A tutorial (+ build recipes) describing how to use conda for C, C++, and python package management
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ags_example_cpp_app-0.1.0
ags_example_cpp_lib-0.1.0
ags_example_py_wrapper-0.1.0-np18
ansible-1.7.2
ant-1.9.4
ant-1.9.6
assertpy-0.10
assertpy-0.11
assertpy-0.12
assertpy-0.13
assertpy-0.8
assertpy-0.9
autoconf-2.69
automake-1.15
autopep8-1.0.4
autopep8-1.1.1
autopep8-1.2.2
avro-python3-1.8.2
awesome-slugify-1.6.4
awesome-slugify-1.6.5
awesome-slugify-1.6
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beaker-1.7.0
beaker-1.8.0
betamax-0.5.1
betamax-matchers-0.2.0
betamax-serializers-0.1.1
blinker-1.4
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bottle-0.12.13
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bz2file-0.98
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c-blosc-1.7.1
caffe-rc3
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cherrypy-3.5.0
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cmake-3.1.0
cmake-3.2.3
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confluent-kafka-0.11.4
confluent-kafka-0.11.5
datadog
docker-2.5.1
elasticsearch-1.4.0
elasticsearch-2.3.0
env-excavator-1.5.0
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gensim-0.10.1-np18
gensim-0.10.1-np19
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geoip2-2.2.0
geopy-1.11.0
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ggplot-0.6.5-np19
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git-2.2.2
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google-apputils-0.4.2
httplib2-0.9.1
httplib2-0.9.2
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ipy_table-1.12
jdk-1.7.0.67.01
jdk-1.7.0.71.14
jdk-1.8.0.74.02
jenkins-1.583
kazoo-2.2.1
langid-1.1.4dev-np18
langid-1.1.4dev-np19
langid-1.1.5-np110
ldap3-0.9.8.2
ldap3-1.2.2
leveldb-1.19
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librdkafka-0.11.3
librdkafka-0.11.4
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librdkafka-0.8.6
librdkafka-0.9.0.99
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librdkafka-0.9.5
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networkx_viewer-0.2.0
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p4python-14.2
pandasql-0.6.1-np18
pandasql-0.6.2
pandasql-0.7.1
pattern-2.6
pkg-config-0.25
praw-2.1.18
praw-3.4.0
prettyplotlib-0.1.7
protobuf-3.0.0.b3
protobuf-3.0.2
protobuf-3.1.0
protobuf-3.5.1
pweave-0.21.2
pweave-0.23
pweave-0.24
py-radix-0.9.3
pyaml-15.8.2
pycapnp-0.5.7
pydruid-0.2.0
pygal-1.5.1
pygal-1.7.0
pygal-2.1.1
pyjq-2.1.0
pyjwt-1.4.0
pykafka-2.1.1 added pykafka, librdkafka, and other deps Dec 15, 2015
pykafka-2.1.2
pykafka-2.3.0
pyquery-1.2.11
pyrocksdb-0.5
pytest-cov-2.2.1
pytest-runner-2.7
pytest-runner-3.0
python-dateutil-2.5.3
python-gflags-3.0.5
python-nvd3-0.12.2
python-nvd3-0.14.2
python-rocksdb-0.6.7
python-slugify-1.1.4
python-snappy-0.5
pytz-2016.4
pyyaml-3.11
r-project-3.1.3
regex-2014.08.28
regex-2015.03.18
regex-2016.03.02
requests-oauthlib-0.4.2
requests-toolbelt-0.6.0
rocksdb-4.1
rocksdb-4.3.1
rocksdb-5.3.6
seaborn-0.4.0-np18
seaborn-0.5.1
setuptools_scm-1.10.1
setuptools_scm-1.15.6
simplejson-3.6.4
simplejson-3.6.5
smart_open-1.2.1
snappy-1.1.3
snappy-1.1.7
tablib-0.10.0
tabulate-0.7.5
thrift-0.9.1-np18
thrift-0.9.2
thrift-0.9.3
tmux-1.9a
tmux-2.1
tweepy-2.3
tweepy-3.3.0
tweepy-3.5.0
twiggy-0.4.5
twiggy-0.4.7
unidecode-0.04.16
update_checker-0.10
update_checker-0.11
urllib3-1.10.3
urllib3-1.14
vertica-python-0.3.0
vim-7.4.443_py27
vim-7.4.443_py34
vim-7.4.443_py35
wsgiref-0.1.2
zsh-5.0.7
zsh-5.2
README.rst
agsdev-0.1.0-linux-64.export
agsdev-0.2.0-linux-64.export
agsdev-0.3.0-linux-64.export
agsdev-0.3.1-linux-64.export
agsdev3-0.1.0-linux-64.export
agsdev3-0.1.1-linux-64.export
agsdev3-0.2.0-linux-64.export
build_order.txt

README.rst

Motivation

If you are developing in C, C++, or python then you should use conda (https://github.com/conda/conda.git) for your package/dependency management. For other languages you might consider it as well.

To justify this, consider other package managers:

  • python has pip + virtualenv
  • C/C++ has apt-get, yum, nuget, homebrew, etc

pip works well if you are only dealing with pure python packages. However, many of the best python packages (e.g. numpy) are written in C, and pip is not a good tool for managing native binaries.

System-level package managers like apt-get and yum work much better with native binaries, but they have serious drawbacks of their own:

  1. need admin privileges to install packages (and changes are system-wide)
  2. limited to packages that are available (and allowed by your IT department)
  3. your builds will be tightly coupled to the platform
  4. every platform has its own package manager
  5. how do you manage dependencies within your own codebase?

Point (2) is particularly nasty. How many of us have been told that our deploy environment is RHEL6 with python 2.6 and gcc 4.4 (or, worse, RHEL5)? Your favorite libraries will not be available.

Point (3) is ugly if you need to deploy across many environments. Maintaining Windows and Mac builds is hard enough. Do you want to maintain separate builds for RHEL5, RHEL6, Ubuntu12.04, and Ubuntu14.04 as well?

Point (4) is a problem even for the Linux-only crowd. Which distro?

Point (1) is at least no longer a problem for Linux developers who use docker. Unfortunately there is not a similar solution for Windows and Mac projects.

The python world mitigated (1) with the introduction of virtualenv. Unfortunately this only works well for pure python code.

What is conda?

conda keeps the good parts of these package managers and jettisons the bad. Like virtualenv, conda avoids the system-wide trap through "environments". No admin privileges are necessary. Unlike virtualenv, it can handle native packages as well.

There is no reliance on platform-specific containers like docker or VMs. The same system works natively on Linux, Windows, and Mac.

conda's package repository (analogous to PyPI) is called anaconda.org (formerly binstar). anaconda.org allows you to upload your own public and private packages, and group yourselves into organizations (similar to how github works).

For proprietary packages (that cannot leave your firewall) you can maintain your own package repository. This can be as simple as a directory containing tarballs. The enterprisey solution is to purchase Anaconda Server from Continuum Analytics (http://www.continuum.io). If your needs are simple then we wrote a silly "poor-man's Anaconda Server" https://github.com/ActivisionGameScience/poboys_conda_package_server.git.

Finally, you can pull from several repositories simultaneously. conda refers to repositories as "package channels".

What is Anaconda?

Anaconda is a specific set of useful packages being maintained by Continuum Analytics (http://www.continuum.io). It is free, but there are several add-on packages (mkl, iopro, and accelerate) that require a license.

The examples below build on top of non-free Anaconda, but you don't need to buy a license if you don't want to. We will show you how to remove mkl, iopro, and accelerate if you don't need them.

About this tutorial

This tutorial is an end-to-end example showing how to use conda in production. We use it to manage both public packages (on anaconda.org) and proprietary packages (behind our firewall).

We will use our own dev environment as a kitchen-sink example. It is a mix of Anaconda packages and our own builds of popular software. Anything not pulled from the default channels will be pulled from our channel https://anaconda.org/ActivisionGameScience. You are invited to contribute to our environment, but otherwise you can use it as an example.

Although Anaconda is fully cross-platform, our channel is lagging in Windows and Mac support (we are slowly working on it). The techniques presented, however, will be nearly identical across all platforms. It should take about 5 minutes to get going (followed by a 30-minute nap while the packages download).

After briefly playing with our environment, we will describe how we created it. In particular, we will pick a package at random and show how it was built (using a "recipe") and uploaded to anaconda.org.

Finally, we will describe 3 example projects that we posted on github. You should pretend that these are your proprietary projects. You will learn how to build them and publish the packages behind your firewall.

Besides learning how to publish packages behind your firewall, you will see that conda handles in-house dependencies in the same way that it handles third-party dependencies.

As a bonus, we will demonsrate how to use cmake to build C++ projects, and how to use cffi to create python wrappers around C/C++ libraries.

First steps: install conda

Before starting, find the most spartan machine possible. Really annoy yourself. A Windows or Mac box will work fine for now. I started with this barebones CentOS5 vagrant box: http://tag1consulting.com/files/centos-5.9-x86-64-minimal.box.

It has no git and no java (let alone javac, mvn, or ant). It doesn't even have vim! It's missing tmux and zsh, and the system python is 2.4. You can forget your favorite python libraries. It doesn't have autotools (good luck building anything).

Let's turn this machine into a joy. Download the Miniconda installer from http://conda.pydata.org/miniconda.html and run it. In Linux:

sh Miniconda-3.7.0-Linux_x86_64.sh

Mac is nearly identical:

sh Miniconda-3.7.0-MacOSX_x86_64.sh

or in Windows:

Miniconda-3.7.0-Windows_x86_64.exe

Feel free to use the python 3 installer (Miniconda3-*) instead if you have already made the switch. Both Anaconda and our dev environment are up-to-date (we use python 3 more often, in fact).

Install it wherever you like (I chose ~/miniconda for Linux and Mac and C:\miniconda for Windows). You can allow the installer to permanently modify your PATH if you want. If so then close and reopen your terminal. If not then you'll always have to enable conda manually. In Linux or Mac:

export PATH=~/miniconda/bin:$PATH

or in Windows:

set PATH=C:\miniconda;C:\miniconda\Scripts;%PATH%

Either way, in Linux or Mac typing which python should show ~/miniconda/bin/python (in Windows where python should show C:\miniconda\python.exe). This is your "root" environment.

Only admin packages are allowed in the root environment. Don't pollute it with anything else. Your real environments will live below the envs/ subdirectory.

Now edit your ~/.condarc file and add our channel and the default channels:

channels:
  - https://conda.anaconda.org/ActivisionGameScience
  - defaults

(in Windows your .condarc file lives in your home directory).

Remember that in YAML indents are 2 spaces (conda will complain otherwise). Since our ActivisionGameScience channel is listed first, packages will be pulled from there preferentially.

Now update everything in your root environment and install a couple of utility packages:

conda update --all
conda install jinja2 git conda-build anaconda-client

(in Windows and Mac please omit git because we do not have it packaged there yet).

Your first environment

If you were in a hurry then you could create a full-fledged Anaconda environment (on any platform) with the command:

conda create -n myenv anaconda

Instead, let's create a minimal environment containing only python and flask:

conda create -n myenv python flask

The new environment will be in the subdirectory envs/mydev/. You can "activate" it like this (Linux or Mac):

source activate mydev

or in Windows:

activate mydev

The new environment contains its own instance of python and flask, i.e. the following import should work:

from flask import Flask

It is easy to install more packages. For example, to install ipython from within an activated environment you would use the command:

conda install ipython

Some environments can have hundreds of packages, so we need a way to reproduce them exactly. You can export the current environment to a text file:

conda list --export > myenv.export

Then, from another machine, recreate the environment exactly:

conda create -n myenv --file myenv.export

(note: export files will be platform-specific, i.e. a Windows export file will not work on a Linux box).

Finally, in Linux or Mac you can deactivate the environment like this (this puts you back into the root environment):

source deactivate

or in Windows:

deactivate

Try out our environment!

You are ready to try out our ActivisionGameScience dev environment. Even if you don't like it, it should give you an idea of the possibilities.

Unfortunately, our dev environment only supports Linux currently. However, the concepts translate to Windows and Mac with almost no change

Clone the current repository (that you are reading):

git clone https://github.com/ActivisionGameScience/ags_conda_recipes.git

or, alternatively, just grab our latest exported file:

ags_dev?-<latest version>-linux-64.export

This contains an exact specification of packages that we like. Some of them come from Anaconda, but many of them come from our own channel. Now you can create your own agsdev environment (name it whatever you want):

conda create -n agsdev --file agsdev?-<latest version>-linux-64.export

Go for a walk to let it download (takes about 30 minutes). Future installs will be almost instantaneous because conda keeps a cache of downloaded tarballs.

After your walk, check out the directory envs/agsdev/. There's your new environment.

You can "activate" it like this:

source activate agsdev

Go ahead, test some things out! You'll notice that everything is there that I complained about (git, cmake, vim, tmux, zsh, java, javac, ant, mvn, and much more!).

Remove licensed components if you don't need them

If you don't want to buy licenses then you need to remove the non-free packages:

conda remove accelerate iopro mkl mkl-rt mkl-service mklfft numbapro cudatoolkit

You'll need a BLAS replacement for MKL, though:

conda install openblas

How did we build and upload our packages to anaconda.org?

Now that you have our environment loaded and running, you might want to know how we built it.

In order to build a package for conda you'll need to write a "recipe". Fortunately, some recipes are so trivial that they can be auto-generated by conda (this is true for most packages in PyPI). For example, to generate a recipe for the library tweepy we use the following command:

conda skeleton pypi tweepy

This creates a directory, tweepy/, that contains the following files:

meta.yaml
build.sh
bld.bat

You should rename the directory to clarify the version of the library that it builds (i.e. tweepy/ becomes tweepy-2.3/) You can find the version in the meta.yaml file.

Pro tip: for packages that link against numpy I have found it necessary to edit meta.yaml and pin the numpy version explicitly:

- numpy 1.8.2

then rename the directory to remind us that we pinned the version, i.e. gensim/ becomes gensim-0.10.1-np18/.

We are not so lucky with other packages (e.g. jdk and vim). Their recipes must be handwritten and often require considerable knowledge of various compilers (e.g. gcc, clang, cl), options, environment variables, and build tools (e.g. cmake, make, nmake, Visual Studio projects, etc).

Because of these difficulties, it is important for us to publish our recipes and encourage pull requests. Our goal is to work together to build a comprehensive library of third-party packages. We especially encourage adding Windows and Mac support.

Build and upload

Make sure that you are in the root environment for this step. Do a source deactivate to make sure.

You can build tweepy-2.3/ with the following command (from one directory above):

conda build tweepy-2.3

Assuming that everything built correctly there will now be a tarball in ~/miniconda/conda-bld/linux-64/ (or in similar directories for Mac and Windows).

Pro tip: for packages that compile C/C++ code (including cython), you should always build with the oldest compiler possible (at least for gcc). I use a RHEL5 box to build our packages because more modern versions of libc will be able to use those binaries (but not the other way around).

Unfortunately, MSVC binaries are not always forward ABI compatible, so the same advice may not apply there

Since our organization on anaconda.org is called ActivisionGameScience I uploaded the package with the following command:

anaconda upload -u ActivisionGameScience ~/miniconda/conda-bld/linux-64/tweepy-2.3-py27.tar.bz2

Obviously I needed to input my personal credentials (and be a member of the ActivisionGameScience organization).

How to manage your codebase with conda

The real power of conda manifests itself when you want to manage your own code. Most shops (especially C/C++ groups) suffer from their own home-brewed Rube Goldberg machines.

With conda we can escape this mess in a cross-platform manner. You can build code however you want, but use conda to handle the package and dependency management.

Pro tip: although you can build using autotools or whatever, we strongly suggest building C/C++ projects with cmake, and python projects with setuptools. Combined with conda this gives a fully cross-platform solution that requires very little platform-specific code.

Project 1: a C++ wrapper library around c-blosc

Look at the repo https://github.com/ActivisionGameScience/ags_example_cpp_lib.git. This is a dumb C++ wrapper around the popular c-blosc compression library. You could clone that repo and build it by hand using cmake (the README contains instructions).

However, we have written a conda recipe to handle it. Clone the current repo (that you are reading):

git clone https://github.com/ActivisionGameScience/ags_conda_recipes.git
cd ags_conda_recipes

and build the recipe:

conda build ags_example_cpp_lib-0.1.0

As always, when building packages, make sure that you have run source deactivate beforehand so that you are in the root environment.

What just happened? This recipe created a sandbox environment, downloaded all dependencies (c-blosc and cmake are in our ActivisionGameScience channel), cloned ags_example_cpp_lib from github, ran cmake, ran the C++ compiler, and then ran the installer. Finally, it created a tarball from the installed files.

The new package is now in ~/miniconda/conda-bld/linux-64/. Check out the README in the https://github.com/ActivisionGameScience/ags_example_cpp_lib.git to see what files are included in the package.

Since we are pretending that this is your proprietary package, we do not want to upload this to anaconda.org. We want to publish the package to your own repository behind the firewall. Let's see how to do this.

Behind-the-firewall conda repository

We'll make the simplest conda repository possible: a directory of tarballs. If you need something enterprisey then consider purchasing Anaconda Server. If you need something in-between then look at our silly project https://github.com/ActivisionGameScience/poboys_conda_package_server.git.

First create some directory to hold your packages:

mkdir /some/path/pkgs_inhouse

Then add it to your .condarc:

channels:
  - file:///some/path/pkgs_inhouse
  - https://conda.anaconda.org/ActivisionGameScience
  - defaults

Next add a platform subdirectory and copy your new package into it:

mkdir /some/path/pkgs_inhouse/linux-64
cp ~/miniconda/conda-bld/ags_example_cpp_lib-0.1.0.tar.bz2 /some/path/pkgs_inhouse/linux-64

Go into the platform subdirectory and index it (this must be repeated whenever adding a new package):

cd /some/path/pkgs_inhouse/linux-64
conda index

We are done. We can install the package in the usual conda way:

conda install ags_example_cpp_lib

and remove it just as easily:

conda remove ags_example_cpp_lib

How it works

To see how conda handled the package management, it is easiest to start with the README in the repo https://github.com/ActivisionGameScience/ags_example_cpp_lib.git.

There you will find details describing how to build and install the library manually using cmake. The most important thing to notice is that cmake needs c-blosc to be already installed. The location is passed on the cmake command line using the argument -DCBLOSC_ROOT=....

For completeness, you should have a look at the cmake scripts:

CMakeLists.txt
cmake/Modules/FindCBLOSC.cmake

to see how the headers and binaries are actually found (this is what the compiler wants). cmake is the best tool for handling the build itself.

But how can we ensure that c-blosc will be installed? For that matter, how can we ensure that cmake will be installed?

This is a dependency problem that is best left to conda. Look at the current repo (that you are reading now) in the directory ags_example_cpp_lib-0.1.0/. In meta.yaml you will see that both cmake and c-blosc are listed as build dependencies, and that c-blosc is repeated as a runtime dependency:

requirements:
  build:
    - cmake
    - c-blosc

  run:
    - c-blosc

Fortunately, both cmake and c-blosc happen to be packages in our anaconda.org channel https://conda.anaconda.org/ActivisionGameScience. Hence conda will know how to install them before attempting a build of ags_example_cpp_lib.

Aside: we wrote recipes for c-blosc and cmake as well. Look in their respective recipe directories c-blosc-1.5.2/ and cmake-3.1.0/ at meta.yaml. You will see that c-blosc also uses cmake to build, but requires no further dependencies. cmake requires no dependencies. We were able to add these packages to our channel by first building and uploading cmake, then building and uploading c-blosc.

Back in the recipe for ags_example_cpp_lib-0.1.0/, look at the Linux/Mac build script build.sh. It contains the exact cmake commands that are described in the README:

mkdir build
cd build
cmake ../ -DCBLOSC_ROOT=$PREFIX  -DCMAKE_INSTALL_PREFIX=$PREFIX

make
make install

($PREFIX will be filled in by conda at build time).

So we see that cmake handles the build beautifully, and conda ensures that the necessary dependencies will be there when cmake goes looking for them.

Project 2: a C++ application using our library

We can repeat this game with the repo https://github.com/ActivisionGameScience/ags_example_cpp_app.git. This project builds two executables: ags_blosc_compress and ags_blosc_decompress. They are command-line utilities that perform blosc compression/decompresson.

This project compiles against the library that we just built (ags_example_cpp_lib).

Aside: by transitivity it also links against c-blosc (but does not compile against it). We could've side-stepped this transitivity complication by having cmake build our library as a MODULE. Modules are self-contained: they have their dependencies linked in already. To keep the example simple, however, I restrained myself to only STATIC and SHARED versions of the library.

As before, if you wanted then you could clone the repo and build it by hand using cmake (the README contains instructions).

Again, we have written a conda recipe to handle it. Assuming that you already cloned this repo (that you are reading):

git clone https://github.com/ActivisionGameScience/ags_conda_recipes.git
cd ags_conda_recipes

you can build the package:

conda build ags_example_cpp_app-0.1.0

(remember to run source deactivate beforehand so that you are in the root environment).

The new package is now in ~/miniconda/conda-bld/linux-64/. Like before, you can put it in your behind-the-firewall conda repository:

cp ~/miniconda/conda-bld/ags_example_cpp_app-0.1.0.tar.bz2 /some/path/pkgs_inhouse/linux-64
cd /some/path/pkgs_inhouse/linux-64
conda index

I highly recommend that you read both the conda recipe and the cmake scripts to understand how this build and dependency management worked.

Project 3: a python wrapper around our C++ library

We do the same thing with the repo https://github.com/ActivisionGameScience/ags_example_py_wrapper.git. This project installs a python module, ags_py_blosc_wrapper, that wraps our C++ library. Look at the README for details how to use it.

Since this is pure python (the binding is done via cffi), no linking is necessary. There is no cmake because there is no C/C++ to build. The build is handled by setuptools.

However, we need our C++ library to be available at runtime. Again, conda handles this dependency. Here is the relevant section in ags_example_py_wrapper_0.1.0/meta.yaml:

requirements:
  build:
    - python
    - setuptools

  run:
    - python
    - numpy 1.8.2
    - cffi
    - ags_example_cpp_lib

Assuming you've already cloned this repo (that you are reading):

git clone https://github.com/ActivisionGameScience/ags_conda_recipes.git
cd ags_conda_recipes

you can build the package in seconds:

conda build ags_example_py_wrapper-0.1.0

The new tarball, located in ~/miniconda/conda-bld/linux-64/, can be added to your behind-the-firewall conda repository like the others:

cp ~/miniconda/conda-bld/ags_example_py_wrapper-0.1.0.tar.bz2 /some/path/pkgs_inhouse/linux-64
cd /some/path/pkgs_inhouse/linux-64
conda index

and installed the conda way:

conda install ags_example_py_wrapper
ipython
    In[0]: from ags_py_blosc_wrapper import BloscWrapper
    In[1]: b = BloscWrapper()

    ...

See the README for usage instructions.

License

All files are licensed under the BSD 3-Clause License as follows:

Copyright (c) 2015, Activision Publishing, Inc.
All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.