lag_calc unexpected keyword argument #71
Comments
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You are looking at the docs for the develop branch, but have the current release installed. horizontal_chans is not in a released version. Either not use this argument, or, if you trust the develop branch (there shouldn't be bugs) then come the repo and install that branch using: |
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Awesome, thanks, I will try with dev. thank you, |
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The current master uses the defaults in develop, the new options just give you more control for non standard networks. |
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Hi! I found another error that I dont know what it is. So, basicly my scipt runs without problems - on first day I get some detections and lag_calc does its work as intendend, while on the next day, it dies with:
on the link, you can find the day of sucessful detection and sucessful lag_calc and the next day that brakes the code. Any idea? |
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It's an error that happens running in parallel, but didn't get raised properly because it is running in parallel, try setting parallel=False. The error message should be raised properly. |
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yes, thanks, this works...now I get this error: LagCalcError: lag-calc has decreased cccsum from 3.724045 to 2.530662 - report this error thanks |
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You should check that your are using the save data for lag calc that you used for match filtering. Running lag calc should never decrease the cccsum if the data are correct.
…Sent from my Vodafone Smart
On Nov 24, 2016 11:00 PM, blazvicic <notifications@github.com> wrote:
yes, thanks, this works...now I get this error:
LagCalcError: lag-calc has decreased cccsum from 3.724045 to 2.530662 - report this error
I can just try/except it, but I would like to know what is happening here, preferably still keeping the lag_calced event.
thanks
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this is my script... the only thing here that I would understand as "not being the same data" is that I am trying to get lag_calc for unique_detections, but if i try this for detections, the same error occurs. or the error comes from "template_names=template_files, templates=templates" part of lag_calc? Thanks! |
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I will try and have a look at this next week, weekend off after earthquake response here in nz, sorry I can't help straight away!
…Sent from my phone
On Nov 25, 2016 9:00 PM, blazvicic <notifications@github.com> wrote:
detections = match_filter.match_filter(template_names=template_files, template_list=templates, st=st, threshold=9, threshold_type='MAD', trig_int=3, plotvar=False, plotdir=('./plots'), cores=4) #### for detection in detections: detection.write("detections/"+day+'_detections',
append=True) #### unique_detections = [] for master in detections: if float(master.threshold) != 0 and (abs(float(master.detect_val))/float(master.threshold) > (1.1)): keep = True for slave in detections: if not master == slave and\ abs(master.detect_time
- slave.detect_time) <= 6.0: # If the events are within 6s of each other then test which # was the 'best' match, strongest det if not master.detect_val > slave.detect_val: keep = False break if keep: unique_detections.append(master) for detw in unique_detections:
detw.write("detections/6s/"+day+'_detections_unique', append=True) print detw.template_name #### cat_cc = lag_calc.lag_calc(detections=unique_detections, detect_data=st, template_names=template_files, templates=templates, shift_len=0.2, min_cc=0.4, cores=4,
interpolate=False, plot=False, parallel=False) if not os.path.isdir("events_cc/") == True: os.system("mkdir events_cc") for i, event in enumerate(sorted(cat_cc)): event.write("events_cc/"+str(i)+".xml", format="QUAKEML") event.write("events_cc/"+str(i)+".obs",
format="NLLOC_OBS")
this is my script... the only thing here that I would understand as "not being the same data" is that I am trying to get lag_calc for unique_detections, but if i try this for detections, the same error occurs.
or the error comes from "template_names=template_files, templates=templates" part of lag_calc?
I did sorted(template_files), but if I try to sorted(templates), it says that this is not possible.
Thanks!
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You are receiving this because you modified the open/close state.
Reply to this email directly, view it on GitHub<#71 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AKaGc9z_SJhaAc5LF8Frv3PzDkoSRRGRks5rBpWRgaJpZM4K37a2>.
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sure, no problem, take your time. thanks |
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Hi @blazvicic just getting to have a look at this - the above script is either not formatted correctly or missing top lines? Can you check and repost please? |
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sure... here it is: from eqcorrscan.core import match_filter, lag_calc
from eqcorrscan.utils import pre_processing
from eqcorrscan.utils.plotting import detection_multiplot
from obspy import read, Catalog
import glob
import os
outfile = open("failed_days", "w")
# Read in the templates
template_path = 'templates/'
template_files = glob.glob(os.path.join(template_path, '*'))
template_files = sorted(template_files)
templates = []
for template_file in template_files:
templates.append(read(template_file))
for template in templates:
for tr in template:
# Change channel names to two letters because EQcorrscan uses Seisan
# style channel names, which are not standard, and should be changed!
# Note that I might change EQcorrscan to use proper three letter
# chanel names in a future release, but I will ensure it works with
# two letter channel names too.
tr.stats.channel = tr.stats.channel[0] + tr.stats.channel[-1]
#pre_processing.shortproc(st=tr, lowcut=3.0, highcut=6.0, filt_order=3, samp_rate=20)
days =os.listdir('./24h/')
for day in sorted(days):
# Create detections and detections/6s folder that are used for detection files
if not os.path.isdir("detections/") == True:
os.system("mkdir detections")
os.system("mkdir detections/6s")
print "Days with data: ", sorted(days)
print "Number of days with data: ", len(days)
print "Match filter started for day: ", day
# Read in and process the daylong data
# There is something wrong with the way, Antelope is setting sampling rate to the miniseeds...
# This is why you need to manualy set to 200Hz - miniseeds are at 199.99Hz.
try:
st = read('24h/'+day+'/*')
print "Setting sampling rates of the traces"
for tr in st:
tr.stats.channel = tr.stats.channel[0] + tr.stats.channel[-1]
if not tr.stats.station == "VINO":
if tr.stats.sampling_rate != 200.0:
tr.stats.sampling_rate=200.0
else:
if tr.stats.sampling_rate != 100.0:
tr.stats.sampling_rate = 100.0
print "Merging traces"
st = st.merge(method=1, fill_value=0)
print "Detrending traces"
st = st.detrend('constant')
# Remove traces that are shorter then 17280000 samples (otherwise this will not work). What can be done?
print "Removing traces shorter then 17270000 samples"
for tr in st:
if not tr.stats.station == "VINO":
if len(tr) < 17270000:
st.remove(tr)
print "trace with less then 17280000 and not VINO:", tr.stats.station
else:
if len(tr) < 8640000:
st.remove(tr)
print "trace with less then 8640000 samples removed:", tr.stats.station
# Some streams will be empty after st.remove, so we cant do anything with them...
if len(st) == 0:
pass
else:
# Use the same filtering and sampling parameters as your template!
print "Preprocessing stream"
#st = pre_processing.shortproc(st, lowcut=3.0, highcut=6.0, filt_order=3,
# samp_rate=20,
# starttime=st[0].stats.starttime)
# Forced Daylong
st = pre_processing.dayproc(st, lowcut=3.0, highcut=6.0, filt_order=3,
samp_rate=20,
starttime=st[0].stats.starttime)
print(st.__str__(extended=True))
####
print "Starting with match filter"
detections = match_filter.match_filter(template_names=template_files,
template_list=templates, st=st,
threshold=9, threshold_type='MAD',
trig_int=3, plotvar=False, plotdir=('./plots'),
cores=4)
####
for detection in detections:
detection.write("detections/"+day+'_detections', append=True)
####
unique_detections = []
for master in detections:
if float(master.threshold) != 0 and (abs(float(master.detect_val))/float(master.threshold) > (1.1)):
keep = True
for slave in detections:
if not master == slave and\
abs(master.detect_time - slave.detect_time) <= 6.0:
# If the events are within 6s of each other then test which
# was the 'best' match, strongest det
if not master.detect_val > slave.detect_val:
keep = False
break
if keep:
unique_detections.append(master)
for detw in unique_detections:
detw.write("detections/6s/"+day+'_detections_unique', append=True)
print detw.template_name
####
cat_cc = lag_calc.lag_calc(detections=unique_detections, detect_data=st, template_names=template_files, templates=templates, shift_len=0.2, min_cc=0.4, cores=4, interpolate=False, plot=False, parallel=False)
if not os.path.isdir("events_cc/") == True:
os.system("mkdir events_cc")
for i, event in enumerate(sorted(cat_cc)):
event.write("events_cc/"+str(i)+".xml", format="QUAKEML")
event.write("events_cc/"+str(i)+".obs", format="NLLOC_OBS")
except TypeError:
print >>outfile, dayon the link you can find one day data, templates and the script in case if c/p is not correctly formated. Thank you for your help and time |
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I created a gist of this here: https://gist.github.com/calum-chamberlain/f4a8fb41f51dca9f91dd8fb03205ae48 And will edit this. |
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Hey @blazvicic sorry I have been so slow with this one - I'm hoping to have a proper look and test in the next couple of days. Just wanted you to know that I haven't forgotten! |
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Sure thing no problem! I am already getting interesting results without lag-calc, but that will add some value :) Thanks ---edited |
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Hey Blaz, |
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Looks like a strange issue in the depths of the correlations, it is going to take a while to work this one out. |
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Thanks. If there is someway I can help I would be gladd to do it... I get the same error also on other days (if I use more then one template and more then one template makes detections). Cheers, |
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Hi Blaz, I have ruled out a few things, it's not an issue related to multiple templates being used, the issue only seems to be there when station VINO is included... Looking more closely at the data, VINO has a significant artefact in it (there are a couple of spikes up to 10e7, which are the issue. Removing those spikes results in no issue. So, to explain: You can see this if you plot up the correlation of the template with one channel of VINO data - the correlation values after the spikes are much higher amplitude, and appear clipped. However if you just correlate the template with the same data period after the spikes (as done in lag_calc) the amplitudes are much lower (which is the cause of the error raised). So... |
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Looks like it's not a spike, but a box, around 20s long in the raw data, which ends up generating two spikes when filtered. |
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thanks for your time with that and comments about the problem. the "near real time" works on my pc now :) |
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near real-time? Would be curious to hear more about what you are up to! |
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i am sorry, nothing fancy. That is why " "... although we did discuss at the departement that this would indeed be a good thing...but it did not go fourther then this. if we go into it, i will discuss it with you, since i saw your plan is the same. does obspy have modul for continuous stream? |
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Cool, I have never got round to doing that, but always wanted to! |
Hi!
I am trying to get catalog of lag_calc events but I have a problem with it.
On the link you can find the script, one day data and templates.
https://drive.google.com/open?id=0BzMcKedS7yadZjY4Zkk4U1hEOFE
The error that I get is:
TypeError: lag_calc() got an unexpected keyword argument 'horizontal_chans'
lines for lag_calc are at the end of the script.
Why am I getting this error?
Thanks,
blaz
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