Use np.take() rather than fancy indexing on large arrays. This accelerates the...

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

CHANGELOG.md

@@ -21,6 +21,7 @@
 - By default the MKL library created many threads for each for the parallel processes. Not only was this not necessary as a high-level parallelisation already exists, but it generated a strong oversubscription on the CPU and on the RAM. The slowdown was over a factor of ~2 in some cases. Now we mandate KML to use only 1 thread fo each process. (Médéric Boquien & Yannick Roehlly)
 - The generic numpy interpolation function was used. As we are in a well-controlled environment, this generated unnecessary verifications on the type and shape of the arrays. The compiled numpy interpolation function is now used, bypassing those checks. This generates a gain of 5-10% in computing speed for the generation of the models. (Médéric Boquien)
 - The interpolation of the spectra of the different physical components on a new wavelength grid was not optimal as the interpolation was done separately for each component. Now a specific function has been implemented caching repetitive computations and vectorising the interpolation to compute it for all the components in a single step. This generates a gain of 10% in computing speed for the generation of the models. (Médéric Boquien)
+- An optimisation in the sorting of a 2D array led to a gain of 10% in computing speed for the generation of the models. (Médéric Boquien)
 
 ## 0.10.0 (2016-09-15)
 ### Added

pcigale/sed/utils.py

@@ -364,7 +364,7 @@ def interpolate_lumin(wl, lumin, wl_new, lumin_new):
         wl_best = np.concatenate((wl, wl_unique))
         s = argsort_wl(wl_best)
         wl_best = wl_best[s]
-        lumin_out = lumin_out[:, s]
+        lumin_out = np.take(lumin_out, s, axis=-1)
     else:
         wl_best = wl
         lumin_out[:-1, :] = lumin