1600612080

Static site generator for Deno

🔥Lume (Work in progress)

lume is the galician word for fire, but also a (yet another) static site generator for Deno.

It’s inspired in other general purpose static site generators such Jekyll or Eleventy but it’s faster, simpler and easier to use and configure, besides being super flexible.

Support for multiple file formats like markdown, yaml, javascript, typescript, jsx, nunjucks and it’s easy to extend.
You can hook any processor to transform assets, like sass or postcss for CSS.
It’s Deno: forget about manage thousand of packages in node_modules or complex bundlers. Lume only install that you need. Clean, fast and secure.

Quick start

Make sure you have Deno installed.

Install lume as a Deno executable:

deno install --unstable --allow-read --allow-write --allow-net https://deno.land/x/lume/cli.js

Create your first page, for example using a nunjucks template:

---
title: Welcome to my page
---

<html>
  <head>
    <title>{{ title }}</title>
  </head>

  <body>
    <h1>{{ title }}</h1>
  </body>
</html>

Run it: lume
This command will compile your documents to HTML and saved into the output folder (defaults to _site)
You can run lume --serve to start up a webserver. Then open http://localhost:3000 in your web browser to see your website.

Download Details:

Author: oscarotero

Demo: https://deno.land/x/lume

Source Code: https://github.com/oscarotero/lume

#deno #nodejs #node #javascript

Building a Static Website with Hugo

#hugo #static #site #generator #markup #static site generator

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7, Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

import io

import tokenize

code = b"color = input('Enter your favourite color: ')"

for token in tokenize.tokenize(io.BytesIO(code).readline):

    print(token)

Python

TokenInfo(type=62 (ENCODING),  string='utf-8')

TokenInfo(type=1  (NAME),      string='color')

TokenInfo(type=54 (OP),        string='=')

TokenInfo(type=1  (NAME),      string='input')

TokenInfo(type=54 (OP),        string='(')

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

TokenInfo(type=54 (OP),        string=')')

TokenInfo(type=4  (NEWLINE),   string='')

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

amelia jones

1591340335

How To Take Help Of Referencing Generator

APA Referencing Generator

Many students use APA style as the key citation style in their assignment in university or college. Although, many people find it quite difficult to write the reference of the source. You ought to miss the names and dates of authors. Hence, APA referencing generator is important for reducing the burden of students. They can now feel quite easy to do the assignments on time.

The functioning of APA referencing generator

If you are struggling hard to write the APA referencing then you can take the help of APA referencing generator. It will create an excellent list. You are required to enter the information about the source. Just ensure that the text is credible and original. If you will copy references then it is a copyright violation.

You can use a referencing generator in just a click. It will generate the right references for all the sources. You are required to organize in alphabetical order. The generator will make sure that you will get good grades.

How to use APA referencing generator?

Select what is required to be cited such as journal, book, film, and others. You can choose the type of required citations list and enter all the required fields. The fields are dates, author name, title, editor name, and editions, name of publishers, chapter number, page numbers, and title of journals. You can click for reference to be generated and you will get the desired result.

Chicago Referencing Generator

Do you require the citation style? You can rely on Chicago Referencing Generator and will ensure that you will get the right citation in just a click. The generator is created to provide solutions to students to cite their research paper in Chicago style. It has proved to be the quickest and best citation generator on the market. The generator helps to sort the homework issues in few seconds. It also saves a lot of time and energy.

This tool helps researchers, professional writers, and students to manage and generate text citation essays. It will help to write Chicago style in a fast and easy way. It also provides details and directions for formatting and cites resources.

So, you must stop wasting the time and can go for Chicago Referencing Generator or APA referencing generator. These citation generators will help to solve the problem of citation issues. You can easily create citations by using endnotes and footnotes.

So, you can generate bibliographies, references, in-text citations, and title pages. These are fully automatic referencing style. You are just required to enter certain details about the citation and you will get the citation in the proper and required format.

So, if you are feeling any problem in doing assignment then you can take the help of assignment help.
If you require help for Assignment then livewebtutors is the right place for you. If you see our prices, you will observe that they are actually very affordable. Also, you can always expect a discount. Our team is capable and versatile enough to offer you exactly what you need, the best services for the prices you can afford.

#apa referencing generator #harvard referencing generator #chicago referencing generator #mla referencing generator #deakin referencing generator #oxford referencing generator

Carmen Grimes

1599527554

An Introduction to Static Site Generators

Websites can be of 2 types: static and dynamic. For static types, when user requests a file, server sends the file and user can see it. Every page is hand coded in html. In a dynamic type, When user requests, content is generated and server builds the page then sends it to the user. Generally managed by CMS like Wordpress or Joomla.

Static site generator (SSG) bridges the gap between static HTML sites and CMS based sites (like Wordpress). It provides better performance of static sites and we do not have to write HTML for content of our site.

Think of a static site generator as a script which takes in data, content and templates, processes them, and outputs a folder full of all the resultant pages and assets.

-Phil Hawksworth

In short, SSGs bring good of both worlds together.

Why use SSG ?

1. Security

Static websites do not have a database and all our server does is returns the file asked by user.

Our server becomes dumb (doesn’t use logic to construct page) so no security issue to exploit.

2. Performance

Static websites perform better and load faster because the page isn’t constructed at run time.

3. Version Control

Our website can live in a version controlled environment, meaning if you make a mistake you can go back to a previous version in one command.

4. Scaling

When the number of users on a dynamic website increase it would mean to efficiently scale the website to ensure every user’s page is served as quickly as possible while for static sites thats not a issue because our work is pre-done. Our website pages are already made and we just have to provide them.

#webdevelopment #programming #static-site-generator #web-development #static-website #html #ssg #website

Royce Reinger

1658977500

A Ruby Library for Generating Text with Recursive Template Grammars

Calyx

Calyx provides a simple API for generating text with declarative recursive grammars.

Install

Command Line

gem install calyx

Gemfile

gem 'calyx'

Examples

The best way to get started quickly is to install the gem and run the examples locally.

Any Gradient

Requires Roda and Rack to be available.

gem install roda

Demonstrates how to use Calyx to construct SVG graphics. Any Gradient generates a rectangle with a linear gradient of random colours.

Run as a web server and preview the output in a browser (http://localhost:9292):

ruby examples/any_gradient.rb

Or generate SVG files via a command line pipe:

ruby examples/any_gradient > gradient1.xml

Tiny Woodland Bot

Requires the Twitter client gem and API access configured for a specific Twitter handle.

gem install twitter

Demonstrates how to use Calyx to make a minimal Twitter bot that periodically posts unique tweets. See @tiny_woodland on Twitter and the writeup here.

TWITTER_CONSUMER_KEY=XXX-XXX
TWITTER_CONSUMER_SECRET=XXX-XXX
TWITTER_ACCESS_TOKEN=XXX-XXX
TWITTER_CONSUMER_SECRET=XXX-XXX
ruby examples/tiny_woodland_bot.rb

Faker

Faker is a popular library for generating fake names and associated sample data like internet addresses, company names and locations.

This example demonstrates how to use Calyx to reproduce the same functionality using custom lists defined in a YAML configuration file.

ruby examples/faker.rb

Usage

Require the library and inherit from Calyx::Grammar to construct a set of rules to generate a text.

require 'calyx'

class HelloWorld < Calyx::Grammar
  start 'Hello world.'
end

To generate the text itself, initialize the object and call the generate method.

hello = HelloWorld.new
hello.generate
# > "Hello world."

Obviously, this hardcoded sentence isn’t very interesting by itself. Possible variations can be added to the text by adding additional rules which provide a named set of text strings. The rule delimiter syntax ({}) can be used to substitute the generated content of other rules.

class HelloWorld < Calyx::Grammar
  start '{greeting} world.'
  greeting 'Hello', 'Hi', 'Hey', 'Yo'
end

Each time #generate runs, it evaluates the tree and randomly selects variations of rules to construct a resulting string.

hello = HelloWorld.new

hello.generate
# > "Hi world."

hello.generate
# > "Hello world."

hello.generate
# > "Yo world."

By convention, the start rule specifies the default starting point for generating the final text. You can start from any other named rule by passing it explicitly to the generate method.

class HelloWorld < Calyx::Grammar
  hello 'Hello world.'
end

hello = HelloWorld.new
hello.generate(:hello)

Block Constructors

As an alternative to subclassing, you can also construct rules unique to an instance by passing a block when initializing the class:

hello = Calyx::Grammar.new do
  start '{greeting} world.'
  greeting 'Hello', 'Hi', 'Hey', 'Yo'
end

hello.generate

Template Expressions

Basic rule substitution uses single curly brackets as delimiters for template expressions:

fruit = Calyx::Grammar.new do
  start '{colour} {fruit}'
  colour 'red', 'green', 'yellow'
  fruit 'apple', 'pear', 'tomato'
end

6.times { fruit.generate }
# => "yellow pear"
# => "red apple"
# => "green tomato"
# => "red pear"
# => "yellow tomato"
# => "green apple"

Nesting and Substitution

Rules are recursive. They can be arbitrarily nested and connected to generate larger and more complex texts.

class HelloWorld < Calyx::Grammar
  start '{greeting} {world_phrase}.'
  greeting 'Hello', 'Hi', 'Hey', 'Yo'
  world_phrase '{happy_adj} world', '{sad_adj} world', 'world'
  happy_adj 'wonderful', 'amazing', 'bright', 'beautiful'
  sad_adj 'cruel', 'miserable'
end

Nesting and hierarchy can be manipulated to balance consistency with novelty. The exact same word atoms can be combined in a variety of ways to produce strikingly different resulting texts.

module HelloWorld
  class Sentiment < Calyx::Grammar
    start '{happy_phrase}', '{sad_phrase}'
    happy_phrase '{happy_greeting} {happy_adj} world.'
    happy_greeting 'Hello', 'Hi', 'Hey', 'Yo'
    happy_adj 'wonderful', 'amazing', 'bright', 'beautiful'
    sad_phrase '{sad_greeting} {sad_adj} world.'
    sad_greeting 'Goodbye', 'So long', 'Farewell'
    sad_adj 'cruel', 'miserable'
  end

  class Mixed < Calyx::Grammar
    start '{greeting} {adj} world.'
    greeting 'Hello', 'Hi', 'Hey', 'Yo', 'Goodbye', 'So long', 'Farewell'
    adj 'wonderful', 'amazing', 'bright', 'beautiful', 'cruel', 'miserable'
  end
end

Random Sampling

By default, the outcomes of generated rules are selected with Ruby’s built-in pseudorandom number generator (as seen in methods like Kernel.rand and Array.sample). To seed the random number generator, pass in an integer seed value as the first argument to the constructor:

grammar = Calyx::Grammar.new(seed: 12345) do
  # rules...
end

Alternatively, you can pass a preconfigured instance of Ruby’s stdlib Random class:

random = Random.new(12345)

grammar = Calyx::Grammar.new(rng: random) do
  # rules...
end

When a random seed isn’t supplied, Time.new.to_i is used as the default seed, which makes each run of the generator relatively unique.

Weighted Choices

Choices can be weighted so that some rules have a greater probability of expanding than others.

Weights are defined by passing a hash instead of a list of rules where the keys are strings or symbols representing the grammar rules and the values are weights.

Weights can be represented as floats, integers or ranges.

Floats must be in the interval 0..1 and the given weights for a production must sum to 1.
Ranges must be contiguous and cover the entire interval from 1 to the maximum value of the largest range.
Integers (Fixnums) will produce a distribution based on the sum of all given numbers, with each number being a fraction of that sum.

The following definitions produce an equivalent weighting of choices:

Calyx::Grammar.new do
  start 'heads' => 1, 'tails' => 1
end

Calyx::Grammar.new do
  start 'heads' => 0.5, 'tails' => 0.5
end

Calyx::Grammar.new do
  start 'heads' => 1..5, 'tails' => 6..10
end

Calyx::Grammar.new do
  start 'heads' => 50, 'tails' => 50
end

There’s a lot of interesting things you can do with this. For example, you can model the triangular distribution produced by rolling 2d6:

Calyx::Grammar.new do
  start(
    '2' => 1,
    '3' => 2,
    '4' => 3,
    '5' => 4,
    '6' => 5,
    '7' => 6,
    '8' => 5,
    '9' => 4,
    '10' => 3,
    '11' => 2,
    '12' => 1
  )
end

Or reproduce Gary Gygax’s famous generation table from the original Dungeon Master’s Guide (page 171):

Calyx::Grammar.new do
  start(
    :empty => 0.6,
    :monster => 0.1,
    :monster_treasure => 0.15,
    :special => 0.05,
    :trick_trap => 0.05,
    :treasure => 0.05
  )
  empty 'Empty'
  monster 'Monster Only'
  monster_treasure 'Monster and Treasure'
  special 'Special'
  trick_trap 'Trick/Trap.'
  treasure 'Treasure'
end

String Modifiers

Dot-notation is supported in template expressions, allowing you to call any available method on the String object returned from a rule. Formatting methods can be chained arbitrarily and will execute in the same way as they would in native Ruby code.

greeting = Calyx::Grammar.new do
  start '{hello.capitalize} there.', 'Why, {hello} there.'
  hello 'hello', 'hi'
end

4.times { greeting.generate }
# => "Hello there."
# => "Hi there."
# => "Why, hello there."
# => "Why, hi there."

You can also extend the grammar with custom modifiers that provide useful formatting functions.

Filters

Filters accept an input string and return the transformed output:

greeting = Calyx::Grammar.new do
  filter :shoutycaps do |input|
    input.upcase
  end

  start '{hello.shoutycaps} there.', 'Why, {hello.shoutycaps} there.'
  hello 'hello', 'hi'
end

4.times { greeting.generate }
# => "HELLO there."
# => "HI there."
# => "Why, HELLO there."
# => "Why, HI there."

Mappings

The mapping shortcut allows you to specify a map of regex patterns pointing to their resulting substitution strings:

green_bottle = Calyx::Grammar.new do
  mapping :pluralize, /(.+)/ => '\\1s'
  start 'One green {bottle}.', 'Two green {bottle.pluralize}.'
  bottle 'bottle'
end

2.times { green_bottle.generate }
# => "One green bottle."
# => "Two green bottles."

Modifier Mixins

In order to use more intricate rewriting and formatting methods in a modifier chain, you can add methods to a module and embed it in a grammar using the modifier classmethod.

Modifier methods accept a single argument representing the input string from the previous step in the expression chain and must return a string, representing the modified output.

module FullStop
  def full_stop(input)
    input << '.'
  end
end

hello = Calyx::Grammar.new do
  modifier FullStop
  start '{hello.capitalize.full_stop}'
  hello 'hello'
end

hello.generate
# => "Hello."

To share custom modifiers across multiple grammars, you can include the module in Calyx::Modifiers. This will make the methods available to all subsequent instances:

module FullStop
  def full_stop(input)
    input << '.'
  end
end

class Calyx::Modifiers
  include FullStop
end

Monkeypatching String

Alternatively, you can combine methods from existing Gems that monkeypatch String:

require 'indefinite_article'

module FullStop
  def full_stop
    self << '.'
  end
end

class String
  include FullStop
end

noun_articles = Calyx::Grammar.new do
  start '{fruit.with_indefinite_article.capitalize.full_stop}'
  fruit 'apple', 'orange', 'banana', 'pear'
end

4.times { noun_articles.generate }
# => "An apple."
# => "An orange."
# => "A banana."
# => "A pear."

Memoized Rules

Rule expansions can be ‘memoized’ so that multiple references to the same rule return the same value. This is useful for picking a noun from a list and reusing it in multiple places within a text.

The @ sigil is used to mark memoized rules. This evaluates the rule and stores it in memory the first time it’s referenced. All subsequent references to the memoized rule use the same stored value.

# Without memoization
grammar = Calyx::Grammar.new do
  start '{name} <{name.downcase}>'
  name 'Daenerys', 'Tyrion', 'Jon'
end

3.times { grammar.generate }
# => Daenerys <jon>
# => Tyrion <daenerys>
# => Jon <tyrion>

# With memoization
grammar = Calyx::Grammar.new do
  start '{@name} <{@name.downcase}>'
  name 'Daenerys', 'Tyrion', 'Jon'
end

3.times { grammar.generate }
# => Tyrion <tyrion>
# => Daenerys <daenerys>
# => Jon <jon>

Note that the memoization symbol can only be used on the right hand side of a production rule.

Unique Rules

Rule expansions can be marked as ‘unique’, meaning that multiple references to the same rule always return a different value. This is useful for situations where the same result appearing twice would appear awkward and messy.

Unique rules are marked by the $ sigil.

grammar = Calyx::Grammar.new do
  start "{$medal}, {$medal}, {$medal}"
  medal 'Gold', 'Silver', 'Bronze'
end

grammar.generate
# => Silver, Bronze, Gold

Dynamically Constructing Rules

Template expansions can be dynamically constructed at runtime by passing a context map of rules to the #generate method:

class AppGreeting < Calyx::Grammar
  start 'Hi {username}!', 'Welcome back {username}...', 'Hola {username}'
end

context = {
  username: UserModel.username
}

greeting = AppGreeting.new
greeting.generate(context)

External File Formats

In addition to defining grammars in pure Ruby, you can load them from external JSON and YAML files:

hello = Calyx::Grammar.load('hello.yml')
hello.generate

The format requires a flat map with keys representing the left-hand side named symbols and the values representing the right hand side substitution rules.

In JSON:

{
  "start": "{greeting} world.",
  "greeting": ["Hello", "Hi", "Hey", "Yo"]
}

In YAML:

---
start: "{greeting} world."
greeting:
  - Hello
  - Hi
  - Hey
  - Yo

Accessing the Raw Generated Tree

Calling #evaluate on the grammar instance will give you access to the raw generated tree structure before it gets flattened into a string.

The tree is encoded as an array of nested arrays, with the leading symbols labeling the choices and rules selected, and the trailing terminal leaves encoding string values.

This may not make a lot of sense unless you’re familiar with the concept of s-expressions. It’s a fairly speculative feature at this stage, but it leads to some interesting possibilities.

grammar = Calyx::Grammar.new do
  start 'Riddle me ree.'
end

grammar.evaluate
# => [:start, [:choice, [:concat, [[:atom, "Riddle me ree."]]]]]

Roadmap

Rough plan for stabilising the API and features for a 1.0 release.

Version	Features planned
`0.6`	~~block constructor~~
`0.7`	~~support for template context map passed to generate~~
`0.8`	~~method missing metaclass API~~
`0.9`	~~return grammar tree from~~ `#evaluate`~~, with flattened string from~~ `#generate` ~~being separate~~
`0.10`	~~inject custom string functions for parameterised rules, transforms and mappings~~
`0.11`	~~support YAML format (and JSON?)~~
`0.12`	~~API documentation~~
`0.13`	~~Support for unique rules~~
`0.14`	~~Support for Ruby 2.4~~
`0.15`	~~Options config and ‘strict mode’ error handling~~
`0.16`	~~Improve representation of weighted probability selection~~
`0.17`	~~Return result object from~~ `#generate` ~~calls~~

Credits

Author & Maintainer

Mark Rickerby

Contributors

Tariq Ali

Author: Maetl
Source Code: https://github.com/maetl/calyx
License: MIT license

#ruby #text