2D tranformation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71adf868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the vertices of the original triangle\n",
    "triangle = np.array([[0, 0], [0, 2], [2, 0], [0,0]])\n",
    "\n",
    "# Define the translation vector\n",
    "translation = np.array([2, 1])\n",
    "\n",
    "# Translate the triangle by adding the translation vector to each vertex\n",
    "new_triangle = triangle + translation\n",
    "\n",
    "# Plot the original and translated triangles\n",
    "plt.plot(triangle[:,0], triangle[:,1], 'bo-', label='Original Triangle')\n",
    "plt.plot(new_triangle[:,0], new_triangle[:,1], 'go-', label='Translated Triangle')\n",
    "plt.axis('equal')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b41e71f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROTATION\n",
    "\n",
    "# Define the vertices of the original triangle\n",
    "triangle = np.array([[0, 0], [0, 2], [2, 0], [0,0]])\n",
    "\n",
    "# Define the rotation angle in degrees\n",
    "angle_deg = 45\n",
    "\n",
    "# Convert the rotation angle to radians\n",
    "angle_rad = np.deg2rad(angle_deg)\n",
    "\n",
    "# Define the rotation matrix\n",
    "rotation = np.array([[np.cos(angle_rad), -np.sin(angle_rad)],\n",
    "                     [np.sin(angle_rad), np.cos(angle_rad)]])\n",
    "\n",
    "# Rotate the triangle by multiplying each vertex by the rotation matrix\n",
    "new_triangle = np.dot(triangle, rotation)\n",
    "\n",
    "# Plot the original and rotated triangles\n",
    "plt.plot(triangle[:,0], triangle[:,1], 'yo-', label='Original Triangle')\n",
    "plt.plot(new_triangle[:,0], new_triangle[:,1], 'go-', label='Rotated Triangle')\n",
    "plt.axis('equal')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad7167e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MmUNTUxObN2/m2WefpbCwMNMjxRd0dz/h7lVAETDRzLoeQf9LoMTdK4DngJXdPE+9u1e7e3UQ3nwuUdTDr7fdp5Jd+vLzTOgoF3c/CKwBZnVZfsDdTx3E+mMg9rFKklGKengVFBRw4MABRT0k3J0DBw7EPJyzJ70eh25mhcBxdz9oZoOAy4HvdllnhLvvi96dDWxNaApJGx2nHk5FRUW0tLSgXZnhUVBQQFFRUULfE88Hi0YAK80sj8gW/ePu/oyZ3UfkYqVPA181s9lAO/BnYF5CU0haKerhk5+fH/MTkJJb4jnK5VV3v8TdK9x9nLvfF11+TzTmuPs33L3M3SvdfZq7v57qwaV/tPtFJHz0SdEcpqiLhIuCnuMUdZHwUNBFURcJCQVdAEVdJAwUdOmgqItkNwVdTqOoi2QvBV3OoKiLZCcFXWJS1EWyj4Iu3VLURbKLgi49UtRFsoeCLr1S1EWyg4IucVHURYJPQZe4KeoiwaagS0IUdZHgUtAlYYq6SDAp6NInirpI8PQadDMrMLM/mtkmM9tiZt+Osc7ZZrbKzHaY2ctmVpKSaSVQFHWRYIlnC/0YMN3dK4EqYJaZ1XRZ5xbg/9z9I8CDdLnmqISXoi4SHPFcgs7d/Uj0bn70q+ulxa8GVkZvPwHMMDNL2pQSaIq6SDDEtQ/dzPLMrAnYDzzn7i93WeUCYA+Au7cDh4BhMZ6n1swazaxRVycPF0VdJPPiCrq7n3D3KqAImGhm4/ryYu5e7+7V7l5dWFjYl6eQAFPURTIroaNc3P0gsAaY1eWhvcCFAGY2EBgCHEjCfJJlFHWRzInnKJdCMxsavT0IuBx4vctqTwM3R29fBzzv7l33s0uOUNRFMiOeLfQRwBozexVYT2Qf+jNmdp+ZzY6uswwYZmY7gL8H7k7NuJItFHWR9BvY2wru/ipwSYzl93S6/S5wfXJHk2x3KupTV0xl2spprLl5DWXnl2V6LJHQ0idFJaW0pS6SPgq6pJyiLpIeCrqkhaIuknoKuqSNoi6SWgq6pJWiLpI6CrqknaIukhoKumSEoi6SfAq6ZIyiLpJcCrpklKIukjwKumScoi6SHAq6BIKiLtJ/CroEhqIu0j8KugSKoi7Sdwq6BI6iLtI3CroEkqIukrh4rlh0oZmtMbNmM9tiZgtirDPVzA6ZWVP0655YzyWSCEVdJDHxbKG3A3e5eylQA9xuZqUx1vu9u1dFv+5L6pSSsxR1kfj1GnR33+fuG6O3DwNbgQtSPZjIKYq6SHwS2oduZiVELkf3coyHJ5nZJjP7DzOLeZ0xM6s1s0Yza2xtbU18WslZirpI7+IOupmdAzwJ3Onub3d5eCNQ7O6VwEPAz2M9h7vXu3u1u1cXFhb2cWTJVYq6SM/iCrqZ5ROJeYO7/3vXx939bXc/Er29Gsg3s+FJnVQERV2kJ/Ec5WLAMmCruz/QzTofiq6HmU2MPu+BZA4qcoqiLhJbPFvok4HPA9M7HZb4CTO7zcxui65zHbDZzDYBS4Ab3N1TNLOIoi4Sw8DeVnD3FwHrZZ1/Bf41WUOJxONU1KeumMq0ldNYc/Mays6P+ft4kZygT4pKVtOWushfKOiS9RR1kQgFXUJBURdR0CVEFHXJdQq6hIqiLrlMQZfQUdQlVynoEkqKuuQiBV1CS1GXXKOgS6gp6pJLFHQJPUVdcoWCLjlBUZdcoKBLzlDUJewUdMkpirqEmYIuOUdRl7BS0CUnKeoSRgq65CxFXcImnkvQXWhma8ys2cy2mNmCGOuYmS0xsx1m9qqZjU/NuCHV0AAnTkLdP0JJSeS+pIWiLmESzxZ6O3CXu5cCNcDtZlbaZZ2PA6OjX7XAI0mdMswaGqC2NnLbgV27IvcV9bRR1CUseg26u+9z943R24eBrcAFXVa7GnjUI14ChprZiKRPG0YLF0Jb2+nL2toiyyVtFHUJg4T2oZtZCXAJ8HKXhy4A9nS638KZ0cfMas2s0cwaW1tbExw1pHbvTmy5pIyiLtku7qCb2TnAk8Cd7v52X17M3evdvdrdqwsLC/vyFOEzalRiyyWlFHXJZnEF3czyicS8wd3/PcYqe4ELO90vii6T3tTVweDBZy6fMyf9swigqEv2iucoFwOWAVvd/YFuVnsauCl6tEsNcMjd9yVxzvCaOxfq6/9yv6gosnX+/e/Dr36VublynKIu2SieLfTJwOeB6WbWFP36hJndZma3RddZDfwJ2AEsBeanZtyQmjs38ueUv4Y9e2DjRhg7Fq6+WlHPIEVdsk08R7m86O7m7hXuXhX9Wu3uP3T3H0bXcXe/3d0/7O7l7t6Y+tFDbNgw+M1vFPUAUNQlm+iTokGlqAeGoi7ZQkEPMkU9MBR1yQYKetAp6oGhqEvQKejZQFEPDEVdgkxBzxaKemAo6hJUCno2UdQDQ1GXIFLQs42iHhiKugSNgp6NFPXAUNQlSBT0bKWoB4aiLkGhoGczRT0wFHUJAgU92ynqgaGoS6Yp6GGgqAeGoi6ZpKCHhaIeGIq6ZIqCHiaKemAo6pIJCnrYKOqBoahLuinoYaSoB4aiLukUzyXolpvZfjPb3M3jU83sUKerGd2T/DElYYp6YCjqki7xbKGvAGb1ss7vO13N6L7+jyVJoagHhqIu6RDPJeheAP6chlkkFRT1wFDUJdWStQ99kpltMrP/MLOy7lYys1ozazSzxtbW1iS9tPRKUQ8MRV1SKRlB3wgUu3sl8BDw8+5WdPd6d6929+rCwsIkvLTETVEPDEVdUqXfQXf3t939SPT2aiDfzIb3ezJJPkU9MBR1SYV+B93MPmRmFr09MfqcB/r7vJIiinpgKOqSbPEctvgYsA4YY2YtZnaLmd1mZrdFV7kO2Gxmm4AlwA3u7qkbWfpNUQ8MRV2SKZ6jXG509xHunu/uRe6+zN1/6O4/jD7+r+5e5u6V7l7j7n9I/djSb4p6YCjqkiz6pGguU9QDQ1GXZFDQc52iHhiKuvSXgi6KeoAo6tIfCrpEKOqBoahLXyno8heKemAo6tIXCrqcTlEPDEVdEqWgy5kU9cBQ1CURCrrEpqgHhqIu8VLQpXuKemAo6hIPBV16pqgHhqIuvVHQpXeKemAo6tITBV3io6gHhqIu3VHQJX6KemAo6hKLgi6JUdQDQ1GXrhR0SZyiHhiKunQWzwUulpvZfjPb3M3jZmZLzGyHmb1qZuOTP6YEjqIeGIq6nBLPFvoKYFYPj38cGB39qgUe6f9YkhUU9cBQ1AXiu2LRC8Cfe1jlauBRj3gJGGpmI5I1oAScoh4YirokYx/6BcCeTvdbosvOYGa1ZtZoZo2tra1JeGkJBEU9MBT13JbWX4q6e727V7t7dWFhYTpfWlJNUQ8MRT13JSPoe4ELO90vii6TXKOoB4ainpuSEfSngZuiR7vUAIfcfV8SnleykaIeGIp67onnsMXHgHXAGDNrMbNbzOw2M7stuspq4E/ADmApMD9l00p2UNQDQ1HPLfEc5XKju49w93x3L3L3Ze7+Q3f/YfRxd/fb3f3D7l7u7o2pH1sCT1EPDEU9d+iTopI6inpgKOq5QUGX1FLUA0NRDz8FXVJPUQ8MRT3cFHRJD0U9MBT18FLQJX0U9cBQ1MNJQZf0UtQDQ1EPHwVd0k9RDwxFPVwUdMkMRT0wFPXwUNAlcxT1wFDUw0FBl8xS1ANDUc9+CrpknqIeGIp6dlPQJRgU9cBQ1LOXgi7BoagHhqKenRR0CRZFPTAU9eyjoEvwKOqBoahnFwVdgklRDwxFPXvEFXQzm2Vm28xsh5ndHePxeWbWamZN0a8vJX9UyTmKemAo6tkhnkvQ5QE/AD4OlAI3mllpjFVXuXtV9OvHSZ5TcpWiHhiKevDFs4U+Edjh7n9y9/eAnwJXp3YskU4U9cBQ1IMtnqBfAOzpdL8luqyrT5vZq2b2hJldGOuJzKzWzBrNrLG1tbUP40rOUtQDQ1EPrmT9UvSXQIm7VwDPAStjreTu9e5e7e7VhYWFSXppyRmKemAo6sEUT9D3Ap23uIuiyzq4+wF3Pxa9+2NgQnLGE+lCUQ8MRT144gn6emC0mV1kZmcBNwBPd17BzEZ0ujsb2Jq8EUW6UNQDQ1EPll6D7u7twB3Ar4iE+nF332Jm95nZ7OhqXzWzLWa2CfgqMC9VA4sAinqAKOrBEdc+dHdf7e4Xu/uH3b0uuuwed386evsb7l7m7pXuPs3dX0/l0CKAoh4ginow6JOikt0U9cBQ1DNPQZfsp6gHhqKeWQq6hIOiHhiKeuYo6BIeinpgKOqZoaBLuCjqgaGop5+CLuGjqAeGop5eCrqEk6IeGIp6+ijoEl6KemAo6umhoEu4KeqBoainnoIu4aeoB4ainloKuuQGRT0wFPXUUdAldyjqgaGop4aCLrlFUQ8MRT35FHTJPYp6YCjqyaWgS25S1ANDUU8eBV1yl6IeGIp6csQVdDObZWbbzGyHmd0d4/GzzWxV9PGXzawk6ZOG2Pyvl3HS4B/tRQbeY8z/elmmR8odXaP+9a9DSQkMGBD5s6Eh0xPmjM5Rn/TjSYz8l5EM+PYAShaX0PCafg7x6DXoZpYH/AD4OFAK3GhmpV1WuwX4P3f/CPAg8N1kDxpW879exiODmsEAgxN58MigZkU9nU5F/YMfhH/+Z9i1C9wjf9bWKuppdPGwi7lr0l0cPn6YfUf24Ti7Du2i9pe1inoczN17XsFsEnCvu18Zvf8NAHf/f53W+VV0nXVmNhD4X6DQe3jy6upqb2xsTMJbyG4D7zFO5MV4wKH07bPTPk9Oe+8YxPo3Nj8fLh6d9nFy1RsH3qD9ZPsZy4uHFLPzzp3pHyhgzGyDu1fHemxgHN9/AbCn0/0W4LLu1nH3djM7BAwD3uoySC1QCzBq1Ki4hg+7Ez38HamU4ekbRGD/3m4eOA6Tu/6lVFKlubU55vLdh3aneZLsE0/Qk8bd64F6iGyhp/O1gyrvJDG30PNOws8eaEn/QLmspCSym6Wr4mJ4/GdpHydXlSwuYdehM38Oo4ZoI7A38fxSdC9wYaf7RdFlMdeJ7nIZAhxIxoBhV3us9My/5nt0uaRXXR0MHnz6ssGDI8slbepm1DE4//Sfw+D8wdTN0M+hN/EEfT0w2swuMrOzgBuAp7us8zRwc/T2dcDzPe0/l794+Ltb+Mo7peSdABzyTsBX3inl4e/qsK20mzsX6usjW+RmkT/r6yPLJW3mls+l/pP1FA8pxjCKhxRT/8l65pbr59CbXn8pCmBmnwAWA3nAcnevM7P7gEZ3f9rMCoCfAJcAfwZucPc/9fSc+qWoiEji+vtLUdx9NbC6y7J7Ot1+F7i+P0OKiEj/6JOiIiIhoaCLiISEgi4iEhIKuohISMR1lEtKXtisFYjxKY4+GU6XT6VmqTC8jzC8BwjH+9B7CI5kvo9idy+M9UDGgp5MZtbY3WE82SQM7yMM7wHC8T70HoIjXe9Du1xEREJCQRcRCYmwBL0+0wMkSRjeRxjeA4Tjfeg9BEda3kco9qGLiEh4ttBFRHKegi4iEhKhCbqZXW9mW8zspJll1WFOvV2EOxuY2XIz229mmzM9S1+Z2YVmtsbMmqP/Li3I9Ex9YWYFZvZHM9sUfR/fzvRMfWVmeWb2ipk9k+lZ+sLMdprZa2bWZGYpP71saIIObAauBV7I9CCJiPMi3NlgBTAr00P0Uztwl7uXAjXA7Vn6szgGTHf3SqAKmGVmNZkdqc8WAFszPUQ/TXP3Kh2HngB33+ru2zI9Rx9MBHa4+5/c/T3gp8DVGZ4pYe7+ApFz4Wctd9/n7hujtw8TCckFmZ0qcR5xJHo3P/qVdUc/mFkR8LfAjzM9S7YITdCzWKyLcGddRMLGzEqIXLDl5QyP0ifRXRVNwH7gOXfPxvexGPgH4GSG5+gPB35tZhvMrDbVL5bWi0T3l5n9BvhQjIcWuvsv0j2PhJOZnQM8Cdzp7m9nep6+cPcTQJWZDQWeMrNx7p41v98ws6uA/e6+wcymZnic/vhrd99rZucDz5nZ69G/zaZEVgXd3WdmeoYUiOci3JImZpZPJOYN7v7vmZ6nv9z9oJmtIfL7jawJOjAZmB29/GUBcJ6Z/Zu7fy7DcyXE3fdG/9xvZk8R2cWasqBrl0vmxXMRbkkDMzNgGbDV3R/I9Dx9ZWaF0S1zzGwQcDnwekaHSpC7f8Pdi9y9hMh/E89nW8zN7H1mdu6p28AVpPh/qqEJupldY2YtwCTgWTP7VaZnioe7twN3AL8i8ku4x919S2anSpyZPQasA8aYWYuZ3ZLpmfpgMvB5YHr0MLOm6BZithkBrDGzV4lsMDzn7ll52F+W+yDwopltAv4IPOvu/5nKF9RH/0VEQiI0W+giIrlOQRcRCQkFXUQkJBR0EZGQUNBFREJCQRcRCQkFXUQkJP4/OPFkovYKC1sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SCALING\n",
    "\n",
    "# Define the vertices of the original triangle\n",
    "triangle = np.array([[0, 0], [0, 2], [2, 0], [0,0]])\n",
    "\n",
    "# Define the scaling factor\n",
    "scale_factor = 2\n",
    "\n",
    "# Define the scaling matrix\n",
    "scaling = np.array([[scale_factor, 0],\n",
    "                    [0, scale_factor]])\n",
    "\n",
    "# Scale the triangle by multiplying each vertex by the scaling matrix\n",
    "new_triangle = np.dot(triangle, scaling)\n",
    "\n",
    "# Plot the original and scaled triangles\n",
    "plt.plot(triangle[:,0], triangle[:,1], 'ro-', label='Original Triangle')\n",
    "plt.plot(new_triangle[:,0], new_triangle[:,1], 'go-', label='Scaled Triangle')\n",
    "plt.axis('equal')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e93a55c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SHEARING\n",
    "\n",
    "# Define the vertices of the original triangle\n",
    "triangle = np.array([[0, 0], [0, 2], [2, 0], [0,0]])\n",
    "\n",
    "# Define the shearing factor\n",
    "shear_factor = 2\n",
    "\n",
    "# Define the shearing matrix\n",
    "shearing = np.array([[1, shear_factor],\n",
    "                     [0, 1]])\n",
    "\n",
    "# Shear the triangle by multiplying each vertex by the shearing matrix\n",
    "new_triangle = np.dot(triangle, shearing)\n",
    "\n",
    "# Plot the original and sheared triangles\n",
    "plt.plot(triangle[:,0], triangle[:,1], 'bo-', label='Original Triangle')\n",
    "plt.plot(new_triangle[:,0], new_triangle[:,1], 'go-', label='Sheared Triangle')\n",
    "plt.axis('equal')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee68a9d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#REFLECTION\n",
    "\n",
    "# Define the vertices of the original triangle\n",
    "triangle = np.array([[0, 0], [0, 2], [2, 0], [0,0]])\n",
    "\n",
    "# Define the reflection axis\n",
    "reflection_axis = np.array([[1, 0], [0, -1]])\n",
    "\n",
    "# Reflect the triangle by multiplying each vertex by the reflection axis\n",
    "new_triangle = np.dot(triangle, reflection_axis)\n",
    "\n",
    "# Plot the original and reflected triangles\n",
    "plt.plot(triangle[:,0], triangle[:,1], 'bo-', label='Original Triangle')\n",
    "plt.plot(new_triangle[:,0], new_triangle[:,1], 'go-', label='Reflected Triangle')\n",
    "plt.axis('equal')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "058b1291",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51337631",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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