semvec/pypi-packages huggingface dataset:
LCU-Connector 1.0.1
Easy-to-use wrapper for the League Client API.
License: MIT
Keywords: league client,league client api,league client api wrapper,api wrapperleague of legends,league of legends api,lcu-driver,lcu driver,lcu-connector,lcu connector

# LCU Connector

This library serves to make the connection with the League Client API in a simple way, although there are others, such as [lcu-driver](https://github.com/sousa-andre/lcu-driver) (which by the way is very good), but with lcu-driver, for example, I couldn't work and structure my code the way I wanted it to, so I decided to make my own wrapper.


## Quick start
I will be brief in the explanation but I guarantee that it will be enough, as this library is simple to use. Among other things, I'm working on more elaborate documentation.

So, hand in the dough! Or rather, on the keyboard...


### Installation
Just use pip and it's fine.
```powershell
pip install lcu-connector
```

### How to use
Import the `Connector` from the `lcu_connector` module, instantiate it and start the session via the `.start()` method or passing the `start=True` parameter directly in the instance.
```python
from lcu_connector import Connector

# Method 01
conn = Connector(start=True)

# Method 02
conn = Connector()
conn.start()
```

Now have fun, the `Connector` object has the same attributes as the `requests` library and derivatives.
```python
from lcu_connector import Connector

conn = Connector(start=True)

# Getting the data of the currently connected summoner
res = conn.get('/lol-summoner/v1/current-summoner')
print(res.json())

# Getting a summoner's data by name
summoner_name = 'JohnDoe'
res = conn.get('/lol-summoner/v1/summoners?name={summoner_name}')
print(res.json())

# Performing POST request
data = {
    'foo': 'bar'
}
res = conn.post('API_URL', data=data)
if res.status_code == 200:
    do_something()
```

VocÃª pode ver todos os links disponÃ­veis para o League Client atravÃ©s do [LCU Explorer](https://github.com/HextechDocs/lcu-explorer).


## To-do
- [ ] Observador de eventos da API
- [ ] FunÃ§Ãµes integradas para tarefas mais utilizadas (como um get_summoner_by_name())
- [ ] DocumentaÃ§Ã£o mais detalhada
locate-pixelcolor-numbacuda 0.12
RGB search with numba.cuda - 10 x faster than numpy
License: MIT
Keywords: numpy,numba,CUDA,rgb,search

# RGB search with numba.cuda - 10 x faster than numpy 

### pip install locate-pixelcolor-numbacuda


```python
from locate_pixelcolor_numbacuda import search_colors
from a_cv_imwrite_imread_plus import open_image_in_cv
import numpy as np
colors=[(66,  71,  69),(62,  67,  65),(144, 155, 153),(52,  57,  55),(127, 138, 136),(53,  58,  56),(51,  56,  54),(32,  27,  18),(24,  17,   8),]
```

#### image'https://www.pexels.com/pt-br/foto/foto-da-raposa-sentada-no-chao-2295744/'

```python
picnp = open_image_in_cv('pexels-alex-andrews-2295744.jpg',channels_in_output=3)
coords=search_colors(pic=picnp,colors=colors,threadsperblock=(18, 18,3),dtypetouse = np.int32)
print(coords)
%timeit search_colors(pic=picnp,colors=colors,threadsperblock=(18, 18,3),dtypetouse = np.int32)


# [[[  19   14]
#   [  19   14]
#   [  11   17]
#   ...
#   [6613 4524]
#   [6614 4524]
#   [6615 4524]]]
# 135 ms Â± 3.5 ms per loop (mean Â± std. dev. of 7 runs, 10 loops each)

# More benchmarks: https://github.com/hansalemaos/locate_pixelcolor_cpp

```
locate-pixelcolor-numba 0.11
RGB search with numba - 2-3 x faster than numpy
License: MIT
Keywords: numba,aot,compiled,rgb,search

# RGB search with numba - 2-3 x faster than numpy 

### pip install locate-pixelcolor-numba


### Important!
This is a compiled .pyd file (Numba AOT), if you can't import it, run the following code to generate a new pyd file, and replace it with the old .pyd file.


```python
from numba_aot_compiler import compnumba #pip install numba-aot-compiler
import numpy as np
from numba import uint8, uint16


def search_colors(r, g, b, rgbs, divider):
    res = np.zeros(b.shape, dtype=np.uint16)
    res2 = np.zeros(b.shape, dtype=np.uint16)
    zaehler = 0
    for rgb in rgbs:
        rr, gg, bb = rgb
        for i in range(r.shape[0]):
            if r[i] == rr:
                if g[i] == gg:
                    if b[i] == bb:
                        dvquot, dvrem = divmod(i, divider)
                        res[zaehler] = dvquot
                        res2[zaehler] = dvrem
                        zaehler = zaehler + 1
    results = np.dstack((res[:zaehler], res2[:zaehler]))
    return results


compi2 = compnumba(
    fu=search_colors,
    funcname="search_colors",
    file="searchcolorsnumba",
    folder="locate_pixelcolor_numba",
    signature=(uint8[:], uint8[:], uint8[:], uint8[:, :], uint16),
    parallel=True,
    fastmath=True,
    nogil=True,
)
```

### How to use it

```python
from locate_pixelcolor_numba import search_colors
import cv2
import time
import numpy as np
pic = cv2.imread(r"pexels-alex-andrews-2295744.jpg") # https://www.pexels.com/pt-br/foto/foto-da-raposa-sentada-no-chao-2295744/
colors = np.array([(66,  71,  69),(62,  67,  65),(144, 155, 153),(52,  57,  55),(127, 138, 136),(53,  58,  56),(51,  56,  54),(32,  27,  18),(24,  17,   8),],dtype=np.uint8)
search_colors(pic,colors)
Out[2]: 
array([[[   0, 4522],
        [   3, 4522],
        [   3, 4523],
        ...,
        [6622, 4522],
        [6622, 4523],
        [6622, 4524]]], dtype=uint16)
%timeit search_colors(pic,colors)
413 ms Â± 1.22 ms per loop (mean Â± std. dev. of 7 runs, 1 loop each)

# More benchmarks: https://github.com/hansalemaos/locate_pixelcolor_cpp

```
locate-pixelcolor-cupy 0.11
Detects colors in images up to 8 times as fast as NumPy
License: MIT
Keywords: Cupy,image,search,rgb

# Detects colors in images up to 8 times as fast as NumPy  

### pip install locate-pixelcolor-cupy
If you haven't installed cupy yet, I recommend you installing it using conda:
conda install -c conda-forge cupy

#### Tested against Windows 10 / Python 3.10 / Anaconda



### Usage

```python

import numpy as np
import cv2
from locate_pixelcolor_cupy import search_colors
# 4525 x 6623 x 3 picture https://www.pexels.com/pt-br/foto/foto-da-raposa-sentada-no-chao-2295744/
picx = r"C:\Users\hansc\Downloads\pexels-alex-andrews-2295744.jpg"
pic = cv2.imread(picx)
colors0 = np.array([[255, 255, 255]], dtype=np.uint8)
resus0 = search_colors(pic=pic, colors=colors0)
colors1 = np.array(
    [
        (66, 71, 69),
        (62, 67, 65),
        (144, 155, 153),
        (52, 57, 55),
        (127, 138, 136),
        (53, 58, 56),
        (51, 56, 54),
        (32, 27, 18),
        (24, 17, 8),
    ],
    dtype=np.uint8,
)
resus1 = search_colors(pic=pic, colors=colors1)
####################################################################
%timeit resus0 = search_colors(pic=pic, colors=colors0)
78.2 ms Â± 1.29 ms per loop (mean Â± std. dev. of 7 runs, 1 loop each)

b,g,r = pic[...,0],pic[...,1],pic[...,2]
%timeit np.where(((b==255)&(g==255)&(r==255)))
150 ms Â± 209 Âµs per loop (mean Â± std. dev. of 7 runs, 10 loops each)
####################################################################
%timeit resus1 = search_colors(pic=pic, colors=colors1)
139 ms Â± 9.78 ms per loop (mean Â± std. dev. of 7 runs, 1 loop each)

%timeit np.where(((b==66)&(g==71)&(r==69))|((b==62)&(g==67)&(r==65))|((b==144)&(g==155)&(r==153))|((b==52)&(g==57)&(r==55))|((b==127)&(g==138)&(r==136))|((b==53)&(g==58)&(r==56))|((b==51)&(g==56)&(r==54))|((b==32)&(g==27)&(r==18))|((b==24)&(g==17)&(r==8)))
1 s Â± 16.1 ms per loop (mean Â± std. dev. of 7 runs, 1 loop each)
####################################################################
```
pinax-invitations 8.0.0
a user to user join invitations app
License: MIT


.. image:: http://pinaxproject.com/pinax-design/patches/pinax-invitations.svg
    :target: https://pypi.python.org/pypi/pinax-invitations/

=================
Pinax Invitations
=================

.. image:: https://img.shields.io/pypi/v/pinax-invitations.svg
    :target: https://pypi.python.org/pypi/pinax-invitations/


.. image:: https://img.shields.io/circleci/project/github/pinax/pinax-invitations.svg
    :target: https://circleci.com/gh/pinax/pinax-invitations
.. image:: https://img.shields.io/codecov/c/github/pinax/pinax-invitations.svg
    :target: https://codecov.io/gh/pinax/pinax-invitations
.. image:: https://img.shields.io/github/contributors/pinax/pinax-invitations.svg
    :target: https://github.com/pinax/pinax-invitations/graphs/contributors
.. image:: https://img.shields.io/github/issues-pr/pinax/pinax-invitations.svg
    :target: https://github.com/pinax/pinax-invitations/pulls
.. image:: https://img.shields.io/github/issues-pr-closed/pinax/pinax-invitations.svg
    :target: https://github.com/pinax/pinax-invitations/pulls?q=is%3Apr+is%3Aclosed


.. image:: http://slack.pinaxproject.com/badge.svg
    :target: http://slack.pinaxproject.com/
.. image:: https://img.shields.io/badge/license-MIT-blue.svg
    :target: https://opensource.org/licenses/MIT/


``pinax-invitations`` is a user to user join invitations app.

Supported Django and Python Versions
------------------------------------

+-----------------+-----+-----+-----+------+
| Django / Python | 3.7 | 3.8 | 3.9 | 3.10 |
+=================+=====+=====+=====+======+
|  3.2            |  *  |  *  |  *  |  *   |
+-----------------+-----+-----+-----+------+
|  4.0            |  *  |  *  |  *  |  *   |
+-----------------+-----+-----+-----+------+
|  4.1            |  *  |  *  |  *  |  *   |
+-----------------+-----+-----+-----+------+