Merge pull request #37 from hugovk/rm-2

Upgrade Python syntax with pyupgrade

pyirf/io/io.py

@@ -45,7 +45,7 @@ def load_config(name):
 
     """
     try:
-        with open(name, "r") as stream:
+        with open(name) as stream:
             cfg = yaml.load(stream, Loader=yaml.FullLoader)
     except FileNotFoundError as e:
         print(e)

pyirf/perf/cut_optimisation.py

@@ -50,7 +50,7 @@ def apply_cuts(self, debug):
         for particle in self.evt_dict.keys():
             data = self.apply_cuts_on_data(self.evt_dict[particle].copy(), debug)
             data.to_hdf(
-                os.path.join(self.outdir, "{}_processed.h5".format(particle)),
+                os.path.join(self.outdir, f"{particle}_processed.h5"),
                 key="dl2",
                 mode="w",
             )
@@ -499,7 +499,7 @@ def find_best_cutoff(self, energy_values, angular_values):
         for ibin in range(len(energy_values) - 1):
             emin = energy_values[ibin]
             emax = energy_values[ibin + 1]
-            print(" ==> {}) Working in E=[{:.3f},{:.3f}]".format(ibin, emin, emax))
+            print(f" ==> {ibin}) Working in E=[{emin:.3f},{emax:.3f}]")
 
             # OLDER PANDAS EQUIVALENT FROM PROTOPIPE
 
@@ -552,7 +552,7 @@ def find_best_cutoff(self, energy_values, angular_values):
             # Loop on angular cut
             for th_cut in theta_to_loop_on:
                 if self.verbose_level > 0:
-                    print("- Theta={:.2f}".format(th_cut))
+                    print(f"- Theta={th_cut:.2f}")
 
                 # Select gamma-rays in ON region
 
@@ -626,8 +626,8 @@ def find_best_cutoff(self, energy_values, angular_values):
             key_list = []
             for key in results_th_cut_dict:
                 key_list.append(key)
-                flux_list.append((results_th_cut_dict[key]["result"]["min_flux"]))
-                eff_sig.append((results_th_cut_dict[key]["result"]["eff_sig"]))
+                flux_list.append(results_th_cut_dict[key]["result"]["min_flux"])
+                eff_sig.append(results_th_cut_dict[key]["result"]["eff_sig"])
                 th.append(results_th_cut_dict[key]["th_cut"])
 
             # In case of equal min fluxes, take the one with bigger sig efficiency
@@ -804,11 +804,11 @@ def _get_sigma_flux(cls, excess, bkg, alpha, min_sigma):
 
     @classmethod
     def _get_energy_key(cls, emin, emax):
-        return "{:.3f}-{:.3f}TeV".format(emin, emax)
+        return f"{emin:.3f}-{emax:.3f}TeV"
 
     @classmethod
     def _get_angular_key(cls, ang):
-        return "{:.3f}deg".format(ang)
+        return f"{ang:.3f}deg"
 
     def get_binned_data(self, g, p, e, nbins=100, score_range=[-1, 1]):
         """Returns binned data as a dictionnary"""

pyirf/scripts/lst_performance.py

@@ -162,7 +162,7 @@ def main(args):
         # Set 0.05 as a lower value
         idx = np.where(thsq_values.value < 0.05)
         thsq_values[idx] = 0.05 * u.deg
-        print('Using theta cut: {}'.format(thsq_values))
+        print(f'Using theta cut: {thsq_values}')
 
     # Cuts optimisation
     print('### Finding best cuts...')

pyirf/scripts/make_DL3.py

@@ -211,10 +211,10 @@ def main():
     thsq_opt_type = cfg["analysis"]["thsq_opt"]["type"]
     if thsq_opt_type == "fixed":
         thsq_values = np.array([cfg["analysis"]["thsq_opt"]["value"]]) * u.deg
-        print("Using fixed theta cut: {}".format(thsq_values))
+        print(f"Using fixed theta cut: {thsq_values}")
     elif thsq_opt_type == "opti":
         thsq_values = np.arange(0.05, 0.40, 0.01) * u.deg
-        print("Optimising theta cut for: {}".format(thsq_values))
+        print(f"Optimising theta cut for: {thsq_values}")
     elif thsq_opt_type == "r68":
         print("Using R68% theta cut")
         print("Computing...")
@@ -275,7 +275,7 @@ def main():
         # Set 0.05 as a lower value
         idx = np.where(thsq_values.value < 0.05)
         thsq_values[idx] = 0.05 * u.deg
-        print("Using theta cut: {}".format(thsq_values))
+        print(f"Using theta cut: {thsq_values}")
 
     # Cuts optimisation
     print("### Finding best cuts...")