semvec/pypi-packages huggingface dataset:
whatis 0.0.1
A small Python utility to tell you what an object is
Requires Python: >=3.5

# WhatIs package

Gives a concise summary of any object's structure and avoids accidentally printing a huge object to stdout.

Works with various libraries (NumPy, PyTorch etc.) via duck typing.

## Examples
`whatis` is useful for inspecting large objects quickly:
```python
>> x = list(enumerate(range(7,4129,3)))
>> x
# ... (veeery large output) ...
>> whatis(x)
list len=1374
  ├ tuple len=2
  │  ├ 0: int
  │  └ 7: int
  │
  ├ tuple len=2
  │  ├ 1: int
  │  └ 10: int
  │
  ├ tuple len=2
  │  ├ 2: int
  │  └ 13: int
  │
  ├ tuple len=2
  │  ├ 3: int
  │  └ 16: int
  │
  └ ...
>>
```
And makes it easy to view the format of objects like this
```python
>>> x = np.ones((5, 5))
>>> out = np.linalg.eig(x)
>>> out
# A lot of information...
(array([5.00000000e+00, 5.65333918e-49, 0.00000000e+00, 0.00000000e+00,
       1.73014109e-64]), array([[-4.47213595e-01,  1.13586253e-16,  1.13586253e-16,
         1.38161505e-31, -1.11062157e-16],
       [-4.47213595e-01,  8.66025404e-01,  8.66025404e-01,
         1.05339661e-15, -8.46780726e-01],
       [-4.47213595e-01, -2.88675135e-01, -2.88675135e-01,
        -2.39769783e-16,  1.11088069e-01],
       [-4.47213595e-01, -2.88675135e-01, -2.88675135e-01,
        -7.07106781e-01,  3.67846328e-01],
       [-4.47213595e-01, -2.88675135e-01, -2.88675135e-01,
         7.07106781e-01,  3.67846328e-01]]))

>> whatis(out)
tuple len=2
  ├ ndarray shape=(5,) dtype=float64
  └ ndarray shape=(5, 5) dtype=float64
>>
```
or this
```python
>> x = torch.randn((103, 75))
>> out = torch.topk(x, 5)
# what's the format of the output again..?
>> out
# ... (very large output) ...
>> whatis(out)
topk len=2
  ├ Tensor shape=torch.Size([103, 5]) dtype=torch.float32 device=cpu
  └ Tensor shape=torch.Size([103, 5]) dtype=torch.int64 device=cpu
>>
```

## Usage
```python
from whatis import whatis
```

## Setup
```pip install whatis```
gene-connector-cli 0.6.5
The Gene-Connector command line interface
Requires Python: >=3.11
Keywords: DNA,Bioinformatics,Phylogeny,Genomics,Genetics

# Gene Connector (gcon)

___

Genbank currently stands as one of the most significant global repositories of genetic information. Nevertheless, it is not always easy to find the information we need in the format we need. Gene Connector (gcon) is a command line interface (CLI) tool that aims to facilitate the metadata extraction from nucleotide records from Entrez Genbank API's. It is a tool that can be used by researchers, students and anyone interested in genetic associated metadata information.

[![Licence](https://img.shields.io/pypi/l/gene-connector-cli?style=for-the-badge)](LICENSE)
[![Github Version](https://img.shields.io/github/v/tag/sgelias/gene-connector-cli?style=for-the-badge)](pyproject.toml)
[![PyPI Version](https://img.shields.io/pypi/v/gene-connector-cli?style=for-the-badge)](pyproject.toml)

___

See [project source code](https://github.com/sgelias/gene-connector-cli) for details.

See [documentation](https://github.com/sgelias/gene-connector-cli/blob/main/docs/README.md) for details.

___

## Installation ⚙️

Recommended installation is using `pip`. However alternative installations are also possible. See [installation guide](https://github.com/sgelias/gene-connector-cli/blob/main/docs/book/01_installation.md) for details.

## Usage 👨🏽‍💻

Gene Connector is a command line interface (CLI) tool, them main command being `gcon`, available after installation. See [usage guide](https://github.com/sgelias/gene-connector-cli/blob/main/docs/book/02_usage.md) for details.

## Project Architecture and Structure 🏛️

See [project structure](https://github.com/sgelias/gene-connector-cli/blob/main/docs/book/03_project_structure.md) for details.

## Data Modelling 🏗️

See [data modelling](https://github.com/sgelias/gene-connector-cli/blob/main/docs/book/04_data_modelling.md) for details.
firstlib 0.1
This is myh first module on python.
Requires Python: >=3.6
Keywords: files speedfiles

# Speed File Library #

## What is this? ##
The module allows you to work with files in just one line of code, without the need to manually open and close the file each time

## Quick Guide ##
The module is based on the following structure:

    
    f = open('data.txt')
    data = f.readlines()
    f.close()
    
Which Python provides by standard.


----------


### Using ###


Using the library is as simple and convenient as possible:

Let's import it first:
First, import everything from the library (use the `from `...` import *` construct).

Examples of all operations:

Writing the contents of an entire file to a variable using the `read()` function:

    temp = File(path='test.txt').read()


Writing the contents of an entire file to a variable line by line using the `readlines()` function:

    temp = File(path='test.txt').readlines()


Write only the first line from a file using the `readline()` function:

    temp = File(path='test.txt').readline()


Writing data from a variable to a file using write() (overwriting or creating a file):

    temp = "Test data"
    File(path='test.txt', data=temp).write()
    

Adding data from a variable to a file using write() (or creating a file):

    temp = "Test data"
    File(path='test.txt', data=temp).add()


----------


## Developer ##
My site: [link](https://y-a-r-i-k.github.io/)
obris 0.1.10
Obris CLI for managing infrastructure independent of your cloud provider.
Requires Python: >=3.7

# Obris CLI
![Obris logo](./assets/images/obris-logo.svg)

The Obris CLI is used to manage Obris apps from the command line. 
It is built using [click](https://click.palletsprojects.com/en/8.1.x/) 
as the underlying cli framework.  

Learn more about Obris @[obris.io](https://obris.io).

Use our [quickstart guide](https://www.obris.io/docs/quickstart) to start building apps.

## Overview

This project aims to make managing your infrastructure independent from the cloud it's deployed on.  We want developers 
to be able to leverage the tools they need and not have to make compromises when choosing between a scoped, managed 
PaaS offering or a customizable, terse IaaS offering.

## Installing

```bash
pip install obris
```

## Using

### Setup
1. Create an API token within your [Obris account](https://obris.io/credentials)
1. Add `export OBRIS_TOKEN=<YOUR-GENERATED-TOKEN>` to your `~/.zshrc` or `~/.bashrc` file

### Commands
* `obris application`
* `obris certificate`
* `obris credential`
* `obris env-var`
* `obris process`
* `obris repo`
* `obris webserver`

## Developing

### Install Dependencies

```bash
pyenv install 3.12.0
pyenv virtualenv 3.12.0 obris-cli

pyenv local obris-cli
pip install -r requirements.txt
```

### Configure Executable

### Entry Point

Start running Obris commands locally!

```bash
python -m obriscli <command>
```

## Contributing

* [Branch Guidelines](docs/contributing/branch_guidelines.md)
* [Pull Request Guidelines](./.github/PULL_REQUEST_TEMPLATE.md)