semvec/pypi-packages huggingface dataset:
D47crunch 2.5.3
Standardization and analytical error propagation of Δ47 and Δ48 clumped-isotope measurements
Requires Python: >=3.12

# D47crunch

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4314550.svg)](https://doi.org/10.5281/zenodo.4314550)
[![PyPI Version](https://img.shields.io/pypi/v/D47crunch.svg)](https://pypi.python.org/pypi/D47crunch)

Python library for processing and standardizing carbonate clumped-isotope analyses, from low-level data out of a dual-inlet mass spectrometer to final, “absolute” Δ<sub>47</sub>, Δ<sub>48</sub> and  Δ<sub>49</sub> values with fully propagated analytical error estimates.

This also provides a command-line interface making it possible to process a multi-session Δ<sub>47</sub> (and potentially Δ<sub>48</sub>, Δ<sub>49</sub>) dataset by simply calling `D47crunch rawdata.csv`. See the [CLI documentation](https://mdaeron.github.io/D47crunch/#3-command-line-interface-cli) for more options.

## Documentation

For the full API and a short tutorial, see [https://mdaeron.github.io/D47crunch].

[https://mdaeron.github.io/D47crunch]: https://mdaeron.github.io/D47crunch

## Installation

This should do the trick:

```bash
pip install D47crunch
```

Alternatively:

1. download the [dev branch] or the [latest release] and unzip it
2. rename the resulting directory to `D47crunch`
3. chose one of one of the following options:
	+ move the `D47crunch` directory to somewhere in your Python path
	+ move the `D47crunch` directory to your desired working directory
	+ move the `D47crunch` directory to any other location (e.g., `/foo/bar`) and include the following code snippet in your scripts:

```py
import sys
sys.path.append('/foo/bar')
```
Having done any of the above you should now be able to `import D47crunch`, with the following requirements: [Python 3], [numpy], [matplotlib], [scipy], [lmfit], and [typer].

[Python 3]: https://www.python.org
[numpy]: https://numpy.org
[lmfit]: https://lmfit.github.io
[matplotlib]: https://matplotlib.org
[scipy]: https://www.scipy.org
[typer]: https://typer.tiangolo.com
[dev branch]: https://github.com/mdaeron/D47crunch/archive/dev.zip
[latest release]: https://github.com/mdaeron/D47crunch/releases/latest

## Contact

All questions and suggestions are welcome and should be directed at [Mathieu Daëron](mailto:daeron@lsce.ipsl.fr?subject=[D47crunch]).
python3-cyberfusion-borg-support 1.6
Library for Borg.

# python3-cyberfusion-borg-support

Library for [Borg](https://www.borgbackup.org/).

# Install

## Debian

Run the following commands to build a Debian package:

    mk-build-deps -i -t 'apt -o Debug::pkgProblemResolver=yes --no-install-recommends -y'
    dpkg-buildpackage -us -uc

## PyPI

Run the following command to install the package from PyPI:

    pip3 install python3-cyberfusion-borg-support

Next, install Borg according to the [documentation](https://borgbackup.readthedocs.io/en/stable/installation.html#distribution-package).

# Configure

No configuration is supported.

# Usage

## Example

```python
import os

from cyberfusion.BorgSupport.repositories import Repository
from cyberfusion.BorgSupport.archives import Archive

repository = Repository(path="/home/example/repository", passphrase="test", identity_file_path=None, create_if_not_exists=True)
archive = Archive(repository=repository, name="example", comment="Example")
```
haptools 0.6.2
Ancestry and haplotype aware simulation of genotypes and phenotypes for complex trait analysis
Requires Python: >=3.7

# haptools
[![pypi version](https://img.shields.io/pypi/v/haptools)](https://pypi.org/project/haptools)
[![image](https://anaconda.org/bioconda/haptools/badges/version.svg)](https://anaconda.org/bioconda/haptools)
[![license](https://img.shields.io/pypi/l/haptools)](LICENSE)
![status](https://github.com/CAST-genomics/haptools/workflows/Tests/badge.svg)
[<img src=https://github.com/codespaces/badge.svg width=160>](https://codespaces.new/CAST-genomics/haptools)

Haptools is a collection of tools for simulating and analyzing genotypes and phenotypes while taking into account haplotype information. Haptools supports fast simulation of admixed genomes (with `simgenotype`), visualization of admixture tracks (with `karyogram`), simulating haplotype- and local ancestry-specific phenotype effects (with `transform` and `simphenotype`), and computing a variety of common file operations and statistics in a haplotype-aware manner.

Homepage: [https://haptools.readthedocs.io/](https://haptools.readthedocs.io/)

Visit our homepage for installation and usage instructions.

![haptools commands](https://github.com/CAST-genomics/haptools/assets/23412689/7de95547-3138-45c6-99d8-46bca5716c26)

## citation
There is an option to _"Cite this repository"_ on the right sidebar of [the repository homepage](https://github.com/CAST-genomics/haptools)

> Arya R Massarat, Michael Lamkin, Ciara Reeve, Amy L Williams, Matteo D’Antonio, Melissa Gymrek, Haptools: a toolkit for admixture and haplotype analysis, Bioinformatics, 2023;, btad104, https://doi.org/10.1093/bioinformatics/btad104
foundry-sdk 0.0.279
A collection of tools and utilities for ETL and training pipelines
Requires Python: >=3.12

![Uni Wuerzburg - Data Driven Decisions](https://raw.githubusercontent.com/d3group/.github/refs/heads/main/assets/unilogo4c_combined.png)

# Foundry SDK
## Info

This repository contains the Foundry SDK, a collection of tools and utilities to help with the development of ETL and training pipelines.

For various documentations, please refer to the foundry-master repository: [Foundry Master](https://github.com/d3group/foundry-master)

## Documentation

- **[Package Publishing Guide](./documentation/publish_package.md)**: Complete workflow for building, versioning, and publishing to PyPI
- **[Foundry Master](https://github.com/d3group/foundry-master)**: Main project documentation and setup guides

## Contributing

If you’d like to contribute to this project, please check out the [Contribution Guide](./CONTRIBUTING.md).
db-dtypes 1.5.0
Pandas Data Types for SQL systems (BigQuery, Spanner)
Requires Python: >=3.9
Keywords: sql, pandas

Pandas Data Types for SQL systems (BigQuery, Spanner)
=====================================================

|ga| |pypi| |versions|

`Pandas extension data types`_ for data from SQL systems such as `BigQuery`_.

- `Library Documentation`_

.. |ga| image:: https://img.shields.io/badge/support-GA-gold.svg
   :target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#general-availability
.. |pypi| image:: https://img.shields.io/pypi/v/db-dtypes.svg
   :target: https://pypi.org/project/db-dtypes/
.. |versions| image:: https://img.shields.io/pypi/pyversions/db-dtypes.svg
   :target: https://pypi.org/project/db-dtypes/
.. _Pandas extension data types: https://pandas.pydata.org/docs/development/extending.html#extension-types
.. _BigQuery: https://cloud.google.com/bigquery/docs/
.. _Library Documentation: https://googleapis.dev/python/db-dtypes/latest


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

Install this library in a `virtualenv`_ using pip. `virtualenv`_ is a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.

With `virtualenv`_, it's possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.

.. _`virtualenv`: https://virtualenv.pypa.io/en/latest/


Supported Python Versions
^^^^^^^^^^^^^^^^^^^^^^^^^
Python >= 3.9

Unsupported Python Versions
^^^^^^^^^^^^^^^^^^^^^^^^^^^
Python <= 3.8.


Mac/Linux
^^^^^^^^^

.. code-block:: console

    pip install virtualenv
    virtualenv <your-env>
    source <your-env>/bin/activate
    <your-env>/bin/pip install db-dtypes


Windows
^^^^^^^

.. code-block:: console

    pip install virtualenv
    virtualenv <your-env>
    <your-env>\Scripts\activate
    <your-env>\Scripts\pip.exe install db-dtypes