semvec/pypi-packages huggingface dataset:
mexp 0.0.1
Filter list using properties
Requires Python: >=3.6

*You can use mexp to help in filtering a list of objects using find and find_any of elements that mactch properties conditions*

**Exemple:**

file `test_mexp.py`:
```
from car_class import Car
from mexp import *


if __name__ == "__main__":
    listTesti = []
    listTesti.append(Car('Jack', 'benlalla', True, 1))
    listTesti.append(Car('Jack2', 'elarrs', False, 5))
    listTesti.append(Car('Marc', 'roro', False, 2))
    result = find(listTesti, lname = 'roro')
    print(result)
```

File `car_class.py`
```
class Car(object):
    def __init__(self, name, lname, active, rank):
        self.name = name
        self.lname = lname
        self.active = active
        self.rank = rank
```


find_any(list_of_object, **kwargs)
 ```
    Args:
        list_of_object: provide a list of objects you want to filter
        kwargs: properties with values you want to use as filter
    Returns:
        A filtered list of objects that match any of the filter properties
```

def find(list_of_object, **kwargs):   
```
    Args:
        list_of_object: provide a list of objects you want to filter
        kwargs: properties with values you want to use as filter
    Returns:
        A filtered list of objects that match the filter properties
```
[**mexp repo**](https://github.com/IbrahimABBAS85/mexp)
mail-tracking-number-voice-recognition-post-processing 0.0.0.1
Library contains functionality for converting given CTC-trained Neural Networks output into tracking number (Russian Post or International mail) format

This package allows you to convert CTC-trained Neural Networks output into mail tracking number in *International mail* or *Russian Post* format  


Format example for *International mail*: ``RO260964943RU``  
Format example for *Russian Post*: ``1421171600738``

Class ``TrackingNumberRecognizer`` has method ``convert(matrix: np.array) -> list``, which takes 2d matrix and returns list of possible tracking numbers in decreasing order of probability


Matrix first dimension corresponds to the time moment. Second dimension corresponds to a letter, last index has to correspond to *blank* (see CTC).
**Russian letters supported only**.

Value is the probability of pronunciation letter at the moment.

Format (*International mail* or *Russian Post*) is set in  ``TrackingNumberRecognizer`` constructor.

You can also set the letters order, the probability of your model making mistake, and the prior probabilities for letters in case of *International mail* format