codeclimate details

qutip/solver/heom/bofin_baths.py

@@ -1281,4 +1281,4 @@ def _calc_chi(self, Nk):
         alpha_p += alpha_p.transpose()
         evals = eigvalsh(alpha_p)
         chi = [-2. / val for val in evals[0: Nk - 1]]
-        return chi
+        return chi

qutip/solver/heom/bofin_fit.py

@@ -117,10 +117,9 @@ def get_fit(
         N : optional,int
             Number of underdamped oscillators to use,
             if set to False it is found automatically.
-        
         Nk : optional,int
-            Number of exponential terms used to approximate the bath correlation
-            functions. Defaults to 5
+            Number of exponential terms used to approximate the bath 
+            correlation functions. Defaults to 5
         final_rmse : float
             Desired normalized root mean squared error .
         lower : list
@@ -147,7 +146,7 @@ def get_fit(
         spec_n = len(params[0])
         end = time()
         fit_time = end - start
-        bath = self._generate_bath(params,Nk)
+        bath = self._generate_bath(params, Nk)
         bath.spectral_density = self._J_fun
         summary = gen_summary(
             fit_time, rmse, N, "The Spectral Density", *params)
@@ -678,7 +677,8 @@ def _fit(
     except Exception:
         sigma = 1e-4
         C_max = abs(max(C, key=abs))
-        if C_max == 0:  ## When The correlation does not have imaginary or real part
+        if C_max == 0:  
+            # When The correlation does not have imaginary or real part
             rmse = 0
             params = [0, 0, 0]
             return rmse, params
@@ -833,4 +833,4 @@ def two_column_summary(
         formatted_line1 = f"{line1:<{max_length1}} |"
         formatted_line2 = f"{line2:<{max_length2}}"
         full_summary += formatted_line1 + formatted_line2 + "\n"
-    return full_summary
+    return full_summary