timestream-ops
Mapped operation Transforms for sequential objectMode streams (e.g. timeseries data).
npm install timestream-ops
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Version | 0.2.0 last updated 3 months ago |
License | MIT |
Keywords | stream, map, timeseries, timestream |
Repository | git://github.com/brycebaril/timestream-ops.git (git) |
Homepage | https://github.com/brycebaril/timestream-ops |
Bugs | https://github.com/brycebaril/timestream-ops/issues |
Dependencies (7) | isnumber, through2-map, through2, array-pivot, stats-lite, flatnest, xtend |
Dependents | timestream |
timestream-ops
Mapped operation Transforms for sequential objectMode streams (e.g. timeseries data). Contains a set of stream Transforms that accept ordered objectMode streams with a sequenceKey perform an operation on each value in each record.
Most of these operations are shallow, that is they will not descend into nested keys at each record.
var tsops = require("timestream-ops")
var spigot = require("stream-spigot")
var concat = require("concat-stream")
var source = spigot({objectMode: true}, [
{v: 0, foo: 0, bar: "hi"},
{v: 1, foo: -1.1},
{v: 2, foo: 2.2},
{v: 3, foo: 3.3},
{v: 4, foo: 4.4},
{v: 5, foo: 5.5},
{v: 7, foo: 6.6006},
])
source.pipe(tsops.sin("v")).pipe(concat(console.log))
/*
[ { v: 0, foo: 0, bar: 'hi' },
{ v: 1, foo: -0.8912073600614354 },
{ v: 2, foo: 0.8084964038195901 },
{ v: 3, foo: -0.1577456941432482 },
{ v: 4, foo: -0.951602073889516 },
{ v: 5, foo: -0.7055403255703919 },
{ v: 7, foo: 0.3121114469569012 } ]
*/
API
This library includes a whole pile of transforms that operate on each record. All operations skip the specified sequence key, if appliccable or unless otherwise noted.
- each
- ceil
- floor
- round
- abs
- log
- exp
- pow
- sqrt
- sin
- cos
- plus
- minus
- times
- divide
- elapsed
- dt
- cumsum
- sma
- keep
- into
- rename
- numbers
- flatten
- nest
- slide
- map
each(seqKey, fn)
Apply fn
to each value in each record, leaving the sequence key seqKey
alone. Walks through each record calling fn
for each value, so fn
should accept a value and return what you would like the new value to be.
ceil(seqKey)
Apply Math.ceil
to each numeric value in each record.
floor(seqKey)
Apply Math.floor
to each numeric value in each record.
round(seqKey, factor)
Round each numeric value in each record to the specified factor. E.g. if the factor is 10
it will round to the tens place 333 -> 330
.
abs(seqKey)
Apply Math.abs
to each numeric value in each record.
log(seqKey)
Apply Math.log
to each numeric value in each record.
exp(seqKey)
Apply Math.exp
to each numeric value in each record.
pow(seqKey, factor)
Apply Math.pow(number, factor)
to each numeric value in each record.
sqrt(seqKey)
Apply Math.sqrt
to each numeric value in each record.
sin(seqKey)
Apply Math.sin
to each numeric value in each record.
cos(seqKey)
Apply Math.cos
to each numeric value in each record.
plus(seqKey, addend)
Add the value addend
to each numeric value in each record.
minus(seqKey, addend)
Subtract the value addend
from each numeric value in each record.
times(seqKey, factor)
Multiply the value factor
by each numeric value in each record.
divide(seqKey, factor)
Divide each numeric value in each record by the value factor
.
elapsed(seqKey)
Insert a new key elapsed
in each record, which is the difference in time since the previous record in the timeseries.
dt(seqKey)
For each numeric value in each record, replace the value with its difference from the previous value. This can be considered similar to a differential.
cumsum(seqKey)
Replace each numeric value with the cumulative sum of all numeric values at that key prior to this record.
sma(seqKey, n)
Replace each numeric value with the Simple Moving Average (mean) of that value for the previous n
records.
keep(seqKey, keys)
Keep only the keys specified by the array keys
in each record.
into(seqKey, path [,name])
Replace the record with a new record which is at the key or key path specified by path
and optionally rename the key to name
. Use this to convert timeseries with partitioned or nested data into specific portions of each record only. path
accepts js dot notation, e.g. into("v", "foo.bar[2]")
would find in each record a property named foo
, in each of those objects a property named bar
which stores an array, then from that array take the 3rd element only.
rename(from, to)
Rename the key from
to the name to
at each record. This will operate on any property of the record, including the sequence key.
numbers(seqKey)
Remove all non-numeric values from each record.
flatten()
Flatten the record (using flatnest) into a record with no nested structures, preserving content.
E.g.
[
{v: 0, abc: {def: ["v0", "v0.1"]}, zyx: ["aa", "ab"]},
{v: 1, abc: {def: ["v1", "v1.1"]}, zyx: ["ba", "bb"]},
{v: 2, abc: {def: ["v2", "v2.1"]}, zyx: ["ca", "cb"]},
{v: 3, abc: {def: ["v3", "v3.1"]}, zyx: ["da", "db"]},
{v: 4, abc: {def: ["v4", "v4.1"]}, zyx: ["ea", "eb"]},
{v: 5, abc: {def: ["v5", "v5.1"]}, zyx: ["fa", "fb"]},
{v: 6},
]
Becomes:
[
{"v":0,"abc.def[0]":"v0","abc.def[1]":"v0.1","zyx[0]":"aa","zyx[1]":"ab"},
{"v":1,"abc.def[0]":"v1","abc.def[1]":"v1.1","zyx[0]":"ba","zyx[1]":"bb"},
{"v":2,"abc.def[0]":"v2","abc.def[1]":"v2.1","zyx[0]":"ca","zyx[1]":"cb"},
{"v":3,"abc.def[0]":"v3","abc.def[1]":"v3.1","zyx[0]":"da","zyx[1]":"db"},
{"v":4,"abc.def[0]":"v4","abc.def[1]":"v4.1","zyx[0]":"ea","zyx[1]":"eb"},
{"v":5,"abc.def[0]":"v5","abc.def[1]":"v5.1","zyx[0]":"fa","zyx[1]":"fb"},
{"v":6}
]
nest()
Nest the record (using flatnest) into a nested structure based on the key names. Typically used to undo a flatten()
operation.
slide(seqKey, value)
Add value
to seqKey
at each record, effectively sliding it in time.
map(fn)
Do it yourself! Full control of each record, using through2-map. Provide a function that accepts a record, and return a new record to send downstream.
LICENSE
MIT