Be more resilient by converting the array of SFR to float. This avoids a...

Be more resilient by converting the array of SFR to float. This avoids a possible crash down the road when doing an in-place assignment of an integer with a float.

CHANGELOG.md

@@ -20,6 +20,7 @@
 - For sfh2exp, when setting the scale of the SFH with sfr0, the normalisation was incorrect by a factor exp(-1/tau_main). (Médéric Boquien)
 - The mass-dependent physical properties are computed assuming the redshift of the model. However because we round the observed redshifts to two decimals, there can be a difference of 0.005 in redshift between the models and the actual observation if CIGALE computes the list of redshifts itself. At low redshift, this can cause a discrepancy in the mass-dependent physical properties: ~0.35 dex at z=0.010 vs 0.015 for instance. Therefore we now evaluate these physical quantities at the observed redshift at full precision. (Médéric Boquien, issue reported by Samir Salim)
 - In the sfhfromfile module, an extraneous offset in the column index made that it took the previous column as the SFR rather than the selected column. (Médéric Boquien)
+- In sfhfromfile, if the SFR is made of integers cigale crashed. Now we systematically convert it to float. (Médéric Boquien)
 
 ### Optimised
 - Prior to version 0.7.0, we needed to maintain the list of redshifts for all the computed models. Past 0.7.0 we just infer the redshift from a list unique redshifts. This means that we can now discard the list of redshifts for all the models and only keep the list of unique redshifts. This saves ~8 MB of memory for every 10⁶ models. the models should be computed slightly faster but it is in the measurement noise. (Médéric Boquien)

pcigale/creation_modules/sfhfromfile.py

@@ -71,7 +71,7 @@ class SfhFromFile(CreationModule):
         time_grid = table.columns[0].data
 
         sfr_column_number = int(self.parameters['sfr_column'])
-        sfr = table.columns[sfr_column_number].data
+        sfr = table.columns[sfr_column_number].data.astype(np.float)
 
         age = int(self.parameters['age'])
         normalise = (self.parameters["normalise"].lower() == "true")