semvec/pypi-packages huggingface dataset:
sinemarc-api 0.0.1
A libary for the rest api of SINEMA RC
Requires Python: >=3.7
Keywords: network,sinemarc

This package for python, which can be used to connect to your SINEMA RC RestAPI.
You can use it to manage your server, devices and users. It provides an easy access to SINEMA RC.


It is using the SINEMA RC API. It can be bought with the MLFB 6GK1724-3VH03-0BV0

Manual
https://support.industry.siemens.com/cs/de/en/view/109793829
timeslime 2.3.0
⌚ This is timeslime! Have fun tracking your time ⌚
Requires Python: >=3.11

# ⌚ timeslime

⌚ This is timeslime! Have fun tracking your time ⌚

## Getting started

Install python and pip and execute the following

~~~bash
pip install timeslime
~~~

🚀 Start your time

~~~bash
timeslime start
~~~

🛑 Stop your time

~~~bash
timeslime stop
~~~

🔧 Configure your weekly working hours (e.g for 40 hours per week)

~~~bash
timeslime config --weekly-working-hours=40
# Get more configuration options with the following.
timeslime config --help
~~~

📊 Get report 

~~~bash
timeslime report
~~~

🤝 Synchronize your time and configuration 

~~~bash
timeslime sync
~~~

💡 Get help

~~~bash
timeslime --help
~~~

## Contribute

~~~bash
git clone https://gitlab.com/lookslikematrix/timeslime.git
cd timeslime
pipenv install --dev
pipenv shell
pytest tests
pip install -e .
timeslime --help
~~~
gretta 0.0.1
Tensor-based SSA for sparse datasets with spatiotemporal information

# GRETTA
**G**eneralized **RE**stricted **T**ensor **T**imeseries **A**nalysis.

This package is designed to perform multivariate analysis of incomplete timeseries based on the generalization of the restricted SSA method to sparse higher order (3D) data.
See an example on the analysis of spatiotemporal humidity data in the [Example-1.ipynb](Example-1.ipynb) jupyter notebook.

# Requirements
- numpy
- scipy
- pandas
- numba

# Citation
If you use `gretta` in published research, please cite:
> Frolov E, Oseledets I. 2023. Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations. IEEE Access. 2023 Jan 5; 11:6357-71. DOI: [10.1109/ACCESS.2023.3234863](https://doi.org/10.1109/ACCESS.2023.3234863). arXiv: [2212.05720](https://arxiv.org/abs/2212.05720).

BibTex entry:
```
@ARTICLE{Frolov2023,
  author={Frolov, Evgeny and Oseledets, Ivan},
  journal={IEEE Access}, 
  title={Tensor-Based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations}, 
  year={2023},
  volume={11},
  number={},
  pages={6357-6371},
  doi={10.1109/ACCESS.2023.3234863}}
```