semvec/pypi-packages huggingface dataset:
ensembl-converter 0.0.1
Convert Ensembl IDs to gene symbols
Requires Python: >=3.6

# ***Ensembl_converter**

The **`Ensembl_converter`** package is a Python library that allows you to convert Ensembl IDs to gene symbols. It provides a simple and convenient way to retrieve gene symbols for a given list of Ensembl IDs using the Ensembl REST API.

## **Installation**

You can install **`Ensembl_converter`** using pip:

```shell
pip install Ensembl_converter
```

## **Usage**
To convert Ensembl IDs to gene symbols, follow these steps:

1. Import the **`EnsemblConverter`** class from the Ensembl_converter package.
2. Create an instance of **`EnsemblConverter`**
3. Call the **`convert_ids`** method with a list of Ensembl IDs as input.

Here's an example:

```python
from Ensembl_converter import EnsemblConverter

# Create an instance of EnsemblConverter
converter = EnsemblConverter()

# Provide a list of Ensembl IDs
ensembl_ids = ['ENSG00000000457', 'ENSG00000273486', 'ENSG00000273493']

# Convert Ensembl IDs to gene symbols
result = converter.convert_ids(ensembl_ids)

# Print the resulting DataFrame
print(result)
```

This will output a DataFrame with two columns: ENSG (Ensembl IDs) and Symbol (gene symbols).

## **Options**
The **`EnsemblConverter`** class supports the following options:

* **`use_progressbar`** (default=False): Set this to **`True`** to display a progress bar during the conversion process. This can be useful for larger gene lists.

## **Dependencies**
The Ensembl_converter package depends on the following libraries:

* requests
* pandas
* tqdm

These dependencies will be automatically installed when you install the **`Ensembl_converter`** package (If you don't already have these installed).

## **Contributing**
Contributions to **`Ensembl_converter`** are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.

## **License**
This project is licensed under the MIT License. See the [**LICENSE**](/LICENSE) file for details.
jws2txt 1.0.0
A JASCO file (*.jws and *.jwb) to text file converter.
Requires Python: >=3.8

# jws2txt

A program to convert binary JASCO SpectraManager (JWS and JWB) files to text files.

## Introduction
------------
jws2txt is a simple command line tool that allows for the conversion of JWS and JWB files from JASCO SpectraManager software to text files. These text files can be used in any data analysis workflows and software.

Unlike JASCO SpectraManager software, jws2txt enables batch conversion of JWS/JWB files.

Data unpacking is based on Víctor M. Hernández-Rocamora's jwsProcessor (https://github.com/vhernandez/jwsProcessor).

## Installation and Usage
------------
1. Download Python.
2. Run the `pip install jws2txt` command in the command line.
3. To convert the files, run the `jws2txt --in-path path_to_folder --out-dir output_folder_path` command, where path_to_folder is the folder containing the JWS or JWB files.
4. The converted files will be located in the folder containing the source JWS or JWB files.
porf 0.0.6
Parse OpenRoad Files - RPT file parser for Openlane OpenROAD Static Timing Analysis output files.
Requires Python: >=3.7

# PORF

Parse OpenRoad Files - A Python Pandas-based report file (RPT) parser for Openlane OpenROAD Static Timing Analysis (STA) output files. 



## Examples

Basic Usage

```python
import porf

parse_file = porf.OpenSTAParser(file_address="./docs/examples/25-rcx_sta.rpt")
parse_run = porf.RunAnalyser(run_directory="./docs/examples/")
```

## Installation

### Quick Start

Install via `pip`

```bash
$ pip install porf
```

Then you can:

```python
import porf
```

Local development install for most recent version. Contributions very welcome.
```bash
$ git clone https://github.com/daquintero/porf.git
$ cd qups
$ pip install -e .[develop]
```
scrapy-s3logstorage 0.1.1
Upload scrapy logs to S3
Requires Python: >=3.7,<4.0


# Scrapy S3 Log Storage

# **DEPRECATED**

This package is deprecated. Please use [scrapy-logexport](https://pypi.org/project/scrapy-logexport/) instead.


## Description
A Scrapy extension to upload log files to S3.

If you're already exporting your feeds to S3 this extension uses many of the same settings. After adding this extension to your `EXTENSIONS` setting, just set the `LOG_FILE`, `S3_LOG_BUCKET` and an optional `S3_LOG_ACL`settings and you're good to go.

## Installation
You can install scrapy-s3logstorage using pip:
```
    pip install scrapy-s3logstorage
```

## Configuration

This extension still requires that a local log file is written. Once scrapy's engine has stopped, the extension will upload the log file to S3 and optionally delete the local file.

Enable the extension by adding it to your `settings.py`:
```
    from environs import Env

    env = Env()  
    env.read_env() 

    EXTENSIONS = {
        'scrapy_s3logstorage.extension.S3LogStorage': 0,
    }

    LOG_FILE = 'scrapy.log' # Must be a local file
    S3_LOG_BUCKET = 'my-bucket' # Bucket name to store logs
    S3_LOG_DELETE_LOCAL = True # Delete local log file after upload, defaults to False

    # If AWS CLI is configured, and you're using the same credentials the following settings are optional
    AWS_ACCESS_KEY_ID = env("AWS_ACCESS_KEY_ID")
    AWS_SECRET_ACCESS_KEY = env("AWS_SECRET_ACCESS_KEY")
    AWS_SESSION_TOKEN = env("AWS_SESSION_TOKEN") # if required
    AWS_ENDPOINT_URL = None  # or your endpoint URL

    # S3_LOG_ACL takes priority over FEED_STORAGE_S3_ACL.
    # If S3_LOG_ACL is not set, FEED_STORAGE_S3_ACL will be used.
    # Setting one or both of these settings is optional.

    S3_LOG_ACL = ''  # or other S3 ACL
    FEED_STORAGE_S3_ACL = '' # or other S3 ACL
```
kboxx 1.0.3
Implementation of pre-trained model based on Keras
Requires Python: >=3.7

# kboxx

This is a package that implements various pre trained models based on Keras. It includes Computer Vision, natural language processing and multimodal correlation models, to help you quickly build and apply to downstream tasks.

1. Dependencies and Installation.

2. What pre trained models are currently included?

3. How to use?

To be continued...
jekyll-to-hugo 0.0.9
Python library for converting jekyll md files to Hugo.
Requires Python: >=3.10

# Jekyll to Hugo Converter

Jekyll to Hugo Converter is a simple tool to convert Jekyll posts to Hugo posts.

I've used this tool to convert [my blog](https://blog.nuculabs.dev) from WordPress to Jekyll and finally to Hugo.

The tool has the following features:
- Batch conversion.
- Post header fields drop/ignore.
- Links rewrite. (Search and Replace)
- Author name rewrite.

It was written in a way that is easy to extend, while I wrote it for my own personal use, I hope you'll find some use for it as well.

Note: This tool is not perfect, it will not convert everything. If you find a bug, please open a PR.

## Table of Contents

* [Usage](#usage)
  * [PiPy](#pipy)
  * [Python From Source](#python-from-source)
  * [Docker](#docker)
* [Configuration](#configuration)
  * [Example Configuration](#example-configuration)
* [License](#license)

## Usage

### PiPy or Pipx

If you have Python installed, you can use the following commands:

```bash
pip install jekyll-to-hugo
jekyll-to-hugo
```

You will need to create a `config.yaml` file in the current directory. See example [here](./config.yaml).

_`pipx` is a tool to install Python CLI tools in isolated environments_

### Python From Source

If you have Python installed, you can use the following commands:

```bash
pip install -r requirements.txt
python3 jekyll-to-hugo.py <jekyll_post_path> <hugo_post_path>
```

To change the config, edit `config.yaml`.

The configuration file path can be configured with the `CONFIG_PATH` environment variable.

### Docker

If you don't have Python installed, you can use Docker:

1. Build the image.

```bash
docker build -t jekyll-to-hugo -f ./docker/Dockerfile .
```

2. Run the image. You will need to mount the following directories: config file, Jekyll posts directory, Hugo posts directory.

```bash
docker run -it --rm -v $(pwd):/app jekyll-to-hugo
```

## Configuration

The configuration file is a YAML file. See example [here](./config.yaml).
The configuration file path can be configured with the `CONFIG_PATH` environment variable.

### Example Configuration

```yaml
logging_level: "INFO"
source_path: "/Users/dnutiu/PycharmProjects/jekyll-to-hugo/my_test_data/_posts"
output_path: "/Users/dnutiu/NucuLabsProjects/NucuLabsDevBlog/content/posts"
converter: "wordpress_markdown_converter"
converter_options:
  enable_regex_heuristics: true
  author_rewrite: "Denis Nuțiu"
  links_rewrite:
    - source: "http://localhost/"
      target: "/"
    - source: "https://nuculabs.wordpress.com/"
      target: "https://nuculabs.dev/posts/"
    - source: "https://twitter.com/metonymyqt"
      target: "https://twitter.com/nuculabs"
  header_fields_drop:
    - restapi_import_id
    - original_post_id
    - timeline_notification
    - wordads_ufa
    - spay_email
    - amp_status
    - advanced_seo_description
    - publicize_twitter_user
```

## License

This project is licensed under the GPL-3.0 license - see the [LICENSE](LICENSE) file for details.

---
Made with ❤️ by [NucuLabs.dev](https://blog.nuculabs.dev)
proptable 0.0.1
For generating property table values for given input
Requires Python: >=3.7

[![PyPI version](https://badge.fury.io/py/R134aPT.svg)](https://badge.fury.io/py/R134aPT)
[![pypi supported versions](https://img.shields.io/pypi/pyversions/R134aPTs.svg)](https://pypi.python.org/pypi/R134aPT)
# PropertyTables_Python
This is a digital Property Table of R134a
You can add some data and obtain relavant data for R134a

# To Install

    pip install PropTable

## Sample Calculation (Before Packaging)

![Screenshot 2023-06-05 001930](https://github.com/Buddhi19/PropertyTables_Python/assets/119914594/2ae5d53a-13ea-41e0-a487-507a3bc1bace)

## Required Data Can be Obtained

![Screenshot 2023-06-01 180639](https://github.com/Buddhi19/PropertyTables_Python/assets/119914594/435f290a-3e75-4ebf-b81a-007314f0691d)