def smooth_lowess(self,
smoothing_parameter=None,
number_of_iterations=None,
show_progressbar=None):
"""Lowess data smoothing in place.
If `smoothing_parameter` or `number_of_iterations` are None the method
is run in interactive mode.
Parameters
----------
smoothing_parameter: float or None
Between 0 and 1. The fraction of the data used
when estimating each y-value.
number_of_iterations: int or None
The number of residual-based reweightings
to perform.
show_progressbar : None or bool
If True, display a progress bar. If None the default is set in
`preferences`.
Raises
------
SignalDimensionError if the signal dimension is not 1.
ImportError if statsmodels is not installed.
Notes
-----
This method uses the lowess algorithm from statsmodels. statsmodels
is required for this method.
"""
if not statsmodels_installed:
raise ImportError("statsmodels is not installed. This package is "
"required for this feature.")
self._check_signal_dimension_equals_one()
if smoothing_parameter is None or number_of_iterations is None:
smoother = SmoothingLowess(self)
if smoothing_parameter is not None:
smoother.smoothing_parameter = smoothing_parameter
if number_of_iterations is not None:
smoother.number_of_iterations = number_of_iterations
smoother.edit_traits()
else:
self.map(lowess,
exog=self.axes_manager[-1].axis,
frac=smoothing_parameter,
it=number_of_iterations,
is_sorted=True,
return_sorted=False,
show_progressbar=show_progressbar)
def smooth_lowess(self, smoothing_parameter = None,
number_of_iterations=None, differential_order = 0):
'''Lowess data smoothing'''
smoother = SmoothingLowess(self)
smoother.differential_order = differential_order
if smoothing_parameter is not None:
smoother.smoothing_parameter = smoothing_parameter
if number_of_iterations is not None:
smoother.number_of_iterations = number_of_iterations
if smoothing_parameter is None or smoothing_parameter is None:
smoother.edit_traits()
else:
smoother.apply()
def smooth_lowess(self,
smoothing_parameter=None,
number_of_iterations=None,
show_progressbar=None,
parallel=None):
"""Lowess data smoothing in place.
If `smoothing_parameter` or `number_of_iterations` are None the method
is run in interactive mode.
Parameters
----------
smoothing_parameter: float or None
Between 0 and 1. The fraction of the data used
when estimating each y-value.
number_of_iterations: int or None
The number of residual-based reweightings
to perform.
show_progressbar : None or bool
If True, display a progress bar. If None the default is set in
`preferences`.
parallel : {Bool, None, int}
Perform the operation parallely
Raises
------
SignalDimensionError if the signal dimension is not 1.
ImportError if statsmodels is not installed.
Notes
-----
This method uses the lowess algorithm from statsmodels. statsmodels
is required for this method.
"""
if not statsmodels_installed:
raise ImportError("statsmodels is not installed. This package is "
"required for this feature.")
self._check_signal_dimension_equals_one()
if smoothing_parameter is None or number_of_iterations is None:
smoother = SmoothingLowess(self)
if smoothing_parameter is not None:
smoother.smoothing_parameter = smoothing_parameter
if number_of_iterations is not None:
smoother.number_of_iterations = number_of_iterations
smoother.edit_traits()
else:
self.map(lowess,
exog=self.axes_manager[-1].axis,
frac=smoothing_parameter,
it=number_of_iterations,
is_sorted=True,
return_sorted=False,
show_progressbar=show_progressbar,
parallel=parallel)