semvec/pypi-packages huggingface dataset:
pacote-imagem-python 0.0.1
Projeto Dio. Python

# package_name

Description. 
The package package_name is used to:
	Processing:
		- Histogram matching 
		- Structural similarity
		- Resize Image
    Utils:
		- Read image
		- Save image
		- Plot image
		- Plot result
		- Plot histogram
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install package_name
```

## Author
Carlos Campos

## License
[MIT](https://choosealicense.com/licenses/mit/)
uvnb 0.1.0
A utility to run UV against Jupyter notebooks
Requires Python: >=3.11

<img src="img.png" width="125" height="125" align="right" />

### uvnb

> A utility to run UV against Jupyter notebooks.

## Quickstart 

You can install this tool via: 

```
uv pip install uvnb
```

## Usage 

The goal of `uvnb` is to allow you to run Jupyter notebooks with dependencies via UV by leveraging the script metadata. By doing this, you can just define your dependencies in the notebook metadata and quickly run it. 

This tool assumes that you have metadata in the first cell that looks like this: 

```python
# /// script
# requires-python = ">=3.11"
# dependencies = [
#   "requests<3",
#   "rich",
# ]
# ///

import requests
from rich.pretty import pprint

resp = requests.get("https://peps.python.org/api/peps.json")
data = resp.json()
pprint([(k, v["title"]) for k, v in data.items()][:10])
```

From here you can run the notebook, or start a Jupyter server via the command line. 

```
python -m uvnb
Usage: python -m uvnb [OPTIONS] COMMAND [ARGS]...

  CLI for working with Jupyter notebooks.

Options:
  --help  Show this message and exit.

Commands:
  run    Run a Jupyter notebook.
  start  Start Jupyter notebook server.
```

## UV to the max 

Fun fact, you don't have to install this tool beforehand. You can just run it with `uv`: 

```
uv run --with nbuv python -m nbuv
```
mosaicperm 0.1.5
Exact inference via mosaic permutations
Requires Python: >=3.9

A python implementation of the mosaic permutation testing framework.

# Installation

To install ``mosaicperm``, just use pip:

``pip install mosaicperm``

# Documentation

Documentation and tutorials are available at https://mosaicperm.readthedocs.io/.

# Quickstart

Below, we give a simple example showing how to use ``mosaicperm`` to test whether a set of factor exposures explain the correlations among a matrix of outcomes variables.

```
	import numpy as np
	import mosaicperm as mp

	# synthetic outcomes and exposures
	n_obs, n_subjects, n_factors = 100, 200, 20
	outcomes = np.random.randn(n_obs, n_subjects)
	exposures = np.random.randn(n_obs, n_subjects, n_factors)
	# example of missing data
	outcomes[0:10][:, 0:5] = np.nan
	exposures[0:10][:, 0:5] = np.nan

	# fit mosaic permutation test
	mpt = mp.factor.MosaicFactorTest(
		outcomes=outcomes,
		exposures=exposures,
		test_stat=mp.statistics.mean_maxcorr_stat,
	)
	print(mpt.fit().summary())

	# produce a time series plot of this analysis
	mpt.fit_tseries(
		nrand=100, n_timepoints=20,
	).plot_tseries()
```

See the [documentation](https://mosaicperm.readthedocs.io/) for more details.


# Reference

If you use ``mosaicperm`` in an academic publication, please consider citing our paper:

```
@article{mosaic2024,
  author = {Spector, Asher and Barber, Rina Foygel and Hastie, Trevor and Kahn, Ronald N. and Candès, Emmanuel},
  title = {The mosaic permutation test: an exact and nonparametric goodness-of-fit test for factor models},
  date = {2024},
  annotation = {2024f},
  eprint = {2404.15017},
  eprintclass = {stat.ME},
  eprinttype = {arXiv},
  url={https://arxiv.org/abs/2404.15017},
}
```
dataclass_io 0.2.0
Dataclass IO
Requires Python: <4.0,>=3.10

# dataclass_io

[![CI](https://github.com/msto/dataclass_io/actions/workflows/python_package.yml/badge.svg?branch=main)](https://github.com/msto/dataclass_io/actions/workflows/python_package.yml?query=branch%3Amain)
[![Python Versions](https://img.shields.io/badge/python-3.10_|_3.11_|_3.12-blue)](https://github.com/msto/dataclass_io)
[![MyPy Checked](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://docs.astral.sh/ruff/)

Read and write dataclasses.

`dataclass_io` provides similar functionality to the standard library's `csv.DictReader` and `csv.DictWriter`, and adds type safety.

## Installation

`dataclass_io` may be installed via `pip`:

```console
pip install dataclass_io
```

## Quickstart

### Reading

```py
from dataclasses import dataclass
from dataclass_io import DataclassReader


@dataclass
class MyData:
    foo: int
    bar: str


with open("test.tsv", "w") as testfile:
    testfile.write("foo\tbar\n")
    testfile.write("1\tabc\n")
    testfile.write("2\tdef\n")

with DataclassReader.open("test.tsv", MyData) as reader:
    for record in reader:
        print(record.foo)
```

### Writing
```py
from dataclasses import dataclass
from dataclass_io import DataclassWriter


@dataclass
class MyData:
    foo: int
    bar: str


with DataclassWriter.open("test.tsv", MyData) as writer:
    for i in range(3):
        record = MyData(foo=i, bar="something")
        writer.write(record)
```