semvec/pypi-packages huggingface dataset:
micropython-sht4x 0.1.0
MicroPython Driver fot the Sensirion Temperature and Humidity SHT40 and SHT45 Sensor
License: MIT
Keywords: sensor,micropython,sht4x,temperature,humidity,driver,sensirion,sht40,sht45,micropython,sensor

Introduction
============


.. image:: https://readthedocs.org/projects/micropython-sht4x/badge/?version=latest
    :target: https://micropython-sht4x.readthedocs.io/en/latest/
    :alt: Documentation Status


.. image:: https://img.shields.io/badge/micropython-Ok-purple.svg
    :target: https://micropython.org
    :alt: micropython

.. image:: https://img.shields.io/pypi/v/micropython-sht4x.svg
    :alt: latest version on PyPI
    :target: https://pypi.python.org/pypi/micropython-sht4x

.. image:: https://static.pepy.tech/personalized-badge/micropython-sht4x?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Pypi%20Downloads
    :alt: Total PyPI downloads
    :target: https://pepy.tech/project/micropython-sht4x

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/psf/black
    :alt: Code Style: Black

MicroPython Driver fot the Sensirion Temperature and Humidity SHT40 and SHT45 Sensor


Installing with mip
====================
To install using mpremote

.. code-block:: shell

    mpremote mip install github:jposada202020/MicroPython_SHT4X

To install directly using a WIFI capable board

.. code-block:: shell

    mip install github:jposada202020/MicroPython_SHT4X


Installing Library Examples
============================

If you want to install library examples:

.. code-block:: shell

    mpremote mip install github:jposada202020/MicroPython_SHT4X/examples.json

To install directly using a WIFI capable board

.. code-block:: shell

    mip install github:jposada202020/MicroPython_SHT4X/examples.json


Installing from PyPI
=====================

On supported GNU/Linux systems like the Raspberry Pi, you can install the driver locally `from
PyPI <https://pypi.org/project/micropython-sht4x/>`_.
To install for current user:

.. code-block:: shell

    pip3 install micropython-sht4x

To install system-wide (this may be required in some cases):

.. code-block:: shell

    sudo pip3 install micropython-sht4x

To install in a virtual environment in your current project:

.. code-block:: shell

    mkdir project-name && cd project-name
    python3 -m venv .venv
    source .env/bin/activate
    pip3 install micropython-sht4x


Usage Example
=============

Take a look at the examples directory

Documentation
=============
API documentation for this library can be found on `Read the Docs <https://micropython-sht4x.readthedocs.io/en/latest/>`_.
usu 0.1.0
usu parser/writer
License: MIT
Requires Python: >=3.8

# usu-py

WIP `usu` parser/~~writer~~ for `python`

See [`test/pyproject.usu`](https://github.com/usu-dev/usu-py/blob/main/tests/pyproject.usu)for an example.

<hr>
<a href="https://github.com/usu-dev">
<img src="https://raw.githubusercontent.com/usu-dev/usu/main/assets/footer.svg" width=250>
</a>
sqlalchemy-airtable 0.0.1.dev3
Python DB-API and SQLAlchemy interface for Airtable.
License: MIT

# airtable-db-api [![PyPI version](https://badge.fury.io/py/sqlalchemy-airtable.svg)](https://badge.fury.io/py/sqlalchemy-airtable) ![main workflow](https://github.com/cancan101/airtable-db-api/actions/workflows/main.yml/badge.svg) [![codecov](https://codecov.io/gh/cancan101/airtable-db-api/branch/main/graph/badge.svg?token=S8XR68NZCU)](https://codecov.io/gh/cancan101/airtable-db-api)

A Python DB API 2.0 for Airtable

This module allows you to query Airtable using SQL. It exposes:

- a [Python DB API 2.0](https://peps.python.org/pep-0249/) (per PEP 249)
- a [SQLAlchemy Dialect](https://docs.sqlalchemy.org/en/14/dialects/) (see also ["Developing new Dialects"](https://github.com/zzzeek/sqlalchemy/blob/master/README.dialects.rst))
- a [Superset Engine Spec](https://preset.io/blog/building-database-connector/)

## SQLAlchemy support

This module provides a SQLAlchemy dialect.

```python
from sqlalchemy.engine import create_engine

engine = create_engine(
    'airtable://:keyXXXX@appYYY?peek_rows=10&tables=tableA&tables=tableB',
    date_columns={"tableA": ["My Date Field"]},
)
```

## Metadata

At various points we need to know:

1. The list of Tables supported in the Base
2. The list of columns (Fields) supported on a given Table
3. The type information for each Field

As of now we solve 1) by passing in a list of Tables using the `tables` query parameter on the URL.
We solve 2) and 3) using some combination of the `peek_rows` query parameter specifying the number of rows to fetch from Airtable to guess Field types and a `date_columns` engine parameter to specify which columns should be parsed as `Date`s.

Alternatively, 1-3 could all be solved with a comprehensive `base_metadata` engine parameter that specifies the Tables and Fields. There are a number of ways to generate this, but one approach is scraping the Base's API docs page using [a technique like this](https://github.com/aivantg/airtable-schema-generator/issues/47#issue-1165801153).

Further options are [documented here](https://github.com/cancan101/airtable-db-api/wiki/Metadata)

## Development

### Python

```bash
$ pip install -r requirements-dev.txt
```

### `pre-commit`

```bash
$ pre-commit install
```

### `black`

Can be run manually as:

```bash
black --target-version py37
```

## Roadmap

- [ ] Support for [Airtable's Metadata API](https://airtable.com/api/meta)
- [ ] Support passed in Airtable Metadata (w/ types)
- [ ] Cleanup configuration (passed as [query param on URL](https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls) vs [engine parameters](https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine))
- [ ] Built in Metadata scraper (not using Metadata API)
- [ ] Caching of field type "peeking"
- [ ] Datetime support
- [ ] More comprehensive testing