semvec/pypi-packages huggingface dataset:
pons.py 1.1.1
An API wrapper for the PONS dictionary
License: MIT
Keywords: API,dictionary

<div align="center">
  <h1>pons.py</h1>
  An API wrapper for the PONS dictionary written in Python
</div>

## Installation
```sh
$ pip install pons.py
```

## Usage
```py
from pons import Client

pons = Client("YOUR_SECRET")  # see PONS API reference

# get dictionaries
# params: language (de|el|en|es|fr|it|pl|pt|ru|sl|tr|zh)
dictionaries = pons.get_dictionaries("en")        # returns a list of Dictionary objects

# get translations
# required params: term, dictionary, source language
# optional params: output language, fuzzy (bool), references (bool)
entries = pons.query("term", "deen", "en")["en"]  # returns a list of EntryHit objects
entries[0].translations                           # returns a list of translations (strings)
```

## References
- [PONS API Reference](https://en.pons.com/p/online-dictionary/developers/api)
- [API Documentation](https://en.pons.com/p/files/uploads/pons/api/api-documentation.pdf)
crispy-bootstrap3 2024.1
Bootstrap3 template pack for django-crispy-forms
License: MIT
Requires Python: >=3.7

# crispy-bootstrap3

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/django-crispy-forms/crispy-bootstrap3/blob/main/LICENSE)

Bootstrap3 template pack for django-crispy-forms. This template pack was 
included with the core django-crispy-forms package until version 2.0.

## Installation

Install this plugin using `pip`:

```bash
    $ pip install crispy-bootstrap3
```

## Usage

You will need to update your project's settings file to add `crispy_forms`
and `crispy_bootstrap3` to your projects `INSTALLED_APPS`. Also set
`bootstrap3` as and allowed template pack and as the default template pack
for your project:

```python
    INSTALLED_APPS = (
        ...
        "crispy_forms",
        "crispy_bootstrap3",
        ...
    )

    CRISPY_ALLOWED_TEMPLATE_PACKS = "bootstrap3"

    CRISPY_TEMPLATE_PACK = "bootstrap3"
```
crossmdf 0.1.0
An utility for fusing data across different modalities like text and image
License: MIT
Requires Python: >=3.6
Keywords: data fusion,multimodal,text features,image features

# cromdf
A library for integrating and analyzing data from different modalities (text, images, audio, etc.). This library helps to combine information from different types of data sources for richer insights and predictions. It provides a way to fuse data to improve the performance of machine learning models
PubmedZenbu 0.3.7
PubmedZenbu
License: MIT
Keywords: pubmed scraping article dogrun

# pubmed-zenbu
1. This tool collects literature data (title_only or title_and_abstract) of your interest.
2. This tool also extracts metadata from literature data using ChatGPT if you set the parameters.

## How to use 
- `conda create -n pubmedzenbu python=3.9`
- Create a `config.yml` 
```
# Sample config.yml

pubmed_search:
  # (Required) NCBI API Key
  ncbi_api_key: YOUR_NCBI_API_KEY
  # (Required) Search query to obtain the PubMed articles of your interest
  search_query: prime editing pig
  # (Required) How far back in time you want to search. 
  search_oldest_year: 2010
  # (Required) `title` or `abstract`. If choose `abstract`, it means you get the joined string of title and abstract.
  which_text_to_use: title
openai:
  # (Required) if use, add 'yes'. If not, keep it empty.
  use_openai: yes
  # (Optional) if use_openai is true, add your openai_api_key. Otherwise, keep it empty.
  openai_api_key: YOUR_OPENAI_API_KEY
  # (Optional) Prompt to ask ChatGPT. If you don't use it, keep it empty.
  prompt: "extract gene and species from the following text \n"
  # (Required) Set the output path. If use_openai is false, literature data will be written out.
  output_path: ./extract_result_20231004.csv
```

- `pubmedzenbu PATH_TO_YOUR_config.yml_FILE`