Added N flag inside node-hub: E flag is pending

examples/aloha/benchmark/python/dynamixel.py

@@ -377,29 +377,29 @@ class Dynamixel:
         )
         self._process_response(dxl_comm_result, dxl_error, motor_id)
 
-    def set_P(self, motor_id: int, P: int):
+    def set_p(self, motor_id: int, p: int):
         """Set the position P gain for the specified servo.
 
         Args:
-            motor_id: ID of the servo to control.
-            P: Position P gain value to set.
+            motor_id (int): The ID of the servo motor
+            p (int): The position P gain value to set
 
         """
         dxl_comm_result, dxl_error = self.packetHandler.write2ByteTxRx(
-            self.portHandler, motor_id, self.POSITION_P, P,
+            self.portHandler, motor_id, self.POSITION_P, p,
         )
         self._process_response(dxl_comm_result, dxl_error, motor_id)
 
-    def set_I(self, motor_id: int, I: int):
+    def set_i(self, motor_id: int, i: int):
         """Set the position I gain for the specified servo.
 
         Args:
-            motor_id: ID of the servo to control.
-            I: Position I gain value to set.
+            motor_id (int): The ID of the servo motor
+            i (int): The position I gain value to set
 
         """
         dxl_comm_result, dxl_error = self.packetHandler.write2ByteTxRx(
-            self.portHandler, motor_id, self.POSITION_I, I,
+            self.portHandler, motor_id, self.POSITION_I, i,
         )
         self._process_response(dxl_comm_result, dxl_error, motor_id)
 

examples/piper/convert.py

@@ -1,29 +1,29 @@
 """TODO: Add docstring."""
 
 import numpy as np
-from scipy.spatial.transform import Rotation as R
+from scipy.spatial.transform import Rotation
 
 
 def convert_quaternion_to_euler(quat):
     """Convert Quaternion (xyzw) to Euler angles (rpy)."""
     # Normalize
     quat = quat / np.linalg.norm(quat)
-    return R.from_quat(quat).as_euler("xyz")
+    return Rotation.from_quat(quat).as_euler("xyz")
 
 
 def convert_euler_to_quaternion(euler):
     """Convert Euler angles (rpy) to Quaternion (xyzw)."""
-    return R.from_euler("xyz", euler).as_quat()
+    return Rotation.from_euler("xyz", euler).as_quat()
 
 
 def convert_euler_to_rotation_matrix(euler):
     """Convert Euler angles (rpy) to rotation matrix (3x3)."""
-    return R.from_euler("xyz", euler).as_matrix()
+    return Rotation.from_euler("xyz", euler).as_matrix()
 
 
 def convert_rotation_matrix_to_euler(rotmat):
     """Convert rotation matrix (3x3) to Euler angles (rpy)."""
-    r = R.from_matrix(rotmat)
+    r = Rotation.from_matrix(rotmat)
     return r.as_euler("xyz", degrees=False)
 
 

node-hub/dora-argotranslate/dora_argotranslate/main.py

@@ -32,15 +32,15 @@ def main():
             break
         if event["type"] == "INPUT" and event["id"] == "text":
             text = event["value"][0].as_py()
-            translatedText = argostranslate.translate.translate(
+            translated_text = argostranslate.translate.translate(
                 text,
                 from_code,
                 to_code,
             )
             print(text, flush=True)
-            print("translated: " + translatedText, flush=True)
+            print("translated: " + translated_text, flush=True)
             node.send_output(
                 "text",
-                pa.array([translatedText]),
+                pa.array([translated_text]),
                 {"language": to_code},
             )

node-hub/dora-distil-whisper/dora_distil_whisper/main.py

@@ -90,26 +90,26 @@ def load_model():
     """TODO: Add docstring."""
     from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 
-    MODEL_NAME_OR_PATH = os.getenv("MODEL_NAME_OR_PATH", DEFAULT_PATH)
+    model_name_or_path = os.getenv("MODEL_NAME_OR_PATH", DEFAULT_PATH)
 
     if bool(os.getenv("USE_MODELSCOPE_HUB") in ["True", "true"]):
         from modelscope import snapshot_download
 
-        if not Path(MODEL_NAME_OR_PATH).exists():
-            MODEL_NAME_OR_PATH = snapshot_download(MODEL_NAME_OR_PATH)
+        if not Path(model_name_or_path).exists():
+            model_name_or_path = snapshot_download(model_name_or_path)
 
     device = "cuda:0" if torch.cuda.is_available() else "cpu"
     torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
 
     model = AutoModelForSpeechSeq2Seq.from_pretrained(
-        MODEL_NAME_OR_PATH,
+        model_name_or_path,
         torch_dtype=torch_dtype,
         low_cpu_mem_usage=True,
         use_safetensors=True,
     )
     model.to(device)
 
-    processor = AutoProcessor.from_pretrained(MODEL_NAME_OR_PATH)
+    processor = AutoProcessor.from_pretrained(model_name_or_path)
     return pipeline(
         "automatic-speech-recognition",
         model=model,

node-hub/dora-internvl/dora_internvl/main.py

@@ -5,9 +5,9 @@ import os
 import numpy as np
 import pyarrow as pa
 import torch
-import torchvision.transforms as T
 from dora import Node
 from PIL import Image
+from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 from transformers import AutoModel, AutoTokenizer
 
@@ -17,13 +17,13 @@ IMAGENET_STD = (0.229, 0.224, 0.225)
 
 def build_transform(input_size):
     """TODO: Add docstring."""
-    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
-    return T.Compose(
+    mean, std = IMAGENET_MEAN, IMAGENET_STD
+    return transforms.Compose(
         [
-            T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
-            T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
-            T.ToTensor(),
-            T.Normalize(mean=MEAN, std=STD),
+            transforms.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
+            transforms.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std),
         ],
     )
 

node-hub/dora-magma/dora_magma/main.py

@@ -22,33 +22,33 @@ magma_dir = current_dir.parent / "Magma" / "magma"
 
 def load_magma_models():
     """TODO: Add docstring."""
-    DEFAULT_PATH = str(magma_dir.parent / "checkpoints" / "Magma-8B")
-    if not os.path.exists(DEFAULT_PATH):
-        DEFAULT_PATH = str(magma_dir.parent)
-        if not os.path.exists(DEFAULT_PATH):
+    default_path = str(magma_dir.parent / "checkpoints" / "Magma-8B")
+    if not os.path.exists(default_path):
+        default_path = str(magma_dir.parent)
+        if not os.path.exists(default_path):
             logger.warning(
                 "Warning: Magma submodule not found, falling back to HuggingFace version",
             )
-            DEFAULT_PATH = "microsoft/Magma-8B"
+            default_path = "microsoft/Magma-8B"
 
-    MODEL_NAME_OR_PATH = os.getenv("MODEL_NAME_OR_PATH", DEFAULT_PATH)
-    logger.info(f"Loading Magma model from: {MODEL_NAME_OR_PATH}")
+    model_name_or_path = os.getenv("MODEL_NAME_OR_PATH", default_path)
+    logger.info(f"Loading Magma model from: {model_name_or_path}")
 
     try:
         model = AutoModelForCausalLM.from_pretrained(
-            MODEL_NAME_OR_PATH,
+            model_name_or_path,
             trust_remote_code=True,
             torch_dtype=torch.bfloat16,
             device_map="auto",
         )
         processor = AutoProcessor.from_pretrained(
-            MODEL_NAME_OR_PATH, trust_remote_code=True,
+            model_name_or_path, trust_remote_code=True,
         )
     except Exception as e:
         logger.error(f"Failed to load model: {e}")
         raise
 
-    return model, processor, MODEL_NAME_OR_PATH
+    return model, processor, model_name_or_path
 
 
 model, processor, MODEL_NAME_OR_PATH = load_magma_models()

node-hub/dora-piper/dora_piper/main.py

@@ -47,8 +47,8 @@ def enable_fun(piper: C_PiperInterface):
 def main():
     """TODO: Add docstring."""
     elapsed_time = time.time()
-    CAN_BUS = os.getenv("CAN_BUS", "")
-    piper = C_PiperInterface(CAN_BUS)
+    can_bus = os.getenv("CAN_BUS", "")
+    piper = C_PiperInterface(can_bus)
     piper.ConnectPort()
 
     if not TEACH_MODE:

node-hub/dora-pyrealsense/dora_pyrealsense/main.py

@@ -14,8 +14,8 @@ RUNNER_CI = True if os.getenv("CI") == "true" else False
 
 def main():
     """TODO: Add docstring."""
-    FLIP = os.getenv("FLIP", "")
-    DEVICE_SERIAL = os.getenv("DEVICE_SERIAL", "")
+    flip = os.getenv("FLIP", "")
+    device_serial = os.getenv("DEVICE_SERIAL", "")
     image_height = int(os.getenv("IMAGE_HEIGHT", "480"))
     image_width = int(os.getenv("IMAGE_WIDTH", "640"))
     encoding = os.getenv("ENCODING", "rgb8")
@@ -26,15 +26,15 @@ def main():
 
     # Serial list
     serials = [device.get_info(rs.camera_info.serial_number) for device in devices]
-    if DEVICE_SERIAL and (DEVICE_SERIAL in serials):
+    if device_serial and (device_serial in serials):
         raise ConnectionError(
-            f"Device with serial {DEVICE_SERIAL} not found within: {serials}.",
+            f"Device with serial {device_serial} not found within: {serials}.",
         )
 
     pipeline = rs.pipeline()
 
     config = rs.config()
-    config.enable_device(DEVICE_SERIAL)
+    config.enable_device(device_serial)
     config.enable_stream(rs.stream.color, image_width, image_height, rs.format.rgb8, 30)
     config.enable_stream(rs.stream.depth, image_width, image_height)
 
@@ -79,11 +79,11 @@ def main():
 
                 ## Change rgb to bgr
 
-                if FLIP == "VERTICAL":
+                if flip == "VERTICAL":
                     frame = cv2.flip(frame, 0)
-                elif FLIP == "HORIZONTAL":
+                elif flip == "HORIZONTAL":
                     frame = cv2.flip(frame, 1)
-                elif FLIP == "BOTH":
+                elif flip == "BOTH":
                     frame = cv2.flip(frame, -1)
 
                 # resize the frame

node-hub/dora-rdt-1b/dora_rdt_1b/main.py

@@ -81,7 +81,7 @@ def get_language_embeddings():
 
     return lang_embeddings.unsqueeze(
         0,
-    )  # Size:  (B, L_lang, D) or None, language condition tokens (variable length),    dimension D is assumed to be the same as the hidden size.
+    )  # Size:  (b, L_lang, D) or None, language condition tokens (variable length),    dimension D is assumed to be the same as the hidden size.
 
 
 def expand2square(pil_img, background_color):
@@ -144,7 +144,7 @@ def process_image(rgbs_lst, image_processor, vision_encoder):
 def get_states(proprio):
     # suppose you control in 7DOF joint position
     """TODO: Add docstring."""
-    STATE_INDICES = [
+    state_indices = [
         STATE_VEC_IDX_MAPPING["left_arm_joint_0_pos"],
         STATE_VEC_IDX_MAPPING["left_arm_joint_1_pos"],
         STATE_VEC_IDX_MAPPING["left_arm_joint_2_pos"],
@@ -161,32 +161,32 @@ def get_states(proprio):
         STATE_VEC_IDX_MAPPING["right_gripper_open"],
     ]
 
-    B, N = 1, 1  # batch size and state history size
+    b, n = 1, 1  # batch size and state history size
     states = torch.zeros(
-        (B, N, config["model"]["state_token_dim"]), device=DEVICE, dtype=DTYPE,
+        (b, n, config["model"]["state_token_dim"]), device=DEVICE, dtype=DTYPE,
     )
     # suppose you do not have proprio
     # it's kind of tricky, I strongly suggest adding proprio as input and further fine-tuning
     proprio = torch.tensor(proprio, device=DEVICE, dtype=DTYPE).reshape(
         (1, 1, -1),
-    )  # B, N = 1, 1  # batch size and state history size
+    )  # b, n = 1, 1  # batch size and state history size
 
     # if you have proprio, you can do like this
     # format like this: [arm_joint_0_pos, arm_joint_1_pos, arm_joint_2_pos, arm_joint_3_pos, arm_joint_4_pos, arm_joint_5_pos, arm_joint_6_pos, gripper_open]
     # proprio = torch.tensor([0, 1, 2, 3, 4, 5, 6, 0.5]).reshape((1, 1, -1))
-    states[:, :, STATE_INDICES] = proprio
+    states[:, :, state_indices] = proprio
 
     state_elem_mask = torch.zeros(
         (1, config["model"]["state_token_dim"]), device=DEVICE, dtype=torch.bool,
     )
 
-    state_elem_mask[:, STATE_INDICES] = True
+    state_elem_mask[:, state_indices] = True
     states, state_elem_mask = (
         states.to(DEVICE, dtype=DTYPE),
         state_elem_mask.to(DEVICE, dtype=DTYPE),
     )
     states = states[:, -1:, :]  # only use the last state
-    return states, state_elem_mask, STATE_INDICES
+    return states, state_elem_mask, state_indices
 
 
 def main():

node-hub/dora-rdt-1b/tests/test_dora_rdt_1b.py

@@ -172,9 +172,9 @@ def test_dummy_states():
 
     # suppose you do not have proprio
     # it's kind of tricky, I strongly suggest adding proprio as input and further fine-tuning
-    B, N = 1, 1  # batch size and state history size
+    b, n = 1, 1  # batch size and state history size
     states = torch.zeros(
-        (B, N, config["model"]["state_token_dim"]),
+        (b, n, config["model"]["state_token_dim"]),
         device=DEVICE,
         dtype=DTYPE,
     )
@@ -182,10 +182,10 @@ def test_dummy_states():
     # if you have proprio, you can do like this
     # format like this: [arm_joint_0_pos, arm_joint_1_pos, arm_joint_2_pos, arm_joint_3_pos, arm_joint_4_pos, arm_joint_5_pos, arm_joint_6_pos, gripper_open]
     # proprio = torch.tensor([0, 1, 2, 3, 4, 5, 6, 0.5]).reshape((1, 1, -1))
-    # states[:, :, STATE_INDICES] = proprio
+    # states[:, :, state_indices] = proprio
 
     state_elem_mask = torch.zeros(
-        (B, config["model"]["state_token_dim"]),
+        (b, config["model"]["state_token_dim"]),
         device=DEVICE,
         dtype=torch.bool,
     )
@@ -194,7 +194,7 @@ def test_dummy_states():
     )
 
     # suppose you control in 7DOF joint position
-    STATE_INDICES = [
+    state_indices = [
         STATE_VEC_IDX_MAPPING["arm_joint_0_pos"],
         STATE_VEC_IDX_MAPPING["arm_joint_1_pos"],
         STATE_VEC_IDX_MAPPING["arm_joint_2_pos"],
@@ -205,7 +205,7 @@ def test_dummy_states():
         STATE_VEC_IDX_MAPPING["gripper_open"],
     ]
 
-    state_elem_mask[:, STATE_INDICES] = True
+    state_elem_mask[:, state_indices] = True
     states, state_elem_mask = (
         states.to(DEVICE, dtype=DTYPE),
         state_elem_mask.to(DEVICE, dtype=DTYPE),
@@ -213,7 +213,7 @@ def test_dummy_states():
     states = states[:, -1:, :]  # only use the last state
     pytest.states = states
     pytest.state_elem_mask = state_elem_mask
-    pytest.STATE_INDICES = STATE_INDICES
+    pytest.state_indices = state_indices
 
 
 def test_dummy_input():
@@ -227,7 +227,7 @@ def test_dummy_input():
     image_embeds = pytest.image_embeds
     state_elem_mask = pytest.state_elem_mask
     states = pytest.states
-    STATE_INDICES = pytest.STATE_INDICES
+    state_indices = pytest.state_indices
 
     actions = rdt.predict_action(
         lang_tokens=lang_embeddings,
@@ -242,10 +242,10 @@ def test_dummy_input():
         ctrl_freqs=torch.tensor([25.0], device=DEVICE),  # would this default work?
     )  # (1, chunk_size, 128)
 
-    # select the meaning action via STATE_INDICES
+    # select the meaning action via state_indices
     action = actions[
         :,
         :,
-        STATE_INDICES,
-    ]  # (1, chunk_size, len(STATE_INDICES)) = (1, chunk_size, 7+ 1)
+        state_indices,
+    ]  # (1, chunk_size, len(state_indices)) = (1, chunk_size, 7+ 1)
     print(action)

node-hub/dora-reachy1/dora_reachy1/main.py

@@ -10,7 +10,7 @@ from reachy_sdk.trajectory import goto
 
 def r_arm_inverse_kinematics(reachy, pose, action) -> list:
     """TODO: Add docstring."""
-    A = np.array(
+    a = np.array(
         [
             [0, 0, -1, pose[0] + action[0]],
             [0, 1, 0, pose[1] + action[1]],
@@ -18,7 +18,7 @@ def r_arm_inverse_kinematics(reachy, pose, action) -> list:
             [0, 0, 0, 1],
         ],
     )
-    return reachy.r_arm.inverse_kinematics(A)
+    return reachy.r_arm.inverse_kinematics(a)
 
 
 def happy_antennas(reachy):
@@ -54,9 +54,9 @@ def main():
     """TODO: Add docstring."""
     node = Node()
 
-    ROBOT_IP = os.getenv("ROBOT_IP", "10.42.0.24")
+    robot_ip = os.getenv("ROBOT_IP", "10.42.0.24")
 
-    reachy = ReachySDK(ROBOT_IP, with_mobile_base=False)
+    reachy = ReachySDK(robot_ip, with_mobile_base=False)
     reachy.turn_on("r_arm")
     reachy.turn_on("head")
 

node-hub/dora-reachy1/dora_reachy1_vision/main.py

@@ -11,14 +11,14 @@ def main():
     """TODO: Add docstring."""
     node = Node()
 
-    ROBOT_IP = os.getenv("ROBOT_IP", "10.42.0.24")
-    CAMERA = os.getenv("CAMERA", "right")
+    robot_ip = os.getenv("ROBOT_IP", "10.42.0.24")
+    camera = os.getenv("CAMERA", "right")
 
-    reachy = ReachySDK(ROBOT_IP, with_mobile_base=False)
+    reachy = ReachySDK(robot_ip, with_mobile_base=False)
 
     for event in node:
         if event["type"] == "INPUT":
-            if CAMERA == "right":
+            if camera == "right":
                 frame = reachy.right_camera.last_frame
             else:
                 frame = reachy.left_camera.last_frame

node-hub/dora-reachy2/dora_reachy2/camera.py

@@ -11,10 +11,10 @@ from reachy2_sdk.media.camera import CameraView
 
 def main():
     """TODO: Add docstring."""
-    ROBOT_IP = os.getenv("ROBOT_IP", "10.42.0.80")
+    robot_ip = os.getenv("ROBOT_IP", "10.42.0.80")
 
     for _ in range(10):
-        reachy = ReachySDK(ROBOT_IP)
+        reachy = ReachySDK(robot_ip)
         try:
             reachy.cameras.teleop.get_frame(view=CameraView.LEFT)
             params = reachy.cameras.depth.get_parameters(view=CameraView.DEPTH)
@@ -28,7 +28,7 @@ def main():
 
     reachy.cameras.teleop.get_frame(view=CameraView.LEFT)
     params = reachy.cameras.depth.get_parameters(view=CameraView.DEPTH)
-    height, width, _distortion_model, _D, K, _R, _P = params
+    height, width, _distortion_model, _d, k, _r, _p = params
 
     node = Node()
 
@@ -90,8 +90,8 @@ def main():
                         metadata={
                             "width": width,
                             "height": height,
-                            "focal": [int(K[0, 0]), int(K[1, 1])],
-                            "resolution": [int(K[0, 2]), int(K[1, 2])],
+                            "focal": [int(k[0, 0]), int(k[1, 1])],
+                            "resolution": [int(k[0, 2]), int(k[1, 2])],
                         },
                     )
 

node-hub/dora-reachy2/dora_reachy2/head.py

@@ -9,17 +9,17 @@ from reachy2_sdk import ReachySDK
 
 def main():
     """TODO: Add docstring."""
-    ROBOT_IP = os.getenv("ROBOT_IP", "10.42.0.80")
+    robot_ip = os.getenv("ROBOT_IP", "10.42.0.80")
 
     for _i in range(5):
-        reachy = ReachySDK(ROBOT_IP)
+        reachy = ReachySDK(robot_ip)
 
         if reachy.head is not None:
             reachy.head.turn_on()
             reachy.head.goto([0, 0, 0])
             break
-    FOV_H = 107
-    FOV_V = 91
+    fov_h = 107
+    fov_v = 91
     resolution = [720, 960]
 
     roll, _pitch, yaw = reachy.head.get_current_positions()
@@ -37,8 +37,8 @@ def main():
                     x_min + x_max
                 ) / 2 - 10  # Deviate a bit to take into account the off centered camera
                 y = (3 * y_min + y_max) / 4
-                ry = (x - resolution[1] / 2) * FOV_H / 2 / resolution[1]
-                rz = (y - resolution[0] / 2) * FOV_V / 2 / resolution[0]
+                ry = (x - resolution[1] / 2) * fov_h / 2 / resolution[1]
+                rz = (y - resolution[0] / 2) * fov_v / 2 / resolution[0]
                 if np.abs(yaw) > 45 and yaw * -ry > 0:
                     reachy.head.cancel_all_goto()
                     roll, _pitch, yaw = reachy.head.get_current_positions()

node-hub/dora-reachy2/dora_reachy2/left_arm.py

@@ -7,7 +7,7 @@ import numpy as np
 import pyarrow as pa
 from dora import Node
 from reachy2_sdk import ReachySDK
-from scipy.spatial.transform import Rotation as R
+from scipy.spatial.transform import Rotation
 
 ROBOT_IP = os.getenv("ROBOT_IP", "10.42.0.80")
 
@@ -26,7 +26,7 @@ l_default_pose = [
 def l_arm_go_to_mixed_angles(reachy, x, y, z):
     """TODO: Add docstring."""
     for theta in range(-80, -60, 10):
-        r = R.from_euler("zyx", [0, theta, 0], degrees=True)
+        r = Rotation.from_euler("zyx", [0, theta, 0], degrees=True)
         transform = np.eye(4)
         transform[:3, :3] = r.as_matrix()
         transform[:3, 3] = [x, y, z]
@@ -41,7 +41,7 @@ def l_arm_go_to_mixed_angles(reachy, x, y, z):
 
         ## First try turning left
         pitch = -90
-        r = R.from_euler("ZYX", (-yaw, 0, 0), degrees=True) * R.from_euler(
+        r = Rotation.from_euler("ZYX", (-yaw, 0, 0), degrees=True) * Rotation.from_euler(
             "ZYX",
             (0, pitch, 0),
             degrees=True,

node-hub/dora-reachy2/dora_reachy2/mobile_base.py

@@ -11,9 +11,9 @@ from reachy2_sdk import ReachySDK
 
 def main():
     """TODO: Add docstring."""
-    ROBOT_IP = os.getenv("ROBOT_IP", "10.42.0.80")
+    robot_ip = os.getenv("ROBOT_IP", "10.42.0.80")
 
-    reachy = ReachySDK(ROBOT_IP)
+    reachy = ReachySDK(robot_ip)
 
     if reachy.mobile_base is not None:
         reachy.mobile_base.turn_on()

node-hub/dora-reachy2/dora_reachy2/right_arm.py

@@ -7,7 +7,7 @@ import numpy as np
 import pyarrow as pa
 from dora import Node
 from reachy2_sdk import ReachySDK
-from scipy.spatial.transform import Rotation as R
+from scipy.spatial.transform import Rotation
 
 ROBOT_IP = os.getenv("ROBOT_IP", "10.42.0.80")
 
@@ -25,7 +25,7 @@ r_default_pose = [
 def r_arm_go_to_mixed_angles(reachy, x, y, z):
     """TODO: Add docstring."""
     for theta in range(-80, -60, 10):
-        r = R.from_euler("zyx", [0, theta, 0], degrees=True)
+        r = Rotation.from_euler("zyx", [0, theta, 0], degrees=True)
         transform = np.eye(4)
         transform[:3, :3] = r.as_matrix()
         transform[:3, 3] = [x, y, z]
@@ -40,7 +40,7 @@ def r_arm_go_to_mixed_angles(reachy, x, y, z):
 
         ## First try turning left
         pitch = -90
-        r = R.from_euler("ZYX", (yaw, 0, 0), degrees=True) * R.from_euler(
+        r = Rotation.from_euler("ZYX", (yaw, 0, 0), degrees=True) * Rotation.from_euler(
             "ZYX",
             (0, pitch, 0),
             degrees=True,