semvec/pypi-packages huggingface dataset:
bc-configs 0.3.3
Configuration module, that provides a way to manage configs structure, validation and casting to type inside your code. Based on pedantic models. Additionally it can automatically getting config from environment variables, .env, HashiCorp Vault, etc.
License: MIT
Requires Python: >=3.11
Keywords: config, dotenv, environment, pydantic

# bc-config

![](./docs/source/_static/coverage-badge.svg) ![](./docs/source/_static/unittests-badge.svg) ![](./docs/source/_static/mypy-badge.svg) ![](./docs/source/_static/ruff-badge.svg)

*Make configuring your application easier.*

# Installing

```bash
pip install bc-configs
```

# Make your custom config class

```python
import os
from bc_configs import BaseConfig, define

define()

class MyConfig(BaseConfig):
    some_int: int
    some_string: str
    some_bool: bool

my_config = MyConfig()  # type: ignore[call-arg]

assert int(os.getenv("MY_SOME_INT")) == my_config.some_int  # True
assert os.getenv("MY_SOME_STRING") == my_config.some_string  # True
assert bool(os.getenv("MY_SOME_BOOL")) == my_config.some_bool  # True
```

The name of the environment variable is formed based on the names of the class and field.

# Features

- Load environment variables from `.env` files (using `python-dotenv`).
- Load environment variables from YAML files (e.g., `.env.yml` or custom path via `YAML_CONFIG_FILE` environment variable).
- Load secrets from HashiCorp Vault (using `hvac`).
SUITPy 2.0.0
Analysis toolbox for cerebellar fMRI data in Python
License: MIT
Requires Python: >=3.10

SUITPy: Analysis and visualization of cerebellar imaging data.
==============================================================

The package provides some basic functionality of the original SUIT toolbox for Matlab (https://github.com/jdiedrichsen/suit).
Currently, only mapping of volume data to the flatmap and display of the flatmap are implemented.

Important links
---------------

- SUIT website: http://diedrichsenlab.org/imaging/suit.htm
- Official source code repository: https://github.com/SUITPy/
- HTML documentation (latest release): https://suitpy.readthedocs.io/

Dependencies
------------

The required dependencies to use the software are:

* python >= 3.10,
* setuptools
* numpy >= 1.22.0
* nibabel >= 3.2.1
* pandas >= 2.0.0
* matplotlib >= 3.5.0
* plotly >= 5.10.0
* scipy >= 1.9.0
* neuroimagingtools >= 1.1.1
* antspyx >= 0.6.1
* torch >= 2.0.0

Install
-------

First make sure you have installed all the dependencies listed above.
Then you can install SUITPy by running the following command in
a command prompt::

    pip install -U --user SUITPy

More detailed instructions are available at
https://suitpy.readthedocs.io/en/latest/install.html


Licence and Acknowledgements
----------------------------
The Python version of the SUIT toolbox has been developed by the Diedrichsenlab including J. Diedrichsen, M. King, D. Zhi, C. Hernandez-Castillo, S. Witt and others. It is distributed under MIT License, meaning that it can be freely used and re-used, as long as proper attribution in form of acknowledgments and links (for online use) or citations (in publications) are given. The relevant references are:

SUIT normalisation and template:

- Diedrichsen, J. (2006). A spatially unbiased atlas template of the human cerebellum. Neuroimage. 33(1), 127-138. doi: 10.1016/j.neuroimage.2006.05.056

Surface-based representation and flatmap

- Diedrichsen, J. & Zotow, E. (2015). Surface-based display of volume-averaged cerebellar data. PLOSOne. doi: 10.1371/journal.pone.0133402
pip-remove 0.1.1
Removes both the specified package and its orphans from the current environment.
License: MIT
Requires Python: >=3.10

Removes both the specified package and its orphans from the current environment.

## Install

- `pipx install pip-remove`

It is recommended you use `pipx` (pip install pipx) so that you can use `pip-remove` in any environment without having to install it on it

## Usage

`pip-remove package_name`

You can add the `-y` flag to skip the confirmation prompts

`pip-remove -y package_name`


Run `pip-remove` in the environment you want to remove the package from
Also, if you run `pip-remove` from the global environment it will not check for used orphans and simply ask to remove all of them


## Features
- ✅ Detects Python environment from the shell you run it in, only need to install once
- ✅ Ignores files ignored by git
- ✅ Checks for orphans still being used in the codebase (if in virtual environment)
mlprac 0.1.0
A collection of Machine Learning practice notebooks
License: MIT
Requires Python: >=3.7
Keywords: machine-learning, jupyter, notebooks, education

# mlprac - Machine Learning Practice Notebooks

A comprehensive collection of Jupyter notebooks for practicing machine learning concepts with Python, NumPy, Pandas, and Scikit-learn.

## Installation

Install the package using pip:

```bash
pip install mlprac
```

## Usage

### Download Notebooks

After installation, you can download all practice notebooks to your current working directory:

```bash
mlprac download
```

Or specify a custom destination:

```bash
mlprac download --dest my-notebooks
```

### List Available Notebooks

To see all available notebooks without downloading:

```bash
mlprac download --list
```

### Package Information

Get information about the package:

```bash
mlprac info
```

## Notebook Contents

This package includes 30+ Jupyter notebooks covering:

- **NumPy Basics** (2.ipynb, 2a-2d.ipynb)
  - Array creation and manipulation
  - Indexing and slicing
  - Mathematical operations
  - Broadcasting

- **Linear Regression** (3.ipynb, 3a-3b.ipynb, 3multi.ipynb)
  - Simple linear regression
  - Multiple linear regression
  - Model evaluation

- **Classification Algorithms** (4.ipynb - 7.ipynb series)
  - Logistic regression
  - K-Nearest Neighbors (KNN)
  - Support Vector Machines (SVM)
  - Decision Trees

- **Clustering** (8.ipynb series)
  - K-Means clustering
  - Hierarchical clustering

- **Neural Networks** (9.ipynb series)
  - Basic neural network implementations
  - Deep learning concepts

- **Advanced Topics** (10.ipynb series)
  - Ensemble methods
  - Model optimization

## Requirements

The notebooks use the following Python libraries:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn

Install them separately:

```bash
pip install numpy pandas matplotlib seaborn scikit-learn jupyter
```

## Python API

You can also use the package programmatically in Python:

```python
import mlprac

# Get the path to notebooks
notebooks_path = mlprac.get_notebooks_path()

# List all available notebooks
notebooks = mlprac.list_notebooks()
for nb in notebooks:
    print(nb)
```

## License

MIT License