semvec/pypi-packages huggingface dataset:
img-convert 0.0.1
Projeto para conversao de imagem RGB.
License: MIT

# img-convert

Este pacote Ã© usado para converter uma imagem RGB para Pecil Sketch.
	
## InstalaÃ§Ã£o

Use o gerenciador de pacotes [pip](https://pip.pypa.io/en/stable/) para instalar este pacote.

```bash
pip install img_convert
```

## Uso

```python
from img_convert.utils.io import ConvertImg
image_path = ''

img = ConvertImg()
img.to_pencil_sketch(image_path)
```

## Autor
Thiago Oliveira

## License
[MIT](https://choosealicense.com/licenses/mit/)
anngtf 0.0.11
anngtf - lift annotations from a `.gtf` file to your AnnData object.
License: MIT
Requires Python: >3.6.0

# anngtf

[![PyPI pyversions](https://img.shields.io/pypi/pyversions/licorice.svg)](https://pypi.python.org/pypi/anngtf/)
[![PyPI version](https://badge.fury.io/py/anngtf.svg)](https://badge.fury.io/py/anngtf)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

Lift annotations from a **[`gtf`](https://en.wikipedia.org/wiki/Gene_transfer_format)** to your **[`adata`](https://anndata.readthedocs.io/en/stable/)** object. 

### Installation

To install via [pip](https://pypi.org/project/anngtf):
```BASH
pip install anngtf
```

To install the development version: 
```BASH
git clone https://github.com/mvinyard/anngtf.git

cd anngtf; pip install -e .
```

## Example usage

### Parsing a `.gtf` file
```python
import anngtf

gtf_filepath = "/path/to/ref/hg38/refdata-cellranger-arc-GRCh38-2020-A-2.0.0/genes/genes.gtf"

```

If this is your first time using `anngtf`, run:
```python
gtf = anngtf.parse(path=gtf_filepath, genes=False, force=False, return_gtf=True)
```
Running this function will create two `.csv` files from the given `.gtf` files - one containing all feature types and one containing only genes. Both of these files are smaller than a `.gtf` and can be loaded into memory much faster using `pandas.read_csv()` (shortcut implemented in the next function). Additionally, this function leaves a paper trail for `anngtf` to find the newly-created `.csv` files again in the future such that one does not need to pass a path to the gtf. 

In the scenario in which you've already run the above function, run:
```python
gtf = anngtf.load() # no path necessary! 
```

### Updating the `adata.var` table. 
```python
import anndata as a
import anngtf

adata = anndata.read_h5ad("/path/to/singlecell/data/adata.h5ad")
gtf = anngtf.load(genes=True)

anngtf.add(adata, gtf)
```

Since the `anngtf` distribution already knows where the `.csv / .gtf` files are, we could directly annotate `adata` without first specifcying `gtf` as a DataFrame, saving a step but I think it's more user-friendly to see what each one looks like, first. 


### Working advantage

Let's take a look at the time difference of loading a `.gtf` into memory as a `pandas.DataFrame`: 

```python
import anngtf
import gtfparse
import time

start = time.time()
gtf = gtfparse.read_gtf("/home/mvinyard/ref/hg38/refdata-cellranger-arc-GRCh38-2020-A-2.0.0/genes/genes.gtf")
stop = time.time()

print("baseline loading time: {:.2f}s".format(stop - start), end='\n\n')

start = time.time()
gtf = anngtf.load()
stop = time.time()

print("anngtf loading time: {:.2f}s".format(stop - start))
```
```
baseline loading time: 87.54s

anngtf loading time: 12.46s
```
~ 7x speed improvement. 

* **Note**: This is not meant to criticize or comment on anything related to [`gtfparse`](https://github.com/openvax/gtfparse) - in fact, this library relies solely on `gtfparse` for the actual parsing of a `.gtf` file into memory as `pandas.DataFrame`.
akismet-async 0.0.1
An async Python interface to the Akismet API.
License: MIT
Requires Python: >=3.7,<4.0

akismet-async
=========

An asyncronous Python 3 Akismet client library.

## Installation
```
pip install akismet-async
```

## API key verification
Get your Akismet API key [here](http://akismet.com/plans/).
```python
from akismet import Akismet, Comment

akismet_client = Akismet(api_key="YOUR_AKISMET_API_KEY" blog="http://your.blog/",
                user_agent="My App/1.0.0")

await akismet_client.verify_key()
```

## Example usage
You can check a comment's spam score by creating a dictionary or a `Comment()` object
for greater type safety:
```python
from akismet import Akismet, Comment

akismet_client = Akismet(api_key="YOUR_AKISMET_API_KEY" blog="http://your.blog/",
                user_agent="My App/1.0.0")

comment = Comment(
    comment_content="This is the body of the comment",
    user_ip="127.0.0.1",
    user_agent="some-user-agent",
    referrer="unknown"
)

first_spam_status = await akismet_client.check(comment)

second_spam_status = await akismet_client.check(
    {
        "user_ip": "127.0.0.2",
        "user_agent": "another-user-agent",
        "referrer": "unknown",
        "comment_content": "This is the body of another comment",
        "comment_author": 'John Doe',
        "is_test": True,
    }
)
```
`check()` returns one of the following strings:
* `ham`
* `probable_spam`
* `definite_spam`
* `unknown`

### Submit Ham
If you have determined that a reported comment is not spam, you can report
the false positive to Akismet:
```python
await akismet_client.submit_ham(comment)
```

### Submit Spam
If a spam comment passes the Akismet check, report it to Akismet:
```python
await akismet_client.submit_spam(comment)
```
twitch-info 2.4.5
Get information about a twitch channels and streams
License: MIT
Keywords: python,stream,twitch,channel,information,info

# Twitch Informations

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)
![GitHub repo size](https://img.shields.io/github/repo-size/feytus/twitch-info?style=for-the-badge&logo=appveyor)
[![License](https://img.shields.io/github/license/feytus/twitch-info?style=for-the-badge)](https://github.com/feytus/twitch-info/blob/master/LICENSE)

Get informations about a **streamer** from **twitch.tv**.

## Install the package

``pip install twitch-info``

## Exemple

```python
from twitch_info import get_user_id, get_stream, get_access_token

# To get client_id and client_secret -> https://youtu.be/JK06TumS6ho

client_id = "<CLIENT ID>"
client_secret = "<CLIENT SECRET>" 


acces_token = get_access_token(client_id=client_id, client_secret=client_secret)

user_id = get_user_id(user_name="<CHANNEL_NAME>", client_id=client_id, acces_token=acces_token)

stream = get_stream(user_id=user_id, client_id=client_id, acces_token=acces_token)
```