This project is a standalone Jupyter environment for doing data science using Python. It aims to include many useful working libraries and packages, while remaining super easy to install and use.
General requirements:
For wxWidgets, I downloaded the source then used:
mkdir build-release
cd build-release
../configure --enable-shared --enable-monolithic --with-osx_cocoa CXX='clang++ -std=c++11 -stdlib=libc++' CC=clang --with-macosx-version-min=10.8 --disable-debug --without-liblzma
make -j4
sudo make install
To get g++-4.9 (required) use the Ubuntu Toolchain PPA.
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install g++-4.9
You'll need various development files to compile.
sudo apt-get install build-essential libz-dev libgtk2.0-dev
libreadline-dev libssl-dev libncursesw5-dev
libgdbm-dev libsqlite3-dev libbz2-dev liblzma-dev
libreadline-gplv2-dev
libc6-dev
Get the wxWidget source and compile:
mkdir buildgtk
cd buildgtk
../configure --with-gtk
make -j4
sudo make install
If prerequisites are met, you should be able to do:
mkdir build
cd build
cmake ..
make
This builds python and various libraries and builds the main application.
make custom-install
This installs pip requirements and then copies custom
files into the notebook static directory. Now you can
try the local application with make local-test
.
Redistributable packages are built using CPack.
make install
make package
The final redistributable files will be placed at the top level of the build directory. Final packages will be compressed tar files for Linux, DMG images for Mac.
If you want to change the icon images, you'll need to regenerate the icns files. I did this using iconverticons because the command-line tools I found were out of date and I didn't want to hassle with automating it.
Pineapple is a project of Nathan Whitehead, copyright 2015. Let me know what you think at nwhitehe [at] gmail.com.