semvec/pypi-packages huggingface dataset:
sqlalchemy-repr 0.1.0
Automatically generates pretty repr of a SQLAlchemy model.
Keywords: pprint pretty print repr sqlalchemy extension

sqlalchemy-repr
===============

.. image:: https://travis-ci.org/manicmaniac/sqlalchemy-repr.svg?branch=master
    :target: https://travis-ci.org/manicmaniac/sqlalchemy-repr

Automatically generates pretty ``repr`` of a SQLAlchemy model.

Install
-------

.. code:: sh

    pip install sqlalchemy-repr


Usage
-----

.. code:: python

    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy_repr import RepresentableBase

    Base = declarative_base(cls=RepresentableBase)


Example
-------

``sqlalchemy_repr.RepresentableBase`` is mixin to add simple representation of columns.

.. code:: python

    >>> from datetime import datetime

    >>> from sqlalchemy import Column, DateTime, Integer, Unicode, create_engine
    >>> from sqlalchemy.ext.declarative import declarative_base
    >>> from sqlalchemy.orm import sessionmaker
    >>> from sqlalchemy_repr import RepresentableBase

    >>> Base = declarative_base(cls=RepresentableBase)

    >>> class User(Base):
    ...    __tablename__ = 'users'
    ...    id = Column(Integer, primary_key=True)
    ...    name = Column(Unicode(255), nullable=False, unique=True)
    ...    created = Column(DateTime, nullable=False)

    >>> engine = create_engine('sqlite://')
    >>> Base.metadata.create_all(engine)

    >>> Session = sessionmaker(bind=engine)
    >>> session = Session()

    >>> user = User(name='spam', created=datetime(2016, 6, 1))
    >>> session.add(user)
    >>> session.commit()

    >>> print(user)
    <User id=1, name='spam', created='2016-06-01T00:00:00'>

``sqlalchemy_repr.PrettyRepresentableBase`` brings pretty, indented multi-line representation.

.. code:: python

    >>> from sqlalchemy_repr import PrettyRepresentableBase
    >>> Base = declarative_base(cls=PrettyRepresentableBase)

    >>> class User(Base):
    ...    __tablename__ = 'users'
    ...    id = Column(Integer, primary_key=True)
    ...    first_name = Column(Unicode(255), nullable=False, unique=True)
    ...    last_name = Column(Unicode(255), nullable=False, unique=True)
    ...    email = Column(Unicode(255), nullable=False)
    ...    created = Column(DateTime, nullable=False)
    ...    modified = Column(DateTime, nullable=False)

    >>> engine = create_engine('sqlite://')
    >>> Base.metadata.create_all(engine)

    >>> Session = sessionmaker(bind=engine)
    >>> session = Session()

    >>> user = User(first_name='spam', last_name='ham',  email='spam@example.com', created=datetime(2016, 6, 1), modified=datetime(2016, 6, 1))
    >>> session.add(user)
    >>> session.commit()

    >>> print(user)
    <User
        id=1,
        first_name='spam',
        last_name='ham',
        email='spam@example.com',
        created='2016-06-01T00:00:00',
        modified='2016-06-01T00:00:00'>
mitosheet-helper-config 0.0.1
A configuration package for the mitosheet package, available to enterprise users only
Requires Python: >=3.7

# Mitosheet Helper Config

This repository is home to the `mitosheet_helper_config` Python package, which is used to configure enterprise installations of the `mitosheet` package. 
Currently, the package allows you to configure the:
- Support email

In the future, enterprises will have many additional configuration options. If you are an enterprise with a specific configuration request, let us know by [opening an issue](https://github.com/mito-ds/monorepo/issues) in the Mito monorepo or sending us an [email](mailto:founders@sagacollab.com). 

This package should only be used with a valid Mito Enterprise liscence or written consent from Mito. Learn more about Mito Enterprise [here](https://www.trymito.io/plans).

# How it works 
The `mitosheet_helper_config` package is designed for admins to set the Mito configuration settings for their entire organization. To do so: 
- Clone this repository
- Navigate to the `mito_config.py` file and follow the instructions to set your enterprise settings
- Upload the repository to your organization's GitHub

Now that you've chosen your configuration options, the last step is to make the `mitosheet_helper_config` package accessible to the `mitosheet` pacakge. 

### Docker Deployment
If you're using Docker to deploy mitosheet to your users, follow these instructions:
- Retrieve the git clone URL of the configured `mitosheet_helper_config` package that you just uploaded to your organization's GitHub. 
    - It shoud look something like this: `https://github.com/mito-ds/mitosheet_helper_config.git`. 
- Add this command to your Docker file: `RUN pip install git+<REPO_URL>#egg=mitosheet_helper_config`. 
    - Make sure to fill in the `<REPO_URL>` with the git clone URL you just collected. 
    - The final command should look something like this: `RUN pip install git+https://github.com/mito-ds/mitosheet_helper_config.git#egg=mitosheet_helper_config`
- Rebuild the base Docker image

### Repository clone
If your users clone a repository to create their Jupyter / Mito environment, follow these instructions:
[TODO] add these instructions 

# For development 
This package is only uploaded to pypi and testpypi as a placeholder. 

### Release to PyPi
- Delete the dist folder, and the .egg-info folder
- Bump the version in setup.cfg and pyproject.toml
- Run `python3 -m build`


###  Release to Test PyPi
- Delete the dist folder, and the .egg-info folder
- Bump the version in setup.cfg and pyproject.toml
- Run `python3 -m build`
- Run `python3 -m twine upload --repository testpypi dist/*`
mlflow-databricks-artifacts 2.0.1
Plugin to create and access MLflow-managed artifacts on Databricks

# mlflow-databricks-artifacts
An artifact repository plugin for MLflow that extends ACL'ed artifacts support to older MLflow versions
### Version 2.0
Backport SSE-CPK support to older version Mlflow.

Related PR https://github.com/mlflow/mlflow/pull/6927/files

Doc https://docs.google.com/document/d/1EXrv4-bYoAWl5vRQ1ksRgwqUjheNYgIgeKhfLZQOtRQ/edit#

The mlflow-databricks-artifacts is based on mlflow version v1.29.0
xia-authenticator 0.1.4
X-I-A Authenticator
Requires Python: >=3.9

.. image:: https://img.shields.io/pypi/v/xia-authenticator.svg?color=blue
   :alt: PyPI-Server
   :target: https://pypi.org/project/xia-authenticator/

====================================
X-I-A Authenticator
====================================

OAuth2.0 Compatible Authenticator Design


Quick start
=============================

Install the package::

    pip install xia-authenticator
sphinx_plausible 0.1.0
Sphinx extension for plausible.io analytics

# Sphinx extension for plausible.io

This extension adds the <https://plausible.io> script tags to a Sphinx
project.  It doesn't change the template, it uses the standard Sphinx
hooks to insert javascript directly into any template (that uses the
standard Sphinx stuff).

This is currently being tested but in production, please verify it
does the correct thing for you.



## Installation

Install the Python package: on pip it is `sphinx-plausible`.



## Usage

Add `sphinx_plausible` to `extensions`:

```python
extensions = [
    "sphinx_plausible",
]
```

Configuration options:
* `plausible_domain` (required): The domain name, like
  `my.domain.org`.  Can be a list which will be joined by commas.

* `plausible_script` (default `https://plausible.io/js/script.js`):
  The URL to the script to load If you want to use the "proxying the
  script" idea, download the script and put it in `html_static_path`
  and add the filename (relative to `html_static_path`) here instead
  of the absolute path.  (in the future we can automatically download
  and cache)

* `plausible_enabled` (default `True`): Should plausible be active?
  You might want to disable it on all you test deployments and so on.
  Default `True`.  You can limit to only the official deployment with
  something such as:

  ```python
  import os
  plausible_enabled = (
      'GITHUB_ACTION' in os.environ
      and os.environ.get('GITHUB_REPOSITORY', '').lower() == 'aaltoscicomp/scicomp-docs'
      and os.environ.get('GITHUB_REF') == 'refs/heads/master'
	  )
  ```


## Status and development

Beta but works as of 2022 - improvements and feature requests are
still welcome!
sentimizer 2.0
Sentimizer will measure sentiment around specific entities within text.
Requires Python: >=2.7

# Sentimizer

## About

Sentimizer will measure sentiment around specific entities within text.  It is built on NLTK, Spacy, and NRCLex.  Output is a dictionary that can be analyzed further, graphed, formulated into a wordcloud, etc.

## References
* https://github.com/explosion/spaCy
* https://github.com/nltk
* https://github.com/metalcorebear/NRCLex

## Revision History
* 2022-10-17: initial commit.

## Example Usage
`pip install sentimizer`

### Instantiate SentiMizer Object
`from sentimizer import SentiMizer`<br><br>
`analyzer = SentiMizer()`

### Load Text
Loads initial body of text.<br><br>
`analyzer.load_text(text : str)`<br><br>
attributes:<br><br>
`analyzer.text` - attribute contains loaded text (str).

### Append Text
For appending additional text to the initial input.<br><br>
`analyzer.append_text(text : str)`

### Entity Recognition
For identifying entities within the loaded body of text.<br><br>
`analyzer.find_entities()`<br><br>
optional parameters:<br><br>
`entity_types_of_interest` - list of entity types for recognition.  Default value is `['ORG', 'PERSON', 'FAC', 'GPE', 'LOC', 'EVENT']`  All possible lables include: `CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART` For a description of each, visit https://spacy.io/models/en. <br><br>
attributes:<br><br>
`analyzer.entities` - dictionary of entities and their tags (dict).<br><br>
`analyzer.sentences` - dictionary of entities and concatenated sentences containing each entity (dict).  Keys are entities and values are the concatenated sentences mentioning that entity.

### Measure Emotional Content
For measuring sentiment and emotional affect of sentences that mention each entity.<br><br>
`analyzer.emote()`<br><br>
attributes:<br><br>
`analyzer.sentiments` - Vader composite sentiment scores for each entity (dict).  Keys are entities and values are the composite sentiment score for that entity.<br><br>
`analyzer.affect` - NRCLex affect scores for each entity (dict).  Keys are entities and the values are affect frequency dictionaries.
optional parameters:<br><br>
`entity_type` - string specifying the entity type to analyze. Default value is `None`.  Default action is the analyze all entity types.