def period_standardize(self, axis=None, dtype=None, ddof=1, period=None):
"""
Standardizes data by substracting the average over a reference period,
and by dividing by the standard deviation over the same period.
Parameters
----------
%(axis)s
%(dtype)s
%(ddof)s
%(period)s
Warnings
--------
The default ``ddof`` is 1:
by default, the method returns the unbiased estimate of the standard deviation.
"""
if period is None:
period = self.refperiod
elif not isinstance(period, (tuple, list, ndarray)):
msg = "Period should be a tuple (starting date, ending date)!"
raise ValueError, msg
refdata = mask_outside_period(self, period[0], period[1],
include_edges=False)
refavg = refdata.mean(axis=axis, dtype=dtype)
refstd = refdata.std(axis=axis, dtype=dtype, ddof=ddof)
if not axis:
result = (self - refavg) * 1. / refstd
else:
result = (self - ma.expand_dims(refavg)).astype(float)
result /= ma.expand_dims(refstd)
return result
def period_std(self, axis=None, dtype=None, out=None, ddof=1,
period=None):
"""
Returns the standard deviation over a reference period.
Parameters
----------
%(axis)s
%(dtype)s
%(out)s
%(ddof)s
%(period)s
Warnings
--------
The default ``ddof`` is 1:
by default, the method returns the unbiased estimate of the standard deviation.
"""
if period is None:
period = self.refperiod
elif not isinstance(period, (tuple, list, ndarray)):
msg = "Period should be a tuple (starting date, ending date)!"
raise ValueError, msg
refdata = mask_outside_period(self, period[0], period[1],
include_edges=False)
return refdata.std(axis=axis, dtype=dtype, out=out, ddof=ddof)
def period_std(self, axis=None, dtype=None, out=None, ddof=1, period=None):
"""
Returns the standard deviation over a reference period.
Parameters
----------
%(axis)s
%(dtype)s
%(out)s
%(ddof)s
%(period)s
Warnings
--------
The default ``ddof`` is 1:
by default, the method returns the unbiased estimate of the standard deviation.
"""
if period is None:
period = self.refperiod
elif not isinstance(period, (tuple, list, ndarray)):
msg = "Period should be a tuple (starting date, ending date)!"
raise ValueError, msg
refdata = mask_outside_period(self,
period[0],
period[1],
include_edges=False)
return refdata.std(axis=axis, dtype=dtype, out=out, ddof=ddof)
def period_average(self, axis=None, dtype=None, out=None, period=None):
"""
Returns the series averaged over the reference period, along the given axis.
Parameters
----------
%(axis)s
%(dtype)s
%(out)s
%(period)s
"""
if period is None:
period = self.refperiod
elif not isinstance(period, (tuple, list, ndarray)):
msg = "Period should be a tuple (starting date, ending date)!"
raise ValueError, msg
refdata = mask_outside_period(self, period[0], period[1],
include_edges=False)
return refdata.mean(axis=axis, dtype=dtype, out=out)
def period_average(self, axis=None, dtype=None, out=None, period=None):
"""
Returns the series averaged over the reference period, along the given axis.
Parameters
----------
%(axis)s
%(dtype)s
%(out)s
%(period)s
"""
if period is None:
period = self.refperiod
elif not isinstance(period, (tuple, list, ndarray)):
msg = "Period should be a tuple (starting date, ending date)!"
raise ValueError, msg
refdata = mask_outside_period(self,
period[0],
period[1],
include_edges=False)
return refdata.mean(axis=axis, dtype=dtype, out=out)
def period_standardize(self, axis=None, dtype=None, ddof=1, period=None):
"""
Standardizes data by substracting the average over a reference period,
and by dividing by the standard deviation over the same period.
Parameters
----------
%(axis)s
%(dtype)s
%(ddof)s
%(period)s
Warnings
--------
The default ``ddof`` is 1:
by default, the method returns the unbiased estimate of the standard deviation.
"""
if period is None:
period = self.refperiod
elif not isinstance(period, (tuple, list, ndarray)):
msg = "Period should be a tuple (starting date, ending date)!"
raise ValueError, msg
refdata = mask_outside_period(self,
period[0],
period[1],
include_edges=False)
refavg = refdata.mean(axis=axis, dtype=dtype)
refstd = refdata.std(axis=axis, dtype=dtype, ddof=ddof)
if not axis:
result = (self - refavg) * 1. / refstd
else:
result = (self - ma.expand_dims(refavg)).astype(float)
result /= ma.expand_dims(refstd)
return result
