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    "info": {
        "author": "Franco Donati",
        "author_email": "f.donati@cml.leidenuniv.nl",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3 :: Only"
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        "description": "######\npycirk\n######\n\n\n\n*A python package to model Circular Economy policy and technological interventions in Environmentally Extended Input-Output Analysis starting from SUTs (EXIOBASE V3.3)*\n\n.. image:: https://zenodo.org/badge/157891556.svg\n   :target: https://zenodo.org/badge/latestdoi/157891556\n.. image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg\n   :target: https://www.gnu.org/licenses/gpl-3.0\n.. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg\n   :target: resources/docs/CONTRIBUTING.md)\n\n\n| Documentation: https://cmlplatform.github.io/pycirk/\n\n10.5281/zenodo.1492957\n\n===============\n1. Installation\n===============\n\n\n1.1. Stable release\n-------------------\n\nRun in your terminal:0\n\n\t$ pip install pycirk\n\n\n1.2. From source\n----------------\n\nClone repository:\n\n\t$ git clone https://fdonati@bitbucket.org/CML-IE/pycirk.git\n\nOnce you have a copy of the source, you can install it with:\n\n    $ python setup.py install\n\n1.3 Data\n--------\n\nYou can download the biregional or multiregional database by following this link\n\nhttps://fdonaticml.stackstorage.com/s/OEPbzJQgdIcsAn1\n\nYou need to place the data inside the package\ne.g. /home/UserName/.local/lib/python3.6/site-packages/pycirk/data\n\n========\n2. Usage\n========\n\n2.1. Import package\n-------------------\n\n\timport pycirk\n\n\n2.2. Initialize\n---------------\n\n\tmy_work = pycirk.Launch(method, directory, aggregation)\n\n\n2.3. set your scenarios and analysis\n------------------------------------\n\n1. Open scenarios.xls in the directory that was specified\n2. From there you can specify interventions and parameters for the analysis\n3. save and continue to the following steps\n\n\n\n2.4. Run scenarios\n------------------\n\nRun one specific scenario\n\n    my_work.scenario_results(scen_no, output_dataset)\n    (0 = baseline)\n\nRun all scenarios\n\n    my_work.all_results()\n\n\n2.5. save scenarios\n-------------------\n\nSave your results\n\n    my_work.save_results()\n\n\n2.6. Use from command line\n\n2.6.1. pycirk --help\n\nUsage: pycirk [OPTIONS]\n\nConsole script for pycirk. A software to model policy and technological\ninterventions in Environmentally Extended Input-Output Analysis (EXIOBASE\nV3.3, 2011)\n\nOptions:\n\n+----------------------------+--------------------------------------+\n| Command                    | Variables                            |\n+============================+======================================+\n|  -tm, --transf_method TEXT | 0 = PXP ITA_TC; 1 = PXP ITA_MSC      |\n+----------------------------+--------------------------------------+\n|  -dr, --directory TEXT     | if left black it will be default     |\n+----------------------------+--------------------------------------+\n|  -ag, --aggregation        | 1 = bi-regional (EU-ROW)             |\n|                            | 0 = None (49 regions)                |\n+----------------------------+--------------------------------------+\n|  -sc, --scenario TEXT      | all, 1, 2,... accepted - 0=baseline  |\n+----------------------------+--------------------------------------+\n|  -s, --save TEXT           | False=no, True=yes                   |\n+----------------------------+--------------------------------------+\n|  -od, --output_dataset     | False=no, True=yes                   |\n+----------------------------+--------------------------------------+\n|  --help                    | Show this message and exit.          |\n+----------------------------+--------------------------------------+\n\n2.6.2. Command example\n\n    pycirk -tm 0 -dr \"\" -sc \"1\" -s True -od False\n\n\n\n===========\n3. Features\n===========\n\n\nExamples of policies that can be modelled through the software:\n\n- sharing\n- recycling\n- life extension\n- rebound effects\n- substituion\n- market and value added changes\n- efficiency\n\nThe tables in which it is possible to apply changes:\n\n- total requirement matrix (A)\n- intermediate transactions (Z)\n- final demand (Y)\n- primary inputs (W)\n\n- emission intermediate extentions (E)\n- material intermediate extensions (M)\n- resource intermediate extensions (R)\n- emission final demand extension (EY)\n- material final demand extension (MY)\n- resource final demand extensions (RY)\n\n- primary inputs coefficients (w)\n- emission intermediate extentions coefficients (e)\n- material intermediate extensions coefficients (m)\n- resource intermediate extensions coefficients (r)\n- emission final demand extension coefficients (eY)\n- material final demand extension coefficients (mY)\n- resource final demand extensions coefficients (rY)\n\nIt is possible to specify:\n\n- region of the intervention\n- whether the intervention affects domestic, import transactions or both\n\n\n====================\n4. Important modules\n====================\n\n4.1. scenarios.xls\n------------------\n\nFrom this .xls file it is possible to set different types of interventions and the analysis to perform:\n\n- matrix = specifies in which matrix of IOT the changes are applied\n- change_type = Primary and ancillary are only used to specify the type of intervention from a conceptual level\n- reg_o or reg_d = Regional coordinates (o=origin or row, d=destination or column)\n- cat_o or cat_d = category (e.g. products or extensions ) coordinates (o=origin or row, d=destination or column)\n- kt = technical coefficient (max achievable technically); a negative value means reduction; unit = %\n- ka = absolute values for addition\n- kp = penetration coefficient (level of market penetration of the policy); unit = %\n- copy = allows you to copy a specific transation to a different point in the matrices (useful for proxy creation)\n- substitution = tells the software whether it needs to substitute values among specified categories\n- sk = which intervention should be substituted\n- swk = Substitution weighing factor (how much of the original transaction should be substituted); allows to simulate difference in prices and physical properties between categories; unit = %\n\nThese can be set for:\n\n- product category e.g. C_STEL (basic iron), C_PULP (pulp), etc.\n- final demand category e.g. F_HOUS (households), F_GOVE (government), etc.\n- primary input category e.g. E_HRHS (employment highly skilled), T_TLSA (taxes less subsidies), etc.\n- emissions extensions e.g. E_CO2_c (CO2 - combustion)\n- material extensions e.g. NI.02 (Nature Inputs: Coking Coal)\n- resource extension e.g. L_1.1 (Land use - Arable Land - Rice)\n\nFurthemore, from the analysis sheet you can set the following variables to be compared in the analysis:\n\n- product categories\n- primary input categories\n- emissions extensions\n- material extensions\n- resource extensions\n- region of interest\n- impact categories # Please see the data_validation_list sheet in the scenarios.xls file for the comprehensive list\n\n\n\n==========\n6. Credits\n==========\n\nThanks to dr. Arnold Tukker, dr. Joao Dias Rodriguez for the supervision\ndr. Arjan de Koning for knowledge support in exiobase\nMSc. Glenn Auguilar Hernandez for testing\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.1.0 (2018-05-11)\n------------------\n\n* First release on PyPI.\n\n\n",
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