semvec/pypi-packages huggingface dataset:
PhotosAPI-Client 0.6.0
A client library for accessing Photos API
Requires Python: >=3.7, <4

# PhotosAPI_Client

A client library for accessing Photos API

## Usage

First, create a client:

```python
from photosapi_client import Client

client = Client(base_url="https://api.example.com")
```

If the endpoints you're going to hit require authentication, use `AuthenticatedClient` instead:

```python
from photosapi_client import AuthenticatedClient

client = AuthenticatedClient(base_url="https://api.example.com", token="SuperSecretToken")
```

Now call your endpoint and use your models:

```python
from photosapi_client.models import MyDataModel
from photosapi_client.api.my_tag import get_my_data_model
from photosapi_client.types import Response

my_data: MyDataModel = get_my_data_model.sync(client=client)
# or if you need more info (e.g. status_code)
response: Response[MyDataModel] = get_my_data_model.sync_detailed(client=client)
```

Or do the same thing with an async version:

```python
from photosapi_client.models import MyDataModel
from photosapi_client.api.my_tag import get_my_data_model
from photosapi_client.types import Response

my_data: MyDataModel = await get_my_data_model.asyncio(client=client)
response: Response[MyDataModel] = await get_my_data_model.asyncio_detailed(client=client)
```

By default, when you're calling an HTTPS API it will attempt to verify that SSL is working correctly. Using certificate verification is highly recommended most of the time, but sometimes you may need to authenticate to a server (especially an internal server) using a custom certificate bundle.

```python
client = AuthenticatedClient(
    base_url="https://internal_api.example.com", 
    token="SuperSecretToken",
    verify_ssl="/path/to/certificate_bundle.pem",
)
```

You can also disable certificate validation altogether, but beware that **this is a security risk**.

```python
client = AuthenticatedClient(
    base_url="https://internal_api.example.com", 
    token="SuperSecretToken", 
    verify_ssl=False
)
```

There are more settings on the generated `Client` class which let you control more runtime behavior, check out the docstring on that class for more info.

Things to know:

1. Every path/method combo becomes a Python module with four functions:
    1. `sync`: Blocking request that returns parsed data (if successful) or `None`
    1. `sync_detailed`: Blocking request that always returns a `Request`, optionally with `parsed` set if the request was successful.
    1. `asyncio`: Like `sync` but async instead of blocking
    1. `asyncio_detailed`: Like `sync_detailed` but async instead of blocking

1. All path/query params, and bodies become method arguments.
1. If your endpoint had any tags on it, the first tag will be used as a module name for the function (my_tag above)
1. Any endpoint which did not have a tag will be in `photosapi_client.api.default`
random-words-generator-fr 0.3
Génère un nombre spécifié de mots français au hasard, entre 0 et 20 mots. Par défaut, génère 8 mots. utile pour divers projets nécessitant des mots français aléatoires. Permet de générer des mots pour des jeux, des tests de vocabulaire, des noms de fichiers uniques, et bien plus encore.
Requires Python: >=3.6
Keywords: mots français aléatoires générateur de mots

# Random Words Generator fr

## Description

`random_words_generator` est une bibliothèque Python simple qui génère un nombre spécifié de mots français au hasard, entre 0 et 20 mots. Par défaut, génère 8 mots. Elle est utile pour divers projets nécessitant des mots français aléatoires, tels que les jeux, les tests de vocabulaire, ou même pour générer des noms de fichiers ou des identifiants uniques.

## Installation

Pour installer `random_words_generator`, utilisez pip :

`bash pip install random_words_generator`

## Utilisation

Pour utiliser `random_words_generator`, importez la fonction `generate_random_words` et appelez-la :

`python from random_words_generator.words import generate_random_words`

Générer 8 mots français par defaut au hasard

`words = generate_random_words() print(words)`

Générer 10 mots français (par exemple) au hasard

`words = generate_random_words(10) print(words)`

## Contribution

Les contributions sont les bienvenues. Si vous souhaitez ajouter des fonctionnalités ou signaler des bugs, veuillez créer une issue ou une pull request.

## Licence

`random_words_generator` est distribué sous la licence MIT. Voir le fichier `LICENSE` pour plus d'informations.

## Contact

Si vous avez des questions ou des suggestions, n'hésitez pas à me contacter.
numerous-solver 3.1.1
Numerous ODE solver
Requires Python: >=3.10

## A jitable object-oriented ODE solver for numerical simulations

## Overview:
Numerous solver is a fast, jitable, highly customizable, and object-oriented ODE (ordinary differential equations) 
solver. 

## How to get started

Install the package via `pip install numerous-solver`, or clone the 
[git repository](https://gitlab.com/numerous/numerous-solver) and install using the `setup.py` 
file.

## Documentation
Please see the [gitlab pages](https://numerous.gitlab.io/numerous-solver/) site for documentation.

### Design philosophy
The `numerous solver` is the choice of ODE (ordinary differential equations) solver for `numerous engine` 
(https://github.com/fossilfree/numerous). It is developed following a set of principles:

- **Object-oriented**: A python-based solver should take advantage of the class structure of python. Numerous solver uses 
"models" and "interfaces" to manage the state-flow between the numerical model and the numerical solver.
- **Customizable**: Using "solver events", the solver can be customized to break the solver loop in order to read external
data, save outputs to database, print status messages etc. This allows a great degree of customization.
- **Support time- and state-events**: Built-in support for time- and state-events to support advanced models, controllers 
and physics discontinuities.
- **Jitable**: `Numerous solver` may be compiled with numba (https://numba.pydata.org/) to, in some cases, increase speed. 
The build-in toolchain can assist the user to write (njit) compilable models. 
- **Extendable**: Numerous solver currently supports 3 methods (RK45, BDF, Euler) and can be extended with new solve 
methods.
- **Open-source**: The code base is open for contributions.
autodistill-eva-clip 0.1.0
EvaClip module for use with Autodistill
Requires Python: >=3.7

# Autodistill EvaCLIP Module

This repository contains the code supporting the CLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[EvaCLIP](https://github.com/baaivision/EVA/tree/master/EVA-CLIP),  is a computer vision model trained using pairs of images and text. It can be used for classification of images.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [EvaCLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/evaclip/).

## Installation

To use EvaCLIP with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-evaclip
```

## Quickstart

```python
from autodistill_evaclip import EvaCLIP
from autodistill.detection import CaptionOntology

# define an ontology to map class names to our EvaCLIP prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = EvaCLIP(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)

results = base_model.predict("./context_images/test.jpg")

print(results)

base_model.label("./context_images", extension=".jpeg")
```
## License

The code in this repository is licensed under an [MIT license](LICENSE.md).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!