semvec/pypi-packages huggingface dataset:
phlower 0.3.0
This is a Python package which helps you handle GNN especially for physics problems.
Requires Python: <4.0,>=3.10

# Phlower


![](docs/source/_static/logo.png)


__Phlower__ is a deep learning framework based on PyTorch especially for physical phenomenon such as fluid dynamics.

For more details, please refer to user manual below.

- [User Manual](https://ricosjp.github.io/phlower/)


## Key Features


Tutorials are prepared for key features !

1. [Extended Tensor object which enables you to handle physics dimention](https://ricosjp.github.io/phlower/tutorials/basic_usages/01_phlower_tensor_basic.html)
2. [Model definition by yaml file](https://ricosjp.github.io/phlower/tutorials/basic_usages/02_model_definition_by_yaml_file.html)
3. [High Level API for scaling, training and predicion](https://ricosjp.github.io/phlower/tutorials/basic_usages/03_high_level_api_for_scaling_training_and_prediction.html)



## Installation

**Phlower** is registered at [PyPI](https://pypi.org/project/phlower/).  You can install phlower using pip. Python 3.10 or newer is supported.

```
pip install phlower
```


## License

This project is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)
holocal 0.3.10
This is an iCalendar generator for Holodule.
Requires Python: <4.0,>=3.10
Keywords: schedule, holodule

# holocal

holocalは、[ホロジュール](https://schedule.hololive.tv/)（ホロライブの配信予定）
からカレンダーアプリに登録できるファイルを生成、配信するプログラムです。

![holocal](https://user-images.githubusercontent.com/33576079/76172492-00a80e80-61da-11ea-9590-a6bcc4a4982d.png)

## URL一覧

* [全体](https://gemmaro.github.io/holocal/all.ics)
* [ホロライブ](https://gemmaro.github.io/holocal/hololive.ics)
* [ホロスターズ](https://gemmaro.github.io/holocal/holostars.ics)
* [hololive Indonesia](https://gemmaro.github.io/holocal/indonesia.ics)
* [hololive English](https://gemmaro.github.io/holocal/english.ics)
* [HOLOSTARS English](https://gemmaro.github.io/holocal/holostars_english.ics)
* [hololive DEV\_IS](https://gemmaro.github.io/holocal/dev_is.ics)

## 設定例

※操作方法はOSのバージョンにより異なる可能性があります。

### iOS (iPhone/iPad)

* ホーム画面の「設定」を開く
* 「パスワードとアカウント」→「アカウントを追加」
* 一番下の「その他」
* 一番下の「照会するカレンダーを追加」
* URL を貼り付けて「次へ」
* そのまま右上の「保存」

### Google Calendar (PC)

* 「設定」を開く
* 左側メニュー「カレンダーの追加」→「URL で追加」
* URL を貼り付け
* 「カレンダーを追加」

PC から追加後はモバイル版 Google Calendar でも閲覧できます。

## 雑記

### カレンダーの更新頻度

1時間に1回としています（ホロジュールは15分に1度のようです）。

カレンダーアプリの更新頻度により、カレンダーへの反映が遅くなる可能性があります。
iOS では、設定より更新頻度を設定できます。

### 通知が欲しい

カレンダー側で通知を設定できるものがあります。
[ホロプラス](https://www.holoplus.com/)、YouTubeチャンネルの通知機能等もご検討ください。

### 配信の長さについて

配信の長さがわからないときは、カレンダーの生成を開始した時刻から2時間後を推定します。
ホロジュールでは、配信開始の予定時間のみが得られるためです。

## バグ・要望

* [機能の要望など](https://github.com/gemmaro/holocal/discussions)
* [不具合の報告など](https://github.com/gemmaro/holocal/issues)

## 謝辞

このプログラムは[sarisia/holodule-ics](https://github.com/sarisia/holodule-ics)を元にしています。

## 使用許諾

このプログラムの開発、運用は[ホロライブプロダクションの二次創作ガイドライン](https://hololivepro.com/terms/ "hololive")に則って行います。

このプログラムはMITライセンスの元に配布されます。
詳細は[LICENSEファイル](LICENSE)をご参照ください。
krcf 0.4.0
AWS Random Cut Forest for Python
Requires Python: >=3.10
Keywords: anomaly detection, random cut forest, rcf

# krcf

## Project Description

Python bindings for the [aws/random-cut-forest-by-aws](https://github.com/aws/random-cut-forest-by-aws) library, providing a fast and efficient implementation of Random Cut Forests for anomaly detection.

Key features:

- Fast anomaly detection using Random Cut Forests
- Attribution and scoring for detected anomalies
- Support for shingling and advanced configuration

## API Usage

### Basic Example

```python
from krcf import RandomCutForest, RandomCutForestOptions

# Define forest options
options: RandomCutForestOptions = {
    "dimensions": 3,  # Number of features in each data point
    "shingle_size": 2,  # Shingle size for time series
    "output_after": 1,  # Number of points before output is ready
}
forest = RandomCutForest(options)

# Update the forest with new data points
forest.update([1.0, 2.0, 3.0])
forest.update([2.0, 3.0, 4.0])

# Compute anomaly score
score = forest.score([2.0, 3.0, 4.0])
print("Anomaly score:", score)
# >>> Anomaly score: 1.0

# Get attribution vector
attr = forest.attribution([2.0, 3.0, 4.0])
print("Attribution:", attr)
# >>> Attribution: {'high': [0.3333333333333335, 0.3333333333333335, 0.3333333333333335, 0.0, 0.0, 0.0], 'low': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}
```

### API Reference

#### RandomCutForest(options)

Create a new Random Cut Forest instance.

- `options`: A dictionary or `RandomCutForestOptions` specifying configuration (see below).

#### Methods

- `update(point: Sequence[float]) -> None`: Update the forest with a new data point.
- `score(point: Sequence[float]) -> float`: Compute the anomaly score for a data point.
- `displacement_score(point: Sequence[float]) -> float`: Compute the displacement score.
- `attribution(point: Sequence[float]) -> dict`: Get the attribution vector for a data point.
- `density(point: Sequence[float]) -> float`: Compute the density estimate for a data point.
- `near_neighbor(point: Sequence[float], percentile: int) -> list`: Find near neighbors for a data point.
- `is_output_ready() -> bool`: Check if the forest is ready to output scores.

#### RandomCutForestOptions

Options for configuring the forest (all except `dimensions` and `shingle_size` are optional):

- `dimensions` (int): Number of features in each data point (required)
- `shingle_size` (int): Shingle size for time series (required)
- `num_trees` (int): Number of trees in the forest
- `sample_size` (int): Sample size for each tree
- `output_after` (int): Number of points before output is ready
- `random_seed` (int): Random seed for reproducibility
- ...and more advanced options

For more details, see the docstrings in the code or the API reference.
rstms-mailgun 1.2.6
Top-level package for rstms-mailgun.
Keywords: rstms_mailgun

rstms-mailgun
=============


![Image](https://img.shields.io/github/license/rstms/rstms_mailgun)

![Image](https://img.shields.io/pypi/v/rstms_mailgun.svg)





Mailgun API CLI


* Free software: MIT license
* Documentation: https://rstms-mailgun.readthedocs.io.



Credits
-------

This package was created with Cookiecutter and `rstms/cookiecutter-python-cli`, a fork of the `audreyr/cookiecutter-pypackage` project template.

[audreyr/cookiecutter](https://github.com/audreyr/cookiecutter)
[audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage)
[rstms/cookiecutter-python-cli](https://github.com/rstms/cookiecutter-python-cli)
gnss-dsp 0.2.1
Digital Signal Processing algorithms for GNSS
Requires Python: >=3.11

# gnss-dsp

`gnss-dsp` is a set of signal processing tools for GNSS. It is mainly
implemented in Rust, with some Python for scripting and plotting. It can be used
as a Python package, which contains some CLI tools, and as a Rust crate, for
access to most of the functionality within Rust projects.

Currently this package contains a CUDA high-sensitivity acquisition algorithm
for GPS L1 C/A.

## Development

This repository uses [just](github.com/casey/just) to run common development
tasks. Run `just` to print the available recipes. The recommended way of doing
development is to create a Python virtual environment, activate it, and run
`just develop`, which calls `maturin develop` to build the package and install
it into the virtual environment.

[uv](https://docs.astral.sh/uv/) is required to run several development tasks.

## License

Licensed under either of

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

at your option.

## Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted
for inclusion in the work by you, as defined in the Apache-2.0 license, shall be
dual licensed as above, without any additional terms or conditions.