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        "author": "Kenn Takara",
        "author_email": "kenn.takara@outlook.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": "==========\r\npysolve\r\n==========\r\nSolving systems of equations\r\n-----------------------------------\r\n\r\nThe purpose of this code is to aid in expressing and solving\r\nsets of equations using Python.\r\n\r\nThis tool will take a textual description of the equations \r\nand then run the solver iteratively until it converges to a solution.\r\n\r\nThe solver provides the following choices for solving:\r\n\r\n- Gauss-Seidel\r\n- Newton-Raphson\r\n- Broyden\r\n\r\nIt also uses parts of sympy to aid in parsing the equations.\r\n\r\nThe initial motivation for this tool was to solve economic\r\nmodels based on Stock Flow Consistent (SFC) models.\r\n\r\n\r\nInstallation\r\n--------------\r\n\tpip install pysolve\r\n\r\n\r\nUsage\r\n-------------\r\n\r\n.. code::\r\n\tfrom pysolve.model import Model\r\n\tfrom pysolve.utils import round_solution,is_close\r\n\r\n\tmodel = Model()\r\n\r\n\tmodel.set_var_default(0)\r\n\tmodel.var('Cd', desc='Consumption goods demand by households')\r\n\tmodel.var('Cs', desc='Consumption goods supply')\r\n\tmodel.var('Gs', desc='Government goods, supply')\r\n\tmodel.var('Hh', desc='Cash money held by households')\r\n\tmodel.var('Hs', desc='Cash money supplied by the government')\r\n\tmodel.var('Nd', desc='Demand for labor')\r\n\tmodel.var('Ns', desc='Supply of labor')\r\n\tmodel.var('Td', desc='Taxes, demand')\r\n\tmodel.var('Ts', desc='Taxes, supply')\r\n\tmodel.var('Y', desc='Income = GDP')\r\n\tmodel.var('YD', desc='Disposable income of households')\t\r\n\r\n\t# This is a shorter way to declare multiple variables\r\n\t# model.vars('Y', 'YD', 'Ts', 'Td', 'Hs', 'Hh', 'Gs', 'Cs',\r\n\t#            'Cd', 'Ns', 'Nd')\r\n\tmodel.param('Gd', desc='Government goods, demand', initial=20)\r\n\tmodel.param('W', desc='Wage rate', initial=1)\r\n\tmodel.param('alpha1', desc='Propensity to consume out of income', initial=0.6)\r\n\tmodel.param('alpha2', desc='Propensity to consume o of wealth', initial=0.4)\r\n\tmodel.param('theta', desc='Tax rate', initial=0.2)\r\n\r\n\tmodel.add('Cs = Cd')\r\n\tmodel.add('Gs = Gd')\r\n\tmodel.add('Ts = Td')\r\n\tmodel.add('Ns = Nd')\r\n\tmodel.add('YD = (W*Ns) - Ts')\r\n\tmodel.add('Td = theta * W * Ns')\r\n\tmodel.add('Cd = alpha1*YD + alpha2*Hh(-1)')\r\n\tmodel.add('Hs - Hs(-1) =  Gd - Td')\r\n\tmodel.add('Hh - Hh(-1) = YD - Cd')\r\n\tmodel.add('Y = Cs + Gs')\r\n\tmodel.add('Nd = Y/W')\r\n\r\n\t# solve until convergence\r\n\tfor _ in xrange(100):\r\n\t    model.solve(iterations=100, threshold=1e-3)\r\n\r\n\t    prev_soln = model.solutions[-2]\r\n\t    soln = model.solutions[-1]\r\n\t    if is_close(prev_soln, soln, rtol=1e-3):\r\n\t        break\r\n\r\n\tprint round_solution(model.solutions[-1], decimals=1)\r\n\r\nFor additional examples, view the iPython notebooks at\r\n\thttp://nbviewer.ipython.org/github/kennt/monetary-economics/tree/master/\r\n\r\n\r\nTutorial\r\n--------\r\nA short tutorial with more explanation is available at\r\n\thttp://nbviewer.ipython.org/github/kennt/monetary-economics/blob/master/extra/pysolve%20tutorial.ipynb\r\n\r\nTODO list\r\n---------\r\n- Sparse matrix support (memory improvements for large systems)\r\n- Documentation\r\n\r\n\r\nChangelog\r\n---------\r\n\r\n0.2.0 (in progress)\r\n-------------------\r\n- Tutorial\r\n- Improved documentation\r\n\r\n0.1.7\r\n-----\r\n- Tutorial\r\n\r\n0.1.6\r\n-----\r\n- Added support for solving with Broyden's method\r\n- Optimized the code for Broyden and Newton-Raphson, should be much faster now.\r\n\r\n0.1.5\r\n-----\r\n- Added the d() function.  Implements the difference between the current value and the value from a previous iteration.  d(x) is equivalent to x - x(-1)\r\n- Added support for the following sympy functions: abs, Min, Max, sign, sqrt\r\n- Added some helper functions to aid in debugging larger models\r\n- Added support for solving via Newton-Raphson\r\n\r\n0.1.4\r\n-----\r\n- Improved error reporting when unable to solve an equation (due to variable missing a value).\r\n- Also, evaluate() used to require that all variables have a value, but that may not be true on initialization, so this requirement has been removed.\r\n\r\n0.1.3 (and before)\r\n------------------\r\n- Added support for the exp() and log() functions.\r\n- Fixed a bug where the usage of '>=' within an if_true() would cause an error.",
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