semvec/pypi-packages huggingface dataset:
quantfox-common 0.1.15
QuantFox common utilities: domain (market/finance/ds) + adapters (db/s3/redis/dss)
Requires Python: >=3.12

# qf-common

Common utilities pour QuantFox.

## Setup Instructions

### Prerequisites

- - Python 3.x
  - pipx (for Poetry installation)

### Installation Steps

- 1. Install dependencies using Poetry:
     `bash
 poetry install
`
- 2. Verify Poetry installation:
     `bash
 pipx run poetry --version
`
- 3. Activate Poetry environment:
     `bash
     poetry shell

## Install

Privé via GitHub Packages:

```toml
[tool.poetry.dependencies]
qf-common = { version = "^0.1.0", source = "github" }

[[tool.poetry.source]]
name = "github"
url = "https://maven.pkg.github.com/QuantFox/qf-common"
default = false

```
uarray 0.9.3
Array interface object for Python with pluggable backends and a multiple-dispatch mechanism for defining down-stream functions
Requires Python: >=3.11
Keywords: uarray, scipy, multiple-dispatch

# `uarray` - A back-end mechanism geared towards array computing

[![Join the chat at https://gitter.im/Plures/uarray](https://badges.gitter.im/Plures/uarray.svg)](https://gitter.im/Plures/uarray?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[![Build Status](https://github.com/Quansight-Labs/uarray/actions/workflows/build.yaml/badge.svg)](https://github.com/Quansight-Labs/uarray/actions)
[![PyPI](https://img.shields.io/pypi/v/uarray.svg?style=flat-square)](https://pypi.org/project/uarray/)

<img src="docs/logo.png" style="width: 20em; text-align: center;" alt="uarray logo">

- [Documentation](https://uarray.org)
- [Road Map](https://github.com/orgs/Quansight-Labs/projects/1)
- [Future Meetings](https://calendar.google.com/calendar/embed?src=quansight.com_cg7sf4usbcn18gdhdb3l2c6v1g%40group.calendar.google.com&ctz=America%2FNew_York)
- [Meeting Notes](https://github.com/Quansight-Labs/uarray/wiki/Meeting-Notes)
- [References](https://github.com/Quansight-Labs/uarray/wiki/References)
- [Papers](https://paperpile.com/shared/fHftX5)

## Contributing

See [`CONTRIBUTING.md`](CONTRIBUTING.md) for more information on how to contribute to `uarray`.
iccore 0.2.6
A collection of common data structures and utilities used in other ICHEC tools
Requires Python: >=3.10
Keywords: HPC

# iccore

This package is part of the [Common Tooling Project](https://ichec-handbook.readthedocs.io/en/latest/src/common_tools.html) at the [Irish Centre for High End Computing](https://www.ichec.ie).

It is a collection of common data structures, data types and low-level utilities used in other ICHEC 'common tools'.

# Features #

The package consists of:

* `data structures` (list, strings, dicts etc.) and utilities for working with them
* tooling for interacting with `system resources`, such as external processes, the filesystem and network
* basic database interaction tooling with sqmlmodel 

## Useful Data Types ##

* HPC and Networking: CPU, GPU, Process, SystemEvent, Node, ClusterAllocation, Host

## Filesystem ##

Utilities to find files with pattern matching and replace content inside files.

**CLI Example:**

You can replace all occurences of a string with another recursively in files with:

``` shell
iccore filesystem replace_in_files --target $REPLACE_DIR --search $FILE_WITH_SEARCH_TERM --replace $FILE_WITH_REPLACE_TERM 
```

The `search` and `replace` terms are read from files. This can be handy to avoid shell escape sequences - as might be needed in `sed`.

## Networking ##

Includes a:

* `HttpClient`

**CLI Example:**

You can download a file with:

``` shell
iccore network download --url $RESOURCE_URL --download_dir $WHERE_TO_PUT_DOWNLOAD
```

## Serialization ##

Tools for reading and writing ymal and json with some error handling.

# Install  #

It is available on PyPI:

``` sh
pip install iccore
```

# Copyright and License #

This software is copyright of the Irish Centre for High End Computing (ICHEC).

You may use it under the terms of GPLv3+ license. See the incluced `LICENSE.txt` file for details.
cofferfile 0.0.10
A python xxxFile like (ie GzipFile, BZ2File, ...) for manipulating encrypted files (Fernet, Nacl, ...).
Requires Python: >=3.9

[![CircleCI](https://dl.circleci.com/status-badge/img/gh/bibi21000/CofferFile/tree/main.svg?style=shield)](https://dl.circleci.com/status-badge/redirect/gh/bibi21000/CofferFile/tree/main)
[![CodeQL](https://github.com/bibi21000/CofferFile/actions/workflows/codeql.yml/badge.svg)](https://github.com/bibi21000/CofferFile/actions/workflows/codeql.yml)
[![codecov](https://codecov.io/gh/bibi21000/CofferFile/graph/badge.svg?token=4124GIOJAK)](https://codecov.io/gh/bibi21000/CofferFile)
![PyPI - Downloads](https://img.shields.io/pypi/dm/cofferfile)

# CofferFile

A python xxxFile like (ie TarFile, GzipFile, BZ2File, pyzstd.ZstdFile, ...)
for encrypting files with Fernet, Nacl, ...

 - encrypting / decrypting data using chunks to reduce memory footprint
 - chainable with other python xxxFile interfaces (stream mode)
 - interface to compress/encrypt and decrypt/decompress (with pyzstd) in stream mode
 - look at BENCHMARK.md ... and chain :)

If you're looking for a more powerfull storage for your sensible datas,
look at PyCoffer : https://github.com/bibi21000/PyCoffer.

This is the main library.
Look at https://github.com/bibi21000/NaclFile, https://github.com/bibi21000/FernetFile,
https://github.com/bibi21000/TinkFile or https://github.com/bibi21000/AesFile for implementations with cryptograhics tools.

Look at documentation : https://bibi21000.github.io/CofferFile.
naust 0.1.2
Tools and templates for handling NPI-specific oceanographic data tasks
Requires Python: <3.13,>3.9

![image](documents/graphics/naust_banner.png)

Collection of various Python functions, templates etc for working with oceanography data specific to the [Norwegian Polar Institute](https://www.npolar.no/en/) (NPI).

The idea is more in-house specific things can live here, but more truly general stuff can live in [kval](github.com/NPIOcean/kval).

Example of things to put here:

- Parsers for specific file formats.
- NPI-specific metadata fixing.
- Salinity QC.
- Log sheets, excel sheets and associated parsers or converterts.
____

**USAGE EXAMPLES**

- [QC of CTD salinity against salinometer values](notebooks/examples/salts/Salt_QC_example.ipynb)

____

Maintained by the Oceanography section at NPI.

____

Most recent release: *v0.1.2*




[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17356730.svg)](https://doi.org/10.5281/zenodo.17356730)
tagreader 6.2.0
Tagreader is a Python package for reading trend data from the OSIsoft PI and Aspen Infoplus.21 IMS systems.
Requires Python: <4.0.0,>=3.9.2
Keywords: Aspen InfoPlus.21, OSIsoft PI

# tagreader-python <!-- omit in toc -->

![GitHub Build Status](https://github.com/equinor/tagreader-python/workflows/Test/badge.svg)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tagreader)
![PyPI](https://img.shields.io/pypi/v/tagreader)
[![Downloads](https://pepy.tech/badge/tagreader)](https://pepy.tech/project/tagreader)

Tagreader is a Python package for reading timeseries data from the OSIsoft PI and Aspen Infoplus.21
Information Management Systems (IMS). It is intended to be easy to use, and present as similar interfaces
as possible to the backend plant historians.

## Installation
You can install tagreader directly into your project from pypi by using pip
or another package manager. Supports Python version 3.9.2 and above.

```shell
pip install tagreader
```

## Usage
Tagreader is easy to use for both Equinor internal IMS services, and non-internal usage. For non-internal usage
you simply need to provide the corresponding IMS service URLs and IMSType.
See [data source](https://equinor.github.io/tagreader-python/docs/about/usage/data-source) for details.

### Usage example
```python
import tagreader
c = tagreader.IMSClient("mysource", "aspenone")
print(c.search("tag*"))
df = c.read_tags(["tag1", "tag2"], "18.06.2020 08:00:00", "18.06.2020 09:00:00", 60)
```

Note, you can add a timeout argument to the search method in order to avoid long-running search queries.

### Jupyter Notebook Quickstart
Jupyter Notebook examples can be found in /examples. In order to run these examples, you need to install the
optional dependencies.

```shell
pip install tagreader[notebooks]
```

The quickstart Jupyter Notebook can be found [here](https://github.com/equinor/tagreader-python/blob/main/examples/quickstart.ipynb)

For more details, see the [Tagreader Docs](https://equinor.github.io/tagreader-python/).

## Documentation
The full documentation can be found in [Tagreader Docs](https://equinor.github.io/tagreader-python/)

## Contribute
To starting contributing, please see [Tagreader Docs - Contribute](https://equinor.github.io/tagreader-python/docs/contribute/overview)
varunayan 0.1.1
Analysis-ready climate data downloader and processor for custom regions
Requires Python: >=3.8
Keywords: atmospheric-science, climate, era5, geospatial, netcdf, weather

# Varunayan

<p align="center">
  <img src="https://raw.githubusercontent.com/saketlab/varunayan/main/docs/_static/varunayan_logo.png" alt="Varunayan Logo" width="200"/>
</p>

A Python package for downloading and processing ERA5 climate data.

## Installation

Install the latest release from PyPI:

```bash
pip install varunayan
```

For development, clone the repository and install it locally:

```bash
pip install -e .
```

## Quick Start


Quickly extract ERA5 processed data with the help of *varunayan* from the command line:


1. either by providing a GeoJSON file to define the area of interest
```bash
varunayan geojson --request-id "request_name" --variables "var1,var2,etc." --start "yyyy-mm-dd" --end "yyyy-mm-dd" --geojson "your_file.geojson" --dataset-type "dataset_type" --pressure-levels "pressure_level_1,pressure_level_2,etc." --freq "frequency" --res "resolution"
```

2. or by providing a bounding box to define the area of interest
```bash
varunayan bbox --request-id "request_name" --variables "var1,var2,etc." --start "yyyy-mm-dd" --end "yyyy-mm-dd" --north "north_bound" --south "south_bound" --east "east_bound" --west "west_bound" --dataset-type "dataset_type" --pressure-levels "pressure_level_1,pressure_level_2,etc." --freq "frequency" --res "resolution"
```

3. or by providing coordinates of the location
```bash
varunayan point --request-id "request_name" --variables "var1,var2,etc." --start "yyyy-mm-dd" --end "yyyy-mm-dd" --lat "latitude" --lon "longitude" --dataset-type "dataset_type" --pressure-levels "pressure_level_1,pressure_level_2,etc." --freq "frequency"
```

The arguments dataset-type (single by default), pressure-levels (empty by default), freq (hourly by default) and res (0.25 by default) are optional.

Example command for extracting pressure-level data for a bounding box
```bash
varunayan bbox --request-id "test" --variables "temperature,relative_humidity" --start "2024-01-1" --end "2024-01-15" --north 30 --south 20 --east 80 --west 70 --dataset-type pressure --pressure-levels "1000,900" --freq daily --res 0.25
```

## Documentation

Please visit our [documentation page](http://saketlab.github.io/varunayan) for detailed documentation.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
hive-common 0.4.3
Common code for Hive libraries and services

[![version badge]](https://pypi.org/project/hive-common/)

[version badge]: https://img.shields.io/pypi/v/hive-common?color=limegreen

# hive-common

Common code for Hive libraries and services

## Installation

### With PIP

```sh
pip install hive-common
```

### From source

```sh
git clone https://github.com/gbenson/hive.git
cd hive/libs/common
make setup
. .venv/bin/activate
make check
```