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HARK/Calibration/Income/IncomeProcesses.py

@@ -78,7 +78,7 @@ def __init__(self, sigma, n_approx, neutral_measure=False, seed=0):
         logn_approx = lognormal_dstn.discretize(
             n_approx if sigma > 0.0 else 1, method="equiprobable", tail_N=0
         )
-        
+
         limit = {
             "dist": lognormal_dstn,
             "method": "equiprobable",
@@ -87,7 +87,7 @@ def __init__(self, sigma, n_approx, neutral_measure=False, seed=0):
             "infimum": logn_approx.limit["infimum"],
             "supremum": logn_approx.limit["supremum"],
         }
-        
+
         # Change the pmv if necessary
         if neutral_measure:
             logn_approx.pmv = (logn_approx.atoms * logn_approx.pmv).flatten()
@@ -127,7 +127,7 @@ def __init__(self, sigma, UnempPrb, IncUnemp, n_approx, seed=0):
         dstn_approx = dstn_orig.discretize(
             n_approx if sigma > 0.0 else 1, method="equiprobable", tail_N=0
         )
-        
+
         if UnempPrb > 0.0:
             dstn_approx = add_discrete_outcome_constant_mean(
                 dstn_approx, p=UnempPrb, x=IncUnemp

HARK/distributions/base.py

@@ -43,7 +43,7 @@ def __init__(self, seed: Optional[int] = 0) -> None:
 
         self._seed: int = _seed
         self._rng: random.Generator = random.default_rng(self._seed)
-        
+
         # Bounds of distribution support should be overwritten by subclasses
         self.infimum = np.array([])
         self.supremum = np.array([])

HARK/distributions/continuous.py

@@ -511,7 +511,7 @@ class Weibull(ContinuousFrozenDistribution):
     def __init__(self, scale=1.0, shape=1.0, seed=0):
         self.scale = np.asarray(scale)
         self.shape = np.asarray(shape)
-        
+
         # Set up the RNG
         super().__init__(stats.weibull_min, c=shape, scale=scale, seed=seed)
         self.infimum = np.array([0.0])

HARK/distributions/utils.py

@@ -11,6 +11,7 @@
 )
 from HARK.distributions.continuous import Normal
 
+
 # TODO: This function does not generate the limit attribute
 def approx_lognormal_gauss_hermite(N, mu=0.0, sigma=1.0, seed=0):
     d = Normal(mu, sigma).discretize(N, method="hermite")
@@ -258,7 +259,7 @@ def add_discrete_outcome_constant_mean(distribution, x, p, sort=False):
             ],
             seed=distribution.seed,
         )
-    
+
     else:
         atoms = np.append(x, distribution.atoms * (1 - p * x) / (1 - p))
         pmv = np.append(p, distribution.pmv * (1 - p))
@@ -301,7 +302,6 @@ def add_discrete_outcome_constant_mean(distribution, x, p, sort=False):
         return DiscreteDistribution(pmv, atoms, seed=distribution.seed, limit=limit)
 
 
-
 def add_discrete_outcome(distribution, x, p, sort=False):
     """
     Adds a discrete outcome of x with probability p to an existing distribution,
@@ -426,7 +426,7 @@ def combine_indep_dstns(*distributions, seed=0):
     P_out = np.prod(P_temp, axis=0)
 
     assert np.isclose(np.sum(P_out), 1), "Probabilities do not sum to 1!"
-    
+
     # Make the limit dictionary
     infimum = np.concatenate(
         [distributions[i].limit["infimum"] for i in range(dstn_count)]