Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Test_data1.csv','r') as csvfile:\n",
    "    spamreader=csv.reader(csvfile,delimiter=',')\n",
    "    sno=[]\n",
    "    label=[]\n",
    "    ass1=[]\n",
    "    ass2=[]\n",
    "    ass3=[]\n",
    "    ass4=[]\n",
    "    ass5=[]\n",
    "    ass6=[]\n",
    "    ass7=[]\n",
    "    for row in spamreader:\n",
    "        sno.append(row[0])\n",
    "        label.append(row[1])\n",
    "        ass1.append(row[2])\n",
    "        ass2.append(row[3])\n",
    "        ass3.append(row[4])\n",
    "        ass4.append(row[5])\n",
    "        ass5.append(row[6])\n",
    "        ass6.append(row[7])\n",
    "        ass7.append(row[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dis(a):\n",
    "    temp=[]\n",
    "    for i in a:\n",
    "        if i not in temp:\n",
    "            temp.append(i)\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "labeld=dis(label)\n",
    "labeld.remove(labeld[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A', 'B', 'C', 'D', 'E', 'F']\n"
     ]
    }
   ],
   "source": [
    "print (labeld)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=[1,2,3,4,5,6,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['1', '10', '10', '10', '10', '10', '9', '22'], ['7', '10', '10', '10', '10', '10', '10', '30'], ['13', '10', '10', '10', '10', '10', '8', '28'], ['19', '10', '10', '10', '10', '10', '10', '25'], ['25', '10', '8', '10', '7', '8', '7', '25'], ['31', '10', '10', '10', '7', '7', '4', '22'], ['37', '10', '10', '9', '7', '6', '6', '23'], ['43', '9', '8', '6', '6', '7', '6', '28'], ['49', '9', '7', '10', '7', '4', '8', '22'], ['55', '10', '10', '10', '9', '5', '6', '25'], ['61', '10', '10', '10', '10', '10', '10', '25'], ['67', '10', '10', '10', '10', '10', '10', '30'], ['73', '10', '10', '10', '10', '10', '8', '28'], ['79', '10', '10', '10', '10', '10', '10', '38'], ['85', '10', '10', '10', '10', '10', '5', '31'], ['91', '10', '10', '10', '7', '1', '6', '26'], ['97', '10', '10', '10', '9', '7', '5', '29'], ['103', '0', '0', '10', '10', '10', '9', '24'], ['109', '10', '10', '10', '9', '5', '7', '26'], ['115', '10', '10', '10', '10', '7', '8', '31'], ['121', '10', '10', '10', '10', '10', '9', '35']]\n"
     ]
    }
   ],
   "source": [
    "la=[]\n",
    "lb=[]\n",
    "lc=[]\n",
    "ld=[]\n",
    "le=[]\n",
    "lf=[]\n",
    "for i in range(len(label)):\n",
    "    temp=[]\n",
    "    if label[i]=='A':\n",
    "        temp.append(sno[i])\n",
    "        temp.append(ass1[i])\n",
    "        temp.append(ass2[i])\n",
    "        temp.append(ass3[i])\n",
    "        temp.append(ass4[i])\n",
    "        temp.append(ass5[i])\n",
    "        temp.append(ass6[i])\n",
    "        temp.append(ass7[i])\n",
    "        la.append(temp)\n",
    "    if label[i]=='B':\n",
    "        temp.append(sno[i])\n",
    "        temp.append(ass1[i])\n",
    "        temp.append(ass2[i])\n",
    "        temp.append(ass3[i])\n",
    "        temp.append(ass4[i])\n",
    "        temp.append(ass5[i])\n",
    "        temp.append(ass6[i])\n",
    "        temp.append(ass7[i])\n",
    "        lb.append(temp)\n",
    "    if label[i]=='C':\n",
    "        temp.append(sno[i])\n",
    "        temp.append(ass1[i])\n",
    "        temp.append(ass2[i])\n",
    "        temp.append(ass3[i])\n",
    "        temp.append(ass4[i])\n",
    "        temp.append(ass5[i])\n",
    "        temp.append(ass6[i])\n",
    "        temp.append(ass7[i])\n",
    "        lc.append(temp)\n",
    "    if label[i]=='D':\n",
    "        temp.append(sno[i])\n",
    "        temp.append(ass1[i])\n",
    "        temp.append(ass2[i])\n",
    "        temp.append(ass3[i])\n",
    "        temp.append(ass4[i])\n",
    "        temp.append(ass5[i])\n",
    "        temp.append(ass6[i])\n",
    "        temp.append(ass7[i])\n",
    "        ld.append(temp)\n",
    "    if label[i]=='E':\n",
    "        temp.append(sno[i])\n",
    "        temp.append(ass1[i])\n",
    "        temp.append(ass2[i])\n",
    "        temp.append(ass3[i])\n",
    "        temp.append(ass4[i])\n",
    "        temp.append(ass5[i])\n",
    "        temp.append(ass6[i])\n",
    "        temp.append(ass7[i])\n",
    "        le.append(temp)\n",
    "    if label[i]=='F':\n",
    "        temp.append(sno[i])\n",
    "        temp.append(ass1[i])\n",
    "        temp.append(ass2[i])\n",
    "        temp.append(ass3[i])\n",
    "        temp.append(ass4[i])\n",
    "        temp.append(ass5[i])\n",
    "        temp.append(ass6[i])\n",
    "        temp.append(ass7[i])\n",
    "        lf.append(temp)\n",
    "print (la)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-61-b285b47ab9ad>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mla\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mla\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m35\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "image/png": 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EHNNx2wr3syMimKXh3iWNIZ6en8/7BQVNIT4zJobZcXHdFuKHExGyX85m7/y9iFdIeTqFxLuObU2YxtD3iDA0LIxBNhsFdXW8U1DAqzk57KquJr+NSj/KZmsz8KMa9nJ58pmBD3RjzAjgM2A8MB+4BSgD1gMPdbbaoga66ikiwhtvvME9c++lvLSCO+Q2Jl19Izcs/Rb9B9gD+lrbmrVltjUL99lxcczUcG9S7fHwz6Ii0pqFeIzdzlUxMcyKi+OcHgrxttQcqmHnHTsp/pe/Wl8yhn4jum/eeo3HQ2ZdXasKv3m7p6B56M+YEdhAN8YMBFYCPxeRt40x8fiWzxXgp/jaMre1sZ9OW1Q9Kjc3l7vvvpt33nmHUbYTeYRHifj1qcyaP7bbX/vrykrS8/JIy89nuz/cz4mMbOq5x/eCK8P3pMYQT8/P5/3CQio8HmKaVeLBDPHDiQjZL2Wz9yF/tf5MStBWcCwtXc3m7XNw1xThGXIHN5/yk8AFujHGDnwAfCwiC9t4fgTwgYiM7+g4WqGr7iQivPnmm9x7772UlZZzs/dWLoibzci0k/jWWfE9Pp7Dwz0EODsyktmxscy0cLjXNK/E/SE+xGZr6omfGxnZa0K8LS2q9fMjGfvyWMKH98y1ST2eag4ceAKXayEOh5OxY5cQFXV+wGe5fAGEAdl8M8vlAuBn+FZajAZ2dDZ1UQNddZe8vDzuvvtu3n77bU6IOokni3+A54xULn1rEnHxwV2gSUR84Z6fT1p+Pjv84d5YuVsh3GuaVeJ/bxbijbNTenuIH05EyH7RV60DpPwmhYQ5Cd26zIPHU4PL9Qwu16+Ji7uBlJRnsNl86wIFMtAvB94FvvI/NBqYBzyJb5ZLOVAFeEXk9I6OpYGuAk1ESEtL45577qG8vIKZg27htsJZZM6L5sbfTuhwSmIwdBTujZV7XB8J9xqPh4+Li0nLy+P9wkLKm4X4rNhYZvSxEG9LzUF/tf5/3Vetezw15Oen069fChERp+P11hMS0vI8T0/McnkYWCIibxpjrgW+KyLXdbSvBroKpLy8PObOnctbb73F6JQJPJD9AM7QEYS9OILvXDMi2MPrVGO4p/lPqO6sriYEOLdZ5d7bwr0xxNPz8vh7sxC/slklbu/jIX44ESH7z9nsfTjw1XpZ2Vp27LiFqqodJCTMISVlITZby+vEerwebKG2bp/lkgR8DBggBDi9o5UWQQNdBU5jVV5WVsZ3p3yfu1dfTvYYO6e+czKjT4wK9vCOmIiwtdlsmebh3jhbJjZI4V7j8fCJvxJvDPHowypxq4V4W6oPVLPzjp2U/LuEqG9HMealMYQPO7pq3eOp4cCBJ3G5foPNPpSQ2B+S3ZCEq8yFq9RFraeWM4edySWjLmGQY1DgK/Q2Zrk8B6wUkbeMMbOBOSJyQRv76SwXFTD5+fnMnTuX5cuXM3HiFGbKPM7aksyemf249tVJDBjYuyrao9EY7o2V+y5/uM+IjGRWD4V7rdfLx/6e+HsFBU0hfqV/dsrxEuKHExGy/pTFvh/sAwMpv00h4Y62q3WP10NuZS6uUhfuMjfuMjeuMhd1VVs4s/9/iAur5sNswx/2ClXNLtYUbgsneXAyzsFORg8ZzWWjL+OSMZd07ywXY0wpECkiYnzvplREBnd0HK3Q1bFIT09n7ty5lJWVccfND3H2e+cTVRxKyc/jmPXwiX12XfKOiAhfNavcd1VXE8o3lfuVAQz3Wq+XT5qFeJnHQ5TNxkz/PPHzjtMQb0vlvkq+vu1rqlZWUX9GPbsf3s3+/vt9FXaZL8CzyrNo8H5z8Q+7gTtGhnJ1kodyj4M11WcS0v80nIOdJEf4Ajx5cDLR/aJb/VsO5ElRA7wKFInIA80e3w78G5iBbz2XCCBRRGraO5YGujoa+fn53HvvvaSlpTF16lS+d87jnPzcYMqiIfn1MZw2IyHYQ+wRjeGelpdHen5+U7jPiIpiVmzsUYV7eyHeWIkfjyHuFS+5FblNFbW7zO2rssvdTdV2ZnkmngYP393wXe765C68xsuLF7/I1hlbSY78Jpwb74faS2nI/xm11TtJSLiDlJTfYLN1/dPKgQz0M4FV+Ga5NC48/Bi+aYxvAjuBanyfGP2riCxt71ga6OpILV++nLlz51JSUsJjP3qchC/PY8y79eybbuN/lk9iaOKAzg9iQSLCl83mue9uFu6z/eEe006413q9/Msf4u8eFuKzYmM5PyrKsiHuFS95lXlNwdwU2M2CO7M8s0VlDeAIdeAc7Pymmh70TVWdVJKE9xEvlZ9VEvWdKMa8OIbwZF9v3eut5cCBBRw69DQORyJjxrxEdPR3jnjcPXFN0SRgDTABX5i/CzwnIp+0t48GuuqqgoIC7rnnHtLS0pgyZQoLnlhMySMenDu97L83ghsXTcBms2boHKnGcG+s3BvD/bxmlfsgm60pxN8rKKDUH+JXxMQwOzaW86KiCOvjId4Y1k0VdRuBnVmWSb235ToqYaFhrSrqw9sgMf1jOmzpiVfIeiGLvY/sxYQaUhem0v9qFzt33kpV1TaGDr2d1NTfHlFV3lyPrIdujJkH/Bxfhf6JiFzf0fZWDHSPCFsrKugXGsro/v2p83r7/A9GsL311lvcfffdlJSUsGDBAiaOmI3nLjchAiEvDOPi649slcTjiYiwpaKiaZ77Hn+49w8NpdzjIfKwSrwv/1sVEVa7V7N47WLWZa7rMKw7CuzY/rEBO/9Svb+aHbdtp3RFGZy6DvujrzH27F8zZMjFx3TcnqjQo4C3gGuAEiAdWC4irx22nSVnudR6PBQ0NJAQFkaIMWyvrGSR283S7GyujotjvtPJ1MEdniNWhykoKOC+++7jjTfeYMqUKbz88hI2LfUybHEJ2akhTH3nZMac1PemJAZLY7in5edTWF/PFTExfT7EARq8Dby9/W0Wrl7I2sy1RIVHcVHqRQyLGNYqsGP6xxBieu79lpWtZ/u2W6l+PRXz4lxC7A5SF45i6G1Dj+mXRk8E+izgIhG53f/9TcA0EZnb3j5WqNBrvV7sxhBiDF9VVLCmrIzh4eGcHxWFu7aW37ndvJidTZnHw9kREcxPTubSIUOOeVF+q3vnnXe46667KC4u5oknnuDWW+7j42u3MvLzevZcHs41f5nMoEF9f0qiOnqlNaW8vOllFq9dzKHSQ6RGp/LgtAe5ecLNDAgL7rkUX6/8Jxw69GvCwoYyZsyf6V86gx237aB0ZSnRF0Uz+sXRhDuPbt56TwT6acAS4Fv4Wi5LgfUi8rv29unLgS4iGGPY6v9ztryhgdlxcUyLaN0TK2toYEl2Ns+63RysrSW1Xz8ecDq5ZejQY75yidUUFhZy77338sYbbzBp0iSWLl1KXVk8+2fvIDJfKPxpLLMfOZGQPl5VqqN3oOQAz619jpc2vkR5XTnnDD+H+dPnc8moSwgNCf7PU3n5BnbsuIXKyq0MHXorKSkLsdsjAV9vPfMPmez74T6M3ZC6KJWhtxx5td7VQEdEOrzhW3xrHbAF+Bp4yv/4CYAbqANKgb8Cjo6ONWXKFOlLvF6viIjsraqSx/ftkylffCHzd++W/VVVXdq/3uOR9NxcmbZhg5CRIVGrVsmje/eKu6amO4fdZ7z99tsSFxcndrtdfvKTn0hdXZ2kLdwuH4dlyPK4DPnvv7OCPUQVRKtdq2VW2iwJeSpEbD+xyfVvXS/rM9cHe1hNPJ4a2bv3fyUjI1T+859EKSj4sN1tq/ZUycazN0oGGbLlf7ZIjfvIMgBfsdx5Xne6ge+j/QP9X9uBtcA0IA34nv/xF4C7OztWXwr0wro6edblkhPXrpXh//2vLDx0SErr64/6eP8tKZFZW7dKSEaG2FaskBu2bZMNZWUBHHHfUVBQINddd50AMmnSJNmyZYtUVtTJi7PWSAYZsuTUVZLlrgj2MFUQ1HvqJf3rdJn+0nRhARL5q0j54b9+KK5SV7CH1kJZ2QZZt268ZGQg27bdLHV1RZ3u4/V4xfWcS1b2XymfRXwmWa9kNRWNnQlYoEvLcO8PbAROw3dxC5v/8en4PkXapwO9zuORfxUVyXR/RX3a+vWSlpsr9R5PwF5jX1WVPLh7twz67DMhI0PO2bhR3svPF08X/8f2de+++67Ex8eLzWaTp556Surq6mT3jhJZNm6lZJAhL9+1QerrAvffW/UNpTWlsmj1Ihnx7AhhAZKyOEV+t/Z3Ul5bHuyhteDx1Mq+fT/2V+UJUlDwwREfo2pPlWw8q/Nq3esVWbNG5KGHAhzoQCiwGagAfg3EAHuaPZ8MbG1n3zn4LlG3ftiwYUf85ntCVk2N/HDPHrGtWCEhGRly9dat8t+Skm59zZL6evntoUMy7L//FTIyZNSaNfIHt1sqGhq69XWDpbCwUK6//noBZOLEibJ582YREflk+QF5LyJDPhiQIR8s2xvkUaqedrDkoDz08UMy+JeDhQXIWUvOkne2vyMNnt73c+Cryk/2V+U3dakqb4/X4xXXYpes7Oer1rOXZovX6xWvV2TtWl+IDx/uS2i7vfsq9EggAzirjUD/qrP9e1OF7vV65dOiIjl30yYhI0MGfvaZPLB7t+zrYn88UOo9HnkzN1dOXb9eyMiQ6FWr5LG9eyXTQn325lX5ggULpK6uThoaPLL04c3yb5Mhf01ZIdu2FAZ7mKoHrXWvlWvSr5HQp0Il9KlQuXb5tbLOvS7Yw2qTryp/QlassMl//pMg+fnvB+zYlbsrm6r1N0ZukQlJNU0hfsklIkuXihQXdz3QbV05w9rsBGqJMWaFv4ceaYyxiUgD4ASyjuRYwVLt8bA0J4dFbje7q6tJdjj4TUoKdyQkEGE7ov8cAWELCWF2XByzYmNZXVbGb10ufnnoEM+4XFwbF8eDTicTBw3q8XEFQlFREfPmzeO1115jwoQJ/POf/2TixIkU5Ffz3qx1pKysZ88lDmb/bQqDB+uURKvzeD28t/M9Fq5eyH9c/yHCEcH86fO579T7SI5IDvbw2lRevsk/g+VL4uNvJDV1MXb7sX8WQgTWr4e0tP68dWgik8nkjn37+JXtCyruTOW8X8UTHX3kU507TTBjzAR8F7SIwXdBaAe+KxYVAgeNMYXAIP82vVZWbS2/z8zkhawsihoa+NagQbwxbhwzY2J6xboVxhhOj4jg9IgI9lZX85zbzcvZ2SzLzeW8yEjmJydzcXQ0IX1kPvv777/PnDlzKCgo4Mknn+Sxxx4jLCyMDavz2DtrO8NzhOyfxnLbYzol0erKa8t5ZfMrPLvmWfaX7OeEyBNYfNFibp14K4McvbNY8XrrOHjw5xw69Avs9hjGj/87MTHfPaZjNoZ4errvduAA2Gxw4YWGSxc4mTg+muwHdxL+4g4ys/MZ8KfROBIdR/oinbZZzgN2AF8C2/CdDB0H3IRvOuMe//O/6exYwWi5bCork5u2bRP7ihViMjJk5ldfyeclJV0+uxxMxXV18vTBg+L099nHrFkjL2RmSmUv7rMXFRXJjTfeKICccsopsmnTpqbnli/eIf9wZMhbMRny+cfuII5S9YRDJYfkB5/8QCJ+GSEsQM54+Qx5a9tbvbI/3lxZ2SZZt26Cv1d+g9TVHX070OsV+eILkR/8QGTECF+T22YTufhikVdeESk6rA3vbfDKoUWHZGW/lbIqcpVkL/P11umOHrr4Av494NuHPXYlvpUWe0Wge7xe+aCgQM7z98cHrFwp9+/aJXt6uD8eKHUej/wtJ0em+vvsQ1atkh/v2yfZvazP/v7770tCQoLYbDZ54oknpLa2VkREqqrq5cVr1/qmJE5dJe5DvWvmggqsLzK/kGuXX9vUH78m/RpZ41oT7GF1yuOpk/37F/h75UMlP/+9ozpOY4g/8ojICSe0DPElS0QKu/D7oXJrsWyY8l/JIEO+nPyPLgf6UV+CTkTKmj3+PvCmHLaOi/+5HlvLpcrj4S+5uSxyudhZXY3T4eD+pCTuTEgg0m7v/AC9nIjweWkpi9xu3i0owG4M18XH86DTySkDBwZtXMXFxTzwwAMsW7aMk08+mVdffZVJkyYBsH93KatmbmHYVi977xzETc9PxB4W/E/3qcDyeD28v+t9Fq5eyKrhQ5zDAAANzElEQVRDqxjsGMydk+/kvlPvY3jk8GAPr1MVFVvYseMWKio2Exd3PaNGPYfdHt3l/UVg40ZIS/O1U/bv97VTLrgAZs+Gyy+H6MbD1dZCVha4XOB2t32fl4cQgpuZ7OcOzuGi7r0EXbPH/xeYCsyUTg7WXR/9z6mt5fdZWfwxM5PChgamDBzIQ8nJXB0b2yv6491hT1UVz2VmsiQ7m0qvlwuiopjvdPKdHu6zf/jhh8yZM4fc3Fwee+wxfvzjHxPmX4f703cPUXHrPmx14PldMt+9LaXHxqV6RkVdBUs3L+XZNc+yt3gvwyOG88C0B7ht0m0MdvT+xem83noOHfoFBw/+DJttCGPG/ImYmMu7tG9jiDf2xPftA5tNuOCMamZNc3PFyC+JLtnXOrBzc1sfLDISnE5ITm51X1U/lAH/c3L3XoLO//jNwF3A+SJS1dlxAh3oX1ZUsMjt5m+5udSLcHlMDPOdTs6MiLDk5cjaUlxfz5+zs3nO7Sarro4T+/fnQaeTG+Lj6deN68aUlJTwwAMP8Oqrr3LyySezdOlSJk+eDIDX6+W1x7aS9EwRecMNp7w9npMmDum2saie5y5z8/y65/nThj9RUlPCdOd05k+fzxVjr8AW0vOzxY5GRcWX/qp8E3Fx1/mr8g7+ndbVIZlZbPq0mPS/O0j7PJF9RZHYTAPnD/qC2aRxRdkyoilquV9ERJtB3XSflASdzGTriUvQXYRvQa5ifFcyelFEnu3oWIEIdK8IHxcVsdDt5v+Ki+kfEsJtCQnMS0oitX//Yzp2X1bn9ZKen89Cl4uNFRXE2O3MTUxkblIS8QG+oPBHH33EnXfeSW5uLj/60Y94/PHHm6ryosIa3p69kdRP69hzkYNZr08mIvIIz9SrXmtj9kYWrl7Im1+/iVe8XHXiVTw47UGmJ08P9tC6zFeV/4qDB3+KzRbN6NEvEBt5SbttEHG52bQ/kvTC80jnavaSSigNXMD/Mavfh1wxbCNDThjcdmA7nZ2GdVf0xCXoXgAS8M18EXzTGmeIyO72jnUsgV7t8fBabi6L3G62V1WRGBbG/U4ndyYkEG2B/nigiAiflZay0OXi/cJC7MZwg7/PPv4Y++wlJSU8+OCDLF26lPHjx7N06VKmTJnS9PzmtXnsnLWdIVlC7uMxXPv4OJ2SaAFe8fLBrg9YuHohKw+uZFDYIO6YfAf3n3Y/IyJHBHt4nauv94W1201F9ufs6Pc7KgZkErfTyajXY7HvyoGcHF8PxU+AzQPOJL3fTaRVXsLe6kRCjZfzx2Ux+8JSrphlZ8j4hICEdVf01Hro3xGRO/zfPw7UisjT7e1zNIGeW1fHHzIz+UNWFgX19Uzy98dnxcb2+YX6u9uuqioWu928kpNDtdfLhVFRzE9O5sKoqCNqSZWXl5ORkcHcuXPJycnh0Ucf5fHHH8fh+KbyfuePu+g3P4vqARDzl1TOutjZHW9J9aDKukpe3fIqz655lt1FuxkWMYx5p83j9km3ExF+dJdSC7j6esjO7vgEY04O3hDh0HVw8EawlcPoP/Uj1n1Ci4pakpxsrhtH+uZRpP87ij37QgkNhfPPh1mz4IorICYmOG+zJwL9RHxTGKfjWw/93/im1tzX3j5HEuhb/f3x1/z98e8OGcL85GTOPo7644FSVF/Pn7Ky+F1mJtl1dYzr35/5yclcHxdHfVUVbrcbl8vV7n1ZmW9C00knncTSpUuZOvWbf1c1NQ28NmcTqX+p5MCkUM5/eyLJI3rnh0VU12SVZ/H8uud5Yf0LFNcUc2rSqTw0/SFmnjizZ/vjDQ1NlXW7gZ2d3aKyBnxVc7OgrhhjZ8cpH1HhOESc42JSx/yesOgTAN+uW7b4TmqmpcGePRAaCued55udEswQb66nril6O3APvkW7tgHVIvLgYds0TVscOXLklL1797Z7PBHhk+JiFrpcfFJcTL+QEG4dOpR5Tiejj+P++NGqqKhoEcwHXS5W7trFxv37Kc/OxhQUIBUVLfYxxhAfH4/T6SQ5ObnpfsSIEVx22WUtqvKD+8pYceUWhn/pYe+tA7nhjxNxOPrGCTHV2uaczSxcvZA3tr6BRzxcOfZK5k+fz3Tn9MAXUQ0NXaqs8Xpb7jdwoC+s2+pVNz7uv/Sj19uAy/VrDhx4Cpst0tcrj53ZIsTT02H37m9CfNYsuPLK3hHizfVIoB/2gr8A3CLyh/a2aa9Cr/F4+GteHotcLr6uqiIhLIx7k5L4fmIiQ7Q/3qaKiopOK+vS0tJW+8XHx5OcnIwjPp7MiAgODByILT6ei8aM4f5JkzgnNbXpBGdHVrzvouTmvThqoG5xEpffOao73qbqZl7x8tHuj1i4eiEZBzIYGDaQ2yfdzv2n3c/IqKO8GHdjWHdWWbcX1u3NBnE6fTNGuqCiYqt/BssGYmOvYdSo59m+PaapEm8M8RkzfJV4bwzx5roa6F1Zy2UJcCmQJyLj/Y9FA28CKcBeYD4wE1/7pcvy6ur4Y1YWv8/MJL++ngkDBrBs7FiuiYs7rvvjlZWVHQa12+2mpKSk1X6NlXVqairnnntuiwrb6XSSlJTUKqx3VlXxrNvNqzk5fJCXx0UNDcx3OrmgnT671+vlb098TcIvC6keZkj55CROntqLfxJUm6rqq1i2ZRmL1ixiV+EunIOdPPPtZ7hj8h1Ehke2v2NDg69y7qiybiusBwz4poK+8MK2A3vwYDjGvwR8VfnT/qo8ggEDlvP661eRng67dkFIiK8Sf/hhX4jHxh7Ty/U6XZnlcja+lsqyZoH+NFAEXAKMxndVo2tF5N8dHauxQt9WWckit5u/5ORQK8KlQ4Yw3+nk3MhIy/fHKysrO62s2wrruLi4VgHd/D4xMbFFO+RIFdTV8afsbJ7PzCSnro7xAwYw3+nkuvh4HP5friXFNSy/ZiOp/6pjz4VhXP3GZCKjju6ityo4ssuz+f0Xv+eP6/9IUXURUxOn8tD0h7jqxKuwE9J5ZZ2V1Tqs+/dvuw3S/D4i4pjDujOVlV+zY8ctlJevJzd3Nk8//TwbN8YSEtKyEu+LIR7Qlov/I/8fNAv0ncC5IpJtjEkAVojImM6OM3riRElZtox/FhURHhLCLUOHMi8pibEDgnvF7kBpDOuOAru4uLjVfnFxce0GdWNlfSxhfSRqvV7eyMtjocvFl5WVxNvt3JOUxLk54WR+byexLiH7sSFc99RJOiWxD/kyaxOLVvyKv+15h3pp4ArbeOaXjuOMAx6MO/ObytrjabljY1h31AaJjOz2sO6I19vAF188Q2XlAiorB/Ob3/yRzz+/mhkzvumJx8UFbXgB0d2BXiIikc2eLxaRThcJDjEhEo41KzrBSw21rR6PJIJYE+O/xRJrhhBnYpseizFDCDO9cy1wLyHUY8dLKI5aKI/wkvW/v2fEhLc731n1Tl47eOwg1vllHBragMNRw4oVs9iw4fdcemmsJUK8uV4T6M1nuQDjga2dvmDfFYNveWGrsvL7s/J7A31/fd0YEel0PvDRzjHLNcYkNGu55LW3oYj8GfgzgDFmfVd+y/RV+v76Liu/N9D319cZY7r0AZ6j/bvr78DN/q9vxvcBI6WUUkHUaaAbY14HVgNjjDFu/4eJfgV82xizG/i2/3ullFJB1GnLRUSubeep84/i9f58FPv0Jfr++i4rvzfQ99fXden9BeyTokoppYLLOnOXlFLqONcjgW6MWWKMyTPGWG7KojEm2RiTYYzZboz52hgzL9hjCiRjTLgxZp0xZov//T0V7DF1B2NMqDFmkzHmg2CPJdCMMQeMMV8ZYzZ3dbZEX2KMiTTGLDfG7PD/HPadq210wBgzxv//rPFWZox5oMN9eqLl0tbyAVbhn7aZICIbjTGDgA3AFSKyLchDCwj/FasGiEiF/1KEnwPzRGRNkIcWUMaY+fiujTtYRC4N9ngCyRhzAJgqIpacp22MeRVYJSIvGWPCgP4i0nr9jD7MGBMKZAKnicjB9rbrkQpdRD6Dwy+0Zw0iki0iG/1flwPbgaTgjipwxKdxjV27/2apEy/GGCe+dYleCvZY1JExxgwGzgZeBhCROquFud/5wN6Owhy0hx5Q/k/UTgLWBnckgeVvR2zG9wGyf4mIpd4f8CzwCN9cYtFqBPjEGLPB/8ltKxkJ5AOv+FtmLxljrLE4VEvfA17vbCMN9AAxxgwE3gIeEJGyYI8nkETEIyITASdwqjHGMm0zY0zj0tAbgj2WbnSGiEwGLgbu8bdArcIGTAb+KCKTgErg0eAOKbD8baTLgPTOttVADwB/b/kt4K8iYtmVq/x/yq4ALgryUALpDOAyf5/5DeA8Y8xrwR1SYIlIlv8+D3gHODW4IwooN74L6zT+1bgcX8BbycXARhHJ7WxDDfRj5D9p+DKwXUQWBns8gWaMiTXGRPq/7gdcAOwI7qgCR0R+JCJOERmB78/aT0XkhiAPK2CMMQP8J+vxtyIuxEIL5IlIDuAyxjQu330+vsthWsm1dKHdAke/ONcR8S8fcC4QY4xxA0+KyMs98do94AzgRuArf58Z4DER+SiIYwqkBOBV/1n2ECBNRCw3tc/C4oF3/BeOsQF/E5F/BndIAXcf8Fd/a2IfcGuQxxMwxpj++JZX+X6XttdPiiqllDVoy0UppSxCA10ppSxCA10ppSxCA10ppSxCA10ppSxCA10ppSxCA10ppSxCA10ppSzi/wFTnKyM5NYikgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.subplot(6,1,1)\n",
    "c=['r','b','g','y','c','m','k','w']\n",
    "\n",
    "for i in range (len(la)):\n",
    "    plt.plot(x,la[i][1:],c[i])\n",
    "    if i==0:\n",
    "        plt.axis([1,7,0,35])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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