Skip to main content

The KinMS (KINematic Molecular Simulation) package can be used to simulate observations of arbitary molecular/atomic cold gas distributions.

Project description

Documentation Status Python 3.6 PyPI version ASCL

The KinMS (KINematic Molecular Simulation) package can be used to simulate observations of arbitary molecular/atomic cold gas distributions. The routines are written with flexibility in mind, and have been used in various different applications, including investigating the kinematics of molecular gas in early-type galaxies (Davis et al, MNRAS, Volume 429, Issue 1, p.534-555, 2013), and determining supermassive black-hole masses from CO interfermetric observations (e.g. Davis et al., Nature, 2013). They are also useful for creating input datacubes for further simulation in e.g. CASA's sim_observe tool.

Note: Most users dont need to install KinMS directly, but likely want KinMS_fitter. Find out more at the KinMS website: https://www.kinms.space.

Install

KinMSpy is designed with Python users in mind. Lots of work has gone into making it lightweight and fast. You can install KinMS with pip install kinms. Alternatively you can download the code, navigate to the directory you unpack it too, and run python setup.py install.

It requires the following modules:

  • numpy
  • matplotlib
  • scipy
  • astropy

Documentation

A simple iPython notebook tutorial on the basics of KinMS can be found here: KinMS simple tutorial

A further suite of examples can be found in examples/KinMS_testsuite.py, which can be modified and updated for most use cases. To run these tests you can run the following commands from within python:

from kinms.examples.KinMS_testsuite import *
run_tests()

To get you started fitting observations with KinMS, check out KinMS_fitter, which wraps KinMS and automates many tasks for you! Check it out here: KinMS_fitter.

If you need go through the nuts and bolts of fitting, see the walk through here: Example fitting tutorial

API documentation is available here.

New: KinMS 3.0 is here!

As of August 2022 KinMS 3.0 has been released. This version has subtantial speed improvements, being around 35% faster in my tests. However, to implement this the interface has had to slightly change. Now only the interferometer/observation parameters are passed to the KinMS class at instantiation, while the parameters that can change (e.g. surface brightness/velocity profiles) are passed in the model_cube() call. The tutorial notebooks/examples have been updated to reflect this. If you need to keep using the old version then it can still be imported as from kinms import KinMS2, although this dual support will be removed in future updates.

New non-circular motions capability

As of version 2.2.0 KinMS now has the capability to model lopsided and bisymmetric gas flows, in addition to the pure radial motions included previously. To get started with this you need to add from kinms.radial_motion import radial_motion, and then pass one of the new methods to KinMS with the radial_motion_func keyword. radial_motion.lopsided_flow and radial_motion.bisymmetric_flow both take four arguments (a radial vector, the transverse and radial velocity as a function of that radius, and an angle for the perterbation). radial_motion.pure_radial replicates previous funcationality, and requires two arguments (a radius vector, and a vector for the radial velocity as a function of radius). E.g. if previously you were passing inflowVel=inflowVel then this would now equate to radial_motion_func=radial_motion.pure_radial(radius,inflowVel).

Communication

If you find any bugs, or wish to be kept up to date when new versions of this software are released, please raise an issue here on github, or email us at DavisT -at- cardiff.ac.uk, Zabelnj -at- cardiff.ac.uk, Dawsonj5 -at- cardiff.ac.uk

License

KinMSpy is MIT-style licensed, as found in the LICENSE file.

Many thanks,

Dr Timothy A. Davis, Nikki Zabel, and James M. Dawson

Cardiff, UK

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kinms-3.0.8.tar.gz (49.8 kB view details)

Uploaded Source

Built Distribution

kinms-3.0.8-py3-none-any.whl (51.8 kB view details)

Uploaded Python 3

File details

Details for the file kinms-3.0.8.tar.gz.

File metadata

  • Download URL: kinms-3.0.8.tar.gz
  • Upload date:
  • Size: 49.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/7.0.1 pkginfo/1.10.0 requests/2.32.3 requests-toolbelt/0.9.1 tqdm/4.66.4 CPython/3.9.20

File hashes

Hashes for kinms-3.0.8.tar.gz
Algorithm Hash digest
SHA256 6caaf2d4db496208acc98f60bd33adac8ff5abe12ff605c3f4318d35e8bfcaba
MD5 cdd857e6664a8cf11f0345a258ff1d9c
BLAKE2b-256 9070ab8c57988e1d86985554e6e3c705949e11ac9c3b301cc61bd372ba866110

See more details on using hashes here.

File details

Details for the file kinms-3.0.8-py3-none-any.whl.

File metadata

  • Download URL: kinms-3.0.8-py3-none-any.whl
  • Upload date:
  • Size: 51.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/7.0.1 pkginfo/1.10.0 requests/2.32.3 requests-toolbelt/0.9.1 tqdm/4.66.4 CPython/3.9.20

File hashes

Hashes for kinms-3.0.8-py3-none-any.whl
Algorithm Hash digest
SHA256 6aad892e2c522d2d49aa2623b67fa8444b8ee08e3cbb2edac64b2288434e9d6b
MD5 2f60b60ca65ec4ebcf3c369a004699d0
BLAKE2b-256 d7b563437f3f3e057742f793aef89174647236abe0dbaff01e2332ea6c4a8fec

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page