formatting

qutip/core/environment.py

@@ -352,7 +352,7 @@ def approx_by_cf_fit(
         self, tlist: ArrayLike, target_rsme: float = 2e-5, Nr_max: int = 10,
         Ni_max: int = 10, guess: list[float] = None, lower: list[float] = None,
         upper: list[float] = None, full_ansatz: bool = False, tag: Any = None,
-        combine: bool =True,sigma:float= 1e-4,maxfev:int=100_000
+        combine: bool = True, sigma: float = 1e-4, maxfev: int = 100_000
     ) -> tuple[ExponentialBosonicEnvironment, dict[str, Any]]:
         r"""
         Generates an approximation to this environment by fitting its
@@ -444,10 +444,10 @@ def approx_by_cf_fit(
             operator). See :meth:`combine` for details.
         sigma : float, default 1e-4
             The uncertainty in the date provided, to calculate uncertainty
-            residuals, it is useful to decrease if the values of the data are 
+            residuals, it is useful to decrease if the values of the data are
             too small
         maxfev: int, default 100_000
-            Number of times the parameters of the fit are allowed to vary 
+            Number of times the parameters of the fit are allowed to vary
             during the optimization
         Returns
         -------
@@ -514,7 +514,7 @@ def approx_by_cf_fit(
         start_real = time()
         rmse_real, params_real = iterated_fit(
             _cf_real_fit_model, num_params, tlist, np.real(clist), target_rsme,
-            guess_re, Nr_min, Nr_max, lower_re, upper_re,sigma=sigma,
+            guess_re, Nr_min, Nr_max, lower_re, upper_re, sigma=sigma,
             maxfev=maxfev
         )
         end_real = time()
@@ -525,7 +525,7 @@ def approx_by_cf_fit(
         rmse_imag, params_imag = iterated_fit(
             _cf_imag_fit_model, num_params, tlist, np.imag(clist), target_rsme,
             guess_im, Ni_min, Ni_max, lower_im, upper_im,
-            sigma=sigma,maxfev=maxfev
+            sigma=sigma, maxfev=maxfev
         )
         end_imag = time()
         fit_time_imag = end_imag - start_imag
@@ -567,14 +567,14 @@ def approx_by_cf_fit(
             vkAI.extend([-b - 1j * c, -b + 1j * c])
 
         approx_env = ExponentialBosonicEnvironment(
-            ckAR, vkAR, ckAI, vkAI, T=self.T, tag=tag,combine=combine)
+            ckAR, vkAR, ckAI, vkAI, T=self.T, tag=tag, combine=combine)
         return approx_env, fit_info
 
     def approx_by_sd_fit(
         self, wlist: ArrayLike, Nk: int = 1, target_rsme: float = 5e-6,
         Nmax: int = 10, guess: list[float] = None, lower: list[float] = None,
-        upper: list[float] = None, tag: Any = None,combine: bool=True,
-        sigma:float =1e-4,maxfev:int=1e-6
+        upper: list[float] = None, tag: Any = None, combine: bool = True,
+        sigma: float = 1e-4, maxfev: int = 1e-6
     ) -> tuple[ExponentialBosonicEnvironment, dict[str, Any]]:
         r"""
         Generates an approximation to this environment by fitting its spectral
@@ -631,10 +631,10 @@ def approx_by_sd_fit(
             operator). See :meth:`combine` for details.
         sigma : float, default 1e-4
             The uncertainty in the date provided, to calculate uncertainty
-            residuals, it is useful to decrease if the values of the data are 
+            residuals, it is useful to decrease if the values of the data are
             too small
         maxfev: int, default 100_000
-            Number of times the parameters of the fit are allowed to vary 
+            Number of times the parameters of the fit are allowed to vary
             during the optimization
         Returns
         -------
@@ -676,7 +676,7 @@ def approx_by_sd_fit(
         start = time()
         rmse, params = iterated_fit(
             _sd_fit_model, 3, wlist, jlist, target_rsme, guess,
-            Nmin, Nmax, lower, upper,sigma=sigma,maxfev=maxfev
+            Nmin, Nmax, lower, upper, sigma=sigma, maxfev=maxfev
         )
         end = time()
         fit_time = end - start
@@ -705,7 +705,7 @@ def approx_by_sd_fit(
             vkAI.extend(coeffs[3])
 
         approx_env = ExponentialBosonicEnvironment(
-            ckAR, vkAR, ckAI, vkAI, T=self.T, tag=tag,combine=combine)
+            ckAR, vkAR, ckAI, vkAI, T=self.T, tag=tag, combine=combine)
         return approx_env, fit_info
 
 
@@ -929,11 +929,11 @@ def approx_by_matsubara(
         """
         if tag is None and self.tag is not None:
             tag = (self.tag, "Matsubara Truncation")
-        if self.T==0:
-            raise warnings.warn("The Matsubara coefficients at T=0 do" 
-                            "not improve with the number of exponents." 
-                            " Other approaches such as fitting the "
-                            "correlation function provide better results.")
+        if self.T == 0:
+            raise warnings.warn("The Matsubara coefficients at T=0 do"
+                                "not improve with the number of exponents."
+                                " Other approaches such as fitting the "
+                                "correlation function provide better results.")
 
         lists = self._matsubara_params(Nk)
         approx_env = ExponentialBosonicEnvironment(
@@ -1023,8 +1023,8 @@ def _matsubara_params(self, Nk):
     # --- Pade approx calculation ---
 
     def _corr(self, Nk):
-        if self.T==0:
-            beta=np.inf
+        if self.T == 0:
+            beta = np.inf
         else:
             beta = 1. / self.T
         kappa, epsilon = self._kappa_epsilon(Nk)
@@ -1039,7 +1039,7 @@ def _corr(self, Nk):
                 self.gamma * (epsilon[ll] * self.T)
                 / ((epsilon[ll]**2 * self.T**2) - self.gamma**2)
             )
-            gamma_p.append(epsilon[ll] *self.T)
+            gamma_p.append(epsilon[ll] * self.T)
 
         return eta_p, gamma_p
 
@@ -1220,11 +1220,11 @@ def approx_by_matsubara(
 
     def _matsubara_params(self, Nk):
         """ Calculate the Matsubara coefficients and frequencies. """
-        if self.T==0:
-            raise warnings.warn("The Matsubara coefficients at T=0 do" 
-                            "not improve with the number of exponents." 
-                            " Other approaches such as fitting the "
-                            "correlation function provide better results.")
+        if self.T == 0:
+            raise warnings.warn("The Matsubara coefficients at T=0 do"
+                                "not improve with the number of exponents."
+                                " Other approaches such as fitting the "
+                                "correlation function provide better results.")
         Om = np.sqrt(self.w0**2 - (self.gamma / 2)**2)
         Gamma = self.gamma / 2
 
@@ -1236,7 +1236,7 @@ def _matsubara_params(self, Nk):
         ])
 
         ck_real.extend([
-            (-2 * self.lam**2 * self.gamma *self.T) * (2 * np.pi * k * self.T)
+            (-2 * self.lam**2 * self.gamma * self.T) * (2 * np.pi * k * self.T)
             / (
                 ((Om + 1j * Gamma)**2 + (2 * np.pi * k * self.T)**2)
                 * ((Om - 1j * Gamma)**2 + (2 * np.pi * k * self.T)**2)
@@ -1636,7 +1636,6 @@ def __init__(
             exponents = self.combine(exponents)
         self.exponents = exponents
 
-
     @classmethod
     def combine(
         cls, exponents: Sequence[CFExponent],
@@ -2285,7 +2284,7 @@ def approx_by_matsubara(
         ck_minus, vk_minus = self._matsubara_params(Nk, -1)
 
         return ExponentialFermionicEnvironment(
-            ck_plus, vk_plus, ck_minus, vk_minus, T=self.T, mu=self.mu, 
+            ck_plus, vk_plus, ck_minus, vk_minus, T=self.T, mu=self.mu,
             tag=tag
         )
 

qutip/solver/heom/bofin_solvers.py

@@ -107,6 +107,7 @@ class HierarchyADOs:
     labels: list of tuples
         A list of the ADO labels within the hierarchy.
     """
+
     def __init__(self, exponents, max_depth):
         self.exponents = exponents
         self.max_depth = max_depth
@@ -716,7 +717,7 @@ def _combine_bath_exponents(self, bath):
         """ Combine the exponents for the specified baths. """
         # Only one bath provided, not a list of baths
         if (not isinstance(bath, (list, tuple))
-            or self._is_environment_api(bath)):
+                or self._is_environment_api(bath)):
             bath = [bath]
         exponents = []
         for b in bath:
@@ -725,10 +726,12 @@ def _combine_bath_exponents(self, bath):
                     b = self._env_to_bath(b)
                 except ValueError as ve:
                     raise ValueError(ve.args[0] +
-                        " When passing environments and baths make sure to"
-                        " include the coupling operators for the environments"
-                        " [(env,Q),bath] is correct while [env,bath] is not"
-                    )
+                                     " When passing environments and baths"
+                                     " make sure to include the coupling "
+                                     "operators for the environments"
+                                     " [(env,Q),bath] is correct "
+                                     "while [env,bath] is not"
+                                     )
             exponents.extend(b.exponents)
 
         if not all(exp.Q.dims == exponents[0].Q.dims for exp in exponents):
@@ -740,18 +743,19 @@ def _combine_bath_exponents(self, bath):
         return exponents
 
     def _is_environment_api(self, bath_spec):
-        is_list =isinstance(bath_spec, (list, tuple)) 
+        is_list = isinstance(bath_spec, (list, tuple))
         if is_list:
-            env_syntax= (
+            env_syntax = (
                 isinstance(bath_spec[0], ExponentialBosonicEnvironment)
                 or isinstance(bath_spec[0], ExponentialFermionicEnvironment)
-            ) 
+            )
             is_bath = (
                 isinstance(bath_spec[0], BosonicBath)
-                or isinstance(bath_spec[0],FermionicBath)
+                or isinstance(bath_spec[0], FermionicBath)
             )
         else:
-            if type(bath_spec) in [ExponentialBosonicEnvironment,ExponentialFermionicEnvironment]:
+            if ((type(bath_spec) == ExponentialBosonicEnvironment) or
+                    (type(bath_spec) == ExponentialFermionicEnvironment)):
                 raise ValueError("Enviroments must be passed with their"
                                  " corresponding coupling operator as a list"
                                  " or tuple (env,Q)")
@@ -760,7 +764,6 @@ def _is_environment_api(self, bath_spec):
             return False
         else:
             return is_list and env_syntax
-
 
     def _env_to_bath(self, bath_spec):
         if isinstance(bath_spec[0], ExponentialBosonicEnvironment):
@@ -1297,6 +1300,7 @@ class HSolverDL(HEOMSolver):
         operator). See :meth:`BosonicBath.combine` for details.
         Keyword only. Default: True.
     """
+
     def __init__(
         self, H_sys, coup_op, coup_strength, temperature,
         N_cut, N_exp, cut_freq, *, bnd_cut_approx=False, options=None,
@@ -1345,6 +1349,7 @@ class _GatherHEOMRHS:
         nhe : int
             The number of ADOs in the hierarchy.
     """
+
     def __init__(self, f_idx, block, nhe):
         self._block_size = block
         self._n_blocks = nhe

qutip/utilities.py

@@ -355,7 +355,7 @@ def iterated_fit(
     guess: ArrayLike | Callable[[int], ArrayLike] = None,
     Nmin: int = 1, Nmax: int = 10,
     lower: ArrayLike = None, upper: ArrayLike = None,
-    sigma:float=1e-4,maxfev:int=100_000
+    sigma: float = 1e-4, maxfev: int = 100_000
 ) -> tuple[float, ArrayLike]:
     r"""
     Iteratively tries to fit the given data with a model of the form
@@ -436,7 +436,8 @@ def iterated_fit(
         lower_repeat = np.tile(lower, N)
         upper_repeat = np.tile(upper, N)
         rmse1, params = _fit(fun, num_params, xdata, ydata,
-                             guesses, lower_repeat, upper_repeat,sigma,maxfev)
+                             guesses, lower_repeat,
+                             upper_repeat, sigma, maxfev)
         N += 1
 
     return rmse1, params
@@ -476,8 +477,8 @@ def _rmse(fun, xdata, ydata, params):
     )
 
 
-def _fit(fun, num_params, xdata, ydata, guesses, lower, upper,sigma,
-         maxfev,method='trf'):
+def _fit(fun, num_params, xdata, ydata, guesses, lower, upper, sigma,
+         maxfev, method='trf'):
     # fun: model function
     # num_params: number of parameters in fun
     # xdata, ydata: data to be fit
@@ -495,7 +496,7 @@ def _fit(fun, num_params, xdata, ydata, guesses, lower, upper,sigma,
             fun, x, _unpack(packed_params, num_params)
         ),
         xdata, ydata, p0=_pack(guesses), bounds=(lower, upper),
-        maxfev=maxfev, method=method,sigma=sigma
+        maxfev=maxfev, method=method, sigma=sigma
     )
     params = _unpack(packed_params, num_params)
     rmse = _rmse(fun, xdata, ydata, params)