semvec/pypi-packages huggingface dataset:
flask-mdict 1.4.22
Flask Mdict Server
License: MIT
Requires Python: >=3.6

===========
Flask-MDict
===========

.. NOTE::

    Follow MIT and 996.ICU license.

Screenshot
==========

.. image:: mdict_screenshot.png

Usage
======
Install
--------
::

    git clone https://github.com/liuyug/flask-mdict.git

    cd flask-mdict
    pip3 install -r requirements.txt

    mkdir content
    # copy MDICT dictionary into content directory
    cp <mdict> content/

    # install word frequency database from ecdict csv
    ecdict.sh
    # or copy current flask_mdict_wfd.db to your content directory
    # cp flask_mdict_wfd.db content/


Run
----
Cli::

    python app.py

    python app.py --mdict-dir your_mdict_path

    python app.py --host 127.0.0.1:5248


Window usage::

    # 1. put files into path:
    # path\
    # path\flask_mdict.exe
    # path\flask_mdict_wfd.db
    # path\your_mdict_path

    # 2. run
    flask_mdict.exe

    # 3. Browser
    firefox http://127.0.0.1:5248


Config
-------

+ flask_mdict.json: server settings
+ flask_mdict.db: dictionary settings and searching history
+ flask_mdict_wfd.db: word frequency db [option]

Browser
--------
::

    firefox http://127.0.0.1:5248

.. note::

    +   MDict original query code come from mdx-server_
    +   Python3

.. _mdx-server: https://github.com/ninja33/mdx-server

Donate 鎹愯禒
=============

.. |alipay_pay| image:: alipay_pay.jpg
    :width: 45%

.. |wx_pay| image:: wx_pay.png
    :width: 45%

|wx_pay| |alipay_pay|
bibfusion 1.0.0
A Python package for merging, preprocessing, and enriching bibliographic data from WoS, Scopus, and OpenAlex.
License: MIT
Requires Python: >=3.10
Keywords: bibliometrics, scientometrics, wos, scopus, openalex, tree of science

# Preprocessing Package

This package provides utilities to preprocess, clean, and harmonize bibliographic data from multiple scientific sources, primarily **Web of Science (WoS)** and **Scopus**.  
It is designed to support bibliometric and scientometric analyses by transforming raw exports into structured pandas DataFrames.

⚠️ **Status:** under active development. APIs and internal structures may change.

---

## Features

- Parsing of raw bibliographic exports into structured DataFrames
- Support for multiple data sources (WoS, Scopus, Crossref, OpenAlex)
- Reference enrichment and linkage across sources
- Designed for reproducible research workflows

---

## Core Functions

- **`wos_df()`**  
  Transforms Web of Science `.txt` export files into pandas DataFrames.

- **`scopus_df()`**  
  Converts Scopus `.bib` export files into pandas DataFrames.

- **`doi_crossref()`**  
  Queries the Crossref API to retrieve metadata associated with a given DOI.

- **`scopus_ref()`**  
  Processes and links article references, identifying relationships between cited documents.

---

## Installation

Clone the repository and install the required dependencies:

```bash
pip install -r requirements.txt
del-cache 1.0.0
A terminal tool to clean cache from package managers
License: MIT
Requires Python: >=3.10

# Cache Cleaner 🧹

A beautiful terminal tool to clean cache from various package managers with rich visual feedback.

## Features

- **Beautiful UI**: Uses Rich for terminal beautification
- **Live Status**: See real-time progress with colored indicators
- **Multiple Package Managers**: Supports pip, npm, yarn, cargo, gem, composer, apt, brew, and go
- **Verbose Mode**: See detailed information about what's being cleaned
- **Dry Run**: Preview what would be cleaned without making changes
- **Force Mode**: Clean system caches that require sudo

## Installation

```bash
pip install del-cache
```

Or install from source
```bash
git clone https://github.com/2kDarki/cache-cleaner
cd cache-cleaner
pip install -e .
```

## Usage

```bash
# Clean all caches
clean

# Preview what would be cleaned
clean --dry-run

# Show detailed output
clean --verbose

# Clean system caches (requires sudo)
clean --force

# Show help
clean --help
```

## [Licence](LICENCE)
vsdx-shrinker 0.1.1
Remove unused master shapes from Visio (.vsdx) files to dramatically reduce file size
License: MIT
Requires Python: >=3.8

# vsdx-shrinker

Remove unused master shapes from Visio (.vsdx) files to dramatically reduce file size.

## The Problem

Visio files can become bloated when you copy-paste content from PDFs, vector graphics, or other sources. Each paste operation can create "master shapes" that remain in the file even after you delete the visible content. These orphaned masters can make your file 10x larger than necessary.

## How It Works

VSDX files are ZIP archives containing XML. This tool:

1. Extracts the VSDX file
2. Scans page content to find which masters are actually referenced (via `USE("...")` patterns)
3. Removes unreferenced masters from `masters.xml` and deletes their XML files
4. Repacks the cleaned archive

## Installation

```bash
pip install vsdx-shrinker
```

Or with uv:

```bash
uv tool install vsdx-shrinker
```

## Usage

### Command Line

Shrink a file (creates backup):

```bash
vsdx-shrinker diagram.vsdx
```

Shrink to a new file:

```bash
vsdx-shrinker diagram.vsdx -o diagram_small.vsdx
```

Analyze without modifying:

```bash
vsdx-shrinker diagram.vsdx --analyze
```

### Python API

```python
from vsdx_shrinker import shrink_vsdx, analyze_vsdx

# Analyze a file
result = analyze_vsdx("diagram.vsdx")
print(f"Can save {result['potential_savings_mb']} MB")

# Shrink a file
result = shrink_vsdx("diagram.vsdx", output_path="diagram_small.vsdx")
print(f"Reduced by {result['reduction_percent']}%")
```

## CLI Options

```
usage: vsdx-shrinker [-h] [-o OUTPUT] [--no-backup] [--analyze] [-q] [--version] input

positional arguments:
  input                 Input .vsdx file path

options:
  -h, --help            show this help message and exit
  -o, --output OUTPUT   Output file path (default: overwrite input with backup)
  --no-backup           Do not create backup when overwriting input file
  --analyze             Only analyze the file, do not modify
  -q, --quiet           Suppress output except errors
  --version             show program's version number and exit
```

## Example Output

```
$ vsdx-shrinker presentation.vsdx --analyze
Analysis of: presentation.vsdx
  Total masters:      244
  Used masters:       6
  Unused masters:     238
  Potential savings:  79.17 MB

$ vsdx-shrinker presentation.vsdx -o presentation_clean.vsdx
Shrunk: presentation.vsdx
  Original size:    14.8 MB
  New size:         1.6 MB
  Reduction:        13.2 MB (89.2%)
  Masters removed:  238
  Output:           presentation_clean.vsdx
```

## License

MIT