def test_multiple_sampling_rates(self):
"""
Check for multiple sampling rates
"""
lines = [
"TIMESERIES XX_TEST__BHZ_R, 200 samples, 200 sps, "
"2008-01-15T00:00:00.000000, SLIST, INTEGER, Counts",
"TIMESERIES XX_TEST__BHZ_R, 50 samples, 50 sps, "
"2008-01-15T00:00:00.900000, SLIST, INTEGER, Counts",
"TIMESERIES XX_TEST__BHZ_R, 200 samples, 200 sps, "
"2008-01-15T00:00:02.000000, SLIST, INTEGER, Counts",
]
files = []
expected = [
"XX.TEST..BHZ 2008-01-15T00:00:01.000000Z "
"2008-01-15T00:00:00.899995Z -0.100",
"XX.TEST..BHZ 2008-01-15T00:00:01.899999Z "
"2008-01-15T00:00:02.000000Z 0.100"
]
with NamedTemporaryFile() as f1, NamedTemporaryFile() as f2, \
NamedTemporaryFile() as f3:
for i, fp in enumerate([f1, f2, f3]):
fp.write(("%s\n" % lines[i]).encode('ascii',
'strict'))
fp.flush()
fp.seek(0)
files.append(fp.name)
# make image comparison instance and set manual rms (see #2089)
image_comp = ImageComparison(self.path, 'scan_mult_sampl.png')
image_comp.tol = 40
with image_comp as ic:
obspy_scan(files + ['--output', ic.name, '--print-gaps'])
with CatchOutput() as out:
obspy_scan(files + ['--output', ic.name, '--print-gaps'])
# read output and compare with expected
# only check if datetime objects are close, not exact
output = out.stdout.splitlines()
for ex_line, out_line in zip(expected, output):
ex_split = ex_line.split(' ')
out_split = out_line.split(' ')
for ex_str, out_str in zip(ex_split, out_split):
try:
utc1 = UTCDateTime(ex_str)
utc2 = UTCDateTime(out_str)
except (ValueError, TypeError):
# if str is not a datetime it should be equal
self.assertEqual(ex_str, out_str)
else:
# datetimes just need to be close
t1, t2 = utc1.timestamp, utc2.timestamp
self.assertTrue(abs(t1 - t2) < .001)
def test_plotAzimSection(self):
"""
Tests plotting 10 in a azimuthal distant section
"""
start = UTCDateTime(0)
st = Stream()
for _i in range(10):
st += self._createStream(start, start + 3600, 100)
st[-1].stats.coordinates = AttribDict({
'latitude': _i,
'longitude': _i
})
# create and compare image
with ImageComparison(self.path, 'waveform_azim_section.png') as ic:
st.plot(outfile=ic.name,
type='section',
dist_degree=True,
ev_coord=(0.0, 0.0))
def test_xcorrPickCorrection_images(self):
"""
Test cross correlation pick correction on a set of two small local
earthquakes.
"""
st1 = read(os.path.join(self.path,
'BW.UH1._.EHZ.D.2010.147.a.slist.gz'))
st2 = read(os.path.join(self.path,
'BW.UH1._.EHZ.D.2010.147.b.slist.gz'))
tr1 = st1.select(component="Z")[0]
tr2 = st2.select(component="Z")[0]
t1 = UTCDateTime("2010-05-27T16:24:33.315000Z")
t2 = UTCDateTime("2010-05-27T16:27:30.585000Z")
with ImageComparison(self.path_images, 'xcorr_pick_corr.png') as ic:
dt, coeff = xcorr_pick_correction(
t1, tr1, t2, tr2, 0.05, 0.2, 0.1, plot=True, filename=ic.name)
def test_response_plot(self):
"""
Tests the response plot.
"""
# Bug in matplotlib 1.4.0 - 1.4.x:
# See https://github.com/matplotlib/matplotlib/issues/4012
reltol = 1.0
if [1, 4, 0] <= MATPLOTLIB_VERSION <= [1, 5, 0]:
reltol = 2.0
resp = read_inventory()[0][0][0].response
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore")
with ImageComparison(self.image_dir, "response_response.png",
reltol=reltol) as ic:
rcParams['savefig.dpi'] = 72
resp.plot(0.001, output="VEL", start_stage=1, end_stage=3,
outfile=ic.name)
def test_response_plot(self):
"""
Tests the response plot.
"""
# Bug in matplotlib 1.4.0 - 1.4.x:
# See https://github.com/matplotlib/matplotlib/issues/4012
reltol = 1.0
if [1, 4, 0] <= MATPLOTLIB_VERSION <= [1, 5, 0]:
reltol = 2.0
sta = read_inventory()[0][0]
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore")
with ImageComparison(self.image_dir,
"station_response.png",
reltol=reltol) as ic:
rcParams['savefig.dpi'] = 72
sta.plot(0.05, channel="*[NE]", outfile=ic.name)
def test_location_plot_local(self):
"""
Tests the inventory location preview plot, local projection, some more
non-default parameters, using Cartopy.
"""
inv = read_inventory()
with ImageComparison(self.image_dir,
'inventory_location-cartopy3.png') as ic:
rcParams['savefig.dpi'] = 72
inv.plot(method='cartopy',
projection='local',
resolution='50m',
size=20**2,
color_per_network={
'GR': 'b',
'BW': 'green'
},
outfile=ic.name)
def test_location_plot_ortho(self):
"""
Tests the inventory location preview plot, ortho projection, some
non-default parameters, using Cartopy.
"""
inv = read_inventory()
with ImageComparison(self.image_dir,
'inventory_location-cartopy2.png') as ic:
rcParams['savefig.dpi'] = 72
inv.plot(method='cartopy',
projection='ortho',
resolution='c',
continent_fill_color='0.3',
marker='d',
label=False,
colormap='Set3',
color_per_network=True,
outfile=ic.name)
def test_combined_station_event_plot(self):
"""
Tests the coombined plotting of inventory/event data in one plot,
reusing the basemap instance.
"""
inv = read_inventory()
cat = read_events()
reltol = 1.1
# Coordinate lines might be slightly off, depending on the basemap
# version.
if BASEMAP_VERSION < [1, 0, 7]:
reltol = 3.0
with ImageComparison(self.image_dir,
'basemap_combined_stations-events.png',
reltol=reltol) as ic:
rcParams['savefig.dpi'] = 72
fig = inv.plot(show=False)
cat.plot(outfile=ic.name, fig=fig)
def test_ppsd_plot_frequency(self):
"""
Test plot of ppsd example data, normal (non-cumulative) style.
"""
# Catch underflow warnings due to plotting on log-scale.
with np.errstate(all='ignore'):
with ImageComparison(self.path, 'ppsd_freq.png', reltol=2.0) as ic:
self.ppsd.plot(
show=False, show_coverage=False, show_histogram=True,
show_percentiles=True, percentiles=[20, 40],
show_noise_models=True, grid=False, max_percentage=50,
period_lim=(0.2, 50), show_mode=True, show_mean=True,
xaxis_frequency=True)
fig = plt.gcf()
ax = fig.axes[0]
ax.set_ylim(-160, -130)
plt.draw()
fig.savefig(ic.name, dpi=50)
def test_location_plot_local(self):
"""
Tests the network location preview plot, local projection, some more
non-default parameters, using Basemap.
"""
net = read_inventory()[0]
# Coordinate lines might be slightly off, depending on the basemap
# version.
reltol = 2.0
# Basemap smaller 1.0.4 has a serious issue with plotting. Thus the
# tolerance must be much higher.
if BASEMAP_VERSION < [1, 0, 4]:
reltol = 100.0
with ImageComparison(self.image_dir, 'network_location-basemap3.png',
reltol=reltol) as ic:
rcParams['savefig.dpi'] = 72
net.plot(method='basemap', projection='local', resolution='l',
size=13**2, outfile=ic.name)
def test_catalog_plot_ortho_longitude_wrap(self):
"""
Tests the catalog preview plot, ortho projection, some non-default
parameters, using Cartopy, with longitudes that need the mean to be
computed in a circular fashion.
"""
cat = read_events('/path/to/events_longitude_wrap.zmap', format='ZMAP')
with ImageComparison(self.image_dir,
'catalog-cartopy_long-wrap.png') as ic:
rcParams['savefig.dpi'] = 40
cat.plot(method='cartopy',
outfile=ic.name,
projection='ortho',
resolution='c',
label=None,
title='',
colorbar=False,
water_fill_color='b')
def test_collection(self):
"""
Tests to plot beachballs as collection into an existing axis
object. The moment tensor values are taken form the
test_Beachball unit test. See that test for more information about
the parameters.
"""
reltol = 1
if MATPLOTLIB_VERSION < [1, 2, 0]:
reltol = 20
mt = [[0.91, -0.89, -0.02, 1.78, -1.55, 0.47], [274, 13, 55],
[130, 79, 98], [264.98, 45.00, -159.99], [160.55, 76.00, -46.78],
[1.45, -6.60, 5.14, -2.67, -3.16, 1.36], [235, 80, 35],
[138, 56, 168], [1, 1, 1, 0, 0, 0], [-1, -1, -1, 0, 0, 0],
[1, -2, 1, 0, 0, 0], [1, -1, 0, 0, 0, 0], [1, -1, 0, 0, 0, -1],
[179, 55, -78], [10, 42.5, 90], [10, 42.5, 92], [150, 87, 1],
[0.99, -2.00, 1.01, 0.92, 0.48, 0.15],
[5.24, -6.77, 1.53, 0.81, 1.49, -0.05],
[16.578, -7.987, -8.592, -5.515, -29.732, 7.517],
[-2.39, 1.04, 1.35, 0.57, -2.94, -0.94], [150, 87, 1]]
# Initialize figure
fig = plt.figure(figsize=(6, 6), dpi=300)
ax = fig.add_subplot(111, aspect='equal')
# Plot the stations or borders
ax.plot([-100, -100, 100, 100], [-100, 100, -100, 100], 'rv')
x = -100
y = -100
for i, t in enumerate(mt):
# add the beachball (a collection of two patches) to the axis
ax.add_collection(Beach(t, width=30, xy=(x, y), linewidth=.6))
x += 50
if (i + 1) % 5 == 0:
x = -100
y += 50
# set the x and y limits and save the output
ax.axis([-120, 120, -120, 120])
# create and compare image
with ImageComparison(self.path, 'bb_collection.png',
reltol=reltol) as ic:
fig.savefig(ic.name)
def test_response_plot(self):
"""
Tests the response plot.
"""
# Bug in matplotlib 1.4.0 - 1.4.2:
# See https://github.com/matplotlib/matplotlib/issues/4012
reltol = 1.0
if [1, 4, 0] <= MATPLOTLIB_VERSION <= [1, 4, 2]:
reltol = 2.0
net = read_inventory()[0]
t = UTCDateTime(2008, 7, 1)
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore")
with ImageComparison(self.image_dir, "network_response.png",
reltol=reltol) as ic:
rcParams['savefig.dpi'] = 72
net.plot_response(0.002, output="DISP", channel="B*E",
time=t, outfile=ic.name)
def test_plotBenchmark(self):
"""
Test benchmark plot.
"""
path = os.path.join(os.path.dirname(__file__), 'data')
path_images = os.path.join(os.path.dirname(__file__), "images")
sufiles = sorted(glob.glob(os.path.join(path, 'seismic01_*_vz.su')))
# new temporary file with PNG extension
with ImageComparison(path_images, 'test_plotBenchmark.png') as ic:
# generate plot
with warnings.catch_warnings(record=True) as w:
warnings.resetwarnings()
np_err = np.seterr(all="warn")
plotBenchmark(sufiles, outfile=ic.name, format='PNG')
np.seterr(**np_err)
self.assertEqual(len(w), 1)
self.assertEqual(w[0].category, RuntimeWarning)
self.assertEqual(str(w[0].message),
'underflow encountered in divide')
def test_response_plot(self):
"""
Tests the response plot.
"""
# Bug in matplotlib 1.4.0 - 1.4.x:
# See https://github.com/matplotlib/matplotlib/issues/4012
reltol = 1.0
if [1, 4, 0] <= MATPLOTLIB_VERSION <= [1, 5, 0]:
reltol = 2.0
inv = read_inventory()
t = UTCDateTime(2008, 7, 1)
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore")
with ImageComparison(self.image_dir, "inventory_response.png",
reltol=reltol) as ic:
rcParams['savefig.dpi'] = 72
inv.plot_response(0.01, output="ACC", channel="*N",
station="[WR]*", time=t, outfile=ic.name)
def test_mopad_fallback(self):
"""
Test the fallback to mopad.
"""
mt = [0.000, -1.232e25, 1.233e25, 0.141e25, -0.421e25, 2.531e25]
with warnings.catch_warnings(record=True) as w:
# Always raise warning.
warnings.simplefilter("always")
with ImageComparison(self.path, 'mopad_fallback.png') as ic:
beachball(mt, outfile=ic.name)
# Make sure the appropriate warnings has been raised.
self.assertTrue(w)
# Filter
w = [_i.message.args[0] for _i in w]
w = [_i for _i in w
if "falling back to the mopad wrapper" in _i.lower()]
self.assertTrue(w)
def test_plot_wiggles_positive_section(self):
"""
Tests plotting 10 traces in a horizontal section,
with colored wiggles only on the positive side.
"""
start = UTCDateTime(0)
st = Stream()
for _i in range(10):
this_start = start + 300 * np.sin(np.pi * _i / 9)
st += self._create_stream(this_start, this_start + 3600, 100)
st[-1].stats.distance = _i * 10e3
st[-1].stats.channel = str(_i % 3)
# create and compare image
with ImageComparison(self.path,
'waveform_wiggles_positive_section.png') as ic:
st.plot(outfile=ic.name,
type='section',
orientation='horizontal',
fillcolors=("black", None))
def test_scan(self):
"""
Run obspy-scan on selected tests/data directories
"""
reltol = 1
if MATPLOTLIB_VERSION < [1, 3, 0]:
reltol = 60
# using mseed increases test time by factor 2
waveform_dirs = [
join(self.root, n, 'tests', 'data') for n in ('sac', 'gse2')
]
with ImageComparison(self.path, 'scan.png', reltol=reltol) as ic:
try:
tmp_stdout = sys.stdout
sys.stdout = open(os.devnull, 'wb')
obspy_scan(waveform_dirs + ['--output', ic.name])
finally:
sys.stdout.close()
sys.stdout = tmp_stdout
def test_plot(self):
"""
Run obspy-plot on selected tests
"""
reltol = 1
if MATPLOTLIB_VERSION < [1, 3, 0]:
reltol = 60
# Copy files to a temp folder to avoid wildcard scans.
with TemporaryWorkingDirectory():
all_files = []
for filename in self.all_files:
newname = join(os.curdir, basename(filename))
shutil.copy(filename, newname)
all_files += [newname]
with ImageComparison(self.path, 'plot.png', reltol=reltol) as ic:
with CatchOutput():
obspy_plot(['--outfile', ic.name] + all_files)
def test_plot_complex_gap_few_samples(self):
"""
Plots three hours with a gap.
There are 45 minutes of data at the beginning and 45 minutes of data at
the end.
"""
start = UTCDateTime(0)
st = self._create_stream(start, start + 3600 * 3 / 4, 5.0)
st += self._create_stream(start + 2.25 * 3600, start + 3 * 3600, 5.0)
st[0].stats.location = '01'
st[1].stats.location = '01'
temp_st = self._create_stream(start + 3600 * 3 / 4,
start + 2.25 * 3600, 5.0)
temp_st[0].stats.location = '02'
st += temp_st
# create and compare image
image_name = 'waveform_complex_gap_few_samples.png'
with ImageComparison(self.path, image_name) as ic:
st.plot(outfile=ic.name)
def test_spherical_diff_phases(self):
"""
Spherical plot of ``diff`` phases.
"""
with ImageComparison(self.image_dir,
"spherical_diff_phases.png") as ic:
self.model.get_ray_paths(700,
140,
phase_list=[
"Pdiff", "Sdiff", "pSdiff", "sSdiff",
"pPdiff", "sPdiff"
]).plot_rays(phase_list=[
"Pdiff", "Sdiff", "pSdiff", "sSdiff",
"pPdiff", "sPdiff"
],
plot_type="spherical",
legend=True,
plot_all=True,
show=False)
plt.savefig(ic.name)
def test_ppsd_plot_cumulative(self):
"""
Test plot of ppsd example data, cumulative style.
"""
# Catch underflow warnings due to plotting on log-scale.
with np.errstate(all='ignore'):
with ImageComparison(self.path, 'ppsd_cumulative.png',
reltol=1.5) as ic:
self.ppsd.plot(
show=False, show_coverage=True, show_histogram=True,
show_noise_models=True, grid=True, period_lim=(0.02, 100),
cumulative=True,
# This does not do anything but silences a warning that
# the `cumulative` and `max_percentage` arguments cannot
# be used at the same time.
max_percentage=None)
fig = plt.gcf()
ax = fig.axes[0]
ax.set_ylim(-160, -130)
plt.draw()
fig.savefig(ic.name)
def test_scanTimes(self):
"""
Checks for timing related options
"""
reltol = 1
if MATPLOTLIB_VERSION < [1, 3, 0]:
reltol = 60
# Copy files to a temp folder to avoid wildcard scans.
with TemporaryWorkingDirectory():
for filename in self.all_files:
shutil.copy(filename, os.curdir)
with ImageComparison(self.path, 'scan_times.png',
reltol=reltol) as ic:
with CatchOutput():
obspy_scan([os.curdir] + ['--output', ic.name] +
['--start-time', '2004-01-01'] +
['--end-time', '2004-12-31'] +
['--event-time', '2004-03-14T15:09:26'] +
['--event-time', '2004-02-07T18:28:18'])
def test_plot_day_plot_explicit_event(self):
'''
Plots day plot, starting Jan 1970, with several events.
'''
start = UTCDateTime(0)
event1 = UTCDateTime(30) # Event: Top left; Note: below right
event2 = UTCDateTime(14 * 60) # Event: Top right; Note: below left
event3 = UTCDateTime(46 * 60) # Event: Bottom left; Note: above right
event4 = UTCDateTime(59 * 60) # Event: Bottom right; Note: above left
event5 = UTCDateTime(61 * 60) # Should be ignored
st = self._create_stream(start, start + 3600, 100)
# create and compare image
image_name = 'waveform_dayplot_event.png'
with ImageComparison(self.path, image_name) as ic:
st.plot(outfile=ic.name, type='dayplot',
timezone='EST', time_offset=-5,
events=[{'time': event1, 'text': 'Event 1'},
{'time': event2, 'text': 'Event 2'},
{'time': event3, 'text': 'Event 3'},
{'time': event4, 'text': 'Event 4'},
{'time': event5, 'text': 'Event 5'}])
def test_scanner_manually_add_streams(self):
"""
Test Scanner class, manually adding streams of read data files
"""
scanner = Scanner()
# Copy files to a temp folder to avoid wildcard scans.
with TemporaryWorkingDirectory():
for filename in self.all_files:
shutil.copy(filename, os.curdir)
for file_ in os.listdir(os.curdir):
# some files used in the test cases actually can not
# be read with obspy..
if file_ in ('STA2.testlines_out', 'STA2.testlines',
'seism-shorter.sac', 'seism-longer.sac'):
continue
st = read(file_, headonly=True)
scanner.add_stream(st)
with ImageComparison(self.path, 'scan.png') as ic:
scanner.plot(ic.name)
def test_ppsd_plot(self):
"""
"""
with ImageComparison(self.path, 'ppsd.png') as ic:
self.ppsd.plot(show=False,
show_coverage=True,
show_histogram=True,
show_percentiles=True,
percentiles=[75, 90],
show_noise_models=True,
grid=True,
max_percentage=50,
period_lim=(0.02, 100),
show_mode=True,
show_mean=True)
from matplotlib.pyplot import gcf, draw
fig = gcf()
ax = fig.axes[0]
ax.set_ylim(-160, -130)
draw()
fig.savefig(ic.name)
def test_ppsd_plot(self):
"""
Test plot of ppsd example data, normal (non-cumulative) style.
"""
with ImageComparison(self.path, 'ppsd.png') as ic:
self.ppsd.plot(show=False,
show_coverage=True,
show_histogram=True,
show_percentiles=True,
percentiles=[75, 90],
show_noise_models=True,
grid=True,
max_percentage=50,
period_lim=(0.02, 100),
show_mode=True,
show_mean=True)
fig = plt.gcf()
ax = fig.axes[0]
ax.set_ylim(-160, -130)
plt.draw()
fig.savefig(ic.name)
def test_catalog_plot_local(self):
"""
Tests the catalog preview plot, local projection, some more non-default
parameters, using Basemap.
"""
cat = read_events()
reltol = 1.5
# Basemap smaller 1.0.4 has a serious issue with plotting. Thus the
# tolerance must be much higher.
if BASEMAP_VERSION < [1, 0, 4]:
reltol = 100
with ImageComparison(self.image_dir,
"catalog-basemap3.png",
reltol=reltol) as ic:
rcParams['savefig.dpi'] = 72
cat.plot(method='basemap',
outfile=ic.name,
projection='local',
resolution='i',
continent_fill_color='0.3',
color='date',
colormap='gist_heat')
def test_plotMultipleTraces(self):
"""
Plots multiple traces underneath.
"""
reltol = 1
if [1, 0, 0] < MATPLOTLIB_VERSION < [1, 2, 0]:
reltol = 20
# 1 trace
st = read()[1]
with ImageComparison(self.path, 'waveform_1_trace.png',
reltol=reltol) as ic:
st.plot(outfile=ic.name, automerge=False)
# 3 traces
st = read()
with ImageComparison(self.path, 'waveform_3_traces.png',
reltol=reltol) as ic:
st.plot(outfile=ic.name, automerge=False)
# 5 traces
st = st[1] * 5
with ImageComparison(self.path, 'waveform_5_traces.png',
reltol=reltol) as ic:
st.plot(outfile=ic.name, automerge=False)
# 10 traces
st = st[1] * 10
with ImageComparison(self.path,
'waveform_10_traces.png',
reltol=reltol) as ic:
st.plot(outfile=ic.name, automerge=False)
# 10 traces - huge numbers
st = st[1] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data * 10**i
with ImageComparison(self.path,
'waveform_10_traces_huge.png',
reltol=reltol) as ic:
st.plot(outfile=ic.name, automerge=False, equal_scale=False)
# 10 traces - tiny numbers
st = st[1] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data / (10**i)
with ImageComparison(self.path,
'waveform_10_traces_tiny.png',
reltol=reltol) as ic:
st.plot(outfile=ic.name, automerge=False, equal_scale=False)
def test_multiple_sampling_rates(self):
"""
Check for multiple sampling rates
"""
lines = [
"TIMESERIES XX_TEST__BHZ_R, 200 samples, 200 sps, "
"2008-01-15T00:00:00.000000, SLIST, INTEGER, Counts",
"TIMESERIES XX_TEST__BHZ_R, 50 samples, 50 sps, "
"2008-01-15T00:00:01.000000, SLIST, INTEGER, Counts",
"TIMESERIES XX_TEST__BHZ_R, 200 samples, 200 sps, "
"2008-01-15T00:00:02.000000, SLIST, INTEGER, Counts",
]
files = []
with NamedTemporaryFile() as f1, NamedTemporaryFile() as f2, \
NamedTemporaryFile() as f3:
for i, fp in enumerate([f1, f2, f3]):
fp.write(("%s\n" % lines[i]).encode('ascii', 'strict'))
fp.flush()
fp.seek(0)
files.append(fp.name)
with ImageComparison(self.path, 'scan_mult_sampl.png')\
as ic:
obspy_scan(files + ['--output', ic.name, '--quiet'])
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
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)