def event_magnitude(sfile, min_sta=3):
"""
Function to generate the magnitude of a single event from a seisan s-file
with amplitude picks in it
:type sfile: String
:param sfile: Nordic type s-file name, with full path
:return: Local magnitude, standard deviation of magnitude
"""
from pro import Sfile_util
from par import mag_conv_par as mag_par
# Check that the s-file exists
import glob
if not glob.glob(sfile):
raise NameError('Sfile does not exist: '+sfile)
picks=Sfile_util.readpicks(sfile)
Mag_out=[]
for pick in picks:
if pick.phase=='IAML':
if 'sta_cor' in locals():
del sta_cor
for station in mag_par.station_corrections:
if pick.station==station[0]:
sta_cor=station[1]
if not 'sta_cor' in locals():
sta_cor=1.0
# print '\nStation correction not found for station '+pick.station
# try:
# Note, seisan stores half amplitudes
Magnitude=mag_conv(pick.amplitude*2, pick.distance, sta_cor,\
mag_par.frequency_dependent,\
pick.peri)
if not np.isnan(Magnitude):
Mag_out.append(Magnitude)
# except (ValueError):
# print 'Either earthquake too far away, or frequency is not set'
# print pick.peri
# pass
if len(Mag_out) > min_sta:
Mag_std=np.std(Mag_out)
Mag_out=np.mean(Mag_out) # Take the mean magnitude
print Mag_out
return Mag_out, Mag_std
else:
return np.nan, np.nan
def plot_residuals(path):
"""
Function to read in S-files and make a histogram of the pick residuals for
each station
:type path: Str
"""
import glob, sys
import matplotlib.pyplot as plt
from pro import Sfile_util
import numpy as np
sfilelist=glob.glob(path+'/*/*/*.S*')
if 'stachan_list' in locals():
del stachan_list
for sfile in sfilelist:
picks=Sfile_util.readpicks(sfile)
for pick in picks:
match=0
if not 'stachan_list' in locals():
if abs(pick.timeres) <=4.0 and pick.channel != 'HT':
if pick.phase=='P':
stachan_list=[[pick.station, pick.channel, [pick.timeres],[]]]
elif pick.phase=='S':
stachan_list=[[pick.station, pick.channel, [], [pick.timeres]]]
else:
for stachan in stachan_list:
if pick.station == stachan[0] and \
pick.channel == stachan[1] and not np.isnan(pick.timeres)\
and abs(pick.timeres) <= 4.0:
if pick.phase=='P':
stachan[2].append(pick.timeres)
elif pick.phase=='S':
stachan[3].append(pick.timeres)
match=1
if match == 0 and not np.isnan(pick.timeres)\
and abs(pick.timeres) <= 4.0 and pick.channel != 'HT':
print 'Found picks for: '+pick.station+' '+pick.channel
if pick.phase=='P':
stachan_list.append([pick.station, pick.channel, [pick.timeres], []])
elif pick.phase=='S':
stachan_list.append([pick.station, pick.channel, [], [pick.timeres]])
# Print out some useful stats
ppicks=0
spicks=0
presidual=0
sresidual=0
for stachan in stachan_list:
ppicks+=len(stachan[2])
spicks+=len(stachan[3])
presidual+=sum(stachan[2])
sresidual+=sum(stachan[3])
print 'Total P-picks: '+str(ppicks)
print 'Total S-picks: '+str(spicks)
print 'P RMS mean: '+str(presidual/ppicks)
print 'S RMS mean: '+str(sresidual/spicks)
# Plot the results
i=0
# Get unique list of stations, make one plot for each station
stations=[]
for stachan in stachan_list:
stations+=[stachan[0]]
stations=list(set(stations))
# Concatenate all the picks for each station
stachan_list.sort()
for stachan in stachan_list:
if not 'sta_list' in locals():
sta_list=[[stachan[0], 'all', stachan[2], stachan[3]]]
station=stachan[0]
i=0
else:
if station==stachan[0]:
sta_list[i][3]+=stachan[3]
sta_list[i][2]+=stachan[2]
else:
sta_list.append(stachan)
i+=1
station=stachan[0]
fig, axes = plt.subplots((len(sta_list)), 1, sharex=True)#, sharey=True)
print 'I have picks for '+str(len(sta_list))+' stations'
axes=axes.ravel()
i=0
for stachan in sta_list:
print 'Plotting for station: '+stachan[0]
if len(stachan[2]) != 0:
n, bins, patches=axes[i].hist(stachan[2], bins=np.arange(-4.0, 4.0, 0.025)\
, facecolor='Black', alpha=0.5)
axes[i].text(0.85, 0.8, r'$\ P:\ \mu='+str(np.mean(stachan[2]))[0:4]+\
',\ \sigma='+str(np.std(stachan[2]))[0:4]+\
',\ n='+str(len(stachan[2]))+'$',\
horizontalalignment='center', verticalalignment='center',
transform=axes[i].transAxes)
if len(stachan[3]) != 0:
n, bins, patches=axes[i].hist(stachan[3],bins=np.arange(-4.0, 4.0, 0.025)\
, facecolor='Red', alpha=0.75)
axes[i].text(0.15, 0.8, r'$\ S:\ \mu='+str(np.mean(stachan[3]))[0:4]+\
',\ \sigma='+str(np.std(stachan[3]))[0:4]+\
',\ n='+str(len(stachan[3]))+'$',\
horizontalalignment='center', verticalalignment='center',
transform=axes[i].transAxes, color='Red')
axes[i].set_ylabel(stachan[0])
# axes[i].yaxis.set_label_position("right")
axes[i].yaxis.tick_right()
axes[i].locator_params(axis='y', nbins=2)
i+=1
axes[i-1].set_xlabel('RMS residual (s)')
plt.xlim(-2.0, 2.0)
# plt.ylim(0,40)
fig.subplots_adjust(hspace=0.25)
fig.subplots_adjust(wspace=0)
fig.text(0.94, 0.5, 'Number of picks', ha='center', va='center', rotation=270)
plt.show()
# plt.savefig('residuals.eps')
return stachan_list