semvec/pypi-packages huggingface dataset:
glib-log-bridge 0.0.1
Bridge Python and GLib logging facilities
Requires Python: >=3.5

Python ↔ GLib logger bridge
===========================

This library allows you to redirect either from the [python logger facility][python-logger] to the [glib logging facility][glib-logger], or the other way around.

[python-logger]: https://docs.python.org/3/library/logging.html
[glib-logger]: https://developer.gnome.org/glib/stable/glib-Message-Logging.html

**NOTE: THIS IS STILL IN DEVELOPMENT! IT MIGHT NOT WORK CORRECTLY, AND THE INTERFACE CAN CHANGE!**

Quick Usage
-----------

### GLib → Python
```python
from gi.repository import GLib
import glib_log_bridge.glib2python as glib2python
g2plog = glib2python.GLibToPythonLogger()
GLib.log_set_writer_func(g2plog.glibToPythonLogWriterFunc, None)
```

### Python → GLib
```python
import logging
import glib_log_bridge.python2glib as python2glib
handler = python2glib.PythonToGLibLoggerHandler()
logging.getLogger().addHandler(handler)
# Logger to apply, logger.getLogger() does it for all messages
```
exploratium-quant 0.0.3
ETL library to download and pre-process financial data.
Requires Python: >=3.6

# ETL library to download and pre-process financial data.

This tools process and enrichs data screpped from Yahoo Finance into a sets of CSV files. It was conceived as a CI component to generate static data for testing purposes.

## Installation

### Requirements

- `click`
- `blessed` 
- `pandas-datareader`

### PIP
```console
pip install exploratium-quant
```

## Usage

```console
Usage: quant-cli [OPTIONS]

Options:
  -t, --tickers TEXT   Tickers to analyse.
  -o, --out-dir PATH   Path to save results.
  --verbose / --quiet  Verbosity on/off.
  --help               Show this message and exit.
```

### Example

By calling:
```console
quant-cli -t "BTC-USD,SPY,ETH-USD,GME"
```

The tool will save a daily OHLC timeseries for the ticker along the following generated metrics:

- **ma7**: 7 days moving average
- **ma30**: 30 days moving average
- **ma90**: 90 days moving average
- **upper_bollinger_band**: Upper Bollinger Band
- **lower_bollinger_band**: Lower Bollinger Band
- **daily_returns**: Daily returns
- **sharpe**: Sharpe ratio
jimobama-events 0.0.1
C# like event handler (pubsub)
Requires Python: >=3.6

# messaging
A simple event handling package.

Almost all application are event driven, for instead , when a serial port is open you want to notify other objects that want to use it.
The module provide a clear interface like C# event handlers.
```
from events.event import Event;
from events.eventhandler import EventHandler;
def OnClicked(event):
    print(event.Type);
    
handler  = EventHandler();
handler += OnClicked;
handler(Event('SERIAL_OPEN');

```
events-jimobama 0.0.1
C# like event handler (pubsub)
Requires Python: >=3.6

# messaging
A simple event handling package.

Almost all application are event driven, for instead , when a serial port is open you want to notify other objects that want to use it.
The module provide a clear interface like C# event handlers.
```
from events.event import Event;
from events.eventhandler import EventHandler;
def OnClicked(event):
    print(event.Type);
    
handler  = EventHandler();
handler += OnClicked;
handler(Event('SERIAL_OPEN');

```
libpythonpro-lu1zibra 0.1
Criando esse repositório, como parte do curso Python Pro BR - Pytools

# Libpythonpro
Criando esse repositÃ³rio, como parte do curso Python Pro BR - Pytools

Nesse curso Ã© ensinado como contribuir com projetos de cÃ³digo aberto

Inserido o link do curso Python Pro [Python Pro](https://www.python.pro.br)

[![Build Status](https://travis-ci.org/lu1zibrahim/libpythonpro.svg?branch=main)](https://travis-ci.org/lu1zibrahim/libpythonpro)

Superta a versÃ£o 3 do Python(Utilizada python 3.9)
Para Instalar:


'''console
python -m venv venv
source venv/Scripts/activate
pip install - requirements-dev.txt
'''


Para conferir qualidade de cÃ³digo:


'''console
flake8
'''


[![Updates](https://pyup.io/repos/github/lu1zibrahim/libpythonpro/shield.svg)](https://pyup.io/repos/github/lu1zibrahim/libpythonpro/)

[![Python 3](https://pyup.io/repos/github/lu1zibrahim/libpythonpro/python-3-shield.svg)](https://pyup.io/repos/github/lu1zibrahim/libpythonpro/)



TÃ³picos a serem abordados:
1. Git
2. Virturalenv
3. Pip
enrichment-shim 0.0.3
A small package testing calling out to third party enrichment
Requires Python: >=3.6

# High level description

This tool will 
1. Take in a set of provided data, or use input.json in its place
2. Read in a set of available enrichment REST endpoints, with POST or GET structure defined
3. Read in a set of steps to follow, to call multiple enrichment providers in parallel, serial, percentage,
or fallback means
4. Read in mapping XML, which will take the input data (step 1) and convert it into what the various REST endpoints
need (step 2).
5. Read in outbound mapping XML, which will take the data that comes back from the enrichment tools (step 2) and map
it to data that is named what a customer would expect as output
6. Follows the steps defined (step 3), with the input data (step 1).
   - For each step, 
      - For each enrichment called, map the incoming data to what the rest call needs.  Call the rest interface
      - On failure, capture the failure in the results
      - On success, capture the data that comes back. Map it to the variable names as defined in step 5
7. Return back the set of data from each of the enrichment providers, remapped as requested, to the caller

# Requirements
* pip install pytz
* python -m pip install --upgrade pip
torchort 0.0.8.dev20210317
training Pytorch models with onnxruntime
Requires Python: >=3.6

# training pytorch models with onnxruntime

## to build (you need to update version number in version.txt in order to be able to upload python whl):
    rm dist/*
    python setup.py bdist_wheel

## to publish:
    twine upload dist/*

## to install:
### stable:
    (uninstall onnxruntime version that do not support gpu and training)
    pip install torchort onnxruntime=1.9.0+cu111_training

### nightly:
    (make sure you are not in the ort repo folder - otherwise torchort is taken as already installed)
    pip install --pre torchort ort-gpu-nightly-training

    (eventually we are aiming at: pip install --pre torchort onnxruntime=1.9.0+cu111_training)

## to test:
    python ./ort/tests/bert_for_sequence_classification.py
## to use torchort within PyTorch training scripts:
    import onnxruntime
    from torchort import ORTModule
    model = ORTModule(model)
    # normal PyTorch training script follows