def read_pair(id):
"""
Function to read a Pair/EigM file.
Returns Pair object
"""
data = sw.load(id)
#Check if we are reading an EigenM object or a Pair
if type(data) is 'splitwavepy.measure.eigenM.EigenM':
quality = data.quality
pair = data.data
return pair, quality
else:
Exception('Unexpected Object type')
def test_pickle_CrossM_io(self):
# generate an eigenM object
a = sw.CrossM(delta=0.1)
# save to file
filename = 'temp.crsm'
a.save(filename)
# load
b = sw.load(filename)
# check they are the same
assert a == b
# cleanup
try:
os.remove(filename)
except OSError:
print("Error: cleanup failed for some reason")
def splitting(station,files,switch='off',phase='SKS'):
"""
Measures SKS splitting for all streams listed in a ttext file at the provided path. These streams must be saved as SAC files.abs
This function is the primary part of this module/package/script/thing, all the pther functions support this one.
inpath - string contaiing the path of the textfile which contains the sac file to be read
switch - optional kwarg to specify if you want to manually window the data or use a set of windows (Walpoles windows are availbale for use by default)
"""
outfile = output_init(station,switch,phase)
with open(files,'r') as reader: # NEW_read_stream.txt is a textfile containing filenames of streams which have been read and saved by Split_Read for this station. s
for line in reader.readlines():
line.strip('\n')
st_id = '{}*'.format(str(line[0:-1]))
st = read_sac(st_id)
# Intialise some global variables which I need to pass things between fucntions (this is probably not be best way to do things but it works!)
global pair_glob
global quality
global tt_glob
if st != False and len(st) is 3: # i.e. if the stream is sufficiently populated and has been read.
tt_UTC, t0, traveltime = model_traveltimes(st[0],phase) # Returns SKS arrival as a UTCDateTime object, event origin time and SKS arrival relative to the origin time
tt_glob = traveltime #global variable, allows for function interect to call for a repeat meausrent
# print(t0)
# print('SKS_UTC ={}'.format(SKS_UTC))
quality = [] # variable to hold Callback key entries for estimated quality of splitting measurements
date,time = int(str(t0.year)+str(t0.julday).zfill(3)),int(str(t0.hour).zfill(2)+str(t0.minute).zfill(2)+str(t0.second).zfill(2)) #creates time and date stamps
if switch is 'on':
eig_file ='{}/{}/{}/{}_{:07d}_{:06d}.eigm'.format('/Users/ja17375/Shear_Wave_Splitting/Python/Eigm_Files',phase,station,station,date,time)
elif switch is 'off':
eig_file = '{}/{}/{}/{}_{}_{:07d}_{:06d}'.format('/Users/ja17375/Shear_Wave_Splitting/Python/Eigm_Files',phase,station,station,'JW_Windows',date,time)
if os.path.isfile(eig_file):
split = sw.load(eig_file) #loads eigm file
write_splitting(outfile,station,phase,eigm=split,st=st,date=date,time=time)
else:
pair = st_prep(st = st, f_min = 0.01,f_max = 0.5)
# print(st[0].stats.starttime)
pair_glob = pair
if switch == 'on': # If manual windowing is on
if phase == 'SKKS' and st[0].stats.sac.gcarc < 105.0:
split = None
else:
print('Test Passed. Phase ={}, GCARC = {}'.format(phase,st[0].stats.sac.gcarc))
split, wbeg, wend,fig = split_measure(pair,traveltime)
split.quality = quality
plt.savefig('{}{}_{}_{:07d}_{:06d}'.format('/Users/ja17375/Shear_Wave_Splitting/Python/Figures/Eigm_Surface/',station,phase,date,time))
plt.close()
write_splitting(outfile,station,phase,eigm=split,st=st,date=date,time=time)
split.save(eig_file)
elif switch == 'off': #Manual windowing is off. For now this will just mean Jacks windows will be used. Eventually add automation or support for entering windows.
(wl_fast,wl_dfast,wl_tlag,wl_dtlag,wl_wbeg,wl_wend) = split_match(date,time,station)
pair.set_window(wl_wbeg,wl_wend) # Sets window to that of Jacks
wbeg,wend = pair.wbeg(), pair.wend()
split = sw.EigenM(pair,lags=(4,) ) # Measures splitting
fig = plt.figure(figsize=(12,6))
eigen_plot(split,fig)
plt.savefig('{}/{}_{}_{}_{:07d}_{:06d}'.format('/Users/ja17375/Shear_Wave_Splitting/Python/Figures/Eigm_Surface',station,phase,'JW_Windows',date,time))
plt.close()
split.quality = 'w'
write_splitting(outfile,station,phase,eigm=split,st=st,date=date,time=time) #Call write_splitting where there are measuremtns to output
# save_sac(st,quality[0],date,time,wbeg,wend,switch)
split.save(eig_file) # Saves splitting measurements
else:
write_splitting(outfile,phase,station)
plt.close('all')
outfile.close()
def plotall(filestem):
plt.figure(figsize=(15,10))
gs = gridspec.GridSpec(7, 6)
ax0 = plt.subplot(gs[0, 0:5])
plt.ion()
plt.show()
# plot trace at top (this will put sks windows in automatically)
sks = sw.load(filestem + '_sks.trnm')
sks.srcpoldata()._ptr(ax0)
# add the skks window
skks = sw.load(filestem + '_skks.trnm')
ax0.axvline(skks.data.wbeg(), linewidth=1, color='k')
ax0.axvline(skks.data.wend(), linewidth=1, color='k')
# plot FFT power spectrum from radial component
ax06 = plt.subplot(gs[0,5])
power0 = np.abs(np.fft.fft(sks.srcpoldata().data()[0]))**2
power1 = np.abs(np.fft.fft(sks.srcpoldata().data()[1]))**2
freq = np.fft.fftfreq(sks.srcpoldata().data().shape[1], sks.srcpoldata().delta)
ax06.fill_between(freq, power0, label='rad', alpha=0.5)
ax06.fill_between(freq, power1, label='trans', alpha=0.5)
ax06.set_xlim(0, 0.6)
ax06.set_xlabel('Frequency (Hz)')
ax06.set_ylabel('Power')
ax06.legend()
# now start plotting particle motions and error surf
ax10 = plt.subplot(gs[1,0])
sks.data._ppm(ax10)
ax20 = plt.subplot(gs[2,0])
sks.data_corr()._ppm(ax20)
ax11 = plt.subplot(gs[1:3, 1:3])
sks._psurf(ax11, vals=sks.energy1/sks.energy2, conf95=True, marker=True, info=True)
# ax10.axis('off')
# ax20.axis('off')
# ax11.axis('off')
ax13 = plt.subplot(gs[1,3])
skks.data._ppm(ax13)
ax23 = plt.subplot(gs[2,3])
skks.data_corr()._ppm(ax23)
ax14 = plt.subplot(gs[1:3, 4:6])
skks._psurf(ax14, vals=skks.energy1/skks.energy2, conf95=True, marker=True, info=True)
# now load eigenm files
sks = sw.load(filestem + '_sks.eigm')
skks = sw.load(filestem + '_skks.eigm')
# now start plotting particle motions and error surf
ax30 = plt.subplot(gs[3,0])
sks.data._ppm(ax30)
ax40 = plt.subplot(gs[4,0])
sks.data_corr()._ppm(ax40)
ax31 = plt.subplot(gs[3:5, 1:3])
sks._psurf(ax31, vals=sks.lam1/sks.lam2, conf95=True, marker=True, info=True)
ax33 = plt.subplot(gs[3,3])
skks.data._ppm(ax33)
ax43 = plt.subplot(gs[4,3])
skks.data_corr()._ppm(ax43)
ax34 = plt.subplot(gs[3:5, 4:6])
skks._psurf(ax34, vals=skks.lam1/skks.lam2, conf95=True, marker=True, info=True)
# now load crossm files
sks = sw.load(filestem + '_sks.xcrm')
skks = sw.load(filestem + '_skks.xcrm')
# now start plotting particle motions and error surf
ax50 = plt.subplot(gs[5,0])
sks.data._ppm(ax50)
ax60 = plt.subplot(gs[6,0])
sks.data_corr()._ppm(ax60)
ax51 = plt.subplot(gs[5:7, 1:3])
sks._psurf(ax51, vals=sks.fisher(), conf95=True, marker=True, info=True)
ax53 = plt.subplot(gs[5,3])
skks.data._ppm(ax53)
ax63 = plt.subplot(gs[6,3])
skks.data_corr()._ppm(ax63)
ax54 = plt.subplot(gs[5:7, 4:6])
skks._psurf(ax54, vals=skks.fisher(), conf95=True, marker=True, info=True)
# plt.tick_params(
# axis='both', # changes apply to the x-axis
# which='both', # both major and minor ticks are affected
# bottom='off', # ticks along the bottom edge are off
# top='off', # ticks along the top edge are off
# labelbottom='off')
plt.tight_layout()
plt.draw()
qual = input('Data Quality ([g]ood/[o]k/[p]oor) \n >')
plt.draw()
disc = input('Are SKS/SKKS discrepant (can add comments)? ([y]es/[n]o/[u]ndeterminable) \n ?')
plt.close()
return qual,disc