semvec/pypi-packages huggingface dataset:
tcp-send-first-segment-last 0.0.5
Send the first TCP segment last for large payloads
Requires Python: >=3.8


# tcp-send-first-segment-last

## Purpose

Not all applications receive data off the network correctly, and they
can miss key chunks of data even when they don't mean to. As an example,
Python's [socket.recv](https://docs.python.org/3/library/socket.html#socket.socket.recv)
needs to be called repeatedly until all data has been received. But,
if the receiving application doesn't properly loop, then it may miss
key data later in the buffer.

This library ensures that all data will be in the receiving buffer
before the receiving application begins reading the data. It does
this by sending the TCP segments out of order; and in particular,
it does this by sending the first TCP segment last.

By sending the first TCP segment last, all the later segments will
already be waiting in the receiving buffer by the time that the
first segment arrives. Then, if the receiving application only
calls Python's `socket.recv` function once, it will still read
all of the intended data.

I first encountered this issue while working on the EXP-301 course
from OffSec (a course that focuses on exploiting applications
via buffer overflows). And I created this library to help solve
labs and challenges from this course.

## Usage

PyPi project: https://pypi.org/project/tcp-send-first-segment-last/

```bash
$ pip install tcp-send-first-segment-last
```

```python
from tcp_send_first_segment_last.send import send_first_segment_last


payload = b"A" * 5000

send_first_segment_last("127.0.0.1", 4444, payload)
```

## Current Implementation

* The current implementation waits 5 seconds before sending the final (first) segment, and waits another 5 seconds before closing the connection
* Payloads are currently broken up into 1000 byte chunks

## Known Drawbacks

* Only supports IP addresses, does not support hostnames
* Only runs on Linux/Unix
* Modifies 'iptables' to disable the default RST response from the OS when receiving a response from the target application
* Chunk size is not currently modifiable
* Wait time before sending the first packet is not modifiable
* Must be `root` to run
LookerDF 1.0.1
Simple get data from Looker API into Pandas dataframe
Requires Python: >=3.6

# LookerAPI Python Library

This Python library allows you to connect to Looker, run queries, and retrieve data as pandas DataFrames. This simplifies the process of interacting with Looker's API and makes it easier to analyze data using pandas.

## Installation

To use this library, you need to have the Looker SDK installed. You can install it using pip:

```sh
pip install looker-sdk
pip install pandas
```
# Usage
## Setup
1. Configuration:
    Create a configuration file (e.g., looker.ini) with your Looker API credentials. The configuration file should look something like this:

    ```ini
    [Looker]
    base_url=https://your.looker.instance
    client_id=your_client_id
    client_secret=your_client_secret
    ```

2. Initialize the Connection:
    ```python
    from LookerDF import Connect
    looker_ini_path = 'path_to_your_looker.ini'
    connection = Connect(looker_ini_path)
    ```

# Running Queries
1. Simple Query:
```python
from your_library_name import GetData

df = GetData.get_query(
    sdk=connection.sdk,
    model='your_model',
    view='your_view',
    sort_by='your_sort_field',
    sort_type='desc',
    limit=100,
    fields=['field1', 'field2'],
    filters={'field': 'value'}
)

print(df)
```
2. Look Query:
```python
df = GetData.get_look(
    sdk=connection.sdk,
    look_id=123,
    limit=100
)

print(df)
```
# Classes and Methods
## Connect Class
### Methods:
`__init__(looker_ini_path)`: Initializes the connection to Looker using the provided ini file.
`make_sdk(looker_ini_path)`: Creates the Looker SDK instance.

## GetData Class
### Static Methods:
`get_query(sdk, model, view, sort_by, sort_type='', is_total, fields, filters, limit=500)`: Runs a Looker query and returns the data as a pandas DataFrame.
`get_look(sdk, look_id, limit=500)`: Retrieves data from a Looker Look and returns it as a pandas DataFrame.

# Example
Here's a complete example of how to use the library:

```python
import pandas as pd
from your_library_name import Connect, GetData

# Initialize connection
looker_ini_path = 'path_to_your_looker.ini'
connection = Connect(looker_ini_path)

# Run a query
df = GetData.get_query(
    sdk=connection.sdk,
    model='your_model',
    view='your_view',
    sort_by='your_sort_field',
    sort_type='desc', 
    limit=100,
    fields=['field1', 'field2'],
    filters={'field': 'value'}
)

print(df)

# Get data from a Look
df = GetData.get_look(
    sdk=connection.sdk,
    look_id=123,
    limit=100
)

print(df)
```
hexdoc-hexkeys 0.1.3.1.0.dev0
Python web book docgen and hexdoc plugin for Hex Keys.
Requires Python: >=3.11
Keywords: hexdoc

# hexdoc-hexkeys

Python web book docgen and [hexdoc](https://pypi.org/project/hexdoc) plugin for Hex Keys.

## Version scheme

We use [hatch-gradle-version](https://pypi.org/project/hatch-gradle-version) to generate the version number based on whichever mod version the docgen was built with.

The version is in this format: `mod-version.python-version.mod-pre.python-dev.python-post`

For example:
* Mod version: `0.11.1-7`
* Python package version: `1.0.dev0`
* Full version: `0.11.1.1.0rc7.dev0`

## Setup

Install Python 3.11 and Node 18 (20+ is **not** currently supported).

```sh
python3.11 -m venv venv

.\venv\Scripts\activate   # Windows
. venv/bin/activate.fish  # fish
source venv/bin/activate  # everything else

# run from the repo root, not doc/
pip install -e .[dev]
```

## Usage

For local testing, create a file called `.env` in the repo root following this template:
```sh
GITHUB_REPOSITORY=MoonlitJolteon/hex-keys-addon
GITHUB_SHA=main
GITHUB_PAGES_URL=https://moonlitjolteon.github.io/hex-keys-addon
```

Useful commands:
```sh
# show help
hexdoc -h

# render and serve the web book in watch mode
nodemon --config doc/nodemon.json

# render and serve the web book
hexdoc serve

# export, render, and merge the web book
hexdoc export
hexdoc render
hexdoc merge

# start the Python interpreter with some extra local variables
hexdoc repl
```