semvec/pypi-packages huggingface dataset:
wmwm 0.0.1
A Python package performing Wilcoxon-Mann-Whitney test in the presence of missing data with controlled Type I error
License: MIT
Requires Python: >=3.6
Keywords: Wilcoxon Mann-Whitney test, missing data, statistical analysis


<!-- README.md is generated from README.Rmd. Please edit that file -->

# wmwm

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This package performs the two-sample hypothesis test method proposed in 
(Zeng et al., 2024) for univariate data when data are not fully observed. 
This method is a theoretical extension of Wilcoxon-Mann-Whitney test in 
the presence of missing data, which controls the Type I error regardless 
of values of missing data.

Bounds of the Wilcoxon-Mann-Whitne test statistic and its p-value will be 
computed in the presence of missing data. The p-value of the test method 
proposed in (Zeng et al., 2024) is then returned as the maximum possible 
p-value of the Wilcoxon-Mann-Whitney test.

## Installation

``` sh
pip install wmwm
```

## Example

This is a basic example which shows you how to perform the test with
missing data:

``` python
import numpy as np
from wmwm import wmwm_test

#### Assume all samples are distinct.
X = np.array([6.2, 3.5, np.nan, 7.6, 9.2])
Y = np.array([0.2, 1.3, -0.5, -1.7])
## By default, when the sample sizes of both X and Y are smaller than 50,
## exact distribution will be used.
wmwm_test(X, Y, ties = False, alternative = 'two.sided')


## using normality approximation with continuity correction:
wmwm_test(X, Y, ties = False, alternative = 'two.sided', exact = False, correct = True)


#### Assume samples can be tied.
X = np.array([6, 9, np.nan, 7, 9])
Y = np.array([0, 1, 0, -1])
## When the samples can be tied, normality approximation will be used.
## By default, lower_boundary = -Inf, upper_boundary = Inf.
wmwm_test(X, Y, ties = True, alternative = 'two.sided')


## specifying lower_boundary and upper_boundary:
wmwm_test(X, Y, ties = True, alternative = 'two.sided', lower_boundary = -1, upper_boundary = 9)


```

## References

Zeng Y, Adams NM, Bodenham DA. On two-sample testing for data with
arbitrarily missing values. arXiv preprint arXiv:2403.15327. 2024 Mar
22.

Mann, Henry B., and Donald R. Whitney. “On a test of whether one of two
random variables is stochastically larger than the other.” The annals of
mathematical statistics (1947): 50-60.
airflow-dag-insight 0.1.0a5
An Apache Airflow plugin that visualizes DAG runs in a Gantt chart, predicts future runs, and identifies tasks that won't execute. Enhance your workflow monitoring and planning with intuitive visualizations.
License: MIT
Requires Python: <4.0,>=3.10

# Airflow DAG Insight Plugin

The Airflow DAG Insight Plugin for [Apache Airflow](https://github.com/apache/airflow) allows you to visualize DAG runs in a Gantt chart, predict future runs, and identify DAGs that won't run, providing a seamless and efficient workflow for managing your pipelines. Enhance your workflow monitoring and planning with intuitive visualizations.

[![Tests Status](https://github.com/hipposys-ltd/airflow-dag-insight/workflows/Tests/badge.svg)](https://github.com/hipposys-ltd/airflow-dag-insight/actions)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

## System Requirements

- **Airflow Versions**: 2.4.0 or newer

## How to Install

Add `airflow-dag-insight` to your `requirements.txt` and restart the web server.

## How to Use

1. Navigate to `DAG Insight` in the `Browse` tab to access the plugin:

   ![Menu](https://github.com/hipposys-ltd/airflow-dag-insight/releases/download/v0.1.0-alpha.0/plugin_menu.png)

2. View all DAG runs in a Gantt chart:

   ![Gantt Chart Logs](https://github.com/hipposys-ltd/airflow-dag-insight/releases/download/v0.1.0-alpha.0/gantt_chart_history_logs.png)

3. Toggle the `Show Future Runs?` option to predict the next runs for your DAGs and generate a list of all the DAGs that won't run.

   **Note**: All event-driven DAGs (scheduled by datasets and triggers) are predicted only to their next run.

4. Future DAGs will be highlighted in gray on the Gantt chart:

   ![Gantt Chart Future Runs](https://github.com/hipposys-ltd/airflow-dag-insight/releases/download/v0.1.0-alpha.0/gantt_chart_future_runs.png)

5. A table of future runs will be displayed, with events ordered by their start date:

   ![Future Runs Table](https://github.com/hipposys-ltd/airflow-dag-insight/releases/download/v0.1.0-alpha.0/future_runs_table.png)

6. Below this, you will find a table listing all the DAGs that won't run:

   ![Missing Future Runs Table](https://github.com/hipposys-ltd/airflow-dag-insight/releases/download/v0.1.0-alpha.0/missing_future_runs_table.png)
KS903-animethion-icon-GIF 0.9.8
Animation icon GIF library for KS903
License: MIT
Keywords: animation icon gif KS903

# README.md

# KS903_animethion_icon_GIF

## 概要

`KS903_animethion_icon_GIF` は、KS903 のためのアニメーションアイコンGIFライブラリです。このライブラリを使用することで、簡単にアニメーションGIFをアイコンとして設定できます。

## インストール

このライブラリは、`pip` を使用してインストールできます。

```bash
pip install KS903_animethion_icon_GIF

使い方の例として
import tkinter as tk
from KS903_animetion_icon import KS903_animethion_icon_GIF

# ウィンドウを作成
root = tk.Tk()
root.geometry("300x300")

# アニメーションアイコンを設定
gif_path = "ico.gif"  # GIFファイルへのパスを設定
icon = KS903_animethion_icon_GIF(root, gif_path)

root.mainloop()



Creator/Inventor: NPO_KS_903.lnc (President: Takeshi Kumura, Director: Takuma Ozawa)
Developer: NPO_KS_903.lnc (President: Takeshi Kumura, Director: Takuma Ozawa)
Designer/Architect: NPO_KS_903.lnc (President: Takeshi Kumura, Director: Takuma Ozawa)
Founder: NPO_KS_903.lnc (President: Takeshi Kumura, Director: Takuma Ozawa)