discminer 0.5.5

The Channel Map Modelling Code

discminer is a Python package for fitting three-dimensional parametric models directly to molecular-line channel maps of protoplanetary discs, then using model–data residuals to reveal their kinematic and intensity substructure.

It enables you to:

• Fit intensity and rotation velocity simultaneously across an entire channel-map cube or a selected subset of channels
• Model Keplerian rotation, with optional pressure support and disc self-gravity
• Reconstruct front- and back-side emitting surfaces and use flexible, non-axisymmetric prescriptions for model attributes
• Fit complex line profiles to derive peak-intensity, centroid-velocity, line-width, and line-slope moment maps
• Extract rotation curves and radial profiles of intensity, line width, and azimuthal, radial, and vertical velocity
• Detect and quantify coherent, localised structures in intensity and velocity residuals

Mining tools

Discminer offers a wide range of analysis and visualisation tools to fully explore the physical and dynamical structure of discs.

cube

• Compute moment maps that accurately capture complex line profile morphologies.
• Output moment maps include peak intensity, line width, line slope, and centroid velocity.
• Easily clip, downsample, and convert data to brightness temperature units.
• Quickly visualise model versus data channels and interactively extract spectra.

rail

• Extract azimuthal and radial profiles of intensity, line width, and velocity from moment maps.
• Compute rotation curves and decompose disc velocities into their three-dimensional components.
• Identify large-scale structures and quantify their pitch angle, width, extent, and degree of coherence.

pick

• Identify small-scale perturbations and estimate their degree of localisation.

plottools

• Customise intensity channels, moments, and residual maps.
• Use sky or disc projections interchangeably for improved visualisation of features.
• Easily overlay disc geometry (considering orientation and vertical structure) onto any observable product.
• Load in 1D profiles or 2D maps from external data e.g. to highlight the presence of dust substructures.

Installation

pip install discminer

To upgrade the code,

pip install -U discminer

Optional dependencies

• termplotlib
• FilFinder
• bettermoments
• schwimmbad
• ipython
• beam-boozle

How to use

You can find practical examples demonstrating the main functionality of the code in the ./example folder of this repository.

To run the examples on your local machine, clone this repository and follow the instructions provided in the README file,

git clone https://github.com/andizq/discminer.git
cd discminer/example/mwc480_12co
less README.rst

Citation

If you find discminer useful for your research please cite the work of Izquierdo et al. 2021,

@ARTICLE{2021A&A...650A.179I,
       author = {{Izquierdo}, A.~F. and {Testi}, L. and {Facchini}, S. and {Rosotti}, G.~P. and {van Dishoeck}, E.~F.},
        title = "{The Disc Miner. I. A statistical framework to detect and quantify kinematical perturbations driven by young planets in discs}",
      journal = {\aap},
     keywords = {planet-disk interactions, planets and satellites: detection, protoplanetary disks, radiative transfer, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics},
         year = 2021,
        month = jun,
       volume = {650},
          eid = {A179},
        pages = {A179},
          doi = {10.1051/0004-6361/202140779},
archivePrefix = {arXiv},
       eprint = {2104.09596},
 primaryClass = {astro-ph.EP},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021A&A...650A.179I},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}