Getting Started With Pyparsing
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Getting Started with Pyparsing
Author: Paul McGuire
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2007-10-05
Need to extract data from a text file or aweb page? Or do you want to make yourapplication more flexible with user-definedcommands or search strings? Doregular expressions and lex/yacc makeyour eyes blur and your brain hurt? Pyparsing could be the solution. Pyparsingis a pure-Python class library thatmakes it easy to build recursive-descentparsers quickly. There is no need tohandcraft your own parsing state machine.With pyparsing, you can quicklycreate HTML page scrapers, logfile dataextractors, or complex data structure orcommand processors. This Short Cutshows you how!
Python in a Nutshell
Author: Alex Martelli
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2023-01-09
Python was recently ranked as today's most popular programming language on the TIOBE index, thanks to its broad applicability to design and prototyping to testing, deployment, and maintenance. With this updated fourth edition, you'll learn how to get the most out of Python, whether you're a professional programmer or someone who needs this language to solve problems in a particular field. Carefully curated by recognized experts in Python, this new edition focuses on version 3.10, bringing this seminal work on the Python language fully up to date on five version releases, including preview coverage of upcoming 3.11 features. This handy guide will help you: Learn how Python represents data and program as objects Understand the value and uses of type annotations Examine which language features appeared in which recent versions Discover how to use modern Python idiomatically Learn ways to structure Python projects appropriately Understand how to debug Python code
Zentropy
This book compiles selected publications authored or co-authored by the editor to present a comprehensive understanding of following topics: (1) density functional theory and CALPHAD modeling; (2) computational tools; and (3) applications of computational thermodynamics. It is noted that while entropy at one scale is well represented by standard statistical mechanics in terms of probability of individual configurations at that scale, the theory capable of counting total entropy of a system from different scales is lacking. The zentropy theory provides a nested form for configurational entropy enabling multiscale modeling to account for disorder and fluctuations from the electronic scale based on quantum mechanics to the experimental scale based on statistical mechanics using free energies of individual configurations rather than their total energies in standard statistical mechanics. The predictions from the zentropy theory demonstrate remarkable agreements with experimental observations for magnetic transitions and associated emergent behaviors of strongly correlated metals and oxides, including singularity and instability at critical points and positive and negative thermal expansions, without the need of additional truncated models and fitting model parameters beyond density function theory. This paves the way to provide the predicted phase equilibrium data for high throughput predictive CALPHAD modeling of complex material systems, and those individual configurations may thus be considered as the genomic building blocks of individual phases in the spirit of Materials Genome®.