def _internal_get_ppsd():
"""
Returns ready computed ppsd for testing purposes.
"""
tr, paz = _get_sample_data()
st = Stream([tr])
ppsd = PPSD(tr.stats, paz, db_bins=(-200, -50, 0.5))
ppsd.add(st)
ppsd.calculate_histogram()
return ppsd
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_restricted_stacks(self):
"""
Test PPSD.calculate_histogram() with restrictions to what data should
be stacked. Also includes image tests.
"""
# set up a bogus PPSD, with fixed random psds but with real start times
# of psd pieces, to facilitate testing the stack selection.
ppsd = PPSD(stats=Stats(dict(sampling_rate=150)), metadata=None,
db_bins=(-200, -50, 20.), period_step_octaves=1.4)
ppsd._times_processed = np.load(
os.path.join(self.path, "ppsd_times_processed.npy")).tolist()
np.random.seed(1234)
ppsd._binned_psds = [
arr for arr in np.random.uniform(
-200, -50,
(len(ppsd._times_processed), len(ppsd.period_bin_centers)))]
# Test callback function that selects a fixed random set of the
# timestamps. Also checks that we get passed the type we expect,
# which is 1D numpy ndarray of float type.
def callback(t_array):
self.assertIsInstance(t_array, np.ndarray)
self.assertEqual(t_array.shape, (len(ppsd._times_processed),))
self.assertEqual(t_array.dtype, np.float64)
np.random.seed(1234)
res = np.random.randint(0, 2, len(t_array)).astype(np.bool)
return res
# test several different sets of stack criteria, should cover
# everything, even with lots of combined criteria
stack_criteria_list = [
dict(starttime=UTCDateTime(2015, 3, 8), month=[2, 3, 5, 7, 8]),
dict(endtime=UTCDateTime(2015, 6, 7), year=[2015],
time_of_weekday=[(1, 0, 24), (2, 0, 24), (-1, 0, 11)]),
dict(year=[2013, 2014, 2016, 2017], month=[2, 3, 4]),
dict(month=[1, 2, 5, 6, 8], year=2015),
dict(isoweek=[4, 5, 6, 13, 22, 23, 24, 44, 45]),
dict(time_of_weekday=[(5, 22, 24), (6, 0, 2), (6, 22, 24)]),
dict(callback=callback, month=[1, 3, 5, 7]),
dict(callback=callback)]
expected_selections = np.load(
os.path.join(self.path, "ppsd_stack_selections.npy"))
# test every set of criteria
for stack_criteria, expected_selection in zip(
stack_criteria_list, expected_selections):
selection_got = ppsd._stack_selection(**stack_criteria)
np.testing.assert_array_equal(selection_got, expected_selection)
# test one particular selection as an image test
plot_kwargs = dict(max_percentage=15, xaxis_frequency=True,
period_lim=(0.01, 50))
ppsd.calculate_histogram(**stack_criteria_list[1])
with ImageComparison(self.path_images,
'ppsd_restricted_stack.png', reltol=1.5) as ic:
fig = ppsd.plot(show=False, **plot_kwargs)
# some matplotlib/Python version combinations lack the left-most
# tick/label "Jan 2015". Try to circumvent and get the (otherwise
# OK) test by changing the left x limit a bit further out (by two
# days, axis is in mpl days). See e.g.
# https://tests.obspy.org/30657/#1
fig.axes[1].set_xlim(left=fig.axes[1].get_xlim()[0] - 2)
with np.errstate(under='ignore'):
fig.savefig(ic.name)
# test it again, checking that updating an existing plot with different
# stack selection works..
# a) we start with the stack for the expected image and test that it
# matches (like above):
ppsd.calculate_histogram(**stack_criteria_list[1])
with ImageComparison(self.path_images,
'ppsd_restricted_stack.png', reltol=1.5,
plt_close_all_exit=False) as ic:
fig = ppsd.plot(show=False, **plot_kwargs)
# some matplotlib/Python version combinations lack the left-most
# tick/label "Jan 2015". Try to circumvent and get the (otherwise
# OK) test by changing the left x limit a bit further out (by two
# days, axis is in mpl days). See e.g.
# https://tests.obspy.org/30657/#1
fig.axes[1].set_xlim(left=fig.axes[1].get_xlim()[0] - 2)
with np.errstate(under='ignore'):
fig.savefig(ic.name)
# b) now reuse figure and set the histogram with a different stack,
# image test should fail:
ppsd.calculate_histogram(**stack_criteria_list[3])
try:
with ImageComparison(self.path_images,
'ppsd_restricted_stack.png',
adjust_tolerance=False,
plt_close_all_enter=False,
plt_close_all_exit=False) as ic:
# rms of the valid comparison above is ~31,
# rms of the invalid comparison we test here is ~36
if MATPLOTLIB_VERSION == [1, 1, 1]:
ic.tol = 33
ppsd._plot_histogram(fig=fig, draw=True)
with np.errstate(under='ignore'):
fig.savefig(ic.name)
except ImageComparisonException:
pass
else:
msg = "Expected ImageComparisonException was not raised."
self.fail(msg)
# c) now reuse figure and set the original histogram stack again,
# image test should pass agin:
ppsd.calculate_histogram(**stack_criteria_list[1])
with ImageComparison(self.path_images,
'ppsd_restricted_stack.png', reltol=1.5,
plt_close_all_enter=False) as ic:
ppsd._plot_histogram(fig=fig, draw=True)
with np.errstate(under='ignore'):
fig.savefig(ic.name)
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_restricted_stacks(self):
"""
Test PPSD.calculate_histogram() with restrictions to what data should
be stacked. Also includes image tests.
"""
# set up a bogus PPSD, with fixed random psds but with real start times
# of psd pieces, to facilitate testing the stack selection.
ppsd = PPSD(stats=Stats(dict(sampling_rate=150)),
metadata=None,
db_bins=(-200, -50, 20.),
period_step_octaves=1.4)
# change data to nowadays used nanoseconds POSIX timestamp
ppsd._times_processed = [
UTCDateTime(t)._ns for t in np.load(
os.path.join(self.path, "ppsd_times_processed.npy")).tolist()
]
np.random.seed(1234)
ppsd._binned_psds = [
arr for arr in np.random.uniform(-200, -50, (
len(ppsd._times_processed), len(ppsd.period_bin_centers)))
]
# Test callback function that selects a fixed random set of the
# timestamps. Also checks that we get passed the type we expect,
# which is 1D numpy ndarray of int type.
def callback(t_array):
self.assertIsInstance(t_array, np.ndarray)
self.assertEqual(t_array.shape, (len(ppsd._times_processed), ))
self.assertTrue(np.issubdtype(t_array.dtype, np.integer))
np.random.seed(1234)
res = np.random.randint(0, 2, len(t_array)).astype(np.bool)
return res
# test several different sets of stack criteria, should cover
# everything, even with lots of combined criteria
stack_criteria_list = [
dict(starttime=UTCDateTime(2015, 3, 8), month=[2, 3, 5, 7, 8]),
dict(endtime=UTCDateTime(2015, 6, 7),
year=[2015],
time_of_weekday=[(1, 0, 24), (2, 0, 24), (-1, 0, 11)]),
dict(year=[2013, 2014, 2016, 2017], month=[2, 3, 4]),
dict(month=[1, 2, 5, 6, 8], year=2015),
dict(isoweek=[4, 5, 6, 13, 22, 23, 24, 44, 45]),
dict(time_of_weekday=[(5, 22, 24), (6, 0, 2), (6, 22, 24)]),
dict(callback=callback, month=[1, 3, 5, 7]),
dict(callback=callback)
]
expected_selections = np.load(
os.path.join(self.path, "ppsd_stack_selections.npy"))
# test every set of criteria
for stack_criteria, expected_selection in zip(stack_criteria_list,
expected_selections):
selection_got = ppsd._stack_selection(**stack_criteria)
np.testing.assert_array_equal(selection_got, expected_selection)
# test one particular selection as an image test
# mpl < 2.2 has slightly offset ticks/ticklabels, so needs a higher
# `reltol` tolerance (see e.g. http://tests.obspy.org/102260)
reltol = 1.5
plot_kwargs = dict(max_percentage=15,
xaxis_frequency=True,
period_lim=(0.01, 50))
ppsd.calculate_histogram(**stack_criteria_list[1])
with ImageComparison(self.path_images,
'ppsd_restricted_stack.png',
reltol=reltol) as ic:
fig = ppsd.plot(show=False, **plot_kwargs)
# some matplotlib/Python version combinations lack the left-most
# tick/label "Jan 2015". Try to circumvent and get the (otherwise
# OK) test by changing the left x limit a bit further out (by two
# days, axis is in mpl days). See e.g.
# https://tests.obspy.org/30657/#1
fig.axes[1].set_xlim(left=fig.axes[1].get_xlim()[0] - 2)
with np.errstate(under='ignore'):
fig.savefig(ic.name)
# test it again, checking that updating an existing plot with different
# stack selection works..
# a) we start with the stack for the expected image and test that it
# matches (like above):
ppsd.calculate_histogram(**stack_criteria_list[1])
with ImageComparison(self.path_images,
'ppsd_restricted_stack.png',
reltol=reltol,
plt_close_all_exit=False) as ic:
fig = ppsd.plot(show=False, **plot_kwargs)
# some matplotlib/Python version combinations lack the left-most
# tick/label "Jan 2015". Try to circumvent and get the (otherwise
# OK) test by changing the left x limit a bit further out (by two
# days, axis is in mpl days). See e.g.
# https://tests.obspy.org/30657/#1
fig.axes[1].set_xlim(left=fig.axes[1].get_xlim()[0] - 2)
with np.errstate(under='ignore'):
fig.savefig(ic.name)
# b) now reuse figure and set the histogram with a different stack,
# image test should fail:
ppsd.calculate_histogram(**stack_criteria_list[3])
try:
with ImageComparison(self.path_images,
'ppsd_restricted_stack.png',
adjust_tolerance=False,
plt_close_all_enter=False,
plt_close_all_exit=False) as ic:
# rms of the valid comparison above is ~31,
# rms of the invalid comparison we test here is ~36
ppsd._plot_histogram(fig=fig, draw=True)
with np.errstate(under='ignore'):
fig.savefig(ic.name)
except ImageComparisonException:
pass
else:
msg = "Expected ImageComparisonException was not raised."
self.fail(msg)
# c) now reuse figure and set the original histogram stack again,
# image test should pass agin:
ppsd.calculate_histogram(**stack_criteria_list[1])
with ImageComparison(self.path_images,
'ppsd_restricted_stack.png',
reltol=reltol,
plt_close_all_enter=False) as ic:
ppsd._plot_histogram(fig=fig, draw=True)
with np.errstate(under='ignore'):
fig.savefig(ic.name)
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=[("freq", np.float64), ("power", np.int32),
("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)
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)
