Testing scipy's find_peaks_cwt

testing_find_peaks_cwt.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import multiprocessing as mp\n",
    "\n",
    "import numpy as np\n",
    "from scipy import signal, ndimage\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_spectrum(A=100, W=30, N=1000, noise=0.1):\n",
    "    np.random.seed()\n",
    "    random_offset = int(np.random.uniform(0.1*N, 0.9*N))\n",
    "    line = np.roll(np.roll(A*signal.gaussian(N, W), -N/2), random_offset)\n",
    "    measured = line + np.random.normal(loc=0, scale=noise, size=N)\n",
    "    \n",
    "    peaks = signal.find_peaks_cwt(measured, np.arange(10, 50, 5), min_snr=5, noise_perc=10)\n",
    "    \n",
    "    return measured, random_offset, peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_spectrum_w_peaks(A=10, W=20, N=1000):\n",
    "    spectrum, offset, peaks = get_spectrum(A=A, W=W, N=N)\n",
    "    _, ax = plt.subplots(1, figsize=(15, 3))\n",
    "    ax.plot(spectrum)\n",
    "    ax.set_title(\"Offset: %s, Peaks: %s\" % (offset, peaks))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WGlFNnz6dMWPG1DSODh0zrZSyBrgB+GxEnBQRe0XEe4D3AtWWahcD50TEmREx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\ntReMGlXvSDqP970PHn4Y7rm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9daee57690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spectrum_w_peaks(A=100, W=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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emtjP0yu2AaxbS0tLrUsALmBxcTHD4bB1GTO1sLDQugSAJHoioH8uOkArpRzJ6IyyV54d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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9daf2bd210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spectra = mp.Pool().map(get_spectrum, np.ones(1e3))\n",
    "\n",
    "peak_offsets = []\n",
    "for s in spectra:\n",
    "    offset = s[1]\n",
    "    peaks = s[2]\n",
    "    if len(peaks) == 0:\n",
    "        peak_offsets.append(1000)\n",
    "    else:\n",
    "        for p in peaks:\n",
    "            peak_offsets.append(offset-p)\n",
    "            \n",
    "_, ax = plt.subplots(1, figsize=(15, 5))\n",
    "ax.hist(peak_offsets, bins=np.arange(-1000, 1100, 100))\n",
    "ax.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python2",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}