Formatted 15 Files of test Dir

pymc3/distributions/continuous.py

@@ -134,9 +134,7 @@ def assert_negative_support(var, label, distname, value=-1e-6):
             support = False
 
     if np.any(support):
-        msg = "The variable specified for {} has negative support for {}, ".format(
-            label, distname
-        )
+        msg = f"The variable specified for {label} has negative support for {distname}, "
         msg += "likely making it unsuitable for this parameter."
         warnings.warn(msg)
 
@@ -712,7 +710,7 @@ def random(self, point=None, size=None):
         )
 
     def _random(self, mu, sigma, lower, upper, size):
-        """ Wrapper around stats.truncnorm.rvs that converts TruncatedNormal's
+        """Wrapper around stats.truncnorm.rvs that converts TruncatedNormal's
         parametrization to scipy.truncnorm. All parameter arrays should have
         been broadcasted properly by generate_samples at this point and size is
         the scipy.rvs representation.
@@ -3447,7 +3445,7 @@ def random(self, point=None, size=None):
         )
 
     def _random(self, c, lower, upper, size):
-        """ Wrapper around stats.triang.rvs that converts Triangular's
+        """Wrapper around stats.triang.rvs that converts Triangular's
         parametrization to scipy.triang. All parameter arrays should have
         been broadcasted properly by generate_samples at this point and size is
         the scipy.rvs representation.
@@ -3706,7 +3704,7 @@ def __init__(self, nu=None, sigma=None, b=None, sd=None, *args, **kwargs):
         self.sigma = self.sd = sigma = tt.as_tensor_variable(floatX(sigma))
         self.b = b = tt.as_tensor_variable(floatX(b))
 
-        nu_sigma_ratio = -nu ** 2 / (2 * sigma ** 2)
+        nu_sigma_ratio = -(nu ** 2) / (2 * sigma ** 2)
         self.mean = (
             sigma
             * np.sqrt(np.pi / 2)
@@ -3762,7 +3760,7 @@ def random(self, point=None, size=None):
         return generate_samples(self._random, nu=nu, sigma=sigma, dist_shape=self.shape, size=size)
 
     def _random(self, nu, sigma, size):
-        """ Wrapper around stats.rice.rvs that converts Rice's
+        """Wrapper around stats.rice.rvs that converts Rice's
         parametrization to scipy.rice. All parameter arrays should have
         been broadcasted properly by generate_samples at this point and size is
         the scipy.rvs representation.

pymc3/tests/conftest.py

@@ -20,31 +20,29 @@
 
 @pytest.fixture(scope="function", autouse=True)
 def theano_config():
-    config = theano.configparser.change_flags(compute_test_value='raise')
+    config = theano.configparser.change_flags(compute_test_value="raise")
     with config:
         yield
 
 
-@pytest.fixture(scope='function', autouse=True)
+@pytest.fixture(scope="function", autouse=True)
 def exception_verbosity():
-    config = theano.configparser.change_flags(
-        exception_verbosity='high')
+    config = theano.configparser.change_flags(exception_verbosity="high")
     with config:
         yield
 
 
-@pytest.fixture(scope='function', autouse=False)
+@pytest.fixture(scope="function", autouse=False)
 def strict_float32():
-    if theano.config.floatX == 'float32':
-        config = theano.configparser.change_flags(
-            warn_float64='raise')
+    if theano.config.floatX == "float32":
+        config = theano.configparser.change_flags(warn_float64="raise")
         with config:
             yield
     else:
         yield
 
 
-@pytest.fixture(scope='function', autouse=False)
+@pytest.fixture(scope="function", autouse=False)
 def seeded_test():
     # TODO: use this instead of SeededTest
     np.random.seed(42)

pymc3/tests/helpers.py

@@ -65,7 +65,7 @@ def matches(self, **kwargs):
 
 class Matcher:
 
-    _partial_matches = ('msg', 'message')
+    _partial_matches = ("msg", "message")
 
     def matches(self, d, **kwargs):
         """
@@ -91,7 +91,7 @@ def match_value(self, k, dv, v):
         if isinstance(v, type(dv)):
             result = False
         elif not isinstance(dv, str) or k not in self._partial_matches:
-            result = (v == dv)
+            result = v == dv
         else:
             result = dv.find(v) >= 0
         return result

pymc3/tests/models.py

@@ -26,16 +26,16 @@ def simple_model():
     mu = -2.1
     tau = 1.3
     with Model() as model:
-        Normal('x', mu, tau=tau, shape=2, testval=tt.ones(2) * .1)
+        Normal("x", mu, tau=tau, shape=2, testval=tt.ones(2) * 0.1)
 
-    return model.test_point, model, (mu, tau ** -.5)
+    return model.test_point, model, (mu, tau ** -0.5)
 
 
 def simple_categorical():
     p = floatX_array([0.1, 0.2, 0.3, 0.4])
     v = floatX_array([0.0, 1.0, 2.0, 3.0])
     with Model() as model:
-        Categorical('x', p, shape=3, testval=[1, 2, 3])
+        Categorical("x", p, shape=3, testval=[1, 2, 3])
 
     mu = np.dot(p, v)
     var = np.dot(p, (v - mu) ** 2)
@@ -46,9 +46,9 @@ def multidimensional_model():
     mu = -2.1
     tau = 1.3
     with Model() as model:
-        Normal('x', mu, tau=tau, shape=(3, 2), testval=.1 * tt.ones((3, 2)))
+        Normal("x", mu, tau=tau, shape=(3, 2), testval=0.1 * tt.ones((3, 2)))
 
-    return model.test_point, model, (mu, tau ** -.5)
+    return model.test_point, model, (mu, tau ** -0.5)
 
 
 def simple_arbitrary_det():
@@ -59,50 +59,52 @@ def arbitrary_det(value):
         return value
 
     with Model() as model:
-        a = Normal('a')
+        a = Normal("a")
         b = arbitrary_det(a)
-        Normal('obs', mu=b.astype('float64'), observed=floatX_array([1, 3, 5]))
+        Normal("obs", mu=b.astype("float64"), observed=floatX_array([1, 3, 5]))
 
     return model.test_point, model
 
 
 def simple_init():
     start, model, moments = simple_model()
-    step = Metropolis(model.vars, np.diag([1.]), model=model)
+    step = Metropolis(model.vars, np.diag([1.0]), model=model)
     return model, start, step, moments
 
 
 def simple_2model():
     mu = -2.1
     tau = 1.3
-    p = .4
+    p = 0.4
     with Model() as model:
-        x = pm.Normal('x', mu, tau=tau, testval=.1)
-        pm.Deterministic('logx', tt.log(x))
-        pm.Bernoulli('y', p)
+        x = pm.Normal("x", mu, tau=tau, testval=0.1)
+        pm.Deterministic("logx", tt.log(x))
+        pm.Bernoulli("y", p)
     return model.test_point, model
 
 
 def simple_2model_continuous():
     mu = -2.1
     tau = 1.3
     with Model() as model:
-        x = pm.Normal('x', mu, tau=tau, testval=.1)
-        pm.Deterministic('logx', tt.log(x))
-        pm.Beta('y', alpha=1, beta=1, shape=2)
+        x = pm.Normal("x", mu, tau=tau, testval=0.1)
+        pm.Deterministic("logx", tt.log(x))
+        pm.Beta("y", alpha=1, beta=1, shape=2)
     return model.test_point, model
 
 
 def mv_simple():
-    mu = floatX_array([-.1, .5, 1.1])
-    p = floatX_array([
-        [2., 0, 0],
-        [.05, .1, 0],
-        [1., -0.05, 5.5]])
+    mu = floatX_array([-0.1, 0.5, 1.1])
+    p = floatX_array([[2.0, 0, 0], [0.05, 0.1, 0], [1.0, -0.05, 5.5]])
     tau = np.dot(p, p.T)
     with pm.Model() as model:
-        pm.MvNormal('x', tt.constant(mu), tau=tt.constant(tau),
-                    shape=3, testval=floatX_array([.1, 1., .8]))
+        pm.MvNormal(
+            "x",
+            tt.constant(mu),
+            tau=tt.constant(tau),
+            shape=3,
+            testval=floatX_array([0.1, 1.0, 0.8]),
+        )
     H = tau
     C = np.linalg.inv(H)
     return model.test_point, model, (mu, C)
@@ -145,9 +147,9 @@ def mv_simple_very_coarse():
 def mv_simple_discrete():
     d = 2
     n = 5
-    p = floatX_array([.15, .85])
+    p = floatX_array([0.15, 0.85])
     with pm.Model() as model:
-        pm.Multinomial('x', n, tt.constant(p), shape=d, testval=np.array([1, 4]))
+        pm.Multinomial("x", n, tt.constant(p), shape=d, testval=np.array([1, 4]))
         mu = n * p
         # covariance matrix
         C = np.zeros((d, d))
@@ -180,30 +182,29 @@ def mv_prior_simple():
     std_post = (K - np.dot(v.T, v)).diagonal() ** 0.5
 
     with pm.Model() as model:
-        x = pm.Flat('x', shape=n)
-        x_obs = pm.MvNormal('x_obs', observed=obs, mu=x,
-                            cov=noise * np.eye(n), shape=n)
+        x = pm.Flat("x", shape=n)
+        x_obs = pm.MvNormal("x_obs", observed=obs, mu=x, cov=noise * np.eye(n), shape=n)
 
     return model.test_point, model, (K, L, mu_post, std_post, noise)
 
 
 def non_normal(n=2):
     with pm.Model() as model:
-        pm.Beta('x', 3, 3, shape=n, transform=None)
-    return model.test_point, model, (np.tile([.5], n), None)
+        pm.Beta("x", 3, 3, shape=n, transform=None)
+    return model.test_point, model, (np.tile([0.5], n), None)
 
 
 def exponential_beta(n=2):
     with pm.Model() as model:
-        pm.Beta('x', 3, 1, shape=n, transform=None)
-        pm.Exponential('y', 1, shape=n, transform=None)
+        pm.Beta("x", 3, 1, shape=n, transform=None)
+        pm.Exponential("y", 1, shape=n, transform=None)
     return model.test_point, model, None
 
 
 def beta_bernoulli(n=2):
     with pm.Model() as model:
-        pm.Beta('x', 3, 1, shape=n, transform=None)
-        pm.Bernoulli('y', 0.5)
+        pm.Beta("x", 3, 1, shape=n, transform=None)
+        pm.Bernoulli("y", 0.5)
     return model.test_point, model, None