diff --git a/whiten_signal.ipynb b/whiten_signal.ipynb deleted file mode 100644 index a0f974f393781a832a3ec05c48f20e50a6c7b5bb..0000000000000000000000000000000000000000 --- a/whiten_signal.ipynb +++ /dev/null @@ -1,280 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a notebook to whiten signal genreated from \"h_m16_L0.18_l2m2_r300.dat\"." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "with open('0.json','r') as f1:\n", - " waveform = json.load(f1)\n", - " \n", - " #extract time series at V1,L1,and H1 from waveform file.\n", - " data_V1 = waveform['data']['V1']\n", - " data_L1 = waveform['data']['L1']\n", - " data_H1 = waveform['data']['H1']\n", - " times_V1 = waveform['times']['V1']\n", - " times_L1 = waveform['times']['L1']\n", - " times_H1 = waveform['times']['H1']\n", - "\n", - " \n", - "sample_rate = 256\n", - "dt=1./sample_rate" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Plot the waveform of L1.\n", - "plt.title('Original signal')\n", - "plt.xlabel('Time(s)')\n", - "plt.ylabel('Waveform')\n", - "plt.plot(times_L1,data_L1)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Load ASD file of L1.\n", - "filename = './bilby_gw_detector_noise_curves_aLIGO_ZERO_DET_high_P_asd.txt'\n", - "freq = []\n", - "asd = []\n", - "with open(filename, 'r') as f1:\n", - " while True:\n", - " lines = f1.readline() \n", - " if not lines:\n", - " break\n", - " pass\n", - " freq_tmp, asd_tmp = [float(i) for i in lines.split()] \n", - " freq.append(freq_tmp) \n", - " asd.append(asd_tmp)\n", - " pass\n", - " \n", - " # Transfer data format from list to array.\n", - " freq = np.array(freq) \n", - " asd = np.array(asd)\n", - " pass" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def whiten_signal(signal, asd, dt):\n", - " \n", - " signal_rfft = np.fft.rfft(signal)\n", - " norm = 1./np.sqrt(1./(dt*2))\n", - "\n", - " whitened_signal_rfft = signal_rfft / asd * norm\n", - " whitened_signal = np.fft.irfft(whitened_signal_rfft, n=Nt)\n", - " return whitened_signal" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Interpolate asd data linearly.\n", - "\n", - "\n", - "Nt = len(data_L1)\n", - "freq_rfft = np.fft.rfftfreq(Nt, dt)\n", - "\n", - "asd_interp = np.interp(freq_rfft, freq, asd) \n", - "\n", - "plt.loglog(freq, asd,'-o',label='L1 ASD')\n", - "plt.loglog(freq_rfft, asd_interp, 'x',label='Interpolation')\n", - "\n", - "plt.grid('on')\n", - "plt.ylabel('ASD (strain/rtHz)')\n", - "plt.xlabel('Freq (Hz)')\n", - "plt.legend(loc='upper center')\n", - "\n", - "# The frequency range is set here, which comes from the calculation of the LIGO detection range. \n", - "# The limit of the seismic isolation systems designed by aLIGO is 10Hz, which is actually not good for low frequency noise below 20Hz.\n", - "# According to Nyquist–Shannon sampling theorem, for half of the sampling rate(128Hz) and above,the signal is meaningless.\n", - "f_min = 20.\n", - "f_max = 128. \n", - "plt.axis([f_min, f_max, 1e-24, 1e-19])\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# This step is not necessary. Just to show a potential issue: interpolation of ASD has some points out of the curve in left side,\n", - "# I am not sure if this has a big impact on the calculation of the whitening process.\n", - "\n", - "plt.loglog(freq, asd,'-o',label='L1 ASD')\n", - "plt.loglog(freq_rfft, asd_interp, 'x',label='Interpolation')\n", - "\n", - "plt.grid('on')\n", - "plt.ylabel('ASD (strain/rtHz)')\n", - "plt.xlabel('Freq (Hz)')\n", - "plt.legend(loc='upper center')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "whitened_signal_L1 = whiten_signal(data_L1,asd_interp,dt)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'sigmas away from the mean.')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(times_L1,whitened_signal_L1)\n", - "plt.title('Whitened signal')\n", - "plt.xlabel('Time(s)')\n", - "plt.ylabel('sigmas away from the mean.')" - ] - } - ], - "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": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}