Metadata-Version: 2.1
Name: ptrail
Version: 0.6.1.1.Beta
Summary: PTRAIL: A Mobility-data Preprocessing Library using parallel computation.
Home-page: https://github.com/YakshHaranwala/PTRAIL.git
Maintainer: PTRAIL Developers
Maintainer-email: mobilitylab2021@gmail.com
License: new BSD
Description: <!---------------------- Introduction Section ------------------->
        <h1> PTRAIL:  A <b><i>P</i></b>arallel 
        <b><i>TR</i></b>ajectory 
        d<b><i>A</i></b>ta
        preprocess<b><i>I</i></b>ng
        <b><i>L</i></b>ibrary
        
         </h1>
        
        <h2> Introduction </h2>
        
        <p align='justify'>
        PTRAIL is a state-of-the art Mobility Data Preprocessing Library that mainly deals with filtering data, generating features and interpolation of Trajectory Data.
        
        <b><i> The main features of PTRAIL are: </i></b>
        </p>
        
        <ol align='justify'>
        <li> PTRAIL uses primarily parallel computation based on
             python Pandas and numpy which makes it very fast as compared
             to other libraries available.
        </li>
        
        <li> PTRAIL harnesses the full power of the machine that
             it is running on by using all the cores available in the
             computer.
        </li>
        
        <li> PTRAIL uses a customized DataFrame built on top of python
             pandas for representation and storage of Trajectory Data.
        </li>
        
        <li> PTRAIL also provides several Temporal and spatial features
             which are calculated mostly using parallel computation for very
             fast and accurate calculations.
        </li>
        
        <li> Moreover, PTRAIL also provides several filteration and
             outlier detection methods for cleaning and noise reduction of
             the Trajectory Data.
        </li>
        
        <li> Apart from the features mentioned above, <i><b> four </b></i>
             different kinds of Trajectory Interpolation techniques are
             offered by PTRAIL which is a first in the community.
        </li>
        </ol>
        
        <!------------------------- Documentation Link ----------------->
        <h2> Documentation </h2>
        
        <span> &#8618; </span>
        <a href='https://PTRAIL.readthedocs.io/en/latest/' target='_blank'> <i> PTRAIL Documentation </i> </a>
        
        <!-------------------- Pip Installation ------------------------->
        <h2> Pip Installation </h2>
        
        1. `pip install PTRAIL`
        
        <!------------------------ Usage Examples ----------------------->
        <h2> Examples </h2>
        
        <span> &#8618; </span>
        <a href='https://github.com/YakshHaranwala/PTRAIL/tree/main/examples' target='_blank'> <i> PTRAIL Examples </i> </a>
        
        <!--------------------- Binder Link ---------------------------->
        <h2> Binder </h2>
        
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/YakshHaranwala/PTRAIL.git/HEAD)
        
        <!-------------------- MISC ------------------------------------>
        <h2> Miscellaneous </h2>
        
        [![Downloads](https://static.pepy.tech/personalized-badge/ptrail?period=total&units=international_system&left_color=black&right_color=blue&left_text=Downloads)](https://pepy.tech/project/ptrail)
        
        <!------------------- Citation ---------------------------------->
        <h2> Citation </h2>
        
        To cite PTRAIL in your academic work, please use the following citation: 
        
        ```bibtex
        @article{haidri2021ptrail,
              title={PTRAIL -- A python package for parallel trajectory data preprocessing}, 
              author={Salman Haidri and Yaksh J. Haranwala and Vania Bogorny and Chiara Renso and Vinicius Prado da Fonseca and Amilcar Soares},
              year={2021},
              eprint={2108.13202},
              url={https://arxiv.org/abs/2108.13202},
              archivePrefix={arXiv},
              primaryClass={cs.DC}
        }
        ```
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
