kxsys/jupyterq repository overview

⁠jupyterq

Jupyter kernel for kdb+. Features include

• Syntax Highlighting for q
• Code completion for q keywords, .z/.h/.Q/.j namespace functions, and user defined variables
• Code help for q keywords and basic help (display and type information) for user defined objects
• script like execution of code (mulitline input)
• Inline display of charts created using embedPy and matplotlib
• console stdout/stderr capture and display in notebooks
• Inline loading and saving of scripts into and from notebook cells

⁠Requirements

• kdb+>=? v3.5 64-bit
• Anaconda Python 3.x
• embedPy⁠

⁠Build and Installation

After you have installed embedPy install the Python dependencies with

pip

pip install -r requirements.txt

or with conda

conda install --file requirements.txt

⁠Download

Download the appropriate release archive from the releases⁠ page.

⁠Linux/Mac

Ensure QHOME is set and you have a working version of q in your PATH, and run

./install.sh

⁠Windows

Ensure QHOME is set and you have a working version of q in your PATH, and run

install.bat

Jupyter console, notebook and QtConsole should work, Jupyter lab has not been tested

⁠Mac

On mac there seem to be issues with MKL when using in embedded python mode e.g. (https://github.com/ContinuumIO/anaconda-issues/issues/6423⁠)

If you have a similar issue using Anaconda Python, the command below may resolve the issue.

conda install nomkl

⁠Docker

If you have Docker installed⁠ you can alternatively create a directory called q and place your kc.lic (or k4.lic) and l64.zip files into a q directory and run:

docker run --rm -it -v `pwd`/q:/tmp/q -p 8888:8888 kxsys/jupyterq

Now point your browser at http://localhost:8888/notebooks/kdb%2BNotebooks.ipynb⁠.

N.B. build instructions for the image are available⁠

⁠Running after install

To run the jupyter console

jupyter console --kernel=qpk

To run the example notebook

jupyter notebook kdb+Notebooks.ipynb

⁠Using notebooks

See the notebook kdb+Notebooks.ipynb for full interactive examples and explanation, it should be viewable on github.

⁠Documentation

Documentation is available on the jupyterq⁠ homepage.