semvec/pypi-packages huggingface dataset:
py-ankiconnect 1.1.4
Minimal wrapper to simplify the usage of the awesome ankiconnect anki addon.
Requires Python: >=3.9
Keywords: anki, flashcards, ankiconnect, learning, cli, tool, spaced, repetition, ebbinghaus, addon


# py_ankiconnect
Minimal wrapper to simplify the usage of the **awesome** [ankiconnect](https://git.foosoft.net/alex/anki-connect) anki addon.
I made this in about an hour to make it easy to interact with [anki](https://ankitects.github.io/) from my many python projects (see my other repos), as well as from the command line.

# Installation
* `python -m pip install py-ankiconnect` or git clone followed by `python -m pip install -e .`

# How To
## Using the command line
* You can either call it using `py_ankiconnect` or `python -m py_ankiconnect`.
* To see the help: `py_ankiconnect --help` (this will either print it using `rich` if installed or using the pager.)
* Examples:
    * Get the list of tags: `py_ankiconnect getTags | jq`
    * Get info about [Clozolkor](https://github.com/thiswillbeyourgithub/Clozolkor): `py_ankiconnect findModelsByName --modelNames ["Clozolkor"] | jq`
    * You can even use pipes: `py_ankiconnect findNotes --query '*test*' | jq -c '.[0:10]' | py_ankiconnect notesInfo --notes -` (you have to use '-', if will be replaced by the content of sys.stdin)

## Using python
``` python
from py_ankiconnect import PyAnkiconnect
akc = PyAnkiconnect()
# ^ You can set a different port or host there directly:
# akc = PyAnkiconnect(default_port=your_port)

# trigger a sync:
result = akc("sync")

# Get the list of all tags:
result = akc("getTags")

# Do some more advanced stuff:
akc(
    action="changeDeck",
    params={
        "cards": [
            1502098034045,
            1502098034048,
            1502298033753
            ],
        "deck": "Japanese::JLPT N3"
        },
)

# It supports async mode. By default it will try async or sync depending on who calls it, but you can set force_async_mode to always use async.
import asyncio
akc = PyAnkiconnect(force_async_mode=True, limit_concurrency=50)
async def main():
    return await akc("getTags")
asyncio.run(main())

```
ming-nndl 0.0.1
Neural Network and Deep Learning (NNDL) library based on PaddlePaddle
Requires Python: >=3.7

# ming-nndl - Neural Network and Deep Learning

> **"我们不生产水，我们只是大自然的搬运工"** 🚰

基于 PaddlePaddle 的神经网络与深度学习基础库，包含《神经网络与深度学习》书籍实验的完整实现。

## 📖 关于本项目

**nndl** 并不是一个公开的标准 Python 库（如 numpy 或 paddle），而是《神经网络与深度学习》这本书配套的**自定义教学代码库**。

正如《第1章 实践基础》中所述：
> "为了更深入地理解深度学习的模型和算法，在本书中，我们也手动实现自己的算子库：nndl"

### 为什么要封装到 PyPI？

在原书的教学环境中，学习者需要手动下载源代码文件才能使用 `nndl` 库。这在 **Google Colab** 等云环境中使用时非常不便，每次都要上传或挂载代码文件。

本项目将课程老师提供的原始代码**封装为标准 Python 包**并发布到 PyPI，使得你可以在任何 Python 环境中**一键安装**：

```bash
pip install ming-nndl
```

这样就能直接在 Colab、Jupyter Notebook 或本地环境中运行书中的实验代码，无需手动管理文件。

### 声明

- 本项目代码**来自课程老师提供的原始教学材料**
- 仅进行了打包封装，**未修改核心算法逻辑**
- 目的是方便学习者使用，**非其他用途**


## 安装

```bash
pip install ming-nndl
```

## 功能特性

- **数据集工具** (`dataset`): 内置常用数据集加载器
- **优化器** (`optimizer`): 实现各类优化算法
- **运行器** (`runner`): 简化模型训练流程
- **评估指标** (`metric`): 常用评估指标计算
- **基础算子** (`op`): 神经网络基础操作
- **激活函数** (`activation`): 常用激活函数实现
- **工具函数** (`tools`): 可视化等辅助工具

## 快速开始

```python
from nndl import RunnerV2
from nndl.dataset import MNIST
from nndl.optimizer import SGD

# 加载数据集
train_dataset = MNIST(mode='train')

# 创建运行器
runner = RunnerV2(model, optimizer=SGD(lr=0.01), metric='accuracy')

# 开始训练
runner.train(train_dataset, epochs=10)
```

## 依赖

- PaddlePaddle >= 2.0.0
- NumPy
- Matplotlib
- Pandas
- Pillow

## 项目链接

- GitHub: https://github.com/lgnorant-lu/nndl
- Issues: https://github.com/lgnorant-lu/nndl/issues

## 许可证

MIT License
bounce 0.7.0
Search using special commands
Requires Python: >=3.10

# Bounce

It's a keyword search engine, meaning you can configure it to redirect `yt` to Youtube, so a search like `yt weird al` would redirect right to Youtube's search.


## 1 minute getting started

1. Install it

        $ pip install bounce

2. Start it:

        $ bounce serve --host "127.0.0.1:5000"

3. Query it:

        $ curl "http://127.0.0.1:5000/?q=yt weird al" 

You can also run it using any WSGI server like [uWSGI](http://projects.unbit.it/uwsgi/) using the included `bouncefile.py` as the `wsgi-file`.


## Configuration


### url configuration

Bounce has a built-in [configuration file with generic mappings](https://github.com/Jaymon/bounce/blob/master/bounce/config.py) but you can also create your own that bounce will read when starting by setting the environment variable `BOUNCE_CONFIG` with a path to your custom configuration python file:

```bash
export BOUNCE_CONFIG=/path/to/bounce_config.py
```

The file must import `bounce.config.commands`:

```python
from bounce.config import commands
```

The `commands.add()` method takes a space separated list of commands and a value:

```python
commands.add("foo bar", "http://foo.com?q={}")
```

So, if you called bounce with the input:

    foo blammo

It would redirect to:

    http://foo.com?q=blammo

You could also call it with `bar blammo` and get the same thing because we set up the command keywords as `foo bar` so either _foo_ or _bar_ would redirect.

Notice that the value is a [python format string](https://docs.python.org/2/library/string.html#formatspec).


### callback configuration

value can also be a callback:

```python
def callback(q):
    # manipulat q in some way and then return where you would like to go
    return 'http://some.url.that.needed.manipulation.q.com={}'.format(q)

commands.add("foo bar", callback)
```

That makes it so bounce can do all kinds of crazy things.


### default configuration

By default, Google is the search engine of choice, so if you don't start your request with a command, bounce will redirect to Google search with your search string. If you would like to change this just pass `default=True` to one of your custom commands:

```python
commands.add("keyword", "value", default=True)
```


### Viewing configuration


the command `ls` will list all the commands bounce supports


## Testing

To test locally from the repo:

    $ export BOUNCE_CONFIG=/path/to/bounce/config.py
    $ python -m bounce serve --host "127.0.0.1:5000"

That should produce output that ends with the bound hostname and port:

    ...
    Server is listening on 127.0.0.1:5000

Which you can then use to test:

    $ curl "http://127.0.0.1:5000/?q=..."

And that's it.