def test_remove_noncontiguous(self):
tb1 = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
tb2 = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
tb3 = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
# nonzero signal mean
tsd1 = TimeseriesData(timebase=tb1,
signal=Signal(np.arange(len(tb1))), channels=ChannelList(Channel('ch_01',Coords('dummy',(0,0,0)))))
tsd2 = TimeseriesData(timebase=tb2,
signal=Signal(np.arange(len(tb2))), channels=ChannelList(Channel('ch_01',Coords('dummy',(0,0,0)))))
tsd3 = TimeseriesData(timebase=tb3,
signal=Signal(np.arange(len(tb3))), channels=ChannelList(Channel('ch_01',Coords('dummy',(0,0,0)))))
self.assertTrue(tb1.is_contiguous())
self.assertTrue(tb2.is_contiguous())
self.assertTrue(tb3.is_contiguous())
tsd2.timebase[-50:] += 1.0
self.assertFalse(tb2.is_contiguous())
ds = DataSet('ds')
for tsd in [tsd1, tsd2, tsd3]:
ds.add(tsd)
for tsd in [tsd1, tsd2, tsd3]:
self.assertTrue(tsd in ds)
filtered_ds = ds.remove_noncontiguous()
for tsd in [tsd1, tsd3]:
self.assertTrue(tsd in filtered_ds)
self.assertFalse(tsd2 in filtered_ds)
def fetch(self):
tb = generate_timebase(t0=float(self.t0), n_samples=int(self.n_samples), sample_freq=float(self.sample_freq))
sig = Signal(float(self.shot) * sin(2 * pi * float(self.frequency) * tb))
dummy_channel = Channel("ch_01", Coords("dummy", (0, 0, 0)))
output_data = TimeseriesData(timebase=tb, signal=sig, channels=ChannelList(dummy_channel))
output_data.meta.update({"shot": self.shot})
return output_data
def test_multi_channel_timeseries(self):
tb = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
tsd = TimeseriesData(timebase=tb,
signal=Signal(np.resize(np.arange(3*len(tb)), (3,len(tb)))),
channels=get_n_channels(3))
seg_dataset = tsd.segment(n_samples=10)
self.assertTrue(len(seg_dataset)==10)
def test_timebase(self):
t0=0.3
n_samples=500
sample_freq=1.e6
test_tb = generate_timebase(t0=t0,n_samples=n_samples, sample_freq=sample_freq)
local_tb = np.arange(t0, t0+n_samples/sample_freq, 1./sample_freq)
self.assertTrue((test_tb == local_tb).all())
self.assertAlmostEqual(test_tb.sample_freq, sample_freq, 4)
def test_remove_mean_single_channel(self):
tb = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
# nonzero signal mean
tsd = TimeseriesData(timebase=tb,
signal=Signal(np.arange(len(tb))), channels=ChannelList(Channel('ch_01',Coords('dummy',(0,0,0)))))
filtered_tsd = tsd.subtract_mean()
assert_almost_equal(np.mean(filtered_tsd.signal), 0)
def test_timebase_slice(self):
t0=0.3
n_samples=500
sample_freq=1.e6
test_tb = generate_timebase(t0=t0,n_samples=n_samples, sample_freq=sample_freq)
self.assertTrue(hasattr(test_tb, 'sample_freq'))
sliced_tb = test_tb[:10]
self.assertTrue(hasattr(sliced_tb, 'sample_freq'))
def fetch(self):
tb = generate_timebase(t0=float(self.t0),
n_samples=int(self.n_samples),
sample_freq=float(self.sample_freq))
sig = Signal(
float(self.amplitude) * sin(2 * pi * float(self.frequency) * tb))
dummy_channel = Channel('ch_01', Coords('dummy', (0, 0, 0)))
output_data = TimeseriesData(timebase=tb,
signal=sig,
channels=ChannelList(dummy_channel))
output_data.meta.update({'shot': self.shot})
return output_data
def test_reduce_time_dataset(self):
new_times = [-0.25, 0.25]
tb = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
tsd_1 = TimeseriesData(timebase=tb,
signal=Signal(np.resize(np.arange(5*len(tb)),(5,len(tb)))),
channels=get_n_channels(5))
tsd_2 = TimeseriesData(timebase=tb, signal=Signal(np.resize(np.arange(5*len(tb))+1,(5,len(tb)))),
channels=get_n_channels(5))
test_dataset = DataSet('test_dataset')
test_dataset.add(tsd_1)
test_dataset.add(tsd_2)
test_dataset.reduce_time(new_times)
def test_dataset(self):
tb = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
tsd_1 = TimeseriesData(timebase=tb,
signal=Signal(np.resize(np.arange(3*len(tb)), (3,len(tb)))),
channels=get_n_channels(3))
tsd_2 = TimeseriesData(timebase=tb,
signal=Signal(np.resize(np.arange(3*len(tb)+1),(3,len(tb)))),
channels=get_n_channels(3))
input_dataset = DataSet('test_dataset')
input_dataset.add(tsd_1)
input_dataset.add(tsd_2)
seg_dataset = input_dataset.segment(n_samples=10)
self.assertTrue(len(seg_dataset)==20)
def test_reduce_time_filter_multi_channel_attached_method(self):
new_times = [-0.25, 0.25]
tb = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
tsd = TimeseriesData(timebase=tb,
signal=Signal(np.resize(np.arange(5*len(tb)),(5,len(tb)))),
channels=get_n_channels(5))
new_time_args = np.searchsorted(tb, new_times)
timebase_test = tsd.timebase[new_time_args[0]:new_time_args[1]].copy()
signal_test = tsd.signal[:,new_time_args[0]:new_time_args[1]].copy()
reduced_tsd = tsd.reduce_time(new_times)
self.assertTrue(isinstance(reduced_tsd, TimeseriesData))
assert_array_almost_equal(reduced_tsd.timebase, timebase_test)
assert_array_almost_equal(reduced_tsd.signal, signal_test)
def test_dataset(self):
ch=get_n_channels(5)
new_times = [-0.25, 0.25]
tb = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
tsd_1 = TimeseriesData(timebase=tb, signal=Signal(np.resize(np.arange(5*len(tb)),(5,len(tb)))),
channels=ch)
tsd_2 = TimeseriesData(timebase=tb,
signal=Signal(np.resize(np.arange(5*len(tb))+1, (5,len(tb)))),
channels=ch)
test_dataset = DataSet('test_ds_1')
test_dataset.add(tsd_1)
test_dataset.add(tsd_2)
self.assertTrue(tsd_1 in test_dataset)
"""
def testFilteredDataHistory_nocopy(self):
tb = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
# nonzero signal mean
ch = get_n_channels(1)
tsd = TimeseriesData(timebase=tb,
signal=Signal(np.arange(len(tb))), channels=ch)
filtered_tsd = tsd.subtract_mean()
#self.assertEqual(len(filtered_tsd.history.split('\n')), 3)
self.assertEqual(len(filtered_tsd.history.split('\n')), 5) # bdb thinks extra 2 is OK
output_data = filtered_tsd.normalise(method='rms', copy=False)
#self.assertEqual(filtered_tsd.history.split('> ')[-1], "normalise(method='rms')")
self.assertEqual(filtered_tsd.history.split('> ')[-1].split('\n')[0], "normalise(method='rms')")
#self.assertEqual(output_data.history.split('> ')[-1], "normalise(method='rms')")
self.assertEqual(output_data.history.split('> ')[-1].split('\n')[0], "normalise(method='rms')")
def testFilteredDataHistory_copy(self):
""" make sure that _copy version does NOT alter original
"""
tb = generate_timebase(t0=-0.5, n_samples=1.e2, sample_freq=1.e2)
# nonzero signal mean
ch = get_n_channels(1)
tsd = TimeseriesData(timebase=tb,
signal=Signal(np.arange(len(tb))), channels=ch)
filtered_tsd = tsd.subtract_mean()
# bdb in 4 places, assume that the xtra info (norm_value) is supposed to be there.
#self.assertEqual(len(filtered_tsd.history.split('\n')), 3)
self.assertEqual(len(filtered_tsd.history.split('\n')), 5) # bdb thinks extra 2 is OK
output_data = filtered_tsd.normalise(method='rms', copy=True)
#self.assertEqual(output_data.history.split('> ')[-1], "normalise(method='rms')")
self.assertEqual(output_data.history.split('> ')[-1].split('\n')[0], "normalise(method='rms')")
#self.assertEqual(filtered_tsd.history.split('> ')[-1], "subtract_mean()")
self.assertEqual(filtered_tsd.history.split('> ')[-1].split('\n')[0], "subtract_mean()")
