Slides from my talk at the Stockholm Python User Group's meetup on Best Practices on October 31st, 2013: http://www.meetup.com/pysthlm/events/145658462/
This describes writing command-line processing for Perl scripts. Don't re-invent the wheel - this problem has been solved. Use Getopt::Long or a similar module from CPAN.
This was presented as one of several "lightning talk" mini-presenations at the Silicon Valley Perl (SVPerl) meeting on Thursday, November 12, 2015 where the primary meeting topic was CPAN, the Comprehensive Perl Archive Network with the tag line "Your project is already half done".
Python advanced 3.the python std lib by example – application building blocksJohn(Qiang) Zhang
The document provides an overview of several Python standard library modules for common application building blocks:
1) Modules like getopt, optparse, and argparse can be used to parse command line arguments; readline, cmd, and shlex handle interactive programs and command line processing.
2) The ConfigParser module can be used to manage application configuration files, while the logging module provides APIs for writing log messages to files.
3) Other modules mentioned include atexit for registering shutdown functions and sched for scheduling tasks. Examples are given demonstrating basic usage of the various argument parsing, configuration, and logging modules.
The document provides an overview of key differences between Python and Scala. Some key points summarized:
1. Python is a dynamically typed, interpreted language while Scala is statically typed and compiles to bytecode. Scala supports both object-oriented and functional programming paradigms.
2. Scala has features like case classes, traits, and pattern matching that Python lacks. Scala also has features like type parameters, implicit conversions, and tail call optimization that Python does not support natively.
3. Common data structures like lists and maps are implemented differently between the languages - Scala uses immutable Lists while Python uses mutable lists. Scala also has features like lazy vals.
4. Control
Python advanced 1.handle error, generator, decorator and decriptor John(Qiang) Zhang
This document provides an overview of exception handling, generators, decorators, and descriptors in Python. It discusses how to handle exceptions with try-except blocks and raise user-defined exceptions. Generators are introduced as a way to create iterators using yield. Decorators allow functions to be passed as arguments to wrapper functions, modifying their behavior. Descriptors are object attributes that define binding behavior through __get__, __set__, and __delete__ methods, and can be used to implement properties.
This document provides an introduction to profiling in Python. It discusses three main profilers in the Python standard library: cProfile, profile, and hotshot. It also provides a quick example of how to profile an application using cProfile from the command line or by adding code to the application. The document concludes with a brief discussion of profiling in Cython and reviewing profile results using the pstats module.
Using Jenkins for Continuous Integration of Perl components OSD2011 Jonas Brømsø
Lightning talk presentation of Perl setup for Jenkins Continuous Integration platform. Notes and more information available at: https://logiclab.jira.com/wiki/display/OPEN/Continuous+Integration
[PyCon 2014 APAC] How to integrate python into a scala stack to build realtim...Jerry Chou
This document discusses different tools for integrating Python into a Scala stack to enable building real-time predictive models. It covers Jython, Jepp, Thrift, and REST APIs. Jepp is identified as allowing access to high-quality Python extensions with CPython speed while avoiding the overhead of separate runtimes like Jython. Thrift and REST are also presented as language-independent options that introduce communication overhead. The document concludes by detailing Fliptop's architecture that uses multiple Python servers communicating via Bottle to handle over 4,500 requests per second for predictive modeling.
This document provides an introduction to using Boost libraries. It explains how to set up a basic Boost project and includes code snippets for Boost.LexicalCast, Boost.Optional, and Boost.Any. Boost.LexicalCast allows type conversions and checks bounds. Boost.Optional explicitly marks values as optional to avoid errors. Boost.Any can store values of any type and any_cast retrieves values or returns nullptr.
This describes writing command-line processing for Perl scripts. Don't re-invent the wheel - this problem has been solved. Use Getopt::Long or a similar module from CPAN.
This was presented as one of several "lightning talk" mini-presenations at the Silicon Valley Perl (SVPerl) meeting on Thursday, November 12, 2015 where the primary meeting topic was CPAN, the Comprehensive Perl Archive Network with the tag line "Your project is already half done".
Python advanced 3.the python std lib by example – application building blocksJohn(Qiang) Zhang
The document provides an overview of several Python standard library modules for common application building blocks:
1) Modules like getopt, optparse, and argparse can be used to parse command line arguments; readline, cmd, and shlex handle interactive programs and command line processing.
2) The ConfigParser module can be used to manage application configuration files, while the logging module provides APIs for writing log messages to files.
3) Other modules mentioned include atexit for registering shutdown functions and sched for scheduling tasks. Examples are given demonstrating basic usage of the various argument parsing, configuration, and logging modules.
The document provides an overview of key differences between Python and Scala. Some key points summarized:
1. Python is a dynamically typed, interpreted language while Scala is statically typed and compiles to bytecode. Scala supports both object-oriented and functional programming paradigms.
2. Scala has features like case classes, traits, and pattern matching that Python lacks. Scala also has features like type parameters, implicit conversions, and tail call optimization that Python does not support natively.
3. Common data structures like lists and maps are implemented differently between the languages - Scala uses immutable Lists while Python uses mutable lists. Scala also has features like lazy vals.
4. Control
Python advanced 1.handle error, generator, decorator and decriptor John(Qiang) Zhang
This document provides an overview of exception handling, generators, decorators, and descriptors in Python. It discusses how to handle exceptions with try-except blocks and raise user-defined exceptions. Generators are introduced as a way to create iterators using yield. Decorators allow functions to be passed as arguments to wrapper functions, modifying their behavior. Descriptors are object attributes that define binding behavior through __get__, __set__, and __delete__ methods, and can be used to implement properties.
This document provides an introduction to profiling in Python. It discusses three main profilers in the Python standard library: cProfile, profile, and hotshot. It also provides a quick example of how to profile an application using cProfile from the command line or by adding code to the application. The document concludes with a brief discussion of profiling in Cython and reviewing profile results using the pstats module.
Using Jenkins for Continuous Integration of Perl components OSD2011 Jonas Brømsø
Lightning talk presentation of Perl setup for Jenkins Continuous Integration platform. Notes and more information available at: https://logiclab.jira.com/wiki/display/OPEN/Continuous+Integration
[PyCon 2014 APAC] How to integrate python into a scala stack to build realtim...Jerry Chou
This document discusses different tools for integrating Python into a Scala stack to enable building real-time predictive models. It covers Jython, Jepp, Thrift, and REST APIs. Jepp is identified as allowing access to high-quality Python extensions with CPython speed while avoiding the overhead of separate runtimes like Jython. Thrift and REST are also presented as language-independent options that introduce communication overhead. The document concludes by detailing Fliptop's architecture that uses multiple Python servers communicating via Bottle to handle over 4,500 requests per second for predictive modeling.
This document provides an introduction to using Boost libraries. It explains how to set up a basic Boost project and includes code snippets for Boost.LexicalCast, Boost.Optional, and Boost.Any. Boost.LexicalCast allows type conversions and checks bounds. Boost.Optional explicitly marks values as optional to avoid errors. Boost.Any can store values of any type and any_cast retrieves values or returns nullptr.
Perl::Tidy formats Perl code to make it more readable by indenting, reformatting and applying coding standards. Perl::Critic analyzes Perl code and detects violations of best practices and coding standards to help improve code quality. Both tools can be configured using options and configuration files to customize formatting styles and select which coding standards to enforce.
PyWPS 4 is a restart of the PyWPS project to implement OGC WPS standards in Python. It has been started from scratch using Python 2.7 to allow future compatibility with Python 3. It aims to provide easier process definition through upper and lower level APIs and prepare for the next version of WPS standards. Development is occurring on GitHub with contributors including Jachym Cepicky, Alex Morega, and Jorge de Jesus.
Python advanced 3.the python std lib by example –data structuresJohn(Qiang) Zhang
This document summarizes several Python standard library modules for working with common data structures and objects. It describes the collection module which includes specialized container types like Counter, defaultdict, deque, namedtuple, OrderedDict, heapq and bisect. It also covers the weakref, copy and pprint modules for working with weak references, copying objects, and pretty printing data structures. The collection module provides alternative container types that are often more efficient for large data sets compared to the basic list, tuple, dict and set.
This document discusses machine learning on source code. It begins by defining machine learning on source code as applying machine learning where the input data is source code. It then discusses some of the challenges of applying machine learning to source code, including data retrieval and analysis. Finally, it provides examples of potential use cases like predicting the next token in code, learning to represent programs with graphs, and building tools to assist with code reviews.
Header files contain function declarations and macro definitions that can be included in C programs using the #include preprocessor directive. They allow code and declarations to be reused across multiple source files. There are two ways to include header files - using angle brackets to search system directories and quotes to search the local directory. The #include directive works by having the preprocessor scan the header file before compiling the rest of the source code. Header files can be included once or multiple times conditionally to avoid errors.
The presentation introduces several new features in Java 8 including lambdas, default methods, streams API, and method references. Lambdas allow for anonymous functions and simplify implementing interfaces with single methods. Default methods allow interfaces to define implementations while maintaining backwards compatibility. The streams API provides a functional-style way to process collections through filtering, mapping, matching, and reducing operations. Method references allow referring to methods without anonymous classes.
Introduction to functional programming (In Arabic)Omar Abdelhafith
Functional programming is a declarative programming paradigm where programs are built around mathematical functions and immutable data transformation (1). Key aspects include using pure functions that always return the same output for the same input and avoid side effects, immutable data that does not change state, representing everything as expressions rather than statements, and treating functions as data that can be passed into other functions or returned from them (2). These characteristics allow functional programs to be deterministic, avoid bugs from mutable state, and more easily write parallel and distributed programs (3).
The document provides guidance on how to create and distribute Perl modules. It discusses what Perl modules are, why they are useful, and how to structure, write, test, and package a module for distribution. Key steps include using the h2xs tool to generate module scaffolding, writing the module code in the .pm file with best practices like strict and warnings, testing the module, and creating a compressed archive for distribution.
An Overview Of Python With Functional ProgrammingAdam Getchell
This document provides an overview of the Python programming language and its capabilities for functional programming. It describes Python's attributes such as being portable, object-oriented, and supporting procedural, object-oriented, and functional programming. It also lists several popular Python modules that provide additional functionality and examples of code written in both a procedural and object-oriented style in Python. Finally, it provides examples of functional programming concepts like map, filter and reduce implemented in Python along with references for further information.
Inspirations for this presentation were drawn from a couple of sprints in one of our internal projects in which we had the freedom of choosing our own technical solutions.
We go through premature optimisation, silver-bullet antipattern, duplication and null-pointer hell.
In the end other antipatterns are swiftly mentioned.
This talk discusses abstract syntax trees, parsing, domain specific languages, interpreters, and code generation. It provides examples of turtle and Small Basic languages and their corresponding abstract syntax trees. It also discusses different approaches to language design and lists resources for learning more about functional programming languages like F# through books, websites, and Koans exercises.
Lua is a lightweight scripting language that supports object-oriented, functional and data-driven programming. It is embedded into applications and has a simple syntax and learning curve. Lua compiles to bytecode and integrates well with C/C++. It uses garbage collection and supports features like modules, coroutines, metatables and standard libraries.
Python 3.0 introduces many backwards incompatible changes to the Python syntax and features to remove previous design mistakes and improve the language. Some key changes include print becoming a function, the introduction of bytes as a separate type from str, and the addition of the nonlocal keyword to allow modifying variables in nested functions. The document provides examples of how to port Python 2 code to Python 3 using the 2to3 refactoring tool.
The document discusses open source programming languages and the Dynamic Language Runtime (DLR), noting that the DLR allows hosting "eval" of any language in any language and makes it possible to write a programming language without building a compiler. It also promotes several open source projects related to dynamic languages like IronPython, IronRuby, and the DLR itself. Examples are given of embedding dynamic languages in applications and using REPL environments.
This document provides an overview of Bonobo, a Python framework for writing ETL (extract, transform, load) jobs. It discusses the history and concepts of ETL, existing ETL tools, and how Bonobo provides a Pythonic approach using code as configuration. Key features of Bonobo highlighted include writing transformations as Python functions or classes, using services and plugins, and its focus on being lightweight, extensible, and easy to install.
This document is a summary of Part 1 of a Python Masterclass on functions taught by PythonWhiz. It discusses what functions are and how they are useful for reusability and avoiding code duplication. It explains how to define functions using the def keyword and return values from functions using the return keyword. It also covers adding parameters to functions, scoping rules, default parameters, keyword arguments, and documenting functions. The goal is to explain the basics of functions in Python.
Gore is a Go REPL that uses "go run" to evaluate code. It reads user input, generates a .go file appending the input to a main function, fixes errors using go-quickfix, and runs the code. This avoids reimplementing the Go compiler. Features include code completion using gocode, and commands like :import, :print, and :doc.
This document discusses different options for parsing command line arguments in Python scripts, including raw argv parsing, getopt, argparse, and docopt. It notes that raw argv parsing and getopt are old-style parsing methods, while argparse is built into Python but may be complex. Docopt is introduced as a module that focuses on usage documentation rather than code, allowing the usage to be defined in a script's docstring. Examples are provided for argparse and more complex usages with docopt.
A tour of Python: slides from presentation given in 2012.
[Some slides are not properly rendered in SlideShare: the original is still available at http://www.aleksa.org/2015/04/python-presentation_7.html.]
Perl::Tidy formats Perl code to make it more readable by indenting, reformatting and applying coding standards. Perl::Critic analyzes Perl code and detects violations of best practices and coding standards to help improve code quality. Both tools can be configured using options and configuration files to customize formatting styles and select which coding standards to enforce.
PyWPS 4 is a restart of the PyWPS project to implement OGC WPS standards in Python. It has been started from scratch using Python 2.7 to allow future compatibility with Python 3. It aims to provide easier process definition through upper and lower level APIs and prepare for the next version of WPS standards. Development is occurring on GitHub with contributors including Jachym Cepicky, Alex Morega, and Jorge de Jesus.
Python advanced 3.the python std lib by example –data structuresJohn(Qiang) Zhang
This document summarizes several Python standard library modules for working with common data structures and objects. It describes the collection module which includes specialized container types like Counter, defaultdict, deque, namedtuple, OrderedDict, heapq and bisect. It also covers the weakref, copy and pprint modules for working with weak references, copying objects, and pretty printing data structures. The collection module provides alternative container types that are often more efficient for large data sets compared to the basic list, tuple, dict and set.
This document discusses machine learning on source code. It begins by defining machine learning on source code as applying machine learning where the input data is source code. It then discusses some of the challenges of applying machine learning to source code, including data retrieval and analysis. Finally, it provides examples of potential use cases like predicting the next token in code, learning to represent programs with graphs, and building tools to assist with code reviews.
Header files contain function declarations and macro definitions that can be included in C programs using the #include preprocessor directive. They allow code and declarations to be reused across multiple source files. There are two ways to include header files - using angle brackets to search system directories and quotes to search the local directory. The #include directive works by having the preprocessor scan the header file before compiling the rest of the source code. Header files can be included once or multiple times conditionally to avoid errors.
The presentation introduces several new features in Java 8 including lambdas, default methods, streams API, and method references. Lambdas allow for anonymous functions and simplify implementing interfaces with single methods. Default methods allow interfaces to define implementations while maintaining backwards compatibility. The streams API provides a functional-style way to process collections through filtering, mapping, matching, and reducing operations. Method references allow referring to methods without anonymous classes.
Introduction to functional programming (In Arabic)Omar Abdelhafith
Functional programming is a declarative programming paradigm where programs are built around mathematical functions and immutable data transformation (1). Key aspects include using pure functions that always return the same output for the same input and avoid side effects, immutable data that does not change state, representing everything as expressions rather than statements, and treating functions as data that can be passed into other functions or returned from them (2). These characteristics allow functional programs to be deterministic, avoid bugs from mutable state, and more easily write parallel and distributed programs (3).
The document provides guidance on how to create and distribute Perl modules. It discusses what Perl modules are, why they are useful, and how to structure, write, test, and package a module for distribution. Key steps include using the h2xs tool to generate module scaffolding, writing the module code in the .pm file with best practices like strict and warnings, testing the module, and creating a compressed archive for distribution.
An Overview Of Python With Functional ProgrammingAdam Getchell
This document provides an overview of the Python programming language and its capabilities for functional programming. It describes Python's attributes such as being portable, object-oriented, and supporting procedural, object-oriented, and functional programming. It also lists several popular Python modules that provide additional functionality and examples of code written in both a procedural and object-oriented style in Python. Finally, it provides examples of functional programming concepts like map, filter and reduce implemented in Python along with references for further information.
Inspirations for this presentation were drawn from a couple of sprints in one of our internal projects in which we had the freedom of choosing our own technical solutions.
We go through premature optimisation, silver-bullet antipattern, duplication and null-pointer hell.
In the end other antipatterns are swiftly mentioned.
This talk discusses abstract syntax trees, parsing, domain specific languages, interpreters, and code generation. It provides examples of turtle and Small Basic languages and their corresponding abstract syntax trees. It also discusses different approaches to language design and lists resources for learning more about functional programming languages like F# through books, websites, and Koans exercises.
Lua is a lightweight scripting language that supports object-oriented, functional and data-driven programming. It is embedded into applications and has a simple syntax and learning curve. Lua compiles to bytecode and integrates well with C/C++. It uses garbage collection and supports features like modules, coroutines, metatables and standard libraries.
Python 3.0 introduces many backwards incompatible changes to the Python syntax and features to remove previous design mistakes and improve the language. Some key changes include print becoming a function, the introduction of bytes as a separate type from str, and the addition of the nonlocal keyword to allow modifying variables in nested functions. The document provides examples of how to port Python 2 code to Python 3 using the 2to3 refactoring tool.
The document discusses open source programming languages and the Dynamic Language Runtime (DLR), noting that the DLR allows hosting "eval" of any language in any language and makes it possible to write a programming language without building a compiler. It also promotes several open source projects related to dynamic languages like IronPython, IronRuby, and the DLR itself. Examples are given of embedding dynamic languages in applications and using REPL environments.
This document provides an overview of Bonobo, a Python framework for writing ETL (extract, transform, load) jobs. It discusses the history and concepts of ETL, existing ETL tools, and how Bonobo provides a Pythonic approach using code as configuration. Key features of Bonobo highlighted include writing transformations as Python functions or classes, using services and plugins, and its focus on being lightweight, extensible, and easy to install.
This document is a summary of Part 1 of a Python Masterclass on functions taught by PythonWhiz. It discusses what functions are and how they are useful for reusability and avoiding code duplication. It explains how to define functions using the def keyword and return values from functions using the return keyword. It also covers adding parameters to functions, scoping rules, default parameters, keyword arguments, and documenting functions. The goal is to explain the basics of functions in Python.
Gore is a Go REPL that uses "go run" to evaluate code. It reads user input, generates a .go file appending the input to a main function, fixes errors using go-quickfix, and runs the code. This avoids reimplementing the Go compiler. Features include code completion using gocode, and commands like :import, :print, and :doc.
This document discusses different options for parsing command line arguments in Python scripts, including raw argv parsing, getopt, argparse, and docopt. It notes that raw argv parsing and getopt are old-style parsing methods, while argparse is built into Python but may be complex. Docopt is introduced as a module that focuses on usage documentation rather than code, allowing the usage to be defined in a script's docstring. Examples are provided for argparse and more complex usages with docopt.
A tour of Python: slides from presentation given in 2012.
[Some slides are not properly rendered in SlideShare: the original is still available at http://www.aleksa.org/2015/04/python-presentation_7.html.]
This document summarizes a presentation on command line arguments in Perl. It discusses different types of arguments, criteria for handling them, and several popular Perl modules for parsing arguments:
- Do It Yourself: Manually parsing arguments by iterating over @ARGV and pattern matching
- -s Option: Using Perl's built-in -s option for basic argument handling by assigning variables
- Getopt::Long: Popular module that provides argument checking and allows specifying argument types, but has extensive documentation
- Getopt::Declare: More fully-featured module that generates usage statements from a specification, supports argument checking and parsing, but also has extensive documentation and a steeper learning curve.
The document provides an overview of using the Perl command line and debugger. It discusses common options for the command line like -c to check syntax, -w to enable warnings, and -e to run code. Examples are given for one-liners using -p and -n. For the debugger, common options like l to list lines, n to single step, and p to print expressions are covered. The document demonstrates using these with examples like examining variables and setting breakpoints.
Computations involving geographic coordinates are commonplace today with maps and location-based services. There are many modules on CPAN which can help Perl programmers to do these computations more easily. Examples are provided where Perl was used to generate the search grid for a missing rocket payload on a large dry lakebed in Nevada, and for mapping progress of a high-altitude balloon over Central California and the teams who were chasing it.
This document discusses the usage of command-line arguments in Java programs. Command-line arguments allow information to be passed into a program when it runs. In Java, command-line arguments are stored as strings in the string array passed to the main() method. An example program is provided that loops through the arguments and prints each one. Users are advised to convert arguments to the desired format using wrapper classes if needed. The key differences between command-line arguments in C and Java are also outlined.
The document discusses various structural concepts and designs by Arno Pronk, including inflatable structures, tensile structures, glass structures, and tensegrity structures. It provides details on Pronk's pavilion design for NIMK with Michiel Kluiters, which used inflatable modular elements. It also describes Pronk's work with adjustable mouldings, structural optimization techniques, and his proposal to build a pavilion for the 2015 IASS conference using innovative structural concepts.
Composites are made of fibers set in resin to give strength and shape. Composites have advantages of being light, requiring low maintenance, and allowing double curved forming and functional integration. NPSP Composites' vision is for bio-based composites to become the future low-carbon material. They produce complete façades and other large, light composite structures using production techniques that allow for visual options and elegant, bended designs. Their mission is to make sustainable composites common.
1. The Concept House project aimed to develop a sustainable house through industrialized construction methods. It was led by professors at Delft University of Technology with support from building companies and suppliers.
2. The project brought together various stakeholders including universities, building companies, and local governments. It sought to gain knowledge, conduct research, and stimulate industry through the development of innovative construction techniques.
3. The completed Concept House prototype in Rotterdam demonstrated an industrialized construction approach using prefabricated materials. However, the assembly process revealed integration challenges between the designs of different partners.
This document summarizes the design and operations of a large data center facility. Key aspects include ambient air cooling that saves over 11 million gallons of water per year, water-side economizers that save an additional 2 million gallons per year, and generators that provide backup power within 6 seconds of an outage. The facility has a highly resilient fiber network with connectivity to other major hubs. The pod configuration and cabling pathways were designed for flexibility, manageability, and to accommodate growth over time. Extensive policies and training modules help achieve consistent operations.
This document provides a checklist for evaluating art lessons to avoid an approach focused solely on making art projects. The checklist includes 6 questions teachers should consider: 1) Does the lesson provide opportunities for creative thinking and developing ideas? 2) What art concepts are being taught or practiced? 3) Are there opportunities for imaginative interpretation and self-expression? 4) Does the lesson make cultural connections to the wider world of art? 5) How will completing this lesson help children develop their art literacy skills? 6) Is this lesson part of a broader unit of inquiry?
The document discusses the increasing use of wood in tall buildings and structures. It provides examples of wood buildings and structures over 50 meters high, including a 100-meter tall wind turbine tower in Germany. It then outlines benefits of wood construction such as reduced environmental impact compared to other materials like steel and concrete, as well as faster construction speeds for multi-story wood buildings. The document concludes by discussing future possibilities for wood-concrete composite skyscrapers over 40 stories tall and their environmental and construction benefits.
GOLD - IEEE GOLD Volunteer Information Evening Nov 2013IEEE SCV YP
On Nov 4, 2013 the IEEE SCV Graduates of the Last Decade held their Volunteer Information Evening at LinkedIn which brings in a number of organizations from the local San Francisco Bay Area to talk about their organizations. Each organization is given 5 minutes to present, and after the event they set up a booth in which the attendees can gather more information on, and volunteer with their organizations. These are the slides for IEEE SCV GOLD.
Python is a versatile, object-oriented programming language that can be used for web development, data analysis, and more. It has a simple syntax and is easy to read and learn. Key features include being interpreted, dynamically typed, supporting functional and object-oriented programming. Common data types include numbers, strings, lists, dictionaries, tuples, and files. Functions and classes can be defined to organize and reuse code. Regular expressions provide powerful string manipulation. Python has a large standard library and is used widely in areas like GUIs, web scripting, AI, and scientific computing.
This document summarizes Lecture 1 of Real World Haskell. It introduces functional programming and Haskell, discusses the Haskell Platform and interactive interpreter ghci. It demonstrates defining simple functions and expressions, writing small interactive programs, and using recursion to number lines of text. Resources for learning more about Haskell are provided.
Plunging Into Perl While Avoiding the Deep End (mostly)Roy Zimmer
This document provides an introduction to the Perl programming language. It discusses Perl nomenclature, attributes, variables, scopes, file input/output, string manipulation, regular expressions, and the DBI module for connecting to databases from Perl scripts. Examples are provided for common Perl programming tasks like reading files, splitting strings, formatting output, and executing SQL queries.
This document provides a tutorial on using the argparse module in Python to parse command line arguments. It begins with simple examples of defining positional and optional arguments, and progresses to combining the two and adding more advanced features like different argument types and actions. The goal is to introduce argparse concepts gradually through examples, building up an understanding of how to define, accept, and handle different types of arguments from the command line.
This document provides an overview of Python for bioinformatics. It discusses what Python is, why it is useful for bioinformatics, and how to get started with Python. It covers installing Python on the Athena system, using IDEs like Eclipse and PyDev, code sharing with Git and GitHub, basic Python concepts like strings, control structures, and data types like lists and dictionaries. It also provides examples of bioinformatics tasks that can be done in Python like calculating Pi using random numbers.
Euro python2011 High Performance PythonIan Ozsvald
I ran this as a 4 hour tutorial at EuroPython 2011 to teach High Performance Python coding.
Techniques covered include bottleneck analysis by profiling, bytecode analysis, converting to C using Cython and ShedSkin, use of the numerical numpy library and numexpr, multi-core and multi-machine parallelisation and using CUDA GPUs.
Write-up with 49 page PDF report: http://ianozsvald.com/2011/06/29/high-performance-python-tutorial-v0-1-from-my-4-hour-tutorial-at-europython-2011/
This document discusses using Python for scientific computing. It begins by listing popular programming languages for scientific purposes, including Fortran, MATLAB, Scilab, GNU Octave, Mathematica, and Python. While MATLAB is currently the most popular, it is proprietary software. Python is introduced as a free and open source alternative with many scientific libraries like NumPy, SciPy, scikit-learn, and Matplotlib. These libraries allow Python to perform similarly to MATLAB. Instructions are provided for installing the necessary Python packages on Linux, Unix, and Windows systems. Examples demonstrate basic Python syntax and how to perform tasks like importing data, visualization, and machine learning classification.
Hiveminder - Everything but the Secret SauceJesse Vincent
Ten tools and techniques to help you:
Find bugs faster バグの検出をもっと素早く
Build web apps ウェブアプリの構築
Ship software ソフトのリリース
Get input from users ユーザからの入力を受けつける
Own the Inbox 受信箱を用意する
今日の話
Boost.Python allows extending C++ code with Python by exposing C++ functions, classes, and objects to Python. It provides a simpler approach than other tools by using only C++. Boost.Python handles interfacing C++ and Python types and memory management. The document discusses exposing C++ code like functions, classes with inheritance and special methods, constants, and enums. It also covers passing objects between C++ and Python like lists and custom types. While Boost.Python enables seamless integration, there are still challenges around complex C++ features and performance. The document ends by demonstrating embedding Python in C++.
Perl is a programming language which can be used for a large variety of tasks. A typical simple use of Perl would be for extracting information from a text file and printing out a report or for converting a text file into another form. But Perl provides a large number of tools for quite complicated problems, including systems programming.
Programs written in Perl are called Perl scripts, whereas the term the perl program refers to the system program named perl for executing Perl scripts.
If you have used shell scripts or awk or sed or similar (Unix) utilities for various purposes, you will find that you can normally use Perl for those and many other purposes, and the code tends to be more compact. And if you haven't used such utilities but have started thinking you might have need for them, then perhaps what you really need to learn is Perl instead of all kinds of futilities.
Perl is implemented as an interpreted (not compiled) language. Thus, the execution of a Perl script tends to use more CPU time than a corresponding C program, for instance. On the other hand, computers tend to get faster and faster, and writing something in Perl instead of C tends to save your time.
Why Perl ?
Perl is still used in today's world as like
data mining / statistical analysis - the perl regex engine, even if slightly outdated, (PCRE, a spinned off library, tops it up in any possibile way and it's the default PHP engine) is still good for simple analysis
UNIX system administration - Perl shall always be installed on UNIX.
You can count on it being readily available even on Mac OS X.
network prototyping - many core network experts learned Perl when it was all the rage; and they still do their proofs-of-concept with it.
security - many security experts, too, need fast prototyping. (and fast automated fixes) Perl can, and does, cover for that.
Benefits of Perl Certification-
The Perl Programming certificate series comprises four courses that take students from a complete beginner to an advanced level that includes use of reusable code and creation of objects for network-based applications.
Upon completion of this certificate program, you will be able to:
Create Perl scripts that use variables, conditionals, interpolation, arrays, lists, hashes, subroutines, loops, formatted printing, data mapping, and sorting.
Implement Perl scripts that work with external files.
Utilize regular expressions, exception handling, multiprocessing, objects, and complex data structures.
Develop applications that perform complex text processing, web page scraping and form handling, and database interaction.
Navigate third-party code in the Comprehensive Perl Archive Network (CPAN) and use it in your own applications.
Contact us today to know more about Perl.
www.vibranttechnologies.co.in
The Ring programming language version 1.5.3 book - Part 39 of 184Mahmoud Samir Fayed
The Natural Library allows defining natural languages in Ring with just a few lines of code. It provides classes to (1) define a natural language, (2) set commands and operators, and (3) run natural code from files or strings. Commands are defined as classes specifying syntax and functionality. The library parses input and calls command classes. This allows quickly building domain-specific languages for tasks like data entry through natural language.
This document contains notes from a Python class covering functions, lists, strings, and their methods. It discusses built-in functions like len(), range(), and type conversions. It also covers control flow structures like if/else, for loops, exceptions, modules, and functions in more detail including defining functions, parameters, arguments, returning values, docstring, and variable scopes. Assignments include writing functions to process lists and check for palindromes in strings.
The WiMAX (IEEE 802.16e) standard offers peak data rates of 128Mbps downlink and
56Mbps uplink over 20MHz wide channels whilst the new standard in development, 4G
WiMAN-Advanced (802.16m) is targeting the requirements to be fully 4G using 64Q QAM,
BPSK and MIMO technologies to reach the 1Gbps rate. It is predicted that in an actual
deployment, using 4X2 MIMO in an urban microcell application using a 20 MHz TDD
channel, the 4G WiMAN-Advanced system will be able to support 120Mbps downlink and
60Mbps uplink per site concurrently. WiMAX applications are already in use in many countries
globally but research in 2010 gave results that showed only just over 350 set ups were actually
in use. Many previous WiMAX operators were found to have moved to LTE along with Yota,
who were the largest WiMAX operator in the world.
Sometimes you see code that is perfectly OK according to the definition of the language, but which is flawed because it breaks too many established idioms and conventions. On the other hand, a solid piece of code is something that looks like it is written by an experienced person who cares about professionalism in programming.
A presentation at Norwegian Developer Conference 2010
The document discusses concurrency models and patterns in programming languages. It describes how features like first-class functions allow some patterns to be invisible in languages. Common patterns like threading and actors are discussed, along with implementations using Communicating Sequential Processes and the actor model in different languages. The goal is to irritate the reader by discussing these concepts.
The document summarizes key points from a presentation titled "Effective Python Programming - OSCON 2005". It discusses Python fundamentals like namespaces, duck typing and exceptions. It also covers structured programming techniques in Python like iterators, generators, for/else loops and try/finally blocks. The presentation emphasizes writing effective code that makes use of Python features and follows best practices.
Python Workshop - Learn Python the Hard WayUtkarsh Sengar
This document provides an introduction to learning Python. It discusses prerequisites for Python, basic Python concepts like variables, data types, operators, conditionals and loops. It also covers functions, files, classes and exceptions handling in Python. The document demonstrates these concepts through examples and exercises learners to practice char frequency counting and Caesar cipher encoding/decoding in Python. It encourages learners to practice more to master the language and provides additional learning resources.
An introduction to the OpenMP parallel programming model.
From the Scalable Computing Support Center at Duke University (http://wiki.duke.edu/display/scsc)
Similar to Command line arguments that make you smile (20)
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
17. $ python prog.py -h
usage: prog.py [-h] [--sum] N [N ...]
!
Process some integers.
!
positional arguments:
N
an integer for the accumulator
!
optional arguments:
-h, --help show this help message and exit
--sum
sum the integers (default:
find the max)
28. $ python prog.py -h
usage: prog.py [-h] [--sum] N [N ...]
!
Process some integers.
!
positional arguments:
N
an integer for the accumulator
!
optional arguments:
-h, --help show this help message and exit
--sum
sum the integers (default:
find the max)
30. """usage: prog.py [-h] [--sum] N [N ...]
!
Process some integers.
!
positional arguments:
N
an integer for the accumulator
!
optional arguments:
-h, --help show this help message and exit
--sum
sum the integers (default:
find the max)
"""
31. """usage: prog.py [-h] [--sum] N [N ...]
!
Process some integers.
!
positional arguments:
N
an integer for the accumulator
!
optional arguments:
-h, --help show this help message and exit
--sum
sum the integers (default:
find the max)
"""
import docopt
32. """usage: prog.py [-h] [--sum] N [N ...]
!
Process some integers.
!
positional arguments:
N
an integer for the accumulator
!
optional arguments:
-h, --help show this help message and exit
--sum
sum the integers (default:
find the max)
"""
import docopt
!
args = docopt.docopt(__doc__)
35. $ python prog.py --sum 1 2 3
{'--help': False,
'--sum': True,
'N': ['1', '2', '3']}
!
$ python prog.py --help
usage: prog.py [-h] [--sum] N [N ...]
!
Process some integers.
!
positional arguments:
N
an integer for the accumulator
!
optional arguments:
-h, --help show this help message and exit
--sum
sum the integers (default:
find the max)