semvec/pypi-packages huggingface dataset:
flpc-v2 0.3.4
A Lightning Fast ⚡ Rust-based regex crate wrapper for Python3 to get faster performance. 👾
Requires Python: >=3.14

<div align="center">
  <img src="https://svgshare.com/i/17F2.svg" height="300" alt="flpc logo">

  # flpc: Lightning-Fast Python Regex

  ![Star](https://img.shields.io/badge/Please%20Give%20A%20Star%20%E2%AD%90-30323D?style=flat-square)
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  🚀 Supercharge your Python regex with Rust-powered performance!
</div>

## 🌟 Why flpc?
Being in experimental stage. The code structure and dependencies may change. If your project is using this. You will have to manually configure the migrations to latest versions.

flpc is a powerful Python library that wraps the blazing-fast [Rust regex crate](https://crates.io/crates/regex), bringing enhanced speed to your regular expression operations. It's designed to be a drop-in replacement for Python's native `re` module, with some minor syntax differences.

## 🚀 Quick Start

1. Install flpc:
   ```
   pip install flpc
   ```

2. Use it in your code as shown in the API

## 🔧 API

flpc mirrors the `re` module's API, with a few small exceptions:

- Use `fmatch()` instead of `match()` (to avoid conflicts with Python's keyword)
- When using `group()` on a match object, always provide an index (e.g., `group(0)` for the entire match)

Common functions include:

- `compile()`
- `search()`
- `findall()`
- `finditer()`
- `split()`
- `sub()`
- `subn()`

## 💡 Pro Tips

- Pre-compile your patterns for faster execution
- Use raw strings (`r''`) for cleaner regex patterns
- Always check if a match is found before accessing groups
- Remember to use `group(0)` to get the entire match

## 🤝 Contributing

We welcome contributions! Whether it's bug reports, feature requests, or code contributions, please feel free to reach out. Check our [contribution guidelines](CONTRIBUTING.md) to get started.

## 📄 License

flpc is open-source software licensed under the MIT license.
edc-microscopy 1.4.2
Microscopy model mixins in clinicedc/edc projects
Requires Python: <3.14,>=3.12
Keywords: CRF, Edc, clinical, clinicedc, django, microscopy, trials

|pypi| |actions| |codecov| |downloads|


edc-microscopy
--------------
Microscopy model mixins in clinicedc/edc projects


.. |pypi| image:: https://img.shields.io/pypi/v/edc-microscopy.svg
    :target: https://pypi.python.org/pypi/edc-microscopy

.. |actions| image:: https://github.com/clinicedc/edc-microscopy/actions/workflows/build.yml/badge.svg
  :target: https://github.com/clinicedc/edc-microscopy/actions/workflows/build.yml

.. |codecov| image:: https://codecov.io/gh/clinicedc/edc-microscopy/branch/develop/graph/badge.svg
  :target: https://codecov.io/gh/clinicedc/edc-microscopy

.. |downloads| image:: https://pepy.tech/badge/edc-microscopy
   :target: https://pepy.tech/project/edc-microscopy
edc-phq9 1.3.1
Classes for PHQ9 in clinicedc/edc projects
Requires Python: <3.14,>=3.12
Keywords: CRF, Edc, clinical, clinicedc, django, phq9, trials

|pypi| |actions| |codecov| |downloads|

edc PHQ9
--------

Classes for PHQ9 (Patient Health Questionnaire)


.. |pypi| image:: https://img.shields.io/pypi/v/edc-phq9.svg
    :target: https://pypi.python.org/pypi/edc-phq9

.. |actions| image:: https://github.com/clinicedc/edc-phq9/actions/workflows/build.yml/badge.svg
  :target: https://github.com/clinicedc/edc-phq9/actions/workflows/build.yml

.. |codecov| image:: https://codecov.io/gh/clinicedc/edc-phq9/branch/develop/graph/badge.svg
  :target: https://codecov.io/gh/clinicedc/edc-phq9

.. |downloads| image:: https://pepy.tech/badge/edc-phq9
   :target: https://pepy.tech/project/edc-phq9
DiSTNet2D 0.2.4
tensorflow/keras implementation of DiSTNet 2D
Requires Python: >=3
Keywords: Segmentation, Tracking, Cell, Tensorflow, Keras

# DistNet2D: Leveraging long-range temporal information for efficient segmentation and tracking

This repository contains python code for training the neural network.

[Link to preprint](https://arxiv.org/abs/2310.19641)

[Link to tutorial](https://github.com/jeanollion/bacmman/wiki/DistNet2D)

Jean Ollion, Martin Maliet, Caroline Giuglaris, Elise Vacher, Maxime Deforet

Extracting long tracks and lineages from videomicroscopy requires an extremely low error rate, which is challenging on complex datasets of dense or deforming cells. Leveraging temporal context is key to overcoming this challenge. We propose DistNet2D, a new deep neural network (DNN) architecture for 2D cell segmentation and tracking that leverages both mid- and long-term temporal information. DistNet2D considers seven frames at the input and uses a post-processing procedure that exploits information from the entire video to correct segmentation errors. DistNet2D outperforms two recent methods on two experimental datasets, one containing densely packed bacterial cells and the other containing eukaryotic cells. It is integrated into an ImageJ-based graphical user interface for 2D data visualization, curation, and training. Finally, we demonstrate the performance of DistNet2D on correlating the size and shape of cells with their transport properties over large statistics, for both bacterial and eukaryotic cells.
dataset-iterator 0.5.7
Keras-style data iterator for images contained in dataset files such as hdf5 or PIL readable files. Images can be contained in several files.
Requires Python: >=3
Keywords: Iterator, Dataset, Image, Numpy

# Dataset Iterator
This repo contains keras iterator classes for multi-channel (time-lapse) images contained in dataset files such as hdf5.

## Dataset structure:
One dataset file can contain several sub-datasets (dataset_name0, dataset_name1, etc...), the iterator will iterate through all of them as if they were concatenated.

    .
    ├── ...
    ├── dataset_name0                    
    │   ├── channel0          
    │   └── channel1   
    │   └── ...
    ├── dataset_name1                    
    │   ├── channel0          
    │   └── channel1   
    │   └── ...
    └── ...

Each dataset contain channels (channel0, channel1 ...) that must have same shape. All datasets must have the same number of channels, and shape (except batch size) must be equal among datasets.

## Groups

There can be more folder level, for instance to have train and test sets in the same file:

    .
    ├── ...
    ├── experiment1                    
    │   ├── train          
    │   │   ├── raw
    │   │   └── labels
    │   └── test   
    │       ├── raw
    │       └── labels
    ├── experiment2                    
    │   ├── train          
    │   │   ├── raw
    │   │   └── labels
    │   └── test   
    │       ├── raw
    │       └── labels
    └── ...
```python
train_it = MultiChannelIterator(dataset_file_path = file_path, channel_keywords = ["/raw", "/labels"], group_keyword="train")
test_it = MultiChannelIterator(dataset_file_path = file_path, channel_keywords = ["/raw", "/labels"], group_keyword="test")
```
# Image formats
- Those iterators are using an object of class `DatasetIO` to access the data.
- There is currently an implementation of DatasetIO for .h5 files (`H5pyIO`), as well as dataset composed of multiple images files supported by PILLOW (`MultipleFileIO`).
- one can also concatenate datasets from different files:
  - if a dataset is split into several files that contain the same channels: use `ConcatenateDatasetIO`
  - if a dataset contains channels in different files, use: `MultipleDatasetIO`

# Demo
See this notebook for a demo: [![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1J-UPivwyNTpyLhOMfzhfG0pIl6gDD9I5)
mcp-songzhe-server 0.1.2
这是一个使用高德地图的天气接口,获取当前城市天气的demo
Requires Python: >=3.11
Keywords: mcp, gaodeweather, server

## 介绍

这是一个使用高德地图的天气接口，获取当前城市天气的demo。

## 使用方法

## MCP 配置

### uvx

```json

{

  "mcpServers": {

    "duckduckgo": {

      "command": "uvx",

      "args": [

        "mcp-gaodeweather-server"

      ]

    }

  }

}
smfun 0.0.4
This is Python Code for Operations on Strings to find Vowels, Consonants, Spaces, Digits and Special Characters

String Character Counter

This is a simple Python package named smfun that analyzes a string and counts the number of vowels consonants digits spaces and special characters The results are displayed in a clean and organized format using pandas

Features

Counts Vowels Detects and counts all vowels regardless of case
Counts Consonants Identifies and counts all consonants
Counts Digits Tallies all numeric characters from zero to nine
Counts Spaces Counts all whitespace characters
Counts Special Characters Flags characters that are not letters digits or spaces

Prerequisites

Python version three point six or higher is required
The pandas library will be installed automatically with the package

Installation and Usage

Install the package using pip

pip install smfun

Import the function in your Python code

from smfun import charFun

Run the function and enter a string when prompted

Enter the String Hello World 123

Example Output

Vowels 3
Consonants 7
Digits 3
Spaces 2
Special Characters 1

License

This package is released under the MIT License