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_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_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_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_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_constant_space_real(self):
# Basic check where there is movement every step
distlims = (self.data.dist[0], self.data.dist[-1])
space = 100.
targ_size = np.ceil((distlims[-1] - distlims[0]) * 1000. / space)
self.data.constant_space(space)
self.assertTrue(self.data.data.shape == (20, targ_size))
self.assertTrue(self.data.x_coord.shape == (targ_size, ))
self.assertTrue(self.data.y_coord.shape == (targ_size, ))
self.assertTrue(self.data.lat.shape == (targ_size, ))
self.assertTrue(self.data.long.shape == (targ_size, ))
self.assertTrue(self.data.elev.shape == (targ_size, ))
self.assertTrue(self.data.decday.shape == (targ_size, ))
# Make sure we can have some bad values from no movement
# This will delete some distance so, be careful with checks
self.setUp()
self.data.constant_space(space, min_movement=35.)
self.assertEqual(self.data.data.shape[0], 20)
self.assertLessEqual(self.data.data.shape[1], targ_size)
self.assertLessEqual(self.data.x_coord.shape[0], targ_size)
self.assertLessEqual(self.data.y_coord.shape[0], targ_size)
self.assertLessEqual(self.data.lat.shape[0], targ_size)
self.assertLessEqual(self.data.long.shape[0], targ_size)
self.assertLessEqual(self.data.elev.shape[0], targ_size)
self.assertLessEqual(self.data.decday.shape[0], targ_size)
# do not fail because flags structure is weird from matlab
self.setUp()
self.data.flags.interp = False
self.data.constant_space(space)
self.assertTrue(self.data.flags.interp.shape == (2, ))
self.assertTrue(self.data.flags.interp[0])
self.assertEqual(self.data.flags.interp[1], space)
# Want to be able to do picks too
self.setUp()
self.data.pick = Picks(self.data)
self.data.picks.samp1 = np.ones((2, self.data.tnum))
self.data.picks.samp2 = np.ones((2, self.data.tnum))
self.data.picks.samp3 = np.ones((2, self.data.tnum))
self.data.picks.power = np.ones((2, self.data.tnum))
self.data.picks.time = np.ones((2, self.data.tnum))
self.data.constant_space(space)
self.assertTrue(self.data.data.shape == (20, targ_size))
self.assertTrue(self.data.x_coord.shape == (targ_size, ))
self.assertTrue(self.data.y_coord.shape == (targ_size, ))
self.assertTrue(self.data.lat.shape == (targ_size, ))
self.assertTrue(self.data.long.shape == (targ_size, ))
self.assertTrue(self.data.elev.shape == (targ_size, ))
self.assertTrue(self.data.decday.shape == (targ_size, ))
self.assertTrue(self.data.picks.samp1.shape == (2, targ_size))
self.assertTrue(self.data.picks.samp2.shape == (2, targ_size))
self.assertTrue(self.data.picks.samp3.shape == (2, targ_size))
self.assertTrue(self.data.picks.power.shape == (2, targ_size))
self.assertTrue(self.data.picks.time.shape == (2, targ_size))
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_output_csv(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
rd.picks = Picks(rd)
rd.picks.add_pick()
# First, export with NaNs
rd.picks.samp2[:] = np.nan
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 have four entries: lat, lon, trace, and the one pick in header and data
self.assertEqual(len(lines[0].split(',')), 4)
self.assertEqual(len(lines[1].split(',')), 4)
# we should have a row per trace, plus a header
self.assertEqual(len(lines), rd.tnum + 1)
# The final header should be in terms of depth
self.assertTrue(lines[0].index('depth') > 0)
# Fill in NaNs
rd.picks.samp2[:] = 1
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 have four entries: lat, lon, trace, and the one pick in header and data
self.assertEqual(len(lines[0].split(',')), 4)
self.assertEqual(len(lines[1].split(',')), 4)
# we should have a row per trace, plus a header
self.assertEqual(len(lines), rd.tnum + 1)
# The final header should be in terms of depth
self.assertTrue(lines[0].index('depth') > 0)
# Check output target for elevation, which is the only weird one
rd.output_csv(os.path.join(THIS_DIR, 'input_data', 'test.csv'),
target_out='elev')
with open(os.path.join(THIS_DIR, 'input_data', 'test.csv')) as fin:
lines = fin.readlines()
# we should have four entries: lat, lon, trace, and the one pick in header and data
self.assertEqual(len(lines[0].split(',')), 4)
self.assertEqual(len(lines[1].split(',')), 4)
# we should have a row per trace, plus a header
self.assertEqual(len(lines), rd.tnum + 1)
# The final header should be in terms of elev
self.assertTrue(lines[0].index('elev') > 0)
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_output_shp_picks(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
rd.picks = Picks(rd)
rd.picks.add_pick()
# First, export with NaNs, both with normal field (depth) and elev
rd.picks.samp2[:] = np.nan
rd.output_shp(os.path.join(THIS_DIR, 'input_data', 'test0.shp'))
rd.output_shp(os.path.join(THIS_DIR, 'input_data', 'test1.shp'), target_out='elev')
# Fill in NaNs
rd.picks.samp2[:] = 1
rd.output_shp(os.path.join(THIS_DIR, 'input_data', 'test2.shp'))
rd.output_shp(os.path.join(THIS_DIR, 'input_data', 'test3.shp'), target_out='elev')
# Check geometry
rd.output_shp(os.path.join(THIS_DIR, 'input_data', 'test4.shp'), t_srs=3413)
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_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_plot_picks(self, mock_show):
# 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.picknums = [2, 10]
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)
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
fig=fig)
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
fig=fig,
ax=ax)
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors='g',
fig=fig,
ax=ax)
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors='gmm',
fig=fig,
ax=ax)
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=['c', 'g'],
fig=fig,
ax=ax)
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=['cmy', 'brb'],
fig=fig,
ax=ax)
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=['cm', 'br'],
fig=fig,
ax=ax)
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=True,
fig=fig,
ax=ax)
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=False,
fig=fig,
ax=ax)
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=['c', 'm', 'b'],
just_middle=False,
fig=fig,
ax=ax)
with self.assertRaises(ValueError):
plot.plot_picks(dat,
np.arange(int(dat.tnum)),
dat.travel_time,
colors=['c', 'm', 'b'],
just_middle=True,
fig=fig,
ax=ax)