def dummy_ts(self):
return timeseries.TimeSeries(np.linspace(0, 10, 10),
dims=("x", ),
coords={
"x": range(10),
"samplerate": 1
})
def setup_class(cls):
times = np.linspace(0, 1, 1000)
ts = np.sin(8 * times) + np.sin(16 * times) + np.sin(32 * times)
cls.timeseries = timeseries.TimeSeries(data=ts,
dims=('time'),
coords={
'time': times,
'samplerate': 1000
})
def time_series():
times = np.linspace(0, 1, 1000)
ts = np.sin(8 * times) + np.sin(16 * times) + np.sin(32 * times)
return timeseries.TimeSeries(data=ts,
dims=('time'),
coords={
'time': times,
'samplerate': 1000
})
def setup_class(self):
self.times = times = np.linspace(0, 1, 1000)
self.data = np.sin(8 * times) + np.sin(16 * times) + np.sin(32 * times)
self.freqs = np.array([10, 20], dtype=float)
self.timeseries = timeseries.TimeSeries(data=self.data[None, :],
coords={
'offsets': [0],
'time': self.times,
'samplerate': 1000
},
dims=('offsets', 'time'))
def test_non_double(self):
"""Test that we can use a TimeSeries that starts out as a dtype other
than double.
"""
lim = 10000
data = np.random.uniform(-lim, lim, (100, 1000)).astype(np.int16)
ts = timeseries.TimeSeries(data=data,
dims=("x", "time"),
coords={
"x":
np.linspace(0, data.shape[0],
data.shape[0]),
"time":
np.arange(data.shape[1]),
"samplerate":
1,
})
mwf = MorletWaveletFilter(ts,
np.array(range(70, 171, 10)),
output="power")
mwf.filter()