semvec/pypi-packages huggingface dataset:
dfipy 9.1.0
Python SDK targeting the DFI API
Requires Python: <4.0,>=3.10

<img src="docs/assets/general-system-dark.svg" align="right">

[![PyPI - Version](https://img.shields.io/pypi/v/dfipy)](https://pypi.python.org/pypi/dfipy/)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![MyPy](https://img.shields.io/badge/%20type_checker-mypy-%231674b1?style=flat)](https://github.com/python/mypy)

# DFI API wrapper

## About

- Python library created by [General System](https://www.generalsystem.com/) and publicly available on [github](https://github.com/thegeneralsystem) to query and experiment with the Data Flow Index (DFI) platform.
- DFI provides the user with an unparalled foundation and scalable solution for processing high velocity, high volume spatiotemporal workloads for any geospatial data science need.
- [Read the code documentation](https://dfipy.docs.generalsystem.com/)
- [Read the whitepaper](https://assets.website-files.com/636ce900cf2e67aab6340642/643807134214aebc2b29849c_General%20System_The%20Data%20Flow%20Index%20Whitepaper%20_%20Nov%2022.pdf)

## Quickstart

- Contact General System to obtain an API token.
- **Install Python SDK:**

  ```bash
  pip install dfipy
  ```

- Start querying with [dfipy Examples](https://github.com/thegeneralsystem/dfipy-examples)

## Licence

- Copyright (c) 2023, General System Group Limited.

- DFIPy is provided as it is and copyrighted under [Apache2 License](LICENCE).

- DFIPy is currently released on public pypi in beta, strictly for testing and evaluation purposes.
almgroad0770i 1.0
Is a FREE asynchronous library from reverse engineered Shazam API written in Python 3.6+ with asyncio and aiohttp. Includes all the methods that Shazam has, including searching for a song by file.

Python is a high-level, interpreted programming language known for its simplicity and readability. Created by Guido van Rossum and first released in 1991, Python emphasizes code readability with its clear and concise syntax, making it an ideal language for beginners and experienced programmers alike.Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming. It offers dynamic typing and automatic memory management, which simplifies development by reducing the amount of code needed to perform tasks compared to lower-level languages.Python has a vast ecosystem of libraries and frameworks, making it suitable for a wide range of applications, from web development and data analysis to artificial intelligence and scientific computing. Popular frameworks such as Django and Flask are widely used for building web applications, while libraries like NumPy and Pandas are essential for data manipulation and analysis.One of Pythons key strengths is its strong community support and active development, with a large number of contributors continuously improving the language and its ecosystem.
jasyllablesep 0.0.0
A package for separating Japanese text into syllables
Requires Python: >=3.6

# jasyllablesep
Japanese text parser to separate Katakana string into syllables.

# Usage

## Install
```
pip install jasyllablesep
```

## Code example

```Python
import jasyllablesep
print(jasyllablesep.parse("シンシュンシャンソンショー"))
```

```
['シン', 'シュン', 'シャン', 'ソン', 'ショー']
```
almgroad0780i 1.0
Is a FREE asynchronous library from reverse engineered Shazam API written in Python 3.6+ with asyncio and aiohttp. Includes all the methods that Shazam has, including searching for a song by file.
Requires Python: >=3.9

Python is a high-level, interpreted programming language known for its simplicity and readability. Created by Guido van Rossum and first released in 1991, Python emphasizes code readability with its clear and concise syntax, making it an ideal language for beginners and experienced programmers alike.Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming. It offers dynamic typing and automatic memory management, which simplifies development by reducing the amount of code needed to perform tasks compared to lower-level languages.Python has a vast ecosystem of libraries and frameworks, making it suitable for a wide range of applications, from web development and data analysis to artificial intelligence and scientific computing. Popular frameworks such as Django and Flask are widely used for building web applications, while libraries like NumPy and Pandas are essential for data manipulation and analysis.One of Pythons key strengths is its strong community support and active development, with a large number of contributors continuously improving the language and its ecosystem.
celltypegenomics 0.0.8
Package for cell type genomics
Requires Python: >=3.7

# CellTypeGenomics

## Overview
CellTypeGenomics is an open-source Python package designed to analyze the cell-type origins of genes using Human Protein Atlas (HPA) data. It helps to identify genes that are potentially over-represented or under-represented in specific cell types, providing insights that are crucial for understanding various biological processes and diseases.

The recent update made it possible to replace our numerical Human Protein Atlas (HPA) marker genes (proteinatlas.tsv) with qualitative marker genes from the human Ensemble Cell Atlas (hECA) or the Human Protein Atlas (HPA). In addition, there is an option to return tissue origins of genes using Human Protein Atlas (HPA) data.

## Key Functionality
- **Gene Analysis:** Analyzes a list of gene Ensembl IDs and returns a sorted pandas DataFrame, highlighting genes that are potentially over- or under-represented in certain cell types.
- **Data Source:** Leverages the comprehensive gene expression data available from the Human Protein Atlas (HPA) and the human Ensemble Cell Atlas (hECA).

## Installation
To install or update CellTypeGenomics, run the following command in your terminal:
```bash
pip install --upgrade --force-reinstall celltypegenomics
```

## Usage
Here's how to use the CellTypeGenomics package to analyze your gene list with numerical Human Protein Atlas (HPA) marker genes:

```python
from celltypegenomics import celltypefishertest

# Specify an optional alpha for significance (default: 0.05)
result = celltypefishertest(list_of_ensembl_ids, alpha=0.05)
print(result)
```
Replace `list_of_ensembl_ids` with your list of gene Ensembl IDs.

Use CellTypeGenomics package to analyze your gene list with qualitative marker genes from the human Ensemble Cell Atlas (hECA):

```python
result = celltypefishertest(list_of_ensembl_ids, heca=True)
print(result)
```

Use CellTypeGenomics package to analyze your gene list with qualitative marker genes from the Human Protein Atlas (HPA):

```python
result = celltypefishertest(list_of_ensembl_ids, hpa_marker_genes=True)
print(result)
```

Use CellTypeGenomics package to analyze your gene list with tissue origins of genes using Human Protein Atlas (HPA):

```python
result = celltypefishertest(list_of_ensembl_ids, tissue=True)
print(result)
```

## Support
For more information, updates, or to contribute to the project, please visit our [GitHub Repository](https://github.com/Zyron/CellTypeGenomics).

## License
CellTypeGenomics is released under the MIT license. See the LICENSE file for more details.