{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Peaks mit Python finden\n",
    "siehe [https://plot.ly/python/peak-finding/](https://plot.ly/python/peak-finding/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(1, 10, 1000) \n",
    "yp = np.sin(x) + np.sin((x-0.5)/2) - np.cos((x-1.0)*6) + 2\n",
    "noise = np.random.normal(0, 0.1, 1000)\n",
    "y = yp #+ noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f73d2f64908>]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kII68yj26Z4IJ9NiNouydRzw2zASVFe5kNo+NZK4ui6uw51Jv6bucroA7UbMG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73d2ff8d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variante 1: SciPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.signal import find_peaks_cwt\n",
    "widths = np.linspace(1, 10, 10)\n",
    "widths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4,  59, 175, 291, 407, 524, 640, 756, 872, 990, 994])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peaks = find_peaks_cwt(y, widths)\n",
    "peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f73d2ffe898>]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OvGZVSbSWdNbbZEdSQ74IHGYDPHblvVyMeh0aa81F9UpZj5NTIfS3OgoWMLms\nNWhymHBuVnvhFsvFN0otdbttMBl0mlxMJoNxRY4SkW63DaF4qujBwaPzUeh1TPFaGmtN0OuYZndq\nI/4oxufjSP/5X+Rz2jaTAXs76/A9+3bggQc0OW+lo61NokjEiEJOv2I1qLPV4NGPvAbJTFbSZBI1\n6XJbsRBLYTGRgkPi3MjBuQh6G+1F93JpcZlVmTS/mkyW48z0It51Tde6z9vZ7ChJxO2VODRAr2PY\n0WTH+TltUiWXtyuvC8j3CQnEZLViXc1oIIb2OguMemUxnEGvQ7NDO0vg74cCAC72/l/X68aXnx5A\nKJaCU2G6qZqp6IhbzL+JcxFLCWOs5KINXCg7ltOgfnAuih0ye3CvhVC5qX6u0huIIpHKon+Dlrg7\nm+wYmIsgrfHk8GFfFE0OkyR75zaPHcM+dYU7lkxjIZYqyuIq7rkUW/qupCvgarSsAXhu0I9Wpxnd\nq1I51/V6wDnwwkhAk/NWOhUu3DEwBnTUa+/hrhTEjVipDeoXokn4I0vY0Vh818LW3MBitfsrj/ik\npSZ2NjuQTGdVyd2uhzcQlbxv0uOxYTIYV9VZMqmgK+BqOhVc4FfDOYc3EFWc3xZpdZk1EW7OOY54\n53Ftr/uiu8nL253Q6xhOTJSu2raSqHDhjqLVKW2Q6mZh+S2wFAZz0eB2lSLupXQW8yr35xCjwo3K\n8cXpQlqnS7z+qOTWANsabOBcvWIX4MIGcDERt9moR7PDXNRFLhhLIZxIF7353+qyYCZU3LzUtZgK\nJeCPJLF3jVYTZqMeOxrtJW2TUElUtHAP+aJFeZOrkVqzEfW2GozNS7sFHpxTV7gB9Ytwhv1RuKzG\nDXOx4v/DkMqpieWE4ikEoknJwi06ddRMl+SrJots49DlthZVhCO6l4pNlbS6LEhnueoe/BO5StrL\nCuwF7G5z4uRkaEtO4KlY4c5mOQbnIqqkAKqNznqr5AhvYDYCi1GvSksAseRe7dteqRGupUaPFqcZ\nXpWnu6xeCyC9E574vGEV1zQVjMOgY2isLa4auMst3zq6nGI93CLiBWgyqG6K65WJEIx6VrCfzO5W\nB/yRJGYXS1O0VUlUrHBPLMQRT2Wwo6n4SLLa6HJLH8I66Iugt1H+uLK1EJtcqW0J9Pqj6JEoDt1u\nG0ZU7DV90Vokpm1E7CYDmhwmDPvUW9NkMJ6vki2GLrcNvvCS4nJzMThQY3MSgOyK3414dTKIS5od\nBVOlYmfChsAPAAAgAElEQVRHcTrVVqJihXtgTmwCtPWEu9stfUNscDas2l1Jva0GJoMOUyqmShKp\nDKZCCVkRrpYR94hfaH8rR6y2eewY9qubKlHjDqm7yGZTo4EYmhwmWGqK20PSIsWWzXKcmAjhsva1\nWyQAy1Jrc9q9XyqVihXu87Ni7nbrpUp2NNnB+ca53shSGlOhhCr5bUCwQKrdX9krs0l/j0fwsYc0\nGkA74hc2vOW0v+1psGHYF1UtlyqM4iteuLvyDiRlwjU2H0WXClZbu8kAp8Woqpd7MhhHOJHG7tbC\nwu2y1sBjr8nv82wlKla4B2bDaHKYLhortRUQI+iN3pADuSpDtYQbEKbNq5kqEaNnOakSAJqlS8bm\n5fuWt3mEKsUFFS4mqUwWs4sJtKsQcYvWUaXOEm8gVrQVUKRV5QlKoltqo1Tptga7ppvZlUrlCvdc\npGCv5M1Oj8cGvY7h/Abl36emhB4aGxW2yEHtIpwRv7zqV9FFpFW6ZGIhLrsuQE1nyUwogSxXZzCI\nw2yE21ajyFkSS6bhCy+pJtxtKnu5pTZO295ox6AvsuWcJRUp3KlMFudnw1tWuGsMOnS7rRt2pTs1\ntQinxajqPM5WlwVz4QRSKlUvev1ReOymdfteL6ej3godU9fFIZJIZeALL6G9QKOrQohpnhEV1jSR\nH6CgjmB2uqU7kJZzobmUOnbbYualrsXgXAT1thrU29a3kPY22BGMpVSvPah0KlK4z82EsZTO5iev\nb0V2NNZiYINUyempEPpbHEX3KFlOq9OMLEe+H3mxjASi6JHRa8Zk0KPVZdEk4lY6vKC9ThhcoMZk\ndXENooOnWLrdtiKFW62I24LFRBrhhDp7E0O+CLZL6HbZm7tDG1LR9VMNVKRwv5Ib0LpnnR3lzU5f\nkx2jgcJN/NOZLM7OhHFpq3ppEkB9h8CIX3p5uUiPx6aKSK5mIl9qLk+sjHod2uvUGVwg5oGL6Qy4\nnC63FVOhOJbS8kryxQ1NNTYngeXTm9R53wiN0zZem7i/s9U2KCtSuE+Mh+CyGkvezrWS2NEkNPEv\n9IYc8kWxlM7i0gKDCZSSH1iswm1vZEnIo8odQtHttqmSlljNxIIgvEp633S71bEpTgZj8NhNioc6\nr6bLbQXnwPi8vL+XNxCD02JUrbOe0rF7azEfTWIhlpLUX77VaUGNQafJhb6SqUjhfnlsAZe3u1RN\nAVQbYnHBq5NrFxeIk2IuXccupYQWFasnRaHbJlO4O+utCCfSCMXVtQROLMRh1CurWOzJ+cuL3QSb\nCiZU2ZgUuTA4WJ5wef1RVac65QcqqPC+EYOVXgluKZ2OoaPOgvEiKkirkcoR7oceAg4fxkwogYG5\nCK7rdQtjiR56qNwrKwvdbiscZgNOTKw9+fyl0QXYTYaipt6shS3nyZ1W4ZZXrodbRIyI1f4wTiwI\nwwuUVCx2u62IJjPwRYorr54MxlWxAoqIaSi5aRyvPyr7groejbUmGPXqDFQQ7X1SctyAcKGXWmm8\nWagc4d6/Hzh4EGe++xMAwG1zZ4CDB4XjWxDGGPZ0uHB8fO2I+6h3AVd21WkyRFkth4DYzlVujlvM\nQYupDbWYWIgpduDk+1/7la8pm+WqFd+I1FmNqDUZZE1NylezqjgSUKdjaHaqYwkcnIvAbNRJri7t\nIOEuIwcOAIcOYd8DH8Ffv/BddH3s/cChQ/lxRVuRy9udOD8bRjy5cuMpFEvh3GwYV3fXaXLeVqdZ\nlbL3kUAUzQ6z7JJqcS6l3LztRkwsxNGu0IbX4yneX+6PLiGZzqpS7i7CGEOXR978yXy/FpU7b7Y6\n1bngD85FsM1jl9x/J59a06jathKpHOEGsPia1+Lf9/wRPvibR8A++tEtLdoAsL+7Hpksx4ve+RXH\nxe+v6qrX5LxqRdxCHlW+UDqtRtSaDapG3Bc83MpEs81lgUHHiqrozLdzVVG4ASHPLSfilFvNKpU2\nlYq3hnwRWdXAHfXyho9sBipKuA9/5fu456WfY/a+TwFf+YqQ497CXNPjRo1Bh9+e9604/uuzs7Cb\nDLiySxufe6vLglA8pbjrnIg3EEOPR1kOvr3OinEVS6jzHm6F05QMeh06661FRdziGtRMlQDCuLvx\n+ZjkkW9yq1ml0uqyYGYxUdTouXgyg8lgXNbejXiHRsJdBiK/fAqv++t78fB/+yyavvSQkCY5eHBL\ni7elRo+ru+vx7MAF4c5mOX51Zg437GzQbDJQvr1rEYODQ7lqNjnFN8tR2ymg1MO9nG5PcTZFtQYo\nrKbbbUM6yyVHu3KrWaXSVmdBJssxW8RAhWF/BJzL678jbmaTcC+DMdbBGDvMGDvNGDvFGLtPi4XY\nTxzD+Fe+ibf/xXuEA7mcN44c0eJ0VcONOxtwfjaSt0i9NLYAX3gJb9jVqNk5W/PWLuW3vSMSJ6kX\noqPeiokF9eZfimmXYtoDiFWKStc0GYyj1myAQ2XBFJtNjUrsEjjil1fNKhU1vNxKJjqJU6PGVd7M\nrmSkRNxpAH/OOe8HcC2AjzPG+lVfyQMPYM9778K25bdIBw4ADzyg+qmqiTuvaINex/CfL4wBAL79\nnBe1JgNu7W/W7JwtzuIHKuTzqAotZx11FsRTGQRU6kFRjIdbpMdjRTyVUTxxRa0+3KuRawkckTEs\nWQ5tKhRvDc1FoGPy0zhbzctt2OgJnPNpANO5r8OMsTMA2gCc1nhtBICGWhPeckUbvvP8KNz2Gjx+\nYhofuaEXNtOGfzrFNDnM0LHiPoDiwIIOhdWv7XlniVBpWCzFeLhFljebanbKvwBMBuOqNgQTaaw1\nwWzUYVRCGkesZlXbUQIsv1MrQrh9UXTWW2WnAVtdlg17+2wmZOW4GWPdAPYCeEGLxRBr8+BtO9Hk\nNOHzT55Df4sDn7h5u6bnM+p1aKwtzhLoDUTR5pI3sGA5ouCrtUE5Pq/cwy1yIbJVlufWKuLW6Zgw\np1RCxCm2S92mcNN4Paw1BtRZjUX15R6ciygqKmvJWRG3SntXyWEbY8wO4IcA/oxzvrjG4x8G8GEA\n6OzsVG2BBNDoMOPxe1+H4xNBXNNTr1qfi/VodZmLjriVpkmAC7lotW5/JxbiRe8LtLosqNHrFDlL\nFhMphJfSqjWXWk2X2yap7P1crsf7zmZtWiYXYyXNZDlG/FHcuLNBwXnNiCUzWIynVeu/UslIirgZ\nY0YIov0I5/xHaz2Hc/41zvk+zvm+hgb5LzyxPk6rETf0NZREtAGgxWVR3CGQc66oK+BybCYD6m01\nqgygTaQy8EeUe7hF9DqGTrdVkbNEK0eJSHeuL3c2u37EeX4mDJNBp1kDt2IGcYzPx5DMZCX1KFnr\nvIA6vVKqASmuEgbgYQBnOOdf0H5JRCXQlps9qeTWcz6aRDiRLrqJUUedRZUinAkVRbPbrazlrFbF\nNyLbG+1YSmc3tMSdmw1jR5Ndk1YJgFiEo0w8xR4lylIlxVtYqwkpEff1AN4D4CbG2PHcvzdpvC6i\nzLQ6zUims4pcHfmS6iItZ+25wpJiuTBAofgos8cjLbIttAatIu7+FqFL5Onpi7KYK9B6slSby4Lw\nkrLOjnkroALhblOxrWw1sKFwc85/xzlnnPPLOedX5P79ohSLI8pHizhQQcFtr1iZp7RqUqSjzorJ\nYBwZmSK5GjU83CJdbhuW0llMy5wQNBmMo8agg8dWvENmLcQo+sw6wh2MJTG7uISdGgp3MV7uwbkI\nPHaTohy1x57rTqjSAJBKp2IqJ4nKopj+yiP+CPQ6VrRQttdZkMrwoseoTSzEYdAV5+EWETdcpVjv\nljMaEGxuUhsnycVs1KO3wYbTU4WF+3xuhmmfRhuTQHGDOIQeJcrSa2J3wmJqD6oJEm5iTYrJGXr9\nMXTUWWDUF/f2Ei2BxW5QTqrg4RbJe7ll5rlHAzF0aTzRqb/FsW6q5HRu+MauZnWnJi1H6QWfc67Y\nCigiWAIp4ia2MPW2GpgMOkWR04hK01U6VLIETizEVNsUbHGYYTLIswRyzjE2H8uXpmtFf6sD06FE\nwYnnr0yE0OQwKSoekorHboLJoMOYzMEO/kgSi4m0rFL31bS5LJiizUliK8MYE6xdMnOGnHN4VSqp\nbquzgDEU3YNCzYpFnY6hy23N5/Gl4IssIZbMaFJmvhxxjN3JAuPuXhkPYk+7Nh0lReQUAy1HSY+S\n1bQ4zZgJJYreE6kGSLiJgigpwpkLCyJVTPGNiMmgR1OtuaiBCktpobeIGo4SEbmWQDH61Dri3tPh\ngo4BR1f1bweAUDyFYX8Uezq0FW4g1x9cZsQ9WIQVUKTFZUE6y+EvcrxcNUDCTRSkxWmR7SoZKbK5\n1Go66i1FRdzi+tW04fV4BGGSGtmJzZ+0znHbTQb0tzpwxLtw0WPHx4XZpVpH3IAweX50Xt5g5aG5\nCKw1+vzeihLUaHJVLZBwEwVpdVkwG04gJaMxvurCXWfFRBE57gt9uFUc0OuxIZnJShaIsUAUOqaO\nj3wj9nXV49j4wkV/s98N+FCj1+GqLm3G3S2ny21FIpXFnIy+3EM+YWNSqPdTRotTtCJu/g1KEm6i\nIO0uCziXF8EM+yKoMehU68nRXm/F9GICybSyqSqih1vNikW5zaZG52NCnxOD9h+3a3rqkUhl8dLo\nyqj7twN+7Ouukz3/UwlduddnVEa65HyuorMYxPfcVqieJOEmCtIlNuiX8QEc8UfR47apVlLdUSf/\n4rGcyWAceh0r6hZ8NXIHB3sDsfxrqTWv62tAjUGHJ0/N5I/NhRM4OxPG9ds9JVmDmBKS0vQKEPLv\ns4tL2NFYnL/cYTbAVqPfEv1KSLiJguQFSsZG3LAvim0q9nq+0N5VWbpkYiGOZocZhiI95ctpcphg\nMeolO0vGAlF01mvrKBGxmwx43XYP/t+p2XyO+ecnpgEAt/Q3lWQNbXWCZ17qBV90lPQVGXEzJhTh\nzGyB6kkSbqIgDbWCQHklClQqIzQ50kS4FTpLJhfiqvcHYUywBEq5oIXiKSzEUiWLuAHg9stbMBmM\n49kBP7JZju8fGcelrQ5Ne5Qsx6jXodVllmwJHJwTWs0WG3EDQrpEaVfLaoKEmyiIKFBSb3nH5mNI\nZ3nRPUqW0+www6hnRUTcMbRr0JGvx2OTlCoppnGSUm6/vAVNDhM++8RZfPXZIZydCeNDr9tWsvMD\nwj7AmMT3zcBsBGajTpULbLODIm6CQLfbJrm8e9gnPE/NiFuvEwqBlFRPpjJZzCwmNBkX1u2xCReq\nDRw3+WiyyDSAHEwGPf73Wy7D2ZlFPPTLc3jdDg/u2NNasvMDQGe9FV6Jg5XP50rd1dgXaXFZMBdO\nbPh3qXZIuIl16fbYMD4vzbM84s8VUag8Fqu9zqKoX8lMKIEs18aG19tgRzrLNxzQez4XTZbCCric\nN/Q34Wf3vhb/ePce/Nt792nWf7sQ2xrsCMVTktoCD86GsaOIisnltDjNyHLIsiIq4qGH4PvpEysb\noB0+DDz0kLbnzUHCTaxLt9uKVIZLcnUM+6Jw22pUHx3VUWdVNFBBTK9o0QN7V4uQj12vjSoADKgY\nTcpld5sTb7uqvWRTk5YjbjSez41KK0Q4kcJUKIEdKuXfm0s0UOFwbSd099yDP//oF/Gr07OCaB88\nCOzfr+l5RUi4iXWR48lV21Ei0lFvhT+SRCyZlvVzkxoU34hsb7TDoGMbDi4YUDGarCbEjdCB2fUn\nr4utZtV6jVqdopdbuzz3+HwMH5l04v9+9DP4l598DkMf+yT4wYPAoUPAgQOanXc5JNzEuvRIbGPK\nOcfAXLioXhOFEIVXbrpkYiEOxi5U1KmJyaDH9kb7uhF3OJHCtIrRZDXRWGtCrdmwYcQttprd3eZU\n5bxixK3lBuU3fj+CLOf4wN/8KXRX7MF/feY/cez2ey6IdglSJiTcxLo01ppgNm7cxnQuvISFWAqX\naNCk/4IlUF66ZHwhhqZas2YVi/0tjnWFW3SUbMWImzGGvqbaDSPuk5OLqLMaVSuQcpgNsNboNSt7\nT6az+OFLE3jjpc1oPfY8al89joTRhL7vf1MQ7BKlTEi4iXXR6Ri2eex5ESrE2RkhsrqkRf0m/R11\nyoR7TOOKxV0tDswuLiFQoBvdqdw0ml0avCbVQF+THefnwus6S05OhbC7zVlUj5LlMCZUyc4sapPj\nPuKdx2Iijf+yNCII9GOP4enPP4ws58jefjtw110lSZmQcBMbcklzLc7OrJ/LPZuLPLWIuD32GliM\neozLTJV4AzFNe2D3twqCfKrAuLATE0HUWY2a5NirgR2NtQjGUvAVuLAl01mcnw3n+4irRYtTuyKc\nX52ZRY1Bh8unB/IC3XfPm/HNq94MXTwO7NtXkjw3CTexIZe01GJ2cQkL61i7zs2E0ewww2WtUf38\njAnzK+VE3JGlNPyRJU17YF/W7gRjwMtjF7dRBYATEyFc1u5SLZqsNi7JOW8KzcE8PxtGKsNxaau6\ndyQtTrOiIddSOHx2Dtf1ulHz3z+dF+jtp47gvcefwBN3fQh45RUhXaIxJNzEhuzMzSgU0yFrcWYm\njJ0aDqHtrLdiTIZwi438tYy4HWYjdjbVXtSJDwDiyQwG5iLY065uNFlNCCkQ4QK2Fq9OqrsxKdLi\nNGtShDO7mIA3EMNrlzfrOnwY7B3vwCOffAh/c+Xd4N//vpBC0Vi8SbiJDRHTH+cKpEtSmSwG58L5\nCEsLuj02jAZiyEocXiCW6WvdI2Rfdx2OjQUvKlA6MSEcu7wEgwsqFYfZiN4GO17JDXFYzRHvPNy2\nGnSr/Ddqdlo0KcI5mhtQsa+7/sLBI0eAQ4fguv2N8EeSmLjiWiGFcuSIqudeDQk3sSGNtSbUWY0F\nI+5zM+Itr3bRZY/Hhngqg9mwtFtgscGR1uPC9nfXI7KUvigd8PtBP3QMuLqnvsBPbg32tLvwykRw\nzQ3KI9557OuuUz2V1OISi3DUTZccHZ2H2ahbmdp54AHgwAFcnruzenUyJKRQHnhA1XOvhoSb2BDG\nGHY21+JMAeE+lsvx7tVwnuG2nJ9c7IeyEaMBoYrTYVa3inM112/3gDHg12fnVhz/7aAfezpccFq0\nPX+lc0WHE/5I8qI010wogfH5OPZ3q39ha9HIy/3S6AL2tLtgXKNF8M7mWhj1LJ/+0RoSbkISe9pd\nODO1iEQqc9Fjx8aC8NhNmrontuUKe4alDi/wxzSPtgHAYzfhys46/OrMbP5YKJbCK+PBlbnQLcp1\nudfg2QH/iuPPDQnfX9PjVv2cLQ71J+GkMlmcnQ4XHLZsMuixs7kWrxbI56sNCTchiau66pDMZNeM\nKI6PB7G3U1v3RJPDBGuNHsO+9f3kImPz2loBl/OGXU14dTKU3xD9+avTyPLSDS6oZLZ5bGhzWfDb\n874Vx586PYsmh0l1RwkAOCxCEY6aqZKB2QiSmey6672szYlXJ0OyhiQrhYSbkIQ4ZPboqgnis4sJ\nDPujmg+hZYyhx2PLDyNej0Qqg6lQvGTDC+7a2wa9juFbz3mRzXL8x/Oj2NFox2UquyWqEcYYXt/n\nwXNDASylhbu1RCqDZ877cEt/E3QaNN8SJ+GoGXGLPWnWE+6P3NCLxz5+vWrnXA8SbkISbrsJ2zw2\nvDgSWHH8mVwkdUNfg+Zr6PHYJOW4RwMxcK6tFXA5zU4z3rq3Df/+By/uP3QcZ6YX8bEDvVvWv72a\nP9rdgshSGr86LewDPHlqBrFkBrdd2qLZOVtVLsI5NRWCxahfd0hIl9uGHo+tJH93Em5CMq/va8Bz\nQwFEly506XvmvA+NtSZNKiZXs81jw8RCLB+5FWKgDMML/vubdmFHUy1+cnwKb7miFXfuaSvZuSud\n67d70Oay4GvPDiGVyeKrzwxjm8eG63rVz2+LqD178tTUIna11JalPe9akHATkrltdzOW0tl8lB1P\nZvDsOR9u3NlQkihjW4MdWX6huKYQ52cj0DFo0qmwEHW2Gvzs3uvx7KcO4IvvuEKTFEC1otcxfPKW\nPrwyEcLN//gMzkwv4pO39mn6GrU4zZhdVKcIh3OOM1OLmtpd5ULCTUhmf3c93LYa/PjYJADgF69O\nI7yUxl1720tyfrHF7NAG6ZLBuTA6660lHyBg0OvQ6bZSimQN3nplG+67eQeynOOB23bi9su0S5MA\nQr+SLEfBPilymAolEF5Ka1pgJhcSbkIyeh3Du67pxFOnZ/HseR/+6dcD2NlUi2tKVGSyo8kOxrBh\nw6vzs5Et2QO7kmGM4f5b+vC7B2/Cx27crvnFrcWpXhFOOQY+bwQJNyGLD7x2GzrqLXjvN17E5EIc\nf3tHf8nSAtYaA3rctnV7YCfTWXj90S3ZA5u4QH6EmQrNpoZywt1bQe8pQ7kXQFQXTqsR3/3QtTh0\ndALX9bpx7TbtNpjWYlerAycm1u59AQDeQBTpLM+PziK2JhdGmBVvCRz0ReCyGuG2qd/5UikUcROy\naa+z4pO39JVctAFh6sz4fByLidSaj4v9VLZXUHRElB6HxQCLUZ0inMG5CLY32Ctq72JD4WaMfYMx\nNscYO1mKBRHEevTnpsmcnV67b8rJyRBq9DqKuLc4jDG0uNSxBA77IiV1KElBSsT9LQC3abwOgpCE\nOAasUJ77lfEgdrU6NJszSVQPLSpUTwZjSfgjyYq7g9vw3c05fxbAfAnWQhAb0uQwwWOvwStr5Lmz\nWY6Tk6EtPbyAuECzo/jqySGfuDFZmipcqagWljDGPswYO8oYO+rz+Tb+AYJQAGMMV3XV4Yj34lhi\n2B9BNJmhHiEEAKDVZcZceKmoIpwLVsDKSr2pJtyc869xzvdxzvc1NGjft4LYuuzvrsf4fPyi/OUf\nhgUx17rhFVEdNDvNyGR5UUU4g3MRmAw6tFXYwGdKBBJVhzhV5oVVDa9+P+BHm8uSr7AktjZqFOEM\n+aLo8dgqpkeJCAk3UXX0tzjgshrxm3MXUnKZLMdzQ368drunomxbRPloyXm5i3GWDM5FKm5jEpBm\nB/wugD8A2MkYm2CMfUD7ZRFEYQx6HW7tb8KvTs/mOwUe8c5jMZHGa3fQ1BlCQIy4p4LKnCWJVAbj\nC7GKswIC0lwl7+Sct3DOjZzzds75w6VYGEGsxx9d1oLwUhqHzwpR949enoCtRo+bdzWWeWVEpeC0\nGGEx6hVH3CP+KDivzGIuSpUQVclrcz2ev/KbQYz4o3js2BTu3NsGaw11cSAEGGOCl3tRmXCLjpKq\njLgJohIx6nX41Bt34pWJEG75wjMwGXS498D2ci+LqDCanWZMK0yVDPkiYAzY1lB5m90UnhBVy1v2\ntiGylMbvB/34rzf0otVVWZYtovy0OC34w5B/4yeuweBcBB11pe/rLgUSbqKqefe1XXj3tV3lXgZR\nobQ4zZgNLyGT5bItfUO+KHorMNoGKFVCEMQmJl+EE5ZXhJPJcgz7KtMKCJBwEwSxiWl1iUU48vLc\nkwtxLKWzFbkxCZBwEwSxiWl2iAMV5DlLxOZSFHETBEGUGKVl75VsBQRIuAmC2MS4rEaYjTrZlsDB\nuQg89hrUVdC4suWQcBMEsWkRinDk9+UemAtXbLQNkHATBLHJaa+zYGIhJvn5nPOKbS4lQsJNEMSm\nprPeitF56cLtiyxhMZHGDhJugiCI8tDttiEYSyEUS0l6/uCs6CiprKk3yyHhJghiU9PptgIARuej\nkp4/WOFWQICEmyCITU63Wyhb9wakpUsG5yKwmwxocpi0XFZRkHATBLGp6awXIu6xgLSIe2BW2Jis\n5ElKJNwEQWxqLDV6NDlMGJUacVdwjxIREm6CIDY9XfU2ScIdiqXgCy+RcBMEQZSbLrcVXgmpkoG5\nMABgewUX3wAk3ARBbAF6G+2YCy8hFF/fEnhmehEA0N/qKMWyFEPCTRDEpqevSYigB2bD6z7v9PQi\nnBZjvjlVpULCTRDEpmdHrpjmfK64phCnpxbR3+KoaEcJQMJNEMQWoM1lga1Gj/PrRNzpTBZnZ8IV\nnyYBSLgJgtgC6HQM25tq1xVubyCKpXQW/S0k3ARBEBVBX6N93VTJqSlhY3IXCTdBEERl0NdUC39k\nCfPR5JqPvzIegsmgq3gPN0DCTRDEFuHSXO761cnQmo+/PLaAy9udqDFUvixW/goJgiBU4LJ2JxgD\njo0tXPRYIpXBqakQruyqK8PK5EPCTRDElqDWbERfYy2OjwcveuzkZAipDMeVnSTcBEEQFcUVHS4c\nGwuCc77i+HNDAQDAPoq4CYIgKot93XUIxVN5B4nIM+d9uLzdCbe9cntwL4eEmyCILcONOxsBAIfP\nzuWPBWNJHBtbwI19DeValmxIuAmC2DI01Jqwp8OFp5cJ91OnZ5HlwI2XNJZxZfIg4SYIYktxa38T\njo8HMZSbLXno6Dh6PDbs7XCVeWXSIeEmCGJLcXBfB2r0Ovzz0wM4fG4OR7wLePe1XRXfWGo5koSb\nMXYbY+wcY2yQMfZprRdFEAShFQ21Jnzkxl48dnwK7//mEfQ22PCuazrLvSxZGDZ6AmNMD+BfANwC\nYALAEcbYTznnp7VeHEEQhBbcd/MOOMwGDPmi+MTN22E26su9JFlsKNwArgYwyDkfBgDG2PcA3AmA\nhJsgiKpEr2P44Ou2lXsZipGSKmkDML7s+4ncMYIgCKIMqLY5yRj7MGPsKGPsqM/nU+vXEgRBEKuQ\nItyTADqWfd+eO7YCzvnXOOf7OOf7Ghqqx8hOEARRbUgR7iMAdjDGehhjNQDuAfBTbZdFEARBFGLD\nzUnOeZoxdi+AJwHoAXyDc35K85URBEEQayLFVQLO+S8A/ELjtRAEQRASoMpJgiCIKoOEmyAIospg\nqxuKq/JLGfMBGFX9F5cWDwB/uRdRIdBrsRJ6PVZCr8cFinktujjnkix5mgj3ZoAxdpRzvq/c66gE\n6LVYCb0eK6HX4wKlei0oVUIQBFFlkHATBEFUGSTchflauRdQQdBrsRJ6PVZCr8cFSvJaUI6bIAii\nyvlTBagAAALWSURBVKCImyAIosog4V4GY6yDMXaYMXaaMXaKMXZfuddUbhhjesbYMcbY4+VeS7lh\njLkYYz9gjJ1ljJ1hjL2m3GsqJ4yx+3Ofk5OMse8yxszlXlMpYYx9gzE2xxg7uexYPWPsKcbYQO6/\ndVqcm4R7JWkAf8457wdwLYCPM8b6y7ymcnMfgDPlXkSF8GUAv+ScXwJgD7bw68IYawPwCQD7OOe7\nIfQxuqe8qyo53wJw26pjnwbwNOd8B4Cnc9+rDgn3Mjjn05zzl3NfhyF8MLfs0AjGWDuA2wF8vdxr\nKTeMMSeA1wN4GAA450nOebC8qyo7BgAWxpgBgBXAVJnXU1I4588CmF91+E4A3859/W0Ab9Hi3CTc\nBWCMdQPYC+CF8q6krHwJwAMAsuVeSAXQA8AH4Ju51NHXGWO2ci+qXHDOJwH8A4AxANMAQpzz/1fe\nVVUETZzz6dzXMwCatDgJCfcaMMbsAH4I4M8454vlXk85YIz9MYA5zvlL5V5LhWAAcCWAr3DO9wKI\nQqPb4Gogl7u9E8IFrRWAjTH27vKuqrLggmVPE9seCfcqGGNGCKL9COf8R+VeTxm5HsCbGWNeAN8D\ncBNj7DvlXVJZmQAwwTkX78B+AEHItypvADDCOfdxzlMAfgTgujKvqRKYZYy1AEDuv3NanISEexmM\nMQYhh3mGc/6Fcq+nnHDO/5Jz3s4574aw6fRrzvmWjag45zMAxhljO3OHbgZwuoxLKjdjAK5ljFlz\nn5ubsYU3a5fxUwDvy339PgA/0eIkJNwruR7AeyBEl8dz/95U7kURFcN/A/AIY+wEgCsAfKbM6ykb\nuTuPHwB4GcCrELRkS1VQMsa+C+APAHYyxiYYYx8A8FkAtzDGBiDclXxWk3NT5SRBEER1QRE3QRBE\nlUHCTRAEUWWQcBMEQVQZJNwEQRBVBgk3QRBElUHCTRAEUWWQcBMEQVQZJNwEQRBVxv8PupyJrL96\nAu0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73d2f93400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(x, y)\n",
    "plt.plot(x[peaks], y[peaks], 'rx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variante 2: peakutils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60, 173, 288, 406, 524, 640, 754, 868])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import peakutils\n",
    "peaks = peakutils.indexes(y, thres=0.5, min_dist=40) \n",
    "# threshold: relative Höhe 0..1 ab der Peaks gelten\n",
    "# min_dist: Anzahl Punkte zwischen 2 Peaks\n",
    "peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Minima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "minima = peakutils.indexes(-y, thres=0.5, min_dist=40) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f73d33e4908>]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6g3jZhpJoLelvtUmOpMb8UTjMBnjs8nu5GPU6tDebFfVK2YpLc2Ec6XZULGBy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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73d2f38cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(x, y)\n",
    "plt.plot(x[peaks], y[peaks], 'rx')\n",
    "plt.plot(x[minima], y[minima], 'gs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integral - Area Under Curve\n",
    "siehe [https://stackoverflow.com/questions/13320262/calculating-the-area-under-a-curve-given-a-set-of-coordinates-without-knowing-t](https://stackoverflow.com/questions/13320262/calculating-the-area-under-a-curve-given-a-set-of-coordinates-without-knowing-t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "area = 2.28417522211\n",
      "area = 2.28417759778\n"
     ]
    }
   ],
   "source": [
    "from scipy.integrate import simps\n",
    "from numpy import trapz\n",
    "\n",
    "peak1 = y[minima[2]:minima[3]]\n",
    "\n",
    "# Compute the area using the composite trapezoidal rule.\n",
    "area = trapz(peak1, dx=0.01)\n",
    "print(\"area =\", area)\n",
    "\n",
    "# Compute the area using the composite Simpson's rule.\n",
    "area = simps(peak1, dx=0.01)\n",
    "print(\"area =\", area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
