DSSS.ipynb

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   "source": [
    "import plotly.express as px\n",
    "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import cufflinks as cf\n",
    "cf.go_offline()\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings \n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2c2b926",
   "metadata": {},
   "source": [
    "> Function"
   ]
  },
  {
   "cell_type": "code",
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   "id": "fdee83d3",
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     "text": [
      "Enter the length of special code :4\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "Enter length of the Data :3\n",
      "-1\n",
      "1\n",
      "-1\n"
     ]
    }
   ],
   "source": [
    "x=int(input('Enter the length of special code :'))\n",
    "l1=[]\n",
    "for i in range(x):\n",
    "    y=int(input())\n",
    "    l1.append(y)\n",
    "\n",
    "def dsss(l1):\n",
    "    for i in range(0,len(l1)):\n",
    "        if l1[i]==0:\n",
    "            l1[i]=-1\n",
    "    x=int(input('Enter length of the Data :'))\n",
    "    l2=[]\n",
    "    for i in range(x):\n",
    "        y=int(input())\n",
    "        l2.append(y)\n",
    "    l3=[]\n",
    "    for i in l2:\n",
    "        for j in l1:\n",
    "            l3.append(i*j) \n",
    "    return l3\n",
    "            \n",
    "l4=dsss(l1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd5cf539",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(l4,columns=['DSSS'])\n",
    "df['arr']=df['DSSS']*0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346adb7a",
   "metadata": {},
   "source": [
    "## Used for manchester encoding to plot the enocoding into the Spread"
   ]
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   "execution_count": 20,
   "id": "090f65ef",
   "metadata": {},
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   "source": [
    "l1=[]\n",
    "for i in range(0,len(df['DSSS'])):\n",
    "    if df['DSSS'][i]==1:\n",
    "        l1.append(1)\n",
    "        l1.append(1)\n",
    "        l1.append(0)\n",
    "        l1.append(0)\n",
    "    else:\n",
    "        l1.append(0)\n",
    "        l1.append(0)\n",
    "        l1.append(1)\n",
    "        l1.append(1)\n",
    "df1=pd.DataFrame(l1,columns=['l1'])"
   ]
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   "execution_count": 21,
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   "source": [
    "df1['ar1']=df1['l1']*0"
   ]
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   "execution_count": 22,
   "id": "eaf469c4",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DSSS</th>\n",
       "      <th>arr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <th>6</th>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <th>10</th>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
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       "    DSSS  arr\n",
       "0     -1    0\n",
       "1      1    0\n",
       "2      1    0\n",
       "3     -1    0\n",
       "4      1    0\n",
       "5     -1    0\n",
       "6     -1    0\n",
       "7      1    0\n",
       "8     -1    0\n",
       "9      1    0\n",
       "10     1    0\n",
       "11    -1    0"
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     "metadata": {},
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   ],
   "source": [
    "df1[['l1','ar1']].iplot(kind='spread');"
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  {
   "cell_type": "markdown",
   "id": "29c5808b",
   "metadata": {},
   "source": [
    "## Above one Spread shows the Manchester Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5fe1d5",
   "metadata": {},
   "source": [
    "> Now user have to decode the manchester encoding to get the actual code with the help of special code.."
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "f18c0313",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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