def lightcurve_moments(ftimes, fmags, ferrs):
'''This calculates the weighted mean, stdev, median, MAD, percentiles, skew,
kurtosis, fraction of LC beyond 1-stdev, and IQR.
Parameters
----------
ftimes,fmags,ferrs : np.array
The input mag/flux time-series with all non-finite elements removed.
Returns
-------
dict
A dict with all of the light curve moments calculated.
'''
ndet = len(fmags)
if ndet > 9:
# now calculate the various things we need
series_median = npmedian(fmags)
series_wmean = (npsum(fmags * (1.0 / (ferrs * ferrs))) /
npsum(1.0 / (ferrs * ferrs)))
series_mad = npmedian(npabs(fmags - series_median))
series_stdev = 1.483 * series_mad
series_skew = spskew(fmags)
series_kurtosis = spkurtosis(fmags)
# get the beyond1std fraction
series_above1std = len(fmags[fmags > (series_median + series_stdev)])
series_below1std = len(fmags[fmags < (series_median - series_stdev)])
# this is the fraction beyond 1 stdev
series_beyond1std = (series_above1std + series_below1std) / float(ndet)
# get the magnitude percentiles
series_mag_percentiles = nppercentile(
fmags, [5.0, 10, 17.5, 25, 32.5, 40, 60, 67.5, 75, 82.5, 90, 95])
return {
'median': series_median,
'wmean': series_wmean,
'mad': series_mad,
'stdev': series_stdev,
'skew': series_skew,
'kurtosis': series_kurtosis,
'beyond1std': series_beyond1std,
'mag_percentiles': series_mag_percentiles,
'mag_iqr': series_mag_percentiles[8] - series_mag_percentiles[3],
}
else:
LOGERROR('not enough detections in this magseries '
'to calculate light curve moments')
return None
def skew(series, bias=True, nan_policy="propagate"):
"""Returns the skewness of a series.
Args:
series (pandas.Series): column to summarize.
bias (bool): Correct for bias?
nan_policy (str): How to handle NaN values:
- "propagate": return NaN
- "raise": throws an error
- "omit": remove NaN before calculating skew
"""
return spskew(series, bias=bias, nan_policy="propagate")