semvec/pypi-packages huggingface dataset:
influxdbpusher 0.1.3
Minimal and smart pusher of samples to InfluxDB for asyncio programs

influxdbpusher
==============

.. image:: https://img.shields.io/pypi/v/influxdbpusher.svg
    :target: https://pypi.python.org/pypi/influxdbpusher
    :alt: Latest PyPI version

Minimal and smart pusher of samples to InfluxDB for asyncio programs

Usage
-----

1. Create a ``InfluxDbPusher`` object
2. Call it to push samples
3. ``InfluxDbPusher`` will try to do intelligent aggregation of samples in the
   background to minimise the number of HTTP request to the InfluxDb server.

Example:

.. code-block:: python

	import asyncio
	import logging
	from influxdbpusher import InfluxDbPusher


	async def test():
	    logging.basicConfig(level=logging.DEBUG)
	    influx = InfluxDbPusher("http://influxdb:8086", "playground")
	    while True:
	        for dummy in range(10):
	            await asyncio.sleep(0.02)
	            influx("test", dummy, {"foo": "bar"})
	            influx("measurement1",
	                   {"fieldname1": 'hello "world"', "value": 2.0},
	                   {"foo": "bar"})
	        await asyncio.sleep(5)
	    await influx.close()


	if __name__ == '__main__':
	    asyncio.get_event_loop().run_until_complete(test())


Installation
------------
.. code-block:: bash

	pip install influxdbpusher


Requirements
^^^^^^^^^^^^

- Python >= 3.5
- aiohttp

Compatibility
-------------

Licence
-------

MIT License

Copyright (c) 2017, Gambit Research

Authors
-------

`influxdbpusher` was written by `Gustavo Carneiro <gjcarneiro@gmail.com>`_.
ezdea 0.0.4
DEA - python interface

# Ezdea promove facilidade ao usar DEA com Python

## Como usar:

```python
# crie a situação a ser analisada. No nosso caso será a seguinte matriz:
from ezdea import DEA
import pandas as pd

matriz = [[20, 151, 100, 90],
          [19, 131, 150, 50],
          [25, 160, 160, 55],
          [27, 168, 180, 72],
          [22, 158, 94, 66],
          [55, 255, 230, 90],
          [33, 235, 220, 88],
          [31, 206, 152, 80],
          [30, 244, 290, 100],
          [50, 268, 250, 100],
          [53, 306, 262, 147],
          [38, 284, 250, 120]]
          
# declare as entradas e saídas do problema
numero_inputs = 2
numero_outputs = 2

# instancie o cenário
cenario = DEA(matriz, numero_inputs, numero_outputs)
resultado_bcc = cenario.bcc_dual_input(matrix=True) # matrix = True força que o retorno seja matriz e não json
df_bcc_dual = pd.DataFrame(resultado_bcc)
print(df_bcc_dual)
metas = cenario.calcular_metas(resultado_bcc)
df_metas = pd.DataFrame(metas)
print(df_metas)

```

## Lista de métodos:

* DEA.ccr_primal_input(matrix=False)

* DEA.ccr_primal_output(matrix=False)

* DEA.ccr_dual_input(matrix=False)

* DEA.ccr_dual_output(matrix=False)

* DEA.bcc_primal_input(matrix=False)

* DEA.bcc_primal_output(matrix=False)

* DEA.bcc_dual_input(matrix=False)

* DEA.bcc_dual_output(matrix=False)

* DEA.calcular_metas(matriz_dual)

# próximo passo: subir para o PyPi