Using SpECTRE's Python modules

Some classes and functions from SpECTRE have Python bindings to make it easier to visualize data, write test code, and provide an introduction to numerical relativity without needing to delve into C++.

Installing the SpECTRE Python modules

First, build SpECTRE with Python bindings enabled by appending the -D BUILD_PYTHON_BINDINGS=ON flag to the cmake command. You can specify the Python version, interpreter and libraries used for compiling and testing the bindings by setting the -D Python_EXECUTABLE to an absolute path such as /usr/bin/python3. You will find that a BUILD_DIR/bin/python directory is created that contains the Python modules. Then, you can install the modules into your Python environment in development mode, which means they are symlinked so that changes to the modules will be reflected in your Python environment, with the command pip install -e path/to/bin/python. Alternatively, remove the -e flag to install the modules normally. You can do this in any Python environment that supports pip, for instance in a virtualenv/venv or in an Anaconda environment. You can also get access to the SpECTRE Python modules by adding BUILD_DIR/bin/python to your PYTHONPATH. This is done automatically by sourcing the file with the command . BUILD_DIR/bin/ By default, SpECTRE uses jemalloc which needs to be pre-loaded for the python bindings to work. Therefore, you need to run LD_PRELOAD=/path/to/ python to execute python scripts or start python consoles. The path to your preferred jemalloc installation is printed out at the end of the cmake configuration or can be found by running the script BUILD_DIR/bin/ Alternatively, you can use your system's memory allocator by appending the flag -D MEMORY_ALLOCATOR=SYSTEM to the cmake command. In this case you will not need to pre-load any libraries.

Running Jupyter within the Docker container

Jupyter lab is installed in the Docker container. You can run it in the container and access it through a browser on the host for a convenient way to work with the SpECTRE Python bindings. To do so, make sure you have exposed a port when running the Docker container, e.g. by appending the option -p 8000:8000 to the docker run command (see Installation). Inside the docker container, it can be convenient to use disown or to apt-get install screen and use screen to obtain a shell that runs the Jupyter server permanently in the background. Within the shell you want to run your Jupyter server, navigate to a directory that will serve as the root for the file system that Jupyter has access to. Make sure it is shared with the host (e.g. SPECTRE_HOME) so your Jupyter notebooks are not lost when the container is deleted. Then, run the following command:

jupyter lab --ip --port 8000 --allow-root

Copy the token that is being displayed. Now you can open a browser on the host machine, point it to http://localhost:8000 and paste in the token. You will have access to the Python environment within the Docker container. If you have followed the instructions above for installing the SpECTRE Python package, you can try importing the Python package in a notebook with:

import spectre

While developing Python code in the spectre packages it can be useful to configure Jupyter to reload packages when they change. Add the following code before any import statements:

%load_ext autoreload
%autoreload 2