From 1640c2f5bc0c7cd209bf2c87415699d2b35365a0 Mon Sep 17 00:00:00 2001 From: Wei Changfeng <2681968849@qq.com> Date: Thu, 3 Sep 2020 07:07:46 +0800 Subject: [PATCH] new file --- whiten_signal.ipynb | 233 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 233 insertions(+) create mode 100644 whiten_signal.ipynb diff --git a/whiten_signal.ipynb b/whiten_signal.ipynb new file mode 100644 index 0000000..4e5cc20 --- /dev/null +++ b/whiten_signal.ipynb @@ -0,0 +1,233 @@ +{ + "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']" + ] + }, + { + "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Plot the waveform of L1.\n", + "plt.title('L1')\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):\n", + " \n", + " signal_rfft = np.fft.rfft(signal)\n", + " whiten_signal_rfft = signal_rfft / asd\n", + " whiten_signal = np.fft.irfft(whiten_signal_rfft, n=Nt)\n", + " return whiten_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", + "sample_rate = 256\n", + "dt=1./sample_rate\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", + "\n", + "# A small problem here: freq_rfft contanins some values which is out of freq.\n", + "# I am not sure if this has a big impact on the calculation of the whitening process." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "whiten_signal_L1 = whiten_signal(data_L1,asd_interp)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "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,whiten_signal_L1)" + ] + } + ], + "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 +} -- GitLab