yaml_json_xlate.py

gr-radar.json

{
    "brief": "A radar toolbox from GSoC '14",
    "contact": {
        "author": "Stefan Wunsch",
        "email": "[email protected]"
    },
    "copyright_owner": "Communications Engineering Lab, KIT",
    "dependencies": [
        "UHD",
        "Qt",
        "Qwt",
        "python-matplotlib"
    ],
    "description": "\nThe basic flowgraph for many radar applications is shown below. First a signal\nis generated and send and received with some hardware. TX and RX signal are\ncompared with an estimator and the result is displayed on the screen.\n\nThe toolbox uses tagged streams for packaging data and to make sure that\ncorresponding data is processed together in one work function call. In most\ncases streams are used up to the evaluation of the signal attributes which are\nused for calculating range, velocity or azimuth. This attributes are most often\npeaks of a FFT spectrum. After this point there is no use for tagged streams\nand it is practical to switch to the message system of GNU Radio. This data is\npacked as PMTs (polymorphic types). Read the subsection 'Message structure and\nidentifiers' for more information.\n\nThe send and receive part of the flowgraph is implemented in two ways. First\nyou can use the USRP Echotimer. This block takes a tagged stream and ensure\nthat this package is send and received synchronously. Further information in\nthe section 'USRP Echotimer'. If you want to test your flowgraphs without the\nneed of hardware you can use a simulator for the propagation effects. A\nsimulator for static targets with constant attributes like range and velocity\nis implemented. It is possible to emulate a moving target if you use sliders\nfor variables in GNU Radio Companion. The static target simulators has\nimplemented a callback that updates the targets attributes in runtime.\n",
    "gr_compat": {
        "max": "v3.7.4",
        "min": "v3.7"
    },
    "repo": "https://github.com/kit-cel/gr-radar.git",
    "stable_release": "master",
    "title": "gr-radar",
    "type": "application",
    "website": "https://grradar.wordpress.com"
}

gr-radar.yaml

---
brief: A radar toolbox from GSoC '14
contact:
  author: Stefan Wunsch
  email: [email protected]
copyright_owner: Communications Engineering Lab, KIT
dependencies:
- UHD
- Qt
- Qwt
- python-matplotlib
gr_compat:
  max: v3.7.4
  min: v3.7
repo: https://github.com/kit-cel/gr-radar.git
stable_release: master
title: gr-radar
type: application
website: https://grradar.wordpress.com
---

The basic flowgraph for many radar applications is shown below. First a signal
is generated and send and received with some hardware. TX and RX signal are
compared with an estimator and the result is displayed on the screen.

The toolbox uses tagged streams for packaging data and to make sure that
corresponding data is processed together in one work function call. In most
cases streams are used up to the evaluation of the signal attributes which are
used for calculating range, velocity or azimuth. This attributes are most often
peaks of a FFT spectrum. After this point there is no use for tagged streams
and it is practical to switch to the message system of GNU Radio. This data is
packed as PMTs (polymorphic types). Read the subsection 'Message structure and
identifiers' for more information.

The send and receive part of the flowgraph is implemented in two ways. First
you can use the USRP Echotimer. This block takes a tagged stream and ensure
that this package is send and received synchronously. Further information in
the section 'USRP Echotimer'. If you want to test your flowgraphs without the
need of hardware you can use a simulator for the propagation effects. A
simulator for static targets with constant attributes like range and velocity
is implemented. It is possible to emulate a moving target if you use sliders
for variables in GNU Radio Companion. The static target simulators has
implemented a callback that updates the targets attributes in runtime.

yaml_json_xlate.py

#!/usr/bin/python2.7

##
# This is a translator between json and md+yaml files that are
# used by static site generators such as jekyll and pelican (with
# a plugin). The post content gets loaded as the description field
# and all other front-matter is preserved as object fields.
#
# Give it one (or possibly more) input files that are either json or
# md+yaml and it will output either md+yaml or json files for you.
##

import argparse
import yaml
import json


def read_yaml(yaml_file):
    '''
    The name is a lie. This actually reads a md file with yaml
    frontmatter. The md content winds up in the description field
    of the generated object. Otherwise the yaml load is straight
    forward.
    ''' 
    f = open(yaml_file, 'r')
    c = f.read()
    yaml_index = c.rfind('---') 
    yaml_content = c[:yaml_index]
    yaml_data = yaml.load(yaml_content)
    description = c[yaml_index+4:]
    yaml_data['description'] = description
    return yaml_data


def read_json(json_file):
    '''
    Pretty straight forward json load since we don't do anything
    fancy with json output.
    '''
    json_data = json.load(json_file)
    print json_data
    return json_data


def read_file(file_name_list):
    '''
    Determine input file type and call the appropriate reader.
    This is just a wrapper so main doesn't have to have logic.
    '''
    metadata = {}
    for file_name in file_name_list:
        if file_name.endswith('md'):
            module_name = file_name.strip('.md')
            metadata[module_name] = read_yaml(file_name)
        elif file_name.endswith('json'):
            module_name = file_name.strip('.json')
            metadata[module_name] = read_json(file_name)
        else:
            print "This file does not end with json or yaml"
        return metadata


def write_output(output_format, output_loc, metadata):
    '''
    Determine the output type and call the appropriate function.
    This is just a wrapper so main doesn't have to have logic.
    '''
    if output_format == 'json':
        write_json(output_loc, metadata)
    elif output_format == 'yaml':
        write_yaml(output_loc, metadata)
    else:
        print "Output format not known"


def write_json(output_loc, metadata):
    '''
    Just a standard json dump. The description field winds up
    looking hideous.
    '''
    for module in metadata.keys():
        json_pp = json.dumps(metadata[module], sort_keys=True,
                    indent=4, separators=(',', ': '))
        print json_pp
        module_file_parts = module.split('/')
        if output_loc is None:
            output_loc = '/'.join(module_file_parts[:-1])
        fname = output_loc + "/" + module_file_parts[-1] + ".json"
        ofile = open(fname, "w")
        print "wrote to file %s" % fname
        ofile.write(json_pp)
        ofile.close()


def write_yaml(output_loc, metadata):
    '''
    The name is a lie. This actually prints a yaml+md document
    that can be used in pelican/jekyll like page generators.
    The description is set as the page content, everything else
    is yaml frontmatter.
    '''
    for module in metadata.keys():
        try:
          description = metadata[module].pop('description')
        except:
          description = ''
        yaml_pp = yaml.dump(metadata[module], default_flow_style=False)
        module_file_parts = module.split('/')
        if output_loc is None:
            output_loc = '/'.join(module_file_parts[:-1])
        fname = output_loc + "/" + module_file_parts[-1] + ".md"
        ofile = open(fname, "w")
        ofile.write('---\n')
        ofile.write(yaml_pp)
        ofile.write('---\n')
        ofile.write(description)
        ofile.close()


def parse_options():
    '''
    Set up our translator options
    '''
    parser = argparse.ArgumentParser(description="Convert between yaml and json files")
    parser.add_argument("--file", dest="infile", type=str, nargs="+",
                            help="The input file(s) to process")
    parser.add_argument("--format", dest="output_format", type=str, default="json",
                            help="The output format (json or yaml)")
    parser.add_argument("--output", dest="output_loc", type=str,
                            help="The output directory")
    return parser.parse_args()


if __name__ == '__main__':
    options = parse_options()
    metadata = read_file(options.infile)
    write_output(options.output_format, options.output_loc, metadata)

The description is somewhat awkward since it makes the yaml part non-standard and looks horrible in json. One option is to not have a description, but it looks nice on CGRAN.