O#Z%HaJfK}UO7>U2B;KYsBFt&m zRS~O|Hkoc#qwV->)wp)ckIE74r7-^B_kP%_9RJ&FK1as4(fT&4!uOc-08=#|TNO8R z?$1Z#os1qWCvE_tof0YLStg=*JKB6tH{-9_@ln>sR3?xl zZxTvZ{0F|2hY~SwX?(Ftx2W_#N0dbW7%yj&KWt0}eG9ps4Pq`>Y?tfWpvVDZ9Y?vg z0~XmZ2AFSJ?9vglVGJ-&3y#uJi%K7Vs%*RmJ!`6%1J-jCbHQS}T-OFg4j6O6I(FT* z4T@YaN3p2qnOP4+))!|&?E@V}5?$68*IP&NGD~Lz8`if$%mM2;iaB6W>?r4eMP0jG#|A|X zSldy|0b_u9wvwZC6nRVIOPP)lvtbOdr`Tjq)NTD*)R*o@=G*xBKGK(Neenmh{yfxq zl2g>9{7omwWix`*7k_pH0m|iV!v;32Z-bZv*0YO6u>;n%VI3P3Ibh5IYdfksU<@!H ztFc>Pk+(FylxY?*zEX~^6O!FFFj29-48lT&^XW`JUve)rO!^o^zNXOx%04`DqMl!Y zkn|6Y&NN$W*AK~!UOU`*@{s