Helps you to parse a Python data structure.

You write your functions. Then you tell parseobj how they're connected. Done

parseobj will traverse the data for you and call your code, when it looks as expected. It will give you nice error messages, when it does not. And it prints you a pseudo-grammar for the expected input.

Disclaimer: This is an experiment, a search for a more pythonic way (for the user, not the library) to traverse data structures.

Here's a trivial object graph: an opera description.

dutchman = {
    "name": "The Flying Dutchman",
    "roles": ["The Dutchman", "Senta", "Daland"]
}

Let's print the names of the operas, and for each role output the number of upper-case characters.

def print_uppercase_count(name):
    print(len([c for c in name if c.isupper()]))


@foreach(print_uppercase_count)
def visit_roles(roles):
    pass


@for_key("roles", visit_roles)
@for_key("name", print)
def visit_opera(opera):
    pass


visit_opera(dutchman)

Output:

2
1
1
The Flying Dutchman

Given invalid input, …

visit_opera({"name": "Das Rheingold"})  # "roles" key missing

… traversing fails (as it should). The good news: parseobj will tell you where it happened and which expectation was not met.

# (stacktrace omitted)
ObjSyntaxError: <root>: Missing key 'roles', found only: ['name']. Expected dict-like object with key 'roles'.

What if your own code surrenders?

def raise_error(value):
    if value == "c":
        raise ValueError("Value is unusable!")

foreach("abcd", raise_error)

parseobj prepends the location to support you in figuring out what went wrong.

ValueError: <root>[2]: Value is unusable!

Want to figure out what valid input might look like? Make parseobj print a pseudo-grammar.

>>> visit_opera.print_grammar()
{
    "name": "<?>", 
    "roles": []
}

• support optional elements
• support more
• Direct usability of all decorator functions
• Grammar: Indicate that other/more elements in collections/dicts are possible.
• Grammar: Consider multiple passes over list
• Proper README ;)
• proper logging (under defined logger)
• Generate random(?) minimal example
• API documentation
• Test suite
• Travis integration
• readthedocs integration
• installable using setup.py

• use paths to skip levels and avoid empty functions
• make exception extension configurable
• support for visitor objects
• consider lengthy values when output
• support for check-only decorators
• support (document) extensibility