fixed bugs from previous commits

data_management.py

@@ -35,7 +35,7 @@ import sqlalchemy as db
 import random
 import warnings
 import settings
-settings.logging.getLogger('sqlalchemy.engine').setLevel(settings.logging.DEBUG)
+
 # We use hashs as identifiers for data points in HDF5 files. These hashs are
 # often not valid python names, which causes a warning. We ignore this warning.
 warnings.simplefilter("ignore", tb.NaturalNameWarning)

gen_data.py

@@ -290,6 +290,8 @@ python -m frtrg_kondo.gen_data \\
                 settings.defaults[name] = value
         except KeyError:
             pass
+    if options["log_level"] == "DEBUG":
+        settings.logging.getLogger("sqlalchemy.engine").setLevel(settings.logging.INFO)
     settings.defaults.logger.setLevel(options.pop("log_level"))
     settings.defaults.update_globals()
 

kondo.py

@@ -966,15 +966,15 @@ class Kondo:
         ### Full current, also includes AC current
         if self.resonant_dc_shift == 0:
             # TODO: implement this with resonant shift
-            mu = np.zeros((2*nmax+1, 2*nmax+1), dtype=np.complex128)
-            mu[np.diag_indices(2*nmax+1)] = self.vdc
+            mu = np.zeros((2*self.nmax+1, 2*self.nmax+1), dtype=np.complex128)
+            mu[np.diag_indices(2*self.nmax+1)] = self.vdc
             if self.fourier_coef is None:
-                mu[np.arange(2*nmax), np.arange(1, 2*nmax+1)] = self.vac/2
-                mu[np.arange(1, 2*nmax+1), np.arange(2*nmax)] = self.vac/2
+                mu[np.arange(2*self.nmax), np.arange(1, 2*self.nmax+1)] = self.vac/2
+                mu[np.arange(1, 2*self.nmax+1), np.arange(2*self.nmax)] = self.vac/2
             else:
                 for i, f in enumerate(self.fourier_coef, 1):
-                    mu[np.arange(2*nmax+1-i), np.arange(i, 2*nmax+1)] = f
-                    mu[np.arange(i, 2*nmax+1), np.arange(2*nmax+1-i)] = f.conjugate()
+                    mu[np.arange(2*self.nmax+1-i), np.arange(i, 2*self.nmax+1)] = f
+                    mu[np.arange(i, 2*self.nmax+1), np.arange(2*self.nmax+1-i)] = f.conjugate()
             mu = RGfunction(self.global_properties, mu, symmetry=-1)
             self.gammaL = mu @ self.deltaGammaL.reduced()
         else: