chrisckwong821 / jupyterq

Jupyter kernel for kdb+

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 (multiline 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
• Python ≥ 3.5
• embedPy

Overview

You can either

• install JupyterQ to run on your local machine; or
• download or build a Docker image in which to run JupyterQ

There are two ways to install JupyterQ on your local machine:

1. download and install a release
2. install with Conda – recommended if you are already using Anaconda Python

Install on local machine

Download and install a release

1. Make sure you have installed embedPy

2. Download a release archive from the releases page, and unzip it.

3. Install the required Python packages with Pip or Conda

   # pip
   pip install -r requirements.txt
   # conda
   conda install --file requirements.txt

4. Ensure QHOME is set and you have a working version of q in your PATH. Note that Jupyter will not pick up Bash aliases when starting q: the location of the q executable needs to be in your PATH.

5. Run the install script

   Linux/macOS

   ./install.sh

   Windows

   install.bat
   

Install with Conda

1. Download and install either the full Anaconda distribution or Miniconda for Python3.

2. Use Conda to install JupyterQ and its dependencies

   conda install -c kx jupyterq

3. Ensure you can run q before running JupyterQ for the first time. You may need to obtain an on-demand licence

q
…
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NOTE

If installing on a system which already has kdb+ we recommended installing JupyterQ, embedPy and the conda packaged version of kdb+ in a conda environment, this can be done using the following steps

// create a new environment and install jupyterq and its dependancies
> conda create -n jupyterqenv -c kx jupyterq
// move into the new environment where jupyterq can be run
> conda activate jupyterqenv

It should be noted that in this case JupyterQ, embedPy and the conda installed kdb can only be run from this activated environment.

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Running after install

To run the Jupyter console

jupyter console --kernel=qpk

To run the example notebook

jupyter notebook kdb+Notebooks.ipynb

Run a Docker image

If you have Docker installed you can alternatively run:

docker run -it --name myjupyterq -p 8888:8888 kxsys/jupyterq

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

For subsequent runs, you will not be prompted to redo the license setup when calling:

docker start -ai myjupyterq

To change the port or use the image to run your own notebooks, see the Docker README.

See instructions regarding headless/presets.

See build instructions for the image.

Using notebooks

See the notebook kdb+Notebooks.ipynb for full interactive examples and explanation. (It should be legible on GitHub.)

Documentation

See documentation on the JupyterQ homepage.