This is a fork of exxeleron/qPython. I have forked this to provide Python 3.7+ support to qPython, since the original project is in "maintenance mode" and seems to be stale. Support to older versions of Python (especially anything below 3.6) is not guaranteed and will probably be dropped.
Currently, no plans to add the project to PyPI, so in case you find this useful, install from Github:
pip install git+https://github.com/gusutabopb/qPython3.gitqPython is a Python library providing support for interprocess communication between Python and kdb+ processes, it offers:
- Synchronous and asynchronous queries
- Convenient asynchronous callbacks mechanism
- Support for kdb+ protocol and types: v3.0, v2.6, v<=2.5
- Uncompression of the IPC data stream
- Internal representation of data via numpy arrays (lists, complex types) and numpy data types (atoms)
- Supported on Python 2.7/3.4/3.5/3.6 and numpy 1.8+
For more details please refer to the documentation.
To install qPython from PyPI:
$ pip install qpython
Please do not use old PyPI package name: exxeleron-qpython.
qPython documentation is generated with help of Sphinx document generator.
In order to build the documentation, including the API docs, execute:
make html from the doc directory.
Documentation is built into the: doc/build/html/ directory.
qPython utilizes Cython to tune performance critical parts of the code.
Instructions:
- Execute:
python setup.py build_ext --inplace
Instructions:
- Execute:
python setup.py bdist
qPython uses py.test as a test runner for unit tests.
Instructions:
- Make sure that top directory is included in the
PYTHONPATH - Execute:
py.test
qPython requires numpy 1.8 to run.
Optional requirements have to be met to provide additional features:
- tune performance of critical parts of the code:
- Cython 0.20.1
- support serialization/deserialization of
pandas.Seriesandpandas.DataFrame- pandas 0.14.0
- run Twisted sample:
- Twisted 13.2.0
- build documentation via Sphinx:
- Sphinx 1.2.3
- mock 1.0.1
Required libraries can be installed using pip.
To install all the required dependencies, execute:
pip install -r requirements.txt
Minimal set of required dependencies can be installed by executing:
pip install -r requirements-minimal.txt