Revert "Stoichiometry matrix is now sparse. This should help memory p…

…roblems."

This reverts commit 401aff2.

Building the LIL sparse matrix was very slow, so testing the dense one again.
Should be a temporary thing...

Conflicts: (resolved)

	source/rmg/model.py

source/rmg/model.py

@@ -36,7 +36,6 @@
 import math
 import numpy
 import scipy.integrate
-import scipy.sparse
 
 
 import constants
@@ -236,28 +235,10 @@ def getStoichiometryMatrix(self):
 		columns represent the reactions in the core and edge in order.
 		"""
 		speciesList, reactionList = self.getLists()
-		#stoichiometry = numpy.zeros((len(speciesList), len(reactionList)), float)
-		stoichiometry = scipy.sparse.lil_matrix((len(speciesList), len(reactionList)), dtype=float)
-		# sparse matrix examples at http://www.scipy.org/SciPy_Tutorial
+		stoichiometry = numpy.zeros((len(speciesList), len(reactionList)), float)
 		for j, rxn in enumerate(reactionList):
 			for i, spec in enumerate(speciesList):
 				stoichiometry[i,j] = rxn.getStoichiometricCoefficient(spec)
-
-# Advantages of the LIL format
-#     - supports flexible slicing
-#     - changes to the matrix sparsity structure are efficient
-# 
-# Disadvantages of the LIL format
-#     - arithmetic operations LIL + LIL are slow (consider CSR or CSC)
-#     - slow column slicing (consider CSC)
-#     - slow matrix vector products (consider CSR or CSC)
-# 
-# Intended Usage
-#     - LIL is a convenient format for constructing sparse matrices
-#     - once a matrix has been constructed, convert to CSR or
-#       CSC format for fast arithmetic and matrix vector operations
-#     - consider using the COO format when constructing large matrices
-		stoichiometry.tocsc()  # convert to CSC format for faster matrix vector operations
 		return stoichiometry
 
 	def getReactionRates(self, T, P, Ci):
@@ -724,15 +705,8 @@ def getResidual(self, t, y, model, stoichiometry):
 		rxnRate = self.getReactionRates(P, V, T, Ni, model)
 
 		# Species balances
-
-		# can't take a zero-width slice of a sparse matrix, so special-case it:
-		if not model.core.reactions:
-			dNidt = numpy.zeros([len(model.core.species)])
-		else:
-			dNidt = ( stoichiometry[0:len(model.core.species), 0:len(model.core.reactions)] * 
-				rxnRate[0:len(model.core.reactions)] ) # stoichiometry matrix is sparse
-		# old version for dense matrix:
-		# dNidt = numpy.dot(stoichiometry.todense()[0:len(model.core.species), 0:len(model.core.reactions)],rxnRate[0:len(model.core.reactions)])
+		dNidt = numpy.dot(stoichiometry[0:len(model.core.species), 0:len(model.core.reactions)],
+		 					rxnRate[0:len(model.core.reactions)])
 
 		# Energy balance (assume isothermal for now)
 		dTdt = 0.0
@@ -794,12 +768,7 @@ def getSpeciesFluxes(self, model, P, V, T, Ni, stoichiometry):
 		matrix for the model.
 		"""
 		rxnRates = self.getReactionRates(P, V, T, Ni, model)
-		return stoichiometry * rxnRates
-		# seems to have the same result as this
-		return numpy.dot(stoichiometry.todense(),rxnRates)
-		# which is the same as this
 		return numpy.dot(stoichiometry, rxnRates)
-		# in the old days of a dense stoichiometry matrix 
 
 	def isModelValid(self, model, dNidt, criticalFlux):
 		"""