semvec/pypi-packages huggingface dataset:
markdown-helper 1.0.2
A simple markdown helper
Requires Python: >=3.10

# python-markdown-helper
[![Coverage Status](https://coveralls.io/repos/github/amahlaka/python-markdown-helper/badge.svg)](https://coveralls.io/github/amahlaka/python-markdown-helper)  
A bunch of helper classes that make generating markdown files easier.

Originally written to make my own life easier. I got tired of writing repetitive code to generate markdown files for my projects.  


# Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Upcoming Features](#upcoming-features)
- [Contributing](#contributing)
- [License](#license)

---

## Installation
```bash
pip install markdown-helper
```

## Usage
Sample code that generated the [EXAMPLE.md](EXAMPLE.md) file

```python
import markdown_helper as mdh

# Create a new markdown Document that includes a table of contents
doc = mdh.Document("Title", "EXAMPLE.md", overwrite=True, table_of_contents=True)

# Add a new section to the document
first_section = doc.add_section("Section 1")
# Add some text to the section
first_section.add("Regular text in the first section")
# Add a list to the section
list_of_fruits = mdh.List(["Apple", "Banana", "Orange"], ordered=True, title="Fruit List")

first_section.add(list_of_fruits)

# Make a new Table with custom value mapping
table = mdh.Table(["Fruit", "Color"], sort_key="Fruit", title="Fruit Table", custom_map={"Fruit": {"Apple": "🍎", "Banana": "🍌"}})
# Add some rows to the table
table.add_row({"Fruit": "Apple", "Color": "Red"})
table.add_row({"Fruit": "Banana", "Color": "Yellow"})

# Add the table to the document
first_section.add(table)

# Add a new section to the document with Smaller title
second_section = mdh.Section(mdh.Header("Section 2", 2))
doc.add_section(second_section)
second_section.add("Regular text in the second section")

# Add a link and an image to the section
link = mdh.Link("https://google.com", "Google")
second_section.add(link)
image = mdh.Image("https://picsum.photos/200", alt="A random image")
second_section.add(image)

# Output the document to as a string
print(doc)

# Save the document to a file
doc.save()
```

# Upcoming Features

-  Add support for code blocks
- ???

# Contributing

Feel free to open a pull request or an issue if you have any suggestions or find any bugs

# License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
scMMT 1.0.1
A package for cell annotation, protein prediction, and low dimensional embedding representation
Requires Python: >=3.10

# scMMT

scMMT (**s**ingle-**c**ell **m**ulti-modal data and **m**ulti-task learning **t**ool) is a powerful deep learning computational tool designed for the analysis of CITE-seq and scRNA-seq data. It offers various functionalities such as cell annotation, protein expression prediction, and low-dimensional embedding. With scMMT, researchers can efficiently explore and interpret complex single-cell datasets, enabling deeper insights into cellular heterogeneity and intercellular interactions.

### Create environment

```shell
conda create -n scMMT python=3.10
conda activate scMMT
```

### Installation

```shell
pip install scMMT
```

Alternatively, you can also install the package directly from GitHub via

```shell
pip install git+https://github.com/SongqiZhou/scMMT.git
```

### Example Demo:

[Guided Tutorial](https://github.com/SongqiZhou/scMMT/tree/main/test)

### Data

(1) Seurat 4 human peripheral blood mononuclear cells (GEO: GSE164378). 

(2) H1N1 influenza PBMC dataset (https://doi.org/10.35092/yhjc.c.4753772). 

(3) COVID dataset(https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10026/).

The University of Pennsylvania has put these data sets together for the convenience of downloading.  [Download Here](https://upenn.app.box.com/s/1p1f1gblge3rqgk97ztr4daagt4fsue5). The reference github link is: https://github.com/jlakkis/sciPENN_codes

### Software Requirements

- Python >= 3.10
- torch >= 2.0.0
- scanpy >= 1.9.3
- scikit-learn >= 1.2.2
- scikit-learn-intelex >= 2023.1.1
- pandas >= 2.0.1
- numpy >= 1.24.3
- scipy >= 1.10.1
- tqdm >= 4.65.0
- anndata >= 0.9.1
DKUtils 2.11.6
DataKitchen Utils Library
Requires Python: >=3.8

# DKUtils

## Description
The DKUtils python package was written to provide utility functions and classes that are used in DataKitchen recipes. 
It serves two main purposes.

1. Provide a Python API Client for interacting with the DataKitchen platform’s REST API (DataKitchenClient)
2. Provide utility classes and methods for interacting with tools commonly orchestrated in recipes in the DataKitchen platform (Alteryx, Jira, Gmail, etc.)

## Development Info
Building and testing this module is conveniently done using Make. Issue the `make` command to see a list
of available targets (shown below for convenience). Note that any target can be suffixed with `-ext` to 
run that target inside a Docker container. This allows testing and development in a standard and portable
environment. To develop inside a running docker container, use the `bash-ext` target. This will drop the 
user into a bash shell inside a running container.

    Add '-ext' to any target to run it inside a docker container

    Versioning:
        bump/major bump/minor bump/patch - bump the version

    Utilities:
        bash         run bash - typically used in conjunction with -ext to enter a docker container
        scan_secrets scan source code for sensitive information

    Linting:
        lint         run flake8 and yapf
        flake8       run flake8
        yapf         run yapf and correct issues in-place
        yapf-diff    run yapf and display diff between existing code and resolution if in-place is used

    Testing:
        test         run all unit tests
        test_unit    run all unit tests
        clean_unit   remove files from last test run (e.g. report_dir, .coverage, etc.)
        tox          run unit tests in python 2 and 3
        clean_tox    clean tox files (e.g. .tox)

    Documentation:
        docs         generate Sphinx documentation
        docs/html    generate Sphinx documentation
        docs/clean   remove generated Sphinx documentation

    Build and Upload:
        build        generate distribution archives (i.e. *.tar.gz and *.whl)
        upload       upload distribution archives to PyPI
        clean_build  remove all the build files (i.e. build, dist, *.egg-info)

    Cleanup:
        clean        run all the clean targets in one go
        clean_pyc    remove all *.pyc files

[Pre-commit](https://pre-commit.com/) is also included to validate and flag commits that contain code 
that does not pass [Flake8](http://flake8.pycqa.org/en/latest/) and [YAPF](https://github.com/google/yapf). 
To use, first install the python package `pip install pre-commit` and then run `pre-commit install`. All 
future commits will run these tools and deny commits that don't pass. When running YAPF, `pre-commit` will
make in-place corrections to your code. Therefore, if it fails the YAPF validation on the first commit 
attempt, simply review the changed files, add, and commit again to resolve.
pyispace 0.3.7
Instance Space Analysis Python package
Requires Python: >=3.7

# PyISpace

_Python Instance Space Analysis Toolkit_

<!--![picture](docs/img/circle-fs.png)-->

## Getting Started

This is the Python version of the [Instance Space Analysis](https://github.com/andremun/InstanceSpace) (ISA) toolkit, originally written in Matlab code. The original ISA toolkit was developed as part of the project [MATILDA](https://matilda.unimelb.edu.au/matilda/about-matilda), at the University of Melbourne, Australia.

PyISpace is a Python package that contains a subset of the tools present in the original Matlab code repository. It is not our intention to add new features to the toolkit. For a complete experience of all tools, we recommend the original code, or the [web version](https://matilda.unimelb.edu.au/matilda/).

### Requirements
Python 3.7 or newer.

### Installation

Via PyPi:

```
pip install pyispace
```

Or directly from the repository code:
```
git clone https://gitlab.com/ita-ml/pyispace.git
cd pyispace/
pip install -e .
```


### Usage

1. Command Line Interface

Inside the folder (`rootdir`) containing the required files [options.json](./options.json) and ``metadata.csv``, run  
```
isa
```

Optionally, the `rootdir` path may be passed with `-r` or `--rootdir`:
```
isa -r /Users/user/workspace/
```

For more information about these files, please refer to the original repository instructions.

2. Python package

```
from pyispace import train_is
model = train_is(metadata, opts)
```

#### Disabling messages
PyISpace uses [logging](https://docs.python.org/3/library/logging.html) to display messages. You can disable the messages, for example, changing the level associated to the package: 

```
logging.getLogger("pyispace").setLevel(logging.ERROR)
```
analysis-tools 0.3.2
Analysis tools for Machine learning projects
Requires Python: >=3.8,<4.0

# Analysis tools for machine learning projects

## 1. Usage
```bash
$ pip install analysis-tools
```

## 2. Tutorial
[examples/titanic/eda.ipynb](https://github.com/alchemine/analysis-tools/blob/main/examples/titanic/eda.ipynb)를 참고

```python
from analysis_tools import eda, metrics

data   = pd.DataFrame(..)
target = 'survived'

num_features       = ['age', 'sibsp', 'parch', 'fare']
cat_features       = data.columns.drop(num_features)
data[num_features] = data[num_features].astype('float32')
data[cat_features] = data[cat_features].astype('string')

eda.plot_missing_value(data)
eda.plot_features(data)
eda.plot_features_target(data, target)
eda.plot_corr(data.corr())
metrics.get_feature_importance(data, target)
```

![](https://github.com/alchemine/analysis-tools/blob/main/examples/titanic/visualization/Missing%20value_1.png?raw=true)
![](https://github.com/alchemine/analysis-tools/blob/main/examples/titanic/visualization/Features_1.png?raw=true)
![](https://github.com/alchemine/analysis-tools/blob/main/examples/titanic/visualization/Features%20vs%20Target_1.png?raw=true)
![](https://github.com/alchemine/analysis-tools/blob/main/examples/titanic/visualization/Correlation%20matrix_1.png?raw=true)
![](https://github.com/alchemine/analysis-tools/blob/main/examples/titanic/visualization/Feature%20importance_1.png?raw=true)
langchain-chatglm 0.0.13
esay use ChatGLM in LangChain.
Requires Python: >=3.10

# langchain-ChatGLM

esay use ChatGLM in LangChain.

[ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B)
[langchain](https://github.com/hwchase17/langchain)

## Install Requirement
```
pip install -r requirements.txt
```

## Usage
```python

from chatglm_pipline import ChatGLMPipeline
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

llm = ChatGLMPipeline.from_model_id(
    model_id="THUDM/chatglm2-6b",
    device=-1, # if use GPU set to 0
    model_kwargs={"temperature": 0, "max_length": 64, "trust_remote_code": True},
    callback_manager=callback_manager, 
    verbose=True,
)

template = """问: {question}

答: 让我们一步一步地思考."""

prompt = PromptTemplate(template=template, input_variables=["question"])

llm_chain = LLMChain(prompt=prompt, llm=llm)

question = "华晨宇和张碧晨是什么关系?"

llm_chain.run(question)

```

## model_kwargs
| key | values | remark |
| --- | ------ | ------ |
| `"device"` | `"cuda"` | 使用cuda加速 |
| `"float"` | `True`, `False` | 使用cpu推理  |
| `"quantize"`| `8` | 量化 |