def mean(tr, params):
"""
Return DC offset of input trace.
Arguments:
tr - Trace4.1 schema trace object <class 'Dbptr'>
params - Empty <dict>
Return Values:
<list> of <dict>s containing field:value pairs. Field values
correspond to a CSS3.0 schema wfmeas table fields.
"""
d = tr.data()
time, endtime, samprate, nsamp = tr.getv("time", "endtime", "samprate", "nsamp")
dt = 1.0 / samprate
nsmps = int(params["twin"] * samprate)
inds = []
for i in range(int(nsamp / nsmps)):
istart = i * nsmps
iend = istart + nsmps
if iend > len(d):
break
m = cs.mean(d[istart:iend])
if abs(m) > params["thresh"]:
inds.append((istart, iend))
if len(inds) == 0:
return None
inds = _flatten_index_tuples(inds)
ret = []
for i in inds:
ret.append(
{"meastype": params["meastype"], "tmeas": time + dt * i[0], "twin": dt * (i[1] - i[0]), "auth": "auto_qc"}
)
return ret
def rms(tr, params):
d = tr.data()
time, endtime, samprate, nsamp = tr.getv("time", "endtime", "samprate",
"nsamp")
dt = 1.0 / samprate
nsmps = int(params["twin"] * samprate)
inds = []
for i in range(int(nsamp / nsmps)):
istart = i * nsmps
iend = istart + nsmps
if iend > len(d):
break
rms = cs.rms(d[istart:iend])
if abs(rms) < params["thresh"]:
inds.append((istart, iend))
if len(inds) == 0:
return None
inds = _flatten_index_tuples(inds)
ret = []
for i in inds:
ret.append({
"meastype": params["meastype"],
"tmeas": time + dt * i[0],
"twin": dt * (i[1] - i[0]),
"auth": "auto_qc",
})
return ret
def std(tr, params):
"""
Test for large spread in data.
Arguments:
tr - Trace4.1 schema trace object <class 'Dbptr'>
params - User-defined parameters <dict>
params['filter'] - Antelope filter string <str>
Return Values:
<list> of <dict>s containing field:value pairs. Field values
correspond to a CSS3.0 schema wfmeas table fields.
"""
from math import sqrt
d = tr.data()
time, endtime, samprate, nsamp = tr.getv("time", "endtime", "samprate",
"nsamp")
dt = 1.0 / samprate
nsmps = int(params["twin"] * samprate)
inds = []
for i in range(int(nsamp / nsmps)):
istart = i * nsmps
iend = istart + nsmps
if iend > len(d):
break
var = cs.var(d[istart:iend])
if sqrt(var) > params["thresh"]:
inds.append((istart, iend))
if len(inds) == 0:
return None
inds = _flatten_index_tuples(inds)
ret = []
for i in inds:
ret.append({
"meastype": params["meastype"],
"tmeas": time + dt * i[0],
"twin": dt * (i[1] - i[0]),
"auth": "auto_qc",
})
return ret
def std(tr, params):
"""
Test for large spread in data.
Arguments:
tr - Trace4.1 schema trace object <class 'Dbptr'>
params - User-defined parameters <dict>
params['filter'] - Antelope filter string <str>
Return Values:
<list> of <dict>s containing field:value pairs. Field values
correspond to a CSS3.0 schema wfmeas table fields.
"""
from numpy import std, float64
from math import sqrt
d = tr.data()
time, endtime, samprate, nsamp = tr.getv("time", "endtime", "samprate", "nsamp")
dt = 1.0 / samprate
nsmps = int(params["twin"] * samprate)
inds = []
for i in range(int(nsamp / nsmps)):
istart = i * nsmps
iend = istart + nsmps
if iend > len(d):
break
var = cs.var(d[istart:iend])
if sqrt(var) > params["thresh"]:
inds.append((istart, iend))
if len(inds) == 0:
return None
inds = _flatten_index_tuples(inds)
ret = []
for i in inds:
ret.append(
{"meastype": params["meastype"], "tmeas": time + dt * i[0], "twin": dt * (i[1] - i[0]), "auth": "auto_qc"}
)
return ret
def mean(tr, params):
"""
Return DC offset of input trace.
Arguments:
tr - Trace4.1 schema trace object <class 'Dbptr'>
params - Empty <dict>
Return Values:
<list> of <dict>s containing field:value pairs. Field values
correspond to a CSS3.0 schema wfmeas table fields.
"""
d = tr.data()
time, endtime, samprate, nsamp = tr.getv("time", "endtime", "samprate",
"nsamp")
dt = 1.0 / samprate
nsmps = int(params["twin"] * samprate)
inds = []
for i in range(int(nsamp / nsmps)):
istart = i * nsmps
iend = istart + nsmps
if iend > len(d):
break
m = cs.mean(d[istart:iend])
if abs(m) > params["thresh"]:
inds.append((istart, iend))
if len(inds) == 0:
return None
inds = _flatten_index_tuples(inds)
ret = []
for i in inds:
ret.append({
"meastype": params["meastype"],
"tmeas": time + dt * i[0],
"twin": dt * (i[1] - i[0]),
"auth": "auto_qc",
})
return ret
def rms(tr, params):
d = tr.data()
time, endtime, samprate, nsamp = tr.getv("time", "endtime", "samprate", "nsamp")
dt = 1.0 / samprate
nsmps = int(params["twin"] * samprate)
inds = []
for i in range(int(nsamp / nsmps)):
istart = i * nsmps
iend = istart + nsmps
if iend > len(d):
break
rms = cs.rms(d[istart:iend])
if abs(rms) < params["thresh"]:
inds.append((istart, iend))
if len(inds) == 0:
return None
inds = _flatten_index_tuples(inds)
ret = []
for i in inds:
ret.append(
{"meastype": params["meastype"], "tmeas": time + dt * i[0], "twin": dt * (i[1] - i[0]), "auth": "auto_qc"}
)
return ret
