Metadata-Version: 2.0
Name: pysolve3
Version: 0.1.5
Summary: A simple interface for solving systems of linear equations
Home-page: https://github.com/gpetrini/pysolve3
Author: Gabriel Petrini da Silveira
Author-email: gpetrinidasilveira@gmail.com
License: MIT
Platform: UNKNOWN
Requires-Dist: sympy (>=1.4)
Requires-Dist: numpy
Requires-Dist: pandas

==========
pysolve
==========
Solving systems of linear equations
-----------------------------------

The purpose of this code is to aid in expressing and solving
sets of equations using Python.

This tool will take a textual description of the equations 
and then run the solver iteratively until it converges to a solution.

The solver provides the following choices for solving:
- Gauss-Seidel
- Newton-Raphson
- Broyden

It also uses parts of sympy to aid in parsing the equations.

The initial motivation for this tool was to solve economic
models based on Stock Flow Consistent (SFC) models.


Installation
--------------
::
	pip3 install pysolve


Usage
-------------

.. code::

	from pysolve3.model import Model
	from pysolve3.utils import round_solution,is_close

	model = Model()

	model.set_var_default(0)
	model.var('Cd', desc='Consumption goods demand by households')
	model.var('Cs', desc='Consumption goods supply')
	model.var('Gs', desc='Government goods, supply')
	model.var('Hh', desc='Cash money held by households')
	model.var('Hs', desc='Cash money supplied by the government')
	model.var('Nd', desc='Demand for labor')
	model.var('Ns', desc='Supply of labor')
	model.var('Td', desc='Taxes, demand')
	model.var('Ts', desc='Taxes, supply')
	model.var('Y', desc='Income = GDP')
	model.var('YD', desc='Disposable income of households')	

	# This is a shorter way to declare multiple variables
	# model.vars('Y', 'YD', 'Ts', 'Td', 'Hs', 'Hh', 'Gs', 'Cs',
	#            'Cd', 'Ns', 'Nd')
	model.param('Gd', desc='Government goods, demand', initial=20)
	model.param('W', desc='Wage rate', initial=1)
	model.param('alpha1', desc='Propensity to consume out of income', initial=0.6)
	model.param('alpha2', desc='Propensity to consume o of wealth', initial=0.4)
	model.param('theta', desc='Tax rate', initial=0.2)

	model.add('Cs = Cd')
	model.add('Gs = Gd')
	model.add('Ts = Td')
	model.add('Ns = Nd')
	model.add('YD = (W*Ns) - Ts')
	model.add('Td = theta * W * Ns')
	model.add('Cd = alpha1*YD + alpha2*Hh(-1)')
	model.add('Hs - Hs(-1) =  Gd - Td')
	model.add('Hh - Hh(-1) = YD - Cd')
	model.add('Y = Cs + Gs')
	model.add('Nd = Y/W')

	# solve until convergence
	for _ in xrange(100):
	    model.solve(iterations=100, threshold=1e-3)

	    prev_soln = model.solutions[-2]
	    soln = model.solutions[-1]
	    if is_close(prev_soln, soln, rtol=1e-3):
	        break

	print round_solution(model.solutions[-1], decimals=1)

For additional examples, view the iPython notebooks at
	http://nbviewer.ipython.org/github/kennt/monetary-economics/tree/master/


Tutorial
--------
A short tutorial with more explanation is available at
	http://nbviewer.ipython.org/github/kennt/monetary-economics/blob/master/extra/pysolve%20tutorial.ipynb



