Other options for Quantiphyse installation¶
Quantiphyse is in PyPi. So, in general, if you have Python and pip working, installation
may be as simple as:
pip install quantiphyse
In practice it not always as simple as this. Below are a number of ‘recipes’ for different platforms which we believe to work. However we cannot guarantee that these will work in all cases.
Note
These instructions are intended for experienced users with a good knowledge of Python environments and packages. If this is not you then please stick to the standard instructions in Installation of Quantiphyse
Note
To use some plugins you’ll need to have a working FSL installation. For more
information go to FSL installation.
Platforms
FSL¶
If you have FSL v6 or later you can install Quantiphyse using fslpython:
fslpython -m pip install quantiphyse --user
And for the plugins:
fslpython -m pip install quantiphyse-cest quantiphyse-asl quantiphyse-qbold quantiphyse-dce quantiphyse-dsc quantiphyse-t1 quantiphyse-fsl quantiphyse-sv quantiphyse-datasim quantiphyse-deeds --user
Some of the plugins may require build tools to be installed. If you get issues, see the additional requirements above for your platform (e.g. Ubuntu, Centos).
Warning
In general we do not recommed using --user with pip. It will install packages in your home
directory but they will be visible to any python environment with the same version as the one you
used to do the install. This often causes problems if you also have other Python environments
on your system (e.g. from Conda).
Ubuntu 16.04 - System python¶
From a terminal window:
sudo easy_install pip
Now install the application:
pip install quantiphyse --user
The recipe above just installs the main application. To install plugins use:
pip install quantiphyse-cest quantiphyse-asl quantiphyse-qbold quantiphyse-dce quantiphyse-dsc quantiphyse-t1 quantiphyse-fsl quantiphyse-sv quantiphyse-datasim quantiphyse-deeds --user
Note
See also the previous comments on the use of --user
Ubuntu 18.04 - System python¶
From a terminal window:
sudo apt install python-pip
Now install the application:
pip install quantiphyse --user
The recipe above just installs the main application. To install plugins use:
pip install quantiphyse-cest quantiphyse-asl quantiphyse-qbold quantiphyse-dce quantiphyse-dsc quantiphyse-t1 quantiphyse-fsl quantiphyse-sv quantiphyse-datasim quantiphyse-deeds --user
Note
In some cases on Ubuntu 18.04 you may find that ‘quantiphyse’ will not run from
the command line until you have either logged out and back in again or run
source $HOME/.profile at the command prompt.
Note
See also the previous comments on the use of --user
Centos 7 - System python¶
This recipe was tested in a Gnome Desktop installation. Open a terminal window and use the following:
sudo easy_install pip
pip install quantiphyse --user
The recipe above just installs the main application. To install plugins use:
sudo yum install gcc-c++ python-devel
sudo pip install setuptools --upgrade
pip install quantiphyse-cest quantiphyse-asl quantiphyse-qbold quantiphyse-dce quantiphyse-dsc quantiphyse-t1 quantiphyse-fsl quantiphyse-sv quantiphyse-datasim quantiphyse-deeds --user
Note
See also the previous comments on the use of --user
Mac OSX¶
Anaconda has been the usual method we have used to install Quantiphyse on Mac due to poor support for recent versions of Python on Mac.
However, on recent releases of OSX (e.g. Mojave) it may be possible to install Quantiphyse into the system Python using:
pip install quantiphyse --user
And for the plugins:
pip install quantiphyse-cest quantiphyse-asl quantiphyse-qbold quantiphyse-dce quantiphyse-dsc quantiphyse-t1 quantiphyse-fsl quantiphyse-sv quantiphyse-datasim quantiphyse-deeds --user
Note
Installation into fslpython is likely to be a more reliable method on Mac if you
have FSL. While the above method has worked for some users, we have also had issues with
incompatible Numpy and Scipy packages that may cause problems with the system python on Mac.
See also the previous comments on the use of --user
One issue with this is that the Quantiphyse executable is installed in a location which is not
in the user’s PATH - typically $HOME/Library/Python2.7/bin/. So you either need to run
Quantiphyse from that folder, or add this folder to your PATH by editing $HOME/.bash_profile:
export PATH=$PATH:$HOME/Library/Python2.7/bin/
Note that currently we do not have an easy way of adding Quantiphyse to the dock - one method is to create an Automator application which runs the executable.
If you have experience of installation using Homebrew please contact us with your recipe and we can add it here.
Windows¶
On Windows we strongly recommend using Anaconda.
FSL does not run natively on Windows, however it can be installed in the Windows Subsystem for Linux (WSL).
If you have FSL installed in Windows and Quantiphyse installed in Anaconda you will need to set FSLDIR
to be a UNC path to the FSLDIR in WSL. You can do this from one of the FSL widgets in Quantiphyse. You
will need to browse to the location \\wsl$\ and from there select your WSL distribution folder
(e.g. Ubuntu-18.04) and then the FSL location in that distribution (e.g. /usr/local/fsl). Once
done, Quantiphyse will use the FSL applications installed in WSL transparently.
Use of virtualenv¶
virtualenv is a tool for creating isolated Python environments. It can be preferable to installing
applications in the system Python environment. You can use virtualenv on most platforms - for example
to install into Ubuntu use:
sudo apt install python-virtualenv
Once installed you have to create and ‘activate’ the environment before installing applications:
virtualenv $HOME/venvs/qp
source $HOME/venvs/qp/bin/activate
pip install quantiphyse
To install Quantiphyse plugins use:
pip install quantiphyse-cest quantiphyse-asl quantiphyse-qbold quantiphyse-dce quantiphyse-dsc quantiphyse-t1 quantiphyse-fsl quantiphyse-sv quantiphyse-datasim quantiphyse-deeds --user
When you have finished using a virtualenv you must ‘deactivate’ it by simply running:
deactivate
To run an application installed in a virtualenv it must be activated first, e.g.:
source $HOME/venvs/qp/bin/activate
quantiphyse
Note
Some Quantiphyse plugins require a C++ compiler to build extensions. You may need to install this before you can install the plugins. See the Ubuntu and Centos sections above for examples of how to install a C++ compiler on these platforms.
Anaconda¶
Anaconda (https://www.anaconda.org) is an easy to install distribution of Python which
also includes the conda tool for installing packages.
You will need to install the Anaconda environment before using any of these recipes.
When selecting a Python version, Python 3.7 is the version on which Quantiphyse
has been most tested, however you can also use other versions. We no longer guarantee
that the application will run under Python 2.7 although we are not aware of any
incompatibilities within quantiphyse itself.
Once Anaconda is installed, follow the instructions in the relevant section below:
Note
In the future we hope to put Quantiphyse into conda itself so the whole
process can consist of conda install quantiphyse.
Anaconda Python 3.7¶
We recommend Python 3.7 as a reasonably up to date version of Python for which dependencies are generally widely available. While Quantiphyse should be compatible with newer Python releases sometimes it is difficult to get matching versions of important dependencies such as Numpy.
On Windows you must first install Visual C++ tools for Python 3 from:
https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019
Then use the following commands:
conda create -n qp python=3.7
conda activate qp
conda install -c conda-forge cython funcsigs matplotlib nibabel numpy pillow pyside2 pyyaml requests scipy scikit-learn scikit-image setuptools six pandas deprecation
pip install pyqtgraph-qp
pip install quantiphyse --no-deps
This installs the basic Quantiphyse app. To install plugins use pip, for example this is to install all current plugins:
pip install quantiphyse-cest quantiphyse-asl quantiphyse-qbold quantiphyse-dce quantiphyse-dsc quantiphyse-t1 quantiphyse-fsl quantiphyse-sv quantiphyse-datasim quantiphyse-deeds
On Mac you will also need to do:
pip install pyobjc
Anaconda Python 3.7 (dependencies from pip)¶
This variation takes dependencies from pip rather than conda. Normally it is preferable to use
conda for dependencies as you can run into problems when using different package managers for the
same package. However you may want to try this recipe if the previous ones do not work for you.
(but please tell us as well so we can fix
the instructions!):
On Windows you must first install Visual C++ tools for Python 3 from:
https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019
Then use the following commands:
conda create -n qp python=3.7
conda activate qp
pip install quantiphyse
This installs the basic Quantiphyse app. To install plugins use pip, for example this is to install all current plugins:
pip install quantiphyse-cest quantiphyse-asl quantiphyse-qbold quantiphyse-dce quantiphyse-dsc quantiphyse-t1 quantiphyse-fsl quantiphyse-sv quantiphyse-datasim quantiphyse-deeds
On Mac you will also need to do:
pip install pyobjc
Anaconda Python 2.7¶
Quantiphyse is compatible with the widely used Python 2.7, although this is now getting rather old and is no longer recommended unless you have a special need for it.
On Windows you must first install Visual C++ for Python 2.7 from:
Then use the following commands:
conda create -n qp python=2.7
conda activate qp
conda install -c conda-forge cython funcsigs matplotlib nibabel numpy pillow pyside2 pyyaml requests scipy scikit-learn scikit-image setuptools six pandas deprecation
pip install pyqtgraph-qp
pip install quantiphyse --no-deps
This installs the basic Quantiphyse app. To install plugins use pip, for example this is to install all current plugins:
pip install quantiphyse-cest quantiphyse-asl quantiphyse-qbold quantiphyse-dce quantiphyse-dsc quantiphyse-t1 quantiphyse-fsl quantiphyse-sv quantiphyse-datasim quantiphyse-deeds
On Mac you will also need to do:
pip install pyobjc
Docker image¶
This is a new and currently experimental method of running Quantiphyse.
If you’ve not used Docker before, it’s a means of running applications in an isolated environment with pre-installed dependencies - rather like a virtual machine but using the existing operating system rather than needing one of its own.
The easiest way to try Quantiphyse through docker is to first install docker itself - e.g. on Ubuntu you’d do:
sudo apt install docker
Then clone the github repository:
https://github.com/physimals/quantiphyse-docker
and run the script:
python quantiphyse-docker.py
This will download and run a Quantiphyse image. Although you need Python to run the script it does not use anything outside the standard library so any version should do.
Currently the Quantiphyse docker image does not have its own copy of FSL - instead it tries to use the one installed on your machine currently. This will only work if your machine is binary compatible with Ubuntu. Centos should be OK, but Mac is not, so you will not be able to use FSL functionality on Mac. We hope to offer an FSL-included version in the future.
Please let us know if you try this method and how you get on with it.