semvec/pypi-packages huggingface dataset:
yarik-django-airflow-api-manager 2.0.20
Airflow api manager django app.
Requires Python: >=3.12

# YARIK-django-auth

## Сборка пакета
```bash
python -m venv .venv
. ./.venv/bin/activate
pip install -r requirements.txt
python -m pip install build
python -m build
```

## Публикация в индексе
```bash
pip install twine
twine upload --repository-url http://<hostname>:<port> dist/*
```

## Установка пакета из индекса
```bash
pip install --index-url http://<hostname>:<port> yarik-django-auth --trusted-host <hostname>
```
httomolib 4.0
Commonly used tomography data processing methods at DLS.
Requires Python: >=3.10

HTTomolib is a library of methods for tomography
-------------------------------------------------

**HTTomolib** is a collection of CPU-only image processing methods in Python for computed tomography.

Purpose of HTTomolib
====================

**HTTomolib** can be used as a stand-alone library, however, it has been specifically developed to 
work together with the `HTTomo <https://diamondlightsource.github.io/httomo/>`_ package.
HTTomo is a user interface (UI) written in Python for fast big data processing using MPI protocols.
**HTTomolib** methods for processing using GPU are accessible in the dedicated
`HTTomolibGPU <https://github.com/DiamondLightSource/httomolibgpu>`_ repository. 

Installation
============

HTTomolib is available on PyPI, so it can be installed into either a virtual environment or a
conda environment.

Virtual environment
~~~~~~~~~~~~~~~~~~~
.. code-block:: console

   $ python -m venv httomolib
   $ source httomolib/bin/activate
   $ pip install httomolib

Conda environment
~~~~~~~~~~~~~~~~~
.. code-block:: console

   $ conda create --name httomolib # create a fresh conda environment
   $ conda activate httomolib # activate the environment
   $ pip install httomolib

Setup the development environment:
==================================

.. code-block:: console
    
   $ git clone git@github.com:DiamondLightSource/httomolib.git # clone the repo
   $ conda create --name httomolib # create a fresh conda environment
   $ conda activate httomolib # activate the environment
   $ pip install -e . # development mode
losoto 2.6.0
LOFAR Solution Tool
Requires Python: >=3.10
Keywords: LOFAR, astronomy, radio astronomy, data analysis

LoSoTo: LOFAR solutions tool
============================

### Main developer:
* Francesco de Gasperin

### Contributed code:
* David Rafferty
* Henrik Edler
* Maaijke Mevius
* Jose Sabater Montes
* Martin Hardcastle
* Andreas Horneffer
* and the LOFAR Survey Key Science Project

### Cite:
* If you use LoSoTo for your data reduction, please cite: de Gasperin et al. 2019, Astronomy & Astrophysics, Volume 622, id.A5
(https://ui.adsabs.harvard.edu/abs/2019A&A...622A...5D/abstract)

### Install:
* Install latest official release of LoSoTo from PyPI:
  `pip install --upgrade losoto`
* Or, install latest development version of LoSoTo from GitHub:
  `pip install --upgrade --user https://github.com/revoltek/losoto/archive/master.zip`
* Or clone the LoSoTo repository from https://github.com/revoltek/losoto, and install LoSoTo using:
  `python setup.py install --prefix=~/mydir/`
* Prepare a `parset` file, which can be based on the file `parset/losoto2.parset`
* In case of problems write to Francesco de Gasperin: astro@voo.it

### H5parm plotter (GUI):
This is an external project maintained by Frits Sweijen: https://github.com/tikk3r/lofar-h5plot

### Documentation:
* Documentation of LoSoTo API is at: __http://revoltek.github.io/losoto/losoto.html__
* A detailed explanation of all the parameters is at: __http://revoltek.github.io/losoto/losoto.operations.html__
* A few important articles/how-to are on the github wiki: __https://github.com/revoltek/losoto/wiki__

### Contents:
* __bin/__: the losoto executable and some manipulators for H5parms
* __docs/__: documentation
* __examples/__: some examples h5parm to use with validation/test parsets
* __losoto/operations/__: contains all the modules for operations
* __parsets/__: some examples parsets
* __tools/__: contains some external tools

### Required packages:
* Python 3
* PyTables version:  >3.4.0
* HDF5 version:      >1.8.4
* NumPy version:     >1.9.0
* Scipy              >1.4 (for interpolatedirections)
wikipedia-tablo-cekici 1.0.4
Wikipedia sayfalarından tablo verilerini çeken Python kütüphanesi
Requires Python: >=3.7
Keywords: wikipedia table scraper data extraction tablo çekici

# Wikipedia Tablo Çekici

Wikipedia sayfalarından tablo verilerini çeken Python kütüphanesi.

## Kurulum
```bash
pip install wikipedia-tablo-cekici
```

## Kullanım

### Temel Kullanım
```python
from wikipedia_tablo_cekici import wikipedia_tablo_cek, tablo_kaydet

url = "https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)"
df = wikipedia_tablo_cek(url, tablo_index=0)
tablo_kaydet(df, 'veriler', 'csv')
```

### Tüm Tabloları Görüntüleme
```python
from wikipedia_tablo_cekici import tum_tablolari_goster

url = "https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)"
tum_tablolari_goster(url)
```

### Farklı Formatlarda Kaydetme
```python
# CSV
tablo_kaydet(df, 'veriler', 'csv')

# Excel
tablo_kaydet(df, 'veriler', 'excel')

# JSON
tablo_kaydet(df, 'veriler', 'json')
```

## Özellikler

- Wikipedia tablolarını Pandas DataFrame'e dönüştürme
- CSV, Excel ve JSON formatlarında kaydetme
- Basit ve kullanımı kolay API

## Gereksinimler

- Python 3.7+
- requests
- beautifulsoup4
- pandas
- lxml
- openpyxl

## Lisans

MIT

## Yazar

Özgür Özen

## GitHub

https://github.com/ozgurrozennn/my_project
xara-astro 1.0.1
A package for eXtreme Angular Resolution Astronomy
Requires Python: >=3.9

# XARA: a python package for eXtreme Angular Resolution Astronomy

**XARA** is a python module that was initially designed to create, extract and manipulate Fourier-phase out of diffraction-dominated astronomical images, using the theory introduced by several journal papers:

- [Martinache (2010), ApJ, 724, 464](https://ui.adsabs.harvard.edu/abs/2010ApJ...724..464M)
- [Martinache (2013), PASP, 125, 422](https://ui.adsabs.harvard.edu/abs/2013PASP..125..422M)
- [Martinache et al (2020), A&A, 636, A72](https://doi.org/10.1051/0004-6361/201936981)

Over the years, the package has been used by several graduate students, motivated by obsevring projects using a wide range of instruments (ground based adaptive optics assisted cameras as well as space borne instruments). The same students  have often made their own fork of the project.

## Tutorial/documentation

A documentation featuring several tutorial examples and a fair amount of howto explanation is available here: http://frantzmartinache.eu/xara_doc/

## Acknowledgement

From 2016 to 2022, the development of XARA was supported by the ERC KERNEL project is a development carried out in the context of the KERNEL project. KERNEL has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement CoG - 683029).

## Installation

> pip install xara-astro
traceAI-google-genai 0.1.1
OpenTelemetry instrumentation for Google Gen AI
Requires Python: <3.14,>3.9

# Google Gen AI OpenTelemetry Integration

## Overview
This integration provides support for using OpenTelemetry with the Google Gen AI framework. It enables tracing and monitoring of applications built with Google Gen AI.

## Installation

1. **Install traceAI Google Gen AI**

```bash
pip install traceAI-google-genai
```


### Set Environment Variables
Set up your environment variables to authenticate with FutureAGI

```python
import os

os.environ["FI_API_KEY"] = FI_API_KEY
os.environ["FI_SECRET_KEY"] = FI_SECRET_KEY
```

## Quickstart

### Register Tracer Provider
Set up the trace provider to establish the observability pipeline. The trace provider:

```python
from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="google_genai_project"
)
```

### Configure Google Gen AI Instrumentation
Instrument the Google Gen AI client to enable telemetry collection. This step ensures that all interactions with the Google Gen AI SDK are tracked and monitored.

```python
from traceai_google_genai import GoogleGenAIInstrumentor

GoogleGenAIInstrumentor().instrument(tracer_provider=trace_provider)
```

### Make a request
Set up your Google Gen AI client with built-in observability.

```python
from google import genai
from google.genai import types

client = genai.Client(vertexai=True, project="your_project_name", location="global")

content = types.Content(
    role="user",
    parts=[
        types.Part.from_text(text="Hello how are you?"),
    ],
)
response = client.models.generate_content(
    model="gemini-2.0-flash-001", contents=content
)

print(response)
```
codeguru-profiler-agent 1.2.6
The Python agent to be used for Amazon CodeGuru Profiler
Requires Python: >=3.6

## Amazon CodeGuru Profiler Python Agent

For more details, check the documentation: https://docs.aws.amazon.com/codeguru/latest/profiler-ug/what-is-codeguru-profiler.html

## How to use it

This package is being released to PyPI as [codeguru-profiler-agent](https://pypi.org/project/codeguru-profiler-agent), so use it as any Python package from PyPI.

For a demo application that uses this agent, check our [demo application](https://github.com/aws-samples/aws-codeguru-profiler-python-demo-application).

## How to contribute

Check the GitHub repository at [aws/amazon-codeguru-profiler-python-agent](https://github.com/aws/amazon-codeguru-profiler-python-agent).

See [CONTRIBUTING](CONTRIBUTING.md) for details.

## How to release

See [DEVELOPMENT](DEVELOPMENT.md) for more information.

## Security

See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.

## License

This project is licensed under the Apache-2.0 License. See [LICENSE](LICENSE) for details.
imas-muscle3 1.0.0
IMAS MUSCLE3 tools
Requires Python: >=3.7

# IMAS tools for MUSCLE3
This repository contains helper actors for working with IMAS data in MUSCLE3.
It contains:

* **Data source**: Load and send timeslices from a DBEntry on the `O_I` port.
* **Data sink**: Save timeslices to a DBEntry on the `F_INIT` port.
* **Data sink_source**: Load and send a timeslice from a DBEntry on the `O_F` port based on an incoming timeslice on the `F_INIT` port.
* **Accumulator (buffer) actors**: Gather messages on `S` port and send out the combination on `O_F`.
* **IMAS-Validator actor**: Perform validation checks on data on the `F_INIT` port, generate reports.
* **Visualization actor**: Visualize incoming IDS data in real-time, receiving timeslices 
from a DBEntry on the `S` port and machine description IDSs on the `F_INIT` port.

# Documentation
Documentation is autogenerated from the source using [Sphinx](http://sphinx-doc.org/).

The documentation can be manually generated by installing sphinx and running:

```bash
make -C docs html
```

# Installation
Important: IMAS-Core is needed for IMAS-MUSCLE3 to work (which is not publicly available yet).
As a user, simply install IMAS-MUSCLE3 with pip:

```bash
pip install imas-muscle3
```

Quick developer installation guide

```bash
git clone git@github.com:iterorganization/IMAS-MUSCLE3.git
cd IMAS-MUSCLE3
python3 -m venv ./venv
. venv/bin/activate
pip install -e .[all]
pytest
```

# How to use
To add an actor to your MUSCLE3 workflow, add the following to the implementations in your ymmsl file:

```bash
implementations:
  *component_name*:
    executable: python
    args: -u -m imas_muscle3.actors.*component_name*
```
Check the actor specific documentation pages to find the relevant ports, settings, etc.
pqcli 1.0.4
Progress Quest: the CLI edition
Requires Python: <4.0,>=3.10

<p align="center">
  <img alt="Progress Quest" src="http://progressquest.com/pq.png">
</p>

Relive the great adventure… this time in the terminal realm!

- Progress Quest site:  http://progressquest.com/
- Online version:       http://progressquest.com/play/
- Original version:     https://bitbucket.org/grumdrig/pq

## Features

- Faithful port of the game logic
- Saves (with backups) to `$XDG_CONFIG_HOME/pqcli/save.dat`
- Terminal interface that comes in 2 flavors:
    - Rich and colorful (`--curses`, default)
    - Minimal, suitable for raw grind (`--basic`)
- Ideal to run on your server

## How it looks like

Curses interface:

![Screenshot](screen-curses-logo.png)
![Screenshot](screen-curses.png)

Basic interface:

![Screenshot](screen-basic.png)

## How to install

If you have Python 3.7, just run `pip install --user pqcli` and you're good to go!
Then type `pqcli` to run the game.

In case if you want to use the git version, the process is just a bit more complex:

```console
$ git clone https://github.com/rr-/pq-cli.git
$ cd pq-cli
$ pip install --user .
```

## Contributing

```sh
# Clone the repository:
git clone https://github.com/rr-/pqcli.git
cd pqcli

# Install to a local venv:
poetry install

# Install pre-commit hooks:
poetry run pre-commit install

# Enter the venv:
poetry shell
```

This project uses [poetry](https://python-poetry.org/) for packaging.
Install instructions are available at [poetry#installation](https://python-poetry.org/docs/#installation).

## Troubleshooting

### `_curses.error: init_pair() returned ERR`

If running on Linux and you get the error `_curses.error: init_pair() returned ERR`,
try making sure that your `$TERM` variable is set to a value which supports
256 colors, such as via the following:

    TERM=xterm-256color pqcli
dropbox-paper-export 0.1.0
Export Dropbox Paper documents to markdown
Requires Python: >=3.12
Keywords: dropbox, paper, markdown, export, backup

# Dropbox Paper Export

Tool to export Dropbox Paper documents to Markdown files with embedded images downloaded locally.

## Features

- Exports all `.paper` documents from a Dropbox folder (including subfolders) to Markdown format
- Downloads and saves images locally, updating markdown links to reference local files
- Supports both personal and team Dropbox accounts
- Shows progress with a progress bar during export
- Preserves folder structure from Dropbox

## How It Works

1. Connects to Dropbox using the API
2. Recursively scans for `.paper` files in the specified directory
3. Exports each document to Markdown format using Dropbox's export API
4. Scans the exported markdown for image links
5. Downloads each image and saves it to a local `assets/` folder
6. Updates the markdown to reference the local image paths

## Requirements

- Python 3.12+
- Dropbox API access token with appropriate permissions

## Usage

1. Create a Dropbox App at https://www.dropbox.com/developers/apps
2. Generate an access token with the following permissions:
   - `account_info.read`
   - `files.metadata.read`
   - `files.content.read`
   - `sharing.read`
3. Install:

```bash
pip install dropbox-paper-export
```

4. Run

```bash
dropbox-paper-export --access-token ACCESS_TOKEN
```

Sample output:

```plaintext
Dropbox Paper to Markdown + Images Exporter ---
   * Detected Team Account. Namespace ID: 1862260163
   * Switching API context to Team Root...
1. Scanning Dropbox for .paper files... (This takes a moment)
2. Found 138 Paper documents. Starting export...

Exporting:  28%|██        | 39/138 [01:43<02:35,  1.57s/doc]
   -> Downloading 15 images for 'Meeting summary'...
```

## Development

```bash
# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install dependencies
uv sync

# Running locally
uv run main.py --access-token YOUR_TOKEN

# Linting
uv run ruff check .
uv run ruff format .

# Building release
uv build
```
opentelemetry-util-http 0.60b1
Web util for OpenTelemetry
Requires Python: >=3.9

OpenTelemetry Util HTTP
=======================

|pypi|

.. |pypi| image:: https://badge.fury.io/py/opentelemetry-util-http.svg
   :target: https://pypi.org/project/opentelemetry-util-http/


This library provides ASGI, WSGI middleware and other HTTP-related
functionality that is common to instrumented web frameworks (such as Django,
Starlette, FastAPI, etc.) to track requests timing through OpenTelemetry.

Installation
------------

::

    pip install opentelemetry-util-http


Usage (Quart)
-------------

.. code-block:: python

    from quart import Quart
    from opentelemetry.instrumentation.asgi import OpenTelemetryMiddleware

    app = Quart(__name__)
    app.asgi_app = OpenTelemetryMiddleware(app.asgi_app)

    @app.route("/")
    async def hello():
        return "Hello!"

    if __name__ == "__main__":
        app.run(debug=True)


Usage (Django 3.0)
------------------

Modify the application's ``asgi.py`` file as shown below.

.. code-block:: python

    import os
    from django.core.asgi import get_asgi_application
    from opentelemetry.instrumentation.asgi import OpenTelemetryMiddleware

    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'asgi_example.settings')

    application = get_asgi_application()
    application = OpenTelemetryMiddleware(application)


Usage (Raw ASGI)
----------------

.. code-block:: python

    from opentelemetry.instrumentation.asgi import OpenTelemetryMiddleware

    app = ...  # An ASGI application.
    app = OpenTelemetryMiddleware(app)


References
----------

* `OpenTelemetry Project <https://opentelemetry.io/>`_
opentelemetry-resource-detector-containerid 0.60b1
Container Resource Detector for OpenTelemetry
Requires Python: >=3.9

OpenTelemetry Resource detectors for containers
==========================================================

|pypi|

.. |pypi| image:: TODO
   :target: TODO


This library provides custom resource detector for container platforms

Installation
------------

::

    pip install opentelemetry-resource-detector-containerid

---------------------------

Usage example for `opentelemetry-resource-detector-containerid`

.. code-block:: python

    from opentelemetry import trace
    from opentelemetry.sdk.trace import TracerProvider
    from opentelemetry.resource.detector.containerid import (
        ContainerResourceDetector,
    )
    from opentelemetry.sdk.resources import get_aggregated_resources


    trace.set_tracer_provider(
        TracerProvider(
            resource=get_aggregated_resources(
                [
                    ContainerResourceDetector(),
                ]
            ),
        )
    )

References
----------

* `OpenTelemetry Project <https://opentelemetry.io/>`_
opentelemetry-propagator-ot-trace 0.60b1
OT Trace Propagator for OpenTelemetry
Requires Python: >=3.9

OpenTelemetry OT Trace Propagator
=================================

|pypi|

.. |pypi| image:: https://badge.fury.io/py/opentelemetry-propagator-ot-trace.svg
   :target: https://pypi.org/project/opentelemetry-propagator-ot-trace/

Installation
------------

::

    pip install opentelemetry-propagator-ot-trace

.. _OpenTelemetry: https://github.com/open-telemetry/opentelemetry-python/

OTTrace Format
--------------

So far there is no "formal" specification of the OTTrace format. The best
document that servers this purpose that exists now is this_ implementation.

.. _this: https://github.com/opentracing/basictracer-python/blob/master/basictracer/text_propagator.py

===================== ======================================================================================================================================= =====================
Header Name           Description                                                                                                                             Required
===================== ======================================================================================================================================= =====================
``ot-tracer-traceid`` uint64 encoded as a string of 16 hex characters                                                                                         yes
``ot-tracer-spanid``  uint64 encoded as a string of 16 hex characters                                                                                         yes
``ot-tracer-sampled`` boolean encoded as a string with the values ``true`` or ``false``                                                                       no
``ot-baggage-*``      repeated string to string key-value baggage items; keys are prefixed with ``ot-baggage-`` and the corresponding value is the raw string if baggage is present
===================== ======================================================================================================================================= =====================

Interop and trace ids
---------------------

The OT Trace propagation format expects trace ids to be 64-bits. In order to
interop with OpenTelemetry, trace ids need to be truncated to 64-bits before
sending them on the wire. When truncating, the least significant (right-most)
bits MUST be retained. For example, a trace id of
``3c3039f4d78d5c02ee8e3e41b17ce105`` would be truncated to
``ee8e3e41b17ce105``.

References
----------

* `OpenTelemetry Project <https://opentelemetry.io/>`_
opentelemetry-processor-baggage 0.60b1
OpenTelemetry Baggage Span Processor
Requires Python: >=3.9

OpenTelemetry Baggage Span Processor
====================================

|pypi|

.. |pypi| image:: https://badge.fury.io/py/opentelemetry-processor-baggage.svg
   :target: https://pypi.org/project/opentelemetry-processor-baggage/

The BaggageSpanProcessor reads entries stored in Baggage
from the parent context and adds the baggage entries' keys and
values to the span as attributes on span start.

Installation
------------

::

    pip install opentelemetry-processor-baggage

Add this span processor to a tracer provider.

Keys and values added to Baggage will appear on subsequent child
spans for a trace within this service *and* be propagated to external
services in accordance with any configured propagation formats
configured. If the external services also have a Baggage span
processor, the keys and values will appear in those child spans as
well.

[!WARNING]

Do not put sensitive information in Baggage.

To repeat: a consequence of adding data to Baggage is that the keys and
values will appear in all outgoing HTTP headers from the application.

Usage
-----

Add the span processor when configuring the tracer provider.

To configure the span processor to copy all baggage entries during configuration:

::

    from opentelemetry.processor.baggage import BaggageSpanProcessor, ALLOW_ALL_BAGGAGE_KEYS

    tracer_provider = TracerProvider()
    tracer_provider.add_span_processor(BaggageSpanProcessor(ALLOW_ALL_BAGGAGE_KEYS))


Alternatively, you can provide a custom baggage key predicate to select which baggage keys you want to copy.

For example, to only copy baggage entries that start with `my-key`:

::

    starts_with_predicate = lambda baggage_key: baggage_key.startswith("my-key")
    tracer_provider.add_span_processor(BaggageSpanProcessor(starts_with_predicate))


For example, to only copy baggage entries that match the regex `^key.+`:

::

    regex_predicate = lambda baggage_key: baggage_key.startswith("^key.+")
    tracer_provider.add_span_processor(BaggageSpanProcessor(regex_predicate))


References
----------
* `OpenTelemetry Project <https://opentelemetry.io/>`_
opentelemetry-instrumentation-wsgi 0.60b1
WSGI Middleware for OpenTelemetry
Requires Python: >=3.9

OpenTelemetry WSGI Middleware
=============================

|pypi|

.. |pypi| image:: https://badge.fury.io/py/opentelemetry-instrumentation-wsgi.svg
   :target: https://pypi.org/project/opentelemetry-instrumentation-wsgi/


This library provides a WSGI middleware that can be used on any WSGI framework
(such as Django / Flask) to track requests timing through OpenTelemetry.

Installation
------------

::

    pip install opentelemetry-instrumentation-wsgi

References
----------

* `OpenTelemetry WSGI Middleware <https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/wsgi/wsgi.html>`_
* `OpenTelemetry Project <https://opentelemetry.io/>`_
* `WSGI <https://www.python.org/dev/peps/pep-3333>`_
* `OpenTelemetry Python Examples <https://github.com/open-telemetry/opentelemetry-python/tree/main/docs/examples>`_