welch

// Predict:
// Trend, seasonality, and noise estimation for metric streams.
//
// Predict is a sub which consumes a metric stream and emits a
// prediction stream based on estimated trend, seasonality, and a
// smoothed version of the input. Prediction errors (the difference
// between predict's output and the input) can signal anomalies in the
// stream.
//
// usage: predict  [options]

welch

The juttle predict proc combines trend, seasonal, and level prediction. Although it is native javascript, many of its components can also be written in juttle, and those examples are here.

welch

[
    {"time":"2016-01-01T00:00:00.000Z", "cust_id":1, "email":"[email protected]"},
    {"time":"2016-01-01T00:00:02.000Z", "cust_id":2, "email":"[email protected]"},
    {"time":"2016-01-01T00:00:03.000Z", "cust_id":3, "email":"[email protected]"}
]

welch

// example of ingesting multiple metrics at a regular 20s pulse from
// collectd (timestamps are aligned across streams, simulated here),
// and merging them onto a single point so relative percentages can be computed.
//
// Useful patterns in add_streams:
//   put *name = value applied to points having {name=foo, value=bar} layout,
//     gives the values unique names in preparation for joining
//   use of single-stream join to combine a variable number of input streams
//   reduce -groupby by, which is the same as reduce by, but is convenient inside a sub.
//   reduce time=first(time) gives reducer results same timestamp as their inputs

welch

// stdlib.random demos:
//
import "random.juttle" as random;

export sub poissonHisto() {
    // render draws from 3 poisson distributions as a scatter chart.
    // see https://en.wikipedia.org/wiki/Poisson_distribution
    emit -limit 10000 -from 0
        | put lambda_1=random.poisson(1), lambda_4=random.poisson(4), lambda_10=random.poisson(10)
        | split lambda_1, lambda_4, lambda_10

welch

// stats demos: statistics 101 for juttle data flows
//
// Each of the stream standardization approaches offered in stats is
// appropriate in a different setting. The demos here show some good
// and some bad choices.
//
import "stats.juttle" as stats;

// cpuZ: visualize z-scores for a cpu metric.
//

welch

// Alerting with context:
//
// This example shows how to annotate an alert event with recent lines from
// a logging stream, to give context for the alert. This is accomplished with
// a custom reducer that tails the log stream, and a join of the current tail
// against each alert event as it occurs. 
//
// The log-tailing stream is joined using the -table option, which means the
// join will fire only when the alert stream gets a new point, but not fire
// when the log stream gets new points.

welch

function pairs = readzmax(FileName)
fileID = fopen(FileName);
% read the file data
vals = fileread(FileName);
fclose(fileID);
% Convert to string
dstr = sprintf('%s',vals);
% keep only lens data
GLDstart = regexp(dstr, 'GENEARL LENS DATA:','end');
dstr = dstr(GLDstart+2:end);

welch

#!/usr/bin/env python
"""
zmx2csv.py: convert zemax output to csv for matlab import

usage: zmx2csv.py filename.txt ... 

reads each filename.txt, outputting filename.csv
sample input file: forPupilMapping_Prescription.txt
"""
import sys

welch

WTF?

The basic set, fed a steady stream of two groups; A group

Juttle

emit -from Date.new(0) -limit 10
| put T = Duration.seconds(time - Date.new(0))
| ( put name = "A" ; put name = "B", T = -T)

| (