change argument name from sigma_clip to sigma
@@ -598,7 +598,7 @@ class LightCurve(object):
return self.estimate_cdpp(**kwargs)
def estimate_cdpp(self, transit_duration=13, savgol_window=101,
- savgol_polyorder=2, sigma_clip=5.):
+ savgol_polyorder=2, sigma=5.):
"""Estimate the CDPP noise metric using the Savitzky-Golay (SG) method.
A common estimate of the noise in a lightcurve is the scatter that
@@ -614,7 +614,7 @@ class LightCurve(object):
1. Remove low frequency signals using a Savitzky-Golay filter with
window length `savgol_window` and polynomial order `savgol_polyorder`.
2. Remove outliers by rejecting data points which are separated from
- the mean by `sigma_clip` times the standard deviation.
+ the mean by `sigma` times the standard deviation.
3. Compute the standard deviation of a running mean with
a configurable window length equal to `transit_duration`.
@@ -636,7 +636,7 @@ class LightCurve(object):
savgol_polyorder : int, optional
Polynomial order of the Savitsky-Golay filter.
The recommended value is 2.
- sigma_clip : float, optional
+ sigma : float, optional
The number of standard deviations to use for clipping outliers.
The default is 5.
@@ -657,7 +657,7 @@ class LightCurve(object):
detrended_lc = self.flatten(window_length=savgol_window,
polyorder=savgol_polyorder)
- cleaned_lc = detrended_lc.remove_outliers(sigma=sigma_clip)
+ cleaned_lc = detrended_lc.remove_outliers(sigma=sigma)
mean = running_mean(data=cleaned_lc.flux, window_size=transit_duration)
cdpp_ppm = np.std(mean) * 1e6
return cdpp_ppm
