def test_boxcar_filter():
a = np.random.rand(100)
b = tsa.boxcar_filter(a)
npt.assert_equal(a,b)
#Should also work for odd number of elements:
a = np.random.rand(99)
b = tsa.boxcar_filter(a)
npt.assert_equal(a,b)
b = tsa.boxcar_filter(a,ub=0.25)
npt.assert_equal(a.shape,b.shape)
b = tsa.boxcar_filter(a,lb=0.25)
npt.assert_equal(a.shape,b.shape)
def test_boxcar_filter():
a = np.random.rand(100)
b = tsa.boxcar_filter(a)
npt.assert_equal(a, b)
#Should also work for odd number of elements:
a = np.random.rand(99)
b = tsa.boxcar_filter(a)
npt.assert_equal(a, b)
b = tsa.boxcar_filter(a, ub=0.25)
npt.assert_equal(a.shape, b.shape)
b = tsa.boxcar_filter(a, lb=0.25)
npt.assert_equal(a.shape, b.shape)
def filtered_boxcar(self):
"""
Filter the time-series by a boxcar filter.
The low pass filter is implemented by convolving with a boxcar function
of the right length and amplitude and the high-pass filter is
implemented by subtracting a low-pass version (as above) from the
signal
"""
if self.ub is not None:
ub = self.ub / self.sampling_rate
else:
ub = 1.0
lb = self.lb / self.sampling_rate
data_out = tsa.boxcar_filter(np.copy(self.data),
lb=lb,
ub=ub,
n_iterations=self._boxcar_iterations)
return ts.TimeSeries(data=data_out,
sampling_rate=self.sampling_rate,
time_unit=self.time_unit)
def filtered_boxcar(self):
"""
Filter the time-series by a boxcar filter.
The low pass filter is implemented by convolving with a boxcar function
of the right length and amplitude and the high-pass filter is
implemented by subtracting a low-pass version (as above) from the
signal
"""
if self.ub is not None:
ub = self.ub / self.sampling_rate
else:
ub = 1.0
lb = self.lb / self.sampling_rate
data_out = tsa.boxcar_filter(np.copy(self.data),
lb=lb, ub=ub,
n_iterations=self._boxcar_iterations)
return ts.TimeSeries(data=data_out,
sampling_rate=self.sampling_rate,
time_unit=self.time_unit,
t0=self._ts.t0)
