semvec/pypi-packages huggingface dataset:
sf-gear 0.1.0
A Python library for Salesforce API and CLI integration
Requires Python: >=3.10
Keywords: salesforce, api, cli, tooling

# SF Gear (sfgear)

A Python library for Salesforce CLI integration. SF Gear provides convenient wrappers around Salesforce CLI commands to simplify Salesforce development workflows.

## Features

- **CLI Module**: Wrapper around Salesforce CLI commands for data operations and org management
- **Cross-platform**: Works on Windows, macOS, and Linux
- **Zero dependencies**: Uses only Python standard library

## Installation

```bash
pip install sf-gear
```

## Quick Start

```python
from sfgear.cli import data, org

# Query data using SOQL
result = data.query("org-alias", "SELECT Id, Name FROM Account LIMIT 10")

# Get a specific record
record = data.get_record("org-alias", "Account", record_id="001000000000000")

# Get org information
org_info = org.display("org-alias")
```

## Requirements

- Python 3.10+
- Salesforce CLI

### Installing Salesforce CLI

**macOS/Linux:**
```bash
npm install -g @salesforce/cli
```

**Windows:**
```bash
npm install -g @salesforce/cli
```

**Alternative (all platforms):**
Download from [https://developer.salesforce.com/tools/sfcli](https://developer.salesforce.com/tools/sfcli)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
py-volumn-test 0.0.2
A small package targeting to help on python project packaging process
Requires Python: >=3.11


# py_volumn_test
This simple volumn testing framework for linux platform.

## Requirements
The application requires
1. python 3.12+
2. requires `df` command
3. hashicorp vault

## Setup
### Hashicorp Vault
Assume
1. we create secret on mount point (mp)
2. we create secret on path (abc)
3. we create a policy of `policy-mp-abc`
3. we create a approle of `ar-mp-abc`


#### Create policy
by creating a hashicorp policy `xxx.hcl`
```hcl
# assume `{mount_point}` is `mp`
# assume `{path}` is `abc`
# access the secret in cli `vault kv get mp/abc`
# The actual path of the secret is `mp/data/abc`
path "{mount_point}/data/{path}/*" {
        capabilities = ["read"]
}
```

Insert or update the policy by 
```
vault policy write {policy_name} {policy_file}
# vault policy write policy-mp-abc xxx.hcl
```

#### Create app role
```sh
# ------ create app role
vault write auth/approle/role/{app role name} token_policies="{policy name}" token_ttl="15m" token_max_ttl="2h"
# vault write auth/approle/role/ar-mp-abc token_policies="policy-mp-abc" token_ttl="15m" token_max_ttl="2h"
vault write auth/approle/role/{app role name} token_policies="{policy name}" token_ttl="15m" token_max_ttl="2h"

# ------ get role id
# The command return the role id
vault read auth/approle/role/{app role name}/role-id
# vault read auth/approle/role/ar-mp-abc/role-id

# ------ get secret id
# The command return the secret id (the secret is force to be generated, without re-view the secret id)
vault write -f auth/approle/role/{app role name}/secret-id
# vault write -f auth/approle/role/ar-mp-abc/secret-id

# ------ login the vault in env
# assume the role_id is `vault_role_id`
# assume the secret_id is `vault_secret_id`
export VAULT_TOKEN="$(vault write -format=json auth/approle/login role_id=$vault_role_id secret_id=$vault_secret_id | jq -r '.auth.client_token')"

```

#### Create secret

```bash
vault kv put mp/abc \
    api_key=$api_key \                     # api key is the api key for restdb.io
    host=$host \                           # host is url
    collection_name=$collection_name \     # collection name is the collection name
    filter_name=$filter_name \             # filter name is the configuration name to filter
    bootstrap_id=$bootstrap_id             # the entry point to access
```

#### Run application
```bash
df | \
    docker run \
        -e VAULT_ADDR="$VAULT_ADDR" \
        -e vault_role_id="$vault_role_id" \
        -e vault_secret_id=$vault_secret_id \
        -e vault_mount_point=$vault_mount_point \
        -e vault_secret_path=$vault_secret_path \
        -i py-volumn-test  
```
astrodynx 0.9.12
A modern astrodynamics library powered by JAX.
Requires Python: >=3.10
Keywords: astrodynamics, celestial mechanics

[![PyPI - Version](https://img.shields.io/pypi/v/astrodynx)](https://pypi.org/project/astrodynx/)
[![GitHub License](https://img.shields.io/github/license/adxorg/astrodynx)](https://github.com/adxorg/astrodynx/blob/main/LICENSE)
[![Github CI](https://github.com/adxorg/astrodynx/actions/workflows/ci.yml/badge.svg)](https://github.com/adxorg/astrodynx/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/adxorg/astrodynx/graph/badge.svg?token=azxgWzPIIU)](https://codecov.io/gh/adxorg/astrodynx)
[![Docs](https://app.readthedocs.org/projects/astrodynx/badge/?version=latest)](https://app.readthedocs.org/projects/astrodynx/builds/)

# Welcome to AstroDynX! 🚀

AstroDynX is a modern astrodynamics library powered by JAX, designed for high-performance scientific computing, automatic differentiation, and GPU/TPU acceleration.

## Features
- JAX support: automatic differentiation, vectorization, JIT compilation
- Modern Python code style and type checking
- Continuous integration and automated testing
- Easy to extend and contribute

> [!WARNING]
> This project is still experimental, APIs could change between releases without notice.

## Installation
You can install AstroDynX from PyPI using pip:
```bash
pip install astrodynx
```
> [!NOTE]
> AstroDynX is written in pure Python build with JAX, so it is compatible with any platform that supports JAX, including CPU, GPU, and TPU. By default, it installs the CPU version. If you want to use AstroDynX on GPU/TPU, follow the [**instructions**](https://jax.readthedocs.io/en/latest/installation.html) to install the appropriate JAX backend for your hardware.

## Documentation
The documentation is available at [Read the Docs](https://astrodynx.readthedocs.io/)

## Contributing

We welcome contributions from the community! Please see our comprehensive contribution guides:

- **[CONTRIBUTING.md](CONTRIBUTING.md)** - Complete guide for contributors
- **[DEVELOPER_GUIDE.md](DEVELOPER_GUIDE.md)** - Detailed development guidelines
- **[NEWCOMER_GUIDE.md](NEWCOMER_GUIDE.md)** - Perfect starting point for new contributors

Whether you're fixing bugs, adding features, improving documentation, or helping with testing, your contributions are valued!

<!-- TODO: ADD CITATION FILES -->
dibisoreporting 0.8
A Python library for generating bibliometric reports, developed at the DiBISO.
Requires Python: >=3.10
Keywords: DiBISO, Data analysis, Scientific research, Plotly, LaTeX

# DiBISO reporting

A Python library for generating bibliometric reports, developed at the [DiBISO](https://www.bibliotheques.universite-paris-saclay.fr/en/department-libraries-information-and-open-science-dibiso-and-its-missions).

Install with:

```bash
pip install dibisoreporting
```

The library contains the following submodules:
  - `biso`: reporting for the BiSO (Open-Science Report)
  - `pubpart`: reporting about publications and partnerships


Homepage: https://pypi.org/project/dibisoreporting/

Documentation: https://dibiso-upsaclay.github.io/dibisoreporting/

Repository URL: https://github.com/dibiso-upsaclay/dibisoreporting

Romain THOMAS 2025  
Department of Libraries, Information and Open Science (DiBISO)  
Université Paris-Saclay
icflow 0.2.4
A collection of simple utilities for machine learning workflows.
Requires Python: >=3.10
Keywords: Machine Learning, Workflow, HPC

`icflow` is a Python package with some prototype workflow tools for use at the Irish Centre for High End Computing (ICHEC).

It is intended to help standardize some of our workflows in areas like Machine Learning by using common utilities, data-formats and data-structures. 

See the project documentation on [ReadTheDocs](https://icflow.readthedocs.io/en/latest/).

# Features #

## Parameter Sweep ##

You can define a parameter sweep in a `yaml` file as follows:

``` yaml
title: "my_parameter_sweep"
program: "launch_program"  

parameters:
    param_0 : 1
    param_1 : "my_value"
    param_2 : [1, 2]
    param_3 : ["a", "b"]
```

Running:

``` shell
icflow sweep --config my_config.yaml
```

with this fill will launch the program or script defined by `program`, which should be in the system `PATH`.

The listed parameters are passed as command line arguments to the `program` in the form `--key value`. Parameter value lists are expanded such that there is a program launch for each combination of values in the list. In the above example this will result in the following program launches:

``` shell
launch_program --param_0 1 --param_1 my_value --param_2 1 --param_3 a
launch_program --param_0 1 --param_1 my_value --param_2 2 --param_3 a
launch_program --param_0 1 --param_1 my_value --param_2 1 --param_3 b
launch_program --param_0 1 --param_1 my_value --param_2 2 --param_3 b
```

Program launching is handled internally by ICHEC's [ictasks](https://git.ichec.ie/performance/toolshed/ictasks) library, with each of these program launches handled as a 'task'.


### Running sweeps in parallel ###
You can also specify details about the number of gpu/cpu s available for running tasks by including the `config` and inner `task_distribution` sections in the sweep config This can allow for tasks to be run in parallel, see a description in [ictasks](https://git.ichec.ie/performance/toolshed/ictasks) covering this `task_distribution` and using gpus in your tasks.

``` yaml
title: ...
program: ... 

parameters:
    ...

config:
    task_distribution:
        ...
```

# Installation #

It is available on PyPI:

``` shell
pip install icflow
```

# Copyright

This software is Copyright of the Irish Centre for High End Computing 2024. You can use it under the terms of the GPLv3+. See the included `LICENSE` file for details.
pyncm-async 1.8.2
NeteaseCloudMusic APIs for Python 3.x 适用于 Python 3 的网易云音乐异步 API
Requires Python: >=3.8

![Logo](https://github.com/greats3an/pyncm/raw/master/demos/_logo.png)

# PyNCM Async!

# 安装
    pip install pyncm-async

# API 使用示例 (IPython)
```python
In [1]: from pyncm_async import apis
# 获取歌曲信息    
In [2]: await apis.track.GetTrackAudio(29732235)
Out[2]:
{'data': [{'id': 29732235, 'url': 'http://m701.music...
# 获取歌曲详情
In [3]: await apis.track.GetTrackDetail(29732235)
Out[3]:
{'songs': [{'name': 'Supernova', 'id': 2...
# 获取歌曲评论
In [4]: await apis.track.GetTrackComments(29732235)
Out[4]:
{'isMusician': False, 'userId': -1, 'topComments': [], 'moreHot': True, 'hotComments': [{'user': {'locationInfo': None, 'liveIn ...
```
- 多 Session 示例

```python
await LoginViaEmail(...) 
# 利用全局 Session 完成该 API Call
session = CreateNewSession() # 建立新的 Session
async with session: # 进入该 Session, 在 `with` 内的 API 将由该 Session 完成
    await LoginViaCellPhone(...)
    result = await GetTrackAudio(...)
# 离开 Session. 此后 API 将继续由全局 Session 管理
await GetTrackComments(...)
```
使用 `async with` 时...
- 注：Session 各*线程*独立，各线程利用 `async with` 设置的 Session 不互相影响
- 注：Session 离开 `async with` clause 时，**Session 会被销毁**，但不会影响全局 Session
- 注：Session 生命周期细节请参阅 https://www.python-httpx.org/async/

同时，你也可以在 API Call 中 指定 Session
```python
await GetTrackComments(..., session=session)
```

详见 [Session 说明](https://github.com/mos9527/pyncm/blob/async/pyncm_async/__init__.py#L67)
## API 说明
大部分 API 函数已经详细注释，可读性较高。推荐参阅 [API 源码](https://github.com/mos9527/pyncm/tree/async/pyncm_async) 获得支持

## FAQ
- 为什么 `GetTrackAudio` 几乎拿不到音频 URL？

你需要[进行登陆](https://github.com/mos9527/pyncm/blob/async/pyncm_async/apis/login.py)。若身边没有合适的账号，也可选择匿名登陆 (IPython内示例)：
```python
In [1]: from pyncm_async.apis.login import LoginViaAnonymousAccount
In [2]: await LoginViaAnonymousAccount()
Out[2]:
{'tick': 1662870122.1159196,
 'content': {'code': 200,
  'userId': 8023914528,
  'createTime': 1662868134354,
  'profile': {'nickname': 'Ano...
```
# 感谢
[Android逆向——网易云音乐排行榜api(上)](https://juejin.im/post/6844903586879520775)

[Binaryify/NeteaseCloudMusicApi](https://github.com/Binaryify/NeteaseCloudMusicApi)
Simultipac 2.1.0
Treat data from CST Particle Studio and SPARK3D for multipactor
Requires Python: >=3.12

# About this package
This package is a set of utils oriented towards multipacting analysis.
In particular:
 - Load parameter sweeps and PIC Position Monitor data from CST Particle Studio.
 - Load results from SPARK3D.
 - Post-treat electron vs time results from these tools: multipactor trend, (TODO: multipactor order).
 - Post-treat CST's PIC Position Monitor:
  - Distribution of emission energies.
  - Distribution of impact energies.
  - Distribution of impact angles.
  - Visualize trajectories.

# Installation
## Requirements
You will need a recent version of Python (at least 3.12).

## Installation
### Simple installation
1. Create a dedicated Python environment, activate it.
2. Run `pip install simultipac`

> [!NOTE]
> If you are completely new to Python and these instructions are unclear, check [this tutorial](https://python-guide.readthedocs.io/en/latest/).
> In particular, you will want to:
> 1. [Install Python](https://python-guide.readthedocs.io/en/latest/starting/installation/) 3.12 or higher.
> 2. [Learn to use Python environments](https://python-guide.readthedocs.io/en/latest/dev/virtualenvs/), `pipenv` or `virtualenv`.
> 3. [Install a Python IDE](https://python-guide.readthedocs.io/en/latest/dev/env/#ides) such as Spyder or VSCode.

### Building form source
1. Navigate to the library installation folder.
2. `git clone git@github.com:AdrienPlacais/Simulia_Multipactor_lib.git` (or download it a `zip`).
3. Navigate to `Simulia_Multipactor_lib`
4. Create a dedicated python environment.
5. `pip install -e .`

# How to use
## Documentation
Documentation is available at [this link](https://simultipac.readthedocs.io/en/latest/).

## Tutorial
Examples are provided in the `examples` folder and [in the documentation](https://simultipac.readthedocs.io/en/latest/manual/examples_jupyter_notebooks.html).

# Gallery
## Compute exponential growth factor
### From SPARK3D
![Evolution of exponential growth factor with accelerating field](docs/manual/images/exp_growth_spark.png)

### From CST
Results of a parametric study on the number of seed electrons.
![Evolution of exponential growth factor with accelerating field](docs/manual/images/exp_growth_cst.png)

## Treat CST PIC Monitor data
### Emission energies
![Distribution of emission energies](docs/manual/images/emission_energy_distribution.png)

### Collision energies
![Distribution of collision energies](docs/manual/images/collision_energy_distribution.png)

### Collision angles
![Distribution of collision angles](docs/manual/images/collision_angle_distribution.png)

### Trajectory plots
Here we represented in red the collision points and in green the emission points.
Electrons without a green point are seed electrons.

![Plot of some trajectories](docs/manual/images/trajectories_1.png)

![Plot of some trajectories](docs/manual/images/trajectories_2.png)

[See also: interactive trajectory plot](https://simultipac.readthedocs.io/en/latest/_static/k3d_tesla_example.html)

# TO DO

- [ ] Avoid git warnigns