Extrapolating beyond the maximum x-value in the data.

[bla...@tcd.ie]

Thanks for the useful Python package!

I have a dataset that contains amplitudes of geomagnetic events for 25 years. I am interested in fitting a powerlaw to the data, and extrapolating to larger X values, estimating the frequency of occurrence of rare events.

I'm only interested in the tail of the data (i.e., values greater than X). Suppose I am interested in the largest 25% of the data (call this data2).

I can calculate the CCDF of data2, then multiply this by len(data2) to get the # events > X expected per 25 years.

The best fit CCDF can be found from "fit.power_law.ccdf()". I can multiply this by len(data2) to get the estimated # of events for per 25 years.

Is there a simple way to extrapolate the best fit CCDF (dashed green line in above plot) for greater values of X? I can get the slope manually, but I am wondering if there is a more elegant way to do it.

Thanks!

[Jeff Alstott]

Try:

`fit.power_law.ccdf(data=[whatever x values you are interested in])`

Same thing can be done for PDF.

Check out the documentation for the `ccdf` method:

The complementary cumulative distribution function (CCDF) of the theoretical distribution. Calculated for the values given in data within xmin and xmax, if present. 

Parameters 

---------- 

data : list or array, optional 

If not provided, attempts to use the data from the Fit object in which the Distribution object is contained.

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[bla...@tcd.ie]

Hi Jeff

That's brilliant, thank you. I must have misread the docs.

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