WWW::MermaidInk

Get images corresponding to Mermaid-js specifications via the web Mermaid-ink interface of Mermaid-js.

WWW::MermaidInk Raku package

The function mermaid-ink of the Raku package "WWW::MermaidInk" gets images corresponding to Mermaid-js specifications via the web Mermaid-ink interface of Mermaid-js.

Usage

use WWW::MermaidInkloads the package.

mermaid-ink($spec)retrieves an image defined by the spec $spec from Mermaid's Ink Web interface.

mermaid-ink($spec format => 'md-image')returns a string that is a Markdown image specification in Base64 format.

mermaid-ink($spec file => fileName)exports the retrieved image into a specified PNG file.

mermaid-ink($spec file => Whatever)exports the retrieved image into the file $*CMD ~ /out.png.

Details & Options

  • Mermaid lets you create diagrams and visualizations using text and code.

  • Mermaid has different types of diagrams: Flowchart, Sequence Diagram, Class Diagram, State Diagram, Entity Relationship Diagram, User Journey, Gantt, Pie Chart, Requirement Diagram, and others. It is a JavaScript based diagramming and charting tool that renders Markdown-inspired text definitions to create and modify diagrams dynamically.

  • mermaid-ink uses the Mermaid's functionalities via the Web interface "https://mermaid.ink/img".

  • The first argument can be a string (that is, a mermaid-js specification) or a list of pairs.

  • The option "directives" can be used to control the layout of Mermaid diagrams if the first argument is a list of pairs.

  • mermaid-ink produces images only.

Examples

Basic Examples

Generate a flowchart from a Mermaid specification:

use WWW::MermaidInk;

'graph TD 
   WL --> |ZMQ|Python --> |ZMQ|WL' 
 ==> mermaid-ink(format=>'md-image')

image data:image/png;base64,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not found

Create a Markdown image expression from a class diagram:

my $spec = q:to/END/;
classDiagram
    Animal <|-- Duck
    Animal <|-- Fish
    Animal <|-- Zebra
    Animal : +int age
    Animal : +String gender
    Animal: +isMammal()
    Animal: +mate()
    class Duck{
        +String beakColor
        +swim()
        +quack()
    }
    class Fish{
        -int sizeInFeet
        -canEat()
    }
    class Zebra{
        +bool is_wild
        +run()
    }
END

mermaid-ink($spec, format=>'md-image')    

image 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not found

Scope

The first argument can be a list of pairs -- the corresponding Mermaid-js graph is produced. Here are the edges of directed graph:

my @edges = ['1' => '3', '3' => '1', '1' => '4', '2' => '3', '2' => '4', '3' => '4'];

[1 => 3 3 => 1 1 => 4 2 => 3 2 => 4 3 => 4]

Here is the corresponding mermaid-js image:

mermaid-ink(@edges, format=>'md-image')

image 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 not found

Flowchart

This flowchart summarizes the execution path of obtaining Mermaid images in a Markdown document:

References

WWW::MermaidInk v0.1.0

Get images corresponding to Mermaid-js specifications via the web Mermaid-ink interface of Mermaid-js.

Authors

  • Anton Antonov

License

Artistic-2.0

Dependencies

HTTP::Tiny:ver<0.2.5+>Base64:ver<0.1.0>

Test Dependencies

Provides

  • WWW::MermaidInk

The Camelia image is copyright 2009 by Larry Wall. "Raku" is trademark of the Yet Another Society. All rights reserved.