hypercat 0.1.7

Hypercube of clumpy AGN tori

HYPERCAT

Hypercubes of (clumpy) AGN tori

Hypercat images at 2.2 and 30 micron, and their composite.

Synopsis

Handle a hypercube of CLUMPY brightness maps and 2D projected dust maps. Easy-to-use classes and functions are provided to interpolate images in many dimensions (spanned by the model parameters), extract monochromatic or multi-wavelength images, as well as rotate images, zoom in and out, apply PSFs, extract interferometric signals, quantify morphologies, etc.

Authors

Robert Nikutta <[e-mail]>, Enrique Lopez-Rodriguez, Kohei Ichikawa

Version

Version of this document: 2022-09-20

Current version of the HYPERCAT package:

License and Attribution

HYPERCAT is open-source software and freely available at https://github.com/rnikutta/hypercat/ and https://pypi.org/project/hypercat/ under a permissive BSD 3-clause license .

In short, if you are using in your research any of the HYPERCAT software or its components, and/or the HYPERCAT model data hypercubes, and/or telescope pupil images, please cite these two papers:

• Nikutta, Lopez-Rodriguez, Ichikawa, Levenson, Packham, Hönig, Alonso-Herrero, "Hypercubes of AGN Tori (Hypercat) -- I. Models and Image Morphology", ApJ (2021, accepted)

• Nikutta, Lopez-Rodriguez, Ichikawa, Levenson, Packham, Hönig, Alonso-Herrero, "Hypercubes of AGN Tori (Hypercat) -- II. Resolving the torus with Extremely Large Telescopes", ApJ (2021, under review)

Minimal install instructions

If you don't mind installing HYPERCAT and its dependencies into your current environment (real or virtual), simply run:

pip install hypercat

If you prefer to install HYPERCAT into a fresh new environment without affecting your existing Python installation, you can create a new environment in various ways.

If you are a user of conda / anaconda / miniconda / astroconda:

We recommend to update the conda-installled packages first (but you also first try to install HYPERCAT without updating):

conda update --all

After that:

conda create -n hypercat-env python=3.7.2
conda activate hypercat-env

pip install hypercat

If you are a user of pyenv:

pyenv install 3.7.2
. .venv/bin/activate

pip install hypercat

Installation trouble-shooting

tbw

HYPERCAT / CLUMPY model images and 2D dust cloud maps

Hypercat needs to access the hypercubes of Clumpy images and dust maps. They can be downloaded as hdf5 files from the link given at https://www.clumpy.org/images/ (which currently is ftp://ftp.tuc.noirlab.edu/pub/nikutta/hypercat/).

We offer several model files, which only differ in the wavelength range they cover:

File name	Size compressed / raw (GB)	Nwave	Wavelengths (micron)
hypercat_20200830_all.hdf5	271 / 913	25	all of the below
hypercat_20200830_nir.hdf5	44 / 146	4	1.2, 2.2, 3.5, 4.8
hypercat_20200830_mir.hdf5	120 / 402	11	8.7, 9.3, 9.8, 10, 10.3, 10.6, 11.3, 11.6, 12, 12.5, 18.5
hypercat_20200830_fir.hdf5	65 / 219	6	31.5, 37.1, 53, 89, 154, 214
hypercat_20200830_submm.hdf5	42 / 146	4	350, 460, 690, 945

Download and unpacking

For example, the *_all.hdf5.gz file contains the image hypercube at all sampled wavelengths. This is the maximally compressed version of the hdf5 file, which must be uncompressed on the user’s computer system. To reduce the peak storage required on the target computer, both steps can be executed in one go (all commands in a single line):

lftp -e 'set net:timeout 10; cat /pub/nikutta/hypercat/hypercat_20200830_all.hdf5.gz; bye' ftp.tuc.noirlab.edu | gunzip >
hypercat_20200830_all.hdf5

The program lftp must be installed on the target system, and 913 GB of space must be available on it (but only 271 GB of compressed data will be downloaded).

File validation

One should also download the checksums file ftp://ftp.tuc.noirlab.edu/pub/nikutta/hypercat/hypercat_20200830.md5 and verify the hypercube file:

# this can take 30 minutes even on a modern computer
md5sum --ignore-missing -c hypercat_20200830.md5
hypercat_20200830_all.hdf5: OK

# or on MacOS and BSD variants
md5 hypercat_20200830_all.hdf5

#... and compare the printed hash with the one in the .md5 file

Pointing HYPERCAT to a model file

The software, and the example Jupyter notebooks (see below) will need to be instructed about the location of the model file(s). This is very easy to do upon loading the model file; the notebooks have several examples on how to accomplish this, e.g.

import hypercat as hc
fname = 'hypercat_20200830_all.hdf5' # use your local location to the HDF5 model file
cube = hc.ModelCube(fname,hypercube='imgdata')  # use 'imgdata' for brightness maps, and 'clddata' for 2D cloud maps

Example Jupyter notebooks

Several Jupyter example notebooks demonstrate some of HYPERCAT's functionality:

• 01-hypercat-basics.ipynb: Loading a model hypercube, generating model images, images at multiple wavelengths, images at multiple values of other model parameters, accessing cloud maps

• 02-hypercat-astro.ipynb: Adding physical units to images, world coordinate system, field of view and pixel scale operations, image rotation / position angle, saving to FITS files

• 03-hypercat-singledish.ipynb: Telescope pupil images (JWST, Keck, GMT, TMT, ELT), simulating observations with single-dish telescopes, noisy observations, Richardson-Lucy deconvolotuion, detector pixel scale, flux preservation, observations at multiple wavelengths

• 04-hypercat-morphology-intro.ipynb: Introduction to morphological measurements (on 2D Gaussians), image centroid, rotation, measuring size of emission features, elongation, half-light radius, Gini coefficient

• 05-hypercat-morphology-clumpy.ipynb: Morphology of the HYPERCAT model images; morphological sizes, elongation, centroid location; compare morphologies of of emission and their underlying dust distributions; from 2D cloud maps to real cloud numbers per LOS; photon escape probability along a LOS

User Manual

WARNING -- the User Manual is still work-in-progress:

For more detailed installation instructions and other usage examples, please see the HYPERCAT User Manual User Manual (in addition to the example Jupyter notebooks )

Contributing

Bug fixes and feature contributions to HYPERCAT are welcome. Please make a pull request against the 'master' branch.