npy

npy allows to read and write files in NPY npy and [NPZ] npy formats on the JVM.

Installation

The latest version of npy is available on jCenter jcenter. If you're using Gradle just add the following to your build.gradle:

repositories {
    jcenter()
}

dependencies {
    compile 'org.jetbrains.bio:npy:0.3.3'
}

Examples

NPY

val values = intArrayOf(1, 2, 3, 4, 5, 6)
val path = Paths.get("sample.npy")
NpyFile.write(path, values, shape = intArrayOf(2, 3))

println(NpyFile.read(path))
// => NpyArray{data=[1, 2, 3, 4, 5, 6], shape=[2, 3]}

NPZ

val values1 = intArrayOf(1, 2, 3, 4, 5, 6)
val values2 = booleanArrayOf(true, false)
val path = Paths.get("sample.npz")

NpzFile.write(path).use {
    it.write("xs", values1, shape = intArrayOf(2, 3))
    it.write("mask", values2)
}

NpzFile.read(path).use {
    println(it.introspect())
    // => [NpzEntry{name=xs, type=int, shape=[2, 3]},
    //     NpzEntry{name=mask, type=boolean, shape=[2]}]

    println("xs   = ${it["xs"]}")
    println("mask = ${it["mask"]}")
    // => xs   = NpyArray{data=[1, 2, 3, 4, 5, 6], shape=[2, 3]}
    //    mask = NpyArray{data=[true, false], shape=[2]}
}

Limitations

The implementation is rather minimal at the moment. Specifically it does not support the following types:

• unsigned integral types (treated as signed),
• bit field,
• complex,
• object,
• Unicode
• void*
• intersections aka types for structured arrays.

Building from source

The build process is as simple as

$ ./gradlew assemble

Testing

No extra configuration is required for running the tests from Gradle

$ ./gradlew test

However, some tests require Python and NumPy to run and will be skipped unless you have these.

Publishing

You can publish a new release with a one-liner

./gradlew clean assemble test generatePomFileForMavenJavaPublication bintrayUpload

Make sure to set Bintray credentials (see API key section here) in $HOME/.gradle/gradle.properties.

$ cat $HOME/.gradle/gradle.properties
bintrayUser=CHANGEME
bintrayKey=CHANGEME