[pre-commit.ci] pre-commit autoupdate (#110)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/psf/black: 23.12.1 → 24.1.1](psf/black@23.12.1...24.1.1)

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

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

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -12,7 +12,7 @@ repos:
         additional_dependencies: [toml]
         exclude: docs/tutorials
   - repo: https://github.com/psf/black
-    rev: "23.12.1"
+    rev: "24.1.1"
     hooks:
       - id: black-jupyter
   - repo: https://github.com/kynan/nbstripout

python/celerite2/pymc/ops.py

@@ -130,9 +130,11 @@ def perform(self, node, inputs, outputs):
     def grad(self, inputs, gradients):
         outputs = self(*inputs)
         grads = (
-            pt.zeros_like(outputs[n])
-            if isinstance(b.type, pytensor.gradient.DisconnectedType)
-            else b
+            (
+                pt.zeros_like(outputs[n])
+                if isinstance(b.type, pytensor.gradient.DisconnectedType)
+                else b
+            )
             for n, b in enumerate(gradients[: len(self.spec["outputs"])])
         )
         return self.rev_op(*chain(inputs, outputs, grads))

python/celerite2/pymc3/distribution.py

@@ -21,9 +21,9 @@ class CeleriteNormal(Continuous):
     def __init__(self, gp, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.gp = gp
-        self.mean = (
-            self.median
-        ) = self.mode = self.gp.mean_value + tt.zeros_like(self.gp._t)
+        self.mean = self.median = self.mode = (
+            self.gp.mean_value + tt.zeros_like(self.gp._t)
+        )
 
     def random(self, point=None, size=None):
         if size is None:

python/celerite2/pymc3/ops.py

@@ -130,9 +130,11 @@ def perform(self, node, inputs, outputs):
     def grad(self, inputs, gradients):
         outputs = self(*inputs)
         grads = (
-            tt.zeros_like(outputs[n])
-            if isinstance(b.type, theano.gradient.DisconnectedType)
-            else b
+            (
+                tt.zeros_like(outputs[n])
+                if isinstance(b.type, theano.gradient.DisconnectedType)
+                else b
+            )
             for n, b in enumerate(gradients[: len(self.spec["outputs"])])
         )
         return self.rev_op(*chain(inputs, outputs, grads))

python/celerite2/pymc3/terms.py

@@ -218,9 +218,11 @@ def get_coefficients(self):
         dc.append(tt.flatten(dj[:, None] + dk[None, :]))
 
         return [
-            tt.concatenate(vals, axis=0)
-            if len(vals)
-            else tt.zeros(0, dtype=self.dtype)
+            (
+                tt.concatenate(vals, axis=0)
+                if len(vals)
+                else tt.zeros(0, dtype=self.dtype)
+            )
             for vals in (ar, cr, ac, bc, cc, dc)
         ]