peak_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.signal import find_peaks, butter,lfilter,cheby2,buttord\n",
    "from scipy.fft import fft, ifft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Data\n",
    "df = pd.read_csv(\"testScan2.txt\", header=None, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['Saturated Absorption','NA', 'Error', 'NaN', 'Voltage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(3):\n",
    "    df['Saturated Absorption'][i] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "test =fft(np.array(df['Saturated Absorption']))\n",
    "plt.plot(df['Saturated Absorption'])\n",
    "plt.grid()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(4, 0.15617655978261058)\n"
    }
   ],
   "source": [
    "# Apply Filter\n",
    "N = buttord(.1,.16,.11,2)\n",
    "print(N)\n",
    "Wn = 0.1\n",
    "rs = 5\n",
    "a,b= butter(N[0],N[1])\n",
    "filtered= lfilter(a,b,df['Saturated Absorption'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x138d0928>]"
     },
     "metadata": {},
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "950\n30\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x139118b0>]"
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#Find Peaks\n",
    "for i in range(30):\n",
    "    filtered[i] = 0\n",
    "peaks, properties = find_peaks(filtered, height=0.10)\n",
    "peaks_fitted, properties = find_peaks(df['Saturated Absorption'], prominence=(None,0.1),height=0.10,wlen=11,distance=8)\n",
    "plt.plot(filtered)\n",
    "plt.plot(peaks,filtered[peaks],'x')\n",
    "c = filtered[::-1]\n",
    "print(len(c)-np.argmax(c>0.04))\n",
    "print(np.argmax(filtered>0.04))\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(df['Saturated Absorption'])\n",
    "plt.plot(peaks_fitted,df['Saturated Absorption'][peaks_fitted],'x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fingers = peaks[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x13932748>]"
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
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rsIXosGCdLdqLBrpSasSOVbYQZBFmJI9/Cx10clF/bgW6iKwRkcMiUiAi9w7w/DoR2Ssiu0UkT0RWer5UpZSvOV7TQmZ8OCFB3mkbaqD3NeS/gogEAY8Ca4G5wE0iMrffaW8Bi4wxi4GvAE94ulCllO85Ud3CtCTPbzfnroQIDfTe3PmxugwoMMYUGmM6gY3Aut4nGGOajTHG9TASMCilAlqX3cHx6hamjcH+oe7SFnpf7gR6BlDc63GJ61gfInKNiBwCXsXZSj+DiNzp6pLJq6qqGkm9E94T7xfyk5f302nT9SuUd31yvJbWTjvnz0j0Wg0JkVZqWzs53Z6c2NwJ9IG2Hznjq2eM+ZsxZjZwNXD/QC9kjHncGJNrjMlNTk4eXqUKu8PwwKsHeWprEf/3fqG3y1ETWGunjSc/OA7A8uneDfROm4OWTrvXavAl7gR6CZDZ6/FkoGywk40x7wEzRCRplLWpfvaU1Pd8/O5h/Q1Hec9PXs7nrUOVLMqMIzY8xGt1pMeFA86+fOVeoG8HskUkS0SswI3Apt4niMhMcW0kKCLnAlagxtPFTnQ7i+oAWJWTTFGtfgMr7yiubeXl3aVYBH59w2Kv1rIgIxaA/LIGr9bhK4KHOsEYYxORu4EtQBDwpDEmX0TWu57fAHwOuFVEuoA24AajnVoed7C8ieToUJZOi+fdI1W0dtqIsA75T6iUxzgchh+8sBdrkIVXv3OhV0e4AGQmRBBsEYpqWr1ah69wKw2MMZuBzf2Obej18UPAQ54tTfV3oLyRuWkxTHWNKjhZ28rsSTFerkpNJFsLa9haWMN9n53r9TAHCLII6XHhnKjR31hBZ4r6DWMMhVXN5KRGMdW1EJK2StR4K61rA+CyOaleruS086Yn8NbBSlo7bd4uxes00P1EQ1sXHTYHk2LDmZrgbBkVaatEjbPudVPiI8dvM+ihfGZhOh02Bztc95gmMg10P1HR6NzmKzUmlNiIEOIiQrSFrsZdXWsXIUFCpHV8dyc6m9yp8QRbhI+O6TgMDXQ/0bMRb4xzI4G02PCeY0qNl7qWTuIirLgGtfmEyNBg5mXEsuukttA10P3EqX47q6fGhPa02pUaL0cqm8jygZuh/S3MiGV/aSMOx8QeXKeB7icqXYHevbN6WmwYJ6pbaNMZcmqcNLR1kV/ayDlT4rxdyhkWTI6lucNG4QSfYKSB7ieOV7eSFBXas7P6ZbNTaeqwsatYf81U42PrsWo67Q5W+9AIl27nun7I5J2o9XIl3qWB7kVl9W28nn/KrYWFdhfXsSDj9Jjz6cnOX3vL67UfXY2PHUV1WIMtLJgc6+1SzjAjOYqkKCsfH9dAV15y65OfcOfTO3j7cOVZzyusauZYVQsX5Zxe0Cwt1rmGRXlD25jWqFS3vKI6FmbEEhrsOyNcuokIy7MS+biwZkKvvKiB7iV/+riIgspmAL7yhzz++NGJQc/dkl8BwKfnTeo5Fm4NIj4ihLIGbaGrsVff2sme4npWeHGp3KEsn55AWUM7JXUTt5Gjge4lj79XSO7UeB69+VwA/n1TPgfKGgc89x/5p1g4ObZnZbluabHhlNdP3G9eNX5e2VuOw8Als1O8Xcqglmc5f9hsLZy449E10L2guLaVoppWrlyYxmcWpvHUV5YB8M6RM7teyhva2FNc36d13i09LoxybaGrcfCHj04wKzWaRZN9b4RLt+yUKBIirXxcOHH70TXQveDDgmoAVmY7l4y/KCeZ2ZOiee/ImWucv3ngzO6WbpNiw3RykRpzRyqaKKhs5rolkwmy+M6Eov4sFmHZtAQ+Pq4tdDWO3i+oJjUmlBnJUT3HVs1KZkdRHZVNpwPaGMNf8oqZlhjBzJSoM14nNTqMutYuOmw6Fl2Nne7NVNadk+7lSoa2fHoCJXVtFFQ2ebsUr9BAHwMOh+GjY9V87/k9fHvjrj6z1xwOw0cF1VwwM6nP9Okbl07B5jA8+3ExxhgOnWrkxse3sb+0kUtnDzzuNzXWOWu0TIcuqjG0t7SB5OhQUlyzlH3ZBTOdv/V+/4W9Xq7EO3R3hDHw5IfHeeDVgz2P77pkJjmp0YBzTfO61i5Wzuy7Q19WUiQXzEjiV28e4U8fF1HZ5JzWvygzjntWZw/4PnPTnOPS88safHI6tvJ/B8oaeWVvGTcvm+LtUtySkxrNTcsyefaTYoprW8lMiPB2SeNKW+hj4L2j1aTHhvH7Ly8FIO/E6dmc77r6yfsHOsD1uZMBesL8thVTefmuC4gJG3jPxpzUaKxBFvaV6PZbamxsP1GLMfD1i2d4uxS33XLeVAD2TsD/F9pCHwMHyxtZlZPMxTnJTEuM4OEth1g1K5mMuHCeyytm2bQEUmLO/PX1qkXpJEWFcs6UOHYU1bF0WsJZ38cabGFOesyE/MZV4+Nvu0pJirKS0W/IrC+b4mqVF9dNvOWltYXuYU3tXVQ1dTAjOQoR4f6r51Pf2sUFD/6T+zblU1TTyhULzhyxAs7ZbhfMTCLCGsyF2ck967acjXOVuYYJv8qc8ryCyiZ2F9dz50XTfWq53KFEh4WQGGnlmGvi3kSige5hx12rvXWvtXJhdjL3Xz0fcI7lBTh/gO6WkZqXHkNTh41SnWCkPOyNA855EVcvzvByJcO3KDOOXcX13i5j3LkV6CKyRkQOi0iBiNw7wPNfEJG9rj8ficgiz5fqHwqrXIHe6yblF8+byp/vWA7ArNRosgcYgjhS3cMZCyZga0SNrZO1LSRFWQfsHvR1S6bGU1DZTNkEa+gMGegiEgQ8CqwF5gI3icjcfqcdB1YZYxYC9wOPe7pQf1FY3YJFYEpi37vr589MYvdPVvPc11Z49NdXDXQ1FowxvLKnvGcROH+zem4qIvDU1iJvlzKu3LkpugwoMMYUAojIRmAdcKD7BGPMR73O3wZM9mSR/qSwqpnJ8REDrkgXF+H5jXXjIqwkRVk10JVH7S9tpKnD5lc3Q3vLSY0md2o8Hx2r9nYp48qdLpcMoLjX4xLXscHcDrw20BMicqeI5IlIXlXVmdPcA8Hx6pae/vPxMjMliqMTdGacGhvdSzo/cM18L1cycitmJLG/tIGGti5vlzJu3An0gfoHBhxSISKX4Az0Hw70vDHmcWNMrjEmNzk5eaBT/JoxhuPVLeM+ySc7JZqjFc102R3j+r4qMDkchhd3lrBkajxJUaHeLmfEVkxPxGFg875yb5cybtwJ9BIgs9fjyUBZ/5NEZCHwBLDOGDMhV8epaOygtdPO9GTP3fR0x4XZSTR12Pjo2IT8sisPO1zRxImaVm7IzRz6ZB927tQ4kqKs/OjFfRTXTowx6e4E+nYgW0SyRMQK3Ahs6n2CiEwBXgS+aIw54vky/UNhlbMfe/o4t9Avykkm0hrUszKjUqOxr9Q5UW1p1tkntvm60OAgfnqVs8voF1sOe7ma8TFkoBtjbMDdwBbgIPCcMSZfRNaLyHrXaT8BEoHfishuEckbs4p92IFy5wYVnhyW6I6wkCAWTo5j50ndMFqN3vHqFoItQma8f94Q7e0zC9NYNDmW/WUTYza1W+PQjTGbjTE5xpgZxpifuY5tMMZscH18hzEm3hiz2PUndyyL9lXbCmuYlhjhlXG7l89NJb+skV0a6moUmtq7eHVvOXPTYwgOCox5h5fPSaWwqoWm9sC/ORoY/2I+wO4wfHy81mt7Lt6wNJNIaxB3/3mXro+uRuwv24s5WdvKD9fM9nYpHrMo07nL0s6TgT9zVAPdAzpsdn7693ya2m2cN907gR4VGsztK7MorW9j0+4z7lkrNSSb3cHT24pYMjW+Z13xQLB0WgKhwRbePnTmFo+BRgPdA27/Qx5PbS0iOyWKS724ie53VueQmRDOqxNomJbynFf2llNU08r6Vf6zVK47wq1BnD8jkbcPV2JMYC9ip4E+Si/tKuWDgmrOm57AG99dRfQga5ePBxFh7fw0PiyonlCTKdTo2R2Gf3t5PynRoVw+x3uNkrFy6ewUimpaOVge2BPwNNBHwe4wPPZeIZHWIB67xTfuA6+ZP4kuu+GtgzqEUbnvaGUTTe02VvbbGjFQXDzL+UPqx3/b5+VKxpYG+ii8tKuUg+WN3HfVPGIjvNcy723x5DjSYsN4bf+pYX3evpIG2jr1ZupEtbfYOazv7ktnermSsZGZEMGK6YkcPtWELYBnVGugj9CbByr4l+f3MD0pkuuW+M5aZBaL8Ol5k3j3SBXNHTa3PqeopoXP/uYDvvyHT8a4OuWr9pbWEx0azLTEwN2b9qblU2jrsrO/rNHbpYwZDfQROFLRxB1POedOPXzdQp/7FXXt/El02hxu39Xv3sJuW2HtWJalfNjekgbmZ8RisfjW97InnZflHO3yyJuBO5ldA30Evr1xNyKw8c7zyB1i309vyJ2WQHxECG8fqsTuxtZ0+b1aLA2tejN1omls7+JgeWPPeO1AlRITxtdWzeDtw1VUuTZiDzQa6MNgjOH5vGIOljfy06vmeW3M+VCCLMLK7GRe3FXKV58aehWG/F7TovPLJ8YUaXXaO4er6LIbVs8NvNEt/a2ekwrA+0cDc/luDXQ3/HLLYdY/vYMH/3GI77+wl6QoK9cv8e2V6L59mfPm1j8PVfLz1w4Oep4xhgNljT1D1fJLA7d/UQ3s3cNVxEWEsDgz3tuljLl56TEkRlp574gG+oRUUtfKb94u4B/5p3js3UIyE8J57msrCLeeuSORL5mZEs1b/7IKgMfeLRx0E+mKxg5qWjpZOTOJ9NiwCbOIkXIyxvDe0SpWzkwiKID7z7tZLMKqWclsya+gpjnwul38MtAPlDVy15920mkb2+FHxhju+GMeIvDzaxew4ZYlvPndVeO+3vlIzUiO4v518wD407aB91bs7m6ZlxHLvIxY9pdqoE8kW/JPUdXUwaqcwNtwZjB3rJxOW5edz/z3B7R3BdZQXb8M9Ps25fPqvvIxXy52S34Fh041cUNuJjctm8Ka+ZMG3CvUl31xxTQ+PS+VJ94/Tl1L5xnP55c1IgJz0mKYkxbD8eqWgPsmV4N7ZttJJseH89lF6d4uZdzMTY/hh2tmc6qxne8+tzuglgPwy0Cf7FqnuaimZUzf58WdJaTFhvHA1f67ryLAty/LodPu4GtP7zjjufyyBqYlRhIVGkxOahQOA4dPBfb0aOV0qqGdDwqquXpxBmEh/tVQGa3bzp9KTFgwm/edCqh9BPwy0NNdO5Gfahi7PrAuu4OPjtVw8axkv18Xem56DEumxrPzZB2nGtr7/CDcX9rIvPQYwLkHo0VObxCsAtu3Nu4C4MpFaV6uZPxFWIN57weXEB8Rwm/fPubtcjzGL5MqJjwYgLrWM7sQPGXT7jKaO2wB07f44LULsDkM5/38LVb94h0+LKjmWFUzpfVtLHWNpU+MCmVaUiQHAngmnXKqbelkR1Edl89JYfakGG+X4xVxEVbWLc7gw2PVAdPN6JeB3m2sAt0Yw8NbDgFwfoCsC52dGs2152b0PP7CEx/znb/sBpy7HXWbkxbD3pKGgOpXVGf6x/5T2B2G76zO8XYpXnXJ7BTauxy8GyDDGP0y0LsnP9YOcJPPEzbtKaOisYM7VmYR48XlcD3t/12/iL33fYrHv7gEcE73npceQ0bc6b0jL5yZxKnGdg5XaD96oDLG8LddJUxPimRu2sRsnXdbMT2R1JhQfrnlcEA0Yvw00J1f+LFqob9+wLn0bKC1XkSEmLAQPjVvUk9r/fp+C4t1LzP6/pHqca9PjY/3j1az/UQdn1+a6XPrEI03a7CF71yew9HKZvaU+P+QXb8M9O4fpHUtnl935OXdpWzeV85XL8wiMjTY46/vKx763EL+fvdKbl0xrc/xSbFhZMSFs7sk8PdfnKie3lZEbHgIX75gmrdL8QlXLEwjNNjCiztLvF3KqLkV6CKyRkQOi0iBiNw7wPOzRWSriHSIyPc8X2ZfDlefi6e7XIprW/nuc3uICQsJuG24+gsJsrBg8sCr681Nj9GhiwHq/94r5I0DFdy6YqrfzakYKzFhIayem8rf95SN+WTFsTZkoItIEPAosBaYC9wkInP7nVYLfAv4pccrHEB3H3pbl91jd6edN0IPEyTCP+65kMSoUF5tskAAABKPSURBVI+8rj+alRrN8eoWOmyBcedfOZXUtfKzzc51fW5ePsXL1fiWa8/NoK61i79sP+ntUkbFnRb6MqDAGFNojOkENgLrep9gjKk0xmwHxmXtVUevmxfVo1yPob3Lzp8/PsmTH57g73vKWL9qOmmx4UN/YgDLTo3C7jAcrx7biVtqfL2617l5+IvfOH/Cf4/3tyonhWVZCfzqzaM9PQD+yJ1AzwCKez0ucR0bNhG5U0TyRCSvqmrkw4R6340ezbrGpfVt3PDYVn78t33c/8oBFmXGcfel2SN+vUDRPS55v668GDD2lzbwy9cPs2hyLOdOCfxVFYcryCJcd+5kals62by/3NvljJg7gT7QbfAR/QgzxjxujMk1xuQmJ498wk7vN69uHl4/eofNTnlDG//+8n4uePCfPXe2l2UlsPGr52EN9sv7xB6VnRJFUlQo7+iM0YCQd6KWzz+2lS674Ssrs7xdjs9ame2cc/LIm0f9dgMMd4ZxlAC9F/+eDJSNTTnucYyghd7eZefFnaV9dv22BltYmBHLQ9ctZIafrKA4HiwW4eJZybxxoAJjzIQf2ubPTjW0c/sf82jttPPXr69gyVTf22HLV6THhfOf1yzgx3/bx11/3slzX1vh7ZKGzZ1A3w5ki0gWUArcCNw8plUNoXcX11CBbozhm8/u4pW9fX+N+tpF0/nep2cR4ufrtIyVeekxvLCjhOrmTpKjJ+4NYn/3X28cpsNm55VvrmR+Rqy3y/F5Ny+fwsnaVh577xh1LZ3ER1q9XdKwDBnoxhibiNwNbAGCgCeNMfkist71/AYRmQTkATGAQ0TuAeYaY8akE9ZhDKHBFiKsQVQ0tZ/13I3bi/uE+cc/vozUmLCxKCugZCU5d38vrGrWQPdTxhg+LKjh0tkpGubD8Kl5qWx49xgfFFT73bLCbs2cMcZsBjb3O7ah18encHbFjAtjwCLCjOQojp5liroxhqe3FjErNZp/3HOhdh0MwwJXAOwqrme5j+6dqs7uxZ2llNa3sf7iwJ5T4WmLJscRGx7CO4er/C7Q/bK/weEwWFybMhwsbxp0mNGxqhYOlDdyy3lTNMyHKTEqlOnJkWw/XuvtUtQI2OwO7n/1AAsyYvnCMh1zPhxBFuHS2SlsyT/FqYaz9wD4Gv8MdFcLfW56DM0dNkrqBt4vc0eRM4wCZcXE8bZ0agJvHaqkucPm7VLUMDgchp+/doj61i7uvGj6gLOB1dl94+IZdNod/OqNI94uZVj8NNANIpCT6hyZUlB1ZrfLJ8dr+eFf9xEXEcJ0V3+wGp7zZjhHRNz8f9u8XIkajr/vLeN3HxwnMdLKZXNSvF2OX8pOjebmZVN4YWcJxbWt3i7HbX4Z6N1D6bKSnIFeWNV3RuN217jb+IgQ/vWKOdrdMkJXLkxn5cwk9pY0UDnEzWflG2x2B4+8eZSZKVFs+/FlRFgDd4G5sfbVi6Zjdxhue/ITv1la1z8DHbAIxEeEkBIdyrbCGto67fxj/ymu+s0HXL9hKwAPX7eI63Mzz/5ialAhQRbuXTsbgPVP7/Cbb+qJ7PkdJRRWt/C9T+mQ3NHKiAvnC8unUFjdwjuH/WMDDL/8F3cYg0UEEWFVTjJvHqzkgof+yfpndrC3pIHzZySy6e4LWN1rJx41MvMzYrluyWR2nqzn+Tz/X140kL22r5wfvbiPkCDR730Pue+qeSRFWXlhh3987/vl72MOQ083yj2rc6ho6iDvRC2TYsL44dpZXL04Q7tZPOjhzy2ksKqZf3t5Pwh8eu4kYiMCZyenQNDU3sUDrzpXUvzPaxYQpDdCPSIkyMLa+Wk8v6OYlg6bz++R4NvVDcIY57BFcP5a9NRXlnm3oABnsQgPfW4hq3/1Hj94YS9PZZzgd7ct1QlaPuJgeSNrH3kfgKdvX8aF2YGxsbmv+OyidJ7eVsTG7cXc7uNr4fhnl4vDOWxRjZ/s1GieuX05F+Uks7+0kYsefpvCqmZvlzXhtXTYesI8OyWKlTpE1+Nyp8aTEGnl55sP+vwGGP4Z6L1a6Gr8rMxO4o9fXsoTt+bSYXNw6f97l7qWTh2n7iV7S+qZf98WwBk6z31thXY1jgGLRbjrkpnYHIZX93l1XcIh+Wmgo9+4XiIiXD43lUTXokXn3P8Gy3/2pm5ZN85O1rTy/ef3ArDhlnN54evn+91CUv7ki+dNJSc1iv9955hPj/byy0A3xmDxy8oDx0c/upT1q2aQFhtGS6edT//6PXaerAPw6W94f9dld/DVp/K46Bdvc7iiiV99fjFr5qd5u6yAZw22cPvKLI5UNPNBQbW3yxmUeOs/X25ursnLyxvR596zcRe7iut59/uXeLgqNRKv7i3nrj/v7HPsze+uYmaKrjHvKb1vfHZ75vblPZsyqLHXYbNzyS/eoayhnbsumcE3L80mLGT8N9oWkR3GmNyBnvPLdq7DDLyNkvKOzyxMO2MzgM/+zwccOqVb2HnKc3mnd4HMTAhn909Wa5iPs9DgoJ4tKh99+xjz/30L5Q0DryPlLX4Z6M6ZohrpvmRZVgIbblnCNy+dycOfW0hoiIXb/5BHl923RwX4g7L6Nv62q5TL56Rw9GdreftfLiYuQvvLveH63Mlc6PpBanMY/vedY16uqC+/DPTuxbmUb1kzfxL/8qlZfH5pJr+8bhGl9W2sfeR9apo7sDsMdS3D2/9VQXFtK19/ZgdtnXbuXTubkCALwTql32tCgiw8fftyTjz4GW5cmskz24ooqfOdxbv88jvDuKb+K9912ZwUvnnpTAoqm1nywJvM+PFmzrn/DR27PgxFNS1c8d/vs6ekgZ9ds4CZKdHeLkn18o2LZ+Iwzk2lfYVfBrpOLPJ9IsJ3V+fwwNXz+xz/4V/3+vzkDG/qtDnosNnZ8O4xVv3iHZrabdxzeTbXLRm3DcGUm6YkRnDHyiye31HCv7203ydGd/nl1H/tcvEPIsIt503lC8unUNHYwS9fP8wLO0q48OF/cudFM9i8r5xf37CYzIQIb5fqNd2Tsmx2B7f9fjt7iuv7PP+DNbP4xsUzvVGacsP318zicEUTT28rIr+sgaXTEpibHkNTu43lWQnMTIka1zkzfhro2kL3JyLCpNgw/vOaBbR22ti87xT3v3IAgGt++yGfWZDGvtIGbjt/Gm8fqqSx3caaeZM4UN7IvWtne2Vo2Hg4UtHENY9+SEun/Yznbl0xlZ9eNU8n0Pm40OAgnvzSUh567RBPfHCcnSf7/kA+f0Yi9189nxnJ4zOE1y/Hod/+h+1UNLXzyjcv9HBVaqzZ7A6+9/we9pc1UlA5dH/6BTMT+f2XlmENPt07eKK6hZqWDpZMTfBITXaH4c2DFQDctymfz+dmEh0WzAOvHmRVTjKP3Li4Z1SJw2HotDtG9ENm67Eajle3cP6MRO5+dif7S/sO67x6cTr/ee0CwoKDdNs4P/TWwQreP1rN+TMSqW3p5N4X9/U8Nys1mhe+voLosNGvUnq2cehuBbqIrAEeAYKAJ4wxD/Z7XlzPXwG0Al8yxuw844V6GU2gf/n3n1DT0smmu1eO6POVbzhZ08p/vHKAmSlR3LQskxd2lFBa3wYGTtS09GntvP6di/iooJoPj9XwxgFn+N6Qm8kP1swiMSp0WO9rdxgOlDXyz0OV/OpN9/aMvCgnmfnpMfzWNUzt1W+tZF56rFuf+8HRam77/SfYB9jM/MFrF3D+jCQOlDeyZv4k9y9C+bzmDhtfevIT8orqeo597tzJLMuK59pzJ494A5JRBbqIBAFHgNVACbAduMkYc6DXOVcA38QZ6MuBR4wxy8/2uqMJ9Nue/IT6ti5evuuCEX2+8g+tnTa+85fdbMmv6DlmEWeXW7cZyZHcdclMLp2dQlyEFWMM+0obAOc44blpMYSFBOFwGO58Oo8DZY2UnWUn90duXMzLu8tYnBnHtedmsKe4gf94JZ+Kxo4zzt1wy7ksz0qkoqmd2ZNieo43tXfx7CcnKahspqGtq0/9SVGhfO9TOUyOj9CJQRNEl93B6v96lxM1p4c3/uqGRVxzzshudJ8t0N3pQ18GFBhjCl0vthFYBxzodc464Cnj/OmwTUTiRCTNGFM+oorP4uPCGt49UsXizDhPv7TyMRHWYB77Yi73bNzFS7udq9wV/OwKLBahtqWT375dwBMfHOe7z+0BnOFuOHOP2Yy4cOIjQ/p0cXzrsmwumJFIZGgw8zP6trTXLc7o+XhyfAQX5STx6NvHuHJhGgmRVrafqOXbG3ez/pm+v4T+7rZc3jtSxR+3FvU5fsHMRH64ZjYLMmK1T3wCCgmy8M73L6G9y84fPjrBg68dovwsjYrRcKeFfh2wxhhzh+vxF4Hlxpi7e53zCvCgMeYD1+O3gB8aY/L6vdadwJ0AU6ZMWVJU1Pcb3x0fFVTzi9cPc+XCdJ9fbF55TofNTqfNcUYf5InqFh549QBvHqw86+eHBAl3X5LNZxelYQ22MDl+dCNrbv6/bXx0rGbA55KirPz82oV02R3EhIWwYkai7iCketgdZlTfD6NtoQ/0zv1/CrhzDsaYx4HHwdnl4sZ7n+H8mUn8TRfxn3BCg4MIDT7zRuS0pEieuG0pXXYH7V12QoIshAZbMMa5jnV5Qxu/ffsYP1gzyyM3pLr96Q5nj2L3ukJNHTZ+9cYR9pTU8z83nTPqHxgqcI3lD3d3Ar0EyOz1eDLQf5V3d85RasyEBFn63GTq7tlIiw3n/n6Tmzyhu+skyPU+seEh3HfVPI+/j1LD4c5t1u1AtohkiYgVuBHY1O+cTcCt4nQe0DAW/edKKaUGN2QL3RhjE5G7gS04hy0+aYzJF5H1ruc3AJtxjnApwDls8ctjV7JSSqmBuDVT1BizGWdo9z62odfHBrjLs6UppZQaDr9cnEsppdSZNNCVUipAaKArpVSA0EBXSqkAoYGulFIBwmvL54pIFTD8uf9OSUC1B8vxB3rNE4Ne88QwmmueaoxJHugJrwX6aIhI3mBrGQQqveaJQa95Yhira9YuF6WUChAa6EopFSD8NdAf93YBXqDXPDHoNU8MY3LNftmHrpRS6kz+2kJXSinVjwa6UkoFCL8LdBFZIyKHRaRARO71dj2eICKZIvK2iBwUkXwR+bbreIKIvCEiR11/x/f6nB+5vgaHReTT3qt+dEQkSER2ubYxDPhrdu23+4KIHHL9e6+YANf8Hdf39X4ReVZEwgLtmkXkSRGpFJH9vY4N+xpFZImI7HM9998y3E1ojTF+8wfneuzHgOmAFdgDzPV2XR64rjTgXNfH0cARYC7wMHCv6/i9wEOuj+e6rj0UyHJ9TYK8fR0jvPbvAn8GXnE9DuhrBv4I3OH62ArEBfI1AxnAcSDc9fg54EuBds3ARcC5wP5ex4Z9jcAnwAqcOxu+BqwdTh3+1kJfBhQYYwqNMZ3ARmCdl2saNWNMuTFmp+vjJuAgzv8I63AGAK6/r3Z9vA7YaIzpMMYcx7mxyLLxrXr0RGQy8BngiV6HA/aaRSQG53/83wEYYzqNMfUE8DW7BAPhIhIMRODcnjKgrtkY8x5Q2+/wsK5RRNKAGGPMVuNM96d6fY5b/C3QM4DiXo9LXMcChohMA84BPgZSjWsrP9ffKa7TAuXr8GvgB4Cj17FAvubpQBXwe1c30xMiEkkAX7MxphT4JXASKMe5PeXrBPA19zLca8xwfdz/uNv8LdAH6k8KmHGXIhIF/BW4xxjTeLZTBzjmV18HEbkSqDTG7HD3UwY45lfXjLOlei7wv8aYc4AWnL+KD8bvr9nVb7wOZ9dCOhApIrec7VMGOOZX1+yGwa5x1Nfub4FeAmT2ejwZ569vfk9EQnCG+Z+MMS+6Dle4fg3D9Xel63ggfB0uAK4SkRM4u84uFZFnCOxrLgFKjDEfux6/gDPgA/maLweOG2OqjDFdwIvA+QT2NXcb7jWWuD7uf9xt/hbo24FsEckSEStwI7DJyzWNmutO9u+Ag8aY/+r11CbgNtfHtwEv9zp+o4iEikgWkI3zZorfMMb8yBgz2RgzDee/4z+NMbcQ2Nd8CigWkVmuQ5cBBwjga8bZ1XKeiES4vs8vw3mPKJCvuduwrtHVLdMkIue5vla39voc93j77vAI7iZfgXMUyDHgX71dj4euaSXOX632Artdf64AEoG3gKOuvxN6fc6/ur4GhxnmnXBf+wNczOlRLgF9zcBiIM/1b/0SED8BrvmnwCFgP/A0ztEdAXXNwLM47xF04Wxp3z6SawRyXV+nY8BvcM3md/ePTv1XSqkA4W9dLkoppQahga6UUgFCA10ppQKEBrpSSgUIDXSllAoQGuhKKRUgNNCVUipA/P+eLtZ1N1mvugAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(df['Saturated Absorption'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "attempts = []\n",
    "for i in range(len(peaks)%5):\n",
    "   attempts.append(peaks[i:(5+i)])\n",
    "attempts = np.array(attempts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[101 143 172 215 286]\n [143 172 215 286 808]\n [172 215 286 808 821]]\n"
    }
   ],
   "source": [
    "print(attempts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "thumbs = attempts[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = buttord(.1,.12,.11,2)\n",
    "\n",
    "a,b= butter(N[0],N[1])\n",
    "\n",
    "x = (lfilter(a,b,df['Error']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "peaks_error, properties_error = find_peaks(x, prominence=(None,0.3),height=0.10,wlen=100,distance=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x137e98b0>]"
     },
     "metadata": {},
     "execution_count": 17
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(x)\n",
    "plt.plot(peaks,x[peaks],'x')\n",
    "plt.plot(peaks_error,x[peaks_error],'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "101   -5.242\n143   -4.928\n172   -4.710\n215   -4.388\n286   -3.855\n808    0.060\n821    0.157\n849    0.367\nName: Voltage, dtype: float64\n"
    }
   ],
   "source": [
    "print(df['Voltage'][peaks])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[  3  66  94 111 140 149 167 185 209 245 263 280 682 742 775 787 802 815\n 844]\n[101 143 172 215 286 808 821 849]\n"
    }
   ],
   "source": [
    "# Place Error Signal Voltage 10MHz to the left input to Jakes\n",
    "print(peaks_fitted)\n",
    "print(peaks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shift signals to the left (constant distance)\n",
    "def import_data(data):\n",
    "    df = pd.read_csv(\"testScan2.txt\", header=None, sep='\\t')\n",
    "    df.columns = ['Saturated Absorption','NA', 'Error', 'NaN', 'Voltage']\n",
    "    for i in range(3):\n",
    "        \tdf['Saturated Absorption'][i] = 0\n",
    "    return df\n",
    "\n",
    "def butterworth_filter(signal): \n",
    "    N = buttord(.1,.16,.11,2)\n",
    "    a,b= butter(N[0],N[1])\n",
    "    filtered= lfilter(a,b,signal)\n",
    "    for i in range(30):\n",
    "        filtered[i] = 0\n",
    "    return filtered\n",
    "\n",
    "def generate_all_attempts(peaks):\n",
    "    attempts = []\n",
    "    for i in range(len(peaks)%5):\n",
    "        attempts.append(peaks[i:(5+i)])\n",
    "    attempts = np.array(attempts)\n",
    "    return attempts\n",
    "\n",
    "def find_saturated_abs_peaks(signal):\n",
    "    peaks, properties = find_peaks(signal, height=0.10)\n",
    "    return peaks, properties\n",
    "def find_error_peaks(signal):\n",
    "    peaks_error, properties_error = find_peaks(signal, prominence=(None,0.3),height=0.10,wlen=100,distance=100) \n",
    "    return peaks_error, properties_error\n",
    "\n",
    "def error_filter(signal): \n",
    "    N = buttord(.1,.12,.11,2)\n",
    "    a,b= butter(N[0],N[1])\n",
    "    filtered = lfilter(a,b,signal)\n",
    "    return filtered\n",
    "\n",
    "def shift(peaks):\n",
    "    for i in range(len(peaks)):\n",
    "        peaks[i] = peaks[i] - 6\n",
    "    return peaks\n",
    "def get_thumb(peaks):\n",
    "    return peaks[-1]"
   ]
  }
 ],
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