semvec/pypi-packages huggingface dataset:
carveman 0.2.0
File carving for pictures, documents and other files based on it's file signatures
License: MIT

# Description

File carving for pictures, documents and other files based on it's header an footer signatures. Does work for non fragmented files only at the moment.

# Available Files:

- .jpg
- .png
- .pdf

# Installation

`pip install carveman`

# Usage

**From command line:**

`python -m carveman --path PATH [--outdir OUTDIR]`

| Option | Short | Type | Default | Description |
|---|---|---|---|---|
|--path | -p | String | - | Path to carving source (dd, raw) |
|--outdir | -o | String | carveman-result | Path to dir where carved files are located |


# Example

`python -m carveman -p path/to/carving-source/example.dd`

The carved files will be located in `carveman-result`

```

###########################################################################################

Carveman by 5f0
File carving for pictures, documents and other files based on it's file signatures

Current working directory: path/to/carveman
        Investigated file: example.dd

                      MD5: 9ff0c5b220e4345133181e7101571374
                   SHA256: b4864ce518b51e0fe6ee43cf705a5a39bedc97c25c97e857314b22b7d5e500a7

     Path to carved files: carveman-result

 Datetime: 01/01/1970 10:11:12

###########################################################################################

--> Carving started

--> Carving for: .jpg

------> .jpg detected! - Start Hex: 0x7000 - End Hex: 0xc1b0 - Nr: 1
------> .jpg detected! - Start Hex: 0xf000 - End Hex: 0x22d77 - Nr: 2
------> .jpg detected! - Start Hex: 0x2e000 - End Hex: 0x2f234 - Nr: 3
------> .jpg detected! - Start Hex: 0x30000 - End Hex: 0x326e4 - Nr: 4
------> .jpg detected! - Start Hex: 0x33000 - End Hex: 0x33e73 - Nr: 5
------> .jpg detected! - Start Hex: 0x34000 - End Hex: 0x362c0 - Nr: 6
------> .jpg detected! - Start Hex: 0x37000 - End Hex: 0x38b20 - Nr: 7
------> .jpg detected! - Start Hex: 0x39000 - End Hex: 0x3b7e7 - Nr: 8
------> .jpg detected! - Start Hex: 0xb7000 - End Hex: 0xb8234 - Nr: 9
------> .jpg detected! - Start Hex: 0xb9000 - End Hex: 0xbb6e4 - Nr: 10
------> .jpg detected! - Start Hex: 0xbc000 - End Hex: 0xbce73 - Nr: 11
------> .jpg detected! - Start Hex: 0xbe000 - End Hex: 0xc02c0 - Nr: 12
------> .jpg detected! - Start Hex: 0xc1000 - End Hex: 0xc2b20 - Nr: 13
------> .jpg detected! - Start Hex: 0xc3000 - End Hex: 0xc57e7 - Nr: 14
------> .jpg detected! - Start Hex: 0x338219 - End Hex: 0x339be0 - Nr: 15
------> .jpg detected! - Start Hex: 0x33a498 - End Hex: 0x33bc9b - Nr: 16

--> Carving for: .png


--> Carving for: .pdf

------> .pdf detected! - Start Hex: 0xb4000 - End Hex: 0xb618c - Nr: 1
------> .pdf detected! - Start Hex: 0x530000 - End Hex: 0x53018f - Nr: 2

--> Carving finished

###########################################################################################

Execution Time: 0.059799 sec
```


# License

MIT
pyhub-stubs 0.0.2
Python API Stub for Lego® Spike Prime and LEGO® MINDSTORMS® Robot Inventor
License: MIT
Keywords: lego,spike,prime,mindstorms

# pyhub-stubs
The unofficial stubbed MicroPython API for LEGO SPIKE Prime and LEGO Mindstorm Robot Inventor. This also includes the low-level modules like `hub`.

## Usage
This project is best working Visual Studio Code extension  [PeterStaev.lego-spikeprime-mindstorms-vscode](https://marketplace.visualstudio.com/items?itemName=PeterStaev.lego-spikeprime-mindstorms-vscode)

## Credits
This project is forked from https://github.com/fluffyhuskystudio/spike-prime-api (See `Credits` section from there as well)

MIT License

Copyright (c) 2022 fluffyhuskystudio
Copyright (c) 2022 Xense Education

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
ppulp 0.1.0
An extension of PuLP, a linear programming problem modeling tool
License: MIT
Keywords: optimization LP linear programming modeling

# ppulp

P(retty)PuLP is an extension of PuLP, linear programming problem modeling tool.

You can use the basic features of pulp plus the following useful extensions to make modeling simpler.

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

<br>

## Install

**PyPI**

```
pip install ppulp
```

**GitHub**

```
git clone https://github.com/nariaki3551/ppulp.git
```

<br>

## Features

- Variable product
- If-then constraints
- Absolute values
- Piecewise linear approximation of nonlinear functions
- Logical operations (And, Or, Xor)
- Reduction (Sum, Prod)


## Examples

### Variables productions

```python
# from pulp import *
from ppulp import *

# create variables
x = LpVariable("x", cat="Binary")
y = LpVariable("y", cat="Binary")

# create variable production
z = x * y
```

### If-then constriant

```python
from ppulp import *

x = LpVariable("x", lowBound=-1)
y = LpVariable("y", lowBound=-1)

prob = LpProblem(sense="Minimize")

# add if-then constraints
prob += (x <= 0) >> (y >= 0)  # if (x <= 0) then (y >= 0)
prob += (y <= 0) >> (x >= 0)  # if (y <= 0) then (x >= 0)
```

### Absolution value

```python
x = LpVariable("x")
y = LpVariable("y")
Abs(x+y)
```

### Approximation of nonlinear functions

```python
from ppulp import *
import math

x = LpVariable("x", lowBound=3)
y = LpVariable("y", lowBound=4)

# create non-linear function
f = PiecewiseLinear(math.log, xl=7, xu=100, num=3)

prob = LpProblem()
prob += f(x + y)
prob += f(x) >= 10
```


### Reduction

```python
from ppulp import *

x = [LpVariable(name=f"x{i}", ini_value=2) for i in range(5)]

# summation
lpSum(x)

# production
lpProd(x)
```
torch-composer 0.0.4rc0
API to compose pytorch neural networks
License: MIT
Requires Python: >3.6.0

# ![logo](/docs/imgs/torch_composer.logo.large.svg)

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

# torch-composer
Compose pytorch neural networks with ease.

### Installation (current version: [`v0.1.0`](https://pypi.org/project/torch-composer/))
```python
pip install torch-composer
```

### Sample use-cases of the API
```python
import torch_composer
import torch

net = torch_composer.TorchNet(in_dim=2500, out_dim=10)
```
```
Sequential(
  (input): Linear(in_features=2500, out_features=200, bias=True)
  (activation_1): LeakyReLU(negative_slope=0.01)
  (hidden_1): Linear(in_features=200, out_features=200, bias=True)
  (output_activation): LeakyReLU(negative_slope=0.01)
  (output): Linear(in_features=200, out_features=10, bias=True)
)
```

As simple as you want (see above) or more complex with optional parameters:
```python
torch_composer.TorchNet(
    in_dim=2500,
    out_dim=10,
    hidden={1: [800, 800], 2: [200, 200]},
    activation_function=torch.nn.LeakyReLU(negative_slope=0.01),
    dropout=0.2,
    input_bias=True,
    output_bias=True,
)
```
```
Sequential(
  (input): Linear(in_features=2500, out_features=800, bias=True)
  (activation_1): LeakyReLU(negative_slope=0.01)
  (hidden_1): Linear(in_features=800, out_features=800, bias=True)
  (dropout_1): Dropout(p=0.2, inplace=False)
  (activation_2): LeakyReLU(negative_slope=0.01)
  (hidden_2): Linear(in_features=200, out_features=200, bias=True)
  (dropout_2): Dropout(p=0.2, inplace=False)
  (output_activation): LeakyReLU(negative_slope=0.01)
  (output): Linear(in_features=200, out_features=10, bias=True)
)
```

### Make an encoder
```python=
torch_composer.TorchNetDecoder(data_dim=2500, latent_dim=10)
```

### Make a decoder
```python=
torch_composer.TorchNetDecoder(data_dim=2500, latent_dim=10)
```

Access and set initial parameters for the output layer:
```python=
torch_composer.tools.init_output_params(net)
```

#### Potential future plans

- Composition of `torch.optim` funcs.
aio-q3-rcon 1.0.0
An async Quake 3 RCON implementation with a CLI
License: MIT
Requires Python: >=3.10,<4.0
Keywords: quake3rcon,quake3,rcon,q3,q3rcon,async,asyncio

# aio-q3-rcon
*An async Quake 3 RCON implementation for Python*

## Installation
```
pip install aio-q3-rcon
```
or with the cli extra
```
pip install aio-q3-rcon[cli]
```

## CLI Usage
```
Usage: q3rcon [OPTIONS] ADDRESS PASSWORD

Options:
  -p, --port INTEGER RANGE        [1<=x<=65535]
  --timeout FLOAT RANGE           [x>=0.01]
  --fragment-read-timeout, --fr-timeout FLOAT RANGE
                                  [x>=0.01]
  --retries INTEGER RANGE         [x>=1]
  --debug
  --help                          Show this message and exit.
```

## API Reference
#### [Examples Folder](examples)

#### *class* [`Client`](aioq3rcon/client.py)(`host`: *`str`*, `port`: *`int`*, `timeout`: *`float`*, `fragment_read_timeout`: *`float`*, `retries`: *`int`*, `logger`: *`Logger | None`*)
- Parameters:
  - `host`: *`str`* - *the host / IP / domain of the server to connect to*
  - `port`: *`port`* - *the port of the server to connect to*
    - default value is `27960`
  - `timeout`: *`float`* - *the timeout for network operations*
    - default value is `2.0`
    - for network operations with retries, the timeout applies to the rewrite attempts as a whole, rather than being per retry
  - `fragment_read_timeout`: *`float`* - *the timeout for waiting on potentially fragmented responses*
    - default value is `.25`
    - the Quake 3 server can sometimes send fragmented responses, since there is no consistent way to tell if a response is fragmented or not, the best solution is to wait for fragmented responses from the server whether they exist or not. This value is the timeout for waiting for those responses.
  - `retries`: *`int`* - *the amount of retries per network operation*
    - default value is `2`
    - all network operations except for reads are wrapped in retry logic
  - `logger`: *`Logger | None`* - *the logger instance*
    - default value is `None`
    - if there is no logger specified, a logger that has `disabled` set to `True` will be used instead
    - currently only some debug information is logged
- Methods:
  - `connect`(`verify`: *`bool`* = `True`) -> *`None`*
    - *connects to the server*
    - *if `verify` is `True`, then the `heartbeat` RCON command is sent and the password is checked as well*
    - *if `Client` is being used as a context manager, this will be called automatically upon enter*
  - `close`() -> *`None`*
    - *closes the connection to the server*
    - *if `Client` is being used as a context manager, this will be called automatically upon exit*
#### *exception* [`RCONError`](aioq3rcon/errors.py)
- Base exception all aio-q3-rcon errors derive from
#### *exception* [`IncorrectPasswordError`](aioq3rcon/errors.py)
- Raised when the provided password is incorrect