semvec/pypi-packages huggingface dataset:
mdeq 2025.6.30
mdeq: mutation disequilibrium analysis tools.
Requires Python: <3.14,>=3.10
Keywords: biology, genomics, statistics, phylogeny, evolution, bioinformatics

[![Coverage Status](https://coveralls.io/repos/github/HuttleyLab/MutationDiseq/badge.svg)](https://coveralls.io/github/HuttleyLab/MutationDiseq)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

# mdeq: a tool for analysing mutation disequilibrium

A manuscript describing the methods will be made available as a preprint soon.

## Installation

```
$ pip install mdeq
```

> **Note**
> `accupy` is an optional package for the most numerically accurate routines involving matrices (e.g. dot products). These routines are explicitly employed for computing the nabla related statistics and are used if this package is installed. Unfortunately it is not easily installed and (at the time of writing) is not under active development.

## The available commands

<!-- [[[cog
import cog
from mdeq import main
from click.testing import CliRunner
runner = CliRunner()
result = runner.invoke(main, ["--help"])
help = result.output.replace("Usage: main", "Usage: mdeq")
cog.out(
    "```\n{}\n```".format(help)
)
]]] -->
```
Usage: mdeq [OPTIONS] COMMAND [ARGS]...

  mdeq: mutation disequilibrium analysis tools.

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  tui              Open Textual TUI.
  prep             pre-process alignment data.
  make-adjacent    makes sqlitedb of adjacent alignment records.
  toe              test of existence of mutation equilibrium.
  teop             between branch equivalence of mutation process test
  aeop             between loci equivalence of mutation process test
  convergence      estimates convergence towards mutation equilibrium.
  make-controls    simulate negative and positive controls
  db-summary       displays summary information about a db
  extract-pvalues  extracts p-values from TOE sqlitedb results
  extract-nabla-c  extracts nabla-c from convergence sqlitedb results
  slide            generate window sized sub-alignments.

```
<!-- [[[end]]] -->
t-record-lib 0.3.2
Cookiecutter template for Thoughtful pip package
Requires Python: >=3.11
Keywords: t_record

T-Record
========

A lightweight Python library for robust record tracing, status tracking, and detailed logging —
built for automation pipelines and data integrity.

Features
--------

* **ThoughtfulRecord model**
  Track each record's status, lifecycle, and traces with robust JSON-serializable objects.

* **Automatic history**
  Dumps detailed logs, merges previous runs seamlessly, preserving full audit history.

* **Enhanced HTML reports**
  Auto-generates beautiful HTML reports with status summary, traces, diffs, and screenshots.

* **Portable archive**
  Packs all data (logs + images + HTML) into a shareable ZIP file.

* **Fail-safe logging**
  Internally uses ``try/except`` to ensure tracing never crashes your main process.

Installation
------------

Install via pip::

    pip install t-record-lib

Or for local development::

    git clone https://github.com/YOUR_ORG/t_record.git
    cd t_record
    pip install -e .

Quickstart
----------

.. code-block:: python

    from t_record.record import configure_records_tracing, ThoughtfulRecord, dump_records, pack_sharable_zip, RecordStatus

    # 1. Configure
    configure_records_tracing("records_folder")

    # 2. Create your record
    class MyRecord(ThoughtfulRecord):
        id: str
        status: RecordStatus = ""

    record = MyRecord(id="rec_001")

    # 3. Change status or add traces
    record.status = Status.RUNNING
    record.log_trace("Started processing", reason="Data loaded")

    # 4. Save everything
    dump_records()

    # 5. Create shareable ZIP
    pack_sharable_zip("records.zip")

Output files
------------

By default, each run will produce::

    /records_folder/{process_name}/{run_id}/
        records_logs.json     # full structured logs (merged from previous runs)
        records_report.html   # human-readable dashboard
        /images/              # attached screenshots

Plus a zip archive on demand.

Advanced usage
--------------

* Continue an existing run (merges history automatically)::

    configure_record_tracing("records_folder")
    record = MyRecord(id="rec_001")  # picks up previous traces
    record.status = Status.COMPLETED
    dump_records()

* Add an image trace::

    record.log_trace("Reviewed", reason="Visual QC", image="path/to/image.png")
yunhu_usdk 0.2.0.1
云湖SDK - 用于与云湖平台进行交互的Python SDK

![](https://socialify.git.ci/shanfishapp/yunhu_usdk/image?custom_description=%E5%BC%80%E6%BA%90%E7%9A%84%E4%BA%91%E6%B9%96%E7%94%A8%E6%88%B7API+SDK&description=1&font=Raleway&forks=1&issues=1&language=1&name=1&owner=1&pulls=1&stargazers=1&theme=Auto)
<p align="center">✨异步·开源·实用✨</p>

<div align='center'><img src="https://img.shields.io/badge/Python-3.8+-green" />&nbsp;&nbsp;&nbsp;<img src="https://img.shields.io/badge/License-MIT-blue" />&nbsp;&nbsp;&nbsp;<img src="https://img.shields.io/badge/Releases-None-red" /></div>
ses-conda 2.1.0
A simple deeplearning kit, implementing the module concept of SES Conda
Requires Python: >=3.0


# SES Conda 2.1.0 自述文件/README
> 中英双语 | Bilingual  
> SES Conda是一个轻量**深度学习套件**，利用EricasZ的思路，实现简单的数据推断。  
> SES Conda is a lightweight **deeplearning toolkit**, implementing simple data prediction following the idea of EricasZ  
---

## 1. 30 秒上手 / 30-Second Start

```python
# 深度学习回归 / Deep regression
from sesconda.dlkit import RegressionKit
X, y = np.loadtxt('X.txt'), np.loadtxt('y.txt')
model_fn = RegressionKit('numpy', X_path='X.npy', y_path='y.npy').run()
print(model_fn([[0.5, 1.2]]))

# 经典多项式拟合 / Classic polynomial fit
from sesconda import expo
e = expo()
e.train([(0, 1), (1, 2.7), (2, 7.4)])
e.display()
```

---

## 2. 功能总览 / Features Overview

| 模块 / Module | 能力 / Capability | 适用场景 / Use-Case |
|---|---|---|
| **RegressionKit** | 任意非线性黑盒逼近 / Universal nonlinear approximation | 大数据、未知解析式 / Big data & unknown formula |
| **expo** | 最优多项式分段拟合 / Optimal polynomial segment fit | 小样本、快速可视化 / Small sample & quick plot |

---

## 3. API 速查 / Quick API

### 3.1 RegressionKit（深度学习 / DL）

```python
kit = RegressionKit(source, **kwargs)
```

| 数据源 / Source | 示例 / Example |
|---|---|
| csv | `RegressionKit('csv', path='data.csv', x_cols=[0,1], y_col=2)` |
| numpy | `RegressionKit('numpy', X_path='X.npy', y_path='y.npy')` |
| sklearn | `RegressionKit('sklearn', name='california')` |
| function | `RegressionKit('func', func=lambda x: np.sin(x))` |

可调关键参数 / Key args  
```python
model_fn = kit.run(
    hidden_dims=(256, 256, 128),
    epochs=800,
    patience=50,
    lr=1e-3,
    batch_size=128,
    standardize=True
)
```

### 3.2 expo（经典 / Classic）

```python
e = expo()
e.train([(x1, y1), (x2, y2), ...])  # 支持批量 / Batch input
e.display()                           # 自动选阶 & 绘图 / Auto degree & plot
```

---

## 4. 更新日志 / Changelog

| 版本 / Version | 日期 / Date | 亮点 / Highlights |
|---|---|---|
| 2.1.0 | 2025-08-13 | 并入 dlkit，新增 RegressionKit / Integrated dlkit, added RegressionKit |
| 2.0.1 | 2025-07-xx | Bug 修复 / Bug fixes |
| 2.0.0-dev | 2025-06-xx | scikit-learn 情感分析优化 / Sentiment analysis improvement |
| 1.x | … | 早期多项式与关系解析 / Early polynomial & relation parse |

---

## 5. 安装 / Installation

```bash
pip install -U ses-conda
```

依赖 / Requirements  
`torch ≥ 1.9`, `scikit-learn ≥ 1.3`, `numpy`, `matplotlib`, `pandas`

---

## 6. 许可证 / License

MIT © 2024-End of the Time EricasZ & 街角的猫_wjz  
详见 LICENSE.txt / See LICENSE.txt for details.

---
django-zero-migrations-commands 1.0.3
Management commands for creating and applying zero migrations.
Requires Python: >=3.6

# django-zero-migrations-commands

Provides a `zeromigrations` management-command that creates and applies zero migrations
## Quick start
1. Install with `pip install django-zero-migrations-commands`
2. Add `"django_zero_migrations_commands"` to your `INSTALLED_APPS` setting like this:
    ```python
    INSTALLED_APPS = [
        # ...
        "django_zero_migrations_commands",
    ]
    ```
3. (Optional) Change the settings described in the next section in your `settings.py`
4. Create zero migrations using `python manage.py zeromigrations create`
5. Apply them on other systems using `python manage.py zeromigrations apply`

## Available settings
```python
ZERO_MIGRATIONS_APPS: Optional[list[str]] = None
"""The apps to reset migrations for. default: `None`.
Will reset migrations for all local apps if `None`."""

ZERO_MIGRATIONS_ALLOW_CREATE_DEBUG_FALSE: bool = False
"""Whether the create action can be run in `DEBUG = False` mode. default: `False`"""

ZERO_MIGRATIONS_CONFIRM_CREATE_DEBUG_FALSE: bool = False
"""Whether to ask for confirmation before running the create action in `DEBUG = False` mode.
default: `False`"""

ZERO_MIGRATIONS_ALLOW_APPLY_DEBUG_FALSE: bool = True
"""Whether the apply action can be run in `DEBUG = False` mode. default: `True`"""

ZERO_MIGRATIONS_CONFIRM_APPLY_DEBUG_FALSE: bool = True
"""Whether to ask for confirmation before running the apply action in `DEBUG = False` mode.
default: `True`"""

ZERO_MIGRATIONS_BEFORE_CREATE_HOOK: Optional[Callable[[], None]] = None
"""The hook to run before creating new migrations. default: `None`."""
```

## What it does
- `create` removes the migrations of your apps and replaces them by a new initial migration.
- `apply` resets the migration history after creating or receiving the new migrations by other means (i.e. version control)
use-notify 0.3.5
一个简单可扩展的异步消息通知库
Requires Python: <4.0,>=3.8

### 一个简单可扩展的异步消息通知库

<a href="https://pypi.org/project/use-notify" target="_blank">
    <img src="https://img.shields.io/pypi/v/use-notify.svg" alt="Package version">
</a>

<a href="https://pypi.org/project/use-notify" target="_blank">
    <img src="https://img.shields.io/pypi/pyversions/use-notify.svg" alt="Supported Python versions">
</a>

#### 安装

> pip install use-notify

#### 使用

```python
from use_notify import useNotify, useNotifyChannel

notify = useNotify()
notify.add(
    # 添加多个通知渠道
    useNotifyChannel.Bark({"token": "xxxxxx"}),
    useNotifyChannel.Ding({
        "token": "xxxxx",
        "at_all": True
    })
)

notify.publish(title="消息标题", content="消息正文")

```

#### 装饰器使用（推荐）

使用 `@notify` 装饰器可以自动为函数执行发送通知：

```python
from use_notify import useNotify, useNotifyChannel, notify, set_default_notify_instance

# 创建并设置全局默认通知实例
default_notify = useNotify()
default_notify.add(
    useNotifyChannel.Bark({"token": "your_bark_token"}),
    useNotifyChannel.Ding({"token": "your_ding_token"})
)
set_default_notify_instance(default_notify)

# 现在可以直接使用装饰器，无需传递 notify_instance
@notify()
def data_processing():
    # 数据处理逻辑
    return "数据处理完成"

@notify(title="重要任务")
def important_task():
    # 重要任务逻辑
    return "重要任务完成"

@notify(notify_on_error=True, notify_on_success=False)
def risky_operation():
    # 只在出错时通知
    if some_condition:
        raise Exception("操作失败")
    return "操作成功"

# 异步函数支持
@notify()
async def async_task():
    await some_async_operation()
    return "异步任务完成"
```

**装饰器特性：**
- ✅ 自动发送成功/失败通知
- ✅ 支持同步和异步函数
- ✅ 可配置通知条件和自定义消息
- ✅ 全局默认实例，简化使用
- ✅ 执行上下文信息收集

> 📖 **详细文档**: [装饰器完整使用指南](docs/decorator.md)

#### 支持的消息通知渠道列表

- Wechat(企微)
- Ding(钉钉)
- Bark
- Email
- Chanify
- Pushdeer
- Pushover
- FeiShu(飞书)
- Ntfy

#### 自己开发消息通知

```python
from use_notify import useNotifyChannel


class Custom(useNotifyChannel.BaseChannel):
    """自定义消息通知"""

    def send(self, *args, **kwargs):
        ...

    async def send_async(self, *args, **kwargs):
        ...
```

#### 更多示例

- [基础使用示例](example/demo.py)
- [装饰器演示](example/decorator_demo.py)
- [装饰器真实场景应用](example/decorator_real_usage.py)
- [全局默认实例使用](example/decorator_default_instance.py)
- [配置文件使用](example/from_setting.py)

#### 文档

- [装饰器完整使用指南](docs/decorator.md) - 详细的装饰器功能说明和最佳实践

#### 特性

- 🚀 **简单易用**: 几行代码即可集成多种通知渠道
- 🔄 **异步支持**: 完整的异步/同步双模式支持
- 🎯 **装饰器模式**: 使用 `@notify` 装饰器自动化通知
- 🔧 **高度可扩展**: 轻松添加自定义通知渠道
- 📱 **多渠道支持**: 支持微信、钉钉、Bark、邮件等多种通知方式
- ⚙️ **灵活配置**: 支持条件通知、自定义模板、全局默认实例等高级功能