arrayviewer

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