semvec/pypi-packages huggingface dataset:
nephelai 0.2.0
A helper library to transport your data into the (next)cloud
License: MIT
Requires Python: >=3.8,<4.0
Keywords: nextcloud,mlops

<p align="center">
<img src="https://github.com/dobraczka/nephelai/raw/main/docs/assets/logo.png" alt="nephelai logo", width=200/>
<h2 align="center">nephelai</h2>
</p>

<p align="center">
<a href="https://github.com/dobraczka/nephelai/actions/workflows/main.yml"><img alt="Actions Status" src="https://github.com/dobraczka/nephelai/actions/workflows/main.yml/badge.svg?branch=main"></a>
<a href='https://nephelai.readthedocs.io/en/latest/?badge=latest'><img src='https://readthedocs.org/projects/nephelai/badge/?version=latest' alt='Documentation Status' /></a>
<a href="https://pypi.org/project/nephelai"/><img alt="Stable python versions" src="https://img.shields.io/pypi/pyversions/nephelai"></a>
<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
</p>

A helper library to upload/download files to/from a password-protected shared nextcloud folder. The link and password are read from your `.env` to enable project-specific shared folders.
Because Nextcloud does not enable chunked uploads for shared folders and your files can hit the size limit, your files are uploaded in chunks if needed and reconstructed after download.

Usage
=====
Create a `.env` file in your project root.
Remember to add this file to your `.gitignore` and always keep it secure to keep your secrets!
Your `.env` should contain:
```bash
NEXTCLOUD_FOLDER_URI="uri_of_the_shared_folder"
NEXTCLOUD_FOLDER_PW="pw_of_the_folder"
```
Then you can interact with the folder in a variety of ways.
Alternatively, you can set this environment variables yourself with your preferred method.

Via CLI:
--------
```bash
nephelai upload mytestfile.txt anextcloud/path/thatwillbecreatedifneeded/
nephelai download anextcloud/path/thatwillbecreatedifneeded/mytestfile.txt
```
You can also upload folders including the file structure:
```bash
tests/resources
├── mymatrix.npy
└── subfolder
    └── testfile.txt
```
Using the `upload-with-fs` command:
```bash
nephelai upload-with-fs tests/resources
```

Which is just syntactic sugar for:

```bash
nephelai upload tests/resources tests/resources
```

Downloading can be done accordingly:
```bash
nephelai download tests
```
Which will download it to your current directory. You can also specify the download path:

```bash
nephelai download tests --local-path /tmp/
```
This download the folder as:
```bash
/tmp/tests
└── resources
    ├── mymatrix.npy
    └── subfolder
        └── testfile.txt
```

Using 

```bash
nephelai ls tests
```
you can show the files in the `tests` directory.

You can get help for each command via the `--help` flag.

Via Python:
----------
```python
from nephelai import upload, download

upload("tests/resources", "tests/resources")
file_dl_path = "/tmp/mymatrix.npy"
download("tests/resources/mymatrix.npy",file_dl_path)

import numpy as np

mymatrix = np.load(file_dl_path)
```

Installation
============

`pip install nephelai`
vima 0.0.2
vima - Pytorch
License: MIT
Requires Python: >=3.6,<4.0
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# VIM
A simple implementation of "VIMA: General Robot Manipulation with Multimodal Prompts"

[Original implementation Link](https://github.com/vimalabs/VIMA)

# Appreciation
* Lucidrains
* Agorians

# Install
`pip install vima`

---

# Usage
```python
import torch
from vima import Vima

# Generate a random input sequence
x = torch.randint(0, 256, (1, 1024)).cuda()

# Initialize VIMA model
model = Vima()

# Pass the input sequence through the model
output = model(x)
```

## MultiModal Iteration
* Pass in text and and image tensors into vima
```python
import torch
from vima.vima import VimaMultiModal

#usage
img = torch.randn(1, 3, 256, 256)
text = torch.randint(0, 20000, (1, 1024))


model = VimaMultiModal()
output = model(text, img)

```

# License
MIT

# Citations
```latex
@inproceedings{jiang2023vima,
  title     = {VIMA: General Robot Manipulation with Multimodal Prompts},
  author    = {Yunfan Jiang and Agrim Gupta and Zichen Zhang and Guanzhi Wang and Yongqiang Dou and Yanjun Chen and Li Fei-Fei and Anima Anandkumar and Yuke Zhu and Linxi Fan},
  booktitle = {Fortieth International Conference on Machine Learning},
  year      = {2023}
}
```
netstat2df 0.10
Retrieves and parses network statistics related to IPv4 TCP and UDP connection returns this information as a Pandas DataFrame
License: MIT
Keywords: netstat,ipv4,tcp,udp


# Retrieves and parses network statistics related to IPv4 TCP and UDP connection returns this information as a Pandas DataFrame

## Tested against Windows 10 / Python 3.10 / Anaconda

### pip install netstat2df


```python
Retrieve and parse network statistics related to IPv4 TCP and UDP connections.

This function uses the 'netstat' command-line utility to gather information about
IPv4 TCP and UDP connections, including local and foreign addresses, ports, process
IDs (PIDs), and executable names. It then processes and returns this information
as a Pandas DataFrame.

Returns:
	pd.DataFrame: A DataFrame containing the following columns:
		- 'aa_proto': Protocol type ('TCP' or 'UDP').
		- 'aa_local_address': Local IP address.
		- 'aa_local_port': Local port number.
		- 'aa_foreign_address': Foreign IP address.
		- 'aa_foreign_port': Foreign port number.
		- 'aa_pid': Process ID (PID) of the associated process.
		- 'aa_executable': Executable name of the associated process.
		- 'aa_status': Status of the connection (may be empty or NaN if unavailable).

Note:
	- If any error occurs during the retrieval or parsing of network statistics,
	  the function returns an empty DataFrame.

Example:
	>>> from netstat2df import get_netstat_ipv4_df
	>>> df = get_netstat_ipv4_df()
	>>> print(df.head())

```