Run black on distributions/ and */__init__.py (#4139)

* Run black on distributions/ and */__init__.py

* Run black on step_methods/

* Revert two files

* Update pymc3/distributions/bound.py

Co-authored-by: Marco Gorelli <[email protected]>

* Revert blackened metropolis.py

* Revert blackened files

* Fix error in quadpotential.py

Co-authored-by: Marco Gorelli <[email protected]>
Co-authored-by: Ali Septiandri <[email protected]>

Author: 3 people

pymc3/distributions/__init__.py

@@ -100,78 +100,79 @@
 
 from .bound import Bound
 
-__all__ = ['Uniform',
-           'Flat',
-           'HalfFlat',
-           'TruncatedNormal',
-           'Normal',
-           'Beta',
-           'Kumaraswamy',
-           'Exponential',
-           'Laplace',
-           'StudentT',
-           'Cauchy',
-           'HalfCauchy',
-           'Gamma',
-           'Weibull',
-           'Bound',
-           'Lognormal',
-           'HalfStudentT',
-           'ChiSquared',
-           'HalfNormal',
-           'Wald',
-           'Pareto',
-           'InverseGamma',
-           'ExGaussian',
-           'VonMises',
-           'Binomial',
-           'BetaBinomial',
-           'Bernoulli',
-           'Poisson',
-           'NegativeBinomial',
-           'ConstantDist',
-           'Constant',
-           'ZeroInflatedPoisson',
-           'ZeroInflatedNegativeBinomial',
-           'ZeroInflatedBinomial',
-           'DiscreteUniform',
-           'Geometric',
-           'Categorical',
-           'OrderedLogistic',
-           'DensityDist',
-           'Distribution',
-           'Continuous',
-           'Discrete',
-           'NoDistribution',
-           'TensorType',
-           'MvNormal',
-           'MatrixNormal',
-           'KroneckerNormal',
-           'MvStudentT',
-           'Dirichlet',
-           'Multinomial',
-           'Wishart',
-           'WishartBartlett',
-           'LKJCholeskyCov',
-           'LKJCorr',
-           'AR1',
-           'AR',
-           'GaussianRandomWalk',
-           'MvGaussianRandomWalk',
-           'MvStudentTRandomWalk',
-           'GARCH11',
-           'SkewNormal',
-           'Mixture',
-           'NormalMixture',
-           'Triangular',
-           'DiscreteWeibull',
-           'Gumbel',
-           'Logistic',
-           'LogitNormal',
-           'Interpolated',
-           'Bound',
-           'Rice',
-           'Moyal',
-           'Simulator',
-           'fast_sample_posterior_predictive'
-           ]
+__all__ = [
+    "Uniform",
+    "Flat",
+    "HalfFlat",
+    "TruncatedNormal",
+    "Normal",
+    "Beta",
+    "Kumaraswamy",
+    "Exponential",
+    "Laplace",
+    "StudentT",
+    "Cauchy",
+    "HalfCauchy",
+    "Gamma",
+    "Weibull",
+    "Bound",
+    "Lognormal",
+    "HalfStudentT",
+    "ChiSquared",
+    "HalfNormal",
+    "Wald",
+    "Pareto",
+    "InverseGamma",
+    "ExGaussian",
+    "VonMises",
+    "Binomial",
+    "BetaBinomial",
+    "Bernoulli",
+    "Poisson",
+    "NegativeBinomial",
+    "ConstantDist",
+    "Constant",
+    "ZeroInflatedPoisson",
+    "ZeroInflatedNegativeBinomial",
+    "ZeroInflatedBinomial",
+    "DiscreteUniform",
+    "Geometric",
+    "Categorical",
+    "OrderedLogistic",
+    "DensityDist",
+    "Distribution",
+    "Continuous",
+    "Discrete",
+    "NoDistribution",
+    "TensorType",
+    "MvNormal",
+    "MatrixNormal",
+    "KroneckerNormal",
+    "MvStudentT",
+    "Dirichlet",
+    "Multinomial",
+    "Wishart",
+    "WishartBartlett",
+    "LKJCholeskyCov",
+    "LKJCorr",
+    "AR1",
+    "AR",
+    "GaussianRandomWalk",
+    "MvGaussianRandomWalk",
+    "MvStudentTRandomWalk",
+    "GARCH11",
+    "SkewNormal",
+    "Mixture",
+    "NormalMixture",
+    "Triangular",
+    "DiscreteWeibull",
+    "Gumbel",
+    "Logistic",
+    "LogitNormal",
+    "Interpolated",
+    "Bound",
+    "Rice",
+    "Moyal",
+    "Simulator",
+    "fast_sample_posterior_predictive",
+]

pymc3/distributions/bound.py

@@ -82,16 +82,13 @@ def _random(self, lower, upper, point=None, size=None):
         upper = np.asarray(upper)
         if lower.size > 1 or upper.size > 1:
             raise ValueError(
-                "Drawing samples from distributions with "
-                "array-valued bounds is not supported."
+                "Drawing samples from distributions with array-valued bounds is not supported."
             )
         total_size = np.prod(size).astype(np.int)
         samples = []
         s = 0
         while s < total_size:
-            sample = np.atleast_1d(
-                self._wrapped.random(point=point, size=total_size)
-            ).flatten()
+            sample = np.atleast_1d(self._wrapped.random(point=point, size=total_size)).flatten()
 
             select = sample[np.logical_and(sample >= lower, sample <= upper)]
             samples.append(select)
@@ -128,7 +125,7 @@ def random(self, point=None, size=None):
                 upper,
                 dist_shape=self.shape,
                 size=size,
-                not_broadcast_kwargs={'point': point},
+                not_broadcast_kwargs={"point": point},
             )
         elif self.lower is not None:
             lower = draw_values([self.lower], point=point, size=size)
@@ -138,7 +135,7 @@ def random(self, point=None, size=None):
                 np.inf,
                 dist_shape=self.shape,
                 size=size,
-                not_broadcast_kwargs={'point': point},
+                not_broadcast_kwargs={"point": point},
             )
         else:
             upper = draw_values([self.upper], point=point, size=size)
@@ -148,7 +145,7 @@ def random(self, point=None, size=None):
                 upper,
                 dist_shape=self.shape,
                 size=size,
-                not_broadcast_kwargs={'point': point},
+                not_broadcast_kwargs={"point": point},
             )
 
 
@@ -168,9 +165,7 @@ def __init__(self, distribution, lower, upper, transform="infer", *args, **kwarg
         if lower is not None:
             default = lower + 1
 
-        super().__init__(
-            distribution, lower, upper, default, *args, transform=transform, **kwargs
-        )
+        super().__init__(distribution, lower, upper, default, *args, transform=transform, **kwargs)
 
 
 class _ContinuousBounded(_Bounded, Continuous):
@@ -215,9 +210,7 @@ def __init__(self, distribution, lower, upper, transform="infer", *args, **kwarg
         else:
             default = None
 
-        super().__init__(
-            distribution, lower, upper, default, *args, transform=transform, **kwargs
-        )
+        super().__init__(distribution, lower, upper, default, *args, transform=transform, **kwargs)
 
 
 class Bound:
@@ -283,23 +276,11 @@ def __call__(self, name, *args, **kwargs):
         transform = kwargs.pop("transform", "infer")
         if issubclass(self.distribution, Continuous):
             return _ContinuousBounded(
-                name,
-                self.distribution,
-                self.lower,
-                self.upper,
-                transform,
-                *args,
-                **kwargs
+                name, self.distribution, self.lower, self.upper, transform, *args, **kwargs
             )
         elif issubclass(self.distribution, Discrete):
             return _DiscreteBounded(
-                name,
-                self.distribution,
-                self.lower,
-                self.upper,
-                transform,
-                *args,
-                **kwargs
+                name, self.distribution, self.lower, self.upper, transform, *args, **kwargs
             )
         else:
             raise ValueError("Distribution is neither continuous nor discrete.")
@@ -311,8 +292,6 @@ def dist(self, *args, **kwargs):
             )
 
         elif issubclass(self.distribution, Discrete):
-            return _DiscreteBounded.dist(
-                self.distribution, self.lower, self.upper, *args, **kwargs
-            )
+            return _DiscreteBounded.dist(self.distribution, self.lower, self.upper, *args, **kwargs)
         else:
             raise ValueError("Distribution is neither continuous nor discrete.")

pymc3/distributions/shape_utils.py

@@ -64,9 +64,7 @@ def _check_shape_type(shape):
                 raise TypeError(f"Value {s} is not a valid integer")
             out.append(o)
     except Exception:
-        raise TypeError(
-            f"Supplied value {shape} does not represent a valid shape"
-        )
+        raise TypeError(f"Supplied value {shape} does not represent a valid shape")
     return tuple(out)
 
 
@@ -172,10 +170,7 @@ def broadcast_dist_samples_shape(shapes, size=None):
     shapes = [_check_shape_type(s) for s in shapes]
     _size = to_tuple(size)
     # samples shapes without the size prepend
-    sp_shapes = [
-        s[len(_size) :] if _size == s[: min([len(_size), len(s)])] else s
-        for s in shapes
-    ]
+    sp_shapes = [s[len(_size) :] if _size == s[: min([len(_size), len(s)])] else s for s in shapes]
     try:
         broadcast_shape = shapes_broadcasting(*sp_shapes, raise_exception=True)
     except ValueError:
@@ -277,8 +272,7 @@ def get_broadcastable_dist_samples(
     out_shape = broadcast_dist_samples_shape(p_shapes, size=size)
     # samples shapes without the size prepend
     sp_shapes = [
-        s[len(_size) :] if _size == s[: min([len(_size), len(s)])] else s
-        for s in p_shapes
+        s[len(_size) :] if _size == s[: min([len(_size), len(s)])] else s for s in p_shapes
     ]
     broadcast_shape = shapes_broadcasting(*sp_shapes, raise_exception=True)
     broadcastable_samples = []

pymc3/distributions/simulator.py

@@ -130,7 +130,6 @@ def _str_repr(self, name=None, dist=None, formatting="plain"):
             return f"{name} ~ Simulator({function}({params}), {distance}, {sum_stat})"
 
 
-
 def identity(x):
     """Identity function, used as a summary statistics."""
     return x

pymc3/distributions/special.py

@@ -17,10 +17,10 @@
 from theano.scalar.basic_scipy import GammaLn, Psi
 from theano import scalar
 
-__all__ = ['gammaln', 'multigammaln', 'psi', 'log_i0']
+__all__ = ["gammaln", "multigammaln", "psi", "log_i0"]
 
-scalar_gammaln = GammaLn(scalar.upgrade_to_float, name='scalar_gammaln')
-gammaln = tt.Elemwise(scalar_gammaln, name='gammaln')
+scalar_gammaln = GammaLn(scalar.upgrade_to_float, name="scalar_gammaln")
+gammaln = tt.Elemwise(scalar_gammaln, name="gammaln")
 
 
 def multigammaln(a, p):
@@ -33,21 +33,33 @@ def multigammaln(a, p):
        degrees of freedom. p > 0
     """
     i = tt.arange(1, p + 1)
-    return (p * (p - 1) * tt.log(np.pi) / 4.
-            + tt.sum(gammaln(a + (1. - i) / 2.), axis=0))
+    return p * (p - 1) * tt.log(np.pi) / 4.0 + tt.sum(gammaln(a + (1.0 - i) / 2.0), axis=0)
 
 
 def log_i0(x):
     """
     Calculates the logarithm of the 0 order modified Bessel function of the first kind""
     """
-    return tt.switch(tt.lt(x, 5), tt.log1p(x**2. / 4. + x**4. / 64. + x**6. / 2304.
-                                           + x**8. / 147456. + x**10. / 14745600.
-                                           + x**12. / 2123366400.),
-                                  x - 0.5 * tt.log(2. * np.pi * x) + tt.log1p(1. / (8. * x)
-                                  + 9. / (128. * x**2.) + 225. / (3072. * x**3.)
-                                  + 11025. / (98304. * x**4.)))
+    return tt.switch(
+        tt.lt(x, 5),
+        tt.log1p(
+            x ** 2.0 / 4.0
+            + x ** 4.0 / 64.0
+            + x ** 6.0 / 2304.0
+            + x ** 8.0 / 147456.0
+            + x ** 10.0 / 14745600.0
+            + x ** 12.0 / 2123366400.0
+        ),
+        x
+        - 0.5 * tt.log(2.0 * np.pi * x)
+        + tt.log1p(
+            1.0 / (8.0 * x)
+            + 9.0 / (128.0 * x ** 2.0)
+            + 225.0 / (3072.0 * x ** 3.0)
+            + 11025.0 / (98304.0 * x ** 4.0)
+        ),
+    )
 
 
-scalar_psi = Psi(scalar.upgrade_to_float, name='scalar_psi')
-psi = tt.Elemwise(scalar_psi, name='psi')
+scalar_psi = Psi(scalar.upgrade_to_float, name="scalar_psi")
+psi = tt.Elemwise(scalar_psi, name="psi")