semvec/pypi-packages huggingface dataset:
scperturb 0.1.0
Python package with E-distance tools for single-cell perturbation data analysis.
Requires Python: >=3.6

# scperturb
A python package to compute E-distances in single-cell perturbation data and
perform E-tests.

# Install
Just install via pip:

```
pip install scperturb
```

# Usage example

```
# E-distances
estats = edist(adata, obs_key='perturbation')
# E-distances to a specific group (e.g. 'control')
estats_control = estats.loc['control']
# E-test for difference to control
df = etest(adata, obs_key='perturbation', obsm_key='X_pca', dist='sqeuclidean', control='control', alpha=0.05, runs=100)
```

Check out notebooks --> e-distance for tutorial.
pynutrition 0.0.1
LLM-driven Python daily nutrition app
Requires Python: >=3.11

# pynutrition
**LLM-driven Python daily nutrition app**

## Installation
```
pip install pynutrition
```

## Usage

```python
from pynutrition import Ingredient, Quantity, Calories, Composition
from pynutrition.nutrients import Fat, Saturates, Carbohydrate, Sugar, Protein, Salt

yoghurt = Ingredient(name="Greek Yoghurt Bakoma (5900197012723)", quantity=Quantity(250), calories=Calories(100), composition=Composition({
    Fat(7.5), Saturates(4.5), Carbohydrate(4.7), Sugar(4.7), Protein(3.5), Salt(0.12) 
}), base=100)
walnuts = Ingredient("Walnuts Carrefour (5905617004623)", Quantity(30), Calories(666), composition=Composition({
    Fat(60.3), Saturates(6.6), Carbohydrate(11.5), Sugar(9.9), Protein(16)
}), base=100)

meal = yoghurt + walnuts
print(meal.calories.as_int())  # round(2.5 * 100 + 0.3 * 666)
>>> 450
```

The `base` is used for calculations of nutritional information which are expressed as amounts per `base`,  often (as in the example) equal to 100g. All quantities are expressed in grams.

You can use `.get_data` and `.load_data` to fetch nutrional data with GPT-4 in the format accepted by the app and load each retrieved row as `Ingredient` that can be used to compose meals.

## Info

> Software engineers, plagued by the stereotype of poor eating and collecting bad habits

<small>Source: [HackerNoon](https://hackernoon.com/the-surprising-benefits-of-intermittent-fasting-for-software-engineers)</small>

This is a one-day project I quickly built (and tuned) for myself to improve my diet and nutrition. While you can see the haste in the code and design, I have realised even such rudimentary version might be useful to you. I want to contribute to supporting others in the community in their goal of becoming healthier, happier and stronger.


I hope I will find some time to make this project grow. If you are interested in extending its functionalities, found bugs or want to help, hit me up through [issues](https://github.com/haiyangdeperci/pynutrition/issues).
calculator-mgaidy 0.0.7
A small example package
Requires Python: >=3.8

# Calculator_mgaidy

## Overview

A basic calculator class that supports arithmetic operations and maintains a history log.

## Installation

You can install the Calculator_mgaidy package using pip:

```
pip install calculator-mgaidy==0.0.7
```
## Attributes

- `memory` (float): The current memory value of the calculator.
- `logs` (List[str]): A list to store the history log of operations.
- `precision` (int): The precision to round results to.

## Constants

- `MAX_NUMBER` (float): The maximum allowed absolute value of number in calculations.
- `MAX_POWER_LEVEL` (int): The maximum allowed power level for exponentiation.
- `MAX_PRECISION` (int): The maximum allowed power level for exponentiation.

## Methods

Each of the functions below have validations set in place to ensure that inputs and results are allowed values.
Precision must be more than zero but less than 300. By default 10.
Numbers maximum allowed absolute value is 1e300. (applies to input and output)
Power and root inputs must be more than zero but less than 300.

### `set_precision(self, precision: int) -> None`

Set the precision for rounding results.

### `reset_memory(self) -> float`

Reset the calculator memory to zero and clear the history log.

### `print_history_logs(self) -> None`

prints out log of operations since last reset.

### `add(self, number: float) -> float`

Add a number to the calculator memory.

### `subtract(self, number: float) -> float`

Subtract a number from the calculator memory.

### `multiply(self, number: float) -> float`

Multiply the calculator memory by a number.

### `divide(self, number: float) -> float`

Divide the calculator memory by a number.

### `power(self, number: float) -> float`

Raise the calculator memory to the power of a given number.

### `root(self, number: float) -> float`

Calculate the root of the calculator memory with a given exponent.

## Usage Example

```python
from calculator import Calculator

# Create a calculator instance
calc = Calculator()

# Perform operations
result = calc.add(5)
print(result)  # Output: 5.0

result = calc.multiply(3)
print(result)  # Output: 15.0
```

## Development

For development, ensure that you have Python and pip installed. You can set up a virtual environment:
```python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
```

## Contributing

Feel free to contribute to this project by submitting bug reports, feature requests, or pull requests.

## Testing

Run the tests to ensure everything is working as expected:
```python
pytest
```
## License

This project is licensed under the MIT License- see the [LICENSE](LICENSE) file for details.