def test_SaveFails(self):
print(SEGY)
data = NoInitRadarData()
with self.assertRaises(ImportError):
data.save_as_segy(
os.path.join(THIS_DIR, 'input_data',
'shots0001_0200_resave.segy'))
def test_plot_power(self, mock_show):
# Only checking that these do not throw errors
dat = NoInitRadarData(big=True)
with self.assertRaises(TypeError):
plot.plot_power(dat, [12, 14])
with self.assertRaises(ValueError):
plot.plot_power(dat, 0)
dat.picks = Picks(dat)
dat.picks.add_pick(10)
dat.picks.power[:] = 10.5
# works with constant power
fig, ax = plot.plot_power(dat, 10)
# works with various inputs
fig, ax = plt.subplots()
plot.plot_power(dat, 10, fig=fig)
plot.plot_power(dat, 10, fig=fig, ax=ax)
plot.plot_power(dat, 10, clims=(-100, 100), fig=fig, ax=ax)
# works with multiple inputs
plot.plot_power([dat, dat], 10, fig=fig, ax=ax)
# works with projected coordinates
dat.x_coord = np.arange(dat.data.shape[1])
dat.y_coord = np.arange(dat.data.shape[1])
plot.plot_power(dat, 10, fig=fig, ax=ax)
plot.plot_power([dat, dat], 10, fig=fig, ax=ax)
with self.assertRaises(ValueError):
plot.plot_power(dat, 0, fig=fig, ax=ax)
# gets ok lims with variable power?
dat.picks.power[:, 0] = 1
plot.plot_power(dat, 10, fig=fig, ax=ax)
def test_plot_radargram_flattenlayer(self, mock_show):
dat = NoInitRadarData(big=True)
dat.picks = Picks(dat)
dat.picks.add_pick(10)
dat.picks.power[:] = 10
dat.picks.samp1[:] = 0
dat.picks.samp2[:] = 1 # make sure no bugs if this is actually constant
dat.picks.samp3[:] = 3
# works with constant power
fig, ax = plot.plot_radargram(dat, flatten_layer=10)
# make sure we can actually follow a variable layer
dat.picks.samp2[:, 1:] = 2
dat.picks.samp2[:, -1] = 4
# works with constant power
fig, ax = plot.plot_radargram(dat, flatten_layer=10)
dat.picks.samp2[:] = 0 # make sure no bugs if this is at the top
fig, ax = plot.plot_radargram(dat, flatten_layer=10)
dat.picks.samp2[:] = dat.data.shape[
0] - 1 # make sure no bugs if this is at the bottom
fig, ax = plot.plot_radargram(dat, flatten_layer=10)
dat.picks.samp2[:,
1] = np.NaN # make sure no bugs if this is at the bottom
fig, ax = plot.plot_radargram(dat, flatten_layer=10)
with self.assertRaises(ValueError):
fig, ax = plot.plot_radargram(dat, flatten_layer=1)
def test_plot_traces(self, mock_show):
# Only checking that these do not throw errors
dat = NoInitRadarData(big=True)
fig, ax = plot.plot_traces(dat, 0)
fig, ax = plt.subplots()
plot.plot_traces(dat, 0, fig=fig)
plot.plot_traces(dat, 0, fig=fig, ax=ax)
plot.plot_traces(dat, [1, 1], fig=fig, ax=ax)
plot.plot_traces(dat, [1, 18], fig=fig, ax=ax)
with self.assertRaises(ValueError):
plot.plot_traces(dat, np.arange(10), fig=fig, ax=ax)
with self.assertRaises(IndexError):
plot.plot_traces(dat, 999, fig=fig, ax=ax)
# no nmo
plot.plot_traces(dat, 0, ydat='depth', fig=fig, ax=ax)
# with nmo
dat.nmo_depth = np.arange(10)
plot.plot_traces(dat, 0, ydat='depth', fig=fig, ax=ax)
with self.assertRaises(ValueError):
plot.plot_traces(dat, 0, ydat='dum', fig=fig, ax=ax)
# Make sure we handle axes rescaling ok
dat.data[:, 0] = 10
dat.data[:, 1] = -10
plot.plot_traces(dat, (0, 2), fig=fig, ax=ax)
def test_sustolt_seisunix(self):
data = NoInitRadarData(big=True)
data.dt = 1.0e-9
data.travel_time = data.travel_time * 1.0e-9
data.fn = os.path.join(THIS_DIR, 'input_data', 'rectangle_sustolt.mat')
with self.assertRaises(FileNotFoundError):
migrationlib.migrationSeisUnix(data)
def test_plot_spectrogram(self, mock_show):
# Only checking that these do not throw errors
dat = NoInitRadarData(big=True)
dat.picks = Picks(dat)
dat.picks.samp1 = np.ones((2, len(dat.lat)))
dat.picks.samp2 = np.ones((2, len(dat.lat)))
dat.picks.samp3 = np.ones((2, len(dat.lat)))
fig, ax = plot.plot_spectrogram(dat, (0., 5.0))
plot.plot_spectrogram(dat, (0., 5.0), fig=fig)
plot.plot_spectrogram(dat, (0., 5.0), fig=fig, ax=ax)
plot.plot_spectrogram(dat, (0., 5.0), window='hamming')
plot.plot_spectrogram(dat, (0., 5.0), scaling='density')
# no error if freq high
plot.plot_spectrogram(dat, 100)
# freq too low
with self.assertRaises(ValueError):
plot.plot_spectrogram(dat, (0., -100))
with self.assertRaises(ValueError):
plot.plot_spectrogram(dat, (0., 5), scaling='dummy')
with self.assertRaises(ValueError):
plot.plot_spectrogram(dat, (0., 5), window='dummy')
def test_failure_3(self):
dat = NoInitRadarData(big=True)
dat.picks = Picks(dat)
dat.picks.samp1 = np.ones((2, len(dat.lat)))
dat.picks.samp2 = np.ones((2, len(dat.lat)))
dat.picks.samp3 = np.ones((2, len(dat.lat)))
with self.assertRaises(AttributeError):
plot.plot_specdense(dat, 'bad')
def test_compKirchoff_cython(self):
data = NoInitRadarData(big=True)
pdata = NoInitRadarData(big=True)
data = mig_cython.migrationKirchhoff(data)
pdata = mig_python.migrationKirchhoff(pdata)
data.data[np.isnan(data.data)] = 0.
self.assertTrue(np.allclose(data.data, pdata.data))
def test_plot_picks_via_radargram(self, mock_show):
"""We want to be able to call this via plot_radargram"""
dat = NoInitRadarData(big=True)
dat.picks = Picks(dat)
dat.picks.samp1 = np.ones((2, len(dat.lat)))
dat.picks.samp2 = np.ones((2, len(dat.lat)))
dat.picks.samp3 = np.ones((2, len(dat.lat)))
dat.picks.picknums = [0, 9]
plot.plot_radargram(dat, pick_colors='mgm')
def test_check_data_shape(self):
data = NoInitRadarData(big=True)
# should pass, i.e. nothing happens
migrationlib._check_data_shape(data)
# make it fail
data.data = np.ones((1, 1))
with self.assertRaises(ValueError):
migrationlib._check_data_shape(data)
def test_output_csv_nolayers(self):
rd = NoInitRadarData()
rd.output_csv(os.path.join(THIS_DIR, 'input_data', 'test.csv'))
with open(os.path.join(THIS_DIR, 'input_data', 'test.csv')) as fin:
lines = fin.readlines()
# we should only have three entries: lat, lon, trace in header and data
self.assertEqual(len(lines[0].split(',')), 3)
self.assertEqual(len(lines[1].split(',')), 3)
# we should have a row per trace, plus a header
self.assertEqual(len(lines), rd.tnum + 1)
def testWriteWithPicksFull(self):
rd = NoInitRadarData()
rd.picks = Picks(rd)
rd.picks.add_pick()
rd.save(os.path.join(THIS_DIR, 'input_data', 'test_out.mat'))
data = RadarData(os.path.join(THIS_DIR, 'input_data', 'test_out.mat'))
self.assertTrue(data.picks is not None)
self.assertTrue(data.picks.lasttrace is not None)
self.assertTrue(data.picks.samp1 is not None)
self.assertTrue(data.picks.samp2 is not None)
self.assertTrue(data.picks.samp3 is not None)
def test_getVelocityProfile(self):
data = NoInitRadarData(big=True)
self.assertEqual(1.68e8, migrationlib.getVelocityProfile(data, 1.68e8))
# need reasonable input here for 2d. Needs a different travel time.
data.travel_time = data.travel_time / 10.
migrationlib.getVelocityProfile(
data,
np.genfromtxt(
os.path.join(THIS_DIR, 'input_data', 'velocity_layers.txt')))
# this should still work since we are close
data.travel_time = data.travel_time / 10.
twod = np.genfromtxt(
os.path.join(THIS_DIR, 'input_data', 'velocity_layers.txt'))
twod = twod * 0.0045 + 1.0e-7 * twod[1]
migrationlib.getVelocityProfile(data, twod)
# need reasonable input here for 3d
data = NoInitRadarData(big=True)
migrationlib.getVelocityProfile(
data,
np.genfromtxt(
os.path.join(THIS_DIR, 'input_data', 'velocity_lateral.txt')))
# Bad distance with good 3d grid
data.dist = None
with self.assertRaises(ValueError):
migrationlib.getVelocityProfile(
data,
np.genfromtxt(
os.path.join(THIS_DIR, 'input_data',
'velocity_lateral.txt')))
data = NoInitRadarData(big=True)
# this should fail on bad z
twod_vel = 1.68e8 * np.ones((10, 2))
twod_vel[:, 1] = 0.
with self.assertRaises(ValueError):
migrationlib.getVelocityProfile(data, twod_vel)
# Use some bad x values
with self.assertRaises(ValueError):
migrationlib.getVelocityProfile(data, 1.68e8 * np.ones((10, 3)))
# bad z values
threed_vel = 1.68e8 * np.ones((10, 3))
threed_vel[:, -1] = np.arange(10) * 1000.
with self.assertRaises(ValueError):
migrationlib.getVelocityProfile(data, threed_vel)
# Make sure we reject bad input shapes
with self.assertRaises(ValueError):
migrationlib.getVelocityProfile(data, 1.68e8 * np.ones((8, )))
with self.assertRaises(ValueError):
migrationlib.getVelocityProfile(data, 1.68e8 * np.ones((8, 1)))
with self.assertRaises(ValueError):
migrationlib.getVelocityProfile(data, 1.68e8 * np.ones((1, 2)))
with self.assertRaises(ValueError):
migrationlib.getVelocityProfile(data, 1.68e8 * np.ones((8, 4)))
def test_concat(self):
dats = process.concat([NoInitRadarData(), NoInitRadarData()])
self.assertTrue(dats[0].data.shape == (2, 4))
with self.assertRaises(ValueError):
d2 = NoInitRadarData()
d2.snum = 3
dats = process.concat([NoInitRadarData(), d2])
with self.assertRaises(ValueError):
d2 = NoInitRadarData()
d2.travel_time = np.array((2, 3))
dats = process.concat([NoInitRadarData(), d2])
def test_PhaseShiftVariable(self):
data = NoInitRadarData(big=True)
data.travel_time = data.travel_time / 10.
data = migrationlib.migrationPhaseShift(data,
vel_fn=os.path.join(
THIS_DIR, 'input_data',
'velocity_layers.txt'))
data = NoInitRadarData(big=True)
with self.assertRaises(TypeError):
data = migrationlib.migrationPhaseShift(data,
vel_fn=os.path.join(
THIS_DIR, 'input_data',
'notafile.txt'))
def test_plot_hft(self, mock_show):
# Only checking that these do not throw errors
dat = NoInitRadarData(big=True)
plot.plot_hft(dat)
fig, ax = plt.subplots()
plot.plot_hft(dat, fig=fig, ax=ax)
plot.plot_hft(dat, fig=fig)
def test_interp(self, mock_kgc, mock_kgm):
dats = [NoInitRadarData(big=True)]
dats[0].constant_space = MagicMock()
gpslib.interp(dats, 10., fn='dum.csv')
self.assertTrue(len(mock_kgc.mock_calls) > 0)
self.assertTrue(len(dats[0].constant_space.mock_calls) > 0)
dats[0].constant_space = MagicMock()
gpslib.interp(dats, 10., fn='dum.mat')
self.assertTrue(len(mock_kgm.mock_calls) > 0)
self.assertTrue(len(dats[0].constant_space.mock_calls) > 0)
with self.assertRaises(Exception):
gpslib.interp(dats, 10., fn='dum.badext')
dats = [NoInitRadarData(big=True)]
dats[0].constant_space = MagicMock()
gpslib.interp(dats, 10.)
self.assertTrue(len(dats[0].constant_space.mock_calls) > 0)
def test_HighPassBadcutoff(self):
radardata = NoInitRadarData()
# fails without constant-spaced data
radardata.flags.interp = np.ones((2, ))
with self.assertRaises(ValueError):
# We have a screwed up filter here because of sampling vs. frequency used
radardata.highpass(1.0e-4)
def test_vbp_fir(self):
radardata = NoInitRadarData()
radardata.vertical_band_pass(1., 10., filttype='fir', order=100)
radardata.vertical_band_pass(1.,
10.,
filttype='fir',
order=2,
fir_window='hanning')
def test_plot_radargram_figaxin(self):
# Only checking that these do not throw errors
dat = NoInitRadarData(big=True)
fig, ax = plot.plot_radargram(dat)
fig, ax = plt.subplots()
fig, ax = plot.plot_radargram(dat, fig=fig, ax=ax)
fig, ax = plt.subplots()
fig, ax = plot.plot_radargram(dat, fig=fig)
def test_plot_power(self):
# Only checking that these do not throw errors
dat = NoInitRadarData(big=True)
with self.assertRaises(TypeError):
fig, ax = plot.plot_power(dat, [12, 14])
with self.assertRaises(ValueError):
fig, ax = plot.plot_power(dat, 0)
dat.picks = Picks(dat)
dat.picks.add_pick(10)
dat.picks.power[:] = 10
# works with constant power
fig, ax = plot.plot_power(dat, 10)
with self.assertRaises(ValueError):
fig, ax = plot.plot_power(dat, 0)
# gets ok lims with variable power?
dat.picks.power[:, 0] = 1
fig, ax = plot.plot_power(dat, 10)
def test_plot_picks(self):
# Only checking that these do not throw errors
dat = NoInitRadarData(big=True)
fig, ax = plot.plot_picks(dat, np.arange(int(dat.tnum)),
dat.travel_time)
dat.picks = Picks(dat)
dat.picks.samp1 = np.ones((2, len(dat.lat)))
dat.picks.samp2 = np.ones((2, len(dat.lat)))
dat.picks.samp3 = np.ones((2, len(dat.lat)))
fig, ax = plot.plot_picks(dat, np.arange(int(dat.tnum)),
dat.travel_time)
fig, ax = plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors='g')
fig, ax = plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors='gmm')
fig, ax = plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=['c', 'g'])
fig, ax = plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=['cmy', 'brb'])
fig, ax = plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=['cm', 'br'])
with self.assertRaises(ValueError):
fig, ax = plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=['c', 'm', 'b'])
def test_HighPass(self):
radardata = NoInitRadarData()
# fails without constant-spaced data
radardata.flags.interp = np.ones((2, ))
radardata.highpass(1000.0)
# There is no high-frequency variability, so this result should be small
# We only have residual variability from the quality of the filter
print(
np.abs((radardata.data - radardata.data[0, 0]) /
radardata.data[0, 0]))
self.assertTrue(
np.all(
np.abs((radardata.data - radardata.data[0, 0]) /
radardata.data[0, 0]) < 1.0e-3))
def test_plot_traces(self):
# Only checking that these do not throw errors
dat = NoInitRadarData(big=True)
fig, ax = plot.plot_traces(dat, 0)
fig, ax = plot.plot_traces(dat, [1, 1])
fig, ax = plot.plot_traces(dat, [1, 18])
with self.assertRaises(ValueError):
fig, ax = plot.plot_traces(dat, np.arange(10))
with self.assertRaises(IndexError):
fig, ax = plot.plot_traces(dat, 999)
# no nmo
fig, ax = plot.plot_traces(dat, 0, ydat='depth')
# with nmo
dat.nmo_depth = np.arange(10)
fig, ax = plot.plot_traces(dat, 0, ydat='depth')
with self.assertRaises(ValueError):
fig, ax = plot.plot_traces(dat, 0, ydat='dum')
# Make sure we handle axes rescaling ok
dat.data[:, 0] = 10
dat.data[:, 1] = -10
fig, ax = plot.plot_traces(dat, (0, 2))
def test_plot_specdense(self):
# Only checking that these do not throw errors
dat = NoInitRadarData(big=True)
dat.picks = Picks(dat)
dat.picks.samp1 = np.ones((2, len(dat.lat)))
dat.picks.samp2 = np.ones((2, len(dat.lat)))
dat.picks.samp3 = np.ones((2, len(dat.lat)))
fig, ax = plot.plot_specdense(dat, 3.0e-7)
plot.plot_specdense(dat, 3.0e-7, fig=fig)
plot.plot_specdense(dat, 3.0e-7, fig=fig, ax=ax)
plot.plot_specdense(dat, 3.0e-7, window='hamming')
plot.plot_specdense(dat, 3.0e-7, scaling='density')
# freq too low
with self.assertRaises(ValueError):
plot.plot_specdense(dat, -100)
with self.assertRaises(ValueError):
plot.plot_specdense(dat, 3.0e-7, scaling='dummy')
with self.assertRaises(ValueError):
plot.plot_specdense(dat, 3.0e-7, window='dummy')
def test__get_pick_targ_infoBadSelections(self):
# Make sure that we are selecting the proper output format
rd = NoInitRadarData()
# Try depth when there is no depth
rd.nmo_depth = None
with self.assertRaises(AttributeError):
out_name, tout = rd._get_pick_targ_info('depth')
# Elevation with no depth or elevation
with self.assertRaises(AttributeError):
out_name, tout = rd._get_pick_targ_info('elev')
# Elevation with depth but not elevation
rd.nmo_depth = np.arange(len(rd.travel_time)) * 1.1
with self.assertRaises(AttributeError):
out_name, tout = rd._get_pick_targ_info('elev')
# Now try to pass a bad value for the selection
with self.assertRaises(ValueError):
out_name, tout = rd._get_pick_targ_info('dummy')
with self.assertRaises(ValueError):
out_name, tout = rd._get_pick_targ_info(['dummy', 'snum'])
def test_kinematic_gps_mat(self, mock_kgc):
dats = [NoInitRadarData(big=True)]
gpslib.kinematic_gps_mat(
dats, os.path.join(THIS_DIR, 'input_data', 'gps_control.mat'))
self.assertTrue(
np.allclose(np.arange(0, 2.0, 0.1), mock_kgc.call_args[0][1]))
self.assertTrue(
np.allclose(np.arange(0, 200, 10), mock_kgc.call_args[0][2]))
self.assertTrue(
np.allclose(np.arange(0, 2000, 100), mock_kgc.call_args[0][3]))
self.assertTrue(
np.allclose(np.arange(0, 20, 1), mock_kgc.call_args[0][4]))
with self.assertRaises(ValueError):
gpslib.kinematic_gps_mat(dats,
os.path.join(THIS_DIR, 'input_data',
'gps_control_badfields.mat'),
extrapolate=False)
def test__get_pick_targ_infoGoodSelections(self):
# Make sure that we are selecting the proper output format
rd = NoInitRadarData()
rd.nmo_depth = np.arange(len(rd.travel_time)) * 1.1
rd.elev = np.arange(rd.tnum) * 1001
out_name, tout = rd._get_pick_targ_info('twtt')
self.assertEqual(out_name, 'twtt')
self.assertTrue(np.all(tout == rd.travel_time))
out_name, tout = rd._get_pick_targ_info('depth')
self.assertEqual(out_name, 'depth')
self.assertTrue(np.all(tout == rd.nmo_depth))
out_name, tout = rd._get_pick_targ_info('snum')
self.assertEqual(out_name, 'snum')
self.assertTrue(np.all(tout == np.arange(rd.snum)))
out_name, tout = rd._get_pick_targ_info('elev')
self.assertEqual(out_name, 'elev')
def test__get_pick_targ_infoAutoselect(self):
# Make sure that we are selecting the proper output format
rd = NoInitRadarData()
# With no depth, we should output travel time
rd.nmo_depth = None
out_name, tout = rd._get_pick_targ_info(None)
self.assertEqual(out_name, 'twtt')
self.assertTrue(np.all(tout == rd.travel_time))
# With depth, return depth
rd.nmo_depth = np.arange(len(rd.travel_time)) * 1.1
out_name, tout = rd._get_pick_targ_info(None)
self.assertEqual(out_name, 'depth')
self.assertTrue(np.all(tout == rd.nmo_depth))
def test_kinematic_gps_csv(self, mock_kgc):
dats = [NoInitRadarData(big=True)]
gpslib.kinematic_gps_csv(
dats, os.path.join(THIS_DIR, 'input_data', 'gps_control.csv'))
self.assertTrue(
np.allclose(np.arange(0, 2.0, 0.1), mock_kgc.call_args[0][1]))
self.assertTrue(
np.allclose(np.arange(0, 200, 10), mock_kgc.call_args[0][2]))
self.assertTrue(
np.allclose(np.arange(0, 2000, 100), mock_kgc.call_args[0][3]))
self.assertTrue(
np.allclose(np.arange(0, 20, 1), mock_kgc.call_args[0][4]))
with self.assertRaises(ValueError):
gpslib.kinematic_gps_csv(dats,
os.path.join(THIS_DIR, 'input_data',
'gps_control.csv'),
names='dumbanddummer')
with self.assertRaises(ValueError):
gpslib.kinematic_gps_csv(dats,
os.path.join(THIS_DIR, 'input_data',
'gps_control.csv'),
names='dumb,dumb,and,dummer')
