semvec/pypi-packages huggingface dataset:
django-healthpoint 1.0.0
Easily create an endpoint for health checks

==============================
Welcome to django-healthpoint!
==============================

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Framework for adding an endpoint for health checks to your project.

Source code
  http://github.com/pennersr/django-healthpoint


Quickstart
==========

Install the app::

    # settings.py
    INSTALLED_APPS = [
        ...
        'healthpoint'
    ]

    # If specified, this user is able to see the details for each
    # individual check in the endpoint.
    HEALTHPOINT_BASICAUTH_USERNAME = 'john'
    HEALTHPOINT_BASICAUTH_PASSWORD = 'doe'

    # If set to True authentication is required for executing the
    # health checks.
    HEALTHPOINT_AUTH_REQUIRED = True

    # urls.py
    urlpatterns = [
        ...
        url(r'^', include('healthpoint.urls')),
    ]

Add a module named ``health.py`` to any of your apps. For example::

    from datetime import timedelta

    from django.contrib.auth.models import User
    from django.utils import timezone

    from healthpoint.decorators import health_check


    @health_check
    def user_signup():
        last_user = User.objects.last()
        time_since_last_signup = timezone.now() - last_user.date_joined
        # Return True/False, throw an exception, or return a tuple with a
        # detail message.
        return (
            time_since_last_signup <= timedelta(days=1),
            "last signup was: {}".format(last_user.date_joined))


The health checks can be accessed via the ``/health/`` endpoint:

- It executes all health checks, and reports status 200 if all checks succeed, status 500 otherwise.

- If a staff user is logged in, the endpoint reports the result for each individual check::

    {
     "success": {},
     "error": {
       "myproject.myapp.health.user_signup": "Last signup was: 2017-10-29 08:45:51"
     }
    }

- To provide more detail on the result, the ``@health_check`` can return a tuple ``(success:bool, detail:str)``. The detail message will be listed in the result.
uniparser-classical-armenian 1.0.0
Rule-based morphological analysis for Classical Armenian
Requires Python: >=3.7

# Classical Armenian morphological analyzer
This is a rule-based morphological analyzer for Classical Armenian (``xcl``). It is based on a formalized description of literary Classical Armenian morphology and uses [uniparser-morph](https://github.com/timarkh/uniparser-morph) for parsing. It performs full morphological analysis of Classical Armenian words (lemmatization, POS tagging, grammatical tagging, glossing).

## How to use
### Python package
The analyzer is available as a Python package. If you want to analyze Classical Armenian texts in Python, install the module:

```
pip3 install uniparser-classical-armenian
```

Import the module and create an instance of ``ClassicalArmenianAnalyzer`` class. After that, you can either parse tokens or lists of tokens with ``analyze_words()``, or parse a frequency list with ``analyze_wordlist()``. Here is a simple example:

```python
from uniparser_classical_armenian import ClassicalArmenianAnalyzer
a = ClassicalArmenianAnalyzer()

analyses = a.analyze_words('զՔրիստոսի')
# The parser is initialized before first use, so expect
# some delay here (usually several seconds)

# You will get a list of Wordform objects
# The analysis attributes are stored in its properties
# as string values, e.g.:
for ana in analyses:
        print(ana.wf, ana.lemma, ana.gramm, ana.gloss)

# You can also pass lists (even nested lists) and specify
# output format ('xml' or 'json')
# If you pass a list, you will get a list of analyses
# with the same structure
analyses = a.analyze_words([['եւ'], ['Սիրեմ', 'զքեզ', ':']],
	                       format='xml')
analyses = a.analyze_words(['զՔրիստոսի', [['եւ'], ['Սիրեմ', 'զքեզ', ':']]],
	                       format='json')
```

Refer to the [uniparser-morph documentation](https://uniparser-morph.readthedocs.io/en/latest/) for the full list of options.