def hurst_analysis2(ts, duration = None, advance = None,**kwargs):
hurst2_results = []
# Pick the duration of the sub-time series
#duration = timedelta(seconds=10)
# Pick how far to jump forward in time for the next sub time-series
# Can use this to overlap with the previous time-series
#advance = duration/2
# Start the loop
extent = TimeRange(ts.data.index[0],
ts.data.index[0] + duration)
while extent.end() <= ts.data.index[-1]:
hurst2 = hurst_fArma([extent.start(),extent.end()],kwargs)
# Store the analyzed time-series and its Hurst analysis
hurst2_results.append( {"extent":extent,"hurst2":hurst2} )
extent.extend(advance,advance)
def plot(self, extract=None, show_frm=None, show_spike=False, show_before_after=False):
if extract is None:
self.lyra.plot()
else:
lc = self.lyra.extract(extract)
lc.plot()
if show_frm is not None:
times = self.onoff(frm_name=show_frm).times()
for tr in times:
plt.axvspan(tr.start(), tr.end(), facecolor='green', alpha=0.5)
if show_spike:
for tr in self.all_spike_times:
plt.axvspan(tr.start(), tr.end(), facecolor='black', alpha=0.75)
if show_before_after:
for event in self.event_times:
current_minimum = datetime.timedelta(days=3)
for spike_time in self.all_spike_times:
event_end_to_spike_start = spike_time.start() - event.end()
print current_minimum
if event_end_to_spike_start > datetime.timedelta():
if event_end_to_spike_start < current_minimum:
current_minimum = event_end_to_spike_start
after_flare = TimeRange(event.end(), spike_time.start())
lc.truncate(after_flare.start(), after_flare.end()).plot(style ='c-')
for event in self.event_times:
current_minimum = datetime.timedelta(days=3)
for spike_time in self.all_spike_times:
spike_end_to_event_start = event.start() - spike_time.end()
if spike_end_to_event_start > datetime.timedelta():
if spike_end_to_event_start < current_minimum:
current_minimum = event.start() - spike_time.end()
before_flare = TimeRange(spike_time.end(),event.start())
lc.truncate(before_flare.start(), before_flare.end()).plot(style ='r-')
