def volume_plot(stationpath, database, limits):
"""
Function to read in station information from a file and earthquake info
from sfiles.
:type stationpath: str
:type databse: str
"""
from eqcorrscan.utils import Sfile_util
import glob
sfiles = glob.glob(database + "/*/*/*")
eqlocs = []
for sfile in sfiles:
try:
eqlocs += [
(
Sfile_util.readheader(sfile).latitude,
Sfile_util.readheader(sfile).longitude,
Sfile_util.readheader(sfile).depth,
)
]
except:
continue
stalocs = []
f = open(stationpath, "r")
for line in f:
stalocs += [(float(line.split(",")[1]), float(line.split(",")[0]), float(line.split(",")[4]) / 1000)]
f.close()
from utils import EQcorrscan_plotting
EQcorrscan_plotting.threeD_seismplot(stalocs, eqlocs, limits)
return
def volume_plot(stationpath, database, limits):
"""
Function to read in station information from a file and earthquake info
from sfiles.
:type stationpath: str
:type databse: str
"""
from eqcorrscan.utils import Sfile_util
import glob
sfiles = glob.glob(database + '/*/*/*')
eqlocs = []
for sfile in sfiles:
try:
eqlocs+=[(Sfile_util.readheader(sfile).latitude,\
Sfile_util.readheader(sfile).longitude,\
Sfile_util.readheader(sfile).depth)]
except:
continue
stalocs = []
f = open(stationpath, 'r')
for line in f:
stalocs+=[(float(line.split(',')[1]),\
float(line.split(',')[0]), float(line.split(',')[4])/1000)]
f.close()
from utils import EQcorrscan_plotting
EQcorrscan_plotting.threeD_seismplot(stalocs, eqlocs, limits)
return
template = template_dummy
for tr in template:
for i in xrange(len(stations)):
if tr.stats.station == stations[i]:
if not "alllags" in locals():
alllags = [lags[i]]
else:
# print stations[i]
alllags = np.concatenate((alllags, [lags[i]]), axis=0)
# print np.shape(alllags)
lags = alllags
print "Lags is shaped: " + str(np.shape(lags))
print "I have " + str(len(template)) + " channels of data"
# Indexing will be an issue, currently don't check that stations match between data and lags
possible_locations = moveout_check(
template,
nodes,
lags,
defaults.threshold,
defaults.threshtype,
tempdef.lowcut,
tempdef.highcut,
tempdef.filter_order,
)
from utils import EQcorrscan_plotting as plotting
if not len(possible_locations) == 0:
plotting.threeD_gridplot(possible_locations)
else:
raise ValueError("No possible location found")
for tr in stream)
stream=Stream(stream)
print stream
if not Prep:
#stream_copy=stream.copy() # Keep the stream safe
print "Running the detection routine"
# Check that the data are okay
detect_templates, detect_nodes=bright_lights.brightness(stations, \
nodes, lags, stream,
brightdef.threshold, brightdef.thresh_type,\
brightdef.coherance, instance, matchdef, templatedef)
del detect_templates#, stream # Delete templates from memory to conserve RAM!
#stream=stream_copy
nodesout+=detect_nodes
if Split:
plotting.threeD_gridplot(nodesout, save=brightdef.plotsave,\
savefile='Detected_nodes_'+str(instance)+'.png')
else:
plotting.threeD_gridplot(nodesout, save=brightdef.plotsave,\
savefile='Detected_nodes.png')
else:
for tr in stream:
print "Writing data as: test_data/"+tr.stats.station+'-'+tr.stats.channel+\
'-'+str(tr.stats.starttime.year)+\
'-'+str(tr.stats.starttime.month).zfill(2)+\
'-'+str(tr.stats.starttime.day).zfill(2)+\
'-processed.ms'
tr.write('test_data/'+tr.stats.station+'-'+tr.stats.channel+\
'-'+str(tr.stats.starttime.year)+\
'-'+str(tr.stats.starttime.month).zfill(2)+\
'-'+str(tr.stats.starttime.day).zfill(2)+\
for tr in stream)
stream = Stream(stream)
print stream
if not Prep:
#stream_copy=stream.copy() # Keep the stream safe
print "Running the detection routine"
# Check that the data are okay
detect_templates, detect_nodes=bright_lights.brightness(stations, \
nodes, lags, stream,
brightdef.threshold, brightdef.thresh_type,\
brightdef.coherance, instance, matchdef, templatedef)
del detect_templates #, stream # Delete templates from memory to conserve RAM!
#stream=stream_copy
nodesout += detect_nodes
if Split:
plotting.threeD_gridplot(nodesout, save=brightdef.plotsave,\
savefile='Detected_nodes_'+str(instance)+'.png')
else:
plotting.threeD_gridplot(nodesout, save=brightdef.plotsave,\
savefile='Detected_nodes.png')
else:
for tr in stream:
print "Writing data as: test_data/"+tr.stats.station+'-'+tr.stats.channel+\
'-'+str(tr.stats.starttime.year)+\
'-'+str(tr.stats.starttime.month).zfill(2)+\
'-'+str(tr.stats.starttime.day).zfill(2)+\
'-processed.ms'
tr.write('test_data/'+tr.stats.station+'-'+tr.stats.channel+\
'-'+str(tr.stats.starttime.year)+\
'-'+str(tr.stats.starttime.month).zfill(2)+\
'-'+str(tr.stats.starttime.day).zfill(2)+\
sys.path.insert(0,path[0:len(path)-5])
from par import template_gen_par as templatedef
from par import bright_lights_par as brightdef
from core import bright_lights
from utils import EQcorrscan_plotting as plotting
# Use the brightness function to search for possible templates
# First read in the travel times
print 'Reading in the original grids'
stations, allnodes, alllags = \
bright_lights._read_tt(brightdef.nllpath,brightdef.stations,\
brightdef.phase)
# Resample the grid to allow us to run it quickly!
print 'Cutting the grid'
stations, nodes, lags = bright_lights._resample_grid(stations, allnodes,
alllags,
brightdef.volume,
brightdef.resolution)
# Remove lags that have a similar network moveout, e.g. the sum of the
# differences in moveouts is small.
print "Removing simlar lags"
stations, nodes, lags = bright_lights._rm_similarlags(stations, nodes, lags,
brightdef.nodesimthresh)
print "Plotting new grid"
plotting.threeD_gridplot(nodes)
dailycoherence = coherence_test(stream, stations, nodes, lags, \
templatedef.length)
else:
raise IOError("No traces read in for this day, are they processed?")
else:
raise IOError("I only know --coherence at the moment")
def find_peaks2(arr,thresh, trig_int, debug=0, maxwidth=10,\
starttime=UTCDateTime('1970-01-01'), samp_rate=1.0):
"""
Function to determine peaks in an array of data using scipy find_peaks_cwt,
works fast in certain cases, but for match_filter cccsum peak finding,
find_peaks2_short works better. Test it out and see which works best for
your application.
:type arr: ndarray
:param arr: 1-D numpy array is required
:type thresh: float
:param thresh: The threshold below which will be considered noise and peaks\
will not be found in.
:type trig_int: int
:param trig_int: The minimum difference in samples between triggers,\
if multiple peaks within this window this code will find the highest.
:type debug: int
:param debug: Optional, debug level 0-5
:type maxwidth: int
:param maxwidth: Maximum peak width to look for in samples
:return: peaks, locs: Lists of peak values and locations.
"""
from scipy.signal import find_peaks_cwt
# Set everything below the threshold to zero
image=np.copy(arr)
image=np.abs(image)
image[image<thresh]=thresh
# We need to check if the number of samples in the image is prime, if it
# is this method will be really slow, so we add a pad to the end to make
# it not of prime length!
if is_prime(len(image)):
image=np.append(image, 0.0)
print 'Input array has a prime number of samples, appending a zero'
print len(image)
if len(image[image>thresh])==0:
print 'No values over threshold found'
return []
if debug > 0:
print 'Found '+str(len(image[image>thresh]))+' samples above the threshold'
initial_peaks=[]
peaks=[]
# Find the peaks
print 'Finding peaks'
peakinds = find_peaks_cwt(image, np.arange(1,maxwidth))
initial_peaks=[(image[peakind], peakind) for peakind in peakinds]
# Sort initial peaks according to amplitude
print 'sorting peaks'
peaks_sort=sorted(initial_peaks, key=lambda amplitude:amplitude[0],\
reverse=True)
# Debugging
if debug>=4:
for peak in initial_peaks:
print peak
if initial_peaks:
peaks.append(peaks_sort[0]) # Definitely take the biggest peak
if debug > 3:
print 'Added the biggest peak of '+str(peaks[0][0])+' at sample '+\
str(peaks[0][1])
if len(initial_peaks) > 1:
if debug>3:
print 'Multiple peaks found, checking them now to see if they overlap'
for next_peak in peaks_sort:#i in xrange(1,len(peaks_sort)): # Loop through the amplitude sorted peaks
# if the next highest amplitude peak is within trig_int of any
# peak already in peaks then we don't want it, else, add it
#next_peak=peaks_sort[i]
if debug>3:
print next_peak
for peak in peaks:
add=False # Use add as a switch for whether or not to append
# next peak to peaks, if once gone through all the peaks
# it is True, then we will add it, otherwise we won't!
if abs(next_peak[1]-peak[1]) < trig_int:
if debug>3:
print 'Difference in time is '+str(next_peak[1]-peak[1])
print 'Which is less than '+str(trig_int)
add=False
# Need to exit the loop here if false
break
else:
add=True
if add:
if debug>3:
print 'Adding peak of '+str(next_peak[0])+' at sample '+\
str(next_peak[1])
peaks.append(next_peak)
elif debug >3:
print 'I did not add peak of '+str(next_peak[0])+\
' at sample '+str(next_peak[1])
if debug >= 3:
from utils import EQcorrscan_plotting
EQcorrscan_plotting.peaks_plot(image, starttime, samp_rate, True, peaks,
'debug_output/peaks_'+\
str(starttime.year)+'-'+\
str(starttime.month)+'-'+\
str(starttime.day)+'.pdf')
peaks=sorted(peaks, key=lambda time:time[1], reverse=False)
return peaks
else:
print 'No peaks for you!'
return peaks
def find_peaks_dep(arr, thresh, trig_int, debug=0,\
starttime=UTCDateTime('1970-01-01'), samp_rate=1.0):
"""
Function to determine peaks in an array of data above a certain threshold.
Depreciated peak-finding routine, very slow, but accurate. If all else fails
this one should work.
:type arr: ndarray
:param arr: 1-D numpy array is required
:type thresh: float
:param thresh: The threshold below which will be considered noise and peaks\
will not be found in.
:type trig_int: int
:param trig_int: The minimum difference in samples between triggers,\
if multiple peaks within this window this code will find the highest.
:return: peaks, locs: Lists of peak values and locations.
"""
# Perform some checks
if trig_int < 3:
import sys
print 'Trigger interval must be greater than two samples to find maxima'
sys.exit()
#from joblib import Parallel, delayed
# Will find peaks in the absolute then transfer these to the true values
sig=np.abs(arr)-thresh
true_peaks=[]
for i in xrange(int(trig_int),int(len(sig)-trig_int), int(trig_int)):
window=sig[i-trig_int:i+trig_int] # Define a moving window containing
# data from +/- the trigger iterval
peaks=[]
locs=[]
for j in xrange(1,len(window)-1):
# Find all turning points within the window
if window[j] > 0.0 and window[j] > window[j+1] and window[j] > window[j-1]:
peaks.append(window[j])
locs.append(i-trig_int+j)
# Find maximum peak in window
if peaks:
true_peaks.append((np.max(np.array(peaks)),\
locs[np.argmax(np.array(peaks))]))
# Get unique values
peaks=sorted(list(set(true_peaks)), key=lambda loc: loc[1])
# Find highest peak in peaks within trig_int of each other
for i in xrange(1,len(peaks)-1):
if peaks[i+1][1]-peaks[i][1] < trig_int:
if peaks[i][0] < peaks[i+1][0]:
peaks[i]=peaks[i+1]
else:
peaks[i+1]=peaks[i]
elif peaks[i][1]-peaks[i-1][1] < trig_int:
if peaks[i][0] < peaks[i-1][0]:
peaks[i]=peaks[i-1]
else:
peaks[i-1]=peaks[i]
peaks=sorted(list(set(peaks)), key=lambda loc: loc[1])
if debug >= 3:
from utils import EQcorrscan_plotting
EQcorrscan_plotting.peaks_plot(arr, starttime, samp_rate, True, peaks,
'debug_output/peaks_'+\
str(starttime.year)+'-'+\
str(starttime.month)+'-'+\
str(starttime.day)+'.pdf')
return peaks
def find_peaks2_short(arr,thresh, trig_int, debug=0, \
starttime=UTCDateTime('1970-01-01'), samp_rate=1.0):
"""
Function to determine peaks in an array of data above a certain threshold.
Uses a mask to remove data below threshold and finds peaks in what is left.
:type arr: ndarray
:param arr: 1-D numpy array is required
:type thresh: float
:param thresh: The threshold below which will be considered noise and peaks\
will not be found in.
:type trig_int: int
:param trig_int: The minimum difference in samples between triggers,\
if multiple peaks within this window this code will find the highest.
:type debug: int
:param debug: Optional, debug level 0-5
:return: peaks, locs: Lists of peak values and locations.
"""
from scipy import ndimage
# Set everything below the threshold to zero
image=np.copy(arr)
image=np.abs(image)
image[image<thresh]=0
if len(image[image>thresh])==0:
print 'No values over threshold found'
return []
if debug > 0:
print 'Found '+str(len(image[image>thresh]))+' samples above the threshold'
initial_peaks=[]
peaks=[]
# Find the peaks
labeled_image, number_of_objects= ndimage.label(image)
peak_slices = ndimage.find_objects(labeled_image)
for peak_slice in peak_slices:
#print 'Width of peak='+str(peak_slice[0].stop-peak_slice[0].start)
window=arr[peak_slice[0].start:peak_slice[0].stop]
initial_peaks.append((max(window),peak_slice[0].start+np.argmax(window)))
# Sort initial peaks according to amplitude
peaks_sort=sorted(initial_peaks, key=lambda amplitude:amplitude[0],\
reverse=True)
# Debugging
if debug>=4:
for peak in initial_peaks:
print peak
if initial_peaks:
peaks.append(peaks_sort[0]) # Definitely take the biggest peak
if debug > 3:
print 'Added the biggest peak of '+str(peaks[0][0])+' at sample '+\
str(peaks[0][1])
if len(initial_peaks) > 1:
if debug>3:
print 'Multiple peaks found, checking them now to see if they overlap'
for next_peak in peaks_sort:#i in xrange(1,len(peaks_sort)): # Loop through the amplitude sorted peaks
# if the next highest amplitude peak is within trig_int of any
# peak already in peaks then we don't want it, else, add it
#next_peak=peaks_sort[i]
if debug>3:
print next_peak
for peak in peaks:
add=False # Use add as a switch for whether or not to append
# next peak to peaks, if once gone through all the peaks
# it is True, then we will add it, otherwise we won't!
if abs(next_peak[1]-peak[1]) < trig_int:
if debug>3:
print 'Difference in time is '+str(next_peak[1]-peak[1])
print 'Which is less than '+str(trig_int)
add=False
# Need to exit the loop here if false
break
else:
add=True
if add:
if debug>3:
print 'Adding peak of '+str(next_peak[0])+' at sample '+\
str(next_peak[1])
peaks.append(next_peak)
elif debug >3:
print 'I did not add peak of '+str(next_peak[0])+\
' at sample '+str(next_peak[1])
if debug >= 3:
from utils import EQcorrscan_plotting
EQcorrscan_plotting.peaks_plot(image, starttime, samp_rate, True, peaks,
'debug_output/peaks_'+\
str(starttime.year)+'-'+\
str(starttime.month)+'-'+\
str(starttime.day)+'.pdf')
peaks=sorted(peaks, key=lambda time:time[1], reverse=False)
return peaks
else:
print 'No peaks for you!'
return peaks
stations, nodes, lags = _resample_grid(stations, nodes, lags, brightdef.volume,\
brightdef.resolution)
# print np.shape(lags)
for station in stations:
if not 'template_dummy' in locals():
template_dummy = template.select(station=station)
else:
template_dummy += template.select(station=station)
template = template_dummy
for tr in template:
for i in xrange(len(stations)):
if tr.stats.station == stations[i]:
if not 'alllags' in locals():
alllags = [lags[i]]
else:
# print stations[i]
alllags = np.concatenate((alllags, [lags[i]]), axis=0)
# print np.shape(alllags)
lags = alllags
print 'Lags is shaped: ' + str(np.shape(lags))
print 'I have ' + str(len(template)) + ' channels of data'
# Indexing will be an issue, currently don't check that stations match between data and lags
possible_locations = moveout_check(template, nodes, lags, defaults.threshold,\
defaults.threshtype, tempdef.lowcut,\
tempdef.highcut, tempdef.filter_order)
from utils import EQcorrscan_plotting as plotting
if not len(possible_locations) == 0:
plotting.threeD_gridplot(possible_locations)
else:
raise ValueError("No possible location found")
