Installation#

This section provides information on how to install DataLab on your system. Once installed, you can start DataLab by running the cdl command in a terminal, or by clicking on the DataLab shortcut in the Start menu (on Windows).

See also

For more details on how to execute DataLab and its command-line options, see Command line features.

How to install#

DataLab is available in several forms:

See also

Impatient to try the next version of DataLab? You can also install the latest development version of DataLab from the master branch of the Git repository. See Development version for more information.

Package manager pip#

GNU/Linux Windows macOS

DataLab’s package cdl is available on the Python Package Index (PyPI) on the following URL: https://pypi.python.org/pypi/cdl.

Installing DataLab from PyPI with Qt is as simple as running this command (you may need to use pip3 instead of pip on some systems):

$ pip install cdl[qt]

Or, if you prefer, you can install DataLab without the Qt library (not recommended):

$ pip install cdl

Note

If you already have a previous version of DataLab installed, you can upgrade it by running the same command with the --upgrade option:

$ pip install --upgrade cdl[qt]

All-in-one installer#

Windows

DataLab is available as a stand-alone application for Windows, which does not require any Python distribution to be installed. Just run the installer and you’re good to go!

../_images/windows_installer.png

DataLab all-in-one installer for Windows#

The installer package is available in the Releases section. It supports automatic uninstall and upgrade feature (no need to uninstall DataLab before runinng the installer of another version of the application).

Warning

DataLab Windows installer is available for Windows 8, 10 and 11 (main release, based on Python 3.11) and also for Windows 7 SP1 (Python 3.8 based release, see file ending with -Win7.exe).

On Windows 7 SP1, before running DataLab (or any other Python 3 application), you must install Microsoft Update KB2533623 (Windows6.1-KB2533623-x64.msu) and also may need to install Microsoft Visual C++ 2015-2022 Redistribuable package.

Python distribution#

Windows

DataLab is also available within a ready-to-use Python distribution, based on WinPython. This distribution is called DataLab-WinPython and is available in the DataLab-WinPython Releases section.

../_images/DataLab-WinPython.png

DataLab-WinPython is a ready-to-use Python distribution including the DataLab platform.#

The main difference with the all-in-one installer is that you can use the Python distribution for other purposes than running DataLab, and you may also extend it with additional packages. On the downside, it is also much bigger than the all-in-one installer because it includes a full Python distribution.

../_images/wpcp.png

DataLab-WinPython Control Panel#

Warning

Whereas the all-in-one installer provides a monolithic package that guarantees the compatibility of all its components because it cannot be modified by the user, the WinPython distribution is more flexible and thus can be broken by a bad manipulation of the Python distribution by the user. This should be taken into account when choosing the installation method.

Wheel package#

GNU/Linux Windows macOS

On any operating system, using pip and the Wheel package is the easiest way to install DataLab on an existing Python distribution:

$ pip install --upgrade DataLab-0.11.1-py2.py3-none-any.whl

Source package#

GNU/Linux Windows macOS

Installing DataLab directly from the source package may be done using pip:

$ pip install --upgrade cdl-0.11.1.tar.gz

Or, if you prefer, you can install it manually by running the following command from the root directory of the source package:

$ pip install --upgrade .

Finally, you can also build your own Wheel package and install it using pip, by running the following command from the root directory of the source package (this requires the build and wheel packages to be installed):

$ pip install build wheel  # Install build and wheel packages (if needed)
$ python -m build  # Build the wheel package
$ pip install --upgrade dist/cdl-0.11.1-py2.py3-none-any.whl  # Install the wheel package

Development version#

GNU/Linux Windows macOS

If you want to try the latest development version of DataLab, you can install it directly from the master branch of the Git repository.

The first time you install DataLab from the Git repository, enter the following command:

$ pip install git+https://github.com/DataLab-Platform/DataLab.git

Then, if at some point you want to upgrade to the latest version of DataLab, just run the same command with options to force the reinstall of the package without handling dependencies (because it would reinstall all dependencies):

$ pip install --force-reinstall --no-deps git+https://github.com/DataLab-Platform/DataLab.git

Note

If dependencies have changed, you may need to execute the same command as above, but without the --no-deps option.

Dependencies#

Note

The DataLab all-in-one installer already include all those required libraries as well as Python itself.

The cdl package requires the following Python modules:

Name

Version

Summary

Python

>=3.8, <4

Python programming language

h5py

>= 3.0

Read and write HDF5 files from Python

NumPy

>= 1.21, < 2

Fundamental package for array computing in Python

SciPy

>= 1.7

Fundamental algorithms for scientific computing in Python

scikit-image

>= 0.18

Image processing in Python

opencv-python-headless

>= 4.5

Wrapper package for OpenCV python bindings.

pandas

>= 1.3

Powerful data structures for data analysis, time series, and statistics

PyWavelets

>= 1.1

PyWavelets, wavelet transform module

psutil

>= 5.5

Cross-platform lib for process and system monitoring in Python.

guidata

>= 3.5

Automatic GUI generation for easy dataset editing and display

PlotPy

>= 2.3

Curve and image plotting tools for Python/Qt applications

QtPy

>= 1.9

Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6).

PyQt5

>=5.11

Python bindings for the Qt cross platform application toolkit

Optional modules for development:

Name

Version

Summary

ruff

An extremely fast Python linter and code formatter, written in Rust.

pylint

python code static checker

Coverage

Code coverage measurement for Python

pyinstaller

>=6.0

PyInstaller bundles a Python application and all its dependencies into a single package.

Optional modules for building the documentation:

Name

Version

Summary

PyQt5

Python bindings for the Qt cross platform application toolkit

sphinx

Python documentation generator

sphinx_intl

Sphinx utility that make it easy to translate and to apply translation.

sphinx-sitemap

Sitemap generator for Sphinx

myst_parser

An extended [CommonMark](https://spec.commonmark.org/) compliant parser,

sphinx_design

A sphinx extension for designing beautiful, view size responsive web components.

sphinx-copybutton

Add a copy button to each of your code cells.

pydata-sphinx-theme

Bootstrap-based Sphinx theme from the PyData community

Optional modules for running test suite:

Name

Version

Summary

pytest

pytest: simple powerful testing with Python

pytest-xvfb

A pytest plugin to run Xvfb (or Xephyr/Xvnc) for tests.

Note

Python 3.11 and PyQt5 are the reference for production release