This document provides an overview of regular expressions and their use. It discusses common regular expression commands like grep and egrep. It explains various regular expression metacharacters such as period, question mark, asterisk, plus, and brackets for character classes. It also covers grouping, back references, quantifiers, anchors and word boundaries. Examples are provided to illustrate how each concept works in matching text patterns.
This document provides an introduction to regular expressions (RegEx). It explains that RegEx allows you to find, match, compare or replace text patterns. It then discusses the basic building blocks of RegEx, including characters, character classes, quantifiers, and assertions. It provides several examples of RegEx patterns to match names, words, ports numbers, and other patterns. It concludes with an overview of common RegEx match types like beginning/end of line, word boundaries, grouping, alternatives, and repetition.
The document discusses regular expressions and text processing in Python. It covers various components of regular expressions like literals, escape sequences, character classes, and metacharacters. It also discusses different regular expression methods in Python like match, search, split, sub, findall, finditer, compile and groupdict. The document provides examples of using these regular expression methods to search, find, replace and extract patterns from text.
Regular expressions (regex) allow complex pattern matching in text. The document discusses regex basics like literals, character classes, quantifiers, and flags in Python. It explains how to use the re module to compile patterns into RegexObjects and search/match strings. RegexObjects provide reusable patterns while re module functions provide shortcuts but cache compiled patterns.
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This document provides an introduction to regular expressions (regex) in R. It discusses literal regex which match text exactly, and metacharacters which have special meanings like ., *, ?, etc. It also covers character classes [ ], anchors ^ and $, quantifiers like ?, *, +, {}, alternations |, and capturing groups () in regex. The document uses examples of matching file names and dates to illustrate regex patterns and their uses in text matching and replacement.
Regular expressions (regexes) allow complex pattern matching in text. They are used to find, extract, or replace substrings. Key regex concepts covered in the document include:
- Common regex syntax like literals, character classes, quantifiers, greedy/lazy matching, anchors, word boundaries and subpatterns.
- How a regex engine works by trying to match a pattern to a subject string and returning the earliest match.
- Uses of regex like searching text, extracting/removing/replacing strings, and validating formats.
- Terminology like regex, subject string, match, and engine.
101 3.7 search text files using regular expressionsAc¨¢cio Oliveira
?
The document provides information about using regular expressions and common Linux utilities like grep and sed to search text files. It covers the basic syntax and options for grep and sed, examples of how to use them to search and make substitutions in files, and an overview of common regular expression metacharacters. The key topics discussed are using grep to search for patterns in files, the s command and flags in sed for substitutions, and metacharacters in regular expressions like *, ., ^, $, [], etc. and what they are used to match in text searches.
This document provides an overview of regular expressions in Python. It defines regular expressions as sequences of characters used to search for patterns in strings. The re module allows using regular expressions in Python programs. Metacharacters like [], ., ^, $, *, + extend the matching capabilities of regular expressions beyond basic text. Examples demonstrate using re functions like search and special characters to extract lines from files based on patterns.
The document discusses regular expressions (regexes) in Python. It defines regexes as sequences of characters used to match patterns in strings. The re module provides full support for regexes. It describes various regex patterns like literals, concatenation, alternation, repetition. It also covers metacharacters like brackets, caret, backslash, dot and special sequences. Finally, it explains the search() and match() methods to perform regex queries on strings, with search() finding matches anywhere and match() only at the start.
This document provides an overview of regular expressions (regex or regexps), including what they are, common uses, and examples in different programming languages. Regular expressions are strings used to search for patterns in text. They are more powerful than wildcards and are available in many languages and programs. The document explains basic regex syntax like characters, anchors, quantifiers, character classes and grouping and provides examples of regex patterns for validating postal codes and URLs.
Don't Fear the Regex - CapitalCamp/GovDays 2014Sandy Smith
?
Have you been scared off by Klingon-looking one-liners in Perl? Do you resort to writing complicated recursive functions just to parse some HTML? Don't!
I'll demystify regular expressions and show you how best to do them in PHP. We'll cover the syntax and functions that make PHP a great text-parsing language, and give you the foundation to learn more.
As a bonus, I'll give you two cases people often use as examples for regexes that PHP gives you better native ways to accomplish.
Given at CapitalCamp & GovDays 2014
The document discusses string manipulation and regular expressions. It provides explanations of regular expression syntax including brackets, quantifiers, predefined character ranges, and flags. It also summarizes PHP functions for regular expressions like ereg(), eregi(), ereg_replace(), split(), and sql_regcase(). Practical examples of using these functions are shown.
This document discusses regular expressions (regexes) in Perl programming. It provides three examples that demonstrate extracting date values from a string using different regex techniques. It then discusses some key regex concepts like quantifiers, grouping, anchors, character classes and more. Code examples are provided to illustrate each concept. The goal is to help readers better understand how to leverage the powerful regex engine in Perl to perform complex text matching and manipulation tasks.
Oracle database supports perl- and POSIX-compatible regular expressions with five elegant and powerful functions: REGEXP_REPLACE, REGEXP_SUBSTR, REGEXP_INSTR, REGEXP_LIKE, and REGEXP_COUNT.
This session will demonstrate their nuances and how to use them effectively for data cleansing, manipulation and selection, for validating things such as Social Security Numbers, credit cards, IP addresses, phone numbers, DNAs, XMLs, for extracting things such as email-ids, hostnames from URLs and strings, and for transposing delimited columns to rows. There will be a demo of a few tricky examples taken from forums.oracle.com and asktom.oracle.com.
The session will end with fuzzy matching and optimization techniques, and things to watch out for.
http://docs.oracle.com/cd/E11882_01/appdev.112/e25518/adfns_regexp.htm
This lecture discusses the concept of Regular Expressions along with its usage in different tools such as grep, sed, and awk
Check the other Lectures and courses in
http://Linux4EnbeddedSystems.com
or Follow our Facebook Group at
- Facebook: @LinuxforEmbeddedSystems
Lecturer Profile:
- https://www.linkedin.com/in/ahmedelarabawy
Do you have data and lists you keep having to massage to make it useful for your project? Have you heard of regular expressions but been frightened by the Klingon-looking examples? Fear no longer!
I¡¯ll demystify regular expressions and show you how best to do them in PHP. We¡¯ll cover the syntax and functions that make PHP a great text-parsing language, and give you the foundation to learn more.
As a bonus, I¡¯ll give you two cases people often use as examples for regexes that PHP gives you better native ways to accomplish.
This document provides an overview of regular expressions (regex). It defines regex as patterns that define classes of strings. Regex are used by utilities like grep, sed, awk, vi and emacs to search for patterns in text. The document discusses the syntax of regex like alternation, grouping and quantification. It provides examples of regex patterns and explains how commands like grep can be used with regex to search files.
This document discusses strings in Python. It covers how strings are treated as a contiguous series of characters that can be indexed, sliced, concatenated, appended, and multiplied. It also discusses that strings are immutable in Python. Various string methods and functions are described, including formatting strings using the % operator. The document also covers regular expressions for string pattern matching and manipulation using the re module.
Don't Fear the Regex - Northeast PHP 2015Sandy Smith
?
Have you been scared off by Klingon-looking one-liners in Perl? Do you resort to writing complicated recursive functions just to parse some HTML? Don¡¯t!
I¡¯ll demystify regular expressions and show you how best to do them in PHP. We¡¯ll cover the syntax and functions that make PHP a great text-parsing language, and give you the foundation to learn more.
As a bonus, I¡¯ll give you two cases people often use as examples for regexes that PHP gives you better native ways to accomplish.
This document provides an introduction to regular expressions (regex) in PHP. It begins with a basic explanation of what regex is for - matching patterns in strings. It then covers the basics of regex syntax, including delimiters, character classes, quantifiers, escaping special characters, and using regex in PHP functions like preg_match() and preg_replace(). It also discusses more advanced topics like character classes, subpatterns, backreferences, modifiers, and when not to use regex. The overall message is that regex is a powerful tool for text manipulation but needs to be used appropriately.
The document discusses topics related to practicing bioinformatics including:
- Installing and working with the TextPad text editor
- Regular expressions (regex), including patterns, quantifiers, anchors, grouping, alternation, and variable interpolation
- Using regex memory variables ($1, $2, etc.) to extract matched substrings
- The s/// substitution operator and tr/// translation operator
- Applying these skills to tasks like finding restriction enzyme cut sites in DNA sequences
Regular expressions are patterns used to match character combinations in strings. They allow concise testing of string properties and manipulation of strings through search, match, and replacement. The document outlines basic regular expression syntax like wildcards, character sets, and flags. It provides examples of using regex to validate input format and extract postal codes and phone numbers through capturing groups. Search finds matches, match returns an array of all matches, and replace substitutes matches using a function.
Regular Expressions: every developer's best friend and worst nightmare! Join Andrei Zmievski, PHP developer and author of the PHP Regex (PCRE) extension, on a journey that will take you from your first steps into the world of regular expressions to the mastery of this most useful of tools. A must for everyone who's ever wondered what /(?=\d+)bar/ means.
The document discusses the components of regular expressions - atoms, quantifiers, and assertions. Atoms are individual characters, quantifiers control how many atoms are seen, and assertions control where atoms are found. Together they form a powerful pattern matching language. Common quantifiers like ?, +, and * are explained alongside more flexible quantifiers using curly braces. Assertions like ^, $, \b and \B are also summarized.
Regular expressions in Java allow for powerful text manipulation and extraction of parts of strings. A regular expression is a pattern that can match part or all of a string. In Java, a regular expression is compiled into a Pattern object, which is then used to create a Matcher object for a specific string. The Matcher provides methods to find matches and extract matched substrings. Mastering regular expressions requires learning a new "programming language" of punctuation symbols, but they are a useful tool for manipulating text.
SAP Automation with UiPath: Solution Accelerators and Best Practices - Part 6...DianaGray10
?
Join us for a comprehensive webinar on SAP Solution Accelerators and best practices for implementing them using UiPath. This session is designed to help SAP professionals and automation enthusiasts understand how to effectively leverage UiPath¡¯s SAP Solution Accelerators to automate standard SAP process quickly. Learn about the benefits, best ways to do it, and real-world success stories to speed up.
The document discusses regular expressions (regexes) in Python. It defines regexes as sequences of characters used to match patterns in strings. The re module provides full support for regexes. It describes various regex patterns like literals, concatenation, alternation, repetition. It also covers metacharacters like brackets, caret, backslash, dot and special sequences. Finally, it explains the search() and match() methods to perform regex queries on strings, with search() finding matches anywhere and match() only at the start.
This document provides an overview of regular expressions (regex or regexps), including what they are, common uses, and examples in different programming languages. Regular expressions are strings used to search for patterns in text. They are more powerful than wildcards and are available in many languages and programs. The document explains basic regex syntax like characters, anchors, quantifiers, character classes and grouping and provides examples of regex patterns for validating postal codes and URLs.
Don't Fear the Regex - CapitalCamp/GovDays 2014Sandy Smith
?
Have you been scared off by Klingon-looking one-liners in Perl? Do you resort to writing complicated recursive functions just to parse some HTML? Don't!
I'll demystify regular expressions and show you how best to do them in PHP. We'll cover the syntax and functions that make PHP a great text-parsing language, and give you the foundation to learn more.
As a bonus, I'll give you two cases people often use as examples for regexes that PHP gives you better native ways to accomplish.
Given at CapitalCamp & GovDays 2014
The document discusses string manipulation and regular expressions. It provides explanations of regular expression syntax including brackets, quantifiers, predefined character ranges, and flags. It also summarizes PHP functions for regular expressions like ereg(), eregi(), ereg_replace(), split(), and sql_regcase(). Practical examples of using these functions are shown.
This document discusses regular expressions (regexes) in Perl programming. It provides three examples that demonstrate extracting date values from a string using different regex techniques. It then discusses some key regex concepts like quantifiers, grouping, anchors, character classes and more. Code examples are provided to illustrate each concept. The goal is to help readers better understand how to leverage the powerful regex engine in Perl to perform complex text matching and manipulation tasks.
Oracle database supports perl- and POSIX-compatible regular expressions with five elegant and powerful functions: REGEXP_REPLACE, REGEXP_SUBSTR, REGEXP_INSTR, REGEXP_LIKE, and REGEXP_COUNT.
This session will demonstrate their nuances and how to use them effectively for data cleansing, manipulation and selection, for validating things such as Social Security Numbers, credit cards, IP addresses, phone numbers, DNAs, XMLs, for extracting things such as email-ids, hostnames from URLs and strings, and for transposing delimited columns to rows. There will be a demo of a few tricky examples taken from forums.oracle.com and asktom.oracle.com.
The session will end with fuzzy matching and optimization techniques, and things to watch out for.
http://docs.oracle.com/cd/E11882_01/appdev.112/e25518/adfns_regexp.htm
This lecture discusses the concept of Regular Expressions along with its usage in different tools such as grep, sed, and awk
Check the other Lectures and courses in
http://Linux4EnbeddedSystems.com
or Follow our Facebook Group at
- Facebook: @LinuxforEmbeddedSystems
Lecturer Profile:
- https://www.linkedin.com/in/ahmedelarabawy
Do you have data and lists you keep having to massage to make it useful for your project? Have you heard of regular expressions but been frightened by the Klingon-looking examples? Fear no longer!
I¡¯ll demystify regular expressions and show you how best to do them in PHP. We¡¯ll cover the syntax and functions that make PHP a great text-parsing language, and give you the foundation to learn more.
As a bonus, I¡¯ll give you two cases people often use as examples for regexes that PHP gives you better native ways to accomplish.
This document provides an overview of regular expressions (regex). It defines regex as patterns that define classes of strings. Regex are used by utilities like grep, sed, awk, vi and emacs to search for patterns in text. The document discusses the syntax of regex like alternation, grouping and quantification. It provides examples of regex patterns and explains how commands like grep can be used with regex to search files.
This document discusses strings in Python. It covers how strings are treated as a contiguous series of characters that can be indexed, sliced, concatenated, appended, and multiplied. It also discusses that strings are immutable in Python. Various string methods and functions are described, including formatting strings using the % operator. The document also covers regular expressions for string pattern matching and manipulation using the re module.
Don't Fear the Regex - Northeast PHP 2015Sandy Smith
?
Have you been scared off by Klingon-looking one-liners in Perl? Do you resort to writing complicated recursive functions just to parse some HTML? Don¡¯t!
I¡¯ll demystify regular expressions and show you how best to do them in PHP. We¡¯ll cover the syntax and functions that make PHP a great text-parsing language, and give you the foundation to learn more.
As a bonus, I¡¯ll give you two cases people often use as examples for regexes that PHP gives you better native ways to accomplish.
This document provides an introduction to regular expressions (regex) in PHP. It begins with a basic explanation of what regex is for - matching patterns in strings. It then covers the basics of regex syntax, including delimiters, character classes, quantifiers, escaping special characters, and using regex in PHP functions like preg_match() and preg_replace(). It also discusses more advanced topics like character classes, subpatterns, backreferences, modifiers, and when not to use regex. The overall message is that regex is a powerful tool for text manipulation but needs to be used appropriately.
The document discusses topics related to practicing bioinformatics including:
- Installing and working with the TextPad text editor
- Regular expressions (regex), including patterns, quantifiers, anchors, grouping, alternation, and variable interpolation
- Using regex memory variables ($1, $2, etc.) to extract matched substrings
- The s/// substitution operator and tr/// translation operator
- Applying these skills to tasks like finding restriction enzyme cut sites in DNA sequences
Regular expressions are patterns used to match character combinations in strings. They allow concise testing of string properties and manipulation of strings through search, match, and replacement. The document outlines basic regular expression syntax like wildcards, character sets, and flags. It provides examples of using regex to validate input format and extract postal codes and phone numbers through capturing groups. Search finds matches, match returns an array of all matches, and replace substitutes matches using a function.
Regular Expressions: every developer's best friend and worst nightmare! Join Andrei Zmievski, PHP developer and author of the PHP Regex (PCRE) extension, on a journey that will take you from your first steps into the world of regular expressions to the mastery of this most useful of tools. A must for everyone who's ever wondered what /(?=\d+)bar/ means.
The document discusses the components of regular expressions - atoms, quantifiers, and assertions. Atoms are individual characters, quantifiers control how many atoms are seen, and assertions control where atoms are found. Together they form a powerful pattern matching language. Common quantifiers like ?, +, and * are explained alongside more flexible quantifiers using curly braces. Assertions like ^, $, \b and \B are also summarized.
Regular expressions in Java allow for powerful text manipulation and extraction of parts of strings. A regular expression is a pattern that can match part or all of a string. In Java, a regular expression is compiled into a Pattern object, which is then used to create a Matcher object for a specific string. The Matcher provides methods to find matches and extract matched substrings. Mastering regular expressions requires learning a new "programming language" of punctuation symbols, but they are a useful tool for manipulating text.
SAP Automation with UiPath: Solution Accelerators and Best Practices - Part 6...DianaGray10
?
Join us for a comprehensive webinar on SAP Solution Accelerators and best practices for implementing them using UiPath. This session is designed to help SAP professionals and automation enthusiasts understand how to effectively leverage UiPath¡¯s SAP Solution Accelerators to automate standard SAP process quickly. Learn about the benefits, best ways to do it, and real-world success stories to speed up.
Struggling to get real value from HubSpot Sales Hub? Learn 5 mighty methods to close more deals without more leads or headcount (even on Starter subscriptions)!
These slides accompanied a webinar run by Hampshire's HubSpot User Group (HUG) on 2nd April, 2025.
HubSpot subscribers can watch the recording here: https://events.hubspot.com/events/details/hubspot-hampshire-presents-5-ways-to-close-more-deals-from-your-existing-sales-pipeline/
ABOUT THE EVENT:
Unlock hidden revenue in your CRM with our practical HubSpot tactics
Are you struggling to get real value from your HubSpot Sales Hub?
If your HubSpot feels like more of an admin burden than a revenue enabler, you¡¯re not alone. Many sales leaders find that their team isn't updating records consistently, pipeline visibility is poor, and reporting doesn¡¯t deliver the insights they need to drive strategy.
The good news? You don¡¯t need to upgrade your HubSpot subscription to sort these issues.
Join us for this webinar to learn 5 mighty tactics that will help you streamline your sales process, improve pipeline visibility, and extract more revenue from your existing pipeline, without spending more on marketing or hiring extra sales reps.
What You¡¯ll Learn
? Customising Records ¨C Increase sales momentum with more useful CRM data for your salespeople
? Pipeline Rules ¨C Improve deal stage consistency and data accuracy for improved prioritisation and forecasting
? Team Permissions & Defaults ¨C Control access and streamline processes. Spend more time selling, less on admin
? Pipeline View Customisation ¨C Get clearer sales insights, faster, to deal with revenue leaks
? Simple Sales Reports ¨C Build actionable dashboards to drive strategy with data
? Bonus: Successful Sales Hub users will share their experiences and the revenue impact it has delivered for them.
Who is this webinar for?
Sales leaders using HubSpot Sales Hub Starter, or those new to HubSpot
Sales managers who need better CRM adoption from their team
Anyone struggling with pipeline visibility, reporting, or forecasting
Teams who want to close more deals without extra sales headcount
Automated Engineering of Domain-Specific Metamorphic Testing EnvironmentsPablo G¨®mez Abajo
?
Context. Testing is essential to improve the correctness of software systems. Metamorphic testing (MT) is an approach especially suited when the system under test lacks oracles, or they are expensive to compute. However, building an MT environment for a particular domain (e.g., cloud simulation, model transformation, machine learning) requires substantial effort.
Objective. Our goal is to facilitate the construction of MT environments for specific domains.
Method. We propose a model-driven engineering approach to automate the construction of MT environments. Starting from a meta-model capturing the domain concepts, and a description of the domain execution environment, our approach produces an MT environment featuring comprehensive support for the MT process. This includes the definition of domain-specific metamorphic relations, their evaluation, detailed reporting of the testing results, and the automated search-based generation of follow-up test cases.
Results. Our method is supported by an extensible platform for Eclipse, called Gotten. We demonstrate its effectiveness by creating an MT environment for simulation-based testing of data centres and comparing with existing tools; its suitability to conduct MT processes by replicating previous experiments; and its generality by building another MT environment for video streaming APIs.
Conclusion. Gotten is the first platform targeted at reducing the development effort of domain-specific MT environments. The environments created with Gotten facilitate the specification of metamorphic relations, their evaluation, and the generation of new test cases.
GDG Cloud Southlake #41: Shay Levi: Beyond the Hype:How Enterprises Are Using AIJames Anderson
?
Beyond the Hype: How Enterprises Are Actually Using AI
Webinar Abstract:
AI promises to revolutionize enterprises - but what¡¯s actually working in the real world? In this session, we cut through the noise and share practical, real-world AI implementations that deliver results. Learn how leading enterprises are solving their most complex AI challenges in hours, not months, while keeping full control over security, compliance, and integrations. We¡¯ll break down key lessons, highlight recent use cases, and show how Unframe¡¯s Turnkey Enterprise AI Platform is making AI adoption fast, scalable, and risk-free.
Join the session to get actionable insights on enterprise AI - without the fluff.
Bio:
Shay Levi is the Co-Founder and CEO of Unframe, a company redefining enterprise AI with scalable, secure solutions. Previously, he co-founded Noname Security and led the company to its $500M acquisition by Akamai in just four years. A proven innovator in cybersecurity and technology, he specializes in building transformative solutions.
En esta charla compartiremos la experiencia del equipo de Bitnami en la mejora de la seguridad de nuestros Helm Charts y Contenedores utilizando Kubescape como herramienta principal de validaci¨®n. Exploraremos el proceso completo, desde la identificaci¨®n de necesidades hasta la implementaci¨®n de validaciones automatizadas, incluyendo la creaci¨®n de herramientas para la comunidad.
Compartiremos nuestra experiencia en la implementaci¨®n de mejoras de seguridad en Charts y Contenedores, bas¨¢ndonos en las mejores pr¨¢cticas del mercado y utilizando Kubescape como herramienta de validaci¨®n. Explicaremos c¨®mo automatizamos estas validaciones integr¨¢ndolas en nuestro ciclo de vida de desarrollo, mejorando significativamente la seguridad de nuestros productos mientras manten¨ªamos la eficiencia operativa.
Durante la charla, los asistentes aprender¨¢n c¨®mo implementar m¨¢s de 60 validaciones de seguridad cr¨ªticas, incluyendo la configuraci¨®n segura de contenedores en modo no privilegiado, la aplicaci¨®n de buenas pr¨¢cticas en recursos de Kubernetes, y c¨®mo garantizar la compatibilidad con plataformas como OpenShift. Adem¨¢s, demostraremos una herramienta de self-assessment que desarrollamos para que cualquier usuario pueda evaluar y mejorar la seguridad de sus propios Charts bas¨¢ndose en esta experiencia.
Convert EML files to PST on Mac operating systemRachel Walker
?
Mailvita EML to PST Converter for Mac is a useful program for Mac users, it can easily change several EML files into Outlook PST files with all attachments. This tool works with a lot of email programs, like Windows Live Mail, Thunderbird, and others. With its simple GUI, it's easy for both technical and non-technical people to convert files. Visit the official website to learn more about this program.
visit here: https://www.mailvita.com/eml-to-pst-converter-for-mac/
How Telemedicine App Development is Revolutionizing Virtual Care.pptxDash Technologies Inc
?
Telemedicine app development builds software for remote doctor consultations and patient check-ups. These apps bridge healthcare professionals with patients via video calls, secure messages, and interactive interfaces. That helps practitioners to provide care without immediate face-to-face interactions; hence, simplifying access to medical care. Telemedicine applications also manage appointment scheduling, e-prescribing, and sending reminders.
Telemedicine apps do not only conduct remote consultations. They also integrate with entire healthcare platforms, such as patient forums, insurance claims processing, and providing medical information libraries. Remote patient monitoring enables providers to keep track of patients' vital signs. This helps them intervene and provide care whenever necessary. Telehealth app development eliminates geographical boundaries and facilitates easier communication.
In this blog, we will explore its market growth, essential features, and benefits for both patients and providers.
Recruiting Tech: A Look at Why AI is Actually OGMatt Charney
?
A lot of recruiting technology vendors out there are talking about how they're offering the first ever (insert AI use case here), but turns out, everything they're selling as innovative or cutting edge has been around since Yahoo! and MySpace were category killers. Here's the receipts.
Why Outsource Accounting to India A Smart Business Move!.pdfanjelinajones6811
?
Outsource Accounting to India to reduce costs, access skilled professionals, and streamline financial operations. Indian accounting firms offer expert services, advanced technology, and round-the-clock support, making it a smart choice for businesses looking to improve efficiency and focus on growth.
Mastering Azure Durable Functions - Building Resilient and Scalable WorkflowsCallon Campbell
?
The presentation aims to provide a comprehensive understanding of how Azure Durable Functions can be used to build resilient and scalable workflows in serverless applications. It includes detailed explanations, application patterns, components, and constraints of Durable Functions, along with performance benchmarks and new storage providers.
Most people might think of a water faucet or even the tap on a keg of beer. But in the world of networking, "TAP" stands for "Traffic Access Point" or "Test Access Point." It's not a beverage or a sink fixture, but rather a crucial tool for network monitoring and testing. Khushi Communications is a top vendor in India, providing world-class Network TAP solutions. With their expertise, they help businesses monitor, analyze, and secure their networks efficiently.
Research Data Management (RDM): the management of dat in the research processHeilaPienaar
?
Presented as part of the M.IT degree at the Department of Information Science, University of Pretoria, South Africa. Module: Data management. 2023, 2024.
AuthZEN The OpenID Connect of Authorization - Gartner IAM EMEA 2025David Brossard
?
Today, the authorization world is fractured - each vendor supports its own APIs & protocols. But this is about to change: OpenID AuthZEN was created in late 2023 to establish much-needed modern authorization standards. As of late 2024, AuthZEN has a stable Implementers Draft, and is expected to reach Final Specification in 2025.
With AuthZEN, IAM teams can confidently externalize and standardize authorization across their application estate without being locked in to a proprietary API.
This session will describe the state of modern authorization, review the AuthZEN API, and demo our 15 interoperable implementations.
All-Data, Any-AI Integration: FME & Amazon Bedrock in the Real-WorldSafe Software
?
Join us for an exclusive webinar featuring special guest speakers from Amazon, Amberside Energy, and Avineon-Tensing as we explore the power of Amazon Bedrock and FME in AI-driven geospatial workflows.
Discover how Avineon-Tensing is using AWS Bedrock to support Amberside Energy in automating image classification and streamlining site reporting. By integrating Bedrock¡¯s generative AI capabilities with FME, image processing and categorization become faster and more efficient, ensuring accurate and organized filing of site imagery. Learn how this approach reduces manual effort, standardizes reporting, and leverages AWS¡¯s secure AI tooling to optimize their workflows.
If you¡¯re looking to enhance geospatial workflows with AI, automate image processing, or simply explore the potential of FME and Bedrock, this webinar is for you!
2. 2
? Regular Expression
? Commands in use
? grep and egrep
? Regular Expression Metacharacters
? Grouping
? Brackets and Character Classes
? Back References
? Quantifiers
? Anchors and Word Boundaries
? Practical Examples
Outline
3. 3
RegularExpression
? Regular expressions (regex) are a powerful method for
describing a text pattern to be matched by various tools.
? There is only one place in bash where regular
expressions are valid, using the =~ comparison in the [[
compound command, as in an if statement.
? Regular expressions are a crucial part of the larger toolkit
for commands like grep, awk, and sed in particular.
4. 4
RegularExpressionvs. PatternMatching
? Pattern matching is used by the shell commands such
as the ls command.
? Regular expressions are used to search for strings of
text in a file by using commands, such as the grep
command.
? The use of regular expressions is generally associated
with text processing.
5. 5
CommandsinUse
? grep: The grep command searches the content of the files for a given
pattern and prints any line where the pattern is matched.
? To use grep, you need to provide it with a pattern and one or more
filenames (or piped data).
? Common command options:
? -c: Count the number of lines that match the pattern.
? -E: Enable extended regular expressions.
? -f: Read the search pattern from a provided file. A file can contain
more than one pattern, with each line containing a single pattern.
? -i: Ignore character case.
? -l: Print only the filename and path where the pattern was found.
? -n: Print the line number of the file where the pattern was found.
? -p: Enable the Perl regular expression engine.
? -R, -r: Recursively search subdirectories.
6. 6
CommandsinUse
? In general, grep is used like this:
? grep options pattern filenames
? To search the /home directory and all subdirectories for files containing
the word password, regardless of uppercase/lowercase distinctions:
7. 7
grepandegrep
? The grep command supports some variations, notably extended syntax
for the regex patterns
? There are three ways to tell grep that you want special meaning on
certain characters:
1. by preceding those characters with a backslash.
2. by telling grep that you want the special syntax (without the need for
a backslash) by using the -E option when you invoke grep.
3. by using the command named egrep, which is a script that simply
invokes grep as grep ¨CE so you don¡¯t have to.
? The only characters that are affected by the extended syntax are? + { | (
and ).
8. Regular Expression Metacharacters
? Regular expressions are patterns that are created
using a series of characters and metacharacters.
? Metacharacters such as the questions mark (?) and
asterisk (*) have special meaning beyond their literal
meanings in regex.
? The 7 lines of frost.txt file will be used in the next
slides examples.
9. Regular Expression Metacharacters
? 1 Two roads diverged in a yellow wood,
? 2 And sorry I could not travel both
? 3 And be one traveler, long I stood
? 4 And looked down one as far as I could
? 5 To where it bent in the undergrowth;
? 6
? 7 Excerpt from The Road Not Taken by
Robert Frost
10. 10
Regular Expression Metacharacters
? The ¡°.¡± Metacharacter:
? The period (.) represents a single wildcard character.
? It will match on any single character except for a newline.
? If you want to treat this metacharacter as a period character rather
than a wildcard, precede it with a backslash (.) to escape its special
meaning.
? If we try to match on the
pattern T.o, the first line of
the frost.txt file is returned
because it contains the
word Two
? Regex patterns are also case
sensitive, which is why line
3 of the file is not returned
even though it contains the
string too
11. 11
Regular Expression Metacharacters
? The ¡°?¡± Metacharacter:
? The question mark (?) character makes any item that precedes it
optional.
? It matches it zero or one time.
? This pattern will match on any three-character sequence that begins with T and ends
with o as well as the two-character sequence To.
? Note that we are using egrep here.
? We could have used grep ¨CE,
? or we could have used ¡°plain¡± grep with a slightly different pattern: T.?o, putting
the backslash on the question mark to give it the extended meaning.
12. 12
Regular Expression Metacharacters
? The ¡°*¡± Metacharacter:
? The asterisk (*) is a special character that matches the preceding item
zero or more times.
? It is similar to ?, the main difference being that the previous item may
appear more than once.
? The .* in the preceding pattern allows any number of any character to
appear between the T and o.
? Thus, the last line also matches because it contains the pattern The
Ro.
13. 13
Regular Expression Metacharacters
? The ¡°+¡± Metacharacter:
? The plus sign (+) metacharacter is the same as the * except it requires
the preceding item to appear at least once.
? The preceding pattern specifies one or more of any character to
appear in between the T and o.
? The first line of text matches because of Two ¡ª the w is one character
between the T and the o.
? The second line doesn¡¯t match the To, as in the previous example;
rather, the pattern matches a much larger string ¡ª all the way to the o
in undergrowth.
? The last line also matches because it contains the pattern The Ro.
14. 14
Grouping
? We can use parentheses to group characters.
? Among other things, this allows us to treat the characters appearing inside
the parentheses as a single item that we can later reference.
? Here, we use parentheses and the Boolean OR operator (|) to create a
pattern that will match on line 3.
? Line 3 as written has the word traveler in it, but this pattern would match
even if traveler was replaced by the word stranger.
15. 15
Brackets and Character Classes
? The square brackets, [ ] , are used to define character classes and lists of
acceptable characters.
? Using this construct, you can list exactly which characters are matched at
this position in the pattern.
? This is particularly useful when trying to perform user-input validation.
? As shorthand, you can specify ranges with a dash, such as [a-j].
? These ranges are in your locale¡¯s collating sequence and alphabet.
? The pattern [a-j] will match one of the letters a through j.
16. 16
Brackets and Character Classes
? Table 3-1 provides a list of common examples when using character
classes and ranges.
? Be careful when defining a range for digits; the range can at most go from 0 to 9.
For example, the pattern [1-475] does not match on numbers between 1 and 475;
it matches on any one of the digits (characters) in the range 1¨C4 or the character 7
or the character 5.
17. 17
Brackets and Character Classes
? There are also predefined character classes known as shortcuts.
? These can be used to indicate common character classes such as
numbers or letters.
18. 18
Brackets and Character Classes
? Note that the shortcuts are not supported by egrep.
? In order to use them, you must use grep with the -p option.
? That option enables the Perl regular expression engine to support the
shortcuts.
? Note: -p (small letter)
19. 19
Brackets and Character Classes
? Other character classes (are valid only within
the bracket syntax, as shown in Table 3-3.
? They match a single character, so if you need
to match many in a row, use the * or + to get
the repetition you need.
? To use one of these classes, it has to be inside
the brackets, so you end up with two sets of
brackets.
? This will match any line with an X followed by
any uppercase letter or digit. It would match
these lines:
21. 21
Back References
? Regex back references are one of the most powerful and often confusing
regex operations.
? Consider the following file, tags.txt:
? Suppose you want to write a regular expression that will extract any line
that contains a matching pair of complete HTML tags.
? The start tag has an HTML tag name; the ending tag has the same tag
name but with a leading slash. <div> and </div> are a matching pair.
You can search for these by writing a lengthy regex that contains all
possible HTML tag values, or you can focus on the format of an
? HTML tag and use a regex back reference, as follows:
22. 22
Back References
? In this example, the back reference is the 1 appearing in the latter part of
the regular expression.
? It is referring back to the expression enclosed in the first set of
parentheses, [A-Za-z]*, which has two parts.
? The letter range in brackets denotes a choice of any letter, uppercase or
lowercase.
? The * that follows it means to repeat that zero or more times.
? Therefore, the 1 refers to whatever was matched by that pattern in
parentheses.
? If [A-Za-z]* matches div, then the 1 also refers to the pattern div.
23. 23
Back References
? You can have more than one back reference in an expression and refer
to each with a 1 or 2 or 3 depending on its order in the regular
expression
? A 1 refers to the first set of parentheses, 2 to the second, and so on
? Note that the parentheses are metacharacters; they have a special
meaning.
? If you just want to match a literal parenthesis, you need to escape its
special meaning by preceding it with a backslash, as in sin([0-9.]*) to
match expressions like sin(6.2) or sin(3.14159).
24. 24
Quantifiers
? Quantifiers specify the number of times an item must appear in a string.
? Quantifiers are defined by curly braces { }.
? For example, the pattern T{5} means that the letter T must appear
consecutively exactly five times.
? The pattern T{3,6} means that the letter T must appear consecutively
three to six times.
? The pattern T{5,} means that the letter T must appear five or more times.
25. 25
Anchors and Word Boundaries
? You can use anchors to specify that a pattern must exist at the beginning
or the end of a string.
? The caret (^) character is used to anchor a pattern to the beginning of a
string.
? For example, ^[1-5] means that a matching string must start with one of
the digits 1 through 5, as the first character on the line.
? The $ character is used to anchor a pattern to the end of a string or line.
? For example, [1-5]$ means that a string must end with one of the digits 1
through 5.
? In addition, you can use b to identify a word boundary (i.e., a space).
? The pattern b[1-5]b will match on any of the digits 1 through 5, where
the digit appears as its own word.
#4: Awk? Aho, Weinberger and Kernighan
The awk command is a Linux tool and programming language that allows users to process and manipulate data and produce formatted reports
SED is a text stream editor used on Unix systems to edit files quickly and efficiently. The tool searches through, replaces, adds, and deletes lines in a text file without opening the file in a text editor.
#18: \w ? [A-Za-z0-9_]
\s matches a space, a tab, a carriage return, a line feed, or a form feed. [?\t\r\n\f]. \f? page separator
\D is the same as [^\d]