semvec/pypi-packages huggingface dataset:
vk-urls-validator 0.0.4
Validator for VK URLs.
Keywords: python,vk urls,validation

# vk_urls_validator

Library for validating VK urls and brings them to one standart.

# How method works?

1. Add prefix "https://"
2. Change all hostnames to "vk.com"
3. Check URL hash for VK rules


# Examples

```python
from vk_urls_validator import validate_url

validated_url = validate_url(url='vk.com/id1')
# >> https://vk.com/id1

validated_url = validate_url(url='https://m.vk.com/id1')
# >> https://vk.com/id1
```


# Installing

```commandline
pip3 install vk_urls_validator
```
userapiLib 0.0.2
With This Package Can You GET Info Username And Checking
Keywords: telegram,username,checker,fragment,api,telegram-username


# UserApilib [![Version](https://img.shields.io/pypi/v/UserApilib?style=flat&logo=pypi)](https://pypi.org/project/UserAPilib) 
### UserApilib
```
This Semple Pkg For Telegram UserNames
Advantages :
    Check-UserName . Get-Info-UserName 

```
### Features
- **Easy** and **Fast**

### Requirements

- Python 3.9+
- Fragment [NFT - AUCTION](https://fragment.com)
- [telegram](https://telegram.org)

### Installation

You can install UserApiLib using pip:
```bash
pip install userapilib
```
To install the development version from Github, use the following command:
```bash
pip install git+https://github.com/muamelameer/userapi/userapilib.git
```

### Examples
Basic example:
```python

from userapilib import *

print(check().Nft(user)) #To Check If UserName Is 'NFT' Or not, Result >>> True - False

print(check().Fragment(user)) #To Check If UserName Is 'Fragment.com' Or not, Result >>> True - False

print(check().Fragment(user,proxy='123.123.123')) #Check If UserName Is 'Fragment.com' Or not 'With Proxis!', Result >>> True - False

print(info().auction(user))  #To Get Information UserName If in 'Fragment.com' , Result >>> Json Data

print(info().fragment(user)) #To Get Status UserName in 'Fragment.com' , Result >>> Onauction - Sold - For Sell - ect...

print(info().fragment(user,proxy='123.123.123')) #To Get Status UserName in 'Fragment.com' With Proxis! , Result >>> Onauction - Sold - For Sell - ect...

print(info().Nft(user)) #To Get Information UserName If is 'NFT' , Result >>> Json Data



```
For examples, check the [examples](https://github.com/muamelameer/userapi/test.py).

# Thanks to
- You for viewing or using this project.

- [@MuamelAmeer](https://github.com/muamelameer)
# License

MIT [License](https://github.com/muamelameer/userapi/blob/main/LICENSE)
modelfree-protein15n 0.0.7
Model free analysis of protein backbone amide 15N spin relaxation rates.

# modelfree-protein15n
Model-Free analysis framework for protein backbone amide 15N NMR spin relaxation rates.

This tool fits the relaxation data to a multi-Lorentzian spectral density function.
One can choose the number of Lorentzians (dynamic modes) for the fit.
Typically, one can perform 1, 2, and 3 dynamic mode MF analysis and see which model is most relevant for the data.
IMPACT analysis is also possible by fixing the correlation times and fitting the amplitudes.

## Relevant litterature

Lipari & Szabo, Journal of the American Chemical Society (1982);
Halle, The Journal of chemical physics (2009);
Khan et al., Biophysical journal (2015)

# installation

    $ pip install modelfree-protein15n

# usage

The program is able to generate random relaxation data with the command 'modfree generate'.
The program is able to fit relaxation data to a model with the command 'modfree fit'.
The program is able to plot the results with the command 'modfree plot'.

## Data generation

To generate relaxation data, type the following command:

    $ modfree generate
    
The following flags are available:
-o (str): Output directory containing the generated data.
-modes (int): number of dynamic modes used to generate the data
-n (int): number of residues in the data
-noise (float): between 0 and 1, indicates the proportion of noise to put in the data. (0.03 by default)
-fields (list, int or float): Magnetic fields used for the rate generation in MHz.
-rates: relaxation rates to generate. by default R1, R2, NOE, etaXY.

For example, you can type:

    $ modfree generate -o Generated -modes 2 -n 70 -noise 0.05 -fields 600 700 850 950 1200 -rates R1 R2 NOE etaXY etaZ
    
## Data fitting

To fit relaxation data, you will need your data in a specific format akin to the generated data. Generate some data to see how it's done. You will also need a directory file and a parameter file. type the following command to fit the data generated in the previous section:

    $ modfree fit -o Generated_fit -d Generated/directories.toml -p Generated/parameters.toml
    
You can also fit only part of the data with the flag -r:

    $ modfree fit -o Generated_fit -d Generated/directories.toml -p Generated/parameters.toml -r 10 11 12 13 14 15 16 17
    
Or

    $ modfree fit -o Generated_fit -d Generated/directories.toml -p Generated/parameters.toml -r 15

## Data plotting

To plot the fitted data, just type:

    $ modfree plot -o Generated_fit -p all

The following flags are available:
-o (str): Output directory containing the data.
-p: What to plot (all, relaxation, parameters, statistics, correlation)
-format: Format of the plot files (pdf, png, jpg, svg...)
-dpi (600 by default)
dsr-agent 0.0.5
A agent helper support send data out the service
Requires Python: >=3.10

### Generate gRPC code
```
python -m grpc_tools.protoc -I./protos --python_out=. --pyi_out=. --grpc_python_out=. ./protos/dsr_agent.proto
```

### Build Package
```
python -m build
```


### Upload Package
```
py -m twine upload dist/*
```