Project page | Paper | Supplemental material
Aviad Levis, Daeyoung Lee, Joel A. Tropp, Charles F. Gammie, and Katherine L. Bouman (2021). "Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements." In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2340-2349. 2021.
@inproceedings{levis2021inference,
title={Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements},
author={Levis, Aviad and Lee, Daeyoung and Tropp, Joel A and Gammie, Charles F and Bouman, Katherine L},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={2340--2349},
year={2021}
}
pynoisy is:
-
A python wrapper for a modified version of the
inoisy
code [1] that supports arbitrary xarray matrices as diffusion tensor fields. pynoisy can be used to generate 3D (spatio-temporal) Gaussian Random Fields (GRFs) as solutions to a stochastic partial differential equation [2,3] (SPDE), which is solved usingHYPRE
computing library [4].Tutorial1
within the tutorials directory gives a notebook example on generation of GRFs. -
A tool for inferring parameters of stochastic fluid dynamics of black-hole accretion from Event Horizon Telescope (EHT) measurements. EHT measurements are Very Large Baseline Intereferometric (VLBI) measurements which are synthesized using
eht-imaging
[5].
Installation using using anaconda package management.
Prerequisites:
The installation steps assume that MPI (e.g. openmpi, mpich) is installed and was tested on Linux Ubuntu 18.04.5. For a self-contained list of instructions see the Singularity .def
file which can be used to generate a container with MPI and conda as explained below. A partial list of the prerequisites include gcc
, gsl
, and hdf5
which can be installed using
sudo apt-get install libgsl-dev
sudo apt-get install gcc gfortran g++ make
Installing OpenMPI with HDF5 on Ubuntu using apt worked (dated: 11/09/2021)
sudo apt update
sudo apt install openmpi-bin openmpi-common openmpi-doc libopenmpi-dev
sudo apt install libhdf5-openmpi-dev
Installation:
Clone pynoisy repository with the inoisy submodule
git clone --recurse-submodules https://github.com/aviadlevis/pynoisy.git
cd pynoisy
Clone and install HYPRE library. If HYPRE was previously installed make sure to have HYPRE_DIR
point to the right path.
git clone https://github.com/hypre-space/hypre.git
cd hypre/src
./configure
make install
cd ../../
Start a virtual environment with new environment variables
conda create -n pynoisy python=3.7.4
conda activate pynoisy
conda env config vars set INOISY_DIR=$(pwd)/inoisy HYPRE_DIR=$(pwd)/hypre/src/hypre
conda env config vars set LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$(pwd)/hypre/src/hypre/lib/
conda activate pynoisy
Install pynoisy
conda install --file requirements.txt
pip install .
Install xarray and its dependencies
conda install -c conda-forge xarray dask netCDF4 bottleneck
Install eht-imaging
conda install -c conda-forge pynfft requests scikit-image
git clone https://github.com/achael/eht-imaging.git
cd eht-imaging
pip install .
cd ../
The easiest way to get started is through the jupyter notebooks in the tutorials
directory.
These notebooks cover both the generation (forward) and estimation (inverse) methods and procedures. Furthermore,
basic utility and visualization methods are introduced and used throughout.
Login and enter API access token
singularity remote login
Build the image to a .sif file
singularity build --remote pynoisy_mpi.sif pynoisy_mpi.def
Run a singularity shell
singularity shell pynoisy_mpi.sif
Proceed with the installation instruction (above) cloning and installing pynoisy and the required dependencies (HYPRE, xarray, eht-imaging etc).
inoisy
code- Lee, D. and Gammie, C.F., 2021. Disks as Inhomogeneous, Anisotropic Gaussian Random Fields. The Astrophysical Journal, 906(1), p.39.
- Lindgren, F., Rue, H. and Lindström, J., 2011. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(4), pp.423-498.
HYPRE
computing libraryeht-imaging
code
© Aviad Levis, California Institute of Technology, 2020.