this is actually not useful -- just keeping it here in case it is handy later

time_sampler.py

import attr
import numpy as np

class TimeSampler(object)
    '''
    samples from a time indexed series based on time -- pick a random
    time within the range of the series; returns the time-delta
    and value-delta of the point to the right of the sample with the
    point before it
    
    this is needed so that small / incremental steps
    do not dominate the results when trying to sample a time
    sequence in order to generate a forecasted future
    '''
    s = attr.ib()  # time-indexed series to sample from
    ts_index, begin, end = attr.ib(), attr.ib(), attr.ib()
    
    @classmethod
    def from_s(cls, s):
        ts_index = s.index.astype('int64')
        return cls(s, ts_index, ts_index[0], ts_index[-1])
    
    def sample(self):
        '''
        returns (time width, value delta) for the step that
        was occurring at a random point over the duration of
        the series
        '''
        # sample evenly across time so that "small" steps aren't
        # overrepresented in the simulation
        sample_time = random.randint(self.begin, self.end)
        sample_ix = np.searchsorted(self.ts_index, sample_time)
        cur_ts, prev_ts = self.ts_index[sample_ix], self.ts_index[sample_ix - 1]
        cur_val, prev_val = self.s[cur_ts], self.s[prev_ts]
        width = cur_ts - prev_ts
        delta = cur_val - prev_val
        return width, delta