idea for improving disturbance module

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Created by: bziniti

@rbraswell can you comment on the feasible of this idea? (in other words would this be too slow?)

1. first transform the input data to probabilities: for empirical probabilities one could: get the order (in magnitude not time) of the data then divide by count of time points

2. then take probabilities as input and output a normal z score see page 1576 equations 14 - 18 for an approximation method in this paper: https://github.com/e-baumer/standard_precip/blob/master/docs/lloyd_hughes.pdf

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Author: Bobby Braswell

Timestamp: 2017-10-21T11:18:52.412Z

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Created by: rbraswell

Module anomindex now done. See a5eeff0

@bziniti please take a look when you have a chance and I'll make some test outputs.

By Ian Cooke on 2017-10-21T11:18:52 (imported from GitLab project)

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Author: Bobby Braswell

Timestamp: 2017-10-23T23:24:53.432Z

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Created by: bziniti

I don't know how to read line 87 ts[t] = idx[t]/<float>(t+1) but it seems to be about calculating the probabilities. In the rain case, would it mean every number in idx is divided by 13140+1?

By Ian Cooke on 2017-10-23T23:24:53 (imported from GitLab project)

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Author: Bobby Braswell

Timestamp: 2017-10-24T01:13:38.192Z

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Created by: rbraswell

I found a few bugs and the code has changed a bit. One remaining thing is that I'm not dealing with the missing values appropriately. Let me fix that and get back to you. It's pretty close.

By Ian Cooke on 2017-10-24T01:13:38 (imported from GitLab project)

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Author: Bobby Braswell

Timestamp: 2017-10-27T18:03:25.330Z

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Created by: rbraswell

This is module "anomindex".

Seems to work ok finally.

By Ian Cooke on 2017-10-27T18:03:25 (imported from GitLab project)