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        "author": "Benny Chin",
        "author_email": "wcchin.88@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: GIS"
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        "description": "# TaPiTaS algorithm\nA data exploration and visualization algorithm for understanding diffusion process.\n\n# Method description and citation:\n## Ref.:\nChin 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\n\nPaper Link:\nhttps://www.nature.com/articles/s41598-017-12852-z\n\nAbstract Link:\nhttps://wcchin.github.io/a-geo-computational-algorithm-for-exploring-the-structure-of-diffusion-progression-in-time-and-space.html\n\n====================\n\n## dependencies\n- pandas\n- geopandas\n- scipy\n- numpy\n- descartes\n- matplotlib\n- seaborn\n- shapely\n\n\n====================\n\n## Usage\nsimilar to the example file\n\n**column settings**\n\npts_setting (about the data, should be set according to data frame):\n\n- xcor: x coordinate column,\n- ycor: y coordinate column, and\n- time: the time column, integer\n\nxcor and ycor will be used to calculate distance, so probably not longitude and latitude, should be projected according to the region\n\n```python\npts_setting = {'xcor':'xcor', 'ycor':'ycor', 'time':'days'}\n```\n\n**main parameters**\n\nsetting the three major parameter (should be set by user):\n- s_radius: spatial searching radius\n- T1: the time buffer, neighboring pair relationship\n- T2: the time threshold, the shifting link relationship\n\n```python\nimport pandas as pd\nimport tapitas\n\nadf = pd.read_csv('test_data/demo_0905.csv', index_col=0)\npts_setting = {'xcor':'xx', 'ycor':'yy', 'time':'time'}\ns_radius = 500\nT1 = 6\nT2 = 23\nPG_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)\nprint(\"calculation done\")\n\nres = PG_graph.results\nprint(res.get_summary_df())\nprint(res.get_node_df())\nprint(res.get_cluster_df())\nprint(res.get_progress_df())\nprint(res.get_final_slinks_df())\nprint(res.get_final_nlinks_df())\n```\n\n\n",
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        "keywords": "point diffusion subcluster progress",
        "license": "LICENSE.txt",
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        "name": "tapitas",
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