高能物理分析软件ROOT的入门使用方法
root是cern开发的数据分析软件，根据cern官网的A ROOT Guide For Beginners英文版翻译的中文文档，适合初学者了解root软件的使用723存储任意类型的 N-tuples…724处理跨文件的 n-tuple7.2.5对进阶用户:使用送择器即本处理树…547.2.6对干进阶价用户:使用 PROOF lite进行多核处理72.7关于 N-tuples的优化8 ROOT in python:::::::::::t·::::··:598. 1 PYROOT598.1. 1 More Python-less C++8.2自定义代码:从C+到 Python9结束语…64References64摘要ROOT是一个用于数据分析和I/O的软件框架:一个强大的工只,可以应对最先进的科学数据分析的典型任务。它的突出特点包括高级图形用户界面,非常适合交互式分析,C++编程语言的解释器,快速高效的原型设计和C艹+对象的持久性杋制,还用于写入大型强了对撞机实验记录的每年PB级数据(1PB=1024TB译者注)。本入门指南说明了ROOT的主要特征,这些特征与数据分析的典型问题相关:输入和绘制测量数据和分析功能的拟合。原创作者-D. Piparo-G. Quast-M,cisc译者注:本文均是 Google翻译结果,仅对代码和板式作调整,欢迎修改分享软件背景与简介欢迎来到数据分析ROOT!测量与理论模型的比较是实验物理学中的标准仟务之一。在最简单的情况下,“模型”只是提供测量数据预测的函数。通常,模垩取决于参数。这种模型可以简单地表示“电流I与电压U成比例”,并且实验者的任务包括从一组测量中确定电阻R作为第一步,需要数据的可视化。接下来,通常必须应用一些操作,例如,校正或参数转换。通常,这些操作是复杂的,并且应该提供强大的数学函数和程序库-例如,考虑应用于输入光谱的积分或峰值搜索或傅立叶变换以获得模型描述的实际测量偵。实验物理学的一个特点是影响每个测量的不可避免的不确定性,可视化工具必须包括这些。在随后的分析中,必须正确处理错误的统计性质作为最后一步,将测量值与模型进行比较,并且需要在此过程中确定自由模型参数。有关适合数据点的函数(模型)的示例,请参见图1.1。有儿种标准方法可供使用,数据分析工具应能方便地访问其中一种以上。还必须提供量化测量和模型之间一致性水平的方法。通常,要分析的数据量很大-考虑借助计算机累积的细粒度测量。因此,可用工具必须包含易于使用且有效的方法来存储和处理数据在量子力学中,模型通常仅根据许多参数预测测量的概率密度函数(“pdf),并且实验分析的目的是从观察到的频率分布中提取参数,其中观察测量。这种测量需要生成和可视化频率分布的装置,所谓的直方图和严格的统计处理,以从纯粹的统计分布中提取模型参数。预期数据的模拟是数据分析的另一个重要方面。通过重复生成“伪数据”,其以与用于真实数据的预期相同的方式进行分析,可以验证或比较分析过程。在许多情况卜,测量误差的分布并不是精确已知的,并且模拟提供了测试不同假设的景响的可能性。满足上述所有要求的强大软件框架是ROOT,这是个由日内瓦欧洲核了研究中心欧洲核研究组织协调的开源项目ROOT非常灵活,既可以在自己的应用程序中使用编程接口,也可以提供用于交互式数据分析的图腦用户界面。木文档的目的是作为初学者指南,并根据学生实验室中解决的典型问题为您自己的用例提供可扩展的示例。本指南有望为您未来科学工作中更复杂的应用奠定基础,建立在现代,最先进的数据分析上具之上本指南以教程的形式向您介绍ROOT包。根据“边做边学”的原则,这个目标将通过具体的例子来完成。也正因为这个原因,本指南无法涵盖ROOT包的所有复杂性。然而,一日您对以卜章节中介绍的概念有信心,您将能够欣赏ROOT用户指南( The Root Users guide2015)并浏览类参考(根参考指南2013)以査找所有详细信息您可能会感兴。您甚至可以查看代码本身,因为ROOT是一个免费的开源广品。与本教程并行使用这些文档!ROOT数据分析框架本身是编写的,并且在很大程度上依赖于C++编程语言:需要些关于C++的知识。如果您不了解这种语言的含义,Js可以利用有关C++的大量文献。ROOT可用于许多平台( Linux, Mac osx, Windows….),但在本指南中我们将隐含地假设您使用的是 Linux。你需要做的第一件事就是安装ROOT,不是吗?获取最新的ROOT版本非常简单。只需在此网页htp:/ root, cern.ch/ downloading-root上寻找“专业版”。您将找到针对不同体系结构的预编译版木,或者您自凵编译的ROOT源代码。只需拿起您需要的味道并按照安装说明操作即可。让我们深入了解ROOT!ROOT基础既然你凵经安装了ROOT,那么你止在运行的这个交互式 shell是什么?就像这样:ROOT带来了双重功能。它有一个宏的解释器(Cing( What is Cling”2015)),您可以从命令行运行或像应用程序一样运行。但它也是一个可以评估任意语句和表达式的交互式 shell这对于调试,快速黑客攻击和测试非常有用。我们先来看一些非常简单的例子2.1ROOT作为计算器您甚至可以使用ROOT交互式she代替计算器!使用该命令启动ROOT交互式shelroot在你的Liux机器上。提示应该很快出现:root「8让我们来看看这里显示的步骤root [0] 1+1(int)2root[1]2*(4+2)/12(doub1e)1.0000root [2] sqrt(3.( double)1.732051root[3]1>2(bool) falseroot [4] TMath: Pi()( double)3.141593root [5] TMath: Erf( 2)( double).222703不错。您可以看到,ROOT不仅可以输入C++语句,还可以输入存在于 MAth命名空间中的高级数学函数。现在让我们做一些更详尽的事情。一个众所周知的几何系列的数字小例root [6 double X=5(double)0.500000root [7] int N=30(int)30root [8] double geom series=0(doub1e)8.099root [9] for (int i=0; i
- 2020-06-28下载
- 积分：1
Lectures on Stochastic Programming-Model
这是一本关于随机规划比较全面的书！比较难，不太容易啃，但是读了之后收获很大。这是高清版的！To Julia, Benjamin, Daniel, Nalan, and Yael;to Tsonka Konstatin and Marekand to the memory of feliks, Maria, and dentcho2009/8/20pagContentsList of notationserace1 Stochastic Programming ModelsIntroduction1.2 Invento1.2.1The news vendor problem1.2.2Constraints12.3Multistage modelsMultiproduct assembl1.3.1Two-Stage Model1.3.2Chance Constrained ModeMultistage modelPortfolio selection131.4.1Static model14.2Multistage Portfolio selection14.3Decision rule211.5 Supply Chain Network Design22Exercises2 Two-Stage Problems272.1 Linear Two-Stage Problems2.1.1Basic pi272.1.2The Expected Recourse Cost for Discrete Distributions 302.1.3The Expected Recourse Cost for General Distributions.. 322.1.4Optimality Conditions垂Polyhedral Two-Stage Problems422.2.1General Properties422.2.2Expected recourse CostOptimality conditions2.3 General Two-Stage Problems82.3.1Problem Formulation, Interchangeability482.3.2Convex Two-Stage Problems2.4 Nonanticipativity2009/8/20page villContents2.4.1Scenario formulation2.4.2Dualization of Nonanticipativity Constraints2.4.3Nonanticipativity duality for general Distributions2.4.4Value of perfect infExercises3 Multistage problems3. 1 Problem Formulation633.1.1The general setting3.1The Linear case653.1.3Scenario trees3.1.4Algebraic Formulation of nonanticipativity constraints 7lDuality....763.2.1Convex multistage problems·763.2.2Optimality Conditions3.2.3Dualization of Feasibility Constraints3.2.4Dualization of nonanticipativity ConstraintsExercises4 Optimization models with Probabilistic Constraints874.1 Introduction874.2 Convexity in Probabilistic Optimization4.2Generalized Concavity of Functions and measures4.2.2Convexity of probabilistically constrained sets1064.2.3Connectedness of Probabilistically Constrained Sets... 113Separable probabilistic Constraints.1144.3Continuity and Differentiability Properties ofDistribution functions4.3.2p-Efficient Points.1154.3.3Optimality Conditions and Duality Theory1224 Optimization Problems with Nonseparable Probabilistic Constraints.. 1324.4Differentiability of Probability Functions and OptimalityConditions13344.2Approximations of Nonseparable ProbabilisticConstraints134.5 Semi-infinite Probabilistic Problems144E1505 Statistical Inference155Statistical Properties of Sample Average Approximation Estimators.. 1555.1.1Consistency of SAA estimators1575.1.2Asymptotics of the saa Optimal value1635.1.3Second order asStochastic Programs5.2 Stoch1745.2.1Consistency of solutions of the SAA GeneralizedEquatio1752009/8/20pContents5.2.2Atotics of saa generalized equations estimators 1775.3 Monte Carlo Sampling Methods180Exponential Rates of Convergence and Sample sizeEstimates in the Case of a finite Feasible se1815.3.2Sample size estimates in the General Case1855.3.3Finite Exponential Convergence1915.4 Quasi-Monte Carlo Methods1935.Variance-Reduction Techniques198Latin hmpling1985.5.2Linear Control random variables method200ng and likelihood ratio methods 205.6 Validation analysis5.6.1Estimation of the optimality g2025.6.2Statistical Testing of Optimality Conditions2075.7Constrained Probler5.7.1Monte Carlo Sampling Approach2105.7.2Validation of an Optimal solution5.8 SAA Method Applied to Multistage Stochastic Programmin205.8.1Statistical Properties of Multistage SAA Estimators22l5.8.2Complexity estimates of Multistage Programs2265.9 Stochastic Approximation Method2305.9Classical Approach5.9.2Robust sA approach..23359.3Mirror Descent sa method235.9.4Accuracy Certificates for Mirror Descent Sa Solutions.. 244Exercis6 Risk Averse Optimi2536.1 Introductio6.2 Mean-Risk models.2546.2.1Main ideas of mean -Risk analysis546.2.2Semideviation6.2.3Weighted Mean Deviations from Quantiles.2566.2.4Average value-at-Risk2576.3 Coherent risk measures2616.3.1Differentiability Properties of Risk Measures2656.3.2Examples of risk Measures..2696.3.3Law invariant risk measures and Stochastic orders2796.3.4Relation to Ambiguous Chance Constraints2856.4 Optimization of risk measures.2886.4.1Dualization of Nonanticipativity Constraints2916.4.2Examples...2956.5 Statistical Properties of Risk measures6.5.IAverage value-at-Ris6.52Absolute semideviation risk measure301Von mises statistical functionals3046.6The problem of moments306中2009/8/20page xContents6.7 Multistage Risk Averse Optimization3086.7.1Scenario tree formulation3086.7.2Conditional risk mappings3156.7.3Risk Averse multistage Stochastic Programming318Exercises3287 Background material3337.1 Optimization and Convex Analysis..334Directional Differentiability3347.1.2Elements of Convex Analysis3367.1.3Optimization and duality3397.1.4Optimality Conditions.............3467.1.5Perturbation analysis3517.1.6Epiconvergence3572 Probability3597.2.1Probability spaces and random variables7.2.2Conditional Probability and Conditional Expectation... 36372.3Measurable multifunctions and random functions3657.2.4Expectation Functions.3687.2.5Uniform Laws of Large Numbers...,,3747.2.6Law of Large Numbers for Random Sets andSubdifferentials3797.2.7Delta method7.2.8Exponential Bounds of the Large Deviations Theory3877.2.9Uniform Exponential Bounds7.3 Elements of Functional analysis3997.3Conjugate duality and differentiability.......... 4017.3.2Lattice structure4034058 Bibliographical remarks407Biibliography415Index4312009/8/20pageList of Notationsequal by definition, 333IR", n-dimensional space, 333A, transpose of matrix(vector)A, 3336I, domain of the conjugate of risk mea-C(X) space of continuous functions, 165sure p, 262CK, polar of cone C, 337Cn, the space of nonempty compact sub-C(v,R"), space of continuously differ-sets of r 379entiable mappings,176set of probability density functions,I Fr influence function. 3042L, orthogonal of (linear) space L, 41Sz, set of contact points, 3990(1), generic constant, 188b(k; a, N), cdf of binomial distribution,Op(), term, 382214S, the set of &-optimal solutions of theo, distance generating function, 236true problem, 18g(x), right-hand-side derivative, 297Va(a), Lebesgue measure of set A C RdCl(A), topological closure of set A, 334195conv(C), convex hull of set C, 337W,(U), space of Lipschitz continuousCorr(X, Y), correlation of X and Y 200functions. 166. 353CoV(X, Y, covariance of X and y, 180[a]+=max{a,0},2ga, weighted mean deviation, 256IA(, indicator function of set A, 334Sc(, support function of set C, 337n(n.f. p). space. 399A(x), set ofdist(x, A), distance from point x to set Ae multipliers vectors334348dom f, domain of function f, 333N(μ,∑), nonmal distribution,16Nc, normal cone to set C, 337dom 9, domain of multifunction 9, 365IR, set of extended real numbers. 333o(z), cdf of standard normal distribution,epif, epigraph of function f, 333IIx, metric projection onto set X, 231epiconvergence, 377convergence in distribution, 163SN, the set of optimal solutions of the0(x,h)d order tangent set 348SAA problem. 156AVOR. Average value-at-Risk. 258Sa, the set of 8-optimal solutions of thef, set of probability measures, 306SAA problem. 181ID(A, B), deviation of set A from set Bn,N, optimal value of the Saa problem,334156IDIZ], dispersion measure of random vari-N(x), sample average function, 155able 7. 2541A(, characteristic function of set A, 334吧, expectation,361int(C), interior of set C, 336TH(A, B), Hausdorff distance between setsLa」, integer part of a∈R,219A and B. 334Isc f, lower semicontinuous hull of funcN, set of positive integers, 359tion f, 3332009/8/20pageList of notationsRc, radial cone to set C, 337C, tangent cone to set C, 337V-f(r), Hessian matrix of second orderpartial derivatives, 179a. subdifferential. 338a, Clarke generalized gradient, 336as, epsilon subdifferential, 380pos w, positive hull of matrix W, 29Pr(A), probability of event A, 360ri relative interior. 337upper semideviation, 255Le, lower semideviation, 255@R. Value-at-Risk. 25Var[X], variance of X, 149, optimal value of the true problem, 1565=(51,……,5), history of the process,{a,b},186r, conjugate of function/, 338f(x, d), generalized directional deriva-g(x, h), directional derivative, 334O,(, term, 382p-efficient point, 116lid, independently identically distributed,1562009/8/20page xlllPrefaceThe main topic of this book is optimization problems involving uncertain parametersfor which stochastic models are available. Although many ways have been proposed tomodel uncertain quantities stochastic models have proved their flexibility and usefulnessin diverse areas of science. This is mainly due to solid mathematical foundations andtheoretical richness of the theory of probabilitystochastic processes, and to soundstatistical techniques of using real dataOptimization problems involving stochastic models occur in almost all areas of scienceand engineering, from telecommunication and medicine to finance This stimulates interestin rigorous ways of formulating, analyzing, and solving such problems. Due to the presenceof random parameters in the model, the theory combines concepts of the optimization theory,the theory of probability and statistics, and functional analysis. Moreover, in recent years thetheory and methods of stochastic programming have undergone major advances. all thesefactors motivated us to present in an accessible and rigorous form contemporary models andideas of stochastic programming. We hope that the book will encourage other researchersto apply stochastic programming models and to undertake further studies of this fascinatinand rapidly developing areaWe do not try to provide a comprehensive presentation of all aspects of stochasticprogramming, but we rather concentrate on theoretical foundations and recent advances inselected areas. The book is organized into seven chapters The first chapter addresses modeling issues. The basic concepts, such as recourse actions, chance(probabilistic)constraintsand the nonanticipativity principle, are introduced in the context of specific models. Thediscussion is aimed at providing motivation for the theoretical developments in the book,rather than practical recommendationsChapters 2 and 3 present detailed development of the theory of two-stage and multistage stochastic programming problems. We analyze properties of the models and developoptimality conditions and duality theory in a rather general setting. Our analysis coversgeneral distributions of uncertain parameters and provides special results for discrete distributions, which are relevant for numerical methods. Due to specific properties of two- andmultistage stochastic programming problems, we were able to derive many of these resultswithout resorting to methods of functional analvsisThe basic assumption in the modeling and technical developments is that the proba-bility distribution of the random data is not influenced by our actions(decisions). In someapplications, this assumption could be unjustified. However, dependence of probability dis-tribution on decisions typically destroys the convex structure of the optimization problemsconsidered, and our analysis exploits convexity in a significant way
- 2020-12-09下载
- 积分：1
