semvec/pypi-packages huggingface dataset:
pendot 0.1.1
Utility for creating dotted and stroked handwriting fonts

# Pendot

This is a Python module used to add "effects" to Glyphs font source files;
it will take an existing source file; add a number of transformations to the
paths such as stroking or dotting open paths, adding guidelines, showing
starting positions and so on; and save the result to a new Glyphs file.

It is designed to be used in conjunction with the Glyphs [Pendot Designer](https://github.com/simoncozens/Pendot)
script, but is available as a standalone Python package so that Dotter files
can be built from the command line. The effects have various parameters
which can be specified on the command line, or by using custom parameter
values attached to a Glyphs instance; the parameters can also be overriden
per glyph.

## Simple use

To add dots to all open paths in a Glyphs font, with no custom settings
added by the Pendot Designer script:

```
$ pendot dot -o Font-dots.glyphs --dot-size 30 --dot-spacing 20 Font.glyphs
```

To add strokes to all open paths in a Glyphs font, with no custom settings
added by the Pendot Designer script:

```
$ pendot stroke -o Font-stroke.glyphs --stroke-width 30 Font.glyphs
```

However, _these will not give optimal results_. For better results, use
Pendot Designer to tune the parameters on a per-glyph and per-instance basis,
and run with

```
$ pendot -o Font-dots.glyphs Font.glyphs Regular
```

which will look for custom parameters stored in the Regular instance.

To run a combination of effects (small strokes, big dots, and guidelines!)
either use Pendot Designer, or provide a JSON string or file on the command
line:

```
$ pendot --config '{"effects": ["Stroker", "Dotter", "Guidelines"], \
  "strokeWidth": 10, "dotSize": 50 }' --output Font-fancy.glyphs Font.glyphs
```
RAGDM-client 0.0.0
Client library for interacting with RAGDM server
Requires Python: >=3.9

# Intro
用於與 RAG workflow Data Manager(RAGDM) 伺服器溝通的客戶端。

# API 端口
RAGDM伺服器提供的服務可從以下兩個端口查詢：
1. `http://<RAGDM伺服器IP>:<RAGDM伺服器端口>/docs`
2. `http://<RAGDM伺服器IP>:<RAGDM伺服器端口>/redoc`

例如，RAGDM伺服器運行在 `172.17.219.35` 的 `2000` 端口，則可直接在瀏覽器中輸入 `http://172.17.219.35:2000/docs` 或 `http://172.17.219.35:2000/redoc` 來查詢可用的服務端口。

## API 服務概述
RAGDM伺服器 提供的服務包含以下 5 種類別：
1. Health
   - 檢查RAGDM伺服器是否存活
   - 檢查Chroma伺服器是否存活
2. Loader
   - 提供可用的載入器資訊
   - 載入指定目錄下的所有文件並進行文本分割
   - 載入指定文件並進行文本分割
3. Collection
   - 取得Chroma伺服器中當前的資料集(collection)資訊
   - 新建資料集
   - 刪除資料集
   - 取得指定資料集中文本片段總數量
4. Documents (in collection)
   - 新增文本片段到指定的資料集
   - 新增文本片段到指定的資料集(會覆蓋掉metadata中有相同'source'的文本片段)
   - 獲取指定資料集中的所有文本片段
   - 從指定的資料集中修改特定的文本片段的內容
   - 刪除指定的資料集中的特定ID的文本片段
5. BM25
   - 建立BM25索引
   - 取得BM25索引表中所有文本片段
   - 取得BM25索引表中特定ID的文本片段
   - 更新BM25索引表中特定ID的文本片段
   - 刪除BM25索引表中特定ID的文本片段
   - 新增文本片段到BM25索引表中


# Usage
有多種方式使用這些端口：
1. 使用 `RAGDMClient`
   ```python
   from K_ragdm.pages.user.manager_client.client import RAGDMClient
   ```
2. 各種API call方式都可以，例如使用 Python的 `requests` 庫、或者直接在命令行中使用 `cURL` 等方式來調用。

# Example
使用範例請參考 [Example](./examples)

# 錯誤碼
完整 ErrorCode 資訊請參考 [ErrorTable](./docs/ErrorTable.md)


# Create a Container for RAGDM client testing
```bash
git clone http://172.17.219.35:3333/kim.chen/ragdm_client.git
cd ragdm_client

sudo docker run -it \
--init \
--rm \
--add-host=host.docker.internal:host-gateway \
-v ${PWD}:/home/workspace \
-v /data/ragdm:/data/ragdm \
-w /home/workspace \
llm-devl
```
pyhopf 1.0.1
Small Python package to calculate the Hopf index of vector fields from finite-difference simulations.
Requires Python: >=3.8

# Python Finite-Difference Hopf Index Calculation

This package provides Python implementations of the calculation of the Hopf index of the magnetisation texture for finite-difference micromagnetic simulations. The four methods implemented are `twopointstencil`, `fivepointstencil`, `solidangle`, and `solidanglefourier`. The package assumes NumPy arrays of the shape `(Nx, Ny, Nz, 3)`, where $N_i$ are the number of cells in each direction, and the final dimension is for the three components. It is assumed that the vector field is normalised.

Example usage to calculate the Hopf index, Hopf density $\boldsymbol{F} \cdot \boldsymbol{A}$, and emergent magnetic field of a vector field stored in a file `Magnetisation.npy` using the solid angle method is shown in the following.

```python
from pyhopf.hopfindex import HopfIndexCalculator

m = np.load('Magnetisation.npy')

hopf_index_calculator = HopfIndexCalculator(m, 'solidangle')
hopf_index            = hopf_index_calculator.hopf_index()
hopf_density          = hopf_index_calculator.hopf_density()
emergent_field        = hopf_index_calculator.emergent_field()
```