View binary array data stored in files, from node, the browser and the command line.

Install

npm install -g arrayviewer

Usage

Show the 4th element (-i 3) in an array stored in a file at ./data/a.f32:

arrayviewer ./data/a.f32 -i 3

produces something like,

[126.48208618164062, 127.23143005371094, 136.79074096679688,
-->126.48942565917969
127.26338195800781, 136.84552001953125, 126.47312927246094, 127.27062225341797, ...]

Show the 10th element, with more context (-c 5).

arrayviewer ./data/a.f32 -i 9 -c 5

might produce,

[..., 136.84552001953125, 126.47312927246094, 127.27062225341797, 136.86407470703125, 126.44374084472656,
-->127.25633239746094
136.88494873046875, 126.36851501464844, 127.19410705566406, 136.8431396484375, 126.31245422363281, ...]

Show some extra information with -v,

arrayviewer ./data/a.f32 -i 9 -c 5 -v

Length: 150528
[..., 136.84552001953125, 126.47312927246094, 127.27062225341797, 136.86407470703125, 126.44374084472656,
-->127.25633239746094
136.88494873046875, 126.36851501464844, 127.19410705566406, 136.8431396484375, 126.31245422363281, ...]

Types

Array type is inferred from a file's extension and can be overridden with the -t option.

arrayviewer ./data/a.arr -t int32

Extensions are mapped to a TypedArray in the following way,

extension | TypedArray | type ---------|------------|------------ i8 | Int8Array | int8 u8 | Uint8Array | uint8 i16 | Int16Array | int16 u16 | Uint16Array| uint16 i32 | Int32Array | int32 u32 | Uint32Array | uint32 f32 | Float32Array| float32 f64 | Float64Array | float64

or (for non-binary types) to Javascript types like this,

extension | Result Type | type ---------|-----------|------ json | Object | json key | Object | json txt | String | str csv | String | str tsv | String | str

Any value from the type column may be supplied with the -t option.

If none of these match the file extension (and no explicit type or metadata file is provided), the data will be interpreted as a Uint8Array.

Metadata

arrayviewer ./data/a.f32 -i 9 -c 6 -m

If the -m option is specified it will also look for a file of the same name as the array with the .meta extension. The meta file has the following format

{
	"shape" : [224, 224, 3],
	"type" : "float32"
}

type should be a string containing any of the values listed in the "type" column from the tables above.

These values match the numpy dtypes

In addition to allowing you to specify a type, providing a meta file allows you to index into an array using -i, -j, -k to specify row, column and channel, respectively.

Numpy

Write compatible arrays from numpy like this,

# given array 'a'
f = open('./data/a.f32', 'wb')
f.write(a.astype(np.float32).tostring())
f.close