semvec/pypi-packages huggingface dataset:
Xigt 1.1.1
A framework for eXtensible Interlinear Glossed Text
License: MIT
Keywords: nlp igt linguistics

Xigt is a framemwork for working with interlinear glossed text (IGT). It
provides a data model and XML format as well as an API for
programmatically interacting with Xigt data. The format has a flat
structure and makes use of IDs and references to encode the annotation
structure of an IGT. This architecture allows for interesting extensions
to the standard IGT tiers, such as for parse trees, dependencies,
bilingual alignments, and more.
frozen-box 0.0.1
Store Python objects in a immutable manner and provide fast attribute lookup
License: MIT
Requires Python: >=3.6

Frozen is a container that:

* makes internal object immutable;
* supports dot notation access.

Example:

.. code-block:: python

    from frozen import freeze

    person = {
        'name': 'Mike',
        'age': 17,
        'scores': {
            'math': [{
                'name': 'test 1',
                'score': 87,
                'sub_score': {
                    'part 1': 25,
                    'part 2': 22,
                    'part 3': 40
                }
            }, {
                'name': 'test 2',
                'score': 92,
                'sub_score': {
                    'part 1': 30,
                    'part 2': 23,
                    'part 3': 39
                }
            }]
        }
    }

    frozen = freeze(person)
    print(frozen['scores.math.1.sub_score.part 3'])  # 39
    print(frozen.scores.math[1].sub_score['part 3'])  # 39
pyd-mon 0.2.0
A super small package for validating and converting MongoDB ObjectId's to Pydantic models.
License: MIT
Requires Python: >=3.9,<4.0

# Pyd_Mon

An easy way to convert and validate Pymongo ObjectId's with Pydantic model fields.

# Usage

## Creating a MongoModel

Creating Pydantic models with a mapping id is as easy as inheriting from MongoModel and creating an `id` field with type of `MongoId`. All of your other fields can be created as normal pydantic fields.

```python
from pyd_mon import MongoModel, MongoId

class Item(MongoModel):
    id: MongoId # Will map to '_id'
    name: str # The rest of the fields are standard Pydantic fields
```

## Create Pydantic instance from Pymongo dict (`_id` -> `id`)

To map from a Pymongo dict you can use the `from_mongo` class method on your model.

```python
result = collection.find_one() # Get item from Pymongo
item = Item.from_mongo(result) # Create a Pydantic instance from the Pymongo dict.
```

## Create Pymongo dict from Pydantic instance (`id` -> `_id`)

To map back from a pydantic instance to a Pymongo dict call the `mongo` method on your model instance.

```python
item = Item(id=MongoId(), name='Example')
collection.insert_one(item.mongo())
```