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Gravitational Wave Open Science Center data analysis¶

数据分析

可视化

数据分析可视化

wenh@dp.tech

发布于 2023-11-12

推荐镜像 :Basic Image:bohrium-notebook:2023-04-07

推荐机型 :c2_m4_cpu

Gravitational Wave Open Science Center data analysis

SET PARAMETERS: Detector and GPS time

You might try some of these examples times in the H1 detector:

Import some packages

Query and download data file

Plot the raw time-series data

Plot the ASD

Whiten and band-pass the data

Plot a q-transform of the data

Gravitational Wave Open Science Center data analysis

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📖 上手指南/Introduction
本文档可在 Bohrium Notebook 上直接运行。你可以点击界面上方按钮开始连接，选择 bohrium-notebook:2023-04-07 基础镜像及任意节点配置（无需GPU)，稍等片刻即可运行。

A quick look at short segments of data from the Gravitational Wave Open Science Center

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SET PARAMETERS: Detector and GPS time

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[1]

# -- Set a GPS time:

t0 = 1126259462.4 # -- GW150914

#-- Choose detector as H1, L1, or V1

detector = 'H1'

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You might try some of these examples times in the H1 detector:

t0 = 1126259462.4 # -- GW150914

t0 = 1187008882.4 # -- GW170817

t0 = 933200215 # -- Loud hardware injection

t0 = 1132401286.33 # -- Koi Fish Glitch

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Import some packages

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[2]

import requests, os

import matplotlib.pyplot as plt

%config InlineBackend.figure_format = 'retina'

try:

from gwpy.timeseries import TimeSeries

except:

! pip install -q "gwpy==3.0.4"

! pip install -q "matplotlib==3.5.1"

from gwpy.timeseries import TimeSeries

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Query and download data file

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[3]

from gwosc.locate import get_urls

url = get_urls(detector, t0, t0)[-1]

print('Downloading: ' , url)

fn = os.path.basename(url)

with open(fn,'wb') as strainfile:

straindata = requests.get(url)

strainfile.write(straindata.content)

Downloading:  https://gwosc.org/eventapi/json/GWTC-1-confident/GW150914/v3/H-H1_GWOSC_4KHZ_R1-1126257415-4096.hdf5

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Plot the raw time-series data

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[3]

# -- Read strain data

strain = TimeSeries.read(fn,format='hdf5.gwosc')

center = int(t0)

strain = strain.crop(center-16, center+16)

fig1 = strain.plot()

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Plot the ASD

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[12]

# -- Plot ASD

fig2 = strain.asd(fftlength=8).plot()

plt.xlim(10,2000)

plt.ylim(1e-24, 1e-19)

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Whiten and band-pass the data

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[6]

# -- Whiten and bandpass data

white_data = strain.whiten()

bp_data = white_data.bandpass(30, 400)

fig3 = bp_data.plot()

plt.xlim(t0-0.2, t0+0.1)

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Plot a q-transform of the data

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[9]

dt = 1 #-- Set width of q-transform plot, in seconds

hq = strain.q_transform(outseg=(t0-dt, t0+dt))

fig4 = hq.plot()

ax = fig4.gca(color = 'rainbow')

fig4.colorbar(label="Normalized energy")

ax.grid(False)

ax.set_yscale('log')

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数据分析

可视化

数据分析可视化

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