def downsample(self, target, **kwargs):
""" Downsample Fex columns. Relies on nltools.stats.downsample,
but ensures that returned object is a Fex object.
Args:
target(float): downsampling target, typically in samples not
seconds
kwargs: additional inputs to nltools.stats.downsample
"""
df_ds = downsample(self,
sampling_freq=self.sampling_freq,
target=target,
**kwargs)
if self.features is not None:
ds_features = downsample(self.features,
sampling_freq=self.sampling_freq,
target=target,
**kwargs)
else:
ds_features = self.features
return self.__class__(df_ds,
sampling_freq=target,
features=ds_features)
def downsample(self, target, **kwargs):
"""Downsample columns of design matrix. Relies on
nltools.stats.downsample, but ensures that returned object is a
design matrix.
Args:
target(float): desired frequency in hz
kwargs: additional inputs to nltools.stats.downsample
"""
if target > self.sampling_freq:
raise ValueError(
"Target must be longer than current sampling rate")
df = Design_Matrix(
downsample(self,
sampling_freq=self.sampling_freq,
target=target,
target_type='hz',
**kwargs))
# convert df to a design matrix
newMat = self._inherit_attributes(df)
newMat.sampling_freq = target
return newMat
def test_downsample():
dat = pd.DataFrame()
dat["x"] = range(0, 100)
dat["y"] = np.repeat(range(1, 11), 10)
assert (dat.groupby("y").mean().values.ravel() == downsample(
data=dat["x"],
sampling_freq=10,
target=1,
target_type="hz",
method="mean").values).all
assert (dat.groupby("y").median().values.ravel() == downsample(
data=dat["x"],
sampling_freq=10,
target=1,
target_type="hz",
method="median").values).all
def test_downsample():
dat = pd.DataFrame()
dat['x'] = range(0, 100)
dat['y'] = np.repeat(range(1, 11), 10)
assert ((dat.groupby('y').mean().values.ravel() == downsample(
data=dat['x'],
sampling_freq=10,
target=1,
target_type='hz',
method='mean').values).all)
assert ((dat.groupby('y').median().values.ravel() == downsample(
data=dat['x'],
sampling_freq=10,
target=1,
target_type='hz',
method='median').values).all)
