Python and ODM Platform Introduction

Python数据库ORM框架介绍Python是一种功能强大的编程语言，而数据库是在现代应用程序中至关重要的一部分。

在Python中使用数据库可以帮助我们有效地管理数据和进行数据操作。

然而，直接使用数据库API来进行编程可能会变得非常繁琐和复杂。

为了解决这个问题，Python开发者们开发了各种数据库ORM（对象关系映射）框架，以简化与数据库的交互。

本文将介绍几个常用的Python数据库ORM框架，并分析它们的特点和适用场景。

一、SQLAlchemySQLAlchemy是Python中最常用的ORM框架之一。

它提供了强大的数据库抽象层，使得开发者可以使用简单的Python对象来表示数据库中的表和数据。

SQLAlchemy支持多种数据库后端，包括SQLite、MySQL、PostgreSQL等，使得开发者可以轻松切换数据库。

SQLAlchemy提供了两种主要的查询语言接口：SQL表达式语言和ORM。

SQL表达式语言允许开发者以面向对象的方式构建SQL查询，并通过Python代码的方式进行数据库操作。

而ORM则提供了更高级的抽象，通过定义Python类和对象来映射数据库表和数据。

二、Django ORMDjango是一个流行的Python Web框架，它内置了一个强大的ORM 框架。

Django ORM是建立在SQLAlchemy之上的，提供了一些额外的功能和便利。

Django ORM支持多种数据库后端，并提供了一套面向对象的API，使得开发者可以更加简洁地进行数据库操作。

使用Django ORM，我们可以通过定义Django的模型类来创建数据库表，然后通过对象的方式进行数据的增删改查。

三、PeeweePeewee是一个轻量级的Python ORM框架，通过简洁的API和优雅的语法，让开发者更加容易上手。

虽然Peewee相对于SQLAlchemy和Django ORM来说功能较为简单，但它仍然提供了足够的功能来满足大部分日常的数据库操作需求。

10分钟⽤python搭建⼀个超好⽤的CMDB系统CMDB 是什么，作为 IT ⼯程师的你想必已经听说过了，或者已经烂熟了，容我再介绍⼀下，以防有读者还不知道。

CMDB 的全称是 Configuration Management Data Base，翻译下就是配置管理数据库，它存储与管理企业 IT 架构中设备的各种配置信息，它⽀撑服务流程的运转、发挥着配置信息的价值。

在今天，⽆论是⾃动化运维、标准化运维、DevOps、甚⾄是时髦的智能运维，其实都离开不CMDB，可以说 CMDB 是运维体系的基⽯，有了配置信息数据库，后⾯各种标准、流程都可以建⽴在 CMDB 基础之上，从⽽实现真正的标准化、⾃动化、智能化运维，节约运维成本的同时，也降低运维流程混乱带来的操作风险。

今天分享⼀个开源的 cmdb 系统的搭建过程，通过这⼀系列搭建的过程你不仅可以获得⼀个⽀持全⽂检索、⾃带 restful api 的 cmdb 系统，⽽且还可以学到不少时髦的技术。

后端技术：Python3DjangoDjango REST frameworkElasticsearchuwsgiNginxDocker前端技术：VueElement-uiVue-RouterVuexAxios先看⼀下这个 CMDB 系统的容颜，设计上参考了饿了么内部的 cmdb 系统:基本功能有：热添加删除表、⾃定义字段类型，⽅便增删改查的前端界⾯，强⼤的搜索查找能⼒（后端使⽤elasticsearch存储数据）可以配合 kibana 使⽤，查看数据的删除修改记录、历史版本等，还带有表级权限管理，开放所有 API。

下⾯介绍两种⽅法搭建此开源 cmdb 系统，⼀个是使⽤ Docker，适⽤于 linux 操作系统，另⼀个是不使⽤ Docker，适⽤于 windows 和 linux 。

最后介绍下 vue 环境的搭建。

1. 使⽤ Docker如果你熟悉容器技术，推荐使⽤此⽅法，不过最新的 Docker ⽬前还不⽀持⼤多数的 windows 版本，因此如果使⽤容器，请使⽤ ubuntu 或 centos 等 Linux 操作系统。

odoo简介
odoo简介
⼀、概述
odoo是基于Python语⾔开发的开源企业应⽤套件，是⼀款跨平台模块化设计的开源软件，同时⽀持C/S和B/S架构，前端采⽤HTML5，客户端可直接⽤浏览器登陆，提供了⾮常友好的操作界⾯。

⼆、重要事件时间点
2002 - openERP
2014 - odoo
三、系统特点
免费且开放源代码
丰富的功能模块
世界领先的模块化技术
⾼度可定制性
多管理客户端
友好的操作视图
强⼤的搜素过滤器
四、总体结构
1、系统架构
数据库服务器： PostgreSQL开源数据库
数据库服务器存放所有业务数据和绝⼤部分配置数据。

在odoo中，⽤户界⾯不是通过HTML或其他程序代码⽣成，⽽是直接存放在数据库中，运⾏时直接从数据库中提取数据，通过xml 渲染成客户端看到的界⾯。

应⽤服务器：所有业务逻辑代码
客户端： B/S 和 C/S
2、应⽤服务器架构
ORM ：对象关系映射
BMD ：底层模块
Report Engine ：报表引擎
Workflow Engine ：⼯作流引擎
WebService ：提供⽹络调⽤接⼝。

python 浅奥
Python由荷兰数学和计算机科学研究，作为一门叫做ABC语言的替代品。

Python浅奥提供了高效的高级数据结构，还能简单有效地面向对象编程。

Python语法和动态类型，以及解释型语言的本质，使它成为多数平台上写脚本和快速开发应用的编程语言，随着版本的不断更新和语言新功能的添加，逐渐被用于独立的、大型项目的开发。

Python浅奥，解释器易于扩展，可以使用C或C++(或者其他可以通过C调用的语言)扩展新的功能和数据类型。

Python也可用于可定制化软件中的扩展程序语言。

Python丰富的标准库，提供了适用于各个主要系统平台的源码或机器码。

语言流行指数的编译器将Python加冕为最受欢迎的编程语言，20年来首次将其置于Java、C和JavaScript之上。

由于Python浅奥，语言的简洁性、易读性以及可扩展性，在国外用Python做科学计算的研究机构日益增多，一些知名大学已经采用Python来教授程序设计课程。

众多开源的科学计算软件包都提供了Python的调用接口，例如著名的计算机视觉库OpenCV、三维可视化库VTK、医学图像处理库ITK。

而Python浅奥专用的科学计算扩展库就更多了，因此Python 语言及其众多的扩展库所构成的开发环境十分适合工程技术、科研人
员处理实验数据、制作图表，甚至开发科学计算应用程序。

python库pydantic的简易⼊门教程⽬录⼀、简介⼆、安装三、常见模型1、BaseModel 基本模型2、递归模型3、GenericModel 通⽤模型（泛型）：四、常⽤类型五、验证器六、配置七、与 mypy ⼀起使⽤总结⼀、简介pydantic 库是 python 中⽤于数据接⼝定义检查与设置管理的库。

pydantic 在运⾏时强制执⾏类型提⽰，并在数据⽆效时提供友好的错误。

它具有如下优点：与 IDE/linter 完美搭配，不需要学习新的模式，只是使⽤类型注解定义类的实例多⽤途，BaseSettings 既可以验证请求数据，也可以从环境变量中读取系统设置快速可以验证复杂结构可扩展，可以使⽤validator装饰器装饰的模型上的⽅法来扩展验证数据类集成，除了BaseModel，pydantic还提供了⼀个dataclass装饰器，它创建带有输⼊数据解析和验证的普通 Python 数据类。

⼆、安装pip install pydantic要测试 pydantic 是否已编译，请运⾏：import pydanticprint('compiled:', piled)⽀持使⽤dotenv⽂件获取配置，需要安装pip install pydantic[dotenv]三、常见模型pydantic中定义对象都是通过模型的，你可以认为模型就是类型语⾔中的类型。

1、BaseModel 基本模型from pydantic import BaseModelclass User(BaseModel):id: intname = 'Jane Doe'上⾯的例⼦，定义了⼀个User模型，继承⾃BaseModel，有2个字段，id是⼀个整数并且是必需的，name是⼀个带有默认值的字符串并且不是必需的实例化使⽤：user = User(id='123')实例化将执⾏所有解析和验证，如果有错误则会触发 ValidationError 报错。

PythonOCC（OpenCascade Community Edition）是一个基于Python的开源CAD/CAE/PLM软件开发评台，它提供了一系列功能强大的工具和库，可以帮助用户快速实现CAD（Computer-Aided Design，计算机辅助设计）、CAE（Computer-Aided Engineering，计算机辅助工程）、PLM（Product Lifecycle Management，产品生命周期管理）等相关领域的开发需求。

PythonOCC中文参考手册是PythonOCC的重要文档之一，它为用户提供了全面的PythonOCC相关信息，帮助用户更好地理解和应用PythonOCC。

一、PythonOCC简介PythonOCC是基于开源CAD内核OpenCascade的Python封装，它提供了一系列可以用于CAD/CAE/PLM软件开发的Python接口和工具。

PythonOCC的主要特点包括开源免费、功能强大、易于学习和使用等。

通过PythonOCC，用户可以利用Python语言的简单易用和OpenCascade内核的强大功能，快速开发出符合自身需求的CAD/CAE/PLM软件，为工程设计和制造领域的开发者提供了一个高效便捷的开发评台。

二、PythonOCC中文参考手册的重要性PythonOCC中文参考手册是PythonOCC的冠方文档，它包含了PythonOCC库的详细说明、使用方法、示例代码等。

PythonOCC中文参考手册的重要性主要体现在以下几个方面：1.为用户提供了全面的PythonOCC相关信息PythonOCC中文参考手册为用户提供了全面的PythonOCC相关信息，涵盖了PythonOCC库的所有内容和功能。

用户可以通过阅读PythonOCC中文参考手册，了解PythonOCC的各种功能和接口，并快速掌握如何使用PythonOCC进行CAD/CAE/PLM软件开发。

1引言Python 是一种动态的高级编程语言，具有简单性、可读性的特点和优势，其功能跨越不同领域，使其成为软件开发、数据分析和自动化领域的重要选择。

Python 广泛的标准库和第三方模块使用户能够轻松解决复杂的任务，从而营造协作且高效的编码环境。

Python 在企业财务分析中的应用价值在于其能够无缝处理复杂的财务任务，其强大的库（如用于数据操作的Pandas 和用于数值计算的NumPy ）增强了数据处理能力。

Python 有助于简化财务建模、风险管理和重复性任务的自动化，从而有助于提高效率和准确性。

很长一段时间以来，Python 已经成为专业人士在企业财务动态环境中寻求数据驱动的见解和战略决策的宝贵工具。

2Python 在企业财务分析中的应用优势2.1提升效率Python 通过简化流程和自动化日常任务，有利于提高企业财务效率。

Python 语言的简单性和可读性使财务专业人员能够编写简洁且有效的代码，从而减少开发和维护所需的时间。

Python 丰富的标准库（如Pandas 和NumPy ）为数据操作和分析提供了强大的工具，可以快速处理大型数据集。

此外，专业金融库（如QuantLib 和Pyfolio ）的可用性允许快速使用复杂的财务分析模型和进行计算。

2.2提升准确性准确性在企业财务分析中至关重要，因为在该项工作中即使是微小的失误也会产生重大的影响。

Python 强调可读性和简单性，加上强大的测试框架，支持开发准确可靠的财务分析模型。

同时，Python 语言广泛的库生态系统有助于开展精确的数值计算，最大限度地减少计算错误。

Python 与强大的统计和机器学习库（如Scikit-learn 和Statsmodels ）集成，使财务专业人士能够构建用于风险评估和预测分析的高级模型。

在Python 应用视角下，蒙特卡洛模拟和其他定量技术的使用变得更具简洁性和准确性，可以更真实地展示潜在的金融场景。

此外，Python 对单元测试和调试的支持有助于在开发过程的早期识别和纠正错误，从而提高财务分析应用程序的应用质量。

Python Optimization Modeling Objects(Pyomo) William E.HartAbstract We describe Pyomo,an open-source tool for modeling optimization appli-cations in Python.Pyomo can be used to define abstract problems,create concrete problem instances,and solve these instances with standard solvers.Pyomo provides a capability that is commonly associated with algebraic modeling languages like AMPL and GAMS.Pyomo leverages the capabilities of the Coopr software,which integrates Python packages for defining optimizers,modeling optimization applica-tions,and managing computational experiments.Key words:Python,Modeling language,Optimization,Open Source Software1IntroductionAlthough high quality optimization solvers are commonly available,the effective integration of these tools with an application model is often a challenge for many users.Optimization solvers are typically written in low-level languages like Fortran or C/C++because these languages offer the performance needed to solve large nu-merical problems.However,direct development of applications in these languages is quite challenging.Low-level languages like these can be difficult to program;they have complex syntax,enforce static typing,and require a compiler for development.There are several ways that optimization technologies can be more effectively integrated with application models.For restricted problem domains,optimizers can be directly interfaced with application modeling tools.For example,modern spread-sheets like Excel integrate optimizers that can be applied to linear programming and simple nonlinear programming problems in a natural way.Similarly,engineering design frameworks like the Dakota toolkit(Eldred et al,2006)can apply optimizers William E.HartSandia National Laboratories,Discrete Math and Complex Systems Department,PO Box5800, Albuquerque,NM87185e-mail:wehart@12William E.Hart to nonlinear programming problems by executing separate application codes via a system call interface that use standardizedfile I/O.Algebraic Modeling Languages(AMLs)are alternative approach that allows applications to be interfaced with optimizers that can exploit problem structure. AMLs are high-level programming languages for describing and solving mathemat-ical problems,particularly optimization-related problems(Kallrath,2004).AMLs like AIMMS(AIMMS,2008),AMPL(AMPL,2008;Fourer et al,2003)and GAMS(GAMS,2008)have programming languages with an intuitive mathemati-cal syntax that supports concepts like sparse sets,indices,and algebraic expressions. AMLs provide a mechanism for defining variables and generating constraints with a concise mathematical representation,which is essential for large-scale,real-world problems that involve thousands of constraints and variables.A related strategy is to use a standard programming language in conjunction with a software library that uses object-oriented design to support similar math-ematical concepts.Although these modeling libraries sacrifice some of the intu-itive mathematical syntax of an AML,they allow the user to leverage the greater flexibility of standard programming languages.For example,modeling tools like FlopC++(FLOPC++,2008),OPL(OPL,2008)and OptimJ(OptimJ,2008)enable the solution of large,complex problems with application models defined within a standard programming language.The Python Optimization Modeling Objects(Pyomo)package described in this paper represents a fourth strategy,where a high level programming language is used to formulate a problem that can be solved by optimizers written in low-level lan-guages.This two-language approach leverages theflexibility of the high-level lan-guage for formulating optimization problems and the efficiency of the low-level lan-guage for numerical computations.This approach is increasingly common in scien-tific computing tools,and the Matlab TOMLAB Optimization Environment(TOM-LAB,2008)is probably the most mature optimization software using this approach.Pyomo supports the definition and solution of optimization applications using the Python scripting language.Python is a powerful dynamic programming language that has a very clear,readable syntax and intuitive object orientation.Pyomo was strongly influenced by the design of AMPL.It includes Python classes that can concisely represent mixed-integer linear programming(MILP)models.Pyomo is interated into Coopr,a COmmon Optimization Python Repository.The Coopr Opt package supports the execution of models developed with Pyomo using standard MILP solvers.Section2describes the motivation and design philosophy behind Pyomo,includ-ing why Python was chosen for the design of Pyomo.Section3describes Pyomo and contrasts Pyomo with AMPL.Section4reviews other Python optimization pack-ages that have been developed,and discusses the high-level design decisions that distinguish Coopr.Section5describes the Coopr Opt package and contrasts its ca-pabilities with other Python optimization tools.Finally,Section6describes future Coopr developments that are planned.Python Optimization Modeling Objects(Pyomo)3 2Pyomo Motivation and Design PhilosophyThe design of Pyomo is motivated by a variety of factors that have impacted appli-cations at Sandia National Laboratories.Sandia’s discrete mathematics group has successfully used AMPL to model and solve large-scale integer programs for many years.This application experience has highlighted the value of AMLs for real-world applications,which are now an integral part of operations research solutions at San-dia.Pyomo was developed to provide an alternative platform for developing math programming models that facilitates the application and deployment of optimiza-tion capabilities.Consequently,Pyomo is not intended to perform modeling better than existing tools.Instead,it supports a different modeling approach for which the software is designed forflexibility,extensibility,portability,and maintainability. 2.1Design Goals and Requirements2.1.1Open SourceA key goal of Pyomo is to provide an open-source math programming modeling capability.Although open-source optimization solvers are widely available in pack-ages like COIN-OR,surprisingly few open-source tools have been developed to model optimization applications.An open-source capability for Pyomo is motivated by several factors:•Transparency and Reliability:When managed well,open-source projects facil-itate transparency in the software design and implementation.Since any devel-oper can study and modify the software,bugs and performance limitations can be identified and resolved by a wide range of developers with diverse software experience.Consequently,there is growing evidence that managing software as open-source can improve its reliability.•Customizable Capability:A key limitation of commercial modeling tools is the ability to customize the modeling or optimization process.An open-source project allows a diverse range of developers to prototype new capabilities.These extensions can customize the software for specific applications,and they can motivate capabilites that are integrated into future software releases.•Flexible Licensing:A variety of significant operations research applications at Sandia National Laboratories have required the use of a modeling tool with a non-commercial license.Open-source license facilitate the free distribution of Pyomo within other open-source projects.Of course,the use of an open-source model is not a panacea.Ensuring high reliabil-ity of the software requires careful software management and a commited developer community.However,flexible licensing appears to be a distinct feature of open-4William E.Hart source software.The Coopr software,which contains Pyomo,is licensed under the BSD.2.1.2Flexible Modeling LanguageAnother goal of Pyomo is to directly use a modern programming language to sup-port the definition of math programming models.In this manner,Pyomo is similar to tools like FlopC++and OptimJ,which support modeling in C++and Java respec-tively.The use of an existing programming language has several advantages:•Extensibility and Robustness:A well-used modern programming language pro-vides a robust foundation for developing and applying models,because the lan-guage has been well-tested in a wide variety of contexts.Further,extensions typi-cally do not require changes to the language but instead involve additional classes and modeling routines that can be used in the modeling process.Thus,support of the modeling language is not a long-term factor when managing the software.•Documentation:Modern programming languages are typically well-documented, and there is often a large on-line community to provide feedback to new users.•Standard Libraries:Languages like Java and Python have a rich set of libraries for tackling just about every programming task.For example,standard libraries can support capabilities like data integration(e.g.working with spreadsheets), thereby avoiding the need to directly support this in a modeling tool.An additional aspect of general-purpose programming languages is that they can support modern language features,like classes andfirst-class functions,that can be critical when defining complex models.Pyomo is implemented in Python,a powerful dynamic programming language that has a very clear,readable syntax and intuitive object orientation.When com-pared with AMLs like AMPL,Pyomo has a more verbose and complex syntax. Thus,a key issue with this approach concerns the target user community and their level of comfort with standard programming concepts.Our examples in this paper compare and contrast AMPL and Pyomo models,which illustrate this trade-off. 2.1.3PortabilityA requirement of Pyomo’s design is that it work on a diverse range of compute platforms.In particular,working well on both MS Windows and Linux platforms is a key requirement for many Sandia applications.The main impact of this require-ment has been to limit the choice of programming languages.For example, languages were not considered for the design of Pyomo due to portability consider-ations.Python Optimization Modeling Objects(Pyomo)5 2.1.4Solver IntegrationModeling tools can be roughly categorized into two classes based on how they in-tegrate with optimization solvers:tightly coupled modeling tools directly link in optimization solver libraries(including dynamic linking),and loosely coupled mod-eling tools apply external optimization executables(e.g.through system calls).Of course,these options are not exclusive,and a goal of Pyomo is to support both types of solver interfaces.This design goal has led to a distinction in Pyomo between model formulation and optimization execution.Pyomo uses a high level programming language to for-mulate a problem that can be solved by optimizers written in low-level languages. This two-language approach leverages theflexibility of the high-level language for formulating optimization problems and the efficiency of the low-level language for numerical computations.2.1.5Abstract ModelsA requirement of Pyomo’s design is that it support the definition of abstract mod-els in a manner similar to the AMPL.AMPL separates the declaration of a model from the data that generates a model instance.This is supports an extremelyflexible modeling capability,which has been leveraged extensively in applications at Sandia.To mimic this capability,Pyomo uses a symbolic representation of data,vari-ables,constraints,etc.Model instances are then generated from external data sets using construction routines that are provided by the user when defining sets,pa-rameters,etc.Further,Pyomo is designed to use data sets in the AMPL format to facilitate translation of models between AMPL and Pyomo.2.2Why Python?Pyomo has been developed in Python for a variety of reasons.First,Python meets the criteria outlined in the previous section:•Open Source License:Python is freely available,and its liberal open source license lets you modify and distribute a Python-based application with few re-strictions.•Features:Python has a rich set of datatypes,support for object oriented pro-gramming,namespaces,exceptions,and dynamic loading.•Support and Stability:Python is highly stable,and it is well supported through newsgroups and special interest groups.•Documentation:Users can learn about Python from extensive online documen-tation,and a number of excellent books that are commonly available.•Standard Library:Python includes a large number of useful modules.6William E.Hart •Extendability and Customization:Python has a simple model for loading Python code developed by a user.Additionally,compiled code packages that optimize computational kernels can be easily used.Python includes support for shared libraries and dynamic loading,so new capabilities can be dynamically integrated into Python applications.•Portability:Python is available on a wide range of compute platforms,so porta-bility is typically not a limitation for Python-based applications.Another factor,not to be overlooked,is the increasing acceptance of Python in the scientific community(Oliphant,2007).Large Python projects like SciPy(Jones et al,2001–)and SAGE(Stein,2008)strongly leverage a diverse set of Python packages.Finally,we note that several other popular programming languages were also considered for Pyomo.However,in most cases Python appears to have distinct ad-vantages:•.Net:As mentioned earlier, languages are not portable to Linux plat-forms,and thus they were not suitable for Pyomo.•Ruby:At the moment,Python and Ruby appear to be the two most widely rec-ommended scripting languages that are portable to Linux platforms,and compar-isons suggest that their core functionality is similar.Our preference for Python is largely based on the fact that it has a nice syntax that does not require users to type weird symbols(e.g.$,%,@).Thus,we expect this will be a more natural language for expressing math programming models.•Java:Java has a lot of the same strengths as Python,and it is arguably as good a choice for Pyomo.However,two aspects of Python recommended it for Pyomo instead of Java.First,Python has a powerful interactive interpreter that allows realtime code development and encourages experimentation with Python soft-ware.Thus,users can work interactively with Pyomo models to become familiar with these objects and to diagnose bugs.Second,it is widely acknowledged that Python’s dynamic typing and compact,concise syntax makes software devel-opment quick and easy.Although some very interesting optimization modeling tools have been developed in languages like C++and Java,there is anecdotal ev-idence that users will not be as productive in these languages as they will when using tools developed in languages like Python(PythonVSJava,2008).•C++:Models formulated with the FlopC++package are similar to models de-veloped with Pyomo.They are be specified in a declarative style using classes to represent model components(e.g.sets,variables and constraints).However, C++requires explicit compilation to execute code,and it does not support an interactive interpreter.Thus,we believe that Python will provide a moreflexible language for users.Python Optimization Modeling Objects(Pyomo)7 3Pyomo OverviewPyomo can be used to define abstract problems,create concrete problem instances, and solve these instances with standard solvers.Pyomo can generate problem in-stances and apply optimization solvers with a fully expressive programming lan-guage.Python’s clean syntax allows Pyomo to express mathematical concepts with a reasonably intuitive syntax.Further,Pyomo can be used within an interactive Python shell,thereby allowing a user to interactively interrogate Pyomo-based mod-els.Thus,Pyomo has many of the advantages of both AML interfaces and modeling libraries.3.1A Simple ExampleIn this section we illustrate Pyomo’s syntax and capabilities by demonstrating how a simple AMPL example can be replicated with Pyomo Python code.Consider the AMPL model,prod.mod:To translate this into Pyomo,the user mustfirst import the Pyomo module and create a Pyomo Model object:##I m p o r t Pyomo#from c o o p r.pyomo i m p o r t∗##C r e a t e model#8William E.Hart model=Model()This import assumes that Pyomo is available on the users’s Python path(see Python documentation for PYTHONPATH for further details).Next,we create the sets and parameters that correspond to the data used in the AMPL model.This can be done very intuitively using the Set and Param classes.model.P=S e t()model.a=Param(model.P)model.b=Param()model.c=Param(model.P)model.u=Param(model.P)Note that parameter b is a scalar,while parameters a,c and u are arrays indexed by the set P.Next,we define the decision variables in this model.model.X=Var(model.P)Decision variables and model parameters are used to define the objectives and con-straints in the model.Parameters define constants and the variables are the values that are optimized.Parameter values are typically defined by a datafile that is pro-cessed by Pyomo.Objectives and constraints are explicitly defined expressions in Pyomo.The Ob-jective and Constraint classes require a rule option that specifies how these ex-pressions are constructed.This is a function that takes one or more arguments:the first arguments are indices into a set that defines the set of objectives or constraints that are being defined,and the last argument is the model that is used to define the expression.Python Optimization Modeling Objects(Pyomo)9The rules used to construct these objects use standard Python functions.The Time rule function includes the use of<and>operators on the expression,which define upper and lower bounds on the constraints.The Limit rule function illus-trates another convention that is supported by Pyomo;a rule can return a tuple that defines the lower bound,body and upper bound for a constraint.The value’None’can be returned for one of the limit values if a bound is not enforced.Once an abstract model has been created,it can be printed as follows: model.p p r i n t()This summarize the information in the Pyomo model,but it does not print out ex-plicit expressions.This is due to the fact that an abstract model needs to be instanted with data to generate the model objectives and constraints:i n s t a n c e=model.c r e a t e(”prod.d a t”)i n s t a n c e.p p r i n t()Once a model instance has been constructed,an optimizer can be applied to it to find an optimal solution.For example,the PICO integer programming solver can be used within Pyomo as follows:o p t=s o l v e r s.S o l v e r F a c t o r y(”p i c o”)o p t.k e e p F i l e s=Truer e s u l t s=o p t.s o l v e(i n s t a n c e)This creates an optimizer object for the PICO executable,and it indicates that tem-poraryfiles should be kept.The Pyomo model instance is optimized,and the opti-mizer returns an object that contains the solutions generated during optimization.3.2Pyomo Commandline ScriptAppendix7provides a complete Python script for the model described in the previ-ous section.Although this Python script can be executed directly,Coopr includes a pyomo script that can construct this model,apply an optimizer and summarize the results.For example,the following command line executes Pyomo using a datafile in a format consistent with AMPL:pyomo prod.py prod.d a t10William E.Hart The pyomo script has a variety of command line options to provide information about the optimization process.Options can control how debugging information is printed,including logging information generated by the optimizer and a summary of the model generated by Pyomo.Further,Pyomo can be configured to keep all intermediatefiles used during optimization,which can support debugging of the model construction process.4Related Python Optimization ToolsA variety of related optimization packages have been developed in Python that are designed to support the formulation and solution of specific classes of structure optimization applications:•CVXOPT:A Python package for convex optimization(CVXOPT,2008).•PuLP:A Python package that can be used to describe linear programming and mixed-integer linear programming optimization problems(PuLP,2008).•POAMS:A Python modeling tool for linear and mixed-integer linear programs that defines Python objects for abstract sets,constraints,objectives,decision vari-ables,and solver interfaces.•OpenOpt:A relatively new numerical optimization framework that is closely coupled with the SciPy scientific Python package(OpenOpt,2008).•NLPy:A Python optimization framework that leverages AMPL to create prob-lem instances,which can then be processed in Python(NLPy,2008).•Pyiopt:A Python interface to the COIN-OR Ipopt solver(Pyipopt,2008). Pyomo is closely related to the modeling capabilities of PuLP and POAMS.Pyomo defines Python objects that can be used to express models,and like POAMS,Pyomo supports a clear distinction between abstract models and problem instances.The main distinguishing feature of Pyomo is support for an instance construction process that is automated by object properties.This is akin to the capabilities of AML’s like AMPL and GAMS,and it provides a standardized technique for constructing model instances.Pyomo models can be initialized with a generic data object,which can be initialized with a variety of data sources(including AMPL*.datfiles).Like NLPy and OpenOpt,the goal of Coopr Opt is to support a diverse set of optimization methods and applications.Coopr Opt includes a facility for transform-ing problem formats,which allows optimizers to solve problems without the user worrying about solver-specific implementation details.Further,Coopr Opt supports mechanisms for reporting detailed information about optimization solutions,in a manner akin to the OSrL data format supported by the COIN-OR OS project(Fourer et al,2008).In the remainder of this section we use the following example to illustrate the differences between PuLP,POAMS and Pyomo:minimize−4x1−5x2subject to2x1+x2≤3(1)x1+2x2≤3x1,x2≥04.1PuLPPuLP relies on overloading operators and commonly used mathematical functions to define expression objects that define objectives and constraints.A problem object is defined,and the objective and constraints are added using the+=operator.Further, problem variables can be defined over index sets to enable compact specification of constraints and objectives.The following PuLP example minimizes the LP(1):from p u l p i m p o r t∗x1=L p V a r i a b l e(”x1”,0)x2=L p V a r i a b l e(”x2”,0)prob=LpProblem(”Example”,LpMinimize)prob+=−4∗x1−5∗x2prob+=2∗x1+x2<=3prob+=x1+2∗x2<=3prob.s o l v e()4.2POAMSPOAMS is a Python modeling tool for linear and mixed-integer linear programs that defines Python objects for abstract sets,constraints,objectives,decision variables, and solver interfaces.These objects can be used to compose an abstract model defi-nition,which is then used to construct a concrete problem instance from a given data set.This separation of the problem instance from the data facilitates the definition of abstract models that can be populated from a diverse range of data sources.POAMS models are managed by classes derived from the POAMS LP object. The following POAMS example minimizes the LP(1)by deriving a class,instanti-ating it,and then running the model:from poams i m p o r t∗c l a s s Example(LP):i n d e x=S e t(1,2)x=Var(i n d e x)o b j=O b j e c t i v e()c1=C o n s t r a i n t()c2=C o n s t r a i n t()d e f model(s e l f):s e l f.o b j.min(−4∗s e l f.x[1]−5∗s e l f.x[2])s e l f.c1.l o a d(2∗s e l f.x[1]+s e l f.x[2]<= 3.0)s e l f.c2.l o a d(s e l f.x[1]+2∗s e l f.x[2]<= 3.0) prob=Example().model()prob.s o l v e()4.3PyomoThe following Pyomo example minimizes LP(1)by instantiating an abstract model, populating the model with symbols,generating an instance,and then applying the PICO MIP optimizer:r e s u l t s=o p t.s o l v e(i n s t a n c e)5The Coopr Opt PackageThe goal of the Coopr Opt package is to support the execution of optimizers in a generic manner.Although Pyomo uses this package,Coopr Opt is designed to support a wide range of optimizers.However,Coopr Opt is not as mature as the OpenOpt package;it currently only supports interfaces to a limited number of opti-mizers aside from the LP and MILP solvers used by Pyomo.Coopr Opt is supports a simple strategy for setting up and executing an optimizer, which is illustrated by the following script:o p t=S o l v e r F a c t o r y(name)o p t.r e s e t()r e s u l t s=o p t.s o l v e(problem)r e s u l t s.w r i t e()This script illustrates several design principles that Coopr follows:•Dynamic Registration of Optimizers:Optimizers are registered via a plugin mechanism that provides an extensible architecture for developers of third-party optimizers.This plugin mechanism includes the specification of parameters that can be initialized from a configurationfile.•Separation of Problems and Solvers:Coopr Opt treats problems and solvers as separate entities.This promotes the development of tools like Pyomo that supportflexible definition of optimization applications,and it enables automatic transformation of problem instances.•Problem Transformation:A key challenge for optimization packages is the need to support a diverse set of problem formats.This is an issue even for LP and MILP solver packages,where MPS is the least common denominator for users.Coopr Opt supports an automatic problem transformation mechanism that enables the application of optimizers to problems with a wide range of formats.•Generic Representation of Optimizer Results:Coopr Opt borrows and extends the representation used by the COIN-OR OS project to support a general repre-sentation of optimizer results.The results object returned by a Coopr optimizer includes information about the problem,the solver execution,and one or more solutions generated during optimization.If the problem in Appendix7is being solved,this script would print the following information that is contained in the results object:=====================================================−−−S o l v e r R e s u l t s−−−=====================================================It is worth noting that Coopr Opt currently does not support direct library inter-faces to optimizers,which is a feature that is strongly supported by Python.How-ever,this is not a design limitation,but instead has been a matter of development priorities.Efforts are planned with the POAMS and PuLP developers to adapt the direct solver interfaces used in these packages for use within Coopr.Although Coopr Opt development has focused on developing interfaces to LP and MILP solvers,we have recently begun developing interfaces to general-purpose nonlinear programming methods.One of the goals of this effort is to develop appli-cation interfaces that are consistent with the interfaces supported by Acro’s COLIN optimization library(ACRO,2008).COLIN has recently been extended to support a system call interface that uses standardizedfile I/O.An XML format has been developed that can be more rigorously checked than thefile format used by theDakota toolkit(Eldred et al,2006),and this format can be readily extended to new application results.Coopr Opt supports applications defined using this system call interface,which will simplify the integration of COLIN optimizers into Coopr Opt. 6DiscussionCoopr is being actively developed to support real-world applications at Sandia Na-tional Laboratories.This experience has validated our assessment that Python is an effective language for supporting the solution of optimization applications.Al-though it is clear that custom languages can support a much more mathematically intuitive syntax,Python’s clean syntax and programming model make it a natural choice for optimization tools like Coopr.Coopr will be publicly released as an open source project in2008.Future devel-opment will focus on several key design issues:•Interoperable with commonly available optimization solvers,and the relationship of Coopr and OpenOpt.•Exploiting synergy with POAMS and PuLP.Developers of Coopr,POAMS and PuLP are assessing this intersection to identify where synergistic efforts can be leveraged.For example,the direct solver interface used by POAMS and PuLP can be adapted for use in Pyomo.•Extending Pyomo to support the definition of general nonlinear models.Concep-tually,this is straightforward,but the model generation and expression mecha-nisms need to be re-designed to support capabilities like automatic differentia-tion.Acknowledgements We thank the ICS reviewers for their critical feedback.We also thank Jon Berry,Robert Carr and Cindy Phillips for their critical feedback on the design of Pyomo,and David Gay for developing the Coopr interface to AMPL NL and SOLfiles.Sandia is a multiprogram laboratory operated by Sandia Corporation,a Lockheed Martin Company,for the United States Department of Energy’s National Nuclear Security Administration under Contract DE-AC04-94-AL85000.ReferencesACRO(2008)ACRO optimization framework.http://software.sandia. gov/acroAIMMS(2008)AIMMS home page.AMPL(2008)AMPL home page./CVXOPT(2008)CVXOPT home page./ cvxopt。

第3章￿系统与软件目录1.操作系统2.脚本开发环境Python3.其他依赖脚本开发环境Python•Python 是一种面向对象的、解释型的通用计算机程序设计语言。

它以对象为核心组织代码，支持多种编程范式，采用动态类型，自动进行内存回收。

•它既具有强大的标准库，也拥有丰富的第三方扩展包。

•目前Python 已进入到3.x 的时代。

由于Python 3.x 向后不兼容，许多利用Python2.x 开发的第三方包在3.x 版本中无法使用，从2.x 到3.x 的过渡是一个漫长的过程。

•Python 在TIOBE 编程语言排行榜的名次不断上升，2020年1月的排名升至第3位。

Python•Python 的早期发展始于阿姆斯特丹一家名为CWI 的研究机构，是Guido vanRossum 在CWI 工作经验的直接产物。

•ABC 语言给了Python 的主要灵感，Amoeba 给了Guido van Rossum 直接的动力，而多媒体组促进了Python 发展。

CWI 没有任何资金被官方指定用于Python 发展，Python 只是作为一种重要的工具，在阿米巴和多媒体群体中使用。

•Guido van Rossum 对编程语言语法的品味受到了Algol 60、Pascal 、Algol 68以及ABC 等语言的强烈影响。

由于Guido van Rossum 在ABC 语言上花了4年的时间，因此，他决定设计一种语言，可以从ABC 语言借用他喜欢的一切，同时解决所有的问题。

这就是Python 语言的由来。

Python 语言的由来l Python这个名字来自于Guido van Rossum所挚爱的电视剧Monty Python’s Flying Circus。

l随着命名问题的解决，Guido van Rossum在1989年12月下旬开始了Python的工作，并在1990年的前几个月推出一个工作版本。

•在1990年，这个早期版本的Python被CWI的许多人使用，但并不仅限于Amoeba组。