From 6d1a615ef779547ab014933735e6417c0c5eaa60 Mon Sep 17 00:00:00 2001 From: Wei Changfeng <2681968849@qq.com> Date: Tue, 8 Sep 2020 07:29:54 +0100 Subject: [PATCH] Delete whiten_signal.ipynb --- whiten_signal.ipynb | 279 -------------------------------------------- 1 file changed, 279 deletions(-) delete mode 100644 whiten_signal.ipynb diff --git a/whiten_signal.ipynb b/whiten_signal.ipynb deleted file mode 100644 index 8e4f2b1..0000000 --- a/whiten_signal.ipynb +++ /dev/null @@ -1,279 +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", - "text/plain": [ - "
" - ] - }, - "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('Strain')\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", - "\n", - " whitened_signal_rfft = signal_rfft / asd\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)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Strain')" - ] - }, - "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('Strain')" - ] - } - ], - "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