def plot_dist_RMS(path, origin):
"""
Function to recalculate the magnitudes for a given database and plot the
RMS against distance.
:type path: Str
:param path: Database to convert
:type origin: Tuple
:param origin: Lat, Long and Depth of origin
"""
import glob
from pro import Sfile_util
sfilelist=glob.glob(path+'/*/*/*.S*')
RMS=[]
dist=[]
for sfile in sfilelist:
header=Sfile_util.readheader(sfile)
RMS.append(header.t_RMS)
dist.append(dist_calc(origin, (header.latitude, header.longitude, header.depth)))
import matplotlib.pyplot as plt
# plt.semilogy(dist,RMS, c='red', marker='o', ls='None')
plt.plot(dist,RMS, c='red', marker='o', ls='None')
plt.xlabel('Distance in km')
plt.ylabel('RMS (s)')
plt.xlim([0,350])
plt.ylim([0,10])
plt.title('RMS with distance from '+str(origin[0])+', '+str(origin[1]))
# plt.legend()
plt.show()
return
def summary_table(path, output='csv'):
"""
Function to generate a summary table of earthquake information. Can output
either as a .csv or a .tex file.
:type path: str
:param path: Database directory
:type output: str
:param output: Either 'csv' or 'tex'
"""
# Check that the output is correct
if not output in ['csv', 'tex']:
print output
raise ValueError('Output format not recognised')
import glob
from pro import Sfile_util
sfilelist=glob.glob(path+'/*/*/*.S*')
f=open('Summary.'+output, 'w')
# Write header
if output == 'csv':
f.write('Date, Origin time (UTC), Latitude (deg), Longitude (deg), Depth (km), Magnitude(seisan), Magnitude(local)\n')
elif output == 'tex':
f.write('\begin{table}{c c c c c c c}\n')
f.write('\textbf{Date} & \textbf{Origin time (UTC)} & \textbf{Latitude (deg)}'+\
'\textbf{Longitude (deg)} & \textbf{Depth (km)} & \textbf{Magnitude(seisan)}'+\
'\textbf{Magnitude (local)}\\ \n')
f.write('\hline\n')
# Write contents
for sfile in sfilelist:
EQ_info=Sfile_util.readheader(sfile)
Mag_out, Mag_std = event_magnitude(sfile)
if output == 'csv':
f.write(str(EQ_info.time.year)+'/'+str(EQ_info.time.month).zfill(2)+'/'+\
str(EQ_info.time.day).zfill(2)+', '+\
str(EQ_info.time.hour).zfill(2)+':'+\
str(EQ_info.time.minute).zfill(2)+':'+\
str(EQ_info.time.second).zfill(2)+'.'+\
str(EQ_info.time.microsecond).zfill(2)+', '+\
str(EQ_info.latitude)+', '+str(EQ_info.longitude)+', '+\
str(EQ_info.depth)+','+str(EQ_info.Mag_1)+','+\
str(Mag_out)+'\n')
elif output == 'tex':
f.write(str(EQ_info.time.year)+'/'+str(EQ_info.time.month).zfill(2)+'/'+\
str(EQ_info.time.day).zfill(2)+' & '+\
str(EQ_info.time.hour).zfill(2)+':'+\
str(EQ_info.time.minute).zfill(2)+':'+\
str(EQ_info.time.second).zfill(2)+'.'+\
str(EQ_info.time.microsecond).zfill(2)+' & '+\
str(EQ_info.latitude)+' & '+str(EQ_info.longitude)+' & '+\
str(EQ_info.depth)+' & '+str(EQ_info.Mag_1)+' & '+\
str(Mag_out)+'\\ \n')
# Write end of table for latex
if output == 'tex':
f.write('\end{table}')
f.close()
print 'Written summary file: Summary.'+output
return
def recalc_database(path, plot=True):
"""
Overarching code to recalculate and plot all the magnitudes for a given
database in seisan. Must be a databse of S-files in a SEISAN REA structure
:type path: String
:param path: Path to the top of the rea tree (above the year directories)
:return: Event info, list of tuples (Mag_out, Mag_in, Date, Location)
"""
import glob, sys, warnings
from par import mag_conv_par as mag_par
from pro import Sfile_util
from obspy import UTCDateTime
if not glob.glob(path):
raise NameError('Path does not exist '+path)
sfilelist=glob.glob(path+'/*/*/*.S??????')
sfilelist.sort()
Mag_in=[] # Only want to take the local magnitudes
Mag_out=[]
Mag_clipped=[]
Date=[]
Event_info=[]
for sfile in sfilelist:
# sys.stdout.write('Working on sfile: '+sfile+'\r')
print('Working on sfile: '+sfile+'\r')
try:
header=Sfile_util.readheader(sfile)
del header
except IndexError:
warnings.warn(sfile+' is corrupt')
break
# sys.stdout.flush()
Date.append(Sfile_util.readheader(sfile).time)
if Sfile_util.readheader(sfile).time == UTCDateTime(0):
raise ValueError(sfile+' has 0 date')
if not np.isnan(Sfile_util.readheader(sfile).Mag_1) and\
Sfile_util.readheader(sfile).Mag_1_type=='L':
Mag_in.append(Sfile_util.readheader(sfile).Mag_1)
Magnitude=np.nan
if Sfile_util.readheader(sfile).Mag_1_type=='L' and\
Sfile_util.readheader(sfile).ev_id != 'E':
Magnitude=(event_magnitude(sfile)[0])
if not np.isnan(Magnitude):
Mag_out.append(Magnitude)
Mag_clipped.append(Sfile_util.readheader(sfile).Mag_1)
Event_info.append([Magnitude, Sfile_util.readheader(sfile).Mag_1, \
Sfile_util.readheader(sfile).time, \
(Sfile_util.readheader(sfile).latitude, \
Sfile_util.readheader(sfile).longitude, \
Sfile_util.readheader(sfile).depth)])
if plot:
import matplotlib.pyplot as plt
try:
fig, ax1 = plt.subplots()
# Plot histogram
bins=np.arange(-1,7,0.2)
n, bins, patches=ax1.hist(Mag_in,bins, facecolor='Black', \
alpha=0.5, label='Previous, n='+str(len(Mag_in)))
n, bins, patches=ax1.hist(Mag_clipped,bins, facecolor='Black', \
alpha=0.7, label='Previous, n='+str(len(Mag_clipped)))
n, bins, patches=ax1.hist(Mag_out, bins, facecolor='Red', \
alpha=0.7, label='Recalculated, n='+str(len(Mag_out)))
plt.legend()
ax1.set_ylabel('Number of events')
ax1.set_ylim([0, max(n)+0.5*max(n)])
plt.xlabel('Local Magnitude $M_L$')
Mag_out=np.sort(Mag_out)
cdf=np.arange(len(Mag_out))/float(len(Mag_out)) # normalized, useful in a mo
cdf=((cdf*-1.0)+1.0)*len(Mag_out)
ax2 = ax1.twinx()
ax2.plot(Mag_out,np.log10(cdf), 'r', linewidth=2.0, label='Recalculated')
Mag_in=np.sort(Mag_in)
cdf=np.arange(len(Mag_in))/float(len(Mag_in)) # normalized, useful in a mo
cdf=((cdf*-1.0)+1.0)*len(Mag_in)
ax2.plot(Mag_in,np.log10(cdf), 'k', linewidth=2.0, label='Previous')
ax2.set_ylabel('$Log_{10}$ of cumulative density')
plt.show()
return Event_info
except (AttributeError):
print '\nError plotting'
return Event_info
else:
return Event_info
