semvec/pypi-packages huggingface dataset:
terragen 0.4.0
Configure, generate and apply Terraform modules to create and destroy Cloud Infrastructure
License: MIT
Requires Python: >=3.8.0


# terragen
Terragen is a framework for generating and automatically applying Terraform modules to simplify the management of sophisticated Cloud Architectures

## Key Features
 * Simplify the creation and destruction of infra across multiple accounts and regions, using one command
 * Fine grained CLI control, all config elements can be overridden on the CLI
 * View generated Terraform files before they are applied.  Each run creates a timestamped output directory
 * Control common and app specific infra separately.  Infra that is shared, eg VPCs, Databases can be controlled separately but still referenced by app specific infra
 * Create default config files you can "sub-class" for specific implementations, allows you to enforce config patterns across your estate
 * Boiler plate Terraform files, like config, tfvars automatically generated
 * Variable interpolation.  You can lookup values from other config files.

## Installing
Terragen can be installed via pip:

```commandline
pip install terragen
```

## ReadTheDocs
[Full details on configuring and running Terragen on ReadTheDocs](https://terragen.readthedocs.io/en/latest/)

## Terragen Workspace
Terragen expects to be run in its own workspace containing its configuration and the Terraform modules you want to configure and apply.

* [Example Workspace is available here](https://github.com/hunt3ri/terragen-example-configs)

## Demo Commands
Once configured as defined in docs, Terragen is very powerful.  Below we demonstrate some commands

### Applying a Named Config
Terragen lets you define multiple apps and configs, just apply them by name
```commandline
terragen --config-dir ./config --config-name sandbox
```

### Specifying the environment we want to deploy to
Terragen lets you create the same infrastructure across multiple accounts by supplying the environment you want to deploy to
```commandline
terragen --config-dir ./config build.environment=test
```

## Debugging
Validate and resolve your config before you run it.
```commandline
terragen -cd ./config --cfg job --resolve
```
### Verbose debugging
You can enable verbose debug logging by passing the following:
```commandline
terragen -cd ./config hydra.verbose=true
```
ilp-keyboard-layout-optimization 0.0.4a3
The QAP variant of keyboard layout optimization, i.e. character to key assignments
License: MIT
Requires Python: >=3.10
Keywords: ilp,qap,keyboard layout optimization

# Keyboard layout optimization via ILP

This repository contains my implementation of the task to optimize a keyboard layout 
in terms of assigning the available characters to the keys in a clever way. This is 
part of my participation in the _Seminar: Integer Programming modeling of puzzles, 
games, and real-world problems_ during the winter term 21/22 at Technische 
Universität zu Berlin.

## Usage

This code requires the [SCIP Optimization Suite
](https://www.scipopt.org/index.php#download) to be installed on the machine to be 
used, which we prepared in a Docker image at [docker_pyscipopt
](https://github.com/BjoernLudwigPTB/docker_pyscipopt). The proper use of this image 
is well documented in its [README.md
](https://github.com/BjoernLudwigPTB/docker_pyscipopt#how-to-build-image). The 
actual code can then be found in the [_src/ilp_keyboard_layout_optimization_ subfolder
](https://git.tu-berlin.de/blutub3d/ilp_keyboard_layout_optimization/-/tree/main/src/ilp_keyboard_layout_optimization).

## Remote development

We included [a bash script _pull_and_optimize.sh_
](https://git.tu-berlin.de/blutub3d/ilp_keyboard_layout_optimization/-/blob/main/pull_and_optimize.sh)
in our codebase to streamline a remote development workflow. We work on the code on a 
computer, that is well-equipped for that task. The committed and pushed code then 
gets processed on another machine, which uses this script, to update its code base 
and run the parameters handed over. It is designed to be called without parameters
to execute the _optimize_ module of the [latest version released on Test.PyPI.org
](https://test.pypi.org/project/ilp-keyboard-layout-optimization/).

```shell
$ ./pull_and_optimize.sh
```

The execution requires the Docker image of our repository [docker_pyscipopt
](https://github.com/BjoernLudwigPTB/docker_pyscipopt) to be built in advance, but it 
could be easily adapted for a local installation of the SCIP Optimization Suite.