chore: build dependencies and clean up

- added linting
- moved requirements into build dependencies
- cleaned up tests
- added new dev section to readme

Author: juelg

.github/workflows/build.yaml

@@ -42,8 +42,8 @@ jobs:
         run: python -m pip install --no-build-isolation '.[dev]'
       - name: Code formatting
         run: make pyformat
-      # - name: Code linting
-      #   run: make pylint
+      - name: Code linting
+        run: make pylint
       - name: Check that stub files are up-to-date
         run: make stubgen && git diff --exit-code
       - name: Ensure all unittests(pytest) are passing

README.md

@@ -29,9 +29,24 @@ print(q)
 sudo apt install libeigen3-dev
 git clone https://github.com/juelg/frankik.git
 cd frankik
-pip install requirements.txt
-pip install -ve '.[dev]' --no-build-isolation
+pip install .
 ```
+### Development installation
+```shell
+pip install -ve '.[dev]'
+```
+### Development Tools
+```shell
+# python code formatting
+make pyformat
+# python code linting
+make pylint
+# cpp code linting
+make cpplint
+# automatic stubfile generation (for changes in bindings)
+make stubgen
+```
+
 
 ## Installation from PyPI
 Coming soon...

pyproject.toml

@@ -1,5 +1,12 @@
 [build-system]
-requires = []
+requires = [
+    "build",
+    "wheel",
+    "scikit-build-core>=0.3.3",
+    "pybind11",
+    "cmake",
+    "ninja",
+]
 build-backend = "scikit_build_core.build"
 
 [project]
@@ -26,9 +33,7 @@ dev = [
     "isort==5.13.2",
     "mypy==1.10.1",
     "types-requests~=2.31",
-    # temporarily use fixed version until PR #275 is merged
-    # pybind11-stubgen==2.5.5
-    "pybind11-stubgen @ git+https://github.com/juelg/pybind11-stubgen@fix/class-sorting",
+    "pybind11-stubgen==2.5.5",
     "pytest==8.1.1",
     "commitizen~=3.28.0",
     "clang",

requirements.txt

@@ -30,7 +30,8 @@ def __init__(self, robot_type: str = RobotType.FR3):
             ValueError: If an unsupported robot type is provided.
         """
         if robot_type not in (RobotType.PANDA, RobotType.FR3):
-            raise ValueError(f"Unsupported robot type: {robot_type}. Choose 'panda' or 'fr3'.")
+            msg = f"Unsupported robot type: {robot_type}. Choose 'panda' or 'fr3'."
+            raise ValueError(msg)
         self.robot_type = robot_type
         self.q_min = q_min_fr3 if robot_type == RobotType.FR3 else q_min_panda
         self.q_max = q_max_fr3 if robot_type == RobotType.FR3 else q_max_panda
@@ -51,7 +52,7 @@ def pose_inverse(
         T_inv = np.eye(4)
         T_inv[:3, :3] = R.T
         T_inv[:3, 3] = -R.T @ t
-        return T_inv
+        return T_inv  # type: ignore
 
     def forward(
         self,
@@ -67,7 +68,7 @@ def forward(
             np.ndarray: A 4x4 homogeneous transformation matrix representing the end-effector pose.
         """
         pose = fk(q0)
-        return pose @ self.pose_inverse(tcp_offset) if tcp_offset is not None else pose
+        return pose @ self.pose_inverse(tcp_offset) if tcp_offset is not None else pose  # type: ignore
 
     def inverse(
         self,
@@ -88,13 +89,13 @@ def inverse(
             np.ndarray | None: A 7-element array representing the joint angles if a solution is found; otherwise, None.
         """
         new_pose = pose @ self.pose_inverse(tcp_offset) if tcp_offset is not None else pose
-        q = ik(new_pose, q0, q7=q7, is_fr3=(self.robot_type == RobotType.FR3))
+        q = ik(new_pose, q0, q7=q7, is_fr3=(self.robot_type == RobotType.FR3))  # type: ignore
 
         if not np.isnan(q).any():
             return q
         if not allow_elbow_flips:
             return None
-        qs = ik_full(new_pose, q0, q7=q7, is_fr3=(self.robot_type == RobotType.FR3))
+        qs = ik_full(new_pose, q0, q7=q7, is_fr3=(self.robot_type == RobotType.FR3))  # type: ignore
         qs_list = list(qs)
         # drop nan solutions
         qs_list = [q for q in qs_list if not np.isnan(q).any()]
@@ -104,8 +105,7 @@ def inverse(
             q0 = self.q_home
 
         # find the closest solution
-        best_q = min(qs_list, key=lambda q_sol: np.linalg.norm(q_sol - q0))
-        return best_q
+        return min(qs_list, key=lambda q_sol: np.linalg.norm(q_sol - q0))  # type: ignore
 
 
 __all__ = [

src/frankik/__init__.py

@@ -8,6 +8,6 @@
 def test_example(robot_type):
     kinematics = FrankaKinematics(robot_type=robot_type)
     q_home = np.array([0.0, -np.pi / 4, 0.0, -3 * np.pi / 4, 0.0, np.pi / 2, np.pi / 4])
-    pose_home = kinematics.forward(q_home)
+    pose_home = kinematics.forward(q_home)  # type: ignore
     q = kinematics.inverse(pose_home)
-    assert np.allclose(q, q_home)
+    assert np.allclose(q, q_home)  # type: ignore

src/tests/test_example.py

@@ -13,18 +13,18 @@
 
 
 @pytest.fixture(autouse=True)
-def set_seed():
+def _set_seed():
     """Automatically seed the RNG before every test to ensure reproducibility."""
     np.random.seed(42)
 
 
-def generate_random_q(q_min, q_max, n=1):
+def generate_random_q(q_min, q_max):
     q_min = np.array(q_min)
     q_max = np.array(q_max)
     range_span = q_max - q_min
 
     # 1. Generate base random values (centered 90% of range)
-    unit_rand = np.random.rand(n, 7)
+    unit_rand = np.random.rand(1, 7)
     q_rand = q_min + (range_span * 0.05) + unit_rand * (range_span * 0.90)
 
     # 2. Singularity Avoidance
@@ -35,33 +35,18 @@ def generate_random_q(q_min, q_max, n=1):
     # Threshold in radians (approx 5 degrees to be safe)
     singularity_threshold = 0.08
 
-    # Handle single sample case (1D array)
-    if n == 1:
-        # Avoid Shoulder Singularity (q[1] ≈ 0)
-        if abs(q_rand[0, 1]) < singularity_threshold:
-            # Push it away from 0, preserving the sign (default to positive if 0)
-            sign = np.sign(q_rand[0, 1]) if q_rand[0, 1] != 0 else 1.0
-            q_rand[0, 1] = sign * singularity_threshold
+    # Avoid Shoulder Singularity (q[1] ≈ 0)
+    if abs(q_rand[0, 1]) < singularity_threshold:
+        # Push it away from 0, preserving the sign (default to positive if 0)
+        sign = np.sign(q_rand[0, 1]) if q_rand[0, 1] != 0 else 1.0
+        q_rand[0, 1] = sign * singularity_threshold
 
-        # Avoid Wrist Singularity (q[5] ≈ 0) - though less critical for this specific bug
-        if abs(q_rand[0, 5]) < singularity_threshold:
-            sign = np.sign(q_rand[0, 5]) if q_rand[0, 5] != 0 else 1.0
-            q_rand[0, 5] = sign * singularity_threshold
+    # Avoid Wrist Singularity (q[5] ≈ 0) - though less critical for this specific bug
+    if abs(q_rand[0, 5]) < singularity_threshold:
+        sign = np.sign(q_rand[0, 5]) if q_rand[0, 5] != 0 else 1.0
+        q_rand[0, 5] = sign * singularity_threshold
 
-        return q_rand[0]
-
-    # Handle batch case (2D array)
-    else:
-        # Vectorized fix for Shoulder (Index 1)
-        # Find indices where q is too close to 0
-        shoulder_mask = np.abs(q_rand[:, 1]) < singularity_threshold
-        # Create a sign vector (replace 0s with 1s)
-        signs = np.sign(q_rand[:, 1])
-        signs[signs == 0] = 1.0
-        # Apply shift
-        q_rand[shoulder_mask, 1] = signs[shoulder_mask] * singularity_threshold
-
-        return q_rand
+    return q_rand[0]
 
 
 def get_random_pose_in_isocube():
@@ -177,7 +162,7 @@ def test_ik_full_consistency(robot_type):
                 continue
 
             valid_sols_count += 1
-            pose_check = frankik.fk(sol)
+            pose_check = frankik.fk(sol)  # type: ignore
 
             # Every returned solution must result in the target pose
             np.testing.assert_allclose(
@@ -193,6 +178,7 @@ def test_ik_full_consistency(robot_type):
 def test_limits_respected():
     """Sanity check that our random generator respects limits."""
     q_min, q_max = frankik.q_min_panda, frankik.q_max_panda
-    q = generate_random_q(q_min, q_max, n=100)
-    assert np.all(q >= q_min)
-    assert np.all(q <= q_max)
+    for _ in range(100):
+        q = generate_random_q(q_min, q_max)
+        assert np.all(q >= q_min)
+        assert np.all(q <= q_max)