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mhjensen a4a41ce5d4 hints on project 2
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<a class="navbar-brand" href="Regression-bs.html">Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis</a>
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<!-- navigation toc: --> <li><a href="._Regression-bs001.html#___sec0" style="font-size: 80%;">Regression analysis, overarching aims</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs002.html#___sec1" style="font-size: 80%;">Regression analysis, overarching aims II</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs003.html#___sec2" style="font-size: 80%;">General linear models</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs004.html#___sec3" style="font-size: 80%;">Rewriting the fitting procedure as a linear algebra problem</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs005.html#___sec4" style="font-size: 80%;">Rewriting the fitting procedure as a linear algebra problem, follows</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs006.html#___sec5" style="font-size: 80%;">Generalizing the fitting procedure as a linear algebra problem</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs007.html#___sec6" style="font-size: 80%;">Generalizing the fitting procedure as a linear algebra problem</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs008.html#___sec7" style="font-size: 80%;">Optimizing our parameters</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs009.html#___sec8" style="font-size: 80%;">Optimizing our parameters, more details</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs010.html#___sec9" style="font-size: 80%;">Interpretations and optimizing our parameters</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs011.html#___sec10" style="font-size: 80%;">Interpretations and optimizing our parameters</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs012.html#___sec11" style="font-size: 80%;">Interpretations and optimizing our parameters</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs013.html#___sec12" style="font-size: 80%;">The \( \chi^2 \) function</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs014.html#___sec13" style="font-size: 80%;">The \( \chi^2 \) function</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs015.html#___sec14" style="font-size: 80%;">The \( \chi^2 \) function</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs016.html#___sec15" style="font-size: 80%;">The \( \chi^2 \) function</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs017.html#___sec16" style="font-size: 80%;">The \( \chi^2 \) function</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs018.html#___sec17" style="font-size: 80%;">The \( \chi^2 \) function</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs019.html#___sec18" style="font-size: 80%;">Simple regression model</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs020.html#___sec19" style="font-size: 80%;">Simple regression model, now using <b>scikit-learn</b></a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs021.html#___sec20" style="font-size: 80%;">Simple linear regression model using <b>scikit-learn</b></a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs022.html#___sec21" style="font-size: 80%;">Simple linear regression model</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs023.html#___sec22" style="font-size: 80%;">Less noise</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs024.html#___sec23" style="font-size: 80%;">How to study our fits</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs025.html#___sec24" style="font-size: 80%;">Minimizing the cost function</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs026.html#___sec25" style="font-size: 80%;">Relative error</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs027.html#___sec26" style="font-size: 80%;">The richness of <b>scikit-learn</b></a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs028.html#___sec27" style="font-size: 80%;">Functions in <b>scikit-learn</b></a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs029.html#___sec28" style="font-size: 80%;">Other functions in <b>scikit-learn</b></a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs030.html#___sec29" style="font-size: 80%;">The mean absolute error and other functions in <b>scikit-learn</b></a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs031.html#___sec30" style="font-size: 80%;">Cubic polynomial in <b>scikit-learn</b></a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs032.html#___sec31" style="font-size: 80%;">Polynomial Regression</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs033.html#___sec32" style="font-size: 80%;">Linking the regression analysis with a statistical interpretation</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs034.html#___sec33" style="font-size: 80%;">Expectation value and variance</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs035.html#___sec34" style="font-size: 80%;">The singular value decompostion</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs036.html#___sec35" style="font-size: 80%;">From standard regression to Ridge regressions</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs037.html#___sec36" style="font-size: 80%;">Fixing the singularity</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs038.html#___sec37" style="font-size: 80%;">Fitting vs. predicting when data is in the model class</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs039.html#___sec38" style="font-size: 80%;">Fitting versus predicting when data is not in the model class</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs040.html#___sec39" style="font-size: 80%;">An example code without the model assessment part</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs041.html#___sec40" style="font-size: 80%;">Generating test data</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs042.html#___sec41" style="font-size: 80%;">How can we effectively evaluate the various models?</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs043.html#___sec42" style="font-size: 80%;">Code examples for Ridge and Lasso Regression</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs044.html#___sec43" style="font-size: 80%;">A second-order polynomial with Ridge and Lasso</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs045.html#___sec44" style="font-size: 80%;">Resampling methods</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs046.html#___sec45" style="font-size: 80%;">Resampling approaches can be computationally expensive</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs047.html#___sec46" style="font-size: 80%;">Why resampling methods ?</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs048.html#___sec47" style="font-size: 80%;">Statistical analysis</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs049.html#___sec48" style="font-size: 80%;">Statistics</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs050.html#___sec49" style="font-size: 80%;">Statistics, moments</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs051.html#___sec50" style="font-size: 80%;">Statistics, central moments</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs052.html#___sec51" style="font-size: 80%;">Statistics, covariance</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs053.html#___sec52" style="font-size: 80%;">Statistics, more covariance</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs054.html#___sec53" style="font-size: 80%;">Statistics, independent variables</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs055.html#___sec54" style="font-size: 80%;">Statistics, more variance</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs056.html#___sec55" style="font-size: 80%;">Statistics and stochastic processes</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs057.html#___sec56" style="font-size: 80%;">Statistics and sample variables</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs058.html#___sec57" style="font-size: 80%;">Statistics, sample variance and covariance</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs059.html#___sec58" style="font-size: 80%;">Statistics, law of large numbers</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs060.html#___sec59" style="font-size: 80%;">Statistics, more on sample error</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs061.html#___sec60" style="font-size: 80%;">Statistics</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs062.html#___sec61" style="font-size: 80%;">Statistics, central limit theorem</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs063.html#___sec62" style="font-size: 80%;">Statistics, more technicalities</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs064.html#___sec63" style="font-size: 80%;">Statistics</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs065.html#___sec64" style="font-size: 80%;">Statistics and sample variance</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs066.html#___sec65" style="font-size: 80%;">Statistics, uncorrelated results</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs067.html#___sec66" style="font-size: 80%;">Statistics, computations</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs068.html#___sec67" style="font-size: 80%;">Statistics, more on computations of errors</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs069.html#___sec68" style="font-size: 80%;">Statistics, wrapping up 1</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs070.html#___sec69" style="font-size: 80%;">Statistics, final expression</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs071.html#___sec70" style="font-size: 80%;">Statistics, effective number of correlations</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs072.html#___sec71" style="font-size: 80%;">Log-likelihood</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs073.html#___sec72" style="font-size: 80%;">Cross-validation</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs074.html#___sec73" style="font-size: 80%;">Computationally expensive</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs075.html#___sec74" style="font-size: 80%;">Various steps in cross-validation</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs076.html#___sec75" style="font-size: 80%;">How to set up the cross-validation for Ridge and/or Lasso</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs077.html#___sec76" style="font-size: 80%;">Predicted Residual Error Sum of Squares</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs078.html#___sec77" style="font-size: 80%;">Resampling methods: Jackknife and Bootstrap</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs079.html#___sec78" style="font-size: 80%;">Resampling methods: Jackknife</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs080.html#___sec79" style="font-size: 80%;">Resampling methods: Jackknife estimator</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs081.html#___sec80" style="font-size: 80%;">Jackknife code example</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs082.html#___sec81" style="font-size: 80%;">Resampling methods: Bootstrap</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs083.html#___sec82" style="font-size: 80%;">Resampling methods: Bootstrap background</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs084.html#___sec83" style="font-size: 80%;">Resampling methods: More Bootstrap background</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs085.html#___sec84" style="font-size: 80%;">Resampling methods: Bootstrap approach</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs086.html#___sec85" style="font-size: 80%;">Resampling methods: Bootstrap steps</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs087.html#___sec86" style="font-size: 80%;">Code example for the Bootstrap method</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs088.html#___sec87" style="font-size: 80%;">Resampling methods: Blocking</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs089.html#___sec88" style="font-size: 80%;">Blocking Transformations</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs090.html#___sec89" style="font-size: 80%;">Blocking Transformations</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs091.html#___sec90" style="font-size: 80%;">Blocking Transformations, getting there</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs092.html#___sec91" style="font-size: 80%;">Blocking Transformations, final expressions</a></li>
<!-- navigation toc: --> <li><a href="#___sec92" style="font-size: 80%;">"Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs094.html#___sec93" style="font-size: 80%;">The bias-variance tradeoff</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs095.html#___sec94" style="font-size: 80%;">Training and testing data</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs096.html#___sec95" style="font-size: 80%;">Procedure to find a predictor</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs097.html#___sec96" style="font-size: 80%;">What we want</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs098.html#___sec97" style="font-size: 80%;">The expected generalization error</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs099.html#___sec98" style="font-size: 80%;">Elaborating a little bit more</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs100.html#___sec99" style="font-size: 80%;">The bias</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs101.html#___sec100" style="font-size: 80%;">The variance</a></li>
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<!-- navigation toc: --> <li><a href="._Regression-bs103.html#___sec102" style="font-size: 80%;">The one-dimensional Ising model, project 2</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs104.html#___sec103" style="font-size: 80%;">Example: The one-dimensional Ising model</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs105.html#___sec104" style="font-size: 80%;">Reformulating the problem to suit regression</a></li>
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<!-- navigation toc: --> <li><a href="._Regression-bs109.html#___sec108" style="font-size: 80%;">Fitting with scikit-learn</a></li>
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<!-- navigation toc: --> <li><a href="._Regression-bs112.html#___sec111" style="font-size: 80%;">Performance of the different models</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs113.html#___sec112" style="font-size: 80%;">Performance as function of the regularization parameter</a></li>
<!-- navigation toc: --> <li><a href="._Regression-bs114.html#___sec113" style="font-size: 80%;">Finding the optimal value of \( \lambda \)</a></li>
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<h2 id="___sec92" class="anchor"><a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts" target="_self">Code examples for Blocking, Jackknife and bootstrap</a> </h2>
<p>
<!-- code=python (!bc pycod) typeset with pygments style "default" -->
<div class="highlight" style="background: #f8f8f8"><pre style="line-height: 125%"><span></span><span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sys</span> <span style="color: #008000; font-weight: bold">import</span> argv
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">os</span> <span style="color: #008000; font-weight: bold">import</span> mkdir, path
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">time</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">numpy</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">np</span>
<span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">matplotlib.ticker</span> <span style="color: #008000; font-weight: bold">import</span> FormatStrFormatter
<span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">matplotlib.font_manager</span> <span style="color: #008000; font-weight: bold">import</span> FontProperties
<span style="color: #408080; font-style: italic"># Timing Decorator</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">timeFunction</span>(f):
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">wrap</span>(<span style="color: #666666">*</span>args):
time1 <span style="color: #666666">=</span> time<span style="color: #666666">.</span>time()
ret <span style="color: #666666">=</span> f(<span style="color: #666666">*</span>args)
time2 <span style="color: #666666">=</span> time<span style="color: #666666">.</span>time()
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&#39;</span><span style="color: #BB6688; font-weight: bold">%s</span><span style="color: #BA2121"> Function Took: </span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121"> </span><span style="color: #BB6688; font-weight: bold">%0.3f</span><span style="color: #BA2121"> s&#39;</span> <span style="color: #666666">%</span> (f<span style="color: #666666">.</span>func_name<span style="color: #666666">.</span>title(), (time2<span style="color: #666666">-</span>time1))
<span style="color: #008000; font-weight: bold">return</span> ret
<span style="color: #008000; font-weight: bold">return</span> wrap
<span style="color: #008000; font-weight: bold">class</span> <span style="color: #0000FF; font-weight: bold">dataAnalysisClass</span>:
<span style="color: #408080; font-style: italic"># General Init functions</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">__init__</span>(<span style="color: #008000">self</span>, fileName, size<span style="color: #666666">=0</span>):
<span style="color: #008000">self</span><span style="color: #666666">.</span>inputFileName <span style="color: #666666">=</span> fileName
<span style="color: #008000">self</span><span style="color: #666666">.</span>loadData(size)
<span style="color: #008000">self</span><span style="color: #666666">.</span>createOutputFolder()
<span style="color: #008000">self</span><span style="color: #666666">.</span>avg <span style="color: #666666">=</span> np<span style="color: #666666">.</span>average(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)
<span style="color: #008000">self</span><span style="color: #666666">.</span>var <span style="color: #666666">=</span> np<span style="color: #666666">.</span>var(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)
<span style="color: #008000">self</span><span style="color: #666666">.</span>std <span style="color: #666666">=</span> np<span style="color: #666666">.</span>std(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">loadData</span>(<span style="color: #008000">self</span>, size<span style="color: #666666">=0</span>):
<span style="color: #008000; font-weight: bold">if</span> size <span style="color: #666666">!=</span> <span style="color: #666666">0</span>:
<span style="color: #008000; font-weight: bold">with</span> <span style="color: #008000">open</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>inputFileName) <span style="color: #008000; font-weight: bold">as</span> inputFile:
<span style="color: #008000">self</span><span style="color: #666666">.</span>data <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(size)
<span style="color: #008000; font-weight: bold">for</span> x <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">xrange</span>(size):
<span style="color: #008000">self</span><span style="color: #666666">.</span>data[x] <span style="color: #666666">=</span> <span style="color: #008000">float</span>(<span style="color: #008000">next</span>(inputFile))
<span style="color: #008000; font-weight: bold">else</span>:
<span style="color: #008000">self</span><span style="color: #666666">.</span>data <span style="color: #666666">=</span> np<span style="color: #666666">.</span>loadtxt(<span style="color: #008000">self</span><span style="color: #666666">.</span>inputFileName)
<span style="color: #408080; font-style: italic"># Statistical Analysis with Multiple Methods</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">runAllAnalyses</span>(<span style="color: #008000">self</span>):
<span style="color: #008000; font-weight: bold">if</span> <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data) <span style="color: #666666">&lt;=</span> <span style="color: #666666">100000</span>:
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Autocorrelation...&quot;</span>
<span style="color: #008000">self</span><span style="color: #666666">.</span>autocorrelation()
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Bootstrap...&quot;</span>
<span style="color: #008000">self</span><span style="color: #666666">.</span>bootstrap()
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Jackknife...&quot;</span>
<span style="color: #008000">self</span><span style="color: #666666">.</span>jackknife()
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Blocking...&quot;</span>
<span style="color: #008000">self</span><span style="color: #666666">.</span>blocking()
<span style="color: #408080; font-style: italic"># Standard Autocorrelation</span>
<span style="color: #AA22FF">@timeFunction</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">autocorrelation</span>(<span style="color: #008000">self</span>):
<span style="color: #008000">self</span><span style="color: #666666">.</span>acf <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(<span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)<span style="color: #666666">/2</span>)
<span style="color: #008000; font-weight: bold">for</span> k <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #666666">0</span>, <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)<span style="color: #666666">/2</span>):
<span style="color: #008000">self</span><span style="color: #666666">.</span>acf[k] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>corrcoef(np<span style="color: #666666">.</span>array([<span style="color: #008000">self</span><span style="color: #666666">.</span>data[<span style="color: #666666">0</span>:<span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)<span style="color: #666666">-</span>k], \
<span style="color: #008000">self</span><span style="color: #666666">.</span>data[k:<span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)]]))[<span style="color: #666666">0</span>,<span style="color: #666666">1</span>]
<span style="color: #408080; font-style: italic"># Bootstrap</span>
<span style="color: #AA22FF">@timeFunction</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">bootstrap</span>(<span style="color: #008000">self</span>, nBoots <span style="color: #666666">=</span> <span style="color: #666666">1000</span>):
bootVec <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(nBoots)
<span style="color: #008000; font-weight: bold">for</span> k <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #666666">0</span>,nBoots):
bootVec[k] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>average(np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>choice(<span style="color: #008000">self</span><span style="color: #666666">.</span>data, <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)))
<span style="color: #008000">self</span><span style="color: #666666">.</span>bootAvg <span style="color: #666666">=</span> np<span style="color: #666666">.</span>average(bootVec)
<span style="color: #008000">self</span><span style="color: #666666">.</span>bootVar <span style="color: #666666">=</span> np<span style="color: #666666">.</span>var(bootVec)
<span style="color: #008000">self</span><span style="color: #666666">.</span>bootStd <span style="color: #666666">=</span> np<span style="color: #666666">.</span>std(bootVec)
<span style="color: #408080; font-style: italic"># Jackknife</span>
<span style="color: #AA22FF">@timeFunction</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">jackknife</span>(<span style="color: #008000">self</span>):
jackknVec <span style="color: #666666">=</span> np<span style="color: #666666">.</span>zeros(<span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data))
<span style="color: #008000; font-weight: bold">for</span> k <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #666666">0</span>,<span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)):
jackknVec[k] <span style="color: #666666">=</span> np<span style="color: #666666">.</span>average(np<span style="color: #666666">.</span>delete(<span style="color: #008000">self</span><span style="color: #666666">.</span>data, k))
<span style="color: #008000">self</span><span style="color: #666666">.</span>jackknAvg <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>avg <span style="color: #666666">-</span> (<span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data) <span style="color: #666666">-</span> <span style="color: #666666">1</span>) <span style="color: #666666">*</span> (np<span style="color: #666666">.</span>average(jackknVec) <span style="color: #666666">-</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>avg)
<span style="color: #008000">self</span><span style="color: #666666">.</span>jackknVar <span style="color: #666666">=</span> <span style="color: #008000">float</span>(<span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data) <span style="color: #666666">-</span> <span style="color: #666666">1</span>) <span style="color: #666666">*</span> np<span style="color: #666666">.</span>var(jackknVec)
<span style="color: #008000">self</span><span style="color: #666666">.</span>jackknStd <span style="color: #666666">=</span> np<span style="color: #666666">.</span>sqrt(<span style="color: #008000">self</span><span style="color: #666666">.</span>jackknVar)
<span style="color: #408080; font-style: italic"># Blocking</span>
<span style="color: #AA22FF">@timeFunction</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">blocking</span>(<span style="color: #008000">self</span>, blockSizeMax <span style="color: #666666">=</span> <span style="color: #666666">500</span>):
blockSizeMin <span style="color: #666666">=</span> <span style="color: #666666">1</span>
<span style="color: #008000">self</span><span style="color: #666666">.</span>blockSizes <span style="color: #666666">=</span> []
<span style="color: #008000">self</span><span style="color: #666666">.</span>meanVec <span style="color: #666666">=</span> []
<span style="color: #008000">self</span><span style="color: #666666">.</span>varVec <span style="color: #666666">=</span> []
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(blockSizeMin, blockSizeMax):
<span style="color: #008000; font-weight: bold">if</span>(<span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data) <span style="color: #666666">%</span> i <span style="color: #666666">!=</span> <span style="color: #666666">0</span>):
<span style="color: #008000; font-weight: bold">pass</span><span style="color: #408080; font-style: italic">#continue</span>
blockSize <span style="color: #666666">=</span> i
meanTempVec <span style="color: #666666">=</span> []
varTempVec <span style="color: #666666">=</span> []
startPoint <span style="color: #666666">=</span> <span style="color: #666666">0</span>
endPoint <span style="color: #666666">=</span> blockSize
<span style="color: #008000; font-weight: bold">while</span> endPoint <span style="color: #666666">&lt;=</span> <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data):
meanTempVec<span style="color: #666666">.</span>append(np<span style="color: #666666">.</span>average(<span style="color: #008000">self</span><span style="color: #666666">.</span>data[startPoint:endPoint]))
startPoint <span style="color: #666666">=</span> endPoint
endPoint <span style="color: #666666">+=</span> blockSize
mean, var <span style="color: #666666">=</span> np<span style="color: #666666">.</span>average(meanTempVec), np<span style="color: #666666">.</span>var(meanTempVec)<span style="color: #666666">/</span><span style="color: #008000">len</span>(meanTempVec)
<span style="color: #008000">self</span><span style="color: #666666">.</span>meanVec<span style="color: #666666">.</span>append(mean)
<span style="color: #008000">self</span><span style="color: #666666">.</span>varVec<span style="color: #666666">.</span>append(var)
<span style="color: #008000">self</span><span style="color: #666666">.</span>blockSizes<span style="color: #666666">.</span>append(blockSize)
<span style="color: #008000">self</span><span style="color: #666666">.</span>blockingAvg <span style="color: #666666">=</span> np<span style="color: #666666">.</span>average(<span style="color: #008000">self</span><span style="color: #666666">.</span>meanVec[<span style="color: #666666">-200</span>:])
<span style="color: #008000">self</span><span style="color: #666666">.</span>blockingVar <span style="color: #666666">=</span> (np<span style="color: #666666">.</span>average(<span style="color: #008000">self</span><span style="color: #666666">.</span>varVec[<span style="color: #666666">-200</span>:]))
<span style="color: #008000">self</span><span style="color: #666666">.</span>blockingStd <span style="color: #666666">=</span> np<span style="color: #666666">.</span>sqrt(<span style="color: #008000">self</span><span style="color: #666666">.</span>blockingVar)
<span style="color: #408080; font-style: italic"># Plot of Data, Autocorrelation Function and Histogram</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">plotAll</span>(<span style="color: #008000">self</span>):
<span style="color: #008000">self</span><span style="color: #666666">.</span>createOutputFolder()
<span style="color: #008000; font-weight: bold">if</span> <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data) <span style="color: #666666">&lt;=</span> <span style="color: #666666">100000</span>:
<span style="color: #008000">self</span><span style="color: #666666">.</span>plotAutocorrelation()
<span style="color: #008000">self</span><span style="color: #666666">.</span>plotData()
<span style="color: #008000">self</span><span style="color: #666666">.</span>plotHistogram()
<span style="color: #008000">self</span><span style="color: #666666">.</span>plotBlocking()
<span style="color: #408080; font-style: italic"># Create Output Plots Folder</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">createOutputFolder</span>(<span style="color: #008000">self</span>):
<span style="color: #008000">self</span><span style="color: #666666">.</span>outName <span style="color: #666666">=</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>inputFileName[:<span style="color: #666666">-4</span>]
<span style="color: #008000; font-weight: bold">if</span> <span style="color: #AA22FF; font-weight: bold">not</span> path<span style="color: #666666">.</span>exists(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName):
mkdir(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName)
<span style="color: #408080; font-style: italic"># Plot the Dataset, Mean and Std</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">plotData</span>(<span style="color: #008000">self</span>):
<span style="color: #408080; font-style: italic"># Far away plot</span>
font <span style="color: #666666">=</span> {<span style="color: #BA2121">&#39;fontname&#39;</span>:<span style="color: #BA2121">&#39;serif&#39;</span>}
plt<span style="color: #666666">.</span>plot(<span style="color: #008000">range</span>(<span style="color: #666666">0</span>, <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)), <span style="color: #008000">self</span><span style="color: #666666">.</span>data, <span style="color: #BA2121">&#39;r-&#39;</span>, linewidth<span style="color: #666666">=1</span>)
plt<span style="color: #666666">.</span>plot([<span style="color: #666666">0</span>, <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)], [<span style="color: #008000">self</span><span style="color: #666666">.</span>avg, <span style="color: #008000">self</span><span style="color: #666666">.</span>avg], <span style="color: #BA2121">&#39;b-&#39;</span>, linewidth<span style="color: #666666">=1</span>)
plt<span style="color: #666666">.</span>plot([<span style="color: #666666">0</span>, <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)], [<span style="color: #008000">self</span><span style="color: #666666">.</span>avg <span style="color: #666666">+</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>std, <span style="color: #008000">self</span><span style="color: #666666">.</span>avg <span style="color: #666666">+</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>std], <span style="color: #BA2121">&#39;g--&#39;</span>, linewidth<span style="color: #666666">=1</span>)
plt<span style="color: #666666">.</span>plot([<span style="color: #666666">0</span>, <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)], [<span style="color: #008000">self</span><span style="color: #666666">.</span>avg <span style="color: #666666">-</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>std, <span style="color: #008000">self</span><span style="color: #666666">.</span>avg <span style="color: #666666">-</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>std], <span style="color: #BA2121">&#39;g--&#39;</span>, linewidth<span style="color: #666666">=1</span>)
plt<span style="color: #666666">.</span>ylim(<span style="color: #008000">self</span><span style="color: #666666">.</span>avg <span style="color: #666666">-</span> <span style="color: #666666">5*</span><span style="color: #008000">self</span><span style="color: #666666">.</span>std, <span style="color: #008000">self</span><span style="color: #666666">.</span>avg <span style="color: #666666">+</span> <span style="color: #666666">5*</span><span style="color: #008000">self</span><span style="color: #666666">.</span>std)
plt<span style="color: #666666">.</span>gca()<span style="color: #666666">.</span>yaxis<span style="color: #666666">.</span>set_major_formatter(FormatStrFormatter(<span style="color: #BA2121">&#39;</span><span style="color: #BB6688; font-weight: bold">%.4f</span><span style="color: #BA2121">&#39;</span>))
plt<span style="color: #666666">.</span>xlim(<span style="color: #666666">0</span>, <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data))
plt<span style="color: #666666">.</span>ylabel(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName<span style="color: #666666">.</span>title() <span style="color: #666666">+</span> <span style="color: #BA2121">&#39; Monte Carlo Evolution&#39;</span>, <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&#39;MonteCarlo History&#39;</span>, <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>title(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName<span style="color: #666666">.</span>title(), <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>savefig(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;/data.eps&quot;</span>)
plt<span style="color: #666666">.</span>savefig(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;/data.png&quot;</span>)
plt<span style="color: #666666">.</span>clf()
<span style="color: #408080; font-style: italic"># Plot Histogram of Dataset and Gaussian around it</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">plotHistogram</span>(<span style="color: #008000">self</span>):
binNumber <span style="color: #666666">=</span> <span style="color: #666666">50</span>
font <span style="color: #666666">=</span> {<span style="color: #BA2121">&#39;fontname&#39;</span>:<span style="color: #BA2121">&#39;serif&#39;</span>}
count, bins, ignore <span style="color: #666666">=</span> plt<span style="color: #666666">.</span>hist(<span style="color: #008000">self</span><span style="color: #666666">.</span>data, bins<span style="color: #666666">=</span>np<span style="color: #666666">.</span>linspace(<span style="color: #008000">self</span><span style="color: #666666">.</span>avg <span style="color: #666666">-</span> <span style="color: #666666">5*</span><span style="color: #008000">self</span><span style="color: #666666">.</span>std, <span style="color: #008000">self</span><span style="color: #666666">.</span>avg <span style="color: #666666">+</span> <span style="color: #666666">5*</span><span style="color: #008000">self</span><span style="color: #666666">.</span>std, binNumber))
plt<span style="color: #666666">.</span>plot([<span style="color: #008000">self</span><span style="color: #666666">.</span>avg, <span style="color: #008000">self</span><span style="color: #666666">.</span>avg], [<span style="color: #666666">0</span>,np<span style="color: #666666">.</span>max(count)<span style="color: #666666">+10</span>], <span style="color: #BA2121">&#39;b-&#39;</span>, linewidth<span style="color: #666666">=1</span>)
plt<span style="color: #666666">.</span>ylim(<span style="color: #666666">0</span>,np<span style="color: #666666">.</span>max(count)<span style="color: #666666">+10</span>)
plt<span style="color: #666666">.</span>ylabel(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName<span style="color: #666666">.</span>title() <span style="color: #666666">+</span> <span style="color: #BA2121">&#39; Histogram&#39;</span>, <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>xlabel(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName<span style="color: #666666">.</span>title() , <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&#39;Counts&#39;</span>, <span style="color: #666666">**</span>font)
<span style="color: #408080; font-style: italic">#gaussian</span>
norm <span style="color: #666666">=</span> <span style="color: #666666">0</span>
<span style="color: #008000; font-weight: bold">for</span> i <span style="color: #AA22FF; font-weight: bold">in</span> <span style="color: #008000">range</span>(<span style="color: #666666">0</span>,<span style="color: #008000">len</span>(bins)<span style="color: #666666">-1</span>):
norm <span style="color: #666666">+=</span> (bins[i<span style="color: #666666">+1</span>]<span style="color: #666666">-</span>bins[i])<span style="color: #666666">*</span>count[i]
plt<span style="color: #666666">.</span>plot(bins, norm<span style="color: #666666">/</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>std <span style="color: #666666">*</span> np<span style="color: #666666">.</span>sqrt(<span style="color: #666666">2</span> <span style="color: #666666">*</span> np<span style="color: #666666">.</span>pi)) <span style="color: #666666">*</span> np<span style="color: #666666">.</span>exp( <span style="color: #666666">-</span> (bins <span style="color: #666666">-</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>avg)<span style="color: #666666">**2</span> <span style="color: #666666">/</span> (<span style="color: #666666">2</span> <span style="color: #666666">*</span> <span style="color: #008000">self</span><span style="color: #666666">.</span>std<span style="color: #666666">**2</span>) ), linewidth<span style="color: #666666">=1</span>, color<span style="color: #666666">=</span><span style="color: #BA2121">&#39;r&#39;</span>)
plt<span style="color: #666666">.</span>savefig(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;/hist.eps&quot;</span>)
plt<span style="color: #666666">.</span>savefig(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;/hist.png&quot;</span>)
plt<span style="color: #666666">.</span>clf()
<span style="color: #408080; font-style: italic"># Plot the Autocorrelation Function</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">plotAutocorrelation</span>(<span style="color: #008000">self</span>):
font <span style="color: #666666">=</span> {<span style="color: #BA2121">&#39;fontname&#39;</span>:<span style="color: #BA2121">&#39;serif&#39;</span>}
plt<span style="color: #666666">.</span>plot(<span style="color: #008000">range</span>(<span style="color: #666666">1</span>, <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)<span style="color: #666666">/2</span>), <span style="color: #008000">self</span><span style="color: #666666">.</span>acf[<span style="color: #666666">1</span>:], <span style="color: #BA2121">&#39;r-&#39;</span>)
plt<span style="color: #666666">.</span>ylim(<span style="color: #666666">-1</span>, <span style="color: #666666">1</span>)
plt<span style="color: #666666">.</span>xlim(<span style="color: #666666">0</span>, <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)<span style="color: #666666">/2</span>)
plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">&#39;Autocorrelation Function&#39;</span>, <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&#39;Lag&#39;</span>, <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&#39;Autocorrelation&#39;</span>, <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>savefig(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;/autocorrelation.eps&quot;</span>)
plt<span style="color: #666666">.</span>savefig(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;/autocorrelation.png&quot;</span>)
plt<span style="color: #666666">.</span>clf()
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">plotBlocking</span>(<span style="color: #008000">self</span>):
font <span style="color: #666666">=</span> {<span style="color: #BA2121">&#39;fontname&#39;</span>:<span style="color: #BA2121">&#39;serif&#39;</span>}
plt<span style="color: #666666">.</span>plot(<span style="color: #008000">self</span><span style="color: #666666">.</span>blockSizes, <span style="color: #008000">self</span><span style="color: #666666">.</span>varVec, <span style="color: #BA2121">&#39;r-&#39;</span>)
plt<span style="color: #666666">.</span>ylabel(<span style="color: #BA2121">&#39;Variance&#39;</span>, <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>xlabel(<span style="color: #BA2121">&#39;Block Size&#39;</span>, <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>title(<span style="color: #BA2121">&#39;Blocking&#39;</span>, <span style="color: #666666">**</span>font)
plt<span style="color: #666666">.</span>savefig(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;/blocking.eps&quot;</span>)
plt<span style="color: #666666">.</span>savefig(<span style="color: #008000">self</span><span style="color: #666666">.</span>outName <span style="color: #666666">+</span> <span style="color: #BA2121">&quot;/blocking.png&quot;</span>)
plt<span style="color: #666666">.</span>clf()
<span style="color: #408080; font-style: italic"># Print Stuff to the Terminal</span>
<span style="color: #008000; font-weight: bold">def</span> <span style="color: #0000FF">printOutput</span>(<span style="color: #008000">self</span>):
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">Sample Size: </span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">len</span>(<span style="color: #008000">self</span><span style="color: #666666">.</span>data)
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">=========================================</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">&quot;</span>
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Sample Average: </span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>avg
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Sample Variance:</span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>var
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Sample Std: </span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>std
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">=========================================</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">&quot;</span>
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Bootstrap Average: </span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>bootAvg
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Bootstrap Variance:</span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>bootVar
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Bootstrap Error: </span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>bootStd
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">=========================================</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">&quot;</span>
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Jackknife Average: </span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>jackknAvg
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Jackknife Variance:</span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>jackknVar
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Jackknife Error: </span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>jackknStd
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">=========================================</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">&quot;</span>
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Blocking Average: </span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>blockingAvg
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Blocking Variance:</span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>blockingVar
<span style="color: #008000; font-weight: bold">print</span> <span style="color: #BA2121">&quot;Blocking Error: </span><span style="color: #BB6622; font-weight: bold">\t</span><span style="color: #BA2121">&quot;</span>, <span style="color: #008000">self</span><span style="color: #666666">.</span>blockingStd, <span style="color: #BA2121">&quot;</span><span style="color: #BB6622; font-weight: bold">\n</span><span style="color: #BA2121">&quot;</span>
<span style="color: #408080; font-style: italic"># Initialize the class</span>
<span style="color: #008000; font-weight: bold">if</span> <span style="color: #008000">len</span>(argv) <span style="color: #666666">&gt;</span> <span style="color: #666666">2</span>:
dataAnalysis <span style="color: #666666">=</span> dataAnalysisClass(argv[<span style="color: #666666">1</span>], <span style="color: #008000">int</span>(argv[<span style="color: #666666">2</span>]))
<span style="color: #008000; font-weight: bold">else</span>:
dataAnalysis <span style="color: #666666">=</span> dataAnalysisClass(argv[<span style="color: #666666">1</span>])
<span style="color: #408080; font-style: italic"># Run Analyses</span>
dataAnalysis<span style="color: #666666">.</span>runAllAnalyses()
<span style="color: #408080; font-style: italic"># Plot the data</span>
dataAnalysis<span style="color: #666666">.</span>plotAll()
<span style="color: #408080; font-style: italic"># Print Some Output</span>
dataAnalysis<span style="color: #666666">.</span>printOutput()
</pre></div>
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