[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/emcee/backends/hdf.py

@@ -2,9 +2,9 @@
 
 from __future__ import division, print_function
 
+import json
 import os
 from tempfile import NamedTemporaryFile
-import json
 
 import numpy as np
 
@@ -202,7 +202,7 @@ def accepted(self):
     def random_state(self):
         with self.open() as f:
             try:
-                dct = json.loads(f[self.name].attrs['random_state'])
+                dct = json.loads(f[self.name].attrs["random_state"])
             except KeyError:
                 return None
         return dct
@@ -269,8 +269,7 @@ def save_step(self, state, accepted):
             g["accepted"][:] += accepted
 
             g.attrs["random_state"] = json.dumps(
-                state.random_state,
-                cls=NumpyEncoder
+                state.random_state, cls=NumpyEncoder
             )
 
             g.attrs["iteration"] = iteration + 1

src/emcee/ensemble.py

@@ -222,16 +222,18 @@ def random_state(self):
         so silently.
 
         """
+
         def rng_dict(rng):
             bg_state = rng.bit_generator.state
             ss = rng.bit_generator.seed_seq
             ss_dict = dict(
                 entropy=ss.entropy,
                 spawn_key=ss.spawn_key,
                 pool_size=ss.pool_size,
-                n_children_spawned=ss.n_children_spawned
+                n_children_spawned=ss.n_children_spawned,
             )
             return dict(bg_state=bg_state, seed_seq=ss_dict)
+
         return rng_dict(self._random)
         # return self._random.bit_generator.state
 
@@ -242,13 +244,15 @@ def random_state(self, state):
         if it doesn't work. Don't say I didn't warn you...
 
         """
+
         def _rng_fromdict(d):
-            bg_state = d['bg_state']
-            ss = np.random.SeedSequence(**d['seed_seq'])
-            bg = getattr(np.random, bg_state['bit_generator'])(ss)
+            bg_state = d["bg_state"]
+            ss = np.random.SeedSequence(**d["seed_seq"])
+            bg = getattr(np.random, bg_state["bit_generator"])(ss)
             bg.state = bg_state
             rng = np.random.Generator(bg)
             return rng
+
         try:
             self._random = _rng_fromdict(state)
             # self._random.bit_generator = state

src/emcee/moves/de.py

@@ -53,7 +53,9 @@ def get_proposal(self, s, c, random):
         diffs = np.diff(c[pairs], axis=1).squeeze(axis=1)  # (ns, ndim)
 
         # Sample a gamma value for each walker following Nelson et al. (2013)
-        gamma = self.g0 * (1 + self.sigma * random.standard_normal((ns, 1)))  # (ns, 1)
+        gamma = self.g0 * (
+            1 + self.sigma * random.standard_normal((ns, 1))
+        )  # (ns, 1)
 
         # In this way, sigma is the standard deviation of the distribution of gamma,
         # instead of the standard deviation of the distribution of the proposal as proposed by Ter Braak (2006).

src/emcee/moves/gaussian.py

@@ -87,7 +87,9 @@ def get_factor(self, rng):
         return np.exp(rng.uniform(-self._log_factor, self._log_factor))
 
     def get_updated_vector(self, rng, x0):
-        return x0 + self.get_factor(rng) * self.scale * rng.standard_normal((x0.shape))
+        return x0 + self.get_factor(rng) * self.scale * rng.standard_normal(
+            (x0.shape)
+        )
 
     def __call__(self, x0, rng):
         nw, nd = x0.shape
@@ -106,7 +108,9 @@ def __call__(self, x0, rng):
 
 class _diagonal_proposal(_isotropic_proposal):
     def get_updated_vector(self, rng, x0):
-        return x0 + self.get_factor(rng) * self.scale * rng.standard_normal((x0.shape))
+        return x0 + self.get_factor(rng) * self.scale * rng.standard_normal(
+            (x0.shape)
+        )
 
 
 class _proposal(_isotropic_proposal):

src/emcee/tests/unit/test_backends.py

@@ -124,7 +124,7 @@ def test_backend(backend, dtype, blobs):
         last2 = sampler2.get_last_sample()
         assert np.allclose(last1.coords, last2.coords)
         assert np.allclose(last1.log_prob, last2.log_prob)
-        assert last1.random_state['bg_state'] == last2.random_state['bg_state']
+        assert last1.random_state["bg_state"] == last2.random_state["bg_state"]
         if blobs:
             _custom_allclose(last1.blobs, last2.blobs)
         else:
@@ -137,7 +137,7 @@ def test_backend(backend, dtype, blobs):
 
 @pytest.mark.parametrize("backend,dtype", product(other_backends, dtypes))
 def test_reload(backend, dtype):
-    with (backend() as backend1):
+    with backend() as backend1:
         run_sampler(backend1, dtype=dtype)
 
         # Test the state
@@ -192,7 +192,7 @@ def test_restart(backend, dtype):
         last2 = sampler2.get_last_sample()
         assert np.allclose(last1.coords, last2.coords)
         assert np.allclose(last1.log_prob, last2.log_prob)
-        assert last1.random_state['bg_state'] == last2.random_state['bg_state']
+        assert last1.random_state["bg_state"] == last2.random_state["bg_state"]
         _custom_allclose(last1.blobs, last2.blobs)
 
         a = sampler1.acceptance_fraction

src/emcee/tests/unit/test_sampler.py

@@ -137,7 +137,9 @@ def run_sampler(
 ):
     rng = np.random.default_rng(seed)
     coords = rng.standard_normal((nwalkers, ndim))
-    sampler = EnsembleSampler(nwalkers, ndim, normal_log_prob, rng=rng, backend=backend)
+    sampler = EnsembleSampler(
+        nwalkers, ndim, normal_log_prob, rng=rng, backend=backend
+    )
     sampler.run_mcmc(
         coords,
         nsteps,