def test_PPSD_w_IRIS(self):
# Bands to be used this is the upper and lower frequency band pairs
fres = zip([0.1, 0.05], [0.2, 0.1])
file_dataANMO = os.path.join(self.path, 'IUANMO.seed')
# Read in ANMO data for one day
st = read(file_dataANMO)
# Use a canned ANMO response which will stay static
paz = {'gain': 86298.5, 'zeros': [0, 0],
'poles': [-59.4313, -22.7121 + 27.1065j, -22.7121 + 27.1065j,
-0.0048004, -0.073199], 'sensitivity': 3.3554*10**9}
# Make an empty PPSD and add the data
ppsd = PPSD(st[0].stats, paz)
ppsd.add(st)
ppsd.calculate_histogram()
# Get the 50th percentile from the PPSD
(per, perval) = ppsd.get_percentile(percentile=50)
# Read in the results obtained from a Mustang flat file
file_dataIRIS = os.path.join(self.path, 'IRISpdfExample')
freq, power, hits = np.genfromtxt(file_dataIRIS, comments='#',
delimiter=',', unpack=True)
# For each frequency pair we want to compare the mean of the bands
for fre in fres:
pervalGoodOBSPY = []
# Get the values for the bands from the PPSD
perinv = 1 / per
mask = (fre[0] < perinv) & (perinv < fre[1])
pervalGoodOBSPY = perval[mask]
# Now we sort out all of the data from the IRIS flat file
mask = (fre[0] < freq) & (freq < fre[1])
triples = list(zip(freq[mask], hits[mask], power[mask]))
# We now have all of the frequency values of interest
# We will get the distinct frequency values
freqdistinct = sorted(list(set(freq[mask])), reverse=True)
percenlist = []
# We will loop through the frequency values and compute a
# 50th percentile
for curfreq in freqdistinct:
tempvalslist = []
for triple in triples:
if np.isclose(curfreq, triple[0], atol=1e-3, rtol=0.0):
tempvalslist += [int(triple[2])] * int(triple[1])
percenlist.append(np.percentile(tempvalslist, 50))
# Here is the actual test
np.testing.assert_allclose(np.mean(pervalGoodOBSPY),
np.mean(percenlist), rtol=0.0, atol=1.0)
def test_ppsd_w_iris(self):
# Bands to be used this is the upper and lower frequency band pairs
fres = zip([0.1, 0.05], [0.2, 0.1])
file_data_anmo = os.path.join(self.path, 'IUANMO.seed')
# Read in ANMO data for one day
st = read(file_data_anmo)
# Use a canned ANMO response which will stay static
paz = {
'gain':
86298.5,
'zeros': [0, 0],
'poles': [
-59.4313, -22.7121 + 27.1065j, -22.7121 + 27.1065j, -0.0048004,
-0.073199
],
'sensitivity':
3.3554 * 10**9
}
# Make an empty PPSD and add the data
# use highest frequency given by IRIS Mustang noise-pdf web service
# (0.475683 Hz == 2.10224036 s) as center of first bin, so that we
# end up with the same bins.
ppsd = PPSD(st[0].stats, paz, period_limits=(2.10224036, 1400))
ppsd.add(st)
ppsd.calculate_histogram()
# Get the 50th percentile from the PPSD
(per, perval) = ppsd.get_percentile(percentile=50)
perinv = 1 / per
# Read in the results obtained from a Mustang flat file
file_data_iris = os.path.join(self.path, 'IRISpdfExample')
data = np.genfromtxt(file_data_iris,
comments='#',
delimiter=',',
dtype=[(native_str("freq"), np.float64),
(native_str("power"), np.int32),
(native_str("hits"), np.int32)])
freq = data["freq"]
power = data["power"]
hits = data["hits"]
# cut data to same period range as in the ppsd we computed
# (Mustang returns more long periods, probably due to some zero padding
# or longer nfft in psd)
num_periods = len(ppsd.period_bin_centers)
freqdistinct = np.array(sorted(set(freq), reverse=True)[:num_periods])
# just make sure that we compare the same periods in the following
# (as we access both frequency arrays by indices from now on)
np.testing.assert_allclose(freqdistinct,
1 / ppsd.period_bin_centers,
rtol=1e-4)
# For each frequency pair we want to compare the mean of the bands
for fre in fres:
# determine which bins we want to compare
mask = (fre[0] < perinv) & (perinv < fre[1])
# Get the values for the bands from the PPSD
per_val_good_obspy = perval[mask]
percenlist = []
# Now we sort out all of the data from the IRIS flat file
# We will loop through the frequency values and compute a
# 50th percentile
for curfreq in freqdistinct[mask]:
mask_ = curfreq == freq
tempvalslist = np.repeat(power[mask_], hits[mask_])
percenlist.append(np.percentile(tempvalslist, 50))
# Here is the actual test
np.testing.assert_allclose(np.mean(per_val_good_obspy),
np.mean(percenlist),
rtol=0.0,
atol=1.2)
def test_ppsd_w_iris(self):
# Bands to be used this is the upper and lower frequency band pairs
fres = zip([0.1, 0.05], [0.2, 0.1])
file_data_anmo = os.path.join(self.path, 'IUANMO.seed')
# Read in ANMO data for one day
st = read(file_data_anmo)
# Use a canned ANMO response which will stay static
paz = {'gain': 86298.5, 'zeros': [0, 0],
'poles': [-59.4313, -22.7121 + 27.1065j, -22.7121 + 27.1065j,
-0.0048004, -0.073199],
'sensitivity': 3.3554 * 10 ** 9}
# Make an empty PPSD and add the data
# use highest frequency given by IRIS Mustang noise-pdf web service
# (0.475683 Hz == 2.10224036 s) as center of first bin, so that we
# end up with the same bins.
ppsd = PPSD(st[0].stats, paz, period_limits=(2.10224036, 1400))
ppsd.add(st)
ppsd.calculate_histogram()
# Get the 50th percentile from the PPSD
(per, perval) = ppsd.get_percentile(percentile=50)
perinv = 1 / per
# Read in the results obtained from a Mustang flat file
file_data_iris = os.path.join(self.path, 'IRISpdfExample')
data = np.genfromtxt(
file_data_iris, comments='#', delimiter=',',
dtype=[(native_str("freq"), np.float64),
(native_str("power"), np.int32),
(native_str("hits"), np.int32)])
freq = data["freq"]
power = data["power"]
hits = data["hits"]
# cut data to same period range as in the ppsd we computed
# (Mustang returns more long periods, probably due to some zero padding
# or longer nfft in psd)
num_periods = len(ppsd.period_bin_centers)
freqdistinct = np.array(sorted(set(freq), reverse=True)[:num_periods])
# just make sure that we compare the same periods in the following
# (as we access both frequency arrays by indices from now on)
np.testing.assert_allclose(freqdistinct, 1 / ppsd.period_bin_centers,
rtol=1e-4)
# For each frequency pair we want to compare the mean of the bands
for fre in fres:
# determine which bins we want to compare
mask = (fre[0] < perinv) & (perinv < fre[1])
# Get the values for the bands from the PPSD
per_val_good_obspy = perval[mask]
percenlist = []
# Now we sort out all of the data from the IRIS flat file
# We will loop through the frequency values and compute a
# 50th percentile
for curfreq in freqdistinct[mask]:
mask_ = curfreq == freq
tempvalslist = np.repeat(power[mask_], hits[mask_])
percenlist.append(np.percentile(tempvalslist, 50))
# Here is the actual test
np.testing.assert_allclose(np.mean(per_val_good_obspy),
np.mean(percenlist), rtol=0.0, atol=1.2)
def test_PPSD_w_IRIS(self):
# Bands to be used this is the upper and lower frequency band pairs
fres = zip([0.1, 0.05], [0.2, 0.1])
file_dataANMO = os.path.join(self.path, 'IUANMO.seed')
# Read in ANMO data for one day
st = read(file_dataANMO)
# Use a canned ANMO response which will stay static
paz = {
'gain':
86298.5,
'zeros': [0, 0],
'poles': [
-59.4313, -22.7121 + 27.1065j, -22.7121 + 27.1065j, -0.0048004,
-0.073199
],
'sensitivity':
3.3554 * 10**9
}
# Make an empty PPSD and add the data
ppsd = PPSD(st[0].stats, paz)
ppsd.add(st)
# Get the 50th percentile from the PPSD
(per, perval) = ppsd.get_percentile(percentile=50)
# Read in the results obtained from a Mustang flat file
file_dataIRIS = os.path.join(self.path, 'IRISpdfExample')
freq, power, hits = np.genfromtxt(file_dataIRIS,
comments='#',
delimiter=',',
unpack=True)
# For each frequency pair we want to compare the mean of the bands
for fre in fres:
pervalGoodOBSPY = []
# Get the values for the bands from the PPSD
perinv = 1 / per
mask = (fre[0] < perinv) & (perinv < fre[1])
pervalGoodOBSPY = perval[mask]
# Now we sort out all of the data from the IRIS flat file
mask = (fre[0] < freq) & (freq < fre[1])
triples = list(zip(freq[mask], hits[mask], power[mask]))
# We now have all of the frequency values of interest
# We will get the distinct frequency values
freqdistinct = sorted(list(set(freq[mask])), reverse=True)
percenlist = []
# We will loop through the frequency values and compute a
# 50th percentile
for curfreq in freqdistinct:
tempvalslist = []
for triple in triples:
if np.isclose(curfreq, triple[0], atol=1e-3, rtol=0.0):
tempvalslist += [int(triple[2])] * int(triple[1])
percenlist.append(np.percentile(tempvalslist, 50))
# Here is the actual test
np.testing.assert_allclose(np.mean(pervalGoodOBSPY),
np.mean(percenlist),
rtol=0.0,
atol=1.0)
