data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T13:06:17.579868Z",
     "start_time": "2023-07-18T13:06:17.560065Z"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## gen_greyscale_image_tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T12:56:03.484604Z",
     "start_time": "2023-07-18T12:56:03.237764Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size:  (300, 520)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=300x520>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = Image.open('Austausch_IPD_Optimierung/gen_greyscale_image_tool/gs_img_doubledome.png')\n",
    "print('size: ', image.size)\n",
    "display(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T12:59:02.491602Z",
     "start_time": "2023-07-18T12:59:02.482572Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:  (136983, 3)\n",
      "[[-5.00000000e+02  4.97005988e+02  6.00162027e-05]\n",
      " [-5.00000000e+02 -9.88023952e+01 -1.45523219e-05]\n",
      " [-5.00000000e+02  2.93413174e+02 -6.60868638e-05]\n",
      " ...\n",
      " [ 5.00000000e+02  2.93413174e+02 -6.60868501e-05]\n",
      " [ 5.00000000e+02  4.01197605e+02 -3.16407108e-06]\n",
      " [ 5.00000000e+02  4.97005988e+02  6.00164115e-05]]\n"
     ]
    }
   ],
   "source": [
    "data = np.load('Austausch_IPD_Optimierung/gen_greyscale_image_tool/nodes_doubledome.npy')\n",
    "#data = np.load('2023_06/01_Generate_Tool_Greyscale_Image/nodes_L_shape.npy')\n",
    "print('shape: ', data.shape)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T12:59:03.132005Z",
     "start_time": "2023-07-18T12:59:03.103521Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: [-500.0000000000002, 500.0000000000002]\n",
      "y: [-500.0000000000003, 500.0000000000003]\n",
      "z: [-0.01741654834282872, 60.049438910653365]\n"
     ]
    }
   ],
   "source": [
    "print(f'x: [{np.min(data[:, 0])}, {np.max(data[:, 0])}]')\n",
    "print(f'y: [{np.min(data[:, 1])}, {np.max(data[:, 1])}]')\n",
    "print(f'z: [{np.min(data[:, 2])}, {np.max(data[:, 2])}]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## gen_greyscale_shear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-19T18:10:21.172761Z",
     "start_time": "2023-04-19T18:10:20.890784Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size:  (300, 520)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=300x520>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = Image.open('Austausch_IPD_Optimierung/gen_greyscale_shear/shear.png')\n",
    "print('size: ', image.size)\n",
    "display(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-19T18:10:21.172903Z",
     "start_time": "2023-04-19T18:10:21.160038Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pixel value: [0, 125]\n"
     ]
    }
   ],
   "source": [
    "pixel_array = image.convert(\"L\").load()\n",
    "\n",
    "min_value = pixel_array[0, 0]\n",
    "max_value = pixel_array[0, 0]\n",
    "\n",
    "for y in range(image.size[1]):\n",
    "    for x in range(image.size[0]):\n",
    "        pixel_value = pixel_array[x, y]\n",
    "        if pixel_value < min_value:\n",
    "            min_value = pixel_value\n",
    "        if pixel_value > max_value:\n",
    "            max_value = pixel_value\n",
    "\n",
    "print(f\"pixel value: [{min_value}, {max_value}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T12:59:47.551284Z",
     "start_time": "2023-07-18T12:59:47.535306Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:  (3, 22080)\n",
      "[[-1.4916667e+02 -1.4750000e+02 -1.4583333e+02 ...  1.3250000e+02\n",
      "   1.3750000e+02  1.4250000e+02]\n",
      " [-2.2750000e+02 -2.2916667e+02 -2.2750000e+02 ...  2.2583333e+02\n",
      "   2.2583333e+02  2.2583333e+02]\n",
      " [ 1.6752557e-01  1.6752557e-01  1.6752557e-01 ...  1.6752557e-01\n",
      "   1.6752557e-01  1.6752557e-01]]\n"
     ]
    }
   ],
   "source": [
    "data = np.load('Austausch_IPD_Optimierung/gen_greyscale_shear/el_coords_xyz.npy')\n",
    "#data = np.load('2023_06/02_Generate_Shear_Greyscale_Image/el_coords_xyz.npy')\n",
    "print('shape: ', data.shape)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T12:59:48.004158Z",
     "start_time": "2023-07-18T12:59:47.979088Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: [-149.16667, 149.16667]\n",
      "y: [-229.16667, 229.16667]\n",
      "z: [0.16752557, 0.16752557]\n"
     ]
    }
   ],
   "source": [
    "print(f'x: [{np.min(data[0])}, {np.max(data[0])}]')\n",
    "print(f'y: [{np.min(data[1])}, {np.max(data[1])}]')\n",
    "print(f'z: [{np.min(data[2])}, {np.max(data[2])}]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-19T18:10:21.292865Z",
     "start_time": "2023-04-19T18:10:21.206545Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:  (10, 22080)\n",
      "[[0.41195634 0.37418076 0.41778436 ... 0.49701878 0.51105613 0.51210517]\n",
      " [0.39384875 0.37308413 0.39670992 ... 0.5773297  0.59098536 0.59397733]\n",
      " [0.95045775 0.92989182 0.92281336 ... 0.60081255 0.59636873 0.5848304 ]\n",
      " ...\n",
      " [0.6096887  0.59772968 0.61147469 ... 0.89284301 0.94110352 0.96263015]\n",
      " [0.62388206 0.59035385 0.62429917 ... 0.79761481 0.79819494 0.80264837]\n",
      " [0.79114717 0.74500221 0.7543807  ... 0.34595782 0.36561573 0.36180529]]\n",
      "y: [3.927862962882499e-06, 48.5947456359863]\n"
     ]
    }
   ],
   "source": [
    "data = np.load('Austausch_IPD_Optimierung/gen_greyscale_shear/y_vals_short.npy')\n",
    "print('shape: ', data.shape)\n",
    "print(data)\n",
    "print(f'y: [{np.min(data)}, {np.max(data)}]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-19T18:10:21.338956Z",
     "start_time": "2023-04-19T18:10:21.292700Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y from image: [0.000996350077912211, 44.160717010498]\n"
     ]
    }
   ],
   "source": [
    "print(f'y from image: [{np.min(data[1])}, {np.max(data[1])}]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-19T18:10:21.387163Z",
     "start_time": "2023-04-19T18:10:21.338294Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.49066666666666664 ≈ 0.49019607843137253\n"
     ]
    }
   ],
   "source": [
    "print(44.16 / 90, '≈', 125 / 255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## spring_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-19T18:10:21.473311Z",
     "start_time": "2023-04-19T18:10:21.383278Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:  (3, 60)\n",
      "[[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  10. -10.\n",
      "   10. -10.  10. -10.  10. -10.  10. -10.  10. -10.  10. -10.  10. -10.\n",
      "   10. -10.  10. -10.  10. -10.  10. -10.  10. -10.  10. -10.  10. -10.\n",
      "   10. -10.  10. -10.  10. -10.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.]\n",
      " [-10. -10. -10. -10. -10. -10. -10. -10. -10. -10. -10. -10.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.  10.  10.  10.  10.  10.  10.  10.  10.\n",
      "   10.  10.  10.  10.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.]]\n"
     ]
    }
   ],
   "source": [
    "data = np.load('Austausch_IPD_Optimierung/spring_coords/spring_dir_of_attack_coords.npy')\n",
    "print('shape: ', data.shape)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-06T13:27:18.126075Z",
     "start_time": "2023-06-06T13:27:18.105120Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:  (3, 60)\n",
      "[[-1.40000000e+02 -1.15000000e+02 -9.00000000e+01 -6.25000000e+01\n",
      "  -3.50000000e+01 -1.00000000e+01  1.50000000e+01  4.00000000e+01\n",
      "   6.50000000e+01  9.00000000e+01  1.15000000e+02  1.40000000e+02\n",
      "   1.50000000e+02 -1.50000000e+02  1.50000000e+02 -1.50000000e+02\n",
      "   1.50000000e+02 -1.50000000e+02  1.50000000e+02 -1.50000000e+02\n",
      "   1.50000000e+02 -1.50000000e+02  1.50000000e+02 -1.50000000e+02\n",
      "   1.50000000e+02 -1.50000000e+02  1.50000000e+02 -1.50000000e+02\n",
      "   1.50000000e+02 -1.50000000e+02  1.50000000e+02 -1.50000000e+02\n",
      "   1.50000000e+02 -1.50000000e+02  1.50000000e+02 -1.50000000e+02\n",
      "   1.50000000e+02 -1.50000000e+02  1.50000000e+02 -1.50000000e+02\n",
      "   1.50000000e+02 -1.50000000e+02  1.50000000e+02 -1.50000000e+02\n",
      "   1.50000000e+02 -1.50000000e+02  1.50000000e+02 -1.50000000e+02\n",
      "  -1.40000000e+02 -1.15000000e+02 -9.00000000e+01 -6.25000000e+01\n",
      "  -3.50000000e+01 -1.00000000e+01  1.50000000e+01  4.00000000e+01\n",
      "   6.50000000e+01  9.00000000e+01  1.15000000e+02  1.40000000e+02]\n",
      " [-2.30000000e+02 -2.30000000e+02 -2.30000000e+02 -2.30000000e+02\n",
      "  -2.30000000e+02 -2.30000000e+02 -2.30000000e+02 -2.30000000e+02\n",
      "  -2.30000000e+02 -2.30000000e+02 -2.30000000e+02 -2.30000000e+02\n",
      "  -2.15000000e+02 -2.15000000e+02 -1.90000000e+02 -1.90000000e+02\n",
      "  -1.65000000e+02 -1.65000000e+02 -1.40000000e+02 -1.40000000e+02\n",
      "  -1.15000000e+02 -1.15000000e+02 -9.00000000e+01 -9.00000000e+01\n",
      "  -6.50000000e+01 -6.50000000e+01 -4.00000000e+01 -4.00000000e+01\n",
      "  -1.50000000e+01 -1.50000000e+01  1.00000000e+01  1.00000000e+01\n",
      "   3.75000000e+01  3.75000000e+01  6.50000000e+01  6.50000000e+01\n",
      "   9.00000000e+01  9.00000000e+01  1.15000000e+02  1.15000000e+02\n",
      "   1.40000000e+02  1.40000000e+02  1.65000000e+02  1.65000000e+02\n",
      "   1.90000000e+02  1.90000000e+02  2.15000000e+02  2.15000000e+02\n",
      "   2.30000000e+02  2.30000000e+02  2.30000000e+02  2.30000000e+02\n",
      "   2.30000000e+02  2.30000000e+02  2.30000000e+02  2.30000000e+02\n",
      "   2.30000000e+02  2.30000000e+02  2.30000000e+02  2.30000000e+02]\n",
      " [ 1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01\n",
      "   1.67525575e-01  1.67525575e-01  1.67525575e-01  1.67525575e-01]]\n"
     ]
    }
   ],
   "source": [
    "data = np.load('Austausch_IPD_Optimierung/spring_coords/spring_pnt_of_attack_coords.npy')\n",
    "print('shape: ', data.shape)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-06T13:27:21.962246Z",
     "start_time": "2023-06-06T13:27:21.955130Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x: [-150.0, 150.0]\n",
      "y: [-230.0, 230.0]\n",
      "z: [0.167525574564934, 0.167525574564934]\n"
     ]
    }
   ],
   "source": [
    "print(f'x: [{np.min(data[0])}, {np.max(data[0])}]')\n",
    "print(f'y: [{np.min(data[1])}, {np.max(data[1])}]')\n",
    "print(f'z: [{np.min(data[2])}, {np.max(data[2])}]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## XY-Simulationsdaten_1000samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T13:03:06.628656Z",
     "start_time": "2023-07-18T13:03:06.525917Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:  (1000, 60)\n",
      "[[0.799101 0.280666 0.105951 ... 0.180111 0.112275 0.401548]\n",
      " [0.390053 0.850338 0.366799 ... 0.242512 0.455161 0.403142]\n",
      " [0.167864 0.181285 0.756506 ... 0.916211 0.779371 0.511979]\n",
      " ...\n",
      " [0.284465 0.957517 0.413402 ... 0.163857 0.263485 0.038158]\n",
      " [0.078085 0.734296 0.588312 ... 0.962643 0.90567  0.652191]\n",
      " [0.313136 0.114475 0.739406 ... 0.969066 0.12245  0.049659]]\n",
      "x: [0.010003, 0.999986]\n"
     ]
    }
   ],
   "source": [
    "data = np.load('Austausch_IPD_Optimierung/XY-Simulationsdaten_train/x_vals.npy')\n",
    "#data = np.load('2023_06/03_Spring_Stiffnesses/X-Vals_-_Spring_stiffnesses.npy')\n",
    "print('shape: ', data.shape)\n",
    "print(data)\n",
    "print(f'x: [{np.min(data)}, {np.max(data)}]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T13:04:23.903485Z",
     "start_time": "2023-07-18T13:04:10.545359Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape:  (1000, 22080)\n",
      "[[0.41195634 0.37418076 0.41778436 ... 0.49701878 0.51105613 0.51210517]\n",
      " [0.39384875 0.37308413 0.39670992 ... 0.5773297  0.59098536 0.59397733]\n",
      " [0.95045775 0.92989182 0.92281336 ... 0.60081255 0.59636873 0.5848304 ]\n",
      " ...\n",
      " [0.99280035 0.97103995 0.96402478 ... 0.74777329 0.74909049 0.75640947]\n",
      " [0.83415937 0.84890544 0.84179497 ... 0.01804365 0.01288089 0.00644183]\n",
      " [0.48254395 0.45938691 0.47547463 ... 0.97329539 0.98011816 0.990291  ]]\n",
      "y: [1.72298939560278e-08, 50.822849273681605]\n",
      "(array([381]), array([14839]))\n"
     ]
    }
   ],
   "source": [
    "data = np.load('Austausch_IPD_Optimierung/XY-Simulationsdaten_train/y_vals.npy')\n",
    "#data = np.load('2023_06/02_Generate_Shear_Greyscale_Image/Y-Vals_-_ShearAngles_L-Winkel.npy')\n",
    "print('shape: ', data.shape)\n",
    "print(data)\n",
    "print(f'y: [{np.min(data)}, {np.max(data)}]')\n",
    "print(np.where(data == np.max(data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T13:14:40.068374Z",
     "start_time": "2023-07-18T13:14:39.713134Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u:  3.3886 29.7364\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('Austausch_IPD_Optimierung/u_spring/U_spring_doubledome.csv')\n",
    "data = pd.read_csv('2023_06/04_Spring_Elongation/U_spring_L_shape.csv')\n",
    "print('u: ', data['u'].min(), data['u'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-18T13:14:41.224509Z",
     "start_time": "2023-07-18T13:14:41.204572Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11.1877, 53.8868)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grippers_coordinates = np.load('Austausch_IPD_Optimierung/spring_coords/spring_pnt_of_attack_coords.npy')[:2]\n",
    "pair_lengths = []\n",
    "for i in range(len(data)):\n",
    "    for j in range(i + 1, len(data)):\n",
    "        if i != j:\n",
    "            if (grippers_coordinates[0][i] == grippers_coordinates[0][j]) and (\n",
    "                    abs(grippers_coordinates[1][i] - grippers_coordinates[1][j]) == 460):\n",
    "                pair_lengths.append(data.iloc[i]['u'] + data.iloc[j]['u'])\n",
    "            if (grippers_coordinates[1][i] == grippers_coordinates[1][j]) and (\n",
    "                    abs(grippers_coordinates[0][i] - grippers_coordinates[0][j]) == 300):\n",
    "                pair_lengths.append(data.iloc[i]['u'] + data.iloc[j]['u'])\n",
    "\n",
    "min(pair_lengths), max(pair_lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}