grass.script: Automatically parse JSON and CSV in parse_command

[petrasovaa]

Given several tools have options format, this adds automatic parsing to parse_command:

>>> data = gs.parse_command("r.what", map='elevation@PERMANENT', coordinates=[641567, 223962], format='json')
[{'easting': 641567, 'northing': 223962, 'site_name': '', 'elevation@PERMANENT': {'value': 75.82444763183594}}]

>>> data = gs.parse_command("v.db.select", map='busroute1@PERMANENT', format='csv')
[{'cat': '1', 'ROUTE': '1'}, {'cat': '2', 'ROUTE': '1'}]
>>> pd.DataFrame(data)
  cat ROUTE
0   1     1
1   2     1

For csv, I use DictReader, which makes sense only when the csv has a header, but that seems like reasonable assumption, and then it works nicely with pandas.

Option delimiter does not fit well into it conceptually, so I deprecated it. Also, it's now taken into account only when parameter parse is not specified, it assumed that anyway.

[wenzeslaus]

Great update overall. The heuristic should work well the format parameter which in combination with the parse_command function will almost always refer to output text format.

A short change for what it accomplishes!

Thanks for changing to csv.DictReader, the purpose here is convenience, so DictReader seems more appropriate than plain reader.

I agree with converting the generator to a list. json.loads also returns plain objects, so it is consistent with that.

>>> type(json.loads("""{"a": 1}"""))
<class 'dict'>
>>> type(json.loads("""[1,2]"""))
<class 'list'>

The delimiter change is a good clean up.

Please, add tests.

[wenzeslaus]

Thanks for the tests! They nicely show how powerful this is.

[wenzeslaus]

Awesome. Looking forward to using this. It will work great together with the JSON GSoC project.