Security News
The Risks of Misguided Research in Supply Chain Security
Snyk's use of malicious npm packages for research raises ethical concerns, highlighting risks in public deployment, data exfiltration, and unauthorized testing.
partial.lenses
Advanced tools
This library provides a collection of Ramda compatible
partial (dealing with undefined
results) lenses. The intention is to make
modification or editing of JSON data convenient and compositional.
Consider the following REPL session using Ramda 0.19.1:
> R.set(R.lensPath(["x", "y"]), 1, {})
{ x: { y: 1 } }
> R.set(R.compose(R.lensProp("x"), R.lensProp("y")), 1, {})
TypeError: Cannot read property 'y' of undefined
> R.view(R.lensPath(["x", "y"]), {})
undefined
> R.view(R.compose(R.lensProp("x"), R.lensProp("y")), {})
TypeError: Cannot read property 'y' of undefined
> R.set(R.lensPath(["x", "y"]), undefined, {x: {y: 1}})
{ x: { y: undefined } }
> R.set(R.compose(R.lensProp("x"), R.lensProp("y")), undefined, {x: {y: 1}})
{ x: { y: undefined } }
One might assume that R.lensPath([p1, ..., pN])
is equivalent to
R.compose(R.lensProp(p1), ..., R.lensProp(pN))
, but that is not the case.
The lenses and operations on lenses are accessed via the default import:
import L from "partial.lenses"
For convenience, you can access basic operations on lenses via the default
import L
:
L.lens(get, set)
is the same as R.lens(get, set)
(see lens).L.over(lens, x2x, s)
is the same as R.over(lens, x2x, s)
(see over).L.set(lens, x, s)
is the same as R.set(lens, x, s)
(see set).L.view(lens, s)
is the same as R.view(lens, s)
(see view).The default import, L
, is also a shorthand function for lens composition (see
compose) and lifting. The semantics
can be described as
L(l1, ..., lN) === R.compose(lift(l1), ..., lift(lN))
where
const lift = l => {
switch (typeof l) {
case "string": return L.prop(l)
case "number": return L.index(l)
default: return l
}
}
L.prop(string)
is much like R.lensProp(string)
(see
lensProp), but composes as a partial
lens:
Examples:
> L.set(L("x", "y"), undefined, {x: {y: 1}})
undefined
> L.set(L("x", "y"), 2, {x: {y: 1}})
{ x: { y: 2 } }
> L.set(L("x", "y"), undefined, {x: {y: 1}, z: 3})
{ z: 3 }
> L.set(L("x", "y"), 2, {x: {y: 1}, z: 3})
{ x: { y: 2 }, z: 3 }
> L.view(L("x", "y"), undefined)
undefined
L.index(integer)
is like R.lensIndex(integer)
(see
lensIndex), but composes as a
partial lens:
L.find(predicate)
operates on arrays like L.index
, but the index to be
viewed is determined by finding the first element from the input array that
matches the given unary predicate. When no matching element is found the effect
is same as with R.index
with the index set to the length of the array.
L.normalize(transform)
maps the value with same given transform
when viewed
and set and implicitly maps undefined to undefined. More specifically,
L.normalize(transform)
is equivalent to R.lens(toPartial(transform), toPartial(transform))
where
const toPartial = transform => x => undefined === x ? x : transform(x)
The use case for normalize
is to make it easy to determine whether, after a
change, the data has actually changed. By keeping the data normalized, a simple
R.equals
comparison will do.
L.replace(inn, out)
, when viewed, replaces the value inn
with out
and vice
versa when set. Values are compared using R.equals
(see
equals).
Examples:
> L.view(L(L.replace(undefined, {type: "title", text: ""}),
"text"),
undefined)
""
> L.set(L(L.replace(undefined, {type: "title", text: ""}),
"text"),
"",
{type: "title", text: "not empty"})
undefined
The use case for replace
is to handle optional properties and elements.
FAQs
Partial lenses is a comprehensive, high-performance optics library for JavaScript
The npm package partial.lenses receives a total of 3,578 weekly downloads. As such, partial.lenses popularity was classified as popular.
We found that partial.lenses demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Snyk's use of malicious npm packages for research raises ethical concerns, highlighting risks in public deployment, data exfiltration, and unauthorized testing.
Research
Security News
Socket researchers found several malicious npm packages typosquatting Chalk and Chokidar, targeting Node.js developers with kill switches and data theft.
Security News
pnpm 10 blocks lifecycle scripts by default to improve security, addressing supply chain attack risks but sparking debate over compatibility and workflow changes.