CPROPS, written in IDL, processes FITS data cubes containing molecular line emission and returns the properties of molecular clouds contained within it. Without corrections for the effects of beam convolution and sensitivity to GMC properties, the resulting properties may be severely biased. This is particularly true for extragalactic observations, where resolution and sensitivity effects often bias measured values by 40% or more. We correct for finite spatial and spectral resolutions with a simple deconvolution and we correct for sensitivity biases by extrapolating properties of a GMC to those we would expect to measure with perfect sensitivity. The resulting method recovers the properties of a GMC to within 10% over a large range of resolutions and sensitivities, provided the clouds are marginally resolved with a peak signal-to-noise ratio greater than 10. We note that interferometers systematically underestimate cloud properties, particularly the flux from a cloud. The degree of bias depends on the sensitivity of the observations and the (u,v) coverage of the observations. In the Appendix to the paper we present a conservative, new decomposition algorithm for identifying GMCs in molecular-line observations. This algorithm treats the data in physical rather than observational units, does not produce spurious clouds in the presence of noise, and is sensitive to a range of morphologies. As a result, the output of this decomposition should be directly comparable among disparate data sets.

The CPROPS package contains within it a distribution of the CLUMPFIND code (ascl:1107.014) written by Jonathan Williams and described in Williams, de Geus, and Blitz (1994). If you make use of the CLUMPFIND functionality in the CPROPS package for a publication, please cite Jonathan's original article.

Spectral-cube provides an easy way to read, manipulate, analyze, and write data cubes with two positional dimensions and one spectral dimension, optionally with Stokes parameters. It is a versatile data container for building custom analysis routines. It provides a uniform interface to spectral cubes, robust to the wide range of conventions of axis order, spatial projections, and spectral units that exist in the wild, and allows easy extraction of cube sub-regions using physical coordinates. It has the ability to create, combine, and apply masks to datasets and is designed to work with datasets too large to load into memory, and provide basic summary statistic methods like moments and array aggregates.

The PHANGS-ALMA pipeline process data from radio interferometer observations. It uses CASA (ascl:1107.013), AstroPy (ascl:1304.002), and other affiliated packages to process data from calibrated visibilities to science-ready spectral cubes and maps. The PHANGS-ALMA pipeline offers a flexible alternative to the scriptForImaging script distributed by ALMA. The pipeline runs in two separate software environments: CASA 5.6 or 5.7 (staging, imaging and post-processing) and Python 3.6 or later (derived products) with modern versions of several packages.