semvec/pypi-packages huggingface dataset:
scipion-chem-coprinet 0.2
Scipion framework plugin for the use of CoPriNet software tools
Keywords: scipion CoPriNet scipion-3.0 cheminformatics

.. |organization| replace:: scipion-chem
.. |repository| replace:: scipion-chem-CoPriNet

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CoPriNet scipion plugin
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**Documentation under development, sorry for the inconvenience**
Scipion framework plugin for the use of CoPriNet software tools.
  
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Install this plugin
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You will need to use `Scipion3 <https://scipion-em.github.io/docs/docs/scipion
-modes/how-to-install.html>`_ to run these protocols.

CoPriNet, is installed automatically by Scipion.

- **Install the stable version**

    Through the plugin manager GUI by launching Scipion and following **Others** >> **Plugin Manager**

    or

.. parsed-literal::

    scipion3 installp -p \ |repository|\ 


- **Developer's version**

    1. Download repository:

    .. parsed-literal::

        git clone \https://github.com/\ |organization|\ /\ |repository|\ .git

    2. Install:

    .. parsed-literal::

        scipion3 installp -p /path/to/\ |repository|\  --devel
  
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Protocols
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This plugin contains the following protocols:

**None for now**

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Packages & enviroments
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Packages installed by this plugin can be located in ``/path/to/scipion/software/em/``.

The following packages will be created:

- CoPriNet-``version``

Where ``version`` is the current version of that specific package.

Also, the following conda enviroments will be created:

- CoPriNet-``version``

As of today, Scipion does not automatically uninstall the conda enviroments created in the installation process when uninstalling a plugin, so keep this list in mind if you want to clean up some disk space if you need to uninstall this one.

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External software
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This plugin integrates the following software:
  
.. _CoPriNet: https://github.com/MolecularAI/CoPriNet
.. |CoPriNet| replace:: **CoPriNet** 

- |CoPriNet|_: Tool for CoPriNet planning.

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Changelog
========================================
All the recent version changes can be found `here <https://github.com/scipion-chem/scipion-chem-CoPriNet/blob/devel/CHANGES.rst>`_.
ewha-parking-dynamicP 0.1.1
Analyze frequent and illegal parking patterns using trajectory and GIS data
Requires Python: >=3.9

# ewha_parking_dynamicP

A Python module for detecting **Frequent Parking** spots from CCTV trajectory data  
and identifying **Illegal Parking** events using GIS layers.

---

## How to use

```python
from ewha_parking_dynamicP.frequent_parking import FrequentParking
from ewha_parking_dynamicP.illegal_parking import IllegalParking

# df: trajectory data (required fields: snr_id, traj_id, dtct_dt, lon/lat or geometry)
# road_df: road data (required fields: CCTV_ID, ufid, geometry)

# frequent parking
frequent_result = FrequentParking(df.copy(), road_df).call()

# illegal parking
illegal_parking = IllegalParking(
    zip_path="data/illegal_parking.zip",          # ZIP archive containing SHP files
    extract_dir="data/illegal_parking_extracted"  # Directory to extract SHP files
)
illegal_result = illegal_parking.call(frequent_result)

```

## Output

- **FrequentParking result**
  - Columns: `CCTV_ID`, `time`, `Geometry`, `Leaving_time`, `Traj_ID`, `Duration`, `ufid`

- **IllegalParking result**
  - Subset of the above events flagged as illegal
  - Rules:
    - ≥ 5 minutes: within custom_area, yellow solid line, yellow dashed line, etc.
    - ≥ 1 minute: within crosswalk, sidewalk, yellow double line (excluding custom_area)


## Requirements
pandas, numpy, geopandas, shapely

> All SHP layers are assumed to use EPSG:4326 (WGS84).
> If your data uses a different CRS, please reproject it before analysis.

## License
This project is licensed under JiwonKim License.

## Contact
kimjiwon4007@ewha.ac.kr