Installation¶

The installation of data-slicer has been tested on Linux, macOS and Windows.

The easiest way to install the package is to use pip. Just type the following on a command line:

pip install data_slicer

If the above does not appear to work, it might help to try installation from a virtual environment.

Anaconda¶

Detailed instructions for Anaconda users follow:

Open “Anaconda Prompt”
In order not to mess up your dependencies, create a virtual environment with python version 3.7.5:
```
$ conda create --name testenv python==3.7.5
[some output]
$ conda activate testenv
(testenv) $
```
Inside your virtual environment, run the following commands to download and install data_slicer with all its dependencies (the first one is just to upgrade pip to the latest version):
```
(testenv) $ pip install --upgrade pip
(testenv) $ pip install data_slicer
```
This will create a lot of console output. If everything succeeded, you should see something like Successfully installed data_slicer towards the end.
Test the installation by starting up PIT:
```
(testenv) $ pit
```
This should bring up a window with some example data. See also below, for how to run automated tests to verify your installation.

Verifying your installation¶

Once installed, you can run a set of automated tests in order to check if the main features work as expected. To do this, issue the following on the command line:

python -m data_slicer.tests.run_tests

The result should be that some text is printed to the console and some windows open, with a few things happening in them before they quickly close again. Basically, these tests simulate a few interactions that the user could have with these windows and verify that they worked with some checks. If all went well you might see some warnings, but no notifications of any failures. It could, for example, look like this:

================== 4 passed, 16 warnings in 14.92 s ==================

Note

The fact that all tests passed does not guarantee that everything is in working order - but it’s a very good sign.

If interested, you can also run these tests individually and interact with the respective windows by calling them like so:

python -m data_slicer.tests.test_XXX

where XXX is any of pit, freeslice, threedwidget.

Upgrading¶

The following command will attempt to upgrade data-slicer to the latest published version:

pip install --upgrade data_slicer

It is usually a good idea to upgrade pip itself before running above command:

pip install --upgrade pip

Note

Run these commands from within the same (virtual) environment as you’ve installed data-slicer in.

Dependencies¶

This software is built upon on a number of other open-source frameworks. The complete list of packages is:

matplotlib>=3.1.1
numpy>=1.17.3
PyOpenGL>=3.1.1a1
pyqtgraph>=0.11.0
PyQt5>=5.13.2
qtconsole>=4.6.0
pytest>=6.2.2
pytest-qt>=3.3.0

Most notably, this includes pyqtgraph for fast live visualizations and widgets, numpy for numerical operations and matplotlib for plot exporting functionalities.