Metadata-Version: 1.1
Name: tapitas
Version: 0.0.2
Summary: A data exploration and visualization algorithm for understanding diffusion process.
Home-page: https://bitbucket.org/wcchin/TaPiTaS
Author: Benny Chin
Author-email: wcchin.88@gmail.com
License: LICENSE.txt
Description: # TaPiTaS algorithm
        A data exploration and visualization algorithm for understanding diffusion process.
        
        # Method description and citation:
        ## Ref.:
        Chin W. C. B., Wen T. H., Sabel C. E. & Wang I. H. (2017). A geo-computational algorithm for exploring the structure of diffusion progression in time and space. Scientific Reports 7: 12565. DOI http://dx.doi.org/10.1038/s41598-017-12852-z
        
        Paper Link:
        https://www.nature.com/articles/s41598-017-12852-z
        
        Abstract Link:
        https://wcchin.github.io/a-geo-computational-algorithm-for-exploring-the-structure-of-diffusion-progression-in-time-and-space.html
        
        ====================
        
        ## dependencies
        - pandas
        - geopandas
        - scipy
        - numpy
        - descartes
        - matplotlib
        - seaborn
        - shapely
        
        
        ====================
        
        ## Usage
        similar to the example file
        
        **column settings**
        
        pts_setting (about the data, should be set according to data frame):
        
        - xcor: x coordinate column,
        - ycor: y coordinate column, and
        - time: the time column, integer
        
        xcor and ycor will be used to calculate distance, so probably not longitude and latitude, should be projected according to the region
        
        ```python
        pts_setting = {'xcor':'xcor', 'ycor':'ycor', 'time':'days'}
        ```
        
        **main parameters**
        
        setting the three major parameter (should be set by user):
        - s_radius: spatial searching radius
        - T1: the time buffer, neighboring pair relationship
        - T2: the time threshold, the shifting link relationship
        
        ```python
        import pandas as pd
        import tapitas
        
        adf = pd.read_csv('test_data/demo_0905.csv', index_col=0)
        pts_setting = {'xcor':'xx', 'ycor':'yy', 'time':'time'}
        s_radius = 500
        T1 = 6
        T2 = 23
        PG_graph = tapitas.Point_Diffusion(adf, pts_setting=pts_setting, s_radius=s_radius, T1=T1, T2=T2, resample_time=99, confidence_level=0.8, critical_value=None)
        print("calculation done")
        
        res = PG_graph.results
        print(res.get_summary_df())
        print(res.get_node_df())
        print(res.get_cluster_df())
        print(res.get_progress_df())
        print(res.get_final_slinks_df())
        print(res.get_final_nlinks_df())
        ```
        
Keywords: point diffusion subcluster progress
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
