Metadata-Version: 2.1
Name: waste-flow
Version: 1.1.3
Summary: A package for retrieving data concerning waste management on the European continent.
Home-page: http://github.com/xapple/waste_flow/
Author: Lucas Sinclair
Author-email: lucas.sinclair@me.com
License: MIT
Description: # `waste_flow` version 1.1.3
        
        `waste_flow` is a python package for retrieving and analyzing data concerning the waste management of European countries.
        
        <p align="center">
        <img height="200" src="waste_flow/reports/template/logo.png?raw=true">
        </p>
        
        ## Installing
        
        Since `waste_flow` is written in python it is compatible with all operating systems: Linux, macOS and Windows. The only prerequisite is python3 which is often installed by default. Simply type the following commands on your terminal:
        
            $ pip3 install --user waste_flow
        
        Alternatively if you want to install it for all users of the system:
        
            $ sudo pip3 install waste_flow
        
        If you do not have `pip` on your system you can usually get it with these commands (fresh Ubuntu 18-LTS):
        
            $ sudo apt-get update
            $ sudo apt-get install python3-distutils
            $ curl -O https://bootstrap.pypa.io/get-pip.py
            $ python3 get-pip.py --user
        
        ## Usage
        
        Bellow are some examples to illustrate the various ways there are to use this package.
        
        To retrieve the large dataframe with dry mass for all years and all countries you can do the following:
        
            from waste_flow.analysis import waste_ana
            print(waste_ana.dry_mass)
        
        If you just want to see how much rubber waste did the UK generate in 2008, you can do the following:
        
            from waste_flow.generation import waste_gen
            params = ("waste   == 'W073' & "
                      "country == 'UK' & "
                      "year    == '2008'")
            result = waste_gen.long_format.query(params)
            print(result)
        
        To create the waste generation plots do the following:
        
            from waste_flow.viz.gen_by_country import legend
            print(legend.plot(rerun=True))
            from waste_flow.viz.gen_by_country import countries
            for gen_viz in countries.values():
                print(gen_viz.plot(rerun=True))
        
        ## Cache
        
        When you import `waste_flow`, we will check the `$WASTE_FLOW_CACHE` environment variable to see where to download and store the cached data. If this variable is not set, we will default to the platform's temporary directory and clone a repository there. This could result in re-downloading the cache after every reboot.
        
        ## Features
        
        The first time you run `waste_flow`, it will automatically download the raw CSVs from the EUROSTAT website to disk and parse the resulting data. On later runs, `waste_flow` will simply retrieve this information directly from the disk. This means that the first time you execute the pipeline things will be noticeably slower: this is normal.
        
        ## Source
        
        The two datasets used in this pipeline are available at the following addresses:
        
        * https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_wasgen&lang=en
        
        * https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_wastrt&lang=en
        
        These are obtained by starting at https://ec.europa.eu/eurostat/data/database
         and following "Database by themes -> Environment -> Waste -> Waste treatment"
        
        The full name of the datasets are:
        
        * Generation of waste by waste category, hazardousness and NACE Rev. 2 activity (env_wasgen)                                  
        * Treatment of waste by waste category, hazardousness and waste management operations (env_wastrt)
        
        ## Customizing
        
        The pipeline is flexible as the user can specify what coefficients they desire or even what custom waste categories they want to create. These input parameters are in the files under the `waste_flow/extra_data_xls` directory.
        
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