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Cheetah (or CheetahTemplate) is a template engine that uses the Python programming language.It can be used standalone or combined with other tools and frameworks. It is often used for server-side scripting and dynamic web content by generating HTML, but can also be used to generate source code.
Mako is a template library written in Python. Mako is an embedded Python (i.e. Python Server Page) language, which refines the familiar ideas of componentized layout and inheritance. The Mako template is used by Reddit. [4] It is the default template language included with the Pylons [5] and Pyramid [6] web frameworks.
Jinja is a web template engine for the Python programming language. It was created by Armin Ronacher and is licensed under a BSD License. Jinja is similar to the Django template engine, but provides Python-like expressions while ensuring that the templates are evaluated in a sandbox. It is a text-based template language and thus can be used to ...
This template is used to display Coxeter–Dynkin diagrams (CDDs). To use the template, list the parts of the diagram from left to right, separated by vertical-bar (pipe) characters (" | "). For example:
Dia has special objects to help draw entity-relationship models, Unified Modeling Language (UML) diagrams, flowcharts, network diagrams, and simple electrical circuits. It is also possible to add support for new shapes by writing simple XML files, using a subset of Scalable Vector Graphics (SVG) to draw the shape.
KNIME (/ n aɪ m / ⓘ), the Konstanz Information Miner, [2] is a free and open-source data analytics, reporting and integration platform.KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept.
The plug flow reactor model (PFR, sometimes called continuous tubular reactor, CTR, or piston flow reactors) is a model used to describe chemical reactions in continuous, flowing systems of cylindrical geometry.
The outer circle in the diagram symbolizes the cyclic nature of data mining itself. A data mining process continues after a solution has been deployed. The lessons learned during the process can trigger new, often more focused business questions, and subsequent data mining processes will benefit from the experiences of previous ones.