Recursively convert list to tuple, set to frozenset, dict to mappingproxy etc.

Example usage:

   import freezedata

   data = [{'a': [1,2,3], 'b': {1,2,3}}, {1:1, 2:2, 3:3}]
   frozendata = freezedata.freeze_data(data)
   print(frozendata)
   >> (mappingproxy({'a': (1, 2, 3), 'b': frozenset({1, 2, 3})}),
mappingproxy({1: 1, 2: 2, 3: 3}))

This is a read-only data structure, that is; there is no direct way to alter this data structure from within frozendata itself (without using some special modules (gc, inspect)).

For example:

frozendata[0]['a'][0] = 4
>> TypeError: 'tuple' object does not support item assignment
del frozendata[1][1]
>> TypeError: 'mappingproxy' object does not support item deletion

Notice: Since a mappingproxy is not hashable, frozen data structures containing mappingproxy (i.e. based on dict) will not be hashable either:

hash(frozendata)
>> TypeError: unhashable type: 'mappingproxy'

On the other hand, if the frozen data structure contains only hashable elements, the whole structure will be hashable (and immutable) as well:

frozendata = freezedata.freeze_data([[1,2,3], {4,5,6}])
print(frozendata)
>> ((1, 2, 3), frozenset({4, 5, 6}))
hash(frozendata)
>> -11948691520864899

Relaxing requirements (accepting functions, modules, classes and instances):

Functions, modules, (user-created) classes and instances are mutable in Python, and therefore neither immutable or read-only. By default, using these will result in errors, but setting parameter allow as one, several or all of functions, modules , classes and instances, these can be used in the new new data structure.

Functions have mutable attributes in Python, but sometimes you still want a function in a new data structure that won't affect the parent data structure / parent function. By setting allow='functions' or allow=['functions'], the new data structure will contain a copy of the included functions and its public attributes:

def func(n):
    return n*2
func.a = 'a'
data = [func]
frozendata = freezedata.freeze_data(data, allow='functions')
data[0] == frozendata[0]
>> False
frozendata[0].a = 'b'
print(data[0].a, frozendata[0].a)
>> a b

modules will be converted to a namedtuple, if you're freezing a module. If a module is in the data structure, but it's not top level, an error will by default be raised. If allow={'modules'} is set, non-top-level modules will be allowed and kept unchanged.

classes and class instances may be converted into namedtuple and used in the frozen data structure by setting allow={'classes', 'instances} or only one, e.g. allow={'classes'}, as needed. By converting to namedtuple, information may be lost, as attributes with leading underscores will be ignored:

class Test:
    a = 1
    def __init__(self, a):
        self.a = a
test = Test(2)
frozendata = freezedata.freeze_data([Test, test], allow={'classes', 'instances'})
print(frozendata)
>> (Test(a=1), Test(a=2))
print(type(frozendata[0]), type(frozendata[1]))
>> <class 'freezedata.freezedata.Test'> <class 'freezedata.freezedata.Test'>  # two namedtuples