Metadata-Version: 1.2
Name: econci
Version: 0.1.2
Summary: Calculates Economic Complexity Indexes
Home-page: https://github.com/phcsoares/econci
Author: Pedro Henrique Castro Soares
Author-email: phcastrosoares@gmail.com
License: MIT license
Description: ======
        econci
        ======
        
        
        .. image:: https://img.shields.io/pypi/v/econci.svg
                :target: https://pypi.python.org/pypi/econci
        
        
        
        
        Calculates Economic Complexity Indexes
        
        
        * Free software: MIT license
        
        This package implements the indexes found in the Atlas of Economic Complexity [HaRH2014]_.
        It also creates the Product Space.
        
        Installation
        ------------
        
                pip install econci
        
        Usage
        -----
        
        .. code-block:: python
        
                import econci
                
                comp = econci.Complexity(df, c='country', p='product', values='export')
                comp.calculate_indexes()
                eci = comp.eci
                pci = comp.pci
        
                # creating the product space
                comp.create_product_space()
                
                # the graphs are networkx.Graph objects
                complete_graph = comp.complete_graph  # complete product space
                max_spanning_tree = comp.maxst  # maximum spanning tree
                prod_space = comp.product_space  # product space
        
                # edges_nodes_to_csv saves one csv file with edges and weights
                # and another file with nodes information
                econci.edges_nodes_to_csv(prod_space, graph_name='prod_space', dir_path='./data/')
        
        Complete list of calculated indexes:
        
        * Economic Complexity Index: :code:`comp.eci`
        * Product Complexity Index: :code:`comp.pci`
        * Country Diversity: :code:`comp.diversity`
        * Product Ubiquity: :code:`comp.ubiquity`
        * Balassa's RCA: :code:`comp.rca`
        * Proximity: :code:`comp.proximity`
        * Density: :code:`comp.density`
        * Distance: :code:`comp.distance`
        
        You can also vary the threshold of RCA value when creating the Mcp matrix.
        The :code:`Complexity` class accepts the parameter :code:`m_cp_thresh`, which by default is :code:`1.0`.
        
        :code:`comp.create_product_space()` also accepts the argument :code:`edge_weight_thresh`, by default :code:`0.65`.
        This argument filters edges to be added to the maximum spanning tree by weight.
        
        References
        ----------
        
        .. [HaRH2014] Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Chung, S., Jimenez, J., … Yildirim, M. A. (2014). The Atlas of Economic Complexity: Mapping Paths to Prosperity. MIT Press.
        
        Credits
        -------
        
        This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
        
        .. _Cookiecutter: https://github.com/audreyr/cookiecutter
        .. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
        
        
        =======
        History
        =======
        
        0.1.0 (2020-06-06)
        ------------------
        
        * First release on PyPI.
        
Keywords: econci
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
