ruff and black

dynax/linearize.py

@@ -170,11 +170,10 @@ def discrete_input_output_linearize(
     reldeg: int,
     ref: DynamicalSystem,
     output: Optional[int] = None,
-    solver: Optional[optx.AbstractRootFinder]  = None,
+    solver: Optional[optx.AbstractRootFinder] = None,
 ) -> Callable[[Array, Array, float, float], float]:
-    """Construct the input-output linearizing feedback law for a discrete-time system.
-    """
-
+    """Construct the input-output linearizing feedback for a discrete-time system."""
+
     # Lee 2022, Chap. 7.4
     f = lambda x, u: sys.vector_field(x, u)
     h = sys.output
@@ -186,7 +185,7 @@ def discrete_input_output_linearize(
         _output = lambda x: x
     else:
         _output = lambda x: x[output]
-        
+
     if solver is None:
         solver = optx.Newton(rtol=1e-6, atol=1e-6)
 
@@ -202,7 +201,10 @@ def y_reldeg_ref(z, v):
 
     def feedbacklaw(x: Array, z: Array, v: float, u_prev: float):
         def fn(u, args):
-            return (_output(h(propagate(f, reldeg, x, u))) - y_reldeg_ref(z, v)).squeeze()
+            return (
+                _output(h(propagate(f, reldeg, x, u))) - y_reldeg_ref(z, v)
+            ).squeeze()
+
         u = optx.root_find(fn, solver, u_prev).value
         return u
 

dynax/util.py

@@ -1,10 +1,10 @@
 import functools
+from typing import Literal
 
 import equinox
 import jax
 import jax.numpy as jnp
 from jaxtyping import Array, ArrayLike
-from typing import Literal
 
 
 def ssmatrix(data: ArrayLike, axis: int = 0) -> Array:

examples/linearize_discrete_time.py

@@ -1,17 +1,16 @@
+import jax.numpy as jnp
 import matplotlib.pyplot as plt
+import numpy as np
+from equinox.nn import GRUCell
+from jax.random import PRNGKey
 
 from dynax import (
-    DynamicalSystem,
-    Map,
     discrete_relative_degree,
     DiscreteLinearizingSystem,
+    DynamicalSystem,
     LinearSystem,
+    Map,
 )
-from equinox.nn import GRUCell
-import jax
-import jax.numpy as jnp
-import numpy as np
-from jax.random import PRNGKey
 
 
 # A nonlinear discrete-time system.
@@ -89,5 +88,3 @@ def output(self, x, u=None, t=None):
 plt.plot(linearizing_inputs, label="linearizing input")
 plt.legend()
 plt.show()
-
-

tests/test_linearize.py

@@ -94,7 +94,7 @@ def test_linearize_dyn2lin():
     class ScalarScalar(DynamicalSystem):
         n_states = "scalar"
         n_inputs = "scalar"
-        
+
         def vector_field(self, x, u, t):
             return -1 * x + 2 * u
 
@@ -164,7 +164,7 @@ def vector_field(self, x, u, t=None):
     def output(self, x, u=None, t=None):
         return x[0]
 
-    
+
 def test_discrete_input_output_linearize():
     sys = Lee7_4_5()
     refsys = sys.linearize()
@@ -176,7 +176,7 @@ def test_discrete_input_output_linearize():
     feedback_sys = DiscreteLinearizingSystem(sys, refsys, reldeg)
     t = np.linspace(0, 0.001, 10)
     u = np.cos(t) * 0.1
-    _, v = Map(feedback_sys)(np.zeros(2+2+1), t, u)
+    _, v = Map(feedback_sys)(np.zeros(2 + 2 + 1), t, u)
     _, y = Map(sys)(np.zeros(2), t, u)
     _, y_ref = Map(refsys)(np.zeros(2), t, u)
 

tests/test_util.py

@@ -1,4 +1,3 @@
-import jax
 import jax.numpy as jnp
 import numpy.testing as npt