Metadata-Version: 2.1
Name: mediaviz
Version: 0.1.1
Summary: Visualize Networks With Force Atlas 2 Layout
Home-page: https://github.com/Tahsin-Mayeesha/Mediaviz
Author: Tahsin Mayeesha
Author-email: tasmiah.tahsin@hotmail.com
License: MIT
Description: ![](readmegif.gif)
        
        # Mediaviz
        
        Mediaviz is a network visualization library created for my project  [Automating Network Visualization and community detection of Media Sources Network from Mediacloud data](https://summerofcode.withgoogle.com/projects/#6265196406898688) done with [Berkman Klein Center of Internet And Society](http://cyber.law.harvard.edu/) at Harvard University while participating at [Google Summer Of Code 2018](https://summerofcode.withgoogle.com/) under the guidance of mentor [Hal Roberts](http://cyber.harvard.edu/people/hroberts).
        
        [Mediacloud](http://mediacloud.org/) is an open source, open data platform for researchers that provides data and statistics for quantitative analysis. Mediaviz has been originally developed to automate the network visualization of the media source networks for different topics such as network neutrality, ebola and more. 
        
        However, beyond the mediacloud usage, Mediaviz is also a general purpose network visualization library provides functionalities for force based layout such as [force atlas 2](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098679), automatic scaling to prevent node overlap, Gephi like features for network filtering, coloring, node resizing, prevention of label overlap and community visualization. 
        
        Mediaviz uses force atlas 2 layout as default and scales the layout automatically for graphs with 100-1000 nodes that has a power law linking structure.  Having sensible defaults, Mediaviz can be used for general graph visualization as well as publishable graphs with many customization parameters including using the drawing function with any other network layout function in networkx or beyond. 
        
        Examples and demo visualizations are provided in the usage section.
        
        # Documentation
        
        Complete Documentation is available at https://mediaviz.readthedocs.io/en/latest/ 
        
        
        
        ![](assets/deep_state.png)
        
        
        
        
        
        
        
        
        
        
        
        # Installation
        
        To install the package from pip :
        
        ```python
        pip install mediaviz
        ```
        
        To build from source , download the repository and go to the Mediaviz top level directory. Then use
        
        ```python
        python setup.py install 
        ```
        
        or 
        
        ```python
        pip install .
        ```
        
        
        
        # Dependencies  
        
        * [networkx](https://networkx.github.io)
        * [fa2l](https://github.com/bosiakov/fa2l/tree/master/fa2l)
        * [adjusttext](http://adjusttext.readthedocs.io)
        * [matplotlib](https://matplotlib.org)
        * [numpy](http://www.numpy.org/)
        * [python-louvain](https://python-louvain.readthedocs.io/en/latest/)
        
        
        
        # Blog Posts
        
        - [GSOC 2018 Experience : Visualizing Media Data With Network Analysis (PART 1 )](https://medium.com/learning-machine-learning/gsoc-2018-experience-visualizing-media-data-with-network-analysis-part-1-c4ba4b76b1aa)
        - [GSOC 2018 : Network Visualization Of MediaCloud Topic Network + 1st evaluation (Part 2)](https://medium.com/learning-machine-learning/gsoc-2018-network-visualization-of-mediacloud-topic-network-1st-evaluation-part-2-ca72e25a88d5)
        
        
        # Usage
        
        ####  Draw a Network with Force Atlas 2 Layout With Default Parameters
        
        ```python
        import networkx as nx
        from mediaviz.draw import draw_forceatlas2_network
        
        path= 'graphname.gexf'
        G = nx.erdos_renyi_graph(200,0.7)
        
        draw_forceatlas2_network(G,node_colors='purple', node_sizes=10, edge_color='gray',filename="random.png")
        ```
        
        ![](assets/random.png)
        
        #### Drawing Network with Force Atlas 2 Layout with customization
        
        Here we parse color codes from .gexf visual attributes.
        
        ```python
        import networkx as nx
        from mediaviz.draw import draw_forceatlas2_network
        from mediaviz.viz_parser import parse_colors, parse_size
        
        # 1000 node graph from mediacloud on network neutrality topic
        path = "network_neutrality.gexf" 
        G = nx.read_gexf(path)
        node_colors = list(parse_colors(path,hex=True).values())
        draw_forceatlas2_network(G,
             num_labels = 30, # num_labels indicates to only label top 30 largest nodes by node_size
             fa2l_scaling_ratio=40,fa2l_iterations=100, # parameters for the force atlas 2 layout
             node_colors = node_colors, 
             with_labels=True, label_field="label",
             filter_by="inlink_count", top=200, # filter to get top 200 nodes sorted by inlink_count
             size_field = "inlink_count",min_size=0.1,max_size=200, # resize by inlink_count
             adjust_labels=True, # adjusts labels to prevent label overlap
             node_opacity=0.8, edge_opacity=0.01, 
             font_size=6, # size of label font_size 
             filename= "network_neutrality.png", title="network_neutrality",
             edge_color_by_source=True)
        ```
        
        ![](assets/network_neutrality.png)
        
        
        
        #### Drawing Network With Community Detection and Coloring By Community Partitions
        
        ```python
        import community
        import networkx as nx
        from mediaviz.community_utils import get_community_graph, get_community_colormap
        from mediaviz.draw import draw_forceatlas2_network
        
        G = nx.florentine_families_graph() 
        # get the community partitions and set partition as an attribute for the nodes 
        G, partitions = get_community_graph(G) 
        # colormaps are automatically assigned for each partition as randomly genererated hex colors
        colormap = get_community_colormap(partitions)
        # use the draw function as usual with forceatlas2 layout as default
        draw_forceatlas2_network(
                G,
                color_by="partition", colormap=colormap,
                node_sizes = 10,
                with_labels=True, 
                edge_color_by_source=True, node_opacity = 1, edge_opacity = 1,
                font_size=10, filename = "community.png",
                figsize=(10, 10));
        ```
        
        ![ ](assets/community.png)
        
        #### Only Using Draw Function for Customized Visualization With Other Layout Algorithms
        
        ```python
        import networkx as nx
        from mediaviz.draw import draw_forceatlas2_network
        G = nx.karate_club_graph()
        pos = nx.spring_layout(G)
        draw_forceatlas2_network(G,
                                 pos = pos,
                                 node_sizes=10,
                                 color_by="club",
                                 colormap={"Officer":"r","Mr. Hi":"b"},
                                 node_opacity=1,edge_opacity=1, filename="karate_club.png",
                                 edge_color="lightgray")
        ```
        
        ![](assets/karate_club.png)
        
        
        
        # Contribution and Further Improvement
        
        If you want to contribute please send a pull request with listing the changes you have made and the reasoning clearly stated. Integrating the drawing function with matplotlib better and trying out different heuristics for automated scaling would be a priority.
        
        # License
        
        This project is licensed under the MIT License - see the License.txt file for details.
        
        
        
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
