semvec/pypi-packages huggingface dataset:
galaxyxml 0.5.5
Galaxy XML generation library

Galaxy XML Generation Libraries |Build Status| |PyPI|
=====================================================

These libraries will support building of Tool XML and Tool Dependencies
XML. We'd be happy to support any other XML that Galaxy supports, just
make an issue or PR if you're feeling motivated.

Known Bugs
----------

-  no validation of unique names
-  repeats aren't named properly
-  conditional/whens aren't named properly
-  conditionals not handled in CLI

License
-------

-  Apache License, v2

Changelog
---------
-  0.5.2

   - Debugging Build

-  0.5.1

   - Debugging Build

-  0.5.0

   - Added support for toolprofile (@fubar2)
   - Removed default stdio addition (@fubar2)
   - Github Actions (@bernt-matthias)

-  0.4.14

   - Fix for TestRepeats (@fubar2)

-  0.4.13

   - Add TestOutputCollection, TestRepeat and tests (thanks @fubar2)

-  0.4.12

   - Correct the ordering of elements for planemo (thanks @fubar2)
   - Properly read stdio on loading tools (thanks @fubar2)

-  0.4.11

   - Update to command line override (thanks @fubar2)

-  0.4.10

   - Allow overriding of command line in support of positional args (thanks @fubar2)

-  0.4.9

   - Fix quoting of text params (thanks @fubar2!)

-  0.4.8

   - Fix travis deploy process
   - Fix testing
   - py36 only

-  0.4.6

   -  Deprecate py2
   -  Wrap version command in CDATA

-  0.4.5

   -  Bug fixes:

      -  Write catched error to logger instead of STDOUT
      -  Fix Travis: install ``xmllint``
      -  Fix Travis: Deal with new ``flake8`` restrictions for
         exceptions

-  0.4.3

   -  Bug fixes:

      -  Allow ``<discover_dataset>`` within ``<data>`` in ``<outputs>``
      -  Allow import of existing XML with no description

-  0.4.2

   -  Add methods to check presence of EDAM and citations (thanks @khillion)

-  0.4.0

   -  Add feature to import existing Galaxy xml

-  0.3.3

   -  @khillion implemented the
      following:

      -  ``<options>`` with ``<filter>`` and ``<column>``
      -  ``<container>`` for ``<requirements>``
      -  Started to add ``<tests>`` section
      -  ``<section>`` in ``<inputs>``
      -  ``<collection>`` with ``<discover_datasets>`` in ``<outputs>``

   -  Please note that ``tool.parameters.OutputParam()`` has been
      changed to ``tool.parameters.OutputData()``

-  0.3.2

   -  configfiles
      (`#8 <https://github.com/hexylena/galaxyxml/pull/8>`__)

-  0.3.0

   -  Travis auto-deploys on new tags
   -  Testing
   -  p3k

-  0.2.3

   -  First widely used/stable version

.. |Build Status| image:: https://travis-ci.org/hexylena/galaxyxml.svg?branch=master
   :target: https://travis-ci.org/hexylena/galaxyxml
.. |PyPI| image:: https://img.shields.io/pypi/v/galaxyxml.svg
   :target: https://pypi.python.org/pypi/galaxyxml/
imgit 0.1.0
Git仓库管理自动化工具
Requires Python: >=3.6

<!--
 * @Description: 
 * @Date: 2023-02-09 18:18:16
 * @LastEditTime: 2023-02-15 14:42:18
 * @FilePath: /m-git-master/yhgit.md
-->
# yhgit

<!-- <div style="background-color: lightgreen; color: white; padding: 0 30px; height: 40px; line-height: 40px; display: inline-block; width: auto; font-size: 20px; font-weight: bold;">run success</div> -->




yhgit 是一款基于 Python 封装的关于 Git 的多仓库管理工具，可以高效，方便的对多个 Git 仓库执行 Git 命令。
适合于在多个仓库中进行关联开发的项目，提高 Git 操作的效率，避免逐个项目执行 Git 命令带来的误操作风险。

- **易用的命令**
封装 Git 命令，命令和参数均由 Git 衍生而来，会使用 Git 就可以成本低上手 MGit。

- **直观高效的执行命令**
提供图表化的结果展示，开发者可以快速查看命令在多个仓库的执行结果；
提供进度展示，开发者可以直观看到当前执行到哪个仓库；

- **安全的执行命令**
在执行命令前对多仓库状态进行安全检查：分支是否异常，工作区是否未提交代码，是否有冲突，等；

## 快速开始

1. &#9888; 确保Python版本最低为 3.7
2. &#9888; 手动创建PodfileModule; 确保按[PodfileModule文件介绍](md/podfilemodule.md)配置文件PodfileModule
3. &#9888; 自动创建PodfileModule; 请执行[yhgit init](md/common-commands.md)
4. &#9888; 确保按[配置podfile](md/podfile.md) 配置Podfile文件

#### 1、安装 yhgit 工具


```ruby
$ pip3 install yhgit
```
 &#9888; 注意第一次安装使用以上命令，要想安装最新版本请使用[Pypi](https://pypi.org/)或者`pip install --upgrade yhgit`安装最新版本
#### 2、准备

- 用Git 将项目 Clone 到本地

- 在根目录下，执行[yhgit init](md/common-commands.md)或者新建PodfileModule.yaml 文件，格式如下

```yaml

version: 1.0.0
branch:
dependencies:
- module: A
  pod: A
  version:
  git: git@xxx.xxx.cn:xxx/a.git
  branch:
  tag: 1.0.0
  configurations:
  path:
  inhibit_warnings: false
  ```

- 修改Podfile文件
  [配置Podfile](md/podfile.md)


#### 2、初始化多仓库 

初始化多仓库使用 `yghit install` 命令;

类似于 Git 从远程 clone 新仓库, 会将多个仓库 clone 到本地;

下面通过一个 demo 体验一下 yhgit 命令：

```ruby
# 2.1 根据PodfileModule.yaml中组件A配置的git，tag或者branch，新建开发分支 test
$ yhgit install -b test A

# 2.2 体验一下mgit命令
$ yhgit init -b xxx.git       切新的开发分支，并自动创建yaml文件
$ yhgit status                查看多个仓库状态
$ yhgit commit -m '提交信息'   提交多仓库的代码
$ yhgit pull                  拉取远端代码
$ yhgit push                  推送代码
$ yhgit release               自动release代码
$ yhgit merge -b master A     merge分支master到当前的开发分支
```


#### 3、已有多仓库如何迁移到 yhgit 管理

- 根据文档[配置 PodfileModule.yaml](md/podfilemodule.md)

  将要管理的仓库都配置到 PodfileModule.yaml 中
  
- 根据文档[配置 Podfile](md/podfile.md)

     
- 使用 `yhgit install -b test A B` 初始化多仓库

  - 新建modules文件
  - 基于`PodfileModule.yaml`中组件组件`A`, `B`的配置信息, 新建开发分支`test`
  - 更新`PodfileModule.yaml`中组件组件`A`, `B`的配置信息, 依赖信息branch为`test`
  - 更新`PodfileLocal.yaml`中组件组件`A`, `B`的配置信息，依赖为path分别为`modules/A`及`modules/B`

  

#### 4、进一步了解 yhgit

[常用命令](md/common-commands.md)

[PodfileModule文件介绍](md/podfilemodule.md)

[配置podfile](md/podfile.md)
ben-memre 0.1.4
A special library by Emre with a few functions for AI projects.
Requires Python: >=3.7

# ben-memre

This is a special demonstration library created by Memre Ozkan. It provides a few simple functions to show how a Python package can be built, and now includes several helper functions useful for common data processing tasks in AI and Machine Learning projects.

## Installation

You can install this library from PyPI using pip:

```bash
pip install ben-memre
```

## Usage

After installation, you can import and use the functions in your Python code. Note that while the package name on PyPI is `ben-memre` (with a hyphen), you must use an underscore (`_`) to import it in Python.

### Basic Functions
```python
from ben_memre import greet, add_numbers

# Use the greet function
message = greet("User")
print(message)  # Output: Hello, User! This is a message from ben-memre.

# Use the add_numbers function
result = add_numbers(10, 5)
print(f"The sum is: {result}")  # Output: The sum is: 15
```

### AI & Data Processing Functions

These functions require `numpy`. It will be automatically installed as a dependency when you install the library.

```python
import numpy as np
from ben_memre import normalize, one_hot_encode, calculate_accuracy

# --- Normalize Data ---
# Insight: Normalizing data to a 0-1 scale is crucial for many ML algorithms 
# (like neural networks and SVMs) to ensure stable and efficient training.
data = [10, 20, 30, 40, 50]
normalized_data = normalize(data)
print(f"Normalized Data: {normalized_data}")
# Output: Normalized Data: [0.   0.25 0.5  0.75 1.  ]

# --- One-Hot Encode Labels ---
# Insight: Categorical labels (like 'cat', 'dog', 'bird') must be converted
# to a numerical format for most models. One-hot encoding creates a binary
# vector for each label, preventing the model from assuming an incorrect
# ordinal relationship between categories.
labels = ['cat', 'dog', 'cat', 'bird']
encoded_labels = one_hot_encode(labels)
print(f"One-Hot Encoded Labels:\n{encoded_labels}")
# Output:
# One-Hot Encoded Labels:
# [[0. 1. 0.]
#  [0. 0. 1.]
#  [0. 1. 0.]
#  [1. 0. 0.]]

# --- Calculate Accuracy ---
# Insight: Accuracy is the most straightforward metric for evaluating a
# classification model. It measures the proportion of correct predictions.
y_true = [1, 0, 1, 1, 0]
y_pred = [1, 0, 1, 0, 0]
accuracy = calculate_accuracy(y_true, y_pred)
print(f"Accuracy: {accuracy:.2f}") # Output: Accuracy: 0.80
```

## Version History

*   **0.1.4 (Current):**
    *   **Feature Add:** Added three new functions for common AI/ML data processing tasks: `normalize`, `one_hot_encode`, and `calculate_accuracy`.
    *   Added `numpy` as a project dependency.

*   **0.1.3:**
    *   **Major Fix:** Restructured the project to use a `src/` layout, which is the standard for modern Python packages. This resolved the persistent `ModuleNotFoundError`.

*   **0.1.2:**
    *   **Attempted Fix:** Modified `__init__.py` to explicitly import functions. This was insufficient without the correct project structure.

*   **0.1.1:**
    *   **Initial Release:** First version with a structural issue causing import errors.