Add zeros_like and ones_like methods to ArrayLikeFactory subclasses

src/adam/casadi/casadi_like.py

@@ -93,7 +93,6 @@ def __setitem__(self, idx, value: Union["CasadiLike", npt.ArrayLike]):
         else:
             self.array[idx] = value.array if isinstance(value, CasadiLike) else value
 
-
     def __getitem__(self, idx) -> "CasadiLike":
         """Overrides get item operator"""
         if idx is Ellipsis:
@@ -145,6 +144,68 @@ def array(*x) -> "CasadiLike":
         """
         return CasadiLike(cs.SX(*x))
 
+    @staticmethod
+    def zeros_like(x) -> CasadiLike:
+        """
+        Args:
+            x (npt.ArrayLike): matrix
+
+        Returns:
+            npt.ArrayLike: zero matrix of dimension x
+        """
+
+        kind = (
+            cs.DM
+            if (isinstance(x, CasadiLike) and isinstance(x.array, cs.DM))
+            or isinstance(x, cs.DM)
+            else cs.SX
+        )
+
+        return (
+            CasadiLike(kind.zeros(x.array.shape))
+            if isinstance(x, CasadiLike)
+            else (
+                CasadiLike(kind.zeros(x.shape))
+                if isinstance(x, (cs.SX, cs.DM))
+                else (
+                    TypeError(f"Unsupported type for zeros_like: {type(x)}")
+                    if isinstance(x, CasadiLike)
+                    else CasadiLike(kind.zeros(x.shape))
+                )
+            )
+        )
+
+    @staticmethod
+    def ones_like(x) -> CasadiLike:
+        """
+        Args:
+            x (npt.ArrayLike): matrix
+
+        Returns:
+            npt.ArrayLike: Identity matrix of dimension x
+        """
+
+        kind = (
+            cs.DM
+            if (isinstance(x, CasadiLike) and isinstance(x.array, cs.DM))
+            or isinstance(x, cs.DM)
+            else cs.SX
+        )
+
+        return (
+            CasadiLike(kind.ones(x.array.shape))
+            if isinstance(x, CasadiLike)
+            else (
+                CasadiLike(kind.ones(x.shape))
+                if isinstance(x, (cs.SX, cs.DM))
+                else (
+                    TypeError(f"Unsupported type for ones_like: {type(x)}")
+                    if isinstance(x, CasadiLike)
+                    else CasadiLike(kind.ones(x.shape))
+                )
+            )
+        )
+
 
 class SpatialMath(SpatialMath):
 

src/adam/core/spatial_math.py

@@ -68,8 +68,9 @@ def T(self):
 class ArrayLikeFactory(abc.ABC):
     """Abstract class for a generic Array wrapper. Every method should be implemented for every data type."""
 
+    @staticmethod
     @abc.abstractmethod
-    def zeros(self, x: npt.ArrayLike) -> npt.ArrayLike:
+    def zeros(x: npt.ArrayLike) -> npt.ArrayLike:
         """
         Args:
             x (npt.ArrayLike): matrix dimension
@@ -79,8 +80,9 @@ def zeros(self, x: npt.ArrayLike) -> npt.ArrayLike:
         """
         pass
 
+    @staticmethod
     @abc.abstractmethod
-    def eye(self, x: npt.ArrayLike) -> npt.ArrayLike:
+    def eye(x: npt.ArrayLike) -> npt.ArrayLike:
         """
         Args:
             x (npt.ArrayLike): matrix dimension
@@ -90,6 +92,30 @@ def eye(self, x: npt.ArrayLike) -> npt.ArrayLike:
         """
         pass
 
+    @staticmethod
+    @abc.abstractmethod
+    def zeros_like(x: npt.ArrayLike) -> npt.ArrayLike:
+        """
+        Args:
+            x (npt.ArrayLike): matrix
+
+        Returns:
+            npt.ArrayLike: zero matrix of dimension x
+        """
+        pass
+
+    @staticmethod
+    @abc.abstractmethod
+    def ones_like(x: npt.ArrayLike) -> npt.ArrayLike:
+        """
+        Args:
+            x (npt.ArrayLike): matrix
+
+        Returns:
+            npt.ArrayLike: ones matrix of dimension x
+        """
+        pass
+
 
 class SpatialMath:
     """Class implementing the main geometric functions used for computing rigid-body algorithm

src/adam/jax/jax_like.py

@@ -132,6 +132,36 @@ def array(x) -> "JaxLike":
         """
         return JaxLike(jnp.array(x))
 
+    @staticmethod
+    def zeros_like(x) -> JaxLike:
+        """
+        Args:
+            x (npt.ArrayLike): matrix
+
+        Returns:
+            npt.ArrayLike: zero matrix of dimension x
+        """
+        return (
+            JaxLike(jnp.zeros_like(x.array))
+            if isinstance(x, JaxLike)
+            else JaxLike(jnp.zeros_like(x))
+        )
+
+    @staticmethod
+    def ones_like(x) -> JaxLike:
+        """
+        Args:
+            x (npt.ArrayLike): matrix
+
+        Returns:
+            npt.ArrayLike: Ones matrix of dimension x
+        """
+        return (
+            JaxLike(jnp.ones_like(x.array))
+            if isinstance(x, JaxLike)
+            else JaxLike(jnp.ones_like(x))
+        )
+
 
 class SpatialMath(SpatialMath):
     def __init__(self):

src/adam/numpy/numpy_like.py

@@ -134,6 +134,36 @@ def array(x) -> "NumpyLike":
         """
         return NumpyLike(np.array(x))
 
+    @staticmethod
+    def zeros_like(x) -> NumpyLike:
+        """
+        Args:
+            x (npt.ArrayLike): matrix
+
+        Returns:
+            npt.ArrayLike: zero matrix of dimension x
+        """
+        return (
+            NumpyLike(np.zeros_like(x.array))
+            if isinstance(x, NumpyLike)
+            else NumpyLike(np.zeros_like(x))
+        )
+
+    @staticmethod
+    def ones_like(x) -> NumpyLike:
+        """
+        Args:
+            x (npt.ArrayLike): matrix
+
+        Returns:
+            npt.ArrayLike: Ones matrix of dimension x
+        """
+        return (
+            NumpyLike(np.ones_like(x.array))
+            if isinstance(x, NumpyLike)
+            else NumpyLike(np.ones_like(x))
+        )
+
 
 class SpatialMath(SpatialMath):
     def __init__(self):

src/adam/pytorch/torch_like.py

@@ -149,6 +149,36 @@ def array(x: ntp.ArrayLike) -> "TorchLike":
         """
         return TorchLike(torch.tensor(x))
 
+    @staticmethod
+    def zeros_like(x) -> TorchLike:
+        """
+        Args:
+            x (npt.ArrayLike): matrix
+
+        Returns:
+            npt.ArrayLike: zero matrix of dimension x
+        """
+        return (
+            TorchLike(torch.zeros_like(x.array))
+            if isinstance(x, TorchLike)
+            else TorchLike(torch.zeros_like(x))
+        )
+
+    @staticmethod
+    def ones_like(x) -> TorchLike:
+        """
+        Args:
+            x (npt.ArrayLike): matrix
+
+        Returns:
+            npt.ArrayLike: Identity matrix of dimension x
+        """
+        return (
+            TorchLike(torch.ones_like(x.array))
+            if isinstance(x, TorchLike)
+            else TorchLike(torch.ones_like(x))
+        )
+
 
 class SpatialMath(SpatialMath):
     def __init__(self):

tests/test_casadi.py

@@ -4,6 +4,7 @@
 from conftest import RobotCfg, State
 
 from adam.casadi import KinDynComputations
+from adam.casadi.casadi_like import CasadiLike, CasadiLikeFactory
 
 
 @pytest.fixture(scope="module")
@@ -173,3 +174,15 @@ def test_gravity_term(setup_test):
     assert idyn_gravity - adam_gravity == pytest.approx(0.0, abs=1e-4)
     adam_gravity = cs.DM(adam_kin_dyn.gravity_term_fun()(state.H, state.joints_pos))
     assert idyn_gravity - adam_gravity == pytest.approx(0.0, abs=1e-4)
+
+
+def test_casadi_like():
+    B = cs.DM([[1.0, 2.0], [3.0, 4.0]])
+    B_like = CasadiLike(B)
+    assert B_like[...].array - B == pytest.approx(0.0, abs=1e-5)
+
+    ones = CasadiLikeFactory.ones_like(B)
+    assert ones[...].array - cs.DM.ones(2, 2) == pytest.approx(0.0, abs=1e-5)
+
+    zeros = CasadiLikeFactory.zeros_like(B)
+    assert zeros[...].array - cs.DM.zeros(2, 2) == pytest.approx(0.0, abs=1e-5)

tests/test_jax.py

@@ -2,8 +2,11 @@
 import pytest
 from conftest import RobotCfg, State
 from jax import config
+import jax.numpy as jnp
+
 
 from adam.jax import KinDynComputations
+from adam.jax.jax_like import JaxLike, JaxLikeFactory
 
 config.update("jax_enable_x64", True)
 
@@ -119,3 +122,14 @@ def test_gravity_term(setup_test):
     idyn_gravity = robot_cfg.idyn_function_values.gravity_term
     adam_gravity = adam_kin_dyn.gravity_term(state.H, state.joints_pos)
     assert idyn_gravity - adam_gravity == pytest.approx(0.0, abs=1e-4)
+
+def test_jax_like():
+    B = jnp.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
+    B_like = JaxLike(B)
+    assert B_like[...].array - B == pytest.approx(0.0, abs=1e-5)
+
+    ones = JaxLikeFactory.ones_like(B_like)
+    assert ones.array - jnp.ones_like(B) == pytest.approx(0.0, abs=1e-5)
+
+    zeros = JaxLikeFactory.zeros_like(B_like)
+    assert zeros.array - jnp.zeros_like(B) == pytest.approx(0.0, abs=1e-5)

tests/test_numpy.py

@@ -3,6 +3,7 @@
 from conftest import RobotCfg, State
 
 from adam.numpy import KinDynComputations
+from adam.numpy.numpy_like import NumpyLike, NumpyLikeFactory
 
 
 @pytest.fixture(scope="module")
@@ -116,3 +117,15 @@ def test_gravity_term(setup_test):
     idyn_gravity = robot_cfg.idyn_function_values.gravity_term
     adam_gravity = adam_kin_dyn.gravity_term(state.H, state.joints_pos)
     assert idyn_gravity - adam_gravity == pytest.approx(0.0, abs=1e-4)
+
+
+def test_numpy_like():
+    B = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
+    B_like = NumpyLike(B)
+    assert B_like[...].array - B == pytest.approx(0.0, abs=1e-5)
+
+    ones = NumpyLikeFactory.ones_like(B_like)
+    assert ones.array - np.ones_like(B) == pytest.approx(0.0, abs=1e-5)
+
+    zeros = NumpyLikeFactory.zeros_like(B_like)
+    assert zeros.array - np.zeros_like(B) == pytest.approx(0.0, abs=1e-5)

tests/test_pytorch.py

@@ -4,6 +4,7 @@
 from conftest import RobotCfg, State
 
 from adam.pytorch import KinDynComputations
+from adam.pytorch.torch_like import TorchLike, TorchLikeFactory
 
 torch.set_default_dtype(torch.float64)
 
@@ -128,3 +129,15 @@ def test_gravity_term(setup_test):
     idyn_gravity = robot_cfg.idyn_function_values.gravity_term
     adam_gravity = adam_kin_dyn.gravity_term(state.H, state.joints_pos)
     assert idyn_gravity - adam_gravity.numpy() == pytest.approx(0.0, abs=1e-4)
+
+
+def test_torch_like():
+    B = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
+    B_like = TorchLike(B)
+    assert B_like[...].array - B == pytest.approx(0.0, abs=1e-5)
+
+    ones = TorchLikeFactory.ones_like(B_like)
+    assert ones.array - np.ones_like(B) == pytest.approx(0.0, abs=1e-5)
+
+    zeros = TorchLikeFactory.zeros_like(B_like)
+    assert zeros.array - np.zeros_like(B) == pytest.approx(0.0, abs=1e-5)