set wave_log=False by default to avoid issues with extrapolation

src/piscola/gaussian_process.py

@@ -61,7 +61,7 @@ def prepare_gp_inputs(times, wavelengths, fluxes, flux_errors, fit_log, wave_log
 
     return X, y, yerr, y_norm
 
-def fit_gp_model(times, wavelengths, fluxes, flux_errors, k1='Matern52', fit_mean=False, fit_log=False, wave_log=True):
+def fit_gp_model(times, wavelengths, fluxes, flux_errors, k1='Matern52', fit_mean=False, fit_log=False, wave_log=False):
     """Fits a Gaussian Process model to a SN multi-colour light curve.
     
     All input arrays MUST have the same length.
@@ -81,7 +81,7 @@ def fit_gp_model(times, wavelengths, fluxes, flux_errors, k1='Matern52', fit_mea
         ``Matern32`` or ``ExpSquared``.
     fit_log: bool, default ``False``.
         Whether to fit the light curves in logarithmic (base 10) scale.
-    wave_log: bool, default ``True``.
+    wave_log: bool, default ``False``.
         Whether to use logarithmic (base 10) scale for the 
         wavelength axis.
         

src/piscola/sn_class.py

@@ -275,24 +275,28 @@ def init_gp_predict(self, times_pred, wavelengths_pred, return_cov=True):
             mu, cov = self.init_gp_model.predict(y, X_test=X_test, return_cov=True)
             # renormalise outputs
             if self.init_fit_log is False:
+                # flux space
                 mu *= y_norm
                 cov *= y_norm ** 2
             else:
+                # convert logarithmic to flux space
                 mu = 10 ** (mu - y_norm)
                 cov *= np.abs(np.outer(mu, mu)) * np.log(10) ** 2
             return mu, cov
         else:
             mu, var = self.init_gp_model.predict(y, X_test=X_test, return_var=True)
             # renormalise outputs
             if self.init_fit_log is False:
+                # flux space
                 mu *= y_norm
                 var *= y_norm ** 2
             else:
+                # convert logarithmic to flux space
                 mu = 10 ** (mu - y_norm)
                 var *= np.abs(mu) ** 2 * np.log(10) ** 2
             return mu, var
 
-    def fit_lcs(self, bands=None, k1='Matern52', fit_log=False, wave_log=True):
+    def fit_lcs(self, bands=None, k1='Matern52', fit_log=False, wave_log=False):
         """Fits the multi-colour observed light-curve data with Gaussian Process.
 
         The time of rest-frame B-band peak luminosity is estimated by finding where the derivative is equal to zero.
@@ -306,7 +310,7 @@ def fit_lcs(self, bands=None, k1='Matern52', fit_log=False, wave_log=True):
             ``Matern32`` or ``ExpSquared``.
         fit_log: bool, default ``False``.
             Whether to fit the light curves in logarithmic (base 10) scale.
-        wave_log: bool, default ``True``.
+        wave_log: bool, default ``False``.
             Whether to use logarithmic (base 10) scale for the 
             wavelength axis.
         """
@@ -542,7 +546,7 @@ def gp_predict(self, times_pred, wavelengths_pred, return_cov=True):
             var *= y_norm ** 2
             return mu, var
 
-    def fit(self, bands=None, k1='Matern52', fit_log=False, wave_log=True, skip_lcs_fit=False):
+    def fit(self, bands=None, k1='Matern52', fit_log=False, wave_log=False, skip_lcs_fit=False):
         """Fits and corrects the multi-colour light curves with an SED template.
 
         The corrections include Milky-Way dust extinction and mangling of the SED 
@@ -558,7 +562,7 @@ def fit(self, bands=None, k1='Matern52', fit_log=False, wave_log=True, skip_lcs_
         fit_log: bool, default ``False``.
             Whether to initiallly fit the light curves in logarithmic (base 10) scale.
             The mangling is always done in flux space.
-        wave_log: bool, default ``True``.
+        wave_log: bool, default ``False``.
             Whether to use logarithmic (base 10) scale for the 
             wavelength axis.
         skip_lcs_fit: bool, default ``False``.