'Refactored by Sourcery'

dddm/samplers/multi_detectors.py

@@ -48,9 +48,9 @@ def save_sub_configs(self, force_index=False):
             if 'logging' not in c.config:
                 raise ValueError(f'{c} does not have logging in config ({list(c.config.keys())})')
             save_as = os.path.join(f'{save_dir}', f'{c.config["detector"]}_')
-            with open(save_as + 'config.json', 'w') as file:
+            with open(f'{save_as}config.json', 'w') as file:
                 json.dump(convert_dic_to_savable(c.config), file, indent=4)
-            np.save(save_as + 'config.npy', convert_dic_to_savable(c.config))
+            np.save(f'{save_as}config.npy', convert_dic_to_savable(c.config))
             shutil.copy(c.config['logging'], save_as +
                         c.config['logging'].split('/')[-1])
             self.log.info('save_sub_configs::\tdone_saving')

dddm/samplers/ultranest.py

@@ -15,7 +15,7 @@ class UltraNestSampler(MultiNestSampler):
     """
 
     def run(self):
-        warnings.warn(f'Ultranest sampler is not completely implemented yet')
+        warnings.warn('Ultranest sampler is not completely implemented yet')
         self._fix_parameters()
         self._print_before_run()
 

tests/test_samplers/test_ultranest.py

@@ -20,19 +20,18 @@ def test(self, halo_name='shm', max_sigma_off=5, **kwargs, ):
                            sampler_kwargs=dict(nlive=100, tol=0.9, verbose=1),
                            fit_parameters=('log_mass', 'log_cross_section',),
                            )
-        config = {**base_config, **kwargs}
+        config = base_config | kwargs
         sampler = self.ct.get_sampler_for_detector(**config)
 
         results, _ = sampler.run()
 
         fails = []
-        for i, (thing, expected, avg, std) in enumerate(
-                zip(
+        for thing, expected, avg, std in zip(
                     base_config.get('fit_parameters'),
                     [getattr(sampler, f) for f in base_config.get('fit_parameters')],
                     results['posterior']['mean'],
                     results['posterior']['stdev']
-                )):
+                ):
             nsigma_off = np.abs(expected - avg) / std
             # assert False, dict(thing=thing, expected=expected, avg=avg, nsigma_off=nsigma_off)
             message = (f'For {thing}: expected {expected:.2f} yielded '