semvec/pypi-packages huggingface dataset:
juan-almeida-image-processing 0.0.1
Image Processing Package using scikit-image
Requires Python: >=3.8

# Projeto DIO: Pacote de Processamento de Imagens em Python

### Autora: Karina Kato
### Disponível em: https://pypi.org/project/wallet-python-pacotes-imagens/
### Repositório de exemplos do template: https://github.com/tiemi/

---

## Descomplicando a criação de pacotes de processamento de imagens em Python


### O pacote "image_processing" é usado para:

Módulo "Processing":
- Correspondência de histograma;
- Similaridade estrutural;
- Redimensionar imagem;

Módulo "Utils":
- Ler imagem;
- Salvar imagem;
- Plotar imagem;
- Plotar gráfico;
- Plotar histograma;


### Resultado do processamento:

<p align="left"><img src="./ex_img.jpg" width="800"></p>
pyPhyNR 0.1.0
Python toolkit for 5G NR physical layer simulations
Requires Python: >=3.9
Keywords: 5G NR, physical layer, telecommunications, signal processing

# pyPhyNR

Python toolkit for 5G NR physical layer simulations. This package provides tools and utilities for working with 5G NR physical layer concepts, including resource grid visualization, waveform generation, and signal processing techniques.

## Installation

```bash
pip install pyPhyNR
```

## Features

Current working features:
- Resource grid visualization
- Waveform generation
- OFDM signal processing
- PDSCH (Physical Downlink Shared Channel) implementation
- DMRS (Demodulation Reference Signals) for PDSCH
- Flexible numerology support

Note: Other physical channels (PBCH, PDCCH, PSS/SSS) are under development and not fully tested yet.

## Quick Start

```python
import pyPhyNR as pynr

# TDD slot configuration (20 slots per frame)
# 0: Downlink, 1: Uplink, 2: Special slot
TDD_SLOT_PATTERN = [0, 0, 0, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

# Create signal builder for 20MHz carrier with 30kHz subcarrier spacing (numerology 1)
signal = pynr.NRSignalBuilder(bandwidth_mhz=20, numerology=1, cell_id=1)

# Configure carrier parameters
signal.configure_carrier(
    sample_rate=23.04e6,
    fft_size=768
).initialize_grid()

# Add PDSCH to downlink slots with DMRS
dl_slots = [i for i, slot in enumerate(TDD_SLOT_PATTERN) if slot == 0]
signal.add_pdsch(
    start_rb=0,
    num_rb=51,  # Full bandwidth
    start_symbol=0,
    num_symbols=14,  # Full slot
    slot_pattern=dl_slots,
    modulation="QAM256",
    power=100.8
).add_dmrs(
    dmrs_positions=[2, 11],  # DMRS symbols within slot
    clear_full_symbol=False,
    subcarrier_pattern="even",  # DMRS on even subcarriers
    power_offset_db=0.0
)

# Visualize resource grid
pynr.utils.plot_grid_dl(signal.carrier_config, signal.grid)

# Generate and analyze waveform
iq_samples = signal.generate_signal()
pynr.utils.plot_time_domain(iq_samples, signal.carrier_config)
pynr.utils.plot_frequency_domain(iq_samples, signal.carrier_config)
```

This example demonstrates:
- Configuring a 20 MHz TDD carrier with 30 kHz subcarrier spacing
- Setting up a realistic TDD slot pattern
- Adding PDSCH with 256-QAM modulation to downlink slots
- Configuring DMRS with proper symbol positions and power
- Visualizing and analyzing the generated signal

## Requirements

- Python >= 3.9
- NumPy
- Matplotlib
- SciPy

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Author

kir812

## Project Status

This project is in alpha stage (version 0.1.0). APIs may change in future releases.
Encryptors 1.8.49
End-to-end algorithm library
Requires Python: >=3.10

# osdental-library

`osdental-library` is a versatile and easy-to-use library for handling common tasks related to **encryption**, **hashing**, and **JWT token management**. Ideal for projects that require a secure and efficient approach to handling sensitive data.

## Features

- Generation and validation of **hashes** using modern algorithms.
- Encryption and decryption of data using secure keys.
- Creation and verification of **JWT tokens** for authentication and authorization.
- Implementations that are easy to integrate into any Python project.

## Installation

You can easily install `osdental-library` using `pip`:

```bash
pip install osdental-library
fitdistcp 0.1.1
A Python package for making reliable predictions using calibrating priors.
Requires Python: >=3.9

# fitdistcp

fitdistcp is a free Python package for fitting statistical models using calibrating priors, with the goal of making reliable predictions. Install using >pip install fitdistcp.

fitdistcp implements the method developed in *Reducing Reliability Bias in Assessments of Extreme Weather Risk using Calibrating Priors*, S. Jewson, T. Sweeting and L. Jewson (2024): https://doi.org/10.5194/ascmo-11-1-2025.

More information and examples are available at https://www.fitdistcp.info, including the equivalent (more comprehensive) R package.

Development of this package was funded by the Lighthill Risk Network: https://lighthillrisknetwork.org.


### Example: Fitting a GEV distribution
```python
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
import fitdistcp.genextreme

x = scipy.stats.genextreme.rvs(0, size=20)                  # make some example training data 
p = np.arange(0.001,0.999,0.001)                            # define the probabilities at which we wish to calculate the quantiles
q = fitdistcp.genextreme.ppf(x,p)                           # this command calculates two sets of predictive quantiles for the GEV, 
                                                            # one based on maxlik, and one that includes parameter uncertainty based on a calibrating prior
print(q['ml_params'])                                       # have a look at the maxlik parameters
plt.plot(q['ml_quantiles'],p, label='ML')                   # plot the maxlik quantiles
plt.plot(q['cp_quantiles'],p,color='red', label='CP')       # plot the quantiles that include parameter uncertainty
plt.legend()
plt.show()
```


### Models
The following models are currently supported. Let us know if you have any suggestions for other models to include.
- expon: Exponential distribution
- gamma: Gamma distribution
- genextreme: Generalised Extreme Value (GEV) distribution
- genextreme_p1 & genextreme_p12: Generalised Extreme Value (GEV) distribution, with 1 predictor, 2 predictors
- genpareto: Generalised Pareto distribution
- gumbel: Gumbel distribution
- lnorm: Lognormal distribution
- norm:	Normal distribution
- weibull:	Weibull distribution


### Methods
Four methods are provided for each model: ppf(x) (quantiles, 'percentage point function'), rvs(n, x) (random variates), pdf(x) and cdf(x), where x is the data to fit.
To use, e.g. ppf for the normal distribution, import fitdistcp.norm and call fitdistcp.norm.ppf(x).
To test the methods use e.g. fitdistcp.norm.reltest(). Reltests (reliability tests) are used to test the reliability of quantile calculations for making predictions.