semvec/pypi-packages huggingface dataset:
flextream 0.1.2
Handler for batching messages to azure eventhubs
Requires Python: >=3.9

# Flextream 🏞️

Utility library forstreaming to Azure Event hubs. Handles background threaded process, and connection caching to minimise bottlenecks when streaming data over hubs.

Can be pip installed with `pip install flextream`.

To use, you'll mostly, just need a single function:

```python
from flextream import send_to_event_hub

send_to_event_hub(
  {"message": "hello world!", "also_some_numbers": [1, 2, 3]},
  namespace="namestapce-name.servicebus.windows.net",
  eventhub="my-first-eventhub",
  latency=10,
)
```

In the above example, bespoke credentials haven't been passed into the `credential` keyword, so it will fall back to authenticating with `DefaultAzureCredentials`. The `latency` parameter is the maximum amount of time in seconds that a message will be held onto before being sent in a background triggered thread.

Any other messages sent to the eventhub during the wait time will be bundled alongside in a batch for efficiency.
leaf-toolkit 1.0.0
Leaf Analysis and Evaluation Toolkit
Requires Python: >=3.12

# leaf-toolkit

This package is for testing and evaluating the Leaf Evaluation and Analysis Framework (LEAF). Please note that especially the GPU version is not runtime optimized. The goal is rather to provide an easy to use tool.
Pull requests for better runtime are welcome.

## Documentation
For more details on how to use the code, please feel free to browse the [Documentation](https://radekzenkl.github.io/leaf-toolkit/home.html)

## Installation 
Please note that this package requires python 3.12 or higher.

You can install the leaf-toolkit directly from pip:
```
pip install leaf-toolkit
```

or from source:
```
git clone https://github.com/RadekZenkl/leaf-toolkit.git
cd leaf-toolkit
pip install -e .
```

## Citation
@software{Leaf-Toolkit,
author = {Zenkl, Radek and Anderegg, Jonas and McDonald, Bruce},
license = {GPLv3},
month = april,
title = {{leaf-toolkit}},
url = {https://github.com/RadekZenkl/leaf-toolkit},
version = {1.0.0},
year = {2025}
}

@article{zenkl2025towards,
  title={Towards high throughput in-field detection and quantification of wheat foliar diseases using deep learning},
  author={Zenkl, Radek and McDonald, Bruce A and Walter, Achim and Anderegg, Jonas},
  journal={Computers and Electronics in Agriculture},
  volume={232},
  pages={109854},
  year={2025},
  publisher={Elsevier}
}
eo4eu-ddsapi 0.7.2
EO4EU fork of official ddsapi package
Requires Python: >=3.11

# DDSAPI-Client
Python Client to access and download data from [CMCC Data Delivery System (DDS)](https://dds.cmcc.it)

## Requirements
Python 3.7, Python 3.8

### Installation  
Conda Installation
```bash
$ conda install -c fondazione-cmcc ddsapi 
```

Pip installation
```bash
$ pip install ddsapi
```

### Configuration
To use the tool a file `$HOME/.ddsapirc` must be created as following

```bash
url: https://ddshub.cmcc.it/api/v2
key: <api-key>
```