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"Python (Computer program language) Mathematical models."
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FreeCAD how-to
Written in cookbook style, this book offers many recipes to create objects, import and export data, create 3D solid objects. Each recipe contains step-by-step instructions followed by analysis of what was done in each task and other useful information. If you've been toying around with FreeCAD and want to have more control over your work flow then this book is for you. The reader needs to have basic knowledge of modeling.
eBook
Python for Finance
Python is a free and powerful tool which can be used to build a financial calculator and price options, and can also explain many trading strategies and test various hypotheses. In addition to that, real-world data can be used to run CAPM (Capital Asset Pricing Model), the Fama-French 3-factor model, estimate VaR (Value at Risk), and estimate spread, illiquidity, and liquidity. This book explores the basics of programming in Python. It is a step-by-step tutorial that will teach you, with the help of concise, practical programs, how to run various statistic tests. With this book, you will learn how to implement financial concepts using python libraries like NumPy, SciPy and Matplotlib. This book introduces you to the basic concepts and operations related to Python. After that, we move on to how you can use Python to build a financial calculator. After going through several key modules (packages), you will learn how to run various statistics tests such as T-tests, F-tests, and normality tests. The book will then cover how to run CAPM (Capital Asset Pricing Model) and the Fama-French 3-factor model, as well as how to form various portfolios and estimate individual stock and portfolios' VaR (Value at Risk). You will learn about various financial concepts using python libraries like NumPy, SciPy and Matplotlib, and trading strategies through illustrations and graphs. You will also learn how to retrieve the historical trading data of stock, option data, and other data from public data sources. You will then discover how you can process the data sources that you obtain to estimate returns and convert daily returns into monthly or annual returns, as well as how to estimate a portfolio's variance-covariance matrix. A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series
using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
eBook
Python for finance
Python is a free and powerful tool that can be used to build a financial calculator and price options, and can also explain many trading strategies and test various hypotheses. This book details the steps needed to retrieve time series data from differ
eBook
Computational physics
The use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming.
This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific materials as well as with the Python programming language. Python has become very popular, particularly for physics education and large scientific projects. It is probably the easiest programming language to learn for beginners, yet is also used for mainstream scientific computing, and has packages for excellent graphics and even symbolic manipulations.
The text is designed for an upper-level undergraduate or beginning graduate course and provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. As part of the teaching of using computers to solve scientific problems, the reader is encouraged to work through a sample problem stated at the beginning of each chapter or unit, which involves studying the text, writing, debugging and running programs, visualizing the results, and the expressing in words what has been done and what can be concluded. Then there are exercises and problems at the end of each chapter for the reader to work on their own (with model programs given for that purpose).
eBook
Mastering pandas for finance
This book will teach you to use Python and the Python Data Analysis Library (pandas) to solve real-world financial problems. Starting with a focus on pandas data structures, you will learn to load and manipulate time-series financial data and then calculate common financial measures, leading into more advanced derivations using fixed- and moving-windows. This leads into correlating time-series data to both index and social data to build simple trading algorithms. From there, you will learn about more complex trading algorithms and implement them using open source back-testing tools. Then, you will examine the calculation of the value of options and Value at Risk. This then leads into the modeling of portfolios and calculation of optimal portfolios based upon risk. All concepts will be demonstrated continuously through progressive examples using interactive Python and IPython Notebook. By the end of the book, you will be familiar with applying pandas to many financial problems, giving you the knowledge needed to leverage pandas in the real world of finance.
eBook
Biotite: new tools for a versatile Python bioinformatics library
Background
Biotite is a program library for sequence and structural bioinformatics written for the Python programming language. It implements widely used computational methods into a consistent and accessible package. This allows for easy combination of various data analysis, modeling and simulation methods.
Results
This article presents major functionalities introduced into Biotite since its original publication. The fields of application are shown using concrete examples. We show that the computational performance of Biotite for bioinformatics tasks is comparable to individual, special purpose software systems specifically developed for the respective single task.
Conclusions
The results show that Biotite can be used as program library to either answer specific bioinformatics questions and simultaneously allow the user to write entire, self-contained software applications with sufficient performance for general application.
Journal Article
CosinorPy: a python package for cosinor-based rhythmometry
Background
Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats.
Results
We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats.
Conclusion
CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from
https://github.com/mmoskon/CosinorPy
. CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.
Journal Article
A Mixed‐Integer Linear Programming Framework for Optimization of Water Network Operations Problems
Water distribution systems (WDSs) are critical infrastructure used to convey water from sources to consumers. The mathematical framework governing the distribution of flows and heads in extended period simulations of WDSs lends itself to application in a wide range of optimization problems. Applying the classical mixed integer linear programming (MILP) approach to model WDSs hydraulics within an optimization framework can contribute to higher solution accuracy with lower computational effort. However, adapting WDSs models to conform to a MILP formulation has proven challenging because of the intrinsic non‐linearity of system hydraulics and the complexity associated with modeling hydraulic devices that influence the state of the WDS. This paper introduces MILPNet, an adjustable framework for WDSs that can be used to build and solve an extensive array of MILP optimization problems. MILPNet includes constraints that represent the mass balance and energy conservation equations, hydraulic devices, control rules, and status checks. To conform to MILP structure, MILPNet employs piece‐wise linear approximation and integer programming. MILPNet was implemented and tested using Gurobi Python API. Modeling accuracy was shown to be comparable to EPANET, a public domain software for hydraulic modeling, and sensitivity analyses were conducted to examine the impacts of the modeling assumptions on the performance of MILPNet. Additionally, application of the framework was demonstrated using pump scheduling optimization examples in single and rolling horizon scenarios. Our results show that MILPNet can facilitate the construction and solution of optimization problems for a range of applications in WDSs operations. Key Points A mixed‐integer linear programming framework (MILPNet) for formulating and solving water distribution system optimization problems is presented MILPNet models system dynamics, hydraulic devices, control rules, and status checks and is flexible to adding more devices and conditions The optimization model can be generated from a .INP file and case‐specific objectives and constraints can be specified via Python interface
Journal Article