Ground Motion Displacement RMS vs Time¶
an example simple tutorial for getting seismic data, computing the power spectral densities, extracting the RMS and plotting
Required:
- python
- obspy (and its dependencies)
- pandas
- jupyter
- notebook
this should be easy to set up in a conda env: conda create -n covid python=3.7 obspy pandas jupyter notebook
Author: Thomas Lecocq @seismotom, Fred Massin @fmassin
Step 1: imports¶
In [1]:
import imp
import seismosocialdistancing
import datetime
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.patheffects as pe
import numpy as np
import pandas as pd
from obspy import UTCDateTime
Step 2: Define Start/End dates and Seismic Channel¶
You'll have to make sure the seed_id you request is indeed available from the data_provider
In [29]:
start = UTCDateTime("2020-03-01")
end = UTCDateTime() # () means "now"
network = "CH"
station = "SLOP"#,SZUZ,SGEV,SBERN,SUSI"#,SEPFL,SBAM2" # Urban stations
location = ""
channel = "HGZ,HGE,HGN"
dataset = 'seismoRMSdata/urban-'
data_provider = "ETH"
logo = 'https://upload.wikimedia.org/wikipedia/commons/thumb/4/44/Logo_SED_2014.png/220px-Logo_SED_2014.png'
bans = {"2020-03-13":'Groups >100 banned',
"2020-03-20":'Groups >5 banned'}
Step 2: Process PSDs to extract the RMS(displacement)¶
This can be done for multiple filters at once (freqs below):
In [20]:
myPSDs = seismosocialdistancing.PSDs() #reloadme=myPSDs)
# 'user@hostname' import ssh-key before via `ssh-copy-id user@hostname`
myPSDs.clientpqlx(sshuserhost='SQLX',
network=network,
station=station,
location=location,
channel=channel,
start=start,
end=end)
# Define frequency bands of interest:
freqs = [(0.1,1.0),(1.0,20.0),(4.0,14.0),(4.0,20.0)]
myPSDs.dRMS(freqs)
Step 3: Custom plot for a single frequency band:¶
In [43]:
o=myPSDs.plot(mode='*',
band = "4.0-14.0",
bans=bans,
logo=logo)
In [ ]:
%autosave 1
import time
time.sleep(2)
%autosave 120
!jupyter nbconvert --to html sqlxSocialDistancing.ipynb
!nbstripout sqlxSocialDistancing.ipynb