Module: features.cadence_features
Given the observed distribution of time lags cads, compute the probability that the next observation occurs within time minutes of an arbitrary epoch. 

Build histogram of all possible 


Ratios 
Find peaks, i.e. local maxima, of an array. 

Normalize histogram such that integral from 

Return the (bin) index of the ith largest peak. 

Compute the ratio of the values of the ith and jth largest peaks. 
cad_prob
 cesium.features.cadence_features.cad_prob(cads, time)
Given the observed distribution of time lags cads, compute the probability that the next observation occurs within time minutes of an arbitrary epoch.
delta_t_hist
 cesium.features.cadence_features.delta_t_hist(t, nbins=50, conv_oversample=50)
Build histogram of all possible
t_i  t_j
’s.For efficiency, we construct the histogram via a convolution of the PDF rather than by actually computing all the differences. For better accuracy we use a factor conv_oversample more bins when performing the convolution and then aggregate the result to have nbins total values.
double_to_single_step
 cesium.features.cadence_features.double_to_single_step(cads)
Ratios
(t[i+2]  t[i]) / (t[i+1]  t[i]
).
find_sorted_peaks
 cesium.features.cadence_features.find_sorted_peaks(x)
Find peaks, i.e. local maxima, of an array. Interior points are peaks if they are greater than both their neighbors, and edge points are peaks if they are greater than their only neighbor. In the case of ties, we (arbitrarily) choose the first index in the sequence of equal values as the peak. Returns a list of tuples
(i, x[i])
of peak indicesi
and valuesx[i]
, sorted in decreasing order by peak value.
normalize_hist
 cesium.features.cadence_features.normalize_hist(hist, total_time)
Normalize histogram such that integral from
t_min
tot_max
equals 1. cf.np.histogram(..., density=True)
.
peak_bin
 cesium.features.cadence_features.peak_bin(peaks, i)
Return the (bin) index of the ith largest peak. Peaks is a list of tuples
(i, x[i])
of peak indicesi
and valuesx[i]
, sorted in decreasing order by peak value.
peak_ratio
 cesium.features.cadence_features.peak_ratio(peaks, i, j)
Compute the ratio of the values of the ith and jth largest peaks. Peaks is a list of tuples
(i, x[i])
of peak indicesi
and valuesx[i]
, sorted in decreasing order by peak value.