def upsample(self, target, target_type='hz', **kwargs):
""" Upsample Fex columns. Relies on nltools.stats.upsample,
but ensures that returned object is a Fex object.
Args:
target(float): upsampling target, default 'hz' (also 'samples',
'seconds')
kwargs: additional inputs to nltools.stats.upsample
"""
df_us = upsample(self,
sampling_freq=self.sampling_freq,
target=target,
target_type=target_type,
**kwargs)
if self.features is not None:
us_features = upsample(self.features,
sampling_freq=self.sampling_freq,
target=target,
target_type=target_type,
**kwargs)
else:
us_features = self.features
return self.__class__(df_us,
sampling_freq=target,
features=us_features)
def test_upsample():
dat = pd.DataFrame()
dat["x"] = range(0, 100)
dat["y"] = np.repeat(range(1, 11), 10)
fs = 2
us = upsample(dat, sampling_freq=1, target=fs, target_type="hz")
assert dat.shape[0] * fs - fs == us.shape[0]
fs = 3
us = upsample(dat, sampling_freq=1, target=fs, target_type="hz")
assert dat.shape[0] * fs - fs == us.shape[0]
def test_upsample():
dat = pd.DataFrame()
dat['x'] = range(0, 100)
dat['y'] = np.repeat(range(1, 11), 10)
fs = 2
us = upsample(dat, sampling_freq=1, target=fs, target_type='hz')
assert (dat.shape[0] * fs - fs == us.shape[0])
fs = 3
us = upsample(dat, sampling_freq=1, target=fs, target_type='hz')
assert (dat.shape[0] * fs - fs == us.shape[0])
def upsample(self, target, **kwargs):
"""Upsample columns of design matrix. Relies on
nltools.stats.upsample, but ensures that returned object is a
design matrix.
Args:
target(float): desired frequence in hz
kwargs: additional inputs to nltools.stats.downsample
"""
if target < self.sampling_freq:
raise ValueError(
"Target must be shorter than current sampling rate")
df = Design_Matrix(
upsample(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