Metadata-Version: 2.1
Name: testingtool-canvsUser
Version: 0.0.1
Summary: A package for custom canvs analytics
Home-page: https://github.com/dbproductionsLTD/canvs_tools
Author: Janet Huang
Author-email: janet.huang@canvs.ai
License: UNKNOWN
Description: # Canvs Toolbox Package
        
        ### Date Formatting
        "MM/DD/YY" 
        
        ## General
        from canvs_toolbox import general as gen
        - gen.consolidate_data(file_path, file_type='csv')
        
        ## API Tools
        
        ### Canvs TV
        from canvs_toolbox.api import tv as tvAPI
        - tvAPI.twitter_daily_export(api_key, data_mode, start_date, end_date)
        - tvAPI.twitter_emotional_authors(api_key, series_id, start_date, end_date)
        - tvAPI.airings_backfill(api_key, data_mode, start_date, end_date)
        - tvAPI.facebook_backfill(api_key, data_mode, start_date, end_date)
        
        ### Canvs Watch
        from canvs_toolbox.api import watch as watchAPI
        - watchAPI.post_backfill(api_key, data_mode, start_date, end_date)
        - watchAPI.series_backfill(api_key, data_mode, start_date, end_date)
        
        ### Canvs Social
        from canvs_toolbox.api import social as socialAPI
        - socialAPI.get_facebook_posts(api_key, fb_id, org_id, start_date, end_date, query_increment=None)
        - socialAPI.get_page_collection(api_key, org_id, start_date, end_date, fb_pages, query_increment=None)
        
        ## Analytics Tools
        
        ### Canvs TV
        from canvs_toolbox.analytics import tv as tvAnalytics
        
        #### Audience Overlap Analysis
        - implementation: tvAnalytics.audience_overlap_analysis(directory)
        - input directory should contain audience csv files from calling tvAPI.twitter_emotional_authors()
        - analysis will find the overlapping audiences across those csv files
        - for best results, rename the csv files to desired series names (e.g. showA, showB)
        
        #### Audience Erosion Analysis
        - implementation: tvAnalytics.audience_erosion_analysis(filename)
        - input file should be a single audience csv file from calling tvAPI.twitter_emotional_authors()
        - creates an episode-over-episode drop-off analysis
        
        #### Emotional Fingerprinting Analysis
        - implementation: emotional_fingerprinting_analysis(source, filename, format)
        - input file should be either from a direct Explore Programs export from the Canvs App
        or from any of the API exports except twitter_emotional_authors
        - computes an emotional similarity score for all possible combinations of passed-in content
        - can choose to either return a stacked view of all pairings and their scores (format = 'stacked)
        or a matrix view containing similarity scores at content intersections (format = 'matrix')
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
