PoPPy point process object
Initialize object with series1 and series2. These should be timeseries of events, given as lists, arrays, or lists of datetime objects. Includes method to perform association analysis of input series
Output can be nicely plotted with plot().
aa_ci(inter[, n_boots, seed]) | Get bootstrap confidence intervals for association number |
assoc([u, h]) | Perform association analysis on input series |
assoc_mult(windows[, inter, n_boots, seed]) | Association analysis w/confidence interval on multiple windows |
ci | |
conf_above | |
plot([figsize, dpi, asympt, show, norm, ...]) | Create basic plot of association analysis. |
plot_mult(windows, data[, min, max, ...]) | Plots a 2D function of window size and lag |
swap() | Swaps process 1 and process 2 |
Get bootstrap confidence intervals for association number
>>> obj.aa_ci(95)
Upper and lower confidence limits are added to ci.
After calling, conf_above will contain the confidence (in percent) that the association number at that lag is above the asymptotic association number. (The confidence of being below is 100 - conf_above) For minor variations in conf_above to be meaningful, a large number of bootstraps is required. (Rougly, 1000 to be meaningful to the nearest percent; 10000 to be meaningful to a tenth of a percent.) A conf_above of 100 usually indicates an insufficient sample size to resolve, not perfect certainty.
Note also that a 95% chance of being above indicates an exclusion from the 90% confidence interval!
Parameters: | inter : float
n_boots : int, optional
seed : int, optional
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Warning
If seed is specified on numpy 1.5 and earlier, the available entropy is reduced to work around a random number generator bug. Upgrade to numpy 1.6 to avoid this limitation. Because of this workaround, if a seed is specified, results from numpy 1.5 are not reproducible with numpy 1.6
Perform association analysis on input series
Parameters: | u : list, optional
h : :
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Association analysis w/confidence interval on multiple windows
Using the time sequence and lags stored in this object, perform full association analysis, including bootstrapping of confidence intervals, for every listed window half-size
Parameters: | windows : sequence
inter : float, optional
n_boots : int, optional
seed : int, optional
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Returns: | out : three numpy array
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Warning
This function is likely to take a LOT of time.
Contains the upper and lower confidence limits for the association number as a function of lag. The first element is the array of lower limits; the second, the array of upper limits. Not available until after calling aa_ci().
Contains the confidence that the association number, as a function of lag, is above the asymptotic association number. (The confidence of being below is 100 - conf_above.) Not available until after calling aa_ci().
Create basic plot of association analysis.
Uses object attributes created by assoc() and, optionally, aa_ci().
Parameters: | figsize : , optional
dpi : int, optional
asympt : boolean, optional
show : boolean, optional
norm : boolean, optional
title : string, optional
xlabel : string, optional
xscale : float, optional
ylabel : string, optional
transparent : boolean, optional
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Plots a 2D function of window size and lag
Parameters: | windows : list
data : list
min : float, optional
max : float, optional
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