semvec/pypi-packages huggingface dataset:
lcwc 0.12.3
Python library for fetching the Lancaster County-Wide Communications live incident list.
Requires Python: >=3.9
Keywords: lcwc,lancaster,police,fire,ems,dispatch,911,incident

# python-lcwc

python-lcwc is a Python library for the [LCWC](https://www.lcwc911.us/live-incident-list) incident feed.

The library features multiple clients for retrieving incidents: a web scraper, an RSS feed parser, and an ArcGIS REST client. See notes below for more information.

## Installation

    pip install lcwc

## Example

```python

import aiohttp
from lcwc.feed import Client

client = Client()

async with aiohttp.ClientSession() as session:
    incidents = await client.get_incidents(session)

    for incident in incidents:
        print(f'{incident.date} - {incident.description}')
```

## Notes

### Web Client

The web client uses web-scraping which can be a bit limited for identification purposes but is the most reliable. The web client is the recommended client for use unless you need more granular information such as static identifers.

### Feed Client

The feed client uses the RSS feed and is similar to the web client except it overs an (admittedly arbitrary) GUID for each incident. Categorization can be a bit off since the feed doesn't supply that in formation so we have to attempt to extrapolate the category from the incident description/units assigned. Use the web client if you need more accurate categorization.

### ArcGIS REST Client

The ArcGIS REST client uses the ArcGIS REST API to retrieve incidents. This is the most accurate client since it uses the same data source as the LCWC website. This is still a bit of a prototype and may be subject to change. The ArcGIS REST client is the recommended client if you need more granular information such as static identifiers and coordinates.
tpfunc 1.2.6
ThingsPro Edge Function SDK
Requires Python: >=3.9

# ThingsPro Edge Function SDK

ThingsPro Edge Function SDK provides several APIs to interact with ThingsPro Edge features.

- Tag (v1)
  - PubSub
  - Direct Access
- Http Server (v1)
  - Get
  - Post
  - Put
  - Delete

Besides, SDK is tested with Python3.9, theoretically compatible with all other modules which you can refer to [Python3.9 official website](https://docs.python.org/3.9/library/index.html). Python3.6 is no longer in the support list.

# Release Note
## 2023-12-21 V1.2.6
### Changes:
- chore: upgrade to python3.9

## 2023-05-03 V1.2.5
### Bug Fix:
- fix: http function missing essential module folder

## 2023-04-26 V1.2.4
### Features:
- feat: support unsubscribe all

## 2021-07-26 V1.2.3
### Bug Fix:
- fix: can't receive messages after tag_v1 subscribe callback exception

## 2021-07-23 V1.2.2
### Bug Fix:
- fix: tag_v1 subscribe init always run into exception
Kyle-Smartsheet 0.0.3
Connect Smartsheet with Dataframe
Keywords: smartsheet,kyle


# Smartsheet of Kyle

It was created Khang Vuong

## Examples of How To Use

Ksheet

```python

from Kyle_Smartsheet import KSheet

sheet = KSheet(SheetID,token)
# Get dataframe
df = sheet.get_dataframe()
# Get column ID
columnID = sheet.get_columnID()
# Get column type
columntype = sheet.get_columnType()
# Convert df to dictionary
dic = convert_df_to_dict(df)
# Add rows
sheet.add_rows(df)
# Update rows
sheet.update_rows(df,list_rowID)
# Delete rows
sheet.delete_rows(df,lst_rowID)

```

KSmartsheet

```python

from Kyle_Smartsheet import KSmartsheet

smart = KSmartSheet(token).smart

```
swedeb-explorer 0.1.15
Swedish parliamentary debate explorer
Requires Python: >=3.11,<3.12.dev0

## Prototype for Swedeb Demo.

Based on Streamlit

To run


`streamlit run swedeb_demo/main_page_shared_filter.py`

To run with .env file (specifying a corpus)


`streamlit run swedeb_demo/main_page_shared_filter.py -- --env_file .env`

To run without displaying the "debug" tab


`streamlit run swedeb_demo/main_page_shared_filter.py -- --debug False`
bertopic-base-chinese 0.0.1
基于bertopic对中文文档进行主题建模

# About
一个基于bertopic对中文文档进行主题建模的包。

# Install
`$ pip3 install -U bertopic_base_chinese`

# Director 
+ bertopic\_base\_chinese
    + \_model.py

## \_model.py
- BERTopic类
  重写了__init__()，设置embedding\_model为"paraphrase-multilingual-MiniLM-L12-v2"，以及选取tokenizer为jieba.lcut，初始化类参数。 

# Usage
```Python3
from bertopic_base_chinese import BERTopic

docs = ["我爱北京天安门", "我家大门常打开，开放怀抱等你"]
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
```

# Contact us
<may.xiaoya.zhang@gmail.com>