Change version to 0.1.0

Author: solangegbv

pyproject.toml

@@ -17,7 +17,7 @@ name = "jsrm"  # Required
 #
 # For a discussion on single-sourcing the version, see
 # https://packaging.python.org/guides/single-sourcing-package-version/
-version = "0.0.17"  # Required
+version = "0.1.0"  # Required
 
 # This is a one-line description or tagline of what your project does. This
 # corresponds to the "Summary" metadata field:

src/jsrm/systems/pcs.py

@@ -79,6 +79,8 @@ class PCS(eqx.Module):
         Computes the dynamical matrix for the system.
     resolve_upon_time(q0: Array, qd0: Array, actuation_args: Tuple[Array], t0: float, t1: float, dt: float, skip_steps: int, solver: AbstractSolver, max_steps: Optional[int] = None) -> Tuple[Array, Array, Array]:
         Simulates the robot dynamics over time using the specified solver.
+    forward_dynamics(t: float, y: Array, actuation_args: Optional[Tuple]) -> Array:
+        Computes the forward dynamics of the system at a given time t.
 
     kinetic_energy(q: Array, qd: Array) -> float:
         Computes the kinetic energy of the system.
@@ -1382,7 +1384,7 @@ def operational_space_dynamical_matrices(
         return Lambda, mu, J, J_d, JB_pinv
 
     @eqx.filter_jit
-    def _forward_dynamics(
+    def forward_dynamics(
         self,
         t: float,
         y: Array,
@@ -1458,7 +1460,7 @@ def resolve_upon_time(
         """
         y0 = jnp.concatenate([q0, qd0])  # Initial state vector
 
-        term = ODETerm(self._forward_dynamics)
+        term = ODETerm(self.forward_dynamics)
 
         t = jnp.arange(t0, t1, dt)  # Time points for the solution
         saveat = SaveAt(ts=t[::skip_steps])  # Save at specified time points

src/jsrm/systems/planar_pcs.py

@@ -79,6 +79,8 @@ class PlanarPCS(eqx.Module):
         Computes the dynamical matrix for the system.
     resolve_upon_time(q0: Array, qd0: Array, actuation_args: Tuple[Array], t0: float, t1: float, dt: float, skip_steps: int, solver: AbstractSolver, max_steps: Optional[int] = None) -> Tuple[Array, Array, Array]:
         Simulates the robot dynamics over time using the specified solver.
+    forward_dynamics(t: float, y: Array, actuation_args: Optional[Tuple]) -> Array:
+        Computes the forward dynamics of the system at a given time t.
 
     kinetic_energy(q: Array, qd: Array) -> float:
         Computes the kinetic energy of the system.
@@ -1373,7 +1375,7 @@ def operational_space_dynamical_matrices(
         return Lambda, mu, J, J_d, JB_pinv
 
     @eqx.filter_jit
-    def _forward_dynamics(
+    def forward_dynamics(
         self,
         t: float,
         y: Array,
@@ -1449,7 +1451,7 @@ def resolve_upon_time(
         """
         y0 = jnp.concatenate([q0, qd0])  # Initial state vector
 
-        term = ODETerm(self._forward_dynamics)
+        term = ODETerm(self.forward_dynamics)
 
         t = jnp.arange(t0, t1, dt)  # Time points for the solution
         saveat = SaveAt(ts=t[::skip_steps])  # Save at specified time points