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Pi_Daily_processing.py
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Pi_Daily_processing.py
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"""
This is a Python 3 program for the Seisberry, running in server mode.
This program is stripped down and optimized to run on light hardware, like the Raspberry.
Raspian is 32 bits limitting a numpy array to 2G, past this limit we run into a memory error.
This program runs automatically daily (scheduled in crontab) to process the
daily production from the 3 components seismometer and generate:
-dayplot for display on the seisberry Apache server.
-miniseed for upload to IRIS
Usage:
Daily rawdata in: /media/pi/xxxx/2020-04-01
Daily miniseed in : /media/pi/yyyy/miniseed
Update the user/station valiables at the beginning of this script.
Run
If the program is killed by the OS, check: /var/log/kern.log
Date: 2020-04-23
For tutorial visit:
https://www.erellaz.com
Original idea from:
https://github.com/will127534/RaspberryPi-seismograph
https://will-123456.blogspot.com/2019/04/diy-seismograph.html
"""
#______________________________________________________________________________
import numpy as np
from datetime import datetime,timedelta
import os
from obspy.core import Trace,Stream,UTCDateTime
from obspy.core.event import read_events
import requests
import sys
#______________________________________________________________________________
# User adjustable variables - a normal user only needs to edit this block
# to have the program run anywhere around the globe.
File_date = datetime.now()- timedelta(days=1)
datadir=r"/media/pi/92ED-675B"
#Optional, if you plan to output traces as SEGY or miniseed
miniseeddir=r"/media/pi/PAUL/miniseed"
# Plot dir
plotdir=r"/home/pi/Desktop/Images"
# Url to download the official seismic events from - used to label your plots
# Quakeml is the defacto official format for seismic event
url = 'https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_week.quakeml'
#Dictionary in Obspy stats format to document your station, is updated later in the code
#For channel naming see: http://www.fdsn.org/pdf/SEEDManual_V2.4_Appendix-A.pdf
#BH= broad band, High gain seismometer, 1 2 3 othogonal components
statsx= {'network': 'USA',
'station': 'SeisBerry',
'location': 'Houston',
'channel': 'BH',
'mseed' : {'dataquality' : 'D'},
}
#Sampling rate from the seisberry, in samples per second
sampling_rate =750 #0.0013ms sample interval
decimation=5
# Your latitude and longitude in decimal degrees, West or South are negative
# Example Houston is stationlat=29.0 stationlon=-95.0
stationlat=29.0
stationlon=-95.0
# box search: half size in decimal degrees (to filter catalog events close to you)
box=10.0
# Magnitude of the world-wide biggest events to be shown on plot
bigeventsmag=5.5
# How big do you want your plots?
size = (210*10,210*10)
#______________________________________________________________________________
# DO NOT MODIFY BELOW, UNLESS YOU KNOW WHAT YOU ARE DOING
#______________________________________________________________________________
# default is component 1
comp=1
# however if the component is passed as argument to the program, we update the component
arguments = len(sys.argv)
if(len(sys.argv)>1):
try:
comp=int(sys.argv[1])
print("Updating component to ",comp)
except:
pass
# Sanity check on the component
if(comp not in [1,2,3]):
print("Bad component passed as argument. Comp needs to be 1, 2 or 3. Defaulting to 1")
comp=1
# Make the process date from the data directory
File_date = os.path.basename(datadir)
print("Processing:",File_date," Component:",comp)
#______________________________________________________________________________
# For seismic event metadata, the de-facto standard is QuakeML (an xml document structure)
# set up a seach box:
minlat=stationlat-box
maxlat=stationlat+box
minlon=stationlon-box
maxlon=stationlon+box
# Load catalog from the web:
print("Events of the week downloaded from:\n",url+"\n UTC Time (Zulu) and filter for location.")
Thisweek_quakeml=requests.get(url).content
cat = read_events(Thisweek_quakeml)
# everything local
cat_local = cat.filter("latitude > " + str(minlat), "latitude < " + str(maxlat), "longitude > " + str(minlon), "longitude < " + str(maxlon))
# and everything world-wide with a magnitude > 5.5
cat_strong=cat.filter("magnitude >= "+ str(bigeventsmag))
# concatenate the local events with the strong events to make our label catalog
cat_all=cat_local
cat_all.extend(cat_strong)
#print(catall)
print(cat_all.__str__(print_all=True))
#______________________________________________________________________________
# Let's start by "locking" the files we will be workin on.
# Since the processing of the 3 components are run in succession due to hardware
# limits, we have to ensure they are run on the same set of underlying files.
if(comp==1):
for filename in sorted(os.listdir(datadir)):
if filename.endswith(".txt"):
try:
os.rename(os.path.join(datadir, filename),os.path.join(datadir, filename+".process"))
except:
pass
#______________________________________________________________________________
#Reading the raw data
starttime=datetime.now()
print("Loading form raw data:")
data=np.empty((0,),float)
for filename in sorted(os.listdir(datadir)):
if filename.endswith(".process"):
filename_date = filename
#print(filename,data.shape)#,data2.shape)
try:
data2 = np.genfromtxt(os.path.join(datadir, filename), delimiter=',',invalid_raise='false')
except:
print("Error reading:",filename)
# decimation by xX factor to save memory for 32 bit systems, add acts as antialias
#print(filename,data.shape,data2.shape)
try:
data3=np.add.reduceat(data2, np.arange(0,data2.shape[0],decimation))
print(filename,data.shape,data2.shape,"decimated to:",data3.shape,"start at, UTC:",UTCDateTime(data2[0][0]))#,starttime)
if(UTCDateTime(data2[0][0])<starttime):
starttime=UTCDateTime(data2[0][0])
print("Updating starttime to ",starttime, "- UTC")
data = np.append(data,data3[:,comp],axis=0)
del data2, data3
except:
print("Exception adding data from: ",filename)
# After the 3rd component is done, rename the files as .done
try:
if(comp==3):
os.rename(os.path.join(datadir, filename),os.path.join(datadir, filename+".done"))
except:
pass
#______________________________________________________________________________
# Updating variables from what we just learnt from reading the data
length=data.shape[0]
# Update the Obspy structure for plots, from the data read
print("Updating trace stats...")
statsx.update({'npts': length})
statsx.update({'sampling_rate': int(sampling_rate/decimation)})
statsx.update({'starttime': starttime})
#______________________________________________________________________________
# Generate the dayplot and write to miniSeed format, for each component
print("Generating trace...")
statsx.update({'channel': 'BH'+str(comp)+"."+filename_date[4:6]+"-"+filename_date[6:8]})
Xt = Trace(data=data[:], header=statsx)
#Xt.filter('lowpass', freq=50, corners=2, zerophase=True)
del data
stream = Stream(traces=[Xt])
del Xt
# Plot output
print("Generating plot...")
outfile=os.path.join(plotdir,filename_date[0:8]+'-dayplotFilter'+str(comp)+'.png')
stream.plot(type='dayplot',outfile=outfile,size=size,events=cat_all)
print("Miniseed writing...")
outminiseed=os.path.join(miniseeddir,filename_date[0:8]+"-comp"+str(comp)+'.mseed')
stream.write(outminiseed,format='MSEED')