semvec/pypi-packages huggingface dataset:
streamlit-session-manager 0.1.1.dev0
this component allows you to manager you session storage
Requires Python: >=3.7

# streamlit-session-manager

this component allows you to manager you session storage

## Installation instructions 

```sh
pip install streamlit-session-manager
```

## Usage instructions

```python
import streamlit as st

from streamlit_session_manager import streamlit_session_manager

value = streamlit_session_manager()

st.write(value)
sparkfun-qwiic-serlcd 2.0.0
SparkFun Electronics Qwiic SerLCD
Keywords: electronics, maker

Qwiic Dual Encoder Reader
==========================

This package provides support to use three varieties of the SparkFun SerLCD screens: 16x2 SerLCD - RGB Backlight (QWIIC), 16x2 SerLCD - RGB Text (QWIIC), and 20x4 SerLCD - RGB Backlight (QWIIC).
make-moments 1.1.1
Development Status :: 4 - Beta
Requires Python: >=3.6

# make_moments
A stand alone version of the pyFAT make_moments and create xv_diagram function. The function can be imported to your python code or you can run it by itself from command line after a pip install:

  pip install make-moments


For moment maps:

use as:
  make_moments filename=<Cub.fits> mask=<mask.fits>

If the maps already exist add overwrite=True

For an overview of the possible input type 'make_moments -e' to print the default yaml file.

to configure setting from a yaml file

  make_moments -c my_input_file.yml

To use in python script:

from make_moments.functions import moments

And then use the moments() function

For PV-diagrams:

create_PV_diagram filename=Cub.fits PA=16.

to configure setting from a yaml file

  create_PV_diagram -c my_input_file.yml

To use in python script:

from make_moments.functions import pv_diagram

And then use the pv_diagram() function
mkdocs-pre-build-script 0.1.0
A MkDocs plugin to run a Python script before build

# mkdocs-pre-build-script

## 安装

```bash
pip intall mkdocs-pre-build-script
```

## 使用

### mkdoc.yal 配置

```yaml
site_name: 我的文档

plugins:
- pre_build_script:
    script:
      - print1.py #可以替换为其他python文件
      - ./src/print2.py
```

> [!tip]
>
> python文件的位置时相对于项目根目录的。

### 示例

示例项目地址：[StudyPavilion/mkdocs_pre_build_script_example: mkdocs_pre_build_script 示例项目](https://github.com/StudyPavilion/mkdocs_pre_build_script_example)

项目结构：

```
mkdocs_pre_build_script_example
├── docs
│   └── index.md
├── src
│   └── print2.py
├── README.md
├── mkdocs.yml
└── print1.py
```

## 编译项目

### windows

安装依赖

```bash
pip install -r requirements.txt
```

打包项目

```bash
python setup.py sdist bdist_wheel
```
blockrand-q-designer 0.1.0
A tool for generating block randomized LC-MS queue designs for full factorial experiments.
Requires Python: >=3.6

# Block randomized queue designer for LCMS experiments

[![DOI](https://zenodo.org/badge/DOI/your_doi_here.svg)](https://doi.org/10.5281/zenodo.15019270)

## Overview

**Block randomized queue designer** is a Python utility to automatically generate an optimal acquisition queue for sequential experiments such as LCMS experiments run on a single instrument. It accepts any full factorial experimental design with arbitrary number of factors, each of which can have a different number of levels, and an independently chosen block size. Given these tools, the tool will provide users with a block-randomized queue that respects the following constraints. For factors with a number of levels equal to the block size, it will perform strict blocking (each and all levels will be present in each block). For factors with less levels than block size, it will allocate levels so that, for any two levels, the difference between the number of samples holding those levels is at most one. For factors with more levels than the block size, it will enforce that no level be repeated in a block. Should the total number of samples not be divisible by the block size, the remaining samples at the end of the procedure will make up the last block, with relaxed constraints. An intra-block reshuffling method optimizes the position of samples in each block to make sure levels across factors are evenly spread across intra-block positions, and to minimize cases in which two or more samples with the same level of the same factor are aquired one after the other.

## Installation

You can install the package using pip:

```bash
pip install blockrand_q_designer
```
Alternatively you can clone the repository and install it locally

```bash
git clone https://github.com/nar-g/blockrand_q_designer.git
cd blockrand_q_designer
pip install .
```

```bash
conda install -c conda-forge blockrand_q_designer
```

## Usage example

A simple example of how to use this tool:

```python

from blockrand_q_designer import LCMSQueueDesign

# Define experimental parameters
factors = ['Fraction', 'Treatment', 'Replicate']
levels = [3, 2, 4]
block_size = 3

# Create a design instance
design = LCMSQueueDesign(factors, levels, block_size, base_name="GN01_01_", starting_index=1)

# Optionally, define labels for better readability
design.labels = {
    "Fraction": ("f1", "f2", "f3"),
    "Treatment": ("Nocodazole", "Control"),
    "Replicate": (1, 2, 3, 4)
}

# Print the generated blocks
design.print_blocks_pretty()

# Save the design to a CSV file
design.save_to_csv("queue_design.csv")

```

## Contributing
Contributions are welcome! 

## License
This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

## Contact
For questions, suggestions, or feedback, please contact guido.narduzzi@bc.biol.ethz.ch