semvec/pypi-packages huggingface dataset:
pymetadata 0.5.11
pymetadata are python utilities for working with metadata.
Requires Python: >=3.10
Keywords: COMBINE, annotation, archive, metadata, modeling, standardization

![pymetadata logo](./_docs/images/favicon/pymetadata-100x100-300dpi.png)

# pymetadata: python utilities for metadata and COMBINE archives
[![GitHub Actions CI/CD Status](https://github.com/matthiaskoenig/pymetadata/workflows/CI-CD/badge.svg)](https://github.com/matthiaskoenig/pymetadata/actions/workflows/main.yml)
[![Version](https://img.shields.io/pypi/v/pymetadata.svg)](https://pypi.org/project/pymetadata/)
[![Python Versions](https://img.shields.io/pypi/pyversions/pymetadata.svg)](https://pypi.org/project/pymetadata/)
[![MIT License](https://img.shields.io/pypi/l/pymetadata.svg)](https://opensource.org/licenses/MIT)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5308801.svg)](https://doi.org/10.5281/zenodo.5308801)

pymetadata is a collection of python utilities for working with
metadata in the context of COMBINE standards with source code available from
[https://github.com/matthiaskoenig/pymetadata](https://github.com/matthiaskoenig/pymetadata).

Features include among others

- COMBINE archive version 1 support (OMEX)
- annotation classes and helpers
- SBO, KISAO and ECO ontology enums

If you have any questions or issues please [open an issue](https://github.com/matthiaskoenig/pymetadata/issues).

Documentation is available from https://matthiaskoenig.github.io/pymetadata.

A presentation from HARMONY2026 is available [here](https://matthiaskoenig.github.io/pymetadata/presentations/HARMONY2026/pymetadata.html).

# Installation
`pymetadata` is available from [pypi](https://pypi.python.org/pypi/pymetadata) and
can be installed via

```bash
pip install pymetadata
```

# Cache path
`pymetadata` caches some information for faster retrieval. The cache path is set to

```python
CACHE_PATH: Path = Path.home() / ".cache" / "pymetadata"
```

To use a custom cache path use

```python
import pymetadata
pymetadata.CACHE_PATH = <cache_path>
```


# How to cite
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5308801.svg)](https://doi.org/10.5281/zenodo.5308801)

# License
- Source Code: [MIT](https://opensource.org/license/MIT)
- Documentation: [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/)

# Funding
Matthias König is supported and by the German Research Foundation (DFG) within the Research Unit Programme FOR 5151
"QuaLiPerF (Quantifying Liver Perfusion-Function Relationship in Complex Resection -
A Systems Medicine Approach)" by grant number 436883643 and by grant number
465194077 (Priority Programme SPP 2311, Subproject SimLivA).

Matthias König was supported by the Federal Ministry of Education and Research (BMBF, Germany)
within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054).

© 2021-2026 Matthias König
neco-ping 1.0.2
Minimalistic silent Telegram notifier for ML pipelines
Requires Python: >=3.7

# 🐾 Neco Ping

**Minimalistic, silent Telegram notifier for your weary ML pipelines.**  
*Because your model training takes forever, nya!* 😺💤

---

`neco_ping` is a tiny, fire-and-forget Python library designed for data scientists and ML engineers who just want a notification when their 10-hour training job is done (or crashed). No polling, no async loops, no complex bot setup—just one simple `post` request.

If the internet is down or the Telegram API is grumpy, `neco_ping` stays silent and won't crash your precious script. Safety first, nya!

## 📦 Installation

Install it via pip (once I publish it, nya!):

```bash
pip install neco_ping
```


## 🚀 Usage

It's super simple. Just import and send.

```python
from neco_ping import send

# Your secret stuff (shhh!)
BOT_TOKEN = "123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11"
MY_CHAT_ID = "123456789"

# ... doing heavy math ...
# ... waiting for GPU to go brrr ...

# Send a message when done!
send(BOT_TOKEN, MY_CHAT_ID, "Master, the training is complete! Accuracy: 99.9% Nya! 😻")
```


## 🛠 Features

- **Zero configuration**: Just pass the token and ID.
- **Fail-safe**: Wraps everything in a broad try-except block. It will never raise an exception, ensuring your script finishes even if the message fails.
- **Sync \& Simple**: Uses `requests` under the hood. No `asyncio` headache for simple scripts.
- **Cute**: Verified to increase model accuracy by 0.00% (statistically insignificant, but spiritually powerful).


## 📝 Example for Jupyter Notebook

```python
# Cell 1
from neco_ping import send
token = "..."
uid = "..."

# Cell 2 (The heavy one)
try:
    model.fit(X_train, y_train, epochs=100)
    send(token, uid, "Training success! Time for Chocola! 🍫")
except Exception as e:
    send(token, uid, f"Nyooo! Training failed: {e} 😿")
    raise e
```
---

*Made with love, 🐟 and Python.*

---

## Commit message rules (for auto-versioning)

We use **Conventional Commits** so the release tool can decide how to bump the version automatically. [web:99]

### Version bumps

- No release: dont use prefixes

- Patch (`1.0.0 -> 1.0.1`): use `fix:` for bugfixes and small corrections.  
  Example: `fix: add retry for telegram send`

- Minor (`1.0.0 -> 1.1.0`): use `feat:` for new features or new public functions.  
  Example: `feat: add send_markdown helper`

- Major (`1.0.0 -> 2.0.0`): mark breaking changes with `feat!` in the type/scope, or add a `BREAKING CHANGE:` footer.
invenio-subjects-mesh-common 0.2.0
Common MeSH subject terms functionality
Requires Python: >=3.11
Keywords: invenio, inveniordm, subjects, MeSH

# invenio-subjects-mesh-common

*Common features surrounding MeSH subject terms for InvenioRDM*

<a href="https://pypi.org/project/invenio-subjects-mesh-common/">
  <img src="https://img.shields.io/pypi/v/invenio-subjects-mesh-common.svg">
</a>

This groups common MeSH features and lets downstream distribution packages (e.g., invenio-subjects-mesh-lite)
provide vocabulary fixtures with targeted filtering. This distribution package is meant to be
a dependency to those downstream distribution packages.


## Installation

```bash
pip install invenio-subjects-mesh-common
```


## Development

Install the project in editable mode with `dev` dependencies in an isolated virtualenv. We recommend using `uv`:

```bash
uv pip install -e .[dev]
# or if using pipenv
pipenv run pip install -e .[dev]
```

Run tests:

```bash
uv run invoke test
# or shorter
uv run inv test
# or if using pipenv
pipenv run inv test
```

Test compatibility with the pre-release version of InvenioRDM (invenio-app-rdm):

```bash
uv run --extra dev_pre --prerelease=allow inv test
```

Check manifest:

```bash
uv run inv check-manifest
# or if using pipenv
pipenv run inv check-manifest
```

Clean out artefacts:

```bash
uv run inv clean
# or if using pipenv
pipenv run inv clean
```
fresco 3.9.0
A Web/WSGI micro-framework
Keywords: wsgi, web, www, framework

Fresco, a web micro-framework for Python
========================================

The fresco web framework is:

- Fast, simple and powerful, with sophisticated URL routing, request and response objects.
- Lightweight and open to integration: you pick the templating and database libraries you want.
- WSGI compliant: easy integration with your choice of web server, apps and middleware.

A minimal fresco web framework application:

.. code-block:: python

    from fresco import FrescoApp, GET, Response

    def helloworld():
        return Response("<h1>Hello World!</h1>")

    app = FrescoApp()
    app.route('/', GET, helloworld)


Read the
`fresco web framework documentation
<https://ollycope.com/software/fresco/latest/>`_ for
more about the framework, or
visit the `source repo <https://sr.ht/~olly/fresco/>`_.
Topsis-Chahat-102303831 1.0.2
A Python package implementing TOPSIS for MCDM problems

\## Project Description



Topsis-Chahat-102303831



For: Project-1 (UCS633)

Submitted by: Chahat  

Roll No: 102303831



Topsis-Chahat-102303831 is a Python package for solving Multiple Criteria Decision Making (MCDM) problems using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).  

The package ranks alternatives based on their distance from the ideal best and ideal worst solutions.



---



Installation:



Install the package using pip:



pip install Topsis-Chahat-102303831



---



Usage



Provide the input file name, followed by the weights vector, impacts vector, and the output file name.



topsis data.xlsx "1,1,1,1,1" "+,+,+,+,+" output.csv



Vectors can also be entered without double quotes:



topsis data.xlsx 1,1,1,1,1 +,+,+,+,+ output.csv



If the vectors contain spaces, they must be enclosed in double quotes.



To view help:



topsis /h



---



Example



Input File (data.xlsx)



| Fund Name | P1   | P2  | P3  | P4   | P5   |

|---------- |----- |-----|---- |----- |------|

| M1        | 0.62 | 0.38| 7.0 | 42.6 | 12.65|

| M2        | 0.79 | 0.62| 4.8 | 65.5 | 17.93|

| M3        | 0.75 | 0.56| 4.6 | 63.7 | 17.40|

| M4        | 0.78 | 0.61| 4.9 | 66.3 | 18.15|

| M5        | 0.61 | 0.37| 6.4 | 32.3 | 9.92 |

| M6        | 0.78 | 0.61| 4.2 | 45.4 | 12.75|

| M7        | 0.90 | 0.81| 3.5 | 63.3 | 17.13|

| M8        | 0.63 | 0.40| 4.1 | 61.7 | 16.71|



Weights vector = \[ 1 , 1 , 1 , 1 , 1 ]  

Impacts vector = \[ + , + , + , + , + ]



---



Input Command



topsis data.xlsx "1,1,1,1,1" "+,+,+,+,+" output.csv



---



Output (output.csv)



| Fund Name | P1   | P2   | P3  | P4   | P5    | Topsis Score | Rank |

|---------- |---- -|----- |---- |----- |------ |--------------|------|

| M1        | 0.84 | 0.71 | 6.7 | 42.1 | 12.59 | 0.563692     | 3    |

| M2        | 0.91 | 0.83 | 7.0 | 31.7 | 10.11 | 0.513032     | 4    |

| M3        | 0.79 | 0.62 | 4.8 | 46.7 | 13.23 | 0.439177     | 6    |

| M4        | 0.78 | 0.61 | 6.4 | 42.4 | 12.55 | 0.491956     | 5    |

| M5        | 0.94 | 0.88 | 3.6 | 62.2 | 16.91 | 0.641886     | 2    |

| M6        | 0.88 | 0.77 | 6.5 | 51.5 | 14.91 | 0.738148     | 1    |

| M7        | 0.66 | 0.44 | 5.3 | 48.9 | 13.83 | 0.407390     | 8    |

| M8        | 0.93 | 0.86 | 3.4 | 37.0 | 10.55 | 0.408499     | 7    |



---





Notes



\- The first column represents the alternatives and is not used in calculations.

\- All criteria columns must contain numeric values only.

\- The number of weights, impacts, and criteria columns must be equal.

\- The input file must contain at least three columns.



---



License



MIT