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tokens: BYF40 HCN ( l , b , V ) data cube we chose as a test area for our code development, and has the advantage of being scriptable in a modern computing environment, as well as having a built-in tool for fitting the model across all spectra in a cube. Assuming LTE, it solves for the common parameters of all hyperfine components. In order to improve the fit quality, the original fits cube input for the package was binned by two spectral channels and convolved from an original resolution of 4060arcsec. Once a minimum signal-to-noise ratio (S/N) threshold is set for the hyperfine fitting, PySpecKit yields separate 2D maps of the excitation temperature T ex , opacity , mean velocity V LSR , and velocity dispersion V , as well as error maps for each parameter map. A sample fit is shown in Fig. 3 , while the parameter maps appear in AppendixA. 3.2 Column density calculation After obtaining the parameter maps and uncertainties as outputs from PySpecKit , the column density N was evaluated using a mix of C shell and miriad (Sault, Teuben Wright 1995 ) software. and T ex were measured in each voxel and then converted into N HCN using the equation Here, Q refers to the partition function, E up is the energy of the upper level above the ground state of the J upper J lower transition, h is the energy of the transition, and k is the Boltzmann constant. The derived N HCN map is shown in Fig. 2 , and has a flatter distribution compared to the overlayed I HCN contours. Figure 2. Maps of N HCN (a) and its uncertainty (b) across BYF40, plotted with contours at intervals of 10percent of the peak I HCN . Figure 3. Sample spectra from the original, model, and residual data cubes. Each was measured at the coordinate l = 284.002(3), b = 0.846(3). Under traditional methods, the X factor for 12 CO is often used to estimate column densities with a simple linear conversion. The accuracy of such conversions has frequently been debated, and recent work by our group (Barnes etal. 2015 , 2016 ) has shown that, in the case of CO at least, both the lines T ex and need to be properly accounted for, and must be self-consistently measured. In this paper, we calculate the uncertainty in N HCN by propagating all of the formal fit errors, and applying the constraints T ex 2.73K and 0 on the PySpecKit solutions. By using the error images and the output images together, relative errors were also determined for several intermediate quantities in the calculation, and for N HCN . The V LSR values fall in the range of 810kms 1 , V = 1.22.2kms 1 , T ex = 49K, and = 0.52.5. 3.3 Clump masses In Table 1 we give the observed and physical parameters of the BYF40 clumps used to derive these results. The values appear similar to table 4 from Paper I , due to the high correlation present between HCN and HCO in the clump. Additionally, Fig. 4 (b) shows that there is a high correlation between the integrated intensities of HCN and HCO . This can be seen in the Fig. 1 (a) Mopra map, where the blue of HCO