semvec/pypi-packages huggingface dataset:
pyfrdas2 0.10
Generate DAS2 for the french tax administration
Requires Python: >=3.7

This python library help generate the **TéléTD files** for the french fiscal administration, which include **DAS2**, also called *Déclaration d'honoraires*. It is used by the OCA module l10n\_fr\_das2 from [OCA/l10n-france](https://github.com/OCA/l10n-france).

The French fiscal administration updates the encryption keys for DAS2 files every year. The encryption keys are updated in this module, which avoids updating the OCA module l10n\_fr\_das2 for every Odoo version. The specifications of the DAS2 file and the encryption keys are available on [www.impots.gouv.fr/tiers-declarants-0](https://www.impots.gouv.fr/tiers-declarants-0).

## Licence

This library is published under the [GNU Lesser General Public License v2.1](https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html) or, at your option, any later version.

## Contributors

* Alexis de Lattre <alexis.delattre@akretion.com>

## Changelog

* version 0.10 dated 2025-11-19: PGP keys for 2025 are the same as last year.
* version 0.9 dated 2025-06-17: drop pgpy because it is not maintained. Replace by python-gnupg, a python wrapper on gpg. Add pyfrdas2.\_\_version\_\_ to get the version of the lib.
* version 0.8 dated 2025-06-06: switch back to pgpy because pgpy13 solves the warning but requires a minimum version of cryptography which breaks odoo tests on python 3.10.
* version 0.7 dated 2025-06-06: switch to pgpy13 to avoid warning. Remove use of pkg\_resources.
* version 0.6 dated 2025-03-21: threshold for year 2024+ updated from 1200 € to 2400 € ([source](https://bofip.impots.gouv.fr/bofip/14327-PGP.html/ACTU-2024-00154))
* version 0.5 dated 2024-12-14: modernize packaging and version management
* version 0.4 dated 2024-06-06: new release to fix bad build
* version 0.3 dated 2024-06-06: add method **get\_partner\_declaration\_threshold()**
* version 0.2 dated 2024-03-12: declare pyfrdas2 as compatible with python 3.6+
* version 0.1 dated 2024-03-11: initial release
fiveD-NN 0.0.3
A neural network to perform interpolation on 5 dimensional data using a neural network.
Requires Python: >=3.8

# Dummy Package Project

This is an example Python package structure with a simple dummy module and class.

## Project Structure

```
dummy_package_project/
├── dummy_package/
│   ├── __init__.py
│   └── dummy_module/
│       ├── __init__.py
│       └── dummy_class.py
├── README.md
```

## Installation

Clone this repository, navigate to the `dummy_package_project` folder and you can install the package locally:

```bash
pip install .
```

## Usage

Example usage of the `DummyClass`:

```python
from dummy_package.dummy_module import DummyClass

obj = DummyClass()
print(obj.__name__)  # Output: DummyClass
print(obj.age)       # Output: Not very old
print(obj.sum(2, 3)) # Output: 5
```

## Package Contents

- **dummy_package.dummy_module.dummy_class.DummyClass**
  A dummy class with a `sum(a, b)` method and example attributes.

## Development

To install development dependencies:

```bash
pip install -r requirements.txt
```

## License

This project is provided for demonstration purposes.
unimath 1.4.5
Mathematical Environment Library in Python
Requires Python: >=3.8

# unimath — Mathematical Environment Library in Python

  

  

**unimath** is a Python library that provides an abstract mathematical environment designed to represent and manipulate sets, intervals, and number systems in a symbolic and intuitive way.

  

The project is continuously growing, with new mathematical structures and operations being added every day.

  

  

Developed and maintained by a **Mathematics undergraduate student at Marmara University**, unimath aims to combine mathematical theory with computational representation, creating a bridge between abstract reasoning and code implementation.

  

  

---

  

  

## Features

  

  

- Abstract representation of **Sets**, **Intervals**, and **Number Systems**

  

- Support for **Vector operations** and **linear algebra concepts**

  

- Symbolic and numerical computation environment

  

- Integration with **matplotlib** for visualization

  

- Intuitive class structures and error handling for mathematical rigor

  

  

---

  

  

## Vector

  

  

- Magnitude

  

- Specific vector information

  

- Inner product

  

- Angle between two vectors

  

- Visualization with matplotlib

  

- Find unit vector

  

- Cosine value between two vectors

  

- Projection around two vectors

  

  

---

  

  

## Matrix

  

  

- Precisely defined mathematically

  

- Transpose

  

- Special matrix definitions

  

- Determinant

  

- Hadamard and classical product

  

- Symbolic Matris

  

- (3,3) Sarrus Method

  

  

---

  

  

## Combinatorics

  

  

- Permutation

  

- Combination

  

- Posibilty

  

  

---

  

  

## Calculus

  

  

- Find Supremum İnfrimum value

  

- Σ and Π operations

  

- Transformation

  

- Monotonicity of the series

  

- Symbolid sequance

  

  

---

  

## Physics

  

  

- Wave equation analysis and visualization

$$
\psi(x,y) = \frac{2}{\sqrt{L_x L_y}} \sin\left(\frac{n_x \pi x}{L_x}\right) \sin\left(\frac{n_y \pi y}{L_y}\right)
$$

-  Energy levels of a particle in a two-dimensional cubic box

$$
E = \frac{\pi^2 \hbar^2}{2m} (n_x^2 + n_y^2 + n_z^2)
$$

```bash

Wave_Equation(Lx=2.0, Ly=1.5, nx=4, ny=1, N=100)

```
  

---

  

  

## Visualization

  

  

unimath includes built-in vector visualization features using **matplotlib**, allowing users to plot and analyze vector relationships directly in Python. This helps bridge the gap between symbolic manipulation and geometric intuition.

  

## Installation / Download

  

  

### PyPI (Recommended)

  

```bash

pip install unimath

```

### Git

  

```bash

git clone https://github.com/Jolankaa/unimath

cd unimath

pip install .

```

  

### Few Examples

```bash

import unimath

# Example: Creating a vector

  

v = unimath.Vector([1,2,3])

  

print("Magnitude:", v.magnitude())

# For visualization

v.Visualization()

  

# Example: Creating a matrix

  

m = unimath.Matrix([[1,2],[3,4]])

print("Determinant:", m.determinant())

```