The PyLops project strives to create a library that is easy to install in any environment and has a very limited number of dependencies. However, since Python2 will retire soon, we have decided to only focus on a Python3 implementation. If you are still using Python2, hurry up!
For this reason you will need Python 3.6 or greater to get started.
Our mandatory dependencies are limited to:
We advise using the Anaconda Python distribution
to ensure that these dependencies are installed via the
Conda package manager. This
is not just a pure stylistic choice but comes with some hidden advantages, such as the linking to
Intel MKL library (i.e., a highly optimized BLAS library created by Intel).
If you simply want to use PyLops for teaching purposes or for small-scale examples, this should not really affect you. However, if you are interested in getting better code performance, read carefully the Advanced installation page.
PyLops’s optional dependencies refer to those dependencies that we do not include
environment.yml files and thus are not strictly
needed nor installed directly as part of a standar installation (see below for details)
However, we sometimes implement additional back-ends (referred to as
engine in the code)
for some of our operators in order to improve their performance.
To do so, we rely on third-party libraries. Those libraries are generally added to the
list of our optional dependencies.
If you are not after code performance, you may simply stick to the mandatory dependencies
and pylops will ensure to always fallback to one of those for any linear operator.
If you are instead after code performance, take a look at the Optional dependencies section in the Advanced installation page.
Step-by-step installation for users¶
Activate your Python environment, and simply type the following command in your terminal to install the PyPi distribution:
>> pip install pylops
If using Conda, you can also install our conda-forge distribution via:
>> conda install -c conda-forge pylops
Note that using the
conda-forge distribution is recommended as all the dependencies (both mandatory
and optional) will be correctly installed for you, while only mandatory dependencies are installed
Alternatively, to access the latest source from github:
>> pip install git+https://firstname.lastname@example.org/PyLops/pylops.git@master
or just clone the repository
>> git clone https://github.com/PyLops/pylops.git
or download the zip file from the repository (green button in the top right corner of the main github repo page) and install PyLops from terminal using the command:
>> make install
If you want to try PyLops but do not have Python in your local machine, you can use our Docker image instead.
After installing Docker in your computer, type the following command in your terminal (note that this will take some time the first time you type it as you will download and install the docker image):
>> docker run -it -v /path/to/local/folder:/home/jupyter/notebook -p 8888:8888 mrava87/pylops:notebook
This will give you an address that you can put in your browser and will open a jupyter-notebook enviroment with PyLops and other basic Python libraries installed. Here /path/to/local/folder is the absolute path of a local folder on your computer where you will create a notebook (or containing notebooks that you want to continue working on). Note that anything you do to the notebook(s) will be saved in your local folder.
A larger image with Conda distribution is also available. Simply use conda_notebook instead of notebook in the previous command.
Step-by-step installation for developers¶
Fork and clone the repository by executing the following in your terminal:
>> git clone https://github.com/your_name_here/pylops.git
The first time you clone the repository run the following command:
>> make dev-install
If you prefer to build a new Conda enviroment just for PyLops, run the following command:
>> make dev-install_conda
To ensure that everything has been setup correctly, run tests:
>> make tests
Make sure no tests fail, this guarantees that the installation has been successfull.
If using Conda environment, always remember to activate the conda environment every time you open a new bash shell by typing:
>> source activate pylops