A modular radio interferometer array simulator, including the radio sky and instrumental effects.
Note that currently this package only supports Python 2.6+, and not Python 3.
The only non-python dependencies are openmpi
and xterm
.
These are not required, but do provide extra functionality.
These can usually be installed via a distro package manager (eg. for Arch Linux,
the package names are exactly openmpi
and xterm
).
If using the Anaconda python distribution, many of the packages may be installed using
conda
. While these dependencies will be installed automatically with the installation
procedure below (i.e. with pip), usually conda users will want to install these with
conda explicitly before installing prisim
.
The conda-appropriate packages can be installed with
conda install mpi4py progressbar psutil pyyaml h5py astropy matplotlib numpy scipy scikit-image
NOTE: at this time, you must install scikit-image
via conda, or else it will
try to install packages that are incompatible with python 2. Full python 3
support is coming soon.
Finally, either install PRISim directly:
pip install git+https://github.com/nithyanandan/PRISim
or clone it into a directory and from inside that directory issue the command:
pip install .
First try using the following (from anywhere on your computer, but inside your env):
setup_prisim_data.py
If this does not work, try a manual download, as follows:
Find the data/
directory under PRISim installation folder which is usually in
/path/to/Anaconda/envs/YOURENV/lib/python-2.7/site-packages/prisim/
Download the contents of PRISim Data from either Google Drive
or the zipped version from Zenodo (.zip or tar.gz) or Google Drive (.tar.gz)
Extract the contents of the zipped file and place it under
/path/to/Anaconda/envs/YOURENV/lib/python-2.7/site-packages/prisim/data/
On terminal, run:
mpirun -n 2 test_mpi4py_for_prisim.py
and you must see a message that the test was successful. Otherwise you will have
to ask your system administrator to install openmpi
It has also been noted that it is preferable to install mpi4py
using
conda install mpi4py
rather than
pip install mpi4py
because the pip installation seems to get the paths to the MPI
libraries
mixed up.
Run on terminal:
mpirun -n nproc run_prisim.py -i parameterfile.yaml
or
mpirun -n nproc xterm -e run_prisim.py -i parameterfile.yaml
where, nproc
is the number of processors (say, 4), and use of option
xterm -e
opens an xterm window where you can view the progress of each of the processes.
Data size is proportional to n_bl x nchan x n_acc
If you use PRISim for your research, please acknowledge and cite it using the bibtex entry from https://doi.org/10.5281/zenodo.2548116