semvec/pypi-packages huggingface dataset:
pandas-nhanes 0.1.8
A Python API for accessing NHANES data
Requires Python: >=3.8

# Pandas-Nhanes

A Python package for accessing a cleaned subset of NHANES 
*National Health and Nutrition Examination Survey* data for quick prototyping—no API key required.

Caching is implemented to avoid re-downloading datasets.

## Installation

Install from PyPI:

```bash
pip install pandas_nhanes
```

Or from source:

```bash
git clone https://github.com/jeromevde/pandas_nhanes.git
cd pandas_nhanes
pip install -e .
```

## Usage

```python
from pandas_nhanes import get_variables, get_dataset, explore
```
```python
# Get the full NHANES variable table
variables = get_variables()
```

```python
# Explore the variables table in an interactive HTML table in browser
explore()
```
```python
# Download a dataset as a pandas DataFrame
TST_L = get_dataset("TST_L")
```

## Results

### Cycle 2021-2023 Estradiol
![estradiol](Examples/estradiol.png)

### Cycle 2021-2023 Testosterone
![testosterone](Examples/testosterone.png)
gadsqlalchemy 0.0.3
Wrapper around SQLAlchemy AsyncSession with built-in query execution profiling and connection context management.
Requires Python: >=3.8

<p align="center">
  <a href="https://github.com/AlexDemure/gadsqlalchemy">
    <a href="https://ibb.co/QFMLpQhC"><img src="https://i.ibb.co/Lz1mYRBJ/logo.png" alt="logo" border="0"></a>
  </a>
</p>

<p align="center">
  Wrapper around SQLAlchemy AsyncSession with built-in query execution profiling and connection context management.
</p>

---

### Installation

```
pip install gadsqlalchemy
```

### Usage

```python
from gadsqlalchemy import Sqlalchemy, Base, CRUD

alchemy = Sqlalchemy("postgresql+asyncpg://postgres:postgres@localhost:5432/db")


class Table(Base):
    ...


class Crud(CRUD):
    table = Table


class Service:

    @classmethod
    async def get(cls):
        async with alchemy.connect() as session:
            ...

    @classmethod
    async def create(cls,):
        async with alchemy.connect(transaction=True) as session:
           ...

# testing
import faker

from gadsqlalchemy.testing import Table

fake = faker.Faker()

class Dummy(Table):
    class Meta:
        model = Table

    name = fake.name()

pytest_plugins = [
    "gadsqlalchemy.testing.fixtures",
]

```
finam-mhm 0.2.0
FINAM module for mHM.
Requires Python: >=3.8

# FINAM-mHM component

FINAM component for the mesoscale Hydrological Model - [mHM](https://mhm-ufz.org/)

<a href="https://finam.pages.ufz.de/finam-mhm/" title="FINAM-mHM homepage" target="_blank">
  <img width="300" src="https://git.ufz.de/FINAM/finam-mhm/-/raw/main/docs/source/_static/logo_large.svg" />
</a>

## Installation

```shell
$ pip install finam-mhm
```

## Resources

* FINAM-mHM [documentation](https://finam.pages.ufz.de/finam-mhm/)
* FINAM-mHM [source code](https://git.ufz.de/FINAM/finam-mhm)
* FINAM [homepage](https://finam.pages.ufz.de)
* FINAM [documentation](https://finam.pages.ufz.de/finam/)
* FINAM [source code](https://git.ufz.de/FINAM/finam)
* FINAM [GitLab group](https://git.ufz.de/FINAM), containing further related projects

## License

LGPLv3 (c) 2021-2025 CHS- and FINAM-Developers from Helmholtz-Zentrum für Umweltforschung GmbH - UFZ. All rights reserved.
pytorch-rdc-net 0.1.0
PyTorch implementation of the RDC-Net for 2D and 3D instance segmentation.
Requires Python: >=3.11

# PyTorch RDC-Net

This is a PyTorch implementation of the [RDC-Net](https://github.com/fmi-basel/RDCNet) for instance segmentation of 2D and 3D images. Some demo training scripts can be found [here](https://github.com/fmi-faim/faim_rdcnet).


## Citation
If you find this work useful, please consider citing:

```bibtex
@inproceedings{ortiz2020,
  title={RDCNet: Instance segmentation with a minimalist recurrent residual network},
  author={Ortiz, Raphael and de Medeiros, Gustavo and Peters H.F.M., Antoine and Liberali, Prisca and Rempfler, Markus},
  booktitle={International Workshop on Machine Learning in Medical Imaging},
  year={2020},
}
```
NumRender 0.1.1
A library to convert numbers to words and vice versa
Requires Python: >=3.6

# NumRender Library

A simple Python library to convert numbers to their word representations and vice versa.

## Installation

```bash
# Installation instructions will be added here once published
# For now, you can install it locally:
pip install NumRender
```

## Usage

```python
import NumRender

# Convert words to number
number = NumRender.words_to_number("three hundred and twenty five")
print(number)  # Output: 325

number = NumRender.words_to_number("five thousand")
print(number)  # Output: 5000

# Convert number to words
words = NumRender.number_to_words(1234)
print(words) # Output: one thousand two hundred and thirty-four (example, actual output might vary slightly based on implementation)

words = NumRender.number_to_words(5000)
print(words) # Output: five thousand
```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

This project is licensed under the MIT License - see the LICENSE file for details (if added).
cr-pulse-interpolator 1.1.1
Full waveform interpolation for air shower simulations
Requires Python: >=3.7

# cr-pulse-interpolator
Full electric-field waveform interpolation for air shower simulations

For elaborate information on the methods and their performance, see  
Corstanje et al. (2023), JINST 18 P09005, arXiv **2306.13514**  
Please cite this when using code and/or method in your analysis, publication etc.

## Contents
The package contains two interpolation submodules:
- **interpolation_fourier**: interpolates a 2D scalar function f(x, y) defined on a radial (polar) grid.
  This includes for instance air shower radio energy fluence or amplitude along a radio footprint in the shower plane.
- **signal_interpolation_fourier**: interpolates full time traces (waveforms) of electric-field signals simulated by air shower simulation codes such as CoREAS, in antennas placed on a radial grid.

## Installation and Dependencies
This package can be installed with the following command:  
```sh
  pip install git+https://github.com/nu-radio/cr-pulse-interpolator
```
To automatically install the requirements for the demo scripts, one can instead use  
```sh
  pip install "cr_pulse_interpolator[demo] @ git+https://github.com/nu-radio/cr-pulse-interpolator"  
```

## Usage 
The interpolation modules have an interface very similar to e.g. Scipy's **interp1d** class.  
Runnable demo scripts are:
- **demo_interpolation_fourier.py**: a demo of the interpolation_fourier method, plotting a radio energy footprint heatmap.
- **demo_signal_interpolation.py**: demonstrates full signal interpolation, doing cross-correlations with true simulated signals on "random" test positions.
- **minimal_usage_demo.py**: a smaller demo of full signal interpolation, aimed at getting the user started quickly.

The full signal interpolation demos require example data files to be downloaded:  
run **download_demo_data.sh** before using the demo scripts.  
The h5py package is required to read the demo data.

The example data files contain E-field traces from a CoREAS shower, converted to two "on-sky" polarizations.
They can be given as input in any order when creating the interpolator object.
This is the recommended usage in the interpolator.  
NB. in the full pulse interpolator, the two polarizations should _not_ be aligned to vxB and vx(vxB), as the near-zero amplitudes along circles in the footprint lead to poor accuracy. In the interpolation for amplitude or fluence only, this is not an issue.
It is recommended to rotate the polarizations by 45 degrees when this happens (e.g. zenith showers, or close to north-south axis in general), and rotate back after interpolating.

Another recommendation is to test the accuracy of interpolation by simulating some 10 or 20 additional antennas placed at strategic positions 'in between' the radial-grid positions.
hieudzbahon 0.1.4
A Python library with mathematical and string utilities
Requires Python: >=3.6

**A Python library**  
Thư viện hỗ trợ toán học do Nguyễn Mạnh Hiếu (Học Sinh Trường THCS & THPT BA HÒN ) phát triển, giúp xử lý thông tin nhanh chóng, hỗ trợ lập trình hiệu quả.

```bash
pip install hieudz hoặc pip install hieudzbahon ( chọn 1 trong 2 nếu lỗi ) 
Usage
python

import hieudz
HOW TO USE : hieudz.funcion() ( example : hieudz.help() , hieudz.songuyento(n,m) )
# Tìm số nguyên tố từ 1 đến 10
print(hieudz.songuyento(1, 10))  # Output: [2, 3, 5, 7]

# Tách chữ số
m = []
print(hieudz.tachds(345, m))  # Output: [3, 4, 5]

# Chuẩn hóa chuỗi
print(hieudz.dinhgiang(" ( Hoc , hoc nua , hoc mai ) "))  # Output: (Hoc, hoc nua, hoc mai)

# Xem danh sách hàm
import hdz<help
hieudz.help()  # In danh sách hàm

# Xem mã nguồn hàm
import hdz<code
hieudz.code("dinhgiang")  # In mã nguồn hàm dinhgiang
Functions
songuyento(n, m): Trả về danh sách các số nguyên tố từ n đến m.

tachds(n, m): Tách từng chữ số của n vào danh sách m.

uocchung(n, m): Tìm các ước chung của n và m.

boichung(n, m): Tìm các bội chung của n và m.

uoclonnhat(n): Tìm ước lớn nhất của n.

nguyento(n): Kiểm tra xem n có phải số nguyên tố không.

sohoanhao(n): Kiểm tra xem n có phải số hoàn hảo không.

phantich(n): Phân tích n thành các thừa số nguyên tố.

daonguoc(n): Đảo ngược số n.

tamgiac(a, b, c): Kiểm tra ba cạnh có tạo thành tam giác hợp lệ không.

dinhgiang(n): Chuẩn hóa chuỗi n.

hieudz.help(): Liệt kê tất cả các hàm.

hieudz.code("func_name"): Hiển thị mã nguồn của một hàm.

License
MIT License