https://github.com/AdaCore/ada-traits-containers

Generic Ada Library for Algorithms and Containers
https://github.com/AdaCore/ada-traits-containers

Last synced: 2 months ago
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Generic Ada Library for Algorithms and Containers

• Host: GitHub
• URL: https://github.com/AdaCore/ada-traits-containers
• Owner: AdaCore
• License: other
• Created: 2016-01-21T11:07:13.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2023-03-21T12:02:17.000Z (over 1 year ago)
• Last Synced: 2024-04-22T10:14:46.903Z (2 months ago)
• Language: Ada
• Size: 1.18 MB
• Stars: 28
• Watchers: 16
• Forks: 13
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-ada - ada-traits-containers - Generic Ada Library for Algorithms and Containers. (Libraries / Algorithms, Containers and Protocols)
• awesome-ada - ada-traits-containers - Generic Ada Library for Algorithms and Containers. (Libraries / Algorithms, Containers and Protocols)

README

        

# Generic Ada Library for Algorithms and Containers

## Goals

This library is another containers library for Ada. Although it

provides containers that do not exist in the standard Ada

runtime (graphs for instance), it is more interesting for the

flexibility it proposes:

  - [X] Bounded/Unbounded containers and even more variants suitable

        for use with the SPARK language.

  - [X] Finite/Indefinite elements, and even more specialized

        variants optimized for specific types

  - [X] Pre and Post conditions, compatible with SPARK, so that some

        variants of the containers can be used for proof.

  - [X] Highly efficient; the user has full control over memory

        allocations, checks, locks, ...

All this flexibility is done via the intensive use of generic

packages, themselves used to instantiate other generic packages.

Check the [documentation](doc/making_of.rst) for more details on the

design of the API, and its current usage.

## Compiling

The library itself is pure Ada code, and only requires a working

Ada compiler to be available in your environment.

This library comes with a testsuite which measures the performance

of the various variants of the containers, and compares them with

C++ equivalent (or near equivalents). This testsuite generates a

nice interactive HTML file.

Compiling and running the testsuite requires that you also have a

C++ compiler in your environment. In addition, you must install the

Boost Graph Library (http://www.boost.org).

You must also download and install the

[GNAT Components Collection](http://libre.adacore.com).

Finally, in order to run the testsuite, you need to install GNATpython and

PyYAML in your Python2 environment. You can install both with the following

command:

```sh

pip install -r REQUIREMENTS.txt

```

Once this is done, modify the [shared.gpr](src/shared.gpr) file.

Set the variable ```Boost_Include''' to point to the install prefix

for Boost:

```

   Boost_Include := ("-I/usr/include");

```

Finally, compile and run the test with

```

make all perfs

```

and finally open the file [tests/perfs/index.html](index.html)

in a browser to view the performance comparison.

## Editing with GNAT Programming Studio

To edit with GPS, including the tests, you must first run:

    make projects

Then you can edit by launching GPS from the top directory, which will

automatically load the aggregate project 'root.gpr'