semvec/pypi-packages huggingface dataset:
qtmodernb 0.3.2
Qt Widgets Modern User Interface
License: MIT

=========
qtmodernb
=========

.. image:: https://travis-ci.org/gmarull/qtmodern.svg?branch=master
    :target: https://travis-ci.org/gmarull/qtmodern
    :alt: Travis build

.. image:: https://img.shields.io/pypi/v/qtmodern.svg
    :target: https://pypi.python.org/pypi/qtmodern
    :alt: PyPI Version

``qtmodernb`` is a test of "qtmodern" package by gmarull, it
is a Python package aimed to make PyQt/PySide applications look
better and consistent on multiple platforms. It provides a custom frameless
window and a dark theme. In order to be compatible with multiple Python Qt
wrappers `QtPy <https://github.com/spyder-ide/qtpy>`_ is used. The initial idea
comes from `this project <https://github.com/Jorgen-VikingGod/Qt-Frameless-Window-DarkStyle>`_.

.. image:: examples/mainwindow.png
    :width: 450px
    :align: center
    :alt: Example

Installation
------------

The recommended way to install is by using ``pip``, i.e::

    pip install qtmodernb

Usage
-----

In order to use ``qtmodernb``, simply apply the style you want to your
application and then, create a ``ModernWindow`` enclosing the window you want to
*modernize*::

    import qtmodernb.styles
    import qtmodernb.windows

    ...

    app = QApplication()
    win = YourWindow()

    qtmodernb.styles.dark(app)
    mw = qtmodernb.windows.ModernWindow(win)
    mw.show()

    ...
mask-recognition 0.0.1
It recognize the face mask on human on the image
License: MIT
Requires Python: >=3.6
Keywords: Recognition,Face Mask,Neural Network,Pytorch

### Project Description

    Face Mask Recognition using PyTorch.

    It creates a bounding box around the face of the person present in the image and put a text
    at the top of the bounding box shows probability of mask on the face.

#


### Install:

    pip install mask_recognition

#

### Requirements:

pytorch >= 1.2.0

torchvision >= 0.3.0

#

### Usage:

    from mask_recognition import MaskRecognition

    import cv2 as cv

    mr = MaskRecognition(device=0)  # -1 for cpu, 0 for gpu

    frame = mr.recognize(frame, color="rgb")    # color="bgr" for BGR color scheme. frame is the RGB image.

    ########################## To use webcam ##########################

    mr.cam_capture(device_id=0)     # 0 for default or 1, 2, 3, if more than one webcam

    ## press esc to stop    
#
validCSV 0.0.1
A small and performance-oriented CSV header linter / verifier
License: MIT

#   validCSV

A small and performance-oriented CSV header verifier / linter with no external Python package dependencies. Just simple.

##  1.	Usage

```python
CSVheader(validator: dict, default: dict = None, ordered = 0, case = 0):
```

-   `ordered`: set `1` if the header need to ordered as the same as your validator. Set `O` otherwise. 
-   `case`: set `0` if for ignoring the letter case. `1` otherwise.
-   `validator`: A dictionary with a `header` key. The `header` should be an list that will represent your csv header

### 1.2.  	Verifier methods

```python
CSVheader.verify_header(file) # Uses all config combinations (ordered, case) used at instantiation;
```

```python
CSVheader.simple_verify_header(file,size = None, restrict: bool = True) # Simple leads to only check if the validator elements exists at CSV header; If size is defined, the verifier will check if the header have the length equal to size; restrict will invalidate headers with smaller lengths than your validator;
```

```python
CSVheader.simple_verify_optional_header(file,size = None, restrict: bool = False, options=[]) # It is similiar to simple_verify_header() but, have set optional header columns. If the header have the same validator elements and at least one optional element, the verifier will return true (passed);
```


### 1.3. 	Example	
```python

from validCSV import CSVheader

validHydroNodes = CSVheader(validator={'header': ["id","name","type","block","watersystem","tia","min_alt","max_alt"]})
if validHydroNodes.verify_header(files[filename]):
    # Do something
    print("Test 1 Passed!")

validQualityStations = CSVheader(validator={'header': ["id","snirh","latitude","longitude"]})
if validQualityStations.simple_verify_header(files[filename]):
    # Do something
    print("Test 2 Passed!")

validQualityData = CSVheader(validator={'header': ["date"]})
if validQualityData.simple_verify_optional_header(files[filename], options=['id','snirh']):
    # Do something
    print("Test 3 Passed!")

```

## 2.	Installation

` pip3 install validCSV `

### 2.1	Local installation

1. `git clone https://github.com/bmalbusca/validCSV.git`
2.  `cd validCSV`
3. `python3 setup.py bdist_wheel `
4. `pip3 install -e .  `

## 3. Dependencies

`python >= 3.0.0`

## 4. Test

### 4.1 `Setup.py`

- `python3 setup.py sdist `
- `check-manifest --create`
juungle 0.6.2
Juungle python package for juungle.net
License: MIT
Requires Python: >=3.6
Keywords: juungle.net api nfts

[![Python application](https://github.com/Juungle/juungle-python/actions/workflows/run_tests.yml/badge.svg)](https://github.com/Juungle/juungle-python/actions/workflows/run_tests.yml)
## NOTE: ALTHOUGH THIS PROJECT WORKS, IT IS STILL UNDER HEAVY DEVELOPMENT AND THINGS WILL CHANGE BEFORE v1.0


## juungle-python
Python package to access Juungle.net API

## Installation
`$ python -m pip install juungle`

## Running from source
```
$ git clone https://github.com/Juungle/juungle-python.git juungle
$ cd juungle
$ python -m pip -r requirements.txt
```
## User credentials for Juungle.net
### Using a config file
Create a file `user-config.ini` with juungle.net credentials
in the same directory of your code:
```
LOGIN_USERNAME="username@email"
LOGIN_PASSWORD="password"
```
### OR While creaing the object
Inside the code

```python
from juungle.nfts import NFTs

nfts = NFTs('username@email', 'password')
```
## Usage
### List all WAIFUs nfts that are being sale for 0.01 BCH or less
```python
from juungle.nft import NFTs

nfts = NFTs()

nfts.available_to_buy = True
nfts.purchased = False
nfts.token_group = 'WAIFU'

nfts.get_nfts()

for nft in nfts.list_nfts:
    if nft.price_bch <= 0.01:
        print(nft.token_name)
```

### List all my NFTs

```python
from juungle.nft import NFTS

nfts = NFTs()
nfts.get_my_nfts()

for nft in nfts.list_nfts:
    print(nft.token_name)
```

### List all my NFTs prices in USD and EUR
**To query prices we going to use coingecko api**

`pip install pycoingecko`

```python
from juungle.nft import NFTS
from pycoingecko import CoinGeckoAPI

nfts = NFTs()
nfts.get_my_nfts()

# Query price
cg = CoinGeckoAPI()

bch_price = cg.get_price(ids='bitcoin-cash',
            vs_currencies=['usd', 'eur'])['bitcoin-cash']

msg = "NFT: {} with price {} USD or {} EUR"

for nft in nfts.list_nfts:
    print(msg.format(nft.token_name, bch_price['usd'], bch_price['eur'])
```

## Tokens Group IDs
Because tokens/group name are not unique we have to use the HEX id that can be
found at the [simpleledger.info](https://simpleledger.info).

Juungle-python package provides a list of IDs just to make easier of the most
common toekns:

Token Name | Token ID
---------- | --------
WAIFU | [a2987562a405648a6c5622ed6c205fca6169faa8afeb96a994b48010bd186a66](https://simpleledger.info/token/a2987562a405648a6c5622ed6c205fca6169faa8afeb96a994b48010bd186a66)