semvec/pypi-packages huggingface dataset:
fastrequest 1.6.3b0
fastrequest fast http requests extension
Requires Python: >=3

# fastrequest
A Python C extension module for fast HTTP requests with SSL/TLS support

## Installation
To build and install the extension, you just need to run the `install.sh` script. 
The script then installs required packages, builds the extension using `setuptools` 
and then installs it using `pip`.

```shell
chmod +x install.sh
./install.sh
```

## Example Usage
Just import `fastrequest` module. It's usage is as easy as ABC!

```python
import fastrequest.http

res = fastrequest.http.get("https://www.google.com")
res.save_to("index.html")
```

Also, JSON (`application/json`) responses can be handled easily too.

```python
import json
import fastrequest.http

res = fastrequest.http.get("https://jsonplaceholder.typicode.com/comments?postId=1")

with open("data.json", "w") as fp:
  json.dump(fp, res.json())
 ```

## Module Reference

### HTTPResponse

HTTPResponse is the generic HTTP/HTTPS response object returned from the GET/POST functions.

Pythonic definition of HTTPResponse is as follows,

```Python
class HTTPResponse(object):
  def __init__(self, data, size, url=""):
    self.data = data
    self.size = size
    self.url = url

  def text(self):
    return self.data.decode()

  def json(self):
    return json.loads(self.text())
```

### http.get

`http.get(url: str[, headers:dict]) -> HTTPResponse`

HTTP/GET request function using `libcurl` as backend.

## API Reference

### HTTPResponse

HTTPResponse is the generic HTTP/HTTPS response object returned from the GET/POST functions.

The data structure of HTTPResponse object is as follows,

```C
typedef struct {
    PyObject_HEAD
    PyObject *response_size;
    PyObject *response_data;
    PyObject *request_url;
} HTTPResponseObject;
```

Where
* `response_size` is byte-size of the response body (PyLong)
* `response_body` is the response body (PyBytes)
* `request_url` is the requested URI (PyUnicode)

### StringBuffer

StringBuffer is the generic 'string' object implementation to be used in Fast Request API functions.

The data structure of StringBuffer object is as follows,

```C
typedef struct {
    char *buf;
    int_fast64_t index;
    int_fast64_t size;
} StringBuffer;
```

### FastRequestAPI_LibcurlHttpGet

`StringBuffer *FastRequestAPI_LibcurlHttpGet(char *url, PyObject *headers)`

Send HTTP/GET request to machine with the `url` specified with optional custom `headers`. This function uses `libcurl` as the backend.
vk-bots 0.5
VK API wrapper
Requires Python: >=3.6

# VKBotsAPI
[![](https://img.shields.io/badge/VK-termisaal-4a76a8)](https://vk.com/termisaal)
Библиотека для работы с событиями из сообщества ВКонтакте в режиме реального времени
hello-rajnish-307-2020 0.0.1
Say Hello....

#Hello World
This is an example project demonstrating how to publish a python module to PyPI.

##Installation
Run the following to install:
```python pip install hello_world```

##Usage
```python from hello_world import say_hello

#Generate "Hello World"
say_hello()
#Generate "Hello Raj"
say_hello("Raj)
```

#Developing Hello World
To install helloworld, along with the tools you need to develop and run tests, run the following in your virtualenv:
```bash
$ pip install -e .[dev]
```
nss3 4.5.16
Simple and fast database operation

NSS3 is a database operation module made by Nanshan team.
It is simple and fast to use nssql.
The module can create database, create list, read database, change database, associate operation, import and export.
Functions included in this module: 
new_sql(sql_name='default_sql',password='None',existet=False): create a new database 
cursor(name=1): create cursor 
new_list(sql_name='default_sql',list_name='list0',existet=False,password='None'): create list 
new_position(sql_name='default_sql',list_name='list0',position_name='position0',data='NanShanSQL',password='None'): create location and add data 
new_position_file(sql_name='default_sql',list_name='list0',position_name='position2',file='File',password='None'):Add files to a position
open_position_file(sql_name='default_sql',list_name='list0',position_name='position2',password='None'):Open a file in a position
del_position(sql_name='default_sql',obj='default_sql\\list1\\position0',password='None'):Delete a position
close_sql(sql='default_sql'): close the database to open a new database 
open_sql(sql='default_sql',password='None'): get the contents of the database 
change_sql(sql_name='default_sql',list_name='list0',position_name='position0',new_data='NanShanSQL',password='None'): change the contents of the database 
change_password(sql='default_sql',new_password='None'): if there is a password, this function can change the password 
new_relation(sql='default_sql',obj1='default_sql\\list0\\position0',obj2='default_sql\\list1\\position0',password='None'): create a new relation 
open_relation(sql='default_sql',obj='default_sql\\list0\\position0',password='None'): open the data corresponding to the Association 
del_relation(sql='default_sql',obj='default_sql\\list0\\position0',password='None'):Delete a relation
del_sql(sql_name='default_sql',password='None'): delete database 
export_sql(ea='nssea',sql_name='default_sql',file_position='D:\\',password='None'): export the database to a file 
import_sql(file='D:\\default_sql.nss',sql_name='default_sql',existet=False,password='None'): import the database file 
open_sql_set():Get which databases in the database set
del_every_sql(): delete all databases
nf-batch-runner 0.1.13
Installs nf-batch-runner in your AWS account
Requires Python: >=3
Keywords: aws batch nextflow bioinformatics pipeline

# nf-batch-runner

nf-batch-runner is a tool which makes it easier to run Nextflow pipelines in your AWS account.

*This tool is not endorsed by Nextflow or AWS*

Using AWS Batch, you can run pipelines in the cloud which scale up and down quickly so that 
you don't pay for servers to sit idle.

## How it works

nf-batch-runner pulls down an AWS CloudFormation template and deploys it into your AWS account.  This
creates resources which are needed to run a Nextflow pipeline in AWS.

It also creates an optional web frontend so that you can starts pipelines from your web browser.

You can [find out more](https://gitlab.com/cgps/nf-batch-runner) in our README.

## Usage

Create an AWS account and create credentials for an Admin user.  It may be posssible to run this tool
with lower priviledges but it needs to create IAM resources so it won't help a lot.

Setup your default credentials as if you were going to [use the AWS cli](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html).

This tool has been tested running from OSX and Windows machines with Python 3.  Linux is probably also fine.

```
$ pip install nf-batch-runner
$ nf-batch-runner create my-environment
Please give us a password you will use to login to the frontend
Password:
Repeat:
Are you happy to install nf-batch-runner in account 012345678910 [y/N]: y
Does you user (ben) have administrator priviledges [y/N]: y
Do you want to deploy the web interface [Y/n]:
Fetching the latest version (v0.0.1)
Downloading the infrastructure template ✓
Waiting for the S3 bucket for the frontend (takes around a minute) ✓
...
Waiting for the users database (takes around a minute) ✓
Creating the users in the database ✓
It can take 15-20 minutes to create your envionment
  [###############---------------------]   42%  00:12:51
Visit https://d1q83rcyz9fxwf.cloudfront.net and login with the user 'admin'
You can upload your files here: https://s3.console.aws.amazon.com/s3/buckets/bt5-cli-test-filesbucket-4t1whgy2vc16/
```

You will be asked to create a password for the web interface and to confirm that you're happy for us to proceed.

After 15-20 minute the command will complete and it will give you a URL to log into the frontend and
a URL to upload files for analysis.

*More details to come*