hdfq 0.4.2

Command-line HDF5 processor

hdfq

hdfq is a CLI tool for displaying and manipulating hdf5 files. It uses syntax similar to jq although it does not yet support everything jq does. hdfq is written in Python.

Installation

with pipx

The recommended way of installing hdfq is trough the pipx installer :

pipx install hdfq

hdfq will be installed in an isolated environment but will be available globally as a shell application.

from source

You can download the source code from gitlab with :

git clone [e-mail]:MatteoBouvier/hdfq.git

Usage

Basics

hdfq requires both a filter (a command to be evaluated) and a path to a file saved in hdf5 format. A typical hdfq command would look like :

hdfq "." path/to/hdf5/file

👆 Tip : you can also invoke hdfq as :

echo path/to/hdf5/file | hdfq "."

The filter argument can be used to :

• read a specific object (stored under the <name> identifier) from the file with .<name>
• read a specific attribute from an object with #<attr_name>
• get the list of identifiers with keys, attributes with attrs and attribute identifiers with kattrs
• set an object's value with <object>=<value>
• delete an object with del(<object>)

Commands can be chained in the filter argument using a | symbol.

Examples

View all contents in a file :

hdfq '.' file.h5

Read the contents of an object named b in a

hdfq '.a.b' file.h5

Read the z attribute of an object's (a.b) :

hdfq '.a.b#z' file.h5

Chain commands (select a.b and ...):

# list keys
hdfq '.a.b | keys' file.h5

# list attribute names
hdfq '.a.b | kattrs' file.h5

# list attribute key-value pairs
hdfq '.a.b | attrs' file.h5

Update an object's value :

hdfq '.a#version = 2' file.h5

Delete an object :

hdfq 'del(.obj)' file.h5

List object sizes (and compute total) in a group :

hdfq '.a | sizes' file.h5

Create a dataset :

# create a dataset from values
hdfq '.a = [1,2,3]' file.h5

# create a 10 x 10 matrix filled with ones
hdfq '.a = [1](10, 10)' file.h5

# specify the data type
hdfq '.a = [1,2,3]<float64>' file.h5

# create an empty dataset with shape (3, 4)
hdfq '.a = [](3, 4)' file.h5

# specify additional parameters of h5 datasets
hdfq '.a = [1,2,3, chunks=True, maxshape=(100, 100)]' file.h5