semvec/pypi-packages huggingface dataset:
fontin 0.0.10
A better font extractor and installer for bulk fonts in one archive.
Requires Python: >=3.6

# font-in
A better font extractor and installer for bulk fonts in one archive.

### font-in installs all the fonts in different archives automatically

# Installation

```
pip install fontin
```

# How to use
To start using it, open command prompt in the folder which has archive of fonts (For example: Downloads). This can be done by simply changing the path in the file explorer to cmd.
Run it by typing ```fontin``` in cmd and then follow along.

# Features

Automatically show all zips in the directory so it's easier for the user to choose.

Automates the task of installing every variant of the font.

Very fast.

# How to contribute

You can simply help contribute by opening an issue whenever you find a bug. 

You can also contribute by forking this repository and then opening a pull request.
NEROnlp 0.0.3
A biomedical Named Entity (Recognition) Ontology
Requires Python: >=3.6

NERO is a biomedical Named Entity (Recognition) Ontology.


Using NERO, we annotated a large biomedical corpus to enable a broad spectrum of natural language processing and
biomedical machine learning tasks.


NERO-nlp is a wrapper around this corpus.


Sample usage:



	# access basic dataframe attributes directly
	In [4]: corpus.columns 

	In [5]: corpus.shape

	# access to the whole dataframe
	In [6]: corpus._data

	# Having directly access to columns by calling then as an attribute
	In [7]: corpus.semantic_class_actionType

	In [8]:                                                                                                                                                                                                     

	# Using documentation in order to gain more insight into
	# functionality of the attribute.
	In [8]: corpus.procset_topic_bd.__doc__                                                                                                                                                        

	# other generic and multipurpose functionalions
	In [9]: corpus.procset_topic_bd('aut')                                                                                                                                                               

	In [10]: corpus.plot_protein_domain_entity() 



Installation:

For running the NERO-nlp you need to have python3.7+ and pandas installed. For installation you can use `pip` or `pip3` for installation. 

    # Install
	sudo pip3 install NERO-nlp
	# or
	sudo python3 -m pip install NERO-nlp
	# Upgrade
	sudo pip3 install NERO-nlp --upgrade
django-describer 0.0.24
A tool for automated generation of several APIs from a Django webapp.
Requires Python: >=3.6

# django_describer

An easy-to-use tool to auto-generate GraphQL API from Django models. More APIs TBD.

## Get started

- Install `django_describer` via PyPI, e. g. `pip install django_describer`.
- Add `graphene_django` to your `INSTALLED_APPS` in Django settings. Otherwise, the template for GraphQL would be invisible.

## Usage

Write your Django models:

```python
from django.db import models


class Publisher(models.Model):
    name = models.CharField(max_length=50)

    @property
    def short_books(self):
        return self.books.all().filter(page_count__lt=300)

    def __str__(self):
        return self.name


class Book(models.Model):
    name = models.CharField(max_length=50)
    page_count = models.IntegerField()
    publisher = models.ForeignKey(Publisher, on_delete=models.CASCADE, blank=True, null=True, related_name="books")

    def __str__(self):
        return "{} ({})".format(self.name, self.publisher)
```

Now write a Describer for it. You can specify:

- which fields (and model properties) are exposed to the API and who can access them
- which CRUD operations are allowed for each model and who can perform them
- extra actions on each model

Per-request field specification, ordering, filtering and pagination are for granted.

```python
from django_describer.actions import DetailAction
from django_describer.datatypes import QuerySet
from django_describer.describers import Describer
from django_describer.permissions import IsAuthenticated

from user.models import User


class UserDescriber(Describer):
    model = User

    extra_actions = {
        "myself": DetailAction(permissions=IsAuthenticated, fetch_fn=lambda request, pk: request.user, id_arg=False)
    }
```

Import all describers into your `urls.py` and create a URL for the api:

```python
from django.contrib import admin
from django.urls import path
from django_describer.adapters.base import generate
from django_describer.adapters.graphql.main import GraphQL

from course.describers import *
from user.describers import *

urlpatterns = [
    path('admin/', admin.site.urls),
    path("graphql/", generate(GraphQL)),
]
```

Now you can do things such as:

```
query q{
  UserMyself{
    id
    username
  }
}

mutation m{
  UserCreate(data: {username: "John", password: "asdf"}){
    object{
      id
    }
  }
}
```
NERO-nlp-bohdan 0.0.1
A biomedical Named Entity (Recognition) Ontology
Requires Python: >=3.6

NERO is a biomedical Named Entity (Recognition) Ontology.


Using NERO, we annotated a large biomedical corpus to enable a broad spectrum of natural language processing and
biomedical machine learning tasks.


NERO-nlp is a wrapper around this corpus.


Sample usage:


.. code-block:: python

	# access basic dataframe attributes directly
	In [4]: corpus.columns 

	In [5]: corpus.shape

	# access to the whole dataframe
	In [6]: corpus._data

	# Having directly access to columns by calling then as an attribute
	In [7]: corpus.semantic_class_actionType

	In [8]:                                                                                                                                                                                                     

	# Using documentation in order to gain more insight into
	# functionality of the attribute.
	In [8]: corpus.procset_topic_bd.__doc__                                                                                                                                                        

	# other generic and multipurpose functionalions
	In [9]: corpus.procset_topic_bd('aut')                                                                                                                                                               

	In [10]: corpus.plot_protein_domain_entity() 



Installation:

For running the NERO-nlp you need to have python3.7+ and pandas installed. For installation you can use `pip` or `pip3` for installation. 

.. code-block:: python
    
    # Install
	sudo pip3 install NERO-nlp
	# or
	sudo python3 -m pip install NERO-nlp
	# Upgrade
	sudo pip3 install NERO-nlp --upgrade