semvec/pypi-packages huggingface dataset:
ewha-db-dynamicP 0.1.1
A database manager for PostgreSQL with trajectory processing.
Requires Python: >=3.8

# ewha-db-dynamicP

A Python package for managing PostgreSQL database connections and processing trajectory data collected from CCTV systems.


## How to use

'''python
from ewha_db_manager.db_manager import DBManager

db = DBManager(
    host = "localhost",
    port = your port number,
    dbname = "your_database",
    user = "your_username",
    password = "your_password"
)

df_group = db.dataframe_groups("your_table")
'''

db.dataframe_groups automatically fetches data from a PostgreSQL database, processes it into trajectory datasets, and groups the results by the CCTV ID where each trajectory was recorded.

## Requirements

psycopg2-binary
pandas
geopandas
movingpandas
tqdm
pyyaml

## License
This project is licensed under the JiyoonLee License.

## Contact
0197black@gmail.com
ewha-cong-dynamicP 0.1.0
A Python module for calculating and analyzing three types of congestion—Road, PM, and Pedestrian congestion.
Requires Python: >=3.8

# ewha-cong_dynamicP

A Python module for calculating and analyzing three types of congestion—Road, PM, and Pedestrian congestion—using trajectory data collected from CCTV systems. It includes safety-aware adjustments based on pedestrian-vehicle and pedestrian-pedestrian interactions.


## How to use

'''python
from ewha_congestions.three_congestions import processing_congestions

df = your data.
data index must be DatetimeIndex as "dtct_dt".
data must has point geometry as "geomatry", "snr_id", "distance", "speed", "acceleration", "traj_id", "direction", "mf_type", and "apr_code"

road_shp = your road data.
road data must has Polygon geometry of roads as "geometry" and CCTV ID as "snr_id".

road_shp = road_shp.to_crs("EPSG:5179") for calculate road area

road_cong, ped_cong, pm_cong = processing_congestions(df, road_shp, 3600).call()

'''
df: includes object tracking data with fields like snr_id, mf_type, apr_code, geometry, dtct_dt, speed, and acceleration.

road_df: includes road information with fields snr_id and geometry.

interval: time window (in seconds) over which congestion is aggregated and computed.

## Output
Each output (road_cong, ped_cong, pm_cong) is a DataFrame with:

CCTV_ID: unique identifier for each camera

time: timestamp of the congestion record

congestion_level: integer from 0 to 4

0: Excellent (≤ 0.2)

1: Good (≤ 0.4)

2: Normal (≤ 0.6)

3: Crowded (≤ 0.8)

4: Very Crowded (> 0.8)

Congestion Types
Type |	Description
Road Congestion |	Combined area occupied by all moving objects divided by the road polygon area
PM Congestion |	PM area only, normalized by unoccupied space (excluding pedestrian & vehicle)
Pedestrian Congestion |	Road congestion adjusted using pedestrian-pedestrian and pedestrian-vehicle conflicts

## Requirements
numpy
pandas
geopandas
movingpandas
tqdm
shapely
geopy

## License
This project is licensed under the JiyoonLee License.

## Contact
0197black@gmail.com
django-oauth2 5.0
Provide OAuth2 access to your app (fork of django-oauth2-provider)
Requires Python: >=3.8

django-oauth2
======================

.. image:: https://travis-ci.org/stormsherpa/django-oauth2-provider.png?branch=master

*django-oauth2* is a Django application that provides
customizable OAuth2\-authentication for your Django projects.

`Documentation <https://new-django-oauth2.readthedocs.io/en/latest/>`_

License
=======

*django-oauth2* is a fork of *django-oauth2-provider* which is released under the MIT License. Please see the LICENSE file for details.


Packaging
=========

 $ python -m build