def filtered(self,freq_range,filt_type='stop',order=4):
"""
Filter the data using a Butterworth filter and return a new
TimeSeries instance.
Parameters
----------
freq_range : {array_like}
The range of frequencies to filter.
filt_type = {scipy.signal.band_dict.keys()},optional
Filter type.
order = {int}
The order of the filter.
Returns
-------
ts : {TimeSeries}
A TimeSeries instance with the filtered data.
"""
filtered_array = filt.buttfilt(np.asarray(self),
freq_range,self.samplerate,filt_type,
order,axis=self.taxis)
attrs = self._attrs.copy()
for k in self._required_attrs.keys():
attrs.pop(k,None)
return TimeSeries(filtered_array,self.tdim, self.samplerate,
dims=self.dims.copy(), **attrs)
def filtered(self,freq_range,filt_type='stop',order=4):
"""
Filter the data using a Butterworth filter and return a new
TimeSeries instance.
Parameters
----------
freq_range : {array_like}
The range of frequencies to filter.
filt_type = {scipy.signal.band_dict.keys()},optional
Filter type.
order = {int}
The order of the filter.
Returns
-------
ts : {TimeSeries}
A TimeSeries instance with the filtered data.
"""
filtered_array = filt.buttfilt(np.asarray(self),
freq_range,self.samplerate,filt_type,
order,axis=self.taxis)
attrs = self._attrs.copy()
for k in self._required_attrs.keys():
attrs.pop(k,None)
return TimeSeries(filtered_array,self.tdim, self.samplerate,
dims=self.dims.copy(), **attrs)
def filter(self):
from ptsa.filt import buttfilt
# find index of the axis called 'time'
if self.time_axis<0:
time_index_array = np.where(np.array(self.time_series.dims) == 'time')
if len(time_index_array)>0:
self.time_axis =time_index_array[0] # picking first index that corresponds to the dimension
else:
raise RuntimeError("Could not locate 'time' axis in your time series."
" Make sure to either label appropriate axis of your time series 'time' or specify"
"time axis explicitely as a non-negative integer '")
filtered_array = buttfilt(self.time_series,
self.freq_range, self.samplerate, self.filt_type,
self.order,axis=self.time_axis)
self.filtered_time_series = xray.DataArray(
filtered_array,
coords = [xray.DataArray(coord.copy()) for coord_name, coord in self.time_series.coords.items() ]
)
# l = [xray.DataArray(coord.copy()) for coord_name, coord in self.time_series.coords.items() ]
return self.filtered_time_series
# attrs = self._attrs.copy()
# for k in self._required_attrs.keys():
# attrs.pop(k,None)
# return TimeSeries(filtered_array,self.tdim, self.samplerate,
# dims=self.dims.copy(), **attrs)
def filtered(self, freq_range, filt_type='stop', order=4):
"""
Filter the data using a Butterworth filter and return a new
TimeSeries instance.
Parameters
----------
freq_range : array-like
The range of frequencies to filter.
filt_type : str
Filter type (default: ``'stop'``).
order : int
The order of the filter (default: 4).
Returns
-------
ts : TimeSeriesX
A TimeSeriesX instance with the filtered data.
"""
warnings.warn("The filtered method is not very flexible. "
"Consider using filters in ptsa.data.filters instead.")
time_axis_index = get_axis_index(self, axis_name='time')
filtered_array = buttfilt(self.values, freq_range, float(self['samplerate']), filt_type,
order, axis=time_axis_index)
new_ts = self.copy()
new_ts.data = filtered_array
return new_ts
def filter(self):
"""
Applies Butterwoth filter to input time series and returns filtered TimeSeriesX object
Returns
-------
filtered: TimeSeriesX
The filtered time series
"""
time_axis_index = get_axis_index(self.time_series, axis_name='time')
filtered_array = buttfilt(self.time_series,
self.freq_range,
float(self.time_series['samplerate']),
self.filt_type,
self.order,
axis=time_axis_index)
coords_dict = {
coord_name: DataArray(coord.copy())
for coord_name, coord in list(self.time_series.coords.items())
}
coords_dict['samplerate'] = self.time_series['samplerate']
dims = [dim_name for dim_name in self.time_series.dims]
filtered_time_series = TimeSeriesX(filtered_array,
dims=dims,
coords=coords_dict)
# filtered_time_series = TimeSeriesX(filtered_time_series)
filtered_time_series.attrs = self.time_series.attrs.copy()
return filtered_time_series
def filtered(self,freq_range,filt_type='stop',order=4):
"""
Filter the data using a Butterworth filter and return a new
TimeSeries instance.
Parameters
----------
freq_range : {array_like}
The range of frequencies to filter.
filt_type = {scipy.signal.band_dict.keys()},optional
Filter type.
order = {int}
The order of the filter.
Returns
-------
ts : {TimeSeries}
A TimeSeries instance with the filtered data.
"""
from ptsa.filt import buttfilt
filtered_array = buttfilt(self.values, freq_range, self.attrs['samplerate'], filt_type,
order,axis=self.time_axis_index)
filtered_time_series = TimeSeriesXray(
filtered_array,
coords = [xr.DataArray(coord.copy()) for coord_name, coord in list(self.coords.items())]
)
filtered_time_series.attrs = self.attrs.copy()
return filtered_time_series
def filter(self):
"""
Applies Butterwoth filter to input time series and returns filtered TimeSeriesX object
Returns
-------
filtered: TimeSeriesX
The filtered time series
"""
time_axis_index = get_axis_index(self.time_series, axis_name='time')
filtered_array = buttfilt(self.time_series,
self.freq_range, float(self.time_series['samplerate']), self.filt_type,
self.order, axis=time_axis_index)
coords_dict = {coord_name: DataArray(coord.copy()) for coord_name, coord in list(self.time_series.coords.items())}
coords_dict['samplerate'] = self.time_series['samplerate']
dims = [dim_name for dim_name in self.time_series.dims]
filtered_time_series = TimeSeriesX(
filtered_array,
dims=dims,
coords=coords_dict
)
# filtered_time_series = TimeSeriesX(filtered_time_series)
filtered_time_series.attrs = self.time_series.attrs.copy()
return filtered_time_series
def test_filter(self):
samplerate = 200
filtType='stop'
freqRange = [10,20]
order = 4
ts = TimeSeries(self.dat200,'time',samplerate,dims=self.dims200)
ts_filt = ts.filtered(freqRange, filtType, order)
test = filt.buttfilt(self.dat200,freqRange,samplerate,filtType,
order,axis=ts.taxis)
np.testing.assert_array_almost_equal(ts_filt[:],test[:],decimal=6)
def test_filter(self):
samplerate = 200
filtType='stop'
freqRange = [10,20]
order = 4
ts = TimeSeries(self.dat200,'time',samplerate,dims=self.dims200)
ts_filt = ts.filtered(freqRange, filtType, order)
test = filt.buttfilt(self.dat200,freqRange,samplerate,filtType,
order,axis=ts.taxis)
np.testing.assert_array_almost_equal(ts_filt[:],test[:],decimal=6)
def filtered(self, freq_range, filt_type='stop', order=4):
"""
Filter the data using a Butterworth filter and return a new
TimeSeries instance.
Parameters
----------
freq_range : {array_like}
The range of frequencies to filter.
filt_type = {scipy.signal.band_dict.keys()},optional
Filter type.
order = {int}
The order of the filter.
Returns
-------
ts : {TimeSeries}
A TimeSeries instance with the filtered data.
"""
from ptsa.filt import buttfilt
filtered_array = buttfilt(self.values,
freq_range,
self.attrs['samplerate'],
filt_type,
order,
axis=self.time_axis_index)
# filtered_time_series = xray.DataArray(
# filtered_array,
# coords = [xray.DataArray(coord.copy()) for coord_name, coord in self.coords.items() ]
# )
filtered_time_series = TimeSeriesXray(
filtered_array,
coords=[
xray.DataArray(coord.copy())
for coord_name, coord in self.coords.items()
])
filtered_time_series.attrs = self.attrs.copy()
return filtered_time_series
def filtered(self, freq_range, filt_type='stop', order=4):
"""
Filter the data using a Butterworth filter and return a new
TimeSeries instance.
Parameters
----------
freq_range : {array_like}
The range of frequencies to filter.
filt_type = {scipy.signal.band_dict.keys()},optional
Filter type.
order = {int}
The order of the filter.
Returns
-------
ts : {TimeSeries}
A TimeSeries instance with the filtered data.
"""
from ptsa.filt import buttfilt
time_axis_index = get_axis_index(self, axis_name='time')
filtered_array = buttfilt(self.values,
freq_range,
float(self['samplerate']),
filt_type,
order,
axis=time_axis_index)
coords = {}
for coord_name, coord in self.coords.items():
if len(coord.shape):
coords[coord_name] = coord
filtered_time_series = TimeSeriesX(
filtered_array,
dims=[dim_name for dim_name in self.dims],
coords=coords)
filtered_time_series.attrs = self.attrs.copy()
return filtered_time_series
def filtered(self, freq_range, filt_type='stop', order=4):
"""
Filter the data using a Butterworth filter and return a new
TimeSeries instance.
Parameters
----------
freq_range : {array_like}
The range of frequencies to filter.
filt_type = {scipy.signal.band_dict.keys()},optional
Filter type.
order = {int}
The order of the filter.
Returns
-------
ts : {TimeSeries}
A TimeSeries instance with the filtered data.
"""
from ptsa.filt import buttfilt
time_axis_index = get_axis_index(self, axis_name='time')
filtered_array = buttfilt(self.values, freq_range, float(self['samplerate']), filt_type,
order, axis=time_axis_index)
coords={}
for coord_name, coord in self.coords.items():
if len(coord.shape):
coords[coord_name] = coord
filtered_time_series = TimeSeriesX(
filtered_array,
dims=[dim_name for dim_name in self.dims],
coords=coords
)
filtered_time_series.attrs = self.attrs.copy()
return filtered_time_series