spacepy.poppy.PPro¶
- class spacepy.poppy.PPro(process1, process2, lags=None, winhalf=None, verbose=False)[source]¶
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
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
- aa_ci(inter, n_boots=1000, seed=None)[source]¶
Get bootstrap confidence intervals for association number
- Requires input of desired confidence interval, e.g.:
>>> 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
- interfloat
percentage confidence interval to calculate
- n_bootsint, optional
number of bootstrap iterations to run
- seedint, optional
seed for the random number generator. If not specified, Python code will use numpy’s RNG and its current seed; C code will seed from the clock.
Warning
If
seed
is specified, results may not be reproducible between systems with different sizes for C long type. Note that 64-bit Windows uses a 32-bit long and so results will be the same between 64 and 32-bit Windows, but not between 64-bit Windows and other 64-bit operating systems. Ifseed
is not specified, results are not reproducible anyhow.
- assoc(u=None, h=None)[source]¶
Perform association analysis on input series
- Parameters
- ulist, optional
the time lags to use
- h :
association window half-width, same type as process1
- assoc_mult(windows, inter=95, n_boots=1000, seed=None)[source]¶
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
- windowssequence
window half-size for each analysis
- interfloat, optional
desired confidence interval, default 95
- n_bootsint, optional
number of bootstrap iterations, default 1000
- seedint, optional
Random number generator seed. It is STRONGLY recommended not to specify (i.e. leave None) to permit multithreading.
- Returns
- outthree numpy array
Three numpy arrays, (windows x lags), containing (in order) low values of confidence interval, high values of ci, percentage confidence above the asymptotic association number
Warning
This function is likely to take a LOT of time.
- ci¶
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()
.
- conf_above¶
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 callingaa_ci()
.
- plot(figsize=None, dpi=80, asympt=True, show=True, norm=True, xlabel='Time lag', xscale=None, ylabel=None, title=None, transparent=True)[source]¶
Create basic plot of association analysis.
Uses object attributes created by
assoc()
and, optionally,aa_ci()
.- Parameters
- figsize, optional
passed through to matplotlib.pyplot.figure
- dpiint, optional
passed through to matplotlib.pyplot.figure
- asymptboolean, optional
True to overplot the line of asymptotic association number
- showboolean, optional
Show the plot? (if false, will create without showing)
- normboolean, optional
Normalize plot to the asymptotic association number
- titlestring, optional
label/title for the plot
- xlabelstring, optional
label to put on the X axis of the resulting plot
- xscalefloat, optional
scale x-axis by this factor (e.g. 60.0 to convert seconds to minutes)
- ylabelstring, optional
label to put on the Y axis of the resulting plot
- transparentboolean, optional
make c.i. patch transparent (default)
- plot_mult(windows, data, min=None, max=None, cbar_label=None, figsize=None, dpi=80, xlabel='Lag', ylabel='Window Size')[source]¶
Plots a 2D function of window size and lag
- Parameters
- windowslist
list of window sizes (y axis)
- datalist
list of data, dimensioned (windows x lags)
- minfloat, optional
clip L{data} to this minimum value
- maxfloat, optional
clip L{data} to this maximum value