examples/generate_noise_in_frequency_domain.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Noise Directly in the Frequency Domain ###\n",
    "\n",
    "It is sometimes useful to generate noise directly in the frequency domain. PyCBC provides functionality to generate noise that has the same spectrum as a PSD you provide. Note that due to how this is made, the equivelant time series will have circular boundary conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pycbc in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (1.13.5)\n",
      "Requirement already satisfied: lalsuite in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (6.53)\n",
      "Requirement already satisfied: numpy<1.15.3,>=1.13.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (1.15.2)\n",
      "Requirement already satisfied: Mako>=1.0.1 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (1.0.8)\n",
      "Requirement already satisfied: cython in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (0.28.5)\n",
      "Requirement already satisfied: decorator>=3.4.2 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (4.3.0)\n",
      "Requirement already satisfied: scipy>=0.16.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (1.1.0)\n",
      "Requirement already satisfied: matplotlib>=1.5.1 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (2.2.3)\n",
      "Requirement already satisfied: pillow in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (5.3.0)\n",
      "Requirement already satisfied: h5py>=2.5 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (2.8.0)\n",
      "Requirement already satisfied: jinja2 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (2.10)\n",
      "Requirement already satisfied: astropy<3.0.0,>=2.0.3 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (2.0.8)\n",
      "Requirement already satisfied: mpld3>=0.3 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (0.3)\n",
      "Requirement already satisfied: lscsoft-glue>=1.59.3 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (2.0.0)\n",
      "Requirement already satisfied: kombine>=0.8.2 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (0.8.3)\n",
      "Requirement already satisfied: emcee==2.2.1 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (2.2.1)\n",
      "Requirement already satisfied: requests>=1.2.1 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (2.20.1)\n",
      "Requirement already satisfied: beautifulsoup4>=4.6.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (4.6.3)\n",
      "Requirement already satisfied: six>=1.10.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (1.11.0)\n",
      "Requirement already satisfied: ligo-segments in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (1.2.0)\n",
      "Requirement already satisfied: weave>=0.16.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pycbc) (0.17.0)\n",
      "Requirement already satisfied: python-dateutil in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from lalsuite) (2.7.5)\n",
      "Requirement already satisfied: MarkupSafe>=0.9.2 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from Mako>=1.0.1->pycbc) (1.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->pycbc) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->pycbc) (2.3.0)\n",
      "Requirement already satisfied: pytz in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->pycbc) (2018.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->pycbc) (1.0.1)\n",
      "Requirement already satisfied: backports.functools_lru_cache in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->pycbc) (1.5)\n",
      "Requirement already satisfied: subprocess32 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from matplotlib>=1.5.1->pycbc) (3.5.3)\n",
      "Requirement already satisfied: pytest>=2.8 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from astropy<3.0.0,>=2.0.3->pycbc) (3.8.0)\n",
      "Requirement already satisfied: pyOpenSSL in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from lscsoft-glue>=1.59.3->pycbc) (18.0.0)\n",
      "Requirement already satisfied: idna<2.8,>=2.5 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from requests>=1.2.1->pycbc) (2.7)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from requests>=1.2.1->pycbc) (3.0.4)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from requests>=1.2.1->pycbc) (1.23)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from requests>=1.2.1->pycbc) (2018.10.15)\n",
      "Requirement already satisfied: setuptools in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=1.5.1->pycbc) (40.8.0)\n",
      "Requirement already satisfied: py>=1.5.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pytest>=2.8->astropy<3.0.0,>=2.0.3->pycbc) (1.6.0)\n",
      "Requirement already satisfied: attrs>=17.4.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pytest>=2.8->astropy<3.0.0,>=2.0.3->pycbc) (18.2.0)\n",
      "Requirement already satisfied: more-itertools>=4.0.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pytest>=2.8->astropy<3.0.0,>=2.0.3->pycbc) (4.3.0)\n",
      "Requirement already satisfied: atomicwrites>=1.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pytest>=2.8->astropy<3.0.0,>=2.0.3->pycbc) (1.2.1)\n",
      "Requirement already satisfied: pluggy>=0.7 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pytest>=2.8->astropy<3.0.0,>=2.0.3->pycbc) (0.7.1)\n",
      "Requirement already satisfied: funcsigs in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pytest>=2.8->astropy<3.0.0,>=2.0.3->pycbc) (1.0.2)\n",
      "Requirement already satisfied: pathlib2>=2.2.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pytest>=2.8->astropy<3.0.0,>=2.0.3->pycbc) (2.3.2)\n",
      "Requirement already satisfied: cryptography>=2.2.1 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pyOpenSSL->lscsoft-glue>=1.59.3->pycbc) (2.3.1)\n",
      "Requirement already satisfied: scandir in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from pathlib2>=2.2.0->pytest>=2.8->astropy<3.0.0,>=2.0.3->pycbc) (1.9.0)\n",
      "Requirement already satisfied: asn1crypto>=0.21.0 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from cryptography>=2.2.1->pyOpenSSL->lscsoft-glue>=1.59.3->pycbc) (0.24.0)\n",
      "Requirement already satisfied: cffi!=1.11.3,>=1.7 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from cryptography>=2.2.1->pyOpenSSL->lscsoft-glue>=1.59.3->pycbc) (1.11.5)\n",
      "Requirement already satisfied: enum34 in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from cryptography>=2.2.1->pyOpenSSL->lscsoft-glue>=1.59.3->pycbc) (1.1.6)\n",
      "Requirement already satisfied: ipaddress in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from cryptography>=2.2.1->pyOpenSSL->lscsoft-glue>=1.59.3->pycbc) (1.0.22)\n",
      "Requirement already satisfied: pycparser in /home/nbuser/anaconda2_501/lib/python2.7/site-packages (from cffi!=1.11.3,>=1.7->cryptography>=2.2.1->pyOpenSSL->lscsoft-glue>=1.59.3->pycbc) (2.19)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install pycbc ligo-common --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pycbc.noise\n",
    "import pycbc.psd\n",
    "import pylab\n",
    "\n",
    "# Generate a PSD using an analytic expression for \n",
    "# the full design Advanced LIGO noise curve\n",
    "f_lower = 10\n",
    "duration = 128\n",
    "sample_rate = 4096\n",
    "tsamples = sample_rate * duration\n",
    "fsamples = tsamples // 2 + 1\n",
    "df = 1.0 / duration\n",
    "psd = pycbc.psd.from_string('aLIGOZeroDetHighPower', fsamples, df, f_lower)\n",
    "\n",
    "# Let's take a look at the spectrum\n",
    "pylab.loglog(psd.sample_frequencies, psd)\n",
    "pylab.xlim(20, 1024)\n",
    "pylab.ylim(1e-48, 1e-45)\n",
    "pylab.xlabel('Frequency (Hz)')\n",
    "pylab.ylabel('Strain^2 / Hz')\n",
    "pylab.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, let's generate noise that has the same spectrum\n",
    "htilde = pycbc.noise.frequency_noise_from_psd(psd, seed=857)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3a29d5d490>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Equivelantly in the time domain\n",
    "hoft = htilde.to_timeseries()\n",
    "\n",
    "# Well zoom in around a short time\n",
    "hoft_zoom = hoft.time_slice(43.5, 44)\n",
    "pylab.plot(hoft_zoom.sample_times, hoft_zoom)\n",
    "\n",
    "# Notice the extreme difference in amplitudes of noise at different frequencies.\n",
    "# You can see clearly the loud low frequency noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1e-48, 1e-45)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now we can verify that the PSD of this noise is as expected. \n",
    "\n",
    "# Use Welch's method with 4s segments\n",
    "psd_estimated = hoft.psd(4)\n",
    "\n",
    "pylab.loglog(psd_estimated.sample_frequencies, psd_estimated, label='Estimated')\n",
    "pylab.loglog(psd.sample_frequencies, psd, label='Original', linewidth=3)\n",
    "pylab.legend()\n",
    "pylab.xlim(20, 1024)\n",
    "pylab.ylim(1e-48, 1e-45)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "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": "2.7.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}