Installation¶
Prerequisites¶
Running RAMP requires a working Python 3 installation. If you are unfamiliar with Python, this can be easily obtianed from the Python website or as part of the Anaconda package which comes preloaded with many useful libraries.
In addition to Python 3, RAMP is parallelized using the OpenCL API. To install the Python port of the OpenCL bindings and compile the OpenCL kernels used by RAMP, it will be necessary to install the OpenCL SDK appropriate to the vendor of the hardware you intend to run RAMP on. At the time of writing, these SDKs can be obtained from
If during the installation or execution of RAMP you are recieving errors relating to the header file ‘CL\cl.h’ it is likely your OpenCL SDK is incorrectly installed.
Obtaining RAMP¶
RAMP is most easily obtained by cloning the public GitHub repository:
$ git clone https://github.com/gcassella/RAMP.git
Otherwise the package can be downloaded from the GitHub page in a .zip archive. The package can then be installed via:
$ cd RAMP
$ python setup.py install
To ensure the package has been installed correctly, try running one of the examples found in the ‘examples’ directory:
$ cd examples
$ python LET_RAMP.py
If this prompts you to select your OpenCL device and then spits out a handful of detector image plots, everything is installed correctly!
Jupyter notebooks¶
Some of the instructional examples for RAMP are presented as Jupyter notebooks. These are interactive Python environments that allow code to be presented, edited, and ran alongside relevant text and graphical outputs. The easiest way to get up and running with RAMP notebooks is via Google Colab, see Quick start, however if you wish to run the notebooks locally follow the instructions here.
To install Jupyter (assuming you followed the instructions above and have a functional Python installation) run:
$ pip install jupyterlab
Jupyter can then be run via:
$ cd %your_ramp_directory_here%/examples/%example_you_want_to_run%
$ jupyter notebook
This will open a page in your default web browser from which you can select the example notebook and run the interactive Python therein.