def read_geofon_hyp(filename, phase_data=False):
"""
Read a GEOFON hypocenter-phase file.
:param str filename:
Full or relative path and name for hypocenter-phase file.
:param bool phase_data:
If True, the phase block in the file is read as well and returned.
"""
# error handling
if not isinstance(filename, basestring):
raise InputError(filename, "Need string or buffer")
if not op.exists(filename):
raise InputError(filename, "No such file or directory")
with open(filename, "r") as f:
data = f.read()
# --- READ ORIGIN BLOCK ---
public_ID = re.search(r'\s+Public ID\s+(gfz\d\d\d\d[a-z]+)', data)
public_ID = public_ID.group(1)
odate = re.search(r'\s+Date\s+(\d\d\d\d-\d\d-\d\d)', data)
otime = re.search(r'\s+Time\s+(\d\d:\d\d:\d\d\.\d+)', data)
origin_time_str = ' '.join((odate.group(1), otime.group(1)))
origin_time = datetime.strptime(origin_time_str,
"%Y-%m-%d %H:%M:%S.%f")
Latitude = re.search(r'\s+Latitude\s+(-?\d{1,2}\.\d\d)\sdeg', data)
latitude = float(Latitude.group(1))
Longitude = re.search(r'\s+Longitude\s+(-?\d{1,3}\.\d\d)\sdeg',
data)
longitude = float(Longitude.group(1))
Depth = re.search(r'\s+Depth\s+(\d{1,3})\skm', data)
depth = float(Depth.group(1))
ResRMS = re.search(r'\s+Residual RMS\s+(\d+\.\d\d)\ss', data)
residual_rms = float(ResRMS.group(1))
AzmGap = re.search(r'\s+Azimuthal gap\s+(\d+)\sdeg', data)
azimuthal_gap = int(AzmGap.group(1))
# --- READ MAGNITUDE BLOCK ---
mag = float(re.search('\s+.(\d.\d\d).+preferred', data).group(1))
# --- READ PHASE BLOCK ---
used_picks = None
if phase_data is True:
# exclude picks with MX and AX qulity (picks weighted to zero)!
phase_pattern = re.compile(r'\s+([A-Z0-9]+)\s+([A-Z]+)\s+'
'(\d+\.\d+)\s+(\d+)\s+([a-zA-Z]+)\s+'
'(\d\d:\d\d:\d\d\.\d)\s+'
'(-?\d+\.\d|N/A)\s(M|A)\s+'
'\d\.\d\s+[A-Z0-9]+')
phase_block = phase_pattern.findall(data)
narr = len(phase_block)
index = xrange(narr)
columns = ['Phase', 'Time', 'Quality', 'Residual', 'Distance',
'Azimuth']
used_picks = pd.DataFrame(index=index, columns=columns)
for i, q in enumerate(phase_block):
sta, net = q[0].strip(), q[1].strip()
if net == 'AD':
net = 'RM'
if net == 'EV':
net = 'IO'
staCode = "_".join((net, sta))
dist = deg2m(float(q[2]))
azi = float(q[3])
phase = q[4].strip()
arrtime = datetime.strptime(q[5], "%H:%M:%S.%f")
if arrtime.time() < origin_time.time():
arrdate = origin_time.date() + timedelta(days=1)
else:
arrdate = origin_time.date()
arrDateTime = datetime.combine(arrdate, arrtime.time())
try:
res = float(q[6])
except ValueError:
res = np.nan
quality = q[7]
used_picks.ix[i, columns] = (phase, arrDateTime, quality, res,
dist, azi)
# Rename indexes to station codes
used_picks.rename(index={i: staCode}, inplace=True)
# Just to be on the safe side!
used_picks.dropna(inplace=True)
# --- make a DataFrame with 2-level MultiIndex indexes
stations = used_picks.index
phases = used_picks['Phase']
tuples = zip(stations, phases)
# drop the `Phase` column
used_picks.drop(labels='Phase', axis=1, inplace=True)
# construct a 2-level MultiIndex
new_idx = pd.MultiIndex.from_tuples(tuples,
names=['Station', 'Phase'])
used_picks.index = new_idx
# Convert columns to numeric and datetime types
for column in used_picks.columns:
if column in ['Residual', 'Distance', 'Azimuth']:
used_picks[column] = pd.to_numeric(used_picks[column])
elif column == 'Time':
used_picks[column] = pd.to_datetime(used_picks[column])
return GeofonEvent(public_ID, origin_time, origin_time_str, latitude,
longitude, depth, residual_rms, azimuthal_gap, mag,
used_picks)
def read_nlloc_hyp(filename, phase_data=False):
"""Read a NonLinLoc Hypocenter-phase file."""
if not isinstance(filename, basestring):
raise InputError(filename, "need string or buffer")
if not os.path.exists(filename):
raise InputError(filename, "no such file or directory")
with open(filename, 'r') as f:
data = f.read()
data = data.splitlines()
# remove the empty lines
data = filter(None, data)
# determine indices of block start/end of the NLLOC output file
indices_hypo_block = [None, None]
indices_phase_block = [None, None]
for i, line in enumerate(data):
if line.startswith("NLLOC "):
indices_hypo_block[0] = i
elif line.startswith("END_NLLOC"):
indices_hypo_block[1] = i
elif line.startswith("PHASE "):
indices_phase_block[0] = i
elif line.startswith("END_PHASE"):
indices_phase_block[1] = i
if any([i is None for i in indices_hypo_block]):
msg = 'Input NLLOC Hypocenter-Phase file is corrupt'
raise InputError(filename, msg)
# To skip any other lines around NLLOC block
lines = data[indices_hypo_block[0]:indices_hypo_block[1]]
i1, i2 = indices_phase_block
hypo_lines, phase_lines = lines[:i1] + lines[i2+1:], lines[i1+1:i2]
# --- HYPOCENTER INFORMATION ---
hypo_lines = dict([line.split(None, 1) for line in hypo_lines])
# NLLOC line: Specifies the beginning of a NLLoc Hypocenter-Phase
# description block and gives the location status.
line = hypo_lines["NLLOC"]
status = filter(None, line.split('"'))[-1].strip('.')
# SIGNATURE Line: Signature text and program run stamp.
line = hypo_lines["SIGNATURE"]
obsfile = re.search('obs:(.+)\s+NLLoc', line).group(1).strip(None)
# TRANSFORM line:
# Geographic to rectangular transformation parameters.
line = hypo_lines["TRANSFORM"]
trans = line.split(None)[0]
# GEOGRAPHIC line:
# Maximum likelihood hypocenter - Geographic coordinates.
line = hypo_lines["GEOGRAPHIC"]
items = line.split()
d = '-'.join(items[1:4])
t = ':'.join(items[4:7])
origin_time_str = ' '.join((d, t))
try:
origin_time = dt.datetime.strptime(origin_time_str,
"%Y-%m-%d %H:%M:%S.%f")
except ValueError:
# there is a bug (simplification) in NLLoc; negative
# origin-time for those events occurred just before midnight!
year, month, day = map(int, items[1:4])
microsec, sec = np.modf(float(items[6]))
sec = abs(int(sec))
microsec = abs(int(microsec * 1.0e+6))
midnight = dt.datetime(year, month, day, 0, 0, 0)
origin_time = midnight - dt.timedelta(days=0,
seconds=sec,
microseconds=microsec)
latitude, longitude = map(float, items[-5:-2:2])
depth = float(items[-1]) * km2m
if trans == 'GLOBAL':
X, Y, Z = geodetic2ecef(latitude, longitude, (-1*depth))
else:
# HYPOCENTER line: Maximum likelihood hypocenter - xyz coordinates.
line = hypo_lines["HYPOCENTER"]
items = line.split(None)
X, Y, Z = map(lambda q: float(q)*km2m, items[1:6:2]) # meters!
# STATISTICS line: "Traditional" Gaussian (normal) statistics
# of PDF (evaluated for PROB_DENSITY grids only).
line = hypo_lines["STATISTICS"]
items = line.split(None)
covXX = float(items[7].strip())
covYY = float(items[13].strip())
covZZ = float(items[17].strip())
latUnc = np.sqrt(covXX)*km2m # meters!
longUnc = np.sqrt(covYY)*km2m # meters!
depthUnc = np.sqrt(covZZ) * km2m # meters!
# QML_OriginQuality line: Specifies QuakeML OriginQuality measures.
line = hypo_lines["QML_OriginQuality"]
items = line.split(None)
assocPhCt, usedPhCt = map(int, items[1:4:2])
resRms, aziGap, secAziGap = map(float, items[11:16:2])
minDist, maxDist, medianDist = [(x*km2m) for x in
map(float, items[-5::2])] # meters!
# QML_OriginUncertainty line: Specifies QuakeML
# OriginUncertainty measures.
line = hypo_lines["QML_OriginUncertainty"]
items = line.split()
horUnc, minHorUnc, maxHorUnc = [(x*km2m) for x in
map(float, items[1:6:2])] # meters!
aziMaxHorUnc = float(items[-1])
# --- PHASE PICK INFORMATION ---
used_picks = None
if phase_data:
index = xrange(len(phase_lines))
columns = ['Phase', 'Residual', 'Distance', 'Azimuth', 'TimeCorr']
used_picks = pd.DataFrame(index=index, columns=columns)
for i, line in enumerate(phase_lines):
items = line.split()
ttpred, ttres, ttweight = map(float, items[15:18])
if ttpred != 0.0 and ttweight != 0.0:
staCode = items[0].strip()
phase = items[4].strip()
staDist = deg2m(float(items[21])) # meters!
staAzi = float(items[22])
ttcorr = float(items[26])
used_picks.ix[i, columns] = (phase, ttres, staDist, staAzi,
ttcorr)
used_picks.rename(index={i: staCode}, inplace=True)
used_picks.dropna(inplace=True)
# --- make a DataFrame with 2-level MultiIndex indexes
stations = used_picks.index
phases = used_picks['Phase']
tuples = zip(stations, phases)
# drop the `Phase` column
used_picks.drop(labels='Phase', axis=1, inplace=True)
# construct a 2-level MultiIndex
new_idx = pd.MultiIndex.from_tuples(tuples,
names=['Station', 'Phase'])
used_picks.index = new_idx
# convert objects to numeric types. possible for all columns.
used_picks = used_picks.apply(pd.to_numeric)
return NLLocEvent(status, obsfile, X, Y, Z, latitude, longitude, depth,
origin_time, origin_time_str, latUnc, longUnc, depthUnc,
assocPhCt, usedPhCt, resRms, aziGap, secAziGap, minDist,
maxDist, medianDist, horUnc, minHorUnc, maxHorUnc,
aziMaxHorUnc, used_picks)
