Anvil

Just another autorigger Online Documentation (ReadTheDocs)

Introduction

My Autorigger. Ain't yo business..yet!

Features

• Caching
• Automated naming conventions
• YAML Config
• Online Documentation
• Temp file generator
• JSON file output
• CLI access
• Dict output
• Automated Custom Docker Image Builds
• Test suite automation using CircleCI
• Automated testing in Maya versions 2015, 2016, 2017, 2018

Prerequisites

To run Anvil locally you must have these installed:

• Maya (2015-2017=8)
• Python2.7 (or sudo access to pip install via mayapy)

Installation

Windows, etc.

A universal installation method (that works on Windows, Mac OS X, Linux, …, and always provides the latest version) is to use pip:

.. code-block:: bash

# Make sure we have an up-to-date version of pip and setuptools:
$ pip install --upgrade pip setuptools
$ pip install Anvil

(If pip installation fails for some reason, you can try easy_install anvil as a fallback.)

Usage

Python Package Usage

Use this tool via package level functions

.. code-block:: python

import anvil
anvil.lorem_ipsum()

Version Support

This package supports only Maya 2015, 2016 and 2017 so far so please be aware...plans for 2018 are in the works.

Acknowledgments

• Stu Schwartz - For being my constant sounding board and advice giver.
• Marcus Ottosson - Without him I would not have been able to complete the docker side of things.

Docker Image Documentation

Adapted README from Marcus Ottosson

Supported tags

• maya2015, maya2016, maya2017, maya2018

Each tag represents a particular version of Maya, such as maya2016. In this image, python is an alias to maya/bin/mayapy which has the following Python packages installed via pip. For more information about this image and its history, please see its the GitHub repository

Usage

To use this image and any of it's supported tags, use docker run.

$ docker run -ti --rm daemonecles/anvil

Without a "tag", this would download the latest available image of Maya. You can explicitly specify a version with a tag.

$ docker run -ti --rm daemonecles/anvil:2018

Images occupy around 5 gb of virtual disk space once installed, and about 1.5 gb of bandwidth to download.

Example

This example will run the latest available version of Maya, create a new scene and save it in your current working directory.

$ docker run -ti -v $(pwd):/root/workdir --rm daemonecles/maya2016
$ mayapy
>>> from maya import standalone, cmds
>>> standalone.initialize()
>>> cmds.file(new=True)
>>> cmds.polySphere(radius=2)
>>> cmds.file(rename="my_scene.ma")
>>> cmds.file(save=True, type="mayaAscii")
>>> exit()
$ cp /root/maya/projects/default/scenes/my_scene.ma workdir/my_scene.ma
$ exit
$ cat my_scene.ma

What's in this image?

This image builds on daemonecles/anvil which has the following software installed.

• Centos6
• git - old version
• pip2.6
• python2.6

Additional installations include.

• python2.7
• pip2.7
• git - up to date (for CircleCI checkout)

Environment variables

• $MAYA_VERSION=####: for getting the installed maya version quickly in the format #### - e.g. - 2018.
• $PYTHON_PATH=$HOME/nvenv/lib/python2.7/site-packages: Placeholder for your test runner virtualenv named "nvenv" so maya can access the installed packages.
• $TEST_PATH=$HOME/test-results: A default directory where the user can place test resulting xml files etc.

Feedback

Issues/Contributing

If you have any problems with or questions about contributing to this image, please contact Marcus Ottosson through a GitHub issue (since the image is basically his with minor additions)