problem1_mini1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b73d60f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the excel file name (for e.g. 'demo.xlsx') which have value of x and ycircle.xlsx\n",
      " Enter the speed of the end effector  or Enter 100 for no effect of dynamics: 100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[380.         400.        ]\n",
      " [379.99993683 400.10053094]\n",
      " [379.99974734 400.20106172]\n",
      " ...\n",
      " [379.99943151 399.69840782]\n",
      " [379.99974734 399.79893828]\n",
      " [379.99993683 399.89946906]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import scipy\n",
    "import cv2\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "from render import Renderer\n",
    "# Renderer is a class(package) which helps to plot in cv2\n",
    "class object(Renderer):\n",
    "    def __init__(self,traject, recordLocation = None):\n",
    "        super().__init__(recordLocation=recordLocation)\n",
    "        self.its = 0\n",
    "        self.q1 = 0     # q1 is the angle made by the first limb of manipulator with x axis\n",
    "        self.q2 = 0     # q2 is the angle made by the second limb of manipulator with x axis\n",
    "        self.theta = 0  # theta is the angle made by the limb 2 w.r.t to limb 1\n",
    "        self.l1 = 200   # length of the first limb\n",
    "        self.l2 = 380   # length of the second limb\n",
    "        self.points = set()\n",
    "        self.traject = traject\n",
    "    def getInfo(self):\n",
    "        info = {\n",
    "            'q1':round(self.q1,4),\n",
    "            'q2':round(self.q2,4), \n",
    "            'x': round(self.x,4),\n",
    "            'y': round(self.y,4),\n",
    "        }\n",
    "        return info\n",
    "    def plot(self):\n",
    "        data = self.traject\n",
    "        plt.scatter(data[:,0],data[:,1])\n",
    "        plt.show()\n",
    "        return data\n",
    "        \n",
    "    def step(self):\n",
    "        self.x = self.traject[self.its,0]\n",
    "        self.y = self.traject[self.its,1]\n",
    "        \n",
    "        self.theta=(math.acos(((self.x)**2+(self.y)**2-(self.l1)**2-(self.l2)**2)/(2*self.l1*self.l2))) \n",
    "        self.q1=math.atan((self.y)/(self.x))-math.atan((self.l2*math.sin(self.theta))/(self.l1+(self.l2*math.cos(self.theta))))\n",
    "        self.q2=(self.q1+self.theta)\n",
    "        \n",
    "        # (x1,y1) is the coordinate of the joint 1\n",
    "        self.x1 = self.l1*math.cos(self.q1)\n",
    "        self.y1 = self.l1*math.sin(self.q1)\n",
    "        \n",
    "        # (x2,y2) is the coordinate of the joint 2\n",
    "        self.x2 = self.x1+self.l2*math.cos(self.q2)\n",
    "        self.y2 = self.y1+self.l2*math.sin(self.q2)\n",
    "        self.its+=1\n",
    "        \n",
    "        self.points.add((self.q1,self.q2,self.x1,self.y1,self.x2,self.y2))\n",
    "    def draw(self,image):\n",
    "        for q1,q2,x1,y1,x2,y2 in self.points:\n",
    "\n",
    "            cv2.line(image,(0,0),(int(self.x1),int(self.y1)),(0,255,0),2) # limb1\n",
    "            cv2.line(image,(int(self.x1),int(self.y1)),(int(self.x2),int(self.y2)),(0,0,255),2) # limb2\n",
    "            cv2.circle(image,(int(x2),int(y2)),2,(255,0,0),-1)  # trajectory traced by our manipulator\n",
    "            \n",
    "        return image \n",
    "\n",
    "user = input(\"Enter the excel file name (for e.g. 'circle.xlsx') which have value of x and y: \")\n",
    "pf = pd.read_excel(user)\n",
    "data_x_y = pf.to_numpy() # converting the dataframe into numpy array\n",
    "anim = object(data_x_y, recordLocation ='anim.mp4')\n",
    "speed = int(input(\" Enter the speed of the end effector  or Enter 100 for no effect of dynamics: \"))\n",
    "# print(data_x_y.shape[0])\n",
    "k=speed\n",
    "if speed >100:\n",
    "    k=100\n",
    "for i in range(0,data_x_y.shape[0]):\n",
    "    anim.step()\n",
    "    \n",
    "    # drawing the every 5th points to run faster (IF you want to run all your points then please ccomment if statement and run anim.render)\n",
    "    if i%5==0:\n",
    "        anim.render(height=600,pause=int(100/k))\n",
    "print(anim.plot())  # plotting the actual trajectory from the given data points\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e84f3915",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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