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(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_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
