doc.IPYNB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python3\n",
    "\n",
    "from ManipulaPy.urdf_processor import URDFToSerialManipulator\n",
    "from math import pi\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to your URDF file\n",
    "urdf_file_path = \"ur5/ur5/ur5.urdf\"\n",
    "\n",
    "# Initialize the URDFToSerialManipulator with the URDF file\n",
    "urdf_processor = URDFToSerialManipulator(urdf_file_path)\n",
    "\n",
    "# Extract the SerialManipulator object\n",
    "ur5 = urdf_processor.serial_manipulator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Screw Axis Theory in Robotics\n",
    "\n",
    "Screw axis theory is a fundamental concept in robotics and mechanical engineering, providing a powerful framework for describing and analyzing the motion of rigid bodies in three-dimensional space. This theory is particularly useful in the context of robotic manipulators, where understanding and controlling the motion of joints and links is crucial.\n",
    "\n",
    "## Understanding Screw Axis\n",
    "\n",
    "A screw motion in three-dimensional space can be thought of as a combination of a rotation about an axis and a translation along the same axis. This concept is encapsulated in the screw axis, which is defined by the following parameters:\n",
    "\n",
    "- **Axis of Rotation**: A line in space about which the rotation occurs.\n",
    "- **Angle of Rotation**: The magnitude of rotation about the axis.\n",
    "- **Direction of Translation**: The direction along the axis in which the translation occurs.\n",
    "- **Magnitude of Translation**: The distance moved along the axis.\n",
    "\n",
    "## Representation of Screw Axis\n",
    "\n",
    "In robotics, the screw axis is often represented as a 6-dimensional vector, known as a twist. The twist vector combines the angular velocity (representing rotation) and linear velocity (representing translation) of a rigid body:\n",
    "\n",
    "$$\n",
    "\\text{T} = \\begin{bmatrix} \\omega \\\\ v \\end{bmatrix}\n",
    "$$\n",
    "where:\n",
    "- $\\omega$ is a 3-dimensional vector representing the angular velocity.\n",
    "- $v$ is a 3-dimensional vector representing the linear velocity.\n",
    "\n",
    "# Twist Expression in Terms of Screw Axis\n",
    "\n",
    "The twist vector associated with motion about a screw axis can be expressed in terms of the screw axis parameters:\n",
    "\n",
    "- **Unit Vector ( $\\hat{s}$ )**: A unit vector along the screw axis, representing the direction of the axis.\n",
    "- **Point on the Axis \\( $q$ \\)**: A point through which the screw axis passes.\n",
    "- **Screw Pitch \\( $h$ \\)**: The ratio of translation to rotation along the screw axis.\n",
    "\n",
    "Given these parameters and an angular velocity  $\\theta_{\\dot{}}$ about the screw axis, the twist $V$ can be expressed as:\n",
    "\n",
    "$$\n",
    "V = \\begin{bmatrix} \\omega \\\\ v \\end{bmatrix} = \\begin{bmatrix} \\hat{s} \\theta_{\\dot{}} \\\\ (q \\times \\hat{s}) \\theta_{\\dot{}} + h \\hat{s} \\theta_{\\dot{}} \\end{bmatrix}\n",
    "$$\n",
    "\n",
    "Here:\n",
    "-  $\\omega$ = $\\hat{s}$ $\\theta_{\\dot{}}$  is the angular velocity component of the twist, representing rotation about the screw axis.\n",
    "- $v$ = -$\\hat{s}$ $\\theta_{\\dot{}}$   $\\times$ $q$ + $h$ $\\hat{s}$ $\\theta_{\\dot{}}$  is the linear velocity component of the twist, representing translation along the screw axis.\n",
    "\n",
    "\n",
    "## Mathematical Tools\n",
    "\n",
    "### Matrix Exponential and Logarithm\n",
    "- **Matrix Exponential (`MatrixExp6`)**: Converts a twist (screw axis representation) into a transformation matrix, which is used to describe the pose of a rigid body.\n",
    "- **Matrix Logarithm (`MatrixLog6`)**: The inverse operation, used to convert a transformation matrix back into a twist.\n",
    "\n",
    "These mathematical tools are key to applying screw theory in practical robotics applications, enabling the conversion between different representations of motion and facilitating the computation of robot dynamics and kinematics.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward Kinematics Using Screw Axis Theory\n",
    "\n",
    "Forward kinematics in robotics is the process of determining the position and orientation of the end-effector given the joint parameters (like angles for revolute joints or displacements for prismatic joints). Screw axis theory provides a powerful framework for analyzing and computing forward kinematics, especially for robotic arms.\n",
    "\n",
    "## Screw Axis Theory in Robotics\n",
    "\n",
    "Screw axis theory describes the motion of a rigid body as a combination of rotation and translation along a screw axis. This motion can be represented by a twist vector:\n",
    "\n",
    "$$\n",
    "\\xi = \\begin{bmatrix} \\omega \\\\ v \\end{bmatrix}\n",
    "$$\n",
    "\n",
    "where $( \\omega )$ is the angular velocity vector and $( v )$ is the linear velocity vector.\n",
    "\n",
    "## Application in Forward Kinematics\n",
    "\n",
    "In the context of forward kinematics, each joint of a robotic manipulator can be associated with a screw axis. The motion of the joint results in a rigid body transformation that can be described using the exponential map of the twist associated with the joint.\n",
    "\n",
    "### Exponential Map of a Twist\n",
    "\n",
    "The exponential map of a twist $( x_i )$ for a joint with an angle $( \\theta )$ is given by:\n",
    "\n",
    "$$\n",
    "T = \\exp(\\hat{\\xi} \\theta)\n",
    "$$\n",
    "\n",
    "where $( \\hat{\\xi} )$ is the twist in matrix form and $( \\exp )$ denotes the matrix exponential.\n",
    "\n",
    "### Computing the End-Effector Pose\n",
    "\n",
    "The pose of the end-effector is obtained by chaining the transformations of each joint:\n",
    "\n",
    "$$\n",
    "T_{\\text{end-effector}} = T_1 T_2 \\cdots T_n\n",
    "$$\n",
    "\n",
    "where $( T_i )$ is the transformation matrix for the \\( i \\)-th joint, computed using the exponential map of its associated twist.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example joint angles (thetalist) for the manipulator\n",
    "thetalist = np.array([pi, pi/6, pi/4, -pi/3, -pi/2, (-2*pi/3)])\n",
    "dthetalist = np.array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])\n",
    "ddthetalist = np.array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.24143868e-01  9.65925826e-01  1.29409523e-01 -3.66623985e-01]\n",
      " [ 5.00000000e-01 -2.29314100e-16  8.66025404e-01 -5.13019655e-01]\n",
      " [ 8.36516304e-01  2.58819045e-01 -4.82962913e-01 -1.30819528e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "T=ur5.forward_kinematics(thetalist,frame='space')\n",
    "print(T)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ur5_d=urdf_processor.dynamics\n",
    "tau=ur5_d.inverse_dynamics(thetalist, dthetalist, ddthetalist, [0,0,-9.81], [0]*6)\n",
    "urdf_processor.visualize_robot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ManipulaPy.path_planning import TrajectoryPlanning as tp\n",
    "\n",
    "\n",
    "Joint_limits = [\n",
    "    (-np.pi, np.pi),  # Joint 1\n",
    "    (-np.pi/2, np.pi/2),  # Joint 2\n",
    "    (-np.pi/2, np.pi/2),  # Joint 3\n",
    "    (-np.pi, np.pi/3),  # Joint 4\n",
    "    (-np.pi/2, np.pi),  # Joint 5\n",
    "    (-np.pi, np.pi)   # Joint 6\n",
    "]\n",
    "trr=tp(ur5,dynamics=urdf_processor.dynamics,joint_limits=Joint_limits,torque_limits=None)\n",
    "traj = trr.JointTrajectory(thetastart=[0]*6,thetaend=thetalist,Tf=5,N=1000,method=5)\n",
    "\n",
    "#tp.plot_trajectory(trajectory_data=traj,Tf=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypingError",
     "evalue": "Failed in cuda mode pipeline (step: nopython frontend)\n\u001b[1m\u001b[1m\u001b[1mNo implementation of function Function(<built-in function zeros>) found for signature:\n \n >>> zeros(UniTuple(Literal[int](6) x 2))\n \nThere are 2 candidate implementations:\n\u001b[1m      - Of which 2 did not match due to:\n      Overload in function 'ol_np_zeros': File: numba/np/arrayobj.py: Line 4441.\n        With argument(s): '(UniTuple(int64 x 2))':\u001b[0m\n\u001b[1m       Rejected as the implementation raised a specific error:\n         TypeError: Failed in cuda mode pipeline (step: fix up args)\n       Signature mismatch: 1 argument types given, but function takes 2 arguments\u001b[0m\n  raised from /home/abo/.local/lib/python3.10/site-packages/numba/core/untyped_passes.py:125\n\u001b[0m\n\u001b[0m\u001b[1mDuring: resolving callee type: Function(<built-in function zeros>)\u001b[0m\n\u001b[0m\u001b[1mDuring: typing of call at /home/abo/Desktop/ManipulaPy/ManipulaPy/path_planning.py (45)\n\u001b[0m\n\u001b[1m\nFile \"ManipulaPy/path_planning.py\", line 45:\u001b[0m\n\u001b[1mdef inverse_dynamics_kernel(thetalist_trajectory, dthetalist_trajectory, ddthetalist_trajectory, gravity_vector, Ftip, Glist, Slist, M, torques_trajectory):\n    <source elided>\n        # Mass matrix computation\n\u001b[1m        M_temp = np.zeros((6, 6))\n\u001b[0m        \u001b[1m^\u001b[0m\u001b[0m\n",
     "output_type": "error",
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypingError\u001b[0m                               Traceback (most recent call last)",
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      "\u001b[0;32m~/Desktop/ManipulaPy/ManipulaPy/path_planning.py\u001b[0m in \u001b[0;36mInverseDynamicsTrajectory\u001b[0;34m(self, thetalist_trajectory, dthetalist_trajectory, ddthetalist_trajectory, gravity_vector, Ftip)\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0md_ddthetalist_trajectory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mddthetalist_trajectory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0md_gravity_vector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgravity_vector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m         \u001b[0md_Ftip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFtip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m         \u001b[0md_Glist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdynamics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0md_Slist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdynamics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mS_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/cuda/dispatcher.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 539\u001b[0;31m         return self.dispatcher.call(args, self.griddim, self.blockdim,\n\u001b[0m\u001b[1;32m    540\u001b[0m                                     self.stream, self.sharedmem)\n\u001b[1;32m    541\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/cuda/dispatcher.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, args, griddim, blockdim, stream, sharedmem)\u001b[0m\n\u001b[1;32m    679\u001b[0m             \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverloads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 681\u001b[0;31m             \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_dispatcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDispatcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    683\u001b[0m         \u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgriddim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblockdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharedmem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/cuda/dispatcher.py\u001b[0m in \u001b[0;36m_compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m    687\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkws\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m         \u001b[0margtypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypeof_pyval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 689\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtypeof_pyval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/cuda/dispatcher.py\u001b[0m in \u001b[0;36mcompile\u001b[0;34m(self, sig)\u001b[0m\n\u001b[1;32m    930\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Compilation disabled\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 932\u001b[0;31m             \u001b[0mkernel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Kernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpy_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargetoptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    933\u001b[0m             \u001b[0;31m# We call bind to force codegen, so that there is a cubin to cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    934\u001b[0m             \u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler_lock.py\u001b[0m in \u001b[0;36m_acquire_compile_lock\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m_acquire_compile_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_acquire_compile_lock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/cuda/dispatcher.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, py_func, argtypes, link, debug, lineinfo, inline, fastmath, extensions, max_registers, opt, device)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0mcc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_current_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_capability\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m         cres = compile_cuda(self.py_func, types.void, self.argtypes,\n\u001b[0m\u001b[1;32m     84\u001b[0m                             \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m                             \u001b[0mlineinfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlineinfo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler_lock.py\u001b[0m in \u001b[0;36m_acquire_compile_lock\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m_acquire_compile_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_acquire_compile_lock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/cuda/compiler.py\u001b[0m in \u001b[0;36mcompile_cuda\u001b[0;34m(pyfunc, return_type, args, debug, lineinfo, inline, fastmath, nvvm_options, cc)\u001b[0m\n\u001b[1;32m    194\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_extension\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtarget_override\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtarget_override\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cuda'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m         cres = compiler.compile_extra(typingctx=typingctx,\n\u001b[0m\u001b[1;32m    197\u001b[0m                                       \u001b[0mtargetctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtargetctx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                                       \u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpyfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler.py\u001b[0m in \u001b[0;36mcompile_extra\u001b[0;34m(typingctx, targetctx, func, args, return_type, flags, locals, library, pipeline_class)\u001b[0m\n\u001b[1;32m    749\u001b[0m     pipeline = pipeline_class(typingctx, targetctx, library,\n\u001b[1;32m    750\u001b[0m                               args, return_type, flags, locals)\n\u001b[0;32m--> 751\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile_extra\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    752\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler.py\u001b[0m in \u001b[0;36mcompile_extra\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m    443\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlifted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlifted_from\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compile_bytecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcompile_ir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc_ir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlifted\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlifted_from\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler.py\u001b[0m in \u001b[0;36m_compile_bytecode\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    511\u001b[0m         \"\"\"\n\u001b[1;32m    512\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc_ir\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compile_core\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_compile_ir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler.py\u001b[0m in \u001b[0;36m_compile_core\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    490\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfail_reason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mis_final_pipeline\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 492\u001b[0;31m                         \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mCompilerError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"All available pipelines exhausted\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler.py\u001b[0m in \u001b[0;36m_compile_core\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    477\u001b[0m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m                     \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    480\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m                         \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler_machinery.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m    366\u001b[0m                     \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipeline_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpass_desc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m                 \u001b[0mpatched_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_patch_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 368\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mpatched_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdependency_analysis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler_machinery.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m    354\u001b[0m                 \u001b[0mpass_inst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_pass_registry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpass_inst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpass_inst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCompilerPass\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 356\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_runPass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpass_inst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    357\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Legacy pass in use\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler_lock.py\u001b[0m in \u001b[0;36m_acquire_compile_lock\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m_acquire_compile_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_acquire_compile_lock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler_machinery.py\u001b[0m in \u001b[0;36m_runPass\u001b[0;34m(self, index, pss, internal_state)\u001b[0m\n\u001b[1;32m    309\u001b[0m                 \u001b[0mmutated\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_initialization\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minternal_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mSimpleTimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpass_time\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m                 \u001b[0mmutated\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_pass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minternal_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mSimpleTimer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfinalize_time\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m                 \u001b[0mmutated\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_finalizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minternal_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/compiler_machinery.py\u001b[0m in \u001b[0;36mcheck\u001b[0;34m(func, compiler_state)\u001b[0m\n\u001b[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompiler_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 273\u001b[0;31m             \u001b[0mmangled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompiler_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    274\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmangled\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m                 msg = (\"CompilerPass implementations should return True/False. \"\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/typed_passes.py\u001b[0m in \u001b[0;36mrun_pass\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m    110\u001b[0m                               % (state.func_id.func_name,)):\n\u001b[1;32m    111\u001b[0m             \u001b[0;31m# Type inference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m             typemap, return_type, calltypes, errs = type_inference_stage(\n\u001b[0m\u001b[1;32m    113\u001b[0m                 \u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypingctx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m                 \u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargetctx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/typed_passes.py\u001b[0m in \u001b[0;36mtype_inference_stage\u001b[0;34m(typingctx, targetctx, interp, args, return_type, locals, raise_errors)\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0minfer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_constraint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m         \u001b[0;31m# return errors in case of partial typing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m         \u001b[0merrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minfer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpropagate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraise_errors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_errors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     94\u001b[0m         \u001b[0mtypemap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrestype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcalltypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minfer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraise_errors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_errors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.10/site-packages/numba/core/typeinfer.py\u001b[0m in \u001b[0;36mpropagate\u001b[0;34m(self, raise_errors)\u001b[0m\n\u001b[1;32m   1089\u001b[0m                                   if isinstance(e, ForceLiteralArg)]\n\u001b[1;32m   1090\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mforce_lit_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1091\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1092\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1093\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mor_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_lit_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypingError\u001b[0m: Failed in cuda mode pipeline (step: nopython frontend)\n\u001b[1m\u001b[1m\u001b[1mNo implementation of function Function(<built-in function zeros>) found for signature:\n \n >>> zeros(UniTuple(Literal[int](6) x 2))\n \nThere are 2 candidate implementations:\n\u001b[1m      - Of which 2 did not match due to:\n      Overload in function 'ol_np_zeros': File: numba/np/arrayobj.py: Line 4441.\n        With argument(s): '(UniTuple(int64 x 2))':\u001b[0m\n\u001b[1m       Rejected as the implementation raised a specific error:\n         TypeError: Failed in cuda mode pipeline (step: fix up args)\n       Signature mismatch: 1 argument types given, but function takes 2 arguments\u001b[0m\n  raised from /home/abo/.local/lib/python3.10/site-packages/numba/core/untyped_passes.py:125\n\u001b[0m\n\u001b[0m\u001b[1mDuring: resolving callee type: Function(<built-in function zeros>)\u001b[0m\n\u001b[0m\u001b[1mDuring: typing of call at /home/abo/Desktop/ManipulaPy/ManipulaPy/path_planning.py (45)\n\u001b[0m\n\u001b[1m\nFile \"ManipulaPy/path_planning.py\", line 45:\u001b[0m\n\u001b[1mdef inverse_dynamics_kernel(thetalist_trajectory, dthetalist_trajectory, ddthetalist_trajectory, gravity_vector, Ftip, Glist, Slist, M, torques_trajectory):\n    <source elided>\n        # Mass matrix computation\n\u001b[1m        M_temp = np.zeros((6, 6))\n\u001b[0m        \u001b[1m^\u001b[0m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "positions = traj['positions']\n",
    "velocities = traj['velocities']\n",
    "accelerations = traj['accelerations']\n",
    "traj_tau = trr.InverseDynamicsTrajectory(positions,velocities,accelerations)\n"
   ]
  }
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