semvec/pypi-packages huggingface dataset:
gffutils 0.13
Work with GFF and GTF files in a flexible database framework

gffutils
========

``gffutils`` is a Python package for working with and manipulating the GFF and
GTF format files typically used for genomic annotations.

Files are loaded into a sqlite3 database, allowing much more complex
manipulation of hierarchical features (e.g., genes, transcripts, and exons)
than is possible with plain-text methods alone.

See documentation at https://daler.github.io/gffutils, and GitHub repo at
https:/github.com/daler/gffutils.
mcaption 0.0.6
A modern implementation of simple image captioning
Requires Python: >=3.10

<h1>Modern-Caption</h1>

---

**Description:**

This Python script provides functionality for generating image captions using the "modern-caption" library. It utilizes CLIP (Contrastive Language-Image Pretraining) and GPT-2 models for image encoding and caption generation, respectively. Users can generate captions for images using different pre-trained models and decoding methods.

---

**Installation:**

1. **Dependencies:**
   - `torch`
   - `clip`
   - `transformers`
   - `numpy`
   - `PIL`
   - `scikit-image`

2. **Setup:**
   - Ensure you have Python installed on your system.
   - Dependencies will be installed during setup.
   - Setup will **not** overwrite existing installs of PyTorch or Torchvision.
   - Install using pip:
     ```bash
     pip install modern-caption
     ```

---

**Usage:**

1. **Importing the Module:**
   ```python
   from mcaption import Caption
   ```

2. **Initializing the Caption Generator:**
   ```python
   cap = Caption(model='conceptual', device='cpu', prefix_length=10)
   ```

3. **Generating Captions:**
   ```python
   import skimage.io as io
   
   image = io.imread("images/cover.jpg")  # Load the image
   caption_conceptual = cap.predict(image, beam=True)  # Generate caption with beam search
   print("Conceptual Model Caption:", caption_conceptual)
   ```

---

**Explanation:**

- The `Caption` class initializes the image-captioning functionality with options to specify the model, device, and other parameters.
- Users can create multiple instances of the `Caption` class to compare different models or share common resources.
- The `predict` method generates captions for images using the specified model and decoding method.

---

**Examples:**

```python
from mcaption import Caption
import skimage.io as io

# Initialize Caption generator with the 'conceptual' model
cap = Caption(model='conceptual', device='cpu', prefix_length=10)

# Load an image and generate a caption using beam search
image = io.imread("images/cover.jpg")
caption_conceptual = cap.predict(image, beam=True)
print("Conceptual Model Caption:", caption_conceptual)

# Initialize another Caption generator with the 'coco' model, inheriting from the previous one
cap2 = Caption(model='coco', inherit=cap)

# Generate a caption using greedy decoding
caption_coco = cap2.predict(image, beam=False)
print("COCO Model Caption:", caption_coco)
```

---

**Notes:**
- The script demonstrates how to initialize and use the image-captioning functionality provided by the "modern-caption" library.
- Users can experiment with different models and decoding methods to obtain varied captions.
- Ensure that the image paths are correct and accessible.
- This library used the following [repository](https://github.com/rmokady/CLIP_prefix_caption) for conceptual reference.

---

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
deeplx-tr 0.1.0a1
TODO
Requires Python: >=3.8

# deeplx-tr
[![pytest](https://github.com/ffreemt/deeplx-tr/actions/workflows/routine-tests.yml/badge.svg)](https://github.com/ffreemt/deeplx-tr/actions)[![python](https://img.shields.io/static/v1?label=python+&message=3.8%2B&color=blue)](https://www.python.org/downloads/)[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)[![PyPI version](https://badge.fury.io/py/deeplx_tr.svg)](https://badge.fury.io/py/deeplx_tr)

deeplx in python

## Install it

```shell
pip install deeplx_tr --pre
```

## Use it

### from command line
```bash
deeplx-tr hello world
# 哈罗世界

deeplx-tr hello world -t de
# Hallo Welt

deeplx-tr --help
```
or
```bash
python -m deeplx_tr hello world
python -m deeplx_tr hello world -d
python -m deeplx_tr --help
```
### from python
```python
from deeplx_tr import deeplx_tr

res = deeplx_tr("hello world")
print(res)
# 哈罗世界

res = deeplx_tr("hello world", to_lang="de")
print(res)
# Hallo Welt
```
N.B. `deeplx-tr` will likely spit out `too many requestes` if you call it too often before long. But it's sufficient for ordinary average daily translation.
If you have a higher demand, try `deeplx.org` for which we provided two clients for your convenience.

## clients to query a `deeplx` server (default `deepx.org`)
```
from deeplx_tr import deeplx_client, deeplx_client_async

res = deeplx_client("hello world")
print(res)
# '哈罗世界'

res = deeplx_client("hello world", target_lang="de")
print(res)
# 'Hallo Welt'

# if you host your own deeplx, for example, at `127.0.0.1:1188'
# res = deeplx_client("hello world", url="http://127.0.0.1:1188/translate")
```

An async client is also available, e.g.
```python
import asyncio
from deeplx_tr import deeplx_client_async

async def main():
  res = await asyncio.gather(deeplx_client_async("hello world"), deeplx_client_async("test"))
  print(res)

asyncio.run(main())
# ['哈罗世界', '测试']
```
The default concurrency limit is `5` but can be altered by setting the environ variable CONCURRENCY_LIMIT, e.g.
```
set CONCURRENCY_LIMIT=8  # in Windows

# export CONCURRENCY_LIMIT=8 in Linux
```
snake-environment 0.3.6
A simple snake environment with 18 states and 4 actions
Requires Python: >=3.8

# Snake Environment

The Snake Environment is a Python package designed for training machine learning models using reinforcement learning techniques. This environment mimics the OpenAI Gym interface, providing a familiar setup for those who are accustomed to using Gym for developing and testing AI models.

## Features

- **Game Environment**: Built using Pygame, this environment allows for on-screen rendering of the snake game, which is useful for visual feedback while training models.
- **Compatibility with AI Training Workflows**: The `SnakeGame` class features a `step` function that returns a tuple of (state, done, reward, score), similar to environments found in OpenAI Gym.
- **State Representation**: The state is represented as a numpy array with 18 dimensions, providing comprehensive information about the game environment at any given step.
- **Utility Methods**: Includes `reset` and `state_dimensions` methods for resetting the game state and retrieving the dimensions of the state space, respectively.

## Installation

To install the Snake Environment, you can use pip:

```bash
pip install snake_environment
```

## Example Usage
```python
from snake_environment import SnakeGame

# Initialize the environment
env = SnakeGame(render=True)  # Set render=False if you do not need to visualize the training process

# Start a new episode
state = env.reset()

# Loop until the episode is finished
done = False
while not done:
    action = model.predict(state) 
    next_state, done, reward, score = env.step(action)
    state = next_state

# Get the dimensions of the state for input layer configuration or debugging
state_dim = env.state_dimensions
print("State dimensions:", state_dim)

```

## Contributions
Contributions are welcome! If you'd like to improve the Snake Environment, please fork this repository and submit a pull request with your proposed changes.

## License
This project is licensed under the MIT License - see the LICENSE file for details.
Calclab 0.1.0
Description of your library
Requires Python: >=3.7

# Calclab

This Python library provides mathematical functions and utilities, including:

- Solving quadratic equations
- Analyzing parabolas
- Calculating distances between points
- Performing basic arithmetic operations
- Finding equations of lines

More functions will be add in the future

## Installation

You can install this library using pip:

```bash
pip install calculator-library
```
## Usage

```python
from calclab import *

# Initialize the calculator
calc = Calculator()

# Solve quadratic equation: Ax^2 + Bx + C = 0
root1, root2 = calc.quadratic_equation(A, B, C)

# Analyze parabola: y = Ax^2 + Bx + C
root1, root2, axis_of_symmetry, vertex = calc.parabola(A, B, C)

# Calculate distance between two points (X1, Y1) and (X2, Y2)
distance = calc.graph_distance(X1, Y1, X2, Y2)

# Perform square root operation
sqrt_result = calc.sqrt(a)

# Perform square operation
square_result = calc.square(a)

#Perform cube operartion 
cube_result = calc.cube(a)

#Perform line-find operation 
line_equation = calc.line_find(X1, Y1, X2, Y2)

```

## Example

```python

from calculator import Calculator

# Initialize the calculator
calc = Calculator()

# Example: Solving quadratic equation
root1, root2 = calc.quadratic_equation(1, -3, 2)
print("Roots of the equation x^2 - 3x + 2 = 0 are:", root1, "and", root2)
```

## Note:
The quadratic equation and the parabola Function Do MALFUNCTION when the 2 roots are not real, imaginary numbers will be added to those functions in a future update
Please read the Usage/Examples above to use the functions correctly

## Contribute
Please feel free to suggest any ways to make this library better!!