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Resample the m2005 stellar models at long wavelengths. The reason is that as the sampling is too sparse, the linear interpolation carried out in cigale creates waves that are not only disgracious but that also artificially boost the broadband fluxes flux. Patch based on previous commit that fixed the same issue for the bc03 models.

Resample the bc03 stellar models at long wavelengths. The reason is that as the sampling is too sparse, the linear interpolation carried out in cigale creates waves that are not only disgracious but that also artificially boost the broadband fluxes flux.

Last (?) modification of the Fritz 2006 parameters. The opening angle is in agreement with Fig. 1 in Fritz (2006). The viewing angle is in agreement with Hatziminaoglou (2008) and Fritz (2010) models.

Large revamp of the SED plotting procedure by Denis and I. Among the main changes: 1. option to plot in mJy in observed frame or un luminosity in rest frame 2. different components are now plotted automatically 3. a logo is now diplayed but can be removed with an option.

Commit Denis' change to make sure that pcigale-plots does not crash when pcigale did not find a best fit.

Add a carriage return after we have computed the last model or analysed the last object so that the information does not get erased.

Add the IRAS, SCUBA and VLA filters from Barbara.

Add some filters from Barbara.

The "galaxy_mass" parameter must not be set to 1.0 in the stellar
emission modules because when using CIGALE to generate SED without
normalising the 1.0 is wrong and misleading.

Instead, we compute the real galaxy mass (the integration of the SFH) in
the star formation history modules.

Now that we handle the luminosity and the individual component separately for speed reasons, we must not forget to scale the total luminosity appropriately when redshifting. Otherwise the fluxes in the filters will be overestimated by a factor 1+z.

Let's avoid using costly np.max() functions on ordered arrays. The max will always be the last element. Also use the fact that the arrays are sorted to select the last 100 elements to compute the average SFR.

Setters and getters are nice but they come with a significant overhead. Because we initialise class members to None anyway, they should not be needed at all. I leave the setter/getter for the sfh as it is actually doing something more. It now accounts for a non negligible fraction of the runtime though.

Rather than relying on deepcopy, let's build the new SED objects ourselves. As we know the structure perfectly we can do that much more efficiently. To do se we add a copy() member to the SED class. This function creates a new object and initialises its members with proper copies.

Call the copy() class member rather than the base numpy function. Quick testing shows it is faster: 1.1 μs vs 2.7 μs for an array of 2000 elements.

Now that we do not add an AGN component anymore when the AGN fraction is 0, the wavelength grid may change from one model to the other. To work around this issue we save the IGM component into a dictionary with the key being the size of the grid. This is not completely foolproof as two grids of same length may have different wavelengths, though that is quite unlikely given pcigale's architecture. It should be good enough for now.

If the AGN fraction is 0, then there is no need to add a component to the SED. It would have a value of 0 everywhere. It looks like we gain around 30% in computation time in this case.

Computing the luminosity each time we integrate in a filter (for instance) is not efficient. As the luminosity is in effect modified in only one place (add_contribution), let's just compute it manually each we add a component. This yields a ~10% improvement.