PoPPy  Point Processes in Python¶
PoPPy – Point Processes in Python.
This module contains point process class types and a variety of functions for association analysis. The routines given here grew from work presented by Morley and Freeman (Geophysical Research Letters, 34, L08104, doi:10.1029/ 2006GL028891, 2007), which were originally written in IDL. This module is intended for application to discrete time series of events to assess statistical association between the series and to calculate confidence limits. Any misapplication or misinterpretation by the user is the user’s own fault.
>>> import datetime as dt
>>> import spacepy.time as spt
Since association analysis is rather computationally expensive, this example shows timing.
>>> t0 = dt.datetime.now()
>>> onsets = spt.Ticktock(onset_epochs, 'CDF')
>>> ticksR1 = spt.Ticktock(tr_list, 'CDF')
Each instance must be initialized
>>> lags = [dt.timedelta(minutes=n) for n in range(400,401,2)]
>>> halfwindow = dt.timedelta(minutes=10)
>>> pp1 = poppy.PPro(onsets.UTC, ticksR1.UTC, lags, halfwindow)
To perform association analysis
>>> pp1.assoc()
Starting association analysis
calculating association for series of length [3494, 1323] at 401 lags
>>> t1 = dt.datetime.now()
>>> print("Elapsed: " + str(t1t0))
Elapsed: 0:35:46.927138
Note that for calculating associations between long series at a large number of lags is SLOW!!
To plot
>>> pp1.plot(dpi=80)
Error: No confidence intervals to plot  skipping
To add 95% confidence limits (using 4000 bootstrap samples)
>>> pp1.aa_ci(95, n_boots=4000)
The plot method will then add the 95% confidence intervals as a semi transparent patch.
Authors: Steve Morley and Jon Niehof Institution: Los Alamos National Laboratory Contact: smorley@lanl.gov, jniehof@lanl.gov
Copyright 2010 Los Alamos National Security, LLC.
Classes

PoPPy point process object 
Functions

Overplots two PPro objects 

Construct bootstrap confidence interval 

Find the percentile of a particular value in a sequence 