semvec/pypi-packages huggingface dataset:
velodata 1.7.1
Python library for interacting with the Velo Data API
Requires Python: >=3.0

# velo-python
Python library for Velo API access. [See full documentation here](https://velodata.gitbook.io/velo-data-api/python).

## Install
```
pip install velodata
```

## Usage
```python
from velodata import lib as velo

# new velo client
client = velo.client('api_key')

# get futures and pick one
future = client.get_futures()[0] 

# get futures columns and pick two
columns = client.get_futures_columns()[:2]

# last 10 minutes in 1 minute resolution
params = {
      'type': 'futures',
      'columns': columns,
      'exchanges': [future['exchange']],
      'products': [future['product']],
      'begin': client.timestamp() - 1000 * 60 * 11,
      'end': client.timestamp(),
      'resolution': '1m'
    }
    
# returns dataframe
print(client.get_rows(params))
```

## Streaming rows
Row requests are broken up into batches past a certain size. If you don't want to wait for all your requests to finish before receiving any data, you can use this:


```python
batches = client.batch_rows(params)  
for df in client.stream_rows(batches):
  print(df)
```

## License
Copyright 2023 Velo Data, license MIT
pynina 1.0.2
A Python API wrapper to retrieve warnings from the german NINA app.
Requires Python: >=3.13.2
Keywords: api, wrapper, nina, warning

# Nina-API
A Python API wrapper to retrieve warnings from the german NINA app.

## How to use package

```python
import asyncio

from pynina import Nina, ApiError


async def main():
    try:
        n: Nina = Nina()
        n.add_region("146270000000")
        await n.update()

        for i in n.warnings["146270000000"]:
            print(i)
            print(i.is_valid())

    except ApiError as error:
        print(f"Error: {error}")


loop = asyncio.get_event_loop()
loop.run_until_complete(main())
loop.close()
```
lm-pub-quiz 0.3.3
Evaluate language models using multiple choice items
Requires Python: >=3.9

# LM Pub Quiz
*Evaluate language models using multiple choice items*


[![Build status](https://img.shields.io/github/actions/workflow/status/lm-pub-quiz/lm-pub-quiz/test.yml?logo=github&label=Tests)](https://github.com/lm-pub-quiz/lm-pub-quiz/actions)
[![PyPI - Version](https://img.shields.io/pypi/v/lm-pub-quiz.svg?logo=pypi&label=Version&logoColor=gold)](https://pypi.org/project/lm-pub-quiz/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/lm-pub-quiz?logo=python&label=Python&logoColor=gold)](https://pypi.org/project/lm-pub-quiz/)
[![License](https://img.shields.io/github/license/lm-pub-quiz/lm-pub-quiz?logo=pypi&logoColor=gold)](https://github.com/lm-pub-quiz/lm-pub-quiz/blob/main/LICENSE)

---

This library implements a knoweledge probing approach which uses LM's inherent ability to estimate the log-likelihood of any given textual statement.
For more information visit the [LM Pub Quiz website](https://lm-pub-quiz.github.io/).

### See also
- [Website](https://lm-pub-quiz.github.io/)
- [Documentation](https://lm-pub-quiz.github.io/lm-pub-quiz)
- [BEAR Dataset](https://github.com/lm-pub-quiz/BEAR)
- [Paper](https://arxiv.org/abs/2404.04113)


## Getting started

This short guide should get you started. For more detailed information visit the [documentation](https://lm-pub-quiz.github.io/lm-pub-quiz). 

### Installing the Package

You can install the package via *pip*:

```shell
pip install lm-pub-quiz
```

For alternatives methods of installing the package, visit the [documentation](https://lm-pub-quiz.github.io/lm-pub-quiz).


### Example Usage

```python
from lm_pub_quiz import Dataset, Evaluator

# Load the dataset
dataset = Dataset.from_name("BEAR")

# Load the model
evaluator = Evaluator.from_model(
    "gpt2",
    model_type="CLM",
)

# Run the evaluation and save the
results = evaluator.evaluate_dataset(
    dataset,
    save_path="gpt2_results",
    batch_size=32,
)

# If the results are analyzed in a different session, they can be loaded from the file system
# results = DatasetResults.from_path("gpt2_results")

print("=== Overall score ===")
print(results.get_metrics("accuracy"))
```


## Contributing
We welcome any questions, comments, or even PRs to this project to improve the package.

We use [hatch](https://hatch.pypa.io) to manage this project. For the most comfortable development experience, please first install hatch using [`pip`](https://hatch.pypa.io/latest/install/#pipx) or [`pipx`](https://hatch.pypa.io/latest/install/#pipx).

Then, to propose a change to the library,

- test your code locally using `hatch run all:test`
- format the code according to our formatting guidelines using `hatch run lint:fmt`,
- check type- and style-consistency using `hatch run lint:all`, and
- finally create a pull request describing the changes you propose.

For work on the documentation, use `hatch run serve-docs` to run a local documentation server.
imas-data-dictionaries 4.1.1
This package all released versions (IDSDef.xml + identifiers) of the Data Dictionary of ITER's Integrated Modelling & Analysis Suite (IMAS)
Keywords: IMAS, Data Dictionary, IDS

# IMAS Data Dictionary Archive

This repository contains an archive of released versions of the [IMAS Data
Dictionary](https://github.com/iterorganization/IMAS-Data-Dictionary) and
corresponding identifiers. The oldest version included is `3.22.0`. The version
of this repository corresponds to the latest included Data Dictionary release.
For example, version `4.0.0` of this repository will include all Data Dictionary
versions from `3.22.0` to `4.0.0`.

The goals of this repository are:
1. Allow easy access to released IMAS Data Dictionary definitions, such that
   they can be used by other tools which rely on them.
2. Make an archive of the definitions available as a Python package, which other
   tools (like [IMAS-Python](https://github.com/iterorganization/IMAS-Python))
   can depend on.


## Legal

Copyright 2012-2025, ITER Organization, Route de Vinon-sur-Verdon, CS 90 046, 
13067 St-Paul-lez-Durance Cedex, France.

This repository contains data schemas and software that are subject to different 
licenses:

* The data schemas and identifiers (all files in the
  [data-dictionary](data-dictionary) and [identifiers](identifiers) folders) are
  licensed under the Creative Commons Attribution-Noderivatives 4.0
  International License (CC-BY-ND 4.0) which permits use and distribution as
  long as appropriate credit is given to the original source, but does not
  permit adaptations, as the main goal of these schemas is to serve as a
  standard for the community. Details can be found in
  [LICENSE-CC-BY-ND](LICENSE-CC-BY-ND).

* The software is licensed under the LGPLv3 License which allows for extensive
  freedom in using, modifying, and distributing it, provided that the license
  terms are met. Details can be found in [LICENSE-LGPL](LICENSE-LGPL).
g3wsuite-config-scripts 0.6.3
Configuration scripts for the g3w suite setup
Requires Python: >=3.7

## G3W Configuration Scripts

This repository contains the scripts to quickly configure a G3W-SUITE instance.


### Installation

Install the module via pip:

```bash
pip install g3w-config-scripts
```

### Usage

#### Development or Production Setup

```bash
g3w_dev_setup configure.properties
```

Where the configuration file is a properties file with the following structure:

```properties
SUITE_REPO_TAG: v3.8.0
SUITE_DOCKER_IMAGE: g3wsuite/g3w-suite:v3.8.x
MY_NEW_BRANCH: v3.8.0_myniceproject
SUITE_SHARED_VOLUME: /myniceproject/
DEBUG: True
FRONTEND: False

# optional plugin setup. Comment out completely to skip
PLUGIN_APP_NAME=myplugin
PLUGIN_REPO=https://github.com/myname/myplugin.git

WEBGIS_PUBLIC_HOSTNAME: v38.g3wsuite.it
G3WSUITE_POSTGRES_PASS: myPgPasswd
PG_SERVICE_CONF: [service@111.111.11.111]|host=111.111.11.111user=myuser|password=mypwd|dbname=mydb|port=5432
```

#### Configuration generation

Don't have a config.properties file? No problem, you can generate one with the following command:

```bash
g3w_config_gen
```

This will generate a config.properties file in the current directory based on the answers given by the user.

#### Branding

Place yourself in the same folder in which you ran the setup scripts and just run the 
branding module:

```bash
g3w_branding
```

You will be asked for all the possible branding parameters.
langchain-mongodb 0.11.0
An integration package connecting MongoDB and LangChain
Requires Python: >=3.10

from libs.community.tests.unit_tests.chains.test_pebblo_retrieval import retriever

# langchain-mongodb

# Installation
```
pip install -U langchain-mongodb
```

# Usage
- [Integrate Atlas Vector Search with LangChain](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/#get-started-with-the-langchain-integration) for a walkthrough on using your first LangChain implementation with MongoDB Atlas.

## Using MongoDBAtlasVectorSearch
```python
import os
from langchain_mongodb import MongoDBAtlasVectorSearch
from langchain_openai import OpenAIEmbeddings

# Pull MongoDB Atlas URI from environment variables
MONGODB_ATLAS_CONNECTION_STRING = os.environ["MONGODB_CONNECTION_STRING"]
DB_NAME = "langchain_db"
COLLECTION_NAME = "test"
VECTOR_SEARCH_INDEX_NAME = "index_name"

MODEL_NAME = "text-embedding-3-large"
OPENAI_API_KEY =  os.environ["OPENAI_API_KEY"]


vectorstore = MongoDBAtlasVectorSearch.from_connection_string(
    connection_string=MONGODB_ATLAS_CONNECTION_STRING,
    namespace=DB_NAME + "." + COLLECTION_NAME,
    embedding=OpenAIEmbeddings(model=MODEL_NAME),
    index_name=VECTOR_SEARCH_INDEX_NAME,
)

retrieved_docs = vectorstore.similarity_search(
    "How do I deploy MongoDBAtlasVectorSearch in our production environment?")
```
globus-cli 3.41.0
Globus CLI
Requires Python: >=3.9
Keywords: globus, cli, command line

Globus CLI
==========

A Command Line Interface to `Globus <https://www.globus.org/>`_.

Source Code: https://github.com/globus/globus-cli

Installation, Running, Other Documentation: https://docs.globus.org/cli

Bugs and Feature Requests
-------------------------

Bugs reports and feature requests are open submission, and should be filed at
https://github.com/globusonline/globus-cli/issues
rozklad-ontu-parser-makisukurisu 0.1.3.0
Package for parsing data from rozklad.ontu.edu.ua
Requires Python: >=3.11

# Rozklad ONTU Pareser

## Badges
[![Pylint](https://github.com/Wandering-Cursor/rozklad-ontu-parser/actions/workflows/lint.yml/badge.svg?branch=master)](https://github.com/Wandering-Cursor/rozklad-ontu-parser/actions/workflows/lint.yml)
[![Upload Python Package](https://github.com/Wandering-Cursor/rozklad-ontu-parser/actions/workflows/publish.yml/badge.svg)](https://github.com/Wandering-Cursor/rozklad-ontu-parser/actions/workflows/publish.yml)
[![CodeQL](https://github.com/Wandering-Cursor/rozklad-ontu-parser/actions/workflows/github-code-scanning/codeql/badge.svg?branch=master)](https://github.com/Wandering-Cursor/rozklad-ontu-parser/actions/workflows/github-code-scanning/codeql)

## Links
Available on [PyPi](https://pypi.org/project/rozklad-ontu-parser-MakisuKurisu/)

## Description (ENG)

This library is designed to get the schedule from the [ONTU schedule site](https://rozklad.ontu.edu.ua).

You can find a small example in the [example.py](/ontu_parser/example.py) file.

You can also find our other project that uses this library [here](https://github.com/Wandering-Cursor/ontu-schedule-bot).

## Опис (UKR)

Ця бібліотека призначена для отримання розкладу з [сайту з розкладом ОНТУ](https://rozklad.ontu.edu.ua)

На поточний момент бібліотека може повернути розклад на поточний тиждень, чи на весь семестр. В подальшому планується додати підтримку розкладу екзаменів, повідомлень та інших розділів сайту.

### А як користуватися?
Ви можете подивитися приклад використання в файлі [example.py](/ontu_parser/example.py). Також наразі є окремий метод для отримання розкладу з CLI - parse.
Приклад використання також доступний у нашому боті, який використовує цю бібліотеку, [код - тут](https://github.com/Wandering-Cursor/ontu-schedule-bot).

## Requirements
- [Python](https://python.org) 3.11 or higher
- [PDM Package Manager](https://pdm-project.org/)

## Honorable Mentions
- [MarshalX](https://github.com/MarshalX) за [дозвіл](https://t.me/yandex_music_api/29677) позичити метод `to_dict` з його ліби: [yandex-music-api](https://github.com/MarshalX/yandex-music-api). (Було внесено мінімальні зміни через bs4 теги)
cfgx 0.4.0
Python-first config loader with composition and CLI overrides.
Requires Python: >=3.10

# cfgx

[![PyPI version](https://img.shields.io/pypi/v/cfgx.svg)](https://pypi.org/project/cfgx/)

Python-first config loader with parent chaining, lazy computed values, and CLI-style overrides.

Docs: https://kabouzeid.github.io/cfgx/

## Install

```bash
pip install cfgx
```

## Quick start

Example config file:

```python
# configs/model.py
config = {
    "model": {"name": "resnet18"},
    "optimizer": {"lr": 3e-4},
}
```

```python
from cfgx import load

cfg = load("configs/model.py", overrides=["optimizer.lr=1e-3"])
```

Works well with [`specbuild`](https://github.com/kabouzeid/specbuild) when you want to build your model and other classes from config dictionaries.

## Advanced example

Base config:

```python
# configs/base.py
from cfgx import Lazy

config = {
    "model": {"depth": 8, "width": 512},
    "optimizer": {
        "lr": 3e-4,
        "weight_decay": 0.01,
        "schedule": {"type": "linear", "warmup_steps": 1_000},
    },
    "trainer": {
        "max_steps": 50_000,
        "hooks": ["progress", "checkpoint"],
        "stages": [{"name": "warmup", "max_steps": 5_000}],
        "log_every": Lazy("c.trainer.max_steps // 100"),
    },
}
```

Derived config:

```python
# configs/finetune.py
from cfgx import Delete, Lazy, Replace

parents = ["base.py"]

config = {
    "model": {"depth": 12},
    "optimizer": {
        "weight_decay": Delete(),
        "schedule": Replace({"type": "cosine", "t_max": 40_000}),
    },
    "trainer": {"max_steps": 10_000},
    "scheduler": {
        "warmup_steps": 500,
        "decay_steps": Lazy(lambda c: c.trainer.max_steps - c.scheduler.warmup_steps),
    },
}
```

Load, override, and snapshot:

```python
from cfgx import dump, format, load

cfg = load(
    "configs/finetune.py",
    overrides=[
        "optimizer.lr=1e-4",
        "trainer.hooks+=wandb",
        "trainer.hooks-='checkpoint'",
        "trainer.stages[0].max_steps=2_000",
        "scheduler.warmup_steps=lazy:c.trainer.max_steps * 0.1",
    ],
)

print(format(cfg))
with open("runs/finetune_config.py", "w") as f:
    dump(cfg, f, format="ruff")
```
cwl2puml 0.32.0
CWL Worflows processor to generate PlantUML diagrams
Requires Python: >=3.8

# CWL Worflows processor to generate PlantUML diagrams

This project aims to deliver a simple yet powerful CLI tool to ingest [CWL Workflows](https://www.commonwl.org/) and generate [PlantUML diagrams](https://plantuml.com/).

## Contribute

Submit a [Github issue](https://github.com/Terradue/cwl2puml/issues) if you have comments or suggestions.

## Documentation

See the documentation at https://Terradue.github.io/cwl2puml/

## License

[![License: CC BY-SA 4.0](https://img.shields.io/badge/License-CC_BY--SA_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by-sa/4.0/)