FRETBursts can be installed as a standard python package either via conda
or PIP (see below). Being written in python, FRETBursts runs on OS X,
Windows and Linux.
For updates on the latest FRETBursts version please refer to the Release Notes (What’s new?).
The preferred way to to install and keep FRETBursts updated is through
conda, a package manager used by Anaconda scientific python distribution.
If you haven’t done it already, please install the python3 version of
Continuum Anaconda distribution
(legacy python2 works too but is less updated).
Then, you can install or upgrade FRETBursts with:
conda install fretbursts -c conda-forge
After the installation, it is recommended that you download and run the FRETBursts notebooks to get familiar with the workflow. If you don’t know what a Jupyter Notebooks is and how to launch it please see:
See also the FRETBursts documentation section: Running FRETBursts.
Users that prefer using PIP, have to make sure that all the non-pure python dependencies are properly installed (i.e. numpy, scipy, pandas, matplotlib, pyqt, pytables), then use the usual:
pip install fretbursts --upgrade
The previous command installs or upgrades FRETBursts to the latest stable release.
As a rule, all new development takes place on separate “feature branches”. The master branch should always be stable and releasable. The advantage of installing from the master branch is that you can get updates without waiting for a formal release. If there are some errors you can always roll back to the latest released version to get your job done. Since you have the full version down to the commit level printed in the notebook you will know which version works and which does not.
You can install the latest development version directly from GitHub with:
pip install git+git://github.com/tritemio/FRETBursts.git
Note
Note that the previous command fails if git is not installed.
Alternatively you can do an “editable” installation, i.e. executing FRETBursts from the source folder. In this case, modifications in the source files are immediately available on the next FRETBursts import. To do so, clone FRETBursts and install it as follows:
git clone https://github.com/OpenSMFS/FRETBursts.git
cd FRETBursts
pip install -e .
It is recommended that you install cython before
FRETBursts so that the optimized C routines are installed as well.
Also, make sure you have lmfit and seaborn installed before running
FRETBursts.
If you want to install multiple versions of FRETBursts, you can create separate environments with conda. Each conda environments can contain a totally different set of packages, so you can have an environment with the latest released FRETBursts and one with the latest master version, for example.
FRETBursts is not in the default conda channel, but in the `conda-forge channel <https://conda-forge.github.io/ >`__. You can add conda-forge to the channel list with:
conda config --append channels conda-forge
This appends conda-forge to the channel list, with a lower
priority than the default channel. It means that a package available,
with the same version, in both conda-forge and the default channel,
will be installed from default.
To make a new environment called fbmaster containing python 3.6 and
fretbursts:
conda create -n fbmaster python=3.6 fretbursts
The environment needs to be activated:
. activate fbmaster
(on windows remove the leading “dot”).
Once the environment is activated you can install/remove more packages in it.
For example you can replace the stable FRETBursts with the version from github master using
pip install -e . in the same terminal where the environment has been activated.
Installing the stable FRETBursts first allows installing all the dependencies through conda.
Conda adds the environment to the notebook menu. So when you open a notebook, you can go to the
menu Kernel -> Change kernel and select fbmaster instead of default (or vice versa).
The latest used kernel is saved in the notebook so you don’t have to switch every time.
Environments help to be more reproducible in computations. They can be “saved” or exported to a text file for recreation on a different machine. For example, you can have the analysis for an old paper that fails to run or gives different results on an updated python installation. If you saved the environment file, you can restore the old environment with the exact version of all packages. It saves you time, trouble and makes the analysis more reproducible.
Refer to the conda documentation Managing environments for details.