semvec/pypi-packages huggingface dataset:
scine-utilities 10.1.0
Utilities used in all SCINE modules

SCINE - Utilities
=================

Introduction
------------

This repository contains functionality which is used in most SCINE modules.
It is vital for the correct functioning of all SCINE modules, but it does not
directly provide any functionality for end users. Therefore, only developers
of SCINE need to directly interact with this repository.

License and Copyright Information
---------------------------------

For license and copyright information, see the file ``LICENSE.txt`` in this
directory.

Installation and Usage
----------------------

As a user of one of the SCINE modules (such as Sparrow), you do not need
to set up SCINE Utilities yourself. Instead, this is done as part of the
installation process of the respective SCINE module. Therefore, the following
instructions are only necessary for developers of SCINE, or those interfacing
this library directly.

Dependencies
............

Required software, minimum required versions in brackets, for this SCINE project are:

- A C++ compiler supporting the C++17 standard
- CMake (3.9)
- Boost (1.65.0)
- Eigen3 (3.3.2)

Installation
............

In order to compile this as a SCINE developer, execute the following
commands::

    git submodule init
    git submodule update
    mkdir build
    cd build
    cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=../inst -DSCINE_BUILD_PYTHON_BINDINGS=ON ..
    make -j 4
    make UtilsOSDocumentation
    make test
    make install

Known Issues
------------

Due to a `bug <https://github.com/boostorg/process/issues/62>`_ in boost 
version 1.69.0, linkage against this version will result in a leakage
of file handles if the ORCA and Gaussian Calculators are called repeatedly.

How to Cite
-----------

When publishing results obtained with any SCINE module, please cite the following paper:

T. Weymuth, J. P. Unsleber, P. L. Türtscher, M. Steiner, J.-G. Sobez, C. H. Müller, M. Mörchen,
V. Klasovita, S. A. Grimmel, M. Eckhoff, K.-S. Csizi, F. Bosia, M. Bensberg, M. Reiher,
"SCINE—Software for chemical interaction networks", *J. Chem. Phys.*, **2024**, *160*, 222501
(DOI `10.1063/5.0206974 <https://doi.org/10.1063/5.0206974>`_).

Support and Contact
-------------------

In case you should encounter problems or bugs, please write a short message
to scine@phys.chem.ethz.ch.

Third-Party Libraries Used
--------------------------

SCINE Utilities makes use of the following third-party libraries:

- `Boost <https://www.boost.org/>`_
- `Eigen <http://eigen.tuxfamily.org>`_
- `Google Test <https://github.com/google/googletest>`_
- `IRC <https://github.com/rmeli/irc>`_
- `LBFGSB <https://github.com/yixuan/LBFGSpp>`_
- `pybind11 <https://github.com/pybind/pybind11>`_
- `Spglib <https://github.com/spglib/spglib>`_
- `yaml-cpp <https://github.com/jbeder/yaml-cpp>`_
scine-sparrow 5.2.0
Open source semi-empirical quantum chemistry code

SCINE - Sparrow
===============

Introduction
------------

Sparrow is a code for fast semiempirical quantum chemical calculations. It
provides the following methods:

- MNDO
- AM1
- RM1
- PM3
- PM6
- non-SCC DFTB (DFTB0)
- DFTB2
- DFTB3

Sparrow can calculate electronic energies, nuclear gradients and
Hessians for the electronic ground state, as well as electronic vertical
transition energies and the electronic transition dipoles.

License and Copyright Information
---------------------------------

Sparrow is distributed under the BSD 3-clause "New" or "Revised" License.
For more license and copyright information, see the file ``LICENSE.txt`` in the
repository.

Installation and Usage
----------------------

For instructions on how to install and use Sparrow as well as for a detailed
documentation of the entire functionality of Sparrow, please consult the user
manual found in the ``manual`` directory in the repository.
Alternatively the manual can also be found on the official GitHub website,
SCINE website and in the hosted documentation.

How to Cite
-----------

When publishing results obtained with Sparrow, please cite the corresponding
release as archived on `Zenodo <https://zenodo.org/record/3244105>`_ (DOI
10.5281/zenodo.3244105; please use the DOI of the respective release).

In addition, we kindly request you to cite the following article when using Sparrow:

T. Husch, A. C. Vaucher, M. Reiher, "Semiempirical molecular orbital models
based on the neglect of diatomic differential overlap approximation", *Int.
J. Quantum Chem.*, **2018**, *118*, e25799.

Furthermore, when publishing results obtained with any SCINE module, please cite the following paper:

T. Weymuth, J. P. Unsleber, P. L. Türtscher, M. Steiner, J.-G. Sobez, C. H. Müller, M. Mörchen,
V. Klasovita, S. A. Grimmel, M. Eckhoff, K.-S. Csizi, F. Bosia, M. Bensberg, M. Reiher,
"SCINE—Software for chemical interaction networks", *J. Chem. Phys.*, **2024**, *160*, 222501
(DOI `10.1063/5.0206974 <https://doi.org/10.1063/5.0206974>`_).

Support and Contact
-------------------

In case you should encounter problems or bugs, please write a short message
to scine@phys.chem.ethz.ch.

Third-Party Libraries Used
--------------------------

SCINE Sparrow makes use of the following third-party libraries:

- `Boost <https://www.boost.org/>`_
- `Cereal <https://uscilab.github.io/cereal/>`_
- `Eigen <http://eigen.tuxfamily.org>`_
- `Google Test <https://github.com/google/googletest>`_
- `pybind11 <https://github.com/pybind/pybind11>`_
- `yaml-cpp <https://github.com/jbeder/yaml-cpp>`_
scine-readuct 6.1.0
Open source quantum chemistry library

SCINE - ReaDuct
===============

Introduction
------------

SCINE ReaDuct is a command-line tool that allows you to carry out

- single point calculations,
- bond order calculations,
- Hessian calculations,
- structure optimizations,
- single-ended transition state searches,
- double-ended B-Spline transition state searches,
- intrinsic reaction coordinate (IRC) calculations,
- artificial force induced reaction (AFIR) calculations, and
- Newton trajectory scans searching for transition state guesses.

For these calculations, it relies on a backend program to provide the necessary
quantum chemical properties (such as nuclear gradients). Currently, SCINE Sparrow,
XTB, CP2K, Gaussian, ORCA, Serenity, and Turbomole are supported as backend programs.

License and Copyright Information
---------------------------------

ReaDuct is distributed under the BSD 3-clause "New" or "Revised" License.
For more license and copyright information, see the file ``LICENSE.txt`` in the
repository.

Installation and Usage
----------------------

For instructions on how to install and use ReaDuct as well as for a detailed
documentation of the entire functionality of ReaDuct, please consult the user
manual found in the ``manual`` directory in in the repository.
Alternatively the manual can also be found on the official GitHub website
and on the SCINE website.

How to Cite
-----------

When publishing results obtained with ReaDuct, please cite the corresponding
release as archived on `Zenodo <https://zenodo.org/record/3244107>`_ (DOI
10.5281/zenodo.3244107; please use the DOI of the respective release).

In addition, we kindly request you to cite the following article when using ReaDuct:

A. C. Vaucher, M. Reiher, "Minimum Energy Paths and Transition States by Curve
Optimization", *J. Chem. Theory Comput.*, **2018**, *16*, 3091.

Furthermore, when publishing results obtained with any SCINE module, please cite the following paper:

T. Weymuth, J. P. Unsleber, P. L. Türtscher, M. Steiner, J.-G. Sobez, C. H. Müller, M. Mörchen,
V. Klasovita, S. A. Grimmel, M. Eckhoff, K.-S. Csizi, F. Bosia, M. Bensberg, M. Reiher,
"SCINE—Software for chemical interaction networks", *J. Chem. Phys.*, **2024**, *160*, 222501
(DOI `10.1063/5.0206974 <https://doi.org/10.1063/5.0206974>`_).

Support and Contact
-------------------

In case you should encounter problems or bugs, please write a short message
to scine@phys.chem.ethz.ch.

Third-Party Libraries Used
--------------------------

SCINE ReaDuct makes use of the following third-party libraries:

- `Boost <https://www.boost.org/>`_
- `Cereal <https://uscilab.github.io/cereal/>`_
- `Eigen <http://eigen.tuxfamily.org>`_
- `IRC <https://github.com/rmeli/irc>`_
- `Google Test <https://github.com/google/googletest>`_
- `pybind11 <https://github.com/pybind/pybind11>`_
- `yaml-cpp <https://github.com/jbeder/yaml-cpp>`_
scine-molassembler 3.0.1
Inorganic molecular graph interpretation, modification and conformer generation


# Molassembler library
## Overview

Molassembler is a C++ library that aims to facilitate crossings between
Cartesian and graph representations of molecules. It provides the necessary
functionality to represent a molecule as a graph, modify it in graph space, and
generate coordinates from graphs. It can capture the absolute configuration
of multidentate and haptic inorganic molecules from positional data and
generate non-superposable stereopermutations as output.
scine-kinetx 3.1.0
Kinetic Modelling of Reaction Networks

SCINE - KiNetx Python Bindings
==============================

Introduction
------------

This repository contains functionality which is used in SCINE to model reaction
kinetics and concentration flows in reaction networks.


Installation and Usage
----------------------

We currently offer only a ``manylinux2014`` build for Linux platforms. This may
be expanded in the future. If you are on a different platform, you can build 
the Python bindings from source (https://github.com/qcscine/kinetx/).


Support and Contact
-------------------

In case you should encounter problems or bugs, please write a short message
to scine@phys.chem.ethz.ch.
pozansky-stock-server 1.4.0
Advanced stock analysis server with candlestick pattern detection and chart generation
Requires Python: >=3.10

# Pozansky Stock Analysis Server

高级股票分析服务器，提供K线形态识别、图表模式检测和技术指标分析。

## 功能特点

- 📊 多周期股票数据分析
- 🕯️ K线形态自动识别
- 📈 图表模式检测
- 📉 技术指标计算
- 🖼️ 自动生成K线图表
- 🌍 支持全球主要市场股票

## 安装

```bash
pip install pozansky-stock-server
wikidataloader 0.0.6
Pythonic wrapper around the Wikidata SPARQL API for quick creation of datasets from Wikidata
Requires Python: >=3.9
Keywords: datasets, wikidata, sparql

# wikidataloader

Easy pythonic wrapper around the [Wikidata SPARQL API](https://query.wikidata.org/) for quick creation of datasets from Wikidata.

Only supports simple, non-recursive queries - for complex queries please directly use the [SPARQL API](https://query.wikidata.org/) provided by Wikidata.

It does not support complex operators (ordering, datetime conversion, string/numeric filtering etc.), because these can be substituted by preprocessing the dataset in Python after retrieval.

## Usage

Look up the URIs for properties (e.g. _P31_) and objects (e.g. _Q5_) on [Wikidata's search engine](https://www.wikidata.org/).

```python
from wikidataloader import WikidataQuery

# Linguists from Germany with birth places and gender

results = WikidataQuery.search(
    # {is_instance:human, country_of_origin:Germany, profession:linguist}
    filters={"P31": "Q5", "P27": "Q183", "P106": "Q14467526"}, 

    # selects the properties "Gender" and "Birth Place" as columns in the dataframe and names them "Gender" and "City of Birth"
    select=[("P21", "Gender"), ("P19", "City of Birth")],

    # returns a maximum of 5 results
    limit=5,

    # retrieves labels in English, if available
    default_language="en" 
).to_pandas()

results

>>>                                    item Gender        City_of_Birth
>>> 0                        Hermann Weller   male     Schwäbisch Gmünd
>>> 1                             Hans Wehr   male              Leipzig
>>> 2                       Theodor Haecker   male            Mulfingen
>>> 3                   Gottfried Bernhardy   male  Gorzów Wielkopolski
>>> 4                    Wilhelm Streitberg   male   Rüdesheim am Rhein

```

For more examples, see [example.ipynb](./example.ipynb)

## Install

Install using pip:

```pip install wikidataloader```

## Limitations

- Does not support recursive queries
- Does not support Senses and Forms for Lexeme queries
nt25 1.0.5
Neo's Tools of Python
Requires Python: >=3.10

# nt25.py

Neo's Tools of Python in 2025

## TODO

- [x] fio
- [x] sqlite
- [x] ex: EXIF tools with Magic
- [x] mt: MapXYZ Transfer tool
- [x] pgogs: Purge Gogs orphan files
- [ ] mysql
- [ ] redis

## Usage

```sh
# help of EXIF tools
ex -h

# help of MapXYZ
mt -h

# Purge Gogs orphan files
./pgogs
```

## have fun

$$
\sqrt{-1},2^{3},\sum,\pi
$$
anyio-sqlite 0.3.0
anyio bridge to the standard sqlite3 module
Requires Python: >=3.9

# anyio-sqlite

[![Python versions](https://img.shields.io/pypi/pyversions/anyio-sqlite.svg)](https://pypi.org/project/anyio-sqlite)
[![PyPI release](https://img.shields.io/pypi/v/anyio-sqlite.svg)](https://pypi.org/project/anyio-sqlite)
[![License](https://img.shields.io/pypi/l/anyio-sqlite.svg)](https://github.com/beer-psi/anyio-sqlite/tree/trunk?tab=readme-ov-file#license)


`anyio-sqlite` is an AnyIO bridge to the standard `sqlite3` database module, providing
async versions of all connection, cursor and blob (Python 3.11+) methods that runs
on both [asyncio](https://docs.python.org/3/library/asyncio.html) and [Trio](https://github.com/python-trio/trio).
Asynchronous context management is also supported.

## Install

`anyio-sqlite` is compatible with Python 3.9 and newer. Install it from PyPI:

```
pip install anyio-sqlite
```

## Examples

Use it the same way as the standard `sqlite3` module, simply scatter the `await` keyword when needed:

```python
import anyio
import anyio_sqlite

async def main():
    async with anyio_sqlite.connect("example.sqlite3") as con:
        async with await con.cursor() as cur:
            await cur.execute("CREATE TABLE movie(title, year, score)")
            await cur.execute("""
                INSERT INTO movie VALUES
                    ('Monty Python and the Holy Grail', 1975, 8.2),
                    ('And Now for Something Completely Different', 1971, 7.5)
            """)
            await con.commit()

        async with await con.execute("SELECT score FROM movie") as cur:
            async for row in cur:
                ...

anyio.run(main)
```

It can also be used mostly procedurally, if you provide an external task group:

```python
import anyio
import anyio_sqlite

async def main():
    async with anyio.create_task_group() as tg:
        con = await anyio_sqlite.Connection.connect(tg, "example.sqlite3")
        cur = await con.cursor()

        await cur.execute("CREATE TABLE movie(title, year, score)")
        await cur.execute("""
            INSERT INTO movie VALUES
                ('Monty Python and the Holy Grail', 1975, 8.2),
                ('And Now for Something Completely Different', 1971, 7.5)
        """)
        await con.commit()
        await cur.aclose()

        cur = await con.execute("SELECT score FROM movie")

        async for row in cur:
            ...

        await cur.aclose()
        await con.aclose()

anyio.run(main)
```

> [!WARNING]
> Since each connection spawns a long-running worker in another thread,
> the connection must be closed after you're done using it! Otherwise,
> the process will hang indefinitely waiting for the worker thread
> to finish.

## License

Licensed under either of

- Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or https://www.apache.org/licenses/LICENSE-2.0)
- MIT license ([LICENSE-MIT](LICENSE-MIT) or https://opensource.org/licenses/MIT)

at your option.
sfttoolbox 1.0.1
Useful tools for sft data science
Requires Python: >=3.8

# SFT Data Science Tool Kit

Welcome to the SFT Data Science Toolkit! This toolkit is designed to empower data scientists and analysts within SFT to efficiently analyse and derive insights from healthcare data.

## Installation

If trying to install this package locally (i.e. using DataSpell), open up a terminal and type:

`pip install -e  "PATH\TO\FOLDER"`

If this is successful, it's then as simple as using:

`import sfttoolbox`

as you would with any other package.

## Notebooks

Notebooks contain ideas for new tools with example useage. The more documentation for the intentional use and interface, the faster the code can be implemented in the module!