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Fν was computed by calculating Fλ and then converting to Fν, which led to typical differences in fluxes of typically 1-2% and a bit more for a handful of pathological filters. Now Fν is computed directly and a bit faster. At the same time rather than computing an effective wavelength (which was more a mean wavelength), we compute the pivot wavelength, which is more appropriate when converting between Fν and Fλ.

When computing the attenuation curve, the bump is now added so that its relative strength does not depend on δ.

Ensure that we do not try to fit bands that have an error bar of 0 mJy. We now simply discard such bands.

In order to capture short evolutionary phases we assume that over 1 Myr star formation occurs in 10 small episodes every 0.1 Myr, rather than 1 large episode every 1 Myr and nothing in-between.

Add the option to build the database with the high resolution BC03 models rather than the low resolution ones.

The computation of the fit taking into the upper limits would only work for the first few models because the number of bands was taken as the number of models.

Read the list of sed modules from pcigale.ini. This is more flexible than inferring this list from the input parameters file as some modules can be missing (for instance when using the default values for all the parameters of a given module).

Compute the best fit at the at the exact distance of the analysed object and not at the distance of the rounded redshift. This eliminates the need for a correction factor.

Implement an observations manager. For now it is mainly used as a simple container for the observations table but upcoming rearchitecturing will make full use of it.

Do not crash when trying to compute equivalent widths when the nebular component emission is missing.

Simplify the cache architecture as we always use the in-memory one. The removes one level of indirection and makes the model generation faster by 1%.

Implement the possibility of not caching a module in order to save memory. A typical use case is when having many different stellar populations at different metallicities. It can take a substantial amount of memory, especially when using many parallel processes.

Correct a bug that was preventing the module restframe_parameters from being listed among the available modules.

Fix a crash in the computation of the χ² because previous optimisations had been been not fully propagated to the case of upper limits.

Give a more reasonable amplitude for the UV bump with dustatt_powerlaw. Otherwise it is a factor Rv too large compared to dustatt_calzleit.

Add the new restframe_param module. It replaces most of the functionality provided in the unofficial param module, which has been trimmed down to only computing fluxes in the observed frame and has been renamed fluxes. Unlike param, the restframe_param module is to be called before the redshifting module.

When calling np.sum(), the axis should be an integer, not a float, otherwise it triggers a very disgracious Numpy warning. Now it is gone.

Use np.take() rather than fancy indexing on large arrays. This accelerates the computation of the models by 10%.

Optimise the interpolation of the luminosity components 1. computing the interpolation of all the components in one step rather than interpolating separately for each component, and 2. caching some intermediate computations that only depend on the grid sampling and therefore apply to all physical components.

Speed up the computation of the models using the compiled interp function rather than the regular numpy one so we bypass checks on the size and type of the arrays.