Using_signal_library.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from itertools import takewhile\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import argrelextrema, argrelmin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 1, 2, 3, 4,\n",
    "\n",
    "        5, 6, 7, 8, 9, 5, 6, 7, 8, 9, 5, 6, 7, 8, 9,\n",
    "\n",
    "        12,\n",
    "\n",
    "        15, 16, 17, 18, 19, 15, 16, 17, 18, \n",
    "\n",
    "        19, 20, 21, 22, 23, 24,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = [np.random.randint(0,30) for i in range(0,50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "freq = Counter(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 4),\n",
       " (2, 4),\n",
       " (3, 4),\n",
       " (4, 4),\n",
       " (0, 3),\n",
       " (5, 3),\n",
       " (6, 3),\n",
       " (7, 3),\n",
       " (8, 3),\n",
       " (9, 3),\n",
       " (15, 2),\n",
       " (16, 2),\n",
       " (17, 2),\n",
       " (18, 2),\n",
       " (19, 2),\n",
       " (12, 1),\n",
       " (20, 1),\n",
       " (21, 1),\n",
       " (22, 1),\n",
       " (23, 1),\n",
       " (24, 1)]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mostfreq = freq.most_common()\n",
    "mostfreq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 4), (2, 4), (3, 4), (4, 4)]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modes = list(takewhile(lambda x_f: x_f[1] == mostfreq[0][1], mostfreq))\n",
    "modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 4., 4., 4., 4., 3., 3., 3., 3., 3., 0., 0., 1., 0., 0., 2., 2.,\n",
       "        2., 2., 2., 1., 1., 2.]),\n",
       " array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23]),\n",
       " <a list of 23 Patch objects>)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.style.use('seaborn-white')\n",
    "plt.hist(data, bins=range(min(data),max(data),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 4,  9, 14, 23, 28, 39]),)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argrelextrema(np.array(data), np.greater)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5, 10, 15, 24, 29, 40]),)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argrelextrema(np.array(data), np.less)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  9, 19])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(np.array(data)[argrelextrema(np.array(data), np.greater)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  5, 15])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(np.array(data)[argrelextrema(np.array(data), np.less)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5, 10, 15, 24, 29, 40]),)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argrelmin(np.array(data))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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